Diabetes & Metabolism Journal

Original Articles

Genetics
Evaluation of Sex-Stratified Polygenic Risk Scores for Type 2 Diabetes Mellitus and Glycemic Traits in the Framingham Heart Study: Ningyuan Wang, Yixin Zhang, Philip Schroeder, Alicia Huerta-Chagoya, Ravi Mandla, James B. Meigs, Alisa K. Manning, Ching-Ti Liu, Josée Dupuis, Josep M. Mercader; Received June 25, 2025 Accepted October 14, 2025 Published online December 9, 2025; DOI: https://doi.org/10.4093/dmj.2025.0557 [Epub ahead of print]

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Abstract PDF Supplementary Material PubReader ePub: Background
Diabetes is a multifactorial disease with significant genetic predisposition. Polygenic risk scores (PRS) have been developed to estimate an individual’s genetic risk of a disease. Traditionally, PRS utilize sex-combined genome-wide association studies (GWAS) due to the limited availability of sex-stratified summary statistics. This study explores sex-dimorphic genetic effects and evaluates the potential benefits of incorporating sex-stratified effects in PRS for type 2 diabetes mellitus (T2DM) and glycemic traits by comparing PRS performance derived from sex-combined versus sex-stratified GWAS.
Methods
We performed a sex-heterogeneity test across sex-specific GWAS and identified nine signals with sex-dimorphic effects for T2DM. PRS[sex-combined] and PRS[sex-stratified] were developed using sex-combined and sex-stratified GWAS results for T2DM (41,444 cases and 354,539 controls), fasting glucose (n=120,595) and fasting insulin (n=98,210). We evaluated these PRS models in 8,379 participants (1,303 cases and 7,076 controls) from the Framingham Heart Study not included in the PRS derivation.
Results
Our findings suggest that sex-combined PRS currently offer better predictive performance for T2DM and glycemic traits.
Conclusion
These results highlight the need for larger sex-stratified studies and the optimization of sex-stratified risk models for clinical practice.

Genetics
SLC30A8 Rare Variant Modify Contribution of Common Genetic and Lifestyle Factors toward Type 2 Diabetes Mellitus: Hye-Mi Jang, Mi Yeong Hwang, Yi Seul Park, Bong-Jo Kim, Young Jin Kim; Received December 20, 2024 Accepted May 6, 2025 Published online August 13, 2025; DOI: https://doi.org/10.4093/dmj.2024.0830 [Epub ahead of print]

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Background
This study aimed to investigate the modifying effects of rare genetic variants on the risk of type 2 diabetes mellitus (T2DM) in the context of common genetic and lifestyle factors.
Methods
We conducted a comprehensive analysis of genetic and lifestyle factors associated with T2DM in a cohort of 146,284 Korean individuals. Among them, 4,603 individuals developed T2DM during the follow-up period of up to 17 years. We calculated a polygenic risk score (PRS) for T2DM and identified carriers of the rare allele I349F at SLC30A8. A Healthy Lifestyle Score (HLS) was also derived from physical activity, obesity, smoking, diet, and sodium intake levels. Using Cox proportional hazards models, we analyzed how PRS, HLS, and I349F influenced T2DM incidence.
Results
Results showed that high PRS and poor lifestyle were associated with increased risk. Remarkably, I349F carriers exhibited a lower T2DM prevalence (5.7% compared to 11.7% in non-carriers) and reduced the impact of high PRS from 23.18% to 12.70%. This trend was consistent across different HLS categories, with I349F carriers displaying a lower risk of T2DM.
Conclusion
The integration of common and rare genetic variants with lifestyle factors enhanced T2DM predictability in the Korean population. Our findings highlight the critical role of rare genetic variants in risk assessments and suggest that standard PRS and HLS metrics alone may be inadequate for predicting T2DM risk among carriers of such variants.

Citations

Citations to this article as recorded by

Differential contributions of cardiovascular health-related lifestyle factors to epigenetic ageing: implications for healthy longevity
Da-eun Lee, Yi Seul Park, Hye-Mi Jang, Bong-Jo Kim, Young Jin Kim, Sung-il Cho, Kyeezu Kim
BMC Medicine.2025;[Epub] CrossRef

Metabolic Risk/Epidemiology
Predictive Models for Type 2 Diabetes Mellitus in Han Chinese with Insights into Cross-Population Applicability and Demographic Specific Risk Factors: Ying-Erh Chen, Djeane Debora Onthoni, Shao-Yuan Chuang, Guo-Hung Li, Yong-Sheng Zhuang, Hung-Yi Chiou, Wayne Huey-Herng Sheu, Ren-Hua Chung; Diabetes Metab J. 2025;49(6):1272-1286. Published online May 21, 2025; DOI: https://doi.org/10.4093/dmj.2024.0319

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Background
The rising global incidence of type 2 diabetes mellitus (T2DM) underscores the need for predictive models that enhance early detection and prevention across diverse populations. This study aimed to identify predictors of incident T2DM within a Han Chinese population, assess their impact across various age and sex demographics, and explore their applicability to European populations.
Methods
Using data from about 65,000 participants in the Taiwan Biobank (TWB), we developed a predictive model, achieving an area under the receiver operating characteristic curve of 90.58%. Key predictors were identified through LASSO regression within the TWB cohort and validated using over 4 million records from Taiwan’s Adult Preventive Healthcare Services (APHS) program and the UK Biobank (UKB).
Results
Our analysis highlighted 13 significant predictors, including established factors like glycosylated hemoglobin (HbA1c) and blood glucose levels, and less conventionally considered variables such as peak expiratory flow. Notable differences in the effects of HbA1c levels and polygenic risk scores between the TWB and UKB cohorts were observed. Additionally, age and sex-specific impacts of these predictors, detailed through APHS data, revealed significant variances; for instance, waist circumference and diagnosed mixed hyperlipidemia showed greater impacts in younger females than in males, while effects remained uniform across male age groups.
Conclusion
Our findings offer novel insights into the diagnosis and management of diabetes for the Han Chinese and potentially for broader East Asian populations, highlighting the importance of ethnic and demographic diversity in developing predictive models for early detection and personalized intervention strategies.

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The Relationship Between High-Density Lipoprotein (HDL) and Glycated Hemoglobin (HbA1C) in Type 2 Diabetes Mellitus Patients: Implications for Cardiovascular Risk
Setyoadi Setyoadi, Dina Dewi Sartika Lestari Ismail, Annisa Wuri Kartika, Dewi Purnama Sari, Angel Dwi Septian, Adelina Stefanie Lallo, Rara Kurniasari
Journal of Rural Community Nursing Practice.2025; 3(2): 234. CrossRef

Genetics
Identification and Potential Clinical Utility of Common Genetic Variants in Gestational Diabetes among Chinese Pregnant Women: Claudia Ha-ting Tam, Ying Wang, Chi Chiu Wang, Lai Yuk Yuen, Cadmon King-poo Lim, Junhong Leng, Ling Wu, Alex Chi-wai Ng, Yong Hou, Kit Ying Tsoi, Hui Wang, Risa Ozaki, Albert Martin Li, Qingqing Wang, Juliana Chung-ngor Chan, Yan Chou Ye, Wing Hung Tam, Xilin Yang, Ronald Ching-wan Ma; Diabetes Metab J. 2025;49(1):128-143. Published online September 20, 2024; DOI: https://doi.org/10.4093/dmj.2024.0139

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Background
The genetic basis for hyperglycaemia in pregnancy remain unclear. This study aimed to uncover the genetic determinants of gestational diabetes mellitus (GDM) and investigate their applications.
Methods
We performed a meta-analysis of genome-wide association studies (GWAS) for GDM in Chinese women (464 cases and 1,217 controls), followed by de novo replications in an independent Chinese cohort (564 cases and 572 controls) and in silico replication in European (12,332 cases and 131,109 controls) and multi-ethnic populations (5,485 cases and 347,856 controls). A polygenic risk score (PRS) was derived based on the identified variants.
Results
Using the genome-wide scan and candidate gene approaches, we identified four susceptibility loci for GDM. These included three previously reported loci for GDM and type 2 diabetes mellitus (T2DM) at MTNR1B (rs7945617, odds ratio [OR], 1.64; 95% confidence interval [CI], 1.38 to 1.96), CDKAL1 (rs7754840, OR, 1.33; 95% CI, 1.13 to 1.58), and INS-IGF2-KCNQ1 (rs2237897, OR, 1.48; 95% CI, 1.23 to 1.79), as well as a novel genome-wide significant locus near TBR1-SLC4A10 (rs117781972, OR, 2.05; 95% CI, 1.61 to 2.62; P_meta=7.6×10^-9), which has not been previously reported in GWAS for T2DM or glycaemic traits. Moreover, we found that women with a high PRS (top quintile) had over threefold (95% CI, 2.30 to 4.09; P_meta=3.1×10^-14) and 71% (95% CI, 1.08 to 2.71; P=0.0220) higher risk for GDM and abnormal glucose tolerance post-pregnancy, respectively, compared to other individuals.
Conclusion
Our results indicate that the genetic architecture of glucose metabolism exhibits both similarities and differences between the pregnant and non-pregnant states. Integrating genetic information can facilitate identification of pregnant women at a higher risk of developing GDM or later diabetes.

Citations

Citations to this article as recorded by

GWAS in Gestational Diabetes Mellitus: Research Advances
Dikun Zhou, Z. Shi, A.H. Hashash, Z.H. Khan
BIO Web of Conferences.2025; 174: 01018. CrossRef
Advancing Early Prediction of Gestational Diabetes Mellitus with Circular RNA Biomarkers
Joon Ho Moon, Sung Hee Choi
Diabetes & Metabolism Journal.2025; 49(3): 403. CrossRef
Association between maternal glucose levels in pregnancy and offspring’s metabolism and adiposity: an 18-year birth cohort study
Yuzhi Deng, Hanbin Wu, Noel Y. H. Ng, Claudia H. T. Tam, Atta Y. T. Tsang, Michael H. M. Chan, Kenneth Ka Hei Lo, Chi Chiu Wang, Wing Hung Tam, Ronald C. W. Ma
Diabetologia.2025; 68(10): 2205. CrossRef
DNA Methylation Biomarkers Predict Offspring Metabolic Risk From Mothers With Hyperglycemia in Pregnancy
Johnny Assaf, Ishant Khurana, Ram Abou Zaki, Claudia H.T. Tam, Ilana Correa, Scott Maxwell, Julie Kinnberg, Malou Christiansen, Caroline Frørup, Heung Man Lee, Harikrishnan Kaipananickal, Jun Okabe, Safiya Naina Marikar, Kwun Kiu Wong, Cadmon K.P. Lim, La
Diabetes.2025; 74(9): 1695. CrossRef
Polygenic Risk Score Associated with Gestational Diabetes Mellitus in an AmericanIndian Population
Karrah Peterson, Camille E. Powe, Quan Sun, Crystal Azure, Tia Azure, Hailey Davis, Kennedy Gourneau, Shyanna LaRocque, Craig Poitra, Sabra Poitra, Shayden Standish, Tyler J. Parisien, Kelsey J. Morin, Lyle G. Best
Journal of Personalized Medicine.2025; 15(9): 395. CrossRef
Apolipoprotein C1 -317H1/H2 and the rs4420638 genetic variations and risk of gestational diabetes mellitus in Chinese women: a case-control study
Wandi Ma, Linbo Guan, Xinghui Liu, Yujie Wu, Zhengting Zhu, Yuwen Guo, Ping Fan, Huai Bai
Frontiers in Endocrinology.2025;[Epub] CrossRef
Hexokinase Domain Containing 1 (HKDC1) Gene Variants and Their Association With Gestational Diabetes Mellitus: A Mini-Review
Sekar Kanthimathi, Polina Popova, Viswanathan Mohan, Wesley Hannah, Ranjit Mohan Anjana, Venkatesan Radha
Journal of Diabetology.2024; 15(4): 354. CrossRef

Epidemiology
Performance of the Achutha Menon Centre Diabetes Risk Score in Identifying Prevalent Diabetes in Tamil Nadu, India: Anu Mary Oommen, Vinod Joseph Abraham, Thirunavukkarasu Sathish, V. Jacob Jose, Kuryan George; Diabetes Metab J. 2017;41(5):386-392. Published online August 25, 2017; DOI: https://doi.org/10.4093/dmj.2017.41.5.386

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Background

The Achutha Menon Centre Diabetes Risk Score (AMCDRS), which was developed in rural Kerala State, South India, had not previously been externally validated. We examined the performance of the AMCDRS in urban and rural areas in the district of Vellore in the South Indian state of Tamil Nadu, and compared it with other diabetes risk scores developed from India.

Methods

We used the data from 4,896 participants (30 to 64 years) of a cross-sectional study conducted in Vellore (2010 to 2012), to calculate the AMCDRS scores using age, family history, and waist circumference. Sensitivity, specificity, positive predictive value (PPV), and negative predictive values (NPV), and the area under the receiver operating characteristic curve (AROC) were calculated for undiagnosed and total diabetes.

Results

Of the 4,896 individuals surveyed, 274 (5.6%) had undiagnosed diabetes and 759 (15.5%) had total diabetes. The AMCDRS, with an optimum cut-point of ≥4, identified 45.0% for further testing with 59.5% sensitivity, 60.5% specificity, 9.1% PPV, 95.8% NPV, and an AROC of 0.639 (95% confidence interval [CI], 0.608 to 0.670) for undiagnosed diabetes. The corresponding figures for total diabetes were 75.1%, 60.5%, 25.9%, 93.0%, and 0.731 (95% CI, 0.713 to 0.750), respectively. The AROC for the AMCDRS was not significantly different from that of the Indian Diabetes Risk Score, the Ramachandran or the Chaturvedi risk scores for total diabetes, but was significantly lower than the AROC of the Chaturvedi score for undiagnosed diabetes.

Conclusion

The AMCDRS is a simple diabetes risk score that can be used to screen for undiagnosed and total diabetes in low-resource primary care settings in India. However, it probably requires recalibration to improve its performance for undiagnosed diabetes.

Citations

Citations to this article as recorded by

Predictive Ability of the Indian Diabetes Risk Score in the Evaluation of Diabetes Risk among Urban Adults of Raipur: A Cross-sectional Study
Ekta Krishna, Anjali Pal, Abhiruchi Galhotra, Arvind Shukla, Madhusudan Prasad Singh, Vijay Kumar
Indian Journal of Community Medicine.2025; 50(6): 957. CrossRef
Evaluation of Madras Diabetes Research Foundation-Indian Diabetes Risk Score in detecting undiagnosed diabetes in the Indian population: Results from the Indian Council of Medical Research-INdia DIABetes population-based study (INDIAB-15)
Mohan Deepa, Nirmal Elangovan, Ulagamathesan Venkatesan, Hiranya Kumar Das, Lobsang Jampa, Prabha Adhikari, Prashant P. Joshi, Richard O. Budnah, Vizolie Suokhrie, Mary John, Karma Jigme Tobgay, Radhakrishnan Subashini, Rajendra Pradeepa, Ranjit Mohan Anj
Indian Journal of Medical Research.2023; 157(4): 239. CrossRef
Evaluating the Performance of the Indian Diabetes Risk Score in Different Ethnic Groups
Manjula D. Nugawela, Sobha Sivaprasad, Viswanathan Mohan, Ramachandran Rajalakshmi, Gopalakrishnan Netuveli
Diabetes Technology & Therapeutics.2020; 22(4): 285. CrossRef
Targeted screening for prediabetes and undiagnosed diabetes in a community setting in India
Thirunavukkarasu Sathish, Jonathan E. Shaw, Robyn J. Tapp, Rory Wolfe, Kavumpurathu R. Thankappan, Sajitha Balachandran, Brian Oldenburg
Diabetes & Metabolic Syndrome: Clinical Research & Reviews.2019; 13(3): 1785. CrossRef
OBSERVATIONAL STUDY EVALUATING ASSOCIATION OF TYPE 2 DIABETES MELLITUS AND THYROID DYSFUNCTION
Elizabeth Jacob, Vivek Koshy Varghese, Tittu Oommen
Journal of Evidence Based Medicine and Healthcare.2018; 5(31): 2285. CrossRef

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