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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
  • 2,925 View
  • 158 Download
  • 1 Crossref
AbstractAbstract PDFSupplementary MaterialPubReader   ePub   
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; Pmeta=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; Pmeta=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  
  • 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
  • 4,099 View
  • 36 Download
  • 3 Web of Science
  • 4 Crossref
AbstractAbstract PDFPubReader   
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  
  • 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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