Sleep disturbances—including insomnia, obstructive sleep apnea, and circadian rhythm disruption—are increasingly recognized as important yet understudied contributors to Type 2 Diabetes Mellitus (T2DM). Prior reviews typically focus on single disorders or mechanisms, limiting understanding of how different scientific traditions conceptualize the sleep–T2DM relationship. This narrative review followed RAMESES publication standards to synthesize how four research disciplines—endocrinology, sleep medicine, behavioral science, and chronobiology—have investigated sleep disturbances in T2DM. Searches were conducted in PubMed, Scopus, Web of Science, ProQuest, and EBSCO (final search: 17 January 2025). Of 2,074 records identified, 62 English-language documents met inclusion criteria. Studies were appraised using tradition-specific criteria, and findings were integrated through chronological storyline development and cross-tradition synthesis. Across traditions, insufficient, fragmented, or mistimed sleep was consistently linked to insulin resistance, impaired glucose tolerance, appetite dysregulation, and adverse body composition. Endocrinology emphasized hormonal, inflammatory, and oxidative pathways; sleep medicine focused on obstructive sleep apnea (OSA) and insomnia, with mixed evidence for continuous positive airway pressure (CPAP) effects on HbA1c. Behavioral research highlighted the influence of disturbed sleep on appetite, self-regulation, and treatment adherence. Chronobiology demonstrated the metabolic consequences of circadian misalignment and variability in melatonin signaling. Although key mechanistic themes converged, the majority of studies were observational or short-term experimental, limiting causal inference; instead, each tradition emphasized distinct explanatory frameworks and variables of interest in linking sleep with metabolic outcomes. Sleep disturbances appear to be modifiable components of a broader metabolic and psychosocial network in T2DM. Incorporating structured sleep assessment and targeted interventions—such as cognitive behavioral therapy for insomnia (CBT-I), CPAP for obstructive sleep apnea, and circadian-based strategies including scheduled light exposure and sleep–wake regularization—may enhance clinical outcomes. A tradition-sensitive synthesis clarifies sources of divergence in the literature and provides a foundation for more coherent, mechanism-informed research.
Patients may be less likely to be screened by their PCP for OSA compared to CVD. The ASCVD risk score and STOP-BANG questionnaire assess overlapping cardiometabolic risk factors. This study examined the correlation between ASCVD and STOP-BANG scores and whether combining them alters OSA screening performance compared to STOP-BANG alone. A cross-sectional secondary analysis of 201 participants (ages 40–75) from two National Sleep Research Resource datasets were utilized. Spearman correlations were calculated between ASCVD, STOP-BANG, and apnea–hypopnea index (AHI). Diagnostic performance metrics (sensitivity, specificity, positive and negative predictive values, likelihood ratios, area under the curve) were calculated for ASCVD (≥ 7.5
Sleep is fundamental for optimal health and well-being. Many adults do not attain adequate sleep due to detrimental sleep habits. An instrument to assess sleep hygiene could be valuable in evaluating patients’ sleep and might facilitate improvements in sleep and sleep-related health. We developed a comprehensive, 20-item sleep hygiene survey, the Atlantic Sleep Hygiene Questionnaire (ASHQ), based on an extensive review and critical analysis of the current sleep hygiene literature. ASHQ was administered to more than 750 consecutive adult patients in a sleep clinic. Patients also completed surveys measuring sleepiness (Epworth Sleepiness Scale, ESS) and fatigue (Fatigue Severity Scale, FSS), as well as a single-item analog survey of sleep quality (Sleep Quality Scale, SQS), and these were compared to the ASHQ. An exploratory ASHQ score of 8 or more (detrimental sleep habits) was significantly associated with abnormal sleepiness (ESS score of 10 or greater, out of a maximum of 24) and abnormal fatigue (FSS score of 36 or greater, out of a maximum of 63). Worse sleep hygiene (higher ASHQ scores) was also associated with lower subjective sleep quality based on SQS scores. Hence, ASHQ scores directly correlated with subjective sleepiness, fatigue and overall sleep quality. ASHQ may provide a novel approach to assess sleep hygiene and could potentially improve sleep, quality of life and general health, as well as specific medical conditions in which sleep may play a role.
Atopic dermatitis (AD) is a common inflammatory skin disorder in infants and is frequently associated with sleep disturbances. Although the relationship between disease severity and sleep quality is well established, the effect of lesion localization on sleep has not been thoroughly investigated. This retrospective cross-sectional study included 60 infants aged 6–36 months diagnosed with AD and 60 age-matched healthy controls. Sleep quality was assessed using the Children’s Sleep Habits Questionnaire (CSHQ), which, although originally developed for older children, has been used in younger populations in previous studies. Disease severity was evaluated using the Scoring Atopic Dermatitis (SCORAD) and Patient-Oriented Eczema Measure (POEM) indices. Lesion localization was determined through clinical examination and categorized as face/neck, trunk/extremities, or generalized (involvement of two or more regions). Parametric and nonparametric tests were used for intergroup comparisons. Relationships between variables were examined using Spearman’s correlation analysis, and multivariate regression analysis was employed to identify independent associations. The AD group exhibited significantly higher total CSHQ scores (p < 0.001). Sleep onset latency, night awakenings, bedtime resistance, and sleep duration problems were more pronounced in infants with AD. Positive correlations were observed between SCORAD/POEM scores and CSHQ scores (p < 0.001). Infants with facial and neck involvement had lower sleep quality compared to those with lesions confined to the trunk and extremities (p < 0.01). The greatest degree of sleep disturbance was observed in infants with generalized involvement. In infants with AD, both disease severity and lesion localization are associated with poorer sleep quality. The finding that facial and neck involvement is linked to greater sleep disturbance highlights the importance of considering anatomical distribution during clinical assessment. Early interventions, including regular emollient use and proactive anti-inflammatory treatment, may help mitigate potential cognitive, emotional, and behavioral risks in this vulnerable age group.
Abstract Background Electrocardiogram (ECG) and photoplethysmogram (PPG) signals offer moderate ability to distinguish Obstructive Sleep Apnea (OSA) patients, including those with comorbid Major Depressive Disorder (MDD), from healthy individuals. This study investigated whether analyzing these signals during specific sleep stages could improve the detection of MDD within an OSA cohort. Methods Polysomnography data from 53 participants (OSA-only, OSA with MDD, and healthy controls) was analyzed. Recordings were segmented into 5-min intervals of pure sleep stages. Results Deep-Sleep was identified as the most discriminative stage, yielding the highest number of significant features and with strong effect sizes for both ECG and PPG. Using Deep-Sleep PPG features related to vascular stiffness, distinguishing Controls from OSA was performed with 100% accuracy. For the more complex task of identifying MDD within the OSA group, combining PPG timing delays in the peripheral pulse wave and one ECG entropy feature achieved 89.33% accuracy, with an AUC of 0.91. Conclusion This single-site research demonstrates that deep-sleep-stage-specific analysis improves the power of ECG and PPG signals to differentiate between Controls, OSA, and OSA with MDD. This framework has the potential for developing an accurate ECG and PPG-based system to screen for depression in patients with sleep disorders, overcoming delays in the assessment due to long waiting periods in sleep clinics. The translation of this research requires a large, multi-site database for the evaluation of wearable devices for this application.
