FTO genotype and weight loss in diet and lifestyle interventions: a systematic review and meta-analysis

Xiang, Lingwei; H, Wu; Pan, An; Patel, Bhakti; Xiang, Guangda; Qi, Lu; Kaplan, Robert C.; Hu, Frank B.; Wylie‐Rosett, Judith; Qi, Qibin

doi:10.3945/ajcn.115.123448

Cited by 86 publications

(76 citation statements)

References 70 publications

(93 reference statements)

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“…Figure 1B is compared with children in low polygenic risk score (PRS) group. 33,34 de Lauzon-Guillain reported that the effect of the body mass index genetic risk score (BMI-GRS) on BMI was weakest among those with the highest levels of cognitive restraint (CR), which indicated that increasing CR might help to mitigate genetic susceptibility to obesity. 33,34 de Lauzon-Guillain reported that the effect of the body mass index genetic risk score (BMI-GRS) on BMI was weakest among those with the highest levels of cognitive restraint (CR), which indicated that increasing CR might help to mitigate genetic susceptibility to obesity.…”

Section: Discussionmentioning

confidence: 99%

Polygenic risk, adherence to a healthy lifestyle, and childhood obesity

et al. 2018

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Summary Background Both polygenic and unhealthy lifestyle factors contribute to individual‐level risk of obesity during childhood. The extent to which increased polygenic risk can be offset by healthy lifestyle is unknown. Objective To examine whether increased polygenic risk of obesity is modified by a healthy lifestyle during childhood. Methods A total of 997 children (418 boys and 579 girls) who had complete DNA genotyping and body mass index (BMI) were eligible for the study were analyzed from an established prospective puberty cohort. The polygenic risk score (PRS) was computed based on 11 BMI‐increasing single nucleotide polymorphisms (SNPs) derived from a published genome‐wide association study (GWAS) for child obesity. Seven lifestyle factors were selected to determine a composite healthy lifestyle, such as food responsiveness, slowness in eating, satiety responsiveness, physical activity, screen time, sleep duration, and sugar‐sweetened beverages consumption, and which was classified as healthy (P75) lifestyle. Linear and logistic regression analyses were conducted. Results Children carrying additional BMI susceptibility alleles increased the BMI by 0.11 kg/m2, after adjusting gender, age and socio‐economic status (SES). A healthy lifestyle was associated with a substantially lower risk of obesity than an unhealthy lifestyle, regardless of the polygenic risk category. Among children at high polygenic risk, a healthy lifestyle was associated with an 85% lower risk of obesity than an unhealthy lifestyle (odds ratio, 0.15; 95% CI, 0.04 to 0.59; p = 0.007). Conclusions Our findings suggest that increased polygenic risk might be largely offset by healthy lifestyle as early as in childhood.

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Section: Discussionmentioning

confidence: 99%

Polygenic risk, adherence to a healthy lifestyle, and childhood obesity

et al. 2018

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“…In agreement with Delahanty et al [4] , we did not find associations between these SNPs and weight loss, although the FTO SNP showed a treatment-specific effect. A recent meta-analysis [5] suggested that the AA genotype of the FTO SNP, but not the TA genotype, was associated with significantly more weight loss than the TT genotype in diet and lifestyle interventions (difference -0.72 kg [95% CI: -1.21, +0.23 kg]; p = 0.004).…”

Section: Discussionmentioning

confidence: 99%

“…The first evidence for a role of genetic predisposition in weight loss and weight regain came from a study by Delahanty et al [4] , which showed that several gene variants were associated with short-and long-term weight loss and weight regain. Based on a meta-analysis of available studies, Xiang et al [5] concluded that individuals carrying the homozygous fat mass and obesity-associated (FTO) obesity-predisposing allele lose more weight by means of diet or lifestyle interventions than noncarriers. Knowledge of such predictors might help to explain interindividual variations in weight loss success and lead to more personalized treatments for weight loss and weight loss maintenance.…”

Section: Introductionmentioning

confidence: 99%

Genetic Predictors of ≥5% Weight Loss by Multidisciplinary Advice to Severely Obese Subjects

Aller¹,

Mariman²,

Bouwman³

et al. 2017

Lifestyle Genomics

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Background:Weight loss success is determined by genetic factors, which may differ according to treatment strategy. Methods: From a multidisciplinary obesity treatment program involving dietary advice, psychological counseling, and increased physical activity, 587 subjects (68% female; 46.1 ± 12.4 years; BMI 39.9 ± 6.3) were recruited. At baseline, a blood sample was drawn for DNA isolation. Genotypes were determined for 30 polymorphisms in 25 candidate genes. The association between genotypes and weight loss was assessed after 3 months (short-term) and after 12 months of treatment (long-term). Weight loss was categorized as ≥ 5% or <5% of initial weight. Results: The G/G genotype of PLIN1 (rs2289487) and PLIN1 (rs2304795), the T/T genotype of PLIN1 (rs1052700), and the C/C genotype of MMP2 predicted ≥ 5% weight loss in the first 3 months. The C/G-G/G genotype of PPARγ (rs1801282) and the T/C genotype of TIMP4 (rs3755724) predicted ≥ 5% weight loss after 12 months. Subjects with the combination of PPARγ (rs1801282) C/G-G/G and TIMP4 (rs3755724) T/C lost even more weight. Conclusion: Polymorphisms in genes related to regulation of fat storage and structural adaptation of the adipocytes are predictors for weight loss success with different genes being relevant for short-term and long-term weight loss success.

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“…Component scores were used as neural predictors of weight loss. For the FTO genotype predictor, A/A and T/A of the FTO rs9939609 were coded 0 while the T/T genotype was coded 1 because a meta‐analysis of BWL interventions showed that carriers of the FTO A/A and T/A genotypes have greater weight loss than those with the T/T . The neural and genotype predictors were simultaneously entered into separate hierarchical linear regressions predicting weight loss in BWL from baseline to 12, 36, and 60 weeks post‐baseline and from 12 to 36 weeks, 12 to 60 weeks, and 36 to 60 weeks.…”

Section: Methodsmentioning

confidence: 99%

Genetic and neural predictors of behavioral weight loss treatment: A preliminary study

Chen

Olino

Conklin

et al. 2016

Obesity

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Objective To examine neural mechanisms of action in behavioral weight loss treatment (BWL) and explore neural and genetic predictors of BWL. Methods Neural activation to palatable food receipt and genetics were compared in 17 women with obesity who received 12-weeks of BWL and 17 women who received no intervention. Participants were scanned twice using functional magnetic resonance imaging at baseline and 12 weeks. Weight was assessed at baseline, 12, 36, and 60 weeks. Results BWL participants lost more weight than controls at 12 weeks (−4.82 vs. −0.70 %). After 12 weeks, BWL had greater reduction in right caudate activation response to milkshake receipt than did controls. Among BWL participants, baseline to 12 weeks reduction in frontostriatal activation to milkshake predicted greater weight loss at 12, 36 and 60 weeks and possessing the A/A or T/A genotype of the fat mass and obesity–associated (FTO) variant rs9939609 predicted greater weight loss at 12 and 36 weeks. Conclusions These preliminary data reveal that reduction in right caudate activation may be a neural mechanism of weight loss in BWL and baseline FTO variant and reduction in frontostriatal activation during BWL predict short- and long-term weight loss. These findings require replication in larger samples.

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FTO genotype and weight loss in diet and lifestyle interventions: a systematic review and meta-analysis

Cited by 86 publications

References 70 publications

Polygenic risk, adherence to a healthy lifestyle, and childhood obesity

Polygenic risk, adherence to a healthy lifestyle, and childhood obesity

Genetic Predictors of ≥5% Weight Loss by Multidisciplinary Advice to Severely Obese Subjects

Genetic and neural predictors of behavioral weight loss treatment: A preliminary study

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