Rationale & Objective: Biomarkers that provide reliable evidence of future diabetic kidney disease (DKD) are needed to improve disease management. In a cross-sectional study, we previously identified thirteen urine metabolites that were reduced in DKD compared with healthy controls. Here, we evaluated associations of these thirteen metabolites with future DKD progression.
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<b><i>Introduction:</i></b> Metabolomics could offer novel prognostic biomarkers and elucidate mechanisms of diabetic kidney disease (DKD) progression. Via metabolomic analysis of urine samples from 995 CRIC participants with diabetes and state-of-the-art statistical modeling, we aimed to identify metabolites prognostic to DKD progression. <b><i>Methods:</i></b> Urine samples (<i>N</i> = 995) were assayed for relative metabolite abundance by untargeted flow-injection mass spectrometry, and stringent statistical criteria were used to eliminate noisy compounds, resulting in 698 annotated metabolite ions. Utilizing the 698 metabolites’ ion abundance along with clinical data (demographics, blood pressure, HbA1c, eGFR, and albuminuria), we developed univariate and multivariate models for the eGFR slope using penalized (lasso) and random forest models. Final models were tested on time-to-ESKD (end-stage kidney disease) via cross-validated C-statistics. We also conducted pathway enrichment analysis and a targeted analysis of a subset of metabolites. <b><i>Results:</i></b> Six eGFR slope models selected 9–30 variables. In the adjusted ESKD model with highest C-statistic, valine (or betaine) and 3-(4-methyl-3-pentenyl)thiophene were associated (<i>p</i> < 0.05) with 44% and 65% higher hazard of ESKD per doubling of metabolite abundance, respectively. Also, 13 (of 15) prognostic amino acids, including valine and betaine, were confirmed in the targeted analysis. Enrichment analysis revealed pathways implicated in kidney and cardiometabolic disease. <b><i>Conclusions:</i></b> Using the diverse CRIC sample, a high-throughput untargeted assay, followed by targeted analysis, and rigorous statistical analysis to reduce false discovery, we identified several novel metabolites implicated in DKD progression. If replicated in independent cohorts, our findings could inform risk stratification and treatment strategies for patients with DKD.
Background: We compared the relationship between the third heart sound (S3) measured by an implantable cardiac device (devS3) and auscultation (ausS3) and evaluated their prognostic powers for predicting heart failure events (HFEs). Methods and Results: In the MultiSENSE study, devS3 was measured daily with continuous values, whereas ausS3 was assessed at study visits with discrete grades. They were compared among patients with and without HFEs at baseline and against each other directly. Cox proportional hazard models were developed between follow-up visits and over the whole study. Simulations were performed on devS3 to match the limitations of auscultation. We studied 900 patients, of whom 106 patients experienced 192 HFEs. Two S3 sensing modalities correlated with each other, but at baseline, only devS3 differentiated patients with or without HFEs (P < 0.0001). The prognostic power of devS3 was superior to that of ausS3 both between follow-up visits (HR = 5.7, P < 0.0001, and 1.7, P = 0.047, respectively) and over the whole study (HR = 2.9, P < 0.0001, and 1.4, P = 0.216, respectively). Simulation results suggested this superiority may be attributed to continuous monitoring and to subaudible measuring capability. Conclusions: S3 measured by implantable cardiac devices has stronger prognostic power to predict episodes of future HFEs than that of auscultation.
Background: Insomnia is common in service members and associated with many mental and physical health problems. Recently, longitudinal data have been used to assess the impact of disturbed sleep on mental health outcomes. These studies have consistently shown relationships between sleep disturbance and development of mental illness. Objective: The present study examined the longitudinal relationship between sleep disturbance and PTSD symptomatology in a cohort of Marines and Navy Corpsmen deployed to Iraq and Afghanistan (n = 2,404) assessed prior to deployment, as well as at −3 and 6 months post-deployment. Additionally, we aimed to investigate the extent to which these relationships are moderated by combat-stress severity, and to what extent these findings are replicated in a second, separate cohort of Marines and Navy corpsmen (n = 938) assessed with identical measures prior to deployment and within 3 months of return. Method: The present study employed latent variable path models to examine the relationships between pre-deployment sleep disturbance and post-deployment reexperiencing symptoms. Initial cross-lagged path models were conducted on discovery and replication samples to validate the hypothesized predictive relationships. Follow up moderation path models were then conducted to include the effect of combat-stress severity on these relationships. Results: Initial cross-lagged models supported a significant relationship between predeployment sleep disturbance and future re-experiencing PTSD symptoms at all time points. Initial moderation models showed a small moderator effect of combat-stress severity, though the main predictive relationship between pre-deployment sleep disturbance and PTSD symptoms remained significant. The moderator effect was not significant in the replication sample. Conclusions: The results of this study support pre-deployment sleep disturbance as a risk factor for development of post-deployment PTSD symptoms. Interventions aimed at normalizing sleep may be important in preventive measures for PTSD.
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