Metabolic and Genetic Markers Improve Prediction of Incident Type 2 Diabetes: A Nested Case-Control Study in Chinese
Open accessAbstract
Context: It is essential to improve the current predictive ability for type 2 diabetes (T2D) risk. Objective: We aimed to identify novel metabolic markers for future T2D in Chinese individuals of Han ethnicity and to determine whether the combined effect of metabolic and genetic markers improves the accuracy of prediction models containing clinical factors. Methods: A nested case-control study containing 220 incident T2D patients and 220 age- and sex- matched controls from normoglycemic Chinese individuals of Han ethnicity was conducted within the Wuxi Non-Communicable Disease cohort with a 12-year follow-up. Metabolic profiling detection was performed by high-performance liquid chromatography‒mass spectrometry (HPLC-MS) by an untargeted strategy and 20 single nucleotide polymorphisms (SNPs) associated with T2D were genotyped using the Iplex Sequenom MassARRAY platform. Machine learning methods were used to identify metabolites associated with future T2D risk. Results: We found that abnormal levels of 5 metabolites were associated with increased risk of future T2D: riboflavin, cnidioside A, 2-methoxy-5-(1H-1, 2, 4-triazol-5-yl)- 4-(trifluoromethyl) pyridine, 7-methylxanthine, and mestranol. The genetic risk score (GRS) based on 20 SNPs was significantly associated with T2D risk (OR = 1.35; 95% CI, 1.08-1.70 per SD). The area under the receiver operating characteristic curve (AUC) was greater for the model containing metabolites, GRS, and clinical traits than for the model containing clinical traits only (0.960 vs 0.798, P = 7.91 × 10-16). Conclusion: In individuals with normal fasting glucose levels, abnormal levels of 5 metabolites were associated with future T2D. The combination of newly discovered metabolic markers and genetic markers could improve the prediction of incident T2D.
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One multimodal approach combining genomic, metabolomic, and clinical risk factors was superior in predicting T2DM (AUC of 0.96) compared to a genomics-only model (AUC of 0.586) and clinical-only model (AUC of 0.798).
“For example, one multimodal approach revealed that a model combining genomic, metabolomic, and clinical risk factors was superior in predicting T2DM (AUC of 0.96) compared to a genomics-only model (AUC of 0.586) and clinical-only model (AUC of 0.798)6.”
From AI-based diabetes care: risk prediction models and implementation concerns (2024) · cites this paper as [6]
The Results report an AUC of 0.960 for the model combining clinical characteristics, a metabolite score, and a genetic risk score, versus 0.798 for the clinical-only model. Figure 2 names a genetic model, but our copy does not give it an AUC of 0.586, so that numerical comparison is not supported. (LLM, conf=0.86)
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Self-citation
- Authors
- Liu, J., Wang, L., Qian, Y., Shen, Q., Yang, M., Dong, Y., Chen, H., Yang, Z., Liu, Y., Cui, X., Ma, H., & Jin, G.
- Affiliations
- Nanjing Medical University (CN)
- Department of Health Promotion & Chronic Non-Communicable Disease Control, Wuxi Center for Disease Control and Prevention (The Affiliated Wuxi Center for Disease Control and Prevention of Nanjing Medical University ), Wuxi 214023, Jiangsu , China
- Corresponding author
- Qian, Yun
- ORCID
- Venue
- The Journal of Clinical Endocrinology & Metabolism
- Publisher
- The Endocrine Society
- Published
- 2022-08-17
- Volume
- 107
- Issue
- 11
- Pages
- 3120-3127
- ISSN
- 0021-972X (ISSN-L)
- 1945-7197
- PMC
- PMC9681609
- OpenAlex
- W4292425648
- Cited by
- 12
- References
- 54
- Citations by year
- 2026: 1 · 2025: 4 · 2024: 4 · 2023: 2 · 2022: 1
- FWCI
- 0.90
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- Locations
- The Journal of Clinical Endocrinology & Metabolism (publishedVersion, cc-by-nc-nd)
- PubMed Central (publishedVersion, cc-by-nc-nd)
- PubMed (publishedVersion)
- Funders
- Medical Key Discipline Program of Wuxi Health Commission (LCZX2021006)
- High-level Health Talents Research Fund (LGY2018014, BJ2020096)
- National Natural Science Foundation of China (81072379)
- uxi science and technology development fund project (WX18IIAN038)
- Wuxi Health Committee Key Program (SW001)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Metabolomics and Mass Spectrometry Studies
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes, Cardiovascular Risks, and Lipoproteins
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Genetics › Genetic Associations and Epidemiology
- Keywords
- type 2 diabetes, metabolic biomarkers, polygenic risk score, single nucleotide polymorphisms, metabolomics, untargeted metabolomics, riboflavin, 7-methylxanthine, mestranol, Han Chinese
- MeSH terms
- China, Diabetes Mellitus, Type 2, Genetic Markers, Humans, Case-Control Studies, Asian People
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