Oren Cohen’s research while affiliated with Icahn School of Medicine at Mount Sinai and other places

Monocytes and neutrophils from the myeloid lineage contribute to multiple sclerosis (MS), but the dynamics of myelopoiesis during MS are unclear. Here we uncover a disease stage-specific relationship between lifestyle, myelopoiesis and neuroinflammation. In mice with relapsing-remitting experimental autoimmune encephalomyelitis (RR-EAE), myelopoiesis in the femur, vertebrae and spleen is elevated prior to disease onset and during remission, preceding the peaks of clinical disability and neuroinflammation. In progressive EAE (P-EAE), vertebral myelopoiesis rises steadily throughout disease, while femur and splenic myelopoiesis is elevated early before waning later during disease height. In parallel, sleep disruption or hyperlipidemia and cardiometabolic syndrome augment M-CSF generation and multi-organ myelopoiesis to worsen P-EAE clinical symptoms, neuroinflammation, and spinal cord demyelination, with M-CSF blockade abrogating these symptoms. Lastly, results from a previous trial show that Mediterranean diet restrains myelopoietic activity and myeloid lineage progenitor skewing and improves clinical symptomology of MS. Together, our data suggest that myelopoiesis in MS is dynamic and dependent on disease stage and location, and that lifestyle factors modulate disease by influencing M-CSF-mediated myelopoiesis.

Purpose We designed a study investigating the cardioprotective role of sleep apnea (SA) in patients with acute myocardial infarction (AMI), focusing on its association with infarct size and coronary collateral circulation. Methods We recruited adults with AMI, who underwent Level-III SA testing during hospitalization. Delayed-enhancement cardiac magnetic resonance (CMR) imaging was performed to quantify AMI size (percent-infarcted myocardium). Rentrop Score quantified coronary collateralization (scores 0–3, higher scores indicating augmented collaterals). Group differences in Rentrop grade and infarct size were compared using the Wilcoxon Rank-Sum test and Fisher’s Exact test as appropriate, with a significance threshold set at p <0.05. Results Among 33 adults, mean age was 54.4±11.5 and mean BMI was 28.4±5.9. 8 patients (24%) had no SA, and 25 (76%) had SA (mild n=10, moderate n=8, severe n=7). 66% (n=22) underwent CMR, and all patients had Rentrop scores. Median infarct size in the no-SA group was 22% versus 28% in the SA group (p=0.79). While we did not find statistically significant differences, moderate SA had a trend toward a smaller infarct size (median 15.5%; IQR 9.23) compared to the other groups (no SA [22.0%; 16.8,31.8], mild SA [27%; 23.8,32.5], and severe SA [34%; 31.53], p=0.12). A higher proportion of moderate SA patients had a Rentrop grade >0, with a trend toward significance (moderate SA versus other groups: 62.5% versus 28%, p=0.08). Conclusion Our study did not find statistically significant differences in cardiac infarct size and the presence of coronary collaterals by sleep apnea severity among patients with AMI. However, our results are hypothesis-generating, and suggest that moderate SA may potentially offer cardioprotective benefits through enhanced coronary collaterals. These insights call for future research to explore the heterogeneity in ischemic preconditioning by SA severity and hypoxic burden to guide tailored clinical strategies for SA management in patients with AMI.

Sleep is integral to cardiovascular health1,2. Yet, the circuits that connect cardiovascular pathology and sleep are incompletely understood. It remains unclear whether cardiac injury influences sleep and whether sleep-mediated neural outputs contribute to heart healing and inflammation. Here we report that in humans and mice, monocytes are actively recruited to the brain after myocardial infarction (MI) to augment sleep, which suppresses sympathetic outflow to the heart, limiting inflammation and promoting healing. After MI, microglia rapidly recruit circulating monocytes to the brain’s thalamic lateral posterior nucleus (LPN) via the choroid plexus, where they are reprogrammed to generate tumour necrosis factor (TNF). In the thalamic LPN, monocytic TNF engages Tnfrsf1a-expressing glutamatergic neurons to increase slow wave sleep pressure and abundance. Disrupting sleep after MI worsens cardiac function, decreases heart rate variability and causes spontaneous ventricular tachycardia. After MI, disrupting or curtailing sleep by manipulating glutamatergic TNF signalling in the thalamic LPN increases cardiac sympathetic input which signals through the β2-adrenergic receptor of macrophages to promote a chemotactic signature that increases monocyte influx. Poor sleep in the weeks following acute coronary syndrome increases susceptibility to secondary cardiovascular events and reduces the heart’s functional recovery. In parallel, insufficient sleep in humans reprogrammes β2-adrenergic receptor-expressing monocytes towards a chemotactic phenotype, enhancing their migratory capacity. Collectively, our data uncover cardiogenic regulation of sleep after heart injury, which restricts cardiac sympathetic input, limiting inflammation and damage.

Obstructive sleep apnea (OSA) affects almost a billion people worldwide and is associated with a myriad of adverse health outcomes. Among the most prevalent and morbid are cardiovascular diseases (CVDs). Nonetheless, randomized controlled trials (RCTs) of OSA treatment have failed to show improvements in CVD outcomes. A major limitation in our field is the lack of precision in defining OSA and specifically subgroups with the potential to benefit from therapy. Further, this has called into question the validity of using the time-honored apnea–hypopnea index as the ultimate defining criteria for OSA. Recent applications of advanced statistical methods and machine learning have brought to light a variety of OSA endotypes and phenotypes. These methods also provide an opportunity to understand the interaction between OSA and comorbid diseases for better CVD risk stratification. Lastly, machine learning and specifically heterogeneous treatment effects modeling can help uncover subgroups with differential outcomes after treatment initiation. In an era of data sharing and big data, these techniques will be at the forefront of OSA research. Advanced data science methods, such as machine-learning analyses and artificial intelligence, will improve our ability to determine the unique influence of OSA on CVD outcomes and ultimately allow us to better determine precision medicine approaches in OSA patients for CVD risk reduction. In this narrative review, we will highlight how team science via machine learning and artificial intelligence applied to existing clinical data, polysomnography, proteomics, and imaging can do just that.