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  1. Circulating branched-chain amino acids and incident heart failure in type 2 diabetes: The Hong Kong Diabetes Register
    Lim LL, Lau ESH, Fung E, Lee HM, Ma RCW, Tam CHT, et al.
    Diabetes Metab Res Rev, 2020 03;36(3):e3253.
    PMID: 31957226 DOI: 10.1002/dmrr.3253
    AIM: Levels of branched-chain amino acids (BCAAs, namely, isoleucine, leucine, and valine) are modulated by dietary intake and metabolic/genetic factors. BCAAs are associated with insulin resistance and increased risk of type 2 diabetes (T2D). Although insulin resistance predicts heart failure (HF), the relationship between BCAAs and HF in T2D remains unknown.

    METHODS: In this prospective observational study, we measured BCAAs in fasting serum samples collected at inception from 2139 T2D patients free of cardiovascular-renal diseases. The study outcome was the first hospitalization for HF.

    RESULTS: During 29 103 person-years of follow-up, 115 primary events occurred (age: 54.8 ± 11.2 years, 48.2% men, median [interquartile range] diabetes duration: 5 years [1-10]). Patients with incident HF had 5.6% higher serum BCAAs than those without HF (median 639.3 [561.3-756.3] vs 605.2 [524.8-708.7] μmol/L; P = .01). Serum BCAAs had a positive linear association with incident HF (per-SD increase in logarithmically transformed BCAAs: hazard ratio [HR] 1.22 [95% CI 1.07-1.39]), adjusting for age, sex, and diabetes duration. The HR remained significant after sequential adjustment of risk factors including incident coronary heart disease (1.24, 1.09-1.41); blood pressure, low-density lipoprotein cholesterol, and baseline use of related medications (1.31, 1.14-1.50); HbA1c , waist circumference, triglyceride, and baseline use of related medications (1.28, 1.11-1.48); albuminuria and estimated glomerular filtration rate (1.28, 1.11-1.48). The competing risk of death analyses showed similar results.

    CONCLUSIONS: Circulating levels of BCAAs are independently associated with incident HF in patients with T2D. Prospective cohort analysis and randomized trials are needed to evaluate the long-term safety and efficacy of using different interventions to optimize BCAAs levels in these patients.

  2. Integrated image-based deep learning and language models for primary diabetes care
    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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