Clinical trial NCT06968728: Sleep, Glucose, and Brain Health in Obesity and Overweight Individuals
Official title: The Interplay Between Sleep Disturbances, Glucose Regulation, and Brain Health in Individuals With Overweight and Obesity
Study type: OBSERVATIONAL
Overall status: NOT_YET_RECRUITING
Conditions: Obesity and Overweight
Type 2 Diabetes
Sleep
Brain Health
Keywords: obesity
overweight
type 2 diabetes
glucose control
brain health
diet
sleep disruption
gut microbiota
Lead sponsor: Uppsala University
Sponsor class: OTHER
First posted: 2025-05-13
Enrollment: 400
Sex: ALL
Minimum age (years): 40
Maximum age (years): 65
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Observational model: COHORT
Time perspective: PROSPECTIVE
Sampling method: NON_PROBABILITY_SAMPLE
Study population: The study will include participants across a range of body weight categories, specifically normal weight, overweight, and obese individuals.

Brief summary: The primary aim of the NNF study is to investigate both cross-sectional and longitudinal associations between sleep patterns-measured over two consecutive weeks at baseline and again one year later-and indicators of glycemic control and brain health in a cohort of middle-aged adults. Through this effort, the investigators hope to identify potential sleep-related biomarkers and behavioral targets for early intervention to support metabolic and cognitive health.

Detailed description: This study investigates the relationship between sleep patterns, glycemic control, and brain health in adults, with a particular focus on middle-aged individuals, including those with normal weight as well as those with overweight or obesity-the latter representing a demographic at elevated risk for type 2 diabetes (T2D). Individual sleep exposures such as sleep duration, sleep quality, sleep timing, and sleep regularity have all been associated with impaired glucose metabolism and indicators of compromised brain health. However, well-controlled studies that comprehensively examine how overall sleep health-considering all these dimensions-is related to both glycemic control and brain health remain limited.

To address this research aim, participants will be recruited from two Swedish sites: Dalarna and Uppsala. Following enrollment and an on-site baseline assessment session, participants' lifestyle behaviors will be monitored over 14 consecutive days. Specifically, nighttime sleep will be assessed using a wearable, FDA-cleared sleep device (SleepImage); 24-hour interstitial glucose levels will be tracked via a continuous glucose monitor (CGM); and physical activity will be recorded using a wrist-worn activity tracker (Fitbit). During this monitoring period, participants will also log their dietary intake and mood through smartphone-based assessments. After the 14-day monitoring period, participants will return for a concluding on-site session.

During the on-site sessions or the 14-day monitoring period, biological samples will be collected to assess metabolic, hormonal, inflammatory, and neurodegenerative biomarkers. Blood samples will be analyzed for markers such as sex hormones, HbA1c, C-reactive protein (CRP), leptin, ghrelin, adiponectin, and brain health indicators including brain-derived tau and neurofilament light chain (NfL), in addition to broader proteomic and metabolomic profiles. Stool samples will also be collected for further analyses. In addition to providing biological samples, all participants-provided they meet inclusion criteria and give informed consent-will undergo structural brain imaging using magnetic resonance imaging (MRI). Cognitive assessments will include verbal fluency tasks and validated psychological instruments to evaluate mood and cognitive function.

The entire protocol will be repeated after one year, allowing for the assessment of longitudinal changes in sleep patterns, metabolic health, and brain function over time.

The primary aim of the NNF study is to investigate both cross-sectional and longitudinal associations between sleep patterns-measured over two consecutive weeks at baseline and again one year later-and indicators of glycemic control and brain health in a cohort of middle-aged adults. Secondary aims include, for example, exploring how sleep patterns relate to variability in protein and metabolite abundances in blood, among other metabolic, cognitive, and biological outcomes detailed below.

Eligibility criteria: * Be between 40 and 65 years of age
* Be classified as having normal weight, overweight, or obesity
* Not be pregnant
* Not be currently using glucose-lowering medications or undergoing glucose therapy
* Not have a diagnosis of type 1 or type 2 diabetes mellitus
* Not have kidney disease, thyroid disorders, or inflammatory bowel disease
* Not have undergone bariatric surgery
* Not actively participating in a weight-loss diet
* Not using medications that affect glucose levels or taking sleep-inducing pills
* Not working night shifts
* Not have traveled across time zones in the past 3 months
* Not have any severe chronic medical illnesses or severe psychiatric disorders
* Be able and willing to wear all required study monitoring devices
* Own a smartphone with Bluetooth capability

Primary outcomes:
measure: Correlation between objective sleep health status and 24-hour glycemic variability
timeFrame: 4 years
measure: Correlation Between Sleep Patterns and Brain Health
timeFrame: 4 years
Secondary outcomes:
measure: Correlation Between 24-Hour Glycemic Variability and Brain Health
timeFrame: 4 years
measure: Correlation Between Sleep Patterns,Gut Microbiota Composition and Brain Health
timeFrame: 4 years
measure: Correlation Between 24-Hour Glycemic Variability and Gut Microbiota Composition
timeFrame: 4 years
measure: Correlation Between Sleep Patterns, Adiposity, Food Choices, and Blood Levels of Appetite-Regulating Hormones
timeFrame: 4 years
measure: Correlation Between Meal Timing,Sleep Health, and Glycemic Control
timeFrame: 4 years
measure: Correlation Between Sleep Health and Mood
timeFrame: 4 years
measure: Correlation Between 24-Hour Glycemic Variability and Mood
timeFrame: 4 years
measure: Quantification of circulating cancer-related biomarkers via Olink Flex Panel
timeFrame: 4 years
measure: Broad proteomic profiling using Olink Reveal Panel
timeFrame: 4 years
measure: Disease prediction by quantification of exosomes
timeFrame: 4 years
measure: Physical Activity Measured by Fitbit and Its Associations with Glycemic Control and Brain Health
timeFrame: 4 years
measure: Association Between Screen Time Patterns and Health Outcomes: Investigating Links with Glycemic Control, Brain Health, and Sleep Patterns
timeFrame: 4 years
measure: 15. Association Between Subjective Sleep Health and 24-Hour Glycemic Variability and Brain Health
timeFrame: 4 years
Other outcomes:
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