Search

To investigate longitudinal changes in peripapillary choroidal thickness (pCT) and retinal nerve fiber thickness (pRNFLT) in patients with Type 2 diabetes mellitus.This was a prospective observational cohort study. Patients with Type 2 diabetes mellitus without diabetic retinopathy (DR) at baseline were recruited, followed up for three years, and further divided into an incident DR group and a non-DR group according to the outcome. The pCT and pRNFLT were measured through swept-source optical coherence tomography at 1-year interval, and the mean rates of pCT and pRNFLT thinning were compared between the DR groups.A total of 682 patients (682 eyes) were included in the final analysis. After 3-years follow-up, 122 (17.89%) developed DR. Both pCT and pRNFLT progressively thinned (-2.37 [-2.80 to -1.95] µm/year; -0.40 [-0.55 to -0.25] µm/year, respectively, P0.05) and accelerated thinning was observed in the incident DR group. The rates of pCT thinning (-3.92 [-4.96 to -2.88] µm/year, -2.03 [-2.49 to -1.57] µm/year, respectively) and pRNFLT loss (-1.03 [-1.31 to -0.76] µm/year, -0.26 [-0.43 to -0.09] µm/year, respectively) in the incident DR group were 1.93 and 3.96 times faster than those in the non-DR group, respectively. In addition, pCT and pRNFLT thinning were negatively related in Type 2 diabetes mellitus population, and faster pCT thinning indicated slower pRNFLT loss.Patients with Type 2 diabetes mellitus were at a higher risk of developing DR when accelerated pCT and pRNFLT thinning were present, indicating that heavier choroidal damage and retinal neurodegeneration precede clinical DR. The pCT and pRNFLT have the potential to serve as novel sensitive biomarkers of preclinical and early DR.

Background The integration of artificial intelligence (AI) and machine learning (ML) technologies into healthcare is transforming patient-practitioner interaction and could offer an additional platform for patient education and support. Objectives This study investigates whether ChatGPT-4 could provide safe and up-to date medical information about breast augmentation which is comparable to other patient information sources. Methods ChatGPT-4 was asked to generate 6 commonly asked questions regarding breast augmentation and respond to them. Its responses were qualitatively evaluated by a panel of specialist plastic and reconstructive surgeons and reconciled with a literature search of two large medical databases for accuracy, informativeness and accessibility. Results ChatGPT-4 provided well-structured, grammatically accurate, and comprehensive responses to the questions posed however it was limited in providing personalised advice and sometimes generated inappropriate or outdated references. ChatGPT consistently encouraged engagement with a specialist for specific information. Conclusions Although ChatGPT-4 showed promise as an adjunct tool in patient education regarding breast augmentation, there are areas requiring improvement. Additional advancements and software engineering are needed to enhance the reliability and applicability of AI-driven chatbots in patient education and support systems.

AimsTo investigate the correlation between the retinal microvasculature using optical coherence tomography angiography (OCTA) and systemic factors in type 2 diabetes mellitus (T2DM) patients.MethodsThis cross-sectional study obtained OCTA data from patients with T2DM administered at hospital and referred to ophthalmic services. Patient data about demographics, comorbid conditions, and blood biomarkers were extracted from electronic medical records. Data from OCTA scans obtained by CIRRUS HD-OCT Model 5,000 were obtained. Vessel density (VD) and perfusion density (PD) within the superficial capillary plexus, and foveal avascular zone (FAZ) area were automatically segmented. These parameters were tested for their correlations with systemic factors by univariate and multivariable linear regression analyses.ResultsA total of 144 T2DM patients (236 eyes) were available for analysis, with mean age of 53.6 (SD = 10.34) and 56.9% were male. Chronic kidney disease, cardiovascular disease, increased serum creatinine (Scr), red blood cell count (RBC), platelets (PLT), apolipoprotein B (APOB), and decreased urine albumin to creatinine ratio (UACR) were significantly associated with lower VD and PD (all p p ConclusionWe found several systemic risk factors, such as PLT, renal function and lipid profiles were associated with PD, VD, and FAZ area among Chinese T2DM patients.

Background Plasma metabolomic profile is disturbed in dementia patients, but previous studies have discordant conclusions. Methods Circulating metabolomic data of 110,655 people in the UK Biobank study were measured with nuclear magnetic resonance technique, and incident dementia records were obtained from national health registers. The associations between plasma metabolites and dementia were estimated using Cox proportional hazard models. The 10-fold cross-validation elastic net regression models selected metabolites that predicted incident dementia, and a 10-year prediction model for dementia was constructed by multivariable logistic regression. The predictive values of the conventional risk model, the metabolites model, and the combined model were discriminated by comparison of area under the receiver operating characteristic curves (AUCs). Net reclassification improvement (NRI) was used to estimate the change of reclassification ability when adding metabolites into the conventional prediction model. Results Amongst 110,655 participants, the mean (standard deviation) age was 56.5 (8.1) years, and 51 186 (46.3%) were male. A total of 1439 (13.0%) developed dementia during a median follow-up of 12.2 years (interquartile range: 11.5–12.9 years). A total of 38 metabolites, including lipids and lipoproteins, ketone bodies, glycolysis-related metabolites, and amino acids, were found to be significantly associated with incident dementia. Adding selected metabolites (n=24) to the conventional dementia risk prediction model significantly improved the prediction for incident dementia (AUC: 0.824 versus 0.817, p =0.042) and reclassification ability (NRI = 4.97%, P = 0.009) for identifying high risk groups. Conclusions Our analysis identified various metabolomic biomarkers which were significantly associated with incident dementia. Metabolomic profiles also provided opportunities for dementia risk reclassification. These findings may help explain the biological mechanisms underlying dementia and improve dementia prediction.

ImportanceThe neural retina is considered a unique window to systemic health, but its biological link with systemic health remains unknown.ObjectiveTo investigate the independent associations of retinal ganglion cell–inner plexiform layer thickness (GCIPLT) metabolic profiles with rates of mortality and morbidity of common diseases.Design, Setting, and ParticipantsThis cohort study evaluated UK Biobank participants enrolled between 2006 and 2010, and prospectively followed them up for multidisease diagnosis and mortality. Additional participants from the Guangzhou Diabetes Eye Study (GDES) underwent optical coherence tomography scanning and metabolomic profiling and were included for validation.Main Outcomes and MeasuresSystematic analysis of circulating plasma metabolites to identify GCIPLT metabolic profiles; prospective associations of these profiles with mortality and morbidity of 6 common diseases with their incremental discriminative value and clinical utility.ResultsAmong 93 838 community-based participants (51 182 [54.5%] women), the mean (SD) age was 56.7 (8.1) years and mean (SD) follow-up was 12.3 (0.8) years. Of 249 metabolic metrics, 37 were independently associated with GCIPLT, including 8 positive and 29 negative associations, and most were associated with the rates of future mortality and common diseases. These metabolic profiles significantly improved the models for discriminating type 2 diabetes over clinical indicators (C statistic: 0.862; 95% CI, 0.852-0.872 vs clinical indicators only, 0.803; 95% CI, 0.792-0.814; P P P P P P Conclusions and RelevanceIn this prospective study of multinational participants, GCIPLT-associated metabolites demonstrated the potential to inform mortality and morbidity risks. Incorporating information on these profiles may facilitate individualized risk stratification for these health outcomes.

Introduction retinal age derived from fundus images using deep learning has been verified as a novel biomarker of ageing. We aim to investigate the association between retinal age gap (retinal age–chronological age) and incident Parkinson’s disease (PD). Methods a deep learning (DL) model trained on 19,200 fundus images of 11,052 chronic disease-free participants was used to predict retinal age. Retinal age gap was generated by the trained DL model for the remaining 35,834 participants free of PD at the baseline assessment. Cox proportional hazards regression models were utilised to investigate the association between retinal age gap and incident PD. Multivariable logistic model was applied for prediction of 5-year PD risk and area under the receiver operator characteristic curves (AUC) was used to estimate the predictive value. Results a total of 35,834 participants (56.7 ± 8.04 years, 55.7% female) free of PD at baseline were included in the present analysis. After adjustment of confounding factors, 1-year increase in retinal age gap was associated with a 10% increase in risk of PD (hazard ratio [HR] = 1.10, 95% confidence interval [CI]: 1.01–1.20, P = 0.023). Compared with the lowest quartile of the retinal age gap, the risk of PD was significantly increased in the third and fourth quartiles (HR = 2.66, 95% CI: 1.13–6.22, P = 0.024; HR = 4.86, 95% CI: 1.59–14.8, P = 0.005, respectively). The predictive value of retinal age and established risk factors for 5-year PD risk were comparable (AUC = 0.708 and 0.717, P = 0.821). Conclusion retinal age gap demonstrated a potential for identifying individuals at a high risk of developing future PD.