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ObjectiveTo develop and validate a prediction nomogram based on motoric cognitive risk syndrome for cognitive impairment in healthy older adults.MethodsUsing two longitudinal cohorts of participants (aged ≥ 60 years) with 4-year follow-up, we developed (n = 1,177) and validated (n = 2,076) a prediction nomogram. LASSO (least absolute shrinkage and selection operator) regression model and multivariable Cox regression analysis were used for variable selection and for developing the prediction model, respectively. The performance of the nomogram was assessed with respect to its calibration, discrimination, and clinical usefulness.ResultsThe individualized prediction nomogram was assessed based on the following: motoric cognitive risk syndrome, education, gender, baseline cognition, and age. The model showed good discrimination [Harrell’s concordance index (C-index) of 0.814; 95% confidence interval, 0.782–0.835] and good calibration. Comparable results were also seen in the validation cohort, which includes good discrimination (C-index, 0.772; 95% confidence interval, 0.776–0.818) and good calibration. Decision curve analysis demonstrated that the prediction nomogram was clinically useful.ConclusionThis prediction nomogram provides a practical tool with all necessary predictors, which are accessible to practitioners. It can be used to estimate the risk of cognitive impairment in healthy older adults.