Investigating the Impact of Smoking, BMI, and Metabolic Markers as Predictors for PSQI-Derived Sleep Phenotypes

Authors: Zaid Ashar, Siena Jacobson, Nida Saif, Kelly Ahn, Rania Khan, Sara Saleh, Nema Sayeed, Maxime Argenson

Field of Study: Biological Sciences

Program Affiliation: Metabolic and Exercise Physiology Lab

Faculty Mentors: Daniel Miller, Lina Begdache

Easel: 7

Timeslot: Afternoon

Abstract: Sleep quality is commonly assessed in clinical settings using the Pittsburgh Sleep Quality Index (PSQI), which provides an overall score but may overlook differences in how sleep dysfunction presents. 129 participants completed the PSQI with physiological and behavioral assessments. Principal component analysis (PCA) was performed in Python 3.14.3 to derive sleep phenotypes, and regression models assessed associations with lifestyle factors. Three sleep phenotypes were analyzed: lower global burden (n=56), disturbances (n=60), and short sleep latency (n=13). Health score (p<0.05), female sex (p<0.01), and smoking (p<0.05) predicted phenotype membership. Smokers were ~10 times more likely to have short sleep latency (p<0.01). Smoking was associated with sleep initiation (p<0.001) in models including respiratory quotient (RQ). These findings support a phenotype-based approach to sleep and suggest metabolic and lifestyle factors contribute to differences in sleep patterns.