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.

Retinal and Choroidal Phenotypes Across Novel Subtypes of Type 2 Diabetes Mellitus.

PURPOSE: To investigate longitudinal changes in choroidal thickness (CT) and ganglion cell-inner plexiform layer thickness (GC-IPLT) across distinct phenotypes of type 2 diabetes mellitus (T2DM) patients. DESIGN: Prospective cohort study. METHODS: T2DM patients were categorized into 5 groups (SAID, SIDD, SIRD, MOD, and MARD) using K-means clustering based on ß-cell function and insulin resistance. Swept-source optical coherence tomography measured baseline and 4-year follow-up CT and GC-IPLT. Linear mixed-effects models assessed absolute and relative changes in CT and GC-IPLT across subtypes. RESULTS: Over a median 4.11-year follow-up, CT and GC-IPLT decreased significantly across all groups. Choroidal thinning rates were most pronounced in SIDD (-6.5 ± 0.53 µm/year and -3.5 ± 0.24%/year) and SAID (-6.27 ± 0.8 µm/year and -3.19 ± 0.37%/year), while MARD showed the slowest thinning rates (-3.63 ± 0.34 µm/year and -1.98 ± 0.25%/year). SIRD exhibited the greatest GC-IPLT loss (-0.66 ± 0.05 µm/year and -0.91 ± 0.07%/year), with the least in SIDD (-0.36 ± 0.05 µm/year and -0.49 ± 0.07%/year), all statistically significant (all P < 0.001). Adjusted for confounding variables, SIDD and SAID groups showed faster CT thinning than MARD [-2.57 µm/year (95% CI: -4.16 to -0.97; P = 0.002) and -2.89 µm/year (95% CI: -4.12 to -1.66; P < 0.001), respectively]. GC-IPLT thinning was notably accelerated in SIRD versus MARD, but slowed in SIDD relative to MARD [differences of -0.16 µm/year (95% CI: -0.3 to -0.03; P = 0.015) and 0.15 µm/year (95% CI: 0.03 to 0.27; P = 0.015), respectively]. CONCLUSIONS: Microvascular damage in the choroid is associated with SIDD patients, whereas early signs of retinal neurodegeneration are evident in SIRD patients. All these changes may precede the onset of DR.

Forecasting Myopic Maculopathy Risk Over a Decade: Development and Validation of an Interpretable Machine Learning Algorithm.

Purpose: The purpose of this study was to develop and validate prediction model for myopic macular degeneration (MMD) progression in patients with high myopia. Methods: The Zhongshan High Myopia Cohort for model development included 660 patients aged 7 to 70 years with a bilateral sphere of ≤-6.00 diopters (D). Two hundred twelve participants with an axial length (AL) ≥25.5 mm from the Chinese Ocular Imaging Project were used for external validation. Thirty-four clinical variables, including demographics, lifestyle, myopia history, and swept source optical coherence tomography data, were analyzed. Sequential forward selection was used for predictor selection, and binary classification models were created using five machine learning algorithms to forecast the risk of MMD progression over 10 years. Results: Over a median follow-up of 10.9 years, 133 patients (20.2%) showed MMD progression in the development cohort. Among them, 69 (51.9%) developed newly-onset MMD, 11 (8.3%) developed patchy atrophy from diffuse atrophy, 54 (40.6%) showed an enlargement of lesions, and 9 (6.8%) developed plus signs. Top six predictors for MMD progression included thinner subfoveal choroidal thickness, longer AL, worse best-corrected visual acuity, older age, female gender, and shallower anterior chamber depth. The eXtreme Gradient Boosting algorithm yielded the best discriminative performance (area under the receiver operating characteristic curve [AUROC] = 0.87 ± 0.02) with good calibration in the training cohort. In a less myopic external validation group (median -5.38 D), 48 patients (22.6%) developed MMD progression over 4 years, with the model's AUROC validated at 0.80 ± 0.008. Conclusions: Machine learning model effectively predicts MMD progression a decade ahead using clinical and imaging indicators. This tool shows promise for identifying "at-risk" high myopes for timely intervention and vision protection.

Long-Term Prediction and Risk Factors for Incident Visual Field Defect in Nonpathologic High Myopia.

Purpose: To investigate the long-term patterns and risk factors of visual field defect (VFD) development in nonpathologic high myopia (HM) over an 8-year follow-up. Methods: This was an observational cohort study. The VFD classification adhered to the Glaucoma Suspects with High Myopia Study Group. Logistic regression models with generalized estimating equations were used to identify risk factors for VFD development. Results: A total of 330 eyes from 194 patients were included. Among them, 49.4% of eyes developed VFD, with enlarged blind spot and nonspecific defect ranked as the most common VFDs, followed by partial arcuate defect, vertical step, nasal step, paracentral defect, and combined defects. Longer axial length (odds ratio [OR] = 1.43 per 1-mm increase; 95% CI, 1.04-1.95; P = 0.026), thinner central corneal thickness (OR = 1.01 per 1-µm decrease; 95% CI, 1.003-1.02; P = 0.013), worse mean deviation of visual field (OR = 1.51 per 1-dB decrease; 95% CI, 1.14-2.00; P = 0.004), and the presence of peripapillary γ-zone (OR = 5.57; 95% CI, 3.06-10.15; P < 0.001) at baseline correlated with the development of any VFD. By incorporating these factors, the prediction models achieved area under the curves of 0.789 (95% CI, 0.726-0.853) and 0.828 (95% CI, 0.714-0.943) for discriminating the development of any VFD and moderate/severe VFD, respectively, with good calibration power. Conclusions: The development of VFD occurred frequently in individuals with nonpathologic HM and can be effectively predicted using relevant metrics. The findings will aid in expanding our knowledge of optic neuropathy in HM.

Identification of major QTLs for soybean seed size and seed weight traits using a RIL population in different environments.

Introduction: The seed weight of soybean [Glycine max (L.) Merr.] is one of the major traits that determine soybean yield and is closely related to seed size. However, the genetic basis of the synergistic regulation of traits related to soybean yield is unclear. Methods: To understand the molecular genetic basis for the formation of soybean yield traits, the present study focused on QTLs mapping for seed size and weight traits in different environments and target genes mining. Results: A total of 85 QTLs associated with seed size and weight traits were identified using a recombinant inbred line (RIL) population developed from Guizao1×B13 (GB13). We also detected 18 environmentally stable QTLs. Of these, qSL-3-1 was a novel QTL with a stable main effect associated with seed length. It was detected in all environments, three of which explained more than 10% of phenotypic variance (PV), with a maximum of 15.91%. In addition, qSW-20-3 was a novel QTL with a stable main effect associated with seed width, which was identified in four environments. And the amount of phenotypic variance explained (PVE) varied from 9.22 to 21.93%. Five QTL clusters associated with both seed size and seed weight were summarized by QTL cluster identification. Fifteen candidate genes that may be involved in regulating soybean seed size and weight were also screened based on gene function annotation and GO enrichment analysis. Discussion: The results provide a biologically basic reference for understanding the formation of soybean seed size and weight traits.