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Chaiyasoot K, Sakai NS, Zakeri R, Makaronidis J, Crisóstomo L, Alves MG, Gan W, Firman C, Jassil FC, Hall-Craggs MA, Taylor SA, Batterham RL. Weight-loss Independent Clinical and Metabolic Biomarkers Associated with Type 2 Diabetes Remission Post-bariatric/metabolic Surgery. Obes Surg 2023; 33:3988-3998. [PMID: 37910328 PMCID: PMC10687127 DOI: 10.1007/s11695-023-06905-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/10/2023] [Revised: 10/04/2023] [Accepted: 10/11/2023] [Indexed: 11/03/2023]
Abstract
PURPOSE Remission of type 2 diabetes (T2D) can be achieved by many, but not all, people following bariatric/metabolic surgery. The mechanisms underlying T2D remission remain incompletely understood. This observational study aimed to identify novel weight-loss independent clinical, metabolic and genetic factors that associate with T2D remission using comprehensive phenotyping. MATERIALS AND METHODS Ten patients without T2D remission (non-remitters) were matched to 10 patients with T2D remission (remitters) for age, sex, type of surgery, body weight, BMI, post-operative weight loss, duration from surgery and duration of T2D. Detailed body composition assessed using magnetic resonance imaging, gut hormones, serum metabolomics, insulin sensitivity, and genetic risk scores for T2D and anthropometric traits were assessed. RESULTS Remitters had significantly greater β-cell function and circulating acyl ghrelin levels, but lower visceral adipose tissue (VAT): subcutaneous adipose tissue (SAT) ratio than non-remitters. Branched-chain amino acids (BCAAs) and VLDL particle size were the most discriminant metabolites between groups. A significant positive correlation between, VAT area, VAT:SAT ratio and circulating levels of BCAAs was observed, whereas a significant negative correlation between BCAAs and β-cell function was revealed. CONCLUSION We highlight a potentially novel relationship between VAT and BCAAs, which may play a role in glucoregulatory control. Improvement in β-cell function, and the role ghrelin plays in its recovery, is likely another key factor influencing T2D remission post-surgery. These findings suggest that adjunctive approaches that target VAT loss and restoration of BCAA metabolism might achieve higher rates of long-term T2D remission post-surgery.
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Affiliation(s)
- Kusuma Chaiyasoot
- Department of Medicine, Centre for Obesity Research, University College London, London, UK
- Division of Nutrition, Department of Medicine, Faculty of Medicine Siriraj Hospital, Mahidol University, Bangkok, Thailand
- The Siriraj Center of Research Excellence for Diabetes and Obesity (SiCORE-DO), Mahidol University, Bangkok, Thailand
| | | | - Roxanna Zakeri
- Department of Medicine, Centre for Obesity Research, University College London, London, UK
| | - Janine Makaronidis
- Department of Medicine, Centre for Obesity Research, University College London, London, UK
- National Institute of Health Research, University College London Hospitals Biomedical Research Centre, London, UK
| | - Luís Crisóstomo
- Department of Immunophysiology and Pharmacology, ICBAS - School of Medicine and Biomedical Sciences, UMIB - Unit for Multidisciplinary Research in Biomedicine, University of Porto, Porto, Portugal
- ITR - Laboratory for Integrative and Translational Research in Population Health, Porto, Portugal
- Institute of Biomedicine, University of Turku, Turku, Finland
| | - Marco G Alves
- Department of Immunophysiology and Pharmacology, ICBAS - School of Medicine and Biomedical Sciences, UMIB - Unit for Multidisciplinary Research in Biomedicine, University of Porto, Porto, Portugal
- ITR - Laboratory for Integrative and Translational Research in Population Health, Porto, Portugal
| | - Wei Gan
- Genetics Department, Novo Nordisk Research Centre Oxford, Innovation Building, Old Road Campus, Headington, OX37LQ, UK
| | - Chloe Firman
- Department of Medicine, Centre for Obesity Research, University College London, London, UK
| | - Friedrich C Jassil
- Department of Medicine, Centre for Obesity Research, University College London, London, UK
| | - Margaret A Hall-Craggs
- UCL Centre for Medical Imaging, London, UK
- National Institute of Health Research, University College London Hospitals Biomedical Research Centre, London, UK
| | - Stuart A Taylor
- UCL Centre for Medical Imaging, London, UK
- National Institute of Health Research, University College London Hospitals Biomedical Research Centre, London, UK
| | - Rachel L Batterham
- Department of Medicine, Centre for Obesity Research, University College London, London, UK.
- National Institute of Health Research, University College London Hospitals Biomedical Research Centre, London, UK.
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Kim H, Westerman KE, Smith K, Chiou J, Cole JB, Majarian T, von Grotthuss M, Kwak SH, Kim J, Mercader JM, Florez JC, Gaulton K, Manning AK, Udler MS. High-throughput genetic clustering of type 2 diabetes loci reveals heterogeneous mechanistic pathways of metabolic disease. Diabetologia 2023; 66:495-507. [PMID: 36538063 PMCID: PMC10108373 DOI: 10.1007/s00125-022-05848-6] [Citation(s) in RCA: 17] [Impact Index Per Article: 17.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/05/2022] [Accepted: 10/28/2022] [Indexed: 12/24/2022]
Abstract
AIMS/HYPOTHESIS Type 2 diabetes is highly polygenic and influenced by multiple biological pathways. Rapid expansion in the number of type 2 diabetes loci can be leveraged to identify such pathways. METHODS We developed a high-throughput pipeline to enable clustering of type 2 diabetes loci based on variant-trait associations. Our pipeline extracted summary statistics from genome-wide association studies (GWAS) for type 2 diabetes and related traits to generate a matrix of 323 variants × 64 trait associations and applied Bayesian non-negative matrix factorisation (bNMF) to identify genetic components of type 2 diabetes. Epigenomic enrichment analysis was performed in 28 cell types and single pancreatic cells. We generated cluster-specific polygenic scores and performed regression analysis in an independent cohort (N=25,419) to assess for clinical relevance. RESULTS We identified ten clusters of genetic loci, recapturing the five from our prior analysis as well as novel clusters related to beta cell dysfunction, pronounced insulin secretion, and levels of alkaline phosphatase, lipoprotein A and sex hormone-binding globulin. Four clusters related to mechanisms of insulin deficiency, five to insulin resistance and one had an unclear mechanism. The clusters displayed tissue-specific epigenomic enrichment, notably with the two beta cell clusters differentially enriched in functional and stressed pancreatic beta cell states. Additionally, cluster-specific polygenic scores were differentially associated with patient clinical characteristics and outcomes. The pipeline was applied to coronary artery disease and chronic kidney disease, identifying multiple overlapping clusters with type 2 diabetes. CONCLUSIONS/INTERPRETATION Our approach stratifies type 2 diabetes loci into physiologically interpretable genetic clusters associated with distinct tissues and clinical outcomes. The pipeline allows for efficient updating as additional GWAS become available and can be readily applied to other conditions, facilitating clinical translation of GWAS findings. Software to perform this clustering pipeline is freely available.
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Affiliation(s)
- Hyunkyung Kim
- Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Kenneth E Westerman
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Kirk Smith
- Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Joshua Chiou
- Department of Pediatrics, University of California San Diego, San Diego, CA, USA
| | - Joanne B Cole
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
- Division of Endocrinology, Boston Children's Hospital, Boston, MA, USA
- Department of Biomedical Informatics, University of Colorado School of Medicine, Aurora, CO, USA
| | | | - Marcin von Grotthuss
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Takeda Pharmaceuticals, Cambridge, MA, USA
| | - Soo Heon Kwak
- Department of Internal Medicine, Seoul National University Hospital, Seoul, Republic of Korea
| | - Jaegil Kim
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- GlaxoSmithKline, Cambridge, MA, USA
| | - Josep M Mercader
- Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Jose C Florez
- Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Kyle Gaulton
- Department of Pediatrics, University of California San Diego, San Diego, CA, USA
| | - Alisa K Manning
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Miriam S Udler
- Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA.
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.
- Department of Medicine, Harvard Medical School, Boston, MA, USA.
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3
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Steffen BT, Tang W, Lutsey PL, Demmer RT, Selvin E, Matsushita K, Morrison AC, Guan W, Rooney MR, Norby FL, Pankratz N, Couper D, Pankow JS. Proteomic analysis of diabetes genetic risk scores identifies complement C2 and neuropilin-2 as predictors of type 2 diabetes: the Atherosclerosis Risk in Communities (ARIC) Study. Diabetologia 2023; 66:105-115. [PMID: 36194249 PMCID: PMC9742300 DOI: 10.1007/s00125-022-05801-7] [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: 05/17/2022] [Accepted: 08/15/2022] [Indexed: 12/14/2022]
Abstract
AIMS/HYPOTHESIS Genetic predisposition to type 2 diabetes is well-established, and genetic risk scores (GRS) have been developed that capture heritable liabilities for type 2 diabetes phenotypes. However, the proteins through which these genetic variants influence risk have not been thoroughly investigated. This study aimed to identify proteins and pathways through which type 2 diabetes risk variants may influence pathophysiology. METHODS Using a proteomics data-driven approach in a discovery sample of 7241 White participants in the Atherosclerosis Risk in Communities Study (ARIC) cohort and a replication sample of 1674 Black ARIC participants, we interrogated plasma levels of 4870 proteins and four GRS of specific type 2 diabetes phenotypes related to beta cell function, insulin resistance, lipodystrophy, BMI/blood lipid abnormalities and a composite score of all variants combined. RESULTS Twenty-two plasma proteins were identified in White participants after Bonferroni correction. Of the 22 protein-GRS associations that were statistically significant, 10 were replicated in Black participants and all but one were directionally consistent. In a secondary analysis, 18 of the 22 proteins were found to be associated with prevalent type 2 diabetes and ten proteins were associated with incident type 2 diabetes. Two-sample Mendelian randomisation indicated that complement C2 may be causally related to greater type 2 diabetes risk (inverse variance weighted estimate: OR 1.65 per SD; p=7.0 × 10-3), while neuropilin-2 was inversely associated (OR 0.44 per SD; p=8.0 × 10-3). CONCLUSIONS/INTERPRETATION Identified proteins may represent viable intervention or pharmacological targets to prevent, reverse or slow type 2 diabetes progression, and further research is needed to pursue these targets.
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Affiliation(s)
- Brian T Steffen
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN, USA
| | - Weihong Tang
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN, USA
| | - Pamela L Lutsey
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN, USA
| | - Ryan T Demmer
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN, USA
- Department of Epidemiology, Mailman School of Public Health, Columbia University, New York, NY, USA
| | - Elizabeth Selvin
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | - Kunihiro Matsushita
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Welch Center for Prevention, Epidemiology and Clinical Research, Baltimore, MD, USA
| | - Alanna C Morrison
- Human Genetics Center, Department of Epidemiology, Human Genetics, and Environmental Sciences, School of Public Health, The University of Texas Health Science Center at Houston, Houston, TX, USA
| | - Weihua Guan
- Division of Biostatistics, University of Minnesota School of Public Health, Minneapolis, MN, USA
| | - Mary R Rooney
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
- Welch Center for Prevention, Epidemiology and Clinical Research, Baltimore, MD, USA
| | - Faye L Norby
- Department of Cardiology, Smidt Heart Institute, Cedars-Sinai Health System, Los Angeles, CA, USA
| | - Nathan Pankratz
- Department of Laboratory Medicine & Pathology, University of Minnesota, Minneapolis, MN, USA
| | - David Couper
- University of North Carolina Gillings School of Global Public Health, Chapel Hill, NC, USA
| | - James S Pankow
- Division of Epidemiology and Community Health, School of Public Health, University of Minnesota, Minneapolis, MN, USA.
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Domingo-Relloso A, Gribble MO, Riffo-Campos AL, Haack K, Cole SA, Tellez-Plaza M, Umans JG, Fretts AM, Zhang Y, Fallin MD, Navas-Acien A, Everson TM. Epigenetics of type 2 diabetes and diabetes-related outcomes in the Strong Heart Study. Clin Epigenetics 2022; 14:177. [PMID: 36529747 PMCID: PMC9759920 DOI: 10.1186/s13148-022-01392-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Accepted: 11/30/2022] [Indexed: 12/23/2022] Open
Abstract
BACKGROUND The prevalence of type 2 diabetes has dramatically increased in the past years. Increasing evidence supports that blood DNA methylation, the best studied epigenetic mark, is related to diabetes risk. Few prospective studies, however, are available. We studied the association of blood DNA methylation with diabetes in the Strong Heart Study. We used limma, Iterative Sure Independence Screening and Cox regression to study the association of blood DNA methylation with fasting glucose, HOMA-IR and incident type 2 diabetes among 1312 American Indians from the Strong Heart Study. DNA methylation was measured using Illumina's MethylationEPIC beadchip. We also assessed the biological relevance of our findings using bioinformatics analyses. RESULTS Among the 358 differentially methylated positions (DMPs) that were cross-sectionally associated either with fasting glucose or HOMA-IR, 49 were prospectively associated with incident type 2 diabetes, although no DMPs remained significant after multiple comparisons correction. Multiple of the top DMPs were annotated to genes with relevant functions for diabetes including SREBF1, associated with obesity, type 2 diabetes and insulin sensitivity; ABCG1, involved in cholesterol and phospholipids transport; and HDAC1, of the HDAC family. (HDAC inhibitors have been proposed as an emerging treatment for diabetes and its complications.) CONCLUSIONS: Our results suggest that differences in peripheral blood DNA methylation are related to cross-sectional markers of glucose metabolism and insulin activity. While some of these DMPs were modestly associated with prospective incident type 2 diabetes, they did not survive multiple testing. Common DMPs with diabetes epigenome-wide association studies from other populations suggest a partially common epigenomic signature of glucose and insulin activity.
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Affiliation(s)
- Arce Domingo-Relloso
- Department of Chronic Diseases Epidemiology, National Center for Epidemiology, Carlos III Health Institute, Madrid, Spain. .,Department of Environmental Health Sciences, Columbia University Mailman School of Public Health, New York, NY, USA. .,Department of Statistics and Operations Research, University of Valencia, Valencia, Spain.
| | - Matthew O. Gribble
- grid.265892.20000000106344187Department of Epidemiology, University of Alabama at Birmingham School of Public Health, Birmingham, AL USA
| | - Angela L. Riffo-Campos
- grid.412163.30000 0001 2287 9552Millennium Nucleus On Sociomedicine (SocioMed) and Vicerrectoría Académica, Universidad de La Frontera, Temuco, Chile ,grid.5338.d0000 0001 2173 938XDepartment of Computer Science, ETSE, University of Valencia, Valencia, Spain
| | - Karin Haack
- grid.250889.e0000 0001 2215 0219Population Health Program, Texas Biomedical Research Institute, San Antonio, TX USA
| | - Shelley A. Cole
- grid.250889.e0000 0001 2215 0219Population Health Program, Texas Biomedical Research Institute, San Antonio, TX USA
| | - Maria Tellez-Plaza
- grid.413448.e0000 0000 9314 1427Department of Chronic Diseases Epidemiology, National Center for Epidemiology, Carlos III Health Institute, Madrid, Spain
| | - Jason G. Umans
- grid.415232.30000 0004 0391 7375MedStar Health Research Institute, Hyattsville, MD USA ,grid.440590.cGeorgetown-Howard Universities Center for Clinical and Translational Science, Washington, DC USA
| | - Amanda M. Fretts
- grid.34477.330000000122986657Department of Epidemiology, Cardiovascular Health Research Unit, University of Washington, Seattle, WA USA
| | - Ying Zhang
- grid.266902.90000 0001 2179 3618Department of Biostatistics and Epidemiology, The University of Oklahoma Health Sciences Center, Oklahoma City, OK USA
| | - M. Daniele Fallin
- grid.189967.80000 0001 0941 6502Emory University Rollins School of Public Health, Atlanta, GA USA ,grid.189967.80000 0001 0941 6502Department of Epidemiology, Emory University Rollins School of Public Health, Atlanta, GA USA
| | - Ana Navas-Acien
- grid.21729.3f0000000419368729Department of Environmental Health Sciences, Columbia University Mailman School of Public Health, New York, NY USA
| | - Todd M. Everson
- grid.189967.80000 0001 0941 6502Gangarosa Department of Environmental Health, Emory University Rollins School of Public Health, Atlanta, GA USA ,grid.189967.80000 0001 0941 6502Department of Epidemiology, Emory University Rollins School of Public Health, Atlanta, GA USA
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Hubacek JA, Dlouha L, Adamkova V, Dlouha D, Pacal L, Kankova K, Galuska D, Lanska V, Veleba J, Pelikanova T. Genetic risk score is associated with T2DM and diabetes complications risks. Gene X 2022; 849:146921. [PMID: 36174902 DOI: 10.1016/j.gene.2022.146921] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/29/2022] [Revised: 09/09/2022] [Accepted: 09/21/2022] [Indexed: 10/14/2022] Open
Abstract
BACKGROUND Type 2 diabetes mellitus (T2DM) is a prototypical complex disease with polygenic architecture playing an important role in determining susceptibility to develop the disease (and its complications) in subjects exposed to modifiable lifestyle factors. A current challenge is to quantify the degree of the individual's genetic risk using genetic risk scores (GRS) capturing the results of genome-wide association studies while incorporating possible ethnicity- or population-specific differences. METHODS This study included three groups of T2DM (T2DM-I, N=1,032; T2DM-II, N=353; and T2DM-III, N=399) patients and 2,481 diabetes-free subjects. The status of the microvascular and macrovascular diabetes complications were known for the T2DM-I patients. Overall, 21 single nucleotide polymorphisms (SNPs) were analyzed, and selected subsets were used to determine the GRS (both weighted - wGRS and unweighted - uGRS) for T2DM risk predictions (6 SNPs) and for predicting the risks of complications (7 SNPs). RESULTS The strongest T2DM markers (P<0.0001) were within the genes for TCF7L2 (transcription factor 7-like 2), FTO (fat mass and obesity associated protein) and ARAP1 (ankyrin repeat and PH domain 1). The T2DM-I subjects with uGRS values greater (Odds Ratio, 95% Confidence Interval) than six had at least twice (2.00, 1.72-2.32) the risk of T2DM development (P<0.0001), and these results were confirmed in the independent groups (T2DM-II 1.82, 1.45-2.27; T2DM-III 2.63, 2.11-3.27). The wGRS (>0.6) further improved (P<0.000001) the risk estimations for all three T2DM groups. The uGRS was also a significant predictor of neuropathy (P<0.0001), nephropathy (P<0.005) and leg ischemia (P<0.0005). CONCLUSIONS If carefully selected and specified, GRS, both weighted and unweighted, could be significant predictors of T2DM development, as well as the diabetes complications development.
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Affiliation(s)
- Jaroslav A Hubacek
- Experimental Medicine Centre, Institute for Clinical and Experimental Medicine, Prague, Czech Republic; 3rd Department of Internal Medicine, 1(st) Faculty of Medicine, Charles University, Prague, Czech Republic.
| | - Lucie Dlouha
- Experimental Medicine Centre, Institute for Clinical and Experimental Medicine, Prague, Czech Republic; Department of Anthropology and Human Genetics, Faculty of Science, Charles University, Prague, Czech Republic
| | - Vera Adamkova
- Department of Preventive Cardiology, Institute for Clinical and Experimental Medicine, Prague, Czech Republic; Czech Technical University of Prague, Faculty of Biomedical Engineering, Prague, Czech Republic
| | - Dana Dlouha
- Experimental Medicine Centre, Institute for Clinical and Experimental Medicine, Prague, Czech Republic
| | - Lukas Pacal
- Department of Pathophysiology, Faculty of Medicine, Masaryk University, Brno, Czech Republic
| | - Katerina Kankova
- Department of Pathophysiology, Faculty of Medicine, Masaryk University, Brno, Czech Republic
| | - David Galuska
- Department of Pathophysiology, Faculty of Medicine, Masaryk University, Brno, Czech Republic
| | - Vera Lanska
- Statistical Unit, Institute for Clinical and Experimental Medicine, Prague, Czech Republic
| | - Jiri Veleba
- Diabetes Centre, Institute for Clinical and Experimental Medicine, Prague, Czech Republic
| | - Terezie Pelikanova
- Diabetes Centre, Institute for Clinical and Experimental Medicine, Prague, Czech Republic
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Sugiyama S, Jinnouchi H, Hieshima K, Kurinami N, Jinnouchi K, Yoshida A, Suzuki T, Kajiwara K, Miyamoto F, Jinnouchi T. Potential Identification of Type 2 Diabetes with Elevated Insulin Clearance. NEJM EVIDENCE 2022; 1:EVIDoa2100052. [PMID: 38319210 DOI: 10.1056/evidoa2100052] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/07/2024]
Abstract
BACKGROUND: Decreased blood insulin concentrations resulting from reduced pancreatic β-cell insulin secretion and elevated insulin clearance (IC) could be involved in impaired glucose metabolism in diabetes. Recently, we reported a patient with type 2 diabetes mellitus (T2DM) who had decreased blood insulin concentrations and elevated IC. METHODS: For this study, we recruited patients with newly diagnosed, treatment-naïve T2DM and measured the metabolic clearance rate of insulin (MCRI) determined by a hyperinsulinemic-euglycemic clamp examination. We defined elevated IC as an MCRI of more than 700 ml/min/m2. Using this tentative cutoff, we identified patients with T2DM with elevated IC and investigated their clinical characteristics. RESULTS: We enrolled 101 patients in this study; 78.2% were men. Patients had a mean age of 54.1 years, a median body-mass index (BMI) of 25.1 kg/m2 (interquartile range [IQR], 22.9 to 28.4 kg/m2), a median hemoglobin A1c of 10.0% (IQR, 8.0 to 12.3%), and a median MCRI of 655 ml/min/m2 (IQR, 562 to 810 ml/min/m2). Our case definition for elevated IC was met by 44 patients whose median MCRI was 842 ml/min/m2 (IQR, 747 to 975 ml/min/m2) compared with those without elevated IC (570 ml/min/m2; IQR, 500 to 628 ml/min/m2). On the basis of this division, fasting blood glucose and insulin levels were 178 mg/dl (IQR, 140 to 218 mg/dl) and 4.2 mU/l (IQR, 2.7 to 5.5 mU/l), respectively, in patients with elevated IC compared with 146 mg/dl (IQR, 128 to 188 mg/dl) and 9.6 mU/l (IQR, 6.6 to 14.9 mU/l), respectively, in patients without elevated IC. The BMI of patients with elevated IC was 22.9 kg/m2 (IQR, 20.7 to 24.2 kg/m2) compared with 27.3 kg/m2 (IQR, 25.2 to 29.4 kg/m2) in patients who did not have elevated IC. There were no clinically significant differences in renal or hepatic function test results. CONCLUSIONS: Our data suggest that there is a group of patients with T2DM with elevated IC, and that they are nonobese and have decreased blood insulin concentrations. If confirmed, this novel form of T2DM could affect the treatment of such patients. (UMIN Clinical Trials Registry number, UMIN000032014.)
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Affiliation(s)
- Seigo Sugiyama
- Diabetes Care Center, Jinnouchi Hospital, Kumamoto, Japan
- Division of Cardiovascular Medicine, Diabetes Care Center, Jinnouchi Hospital, Kumamoto, Japan
| | - Hideaki Jinnouchi
- Diabetes Care Center, Jinnouchi Hospital, Kumamoto, Japan
- Division of Cardiovascular Medicine, Diabetes Care Center, Jinnouchi Hospital, Kumamoto, Japan
- Division of Preventive Cardiology, Department of Cardiovascular Medicine, Kumamoto University Hospital, Kumamoto, Japan
| | - Kunio Hieshima
- Diabetes Care Center, Jinnouchi Hospital, Kumamoto, Japan
- Infectious Disease Division, Diabetes Care Center, Jinnouchi Hospital, Kumamoto, Japan
| | - Noboru Kurinami
- Diabetes Care Center, Jinnouchi Hospital, Kumamoto, Japan
- Obesity Treatment Division, Diabetes Care Center, Jinnouchi Hospital, Kumamoto, Japan
| | - Katsunori Jinnouchi
- Diabetes Care Center, Jinnouchi Hospital, Kumamoto, Japan
- Division of Gastroenterology and Nephrology, Diabetes Care Center, Jinnouchi Hospital, Kumamoto, Japan
| | - Akira Yoshida
- Diabetes Care Center, Jinnouchi Hospital, Kumamoto, Japan
- Pharmacology Division, Diabetes Care Center, Jinnouchi Hospital, Kumamoto, Japan
| | - Tomoko Suzuki
- Diabetes Care Center, Jinnouchi Hospital, Kumamoto, Japan
- Division of Cardiovascular Medicine, Diabetes Care Center, Jinnouchi Hospital, Kumamoto, Japan
| | - Keizo Kajiwara
- Diabetes Care Center, Jinnouchi Hospital, Kumamoto, Japan
- Division of Cardiovascular Medicine, Diabetes Care Center, Jinnouchi Hospital, Kumamoto, Japan
| | - Fumio Miyamoto
- Diabetes Care Center, Jinnouchi Hospital, Kumamoto, Japan
- Ophthalmology Division, Diabetes Care Center, Jinnouchi Hospital, Kumamoto, Japan
| | - Tomio Jinnouchi
- Diabetes Care Center, Jinnouchi Hospital, Kumamoto, Japan
- Division of Cardiovascular Medicine, Diabetes Care Center, Jinnouchi Hospital, Kumamoto, Japan
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7
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Zhou Q, Wang Y, Gu Y, Li J, Wang H, Leng J, Li W, Yu Z, Hu G, Ma RCW, Fang ZZ, Yang X, Jiang G. Genetic variants associated with beta-cell function and insulin sensitivity potentially influence bile acid metabolites and gestational diabetes mellitus in a Chinese population. BMJ Open Diabetes Res Care 2021; 9:9/1/e002287. [PMID: 34518156 PMCID: PMC8438732 DOI: 10.1136/bmjdrc-2021-002287] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/22/2021] [Accepted: 08/17/2021] [Indexed: 11/10/2022] Open
Abstract
INTRODUCTION To investigate associations between genetic variants related to beta-cell (BC) dysfunction or insulin resistance (IR) in type 2 diabetes (T2D) and bile acids (BAs), as well as the risk of gestational diabetes mellitus (GDM). RESEARCH DESIGN AND METHODS We organized a case-control study of 230 women with GDM and 217 without GDM nested in a large prospective cohort of 22 302 Chinese women in Tianjin, China. Two weighted genetic risk scores (GRSs), namely BC-GRS and IR-GRS, were established by combining 39 and 23 single nucleotide polymorphisms known to be associated with BC dysfunction and IR, respectively. Regression and mediation analyses were performed to evaluate the relationship of GRSs with BAs and GDM. RESULTS We found that the BC-GRS was inversely associated with taurodeoxycholic acid (TDCA) after adjustment for confounders (Beta (SE)=-0.177 (0.048); p=2.66×10-4). The BC-GRS was also associated with the risk of GDM (OR (95% CI): 1.40 (1.10 to 1.77); p=0.005), but not mediated by TDCA. Compared with individuals in the low tertile of BC-GRS, the OR for GDM was 2.25 (95% CI 1.26 to 4.01) in the high tertile. An interaction effect of IR-GRS with taurochenodeoxycholic acid (TCDCA) on the risk of GDM was evidenced (p=0.005). Women with high IR-GRS and low concentration of TCDCA had a markedly higher OR of 14.39 (95% CI 1.59 to 130.16; p=0.018), compared with those with low IR-GRS and high TCDCA. CONCLUSIONS Genetic variants related to BC dysfunction and IR in T2D potentially influence BAs at early pregnancy and the development of GDM. The identification of both modifiable and non-modifiable risk factors may facilitate the identification of high-risk individuals to prevent GDM.
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Affiliation(s)
- Qiulun Zhou
- School of Public Health (Shenzhen), Sun Yat-Sen University, Shenzhen, Guangdong, China
| | - Ying Wang
- The Second School of Clinical Medicine, Key Laboratory of 3D Printing Technology in Stomatology, Guangdong Medical University, Dongguan, Guangdong, China
| | - Yuqin Gu
- School of Public Health (Shenzhen), Sun Yat-Sen University, Shenzhen, Guangdong, China
| | - Jing Li
- Department of Epidemiology and Biostatistics, School of Public Health, Tianjin Medical University, Tianjin, China
| | - Hui Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Tianjin Medical University, Tianjin, China
| | - Junhong Leng
- Project Office, Tianjin Women and Children's Health Center, Tianjin, China
| | - Weiqin Li
- Project Office, Tianjin Women and Children's Health Center, Tianjin, China
| | - Zhijie Yu
- Population Cancer Research Program and Department of Pediatrics, Dalhousie University, Halifax, Nova Scotia, Canada
| | - Gang Hu
- Chronic Disease Epidemiology Laboratory, Pennington Biomedical Research Center, Baton Rouge, Louisiana, USA
| | - Ronald Ching Wan Ma
- Department of Medicine and Therapeutics, The Chinese University of Hong Kong, Hong Kong, China
- Li Ka Shing Institute of Health Sciences, The Chinese University of Hong Kong, Hong Kong, China
| | - Zhong-Ze Fang
- Department of Toxicology and Sanitary Chemistry, School of Public Health, Tianjin Medical University, Tianjin, China
| | - Xilin Yang
- Department of Epidemiology and Biostatistics, School of Public Health, Tianjin Medical University, Tianjin, China
| | - Guozhi Jiang
- School of Public Health (Shenzhen), Sun Yat-Sen University, Shenzhen, Guangdong, China
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8
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Defining the Relative Role of Insulin Clearance in Early Dysglycemia in Relation to Insulin Sensitivity and Insulin Secretion: The Microbiome and Insulin Longitudinal Evaluation Study (MILES). Metabolites 2021; 11:metabo11070420. [PMID: 34206745 PMCID: PMC8304591 DOI: 10.3390/metabo11070420] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/25/2021] [Revised: 06/07/2021] [Accepted: 06/16/2021] [Indexed: 11/30/2022] Open
Abstract
Insulin resistance and insufficient insulin secretion are well-recognized contributors to type 2 diabetes. A potential role of reduced insulin clearance has been suggested, but few studies have investigated the contribution of insulin clearance while simultaneously examining decreased insulin sensitivity and secretion. The goal of this study was to conduct such an investigation in a cohort of 353 non-Hispanic White and African American individuals recruited in the Microbiome and Insulin Longitudinal Evaluation Study (MILES). Participants underwent oral glucose tolerance tests from which insulin sensitivity, insulin secretion, insulin clearance, and disposition index were calculated. Regression models examined the individual and joint contributions of these traits to early dysglycemia (prediabetes or newly diagnosed diabetes). In separate models, reduced insulin sensitivity, reduced disposition index, and reduced insulin clearance were associated with dysglycemia. In a joint model, only insulin resistance and reduced insulin secretion were associated with dysglycemia. Models with insulin sensitivity, disposition index, or three insulin traits had the highest discriminative value for dysglycemia (area under the receiver operating characteristics curve of 0.82 to 0.89). These results suggest that in the race groups studied, insulin resistance and compromised insulin secretion are the main independent underlying defects leading to early dysglycemia.
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9
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Miranda-Lora AL, Vilchis-Gil J, Juárez-Comboni DB, Cruz M, Klünder-Klünder M. A Genetic Risk Score Improves the Prediction of Type 2 Diabetes Mellitus in Mexican Youths but Has Lower Predictive Utility Compared With Non-Genetic Factors. Front Endocrinol (Lausanne) 2021; 12:647864. [PMID: 33776940 PMCID: PMC7994893 DOI: 10.3389/fendo.2021.647864] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/30/2020] [Accepted: 02/18/2021] [Indexed: 01/07/2023] Open
Abstract
Background Type 2 diabetes (T2D) is a multifactorial disease caused by a complex interplay between environmental risk factors and genetic predisposition. To date, a total of 10 single nucleotide polymorphism (SNPs) have been associated with pediatric-onset T2D in Mexicans, with a small individual effect size. A genetic risk score (GRS) that combines these SNPs could serve as a predictor of the risk for pediatric-onset T2D. Objective To assess the clinical utility of a GRS that combines 10 SNPs to improve risk prediction of pediatric-onset T2D in Mexicans. Methods This case-control study included 97 individuals with pediatric-onset T2D and 84 controls below 18 years old without T2D. Information regarding family history of T2D, demographics, perinatal risk factors, anthropometric measurements, biochemical variables, lifestyle, and fitness scores were then obtained. Moreover, 10 single nucleotide polymorphisms (SNPs) previously associated with pediatric-onset T2D in Mexicans were genotyped. The GRS was calculated by summing the 10 risk alleles. Pediatric-onset T2D risk variance was assessed using multivariable logistic regression models and the area under the receiver operating characteristic curve (AUC). Results The body mass index Z-score (Z-BMI) [odds ratio (OR) = 1.7; p = 0.009] and maternal history of T2D (OR = 7.1; p < 0.001) were found to be independently associated with pediatric-onset T2D. No association with other clinical risk factors was observed. The GRS also showed a significant association with pediatric-onset T2D (OR = 1.3 per risk allele; p = 0.006). The GRS, clinical risk factors, and GRS plus clinical risk factors had an AUC of 0.66 (95% CI 0.56-0.75), 0.72 (95% CI 0.62-0.81), and 0.78 (95% CI 0.70-0.87), respectively (p < 0.01). Conclusion The GRS based on 10 SNPs was associated with pediatric-onset T2D in Mexicans and improved its prediction with modest significance. However, clinical factors, such the Z-BMI and family history of T2D, continue to have the highest predictive utility in this population.
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Affiliation(s)
- América Liliana Miranda-Lora
- Epidemiological Research Unit in Endocrinology and Nutrition, Hospital Infantil de México Federico Gómez, Mexico City, Mexico
| | - Jenny Vilchis-Gil
- Epidemiological Research Unit in Endocrinology and Nutrition, Hospital Infantil de México Federico Gómez, Mexico City, Mexico
| | | | - Miguel Cruz
- Medical Research Unit in Biochemistry, Hospital de Especialidades Centro Médico Nacional SXXI, Instituto Mexicano del Seguro Social, Mexico City, Mexico
| | - Miguel Klünder-Klünder
- Research Subdirectorate, Hospital Infantil de México Federico Gómez, Mexico City, Mexico
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10
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Bykhovskaya Y, Rabinowitz YS. Update on the genetics of keratoconus. Exp Eye Res 2020; 202:108398. [PMID: 33316263 DOI: 10.1016/j.exer.2020.108398] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2020] [Revised: 12/04/2020] [Accepted: 12/07/2020] [Indexed: 02/07/2023]
Abstract
In the past few years we have seen a great acceleration of discoveries in the field of keratoconus including new treatments, diagnostic tools, genomic and molecular determinants of disease risk. Recent genome-wide association studies (GWAS) of keratoconus cases and population wide studies of variation in central corneal thickness and in corneal biomechanical properties confirmed already identified genes and found many new susceptibility variants and biological pathways. Recent findings in genetic determinants of familial keratoconus revealed functionally important variants and established first mouse model of keratoconus. Latest transcriptomic and expression studies started assessing novel non-coding RNA targets in addition to identifying tissue specific effects of coding genes. First genomic insights into better prediction of treatment outcomes are bringing the advent of genomic medicine into keratoconus clinical practice.
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Affiliation(s)
- Yelena Bykhovskaya
- Cornea Genetic Eye Institute, Department of Surgery and Board of the Governors Regenerative Medicine Institute, Beverly Hills, Cedars-Sinai, Los Angeles, CA, United States.
| | - Yaron S Rabinowitz
- Cornea Genetic Eye Institute, Department of Surgery and Board of the Governors Regenerative Medicine Institute, Beverly Hills, Cedars-Sinai, Los Angeles, CA, United States
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11
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Abstract
Diabetes mellitus is a major risk factor for coronary heart disease (CHD). The major form of diabetes mellitus is type 2 diabetes mellitus (T2D), which is thus largely responsible for the CHD association in the general population. Recent years have seen major advances in the genetics of T2D, principally through ever-increasing large-scale genome-wide association studies. This article addresses the question of whether this expanding knowledge of the genomics of T2D provides insight into the etiologic relationship between T2D and CHD. We will investigate this relationship by reviewing the evidence for shared genetic loci between T2D and CHD; by examining the formal testing of this interaction (Mendelian randomization studies assessing whether T2D is causal for CHD); and then turn to the implications of this genetic relationship for therapies for CHD, for therapies for T2D, and for therapies that affect both. In conclusion, the growing knowledge of the genetic relationship between T2D and CHD is beginning to provide the promise for improved prevention and treatment of both disorders.
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Affiliation(s)
- Mark O Goodarzi
- From the Division of Endocrinology, Diabetes, and Metabolism, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA (M.O.G.)
| | - Jerome I Rotter
- Institute for Translational Genomics and Population Sciences and Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA (J.I.R.)
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12
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Colca J. NASH (nonalcoholic steatohepatitis), diabetes, and macrovascular disease: multiple chronic conditions and a potential treatment at the metabolic root. Expert Opin Investig Drugs 2020; 29:191-196. [PMID: 31928475 DOI: 10.1080/13543784.2020.1715940] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Abstract
Introduction: NASH and type 2 diabetes (T2D) are clinical definitions that overlap and result from metabolic dysfunction caused by over-nutrition relative to metabolic need. This volume details drug development programs aimed at specific NASH pathology with a focus on liver outcomes; this commentary suggests a metabolic approach that should not be overlooked based on a new understanding of insulin sensitizers.Areas covered: The overlap of NASH and T2D with respect to metabolic syndrome is discussed in the context of new understandings of insulin sensitizers. Adverse clinical outcomes in subjects with advanced NAFLD (e.g. NASH) and advanced metabolic dysfunction (e.g., T2D) are primarily due to cardiovascular issues. Clinical evidence suggests that insulin resistance and hyperinsulinemia predict adverse cardiovascular outcomes. NALFD/NASH significantly contributes to insulin resistance and hyperinsulinemia. A new insulin sensitizer that targets the newly identified mitochondrial pyruvate carrier could provide an approach.Expert opinion: A metabolic approach is needed for the treatment of NASH. Clinical studies are underway to determine whether a new insulin sensitizer that targets pyruvate metabolism can impact NASH, T2D, and cardiovascular disease. A broader view of metabolic disease may provide a more assessable way to track therapeutic benefit.
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Affiliation(s)
- Jerry Colca
- Cirius Therapeutics, Kalamazoo, MI, USA.,Cirius Therapeutics, San Diago, CA, USA
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