Cognitive impairment is often diagnosed years after symptoms begin, motivating earlier, noninvasive risk assessment. We investigated whether multimodal features derived from overnight polysomnography can predict cognitive impairment 1-6 years after a sleep study. Each 30-second epoch was represented by 105 features, including EEG, EMG, SpO2, heart-rate variability, and automated sleep annotations. We then fused the epoch-level features with whole-night aggregated features and demographics using a temporal convolutional network for binary prediction of cognitive impairment. As part of the George B. Moody PhysioNet Challenge 2026, our team (CLECLINIC) achieved an age-conditioned AUROC of 0.696 and a prevalence-weighted reward of 0.125 on the official validation set. These results support the potential of routine polysomnography for opportunistic cognitive-risk assessment.