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Tanabe H, Sato M, Miyake A, Shimajiri Y, Ojima T, Narita A, Saito H, Tanaka K, Masuzaki H, Kazama JJ, Katagiri H, Tamiya G, Kawakami E, Shimabukuro M. Machine learning-based reproducible prediction of type 2 diabetes subtypes. Diabetologia 2024:10.1007/s00125-024-06248-8. [PMID: 39168869 DOI: 10.1007/s00125-024-06248-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Accepted: 06/12/2024] [Indexed: 08/23/2024]
Abstract
AIMS/HYPOTHESIS Clustering-based subclassification of type 2 diabetes, which reflects pathophysiology and genetic predisposition, is a promising approach for providing personalised and effective therapeutic strategies. Ahlqvist's classification is currently the most vigorously validated method because of its superior ability to predict diabetes complications but it does not have strong consistency over time and requires HOMA2 indices, which are not routinely available in clinical practice and standard cohort studies. We developed a machine learning (ML) model to classify individuals with type 2 diabetes into Ahlqvist's subtypes consistently over time. METHODS Cohort 1 dataset comprised 619 Japanese individuals with type 2 diabetes who were divided into training and test sets for ML models in a 7:3 ratio. Cohort 2 dataset, comprising 597 individuals with type 2 diabetes, was used for external validation. Participants were pre-labelled (T2Dkmeans) by unsupervised k-means clustering based on Ahlqvist's variables (age at diagnosis, BMI, HbA1c, HOMA2-B and HOMA2-IR) to four subtypes: severe insulin-deficient diabetes (SIDD), severe insulin-resistant diabetes (SIRD), mild obesity-related diabetes (MOD) and mild age-related diabetes (MARD). We adopted 15 variables for a multiclass classification random forest (RF) algorithm to predict type 2 diabetes subtypes (T2DRF15). The proximity matrix computed by RF was visualised using a uniform manifold approximation and projection. Finally, we used a putative subset with missing insulin-related variables to test the predictive performance of the validation cohort, consistency of subtypes over time and prediction ability of diabetes complications. RESULTS T2DRF15 demonstrated a 94% accuracy for predicting T2Dkmeans type 2 diabetes subtypes (AUCs ≥0.99 and F1 score [an indicator calculated by harmonic mean from precision and recall] ≥0.9) and retained the predictive performance in the external validation cohort (86.3%). T2DRF15 showed an accuracy of 82.9% for detecting T2Dkmeans, also in a putative subset with missing insulin-related variables, when used with an imputation algorithm. In Kaplan-Meier analysis, the diabetes clusters of T2DRF15 demonstrated distinct accumulation risks of diabetic retinopathy in SIDD and that of chronic kidney disease in SIRD during a median observation period of 11.6 (4.5-18.3) years, similarly to the subtypes using T2Dkmeans. The predictive accuracy was improved after excluding individuals with low predictive probability, who were categorised as an 'undecidable' cluster. T2DRF15, after excluding undecidable individuals, showed higher consistency (100% for SIDD, 68.6% for SIRD, 94.4% for MOD and 97.9% for MARD) than T2Dkmeans. CONCLUSIONS/INTERPRETATION The new ML model for predicting Ahlqvist's subtypes of type 2 diabetes has great potential for application in clinical practice and cohort studies because it can classify individuals with missing HOMA2 indices and predict glycaemic control, diabetic complications and treatment outcomes with long-term consistency by using readily available variables. Future studies are needed to assess whether our approach is applicable to research and/or clinical practice in multiethnic populations.
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Affiliation(s)
- Hayato Tanabe
- Department of Diabetes, Endocrinology, and Metabolism, Fukushima Medical University School of Medicine, Fukushima, Japan
- Department of Diabetes, Metabolism and Endocrinology, Tohoku University Graduate School of Medicine, Miyagi, Japan
| | - Masahiro Sato
- Department of Diabetes, Endocrinology, and Metabolism, Fukushima Medical University School of Medicine, Fukushima, Japan
| | - Akimitsu Miyake
- Department of AI and Innovative Medicine, Tohoku University School of Medicine, Miyagi, Japan
| | | | - Takafumi Ojima
- Department of AI and Innovative Medicine, Tohoku University School of Medicine, Miyagi, Japan
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Osaka, Japan
| | - Akira Narita
- Tohoku Medical Megabank Organization, Tohoku University, Miyagi, Japan
| | - Haruka Saito
- Department of Diabetes, Endocrinology, and Metabolism, Fukushima Medical University School of Medicine, Fukushima, Japan
| | - Kenichi Tanaka
- Department of Nephrology and Hypertension, Fukushima Medical University School of Medicine, Fukushima, Japan
| | - Hiroaki Masuzaki
- Division of Endocrinology and Metabolism, Second Department of Internal Medicine, University of the Ryukyus Graduate School of Medicine, Okinawa, Japan
| | - Junichiro J Kazama
- Department of Nephrology and Hypertension, Fukushima Medical University School of Medicine, Fukushima, Japan
| | - Hideki Katagiri
- Department of Diabetes, Metabolism and Endocrinology, Tohoku University Graduate School of Medicine, Miyagi, Japan
| | - Gen Tamiya
- Department of AI and Innovative Medicine, Tohoku University School of Medicine, Miyagi, Japan
- Tohoku Medical Megabank Organization, Tohoku University, Miyagi, Japan
| | - Eiryo Kawakami
- Department of Artificial Intelligence Medicine, Graduate School of Medicine, Chiba University, Chiba, Japan.
- Advanced Data Science Project, RIKEN Information R&D and Strategy Headquarters, RIKEN, Yokohama, Japan.
| | - Michio Shimabukuro
- Department of Diabetes, Endocrinology, and Metabolism, Fukushima Medical University School of Medicine, Fukushima, Japan.
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Gurung RL, Zheng H, Lee BTK, Liu S, Liu JJ, Chan C, Ang K, Subramaniam T, Sum CF, Coffman TM, Lim SC. Proteomics profiling and association with cardiorenal complications in type 2 diabetes subtypes in Asian population. Diabetes Res Clin Pract 2024; 214:111790. [PMID: 39059739 DOI: 10.1016/j.diabres.2024.111790] [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: 05/14/2024] [Revised: 07/09/2024] [Accepted: 07/22/2024] [Indexed: 07/28/2024]
Abstract
AIM Among multi-ethnic Asians, type 2 diabetes (T2D) clustered in three subtypes; mild obesity-related diabetes (MOD), mild age-related diabetes with insulin insufficiency (MARD-II) and severe insulin-resistant diabetes with relative insulin insufficiency (SIRD-RII) had differential cardio-renal complication risk. We assessed the proteomic profiles to identify subtype specific biomarkers and its association with diabetes complications. METHODS 1448 plasma proteins at baseline were measured and compared across the T2D subtypes. Multivariable cox regression was used to assess associations between significant proteomics features and cardio-renal complications. RESULTS Among 645 T2D participants (SIRD-RII [19%], MOD [45%], MARD-II [36%]), 295 proteins expression differed significantly across the groups. These proteins were enriched in cell adhesion, neurogenesis and inflammatory response processes. In SIRD-RII group, ADH4, ACY1, THOP1, IGFBP2, NEFL, ENTPD2, CALB1, HAO1, CTSV, ITGAV, SCLY, EDA2R, ERBB2 proteins significantly associated with progressive CKD and LILRA5 protein with incident heart failure (HF). In MOD group, TAFA5, RSPO3, EDA2R proteins significantly associated with incident HF. In MARD-II group, FABP4 protein significantly associated with progressive CKD and PTPRN2 protein with major adverse cardiovascular events. Genetically determined NEFL and CALB1 were associated with kidney function decline. CONCLUSIONS Each T2D subtype has unique proteomics signature and association with clinical outcomes and underlying mechanisms.
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Affiliation(s)
- Resham Lal Gurung
- Clinical Research Unit, Khoo Teck Puat Hospital, Singapore; Cardiovascular and Metabolic Disorders Signature Research Program, Duke-NUS Medical School, Singapore
| | - Huili Zheng
- Clinical Research Unit, Khoo Teck Puat Hospital, Singapore
| | | | - Sylvia Liu
- Clinical Research Unit, Khoo Teck Puat Hospital, Singapore
| | - Jian-Jun Liu
- Clinical Research Unit, Khoo Teck Puat Hospital, Singapore
| | - Clara Chan
- Clinical Research Unit, Khoo Teck Puat Hospital, Singapore
| | - Keven Ang
- Clinical Research Unit, Khoo Teck Puat Hospital, Singapore
| | | | - Chee Fang Sum
- Diabetes Centre, Admiralty Medical Centre, Singapore
| | - Thomas M Coffman
- Cardiovascular and Metabolic Disorders Signature Research Program, Duke-NUS Medical School, Singapore
| | - Su Chi Lim
- Clinical Research Unit, Khoo Teck Puat Hospital, Singapore; Lee Kong Chian School of Medicine, Nanyang Technology University, Singapore; Diabetes Centre, Admiralty Medical Centre, Singapore; Saw Swee Hock School of Public Heath, Singapore.
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Thiele M, Villesen IF, Niu L, Johansen S, Sulek K, Nishijima S, Espen LV, Keller M, Israelsen M, Suvitaival T, Zawadzki AD, Juel HB, Brol MJ, Stinson SE, Huang Y, Silva MCA, Kuhn M, Anastasiadou E, Leeming DJ, Karsdal M, Matthijnssens J, Arumugam M, Dalgaard LT, Legido-Quigley C, Mann M, Trebicka J, Bork P, Jensen LJ, Hansen T, Krag A. Opportunities and barriers in omics-based biomarker discovery for steatotic liver diseases. J Hepatol 2024; 81:345-359. [PMID: 38552880 DOI: 10.1016/j.jhep.2024.03.035] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/20/2023] [Revised: 02/16/2024] [Accepted: 03/19/2024] [Indexed: 07/26/2024]
Abstract
The rising prevalence of liver diseases related to obesity and excessive use of alcohol is fuelling an increasing demand for accurate biomarkers aimed at community screening, diagnosis of steatohepatitis and significant fibrosis, monitoring, prognostication and prediction of treatment efficacy. Breakthroughs in omics methodologies and the power of bioinformatics have created an excellent opportunity to apply technological advances to clinical needs, for instance in the development of precision biomarkers for personalised medicine. Via omics technologies, biological processes from the genes to circulating protein, as well as the microbiome - including bacteria, viruses and fungi, can be investigated on an axis. However, there are important barriers to omics-based biomarker discovery and validation, including the use of semi-quantitative measurements from untargeted platforms, which may exhibit high analytical, inter- and intra-individual variance. Standardising methods and the need to validate them across diverse populations presents a challenge, partly due to disease complexity and the dynamic nature of biomarker expression at different disease stages. Lack of validity causes lost opportunities when studies fail to provide the knowledge needed for regulatory approvals, all of which contributes to a delayed translation of these discoveries into clinical practice. While no omics-based biomarkers have matured to clinical implementation, the extent of data generated has enabled the hypothesis-free discovery of a plethora of candidate biomarkers that warrant further validation. To explore the many opportunities of omics technologies, hepatologists need detailed knowledge of commonalities and differences between the various omics layers, and both the barriers to and advantages of these approaches.
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Affiliation(s)
- Maja Thiele
- Center for Liver Research, Department of Gastroenterology and Hepatology, Odense University Hospital, Odense, Denmark; Department for Clinical Research, University of Southern Denmark, Odense, Denmark
| | - Ida Falk Villesen
- Center for Liver Research, Department of Gastroenterology and Hepatology, Odense University Hospital, Odense, Denmark; Department for Clinical Research, University of Southern Denmark, Odense, Denmark
| | - Lili Niu
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark; Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany
| | - Stine Johansen
- Center for Liver Research, Department of Gastroenterology and Hepatology, Odense University Hospital, Odense, Denmark
| | | | - Suguru Nishijima
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Lore Van Espen
- KU Leuven, Department of Microbiology, Immunology, and Transplantation, Rega Institute, Laboratory of Viral Metagenomics, Leuven, Belgium
| | - Marisa Keller
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| | - Mads Israelsen
- Center for Liver Research, Department of Gastroenterology and Hepatology, Odense University Hospital, Odense, Denmark; Department for Clinical Research, University of Southern Denmark, Odense, Denmark
| | | | | | - Helene Bæk Juel
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Science, University of Copenhagen, Copenhagen, Denmark
| | - Maximilian Joseph Brol
- Medizinische Klinik B (Gastroenterologie, Hepatologie, Endokrinologie, Klinische Infektiologie), Universitätsklinikum Münster Westfälische, Wilhelms-Universität Münster, Germany
| | - Sara Elizabeth Stinson
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Science, University of Copenhagen, Copenhagen, Denmark
| | - Yun Huang
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Science, University of Copenhagen, Copenhagen, Denmark
| | - Maria Camilla Alvarez Silva
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Science, University of Copenhagen, Copenhagen, Denmark
| | - Michael Kuhn
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany
| | | | - Diana Julie Leeming
- Fibrosis, Hepatic and Pulmonary Research, Nordic Bioscience, Herlev, Denmark
| | - Morten Karsdal
- Fibrosis, Hepatic and Pulmonary Research, Nordic Bioscience, Herlev, Denmark
| | - Jelle Matthijnssens
- KU Leuven, Department of Microbiology, Immunology, and Transplantation, Rega Institute, Laboratory of Viral Metagenomics, Leuven, Belgium
| | - Manimozhiyan Arumugam
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Science, University of Copenhagen, Copenhagen, Denmark
| | | | | | - Matthias Mann
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark; Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany
| | - Jonel Trebicka
- Medizinische Klinik B (Gastroenterologie, Hepatologie, Endokrinologie, Klinische Infektiologie), Universitätsklinikum Münster Westfälische, Wilhelms-Universität Münster, Germany
| | - Peer Bork
- Structural and Computational Biology Unit, European Molecular Biology Laboratory, Heidelberg, Germany; Max Delbrück Centre for Molecular Medicine, Berlin, Germany; Department of Bioinformatics, Biocenter, University of Würzburg, Würzburg, Germany
| | - Lars Juhl Jensen
- Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Torben Hansen
- Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Science, University of Copenhagen, Copenhagen, Denmark
| | - Aleksander Krag
- Center for Liver Research, Department of Gastroenterology and Hepatology, Odense University Hospital, Odense, Denmark; Department for Clinical Research, University of Southern Denmark, Odense, Denmark.
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Li X, Donnelly LA, Slieker RC, Beulens JWJ, 't Hart LM, Elders PJM, Pearson ER, van Giessen A, Leal J, Feenstra T. Trajectories of clinical characteristics, complications and treatment choices in data-driven subgroups of type 2 diabetes. Diabetologia 2024; 67:1343-1355. [PMID: 38625583 PMCID: PMC11153261 DOI: 10.1007/s00125-024-06147-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/29/2023] [Accepted: 02/12/2024] [Indexed: 04/17/2024]
Abstract
AIMS/HYPOTHESIS This study aimed to explore the added value of subgroups that categorise individuals with type 2 diabetes by k-means clustering for two primary care registries (the Netherlands and Scotland), inspired by Ahlqvist's novel diabetes subgroups and previously analysed by Slieker et al. METHODS: We used two Dutch and Scottish diabetes cohorts (N=3054 and 6145; median follow-up=11.2 and 12.3 years, respectively) and defined five subgroups by k-means clustering with age at baseline, BMI, HbA1c, HDL-cholesterol and C-peptide. We investigated differences between subgroups by trajectories of risk factor values (random intercept models), time to diabetes-related complications (logrank tests and Cox models) and medication patterns (multinomial logistic models). We also compared directly using the clustering indicators as predictors of progression vs the k-means discrete subgroups. Cluster consistency over follow-up was assessed. RESULTS Subgroups' risk factors were significantly different, and these differences remained generally consistent over follow-up. Among all subgroups, individuals with severe insulin resistance faced a significantly higher risk of myocardial infarction both before (HR 1.65; 95% CI 1.40, 1.94) and after adjusting for age effect (HR 1.72; 95% CI 1.46, 2.02) compared with mild diabetes with high HDL-cholesterol. Individuals with severe insulin-deficient diabetes were most intensively treated, with more than 25% prescribed insulin at 10 years of diagnosis. For severe insulin-deficient diabetes relative to mild diabetes, the relative risks for using insulin relative to no common treatment would be expected to increase by a factor of 3.07 (95% CI 2.73, 3.44), holding other factors constant. Clustering indicators were better predictors of progression variation relative to subgroups, but prediction accuracy may improve after combining both. Clusters were consistent over 8 years with an accuracy ranging from 59% to 72%. CONCLUSIONS/INTERPRETATION Data-driven subgroup allocations were generally consistent over follow-up and captured significant differences in risk factor trajectories, medication patterns and complication risks. Subgroups serve better as a complement rather than as a basis for compressing clustering indicators.
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Affiliation(s)
- Xinyu Li
- Groningen Research Institute of Pharmacy, Faculty of Science and Engineering, University of Groningen, Groningen, the Netherlands.
| | - Louise A Donnelly
- Division of Population Health and Genomics, Ninewells Hospital and School of Medicine, University of Dundee, Dundee, UK
| | - Roderick C Slieker
- Department of Cell and Chemical Biology, Leiden University Medical Center, Leiden, the Netherlands
- Department of Epidemiology and Data Science, Amsterdam UMC, Location Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Amsterdam Public Health, Amsterdam Cardiovascular Sciences, Amsterdam, the Netherlands
| | - Joline W J Beulens
- Department of Epidemiology and Data Science, Amsterdam UMC, Location Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Amsterdam Public Health, Amsterdam Cardiovascular Sciences, Amsterdam, the Netherlands
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht University, Utrecht, Netherlands
| | - Leen M 't Hart
- Department of Cell and Chemical Biology, Leiden University Medical Center, Leiden, the Netherlands
- Department of Epidemiology and Data Science, Amsterdam UMC, Location Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
- Amsterdam Public Health, Amsterdam Cardiovascular Sciences, Amsterdam, the Netherlands
- Department of Biomedical Data Sciences, Section Molecular Epidemiology, Leiden University Medical Center, Leiden, the Netherlands
| | - Petra J M Elders
- Amsterdam Public Health, Amsterdam Cardiovascular Sciences, Amsterdam, the Netherlands
- Department of General Practice, Amsterdam University Medical Center, Location Vrije Universiteit Amsterdam, Amsterdam, the Netherlands
| | - Ewan R Pearson
- Division of Population Health and Genomics, Ninewells Hospital and School of Medicine, University of Dundee, Dundee, UK
| | - Anoukh van Giessen
- National Institute of Public Health and the Environment, Bilthoven, the Netherlands
| | - Jose Leal
- Health Economics Research Centre, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Talitha Feenstra
- Groningen Research Institute of Pharmacy, Faculty of Science and Engineering, University of Groningen, Groningen, the Netherlands
- National Institute of Public Health and the Environment, Bilthoven, the Netherlands
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Kaplan SJ, Wong W, Yan J, Pulecio J, Cho HS, Li Q, Zhao J, Leslie-Iyer J, Kazakov J, Murphy D, Luo R, Dey KK, Apostolou E, Leslie CS, Huangfu D. CRISPR Screening Uncovers a Long-Range Enhancer for ONECUT1 in Pancreatic Differentiation and Links a Diabetes Risk Variant. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.04.26.591412. [PMID: 38746154 PMCID: PMC11092487 DOI: 10.1101/2024.04.26.591412] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/16/2024]
Abstract
Functional enhancer annotation is a valuable first step for understanding tissue-specific transcriptional regulation and prioritizing disease-associated non-coding variants for investigation. However, unbiased enhancer discovery in physiologically relevant contexts remains a major challenge. To discover regulatory elements pertinent to diabetes, we conducted a CRISPR interference screen in the human pluripotent stem cell (hPSC) pancreatic differentiation system. Among the enhancers uncovered, we focused on a long-range enhancer ∼664 kb from the ONECUT1 promoter, since coding mutations in ONECUT1 cause pancreatic hypoplasia and neonatal diabetes. Homozygous enhancer deletion in hPSCs was associated with a near-complete loss of ONECUT1 gene expression and compromised pancreatic differentiation. This enhancer contains a confidently fine-mapped type 2 diabetes associated variant (rs528350911) which disrupts a GATA motif. Introduction of the risk variant into hPSCs revealed substantially reduced binding of key pancreatic transcription factors (GATA4, GATA6 and FOXA2) on the edited allele, accompanied by a slight reduction of ONECUT1 transcription, supporting a causal role for this risk variant in metabolic disease. This work expands our knowledge about transcriptional regulation in pancreatic development through the characterization of a long-range enhancer and highlights the utility of enhancer discovery in disease-relevant settings for understanding monogenic and complex disease.
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't Hart LM, de Klerk JA, Bouland GA, Peerlings JHD, Blom MT, Cramer SJ, Bijkerk R, Beulens JWJ, Slieker RC. Small RNA sequencing reveals snoRNAs and piRNA-019825 as novel players in diabetic kidney disease. Endocrine 2024:10.1007/s12020-024-03884-3. [PMID: 38801599 DOI: 10.1007/s12020-024-03884-3] [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: 03/20/2024] [Accepted: 05/16/2024] [Indexed: 05/29/2024]
Abstract
INTRODUCTION Micro- and macrovascular complications are common among persons with type 2 diabetes. Recently there has been growing interest to investigate the potential of circulating small non-coding RNAs (sncRNAs) as contributors to the development of diabetic complications. In this study we investigate to what extent circulating sncRNAs levels associate with prevalent diabetic kidney disease (DKD) in persons with type 2 diabetes. METHODS Plasma sncRNAs levels were determined using small RNA-seq, allowing detection of miRNAs, snoRNAs, piRNAs, tRNA fragments, and various other sncRNA classes. We tested for differentially expressed sncRNAs in persons with type 2 diabetes, with DKD (n = 69) or without DKD (n = 405). In secondary analyses, we also tested the association with eGFR, albuminuria (UACR), and the plasma proteome. RESULTS In total seven sncRNAs were negatively associated with prevalent DKD (all PFDR ≤ 0.05). Including one microRNA (miR-143-5p), five snoRNAs (U8, SNORD118, SNORD24, SNORD107, SNORD87) and a piRNA (piR-019825 | DQ597218). Proteomic analyses showed that the seven sncRNAs, and especially the piRNA piR-019825, were associated with plasma levels of 24 proteins of which several have known associations with kidney function including TNF sR-I (TNFRFS1A), DAN (NBL1) and cystatin C (CST3). CONCLUSION We have identified novel small non-coding RNAs, primarily from classes other than microRNAs, that are associated with diabetic kidney disease. Our results show that the involvement of small non-coding RNAs in DKD goes beyond the already known microRNAs and also involves other classes of sncRNA, in particular snoRNAs and the piRNA piR-019825, that have never been studied before in relation to kidney function.
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Affiliation(s)
- L M 't Hart
- Department of Cell and Chemical Biology, Leiden University Medical Center, Leiden, The Netherlands.
- Department of Epidemiology and Data Sciences, Amsterdam University Medical Center, Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands.
- Health Behaviors & Chronic Diseases Research Program & Personalised Medicine Research Program, Amsterdam Public Health, Amsterdam, The Netherlands.
- Department of Biomedical Data Sciences, Section Molecular Epidemiology, Leiden University Medical Center, Leiden, The Netherlands.
| | - J A de Klerk
- Department of Cell and Chemical Biology, Leiden University Medical Center, Leiden, The Netherlands
- Department of Internal Medicine, Division of Nephrology, Leiden University Medical Center, Leiden, The Netherlands
- Einthoven Laboratory for Vascular and Regenerative Medicine, Leiden University Medical Center, Leiden, The Netherlands
| | - G A Bouland
- Department of Cell and Chemical Biology, Leiden University Medical Center, Leiden, The Netherlands
| | - J H D Peerlings
- Department of Cell and Chemical Biology, Leiden University Medical Center, Leiden, The Netherlands
| | - M T Blom
- Health Behaviors & Chronic Diseases Research Program & Personalised Medicine Research Program, Amsterdam Public Health, Amsterdam, The Netherlands
- Department of General Practice, Amsterdam University Medical Center, Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
| | - S J Cramer
- Department of Cell and Chemical Biology, Leiden University Medical Center, Leiden, The Netherlands
| | - R Bijkerk
- Department of Internal Medicine, Division of Nephrology, Leiden University Medical Center, Leiden, The Netherlands
- Einthoven Laboratory for Vascular and Regenerative Medicine, Leiden University Medical Center, Leiden, The Netherlands
| | - J W J Beulens
- Department of Epidemiology and Data Sciences, Amsterdam University Medical Center, Location Vrije Universiteit Amsterdam, Amsterdam, The Netherlands
- Health Behaviors & Chronic Diseases Research Program & Personalised Medicine Research Program, Amsterdam Public Health, Amsterdam, The Netherlands
| | - R C Slieker
- Department of Cell and Chemical Biology, Leiden University Medical Center, Leiden, The Netherlands
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Kurgan N, Kjærgaard Larsen J, Deshmukh AS. Harnessing the power of proteomics in precision diabetes medicine. Diabetologia 2024; 67:783-797. [PMID: 38345659 DOI: 10.1007/s00125-024-06097-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/14/2023] [Accepted: 12/20/2023] [Indexed: 03/21/2024]
Abstract
Precision diabetes medicine (PDM) aims to reduce errors in prevention programmes, diagnosis thresholds, prognosis prediction and treatment strategies. However, its advancement and implementation are difficult due to the heterogeneity of complex molecular processes and environmental exposures that influence an individual's disease trajectory. To address this challenge, it is imperative to develop robust screening methods for all areas of PDM. Innovative proteomic technologies, alongside genomics, have proven effective in precision cancer medicine and are showing promise in diabetes research for potential translation. This narrative review highlights how proteomics is well-positioned to help improve PDM. Specifically, a critical assessment of widely adopted affinity-based proteomic technologies in large-scale clinical studies and evidence of the benefits and feasibility of using MS-based plasma proteomics is presented. We also present a case for the use of proteomics to identify predictive protein panels for type 2 diabetes subtyping and the development of clinical prediction models for prevention, diagnosis, prognosis and treatment strategies. Lastly, we discuss the importance of plasma and tissue proteomics and its integration with genomics (proteogenomics) for identifying unique type 2 diabetes intra- and inter-subtype aetiology. We conclude with a call for action formed on advancing proteomics technologies, benchmarking their performance and standardisation across sites, with an emphasis on data sharing and the inclusion of diverse ancestries in large cohort studies. These efforts should foster collaboration with key stakeholders and align with ongoing academic programmes such as the Precision Medicine in Diabetes Initiative consortium.
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Affiliation(s)
- Nigel Kurgan
- Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark
| | - Jeppe Kjærgaard Larsen
- Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark
| | - Atul S Deshmukh
- Novo Nordisk Foundation Center for Basic Metabolic Research, University of Copenhagen, Copenhagen, Denmark.
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8
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Ali AS, Pham C, Morahan G, Ekinci EI. Genetic Risk Scores Identify People at High Risk of Developing Diabetic Kidney Disease: A Systematic Review. J Clin Endocrinol Metab 2024; 109:1189-1197. [PMID: 38039081 PMCID: PMC11031242 DOI: 10.1210/clinem/dgad704] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/19/2023] [Revised: 11/20/2023] [Accepted: 11/29/2023] [Indexed: 12/03/2023]
Abstract
CONTEXT Diabetic kidney disease (DKD) is the leading cause of end-stage renal disease. Measures to prevent and treat DKD require better identification of patients most at risk. In this systematic review, we summarize the existing evidence of genetic risk scores (GRSs) and their utility for predicting DKD in people with type 1 or type 2 diabetes. EVIDENCE ACQUISITION We searched MEDLINE, Embase, Web of Science, and Cochrane Reviews in June 2022 to identify all existing and relevant literature. Main data items sought were study design, sample size, population, single nucleotide polymorphisms of interest, DKD-related outcomes, and relevant summary measures of result. The Critical Appraisal Skills Programme checklist was used to evaluate the methodological quality of studies. EVIDENCE SYNTHESIS We identified 400 citations of which 15 are included in this review. Overall, 7 studies had positive results, 5 had mixed results, and 3 had negative results. Most studies with the strongest methodological quality (n = 9) reported statistically significant and favourable findings of a GRS's association with at least 1 measure of DKD. CONCLUSION This systematic review presents evidence of the utility of GRSs to identify people with diabetes that are at high risk of developing DKD. In practice, a robust GRS could be used at the first clinical encounter with a person living with diabetes in order to stratify their risk of complications. Further prospective research is needed.
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Affiliation(s)
- Aleena Shujaat Ali
- Department of Medicine, The University of Melbourne, Melbourne 3084, Australia
- Australian Centre for Accelerating Diabetes Innovations, The University of Melbourne, Melbourne 3000, Australia
| | - Cecilia Pham
- Department of Medicine, The University of Melbourne, Melbourne 3084, Australia
- Australian Centre for Accelerating Diabetes Innovations, The University of Melbourne, Melbourne 3000, Australia
| | - Grant Morahan
- Australian Centre for Accelerating Diabetes Innovations, The University of Melbourne, Melbourne 3000, Australia
- Diabetes Research Foundation, The University of Western Australia, Perth 6009, Australia
| | - Elif Ilhan Ekinci
- Department of Medicine, The University of Melbourne, Melbourne 3084, Australia
- Australian Centre for Accelerating Diabetes Innovations, The University of Melbourne, Melbourne 3000, Australia
- Department of Endocrinology, Austin Health, Melbourne 3084, Australia
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9
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Li S, Dragan I, Tran VDT, Fung CH, Kuznetsov D, Hansen MK, Beulens JWJ, Hart LM‘, Slieker RC, Donnelly LA, Gerl MJ, Klose C, Mehl F, Simons K, Elders PJM, Pearson ER, Rutter GA, Ibberson M. Multi-omics subgroups associated with glycaemic deterioration in type 2 diabetes: an IMI-RHAPSODY Study. Front Endocrinol (Lausanne) 2024; 15:1350796. [PMID: 38510703 PMCID: PMC10951062 DOI: 10.3389/fendo.2024.1350796] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/06/2023] [Accepted: 02/22/2024] [Indexed: 03/22/2024] Open
Abstract
Introduction Type 2 diabetes (T2D) onset, progression and outcomes differ substantially between individuals. Multi-omics analyses may allow a deeper understanding of these differences and ultimately facilitate personalised treatments. Here, in an unsupervised "bottom-up" approach, we attempt to group T2D patients based solely on -omics data generated from plasma. Methods Circulating plasma lipidomic and proteomic data from two independent clinical cohorts, Hoorn Diabetes Care System (DCS) and Genetics of Diabetes Audit and Research in Tayside Scotland (GoDARTS), were analysed using Similarity Network Fusion. The resulting patient network was analysed with Logistic and Cox regression modelling to explore relationships between plasma -omic profiles and clinical characteristics. Results From a total of 1,134 subjects in the two cohorts, levels of 180 circulating plasma lipids and 1195 proteins were used to separate patients into two subgroups. These differed in terms of glycaemic deterioration (Hazard Ratio=0.56;0.73), insulin sensitivity and secretion (C-peptide, p=3.7e-11;2.5e-06, DCS and GoDARTS, respectively; Homeostatic model assessment 2 (HOMA2)-B; -IR; -S, p=0.0008;4.2e-11;1.1e-09, only in DCS). The main molecular signatures separating the two groups included triacylglycerols, sphingomyelin, testican-1 and interleukin 18 receptor. Conclusions Using an unsupervised network-based fusion method on plasma lipidomics and proteomics data from two independent cohorts, we were able to identify two subgroups of T2D patients differing in terms of disease severity. The molecular signatures identified within these subgroups provide insights into disease mechanisms and possibly new prognostic markers for T2D.
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Affiliation(s)
- Shiying Li
- Centre de Recherche du CHUM, Faculty of Medicine, University of Montreal, Montreal, QC, Canada
| | - Iulian Dragan
- Vital-IT Group, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Van Du T. Tran
- Vital-IT Group, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Chun Ho Fung
- Section of Cell Biology and Functional Genomics, Department of Metabolism, Diabetes and Reproduction, Imperial College of London, London, United Kingdom
| | - Dmitry Kuznetsov
- Vital-IT Group, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | | | - Joline W. J. Beulens
- Department of Epidemiology and Data Sciences, Amsterdam University Medical Center, Amsterdam, Netherlands
- Amsterdam Public Health, Amsterdam, Netherlands
| | - Leen M. ‘t Hart
- Department of Epidemiology and Data Sciences, Amsterdam University Medical Center, Amsterdam, Netherlands
- Amsterdam Public Health, Amsterdam, Netherlands
- Department of Cell and Chemical Biology, Leiden University Medical Center, Leiden, Netherlands
- Department of Biomedical Data Sciences, Section Molecular Epidemiology, Leiden University Medical Center, Leiden, Netherlands
| | - Roderick C. Slieker
- Department of Cell and Chemical Biology, Leiden University Medical Center, Leiden, Netherlands
| | - Louise A. Donnelly
- Division of Population Health & Genomics, School of Medicine, University of Dundee, Dundee, United Kingdom
| | | | | | - Florence Mehl
- Vital-IT Group, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | | | - Petra J. M. Elders
- Department of General Practice and Elderly Care Medicine, Amsterdam Public Health Research Institute, Amsterdam UMC–location VUmc, Amsterdam, Netherlands
| | - Ewan R. Pearson
- Division of Population Health & Genomics, School of Medicine, University of Dundee, Dundee, United Kingdom
| | - Guy A. Rutter
- Centre de Recherche du CHUM, Faculty of Medicine, University of Montreal, Montreal, QC, Canada
- Section of Cell Biology and Functional Genomics, Department of Metabolism, Diabetes and Reproduction, Imperial College of London, London, United Kingdom
- Lee Kong Chian School of Medicine, Nan Yang Technological University, Singapore, Singapore
| | - Mark Ibberson
- Vital-IT Group, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
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10
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Delfin C, Dragan I, Kuznetsov D, Tajes JF, Smit F, Coral DE, Farzaneh A, Haugg A, Hungele A, Niknejad A, Hall C, Jacobs D, Marek D, Fraser DP, Thuillier D, Ahmadizar F, Mehl F, Pattou F, Burdet F, Hawkes G, Arts ICW, Blanch J, Van Soest J, Fernández-Real JM, Boehl J, Fink K, van Greevenbroek MMJ, Kavousi M, Minten M, Prinz N, Ipsen N, Franks PW, Ramos R, Holl RW, Horban S, Duarte-Salles T, Tran VDT, Raverdy V, Leal Y, Lenart A, Pearson E, Sparsø T, Giordano GN, Ioannidis V, Soh K, Frayling TM, Le Roux CW, Ibberson M. A Federated Database for Obesity Research: An IMI-SOPHIA Study. Life (Basel) 2024; 14:262. [PMID: 38398771 PMCID: PMC10890572 DOI: 10.3390/life14020262] [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: 12/20/2023] [Revised: 01/12/2024] [Accepted: 02/08/2024] [Indexed: 02/25/2024] Open
Abstract
Obesity is considered by many as a lifestyle choice rather than a chronic progressive disease. The Innovative Medicines Initiative (IMI) SOPHIA (Stratification of Obesity Phenotypes to Optimize Future Obesity Therapy) project is part of a momentum shift aiming to provide better tools for the stratification of people with obesity according to disease risk and treatment response. One of the challenges to achieving these goals is that many clinical cohorts are siloed, limiting the potential of combined data for biomarker discovery. In SOPHIA, we have addressed this challenge by setting up a federated database building on open-source DataSHIELD technology. The database currently federates 16 cohorts that are accessible via a central gateway. The database is multi-modal, including research studies, clinical trials, and routine health data, and is accessed using the R statistical programming environment where statistical and machine learning analyses can be performed at a distance without any disclosure of patient-level data. We demonstrate the use of the database by providing a proof-of-concept analysis, performing a federated linear model of BMI and systolic blood pressure, pooling all data from 16 studies virtually without any analyst seeing individual patient-level data. This analysis provided similar point estimates compared to a meta-analysis of the 16 individual studies. Our approach provides a benchmark for reproducible, safe federated analyses across multiple study types provided by multiple stakeholders.
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Affiliation(s)
| | - Iulian Dragan
- Vital-IT Group, SIB Swiss Institute of Bioinformatics, CH-1015 Lausanne, Switzerland
| | - Dmitry Kuznetsov
- Vital-IT Group, SIB Swiss Institute of Bioinformatics, CH-1015 Lausanne, Switzerland
| | - Juan Fernandez Tajes
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Sciences, Clinical Research Centre (CRC), Lund University, Jan Waldenströmsgata 35, SE-20502 Malmö, Sweden
| | - Femke Smit
- Maastricht Center for Systems Biology, Faculty of Science and Engineering, Maastricht University, Paul Henri Spaaklaan 1, 6229 EN Maastricht, The Netherlands
| | - Daniel E. Coral
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Sciences, Clinical Research Centre (CRC), Lund University, Jan Waldenströmsgata 35, SE-20502 Malmö, Sweden
| | - Ali Farzaneh
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, 3000 CA Rotterdam, The Netherlands
| | - André Haugg
- Global Biostatistics & Data Sciences, Boehringer Ingelheim Pharma GmbH & Co. KG, 88400 Biberach, Germany
| | - Andreas Hungele
- Institute of Epidemiology and Medical Biometry, CAQM, University of Ulm, 89081 Ulm, Germany
- German Center for Diabetes Research (DZD), 85764 Neuherberg, Germany
| | - Anne Niknejad
- Vital-IT Group, SIB Swiss Institute of Bioinformatics, CH-1015 Lausanne, Switzerland
| | - Christopher Hall
- Division of Population Health and Genomics, Ninewells Hospital and School of Medicine, University of Dundee, Dundee DD1 4HN, UK
| | - Daan Jacobs
- Nederlandse Obesitas Kliniek, Huis Ter Heide, 3712 BA Utrecht, The Netherlands
| | - Diana Marek
- Vital-IT Group, SIB Swiss Institute of Bioinformatics, CH-1015 Lausanne, Switzerland
| | - Diane P. Fraser
- University of Exeter Medical School, University of Exeter, Exeter EX1 2LU, UK
| | - Dorothee Thuillier
- Univ Lille, Inserm, CHU Lille, Pasteur Institute Lille, U1190 Translational Research for Diabetes, European Genomic Institute of Diabetes, 59000 Lille, France; (D.T.)
| | - Fariba Ahmadizar
- Data Science and Biostatistics Department, Julius Global Health, University Medical Center Utrecht, 3508 GA Utrecht, The Netherlands
| | - Florence Mehl
- Vital-IT Group, SIB Swiss Institute of Bioinformatics, CH-1015 Lausanne, Switzerland
| | - Francois Pattou
- Univ Lille, Inserm, CHU Lille, Pasteur Institute Lille, U1190 Translational Research for Diabetes, European Genomic Institute of Diabetes, 59000 Lille, France; (D.T.)
| | - Frederic Burdet
- Vital-IT Group, SIB Swiss Institute of Bioinformatics, CH-1015 Lausanne, Switzerland
| | - Gareth Hawkes
- University of Exeter Medical School, University of Exeter, Exeter EX1 2LU, UK
| | - Ilja C. W. Arts
- Maastricht Center for Systems Biology, Faculty of Science and Engineering, Maastricht University, Paul Henri Spaaklaan 1, 6229 EN Maastricht, The Netherlands
| | - Jordi Blanch
- Fundació Institut Universitari per a la Recerca a l’Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), 08007 Barcelona, Spain
- ISV-Girona Research Group, Research Unit in Primary Care, Primary Care Services, Catalan Institute of Health (ICS), 08908 Barcelona, Spain
| | - Johan Van Soest
- Brightlands Institute for Smart Society (BISS), Faculty of Science and Engineering, Maastricht University, 6229 EN Maastricht, The Netherlands
- Department of Radiation Oncology (Maastro), GROW-School for Oncology and Reproduction, Maastricht University Medical Center, 6229 EN Maastricht, The Netherlands
| | - José-Manuel Fernández-Real
- Nutrition, Eumetabolism and Health Group, Institut d’Investigació Biomèdica de Girona (IDIBGI-CERCA), Av. França 30, 17007 Girona, Spain
- Department of Medical Sciences, School of Medicine, University of Girona, 17003 Girona, Spain
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, 28029 Madrid, Spain
- Department of Diabetes, Endocrinology and Nutrition, Dr. Josep Trueta University Hospital, Av. França, s/n, 17007 Girona, Spain
| | - Juergen Boehl
- Global Biostatistics & Data Sciences, Boehringer Ingelheim Pharma GmbH & Co. KG, 88400 Biberach, Germany
| | - Katharina Fink
- Institute of Epidemiology and Medical Biometry, CAQM, University of Ulm, 89081 Ulm, Germany
- German Center for Diabetes Research (DZD), 85764 Neuherberg, Germany
| | - Marleen M. J. van Greevenbroek
- Department of Internal Medicine and CARIM School of Cardiovascular Diseases, Maastricht University, 6229 EN Maastricht, The Netherlands
| | - Maryam Kavousi
- Department of Epidemiology, Erasmus MC, University Medical Center Rotterdam, 3000 CA Rotterdam, The Netherlands
| | - Michiel Minten
- Maastricht Center for Systems Biology, Faculty of Science and Engineering, Maastricht University, Paul Henri Spaaklaan 1, 6229 EN Maastricht, The Netherlands
| | - Nicole Prinz
- Institute of Epidemiology and Medical Biometry, CAQM, University of Ulm, 89081 Ulm, Germany
- German Center for Diabetes Research (DZD), 85764 Neuherberg, Germany
| | | | - Paul W. Franks
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Sciences, Clinical Research Centre (CRC), Lund University, Jan Waldenströmsgata 35, SE-20502 Malmö, Sweden
| | - Rafael Ramos
- Fundació Institut Universitari per a la Recerca a l’Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), 08007 Barcelona, Spain
- Department of Medical Sciences, School of Medicine, University of Girona, 17003 Girona, Spain
- Department of Medical Informatics, Erasmus University Medical Center, 3000 CA Rotterdam, The Netherlands
- Research in Vascular Health Group, Institut d’Investigació Biomèdica de Girona (IDIBGI-CERCA), Parc Hospitalari Martí i Julià, Edifici M2, 17190 Salt, Spain
| | - Reinhard W. Holl
- Institute of Epidemiology and Medical Biometry, CAQM, University of Ulm, 89081 Ulm, Germany
- German Center for Diabetes Research (DZD), 85764 Neuherberg, Germany
| | - Scott Horban
- Division of Population Health and Genomics, Ninewells Hospital and School of Medicine, University of Dundee, Dundee DD1 4HN, UK
| | - Talita Duarte-Salles
- Fundació Institut Universitari per a la Recerca a l’Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), 08007 Barcelona, Spain
- Department of Medical Informatics, Erasmus University Medical Center, 3000 CA Rotterdam, The Netherlands
| | - Van Du T. Tran
- Vital-IT Group, SIB Swiss Institute of Bioinformatics, CH-1015 Lausanne, Switzerland
| | - Violeta Raverdy
- Univ Lille, Inserm, CHU Lille, Pasteur Institute Lille, U1190 Translational Research for Diabetes, European Genomic Institute of Diabetes, 59000 Lille, France; (D.T.)
| | - Yenny Leal
- Nutrition, Eumetabolism and Health Group, Institut d’Investigació Biomèdica de Girona (IDIBGI-CERCA), Av. França 30, 17007 Girona, Spain
- Department of Medical Sciences, School of Medicine, University of Girona, 17003 Girona, Spain
- CIBER Fisiopatología de la Obesidad y Nutrición (CIBEROBN), Instituto de Salud Carlos III, 28029 Madrid, Spain
- Department of Diabetes, Endocrinology and Nutrition, Dr. Josep Trueta University Hospital, Av. França, s/n, 17007 Girona, Spain
| | | | - Ewan Pearson
- Division of Population Health and Genomics, Ninewells Hospital and School of Medicine, University of Dundee, Dundee DD1 4HN, UK
| | | | - Giuseppe N. Giordano
- Genetic and Molecular Epidemiology Unit, Lund University Diabetes Centre, Department of Clinical Sciences, Clinical Research Centre (CRC), Lund University, Jan Waldenströmsgata 35, SE-20502 Malmö, Sweden
| | - Vassilios Ioannidis
- Vital-IT Group, SIB Swiss Institute of Bioinformatics, CH-1015 Lausanne, Switzerland
| | - Keng Soh
- Novo Nordisk A/S, 2860 Søborg, Denmark
| | - Timothy M. Frayling
- University of Exeter Medical School, University of Exeter, Exeter EX1 2LU, UK
- Department of Genetic Medicine and Development, Faculty of Medicine, University of Geneva, 1 Rue Michel-Servet, CH-1211 Geneva, Switzerland
| | - Carel W. Le Roux
- Diabetes Complications Research Centre, University College Dublin, D04 V1W8 Dublin, Ireland
| | - Mark Ibberson
- Vital-IT Group, SIB Swiss Institute of Bioinformatics, CH-1015 Lausanne, Switzerland
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11
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Xourafa G, Korbmacher M, Roden M. Inter-organ crosstalk during development and progression of type 2 diabetes mellitus. Nat Rev Endocrinol 2024; 20:27-49. [PMID: 37845351 DOI: 10.1038/s41574-023-00898-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 08/29/2023] [Indexed: 10/18/2023]
Abstract
Type 2 diabetes mellitus (T2DM) is characterized by tissue-specific insulin resistance and pancreatic β-cell dysfunction, which result from the interplay of local abnormalities within different tissues and systemic dysregulation of tissue crosstalk. The main local mechanisms comprise metabolic (lipid) signalling, altered mitochondrial metabolism with oxidative stress, endoplasmic reticulum stress and local inflammation. While the role of endocrine dysregulation in T2DM pathogenesis is well established, other forms of inter-organ crosstalk deserve closer investigation to better understand the multifactorial transition from normoglycaemia to hyperglycaemia. This narrative Review addresses the impact of certain tissue-specific messenger systems, such as metabolites, peptides and proteins and microRNAs, their secretion patterns and possible alternative transport mechanisms, such as extracellular vesicles (exosomes). The focus is on the effects of these messengers on distant organs during the development of T2DM and progression to its complications. Starting from the adipose tissue as a major organ relevant to T2DM pathophysiology, the discussion is expanded to other key tissues, such as skeletal muscle, liver, the endocrine pancreas and the intestine. Subsequently, this Review also sheds light on the potential of multimarker panels derived from these biomarkers and related multi-omics for the prediction of risk and progression of T2DM, novel diabetes mellitus subtypes and/or endotypes and T2DM-related complications.
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Affiliation(s)
- Georgia Xourafa
- Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany
- German Center for Diabetes Research, Partner Düsseldorf, München-Neuherberg, Düsseldorf, Germany
| | - Melis Korbmacher
- Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany
- German Center for Diabetes Research, Partner Düsseldorf, München-Neuherberg, Düsseldorf, Germany
| | - Michael Roden
- Institute for Clinical Diabetology, German Diabetes Center, Leibniz Center for Diabetes Research at Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany.
- German Center for Diabetes Research, Partner Düsseldorf, München-Neuherberg, Düsseldorf, Germany.
- Department of Endocrinology and Diabetology, Medical Faculty and University Hospital Düsseldorf, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany.
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12
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McNabney SM, Gletsu-Miller N, Rowland DL. Sexual Function and Satisfaction in the Context of Obesity. Curr Diab Rep 2023; 23:315-327. [PMID: 37632680 DOI: 10.1007/s11892-023-01523-1] [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] [Accepted: 08/07/2023] [Indexed: 08/28/2023]
Abstract
PURPOSE OF REVIEW Sexual dysfunction is commonly associated with overweight/obesity, but the underlying physiological and psychosocial mechanisms are not fully understood. This review contextualizes the obesity-sexual (dys)function relationship, describes recent insights from the medical and social science literature, and suggests opportunities for continued research. RECENT FINDINGS Although sexual dysfunction has been historically evaluated as a consequence/outcome of obesity, it is increasingly considered as a harbinger of future metabolic comorbidities, including type 2 diabetes and cardiovascular disease. Body image dissatisfaction is a consistent predictor for lower sexual satisfaction across BMI categories, likely mediated by cognitive distraction during partnered sex. To fully capture the relationship between obesity and sexual dysfunction, multidisciplinary research approaches are warranted. While clinically significant weight loss tends to improve sexual functioning for women and men, higher body image satisfaction may independently promote sexual function and satisfaction without concomitant weight loss.
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Affiliation(s)
- Sean M McNabney
- Department of Applied Health Science, School of Public Health-Bloomington, Indiana University, Bloomington, IN, USA
| | - Nana Gletsu-Miller
- Department of Applied Health Science, School of Public Health-Bloomington, Indiana University, Bloomington, IN, USA
| | - David L Rowland
- Department of Psychology, Valparaiso University, Valparaiso, IN, USA.
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13
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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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14
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Astarita G, Kelly RS, Lasky-Su J. Metabolomics and lipidomics strategies in modern drug discovery and development. Drug Discov Today 2023; 28:103751. [PMID: 37640150 PMCID: PMC10543515 DOI: 10.1016/j.drudis.2023.103751] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Revised: 08/09/2023] [Accepted: 08/21/2023] [Indexed: 08/31/2023]
Abstract
Metabolomics and lipidomics have an increasingly pivotal role in drug discovery and development. In the context of drug discovery, monitoring changes in the levels or composition of metabolites and lipids relative to genetic variations yields functional insights, bolstering human genetics and (meta)genomic methodologies. This approach also sheds light on potential novel targets for therapeutic intervention. In the context of drug development, metabolite and lipid biomarkers contribute to enhanced success rates, promising a transformative impact on precision medicine. In this review, we deviate from analytical chemist-focused perspectives, offering an overview tailored to drug discovery. We provide introductory insight into state-of-the-art mass spectrometry (MS)-based metabolomics and lipidomics techniques utilized in drug discovery and development, drawing from the collective expertise of our research teams. We comprehensively outline the application of metabolomics and lipidomics in advancing drug discovery and development, spanning fundamental research, target identification, mechanisms of action, and the exploration of biomarkers.
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Affiliation(s)
- Giuseppe Astarita
- Georgetown University, Washington, DC, USA; Arkuda Therapeutics, Watertown, MA, USA.
| | - Rachel S Kelly
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
| | - Jessica Lasky-Su
- Channing Division of Network Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, MA, USA
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15
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Bhandari SM, Singh P, Arun N, Sekimitsu S, Raghu V, Rauscher FG, Elze T, Horn K, Kirsten T, Scholz M, Segrè AV, Wiggs JL, Kalpathy-Cramer J, Zebardast N. Automated detection of genetic relatedness from fundus photographs using Siamese Neural Networks. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.08.16.23294183. [PMID: 37662422 PMCID: PMC10473808 DOI: 10.1101/2023.08.16.23294183] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/05/2023]
Abstract
Heritability of common eye diseases and ocular traits are relatively high. Here, we develop an automated algorithm to detect genetic relatedness from color fundus photographs (FPs). We estimated the degree of shared ancestry amongst individuals in the UK Biobank using KING software. A convolutional Siamese neural network-based algorithm was trained to output a measure of genetic relatedness using 7224 pairs (3612 related and 3612 unrelated) of FPs. The model achieved high performance for prediction of genetic relatedness; when computed Euclidean distances were used to determine probability of relatedness, the area under the receiver operating characteristic curve (AUROC) for identifying related FPs reached 0.926. We performed external validation of our model using FPs from the LIFE-Adult study and achieved an AUROC of 0.69. An occlusion map indicates that the optic nerve and its surrounding area may be the most predictive of genetic relatedness. We demonstrate that genetic relatedness can be captured from FP features. This approach may be used to uncover novel biomarkers for common ocular diseases.
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16
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Slieker RC, Donnelly LA, Akalestou E, Lopez-Noriega L, Melhem R, Güneş A, Abou Azar F, Efanov A, Georgiadou E, Muniangi-Muhitu H, Sheikh M, Giordano GN, Åkerlund M, Ahlqvist E, Ali A, Banasik K, Brunak S, Barovic M, Bouland GA, Burdet F, Canouil M, Dragan I, Elders PJM, Fernandez C, Festa A, Fitipaldi H, Froguel P, Gudmundsdottir V, Gudnason V, Gerl MJ, van der Heijden AA, Jennings LL, Hansen MK, Kim M, Leclerc I, Klose C, Kuznetsov D, Mansour Aly D, Mehl F, Marek D, Melander O, Niknejad A, Ottosson F, Pavo I, Duffin K, Syed SK, Shaw JL, Cabrera O, Pullen TJ, Simons K, Solimena M, Suvitaival T, Wretlind A, Rossing P, Lyssenko V, Legido Quigley C, Groop L, Thorens B, Franks PW, Lim GE, Estall J, Ibberson M, Beulens JWJ, 't Hart LM, Pearson ER, Rutter GA. Identification of biomarkers for glycaemic deterioration in type 2 diabetes. Nat Commun 2023; 14:2533. [PMID: 37137910 PMCID: PMC10156700 DOI: 10.1038/s41467-023-38148-7] [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] [Subscribe] [Scholar Register] [Received: 04/19/2021] [Accepted: 04/18/2023] [Indexed: 05/05/2023] Open
Abstract
We identify biomarkers for disease progression in three type 2 diabetes cohorts encompassing 2,973 individuals across three molecular classes, metabolites, lipids and proteins. Homocitrulline, isoleucine and 2-aminoadipic acid, eight triacylglycerol species, and lowered sphingomyelin 42:2;2 levels are predictive of faster progression towards insulin requirement. Of ~1,300 proteins examined in two cohorts, levels of GDF15/MIC-1, IL-18Ra, CRELD1, NogoR, FAS, and ENPP7 are associated with faster progression, whilst SMAC/DIABLO, SPOCK1 and HEMK2 predict lower progression rates. In an external replication, proteins and lipids are associated with diabetes incidence and prevalence. NogoR/RTN4R injection improved glucose tolerance in high fat-fed male mice but impaired it in male db/db mice. High NogoR levels led to islet cell apoptosis, and IL-18R antagonised inflammatory IL-18 signalling towards nuclear factor kappa-B in vitro. This comprehensive, multi-disciplinary approach thus identifies biomarkers with potential prognostic utility, provides evidence for possible disease mechanisms, and identifies potential therapeutic avenues to slow diabetes progression.
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Affiliation(s)
- Roderick C Slieker
- Department of Epidemiology and Data Science, Amsterdam Public Health Institute, Amsterdam Cardiovascular Sciences, Amsterdam UMC, location VUMC, Amsterdam, the Netherlands
- Department of Cell and Chemical Biology, Leiden University Medical Center, Leiden, the Netherlands
| | - Louise A Donnelly
- Population Health & Genomics, School of Medicine, University of Dundee, Dundee, UK
| | - Elina Akalestou
- Section of Cell Biology and Functional Genomics, Division of Diabetes, Endocrinology and Metabolism, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Livia Lopez-Noriega
- Section of Cell Biology and Functional Genomics, Division of Diabetes, Endocrinology and Metabolism, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Rana Melhem
- CHUM Research Centre and University of Montreal, Montreal, QC, Canada
| | - Ayşim Güneş
- IRCM and University of Montreal, Montreal, QC, Canada
| | | | - Alexander Efanov
- Lilly Research Laboratories, Eli Lilly and Company, Indianapolis, US
| | - Eleni Georgiadou
- Section of Cell Biology and Functional Genomics, Division of Diabetes, Endocrinology and Metabolism, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Hermine Muniangi-Muhitu
- Section of Cell Biology and Functional Genomics, Division of Diabetes, Endocrinology and Metabolism, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | - Mahsa Sheikh
- Section of Cell Biology and Functional Genomics, Division of Diabetes, Endocrinology and Metabolism, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
| | | | - Mikael Åkerlund
- Department of Clinical Sciences, Lund University, Malmö, Sweden
| | - Emma Ahlqvist
- Department of Clinical Sciences, Lund University, Malmö, Sweden
| | - Ashfaq Ali
- Steno Diabetes Center Copenhagen, Gentofte, Denmark
| | - Karina Banasik
- Novo Nordisk Foundation Center for Protein Research, Copenhagen, Denmark
| | - Søren Brunak
- Novo Nordisk Foundation Center for Protein Research, Copenhagen, Denmark
| | - Marko Barovic
- Paul Langerhans Institute Dresden (PLID) of the Helmholtz Center Munich at the University Hospital Carl Gustav Carus and Medical Faculty, Dresden, Germany
| | - Gerard A Bouland
- Department of Cell and Chemical Biology, Leiden University Medical Center, Leiden, the Netherlands
| | - Frédéric Burdet
- Vital-IT Group, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Mickaël Canouil
- INSERM U1283, CNRS UMR 8199, European Genomic Institute for Diabetes (EGID), Institut Pasteur de Lille, University of Lille, Lille University Hospital, Lille, F-59000, France
| | - Iulian Dragan
- Vital-IT Group, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Petra J M Elders
- Department of General Practice and Elderly Care Medicine, Amsterdam Public Health Research Institute, Amsterdam UMC-location VUmc, Amsterdam, the Netherlands
| | | | - Andreas Festa
- Eli Lilly Regional Operations GmbH, Vienna, Austria
- 1st Medical Department, LK Stockerau, Niederösterreich, Austria
| | - Hugo Fitipaldi
- Department of Clinical Sciences, Lund University, Malmö, Sweden
| | - Phillippe Froguel
- INSERM U1283, CNRS UMR 8199, European Genomic Institute for Diabetes (EGID), Institut Pasteur de Lille, University of Lille, Lille University Hospital, Lille, F-59000, France
- Division of Systems Biology, Department of Diabetes, Endocrinology and Metabolism, Imperial College London, London, UK
| | - Valborg Gudmundsdottir
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
- Icelandic Heart Association, Kopavogur, Iceland
| | - Vilmundur Gudnason
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
- Icelandic Heart Association, Kopavogur, Iceland
| | | | - Amber A van der Heijden
- Department of General Practice and Elderly Care Medicine, Amsterdam Public Health Research Institute, Amsterdam UMC-location VUmc, Amsterdam, the Netherlands
| | - Lori L Jennings
- Novartis Institutes for Biomedical Research, Cambridge, MA, 02139, USA
| | - Michael K Hansen
- Cardiovascular and Metabolic Disease Research, Janssen Research & Development, Spring House, PA, USA
| | - Min Kim
- Steno Diabetes Center Copenhagen, Gentofte, Denmark
- Institute of Pharmaceutical Science, Faculty of Life Sciences and Medicines, King's College London, London, UK
| | - Isabelle Leclerc
- Section of Cell Biology and Functional Genomics, Division of Diabetes, Endocrinology and Metabolism, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
- CHUM Research Centre and University of Montreal, Montreal, QC, Canada
| | | | - Dmitry Kuznetsov
- Vital-IT Group, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | | | - Florence Mehl
- Vital-IT Group, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Diana Marek
- Vital-IT Group, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Olle Melander
- Department of Clinical Sciences, Lund University, Malmö, Sweden
| | - Anne Niknejad
- Vital-IT Group, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Filip Ottosson
- Department of Clinical Sciences, Lund University, Malmö, Sweden
- Section for Clinical Mass Spectrometry, Danish Center for Neonatal Screening, Department of Congenital Disorders, Statens Serum Institut, Copenhagen, Denmark
| | - Imre Pavo
- Eli Lilly Regional Operations GmbH, Vienna, Austria
| | - Kevin Duffin
- Lilly Research Laboratories, Eli Lilly and Company, Indianapolis, US
| | - Samreen K Syed
- Lilly Research Laboratories, Eli Lilly and Company, Indianapolis, US
| | - Janice L Shaw
- Lilly Research Laboratories, Eli Lilly and Company, Indianapolis, US
| | - Over Cabrera
- Lilly Research Laboratories, Eli Lilly and Company, Indianapolis, US
| | - Timothy J Pullen
- Section of Cell Biology and Functional Genomics, Division of Diabetes, Endocrinology and Metabolism, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK
- Department of Diabetes, Guy's Campus King's College London, London, UK
| | | | - Michele Solimena
- Paul Langerhans Institute Dresden (PLID) of the Helmholtz Center Munich at the University Hospital Carl Gustav Carus and Medical Faculty, Dresden, Germany
- Molecular Diabetology, University Hospital and Medical Faculty Carl Gustav Carus, TU Dresden, Dresden, Germany
| | | | | | - Peter Rossing
- Steno Diabetes Center Copenhagen, Gentofte, Denmark
- Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Valeriya Lyssenko
- Department of Clinical Science, Center for Diabetes Research, University of Bergen, Bergen, Norway
- Genomics, Diabetes and Endocrinology Unit, Department of Clinical Sciences Malmö, Lund University Diabetes Centre, Skåne University Hospital, Malmö, Sweden
| | - Cristina Legido Quigley
- Steno Diabetes Center Copenhagen, Gentofte, Denmark
- Institute of Pharmaceutical Science, Faculty of Life Sciences and Medicines, King's College London, London, UK
| | - Leif Groop
- Department of Clinical Sciences, Lund University, Malmö, Sweden
- Finnish Institute of Molecular Medicine, Helsinki University, Helsinki, Finland
| | - Bernard Thorens
- Center for Integrative Genomics, University of Lausanne, CH-1015, Lausanne, Switzerland
| | - Paul W Franks
- Department of Clinical Sciences, Lund University, Malmö, Sweden
- Department of Nutrition, Harvard School of Public Health, Boston, MA, USA
| | - Gareth E Lim
- CHUM Research Centre and University of Montreal, Montreal, QC, Canada
| | | | - Mark Ibberson
- Vital-IT Group, SIB Swiss Institute of Bioinformatics, Lausanne, Switzerland
| | - Joline W J Beulens
- Department of Epidemiology and Data Science, Amsterdam Public Health Institute, Amsterdam Cardiovascular Sciences, Amsterdam UMC, location VUMC, Amsterdam, the Netherlands
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, the Netherlands
| | - Leen M 't Hart
- Department of Epidemiology and Data Science, Amsterdam Public Health Institute, Amsterdam Cardiovascular Sciences, Amsterdam UMC, location VUMC, Amsterdam, the Netherlands.
- Department of Cell and Chemical Biology, Leiden University Medical Center, Leiden, the Netherlands.
- Department of Biomedical Data Sciences, Section Molecular Epidemiology, Leiden University Medical Center, Leiden, the Netherlands.
| | - Ewan R Pearson
- Population Health & Genomics, School of Medicine, University of Dundee, Dundee, UK.
| | - Guy A Rutter
- Section of Cell Biology and Functional Genomics, Division of Diabetes, Endocrinology and Metabolism, Department of Metabolism, Digestion and Reproduction, Imperial College London, London, UK.
- CHUM Research Centre and University of Montreal, Montreal, QC, Canada.
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Singapore.
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17
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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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18
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Pigeyre M, Gerstein H, Ahlqvist E, Hess S, Paré G. Identifying blood biomarkers for type 2 diabetes subtyping: a report from the ORIGIN trial. Diabetologia 2023; 66:1045-1051. [PMID: 36854916 DOI: 10.1007/s00125-023-05887-7] [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: 10/05/2022] [Accepted: 01/18/2023] [Indexed: 03/02/2023]
Abstract
AIMS/HYPOTHESIS Individuals with diabetes can be clustered into five subtypes using up to six routinely measured clinical variables. We hypothesised that circulating protein levels might be used to distinguish between these subtypes. We recently used five of these six variables to categorise 7017 participants from the Outcome Reduction with an Initial Glargine Intervention (ORIGIN) trial into these subtypes: severe autoimmune diabetes (SAID, n=241), severe insulin-deficient diabetes (SIDD, n=1594), severe insulin-resistant diabetes (SIRD, n=914), mild obesity-related diabetes (MOD, n=1595) and mild age-related diabetes (MARD, n=2673). METHODS Forward-selection logistic regression models were used to identify a subset of 233 cardiometabolic protein biomarkers that were independent determinants of one subtype vs the others. We then assessed the performance of adding identified biomarkers (one after one, from the most discriminant to the least) to predict each subtype vs the others using area under the receiver operating characteristic curve (AUC ROC). Models were adjusted for age, sex, ethnicity, C-peptide level, diabetes duration and glucose-lowering medication usage at blood collection. RESULTS A total of 25 biomarkers were independent determinants of subtypes, including 13 for SIDD, 2 for SIRD, 7 for MOD and 11 for MARD (all p<4.3 × 10-5). The performance of the biomarker sets (comprising 1 to 25 biomarkers), assessed through the AUC ROC, ranged from 0.611 to 0.734, 0.723 to 0.861, 0.672 to 0.742, and 0.651 to 0.751, for SIDD, SIRD, MOD and MARD, respectively. No biomarkers other than GAD antibodies were determinants of SAID. CONCLUSIONS/INTERPRETATION We identified 25 serum biomarkers, as independent determinants of type 2 diabetes subtypes, that could be combined into a diagnostic test for subtyping. TRIAL REGISTRATION ORIGIN trial, ClinicalTrials.gov NCT00069784.
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Affiliation(s)
- Marie Pigeyre
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, ON, Canada.
- Thrombosis and Atherosclerosis Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, ON, Canada.
- Department of Medicine, McMaster University, Michael G. DeGroote School of Medicine, Hamilton, ON, Canada.
| | - Hertzel Gerstein
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, ON, Canada
- Thrombosis and Atherosclerosis Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, ON, Canada
- Department of Medicine, McMaster University, Michael G. DeGroote School of Medicine, Hamilton, ON, Canada
- Department of Clinical Epidemiology & Biostatistics, McMaster University, Hamilton, ON, Canada
| | - Emma Ahlqvist
- Lund University Diabetes Centre, Department of Clinical Sciences, Lund University, Skåne University Hospital, Malmö, Sweden
| | - Sibylle Hess
- Global Medical Diabetes, Sanofi, Frankfurt, Germany
| | - Guillaume Paré
- Population Health Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, ON, Canada
- Thrombosis and Atherosclerosis Research Institute, David Braley Cardiac, Vascular and Stroke Research Institute, Hamilton, ON, Canada
- Department of Clinical Epidemiology & Biostatistics, McMaster University, Hamilton, ON, Canada
- Department of Pathology and Molecular Medicine, McMaster University, Michael G. DeGroote School of Medicine, Hamilton, ON, Canada
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19
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de Klerk JA, Beulens JWJ, Mei H, Bijkerk R, van Zonneveld AJ, Koivula RW, Elders PJM, 't Hart LM, Slieker RC. Altered blood gene expression in the obesity-related type 2 diabetes cluster may be causally involved in lipid metabolism: a Mendelian randomisation study. Diabetologia 2023; 66:1057-1070. [PMID: 36826505 PMCID: PMC10163084 DOI: 10.1007/s00125-023-05886-8] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/04/2022] [Accepted: 01/17/2023] [Indexed: 02/25/2023]
Abstract
AIMS/HYPOTHESIS The aim of this study was to identify differentially expressed long non-coding RNAs (lncRNAs) and mRNAs in whole blood of people with type 2 diabetes across five different clusters: severe insulin-deficient diabetes (SIDD), severe insulin-resistant diabetes (SIRD), mild obesity-related diabetes (MOD), mild diabetes (MD) and mild diabetes with high HDL-cholesterol (MDH). This was to increase our understanding of different molecular mechanisms underlying the five putative clusters of type 2 diabetes. METHODS Participants in the Hoorn Diabetes Care System (DCS) cohort were clustered based on age, BMI, HbA1c, C-peptide and HDL-cholesterol. Whole blood RNA-seq was used to identify differentially expressed lncRNAs and mRNAs in a cluster compared with all others. Differentially expressed genes were validated in the Innovative Medicines Initiative DIabetes REsearCh on patient straTification (IMI DIRECT) study. Expression quantitative trait loci (eQTLs) for differentially expressed RNAs were obtained from a publicly available dataset. To estimate the causal effects of RNAs on traits, a two-sample Mendelian randomisation analysis was performed using public genome-wide association study (GWAS) data. RESULTS Eleven lncRNAs and 175 mRNAs were differentially expressed in the MOD cluster, the lncRNA AL354696.2 was upregulated in the SIDD cluster and GPR15 mRNA was downregulated in the MDH cluster. mRNAs and lncRNAs that were differentially expressed in the MOD cluster were correlated among each other. Six lncRNAs and 120 mRNAs validated in the IMI DIRECT study. Using two-sample Mendelian randomisation, we found 52 mRNAs to have a causal effect on anthropometric traits (n=23) and lipid metabolism traits (n=10). GPR146 showed a causal effect on plasma HDL-cholesterol levels (p = 2×10-15), without evidence for reverse causality. CONCLUSIONS/INTERPRETATION Multiple lncRNAs and mRNAs were found to be differentially expressed among clusters and particularly in the MOD cluster. mRNAs in the MOD cluster showed a possible causal effect on anthropometric traits, lipid metabolism traits and blood cell fractions. Together, our results show that individuals in the MOD cluster show aberrant RNA expression of genes that have a suggested causal role on multiple diabetes-relevant traits.
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Affiliation(s)
- Juliette A de Klerk
- Department of Cell and Chemical Biology, Leiden University Medical Center, Leiden, the Netherlands
- Department of Internal Medicine (Nephrology), Leiden University Medical Center, Leiden, the Netherlands
| | - Joline W J Beulens
- Amsterdam Public Health Institute, Amsterdam UMC, Amsterdam, the Netherlands
- Julius Center for Health Sciences and Primary Care, University Medical Center Utrecht, Utrecht, the Netherlands
- Department of Epidemiology and Data Science, Amsterdam UMC, location VUmc, Amsterdam, the Netherlands
| | - Hailiang Mei
- Sequencing Analysis Support Core, Leiden University Medical Center, Leiden, the Netherlands
| | - Roel Bijkerk
- Department of Internal Medicine (Nephrology), Leiden University Medical Center, Leiden, the Netherlands
| | - Anton Jan van Zonneveld
- Department of Internal Medicine (Nephrology), Leiden University Medical Center, Leiden, the Netherlands
| | - Robert W Koivula
- Department of Clinical Sciences, Lund University, Genetic and Molecular Epidemiology, CRC, Skåne University Hospital Malmö, Malmö, Sweden
| | - Petra J M Elders
- Amsterdam Public Health Institute, Amsterdam UMC, Amsterdam, the Netherlands
- Department of General Practice and Elderly Care Medicine, Amsterdam Public Health Research Institute, Amsterdam UMC, location VUmc, Amsterdam, the Netherlands
| | - Leen M 't Hart
- Department of Cell and Chemical Biology, Leiden University Medical Center, Leiden, the Netherlands
- Amsterdam Public Health Institute, Amsterdam UMC, Amsterdam, the Netherlands
- Department of Epidemiology and Data Science, Amsterdam UMC, location VUmc, Amsterdam, the Netherlands
- Department of Biomedical Data Sciences, Section Molecular Epidemiology, Leiden University Medical Center, Leiden, the Netherlands
| | - Roderick C Slieker
- Department of Cell and Chemical Biology, Leiden University Medical Center, Leiden, the Netherlands.
- Amsterdam Public Health Institute, Amsterdam UMC, Amsterdam, the Netherlands.
- Department of Epidemiology and Data Science, Amsterdam UMC, location VUmc, Amsterdam, the Netherlands.
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20
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Dawed AY, Haider E, Pearson ER. Precision Medicine in Diabetes. Handb Exp Pharmacol 2023; 280:107-129. [PMID: 35704097 DOI: 10.1007/164_2022_590] [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] [Indexed: 10/18/2022]
Abstract
Tailoring treatment or management to groups of individuals based on specific clinical, molecular, and genomic features is the concept of precision medicine. Diabetes is highly heterogenous with respect to clinical manifestations, disease progression, development of complications, and drug response. The current practice for drug treatment is largely based on evidence from clinical trials that report average effects. However, around half of patients with type 2 diabetes do not achieve glycaemic targets despite having a high level of adherence and there are substantial differences in the incidence of adverse outcomes. Therefore, there is a need to identify predictive markers that can inform differential drug responses at the point of prescribing. Recent advances in molecular genetics and increased availability of real-world and randomised trial data have started to increase our understanding of disease heterogeneity and its impact on potential treatments for specific groups. Leveraging information from simple clinical features (age, sex, BMI, ethnicity, and co-prescribed medications) and genomic markers has a potential to identify sub-groups who are likely to benefit from a given drug with minimal adverse effects. In this chapter, we will discuss the state of current evidence in the discovery of clinical and genetic markers that have the potential to optimise drug treatment in type 2 diabetes.
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Affiliation(s)
- Adem Y Dawed
- Division of Population Health and Genomics, School of Medicine, University of Dundee, Dundee, UK
| | - Eram Haider
- Division of Population Health and Genomics, School of Medicine, University of Dundee, Dundee, UK
| | - Ewan R Pearson
- Division of Population Health and Genomics, School of Medicine, University of Dundee, Dundee, UK.
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21
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Wang J, Liu JJ, Gurung RL, Liu S, Lee J, M Y, Ang K, Shao YM, Tang JIS, Benke PI, Torta F, Wenk MR, Tavintharan S, Tang WE, Sum CF, Lim SC. Clinical variable-based cluster analysis identifies novel subgroups with a distinct genetic signature, lipidomic pattern and cardio-renal risks in Asian patients with recent-onset type 2 diabetes. Diabetologia 2022; 65:2146-2156. [PMID: 35763031 PMCID: PMC9630229 DOI: 10.1007/s00125-022-05741-2] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/28/2022] [Accepted: 04/25/2022] [Indexed: 01/11/2023]
Abstract
AIMS/HYPOTHESIS We sought to subtype South East Asian patients with type 2 diabetes by de novo cluster analysis on clinical variables, and to determine whether the novel subgroups carry distinct genetic and lipidomic features as well as differential cardio-renal risks. METHODS Analysis by k-means algorithm was performed in 687 participants with recent-onset diabetes in Singapore. Genetic risk for beta cell dysfunction was assessed by polygenic risk score. We used a discovery-validation approach for the lipidomics study. Risks for cardio-renal complications were studied by survival analysis. RESULTS Cluster analysis identified three novel diabetic subgroups, i.e. mild obesity-related diabetes (MOD, 45%), mild age-related diabetes with insulin insufficiency (MARD-II, 36%) and severe insulin-resistant diabetes with relative insulin insufficiency (SIRD-RII, 19%). Compared with the MOD subgroup, MARD-II had a higher polygenic risk score for beta cell dysfunction. The SIRD-RII subgroup had higher levels of sphingolipids (ceramides and sphingomyelins) and glycerophospholipids (phosphatidylethanolamine and phosphatidylcholine), whereas the MARD-II subgroup had lower levels of sphingolipids and glycerophospholipids but higher levels of lysophosphatidylcholines. Over a median of 7.3 years follow-up, the SIRD-RII subgroup had the highest risks for incident heart failure and progressive kidney disease, while the MARD-II subgroup had moderately elevated risk for kidney disease progression. CONCLUSIONS/INTERPRETATION Cluster analysis on clinical variables identified novel subgroups with distinct genetic, lipidomic signatures and varying cardio-renal risks in South East Asian participants with type 2 diabetes. Our study suggests that this easily actionable approach may be adapted in other ethnic populations to stratify the heterogeneous type 2 diabetes population for precision medicine.
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Affiliation(s)
- Jiexun Wang
- Clinical Research Unit, Khoo Teck Puat Hospital, Singapore, Republic of Singapore
| | - Jian-Jun Liu
- Clinical Research Unit, Khoo Teck Puat Hospital, Singapore, Republic of Singapore
| | - Resham L Gurung
- Clinical Research Unit, Khoo Teck Puat Hospital, Singapore, Republic of Singapore
| | - Sylvia Liu
- Clinical Research Unit, Khoo Teck Puat Hospital, Singapore, Republic of Singapore
| | - Janus Lee
- Clinical Research Unit, Khoo Teck Puat Hospital, Singapore, Republic of Singapore
| | - Yiamunaa M
- Clinical Research Unit, Khoo Teck Puat Hospital, Singapore, Republic of Singapore
| | - Keven Ang
- Clinical Research Unit, Khoo Teck Puat Hospital, Singapore, Republic of Singapore
| | - Yi Ming Shao
- Clinical Research Unit, Khoo Teck Puat Hospital, Singapore, Republic of Singapore
| | - Justin I-Shing Tang
- Department of Medicine, Khoo Teck Puat Hospital, Singapore, Republic of Singapore
| | - Peter I Benke
- Lipidomics Incubator, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Republic of Singapore
| | - Federico Torta
- Lipidomics Incubator, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Republic of Singapore
| | - Markus R Wenk
- Lipidomics Incubator, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Republic of Singapore
| | | | - Wern Ee Tang
- National Healthcare Group Polyclinic, Singapore, Republic of Singapore
| | - Chee Fang Sum
- Diabetes Centre, Admiralty Medical Centre, Singapore, Republic of Singapore
| | - Su Chi Lim
- Diabetes Centre, Admiralty Medical Centre, Singapore, Republic of Singapore.
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Republic of Singapore.
- Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore, Republic of Singapore.
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22
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Ding K, Zhou Z, Ma Y, Li X, Xiao H, Wu Y, Wu T, Chen D. Identification of Novel Metabolic Subtypes Using Multi-Trait Limited Mixed Regression in the Chinese Population. Biomedicines 2022; 10:biomedicines10123093. [PMID: 36551856 PMCID: PMC9775185 DOI: 10.3390/biomedicines10123093] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/27/2022] [Revised: 11/25/2022] [Accepted: 11/29/2022] [Indexed: 12/03/2022] Open
Abstract
The aggregation and interaction of metabolic risk factors leads to highly heterogeneous pathogeneses, manifestations, and outcomes, hindering risk stratification and targeted management. To deconstruct the heterogeneity, we used baseline data from phase II of the Fangshan Family-Based Ischemic Stroke Study (FISSIC), and a total of 4632 participants were included. A total of 732 individuals who did not have any component of metabolic syndrome (MetS) were set as a reference group, while 3900 individuals with metabolic abnormalities were clustered into subtypes using multi-trait limited mixed regression (MFMR). Four metabolic subtypes were identified with the dominant characteristics of abdominal obesity, hypertension, hyperglycemia, and dyslipidemia. Multivariate logistic regression showed that the hyperglycemia-dominant subtype had the highest coronary heart disease (CHD) risk (OR: 6.440, 95% CI: 3.177-13.977) and that the dyslipidemia-dominant subtype had the highest stroke risk (OR: 2.450, 95% CI: 1.250-5.265). Exome-wide association studies (EWASs) identified eight SNPs related to the dyslipidemia-dominant subtype with genome-wide significance, which were located in the genes APOA5, BUD13, ZNF259, and WNT4. Functional analysis revealed an enrichment of top genes in metabolism-related biological pathways and expression in the heart, brain, arteries, and kidneys. Our findings provide directions for future attempts at risk stratification and evidence-based management in populations with metabolic abnormalities from a systematic perspective.
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23
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Zaghlool SB, Halama A, Stephan N, Gudmundsdottir V, Gudnason V, Jennings LL, Thangam M, Ahlqvist E, Malik RA, Albagha OME, Abou-Samra AB, Suhre K. Metabolic and proteomic signatures of type 2 diabetes subtypes in an Arab population. Nat Commun 2022; 13:7121. [PMID: 36402758 PMCID: PMC9675829 DOI: 10.1038/s41467-022-34754-z] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2021] [Accepted: 11/07/2022] [Indexed: 11/20/2022] Open
Abstract
Type 2 diabetes (T2D) has a heterogeneous etiology influencing its progression, treatment, and complications. A data driven cluster analysis in European individuals with T2D previously identified four subtypes: severe insulin deficient (SIDD), severe insulin resistant (SIRD), mild obesity-related (MOD), and mild age-related (MARD) diabetes. Here, the clustering approach was applied to individuals with T2D from the Qatar Biobank and validated in an independent set. Cluster-specific signatures of circulating metabolites and proteins were established, revealing subtype-specific molecular mechanisms, including activation of the complement system with features of autoimmune diabetes and reduced 1,5-anhydroglucitol in SIDD, impaired insulin signaling in SIRD, and elevated leptin and fatty acid binding protein levels in MOD. The MARD cluster was the healthiest with metabolomic and proteomic profiles most similar to the controls. We have translated the T2D subtypes to an Arab population and identified distinct molecular signatures to further our understanding of the etiology of these subtypes.
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Affiliation(s)
- Shaza B Zaghlool
- Department of Physiology and Biophysics, Weill Cornell Medicine-Qatar, Doha, Qatar
| | - Anna Halama
- Department of Physiology and Biophysics, Weill Cornell Medicine-Qatar, Doha, Qatar
| | - Nisha Stephan
- Department of Physiology and Biophysics, Weill Cornell Medicine-Qatar, Doha, Qatar
| | - Valborg Gudmundsdottir
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
- Icelandic Heart Association, Kopavogur, Iceland
| | - Vilmundur Gudnason
- Faculty of Medicine, University of Iceland, Reykjavik, Iceland
- Icelandic Heart Association, Kopavogur, Iceland
| | - Lori L Jennings
- Novartis Institutes for Biomedical Research, Cambridge, MA, USA
| | | | - Emma Ahlqvist
- Department of Clinical Sciences Malmö, Lund University, Lund, Sweden
| | | | - Omar M E Albagha
- College of Health and Life Sciences, Hamad Bin Khalifa University, Education City, Doha, Qatar
- Centre for Genomic and Experimental Medicine, Institute of Genetics and Cancer, University of Edinburgh, Edinburgh, UK
| | | | - Karsten Suhre
- Department of Physiology and Biophysics, Weill Cornell Medicine-Qatar, Doha, Qatar.
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24
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Herder C, Roden M. A novel diabetes typology: towards precision diabetology from pathogenesis to treatment. Diabetologia 2022; 65:1770-1781. [PMID: 34981134 PMCID: PMC9522691 DOI: 10.1007/s00125-021-05625-x] [Citation(s) in RCA: 47] [Impact Index Per Article: 23.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/25/2021] [Accepted: 10/04/2021] [Indexed: 02/07/2023]
Abstract
The current classification of diabetes, based on hyperglycaemia, islet-directed antibodies and some insufficiently defined clinical features, does not reflect differences in aetiological mechanisms and in the clinical course of people with diabetes. This review discusses evidence from recent studies addressing the complexity of diabetes by proposing novel subgroups (subtypes) of diabetes. The most widely replicated and validated approach identified, in addition to severe autoimmune diabetes, four subgroups designated severe insulin-deficient diabetes, severe insulin-resistant diabetes, mild obesity-related diabetes and mild age-related diabetes subgroups. These subgroups display distinct patterns of clinical features, disease progression and onset of comorbidities and complications, with severe insulin-resistant diabetes showing the highest risk for cardiovascular, kidney and fatty liver diseases. While it has been suggested that people in these subgroups would benefit from stratified treatments, RCTs are required to assess the clinical utility of any reclassification effort. Several methodological and practical issues also need further study: the statistical approach used to define subgroups and derive recommendations for diabetes care; the stability of subgroups over time; the optimal dataset (e.g. phenotypic vs genotypic) for reclassification; the transethnic generalisability of findings; and the applicability in clinical routine care. Despite these open questions, the concept of a new classification of diabetes has already allowed researchers to gain more insight into the colourful picture of diabetes and has stimulated progress in this field so that precision diabetology may become reality in the future.
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Affiliation(s)
- Christian Herder
- Institute for Clinical Diabetology, German Diabetes Center (Deutsches Diabetes-Zentrum/DDZ), 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.
- German Center for Diabetes Research (DZD), Partner Düsseldorf, München-Neuherberg, Germany.
| | - Michael Roden
- Institute for Clinical Diabetology, German Diabetes Center (Deutsches Diabetes-Zentrum/DDZ), 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.
- German Center for Diabetes Research (DZD), Partner Düsseldorf, München-Neuherberg, Germany.
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25
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Florez JC, Pearson ER. A roadmap to achieve pharmacological precision medicine in diabetes. Diabetologia 2022; 65:1830-1838. [PMID: 35748917 PMCID: PMC9522818 DOI: 10.1007/s00125-022-05732-3] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/28/2021] [Accepted: 01/17/2022] [Indexed: 12/12/2022]
Abstract
Current pharmacological treatment of diabetes is largely algorithmic. Other than for cardiovascular disease or renal disease, where sodium-glucose cotransporter 2 inhibitors and/or glucagon-like peptide-1 receptor agonists are indicated, the choice of treatment is based upon overall risks of harm or side effect and cost, and not on probable benefit. Here we argue that a more precise approach to treatment choice is necessary to maximise benefit and minimise harm from existing diabetes therapies. We propose a roadmap to achieve precision medicine as standard of care, to discuss current progress in relation to monogenic diabetes and type 2 diabetes, and to determine what additional work is required. The first step is to identify robust and reliable genetic predictors of response, recognising that genotype is static over time and provides the skeleton upon which modifiers such as clinical phenotype and metabolic biomarkers can be overlaid. The second step is to identify these metabolic biomarkers (e.g. beta cell function, insulin sensitivity, BMI, liver fat, metabolite profile), which capture the metabolic state at the point of prescribing and may have a large impact on drug response. Third, we need to show that predictions that utilise these genetic and metabolic biomarkers improve therapeutic outcomes for patients, and fourth, that this is cost-effective. Finally, these biomarkers and prediction models need to be embedded in clinical care systems to enable effective and equitable clinical implementation. Whilst this roadmap is largely complete for monogenic diabetes, we still have considerable work to do to implement this for type 2 diabetes. Increasing collaborations, including with industry, and access to clinical trial data should enable progress to implementation of precision treatment in type 2 diabetes in the near future.
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Affiliation(s)
- Jose C Florez
- Center for Genomic Medicine and Diabetes Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA.
- Department of Medicine, Harvard Medical School, Boston, MA, USA.
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of Harvard & MIT, Cambridge, MA, USA.
| | - Ewan R Pearson
- Department of Population Health & Genomics, School of Medicine, University of Dundee, Dundee, Scotland, UK.
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26
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Schrader S, Perfilyev A, Ahlqvist E, Groop L, Vaag A, Martinell M, García-Calzón S, Ling C. Novel Subgroups of Type 2 Diabetes Display Different Epigenetic Patterns That Associate With Future Diabetic Complications. Diabetes Care 2022; 45:1621-1630. [PMID: 35607770 PMCID: PMC9274219 DOI: 10.2337/dc21-2489] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/30/2021] [Accepted: 04/05/2022] [Indexed: 02/03/2023]
Abstract
OBJECTIVE Type 2 diabetes (T2D) was recently reclassified into severe insulin-deficient diabetes (SIDD), severe insulin-resistant diabetes (SIRD), mild obesity-related diabetes (MOD), and mild age-related diabetes (MARD), which have different risk of complications. We explored whether DNA methylation differs between these subgroups and whether subgroup-unique methylation risk scores (MRSs) predict diabetic complications. RESEARCH DESIGN AND METHODS Genome-wide DNA methylation was analyzed in blood from subjects with newly diagnosed T2D in discovery and replication cohorts. Subgroup-unique MRSs were built, including top subgroup-unique DNA methylation sites. Regression models examined whether MRSs associated with subgroups and future complications. RESULTS We found epigenetic differences between the T2D subgroups. Subgroup-unique MRSs were significantly different in those patients allocated to each respective subgroup compared with the combined group of all other subgroups. These associations were validated in an independent replication cohort, showing that subgroup-unique MRSs associate with individual subgroups (odds ratios 1.6-6.1 per 1-SD increase, P < 0.01). Subgroup-unique MRSs were also associated with future complications. Higher MOD-MRS was associated with lower risk of cardiovascular (hazard ratio [HR] 0.65, P = 0.001) and renal (HR 0.50, P < 0.001) disease, whereas higher SIRD-MRS and MARD-MRS were associated with an increased risk of these complications (HR 1.4-1.9 per 1-SD increase, P < 0.01). Of 95 methylation sites included in subgroup-unique MRSs, 39 were annotated to genes previously linked to diabetes-related traits, including TXNIP and ELOVL2. Methylation in the blood of 18 subgroup-unique sites mirrors epigenetic patterns in tissues relevant for T2D, muscle and adipose tissue. CONCLUSIONS We identified differential epigenetic patterns between T2D subgroups that associated with future diabetic complications. These data support a reclassification of diabetes and the need for precision medicine in T2D subgroups.
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Affiliation(s)
- Silja Schrader
- Epigenetics and Diabetes Unit, Department of Clinical Sciences, Lund University Diabetes Centre, Lund University, Scania University Hospital, Malmö, Sweden
| | - Alexander Perfilyev
- Epigenetics and Diabetes Unit, Department of Clinical Sciences, Lund University Diabetes Centre, Lund University, Scania University Hospital, Malmö, Sweden
| | - Emma Ahlqvist
- Genomics, Diabetes and Endocrinology Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden
| | - Leif Groop
- Genomics, Diabetes and Endocrinology Unit, Department of Clinical Sciences, Lund University, Malmö, Sweden
| | - Allan Vaag
- Type 2 Diabetes Biology Research, Steno Diabetes Center, Copenhagen, Denmark
| | - Mats Martinell
- Department of Public Health and Caring Sciences, Uppsala University, Uppsala, Sweden.,Academic Primary Care Centre, Uppsala, Sweden
| | - Sonia García-Calzón
- Epigenetics and Diabetes Unit, Department of Clinical Sciences, Lund University Diabetes Centre, Lund University, Scania University Hospital, Malmö, Sweden.,Department of Food Science and Physiology, University of Navarra, Pamplona, Spain
| | - Charlotte Ling
- Epigenetics and Diabetes Unit, Department of Clinical Sciences, Lund University Diabetes Centre, Lund University, Scania University Hospital, Malmö, Sweden
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27
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Antonio-Villa NE, Fernández-Chirino L, Vargas-Vázquez A, Fermín-Martínez CA, Aguilar-Salinas CA, Bello-Chavolla OY. Prevalence Trends of Diabetes Subgroups in the United States: A Data-driven Analysis Spanning Three Decades From NHANES (1988-2018). J Clin Endocrinol Metab 2022; 107:735-742. [PMID: 34687306 DOI: 10.1210/clinem/dgab762] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/21/2021] [Indexed: 11/19/2022]
Abstract
CONTEXT Data-driven diabetes subgroups were proposed as an alternative to address diabetes heterogeneity. However, changes in trends for these subgroups have not been reported. OBJECTIVE Here, we analyzed trends of diabetes subgroups, stratified by sex, race, education level, age categories, and time since diabetes diagnosis in the United States. METHODS We used data from consecutive NHANES cycles spanning the 1988-2018 period. Diabetes subgroups (mild obesity-related [MOD], severe insulin-deficient [SIDD], severe insulin-resistant [SIRD], and mild age-related diabetes [MARD]) were classified using validated self-normalizing neural networks. Severe autoimmune diabetes (SAID) was assessed for NHANES-III. Prevalence was estimated using examination sample weights considering bicyclic changes (BCs) to evaluate trends and changes over time. RESULTS Diabetes prevalence in the United States increased from 7.5% (95% CI 7.1-7.9) in 1988-1989 to 13.9% (95% CI 13.4-14.4) in 2016-2018 (BC 1.09%, 95% CI 0.98-1.31, P < .001). Non-Hispanic Black people had the highest prevalence. Overall, MOD, MARD, and SIDD had an increase during the studied period. Particularly, non-Hispanic Black people had sharp increases in MARD and SIDD, Mexican Americans in SIDD, and non-Hispanic White people in MARD. Males, subjects with secondary/high school, and adults aged 40-64 years had the highest increase in MOD prevalence. Trends in diabetes subgroups sustained after stratifying time since diabetes diagnosis. CONCLUSION Prevalence of diabetes and its subgroups in the United States has increased from 1988 to 2018. These trends were different across sex, ethnicities, education, and age categories, indicating significant heterogeneity in diabetes within the US obesity burden, population aging, socioeconomic disparities, and lifestyle aspects could be implicated in the increasing trends of diabetes in the United States.
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Affiliation(s)
- Neftali Eduardo Antonio-Villa
- MD/PhD Program (PECEM), Faculty of Medicine, National Autonomous University of Mexico, Mexico City, Mexico
- Dirección de Investigación, Instituto Nacional de Geriatría, Mexico City, Mexico
| | - Luisa Fernández-Chirino
- Dirección de Investigación, Instituto Nacional de Geriatría, Mexico City, Mexico
- Faculty of Chemistry, National Autonomous University of Mexico, Mexico City, Mexico
| | - Arsenio Vargas-Vázquez
- MD/PhD Program (PECEM), Faculty of Medicine, National Autonomous University of Mexico, Mexico City, Mexico
| | - Carlos A Fermín-Martínez
- MD/PhD Program (PECEM), Faculty of Medicine, National Autonomous University of Mexico, Mexico City, Mexico
- Dirección de Investigación, Instituto Nacional de Geriatría, Mexico City, Mexico
| | - Carlos A Aguilar-Salinas
- División de Nutrición, Instituto Nacional de Ciencias Médicas y Nutrición Salvador Zubiran, Mexico City, Mexico
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