In people on long‑term dialysis, a tiny protein called MOTS‑c is found at higher levels than in healthy folks, and those with the highest levels are more likely to die or have heart problems. Adding MOTS‑c measurements to the usual health checks makes the predictions a bit more accurate, but the improvement is modest.

Uremic patients exhibit remarkably increased rates of mortality and cardiovascular (CV) events, but risk prediction in this setting remains difficult. Systemic mitochondrial dysfunction is pervasive in end-stage kidney disease and may contribute to CV complications. We tested the clinical significance of circulating MOTS-c, a small mitochondrial-derived peptide, as a biomarker for improving mortality and CV risk prediction in hemodialysis (HD) patients. We conducted a prospective, observational, multicenter study on 94 prevalent HD patients. The study endpoint was a composite of all-cause mortality and non-fatal CV events. The diagnostic and prognostic capacities of predictive models based on cohort-related risk factors were tested before and after the inclusion of MOTS-c. MOTS-c levels were higher in HD patients than in controls (p < 0.001) and even more elevated (p = 0.01) in the 53 individuals experiencing the combined endpoint during follow-up (median duration: 26.5 months). MOTS-c was independently associated with the endpoint at either multivariate logistic (OR 1.020; 95% CI: 1.011-1.109; p = 0.03) or Cox regression analyses (HR 1.004; 95% CI: 1.000-1.025; p = 0.05) and the addition of this biomarker to prognostic models including the other cohort-related risk predictors (age, left ventricular mass, evidence of diastolic dysfunction, diabetes, pulse pressure) significantly improved the calibration, risk variability explanation, discrimination (receiver operating characteristic area under the curve from 0.727 to 0.743; C-index from 0.658 to 0.700), and particularly, the overall reclassification capacity (NRI 15.87%; p = 0.01). In HD patients, the mitochondrial-derived peptide MOTS-c may impart significant information to refine CV risk prediction, beyond cohort-related risk factors. Future investigations are needed to generalize these findings in larger and more heterogeneous cohorts.