Plasma Metabolomics Identifies Key Metabolites and Improves Prediction of Diabetic Retinopathy: Development and Validation across Multinational Cohorts.

PURPOSE: To identify longitudinal metabolomic fingerprints of diabetic retinopathy (DR) and to evaluate their usefulness in predicting DR development and progression. DESIGN: Multicenter, multiethnic cohort study. PARTICIPANTS: This study included 17 675 participants from the UK Biobank (UKB) who had baseline prediabetes or diabetes, identified in accordance with the 2021 American Diabetes Association guidelines, and were free of baseline DR and an additional 638 participants with type 2 diabetes mellitus from the Guangzhou Diabetic Eye Study (GDES) for external validation. Diabetic retinopathy was determined by ICD-10 codes in the UKB cohort and revised ETDRS grading criteria in the GDES cohort. METHODS: Longitudinal DR metabolomic fingerprints were identified through nuclear magnetic resonance (NMR) assay in UKB participants. The predictive value of these fingerprints for predicting DR development were assessed in a fully withheld test set. External validation and extrapolation analyses of DR progression and microvascular damage were conducted in the GDES cohort using NMR technology. Model assessments included the concordance (C) statistic, net classification improvement (NRI), integrated discrimination improvement (IDI), calibration, and clinical usefulness in both cohorts. MAIN OUTCOME MEASURES: DR development and progression and retinal microvascular damage. RESULTS: Of 168 metabolites, 118 were identified as candidate metabolomic fingerprints for future DR development. These fingerprints significantly improved the predictability for DR development beyond traditional indicators (C statistic, 0.802 [95% confidence interval (CI), 0.760-0.843] vs. 0.751 [95% CI, 0.706-0.796]; P = 5.56 × 10-4). Glucose, lactate, and citrate were among the fingerprints validated in the GDES cohort. Using these parsimonious and replicable fingerprints yielded similar improvements for predicting DR development (C statistic, 0.807 [95% CI, 0.711-0.903] vs. 0.617 [95% CI, 0.494-0.740]; P = 1.68 × 10-4) and progression (C statistic, 0.797 [95% CI, 0.712-0.882] vs. 0.665 [95% CI, 0.545-0.784]; P = 0.003) in the external GDES cohort. Improvements in NRIs, IDIs, and clinical usefulness also were evident in both cohorts (all P < 0.05). In addition, lactate and citrate were associated with microvascular damage across macular and optic nerve head regions among Chinese GDES (all P < 0.05). CONCLUSIONS: Metabolomic profiling may be effective in identifying robust fingerprints for predicting future DR development and progression, providing novel insights into the early and advanced stages of DR pathophysiology. FINANCIAL DISCLOSURE(S): The author(s) have no proprietary or commercial interest in any materials discussed in this article.

Frailty and risk of microvascular disease in adults with prediabetes.

BACKGROUND AND AIMS: To assess the relationship between frailty phenotypes and the risk of MVD among prediabetics in two prospective cohorts. METHODS: The study included 66,068 and 226 participants with prediabetes from the UK Biobank (UKB) and Chinese Ocular Imaging Project (COIP) in Guangzhou, China, respectively. Frailty was evaluated using the Fried phenotype, which includes weight loss, fatigue, low grip strength, low physical activity, and slow walking pace. The outcome was incident microvascular diseases, including diabetic retinopathy, nephropathy, and neuropathy in UKB, and decline rate of retinal capillary density in COIP. Cox models were used to calculate hazard ratios (HRs) and 95 % confidential intervals (CIs), and mixed linear model was used to determine the ß and 95 % CIs. RESULTS: At baseline, 27,491 (41.6 %) and 3332 (5.0 %) prediabetics were classified as pre-frail and frail, respectively in UKB. During a median follow-up of 8.9 years, 3784 cases of incident microvascular diseases were identified. Pre-frailty and frailty were significantly associated with a higher risk of microvascular diseases (HR 1.21 [1.12, 1.30] for pre-frailty; HR 1.60 [1.42, 1.81] for frailty). Compared to no frailty, the adjusted HRs for frailty were 1.42 (0.73, 2.76) for retinopathy, 1.49 (1.31, 1.70) for nephropathy, and 2.37 (1.69, 3.33) for neuropathy. Fatigue and walking pace were the strongest mediators of frailty and microvascular diseases. In the COIP, the lowest handgrip strength group exhibited 62%-63 % faster annually decline in retinal capillary density compared with the highest group (all P<0.05). CONCLUSIONS: Each frailty point is important for prediabetics because both pre-frailty and frailty phenotypes are strongly associated with an increased risk of microvascular diseases and its subtypes. Lower handgrip strength presents with faster decline in retinal capillary density.

Handgrip strength and risks of diabetic vascular complications: Evidence from Guangzhou Diabetic Eye Study and UK cohorts.

PURPOSE: The purpose is to investigate the association between handgrip strength (HGS) and the risk of future diabetic complications in multicountry cohorts. METHODS: The association between HGS and diabetic complications was evaluated using cox models among 84 453 patients with pre-diabetes and diabetes from the UK Biobank with a 12-year follow-up. The association between HGS and longitudinal microcirculatory damage rates was assessed among 819 patients with diabetes from the Guangzhou Diabetic Eye Study (GDES) with a 3-year follow-up. Participants were divided into three age groups (<56, 56-65 and ≥65 years), and each group was further subdivided into three HGS tertiles. RESULTS: A 5 kg reduction in HGS was associated with increased risk for all-cause mortality (women, HR=1.10, 95% CI: 1.05 to 1.14; p<0.001; men, HR=1.13, 95% CI: 1.11 to 1.15; p<0.001). Women and men in the lowest HGS group exhibited 1.6-times and 1.3-1.5-times higher risk of myocardial infarction and stroke compared with the highest HGS group. In men, there was a higher risk of developing end-stage renal disease (HR=1.83, 95% CI: 1.30 to 2.57; p=0.001), while this was not observed in women. Both sexes in the lowest HGS group had a 1.3-times higher risk of diabetic retinopathy compared with the highest HGS group. In the GDES group, individuals with the lowest HGS showed accelerated microcirculatory damage in retina (all p<0.05). CONCLUSIONS: Reduced HGS is significantly associated with a higher risk of diabetic complications and accelerated microvascular damage. HGS could serve as a practical indicator of vascular health in patients with pre-diabetes and diabetes.

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

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 < 0.05). For patients with referral DR, those in the PCP+DeepDR-LLM arm were more likely to adhere to DR referrals (P < 0.01). Additionally, DeepDR-LLM deployment improved the quality and empathy level of management recommendations. Given its multifaceted performance, DeepDR-LLM holds promise as a digital solution for enhancing primary diabetes care and DR screening.