INTRODUCTION

The global prevalence of obesity and cardiometabolic diseases has increased progressively in the past decades to reach pandemic proportions ((1)). A major risk factor for cardiometabolic diseases is the so-called metabolic syndrome, which is defined as a constellation of metabolic disorders, including insulin resistance and impaired glucose tolerance, atherogenic dyslipidemia (high triglycerides [TG], total cholesterol [TC], and low-density lipoprotein cholesterol [LDL-C], and low high-density lipoprotein cholesterol [HDL-C]), and a proinflammatory state accompanied by excess visceral adipose tissue (VAT) mass ((2)).

Omega-6– and omega-3–derived lipid mediators have a prominent role in inflammation and cardiometabolic diseases as they possess pro- and anti-inflammatory properties ((3, 4)). Omega-6 and omega-3 polyunsaturated fatty acids (PUFAs) are essential fatty acids (i.e., they should be obtained from the diet) because they cannot be synthesized endogenously in humans ((5)). Upon dietary ingestion, circulating omega-6 and omega-3 PUFAs are used by almost all tissues and are oxidized by several enzymatic steps involving cyclooxygenase, lipoxygenase, or cytochrome P450 enzymes ((6)). These enzymatic reactions facilitate the conversion of PUFAs to oxylipins and other bioactive lipids, which are major mediators of the pro- and anti-inflammatory effects of PUFAs on human metabolism ((6)).

Curiously, circulating oxylipin levels might not necessarily mimic dietary PUFA intake, because oxylipin production depends on enzyme preference for specific PUFAs ((6)). Oxylipins are reported to be produced by various tissues, including white adipose tissue, liver, kidney, and ileum ((6)). Additionally, brown adipose tissue (BAT), a metabolically active thermogenic tissue present in adult humans ((7-9)), has been suggested to secrete omega-6 (e.g., 5-HETE, 5,6-EpETrE, 12,13-DiHOME) and omega-3 (e.g., 12-HEPE) oxylipins ((10-13)).

Although omega-6 oxylipins have proinflammatory, vasoconstrictor, and proliferative functions, all of which are involved in the progression of obesity and cardiometabolic diseases ((6, 14)), omega-3 oxylipins have the opposite effects ((6, 14)). Notably, plasma levels of the omega-6 oxylipins 9,10-EpODE and 9,10-EpOME are higher in women with type 2 diabetes than in nondiabetic peers ((15)). Likewise, plasma levels of omega-6 (8,9-DiHETrE) and omega-3 (5-HEPE and 10,11-DiHDPE) oxylipins are higher in men with hyperlipidemia than in normolipidemic men ((16)). These studies support a relationship between omega-6 oxylipins and cardiometabolic disorders; however, the relationship between both omega-6 and -3 oxylipins and cardiometabolic risk factors in young, relatively healthy individuals has not yet been established.

Here we investigated the associations of plasma levels of omega-6 and -3 PUFA–derived oxylipins with body composition and cardiometabolic risk factors in a well-phenotyped cohort of young adults.

METHODS

See online Supporting Information for an extensive description of all methods.

Study design and participants

A total of 136 participants, including 91 women and 45 men (age 22.1 [SD 2.2] years), were included in the present study (ACTIBATE; ClinicalTrials.gov ID: NCT02365129) ((17)). The ACTIBATE study encompassed multiple evaluation waves involving groups of 16 to 24 individuals every 2 to 3 weeks in October, November, and December of 2015 and 2016. The study was approved by the Ethics Committee on Human Research of the University of Granada (n° 924) and the Servicio Andaluz de Salud and was in accordance with the principles of the latest version of the Declaration of Helsinki. Written informed consent was provided from all participants before their enrollment. Inclusion criteria were the following: (1) nonsmoking, (2) sedentary (i.e., <20 minutes of moderate-to-vigorous physical activity on <3 days per week), (3) no acute or chronic illness, (4) no medication that might interfere with the assessments, and (5) not pregnant. Participants were requested to be rested, to abstain from moderate-to-vigorous physical activity, to use bus or car transportation, to refrain from stimulants and/or alcohol on the days of the measurements, and to have not performed any moderate exercise in the previous 24 hours or vigorous exercise in the previous 48 hours. A total of 133 participants were included in the main analyses; 3 participants were excluded because of blood collection issues.

Anthropometric and body composition measurements

Body weight was measured to the nearest 0.1 kg and height to the nearest 0.1 cm, using a model 799 scale and a stadiometer, respectively (both from Seca), without shoes and with light clothing. BMI was calculated from weight and height (kilograms/meters squared). Waist circumference was measured at the minimum perimeter, at the end of a normal expiration, with the arms relaxed on both sides of the body. When the minimum perimeter could not be detected, measurements were taken just above the umbilicus, in a horizontal plane. Waist circumference was measured twice with a plastic tape measure; the two measures were averaged for further analyses.

Lean, fat, and VAT masses were measured by dual-energy x-ray absorptiometry using a Discovery Wi device (Hologic Inc.) equipped with analysis software (APEX version 4.0.2). Lean and fat mass indices were expressed as kilograms/meters squared; fat mass was also expressed as a percentage of body weight. Individuals were categorized according to their BMI as having normal weight (BMI <25.0 kg/m2), overweight (BMI ≥25.0 kg/m2 and <30.0 kg/m2), or obesity (BMI ≥30.0 kg/m2) ((18)).

Assessment of 18F-fluorodeoxyglucose uptake by BAT

Briefly, the shivering threshold of each participant was determined following a personalized cooling protocol ((19)). Then 48 to 72 hours after the shivering threshold determination, participants were exposed to a 2-hour personalized cooling procedure at 3.8°C above their individual shivering threshold. After 1 hour of cold exposure, a bolus of ~185 MBq of 18F-fluorodeoxyglucose (18F-FDG) was intravenously injected, and the water temperature was increased by 1°C. If participants reported shivering, the water temperature was further increased by 1°C. After 2 hours of cold exposure, a static positron emission tomography/computed tomography (PET/CT; Siemens Biograph 16 PET-CT) scan was performed. CT acquisition was performed using a peak of 120 kV and PET acquisition with a scan time of 6 minutes per bed position. PET/CT images were obtained from the atlas (i.e., cervical vertebra 1) to approximately the midchest. BAT-related outcomes were calculated as described ((20-22)).

Blood sample collection and determination of cardiometabolic risk factors

Blood was collected after an overnight (10 hours) fast, between 8:00 and 9:00 am, and at room temperature (20°C to 24°C). Blood was drawn from the antecubital vein and was immediately centrifuged to obtain serum (obtained with Vacutainer SST II Advance tubes, VWR International, LLC) and plasma (obtained with Vacutainer Hemogard tubes, containing the K2 potassium salt of ETDA as anticoagulant). Samples were aliquoted and stored at −80°C.

Traditional cardiometabolic risk factors and inflammatory markers were measured in serum (glucose, insulin, cortisol, TC, HDL-C, TG, apolipoproteins A and B, glutamic pyruvic transaminase [GTP], gamma-glutamyl transferase [GGT], alkaline phosphatase [ALP], C-reactive protein, complement C3, complement C4, leptin, adiponectin, interleukin [IL]-2, IL-4, IL-6, IL-7, IL-8, IL-10, IL-17a, interferon gamma [IFNɣ], and tumor necrosis factor-alpha [TNFα]).

Metabolic syndrome prevalence was calculated following the National Cholesterol Education Program Adult Treatment Panel III (ATP III) ((23)) and International Diabetes Federation (IDF) classifications ((24)). Insulin sensitivity was estimated via the homeostatic model assessment of insulin resistance index (HOMA-IR) ((25)), and the fatty liver index was determined as a proxy of hepatic steatosis ((26)).

Determination of plasma oxylipins

Oxylipins were determined by liquid chromatography-tandem mass spectrometry (LC-MS/MS) ((27)). The LC-MS/MS protocol enabled the relative quantitation of 83 oxylipins from the conversion of the omega-6 PUFAs linoleic acid (LA), dihomo-γ-linolenic acid (DGLA), and arachidonic acid (AA), as well as the omega-3 PUFAs α-linolenic acid (ALA), eicosapentaenoic acid (EPA), and docosahexaenoic acid (DHA). The oxylipins that can be detected by this method are listed in Supporting Information Table S1. Of the 50 oxylipins detected, 40 showed a low analytical variability with relative standard in quality control (QCRSD) ≤ 15%, and 10 showed a moderate variability between 15% < QCRSD ≤ 40% (Supporting Information Table S1).

Statistical analyses

The baseline characteristics and outcomes of the study participants were expressed as mean (SD). The normal distribution assumption was tested using the Shapiro–Wilk test, visual histograms, and Q–Q plots. Non-normally distributed variables (traditional cardiometabolic and inflammatory markers and oxylipins) were log10-transformed before further analysis. We performed all the analyses divided by men and women, and similar patterns were observed. Moreover, we studied whether significant interactions were presented between the plasma oxylipin levels and body composition and cardiometabolic risk parameters using linear regression models, and no sex interactions were observed (all p ≥ 0.05). Thus, data of men and women were analyzed together.

We conducted Pearson correlation analyses to examine the relationship between plasma levels of oxylipins and body composition and cardiometabolic risk parameters. All p values were corrected by the two-stage step-up procedure of Benjamini, Krieger, and Yekutieli for multiple comparisons by controlling the false discovery rate (FDR) ((28)). Plasma omega-6 and omega-3 oxylipins were both weakly correlated with PUFA, fish, and shellfish intake (data not shown). We therefore performed partial correlations of plasma levels of oxylipins with body composition and cardiometabolic risk parameters adjusting for dietary energy, PUFA, fish, and shellfish intake. Pearson correlation analyses were also carried out between plasma levels of oxylipins and traditional inflammatory parameters. As secondary analyses, we examined the relationship between body composition and cardiometabolic risk factors. All correlation analyses and plots were designed using the “corrplot” package in R software version 3.6.0 (R Foundation for Statistical Computing). Forward stepwise regression was performed using fat mass percentage and VAT mass as dependent outcomes in separate models. The measured oxylipins persisting after FDR correction (i.e., 15-HETrE, 5-HETE, 14,15-EpETrE, 8,12-iso-iPF2α-VI, 14,15-DiHETE, 17,18-DiHETE, and 19,20-DiHDPA), together with the traditional cardiometabolic risk factors HOMA-IR, GTP, GGT, ALP, TC, HDL-C, TG, cortisol, adiponectin, and leptin, were introduced as predictors using a forward stepwise procedure, which introduces the predicting components step-by-step into the model (if p < 0.05) according to the strength of their association with the dependent outcome. We repeated the same analyses including plasma levels of oxylipins and traditional pro- and anti-inflammatory markers as predictors of fat mass percentage and VAT mass. The forward stepwise regression was performed using the SPSS Statistics version 25.0 (IBM Corp.).

Differences in plasma levels of oxylipins across BMI categories (normal weight, overweight, and obesity) were assessed with metabolic pathway analyses using Cytoscape software version 3.7.0 (Cytoscape Consortium) ((29)). We calculated a fold change for each oxylipin as the ratio between the oxylipin level in individuals with obesity divided by the oxylipin level in normal-weight individuals, as well as between obesity and overweight and between overweight and normal weight. Independent t test analyses were performed for between-group comparisons. The level of significance was set at p < 0.05, using SPSS version 25.0.

RESULTS

The descriptive characteristics of the participants are presented in Table 1. A total of 50 of the 83 detectable PUFA-derived omega-6 (n = 30) and omega-3 (n = 20) oxylipins, including their precursors, were detected in fasting plasma samples (Supporting Information Table S1). Baseline plasma levels of each oxylipin measured in the participants are listed in Supporting Information Table S2.

TABLE 1. Characteristics of the participants n Total n Men n Women Age (y) 136 22.1 (2.2) 45 22.3 (2.3) 91 21.9 (2.2) Body composition BMI (kg/m2) 136 24.9 (4.6) 45 26.8 (5.5) 91 23.9 (3.7) Lean body mass (kg) 136 41.8 (9.7) 45 52.8 (7.2) 91 36.3 (5.0) Lean mass index (kg/m2) 136 14.7 (2.4) 45 17.2 (2.1) 91 13.5 (1.4) Fat body mass (kg) 136 24.7 (8.8) 45 24.8 (11.0) 91 24.6 (7.5) Fat mass (%) 136 35.5 (7.6) 45 29.7 (7.6) 91 38.3 (5.9) Fat mass index (kg/m2) 136 8.8 (3.0) 45 8.1 (3.6) 91 9.1 (2.7) Visceral adipose tissue mass (g) 136 336.4 (174.1) 45 417.9 (175.9) 91 296.1 (159.2) Waist circumference (cm) 130 81.0 (4.6) 43 89.9 (15.2) 87 76.5 (10.5) BAT BAT volume (mL) 131 68.5 (57.4) 42 78.9 (66.0) 89 63.6 (52.6) BAT metabolic activity 131 332.9 (328.7) 42 326.8 (327.8) 89 335.8 (331.0) BAT SUVmean 131 3.7 (1.9) 42 3.2 (1.3) 89 4.0 (2.1) BAT SUVpeak 131 11.1 (8.2) 42 9.9 (7.3) 89 11.6 (8.6) BAT SUVmax 131 12.2 (9.0) 42 10.8 (8.1) 89 12.8 (9.4) Cardiometabolic risk factors Metabolic syndrome ATP III 128 0.5 (0.9) 42 1.0 (1.3) 86 0.2 (0.5) Metabolic syndrome IDF 128 0.7 (1.1) 42 1.1 (1.5) 86 0.5 (0.7) Fatty liver index 132 20.4 (25.0) 43 36.9 (32.0) 89 12.5 (15.7) GTP (IU/L) 131 19.0 (17.5) 43 28.4 (26.8) 88 14.4 (6.7) GGT (IU/L) 131 19.8 (20.0) 43 29.9 (29.8) 88 14.9 (9.9) ALP (IU/L) 132 71.3 (18.5) 43 79.3 (19.4) 89 67.5 (16.9) C-reactive protein (mg/L) 132 2.4 (3.4) 43 2.1 (2.3) 89 2.5 (3.8) C3 (mg/dL) 132 137.4 (23.8) 43 143.0 (26.2) 89 134.7 (22.2) C4 (mg/dL) 132 28.7 (8.8) 43 30.3 (9.9) 89 27.9 (8.1) Glucose (mg/dL) 132 87.6 (6.6) 43 88.9 (7.4) 89 87.0 (6.1) Insulin (µIU/mL) 132 8.3 (4.9) 43 9.1 (6.4) 89 8.0 (4.0) HOMA-IR 132 1.8 (1.2) 43 2.1 (1.6) 89 1.7 (1.0) Insulin glucose ratio 132 14.1 (7.0) 43 14.8 (8.8) 89 13.8 (6.0) Total cholesterol (mg/dL) 132 165.1 (32.2) 43 160.1 (30.9) 89 167.6 (32.7) HDL-C (mg/dL) 132 52.8 (11.0) 43 46.0 (7.4) 89 56.0 (11.0) LDL-C (mg/dL) 132 96.0 (25.3) 43 96.5 (26.2) 89 95.8 (25.0) APOA1 (mg/dL) 113 144.7 (27.5) 37 130.0 (16.8) 76 151.9 (28.9) APOB (mg/dL) 113 69.7 (19.9) 37 72.7 (24.4) 76 68.3 (17.3) Triglycerides (mg/dL) 132 82.5 (44.6) 43 88.2 (47.2) 89 79.7 (43.2) Leptin (µg/L) 129 6.2 (4.4) 42 4.4 (4.0) 87 7.1 (4.3) Adiponectin (mg/L) 127 11.4 (7.9) 42 7.7 (5.2) 85 13.3 (8.3) Systolic blood pressure (mmHg) 134 116.7 (11.6) 44 125.3 (10.9) 90 112.5 (9.5) Diastolic blood pressure (mmHg) 134 70.9 (7.6) 44 72.2 (9.2) 90 70.3 (6.7) Inflammatory parameters IL-2 (pg/mL) 109 2.4 (1.4) 37 2.0 (1.3) 72 2.6 (1.5) IL-4 (pg/mL) 109 13.1 (9.7) 37 11.7 (9.5) 72 13.8 (9.7) IL-6 (pg/mL) 109 1.6 (1.6) 37 1.6 (1.8) 72 1.6 (1.5) IL-7 (pg/mL) 109 4.0 (2.8) 37 3.3 (2.2) 72 4.4 (3.0) IL-8 (pg/mL) 109 1.6 (0.8) 37 1.5 (0.9) 72 1.6 (0.8) IL-10 (pg/mL) 109 2.9 (3.6) 37 2.3 (2.1) 72 3.2 (4.1) IFN-γ (pg/mL) 109 12.9 (5.4) 37 11.5 (5.5) 72 13.5 (5.3) TNFα (pg/mL) 109 1.8 (1.1) 37 1.5 (0.7) 72 1.9 (1.2) Data are presented as mean (SD). ALP, alkaline phosphatase; APOA1, apolipoprotein A1; APOB, apolipoprotein B; ATP III, Adult Treatment Panel III; BAT, brown adipose tissue; C3, complement C3; C4, complement C4; GGT, gamma-glutamyl transferase; GTP, glutamic pyruvic transaminase; HDL-C, high-density lipoprotein cholesterol; HOMA-IR, homeostatic model assessment of insulin resistance index; IDF, International Diabetes Federation; IFN-γ, interferon gamma; IL, interleukin; LDL-C, low-density lipoprotein cholesterol; SUV, standardized uptake value; TNFα, tumor necrosis factor-alpha. Plasma levels of oxylipins are related to body composition parameters

At the first step, we investigated the relationship between the plasma levels of omega-6 and omega-3 oxylipins and body composition parameters. A total of 12 of the 30 detected omega-6 oxylipins correlated positively with fat mass percentage (all r ≥ 0.18; p < 0.05; Figure 1A), whereas 3 of the 20 detected omega-3 oxylipins correlated negatively with fat mass percentage (all r ≤ −0.29; p < 0.05; Figure 1B). Similar correlation coefficients were obtained between the aforementioned oxylipins and fat mass index or VAT mass (all r ≤ −0.23; p < 0.05; Figure 1A,B). After FDR correction, four omega-6 oxylipins (15-HeTrE, 5-HETE, 14,15-EpETrE, and 8,12-iso-iPF2α-VI) and three omega-3 oxylipins (14,15-DIHETE, 17,18-DIHETE, and 19,20-DiHDPA) remained significantly correlated with adiposity parameters (fat mass percentage, fat mass index, and VAT mass; all p < 0.05; Figure 1A,B).

Pearson correlation analyses between plasma levels of oxylipins (omega-6 and omega-3) with body composition and brown adipose tissue (BAT) parameters in young adults (n = 133). Every box represents a significant correlation coefficient (all p10-transformed prior to data analysis. *Significant after false discovery rate corrections. SUV, standardized uptake value. The abbreviations used for each oxylipin are detailed in Supporting Information Table S1

Given the clear link between plasma oxylipins and dietary PUFA intake ((30)), we next conducted partial correlation analysis adjusting for dietary energy, PUFA, fish, and shellfish intake as cofounders. We found that the significant correlations between the set of four omega-6 and three omega-3 oxylipins with adiposity parameters remained significant (Supporting Information Figure S1), suggesting that these correlations are not affected by dietary energy, PUFA, fish, and shellfish intake.

Because BAT can be a source of oxylipins ((10-13)) and has been linked to a healthier cardiometabolic profile in humans ((31)), we explored whether plasma oxylipin levels correlated with BAT volume and activity. After FDR correction, plasma levels of the omega-3 oxylipins 14,15-DiHETE, 17,18-DiHETE, 19,20-EpDPE, and 19,20-DiHDPA correlated negatively with BAT volume and activity (BAT metabolic activity, SUVmean, SUVpeak, and SUVmax; all r ≤ −0.23; p < 0.05; Figure 1B). By contrast, no significant correlations were found between plasma omega-6 oxylipins and BAT volume or activity (Figure 1A). Of note, the associations remained unaltered when dietary intake (i.e., energy, PUFA, fish, and shellfish intake; Supporting Information Figure S1) and the seasonal variation were included as cofounders, the latter of which is known to affect the determination of BAT volume and activity in humans ((31, 32)) (data not shown).

Plasma levels of oxylipins are related to cardiometabolic risk factors

We next examined whether plasma oxylipin levels were related to traditional cardiometabolic risk factors assessed in serum. Notably, the set of four omega-6 oxylipins that correlated positively with adiposity parameters (15-HeTrE, 5-HETE, 14,15-EpETrE, and 8,12-iso-iPF2α-VI) also correlated positively with an adverse cardiometabolic profile (i.e., higher prevalence of metabolic syndrome, higher fatty liver index, serum glucose, and serum lipid parameters [TC or TG]; r ≥ 0.18; p < 0.05; Figure 2A). By contrast, the set of three omega-3 oxylipins that correlated negatively with adiposity parameters (14,15-DIHETE, 17,18-DIHETE, and 19,20-DiHDPA) correlated with a favorable cardiometabolic profile, specifically lower prevalence of metabolic syndrome, fatty liver index, serum glucose, and serum lipid parameters (TC or TG) (p < 0.05; Figure 2B). The correlations remained significant after adjusting for dietary energy, PUFA, fish, and shellfish intake (Supporting Information Figure S2), although they were not significant after correcting for FDR.

Pearson correlation analyses between plasma levels of oxylipins (omega-6 and omega-3) with cardiometabolic risk factors in young adults (n = 133). Every box represents a significant correlation coefficient (all p10-transformed prior to data analysis. *Significant after false discovery rate corrections. ALP, alkaline phosphatase; APOA1, apolipoprotein A1; APOB, apolipoprotein B; ATP III, National Cholesterol Education Program Adult Treatment Panel III; GGT, gamma-glutamyl transferase; GTP, glutamic pyruvic transaminase; HDL-C, high-density lipoprotein cholesterol; HOMA-IR, homeostatic model assessment of insulin resistance index; IDF, International Diabetes Federation; LDL-C, low-density lipoprotein cholesterol. The abbreviations used for each oxylipin are detailed in Supporting Information Table S1

Based on the pro- and anti-inflammatory effects of the omega-6 and omega-3 oxylipins, respectively, we expected to observe significant correlations with traditional pro- and anti-inflammatory markers (IL-2, IL-4, IL-6, IL-7, IL-8, IL-10, IL-17a, IFNɣ, and TNFα). We found that plasma levels of three of the four omega-6 oxylipins that correlated positively with adiposity parameters and an adverse cardiometabolic profile (15-HeTrE, 5-HETE, and 8,12-iso-iPF2α-VI) also correlated positively with the proinflammatory markers IL-17, IFNɣ, and TNFα and negatively with the anti-inflammatory marker IL-4. Surprisingly, we found that the plasma levels of the three omega-3 oxylipins that correlated negatively with adiposity parameters (14,15-DIHETE, 17,18-DIHETE, and 19,20-DiHDPA) did not correlate with traditional pro- and anti-inflammatory makers (all r ≥ 0.19; p < 0.05; Supporting Information Figure S3B).

Given these findings, we used stepwise linear regression models to assess whether plasma levels of oxylipins were better predictors of adiposity than traditional cardiometabolic risk factors or established pro- and anti-inflammatory markers. We selected the four omega-6 and 3 omega-3 oxylipins that were related to adiposity and cardiometabolic risk parameters (Figures 1, 2, Figure 3). The panel of seven oxylipins was included together with classical cardiometabolic risk factors as independent outcomes, and fat mass percentage or VAT mass was included as dependent outcomes in separate models (Supporting Information Table S3). We repeated these analyses including the panel of oxylipins together with established pro- and anti-inflammatory markers as independent outcomes (Table 2). Overall, the traditional cardiometabolic risk factors, principally leptin and HOMA-IR, were found to be better predictors of adiposity (fat mass percentage and VAT mass) than the panel of seven oxylipins (Supporting Information Table S3). However, the plasma levels of the omega-6 oxylipins 5-HETE and 15-HETrE and the plasma levels of 14,15-DiHETE and 17,18-DiHETE (omega-3 oxylipins) were better predictors of adiposity than established pro- and anti-inflammatory markers (e.g., IL-6, TNFα) (Table 2), predicting up to 27.2% and 22.9% of the explained variance of the fat mass percentage and VAT mass, respectively (Table 2). The stepwise linear regression models were repeated with the inclusion of all oxylipins, resulting in similar results to th