Serum ω-3 Polyunsaturated Fatty Acids and Potential Influence Factors in Elderly Patients with Multiple Cardiovascular Risk Factors

Recent clinical trials failed to demonstrate that ω-3 polyunsaturated fatty acid (PUFA) supplement reduced cardiovascular events, which contradicted previous evidence. However, serum ω-3 PUFA concentrations of participants remained unclear in those studies. We aimed to investigate the definite relationship between serum concentrations of ω-3 PUFAs and coronary artery disease (CAD), and to explore the potential influence factors of ω-3 PUFAs. We selected Chinese in-patients (n = 460) with multiple cardiovascular risk factors or an established diagnosis of CAD. Serum ω-3 PUFAs, including eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA), were measured by liquid chromatography mass spectrometry. Serum concentrations of ω-3 PUFAs in CAD patients were lower than that in patients with cardiovascular risk factors. Furthermore, high serum DHA concentration was an independent protective factor of CAD after adjustment for confounding factors (OR: 0.52, p = 0.014). Alcohol intake (p = 0.036) and proton pump inhibitor (PPI) usage (p = 0.027) were associated with a decreased serum ω-3 PUFA concentration. We conclude that serum concentrations of ω-3 PUFAs may associate with a decreased CAD proportion, and DHA may serve as a protective factor of CAD. Serum ω-3 PUFA concentrations may be reduced by alcohol intake and certain drugs like PPIs.


Measurement of Serum ω-3 PUFAs.
Fasting serum samples were collected early in the morning after the participants had fasted for at least 12 hours overnight, and stored at −80 °C until analyses. Serum ω-3 PUFAs, including EPA and DHA, were measured by liquid chromatography mass spectrometry (LCMS) 19 (Fan-Xing Biological Technology -Beijing Co., Ltd).
10 μL-serum samples were separated by a chromatographic column (BEH C18, 5 μm × 4.6 × 150 mm, Aglient) and eluted with a mobile phase of 40% A (water containing 5 mmol/L ammonium acetate) and 60% B (acetonitrile containing 5 mmol/L ammonium acetate) at a flow rate of 0.1 mL/min. The MS detection (MicroQ-TOFII, Bruker Dalton) was performed with electrospray ionization in the positive ion mode with multiple reaction monitors (Fig. 1). Drying temperature was 250 °C.
Peak intensity was linearly correlated with ω-3 PUFA concentration (R 2 > 0.99). Therefore, serum concentrations of EPA and DHA were calculated according to the equation of linear regression between peak intensity and concentration.
Statistical analyses. The Shapiro-Wilk test was used to determine the distribution normality of each continuous variable. Normally distributed, continuous variables were presented as mean ± SD, and any differences between the two groups were tested by the student t-test. Non-normally distributed, continuous variables were presented as median with inter quartile range (IQR), and the Kruskal-Wallis test was used to discern differences between the two groups. Categorical variables were presented as percentages, and potential differences between two groups were estimated with the chi-square test or Fisher's exact test. Possible relationships between two variables were analyzed by the Pearson test. Binary logistic regression analyses were conducted to estimate the correlationship between ω-3 PUFAs and CAD, and linear regression analyses were used to trace the possible influence factors of ω-3 PUFAs. P values less than 0.05 were regarded as statistically significant. All data analyses were performed using SPSS 20.0 software (Statistical Package for the Social Sciences, SPSS Ins., Chicago, IL). Data Availability. The datasets generated during and analysed during the current study are available from the corresponding author on reasonable request. In addition, the concentrations of fasting blood glucose (mean 6.19 vs. 5.43 mmol/L, p < 0.001) and hemoglobin A1C (HbA1c) (mean 6.60 vs. 6.13%, p < 0.001) were higher in the CAD group than the CAD-Risk group. Moreover, we found that the CAD group took more drugs than the CAD-Risk group [aspirin (74.7 vs. 15   We further allocated the participants into three subgroups (55-65: 55 ≤ age ≤ 65, 65-75: 65 < age ≤ 75, 75 + : age > 75) according to their age ( Fig. 2A,B). In 55-65 subgroup, both EPA and DHA concentrations in CAD patients were significantly lower than that in CAD-Risk patients (

Identification of independent influence factors of CAD.
In order to figure out the independent influence factors of CAD, we set the binary variable "cardiovascular disease" as the dependent variable, CAD-Risk as "0" and CAD as "1". Totally 11 covariates were set as the independent variables, and passed through the binary logistic regression model using the Backward-Wald method. The 11 covariates were age, gender, smoking habit, alcohol intake, BMI, hypertension, hyperlipidemia, fasting blood glucose, white blood cells, EPA_G (0 = EPA ≤ median 381.00 μg/L, 1 = EPA > median 381.00 μg/L) and DHA_G (0 = DHA ≤ median 1480.69 μg/L, 1 = DHA > median 1480.69 μg/L). Eventually, four covariates were entered the equation, shown in Table 2 Baseline characteristics of participants according to ω-3 PUFAs concentrations. We further allocated the participants into different groups according to their median serum EPA and DHA concentrations to analyze the relationship between ω-3 PUFA concentrations and CAD. As shown in Table 3, no significant differences were found in age, gender, smoking/alcoholic habits, or BMI between the two groups (high EPA group vs. low EPA group; high DHA group vs. low DHA group). The CAD proportion was lower in the high EPA group compared with the low EPA group (34.0 vs. 48.7%, p < 0.001), and similar result was found in the high DHA and low DHA groups (35.1 vs. 46.9%, p = 0.010). All serum lipid concentrations were found to be higher in the high DHA group compared with the low DHA group (median TG: 1. .08 mmol/L, p = 0.016) concentrations were higher in the high EPA group compared with the low EPA group. Certain blood biochemical markers, including albumin (ALB), red blood cells (RBC), hemoglobin (Hb) and potassium (K) were significantly higher in the high ω-3 PUFA groups than the low ω-3 PUFA groups (p values < 0.05). However, other biochemical markers, such as creatine kinase (CK) and brain natriuretic peptide (BNP) were significantly lower in the high ω-3PUFA groups than the low ω-3PUFA groups (p values < 0.05). In addition, the high EPA group took less ACEIs (8.2 vs. 17.4%, p = 0.019) and diuretics (14.9 vs. 24.7%, p = 0.013) than the low EPA group; while the high DHA group used less diuretics (16.5 vs. 26.1%, p = 0.045) and PPIs (25.2 vs. 35.8%, p = 0.047) than the low DHA group.

Discussion
To date, whether ω-3 PUFAs are associated with a reduced risk of cardiovascular events remain controversial, and data on serum ω-3 PUFA concentrations are still lacking worldwide. Therefore, we performed this study to elucidate the potential relationship between serum ω-3 PUFA concentrations and CAD in the Chinese elderly. The  Table 2. Independent risk factors of CAD. Binary logistic regression analysis. The dependent variable was CAD (CAD-Risk as "0", CAD as "1"). The 11 covariates were age, gender (0 = male, 1 = female), smoking habit (0 = never smoke or quit smoking more than one year, 1 = still smoking), alcohol intake (0 = no alcohol intake or quit using alcohol more than one month, 1 = still drinking), BMI, hypertension (0 = without hypertension, 1 = with hypertension), hyperlipidemia (0 = without hyperlipidemia, 1 = with hyperlipidemia), fasting blood glucose, white blood cells, EPA_G (0 = EPA ≤ median 381.00 μg/L, 1 = EPA > median 381.00 μg/L), and DHA_G (0 = DHA ≤ median 1480.69 μg/L, 1 = DHA > median 1480.69 μg/L). B, partial regression coefficient; S.E., standard error for B; df, degree of freedom; OR, odds ratio; CI, confidence interval.  comparison between patients with cardiovascular risk factors and established diagnosis of CAD demonstrated that serum concentrations of ω-3 PUFAs, including EPA and DHA, were lower in patients with CAD than those with cardiovascular risk factors. Furthermore, the concentration differences between CAD and CAD-Risk group were significant in 55-65 (55 ≤ age ≤ 65) subgroups. This may result from complicated combined diseases and drugs that the older patients have, which may affect the protective effect of ω-3 PUFAs on CAD.
The CAD proportion was significantly lower in the high ω-3 PUFA group compared with the low ω-3 PUFA group. Unfortunately, no significant differences were found in coronary restenosis or revascularization proportion between different ω-3 PUFA concentration groups (see Table 3), which suggest that ω-3 PUFA may benefit CAD occurrence, while its role in CAD progression is not outstanding. Regression analyses revealed that DHA could serve as a protective factor of CAD, after adjustment for age, gender and co-morbidity conditions (OR: 0.52, 95% CI: 0.31, 0.88; p = 0.014). Although, the Pearson correlation analysis showed serum concentrations of EPA and DHA were positively correlated with each other (r = 0.53, p < 0.001), similar protective effect against CAD was not observed for EPA. It is possible that the sample size in the current study may not be sufficient to detect the beneficial effects of EPA or the protective effect of EPA on CAD may be less than DHA.
ω-3 PUFAs are components of erythrocyte membranes and necessary fatty acids that must be obtained from food. Therefore, we valued several biomarkers associated with red blood cells (RBC, Hb and mean corpuscular haemoglobin concentration/MCHC) and nutrient status (BMI, Vitamin D, potassium, sodium and albumin). The analysis results corresponded to our knowledge that high density of red blood cells and good nutrient status were associated with high ω-3 PUFA concentrations 21,22 . Additionally, we estimated other potential influence factors, such as smoking/alcoholic habit, hepatic (alanine aminotransferase) and renal (plasma creatinine and eGFR) function (see Table 3). Certain factors were found to affect serum ω-3 PUFAs. For example, regular alcohol intake and PPI use were correlated with a decreased serum ω-3 PUFA concentrations after adjustment for confounding factors (see Tables 4 and 5). Alcohol and PPI intake may affect the metabolic absorption of ω-3 PUFA from diet, generating reduced serum ω-3 PUFA concentrations. These findings indicate that reducing alcohol and PPI intake may contribute to a high serum ω-3 PUFA concentration. Considering that it has not been reported before, the findings require further investigations to confirm.
Previous observational studies employed self-reported FFQ to estimate PUFA intake. Food conversion estimations are imprecise as ω-3 PUFA amounts vary by food source and cooking methods and. In addition, different metabolic capabilities of ω-3 PUFAs contribute to altered ω-3 PUFA levels in different populations. Blood-based biomarkers of ω-3 PUFA intake are more objective and more accurate estimates of biological exposure 23 . Only a small number of studies have measured fatty acids, with many of these studies limited by sample size. According to our data, serum ω-3 PUFA concentrations were lower than that in other reports, which may suggest that the Chinese elderly, whose average daily intake of ω-3 PUFAs is relatively lower than that in Western and Japanese populations, may actually have lower baseline serum ω-3 PUFA concentrations. Besides, these results may be secondary to LCMS, a different yet more sensitive and precise measurement method. Additionally, the sample size of the study thus far is the largest that directly measures serum ω-3 PUFA concentrations.
Recent randomized controlled trials (RCTs) and meta-analyses fail to show that ω-3 PUFA intervention has beneficial effects in cardiovascular events [9][10][11] , which contradicts the findings of previous epidemiological studies. Potential reasons for this discrepancy may include the followings: First, the protective effects of ω-3 PUFAs against cardiovascular diseases may take many years to develop, and therefore the follow-up of recent RCTs may not be long enough. Second, doses of ω-3 PUFAs used by these studies (300-900 mg/day) were lower than the recommendation concentration (over 1000 mg/day) 24 , which may be insufficient to induce clinical benefits. Third, the biosynthesis of ω-3 PUFA is inefficient and varies from person to person [25][26][27] . We also found that serum lipid concentrations, including TG, TC, HDL-C, and LDL-C, in the CAD group were markedly lower than that in the CAD-Risk group, which may be attributed to the higher usage rate of statins (89.0 vs. 32.2%, p < 0.001, shown in Table 1) in CAD patients for secondary prevention. Long-term RCTs support the concept that combining statins with ω-3 fatty acids seems to further decrease CAD risk in primary prevention and CAD mortality in secondary prevention 28 . It is obvious that the cardiovascular events have been effectively retard with improved secondary prevention for cardiovascular diseases, which may weaken the protective effect of ω-3 PUFAs on CAD. Despite the inconsistent results obtained by previous studies, the American Heart Association (AHA) still recommend patients with cardiovascular diseases, especially those with CAD to take ω-3 PUFA supplements (IIa), and future multi-center studies with longer follow-up in different populations are needed to better elucidate the actual roles of ω-3 PUFAs in the prevention of cardiovascular diseases 29 .
There is no denying that some restrictions exist in this study. First, this was a cross-sectional study, which precluded us from obtaining a definite conclusion on the cause-effect relationship between ω-3 PUFAs and CAD. Second, in order to obtain the most comprehensive biochemical parameters for tracing potential influence factors of ω-3 PUFAs, the study design was hospital-based. Therefore, we could not rule out the possibility of selection bias. Third, considering that recalling and reporting bias may markedly influence the reliability of baseline ω-3 PUFA intake calculation, we didn't rely on dietary patterns, which may also affect serum ω-3 PUFA concentrations. Larger-scale and long-term studies are still needed to confirm our findings. bilirubin; RBC, red blood cells; Hb, hemoglobin; MCHC, mean corpuscular haemoglobin concentration; Pcr, plasma creatinine; eGFR, estimated glomerular filtration rate; K, potassium; Na, sodium; GLU, fasting blood glucose; CK, creatine kinase; BNP, brain natriuretic peptide.  Table 4. Correlation analysis of serum concentration of EPA. Linear regression analysis. The continuous variable "EPA" was set as the dependent variable (y). The 16 covariates were age, gender, smoking habit, alcohol intake, BMI, hypertension, diabetes mellitus, RBC, Hb, ALB, DHA_G, K, ACEIs (0 = no take, 1 = take), CCBs (0 = no take, 1 = take), Statins (0 = no take, 1 = take), and PPIs (0 = no take, 1 = take). B, unstandardized partial regression coefficient; S.E., standard error for B; Beta, standardized partial regression coefficient; t, t-test value. R 2 = 0.288.