As part of the George B. Moody PhysioNet Challenge 2026, we developed a computational approach based on SleepFM to predict future diagnosis of cognitive impairment from polysomnography (PSG). Our team, Kust_MeAI, proposed a transfer-learning framework combining a pretrained SleepFM foundation model, patient-level temporal modeling of multimodal PSG embeddings, and automated clinical features. PSG recordings were processed through a SleepFM-compatible pipeline with dynamic channel selection, normalization, and token-based segmentation, and CAISR-derived annotations were used to derive patient-level clinical features. A publicly released SleepFM model was used as a frozen multimodal encoder to extract representations from EEG, respiratory, ECG, and EMG signals. Multimodal embeddings were aggregated through spatial attention, bidirectional temporal modeling, and temporal attention pooling, then combined with demographic and clinical features for cognitive impairment prediction. We trained and evaluated the model using per-site cross validation. Our model received a Challenge score of 0.655 (ranked 46th out of 102 teams) on the hidden validation set.