Breathing cessation events that compose the apnea-hypopnea index are distinctively associated with the adverse outcomes in Alzheimer's disease

Predicting Alzheimer's disease CSF core biomarkers: a multimodal Machine Learning approach

Introduction

Alzheimer's disease (AD) is a progressive neurodegenerative disorder. Current core cerebrospinal fluid (CSF) AD biomarkers, widely employed for diagnosis, require a lumbar puncture to be performed, making them impractical as screening tools. Considering the role of sleep disturbances in AD, recent research suggests quantitative sleep electroencephalography features as potential non-invasive biomarkers of AD pathology. However, quantitative analysis of comprehensive polysomnography (PSG) signals remains relatively understudied. PSG is a non-invasive test enabling qualitative and quantitative analysis of a wide range of parameters, offering additional insights alongside other biomarkers. Machine Learning (ML) gained interest for its ability to discern intricate patterns within complex datasets, offering promise in AD neuropathology detection. Therefore, this study aims to evaluate the effectiveness of a multimodal ML approach in predicting core AD CSF biomarkers.

Methods

Mild-moderate AD patients were prospectively recruited for PSG, followed by testing of CSF and blood samples for biomarkers. PSG signals underwent preprocessing to extract non-linear, time domain and frequency domain statistics quantitative features. Multiple ML algorithms were trained using four subsets of input features: clinical variables (CLINVAR), conventional PSG parameters (SLEEPVAR), quantitative PSG signal features (PSGVAR) and a combination of all subsets (ALL). Cross-validation techniques were employed to evaluate model performance and ensure generalizability. Regression models were developed to determine the most effective variable combinations for explaining variance in the biomarkers.

Results

On 49 subjects, Gradient Boosting Regressors achieved the best results in estimating biomarkers levels, using different loss functions for each biomarker: least absolute deviation (LAD) for the Aβ42, least squares (LS) for p-tau and Huber for t-tau. The ALL subset demonstrated the lowest training errors for all three biomarkers, albeit with varying test performance. Specifically, the SLEEPVAR subset yielded the best test performance in predicting Aβ42, while the ALL subset most accurately predicted p-tau and t-tau due to the lowest test errors.

Conclusions

Multimodal ML can help predict the outcome of CSF biomarkers in early AD by utilizing non-invasive and economically feasible variables. The integration of computational models into medical practice offers a promising tool for the screening of patients at risk of AD, potentially guiding clinical decisions.

Effect of Continuous Positive Airway Pressure on Incident Frailty in Elderly Patients with Obstructive Sleep Apnea: A Study Based on Propensity Score Matching

Background

The concomitant rise in the prevalence of obstructive sleep apnea (OSA) and frailty among the elderly population has been linked to an increase in mortality rates. Despite continuous positive airway pressure (CPAP) being the gold standard treatment for OSA, its impact on incident frailty remains inadequately explored.

Methods

In this cohort study, we analyzed data from 1290 patients diagnosed with OSA, aged 60 years and older. A subset of 71 patients who demonstrated high adherence to CPAP therapy were categorized as the CPAP group. Propensity score matching (PSM) was employed at a 1:4 ratio, matching for variables such as age, gender, body mass index (BMI), and sleep apnea-hypopnea index (AHI), to establish a non-CPAP group for comparison. The FRAIL scale was utilized to evaluate the frailty status of participants. Logistic regression analysis examined the relationship between CPAP therapy and incident frailty, as well as its individual components, in elderly patients with OSA.

Results

During a median follow-up period of 52 months, incident frailty was observed in 70 patients (19.7%). Patients with OSA receiving CPAP therapy exhibited a lower incidence of frailty compared to those not receiving CPAP (11.26% vs 21.83%, P=0.045). In the multivariate model, CPAP therapy was significantly correlated with a reduced risk of incident frailty (OR = 0.36, 95% CI, 0.15-0.88; P = 0.025). Subcomponent analyses revealed that CPAP was associated with a lower risk of fatigue (OR=0.35, 95% CI, 0.19-0.63; P < 0.001), resistance (OR = 0.32, 95% CI, 0.14-0.74; P=0.008), and weight loss (OR = 0.38, 95% CI, 0.19-0.75; P = 0.007).

Conclusion

CPAP therapy was associated with a reduced risk of incident frailty among elderly patients with OSA.