From gut health to metabolism: How sleep controls more than you think

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How well you sleep could be impacting your body fat and heart health. Cutting-edge research shows that your sleep patterns could be silently shaping your risk for heart disease, diabetes, and even osteoporosis—scientists now reveal the hidden power of sleep in predicting long-term health.

Study: Phenome-wide associations of sleep characteristics in the Human Phenotype Project. Image Credit: Porvolio / Shutterstock

A recent study published in the journal Nature Medicine analyzed thousands of individuals to uncover how sleep is connected to major physiological systems and body characteristics. By examining a vast dataset, researchers explored links between sleep and cardiovascular health, metabolism, mental well-being, and more, offering new insights into the far-reaching effects of sleep on the body.

Background

Sleep is a fundamental biological process that supports cognitive function, immune response, and metabolic regulation. While researchers have long understood the consequences of sleep deprivation, the broader connections between sleep characteristics and multiple physiological systems remain largely unexplored.

Previous studies have focused on sleep disorders such as insomnia and obstructive sleep apnea and linked them to heart disease, obesity, and diabetes. However, these studies often rely on self-reported data or use limited objective sleep measurements in their assessments.

A comprehensive, high-resolution approach is needed to fully understand the interplay between sleep and overall health. This study aimed to bridge that gap by leveraging large-scale sleep monitoring and extensive physiological profiling data.

The Current Study

The researchers analyzed data from 6,366 individuals (3,043 males and 3,323 females) between the ages of 40 and 75 who participated in the Human Phenotype Project (HPP), a cohort primarily composed of healthy individuals of European (Ashkenazi) Jewish descent. 

They collected 16,812 nights of sleep data using home sleep apnea tests, which recorded metrics such as respiratory patterns (e.g., peripheral apnea-hypopnea index, or pAHI), snoring, sleep positions, sleep stages, and oxygen saturation levels (e.g., mean nadir SpO2). Additionally, pulse rate variability (PRV) was measured as an indicator of heart rate fluctuations during sleep.

The study integrated data from 16 other physiological systems, including cardiovascular health, metabolism, mental health, gut microbiome composition, and immune function, to examine how sleep characteristics interact with overall health.

The researchers employed advanced statistical modeling techniques, including Spearman correlations, mediation analyses, and machine learning models (e.g., LASSO regression and gradient boosting decision trees), to assess these relationships.

The study quantified the strength of associations between sleep traits and body characteristics by comparing sleep data with biomarkers such as blood triglycerides, insulin resistance markers, and bone density measurements.

Furthermore, a longitudinal subset of 574 participants underwent two sleep monitoring series separated by two years, allowing the researchers to examine how sleep patterns evolved over time and their potential long-term health implications. The study also considered lifestyle factors, such as diet and physical activity, to determine their contribution to sleep variations.

Major Insights

The study revealed that sleep patterns are intricately linked to various physiological systems. Poor sleep quality, including reduced deep sleep, increased nighttime disruptions, and lower sleep efficiency, was associated with higher risks of cardiovascular disease, metabolic disorders, and mental health conditions.

Specifically, pAHI (a measure of sleep apnea severity) was more strongly associated with cardiovascular and metabolic diseases, while sleep architecture-related features (e.g., sleep duration and deep sleep percentage) were more predictive of endocrinology diseases.

Moreover, variability in sleep duration and lower sleep efficiency were also linked to increased markers of inflammation and immune system dysregulation.

The findings also reported significant sex differences in sleep-related health risks. Men exhibited a stronger correlation between sleep apnea severity and cardiovascular conditions, while women displayed a pronounced link between sleep disturbances and osteoporosis and osteopenia, especially after menopause, due to decreased levels of estrogen. 

Notably, the study highlighted that visceral adipose tissue (VAT), a type of deep belly fat, was the strongest predictor of sleep apnea severity, surpassing traditional risk factors such as body mass index (BMI) and age. 

The research further demonstrated that 15% of body characteristics across 15 physiological systems could be predicted by sleep traits, supporting the idea that sleep monitoring could serve as a predictive tool for broader health risks.

Additionally, the study uncovered that gut microbiome composition, mental health, and dietary habits, particularly in women, were more predictive of obstructive sleep apnea (OSA) symptoms, such as excessive daytime sleepiness, than traditional factors, including BMI and VAT. However, lifestyle behaviors, including smoking, sedentary time, and physical activity, were also found to significantly influence sleep patterns.

These findings emphasized the critical role of sleep in maintaining overall health and highlighted the importance of personalized sleep interventions.

Conclusions

Overall, the study stressed the extensive impact of sleep on various body systems, reinforcing the need for better sleep health awareness. By integrating sleep monitoring with multi-omic profiling, the researchers provided a roadmap for future studies to explore the causal relationships between sleep and disease.

The findings also highlighted the potential of sleep-based interventions in preventing chronic conditions. However, the study had some limitations, including potential selection bias due to the cohort’s demographic composition (primarily healthy, educated individuals of European (Ashkenazi) Jewish descent) and the lack of respiratory function assessments (e.g., spirometry), which could have provided a more complete picture of respiratory health.