Letícia A. Brondani, MSc, Guilherme C.K. Duarte, Luís H. Canani, PhD, and Daisy Crispim, PhD

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1 MET ver9-Brondani_1P.3d 10/05/13 1:38pm Page 1 MET ver9-Brondani_1P METABOLIC SYNDROME AND RELATED DISORDERS Volume X, Number X, 2013 Ó Mary Ann Liebert, Inc. Pp. 1 9 DOI: /met The Presence of At Least Three Alleles of the ADRB3 Trp64Arg (C/T) and UCP1-3826A/G Polymorphisms Is Associated with Protection to Overweight/Obesity and with Higher High-Density Lipoprotein Cholesterol Levels in Caucasian-Brazilian Patients with Type 2 Diabetes Letícia A. Brondani, MSc, Guilherme C.K. Duarte, Luís H. Canani, PhD, and Daisy Crispim, PhD Abstract Background: We investigated whether the A/G polymorphism (rs ) of the uncoupling protein 1 gene (UCP1) and the Trp64Arg polymorphism (rs4994) of the b3-adrenergic receptor gene (ADRB3) are associated with type 2 diabetes mellitus (T2DM) and features of metabolic syndrome in a Brazilian-Caucasian population. Methods: Both polymorphisms were genotyped in 1015 T2DM patients and 561 nondiabetic subjects. The combined effect of both polymorphisms on T2DM and metabolic syndrome related parameters was analyzed according to a triallelic inheritance pattern, by which at least three minor alleles from two loci are necessary for trait manifestation. Results: UCP1-3826A/G and ADRB3 Trp64Arg polymorphisms were not associated with T2DM (P > 0.05). Patients carrying the ADRB3 64Arg allele had higher fasting plasma glucose and high-density lipoprotein cholesterol (HDL-C) than patients with the Trp64Trp genotype (P = and P = 0.015, respectively). The 64Arg allele was also associated with protection against overweight/obesity (body mass index 25 kg/m 2 ; odds ratio [OR] = 0.598; P = 0.014). Interestingly, prevalence of overweight/obesity was lower among carriers of at least three minor alleles of the A/G and ADRB3 Trp64Arg polymorphisms than among patients with fewer than three minor alleles (54.5% vs. 79.1%; OR = 0.288; P = 0.007, respectively). Subjects with at least three minor alleles also had higher HDL-C levels (P = 0.018). Conclusions: UCP1-3826A/G and ADRB3 Trp64Arg polymorphisms may have a combined effect in the modulation of overweight/obesity and HDL-C levels in type 2 diabetes mellitus (T2DM) Caucasian-Brazilian patients. Introduction Type 2 diabetes mellitus (T2DM) is a heterogeneous group of metabolic disorders characterized by chronic hyperglycemia caused by insulin resistance (IR) in peripheral tissues and insufficient compensatory insulin secretion by pancreatic b-cells. 1 Most T2DM patients are obese, and obesity itself causes some degree of IR. 1 Thus, polymorphisms of genes involved in the pathogenesis of obesity may also predispose to T2DM. Two of the many candidate genes that could be involved in the development of both obesity and T2DM are the uncoupling protein 1 (UCP1) and b3- adrenergic receptor (ADRB3) genes, because they are key modulators of thermogenesis and energy expenditure. 2,3 UCP1 belongs to a family of anion transporters located in the inner mitochondrial membrane. 4,5 It uncouples oxidative metabolism from adenosine triphosphate (ATP) synthesis and dissipates energy as heat in response to cold and excessive calorie intake by allowing proton influx through the inner mitochondrial membrane of brown adipose tissue (BAT). 3,6 Studies in rodents have shown that UCP1-mediated thermogenesis in BAT contributes to controlling body Endocrine Division, Hospital de Clínicas de Porto Alegre, Universidade Federal do Rio Grande do Sul, Porto Alegre, Rio Grande do Sul, Brazil. 1

2 MET ver9-Brondani_1P.3d 10/05/13 1:38pm Page 2 2 BRONDANI ET AL. temperature, body weight, energy expenditure, and glucose and lipid metabolism. 6,7 Although it is reported that the amount of BAT in adult humans is reduced, it is still responsible for 1% 2% of the energy expenditure, preventing weight gain of 1 2 kg per year Thus, a decreased energy expenditure, due to a reduced amount of BAT, could increase the risk of obesity, metabolic syndrome, and T2DM if accumulated over decades. 11 It was originally believed that UCP1 was exclusively expressed in BAT. However, it has recently been reported that UCP1 is detected, albeit at low levels, in other human tissue types, such as white adipose tissue (WAT), skeletal muscle, retinal cells, and pancreatic islets Interestingly, UCP1 mrna expression in WAT appears to be lower in morbidly obese subjects than in lean subjects. 16,17 The results of several studies provide evidence of associations between the G allele of the A/G (rs ) polymorphism in the UCP1 promoter region and obesity, body mass index (BMI), or other obesity-related parameters Furthermore, there are also studies indicating that the G allele may also be associated with reduced highdensity lipoprotein cholesterol (HDL-C) levels, increased triglyceride or low-density lipoprotein cholesterol (LDL-C) levels, increased systolic (SBP) and/or diastolic blood pressure (DBP), and T2DM. 24,26 In common with UCP1, ADRB3 is expressed in BAT and WAT and plays an important role in inducing lipolysis and regulating energy homeostasis. 24,27 It is also the main adrenoreceptor that stimulates UCP1 expression. 27 The Trp64Arg (rs4994) polymorphism of the ADRB3 gene has been associated with weight gain and other obesity-related indices in a number of populations. 28,29 Interestingly, some studies have demonstrated that there are synergistic effects between the -3826A/G polymorphism of the UCP1 gene and the Trp64Arg polymorphism of the ADRB3 gene associated with an increased tendency to weight gain, 22 a lower resting metabolic rate, 30 and resistance to weight loss or failure to maintain weight after a low-calorie diet. 31 In view of the above, we conducted this study to investigate whether the A/G polymorphism (rs , UCP1 gene) and the Trp64Arg polymorphism (rs4994, ADRB3gene), individually or in combination, were associated with T2DM or metabolic syndrome related parameters in a sample of Caucasian T2DM patients from Brazil. Methods Subjects A total of 1576 unrelated subjects were enrolled in the study. The sample population comprised 1015 T2DM patients and 561 nondiabetic subjects. T2DM patients were enrolled in a multicenter study that began recruiting patients in South Brazil in The project was originally designed to study genetic risk factors associated with T2DM and its chronic complications, such as diabetic nephropathy and diabetic retinopathy. It initially had four participating centers located in teaching hospitals in the Brazilian state of Rio Grande do Sul, specifically the Grupo Hospitalar Conceição, the Hospital São Vicente de Paula, the Hospital Universitário de Rio Grande, and the Hospital de Clínicas de Porto Alegre. All patients with T2DM attending the endocrine clinics in these hospitals from 2002 to 2011 were included. A detailed description of the study can be found elsewhere. 32 T2DM was defined as a diagnosis of diabetes after the age of 35 with no insulin therapy during the first year after diagnosis and no previous episodes of ketoacidosis. 1 The nondiabetic group was recruited from healthy blood donors who did not have T2DM or a family history of the disease. Subjects with glycated hemoglobin (HbA1c) < 5.7% were considered as not having diabetes or prediabetes 1 and were included in the nondiabetic sample. The distributions of mean age and gender were similar for T2DM patients and nondiabetic subjects. All subjects had Caucasian ancestry (mostly of Portuguese, Spanish, Italian, and German descent), and were self-defined as white. A standard questionnaire was used to collect information on age, age at T2DM diagnosis, and drug treatment. All patients underwent physical examination and laboratory evaluation. BMI was calculated as weight in kilograms divided by height in meters squared. Obesity was defined as BMI 30 kg/m 2, and overweight/obesity as BMI 25 kg/m 2. Waist circumference was measured at the narrowest point as viewed from the front. Blood pressure was measured twice with a 5-min rest between measurements, the subject seated, and using a mercury sphygmomanometer (Korotkoff phases I and V). The means of both measurements were used to calculate SBP and DBP. Arterial hypertension was defined as blood pressure 140/90 mmhg or use of antihypertensive drugs irrespective of blood pressure at the time of assessment. Serum samples were collected for laboratory testing after a 12-h fast. Glucose levels were determined using the glucose oxidase method; HbA1c was quantified using an ionexchange high-performance liquid chromatography (HPLC) procedure (Merck-Hitachi L-9100 Analyser, Merck, Darmstadt, Germany), and total plasma cholesterol, HDL-C and triglycerides were assayed using enzymatic methods. LDL-C was calculated using the Friedewald equation. The findings of this study did not influence the patients diagnoses or treatment in any way. The study protocol was approved by the ethic committees of the participating centers, and all subjects gave informed consent in writing. Genotyping A standardized salting-out procedure was used to extract DNA from peripheral blood leukocytes. 33 Both UCP1-3826A/G (rs ) and ADRB3 Trp64Arg (T/C; rs4994) polymorphisms were detected using primers and probes contained in Human Custom TaqMan Genotyping Assays 40x (Assays-By-Design Service; Life Technologies, Foster City, CA). The primer and probe sequences used for genotyping the A/G and Trp64Arg polymorphisms were as follows: UCP1, 5 -TTTGTGCAGCGATTTCTGATTGAC-3 (forward primer), UCP1, 5 -CTCCTTTCCTTTTTGAAAATG TAGAACACA-3 (reverse primer), UCP1-FAM, 5 -AATG CACTCGATCAAA-3 ; UCP1-VIC, 5 -CAAATGCACTTGA TCAAA-3 ; ADRB3, 5 -GCAACCTGCTGGTCATCGT-3 (forward primer), ADRB3, 5 -ACGAACACGTTGGTCATG GT-3 (reverse primer), ADRB3-FAM, 5 -CGGAGTCCA GGCGAT-3, ADRB3-VIC, 5 -TCGGAGTCCGGGCGAT-3. Reactions were conducted in a 96-well plate, in a total reaction volume of 5 ml, using 2 ng of genomic DNA, Taq- Man Genotyping Master Mix 1 (Life Technologies), and Custom TaqMan Genotyping Assay 1x. Plates were then

3 MET ver9-Brondani_1P.3d 10/05/13 1:38pm Page 3 ADRB3 AND UCP1 OVERWEIGHT AND HDL-C 3 placed in a real-time PCR thermal cycler (7500 Fast Real PCR System; Life Technologies) and heated for 10 min at 95 C, followed by 40 cycles of 95 C for 15 sec and 60 C for 1 min. Fluorescence data files from each plate were analyzed using automated allele-calling software (SDS 2.1; Life Technologies). Only 938 patients were successfully genotyped for both of the polymorphisms under analysis. All amplification reactions were performed twice. The genotyping success rate was higher than 95%, with a calculated error rate based on PCR duplicates of 0.5%. Statistical analyses Results are expressed as means standard deviations (SD), percentages, or median (range). Allelic frequencies were determined by gene counting and departures from the Hardy Weinberg equilibrium were verified using the chisquared test. Clinical and laboratory characteristics were compared using analysis of variance (ANOVA), the unpaired Student t-test, or chi-squared as appropriate. Associations between UCP1-3826A/G and ADRB3 Trp64Arg polymorphisms and T2DM or different metabolic syndrome related variables were tested using general linear model (GLM) analyses, adjusting for covariates. A triallelic pattern of inheritance (by which at least three minor alleles from two loci are necessary for trait manifestation) was used to evaluate the combined effect of both polymorphisms on T2DM and metabolic syndrome related parameters. 34,35 This is a useful method to analyze the combination between two polymorphisms when one of them is rare (in our case, the ADRB3 Trp64Arg polymorphism) once it dichotomizes the sample to only two groups of polymorphic combinations, increasing the statistical power. These analyses were used to compare two groups: (1) Subjects with at least three minor alleles (less frequent alleles) of the Trp64Arg (T/C) and -3826A/G polymorphisms (C/C-A/G, C/C-G/G, or C/T-G/ G genotypes), and (2) subjects with fewer than three minor alleles (T/T-A/A, T/T-A/G, T/T-G/G, C/T-A/A, or C/T- A/G genotypes). GLM analyses were also used to evaluate the combined effect of at least three minor alleles of the two analyzed polymorphisms in modulating T2DM or metabolic syndrome related characteristics (SBP, DBP, BMI, waist circumference, total cholesterol, HDL-C, LDL-C, and triglyceride levels). Logistic regression analyses were performed to evaluate the independent association of the UCP1-3826A/G and ADRB3 Trp64Arg polymorphisms with T2DM, after adjusting for age and gender. Moreover, in T2DM patients, logistic regression analyses were also performed with obesity or overweight/obesity as dependent variables and age, gender, and presence of at least three minor alleles of the two analyzed polymorphisms as independent variables. Variables such as triglycerides that had skewed distribution were log-transformed before analyses. All analyses were performed with the SPSS 18.0 software (SPSS, Chicago, IL). P values < 0.05 were considered significant. Results The mean age of the 1015 T2DM patients analyzed for this study was years, mean duration of T2DM was years, mean HbA1c was %, and mean BMI was kg/m 2. Males comprised 44.9% (n = 456) of the sample, 71.6% (n = 727) of all patients had arterial hypertension, and 35.9% (n = 364) had obesity. Table 1 lists the allele and genotype frequencies of UCP1-3826A/G and ADRB3 Trp64Arg polymorphisms in patients with T2DM and in nondiabetic subjects. The genotype frequencies of A/G and Trp64Arg polymorphisms were in agreement with those predicted by the Hardy Weinberg equilibrium in nondiabetic subjects (P = and P = 0.999, respectively) and did not differ between T2DM and nondiabetic groups (Table 1). These results did not change after adjusting for age and gender (Table 1). The frequency of the G allele was in T2DM patients and in nondiabetic subjects (P = 0.851), and the frequency of the 64Arg (C) allele was in T2DM patients and in nondiabetic subjects (P = 0.680). Moreover, the frequency of at least three minor alleles of the two analyzed polymorphisms did not differ between T2DM patients and nondiabetic subjects (2.0% vs. 2.0%; P > 0.999), and this result did not change after adjusting for age and gender (OR = 1.307, CI 95% ; P = 0.651). b T1 Table 1. Allele and Genotype Frequencies of UCP1-3826A/G and ADRB3 Trp64Arg Polymorphisms in T2DM Patients and Nondiabetic Subjects T2DM patients Nondiabetic subjects Unadjusted P* Adjusted OR (95% CI)/P { UCP A/G n = 981 n = 534 A/A 489 (49.8) 263 (49.3) A/G 370 (37.7) 211 (39.5) ( )/0.926 G/G 122 (12.4) 60 (11.2) ( )/0.956 A G ADRB3 Trp64Arg (T/C) n = 1015 n = 561 C/C 14 (1.4) 3 (0.5) ( )/0.269 C/T 142 (14.0) 82 (14.6) ( )/0.160 T/T 859 (84.6) 476 (84.8) 1 C T Data are presented as number (%) or proportion. *P values were obtained by chi-squared tests. { P values were obtained from logistic regression analyses adjusted for age and gender. T2DM, type 2 diabetes mellitus; OR, odds ratio; CI, confidence interval.

4 MET ver9-Brondani_1P.3d 10/05/13 1:38pm Page 4 4 BRONDANI ET AL. Table 2. Clinical and Laboratory Characteristics of T2DM Patients, Broken Down by Presence of Different UCP1-3826A/G Genotypes UCP A/G genotypes A/A (n = 489) A/G (n = 370) G/G (n = 122) F/P* P { Age (years) T2DM duration (years) 12 (1 75) 11 (1 54) 11 (1 44) Gender (% males) HbA1c % (mmol/mol) a (54) (56) (55) 0.668/0.513 FPG (mmol/l) a /0.227 SBP (mmhg) b /0.321 DBP (mmhg) b /0.953 BMI (kg/m 2 ) a /0.270 Waist (cm) a /0.689 Total cholesterol (mmol/l) c /0.511 HDL-C (mmol/l) c /0.466 LDL-C (mmol/l) c /0.391 Triglycerides (mmol/l) c 1.69 ( ) 1.72 ( ) 1.86 ( ) 0.673/0.511 Data are mean standard deviation (SD), median (minimum maximum values) or %. *F and P values obtained from general linear model univariate analyses, after adjustment for: a age and gender; b age, gender, use of medication for hypertension, and BMI; c age, gender and use of hypolipidemic medications; { P values are according to chi-squared test or analysis of variance (ANOVA) as appropriate. T2DM, type 2 diabetes mellitus; HbA1c, glycated hemoglobin; FPG, fasting plasma glucose; SBP, systolic blood pressure; DBP, diastolic blood pressure; BMI, body mass index; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol. T2 c T3 c Table 2 summarizes the clinical and laboratory data for the T2DM patients according to the different genotypes of the UCP1-3826A/G polymorphism. There were no significant differences among the three A/G genotypes in terms of SBP and DBP, BMI, waist circumference, total cholesterol, HDL-C, LDL-C, HbA1c, fasting plasma glucose, or triglyceride levels. Table 3 illustrates the clinical and laboratory data for the T2DM patients grouped according to the presence of the ADRB3 64Arg (C) allele. Patients carrying the 64Arg allele (C/C + C/T) had higher fasting plasma glucose and HDL-C levels than patients with the Trp64Trp (T/T) genotype, after adjusting for age and gender for glucose, and age, gender, and use of hypolipidemic medications for HDL-C (P = and P = 0.015, respectively). None of the other variables shown in Table 3 differed between patients carrying the 64Arg (C) allele and patients with the Trp64Trp genotype. Table 4 lists clinical and laboratory characteristics for the T2DM patients grouped according to the presence of at least three minor alleles of the UCP1-3826A/G and ADRB3 Trp64Arg polymorphisms. Interestingly, carriers of at least three minor alleles of the A/G and Trp64Arg b T4 Table 3. Clinical and Laboratory Characteristics of T2DM Patients, Grouped According to the Presence of the ADRB3 64Arg (C) Allele ADRB3 Trp64Arg genotypes (T/C) T/T (n = 859) C/T-C/C (n = 156) F/P* P { Age (years) T2DM duration (years) 12 (1 75) 10 (1 43) Gender (% males) HbA1c % (mmol/mol) a (54) (56) 0.261/0.610 FPG (mmol/l) a / SBP (mmhg) b /0.658 DBP (mmhg) b /0.491 BMI (kg/m 2 ) a /0.369 Waist (cm) a /0.368 Total cholesterol (mmol/l) c /0.266 HDL-C (mmol/l) c /0.015 LDL-C (mmol/l) c /0.583 Triglycerides (mmol/l) c 1.72 ( ) 1.86 ( ) 1.148/0.284 Data are mean standard deviation (SD), median (minimum maximum values) or %. *F and P values obtained from general linear model univariate analyses, after adjustment for: a age and gender; b age, gender, use of hypertension medication, and BMI; c age, gender and use of hypolipidemic medications. { P values are according to the chi-squared test or analysis of variance (ANOVA) as appropriate. T2DM, type 2 diabetes mellitus; HbA1c, glycated hemoglobin; FPG, fasting plasma glucose; SBP, systolic blood pressure; DBP, diastolic blood pressure; BMI, body mass index; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol.

5 MET ver9-Brondani_1P.3d 10/05/13 1:38pm Page 5 ADRB3 AND UCP1 OVERWEIGHT AND HDL-C 5 Table 4. Clinical and Laboratory Characteristics of T2DM Patients, Broken Down by Presence or Absence of Three or More Minor Alleles of the ADRB3 Trp64Arg (T/C) and UCP1-3826A/G Polymorphisms UCP1 3826A/G ADRB3 Trp64Arg (T/C) interaction < 3 alleles (n = 914) 3 alleles (n = 24) F/P* P { Age (years) T2DM duration (years) 12 (1 75) 11 (1 32) Gender (% males) HbA1c % (mmol/mol) a (54) (59) 0.184/0.668 FPG (mmol/l) a /0.665 SBP (mmhg) b /0.621 DBP (mmhg) b /0.646 BMI (kg/m 2 ) a /0.300 Waist (cm) a /0.053 Total cholesterol (mmol/l) c /0.536 HDL-C (mmol/l) c /0.018 LDL-C (mmol/l) c /0.667 Triglycerides (mmol/l) c 1.7 ( ) 1.7 ( ) 0.159/0.690 Data are mean standard deviation (SD), median (minimum maximum values) or %. F and P values obtained from general linear model univariate analyses, after adjustment for: a age and gender; b age, gender, use of medication for hypertension, and BMI; c age, gender and use of hypolipidemic medications. { P values are according to the chi-squared test or analysis of variance (ANOVA) as appropriate. T2DM, type 2 diabetes mellitus; HbA1c, glycated hemoglobin; FPG, fasting plasma glucose; SBP, systolic blood pressure; DBP, diastolic blood pressure; BMI, body mass index; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol. F1 c polymorphisms had higher HDL-C levels than patients with fewer than three minor alleles, after adjustment for age, sex, and use of hypolipidemic medications (P = 0.018). None of the other variables analyzed differed between patients carrying at least three minor alleles of the two polymorphisms and patients with fewer than three minor alleles, after adjustment for covariates. Our data suggest that the A/G and Trp64Arg polymorphisms, individually or in combination, are not independently associated with obesity in T2DM patients (P = and P = 0.478, respectively; Fig. 1). Subjects with different UCP1-3826A/G genotypes had similar overweight/obesity prevalence rates (A/A vs. A/G + G/G; P = 0.714), but overweight/ obesity prevalence was lower among patients carrying the ADRB3 64Arg (C) allele than among those with the Trp64Trp genotype, after adjustment for age and gender (71.7% vs. 80.1%; OR = 0.598, CI 95% ; P = 0.014). Interestingly, T2DM patients carrying at least three minor alleles of the two polymorphisms had a lower prevalence of overweight/obesity than patients carrying fewer than three minor alleles, after adjustment for age and gender (54.5% vs. 79.1%; OR = 0.288, CI 95% ; P = 0.007; Fig. 1). Discussion In this study, we investigated whether the UCP1-3826A/ G and ADRB3 Trp64Arg polymorphisms were associated with T2DM or metabolic syndrome related characteristics in Caucasian subjects from South Brazil. Genotype and allele frequencies of both polymorphisms were similar for T2DM patients and nondiabetic subjects, suggesting that these polymorphisms are not important risk factors for T2DM in our population. T2DM patients carrying the ADRB3 64Arg (C) allele had higher fasting plasma glucose and HDL-C levels than Trp64Trp patients. The 64Arg (C) allele was also associated with a y40% reduction in the risk of overweight/ obesity. Remarkably, the presence of at least three minor alleles of the UCP1-3826A/G and ADRB3 Trp64Arg polymorphisms was associated with a y70% reduction in the risk of overweight/obesity, suggesting a combined effect between these two polymorphisms on modulating overweight/obesity in T2DM patients. Furthermore, a combined effect on HDL-C levels was also observed between the A/G and Trp64Arg polymorphisms. It is worth mentioning that the frequencies of these two polymorphisms in our samples were similar to frequencies that have been observed in other Caucasian populations. 19,22,36 39 The fact that UCP1 has been found to increase energy expenditure 3 has led to the UCP1 gene being regarded as a candidate gene for obesity, T2DM, or related traits. For this reason, a number of genetic studies have investigated the relationship between UCP1 locus and susceptibility to these disorders, with particular attention focussed on the -3826A/G polymorphism. 26 The G allele has been associated with reduced mrna expression in intraperitoneal adipose tissue of obese subjects, indicating that this polymorphism has functional importance. 17 Although some studies have reported an association between the G allele and T2DM, 19,22,40,41 a recent meta-analysis including 3071 T2DM cases and 2561 nondiabetic controls from different populations indicated that this polymorphism in not associated with T2DM 42 and is in agreement with the data reported here. On the other hand, several independent studies have found evidence of an association between the -3826G allele and obesity, BMI, or other obesity-related parameters, 24,25 which conflicts with our results. Control of lipolysis in adipocytes is critical for overall body mass homeostasis. Human adipocytes express all three b-adrenoreceptors, which in turn are believed to participate in regulating the action of catecholamines on fat tissue. Abnormal expression of any subtype, or a change in its ability to recognize ligand and transducer intracellular signals, could have profound effects on adipose tissue metabolism and

6 MET ver9-Brondani_1P.3d 10/05/13 1:38pm Page 6 6 BRONDANI ET AL. FIG. 1. Prevalence of obesity and overweight/obesity in type 2 diabetes mellitus (T2DM) patients by different genotypes of the ADRB3 Trp64Arg and UCP1-3826A/G polymorphism. (A) Prevalence of obesity and overweight/obesity, by different ADRB3 Trp64Arg genotypes. (*) P = 0.478; (**) P = (B) Prevalence of obesity and overweight/obesity by different UCP1-3826A/G genotypes. (*) P = 0.520; (**) P = (C) Prevalence of obesity and overweight/obesity by presence or absence of at least three three minor alleles of the ADRB3 Trp64Arg and UCP1-3826A/G polymorphisms. (*) P = 0.795; (**) P = Please note that the overweight group in the figure includes all patients with body mass index 25 kg/m 2. basal metabolic rate. 43 Therefore, ADRB3 may play a significant role in the control of lipolysis and UCP1-mediated thermogenesis in BAT, influencing energy expenditure. 44 The Trp64Arg (T/C) polymorphism in the ADRB3 gene has been related to abnormal b3-adrenergic signaling 43 and has been associated with obesity and related metabolic disorders in some, but not all, populations. 21,24,29,38,39,45 51 A recent meta-analysis conducted by Kurokawa et al. 28 in 97 studies, involving 44,833 individuals, showed that the Trp64Arg polymorphism is associated with BMI in East Asians, but not in Europeans. Some hypotheses propose that obesity and T2DM are widespread in human populations because susceptibility alleles have been selected for during human evolution and have more recently become unfavorable as a consequence of changed environment conditions and lifestyles. 52 In particular, it has been suggested that alleles responsible for T2DM might have evolved as thrifty variants in ancient populations. 52 In this context, Cagliani et al. 53 showed that the ADRB3 gene has been subjected to a significant selective sweep in African populations and that the Trp64 allele probably represents the selection target. Therefore, we hypothesized that the association between the ADRB3 Trp64Arg polymorphism and overweight/obesity might also be explained by the thrifty genotype theory, with the Trp64 allele possibly being a thrifty variant associated with increased fat accumulation during human evolution. Some studies have shown that HDL-C and LDL-C levels are closely related to energy expenditure. Higuchi et al. 54 conducted a study with 5 healthy male subjects who ran on a treadmill for 50 min, five times a week. After 4 weeks of exercise, HDL-C cholesterol levels were significantly increased in these subjects. Ferguson et al. 55 conducted a study in which 11 male subjects ran on a treadmill at 70% maximal oxygen consumption (VO 2 max), reporting that LDL-C and total cholesterol had decreased and HDL-C increased after exercise. These two studies indicate that energy expenditure can be a potent factor for regulating HDL-C and LDL-C levels. Therefore, the association that we are describing between the Arg64 allele and elevated HDL-C levels appears plausible from a biological perspective, because overweight/obesity prevalence was lower among T2DM patients carrying the Arg64 allele, which could be explained if these patients have increased energy expenditure. An earlier study conducted with Swedish subjects with varying degrees of glucose tolerance showed that in sibling pairs discordant for the Trp64Arg polymorphism, those siblings carrying the Arg64 allele had higher 2-h glucose levels than siblings with the Trp64Trp genotype. 50 Malczewska-Malec et al. 49 reported that patients with BMI < 30 kg/m 2 carrying the Trp64Trp genotype had lower glucose concentrations during oral glucose tolerance tests (OGTTs), which suggests better glucose tolerance. The results of both studies agree with our findings indicating that 64Arg (C) allele carriers had higher fasting plasma glucose than subjects homozygous for the Trp64 allele (T/T). Nevertheless, the mechanism by which this

7 MET ver9-Brondani_1P.3d 10/05/13 1:38pm Page 7 ADRB3 AND UCP1 OVERWEIGHT AND HDL-C 7 polymorphism could influence glucose metabolism is still not clear. The ADRB3 Trp64Arg polymorphism and the UCP1-3826A/G polymorphism have been observed to exert a synergistic effect on weight gain among obese French subjects, 22 on resting metabolic rate in obese Finns, 30 on resistance to weight loss in Finnish women and obese Japanese subjects, 31,56 and on weight maintenance in Finnish women, 31 supporting the hypothesis that obesity may to some extent be the result of additive genetic factors. The association that we report here, suggesting a combined effect between the ADRB3 Trp64Arg and UCP1-3826A/G polymorphisms on modulation of overweight/obesity is in agreement with the view that these variants have an additive pattern of influence on modulation of obesity. On the other hand, other studies did not find any evidence that an interaction between these two polymorphisms had an influence on resistance to a low-calorie diet, BMI, or triglyceride levels, or on several metabolic parameters related to obesity and T2DM. 24 Some factors could have interfered with the findings of the present study. First, we cannot rule out the possibility of population stratification bias when analyzing our samples, even though only Caucasian-Brazilian subjects were studied and both T2DM patients and nondiabetic subjects were recruited from the same hospitals, thus reducing the risk of false-positive/negative associations due to this bias. Second, hypolipidemic and/or hypertension treatment could have played a role on the reported data regarding T2DM patients. However, we minimized such a possibility by including the presence of these treatments as covariates on the multiple linear regression analyses. Third, we cannot fully exclude the possibility of a type II error when analyzing the association between the two analyzed polymorphisms and T2DM. Our power to detect an OR 1.4 was 83% for the UCP1-3826A/ G polymorphism but was less than 80% for the ADRB3 Trp64Arg polymorphism. Nevertheless, taking into account that the frequencies of both polymorphisms were very similar between T2DM patients and nondiabetic subjects, it is unlikely that these variants could have an important role in the pathogenesis of T2DM. In addition, our data regarding metabolic syndrome related features in the T2DM sample must be interpreted with caution because for some of these characteristics we have less than 80% power to detect small differences between the different genotypes of the analyzed polymorphisms. Finally, due to the small number of subjects homozygous for both minor alleles of the two polymorphisms (UCP1 G/G ADRB3 C/C; n = 2), we were not able to compare the analyzed characteristics among subjects carrying zero to four minor alleles, which would be the best way to prove interaction between these polymorphisms. Thus, our data does not answer whether the presence of four minor alleles increases the effect on the analyzed characteristics as compared to the presence of two or three minor alleles. In conclusion, the results of this study suggest that the ADRB3 Trp64Arg polymorphism is associated with fasting glucose and HDL-C levels and with protection against overweight/obesity. Furthermore, the UCP1-3826A/G and ADRB3 Trp64Arg polymorphisms might act in combination in the modulation of overweight/obesity and HDL-C levels. The associations we describe appear to be biologically plausible, taking into account the recognized role that ADRB3 and UCP1 genes play in energy expenditure. It is worth noting that the present data should be interpreted with caution when translated to nondiabetic subjects once we only analyzed the role of ADRB3 and UCP1 polymorphisms on metabolic syndrome related features in T2DM patients. Acknowledgments This study was supported by Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq), Fundo de Incentivo à Pesquisa (FIPE) from Hospital de Clínicas de Porto Alegres, Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES), and Programa de Apoio a Núcleos de Excelência (PRONEX) FAPERGS/CNPQ n 008/2009. Author Disclosure Statement The authors declare they have no conflicts of interest. All authors conceived and designed the study, performed the statistical analysis and interpretation, drafted the manuscript, revised for important intellectual content, and read and approved the final manuscript. References 1. American Diabetes Association. Diagnosis and classification of diabetes mellitus. 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