Population Health Sciences, Bristol Medical School, University of Bristol, UK; MRC Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, UK.
Population Health Sciences, Bristol Medical School, University of Bristol, UK; MRC Integrative Epidemiology Unit, Bristol Medical School, University of Bristol, UK.
Econ Hum Biol. 2022 Jan;44:101088. doi: 10.1016/j.ehb.2021.101088. Epub 2021 Nov 26.
We analyze how measures of adiposity - body mass index (BMI) and waist hip ratio (WHR) - causally influence rates of hospital admission. Conventional analyses of this relationship are susceptible to omitted variable bias from variables that jointly influence both hospital admission and adipose status. We implement a novel quasi-Poisson instrumental variable model in a Mendelian randomization framework, identifying causal effects from random perturbations to germline genetic variation. We estimate the individual and joint effects of BMI, WHR, and WHR adjusted for BMI. We also implement multivariable instrumental variable methods in which the causal effect of one exposure is estimated conditionally on the causal effect of another exposure. Data on 310,471 participants and over 550,000 inpatient admissions in the UK Biobank were used to perform one-sample and two-sample Mendelian randomization analyses. The results supported a causal role of adiposity on hospital admissions, with consistency across all estimates and sensitivity analyses. Point estimates were generally larger than estimates from comparable observational specifications. We observed an attenuation of the BMI effect when adjusting for WHR in the multivariable Mendelian randomization analyses, suggesting that an adverse fat distribution, rather than a higher BMI itself, may drive the relationship between adiposity and risk of hospital admission.
我们分析了肥胖指标——体重指数（BMI）和腰臀比（WHR）——如何对住院率产生因果影响。传统的这种关系分析容易受到共同影响住院和脂肪状况的变量的遗漏变量偏差的影响。我们在孟德尔随机化框架中实施了一种新颖的拟泊松工具变量模型，通过对种系遗传变异的随机干扰来确定因果效应。我们估计了 BMI、WHR 以及 WHR 调整 BMI 后的个体和联合效应。我们还实施了多变量工具变量方法，其中一种暴露的因果效应是在另一种暴露的因果效应的条件下估计的。利用英国生物库中的 310471 名参与者和超过 55 万例住院患者的数据，进行了单样本和两样本孟德尔随机化分析。结果支持肥胖与住院之间存在因果关系，所有估计值和敏感性分析都具有一致性。点估计值通常大于可比观察规范的估计值。我们在多变量孟德尔随机化分析中观察到 BMI 效应的衰减，这表明不良的脂肪分布，而不是更高的 BMI 本身，可能是肥胖与住院风险之间关系的驱动因素。
