Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
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.
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.
Not provided
Not provided
Not provided
Not provided
Not provided
| Measure | Description | Time Frame |
|---|---|---|
| Correlation between objective sleep health status and 24-hour glycemic variability | 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. | 4 years |
| Correlation Between Sleep Patterns and Brain Health | 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. | 4 years |
| Measure | Description | Time Frame |
|---|---|---|
| Correlation Between 24-Hour Glycemic Variability and Brain Health | 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. | 4 years |
Not provided
Not provided
Not provided
The study will include participants across a range of body weight categories, specifically normal weight, overweight, and obese individuals.
| Name | Role | Phone | Extension | |
|---|---|---|---|---|
| Christian Benedict, PhD | Contact | +46 18 471 43 30 | christian.benedict@farmbio.uu.se | |
| Bettina Sielaff, MD | Contact | +46 076-1411449 | bettina.sielaff@regiondalarna.se |
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
Not provided
| ID | Term |
|---|---|
| D009765 | Obesity |
| D050177 | Overweight |
| D003924 | Diabetes Mellitus, Type 2 |
| ID | Term |
|---|---|
| D044343 | Overnutrition |
| D009748 | Nutrition Disorders |
| D009750 | Nutritional and Metabolic Diseases |
| D001835 | Body Weight |
Not provided
Not provided
Not provided
Not provided
Not provided
Blood, fecal sample
| Correlation Between Sleep Patterns,Gut Microbiota Composition and Brain Health | 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. | 4 years |
| Correlation Between 24-Hour Glycemic Variability and Gut Microbiota Composition | 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. | 4 years |
| Correlation Between Sleep Patterns, Adiposity, Food Choices, and Blood Levels of Appetite-Regulating Hormones | 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. | 4 years |
| Correlation Between Meal Timing,Sleep Health, and Glycemic Control | 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. | 4 years |
| Correlation Between Sleep Health and Mood | 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. | 4 years |
| Correlation Between 24-Hour Glycemic Variability and Mood | 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. | 4 years |
| Quantification of circulating cancer-related biomarkers via Olink Flex Panel | 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 | 4 years |
| Broad proteomic profiling using Olink Reveal Panel | 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. | 4 years |
| Disease prediction by quantification of exosomes | 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. | 4 years |
| Physical Activity Measured by Fitbit and Its Associations with Glycemic Control and Brain Health | 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. | 4 years |
| Association Between Screen Time Patterns and Health Outcomes: Investigating Links with Glycemic Control, Brain Health, 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. | 4 years |
| 15. Association Between Subjective Sleep Health and 24-Hour Glycemic Variability and Brain Health | 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. | 4 years |
| D012816 |
| Signs and Symptoms |
| D013568 | Pathological Conditions, Signs and Symptoms |
| D003920 | Diabetes Mellitus |
| D044882 | Glucose Metabolism Disorders |
| D008659 | Metabolic Diseases |
| D004700 | Endocrine System Diseases |