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Departments of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA 02115Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115
The State Key Laboratory of Pharmaceutical Biotechnology and MOE Key Laboratory of Model Animals for Disease Study, Model Animal Research Center, Nanjing University, Nanjing 210061, China
Departments of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA 02115Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115
Shanghai Institute of Nutrition and Health, and Key Laboratory of Systems Biology, Institute of Biochemistry and Cell Biology, Shanghai Institutes for Biological Sciences, Shanghai Institutes for Biological Sciences, Chinese Academy of Sciences, Shanghai 200031, ChinaDepartment of Life Sciences and Technology, ShanghaiTech University, Shanghai 201210, China
1 Q. Sun and X. Lin contributed equally to this work.
Affiliations
Departments of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA 02115Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115
The effects of PUFAs on metabolic syndrome (MetS) remain to be characterized, particularly in Asians. We aimed to investigate the prospective associations of PUFAs with MetS and the role of acylcarnitines in these associations in Chinese individuals. Among 1,245 Chinese men and women aged 50–70 years who completed a 6 year follow-up, baseline erythrocyte FAs and plasma acylcarnitines were profiled using gas chromatography coupled with positive chemical ionization and liquid chromatography-tandem mass spectrometry, respectively. Total n-6 PUFAs and three 22-carbon n-6 PUFAs were significantly associated with lower MetS risk comparing extreme quartiles: relative risks (RRs) (95% CIs) were 0.75 (0.57, 0.97) for total n-6 PUFAs, 0.69 (0.56, 0.85) for 22:2n-6, 0.76 (0.59, 0.99) for 22:4n-6, and 0.74 (0.58, 0.94) for 22:5n-6, while 18:3n-3 and 18:3n-6 were positively associated with MetS risk. In a network analysis, a module mostly consisting of long-chain n-6 PUFAs and very-long-chain saturated FAs was inversely associated with incident MetS (RR per SD: 0.84; 95% CI: 0.76, 0.92), and this module was more strongly associated with lower MetS risk when a short- to medium-chain acylcarnitine (C5–C10) module score was lower (Pinteraction = 0.03). Our data suggested inverse associations of total n-6 and certain long-chain n-6 PUFAs with cardiometabolic disorders, and this association might be modified by certain acylcarnitines.
Dietary Guidelines Advisory Committee Scientific Report of the 2015 Dietary Guidelines Advisory Committee: Advisory Report to the Secretary of Health and Human Services and the Secretary of Agriculture.
Ethnic differences in beta-cell function, dietary intake and expression of the metabolic syndrome among UK adults of South Asian, black African-Caribbean and white-European origin at high risk of metabolic syndrome.
) and variations in the genes involving PUFA metabolism, as indicated by a recent transethnic genome-wide association study in Chinese and European-ancestry populations (
Genome-wide meta-analyses identify novel loci associated with n-3 and n-6 polyunsaturated fatty acid levels in Chinese and European-ancestry populations.
Although associations of objectively measured PUFA biomarkers with type 2 diabetes or CVD have been investigated in several cohorts, findings on specific PUFAs with these cardiometabolic outcomes were inconsistent (
Omega-6 fatty acid biomarkers and incident type 2 diabetes: pooled analysis of individual-level data for 39 740 adults from 20 prospective cohort studies.
). Metabolic syndrome (MetS), a constellation of multiple cardiometabolic conditions such as central obesity, dyslipidemia, and elevated levels of blood pressure and fasting glucose, is known as a precursor of type 2 diabetes or CVD, and it is considered as a critical condition for early intervention to reduce the onset of cardiometabolic diseases (
). However, studies regarding the relations between PUFAs and MetS are sparse, and a limited number of prospective studies conducted in Western populations have shown mixed results (
). Because of the much longer life span of red blood cells, erythrocyte FAs were indicated to be more reproducible over time than FAs from other blood fractions (
); nevertheless, the prospective associations between PUFAs and MetS risk remain to be determined in Asians.
Another knowledge gap that needs to be filled is the effects of acylcarnitines on FA metabolism and related metabolic risk. Acylcarnitines are esterified forms of carnitine and play an essential role in transporting long-chain FAs across the mitochondrial inner membrane for β-oxidation (
). In our previous study conducted in the same cohort population, a panel of plasma acylcarnitines was found to be significantly associated with 6 y incident type 2 diabetes and substantially improved the ability of the disease prediction beyond established risk factors (
). Owing to the complex acylcarnitine metabolism, the overall pattern of acylcarnitines may reflect the degree of mitochondrial stress and dysregulation of fatty acid oxidation (FAO) better than individual acylcarnitines (
). Meanwhile, it is also of interest to explore the potential impacts of FAO status, indicated by specific acylcarnitine profile, on the associations between PUFAs and cardiometabolic outcomes.
Therefore, this study aimed to test whether erythrocyte PUFAs were inversely associated with 6 y incident MetS and its components in middle-aged and elderly Chinese individuals. Moreover, the role of acylcarnitines in the associations of interest was also explored by using a network analysis in this cohort study.
MATERIALS AND METHODS
Study population
Study participants were from the Nutrition and Health of Aging Population in China, a population-based cohort study of 3,289 residents aged 50–70 years living in Beijing and Shanghai (
). Briefly, the study was initiated in 2005, and a follow-up survey was performed in 2011. Most participants were successfully followed up (77%; n = 2,529). The study protocol was approved by the Institutional Review Board of the Institute for Nutritional Sciences, Chinese Academy of Sciences, and abided by Declaration of Helsinki principles. Written informed consent was obtained from all participants.
Data collection
In both baseline and 6 year follow-up surveys, information on demographic variables, health status, lifestyle factors, and physical activities was obtained using a standard questionnaire by trained staff. After overnight fasting, a physical examination was conducted, and venous blood samples were collected from each participant. Body weight, height, waistline, and blood pressure were measured following a standard protocol by trained medical professionals (
Blood samples were collected using EDTA-containing vacuum tubes. After centrifuging at 1,400 g for 15 min, plasma was aliquoted and stored at −80°C before analysis. The measurements of plasma glucose, total cholesterol, HDL-cholesterol, LDL-cholesterol, triglycerides, insulin, C-reactive protein (CRP), and adiponectin have been described previously (
). Homeostatic model assessment of insulin resistance (HOMA-IR) was calculated as insulin (μU/ml) × glucose (mmol/l)/22.5.
Erythrocyte FAs were measured using gas chromatography coupled with positive chemical ionization (Agilent 6890N-5975B; Agilent Technologies, Santa Clara, CA) (
). Percentages of the area under each peak in summed areas of all FAs were determined as relative amounts of each FA. A total of 28 FAs were quantified, including four n-3 PUFAs [18:3n-3 (α-linolenic), 20:5n-3 (eicosapentaenoic), 22:5n-3 (docosapentaenoic), and 22:6n-3 (docosahexaenoic)] and eight n-6 PUFAs [18:2n-6 (linoleic), 18:3n-6 (γ-linolenic), 20:2n-6 (eicosadienoic), 20:3n-6 (dihomo-γ-linolenic), 20:4n-6 (arachidonic), 22:2n-6 (docosadienoic), 22:4n-6 (docosatetraenoic), and 22:5n-6 (docosapentaenoic)].
Plasma acylcarnitines were measured using liquid chromatography-tandem mass spectrometry (
). Chromatographic separation was performed by using a 1260 HPLC system (Agilent Technologies), and an Agilent 6410B QQQ mass spectrometer was applied for the mass spectrometric analysis. Deuterium-labeled carnitine and acylcarnitines (NSK-B Set; Cambridge Isotope Laboratories, Inc., Tewksbury, MA) were purchased for internal standards. A total of 34 acylcarnitines were measured, including free carnitine, 3-dehydroxycarnitine, 3-dehydrocarnitine, short-chain acylcarnitines (C2, C3, C3DC, C4, C5, C5OH, C5:1, C6, C6OH, C6DC, and C7DC), medium-chain acylcarnitines (C8, C8:1, C10, C10DC, C12, C12OH, C12:1, C12DC, C14, C14OH and C14:1OH), and long-chain acylcarnitines (C16, C16:1, C16:2, C18, C18OH, C18:1, C18:2, C20, and C20:4).
Definition of MetS
MetS was defined according to the updated National Cholesterol Education Program Adult Treatment Panel III criteria for Asian Americans (
Diagnosis and management of the metabolic syndrome: an American Heart Association/National Heart, Lung, and Blood Institute scientific statement: Executive Summary.
). Participants with MetS were defined if they had any three or more of the following features: waistline ≥90 cm for men or ≥80 cm for women; triglycerides ≥1.7 mmol/l; HDL-cholesterol ≤1.03 mmol/l for men or ≤1.30 mmol/l for women; blood pressure ≥130/85 mmHg or current use of antihypertensive drugs; and fasting glucose ≥5.6 mmol/l, or taking oral antidiabetic agents (or insulin), or previously diagnosed type 2 diabetes.
Participants with the following conditions were excluded: MetS or type 2 diabetes at baseline, insufficient data to define incident MetS, and lacking baseline data of FAs. When a specific MetS component was analyzed, participants with a corresponding component at baseline were excluded. The final analyses included 1,245 participants for MetS, 1,153 for central obesity, 1,717 for triglycerides, 1,314 for HDL-cholesterol, 705 for blood pressure, and 1,350 for fasting glucose.
Statistical analyses
Variables with skewed distribution were natural log-transformed. Spearman correlation coefficients (rs) among metabolites and between metabolites and clinical risk markers were calculated after adjusting for age, sex, region, and residence. Relative risks (RRs) for MetS or each component were calculated by the log-Poisson model after adjusting for age, sex, region, residence, current smoking status, current drinking status, years of education, physical activity, family history of chronic diseases [a parent or first-degree sibling having coronary heart disease (CHD), stroke, hypertension, or diabetes], and BMI.
) was used for the network analysis of all FAs and acylcarnitines at baseline, which were standardized (mean: 0; SD: 1) before analyses. A soft-thresholding power of five and minimum module size of three were chosen to create metabolite modules. As the module-representative variable, the module eigengenes (MEs) were derived corresponding to the first principal component of each identified module. Module membership strength was evaluated by Pearson correlation coefficients between metabolites and corresponding MEs. RRs for MetS according to quartiles of MEs or per SD increase in MEs were also estimated using log-Poisson models. P for interactions between modules was calculated by using likelihood ratio tests.
All analyses were conducted with Stata version 9.2 (StataCorp LP, College Station, TX) and R software version 3.4.0 (
). Two tailed P < 0.05 was considered statistically significant.
RESULTS
Baseline characteristics of participants
Over a 6 year period, 433 of 1,245 (34.8%) eligible participants developed incident MetS. Compared with noncases, incident cases were more likely to be females, urban, current nonsmokers and nondrinkers, and have a family history of chronic diseases at baseline (Table 1). Incident cases also had adverse profiles of blood pressure, triglycerides, total cholesterol, LDL- and HDL-cholesterol, fasting insulin, HOMA-IR, CRP, and adiponectin, as well as higher BMI and waistline at baseline. Baseline characteristics of all participants who were followed up or lost in 2011 are presented in supplemental Table S1, while those for incident cases and noncases stratified by gender are shown in supplemental Table S2.
TABLE 1Baseline characteristics of participants with and without incident MetS
Without Incident MetS (n = 812)
With Incident MetS (n = 433)
Age, years
58.0 ± 5.9
57.9 ± 6.0
Men, n (%)
428 (52.7)
166 (38.3)
Northern residents, n (%)
331 (40.8)
187 (43.2)
Urban residents, n (%)
287 (35.3)
193 (44.6)
BMI, kg/m2
22.1 ± 2.6
24.4 ± 3.0
Waistline, cm
76.5 ± 8.3
83.1 ± 8.8
Current drinking, yes (%)
258 (31.8)
112 (25.9)
Current smoking, yes (%)
279 (34.4)
111 (25.6)
Education, n (%)
0–6 years
406 (40.0)
196 (45.3)
7–9 years
262 (32.3)
145 (33.5)
≥10 years
144 (17.7)
92 (21.3)
Physical activity, n (%)
Low
58 (7.1)
23 (5.3)
Moderate
271 (33.4)
167 (38.6)
High
483 (59.5)
243 (56.1)
Family history of chronic diseases, n (%)
357 (44.0)
239 (55.2)
Systolic blood pressure, mmHg
130.5 ± 20.7
137.7 ± 20.6
Diastolic blood pressure, mmHg
76.2 ± 10.1
79.9 ± 10.3
Triglycerides, mmol/l
0.8 (0.6, 1.0)
1.1 (0.8, 1.4)
HDL-cholesterol, mmol/l
1.4 ± 0.3
1.3 ± 0.3
LDL-cholesterol, mmol/l
3.0 ± 0.9
3.3 ± 0.9
Total cholesterol, mmol/l
4.5 ± 0.9
4.7 ± 0.9
Fasting glucose, mmol/l
5.2 ± 0.5
5.2 ± 0.4
RBC HbA1c, %
5.6 ± 0.4
5.7 ± 0.4
Fasting insulin, mIU/l
11.6 (8.4, 15.0)
13.3 (9.7, 17.8)
HOMA-IR
2.6 (1.9, 3.5)
3.1 (2.2, 4.1)
Adiponectin, mg/l
17.4 (10.8, 26.6)
13.2 (8.5, 21.1)
CRP, mg/l
0.4 (0.1, 0.9)
0.6 (0.3, 1.2)
Total n-6 PUFAs, %
32.3 ± 4.2
32.5 ± 4.2
18:2n-6 (Linoleic acid)
13.6 ± 2.8
13.7 ± 2.9
18:3n-6 (γ-Linolenic acid)
0.079 (0.049, 0.123)
0.101 (0.066, 0.147)
20:2n-6 (Eicosadienoic acid)
0.40 ± 0.07
0.39 ± 0.06
20:3n-6 (Dihomo-γ-linolenic acid)
1.26 ± 0.28
1.30 ± 0.28
20:4n-6 (Arachidonic acid)
12.9 ± 1.9
12.9 ± 2.0
22:2n-6 (Docosadienoic acid)
0.082 ± 0.019
0.077 ± 0.018
22:4n-6 (Docosatetraenoic acid)
2.62 ± 0.67
2.55 ± 0.63
22:5n-6 (Docosapentaenoic acid)
1.51 ± 0.44
1.48 ± 0.42
Total n-3 PUFAs, %
6.85 (6.01, 7.85)
7.00 (6.25, 7.82)
18:3n-3 (α-Linolenic acid)
0.229 (0.183, 0.297)
0.241 (0.187, 0.314)
20:5n-3 (Eicosapentaenoic acid)
0.411 (0.304, 0.573)
0.424 (0.331, 0.575)
22:5n-3 (Docosapentaenoic acid)
1.76 ± 0.29
1.74 ± 0.30
22:6n-3 (Docosahexaenoic acid)
4.45 ± 1.02
4.56 ± 0.99
Values are means ± SDs for normal distributed variables or medians (interquartile ranges) for skewed distributed variables. Percentages may not sum to 100 as a result of rounding. Adiponectin data are missing for 33 participants. HbA1c, glycated hemoglobin; RBC, red blood cell.
The mean levels (percentage total FAs) of total n-6 and n-3 PUFAs at baseline were 32.4% and 7.1%, respectively. The incident cases had significantly higher proportions of 18:3n-6, 20:3n-6, and 18:3n-3 but lower proportions of 20:2n-6 and 22:2n-6 than those of noncases (Table 1). Meanwhile, incident MetS cases had higher levels of free carnitine and medium- and long-chain acylcarnitines but lower 3-dehydroxycarnitine and 3-dehydrocarnitine than those without incident MetS (supplemental Table S3). Moreover, PUFAs were intercorrelated, with rs values ranging from −0.27 to 0.60 (supplemental Table S4). Correlations were weak to moderate between PUFAs and BMI, waistline, blood lipids, and blood pressure or insulin, with all rs values <0.40. Weak correlations were also detected for PUFAs and acylcarnitines, with no rs values >0.30 (supplemental Fig. S1).
PUFAs and incident MetS risk
Of n-6 PUFAs, total n-6 PUFAs and three 22-carbon n-6 PUFAs were significantly associated with a 24% to 31% lower MetS risk comparing extreme quartiles: the RRs (95% CIs) were 0.75 (0.57, 0.97) for total n-6 PUFAs, 0.69 (0.56, 0.85) for 22:2n-6, 0.76 (0.59, 0.99) for 22:4n-6, and 0.74 (0.58, 0.94) for 22:5n-6 after multivariate adjustment (Table 2). On the other hand, 18:3n-6 showed positive association with incident MetS (RR per SD: 1.15; 95% CI: 1.06, 1.24).
TABLE 2RRs and 95% CIs for MetS according to baseline erythrocyte PUFAs
Quartile 1
Quartile 2
Quartile 3
Quartile 4
RR per SD
P
Total n-6 PUFAs
Median (interquartile range)
26.95 (25.49, 28.32)
31.38 (30.39, 32.36)
34.57 (33.89, 35.23)
36.88 (36.43, 37.66)
Case/noncase
101/210
116/196
111/200
105/206
Model 1
1
1.02 (0.83, 1.27)
0.86 (0.67, 1.10)
0.74 (0.57, 0.97)
0.88 (0.80, 0.97)
0.009
Model 2
1
0.98 (0.80, 1.21)
0.89 (0.70, 1.14)
0.75 (0.57, 0.97)
0.90 (0.81, 0.99)
0.028
18:2n-6
Median (interquartile range)
10.29 (9.55, 10.95)
12.38 (11.97, 12.92)
14.30 (13.85, 14.82)
16.96 (16.04, 18.09)
Case/noncase
94/217
110/202
119/192
110/201
Model 1
1
1.10 (0.88, 1.39)
1.07 (0.83, 1.38)
0.86 (0.65, 1.15)
0.93 (0.84, 1.04)
0.20
Model 2
1
1.11 (0.89, 1.38)
1.01 (0.79, 1.29)
0.85 (0.65, 1.13)
0.95 (0.86, 1.06)
0.37
18:3n-6
Median (interquartile range)
0.038 (0.031, 0.046)
0.068 (0.060, 0.076)
0.106 (0.096, 0.119)
0.172 (0.148, 0.207)
Case/noncase
76/235
103/209
114/197
140/171
Model 1
1
1.31 (1.02, 1.68)
1.48 (1.16, 1.89)
1.70 (1.35, 2.15)
1.21 (1.12, 1.30)
<0.001
Model 2
1
1.22 (0.97, 1.54)
1.32 (1.05, 1.67)
1.48 (1.19, 1.85)
1.15 (1.06, 1.24)
<0.001
20:2n-6
Median (interquartile range)
0.325 (0.301, 0.338)
0.370 (0.360, 0.380)
0.407 (0.398, 0.418)
0.462 (0.446, 0.498)
Case/noncase
117/194
117/195
108/203
91/220
Model 1
1
0.97 (0.79, 1.19)
0.91 (0.74, 1.12)
0.79 (0.63, 0.99)
0.92 (0.85, 1.00)
0.054
Model 2
1
1.08 (0.89, 1.31)
1.02 (0.84, 1.25)
0.89 (0.72, 1.11)
0.97 (0.90, 1.05)
0.42
20:3n-6
Median (interquartile range)
0.968 (0.895, 1.025)
1.168 (1.126, 1.210)
1.331 (1.291, 1.379)
1.615 (1.516, 1.737)
Case/noncase
90/221
114/198
97/214
132/179
Model 1
1
1.28 (1.02, 1.61)
1.08 (0.85, 1.37)
1.47 (1.18, 1.82)
1.09 (1.01, 1.17)
0.024
Model 2
1
1.13 (0.91, 1.39)
0.92 (0.73, 1.15)
1.23 (0.998, 1.53)
1.03 (0.96, 1.11)
0.39
20:4n-6
Median (interquartile range)
10.74 (9.94, 11.22)
12.41 (12.02, 12.72)
13.53 (13.25, 13.88)
15.13 (14.68, 15.71)
Case/noncase
108/203
108/204
111/200
106/205
Model 1
1
0.94 (0.76, 1.17)
0.94 (0.76, 1.16)
0.85 (0.67, 1.09)
0.92 (0.84, 0.996)
0.041
Model 2
1
0.95 (0.78, 1.16)
0.97 (0.79, 1.19)
0.87 (0.68, 1.11)
0.93 (0.86, 1.02)
0.11
22:2n-6
Median (interquartile range)
0.060 (0.055, 0.064)
0.074 (0.071, 0.077)
0.084 (0.082, 0.087)
0.102 (0.095, 0.111)
Case/noncase
132/179
115/197
103/208
83/228
Model 1
1
0.86 (0.71, 1.04)
0.79 (0.65, 0.97)
0.65 (0.52, 0.81)
0.84 (0.77, 0.91)
<0.001
Model 2
1
0.86 (0.72, 1.02)
0.75 (0.62, 0.91)
0.69 (0.56, 0.85)
0.85 (0.78, 0.93)
<0.001
22:4n-6
Median (interquartile range)
1.85 (1.68, 1.99)
2.31 (2.21, 2.41)
2.74 (2.62, 2.91)
3.44 (3.28, 3.66)
Case/noncase
113/198
106/206
114/197
100/211
Model 1
1
0.92 (0.75, 1.14)
0.93 (0.74, 1.18)
0.83 (0.62, 1.09)
0.89 (0.80, 0.99)
0.031
Model 2
1
0.87 (0.72, 1.06)
0.96 (0.77, 1.20)
0.76 (0.59, 0.99)
0.86 (0.78, 0.95)
0.003
22:5n-6
Median (interquartile range)
0.96 (0.81, 1.09)
1.38 (1.30, 1.46)
1.66 (1.59, 1.73)
1.98 (1.88, 2.15)
Case/noncase
112/199
109/203
114/197
98/213
Model 1
1
0.88 (0.70, 1.10)
0.88 (0.69, 1.10)
0.75 (0.59, 0.97)
0.90 (0.82, 0.98)
0.016
Model 2
1
0.81 (0.66, 1.00)
0.82 (0.66, 1.03)
0.74 (0.58, 0.94)
0.88 (0.81, 0.97)
0.008
Total n-3 PUFAs
Median (interquartile range)
5.55 (5.20, 5.85)
6.50 (6.32, 6.71)
7.36 (7.13, 7.60)
8.55 (8.12, 9.12)
Case/noncase
93/218
113/199
120/191
107/204
Model 1
1
1.19 (0.95, 1.48)
1.26 (0.99, 1.59)
1.12 (0.86, 1.44)
1.07 (0.99, 1.16)
0.073
Model 2
1
1.12 (0.91, 1.39)
1.21 (0.97, 1.52)
1.03 (0.81, 1.31)
1.05 (0.97, 1.13)
0.21
18:3n-3
Median (interquartile range)
0.153 (0.135, 0.171)
0.209 (0.197, 0.221)
0.264 (0.247, 0.282)
0.360 (0.326, 0.419)
Case/noncase
102/209
96/216
110/201
125/186
Model 1
1
0.99 (0.79, 1.24)
1.13 (0.90, 1.42)
1.24 (0.98, 1.57)
1.08 (1.01, 1.14)
0.015
Model 2
1
1.04 (0.84, 1.29)
1.18 (0.95, 1.46)
1.31 (1.06, 1.64)
1.09 (1.03, 1.15)
0.002
20:5n-3
Median (interquartile range)
0.252 (0.218, 0.284)
0.361 (0.338, 0.387)
0.489 (0.454, 0.533)
0.693 (0.631, 0.805)
Case/noncase
93/218
116/196
115/196
109/202
Model 1
1
1.23 (0.99, 1.54)
1.31 (1.02, 1.67)
1.24 (0.95, 1.62)
1.10 (1.00, 1.20)
0.043
Model 2
1
1.27 (1.03, 1.58)
1.31 (1.03, 1.65)
1.16 (0.91, 1.49)
1.08 (0.99, 1.18)
0.065
22:5n-3
Median (interquartile range)
1.43 (1.32, 1.50)
1.65 (1.60, 1.69)
1.82 (1.78, 1.87)
2.10 (2.01, 2.22)
Case/noncase
112/199
113/199
95/216
113/198
Model 1
1
1.09 (0.88, 1.34)
0.95 (0.76, 1.19)
1.19 (0.96, 1.48)
1.02 (0.94, 1.11)
0.61
Model 2
1
1.08 (0.89, 1.32)
0.90 (0.72, 1.12)
1.09 (0.87, 1.35)
0.98 (0.91, 1.07)
0.70
22:6n-3
Median (interquartile range)
3.36 (3.02, 3.59)
4.13 (3.97, 4.27)
4.77 (4.60, 4.94)
5.66 (5.38, 6.08)
Case/noncase
93/218
110/202
122/189
108/203
Model 1
1
1.14 (0.91, 1.43)
1.24 (0.98, 1.56)
1.07 (0.83, 1.37)
1.03 (0.95, 1.12)
0.46
Model 2
1
1.11 (0.89, 1.37)
1.12 (0.89, 1.40)
1.00 (0.79, 1.26)
1.01 (0.93, 1.10)
0.77
Model 1 was adjusted for age, sex, region, and residence. Model 2 was further adjusted for current smoking status, current drinking status, years of education, physical activity, family history of chronic diseases, and BMI. P values were for RRs per SD change in exposures derived from log-Poisson models.
Of n-3 PUFAs, higher 18:3n-3 was also positively associated with incident MetS, with a per SD RR of 1.09 (95% CI: 1.03, 1.15). No significant association was detected for total n-3 PUFAs or other n-3 PUFAs, including 20:5n-3, 22:5n-3, or 22:6n-3 (Table 2).
For associations with individual MetS components (supplemental Table S5), total n-6 PUFAs and 20:4n-6 were inversely associated with central obesity, while 18:3n-6 and 20:3n-6 were positively associated with central obesity. Lower levels of 20:4n-6, 22:2n-6, 22:4n-6, and 22:5n-6 but higher concentrations of 18:3n-3 and 20:5n-3 were significantly associated with elevated triglycerides. Moreover, 20:4n-6 and 22:4n-6 were positively associated with HDL-cholesterol levels, while 18:3n-3 was inversely associated with HDL-cholesterol levels. The levels of total n-6 PUFAs and 20:4n-6 were also inversely associated with elevated fasting glucose. Associations stratified by gender are shown in supplemental Table S6.
Network analysis
Six modules (three FA modules and three acylcarnitine modules) were generated by a network analysis performed on baseline values of FAs and acylcarnitines. When module membership strengths >0.8 were considered (supplemental Table S7), module 1 was determined by 22-carbon n-6 PUFAs (22:4n-6 and 22:5n-6) and very-long-chain saturated FAs (22:0 and 24:0); module 2 was driven by monounsaturated FAs (20:1n-9, 22:1n-9, and 24:1n-9); module 3 consisted of trans-FAs (18:2n-6 9c12t and 18:2n-6 9t12c); module 4 was mostly driven by long-chain acylcarnitines (C14OH, C14:1OH, C16:1, C16:2, C18, C18:1, C18:2, C20, and C20:4); module 5 was determined by medium-chain acylcarnitines (C12, C12OH, C12:1, and C14); and module 6 was contributed by short- to medium-chain acylcarnitines (C6, C8, and C10). Figure 1 shows the network heat map and correlations of the six modules as well as the subnetworks of modules 1 and 4. The intercorrelations of FA modules ranged from −0.25 to 0.09, and the intercorrelations of acylcarnitine modules ranged from 0.38 to 0.76 (Fig. 1B, supplemental Table S8).
Fig. 1Network analysis of baseline FAs and acylcarnitines. A: Network heat map, baseline FAs, and acylcarnitines were constructed into six modules (FAs: blue, yellow, and red; acylcarnitines: turquoise, brown, and green). B: Correlations among the six modules: color bars on the left and at the bottom indicate corresponding modules; grids represent Pearson correlation coefficients among MEs. C: Subnetwork plot of module 1 (blue in A): Pearson correlation coefficients >0.3 or <−0.3 were plotted; gray and blue edges indicate positive and inverse correlations, respectively. D: Subnetwork plot of module 4 (turquoise in A): Pearson correlation coefficients >0.5 were plotted.
Modules 1 and 3 were both inversely associated with incident MetS after multivariable adjustment, with per SD RRs (95% CIs) of 0.84 (0.76, 0.92) and 0.90 (0.83, 0.98) (P < 0.05), respectively, whereas module 6 was marginally positively associated with incident MetS, with a per SD RR of 1.07 (95% CI: 0.99, 1.15) (P = 0.08) (Table 3).
TABLE 3RRs and 95% CIs for incident MetS according to MEs
Model 1 was adjusted for age, sex, region, and residence. Model 2 was further adjusted for current smoking status, current drinking status, years of education, physical activity, family history of chronic diseases, and BMI. Data of acylcarnitines are missing for 46 participants. P values were for RRs per SD change in exposures derived from log-Poisson models.
We next focused on the interactions between FA modules that were significantly associated with MetS risk (modules 1 and 3) and acylcarnitine modules. Significant interaction was found between modules 1 and 6 (Pinteraction = 0.03) (supplemental Fig. S2). Adjusted RRs (95% CIs) of MetS according to tertiles of module 1 were 1 (reference), 0.76 (0.57, 1.03), and 0.52 (0.35, 0.75) in the lowest module 6 tertile and 0.91 (0.69, 1.21), 0.86 (0.65, 1.14), and 0.63 (0.45, 0.88) in the highest module 6 tertile, respectively.
DISCUSSION
In this prospective study among Chinese men and women, we found that total n-6 PUFAs and three long-chain (22-carbon) n-6 PUFAs were inversely associated with a 6 year risk of developing incident MetS, while 18:3n-3 and 18:3n-6 showed positive associations. Using network analysis, we identified three FA modules and three acylcarnitine modules. Two FA modules consisting of long-chain n-6 PUFAs or trans-FAs were associated with a lower MetS risk. Although the FA modules and acylcarnitine modules were not strongly correlated with each other, data from an exploratory analysis suggested that the long-chain n-6 PUFA module was more strongly associated with lower MetS risk when the short- to medium-chain acylcarnitine (C5–C10) module score was also lower.
To our knowledge, this is the first prospective study demonstrating that total n-6 and long-chain n-6 PUFA biomarkers were associated with a reduced MetS risk in an Asian population. Our findings are in line with those from two earlier cohort studies in Finnish populations. One of the studies found an inverse association between serum total n-6 PUFAs and incident MetS among 665 Finnish men and women (
). In addition, increased circulating levels of total n-6 PUFAs were associated with decreased incidence of type 2 diabetes or CHD in several European studies (
Serum n-6 polyunsaturated fatty acids, Δ5- and Δ6-desaturase activities, and risk of incident type 2 diabetes in men: the Kuopio Ischaemic Heart Disease Risk Factor Study.
When individual n-6 PUFA biomarkers were considered, our study showed significantly inverse associations of 22-carbon n-6 PUFAs, including 22:2n-6, 22:4n-6, and 22:5n-6, with 6 year incident MetS, although the inverse association did not achieve statistical significance for 18:2n-6. Notably, existing results regarding the associations of specific n-6 PUFAs with cardiometabolic diseases remain controversial among individual studies. For instance, Yary et al. (
) found that both serum 18:2n-6 and 20:4n-6 were inversely associated with future MetS risk in Finnish men. However, in a recent meta-analysis that consisted of 39,740 adults, only higher levels of 18:2n-6, but not 20:4n-6, were associated with a lower risk of type 2 diabetes (
Omega-6 fatty acid biomarkers and incident type 2 diabetes: pooled analysis of individual-level data for 39 740 adults from 20 prospective cohort studies.
). Moreover, inconsistent associations of 22:4n-6 or 22:5n-6 with CHD or type 2 diabetes were also reported in cohort studies among European populations (
). The genetic differences might be one of the primary reasons underlying heterogenetic findings across ethnic groups. In a recent genome-wide association study of circulating PUFA levels, a genetic variant (rs174547) on FADS1, which encodes the Δ5 desaturase, showed different effect sizes of associations with the levels of several n-6 PUFAs between Chinese and European ancestries (
Genome-wide meta-analyses identify novel loci associated with n-3 and n-6 polyunsaturated fatty acid levels in Chinese and European-ancestry populations.
). These lines of evidence suggested that differences in genetic architectures involving PUFA metabolism might partially explain the ethnic differences in the associations between PUFAs and cardiometabolic outcomes.
Our study also evaluated the effects of n-6 PUFAs on each MetS feature and found that higher levels of 22:2n-6, 22:4n-6, and 22:5n-6 were associated with lower triglycerides and/or higher HDL-cholesterol levels. Although underlying mechanisms remain unclear, all of these long-chain n-6 PUFAs are the downstream products of 18:2n-6 (
Fatty acids as biocompounds: their role in human metabolism, health and disease−a review. Part 1: classification, dietary sources and biological functions.
). It is plausible that the processes of converting 18:2n-6 into these long-chain n-6 PUFAs via elongation and desaturation might also play a role in these findings. A previous study in hamsters showed that the administration of a high-cholesterol diet supplemented with 22:5n-6 significantly improved the blood lipoprotein profile and suppressed the gene expressions of SREBP-2 and 3-HMG-CoA reductase in the lipid metabolic pathway (
Omega-6 fatty acids and risk for cardiovascular disease: a science advisory from the American Heart Association Nutrition Subcommittee of the Council on Nutrition, Physical Activity, and Metabolism; Council on Cardiovascular Nursing; and Council on Epidemiology and Prevention.
). Interestingly, in our study, 20:4n-6 was not correlated with CRP but associated with significantly reduced risks of central obesity, dyslipidemia, and elevated fasting glucose. Studies in vitro have also revealed that 20:4n-6 could alleviate chemical-induced cytotoxicity in insulin-secreting cells (
). Overall, our study demonstrated that total and certain n-6 PUFAs exerted a favorable rather than harmful impact on cardiometabolic outcomes.
In addition, neither total nor marine-derived n-3 PUFAs such as 20:5n-3 and 22:6n-3 showed significant associations for incident MetS, which was similar to the finding from the Finnish cohort by Vanhala et al. (
). It is noteworthy that, except for these two studies, most cohorts have focused on associations between n-3 PUFAs and CVD or type 2 diabetes and revealed inconsistent results. For instance, in a recent meta-analysis that included 10 trials and 77,917 participants, n-3 PUFA supplementation did not exert significant effects on reducing CVD risk (
) showed that neither intake nor biomarkers of n-3 PUFAs were significantly associated with incident type 2 diabetes. In the case of 18:3n-3 and 18:3n-6, findings from available studies are still inconclusive (
Fatty acids measured in plasma and erythrocyte-membrane phospholipids and derived by food-frequency questionnaire and the risk of new-onset type 2 diabetes: a pilot study in the European Prospective Investigation into Cancer and Nutrition (EPIC)-Norfolk cohort.
Erythrocyte membrane phospholipid fatty acids, desaturase activity, and dietary fatty acids in relation to risk of type 2 diabetes in the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam Study.
), we also used a network analysis approach. The long-chain n-6 PUFA module derived by the network analysis was consistently associated with a lower MetS risk. Of note, the trans-FA module showed an inverse association with incident MetS risk. This finding was somewhat consistent with the observed inverse association of trans-18:1, a major trans-FA isoform as well as marker of dairy intake, with incident type 2 diabetes in the same Chinese population (
Dairy consumption, type 2 diabetes, and changes in cardiometabolic traits: a prospective cohort study of middle-aged and older Chinese in Beijing and Shanghai.
Our analyses showed a significant interaction between short- and medium-chain acylcarnitine module and the long-chain n-6 PUFA module, suggesting that the favorable effects of n-6 PUFAs could be more pronounced when these acylcarnitine levels are low. Elevated acylcarnitine concentrations were observed in obese individuals, and accumulated body fat might attribute to FA overload and stressed mitochondria with incomplete FAO (
). Although the current study did not show significant associations of any acylcarnitine module with MetS, elevated short- and medium-chain acylcarnitine levels were reported to be independently associated with total CVD and stroke in a Spanish population (
). Overall, these data suggest a potential interplay between n-6 PUFAs and acylcarnitines in modulating cardiometabolic risk. More data are needed, however, to replicate our findings and elucidate relevant mechanisms.
Our study has some limitations that deserve to be discussed. First, approximately 23% of participants dropped out in the 6 year follow-up. Although this rate was similar to other cohort studies (
), we cannot exclude the possibility that the loss to follow-up was linked to exposure or outcome ascertainments. Second, the metabolites were measured only once at baseline. Nevertheless, the erythrocyte FAs were demonstrated to be reproducible over time (
). Third, although PUFA biomarkers may reflect certain dietary fat intake more objectively than that of a questionnaire-based approach, levels of erythrocyte PUFAs could also be influenced by other factors such as genetic background, specific metabolic profile, and health status; thus, implications for dietary intake are limited. Finally, our study was conducted in middle-aged and elderly Chinese individuals, and the findings might not be generalized to other populations with different ages and ethnic backgrounds.
In conclusion, total n-6 PUFAs and 22-carbon n-6 PUFAs in erythrocytes are associated with a reduced 6 year incident MetS risk in Chinese men and women. More studies are merited to confirm our findings and to illuminate underlying mechanisms.
Acknowledgments
The authors thank Gang Liu, Pang Yao, Feijie Wang, Shaofeng Huo, Quan Xiong, Zhenhua Niu, Di Wang, and Yaogan Luo of the Chinese Academy of Sciences for helping to collect the data for this study.
Dietary Guidelines Advisory Committee Scientific Report of the 2015 Dietary Guidelines Advisory Committee: Advisory Report to the Secretary of Health and Human Services and the Secretary of Agriculture.
Ethnic differences in beta-cell function, dietary intake and expression of the metabolic syndrome among UK adults of South Asian, black African-Caribbean and white-European origin at high risk of metabolic syndrome.
Genome-wide meta-analyses identify novel loci associated with n-3 and n-6 polyunsaturated fatty acid levels in Chinese and European-ancestry populations.
Omega-6 fatty acid biomarkers and incident type 2 diabetes: pooled analysis of individual-level data for 39 740 adults from 20 prospective cohort studies.
Diagnosis and management of the metabolic syndrome: an American Heart Association/National Heart, Lung, and Blood Institute scientific statement: Executive Summary.
Serum n-6 polyunsaturated fatty acids, Δ5- and Δ6-desaturase activities, and risk of incident type 2 diabetes in men: the Kuopio Ischaemic Heart Disease Risk Factor Study.
Fatty acids as biocompounds: their role in human metabolism, health and disease−a review. Part 1: classification, dietary sources and biological functions.
Omega-6 fatty acids and risk for cardiovascular disease: a science advisory from the American Heart Association Nutrition Subcommittee of the Council on Nutrition, Physical Activity, and Metabolism; Council on Cardiovascular Nursing; and Council on Epidemiology and Prevention.
Fatty acids measured in plasma and erythrocyte-membrane phospholipids and derived by food-frequency questionnaire and the risk of new-onset type 2 diabetes: a pilot study in the European Prospective Investigation into Cancer and Nutrition (EPIC)-Norfolk cohort.
Erythrocyte membrane phospholipid fatty acids, desaturase activity, and dietary fatty acids in relation to risk of type 2 diabetes in the European Prospective Investigation into Cancer and Nutrition (EPIC)-Potsdam Study.
Dairy consumption, type 2 diabetes, and changes in cardiometabolic traits: a prospective cohort study of middle-aged and older Chinese in Beijing and Shanghai.
This work was supported by Ministry of Science and Technology of China Grants 2017YFC0909700 and 2016YFC1304903; National Natural Science Foundation of China Grants 81471013, 81561128018, and 81700700; and Chinese Academy of Sciences Grants ZDBS-SSW-DQC-02, ZDRW-ZS-2016-8, and KJZD-EW-L14-2-2. Q. Sun reported receiving ad hoc consulting fees from Emavant Solutions GmbH.
Abbreviations:
CHD
coronary heart disease
CRP
C-reactive protein
FAO
fatty acid oxidation
HOMA-IR
homeostatic model assessment of insulin resistance
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