This is the first long-term prospective cohort study to have investigated the associations between ATFAs and incident cardiovascular events. We have shown that relative intakes of monounsaturated and polyunsaturated fatty acids were independent and opposite predictors of CVD. Both for n
-3 and n
-6 PUFA, those with proportionate intakes in the upper 20% had ~20% less CVD than those in the lowest 20%, after allowing for a wide range of known risk factors. Furthermore, we have demonstrated that adding adipose tissue PUFA increased the prognostic capacities of the Framingham and ASSIGN risk equations. We thus give robust evidence to support the common theory that the balance of fats in the diet is important for cardiovascular health, even after allowing for classical risk factors that they affect, such as dyslipidaemia and hypertension. We have found that some of the relative effects of fat intake are likely to be captured by a sensitive measure of socio-economic status. Even after additional allowance for this and family history of CHD, n
-3 PUFA improved classification of CVD by over 6%, which compares favourably to 1–3% for fibrinogen29,30
and 7% for CRP,29
found in similar analyses using the SHHEC.
The protective independent predictive value of n
-3 ATFAs for CVD observed in this study is consistent with results from numerous case–control studies investigating the associations between blood or red cell membrane levels of n
-3 PUFA and CHD.3,31
Further, as observed in our prospective cohort, previous epidemiological studies and intervention trials have observed beneficial effects of n
-6 PUFA on CHD.2
Our study is the first to investigate SFA and MUFA from adipose tissue and incident CVD, and thus no comparison is possible with other published data.
There is no gold standard biomarker for the quantification of the usual dietary intake of total fat, often prone to underreporting.4
Intakes of several fatty acids can be estimated through different biomarkers such as blood (serum, plasma, or red cell) levels and adipose tissue obtained from a skin biopsy punch. Each of these methods have specificities related to their analytical methods and capacity to represent a different duration of exposure of fatty acid intakes (days for serum; weeks for red cells; years for adipose tissue) and level of correlation to fatty acid dietary intakes. Three main types of ATFA biomarkers have been related to dietary intakes: n
-3 and n
-6 PUFAs, trans unsaturated and some subgroups of SFA derived from dairy products. As they are not produced endogeneously, they represent good biomarkers for validation studies. In a recent systematic review, it has been estimated that the level of correlation of n
-3 PUFA intake and n
-3 level in adipose tissue ranged from 0.40 to 0.60, according to the method of dietary assessment.5
Similar correlations were identified with n
-3 blood levels; however, they represent a short-term exposure to n
-3 intakes compared with adipose tissue levels. On the other hand, several factors can influence measured levels of ATFAs, such as weight fluctuations, the use of supplements (e.g. fish oil capsules) and the procedures used. The tissue-sampling site (arm, buttock, or abdomen is known to cause variations in sampling results,19,32
but it is unknown what effects this would have on CVD-risk estimation. The arm was chosen in this study because it is the most practical site for extraction in large clinical studies. Degradation during long-term storage could also affect sample values. To reduce such issues, we used standardized procedures for adipose tissue biopsies and assays were all done within 3 years of sampling.
This study has two strengths that are essential for robust inference: relatively low sampling error (due to the large sample size, leading to narrow CIs) and relatively low chance of measurement error. The latter is a combination of the objective measures of ATFA measurement, the exclusion of those with pre-existing CVD at baseline and the prospective and impersonal nature of event ascertainment. As well as being the only cohort study of ATFAs, as far as we are aware this is also the first study that uses modern statistical methods to investigate whether objective measures of diet add to discrimination and reclassification over and above routine risk scores. To have shown that they do add to all the variables included in the two most appropriate risk scores moves the debate over whether diet is an independent risk factor for CVD one important, positive, step further.
The study has several limitations. First, the ATFAs were only measured once, yet diets are sure to have changed during follow-up. Thus we can estimate how well the accumulated effects of diet over the years preceding examination predict future CVD, after adding to risk scores, but not how changes in diet predict CVD. However, it is only the first of these goals that these analyses seek to address. Risk scores are conventionally based upon single enumerations of risk factors, because they give the risk of CVD in the future according to results collected today. Thus, although the Framingham study has multiple measurements of blood pressure, cholesterol, smoking, etc., the Framingham risk score only uses risk factor data collected at a single time. Hence, for the current purpose, a one-off measurement is sufficient. We cannot, however, say how changes in diet may have affected the risk of CVD. We also cannot estimate how the relative effects of components of the diet may change with the introduction of new treatments over time, most obviously following the widespread introduction of statins from the mid-1990s in Scotland. It is conceivable that statins may affect ATFAs—although the Oxford Cholesterol Study found no significant effects of simvastatin on individual free fatty acids in serum33
—but we have no reason to suppose that statin use would cause important variations in the associations between ATFAs and CVD. Another limitation is that some important ATFAs are missing from the panel that was measured >20 years ago, when technology was less well-developed. This includes the n
-3 variables: 18:3, n
-3 (alpha linolenic acid) and 20:5, n
-3 (eicosapentaenoic acid, EPA) and restricts the scope of our conclusions regarding the effects of ATFAs. Furthermore, modern practice would be to store specimens at −80°C, rather than −40°C, when kept unthawed for >6 months. This may have introduced some measurement error into our analyses, which will have caused associations with CVD to tend to the null. Another issue may be that our ATFA measures are proportional, not absolute. Also, we have no external data set upon which to test our findings—instead we use the most efficient internal method of bias correction that we are aware of.28
Although the bootstrap distributions sometimes showed moderate skewness, in sensitivity analyses, percentile-based CIs based on repeated bootstrapping with 500 resamples, and on 1000 resamples, were always similar to the normal-based CIs, suggesting that the estimates and approximate P
-values quoted in Table
provide robust inferences. Finally, despite their attractiveness as objective estimates of long-term dietary habits, ATFAs have the practical disadvantage that they are invasive and not suitable for widespread use in clinical check-ups. This is emphasized by the large number of missing values (>60%) in our ATFA data, of which about a third were due to subjects refusing the test.