Abstract
Background:
Heart failure (HF) is highly prevalent among older adults and is associated with high costs. Although serum total non-esterified fatty acids (NEFAs) have been positively associated with HF risk, the contribution of each individual NEFA to HF risk has not been examined.
Objective:
To examine the association of individual fasting NEFAs with HF risk in older adults.
Methods:
In this prospective cohort study of older adults, we measured 35 individual NEFAs in 2,140 participants of the Cardiovascular Health Study using gas chromatography. HF was ascertained using review of medical records by an endpoint committee.
Results:
Mean age was 77.7±4.4 years and 38.8% were men. During a median follow-up of 9.7 years (maximum 19.0), 655 new cases of HF occurred. In a multivariable Cox regression model controlling for demographic and anthropometric variables, field center, education, serum albumin, glomerular filtration rate, physical activity, alcohol consumption, smoking, hormone replacement therapy, unintentional weight loss, and all other measured NEFAs, we observed inverse associations [HR (95% CI) per standard deviation] of non-esterified pentadecanoic (15:0)[0.73 (0.57-0.94)], ɤ-linolenic (GLA) [0.87 (0.75-1.00)], and docosahexaenoic (DHA) [0.73 (0.61-0.88)] acids with HF and positive associations of non-esterified stearic (18:0) [1.30 (1.04-1.63)] and nervonic (24:1n-9) [1.17 (1.06-1.29)] acids with HF.
Conclusion:
Our data are consistent with a higher risk of HF with non-esterified stearic and nervonic acids and a lower risk with non-esterified 15:0; GLA; and DHA in older adults. If confirmed in other studies, specific NEFAs may provide new targets for HF prevention.
Keywords: Epidemiology, heart failure, risk factors, fatty acids
Introduction
With improved life expectancy and a growing aging US population, heart failure (HF) remains a major public health issue1. Approximately 5 million Americans live with HF and each year nearly one million Americans are diagnosed with HF1. The prevalence of HF is expected to continue to rise, partly due to improved survival after myocardial infarction, a shift in demographics to an older population, and increase prevalence of type 2 diabetes (T2D) and obesity (two important risk factors for HF2-7). Furthermore, HF remains one of the leading causes of office visits among older adults and is associated with a high mortality8-10. This underscores the need for identification of novel and modifiable risk factors that can be used to improve prevention of HF. Non-esterified fatty acids (NEFAs) are byproducts of lipolysis11 and can impair insulin signaling in skeletal muscle, promote hepatic gluconeogenesis, and reduce insulin secretion12, 13, with subsequent development of T2D. NEFAs can also impair endothelial function14 and might contribute to ischemic cardiomyopathy. Our group15 has previously shown that serum total NEFAs were associated with a higher risk of T2D16 and HF17.Because total serum NEFAs comprise >30 individual fatty acids with various carbon lengths and degree of unsaturation, it is unclear whether the observed elevated risk of HF with serum total NEFAs results from equal contribution of each individual NEFA or from a subset of individual NEFAs. For example, myocardium preferentially uses long-chain esterified/non-esterified fatty acids to meet energy demand via beta-oxidation18. Animal experiments have shown that beta-oxidation of long-chain monounsaturated fatty acids can cause cardiotoxicity19. In humans, phospholipid source of nervonic acid (24:1n-9) has been positively associated with cardiovascular risk factors and with a higher risk of HF,20 while very long-chain saturated fatty acids (20:0, 22:0, and 24:0) have been associated with a lower risk of HF21. No previous study has examined the association of non-esterified nervonic acid or other individual NEFAs with HF. Identification of specific NEFAs that play a major role in the development of HF could provide potential new targets for future dietary and/or pharmacologic interventions. Hence, we sought to comprehensively examine this gap using an agnostic approach to test the primary hypothesis that some but not all individual NEFAs are associated with HF risk in older adults. In a secondary analysis, we tested the hypothesis that the association of individual NEFAs with HF is stronger when restricted to the first five years of follow up as previously reported between serum total NEFAs and incidence of diabetes and HF in the same cohort15, 17.
Materials and methods
We used data collected on participants of the Cardiovascular Health Study (CHS), which is a prospective cohort consisting of 5,888 men and women aged 65 years and older who were selected for recruitment from four US communities (Forsyth County, NC; Washington County, MD; Sacramento County, CA; and Pittsburgh, PA). A description of design, methods, and procedures in the CHS has been previously published22. Briefly, 5,201 adults aged ≥65 years were enrolled between 1989 and 1990. In addition, a supplemental cohort of 687 predominantly African American adults was recruited between 1992 and 1993 from three of the same communities (excepting Washington County). Of the 4,413 participants who attended the 1996-1997 visit (year 9), the current study focused on 2,402 CHS participants who underwent an oral glucose tolerance test at the 1996-97 examination to characterize both individual fasting NEFAs and post OGTT NEFAs. Of the 2,402 people with OGTT, we selected 2,145 participants with unthawed fasting and post-OGTT serum samples due to sensitivity of individual NEFAs to thaw-freeze cycles. After excluding specimens that were oxidized or hemolyzed (n=5), fasting NEFA measurements were available on 2,140. In this report, we focused on fasting NEFAs and we excluded participants with prevalent HF (n =137), resulting in a final analysis sample of 2,003 participants. The institutional review board of each participating center approved the study, and all participants gave informed written consent to participate in the study. A separate exemption approval to use de-identified archived serum samples to measure individual NEFAs was obtained from the Tufts University/Tufts Medical Center Institutional Review Board.
Serum lipids were extracted using previously described techniques23, 24 and the NEFA subfraction was separated via solid-phase chromatography using aminopropyl column25, followed by methylation to form fatty acid methyl esters24, 26. We used an Autosystem XL gas chromatogram (Perkin Elmer, Boston, MA) equipped with a 100 m x 0.25 mm capillary column (HP INNOWax, Agilent Technologies, CA) to separate the fatty acid methyl esters. We identified 35 fatty acids using authenticated standards (NuCheck Prep, MN). Inter-assay coefficients of variation were 0.5% to 4.3% for fatty acids with concentrations >5 mol%; 1.8% to 7.1% for those present at concentrations between 1-5 mol%; and 2.8% to 11.1% for those present at concentrations <1 mol%. All measurements for NEFAs were completed at the Jean Mayer USDA Human Nutrition Research Center on Aging, Tufts University, Boston, MA.
HF events were adjudicated by the CHS Events Committee as previously described27, 28. Briefly, HF validation required symptoms (shortness of breath, fatigue, orthopnea, paroxysmal nocturnal dyspnea); clinical signs including pulmonary rales, peripheral edema, gallop rhythm, and displaced left ventricular apical impulse; pulmonary edema and increasing cardiomegaly on chest X-rays; and use of diuretics, digitalis, or vasodilators for HF treatment. Incident HF was ascertained upon review of pertinent data on hospitalization or outpatient visits such as medical history, physical examination, report of chest X-ray, and medications. HF sub-type (reduced or preserved ejection fraction) was based on left ventricular ejection fraction (<45% and ≥ 45%, respectively).
We collected self-reported information on age, sex, ethnicity, educational attainment, smoking status, unintentional weight-loss >10 pounds, health status, hormone replacement therapy (HRT), and alcohol consumption. Leisure-time activity (kcal/week) was assessed with a modified Minnesota Leisure-Time Activities questionnaire29. Weight, waist circumference, and height were measured using standardized protocols. Serum albumin was measured using standard methods. Hypertension was defined as systolic blood pressure ≥ 140 mm Hg, diastolic blood pressure ≥ 90 mm Hg, or physician diagnosis of hypertension plus treatment with blood pressure lowering medications. T2D was defined if a participant was using insulin or oral hypoglycemic agents; had a fasting glucose level of ≥ 7 mmol/L (126 mg/dL) or a non-fasting glucose level of ≥ 11.1 mmol/L (200 mg/dl). Glomerular filtration rate was estimated based on cystatin C as previously described30.
We used generalized additive models to test for departures from linearity for the association of each individual NEFA with incident HF. Only one individual NEFA (20:5n-3) showed evidence of non-linearity in a model without adjustment for the other NEFAs; however, in the model with adjustment for other individual NEFAs, there was little evidence of meaningful non-linearity. Therefore, we present only the estimates from the linear model for simplicity and clarity. We used Cox proportional hazards regression that included all 35 NEFAs to estimate hazard ratios with 95% confidence intervals for incident HF associated with a 1 standard deviation (SD) increment in each NEFA. Time at risk was calculated as the interval in days from the date of the 1996-97 visit to the earliest of: date of incident HF, date of death, date of loss to follow-up, or administrative censoring date (June 2015). Model 1 adjusted for age, sex, race, field center, and all other NEFAs. Model 2 controlled for all variables in model 1 plus education, serum albumin, body mass index, waist circumference, estimated glomerular filtration rate using cystatin, physical activity, alcohol, smoking status, HRT, and unintentional weight loss. We also examined additional control for potential intermediate factors including prevalent diabetes, coronary artery disease, atrial fibrillation, hypertension, and use of blood pressure medications using model 2. We found no departure from proportional hazards; however, because our previous analysis of total NEFAs and HF showed a stronger association within the first 5 years of follow-up17, we conducted a secondary analysis in the present study limiting follow-up to the first 5 years. Additional secondary analyses included association of fasting serum total NEFAs measured on samples collected during 1996-97 study visit with HF risk. Finally, we conducted sensitivity analyses excluding 370 people with prevalent coronary artery disease and excluding the first two years of follow up.
Results
The mean age of the 2,003 study participants at baseline (1996-97) was 77.7±4.4 years, 38.8% were men, and 14.2% were black. Table 1 presents baseline characteristics by quartiles of non-esterified nervonic acid. Higher serum concentration of nervonic acid was associated with older age, female sex, high blood pressure, and high alcohol consumption (Table 1). Mean (SD) of the most abundant individual NEFAs (μmol/L) were 150.0 (62.9) for 18:1n-9 (oleic); 124.0 (44.5) for 16:0 (palmitic); 78.7 (32.5) for 18:2n-6 (linoleic); and 60.3 (17.2) for 18:0 (stearic)[Supplemental Table 1]. During a median follow up of 9.4 years, 655 (32.7%) participants developed HF, including 152 cases with reduced and 250 with preserved left ventricular ejection fraction. In a multivariable-adjusted Cox regression model that included all 35 NEFAs, each SD increment of non-esterified stearic acid (18:0) was associated with a 30% higher risk of HF (95% CI: 4% to 63%) , adjusting for age, sex, race, field center, education, serum albumin, body mass index, waist circumference, glomerular filtration rate based on cystatin C, leisure time physical activity, alcohol, smoking status, HRT, unintentional weight loss, and all other NEFAs (Table 2); corresponding values for non-esterified nervonic acid (24:1n-9) was 17% (95% CI: 6% to 29%), Table 2. In contrast, non-esterified pentadecanoic acid (15:0), GLA (18:3n-6), and DHA (22:6n-3) were associated with a 27% (95% CI: 6-43%), 13% (95% CI: 0-25%), and 27% (95% CI: 12-39%) lower risk of HF, respectively, in a fully adjusted model (Table 2). Additional adjustment for potential mediators (type 2 diabetes, hypertension and blood pressure lowering medications, CHD, and atrial fibrillation) did not materially alter the results [HR (95% CI) per SD increment: 0.78 (0.60-1.10) for 15:0; 1.30 (1.04-1.63) for 18:0; 1.16 (1.05-1.28) for 24:1n-9; 0.86 (0.74-1.00) for 18:3n-6; and 0.72 (0.60-0.87) for 22:6n-3. In a secondary analysis restricted to the first five years of follow up, we observed associations of non-esterified pentadecanoic acid (15:0) [HR per SD: 0.55 (95% CI: 0.36-0.85)] and non-esterified nervonic acid [HR per SD: 1.24 (95% CI: 1.08-1.42)] with HF in a multivariable adjusted model (Supplemental Table 2). Additionally, we observed associations of individual NEFAs with HF that were not apparent with the longer follow-up period (supplemental Table 2). Exclusion of the first two years of follow up, or of 370 participants with prevalent coronary artery disease did not alter the conclusions. For example, for individual DHA (22:6n-3), [HR (95% CI) per SD increment went from 0.73 (0.61-0.88) in main analysis to 0.73 (0.60-0.89) after exclusion of the first 2 years of follow up and 0.74 (0.60-0.92) after exclusion of prevalent CAD. Corresponding values for stearic acid (18:0) were 1.30 (1.04-1.63); 1.35 (1.06-1.71); and 1.34 (1.03-1.74). In exploratory analyses, fasting serum total NEFAs (per SD increment) measured contemporaneously with individual NEFAs was not associated with HF risk (multivariable adjusted HR: 1.00 (95% CI: 0.91-1.09). Lastly, examination of HF subtypes showed similar direction of effect sizes albeit lack of precision due to limited cases of HF with preserved (n=250) and reduced (n=152) ejection fraction (Supplemental Table 3).
Table 1.
Characteristics of CHS participants by quartiles of non-esterified nervonic acid (24:1n-9)
| Characteristic | Quartiles of serum concentration of 24:1n-9 (umol/L) | |||
|---|---|---|---|---|
| ≤0.27 | >0.27-0.32 | >0.32-0.39 | >0.39 | |
| Age (y) | 77.2 ± 4.0 | 77.6 ± 4.3 | 78.0 ± 4.6 | 78.2 ± 4.7 |
| Male | 208 (41.5%) | 178 (35.5%) | 192 (38.3%) | 200 (40.0%) |
| Black race | 71 (14.2%) | 75 (15.0%) | 65 (13.0%) | 74 (14.8%) |
| Field center | ||||
| North Carolina | 146 (29.1%) | 128 (25.5%) | 103 (20.6%) | 86 (17.2%) |
| California | 153 (30.5%) | 142 (28.3%) | 129 (25.7%) | 155 (31.0%) |
| Maryland | 85 (17.0%) | 97 (19.4%) | 108 (21.6%) | 113 (22.6%) |
| Pennsylvania | 117 (23.4%) | 134 (26.7%) | 161 (32.1%) | 146 (29.2%) |
| Educational attainment | ||||
| <High school | 104 (20.8%) | 102 (20.4%) | 111 (22.2%) | 104 (20.8%) |
| High school | 129 (25.9%) | 150 (30.1%) | 147 (29.4%) | 148 (29.6%) |
| >High school | 266 (53.3%) | 247 (49.5%) | 242 (48.4%) | 248 (49.6%) |
| Smoking status | ||||
| Never | 234 (46.7%) | 222 (44.3%) | 206 (41.1%) | 216 (43.2%) |
| Former | 228 (45.5%) | 252 (50.3%) | 256 (51.1%) | 249 (49.8%) |
| Current | 39 (7.8%) | 27 (5.4%) | 39 (7.8%) | 35 (7.0%) |
| Alcoholic drinks/week | ||||
| 0 | 301 (60.3%) | 288 (57.7%) | 265 (52.9%) | 240 (48.1%) |
| 1-6 | 142 (28.5%) | 158 (31.7%) | 161 (32.1%) | 153 (30.7%) |
| 7-14 | 37 (7.4%) | 27 (5.4%) | 43 (8.6%) | 63 (12.6%) |
| >14 | 19 (3.8%) | 26 (5.2%) | 32 (6.4%) | 43 (8.6%) |
| Physical activity, kcals/week | 1487 ± 1910 | 1392 ± 1712 | 1272 ± 1389 | 1374 ± 1723 |
| Body mass index (kg/m2) | 27.1 ± 4.3 | 26.7 ± 4.0 | 27.0 ± 4.7 | 25.9 ± 4.5 |
| Waist circumference, cm | 97.5 ± 12.3 | 96.0 ± 11.9 | 96.4 ± 12.9 | 95.0 ± 13.4 |
| Systolic BP (mm Hg) | 136.5 ± 20.1 | 134.7 ± 19.6 | 137.3 ± 20.3 | 138.3 ± 21.1 |
| Cholesterol (mg/dl) | 194.4 ± 37.4 | 203.6 ± 37.3 | 206.5 ± 38.6 | 209.3 ± 39.6 |
| Albumin (g/dl) | 3.7 ± 0.3 | 3.8 ± 0.3 | 3.8 ± 0.3 | 3.8 ± 0.3 |
| eGFR-cystatin | 72.3 ± 17.1 | 73.0 ± 19.2 | 72.3 ± 17.8 | 72.3 ± 19.3 |
| C-reactive protein (mg/L) | 4.3 ± 7.8 | 4.0 ± 7.1 | 4.6 ± 7.6 | 4.7 ± 8.6 |
| NTproBNP, pg/ml | 279.3 ± 387.4 | 240.6 ± 299.7 | 316.0 ± 502.7 | 347.3 ± 658.4 |
| Prevalent diabetes | 28 (5.6%) | 22 (4.4%) | 31 (6.2%) | 33 (6.6%) |
| Hypertension | 297 (59.4%) | 293 (58.6%) | 290 (58.2%) | 308 (61.7%) |
| Anti-hypertensive medication | 257 (51.3%) | 262 (52.3%) | 245 (49.0%) | 250 (50.1%) |
| Aspirin use > 2 days in 2 weeks | 216 (44.1%) | 204 (41.3%) | 194 (39.2%) | 173 (35.2%) |
| Unintentional weight loss | 20 (4.3%) | 14 (3.0%) | 27 (5.7%) | 30 (6.3%) |
| Self-reported health | ||||
| Excellent | 38 (7.6%) | 20 (4.0%) | 38 (7.6%) | 21 (4.2%) |
| Very good | 159 (31.7%) | 147 (29.3%) | 131 (26.2%) | 141 (28.2%) |
| Good | 218 (43.5%) | 246 (49.1%) | 246 (49.2%) | 246 (49.2%) |
| Fair | 84 (16.8%) | 86 (17.2%) | 80 (16.0%) | 84 (16.8%) |
| Poor | 2 (0.4%) | 2 (0.4%) | 5 (1.0%) | 8 (1.6%) |
Table 2.
Hazard ratio (95% CI) of incident heart failure per SD increment of individual NEFAs
| Model 1 | Model 2 | |||
|---|---|---|---|---|
| NEFA, μmol/L | Hazard ratio (95% CI) | p | Hazard ratio (95% CI) | p |
| 12:0 | 0.99 (0.90-1.08) | 0.76 | 0.97 (0.88-1.07) | 0.56 |
| 14:0 | 1.15 (0.86-1.55) | 0.35 | 1.08 (0.79-1.48) | 0.62 |
| 15:0 Pentadecanoic acid | 0.72 (0.57-0.92) | 0.01 | 0.73 (0.57-0.94) | 0.01 |
| 16:0 | 1.12 (0.83-1.49) | 0.46 | 1.02 (0.74-1.41) | 0.90 |
| 18:0 stearic acid | 1.18 (0.96-1.45) | 0.13 | 1.30 (1.04-1.63) | 0.02 |
| 20:0 | 0.98 (0.84-1.14) | 0.77 | 0.92 (0.78-1.08) | 0.30 |
| 22:0 | 1.03 (0.94-1.13) | 0.57 | 1.01 (0.91-1.12) | 0.89 |
| 24:0 | 0.98 (0.89-1.07) | 0.59 | 0.98 (0.90-1.07) | 0.65 |
| 14:1n-5 | 0.97 (0.75-1.25) | 0.83 | 0.92 (0.71-1.20) | 0.55 |
| 16:1n-9 | 1.21 (0.94-1.56) | 0.14 | 1.15 (0.88-1.51) | 0.29 |
| 16:1n-7 | 0.98 (0.71-1.37) | 0.92 | 1.19 (0.84-1.69) | 0.32 |
| 18:1n-9 oleic acid | 0.65 (0.40-1.05) | 0.08 | 0.69 (0.42-1.14) | 0.15 |
| 18:1n-7 | 1.27 (0.88-1.85) | 0.20 | 1.05 (0.71-1.56) | 0.82 |
| 20:1n-9 | 0.91 (0.71-1.16) | 0.44 | 0.95 (0.74-1.21) | 0.67 |
| 22:1n-9 | 1.00 (0.92-1.10) | 0.94 | 1.00 (0.91-1.10) | 0.97 |
| 24:1n-9 nervonic acid | 1.12 (1.06-1.19) | 0.0002 | 1.17 (1.06-1.29) | 0.0018 |
| 18:2n-6 | 1.28 (0.94-1.75) | 0.12 | 1.16 (0.85-1.60) | 0.35 |
| 18:3n-6 ɤ-linolenic acid | 0.85 (0.75-0.97) | 0.01 | 0.87 (0.75-1.00) | 0.05 |
| 20:2n-6 | 0.93 (0.80-1.06) | 0.27 | 0.96 (0.83-1.10) | 0.55 |
| 20:3n-6 | 1.11 (0.93-1.33) | 0.26 | 1.09 (0.91-1.32) | 0.35 |
| 20:4n-6 | 0.87 (0.75-1.02) | 0.08 | 0.89 (0.75-1.04) | 0.14 |
| 22:4n-6adrenic acid | 1.07 (0.99-1.16) | 0.07 | 1.08 (0.99-1.17) | 0.07 |
| 22:5n-6 | 0.98 (0.86-1.12) | 0.78 | 1.00 (0.87-1.14) | 0.97 |
| 18:3n-3 | 0.88 (0.72-1.08) | 0.23 | 0.98 (0.80-1.20) | 0.84 |
| 18:4n-3 | 1.03 (0.93-1.13) | 0.59 | 1.01 (0.91-1.12) | 0.90 |
| 20:5n-3 EPA | 1.16 (1.01-1.32) | 0.04 | 1.15 (0.99-1.33) | 0.06 |
| 22:5n-3 DPA | 1.11 (0.90-1.38) | 0.34 | 1.19 (0.95-1.50) | 0.14 |
| 22:6n-3 DHA | 0.75 (0.63-0.89) | 0.0015 | 0.73 (0.61-0.88) | 0.0011 |
| 16:1n-9T | 0.86 (0.68-1.08) | 0.20 | 0.83 (0.64-1.06) | 0.14 |
| 16:1n-7T | 1.14 (0.90-1.44) | 0.28 | 1.13 (0.88-1.45) | 0.33 |
| 18:1n10-12T | 1.25 (0.96-1.62) | 0.10 | 1.19 (0.91-1.57) | 0.21 |
| 18:1n-9T | 1.03 (0.81-1.30) | 0.83 | 1.09 (0.85-1.39) | 0.51 |
| 18:1n-7T | 0.86 (0.68-1.08) | 0.19 | 0.86 (0.67-1.10) | 0.23 |
| 18:2T* | 0.97 (0.90-1.06) | 0.53 | 0.98 (0.90-1.07) | 0.61 |
| 18:2CLA | 0.98 (0.87-1.11) | 0.79 | 1.02 (0.89-1.17) | 0.74 |
Model 1 adjusts for age, sex, race, field center, and other NEFAs (n=2003)
Model 2 adjust for Model 1 covariates plus education, serum albumin, BMI, waist circumference, estimated glomerular filtration rate using cystatin, physical activity, alcohol, smoking status, HRT, unintentional weight loss, and other NEFAs (n=1849); EPA: eicosapentaenoic acid; DPA: docosapentaenoic acid; DHA: docosahexaenoic acid
Discussion
In this large prospective study of US older adults, we found a higher risk of HF with two individual NEFAs (stearic and nervonic acids) and a lower risk with three NEFAs (pentadecanoic acid, GLA, and DHA) in a multivariable adjusted model. Of these five NEFAs associated with HF, only two (pentadecanoic and nervonic acids) showed associations when follow up was restricted to the first five years of the study in a secondary analysis.
To the best of our knowledge, this is the first study to comprehensively examine the association of individual NEFAs with HF risk in a large prospective cohort of older adults. These findings extend the interpretation of our previous work reporting a positive association of serum total NEFAs with HF risk17 in the same cohort of older adults by suggesting that evaluation of the association of serum total NEFAs with HF risk may depend on the relative composition of individual NEFAs and direction of those associations (i.e., lower HF risk with 15:0,18:3n-6, and 22:6n-3 versus higher risk with 24:1n-9 and 18:0 NEFAs). Furthermore, assessment of individual NEFAs can help identify a sub-class of NEFAs associated with higher versus lower risk of HF and inform future dietary and/or pharmacological approaches to alter serum concentrations of those specific NEFAs for HF benefit. Although we observed new individual NEFAs associated with HF when analysis was restricted to the first 5 years of follow up, these findings are only exploratory and merit confirmation in future studies with adequate data to evaluate the impact of updating NEFAs over time on HF outcome. If confirmed in future studies, these findings might provide an opportunity to develop (i) new pharmacologic interventions that can alter the concentration of nervonic acid for example, or (ii) new dietary recommendations to shift the balance between protective (i.e., DHA, pentadecanoic, gamma-linolenic acids) and deleterious (i.e. stearic and nervonic acids) NEFAs to protect against HF.
In two large prospective cohorts, phospholipid very long-chain monounsaturated fatty acids were positively associated with cardiovascular risk factors and with a higher incidence of HF20; comparing the highest to the lowest quintile of plasma phospholipid nervonic acid (24:1n-9), multivariable adjusted hazard ratios were 1.75 (95% CI: 1.23–2.50) in the Cardiovascular Health Study (CHS) and 1.92 (95% CI: 1.22–3.03) in the Atherosclerosis Risk in Communities (ARIC) Study20. It is important to acknowledge that we cannot directly compare associations of phospholipid fraction with NEFAs as former tend to reflect longer term intake while NEFAs reflect a balance between lipolytic factors (i.e., catecholamines) and inhibitors of lipolysis (i.e., insulin or adiponectin). Nonetheless, it is important to note that nervonic acid from two different compartments (phospholipid vs. non-esterified fractions) showed similar association with HF. Of note is that very long-chain fatty acids including nervonic acid (24:1n-9) are predominantly oxidized in peroxisomes, instead of mitochondria and a lack of membrane-transporting enzymes for very long-chain fatty acids in peroxisomes31, 32 can lead to accumulation of reactive oxygen species and other metabolites from beta-oxidation with subsequent apoptosis33, 34. Lower risk of HF observed with non-esterified DHA is consistent with observational and clinical trial data relating red blood cell membrane, phospholipid marine omega-3 fatty acids35, or EPA/DHA supplements with HF36.
The current study has some limitations including the observational study design which cannot preclude unmeasured or residual confounding as source of bias. Second, we only had one measurement of individual NEFAs at baseline and were not able to account for changes in serum NEFAs over time. Third, our cohort consisted of older adults with mean age of 77 years, thereby limiting generalizability of our findings to the general population. Fourth, we had limited statistical power and precision to evaluate associations of individual NEFAs with heart failure type (preserved vs. reduced ejection fraction). Fifth, we did not adjust for multiple comparisons, and our findings should be considered hypothesis-generating. Despite these limitations, our study has numerous strengths including the novel aspect of examining association of individual NEFAs with HF risk; availability of data on major confounding factors; relatively long follow up in CHS; multi-center design with participants from different geographic regions of the US; standardized approach to adjudicate HF by CHS Endpoint Committee; and objective and accurate measurement of individual NEFAs with excellent coefficients of variation for individual NEFAs with high concentrations.
Given the current burden of HF in the US, our data provide preliminary evidence for potential novel targets for pharmacological and/or dietary intervention to modify HF risk factors and with the ultimate goal to prevent development of HF. Thus, replication of our findings while addressing some of the shortcomings outlined above is warranted.
In conclusion, we have demonstrated in this large prospective study of older adults, that individual NEFAs are differentially associated with HF risk. Specifically, we showed that higher serum levels of non-esterified DHA, GLA, and pentadecanoic acid were associated with lower incident HF risk, while higher levels of non-esterified stearic and nervonic acids were associated with a higher risk of HF.
Supplementary Material
Acknowledgements:
We are indebted to the participants and staff of the Cardiovascular Health Study.
Funding source:
This research was supported by contracts HHSN268201200036C, HHSN268200800007C, HHSN268201800001C, N01HC55222, N01HC85079, N01HC85080, N01HC85081, N01HC85082, N01HC85083, N01HC85086, 75N92021D00006 and grants U01HL080295 and U01HL130114 from the National Heart, Lung, and Blood Institute (NHLBI), with additional contribution from the National Institute of Neurological Disorders and Stroke (NINDS). Additional support was provided by R01AG023629 and R01AG053325-01 from the National Institute on Aging (NIA). A full list of principal CHS investigators and institutions can be found at CHS-NHLBI.org.
Role of the sponsor:
Funding agencies did not have any role in the design and conduct of the study; collection, management, analysis, and interpretation of the data.
Footnotes
Conflict of Interest Disclosures: None
Disclaimer:
The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.
References
- 1.Virani SS, Alonso A, Benjamin EJ, Bittencourt MS, Callaway CW, Carson AP, Chamberlain AM, Chang AR, Cheng S, Delling FN, Djousse L, Elkind MSV, Ferguson JF, Fornage M, Khan SS, Kissela BM, Knutson KL, Kwan TW, Lackland DT, Lewis TT, Lichtman JH, Longenecker CT, Loop MS, Lutsey PL, Martin SS, Matsushita K, Moran AE, Mussolino ME, Perak AM, Rosamond WD, Roth GA, Sampson UKA, Satou GM, Schroeder EB, Shah SH, Shay CM, Spartano NL, Stokes A, Tirschwell DL, VanWagner LB, Tsao CW, American Heart Association Council on E, Prevention Statistics C and Stroke Statistics S. Heart Disease and Stroke Statistics-2020 Update: A Report From the American Heart Association. Circulation. 2020;141:e139–e596. [DOI] [PubMed] [Google Scholar]
- 2.Kannel WB, Hjortland M and Castelli WP. Role of diabetes in congestive heart failure: the Framingham study. Am J Cardiol. 1974;34:29–34. [DOI] [PubMed] [Google Scholar]
- 3.Nichols GA, Hillier TA, Erbey JR and Brown JB. Congestive heart failure in type 2 diabetes: prevalence, incidence, and risk factors. Diabetes Care. 2001;24:1614–9. [DOI] [PubMed] [Google Scholar]
- 4.Dei Cas A, Khan SS, Butler J, Mentz RJ, Bonow RO, Avogaro A, Tschoepe D, Doehner W, Greene SJ, Senni M, Gheorghiade M and Fonarow GC. Impact of diabetes on epidemiology, treatment, and outcomes of patients with heart failure. JACC Heart Fail. 2015;3:136–45. [DOI] [PubMed] [Google Scholar]
- 5.Ohkuma T, Komorita Y, Peters SAE and Woodward M. Diabetes as a risk factor for heart failure in women and men: a systematic review and meta-analysis of 47 cohorts including 12 million individuals. Diabetologia. 2019;62:1550–1560. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Rosengren A, Aberg M, Robertson J, Waern M, Schaufelberger M, Kuhn G, Aberg D, Schioler L and Toren K. Body weight in adolescence and long-term risk of early heart failure in adulthood among men in Sweden. Eur Heart J. 2017;38:1926–1933. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Kenchaiah S, Sesso HD and Gaziano JM. Body mass index and vigorous physical activity and the risk of heart failure among men. Circulation. 2009;119:44–52. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 8.Laribi S, Aouba A, Nikolaou M, Lassus J, Cohen-Solal A, Plaisance P, Pavillon G, Jois P, Fonarow GC, Jougla E, Mebazaa A and network G. Trends in death attributed to heart failure over the past two decades in Europe. Eur J Heart Fail. 2012;14:234–9. [DOI] [PubMed] [Google Scholar]
- 9.Lassus JP, Siirila-Waris K, Nieminen MS, Tolonen J, Tarvasmaki T, Peuhkurinen K, Melin J, Pulkki K, Harjola VP and group F-As. Long-term survival after hospitalization for acute heart failure--differences in prognosis of acutely decompensated chronic and new-onset acute heart failure. Int J Cardiol. 2013;168:458–62. [DOI] [PubMed] [Google Scholar]
- 10.Younis A, Mulla W, Goldkorn R, Klempfner R, Peled Y, Arad M, Freimark D and Goldenberg I. Differences in Mortality of New-Onset (De-Novo) Acute Heart Failure Versus Acute Decompensated Chronic Heart Failure. Am J Cardiol. 2019;124:554–559. [DOI] [PubMed] [Google Scholar]
- 11.Koutsari C and Jensen MD. Thematic review series: patient-oriented research. Free fatty acid metabolism in human obesity. J Lipid Res. 2006;47:1643–50. [DOI] [PubMed] [Google Scholar]
- 12.Delarue J and Magnan C. Free fatty acids and insulin resistance. Curr Opin Clin Nutr Metab Care. 2007;10:142–8. [DOI] [PubMed] [Google Scholar]
- 13.Nolan CJ, Madiraju MS, Delghingaro-Augusto V, Peyot ML and Prentki M. Fatty acid signaling in the beta-cell and insulin secretion. Diabetes. 2006;55 Suppl 2:S16–23. [DOI] [PubMed] [Google Scholar]
- 14.Steinberg HO, Paradisi G, Hook G, Crowder K, Cronin J and Baron AD. Free fatty acid elevation impairs insulin-mediated vasodilation and nitric oxide production. Diabetes. 2000;49:1231–8. [DOI] [PubMed] [Google Scholar]
- 15.Djousse L, Khawaja O, Bartz TM, Biggs ML, Ix JH, Zieman SJ, Kizer JR, Tracy RP, Siscovick DS and Mukamal KJ. Plasma fatty acid-binding protein 4, nonesterified fatty acids, and incident diabetes in older adults. Diabetes Care. 2012;35:1701–7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Djousse L, Dorgan JF, Zhang Y, Schatzkin A, Hood M, D'Agostino RB, Copenhafer DL, Kreger BE and Ellison RC. Alcohol consumption and risk of lung cancer: the Framingham Study. J Natl Cancer Inst. 2002;94:1877–82. [DOI] [PubMed] [Google Scholar]
- 17.Djousse L, Benkeser D, Arnold A, Kizer JR, Zieman SJ, Lemaitre RN, Tracy RP, Gottdiener JS, Mozaffarian D, Siscovick DS, Mukamal KJ and Ix JH. Plasma free fatty acids and risk of heart failure: the Cardiovascular Health Study. Circ Heart Fail. 2013;6:964–9. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Lopaschuk GD, Ussher JR, Folmes CD, Jaswal JS and Stanley WC. Myocardial fatty acid metabolism in health and disease. Physiol Rev. 2010;90:207–58. [DOI] [PubMed] [Google Scholar]
- 19.Beare-Rogers JL and Nera EA. Cardiac fatty acids and histopathology of rats, pigs, monkeys and gerbils fed rapeseed oil. Comp Biochem Physiol B. 1972;41:793–800. [DOI] [PubMed] [Google Scholar]
- 20.Imamura F, Lemaitre RN, King IB, Song X, Steffen LM, Folsom AR, Siscovick DS and Mozaffarian D. Long-chain monounsaturated Fatty acids and incidence of congestive heart failure in 2 prospective cohorts. Circulation. 2013;127:1512-21, 1521e1–18. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Lemaitre RN, McKnight B, Sotoodehnia N, Fretts AM, Qureshi WT, Song X, King IB, Sitlani CM, Siscovick DS, Psaty BM and Mozaffarian D. Circulating Very Long-Chain Saturated Fatty Acids and Heart Failure: The Cardiovascular Health Study. J Am Heart Assoc. 2018;7:e010019. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Fried LP, Borhani NO, Enright P, Furberg CD, Gardin JM, Kronmal RA, Kuller LH, Manolio TA, Mittelmark MB, Newman A and et al. The Cardiovascular Health Study: design and rationale. Ann Epidemiol. 1991;1:263–76. [DOI] [PubMed] [Google Scholar]
- 23.Folch J, Lees M and Sloane Stanley GH. A simple method for the isolation and purification of total lipides from animal tissues. J Biol Chem. 1957;226:497–509. [PubMed] [Google Scholar]
- 24.Matthan NR, Ip B, Resteghini N, Ausman LM and Lichtenstein AH. Long-term fatty acid stability in human serum cholesteryl ester, triglyceride, and phospholipid fractions. J Lipid Res. 2010;51:2826–32. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 25.Agren JJ, Julkunen A and Penttila I. Rapid separation of serum lipids for fatty acid analysis by a single aminopropyl column. J Lipid Res. 1992;33:1871–6. [PubMed] [Google Scholar]
- 26.Morrison WR and Smith LM. Preparation of Fatty Acid Methyl Esters and Dimethylacetals from Lipids with Boron Fluoride--Methanol. J Lipid Res. 1964;5:600–8. [PubMed] [Google Scholar]
- 27.Ives DG, Fitzpatrick AL, Bild DE, Psaty BM, Kuller LH, Crowley PM, Cruise RG and Theroux S. Surveillance and ascertainment of cardiovascular events. The Cardiovascular Health Study. Ann Epidemiol. 1995;5:278–85. [DOI] [PubMed] [Google Scholar]
- 28.Gottdiener JS, Arnold AM, Aurigemma GP, Polak JF, Tracy RP, Kitzman DW, Gardin JM, Rutledge JE and Boineau RC. Predictors of congestive heart failure in the elderly: the Cardiovascular Health Study. J Am Coll Cardiol. 2000;35:1628–37. [DOI] [PubMed] [Google Scholar]
- 29.Taylor HL, Jacobs DR Jr., Schucker B, Knudsen J, Leon AS and Debacker G. A questionnaire for the assessment of leisure time physical activities. J Chronic Dis. 1978;31:741–55. [DOI] [PubMed] [Google Scholar]
- 30.Stevens LA, Coresh J, Schmid CH, Feldman HI, Froissart M, Kusek J, Rossert J, Van Lente F, Bruce RD 3rd, Zhang YL, Greene T and Levey AS. Estimating GFR using serum cystatin C alone and in combination with serum creatinine: a pooled analysis of 3,418 individuals with CKD. Am J Kidney Dis. 2008;51:395–406. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Reddy JK and Hashimoto T. Peroxisomal beta-oxidation and peroxisome proliferator-activated receptor alpha: an adaptive metabolic system. Annu Rev Nutr. 2001;21:193–230. [DOI] [PubMed] [Google Scholar]
- 32.Bremer J and Norum KR. Metabolism of very long-chain monounsaturated fatty acids (22:1) and the adaptation to their presence in the diet. J Lipid Res. 1982;23:243–56. [PubMed] [Google Scholar]
- 33.Lesnefsky EJ, Moghaddas S, Tandler B, Kerner J and Hoppel CL. Mitochondrial dysfunction in cardiac disease: ischemia--reperfusion, aging, and heart failure. J Mol Cell Cardiol. 2001;33:1065–89. [DOI] [PubMed] [Google Scholar]
- 34.Goldberg IJ, Trent CM and Schulze PC. Lipid metabolism and toxicity in the heart. Cell Metab. 2012;15:805–12. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 35.Djousse L, Akinkuolie AO, Wu JH, Ding EL and Gaziano JM. Fish consumption, omega-3 fatty acids and risk of heart failure: a meta-analysis. Clin Nutr. 2012;31:846–53. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Djousse L, Cook NR, Kim E, Bodar V, Walter J, Bubes V, Luttmann-Gibson H, Mora S, Joseph J, Lee IM, Albert CM, Buring JE, Gaziano JM, Manson JE and Group VR. Supplementation With Vitamin D and Omega-3 Fatty Acids and Incidence of Heart Failure Hospitalization: VITAL-Heart Failure. Circulation. 2020;141:784–786. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.