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The prognostic value of adipose tissue fatty acids for incident cardiovascular disease: results from 3944 subjects in the Scottish Heart Health Extended Cohort Study

Mark Woodward, Hugh Tunstall-Pedoe, G. David Batty, Roger Tavendale, Frank B. Hu, Sébastien Czernichow
DOI: http://dx.doi.org/10.1093/eurheartj/ehr036 1416-1423 First published online: 23 February 2011

Abstract

Aims Dietary fats are routinely considered key determinants of cardiovascular risk, yet the scientific basis of this association has never been demonstrated using objective measures of fat intakes in a large prospective study in a general population.

Methods and results Adipose tissue was taken from 3944 participants, predominantly aged 40–59 years, in Scotland, 1984–87. Percentages of individual fatty acids were measured using gas chromatography. Over a median of 19.5 years, 870 incident cardiovascular disease (CVD) events occurred. Hazard ratios (HRs) were obtained from Cox models and the additional prognostic value, accounting for variables in the Framingham and ASSIGN CVD risk scores, were assessed using discrimination indices. Adjusting for age, sex, total and HDL-cholesterol, systolic blood pressure, smoking, hypertensive medication use, diabetes, socio-economic status and family history, the percentage of monounsaturated adipose tissue fatty acids had a positive log-linear relationship with incident CVD: the HR comparing risk between the fourth and first quartiles was 1.29 (95% confidence interval: 1.05, 1.59). n-3 polyunsaturated fat showed the reverse trend, the corresponding result being 0.77 (0.63, 0.94). These two composite variables improved the classification of incident CVD events by 1.0 and 6.4%, respectively, with only the latter being significant at the 5% level.

Conclusions A diet which is proportionately rich in polyunsaturated fat, as opposed to other fats, is expected to decrease the risk of CVD independently of the effects of common CVD risk factors, including social deprivation. Taking account of such diets improves the classification of future CVD events.

  • Fatty acids
  • Myocardial infarction
  • Stroke
  • Risk score

Introduction

Although diet is widely recognized as an important determinant of cardiovascular disease (CVD), this health behaviour is difficult to quantify precisely due to variations in portion size, cooking methods, and the balance of ingredients in food items, particularly processed food. Usual methods of dietary assessment, such as food frequency questionnaires, diet history or repeated 24-h recalls, are often used in large epidemiological studies, yet these suffer from all the above problems, as well as errors in recall.1 These problems are especially true when quantifying intake of individual fatty acids, which are thought to be of paramount importance in chronic disease.2 Accordingly, there is considerable interest in the utilization of biomarkers for fatty acid intake, through blood samples or subcutaneous adipose tissue.3 An important strength of adipose tissue fatty acids (ATFAs) is that they represent long-term (months to years) exposure to intakes of fatty acids, compared with days or weeks of exposure for blood or red blood cells levels.4 The relatively few studies of ATFA conducted to date have been hampered by the small numbers of subjects,5 and all have used cross-sectional or case–control designs617 which are suboptimal for investigating potential causality.18 Since tissue concentrations of fatty acids may be influenced by pathophysiological changes induced by diseases, prospective cohort studies are more suitable for examining the relationship between these biomarkers and CVD risk.

To our knowledge, the largest sample of adipose tissue used to quantify fatty acids was collected in the Scottish Heart Health Extended Cohort Study (SHHEC). Previous reports from the cross-sectional phase of SHHEC have shown cross-sectional associations between ATFAs and coronary heart disease (CHD) at a regional level19 and changes in ATFAs with ageing.20 In both cases, only the first tranche of baseline SHHEC data were utilized. Here, we use all the available baseline data and, for the first time, report analyses of ATFAs in the prospective phase of the SHHEC. Since the major risk factors for CVD are well established, the primary aim of the current work is to ascertain whether ATFAs have any extra prognostic value, over and above standard risk engines. Of these, the Framingham risk score21 is the most commonly used world-wide for estimating individuals’ 10-year risk of CVD, while the ASSIGN CVD risk score22 is being implemented in Scotland.

Methods

The study population for the SHHEC includes participants in the Scottish Heart Health Study (SHHS), a national random population survey between 1984 and 1986, and six associated random population surveys in Scotland.22,23 Subjects were recruited through the lists of General Practitioners. At the time of inception, Scotland had particularly high levels of coronary mortality in both sexes and internationally low intakes of polyunsaturated fats. A pre-specified aim of the study was, thus, to investigate whether dietary risk factors would add to existing, known, risk factors in the prediction of CVD.

Adipose tissue was extracted from subjects in the SHHS and in two of the associated surveys: the 1986 MONICA study in north Glasgow and Edinburgh and a 1987 survey in Aryshire and Arran—all cities or regions of Scotland. Both these surveys used exactly the same questionnaire and clinic procedures as in the SHHS. Each random sample included subjects aged 40–59 years, with a small number outside this range also included in the MONICA surveys. All participants received questions on demographics, medical history, and lifestyle, including tobacco use, and a food frequency questionnaire, from which daily intakes of macronutrients were estimated.24 Participants were invited to attend clinics where blood pressure, weight, and height were measured and a 12-lead electrocardiogram was applied and a specimen of adipose tissue was taken from the outer upper arm using a 3 mm skin biopsy punch and frozen, initially at −20°C for up to 5 days and then for a median of 2 years (minimum 16 months; maximum 35 months) at −40°C. The fatty acid methyl esters were separated using gas chromatography and individual fatty acids were expressed as a percentage of the total amount of fatty acids present in the sample. Further details of the assay method were given previously.19,25 The specific ATFAs measured are shown in Table 1.

View this table:
Table 1

Baseline summary statistics for 3944 men and women in the Scottish Heart Health Extended Cohort Study

VariableMen (n = 2095)Women (n = 1849)
Continuous variables: mean (standard deviation) or median (first to third quartile)a
 Age (years)49.0 (6.9)48.9 (6.6)
 Systolic blood pressure (mmHg)133.2 (18.5)130.0 (20.0)
 Total serum cholesterol (mmol/L)6.29 (1.13)6.49 (1.31)
 High-density lipoprotein cholesterol (mmol/L)1.38 (0.37)1.68 (0.42)
 Scottish index of multiple deprivation score16.4 (8.52–31.1)16.0 (8.55–31.1)
 Cigarettes per day (for smokers)20 (15–25)18 (12–20)
Nutrient intakes per dayb
 Fat (g)88.0 (73.7–106.7)74.3 (61.1–90.3)
 Saturated fat (g)38.9 (31.6–48.0)33.5 (26.3–41.6)
 Polyunsaturated fat (g)10.6 (8.10–14.1)8.50 (6.40–11.4)
 Protein (g)85.8 (73.9–99.2)73.3 (63.7–84.9)
 Carbohydrates (g)270.6 (222.1–329.2)185.9 (149.8–229.7)
 Alcohol (g)14.9 (4.10–31.4)3.70 (0.00–9.50)
 Energy (kcals)2316 (1953–2710)1706 (1448–2019)
Adipose tissue fatty acidsc
 Saturated fat (SFA) (%)27.0 (24.9–29.2)25.2 (22.6–27.7)
  C14:0 Myristic2.31 (1.81–2.83)2.28 (1.75–2.76)
  C16:0 Palmitic21.0 (19.7–22.4)19.4 (17.9–21.0)
  C18:0 Stearic3.64 (2.97–4.41)3.39 (2.66–4.29)
 Monounsaturated fat (MUFA) (%)62.7 (60.2–65.2)64.0 (61.3–67.0)
  C16:1, n-7 Palmitoleic8.66 (7.45–9.98)9.23 (7.96–10.56)
  C18:1, n-9 Oleic50.6 (49.0–52.2)51.3 (50.0–53.1)
  C20:1, n-9 Eicosaenoic3.08 (2.76–3.48)3.05 (2.75–3.42)
 Polyunsaturated fat (PUFA) (%)9.93 (8.45–11.64)10.4 (9.15–12.1)
  n-6 polyunsaturated fat9.51 (8.04–11.01)9.94 (8.67–11.61)
   C18:2, n-6 Linoleic8.57 (7.13–10.20)8.99 (7.75–10.62)
   C18:3, n-6 γ-Linolenic0.20 (0.15–0.26)0.17 (0.12–0.22)
   C20:3, n-6 Dihomo-γ- linolenic0.10 (0.06–0.12)0.15 (0.11–0.18)
   C20:4, n-6 Arachidonic0.63 (0.54–0.72)0.60 (0.51–0.69)
  n-3 polyunsaturated fat0.42 (0.33–0.52)0.46 (0.36–0.58)
   C22:5, n-3 Docosapentaenoic0.25 (0.20–0.30)0.26 (0.21–0.32)
   C22:6, n-3 Docosahexaenoic0.17 (0.13–0.22)0.19 (0.15–0.26)
 PUFA/SFA0.37 (0.31–0.44)0.42 (0.35–0.51)
Binary variables, n (%)
 Smokers765 (37)640 (35)
 Diabetes21 (1)19 (1)
 Family history of coronary disease547 (26)569 (31)
 Blood pressure lowering medication108 (5)196 (11)
  • aAs appropriate (for skewed variables quartiles are shown).

  • bEstimated from a food frequency questionnaire.

  • cThe relative amount of this fatty acid in relation to the total adipose fat, per person.

From the three constituent surveys, 12 534 subjects were asked to provide adipose tissue samples; after removing those who refused (16%) and failed extractions (46%), there were 4763 (38%) from whom valid fatty acid analyses could be drawn. Failed extractions were generally due to the punch having extracted skin only—to minimize discomfort, a relatively non-invasive method of biopsy punching was used25 and repeat biopsies were not allowed in the study protocol. There were no important differences between those included (n = 4763) and excluded (n = 7771) in any of the variables used in this manuscript: mean age at baseline was 48.8 vs. 48.5 years; mean systolic blood pressure, 132.0 vs. 132.7 mmHg; mean serum total cholesterol, 6.41 vs. 6.45 mmol/L; HDL-cholesterol 1.51 vs. 1.52; 47 vs. 52% were women; 37 vs. 39% were smokers; 1.3 vs. 1.7% had a history of diabetes; and 24.5 vs. 24.5% had CVD events, 19.6 vs. 19.8% had CHD events, and 6.7 vs. 6.9% had cerebrovascular events, during follow-up.

Subjects who gave written permission were followed up through death registrations and the national record linkage database,22 up to the end of 2005. The primary outcome studied here is incident CVD: deaths attributed to a cardiovascular cause [International Classification of Diseases (ICD) 9 codes 390–459, ICD 10 codes I00-I99]; any hospital discharge diagnosis post-recruitment of CHD (ICD 9 410-414, ICD 10 I20-I25) or cerebrovascular disease (ICD 9 430–438, ICD10 G45, I60-I69); or surgical codes for coronary artery bypass graft or percutaneous coronary angioplasty. Secondary outcomes were CHD and cerebrovascular disease.

So as to analyse incident events, anyone with evidence of CVD at baseline was excluded from all the analyses reported here. This was determined from questions on previous doctor diagnoses of angina, heart attack, or stroke at baseline; whether the ECG was suggestive of myocardial infarction using Minnesota codes26; and by extending the search of the national linkage database back to 1981 to look for hospitalizations for CHD or stroke or any coronary surgical procedures prior to recruitment.

Statistical methods

Since several of the ATFAs had highly skewed distributions, quartiles, and Spearman rank correlations were used to describe the data. Cox proportional hazards models were used to estimate hazard ratios (HRs) and 95% confidence intervals (CIs) for CVD by the quarters of each ATFA and for some appropriate combined variables: saturated (SFA: C14:0 + C16:0 + C18:0), monounsaturated (MUFA: 16:1, n-7 + C18:1, n-9 + C20:1, n-9), n-3 polyunsaturated (C22:5, n-3 + C22:6, n-3), n-6 polyunsaturated (C18:2, n-6 + C18:3, n-6 + C20:3, n-6 + C20:4, n-6) and polyunsaturated (n-6 PUFA + n-3 PUFA) fatty acids, and the PUFA to SFA ratio. Associations with ATFAs were adjusted for two sets of potential confounding factors: those used in the Framingham CVD score21 (age, total and HDL-cholesterol, systolic blood pressure, diabetes, history of blood pressure medication, and current smoking status) and those used in either the Framingham or the Scottish ASSIGN score22 (adding family history of CHD and socio-economic status to Framingham, and refining the variable used to measure current smoking as number of cigarettes smoked per day). Socio-economic status was measured by the Scottish index of multiple deprivation (SIMD),22 which increases with increasing deprivation. Anyone with missing values for any of these variables was excluded from all analyses. Further adjustment sets, not reported here, substituted highest level of education achieved for SIMD and added body mass index (weight divided by the square of height, in kg/m2)—neither changed the HRs in any important way. Since no interactions between ATFAs and sex were significant at the 1% level, results are presented across the sexes, with sex added to the two risk adjustment sets. Tests for linear and quadratic effects, on the logarithmic scale, were carried out using log likelihood tests.18

To ascertain whether ATFAs have any prognostic value for CVD, having allowed for the risk factors included in the Framingham and ASSIGN scores, areas under the receiver operating characteristic curves (c-statistics) and relative integrated discrimination indices (RIDIs),27 after adding ATFAs to the two adjustment sets considered, were estimated using bootstrap resampling (500 times) to correct for self-testing.28 Approximate P-values and 95% CIs were obtained by assuming normal distributions for these parameters. In these analyses, only those ATFAs which were independently significant after multiple adjustments in the Cox models were included. Adipose tissue fatty acids were measured by continuous terms in these analyses, taking linear terms only unless quadratic terms were found significant in the Cox analyses, in which case these were added. Non-ATFA continuous variables were treated as in the Framingham and ASSIGN scores—i.e. the Framingham variables were log transformed. Every such variable was taken together with its interaction with sex: that is, effect modification by sex was allowed for in every case, as in the Framingham21 and ASSIGN22 scores. All analyses were performed using SAS version 9.1.

Results

Of the 4763 with adipose samples, 4433 had no evidence of pre-existing CVD at baseline. Of these, 3944 (47% women) had no missing values for any of the covariates chosen for multivariable analyses. The mean age at baseline was 49 years in each sex and, by modern standards, the prevalence of smoking was high, and that of diabetes was low, in both sexes (Table 1). Almost two-thirds of adipose fat was monounsaturated and only ∼10% polyunsaturated, with virtually the same percentages for each sex.

In the general context of observational epidemiology, there were high age/sex-adjusted correlations (of ±0.18 or more) between both the SIMD score (increasing social deprivation) and cigarette dose and both MUFA (precisely, C16:1, n-7 and C18:1, n-9, in a positive direction) and PUFA (precisely, C18:2, in a negative direction) (Supplementary material online, Table S1). The only other correlations of this order of magnitude were between C18:3, n-6 SFA and HDL-cholesterol (positively) and between cigarette dose and the PUFA to SFA ratio (negatively).

During follow-up (median of 19.5 years), 870 incident CVD events occurred; 651 of these 870 had a CHD event and 219 had a cerebrovascular event (44 had both). Many of the ATFAs were independently associated with CVD after adjustment for Framingham variables (Table 2). For the HRs comparing the extreme quarters, only C20:1, n-9, C18:3, n-6, C20:3, n-6, C20:4, n-6 and the PUFA to SFA ratio were non-significant at the 5% significance level. In most cases, there was evidence of a linear trend, sometimes together with curvature to suggest an arrested trend (threshold effect). For example, n-6 PUFA shows similarity between the risks in the first two-quarters and then a drop in risk to the third- and fourth-quarters, which have similar risk. As the percentage of SFA and PUFA (n-3 and n-6) increased, the risk of CVD tended to go down, while MUFA had a positive relationship with the risk of CVD. Further adjustment for family history, SIMD score and cigarette dose accounted for the effects of SFA and n-6 PUFA, but left a residual positive effect of MUFA and negative effect of n-3 PUFA. Figure 1 summarizes the associations for the major subtypes of ATFAs, showing HRs contrasting the extreme quarters, after adjustment for all the confounding variables in the two risk scores (ASSIGN and Framingham) combined.

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Table 2

Associations between fatty acids and incident cardiovascular disease after adjustment for Framingham variablesa and after adjustment for Framingham and ASSIGN (‘all’) variablesb

Fatty acid/AdjustmentHazard ratio (95% confidence interval) vs. first quarterP-value
Quarter
SecondThirdFourthLinearQuadratic
Saturated fat (SFA)
 Framingham0.86 (0.71, 1.04)0.87 (0.71, 1.05)0.74 (0.60, 0.90)0.0030.81
 All0.87 (0.72, 1.06)0.94 (0.77, 1.14)0.81 (0.66, 1.00)0.060.99
C14:0 Myristic
 Framingham0.91 (0.76, 1.10)0.78 (0.64, 0.94)0.77 (0.63, 0.93)0.0060.38
 All0.95 (0.79, 1.15)0.84 (0.69, 1.01)0.85 (0.70, 1.03)0.100.31
C16:0 Palmitic
 Framingham0.87 (0.72, 1.07)0.81 (0.66, 0.99)0.76 (0.62, 0.94)0.010.45
 All0.88 (0.72, 1.07)0.86 (0.70, 1.05)0.83 (0.68, 1.02)0.100.74
C18:0 Stearic
 Framingham0.90 (0.74, 1.08)0.85 (0.71, 1.03)0.78 (0.64, 0.96)0.010.14
 All0.90 (0.75, 1.08)0.90 (0.74, 1.09)0.85 (0.69, 1.04)0.110.18
Monounsaturated fat (MUFA)
 Framingham1.22 (0.99, 1.49)1.22 (1.00, 1.50)1.44 (1.18, 1.76)<0.00010.29
 All1.19 (0.97, 1.46)1.15 (0.93, 1.41)1.29 (1.05, 1.59)0.0040.47
C16:1, n-7 Palmitoleic
 Framingham1.02 (0.83, 1.24)1.07 (0.87, 1.31)1.30 (1.07, 1.58)0.0020.04
 All1.00 (0.81, 1.22)1.01 (0.82, 1.24)1.21 (0.99, 1.47)0.030.04
C18:1, n-9 Oleic
 Framingham1.12 (0.91, 1.37)1.27 (1.04, 1.55)1.39 (1.14, 1.71)0.00050.70
 All1.07 (0.87, 1.32)1.19 (0.97, 1.45)1.25 (1.02, 1.54)0.020.77
C20:1, n-9 Eicosaenoic
 Framingham1.00 (0.82, 1.21)0.93 (0.77, 1.14)1.15 (0.95, 1.39)0.190.56
 All1.02 (0.84, 1.23)0.93 (0.76, 1.13)1.11 (0.91, 1.34)0.290.87
Polyunsaturated fat (PUFA)
 Framingham0.95 (0.80, 1.14)0.79 (0.65, 0.96)0.79 (0.65, 0.97)0.020.002
 All0.97 (0.81, 1.16)0.81 (0.66, 0.98)0.84 (0.69, 1.03)0.070.004
n-6 polyunsaturated fat
 Framingham0.96 (0.80, 1.15)0.80 (0.66, 0.97)0.80 (0.66, 0.98)0.030.002
 All0.98 (0.82, 1.17)0.83 (0.68, 1.01)0.85 (0.69, 1.04)0.100.007
C18:2, n-6 Linoleic
 Framingham0.93 (0.78, 1.12)0.79 (0.65, 0.95)0.79 (0.64, 0.96)0.030.001
 All0.95 (0.79, 1.14)0.82 (0.67, 0.99)0.83 (0.68, 1.03)0.100.007
C18:3, n-6 γ-Linolenic
 Framingham0.84 (0.70, 1.02)0.95 (0.79, 1.16)0.92 (0.75, 1.14)0.380.52
 All0.85 (0.70, 1.03)0.96 (0.79, 1.17)0.95 (0.77, 1.17)0.560.62
C20:3, n-6 Dihomo-γ-linolenic
 Framingham0.80 (0.66, 0.96)0.79 (0.65, 0.97)0.81 (0.65, 1.00)0.040.07
 All0.80 (0.66, 0.96)0.81 (0.66, 0.99)0.83 (0.67, 1.03)0.050.08
C20:4, n-6 Arachidonic
 Framingham0.93 (0.76, 1.14)0.96 (0.79, 1.17)1.11 (0.91, 1.34)0.720.59
 All0.93 (0.76, 1.13)0.93 (0.76, 1.13)1.03 (0.85, 1.25)0.970.82
n-3 polyunsaturated fat
 Framingham0.92 (0.76, 1.12)0.80 (0.65, 0.97)0.79 (0.64, 0.96)0.020.002
 All0.94 (0.78, 1.14)0.78 (0.64, 0.95)0.77 (0.63, 0.94)0.010.003
C22:5, n-3 Docosapentaenoic
 Framingham0.89 (0.73, 1.09)0.84 (0.69, 1.03)0.80 (0.65, 0.98)0.030.09
 All0.91 (0.75, 1.11)0.85 (0.70, 1.04)0.77 (0.63, 0.95)0.020.03
C22:6, n-3 Docosahexaenoic
 Framingham0.85 (0.71, 1.04)0.78 (0.64, 0.95)0.78 (0.64, 0.96)0.050.002
 All0.86 (0.71, 1.04)0.75 (0.62, 0.92)0.76 (0.62, 0.93)0.030.0006
PUFA/SFA
 Framingham0.87 (0.73, 1.05)0.88 (0.73, 1.06)0.91 (0.75, 1.11)0.560.16
 All0.84 (0.70, 1.01)0.85 (0.70, 1.03)0.91 (0.74, 1.11)0.580.07
  • aFramingham variables are sex, age, total serum cholesterol, HDL-cholesterol, systolic blood pressure, history of taking blood pressure medication, smoking (current vs. not), diabetes, and sex21.

  • b‘All’ variables are sex, age, total serum cholesterol, HDL-cholesterol, systolic blood pressure, history of taking blood pressure medication, cigarettes smoked per day, diabetes, sex, family history of coronary disease and socio-economic status22.

Figure 1

Hazard ratios for cardiovascular disease comparing highest to lowest quarters of individual adipose tissue fatty acids, after adjustment for sex, age, total serum cholesterol, HDL-cholesterol, systolic blood pressure, history of taking blood pressure medication, diabetes, cigarettes smoked per day, family history of coronary disease, and socio-economic status. Diamonds show results for the summation of all fatty acids within the four subtypes shown. Bars and diamonds extend to the limits of the 95% confidence intervals.

For additional insight, Supplementary material online, Table S2 shows the effects of the ATFAs on CVD after adjustment for only age and sex and adjusting for age, sex, SIMD score, and family history of CHD. Supplementary material online, Tables S3 and S4 show the effects of ATFAs on CHD and cerebrovascular disease, respectively, adjusting as in Table 2. In each case, the broad inferences drawn are the same as for Table 2, although the PUFA to SFA ratio showed some evidence of an effect on CVD after minimal adjustments (Supplementary material online, Table S2).

After correction for internal testing, many MUFA and PUFA (n-3 and n-6) individual ATFAs added significantly (i.e. approximate P-values, after bootstrapping, <0.05) to Framingham variables, both for discrimination and correct classification of who will and will not develop CVD in SHHEC (Table 3; left side). When adding individual ATFAs to the prediction model with all variables considered here (Framingham + ASSIGN), none gave approximate P-values < 0.05 for discrimination, but PUFA variables (not others) appear to improve classification significantly. Total n-3 PUFA adds most to the classification of CVD, increasing the accuracy of distinguishing future cases from non-cases by 6.4% when added to the Framingham variables, and by 6.0% when added to the Framingham and ASSIGN variables together. Confidence intervals for these percentages, using bootstrap percentiles, were 2.5–10.6% and 2.0–10.3%, respectively (normal-based equivalents were similar: 2.3–10.6 and 1.7–10.3%). Total n-6 PUFA (predominantly C18:2, n-6) improves classification of CVD by 4.5% when added to the Framingham variables, and by 3.4% when added to all variables.

View this table:
Table 3

Marginal discrimination and reclassification statistics, with approximate P-values, for incident cardiovascular disease within 10 years when individual adipose fatty acid variablesa are added to only Framingham variablesb and when individual adipose fatty acid variablesa are added to Framingham and ASSIGN (‘all’) variablesb

Added to Framingham variablesAdded to all variables
DiscriminationReclassificationDiscriminationReclassification
ΔcP-valueRP-valueΔcP-valueRP-value
SFA0.00110.101.10.080.00000.990.10.39
 C14:0−0.00060.522.00.02−0.00040.580.50.14
 C16:00.00060.480.90.13−0.00010.960.10.40
 C18:00.00200.020.30.260.00060.750.00.46
MUFA0.00210.0022.70.01−0.00020.871.00.11
 C16:10.00210.021.50.050.00000.911.00.11
 C18:10.00080.362.60.008−0.00020.901.00.09
PUFA0.00240.024.90.0020.00080.483.80.006
 PUFA-60.00230.024.50.0030.00070.473.40.008
 C18:20.00220.034.20.0030.00060.523.00.01
 C20:3−0.00050.541.90.07−0.00120.271.80.06
 PUFA-30.00250.036.00.0030.00130.276.40.001
 C22:50.00140.144.20.020.00080.455.80.003
 C22:60.00240.054.20.010.00100.364.80.004
  • For full names of fatty acid variables and adjustments, see Table 2. Δc, delta c statistic (area under the receiver operating characteristic curve); R, relative integrated discrimination index (%).

  • The c-statistic for Framingham variables is 0.7425 and for ‘all’ variables is 0.7594.

  • aLinear terms + quadratic terms where quadratic effect P-value is <0.05 in Table 2.

  • bFor a list of adjustments see Table 2.

Discussion

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 3 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.

Conclusions

Using biomarkers, such as adipose tissue, allows investigation of the long-term exposure to fatty acids. We have shown that both n-6 and n-3 PUFA are protective for incident CVD and increase the discrimination and classification capacities of standard cardiovascular risk equations. Even if the routine use of tissue biopsy is not warranted, these results help to better understand the mechanisms involved in the protective effects of n-6 and n-3 PUFA on CVD.

Funding

This study was supported by the Chief Scientist Office of the Scottish Health Department and subsequently by the British Heart Foundation. M.W.'s research is supported by the Australian National Health and Medical Research Council's program grant 571281. G.D.B. is a Wellcome Trust Career Development Fellow (WBS U.1300.00.006.00012.01). F.H.'s research is supported by NIH grant HL60712.

Conflict of interest: none declared.

References

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