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Summary: Background: Circadian (24-h) rhythms are important regulators in physiology and disease, but systemic disease may disrupt circadian rhythmicity. Heart failure (HF) is a systemic disease affecting hormonal regulation. We investigate whether HF affects the rhythmic expression of melatonin and cortisol, main endocrine products of the central clock, and cardiac-specific troponin in patients. We corroborate the functionality of the peripheral clock directly in the organs of translational models, inaccessible in human participants. Methods: We included 46 HF patients (71.7% male, median age of 60 years, NYHA class II (32.6%) or III (67.4%), ischemic cardiomyopathy (43.5%), comorbidities: diabetes 21.7%, atrial fibrillation 30.4%), and 24 matched controls. Blood was collected at seven time-points during a 24-h period (totalling 320 HF and 167 control samples) for melatonin, cortisol, and cardiac troponin T (cTnT) measurements after which circadian rhythms were assessed through cosinor analyses, both on the individual and the group level. Next, we analysed peripheral circadian clock functionality using cosinor analysis in male animal HF models: nocturnal mice and diurnal zebrafish, based on expression of core clock genes in heart, kidneys, and liver, every 4 h during a 24-h period in a light/darkness synchronised environment. Findings: Melatonin and cortisol concentrations followed a physiological 24-h pattern in both patients and controls. For melatonin, acrophase occurred during the night for both groups, with significantly decreased amplitude (median 5.2 vs 8.8, P = 0.0001) and circadian variation ([maximum]/[minimum]) in heart failure patients. For cortisol, mesor showed a significant increase for HF patients (mean 331.9 vs 275.1, P = 0.017) with a difference of 56.8 (95% CI 10.3–103.3) again resulting in a relatively lower variation: median 3.9 vs 6.3 (P = 0.0058). A nocturnal blood pressure dip was absent in 77.8% of HF patients.Clock gene expression profiles (Bmal, Clock, Per, Cry) were similar and with expected phase relations in animal HF models and controls, demonstrating preserved peripheral clock functionality in HF. Furthermore, oscillations in diurnal zebrafish were expectedly in opposite phases to those of nocturnal mice. Concordantly, cTnT concentrations in HF patients revealed significant circadian oscillations. Interpretation: Central clock output is dampened in HF patients while the molecular peripheral clock, as confirmed in animal models, remains intact. This emphasises the importance of taking timing into account in research and therapy for HF, setting the stage for another dimension of diagnostic, prognostic and therapeutic approaches. Funding: Hartstichting.

Background: MHealth interventions promise to bridge gaps in clinical care but documentation of their effectiveness is limited. We evaluated the utilization and effect of an mhealth clinical decision-making support intervention that aimed to improve neonatal mortality in Ghana by providing access to emergency neonatal protocols for frontline health workers. Methods: In the Eastern Region of Ghana, sixteen districts were randomized into two study arms (8 intervention and 8 control clusters) in a cluster-randomized controlled trial. Institutional neonatal mortality data were extracted from the District Health Information System-2 during an 18-month intervention period. We performed an intention-to-treat analysis and estimated the effect of the intervention on institutional neonatal mortality (primary outcome measure) using grouped binomial logistic regression with a random intercept per cluster. This trial is registered at ClinicalTrials.gov (NCT02468310) and Pan African Clinical Trials Registry (PACTR20151200109073). Findings: There were 65,831 institutional deliveries and 348 institutional neonatal deaths during the study period. Overall, 47∙3% of deliveries and 56∙9% of neonatal deaths occurred in the intervention arm. During the intervention period, neonatal deaths increased from 4∙5 to 6∙4 deaths and, from 3∙9 to 4∙3 deaths per 1000 deliveries in the intervention arm and control arm respectively. The odds of neonatal death was 2⋅09 (95% CI (1∙00;4∙38); p = 0∙051) times higher in the intervention arm compared to the control arm (adjusted odds ratio). The correlation between the number of protocol requests and the number of deliveries per intervention cluster was 0∙71 (p = 0∙05). Interpretation: The higher risk of institutional neonatal death observed in intervention clusters may be due to problems with birth and death registration, unmeasured and unadjusted confounding, and unintended use of the intervention. The findings underpin the need for careful and rigorous evaluation of mHealth intervention implementation and effects. Funding: Netherlands Foundation for Scientific Research - WOTRO, Science for Global Development; Utrecht University. Keywords: Neonatal mortality, Ghana, mHealth, Low and middle income countries

Objective: Military servicemen deployed to war zones are at increased risk of developing posttraumatic stress disorder (PTSD) and successful adaptation to stress is important. Epigenetic alterations in response to trauma have been identified as mechanism of adaptation and may therefore predict deployment-related PTSD symptoms. To date, human studies of epigenetic marks for traumatic stress have been largely constrained by short-term analyses of one or two time points. Method: This study in a prospective Dutch military cohort (N ​= ​125) examined longitudinal changes of DNA methylation profiles before, as well as one and six months after deployment-related combat exposure in relation to the development of PTSD symptoms over a period of up to five years after deployment. We investigated the predictive value of specific methylation changes for immediate and delayed-onset PTSD symptoms and recovery. This epigenetic prediction was compared to polygenic risk score predictions obtained from the currently available largest genome-wide association study of PTSD. Results: A total of fourteen genomic regions were identified in which PTSD symptom levels were associated with methylation changes over time (pre-deployment, one, and six months post-deployment). Of these regions, four were significant determinants of longitudinal development of PTSD symptoms. In addition, we observed that, together with risk level during deployment (operating inside or outside the military base) and physical childhood trauma, post-deployment decreases in methylation at a genomic region in EP300/miRNA1281 was associated with a delayed onset of PTSD compared to a resilient profile. Polygenic risk, in contrast, was related to PTSD onset within six months after deployment but was not associated with long term outcomes. Conclusion: The present study suggests predictive utility of changes in DNA methylation for the subsequent development of PTSD symptoms and showed that the currently available measure of polygenic risk is primarily related to non-delayed disease onset.