Clusters of fatty acids in the serum triacylglyceride fraction associate with the disorders of type 2 diabetes
2018; Elsevier BV; Volume: 59; Issue: 9 Linguagem: Inglês
10.1194/jlr.p084970
ISSN1539-7262
AutoresLuke Johnston, Zhen Liu, Ravi Retnakaran, Bernard Zinman, Adria Giacca, Stewart B. Harris, Richard P. Bazinet, Anthony J. Hanley,
Tópico(s)Diabetes, Cardiovascular Risks, and Lipoproteins
ResumoOur aim was to examine longitudinal associations of triacylglyceride fatty acid (TGFA) composition with insulin sensitivity (IS) and β-cell function. Adults at risk for T2D (n = 477) had glucose and insulin measured from a glucose challenge at three time points over 6 years. The outcome variables Matsuda insulin sensitivity index, homeostatic model of assessment 2–percent sensitivity (HOMA2-%S), Insulinogenic Index over HOMA-IR (IGI/IR), and Insulin Secretion-Sensitivity Index-2 were computed from the glucose challenge. Gas chromatography quantified TGFA composition from the baseline. We used adjusted generalized estimating equation (GEE) models and partial least squares (PLS) regression for the analysis. In adjusted GEE models, four TGFAs (14:0, 16:0, 14:1n-7, and 16:1n-7 as mol%) had strong negative associations with IS, whereas others (e.g., 18:1n-7, 18:1n-9, 20:2n-6, and 20:5n-3) had strong positive associations. Few associations were seen for β-cell function, except for 16:0, 18:1n-7, and 20:2n-6. PLS analysis indicated four TGFAs (14:0, 16:0, 14:1n-7, and 16:1n-7) that clustered together and strongly related with lower IS. These four TGFAs also correlated highly (r > 0.4) with clinically measured triacylglyceride. We found that higher proportions of a cluster of four TGFAs strongly related with lower IS as well as hypertriglyceridemia, suggesting that only a few FAs within the TGFA composition may primarily explain lipids' role in glucose dysregulation. Our aim was to examine longitudinal associations of triacylglyceride fatty acid (TGFA) composition with insulin sensitivity (IS) and β-cell function. Adults at risk for T2D (n = 477) had glucose and insulin measured from a glucose challenge at three time points over 6 years. The outcome variables Matsuda insulin sensitivity index, homeostatic model of assessment 2–percent sensitivity (HOMA2-%S), Insulinogenic Index over HOMA-IR (IGI/IR), and Insulin Secretion-Sensitivity Index-2 were computed from the glucose challenge. Gas chromatography quantified TGFA composition from the baseline. We used adjusted generalized estimating equation (GEE) models and partial least squares (PLS) regression for the analysis. In adjusted GEE models, four TGFAs (14:0, 16:0, 14:1n-7, and 16:1n-7 as mol%) had strong negative associations with IS, whereas others (e.g., 18:1n-7, 18:1n-9, 20:2n-6, and 20:5n-3) had strong positive associations. Few associations were seen for β-cell function, except for 16:0, 18:1n-7, and 20:2n-6. PLS analysis indicated four TGFAs (14:0, 16:0, 14:1n-7, and 16:1n-7) that clustered together and strongly related with lower IS. These four TGFAs also correlated highly (r > 0.4) with clinically measured triacylglyceride. We found that higher proportions of a cluster of four TGFAs strongly related with lower IS as well as hypertriglyceridemia, suggesting that only a few FAs within the TGFA composition may primarily explain lipids' role in glucose dysregulation. Hypertriglyceridemia is a well-described metabolic disorder resulting in negative health outcomes (1.Chehade J.M. Gladysz M. Mooradian A.D. Dyslipidemia in type 2 diabetes: prevalence, pathophysiology, and management.Drugs. 2013; 73: 327-339Crossref PubMed Scopus (164) Google Scholar). It is a risk factor for CVD (2.D'Agostino R.B. Hamman R.F. Karter A.J. Mykkanen L. Wagenknecht L.E. Haffner S.M. Cardiovascular disease risk factors predict the development of type 2 diabetes: The Insulin Resistance Atherosclerosis Study.Diabetes Care. 2004; 27: 2234-2240Crossref PubMed Scopus (109) Google Scholar, 3.Vergès B. 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However, clinically measured TG is limited, as it represents the full FA spectrum within the TG fraction as a summary measure. There is increasing appreciation for the importance of specific FA composition profiles in different plasma fractions on various health outcomes (7.Forouhi N.G. Koulman A. Sharp S.J. Imamura F. Kröger J. Schulze M.B. Crowe F.L. Huerta J.M. Guevara M. Beulens J.W.J. et al.Differences in the prospective association between individual plasma phospholipid saturated fatty acids and incident type 2 diabetes: the EPIC-InterAct Case-Cohort Study.Lancet Diabetes Endocrinol. 2014; 2: 810-818Abstract Full Text Full Text PDF PubMed Scopus (366) Google Scholar, 8.Ma W. Wu J.H.Y. Wang Q. Lemaitre R.N. Mukamal K.J. Djoussé L. King I.B. Song X. Biggs M.L. Delaney J.A. et al.Prospective association of fatty acids in the de novo lipogenesis pathway with risk of type 2 diabetes: The Cardiovascular Health Study.Am. J. Clin. 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O'Donnell C.J. et al.Lipid profiling identifies a triacylglycerol signature of insulin resistance and improves diabetes prediction in humans.J. Clin. Invest. 2011; 121: 1402-1411Crossref PubMed Scopus (450) Google Scholar, 12.Lankinen M.A. Stančáková A. Uusitupa M. Ågren J. Pihlajamäki J. Kuusisto J. Schwab U. Laakso M. Plasma fatty acids as predictors of glycaemia and type 2 diabetes.Diabetologia. 2015; 58: 2533-2544Crossref PubMed Scopus (73) Google Scholar). The interaction between TG and insulin sensitivity (IS) is complex and involves components of a feedback system (3.Vergès B. Pathophysiology of diabetic dyslipidaemia: where are we?.Diabetologia. 2015; 58: 886-899Crossref PubMed Scopus (343) Google Scholar). Greater resistance to insulin in both the liver and muscle may result in greater production of TG and secretion of lipoproteins that transport TG (13.Yu S.S. Castillo D.C. Courville A.B. Sumner A.E. The triglyceride paradox in people of African descent.Metab. Syndr. Relat. Disord. 2012; 10: 77-82Crossref PubMed Scopus (57) Google Scholar). Likewise, greater TG may contribute to metabolic dysfunction and lipotoxicity in various tissues, affecting IS, and thus continue the cycle (3.Vergès B. Pathophysiology of diabetic dyslipidaemia: where are we?.Diabetologia. 2015; 58: 886-899Crossref PubMed Scopus (343) Google Scholar). Given the complexity and temporal nature of the relationship, long-term studies with multiple data collection time points are paramount to better understanding the underlying biology and subsequent risk. Although several studies have documented prospective associations of hypertriglyceridemia with incident T2D (2.D'Agostino R.B. Hamman R.F. Karter A.J. Mykkanen L. Wagenknecht L.E. Haffner S.M. Cardiovascular disease risk factors predict the development of type 2 diabetes: The Insulin Resistance Atherosclerosis Study.Diabetes Care. 2004; 27: 2234-2240Crossref PubMed Scopus (109) Google Scholar, 14.Chien K. Cai T. Hsu H. Su T. Chang W. Chen M. Lee Y. Hu F.B. A prediction model for type 2 diabetes risk among Chinese people.Diabetologia. 2009; 52: 443-450Crossref PubMed Scopus (119) Google Scholar, 15.Schulze M.B. Weikert C. Pischon T. Bergmann M.M. Al-Hasani H. Schleicher E. Fritsche A. Haring H-U. Boeing H. Joost H-G. Use of multiple metabolic and genetic markers to improve the prediction of type 2 diabetes: The EPIC-Potsdam Study.Diabetes Care. 2009; 32: 2116-2119Crossref PubMed Scopus (112) Google Scholar), only a limited number of longitudinal studies (11.Rhee E.P. Cheng S. Larson M.G. Walford G.A. Lewis G.D. McCabe E. Yang E. Farrell L. Fox C.S. O'Donnell C.J. et al.Lipid profiling identifies a triacylglycerol signature of insulin resistance and improves diabetes prediction in humans.J. Clin. Invest. 2011; 121: 1402-1411Crossref PubMed Scopus (450) Google Scholar, 12.Lankinen M.A. Stančáková A. Uusitupa M. Ågren J. Pihlajamäki J. Kuusisto J. Schwab U. Laakso M. Plasma fatty acids as predictors of glycaemia and type 2 diabetes.Diabetologia. 2015; 58: 2533-2544Crossref PubMed Scopus (73) Google Scholar) have examined the relationship between TG and its composition with the pathophysiological factors underlying T2D, particularly β-cell function. Our objective was to examine the longitudinal role of the specific composition of the serum TG fraction on the oral glucose tolerance test (OGTT)-derived measures of IS and β-cell function compared with clinically measured TG in a Canadian population at risk for T2D. Recruitment for the baseline visit of the Prospective Metabolism and Islet Cell Evaluation (PROMISE) cohort took place between 2004 and 2006 in London and Toronto, Canada. Individuals were selected to participate if they met the eligibility criteria of having one or more risk factors for T2D, including obesity, hypertension, family history of diabetes, and/or a history of gestational diabetes or birth of a macrosomic infant. A total of 736 individuals attended the baseline visit. Subsequent examinations occurred every 3 years, with data from three examination visits available for the present analysis (2004–2006, 2007–2009, and 2010–2013). The present study used data on participants who did not have T2D at baseline, who returned for one or more of the follow-up examinations, and who had samples available for FA measurements (n = 477; see the CONSORT diagram in supplemental Figure S1). Metabolic characterization, anthropometric measurements, and questionnaires on lifestyle and sociodemographics were administered at each examination visit. Research ethics approval was obtained from Mount Sinai Hospital and the University of Western Ontario, and all participants provided written informed consent. Data collection methods were standardized across the two centers, and research nurses were centrally trained. After 8–12 h of overnight fasting, participants completed a 75 g OGTT at each examination visit, with blood samples taken at fasting, 30 min, and 2 h postglucose load. Samples were subsequently processed and frozen at −70°C. Alanine aminotransferase (ALT) was measured using standard laboratory procedures. Cholesterol, HDL, and clinically measured TG were measured using Roche Modular's enzymatic colorimetric tests (Mississauga, ON). Both insulin and glucose were measured from OGTT blood samples at fasting, 30 min, and 2 h time points. Specific insulin was measured with the Elecsys 1010 (Roche Diagnostics, Basel, Switzerland) immunoassay analyzer and electrochemiluminescence immunoassay, which shows 0.05% cross-reactivity to intact human proinsulin and the Des 31,32 circulating split form (Linco Res., Inc) and has a coefficient of variation (CV) of 9.3%. Glucose was determined using an enzymatic hexokinase method (Roche Modular, Roche Diagnostics) with a detection range of 0.11–41.6 mmol/l, an interassay CV of <1.1%, and an intraassay CV of < 1.9%. All assays were performed at the Banting and Best Diabetes Centre Core Lab at Mt. Sinai Hospital. Triacylglyceride FA (TGFA) composition was quantified using stored fasting serum samples from the baseline visit, which had been frozen at −70°C for 4–6 years and had not been exposed to any freeze–thaw cycles. Serum FAs have been documented to be stable at these temperatures for up to 10 years (16.Matthan N.R. Ip B. Resteghini N. Ausman L.M. Lichtenstein A.H. Long-term fatty acid stability in human serum cholesteryl ester, triglyceride, and phospholipid fractions.J. Lipid Res. 2010; 51: 2826-2832Abstract Full Text Full Text PDF PubMed Scopus (85) Google Scholar). A known amount of triheptadecanoin (17:0; Nu-Chek Prep, Inc., Elysian, MN) was added as an internal standard prior to extracting total lipids according to the method of Folch et al. (17.Folch J. Lees M. Sloane Stanley G.H. A simple method for the isolation and purification of total lipides from animal tissues.J. Biol. Chem. 1957; 226: 497-509Abstract Full Text PDF PubMed Google Scholar). Each serum lipid fraction (NEFAs, cholesteryl ester, phospholipid, and TG) was isolated using TLC. FA methyl esters were separated and quantified using a Varian-430 gas chromatograph (Varian, Lake Forest, CA) equipped with a Varian Factor Four capillary column and a flame ionization detector. FA concentrations (nmol/ml) were calculated by proportional comparison of gas chromatography peak areas to that of the internal standards (18.Nishi S. Kendall C.W.C. Gascoyne A-M. Bazinet R.P. Bashyam B. Lapsley K.G. Augustin L.S.A. Sievenpiper J.L. Jenkins D.J.A. Effect of almond consumption on the serum fatty acid profile: a dose-response study.Br. J. Nutr. 2014; 112: 1137-1146Crossref PubMed Scopus (32) Google Scholar). There were 22 FAs measured in the TGFA fraction. Findings for other lipid fractions in this cohort are reported separately (see ref. 9.Johnston L.W. Harris S.B. Retnakaran R. Zinman B. Giacca A. Liu Z. Bazinet R.P. Hanley A.J. Longitudinal associations of phospholipid and cholesteryl ester fatty acids with disorders underlying diabetes.J. Clin. Endocrinol. Metab. 2016; 101: 2536-2544Crossref PubMed Scopus (11) Google Scholar for the phospholipid and cholesteryl ester fraction and ref. 10.Johnston L.W. Harris S.B. Retnakaran R. Giacca A. Liu Z. Bazinet R.P. Hanley A.J. Association of non-esterified fatty acid composition with insulin sensitivity and beta cell function in the Prospective Metabolism and Islet Cell Evaluation (PROMISE) cohort.Diabetologia. 2017; 61: 821-830Crossref PubMed Scopus (29) Google Scholar for the NEFA fraction analysis). Height, weight, and waist circumference (WC) were measured at all clinic examinations using standard procedures. WC was measured at the natural waist, defined as the narrowest part of the torso between the umbilicus and the xiphoid process. BMI was calculated by dividing weight (kilograms) by height (meters) squared. Questionnaires administered at each examination determined sociodemographics. A version of the Modifiable Activity Questionnaire (MAQ) (19.Kriska A.M. Knowler W.C. LaPorte R.E. Drash A.L. Wing R.R. Blair S.N. Bennett P.H. Kuller L.H. 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Insulin sensitivity indices obtained from oral glucose tolerance testing: comparison with the euglycemic insulin clamp.Diabetes Care. 1999; 22: 1462-1470Crossref PubMed Scopus (4355) Google Scholar) and homeostatic model of assessment 2–percent sensitivity (HOMA2-%S) (21.Levy J.C. Matthews D.R. Hermans M.P. Correct homeostasis model assessment (HOMA) evaluation uses the computer program.Diabetes Care. 1998; 21: 2191-2192Crossref PubMed Scopus (1538) Google Scholar) using the HOMA2 Calculator. HOMA largely reflects hepatic IS, whereas ISI reflects whole-body IS (22.Abdul-Ghani M.A. Matsuda M. Balas B. DeFronzo R.A. Muscle and liver insulin resistance indexes derived from the oral glucose tolerance test.Diabetes Care. 2007; 30: 89-94Crossref PubMed Scopus (376) Google Scholar). Beta-cell function was assessed using the Insulinogenic Index (23.Wareham N.J. Phillips D.I. Byrne C.D. Hales C.N. The 30 minute insulin incremental response in an oral glucose tolerance test as a measure of insulin secretion.Diabet. Med. 1995; 12: 931Crossref PubMed Scopus (165) Google Scholar) over HOMA-IR (24.Matthews D.R. Hosker J.P. Rudenski A.S. Naylor B.A. Treacher D.F. Turner R.C. Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man.Diabetologia. 1985; 28: 412-419Crossref PubMed Scopus (25570) Google Scholar) (IGI/IR) and the Insulin Secretion-Sensitivity Index-2 (ISSI-2) (25.Retnakaran R. Qi Y. Goran M.I. Hamilton J.K. Evaluation of proposed oral disposition index measures in relation to the actual disposition index.Diabet. Med. 2009; 26: 1198-1203Crossref PubMed Scopus (214) Google Scholar). IGI/IR is a measure of the early phase of insulin secretion, whereas ISSI-2 is analogous to the disposition index (but is calculated using OGTT values). Each index has been validated against gold-standard measures (20.Matsuda M. DeFronzo R.A. Insulin sensitivity indices obtained from oral glucose tolerance testing: comparison with the euglycemic insulin clamp.Diabetes Care. 1999; 22: 1462-1470Crossref PubMed Scopus (4355) Google Scholar, 24.Matthews D.R. Hosker J.P. Rudenski A.S. Naylor B.A. Treacher D.F. Turner R.C. Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man.Diabetologia. 1985; 28: 412-419Crossref PubMed Scopus (25570) Google Scholar, 25.Retnakaran R. Qi Y. Goran M.I. Hamilton J.K. Evaluation of proposed oral disposition index measures in relation to the actual disposition index.Diabet. Med. 2009; 26: 1198-1203Crossref PubMed Scopus (214) Google Scholar, 26.Hermans M.P. Levy J.C. Morris R.J. Turner R.C. Comparison of insulin sensitivity tests across a range of glucose tolerance from normal to diabetes.Diabetologia. 1999; 42: 678-687Crossref PubMed Scopus (253) Google Scholar). The primary outcome variables for this analysis were HOMA2-%S, ISI, IGI/IR, and ISSI-2, which were log-transformed for the statistical modeling. The primary predictor variables for this analysis were 22 individual TGFAs included as either mol% of the total fraction or as a concentration (nmol/ml). Clinically measured TG was also included as a primary predictor to allow us to test the hypothesis that specific TGFAs better predicted outcomes compared with clinical TG. Pearson correlation coefficients were computed to assess the relationships of individual TGFAs with other continuous variables. Within-TGFA composition correlations were also computed and subsequently analyzed using hierarchical clustering. Generalized estimating equation (GEE) models (27.Zeger S.L. Liang K.Y. Longitudinal data analysis for discrete and continuous outcomes.Biometrics. 1986; 42: 121-130Crossref PubMed Scopus (6279) Google Scholar) were used in the primary analysis to determine the longitudinal associations between the outcome variables and the predictor variables. The predictor variables were scaled (mean-centered and standardized). Given the longitudinal design, an autoregressive of order 1 working correlation matrix was specified in the GEE model. Covariates to adjust for were selected based on the previous literature, from directed acyclic graph (DAG) (28.Greenland S. Pearl J. Robins J.M. Causal diagrams for epidemiologic research.Epidemiology. 1999; 10: 37-48Crossref PubMed Scopus (2499) Google Scholar) recommendations and from quasilikelihood information criteria. DAGs are used to identify the minimum adjustment necessary for a model by using the causal pathways to algorithmically identify potential confounding and colliding variables (see ref. 28.Greenland S. Pearl J. Robins J.M. Causal diagrams for epidemiologic research.Epidemiology. 1999; 10: 37-48Crossref PubMed Scopus (2499) Google Scholar for more details about using DAGs). The DAG structures to understand potential confounding, shown in supplemental Figs. S2 and S3, were processed by the DAGitty software (29.Textor J. Hardt J. Knüppel S. DAGitty: a graphical tool for analyzing causal diagrams.Epidemiology. 2011; 22: 745Crossref PubMed Scopus (775) Google Scholar, 30.Shrier I. Platt R.W. Reducing bias through directed acyclic graphs.BMC Med. Res. Methodol. 2008; 8: 70Crossref PubMed Scopus (842) Google Scholar) to generate the recommended adjustments. These DAG structures were developed based on hypothesized causal pathways between each variable, which were then input into the DAGitty software. The output from DAGitty was used, in conjunction with the other methods, to help inform the final model. The final GEE model (M6; seen in supplemental Table S1) was adjusted for years since baseline, WC, baseline age, ethnicity, sex, ALT, MET, and total NEFA. The variables TGFA, total NEFA, sex, ethnicity, and baseline age were classified as time-independent (held constant), as they were measured only at the baseline visit or do not change throughout the study, whereas the outcome variables and remaining covariates were set as time-dependent. After transformations, the GEE estimates were interpreted as an expected percent difference in the outcome variable for every SD increase in the predictor variable, given the covariates are held constant (including time). We also tested for an interaction with sex, ethnicity, or time by the predictor term for each outcome variable. Although GEE accounts for the longitudinal design of the data, this approach is limited in that it cannot analyze the inherent multivariate nature of the composition of the TGFA fraction. Therefore, to confirm the GEE results in a multivariate environment (i.e., all TGFAs analyzed collectively), partial least squares (PLS) regression was used to identify the patterns of TGFA composition against IS and β-cell function as outcome variables. Briefly, PLS is a technique that extracts latent structures (clusters) underlying a set of predictor variables conditional on a response variable(s) (i.e., the outcome variables). How accurately the clusters within the TGFA composition predict metabolic function is determined by using cross-validation on the PLS models. A more detailed explanation of these statistical techniques and on the analysis process can be found in the supplemental methods for our paper in the NEFA fraction (10.Johnston L.W. Harris S.B. Retnakaran R. Giacca A. Liu Z. Bazinet R.P. Hanley A.J. Association of non-esterified fatty acid composition with insulin sensitivity and beta cell function in the Prospective Metabolism and Islet Cell Evaluation (PROMISE) cohort.Diabetologia. 2017; 61: 821-830Crossref PubMed Scopus (29) Google Scholar). All analyses were performed using R (Version 3.4.4) (31.R Core Team R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria2015Google Scholar), along with the R packages geepack (Version 1.2.1) for GEE (32.Højsgaard S. Halekoh U. Yan J. The R package geepack for generalized estimating equations.J. Stat. Softw. 2006; 15: 1-11Google Scholar) and pls (Version 2.6.0) for PLS. The R code and extra analyses for this manuscript are available at https://doi.org/10.6084/m9.figshare.5143438. Results were considered statistically significant at P < 0.05, after adjusting for multiple testing using the Benjamini–Hochberg (BH) false discovery rate (33.Benjamini Y. Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing.J. R. Stat. Soc. Series B Stat. Methodol. 1995; 57: 289-300Google Scholar). STROBE was used as a guideline for reporting (34.Vandenbroucke J.P. von Elm E. Altman D.G. Gøtzsche P.C. Mulrow C.D. Pocock S.J. Poole C. Schlesselman J.J. Egger M. STROBE Initiative Strengthening the Reporting of Observational Studies in Epidemiology (STROBE): explanation and elaboration.PLoS Med. 2007; 4: e297Crossref PubMed Scopus (2329) Google Scholar). Table 1 shows basic characteristics of the PROMISE cohort. The mean follow-up time was 5.6 (1.0) years, where 88.5% of participants attended all three visits. There were 349 (73.2%) females and 336 (70.4%) who reported European ancestry, with a mean age in years of 50.0 (9.8) and a mean BMI of 31.1 (6.5) kg/m2. As expected from the study's eligibility criteria, the majority of participants, n = 305 (65.3%), had a family history of diabetes. Between the baseline visit and the 6 year visit in this sample, IS and β-cell function measures had a significant median decline of between 14% and 21% (P < 0.001 from GEE; n = 357–470).TABLE 1Basic characteristics of PROMISE participants at each of the three clinic visitsMeasureBaseline3 Year6 YearHOMA2-%S88.8 (54.2–136.7)76.8 (49.1–121.8)73.7 (49.5–110.1)ISI13.6 (8.7–21.8)11.6 (6.9–19.1)11.7 (7.5–17.6)IGI/IR7.1 (4.2–10.6)5.6 (3.6–9.8)5.6 (3.4–9.2)ISSI-2727.5 (570.0–922.5)611.2 (493.0–836.4)624.8 (470.0–813.8)ALT (U/l)29.6 (16.0)28.4 (19.6)25.8 (16.9)TG (mmol/l)1.5 (0.8)1.4 (0.8)1.4 (0.7)Chol (mmol/l)5.2 (0.9)5.1 (1.0)5.1 (0.9)HDL (mmol/l)1.4 (0.4)1.3 (0.4)1.4 (0.4)TGFA (nmol/ml)3,137.5 (1,686.6)NEFA (nmol/ml)383.1 (116.3)MET46.1 (61.4)48.2 (60.4)43.7 (56.7)Age (years)50.0 (9.8)53.2 (9.8)56.2 (9.6)BMI (kg/m2)31.1 (6.5)31.4 (6.5)31.1 (6.6)WC (cm)98.5 (15.5)99.2 (15.7)100.5 (15.8)EthnicityEuropean336 (70%)Latino/a59 (12%)Other50 (10%)South Asian32 (7%)SexFemale349 (73%)Male128 (27%)Values are median (interquartile range), mean (SD), or n (percent). Chol, cholesterol. Open table in a new tab Values are median (interquartile range), mean (SD), or n (percent). Chol, cholesterol. Figure 1 shows the composition of each FA in the TG fraction (see supplemental Table S2 for the raw values). Three TGFAs contributed 82.4% to the total TG concentration: 18:1 n-9 (37.8%); 16:0 (26.6%); and, 18:2 n-6 (18.0%). Figure 2 shows a heatmap of the correlation of individual TGFAs as concentrations with the outcome variables and several basic characteristics. As expected, nearly all TGFAs had very strong positive correlations (r = 0.34–0.92) with clinically measured TG and moderate positive correlations with WC (r = 0.31–0.36). There were also moderate negative correlations with HDL (r = −0.53 to −0.31). For the outcome variables, the correlations for the IS measures were generally higher (HOMA2-%S: r = −0.47 to −0.32, ISI: r = −0.47 to −0.31) than for the β-cell function measures (all r < 0.30). For correlations of individual TGFAs using mol% with the basic participant characteristics, as shown in Fig. 3, differences in correlations between FAs were most evident for 14:0, 14:1n-7, 16:0, and 16:1n-7 that had a moderate positive correlation with clinical TG (r = 0.42–0.52), whereas all other FAs had a negative association (r = −0.5 to −0.34). In particular, those FAs with the negative associations with clinical TG were all the very-long-chain PUFAs (e.g., 20:4n-6 and 20:5n-3). As seen in Fig. 4, four FAs (14:0, 16:0, 14:1n-7, and 16:1n-7) clustered together, each highly positively correlated with each other and negatively correlated with all other FAs.Fig. 2Pearson correlation heatmap of TGFAs (nmol/ml) with continuous basic and metabolic characteristics of PROMISE participants from the baseline visit (2004–2006). Darker orange represents a positive correlation; darker blue represents a negative correlation. Chol., cholesterol.View Large Image Figure ViewerDownload Hi-res image Download (PPT)Fig. 3Pearson correlation heatmap of TGFAs (mol%) with continuous basic and metabolic characteristics of PROMISE participants from the baseline visit (2004–2006). Darker orange represents a positive correlation; darker blue represents a negative correlation. Chol., cholesterol.View Large Image Figure ViewerDownload Hi-res image Download (PPT)Fig. 4Pear
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