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  1. Venkataraman K, Kao SL, Thai AC, Salim A, Lee JJ, Heng D, et al.
    Diabet Med, 2012 Jul;29(7):911-7.
    PMID: 22283416 DOI: 10.1111/j.1464-5491.2012.03599.x
    AIMS: To study whether HbA(1c) , and its relationship with fasting plasma glucose, was significantly different among Chinese, Malays and Indians in Singapore.

    METHODS: A sample of 3895 individuals without known diabetes underwent detailed interview and health examination, including anthropometric and biochemical evaluation, between 2004 and 2007. Pearson's correlation, analysis of variance and multiple linear regression analyses were used to examine the influence of ethnicity on HbA(1c) .

    RESULTS: As fasting plasma glucose increased, HbA(1c) increased more in Malays and Indians compared with Chinese after adjustment for age, gender, waist circumference, serum cholesterol, serum triglyceride and homeostasis model assessment of insulin resistance (P-interaction < 0.001). This translates to an HbA(1c) difference of 1.1 mmol/mol (0.1%, Indians vs. Chinese), and 0.9 mmol/mol (0.08%, Malays vs. Chinese) at fasting plasma glucose 5.6 mmol/l (the American Diabetes Association criterion for impaired fasting glycaemia); and 2.1 mmol/mol (0.19%, Indians vs. Chinese) and 2.6 mmol/mol (0.24%, Malays vs. Chinese) at fasting plasma glucose 7.0 mmol/l, the diagnostic criterion for diabetes mellitus.

    CONCLUSIONS: Using HbA(1c) in place of fasting plasma glucose will reclassify different proportions of the population in different ethnic groups. This may have implications in interpretation of HbA(1c) results across ethnic groups and the use of HbA(1c) for diagnosing diabetes mellitus.

  2. Chai JF, Kao SL, Wang C, Lim VJ, Khor IW, Dou J, et al.
    J Clin Endocrinol Metab, 2020 Dec 01;105(12).
    PMID: 32936915 DOI: 10.1210/clinem/dgaa658
    CONTEXT: Glycated hemoglobin A1c (HbA1c) level is used to screen and diagnose diabetes. Genetic determinants of HbA1c can vary across populations and many of the genetic variants influencing HbA1c level were specific to populations.

    OBJECTIVE: To discover genetic variants associated with HbA1c level in nondiabetic Malay individuals.

    DESIGN AND PARTICIPANTS: We conducted a genome-wide association study (GWAS) analysis for HbA1c using 2 Malay studies, the Singapore Malay Eye Study (SiMES, N = 1721 on GWAS array) and the Living Biobank study (N = 983 on GWAS array and whole-exome sequenced). We built a Malay-specific reference panel to impute ethnic-specific variants and validate the associations with HbA1c at ethnic-specific variants.

    RESULTS: Meta-analysis of the 1000 Genomes imputed array data identified 4 loci at genome-wide significance (P 

  3. Li J, Guan Z, Wang J, Cheung CY, Zheng Y, Lim LL, et al.
    Nat Med, 2024 Jul 19.
    PMID: 39030266 DOI: 10.1038/s41591-024-03139-8
    Primary diabetes care and diabetic retinopathy (DR) screening persist as major public health challenges due to a shortage of trained primary care physicians (PCPs), particularly in low-resource settings. Here, to bridge the gaps, we developed an integrated image-language system (DeepDR-LLM), combining a large language model (LLM module) and image-based deep learning (DeepDR-Transformer), to provide individualized diabetes management recommendations to PCPs. In a retrospective evaluation, the LLM module demonstrated comparable performance to PCPs and endocrinology residents when tested in English and outperformed PCPs and had comparable performance to endocrinology residents in Chinese. For identifying referable DR, the average PCP's accuracy was 81.0% unassisted and 92.3% assisted by DeepDR-Transformer. Furthermore, we performed a single-center real-world prospective study, deploying DeepDR-LLM. We compared diabetes management adherence of patients under the unassisted PCP arm (n = 397) with those under the PCP+DeepDR-LLM arm (n = 372). Patients with newly diagnosed diabetes in the PCP+DeepDR-LLM arm showed better self-management behaviors throughout follow-up (P 
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