Global Burden of Disease analysis dataset of BMI and CVD outcomes, risk factors, and SAS codes

This formatted dataset originates from raw data files from the Institute of Health Metrics and Evaluation Global Burden of Disease (GBD2017). It is population weighted worldwide data on male and female cohorts ages 15-69 years including body mass index (BMI) and cardiovascular disease (CVD) and associated dietary, metabolic and other risk factors. The purpose of creating this formatted database is to explore the univariate and multiple regression correlations of BMI and CVD and other health outcomes with risk factors. Our research hypothesis is that we can successfully apply artificial intelligence to model BMI and CVD risk factors and health outcomes. We derived a BMI multiple regression risk factor formula that satisfied all nine Bradford Hill causality criteria for epidemiology research. We found that animal products and added fats are negatively correlated with CVD early deaths worldwide but positively correlated with CVD early deaths in high quantities. We interpret this as showing that optimal cardiovascular outcomes come with moderate (not low and not high) intakes of animal foods and added fats. For questions, please email [e-mail]. Thanks.本标准化数据集源自健康指标与评估研究所（Institute of Health Metrics and Evaluation）发布的2017年全球疾病负担（Global Burden of Disease 2017, GBD2017）原始数据文件。本数据集为全球范围内15至69岁男女队列的人口加权数据，涵盖体质指数（body mass index, BMI）、心血管疾病（cardiovascular disease, CVD）及相关饮食、代谢与其他危险因素信息。构建该标准化数据库的核心目标，在于探究BMI、CVD及其他健康结局与各类危险因素之间的单变量与多元回归关联。本研究的假设为：可成功运用人工智能（Artificial Intelligence, AI）对BMI、CVD危险因素及健康结局开展建模分析。本研究推导得到了满足流行病学研究中全部9项布拉德福德·希尔（Bradford Hill）因果判定标准的BMI多元回归危险因素公式。研究发现，全球范围内动物源性食品与添加脂肪的摄入与心血管疾病过早死亡呈负相关，但当摄入量过高时则转为正相关。据此我们认为，实现最优心血管健康结局的关键在于动物源性食品与添加脂肪的中等摄入量（既不过低也不过高）。如有疑问，请致信[e-mail]。致谢。