Ya Huang1, Min Xu1, Lan Xie2, Tiange Wang1, Xiaolin Huang1, Xiaofei Lv1, Ying Chen1, Lin Ding1, Lin Lin1, Weiqing Wang1, Yufang Bi1, Yimin Sun3, Yifei Zhang4, Guang Ning1. 1. State Key Laboratory of Medical Genomics, Key Laboratory for Endocrine and Metabolic Diseases of Ministry of Health, National Clinical Research Center for Metabolic Diseases, Collaborative Innovation Center of Systems Biomedicine and Shanghai Clinical Center for Endocrine and Metabolic Diseases, Rui-Jin Hospital, Shanghai Jiao-Tong University School of Medicine, Shanghai, 200025, China; Shanghai Institute of Endocrine and Metabolic Diseases, Department of Endocrine and Metabolic Diseases, Rui-Jin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China. 2. Medical Systems Biology Research Center, Tsinghua University School of Medicine, Beijing, 100084, China. 3. Medical Systems Biology Research Center, Tsinghua University School of Medicine, Beijing, 100084, China; National Engineering Research Center for Beijing Biochip Technology, Beijing, 102206, China. Electronic address: ymsun@capitalbio.com. 4. State Key Laboratory of Medical Genomics, Key Laboratory for Endocrine and Metabolic Diseases of Ministry of Health, National Clinical Research Center for Metabolic Diseases, Collaborative Innovation Center of Systems Biomedicine and Shanghai Clinical Center for Endocrine and Metabolic Diseases, Rui-Jin Hospital, Shanghai Jiao-Tong University School of Medicine, Shanghai, 200025, China; Shanghai Institute of Endocrine and Metabolic Diseases, Department of Endocrine and Metabolic Diseases, Rui-Jin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200025, China. Electronic address: feifei-a@163.com.
Abstract
BACKGROUND AND AIMS: Observational studies showed that obesity is a major risk factor for peripheral arterial disease (PAD). However, conventional epidemiology studies are vulnerable to residual bias from confounding factors. We aimed to explore the causality of obesity in development of PAD using Mendelian Randomization (MR) approach. METHODS: A MR analysis was performed in 11,477 community-dwelling adults aged 40 years and above recruited from two nearby communities during 2011-2013 in Shanghai, China. We genotyped 14 body mass index (BMI) associated common variants identified and validated in East Asians. PAD was defined as ankle-to-brachial index (ABI) <0.90 or >1.40. Weighted BMI genetic risk score (GRS) was used as the Instrumental Variable (IV). RESULTS: After adjusted for confounding factors, we found that each standard deviation (SD, 2.76 points) increase in BMI-GRS was associated with 0.43 (95% confidence interval [CI]: 0.36-0.49) kg/m(2) increase in BMI (P < 0.0001) and an odds ratio (OR) for PAD of 1.17 (95% CI: 1.07-1.27; P = 0.0004). Compared with the lowest quartile of BMI-GRS, the second, third and highest quartile associated with 9%, 19% and 45% increment of PAD risk, respectively (P for trend = 0.002). In the MR analysis, we demonstrated a causal relationship between obesity and PAD (OR = 1.44 per BMI-unit, 95% CI: 1.18-1.75; P = 0.0003). CONCLUSIONS: This study suggested that obesity may be causally associated with PAD after controlling for the potential intermediate factors like hypertension, dyslipidemia and hyperglycemia.
BACKGROUND AND AIMS: Observational studies showed that obesity is a major risk factor for peripheral arterial disease (PAD). However, conventional epidemiology studies are vulnerable to residual bias from confounding factors. We aimed to explore the causality of obesity in development of PAD using Mendelian Randomization (MR) approach. METHODS: A MR analysis was performed in 11,477 community-dwelling adults aged 40 years and above recruited from two nearby communities during 2011-2013 in Shanghai, China. We genotyped 14 body mass index (BMI) associated common variants identified and validated in East Asians. PAD was defined as ankle-to-brachial index (ABI) <0.90 or >1.40. Weighted BMI genetic risk score (GRS) was used as the Instrumental Variable (IV). RESULTS: After adjusted for confounding factors, we found that each standard deviation (SD, 2.76 points) increase in BMI-GRS was associated with 0.43 (95% confidence interval [CI]: 0.36-0.49) kg/m(2) increase in BMI (P < 0.0001) and an odds ratio (OR) for PAD of 1.17 (95% CI: 1.07-1.27; P = 0.0004). Compared with the lowest quartile of BMI-GRS, the second, third and highest quartile associated with 9%, 19% and 45% increment of PAD risk, respectively (P for trend = 0.002). In the MR analysis, we demonstrated a causal relationship between obesity and PAD (OR = 1.44 per BMI-unit, 95% CI: 1.18-1.75; P = 0.0003). CONCLUSIONS: This study suggested that obesity may be causally associated with PAD after controlling for the potential intermediate factors like hypertension, dyslipidemia and hyperglycemia.
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