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

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

Adipose Tissue Fatty Acid Patterns and Changes in Anthropometry: A Cohort Study

INTRODUCTION: Diets rich in n-3 long chain polyunsaturated fatty acids (LC-PUFA), but low in n-6 LC-PUFA and 18:1 trans-fatty acids (TFA), may lower the risk of overweight and obesity. These fatty acids have often been investigated individually. We explored associations between global patterns in adipose tissue fatty acids and changes in anthropometry. METHODS: 34 fatty acid species from adipose tissue biopsies were determined in a random sample of 1100 men and women from a Danish cohort study. We used sex-specific principal component analysis and multiple linear regression to investigate the associations of adipose tissue fatty acid patterns with changes in weight, waist circumference (WC), and WC controlled for changes in body mass index (WC(BMI)), adjusting for confounders. RESULTS: 7 principal components were extracted for each sex, explaining 77.6% and 78.3% of fatty acid variation in men and women, respectively. Fatty acid patterns with high levels of TFA tended to be positively associated with changes in weight and WC for both sexes. Patterns with high levels of n-6 LC-PUFA tended to be negatively associated with changes in weight and WC in men, and positively associated in women. Associations with patterns with high levels of n-3 LC-PUFA were dependent on the context of the rest of the fatty acid pattern. CONCLUSIONS: Adipose tissue fatty acid patterns with high levels of TFA may be linked to weight gain, but patterns with high n-3 LC-PUFA did not appear to be linked to weight loss. Associations depended on characteristics of the rest of the pattern

tt: Treelet transform with Stata

The treelet transform is a recent data reduction technique from the field of machine learning. Sharing many similarities with principal component analysis, the treelet transform can reduce a multidimensional dataset to the projections on a small number of directions or components that account for much of the variation in the original data. However, in contrast to principal component analysis, the treelet transform produces sparse components. This can greatly simplify interpretation. I describe the tt Stata add-on for performing the treelet transform. The add-on includes a Mata implementation of the treelet transform algorithm alongside other functionality to aid in the practical application of the treelet transform. I demonstrate an example of a basic exploratory data analysis using the tt add-on