If you don't remember your password, you can reset it by entering your email address and clicking the Reset Password button. You will then receive an email that contains a secure link for resetting your password
If the address matches a valid account an email will be sent to __email__ with instructions for resetting your password
Department of Nutritional Sciences, Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada M5S 3E2Leadership Sinai Center for Diabetes, Mount Sinai Hospital, Toronto, Ontario, Canada M5G 1X5
Recent evidence has documented distinct effects of individual saturated FAs (SFAs) on cardiometabolic outcomes, with potential protective effects from odd- and very long-chain SFAs (VLSFAs). Cross-sectional and prospective associations of individual serum SFAs (12:0, 14:0, 15:0, 16:0, 18:0, 20:0, 22:0, and total SFA) with proinflammatory biomarkers and adiponectin were investigated in 555 adults from the IRAS. Principal component analysis (PCA) of proinflammatory markers yielded three clusters: principal component (PC) 1: fibrinogen, white cell count, C-reactive protein; PC 2: plasminogen activator inhibitor-1 (PAI-1), TNF-α, IL-18; PC 3: IL-6 and IL-8. Cross-sectional analyses on proinflammatory PCs and adiponectin, and prospective analyses on 5 year PAI-1 and fibrinogen concentrations were conducted with multiple regression. Total SFA and 16:0 were positively associated with PC 1 and PC 2, and negatively associated with adiponectin. The 14:0 was positively associated with PC 1 and negatively associated with adiponectin. In contrast, 15:0, 20:0, and 22:0 were negatively associated with PC 2, and 20:0 and 22:0 were positively associated with adiponectin. The 18:0 was negatively associated with PC 3. Prospectively, 15:0, 18:0, 20:0, and 22:0 were negatively associated with 5 year PAI-1 concentrations. The results demonstrate that individual SFAs have distinct roles in subclinical inflammation, highlighting the unique metabolic impacts of individual SFAs.
American College of Cardiology/American Heart Association Task Force on Practice Guidelines
et al.
2013 ACC/AHA guideline on the treatment of blood cholesterol to reduce atherosclerotic cardiovascular risk in adults: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines.
), a strong independent risk factor for CVD. Dietary guidelines from the American Heart Association recommend limiting the percentage of calories from SFA to 5–6% for LDL reduction (
American College of Cardiology/American Heart Association Task Force on Practice Guidelines
et al.
2013 ACC/AHA guideline on the treatment of blood cholesterol to reduce atherosclerotic cardiovascular risk in adults: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines.
). However, results from recent meta-analyses of observational studies and clinical trials have challenged the strength of evidence for the relationship between SFA intake and CVD risk, and have generated debates on the appropriateness and effectiveness of current SFA consumption guidelines (
Intake of saturated and trans unsaturated fatty acids and risk of all cause mortality, cardiovascular disease, and type 2 diabetes: systematic review and meta-analysis of observational studies.
). A recent meta-analysis of 12 prospective observation studies found no association between SFA intake with all-cause mortality, coronary heart disease (CHD), CHD mortality, and ischemic stroke (
Intake of saturated and trans unsaturated fatty acids and risk of all cause mortality, cardiovascular disease, and type 2 diabetes: systematic review and meta-analysis of observational studies.
), rather these are mainly derived from the consumption of ruminant products, such as dairy and meat; indeed, odd-chained FAs have been previously validated as biomarkers for dairy intake (
). Very long-chain SFAs (VLSFAs), defined as those that are ≥20 carbons in length, may be derived from the diet (canola oil, macadamia nuts, peanuts) or through endogenous elongation from 18:0 (
US Department of Agriculture, Agricultural Research Service, Nutrient Data Laboratory. 2016. USDA National Nutrient Database for Standard Reference, Release 28. Accessed July 24, 2016, at https://ndb.nal.usda.gov/ndb/,
). Moreover, there is increasing evidence suggesting that individual SFAs affect cardiometabolic health differently, with potential protective effects observed for odd-chained SFAs and VLSFAs (
). Results from the same meta-analysis of circulating SFAs found different effects based on SFA chain length, with margaric acid (17:0) having a significant inverse association with coronary outcomes (
). Likewise, a case-cohort analysis within the EPIC-InterAct study found that even-chained SFAs [myristic acid (14:0), palmitic acid (16:0), and stearic acid (18:0)] were positively associated with incident type 2 diabetes (T2DM), while odd-chained SFAs [pentadecanoic acid (15:0) and 17:0] and VLSFAs [arachidic acid (20:0), behenic acid (22:0), tricosanoic acid (23:0), and lignoceric acid (24:0)] were inversely associated with incident T2DM (
Differences in the prospective association between individual plasma phospholipid saturated fatty acids and incident type 2 diabetes: the EPIC-InterAct case-cohort study.
), which in turn stimulates the synthesis of hepatic acute-phase proteins, such as fibrinogen, plasminogen activator inhibitor-1 (PAI-1), and C-reactive protein (CRP) (
). Chronic elevation in proinflammatory markers is observed in people with established CVD and T2DM and is predictive of future cardiovascular events, as well as conversion to T2DM (
Elevated levels of acute-phase proteins and plasminogen activator inhibitor-1 predict the development of type 2 diabetes: the Insulin Resistance Atherosclerosis Study.
Elevated levels of acute-phase proteins and plasminogen activator inhibitor-1 predict the development of type 2 diabetes: the Insulin Resistance Atherosclerosis Study.
). Experimental literature has shown increased proinflammatory markers in cell lines and animal models with increased SFA exposure, with the majority of studies utilizing 16:0 (
). Similarly, studies of SFAs and inflammation in human cohorts have largely focused on even-chained SFAs and, thus, the role of other SFAs, in particular odd-chained SFAs and VLSFAs, on chronic subclinical inflammation is poorly understood (
The objective of this study was, therefore, to investigate in a multi-ethnic observational study: 1) cross-sectional associations of individual serum SFAs with proinflammatory biomarkers and adiponectin; and 2) prospective associations of individual serum SFAs with PAI-1 and fibrinogen after 5 years of follow-up.
MATERIALS AND METHODS
Study design
The IRAS is a multicenter epidemiologic study investigating the relationship between insulin resistance and CVD risk factors in a large tri-ethnic cohort. A detailed methodology for IRAS has been previously published (
). Cohort recruitment in four clinical centers (Los Angeles, CA; Oakland, CA; San Luis Valley, CO; and San Antonio, TX) aimed to enroll equal numbers of participants across age (40–49, 50–59, and 60–69 years), sex, glucose tolerance status (normal, impaired glucose tolerance, and diabetes), and ethnic groups (African American, Hispanic, non-Hispanic Whites). Baseline clinic visits were conducted between October 1992 and April 1994, with follow-up occurring between February 1998 and July 1999, with a follow-up rate of 81%. Written informed consent was obtained from all participants and institutional review boards of the study centers approved the study.
Population
A total of 1,624 participants were recruited in the IRAS. Participants without T2DM at baseline were included in the present analysis (n = 1,065). FA analyses were conducted on a subset of the population who had completed baseline and follow-up visits (n = 648). After further exclusions for participants with missing baseline inflammatory markers (n = 53), as well as CRP concentrations ≥10 mg/l (n = 40), the final sample size for this study was 555. There were no differences in sex between participants included in the study compared with those excluded. However, there were differences in age, ethnicity, and study center, where those included in the analysis were slightly younger and more likely to be non-Hispanic Whites (supplemental Table S1).
Variable measurements
Clinic examinations at baseline and follow-up were conducted during two clinic visits that were 1 week apart (
). Participants were asked to fast for 12 h, avoid alcohol and heavy exercise for 24 h, and avoid smoking the day of the visit. Glucose tolerance status, based on the 2006 World Health Organization criteria, was measured using a 2 h 75 g oral glucose tolerance test (Orange-dex; Custom Laboratories, Baltimore, MD) (
World Health Organization, International Diabetes Federation
Definition and Diagnosis of Diabetes Mellitus and Intermediate Hyperglycemia: Report of a WHO/IDF Consultation. WHO Document Production Services,
Geneva, Switzerland2006
). Diabetes cases were defined by oral glucose tolerance test or current oral hypoglycemic or insulin use.
Inflammatory markers.
Baseline and 5 year follow-up fasting plasma samples were centrifuged and stored at −70°C within 90 min of collection to measure a range of laboratory parameters, including biomarkers for this study (
). At follow-up, only PAI-1 and fibrinogen concentrations were measured. Analysis for the inflammatory markers, PAI-1, TNF-α, CRP, and white cell count (WCC), were conducted at the Laboratory for Clinical Biochemistry Research at the University of Vermont (
). Fibrinogen was measured in citrated plasma with a modified clot-rate assay using the Diagnostica Stago ST4 instrument (American Bioproducts, Parsippany, NJ), with a coefficient of variation (CV) of 3.0% (
). PAI-1 in citrated plasma was measured using a two-site immunoassay, which was sensitive to free PAI-1, but not to PAI-1 complexed with tissue plasminogen activator (CV of 14%) (
). TNF-α in citrated plasma was measured using a Quantikine HS Human TNF-α immunoassay (R&D Systems, Minneapolis, MN) according to the manufacturer's instructions, with a CV of 8.4–11.8% (
Circulating levels of TNF-α are associated with impaired glucose tolerance, increased insulin resistance, and ethnicity: the Insulin Resistance Atherosclerosis Study.
). IL-6, IL-8, and IL-18 were quantified using molecular counting on the ZeptX™ system (Singulex) in a 10 μl volume using a 384-microwell immunoassay format (
). Attached to each microwell were capture antibodies specific for an individual biomarker. After serum samples were added to the microwell, AlexaFluor 647-labeled secondary antibodies were added and bound. Fluorescence-labeled antibody complexes were chemically released from each well and pumped through a capillary flow system to detect laser-induced fluorescence. Each photon signal above the background level represents a labeled antibody molecule (
). The total lipid extract was trans-esterified in 1% sulfuric acid in methanol in a sealed vial under a nitrogen atmosphere at 100°C for 45 min. The samples were then neutralized with 6% potassium carbonate and the FA methyl esters were extracted with hexane and prepared for gas chromatography. Individual FAs were separated using capillary gas chromatography (Agilent Technologies model 6890) equipped with a 30 m DB-88MS capillary column (Agilent Technologies) and quantified using a flame-ionization detector. Each FA peak and area under the peak was compared with the internal standard control to quantify FA concentrations. A total of 35 serum FAs were analyzed and quantified and all results passed internal quality assurance and quality control processes. Seven SFAs were identified in this cohort and were analyzed in the current work: even-chained SFAs (12:0, 14:0, 16:0, and 18:0), odd-chained SFA (15:0), and VLSFAs (20:0 and 22:0). FAs in this work are expressed as a mole percentage (mol%) of total FAs, which normalize any increases or decreases in total serum lipid levels.
Other measurements.
Weight, height, and waist circumference were measured in duplicate to the nearest 0.1 kg (weight) and 0.5 cm (height and waist circumference). A validated physical activity recall assessed total estimated energy expenditure (kilocalories per kilogram per week) as the sum of energy expenditure activities and energy expenditure from sleep during the past year (
). Age, ethnicity, and smoking status were self-reported and medical history was assessed using structured interviews. Usual dietary intake over the past year prior to baseline was assessed using a validated semi-quantitative 114-item food frequency questionnaire. The food frequency questionnaire was modified from the National Cancer Institute Health Habits and History Questionnaire to include ethnic and regional foods, and was validated in a subsample of 186 women (
). Nutrient and energy intakes were estimated using an expanded nutrient database (HHHQ-DIETSYS analysis software, version 3.0; National Cancer Institute, Bethesda, MD). Participants were asked to recall the usual frequency and portion sizes of foods and beverages over the past year. Servings per day were standardized to the medium serving size by multiplying the intake frequency with the portion size after applying a weighting factor (small = 0.5, medium = 1.0, large = 1.5).
Statistical analysis
Baseline characteristics overall and across tertiles (T1–T3) of increasing total SFA are presented. Normally distributed variables are presented as mean ± SD, nonnormally distributed variables are presented as medians with interquartile ranges, and categorical variables are presented as number and percent of participants in each tertile, with differences across tertiles tested using ANOVA, Kruskal-Wallis tests, and Chi-square tests, respectively. Principal component analysis (PCA) was used to identify proinflammatory variable clusters for the purpose of reducing the number of dependent variables in the analyses and optimizing the inflammatory marker signal. PCA is a variable reduction procedure that creates a smaller number of new independent variables, called principal components (PCs), which explain most of the variation in the original variables (
). All proinflammatory variables, except for fibrinogen, were log transformed prior to conducting PCA to achieve normality. PCA was conducted in SAS using PROC FACTOR with eight proinflammatory variable inputs: fibrinogen, PAI-1, TNF-α, CRP, WCC, IL-6, IL-8, and IL-18. The number of PCs to be retained was determined based on eigenvalues >1 and the evaluation of scree plots. Generated PCs were interpreted using variable factor loadings of ≥0.40. A score for each pattern was calculated for each participant and these scores used as were the dependent variables in linear regression analysis. Adiponectin was chosen, a priori, to be analyzed as a separate dependent variable due to its documented anti-inflammatory properties (
Multiple linear regression analyses were conducted to evaluate the cross-sectional and prospective associations of total and individual SFAs with inflammatory markers. The regression models were sequentially adjusted for potential demographic, lifestyle, and dietary confounders: age, sex, ethnicity, center, physical activity, smoking status, alcohol intake, education, total energy intake, fruit and vegetable intake, red meat intake, soft drink intake, and fiber intake. In prospective analyses of 5 year PAI-1 and fibrinogen, baseline PAI-1 or fibrinogen was also added as a covariate. Effect modification analyses were conducted to assess potential interactions between the SFAs and variables of a priori interest, namely sex and ethnicity, in light of observations within this cohort and results of previous literature (
Serum pentadecanoic acid (15:0), a short-term marker of dairy food intake, is inversely associated with incident type 2 diabetes and its underlying disorders.
). Subgroup regression analyses were conducted when interaction terms were significant (P < 0.05). All analyses were conducted using SAS version 9.4 (SAS Institute Inc., Cary, NC). Statistical significance was set at P < 0.05.
RESULTS
Baseline characteristics of the participants overall and across tertiles of total SFA (mol% of total FAs) are presented in %Table 1. Sex differed across tertiles, with a higher proportion of women in the highest tertile, and the highest proportion of males in the lowest tertile. Ethnicity differed significantly, with African Americans having the lowest total SFA compared with non-Hispanic Whites and Hispanics across all tertiles. BMI, waist circumference, and impaired glucose tolerance status prevalence significantly increased across tertiles. Smoking status, alcohol intake, and physical activity did not differ significantly; however, total energy intake increased significantly. All proinflammatory markers, except fibrinogen, increased while adiponectin decreased across tertiles.
TABLE 1Baseline characteristics of participants, overall and by tertiles of total SFAs, in the IRAS
Serum SFA (mole fraction of total FA concentration)
12:0
0.08 (0.05–0.13)
0.06 (0.04–0.09)
0.08 (0.05–0.13)
0.1 (0.07–0.16)
<0.0001
14:0
0.98 (0.77–1.28)
0.77 (0.64–0.93)
0.95 (0.79–1.13)
1.35 (1.11–1.55)
<0.0001
15:0
0.25 ± 0.06
0.24 (0.05)
0.25 (0.05)
0.26 (0.07)
0.0019
16:0
21.55 ± 2
19.69 (0.99)
21.27 (0.87)
23.68 (1.48)
<0.0001
18:0
6.82 ± 0.68
6.69 (0.63)
6.96 (0.71)
6.82 (0.69)
0.0007
20:0
0.11 ± 0.03
0.11 ± 0.03
0.11 ± 0.03
0.10 ± 0.02
<0.0001
22:0
0.22 (0.17–0.27)
0.24 (0.2–0.29)
0.22 (0.19–0.28)
0.19 (0.16–0.23)
<0.0001
Inflammatory markers
PAI-1 (ng/ml)
17 (10–28)
14 (7–22)
15 (10–26)
22 (13–34)
<0.0001
TNF-α (pg/ml)
3.32 (2.61–4.32)
3.04 (2.55–4.1)
3.22 (2.51–4.03)
3.83 (3–4.74)
<0.0001
Fibrinogen (mg/dl)
269.37 ± 47.58
272.32 (48.66)
269 (49.88)
266.79 (44.09)
0.53
CRP (mg/l)
1.55 (0.70–2.71)
1.16 (0.57–2)
1.34 (0.72–2.47)
2.14 (1.20–3.87)
<0.0001
WCC (×103/mm3)
5.5 (4.6–6.5)
5.3 (4.4–6.3)
5.4 (4.4–6.4)
5.8 (5–6.8)
0.0030
IL-6 (counts/ml)
0.006 (0.004–0.009)
0.006 (0.004–0.009)
0.006 (0.004–0.008)
0.007 (0.005–0.01)
0.0015
IL-8 (counts/ml)
0.005 (0.004–0.008)
0.005 (0.004–0.007)
0.005 (0.004–0.008)
0.006 (0.004–0.007)
0.18
IL-18 (counts/ml)
0.24 (0.19–0.33)
0.22 (0.18–0.3)
0.23 (0.18–0.31)
0.3 (0.21–0.38)
<0.0001
Adiponectin (μg/ml)
7.32 (5.51–9.73)
7.82 (6.03–10.26)
7.39 (5.39–9.77)
6.76 (5.29–8.96)
0.0036
Continuous variables are presented as mean ± SD if normally distributed or as median (interquartile range) if non-normally distributed. Categorical variables are presented as n (%). For continuous variables, P values were generated from ANOVA or Kruskal-Wallis comparison across tertiles for normal and skewed variables, respectively. For categorical variables, P values were derived from chi-square tests for comparisons across tertiles. Total SFA is the sum of 12:0, 14:0, 15:0, 16:0, 18:0, 20:0, and 22:0.
PCA yielded three clusters derived from baseline proinflammatory marker concentrations. Factor loadings are presented in %Table 2. Major inflammatory marker contributors to PC 1 were fibrinogen, WCC, and CRP. PC 2 was characterized by high positive loadings of PAI-1, TNF-α, and IL-18, while PC 3 was characterized by IL-6 and IL-8. These three PCs captured 59% of the variance among the proinflammatory variables.
TABLE 2Proinflammatory marker loadings on the first three PCs identified from PCA
Loadings >0.40 indicate the proinflammatory markers that define the PC.
0.14
Eigenvalue
2.19
1.40
1.11
Cumulative explained variance (%)
27
45
59
PC loading is a measure of the contribution of each (baseline) proinflammatory marker to each PC pattern. The number of PCs retained were based on eigenvalues >1 and scree plots.
a Loadings >0.40 indicate the proinflammatory markers that define the PC.
The main results from regression analyses are shown in Figs. 1 and 2. Results of fully adjusted multiple linear regression analyses of cross-sectional data are shown in Fig. 1. Total SFA was positively associated with PC 1 and PC 2, and inversely associated with adiponectin. Similarly, 14:0 was positively associated with PC 2, and 16:0 was positively associated with PC 1 and PC 2, while both of these FAs were inversely associated with adiponectin. On the other hand, 15:0, 20:0, and 22:0 were inversely associated with PC 2, while 20:0 and 22:0 were positively associated with adiponectin. Furthermore, only 18:0 was associated with PC 3, while 12:0 was not significantly associated with proinflammatory clusters or adiponectin.
Fig. 1Multiple linear regression analyses evaluating cross-sectional associations of individual serum SFAs and total SFA (mole fraction of total FA concentration) on baseline proinflammatory marker patterns derived from PCA and adiponectin. Standardized β coefficients are presented. PC 1: fibrinogen, log WCC, and log CRP; PC 2: log PAI-1, log TNF-α, and log IL-18; PC 3: log IL-6 and log IL-8. Total SFA is the sum of 12:0, 14:0, 15:0, 16:0, 18:0, 20:0, and 22:0. Covariates: age, sex, ethnicity, center, physical activity, smoking status, alcohol intake, education, total energy intake, fruit and vegetable intake, red meat intake, soft drink intake, and fiber intake. aP < 0.05, bP < 0.01, cP < 0.001, dP < 0.0001.
Fig. 2Multiple linear regression analyses evaluating prospective associations of individual serum SFAs and total SFA (mole fraction of total FA concentration) on 5 year concentrations of PAI-1. Standardized β coefficients are presented. Total SFA is the sum of 12:0, 14:0, 15:0, 16:0, 18:0, 20:0, and 22:0. Covariates: age, sex, ethnicity, center, physical activity, smoking status, alcohol intake, education, total energy intake, fruit and vegetable intake, red meat intake, soft drink intake, fiber intake, and baseline PAI-1. aP < 0.05, bP < 0.01.
In prospective analyses of baseline SFAs with 5 year PAI-1 and fibrinogen concentrations, 15:0, 18:0, 20:0, and 22:0 were inversely associated with PAI-1 at 5 years (Fig. 2). Total SFA, 12:0, 14:0, and 16:0 were not significantly associated with PAI-1. There was no association between total or individual SFAs with fibrinogen at follow-up.
Sex significantly modified the associations of 12:0 and 14:0 on PC 1 in cross-sectional analyses. In subgroup analyses by sex, 12:0 was inversely associated with PC 1 in females, while 14:0 was positively associated with PC 1 in males (%Table 3). In addition, sex significantly modified the associations of 15:0 with adiponectin and total SFA with fibrinogen prospectively; however, there were no marked differences in the associations in subgroup analyses by sex (%Tables 4, %5). Moreover, ethnicity was a significant effect modifier for 20:0 and 22:0 on PC 2. Ethnicity significantly modified associations of VLSFA with PC2. In Hispanics, 20:0 and 22:0 were inversely associated with PC 2, while 22:0 was inversely associated with PC 2 in non-Hispanic Whites (%Table 6). In addition, sensitivity analyses excluding participants with morbid obesity (n = 20) or CHD (n = 23) at baseline did not change results appreciably (data not shown).
TABLE 3Cross-sectional multiple linear regression analyses evaluating associations of 12:0 and 14:0 on PC 1 by sex
SFA
Male
Female
β ± SEM
P
β ± SEM
P
12:0
0.078 ± 0.068
0.25
−0.137 ± 0.052
0.0082
14:0
0.137 ± 0.056
0.0148
−0.003 ± 0.066
0.96
PC 1: fibrinogen, log WCC, and log CRP (all measured at baseline); Interaction term p values: 12:0 (P = 0.005); 14:0 (P = 0.047). Covariates: age, ethnicity, center, physical activity, smoking status, alcohol intake, education, total energy intake, fruit and vegetable intake, red meat intake, soft drink intake, and fiber intake. SFAs are expressed as mole fractions of total FA concentration. Standardized β coefficients are presented.
TABLE 4Cross-sectional multiple linear regression analyses evaluating associations of 15:0 on baseline adiponectin by sex
SFA
Male
Female
β ± SEM
P
β ± SEM
P
15:0
−0.052 ± 0.048
0.28
0.095 ± (0.069)
0.17
Interaction term P value = 0.014; Covariates: age, ethnicity, center, physical activity, smoking status, alcohol intake, education, total energy intake, fruit and vegetable intake, red meat intake, soft drink intake, and fiber intake. 15:0 is expressed as mole fraction of total FA concentration. Standardized β coefficients are presented.
TABLE 5Prospective multiple linear regression analyses evaluating associations of total SFA on 5 year fibrinogen by sex
SFA
Male
Female
β ± SEM
P
β ± SEM
P
Total SFA
−0.032 ± 0.062
0.60
0.06 ± 80.051
0.18
Interaction term P = 0.040. Covariates: age, ethnicity, center, physical activity, smoking status, alcohol intake, education, total energy intake, fruit and vegetable intake, red meat intake, soft drink intake, and fiber intake. Total SFA is expressed as mole fraction of total FA concentration. Standardized β coefficients are presented.
TABLE 6Cross-sectional multiple linear regression analyses evaluating associations of 20:0 and 22:0 on PC 2 by ethnicity
SFA
Non-Hispanic White
African American
Hispanic
β ± SEM
P
β ± SEM
P
β ± SEM
P
20:0
−0.087 ± 0.064
0.17
−0.103 ± 0.078
0.19
−0.219 ± 0.067
0.0012
22:0
−0.125 ± 0.059
0.0369
−0.122 ± 0.082
0.14
−0.310 ± 0.076
<0.0001
PC 2: log PAI-1, log TNF-α, and log IL18 (all measured at baseline). Interaction term P values: 20:0 (P = 0.028); 22:0 (P = 0.021). Covariates: age, sex, center, physical activity, smoking status, alcohol intake, education, total energy intake, fruit and vegetable intake, red meat intake, soft drink intake, and fiber intake. SFAs are expressed as mole fractions of total FA concentration. Standardized β coefficients are presented.
In the IRAS cohort, we found that among the even-chained SFAs, shorter chain lengths (14:0 and 16:0) were positively related to proinflammatory markers and negatively related to adiponectin, while longer even-chained SFAs (20:0 and 22:0) and odd-chained SFA (15:0) had inverse associations. Our findings are consistent with the emerging literature demonstrating different cardiometabolic effects of individual SFAs, specifically, with potential protective effects observed for odd-chained SFAs and VLSFAs, and detrimental effects of even-chained SFAs (
Differences in the prospective association between individual plasma phospholipid saturated fatty acids and incident type 2 diabetes: the EPIC-InterAct case-cohort study.
). Our results extend this literature in a number of important ways, including measuring a variety of circulating SFAs and inflammatory markers in this cohort and, for the first time, showing heterogeneity in their associations both cross-sectionally and prospectively in a multi-ethnic cohort.
Our results are most likely explained either by a structure-function relationship between FA chain length and inflammatory processes or an increase in de novo lipogenesis in concert with the increased subclinical inflammation. A structure-function relationship based on chain-length would indicate an active role for SFAs in the triggering of subclinical inflammation, perhaps by activating toll-like receptor 4 (
Role of the Toll-like receptor 4/NF-kappaB pathway in saturated fatty acid-induced inflammatory changes in the interaction between adipocytes and macrophages.
). Alternatively, an increase in de novo FA synthesis as a result of inflammation and metabolic stress would lead to production of shorter chain FAs (e.g., 14:0 and 16:0) by FA synthase and, thus, a relative enrichment in their concentrations. The FA 15:0 is unique among the FAs presented here in that: 1) it is not of mammalian origin and likely derives from the intake of ruminant animal products; and 2) despite being a shorter chain SFA, it is inversely associated with subclinical inflammation. This indicates a possible role for 15:0 or the foods containing 15:0 in the suppression of inflammatory processes. In this study, 15:0 was the only odd-chained SFA available, as 17:0 was used as an internal standard in the FA analysis.
In the current study, we used PCA to identify three proinflammatory clusters, not only to reduce the number of outcome variables in order to avoid multiple testing, but also to better understand the covariance structure or interactions between proinflammatory markers in this cohort, especially because diagnostic criteria for chronic systemic inflammation have yet to be established (
). Although the precise functions of individual inflammatory markers likely vary, this method better captures the overall inflammatory state, as it accounts for the combined effects of multiple markers, which might be underestimated in analyses evaluating single markers (
Association between inflammatory components and physical function in the health, aging, and body composition study: a principal component analysis approach.
J. Gerontol. A Biol. Sci. Med. Sci.2009; 64: 581-589
To our knowledge, no other studies have determined the association between multiple individual SFAs with a variety of inflammatory markers. Previous epidemiological studies examining the association between circulating SFAs with inflammatory markers are limited in number and conflicting. In 264 elderly Swedish men and women, 18:0 was inversely correlated with CRP, while there was no significant correlation of 14:0 and 16:0 with markers of inflammation and endothelial function cross-sectionally (
), although there was no association with adiponectin or leptin after multivariate adjustment. The lack of association in females may be due to their higher accumulation of total central fat compared with men, which the authors speculate to be the most important source of inflammation, or from the effects of sex hormones on circulating levels of inflammatory markers.
In terms of odd-chained SFAs, in a nested case-control study, the sum of 15:0 and 17:0 in serum phospholipids was inversely correlated with PAI-1 and leptin (
Estimated intake of milk fat is negatively associated with cardiovascular risk factors and does not increase the risk of a first acute myocardial infarction. A prospective case-control study.
). On the other hand, in the Multi-Ethnic Study of Atherosclerosis (MESA) (n = 2,837), both 14:0 and 15:0 were not cross-sectionally associated with CRP after adjustment for sociodemographic, lifestyle, and dietary factors (
). However, subgroup associations by sex or ethnicity were not analyzed in the MESA cohort. Further investigations on potential differences on the associations of individual SFAs with inflammatory markers by sex, ethnicity, and other subgroups are warranted. Heterogeneity in the current literature may be due to differences in population characteristics, the FA pool in which SFAs were measured, covariates included in statistical analyses, and the SFAs and inflammatory markers evaluated.
Studies examining the mechanisms in which SFA affects concentrations of inflammatory markers have largely focused on even-chained SFAs, most notably 16:0. In vitro studies have demonstrated that high 16:0 exposure in adipocytes and myotubules increased TNF-α, IL-10, and IL-6 mRNA and protein expression (
). Moreover, SFAs can stimulate the inflammatory signaling cascade by binding to and activating toll-like receptor 4 in adipocytes and macrophages, resulting in NFκB and JNK activation and cytokine production (
Role of the Toll-like receptor 4/NF-kappaB pathway in saturated fatty acid-induced inflammatory changes in the interaction between adipocytes and macrophages.
). On the other hand, the mechanism underlying the observed protective effects of odd-chained SFAs and VLSFAs is not known, as these SFAs are largely understudied. Studies have yet to determine whether these SFAs are bioactive or are rather a marker of other potential protective components found in dairy, such as calcium, vitamin D, proteins, or other dairy specific FAs, such as c9,t11-CLA (
Serum pentadecanoic acid (15:0), a short-term marker of dairy food intake, is inversely associated with incident type 2 diabetes and its underlying disorders.
). However, the biological pathways that may link VLSFAs with cardiometabolic health are largely unknown, as there is a lack of mechanistic studies on the metabolism of these SFAs.
The main strength of this study is that the IRAS is a large well-characterized cohort with detailed and precise measurements of outcomes, exposures, and covariates. Multiple SFAs, adiponectin, and inflammatory markers were measured in serum, allowing a more comprehensive evaluation of associations between individual SFAs and the inflammatory phenotype. In addition, we optimized proinflammatory marker clustering using PCA. However, one limitation is that SFAs and most inflammatory markers, except PAI-1 and fibrinogen, were only measured at baseline. Therefore, assessing the temporality of many of the associations reported herein was not possible. Second, because circulating even-chained SFAs reflect both dietary and endogenous production, we were unable to distinguish any differences in effects based on the SFA source; thus, one must be cautious in extrapolating our results to dietary recommendations on SFA intake. Moreover, there is the potential for residual confounding in our analyses beyond the demographic, lifestyle, dietary, and metabolic covariates included in our models.
In conclusion, this study found that individual SFAs in serum affect subclinical inflammation differently, highlighting the unique metabolic impacts of individual SFAs. Lower circulating 15:0 and VLSFAs and higher even-chained SFAs were related to worsened subclinical inflammation status. Further studies on the sources (de-novo lipogenesis or dietary), metabolism, and mechanisms of individual SFAs as they relate to inflammatory and cardiometabolic pathways are warranted, especially for the understudied odd-chained SFAs and VLSFAs. While circulating levels of odd-chained SFAs may be altered through diet, the implications of these findings for intake recommendations for endogenously produced even-chained SFAs are less clear. Further studies on the biology of individual SFAs will help to elucidate the heterogeneity within SFA species and help to inform future public health recommendations on the role of SFA intake on cardiometabolic health.
Acknowledgments
The authors would like to thank the IRAS participants and staff for their participation in the study.
American College of Cardiology/American Heart Association Task Force on Practice Guidelines
et al.
2013 ACC/AHA guideline on the treatment of blood cholesterol to reduce atherosclerotic cardiovascular risk in adults: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines.
Intake of saturated and trans unsaturated fatty acids and risk of all cause mortality, cardiovascular disease, and type 2 diabetes: systematic review and meta-analysis of observational studies.
US Department of Agriculture, Agricultural Research Service, Nutrient Data Laboratory. 2016. USDA National Nutrient Database for Standard Reference, Release 28. Accessed July 24, 2016, at https://ndb.nal.usda.gov/ndb/,
Differences in the prospective association between individual plasma phospholipid saturated fatty acids and incident type 2 diabetes: the EPIC-InterAct case-cohort study.
Elevated levels of acute-phase proteins and plasminogen activator inhibitor-1 predict the development of type 2 diabetes: the Insulin Resistance Atherosclerosis Study.
World Health Organization, International Diabetes Federation
Definition and Diagnosis of Diabetes Mellitus and Intermediate Hyperglycemia: Report of a WHO/IDF Consultation. WHO Document Production Services,
Geneva, Switzerland2006
Circulating levels of TNF-α are associated with impaired glucose tolerance, increased insulin resistance, and ethnicity: the Insulin Resistance Atherosclerosis Study.
Serum pentadecanoic acid (15:0), a short-term marker of dairy food intake, is inversely associated with incident type 2 diabetes and its underlying disorders.
Role of the Toll-like receptor 4/NF-kappaB pathway in saturated fatty acid-induced inflammatory changes in the interaction between adipocytes and macrophages.
Association between inflammatory components and physical function in the health, aging, and body composition study: a principal component analysis approach.
J. Gerontol. A Biol. Sci. Med. Sci.2009; 64: 581-589
Estimated intake of milk fat is negatively associated with cardiovascular risk factors and does not increase the risk of a first acute myocardial infarction. A prospective case-control study.
This work was supported in part by the Dairy Research Cluster Initiative (Dairy Farmers of Canada, Agriculture and Agri-Food Canada, the Canadian Dairy Network, and the Canadian Dairy Commission). Additional funding was provided by the Ontario Graduate Scholarship; the University of Toronto's Banting and Best Diabetes Centre, Novo Nordisk Graduate Studentship (I.D.S.). A.J.H. holds a Tier II Canada Research Chair in Diabetes Epidemiology. IRAS was supported by National Heart, Lung and Blood Institute grants U01-HL47887, U01-HL47889, U01-HL47892, U01-HL47902, DK-29867 and R01-58329 and the National Institutes of Health grant M01-RR-43. The contents of this work are solely the responsibility of the authors and do not necessarily represent the official views of the National Institutes of Health. The authors declare no financial conflicts of interest relevant to this study.
33807-4&id=gr1.jpg){kind=link}
33807-4&id=gr2.jpg){kind=link}