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TaqIA polymorphism in dopamine D2 receptor gene complicates weight maintenance in younger obese patients
Nutrition 28 (2012) 996–1001
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Applied nutritional investigation
TaqIA polymorphism in dopamine D2 receptor gene complicates weight maintenance in younger obese patients Julia K. Winkler a, c, Annika Woehning M.Sc. a, Jobst-Hendrik Schultz M.D. b, Maik Brune M.D. a, Nigel Beaton M.Sc. c, Tenagne Delessa Challa M.Sc. c, Stella Minkova M.D. a, Eva Roeder M.Sc. c, Peter P. Nawroth M.D. a, Hans-Christoph Friederich M.D. b, Christian Wolfrum Ph.D. c, Gottfried Rudofsky M.D. a, * a
Department of Medicine I and Clinical Chemistry, University of Heidelberg, 69120 Heidelberg, Germany Department of Psychosomatic and General Internal Medicine, University of Heidelberg, 69120 Heidelberg, Germany c Swiss Federal Institute of Technology, Institute of Food Nutrition and Health, 8603 Schwerzenbach, Switzerland b
a r t i c l e i n f o
a b s t r a c t
Article history: Received 8 September 2011 Accepted 27 December 2011
Objective: The A1 allele of the TaqIA polymorphism in the dopamine D2 receptor gene (rs1800497) has been associated with obesity. However, the effect of the polymorphism on the success in weight loss and/or weight maintenance during weight-loss programs has not been evaluated thus far. Methods: The rs1800497 was genotyped in 202 (135 female, 67 male) severely obese individuals with an initial body mass index of 41.7 0.5 kg/m2 who participated in a weight-loss program consisting of a weight-loss phase with a formula diet (12 wk) and a weight-maintenance phase (40 wk). Measurements were collected at baseline, after the weight-loss phase, and at the end of the weight-maintenance phase at 1 y. Results: Genotyping revealed 4 A1A1, 67 A1A2, and 131 A2A2 genotype carriers. Of the 202 subjects in the program, 66.8% completed the program and 33.2% terminated prematurely. Neither the attrition rate (P ¼ 0.44) nor the overall weight loss was influenced by the different genotypes (P ¼ 0.96). However, younger A1þ participants (A1A1 and A1A2) had a higher body mass index at all time points (baseline, P ¼ 0.04; after weight loss, P ¼ 0.05; after weight maintenance, P ¼ 0.02). They also showed less overall weight loss (P ¼ 0.05), which derived mainly from a greater weight regain during the maintenance phase (P ¼ 0.02). Conclusion: In this program, younger A1þ participants exhibited problems in maintaining weight loss during a weight-loss program. Ó 2012 Elsevier Inc. All rights reserved.
Keywords: Weight maintenance Weight regain Weight loss Obesity Dopamine D2 receptor gene rs1800497
Introduction Obesity is a serious global health issue because it has become a worldwide epidemic, with at least 1.1 billion adults and 10% of children being overweight or obese [1,2]. Excess body weight can lead to the metabolic syndrome and a decreased life expectancy owing to cardiovascular disease, type 2 diabetes, or certain types of cancer [2,3]. Obesity results mainly from an imbalance between energy intake and energy expenditure, which are influenced by environmental and genetic factors [4]. Polymorphisms in the This work was supported by a grant from the European Foundation of the Study of Diabetes (G. R., C. W.) and the ERC (AdipoDif; C. W.). * Corresponding author. Tel.: þ49-6221-563-8620; fax: þ49-6221-565-793. E-mail address: [email protected] (G. Rudofsky). 0899-9007/$ - see front matter Ó 2012 Elsevier Inc. All rights reserved. doi:10.1016/j.nut.2011.12.018
dopamine D2 receptor (DRD2) and obesity (ob) genes together account for about 20% of the variance in the body mass index (BMI), particularly in younger women [5,6]. One such variant is the A1 allele of the TaqIA single nucleotide polymorphism in the DRD2 gene (rs1800497), for which a decreased brain DRD2 density in the striatum has been described [5,7]. This decreased receptor density has been associated with an increased prevalence of obesity [6,8–13] and substance abuse [14–18]. As an underlying cause, A1 allele carriers have been postulated to have an impaired reward circuitry leading to a state of “reward deficiency syndrome” [18,19]. This condition is aggravated by an increased impulsivity [15,20]. Thus, these behavioral changes make A1þ carriers prone to overeating [21–26] and obesity. However, the effect of the A1 allele on the success of weightloss programs is largely unknown [11]. The purpose of this study was to analyze whether the A1 allele of rs1800497 decreases the
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likelihood of weight loss and/or weight maintenance in severely obese subjects during a 1-y weight-loss program. Materials and methods Weight-loss program OPTIFAST52 The present study was a monocentric, longitudinal investigation involving obese individuals who participated in the multidisciplinary, non-surgical weight, Inc., Vevey, Switzerland). In 1999 the OPTIloss program (OPTIFAST52; Nestle FAST52 program was established in Germany with the aim of treating obese people of at least 18 y of age and with a BMI of 30 kg/m2 or higher. OPTIFAST52 combines the expertise of physicians, dietitians, physical therapists, and psychologists in an outpatient-based, comprehensive, multidisciplinary approach. The program consists of a four phased lifestyle intervention with four modules (psychology, medicine, dietetics, and exercise) and is designed for 52 wk. During the program, closed groups of 10 to 15 people meet for weekly sessions of about 3.5 h. The four program phases include 1) a 1-wk introduction time; (ii) a 12-wk period of a low-calorie formula diet (800 kcal/d; five packets at 160 kcal dissolved in 250–300 mL of water as a meal replacement; OPTIFAST52 800 formula) accompanied by 12 medical examinations, 12 exercise units, 2 lessons of behavior therapy, and 2 sessions of nutrition counseling; 3) a 6-wk refeeding phase in which solid food is reintroduced and the formula diet is gradually replaced by a normal diet with only a small change of total energy intake accompanied by six medical examinations, six exercise units, two lessons of behavior therapy, and six sessions of nutrition counseling; and 4) a 33-wk stabilization phase in which energy intake is increased step by step to an individual level that allows weight stabilization and in which nutritional education and behavior modification are intensified to determine coping strategies and to achieve long-term weight control, accompanied by 9 medical examinations, 17 exercise units, 26 lessons of behavior therapy, and 8 sessions of nutritional counseling. For the purpose of the present analyses, it was sufficient to divide the program into two parts: a weight-loss phase of 12 wk and a weight-maintenance phase of 40 wk. In this study, data from the beginning (T0), from week 12 after the formula-based weight-loss phase (T1), and from week 52 after the weightmaintenance period of 40 wk (T2) were included. Study population The ethics committee at the University of Heidelberg approved the study and written informed consent was given by all participants. A population of 202 obese adults taking part in the OPTIFAST52 program at the University Hospital of Heidelberg from 2005 through 2010 was analyzed. The study sample included 135 female and 67 male subjects 18 to 72 y old. The mean BMI at the beginning of the program was 41.7 0.5 kg/m2. The 1-y program was completed by 135 participants and discontinued by 67. Anthropometric and laboratory measurements During the 52-wk program, weight and blood pressure were monitored regularly. Body weight was determined on a calibrated scale (model 764, Seca, Hamburg, Germany) after an 8-h fast. The BMI was calculated as weight in kilograms divided by the square of the height in meters. To control laboratory values, blood samples were collected at five different time points during the program after a fasting time of at least 8 h. The laboratory parameters were measured in the central laboratory of the University Hospital of Heidelberg. In this study, concentrations of blood glucose, total cholesterol, low-density lipoprotein (LDL), high-density lipoprotein (HDL), and triacylglycerols from T0, T1, and T2 were included. Blood collection tubes contained sodium fluoride (Sarstedt S-Monovette, no. 04.1903, Nümbrecht, Germany) for the assessment of glucose levels and lithium heparin (Sarstedt S-Monovette, no. 01.1634) for the assessment of lipid profiles. Samples were directed to the central laboratory without delay, and plasma was separated out and analyzed on a ADVIA 2400 chemistry analyzer (Siemens, Eschborn, Germany). Concentrations of glucose, total cholesterol, HDL, and triacylglycerols were determined using the appropriate Siemens test kits (nos. B01-4597-01, 04993681, 08058065, and B01-4133-01, respectively) according to the manufacturer’s instructions. Further, concentrations of LDL were calculated according to the Friedewald equation. Questionnaire on body weight and lifestyle As part of the diagnostic procedure, all participants in the OPTIFAST52 program completed a non-standardized psychological questionnaire provided by , Inc.) at the beginning of the program. The questionthe manufacturer (Nestle naire includes information about the development of body weight, previous diets, previous weight loss, causes of overweight, eating behavior, physical
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activity, life crises, self-assessment, smoking and alcohol consumption, education and profession, current life situation, and stress factors. Genotyping The DRD2 TaqIA polymorphism was assayed by polymerase chain reaction (PCR) restriction fragment length polymorphism analyses [27,28]. Genomic DNA was collected and isolated from 250 mL of whole blood anticoagulated with ethylenediaminetetraacetic acid using the peqGOLD Blood DNA Mini Kit (PEQLAB Biotechnologie GmbH, Erlangen, Germany). PCR conditions Genomic DNA, 1 Green GoTaq Flexi Buffer, 37.5 nmol of magnesium chloride solution (Promega GmbH, Mannheim, Germany), deoxynucleotide triphosphates (Fermentas GmbH, St. Leon-Rot, Germany), forward (50 -CCGTCGACGGCTGGC CAAGTTGTCTA-30 ) and reverse (50 -CCGTCGACCCTTCCTGAGTGTCATCA-30 ) oligonucleotide primers (Eurofins MWG Synthesis GmbH, Ebersberg, Germany), and 1 U of GoTaq Flexi DNA Polymerase (Promega GmbH) in a total volume of 25 mL was processed at 94 C for 4 min followed by 40 cycles at 94 C for 30 s, 68 C for 30 s, 72 C for 30 s, and a final extension at 72 C for 3 min. Digestion of 10 mL of the PCR products was accomplished at 65 C for 50 min with 4 U of the TaqaI restriction enzyme (New England BioLabs, Ipswich, MA, USA). The final products were resolved on an ethidium bromide–stained 2% agarose gel. After incubation of the 310-bp PCR product, the A1 allele remained intact, whereas the A2 allele resulted in two pieces of 180 and 130 bp. On each gel, a negative control (water) and one positive control were added. During gel electrophoresis, the GeneRuler 100-bp Plus DNA Ladder (Fermentas GmbH) was used as a DNA size marker. Statistical analysis SPSS 19.0 (SPSS, Inc., Chicago, IL, USA) was used for the statistical analyses. Participants who carried the A1A1 or A1A2 genotype were considered A1þ and participants with the A2A2 genotype were considered A1 [11]. A per-protocol analysis was chosen to analyze the influence of the A1þ and A1 genotypes on weight loss and weight maintenance during the 1-y program. The Wilcoxon– Mann–Whitney test (U test) was used to assess differences between the Aþ and A1 genotype groups. To determine whether the A1þ and A1 genotypes were in Hardy–Weinberg equilibrium, a chi-square test was performed. P 0.05 was considered statistically significant. Variables are expressed as mean SEM or percentage.
Results This study was performed as a monocentric longitudinal study in which 202 unrelated Caucasian subjects with severe obesity were analyzed for the rs1800497 polymorphism in the DRD2 gene. Across all subjects, genotyping showed 4 A1A1 (2%), 67 A1A2 (33.2%), and 131 A2A2 (64.8%) genotype carriers. The minor allele frequency of the A1 allele was 18.6% and allele frequencies were in Hardy–Weinberg equilibrium (P ¼ 0.17). Of the 202 subjects, 135 (66.8%) completed the program and 67 (33.2%) terminated prematurely. The A1 allele did not influence the dropout rate of the program (A1þ versus A1, P ¼ 0.44). In accordance with the per-protocol analysis, only completers of the program were included in further analyses (Table 1). The influence of the single nucleotide polymorphism rs1800497 on BMI was analyzed (Fig. 1). Based on previous findings in young adults, the group was separated according to age and gender [6]. Although no difference between A1þ and A1 was observed for the groups of all completers (men and women), the group of younger completers (21–40 y old) showed significant differences in the BMI at T0 (A1þ, 43.6 1.4 kg/m2; A1, 40.4 1.2 kg/m2; P ¼ 0.039), at T1 (A1þ, 36.9 1.3 kg/m2; A1, 33.8 1.2 kg/m2; P ¼ 0.045), and at T2 (A1þ, 38.7 1.8 kg/m2; A1, 33.4 1.1 kg/m2; P ¼ 0.015; Fig. 1D). However, when younger completers were separated according to gender, this effect was almost lost because of the smaller sample (Fig. 1E,F). No effect was observed for the group of older completers (41–60 y old; Fig. 1G–I).
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Table 1 Baseline characteristics of completers (n ¼ 135) according to genotypes of TaqIA variant rs1800497
Age (y) Women/men Height (m) Weight (kg) BMI (kg/m2) Nicotine use CHD anamnestic Hypertension anamnestic Systolic blood pressure (mmHg) Diastolic blood pressure (mmHg) Glucose (mg/dL) Diabetes Total cholesterol (mg/dL) LDL cholesterol (mg/dL) HDL cholesterol (mg/dL) Triacylglycerols (mg/dL)
Total (n ¼ 135)
A1A1 (n ¼ 2)
A2A2 (n ¼ 90)
A1A2 (n ¼ 43)
P
46.1 1 85/50 (63%/37%) 1.72 0.01 124 1.9 41.8 0.5 23 (17%) 26 (19.3%) 75 (55.6%) 140.6 1.5 88.2 0.9 109.4 2.4 23 (17%) 206.5 3.5 126.6 3 49.7 1.1 148.2 7.4
51 2 1/1 (50%/50%) 1.76 0.11 125.3 22.8 40.2 2.6 0 (0%) 1 (50%) 1 (50%) 149 7 94.5 2.5 98 12 0 (0%) 203.5 15.5 134 15 48.5 9.5 106 50
45.8 1.2 62/28 (68.9%/31.1%) 1.71 0.01 122 2.2 41.5 0.6 14 (15.6%) 18 (20%) 53 (58.9%) 141.1 2 88.2 1.2 110.2 2.7 16 (17.8%) 207.1 4.2 127.6 3.6 51.5 1.4 140.4 8.4
46.5 2 22/21 (51.2%/48.8%) 1.73 0.01 128 3.5 42.6 1 9 (20.9%) 7 (16.3%) 21 (48.8%) 139.1 2.4 88 1.6 108.1 5 7 (16.3%) 205.1 6.6 124.1 5.4 45.8 1.3 167 15
0.49* 0.04yz 0.17* 0.2* 0.37* 0.56y 0.91y 0.43y 0.89* 0.92* 0.4* 0.75y 0.87* 0.68* 0.03*z 0.12*
BMI, body mass index; CHD, coronary heart disease; HDL, high-density lipoprotein; LDL, low-density lipoprotein Values are presented as mean SEM or number of subjects (percentage). P values are listed for the comparison of A1 participants (A2A2) with A1þ participants (A1A1 and A1A2) * Mann–Whitney U test. y Chi-square test. z Significant at P 0.05.
Fig. 1. The BMI (kilograms per meter squared) course for A1 participants (A2A2; dashed line) and A1þ participants (A1A1 and A1A2; solid line) is presented for all completers and according to gender and age (younger, 21–40 y; older, 41–60 y). Comparison between different genotypes was tested by a one-sided Wilcoxon–Mann– Whitney test (U test). Significant (P 0.05) values are presented in bold. BMI, body mass index; T0, beginning of program; T1, from week 12 after the formula-based weightloss phase; T2, from week 52 after the weight-maintenance period of 40 wk.
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The young group was studied more in detail. Compared with A1 participants, A1þ participants exhibited significantly less weight decreases (A1þ, 11.8 1.8%; A1, 17.4 1.1%; P ¼ 0.034) and BMI decreases (A1þ, 5.0 0.7 kg/m2; A1, 7.0 0.5 kg/m2; P ¼ 0.049) through the end of the program (Fig. 2). The A1 allele did not influence weight loss during the weight-loss phase (T0–T1, P ¼ 0.90) but did during the weight-maintenance phase (T1–T2; A1þ, þ1.8 0.7 kg/m2; A1, 0.4 0.4 kg/m2; P ¼ 0.023). Further, for A1þ participants compared with A1 participants, there was a trend to regain weight during this phase (P ¼ 0.08; Table 2). However, in older A1þ and A1 completers, this difference was not observed during the weight-loss phase or during the weight-maintenance phase. With regard to metabolic parameters, younger A1þ carriers showed significantly higher insulin concentrations and homeostasis model assessment indices, which corresponded to increased BMIs at the end of the program (Table 3). The same effect was observed for HDL levels, which tended to be lower in A1þ participants (Table 3). However, A1þ and A1 participants did not differ in weight-related changes of glucose and insulin plasma levels, homeostasis model assessment indices, LDL cholesterol, HDL cholesterol, and triacylglycerols during the 1-y program (Table 3). Discussion The main finding of the present study was that younger A1þ carriers of rs1800497 showed difficulties in weight stabilization during a 1-y weight-loss program. This was demonstrated by identical weight-loss curves for the A1þ and A1 carriers during the formula diet but an increased weight regain in younger A1þ genotype carriers during the maintenance period, when food choice was liberated. Thus, the increased body weight of the A1þ carriers observed in the present and other studies might derive from difficulties in weight stabilization earlier in the lives of these subjects [6]. Participants of the analyzed weight-loss program described the weight-loss phase with a formula diet of a defined energy intake as easier than the weight-maintenance phase when food
Fig. 2. Changes in the body mass index (kilograms per meter squared) for younger completers comparing A1 participants (A2A2; white bars) and A1þ participants (A1A1 and A1A2; black bars) are presented. Comparison between genotype groups was tested by a one-sided Wilcoxon–Mann–Whitney test (U test). Significant (P 0.05) values are presented in bold. T0, beginning of program; T1, from week 12 after the formula-based weight-loss phase; T2, from week 52 after the weightmaintenance period of 40 wk.
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Table 2 Participants 21 to 40 y old (n ¼ 41) with weight loss or weight regain during the weight-maintenance phase (T1 to T2) according to genotypes of TaqIA variant rs1800497
Weight loss Weight regain
Completers 21–40 y old (n ¼ 41)
A1 (n ¼ 29)
A1þ (n ¼ 12)
P
19 22
16 13
3 9
0.08*
T0, beginning of program; T1, from week 12 after the formula-based weight-loss phase; T2, from week 52 after the weight-maintenance period of 40 wk Weight regain was defined as any weight gain from T1 through T2. P values are listed for the comparison of A1 participants (A2A2) with A1þ participants (A1A1 and A1A2). Statistically significant at P 0.05 * Chi-square test.
intake was their own responsibility and was defined according to their personal needs and decisions (data not shown). One might speculate whether A1þ carriers are more vulnerable during weight maintenance owing to a hypofunctioning of the mesolimbic dopamine reward system. These individuals have been shown to have a weaker activation in the striatum and prefrontal regions during food intake. It is plausible that an enhanced stimulation might compensate for this deficiency. Thus, this “reward deficiency syndrome” might contribute to overeating [23–28].
Table 3 Metabolic parameters of completers 21 to 40 y old according to genotypes of TaqIA variant rs1800497 A1 Glucose (mg/dL) T0 T2 DGlucose (mg/dL)/DBMI (kg/m2) T0–T2 Insulin (mU/L) T0 T2 DInsulin (mU/L)/DBMI (kg/m2) T0–T2 HOMA index T0 T2 DHOMA index/DBMI (kg/m2) T0–T2 LDL-C (mg/dL) T0 T2 DLDL-C (mg/dL)/DBMI (kg/m2) T0–T2 HDL-C (mg/dL) T0 T2 DHDL-C (mg/dL)/DBMI (kg/m2) T0–T2 Triacylglycerol (mg/dl) T0 T2 DTriacylglycerol (mg/dL)/DBMI (kg/m2) T0–T2
102 4 93 2
A1þ 105 6 98 3
P 0.75 0.09
1.3 0.7
1.0 1.3
20.1 3.1 12.2 1.5
32.3 8.4 21.4 2.5
0.8 0.4
2.6 1.9
0.47
5.4 0.9 2.9 0.4
8.7 2.7 4.7 0.6
0.15 0.02*
0.3 0.1
0.8 0.6
0.85
126 6 115 6
0.61 0.04* 0.005*
114 8 115 7
0.31 0.97
0.9 1.1
0.9 1.4
0.21
51 3 61 3
42 2 48 2
1.4 0.7
1.9 0.9
0.91
148 14 106 13
154 21 134 24
0.68 0.23
3.3 1.9
2.5 4.0
0.87
0.03* 0.001*
BMI, body mass index; D, difference; HDL-C, high-density lipoprotein cholesterol; HOMA, homeostasis model assessment; LDL-C, low-density lipoprotein cholesterol; T0, beginning of program; T2, from week 52 after the weightmaintenance period of 40 wk Parameters are presented for T0 and T2. Changes in metabolic parameters in relation to changes in BMI are presented for T0-T2. Values are presented as mean SEM. P values are listed for the comparison of A1 participants (A2A2) with A1þ participants (A1A1 and A1A2) * Significant at P 0.05.
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The concern of weight stabilization for individuals with the A1 allele is aggravated by the fact that they are more susceptible to impulsive behavior [15,20], emotional eating [29], and the development of pathologic eating behavior [30]. We observed the effect on weight maintenance only in younger A1þ participants. This might be explained in one of several ways. First, it is known that pathologic eating behavior is more frequent in younger subjects [31] and that impulse control and response-inhibition capacities during aging become more effective in controlling reward-driven eating behavior [32]. Second, genetic factors in the development of obesity seem to be particularly important for younger individuals because with aging acquired factors become more important [6,33]. Third, during aging, several hormonal changes occur, e.g., puberty or menopause, so hormone status may play a role [33]. Therefore, age-dependent analysis is absolutely necessary to detect genetic main effects and time-dependent effects [33,34]. Thus, weaker genetic associations with at least marginal significances may be detected [33]. However, there are some limitations to the present study. Only participants of the weight-loss program were analyzed. Therefore, no control group was included in the study design. Further, physical activity during the program was not measured. Thus, the influence of the different genotypes on physical activity remains unclear. Moreover, the present sample size was limited. Therefore, we cannot exclude that we might have missed weaker effects especially concerning older participants. Aside from the present study, there are some limited data on the analysis of genetic variations in other obesity and weight-loss studies [35–38]. These previous studies confirm our finding that genetic constitution influences the development of obesity and the success of weight loss during interventions [35,36]. Although we cannot fully exclude the possibility that the A1 allele of the DR2D variant rs1800497 is in linkage disequilibrium with an unidentified causative variant in a gene localized near the DR2D gene on chromosome 11, our data indicate that the A1þ variant is associated with less success in weight maintenance in obese patients. Further studies are needed to determine why younger A1þ participants in particular have difficulties with successful weight maintenance. In the future, special screening for pathologic eating behavior and for genetic risk factors before participation in weight-loss programs might improve the short- and long-term outcomes of non-surgical weight-loss strategies. Conclusion Younger A1þ participants showed an increased BMI at baseline and exhibited problems in losing weight and maintaining weight loss during a 1-y weight-loss program. Thus, genotyping might provide an opportunity to individualize programs according to participants’ needs by offering personalized exercise and nutritional regimes, especially because compliance and long-term weight maintenance have been shown to be augmented by diets based on genetic make-up and by the teaching of maintenance skills face to face [39–41]. Therefore, for carriers of the risk allele, we suggest individualized follow-up counseling and psychological support, including major elements of behavioral therapy, to improve impulse control. References [1] James PT, Leach R, Kalamara E, Shayeghi M. The worldwide obesity epidemic. Obes Res 2001;9(suppl 4):228S–33S. [2] Curbing the obesity epidemic. Lancet 2006;367:1549.
[3] Lusis AJ, Attie AD, Reue K. Metabolic syndrome: from epidemiology to systems biology. Nat Rev Genet 2008;9:819–30. [4] Speakman JR. Obesity: the integrated roles of environment and genetics. J Nutr 2004;134:2090S–105S. [5] Wang GJ, Volkow ND, Thanos PK, Fowler JS. Similarity between obesity and drug addiction as assessed by neurofunctional imaging: a concept review. J Addict Dis 2004;23:39–53. [6] Comings DE, Gade R, MacMurray JP, Muhleman D, Peters WR. Genetic variants of the human obesity (OB) gene: association with body mass index in young women, psychiatric symptoms, and interaction with the dopamine D2 receptor (DRD2) gene. Mol Psychiatry 1996;1:325–35. [7] Noble EP, Blum K, Ritchie T, Montgomery A, Sheridan PJ. Allelic association of the D2 dopamine receptor gene with receptor-binding characteristics in alcoholism. Arch Gen Psychiatry 1991;48:648–54. [8] Fetissov SO, Meguid MM. On dopamine, D2 receptor, and Taq1A polymorphism in obesity and anorexia. Nutrition 2009;25:132–3. [9] Noble EP, Noble RE, Ritchie T, Syndulko K, Bohlman MC, Noble LA, et al. D2 dopamine receptor gene and obesity. Int J Eat Disord 1994;15:205–17. [10] Fang YJ, Thomas GN, Xu ZL, Fang JQ, Critchley JA, Tomlinson B. An affected pedigree member analysis of linkage between the dopamine D2 receptor gene TaqI polymorphism and obesity and hypertension. Int J Cardiol 2005;102:111–6. [11] Barnard ND, Noble EP, Ritchie T, Cohen J, Jenkins DJ, Turner-McGrievy G, et al. D2 dopamine receptor Taq1A polymorphism, body weight, and dietary intake in type 2 diabetes. Nutrition 2009;25:58–65. [12] Wang GJ, Volkow ND, Logan J, Pappas NR, Wong CT, Zhu W, et al. Brain dopamine and obesity. Lancet 2001;357:354–7. [13] Comings DE, Flanagan SD, Dietz G, Muhleman D, Knell E, Gysin R. The dopamine D2 receptor (DRD2) as a major gene in obesity and height. Biochem Med Metab B 1993;50:176–85. [14] Blum K, Braverman ER, Wood RC, Gill J, Li C, Chen TJ, et al. Increased prevalence of the Taq I A1 allele of the dopamine receptor gene (DRD2) in obesity with comorbid substance use disorder: a preliminary report. Pharmacogenetics 1996;6:297–305. [15] Esposito-Smythers C, Spirito A, Rizzo C, McGeary JE, Knopik VS. Associations of the DRD2 TaqIA polymorphism with impulsivity and substance use: preliminary results from a clinical sample of adolescents. Pharmacol Biochem Behav 2009;93:306–12. [16] Noble EP. Addiction and its reward process through polymorphisms of the D2 dopamine receptor gene: a review. Eur J Psychiatry 2000;15:79–89. [17] Munafo MR, Matheson IJ, Flint J. Association of the DRD2 gene Taq1A polymorphism and alcoholism: a meta-analysis of case–control studies and evidence of publication bias. Mol Psychiatry 2007;12:454–61. [18] Bowirrat A, Oscar-Berman M. Relationship between dopaminergic neurotransmission, alcoholism, and reward deficiency syndrome. Am J Med Genet B Neuropsychiatr Genet 2005;132B:29–37. [19] Comings DE, Blum K. Reward deficiency syndrome: genetic aspects of behavioral disorders. Prog Brain Res 2000;126:325–41. [20] Eisenberg DT, Mackillop J, Modi M, Beauchemin J, Dang D, Lisman SA, et al. Examining impulsivity as an endophenotype using a behavioral approach: a DRD2 TaqI A and DRD4 48-bp VNTR association study. Behav Brain Funct 2007;3:2. [21] Wang GJ, Volkow ND, Thanos PK, Fowler JS. Imaging of brain dopamine pathways: implications for understanding obesity. J Addict Med 2009; 3:8–18. [22] Stice E, Spoor S, Ng J, Zald DH. Relation of obesity to consummatory and anticipatory food reward. Physiol Behav 2009;97:551–60. [23] Stice E, Yokum S, Blum K, Bohon C. Weight gain is associated with reduced striatal response to palatable food. J Neurosci 2010;30:13105–9. [24] Stice E, Spoor S, Bohon C, Small DM. Relation between obesity and blunted striatal response to food is moderated by TaqIA A1 allele. Science 2008;322:449–52. [25] Felsted JA, Ren X, Chouinard-Decorte F, Small DM. Genetically determined differences in brain response to a primary food reward. J Neurosci 2010;30:2428–32. [26] Volkow ND, Wang GJ, Telang F, Fowler JS, Thanos PK, Logan J, et al. Low dopamine striatal D2 receptors are associated with prefrontal metabolism in obese subjects: possible contributing factors. Neuroimage 2008; 42:1537–43. [27] Grandy DK, Zhang Y, Civelli O. PCR detection of the TaqA RFLP at the DRD2 locus. Hum Mol Genet 1993;2:2197. [28] Noble EP, Berman SM, Ozkaragoz TZ, Ritchie T. Prolonged P300 latency in children with the D2 dopamine receptor A1 allele. Am J Hum Genet 1994;54:658–68. [29] van Strien T, Snoek HM, van der Zwaluw CS, Engels RC. Parental control and the dopamine D2 receptor gene (DRD2) interaction on emotional eating in adolescence. Appetite 2010;54:255–61. [30] Nisoli E, Brunani A, Borgomainerio E, Tonello C, Dioni L, Briscini L, et al. D2 dopamine receptor (DRD2) gene Taq1A polymorphism and the eatingrelated psychological traits in eating disorders (anorexia nervosa and bulimia) and obesity. Eat Weight Disord 2007;12:91–6. [31] Hoek HW, van Hoeken D. Review of the prevalence and incidence of eating disorders. Int J Eat Disord 2003;34:383–96.
J. K. Winkler et al. / Nutrition 28 (2012) 996–1001 [32] Christakou A, Brammer M, Rubia K. Maturation of limbic corticostriatal activation and connectivity associated with developmental changes in temporal discounting. Neuroimage 2011;54:1344–54. [33] Lasky-Su J, Lyon HN, Emilsson V, Heid IM, Molony C, Raby BA, et al. On the replication of genetic associations: timing can be everything! Am J Hum Genet 2008;82:849–58. [34] Gorlova OY, Amos CI, Wang NW, Shete S, Turner ST, Boerwinkle E. Genetic linkage and imprinting effects on body mass index in children and young adults. Eur J Hum Genet 2003;11:425–32. [35] Moreno-Aliaga MJ, Santos JL, Marti A, Martinez JA. Does weight loss prognosis depend on genetic make-up? Obes Rev 2005;6:155–68. [36] Papoutsakis C, Dedoussis GV. Gene diet interactions in childhood obesity: paucity of evidence as the epidemic of childhood obesity continues to rise. Personalized Med 2007;4:133–46.
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[37] Haupt A, Thamer C, Heni M, Ketterer C, Machann J, Schick F, et al. Gene variants of TCF7L2 influence weight loss and body composition during lifestyle intervention in a population at risk for type 2 diabetes. Diabetes 2010;59:747–50. [38] Haupt A, Thamer C, Heni M, Tschritter O, Machann J, Schick F, et al. Impact of variation near MC4R on whole-body fat distribution, liver fat, and weight loss. Obesity (Silver Spring) 2009;17:1942–5. [39] Marti A, Goyenechea E, Martinez JA. Nutrigenetics: a tool to provide personalized nutritional therapy to the obese. World Rev Nutr Diet 2010;101:21–33. [40] Arkadianos I, Valdes AM, Marinos E, Florou A, Gill RD, Grimaldi KA. Improved weight management using genetic information to personalize a calorie controlled diet. Nutr J 2007;6:29. [41] Wing RR, Tate DF, Gorin AA, Raynor HA, Fava JL. A self-regulation program for maintenance of weight loss. N Engl J Med 2006;355:1563–71.
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Applied nutritional investigation
TaqIA polymorphism in dopamine D2 receptor gene complicates weight maintenance in younger obese patients Julia K. Winkler a, c, Annika Woehning M.Sc. a, Jobst-Hendrik Schultz M.D. b, Maik Brune M.D. a, Nigel Beaton M.Sc. c, Tenagne Delessa Challa M.Sc. c, Stella Minkova M.D. a, Eva Roeder M.Sc. c, Peter P. Nawroth M.D. a, Hans-Christoph Friederich M.D. b, Christian Wolfrum Ph.D. c, Gottfried Rudofsky M.D. a, * a
Department of Medicine I and Clinical Chemistry, University of Heidelberg, 69120 Heidelberg, Germany Department of Psychosomatic and General Internal Medicine, University of Heidelberg, 69120 Heidelberg, Germany c Swiss Federal Institute of Technology, Institute of Food Nutrition and Health, 8603 Schwerzenbach, Switzerland b
a r t i c l e i n f o
a b s t r a c t
Article history: Received 8 September 2011 Accepted 27 December 2011
Objective: The A1 allele of the TaqIA polymorphism in the dopamine D2 receptor gene (rs1800497) has been associated with obesity. However, the effect of the polymorphism on the success in weight loss and/or weight maintenance during weight-loss programs has not been evaluated thus far. Methods: The rs1800497 was genotyped in 202 (135 female, 67 male) severely obese individuals with an initial body mass index of 41.7 0.5 kg/m2 who participated in a weight-loss program consisting of a weight-loss phase with a formula diet (12 wk) and a weight-maintenance phase (40 wk). Measurements were collected at baseline, after the weight-loss phase, and at the end of the weight-maintenance phase at 1 y. Results: Genotyping revealed 4 A1A1, 67 A1A2, and 131 A2A2 genotype carriers. Of the 202 subjects in the program, 66.8% completed the program and 33.2% terminated prematurely. Neither the attrition rate (P ¼ 0.44) nor the overall weight loss was influenced by the different genotypes (P ¼ 0.96). However, younger A1þ participants (A1A1 and A1A2) had a higher body mass index at all time points (baseline, P ¼ 0.04; after weight loss, P ¼ 0.05; after weight maintenance, P ¼ 0.02). They also showed less overall weight loss (P ¼ 0.05), which derived mainly from a greater weight regain during the maintenance phase (P ¼ 0.02). Conclusion: In this program, younger A1þ participants exhibited problems in maintaining weight loss during a weight-loss program. Ó 2012 Elsevier Inc. All rights reserved.
Keywords: Weight maintenance Weight regain Weight loss Obesity Dopamine D2 receptor gene rs1800497
Introduction Obesity is a serious global health issue because it has become a worldwide epidemic, with at least 1.1 billion adults and 10% of children being overweight or obese [1,2]. Excess body weight can lead to the metabolic syndrome and a decreased life expectancy owing to cardiovascular disease, type 2 diabetes, or certain types of cancer [2,3]. Obesity results mainly from an imbalance between energy intake and energy expenditure, which are influenced by environmental and genetic factors [4]. Polymorphisms in the This work was supported by a grant from the European Foundation of the Study of Diabetes (G. R., C. W.) and the ERC (AdipoDif; C. W.). * Corresponding author. Tel.: þ49-6221-563-8620; fax: þ49-6221-565-793. E-mail address: [email protected] (G. Rudofsky). 0899-9007/$ - see front matter Ó 2012 Elsevier Inc. All rights reserved. doi:10.1016/j.nut.2011.12.018
dopamine D2 receptor (DRD2) and obesity (ob) genes together account for about 20% of the variance in the body mass index (BMI), particularly in younger women [5,6]. One such variant is the A1 allele of the TaqIA single nucleotide polymorphism in the DRD2 gene (rs1800497), for which a decreased brain DRD2 density in the striatum has been described [5,7]. This decreased receptor density has been associated with an increased prevalence of obesity [6,8–13] and substance abuse [14–18]. As an underlying cause, A1 allele carriers have been postulated to have an impaired reward circuitry leading to a state of “reward deficiency syndrome” [18,19]. This condition is aggravated by an increased impulsivity [15,20]. Thus, these behavioral changes make A1þ carriers prone to overeating [21–26] and obesity. However, the effect of the A1 allele on the success of weightloss programs is largely unknown [11]. The purpose of this study was to analyze whether the A1 allele of rs1800497 decreases the
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likelihood of weight loss and/or weight maintenance in severely obese subjects during a 1-y weight-loss program. Materials and methods Weight-loss program OPTIFAST52 The present study was a monocentric, longitudinal investigation involving obese individuals who participated in the multidisciplinary, non-surgical weight, Inc., Vevey, Switzerland). In 1999 the OPTIloss program (OPTIFAST52; Nestle FAST52 program was established in Germany with the aim of treating obese people of at least 18 y of age and with a BMI of 30 kg/m2 or higher. OPTIFAST52 combines the expertise of physicians, dietitians, physical therapists, and psychologists in an outpatient-based, comprehensive, multidisciplinary approach. The program consists of a four phased lifestyle intervention with four modules (psychology, medicine, dietetics, and exercise) and is designed for 52 wk. During the program, closed groups of 10 to 15 people meet for weekly sessions of about 3.5 h. The four program phases include 1) a 1-wk introduction time; (ii) a 12-wk period of a low-calorie formula diet (800 kcal/d; five packets at 160 kcal dissolved in 250–300 mL of water as a meal replacement; OPTIFAST52 800 formula) accompanied by 12 medical examinations, 12 exercise units, 2 lessons of behavior therapy, and 2 sessions of nutrition counseling; 3) a 6-wk refeeding phase in which solid food is reintroduced and the formula diet is gradually replaced by a normal diet with only a small change of total energy intake accompanied by six medical examinations, six exercise units, two lessons of behavior therapy, and six sessions of nutrition counseling; and 4) a 33-wk stabilization phase in which energy intake is increased step by step to an individual level that allows weight stabilization and in which nutritional education and behavior modification are intensified to determine coping strategies and to achieve long-term weight control, accompanied by 9 medical examinations, 17 exercise units, 26 lessons of behavior therapy, and 8 sessions of nutritional counseling. For the purpose of the present analyses, it was sufficient to divide the program into two parts: a weight-loss phase of 12 wk and a weight-maintenance phase of 40 wk. In this study, data from the beginning (T0), from week 12 after the formula-based weight-loss phase (T1), and from week 52 after the weightmaintenance period of 40 wk (T2) were included. Study population The ethics committee at the University of Heidelberg approved the study and written informed consent was given by all participants. A population of 202 obese adults taking part in the OPTIFAST52 program at the University Hospital of Heidelberg from 2005 through 2010 was analyzed. The study sample included 135 female and 67 male subjects 18 to 72 y old. The mean BMI at the beginning of the program was 41.7 0.5 kg/m2. The 1-y program was completed by 135 participants and discontinued by 67. Anthropometric and laboratory measurements During the 52-wk program, weight and blood pressure were monitored regularly. Body weight was determined on a calibrated scale (model 764, Seca, Hamburg, Germany) after an 8-h fast. The BMI was calculated as weight in kilograms divided by the square of the height in meters. To control laboratory values, blood samples were collected at five different time points during the program after a fasting time of at least 8 h. The laboratory parameters were measured in the central laboratory of the University Hospital of Heidelberg. In this study, concentrations of blood glucose, total cholesterol, low-density lipoprotein (LDL), high-density lipoprotein (HDL), and triacylglycerols from T0, T1, and T2 were included. Blood collection tubes contained sodium fluoride (Sarstedt S-Monovette, no. 04.1903, Nümbrecht, Germany) for the assessment of glucose levels and lithium heparin (Sarstedt S-Monovette, no. 01.1634) for the assessment of lipid profiles. Samples were directed to the central laboratory without delay, and plasma was separated out and analyzed on a ADVIA 2400 chemistry analyzer (Siemens, Eschborn, Germany). Concentrations of glucose, total cholesterol, HDL, and triacylglycerols were determined using the appropriate Siemens test kits (nos. B01-4597-01, 04993681, 08058065, and B01-4133-01, respectively) according to the manufacturer’s instructions. Further, concentrations of LDL were calculated according to the Friedewald equation. Questionnaire on body weight and lifestyle As part of the diagnostic procedure, all participants in the OPTIFAST52 program completed a non-standardized psychological questionnaire provided by , Inc.) at the beginning of the program. The questionthe manufacturer (Nestle naire includes information about the development of body weight, previous diets, previous weight loss, causes of overweight, eating behavior, physical
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activity, life crises, self-assessment, smoking and alcohol consumption, education and profession, current life situation, and stress factors. Genotyping The DRD2 TaqIA polymorphism was assayed by polymerase chain reaction (PCR) restriction fragment length polymorphism analyses [27,28]. Genomic DNA was collected and isolated from 250 mL of whole blood anticoagulated with ethylenediaminetetraacetic acid using the peqGOLD Blood DNA Mini Kit (PEQLAB Biotechnologie GmbH, Erlangen, Germany). PCR conditions Genomic DNA, 1 Green GoTaq Flexi Buffer, 37.5 nmol of magnesium chloride solution (Promega GmbH, Mannheim, Germany), deoxynucleotide triphosphates (Fermentas GmbH, St. Leon-Rot, Germany), forward (50 -CCGTCGACGGCTGGC CAAGTTGTCTA-30 ) and reverse (50 -CCGTCGACCCTTCCTGAGTGTCATCA-30 ) oligonucleotide primers (Eurofins MWG Synthesis GmbH, Ebersberg, Germany), and 1 U of GoTaq Flexi DNA Polymerase (Promega GmbH) in a total volume of 25 mL was processed at 94 C for 4 min followed by 40 cycles at 94 C for 30 s, 68 C for 30 s, 72 C for 30 s, and a final extension at 72 C for 3 min. Digestion of 10 mL of the PCR products was accomplished at 65 C for 50 min with 4 U of the TaqaI restriction enzyme (New England BioLabs, Ipswich, MA, USA). The final products were resolved on an ethidium bromide–stained 2% agarose gel. After incubation of the 310-bp PCR product, the A1 allele remained intact, whereas the A2 allele resulted in two pieces of 180 and 130 bp. On each gel, a negative control (water) and one positive control were added. During gel electrophoresis, the GeneRuler 100-bp Plus DNA Ladder (Fermentas GmbH) was used as a DNA size marker. Statistical analysis SPSS 19.0 (SPSS, Inc., Chicago, IL, USA) was used for the statistical analyses. Participants who carried the A1A1 or A1A2 genotype were considered A1þ and participants with the A2A2 genotype were considered A1 [11]. A per-protocol analysis was chosen to analyze the influence of the A1þ and A1 genotypes on weight loss and weight maintenance during the 1-y program. The Wilcoxon– Mann–Whitney test (U test) was used to assess differences between the Aþ and A1 genotype groups. To determine whether the A1þ and A1 genotypes were in Hardy–Weinberg equilibrium, a chi-square test was performed. P 0.05 was considered statistically significant. Variables are expressed as mean SEM or percentage.
Results This study was performed as a monocentric longitudinal study in which 202 unrelated Caucasian subjects with severe obesity were analyzed for the rs1800497 polymorphism in the DRD2 gene. Across all subjects, genotyping showed 4 A1A1 (2%), 67 A1A2 (33.2%), and 131 A2A2 (64.8%) genotype carriers. The minor allele frequency of the A1 allele was 18.6% and allele frequencies were in Hardy–Weinberg equilibrium (P ¼ 0.17). Of the 202 subjects, 135 (66.8%) completed the program and 67 (33.2%) terminated prematurely. The A1 allele did not influence the dropout rate of the program (A1þ versus A1, P ¼ 0.44). In accordance with the per-protocol analysis, only completers of the program were included in further analyses (Table 1). The influence of the single nucleotide polymorphism rs1800497 on BMI was analyzed (Fig. 1). Based on previous findings in young adults, the group was separated according to age and gender [6]. Although no difference between A1þ and A1 was observed for the groups of all completers (men and women), the group of younger completers (21–40 y old) showed significant differences in the BMI at T0 (A1þ, 43.6 1.4 kg/m2; A1, 40.4 1.2 kg/m2; P ¼ 0.039), at T1 (A1þ, 36.9 1.3 kg/m2; A1, 33.8 1.2 kg/m2; P ¼ 0.045), and at T2 (A1þ, 38.7 1.8 kg/m2; A1, 33.4 1.1 kg/m2; P ¼ 0.015; Fig. 1D). However, when younger completers were separated according to gender, this effect was almost lost because of the smaller sample (Fig. 1E,F). No effect was observed for the group of older completers (41–60 y old; Fig. 1G–I).
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Table 1 Baseline characteristics of completers (n ¼ 135) according to genotypes of TaqIA variant rs1800497
Age (y) Women/men Height (m) Weight (kg) BMI (kg/m2) Nicotine use CHD anamnestic Hypertension anamnestic Systolic blood pressure (mmHg) Diastolic blood pressure (mmHg) Glucose (mg/dL) Diabetes Total cholesterol (mg/dL) LDL cholesterol (mg/dL) HDL cholesterol (mg/dL) Triacylglycerols (mg/dL)
Total (n ¼ 135)
A1A1 (n ¼ 2)
A2A2 (n ¼ 90)
A1A2 (n ¼ 43)
P
46.1 1 85/50 (63%/37%) 1.72 0.01 124 1.9 41.8 0.5 23 (17%) 26 (19.3%) 75 (55.6%) 140.6 1.5 88.2 0.9 109.4 2.4 23 (17%) 206.5 3.5 126.6 3 49.7 1.1 148.2 7.4
51 2 1/1 (50%/50%) 1.76 0.11 125.3 22.8 40.2 2.6 0 (0%) 1 (50%) 1 (50%) 149 7 94.5 2.5 98 12 0 (0%) 203.5 15.5 134 15 48.5 9.5 106 50
45.8 1.2 62/28 (68.9%/31.1%) 1.71 0.01 122 2.2 41.5 0.6 14 (15.6%) 18 (20%) 53 (58.9%) 141.1 2 88.2 1.2 110.2 2.7 16 (17.8%) 207.1 4.2 127.6 3.6 51.5 1.4 140.4 8.4
46.5 2 22/21 (51.2%/48.8%) 1.73 0.01 128 3.5 42.6 1 9 (20.9%) 7 (16.3%) 21 (48.8%) 139.1 2.4 88 1.6 108.1 5 7 (16.3%) 205.1 6.6 124.1 5.4 45.8 1.3 167 15
0.49* 0.04yz 0.17* 0.2* 0.37* 0.56y 0.91y 0.43y 0.89* 0.92* 0.4* 0.75y 0.87* 0.68* 0.03*z 0.12*
BMI, body mass index; CHD, coronary heart disease; HDL, high-density lipoprotein; LDL, low-density lipoprotein Values are presented as mean SEM or number of subjects (percentage). P values are listed for the comparison of A1 participants (A2A2) with A1þ participants (A1A1 and A1A2) * Mann–Whitney U test. y Chi-square test. z Significant at P 0.05.
Fig. 1. The BMI (kilograms per meter squared) course for A1 participants (A2A2; dashed line) and A1þ participants (A1A1 and A1A2; solid line) is presented for all completers and according to gender and age (younger, 21–40 y; older, 41–60 y). Comparison between different genotypes was tested by a one-sided Wilcoxon–Mann– Whitney test (U test). Significant (P 0.05) values are presented in bold. BMI, body mass index; T0, beginning of program; T1, from week 12 after the formula-based weightloss phase; T2, from week 52 after the weight-maintenance period of 40 wk.
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The young group was studied more in detail. Compared with A1 participants, A1þ participants exhibited significantly less weight decreases (A1þ, 11.8 1.8%; A1, 17.4 1.1%; P ¼ 0.034) and BMI decreases (A1þ, 5.0 0.7 kg/m2; A1, 7.0 0.5 kg/m2; P ¼ 0.049) through the end of the program (Fig. 2). The A1 allele did not influence weight loss during the weight-loss phase (T0–T1, P ¼ 0.90) but did during the weight-maintenance phase (T1–T2; A1þ, þ1.8 0.7 kg/m2; A1, 0.4 0.4 kg/m2; P ¼ 0.023). Further, for A1þ participants compared with A1 participants, there was a trend to regain weight during this phase (P ¼ 0.08; Table 2). However, in older A1þ and A1 completers, this difference was not observed during the weight-loss phase or during the weight-maintenance phase. With regard to metabolic parameters, younger A1þ carriers showed significantly higher insulin concentrations and homeostasis model assessment indices, which corresponded to increased BMIs at the end of the program (Table 3). The same effect was observed for HDL levels, which tended to be lower in A1þ participants (Table 3). However, A1þ and A1 participants did not differ in weight-related changes of glucose and insulin plasma levels, homeostasis model assessment indices, LDL cholesterol, HDL cholesterol, and triacylglycerols during the 1-y program (Table 3). Discussion The main finding of the present study was that younger A1þ carriers of rs1800497 showed difficulties in weight stabilization during a 1-y weight-loss program. This was demonstrated by identical weight-loss curves for the A1þ and A1 carriers during the formula diet but an increased weight regain in younger A1þ genotype carriers during the maintenance period, when food choice was liberated. Thus, the increased body weight of the A1þ carriers observed in the present and other studies might derive from difficulties in weight stabilization earlier in the lives of these subjects [6]. Participants of the analyzed weight-loss program described the weight-loss phase with a formula diet of a defined energy intake as easier than the weight-maintenance phase when food
Fig. 2. Changes in the body mass index (kilograms per meter squared) for younger completers comparing A1 participants (A2A2; white bars) and A1þ participants (A1A1 and A1A2; black bars) are presented. Comparison between genotype groups was tested by a one-sided Wilcoxon–Mann–Whitney test (U test). Significant (P 0.05) values are presented in bold. T0, beginning of program; T1, from week 12 after the formula-based weight-loss phase; T2, from week 52 after the weightmaintenance period of 40 wk.
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Table 2 Participants 21 to 40 y old (n ¼ 41) with weight loss or weight regain during the weight-maintenance phase (T1 to T2) according to genotypes of TaqIA variant rs1800497
Weight loss Weight regain
Completers 21–40 y old (n ¼ 41)
A1 (n ¼ 29)
A1þ (n ¼ 12)
P
19 22
16 13
3 9
0.08*
T0, beginning of program; T1, from week 12 after the formula-based weight-loss phase; T2, from week 52 after the weight-maintenance period of 40 wk Weight regain was defined as any weight gain from T1 through T2. P values are listed for the comparison of A1 participants (A2A2) with A1þ participants (A1A1 and A1A2). Statistically significant at P 0.05 * Chi-square test.
intake was their own responsibility and was defined according to their personal needs and decisions (data not shown). One might speculate whether A1þ carriers are more vulnerable during weight maintenance owing to a hypofunctioning of the mesolimbic dopamine reward system. These individuals have been shown to have a weaker activation in the striatum and prefrontal regions during food intake. It is plausible that an enhanced stimulation might compensate for this deficiency. Thus, this “reward deficiency syndrome” might contribute to overeating [23–28].
Table 3 Metabolic parameters of completers 21 to 40 y old according to genotypes of TaqIA variant rs1800497 A1 Glucose (mg/dL) T0 T2 DGlucose (mg/dL)/DBMI (kg/m2) T0–T2 Insulin (mU/L) T0 T2 DInsulin (mU/L)/DBMI (kg/m2) T0–T2 HOMA index T0 T2 DHOMA index/DBMI (kg/m2) T0–T2 LDL-C (mg/dL) T0 T2 DLDL-C (mg/dL)/DBMI (kg/m2) T0–T2 HDL-C (mg/dL) T0 T2 DHDL-C (mg/dL)/DBMI (kg/m2) T0–T2 Triacylglycerol (mg/dl) T0 T2 DTriacylglycerol (mg/dL)/DBMI (kg/m2) T0–T2
102 4 93 2
A1þ 105 6 98 3
P 0.75 0.09
1.3 0.7
1.0 1.3
20.1 3.1 12.2 1.5
32.3 8.4 21.4 2.5
0.8 0.4
2.6 1.9
0.47
5.4 0.9 2.9 0.4
8.7 2.7 4.7 0.6
0.15 0.02*
0.3 0.1
0.8 0.6
0.85
126 6 115 6
0.61 0.04* 0.005*
114 8 115 7
0.31 0.97
0.9 1.1
0.9 1.4
0.21
51 3 61 3
42 2 48 2
1.4 0.7
1.9 0.9
0.91
148 14 106 13
154 21 134 24
0.68 0.23
3.3 1.9
2.5 4.0
0.87
0.03* 0.001*
BMI, body mass index; D, difference; HDL-C, high-density lipoprotein cholesterol; HOMA, homeostasis model assessment; LDL-C, low-density lipoprotein cholesterol; T0, beginning of program; T2, from week 52 after the weightmaintenance period of 40 wk Parameters are presented for T0 and T2. Changes in metabolic parameters in relation to changes in BMI are presented for T0-T2. Values are presented as mean SEM. P values are listed for the comparison of A1 participants (A2A2) with A1þ participants (A1A1 and A1A2) * Significant at P 0.05.
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The concern of weight stabilization for individuals with the A1 allele is aggravated by the fact that they are more susceptible to impulsive behavior [15,20], emotional eating [29], and the development of pathologic eating behavior [30]. We observed the effect on weight maintenance only in younger A1þ participants. This might be explained in one of several ways. First, it is known that pathologic eating behavior is more frequent in younger subjects [31] and that impulse control and response-inhibition capacities during aging become more effective in controlling reward-driven eating behavior [32]. Second, genetic factors in the development of obesity seem to be particularly important for younger individuals because with aging acquired factors become more important [6,33]. Third, during aging, several hormonal changes occur, e.g., puberty or menopause, so hormone status may play a role [33]. Therefore, age-dependent analysis is absolutely necessary to detect genetic main effects and time-dependent effects [33,34]. Thus, weaker genetic associations with at least marginal significances may be detected [33]. However, there are some limitations to the present study. Only participants of the weight-loss program were analyzed. Therefore, no control group was included in the study design. Further, physical activity during the program was not measured. Thus, the influence of the different genotypes on physical activity remains unclear. Moreover, the present sample size was limited. Therefore, we cannot exclude that we might have missed weaker effects especially concerning older participants. Aside from the present study, there are some limited data on the analysis of genetic variations in other obesity and weight-loss studies [35–38]. These previous studies confirm our finding that genetic constitution influences the development of obesity and the success of weight loss during interventions [35,36]. Although we cannot fully exclude the possibility that the A1 allele of the DR2D variant rs1800497 is in linkage disequilibrium with an unidentified causative variant in a gene localized near the DR2D gene on chromosome 11, our data indicate that the A1þ variant is associated with less success in weight maintenance in obese patients. Further studies are needed to determine why younger A1þ participants in particular have difficulties with successful weight maintenance. In the future, special screening for pathologic eating behavior and for genetic risk factors before participation in weight-loss programs might improve the short- and long-term outcomes of non-surgical weight-loss strategies. Conclusion Younger A1þ participants showed an increased BMI at baseline and exhibited problems in losing weight and maintaining weight loss during a 1-y weight-loss program. Thus, genotyping might provide an opportunity to individualize programs according to participants’ needs by offering personalized exercise and nutritional regimes, especially because compliance and long-term weight maintenance have been shown to be augmented by diets based on genetic make-up and by the teaching of maintenance skills face to face [39–41]. Therefore, for carriers of the risk allele, we suggest individualized follow-up counseling and psychological support, including major elements of behavioral therapy, to improve impulse control. References [1] James PT, Leach R, Kalamara E, Shayeghi M. The worldwide obesity epidemic. Obes Res 2001;9(suppl 4):228S–33S. [2] Curbing the obesity epidemic. Lancet 2006;367:1549.
[3] Lusis AJ, Attie AD, Reue K. Metabolic syndrome: from epidemiology to systems biology. Nat Rev Genet 2008;9:819–30. [4] Speakman JR. Obesity: the integrated roles of environment and genetics. J Nutr 2004;134:2090S–105S. [5] Wang GJ, Volkow ND, Thanos PK, Fowler JS. Similarity between obesity and drug addiction as assessed by neurofunctional imaging: a concept review. J Addict Dis 2004;23:39–53. [6] Comings DE, Gade R, MacMurray JP, Muhleman D, Peters WR. Genetic variants of the human obesity (OB) gene: association with body mass index in young women, psychiatric symptoms, and interaction with the dopamine D2 receptor (DRD2) gene. Mol Psychiatry 1996;1:325–35. [7] Noble EP, Blum K, Ritchie T, Montgomery A, Sheridan PJ. Allelic association of the D2 dopamine receptor gene with receptor-binding characteristics in alcoholism. Arch Gen Psychiatry 1991;48:648–54. [8] Fetissov SO, Meguid MM. On dopamine, D2 receptor, and Taq1A polymorphism in obesity and anorexia. Nutrition 2009;25:132–3. [9] Noble EP, Noble RE, Ritchie T, Syndulko K, Bohlman MC, Noble LA, et al. D2 dopamine receptor gene and obesity. Int J Eat Disord 1994;15:205–17. [10] Fang YJ, Thomas GN, Xu ZL, Fang JQ, Critchley JA, Tomlinson B. An affected pedigree member analysis of linkage between the dopamine D2 receptor gene TaqI polymorphism and obesity and hypertension. Int J Cardiol 2005;102:111–6. [11] Barnard ND, Noble EP, Ritchie T, Cohen J, Jenkins DJ, Turner-McGrievy G, et al. D2 dopamine receptor Taq1A polymorphism, body weight, and dietary intake in type 2 diabetes. Nutrition 2009;25:58–65. [12] Wang GJ, Volkow ND, Logan J, Pappas NR, Wong CT, Zhu W, et al. Brain dopamine and obesity. Lancet 2001;357:354–7. [13] Comings DE, Flanagan SD, Dietz G, Muhleman D, Knell E, Gysin R. The dopamine D2 receptor (DRD2) as a major gene in obesity and height. Biochem Med Metab B 1993;50:176–85. [14] Blum K, Braverman ER, Wood RC, Gill J, Li C, Chen TJ, et al. Increased prevalence of the Taq I A1 allele of the dopamine receptor gene (DRD2) in obesity with comorbid substance use disorder: a preliminary report. Pharmacogenetics 1996;6:297–305. [15] Esposito-Smythers C, Spirito A, Rizzo C, McGeary JE, Knopik VS. Associations of the DRD2 TaqIA polymorphism with impulsivity and substance use: preliminary results from a clinical sample of adolescents. Pharmacol Biochem Behav 2009;93:306–12. [16] Noble EP. Addiction and its reward process through polymorphisms of the D2 dopamine receptor gene: a review. Eur J Psychiatry 2000;15:79–89. [17] Munafo MR, Matheson IJ, Flint J. Association of the DRD2 gene Taq1A polymorphism and alcoholism: a meta-analysis of case–control studies and evidence of publication bias. Mol Psychiatry 2007;12:454–61. [18] Bowirrat A, Oscar-Berman M. Relationship between dopaminergic neurotransmission, alcoholism, and reward deficiency syndrome. Am J Med Genet B Neuropsychiatr Genet 2005;132B:29–37. [19] Comings DE, Blum K. Reward deficiency syndrome: genetic aspects of behavioral disorders. Prog Brain Res 2000;126:325–41. [20] Eisenberg DT, Mackillop J, Modi M, Beauchemin J, Dang D, Lisman SA, et al. Examining impulsivity as an endophenotype using a behavioral approach: a DRD2 TaqI A and DRD4 48-bp VNTR association study. Behav Brain Funct 2007;3:2. [21] Wang GJ, Volkow ND, Thanos PK, Fowler JS. Imaging of brain dopamine pathways: implications for understanding obesity. J Addict Med 2009; 3:8–18. [22] Stice E, Spoor S, Ng J, Zald DH. Relation of obesity to consummatory and anticipatory food reward. Physiol Behav 2009;97:551–60. [23] Stice E, Yokum S, Blum K, Bohon C. Weight gain is associated with reduced striatal response to palatable food. J Neurosci 2010;30:13105–9. [24] Stice E, Spoor S, Bohon C, Small DM. Relation between obesity and blunted striatal response to food is moderated by TaqIA A1 allele. Science 2008;322:449–52. [25] Felsted JA, Ren X, Chouinard-Decorte F, Small DM. Genetically determined differences in brain response to a primary food reward. J Neurosci 2010;30:2428–32. [26] Volkow ND, Wang GJ, Telang F, Fowler JS, Thanos PK, Logan J, et al. Low dopamine striatal D2 receptors are associated with prefrontal metabolism in obese subjects: possible contributing factors. Neuroimage 2008; 42:1537–43. [27] Grandy DK, Zhang Y, Civelli O. PCR detection of the TaqA RFLP at the DRD2 locus. Hum Mol Genet 1993;2:2197. [28] Noble EP, Berman SM, Ozkaragoz TZ, Ritchie T. Prolonged P300 latency in children with the D2 dopamine receptor A1 allele. Am J Hum Genet 1994;54:658–68. [29] van Strien T, Snoek HM, van der Zwaluw CS, Engels RC. Parental control and the dopamine D2 receptor gene (DRD2) interaction on emotional eating in adolescence. Appetite 2010;54:255–61. [30] Nisoli E, Brunani A, Borgomainerio E, Tonello C, Dioni L, Briscini L, et al. D2 dopamine receptor (DRD2) gene Taq1A polymorphism and the eatingrelated psychological traits in eating disorders (anorexia nervosa and bulimia) and obesity. Eat Weight Disord 2007;12:91–6. [31] Hoek HW, van Hoeken D. Review of the prevalence and incidence of eating disorders. Int J Eat Disord 2003;34:383–96.
J. K. Winkler et al. / Nutrition 28 (2012) 996–1001 [32] Christakou A, Brammer M, Rubia K. Maturation of limbic corticostriatal activation and connectivity associated with developmental changes in temporal discounting. Neuroimage 2011;54:1344–54. [33] Lasky-Su J, Lyon HN, Emilsson V, Heid IM, Molony C, Raby BA, et al. On the replication of genetic associations: timing can be everything! Am J Hum Genet 2008;82:849–58. [34] Gorlova OY, Amos CI, Wang NW, Shete S, Turner ST, Boerwinkle E. Genetic linkage and imprinting effects on body mass index in children and young adults. Eur J Hum Genet 2003;11:425–32. [35] Moreno-Aliaga MJ, Santos JL, Marti A, Martinez JA. Does weight loss prognosis depend on genetic make-up? Obes Rev 2005;6:155–68. [36] Papoutsakis C, Dedoussis GV. Gene diet interactions in childhood obesity: paucity of evidence as the epidemic of childhood obesity continues to rise. Personalized Med 2007;4:133–46.
1001
[37] Haupt A, Thamer C, Heni M, Ketterer C, Machann J, Schick F, et al. Gene variants of TCF7L2 influence weight loss and body composition during lifestyle intervention in a population at risk for type 2 diabetes. Diabetes 2010;59:747–50. [38] Haupt A, Thamer C, Heni M, Tschritter O, Machann J, Schick F, et al. Impact of variation near MC4R on whole-body fat distribution, liver fat, and weight loss. Obesity (Silver Spring) 2009;17:1942–5. [39] Marti A, Goyenechea E, Martinez JA. Nutrigenetics: a tool to provide personalized nutritional therapy to the obese. World Rev Nutr Diet 2010;101:21–33. [40] Arkadianos I, Valdes AM, Marinos E, Florou A, Gill RD, Grimaldi KA. Improved weight management using genetic information to personalize a calorie controlled diet. Nutr J 2007;6:29. [41] Wing RR, Tate DF, Gorin AA, Raynor HA, Fava JL. A self-regulation program for maintenance of weight loss. N Engl J Med 2006;355:1563–71.