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Sun Y, Mao Q, Zhou D, Tian J, Du H, Yu Q, Zhao J, Duan W, Liu C, Duan Y, Zhou J, Zhang T, Xia Z, Yin Y, Liu Y, Zhao X, Xu S. Associations of multiple blood metals with cardiac structure and function: A cross-sectional study in a CAD population. ENVIRONMENTAL POLLUTION (BARKING, ESSEX : 1987) 2024; 360:124718. [PMID: 39163945 DOI: 10.1016/j.envpol.2024.124718] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/09/2024] [Revised: 08/09/2024] [Accepted: 08/11/2024] [Indexed: 08/22/2024]
Abstract
Coronary artery disease (CAD) is often accompanied by abnormal cardiac structure and function, leading to an increased prognostic risk. However, less is known about the associations of mixed metals with abnormal cardiac structure and function in CAD patients. Here, we aimed to investigate the associations of exposure to metal mixtures with cardiac structure and function and potential interactions in a CAD population. We conducted a cross-sectional study from Southwest China that included 1555 CAD patients. The blood concentrations of 14 metals were measured via inductively coupled plasma spectrometry. CAD was defined as at least one vessel having stenosis ≥50% the vessel diameter. Echocardiography was used for cardiac structural and functional measurements. Bayesian kernel machine regression was applied to explore the overall effect, metal weight, and dose effect. Linear regression analysis was used to analyze the effects of single metals, metal‒metal interactions and metal‒traditional interactions. Finally, we found that the negative associations of mixed metals with cardiac structure was significant when the levels of all metals were below the 60th percentile. For cardiac function, changes in metals from 50th to 75th were associated with 0.954% and 0.683% decrease in left ventricular ejection fraction and left ventricular fractional shortening, respectively. Negative associations of copper and manganese with cardiac structure and function, whereas positive associations of titanium, selenium and molybdenum with several parameters were found. Antagonistic interactions between copper and tin and between selenium and several metals (manganese, copper and aluminum) (all Pinteraction terms < 0.05) were found. In conclusion, mixed metal exposure was negatively associated with cardiac structure and function in CAD patients. The main metals contributing to this negative associations were copper and manganese. Selenium or tin supplementation may reduce the adverse associations of copper and manganese with cardiac structure and function.
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Affiliation(s)
- Yapei Sun
- Center of Laboratory Medicine, Chongqing Prevention and Treatment Center for Occupational Diseases, Chongqing, 400060, China; Chongqing Key Laboratory of Prevention and Treatment for Occupational Diseases and Poisoning, Chongqing, 400060, China
| | - Qi Mao
- Department of Cardiology, Institute of Cardiovascular Research, Xinqiao Hospital, Army Medical University, Chongqing, 400037, China
| | - Denglu Zhou
- Department of Cardiology, Institute of Cardiovascular Research, Xinqiao Hospital, Army Medical University, Chongqing, 400037, China
| | - Jiacheng Tian
- Center of Laboratory Medicine, Chongqing Prevention and Treatment Center for Occupational Diseases, Chongqing, 400060, China; Chongqing Key Laboratory of Prevention and Treatment for Occupational Diseases and Poisoning, Chongqing, 400060, China
| | - Hang Du
- Center of Laboratory Medicine, Chongqing Prevention and Treatment Center for Occupational Diseases, Chongqing, 400060, China; Chongqing Key Laboratory of Prevention and Treatment for Occupational Diseases and Poisoning, Chongqing, 400060, China
| | - Qin Yu
- Center of Laboratory Medicine, Chongqing Prevention and Treatment Center for Occupational Diseases, Chongqing, 400060, China; Chongqing Key Laboratory of Prevention and Treatment for Occupational Diseases and Poisoning, Chongqing, 400060, China
| | - Jianhua Zhao
- Department of Cardiology, Institute of Cardiovascular Research, Xinqiao Hospital, Army Medical University, Chongqing, 400037, China
| | - Weixia Duan
- Center of Laboratory Medicine, Chongqing Prevention and Treatment Center for Occupational Diseases, Chongqing, 400060, China; Chongqing Key Laboratory of Prevention and Treatment for Occupational Diseases and Poisoning, Chongqing, 400060, China
| | - Cong Liu
- Center of Laboratory Medicine, Chongqing Prevention and Treatment Center for Occupational Diseases, Chongqing, 400060, China; Chongqing Key Laboratory of Prevention and Treatment for Occupational Diseases and Poisoning, Chongqing, 400060, China
| | - Yu Duan
- Center of Laboratory Medicine, Chongqing Prevention and Treatment Center for Occupational Diseases, Chongqing, 400060, China; Chongqing Key Laboratory of Prevention and Treatment for Occupational Diseases and Poisoning, Chongqing, 400060, China
| | - Jie Zhou
- Center of Laboratory Medicine, Chongqing Prevention and Treatment Center for Occupational Diseases, Chongqing, 400060, China; Chongqing Key Laboratory of Prevention and Treatment for Occupational Diseases and Poisoning, Chongqing, 400060, China
| | - Tian Zhang
- Center of Laboratory Medicine, Chongqing Prevention and Treatment Center for Occupational Diseases, Chongqing, 400060, China; Chongqing Key Laboratory of Prevention and Treatment for Occupational Diseases and Poisoning, Chongqing, 400060, China
| | - Zhiqin Xia
- Center of Laboratory Medicine, Chongqing Prevention and Treatment Center for Occupational Diseases, Chongqing, 400060, China; Chongqing Key Laboratory of Prevention and Treatment for Occupational Diseases and Poisoning, Chongqing, 400060, China
| | - Yangguang Yin
- Center of Laboratory Medicine, Chongqing Prevention and Treatment Center for Occupational Diseases, Chongqing, 400060, China; Chongqing Key Laboratory of Prevention and Treatment for Occupational Diseases and Poisoning, Chongqing, 400060, China
| | - Yongsheng Liu
- Center of Laboratory Medicine, Chongqing Prevention and Treatment Center for Occupational Diseases, Chongqing, 400060, China; Chongqing Key Laboratory of Prevention and Treatment for Occupational Diseases and Poisoning, Chongqing, 400060, China
| | - Xiaohui Zhao
- Department of Cardiology, Institute of Cardiovascular Research, Xinqiao Hospital, Army Medical University, Chongqing, 400037, China
| | - Shangcheng Xu
- Center of Laboratory Medicine, Chongqing Prevention and Treatment Center for Occupational Diseases, Chongqing, 400060, China; Chongqing Key Laboratory of Prevention and Treatment for Occupational Diseases and Poisoning, Chongqing, 400060, China.
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Wang Q, Leask MP, Lee K, Jaiswal J, Kallingappa P, Dissanayake W, Puli'uvea C, O'Sullivan C, Watson H, Wilcox P, Murphy R, Merry TL, Shepherd PR. The population-specific Thr44Met OCT3 coding variant affects metformin pharmacokinetics with subsequent effects on insulin sensitivity in C57Bl/6J mice. Diabetologia 2024:10.1007/s00125-024-06287-1. [PMID: 39422716 DOI: 10.1007/s00125-024-06287-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/30/2024] [Accepted: 08/19/2024] [Indexed: 10/19/2024]
Abstract
AIMS/HYPOTHESIS Metformin is an important first-line treatment for type 2 diabetes and acts by increasing the body's ability to dispose of glucose. Metformin's efficacy can be affected by genetic variants in the transporters that regulate its uptake into cells. The SLC22A3 gene (also known as EMT; EMTH; OCT3) codes for organic cation transporter 3 (OCT3), which is a broad-specificity cation transporter that also transports metformin. Most SLC22A3 variants reduce the rate of metformin transport but the rs8187715 variant (p.Thr44Met) is reported to increase uptake of metformin in vitro. However, the impact of this on in vivo metformin transport and efficacy is unknown. Very few carriers of this variant have been reported globally, but, notably, all were of Pacific Island descent. Therefore, this study aims to understand the prevalence of this variant in Polynesian peoples (Māori and Pacific peoples) and to understand its impact on metformin transport and efficacy in vivo. METHODS rs8187715 was genotyped in 310 individuals with Māori and Pacific ancestry recruited in Aotearoa New Zealand. To study this variant in a physiological context, an orthologous knockin mouse model with C57BL/6J background was used. Pharmacokinetic analysis compared uptake rate of metformin into tissues. Plasma growth/differentiation factor 15 (GDF-15) was also measured as a marker of metformin efficacy. Glucose and insulin tolerance was assessed after acute or sustained metformin treatment in knockin and wild-type control mice to examine the impact of the variant on metformin's glycaemic control. RESULTS The minor allele frequency of this variant in the Māori and Pacific participants was 15.4%. There was no association of the variant with common metabolic parameters including diabetes status, BMI, blood pressure, lipids, or blood glucose and HbA1c. However, in the orthologous knockin mouse model, the rate of metformin uptake into the blood and tissues was increased. Acute metformin dosing increased insulin sensitivity in variant knockin mice but this effect was lost after longer-term metformin treatment. Metformin's effects on GDF-15 levels were also lost in variant knockin mice with longer-term metformin treatment. CONCLUSIONS/INTERPRETATION These data provide evidence that the SLC22A3 rs8187715 variant accelerates metformin uptake rate in vivo. While this acutely improves insulin sensitivity, there was no increased effect of metformin with longer-term dosing. Thus, our finding of a high prevalence of this variant specifically in Māori and Pacific peoples identifies it as a potential population-specific pharmacogenetic marker with potential to guide metformin therapy in these peoples.
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Affiliation(s)
- Qian Wang
- Department of Molecular Medicine and Pathology, University of Auckland, Auckland, New Zealand
- Maurice Wilkins Centre, Auckland, New Zealand
| | | | - Kate Lee
- Department of Molecular Medicine and Pathology, University of Auckland, Auckland, New Zealand
- Maurice Wilkins Centre, Auckland, New Zealand
| | - Jagdish Jaiswal
- Auckland Cancer Society Research Centre, University of Auckland, Auckland, New Zealand
| | - Prasanna Kallingappa
- Department of Molecular Medicine and Pathology, University of Auckland, Auckland, New Zealand
| | - Waruni Dissanayake
- Department of Molecular Medicine and Pathology, University of Auckland, Auckland, New Zealand
- Maurice Wilkins Centre, Auckland, New Zealand
| | - Chris Puli'uvea
- Maurice Wilkins Centre, Auckland, New Zealand
- Department of Biomedicine and Diagnostics, Auckland University of Technology, Auckland, New Zealand
| | | | - Huti Watson
- Paratene Ngata Research Centre, Ngati Porou Oranga, Te Puia Springs, New Zealand
| | - Phillip Wilcox
- Maurice Wilkins Centre, Auckland, New Zealand
- Department of Statistics, University of Otago, Dunedin, New Zealand
| | - Rinki Murphy
- Maurice Wilkins Centre, Auckland, New Zealand
- Department of Medicine, University of Auckland, Auckland, New Zealand
- Auckland Diabetes Centre, Te Whatu Ora Health New Zealand, Te Toka Tumai, New Zealand
| | - Troy L Merry
- Maurice Wilkins Centre, Auckland, New Zealand
- Department of Nutrition, University of Auckland, Auckland, New Zealand
| | - Peter R Shepherd
- Department of Molecular Medicine and Pathology, University of Auckland, Auckland, New Zealand.
- Maurice Wilkins Centre, Auckland, New Zealand.
- Auckland Cancer Society Research Centre, University of Auckland, Auckland, New Zealand.
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Shang Y, Wang S, Wei C, Xie H. Associations of Cognitive Impairment with All-Cause and Cardiovascular Mortality Among Individuals with Diabetes: A Prospective Cohort Study. J Appl Gerontol 2024; 43:1449-1460. [PMID: 38652679 DOI: 10.1177/07334648241241392] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/25/2024] Open
Abstract
This study explored the association between diabetes, cognitive imFpairment (CI), and mortality in a cohort of 2931 individuals aged 60 and above from the 2011 to 2014 NHANES. Mortality data was gathered through 2019, and multivariable Cox proportional hazards models were used to determine the association between diabetes, CI, and mortality adjusting for sociodemographic characteristics, lifestyle factors, and comorbidity conditions. The study spanned up to 9.17 years, observing 579 deaths, with individuals having both diabetes and CI showing the highest all-cause mortality (23.6 events per 100 patient-years). Adjusted analysis revealed a 2.34-fold higher risk of all-cause mortality for this group, surpassing those with diabetes or CI alone. These results held after a series of stratified and sensitivity analyses. In conclusion, CI was linked to higher all-cause mortality in individuals with diabetes, emphasizing the need to address cognitive dysfunction in diabetic patients.
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Affiliation(s)
- Yanchang Shang
- Department of Geriatric Neurology, The Second Medical Center & National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China
| | - Shuhui Wang
- Department of Neurology, Beijing Friendship Hospital, Capital Medical University, Beijing, China
| | - Chao Wei
- Department of Geriatric Neurology, The Second Medical Center & National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China
| | - Hengge Xie
- Department of Geriatric Neurology, The Second Medical Center & National Clinical Research Center for Geriatric Diseases, Chinese PLA General Hospital, Beijing, China
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Lu B, Li P, Crouse AB, Grimes T, Might M, Ovalle F, Shalev A. Data-driven Cluster Analysis Reveals Increased Risk for Severe Insulin-Deficient Diabetes in Black/African Americans. J Clin Endocrinol Metab 2024:dgae516. [PMID: 39078946 DOI: 10.1210/clinem/dgae516] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/16/2024] [Revised: 06/27/2024] [Accepted: 07/23/2024] [Indexed: 10/05/2024]
Abstract
CONTEXT Diabetes is a heterogenic disease and distinct clusters have emerged, but the implications for diverse populations have remained understudied. OBJECTIVE Apply cluster analysis to a diverse diabetes cohort in the U.S. Deep South. DESIGN Retrospective hierarchical cluster analysis of electronic health records from 89,875 patients diagnosed with diabetes between January 1, 2010, and December 31, 2019, at the Kirklin Clinic of the University of Alabama at Birmingham, an ambulatory referral center. PATIENTS Adult patients with ICD diabetes codes were selected based on available data for 6 established clustering parameters (GAD-autoantibody; HbA1c; BMI; Diagnosis age; HOMA2-B; HOMA2-IR); ∼42% were Black/African American. MAIN OUTCOME MEASURE(S) Diabetes subtypes and their associated characteristics in a diverse adult population based on clustering analysis. We hypothesized that racial background would affect the distribution of subtypes. Outcome and hypothesis were formulated prior to data collection. RESULTS Diabetes cluster distribution was significantly different in Black/African Americans compared to Whites (P<0.001). Black/African Americans were more likely to have severe insulin deficient diabetes (SIDD) (OR 1.83, CI 1.36-2.45, P<0.001), associated with more serious metabolic perturbations and a higher risk for complications (OR 1.42, 95% CI 1.06-1.90, P=0.020). Surprisingly, Black/African Americans specifically had more severe impairment of beta cell function (HOMA2-B, C-peptide) (P<0.001), while not being more obese or insulin resistant. CONCLUSIONS Racial background greatly influences diabetes cluster distribution and Black/African Americans are more frequently and more severely affected by SIDD. This may further help explain the disparity in outcomes and have implications for treatment choice.
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Affiliation(s)
- Brian Lu
- Comprehensive Diabetes Center, Department of Medicine, Division of Endocrinology, Diabetes and Metabolism, University of Alabama at Birmingham
| | - Peng Li
- School of Nursing, University of Alabama at Birmingham
| | - Andrew B Crouse
- Hugh Kaul Precision Medicine Institute, University of Alabama at Birmingham, Birmingham, AL 35294, USA
| | - Tiffany Grimes
- Comprehensive Diabetes Center, Department of Medicine, Division of Endocrinology, Diabetes and Metabolism, University of Alabama at Birmingham
| | - Matthew Might
- Hugh Kaul Precision Medicine Institute, University of Alabama at Birmingham, Birmingham, AL 35294, USA
| | - Fernando Ovalle
- Comprehensive Diabetes Center, Department of Medicine, Division of Endocrinology, Diabetes and Metabolism, University of Alabama at Birmingham
| | - Anath Shalev
- Comprehensive Diabetes Center, Department of Medicine, Division of Endocrinology, Diabetes and Metabolism, University of Alabama at Birmingham
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Naylor RN, Patel KA, Kettunen JLT, Männistö JME, Støy J, Beltrand J, Polak M, Vilsbøll T, Greeley SAW, Hattersley AT, Tuomi T. Precision treatment of beta-cell monogenic diabetes: a systematic review. COMMUNICATIONS MEDICINE 2024; 4:145. [PMID: 39025920 PMCID: PMC11258280 DOI: 10.1038/s43856-024-00556-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Accepted: 06/19/2024] [Indexed: 07/20/2024] Open
Abstract
BACKGROUND Beta-cell monogenic forms of diabetes have strong support for precision medicine. We systematically analyzed evidence for precision treatments for GCK-related hyperglycemia, HNF1A-, HNF4A- and HNF1B-diabetes, and mitochondrial diabetes (MD) due to m.3243 A > G variant, 6q24-transient neonatal diabetes mellitus (TND) and SLC19A2-diabetes. METHODS The search of PubMed, MEDLINE, and Embase for individual and group level data for glycemic outcomes using inclusion (English, original articles written after 1992) and exclusion (VUS, multiple diabetes types, absent/aggregated treatment effect measures) criteria. The risk of bias was assessed using NHLBI study-quality assessment tools. Data extracted from Covidence were summarized and presented as descriptive statistics in tables and text. RESULTS There are 146 studies included, with only six being experimental studies. For GCK-related hyperglycemia, the six studies (35 individuals) assessing therapy discontinuation show no HbA1c deterioration. A randomized trial (18 individuals per group) shows that sulfonylureas (SU) were more effective in HNF1A-diabetes than in type 2 diabetes. Cohort and case studies support SU's effectiveness in lowering HbA1c. Two cross-over trials (each with 15-16 individuals) suggest glinides and GLP-1 receptor agonists might be used in place of SU. Evidence for HNF4A-diabetes is limited. Most reported patients with HNF1B-diabetes (N = 293) and MD (N = 233) are on insulin without treatment studies. Limited data support oral agents after relapse in 6q24-TND and for thiamine improving glycemic control and reducing/eliminating insulin requirement in SLC19A2-diabetes. CONCLUSION There is limited evidence, and with moderate or serious risk of bias, to guide monogenic diabetes treatment. Further evidence is needed to examine the optimum treatment in monogenic subtypes.
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Affiliation(s)
- Rochelle N Naylor
- Departments of Pediatrics and Medicine, University of Chicago, Chicago, IL, USA
| | - Kashyap A Patel
- University of Exeter Medical School, Department of Clinical and Biomedical Sciences, Exeter, Devon, UK
| | - Jarno L T Kettunen
- Helsinki University Hospital, Abdominal Centre/Endocrinology, Helsinki, Finland
- Folkhalsan Research Center, Helsinki, Finland
- Institute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki, Finland
| | - Jonna M E Männistö
- Departments of Pediatrics and Clinical Genetics, Kuopio University Hospital, Kuopio, Finland
- Department of Medicine, University of Eastern Finland, Kuopio, Finland
| | - Julie Støy
- Steno diabetes center Aarhus, Aarhus University Hospital, Aarhus, Denmark
| | - Jacques Beltrand
- APHP Centre Hôpital Necker Enfants Malades Université Paris Cité, Paris, France
| | - Michel Polak
- Inserm U1016 Institut Cochin, Paris, France
- Department of Pediatric Endocrinology, Gynecology and Diabetology, Hôpital Universitaire Necker Enfants Malades, Paris, France
- Université Paris Cité, Paris, France
| | - Tina Vilsbøll
- Department of Clinical Medicine, University of Copenhagen, København, Denmark
| | - Siri A W Greeley
- Departments of Pediatrics and Medicine, University of Chicago, Chicago, IL, USA
| | - Andrew T Hattersley
- University of Exeter Medical School, Department of Clinical and Biomedical Sciences, Exeter, Devon, UK
| | - Tiinamaija Tuomi
- Helsinki University Hospital, Abdominal Centre/Endocrinology, Helsinki, Finland.
- Folkhalsan Research Center, Helsinki, Finland.
- Institute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki, Finland.
- Lund University Diabetes Center, Malmo, Sweden.
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Kaylan KB, Philipson LH. Werner Syndrome and Diabetes: Opportunities for Precision Medicine. Diabetes Care 2024; 47:785-786. [PMID: 38640412 DOI: 10.2337/dci24-0005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/21/2024]
Affiliation(s)
- Kerim B Kaylan
- Department of Medicine, The University of Chicago, Chicago, IL
| | - Louis H Philipson
- Department of Medicine, The University of Chicago, Chicago, IL
- Kovler Diabetes Center, The University of Chicago, Chicago, IL
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Felton JL, Redondo MJ, Oram RA, Speake C, Long SA, Onengut-Gumuscu S, Rich SS, Monaco GSF, Harris-Kawano A, Perez D, Saeed Z, Hoag B, Jain R, Evans-Molina C, DiMeglio LA, Ismail HM, Dabelea D, Johnson RK, Urazbayeva M, Wentworth JM, Griffin KJ, Sims EK. Islet autoantibodies as precision diagnostic tools to characterize heterogeneity in type 1 diabetes: a systematic review. COMMUNICATIONS MEDICINE 2024; 4:66. [PMID: 38582818 PMCID: PMC10998887 DOI: 10.1038/s43856-024-00478-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Accepted: 03/05/2024] [Indexed: 04/08/2024] Open
Abstract
BACKGROUND Islet autoantibodies form the foundation for type 1 diabetes (T1D) diagnosis and staging, but heterogeneity exists in T1D development and presentation. We hypothesized that autoantibodies can identify heterogeneity before, at, and after T1D diagnosis, and in response to disease-modifying therapies. METHODS We systematically reviewed PubMed and EMBASE databases (6/14/2022) assessing 10 years of original research examining relationships between autoantibodies and heterogeneity before, at, after diagnosis, and in response to disease-modifying therapies in individuals at-risk or within 1 year of T1D diagnosis. A critical appraisal checklist tool for cohort studies was modified and used for risk of bias assessment. RESULTS Here we show that 152 studies that met extraction criteria most commonly characterized heterogeneity before diagnosis (91/152). Autoantibody type/target was most frequently examined, followed by autoantibody number. Recurring themes included correlations of autoantibody number, type, and titers with progression, differing phenotypes based on order of autoantibody seroconversion, and interactions with age and genetics. Only 44% specifically described autoantibody assay standardization program participation. CONCLUSIONS Current evidence most strongly supports the application of autoantibody features to more precisely define T1D before diagnosis. Our findings support continued use of pre-clinical staging paradigms based on autoantibody number and suggest that additional autoantibody features, particularly in relation to age and genetic risk, could offer more precise stratification. To improve reproducibility and applicability of autoantibody-based precision medicine in T1D, we propose a methods checklist for islet autoantibody-based manuscripts which includes use of precision medicine MeSH terms and participation in autoantibody standardization workshops.
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Affiliation(s)
- Jamie L Felton
- Department of Pediatrics, Center for Diabetes and Metabolic Diseases, Indianapolis, IN, USA
- Herman B Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Maria J Redondo
- Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA
- Division of Pediatric Diabetes and Endocrinology, Texas Children's Hospital, Houston, TX, USA
| | - Richard A Oram
- NIHR Exeter Biomedical Research Centre (BRC), Academic Kidney Unit, University of Exeter, Exeter, UK
- Department of Clinical and Biomedical Sciences, University of Exeter Medical School, Exeter, UK
- Royal Devon University Healthcare NHS Foundation Trust, Exeter, UK
| | - Cate Speake
- Center for Interventional Immunology, Benaroya Research Institute, Seattle, WA, USA
| | - S Alice Long
- Center for Translational Immunology, Benaroya Research Institute, Seattle, WA, USA
| | - Suna Onengut-Gumuscu
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
| | - Stephen S Rich
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
| | - Gabriela S F Monaco
- Department of Pediatrics, Center for Diabetes and Metabolic Diseases, Indianapolis, IN, USA
- Herman B Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Arianna Harris-Kawano
- Department of Pediatrics, Center for Diabetes and Metabolic Diseases, Indianapolis, IN, USA
| | - Dianna Perez
- Department of Pediatrics, Center for Diabetes and Metabolic Diseases, Indianapolis, IN, USA
| | - Zeb Saeed
- Department of Endocrinology, Diabetes and Metabolism, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Benjamin Hoag
- Department of Pediatrics, Sanford School of Medicine, University of South Dakota, Sioux Falls, SD, USA
| | - Rashmi Jain
- Department of Pediatrics, Sanford School of Medicine, University of South Dakota, Sioux Falls, SD, USA
| | - Carmella Evans-Molina
- Department of Pediatrics, Center for Diabetes and Metabolic Diseases, Indianapolis, IN, USA
- Herman B Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Endocrinology, Diabetes and Metabolism, Indiana University School of Medicine, Indianapolis, IN, USA
- Richard L. Roudebush VAMC, Indianapolis, IN, USA
| | - Linda A DiMeglio
- Department of Pediatrics, Center for Diabetes and Metabolic Diseases, Indianapolis, IN, USA
- Herman B Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Heba M Ismail
- Department of Pediatrics, Center for Diabetes and Metabolic Diseases, Indianapolis, IN, USA
- Herman B Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Dana Dabelea
- Lifecourse Epidemiology of Adiposity and Diabetes (LEAD) Center, Aurora, CO, USA
| | - Randi K Johnson
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
- Department of Epidemiology, Colorado School of Public Health, Aurora, CO, USA
| | | | - John M Wentworth
- Royal Melbourne Hospital Department of Diabetes and Endocrinology, Parkville, VIC, Australia
- Walter and Eliza Hall Institute, Parkville, VIC, Australia
- University of Melbourne Department of Medicine, Parkville, VIC, Australia
| | - Kurt J Griffin
- Department of Pediatrics, Sanford School of Medicine, University of South Dakota, Sioux Falls, SD, USA
- Sanford Research, Sioux Falls, SD, USA
| | - Emily K Sims
- Department of Pediatrics, Center for Diabetes and Metabolic Diseases, Indianapolis, IN, USA.
- Herman B Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN, USA.
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Morettini M, Palumbo MC, Bottiglione A, Danieli A, Del Giudice S, Burattini L, Tura A. Glucagon-like peptide-1 and interleukin-6 interaction in response to physical exercise: An in-silico model in the framework of immunometabolism. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2024; 245:108018. [PMID: 38262127 DOI: 10.1016/j.cmpb.2024.108018] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 07/19/2023] [Revised: 12/27/2023] [Accepted: 01/10/2024] [Indexed: 01/25/2024]
Abstract
BACKGROUND AND OBJECTIVE Glucagon-like peptide 1 (GLP-1) is classically identified as an incretin hormone, secreted in response to nutrient ingestion and able to enhance glucose-stimulated insulin secretion. However, other stimuli, such as physical exercise, may enhance GLP-1 plasma levels, and this exercise-induced GLP-1 secretion is mediated by interleukin-6 (IL-6), a cytokine secreted by contracting skeletal muscle. The aim of the study is to propose a mathematical model of IL-6-induced GLP-1 secretion and kinetics in response to physical exercise of moderate intensity. METHODS The model includes the GLP-1 subsystem (with two pools: gut and plasma) and the IL-6 subsystem (again with two pools: skeletal muscle and plasma); it provides a parameter of possible clinical relevance representing the sensitivity of GLP-1 to IL-6 (k0). The model was validated on mean IL-6 and GLP-1 data derived from the scientific literature and on a total of 100 virtual subjects. RESULTS Model validation provided mean residuals between 0.0051 and 0.5493 pg⋅mL-1 for IL-6 (in view of concentration values ranging from 0.8405 to 3.9718 pg⋅mL-1) and between 0.0133 and 4.1540 pmol⋅L-1 for GLP-1 (in view of concentration values ranging from 0.9387 to 17.9714 pmol⋅L-1); a positive significant linear correlation (r = 0.85, p<0.001) was found between k0 and the ratio between areas under GLP-1 and IL-6 curve, over the virtual subjects. CONCLUSIONS The model accurately captures IL-6-induced GLP-1 kinetics in response to physical exercise.
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Affiliation(s)
- Micaela Morettini
- Department of Information Engineering, Università Politecnica delle Marche, via Brecce Bianche 12, Ancona, 60131, Italy.
| | - Maria Concetta Palumbo
- Institute for Applied Computing (IAC) "Mauro Picone", National Research Council of Italy, via dei Taurini 19, Rome, 00185, Italy.
| | - Alessandro Bottiglione
- Department of Information Engineering, Università Politecnica delle Marche, via Brecce Bianche 12, Ancona, 60131, Italy.
| | - Andrea Danieli
- Department of Information Engineering, Università Politecnica delle Marche, via Brecce Bianche 12, Ancona, 60131, Italy.
| | - Simone Del Giudice
- Department of Information Engineering, Università Politecnica delle Marche, via Brecce Bianche 12, Ancona, 60131, Italy.
| | - Laura Burattini
- Department of Information Engineering, Università Politecnica delle Marche, via Brecce Bianche 12, Ancona, 60131, Italy.
| | - Andrea Tura
- CNR Institute of Neuroscience, Corso Stati Uniti 4, Padova, 35127, Italy.
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9
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Bansal V, Winkelmann BR, Dietrich JW, Boehm BO. Whole-exome sequencing in familial type 2 diabetes identifies an atypical missense variant in the RyR2 gene. Front Endocrinol (Lausanne) 2024; 15:1258982. [PMID: 38444585 PMCID: PMC10913019 DOI: 10.3389/fendo.2024.1258982] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/14/2023] [Accepted: 01/10/2024] [Indexed: 03/07/2024] Open
Abstract
Genome-wide association studies have identified several hundred loci associated with type 2 diabetes mellitus (T2DM). Additionally, pathogenic variants in several genes are known to cause monogenic diabetes that overlaps clinically with T2DM. Whole-exome sequencing of related individuals with T2DM is a powerful approach to identify novel high-penetrance disease variants in coding regions of the genome. We performed whole-exome sequencing on four related individuals with T2DM - including one individual diagnosed at the age of 33 years. The individuals were negative for mutations in monogenic diabetes genes, had a strong family history of T2DM, and presented with several characteristics of metabolic syndrome. A missense variant (p.N2291D) in the type 2 ryanodine receptor (RyR2) gene was one of eight rare coding variants shared by all individuals. The variant was absent in large population databases and affects a highly conserved amino acid located in a mutational hotspot for pathogenic variants in Catecholaminergic polymorphic ventricular tachycardia (CPVT). Electrocardiogram data did not reveal any cardiac abnormalities except a lower-than-normal resting heart rate (< 60 bpm) in two individuals - a phenotype observed in CPVT individuals with RyR2 mutations. RyR2-mediated Ca2+ release contributes to glucose-mediated insulin secretion and pathogenic RyR2 mutations cause glucose intolerance in humans and mice. Analysis of glucose tolerance testing data revealed that missense mutations in a CPVT mutation hotspot region - overlapping the p.N2291D variant - are associated with complete penetrance for glucose intolerance. In conclusion, we have identified an atypical missense variant in the RyR2 gene that co-segregates with diabetes in the absence of overt CPVT.
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Affiliation(s)
- Vikas Bansal
- Department of Pediatrics, University of California San Diego, La Jolla, CA, United States
- Institute of Genomic Medicine, University of California San Diego, La Jolla, CA, United States
| | | | - Johannes W Dietrich
- Diabetes, Endocrinology and Metabolism Section, Department of Internal Medicine I, St. Josef Hospital, Ruhr University Hospitals, Bochum, Germany
- Diabetes Center Bochum-Hattingen, St. Elisabeth-Hospital Blankenstein, Hattingen, Germany
- Center for Rare Endocrine Diseases, Ruhr Center for Rare Diseases (CeSER), Ruhr University Bochum and Witten/Herdecke University, Bochum, Germany
- Center for Diabetes Technology, Catholic Hospitals Bochum, Bochum, Germany
| | - Bernhard O Boehm
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore
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Sun Y, Mao Q, Zhou D, Tian J, Du H, Yu Q, Zhao J, Duan W, Liu C, Duan Y, Zhou J, Zhang T, Xia Z, Yin Y, Liu Y, Zhao X, Xu S. Association of multiple blood metals and systemic atherosclerosis: A cross-sectional study in the CAD population. CHEMOSPHERE 2024; 349:140991. [PMID: 38141683 DOI: 10.1016/j.chemosphere.2023.140991] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 06/25/2023] [Revised: 12/03/2023] [Accepted: 12/17/2023] [Indexed: 12/25/2023]
Abstract
BACKGROUND Coronary atherosclerotic disease (CAD) is often accompanied by peripheral atherosclerosis, resulting in a higher risk of ischemia and cardiovascular death. Exposure to metals is associated with atherosclerotic plaques at specific sites. However, less is known about the effects of mixed metals on systemic atherosclerotic burden in CAD patients. OBJECTIVES To investigate the association of metal mixtures with systemic atherosclerotic burden in a CAD population. METHODS A cross-sectional study including 1562 CAD patients from Southwest China was conducted. The levels of 10 blood metals were measured via inductively coupled plasma spectrometry. More than one vessel with a stenosis ≥50% vessel diameter was defined as CAD. Carotid and lower limb atherosclerosis was assessed by using ultrasound, and coronary atherosclerosis was quantified via arterial angiography. Systemic atherosclerosis was scored according to the presence or absence of lesions at the three sites and the total number of lesions. To investigate the combined impacts and interaction effects of metals, Bayesian kernel machine regression was used. Weighted quantile regression was used to identify the contributions of the metals. RESULTS Significant overall associations of mixed metals with systemic atherosclerotic burden were found. These positive overall associations were mainly driven by Cd, Cu and Pb in systemic atherosclerosis. The main contributing factors were As and Cu for coronary atherosclerosis as well as Cd, Cu and Pb for carotid and lower limb atherosclerosis. Cd and Pb or Cr can interact, and Pb interacts with age, sex and alcohol. CONCLUSIONS In CAD patients, exposure to combinations of metals was highly positively associated with systemic atherosclerotic burden. These significant trends were more pronounced in the peripheral arteries and carotid arteries. Controlling environmental metal exposure can contribute to reducing systemic atherosclerosis in CAD patients.
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Affiliation(s)
- Yapei Sun
- Center of Laboratory Medicine, Chongqing Prevention and Treatment Center for Occupational Diseases, Chongqing 400060, China; Chongqing Key Laboratory of Prevention and Treatment for Occupational Diseases and Poisoning, Chongqing 400060, China; School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - Qi Mao
- Department of Cardiology, Institute of Cardiovascular Research, Xinqiao Hospital, Army Medical University, Chongqing 400037, China
| | - Denglu Zhou
- Department of Cardiology, Institute of Cardiovascular Research, Xinqiao Hospital, Army Medical University, Chongqing 400037, China
| | - Jiacheng Tian
- Center of Laboratory Medicine, Chongqing Prevention and Treatment Center for Occupational Diseases, Chongqing 400060, China; Chongqing Key Laboratory of Prevention and Treatment for Occupational Diseases and Poisoning, Chongqing 400060, China; School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - Hang Du
- Center of Laboratory Medicine, Chongqing Prevention and Treatment Center for Occupational Diseases, Chongqing 400060, China; Chongqing Key Laboratory of Prevention and Treatment for Occupational Diseases and Poisoning, Chongqing 400060, China
| | - Qin Yu
- Center of Laboratory Medicine, Chongqing Prevention and Treatment Center for Occupational Diseases, Chongqing 400060, China; Chongqing Key Laboratory of Prevention and Treatment for Occupational Diseases and Poisoning, Chongqing 400060, China
| | - Jianhua Zhao
- Department of Cardiology, Institute of Cardiovascular Research, Xinqiao Hospital, Army Medical University, Chongqing 400037, China
| | - Weixia Duan
- Center of Laboratory Medicine, Chongqing Prevention and Treatment Center for Occupational Diseases, Chongqing 400060, China; Chongqing Key Laboratory of Prevention and Treatment for Occupational Diseases and Poisoning, Chongqing 400060, China
| | - Cong Liu
- Center of Laboratory Medicine, Chongqing Prevention and Treatment Center for Occupational Diseases, Chongqing 400060, China; Chongqing Key Laboratory of Prevention and Treatment for Occupational Diseases and Poisoning, Chongqing 400060, China
| | - Yu Duan
- Center of Laboratory Medicine, Chongqing Prevention and Treatment Center for Occupational Diseases, Chongqing 400060, China; Chongqing Key Laboratory of Prevention and Treatment for Occupational Diseases and Poisoning, Chongqing 400060, China
| | - Jie Zhou
- Center of Laboratory Medicine, Chongqing Prevention and Treatment Center for Occupational Diseases, Chongqing 400060, China; Chongqing Key Laboratory of Prevention and Treatment for Occupational Diseases and Poisoning, Chongqing 400060, China; School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - Tian Zhang
- Center of Laboratory Medicine, Chongqing Prevention and Treatment Center for Occupational Diseases, Chongqing 400060, China; Chongqing Key Laboratory of Prevention and Treatment for Occupational Diseases and Poisoning, Chongqing 400060, China
| | - Zhiqin Xia
- Center of Laboratory Medicine, Chongqing Prevention and Treatment Center for Occupational Diseases, Chongqing 400060, China; Chongqing Key Laboratory of Prevention and Treatment for Occupational Diseases and Poisoning, Chongqing 400060, China; School of Public Health, Nanjing Medical University, Nanjing 211166, China
| | - Yangguang Yin
- Center of Laboratory Medicine, Chongqing Prevention and Treatment Center for Occupational Diseases, Chongqing 400060, China; Chongqing Key Laboratory of Prevention and Treatment for Occupational Diseases and Poisoning, Chongqing 400060, China
| | - Yongsheng Liu
- Center of Laboratory Medicine, Chongqing Prevention and Treatment Center for Occupational Diseases, Chongqing 400060, China; Chongqing Key Laboratory of Prevention and Treatment for Occupational Diseases and Poisoning, Chongqing 400060, China
| | - Xiaohui Zhao
- Department of Cardiology, Institute of Cardiovascular Research, Xinqiao Hospital, Army Medical University, Chongqing 400037, China.
| | - Shangcheng Xu
- Center of Laboratory Medicine, Chongqing Prevention and Treatment Center for Occupational Diseases, Chongqing 400060, China; Chongqing Key Laboratory of Prevention and Treatment for Occupational Diseases and Poisoning, Chongqing 400060, China; School of Public Health, Nanjing Medical University, Nanjing 211166, China.
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11
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Murphy E, Finucane FM. Addressing uncertainty about the role of structured lifestyle modification for metabolic surgery patients. Metabolism 2024; 151:155739. [PMID: 37984732 DOI: 10.1016/j.metabol.2023.155739] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/30/2023] [Revised: 10/27/2023] [Accepted: 11/13/2023] [Indexed: 11/22/2023]
Abstract
There is good evidence that structured lifestyle modification programmes improve health in patients with metabolic and cardiovascular disorders, but there is no specific evidence that they improve outcomes in patients undergoing metabolic or obesity surgery. Despite expert consensus guidelines stating this fact, some healthcare systems still compel patients to participate in a structured lifestyle modification programme prior to metabolic or obesity surgery. There is a well-established need for individualised multidisciplinary dietetic and physical activity care for metabolic and obesity surgery patients, and the benefits of intentional weight loss prior to surgery are well proven, but these are distinct from potentially harmful requirements for patients to undertake compulsory structured lifestyle programmes of fixed duration, frequency and intensity, which may delay surgery and reinforce obesity stigma. A critical step in rejuvenating metabolic surgery is to reframe patient participation in structured lifestyle modification programmes as an opportunity for education and empowerment, not as an indicator of motivation or suitability for metabolic surgery. Large, well-designed and adequately powered clinical trials are needed to address uncertainties in the evidence base for these programmes. Given genuine equipoise, they will need to determine whether "surgery plus lifestyle" is superior to "surgery plus placebo". Moreover, they will need to determine the cost-effectiveness of these programmes and identify some of the factors giving rise to the substantial heterogeneity in responses to structured lifestyle modification.
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Affiliation(s)
- Enda Murphy
- Centre for Diabetes, Endocrinology and Metabolism, Galway University Hospitals, Saolta Health Care Group, Galway, Ireland; HRB Clinical Research Facility, University of Galway and Saolta University Health Care Group, Ireland; Cúram, University of Galway, Ireland.
| | - Francis M Finucane
- Centre for Diabetes, Endocrinology and Metabolism, Galway University Hospitals, Saolta Health Care Group, Galway, Ireland; HRB Clinical Research Facility, University of Galway and Saolta University Health Care Group, Ireland; Cúram, University of Galway, Ireland
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12
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Ahmad A, Lim LL, Morieri ML, Tam CHT, Cheng F, Chikowore T, Dudenhöffer-Pfeifer M, Fitipaldi H, Huang C, Kanbour S, Sarkar S, Koivula RW, Motala AA, Tye SC, Yu G, Zhang Y, Provenzano M, Sherifali D, de Souza RJ, Tobias DK, Gomez MF, Ma RCW, Mathioudakis N. Precision prognostics for cardiovascular disease in Type 2 diabetes: a systematic review and meta-analysis. COMMUNICATIONS MEDICINE 2024; 4:11. [PMID: 38253823 PMCID: PMC10803333 DOI: 10.1038/s43856-023-00429-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/12/2023] [Accepted: 12/14/2023] [Indexed: 01/24/2024] Open
Abstract
BACKGROUND Precision medicine has the potential to improve cardiovascular disease (CVD) risk prediction in individuals with Type 2 diabetes (T2D). METHODS We conducted a systematic review and meta-analysis of longitudinal studies to identify potentially novel prognostic factors that may improve CVD risk prediction in T2D. Out of 9380 studies identified, 416 studies met inclusion criteria. Outcomes were reported for 321 biomarker studies, 48 genetic marker studies, and 47 risk score/model studies. RESULTS Out of all evaluated biomarkers, only 13 showed improvement in prediction performance. Results of pooled meta-analyses, non-pooled analyses, and assessments of improvement in prediction performance and risk of bias, yielded the highest predictive utility for N-terminal pro b-type natriuretic peptide (NT-proBNP) (high-evidence), troponin-T (TnT) (moderate-evidence), triglyceride-glucose (TyG) index (moderate-evidence), Genetic Risk Score for Coronary Heart Disease (GRS-CHD) (moderate-evidence); moderate predictive utility for coronary computed tomography angiography (low-evidence), single-photon emission computed tomography (low-evidence), pulse wave velocity (moderate-evidence); and low predictive utility for C-reactive protein (moderate-evidence), coronary artery calcium score (low-evidence), galectin-3 (low-evidence), troponin-I (low-evidence), carotid plaque (low-evidence), and growth differentiation factor-15 (low-evidence). Risk scores showed modest discrimination, with lower performance in populations different from the original development cohort. CONCLUSIONS Despite high interest in this topic, very few studies conducted rigorous analyses to demonstrate incremental predictive utility beyond established CVD risk factors for T2D. The most promising markers identified were NT-proBNP, TnT, TyG and GRS-CHD, with the highest strength of evidence for NT-proBNP. Further research is needed to determine their clinical utility in risk stratification and management of CVD in T2D.
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Affiliation(s)
- Abrar Ahmad
- Department of Clinical Sciences, Lund University Diabetes Centre, Lund University, Malmö, Sweden
| | - Lee-Ling Lim
- Department of Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong SAR, China
- Asia Diabetes Foundation, Hong Kong SAR, China
| | - Mario Luca Morieri
- Metabolic Disease Unit, University Hospital of Padova, Padova, Italy
- Department of Medicine, University of Padova, Padova, Italy
| | - Claudia Ha-Ting Tam
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong SAR, China
- Laboratory for Molecular Epidemiology in Diabetes, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong SAR, China
- Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Feifei Cheng
- Health Management Center, The Second Affiliated Hospital of Chongqing Medical University, Chongqing Medical University, Chongqing, China
| | - Tinashe Chikowore
- MRC/Wits Developmental Pathways for Health Research Unit, Department of Paediatrics, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | | | - Hugo Fitipaldi
- Department of Clinical Sciences, Lund University Diabetes Centre, Lund University, Malmö, Sweden
| | - Chuiguo Huang
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong SAR, China
- Laboratory for Molecular Epidemiology in Diabetes, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong SAR, China
- Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong SAR, China
| | | | - Sudipa Sarkar
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Robert Wilhelm Koivula
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, United Kingdom
| | - Ayesha A Motala
- Department of Diabetes and Endocrinology, Nelson R Mandela School of Medicine, University of KwaZulu-Natal, Durban, South Africa
| | - Sok Cin Tye
- Department of Clinical Pharmacy and Pharmacology, University Medical Center Groningen, Groningen, the Netherlands
- Sections on Genetics and Epidemiology, Joslin Diabetes Center, Harvard Medical School, Boston, Massachusetts, USA
| | - Gechang Yu
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong SAR, China
- Laboratory for Molecular Epidemiology in Diabetes, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong SAR, China
- Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Yingchai Zhang
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong SAR, China
- Laboratory for Molecular Epidemiology in Diabetes, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong SAR, China
- Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Michele Provenzano
- Nephrology, Dialysis and Renal Transplant Unit, IRCCS-Azienda Ospedaliero-Universitaria di Bologna, Alma Mater Studiorum University of Bologna, Bologna, Italy
| | - Diana Sherifali
- Heather M. Arthur Population Health Research Institute, McMaster University, Ontario, Canada
| | - Russell J de Souza
- Department of Health Research Methods, Evidence, and Impact, Faculty of Health Sciences, McMaster University, Hamilton, Ontario, Canada
- Population Health Research Institute, Hamilton Health Sciences Corporation, Hamilton, Ontario, Canada
| | | | - Maria F Gomez
- Department of Clinical Sciences, Lund University Diabetes Centre, Lund University, Malmö, Sweden.
- Faculty of Health, Aarhus University, Aarhus, Denmark.
| | - Ronald C W Ma
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong SAR, China.
- Laboratory for Molecular Epidemiology in Diabetes, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong SAR, China.
- Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong SAR, China.
| | - Nestoras Mathioudakis
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
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13
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Semnani-Azad Z, Gaillard R, Hughes AE, Boyle KE, Tobias DK, Perng W. Precision stratification of prognostic risk factors associated with outcomes in gestational diabetes mellitus: a systematic review. COMMUNICATIONS MEDICINE 2024; 4:9. [PMID: 38216688 PMCID: PMC10786838 DOI: 10.1038/s43856-023-00427-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Accepted: 12/12/2023] [Indexed: 01/14/2024] Open
Abstract
BACKGROUND The objective of this systematic review is to identify prognostic factors among women and their offspring affected by gestational diabetes mellitus (GDM), focusing on endpoints of cardiovascular disease (CVD) and type 2 diabetes (T2D) for women, and cardiometabolic profile for offspring. METHODS This review included studies published in English language from January 1st, 1990, through September 30th, 2021, that focused on the above outcomes of interest with respect to sociodemographic factors, lifestyle and behavioral characteristics, traditional clinical traits, and 'omics biomarkers in the mothers and offspring during the perinatal/postpartum periods and across the lifecourse. Studies that did not report associations of prognostic factors with outcomes of interest among GDM-exposed women or children were excluded. RESULTS Here, we identified 109 publications comprising 98 observational studies and 11 randomized-controlled trials. Findings indicate that GDM severity, maternal obesity, race/ethnicity, and unhealthy diet and physical activity levels predict T2D and CVD in women, and greater cardiometabolic risk in offspring. However, using the Diabetes Canada 2018 Clinical Practice Guidelines for studies, the level of evidence was low due to potential for confounding, reverse causation, and selection biases. CONCLUSIONS GDM pregnancies with greater severity, as well as those accompanied by maternal obesity, unhealthy diet, and low physical activity, as well as cases that occur among women who identify as racial/ethnic minorities are associated with worse cardiometabolic prognosis in mothers and offspring. However, given the low quality of evidence, prospective studies with detailed covariate data collection and high fidelity of follow-up are warranted.
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Affiliation(s)
- Zhila Semnani-Azad
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
| | - Romy Gaillard
- Department of Pediatrics, Erasmus MC, University Medical Center, Rotterdam, the Netherlands
| | - Alice E Hughes
- Faculty of Health and Life Sciences, University of Exeter Medical School, Exeter, UK
| | - Kristen E Boyle
- Department of Pediatrics and the Lifecourse Epidemiology of Adiposity and Diabetes (LEAD) Center, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Deirdre K Tobias
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Wei Perng
- Department of Epidemiology and the Lifecourse Epidemiology of Adiposity and Diabetes (LEAD) Center, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
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14
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Francis EC, Powe CE, Lowe WL, White SL, Scholtens DM, Yang J, Zhu Y, Zhang C, Hivert MF, Kwak SH, Sweeting A. Refining the diagnosis of gestational diabetes mellitus: a systematic review and meta-analysis. COMMUNICATIONS MEDICINE 2023; 3:185. [PMID: 38110524 PMCID: PMC10728189 DOI: 10.1038/s43856-023-00393-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Accepted: 10/25/2023] [Indexed: 12/20/2023] Open
Abstract
BACKGROUND Perinatal outcomes vary for women with gestational diabetes mellitus (GDM). The precise factors beyond glycemic status that may refine GDM diagnosis remain unclear. We conducted a systematic review and meta-analysis of potential precision markers for GDM. METHODS Systematic literature searches were performed in PubMed and EMBASE from inception to March 2022 for studies comparing perinatal outcomes among women with GDM. We searched for precision markers in the following categories: maternal anthropometrics, clinical/sociocultural factors, non-glycemic biochemical markers, genetics/genomics or other -omics, and fetal biometry. We conducted post-hoc meta-analyses of a subset of studies with data on the association of maternal body mass index (BMI, kg/m2) with offspring macrosomia or large-for-gestational age (LGA). RESULTS A total of 5905 titles/abstracts were screened, 775 full-texts reviewed, and 137 studies synthesized. Maternal anthropometrics were the most frequent risk marker. Meta-analysis demonstrated that women with GDM and overweight/obesity vs. GDM with normal range BMI are at higher risk of offspring macrosomia (13 studies [n = 28,763]; odds ratio [OR] 2.65; 95% Confidence Interval [CI] 1.91, 3.68), and LGA (10 studies [n = 20,070]; OR 2.23; 95% CI 2.00, 2.49). Lipids and insulin resistance/secretion indices were the most studied non-glycemic biochemical markers, with increased triglycerides and insulin resistance generally associated with greater risk of offspring macrosomia or LGA. Studies evaluating other markers had inconsistent findings as to whether they could be used as precision markers. CONCLUSIONS Maternal overweight/obesity is associated with greater risk of offspring macrosomia or LGA in women with GDM. Pregnancy insulin resistance or hypertriglyceridemia may be useful in GDM risk stratification. Future studies examining non-glycemic biochemical, genetic, other -omic, or sociocultural precision markers among women with GDM are warranted.
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Affiliation(s)
- Ellen C Francis
- Department of Biostatistics and Epidemiology, Rutgers School of Public Health, Piscataway, NJ, USA.
| | - Camille E Powe
- Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
| | - William L Lowe
- Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Sara L White
- Department of Women and Children's Health, King's College London, London, UK
| | - Denise M Scholtens
- Department of Preventive Medicine, Division of Biostatistics, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Jiaxi Yang
- Global Center for Asian Women's Health (GloW), Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Department of Obstetrics and Gynecology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Bia-Echo Asia Centre for Reproductive Longevity & Equality (ACRLE), Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Yeyi Zhu
- Kaiser Permanente Northern California Division of Research, Oakland, CA, USA
| | - Cuilin Zhang
- Global Center for Asian Women's Health (GloW), Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Department of Obstetrics and Gynecology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Bia-Echo Asia Centre for Reproductive Longevity & Equality (ACRLE), Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Marie-France Hivert
- Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
- Department of Population Medicine, Harvard Medical School, Harvard Pilgrim Health Care Institute, Boston, MA, USA
| | - Soo Heon Kwak
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul National University Hospital, Seoul, Republic of Korea
| | - Arianne Sweeting
- Faculty of Medicine and Health, University of Sydney, Sydney, NSW, Australia
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15
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Finucane FM, Gibson I, Hughes R, Murphy E, Hynes L, Harris A, McGuire BE, Hynes M, Collins C, Cradock K, Seery S, Jones J, O’Brien T, O’Donnell MJ. Factors associated with weight loss and health gains in a structured lifestyle modification programme for adults with severe obesity: a prospective cohort study. Front Endocrinol (Lausanne) 2023; 14:1257061. [PMID: 37916153 PMCID: PMC10616877 DOI: 10.3389/fendo.2023.1257061] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/11/2023] [Accepted: 09/27/2023] [Indexed: 11/03/2023] Open
Abstract
Background Individual responses to behavioural weight loss interventions can vary significantly, and a better understanding of the factors associated with successful treatment might help to target interventions for those who will benefit the most. We sought to identify demographic and clinical characteristics that predicted intervention "success" (defined as ≥5% weight loss) and other health gains in patients with severe obesity attending a ten-week structured lifestyle modification programme. Methods We conducted a prospective cohort study of all 1122 patients (751 (66.9%) female, mean age 47.3 ± 11.9 years, mean body mass index (BMI) 46.7 ± 7.8 kgm-2) referred from our hospital-based obesity clinic, who started the structured lifestyle programme between 2012-2019. We compared routine clinical measures such as weight, fitness, blood pressure, lipids and HbA1c at baseline and follow-up. We also used validated questionnaires to quantify anxiety, depression and health-related quality of life. Results Of 1122 patients who started, 877 (78.2%) completed the programme and attended for follow up. Of these, 12.8% lost ≥5% body weight. The amount of weight lost was a strong and consistent predictor of improvements in metabolic, cardiovascular, and mental health, even after adjusting for age, sex, programme attendance and baseline fitness. Older age, male sex, being physically active and having lower anxiety and depression scores at baseline predicted greater weight loss. Younger age, depression and longer wait time to start the intervention were associated with drop-out. Conclusions In adults with severe obesity completing a structured lifestyle modification programme, older age and good mental health were associated with programme completion and attaining ≥5% weight loss. The magnitude of weight lost was a strong predictor of improvements in cardiovascular, metabolic and mental health associated with programme completion.
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Affiliation(s)
- Francis M. Finucane
- Bariatric Medicine Service, Centre for Diabetes, Endocrinology and Metabolism, Galway University Hospitals, Galway, Ireland
- Department of Medicine, College of Medicine, Nursing and Health Sciences, University of Galway, Galway, Ireland
- Cúram, University of Galway, Galway, Ireland
| | - Irene Gibson
- Department of Medicine, College of Medicine, Nursing and Health Sciences, University of Galway, Galway, Ireland
- Croí, The West of Ireland Cardiac Foundation, Galway, Ireland
- National Institute of Preventive Cardiology, University of Galway, Galway, Ireland
| | - Robert Hughes
- Bariatric Medicine Service, Centre for Diabetes, Endocrinology and Metabolism, Galway University Hospitals, Galway, Ireland
- Department of Medicine, College of Medicine, Nursing and Health Sciences, University of Galway, Galway, Ireland
| | - Enda Murphy
- Department of Medicine, College of Medicine, Nursing and Health Sciences, University of Galway, Galway, Ireland
- Cúram, University of Galway, Galway, Ireland
| | - Lisa Hynes
- Croí, The West of Ireland Cardiac Foundation, Galway, Ireland
- National Institute of Preventive Cardiology, University of Galway, Galway, Ireland
| | - Aisling Harris
- Croí, The West of Ireland Cardiac Foundation, Galway, Ireland
- National Institute of Preventive Cardiology, University of Galway, Galway, Ireland
| | - Brian E. McGuire
- Bariatric Medicine Service, Centre for Diabetes, Endocrinology and Metabolism, Galway University Hospitals, Galway, Ireland
- School of Psychology, University of Galway, Galway, Ireland
| | - Mary Hynes
- Bariatric Medicine Service, Centre for Diabetes, Endocrinology and Metabolism, Galway University Hospitals, Galway, Ireland
- School of Psychology, University of Galway, Galway, Ireland
| | - Chris Collins
- Department of Upper Gastrointestinal Surgery, Galway University Hospital, Galway, Ireland
| | - Kevin Cradock
- Department of Health and Nutrition Sciences, Atlantic Technological University, Sligo, Ireland
| | - Suzanne Seery
- Croí, The West of Ireland Cardiac Foundation, Galway, Ireland
- National Institute of Preventive Cardiology, University of Galway, Galway, Ireland
| | - Jennifer Jones
- Department of Medicine, College of Medicine, Nursing and Health Sciences, University of Galway, Galway, Ireland
- Croí, The West of Ireland Cardiac Foundation, Galway, Ireland
- National Institute of Preventive Cardiology, University of Galway, Galway, Ireland
| | - Tim O’Brien
- Bariatric Medicine Service, Centre for Diabetes, Endocrinology and Metabolism, Galway University Hospitals, Galway, Ireland
- Department of Medicine, College of Medicine, Nursing and Health Sciences, University of Galway, Galway, Ireland
- Cúram, University of Galway, Galway, Ireland
| | - Martin J. O’Donnell
- Department of Medicine, College of Medicine, Nursing and Health Sciences, University of Galway, Galway, Ireland
- Cúram, University of Galway, Galway, Ireland
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Smith K, Deutsch AJ, McGrail C, Kim H, Hsu S, Mandla R, Schroeder PH, Westerman KE, Szczerbinski L, Majarian TD, Kaur V, Williamson A, Claussnitzer M, Florez JC, Manning AK, Mercader JM, Gaulton KJ, Udler MS. Multi-ancestry Polygenic Mechanisms of Type 2 Diabetes Elucidate Disease Processes and Clinical Heterogeneity. RESEARCH SQUARE 2023:rs.3.rs-3399145. [PMID: 37886436 PMCID: PMC10602111 DOI: 10.21203/rs.3.rs-3399145/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/28/2023]
Abstract
We identified genetic subtypes of type 2 diabetes (T2D) by analyzing genetic data from diverse groups, including non-European populations. We implemented soft clustering with 650 T2D-associated genetic variants, capturing known and novel T2D subtypes with distinct cardiometabolic trait associations. The twelve genetic clusters were distinctively enriched for single-cell regulatory regions. Polygenic scores derived from the clusters differed in distribution between ancestry groups, including a significantly higher proportion of lipodystrophy-related polygenic risk in East Asian ancestry. T2D risk was equivalent at a BMI of 30 kg/m2 in the European subpopulation and 24.2 (22.9-25.5) kg/m2 in the East Asian subpopulation; after adjusting for cluster-specific genetic risk, the equivalent BMI threshold increased to 28.5 (27.1-30.0) kg/m2 in the East Asian group, explaining about 75% of the difference in BMI thresholds. Thus, these multi-ancestry T2D genetic subtypes encompass a broader range of biological mechanisms and help explain ancestry-associated differences in T2D risk profiles.
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Affiliation(s)
- Kirk Smith
- Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Aaron J. Deutsch
- Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Carolyn McGrail
- Department of Pediatrics, University of California San Diego, San Diego, CA, USA
| | - Hyunkyung Kim
- Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Committee on Genetics, Genomics and Systems Biology, University of Chicago, Chicago, IL, USA
- Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL, USA
| | - Sarah Hsu
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Ravi Mandla
- Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Philip H. Schroeder
- Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Kenneth E. Westerman
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Lukasz Szczerbinski
- Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Endocrinology, Diabetology and Internal Medicine, Medical University of Bialystok, Bialystok, Poland
- Clinical Research Centre, Medical University of Bialystok, Bialystok, Poland
| | - Timothy D. Majarian
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Varinderpal Kaur
- Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Alice Williamson
- Precision Healthcare University Research Institute, Queen Mary University of London, London, UK
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Melina Claussnitzer
- Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- The Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Jose C. Florez
- Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Alisa K. Manning
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Josep M. Mercader
- Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Kyle J. Gaulton
- Department of Pediatrics, University of California San Diego, San Diego, CA, USA
| | - Miriam S. Udler
- Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
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Misra S, Wagner R, Ozkan B, Schön M, Sevilla-Gonzalez M, Prystupa K, Wang CC, Kreienkamp RJ, Cromer SJ, Rooney MR, Duan D, Thuesen ACB, Wallace AS, Leong A, Deutsch AJ, Andersen MK, Billings LK, Eckel RH, Sheu WHH, Hansen T, Stefan N, Goodarzi MO, Ray D, Selvin E, Florez JC, Meigs JB, Udler MS. Precision subclassification of type 2 diabetes: a systematic review. COMMUNICATIONS MEDICINE 2023; 3:138. [PMID: 37798471 PMCID: PMC10556101 DOI: 10.1038/s43856-023-00360-3] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Accepted: 09/15/2023] [Indexed: 10/07/2023] Open
Abstract
BACKGROUND Heterogeneity in type 2 diabetes presentation and progression suggests that precision medicine interventions could improve clinical outcomes. We undertook a systematic review to determine whether strategies to subclassify type 2 diabetes were associated with high quality evidence, reproducible results and improved outcomes for patients. METHODS We searched PubMed and Embase for publications that used 'simple subclassification' approaches using simple categorisation of clinical characteristics, or 'complex subclassification' approaches which used machine learning or 'omics approaches in people with established type 2 diabetes. We excluded other diabetes subtypes and those predicting incident type 2 diabetes. We assessed quality, reproducibility and clinical relevance of extracted full-text articles and qualitatively synthesised a summary of subclassification approaches. RESULTS Here we show data from 51 studies that demonstrate many simple stratification approaches, but none have been replicated and many are not associated with meaningful clinical outcomes. Complex stratification was reviewed in 62 studies and produced reproducible subtypes of type 2 diabetes that are associated with outcomes. Both approaches require a higher grade of evidence but support the premise that type 2 diabetes can be subclassified into clinically meaningful subtypes. CONCLUSION Critical next steps toward clinical implementation are to test whether subtypes exist in more diverse ancestries and whether tailoring interventions to subtypes will improve outcomes.
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Affiliation(s)
- Shivani Misra
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK.
- Department of Diabetes and Endocrinology, Imperial College Healthcare NHS Trust, London, UK.
| | - Robert Wagner
- Department of Endocrinology and Diabetology, University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Moorenstr. 5, 40225, Düsseldorf, Germany
- Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Auf'm Hennekamp 65, 40225, Düsseldorf, Germany
- German Center for Diabetes Research (DZD), Ingolstädter Landstraße 1, 85764, Neuherberg, Germany
| | - Bige Ozkan
- Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Ciccarone Center for the Prevention of Cardiovascular Disease, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Martin Schön
- Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Auf'm Hennekamp 65, 40225, Düsseldorf, Germany
- German Center for Diabetes Research (DZD), Ingolstädter Landstraße 1, 85764, Neuherberg, Germany
- Institute of Experimental Endocrinology, Biomedical Research Center, Slovak Academy of Sciences, Bratislava, Slovakia
| | - Magdalena Sevilla-Gonzalez
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA, USA
- Programs in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Katsiaryna Prystupa
- Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Auf'm Hennekamp 65, 40225, Düsseldorf, Germany
- German Center for Diabetes Research (DZD), Ingolstädter Landstraße 1, 85764, Neuherberg, Germany
| | - Caroline C Wang
- Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Raymond J Kreienkamp
- Programs in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Diabetes Unit, Division of Endocrinology, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Pediatrics, Division of Endocrinology, Boston Children's Hospital, Boston, MA, USA
| | - Sara J Cromer
- Programs in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Diabetes Unit, Division of Endocrinology, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Mary R Rooney
- Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Daisy Duan
- Division of Endocrinology, Diabetes and Metabolism, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Anne Cathrine Baun Thuesen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Amelia S Wallace
- Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Aaron Leong
- Programs in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Diabetes Unit, Division of Endocrinology, Massachusetts General Hospital, Boston, MA, USA
- Division of General Internal Medicine, Massachusetts General Hospital, 100 Cambridge St 16th Floor, Boston, MA, USA
| | - Aaron J Deutsch
- Programs in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Diabetes Unit, Division of Endocrinology, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Mette K Andersen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Liana K Billings
- Division of Endocrinology, Diabetes and Metabolism, NorthShore University Health System, Skokie, IL, USA
- Department of Medicine, Pritzker School of Medicine, University of Chicago, Chicago, IL, USA
| | - Robert H Eckel
- Division of Endocrinology, Metabolism and Diabetes, University of Colorado School of Medicine, Aurora, CO, USA
| | - Wayne Huey-Herng Sheu
- Institute of Molecular and Genomic Medicine, National Health Research Institute, Miaoli County, Taiwan, ROC
- Division of Endocrinology and Metabolism, Taichung Veterans General Hospital, Taichung, Taiwan, ROC
- Division of Endocrinology and Metabolism, Taipei Veterans General Hospital, Taipei, Taiwan, ROC
| | - Torben Hansen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Norbert Stefan
- German Center for Diabetes Research (DZD), Ingolstädter Landstraße 1, 85764, Neuherberg, Germany
- University Hospital of Tübingen, Tübingen, Germany
- Institute of Diabetes Research and Metabolic Diseases (IDM), Helmholtz Center Munich, Neuherberg, Germany
| | - Mark O Goodarzi
- Division of Endocrinology, Diabetes and Metabolism, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Debashree Ray
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Elizabeth Selvin
- Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Jose C Florez
- Programs in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Diabetes Unit, Division of Endocrinology, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - James B Meigs
- Programs in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Division of General Internal Medicine, Massachusetts General Hospital, 100 Cambridge St 16th Floor, Boston, MA, USA
| | - Miriam S Udler
- Programs in Metabolism and Medical & Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Diabetes Unit, Division of Endocrinology, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
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18
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Semple RK, Patel KA, Auh S, Brown RJ. Genotype-stratified treatment for monogenic insulin resistance: a systematic review. COMMUNICATIONS MEDICINE 2023; 3:134. [PMID: 37794082 PMCID: PMC10550936 DOI: 10.1038/s43856-023-00368-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2023] [Accepted: 09/20/2023] [Indexed: 10/06/2023] Open
Abstract
BACKGROUND Monogenic insulin resistance (IR) includes lipodystrophy and disorders of insulin signalling. We sought to assess the effects of interventions in monogenic IR, stratified by genetic aetiology. METHODS Systematic review using PubMed, MEDLINE and Embase (1 January 1987 to 23 June 2021). Studies reporting individual-level effects of pharmacologic and/or surgical interventions in monogenic IR were eligible. Individual data were extracted and duplicates were removed. Outcomes were analysed for each gene and intervention, and in aggregate for partial, generalised and all lipodystrophy. RESULTS 10 non-randomised experimental studies, 8 case series, and 23 case reports meet inclusion criteria, all rated as having moderate or serious risk of bias. Metreleptin use is associated with the lowering of triglycerides and haemoglobin A1c (HbA1c) in all lipodystrophy (n = 111), partial (n = 71) and generalised lipodystrophy (n = 41), and in LMNA, PPARG, AGPAT2 or BSCL2 subgroups (n = 72,13,21 and 21 respectively). Body Mass Index (BMI) is lowered in partial and generalised lipodystrophy, and in LMNA or BSCL2, but not PPARG or AGPAT2 subgroups. Thiazolidinediones are associated with improved HbA1c and triglycerides in all lipodystrophy (n = 13), improved HbA1c in PPARG (n = 5), and improved triglycerides in LMNA (n = 7). In INSR-related IR, rhIGF-1, alone or with IGFBP3, is associated with improved HbA1c (n = 17). The small size or absence of other genotype-treatment combinations preclude firm conclusions. CONCLUSIONS The evidence guiding genotype-specific treatment of monogenic IR is of low to very low quality. Metreleptin and Thiazolidinediones appear to improve metabolic markers in lipodystrophy, and rhIGF-1 appears to lower HbA1c in INSR-related IR. For other interventions, there is insufficient evidence to assess efficacy and risks in aggregated lipodystrophy or genetic subgroups.
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Affiliation(s)
- Robert K Semple
- Centre for Cardiovascular Science, Queen's Medical Research Institute, University of Edinburgh, Edinburgh, UK
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Kashyap A Patel
- Department of Clinical and Biomedical Sciences, University of Exeter Medical School, Exeter, UK
- Department of Diabetes and Endocrinology, Royal Devon and Exeter NHS Foundation Trust, Exeter, UK
| | - Sungyoung Auh
- National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Rebecca J Brown
- National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, USA.
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Murphy R, Colclough K, Pollin TI, Ikle JM, Svalastoga P, Maloney KA, Saint-Martin C, Molnes J, Misra S, Aukrust I, de Franco E, Flanagan SE, Njølstad PR, Billings LK, Owen KR, Gloyn AL. The use of precision diagnostics for monogenic diabetes: a systematic review and expert opinion. COMMUNICATIONS MEDICINE 2023; 3:136. [PMID: 37794142 PMCID: PMC10550998 DOI: 10.1038/s43856-023-00369-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Accepted: 09/21/2023] [Indexed: 10/06/2023] Open
Abstract
BACKGROUND Monogenic diabetes presents opportunities for precision medicine but is underdiagnosed. This review systematically assessed the evidence for (1) clinical criteria and (2) methods for genetic testing for monogenic diabetes, summarized resources for (3) considering a gene or (4) variant as causal for monogenic diabetes, provided expert recommendations for (5) reporting of results; and reviewed (6) next steps after monogenic diabetes diagnosis and (7) challenges in precision medicine field. METHODS Pubmed and Embase databases were searched (1990-2022) using inclusion/exclusion criteria for studies that sequenced one or more monogenic diabetes genes in at least 100 probands (Question 1), evaluated a non-obsolete genetic testing method to diagnose monogenic diabetes (Question 2). The risk of bias was assessed using the revised QUADAS-2 tool. Existing guidelines were summarized for questions 3-5, and review of studies for questions 6-7, supplemented by expert recommendations. Results were summarized in tables and informed recommendations for clinical practice. RESULTS There are 100, 32, 36, and 14 studies included for questions 1, 2, 6, and 7 respectively. On this basis, four recommendations for who to test and five on how to test for monogenic diabetes are provided. Existing guidelines for variant curation and gene-disease validity curation are summarized. Reporting by gene names is recommended as an alternative to the term MODY. Key steps after making a genetic diagnosis and major gaps in our current knowledge are highlighted. CONCLUSIONS We provide a synthesis of current evidence and expert opinion on how to use precision diagnostics to identify individuals with monogenic diabetes.
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Affiliation(s)
- Rinki Murphy
- Department of Medicine, Faculty of Medical and Health Sciences, University of Auckland, Auckland, New Zealand.
- Auckland Diabetes Centre, Te Whatu Ora Health New Zealand, Te Tokai Tumai, Auckland, New Zealand.
| | - Kevin Colclough
- Exeter Genomics Laboratory, Royal Devon University Healthcare NHS Foundation Trust, Exeter, United Kingdom
| | - Toni I Pollin
- Department of Medicine, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Jennifer M Ikle
- Department of Pediatrics, Division of Endocrinology & Diabetes, Stanford School of Medicine, Stanford, CA, USA
- Stanford Diabetes Research Center, Stanford School of Medicine, Stanford, CA, USA
| | - Pernille Svalastoga
- Children and Youth Clinic, Haukeland University Hospital, Bergen, Norway
- Mohn Center for Diabetes Precision Medicine, Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Kristin A Maloney
- Department of Medicine, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Cécile Saint-Martin
- Department of Medical Genetics, AP-HP Pitié-Salpêtrière Hospital, Sorbonne University, Paris, France
| | - Janne Molnes
- Mohn Center for Diabetes Precision Medicine, Department of Clinical Science, University of Bergen, Bergen, Norway
- Department of Medical Genetics, Haukeland University Hospital, Bergen, Norway
| | - Shivani Misra
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
- Department of Diabetes and Endocrinology, Imperial College Healthcare NHS Trust, London, UK
| | - Ingvild Aukrust
- Mohn Center for Diabetes Precision Medicine, Department of Clinical Science, University of Bergen, Bergen, Norway
- Department of Medical Genetics, Haukeland University Hospital, Bergen, Norway
| | - Elisa de Franco
- Department of Clinical and Biomedical Science, Faculty of Health and Life Sciences, University of Exeter, Exeter, UK
| | - Sarah E Flanagan
- Department of Clinical and Biomedical Science, Faculty of Health and Life Sciences, University of Exeter, Exeter, UK
| | - Pål R Njølstad
- Children and Youth Clinic, Haukeland University Hospital, Bergen, Norway
- Mohn Center for Diabetes Precision Medicine, Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Liana K Billings
- Division of Endocrinology, NorthShore University HealthSystem, Skokie, IL, USA
- Department of Medicine, Pritzker School of Medicine, University of Chicago, Chicago, IL, USA
| | - Katharine R Owen
- Oxford Center for Diabetes, Endocrinology & Metabolism, University of Oxford, Oxford, UK
- NIHR Oxford Biomedical Research Centre, Oxford, UK
| | - Anna L Gloyn
- Department of Pediatrics, Division of Endocrinology & Diabetes, Stanford School of Medicine, Stanford, CA, USA.
- Stanford Diabetes Research Center, Stanford School of Medicine, Stanford, CA, USA.
- Department of Genetics, Stanford School of Medicine, Stanford, CA, USA.
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20
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Lim S, Takele WW, Vesco KK, Redman LM, Hannah W, Bonham MP, Chen M, Chivers SC, Fawcett AJ, Grieger JA, Habibi N, Leung GKW, Liu K, Mekonnen EG, Pathirana M, Quinteros A, Taylor R, Ukke GG, Zhou SJ, Josefson J. Participant characteristics in the prevention of gestational diabetes as evidence for precision medicine: a systematic review and meta-analysis. COMMUNICATIONS MEDICINE 2023; 3:137. [PMID: 37794119 PMCID: PMC10551015 DOI: 10.1038/s43856-023-00366-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2023] [Accepted: 09/20/2023] [Indexed: 10/06/2023] Open
Abstract
BACKGROUND Precision prevention involves using the unique characteristics of a particular group to determine their responses to preventive interventions. This study aimed to systematically evaluate the participant characteristics associated with responses to interventions in gestational diabetes mellitus (GDM) prevention. METHODS We searched MEDLINE, EMBASE, and Pubmed to identify lifestyle (diet, physical activity, or both), metformin, myoinositol/inositol and probiotics interventions of GDM prevention published up to May 24, 2022. RESULTS From 10347 studies, 116 studies (n = 40940 women) are included. Physical activity results in greater GDM reduction in participants with a normal body mass index (BMI) at baseline compared to obese BMI (risk ratio, 95% confidence interval: 0.06 [0.03, 0.14] vs 0.68 [0.26, 1.60]). Combined diet and physical activity interventions result in greater GDM reduction in participants without polycystic ovary syndrome (PCOS) than those with PCOS (0.62 [0.47, 0.82] vs 1.12 [0.78-1.61]) and in those without a history of GDM than those with unspecified GDM history (0.62 [0.47, 0.81] vs 0.85 [0.76, 0.95]). Metformin interventions are more effective in participants with PCOS than those with unspecified status (0.38 [0.19, 0.74] vs 0.59 [0.25, 1.43]), or when commenced preconception than during pregnancy (0.21 [0.11, 0.40] vs 1.15 [0.86-1.55]). Parity, history of having a large-for-gestational-age infant or family history of diabetes have no effect on intervention responses. CONCLUSIONS GDM prevention through metformin or lifestyle differs according to some individual characteristics. Future research should include trials commencing preconception and provide results disaggregated by a priori defined participant characteristics including social and environmental factors, clinical traits, and other novel risk factors to predict GDM prevention through interventions.
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Affiliation(s)
- Siew Lim
- Eastern Health Clinical School, Monash University, Melbourne, Victoria, Australia.
| | - Wubet Worku Takele
- Eastern Health Clinical School, Monash University, Melbourne, Victoria, Australia
| | - Kimberly K Vesco
- Kaiser Permanente Northwest, Kaiser Permanente Center for Health Research, Oakland, USA
| | | | - Wesley Hannah
- Madras Diabetes Research Foundation Chennai, Chennai, India
- Deakin University, Melbourne, Australia
| | - Maxine P Bonham
- Department of Nutrition and Dietetics, Monash University, Melbourne, Victoria, Australia
| | - Mingling Chen
- Monash Centre for Health Research and Implementation, Monash University, Clayton, VIC, Australia
| | - Sian C Chivers
- Department of Women and Children's Health, King's College London, London, United Kingdom
| | - Andrea J Fawcett
- Ann & Robert H. Lurie Children's Hospital of Chicago, Chicago, IL, USA
- Department of Clinical & Organizational Development, University of Chicago, Chicago, IL, USA
| | - Jessica A Grieger
- Adelaide Medical School, Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, Australia
| | - Nahal Habibi
- Adelaide Medical School, Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, Australia
| | - Gloria K W Leung
- Department of Nutrition and Dietetics, Monash University, Melbourne, Victoria, Australia
| | - Kai Liu
- Department of Nutrition and Dietetics, Monash University, Melbourne, Victoria, Australia
| | | | - Maleesa Pathirana
- Adelaide Medical School, Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, Australia
| | - Alejandra Quinteros
- Adelaide Medical School, Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, Australia
| | - Rachael Taylor
- School of Health Sciences, University of Newcastle, Newcastle, Australia
| | - Gebresilasea G Ukke
- Eastern Health Clinical School, Monash University, Melbourne, Victoria, Australia
| | - Shao J Zhou
- School of Agriculture, Food and Wine, University of Adelaide, Adelaide, Australia
- American Diabetes Association (ADA) and European Association for the Study of Diabetes (EASD) Precision Medicine in Diabetes Initiative (PMDI) led by Paul Franks, Malmo, Sweden
| | - Jami Josefson
- Northwestern University/ Lurie Children's Hospital of Chicago, Chicago, USA.
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21
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Young KG, McInnes EH, Massey RJ, Kahkoska AR, Pilla SJ, Raghavan S, Stanislawski MA, Tobias DK, McGovern AP, Dawed AY, Jones AG, Pearson ER, Dennis JM. Treatment effect heterogeneity following type 2 diabetes treatment with GLP1-receptor agonists and SGLT2-inhibitors: a systematic review. COMMUNICATIONS MEDICINE 2023; 3:131. [PMID: 37794166 PMCID: PMC10551026 DOI: 10.1038/s43856-023-00359-w] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2023] [Accepted: 09/15/2023] [Indexed: 10/06/2023] Open
Abstract
BACKGROUND A precision medicine approach in type 2 diabetes requires the identification of clinical and biological features that are reproducibly associated with differences in clinical outcomes with specific anti-hyperglycaemic therapies. Robust evidence of such treatment effect heterogeneity could support more individualized clinical decisions on optimal type 2 diabetes therapy. METHODS We performed a pre-registered systematic review of meta-analysis studies, randomized control trials, and observational studies evaluating clinical and biological features associated with heterogenous treatment effects for SGLT2-inhibitor and GLP1-receptor agonist therapies, considering glycaemic, cardiovascular, and renal outcomes. After screening 5,686 studies, we included 101 studies of SGLT2-inhibitors and 75 studies of GLP1-receptor agonists in the final systematic review. RESULTS Here we show that the majority of included papers have methodological limitations precluding robust assessment of treatment effect heterogeneity. For SGLT2-inhibitors, multiple observational studies suggest lower renal function as a predictor of lesser glycaemic response, while markers of reduced insulin secretion predict lesser glycaemic response with GLP1-receptor agonists. For both therapies, multiple post-hoc analyses of randomized control trials (including trial meta-analysis) identify minimal clinically relevant treatment effect heterogeneity for cardiovascular and renal outcomes. CONCLUSIONS Current evidence on treatment effect heterogeneity for SGLT2-inhibitor and GLP1-receptor agonist therapies is limited, likely reflecting the methodological limitations of published studies. Robust and appropriately powered studies are required to understand type 2 diabetes treatment effect heterogeneity and evaluate the potential for precision medicine to inform future clinical care.
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Affiliation(s)
- Katherine G Young
- Exeter Centre of Excellence in Diabetes (EXCEED), University of Exeter Medical School, RILD Building, Royal Devon & Exeter Hospital, Exeter, UK
| | - Eram Haider McInnes
- Division of Population Health & Genomics, School of Medicine, University of Dundee, Dundee, UK
| | - Robert J Massey
- Division of Population Health & Genomics, School of Medicine, University of Dundee, Dundee, UK
| | - Anna R Kahkoska
- Department of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Scott J Pilla
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Sridharan Raghavan
- Section of Academic Primary Care, US Department of Veterans Affairs Eastern Colorado Health Care System, Aurora, CO, USA
| | - Maggie A Stanislawski
- Department of Biomedical Informatics, School of Medicine, University of Colorado, Aurora, USA
| | - Deirdre K Tobias
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA
| | - Andrew P McGovern
- Exeter Centre of Excellence in Diabetes (EXCEED), University of Exeter Medical School, RILD Building, Royal Devon & Exeter Hospital, Exeter, UK
| | - Adem Y Dawed
- Division of Population Health & Genomics, School of Medicine, University of Dundee, Dundee, UK
| | - Angus G Jones
- Exeter Centre of Excellence in Diabetes (EXCEED), University of Exeter Medical School, RILD Building, Royal Devon & Exeter Hospital, Exeter, UK
| | - Ewan R Pearson
- Division of Population Health & Genomics, School of Medicine, University of Dundee, Dundee, UK.
| | - John M Dennis
- Exeter Centre of Excellence in Diabetes (EXCEED), University of Exeter Medical School, RILD Building, Royal Devon & Exeter Hospital, Exeter, UK.
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22
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Bodhini D, Morton RW, Santhakumar V, Nakabuye M, Pomares-Millan H, Clemmensen C, Fitzpatrick SL, Guasch-Ferre M, Pankow JS, Ried-Larsen M, Franks PW, Tobias DK, Merino J, Mohan V, Loos RJF. Impact of individual and environmental factors on dietary or lifestyle interventions to prevent type 2 diabetes development: a systematic review. COMMUNICATIONS MEDICINE 2023; 3:133. [PMID: 37794109 PMCID: PMC10551013 DOI: 10.1038/s43856-023-00363-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/07/2023] [Accepted: 09/18/2023] [Indexed: 10/06/2023] Open
Abstract
BACKGROUND The variability in the effectiveness of type 2 diabetes (T2D) preventive interventions highlights the potential to identify the factors that determine treatment responses and those that would benefit the most from a given intervention. We conducted a systematic review to synthesize the evidence to support whether sociodemographic, clinical, behavioral, and molecular factors modify the efficacy of dietary or lifestyle interventions to prevent T2D. METHODS We searched MEDLINE, Embase, and Cochrane databases for studies reporting on the effect of a lifestyle, dietary pattern, or dietary supplement interventions on the incidence of T2D and reporting the results stratified by any effect modifier. We extracted relevant statistical findings and qualitatively synthesized the evidence for each modifier based on the direction of findings reported in available studies. We used the Diabetes Canada Clinical Practice Scale to assess the certainty of the evidence for a given effect modifier. RESULTS The 81 publications that met our criteria for inclusion are from 33 unique trials. The evidence is low to very low to attribute variability in intervention effectiveness to individual characteristics such as age, sex, BMI, race/ethnicity, socioeconomic status, baseline behavioral factors, or genetic predisposition. CONCLUSIONS We report evidence, albeit low certainty, that those with poorer health status, particularly those with prediabetes at baseline, tend to benefit more from T2D prevention strategies compared to healthier counterparts. Our synthesis highlights the need for purposefully designed clinical trials to inform whether individual factors influence the success of T2D prevention strategies.
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Affiliation(s)
| | - Robert W Morton
- Department of Pathology & Molecular Medicine, McMaster University, Hamilton, ON, Canada
- Population Health Research Institute, Hamilton, ON, Canada
- Department of Translational Medicine, Medical Science, Novo Nordisk Foundation, Tuborg Havnevej 19, 2900, Hellerup, Denmark
| | - Vanessa Santhakumar
- Division of Preventive Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Mariam Nakabuye
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Hugo Pomares-Millan
- Department of Clinical Sciences, Genetic and Molecular Epidemiology Unit, Lund University, Skåne University Hospital Malmö, Malmö, Sweden
- Department of Epidemiology, Geisel School of Medicine at Dartmouth, Hanover, NH, USA
| | - Christoffer Clemmensen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Stephanie L Fitzpatrick
- Institute of Health System Science, Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, USA
| | - Marta Guasch-Ferre
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - James S Pankow
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN, USA
| | - Mathias Ried-Larsen
- Centre for Physical Activity Research, Rigshospitalet, Copenhagen, Denmark
- Institute for Sports and Clinical Biomechanics, University of Southern Denmark, Odense, Denmark
| | - Paul W Franks
- Department of Translational Medicine, Medical Science, Novo Nordisk Foundation, Tuborg Havnevej 19, 2900, Hellerup, Denmark
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Lund University Diabetes Centre, Department of Clinical Sciences, Lund University, Malmo, Sweden
- Oxford Centre for Diabetes, Endocrinology and Metabolism, Radcliffe Department of Medicine, University of Oxford, Oxford, UK
| | - Deirdre K Tobias
- Division of Preventive Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Jordi Merino
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Diabetes Unit, Endocrine Division, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Viswanathan Mohan
- Madras Diabetes Research Foundation, Chennai, India
- Dr. Mohan's Diabetes Specialities Centre, Chennai, India
| | - Ruth J F Loos
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark.
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA.
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23
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Benham JL, Gingras V, McLennan NM, Most J, Yamamoto JM, Aiken CE, Ozanne SE, Reynolds RM. Precision gestational diabetes treatment: a systematic review and meta-analyses. COMMUNICATIONS MEDICINE 2023; 3:135. [PMID: 37794196 PMCID: PMC10550921 DOI: 10.1038/s43856-023-00371-0] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/08/2023] [Accepted: 09/25/2023] [Indexed: 10/06/2023] Open
Abstract
BACKGROUND Gestational Diabetes Mellitus (GDM) affects approximately 1 in 7 pregnancies globally. It is associated with short- and long-term risks for both mother and baby. Therefore, optimizing treatment to effectively treat the condition has wide-ranging beneficial effects. However, despite the known heterogeneity in GDM, treatment guidelines and approaches are generally standardized. We hypothesized that a precision medicine approach could be a tool for risk-stratification of women to streamline successful GDM management. With the relatively short timeframe available to treat GDM, commencing effective therapy earlier, with more rapid normalization of hyperglycaemia, could have benefits for both mother and fetus. METHODS We conducted two systematic reviews, to identify precision markers that may predict effective lifestyle and pharmacological interventions. RESULTS There was a paucity of studies examining precision lifestyle-based interventions for GDM highlighting the pressing need for further research in this area. We found a number of precision markers identified from routine clinical measures that may enable earlier identification of those requiring escalation of pharmacological therapy (to metformin, sulphonylureas or insulin). This included previous history of GDM, Body Mass Index and blood glucose concentrations at diagnosis. CONCLUSIONS Clinical measurements at diagnosis could potentially be used as precision markers in the treatment of GDM. Whether there are other sensitive markers that could be identified using more complex individual-level data, such as omics, and if these can feasibly be implemented in clinical practice remains unknown. These will be important to consider in future studies.
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Affiliation(s)
- Jamie L Benham
- Department of Medicine and Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, AB, Canada
| | - Véronique Gingras
- Department of Nutrition, Université de Montréal, Montreal, QC, Canada
- Research Center, Sainte-Justine University Hospital Center, Montreal, QC, Canada
| | - Niamh-Maire McLennan
- MRC Centre for Reproductive Health, Queens's Medical Research Institute, University of Edinburgh, Edinburgh, UK
- Centre for Cardiovascular Science, Queens's Medical Research Institute, University of Edinburgh, Edinburgh, UK
| | - Jasper Most
- Department of Orthopedics, Zuyderland Medical Center, Sittard-Geleen, The Netherlands
| | | | - Catherine E Aiken
- Department of Obstetrics and Gynaecology, the Rosie Hospital, Cambridge, UK
- NIHR Cambridge Biomedical Research Centre, University of Cambridge, Cambridge, UK
| | - Susan E Ozanne
- University of Cambridge Metabolic Research Laboratories and MRC Metabolic Diseases Unit, Wellcome-MRC Institute of Metabolic Science, Cambridge, UK
| | - Rebecca M Reynolds
- MRC Centre for Reproductive Health, Queens's Medical Research Institute, University of Edinburgh, Edinburgh, UK.
- Centre for Cardiovascular Science, Queens's Medical Research Institute, University of Edinburgh, Edinburgh, UK.
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24
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Jacobsen LM, Sherr JL, Considine E, Chen A, Peeling SM, Hulsmans M, Charleer S, Urazbayeva M, Tosur M, Alamarie S, Redondo MJ, Hood KK, Gottlieb PA, Gillard P, Wong JJ, Hirsch IB, Pratley RE, Laffel LM, Mathieu C. Utility and precision evidence of technology in the treatment of type 1 diabetes: a systematic review. COMMUNICATIONS MEDICINE 2023; 3:132. [PMID: 37794113 PMCID: PMC10550996 DOI: 10.1038/s43856-023-00358-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/28/2023] [Accepted: 09/15/2023] [Indexed: 10/06/2023] Open
Abstract
BACKGROUND The greatest change in the treatment of people living with type 1 diabetes in the last decade has been the explosion of technology assisting in all aspects of diabetes therapy, from glucose monitoring to insulin delivery and decision making. As such, the aim of our systematic review was to assess the utility of these technologies as well as identify any precision medicine-directed findings to personalize care. METHODS Screening of 835 peer-reviewed articles was followed by systematic review of 70 of them (focusing on randomized trials and extension studies with ≥50 participants from the past 10 years). RESULTS We find that novel technologies, ranging from continuous glucose monitoring systems, insulin pumps and decision support tools to the most advanced hybrid closed loop systems, improve important measures like HbA1c, time in range, and glycemic variability, while reducing hypoglycemia risk. Several studies included person-reported outcomes, allowing assessment of the burden or benefit of the technology in the lives of those with type 1 diabetes, demonstrating positive results or, at a minimum, no increase in self-care burden compared with standard care. Important limitations of the trials to date are their small size, the scarcity of pre-planned or powered analyses in sub-populations such as children, racial/ethnic minorities, people with advanced complications, and variations in baseline glycemic levels. In addition, confounders including education with device initiation, concomitant behavioral modifications, and frequent contact with the healthcare team are rarely described in enough detail to assess their impact. CONCLUSIONS Our review highlights the potential of technology in the treatment of people living with type 1 diabetes and provides suggestions for optimization of outcomes and areas of further study for precision medicine-directed technology use in type 1 diabetes.
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Affiliation(s)
| | | | | | | | | | | | | | | | - Mustafa Tosur
- Baylor College of Medicine, Texas Children's Hospital, Houston, TX, USA
- Children's Nutrition Research Center, USDA/ARS, Houston, TX, USA
| | - Selma Alamarie
- Stanford University School of Medicine, Stanford, CA, USA
| | - Maria J Redondo
- Baylor College of Medicine, Texas Children's Hospital, Houston, TX, USA
| | - Korey K Hood
- Stanford University School of Medicine, Stanford, CA, USA
| | - Peter A Gottlieb
- Barbara Davis Center, University of Colorado School of Medicine, Aurora, CO, USA
| | | | - Jessie J Wong
- Children's Nutrition Research Center, USDA/ARS, Houston, TX, USA
| | - Irl B Hirsch
- University of Washington School of Medicine, Seattle, WA, USA
| | | | - Lori M Laffel
- Joslin Diabetes Center, Harvard Medical School, Boston, MA, USA
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25
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Wei Y, Herzog K, Ahlqvist E, Andersson T, Nyström T, Zhan Y, Tuomi T, Carlsson S. All-Cause Mortality and Cardiovascular and Microvascular Diseases in Latent Autoimmune Diabetes in Adults. Diabetes Care 2023; 46:1857-1865. [PMID: 37635682 PMCID: PMC10516249 DOI: 10.2337/dc23-0739] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Accepted: 07/27/2023] [Indexed: 08/29/2023]
Abstract
OBJECTIVE Latent autoimmune diabetes in adults (LADA) is a heterogenous, slowly progressing autoimmune diabetes. We aim to contribute new knowledge on the long-term prognosis of LADA with varying degrees of autoimmunity by comparing it to type 2 diabetes and adult-onset type 1 diabetes. RESEARCH DESIGN AND METHODS This Swedish population-based study included newly diagnosed LADA (n = 550, stratified into LADAlow and LADAhigh by median autoimmunity level), type 2 diabetes (n = 2,001), adult-onset type 1 diabetes (n = 1,573), and control subjects without diabetes (n = 2,355) in 2007-2019. Register linkages provided information on all-cause mortality, cardiovascular diseases (CVDs), diabetic retinopathy, nephropathy, and clinical characteristics during follow-up. RESULTS Mortality was higher in LADA (hazard ratio [HR] 1.44; 95% CI 1.03, 2.02), type 1 (2.31 [1.75, 3.05]), and type 2 diabetes (1.31 [1.03, 1.67]) than in control subjects. CVD incidence was elevated in LADAhigh (HR 1.67; 95% CI 1.04, 2.69) and type 2 diabetes (1.53 [1.17, 2.00]), but not in LADAlow or type 1 diabetes. Incidence of retinopathy but not nephropathy was higher in LADA (HR 2.25; 95% CI 1.64, 3.09), including LADAhigh and LADAlow than in type 2 diabetes (unavailable in type 1 diabetes). More favorable blood pressure and lipid profiles, but higher HbA1c levels, were seen in LADA than type 2 diabetes at baseline and throughout follow-up, especially in LADAhigh, which resembled type 1 diabetes in this respect. CONCLUSIONS Despite having fewer metabolic risk factors than type 2 diabetes, LADA has equal to higher risks of death, CVD, and retinopathy. Poorer glycemic control, particularly in LADAhigh, highlights the need for improved LADA management.
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Affiliation(s)
- Yuxia Wei
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Katharina Herzog
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
- Novo Nordisk A/S, Søborg, Denmark
| | - Emma Ahlqvist
- Department of Clinical Sciences in Malmö, Clinical Research Centre, Lund University, Malmö, Sweden
| | - Tomas Andersson
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
- Centre for Occupational and Environmental Medicine, Stockholm County Council, Stockholm, Sweden
| | - Thomas Nyström
- Department of Clinical Science and Education, Karolinska Institutet, Stockholm, Sweden
| | - Yiqiang Zhan
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
- School of Public Health (Shenzhen), Sun Yat-Sen University, Shenzhen, China
| | - Tiinamaija Tuomi
- Department of Clinical Sciences in Malmö, Clinical Research Centre, Lund University, Malmö, Sweden
- Institute for Molecular Medicine Finland, Helsinki University, Helsinki, Finland
- Department of Endocrinology, Abdominal Center, Helsinki University Hospital, Research Program for Diabetes and Obesity, University of Helsinki, and Folkhälsan Research Center, Helsinki, Finland
| | - Sofia Carlsson
- Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden
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26
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Johansson Å, Andreassen OA, Brunak S, Franks PW, Hedman H, Loos RJ, Meder B, Melén E, Wheelock CE, Jacobsson B. Precision medicine in complex diseases-Molecular subgrouping for improved prediction and treatment stratification. J Intern Med 2023; 294:378-396. [PMID: 37093654 PMCID: PMC10523928 DOI: 10.1111/joim.13640] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 04/25/2023]
Abstract
Complex diseases are caused by a combination of genetic, lifestyle, and environmental factors and comprise common noncommunicable diseases, including allergies, cardiovascular disease, and psychiatric and metabolic disorders. More than 25% of Europeans suffer from a complex disease, and together these diseases account for 70% of all deaths. The use of genomic, molecular, or imaging data to develop accurate diagnostic tools for treatment recommendations and preventive strategies, and for disease prognosis and prediction, is an important step toward precision medicine. However, for complex diseases, precision medicine is associated with several challenges. There is a significant heterogeneity between patients of a specific disease-both with regards to symptoms and underlying causal mechanisms-and the number of underlying genetic and nongenetic risk factors is often high. Here, we summarize precision medicine approaches for complex diseases and highlight the current breakthroughs as well as the challenges. We conclude that genomic-based precision medicine has been used mainly for patients with highly penetrant monogenic disease forms, such as cardiomyopathies. However, for most complex diseases-including psychiatric disorders and allergies-available polygenic risk scores are more probabilistic than deterministic and have not yet been validated for clinical utility. However, subclassifying patients of a specific disease into discrete homogenous subtypes based on molecular or phenotypic data is a promising strategy for improving diagnosis, prediction, treatment, prevention, and prognosis. The availability of high-throughput molecular technologies, together with large collections of health data and novel data-driven approaches, offers promise toward improved individual health through precision medicine.
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Affiliation(s)
- Åsa Johansson
- Department of Immunology, Genetics and Pathology, Science for Life Laboratory, Uppsala university, Sweden
| | - Ole A. Andreassen
- NORMENT Centre, Division of Mental Health and Addiction, Oslo University Hospital & Institute of Clinical Medicine, University of Oslo, Oslo, Norway
- KG Jebsen Centre for Neurodevelopment Research, University of Oslo, Oslo, Norway
| | - Søren Brunak
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, DK-2200 Copenhagen, Denmark
- Copenhagen University Hospital, Rigshospitalet, Blegdamsvej 9, DK-2200 Copenhagen, Denmark
| | - Paul W. Franks
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Science, Lund University, Sweden
- Novo Nordisk Foundation, Denmark
| | - Harald Hedman
- Department of Medical Biosciences, Umeå University, Umeå, Sweden
| | - Ruth J.F. Loos
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- The Charles Bronfman Institute for Personalized Medicine at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Benjamin Meder
- Precision Digital Health, Cardiogenetics Center Heidelberg, Department of Cardiology, University Of Heidelberg, Germany
| | - Erik Melén
- Department of Clinical Sciences and Education, Södersjukhuset, Karolinska Institutet, Stockholm
- Sachś Children and Youth Hospital, Södersjukhuset, Stockholm, Sweden
| | - Craig E Wheelock
- Unit of Integrative Metabolomics, Institute of Environmental Medicine, Karolinska Institutet, 171 77 Stockholm, Sweden
- Department of Respiratory Medicine and Allergy, Karolinska University Hospital, 171 76 Stockholm, Sweden
| | - Bo Jacobsson
- Department of Obstetrics and Gynecology, Institute of Clinical Science, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Department of Obstetrics and Gynaecology, Sahlgrenska University Hospital, Göteborg, Sweden
- Department of Genetics and Bioinformatics, Domain of Health Data and Digitalisation, Institute of Public Health, Oslo, Norway
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27
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Tobias DK, Merino J, Ahmad A, Aiken C, Benham JL, Bodhini D, Clark AL, Colclough K, Corcoy R, Cromer SJ, Duan D, Felton JL, Francis EC, Gillard P, Gingras V, Gaillard R, Haider E, Hughes A, Ikle JM, Jacobsen LM, Kahkoska AR, Kettunen JLT, Kreienkamp RJ, Lim LL, Männistö JME, Massey R, Mclennan NM, Miller RG, Morieri ML, Most J, Naylor RN, Ozkan B, Patel KA, Pilla SJ, Prystupa K, Raghavan S, Rooney MR, Schön M, Semnani-Azad Z, Sevilla-Gonzalez M, Svalastoga P, Takele WW, Tam CHT, Thuesen ACB, Tosur M, Wallace AS, Wang CC, Wong JJ, Yamamoto JM, Young K, Amouyal C, Andersen MK, Bonham MP, Chen M, Cheng F, Chikowore T, Chivers SC, Clemmensen C, Dabelea D, Dawed AY, Deutsch AJ, Dickens LT, DiMeglio LA, Dudenhöffer-Pfeifer M, Evans-Molina C, Fernández-Balsells MM, Fitipaldi H, Fitzpatrick SL, Gitelman SE, Goodarzi MO, Grieger JA, Guasch-Ferré M, Habibi N, Hansen T, Huang C, Harris-Kawano A, Ismail HM, Hoag B, Johnson RK, Jones AG, Koivula RW, Leong A, Leung GKW, Libman IM, Liu K, Long SA, Lowe WL, Morton RW, Motala AA, Onengut-Gumuscu S, Pankow JS, Pathirana M, Pazmino S, Perez D, Petrie JR, Powe CE, Quinteros A, Jain R, Ray D, Ried-Larsen M, Saeed Z, Santhakumar V, Kanbour S, Sarkar S, Monaco GSF, Scholtens DM, Selvin E, Sheu WHH, Speake C, Stanislawski MA, Steenackers N, Steck AK, Stefan N, Støy J, Taylor R, Tye SC, Ukke GG, Urazbayeva M, Van der Schueren B, Vatier C, Wentworth JM, Hannah W, White SL, Yu G, Zhang Y, Zhou SJ, Beltrand J, Polak M, Aukrust I, de Franco E, Flanagan SE, Maloney KA, McGovern A, Molnes J, Nakabuye M, Njølstad PR, Pomares-Millan H, Provenzano M, Saint-Martin C, Zhang C, Zhu Y, Auh S, de Souza R, Fawcett AJ, Gruber C, Mekonnen EG, Mixter E, Sherifali D, Eckel RH, Nolan JJ, Philipson LH, Brown RJ, Billings LK, Boyle K, Costacou T, Dennis JM, Florez JC, Gloyn AL, Gomez MF, Gottlieb PA, Greeley SAW, Griffin K, Hattersley AT, Hirsch IB, Hivert MF, Hood KK, Josefson JL, Kwak SH, Laffel LM, Lim SS, Loos RJF, Ma RCW, Mathieu C, Mathioudakis N, Meigs JB, Misra S, Mohan V, Murphy R, Oram R, Owen KR, Ozanne SE, Pearson ER, Perng W, Pollin TI, Pop-Busui R, Pratley RE, Redman LM, Redondo MJ, Reynolds RM, Semple RK, Sherr JL, Sims EK, Sweeting A, Tuomi T, Udler MS, Vesco KK, Vilsbøll T, Wagner R, Rich SS, Franks PW. Second international consensus report on gaps and opportunities for the clinical translation of precision diabetes medicine. Nat Med 2023; 29:2438-2457. [PMID: 37794253 PMCID: PMC10735053 DOI: 10.1038/s41591-023-02502-5] [Citation(s) in RCA: 45] [Impact Index Per Article: 45.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Accepted: 07/14/2023] [Indexed: 10/06/2023]
Abstract
Precision medicine is part of the logical evolution of contemporary evidence-based medicine that seeks to reduce errors and optimize outcomes when making medical decisions and health recommendations. Diabetes affects hundreds of millions of people worldwide, many of whom will develop life-threatening complications and die prematurely. Precision medicine can potentially address this enormous problem by accounting for heterogeneity in the etiology, clinical presentation and pathogenesis of common forms of diabetes and risks of complications. This second international consensus report on precision diabetes medicine summarizes the findings from a systematic evidence review across the key pillars of precision medicine (prevention, diagnosis, treatment, prognosis) in four recognized forms of diabetes (monogenic, gestational, type 1, type 2). These reviews address key questions about the translation of precision medicine research into practice. Although not complete, owing to the vast literature on this topic, they revealed opportunities for the immediate or near-term clinical implementation of precision diabetes medicine; furthermore, we expose important gaps in knowledge, focusing on the need to obtain new clinically relevant evidence. Gaps include the need for common standards for clinical readiness, including consideration of cost-effectiveness, health equity, predictive accuracy, liability and accessibility. Key milestones are outlined for the broad clinical implementation of precision diabetes medicine.
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Affiliation(s)
- Deirdre K Tobias
- Division of Preventative Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Jordi Merino
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Diabetes Unit, Endocrine Division, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Abrar Ahmad
- Department of Clinical Sciences, Lund University Diabetes Centre, Lund University, Malmö, Sweden
| | - Catherine Aiken
- Department of Obstetrics and Gynaecology, The Rosie Hospital, Cambridge, UK
- NIHR Cambridge Biomedical Research Centre, University of Cambridge, Cambridge, UK
| | - Jamie L Benham
- Departments of Medicine and Community Health Sciences, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
| | - Dhanasekaran Bodhini
- Department of Molecular Genetics, Madras Diabetes Research Foundation, Chennai, India
| | - Amy L Clark
- Division of Pediatric Endocrinology, Department of Pediatrics, Saint Louis University School of Medicine, SSM Health Cardinal Glennon Children's Hospital, St. Louis, MO, USA
| | - Kevin Colclough
- Department of Clinical and Biomedical Sciences, University of Exeter Medical School, Exeter, UK
| | - Rosa Corcoy
- CIBER-BBN, ISCIII, Madrid, Spain
- Institut d'Investigació Biomèdica Sant Pau (IIB SANT PAU), Barcelona, Spain
- Departament de Medicina, Universitat Autònoma de Barcelona, Bellaterra, Spain
| | - Sara J Cromer
- Diabetes Unit, Endocrine Division, Massachusetts General Hospital, Boston, MA, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Daisy Duan
- Division of Endocrinology, Diabetes and Metabolism, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Jamie L Felton
- Department of Pediatrics, Indiana University School of Medicine, Indianapolis, IN, USA
- Herman B Wells Center for Pediatric Research, University School of Medicine, Indianapolis, IN, USA
- Center for Diabetes and Metabolic Diseases, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Ellen C Francis
- Department of Biostatistics and Epidemiology, Rutgers School of Public Health, Piscataway, NJ, USA
| | | | - Véronique Gingras
- Department of Nutrition, Université de Montréal, Montreal, Quebec, Quebec, Canada
- Research Center, Sainte-Justine University Hospital Center, Montreal, Quebec, Quebec, Canada
| | - Romy Gaillard
- Department of Pediatrics, Erasmus Medical Center, Rotterdam, the Netherlands
| | - Eram Haider
- Division of Population Health & Genomics, School of Medicine, University of Dundee, Dundee, UK
| | - Alice Hughes
- Department of Clinical and Biomedical Sciences, University of Exeter Medical School, Exeter, UK
| | - Jennifer M Ikle
- Department of Pediatrics, Stanford School of Medicine, Stanford University, Stanford, CA, USA
- Stanford Diabetes Research Center, Stanford School of Medicine, Stanford University, Stanford, CA, USA
| | | | - Anna R Kahkoska
- Department of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Jarno L T Kettunen
- Helsinki University Hospital, Abdominal Centre/Endocrinology, Helsinki, Finland
- Folkhalsan Research Center, Helsinki, Finland
- Institute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki, Finland
| | - Raymond J Kreienkamp
- Diabetes Unit, Endocrine Division, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute, Cambridge, MA, USA
- Department of Pediatrics, Division of Endocrinology, Boston Children's Hospital, Boston, MA, USA
| | - Lee-Ling Lim
- Department of Medicine, Faculty of Medicine, University of Malaya, Kuala Lumpur, Malaysia
- Asia Diabetes Foundation, Hong Kong SAR, China
- Department of Medicine & Therapeutics, Chinese University of Hong Kong, Hong Kong SAR, China
| | - Jonna M E Männistö
- Departments of Pediatrics and Clinical Genetics, Kuopio University Hospital, Kuopio, Finland
- Department of Medicine, University of Eastern Finland, Kuopio, Finland
| | - Robert Massey
- Division of Population Health & Genomics, School of Medicine, University of Dundee, Dundee, UK
| | - Niamh-Maire Mclennan
- Centre for Cardiovascular Science, Queen's Medical Research Institute, University of Edinburgh, Edinburgh, UK
| | - Rachel G Miller
- Department of Epidemiology, University of Pittsburgh, Pittsburgh, PA, USA
| | - Mario Luca Morieri
- Metabolic Disease Unit, University Hospital of Padova, Padova, Italy
- Department of Medicine, University of Padova, Padova, Italy
| | - Jasper Most
- Department of Orthopedics, Zuyderland Medical Center, Sittard-Geleen, The Netherlands
| | - Rochelle N Naylor
- Departments of Pediatrics and Medicine, University of Chicago, Chicago, IL, USA
| | - Bige Ozkan
- Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Ciccarone Center for the Prevention of Cardiovascular Disease, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Kashyap Amratlal Patel
- Department of Clinical and Biomedical Sciences, University of Exeter Medical School, Exeter, UK
| | - Scott J Pilla
- Department of Medicine, Johns Hopkins University, Baltimore, MD, USA
- Department of Health Policy and Management, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, USA
| | - Katsiaryna Prystupa
- Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
| | - Sridharan Raghavan
- Section of Academic Primary Care, US Department of Veterans Affairs Eastern Colorado Health Care System, Aurora, CO, USA
- Department of Medicine, University of Colorado School of Medicine, Aurora, CO, USA
| | - Mary R Rooney
- Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Martin Schön
- Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- Institute of Diabetes Research and Metabolic Diseases (IDM), Helmholtz Center Munich, Neuherberg, Germany
- Institute of Experimental Endocrinology, Biomedical Research Center, Slovak Academy of Sciences, Bratislava, Slovakia
| | - Zhila Semnani-Azad
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Magdalena Sevilla-Gonzalez
- Programs in Metabolism and Medical & Population Genetics, Broad Institute, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Pernille Svalastoga
- Mohn Center for Diabetes Precision Medicine, Department of Clinical Science, University of Bergen, Bergen, Norway
- Children and Youth Clinic, Haukeland University Hospital, Bergen, Norway
| | - Wubet Worku Takele
- Eastern Health Clinical School, Monash University, Melbourne, Victoria, Australia
| | - Claudia Ha-Ting Tam
- Department of Medicine & Therapeutics, Chinese University of Hong Kong, Hong Kong SAR, China
- Laboratory for Molecular Epidemiology in Diabetes, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong SAR, China
- Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Anne Cathrine B Thuesen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Mustafa Tosur
- Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA
- Division of Pediatric Diabetes and Endocrinology, Texas Children's Hospital, Houston, TX, USA
- Children's Nutrition Research Center, USDA/ARS, Houston, TX, USA
| | - Amelia S Wallace
- Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Caroline C Wang
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Jessie J Wong
- Stanford University School of Medicine, Stanford, CA, USA
| | | | - Katherine Young
- Department of Clinical and Biomedical Sciences, University of Exeter Medical School, Exeter, UK
| | - Chloé Amouyal
- Department of Diabetology, APHP, Paris, France
- Sorbonne Université, INSERM, NutriOmic team, Paris, France
| | - Mette K Andersen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Maxine P Bonham
- Department of Nutrition, Dietetics and Food, Monash University, Melbourne, Victoria, Australia
| | - Mingling Chen
- Monash Centre for Health Research and Implementation, Monash University, Clayton, Victoria, Australia
| | - Feifei Cheng
- Health Management Center, The Second Affiliated Hospital of Chongqing Medical University, Chongqing Medical University, Chongqing, China
| | - Tinashe Chikowore
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- MRC/Wits Developmental Pathways for Health Research Unit, Department of Paediatrics, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
- Channing Division of Network Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Sian C Chivers
- Department of Women and Children's Health, King's College London, London, UK
| | - Christoffer Clemmensen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Dana Dabelea
- Lifecourse Epidemiology of Adiposity and Diabetes (LEAD) Center, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Adem Y Dawed
- Division of Population Health & Genomics, School of Medicine, University of Dundee, Dundee, UK
| | - Aaron J Deutsch
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Laura T Dickens
- Section of Adult and Pediatric Endocrinology, Diabetes and Metabolism, Kovler Diabetes Center, University of Chicago, Chicago, IL, USA
| | - Linda A DiMeglio
- Department of Pediatrics, Indiana University School of Medicine, Indianapolis, IN, USA
- Herman B Wells Center for Pediatric Research, University School of Medicine, Indianapolis, IN, USA
- Center for Diabetes and Metabolic Diseases, Indiana University School of Medicine, Indianapolis, IN, USA
- Department of Pediatrics, Riley Hospital for Children, Indiana University School of Medicine, Indianapolis, IN, USA
| | | | - Carmella Evans-Molina
- Department of Pediatrics, Indiana University School of Medicine, Indianapolis, IN, USA
- Herman B Wells Center for Pediatric Research, University School of Medicine, Indianapolis, IN, USA
- Center for Diabetes and Metabolic Diseases, Indiana University School of Medicine, Indianapolis, IN, USA
- Richard L. Roudebush VAMC, Indianapolis, IN, USA
| | - María Mercè Fernández-Balsells
- Biomedical Research Institute Girona, IdIBGi, Girona, Spain
- Diabetes, Endocrinology and Nutrition Unit Girona, University Hospital Dr Josep Trueta, Girona, Spain
| | - Hugo Fitipaldi
- Department of Clinical Sciences, Lund University Diabetes Centre, Lund University, Malmö, Sweden
| | - Stephanie L Fitzpatrick
- Institute of Health System Science, Feinstein Institutes for Medical Research, Northwell Health, Manhasset, NY, USA
| | - Stephen E Gitelman
- University of California at San Francisco, Department of Pediatrics, Diabetes Center, San Francisco, CA, USA
| | - Mark O Goodarzi
- Division of Endocrinology, Diabetes and Metabolism, Cedars-Sinai Medical Center, Los Angeles, CA, USA
- Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Jessica A Grieger
- Adelaide Medical School, Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, South Australia, Australia
- Robinson Research Institute, The University of Adelaide, Adelaide, South Australia, Australia
| | - Marta Guasch-Ferré
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Public Health and Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Nahal Habibi
- Adelaide Medical School, Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, South Australia, Australia
- Robinson Research Institute, The University of Adelaide, Adelaide, South Australia, Australia
| | - Torben Hansen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Chuiguo Huang
- Department of Medicine & Therapeutics, Chinese University of Hong Kong, Hong Kong SAR, China
- Laboratory for Molecular Epidemiology in Diabetes, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Arianna Harris-Kawano
- Department of Pediatrics, Indiana University School of Medicine, Indianapolis, IN, USA
- Herman B Wells Center for Pediatric Research, University School of Medicine, Indianapolis, IN, USA
- Center for Diabetes and Metabolic Diseases, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Heba M Ismail
- Department of Pediatrics, Indiana University School of Medicine, Indianapolis, IN, USA
- Herman B Wells Center for Pediatric Research, University School of Medicine, Indianapolis, IN, USA
- Center for Diabetes and Metabolic Diseases, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Benjamin Hoag
- Division of Endocrinology and Diabetes, Department of Pediatrics, Sanford Children's Hospital, Sioux Falls, SD, USA
- University of South Dakota School of Medicine, E Clark St, Vermillion, SD, USA
| | - Randi K Johnson
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
- Department of Epidemiology, Colorado School of Public Health, Aurora, CO, USA
| | - Angus G Jones
- Department of Clinical and Biomedical Sciences, University of Exeter Medical School, Exeter, UK
- Royal Devon University Healthcare NHS Foundation Trust, Exeter, UK
| | - Robert W Koivula
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK
| | - Aaron Leong
- Diabetes Unit, Endocrine Division, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Gloria K W Leung
- Department of Nutrition, Dietetics and Food, Monash University, Melbourne, Victoria, Australia
| | | | - Kai Liu
- Adelaide Medical School, Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, South Australia, Australia
| | - S Alice Long
- Center for Translational Immunology, Benaroya Research Institute, Seattle, WA, USA
| | - William L Lowe
- Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Robert W Morton
- Department of Pathology & Molecular Medicine, McMaster University, Hamilton, Ontario, Canada
- Population Health Research Institute, Hamilton, Ontario, Canada
- Department of Translational Medicine, Medical Science, Novo Nordisk Foundation, Hellerup, Denmark
| | - Ayesha A Motala
- Department of Diabetes and Endocrinology, Nelson R. Mandela School of Medicine, University of KwaZulu-Natal, Durban, South Africa
| | - Suna Onengut-Gumuscu
- Center for Public Health Genomics, Department of Public Health Sciences, University of Virginia, Charlottesville, VA, USA
| | - James S Pankow
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN, USA
| | - Maleesa Pathirana
- Adelaide Medical School, Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, South Australia, Australia
- Robinson Research Institute, The University of Adelaide, Adelaide, South Australia, Australia
| | - Sofia Pazmino
- Department of Chronic Diseases and Metabolism, Clinical and Experimental Endocrinologyó, KU Leuven, Leuven, Belgium
| | - Dianna Perez
- Department of Pediatrics, Indiana University School of Medicine, Indianapolis, IN, USA
- Herman B Wells Center for Pediatric Research, University School of Medicine, Indianapolis, IN, USA
- Center for Diabetes and Metabolic Diseases, Indiana University School of Medicine, Indianapolis, IN, USA
| | - John R Petrie
- School of Health and Wellbeing, College of Medical, Veterinary and Life Sciences, University of Glasgow, Glasgow, UK
| | - Camille E Powe
- Diabetes Unit, Endocrine Division, Massachusetts General Hospital, Boston, MA, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Department of Obstetrics, Gynecology, and Reproductive Biology, Massachusetts General Hospital and Harvard Medical School, Boston, MA, USA
| | - Alejandra Quinteros
- Adelaide Medical School, Faculty of Health and Medical Sciences, The University of Adelaide, Adelaide, South Australia, Australia
| | - Rashmi Jain
- Sanford Children's Specialty Clinic, Sioux Falls, SD, USA
- Department of Pediatrics, Sanford School of Medicine, University of South Dakota, Sioux Falls, SD, USA
| | - Debashree Ray
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Mathias Ried-Larsen
- Centre for Physical Activity Research, Rigshospitalet, Copenhagen, Denmark
- Institute for Sports and Clinical Biomechanics, University of Southern Denmark, Odense, Denmark
| | - Zeb Saeed
- Department of Medicine, Division of Endocrinology, Diabetes and Metabolism, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Vanessa Santhakumar
- Division of Preventative Medicine, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Sarah Kanbour
- Department of Medicine, Johns Hopkins University, Baltimore, MD, USA
- AMAN Hospital, Doha, Qatar
| | - Sudipa Sarkar
- Department of Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Gabriela S F Monaco
- Department of Pediatrics, Indiana University School of Medicine, Indianapolis, IN, USA
- Herman B Wells Center for Pediatric Research, University School of Medicine, Indianapolis, IN, USA
- Center for Diabetes and Metabolic Diseases, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Denise M Scholtens
- Department of Preventive Medicine, Division of Biostatistics, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Elizabeth Selvin
- Welch Center for Prevention, Epidemiology, and Clinical Research, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Wayne Huey-Herng Sheu
- Institute of Molecular and Genomic Medicine, National Health Research Institutes, Zhunan, Taiwan
- Divsion of Endocrinology and Metabolism, Taichung Veterans General Hospital, Taichung, Taiwan
- Division of Endocrinology and Metabolism, Taipei Veterans General Hospital, Taipei, Taiwan
| | - Cate Speake
- Center for Interventional Immunology, Benaroya Research Institute, Seattle, WA, USA
| | - Maggie A Stanislawski
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Nele Steenackers
- Department of Chronic Diseases and Metabolism, Clinical and Experimental Endocrinologyó, KU Leuven, Leuven, Belgium
| | - Andrea K Steck
- Barbara Davis Center for Diabetes, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Norbert Stefan
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- Institute of Diabetes Research and Metabolic Diseases (IDM), Helmholtz Center Munich, Neuherberg, Germany
- University Hospital of Tübingen, Tübingen, Germany
| | - Julie Støy
- Steno Diabetes Center Aarhus, Aarhus University Hospital, Aarhus, Denmark
| | | | - Sok Cin Tye
- Sections on Genetics and Epidemiology, Joslin Diabetes Center, Harvard Medical School, Boston, MA, USA
- Department of Clinical Pharmacy and Pharmacology, University Medical Center Groningen, Groningen, the Netherlands
| | | | - Marzhan Urazbayeva
- Division of Pediatric Diabetes and Endocrinology, Texas Children's Hospital, Houston, TX, USA
- Gastroenterology, Baylor College of Medicine, Houston, TX, USA
| | - Bart Van der Schueren
- Department of Chronic Diseases and Metabolism, Clinical and Experimental Endocrinologyó, KU Leuven, Leuven, Belgium
- Department of Endocrinology, University Hospitals Leuven, Leuven, Belgium
| | - Camille Vatier
- Sorbonne University, Inserm U938, Saint-Antoine Research Centre, Institute of Cardiometabolism and Nutrition, Paris, France
- Department of Endocrinology, Diabetology and Reproductive Endocrinology, Assistance Publique-Hôpitaux de Paris, Saint-Antoine University Hospital, National Reference Center for Rare Diseases of Insulin Secretion and Insulin Sensitivity (PRISIS), Paris, France
| | - John M Wentworth
- Royal Melbourne Hospital Department of Diabetes and Endocrinology, Parkville, Victoria, Australia
- Walter and Eliza Hall Institute, Parkville, Victoria, Australia
- University of Melbourne Department of Medicine, Parkville, Victoria, Australia
| | - Wesley Hannah
- Deakin University, Melbourne, Victoria, Australia
- Department of Epidemiology, Madras Diabetes Research Foundation, Chennai, India
| | - Sara L White
- Department of Women and Children's Health, King's College London, London, UK
- Department of Diabetes and Endocrinology, Guy's and St Thomas' Hospitals NHS Foundation Trust, London, UK
| | - Gechang Yu
- Department of Medicine & Therapeutics, Chinese University of Hong Kong, Hong Kong SAR, China
- Laboratory for Molecular Epidemiology in Diabetes, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Yingchai Zhang
- Department of Medicine & Therapeutics, Chinese University of Hong Kong, Hong Kong SAR, China
- Laboratory for Molecular Epidemiology in Diabetes, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong SAR, China
| | - Shao J Zhou
- Robinson Research Institute, The University of Adelaide, Adelaide, South Australia, Australia
- School of Agriculture, Food and Wine, University of Adelaide, Adelaide, South Australia, Australia
| | - Jacques Beltrand
- Institut Cochin, Inserm U 10116, Paris, France
- Pediatric Endocrinology and Diabetes, Hopital Necker Enfants Malades, APHP Centre, Université de Paris, Paris, France
| | - Michel Polak
- Institut Cochin, Inserm U 10116, Paris, France
- Pediatric Endocrinology and Diabetes, Hopital Necker Enfants Malades, APHP Centre, Université de Paris, Paris, France
| | - Ingvild Aukrust
- Mohn Center for Diabetes Precision Medicine, Department of Clinical Science, University of Bergen, Bergen, Norway
- Department of Medical Genetics, Haukeland University Hospital, Bergen, Norway
| | - Elisa de Franco
- Department of Clinical and Biomedical Sciences, University of Exeter Medical School, Exeter, UK
| | - Sarah E Flanagan
- Department of Clinical and Biomedical Sciences, University of Exeter Medical School, Exeter, UK
| | - Kristin A Maloney
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Andrew McGovern
- Department of Clinical and Biomedical Sciences, University of Exeter Medical School, Exeter, UK
| | - Janne Molnes
- Mohn Center for Diabetes Precision Medicine, Department of Clinical Science, University of Bergen, Bergen, Norway
- Department of Medical Genetics, Haukeland University Hospital, Bergen, Norway
| | - Mariam Nakabuye
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Pål Rasmus Njølstad
- Mohn Center for Diabetes Precision Medicine, Department of Clinical Science, University of Bergen, Bergen, Norway
- Children and Youth Clinic, Haukeland University Hospital, Bergen, Norway
| | - Hugo Pomares-Millan
- Department of Clinical Sciences, Lund University Diabetes Centre, Lund University, Malmö, Sweden
- Department of Epidemiology, Geisel School of Medicine at Dartmouth, Hanover, NH, USA
| | - Michele Provenzano
- Nephrology, Dialysis and Renal Transplant Unit, IRCCS-Azienda Ospedaliero-Universitaria di Bologna, Alma Mater Studiorum University of Bologna, Bologna, Italy
| | - Cécile Saint-Martin
- Department of Medical Genetics, AP-HP Pitié-Salpêtrière Hospital, Sorbonne University, Paris, France
| | - Cuilin Zhang
- Global Center for Asian Women's Health, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
- Department of Obstetrics and Gynecology, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore
| | - Yeyi Zhu
- Kaiser Permanente Northern California Division of Research, Oakland, CA, USA
- Department of Epidemiology and Biostatistics, University of California, San Francisco, CA, USA
| | - Sungyoung Auh
- National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Russell de Souza
- Population Health Research Institute, Hamilton, Ontario, Canada
- Department of Health Research Methods, Evidence, and Impact, Faculty of Health Sciences, McMaster University, Hamilton, Ontario, Canada
| | - Andrea J Fawcett
- Ann & Robert H. Lurie Children's Hospital of Chicago, Department of Pediatrics, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
- Department of Clinical and Organizational Development, Chicago, IL, USA
| | | | - Eskedar Getie Mekonnen
- College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia
- Global Health Institute, Faculty of Medicine and Health Sciences, University of Antwerp, Antwerp, Belgium
| | - Emily Mixter
- Department of Medicine and Kovler Diabetes Center, University of Chicago, Chicago, IL, USA
| | - Diana Sherifali
- Population Health Research Institute, Hamilton, Ontario, Canada
- School of Nursing, Faculty of Health Sciences, McMaster University, Hamilton, Ontario, Canada
| | - Robert H Eckel
- Division of Endocrinology, Metabolism, Diabetes, University of Colorado, Aurora, CO, USA
| | - John J Nolan
- Department of Clinical Medicine, School of Medicine, Trinity College Dublin, Dublin, Ireland
- Department of Endocrinology, Wexford General Hospital, Wexford, Ireland
| | - Louis H Philipson
- Department of Medicine and Kovler Diabetes Center, University of Chicago, Chicago, IL, USA
| | - Rebecca J Brown
- National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, USA
| | - Liana K Billings
- Division of Endocrinology, NorthShore University HealthSystem, Skokie, IL, USA
- Department of Medicine, Prtizker School of Medicine, University of Chicago, Chicago, IL, USA
| | - Kristen Boyle
- Lifecourse Epidemiology of Adiposity and Diabetes (LEAD) Center, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Tina Costacou
- Department of Epidemiology, University of Pittsburgh, Pittsburgh, PA, USA
| | - John M Dennis
- Department of Clinical and Biomedical Sciences, University of Exeter Medical School, Exeter, UK
| | - Jose C Florez
- Diabetes Unit, Endocrine Division, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Anna L Gloyn
- Department of Pediatrics, Stanford School of Medicine, Stanford University, Stanford, CA, USA
- Stanford Diabetes Research Center, Stanford School of Medicine, Stanford University, Stanford, CA, USA
- Department of Genetics, Stanford School of Medicine, Stanford University, Stanford, CA, USA
| | - Maria F Gomez
- Department of Clinical Sciences, Lund University Diabetes Centre, Lund University, Malmö, Sweden
- Faculty of Health, Aarhus University, Aarhus, Denmark
| | - Peter A Gottlieb
- Barbara Davis Center for Diabetes, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Siri Atma W Greeley
- Departments of Pediatrics and Medicine and Kovler Diabetes Center, University of Chicago, Chicago, IL, USA
| | - Kurt Griffin
- Department of Pediatrics, Sanford School of Medicine, University of South Dakota, Sioux Falls, SD, USA
- Sanford Research, Sioux Falls, SD, USA
| | - Andrew T Hattersley
- Department of Clinical and Biomedical Sciences, University of Exeter Medical School, Exeter, UK
- Royal Devon University Healthcare NHS Foundation Trust, Exeter, UK
| | - Irl B Hirsch
- University of Washington School of Medicine, Seattle, WA, USA
| | - Marie-France Hivert
- Diabetes Unit, Endocrine Division, Massachusetts General Hospital, Boston, MA, USA
- Department of Population Medicine, Harvard Medical School, Harvard Pilgrim Health Care Institute, Boston, MA, USA
- Department of Medicine, Universite de Sherbrooke, Sherbrooke, Quebec, Canada
| | - Korey K Hood
- Stanford University School of Medicine, Stanford, CA, USA
| | - Jami L Josefson
- Ann & Robert H. Lurie Children's Hospital of Chicago, Department of Pediatrics, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | - Soo Heon Kwak
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul National University Hospital, Seoul, Republic of Korea
| | - Lori M Laffel
- Joslin Diabetes Center, Harvard Medical School, Boston, MA, USA
| | - Siew S Lim
- Eastern Health Clinical School, Monash University, Melbourne, Victoria, Australia
| | - Ruth J F Loos
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
- Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ronald C W Ma
- Department of Medicine & Therapeutics, Chinese University of Hong Kong, Hong Kong SAR, China
- Laboratory for Molecular Epidemiology in Diabetes, Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong SAR, China
- Hong Kong Institute of Diabetes and Obesity, The Chinese University of Hong Kong, Hong Kong SAR, China
| | | | | | - James B Meigs
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA, USA
- Broad Institute, Cambridge, MA, USA
| | - Shivani Misra
- Division of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
- Department of Diabetes & Endocrinology, Imperial College Healthcare NHS Trust, London, UK
| | - Viswanathan Mohan
- Department of Diabetology, Madras Diabetes Research Foundation & Dr. Mohan's Diabetes Specialities Centre, Chennai, India
| | - Rinki Murphy
- Department of Medicine, Faculty of Medicine and Health Sciences, University of Auckland, Auckland, New Zealand
- Auckland Diabetes Centre, Te Whatu Ora Health New Zealand, Auckland, New Zealand
- Medical Bariatric Service, Te Whatu Ora Counties, Health New Zealand, Auckland, New Zealand
| | - Richard Oram
- Department of Clinical and Biomedical Sciences, University of Exeter Medical School, Exeter, UK
- Royal Devon University Healthcare NHS Foundation Trust, Exeter, UK
| | - Katharine R Owen
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK
- Oxford NIHR Biomedical Research Centre, University of Oxford, Oxford, UK
| | - Susan E Ozanne
- University of Cambridge, Metabolic Research Laboratories and MRC Metabolic Diseases Unit, Wellcome-MRC Institute of Metabolic Science, Cambridge, UK
| | - Ewan R Pearson
- Division of Population Health & Genomics, School of Medicine, University of Dundee, Dundee, UK
| | - Wei Perng
- Lifecourse Epidemiology of Adiposity and Diabetes (LEAD) Center, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Toni I Pollin
- Department of Medicine, University of Maryland School of Medicine, Baltimore, MD, USA
- Department of Epidemiology & Public Health, University of Maryland School of Medicine, Baltimore, MD, USA
| | - Rodica Pop-Busui
- Department of Internal Medicine, Division of Metabolism, Endocrinology and Diabetes, University of Michigan, Ann Arbor, MI, USA
| | | | | | - Maria J Redondo
- Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA
- Division of Pediatric Diabetes and Endocrinology, Texas Children's Hospital, Houston, TX, USA
| | - Rebecca M Reynolds
- Centre for Cardiovascular Science, Queen's Medical Research Institute, University of Edinburgh, Edinburgh, UK
| | - Robert K Semple
- Centre for Cardiovascular Science, Queen's Medical Research Institute, University of Edinburgh, Edinburgh, UK
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | | | - Emily K Sims
- Department of Pediatrics, Indiana University School of Medicine, Indianapolis, IN, USA
- Herman B Wells Center for Pediatric Research, University School of Medicine, Indianapolis, IN, USA
- Center for Diabetes and Metabolic Diseases, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Arianne Sweeting
- Faculty of Medicine and Health, University of Sydney, Sydney, New South Wales, Australia
- Department of Endocrinology, Royal Prince Alfred Hospital, Sydney, New South Wales, Australia
| | - Tiinamaija Tuomi
- Helsinki University Hospital, Abdominal Centre/Endocrinology, Helsinki, Finland
- Folkhalsan Research Center, Helsinki, Finland
- Institute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki, Finland
| | - Miriam S Udler
- Diabetes Unit, Endocrine Division, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Kimberly K Vesco
- Kaiser Permanente Northwest, Kaiser Permanente Center for Health Research, Portland, OR, USA
| | - Tina Vilsbøll
- Clinial Research, Steno Diabetes Center Copenhagen, Herlev, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Robert Wagner
- Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- German Center for Diabetes Research (DZD), Neuherberg, Germany
- Department of Endocrinology and Diabetology, University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Stephen S Rich
- Center for Public Health Genomics, Department of Public Health Sciences, University of Virginia, Charlottesville, VA, USA
| | - Paul W Franks
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA.
- Department of Clinical Sciences, Lund University Diabetes Centre, Lund University, Malmö, Sweden.
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Oxford, UK.
- Department of Translational Medicine, Medical Science, Novo Nordisk Foundation, Hellerup, Denmark.
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Pantazis LJ, Frechtel GD, Cerrone GE, García R, Iglesias Molli AE. Phenotype similarities in automatically grouped T2D patients by variation-based clustering of IL-1β gene expression. EJIFCC 2023; 34:228-244. [PMID: 37868088 PMCID: PMC10588079] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 10/24/2023]
Abstract
Background Analyzing longitudinal gene expression data is extremely challenging due to limited prior information, high dimensionality, and heterogeneity. Similar difficulties arise in research of multifactorial diseases such as Type 2 Diabetes. Clustering methods can be applied to automatically group similar observations. Common clinical values within the resulting groups suggest potential associations. However, applying traditional clustering methods to gene expression over time fails to capture variations in the response. Therefore, shape-based clustering could be applied to identify patient groups by gene expression variation in a large time metabolic compensatory intervention. Objectives To search for clinical grouping patterns between subjects that showed similar structure in the variation of IL-1β gene expression over time. Methods A new approach for shape-based clustering by IL-1β expression behavior was applied to a real longitudinal database of Type 2 Diabetes patients. In order to capture correctly variations in the response, we applied traditional clustering methods to slopes between measurements. Results In this setting, the application of K-Medoids using the Manhattan distance yielded the best results for the corresponding database. Among the resulting groups, one of the clusters presented significant differences in many key clinical values regarding the metabolic syndrome in comparison to the rest of the data. Conclusions The proposed method can be used to group patients according to variation patterns in gene expression (or other applications) and thus, provide clinical insights even when there is no previous knowledge on the subject clinical profile and few timepoints for each individual.
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Affiliation(s)
- Lucio José Pantazis
- Centro de Sistemas y Control, Instituto Tecnológico de Buenos Aires (ITBA), Lavardén 315 1437, Ciudad Autónoma de Buenos Aires, Argentina
| | - Gustavo Daniel Frechtel
- CONICET-Universidad de Buenos Aires. Instituto de Inmunología, Genética y Metabolismo (INIGEM). Laboratorio de Diabetes y Metabolismo. Avenida Córdoba 2351, Ciudad Autónoma de Buenos Aires, Argentina
- Universidad de Buenos Aires. Facultad de Medicina. Departamento de Medicina. Cátedra de Nutrición. Avenida Córdoba 2351, Ciudad Autónoma de Buenos Aires, Argentina
| | - Gloria Edith Cerrone
- CONICET-Universidad de Buenos Aires. Instituto de Inmunología, Genética y Metabolismo (INIGEM). Laboratorio de Diabetes y Metabolismo. Avenida Córdoba 2351, Ciudad Autónoma de Buenos Aires, Argentina
- Universidad de Buenos Aires. Facultad de Farmacia y Bioquímica. Departamento de Microbiología, Inmunología, Biotecnología y Genética. Cátedra de Genética. Avenida Córdoba 2351, Ciudad Autónoma de Buenos Aires, Argentina
| | - Rafael García
- Centro de Sistemas y Control, Instituto Tecnológico de Buenos Aires (ITBA), Lavardén 315 1437, Ciudad Autónoma de Buenos Aires, Argentina
| | - Andrea Elena Iglesias Molli
- CONICET-Universidad de Buenos Aires. Instituto de Inmunología, Genética y Metabolismo (INIGEM). Laboratorio de Diabetes y Metabolismo. Avenida Córdoba 2351, Ciudad Autónoma de Buenos Aires, Argentina
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Smith K, Deutsch AJ, McGrail C, Kim H, Hsu S, Mandla R, Schroeder PH, Westerman KE, Szczerbinski L, Majarian TD, Kaur V, Williamson A, Claussnitzer M, Florez JC, Manning AK, Mercader JM, Gaulton KJ, Udler MS. Multi-ancestry Polygenic Mechanisms of Type 2 Diabetes Elucidate Disease Processes and Clinical Heterogeneity. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.09.28.23296294. [PMID: 37808749 PMCID: PMC10557820 DOI: 10.1101/2023.09.28.23296294] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/10/2023]
Abstract
We identified genetic subtypes of type 2 diabetes (T2D) by analyzing genetic data from diverse groups, including non-European populations. We implemented soft clustering with 650 T2D-associated genetic variants, capturing known and novel T2D subtypes with distinct cardiometabolic trait associations. The twelve genetic clusters were distinctively enriched for single-cell regulatory regions. Polygenic scores derived from the clusters differed in distribution between ancestry groups, including a significantly higher proportion of lipodystrophy-related polygenic risk in East Asian ancestry. T2D risk was equivalent at a BMI of 30 kg/m2 in the European subpopulation and 24.2 (22.9-25.5) kg/m2 in the East Asian subpopulation; after adjusting for cluster-specific genetic risk, the equivalent BMI threshold increased to 28.5 (27.1-30.0) kg/m2 in the East Asian group, explaining about 75% of the difference in BMI thresholds. Thus, these multi-ancestry T2D genetic subtypes encompass a broader range of biological mechanisms and help explain ancestry-associated differences in T2D risk profiles.
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Affiliation(s)
- Kirk Smith
- Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Aaron J. Deutsch
- Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Carolyn McGrail
- Department of Pediatrics, University of California San Diego, San Diego, CA, USA
| | - Hyunkyung Kim
- Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Committee on Genetics, Genomics and Systems Biology, University of Chicago, Chicago, IL, USA
- Section of Genetic Medicine, Department of Medicine, University of Chicago, Chicago, IL, USA
| | - Sarah Hsu
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Ravi Mandla
- Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Philip H. Schroeder
- Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Kenneth E. Westerman
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Lukasz Szczerbinski
- Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Endocrinology, Diabetology and Internal Medicine, Medical University of Bialystok, Bialystok, Poland
- Clinical Research Centre, Medical University of Bialystok, Bialystok, Poland
| | - Timothy D. Majarian
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Varinderpal Kaur
- Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Alice Williamson
- Precision Healthcare University Research Institute, Queen Mary University of London, London, UK
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Melina Claussnitzer
- Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- The Novo Nordisk Foundation Center for Genomic Mechanisms of Disease, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Jose C. Florez
- Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Alisa K. Manning
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Josep M. Mercader
- Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Kyle J. Gaulton
- Department of Pediatrics, University of California San Diego, San Diego, CA, USA
| | - Miriam S. Udler
- Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
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Naylor RN, Patel KA, Kettunen JL, Männistö JM, Støy J, Beltrand J, Polak M, Vilsbøll T, Greeley SA, Hattersley AT, Tuomi T. Systematic Review of Treatment of Beta-Cell Monogenic Diabetes. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.05.12.23289807. [PMID: 37214872 PMCID: PMC10197799 DOI: 10.1101/2023.05.12.23289807] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
Background Beta-cell monogenic forms of diabetes are the area of diabetes care with the strongest support for precision medicine. We reviewed treatment of hyperglycemia in GCK-related hyperglycemia, HNF1A-HNF4A- and HNF1B-diabetes, Mitochondrial diabetes (MD) due to m.3243A>G variant, 6q24-transient neonatal diabetes (TND) and SLC19A2-diabetes. Methods Systematic reviews with data from PubMed, MEDLINE and Embase were performed for the different subtypes. Individual and group level data was extracted for glycemic outcomes in individuals with genetically confirmed monogenic diabetes. Results 147 studies met inclusion criteria with only six experimental studies and the rest being single case reports or cohort studies. Most studies had moderate or serious risk of bias.For GCK-related hyperglycemia, six studies (N=35) showed no deterioration in HbA1c on discontinuing glucose lowering therapy. A randomized trial (n=18 per group) showed that sulfonylureas (SU) were more effective in HNF1A-diabetes than in type 2 diabetes, and cohort and case studies supported SU effectiveness in lowering HbA1c. Two crossover trials (n=15 and n=16) suggested glinides and GLP-1 receptor agonists might be used in place of SU. Evidence for HNF4A-diabetes was limited. While some patients with HNF1B-diabetes (n=301) and MD (n=250) were treated with oral agents, most were on insulin. There was some support for the use of oral agents after relapse in 6q24-TND, and for thiamine improving glycemic control and reducing insulin requirement in SLC19A2-diabetes (less than half achieved insulin-independency). Conclusion There is limited evidence to guide the treatment in monogenic diabetes with most studies being non-randomized and small. The data supports: no treatment in GCK-related hyperglycemia; SU for HNF1A-diabetes. Further evidence is needed to examine the optimum treatment in monogenic subtypes.
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Affiliation(s)
- Rochelle N. Naylor
- Departments of Pediatrics and Medicine, University of Chicago, Chicago, Illinois, USA
| | - Kashyap A. Patel
- University of Exeter Medical School, Department of Clinical and Biomedical Sciences, Exeter, Devon, UK
| | - Jarno L.T. Kettunen
- Helsinki University Hospital, Abdominal Centre/Endocrinology, Helsinki, Finland; Folkhalsan Research Center, Helsinki, Finland; Institute for Molecular Medicine Finland FIMM, University of Helsinki, Helsinki, Finland
| | - Jonna M.E. Männistö
- Departments of Pediatrics and Clinical Genetics, Kuopio University Hospital, Kuopio, Finland; Department of Medicine, University of Eastern Finland, Kuopio, Finland
| | - Julie Støy
- Steno diabetes center Aarhus, Aarhus university hospital, Aarhus, Denmark
| | - Jacques Beltrand
- APHP Centre Hôpital Necker Enfants Malades Université Paris Cité, Paris France; Inserm U1016 Institut Cochin Paris France
| | - Michel Polak
- Department of pediatric endocrinology gynecology and diabetology, Hôpital Universitaire Necker Enfants Malades, IMAGINE institute, INSERM U1016, Paris, France; Université Paris Cité, Paris, France
| | - ADA/EASD PMDI
- American Diabetes Association/European Association for the Study of Diabetes Precision Medicine Initiative
| | - Tina Vilsbøll
- Department of Clinical Medicine, University of Copenhagen
| | - Siri A.W. Greeley
- Departments of Pediatrics and Medicine, University of Chicago, Chicago, Illinois, USA
| | - Andrew T. Hattersley
- University of Exeter Medical School, Department of Clinical and Biomedical Sciences, Exeter, Devon, UK
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Kuss O, Opitz ME, Brandstetter LV, Schlesinger S, Roden M, Hoyer A. How amenable is type 2 diabetes treatment for precision diabetology? A meta-regression of glycaemic control data from 174 randomised trials. Diabetologia 2023; 66:1622-1632. [PMID: 37338539 PMCID: PMC10390610 DOI: 10.1007/s00125-023-05951-2] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/06/2023] [Accepted: 05/16/2023] [Indexed: 06/21/2023]
Abstract
AIMS/HYPOTHESIS There are two prerequisites for the precision medicine approach to be beneficial for treated individuals. First, there must be treatment heterogeneity; second, in the case of treatment heterogeneity, we need to detect clinical predictors to identify people who would benefit from one treatment more than from others. There is an established meta-regression approach to assess these two prerequisites that relies on measuring the variability of a clinical outcome after treatment in placebo-controlled randomised trials. Our aim was to apply this approach to the treatment of type 2 diabetes. METHODS We performed a meta-regression analysis using information from 174 placebo-controlled randomised trials with 178 placebo and 272 verum (i.e. active treatment) arms including 86,940 participants with respect to the variability of glycaemic control as assessed by HbA1c after treatment and its potential predictors. RESULTS The adjusted difference in log(SD) values between the verum and placebo arms was 0.037 (95% CI: 0.004, 0.069). That is, we found a small increase in the variability of HbA1c values after treatment in the verum arms. In addition, one potentially relevant predictor for explaining this increase, drug class, was observed, and GLP-1 receptor agonists yielded the largest differences in log(SD) values. CONCLUSIONS/INTERPRETATION The potential of the precision medicine approach in the treatment of type 2 diabetes is modest at best, at least with regard to an improvement in glycaemic control. Our finding of a larger variability after treatment with GLP-1 receptor agonists in individuals with poor glycaemic control should be replicated and/or validated with other clinical outcomes and with different study designs. FUNDING The research reported here received no specific grant from any funding agency in the public, commercial or not-for-profit sectors. DATA AVAILABILITY Two datasets (one for the log[SD] and one for the baseline-corrected log[SD]) to reproduce the analyses from this paper are available on https://zenodo.org/record/7956635 .
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Affiliation(s)
- Oliver Kuss
- Institute for Biometrics and Epidemiology, German Diabetes Center, Leibniz Institute for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany.
- Centre for Health and Society, Faculty of Medicine, Heinrich Heine University Düsseldorf, Düsseldorf, Germany.
- German Center for Diabetes Research, Partner Düsseldorf, München-Neuherberg, Germany.
| | | | | | - Sabrina Schlesinger
- Institute for Biometrics and Epidemiology, German Diabetes Center, Leibniz Institute for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- German Center for Diabetes Research, Partner Düsseldorf, München-Neuherberg, Germany
| | - Michael Roden
- German Center for Diabetes Research, Partner Düsseldorf, München-Neuherberg, Germany
- Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich Heine University Düsseldorf, Düsseldorf, Germany
- Department of Endocrinology and Diabetology, Medical Faculty and University Hospital Düsseldorf, Heinrich Heine University Düsseldorf, Düsseldorf, Germany
| | - Annika Hoyer
- Biostatistics and Medical Biometry, Medical School EWL, Bielefeld University, Bielefeld, Germany
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Kottlan A, Zirkl A, Geistlinger J, Machado Charry E, Glasser BJ, Khinast JG. Single-tablet-scale direct-compression: An on-demand manufacturing route for personalized tablets. Int J Pharm 2023; 643:123274. [PMID: 37507098 DOI: 10.1016/j.ijpharm.2023.123274] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Revised: 07/24/2023] [Accepted: 07/25/2023] [Indexed: 07/30/2023]
Abstract
Today's pharmaceutical industry is facing various challenges. Two of them are issues with supply chain security and the increasing demand for personalized medicine. Both can be addressed by increasing flexibility and a more decentralized approach to pharmaceutical manufacturing. In this study, we present a setup that provides flexibility in terms of supplied raw materials and the product, i.e., a direct-compression setup for personalized tablets operating at a single-tablet-scale. The performance of the implemented single-tablet-scale technology for dosing and mixing was investigated. In addition, an analysis of the critical quality attributes (CQAs) of immediate release ibuprofen and loratadine tablets was performed. The developed dosing device achieved acceptance rates of > 90 % for doses ≥ 20 mg for various pharmaceutical powders. Regarding the vibratory mixing process, a dependency of the performance on the applied frequencies and acceleration was observed, with 100 Hz and ∼ 90 G performing best, yet still exhibiting varying mixing efficacies depending on the granular system. The tablets produced met U.S. Pharmacopeia requirements regarding mechanical stability and dissolution characteristics. Given these results, we consider the developed setup a proof of concept of a tool to provide personalized tablets to patients while minimizing the dependency on complex supply chains.
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Affiliation(s)
- Andreas Kottlan
- Institute for Process- and Particle Engineering, Graz University of Technology, Inffeldgasse 13, A-8010 Graz, Austria
| | - Andrea Zirkl
- Institute for Process- and Particle Engineering, Graz University of Technology, Inffeldgasse 13, A-8010 Graz, Austria
| | - Jakob Geistlinger
- Institute for Process- and Particle Engineering, Graz University of Technology, Inffeldgasse 13, A-8010 Graz, Austria
| | - Eduardo Machado Charry
- Institute of Solid State Physics and NAWI Graz, Graz University of Technology, Petersgasse 16/III, 8010 Graz, Austria
| | - Benjamin J Glasser
- Rutgers, The State University of New Jersey, Department of Chemical and Biochemical Engineering, 98 Brett Road, Piscataway, NJ 08854, USA
| | - Johannes G Khinast
- Institute for Process- and Particle Engineering, Graz University of Technology, Inffeldgasse 13, A-8010 Graz, Austria; Research Center Pharmaceutical Engineering GmbH, Inffeldgasse 13, A-8010 Graz, Austria.
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Ahmad A, Lim LL, Morieri ML, Tam CHT, Cheng F, Chikowore T, Dudenhöffer-Pfeifer M, Fitipaldi H, Huang C, Kanbour S, Sarkar S, Koivula RW, Motala AA, Tye SC, Yu G, Zhang Y, Provenzano M, Sherifali D, de Souza R, Tobias DK, Gomez MF, Ma RCW, Mathioudakis NN. Precision Prognostics for Cardiovascular Disease in Type 2 Diabetes: A Systematic Review and Meta-analysis. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.04.26.23289177. [PMID: 37162891 PMCID: PMC10168509 DOI: 10.1101/2023.04.26.23289177] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/11/2023]
Abstract
Background Precision medicine has the potential to improve cardiovascular disease (CVD) risk prediction in individuals with type 2 diabetes (T2D). Methods We conducted a systematic review and meta-analysis of longitudinal studies to identify potentially novel prognostic factors that may improve CVD risk prediction in T2D. Out of 9380 studies identified, 416 studies met inclusion criteria. Outcomes were reported for 321 biomarker studies, 48 genetic marker studies, and 47 risk score/model studies. Results Out of all evaluated biomarkers, only 13 showed improvement in prediction performance. Results of pooled meta-analyses, non-pooled analyses, and assessments of improvement in prediction performance and risk of bias, yielded the highest predictive utility for N-terminal pro b-type natriuretic peptide (NT-proBNP) (high-evidence), troponin-T (TnT) (moderate-evidence), triglyceride-glucose (TyG) index (moderate-evidence), Genetic Risk Score for Coronary Heart Disease (GRS-CHD) (moderate-evidence); moderate predictive utility for coronary computed tomography angiography (low-evidence), single-photon emission computed tomography (low-evidence), pulse wave velocity (moderate-evidence); and low predictive utility for C-reactive protein (moderate-evidence), coronary artery calcium score (low-evidence), galectin-3 (low-evidence), troponin-I (low-evidence), carotid plaque (low-evidence), and growth differentiation factor-15 (low-evidence). Risk scores showed modest discrimination, with lower performance in populations different from the original development cohort. Conclusions Despite high interest in this topic, very few studies conducted rigorous analyses to demonstrate incremental predictive utility beyond established CVD risk factors for T2D. The most promising markers identified were NT-proBNP, TnT, TyG and GRS-CHD, with the highest strength of evidence for NT-proBNP. Further research is needed to determine their clinical utility in risk stratification and management of CVD in T2D.
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Sachs S, Götz A, Finan B, Feuchtinger A, DiMarchi RD, Döring Y, Weber C, Tschöp MH, Müller TD, Hofmann SM. GIP receptor agonism improves dyslipidemia and atherosclerosis independently of body weight loss in preclinical mouse model for cardio-metabolic disease. Cardiovasc Diabetol 2023; 22:217. [PMID: 37592302 PMCID: PMC10436634 DOI: 10.1186/s12933-023-01940-2] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/09/2023] [Accepted: 07/24/2023] [Indexed: 08/19/2023] Open
Abstract
BACKGROUND Agonism at the receptor for the glucose-dependent insulinotropic polypeptide (GIPR) is a key component of the novel unimolecular GIPR:GLP-1R co-agonists, which are among the most promising drugs in clinical development for the treatment of obesity and type 2 diabetes. The therapeutic effect of chronic GIPR agonism to treat dyslipidemia and thus to reduce the cardiovascular disease risk independently of body weight loss has not been explored yet. METHODS After 8 weeks on western diet, LDL receptor knockout (LDLR-/-) male mice were treated with daily subcutaneous injections of long-acting acylated GIP analog (acyl-GIP; 10nmol/kg body weight) for 28 days. Body weight, food intake, whole-body composition were monitored throughout the study. Fasting blood glucose and intraperitoneal glucose tolerance test (ipGTT) were determined on day 21 of the study. Circulating lipid levels, lipoprotein profiles and atherosclerotic lesion size was assessed at the end of the study. Acyl-GIP effects on fat depots were determined by histology and transcriptomics. RESULTS Herein we found that treatment with acyl-GIP reduced dyslipidemia and atherogenesis in male LDLR-/- mice. Acyl-GIP administration resulted in smaller adipocytes within the inguinal fat depot and RNAseq analysis of the latter revealed that acyl-GIP may improve dyslipidemia by directly modulating lipid metabolism in this fat depot. CONCLUSIONS This study identified an unanticipated efficacy of chronic GIPR agonism to improve dyslipidemia and cardiovascular disease independently of body weight loss, indicating that treatment with acyl-GIP may be a novel approach to alleviate cardiometabolic disease.
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Affiliation(s)
- Stephan Sachs
- Institute for Diabetes and Regeneration, Helmholtz Diabetes Center at Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), 85764, Neuherberg, Germany
- Institute for Diabetes and Obesity, Division of Metabolic Diseases, Helmholtz Diabetes Center at Helmholtz Centre Munich, Munich, Germany
- Technische Universität München, 80333, Munich, Germany
| | - Anna Götz
- Institute for Diabetes and Regeneration, Helmholtz Diabetes Center at Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), 85764, Neuherberg, Germany
- Institute for Diabetes and Obesity, Division of Metabolic Diseases, Helmholtz Diabetes Center at Helmholtz Centre Munich, Munich, Germany
| | - Brian Finan
- Novo Nordisk Research Center Indianapolis, Indianapolis, IN, USA
| | - Annette Feuchtinger
- Research Unit Analytical Pathology, Helmholtz Center Munich, 85764, Neuherberg, Germany
| | | | - Yvonne Döring
- Department of Angiology, Swiss Cardiovascular Center, Inselspital, Bern University Hospital, University of Bern, Bern, Switzerland
- Institute for Cardiovascular Prevention (IPEK), Ludwig-Maximilians-University Munich, Munich, Germany
- DZHK (German Centre for Cardiovascular Research), Partner Site Munich Heart Alliance, Munich, Germany
| | - Christian Weber
- Institute for Cardiovascular Prevention (IPEK), Ludwig-Maximilians-University Munich, Munich, Germany
- DZHK (German Centre for Cardiovascular Research), Partner Site Munich Heart Alliance, Munich, Germany
- Department of Biochemistry, Cardiovascular Research Institute Maastricht (CARIM), Maastricht University, Maastricht, the Netherlands
- Munich Cluster for Systems Neurology (SyNergy), Munich, Germany
| | - Matthias H Tschöp
- Institute for Diabetes and Obesity, Division of Metabolic Diseases, Helmholtz Diabetes Center at Helmholtz Centre Munich, Munich, Germany
- Technische Universität München, 80333, Munich, Germany
- German Center for Diabetes Research (DZD), 85764, Neuherberg, Germany
| | - Timo D Müller
- Institute for Diabetes and Obesity, Division of Metabolic Diseases, Helmholtz Diabetes Center at Helmholtz Centre Munich, Munich, Germany.
- German Center for Diabetes Research (DZD), 85764, Neuherberg, Germany.
| | - Susanna M Hofmann
- Institute for Diabetes and Regeneration, Helmholtz Diabetes Center at Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), 85764, Neuherberg, Germany.
- German Center for Diabetes Research (DZD), 85764, Neuherberg, Germany.
- Department of Medicine IV, University Hospital, LMU Munich, Munich, Germany.
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Bodhini D, Morton RW, Santhakumar V, Nakabuye M, Pomares-Millan H, Clemmensen C, Fitzpatrick SL, Guasch-Ferre M, Pankow JS, Ried-Larsen M, Franks PW, Tobias DK, Merino J, Mohan V, Loos RJF. Role of sociodemographic, clinical, behavioral, and molecular factors in precision prevention of type 2 diabetes: a systematic review. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.05.03.23289433. [PMID: 37205385 PMCID: PMC10187453 DOI: 10.1101/2023.05.03.23289433] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Abstract
The variability in the effectiveness of type 2 diabetes (T2D) preventive interventions highlights the potential to identify the factors that determine treatment responses and those that would benefit the most from a given intervention. We conducted a systematic review to synthesize the evidence to support whether sociodemographic, clinical, behavioral, and molecular characteristics modify the efficacy of dietary or lifestyle interventions to prevent T2D. Among the 80 publications that met our criteria for inclusion, the evidence was low to very low to attribute variability in intervention effectiveness to individual characteristics such as age, sex, BMI, race/ethnicity, socioeconomic status, baseline behavioral factors, or genetic predisposition. We found evidence, albeit low certainty, to support conclusions that those with poorer health status, particularly those with prediabetes at baseline, tend to benefit more from T2D prevention strategies compared to healthier counterparts. Our synthesis highlights the need for purposefully designed clinical trials to inform whether individual factors influence the success of T2D prevention strategies.
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Rossing P, Frimodt-Møller M, Persson F. Precision Medicine and/or Biomarker Based Therapy in T2DM: Ready for Prime Time? Semin Nephrol 2023; 43:151430. [PMID: 37862744 DOI: 10.1016/j.semnephrol.2023.151430] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/22/2023]
Abstract
Approximately 30-40% of people with type 2 diabetes mellitus develop chronic kidney disease. This is characterised by elevated blood pressure, declining kidney function and enhanced cardiovascular morbidity and mortality. Increased albuminuria and decreasing estimated glomerular function has to be evaluated regularly to diagsnose kidney disease. New biomarkers may facilitate early diagnosis and provide infomation on undlying pathology thereby supporting early precision intervention for the optimal benefit. A number of biomarkers have been suggested but are not yet implemented in clinical practice. iI the future such bimarkers may pave the way for personalized treatment.
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Affiliation(s)
- Peter Rossing
- Complications Research, Steno Diabetes Center Copenhagen, Herlev, Denmark; Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark.
| | | | - Frederik Persson
- Complications Research, Steno Diabetes Center Copenhagen, Herlev, Denmark
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Young KG, McInnes EH, Massey RJ, Kahkohska AR, Pilla SJ, Raghaven S, Stanislawski MA, Tobias DK, McGovern AP, Dawed AY, Jones AG, Pearson ER, Dennis JM. Precision medicine in type 2 diabetes: A systematic review of treatment effect heterogeneity for GLP1-receptor agonists and SGLT2-inhibitors. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.04.21.23288868. [PMID: 37131814 PMCID: PMC10153311 DOI: 10.1101/2023.04.21.23288868] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Background A precision medicine approach in type 2 diabetes requires identification of clinical and biological features that are reproducibly associated with differences in clinical outcomes with specific anti-hyperglycaemic therapies. Robust evidence of such treatment effect heterogeneity could support more individualized clinical decisions on optimal type 2 diabetes therapy. Methods We performed a pre-registered systematic review of meta-analysis studies, randomized control trials, and observational studies evaluating clinical and biological features associated with heterogenous treatment effects for SGLT2-inhibitor and GLP1-receptor agonist therapies, considering glycaemic, cardiovascular, and renal outcomes. Results After screening 5,686 studies, we included 101 studies of SGLT2-inhibitors and 75 studies of GLP1-receptor agonists in the final systematic review. The majority of papers had methodological limitations precluding robust assessment of treatment effect heterogeneity. For glycaemic outcomes, most cohorts were observational, with multiple analyses identifying lower renal function as a predictor of lesser glycaemic response with SGLT2-inhibitors and markers of reduced insulin secretion as predictors of lesser response with GLP1-receptor agonists. For cardiovascular and renal outcomes, the majority of included studies were post-hoc analyses of randomized control trials (including meta-analysis studies) which identified limited clinically relevant treatment effect heterogeneity. Conclusions Current evidence on treatment effect heterogeneity for SGLT2-inhibitor and GLP1-receptor agonist therapies is limited, likely reflecting the methodological limitations of published studies. Robust and appropriately powered studies are required to understand type 2 diabetes treatment effect heterogeneity and evaluate the potential for precision medicine to inform future clinical care. Plain language summary This review identifies research that helps understand which clinical and biological factors that are associated with different outcomes for specific type 2 diabetes treatments. This information could help clinical providers and patients make better informed personalized decisions about type 2 diabetes treatments. We focused on two common type 2 diabetes treatments: SGLT2-inhibitors and GLP1-receptor agonists, and three outcomes: blood glucose control, heart disease, and kidney disease. We identified some potential factors that are likely to lessen blood glucose control including lower kidney function for SGLT2-inhibitors and lower insulin secretion for GLP1-receptor agonists. We did not identify clear factors that alter heart and renal disease outcomes for either treatment. Most of the studies had limitations, meaning more research is needed to fully understand the factors that influence treatment outcomes in type 2 diabetes.
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Affiliation(s)
- Katherine G Young
- Exeter Centre of Excellence in Diabetes (EXCEED), University of Exeter Medical School, RILD Building, Royal Devon & Exeter Hospital, Barrack Road, Exeter, UK
| | - Eram Haider McInnes
- Division of Population Health & Genomics, School of Medicine, University of Dundee, Dundee, UK
| | - Robert J Massey
- Division of Population Health & Genomics, School of Medicine, University of Dundee, Dundee, UK
| | - Anna R Kahkohska
- Department of Nutrition, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Scott J Pilla
- Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Sridharan Raghaven
- Section of Academic Primary Care, US Department of Veterans Affairs Eastern Colorado Health Care System, Aurora, CO, USA
| | - Maggie A Stanislawski
- Department of Biomedical Informatics, School of Medicine, University of Colorado, Aurora, USA, 80045
| | - Deirdre K Tobias
- Department of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Medicine, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA, USA
| | - Andrew P McGovern
- Exeter Centre of Excellence in Diabetes (EXCEED), University of Exeter Medical School, RILD Building, Royal Devon & Exeter Hospital, Barrack Road, Exeter, UK
| | - Adem Y Dawed
- Division of Population Health & Genomics, School of Medicine, University of Dundee, Dundee, UK
| | - Angus G Jones
- Exeter Centre of Excellence in Diabetes (EXCEED), University of Exeter Medical School, RILD Building, Royal Devon & Exeter Hospital, Barrack Road, Exeter, UK
| | - Ewan R Pearson
- Division of Population Health & Genomics, School of Medicine, University of Dundee, Dundee, UK
| | - John M Dennis
- Exeter Centre of Excellence in Diabetes (EXCEED), University of Exeter Medical School, RILD Building, Royal Devon & Exeter Hospital, Barrack Road, Exeter, UK
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Semple RK, Patel KA, Auh S, Brown RJ. Systematic review of genotype-stratified treatment for monogenic insulin resistance. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.04.17.23288671. [PMID: 37205502 PMCID: PMC10187355 DOI: 10.1101/2023.04.17.23288671] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Abstract
Objective To assess the effects of pharmacologic and/or surgical interventions in monogenic insulin resistance (IR), stratified by genetic aetiology. Design Systematic review. Data sources PubMed, MEDLINE and Embase, from 1 January 1987 to 23 June 2021. Review methods Studies reporting individual-level effects of pharmacologic and/or surgical interventions in monogenic IR were eligible. Individual subject data were extracted and duplicate data removed. Outcomes were analyzed for each affected gene and intervention, and in aggregate for partial, generalised and all lipodystrophy. Results 10 non-randomised experimental studies, 8 case series, and 21 single case reports met inclusion criteria, all rated as having moderate or serious risk of bias. Metreleptin was associated with lower triglycerides and hemoglobin A1c in aggregated lipodystrophy (n=111), in partial lipodystrophy (n=71) and generalised lipodystrophy (n=41)), and in LMNA , PPARG , AGPAT2 or BSCL2 subgroups (n=72,13,21 and 21 respectively). Body Mass Index (BMI) was lower after treatment in partial and generalised lipodystrophy overall, and in LMNA or BSCL2 , but not PPARG or AGPAT2 subgroups. Thiazolidinedione use was associated with improved hemoglobin A1c and triglycerides in aggregated lipodystrophy (n=13), improved hemoglobin A1c only in the PPARG subgroup (n=5), and improved triglycerides only in the LMNA subgroup (n=7). In INSR -related IR, use of rhIGF-1, alone or with IGFBP3, was associated with improved hemoglobin A1c (n=15). The small size or absence of all other genotype-treatment combinations precluded firm conclusions. Conclusions The evidence guiding genotype-specific treatment of monogenic IR is of low to very low quality. Metreleptin and Thiazolidinediones appear to have beneficial metabolic effects in lipodystrophy, and rhIGF-1 appears to lower hemoglobin A1c in INSR-related IR. For other interventions there is insufficient evidence to assess efficacy and risks either in aggregated lipodystrophy or in genetic subgroups. There is a pressing need to improve the evidence base for management of monogenic IR.
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Affiliation(s)
- Robert K. Semple
- Centre for Cardiovascular Science, University of Edinburgh, Edinburgh, UK
- MRC Human Genetics Unit, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | - Kashyap A. Patel
- Institute of Biomedical and Clinical Science, University of Exeter Medical School, Exeter, UK
- Department of Diabetes and Endocrinology, Royal Devon and Exeter NHS Foundation Trust, Exeter, UK
| | - Sungyoung Auh
- Office of the Clinical Director, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD, USA
| | - ADA/EASD PMDI
- American Diabetes Association/European Association for the Study of Diabetes Precision Medicine in Diabetes Initiative
| | - Rebecca J. Brown
- National Institute of Diabetes and Digestive and Kidney Diseases. National Institutes of Health. Bethesda, MD, USA
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Misra S, Wagner R, Ozkan B, Schön M, Sevilla-Gonzalez M, Prystupa K, Wang CC, Kreienkamp RJ, Cromer SJ, Rooney MR, Duan D, Thuesen ACB, Wallace AS, Leong A, Deutsch AJ, Andersen MK, Billings LK, Eckel RH, Sheu WHH, Hansen T, Stefan N, Goodarzi MO, Ray D, Selvin E, Florez JC, Meigs JB, Udler MS. Systematic review of precision subclassification of type 2 diabetes. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.04.19.23288577. [PMID: 37131632 PMCID: PMC10153304 DOI: 10.1101/2023.04.19.23288577] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Heterogeneity in type 2 diabetes presentation, progression and treatment has the potential for precision medicine interventions that can enhance care and outcomes for affected individuals. We undertook a systematic review to ascertain whether strategies to subclassify type 2 diabetes are associated with improved clinical outcomes, show reproducibility and have high quality evidence. We reviewed publications that deployed 'simple subclassification' using clinical features, biomarkers, imaging or other routinely available parameters or 'complex subclassification' approaches that used machine learning and/or genomic data. We found that simple stratification approaches, for example, stratification based on age, body mass index or lipid profiles, had been widely used, but no strategy had been replicated and many lacked association with meaningful outcomes. Complex stratification using clustering of simple clinical data with and without genetic data did show reproducible subtypes of diabetes that had been associated with outcomes such as cardiovascular disease and/or mortality. Both approaches require a higher grade of evidence but support the premise that type 2 diabetes can be subclassified into meaningful groups. More studies are needed to test these subclassifications in more diverse ancestries and prove that they are amenable to interventions.
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Murphy R, Colclough K, Pollin TI, Ikle JM, Svalastoga P, Maloney KA, Saint-Martin C, Molnes J, Misra S, Aukrust I, de Franco A, Flanagan SE, Njølstad PR, Billings LK, Owen KR, Gloyn AL. A Systematic Review of the use of Precision Diagnostics in Monogenic Diabetes. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.04.15.23288269. [PMID: 37131594 PMCID: PMC10153302 DOI: 10.1101/2023.04.15.23288269] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Monogenic forms of diabetes present opportunities for precision medicine as identification of the underlying genetic cause has implications for treatment and prognosis. However, genetic testing remains inconsistent across countries and health providers, often resulting in both missed diagnosis and misclassification of diabetes type. One of the barriers to deploying genetic testing is uncertainty over whom to test as the clinical features for monogenic diabetes overlap with those for both type 1 and type 2 diabetes. In this review, we perform a systematic evaluation of the evidence for the clinical and biochemical criteria used to guide selection of individuals with diabetes for genetic testing and review the evidence for the optimal methods for variant detection in genes involved in monogenic diabetes. In parallel we revisit the current clinical guidelines for genetic testing for monogenic diabetes and provide expert opinion on the interpretation and reporting of genetic tests. We provide a series of recommendations for the field informed by our systematic review, synthesizing evidence, and expert opinion. Finally, we identify major challenges for the field and highlight areas for future research and investment to support wider implementation of precision diagnostics for monogenic diabetes.
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Affiliation(s)
- Rinki Murphy
- Department of Medicine, Faculty of Medical and Health Sciences, University of Auckland, Auckland, New Zealand
- Auckland Diabetes Centre, Te Whatu Ora Health New Zealand, Te Tokai Tumai, Auckland, New Zealand
| | - Kevin Colclough
- Exeter Genomics Laboratory, Royal Devon University Healthcare NHS Foundation Trust, Exeter, United Kingdom
| | - Toni I Pollin
- Department of Medicine, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Jennifer M Ikle
- Department of Pediatrics, Division of Endocrinology & Diabetes, Stanford School of Medicine, Stanford, CA, USA
- Stanford Diabetes Research Center, Stanford School of Medicine, Stanford, CA, USA
| | - Pernille Svalastoga
- Children and Youth Clinic, Haukeland University Hospital, Bergen, Norway
- Mohn Center for Diabetes Precision Medicine, Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Kristin A Maloney
- Department of Medicine, University of Maryland School of Medicine, Baltimore, Maryland, USA
| | - Cécile Saint-Martin
- Department of Medical Genetics, AP-HP Pitié-Salpêtrière Hospital, Sorbonne University, Paris, France
| | - Janne Molnes
- Mohn Center for Diabetes Precision Medicine, Department of Clinical Science, University of Bergen, Bergen, Norway
- Department of Medical Genetics, Haukeland University Hospital, Bergen, Norway
| | - Shivani Misra
- Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
- Department of Diabetes and Endocrinology, Imperial College Healthcare NHS Trust, London, UK
| | - Ingvild Aukrust
- Mohn Center for Diabetes Precision Medicine, Department of Clinical Science, University of Bergen, Bergen, Norway
- Department of Medical Genetics, Haukeland University Hospital, Bergen, Norway
| | - aiElisa de Franco
- Department of Medicine, Faculty of Medical and Health Sciences, University of Auckland, Auckland, New Zealand
| | - Sarah E Flanagan
- Department of Clinical and Biomedical Science, Faculty of Health and Life Sciences, University of Exeter, UK
| | - Pål R Njølstad
- Children and Youth Clinic, Haukeland University Hospital, Bergen, Norway
- Mohn Center for Diabetes Precision Medicine, Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Liana K Billings
- Division of Endocrinology, NorthShore University HealthSystem, Skokie, IL, USA; Department of Medicine, Pritzker School of Medicine, University of Chicago, Chicago, IL, USA
| | - Katharine R Owen
- Oxford Center for Diabetes, Endocrinology & Metabolism, University of Oxford, UK
- NIHR Oxford Biomedical Research Centre, Oxford, UK
| | - Anna L Gloyn
- Department of Pediatrics, Division of Endocrinology & Diabetes, Stanford School of Medicine, Stanford, CA, USA
- Stanford Diabetes Research Center, Stanford School of Medicine, Stanford, CA, USA
- Department of Genetics, Stanford School of Medicine, Stanford, CA, USA
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Semnani-Azad Z, Gaillard R, Hughes AE, Boyle KE, Tobias DK, Perng W. Predictors and risk factors of short-term and long-term outcomes among women with gestational diabetes mellitus (GDM) and their offspring: Moving toward precision prognosis? MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.04.14.23288199. [PMID: 37131686 PMCID: PMC10153333 DOI: 10.1101/2023.04.14.23288199] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
As part of the American Diabetes Association Precision Medicine in Diabetes Initiative (PMDI) - a partnership with the European Association for the Study of Diabetes (EASD) - this systematic review is part of a comprehensive evidence evaluation in support of the 2 nd International Consensus Report on Precision Diabetes Medicine. Here, we sought to synthesize evidence from empirical research papers published through September 1 st , 2021 to evaluate and identify prognostic conditions, risk factors, and biomarkers among women and children affected by gestational diabetes mellitus (GDM), focusing on clinical endpoints of cardiovascular disease (CVD) and type 2 diabetes (T2D) among women with a history of GDM; and adiposity and cardiometabolic profile among offspring exposed to GDM in utero. We identified a total of 107 observational studies and 12 randomized controlled trials testing the effect of pharmaceutical and/or lifestyle interventions. Broadly, current literature indicates that greater GDM severity, higher maternal body mass index, belonging to racial/ethnic minority group; and unhealthy lifestyle behaviors would predict a woman's risk of incident T2D and CVD, and an unfavorable cardiometabolic profile among offspring. However, the level of evidence is low (Level 4 according to the Diabetes Canada 2018 Clinical Practice Guidelines for diabetes prognosis) largely because most studies leveraged retrospective data from large registries that are vulnerable to residual confounding and reverse causation bias; and prospective cohort studies that may suffer selection and attrition bias. Moreover, for the offspring outcomes, we identified a relatively small body of literature on prognostic factors indicative of future adiposity and cardiometabolic risk. Future high-quality prospective cohort studies in diverse populations with granular data collection on prognostic factors, clinical and subclinical outcomes, high fidelity of follow-up, and appropriate analytical approaches to deal with structural biases are warranted.
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Lim S, Takele WW, Vesco KK, Redman L, Josefson J. A systematic review and meta-analysis of participant characteristics in the prevention of gestational diabetes: a summary of evidence for precision medicine. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.04.16.23288650. [PMID: 37131714 PMCID: PMC10153349 DOI: 10.1101/2023.04.16.23288650] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
Background and aims Precision prevention involves using the unique characteristics of a particular group to determine their responses to preventive interventions. This study aimed to systematically evaluate the participant characteristics associated with interventions in gestational diabetes mellitus (GDM) prevention. Methods We searched MEDLINE, EMBASE, and Pubmed to identify lifestyle (diet, physical activity, or both), metformin, myoinositol/inositol and probiotics interventions of GDM prevention published up to May 24, 2022. Results From 10347 studies, 116 studies (n=40940 women) were included. Physical activity resulted in greater GDM reduction in participants with a normal body mass index (BMI) at baseline compared to obese BMI (risk ratio, 95% confidence interval: 0.06 [0.03, 0.14] vs 0.68 [0.26, 1.60]). Diet and physical activity interventions resulted in greater GDM reduction in participants without polycystic ovary syndrome (PCOS) than those with PCOS (0.62 [0.47, 0.82] vs 1.12 [0.78-1.61]) and in those without a history of GDM than those with unspecified history (0.62 [0.47, 0.81] vs 0.85 [0.76, 0.95]). Metformin interventions were more effective in participants with PCOS than those with unspecified status (0.38 [0.19, 0.74] vs 0.59 [0.25, 1.43]), or when commenced preconception than during pregnancy (0.22 [0.11, 0.45] vs 1.15 [0.86-1.55]). Parity, history of having a large-for-gestational-age infant or family history of diabetes had no effect. Conclusions GDM prevention through metformin or lifestyle differs according to some individual characteristics. Future research should include trials commencing preconception and provide results stratified by participant characteristics including social and environmental factors, clinical traits, and other novel risk factors to predict GDM prevention through interventions. Plain language summary Precision prevention involves using a group’s unique context to determine their responses to preventive interventions. This study aimed to evaluate the participant characteristics associated with interventions in GDM prevention. We searched medical literature databases to identify lifestyle (diet, physical activity), metformin, myoinositol/inositol and probiotics interventions. A total of 116 studies (n=40903 women) were included. Diet and physical activity interventions resulted in greater GDM reduction in participants without polycystic ovary syndrome (PCOS) and those without a history of GDM. Metformin interventions resulted in greater GDM reduction in participants with PCOS or when started during the preconception period. Future research should include trials starting in the preconception period, and provide results stratified by participant characteristics to predict GDM prevention through interventions.
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Narayan KMV, Varghese JS, Beyh YS, Bhattacharyya S, Khandelwal S, Krishnan GS, Siegel KR, Thomas T, Kurpad AV. A Strategic Research Framework for Defeating Diabetes in India: A 21st-Century Agenda. J Indian Inst Sci 2023; 103:1-22. [PMID: 37362852 PMCID: PMC10029804 DOI: 10.1007/s41745-022-00354-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/05/2022] [Accepted: 12/14/2022] [Indexed: 03/24/2023]
Abstract
Indian people are at high risk for type 2 diabetes (T2DM) even at younger ages and lower body weights. Already 74 million people in India have the disease, and the proportion of those with T2DM is increasing across all strata of society. Unique aspects, related to lower insulin secretion or function, and higher hepatic fat deposition, accompanied by the rise in overweight (related to lifestyle changes) may all be responsible for this unrelenting epidemic of T2DM. Yet, research to understand the causes, pathophysiology, phenotypes, prevention, treatment, and healthcare delivery of T2DM in India seriously lags behind. There are major opportunities for scientific discovery and technological innovation, which if tapped can generate solutions for T2DM relevant to the country's context and make leading contributions to global science. We analyze the situation of T2DM in India, and present a four-pillar (etiology, precision medicine, implementation research, and health policy) strategic research framework to tackle the challenge. We offer key research questions for each pillar, and identify infrastructure needs. India offers a fertile environment for shifting the paradigm from imprecise late-stage diabetes treatment toward early-stage precision prevention and care. Investing in and leveraging academic and technological infrastructures, across the disciplines of science, engineering, and medicine, can accelerate progress toward a diabetes-free nation.
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Affiliation(s)
- K. M. Venkat Narayan
- Hubert Department of Global Health, Rollins School of Public Health, Emory University, Atlanta, GA 30322 USA
- Emory Global Diabetes Research Center, Woodruff Health Sciences Center, Emory University, Atlanta, GA 30322 USA
| | - Jithin Sam Varghese
- Hubert Department of Global Health, Rollins School of Public Health, Emory University, Atlanta, GA 30322 USA
- Emory Global Diabetes Research Center, Woodruff Health Sciences Center, Emory University, Atlanta, GA 30322 USA
| | - Yara S. Beyh
- Laney Graduate School, Nutrition and Health Sciences Doctoral Program, Emory University, Atlanta, USA
| | | | | | - Gokul S. Krishnan
- Robert Bosch Centre for Data Science and Artificial Intelligence, Indian Institute of Technology Madras, Chennai, India
| | - Karen R. Siegel
- Hubert Department of Global Health, Rollins School of Public Health, Emory University, Atlanta, GA 30322 USA
- Emory Global Diabetes Research Center, Woodruff Health Sciences Center, Emory University, Atlanta, GA 30322 USA
| | - Tinku Thomas
- Department of Biostatistics, St. John’s Medical College, Bengaluru, India
| | - Anura V. Kurpad
- Department of Physiology, St. John’s Medical College, Bengaluru, India
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Type 2 Diabetes Prevention Programs-From Proof-of-Concept Trials to National Intervention and Beyond. J Clin Med 2023; 12:jcm12051876. [PMID: 36902668 PMCID: PMC10003211 DOI: 10.3390/jcm12051876] [Citation(s) in RCA: 5] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/15/2023] [Revised: 02/19/2023] [Accepted: 02/21/2023] [Indexed: 03/08/2023] Open
Abstract
The prevention of type 2 diabetes (T2D) in high-risk people with lifestyle interventions has been demonstrated by several randomized controlled trials. The intervention effect has sustained up to 20 years in post-trial monitoring of T2D incidence. In 2000, Finland launched the national T2D prevention plan. For screening for high T2D risk, the non-laboratory Finnish Diabetes Risk Score was developed and widely used, also in other countries. The incidence of drug-treated T2D has decreased steadily since 2010. The US congress authorized public funding for a national diabetes prevention program (NDPP) in 2010. It was built around a 16-visit program that relies on referral from primary care and self-referral of persons with either prediabetes or by a diabetes risk test. The program uses a train-the-trainer program. In 2015 the program started the inclusion of online programs. There has been limited implementation of nationwide T2D prevention programs in other countries. Despite the convincing results from RCTs in China and India, no translation to the national level was introduced there. T2D prevention efforts in low-and middle-income countries are still limited, but results have been promising. Barriers to efficient interventions are greater in these countries than in high-income countries, where many barriers also exist. Health disparities by socioeconomic status exist for T2D and its risk factors and form a challenge for preventive interventions. It seems that a stronger commitment to T2D prevention is needed, such as the successful WHO Framework Convention on Tobacco Control, which legally binds the countries to act.
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Mao Q, Zhou D, Sun Y, Zhao J, Xu S, Zhao X. Independent association of blood cadmium with subclinical lower extremity atherosclerosis: An observational study based on dose-response analysis. CHEMOSPHERE 2023; 313:137441. [PMID: 36470359 DOI: 10.1016/j.chemosphere.2022.137441] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/01/2022] [Revised: 11/26/2022] [Accepted: 11/28/2022] [Indexed: 06/17/2023]
Abstract
BACKGROUND Atherosclerosis is an increasingly public health issue globally. Previous studies have showed a causal link between heavy metal exposure and atherosclerosis. However, the association of cadmium concentration with subclinical lower extremity atherosclerosis (SLEA) remains unclear. AIMS To investigate the association of blood cadmium with SLEA and its extent, and further analyze the potential dose-response relationship. METHODS Blood cadmium concentration was measured using inductively coupled plasma mass spectrometry. SLEA and its extent were assessed by ultrasound diagnosis system. Multivariate models were applied to evaluate the association of blood cadmium with SLEA and its extent. Restricted cubic splines were performed to explore the potential dose-response relationship. RESULTS This observational study consisted of 1664 participants from cardiovascular outpatient, with an average age of 62.4 years and 1218 (73.2%) men. When blood cadmium was included as a categorical variable in multivariate models, logistic regression analysis showed that high quartile in blood cadmium was an independent risk factor of SLEA (OR = 2.704, 95%CI 1.866-3.919). After log-transformed for SLEA extent parameters, linear regression analysis indicated that high quartile in blood cadmium was significantly associated with higher Crouse score (GMR = 1.21, 95%CI 1.15-1.28), plaque maximum thickness (GMR = 1.13, 95%CI 1.09-1.18) and diseased vessel count (GMR = 1.14, 95%CI 1.10-1.19), respectively. When blood cadmium was used as a continuous variable in restricted cubic splines, the dose-response relationship presented a positive progression in SLEA (P = 0.302), plaque maximum thickness (P = 0.145) and diseased vessel count (P = 0.055) apparently that did not deviate from linearity. CONCLUSIONS Blood cadmium exhibited an independent association with SLEA, and this dose-response relationship was progressive without significant departure from linearity.
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Affiliation(s)
- Qi Mao
- Department of Cardiology, Institute of Cardiovascular Research, Xinqiao Hospital, Army Medical University, Chongqing 400037, China.
| | - Denglu Zhou
- Department of Cardiology, Institute of Cardiovascular Research, Xinqiao Hospital, Army Medical University, Chongqing 400037, China.
| | - Yapei Sun
- School of Public Health, Nanjing Medical University, Nanjing 211166, China; Center of Laboratory Medicine, Chongqing Prevention and Treatment Center for Occupational Diseases, Chongqing 400060, China; Chongqing Key Laboratory of Prevention and Treatment for Occupational Diseases and Poisoning, Chongqing 400060, China.
| | - Jianhua Zhao
- Department of Cardiology, Institute of Cardiovascular Research, Xinqiao Hospital, Army Medical University, Chongqing 400037, China.
| | - Shangcheng Xu
- School of Public Health, Nanjing Medical University, Nanjing 211166, China; Center of Laboratory Medicine, Chongqing Prevention and Treatment Center for Occupational Diseases, Chongqing 400060, China; Chongqing Key Laboratory of Prevention and Treatment for Occupational Diseases and Poisoning, Chongqing 400060, China.
| | - Xiaohui Zhao
- Department of Cardiology, Institute of Cardiovascular Research, Xinqiao Hospital, Army Medical University, Chongqing 400037, China.
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Mannino GC, Mancuso E, Sbrignadello S, Morettini M, Andreozzi F, Tura A. Chemical Compounds and Ambient Factors Affecting Pancreatic Alpha-Cells Mass and Function: What Evidence? INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2022; 19:16489. [PMID: 36554367 PMCID: PMC9778390 DOI: 10.3390/ijerph192416489] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/28/2022] [Revised: 12/02/2022] [Accepted: 12/04/2022] [Indexed: 06/17/2023]
Abstract
The exposure to different substances present in the environment can affect the ability of the human body to maintain glucose homeostasis. Some review studies summarized the current evidence about the relationships between environment and insulin resistance or beta-cell dysfunction. Instead, no reviews focused on the relationships between the environment and the alpha cell, although in recent years clear indications have emerged for the pivotal role of the alpha cell in glucose regulation. Thus, the aim of this review was to analyze the studies about the effects of chemical, biological, and physical environmental factors on the alpha cell. Notably, we found studies focusing on the effects of different categories of compounds, including air pollutants, compounds of known toxicity present in common objects, pharmacological agents, and compounds possibly present in food, plus studies on the effects of physical factors (mainly heat exposure). However, the overall number of relevant studies was limited, especially when compared to studies related to the environment and insulin sensitivity or beta-cell function. In our opinion, this was likely due to the underestimation of the alpha-cell role in glucose homeostasis, but since such a role has recently emerged with increasing strength, we expect several new studies about the environment and alpha-cell in the near future.
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Affiliation(s)
- Gaia Chiara Mannino
- Department of Medical and Surgical Sciences, Magna Graecia University of Catanzaro, 88100 Catanzaro, Italy
| | - Elettra Mancuso
- Department of Medical and Surgical Sciences, Magna Graecia University of Catanzaro, 88100 Catanzaro, Italy
| | | | - Micaela Morettini
- Department of Information Engineering, Università Politecnica delle Marche, 60131 Ancona, Italy
| | - Francesco Andreozzi
- Department of Medical and Surgical Sciences, Magna Graecia University of Catanzaro, 88100 Catanzaro, Italy
| | - Andrea Tura
- CNR Institute of Neuroscience, 35127 Padova, Italy
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Chemello G, Salvatori B, Morettini M, Tura A. Artificial Intelligence Methodologies Applied to Technologies for Screening, Diagnosis and Care of the Diabetic Foot: A Narrative Review. BIOSENSORS 2022; 12:985. [PMID: 36354494 PMCID: PMC9688674 DOI: 10.3390/bios12110985] [Citation(s) in RCA: 12] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/27/2022] [Revised: 10/26/2022] [Accepted: 11/04/2022] [Indexed: 06/16/2023]
Abstract
Diabetic foot syndrome is a multifactorial pathology with at least three main etiological factors, i.e., peripheral neuropathy, peripheral arterial disease, and infection. In addition to complexity, another distinctive trait of diabetic foot syndrome is its insidiousness, due to a frequent lack of early symptoms. In recent years, it has become clear that the prevalence of diabetic foot syndrome is increasing, and it is among the diabetes complications with a stronger impact on patient's quality of life. Considering the complex nature of this syndrome, artificial intelligence (AI) methodologies appear adequate to address aspects such as timely screening for the identification of the risk for foot ulcers (or, even worse, for amputation), based on appropriate sensor technologies. In this review, we summarize the main findings of the pertinent studies in the field, paying attention to both the AI-based methodological aspects and the main physiological/clinical study outcomes. The analyzed studies show that AI application to data derived by different technologies provides promising results, but in our opinion future studies may benefit from inclusion of quantitative measures based on simple sensors, which are still scarcely exploited.
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Affiliation(s)
- Gaetano Chemello
- CNR Institute of Neuroscience, Corso Stati Uniti 4, 35127 Padova, Italy
| | | | - Micaela Morettini
- Department of Information Engineering, Università Politecnica delle Marche, Via Brecce Bianche, 12, 60131 Ancona, Italy
| | - Andrea Tura
- CNR Institute of Neuroscience, Corso Stati Uniti 4, 35127 Padova, Italy
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Salvatori B, Linder T, Eppel D, Morettini M, Burattini L, Göbl C, Tura A. TyGIS: improved triglyceride-glucose index for the assessment of insulin sensitivity during pregnancy. Cardiovasc Diabetol 2022; 21:215. [PMID: 36258194 PMCID: PMC9580191 DOI: 10.1186/s12933-022-01649-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/25/2022] [Accepted: 09/28/2022] [Indexed: 11/21/2022] Open
Abstract
Background The triglyceride-glucose index (TyG) has been proposed as a surrogate marker of insulin resistance, which is a typical trait of pregnancy. However, very few studies analyzed TyG performance as marker of insulin resistance in pregnancy, and they were limited to insulin resistance assessment at fasting rather than in dynamic conditions, i.e., during an oral glucose tolerance test (OGTT), which allows more reliable assessment of the actual insulin sensitivity impairment. Thus, first aim of the study was exploring in pregnancy the relationships between TyG and OGTT-derived insulin sensitivity. In addition, we developed a new version of TyG, for improved performance as marker of insulin resistance in pregnancy. Methods At early pregnancy, a cohort of 109 women underwent assessment of maternal biometry and blood tests at fasting, for measurements of several variables (visit 1). Subsequently (26 weeks of gestation) all visit 1 analyses were repeated (visit 2), and a subgroup of women (84 selected) received a 2 h-75 g OGTT (30, 60, 90, and 120 min sampling) with measurement of blood glucose, insulin and C-peptide for reliable assessment of insulin sensitivity (PREDIM index) and insulin secretion/beta-cell function. The dataset was randomly split into 70% training set and 30% test set, and by machine learning approach we identified the optimal model, with TyG included, showing the best relationship with PREDIM. For inclusion in the model, we considered only fasting variables, in agreement with TyG definition. Results The relationship of TyG with PREDIM was weak. Conversely, the improved TyG, called TyGIS, (linear function of TyG, body weight, lean body mass percentage and fasting insulin) resulted much strongly related to PREDIM, in both training and test sets (R2 > 0.64, p < 0.0001). Bland–Altman analysis and equivalence test confirmed the good performance of TyGIS in terms of association with PREDIM. Different further analyses confirmed TyGIS superiority over TyG. Conclusions We developed an improved version of TyG, as new surrogate marker of insulin sensitivity in pregnancy (TyGIS). Similarly to TyG, TyGIS relies only on fasting variables, but its performances are remarkably improved than those of TyG. Supplementary Information The online version contains supplementary material available at 10.1186/s12933-022-01649-8.
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Affiliation(s)
| | - Tina Linder
- Department of Obstetrics and Gynaecology, Medical University of Vienna, 1090, Vienna, Austria
| | - Daniel Eppel
- Department of Obstetrics and Gynaecology, Medical University of Vienna, 1090, Vienna, Austria
| | - Micaela Morettini
- Department of Information Engineering, Università Politecnica Delle Marche, 60131, Ancona, Italy
| | - Laura Burattini
- Department of Information Engineering, Università Politecnica Delle Marche, 60131, Ancona, Italy
| | - Christian Göbl
- Department of Obstetrics and Gynaecology, Medical University of Vienna, 1090, Vienna, Austria
| | - Andrea Tura
- CNR Institute of Neuroscience, Corso Stati Uniti 4, 35127, Padua, Italy.
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Precision diabetes is becoming a reality in India. PROCEEDINGS OF THE INDIAN NATIONAL SCIENCE ACADEMY 2022. [DOI: 10.1007/s43538-022-00115-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/27/2022]
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Tosur M, Philipson LH. Precision diabetes: Lessons learned from maturity-onset diabetes of the young (MODY). J Diabetes Investig 2022; 13:1465-1471. [PMID: 35638342 PMCID: PMC9434589 DOI: 10.1111/jdi.13860] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/22/2022] [Revised: 05/20/2022] [Accepted: 05/27/2022] [Indexed: 11/28/2022] Open
Abstract
Maturity-onset of diabetes of the young (MODY) are monogenic forms of diabetes characterized by early onset diabetes with autosomal dominant inheritance. Since its first description about six decades ago, there have been significant advancements in our understanding of MODY from clinical presentations to molecular diagnostics and therapeutic responses. The prevalence of MODY is estimated as at least 1.1-6.5% of the pediatric diabetes population with a high degree of geographic variability that might arise from several factors in the criteria used to ascertain cases. GCK-MODY, HNF1A-MODY, and HNF4A-MODY account for >90% of MODY cases. While some MODY forms do not require treatment (i.e., GCK-MODY), some others are highly responsive to oral agents (i.e., HNF1A-MODY). The risk of micro- and macro-vascular complications of diabetes also differ significantly between MODY forms. Despite its high clinical impact, 50-90% of MODY cases are estimated to be misdiagnosed as type 1 or type 2 diabetes. Although there are many clinical features suggestive of MODY diagnosis, there is no single clinical criterion. An online MODY Risk Calculator can be a useful tool for clinicians in the decision-making process for MODY genetic testing in some situations. Molecular genetic tests with a commercial gene panel should be performed in cases with a suspicion of MODY. Unresolved atypical cases can be further studied by exome or genome sequencing in a clinical or research setting, as available.
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Affiliation(s)
- Mustafa Tosur
- The Division of Diabetes and Endocrinology, Department of Pediatrics, Baylor College of MedicineTexas Children's HospitalHoustonTexasUSA
| | - Louis H Philipson
- Departments of Medicine and Pediatrics, Kovler Diabetes CenterUniversity of ChicagoChicagoIllinoisUSA
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