Adiposity and ischaemic Heart Disease in the UK Biobank: A prospective cohort study of 450 000 adults.

背景缺血性心脏病（Ischaemic heart disease，IHD）是2010年全球范围内死亡和残疾所致的每日调整生命年损失的主要原因。肥胖正日益被认为是心血管疾病（CVD）的主要危险因素，包括IHD。肥胖导致CVD的机制被认为部分是其对公认的血管风险因素（如血脂异常、高血压和糖尿病）的不良影响的结果。然而，高水平肥胖的一些过度风险仍然无法解释。重要的是，随着全球平均BMI每十年增加约1.5kg/m2，以及全球IHD死亡人数的增加，进行高质量的研究，提供与当代人群肥胖相关的IHD风险的详细估计至关重要。目前，肥胖与CVD之间关联的最可靠估计来自前瞻性研究的汇总分析，例如前瞻性研究协作。然而，这些荟萃分析仅限于致死性事件，与单一研究相比，无法进行详细的混杂评估。此外，大多数参与者是在30 - 40年前招募的，这些发现与当前英国人口的相关性尚不清楚。重要的是，常用的肥胖测量方法，如腰臀比（WHR），腰围和BMI是有用的，但不完美的身体脂肪测量。研究UKB参与者的一个优势是生物阻抗的可用性，这是一种可能更具信息性的肥胖测量方法，因为它提供了相对于体重的总体脂和局部体脂的估计值。然而，生物阻抗和IHD风险之间的关联尚未在大型和强大的流行病学研究中进行调查。UKB的另一个优势是使用高精度的双能X射线吸收法（DEXA）和磁共振成像（MRI）对100 000名参与者进行直接测量。这将使得在估计人群亚组中的全身性和中心性肥胖时能够评估每个肥胖测量的可靠性。目的i）肥胖症和IHD：前瞻性关联-在500，000名UKB参与者中调查腰围、WHR、BMI和生物阻抗与IHD事件（致命性和非致命性）发生风险的关联。将针对重要的潜在混杂因素（如吸烟、年龄和性别）调整估计值。此外，所有估计值都将在适当的情况下进行回归稀释偏差校正;使用重新调查数据来评估肥胖指标的人内变异性。-在该人群中以及在人群的重要亚组中，确定对IHD事件风险具有最大预测值的肥胖测量。ii）横截面关联-根据地理位置和各种其他个体参与者特征，研究UKB队列人群亚组中肥胖的分布。- 描述肥胖与已知和潜在的中间因素，包括糖尿病，血压和血脂水平的措施之间的横截面关联。研究不同肥胖指标之间的相关性，特别强调通过DEXA和MRI扫描评估的肥胖指标与体脂分布之间的关系。iii）遗传分析：-使用来自全基因组测定的信息来研究与不同肥胖测量和身体组成估计相关的SNP的模式。随后生成肥胖的个体遗传评分，并研究这些遗传评分与IHD发病风险的相关风险。

BackgroundIschaemic heart disease (IHD) was the leading cause of death and Daily Adjusted Life Years lost to disability worldwide in 2010. Adiposity is becoming increasingly recognized as a leading risk factor for cardiovascular disease (CVD), including IHD. The mechanism through which adiposity causes CVD is thought to be the result, in part, of its adverse effect on well-established vascular risk factors such as dyslipidemia, hypertension and diabetes mellitus. However, some of the excess risk at high levels of adiposity remains unexplained. Importantly, with the global average BMI increasing by around 1.5kg/m2 each decade and with IHD deaths rising worldwide, it is of fundamental importance to conduct high-quality research delivering detailed estimates of the risk of IHD associated with adiposity in contemporary populations. Currently, the most robust estimates of the association between adiposity and CVD are derived from pooled analyses of prospective studies, such as the Prospective Studies Collaboration. However, these meta-analyses were restricted to fatal events, and could not conduct as detailed an assessment of confounding compared to a single study. Furthermore, most participants were recruited 30-40 years ago, and the relevance of these findings to the current UK population is unclear. Importantly, commonly used measures of adiposity such as waist-to-hip ratio (WHR), waist circumference and BMI are useful but imperfect measures of body fat. An advantage of studying UKB participants is the availability of bio-impedance, a potentially more informative measure of adiposity, as it provides an estimate of total and regional body fat relative to body weight. However, the association between bio-impedance and IHD risk has yet to be investigated in large and robust epidemiological studies. A further advantage of UKB is the on-going direct measurement of adiposity in 100 000 participants using highly accurate dual energy X-ray absorptiometry (DEXA) and magnetic resonance imaging (MRI). This will enable the assessment of the reliability of each adiposity measure when estimating general and central adiposity in population sub-groups. Aims i) Adiposity and IHD: Prospective associations- Investigating the association of waist circumference, WHR, BMI and bio-impedance with risk of incident IHD events (both fatal and non-fatal) in 500, 000 UKB participants. Estimates will be adjusted for important potential confounders such as smoking, age and sex. Furthermore, all estimates will be corrected, where appropriate, for regression dilution bias; using resurvey data to assess within-person variability of adiposity measures.- Identifying the adiposity measure with the most predictive value for incident IHD risk in this population and across important sub-groups of the population.ii) Cross-sectional associations - Studying the distribution of adiposity across sub-groups of the UKB cohort population, according to geographical location and various other individual participant characteristics. - Describing cross-sectional associations between measures of adiposity with known and potential intermediate factors, including diabetes mellitus, blood pressure and lipid levels.- Investigating correlations between different adiposity measures, with particular emphasis on the relationship between adiposity measures and body fat distribution as assessed by DEXA and MRI scans. iii) Genetic analysis:- Using information from genome-wide assays to investigate patterns of SNP's associated with different adiposity measures and body composition estimates. Subsequently generating individual genetic scores for adiposity, and investigating the associated risk of these genetic scores for risk of IHD incidence.