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Cited by 82Genetic Associations and EpidemiologyNutrition, Genetics, and DiseaseDiabetes and associated disorders

The Genetic Epidemiology of Type 2 Diabetes: Opportunities for Health Translation

Open access
Meigs, James B.·2019·Current Diabetes Reports
DOI 10.1007/s11892-019-1173-yPMC PMC8059416
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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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  • Risk factors for type 2 diabetes include overweight and obesity, physical inactivity, age, family history, race and ethnicity, history of gestational diabetes, and presence of other conditions (eg, hypertension, dyslipidemia, cardiovascular disease).

    “Risk factors for type 2 diabetes include overweight and obesity, physical inactivity, age, family history, race and ethnicity, history of gestational diabetes, and presence of other conditions (eg, hypertension, dyslipidemia, cardiovascular disease).9–14”

    From Diagnosis and Treatment of Type 2 Diabetes in Adults (2025) · cites this paper as [13]

    The “Genomic Risk Scores for Prediction and Personalization” section describes a type 2 diabetes prediction model using age, parental diabetes, BMI, blood pressure, HDL cholesterol, and triglycerides. The “Genomic Polygenic Prediction” section also links obesity to elevated risk. Our copy does not establish physical inactivity, gestational diabetes, or cardiovascular disease as risk factors, so it does not support the full list. (LLM, conf=0.87)

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Research works printed but not linked: 32

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Cited by

Sorted by citations

Authors
Meigs, J. B.
Affiliations
Broad Institute (US)
Corresponding author
Meigs, James B.
ORCID
Meigs, James B.
Venue
Current Diabetes Reports
Publisher
Springer Science and Business Media LLC
Published
2019-07-22
Volume
19
Issue
8
Pages
62-62
ISSN
  • 1534-4827 (ISSN-L)
  • 1539-0829
DOI
10.1007/s11892-019-1173-y
PMC
PMC8059416
OpenAlex
W2963636941
Cited by
82
References
6
Citations by year
2026: 11 · 2025: 11 · 2024: 15 · 2023: 18 · 2022: 13 · 2021: 6 · 2020: 5 · 2019: 2 · 2018: 1
FWCI
0.88
Type
journal article
Language
en
Open access
Licence
Locations
  • Current Diabetes Reports (publishedVersion)
  • PubMed (publishedVersion)
  • PubMed Central (acceptedVersion)
Funders
National Institute of Diabetes and Digestive and Kidney Diseases (U01DK078616, UM1DK078616)
Topics
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Nutrition, Genetics, and Disease
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Diabetes and associated disorders
Keywords
type 2 diabetes, genetic epidemiology, polygenic risk score, genome-wide association study, diabetes risk prediction, diabetes screening, diabetes prevention, body mass index, insulin resistance, personalized medicine
MeSH terms
Diabetes Mellitus, Type 2, Humans, Insulin Resistance, Molecular Epidemiology, Genetic Predisposition to Disease, Polymorphism, Single Nucleotide, Genome-Wide Association Study

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