Predictive Modeling Using a Composite Index of Sleep and Cognition in the Alzheimer's Continuum: A Decade-Long Historical Cohort Study

Abstract

Background: Sleep disturbances frequently affect Alzheimer's disease (AD), with up to 65% patients reporting sleep-related issues that may manifest up to a decade before AD symptoms.

Objective: To construct a nomogram that synthesizes sleep quality and cognitive performance for predicting cognitive impairment (CI) conversion outcomes.

Methods: Using scores from three well-established sleep assessment tools, Pittsburg Sleep Quality Index, REM Sleep Behavior Disorder Screening Questionnaire, and Epworth Sleepiness Scale, we created the Sleep Composite Index (SCI), providing a comprehensive snapshot of an individual's sleep status. Initially, a CI conversion prediction model was formed via COX regression, fine-tuned by bidirectional elimination. Subsequently, an optimized prediction model through COX regression, depicted as a nomogram, offering predictions for CI development in 5, 8, and 12 years among cognitively unimpaired (CU) individuals.

Results: After excluding CI patients at baseline, our study included 816 participants with complete baseline and follow-up data. The CU group had a mean age of 66.1±6.7 years, with 36.37% males, while the CI group had an average age of 70.3±9.0 years, with 39.20% males. The final model incorporated glial fibrillary acidic protein, Verbal Fluency Test and SCI, and an AUC of 0.8773 (0.792-0.963).

Conclusions: In conclusion, the sleep-cognition nomogram we developed could successfully predict the risk of converting to CI in elderly participants and could potentially guide the design of interventions for rehabilitation and/or cognitive enhancement to improve the living quality for healthy older adults, detect at-risk individuals, and even slow down the progression of AD.

Keywords: Alzheimer’s disease; biomarkers; prediction; sleep.

Fig. 1

The flow chart of this study.

Fig. 2

The nomogram shows the calculation of the final probability of the adverse outcome (CU converting to CI) in 5, 8, and 12 years. A total point needs to be firstly determined based on the value of individual predictors using the nomogram: a vertical line was drawn from each predictor’s line to the “points” line to obtain an individual predictor point, all of which were then summed up to the aforementioned total point. Then, another vertical line was drawn from the “total points” lines to the bottom three risk lines, getting the probability of converting to CI in 5, 8, and 12 years, respectively.

Fig. 3

A, B) ROC curves using the nomogram to predict CI transitions at 5, 8, and 12 years for the training and validation sets, respectively. C, D) Decision curve analyses using nomograms to predict CI transitions in the training and validation cohorts at 5, 8, and 12 years, respectively. The graph plotted the net benefit against threshold probability. The gray line represents the treatment-for-all scenario in which all patients would transform to CI, and the thin black line represents treatment-for-none scenario in which no patient transforms to CI. The net benefit was calculated by subtracting the proportion of all false-positive patients from the proportion of true positives, weighted by the loss brought by no treatment to CI and unnecessary treatment.

Fig. 4

Calibration plot of the optimal model in training (A) and validation (B) cohort. The actual observed rate of conversion is shown on the y-axis, and the nomogram-predicted probability of conversion is shown on the x-axis.