Photoreceptor metabolic window unveils eye-body interactions

Abstract

Photoreceptors are specialized neurons at the core of the retina's functionality, with optical accessibility and exceptional sensitivity to systemic metabolic stresses. Here we show the ability of risk-free, in vivo photoreceptor assessment as a window into systemic health and identify shared metabolic underpinnings of photoreceptor degeneration and multisystem health outcomes. A thinner photoreceptor layer thickness is significantly associated with an increased risk of future mortality and 13 multisystem diseases, while systematic analyses of circulating metabolomics enable the identification of 109 photoreceptor-related metabolites, which in turn elevate or reduce the risk of these health outcomes. To translate these insights into a practical tool, we developed an artificial intelligence (AI)-driven photoreceptor metabolic window framework and an accompanying interpreter that comprehensively captures the metabolic landscape of photoreceptor-systemic health linkages and simultaneously predicts 16 multisystem health outcomes beyond established approaches while retaining interpretability. Our work, replicated across cohorts of diverse ethnicities, reveals the potential of photoreceptors to inform systemic health and advance a multisystem perspective on human health by revealing eye-body connections and shared metabolic influences.

Fig. 1. Overview of the study design and analyses.

a To explore the ability of risk-free, in vivo photoreceptor assessment as a window into systemic health and to identify shared metabolic underpinnings of photoreceptor degeneration and multisystem health outcomes, participants from the UKB and the GDES were categorized into several distinct populations. b The study outcomes include mortality, cardiovascular outcomes, metabolic outcomes, renal outcomes, hepatic outcomes, pulmonary outcomes, and cancer outcomes. c To translate these insights into a practical tool, we developed an analytical framework (PMW) that comprehensively captures the metabolic landscape of photoreceptor–systemic health linkages and simultaneously predicts 16 multisystem health outcomes beyond established models. d Five established models for each outcome were used to assess the performance of PMW in predicting multisystem health risks, with the predictors used in the corresponding system prediction shown. Parts of (a–c) were created from BioRender (biorender.com) and Flaticon (flaticon.com). UKB UK Biobank, GDES Guangzhou Diabetic Eye Study, ResNet residual network, MLP multilayer perception, BMI body mass index, WHR waist-hip ratio, SBP systolic blood pressure, FEV1 forced expiratory volume in one second, eGFR estimated glomerular filtration rate, ACR urine albumin-to-creatinine ratio, SUA serum uric acid, FBG fasting blood glucose, HbA1c hemoglobin A1c, LDL-c low-density lipoprotein cholesterol, HDL-c high-density lipoprotein cholesterol, ALT alanine aminotransferase, AST aspartate aminotransferase, γ-GGT γ-glutamyl-transferase.

Fig. 2. Photoreceptor layer thickness and multisystem outcome risk.

Hazard ratios for incident outcomes per 1-SD photoreceptor layer thinning across subfields were estimated with CPH models. Squares represent the estimated hazard ratios (red for the average photoreceptor layer, pink for the central photoreceptor layer, orange for the inner ring photoreceptor layer, and blue for the outer ring photoreceptor layer), with 95% CIs indicated as lines of error bars. Solid blocks and asterisks indicate significant associations through two-sided Wald tests after controlling FDR for multiple tests. Source data are provided as a Source Data file. T2D type 2 diabetes, CHD coronary heart disease, AAA abdominal aortic aneurysm, PAD peripheral arterial disease, ESRD end-stage renal disease, COPD chronic obstructive pulmonary disease, SD standard deviation, CPH Cox proportional hazard, FDR false discovery rate.

Fig. 3. Photoreceptor-associated metabolites and multisystem outcome risk.

a Heatmap illustrating the association of average photoreceptor layer-related metabolites with multisystem outcome risk (left), and the association of these metabolites with an average photoreceptor layer thickness (right), where red represents negative associations and blue represents positive associations. b Heatmap illustrating findings pertaining to the subfield photoreceptor layer in a similar manner to (a). Source data are provided as a Source Data file. T2D type 2 diabetes, CHD coronary heart disease, AAA abdominal aortic aneurysm, PAD peripheral arterial disease, ESRD end-stage renal disease, COPD chronic obstructive pulmonary disease, HDL high-density lipoprotein, IDL intermediate-density lipoprotein, LDL low-density lipoprotein, VLDL very low-density lipoprotein.

Fig. 4. Profile of photoreceptor metabolic window (PMW) and corresponding interpreter.

a Overall attribution of photoreceptor-related metabolites to the PMW architecture. Individual attributions are aggregated by percentiles, with each dot representing one percentile. The distance of a dot from the circular baseline reflects the strength of the absolute attribution for that percentile. Deviations toward the center and periphery indicate negative and positive contributions, while dot colors represent the normalized values for each photoreceptor-related metabolite. b Stacked bar chart illustrating attribution of each photoreceptor-related metabolites across 16 multisystem outcome risks. Source data are provided as a Source Data file. T2D type 2 diabetes, CHD coronary heart disease, AAA abdominal aortic aneurysm, PAD peripheral arterial disease, ESRD end-stage renal disease, COPD chronic obstructive pulmonary disease, HDL high-density lipoprotein, IDL intermediate-density lipoprotein, LDL low-density lipoprotein, VLDL very low-density lipoprotein.

Fig. 5. Photoreceptor metabolic window (PMW) enables distinct risk stratification and predictive improvement for multisystem outcomes.

a Cumulative event rates throughout the observation period, stratified by PMW state quantiles, with 95% CIs indicated as shades derived from survival proportions. Red represents the top 10%, yellow represents the middle 10%, and blue represents the bottom 10%. b Comparison of model performance, including the Age&Sex model, established models, and models incorporating PMW to predict multisystem outcomes. Different colors denote distinct models incorporating PMW, with horizontal dashed lines indicating the performance benchmarks set by the Age&Sex model and four respective established models for each outcome. Data are presented as medians (center of error bar) and 95% CIs (line of error bar) determined by bootstrapping of 1000 iterations. Source data are provided as a Source Data file. T2D type 2 diabetes, CHD coronary heart disease, AAA abdominal aortic aneurysm, PAD peripheral arterial disease, ESRD end-stage renal disease, COPD chronic obstructive pulmonary disease, FGCRS Framingham General Cardiovascular Risk Score, SCORE2 Systematic Coronary Risk Evaluation 2, WHO-CVD World Health Organization Cardiovascular Disease, AHA/ASCVD American Heart Association/Atherosclerotic Cardiovascular Disease, KFRE Kidney Failure Risk Equation, CLivD score Chronic Liver Disease score, LLP Liverpool Lung Project, PLMO Prostate, Lung, Colorectal, and Ovarian Cancer Scanning Trial, LCRAT Lung Cancer Risk Assessment Tool.

Fig. 6. Validation of Photoreceptor metabolic window (PMW)’s predictive performance and clinical utility in the Guangzhou Diabetic Eye Study (GDES) cohort.

a Comparison of model performance, including the Age&Sex model, established models, and models incorporating PMW to predict multisystem outcomes. Different colors denote distinct models incorporating PMW, with horizontal dashed lines indicating the performance benchmarks set by the Age&Sex model and four respective established models for each outcome. Data are presented as medians (center of error bar) and 95% CIs (line of error bar) determined by bootstrapping of 1000 iterations. b Net benefit of clinical utility standardized by endpoint prevalence, with horizontal dotted gray lines indicating ‘treat none’ and vertical solid gray lines indicating ‘treat all’. Source data are provided as a Source Data file. DR, diabetic retinopathy, VTDR vision-threatening DR, FGCRS Framingham General Cardiovascular Risk Score, SCORE2 Systematic Coronary Risk Evaluation 2, WHO-CVD World Health Organization Cardiovascular Disease, AHA/ASCVD American Heart Association/Atherosclerotic Cardiovascular Disease, KFRE Kidney Failure Risk Equation.