Sleep, Glucose, and Brain Health in Obesity and Overweight Individuals

An assessment will be made to determine whether poorer sleep health-measured using wearable-derived metrics such as sleep duration, sleep efficiency, sleep regularity, and the apnea-hypopnea index-is associated with a greater proportion of time spent outside the recommended glucose range of 70 to 180 mg/dL and with increased glucose variability, as indicated by the coefficient of variation. Additionally, we will evaluate which specific sleep metrics, including total sleep duration, sleep efficiency, sleep onset timing, sleep regularity, and the presence of sleep-disordered breathing, show the strongest associations with glycemic outcomes.

Assessment of whether poorer sleep health is associated with elevated concentrations of brain health biomarkers, including tau and neurofilament light chain (NfL), poorer performance on a verbal fluency task, and variations in total and regional gray matter volume.

This aim will assess whether increased 24-hour glycemic variability is associated with elevated concentrations of brain health biomarkers, including tau and neurofilament light chain (NfL), as well as variations in cognitive performance and brain structure, such as gray matter volume.

This aim will assess whether variations in sleep patterns, including sleep duration, efficiency, and regularity, are associated with differences in gut microbiota composition and diversity. In this context, we will also quantify fecal calprotectin concentrations to assess the presence of subclinical gut inflammation. And explore whether sleep-related changes in the gut microbiome are linked to brain health indicators such as neurodegenerative biomarkers and cognitive performance.

This aim will assess whether increased 24-hour glycemic variability is associated with changes in gut microbiota composition and diversity. In this context, we will also quantify fecal calprotectin concentrations to assess the presence of subclinical gut inflammation.

This aim will assess how variations in sleep patterns are associated with adiposity (body fat, waist circumference), food choices (derived from Food Frequency Questionnaire), and blood levels of appetite-regulating hormones, such as leptin, ghrelin, and adiponectin.

This aim will assess whether regularity in meal timing is associated with sleep health status, including sleep duration, efficiency, and consistency, especially the timing of the last meal before sleep and its association with sleep health. In addition, we will examine how eating patterns and food types influence glycemic variability.

This aim will assess whether poorer sleep health is associated with more negative affect and less positive affect, as measured by the Positive and Negative Affect Schedule (PANAS), increased depressive symptoms as assessed by the Montgomery-Åsberg Depression Rating Scale (MADRS), and lower scores on an app-based mood scale.

This aim will assess whether increased 24-hour glycemic variability is associated with more negative affect and less positive affect, as measured by the Positive and Negative Affect Schedule (PANAS), increased depressive symptoms as assessed by the Montgomery-Åsberg Depression Rating Scale (MADRS), and lower scores on an app-based mood scale administered daily.

This aim will assess the potential association between selected blood-based biomarkers, implicated in the pathogenesis of colorectal, breast, prostate, and lung cancer, and glycemic control (measured by continuous glucose monitoring), brain health (assessed via neurodegenerative biomarkers, MRI, and verbal fluency), and sleep patterns. Up to 30 biomarkers will be quantified using the Olink Flex panel with absolute concentration assays (pg/mL), enabling targeted evaluation of cancer-related molecular signatures

This aim will investigate whether glycemic control (measured by continuous glucose monitoring), brain health (assessed via neurodegenerative biomarkers, MRI, and verbal fluency), and sleep patterns are associated with protein profiles measured using the Olink Reveal panel, an exploratory platform targeting proteins involved in metabolic, immunologic, and neurodegenerative processes.

Determine if glycemic control (measured by continuous glucose monitoring), brain health (assessed via neurodegenerative biomarkers, MRI, and verbal fluency), and sleep patterns are associated with specific exosome signatures.

This aim will evaluate how physical activity levels, measured via Fitbit, are associated with glycemic control (measured by CGM), brain health (neurodegenerative biomarkers, MRI, and verbal fluency), and sleep patterns.

To investigate whether weekly screen time, as well as screen use prior to and during bedtime, are associated with glycemic control (measured by CGM), brain health (neurodegenerative biomarkers, MRI, and verbal fluency), and sleep patterns.

This aim will assess whether subjective sleep health-measured using the Insomnia Severity Index (ISI), a widely used questionnaire for evaluating insomnia severity, and the Pittsburgh Sleep Quality Index (PSQI)-is associated with glycemic control (measured by continuous glucose monitoring), brain health (neurodegenerative biomarkers, MRI, and verbal fluency), and objective sleep patterns.