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Biochemical, Molecular, and Genetic Mechanisms

Interleukin1ß Genetic Polymorphisms Interact with Polyunsaturated Fatty Acids to Modulate Risk of the Metabolic Syndrome^1–3,

⁴ Nutrition and Genomics Laboratory, JM-U.S. Department of Agriculture Human Nutrition Research Center on Aging at Tufts University, Boston, MA 02111; ⁵ Department of Epidemiology, University of Alabama, Birmingham, AL 35294; ⁶ Division of Epidemiology and Community Health, University of Minnesota, Minneapolis, MN 55415; ⁷ Division of Statistical Genomics, Washington University School of Medicine, St. Louis, MO 63110; ⁸ Human Genetics Center, University of Texas, Houston, TX 77225; ⁹ Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN 55415; and ¹⁰ Department of Experimental and Clinical Pharmacology, University of Minnesota, Minneapolis, MN 55455

^* To whom correspondence should be addressed. E-mail: jose.ordovas{at}tufts.edu.

	ABSTRACT

TOP ABSTRACT Introduction Subjects and Methods Results Discussion LITERATURE CITED

 
Chronic inflammation has been identified as an important component of the metabolic syndrome (MetS). Therefore, environmental and genetic factors contributing to the variation of inflammatory responses could affect individuals' susceptibility to MetS. We investigated the association between common IL1ß genetic polymorphisms, inflammation, and the MetS, and the modulation of diet-related variables (i.e., erythrocyte membrane fatty acid composition) in a white U.S. population. IL1ß single nucleotide polymorphisms (SNP) (–1473G > C, –511G > A, –31T > C, 3966C > T, 6054G > A), clinical and biochemical measurements were characterized in a total of 1120 subjects (540 males and 580 females) participating in the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) Study. The 6054 G > A SNP was significantly associated with plasma C-reactive protein (P = 0.054), adiponectin (P = 0.021), and the prevalence of MetS (P = 0.004). Moreover, there was a significant interaction between the 6054G > A SNP and erythrocyte membrane (n-3) PUFA (P = 0.019). Among subjects with low (n-3) PUFA content (below the median), the 6054 G allele was associated with increased risk of the MetS (OR = 3.29, 95%CI = 1.49–7.26 for GG and OR = 1.95, 95%CI = 0.85–4.46 for GA, P < 0.001) compared with the AA genotype, but there were no significant genotype associations among subjects with high (n-3) PUFA content (above the median). Further analyses supported a significant haplotype global effect on the MetS (P = 0.017) among subjects with low (n-3) PUFA content. These results suggested that IL1ß genetic variants were associated with measures of chronic inflammation and the MetS risk, and that genetic influences were more evident among subjects with low (n-3) PUFA intake.

	Introduction

TOP ABSTRACT Introduction Subjects and Methods Results Discussion LITERATURE CITED

Increasing evidence from both experimental and epidemiologic studies supports the role of chronic low-level inflammation underlying each component of the MetS as well as CVD risk (5, 6). However, interindividual variation of inflammatory responses to environmental factors, such as stress and diet, is extensive (7, 8), supporting the notion that genetic susceptibility may play an important role in phenotypic expression. In this regard, family studies have demonstrated significant heritabilities of inflammatory markers, such as C-reactive protein (CRP), interleukin-6 (IL6), and tumor necrosis factor- (TNF) (9, 10). Therefore, it is plausible to hypothesize that genetic variation at inflammatory cytokine genes could interact with environmental exposures, such as diet, to modulate individuals' MetS susceptibility.

The pleiotropic proinflammatory cytokine IL1ß, together with TNF and IL6, plays a central role in the regulation of the immune responses and inflammatory process (11). IL1ß regulates the production of a variety of inflammatory mediators (12, 13) that have been implicated in the atherosclerotic process (14). Circulating IL1ß has also been associated with other CVD risk factors, such as obesity, dyslipidemia, and insulin resistance (15–17).

The human IL1ß gene is a member of the IL1 cluster on chromosome 2q14, and it is flanked by the IL1 and IL1RN genes (18). Previous studies indicate that genetic polymorphisms at this locus are associated with chronic inflammatory disorders, such as autoimmune diseases, cancer, and CVD (19–21). However, few studies have explored the association of polymorphisms at this locus with phenotypes of the MetS (22–24). Therefore, the goal of this study was to investigate the potential relationship between common genetic polymorphisms at the IL1ß locus and the MetS, and to provide new insights into their modulation by dietary fat intake.

	Subjects and Methods

TOP ABSTRACT Introduction Subjects and Methods Results Discussion LITERATURE CITED

 
    Subjects. The study population consisted of 540 men and 580 women (mean ± SD, 49 ± 16 y of age) participating in the Genetics of Lipid Lowering Drugs and Diet Network (GOLDN) Study. Participants were recruited from the ongoing NHLBI Family Heart Study (25) in 2 genetically homogeneous centers (Minneapolis, Minnesota and Salt Lake City, Utah) with predominantly Caucasian populations. The primary aim of the GOLDN study was to characterize the role of genetic and dietary factors on an individual's response to fenofibrate. The detailed design and methodology of the study has been previously described (26). The protocol for this study was approved by the Human Studies Committee of Institutional Review Board at the University of Minnesota, University of Utah, and Tufts University/New England Medical Center. Written informed consent was obtained from all participants.

For GOLDN participants, the clinical examinations at the baseline visit included anthropometric and blood-pressure measurements. Weight was measured with a beam balance and waist was measured at the umbilicus. Height without shoes was measured with fixed stadiometer. BMI was calculated as weight (kg)/height (m²). Blood pressure was determined as the mean of 2 consecutive measurements after a 5-min rest, in the right arm (except for those women who reported right-side mastectomy), with an oscillometric device (Dinamap Pro Series 100, GE Medical Systems). Questionnaires were administered to solicit demographic and lifestyle information and medical and medication history. The habitual dietary intake was assessed by the Diet History Questionnaire developed by the National Cancer Institute (NCI) (27). Physical activity was expressed as metabolic equivalent task (MET) hours based on self-reported types and durations of activities over a period of 24 h. Smoking status was described in 3 categories: current, former, and never smoking. Alcohol consumption was defined as current drinkers and nondrinkers. Self-reported use of hormone therapy by women included contraceptives, conjugated estrogen, estradiol, and progestin.

    Biochemical measurements. Blood samples were drawn after fasting overnight at the baseline visit before entering into the fenofibrate intervention program. Blood collection, plasma separation and processing, and biochemical analyses for inflammatory markers, including high-sensitivity CRP, IL6, TNF, and lipid measurements, including triglycerides, total cholesterol, and HDL cholesterol, have been previously described (28). Fasting plasma insulin was determined by the Human Insulin Specific RIA kit (Linco Research). Fasting plasma glucose was measured using the method of a hexokinase-mediated reaction on a Hitachi 911 (Roche Diagnostics). Plasma adiponectin was measured by competitive RIA (Linco Research).

    Erythrocyte membrane fatty acid determination. Fasting blood samples were collected in EDTA-containing tubes. Erythrocytes separation and fatty acid (FA) extraction followed procedures previously described (29, 30). The final product was dissolved in heptane and injected into a capillary Varian CP7420 100-m column with a Hewlett Packard 5890 GC, equipped with a HP6890A autosampler. Fatty acid methylesters from 12:0 through 24:1(n-9) were separated, identified, and expressed as a percentage of total FA.

    DNA isolation and genotyping. Genomic DNA was extracted from blood samples and purified using commercial Puregene reagents (Gentra Systems) following the manufacturer's instructions. Three promoter single-nucleotide polymorphisms (SNP) (–1473G > C, –511G > A, and –31T > C), one synonymous SNP (3966 C > T) and one intronic SNP (6054G > A) were selected for genotyping. Among them, SNP –511G > A, 3966 C > T, and 6054G > A represent tagging SNP within the IL1ß gene and SNP –1473G > C, –511G > A, and –31G > C have been reported to be functional (20, 31). Genotyping was carried out using the 5'nuclease allelic discrimination Taqman assay with ABI 7900HT system (Applied Biosystems)(32). The descriptions of SNP, primers, probes, and sequences, as well as ABI assay-on-demand ID, are presented in Supplemental Table 1.

    Statistical analysis. Statistical analyses were carried out using SAS for Windows, version 9.0 (SAS Institute). In unrelated subjects, the chi-square test was used to determine whether genotype distribution followed the Hardy-Weinberg equilibrium. A logarithmic transformation was applied for plasma triglyceride, CRP, IL6, TNF, and adiponectin to normalize the distribution of data. The MetS was defined according to the 2005 National Cholesterol Education Program (NCEP) Adult Treatment Panel III (ATP III) guidelines (1). The cutoff point of 3 mg/L was used to dichotomize plasma CRP concentration and low adiponectin was defined as below the median level of plasma adiponectin. Three genotype groups were first considered to check different inherent models. Then the additive model was applied to the data analyses. We used the generalized estimating equation (GEE) approach with exchangeable correlation structure as implemented in the GENMOD procedure in SAS to adjust for the correlated observations due to familial relationship. In this model, the identity link function and the logit link function were used for continuous and binary outcomes, respectively. For binary outcomes, the presented results were the exponentiated OR and 95% CI, which were computed at log-scale. The potential confounding factors included age; gender; smoking status; alcohol consumption; physical activity; use of medications, including antiinflammatory agents, such as aspirin and nonsteroidal antiinflammatory drugs (NSAID); treatment for hypertension, hypercholestemia, and diabetes; and hormone treatment in women. The analyses were performed for the whole sample and for men and women separately to verify the homogeneity of genetic effects among men and women. To determine the interaction between IL1ß SNP and membrane PUFA content, the median levels of PUFA in this population were used as the cutoff point to dichotomize the corresponding variables. The interactions between SNP and membrane PUFA (as dichotomous variables) were tested in the multivariate interaction model. The correlation between RBC membrane PUFA contents and dietary PUFA intake was estimated using partial Pearson correlation controlling for age, gender, BMI, and total energy intake.

The pairwise linkage disequilibrium (LD) between SNP was estimated as correlation coefficient (R) in unrelated subjects using Helixtree software package (Golden Helix). Haplotypes were estimated using the MERLIN program (33), which reconstructs haplotypes for a set of tightly linked markers from extended pedigrees. We used the GEE model to examine the haplotype-phenotype association in which inferred haplotypes were considered as a predictor, and the aforementioned confounding factors were considered as covariates. The interaction of haplotypes and membrane PUFA was tested in the multivariate interaction model. A 2-tailed P-value of <0.05 was considered significant.

	Results

TOP ABSTRACT Introduction Subjects and Methods Results Discussion LITERATURE CITED

 
By design, the phenotypes used in the definition of the MetS were significantly different between MetS and non-MetS subjects (Table 1). In addition, plasma inflammatory markers, including hsCRP, IL6, and TNF, were higher in MetS subjects than non-MetS subjects (P < 0.001). Conversely, plasma adiponectin was lower in MetS than non-MetS subjects (P < 0.001). For the erythrocyte membrane fatty acid composition analysis, -linolenic acid [ALA, 18:3(n-3)], eicosapentaenoic acid [EPA, 20:5 (n-3)], docosahexaenoic acid [DHA, 22:6(n-3)], and docosapentaenoic acid [DPA, 22:5(n-3)] were grouped as (n-3) PUFA; and linoleic acid [LA, 18:2(n-6)], -linolenic acid [GLA, 18:3(n-6)], homo-gamma-linolenic acid [20:3(n-6)], and arachidonic acid [AA, 20:4(n-6)] were grouped as (n-6) PUFA. Total PUFA, (n-3) PUFA, particularly marine-derived (n-3) PUFA (DHA+EPA), and (n-6) PUFA in erythrocyte membrane were lower in MetS than in non-MetS subjects (P < 0.01), whereas SFA content was higher in the MetS subjects (P < 0.001). The monounsaturated fatty acid (MUFA) content did not differ between MetS and non-MetS subjects (Table 1).

For SNP 6054G > A, GG and GA subjects had higher risk of MetS than AA subjects (P = 0.004) in a multivariate adjusted model. There was no significant association between this SNP and each component of the MetS, except for the association with high blood pressure (P = 0.016). Moreover, GG and GA subjects were more likely to have high plasma CRP concentrations (P = 0.054) and low plasma adiponectin concentrations (P = 0.021) than AA subjects. For the –511G > A SNP, the risk of MetS for GG and GA subjects was lower than for AA subjects (P = 0.042). We found similar results for SNP –1473G > C such that GG and GC subjects tended to have a lower risk of the MetS than CC subjects (P = 0.078). Interestingly, the more common genotype for each of these 2 promoter SNP was significantly associated with reduced risk of high blood pressure compared with the less common genotype (P < 0.001 for –511G > A and P = 0.013 for –1473G > C). We did not observe such associations for SNP 3966C > T (Table 2).

	Discussion

TOP ABSTRACT Introduction Subjects and Methods Results Discussion LITERATURE CITED

 
We demonstrated that, among the white population participating in the GOLDN study, common genetic variants at the IL1B locus were associated with risk of MetS and MetS-related phenotypes. Moreover, these genetic associations were modulated by marine-derived (n-3) PUFA as determined by their content in the erythrocyte membrane, suggesting that dietary factors may modulate the genetic susceptibility toward developing MetS.

The concept that inflammation plays a significant role in the pathogenesis of the MetS is gaining support, based on the evidence showing that inflammation 1) impairs insulin sensitivity, 2) contributes to atherogenic dyslipidemia, and 3) deteriorates the function of the arterial wall, which may contribute to hypertension (17, 34, 35). Moreover, adipose tissue acts as an endocrine organ secreting various inflammatory mediators, which provides even greater support to the connection between inflammation, related genetic factors, and the MetS (36).

In our study, both the IL1ß –1473G > C and the –511G > A (in complete LD with –31G > C) SNP were associated with the risk of high blood pressure. The association of –511G > A with blood pressure was previously reported in a Chinese population (24). Despite the fact that both SNP appear to function by altering transcription-factor binding affinity and subsequently affecting the transcriptional activity of the IL1ß gene, only –511G > A was associated with the risk of MetS (20, 31).

We also found significant associations related to the intronic 6054G > A SNP. Subjects carrying the 6054 G allele were at significantly higher risk of MetS than subjects with the 6054AA genotype. The G allele was associated with increased blood pressure and a consistent trend toward increased abdominal obesity, hyperglycemia, and dyslipidemia. Moreover, carriers of the 6054G allele had significantly higher plasma CRP and lower adiponectin concentrations than 6054AA subjects, suggesting a more inflammatory state that may drive these subjects toward the cluster of risk factors associated with MetS. The mechanism by which this SNP may contribute to observed associations is unclear. This SNP is positioned in the noncoding region of intron 6, far from any known regulatory region. Therefore, it is likely that this SNP is a marker linked to another functional variant.

In addition to the genetic susceptibility to MetS, environmental and behavioral factors are involved. Among them, diet, and more specifically, PUFA, provides protection through several mechanisms, including improvements of plasma lipid and inflammation profiles, vascular function, and insulin sensitivity (37–39). Dietary fatty acids or their derivatives are natural ligands for the peroxisome proliferator activated receptors (PPAR) family of transcription factors (40). PPAR regulate multiple genes involved in glucose, lipid metabolism, and energy homeostasis through ligand-dependent activation (40).

Moreover, PPAR modulate the expression of genes involved in inflammation, including IL1, IL6, and TNF (41). The intensity and even directionality of these effects may depend on whether the fatty acids belong to the (n-3) or (n-6) family of PUFA. Epidemiologic evidence suggests that (n-3) PUFA is associated with a protective profile for multiple features of MetS (7, 42). (n-3) PUFA inhibit the production of inflammatory mediators including CRP, IL1, TNF, and IL6 (8, 43). Conversely, the effects of (n-6) PUFA on inflammation and related risk factors are less consistent (44–46). Based on this, we investigated whether erythrocyte membrane PUFA composition, a surrogate of habitual dietary intake (47, 48), could modulate the genotype-phenotype associations observed in our study. Consistent with previous reports (47, 48), our data showed that erythrocyte membrane long chain (n-3) PUFA, EPA and DHA were significantly correlated with dietary fat components. Moreover, consistent with our hypothesis, we found a significant interaction between total PUFA content and the IL1ß 6054G > A on MetS risk. GG and GA subjects with low membrane PUFA content and presumably low PUFA dietary intake, had a significantly increased MetS risk than AA subjects. Interestingly, when we analyzed the membrane PUFA content in more detail, we observed that DHA and EPA strongly influenced the genetic effect. Thus, the potentially deleterious effects of this SNP on MetS risk were only observed among subjects with low membrane DHA+EPA content. Importantly, our data suggest that the increasing genetic predisposition toward the development of MetS could be obliterated by a diet rich in (n-3) PUFA, supporting the notion that more tailored dietary recommendations could be successfully used to prevent chronic diseases.

We further examined the combined effects of SNP on the MetS by conducting haplotype analyses in our population. Five SNP, including 6054G > A, 3966C > T, –31T > C, –511G > A and –1473G > C, were in significant LD, which is consistent with a previous report (49). Haplotype 11222 (6054G/3966C/–31C/–511A /–1473C), representing 25% of observed haplotypes, was associated with a significantly higher concentration of plasma CRP compared with the most common 21111(6054A/3954C/–31T/–511G/–1473G) haplotype representing 33% of observed haplotypes. This observation is consistent with a recent in vitro functional study suggesting that the effect of SNP at the IL1ß promoter region was subject to haplotype context (31). Again, the haplotype effect appeared to be modulated by the membrane DHA+EPA content. Among subjects with low DHA+EPA membrane content, there was significant haplotype association with MetS. The OR of MetS for haplotype 11222 was significantly higher than that for haplotype 21111. However, there was no significant association among subjects with high membrane DHA+EPA content. The interpretation of these findings could be that high intake of DHA and EPA, reflected by their high content in tissue, inhibits expression of genes encoding inflammatory cytokines and thus attenuates the potentially detrimental effects of the genetic susceptibility toward increased risk of MetS. However, due to the moderate correlation of membrane PUFA with the corresponding FA in the diet in our study, it is possible that factors other than diet, such as endogenous FA synthesis and transformation of exogenous FA, which could also influence FA tissue composition, may modulate the genetic susceptibility to MetS.

Despite the evidence, we should be cautious in the interpretation of our results. This is a cross-sectional study, and biomarkers for habitual dietary intake were measured within a reduced period of time and may not be representative of longer time exposures. Therefore, the causal link among genetics, diet, and MetS remains tenuous. To support our findings, additional studies are needed that use more powerful experimental designs, such as longitudinal follow-up, and more reliable biomarkers reflecting long-term dietary habits. In addition, extension to other ethnic populations with increased MetS risk, such as Native Americans and Hispanics, is clearly warranted.

In conclusion, our results support the concept that genetic variants at the IL1ß locus may contribute to individual susceptibility to the development of MetS. Most importantly, this genetic susceptibility could be modulated by dietary fat intake and in particular (n-3) PUFA, such as DHA and EPA. This new information should provide guidance for the design and analysis of studies assessing MetS risk, as well as the implementation of more personalized preventive and therapeutic approaches to fight CVD.

	FOOTNOTES

 
¹ Supported by contract 53-K06-5-10 from NIH and 58-1950-9-001 from the USDA Research Service and by NIH Heart, Lung, and Blood Institute grant U 01 HL72524, Genetic and Environmental Determinants of Triglycerides.

² Author disclosures: J. Shen, D. K. Arnett, J. M. Peacock, L. D. Parnell, A. Kraja, J. E. Hixson, M. Y. Tsai, C. Lai, E. Kabagambe, R. J. Straka, and J. M. Ordovas, no conflicts of interest.

³ Supplemental Tables 1 and 2 are available with the online posting of this paper at jn.nutrition.org.

¹¹ Abbreviations used: CRP, C-reactive protein; CVD, cardiovascular disease; DHA, docosahexaenoic acid; EPA, eicosapentaenoic acid; FA, fatty acid; GOLDN, Genetics of Lipid Lowering Drugs and Diet Network; IL6, interleukin-6; LD, linkage disequilibrium; MetS, metabolic syndrome; SNP, single nucleotide polymorphism; TNF, tumor necrosis factor-.

Manuscript received 19 March 2007. Initial review completed 19 April 2007. Revision accepted 21 May 2007.

	LITERATURE CITED

TOP ABSTRACT Introduction Subjects and Methods Results Discussion LITERATURE CITED

 

1. Grundy SM, Cleeman JI, Daniels SR, Donato KA, Eckel RH, Franklin BA, Gordon DJ, Krauss RM, Savage PJ, et al. Diagnosis and management of the metabolic syndrome: an American Heart Association/National Heart, Lung, and Blood Institute scientific statement. Circulation. 2005;112:2735–52.[Free Full Text]

2. Moller DE, Kaufman KD. Metabolic syndrome: a clinical and molecular perspective. Annu Rev Med. 2005;56:45–62.[Medline]

3. Eckel RH, Grundy SM, Zimmet PZ. The metabolic syndrome. Lancet. 2005;365:1415–28.[Medline]

4. Groop L. Genetics of the metabolic syndrome. Br J Nutr. 2000;83: Suppl 1:S39–48.[Medline]

5. Festa A, D'Agostino R, Jr., Tracy RP, Haffner SM. Elevated levels of acute-phase proteins and plasminogen activator inhibitor-1 predict the development of type 2 diabetes: the insulin resistance atherosclerosis study. Diabetes. 2002;51:1131–7.[Abstract/Free Full Text]

6. Rutter MK, Meigs JB, Sullivan LM, D'Agostino RB Sr, Wilson PW. C-reactive protein, the metabolic syndrome, and prediction of cardiovascular events in the Framingham Offspring Study. Circulation. 2004;110:380–5.[Abstract/Free Full Text]

7. Klein-Platat C, Drai J, Oujaa M, Schlienger JL, Simon C. Plasma fatty acid composition is associated with the metabolic syndrome and low-grade inflammation in overweight adolescents. Am J Clin Nutr. 2005;82:1178–84.[Abstract/Free Full Text]

8. Lopez-Garcia E, Schulze MB, Manson JE, Meigs JB, Albert CM, Rifai N, Willett WC, Hu FB. Consumption of (n-3) fatty acids is related to plasma biomarkers of inflammation and endothelial activation in women. J Nutr. 2004;134:1806–11.[Abstract/Free Full Text]

9. Pankow JS, Folsom AR, Cushman M, Borecki IB, Hopkins PN, Eckfeldt JH, Tracy RP. Familial and genetic determinants of systemic markers of inflammation: the NHLBI family heart study. Atherosclerosis. 2001;154:681–9.[Medline]

10. Retterstol L, Eikvar L, Berg K. A twin study of C-Reactive Protein compared to other risk factors for coronary heart disease. Atherosclerosis. 2003;169:279–82.[Medline]

11. Dinarello CA. Biologic basis for interleukin-1 in disease. Blood. 1996;87:2095–147.[Abstract/Free Full Text]

12. Ng SB, Tan YH, Guy GR. Differential induction of the interleukin-6 gene by tumor necrosis factor and interleukin-1. J Biol Chem. 1994;269:19021–7.[Abstract/Free Full Text]

13. Zhang D, Jiang SL, Rzewnicki D, Samols D, Kushner I. The effect of interleukin-1 on C-reactive protein expression in Hep3B cells is exerted at the transcriptional level. Biochem J. 1995;310:143–8.[Medline]

14. Libby P. Inflammation in atherosclerosis. Nature. 2002;420:868–74.[Medline]

15. Garcia MC, Wernstedt I, Berndtsson A, Enge M, Bell M, Hultgren O, Horn M, Ahren B, Enerback S, et al. Mature-onset obesity in interleukin-1 receptor I knockout mice. Diabetes. 2006;55:1205–13.[Abstract/Free Full Text]

16. Jager J, Gremeaux T, Cormont M, Le Marchand-Brustel Y, Tanti JF. Interleukin-1beta-induced insulin resistance in adipocytes through down-regulation of insulin receptor substrate-1 expression. Endocrinology. 2007;148:241–51.[Abstract/Free Full Text]

17. Khovidhunkit W, Kim MS, Memon RA, Shigenaga JK, Moser AH, Feingold KR, Grunfeld C. Effects of infection and inflammation on lipid and lipoprotein metabolism: mechanisms and consequences to the host. J Lipid Res. 2004;45:1169–96.[Abstract/Free Full Text]

18. Nicklin MJ, Weith A, Duff GW. A physical map of the region encompassing the human interleukin-1 alpha, interleukin-1 beta, and interleukin-1 receptor antagonist genes. Genomics. 1994;19:382–4.[Medline]

19. Camargo JF, Correa PA, Castiblanco J, Anaya JM. Interleukin-1beta polymorphisms in Colombian patients with autoimmune rheumatic diseases. Genes Immun. 2004;5:609–14.[Medline]

20. El-Omar EM, Carrington M, Chow WH, McColl KE, Bream JH, Young HA, Herrera J, Lissowska J, Yuan CC, et al. Interleukin-1 polymorphisms associated with increased risk of gastric cancer. Nature. 2000;404:398–402.[Medline]

21. Iacoviello L, Di Castelnuovo A, Gattone M, Pezzini A, Assanelli D, Lorenzet R, Del Zotto E, Colombo M, Napoleone E, et al. Polymorphisms of the interleukin-1beta gene affect the risk of myocardial infarction and ischemic stroke at young age and the response of mononuclear cells to stimulation in vitro. Arterioscler Thromb Vasc Biol. 2005;25:222–7.[Abstract/Free Full Text]

22. Strandberg L, Lorentzon M, Hellqvist A, Nilsson S, Wallenius V, Ohlsson C, Jansson JO. Interleukin-1 system gene polymorphisms are associated with fat mass in young men. J Clin Endocrinol Metab. 2006;91:2749–54.[Abstract/Free Full Text]

23. Achyut BR, Srivastava A, Bhattacharya S, Mittal B. Genetic association of interleukin-1beta (-511C/T) and interleukin-1 receptor antagonist (86 bp repeat) polymorphisms with Type 2 diabetes mellitus in North Indians. Clin Chim Acta. 2007;377:163–9.[Medline]

24. Huang G, Niu T, Peng S, Ling D, Liu J, Zhang X, Xu X. Association between the interleukin-1beta C(-511)T polymorphism and blood pressure in a Chinese hypertensive population. Immunol Lett. 2004;91:159–62.[Medline]

25. Higgins M, Province M, Heiss G, Eckfeldt J, Ellison RC, Folsom AR, Rao DC, Sprafka JM, Williams R. NHLBI Family Heart Study: objectives and design. Am J Epidemiol. 1996;143:1219–28.[Abstract/Free Full Text]

26. Lai CQ, Arnett DK, Corella D, Straka RJ, Tsai MY, Peacock JM, Adiconis X, Parnell LD, Hixson JE, et al. Fenofibrate effect on triglyceride and postprandial response of apolipoprotein A5 variants. The GOLDNstudy. Arterioscler Thromb Vasc Biol. 2007;27:1417–25.[Abstract/Free Full Text]

27. Morgan D. SAS/AFR, metadata and the National Cancer Institutes Diet History Questionnaire: How to build a computer-assisted interview. Western Users of SAS Software, Eleventh Annual Conference. San Francisco; 2003. Available from: http://www.biostat.wustl.edu/derek/sasindex.html.

28. Tsai MY, Hanson NQ, Straka RJ, Hoke TR, Ordovas JM, Peacock JM, Arends VL, Arnett DK. Effect of influenza vaccine on markers of inflammation and lipid profile. J Lab Clin Med. 2005;145:323–7.[Medline]

29. Reed CF, Swisher SN, Marinetti GV, Enen EG. Studies of the lipids of the erythrocyte. I. Quantitative analysis of the lipids of normal human red blood cells. J Lab Clin Med. 1960;56:281–9.[Medline]

30. Morrison WR, Smith LM. Preparation of fatty acid methyl esters and dimethylacetals from lipids with boron fluoride–methanol. J Lipid Res. 1964;5:600–8.[Medline]

31. Chen H, Wilkins LM, Aziz N, Cannings C, Wyllie DH, Bingle C, Rogus J, Beck JD, Offenbacher S, et al. Single nucleotide polymorphisms in the human interleukin-1B gene affect transcription according to haplotype context. Hum Mol Genet. 2006;15:519–29.[Abstract/Free Full Text]

32. Livak KJ. Allelic discrimination using fluorogenic probes and the 5' nuclease assay. Genet Anal. 1999;14:143–9.[Medline]

33. Abecasis GR, Cherny SS, Cookson WO, Cardon LR. Merlin–rapid analysis of dense genetic maps using sparse gene flow trees. Nat Genet. 2002;30:97–101.[Medline]

34. Pauletto P, Rattazzi M. Inflammation and hypertension: the search for a link. Nephrol Dial Transplant. 2006;21:850–3.[Free Full Text]

35. Shoelson SE, Lee J, Goldfine AB. Inflammation and insulin resistance. J Clin Invest. 2006;116:1793–801.[Medline]

36. Juge-Aubry CE, Henrichot E, Meier CA. Adipose tissue: a regulator of inflammation. Best Pract Res Clin Endocrinol Metab. 2005;19:547–66.[Medline]

37. Riccardi G, Giacco R, Rivellese AA. Dietary fat, insulin sensitivity and the metabolic syndrome. Clin Nutr. 2004;23:447–56.[Medline]

38. Decsi T, Molnar D, Koletzko B. Long-chain polyunsaturated fatty acids in plasma lipids of obese children. Lipids. 1996;31:305–11.[Medline]

39. Simopoulos AP. Essential fatty acids in health and chronic disease. Am J Clin Nutr. 1999;70:560S–9S.[Abstract/Free Full Text]

40. Ferre P. The biology of peroxisome proliferator-activated receptors: relationship with lipid metabolism and insulin sensitivity. Diabetes. 2004;53: Suppl 1:S43–50.[Abstract/Free Full Text]

41. Daynes RA, Jones DC. Emerging roles of PPARs in inflammation and immunity. Nature Rev Immunol. 2002;2:748–59.[Medline]

42. Vessby B. Dietary fat, fatty acid composition in plasma and the metabolic syndrome. Curr Opin Lipidol. 2003;10:15–9.[Medline]

43. Meydani SN, Dinarello CA. Influence of dietary fatty acids on cytokine production and its clinical implications. Nutr Clin Pract. 1993;8:65–72.[Abstract/Free Full Text]

44. Griffin MD, Sanders TA, Davies IG, Morgan LM, Millward DJ, Lewis F, Slaughter S, Cooper JA, Miller GJ, Griffin BA. Effects of altering the ratio of dietary n-6 to n-3 fatty acids on insulin sensitivity, lipoprotein size, and postprandial lipemia in men and postmenopausal women aged 45–70 y: the OPTILIP Study. Am J Clin Nutr. 2006;84:1290–8.[Abstract/Free Full Text]

45. Harbige LS. Fatty acids, the immune response, and autoimmunity: a question of n-6 essentiality and the balance between n-6 and n-3. Lipids. 2003;38:323–41.[Medline]

46. Pischon T, Hankinson SE, Hotamisligil GS, Rifai N, Willett WC, Rimm EB. Habitual dietary intake of n-3 and n-6 fatty acids in relation to inflammatory markers among US men and women. Circulation. 2003;108:155–60.[Abstract/Free Full Text]

47. Fuhrman BJ, Barba M, Krogh V, Micheli A, Pala V, Lauria R, Chajes V, Riboli E, Sieri S, et al. Erythrocyte membrane phospholipid composition as a biomarker of dietary fat. Ann Nutr Metab. 2006;50:95–102.[Medline]

48. Glatz JF, Soffers AE, Katan MB. Fatty acid composition of serum cholesteryl esters and erythrocyte membranes as indicators of linoleic acid intake in man. Am J Clin Nutr. 1989;49:269–76.[Abstract/Free Full Text]

49. Belfer I, Buzas B, Hipp H, Dean M, Evans C, Lorincz I, Max MB, Goldman D. Haplotype structure of inflammatory cytokines genes (IL1B, IL6 and TNF/LTA) in US Caucasians and African Americans. Genes Immun. 2004;5:505–12.[Medline]

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