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Multicenter Study
. 2015 Dec 18:16:114.
doi: 10.1186/s12881-015-0259-x.

Physical activity, smoking, and genetic predisposition to obesity in people from Pakistan: the PROMIS study

Shafqat Ahmad  1 , Wei Zhao  2 , Frida Renström  3 , Asif Rasheed  4 , Maria Samuel  5 , Mozzam Zaidi  6 , Nabi Shah  7   8 , Nadeem Hayyat Mallick  9 , Khan Shah Zaman  10 , Mohammad Ishaq  11 , Syed Zahed Rasheed  12 , Fazal-ur-Rheman Memon  13 , Bashir Hanif  14 , Muhammad Shakir Lakhani  15 , Faisal Ahmed  16 , Shahana Urooj Kazmi  17 , Philippe Frossard  18   19 , Paul W Franks  20   21   22 , Danish Saleheen  23   24   25
Affiliations
Multicenter Study

Physical activity, smoking, and genetic predisposition to obesity in people from Pakistan: the PROMIS study

Shafqat Ahmad et al. BMC Med Genet. .

Abstract

Background: Multiple genetic variants have been reliably associated with obesity-related traits in Europeans, but little is known about their associations and interactions with lifestyle factors in South Asians.

Methods: In 16,157 Pakistani adults (8232 controls; 7925 diagnosed with myocardial infarction [MI]) enrolled in the PROMIS Study, we tested whether: a) BMI-associated loci, individually or in aggregate (as a genetic risk score--GRS), are associated with BMI; b) physical activity and smoking modify the association of these loci with BMI. Analyses were adjusted for age, age(2), sex, MI (yes/no), and population substructure.

Results: Of 95 SNPs studied here, 73 showed directionally consistent effects on BMI as reported in Europeans. Each additional BMI-raising allele of the GRS was associated with 0.04 (SE = 0.01) kg/m(2) higher BMI (P = 4.5 × 10(-14)). We observed nominal evidence of interactions of CLIP1 rs11583200 (P(interaction) = 0.014), CADM2 rs13078960 (P(interaction) = 0.037) and GALNT10 rs7715256 (P(interaction) = 0.048) with physical activity, and PTBP2 rs11165643 (P(interaction) = 0.045), HIP1 rs1167827 (P(interaction) = 0.015), C6orf106 rs205262 (P(interaction) = 0.032) and GRID1 rs7899106 (P(interaction) = 0.043) with smoking on BMI.

Conclusions: Most BMI-associated loci have directionally consistent effects on BMI in Pakistanis and Europeans. There were suggestive interactions of established BMI-related SNPs with smoking or physical activity.

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Figures

Fig. 1
Fig. 1
Effect estimates for the 95 BMI variants obtained in the PROMIS study (N = 16,157) in comparison to those reported by the GIANT Consortium (Locke et al. [8]) (N = 339,224)
Fig. 2
Fig. 2
Association of the genetic risk score (GRS) based on 95 BMI-associated SNPs and BMI in the PROMIS Study (N = 16,157). Data represents mean (95 % CI) BMI per quintile of the GRS, adjusted forage, age2, sex, MI status, and principal components
Fig. 3
Fig. 3
The discriminative power of different models to predict risk of obesity [BMI (kg/m2) ≥27.5 compared with 18.5 kg/m2 < BMI < 23 kg/m2]. The area under the receiver operating characteristic curve (ROC AUC) is investigated: Model 1 = age, age2, sex and case status; model 2 = all 95 single nucleotide polymorphisms; model 3 = model 1 + model 2
Fig. 4
Fig. 4
Association of PTBP2 rs11165643 (a), HIP1 rs1167827 (b), C6orf106 rs205262 (c) and GRID1 rs7899106 (d), on BMI stratified by smoking status in control participants (N = 8219) from PROMIS
Fig. 5
Fig. 5
Association between CLIP1 rs11057405 (a) CADM2 rs13078960 (b) and GALNT10 rs7715256 (c) on BMI stratified by physical activity in control participants (N = 6784) from PROMIS. For CLIP1 rs11057405 variant, there were only N = 7 participants are in the “active” physical activity group, so participants having genotype “AA” and “AG” were combined for the Fig. 5a
See this image and copyright information in PMC

References

    1. Hubert HB, Feinleib M, McNamara PM, Castelli WP. Obesity as an independent risk factor for cardiovascular disease: a 26-year follow-up of participants in the Framingham Heart Study. Circulation. 1983;67(5):968–77. doi: 10.1161/01.CIR.67.5.968. - DOI - PubMed
    1. Pischon T, Boeing H, Hoffmann K, Bergmann M, Schulze MB, Overvad K, et al. General and abdominal adiposity and risk of death in Europe. N Engl J Med. 2008;359(20):2105–20. doi: 10.1056/NEJMoa0801891. - DOI - PubMed
    1. Sullivan PW, Morrato EH, Ghushchyan V, Wyatt HR, Hill JO. Obesity, inactivity, and the prevalence of diabetes and diabetes-related cardiovascular comorbidities in the U.S., 2000–2002. Diabetes Care. 2005;28(7):1599–603. doi: 10.2337/diacare.28.7.1599. - DOI - PubMed
    1. Zhou ZY, Liu YK, Chen HL, Liu F. Body mass index and knee osteoarthritis risk: a dose–response meta-analysis. Obesity (Silver Spring) 2014;22(10):2180–5. doi: 10.1002/oby.20835. - DOI - PubMed
    1. Ng M, Fleming T, Robinson M, Thomson B, Graetz N, Margono C, et al. Global, regional, and national prevalence of overweight and obesity in children and adults during 1980–2013: a systematic analysis for the Global Burden of Disease Study 2013. Lancet. 2014;384(9945):766–81. doi: 10.1016/S0140-6736(14)60460-8. - DOI - PMC - PubMed

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