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Erythrocyte PUFAs, circulating acylcarnitines, and metabolic syndrome risk: a prospective study in Chinese[S]

  • Yiwei Ma
    Affiliations
    CAS Key Laboratory of Nutrition, Metabolism and Food Safety, Chinese Academy of Sciences, Shanghai 200031, China
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  • Liang Sun
    Affiliations
    CAS Key Laboratory of Nutrition, Metabolism and Food Safety, Chinese Academy of Sciences, Shanghai 200031, China
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  • Jun Li
    Affiliations
    Departments of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA 02115

    Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115
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  • Yao Hu
    Affiliations
    CAS Key Laboratory of Nutrition, Metabolism and Food Safety, Chinese Academy of Sciences, Shanghai 200031, China
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  • Zhenji Gan
    Affiliations
    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
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  • Geng Zong
    Affiliations
    CAS Key Laboratory of Nutrition, Metabolism and Food Safety, Chinese Academy of Sciences, Shanghai 200031, China
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  • He Zheng
    Affiliations
    CAS Key Laboratory of Nutrition, Metabolism and Food Safety, Chinese Academy of Sciences, Shanghai 200031, China
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  • Qianlu Jin
    Affiliations
    CAS Key Laboratory of Nutrition, Metabolism and Food Safety, Chinese Academy of Sciences, Shanghai 200031, China
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  • Huaixing Li
    Affiliations
    CAS Key Laboratory of Nutrition, Metabolism and Food Safety, Chinese Academy of Sciences, Shanghai 200031, China
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  • Frank B. Hu
    Affiliations
    Departments of Nutrition, Harvard T.H. Chan School of Public Health, Boston, MA 02115

    Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115

    Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115
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  • Rong Zeng
    Affiliations
    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, China

    Department of Life Sciences and Technology, ShanghaiTech University, Shanghai 201210, China
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  • Author Footnotes
    1 Q. Sun and X. Lin contributed equally to this work.
    Qi Sun
    Correspondence
    To whom correspondence should be addressed. (X.L.); (Q.S.)
    Footnotes
    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 02115

    Channing Division of Network Medicine, Department of Medicine, Brigham and Women's Hospital, Harvard Medical School, Boston, MA 02115
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  • Author Footnotes
    1 Q. Sun and X. Lin contributed equally to this work.
    Xu Lin
    Correspondence
    To whom correspondence should be addressed. (X.L.); (Q.S.)
    Footnotes
    1 Q. Sun and X. Lin contributed equally to this work.
    Affiliations
    CAS Key Laboratory of Nutrition, Metabolism and Food Safety, Chinese Academy of Sciences, Shanghai 200031, China
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  • Author Footnotes
    [S] The online version of this article (available at http://www.jlr.org) contains a supplement.
    1 Q. Sun and X. Lin contributed equally to this work.
Open AccessPublished:December 14, 2018DOI:https://doi.org/10.1194/jlr.P088005
      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.
      A higher consumption of PUFAs has been shown to be associated with reduced risks of type 2 diabetes and CVD in Western populations (
      • Sacks F.M.
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      • Wu J.H.Y.
      • Appel L.J.
      • Creager M.A.
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      • Miller M.
      • Rimm E.B.
      • Rudel L.L.
      • Robinson J.G.
      • et al.
      Dietary fats and cardiovascular disease: a presidential advisory from the American Heart Association.
      ,
      • Salmerón J.
      • Hu F.B.
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      • Stampfer M.J.
      • Colditz G.A.
      • Rimm E.B.
      • Willett W.C.
      Dietary fat intake and risk of type 2 diabetes in women.
      ). Recent dietary guidelines such as the 2015–2020 Dietary Guidelines for Americans also encouraged PUFA intakes (
      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.
      ). However, little is known about whether the guidelines are pertinent to Asians who have different dietary patterns (
      • Goff L.M.
      • Griffin B.A.
      • Lovegrove J.A.
      • Sanders T.A.
      • Jebb S.A.
      • Bluck L.J.
      • Frost G.S.
      • Group R.S.
      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 (
      • Hu Y.
      • Li H.
      • Lu L.
      • Manichaikul A.
      • Zhu J.
      • Chen Y.D.
      • Sun L.
      • Liang S.
      • Siscovick D.S.
      • Steffen L.M.
      • et al.
      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 (
      • Wu J.H.
      • Micha R.
      • Imamura F.
      • Pan A.
      • Biggs M.L.
      • Ajaz O.
      • Djousse L.
      • Hu F.B.
      • Mozaffarian D.
      Omega-3 fatty acids and incident type 2 diabetes: a systematic review and meta-analysis.
      ,
      • Wu J.H.Y.
      • Marklund M.
      • Imamura F.
      • Tintle N.
      • Ardisson Korat A.V.
      • de Goede J.
      • Zhou X.
      • Yang W.S.
      • de Oliveira Otto M.C.
      • Kroger J.
      • et al.
      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.
      ,
      • Chowdhury R.
      • Warnakula S.
      • Kunutsor S.
      • Crowe F.
      • Ward H.A.
      • Johnson L.
      • Franco O.H.
      • Butterworth A.S.
      • Forouhi N.G.
      • Thompson S.G.
      • et al.
      Association of dietary, circulating, and supplement fatty acids with coronary risk: a systematic review and meta-analysis.
      ). 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 (
      • Eckel R.H.
      • Grundy S.M.
      • Zimmet P.Z.
      The metabolic syndrome.
      ). 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 (
      • Vanhala M.
      • Saltevo J.
      • Soininen P.
      • Kautiainen H.
      • Kangas A.J.
      • Ala-Korpela M.
      • Mantyselka P.
      Serum omega-6 polyunsaturated fatty acids and the metabolic syndrome: a longitudinal population-based cohort study.
      ,
      • Warensjö E.
      • Riserus U.
      • Vessby B.
      Fatty acid composition of serum lipids predicts the development of the metabolic syndrome in men.
      ,
      • Warensjö E.
      • Sundstrom J.
      • Lind L.
      • Vessby B.
      Factor analysis of fatty acids in serum lipids as a measure of dietary fat quality in relation to the metabolic syndrome in men.
      ). 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 (
      • Arab L.
      Biomarkers of fat and fatty acid intake.
      ). We previously observed an inverse association between erythrocyte 22:6n-3 (DHA) and MetS prevalence by using baseline data of the current cohort (
      • Zhang G.
      • Sun Q.
      • Hu F.B.
      • Ye X.
      • Yu Z.
      • Zong G.
      • Li H.
      • Zhou Y.
      • Lin X.
      Erythrocyte n-3 fatty acids and metabolic syndrome in middle-aged and older Chinese.
      ); 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 (
      • Reuter S.E.
      • Evans A.M.
      Carnitine and acylcarnitines: pharmacokinetic, pharmacological and clinical aspects.
      ). Several studies have shown alterations of circulating acylcarnitines in cardiometabolic conditions (
      • Adams S.H.
      • Hoppel C.L.
      • Lok K.H.
      • Zhao L.
      • Wong S.W.
      • Minkler P.E.
      • Hwang D.H.
      • Newman J.W.
      • Garvey W.T.
      Plasma acylcarnitine profiles suggest incomplete long-chain fatty acid beta-oxidation and altered tricarboxylic acid cycle activity in type 2 diabetic African-American women.
      ,
      • Mihalik S.J.
      • Goodpaster B.H.
      • Kelley D.E.
      • Chace D.H.
      • Vockley J.
      • Toledo F.G.
      • DeLany J.P.
      Increased levels of plasma acylcarnitines in obesity and type 2 diabetes and identification of a marker of glucolipotoxicity.
      ,
      • Mai M.
      • Tonjes A.
      • Kovacs P.
      • Stumvoll M.
      • Fiedler G.M.
      • Leichtle A.B.
      Serum levels of acylcarnitines are altered in prediabetic conditions.
      ). 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 (
      • Sun L.
      • Liang L.
      • Gao X.
      • Zhang H.
      • Yao P.
      • Hu Y.
      • Ma Y.
      • Wang F.
      • Jin Q.
      • Li H.
      • et al.
      Early prediction of developing type 2 diabetes by plasma acylcarnitines: a population-based study.
      ). 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 (
      • Koves T.R.
      • Ussher J.R.
      • Noland R.C.
      • Slentz D.
      • Mosedale M.
      • Ilkayeva O.
      • Bain J.
      • Stevens R.
      • Dyck J.R.
      • Newgard C.B.
      • et al.
      Mitochondrial overload and incomplete fatty acid oxidation contribute to skeletal muscle insulin resistance.
      ). 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 (
      • Ye X.
      • Yu Z.
      • Li H.
      • Franco O.H.
      • Liu Y.
      • Lin X.
      Distributions of C-reactive protein and its association with metabolic syndrome in middle-aged and older Chinese people.
      ). 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 (
      • Yu Z.
      • Ye X.
      • Wang J.
      • Qi Q.
      • Franco O.H.
      • Rennie K.L.
      • Pan A.
      • Li H.
      • Liu Y.
      • Hu F.B.
      • et al.
      Associations of physical activity with inflammatory factors, adipocytokines, and metabolic syndrome in middle-aged and older chinese people.
      ). BMI was calculated as kg/m2.

      Laboratory measurements

      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 (
      • Ye X.
      • Yu Z.
      • Li H.
      • Franco O.H.
      • Liu Y.
      • Lin X.
      Distributions of C-reactive protein and its association with metabolic syndrome in middle-aged and older Chinese people.
      ,
      • Wang J.
      • Li H.
      • Franco O.H.
      • Yu Z.
      • Liu Y.
      • Lin X.
      Adiponectin and metabolic syndrome in middle-aged and elderly Chinese.
      ). 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) (
      • Zong G.
      • Ye X.
      • Sun L.
      • Li H.
      • Yu Z.
      • Hu F.B.
      • Sun Q.
      • Lin X.
      Associations of erythrocyte palmitoleic acid with adipokines, inflammatory markers, and the metabolic syndrome in middle-aged and older Chinese.
      ). 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 (
      • Sun L.
      • Liang L.
      • Gao X.
      • Zhang H.
      • Yao P.
      • Hu Y.
      • Ma Y.
      • Wang F.
      • Jin Q.
      • Li H.
      • et al.
      Early prediction of developing type 2 diabetes by plasma acylcarnitines: a population-based study.
      ). 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 (
      • Grundy S.M.
      • Cleeman J.I.
      • Daniels S.R.
      • Donato K.A.
      • Eckel R.H.
      • Franklin B.A.
      • Gordon D.J.
      • Krauss R.M.
      • Savage P.J.
      • Smith Jr., S.C.
      • et al.
      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.
      R package WGCNA version 1.51 (
      • Langfelder P.
      • Horvath S.
      WGCNA: an R package for weighted correlation network analysis.
      ) 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 (
      R Core Team R: A language and environment for statistical computing..
      ). Subnetwork visualization was performed by Cytoscape version 3.5.1 (
      • Shannon P.
      • Markiel A.
      • Ozier O.
      • Baliga N.S.
      • Wang J.T.
      • Ramage D.
      • Amin N.
      • Schwikowski B.
      • Ideker T.
      Cytoscape: a software environment for integrated models of biomolecular interaction networks.
      ). 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, years58.0 ± 5.957.9 ± 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/m222.1 ± 2.624.4 ± 3.0
      Waistline, cm76.5 ± 8.383.1 ± 8.8
      Current drinking, yes (%)258 (31.8)112 (25.9)
      Current smoking, yes (%)279 (34.4)111 (25.6)
      Education, n (%)
      0–6 years406 (40.0)196 (45.3)
      7–9 years262 (32.3)145 (33.5)
      ≥10 years144 (17.7)92 (21.3)
      Physical activity, n (%)
      Low58 (7.1)23 (5.3)
      Moderate271 (33.4)167 (38.6)
      High483 (59.5)243 (56.1)
      Family history of chronic diseases, n (%)357 (44.0)239 (55.2)
      Systolic blood pressure, mmHg130.5 ± 20.7137.7 ± 20.6
      Diastolic blood pressure, mmHg76.2 ± 10.179.9 ± 10.3
      Triglycerides, mmol/l0.8 (0.6, 1.0)1.1 (0.8, 1.4)
      HDL-cholesterol, mmol/l1.4 ± 0.31.3 ± 0.3
      LDL-cholesterol, mmol/l3.0 ± 0.93.3 ± 0.9
      Total cholesterol, mmol/l4.5 ± 0.94.7 ± 0.9
      Fasting glucose, mmol/l5.2 ± 0.55.2 ± 0.4
      RBC HbA1c, %5.6 ± 0.45.7 ± 0.4
      Fasting insulin, mIU/l11.6 (8.4, 15.0)13.3 (9.7, 17.8)
      HOMA-IR2.6 (1.9, 3.5)3.1 (2.2, 4.1)
      Adiponectin, mg/l17.4 (10.8, 26.6)13.2 (8.5, 21.1)
      CRP, mg/l0.4 (0.1, 0.9)0.6 (0.3, 1.2)
      Total n-6 PUFAs, %32.3 ± 4.232.5 ± 4.2
      18:2n-6 (Linoleic acid)13.6 ± 2.813.7 ± 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 ± 0.070.39 ± 0.06
      20:3n-6 (Dihomo-γ-linolenic acid)1.26 ± 0.281.30 ± 0.28
      20:4n-6 (Arachidonic acid)12.9 ± 1.912.9 ± 2.0
      22:2n-6 (Docosadienoic acid)0.082 ± 0.0190.077 ± 0.018
      22:4n-6 (Docosatetraenoic acid)2.62 ± 0.672.55 ± 0.63
      22:5n-6 (Docosapentaenoic acid)1.51 ± 0.441.48 ± 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 ± 0.291.74 ± 0.30
      22:6n-3 (Docosahexaenoic acid)4.45 ± 1.024.56 ± 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 1Quartile 2Quartile 3Quartile 4RR per SDP
      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/noncase101/210116/196111/200105/206
      Model 111.02 (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 210.98 (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/noncase94/217110/202119/192110/201
      Model 111.10 (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 211.11 (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/noncase76/235103/209114/197140/171
      Model 111.31 (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 211.22 (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/noncase117/194117/195108/20391/220
      Model 110.97 (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 211.08 (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/noncase90/221114/19897/214132/179
      Model 111.28 (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 211.13 (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/noncase108/203108/204111/200106/205
      Model 110.94 (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 210.95 (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/noncase132/179115/197103/20883/228
      Model 110.86 (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 210.86 (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/noncase113/198106/206114/197100/211
      Model 110.92 (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 210.87 (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/noncase112/199109/203114/19798/213
      Model 110.88 (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 210.81 (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/noncase93/218113/199120/191107/204
      Model 111.19 (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 211.12 (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/noncase102/20996/216110/201125/186
      Model 110.99 (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 211.04 (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/noncase93/218116/196115/196109/202
      Model 111.23 (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 211.27 (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/noncase112/199113/19995/216113/198
      Model 111.09 (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 211.08 (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/noncase93/218110/202122/189108/203
      Model 111.14 (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 211.11 (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).
      Figure thumbnail gr1
      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
      Quartile 1Quartile 2Quartile 3Quartile 4RR per SDP
      Module 1
      16:0, 18:1n-9, 18:3n-3, 20:2n-6, 20:5n-3, 20:4n-6, 22:0, 24:0, 22:4n-6, 22:5n-6Case/noncase112/199110/202113/19898/213
      Model 110.83 (0.67, 1.04)0.77 (0.60, 0.99)0.62 (0.46, 0.84)0.85 (0.78, 0.94)0.001
      Model 210.77 (0.62, 0.95)0.76 (0.60, 0.97)0.63 (0.47, 0.84)0.84 (0.76, 0.92)<0.001
      Module 2
      18:1n-7, 18:2n-6, 20:1n-9, 22:1n-9, 24:1n-9Case/noncase126/185110/202100/21197/214
      Model 110.94 (0.77, 1.15)0.91 (0.72, 1.15)0.96 (0.71, 1.30)1.00 (0.90, 1.12)0.96
      Model 210.93 (0.76, 1.13)0.95 (0.76, 1.19)1.10 (0.82, 1.46)1.06 (0.96, 1.18)0.25
      Module 3
      18:1t isomers, 18:2n-6 9c12t, 18:2n-6 9t12cCase/noncase112/19997/215117/194107/204
      Model 110.84 (0.67, 1.04)0.94 (0.76, 1.16)0.82 (0.65, 1.03)0.92 (0.85, 0.996)0.041
      Model 210.85 (0.69, 1.05)0.93 (0.75, 1.14)0.81 (0.65, 1.01)0.90 (0.83, 0.98)0.013
      Module 4
      C12DC, C14:1OH, C14OH, C16:2, C16:1, C18:2, C18OH, C18:1, C18, C20, C20:4Case/noncase94/20698/202109/191117/182
      Model 111.10 (0.87, 1.39)1.21 (0.96, 1.52)1.30 (1.04, 1.62)1.09 (1.01, 1.18)0.019
      Model 211.01 (0.81, 1.26)1.06 (0.85, 1.31)1.09 (0.88, 1.35)1.03 (0.96, 1.10)0.46
      Module 5
      C2, C6OH, C10DC, C12OH, C12:1, C12, C13DC, C14, C16Case/noncase106/194116/18492/208104/195
      Model 111.10 (0.89, 1.35)0.93 (0.74, 1.16)1.06 (0.86, 1.32)1.06 (0.98, 1.15)0.15
      Model 211.05 (0.86, 1.28)0.87 (0.70, 1.08)1.03 (0.84, 1.27)1.04 (0.95, 1.12)0.40
      Module 6
      C5, C6, C8, C10Case/noncase103/197101/19997/203117/182
      Model 111.01 (0.81, 1.26)1.02 (0.82, 1.28)1.24 (1.01, 1.53)1.13 (1.05, 1.21)<0.001
      Model 210.96 (0.78, 1.19)0.90 (0.72, 1.19)1.14 (0.93, 1.39)1.07 (0.99, 1.15)0.08
      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 (
      • Vanhala M.
      • Saltevo J.
      • Soininen P.
      • Kautiainen H.
      • Kangas A.J.
      • Ala-Korpela M.
      • Mantyselka P.
      Serum omega-6 polyunsaturated fatty acids and the metabolic syndrome: a longitudinal population-based cohort study.
      ). Similar associations were also observed in another study that consisted of 661 middle-aged Finnish men (
      • Yary T.
      • Voutilainen S.
      • Tuomainen T.P.
      • Ruusunen A.
      • Nurmi T.
      • Virtanen J.K.
      Omega-6 polyunsaturated fatty acids, serum zinc, delta-5- and delta-6-desaturase activities and incident metabolic syndrome.
      ). 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 (
      • Khaw K.T.
      • Friesen M.D.
      • Riboli E.
      • Luben R.
      • Wareham N.
      Plasma phospholipid fatty acid concentration and incident coronary heart disease in men and women: the EPIC-Norfolk prospective study.
      ,
      • Yary T.
      • Voutilainen S.
      • Tuomainen T.P.
      • Ruusunen A.
      • Nurmi T.
      • Virtanen J.K.
      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. (
      • Yary T.
      • Voutilainen S.
      • Tuomainen T.P.
      • Ruusunen A.
      • Nurmi T.
      • Virtanen J.K.
      Omega-6 polyunsaturated fatty acids, serum zinc, delta-5- and delta-6-desaturase activities and incident metabolic syndrome.
      ) 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 (
      • Wu J.H.Y.
      • Marklund M.
      • Imamura F.
      • Tintle N.
      • Ardisson Korat A.V.
      • de Goede J.
      • Zhou X.
      • Yang W.S.
      • de Oliveira Otto M.C.
      • Kroger J.
      • et al.
      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 (
      • Khaw K.T.
      • Friesen M.D.
      • Riboli E.
      • Luben R.
      • Wareham N.
      Plasma phospholipid fatty acid concentration and incident coronary heart disease in men and women: the EPIC-Norfolk prospective study.
      ,
      • Forouhi N.G.
      • Imamura F.
      • Sharp S.J.
      • Koulman A.
      • Schulze M.B.
      • Zheng J.
      • Ye Z.
      • Sluijs I.
      • Guevara M.
      • Huerta J.M.
      • et al.
      Association of plasma phospholipid n-3 and n-6 polyunsaturated fatty acids with type 2 diabetes: the EPIC-InterAct Case-Cohort Study.
      ). 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 (
      • Hu Y.
      • Li H.
      • Lu L.
      • Manichaikul A.
      • Zhu J.
      • Chen Y.D.
      • Sun L.
      • Liang S.
      • Siscovick D.S.
      • Steffen L.M.
      • et al.
      Genome-wide meta-analyses identify novel loci associated with n-3 and n-6 polyunsaturated fatty acid levels in Chinese and European-ancestry populations.
      ). Moreover, gene expression levels of FADS1/FADS2 varied between Chinese and other ethnic populations (
      • Xiang M.
      • Rahman M.A.
      • Ai H.
      • Li X.
      • Harbige L.S.
      Diet and gene expression: delta-5 and delta-6 desaturases in healthy Chinese and European subjects.
      ). 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 (
      • Tvrzicka E.
      • Kremmyda L.S.
      • Stankova B.
      • Zak A.
      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 (
      • Chen J.
      • Jiang Y.
      • Liang Y.
      • Tian X.
      • Peng C.
      • Ma K.Y.
      • Liu J.
      • Huang Y.
      • Chen Z.Y.
      DPA n-3, DPA n-6 and DHA improve lipoprotein profiles and aortic function in hamsters fed a high cholesterol diet.
      ). On the other hand, 20:4n-6 is known to be the precursor of both pro- or antiinflammatory eicosanoids (
      • Harris W.S.
      • Mozaffarian D.
      • Rimm E.
      • Kris-Etherton P.
      • Rudel L.L.
      • Appel L.J.
      • Engler M.M.
      • Engler M.B.
      • Sacks F.
      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 (
      • Suresh Y.
      • Das U.N.
      Protective action of arachidonic acid against alloxan-induced cytotoxicity and diabetes mellitus.
      ) and activate the PPAR-α pathway, which is critical in regulating the blood lipid profile (
      • Kliewer S.A.
      • Sundseth S.S.
      • Jones S.A.
      • Brown P.J.
      • Wisely G.B.
      • Koble C.S.
      • Devchand P.
      • Wahli W.
      • Willson T.M.
      • Lenhard J.M.
      • et al.
      Fatty acids and eicosanoids regulate gene expression through direct interactions with peroxisome proliferator-activated receptors alpha and gamma.
      ). 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. (
      • Vanhala M.
      • Saltevo J.
      • Soininen P.
      • Kautiainen H.
      • Kangas A.J.
      • Ala-Korpela M.
      • Mantyselka P.
      Serum omega-6 polyunsaturated fatty acids and the metabolic syndrome: a longitudinal population-based cohort study.
      ) but not the finding from a Swedish study that showed an inverse association between n-3 PUFAs and MetS (
      • Warensjö E.
      • Sundstrom J.
      • Lind L.
      • Vessby B.
      Factor analysis of fatty acids in serum lipids as a measure of dietary fat quality in relation to the metabolic syndrome in men.
      ). 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 (
      • Aung T.
      • Halsey J.
      • Kromhout D.
      • Gerstein H.C.
      • Marchioli R.
      • Tavazzi L.
      • Geleijnse J.M.
      • Rauch B.
      • Ness A.
      • Galan P.
      • et al.
      Associations of omega-3 fatty acid supplement use with cardiovascular disease risks: meta-analysis of 10 trials involving 77917 individuals.
      ). Additionally, in another systematic review and meta-analysis that included 18 independent cohorts and 540,184 individuals, Wu et al. (
      • Wu J.H.
      • Micha R.
      • Imamura F.
      • Pan A.
      • Biggs M.L.
      • Ajaz O.
      • Djousse L.
      • Hu F.B.
      • Mozaffarian D.
      Omega-3 fatty acids and incident type 2 diabetes: a systematic review and meta-analysis.
      ) 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 (
      • Forouhi N.G.
      • Imamura F.
      • Sharp S.J.
      • Koulman A.
      • Schulze M.B.
      • Zheng J.
      • Ye Z.
      • Sluijs I.
      • Guevara M.
      • Huerta J.M.
      • et al.
      Association of plasma phospholipid n-3 and n-6 polyunsaturated fatty acids with type 2 diabetes: the EPIC-InterAct Case-Cohort Study.
      ,
      • Patel P.S.
      • Sharp S.J.
      • Jansen E.
      • Luben R.N.
      • Khaw K.T.
      • Wareham N.J.
      • Forouhi N.G.
      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.
      ,
      • Kröger J.
      • Zietemann V.
      • Enzenbach C.
      • Weikert C.
      • Jansen E.H.
      • Doring F.
      • Joost H.G.
      • Boeing H.
      • Schulze M.B.
      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.
      ).
      To explore biological networks from a system-scale perspective and to identify clusters of highly correlated metabolites (
      • Langfelder P.
      • Horvath S.
      WGCNA: an R package for weighted correlation network analysis.
      ), 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 (
      • Zong G.
      • Sun Q.
      • Yu D.
      • Zhu J.
      • Sun L.
      • Ye X.
      • Li H.
      • Jin Q.
      • Zheng H.
      • Hu F.B.
      • et al.
      Dairy consumption, type 2 diabetes, and changes in cardiometabolic traits: a prospective cohort study of middle-aged and older Chinese in Beijing and Shanghai.
      ), who had a very low intake of trans-fat from partially hydrogenated oils (
      • Yu D.X.
      • Sun Q.
      • Ye X.W.
      • Pan A.
      • Zong G.
      • Zhou Y.H.
      • Li H.X.
      • Hu F.B.
      • Lin X.
      Erythrocyte trans-fatty acids, type 2 diabetes and cardiovascular risk factors in middle-aged and older Chinese individuals.
      ).
      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 (
      • Rauschert S.
      • Uhl O.
      • Koletzko B.
      • Hellmuth C.
      Metabolomic biomarkers for obesity in humans: a short review.
      ). In our previous study, acylcarnitines substantially improved the ability to predict incident type 2 diabetes beyond conventional risk factors (
      • Sun L.
      • Liang L.
      • Gao X.
      • Zhang H.
      • Yao P.
      • Hu Y.
      • Ma Y.
      • Wang F.
      • Jin Q.
      • Li H.
      • et al.
      Early prediction of developing type 2 diabetes by plasma acylcarnitines: a population-based study.
      ). 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 (
      • Guasch-Ferré M.
      • Zheng Y.
      • Ruiz-Canela M.
      • Hruby A.
      • Martinez-Gonzalez M.A.
      • Clish C.B.
      • Corella D.
      • Estruch R.
      • Ros E.
      • Fito M.
      • et al.
      Plasma acylcarnitines and risk of cardiovascular disease: effect of Mediterranean diet interventions.
      ). 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 (
      • Vega S.
      • Benito-Leon J.
      • Bermejo-Pareja F.
      • Medrano M.J.
      • Vega-Valderrama L.M.
      • Rodriguez C.
      • Louis E.D.
      Several factors influenced attrition in a population-based elderly cohort: neurological disorders in Central Spain Study.
      ,
      • Zunzunegui M.V.
      • Beland F.
      • Gutierrez-Cuadra P.
      Loss to follow-up in a longitudinal study on aging in Spain.
      ), 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 (
      • Arab L.
      Biomarkers of fat and fatty acid intake.
      ). 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.

      Supplementary Material

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