RBP4 variants are significantly associated with plasma RBP4 levels and hypertriglyceridemia risk in Chinese Hans
2009; Elsevier BV; Volume: 50; Issue: 7 Linguagem: Inglês
10.1194/jlr.p900014-jlr200
ISSN1539-7262
AutoresYing Wu, Huaixing Li, Ruth J. F. Loos, Qibin Qi, Frank B. Hu, Yong Liu, Xu Lin,
Tópico(s)Adipose Tissue and Metabolism
ResumoWe previously found that plasma RBP4 levels were strongly associated with metabolic syndrome components. This study aimed to determine whether RBP4 variants are associated with the metabolic syndrome components and plasma RBP4 levels, and to investigate whether the associations between plasma RBP4 and the metabolic syndrome components are causal. Five tagSNPs were tested for their associations with plasma RBP4 levels and metabolic syndrome components in a population-based sample of 3,210 Chinese Hans. A possible causal relationship between plasma RBP4 levels and hypertriglyceridemia was explored by Mendelian randomization. Plasma RBP4 levels were significantly associated with rs10882273 (βz −0.10SD[−0.17, −0.03], P = 0.0050), rs3758538 (βz −0.13SD[−0.24, −0.02], P = 0.0249) in all participants, and with rs17108993 in Shanghai participants (βz −0.19SD[−0.32, −0.05], P = 0.0061). The single nucleotide polymorphism (SNP) rs3758538 was significantly associated with hypertriglyceridemia (OR 0.62[0.45–0.85], P = 0.0026) and triglycerides (βz −0.19SD[−0.30, −0.07], P = 0.001) in all participants. In Mendelian randomization analysis, the observed effect size of association between rs3758538 and hypertriglyceridemia was different from the expected effect size (P = 0.0213). This is the first study to show that the RBP4 variants are significantly associated with plasma RBP4 levels and hypertriglyceridemia risk in Chinese Hans. However, results of Mendelian randomization do not support the hypothesis that RBP4 levels are causally related to hypertriglyceridemia risk. We previously found that plasma RBP4 levels were strongly associated with metabolic syndrome components. This study aimed to determine whether RBP4 variants are associated with the metabolic syndrome components and plasma RBP4 levels, and to investigate whether the associations between plasma RBP4 and the metabolic syndrome components are causal. Five tagSNPs were tested for their associations with plasma RBP4 levels and metabolic syndrome components in a population-based sample of 3,210 Chinese Hans. A possible causal relationship between plasma RBP4 levels and hypertriglyceridemia was explored by Mendelian randomization. Plasma RBP4 levels were significantly associated with rs10882273 (βz −0.10SD[−0.17, −0.03], P = 0.0050), rs3758538 (βz −0.13SD[−0.24, −0.02], P = 0.0249) in all participants, and with rs17108993 in Shanghai participants (βz −0.19SD[−0.32, −0.05], P = 0.0061). The single nucleotide polymorphism (SNP) rs3758538 was significantly associated with hypertriglyceridemia (OR 0.62[0.45–0.85], P = 0.0026) and triglycerides (βz −0.19SD[−0.30, −0.07], P = 0.001) in all participants. In Mendelian randomization analysis, the observed effect size of association between rs3758538 and hypertriglyceridemia was different from the expected effect size (P = 0.0213). This is the first study to show that the RBP4 variants are significantly associated with plasma RBP4 levels and hypertriglyceridemia risk in Chinese Hans. However, results of Mendelian randomization do not support the hypothesis that RBP4 levels are causally related to hypertriglyceridemia risk. Retinol-binding protein 4 (RBP4), initially known as the specific carrier of retinol, has recently been identified as a new adipokine that is robustly associated with visceral fat and insulin resistance (1von Eynatten M. Humpert P.M. Retinol-binding protein-4 in experimental and clinical metabolic disease.Expert Rev. Mol. Diagn. 2008; 8: 289-299Crossref PubMed Scopus (39) Google Scholar). The potential link between RBP4 and insulin resistance was first suggested by the observations that serum RBP4 levels were elevated in adipose-Glut4−/− mice and completely normalized by the anti-diabetic agent rosiglitazone (2Yang Q. Graham T.E. Mody N. Preitner F. Peroni O.D. Zabolotny J.M. Kotani K. Quadro L. Kahn B.B. Serum retinol binding protein 4 contributes to insulin resistance in obesity and type 2 diabetes.Nature. 2005; 436: 356-362Crossref PubMed Scopus (1634) Google Scholar), whereas genetic deletion of Rbp4 (Rbp4−/− mice) improved insulin sensitivity (2Yang Q. Graham T.E. Mody N. Preitner F. Peroni O.D. Zabolotny J.M. Kotani K. Quadro L. Kahn B.B. Serum retinol binding protein 4 contributes to insulin resistance in obesity and type 2 diabetes.Nature. 2005; 436: 356-362Crossref PubMed Scopus (1634) Google Scholar). These findings in mice were strongly supported by clinical data in humans from the same group (3Graham T.E. Yang Q. Bluher M. Hammarstedt A. Ciaraldi T.P. Henry R.R. Wason C.J. Oberbach A. Jansson P.A. Smith U. et al.Retinol-binding protein 4 and insulin resistance in lean, obese, and diabetic subjects.N. Engl. J. Med. 2006; 354: 2552-2563Crossref PubMed Scopus (1082) Google Scholar), showing that serum RBP4 levels were elevated in insulin-resistant individuals with obesity, impaired glucose tolerance, type 2 diabetes, and even in lean nondiabetic individuals with a strong family history of type 2 diabetes. Several recent genetic association studies also found an association between common RBP4 variants and insulin resistance or type 2 diabetes (4Munkhtulga L. Nakayama K. Utsumi N. Yanagisawa Y. Gotoh T. Omi T. Kumada M. Erdenebulgan B. Zolzaya K. Lkhagvasuren T. et al.Identification of a regulatory SNP in the retinol binding protein 4 gene associated with type 2 diabetes in Mongolia.Hum. Genet. 2007; 120: 879-888Crossref PubMed Scopus (92) Google Scholar, 5van Hoek M. Dehghan A. Zillikens M.C. Hofman A. Witteman J.C. Sijbrands E.J. An RBP4 promoter polymorphism increases risk of type 2 diabetes.Diabetologia. 2008; 51: 1423-1428Crossref PubMed Scopus (60) Google Scholar, 6Craig R.L. Chu W.S. Elbein S.C. Retinol binding protein 4 as a candidate gene for type 2 diabetes and prediabetic intermediate traits.Mol. Genet. Metab. 2007; 90: 338-344Crossref PubMed Scopus (76) Google Scholar–7Kovacs P. Geyer M. Berndt J. Kloting N. Graham T.E. Bottcher Y. Enigk B. Tonjes A. Schleinitz D. Schon M.R. et al.Effects of genetic variation in the human retinol binding protein-4 gene (RBP4) on insulin resistance and fat depot-specific mRNA expression.Diabetes. 2007; 56: 3095-3100Crossref PubMed Scopus (80) Google Scholar). However, results of subsequent clinical and cross-sectional studies were inconsistent, as many failed to confirm the inverse correlation between RBP4 and insulin resistance, or some found opposite results (8Janke J. Engeli S. Boschmann M. Adams F. Bohnke J. Luft F.C. Sharma A.M. Jordan J. Retinol-binding protein 4 in human obesity.Diabetes. 2006; 55: 2805-2810Crossref PubMed Scopus (298) Google Scholar, 9von Eynatten M. Lepper P.M. Liu D. Lang K. Baumann M. Nawroth P.P. Bierhaus A. Dugi K.A. Heemann U. Allolio B. et al.Retinol-binding protein 4 is associated with components of the metabolic syndrome, but not with insulin resistance, in men with type 2 diabetes or coronary artery disease.Diabetologia. 2007; 50: 1930-1937Crossref PubMed Scopus (148) Google Scholar, 10Takashima N. Tomoike H. Iwai N. Retinol-binding protein 4 and insulin resistance.N. Engl. J. Med. 2006; 355 (author reply 1394–1395).1392Crossref PubMed Scopus (75) Google Scholar, 11Erikstrup C. Mortensen O.H. Pedersen B.K. Retinol-binding protein 4 and insulin resistance.N. Engl. J. Med. 2006; 355 (author reply 1394–1395).: 1393-1394PubMed Google Scholar, 12Broch M. Vendrell J. Ricart W. Richart C. Fernandez-Real J.M. Circulating retinol-binding protein-4, insulin sensitivity, insulin secretion, and insulin disposition index in obese and nonobese subjects.Diabetes Care. 2007; 30: 1802-1806Crossref PubMed Scopus (119) Google Scholar–13Promintzer M. Krebs M. Todoric J. Luger A. Bischof M.G. Nowotny P. Wagner O. Esterbauer H. Anderwald C. Insulin resistance is unrelated to circulating retinol binding protein and protein C inhibitor.J. Clin. Endocrinol. Metab. 2007; 92: 4306-4312Crossref PubMed Scopus (79) Google Scholar). Apparently, the role of RBP4 as a mediator of insulin resistance in humans remains to be clarified. Growing evidence suggests that RBP4 may play a more important role in lipid metabolism than insulin resistance. For example, most of the previous human studies that confirmed the association of RBP4 levels with insulin resistance also observed significant associations with lipid levels, in particular with triglyceride, HDL-cholesterol, and LDL-cholesterol (3Graham T.E. Yang Q. Bluher M. Hammarstedt A. Ciaraldi T.P. Henry R.R. Wason C.J. Oberbach A. Jansson P.A. Smith U. et al.Retinol-binding protein 4 and insulin resistance in lean, obese, and diabetic subjects.N. Engl. J. Med. 2006; 354: 2552-2563Crossref PubMed Scopus (1082) Google Scholar, 9von Eynatten M. Lepper P.M. Liu D. Lang K. Baumann M. Nawroth P.P. Bierhaus A. Dugi K.A. Heemann U. Allolio B. et al.Retinol-binding protein 4 is associated with components of the metabolic syndrome, but not with insulin resistance, in men with type 2 diabetes or coronary artery disease.Diabetologia. 2007; 50: 1930-1937Crossref PubMed Scopus (148) Google Scholar, 14Takebayashi K. Suetsugu M. Wakabayashi S. Aso Y. Inukai T. Retinol binding protein-4 levels and clinical features of type 2 diabetes patients.J. Clin. Endocrinol. Metab. 2007; 92: 2712-2719Crossref PubMed Scopus (176) Google Scholar, 15Lee D.C. Lee J.W. Im J.A. Association of serum retinol binding protein 4 and insulin resistance in apparently healthy adolescents.Metabolism. 2007; 56: 327-331Abstract Full Text Full Text PDF PubMed Scopus (124) Google Scholar). Others observed associations of RBP4 with increased triglycerides levels and with pro-atherogenic lipoproteins or key enzymes of lipoprotein metabolism, but not with insulin resistance marker (9von Eynatten M. Lepper P.M. Liu D. Lang K. Baumann M. Nawroth P.P. Bierhaus A. Dugi K.A. Heemann U. Allolio B. et al.Retinol-binding protein 4 is associated with components of the metabolic syndrome, but not with insulin resistance, in men with type 2 diabetes or coronary artery disease.Diabetologia. 2007; 50: 1930-1937Crossref PubMed Scopus (148) Google Scholar, 16Hutchison S.K. Harrison C. Stepto N. Meyer C. Teede H.J. Retinol-binding protein 4 and insulin resistance in polycystic ovary syndrome.Diabetes Care. 2008; 31: 1427-1432Crossref PubMed Scopus (46) Google Scholar, 17Zugaro A. Pandolfi C. Barbonetti A. Vassallo M.R. D'Angeli A. Necozione S. Colangeli M.S. Francavilla S. Francavilla F. Retinol binding protein 4, low birth weight-related insulin resistance and hormonal contraception.Endocrine. 2007; 32: 166-169Crossref PubMed Scopus (6) Google Scholar–18Aeberli I. Biebinger R. Lehmann R. L'Allemand D. Spinas G.A. Zimmermann M.B. Serum retinol-binding protein 4 concentration and its ratio to serum retinol are associated with obesity and metabolic syndrome components in children.J. Clin. Endocrinol. Metab. 2007; 92: 4359-4365Crossref PubMed Scopus (122) Google Scholar). Since hypertriglyceridemia plays an important role in the pathogenesis of cardiovascular disease, circulating RBP4 levels might emerge as a suitable target for therapeutic intervention in cardiovascular disease if the association between circulating RBP4 and hypertriglyceridemia is causal. However, the observational nature of epidemiological association studies does not allow inference of the causal direction between two related traits. It is, therefore, of interest to determine whether elevated circulating RBP4 levels causally contribute to an unfavorable lipid profile and, therefore, to the pathogenesis of cardiovascular disease. This determination can be achieved by Mendelian randomization (19Sandhu M.S. Debenham S.L. Barroso I. Loos R.J. Mendelian randomisation studies of type 2 diabetes: future prospects.Diabetologia. 2008; 51: 211-213Crossref PubMed Scopus (7) Google Scholar, 20Sheehan N.A. Didelez V. Burton P.R. Tobin M.D. Mendelian randomisation and causal inference in observational epidemiology.PLoS Med. 2008; 5: e177Crossref PubMed Scopus (228) Google Scholar), an epidemiological approach for assessing the direction of causality, in an unbiased way, between putative risk factors and a disease. According to Mendelian randomization, genetic variants in the RBP4 gene are randomly transmitted to the offspring and largely free from reverse causation and confounding. If the association between high circulating RBP4 levels and hypertriglyceridemia or other components of metabolic syndrome is causal, then the genetic variants associated with circulating RBP4 levels also should be associated with risk of hypertriglyceridemia or other components of metabolic syndrome for individuals carrying these variants to the extent predicted by the magnitudes of the associations between genetic variant and circulating RBP4 levels and between the circulating RPB4 and elevated triglyceride levels. Otherwise, the causality is refuted. Therefore, a reliable association between genetic variant in RBP4 gene and plasma RBP4 levels is one of the key assumptions for performing Mendelian randomization. One of our previous studies based on the same population indicated that the increased levels of circulating RBP4 were strongly and positively associated with body mass index (BMI), waist circumference, blood pressure, plasma triglyceride, total- and LDL-cholesterol levels, and negatively with HDL-cholesterol levels (21Qi Q. Yu Z. Ye X. Zhao F. Huang P. Hu F.B. Franco O.H. Wang J. Li H. Liu Y. et al.Elevated retinol-binding protein 4 levels are associated with metabolic syndrome in Chinese people.J. Clin. Endocrinol. Metab. 2007; 92: 4827-4834Crossref PubMed Scopus (175) Google Scholar). The aims of the present study are (1von Eynatten M. Humpert P.M. Retinol-binding protein-4 in experimental and clinical metabolic disease.Expert Rev. Mol. Diagn. 2008; 8: 289-299Crossref PubMed Scopus (39) Google Scholar) to determine whether common variants in the RBP4 gene are associated with circulating RBP4 levels and with the various components of metabolic syndrome, and (2Yang Q. Graham T.E. Mody N. Preitner F. Peroni O.D. Zabolotny J.M. Kotani K. Quadro L. Kahn B.B. Serum retinol binding protein 4 contributes to insulin resistance in obesity and type 2 diabetes.Nature. 2005; 436: 356-362Crossref PubMed Scopus (1634) Google Scholar) to investigate the triangular relationship among plasma RBP4 levels, RBP4 gene variants, and the risk of the metabolic syndrome–related traits to assess the possible causal direction that we observed in a Chinese Han population. The study population consisted of 3,210 unrelated Chinese Hans from 50 to 70 years of age (1,423 men and 1,787 women) from the Study on Nutrition and Health of Aging Population in China. All participants underwent a complete physical examination including standard anthropometric measurements, overnight fasting blood sample collection, and completion of questionnaires about medical history, nutrition, and physical activity. Height and weight were measured with participants dressed in light-weight clothing without shoes, and BMI was calculated as [weight (kg) / height2 (m2)]. Waist circumference (cm) was measured midway between the lowest rib and the iliac crest to the nearest 0.1 cm, after inhalation and exhalation. Blood pressure was measured by using an electronic blood pressure monitor (Omron HEM-705CP, OMRON Healthcare Inc., Vernon Hills, Illinois) on the right arm of the participant, who was in a comfortable seated position after at least a 5-min rest. Participants were asked to avoid vigorous exercise, eating, drinking, smoking, or long exposure to cold or hot temperatures for 1 h before the measurement. Three measurements were taken, and the mean of the last two measurements was used for analysis. The metabolic syndromes were defined by the updated National Cholesterol Education Program Adult Treatment Panel III criteria (NCEP-ATP III) for Asian-Americans as presenting three or more of the following components: (1von Eynatten M. Humpert P.M. Retinol-binding protein-4 in experimental and clinical metabolic disease.Expert Rev. Mol. Diagn. 2008; 8: 289-299Crossref PubMed Scopus (39) Google Scholar) waist circumferences 90 cm or greater in men or 80 cm or greater in women; (2Yang Q. Graham T.E. Mody N. Preitner F. Peroni O.D. Zabolotny J.M. Kotani K. Quadro L. Kahn B.B. Serum retinol binding protein 4 contributes to insulin resistance in obesity and type 2 diabetes.Nature. 2005; 436: 356-362Crossref PubMed Scopus (1634) Google Scholar) triglycerides 1.7 mmol/L or greater; (3Graham T.E. Yang Q. Bluher M. Hammarstedt A. Ciaraldi T.P. Henry R.R. Wason C.J. Oberbach A. Jansson P.A. Smith U. et al.Retinol-binding protein 4 and insulin resistance in lean, obese, and diabetic subjects.N. Engl. J. Med. 2006; 354: 2552-2563Crossref PubMed Scopus (1082) Google Scholar) HDL-C less than 1.03 mmol/L in men or less than 1.30 mmol/L in women; (4Munkhtulga L. Nakayama K. Utsumi N. Yanagisawa Y. Gotoh T. Omi T. Kumada M. Erdenebulgan B. Zolzaya K. Lkhagvasuren T. et al.Identification of a regulatory SNP in the retinol binding protein 4 gene associated with type 2 diabetes in Mongolia.Hum. Genet. 2007; 120: 879-888Crossref PubMed Scopus (92) Google Scholar) fasting plasma 5.6 mmol/L or greater or previously diagnosed type 2 diabetes or on oral anti-diabetic medication; (5van Hoek M. Dehghan A. Zillikens M.C. Hofman A. Witteman J.C. Sijbrands E.J. An RBP4 promoter polymorphism increases risk of type 2 diabetes.Diabetologia. 2008; 51: 1423-1428Crossref PubMed Scopus (60) Google Scholar) blood pressure 130/85 mmHg or greater or current use of anti-hypertensive drugs. The study design and recruitment protocol of this population-based cohort has been described in detail elsewhere (22Ye 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.J. Am. Coll. Cardiol. 2007; 49: 1798-1805Crossref PubMed Scopus (160) Google Scholar) and was approved by the Institutional Review Board of the Institute for Nutritional Sciences. Written informed consent was obtained from all participants. The phenotypic characteristics of the study population are shown (see supplementary Table I). Plasma RBP4 protein levels were measured in duplicate by an in-house–developed sandwich ELISA method, utilizing affinity-chromatography purified polyclonal and monoclonal antibodies generated against recombinant human RBP4 protein. The assay system was subsequently cross-validated by western blotting. The intra-assay CV was 1.8–7.6% and inter-assay was 3.7–8.8% (21Qi Q. Yu Z. Ye X. Zhao F. Huang P. Hu F.B. Franco O.H. Wang J. Li H. Liu Y. et al.Elevated retinol-binding protein 4 levels are associated with metabolic syndrome in Chinese people.J. Clin. Endocrinol. Metab. 2007; 92: 4827-4834Crossref PubMed Scopus (175) Google Scholar). Fasting glucose, triglycerides, and HDL-cholesterol were measured enzymatically on an automatic analyzer (Hitachi 7080, Japan) with reagents purchased from Wako Pure Chemical Industries (Osaka, Japan) (22Ye 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.J. Am. Coll. Cardiol. 2007; 49: 1798-1805Crossref PubMed Scopus (160) Google Scholar). The tagSNPs for RBP4 were selected by using Tagger program implemented in Haploview V3.2 (http://www.broad.mit.edu/mpg/haploview) from the HapMap genotype data/phase II Mar08 for the Asians combined (JPT + CHB populations). According to the HapMap data, there are a total of 25 SNPs in the genomic region from ∼5kb upstream to ∼5kb downstream of RBP4 gene of which 10 are monomorphic. For the remaining 15 polymorphic single nucleotide polymorphisms (SNPs) [minor allele frequency (MAF) ≥ 5%], tagSNPs were selected with Tagger using a pairwise approach with an r2 threshold ≥ 0.8. This yielded 4 tagSNPs (rs17108973, rs3758538, rs17108993, and rs11187549) that capture all common variants in this gene region. We genotyped these 4 tagSNPs and an additional 3 SNPs (rs10882273, nonHapMap SNP rs3758539, and rs34571439) that were previously reported to be associated with type 2 diabetes or its related traits in Caucasians (4Munkhtulga L. Nakayama K. Utsumi N. Yanagisawa Y. Gotoh T. Omi T. Kumada M. Erdenebulgan B. Zolzaya K. Lkhagvasuren T. et al.Identification of a regulatory SNP in the retinol binding protein 4 gene associated with type 2 diabetes in Mongolia.Hum. Genet. 2007; 120: 879-888Crossref PubMed Scopus (92) Google Scholar, 6Craig R.L. Chu W.S. Elbein S.C. Retinol binding protein 4 as a candidate gene for type 2 diabetes and prediabetic intermediate traits.Mol. Genet. Metab. 2007; 90: 338-344Crossref PubMed Scopus (76) Google Scholar, 7Kovacs P. Geyer M. Berndt J. Kloting N. Graham T.E. Bottcher Y. Enigk B. Tonjes A. Schleinitz D. Schon M.R. et al.Effects of genetic variation in the human retinol binding protein-4 gene (RBP4) on insulin resistance and fat depot-specific mRNA expression.Diabetes. 2007; 56: 3095-3100Crossref PubMed Scopus (80) Google Scholar). SNP genotyping was performed with the GenomeLab™ SNPstream® Genotyping System (Beckman Coulter) according to the manufacturer's protocol. The success rate of genotyping was greater than 94%, and the concordance rate was greater than 99% based on 12% duplicate samples for each SNP. All the variants were in Hardy-Weinberg equilibrium (P > 0.21). The estimates of pairwise linkage disequilibrium (LD) between the 7 SNPs are shown (see supplementary Table II). Since the SNP rs17108973, rs10882273, and rs34571439 are in strong LD with each other (r2 > 0.84), only rs10882273 was used for further analyses. Hardy-Weinberg equilibrium was tested using a likelihood ratio test. TagSNP selection and LD estimation were performed with Haploview V3.2. Logistic regression models were applied to assess the association between RBP4 variants and metabolic syndrome components in case-control analyses. Generalized linear models were used to assess the associations between plasma RBP4 and triglyceride levels, as well as the associations of RBP4 variants with circulating RBP4 levels and the metabolic syndrome–related quantitative traits, including BMI, waist circumference, fasting glucose, fasting triglycerides, HDL-cholesterol, systolic blood pressure (SBP) and diastolic blood pressure (DBP). The dominant model was used for analyses of the SNPs (rs3758538 and rs17108993) with the small number of homozygous carriers of minor alleles (n < 12); otherwise, additive genetic model was applied. We excluded individuals with known diabetes or who were receiving glucose-lowering treatment (n = 267) from the quantitative trait analyses for fasting glucose. Individuals with self-reported dyslipidemia diagnoses status or lipid-lowering medication (n = 479) were excluded from the lipid-related quantitative trait analyses. We adjusted the blood pressure by adding 10 mmHg to SBP and 5 mmHg to DBP, respectively, for individuals who were receiving blood pressure–lowering treatment (23Tobin M.D. Sheehan N.A. Scurrah K.J. Burton P.R. Adjusting for treatment effects in studies of quantitative traits: antihypertensive therapy and systolic blood pressure.Stat. Med. 2005; 24: 2911-2935Crossref PubMed Scopus (459) Google Scholar). The association studies were performed either in the whole population for the SNPs with similar genotype distribution between Beijing and Shanghai, or in Beijing and Shanghai subpopulations separately for the remaining three SNPs (rs10882273, rs3758539, and rs17108993) with different genotype distribution between Beijing and Shanghai (see supplementary Table III). Meta-analyses were then conducted to evaluate the combined effects size across the two sub-populations under an additive or dominant genetic model. Triglycerides and BMI was log-transformed to reach normal distribution. All continuous traits were standardized to sex-specific Z-scores before analyses and effect sizes are presented as standardized βs (Z-score). We used the triangulation approach (Mendelian randomization) to explore the potential causal relationship between plasma RBP4 and triglyceride levels. We applied a stepwise approach including the following analyses: (1): plasma RBP4 levels–hypertriglyceridemia risk; (2) RBP4 gene–plasma RBP4 levels; (3) observed RBP4 gene–hypertriglyceridemia risk; and (4) expected RBP4 gene–hypertriglyceridemia risk. The expected effect size of the RBP4 gene–hypertriglyceridemia association was calculated by multiplying the magnitudes of RBP4 gene–plasma RBP4 association and of plasma RBP4–hypertriglyceridemia association. The associations were estimated using logistic regression or generalized linear regression as described above. Data management and statistical analyses were performed with SAS version 9.1 (SAS Institute, Cary, NC) unless otherwise indicated. All reported P values were nominal and two-sided. We first examined whether the genetic variants in the RBP4 gene were associated with plasma RBP4 levels. As shown in Table 1, the minor C-alleles of SNP rs10882273 (βz = −0.10SD [−0.17, −0.03], P = 0.0050) and rs3758538 (βz = −0.13SD [−0.24, −0.02], P = 0.0249) were all significantly associated with reduced plasma RBP4 levels in the total population, whereas the minor G-allele of the SNP rs17108993 was significantly associated with decreased plasma RBP4 levels in the Shanghai subpopulation only (βz = −0.19SD [−0.32, −0.05], P = 0.0061).TABLE 1Associations between RBP4 variants and plasma RBP4 levelsBeijing (n = 1,574)Shanghai (n = 1,636)Beijing + Shanghai (n = 3,210)SNP IDGenotypeMean (SE)β (95% CI)PMean (SE)β (95% CI)PMean (SE)β (95% CI)PP(heter)rs10882273 (+11880 T>C)TT43.0 (0.4)38.5 (0.3)40.7 (0.2)TC41.5 (0.6)−0.10 (−0.20, −0.01)0.036437.4 (0.6)−0.09 (−0.19, 0.00)0.0639.4 (0.4)−0.10 (−0.17, −0.03)0.0050aP values were from fix-effect model in meta-analyses.0.85CC41.7 (2.0)36.4 (2.1)39.1 (1.4)rs3758539(−803 G>A)GG42.7 (0.4)38.4 (0.3)40.5 (0.2)GA42.1 (0.6)−0.05 (−0.16, 0.05)0.3437.4 (0.6)−0.08 (−0.19, 0.02)0.1339.8 (0.4)−0.07 (−0.14, 0.01)0.08aP values were from fix-effect model in meta-analyses.0.69AA41.1 (2.3)36.7 (2.3)38.9 (1.6)rs3758538 (−1265 A>C)bDominant model was applied. Otherwise, additive model was used.AA42.5 (0.3)−0.15 (−0.31, 0.02)0.0838.3 (0.3)−0.10 (−0.26, 0.06)0.2140.4 (0.2)−0.13 (−0.24, −0.02)0.0249cWhen Beijing and Shanghai participants were combined, analyses were performed by pooling all 3,210 subjects together for SNPs (rs3758538 and rs11187549) with no significant difference in genotype distribution. Otherwise, meta-analyses were conducted to evaluate the combined effects size for SNPs with significant differences in genotype distribution.0.67AC+CC40.9 (0.9)37.1 (0.9)38.9 (0.6)rs17108993 (−3248 C>G)bDominant model was applied. Otherwise, additive model was used.CC42.4 (0.3)−0.01 (−0.18, 0.16)0.9338.4 (0.3)−0.19 (−0.32, −0.05)0.006140.4 (0.2)−0.10 (−0.28, 0.07)0.24dP values were from random-effect model in meta-analyses.0.10CG+GG42.3 (1.0)36.2 (0.7)39.1 (0.6)rs11187549(−3839 G>A)GG41.7 (0.6)37.7 (0.5)39.7 (0.4)GA42.3 (0.4)0.06 (−0.01, 0.13)0.1038.2 (0.4)−0.01 (−0.07, 0.06)0.8140.2 (0.3)0.03 (−0.02, 0.07)0.29cWhen Beijing and Shanghai participants were combined, analyses were performed by pooling all 3,210 subjects together for SNPs (rs3758538 and rs11187549) with no significant difference in genotype distribution. Otherwise, meta-analyses were conducted to evaluate the combined effects size for SNPs with significant differences in genotype distribution.0.16AA43.0 (0.6)37.4 (0.6)40.2 (0.4)BMI, body mass index; SNP, single nucleotide polymorphism; RBP4, retinol-binding protein 4. Effect sizes β (95% CI) are relative to the generalized linear regression model when using the normalized trait and represent the change in plasma RBP4 levels in SD units, on average, for each additional copy of minor allele. P values corresponded to standardized effects and were adjusted for age and BMI.a P values were from fix-effect model in meta-analyses.b Dominant model was applied. Otherwise, additive model was used.c When Beijing and Shanghai participants were combined, analyses were performed by pooling all 3,210 subjects together for SNPs (rs3758538 and rs11187549) with no significant difference in genotype distribution. Otherwise, meta-analyses were conducted to evaluate the combined effects size for SNPs with significant differences in genotype distribution.d P values were from random-effect model in meta-analyses. Open table in a new tab BMI, body mass index; SNP, single nucleotide polymorphism; RBP4, retinol-binding protein 4. Effect sizes β (95% CI) are relative to the generalized linear regression model when using the normalized trait and represent the change in plasma RBP4 levels in SD units, on average, for each additional copy of minor allele. P values corresponded to standardized effects and were adjusted for age and BMI. As suggested in our previous study (21Qi Q. Yu Z. Ye X. Zhao F. Huang P. Hu F.B. Franco O.H. Wang J. Li H. Liu Y. et al.Elevated retinol-binding protein 4 levels are associated with metabolic syndrome in Chinese people.J. Clin. Endocrinol. Metab. 2007; 92: 4827-4834Crossref PubMed Scopus (175) Google Scholar), plasma RBP4 levels (sex-specific Z-scores) were strongly associated with increased risk of hypertriglyceridemia (odds ratio [OR] 1.80 [1.64–1.96], P = 2.53E-38), obesity (OR 1.38 [1.24–1.54], P = 4.08E-9)/central obesity (OR 1.35 [1.25–1.45], P = 1.15E-14), low HDL-cholesterol (OR 1.11 [1.03–1.21], P = 0.0066) and hypertension (OR 1.18 [1.08–1.28], P= 0.0001) in this population-based sample of Chinese Hans (Table 2). We next performed six case-control studies for each SNP to examine whether they were associated with individual components of the metabolic syndrome, including obesity/central obesity, hypertriglyceridemia, low HDL-cholesterol, hyperglycemia
Referência(s)