Cognitive impairment has been associated with alter- ations in multiple physiological processes during sleep, motivating the analysis of multimodal polysomnography beyond conventional sleep-stage measures. We devel- oped a pipeline for predicting cognitive impairment from polysomnographic recordings using modality- and sleep- stage-specific handcrafted features. The extracted fea- ture space was evaluated using missingness-based filter- ing and stratified feature selection, while additional ex- periments incorporated learned representations generated by the SleepFM foundation model. XGBoost classifiers were used for prediction. Internal experiments showed predictive information across all investigated physiologi- cal modalities and identified respiratory and Wake-stage features as particularly informative. A respiratory-only model was successfully evaluated on the hidden Challenge validation set, achieving an AUROC of 0.884, an age- weighted AUROC of 0.748, and an age-conditioned AU- ROC of 0.727. These results demonstrate that respiratory physiology alone contains substantial information asso- ciated with cognitive impairment, although the reduction after age adjustment indicates that part of this associa- tion is related to age. The internal feature analysis fur- ther suggests that informative respiratory characteristics are distributed across multiple sensors and signal descrip- tors rather than being confined to a single conventional biomarker.