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This study aims to investigate the association between metabolomic aging acceleration and body mass index (BMI) phenotypes with mortality and obesity‐related morbidities (ORMs). 85,458 participants were included from the UK Biobank. Metabolomic age was determined using 168 metabolites. The Chronological Age‐Adjusted Gap was used to define metabolomically younger (MY) or older (MO) status. BMI categories were defined as normal weight, overweight, and obese. Participants were categorized into MY normal weight (MY‐NW, reference), MY overweight (MY‐OW), MY obesity (MY‐OB), MO normal weight (MO‐NW), MO overweight (MO‐OW), and MO obesity (MO‐OB). Mortality and 43 ORMs were identified through death registries and hospitalization records. Compared with MY‐NW phenotype, MO‐OB phenotype yielded increased risk of mortality and 32 ORMs, followed by MO‐OW with mortality and 27 ORMs, MY‐OB with mortality and 26 ORMs, MY‐OW with 21 ORMs, and MO‐NW with mortality and 14 ORMs. Consistently, MO‐OB phenotype showed the highest risk of developing obesity‐related multimorbidities, followed by MY‐OB phenotype, MO‐OW phenotype, MY‐OW phenotype, and MO‐NW phenotype. Additive interactions were found between metabolomic aging acceleration and obesity on CVD‐specific mortality and 10 ORMs. Additionally, individuals with metabolomic aging acceleration had higher mortality and cardiovascular risk, even within the same BMI category. These findings suggest that metabolomic aging acceleration could help stratify mortality and ORMs risk across different BMI categories. Weight management should also be extended to individuals with overweight or obesity even in the absence of accelerated metabolomic aging, as they face increased healthy risk compared with MY‐NW individuals. Additionally, delaying metabolic aging acceleration is needed for all metabolomically older groups, including those with normal weight [ABSTRACT FROM AUTHOR]

This longitudinal study used diet-wide association studies (DWAS) to investigate the association between diverse dietary food and nutrient intakes and the onset of type 2 diabetes mellitus (T2DM). Out of 502,505 participants from the UK Biobank, 119,040 with dietary data free of T2DM at the baseline were included, and 3241 developed T2DM during a median follow-up of 11.7 years. The DWAS analysis, which is based on Cox regression models, was used to analyse the associations between dietary food or nutrient intake factors and T2DM risk. The study found that 10 out of 225 dietary factors were significantly associated with the T2DM risk. Total alcohol (HR = 0.86, 0.85–0.92, p = 1.26 × 10−32), red wine (HR = 0.89, 0.88–0.94, p = 7.95 × 10−19), and fresh tomatoes (HR = 0.92, 0.89–0.94, p = 2.3 × 10−11) showed a negative association with T2DM risk, whereas sliced buttered bread exhibited a positive association. Additionally, 5 out of 21 nutrient intake variables revealed significant associations with the T2DM risk, with iron having the highest protective effect and starch as a risk factor. In conclusion, DWAS is an effective method for discovering novel associations when exploring numerous dietary variables simultaneously and could provide valuable insight into future dietary guidance for T2DM. [ABSTRACT FROM AUTHOR]

Purpose: We investigated the association between metabolically healthy obesity (MHO) and retinal age gap and explored potential sex differences in this association., Methods: This study included 30,335 participants from the UK Biobank. Body mass index (BMI) was classified into normal weight, overweight, and obesity. Metabolic health (MH) was defined as meeting the following criteria: systolic blood pressure of <130 mm Hg, no antihypertensive drugs, waist-to-hip ratio of <0.95 for women or 1.03 for men, and the absence of diabetes. Participants were categorized as MH normal weight (MHN), MH overweight (MHOW), MHO, metabolically unhealthy normal weight, metabolically unhealthy (MU) overweight, and MU obesity. Retinal age gap was defined as the difference between retinal age and chronological age. Linear regression models were used to investigate the association of metabolic phenotypes of obesity with retinal age gap., Results: Compared with MHN, individuals with MHOW (β, 0.17; 95% confidence interval [CI], 0.01-0.32; P = 0.039) and MHO (β, 0.23; 95% CI, 0.02-0.44; P = 0.031) were associated with increased retinal age gap. Furthermore, individuals classified as metabolic unhealthy were also associated with higher retinal age gap, irrespective of body mass index categories (β for MU normal weight, 0.23; 95% CI, 0.08-0.38; P = 0.003; β for MU overweight: 0.31; 95% CI, 0.18-0.45; P < 0.001; β for MU obesity, 0.50; 95% CI, 0.36-0.65; P < 0.001). No significant sex difference was observed in the association between metabolic phenotypes of obesity and retinal age gap (all P for interaction > 0.05)., Conclusions: MHOW and MHO were associated significantly with an increased retinal age gap compared with MHN individuals. Weight management should be recommended for individuals who are overweight or obese, even in the absence of metabolic unhealth., Translational Relevance: Retinal age gap provides a simple tool for identifying early health risks for MHOW and MHO individuals.

Background/aims: The emerging concept of retinal age, a biomarker derived from retinal images, holds promise in estimating biological age. The retinal age gap (RAG) represents the difference between retinal age and chronological age, which serves as an indicator of deviations from normal ageing. This scoping review aims to collate studies on retinal age to determine its potential clinical utility and to identify knowledge gaps for future research., Methods: Using the Preferred Reporting Items for Systematic Reviews and Meta-Analyses checklist, eligible non-review, human studies were identified, selected and appraised. PubMed, Scopus, SciELO, PsycINFO, Google Scholar, Cochrane, CINAHL, Africa Wide EBSCO, MedRxiv and BioRxiv databases were searched to identify literature pertaining to retinal age, the RAG and their associations. No restrictions were imposed on publication date., Results: Thirteen articles published between 2022 and 2023 were analysed, revealing four models capable of determining biological age from retinal images. Three models, 'Retinal Age', 'EyeAge' and a 'convolutional network-based model', achieved comparable mean absolute errors: 3.55, 3.30 and 3.97, respectively. A fourth model, 'RetiAGE', predicting the probability of being older than 65 years, also demonstrated strong predictive ability with respect to clinical outcomes. In the models identified, a higher predicted RAG demonstrated an association with negative occurrences, notably mortality and cardiovascular health outcomes., Conclusion: This review highlights the potential clinical application of retinal age and RAG, emphasising the need for further research to establish their generalisability for clinical use, particularly in neuropsychiatry. The identified models showcase promising accuracy in estimating biological age, suggesting its viability for evaluating health status., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.)

Purpose: The purpose of this study was to perform a systematic review and meta-analysis to synthesize evidence from studies using deep learning (DL) to predict cardiovascular disease (CVD) risk from retinal images., Methods: A systematic literature search was performed in MEDLINE, Scopus, and Web of Science up to June 2022. We extracted data pertaining to predicted outcomes, model development, and validation and model performance metrics. Included studies were graded using the Quality Assessment of Diagnostic Accuracies Studies 2 tool. Model performance was pooled across eligible studies using a random-effects meta-analysis model., Results: A total of 26 studies were included in the analysis. There were 42 CVD risk-related outcomes predicted from retinal images were identified, including 33 CVD risk factors, 4 cardiac imaging biomarkers, 2 CVD risk scores, the presence of CVD, and incident CVD. Three studies that aimed to predict the development of future CVD events reported an area under the receiver operating curve (AUROC) between 0.68 and 0.81. Models that used retinal images as input data had a pooled mean absolute error of 3.19 years (95% confidence interval [CI] = 2.95-3.43) for age prediction; a pooled AUROC of 0.96 (95% CI = 0.95-0.97) for gender classification; a pooled AUROC of 0.80 (95% CI = 0.73-0.86) for diabetes detection; and a pooled AUROC of 0.86 (95% CI = 0.81-0.92) for the detection of chronic kidney disease. We observed a high level of heterogeneity and variation in study designs., Conclusions: Although DL models appear to have reasonably good performance when it comes to predicting CVD risk, further work is necessary to evaluate the real-world applicability and predictive accuracy., Translational Relevance: DL-based CVD risk assessment from retinal images holds great promise to be translated to clinical practice as a novel approach for CVD risk assessment, given its simple, quick, and noninvasive nature.