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A plasma long-chain acylcarnitine predicts cardiovascular mortality in incident dialysis patients.

ABSTRACT: The marked excess in cardiovascular mortality that results from uremia remains poorly understood.In 2 independent, nested case-control studies, we applied liquid chromatography-mass spectrometry-based metabolite profiling to plasma obtained from participants of a large cohort of incident hemodialysis patients. First, 100 individuals who died of a cardiovascular cause within 1 year of initiating hemodialysis (cases) were randomly selected along with 100 individuals who survived for at least 1 year (controls), matched for age, sex, and race. Four highly intercorrelated long-chain acylcarnitines achieved the significance threshold adjusted for multiple testing (P<0.0003). Oleoylcarnitine, the long-chain acylcarnitine with the strongest association with cardiovascular mortality in unadjusted analysis, remained associated with 1-year cardiovascular death after multivariable adjustment (odds ratio per SD 2.3 [95% confidence interval, 1.4 to 3.8]; P=0.001). The association between oleoylcarnitine and 1-year cardiovascular death was then replicated in an independent sample (n=300, odds ratio per SD 1.4 [95% confidence interval, 1.1 to 1.9]; P=0.008). Addition of oleoylcarnitine to clinical variables improved cardiovascular risk prediction using net reclassification (NRI, 0.38 [95% confidence interval, 0.20 to 0.56]; P<0.0001). In physiologic profiling studies, we demonstrate that the fold change in plasma acylcarnitine levels from the aorta to renal vein and from pre- to posthemodialysis samples exclude renal or dialytic clearance of long-chain acylcarnitines as confounders in our analysis.Our data highlight clinically meaningful alterations in acylcarnitine homeostasis at the time of dialysis initiation, which may represent an early marker, effector, or both of uremic cardiovascular risk.

SUBMITTER: Kalim S

PROVIDER: S-EPMC3886735 | biostudies-literature | 2013 Dec

REPOSITORIES: biostudies-literature

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A plasma long-chain acylcarnitine predicts cardiovascular mortality in incident dialysis patients.

Kalim Sahir S Clish Clary B CB Wenger Julia J Elmariah Sammy S Yeh Robert W RW Deferio Joseph J JJ Pierce Kerry K Deik Amy A Gerszten Robert E RE Thadhani Ravi R Rhee Eugene P EP

Journal of the American Heart Association 20131205 6

<h4>Background</h4>The marked excess in cardiovascular mortality that results from uremia remains poorly understood.<h4>Methods and results</h4>In 2 independent, nested case-control studies, we applied liquid chromatography-mass spectrometry-based metabolite profiling to plasma obtained from participants of a large cohort of incident hemodialysis patients. First, 100 individuals who died of a cardiovascular cause within 1 year of initiating hemodialysis (cases) were randomly selected along with ...[more]

PMID: 24308938

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Project description:Some variables including age, comorbidity of diabetes, and so on at dialysis initiation are associated with patient prognosis. Cardiovascular (CV) events are a major cause of death, and adequate models that predict prognosis in dialysis patients are warranted. Therefore, we created models using some variables at dialysis initiation. We used a database of 1,520 consecutive dialysis patients (median age, 70 years; 492 women [32.4%]) from a multicenter prospective cohort study. We established the primary endpoint as a composite of the incidence of first CV events or all-cause death. A multivariable Cox proportional hazard regression model was used to construct a model. We considered a complex and a simple model. We used area under the receiver operating characteristic curve (AUROC) to assess and compare the predictive performances of the prediction models and evaluated the improvement in discrimination using the complex model versus the simple model using net reclassification improvement (NRI). We then assessed integrated discrimination improvement (IDI) to evaluate improvements in average sensitivity and specificity. Of 392 deaths, 152 were CV-related. Totally, 506 CV events occurred during the follow-up period (median 1,285 days). Finally, 692 patients reached the primary endpoint. Baseline data were set at dialysis initiation. AUROC for the primary endpoint was 0.737 (95% confidence interval [CI], 0.712-0.761) in the simple model and 0.765 (95% CI, 0.741-0.788) in the complex model. There were significant intergroup differences in NRI (0.44; 95% CI, 0.34-0.53; p < 0.001) and IDI (0.02; 95% CI, 0.02-0.03; p < 0.001). We prepared a Shiny R application for each model to automatically calculate the predicted occurrence probability (https://statacademy.shinyapps.io/App_inaguma_20190717/). The complex model made more accurate predictions than the simple model. However, the intergroup difference was not significant. Hence, the simple model was more useful than the complex model. The tool was useful in a real-world clinical setting because it required only routinely available variables. Moreover, we emphasized that the tool could predict the incidence of CV events or all-cause mortality for individual patients. In the future, we must confirm its external validity in other prospective cohorts.

Project description:BackgroundCirculating saturated fatty acids (SFAs) are integrated biomarkers of diet and metabolism that may influence the pathogenesis of diabetes. In epidemiologic studies, circulating levels of palmitic acid (16:0) are associated with diabetes; however, very-long-chain SFAs (VLSFAs), with 20 or more carbons, differ from palmitic acid in their biological activities, and little is known of the association of circulating VLSFA with diabetes.ObjectiveBy using data from the Cardiovascular Health Study, we examined the associations of plasma phospholipid VLSFA levels measured at baseline with subsequent incident diabetes.DesignA total of 3179 older adults, with a mean age of 75 y at study baseline (1992-1993), were followed through 2011. We used multiple proportional hazards regression to examine the associations of arachidic acid (20:0), behenic acid (22:0), and lignoceric acid (24:0) with diabetes.ResultsBaseline levels of each VLSFA were cross-sectionally associated with lower triglyceride levels and lower circulating palmitic acid. We identified 284 incident diabetes cases during follow-up. Compared with the lowest quartile, levels of arachidic acid in the highest quartile of the fatty acid distribution were associated with a 47% lower risk of diabetes (95% CI: 23%, 63%; P-trend: <0.001), after adjustment for demographics, lifestyle factors, and clinical conditions. In analogous comparisons, levels of behenic and lignoceric acid were similarly associated with 33% (95% CI: 6%, 53%; P-trend: 0.02) and 37% (95% CI: 11%, 55%; P-trend: 0.01) lower diabetes risk, respectively. Adjustment for triglycerides and palmitic acid attenuated the associations toward the null, and only the association of arachidic acid remained statistically significant (32% lower risk for fourth vs. first quartile; P-trend: 0.04).ConclusionsThese results suggest that circulating VLSFAs are associated with a lower risk of diabetes, and these associations may be mediated by lower triglycerides and palmitic acid. The study highlights the need to distinguish the effects of different SFAs and to explore determinants of circulating VLSFAs. This trial was registered at clinicaltrials.gov as NCT00005133.

Project description:Background Cardiovascular disease is the leading cause of morbidity and mortality in patients with end-stage renal disease. Heart rhythm complexity analysis has been shown to be useful in predicting outcomes in various diseases; however, data on patients with end-stage renal disease are limited. In this study, we analyzed the association between heart rhythm complexity and long-term cardiovascular outcomes in patients with end-stage renal disease receiving peritoneal dialysis. Methods and Results We prospectively enrolled 133 patients receiving peritoneal dialysis and analyzed linear heart rate variability and heart rhythm complexity variables including detrended fluctuation analysis (DFA) and multiscale entropy. The primary outcome was cardiovascular mortality, and the secondary outcome was the occurrence of major adverse cardiovascular events. After a median of 6.37 years of follow-up, 21 patients (22%) died from cardiovascular causes. These patients had a significantly lower low-frequency band of heart rate variability, low/high-frequency band ratio, total power band of heart rate variability, heart rate turbulence slope, deceleration capacity, short-term DFA (DFA?1); and multiscale entropy slopes 1 to 5, scale 5, area 1 to 5, and area 6 to 20 compared with the patients who did not die from cardiovascular causes. Time-dependent receiver operating characteristic curve analysis showed that DFA?1 had the greatest discriminatory power for cardiovascular mortality (area under the curve: 0.763) and major adverse cardiovascular events (area under the curve: 0.730). The best cutoff value for DFA?1 was 0.98 to predict both cardiovascular mortality and major adverse cardiovascular events. Multivariate Cox regression analysis showed that DFA?1 (hazard ratio: 0.076; 95% CI, 0.016-0.366; P=0.001) and area 1 to 5 (hazard ratio: 0.645; 95% CI, 0.447-0.930; P=0.019) were significantly associated with cardiovascular mortality. Conclusions Heart rhythm complexity appears to be a promising noninvasive tool to predict long-term cardiovascular outcomes in patients receiving peritoneal dialysis.

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A plasma long-chain acylcarnitine predicts cardiovascular mortality in incident dialysis patients.

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A plasma long-chain acylcarnitine predicts cardiovascular mortality in incident dialysis patients.

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