Sleep biosignature of Type 2 diabetes: a case-control study

Altered sleep architecture in diabetes and prediabetes: findings from the Baependi Heart Study

STUDY OBJECTIVES

People with diabetes and prediabetes are more likely to have sleep-disordered breathing (SDB), but few studies examined sleep architecture in people with diabetes or prediabetes in the absence of moderate-severe SDB, which was the aim of our cross-sectional study.

METHODS

This cross-sectional sample is from the Baependi Heart Study, a family-based cohort of adults in Brazil. About 1074 participants underwent at-home polysomnography (PSG). Diabetes was defined as fasting glucose >125 mg/dL or HbA1c > 6.4 mmol/mol or taking diabetic medication, and prediabetes was defined as HbA1c ≥ 5.7 & <6.5 mmol/mol or fasting glucose ≥ 100 & ≤125 mg/dl. We excluded participants with an apnea-hypopnea index (AHI) ≥ 30 in primary analyses and ≥ 15 in secondary analysis. We compared sleep stages among the 3 diabetes groups (prediabetes, diabetes, neither).

RESULTS

Compared to those without diabetes, we found shorter REM duration for participants with diabetes (-6.7 min, 95%CI -13.2, -0.1) and prediabetes (-5.9 min, 95%CI -10.5, -1.3), even after adjusting for age, gender, BMI, and AHI. Diabetes was also associated with lower total sleep time (-13.7 min, 95%CI -26.8, -0.6), longer slow-wave sleep (N3) duration (+7.6 min, 95%CI 0.6, 14.6) and higher N3 percentage (+2.4%, 95%CI 0.6, 4.2), compared to those without diabetes. Results were similar when restricting to AHI < 15.

CONCLUSIONS

People with diabetes and prediabetes had less REM sleep than people without either condition. People with diabetes also had more N3 sleep. These results suggest that diabetes and prediabetes are associated with differences in sleep architecture, even in the absence of moderate-severe sleep apnea.

Altered sleep architecture in diabetes and prediabetes: findings from the Baependi Heart Study

Objective

People with diabetes are more likely to have obstructive sleep apnea, but there are few studies examining sleep architecture in people with diabetes, especially in the absence of moderate-severe sleep apnea. Therefore, we compared sleep architecture among people with diabetes, prediabetes or neither condition, whilst excluding people with moderate-severe sleep apnea.

Research design and methods

This sample is from the Baependi Heart Study, a prospective, family-based cohort of adults in Brazil. 1,074 participants underwent at-home polysomnography (PSG). Diabetes was defined as 1) FBG>125 OR 2) HbA1c>6.4 OR 3) taking diabetic medication, and prediabetes was defined as 1) [(5.7≤HbA1c≤6.4) OR (100≤FBG≤125)] AND 2) not taking diabetic medication. We excluded participants that had an apnea-hypopnea index (AHI)>30 from these analyses to reduce confounding due to severe sleep apnea. We compared sleep stages among the 3 groups.

Results

Compared to those without diabetes, we found shorter REM duration for participants with diabetes (-6.7min, 95%CI -13.2, -0.1) or prediabetes (-5.9min, 95%CI -10.5, -1.3), even after adjusting for age, gender, BMI, and AHI. Diabetes was also associated with lower total sleep time (-13.7min, 95%CI -26.8, -0.6), longer slow-wave sleep (N3) duration (+7.6min, 95%CI 0.6, 14.6) and higher N3 percentage (+2.4%, 95%CI 0.6, 4.2), compared to those without diabetes.

Conclusions

People with diabetes and prediabetes had less REM sleep after taking into account potential confounders, including AHI. People with diabetes also had more N3 sleep. These results suggest that diabetes is associated with different sleep architecture, even in the absence of moderate-severe sleep apnea.

Prediabetes Is Associated with Increased Prevalence of Sleep-Disordered Breathing

Type 2 diabetes leads to severe nocturnal hypoxemia, with an increase in apnea events and daytime sleepiness. Hence, we assessed sleep breathing parameters in the prediabetes stage. A cross-sectional study conducted on 966 middle-aged subjects without known pulmonary disease (311 patients with prediabetes and 655 controls with normal glucose metabolism) was conducted. Prediabetes was defined by glycated hemoglobin (HbA1c), and a nonattended overnight home sleep study was performed. Participants with prediabetes (n = 311) displayed a higher apnea−hypopnea index (AHI: 12.7 (6.1;24.3) vs. 9.5 (4.2;19.6) events/h, p < 0.001) and hypopnea index (HI: 8.4 (4.0;14.9) vs. 6.0 (2.7;12.6) events/h, p < 0.001) than controls, without differences in the apnea index. Altogether, the prevalence of obstructive sleep apnea was higher in subjects with prediabetes than in controls (78.1 vs. 69.9%, p = 0.007). Additionally, subjects with prediabetes presented impaired measurements of the median and minimum nocturnal oxygen saturation, the percentage of time spent with oxygen saturations below 90%, and the 4% oxygen desaturation index in comparison with individuals without prediabetes (p < 0.001 for all). After adjusting for age, sex, and the presence of obesity, HbA1c correlated with the HI in the entire population (r = 0.141, p < 0.001), and the presence of prediabetes was independently associated with the AHI (B = 2.20 (0.10 to 4.31), p = 0.040) as well as the HI (B = 1.87 (0.61 to 3.14), p = 0.004) in the multiple linear regression model. We conclude that prediabetes is an independent risk factor for an increased AHI after adjusting for age, sex, and obesity. The enhanced AHI is mainly associated with increments in the hypopnea events.

Poor Sleep Quality Linked to Decreased Brain Gray Matter Density in Adults with Type 2 Diabetes

BACKGROUND

Poor sleep is common in adults with Type 2 Diabetes Mellitus (T2DM), which may contribute to brain tissue changes. However, the impact of sleep quality on brain tissue in T2DM individuals is unclear. We aimed to evaluate differential sleep quality with brain changes, and brain tissue integrity in T2DM patients.

METHODS

Data were collected from 34 patients with T2DM and included sleep quality (assessed by the Pittsburgh Sleep Quality Index [PSQI], and high-resolution T1-weighted brain images using a 3.0-Tesla MRI scanner. Gray matter density (GMD) maps were compared between subjects with good vs poor sleep quality as assessed by PSQI (covariates: age, sex, BMI).

RESULTS

Of 34 T2DM patients, 17 showed poor sleep quality. Multiple brain sites, including the hippocampus, cerebellum, prefrontal, amygdala, thalamus, hypothalamus, insula, cingulate, and temporal areas, showed reduced gray matter in T2DM patients with poor sleep quality over patients with good sleep quality. Negative associations emerged between PSQI scores and gray matter density in multiple areas.

CONCLUSIONS

T2DM patients with poor sleep quality show brain tissue changes in sites involved in sleep regulation. Findings indicate that improving sleep may help mitigate brain tissue damage, and thus, improve brain function in T2DM patients.

Sleep disorders in people with type 2 diabetes and associated health outcomes: a review of the literature

Sleep disorders are linked to development of type 2 diabetes and increase the risk of developing diabetes complications. Treating sleep disorders might therefore play an important role in the prevention of diabetes progression. However, the detection and treatment of sleep disorders are not part of standardised care for people with type 2 diabetes. To highlight the importance of sleep disorders in people with type 2 diabetes, we provide a review of the literature on the prevalence of sleep disorders in type 2 diabetes and the association between sleep disorders and health outcomes, such as glycaemic control, microvascular and macrovascular complications, depression, mortality and quality of life. Additionally, we examine the extent to which treating sleep disorders in people with type 2 diabetes improves these health outcomes. We performed a literature search in PubMed from inception until January 2021, using search terms for sleep disorders, type 2 diabetes, prevalence, treatment and health outcomes. Both observational and experimental studies were included in the review. We found that insomnia (39% [95% CI 34, 44]), obstructive sleep apnoea (55-86%) and restless legs syndrome (8-45%) were more prevalent in people with type 2 diabetes, compared with the general population. No studies reported prevalence rates for circadian rhythm sleep-wake disorders, central disorders of hypersomnolence or parasomnias. Additionally, several cross-sectional and prospective studies showed that sleep disorders negatively affect health outcomes in at least one diabetes domain, especially glycaemic control. For example, insomnia is associated with increased HbA_1c levels (2.51 mmol/mol [95% CI 1.1, 4.4]; 0.23% [95% CI 0.1, 0.4]). Finally, randomised controlled trials that investigate the effect of treating sleep disorders in people with type 2 diabetes are scarce, based on a small number of participants and sometimes inconclusive. Conventional therapies such as weight loss, sleep education and cognitive behavioural therapy seem to be effective in improving sleep and health outcomes in people with type 2 diabetes. We conclude that sleep disorders are highly prevalent in people with type 2 diabetes, negatively affecting health outcomes. Since treatment of the sleep disorder could prevent diabetes progression, efforts should be made to diagnose and treat sleep disorders in type 2 diabetes in order to ultimately improve health and therefore quality of life.

Physiological sleep measures predict time to 15-year mortality in community adults: Application of a novel machine learning framework

Clarifying whether physiological sleep measures predict mortality could inform risk screening; however, such investigations should account for complex and potentially non-linear relationships among health risk factors. We aimed to establish the predictive utility of polysomnography (PSG)-assessed sleep measures for mortality using a novel permutation random forest (PRF) machine learning framework. Data collected from the years 1995 to present are from the Sleep Heart Health Study (SHHS; n = 5,734) and the Wisconsin Sleep Cohort Study (WSCS; n = 1,015), and include initial assessments of sleep and health, and up to 15 years of follow-up for all-cause mortality. We applied PRF models to quantify the predictive abilities of 24 measures grouped into five domains: PSG-assessed sleep (four measures), self-reported sleep (three), health (eight), health behaviours (four), and sociodemographic factors (five). A 10-fold repeated internal validation (WSCS and SHHS combined) and external validation (training in SHHS; testing in WSCS) were used to compute unbiased variable importance metrics and associated p values. We observed that health, sociodemographic factors, and PSG-assessed sleep domains predicted mortality using both external validation and repeated internal validation. The PSG-assessed sleep efficiency and the percentage of sleep time with oxygen saturation <90% were among the most predictive individual measures. Multivariable Cox regression also revealed the PSG-assessed sleep domain to be predictive, with very low sleep efficiency and high hypoxaemia conferring the highest risk. These findings, coupled with the emergence of new low-burden technologies for objectively assessing sleep and overnight oxygen saturation, suggest that consideration of physiological sleep measures may improve risk screening.

Pulmonary Function and Sleep Breathing: Two New Targets for Type 2 Diabetes Care

Population-based studies showing the negative impact of type 2 diabetes (T2D) on lung function are overviewed. Among the well-recognized pathophysiological mechanisms, the metabolic pathways related to insulin resistance (IR), low-grade chronic inflammation, leptin resistance, microvascular damage, and autonomic neuropathy are emphasized. Histopathological changes are exposed, and findings reported from experimental models are clearly differentiated from those described in humans. The accelerated decline in pulmonary function that appears in patients with cystic fibrosis (CF) with related abnormalities of glucose tolerance and diabetes is considered as an example to further investigate the relationship between T2D and the lung. Furthermore, a possible causal link between antihyperglycemic therapies and pulmonary function is examined. T2D similarly affects breathing during sleep, becoming an independent risk factor for higher rates of sleep apnea, leading to nocturnal hypoxemia and daytime sleepiness. Therefore, the impact of T2D on sleep breathing and its influence on sleep architecture is analyzed. Finally, the effect of improving some pathophysiological mechanisms, primarily IR and inflammation, as well as the optimization of blood glucose control on sleep breathing is evaluated. In summary, the lung should be considered by those providing care for people with diabetes and raise the central issue of whether the normalization of glucose levels can improve pulmonary function and ameliorate sleep-disordered breathing. Therefore, patients with T2D should be considered a vulnerable group for pulmonary dysfunction. However, further research aimed at elucidating how to screen for the lung impairment in the population with diabetes in a cost-effective manner is needed.