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Mandla R, Schroeder P, Porneala B, Florez JC, Meigs JB, Mercader JM, Leong A. Polygenic scores for longitudinal prediction of incident type 2 diabetes in an ancestrally and medically diverse primary care physician network: a patient cohort study. Genome Med 2024; 16:63. [PMID: 38671457 PMCID: PMC11046943 DOI: 10.1186/s13073-024-01337-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Accepted: 04/17/2024] [Indexed: 04/28/2024] Open
Abstract
BACKGROUND The clinical utility of genetic information for type 2 diabetes (T2D) prediction with polygenic scores (PGS) in ancestrally diverse, real-world US healthcare systems is unclear, especially for those at low clinical phenotypic risk for T2D. METHODS We tested the association of PGS with T2D incidence in patients followed within a primary care practice network over 16 years in four hypothetical scenarios that varied by clinical data availability (N = 14,712): (1) age and sex; (2) age, sex, body mass index (BMI), systolic blood pressure, and family history of T2D; (3) all variables in (2) and random glucose; and (4) all variables in (3), HDL, total cholesterol, and triglycerides, combined in a clinical risk score (CRS). To determine whether genetic effects differed by baseline clinical risk, we tested for interaction with the CRS. RESULTS PGS was associated with incident T2D in all models. Adjusting for age and sex only, the Hazard Ratio (HR) per PGS standard deviation (SD) was 1.76 (95% CI 1.68, 1.84) and the HR of top 5% of PGS vs interquartile range (IQR) was 2.80 (2.39, 3.28). Adjusting for the CRS, the HR per SD was 1.48 (1.40, 1.57) and HR of the top 5% of PGS vs IQR was 2.09 (1.72, 2.55). Genetic effects differed by baseline clinical risk ((PGS-CRS interaction p = 0.05; CRS below the median: HR 1.60 (1.43, 1.79); CRS above the median: HR 1.45 (1.35, 1.55)). CONCLUSIONS Genetic information can help identify high-risk patients even among those perceived to be low risk in a clinical evaluation.
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Affiliation(s)
- Ravi Mandla
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Diabetes Unit, Endocrine Division, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Philip Schroeder
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Diabetes Unit, Endocrine Division, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Bianca Porneala
- Division of General Internal Medicine, Department of Medicine, Massachusetts General Hospital, 100 Cambridge St. Fl. 16, Boston, MA, 02114, USA
| | - Jose C Florez
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Diabetes Unit, Endocrine Division, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - James B Meigs
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Division of General Internal Medicine, Department of Medicine, Massachusetts General Hospital, 100 Cambridge St. Fl. 16, Boston, MA, 02114, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Josep M Mercader
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Diabetes Unit, Endocrine Division, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Department of Medicine, Harvard Medical School, Boston, MA, USA
| | - Aaron Leong
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Diabetes Unit, Endocrine Division, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA.
- Division of General Internal Medicine, Department of Medicine, Massachusetts General Hospital, 100 Cambridge St. Fl. 16, Boston, MA, 02114, USA.
- Department of Medicine, Harvard Medical School, Boston, MA, USA.
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Brīvība M, Atava I, Pečulis R, Elbere I, Ansone L, Rozenberga M, Silamiķelis I, Kloviņš J. Evaluating the Efficacy of Type 2 Diabetes Polygenic Risk Scores in an Independent European Population. Int J Mol Sci 2024; 25:1151. [PMID: 38256224 PMCID: PMC10817091 DOI: 10.3390/ijms25021151] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/03/2023] [Revised: 01/04/2024] [Accepted: 01/09/2024] [Indexed: 01/24/2024] Open
Abstract
Numerous type 2 diabetes (T2D) polygenic risk scores (PGSs) have been developed to predict individuals' predisposition to the disease. An independent assessment and verification of the best-performing PGS are warranted to allow for a rapid application of developed models. To date, only 3% of T2D PGSs have been evaluated. In this study, we assessed all (n = 102) presently published T2D PGSs in an independent cohort of 3718 individuals, which has not been included in the construction or fine-tuning of any T2D PGS so far. We further chose the best-performing PGS, assessed its performance across major population principal component analysis (PCA) clusters, and compared it with newly developed population-specific T2D PGS. Our findings revealed that 88% of the published PGSs were significantly associated with T2D; however, their performance was lower than what had been previously reported. We found a positive association of PGS improvement over the years (p-value = 8.01 × 10-4 with PGS002771 currently showing the best discriminatory power (area under the receiver operating characteristic (AUROC) = 0.669) and PGS003443 exhibiting the strongest association PGS003443 (odds ratio (OR) = 1.899). Further investigation revealed no difference in PGS performance across major population PCA clusters and when compared with newly developed population-specific PGS. Our findings revealed a positive trend in T2D PGS performance, consistently identifying high-T2D-risk individuals in an independent European population.
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Affiliation(s)
- Monta Brīvība
- Latvian Biomedical Research and Study Centre, LV-1067 Riga, Latvia; (I.A.); (I.E.); (L.A.); (J.K.)
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Billings LK, Shi Z, Wei J, Rifkin AS, Zheng SL, Helfand BT, Ilbawi N, Dunnenberger HM, Hulick PJ, Qamar A, Xu J. Utility of Polygenic Scores for Differentiating Diabetes Diagnosis Among Patients With Atypical Phenotypes of Diabetes. J Clin Endocrinol Metab 2023; 109:107-113. [PMID: 37560999 DOI: 10.1210/clinem/dgad456] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/04/2023] [Revised: 07/10/2023] [Accepted: 08/08/2023] [Indexed: 08/11/2023]
Abstract
CONTEXT Misclassification of diabetes type occurs in people with atypical presentations of type 1 diabetes (T1D) or type 2 diabetes (T2D). Although current clinical guidelines suggest clinical variables and treatment response as ways to help differentiate diabetes type, they remain insufficient for people with atypical presentations. OBJECTIVE This work aimed to assess the clinical utility of 2 polygenic scores (PGSs) in differentiating between T1D and T2D. METHODS Patients diagnosed with diabetes in the UK Biobank were studied (N = 41 787), including 464 (1%) and 15 923 (38%) who met the criteria for classic T1D and T2D, respectively, and 25 400 (61%) atypical diabetes. The validity of 2 published PGSs for T1D (PGST1D) and T2D (PGST2D) in differentiating classic T1D or T2D was assessed using C statistic. The utility of genetic probability for T1D based on PGSs (GenProb-T1D) was evaluated in atypical diabetes patients. RESULTS The joint performance of PGST1D and PGST2D for differentiating classic T1D or T2D was outstanding (C statistic = 0.91), significantly higher than that of PGST1D alone (0.88) and PGST2D alone (0.70), both P less than .001. Using an optimal cutoff of GenProb-T1D, 23% of patients with atypical diabetes had a higher probability of T1D and its validity was independently supported by clinical presentations that are characteristic of T1D. CONCLUSION PGST1D and PGST2D can be used to discriminate classic T1D and T2D and have potential clinical utility for differentiating these 2 types of diseases among patients with atypical diabetes.
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Affiliation(s)
- Liana K Billings
- Department of Medicine, NorthShore University HealthSystem, Evanston, IL 60201, USA
- Department of Medicine, University of Chicago Pritzker School of Medicine, Chicago, IL 60637, USA
| | - Zhuqing Shi
- Program for Personalized Cancer Care, NorthShore University HealthSystem, Evanston, IL 60201, USA
| | - Jun Wei
- Program for Personalized Cancer Care, NorthShore University HealthSystem, Evanston, IL 60201, USA
| | - Andrew S Rifkin
- Program for Personalized Cancer Care, NorthShore University HealthSystem, Evanston, IL 60201, USA
| | - S Lilly Zheng
- Program for Personalized Cancer Care, NorthShore University HealthSystem, Evanston, IL 60201, USA
| | - Brian T Helfand
- Department of Medicine, University of Chicago Pritzker School of Medicine, Chicago, IL 60637, USA
- Program for Personalized Cancer Care, NorthShore University HealthSystem, Evanston, IL 60201, USA
- Department of Surgery, NorthShore University HealthSystem, Evanston, IL 60201, USA
| | - Nadim Ilbawi
- Department of Family Medicine, NorthShore University HealthSystem, Evanston, IL 60201, USA
| | - Henry M Dunnenberger
- Neaman Center for Personalized Medicine, NorthShore University HealthSystem, Evanston, IL 60201, USA
| | - Peter J Hulick
- Neaman Center for Personalized Medicine, NorthShore University HealthSystem, Evanston, IL 60201, USA
| | - Arman Qamar
- Department of Medicine, NorthShore University HealthSystem, Evanston, IL 60201, USA
| | - Jianfeng Xu
- Department of Medicine, NorthShore University HealthSystem, Evanston, IL 60201, USA
- Department of Medicine, University of Chicago Pritzker School of Medicine, Chicago, IL 60637, USA
- Program for Personalized Cancer Care, NorthShore University HealthSystem, Evanston, IL 60201, USA
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Cui N, Li Y, Huang S, Ge Y, Guo S, Tan L, Hao L, Lei G, Shang X, Xiong G, Yang X. Cholesterol-rich dietary pattern during early pregnancy and genetic variations of cholesterol metabolism genes in predicting gestational diabetes mellitus: a nested case-control study. Am J Clin Nutr 2023; 118:966-976. [PMID: 37923501 DOI: 10.1016/j.ajcnut.2023.08.017] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2023] [Revised: 08/17/2023] [Accepted: 08/24/2023] [Indexed: 11/07/2023] Open
Abstract
BACKGROUND Higher dietary cholesterol intake during pregnancy increases risk of gestational diabetes mellitus (GDM). However, no studies have investigated interindividual variability in cholesterol metabolism and the association of genetics and diet on GDM. OBJECTIVE ; To prospectively evaluate the joint association of cholesterol-rich dietary patterns and polymorphisms of genes coding for cholesterol metabolism pathway proteins with GDM. METHODS A total of 1116 pregnant females from the Tongji Birth Cohort were enrolled. GDM was diagnosed according to a 75-g 2-h oral glucose tolerance test at 24-28 wk of gestation. Dietary data were collected by a validated food frequency questionnaire. The reduced-rank regression method was used to identify dietary patterns using dietary cholesterol as the response variable. Time-of-flight mass spectrometry was used for genotyping. The genetic risk score (GRS) for GDM was constructed with genetic variants in 28 cholesterol metabolism-related single-nucleotide polymorphisms (SNPs). Conditional logistic regression models were used to assess the odds ratio (OR) for GDM. RESULTS The cholesterol-rich dietary pattern was rich in livestock and poultry meat and eggs but lower in cereals. The multivariable-adjusted ORs for GDM were 1.24 (95% confidence interval: 1.06-1.44) per SD increment of cholesterol-rich pattern scores and 1.28 (1.09-1.49) per tertile GRS. The variants of the CYP7A1 rs3808607 G→T/rs8192871 G→A/rs7833904 A→T, as well as AGGG and TTGA haplotypes of 4 CYP7A1-spanning SNPs, were significantly associated with GDM. For the joint effect, the OR was 3.53 (1.71-7.31) in the highest categories of both dietary pattern scores and GRS compared with individuals with the lowest strata without significant interaction (P for interaction = 0.101). CONCLUSIONS Both a cholesterol-rich dietary pattern and genetic variants of cholesterol metabolism genes are associated with risk of GDM. Adherence to a cholesterol-rich dietary pattern during early pregnancy promotes the chance of GDM, especially in women with higher GRS. CLINICAL TRIAL REGISTRY This trial was registered at http://www.chictr.org.cn (Registration number: ChiCTR1800016908). URL: =https://www.chictr.org.cn/showprojEN.html?proj=28081.
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Affiliation(s)
- Ningning Cui
- Department of Nutrition and Food Hygiene, Hubei Key Laboratory of Food Nutrition and Safety, MOE Key Laboratory of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, P.R. China
| | - Yan Li
- Department of Nutrition and Food Hygiene, Hubei Key Laboratory of Food Nutrition and Safety, MOE Key Laboratory of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, P.R. China; Shenzhen Center for Chronic Disease Control, Shenzhen, P.R. China
| | - Shanshan Huang
- Department of Nutrition and Food Hygiene, Hubei Key Laboratory of Food Nutrition and Safety, MOE Key Laboratory of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, P.R. China
| | - Yanyan Ge
- Department of Nutrition and Food Hygiene, Hubei Key Laboratory of Food Nutrition and Safety, MOE Key Laboratory of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, P.R. China
| | - Shu Guo
- Department of Nutrition and Food Hygiene, Hubei Key Laboratory of Food Nutrition and Safety, MOE Key Laboratory of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, P.R. China
| | - Le Tan
- Department of Nutrition and Food Hygiene, Hubei Key Laboratory of Food Nutrition and Safety, MOE Key Laboratory of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, P.R. China
| | - Liping Hao
- Department of Nutrition and Food Hygiene, Hubei Key Laboratory of Food Nutrition and Safety, MOE Key Laboratory of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, P.R. China
| | - Gang Lei
- The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, P.R. China
| | - Xuejun Shang
- Department of Andrology, Jinling Hospital, School of Medicine, Nanjing University/Nanjing School of Clinical Medicine, Southern Medical University, Nanjing, Jiangsu, P.R. China
| | - Guoping Xiong
- The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, P.R. China
| | - Xuefeng Yang
- Department of Nutrition and Food Hygiene, Hubei Key Laboratory of Food Nutrition and Safety, MOE Key Laboratory of Environment and Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, Hubei, P.R. China.
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Srinivasan S, Wu P, Mercader JM, Udler MS, Porneala BC, Bartz TM, Floyd JS, Sitlani C, Guo X, Haessler J, Kooperberg C, Liu J, Ahmad S, van Duijn C, Liu CT, Goodarzi MO, Florez JC, Meigs JB, Rotter JI, Rich SS, Dupuis J, Leong A. A Type 1 Diabetes Polygenic Score Is Not Associated With Prevalent Type 2 Diabetes in Large Population Studies. J Endocr Soc 2023; 7:bvad123. [PMID: 37841955 PMCID: PMC10576255 DOI: 10.1210/jendso/bvad123] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/30/2023] [Indexed: 10/17/2023] Open
Abstract
Context Both type 1 diabetes (T1D) and type 2 diabetes (T2D) have significant genetic contributions to risk and understanding their overlap can offer clinical insight. Objective We examined whether a T1D polygenic score (PS) was associated with a diagnosis of T2D in the Cohorts for Heart and Aging Research in Genomic Epidemiology (CHARGE) consortium. Methods We constructed a T1D PS using 79 known single nucleotide polymorphisms associated with T1D risk. We analyzed 13 792 T2D cases and 14 169 controls from CHARGE cohorts to determine the association between the T1D PS and T2D prevalence. We validated findings in an independent sample of 2256 T2D cases and 27 052 controls from the Mass General Brigham Biobank (MGB Biobank). As secondary analyses in 5228 T2D cases from CHARGE, we used multivariable regression models to assess the association of the T1D PS with clinical outcomes associated with T1D. Results The T1D PS was not associated with T2D both in CHARGE (P = .15) and in the MGB Biobank (P = .87). The partitioned human leukocyte antigens only PS was associated with T2D in CHARGE (OR 1.02 per 1 SD increase in PS, 95% CI 1.01-1.03, P = .006) but not in the MGB Biobank. The T1D PS was weakly associated with insulin use (OR 1.007, 95% CI 1.001-1.012, P = .03) in CHARGE T2D cases but not with other outcomes. Conclusion In large biobank samples, a common variant PS for T1D was not consistently associated with prevalent T2D. However, possible heterogeneity in T2D cannot be ruled out and future studies are needed do subphenotyping.
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Affiliation(s)
- Shylaja Srinivasan
- Division of Pediatric Endocrinology, University of California at San Francisco, San Francisco, CA 94158, USA
| | - Peitao Wu
- Department of Biostatistics, Boston University School of Public Health, Boston, MA 02215, USA
| | - Josep M Mercader
- Department of Medicine, Harvard Medical School, Boston, MA 02115, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of Harvard & Massachusetts Institute of Technology, Cambridge, MA 02142, USA
- Center for Genomic Medicine and Diabetes Unit, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Miriam S Udler
- Department of Medicine, Harvard Medical School, Boston, MA 02115, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of Harvard & Massachusetts Institute of Technology, Cambridge, MA 02142, USA
- Center for Genomic Medicine and Diabetes Unit, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Bianca C Porneala
- Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Traci M Bartz
- Department of Biostatistics, University of Washington, Seattle, WA 98195, USA
- Cardiovascular Health Research Unit, University of Washington, Seattle, WA 98195, USA
| | - James S Floyd
- Cardiovascular Health Research Unit, University of Washington, Seattle, WA 98195, USA
- Department of Medicine, University of Washington, Seattle, WA 98195, USA
- Department of Epidemiology, University of Washington, Seattle, WA 98195, USA
| | - Colleen Sitlani
- Cardiovascular Health Research Unit, University of Washington, Seattle, WA 98195, USA
- Department of Medicine, University of Washington, Seattle, WA 98195, USA
| | - Xiquing Guo
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA 90502, USA
| | - Jeffrey Haessler
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA
| | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA
| | - Jun Liu
- Department of Epidemiology, Erasmus Medical Center, 3015 GD Rotterdam, The Netherlands
- Nuffield Department of Population Health, University of Oxford, Oxford OX1 2JD, UK
| | - Shahzad Ahmad
- Department of Epidemiology, Erasmus Medical Center, 3015 GD Rotterdam, The Netherlands
| | - Cornelia van Duijn
- Department of Epidemiology, Erasmus Medical Center, 3015 GD Rotterdam, The Netherlands
- Nuffield Department of Population Health, University of Oxford, Oxford OX1 2JD, UK
| | - Ching-Ti Liu
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of Harvard & Massachusetts Institute of Technology, Cambridge, MA 02142, USA
| | - Mark O Goodarzi
- Division of Endocrinology, Diabetes and Metabolism, Department of Medicine, Cedars-Sinai Medical Center, Los Angeles, CA 90048, USA
| | - Jose C Florez
- Department of Medicine, Harvard Medical School, Boston, MA 02115, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of Harvard & Massachusetts Institute of Technology, Cambridge, MA 02142, USA
- Center for Genomic Medicine and Diabetes Unit, Massachusetts General Hospital, Boston, MA 02114, USA
| | - James B Meigs
- Department of Medicine, Harvard Medical School, Boston, MA 02115, USA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of Harvard & Massachusetts Institute of Technology, Cambridge, MA 02142, USA
- Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Jerome I Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA 90502, USA
| | - Stephen S Rich
- Center for Public Health Genomics, Department of Public Health Sciences, University of Virginia, Charlottesville, VA 22903, USA
| | - Josée Dupuis
- Department of Biostatistics, Boston University School of Public Health, Boston, MA 02215, USA
| | - Aaron Leong
- Department of Medicine, Harvard Medical School, Boston, MA 02115, USA
- Center for Genomic Medicine and Diabetes Unit, Massachusetts General Hospital, Boston, MA 02114, USA
- Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA 02114, USA
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Mandla R, Schroeder P, Porneala B, Florez JC, Meigs JB, Mercader JM, Leong A. Polygenic Scores for Longitudinal Prediction of Incident Type 2 Diabetes in an Ancestrally and Medically Diverse Primary Care Network. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.09.08.23295276. [PMID: 37732255 PMCID: PMC10508788 DOI: 10.1101/2023.09.08.23295276] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/22/2023]
Abstract
OBJECTIVE The clinical utility of genetic information for type 2 diabetes (T2D) prediction with polygenic score (PGS) in ancestrally diverse, real-world US healthcare systems is unclear, especially for those at low clinical phenotypic risk for T2D. RESEARCH DESIGN AND METHODS We tested the association of PGS with T2D incidence in patients followed within a primary care practice network over 16 years in four hypothetical scenarios that varied by clinical data availability (N = 14,712): 1) age and sex, 2) age, sex, BMI, systolic blood pressure, and family history of diabetes; 3) all variables in (2) and random glucose; 4) all variables in (3), HDL, total cholesterol, and triglycerides, combined in a clinical risk score (CRS). To determine whether genetic effects differed by baseline clinical risk, we tested for interaction with the CRS. RESULTS PGS was associated with incident diabetes in all models. Adjusting for age and sex only, the Hazard Ratio (HR) per PGS standard deviation (SD) was 1.76 (95% CI 1.68, 1.84) and the HR of top 5% of PGS vs interquartile range (IQR) was 2.80 (2.39, 3.28). Adjusting for the CRS, the HR per SD was 1.48 (1.40, 1.57) and HR of top 5% of PGS vs IQR was 2.09 (1.72, 2.55). Genetic effects differed by baseline clinical risk [(PGS-CRS interaction p =0.05; CRS below the median: HR 1.60 (1.43, 1.79); CRS above the median: HR 1.45 (1.35, 1.55)]. CONCLUSIONS Genetic information can help identify high-risk patients even among those perceived to be low risk in a clinical evaluation.
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Goyal S, Rani J, Bhat MA, Vanita V. Genetics of diabetes. World J Diabetes 2023; 14:656-679. [PMID: 37383588 PMCID: PMC10294065 DOI: 10.4239/wjd.v14.i6.656] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/26/2022] [Revised: 03/13/2023] [Accepted: 04/17/2023] [Indexed: 06/14/2023] Open
Abstract
Diabetes mellitus is a complicated disease characterized by a complex interplay of genetic, epigenetic, and environmental variables. It is one of the world's fastest-growing diseases, with 783 million adults expected to be affected by 2045. Devastating macrovascular consequences (cerebrovascular disease, cardiovascular disease, and peripheral vascular disease) and microvascular complications (like retinopathy, nephropathy, and neuropathy) increase mortality, blindness, kidney failure, and overall quality of life in individuals with diabetes. Clinical risk factors and glycemic management alone cannot predict the development of vascular problems; multiple genetic investigations have revealed a clear hereditary component to both diabetes and its related complications. In the twenty-first century, technological advancements (genome-wide association studies, next-generation sequencing, and exome-sequencing) have led to the identification of genetic variants associated with diabetes, however, these variants can only explain a small proportion of the total heritability of the condition. In this review, we address some of the likely explanations for this "missing heritability", for diabetes such as the significance of uncommon variants, gene-environment interactions, and epigenetics. Current discoveries clinical value, management of diabetes, and future research directions are also discussed.
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Affiliation(s)
- Shiwali Goyal
- Department of Ophthalmic Genetics and Visual Function Branch, National Eye Institute, Rockville, MD 20852, United States
| | - Jyoti Rani
- Department of Human Genetics, Guru Nanak Dev University, Amritsar 143005, Punjab, India
| | - Mohd Akbar Bhat
- Department of Ophthalmology, Georgetown University Medical Center, Washington DC, DC 20057, United States
| | - Vanita Vanita
- Department of Human Genetics, Guru Nanak Dev University, Amritsar 143005, Punjab, India
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Wedekind LE, Mahajan A, Hsueh WC, Chen P, Olaiya MT, Kobes S, Sinha M, Baier LJ, Knowler WC, McCarthy MI, Hanson RL. The utility of a type 2 diabetes polygenic score in addition to clinical variables for prediction of type 2 diabetes incidence in birth, youth and adult cohorts in an Indigenous study population. Diabetologia 2023; 66:847-860. [PMID: 36862161 PMCID: PMC10036431 DOI: 10.1007/s00125-023-05870-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/08/2022] [Accepted: 11/29/2022] [Indexed: 03/03/2023]
Abstract
AIMS/HYPOTHESIS There is limited information on how polygenic scores (PSs), based on variants from genome-wide association studies (GWASs) of type 2 diabetes, add to clinical variables in predicting type 2 diabetes incidence, particularly in non-European-ancestry populations. METHODS For participants in a longitudinal study in an Indigenous population from the Southwestern USA with high type 2 diabetes prevalence, we analysed ten constructions of PS using publicly available GWAS summary statistics. Type 2 diabetes incidence was examined in three cohorts of individuals without diabetes at baseline. The adult cohort, 2333 participants followed from age ≥20 years, had 640 type 2 diabetes cases. The youth cohort included 2229 participants followed from age 5-19 years (228 cases). The birth cohort included 2894 participants followed from birth (438 cases). We assessed contributions of PSs and clinical variables in predicting type 2 diabetes incidence. RESULTS Of the ten PS constructions, a PS using 293 genome-wide significant variants from a large type 2 diabetes GWAS meta-analysis in European-ancestry populations performed best. In the adult cohort, the AUC of the receiver operating characteristic curve for clinical variables for prediction of incident type 2 diabetes was 0.728; with the PS, 0.735. The PS's HR was 1.27 per SD (p=1.6 × 10-8; 95% CI 1.17, 1.38). In youth, corresponding AUCs were 0.805 and 0.812, with HR 1.49 (p=4.3 × 10-8; 95% CI 1.29, 1.72). In the birth cohort, AUCs were 0.614 and 0.685, with HR 1.48 (p=2.8 × 10-16; 95% CI 1.35, 1.63). To further assess the potential impact of including PS for assessing individual risk, net reclassification improvement (NRI) was calculated: NRI for the PS was 0.270, 0.268 and 0.362 for adult, youth and birth cohorts, respectively. For comparison, NRI for HbA1c was 0.267 and 0.173 for adult and youth cohorts, respectively. In decision curve analyses across all cohorts, the net benefit of including the PS in addition to clinical variables was most pronounced at moderately stringent threshold probability values for instituting a preventive intervention. CONCLUSIONS/INTERPRETATION This study demonstrates that a European-derived PS contributes significantly to prediction of type 2 diabetes incidence in addition to information provided by clinical variables in this Indigenous study population. Discriminatory power of the PS was similar to that of other commonly measured clinical variables (e.g. HbA1c). Including type 2 diabetes PS in addition to clinical variables may be clinically beneficial for identifying individuals at higher risk for the disease, especially at younger ages.
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Affiliation(s)
- Lauren E Wedekind
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Phoenix, AZ, USA.
- Nuffield Department of Medicine, University of Oxford, Oxford, UK.
| | - Anubha Mahajan
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
- Genentech, San Francisco, CA, USA
| | - Wen-Chi Hsueh
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Phoenix, AZ, USA
| | - Peng Chen
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Phoenix, AZ, USA
- College of Basic Medical Sciences, Jilin University, Changchun, China
| | - Muideen T Olaiya
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Phoenix, AZ, USA
- School of Clinical Sciences, Monash University, Clayton, VIC, Australia
| | - Sayuko Kobes
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Phoenix, AZ, USA
| | - Madhumita Sinha
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Phoenix, AZ, USA
| | - Leslie J Baier
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Phoenix, AZ, USA
| | - William C Knowler
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Phoenix, AZ, USA
| | - Mark I McCarthy
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, UK
- Genentech, San Francisco, CA, USA
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Headington, UK
| | - Robert L Hanson
- Phoenix Epidemiology and Clinical Research Branch, National Institute of Diabetes and Digestive and Kidney Diseases, National Institutes of Health, Phoenix, AZ, USA
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9
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Marushchak M, Mazur L, Krynytska I. Insulin receptor substrate-1 gene polymorphism and lipid panel data in type 2 diabetic patients with comorbid obesity and/or essential hypertension. Endocr Regul 2023; 57:1-11. [PMID: 36753667 DOI: 10.2478/enr-2023-0001] [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] [Indexed: 02/10/2023] Open
Abstract
Objective. The hallmarks of type 2 diabetes mellitus (T2DM) are insulin resistance (IR) and insulin receptor substrate (IRS) proteins essential for the insulin signaling. IRS-1 gene has not only been shown to be associated with T2DM, but also has indicated that it may significantly correlate with diabetic complications, such as coronary heart disease and obesity. The aim of this study was to evaluate changes of the lipid panel data in T2DM patients with comorbid obesity and/or essential hypertension in connection with the IRS-1 (rs2943640) polymorphism. Methods. The study involved 33 T2DM patients and 10 healthy individuals. The IRS-1 (rs2943640) polymorphism was genotyped using a TaqMan real-time polymerase chain reaction method. Blood serum lipid panel data were determined with commercially available kits using a Cobas 6000 analyzer. Results. Analysis of the serum lipid panel data depending on the presence of the C/A alleles of IRS-1 (rs2943640) polymorphism in T2DM patients, regardless of the presence/absence of comorbidities, showed significantly lower level of high-density lipoprotein cholesterol (HDL-C) and significantly higher level of non-HDL-C in the carriers of C allele vs. carriers of A allele. In T2DM patients with comorbid obesity and essential hypertension, proatherogenic lipid changes were found in both C and A alleles carriers. Analysis of the effect of IRS-1 (rs2943640) genotypes on serum lipid panel data in T2DM patients, regardless of the presence/absence of comorbidities, showed that the CC genotype carriers had more pronounced pro-atherogenic changes vs. carriers of СА and АА genotypes. In the comorbid course of T2DM (both in combination with obesity and obesity and essential hypertension), pro-atherogenic changes were found in the carriers of the CA genotype of IRS-1 (rs2943640) polymorphism. Conclusions. The presence of the C allele of IRS-1 (rs2943640) polymorphism in both homo-zygous and heterozygous states indicates increased risk of pro-atherogenic changes in T2DM patients with comorbid obesity and/or essential hypertension.
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Affiliation(s)
- Mariya Marushchak
- Department of Functional and Laboratory Diagnostics, I. Horbachevsky Ternopil National Medical University, Ternopil, Ukraine
| | - Lyudmyla Mazur
- Department of Higher Nursing Education, Patient Care and Clinical Immunology, I. Horbachevsky Ternopil National Medical University, Ternopil, Ukraine
| | - Inna Krynytska
- Department of Functional and Laboratory Diagnostics, I. Horbachevsky Ternopil National Medical University, Ternopil, Ukraine
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10
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Testing the Utility of Polygenic Risk Scores for Type 2 Diabetes and Obesity in Predicting Metabolic Changes in a Prediabetic Population: An Observational Study. Int J Mol Sci 2022; 23:ijms232416081. [PMID: 36555722 PMCID: PMC9787993 DOI: 10.3390/ijms232416081] [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: 11/03/2022] [Revised: 12/03/2022] [Accepted: 12/14/2022] [Indexed: 12/23/2022] Open
Abstract
Prediabetes is an intermediate state of hyperglycemia during which glycemic parameters are above normal levels but below the T2D threshold. T2D and its precursor prediabetes affect 6.28% and 7.3% of the world’s population, respectively. The main objective of this paper was to create and compare two polygenic risk scores (PRSs) versus changes over time (Δ) in metabolic parameters related to prediabetes and metabolic complications. The genetics of 446 prediabetic patients from the Polish Registry of Diabetes cohort were investigated. Seventeen metabolic parameters were measured and compared at baseline and after five years using statistical analysis. Subsequently, genetic polymorphisms present in patients were determined to build a T2D PRS (68 SNPs) and an obesity PRS (21 SNPs). Finally, the association among the two PRSs and the Δ of the metabolic traits was assessed. After a multiple linear regression with adjustment for age, sex, and BMI at a nominal significance of (p < 0.05) and adjustment for multiple testing, the T2D PRS was found to be positively associated with Δ fat mass (FM) (p = 0.025). The obesity PRS was positively associated with Δ FM (p = 0.023) and Δ 2 h glucose (p = 0.034). The comparison of genotype frequencies showed that AA genotype carriers of rs10838738 were significantly higher in Δ 2 h glucose and in Δ 2 h insulin. Our findings suggest that prediabetic individuals with a higher risk of developing T2D experience increased Δ FM, and those with a higher risk of obesity experience increased Δ FM and Δ two-hour postprandial glucose. The associations found in this research could be a powerful tool for identifying prediabetic individuals with an increased risk of developing T2D and obesity.
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11
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Abstract
It is well established from clinical trials that behavioural interventions can halve the risk of progression from prediabetes to type 2 diabetes but translating this evidence of efficacy into effective real-world interventions at scale is an ongoing challenge. A common suggestion is that future preventive interventions need to be more personalised in order to enhance effectiveness. This review evaluates the degree to which existing interventions are already personalised and outlines how greater personalisation could be achieved through better identification of those at high risk, division of type 2 diabetes into specific subgroups and, above all, more individualisation of the behavioural targets for preventive action. Approaches using more dynamic real-time data are in their scientific infancy. Although these approaches are promising they need longer-term evaluation against clinical outcomes. Whatever personalised preventive approaches for type 2 diabetes are developed in the future, they will need to be complementary to existing individual-level interventions that are being rolled out and that are demonstrably effective. They will also need to ideally synergise with, and at the very least not detract attention from, efforts to develop and implement strategies that impact on type 2 diabetes risk at the societal level.
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Affiliation(s)
- Nicholas J Wareham
- Medical Research Council Epidemiology Unit, Institute of Metabolic Science, University of Cambridge Clinical School, Cambridge, UK.
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12
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Ramírez J, van Duijvenboden S, Young WJ, Tinker A, Lambiase PD, Orini M, Munroe PB. Prediction of Coronary Artery Disease and Major Adverse Cardiovascular Events Using Clinical and Genetic Risk Scores for Cardiovascular Risk Factors. CIRCULATION. GENOMIC AND PRECISION MEDICINE 2022; 15:e003441. [PMID: 35861959 PMCID: PMC9584057 DOI: 10.1161/circgen.121.003441] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 11/30/2022]
Abstract
BACKGROUND Coronary artery disease (CAD) and major adverse cardiovascular events (MACE) are the leading causes of death in the general population, but risk stratification remains suboptimal. CAD genetic risk scores (GRSs) predict risk independently from clinical tools, like QRISK3. We assessed the added value of GRSs for a variety of cardiovascular traits (CV GRSs) for predicting CAD and MACE and tested their early-life screening potential by comparing against the CAD GRS only. METHODS We used data from 379 581 participants in the UK Biobank without known cardiovascular conditions (follow-up, 11.3 years; 3.3% CAD cases and 5.2% MACE cases). In a training subset (50%) we built 3 scores: QRISK3; QRISK3 and an established CAD GRS; and QRISK3, the CAD GRS and the CV GRSs. In an independent subset (50%), we evaluated each score's performance using the concordance index, odds ratio and net reclassification index. We then repeated the analyses without considering QRISK3. RESULTS For CAD, the combination of QRISK3 and the CAD GRS had a better performance than QRISK3 alone (concordance index, 0.766 versus 0.753; odds ratio, 5.47 versus 4.82; net reclassification index, 7.7%). Adding the CV GRSs did not significantly improve risk stratification. When only looking at genetic information, the combination of CV GRSs and the CAD GRS had a better performance than the CAD GRS alone (concordance index, 0.637 versus 0.625; odds ratio, 2.17 versus 2.07; net reclassification index, 3.3%). Similar results were obtained for MACE. CONCLUSIONS In individuals without known cardiovascular disease, the inclusion of CV GRSs to a clinical tool and an established CAD GRS does not improve CAD or MACE risk stratification. However, their combination only with the CAD GRS increases prediction performance indicating potential use in early-life screening before the advanced development of conventional cardiovascular risk factors.
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Affiliation(s)
- Julia Ramírez
- Clinical Pharmacology and Precision Medicine Deparment, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom (J.R., S.v.D., W.J.Y., A.T., P.B.M.).,Electronic Engineering and Communications Department, Aragon Institute of Engineering Research, University of Zaragoza, Spain and CIBER's Bioengineering, Biomaterials and Nanomedicine, Spain. (J.R.)
| | - Stefan van Duijvenboden
- Clinical Pharmacology and Precision Medicine Deparment, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom (J.R., S.v.D., W.J.Y., A.T., P.B.M.).,Institute of Cardiovascular Science, University College London, London, United Kingdom (S.v.D., P.D.L., M.O.)
| | - William J Young
- Clinical Pharmacology and Precision Medicine Deparment, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom (J.R., S.v.D., W.J.Y., A.T., P.B.M.).,Barts Heart Centre, St Bartholomew's Hospital, London, United Kingdom (W.J.Y., P.D.L., M.O.)
| | - Andrew Tinker
- Clinical Pharmacology and Precision Medicine Deparment, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom (J.R., S.v.D., W.J.Y., A.T., P.B.M.).,NIHR Barts Cardiovascular Biomedical Research Centre, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, United Kingdom (A.T., P.B.M.)
| | - Pier D Lambiase
- Institute of Cardiovascular Science, University College London, London, United Kingdom (S.v.D., P.D.L., M.O.).,Barts Heart Centre, St Bartholomew's Hospital, London, United Kingdom (W.J.Y., P.D.L., M.O.)
| | - Michele Orini
- Institute of Cardiovascular Science, University College London, London, United Kingdom (S.v.D., P.D.L., M.O.).,Barts Heart Centre, St Bartholomew's Hospital, London, United Kingdom (W.J.Y., P.D.L., M.O.)
| | - Patricia B Munroe
- Clinical Pharmacology and Precision Medicine Deparment, William Harvey Research Institute, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, London, United Kingdom (J.R., S.v.D., W.J.Y., A.T., P.B.M.).,NIHR Barts Cardiovascular Biomedical Research Centre, Barts and The London School of Medicine and Dentistry, Queen Mary University of London, United Kingdom (A.T., P.B.M.)
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13
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Lopez-Pineda A, Vernekar M, Moreno-Grau S, Rojas-Muñoz A, Moatamed B, Lee MTM, Nava-Aguilar MA, Gonzalez-Arroyo G, Numakura K, Matsuda Y, Ioannidis A, Katsanis N, Takano T, Bustamante CD. Validating and automating learning of cardiometabolic polygenic risk scores from direct-to-consumer genetic and phenotypic data: implications for scaling precision health research. Hum Genomics 2022; 16:37. [PMID: 36076307 PMCID: PMC9452874 DOI: 10.1186/s40246-022-00406-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/08/2022] [Accepted: 08/06/2022] [Indexed: 11/29/2022] Open
Abstract
Introduction A major challenge to enabling precision health at a global scale is the bias between those who enroll in state sponsored genomic research and those suffering from chronic disease. More than 30 million people have been genotyped by direct-to-consumer (DTC) companies such as 23andMe, Ancestry DNA, and MyHeritage, providing a potential mechanism for democratizing access to medical interventions and thus catalyzing improvements in patient outcomes as the cost of data acquisition drops. However, much of these data are sequestered in the initial provider network, without the ability for the scientific community to either access or validate. Here, we present a novel geno-pheno platform that integrates heterogeneous data sources and applies learnings to common chronic disease conditions including Type 2 diabetes (T2D) and hypertension.
Methods We collected genotyped data from a novel DTC platform where participants upload their genotype data files and were invited to answer general health questionnaires regarding cardiometabolic traits over a period of 6 months. Quality control, imputation, and genome-wide association studies were performed on this dataset, and polygenic risk scores were built in a case–control setting using the BASIL algorithm.
Results We collected data on N = 4,550 (389 cases / 4,161 controls) who reported being affected or previously affected for T2D and N = 4,528 (1,027 cases / 3,501 controls) for hypertension. We identified 164 out of 272 variants showing identical effect direction to previously reported genome-significant findings in Europeans. Performance metric of the PRS models was AUC = 0.68, which is comparable to previously published PRS models obtained with larger datasets including clinical biomarkers. Discussion DTC platforms have the potential of inverting research models of genome sequencing and phenotypic data acquisition. Quality control (QC) mechanisms proved to successfully enable traditional GWAS and PRS analyses. The direct participation of individuals has shown the potential to generate rich datasets enabling the creation of PRS cardiometabolic models. More importantly, federated learning of PRS from reuse of DTC data provides a mechanism for scaling precision health care delivery beyond the small number of countries who can afford to finance these efforts directly.
Conclusions The genetics of T2D and hypertension have been studied extensively in controlled datasets, and various polygenic risk scores (PRS) have been developed. We developed predictive tools for both phenotypes trained with heterogeneous genotypic and phenotypic data generated outside of the clinical environment and show that our methods can recapitulate prior findings with fidelity. From these observations, we conclude that it is possible to leverage DTC genetic repositories to identify individuals at risk of debilitating diseases based on their unique genetic landscape so that informed, timely clinical interventions can be incorporated.
Supplementary Information The online version contains supplementary material available at 10.1186/s40246-022-00406-y.
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Affiliation(s)
- Arturo Lopez-Pineda
- Galatea Bio, Inc., 975 W 22nd Street, Hialeah, Florida, 33010, USA.,Amphora Health, Batallon Independencia 80, Morelia, Michoacan, 58260, Mexico
| | - Manvi Vernekar
- Genomelink, Inc., 2150 Shattuck Avenue, Berkeley, California, 94704, USA.,Awakens Japan K.K., 2-11-3 Meguro, Meguro-ku, Tokyo, 1530063, Japan
| | | | | | - Babak Moatamed
- Galatea Bio, Inc., 975 W 22nd Street, Hialeah, Florida, 33010, USA
| | | | - Marco A Nava-Aguilar
- Galatea Bio, Inc., 975 W 22nd Street, Hialeah, Florida, 33010, USA.,Amphora Health, Batallon Independencia 80, Morelia, Michoacan, 58260, Mexico
| | - Gilberto Gonzalez-Arroyo
- Galatea Bio, Inc., 975 W 22nd Street, Hialeah, Florida, 33010, USA.,Amphora Health, Batallon Independencia 80, Morelia, Michoacan, 58260, Mexico
| | - Kensuke Numakura
- Genomelink, Inc., 2150 Shattuck Avenue, Berkeley, California, 94704, USA.,Awakens Japan K.K., 2-11-3 Meguro, Meguro-ku, Tokyo, 1530063, Japan
| | - Yuta Matsuda
- Genomelink, Inc., 2150 Shattuck Avenue, Berkeley, California, 94704, USA.,Awakens Japan K.K., 2-11-3 Meguro, Meguro-ku, Tokyo, 1530063, Japan
| | - Alexander Ioannidis
- Galatea Bio, Inc., 975 W 22nd Street, Hialeah, Florida, 33010, USA.,Department of Biomedical Data Science, Stanford University School of Medicine, 1265 Welch Road, Stanford, California, 94305, USA
| | | | - Tomohiro Takano
- Genomelink, Inc., 2150 Shattuck Avenue, Berkeley, California, 94704, USA. .,Awakens Japan K.K., 2-11-3 Meguro, Meguro-ku, Tokyo, 1530063, Japan.
| | - Carlos D Bustamante
- Galatea Bio, Inc., 975 W 22nd Street, Hialeah, Florida, 33010, USA. .,Department of Biomedical Data Science, Stanford University School of Medicine, 1265 Welch Road, Stanford, California, 94305, USA. .,Chan Zuckerberg Biohub, 499 Illinois Street, San Francisco, California, 94158, USA.
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14
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Khan SS, Page C, Wojdyla DM, Schwartz YY, Greenland P, Pencina MJ. Predictive Utility of a Validated Polygenic Risk Score for Long-Term Risk of Coronary Heart Disease in Young and Middle-Aged Adults. Circulation 2022; 146:587-596. [PMID: 35880530 PMCID: PMC9398962 DOI: 10.1161/circulationaha.121.058426] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/23/2023]
Abstract
BACKGROUND Understanding the predictive utility of previously derived polygenic risk scores (PRSs) for long-term risk of coronary heart disease (CHD) and its additive value beyond traditional risk factors can inform prevention strategies. METHODS Data from adults 20 to 59 years of age who were free of CHD from the FOS (Framingham Offspring Study) and the ARIC (Atherosclerosis Risk in Communities) study were analyzed. Because the PRS was derived from samples of predominantly European ancestry, individuals who self-reported White race were included. The sample was stratified by age and cohort: young (FOS, 20-39 years [median, 30 years] of age), early midlife (FOS, 40-59 years [median, 43] years of age), and late midlife (ARIC, 45-59 years [median, 52 years] of age). Two previously derived and validated prediction tools were applied: (1) a 30-year traditional risk factor score and (2) a genome-wide PRS comprising >6 million genetic variants. Hazard ratios for the association between each risk estimate and incident CHD were calculated. Predicted and observed rates of CHD were compared to assess discrimination for each model individually and together with the optimism-corrected C index (95% CI). RESULTS Among 9757 participants, both the traditional risk factor score (hazard ratio per 1 SD, 2.60 [95% CI, 2.08-3.27], 2.09 [95% CI, 1.83-2.40], and 2.11 [95% CI, 1.96-2.28]) and the PRS (hazard ratio, 1.98 [95% CI, 1.70-2.30], 1.64 [95% CI, 1.47-1.84], and 1.22 [95% CI, 1.15-1.30]) were significantly associated with incident CHD in young, early midlife, and late midlife, respectively. Discrimination was similar or better for the traditional risk factor score (C index, 0.74 [95% CI, 0.70-0.78], 0.70 [95% CI, 0.67-0.72], and 0.72 [95% CI, 0.70-0.73]) compared with an age- and sex-adjusted PRS (0.73 [95% CI, 0.69-0.78], 0.66 [95% CI, 0.62-0.69], and 0.66 [95% CI, 0.64-0.67]) in young, early-midlife, and late-midlife participants, respectively. The ΔC index when PRS was added to the traditional risk factor score was 0.03 (95% CI, 0.001-0.05), 0.02 (95% CI, -0.002 to 0.037), and 0.002 (95% CI, -0.002 to 0.006) in young, early-midlife, and late-midlife participants, respectively. CONCLUSIONS Despite a statistically significant association between PRS and 30-year risk of CHD, the C statistic improved only marginally with the addition of PRS to the traditional risk factor model among young adults and did not improve among midlife adults. PRS, an immutable factor that cannot be directly intervened on, has minimal clinical utility for long-term CHD prediction when added to a traditional risk factor model.
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Affiliation(s)
- Sadiya S. Khan
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL
- Division of Cardiology, Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL
| | - Courtney Page
- Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC
| | - Daniel M. Wojdyla
- Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC
| | - Yosef Y. Schwartz
- Department of Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL
| | - Philip Greenland
- Department of Preventive Medicine, Northwestern University Feinberg School of Medicine, Chicago, IL
| | - Michael J. Pencina
- Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC
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Laakso M, Fernandes Silva L. Genetics of Type 2 Diabetes: Past, Present, and Future. Nutrients 2022; 14:nu14153201. [PMID: 35956377 PMCID: PMC9370092 DOI: 10.3390/nu14153201] [Citation(s) in RCA: 27] [Impact Index Per Article: 13.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Revised: 07/30/2022] [Accepted: 08/03/2022] [Indexed: 02/01/2023] Open
Abstract
Diabetes has reached epidemic proportions worldwide. Currently, approximately 537 million adults (20–79 years) have diabetes, and the total number of people with diabetes is continuously increasing. Diabetes includes several subtypes. About 80% of all cases of diabetes are type 2 diabetes (T2D). T2D is a polygenic disease with an inheritance ranging from 30 to 70%. Genetic and environment/lifestyle factors, especially obesity and sedentary lifestyle, increase the risk of T2D. In this review, we discuss how studies on the genetics of diabetes started, how they expanded when genome-wide association studies and exome and whole-genome sequencing became available, and the current challenges in genetic studies of diabetes. T2D is heterogeneous with respect to clinical presentation, disease course, and response to treatment, and has several subgroups which differ in pathophysiology and risk of micro- and macrovascular complications. Currently, genetic studies of T2D focus on these subgroups to find the best diagnoses and treatments for these patients according to the principles of precision medicine.
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Affiliation(s)
- Markku Laakso
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland, 70210 Kuopio, Finland
- Department of Medicine, Kuopio University Hospital, 70210 Kuopio, Finland
- Correspondence: ; Tel.: +358-40-672-3338
| | - Lilian Fernandes Silva
- Institute of Clinical Medicine, Internal Medicine, University of Eastern Finland, 70210 Kuopio, Finland
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16
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Insulin resistance in children. Curr Opin Pediatr 2022; 34:400-406. [PMID: 35796641 DOI: 10.1097/mop.0000000000001151] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
PURPOSE OF REVIEW Insulin resistance (IR) is a clinical condition due to the decline in the efficiency of insulin promoting glucose uptake and utilization. The aim of this review is to provide an overview of the current knowledge on IR in children, focusing on its physiopathology, the most appropriate methods of measurement of IR, the assessment of risk factors, the effects of IR in children, and finally giving indications on screening and treatment. RECENT FINDINGS IR has evolved more and more to be a global public health problem associated with several chronic metabolic diseases. SUMMARY Detecting a correct measurement method and specific risk predictors, in order to reduce the incidence of IR, represents a challenging goal.
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Ge T, Irvin MR, Patki A, Srinivasasainagendra V, Lin YF, Tiwari HK, Armstrong ND, Benoit B, Chen CY, Choi KW, Cimino JJ, Davis BH, Dikilitas O, Etheridge B, Feng YCA, Gainer V, Huang H, Jarvik GP, Kachulis C, Kenny EE, Khan A, Kiryluk K, Kottyan L, Kullo IJ, Lange C, Lennon N, Leong A, Malolepsza E, Miles AD, Murphy S, Namjou B, Narayan R, O'Connor MJ, Pacheco JA, Perez E, Rasmussen-Torvik LJ, Rosenthal EA, Schaid D, Stamou M, Udler MS, Wei WQ, Weiss ST, Ng MCY, Smoller JW, Lebo MS, Meigs JB, Limdi NA, Karlson EW. Development and validation of a trans-ancestry polygenic risk score for type 2 diabetes in diverse populations. Genome Med 2022; 14:70. [PMID: 35765100 PMCID: PMC9241245 DOI: 10.1186/s13073-022-01074-2] [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] [Subscribe] [Scholar Register] [Received: 10/31/2021] [Accepted: 06/16/2022] [Indexed: 02/05/2023] Open
Abstract
BACKGROUND Type 2 diabetes (T2D) is a worldwide scourge caused by both genetic and environmental risk factors that disproportionately afflicts communities of color. Leveraging existing large-scale genome-wide association studies (GWAS), polygenic risk scores (PRS) have shown promise to complement established clinical risk factors and intervention paradigms, and improve early diagnosis and prevention of T2D. However, to date, T2D PRS have been most widely developed and validated in individuals of European descent. Comprehensive assessment of T2D PRS in non-European populations is critical for equitable deployment of PRS to clinical practice that benefits global populations. METHODS We integrated T2D GWAS in European, African, and East Asian populations to construct a trans-ancestry T2D PRS using a newly developed Bayesian polygenic modeling method, and assessed the prediction accuracy of the PRS in the multi-ethnic Electronic Medical Records and Genomics (eMERGE) study (11,945 cases; 57,694 controls), four Black cohorts (5137 cases; 9657 controls), and the Taiwan Biobank (4570 cases; 84,996 controls). We additionally evaluated a post hoc ancestry adjustment method that can express the polygenic risk on the same scale across ancestrally diverse individuals and facilitate the clinical implementation of the PRS in prospective cohorts. RESULTS The trans-ancestry PRS was significantly associated with T2D status across the ancestral groups examined. The top 2% of the PRS distribution can identify individuals with an approximately 2.5-4.5-fold of increase in T2D risk, which corresponds to the increased risk of T2D for first-degree relatives. The post hoc ancestry adjustment method eliminated major distributional differences in the PRS across ancestries without compromising its predictive performance. CONCLUSIONS By integrating T2D GWAS from multiple populations, we developed and validated a trans-ancestry PRS, and demonstrated its potential as a meaningful index of risk among diverse patients in clinical settings. Our efforts represent the first step towards the implementation of the T2D PRS into routine healthcare.
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Affiliation(s)
- Tian Ge
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA.
- Center for Precision Psychiatry, Massachusetts General Hospital, Boston, MA, USA.
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA.
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Marguerite R Irvin
- Department of Epidemiology, School of Public Health, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Amit Patki
- Department of Biostatistics, School of Public Health, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Vinodh Srinivasasainagendra
- Department of Biostatistics, School of Public Health, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Yen-Feng Lin
- Center for Neuropsychiatric Research, National Health Research Institutes, Miaoli, Taiwan
- Department of Public Health & Medical Humanities, School of Medicine, National Yang Ming Chiao Tung University, Taipei, Taiwan
- Institute of Behavioral Medicine, College of Medicine, National Cheng Kung University, Tainan, Taiwan
| | - Hemant K Tiwari
- Department of Biostatistics, School of Public Health, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Nicole D Armstrong
- Department of Epidemiology, School of Public Health, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Barbara Benoit
- Mass General Brigham Research Information Science & Computing, Boston, MA, USA
| | - Chia-Yen Chen
- Translational Biology, Biogen Inc., Cambridge, MA, USA
| | - Karmel W Choi
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Center for Precision Psychiatry, Massachusetts General Hospital, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
| | - James J Cimino
- Informatics Institute, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Brittney H Davis
- Department of Neurology, School of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Ozan Dikilitas
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
- Department of Internal Medicine, Mayo Clinician-Investigator Training Program, Mayo Clinic, Rochester, MN, USA
| | - Bethany Etheridge
- Department of Neurology, School of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Yen-Chen Anne Feng
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Institute of Epidemiology and Preventive Medicine, National Taiwan University, Taipei, Taiwan
| | - Vivian Gainer
- Mass General Brigham Research Information Science & Computing, Boston, MA, USA
| | - Hailiang Huang
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Gail P Jarvik
- Division of Medical Genetics, Department of Medicine, University of Washington, Seattle, WA, USA
| | | | - Eimear E Kenny
- Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Atlas Khan
- Division of Nephrology, Department of Medicine, Vagelos College of Physicians & Surgeons, Columbia University, New York, USA
| | - Krzysztof Kiryluk
- Division of Nephrology, Department of Medicine, Vagelos College of Physicians & Surgeons, Columbia University, New York, USA
| | - Leah Kottyan
- Center for Autoimmune Genomics and Etiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Iftikhar J Kullo
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA
| | - Christoph Lange
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Niall Lennon
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Aaron Leong
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA, USA
- Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
| | | | - Ayme D Miles
- Informatics Institute, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Shawn Murphy
- Department of Neurology, Massachusetts General Hospital, Boston, MA, USA
| | - Bahram Namjou
- Center for Autoimmune Genomics and Etiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Renuka Narayan
- Department of Neurology, School of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA
| | | | - Jennifer A Pacheco
- Center for Genetic Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Emma Perez
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Mass General Brigham Personalized Medicine, Boston, MA, USA
| | - Laura J Rasmussen-Torvik
- Department of Preventive Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL, USA
| | - Elisabeth A Rosenthal
- Division of Medical Genetics, Department of Medicine, University of Washington, Seattle, WA, USA
| | - Daniel Schaid
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA
| | - Maria Stamou
- Division of Endocrinology, Massachusetts General Hospital, Boston, MA, USA
| | - Miriam S Udler
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Wei-Qi Wei
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Scott T Weiss
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
| | - Maggie C Y Ng
- Vanderbilt Genetics Institute, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Jordan W Smoller
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Center for Precision Psychiatry, Massachusetts General Hospital, Boston, MA, USA
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Matthew S Lebo
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Mass General Brigham Personalized Medicine, Boston, MA, USA
- Department of Pathology, Brigham and Women's Hospital, Boston, MA, USA
| | - James B Meigs
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
- Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Nita A Limdi
- Department of Neurology, School of Medicine, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Elizabeth W Karlson
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Mass General Brigham Personalized Medicine, Boston, MA, USA
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18
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Holzapfel C, Waldenberger M, Lorkowski S, Daniel H. Genetics and Epigenetics in Personalized Nutrition: Evidence, Expectations and Experiences. Mol Nutr Food Res 2022; 66:e2200077. [PMID: 35770348 DOI: 10.1002/mnfr.202200077] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/04/2022] [Revised: 05/17/2022] [Indexed: 11/10/2022]
Abstract
With the presentation of the blueprint of the first human genome in 2001 and the advent of technologies for high-throughput genetic analysis, personalized nutrition (PN) became a new scientific field and the first commercial offerings of genotype-based nutrition advice emerged at the same time. Here, we summarize the state of evidence for the effect of genetic and epigenetic factors in the development of obesity, the metabolic syndrome and resulting illnesses such as non-insulin-dependent diabetes mellitus and cardiovascular diseases. We also critically value the concepts of PN that were built around the new genetic avenue from both the academic and a commercial perspective and their effectiveness in causing sustained changes in diet, lifestyle and for improving health. Despite almost 20 years of research and commercial direct-to-consumer offerings, evidence for the success of gene-based dietary recommendations is still generally lacking. This calls for new concepts of future PN solutions that incorporate more phenotypic measures and provide a panel of instruments (e.g., self- and bio-monitoring tools, feedback systems, algorithms based on artificial intelligence) that increases compliance based on the individual´s physical and social environment and value system. This article is protected by copyright. All rights reserved.
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Affiliation(s)
- Christina Holzapfel
- Institute for Nutritional Medicine, Technical University of Munich, School of Medicine, Munich, Germany
| | - Melanie Waldenberger
- Research Unit Molecular Epidemiology, Institute of Epidemiology, Helmholtz Zentrum Munich, German Research Center for Environmental Health, Neuherberg, Germany.,German Center for Cardiovascular Research (DZHK), Partner Site Munich Heart Alliance, Munich, Germany
| | - Stefan Lorkowski
- Institute of Nutritional Sciences and Competence Cluster for Nutrition and Cardiovascular Health (nutriCARD) Halle-Jena-Leipzig, Friedrich Schiller University, Jena, Germany
| | - Hannelore Daniel
- Professor emeritus, Technical University of Munich, Freising, Germany
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Abstract
PURPOSE OF REVIEW Type 2 diabetes (T2D) is a multifactorial, heritable syndrome characterized by dysregulated glucose homeostasis that results from impaired insulin secretion and insulin resistance. Genetic association studies have successfully identified hundreds of T2D risk loci implicating many genes in disease pathogenesis. In this review, we provide an overview of the recent T2D genetic studies from the past 3 years with particular focus on the effects of sample size and ancestral diversity on genetic discovery as well as discuss recent work on the use and limitations of genetic risk scores (GRS) for T2D risk prediction. RECENT FINDINGS Recent large-scale, multi-ancestry genetic studies of T2D have identified over 500 novel risk loci. The genetic variants (i.e., single nucleotide polymorphisms (SNPs)) marking these novel loci in general have smaller effect sizes than previously discovered loci. Inclusion of samples from diverse ancestral backgrounds shows a few ancestry specific loci marked by common variants, but overall, the majority of loci discovered are common across ancestries. Inclusion of common variant GRS, even with hundreds of loci, does not substantially increase T2D risk prediction over standard clinical risk factors such as age and family history. Common variant association studies of T2D have now identified over 700 T2D risk loci, half of which have been discovered in the past 3 years. These recent studies demonstrate that inclusion of ancestrally diverse samples can enhance locus discovery and improve accuracy of GRS for T2D risk prediction. GRS based on common variants, however, only minimally enhances risk prediction over standard clinical risk factors.
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Affiliation(s)
- Natalie DeForest
- Department of Medicine, Division of Endocrinology, University of California San Diego, La Jolla, CA, USA
- Biomedical Sciences Graduate Program, University of California San Diego, La Jolla, CA, USA
| | - Amit R Majithia
- Department of Medicine, Division of Endocrinology, University of California San Diego, La Jolla, CA, USA.
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20
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Hao L, Kraft P, Berriz GF, Hynes ED, Koch C, Korategere V Kumar P, Parpattedar SS, Steeves M, Yu W, Antwi AA, Brunette CA, Danowski M, Gala MK, Green RC, Jones NE, Lewis ACF, Lubitz SA, Natarajan P, Vassy JL, Lebo MS. Development of a clinical polygenic risk score assay and reporting workflow. Nat Med 2022; 28:1006-1013. [PMID: 35437332 PMCID: PMC9117136 DOI: 10.1038/s41591-022-01767-6] [Citation(s) in RCA: 66] [Impact Index Per Article: 33.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2021] [Accepted: 03/02/2022] [Indexed: 12/31/2022]
Abstract
Implementation of polygenic risk scores (PRS) may improve disease prevention and management but poses several challenges: the construction of clinically valid assays, interpretation for individual patients, and the development of clinical workflows and resources to support their use in patient care. For the ongoing Veterans Affairs Genomic Medicine at Veterans Affairs (GenoVA) Study we developed a clinical genotype array-based assay for six published PRS. We used data from 36,423 Mass General Brigham Biobank participants and adjustment for population structure to replicate known PRS-disease associations and published PRS thresholds for a disease odds ratio (OR) of 2 (ranging from 1.75 (95% CI: 1.57-1.95) for type 2 diabetes to 2.38 (95% CI: 2.07-2.73) for breast cancer). After confirming the high performance and robustness of the pipeline for use as a clinical assay for individual patients, we analyzed the first 227 prospective samples from the GenoVA Study and found that the frequency of PRS corresponding to published OR > 2 ranged from 13/227 (5.7%) for colorectal cancer to 23/150 (15.3%) for prostate cancer. In addition to the PRS laboratory report, we developed physician- and patient-oriented informational materials to support decision-making about PRS results. Our work illustrates the generalizable development of a clinical PRS assay for multiple conditions and the technical, reporting and clinical workflow challenges for implementing PRS information in the clinic.
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Affiliation(s)
- Limin Hao
- Laboratory for Molecular Medicine, Mass General Brigham Personalized Medicine, Cambridge, MA, USA
| | - Peter Kraft
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Gabriel F Berriz
- Laboratory for Molecular Medicine, Mass General Brigham Personalized Medicine, Cambridge, MA, USA
| | - Elizabeth D Hynes
- Laboratory for Molecular Medicine, Mass General Brigham Personalized Medicine, Cambridge, MA, USA
| | - Christopher Koch
- Laboratory for Molecular Medicine, Mass General Brigham Personalized Medicine, Cambridge, MA, USA
| | | | - Shruti S Parpattedar
- Laboratory for Molecular Medicine, Mass General Brigham Personalized Medicine, Cambridge, MA, USA
| | - Marcie Steeves
- Laboratory for Molecular Medicine, Mass General Brigham Personalized Medicine, Cambridge, MA, USA
- Medical Genetics, Massachusetts General Hospital, Boston, MA, USA
| | - Wanfeng Yu
- Laboratory for Molecular Medicine, Mass General Brigham Personalized Medicine, Cambridge, MA, USA
| | - Ashley A Antwi
- Veterans Affairs Boston Healthcare System, Boston, MA, USA
| | | | | | - Manish K Gala
- Division of Gastroenterology, Massachusetts General Hospital, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Robert C Green
- Harvard Medical School, Boston, MA, USA
- Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA
- Precision Population Health, Ariadne Labs, Boston, MA, USA
| | - Natalie E Jones
- Veterans Affairs Boston Healthcare System, Boston, MA, USA
- Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Anna C F Lewis
- E J Safra Center for Ethics, Harvard University, Cambridge, MA, USA
| | - Steven A Lubitz
- Cardiovascular Disease Initiative, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
- Demoulas Center for Cardiac Arrhythmias, Massachusetts General Hospital, Boston, MA, USA
| | - Pradeep Natarajan
- Harvard Medical School, Boston, MA, USA
- Cardiovascular Disease Initiative, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, MA, USA
- Cardiovascular Research Center, Massachusetts General Hospital, Boston, MA, USA
| | - Jason L Vassy
- Veterans Affairs Boston Healthcare System, Boston, MA, USA.
- Harvard Medical School, Boston, MA, USA.
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA.
- Precision Population Health, Ariadne Labs, Boston, MA, USA.
| | - Matthew S Lebo
- Laboratory for Molecular Medicine, Mass General Brigham Personalized Medicine, Cambridge, MA, USA
- Harvard Medical School, Boston, MA, USA
- Department of Pathology, Brigham and Women's Hospital, Boston, MA, USA
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21
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Zhuang P, Liu X, Li Y, Li H, Zhang L, Wan X, Wu Y, Zhang Y, Jiao J. Circulating Fatty Acids and Genetic Predisposition to Type 2 Diabetes: Gene-Nutrient Interaction Analysis. Diabetes Care 2022; 45:564-575. [PMID: 35089324 DOI: 10.2337/dc21-2048] [Citation(s) in RCA: 13] [Impact Index Per Article: 6.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/30/2021] [Accepted: 12/22/2021] [Indexed: 02/03/2023]
Abstract
OBJECTIVE To assess the relationship of circulating fatty acids (FA) with risk of type 2 diabetes (T2D) and potential interactions with genetic risk. RESEARCH DESIGN AND METHODS A total of 95,854 participants with complete data on plasma FA from the UK Biobank were enrolled between 2006 and 2010 and were followed up to the end of 2020. Plasma concentrations of saturated fatty acids (SFA), monounsaturated fatty acids (MUFA), and polyunsaturated fatty acids (PUFA) were analyzed by a high-throughput nuclear magnetic resonance-based biomarker profiling platform. The genetic risk scores (GRS) were calculated on the basis of 424 variants associated with T2D. Pathway-specific GRS were calculated based on robust clusters of T2D loci. RESULTS There were 3,052 instances of T2D documented after an average follow-up of 11.6 years. Plasma concentrations of SFA and MUFA were positively associated with T2D risk, while plasma PUFA were inversely associated. After adjustment for major risk factors, hazard ratios (95% CI) of T2D for 1-SD increment were 1.03 (1.02-1.04) for SFA, 1.03 (1.02-1.05) for MUFA, 0.62 (0.56-0.68) for PUFA, 0.67 (0.61-0.73) for n-6 PUFA, 0.90 (0.85-0.95) for n-3 PUFA, and 1.01 (0.98-1.04) for n-6-to-n-3 ratio. Plasma MUFA had significant interactions with the overall GRS and GRS for proinsulin and liver/lipid clusters on T2D risk. The protective associations of n-3 PUFA with T2D risk were weaker among individuals with higher obesity GRS (P interaction = 0.040) and liver/lipid GRS (P interaction = 0.012). Additionally, increased plasma n-3 PUFA concentration was associated with more reductions in T2D risk among participants carrying more docosapentaenoic acid-associated alleles (P interaction = 0.007). CONCLUSIONS Plasma concentrations of SFA and MUFA were associated with a higher T2D risk, whereas plasma PUFA and n-6 and n-3 PUFA were related to a lower risk. Circulating MUFA and n-3 PUFA had significant interactions with genetic predisposition to T2D and FA-associated variants.
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Affiliation(s)
- Pan Zhuang
- College of Biosystems Engineering and Food Science, National-Local Joint Engineering Laboratory of Intelligent Food Technology and Equipment, Zhejiang Key Laboratory for Agro-Food Processing, Integrated Research Base of Southern Fruit and Vegetable Preservation Technology, Zhejiang International Scientific and Technological Cooperation Base of Health Food Manufacturing and Quality Control, Fuli Institute of Food Science, Zhejiang University, Hangzhou, Zhejiang, China
| | - Xiaohui Liu
- Department of Nutrition, School of Public Health, Department of Clinical Nutrition, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Yin Li
- Department of Nutrition, School of Public Health, Department of Clinical Nutrition, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Haoyu Li
- College of Biosystems Engineering and Food Science, National-Local Joint Engineering Laboratory of Intelligent Food Technology and Equipment, Zhejiang Key Laboratory for Agro-Food Processing, Integrated Research Base of Southern Fruit and Vegetable Preservation Technology, Zhejiang International Scientific and Technological Cooperation Base of Health Food Manufacturing and Quality Control, Fuli Institute of Food Science, Zhejiang University, Hangzhou, Zhejiang, China
| | - Lange Zhang
- Department of Nutrition, School of Public Health, Department of Clinical Nutrition, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Xuzhi Wan
- College of Biosystems Engineering and Food Science, National-Local Joint Engineering Laboratory of Intelligent Food Technology and Equipment, Zhejiang Key Laboratory for Agro-Food Processing, Integrated Research Base of Southern Fruit and Vegetable Preservation Technology, Zhejiang International Scientific and Technological Cooperation Base of Health Food Manufacturing and Quality Control, Fuli Institute of Food Science, Zhejiang University, Hangzhou, Zhejiang, China
| | - Yuqi Wu
- College of Biosystems Engineering and Food Science, National-Local Joint Engineering Laboratory of Intelligent Food Technology and Equipment, Zhejiang Key Laboratory for Agro-Food Processing, Integrated Research Base of Southern Fruit and Vegetable Preservation Technology, Zhejiang International Scientific and Technological Cooperation Base of Health Food Manufacturing and Quality Control, Fuli Institute of Food Science, Zhejiang University, Hangzhou, Zhejiang, China
| | - Yu Zhang
- College of Biosystems Engineering and Food Science, National-Local Joint Engineering Laboratory of Intelligent Food Technology and Equipment, Zhejiang Key Laboratory for Agro-Food Processing, Integrated Research Base of Southern Fruit and Vegetable Preservation Technology, Zhejiang International Scientific and Technological Cooperation Base of Health Food Manufacturing and Quality Control, Fuli Institute of Food Science, Zhejiang University, Hangzhou, Zhejiang, China.,Ningbo Research Institute, Zhejiang University, Ningbo, Zhejiang, China
| | - Jingjing Jiao
- Department of Nutrition, School of Public Health, Department of Clinical Nutrition, The Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
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22
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Insulin receptor substrate 1 gene variations and lipid profile characteristics in the type 2 diabetic patients with comorbid obesity and chronic pancreatitis. Endocr Regul 2022; 56:1-9. [PMID: 35180824 DOI: 10.2478/enr-2022-0001] [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] [Indexed: 11/20/2022] Open
Abstract
Objective. Type 2 diabetes mellitus (T2DM) is one of diseases that develops in a setting of polymorbid processes or more often promotes their development, forming in this spectrum the phenomenon of comorbidity. The aim of this study was to evaluate changes in the lipid panel data in T2DM patients with comorbid obesity and chronic pancreatitis (CP) taking into account the C/A polymorphism of the insulin receptor substrate 1 (IRS1) gene (rs2943640). Methods. The study involved 34 T2DM patients and 10 healthy individuals. The rs2943640 IRS1 gene polymorphism was genotyped using the TaqMan real-time polymerase chain reaction (PCR) method. Blood serum lipid panel data were determined with commercially available kits on a Cobas 6000 analyzer. Results. In patients with only T2DM and T2DM + comorbid obesity, an association between IRS1 gene polymorphism (rs2943640) and lipid profile abnormalities with maximum changes of the lipid characteristics recorded in C/C genotype carriers was found. Within the C/C genotype of the IRS1 gene (rs2943640) in type 2 diabetic patients with comorbid obesity and CP, significantly lower high-density lipoprotein cholesterol (HDL-C) levels and significantly higher levels of triglycerides (TG), non-HDL-C and remnant cholesterol (RC) in relation to type 2 diabetic patients with comorbid obesity were found. At the same time, within the C/A genotype of the IRS1 gene (rs2943640), significant changes of lipid panel data were found in type 2 diabetic patients with comorbid obesity relative to the control group (p<0.001). Conclusions. Our data indicate that the presence of the C allele of IRS1 gene (rs2943640) in both homozygous and heterozygous states may indicate increased risk of dyslipidemia in type 2 diabetic patients with comorbidities.
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23
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San-Cristobal R, de Toro-Martín J, Vohl MC. Appraisal of Gene-Environment Interactions in GWAS for Evidence-Based Precision Nutrition Implementation. Curr Nutr Rep 2022; 11:563-573. [PMID: 35948824 PMCID: PMC9750926 DOI: 10.1007/s13668-022-00430-3] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 07/15/2022] [Indexed: 01/31/2023]
Abstract
PURPOSE OF REVIEW This review aims to analyse the currently reported gene-environment (G × E) interactions in genome-wide association studies (GWAS), involving environmental factors such as lifestyle and dietary habits related to metabolic syndrome phenotypes. For this purpose, the present manuscript reviews the available GWAS registered on the GWAS Catalog reporting the interaction between environmental factors and metabolic syndrome traits. RECENT FINDINGS Advances in omics-related analytical and computational approaches in recent years have led to a better understanding of the biological processes underlying these G × E interactions. A total of 42 GWAS were analysed, reporting over 300 loci interacting with environmental factors. Alcohol consumption, sleep time, smoking habit and physical activity were the most studied environmental factors with significant G × E interactions. The implementation of more comprehensive GWAS will provide a better understanding of the metabolic processes that determine individual responses to environmental exposures and their association with the development of chronic diseases such as obesity and the metabolic syndrome. This will facilitate the development of precision approaches for better prevention, management and treatment of these diseases.
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Affiliation(s)
- Rodrigo San-Cristobal
- grid.23856.3a0000 0004 1936 8390Centre Nutrition, Santé Et Société (NUTRISS), Institut Sur La Nutrition Et Les Aliments Fonctionnels (INAF), Université Laval, Québec, QC Canada ,grid.23856.3a0000 0004 1936 8390School of Nutrition, Université Laval, Quebec, QC G1V 0A6 Canada
| | - Juan de Toro-Martín
- grid.23856.3a0000 0004 1936 8390Centre Nutrition, Santé Et Société (NUTRISS), Institut Sur La Nutrition Et Les Aliments Fonctionnels (INAF), Université Laval, Québec, QC Canada ,grid.23856.3a0000 0004 1936 8390School of Nutrition, Université Laval, Quebec, QC G1V 0A6 Canada
| | - Marie-Claude Vohl
- grid.23856.3a0000 0004 1936 8390Centre Nutrition, Santé Et Société (NUTRISS), Institut Sur La Nutrition Et Les Aliments Fonctionnels (INAF), Université Laval, Québec, QC Canada ,grid.23856.3a0000 0004 1936 8390School of Nutrition, Université Laval, Quebec, QC G1V 0A6 Canada
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24
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Padilla-Martinez F, Wojciechowska G, Szczerbinski L, Kretowski A. Circulating Nucleic Acid-Based Biomarkers of Type 2 Diabetes. Int J Mol Sci 2021; 23:ijms23010295. [PMID: 35008723 PMCID: PMC8745431 DOI: 10.3390/ijms23010295] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/10/2021] [Revised: 12/25/2021] [Accepted: 12/26/2021] [Indexed: 11/23/2022] Open
Abstract
Type 2 diabetes (T2D) is a deficiency in how the body regulates glucose. Uncontrolled T2D will result in chronic high blood sugar levels, eventually resulting in T2D complications. These complications, such as kidney, eye, and nerve damage, are even harder to treat. Identifying individuals at high risk of developing T2D and its complications is essential for early prevention and treatment. Numerous studies have been done to identify biomarkers for T2D diagnosis and prognosis. This review focuses on recent T2D biomarker studies based on circulating nucleic acids using different omics technologies: genomics, transcriptomics, and epigenomics. Omics studies have profiled biomarker candidates from blood, urine, and other non-invasive samples. Despite methodological differences, several candidate biomarkers were reported for the risk and diagnosis of T2D, the prognosis of T2D complications, and pharmacodynamics of T2D treatments. Future studies should be done to validate the findings in larger samples and blood-based biomarkers in non-invasive samples to support the realization of precision medicine for T2D.
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Affiliation(s)
- Felipe Padilla-Martinez
- Clinical Research Centre, Medical University of Bialystok, 15276 Białystok, Poland; (F.P.-M.); (L.S.); (A.K.)
| | - Gladys Wojciechowska
- Clinical Research Centre, Medical University of Bialystok, 15276 Białystok, Poland; (F.P.-M.); (L.S.); (A.K.)
- Correspondence:
| | - Lukasz Szczerbinski
- Clinical Research Centre, Medical University of Bialystok, 15276 Białystok, Poland; (F.P.-M.); (L.S.); (A.K.)
- Department of Endocrinology, Diabetology and Internal Medicine, Medical University of Bialystok, 15276 Białystok, Poland
| | - Adam Kretowski
- Clinical Research Centre, Medical University of Bialystok, 15276 Białystok, Poland; (F.P.-M.); (L.S.); (A.K.)
- Department of Endocrinology, Diabetology and Internal Medicine, Medical University of Bialystok, 15276 Białystok, Poland
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Zhuang P, Liu X, Li Y, Wan X, Wu Y, Wu F, Zhang Y, Jiao J. Effect of Diet Quality and Genetic Predisposition on Hemoglobin A 1c and Type 2 Diabetes Risk: Gene-Diet Interaction Analysis of 357,419 Individuals. Diabetes Care 2021; 44:2470-2479. [PMID: 34433621 DOI: 10.2337/dc21-1051] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/15/2021] [Accepted: 07/29/2021] [Indexed: 02/03/2023]
Abstract
OBJECTIVE To assess the interactions between diet quality and genetic predisposition to incident type 2 diabetes (T2D). RESEARCH DESIGN AND METHODS Between 2006 and 2010, 357,419 participants with genetic and complete dietary data from the UK Biobank were enrolled and prospectively followed up to 2017. The genetic risk score (GRS) was calculated on the basis of 424 variants associated with T2D risk, and a higher GRS indicates a higher genetic predisposition to T2D. The adherence to a healthy diet was assessed by a diet quality score comprising 10 important dietary components, with a higher score representing a higher overall diet quality. RESULTS There were 5,663 incident T2D cases documented during an average of 8.1 years of follow-up. A significant negative interaction was observed between the GRS and the diet quality score. After adjusting for major risk factors, per SD increment in the GRS and the diet quality score was associated with a 54% higher and a 9% lower risk of T2D, respectively. A simultaneous increment of 1 SD in both the diet quality score and GRS was additionally associated with a 3% lower T2D risk due to the antagonistic interaction. In categorical analyses, a sharp reduction of 23% in T2D risk associated with a 1-SD increment in the diet quality score was detected among participants in the extremely high GRS group (GRS >95%). We also observed a strong negative interaction between the GRS and the diet quality score on the blood HbA1c level at baseline (P < 0.001). CONCLUSIONS The adherence to a healthy diet was associated with more reductions in blood HbA1c levels and subsequent T2D risk among individuals with a higher genetic risk. Our findings support tailoring dietary recommendations to an individual's genetic makeup for T2D prevention.
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Affiliation(s)
- Pan Zhuang
- Department of Food Science and Nutrition, Zhejiang Key Laboratory for Agro-Food Processing, Fuli Institute of Food Science, College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, Zhejiang, China
| | - Xiaohui Liu
- Department of Nutrition, School of Public Health, and Department of Clinical Nutrition of Affiliated Second Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Yin Li
- Department of Nutrition, School of Public Health, and Department of Clinical Nutrition of Affiliated Second Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Xuzhi Wan
- Department of Food Science and Nutrition, Zhejiang Key Laboratory for Agro-Food Processing, Fuli Institute of Food Science, College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, Zhejiang, China
| | - Yuqi Wu
- Department of Food Science and Nutrition, Zhejiang Key Laboratory for Agro-Food Processing, Fuli Institute of Food Science, College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, Zhejiang, China
| | - Fei Wu
- Department of Nutrition, School of Public Health, and Department of Clinical Nutrition of Affiliated Second Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
| | - Yu Zhang
- Department of Food Science and Nutrition, Zhejiang Key Laboratory for Agro-Food Processing, Fuli Institute of Food Science, College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou, Zhejiang, China
| | - Jingjing Jiao
- Department of Nutrition, School of Public Health, and Department of Clinical Nutrition of Affiliated Second Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang, China
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DNA Methylation and Type 2 Diabetes: Novel Biomarkers for Risk Assessment? Int J Mol Sci 2021; 22:ijms222111652. [PMID: 34769081 PMCID: PMC8584054 DOI: 10.3390/ijms222111652] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Revised: 10/25/2021] [Accepted: 10/25/2021] [Indexed: 12/15/2022] Open
Abstract
Diabetes is a severe threat to global health. Almost 500 million people live with diabetes worldwide. Most of them have type 2 diabetes (T2D). T2D patients are at risk of developing severe and life-threatening complications, leading to an increased need for medical care and reduced quality of life. Improved care for people with T2D is essential. Actions aiming at identifying undiagnosed diabetes and at preventing diabetes in those at high risk are needed as well. To this end, biomarker discovery and validation of risk assessment for T2D are critical. Alterations of DNA methylation have recently helped to better understand T2D pathophysiology by explaining differences among endophenotypes of diabetic patients in tissues. Recent evidence further suggests that variations of DNA methylation might contribute to the risk of T2D even more significantly than genetic variability and might represent a valuable tool to predict T2D risk. In this review, we focus on recent information on the contribution of DNA methylation to the risk and the pathogenesis of T2D. We discuss the limitations of these studies and provide evidence supporting the potential for clinical application of DNA methylation marks to predict the risk and progression of T2D.
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Wu YY, Thompson MD, Youkhana F, Pirkle CM. Interaction Between Physical Activity and Polygenic Score on Type 2 Diabetes Mellitus in Older Black and White Participants From the Health and Retirement Study. J Gerontol A Biol Sci Med Sci 2021; 76:1214-1221. [PMID: 33515027 DOI: 10.1093/gerona/glab025] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/05/2020] [Indexed: 02/07/2023] Open
Abstract
This study investigated the association of lifestyle factors and polygenic risk scores (PGS), and their interaction, on type 2 diabetes mellitus (T2D). We examined data from the U.S. Health and Retirement Study, a prospective longitudinal cohort of adults aged 50 years and older, containing nationally representative samples of Black and White Americans with precalculated PGS for T2D (N = 14 001). Predicted prevalence and incidence of T2D were calculated with logistic regression models. We calculated differences in T2D prevalence and incidence by PGS percentiles and for interaction variables using nonparametric bootstrap method. Black participants had approximately twice the prevalence of Whites (26.2% vs 14.2%), with a larger difference between the 90th and 10th PGS percentile from age 50 to 80 years. Significant interaction (pinteraction = .0096) was detected between PGS and physical activity among Whites. Among Whites in the 90th PGS percentile, T2D prevalence for moderate physical activity was 17.0% (95% CI: 14.8, 19.6), 6.8% lower compared to no/some physical activity (23.8%; 95% CI: 20.4, 27.5). T2D prevalence was similar (~10%) for both groups in the 10th PGS percentile. Incident T2D in Whites followed a similar pattern (pinteraction = .0325). No significant interactions with PGS were detected among Black participants. Interaction of different genetic risk profiles with lifestyle factors may inform understanding of varying inventions' efficacy for different groups of people, potentially improving clinical and prevention interventions.
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Affiliation(s)
- Yan Yan Wu
- Thompson School of Social Work & Public Health, University of Hawai'i at Mānoa, Honolulu, USA
| | - Mika D Thompson
- Thompson School of Social Work & Public Health, University of Hawai'i at Mānoa, Honolulu, USA
| | - Fadi Youkhana
- Thompson School of Social Work & Public Health, University of Hawai'i at Mānoa, Honolulu, USA
| | - Catherine M Pirkle
- Thompson School of Social Work & Public Health, University of Hawai'i at Mānoa, Honolulu, USA
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Liguori F, Mascolo E, Vernì F. The Genetics of Diabetes: What We Can Learn from Drosophila. Int J Mol Sci 2021; 22:ijms222011295. [PMID: 34681954 PMCID: PMC8541427 DOI: 10.3390/ijms222011295] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2021] [Revised: 10/12/2021] [Accepted: 10/16/2021] [Indexed: 12/14/2022] Open
Abstract
Diabetes mellitus is a heterogeneous disease characterized by hyperglycemia due to impaired insulin secretion and/or action. All diabetes types have a strong genetic component. The most frequent forms, type 1 diabetes (T1D), type 2 diabetes (T2D) and gestational diabetes mellitus (GDM), are multifactorial syndromes associated with several genes’ effects together with environmental factors. Conversely, rare forms, neonatal diabetes mellitus (NDM) and maturity onset diabetes of the young (MODY), are caused by mutations in single genes. Large scale genome screenings led to the identification of hundreds of putative causative genes for multigenic diabetes, but all the loci identified so far explain only a small proportion of heritability. Nevertheless, several recent studies allowed not only the identification of some genes as causative, but also as putative targets of new drugs. Although monogenic forms of diabetes are the most suited to perform a precision approach and allow an accurate diagnosis, at least 80% of all monogenic cases remain still undiagnosed. The knowledge acquired so far addresses the future work towards a study more focused on the identification of diabetes causal variants; this aim will be reached only by combining expertise from different areas. In this perspective, model organism research is crucial. This review traces an overview of the genetics of diabetes and mainly focuses on Drosophila as a model system, describing how flies can contribute to diabetes knowledge advancement.
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Affiliation(s)
- Francesco Liguori
- Preclinical Neuroscience, IRCCS Santa Lucia Foundation, 00143 Rome, Italy;
| | - Elisa Mascolo
- Department of Biology and Biotechnology “Charles Darwin”, Sapienza University, 00185 Rome, Italy;
| | - Fiammetta Vernì
- Department of Biology and Biotechnology “Charles Darwin”, Sapienza University, 00185 Rome, Italy;
- Correspondence:
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Balkhiyarova Z, Luciano R, Kaakinen M, Ulrich A, Shmeliov A, Bianchi M, Chioma L, Dallapiccola B, Prokopenko I, Manco M. Relationship between glucose homeostasis and obesity in early life-A study of Italian children and adolescents. Hum Mol Genet 2021; 31:816-826. [PMID: 34590674 PMCID: PMC8895752 DOI: 10.1093/hmg/ddab287] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/18/2021] [Revised: 09/15/2021] [Accepted: 09/16/2021] [Indexed: 01/09/2023] Open
Abstract
OBJECTIVES Epidemic obesity is the most important risk factor for prediabetes and type 2 diabetes (T2D) in youth as it is in adults. Obesity shares pathophysiological mechanisms with T2D and is likely to share part of the genetic background. We aimed to test if weighted genetic risk scores (GRSs) for T2D, fasting glucose (FG) and fasting insulin (FI) predict glycaemic traits and if there is a causal relationship between obesity and impaired glucose metabolism in children and adolescents. DESIGN AND PATIENTS Genotyping of 42 SNPs established by genome-wide association studies for T2D, FG and FI was performed in 1660 Italian youths aged between 2 and 19 years. We defined GRS for T2D, FG and FI and tested their effects on glycaemic traits, including FG, FI, indices of insulin resistance/beta cell function, and body mass index (BMI). We evaluated causal relationships between obesity and FG/FI using one-sample Mendelian Randomization analyses in both directions. RESULTS GRS-FG associated with FG (beta = 0.075 mmol/l, SE = 0.011, P = 1.58 × 10-11) and beta cell function (beta = -0.041, SE = 0.0090 P = 5.13 × 10-6). GRS-T2D also demonstrated an association with beta cell function (beta = -0.020, SE = 0.021 P = 0.030). We detected a causal effect of increased BMI on levels of FI in Italian youths (beta = 0.31 ln (pmol/l), 95%CI [0.078, 0.54], P = 0.0085), while there was no effect of FG/FI levels on BMI. CONCLUSION Our results demonstrate that the glycaemic and T2D risk genetic variants contribute to higher FG and FI levels and decreased beta cell function in children and adolescents. The causal effects of adiposity on increased insulin resistance are detectable from childhood age.
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Affiliation(s)
- Zhanna Balkhiyarova
- Section of Statistical Multi-Omics, Department of Clinical and Experimental Medicine, University of Surrey, Guildford GU2 7XH, UK.,Institute of Biochemistry and Genetics, Ufa Federal Research Centre Russian Academy of Sciences, Ufa 450008, Russian Federation.,Bashkir State Medical University, Ufa 450054, Russian Federation
| | - Rosa Luciano
- Research Area for Multifactorial Disease, Bambino Gesù Children's Hospital, IRCCS, Rome 00146, Italy.,Department of Laboratory Medicine, Bambino Gesù Children's Hospital, IRCCS, Rome 00146, Italy
| | - Marika Kaakinen
- Section of Statistical Multi-Omics, Department of Clinical and Experimental Medicine, University of Surrey, Guildford GU2 7XH, UK.,Section of Genetics and Genomics, Department of Metabolism, Digestion and Reproduction, Imperial College London, London SW7 2AZ, UK
| | - Anna Ulrich
- Section of Statistical Multi-Omics, Department of Clinical and Experimental Medicine, University of Surrey, Guildford GU2 7XH, UK.,Section of Genetics and Genomics, Department of Metabolism, Digestion and Reproduction, Imperial College London, London SW7 2AZ, UK
| | - Aleksey Shmeliov
- Section of Statistical Multi-Omics, Department of Clinical and Experimental Medicine, University of Surrey, Guildford GU2 7XH, UK
| | - Marzia Bianchi
- Research Area for Multifactorial Disease, Bambino Gesù Children's Hospital, IRCCS, Rome 00146, Italy
| | - Laura Chioma
- Unit of Endocrinology, Bambino Gesù Children's Hospital, IRCCS, Rome 00146, Italy
| | - Bruno Dallapiccola
- Genetics and Rare Diseases Research Division, Bambino Gesù Children's Hospital, IRCCS, Rome 00146, Italy
| | - Inga Prokopenko
- Section of Statistical Multi-Omics, Department of Clinical and Experimental Medicine, University of Surrey, Guildford GU2 7XH, UK.,Institute of Biochemistry and Genetics, Ufa Federal Research Centre Russian Academy of Sciences, Ufa 450008, Russian Federation.,UMR 8199 - EGID, Institut Pasteur de Lille, CNRS, University of Lille, Lille 59000, France
| | - Melania Manco
- Research Area for Multifactorial Disease, Bambino Gesù Children's Hospital, IRCCS, Rome 00146, Italy
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Kim DS, Gloyn AL, Knowles JW. Genetics of Type 2 Diabetes: Opportunities for Precision Medicine: JACC Focus Seminar. J Am Coll Cardiol 2021; 78:496-512. [PMID: 34325839 PMCID: PMC8328195 DOI: 10.1016/j.jacc.2021.03.346] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Revised: 03/14/2021] [Accepted: 03/16/2021] [Indexed: 12/30/2022]
Abstract
Type 2 diabetes (T2D) is highly prevalent and is a strong contributor for cardiovascular disease. However, there is significant heterogeneity in disease pathogenesis and the risk of complications. Enormous progress has been made in our ability to catalog genetic variation associated with T2D risk and variation in disease-relevant quantitative traits. These discoveries hold the potential to shed light on tractable targets and pathways for safe and effective therapeutic development, but the promise of precision medicine has been slow to be realized. Recent studies have identified subgroups of individuals with differential risk for intermediate phenotypes (eg, lipid levels, fasting insulin, body mass index) that contribute to T2D risk, helping to account for the observed clinical heterogeneity. These "partitioned genetic risk scores" not only have the potential to identify patients at greatest risk of cardiovascular disease and rapid disease progression, but also could aid patient stratification bridging the gap toward precision medicine for T2D.
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Affiliation(s)
- Daniel Seung Kim
- Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, California, USA
| | - Anna L Gloyn
- Division of Endocrinology, Department of Pediatrics, Stanford University School of Medicine, Stanford, California, USA; Stanford Diabetes Research Center, Stanford University, Stanford, California, USA
| | - Joshua W Knowles
- Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, California, USA; Stanford Diabetes Research Center, Stanford University, Stanford, California, USA; Stanford Cardiovascular Institute, Stanford University, Stanford, California, USA.
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Untangling the genetic link between type 1 and type 2 diabetes using functional genomics. Sci Rep 2021; 11:13871. [PMID: 34230558 PMCID: PMC8260770 DOI: 10.1038/s41598-021-93346-x] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/07/2020] [Accepted: 06/16/2021] [Indexed: 02/06/2023] Open
Abstract
There is evidence pointing towards shared etiological features between type 1 diabetes (T1D) and type 2 diabetes (T2D) despite both phenotypes being considered genetically distinct. However, the existence of shared genetic features for T1D and T2D remains complex and poorly defined. To better understand the link between T1D and T2D, we employed an integrated functional genomics approach involving extensive chromatin interaction data (Hi-C) and expression quantitative trait loci (eQTL) data to characterize the tissue-specific impacts of single nucleotide polymorphisms associated with T1D and T2D. We identified 195 pleiotropic genes that are modulated by tissue-specific spatial eQTLs associated with both T1D and T2D. The pleiotropic genes are enriched in inflammatory and metabolic pathways that include mitogen-activated protein kinase activity, pertussis toxin signaling, and the Parkinson's disease pathway. We identified 8 regulatory elements within the TCF7L2 locus that modulate transcript levels of genes involved in immune regulation as well as genes important in the etiology of T2D. Despite the observed gene and pathway overlaps, there was no significant genetic correlation between variant effects on T1D and T2D risk using European ancestral summary data. Collectively, our findings support the hypothesis that T1D and T2D specific genetic variants act through genetic regulatory mechanisms to alter the regulation of common genes, and genes that co-locate in biological pathways, to mediate pleiotropic effects on disease development. Crucially, a high risk genetic profile for T1D alters biological pathways that increase the risk of developing both T1D and T2D. The same is not true for genetic profiles that increase the risk of developing T2D. The conversion of information on genetic susceptibility to the protein pathways that are altered provides an important resource for repurposing or designing novel therapies for the management of diabetes.
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Raghavan S, Jablonski K, Delahanty LM, Maruthur NM, Leong A, Franks PW, Knowler WC, Florez JC, Dabelea D. Interaction of diabetes genetic risk and successful lifestyle modification in the Diabetes Prevention Programme. Diabetes Obes Metab 2021; 23:1030-1040. [PMID: 33394545 PMCID: PMC8852694 DOI: 10.1111/dom.14309] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/23/2020] [Revised: 12/20/2020] [Accepted: 12/23/2020] [Indexed: 12/13/2022]
Abstract
AIM To test whether diabetes genetic risk modifies the association of successful lifestyle changes with incident diabetes. MATERIALS AND METHODS We studied 823 individuals randomized to the intensive lifestyle intervention (ILS) arm of the Diabetes Prevention Programme who were diabetes-free 1 year after enrolment. We tested additive and multiplicative interactions of a 67-variant diabetes genetic risk score (GRS) with achievement of three ILS goals at 1 year (≥7% weight loss, ≥150 min/wk of moderate leisure-time physical activity, and/or a goal for self-reported total fat intake) on the primary outcome of incident diabetes over 3 years of follow-up. RESULTS A lower GRS and achieving each or all three ILS goals were each associated with lower incidence of diabetes (all P < 0.05). Additive interactions were significant between the GRS and achievement of the weight loss goal (P < 0.001), physical activity goal (P = 0.02), and all three ILS goals (P < 0.001) for diabetes risk. Achievement of all three ILS goals was associated with 1.8 (95% CI 0.3, 3.4), 3.1 (95% CI 1.5, 4.7), and 3.9 (95% CI 1.6, 6.2) fewer diabetes cases/100-person-years in the first, second and third GRS tertiles (P < 0.001 for trend). Multiplicative interactions between the GRS and ILS goal achievement were significant for the diet goal (P < 0.001), but not for weight loss (P = 0.18) or physical activity (P = 0.62) goals. CONCLUSIONS Genetic risk may identify high-risk subgroups for whom successful lifestyle modification is associated with greater absolute reduction in the risk of incident diabetes.
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Affiliation(s)
- Sridharan Raghavan
- Department of Veterans Affairs Eastern Colorado Healthcare System, Aurora, CO
- Division of Hospital Medicine, University of Colorado School of Medicine, Aurora, CO
- Colorado Cardiovascular Outcomes Research Consortium, Aurora, CO
- Center for Lifecourse Epidemiology of Adiposity and Diabetes, Colorado School of Public Health, Aurora, CO
| | - Kathleen Jablonski
- Department of Epidemiology and Biostatistics, Milken Institute School of Public Health, George Washington University, Washington, DC
| | - Linda M. Delahanty
- Diabetes Unit and Endocrine Division, Department of Medicine, Massachusetts General Hospital, Boston, MA
- Department of Medicine, Harvard Medical School, Boston, MA
| | - Nisa M. Maruthur
- Division of General Internal Medicine, Johns Hopkins University School of Medicine, Baltimore, MD
| | - Aaron Leong
- Diabetes Unit and Endocrine Division, Department of Medicine, Massachusetts General Hospital, Boston, MA
| | - Paul W. Franks
- Genetic & Molecular Epidemiology Unit, Lund University Diabetes Center, Department of Clinical Science, Malmö, Sweden
| | - William C. Knowler
- Diabetes Epidemiology and Clinical Research Section, National Institute of Diabetes and Digestive and Kidney Diseases, Phoenix, AZ
| | - Jose C. Florez
- Diabetes Unit and Endocrine Division, Department of Medicine, Massachusetts General Hospital, Boston, MA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA
- Department of Medicine, Harvard Medical School, Boston, MA
- Programs in Metabolism and Medical & Population Genetics, Broad Institute, Cambridge, MA
| | - Dana Dabelea
- Center for Lifecourse Epidemiology of Adiposity and Diabetes, Colorado School of Public Health, Aurora, CO
- Department of Epidemiology, Colorado School of Public Health, Aurora, CO
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Cho SB, Jang JH, Chung MG, Kim SC. Exome Chip Analysis of 14,026 Koreans Reveals Known and Newly Discovered Genetic Loci Associated with Type 2 Diabetes Mellitus. Diabetes Metab J 2021; 45:231-240. [PMID: 32794382 PMCID: PMC8024163 DOI: 10.4093/dmj.2019.0163] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/31/2019] [Accepted: 02/10/2020] [Indexed: 12/14/2022] Open
Abstract
BACKGROUND Most loci associated with type 2 diabetes mellitus (T2DM) discovered to date are within noncoding regions of unknown functional significance. By contrast, exonic regions have advantages for biological interpretation. METHODS We analyzed the association of exome array data from 14,026 Koreans to identify susceptible exonic loci for T2DM. We used genotype information of 50,543 variants using the Illumina exome array platform. RESULTS In total, 7 loci were significant with a Bonferroni adjusted P=1.03×10-6. rs2233580 in paired box gene 4 (PAX4) showed the highest odds ratio of 1.48 (P=1.60×10-10). rs11960799 in membrane associated ring-CH-type finger 3 (MARCH3) and rs75680863 in transcobalamin 2 (TCN2) were newly identified loci. When we built a model to predict the incidence of diabetes with the 7 loci and clinical variables, area under the curve (AUC) of the model improved significantly (AUC=0.72, P<0.05), but marginally in its magnitude, compared with the model using clinical variables (AUC=0.71, P<0.05). When we divided the entire population into three groups-normal body mass index (BMI; <25 kg/m2), overweight (25≤ BMI <30 kg/m2), and obese (BMI ≥30 kg/m2) individuals-the predictive performance of the 7 loci was greatest in the group of obese individuals, where the net reclassification improvement was highly significant (0.51; P=8.00×10-5). CONCLUSION We found exonic loci having a susceptibility for T2DM. We found that such genetic information is advantageous for predicting T2DM in a subgroup of obese individuals.
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Affiliation(s)
- Seong Beom Cho
- Division of Biomedical Informatics, Center for Genome Science, National Institute of Health, Korea Center for Disease Control and Prevention, Cheongju, Korea
| | - Jin Hwa Jang
- Division of Biomedical Informatics, Center for Genome Science, National Institute of Health, Korea Center for Disease Control and Prevention, Cheongju, Korea
| | - Myung Guen Chung
- Division of Biomedical Informatics, Center for Genome Science, National Institute of Health, Korea Center for Disease Control and Prevention, Cheongju, Korea
| | - Sang Cheol Kim
- Division of Biomedical Informatics, Center for Genome Science, National Institute of Health, Korea Center for Disease Control and Prevention, Cheongju, Korea
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Liu W, Zhuang Z, Wang W, Huang T, Liu Z. An Improved Genome-Wide Polygenic Score Model for Predicting the Risk of Type 2 Diabetes. Front Genet 2021; 12:632385. [PMID: 33643391 PMCID: PMC7905203 DOI: 10.3389/fgene.2021.632385] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2020] [Accepted: 01/11/2021] [Indexed: 01/09/2023] Open
Abstract
Polygenic risk score (PRS) has been shown to be predictive of disease risk such as type 2 diabetes (T2D). However, the existing studies on genetic prediction for T2D only had limited predictive power. To further improve the predictive capability of the PRS model in identifying individuals at high T2D risk, we proposed a new three-step filtering procedure, which aimed to include truly predictive single-nucleotide polymorphisms (SNPs) and avoid unpredictive ones into PRS model. First, we filtered SNPs according to the marginal association p-values (p≤ 5× 10-2) from large-scale genome-wide association studies. Second, we set linkage disequilibrium (LD) pruning thresholds (r 2) as 0.2, 0.4, 0.6, and 0.8. Third, we set p-value thresholds as 5× 10-2, 5× 10-4, 5× 10-6, and 5× 10-8. Then, we constructed and tested multiple candidate PRS models obtained by the PRSice-2 software among 182,422 individuals in the UK Biobank (UKB) testing dataset. We validated the predictive capability of the optimal PRS model that was chosen from the testing process in identifying individuals at high T2D risk based on the UKB validation dataset (n = 274,029). The prediction accuracy of the PRS model evaluated by the adjusted area under the receiver operating characteristics curve (AUC) showed that our PRS model had good prediction performance [AUC = 0.795, 95% confidence interval (CI): (0.790, 0.800)]. Specifically, our PRS model identified 30, 12, and 7% of the population at greater than five-, six-, and seven-fold risk for T2D, respectively. After adjusting for sex, age, physical measurements, and clinical factors, the AUC increased to 0.901 [95% CI: (0.897, 0.904)]. Therefore, our PRS model could be useful for population-level preventive T2D screening.
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Affiliation(s)
- Wei Liu
- Department of Statistics and Actuarial Science, The University of Hong Kong, Hong Kong, China
| | - Zhenhuang Zhuang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Wenxiu Wang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China
| | - Tao Huang
- Department of Epidemiology and Biostatistics, School of Public Health, Peking University, Beijing, China.,Center for Intelligent Public Health, Institute for Artificial Intelligence, Peking University, Beijing, China.,Key Laboratory of Molecular Cardiovascular Diseases, Ministry of Education, Peking University, Beijing, China
| | - Zhonghua Liu
- Department of Statistics and Actuarial Science, The University of Hong Kong, Hong Kong, China
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35
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Abstract
PURPOSE OF THE REVIEW Proteins are the central layer of information transfer from genome to phenome and represent the largest class of drug targets. We review recent advances in high-throughput technologies that provide comprehensive, scalable profiling of the plasma proteome with the potential to improve prediction and mechanistic understanding of type 2 diabetes (T2D). RECENT FINDINGS Technological and analytical advancements have enabled identification of novel protein biomarkers and signatures that help to address challenges of existing approaches to predict and screen for T2D. Genetic studies have so far revealed putative causal roles for only few of the proteins that have been linked to T2D, but ongoing large-scale genetic studies of the plasma proteome will help to address this and increase our understanding of aetiological pathways and mechanisms leading to diabetes. Studies of the human plasma proteome have started to elucidate its potential for T2D prediction and biomarker discovery. Future studies integrating genomic and proteomic data will provide opportunities to prioritise drug targets and identify pathways linking genetic predisposition to T2D development.
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Affiliation(s)
| | - Maik Pietzner
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
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36
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Karunamuni RA, Huynh-Le MP, Fan CC, Eeles RA, Easton DF, Kote-Jarai ZS, Amin Al Olama A, Benlloch Garcia S, Muir K, Gronberg H, Wiklund F, Aly M, Schleutker J, Sipeky C, Tammela TLJ, Nordestgaard BG, Key TJ, Travis RC, Neal DE, Donovan JL, Hamdy FC, Pharoah P, Pashayan N, Khaw KT, Thibodeau SN, McDonnell SK, Schaid DJ, Maier C, Vogel W, Luedeke M, Herkommer K, Kibel AS, Cybulski C, Wokolorczyk D, Kluzniak W, Cannon-Albright L, Brenner H, Schöttker B, Holleczek B, Park JY, Sellers TA, Lin HY, Slavov C, Kaneva R, Mitev V, Batra J, Clements JA, Spurdle A, Teixeira MR, Paulo P, Maia S, Pandha H, Michael A, Mills IG, Andreassen OA, Dale AM, Seibert TM. The effect of sample size on polygenic hazard models for prostate cancer. Eur J Hum Genet 2020; 28:1467-1475. [PMID: 32514134 PMCID: PMC7608255 DOI: 10.1038/s41431-020-0664-2] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2019] [Revised: 02/27/2020] [Accepted: 05/22/2020] [Indexed: 11/12/2022] Open
Abstract
We determined the effect of sample size on performance of polygenic hazard score (PHS) models in prostate cancer. Age and genotypes were obtained for 40,861 men from the PRACTICAL consortium. The dataset included 201,590 SNPs per subject, and was split into training and testing sets. Established-SNP models considered 65 SNPs that had been previously associated with prostate cancer. Discovery-SNP models used stepwise selection to identify new SNPs. The performance of each PHS model was calculated for random sizes of the training set. The performance of a representative Established-SNP model was estimated for random sizes of the testing set. Mean HR98/50 (hazard ratio of top 2% to average in test set) of the Established-SNP model increased from 1.73 [95% CI: 1.69-1.77] to 2.41 [2.40-2.43] when the number of training samples was increased from 1 thousand to 30 thousand. Corresponding HR98/50 of the Discovery-SNP model increased from 1.05 [0.93-1.18] to 2.19 [2.16-2.23]. HR98/50 of a representative Established-SNP model using testing set sample sizes of 0.6 thousand and 6 thousand observations were 1.78 [1.70-1.85] and 1.73 [1.71-1.76], respectively. We estimate that a study population of 20 thousand men is required to develop Discovery-SNP PHS models while 10 thousand men should be sufficient for Established-SNP models.
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Affiliation(s)
- Roshan A Karunamuni
- Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, CA, USA.
| | - Minh-Phuong Huynh-Le
- Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, CA, USA
| | - Chun C Fan
- Healthlytix, 4747 Executive Dr. Suite 820, San Diego, CA, USA
| | - Rosalind A Eeles
- The Institute of Cancer Research, London, SM2 5NG, UK
- Royal Marsden NHS Foundation Trust, London, SW3 6JJ, UK
| | - Douglas F Easton
- Department of Public Health and Primary Care, Centre for Cancer Genetic Epidemiology, Strangeways Research Laboratory, University of Cambridge, Cambridge, CB1 8RN, UK
| | | | - Ali Amin Al Olama
- Department of Public Health and Primary Care, Centre for Cancer Genetic Epidemiology, Strangeways Research Laboratory, University of Cambridge, Cambridge, CB1 8RN, UK
- Department of Clinical Neurosciences, Stroke Research Group, R3, Box 83, Cambridge Biomedical Campus, University of Cambridge, Cambridge, CB2 0QQ, UK
| | - Sara Benlloch Garcia
- Department of Public Health and Primary Care, Centre for Cancer Genetic Epidemiology, Strangeways Research Laboratory, University of Cambridge, Cambridge, CB1 8RN, UK
| | - Kenneth Muir
- Division of Population Health, Health Services Research and Primary Care, University of Manchester, Oxford Road, Manchester, M13 9PL, UK
- Warwick Medical School, University of Warwick, Coventry, UK
| | - Henrik Gronberg
- Department of Medical Epidemiology and Biostatistics, Karolinska Institute, SE-171 77, Stockholm, Sweden
| | - Fredrik Wiklund
- Department of Medical Epidemiology and Biostatistics, Karolinska Institute, SE-171 77, Stockholm, Sweden
| | - Markus Aly
- Department of Medical Epidemiology and Biostatistics, Karolinska Institute, SE-171 77, Stockholm, Sweden
- Department of Molecular Medicine and Surgery, Karolinska Institute, SE-171 77, Stockholm, Sweden
- Department of Urology, Karolinska University Hospital, Stockholm, Sweden
| | - Johanna Schleutker
- Institute of Biomedicine, University of Turku, Kiinamyllynkatu 10, FI-20014, Turku, Finland
- Department of Medical Genetics, Genomics, Laboratory Division, Turku University Hospital, PO Box 52, 20521, Turku, Finland
| | - Csilla Sipeky
- Institute of Biomedicine, University of Turku, Kiinamyllynkatu 10, FI-20014, Turku, Finland
| | - Teuvo L J Tammela
- Faculty of Medicine and Health Technology, Prostate Cancer Research Center, Tampere University, FI-33014, Tampere, Finland
- Department of Urology, Tampere University Hospital, Tampere, Finland
| | - Børge G Nordestgaard
- Faculty of Health and Medical Sciences, University of Copenhagen, 2200, Copenhagen, Denmark
- Department of Clinical Biochemistry, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, 2200, Copenhagen, Denmark
| | - Tim J Key
- Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, OX3 7LF, UK
| | - Ruth C Travis
- Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, OX3 7LF, UK
| | - David E Neal
- Nuffield Department of Surgical Sciences, University of Oxford, Room 6603, Level 6, John Radcliffe Hospital, Headley Way, Headington, Oxford, OX3 9DU, UK
- Department of Oncology, Box 279, Addenbrooke's Hospital, University of Cambridge, Hills Road, Cambridge, CB2 0QQ, UK
- Cancer Research UK, Cambridge Research Institute, Li Ka Shing Centre, Cambridge, UK
| | - Jenny L Donovan
- School of Social and Community Medicine, University of Bristol, Canynge Hall, 39 Whatley Road, Bristol, BS8 2PS, UK
| | - Freddie C Hamdy
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, OX1 2JD, UK
- Faculty of Medical Science, John Radcliffe Hospital, University of Oxford, Oxford, UK
| | - Paul Pharoah
- Department of Oncology, Centre for Cancer Genetic Epidemiology, Strangeways Laboratory, University of Cambridge, Worts Causeway, Cambridge, CB1 8RN, UK
| | - Nora Pashayan
- Department of Oncology, Centre for Cancer Genetic Epidemiology, Strangeways Laboratory, University of Cambridge, Worts Causeway, Cambridge, CB1 8RN, UK
- Department of Applied Health Research, University College London, London, UK
- Department of Applied Health Research, University College London, London, WC1E 7HB, UK
| | - Kay-Tee Khaw
- Clinical Gerontology Unit, University of Cambridge, Cambridge, CB2 2QQ, UK
| | - Stephen N Thibodeau
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, 55905, USA
| | - Shannon K McDonnell
- Division of Biomedical Statistics & Informatics, Mayo Clinic, Rochester, MN, 55905, USA
| | - Daniel J Schaid
- Division of Biomedical Statistics & Informatics, Mayo Clinic, Rochester, MN, 55905, USA
| | - Christiane Maier
- Humangenetik Tuebingen, Paul-Ehrlich-Str 23, D-72076, Tuebingen, Germany
| | - Walther Vogel
- Institute for Human Genetics, University Hospital Ulm, 89075, Ulm, Germany
| | - Manuel Luedeke
- Humangenetik Tuebingen, Paul-Ehrlich-Str 23, D-72076, Tuebingen, Germany
| | - Kathleen Herkommer
- Department of Urology, School of Medicine, Klinikum rechts der Isar, Technical University of Munich, München, Germany
| | - Adam S Kibel
- Division of Urologic Surgery, Brigham and Womens Hospital, 75 Francis Street, Boston, MA, 02115, USA
| | - Cezary Cybulski
- Department of Genetics and Pathology, International Hereditary Cancer Center, Pomeranian Medical University, 70-115, Szczecin, Poland
| | - Dominika Wokolorczyk
- Department of Genetics and Pathology, International Hereditary Cancer Center, Pomeranian Medical University, 70-115, Szczecin, Poland
| | - Wojciech Kluzniak
- Department of Genetics and Pathology, International Hereditary Cancer Center, Pomeranian Medical University, 70-115, Szczecin, Poland
| | - Lisa Cannon-Albright
- Division of Genetic Epidemiology, Department of Medicine, University of Utah School of Medicine, Salt Lake City, UT, 84112, USA
- George E. Wahlen Department of Veterans Affairs Medical Center, Salt Lake City, UT, 84148, USA
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), D-69120, Heidelberg, Germany
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), D-69120, Heidelberg, Germany
- Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Im Neuenheimer Feld 460, 69120, Heidelberg, Germany
| | - Ben Schöttker
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), D-69120, Heidelberg, Germany
- Network Aging Research, University of Heidelberg, Heidelberg, Germany
| | - Bernd Holleczek
- Saarland Cancer Registry, D-66119, Saarbrücken, Germany
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany
| | - Jong Y Park
- Department of Cancer Epidemiology, Moffitt Cancer Center, 12902 Magnolia Drive, Tampa, FL, 33612, USA
| | - Thomas A Sellers
- Department of Cancer Epidemiology, Moffitt Cancer Center, 12902 Magnolia Drive, Tampa, FL, 33612, USA
| | - Hui-Yi Lin
- School of Public Health, Louisiana State University Health Sciences Center, New Orleans, LA, 70112, USA
| | - Chavdar Slavov
- Department of Urology and Alexandrovska University Hospital, Medical University of Sofia, 1431, Sofia, Bulgaria
| | - Radka Kaneva
- Department of Medical Chemistry and Biochemistry, Molecular Medicine Center, Medical University of Sofia, Sofia, 2 Zdrave Str., 1431, Sofia, Bulgaria
| | - Vanio Mitev
- Department of Medical Chemistry and Biochemistry, Molecular Medicine Center, Medical University of Sofia, Sofia, 2 Zdrave Str., 1431, Sofia, Bulgaria
| | - Jyotsna Batra
- Institute of Health and Biomedical Innovation and School of Biomedical Sciences, Queensland University of Technology, Brisbane, QLD, 4059, Australia
- Australian Prostate Cancer Research Centre-Qld, Translational Research Institute, Brisbane, QLD, 4102, Australia
| | - Judith A Clements
- Australian Prostate Cancer Research Centre-Qld, Institute of Health and Biomedical Innovation and School of Biomedical Science, Queensland University of Technology, Brisbane, QLD, 4059, Australia
- Translational Research Institute, Brisbane, QLD, 4102, Australia
| | - Amanda Spurdle
- Molecular Cancer Epidemiology Laboratory, QIMR Berghofer Institute of Medical Research, Brisbane, Australia
| | - Manuel R Teixeira
- Department of Genetics, Portuguese Oncology Institute of Porto (IPO-Porto), 4200-072, Porto, Portugal
- Biomedical Sciences Institute (ICBAS), University of Porto, 4050-313, Porto, Portugal
| | - Paula Paulo
- Department of Genetics, Portuguese Oncology Institute of Porto (IPO-Porto), 4200-072, Porto, Portugal
- Cancer Genetics Group, IPO-Porto Research Center (CI-IPOP), Portuguese Oncology Institute of Porto (IPO-Porto), Porto, Portugal
| | - Sofia Maia
- Department of Genetics, Portuguese Oncology Institute of Porto (IPO-Porto), 4200-072, Porto, Portugal
- Cancer Genetics Group, IPO-Porto Research Center (CI-IPOP), Portuguese Oncology Institute of Porto (IPO-Porto), Porto, Portugal
| | - Hardev Pandha
- The University of Surrey, Guildford, Surrey, GU2 7XH, UK
| | | | - Ian G Mills
- Center for Cancer Research and Cell Biology, Queen's University of Belfast, Belfast, UK
- Nuffield Department of Surgical Sciences, John Radcliffe Hospital, University of Oxford, Oxford, UK
| | - Ole A Andreassen
- NORMENT, KG Jebsen Centre, Oslo University Hospital and University of Oslo, Oslo, Norway
| | - Anders M Dale
- Department of Radiology, University of California San Diego, La Jolla, CA, USA
- Department of Cognitive Science, University of California San Diego, La Jolla, CA, USA
- Department of Neurosciences, University of California San Diego, La Jolla, CA, USA
| | - Tyler M Seibert
- Department of Radiation Medicine and Applied Sciences, University of California San Diego, La Jolla, CA, USA.
- Department of Bioengineering, University of California San Diego, La Jolla, CA, USA.
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37
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Bergman M, Abdul-Ghani M, DeFronzo RA, Manco M, Sesti G, Fiorentino TV, Ceriello A, Rhee M, Phillips LS, Chung S, Cravalho C, Jagannathan R, Monnier L, Colette C, Owens D, Bianchi C, Del Prato S, Monteiro MP, Neves JS, Medina JL, Macedo MP, Ribeiro RT, Filipe Raposo J, Dorcely B, Ibrahim N, Buysschaert M. Review of methods for detecting glycemic disorders. Diabetes Res Clin Pract 2020; 165:108233. [PMID: 32497744 PMCID: PMC7977482 DOI: 10.1016/j.diabres.2020.108233] [Citation(s) in RCA: 96] [Impact Index Per Article: 24.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/19/2020] [Accepted: 05/19/2020] [Indexed: 02/07/2023]
Abstract
Prediabetes (intermediate hyperglycemia) consists of two abnormalities, impaired fasting glucose (IFG) and impaired glucose tolerance (IGT) detected by a standardized 75-gram oral glucose tolerance test (OGTT). Individuals with isolated IGT or combined IFG and IGT have increased risk for developing type 2 diabetes (T2D) and cardiovascular disease (CVD). Diagnosing prediabetes early and accurately is critical in order to refer high-risk individuals for intensive lifestyle modification. However, there is currently no international consensus for diagnosing prediabetes with HbA1c or glucose measurements based upon American Diabetes Association (ADA) and the World Health Organization (WHO) criteria that identify different populations at risk for progressing to diabetes. Various caveats affecting the accuracy of interpreting the HbA1c including genetics complicate this further. This review describes established methods for detecting glucose disorders based upon glucose and HbA1c parameters as well as novel approaches including the 1-hour plasma glucose (1-h PG), glucose challenge test (GCT), shape of the glucose curve, genetics, continuous glucose monitoring (CGM), measures of insulin secretion and sensitivity, metabolomics, and ancillary tools such as fructosamine, glycated albumin (GA), 1,5- anhydroglucitol (1,5-AG). Of the approaches considered, the 1-h PG has considerable potential as a biomarker for detecting glucose disorders if confirmed by additional data including health economic analysis. Whether the 1-h OGTT is superior to genetics and omics in providing greater precision for individualized treatment requires further investigation. These methods will need to demonstrate substantially superiority to simpler tools for detecting glucose disorders to justify their cost and complexity.
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Affiliation(s)
- Michael Bergman
- NYU School of Medicine, NYU Diabetes Prevention Program, Endocrinology, Diabetes, Metabolism, VA New York Harbor Healthcare System, Manhattan Campus, 423 East 23rd Street, Room 16049C, NY, NY 10010, USA.
| | - Muhammad Abdul-Ghani
- Division of Diabetes, University of Texas Health Science Center at San Antonio, San Antonio, TX 78229, USA.
| | - Ralph A DeFronzo
- Division of Diabetes, University of Texas Health Science Center at San Antonio, San Antonio, TX 78229, USA.
| | - Melania Manco
- Research Area for Multifactorial Diseases, Bambino Gesù Children Hospital, Rome, Italy.
| | - Giorgio Sesti
- Department of Clinical and Molecular Medicine, University of Rome Sapienza, Rome 00161, Italy
| | - Teresa Vanessa Fiorentino
- Department of Medical and Surgical Sciences, University Magna Græcia of Catanzaro, Catanzaro 88100, Italy.
| | - Antonio Ceriello
- Department of Cardiovascular and Metabolic Diseases, Istituto Ricerca Cura Carattere Scientifico Multimedica, Sesto, San Giovanni (MI), Italy.
| | - Mary Rhee
- Emory University School of Medicine, Department of Medicine, Division of Endocrinology, Metabolism, and Lipids, Atlanta VA Health Care System, Atlanta, GA 30322, USA.
| | - Lawrence S Phillips
- Emory University School of Medicine, Department of Medicine, Division of Endocrinology, Metabolism, and Lipids, Atlanta VA Health Care System, Atlanta, GA 30322, USA.
| | - Stephanie Chung
- Diabetes Endocrinology and Obesity Branch, National Institutes of Diabetes, Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD 20892, USA.
| | - Celeste Cravalho
- Diabetes Endocrinology and Obesity Branch, National Institutes of Diabetes, Digestive and Kidney Diseases, National Institutes of Health, Bethesda, MD 20892, USA.
| | - Ram Jagannathan
- Emory University School of Medicine, Department of Medicine, Division of Endocrinology, Metabolism, and Lipids, Atlanta VA Health Care System, Atlanta, GA 30322, USA.
| | - Louis Monnier
- Institute of Clinical Research, University of Montpellier, Montpellier, France.
| | - Claude Colette
- Institute of Clinical Research, University of Montpellier, Montpellier, France.
| | - David Owens
- Diabetes Research Group, Institute of Life Science, Swansea University, Wales, UK.
| | - Cristina Bianchi
- University Hospital of Pisa, Section of Metabolic Diseases and Diabetes, University Hospital, University of Pisa, Pisa, Italy.
| | - Stefano Del Prato
- Department of Clinical and Experimental Medicine, University of Pisa, Pisa, Italy.
| | - Mariana P Monteiro
- Endocrine, Cardiovascular & Metabolic Research, Unit for Multidisciplinary Research in Biomedicine (UMIB), University of Porto, Porto, Portugal; Institute of Biomedical Sciences Abel Salazar (ICBAS), University of Porto, Porto, Portugal.
| | - João Sérgio Neves
- Department of Surgery and Physiology, Cardiovascular Research and Development Center, Faculty of Medicine, University of Porto, Porto, Portugal; Department of Endocrinology, Diabetes and Metabolism, São João University Hospital Center, Porto, Portugal.
| | | | - Maria Paula Macedo
- CEDOC-Centro de Estudos de Doenças Crónicas, NOVA Medical School, Faculdade de Ciências Médicas, Universidade Nova de Lisboa, Lisboa, Portugal; APDP-Diabetes Portugal, Education and Research Center (APDP-ERC), Lisboa, Portugal.
| | - Rogério Tavares Ribeiro
- Institute for Biomedicine, Department of Medical Sciences, University of Aveiro, APDP Diabetes Portugal, Education and Research Center (APDP-ERC), Aveiro, Portugal.
| | - João Filipe Raposo
- CEDOC-Centro de Estudos de Doenças Crónicas, NOVA Medical School, Faculdade de Ciências Médicas, Universidade Nova de Lisboa, Lisboa, Portugal; APDP-Diabetes Portugal, Education and Research Center (APDP-ERC), Lisboa, Portugal.
| | - Brenda Dorcely
- NYU School of Medicine, Division of Endocrinology, Diabetes, Metabolism, NY, NY 10016, USA.
| | - Nouran Ibrahim
- NYU School of Medicine, Division of Endocrinology, Diabetes, Metabolism, NY, NY 10016, USA.
| | - Martin Buysschaert
- Department of Endocrinology and Diabetology, Université Catholique de Louvain, University Clinic Saint-Luc, Brussels, Belgium.
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38
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Aksit MA, Pace RG, Vecchio-Pagán B, Ling H, Rommens JM, Boelle PY, Guillot L, Raraigh KS, Pugh E, Zhang P, Strug LJ, Drumm ML, Knowles MR, Cutting GR, Corvol H, Blackman SM. Genetic Modifiers of Cystic Fibrosis-Related Diabetes Have Extensive Overlap With Type 2 Diabetes and Related Traits. J Clin Endocrinol Metab 2020; 105:5599821. [PMID: 31697830 PMCID: PMC7236628 DOI: 10.1210/clinem/dgz102] [Citation(s) in RCA: 28] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/04/2019] [Accepted: 10/02/2019] [Indexed: 02/08/2023]
Abstract
CONTEXT Individuals with cystic fibrosis (CF) develop a distinct form of diabetes characterized by β-cell dysfunction and islet amyloid accumulation similar to type 2 diabetes (T2D), but generally have normal insulin sensitivity. CF-related diabetes (CFRD) risk is determined by both CFTR, the gene responsible for CF, and other genetic variants. OBJECTIVE To identify genetic modifiers of CFRD and determine the genetic overlap with other types of diabetes. DESIGN AND PATIENTS A genome-wide association study was conducted for CFRD onset on 5740 individuals with CF. Weighted polygenic risk scores (PRSs) for type 1 diabetes (T1D), T2D, and diabetes endophenotypes were tested for association with CFRD. RESULTS Genome-wide significance was obtained for variants at a novel locus (PTMA) and 2 known CFRD genetic modifiers (TCF7L2 and SLC26A9). PTMA and SLC26A9 variants were CF-specific; TCF7L2 variants also associated with T2D. CFRD was strongly associated with PRSs for T2D, insulin secretion, postchallenge glucose concentration, and fasting plasma glucose, and less strongly with T1D PRSs. CFRD was inconsistently associated with PRSs for insulin sensitivity and was not associated with a PRS for islet autoimmunity. A CFRD PRS comprising variants selected from these PRSs (with a false discovery rate < 0.1) and the genome-wide significant variants was associated with CFRD in a replication population. CONCLUSIONS CFRD and T2D have more etiologic and mechanistic overlap than previously known, aligning along pathways involving β-cell function rather than insulin sensitivity. Two CFRD risk loci are unrelated to T2D and may affect multiple aspects of CF. An 18-variant PRS stratifies risk of CFRD in an independent population.
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Affiliation(s)
- Melis A Aksit
- McKusick-Nathans Institute of the Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Rhonda G Pace
- Marsico Lung Institute/UNC CF Research Center, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | | | - Hua Ling
- Center for Inherited Disease Research, Johns Hopkins University, Baltimore, Maryland
| | - Johanna M Rommens
- The Hospital for Sick Children and the University of Toronto, Toronto, Ontario, Canada
| | - Pierre-Yves Boelle
- Sorbonne Université, INSERM, Institut Pierre Louis d’Épidémiologie et de Santé Publique, iPLESP, AP-HP, Hôpital Saint-Antoine, Paris, France
| | - Loic Guillot
- Sorbonne Université, INSERM, Centre de Recherche Saint-Antoine, CRSA, Paris, France
| | - Karen S Raraigh
- McKusick-Nathans Institute of the Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Elizabeth Pugh
- Center for Inherited Disease Research, Johns Hopkins University, Baltimore, Maryland
| | - Peng Zhang
- Center for Inherited Disease Research, Johns Hopkins University, Baltimore, Maryland
| | - Lisa J Strug
- The Hospital for Sick Children and the University of Toronto, Toronto, Ontario, Canada
| | | | - Michael R Knowles
- Marsico Lung Institute/UNC CF Research Center, School of Medicine, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina
| | - Garry R Cutting
- McKusick-Nathans Institute of the Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland
| | - Harriet Corvol
- Sorbonne Université, INSERM, Centre de Recherche Saint-Antoine, CRSA, Paris, France
| | - Scott M Blackman
- McKusick-Nathans Institute of the Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, Maryland
- Division of Pediatric Endocrinology, Johns Hopkins University School of Medicine, Baltimore, Maryland
- Correspondence and Reprint Requests: Scott M. Blackman, McKusick-Nathans Institute of the Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD 21205. E-mail:
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39
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Crawford DL, Schulte PM, Whitehead A, Oleksiak MF. Evolutionary Physiology and Genomics in the Highly Adaptable Killifish (
Fundulus heteroclitus
). Compr Physiol 2020; 10:637-671. [DOI: 10.1002/cphy.c190004] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/08/2023]
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40
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Padilla-Martínez F, Collin F, Kwasniewski M, Kretowski A. Systematic Review of Polygenic Risk Scores for Type 1 and Type 2 Diabetes. Int J Mol Sci 2020; 21:E1703. [PMID: 32131491 PMCID: PMC7084489 DOI: 10.3390/ijms21051703] [Citation(s) in RCA: 34] [Impact Index Per Article: 8.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/03/2020] [Revised: 02/28/2020] [Accepted: 02/28/2020] [Indexed: 02/07/2023] Open
Abstract
Recent studies have led to considerable advances in the identification of genetic variants associated with type 1 and type 2 diabetes. An approach for converting genetic data into a predictive measure of disease susceptibility is to add the risk effects of loci into a polygenic risk score. In order to summarize the recent findings, we conducted a systematic review of studies comparing the accuracy of polygenic risk scores developed during the last two decades. We selected 15 risk scores from three databases (Scopus, Web of Science and PubMed) enrolled in this systematic review. We identified three polygenic risk scores that discriminate between type 1 diabetes patients and healthy people, one that discriminate between type 1 and type 2 diabetes, two that discriminate between type 1 and monogenic diabetes and nine polygenic risk scores that discriminate between type 2 diabetes patients and healthy people. Prediction accuracy of polygenic risk scores was assessed by comparing the area under the curve. The actual benefits, potential obstacles and possible solutions for the implementation of polygenic risk scores in clinical practice were also discussed. Develop strategies to establish the clinical validity of polygenic risk scores by creating a framework for the interpretation of findings and their translation into actual evidence, are the way to demonstrate their utility in medical practice.
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Affiliation(s)
- Felipe Padilla-Martínez
- Centre for Bioinformatics and Data Analysis, Medical University of Bialystok, 15-276 Bialystok, Poland; (F.C.); (M.K.)
- Clinical Research Centre, Medical University of Bialystok, 15-276 Bialystok, Poland;
| | - Francois Collin
- Centre for Bioinformatics and Data Analysis, Medical University of Bialystok, 15-276 Bialystok, Poland; (F.C.); (M.K.)
| | - Miroslaw Kwasniewski
- Centre for Bioinformatics and Data Analysis, Medical University of Bialystok, 15-276 Bialystok, Poland; (F.C.); (M.K.)
| | - Adam Kretowski
- Clinical Research Centre, Medical University of Bialystok, 15-276 Bialystok, Poland;
- Department of Endocrinology, Diabetology and Internal Medicine, Medical University of Bialystok, 15-276 Bialystok, Poland
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41
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Semnani-Azad Z, Connelly PW, Johnston LW, Retnakaran R, Harris SB, Zinman B, Hanley AJ. The Macrophage Activation Marker Soluble CD163 is Longitudinally Associated With Insulin Sensitivity and β-cell Function. J Clin Endocrinol Metab 2020; 105:5611046. [PMID: 31677389 PMCID: PMC7112970 DOI: 10.1210/clinem/dgz166] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/16/2019] [Accepted: 10/30/2019] [Indexed: 02/01/2023]
Abstract
CONTEXT Chronic inflammation arising from adipose tissue macrophage (ATM) activation may be central in type 2 diabetes etiology. Our objective was to assess the longitudinal associations of soluble CD163 (sCD163), a novel biomarker of ATM activation, with insulin sensitivity, β-cell function, and dysglycemia in high-risk subjects. METHODS Adults at risk for type 2 diabetes in the Prospective Metabolism and Islet Cell Evaluation (PROMISE) study had 3 assessments over 6 years (n = 408). Levels of sCD163 were measured using fasting serum. Insulin sensitivity was assessed by HOMA2-%S and the Matsuda index (ISI). β-cell function was determined by insulinogenic index (IGI) over HOMA-IR and insulin secretion-sensitivity index-2 (ISSI-2). Incident dysglycemia was defined as the onset of impaired fasting glucose, impaired glucose tolerance, or type 2 diabetes. Generalized estimating equations (GEE) evaluated longitudinal associations of sCD163 with insulin sensitivity, β-cell function, and incident dysglycemia adjusting for demographic and lifestyle covariates. Areas under receiver-operating-characteristic curve (AROC) tested whether sCD163 improved dysglycemia prediction in a clinical model. RESULTS Longitudinal analyses showed significant inverse associations between sCD163 and insulin sensitivity (% difference per standard deviation increase of sCD163 for HOMA2-%S (β = -7.01; 95% CI, -12.26 to -1.44) and ISI (β = -7.60; 95% CI, -11.09 to -3.97) and β-cell function (ISSI-2 (β = -4.67; 95 %CI, -8.59 to -0.58) and IGI/HOMA-IR (β = -8.75; 95% CI, -15.42 to -1.56)). Increased sCD163 was associated with greater risk for incident dysglycemia (odds ratio = 1.04; 95% CI, 1.02-1.06; P < 0.001). Adding sCD163 data to a model with clinical variables improved prediction of incident dysglycemia (AROC=0.6731 vs 0.638; P < 0.05). CONCLUSIONS sCD163 was longitudinally associated with core disorders that precede the onset of type 2 diabetes.
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MESH Headings
- Adipose Tissue/cytology
- Adipose Tissue/immunology
- Adult
- Antigens, CD/blood
- Antigens, CD/metabolism
- Antigens, Differentiation, Myelomonocytic/blood
- Antigens, Differentiation, Myelomonocytic/metabolism
- Biomarkers/blood
- Biomarkers/metabolism
- Blood Glucose/analysis
- Blood Glucose/metabolism
- Diabetes Mellitus, Type 2/blood
- Diabetes Mellitus, Type 2/diagnosis
- Diabetes Mellitus, Type 2/immunology
- Diabetes Mellitus, Type 2/physiopathology
- Female
- Glucose Tolerance Test
- Humans
- Insulin Resistance/immunology
- Islets of Langerhans/physiopathology
- Longitudinal Studies
- Macrophage Activation
- Macrophages/immunology
- Macrophages/metabolism
- Male
- Middle Aged
- Prospective Studies
- Receptors, Cell Surface/blood
- Receptors, Cell Surface/metabolism
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Affiliation(s)
- Zhila Semnani-Azad
- Department of Nutritional Sciences, Faculty of Medicine, University of Toronto, Toronto, Canada
| | - Philip W Connelly
- Keenan Research Centre for Biomedical Science, St. Michael’s Hospital, Toronto, Canada
- Division of Endocrinology and Metabolism, University of Toronto, Toronto, Canada
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Canada
| | - Luke W Johnston
- Department of Public Health, Aarhus University, Aarhus, Denmark
| | - Ravi Retnakaran
- Division of Endocrinology and Metabolism, University of Toronto, Toronto, Canada
- Leadership Sinai Centre for Diabetes, Mount Sinai Hospital, Toronto, Canada
- Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, Canada
| | - Stewart B Harris
- Department of Family Medicine, Western University, London, Canada
| | - Bernard Zinman
- Division of Endocrinology and Metabolism, University of Toronto, Toronto, Canada
- Leadership Sinai Centre for Diabetes, Mount Sinai Hospital, Toronto, Canada
- Lunenfeld-Tanenbaum Research Institute, Mount Sinai Hospital, Toronto, Canada
| | - Anthony J Hanley
- Department of Nutritional Sciences, Faculty of Medicine, University of Toronto, Toronto, Canada
- Division of Endocrinology and Metabolism, University of Toronto, Toronto, Canada
- Leadership Sinai Centre for Diabetes, Mount Sinai Hospital, Toronto, Canada
- Correspondence and Reprint Requests: Anthony J. Hanley, PhD. Department of Nutritional Sciences, Faculty of Medicine, University of Toronto, Medical Sciences Building, 1 King’s College Circle, Toronto, ON, Canada M5S 1A8. Tel: 416-978-3616, E-mail: , ORCID ID: 0000-0002-6364-2444
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Udler MS, McCarthy MI, Florez JC, Mahajan A. Genetic Risk Scores for Diabetes Diagnosis and Precision Medicine. Endocr Rev 2019; 40:1500-1520. [PMID: 31322649 PMCID: PMC6760294 DOI: 10.1210/er.2019-00088] [Citation(s) in RCA: 150] [Impact Index Per Article: 30.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/26/2019] [Accepted: 07/08/2019] [Indexed: 12/13/2022]
Abstract
During the last decade, there have been substantial advances in the identification and characterization of DNA sequence variants associated with individual predisposition to type 1 and type 2 diabetes. As well as providing insights into the molecular, cellular, and physiological mechanisms involved in disease pathogenesis, these risk variants, when combined into a polygenic score, capture information on individual patterns of disease predisposition that have the potential to influence clinical management. In this review, we describe the various opportunities that polygenic scores provide: to predict diabetes risk, to support differential diagnosis, and to understand phenotypic and clinical heterogeneity. We also describe the challenges that will need to be overcome if this potential is to be fully realized.
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Affiliation(s)
- Miriam S Udler
- Diabetes Unit, Massachusetts General Hospital, Boston, Massachusetts
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts
- Department of Medicine, Harvard Medical School, Boston, Massachusetts
| | - Mark I McCarthy
- Oxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford, Headington, Oxford, United Kingdom
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
- Oxford NIHR Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, John Radcliffe Hospital, Oxford, United Kingdom
| | - Jose C Florez
- Diabetes Unit, Massachusetts General Hospital, Boston, Massachusetts
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts
- Programs in Metabolism and Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts
- Department of Medicine, Harvard Medical School, Boston, Massachusetts
| | - Anubha Mahajan
- Wellcome Centre for Human Genetics, University of Oxford, Oxford, United Kingdom
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Janssens ACJW. Validity of polygenic risk scores: are we measuring what we think we are? Hum Mol Genet 2019; 28:R143-R150. [PMID: 31504522 PMCID: PMC7013150 DOI: 10.1093/hmg/ddz205] [Citation(s) in RCA: 59] [Impact Index Per Article: 11.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2019] [Revised: 08/14/2019] [Accepted: 08/14/2019] [Indexed: 12/16/2022] Open
Abstract
Polygenic risk scores (PRSs) have become the standard for quantifying genetic liability in the prediction of disease risks. PRSs are generally constructed as weighted sum scores of risk alleles using effect sizes from genome-wide association studies as their weights. The construction of PRSs is being improved with more appropriate selection of independent single-nucleotide polymorphisms (SNPs) and optimized estimation of their weights but is rarely reflected upon from a theoretical perspective, focusing on the validity of the risk score. Borrowing from psychometrics, this paper discusses the validity of PRSs and introduces the three main types of validity that are considered in the evaluation of tests and measurements: construct, content, and criterion validity. This introduction is followed by a discussion of three topics that challenge the validity of PRS, namely, their claimed independence of clinical risk factors, the consequences of relaxing SNP inclusion thresholds and the selection of SNP weights. This discussion of the validity of PRS reminds us that we need to keep questioning if weighted sums of risk alleles are measuring what we think they are in the various scenarios in which PRSs are used and that we need to keep exploring alternative modeling strategies that might better reflect the underlying biological pathways.
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Affiliation(s)
- A Cecile J W Janssens
- Department of Epidemiology, Rollins School of Public Health, Emory University, 1518 Clifton Road NE, Atlanta, GA, USA
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Srinivasan S, Jablonski KA, Knowler WC, Dagogo-Jack S, E. Kahn S, Boyko EJ, Bray GA, Horton ES, Hivert MF, Goldberg R, Chen L, Mercader J, Harden M, Florez JC. A Polygenic Lipodystrophy Genetic Risk Score Characterizes Risk Independent of BMI in the Diabetes Prevention Program. J Endocr Soc 2019; 3:1663-1677. [PMID: 31428720 PMCID: PMC6694040 DOI: 10.1210/js.2019-00069] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/20/2019] [Accepted: 06/18/2019] [Indexed: 01/24/2023] Open
Abstract
CONTEXT There is substantial heterogeneity in insulin sensitivity, and genetics may suggest possible mechanisms by which common variants influence this trait. OBJECTIVES We aimed to evaluate an 11-variant polygenic lipodystrophy genetic risk score (GRS) for association with anthropometric, glycemic and metabolic traits in the Diabetes Prevention Program (DPP). In secondary analyses, we tested the association of the GRS with cardiovascular risk factors in the DPP. DESIGN In 2713 DPP participants, we evaluated a validated GRS of 11 common variants associated with fasting insulin-based measures of insulin sensitivity discovered through genome-wide association studies that cluster with a metabolic profile of lipodystrophy, conferring high metabolic risk despite low body mass index (BMI). RESULTS At baseline, a higher polygenic lipodystrophy GRS was associated with lower weight, BMI, and waist circumference measurements, but with worse insulin sensitivity index (ISI) values. Despite starting at a lower weight and BMI, a higher GRS was associated with less weight and BMI reduction at one year and less improvement in ISI after adjusting for baseline values but was not associated with diabetes incidence. A higher GRS was also associated with more atherogenic low-density lipoprotein peak-particle-density at baseline but was not associated with coronary artery calcium scores in the Diabetes Prevention Program Outcomes Study. CONCLUSIONS In the DPP, a higher polygenic lipodystrophy GRS for insulin resistance with lower BMI was associated with diminished improvement in insulin sensitivity and potential higher cardiovascular disease risk. This GRS helps characterize insulin resistance in a cohort of individuals at high risk for diabetes, independent of adiposity.
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Affiliation(s)
- Shylaja Srinivasan
- Division of Pediatric Endocrinology and Diabetes, Department of Pediatrics, University of California at San Francisco, San Francisco, California
| | - Kathleen A Jablonski
- Department of Epidemiology and Biostatistics, Milken Institute School of Public Health, George Washington University, Washington, DC
| | - William C Knowler
- Diabetes Epidemiology and Clinical Research Section, National Institute of Diabetes and Digestive and Kidney Diseases, Phoenix, Arizona
| | - Samuel Dagogo-Jack
- Division of Endocrinology, Diabetes and Metabolism, University of Tennessee Health Science Center, Memphis, Tennessee
| | - Steven E. Kahn
- Division of Metabolism, Endocrinology and Nutrition, Department of Medicine, VA Puget Sound Health Care System and University of Washington, Seattle, Washington
| | - Edward J Boyko
- Division of General Internal Medicine, University of Washington, Seattle, Washington
| | - George A Bray
- Division of Clinical Obesity and Metabolism, Pennington Biomedical Research Center, Baton Rouge, Louisiana
| | | | - Marie-France Hivert
- Diabetes Research Center, Diabetes Unit, Massachusetts General Hospital, Boston, Massachusetts
- Department of Medicine, Harvard Medical School, Boston, Massachusetts
- Department of Population Medicine, Harvard Pilgrim Health Care Institute, Boston, Massachusetts
| | - Ronald Goldberg
- Diabetes Research Institute, University of Miami Health System, Miami, Florida
| | - Ling Chen
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of Harvard & Massachusetts Institute of Technology, Cambridge, Massachusetts
| | - Josep Mercader
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of Harvard & Massachusetts Institute of Technology, Cambridge, Massachusetts
| | - Maegan Harden
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of Harvard & Massachusetts Institute of Technology, Cambridge, Massachusetts
| | - Jose C Florez
- Diabetes Research Center, Diabetes Unit, Massachusetts General Hospital, Boston, Massachusetts
- Department of Medicine, Harvard Medical School, Boston, Massachusetts
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, Massachusetts
- Programs in Metabolism and Medical & Population Genetics, Broad Institute of Harvard & Massachusetts Institute of Technology, Cambridge, Massachusetts
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Ingelsson E, McCarthy MI. Human Genetics of Obesity and Type 2 Diabetes Mellitus: Past, Present, and Future. CIRCULATION-GENOMIC AND PRECISION MEDICINE 2019; 11:e002090. [PMID: 29899044 DOI: 10.1161/circgen.118.002090] [Citation(s) in RCA: 43] [Impact Index Per Article: 8.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
Type 2 diabetes mellitus (T2D) and obesity already represent 2 of the most prominent risk factors for cardiovascular disease, and are destined to increase in importance given the global changes in lifestyle. Ten years have passed since the first round of genome-wide association studies for T2D and obesity. During this decade, we have witnessed remarkable developments in human genetics. We have graduated from the despair of candidate gene-based studies that generated few consistently replicated genotype-phenotype associations, to the excitement of an exponential harvest of loci robustly associated with medical outcomes through ever larger genome-wide association study meta-analyses. As well as discovering hundreds of loci, genome-wide association studies have provided transformative insights into the genetic architecture of T2D and other complex traits, highlighting the extent of polygenicity and the tiny effect sizes of many common risk alleles. Genome-wide association studies have also provided a critical starting point for discovering new biology relevant to these traits. Expectations are high that these discoveries will foster development of more effective strategies for intervention, through optimization of precision medicine approaches. In this article, we review current knowledge and provide suggestions for the next steps in genetic research for T2D and obesity. We focus on four areas relevant to precision medicine: genetic architecture, pharmacogenetics and other gene-environment interactions, mechanistic inference, and drug development. As we describe, the genetic architecture of complex traits has major implications for the prospects of precision medicine, rendering some anticipated approaches decidedly unrealistic. We highlight obstacles to the translation of human genetic findings into mechanism inference but are optimistic that, as these are overcome, there is untapped potential for novel drugs and more effective strategies for treating and preventing T2D and obesity.
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Affiliation(s)
- Erik Ingelsson
- Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, CA (E.I.) .,Stanford Cardiovascular Institute, Stanford University, CA (E.I.)
| | - Mark I McCarthy
- Wellcome Centre for Human Genetics (M.I.M.).,Oxford Centre for Diabetes, Endocrinology and Metabolism, Churchill Hospital, University of Oxford, United Kingdom (M.I.M.).,Oxford NIHR Biomedical Research Centre, Oxford University Hospitals Trust, United Kingdom (M.I.M.)
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46
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Merino J, Guasch-Ferré M, Ellervik C, Dashti HS, Sharp SJ, Wu P, Overvad K, Sarnowski C, Kuokkanen M, Lemaitre RN, Justice AE, Ericson U, Braun KVE, Mahendran Y, Frazier-Wood AC, Sun D, Chu AY, Tanaka T, Luan J, Hong J, Tjønneland A, Ding M, Lundqvist A, Mukamal K, Rohde R, Schulz CA, Franco OH, Grarup N, Chen YDI, Bazzano L, Franks PW, Buring JE, Langenberg C, Liu CT, Hansen T, Jensen MK, Sääksjärvi K, Psaty BM, Young KL, Hindy G, Sandholt CH, Ridker PM, Ordovas JM, Meigs JB, Pedersen O, Kraft P, Perola M, North KE, Orho-Melander M, Voortman T, Toft U, Rotter JI, Qi L, Forouhi NG, Mozaffarian D, Sørensen TIA, Stampfer MJ, Männistö S, Selvin E, Imamura F, Salomaa V, Hu FB, Wareham NJ, Dupuis J, Smith CE, Kilpeläinen TO, Chasman DI, Florez JC. Quality of dietary fat and genetic risk of type 2 diabetes: individual participant data meta-analysis. BMJ 2019; 366:l4292. [PMID: 31345923 PMCID: PMC6652797 DOI: 10.1136/bmj.l4292] [Citation(s) in RCA: 25] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
OBJECTIVE To investigate whether the genetic burden of type 2 diabetes modifies the association between the quality of dietary fat and the incidence of type 2 diabetes. DESIGN Individual participant data meta-analysis. DATA SOURCES Eligible prospective cohort studies were systematically sourced from studies published between January 1970 and February 2017 through electronic searches in major medical databases (Medline, Embase, and Scopus) and discussion with investigators. REVIEW METHODS Data from cohort studies or multicohort consortia with available genome-wide genetic data and information about the quality of dietary fat and the incidence of type 2 diabetes in participants of European descent was sought. Prospective cohorts that had accrued five or more years of follow-up were included. The type 2 diabetes genetic risk profile was characterized by a 68-variant polygenic risk score weighted by published effect sizes. Diet was recorded by using validated cohort-specific dietary assessment tools. Outcome measures were summary adjusted hazard ratios of incident type 2 diabetes for polygenic risk score, isocaloric replacement of carbohydrate (refined starch and sugars) with types of fat, and the interaction of types of fat with polygenic risk score. RESULTS Of 102 305 participants from 15 prospective cohort studies, 20 015 type 2 diabetes cases were documented after a median follow-up of 12 years (interquartile range 9.4-14.2). The hazard ratio of type 2 diabetes per increment of 10 risk alleles in the polygenic risk score was 1.64 (95% confidence interval 1.54 to 1.75, I2=7.1%, τ2=0.003). The increase of polyunsaturated fat and total omega 6 polyunsaturated fat intake in place of carbohydrate was associated with a lower risk of type 2 diabetes, with hazard ratios of 0.90 (0.82 to 0.98, I2=18.0%, τ2=0.006; per 5% of energy) and 0.99 (0.97 to 1.00, I2=58.8%, τ2=0.001; per increment of 1 g/d), respectively. Increasing monounsaturated fat in place of carbohydrate was associated with a higher risk of type 2 diabetes (hazard ratio 1.10, 95% confidence interval 1.01 to 1.19, I2=25.9%, τ2=0.006; per 5% of energy). Evidence of small study effects was detected for the overall association of polyunsaturated fat with the risk of type 2 diabetes, but not for the omega 6 polyunsaturated fat and monounsaturated fat associations. Significant interactions between dietary fat and polygenic risk score on the risk of type 2 diabetes (P>0.05 for interaction) were not observed. CONCLUSIONS These data indicate that genetic burden and the quality of dietary fat are each associated with the incidence of type 2 diabetes. The findings do not support tailoring recommendations on the quality of dietary fat to individual type 2 diabetes genetic risk profiles for the primary prevention of type 2 diabetes, and suggest that dietary fat is associated with the risk of type 2 diabetes across the spectrum of type 2 diabetes genetic risk.
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Abstract
PURPOSE OF REVIEW Genome-wide association studies have delineated the genetic architecture of type 2 diabetes. While functional studies to identify target transcripts are ongoing, new genetic knowledge can be translated directly to health applications. The review covers several translation directions but focuses on genomic polygenic scores for screening and prevention. RECENT FINDINGS Over 400 genomic variants associated with T2D and its related quantitative traits are now known. Genetic scores comprising dozens to millions of associated variants can predict incident T2D. However, measurement of body mass index is more efficient than genetic scores to detect T2D risk groups, and knowledge of T2D genetic risk alone seems insufficient to improve health. Genetically determined metabolic sub-phenotypes can be identified by clustering variants associated with physiological axes like insulin resistance. Genetic sub-phenotyping may be a way forward to identify specific individual phenotypes for prevention and treatment. Genomic polygenic scores for T2D can predict incident diabetes but may not be useful to improve health overall. Genetic detection of T2D sub-phenotypes could be useful to personalize screening and care.
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Affiliation(s)
- James B Meigs
- Harvard Medical School, Boston, USA.
- Division of General Internal Medicine, Massachusetts General Hospital, 100 Cambridge St 16th Floor, Boston, MA, 02114, USA.
- MGH Division of Clinical Research Clinical Effectiveness Research Unit, Boston, USA.
- Broad Institute, Cambridge, USA.
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Genome-wide Association Study of Change in Fasting Glucose over time in 13,807 non-diabetic European Ancestry Individuals. Sci Rep 2019; 9:9439. [PMID: 31263163 PMCID: PMC6602949 DOI: 10.1038/s41598-019-45823-7] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/10/2018] [Accepted: 05/29/2019] [Indexed: 01/13/2023] Open
Abstract
Type 2 diabetes (T2D) affects the health of millions of people worldwide. The identification of genetic determinants associated with changes in glycemia over time might illuminate biological features that precede the development of T2D. Here we conducted a genome-wide association study of longitudinal fasting glucose changes in up to 13,807 non-diabetic individuals of European descent from nine cohorts. Fasting glucose change over time was defined as the slope of the line defined by multiple fasting glucose measurements obtained over up to 14 years of observation. We tested for associations of genetic variants with inverse-normal transformed fasting glucose change over time adjusting for age at baseline, sex, and principal components of genetic variation. We found no genome-wide significant association (P < 5 × 10-8) with fasting glucose change over time. Seven loci previously associated with T2D, fasting glucose or HbA1c were nominally (P < 0.05) associated with fasting glucose change over time. Limited power influences unambiguous interpretation, but these data suggest that genetic effects on fasting glucose change over time are likely to be small. A public version of the data provides a genomic resource to combine with future studies to evaluate shared genetic links with T2D and other metabolic risk traits.
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49
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O'Connor S, Rudkowska I. Dietary Fatty Acids and the Metabolic Syndrome: A Personalized Nutrition Approach. ADVANCES IN FOOD AND NUTRITION RESEARCH 2019; 87:43-146. [PMID: 30678820 DOI: 10.1016/bs.afnr.2018.07.004] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/10/2023]
Abstract
Dietary fatty acids are present in a wide variety of foods and appear in different forms and lengths. The different fatty acids are known to have various effects on metabolic health. The metabolic syndrome (MetS) is a constellation of risk factors of chronic diseases. The etiology of the MetS is represented by a complex interplay of genetic and environmental factors. Dietary fatty acids can be important contributors of the evolution or in prevention of the MetS; however, great interindividual variability exists in the response to fatty acids. The identification of genetic variants interacting with fatty acids might explain this heterogeneity in metabolic responses. This chapter reviews the mechanisms underlying the interactions between the different components of the MetS, dietary fatty acids and genes. Challenges surrounding the implementation of personalized nutrition are also covered.
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Affiliation(s)
- Sarah O'Connor
- CHU de Québec Research Center, Université Laval, Québec, QC, Canada; Department of Kinesiology, Faculty of Medicine, Université Laval, Québec, QC, Canada
| | - Iwona Rudkowska
- CHU de Québec Research Center, Université Laval, Québec, QC, Canada; Department of Kinesiology, Faculty of Medicine, Université Laval, Québec, QC, Canada.
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50
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Graae AS, Hollensted M, Kloppenborg JT, Mahendran Y, Schnurr TM, Appel EVR, Rask J, Nielsen TRH, Johansen MØ, Linneberg A, Jørgensen ME, Grarup N, Kadarmideen HN, Holst B, Pedersen O, Holm JC, Hansen T. An adult-based insulin resistance genetic risk score associates with insulin resistance, metabolic traits and altered fat distribution in Danish children and adolescents who are overweight or obese. Diabetologia 2018; 61:1769-1779. [PMID: 29855666 PMCID: PMC6061152 DOI: 10.1007/s00125-018-4640-0] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/20/2017] [Accepted: 04/11/2018] [Indexed: 01/22/2023]
Abstract
AIMS/HYPOTHESIS A genetic risk score (GRS) consisting of 53 insulin resistance variants (GRS53) was recently demonstrated to associate with insulin resistance in adults. We speculated that the GRS53 might already associate with insulin resistance during childhood, and we therefore aimed to investigate this in populations of Danish children and adolescents. Furthermore, we aimed to address whether the GRS associates with components of the metabolic syndrome and altered body composition in children and adolescents. METHODS We examined a total of 689 children and adolescents who were overweight or obese and 675 children and adolescents from a population-based study. Anthropometric data, dual-energy x-ray absorptiometry scans, BP, fasting plasma glucose, fasting serum insulin and fasting plasma lipid measurements were obtained, and HOMA-IR was calculated. The GRS53 was examined for association with metabolic traits in children by linear regressions using an additive genetic model. RESULTS In overweight/obese children and adolescents, the GRS53 associated with higher HOMA-IR (β = 0.109 ± 0.050 (SE); p = 2.73 × 10-2), fasting plasma glucose (β = 0.010 ± 0.005 mmol/l; p = 2.51 × 10-2) and systolic BP SD score (β = 0.026 ± 0.012; p = 3.32 × 10-2) as well as lower HDL-cholesterol (β = -0.008 ± 0.003 mmol/l; p = 1.23 × 10-3), total fat-mass percentage (β = -0.143 ± 0.054%; p = 9.15 × 10-3) and fat-mass percentage in the legs (β = -0.197 ± 0.055%; p = 4.09 × 10-4). In the population-based sample of children, the GRS53 only associated with lower HDL-cholesterol concentrations (β = -0.007 ± 0.003 mmol/l; p = 1.79 × 10-2). CONCLUSIONS/INTERPRETATION An adult-based GRS comprising 53 insulin resistance susceptibility SNPs associates with insulin resistance, markers of the metabolic syndrome and altered fat distribution in a sample of Danish children and adolescents who were overweight or obese.
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Affiliation(s)
- Anne-Sofie Graae
- Section for Metabolic Receptology, Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Mette Hollensted
- Section of Metabolic Genetics, Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Blegdamsvej 3B, DK-2200, Copenhagen N, Denmark
| | - Julie T Kloppenborg
- The Children's Obesity Clinic, Department of Pediatrics, Copenhagen University Hospital Holbæk, Holbæk, Denmark
| | - Yuvaraj Mahendran
- Section of Metabolic Genetics, Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Blegdamsvej 3B, DK-2200, Copenhagen N, Denmark
| | - Theresia M Schnurr
- Section of Metabolic Genetics, Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Blegdamsvej 3B, DK-2200, Copenhagen N, Denmark
| | - Emil Vincent R Appel
- Section of Metabolic Genetics, Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Blegdamsvej 3B, DK-2200, Copenhagen N, Denmark
| | - Johanne Rask
- The Children's Obesity Clinic, Department of Pediatrics, Copenhagen University Hospital Holbæk, Holbæk, Denmark
| | - Tenna R H Nielsen
- The Children's Obesity Clinic, Department of Pediatrics, Copenhagen University Hospital Holbæk, Holbæk, Denmark
| | - Mia Ø Johansen
- The Children's Obesity Clinic, Department of Pediatrics, Copenhagen University Hospital Holbæk, Holbæk, Denmark
| | - Allan Linneberg
- Center for Clinical Research and Disease Prevention, Bispebjerg and Frederiksberg Hospital, The Capital Region, Copenhagen, Denmark
- Department of Clinical Experimental Research, Rigshospitalet, Glostrup, Denmark
- Department of Clinical Medicine, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Marit E Jørgensen
- Steno Diabetes Center, Gentofte, Denmark
- Faculty of Health Sciences, University of Southern Denmark, Odense, Denmark
| | - Niels Grarup
- Section of Metabolic Genetics, Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Blegdamsvej 3B, DK-2200, Copenhagen N, Denmark
| | - Haja N Kadarmideen
- Section of Systems Genomics, Department of Bio and Health Informatics, Technical University of Denmark, Kongens Lyngby, Denmark
| | - Birgitte Holst
- Section for Metabolic Receptology, Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark
| | - Oluf Pedersen
- Section of Metabolic Genetics, Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Blegdamsvej 3B, DK-2200, Copenhagen N, Denmark
| | - Jens-Christian Holm
- The Children's Obesity Clinic, Department of Pediatrics, Copenhagen University Hospital Holbæk, Holbæk, Denmark
| | - Torben Hansen
- Section of Metabolic Genetics, Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medical Sciences, University of Copenhagen, Blegdamsvej 3B, DK-2200, Copenhagen N, Denmark.
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