Physical Activity and the Association of Common FTO Gene Variants With Body Mass Index and Obesity ORIGINAL INVESTIGATION Physical Activity and the Association of Common FTO Gene Variants With Body Mass Index and Obesity Evadnie Rampersaud, MSPH, PhD; Braxton D. Mitchell, PhD; Toni I. Pollin, PhD; Mao Fu, PhD; Haiqing Shen, PhD; Jeffery R. O’Connell, PhD; Julie L. Ducharme, MD; Scott Hines, MD; Paul Sack, MD; Rosalie Naglieri, MD; Alan R. Shuldiner, MD; Soren Snitker, MD, PhD Background: Common FTO (fat mass and obesity asso- ciated) gene variants have recently been associated with body mass index (BMI) and obesity in several large studies. The role of lifestyle factors (such as physical activity) in those with an underlying FTO genetic predisposition is unknown. Methods: To determine if FTO variants are associated with BMI in Old Order Amish (OOA) individuals, and to further determine whether the detrimental associa- tions of FTO gene variants can be lessened by increased physical activity, a total of 704 healthy OOA adults were selected from the Heredity and Phenotype Intervention (HAPI) Heart Study, an investigation of gene � environ- ment interactions in cardiovascular disease, for whom ob- jective quantified physical activity measurements were available and for whom 92 single-nucleotide polymor- phisms (SNPs) in FTO were genotyped. Results: Twenty-six FTO SNPs were associated with BMI (P = .04 to �.001), including rs1477196 (P � .001) and rs1861868 (P � .001), 2 SNPs in moderate linkage dis- equilibrium in the OOA (D� = 0.82; r2= 0.36). Stratified analyses of rs1861868 revealed its association with BMI to be restricted entirely to those subjects with low sex- and age-adjusted physical activity scores (P � .001); in contrast, the SNP had no effect on those with above- average physical activity scores (P = .29), with the genotype � physical activity interaction achieving statis- tical significance (P = .01). Similar evidence for interac- tion was also obtained for rs1477196. Conclusions: Our results strongly suggest that the in- creased risk of obesity owing to genetic susceptibility by FTO variants can be blunted through physical activity. These findings emphasize the important role of physi- cal activity in public health efforts to combat obesity, par- ticularly in genetically susceptible individuals. Arch Intern Med. 2008;168(16):1791-1797 O BESITY AND RELATED CO- morbid conditions rep- resent a global public health burden and ac- count for a growing por- tion of health care spending in the indus- trialized world. It is widely acknowledged that there is a substantial genetic contri- bution to body mass index (BMI), and re- cently, robust associations of common vari- ants in intron 1 of the fat mass and obesity associated (FTO) gene with BMI, percent- age of body fat, and obesity were identi- fied in large studies of white adults and chil- dren.1-3 Owing to the high frequency of the obesity-associated FTO variants (about 30% allele frequency for the most strongly associated single-nucleotide polymor- phisms [SNPs] in European populations) and their impact (each “risk allele” is as- sociated with a 1.75-kg increase in body weight), these variants carry a population- attributable risk for obesity of greater than 20% in the studied populations.1,3 The function of FTO is incompletely un- derstood, although recent work4 has dem- onstrated that this gene codes for a protein expressed in the hypothalamus, a center of energy balance, and adipose tissue, where it is localized in cell nuclei and may be in- volved in demethylation of DNA and per- haps other, as-yet unidentified, functions. In addition to genetic factors, lifestyle factors, including diet and physical inac- tivity, are important contributors to weight gain and obesity. Although physical ac- tivity has been shown to facilitate weight loss and weight maintenance in obese sub- jects, there is great interindividual varia- tion in response.5 It is unknown whether lifestyle factors, such as physical activity, can attenuate weight gain and obesity in Author Affiliations: Department of Medicine, University of Maryland (Drs Rampersaud, Mitchell, Pollin, Fu, Shen, O’Connell, Ducharme, Hines, Sack, Naglieri, Shuldiner, and Snitker), and the Geriatric Research and Education Clinical Center, Baltimore Veterans Administration Medical Center (Dr Shuldiner), Baltimore. Dr Rampersaud is now with the Miller School of Medicine, Miami Institute for Human Genetics, University of Miami, Miami, Florida. (REPRINTED) ARCH INTERN MED/ VOL 168 (NO. 16), SEP 8, 2008 WWW.ARCHINTERNMED.COM 1791 ©2008 American Medical Association. All rights reserved. Downloaded From: https://jamanetwork.com/ by a Carnegie Mellon University User on 04/05/2021 those with an underlying FTO genetic predisposition. The specific issues addressed in this report are whether FTO variants are associated with BMI in the Old Order Amish (OOA), a population in which moderate to high levels of physical activity are common and, if so, whether the detrimental associations of BMI-associated FTO gene vari- ants can be lessened by increased physical activity. METHODS STUDY PARTICIPANTS Study participants were members of the OOA community in Lancaster County, Pennsylvania. The OOA are a rural-living, closed, founder population of European origin, well known for eschewing many modern conveniences; they do not own cars or use electricity in their homes. Most OOA men are farmers or work in physically demanding occupations such as black- smithing and carpentry. Women are homemakers, working with- out the aid of modern appliances and often taking care of many children. Similarities in tradition, formal education, and geo- graphic location among the OOA make for a relatively homo- geneous lifestyle, including dietary habits. Individuals in- cluded in this report took part in the Heredity and Phenotype Intervention (HAPI) Heart Study, an investigation of gene � envi- ronment interactions in cardiovascular disease of 868 adult OOA individuals in generally good health, who were recruited from 2003 to 2007.6 Subjects provided blood samples for DNA analy- sis and underwent a wide panel of physiological tests, includ- ing 7-day measurement of physical activity by accelerometry. The protocol was approved by the institutional review board of the University of Maryland, Baltimore. Subjects gave in- formed consent before participation. PHENOTYPIC ASSESSMENT All study subjects underwent a detailed clinical examination at the Amish Research Clinic in Strasburg, Pennsylvania. Height and weight were measured by trained nurses in subjects with- out shoes and in light clothing using a stadiometer and cali- brated scale. Body mass index was calculated as weight in ki- lograms divided by height in meters squared. Subjects with a BMI of 25 or greater and less than 30 were defined as being overweight, and those with a BMI of 30 or greater were de- fined as obese. Additional measures of other obesity-related traits were obtained, including waist circumference, measured to the nearest 0.1 cm using an inelastic tape, and in a subset of 355 individuals, body composition (fat mass, lean mass, and de- rived percentage of body fat) as determined by dual energy x-ray absorptiometry (DEXA). Objective measurement of physical activity was obtained over 7 consecutive 24-hour days using Actical activity monitors (ver- sions 8.2 and 8.3; Mini Mitter Co Inc, Bend, Oregon) worn on the hip. These devices incorporate an accelerometer, sensitive to 0.01 times gravity in multiple directions, electronic cir- cuitry, and a memory. Acceleration of the device is integrated and expressed as a number—activity counts—for each 15- second recording interval that is stored in memory until the device is returned and data uploaded. Activity can be ex- pressed as raw activity counts, inherently independent of body size, or as its associated energy expenditure (in kilocalories per day) using formulas validated against gas exchange.7 Acceler- ometry is a well-established method to quantify physical ac- tivity.8 Of the 868 HAPI Heart Study participants, physical ac- tivity measurements were available for 711 subjects, of whom 704 had valid genotype data. GENOTYPING Genomic DNA isolated from whole blood was genotyped with the Affymetrix GeneChip Human Mapping 500 K Array set (Af- fymetrix, Santa Clara, California). Total genomic DNA (250 ng) was digested with NspI or StyI enzymes and processed accord- ing to the Affymetrix protocol. The GeneChip genotyping analy- sis software (GTYPE version 4.0; Affymetrix) was used for au- tomated genotype calling as part of the GeneChip operating software platform. The GTYPE-generated chip files were re- analyzed using the Bayesian robust linear model with Maha- lanobis (BRLMM) genotype calling algorithm (free, download- able software available at http://www.affymetrix.com/support /technical/whitepapers/brlmm_whitepaper.pdf) and a confidence threshold for call quality of 0.5. As an initial quality control measure, BRLMM-generated chip files with genotype call rates of less than 93% for both enzymes were excluded from further analysis. For this study, SNPs that were either monomorphic or that deviated from Hardy-Weinberg equilibrium using a cut point of P � .001 were excluded from analysis. The FTO gene, located on chromosome 16q12.2, is approxi- mately 430 kilobases (kb) in length and contains 9 exons. The Affymetrix 500 K arrays contain 92 SNPs within the region of this gene. The mean genotype call rate was 98.6% for these 92 SNPs. STATISTICAL ANALYSES Association analyses of FTO SNPs and trait variables were per- formed using a variance component approach. Non–normally distributed variables were natural log-transformed prior to analy- ses. We modeled BMI and related traits as a function of mea- sured environmental covariates, additive genetic associations, and a residual error component. The associations of age, age2, sex, and interaction terms for age and sex were estimated jointly with genotype associations. Genotype was scored using an ad- ditive model to allow for allele dosage effects. Parameter esti- mates were obtained by maximum likelihood methods, and the significance of association was tested by a likelihood ratio test. We accounted for the relatedness of OOA study subjects by es- timating parameter effects conditional on residual correla- tions in BMI (or similar trait) between related individuals. The association analyses were performed using the SOLAR soft- ware program.9 Pairwise linkage disequilibrium (LD) correlation statistics (r2) were computed using Haploview beta software, version 4.0.10 Mul- tilocus haplotype analyses were performed using HaploStats soft- ware,11,12 which estimates haplotype frequencies using an ex- pectation-maximization algorithm in situations in which the haplotype phase is ambiguous. Global and haplotype-specific score statistics and permutation-based P values were calculated after adjustments were made for sex, age, and age2. These analyses did not take into account the relatedness of study subjects. We evaluated the associations of FTO SNPs on BMI after strati- fication of the sample according to “high” and “low” physical activity strata. Because the daily number of physical activity ac- celerometer counts was generally lower in women than in men and decreased with age in both sexes (data not shown), this strati- fication was performed in a sex- and age-specific fashion. After logarithmic transformation of mean total physical activity counts, subjects were dichotomized into the high- or low-activity stra- tum depending on whether their age-, age2- and sex-specific re- siduals were greater than or less than 0. In addition to these strati- fied analyses, we determined whether the association of genotype on BMI was modified by physical activity levels by constructing a regression model that included the following independent variables: sex, age, age2, age � sex, age2 � sex, SNP genotype, ln-transformed physical activity counts, and an interaction term (REPRINTED) ARCH INTERN MED/ VOL 168 (NO. 16), SEP 8, 2008 WWW.ARCHINTERNMED.COM 1792 ©2008 American Medical Association. All rights reserved. Downloaded From: https://jamanetwork.com/ by a Carnegie Mellon University User on 04/05/2021 (ln-transformed physical activity counts � genotype). The pres- ence of an interaction between physical activity and SNP geno- type on BMI was assessed by a likelihood ratio test, in which we compared a model with the interaction term (full model) with a model without the interaction term (nested model). As in pre- vious analyses, these analyses were performed using variance com- ponents analyses in the SOLAR software program to account for the correlations in BMI among family members. For the conversion of accelerometer counts into activity en- ergy expenditure (in kilocalories per day), we used Actical com- puter software (version 2.04), provided by the manufacturer of the accelerometers. This software applies a validated regres- sion equation7 to accelerometer counts on a per-epoch basis. RESULTS A total of 704 subjects with both physical activity and genotype data were included in our analyses. The mean (SD) age was 43.6 (3.4) years, and the sample included slightly more men than women (53% vs 47%). The mean BMI was higher in women (27.8 [5.7]) than in men (25.7 [3.4]). The prevalence of overweight and obesity in OOA men was 54.0% and 10.1%, respectively, and in OOA women was 63.7% and 30.5%, respectively. Mean physi- cal activity in men and women amounted to 519 (271) and 383 (219) � 103 counts/d, respectively. FTO SNP ASSOCIATIONS WITH OBESITY AND RELATED TRAITS We analyzed 92 SNPs falling in a 347-kb interval that spanned FTO (data are available from the authors at http: //medschool.umaryland.edu/endocrinology/afdspublic .asp). Of these SNPs, 26 were associated with BMI (P = .04 to �.001 under an additive genetic model). rs9939609, the associated SNP originally reported by Frayling et al,1 was modestly associated with obesity (P = .03) and not associated with BMI (P = .06) in the OOA, although stron- ger associations with this SNP were seen with total fat mass (P = .007) and percentage of body fat (P = .01). Figure 1 shows 20 SNPs in the region surrounding rs9939609. These 20 SNPs all fall within an 81-kb re- gion spanning from within intron 1 to within intron 3 of the FTO gene. The SNP most strongly associated with BMI in the OOA was rs1861868, a common SNP with a similar risk allele frequency (0.47) as in HapMap (http:// www.hapmap.org) genotype samples of white individu- als (0.50). Each A allele of rs1861868 was associated with a 0.75 increase in BMI (P � .001), corresponding to a mean (SD) increase in weight of 2.0 (0.7) kg per allele (Table 1). Individuals with the A allele for rs1861868 were at increased risk of being obese (odds ratio [OR], 1.26; P = .006) and of being overweight (OR, 1.15; P = .05). The A allele of rs1861868 was also associated with greater waist circumference (P = .003) and weight (P = .002). In a smaller subset of 355 subjects, the same directional associations were seen with total fat mass (P � .001) and with percentage of fat mass (P = .001). Sex-, age-, and age2-adjusted physical activity levels did not dif- fer significantly according to genotype (P = .11). Similar associations were observed with the C allele of rs1477196, the second most significant SNP in the OOA. In the OOA, each risk allele for rs1477196 was as- sociated with a mean (SD) increase in BMI of 0.84 (0.25) (P � .001). The SNPs rs1861868 and rs1477196 are 17.9 kb apart in intron 1 and are in moderate LD in the OOA (D� = 0.82; r2= 0.36). We examined the relationship between the 2 best SNPs identified from our analyses with rs9939609. In the OOA, this SNP is in strong LD with rs1477196 (D� = 1; r2= 0.54) but not with rs1861868 (D� = 0.44; r2= 0.17) (Figure 1). Together, the 3 SNPs (ordered from 5� to 3� rs1861868- rs1477196-rs9939609) defined 6 possible haplotypes (2 with frequencies �5%), 3 of which were associated with BMI (global permuted P = .004) (Table 2). The GTA hap- lotype had a frequency of 33.0% and was associated with decreased BMI (permuted P � .001), whereas the comple- mentary ACT haplotype had a frequency of 35.0% and was associated with increased BMI (permuted P = .08). The less frequent ACA haplotype (10.4%) was also as- sociated with increased BMI (permuted P = .006). These results suggest that in the OOA, regardless of rs9939609 allele (A or T), rs1861868 and rs1477196 define a hap- lotype that is associated with increased BMI. ANALYSES STRATIFIED BY PHYSICAL ACTIVITY LEVELS We next assessed the relationship of rs1861868 with BMI and obesity, stratified according to physical activity level (Figure 2). Among those within the lower half of the physical activity distribution (n = 361), the rs1861868 A allele was strongly associated with greater BMI (a mean [SD] increase of 1.12 [0.33] for each risk allele; P � .001). In contrast, BMI was not significantly associated with the rs1861868 genotype in subjects in the upper half of the physical activity distribution (n = 341) (0.30 [0.28] in- crease in BMI per risk allele; P = .29). rs1861868 was also associated with being obese in the low-activity group (OR, 1.28 for each risk allele; P = .03) but not in the high- activity group (P = .15). A formal test for interaction re- vealed significant evidence that increased physical ac- tivity levels blunted the association between the rs1861868 A allele on BMI (interaction P = .01). In Figure 3, we graphically illustrate, using the predicted values from the linear regression of residualized physical activity on BMI and rs1861868 genotype, that the difference in BMI across rs1861868 genotypes is large in less physically active in- dividuals but small and not statistically significant in more physically active individuals. The impact of increased physical activity on BMI reduction is most apparent for individuals with the rs1861868 AA genotype. Similarly, the rs1477196 C allele was associated with a 1.22 (0.38) increase in BMI per risk allele in the low- activity group (P = .001) but only a 0.27 (0.31) increase in BMI in the high-activity group (P = .38) (interaction P = .004) (Figure 2). The rs1477196 C allele was associ- ated with obesity in the low-activity group (OR, 1.58 for each risk allele; P = .001) but not in the high-activity group (P = .11). The level of physical activity in the 2 strata can be described by the energy expenditure at the 25th and 75th centiles of the distribution, respectively. For women, energy expenditure was 2610 and 3590 kcal/d (REPRINTED) ARCH INTERN MED/ VOL 168 (NO. 16), SEP 8, 2008 WWW.ARCHINTERNMED.COM 1793 ©2008 American Medical Association. All rights reserved. Downloaded From: https://jamanetwork.com/ by a Carnegie Mellon University User on 04/05/2021 3.5 4.0 3.0 2.5 2.0 1.5 1.0 0.5 0 –l og 10 ( P Va lu e) A B C rs 18 61 86 9 rs 18 61 86 8 rs 99 40 70 0 rs 99 40 12 8 rs 99 39 97 3 rs 99 22 04 7 rs 16 95 25 22 rs 17 81 72 88 rs 14 77 19 6 rs 11 21 98 0 rs 71 93 14 4 rs 80 50 13 6 rs 99 26 28 9 rs 99 39 60 9 rs 99 30 50 6 rs 11 07 59 94 rs 14 21 09 0 rs 99 72 71 7 rs 10 85 25 22 rs 17 81 89 02 rs 18 61 86 9 rs 18 61 86 8 rs 99 40 70 0 rs 99 40 12 8 rs 99 39 97 3 rs 99 22 04 7 rs 16 95 25 22 rs 17 81 72 88 rs 14 77 19 6 rs 11 21 98 0 rs 71 93 14 4 rs 80 50 13 6 rs 99 39 60 9 rs 99 30 50 6 rs 11 07 59 94 rs 14 21 09 0 rs 99 72 71 7 rs 10 85 25 22 rs 17 81 89 02 BMI Block 2 (29 kb)Block 1 (5 kb) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 Block 3 (13 kb) 91 X 0 94 3 2 3 85 59 80 99 98 90 0 2 0 3 55 741220 0 3 2 2 7 11220 0 2 1 1 5 51220 3 3 3 1 5 51101 1 3 4 1 5 20211 1 2 0 2 3 1023111721 20 17 0 3 2 2 1 1 2 1202 2 1 0 1 2111 1 0 1 0 0 0 X 2 1 1720 11 217 21 23 11 33 34 31 30 3 1 021 21 18 18 29 21 0 0 21 1 94 82 82 85 90 95 951 1 2 95 83 83 818585 85 82 92 1 2 2 3 2 2 92 84 84 82 84 84 92 92 81 82 92 90 53 53 54 54 53 80 80 79 90 98 93 99 99 99992 7 94 Block 1 (0 kb) Block 2 (20 kb) 56 57 67 80 82 97 98 99 101 104 106 114 125 142 159 160 163 172 200 70 51051 0 0 0 0 0 2 2 2 3 4 3 0 0 3 4 1 1 1 1 1 2 7 9 9 9 9 5 5 5 4 5 0 0 0 2 4 1 2 2 0 2 0 0 0 3 3 3 3 0 0 0 2 7 27 20 0 1 2 5 5700 0 0 1 0 0 1 0 0 0 8 10 0 0 9 11 1 1 11 9 9 8 11 1316 14 0 18 11 10 17 76 12 13 13 0 0 1 1 187 66 54 0 0 56 54 38 35 55 90 0 0 0 0 54 50 35 87 31 3262 64 640 0 0 0 59 58 56 56 64 87 87 83 84 84 83 93 93 19 11 32 84 84 93 87 84 84 96 95 2 0 1 1 15 11 Figure 1. Linkage disequilibrium (LD) structure and FTO single-nucleotide polymorphism (SNP) associations with body mass index (BMI). A, For each SNP surrounding rs9939609, we show –log10 (P value) for the additive model in the Old Order Amish (OOA). B, The LD (r 2) of FTO in OOA white subjects. C, The LD (r 2) of FTO in the HapMap (http://www.hapmap.org) genotype of white individuals. The association of rs9939609 with BMI was first reported in European white individuals1 and subsequently in French white individuals2; rs9930506 was the SNP most highly associated with BMI in a Sardinian population3; rs8050136 was associated with type 2 diabetes mellitus (T2DM) in European white subjects.11-13 Zeggini et al13 later found that the association disappeared after adjustment for BMI. In an independent sample of 419 OOA subjects (of whom 124 had T2DM), rs8050136 was associated with T2DM (P = .007), and this association was abolished after adjustment for BMI (P = .53) (data not shown). rs9939609, rs9930506, and rs8050136 all reside in a cluster of SNPs in high LD in HapMap genotypes of white individuals. Black squares indicate r 2� 0.80; gray squares, 0.10 � r 2� 0.80; white squares, r 2� 0.10. kb Indicates kilobases. (REPRINTED) ARCH INTERN MED/ VOL 168 (NO. 16), SEP 8, 2008 WWW.ARCHINTERNMED.COM 1794 ©2008 American Medical Association. All rights reserved. Downloaded From: https://jamanetwork.com/ by a Carnegie Mellon University User on 04/05/2021 at the 25th and 75th centiles, respectively, whereas for men energy expenditure was 3130 and 3990 kcal/d at the same centiles. Thus, in this population, a mean activity level of 860 calories for women and 980 calories for men separates the high- and low-activity strata, which differ in their phenotypic expression of the at-risk FTO genotypes. COMMENT Our study replicates the association between variants in the FTO gene and obesity-related traits (eg, BMI, body weight, waist circumference, and percentage of body fat) recently reported by others.1-3,13-15 Furthermore, we have shown that the association of genotype on body compo- sition is much smaller and not statistically significant in subjects having higher physical activity levels. The SNPs most strongly associated with BMI in our study were rs1861868 and rs1477196, both of which are common in the OOA (0.49 and 0.35, respectively) and in non- OOA white individuals (0.48 and 0.28, respectively). These SNPs are in higher LD in the OOA (D� = 0.84; r2= 0.36) than in non-OOA European white individuals (HapMap genotypes from white individuals, D� = 0.61, r2= 0.15). Greater LD in the OOA compared with the gen- eral white population might be expected because the OOA are a relatively young founder population. Although an association of rs1477196 with obesity (P = 6.0 � 10−9 based on an additive model) has previously been reported in a study of French adults,2 to our knowledge, the associa- tion of rs1861868 with BMI or obesity has not been de- scribed. In our analyses, a common haplotype defined by both SNPs (rs1861868, A allele; rs1477196, C allele) seems to jointly confer risk to increased BMI. This seems to be independent of rs9939609, the SNP originally found to be associated with BMI in multiple European white populations1,3 (Table 2). At least in the OOA, these SNPs may both be marking a common, yet unknown func- tional allele or may themselves have functional effects on body size through an as-yet undetermined mechanism. Further fine mapping with additional genotyping to help localize the true functional variant was beyond the scope of the current study. A primary finding of this study is that of a gene � envi- ronment interaction between variants in the FTO gene and physical activity. Using objective-, age-, and sex- adjusted physical activity measures obtained for 7 con- secutive 24-hour days, we were able to investigate the genotype associations of FTO variants in OOA subjects with high and low physical activity levels. Unlike ques- tionnaires, the Actical device provides an objective esti- mate of physical activity that is unaffected by study par- ticipant recall bias. We found that SNP associations with BMI and related measures were limited to less physically active individuals. In the less active group, Table 1. Clinical Characteristics of the Study Population by FTO rs1861868 Genotypes a AA (n = 166) AG (n = 333) GG (n = 203) P Value Characteristic Women, % 47.0 49.6 41.9 .42 Age, y 43.4 ± 14.9 43.2 ± 14.1 44.6 ± 13.6 .38 BMI 27.4 ± 4.8 26.5 ± 4.5 25.9 ± 4.1 �.001 Waist circumference, cm 89.3 ± 11.4 86.9 ± 11.0 86.4 ± 10.5 .003 Weight, kg 76.5 ± 12.9 74.3 ± 13.1 73.0 ± 11.8 .002 Height, cm 167.5 ± 8.7 167.4 ± 9.3 167.6 ± 9.7 .81 Overweight (25 � BMI � 30), % 64.5 57.1 56.7 .05 Obese (BMI �30), % 27.1 18.3 15.8 .006 Total daily accelerometer counts/1000, median (25%, 75% percentiles) 362 (288, 538) 392 (273, 572) 427 (285, 610) Physical activity b −0.05 ± 0.43 0.002 ± 0.45 0.04 ± 0.49 .11 Fat and Lean Mass c (n = 82) (n = 169) (n = 103) Total fat mass, kg 21.8 ± 9.9 20.5 ± 9.5 18.5 ± 9.2 �.001 Total lean mass, kg 55.3 ± 9.8 52.6 ± 9.6 51.8 ± 10.8 .02 Total % fat 27.8 ± 10.1 27.5 ± 10.3 25.8 ± 10.7 .001 Abbreviation: BMI, body mass index (calculated as weight in kilograms divided by height in meters squared). a Data are given as mean ± SD except where noted; “n” indicates the number of samples. b Group means for age- and sex-residualized daily physical activity counts. c Measures of body composition as determined by dual-energy x-ray absorptiometry (DEXA). Table 2. Haplotypes for FTO SNPs rs1861868, rs1477196, and rs9939609 Haplotype Frequency Haplotype Score P Value a GTA 0.330 −3.49 �.001 GCA 0.039 −0.25 .80 GCT 0.147 −0.13 .90 ATA 0.030 0.20 .84 ACT 0.349 1.77 .08 ACA 0.104 2.73 .006 Abbreviation: SNP, single-nucleotide polymorphisms. a Global P = .006; df = 5. P values were calculated by score test after adjustment for sex, age, and age2, using HaploStats software.11,12 Statistical analysis was restricted to samples completely genotyped at all 3 SNPs (n = 648). (REPRINTED) ARCH INTERN MED/ VOL 168 (NO. 16), SEP 8, 2008 WWW.ARCHINTERNMED.COM 1795 ©2008 American Medical Association. All rights reserved. Downloaded From: https://jamanetwork.com/ by a Carnegie Mellon University User on 04/05/2021 being AA homozygous for the rs1861868 FTO variant was associated with an increase in BMI of 2.1, a slightly higher estimate than that obtained by Frayling et al1 for rs9939609 in United Kingdom (UK) Wellcome Trust Case Control subjects with type 2 diabetes mellitus, and much higher than the same group’s estimate for rs9939609 in UK type 2 diabetes mellitus genetics con- sortium controls.1 By contrast, in the more physically active OOA stratum, the associations of the FTO vari- ants were much smaller and not statistically significant. Because the FTO genotype was not associated with physical activity, this finding suggests a strong moderat- ing effect of physical activity on the deleterious effects of FTO variants, consistent with published findings16 based on self-reported physical activity data. When men and women were evaluated separately without consid- eration of covariates, the association of rs1861868 genotype reached nominal statistical significance in women only, not in men. However, there was no evi- dence that the effect of genotype on BMI differed sub- stantially between men and women, particularly when the level of physical activity was included in the regres- sion model (ie, the genotype by sex interaction term was not statistically significant; P = .17). Recent work4,17 has demonstrated that FTO codes for a protein expressed in the hypothalamus, the function of which is impacted in many obesity-related genetic defects in humans. Because FTO is down-regulated by Krebs cycle intermediates,4 it is conceivable that this protein is involved in incompletely understood nutrient sensing pathways, which are pivotal to central regula- tion of energy intake. Thus, a mechanism whereby increased physical activity can negate the association of FTO variants with fat accretion could be through pertu- bation of energy flux resulting in alterations in expres- sion of FTO. 35 33 31 29 27 25 23 AA AG GG Genotype M ea n BM I A P < .001 n = 92 n = 172 n = 96 35 33 31 29 27 25 23 AA AG GG Genotype P = .29 n = 74 n = 161 n = 107 35 33 31 29 27 25 23 CC CT TT Genotype P = .38 n = 118 n = 156 n = 44 rs1861868 35 33 31 29 27 25 23 CC CT TT Genotype M ea n BM I B P = .001 n = 114 n = 156 n = 34 rs1477196 Figure 2. Mean body mass index (BMI), calculated as weight in kilograms divided by height in meters squared, in high and low physical activity groups according to FTO single-nucleotide polymorphism genotypes for rs1861868 (A) and rs1477196 (B). High activity was defined as age-, age2-, and sex-specific residuals greater than 0, and low activity was defined by residuals less than 0. Interaction P values: rs1861868, P = .01; rs1477196, P = .004. 29.5 29.0 28.5 28.0 27.5 27.0 26.5 26.0 25.5 25.0 24.5 –2.0 –0.5–1.5 –1.0 0 1.00.5 1.5 2.0 Residualized Physical Activity BM I AA GA GG Genotype Figure 3. Predicted body mass index (BMI), calculated as weight in kilograms divided by height in meters squared, as a function of residualized age- and sex-specific ln-transformed physical activity accelerometer counts according to FTO rs1861868 genotypes. On the left side of the plot (low physical activity), BMI levels are strikingly dissimilar between rs1861868 genotypes. In contrast, on the right side of the plot, similar BMI levels can be seen across genotypes, particularly in subjects with very high levels of physical activity. (REPRINTED) ARCH INTERN MED/ VOL 168 (NO. 16), SEP 8, 2008 WWW.ARCHINTERNMED.COM 1796 ©2008 American Medical Association. All rights reserved. Downloaded From: https://jamanetwork.com/ by a Carnegie Mellon University User on 04/05/2021 Activity levels in the “high-activity” stratum were ap- proximately 900 kcal higher than in the “low-activity” stratum, which, depending on body size, corresponds to about 3 to 4 hours of moderately intensive physical ac- tivity, such as brisk walking, house cleaning, or garden- ing.18 Although this seems to be a large amount of physi- cal activity, the OOA demonstrate that this level of activity was typical of an agrarian lifestyle without modern ma- chinery. Of course, our cross-sectional study is un- suited to determine the amount of activity required to negate the effect of an FTO-related genetic predisposi- tion to weight gain; however, in a retrospective analysis in which weight regain was measured as a function of physical activity energy expenditure, Schoeller et al18 found that the addition of 80 min/d of moderate activity or 35 min/d of vigorous activity to a sedentary lifestyle was sufficient for weight maintenance. Prospective in- tervention studies will be necessary to define these para- meters more accurately. In conclusion, we have replicated the associations of common SNPs in the FTO gene with increased BMI and risk to obesity in the OOA. Furthermore, we provide quan- titative data to show that the weight increase resulting from the presence of these SNPs is much smaller and not statistically significant in subjects who are very physi- cally active. This finding offers some clues to the mecha- nism by which FTO influences changes in BMI and may have important implications in targeting personalized life- style recommendations to prevent obesity in genetically susceptible individuals. Accepted for Publication: March 4, 2008. Correspondence: Evadnie Rampersaud, MSPH, PhD, Miller School of Medicine, Miami Institute for Human Genetics, University of Miami, PO Box 019132 (M-860), Miami, FL 33101 (erampersaud@med.miami.edu). Author Contributions: Study concept and design: Rampersaud, Mitchell, Shen, Shuldiner, and Snitker. Ac- quisition of data: Fu, Ducharme, Hines, Sack, Naglieri, Shuldiner, and Snitker. Analysis and interpretation of data: Rampersaud, Mitchell, Pollin, Fu, O’Connell, Shuldiner, and Snitker. Drafting of the manuscript: Rampersaud, Mitchell, and Snitker. Critical revision of the manuscript for important intellectual content: Rampersaud, Mitchell, Pollin, Fu, Shen, O’Connell, Ducharme, Hines, Sack, Naglieri, Shuldiner, and Snitker. Statistical analysis: Rampersaud, Mitchell, Pollin, Shen, O’Connell, and Snitker. Obtained funding: Shuldiner. Administrative, technical, and material support: Fu, Ducharme, and Sack. Study supervision: Mitchell, Shuldiner, and Snitker. Financial Disclosure: None reported. REFERENCES 1. Frayling TM, Timpson NJ, Weedon MN, et al. A common variant in the FTO gene is associated with body mass index and predisposes to childhood and adult obesity. Science. 2007;316(5826):889-894. 2. Dina C, Meyre D, Gallina S, et al. Variation in FTO contributes to childhood obe- sity and severe adult obesity. Nat Genet. 2007;39(6):724-726. 3. Scuteri A, Sanna S, Chen WM, et al. Genome-wide association scan shows ge- netic variants in the FTO gene are associated with obesity-related traits. PLoS Genet. 2007;3(7):e115. 4. 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Correction Errors in Funding/Support, Role of the Sponsor, and Additional Contributions: The Original Investigation titled “Physical Activity and the Association of Com- mon FTO Gene Variants With Body Mass Index and Obe- sity,” which was published in the September 8, 2008, is- sue of the Archives (2008;168[16]:1791-1797), contained omissions of the Funding/Support, Role of the Sponsor, and Additional Contributions paragraphs. The follow- ing information should have been included: Funding/Support: The HAPI Heart Study receives funding from National Institutes of Health (NIH) grant U01 HL072515. Partial funding for this study was pro- vided by the Clinical Nutrition Research Unit of Mary- land, grant P30 DK072488; the University of Maryland General Clinical Research Center, grant M01 RR 16500; the Johns Hopkins University General Clinical Re- search Center, grant M01 RR 000052; and the Geriatric Research and Education Clinical Center, Baltimore Vet- erans Administration Medical Center. Dr Rampersaud was funded by a postdoctoral NIH/National Heart, Lung, and Blood Institute–sponsored NRSA training grant T32HL072751. Role of the Sponsor: As part of the PROGENI Net- work, this project was overseen by an NIH-appointed Data Safety and Monitoring Board. Additional Contributions: We thank the Amish study participants and our Amish Research Clinic and laboratory staff for their extraordinary efforts. (REPRINTED) ARCH INTERN MED/ VOL 169 (NO. 5), MAR 9, 2009 WWW.ARCHINTERNMED.COM 453 ©2009 American Medical Association. All rights reserved. Downloaded From: https://jamanetwork.com/ by a Carnegie Mellon University User on 04/05/2021