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Trans fatty acids in adipose tissue and risk of myocardial infarction: A case-cohort study.

ABSTRACT: BACKGROUND:The risk of coronary heart disease associated with intake of individual trans fatty acids (TFAs) is not clear. Adipose tissue content of TFAs is a biomarker of TFA intake and metabolism. OBJECTIVE:We investigated the rate of myocardial infarction (MI) associated with the adipose tissue content of total 18:1t, isomers of 18:1t (18:1 Δ6-10t and 18:1 Δ11t) and 18:2 Δ9c, 11t. METHODS:A case-cohort study, nested within the Danish Diet, Cancer and Health cohort (n = 57,053), was conducted, which included a random sample (n = 3156) of the total cohort and all incident MI cases (n = 2148) during follow-up (14 years). Information on MI cases was obtained by linkage with nationwide registers and validated. Adipose tissue was taken from the participants buttocks and the fatty acid composition was determined by gas chromatography. RESULTS:Women with higher adipose tissue content of total 18:1t had a 57% higher MI rate (quintiles 5 versus 1, hazard ratio, 1.57; 95% confidence interval, 1.12-2.20; P-trend = 0.011) and women with higher content of 18:1 Δ6-10t had a 76% higher MI rate (quintiles 5 versus 1, hazard ratio, 1.76; 95% confidence interval, 1.23-2.51; P-trend = 0.002). No association between 18:1 Δ11t content and MI rate was observed. In men, no associations between adipose tissue content of total 18:1t and 18:1 Δ6-10t and MI rate were observed. However, men with higher content of 18:1 Δ11t had a 48% higher MI rate (quintiles 5 versus 1, hazard ratio, 1.48; 95% confidence interval, 1.17-1.86; P-trend = 0.003). Adipose tissue content of 18:2 Δ9c, 11t was not associated with MI rate in women or men. CONCLUSIONS:Adipose tissue content of 18:2 Δ9c, 11t was not associated with MI rate in women or men, whereas higher contents of isomers of 18:1t were associated with higher MI rates but the associations for individual 18:1t isomers differed, however, in women and men.

SUBMITTER: Jakobsen MU

PROVIDER: S-EPMC6104995 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Trans fatty acids (TFAs) have been found to impair flow mediated vasodilation and nitric oxide (NO) production. We sought to examine if serum TFA levels are associated with plasma levels of the NO inhibitor asymmetric dimethylarginine (ADMA) and if possible relationships between serum TFA and cardiovascular morbidity or mortality are mediated or modified by plasma ADMA levels.The cohort included patients who underwent coronary angiography for suspected coronary heart disease in 2000-2001. Serum trans 16:1n7 and trans 18:1 isomers were determined by gas liquid chromatography and the summation of these two TFAs is reported as TFA (percentage by weight (wt%) or concentration). Associations between TFAs and ADMA were estimated by calculating the Spearman's rank correlation coefficient (?), and risk associations with AMI, cardiovascular death and all-cause mortality across quartiles of TFAs (wt% or concentration) were explored by Cox modeling.A total of 1364 patients (75 % men) with median (25(th),75(th) percentile) age 61 (54, 69) years, serum TFA 0.46 (0.36, 0.56) wt% and plasma ADMA 0.59 (0.50, 0.70) ?mol/L were studied. Serum TFA levels (? = 0.21, p < 0.001), trans 16:1n7 (? = 0.22, p < 0.001) and trans 18:1 (? = 0.20, p < 0.001) levels were significantly correlated with plasma ADMA levels. During the median (25(th),75(th) percentile) follow-up time of 5.8 (4.5, 6.4) years, 129 (9.5 %) patients experienced an AMI, 124 (9.1 %) died, whereof 66 (53 %) due to cardiovascular causes. After multivariate adjustments no significant associations between serum TFA levels (wt% or concentration) and incident AMI, CV death and all-cause mortality were observed. Similar results were obtained when repeating the analyses with trans 16:1n7 and trans 18:1 individually. Plasma ADMA levels did not significantly modify the associations between TFA levels and outcomes.Serum TFA levels were positively correlated with plasma ADMA levels. After multivariate adjustments, TFAs were not associated with incident AMI or mortality, and associations were not influenced by ADMA.Clinicaltrials.gov Identifier: NCT00354081.

Project description:ObjectivesThis study sought to determine whether coronary computed tomography angiography (CCTA)-based radiomic analysis of pericoronary adipose tissue (PCAT) could distinguish patients with acute myocardial infarction (MI) from patients with stable or no coronary artery disease (CAD).BackgroundImaging of PCAT with CCTA enables detection of coronary inflammation. Radiomics involves extracting quantitative features from medical images to create big data and identify novel imaging biomarkers.MethodsIn a prospective case-control study, 60 patients with acute MI underwent CCTA within 48 h of admission, before invasive angiography. These subjects were matched to patients with stable CAD (n = 60) and controls with no CAD (n = 60) by age, sex, risk factors, medications, and CT tube voltage. PCAT was segmented around the proximal right coronary artery (RCA) in all patients and around culprit and nonculprit lesions in patients with MI. PCAT segmentations were analyzed using Radiomics Image Analysis software.ResultsOf 1,103 calculated radiomic parameters, 20.3% differed significantly between MI patients and controls, and 16.5% differed between patients with MI and stable CAD (critical p < 0.0006); whereas none differed between patients with stable CAD and controls. On cluster analysis, the most significant radiomic parameters were texture or geometry based. At 6 months post-MI, there was no significant change in the PCAT radiomic profile around the proximal RCA or nonculprit lesions. Using machine learning (XGBoost), a model integrating clinical features (risk factors, serum lipids, high-sensitivity C-reactive protein), PCAT attenuation, and radiomic parameters provided superior discrimination of acute MI (area under the receiver operator characteristic curve [AUC]: 0.87) compared with a model with clinical features and PCAT attenuation (AUC: 0.77; p = 0.001) or clinical features alone (AUC: 0.76; p < 0.001).ConclusionsPatients with acute MI have a distinct PCAT radiomic phenotype compared with patients with stable or no CAD. Using machine learning, a radiomics-based model outperforms a PCAT attenuation-based model in accurately identifying patients with MI.

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Trans fatty acids in adipose tissue and risk of myocardial infarction: A case-cohort study.

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