Patient-reported sleep and physical function during and after hospitalization.

PURPOSE: Poor sleep is associated with morbidity and mortality in the community; however, the health impact of poor sleep during and after hospitalization is poorly characterized. Our purpose was to describe trends in patient-reported sleep and physical function during and after hospitalization and evaluate sleep as a predictor of function after discharge. METHODS: This is a secondary analysis of trial data with 232 adults followed for 3months after hospital discharge. Main measures were patient-reported surveys on sleep (Pittsburgh Sleep Quality Index) and physical function (Katz Activities of Daily Living, Lawton Instrumental Activities of Daily Living, and Nagi Mobility Scale) were collected during hospitalization and at 1, 5, 9, and 13weeks postdischarge. RESULTS: Patient-reported sleep declined significantly during hospitalization and remained worse for 3months postdischarge (median Pittsburgh Sleep Quality Index=8 vs. 6, p < .001). In parallel, mobility declined significantly from baseline and remained worse at each follow-up time (median Nagi score=2 vs. 0, p < .001). Instrumental activities of daily living similarly decreased during and after hospitalization, but basic activities of daily living were unaffected. In adjusted time-series logistic regression models, the odds of mobility impairment were 1.48 times higher for each 1-point increase in Pittsburgh Sleep Quality Index score over time (95% CI 1.27-1.71, p < .001). CONCLUSIONS: Patient-reported sleep worsened during hospitalization, did not improve significantly for 3months after hospitalization, and poor sleep was a significant predictor of functional impairment over this time. Sleep dysfunction that begins with hospitalization may persist and prevent functional recovery after discharge. TRIAL REGISTRATION: The primary study was registered at ClinicalTrials.gov NCT03321279.

Lipidomic profiling identifies signatures of metabolic risk.

BACKGROUND: Metabolic syndrome (MetS), the clustering of metabolic risk factors, is associated with cardiovascular disease risk. We sought to determine if dysregulation of the lipidome may contribute to metabolic risk factors. METHODS: We measured 154 circulating lipid species in 658 participants from the Framingham Heart Study (FHS) using liquid chromatography-tandem mass spectrometry and tested for associations with obesity, dysglycemia, and dyslipidemia. Independent external validation was sought in three independent cohorts. Follow-up data from the FHS were used to test for lipid metabolites associated with longitudinal changes in metabolic risk factors. RESULTS: Thirty-nine lipids were associated with obesity and eight with dysglycemia in the FHS. Of 32 lipids that were available for replication for obesity and six for dyslipidemia, 28 (88%) replicated for obesity and five (83%) for dysglycemia. Four lipids were associated with longitudinal changes in body mass index and four were associated with changes in fasting blood glucose in the FHS. CONCLUSIONS: We identified and replicated several novel lipid biomarkers of key metabolic traits. The lipid moieties identified in this study are involved in biological pathways of metabolic risk and can be explored for prognostic and therapeutic utility.

MicroRNA Signature of Cigarette Smoking and Evidence for a Putative Causal Role of MicroRNAs in Smoking-Related Inflammation and Target Organ Damage.

BACKGROUND: Cigarette smoking increases risk for multiple diseases. MicroRNAs regulate gene expression and may play a role in smoking-induced target organ damage. We sought to describe a microRNA signature of cigarette smoking and relate it to smoking-associated clinical phenotypes, gene expression, and lung inflammatory signaling. METHODS AND RESULTS: Expression profiling of 283 microRNAs was conducted on whole blood-derived RNA from 5023 Framingham Heart Study participants (54.0% women; mean age, 55±13 years) using TaqMan assays and high-throughput reverse transcription quantitative polymerase chain reaction. Associations of microRNA expression with smoking status and associations of smoking-related microRNAs with inflammatory biomarkers and pulmonary function were tested with linear mixed effects models. We identified a 6-microRNA signature of smoking. Five of the 6 smoking-related microRNAs were associated with serum levels of C-reactive protein or interleukin-6; miR-1180 was associated with pulmonary function measures at a marginally significant level. Bioinformatic evaluation of smoking-associated genes coexpressed with the microRNA signature of cigarette smoking revealed enrichment for immune-related pathways. Smoking-associated microRNAs altered expression of selected inflammatory mediators in cell culture gain-of-function assays. CONCLUSIONS: We characterized a novel microRNA signature of cigarette smoking. The top microRNAs were associated with systemic inflammatory markers and reduced pulmonary function, correlated with expression of genes involved in immune function, and were sufficient to modulate inflammatory signaling. Our results highlight smoking-associated microRNAs and are consistent with the hypothesis that smoking-associated microRNAs serve as mediators of smoking-induced inflammation and target organ damage. These findings call for further mechanistic studies to explore the diagnostic and therapeutic use of smoking-related microRNAs.

Metabolite Signatures of Metabolic Risk Factors and their Longitudinal Changes.

CONTEXT: Metabolic dysregulation underlies key metabolic risk factors­obesity, dyslipidemia, and dysglycemia. OBJECTIVE: To uncover mechanistic links between metabolomic dysregulation and metabolic risk by testing metabolite associations with risk factors cross-sectionally and with risk factor changes over time. DESIGN: Cross-sectional­discovery samples (n = 650; age, 36­69 years) from the Framingham Heart Study (FHS) and replication samples (n = 670; age, 61­76 years) from the BioImage Study, both following a factorial design sampled from high vs low strata of body mass index, lipids, and glucose. Longitudinal­FHS participants (n = 554) with 5­7 years of follow-up for risk factor changes. SETTING: Observational studies. PARTICIPANTS: Cross-sectional samples with or without obesity, dysglycemia, and dyslipidemia, excluding prevalent cardiovascular disease and diabetes or dyslipidemia treatment. Age- and sex-matched by group. INTERVENTIONS: None. MAIN OUTCOME MEASURE(S): Gas chromatography-mass spectrometry detected 119 plasma metabolites. Cross-sectional associations with obesity, dyslipidemia, and dysglycemia were tested in discovery, with external replication of 37 metabolites. Single- and multi-metabolite markers were tested for association with longitudinal changes in risk factors. RESULTS: Cross-sectional metabolite associations were identified with obesity (n = 26), dyslipidemia (n = 21), and dysglycemia (n = 11) in discovery. Glutamic acid, lactic acid, and sitosterol associated with all three risk factors in meta-analysis (P < 4.5 × 10−4). Metabolites associated with longitudinal risk factor changes were enriched for bioactive lipids. Multi-metabolite panels explained 2.5­15.3% of longitudinal changes in metabolic traits. CONCLUSIONS: Cross-sectional results implicated dysregulated glutamate cycling and amino acid metabolism in metabolic risk. Certain bioactive lipids were associated with risk factors cross-sectionally and over time, suggesting their upstream role in risk factor progression. Functional studies are needed to validate findings and facilitate translation into treatments or preventive measures.