Metabolic signature of the ganglion cell–inner plexiform layer thickness and the risks of mortality and morbidity: a population-based study in UK Biobank
Authors: Yang Shaopeng , Yuan Yixiong , Chen Yanping , Zhang Shiran , Wang Yujie , Shang Xianwen , Bulloch Gabriella , Liao Huan , Chen Yifan , Zhang Lei , Zhu Zhuoting , He Mingguang , Wang Wei 
Published: September 2022

SummaryBackgroundThe retina is considered a unique window to systemic health, but their biological link remains unknown.MethodsA total of 93,838 UK Biobank participants with metabolomics data were included in the study. Plasma metabolites associated with GCIPLT were identified in 7,824 participants who also underwent retinal optical coherence tomography; prospective associations of GCIPLT-associated metabolites with 12-year risk of mortality and major age-related diseases were assessed in 86,014 participants. The primary outcomes included all- and specific-cause mortality. The secondary outcomes included incident type 2 diabetes mellitus (T2DM), obstructive sleep apnea/hypopnea syndrome (OSAHS), myocardial infarction (MI), heart failure, ischemic stroke, and dementia. C-statistics and net reclassification indexes (NRIs) were calculated to evaluate the added predictive value of GCIPLT metabolites. Calibration was assessed using calibration plots.FindingsSixteen metabolomic signatures were associated with GCIPLT (P< 0.009 [Bonferroni-corrected threshold]), and most were associated with the future risk of mortality and age-related diseases. The constructed meta-GCIPLT scores distinguished well between patients with high and low risks of mortality and morbidity, showing predictive values higher than or comparable to those of traditional risk factors (C-statistics: 0.780[0.771-0.788], T2DM; 0.725[0.707-0.743], OSAHS; 0.711[0.695-0.726], MI; 0.685[0.662-0.707], cardiovascular mortality; 0.657[0.640-0.674], heart failure; 0.638[0.636-0.660], other mortality; 0.630[0.618-0.642], all-cause mortality; 0.620[0.598-0.643], dementia; 0.614[0.593-0.634], stroke; and 0.601[0.585-0.617], cancer mortality). The NRIs confirmed the inclusion of GCIPLT metabolomic signatures to the models based on traditional risk factors resulted in significant improvements in model performance (5.18%, T2DM [P=3.86E-11]; 4.43%, dementia [P=0.003]; 4.20%, cardiovascular mortality [P=6.04E-04]; 3.73%, MI [P=1.72E-07]; 2.93%, OSAHS [P=3.13E-05]; 2.39%, all-cause mortality [P=3.89E-05]; 2.33%, stroke [P=0.049]; 2.09%, cancer mortality [P=0.039]; and 1.59%, heart failure [P=2.72E-083.07E-04]). Calibration plots showed excellent calibration between predicted risk and actual incidence in the new models.InterpretationGCIPLT-associated plasma metabolites captured the residual risk for mortality and major systemic diseases not quantified by traditional risk factors in the general population. Incorporating GCIPLT metabolomic signatures into prediction models may assist in screening for future risks of these health outcomes.FundingNational Natural Science Foundation (China).Research in contextEvidence before this studyRecent studies have recognized that retinal measurements can indicate an accelerated risk of aging and multiple systemic diseases preceding clinical symptoms and signs. Despite these insights, it remains unknown how retinal alterations are biologically linked to systemic health.Added value of this studyUsing the UK Biobank, we identified ganglion cell–inner plexiform layer thickness (GCIPLT) metabolomic signatures, and revealed their association with the risk of all- and specific-cause mortality and six age related diseases: type 2 diabetes, dementia, stroke, myocardial infarction, heart failure, and obstructive sleep apnea/hypopnea syndrome. The meta-GCIPLT score significantly improved the discriminative power of the predictive models for theses health outcomes based on conventional risk factors.Implications of all the available evidenceGCIPLT-associated plasma metabolites have the potential to capture the residual risk of systemic diseases and mortality not quantified by traditional risk factors. Incorporating GCIPLT metabolomic signatures into prediction models may assist in screening for future risks of these health outcomes. Since metabolism is a modifiable risk factor that can be treated medically, the future holds promise for the development of new strategies that reverse or interrupt the onset of these diseases by modifying metabolic factors.