Somers, Virend K.

Background Disruption in rest‐activity rhythm (RAR) is an emerging risk factor for cardiovascular disease (CVD) and mortality, but the strength of evidence is unclear. Therefore, we conducted a systematic review and meta‐analysis to assess associations between RAR characteristics and cardiovascular disease incidence or prevalence, cardiovascular mortality, and all‐cause mortality. Methods A systematic search was conducted in PubMed, Web of Science, Cochrane Library, and Embase for cohort or cross‐sectional studies published up to August 2025. The exposure variables were nonparametric RAR parameters, including interdaily stability (describing day‐to‐day stability of the RAR), intradaily variability (a measure of RAR fragmentation), and relative amplitude (indicating robustness or strength of the RAR). The primary outcomes were cardiovascular mortality and all‐cause mortality. The secondary outcomes included cardiovascular disease prevalence and incidence. Results Seven studies involving 109 365 participants were included. Pooled results showed that higher intradaily variability was associated with a significantly increased risk of all‐cause mortality (hazard ratio [HR], 1.05 [95% CI, 1.02–1.09]) and cardiovascular disease prevalence (odds ratio, 1.73 [95% CI, 1.21–2.47]). Lower relative amplitude was significantly associated with higher cardiovascular mortality (HR, 1.50 [95% CI, 1.13–1.98]) and all‐cause mortality (HR, 1.46 [95% CI, 1.27–1.68]). Interdaily stability showed no significant associations with any outcome. Conclusions Measures of disrupted rest‐activity rhythms were associated with increased cardiovascular mortality, and all‐cause mortality. These findings support using RAR metrics as early, noninvasive risk indicators. Future work should investigate whether appropriately timed physical activity and sleep, as well as other interventions targeting RAR can mitigate cardiovascular risk.

The advent of high-throughput sequencing technologies has revolutionized cardiovascular genetics, generating vast amounts of data. A major challenge in the genomic era is the efficient and accurate identification of causative genetic variants associated with cardiovascular disease. Artificial intelligence (AI)-driven computational approaches offer a powerful solution by enabling the automation of variant classification, improving consistency and reproducibility, and enhancing predictive accuracy. These methods may guide not only variant classification, variant effect prediction, and clinical prioritization, but also open the door to data-driven precision medicine, enabling more accurate and individualized diagnoses and targeted therapeutic strategies tailored to each patient’s unique genetic and clinical profile. This state-of-the-art review provides a structured overview of AI methodologies applied to cardiovascular genomics, beginning with rule-based expert systems that encode standardized guidelines for consistent variant interpretation. Next, we examine machine learning approaches capable of identifying complex patterns in annotated multimodal clinical and multi-omic datasets. The role of deep learning algorithms is highlighted for their ability to extract features from high-dimensional, unstructured data relevant to cardiovascular disease. In addition, the potential of generative AI is explored, including applications in synthetic data generation, variant impact prediction, and automated summarization of biomedical literature. Despite advances, several challenges remain, including data heterogeneity, the need for explainable AI models to elucidate the decision mechanisms, and the complexity linked to the integration of AI-based tools into clinical workflows. Addressing these issues requires interdisciplinary collaboration among clinicians, geneticists, data scientists, and bioinformaticians to ensure the effective translation of AI-generated insights into clinical practice. This review aims to provide a comprehensive perspective on the evolving role of AI in cardiovascular genomics and its implications for advancing precision medicine.

Sleep is an active period of profound autonomic fluctuation, cycling between the parasympathetic dominance of nonrapid eye movement sleep and the sympathetic/parasympathetic volatility of rapid eye movement sleep. Sleep-disordered breathing, a spectrum of disorders marked by recurrent ventilatory instability and intermittent hypoxia during sleep, particularly obstructive sleep apnea, pathologically amplifies this volatility, transforming sleep into a nightly cascade of severe autonomic and hemodynamic stress. The cardinal features of sleep-disordered breathing, intermittent hypoxia, recurrent arousals, and marked intrathoracic pressure swings, act synergistically to drive chronic, 24-hour sympathetic overactivity, chemoreflex sensitization, and maladaptive neuroplasticity. These effects are mediated at a cellular level by oxidative stress, systemic inflammation, endothelial dysfunction, and neuroendocrine dysregulation. This persistent autonomic reset provides a direct mechanistic link to cardiovascular consequences. It is likely a primary driver of hypertension, blunting the nocturnal blood pressure dip and promoting sustained 24-hour sympathoexcitation. It fosters a proarrhythmic substrate for atrial fibrillation through mechanical stress, which drives atrial remodeling and autonomic conflict. Furthermore, sleep-disordered breathing contributes to myocardial ischemia by increasing myocardial oxygen demand and promoting a prothrombotic state. Beyond chronic disease, sleep-related autonomic shifts can act as acute triggers for malignant arrhythmias in individuals with vulnerable substrates, such as inherited channelopathies, a risk that may be significantly amplified by comorbid sleep-disordered breathing. This review delineates the critical neural and cellular pathways connecting sleep, autonomic dysregulation, and cardiovascular risk.

Abstract Background The implications of poor sleep for cardiovascular disease risk are increasingly recognised. Although previous research has focused on clinical sleep disorders, emerging evidence highlights the relevance of subclinical sleep aspects for cardiovascular health. Sleep midpoint, an index of sleep timing and a proxy of circadian phase, has been shown to be associated with markers of cardiometabolic risk, including obesity, in the general population. Purpose We aimed to assess whether sleep midpoint was related to body composition measures in patients with coronary heart disease enrolled in cardiac rehabilitation (CR). Methods Study participants were recruited from the patient population referred to our CR center for acute coronary syndrome or coronary revascularisation. Shift work was exclusionary. Participants underwent sleep testing prior to entering the CR program, including completion of one week of wrist actigraphy and sleep log. Using actigraphy-derived sleep metrics, sleep midpoint was calculated as the middle time (hh:mm) between bedtime and wake up time for each individual night, and averaged across the monitoring week. Based on the median sleep midpoint of the sample, participants were categorised as having early (before 2:42) or late (after 2:42) sleep midpoint. Body composition and regional fat distribution were determined by whole-body dual-energy absorptiometry (DEXA). Between-group characteristics were compared using independent t-test, Mann-Whitney U test and Chi-square statistics as appropriate. Linear regression was used to test the association between sleep midpoint and body fat measures. Results Thirty-one participants (26 males, 64.7±12.5 years) completed both actigraphy and DEXA assessments. Demographics, lifestyle, and clinical characteristics were similar between patients with early sleep midpoint (n=15; sleep midpoint, hh:mm: 2:00, range 0:57 to 2:41) and patients with late sleep midpoint (n=16; sleep midpoint 3:18, range 2:42 to 4:17). Despite comparable body mass index between groups (p=0.21), patients with late sleep midpoint had significantly higher total body fat percentage (p=0.017), android fat percentage (p=0.015), and gynoid fat percentage (p=0.045). Late sleep midpoint remained significantly associated with adiposity measures in age- and sex-adjusted analysis (total body fat percentage, beta = 0.371, 95% CI 0.063 to 0.68; android fat percentage, beta = 0.394, 95% CI 0.078 to 0.71; gynoid fat percentage, beta = 0.301, 95% CI 0.007 to 0.596). Conclusions Late sleep midpoint was associated with excess adiposity in patients with coronary heart disease enrolled in CR. These findings support the predictive value of sleep timing for cardiometabolic risk and emphasise the importance of objective sleep assessment for secondary prevention.

Augmented cardiovascular responses to spontaneous nocturnal arousals were previously shown in chronic short-sleeping adults, without known sex differences. Sustained 4-h sleep restriction produced a sex-dependent alteration in heart rate responses to arousals and resulted in a prolonged reduction in postarousal pulse transit time in females only. These findings yield the first sex-specific characterization of nocturnal cardiovascular responses to spontaneous arousals following sleep restriction, and may place females at greater sleep truncation-related cardiovascular disease risk.

ABSTRACT Background and Purpose White matter hyperintensities (WMH) are a core neuroimaging marker of cerebral small vessel disease (CSVD). Sleep apnoea (SA) is a recognized vascular risk factor, but its associations with regional WMH burden, short‐interval WMH change and cognitive performance in population‐based cohorts remain incompletely defined. We examined these associations in the UK Biobank imaging cohort. Methods We conducted cross‐sectional analyses with an exploratory longitudinal subset in propensity score–matched cohorts excluding major neurologic and cerebrovascular disease before imaging. SA was identified from hospital records and self‐reports prior to brain magnetic resonance imaging (MRI). Head‐size–normalized WMH volumes were analysed on the log1p scale using multivariable linear regression with robust standard errors. Total WMH was the primary outcome; periventricular WMH (PWMH), deep WMH (DWMH) and cognitive measures were secondary outcomes. Mediation analyses evaluated WMH as a mediator of SA–cognition associations. Results In cross‐sectional analyses ( N = 12,890; mean age, 67.4 years; 27.2% women), SA was associated with greater WMH burden. Total WMH volume was 10.7% higher among participants with SA (95% CI, 5.0%–16.6%; p < 0.001). PWMH and DWMH volumes were 10.0% (95% CI, 4.7%–15.7%; q < 0.001) and 15.7% (95% CI, 7.1%–25.1%; q < 0.001) higher, respectively. SA was associated with modestly worse cognitive performance, with effect estimates attenuated after adjustment for WMH burden and cardiometabolic conditions. Mediation analyses indicated that WMH burden, particularly PWMH, mediated associations between SA and cognitive performance. In the longitudinal subset ( N = 698; median follow‐up, 2.26 years), no significant between‐group differences were observed in annualized WMH change. Conclusions In this large population‐based imaging cohort, SA was associated with higher WMH burden, a key neuroimaging marker of CSVD. Cognitive differences were modest, but WMH burden, particularly PWMH, consistently mediated associations between SA and cognitive performance. Longer term studies are needed to clarify temporal relationships and potential treatment effects.

Background: Sleep regularity is emerging as an independent determinant of health. Multiple studies suggest a link between increased variability in sleep duration and timing and adverse cardiovascular, metabolic, and cognitive outcomes. There is, however, a lack of data on the implications of sleep regularity for aerobic capacity and physical endurance, particularly in patients with established cardiovascular disease. This study seeks to investigate the influence of irregularity in sleep duration on functional capacity in patients with coronary artery disease (CAD) undergoing cardiac rehabilitation (CR). Methods: We recruited a sample of patients with CAD who were referred to CR at Mayo Clinic Rochester. Participants completed a 6-minute walk test (6MWT) upon enrollment into CR. 6MWT variables included distance walked (meters), predicted distance walked (percentage) and estimated metabolic equivalents (METs). 24-hour objective sleep quality and quantity were assessed over a 7-day period by wrist actigraphy. Average standard deviation (SD) of sleep duration was used to define sleep regularity, with SD >60 minutes indicating irregular sleep. Chi-square test, independent samples t-test, and linear regression were used for statistical analysis. Results: Twenty-eight participants with valid actigraphy and 6MWT were included in this analysis (eight females; mean age 67.4 ± 11.6 years; body mass index 31.1 ± 5.8 kg/m 2 ). The average total sleep time was 7.1 ± 1.2 hours, and 10 participants (35.7%) exhibited sleep duration SD >60 minutes. Demographic, clinical and sleep characteristics were comparable between the regular and irregular sleep groups. Compared to patients with regular sleep, those with irregular sleep had poorer 6MWT, with shorter distance walked (406.2 ± 81.2 meters vs 338.8 ± 80.7 meters; p = 0.045), lower % of predicted distance walked (87.1 ± 18.7% vs 69.2 ± 11.4%; p = 0.011), and lower METS (2.99 ± 0.36 vs 2.63 ± 0.38; p = 0.022). Irregular sleep remained significantly associated with performance at the 6MWT after taking into account age and sex, with patients walking 14.4 meters fewer with every 10-minute increase in sleep variability (95% CI -28.1, -0.7; p 0.041). Conclusions: Increased day-to-day fluctuation in sleep duration is associated with reduced exercise capacity in patients with CAD undergoing CR. These findings add to the mounting body of evidence linking irregular sleep patterns to heightened cardiovascular risk, and support the integration of sleep monitoring in the CR practice to enhance risk stratification and outcome prediction. Funding Source: Sleep Number Corp. This abstract was presented at the American Physiology Summit 2026 and is only available in HTML format. There is no downloadable file or PDF version. The Physiology editorial board was not involved in the peer review process.

Abstract Introduction AD109, an investigational oral anti-apneic neuromuscular modulator, combines in one tablet the novel antimuscarinic, aroxybutynin (2.5 mg), with the selective norepinephrine reuptake inhibitor, atomoxetine (75 mg). AD109 significantly improved the apnea-hypopnea index and oxygenation vs placebo (PBO) in two phase 3 clinical trials in OSA. This exploratory analysis evaluated concomitant administration of AD109 with glucagon-like peptide-1 receptor agonists (GLP-1 RA) on weight change in OSA. Methods SynAIRgy (NCT05813275; N=646) and LunAIRo (NCT05811247; N=660) were randomized, double-blinded, PBO-controlled, parallel-arm phase 3 trials of AD109 vs PBO in adults with mild-to-severe OSA (AHI &gt;5) who failed or refused positive airway pressure therapy. Participants must have had a body mass index (BMI) 18.5–40 kg/m2 for males / 18.5–42 kg/m2 for females. Participants receiving GLP-1 RA for diabetes were permitted. Body weight was assessed throughout the trials. Results One-hundred and six participants were enrolled in the phase 3 trials taking a GLP-1 RA for diabetes (PBO+GLP-1 RA: n=48; AD109+GLP-1 RA: n=58). In participants receiving a GLP-1 RA with baseline BMI &lt; 30, AD109 reduced weight by 2.7% (vs PBO: -1.4%, P=0.08) and 0.5% (vs PBO: 2.3%, P=0.60) at Week 26 and Week 51, respectively. In participants receiving a GLP-1 RA with baseline BMI 30 to 34.9, AD109 reduced weight by 0.9% (vs PBO: 1.2%, P=0.84) and 2.5% (vs PBO: 3.3%, P=0.85) at Week 26 and Week 51, respectively. In participants receiving a GLP-1 RA with baseline BMI ≥35, AD109 reduced weight by 5.5% (vs PBO: 0.9%, P=0.02) and 11.5% (vs PBO: 3.8%, P=0.04) at Week 26 and Week 51, respectively. Participants not receiving GLP-1 RA (n=979) showed smaller weight reductions across BMI classes; in participants with BMI ≥35, AD109 reduced weight by 1.4% (vs PBO: 0.2%, P=0.002) and 2.8% (vs PBO: 1.4%, P=0.19) at Week 26 and Week 51, respectively. Conclusion In this exploratory analysis using pooled data from SynAIRgy and LunAIRo, results indicate that participants with a baseline BMI ≥35 receiving AD109+GLP-1 RA experienced progressively greater weight loss over time relative to GLP-1 RA alone. Future studies are needed to further evaluate concomitant administration of AD109+GLP-1 RA in OSA. Support (if any) These studies were supported by Apnimed, Inc.

Establishes a robust long-term human adipocyte organoid culture system maintained for over 90 days. Recapitulates key features of OSA-related intermittent hypoxia in human adipocytes in vitro. Demonstrates IH-induced adipocyte aging, including ultrastructural damage and epigenetic reprogramming. Identifies impaired adipogenesis and insulin signaling under intermittent hypoxia. Provides a physiologically relevant platform for mechanistic studies and drug screening.