The authors have declared that no competing interests exist.
Conceived and designed the experiments: EKK JMO PNH MYT DKA. Performed the experiments: EKK JMO PNH MYT DKA. Analyzed the data: EKK. Contributed reagents/materials/analysis tools: EKK JMO PNH MYT DKA. Wrote the paper: EKK. Edited the drafts and approved the manuscript: JMO PNH MYT DKA.
Trans fatty acids (TFA) lower HDL and increase triglyceride concentrations while polyunsaturated fatty acids (PUFA) lower triglycerides and may decrease HDL concentrations. The effect of the interaction between trans fat and PUFA on lipids is uncertain.
Men and women (n = 1032) in the Genetics of Lipid-Lowering Drugs and Diet Network (GOLDN) study were included. Fatty acids in erythrocyte membranes were measured with gas chromatography while data on potential confounders were obtained from questionnaires. To test the interaction between total erythrocyte PUFA (ePUFA) and TFA (eTFA) on lipid concentrations we distributed eTFA into tertiles and dichotomized ePUFA at the median concentration.
For the 1st, 2nd and 3rd tertiles of eTFA, multivariate-adjusted means±s.e.m for HDL were 46.2±1.1, 46.3±1.1 and 45.5±1.0 mg/dL among those with low ePUFA, respectively, while they were 50.0±1.1, 46.9±1.1 and 44.7±1.1 mg/dL among those with high ePUFA, respectively (
The relation between trans fat and HDL, VLDL and triglycerides may depend on PUFA. The benefit of avoiding trans fat may be greater among individuals with higher PUFA intake. Supplementation with PUFA among individuals with relatively high trans fat intake may have limited benefits on lipid profiles.
Dietary fat has varying effects on plasma lipoproteins
In contrast trans fatty acids, e.g., elaidic acid, up-regulate cholesterol ester transfer protein with concomitant increase in very low-density lipoprotein (VLDL) cholesterol, increase inflammation and down-regulate LPL activity, even in presence of linoleic acid
The metabolic effects of individual dietary fats may also be modified by the overall fatty acid composition of the diet. Indeed fatty acid patterns have been associated with dyslipidemia and the metabolic syndrome in cross-sectional and prospective studies
This study was approved by the institutional review boards at the University of Alabama at Birmingham, Tufts University, University of Minnesota and University of Utah. All patients gave written informed consent.
The participants in this study were 1328 white men and women in the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) family study that enrolled patients from two genetically homogeneous centers in Minneapolis, MN and Salt Lake City, Utah
Habitual dietary intake was assessed with a validated National Cancer Institute diet history questionnaire (DHQ)
All samples were centrifuged within 20 minutes of collection at 2000×g for 15 min at 4°C and stored frozen at −70°C. For each analyte, specimens from each participant were assayed in the same batch to eliminate inter-assay imprecision.
Biochemical analyses were performed as previously described
Fatty acids in the erythrocyte membranes were extracted with a mixture of chloroform:methanol (2∶1, by volume) collected in heptane and injected onto a capillary Varian CP7420 100 m column using a Hewlett Packard 5890 gas chromatograph equipped with a HP6890A autosampler. The initial temperature of 190°C was increased to 240°C over 50 minutes to separate fatty acids from 12∶0 through 24∶1n9
SAS Software version 9.2 (SAS Institute, Inc., Cary, NC) was used for statistical analyses. From the 1328 men and women screened for the GOLDN study, we excluded all subjects who did not meet our inclusion criteria
Lipid profiles, namely HDL, LDL, VLDL, total cholesterol or triglycerides were the outcome variables in mixed multivariate models that simultaneously included all four fat subtypes in erythrocyte membranes, covariates and pedigree as a random effect. The covariates included in the final models were age, sex (men vs. women), BMI, study site, smoking (never, past and current smokers), alcohol intake status (current drinker vs. non-current drinker) and physical activity (as quartiles). Income and education did not change the models appreciably and were excluded from the final analyses. The means and standard errors reported are adjusted for age, sex, BMI, study site, smoking, alcohol intake status, physical activity and pedigree. The differences in the distribution of potential confounders across fatty acid categories were considered significant at
Low PUFA | High PUFA | ||||||||||||||
Low trans | Moderatetrans | High trans |
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Low trans | Moderatetrans | High trans |
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n | 151 | 171 | 194 | – | 193 | 173 | 150 | – | |||||||
Age, y | 52±14 | 49±17 | 45±17 | <0.001 | 54±14 | 50±15 | 42±17 | <0.0001 | |||||||
BMI, kg/m2 | 29.5±6.2 | 29.3±6.0 | 27.9±5.7 | 0.04 | 28.9±5.4 | 27.7±5.0 | 26.5±5.0 | 0.0001 | |||||||
Waist circumference, m | 1.01±0.18 | 0.99±0.16 | 0.95±0.17 | 0.002 | 0.98±0.17 | 0.95±0.17 | 0.92±0.14 | 0.01 | |||||||
TV or computer, hr/wkday | 2.64±1.85 | 2.54±1.79 | 2.72±1.71 | 0.44 | 2.78±1.86 | 2.43±1.78 | 2.45±1.97 | 0.03 | |||||||
Women, % | 46 | 50 | 54 | 0.34 | 49 | 56 | 57 | 0.28 | |||||||
Current smokers, % | 12 | 6 | 8 | 0.13 | 7 | 6 | 7 | 0.92 | |||||||
Current drinkers, % | 56 | 49 | 49 | 0.27 | 59 | 53 | 42 | 0.01 | |||||||
Systolic BP, mmHg | 119±17 | 114±17 | 115±15 | 0.04 | 117±18 | 114±17 | 114±14 | 0.18 | |||||||
Diastolic BP, mmHg | 70±9 | 67±10 | 67±9 | 0.01 | 69±9 | 68±10 | 67±9 | 0.15 | |||||||
Insulin, mU/L | 14.64±10.00 | 14.63±8.26 | 13.70±7.20 | 0.55 | 14.23±9.27 | 12.99±7.13 | 12.33±6.83 | 0.44 | |||||||
Glucose, mg/dL | 107±24 | 102±19 | 98±15 | <0.0001 | 105±22 | 99±11 | 98±14 | 0.0001 | |||||||
HOMA insulin resistance | 3.92±2.82 | 3.80±2.72 | 3.41±2.10 | 0.16 | 3.75±2.75 | 3.25±2.03 | 3.08±2.18 | 0.12 | |||||||
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Total saturated fat, % | 35.75±1.41 | 35.34±1.16 | 34.58±1.38 | <0.0001 | 35.33±1.03 | 34.77±1.17 | 34.18±1.11 | <0.0001 | |||||||
Total MUFA, % | 18.34±1.02 | 18.24±1.06 | 18.42±1.05 | 0.27 | 17.55±0.93 | 17.59±1.01 | 17.62±0.98 | 0.81 | |||||||
Total PUFA, % | 32.94±1.08 | 33.14±0.86 | 32.83±1.14 | 0.02 | 35.37±0.85 | 35.31±0.98 | 35.19±0.82 | 0.14 | |||||||
Total cis-n3 fat, % | 5.70±0.96 | 5.41±0.79 | 5.22±0.84 | <0.0001 | 6.61±1.51 | 6.21±1.22 | 5.72±0.91 | <0.0001 | |||||||
Total cis-n6 fat, % | 27.16±1.33 | 27.65±1.06 | 27.54±1.25 | 0.001 | 28.69±1.53 | 29.04±1.44 | 29.41±1.06 | <0.0001 | |||||||
Total trans fat, % | 1.23±0.18 | 1.65±0.12 | 2.29±0.41 | – | 1.19±0.19 | 1.64±0.12 | 2.15±0.29 | – | |||||||
16∶1 trans, % | 0.06±0.02 | 0.08±0.02 | 0.10±0.03 | – | 0.06±0.02 | 0.07±0.03 | 0.08±0.03 | – | |||||||
18∶1 trans, % | 0.99±0.16 | 1.35±0.11 | 1.91±0.37 | – | 0.98±0.17 | 1.35±0.12 | 1.80±0.27 | – | |||||||
18∶2 trans, % | 0.18±0.04 | 0.22±0.04 | 0.29±0.07 | – | 0.16±0.04 | 0.22±0.05 | 0.27±0.05 | – | |||||||
PUFA:Sat fat ratio | 0.92±0.05 | 0.94±0.04 | 0.95±0.05 | <0.0001 | 1.00±0.04 | 1.02±0.05 | 1.03±0.04 | <0.0001 | |||||||
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Total energy, kcal/d | 2062±1010 | 2063±868 | 2106±958 | 0.60 | 2034±883 | 2037±765 | 2068±872 | 0.86 | |||||||
Total sat fat, % energy | 11.78±2.91 | 11.98±2.69 | 11.75±2.56 | 0.54 | 12.05±3.14 | 11.70±2.31 | 12.02±2.44 | 0.51 | |||||||
Total MUFA, % energy | 13.11±3.09 | 12.95±2.68 | 12.97±2.56 | 0.98 | 14.15±3.11 | 13.23±2.42 | 13.40±2.60 | 0.01 | |||||||
Total PUFA, % energy | 7.46±2.26 | 7.24±1.95 | 7.32±1.98 | 0.66 | 8.33±2.49 | 7.79±1.95 | 7.72±2.01 | 0.06 | |||||||
Total trans, % energy | 1.89±0.48 | 2.08±0.55 | 2.35±0.64 | <0.0001 | 1.94±0.54 | 2.15±0.53 | 2.33±0.69 | <0.0001 | |||||||
Carbohydrate, % energy | 47.86±9.34 | 50.27±8.31 | 51.45±7.24 | 0.0004 | 45.45±9.67 | 49.16±7.08 | 49.54±6.70 | 0.0002 | |||||||
Protein, % energy | 15.97±3.04 | 15.53±2.80 | 15.25±2.77 | 0.11 | 16.23±3.03 | 16.23±2.70 | 15.86±2.36 | 0.36 |
Values are means±SD or %. PUFA = Polyunsaturated fat; MUFA = Monounsaturated fat; Sat = Saturated; Trans = Total trans fat in erythrocyte membranes; BP = Blood pressure; FFQ = Food frequency questionnaire.
In the total study sample, the mean (±SD) erythrocyte membrane trans fat content as a percentage of total erythrocyte membrane fat was 1.21±0.19, 1.64±0.12 and 2.23±0.37 for the 1st, 2nd and 3rd tertile of total trans fat, respectively. Thus, individuals in the top tertile had 84% higher trans fat on average compared to those in the lowest tertile. The amount of energy from trans fat estimated from a food frequency questionnaire was also higher in top tertile compared to the lowest tertile of erythrocyte trans fat in both the low and high erythrocyte membrane PUFA groups (
In fully adjusted models that included main effects and an interaction term for eTFA and ePUFA, erythrocyte total trans fat concentrations showed a significant inverse association with HDL (
Lipid values are means ± s.e.m and are adjusted for study site, age, sex, body mass index, physical activity, alcohol intake status, smoking status, erythrocyte monounsaturated fat, erythrocyte saturated fat and pedigree as a random effect.
There was no significant interaction (
The relation between eTFA, ePUFA and triglycerides was similar to that of VLDL
Since saturated fat and MUFA in erythrocytes are not good biomarkers of intake, we performed additional analyses using saturated fat and MUFA variables from the FFQ as covariates. We also performed analyses adjusting for carbohydrate intake. These analyses did not change the inferences from models that were not adjusted for these variables.
Consistent with other studies, we found that higher concentrations of trans fatty acids in erythrocyte membranes are associated with lower HDL concentrations and higher PUFA concentrations are associated with lower triglyceride and VLDL concentrations
Compared to other studies on the relation between fatty acids and lipids, our study had a number of strengths including the large sample size (n = 1032) and use of objectively measured independent (erythrocyte fatty acids) and dependent variables (lipids). Use of erythrocyte membrane fatty acids measured by gas chromatography greatly enhances the quality of trans fat and PUFA assessments. Furthermore, our analyses adjusted for various potential confounders including age, sex, study site, body mass index, physical activity, alcohol intake, smoking, erythrocyte monounsaturated fat, erythrocyte saturated fat and pedigree as a random effect.
When the erythrocyte PUFA concentration is above the median in our study population, we see the expected associations
The exact reasons for these observations are not clear but could be related to trans fat inhibition of LPL activity and up-regulation of CETP activity
Our study has a number of limitations. It was a cross-sectional design thus we are unable to determine whether the observed associations may have been affected by reverse causality since individuals with dyslipidemia may have changed their diet. Secondly, we did not measure LPL or CETP activities so as to better understand the underlying mechanisms. Nonetheless, these findings are interesting in that they show for the first time that the effect of trans fat or PUFA vary depending on relative concentrations of other fatty acids. Our study is unique in that lipids were measured three weeks after patients suspended use of their lipid-lowering drugs. Thus our findings are not confounded by lipid-lowering drugs.
These findings will need to be replicated in prospective studies with lipids and/or cardiovascular events as end-points. Other human or animal studies are needed to elucidate on the mechanism underlying the observed interaction between trans fat, PUFA and lipids (i.e., HDL, triglycerides and VLDL).
The association between trans fat and lipids (HDL, VLDL and triglycerides) may vary depending on PUFA. The benefit of avoiding trans fat may be greater among individuals with higher PUFA intake. Supplementation with PUFA among individuals with relatively high trans fat intake may have limited benefits on lipid profiles.
(DOC)
We are grateful to the staff of the GOLDN study for the assistance in data collection and management. Results from this study were presented at the joint meeting of the American Heart Association’s 50th Cardiovascular Disease Epidemiology and Prevention council and the Nutrition, Physical Activity, and Metabolism council, March 2–5, 2010 at the Hilton San Francisco Union Square in San Francisco, California.