The Framingham Heart Study provided unique insights into cardiovascular disease (CVD) risk factors and ledto the development of the Framingham Risk Score based on 8 baseline standard risk factors—that is, age, sex, smoking, hypertension treatment, diabetes status, systolic blood pressure, total cholesterol, and HDL cholesterol.
In addition to the standard risk factors, biomarkers integrating lifestyle choices might help identify individuals at risk and be useful to assess treatment approaches, prevent morbidity, and delay death.
Among the diet-based biomarkers are fatty acids (FAs), whether measured in plasma or red blood cell (RBC) membranes. The FAs most clearly associated with reduced risk for CVD and for total mortality (i.e., death from any cause) are the omega-3 FAs, EPA (20:5n–3) and DHA (22:6n–3). For instance, in a 2018 report which included 2500 participants in the Framingham Offspring Cohort followed for a median of 7.3 years, the baseline RBC EPA+DHA content [the omega-3 index (O3I)] was significantly and inversely associated with risk for death from all causes. Individuals in the highest quintile were 33% less likely to succumb during the follow-up years compared with those in the lowest quintile.
However, this and other prior investigations evaluated only one FA metric as an exposure variable. In one of the previous studies, the association of various FAs with acute coronary syndrome was systematically evaluated. A suite of 10 FAs was identified and compared with a suite of standard risk factors. The RBC FA profile discriminated cases from controls significantly better than did standard risk factors. However, that approach is not easily translated into clinical use, partly because it was cross-sectional and used a case–control design.
The current study posed a similar question in a prospective setting using the Framingham Offspring Cohort,which was followed for clinical events for 11 years after RBC FAs were measured. The authors explored how a fingerprint or pattern of RBC FAs measured in older Americans compares with standard risk factors as predictors of risk of all-cause mortality.
This study was conducted in the framework of the Framingham Offspring Cohort. 2240 participants aged in mid-60s who attended their 8th examination cycle (2005–2008) were eligible for the current investigation. The primary endpoint was risk of all-cause mortality during 11 years of follow-up. In the final data set, 384 participants died during the follow-up.
In the blood samples of participants, twenty-seven FAs were quantified and expressed as a percentage of total RBC FAs, and the O3I was computed as the sum of EPA and DHA. For modeling purposes, the authors used the O3I (instead of its constituent FAs) and the remaining 25 FAs for a total of 26 FA metrics.
Sample characteristics were summarized using standard statistical metrics. HRs were estimated on a per quintile basis.
Primary analyses related quintiles of FAs to mortality. Specifically, a forward, stepwise approach was used to systematically evaluate, in age- and sex-adjusted models, the association of additional risk factors (i.e., 6 standard risk factors and 26 FA metrics) with risk of death from all causes. The authors used 10-fold cross-validation to build and validate models. This exercise builds 10 pairs of discovery–validation data sets, each composed of 90% (discovery) and 10% (validation).
The model-building process was then applied to each of the 10 discovery sets, each of which identified predictors (i.e., FAs and/or standard risk factors) to be potentially included in the final model. To be included in the final model, a predictor had to be statistically significant in ≥5 of the 10 discovery data sets. Models were constructed in a forward, stepwise approach using a predictor entry criterion of P < 0.05; adjusting for age and sex in all models, and then evaluating the 26 FA metrics and the 6 remaining standard risk factors.
Reported HRs and concordances (via Harrell’s C-statistic) were averaged across the 10 validation data sets. C-statistics were compared between resulting models and the standard risk factors from Framingham using paired t tests across the 10 validation data sets.
Four of the evaluated FA metrics [14:0, 16:1n–7, 22:0, and omega-3 index (O3I; 20:5n–3 + 22:6n–3)] appeared in ≥5 of the discovery models as significant predictors of all-cause mortality.
In age- and sex-adjusted models, a model with 4 FA metrics was at least as good at predicting all-cause mortality as a model including the remaining 6 standard risk factors (C-statistic: 0.778; compared with C-statistic: 0.777).
A model with 4 FA metrics plus smoking and diabetes (FA + Sm + D) had a higher C-statistic (0.790) compared with the FA (P < 0.01) or smoking and diabetes (Sm + D) models alone (C-statistic: 0.766; P < 0.001).
A variety of other highly correlated FAs could be substituted for 14:0, 16:1n–7, 22:0, or O3I with similar predicted outcomes.
In conclusion, in this cohort followed for 11 years, the information carried in the concentrations of 4 RBC FA metrics was as useful as that carried in lipid levels, blood pressure, smoking, and diabetic status with regard to predicting total mortality. The best predictions were made with the FA metrics and smoking/diabetes status.The cross-validation approach was robust and suggests a strong association between this RBC FA fingerprint and risk for all-cause mortality.
McBurney MI et al. (2021) Using an erythrocyte fatty acid fingerprint to predict risk of all-cause mortality: the Framingham Offspring Cohort. J Clin Nutr.
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