Syed-Abdul Shabbir, Moldovan Max, Nguyen Phung-Anh, Enikeev Ruslan, Jian Wen-Shan, Iqbal Usman, Hsu Min-Huei, Li Yu-Chuan
Graduate Institute of Biomedical Informatics, College of Medical Science and Technology, Taipei Medical University, Taipei, Taiwan.
Centre for Clinical Governance Research, Australian Institute of Health Innovation, Faculty of Medicine, University of New South Wales, Sydney, Australia School of Population Health, Sansom Institute for Health Research, University of South Australia, South Australian Health & Medical Research Institute (SAHMRI).
J Am Med Inform Assoc. 2015 Jul;22(4):896-9. doi: 10.1093/jamia/ocu019. Epub 2015 Feb 5.
To objectively characterize phenome-wide associations observed in the entire Taiwanese population and represent them in a meaningful, interpretable way.
In this population-based observational study, we analyzed 782 million outpatient visits and 15 394 unique phenotypes that were observed in the entire Taiwanese population of over 22 million individuals. Our data was obtained from Taiwan's National Health Insurance Research Database.Results We stratified the population into 20 gender-age groups and generated 28.8 million and 31.8 million pairwise odds ratios from male and female subpopulations, respectively. These associations can be accessed online at http://associations.phr.tmu.edu.tw. To demonstrate the database and validate the association estimates obtained, we used correlation analysis to analyze 100 phenotypes that were observed to have the strongest positive association estimates with respect to essential hypertension. The results indicated that association patterns tended to have a strong positive correlation between adjacent age groups, while correlation estimates tended to decline as groups became more distant in age, and they diverged when assessed across gender groups.
The correlation analysis of pairwise disease association patterns across different age and gender groups led to outcomes that were broadly predicted before the analysis, thus confirming the validity of the information contained in the presented database. More diverse individual disease-specific analyses would lead to a better understanding of phenome-wide associations and empower physicians to provide personalized care in terms of predicting, preventing, or initiating an early management of concomitant diseases.
客观描述在整个台湾人群中观察到的全表型关联,并以有意义、可解释的方式呈现这些关联。
在这项基于人群的观察性研究中,我们分析了超过2200万台湾人群中的7.82亿次门诊就诊和15394种独特的表型。我们的数据来自台湾国民健康保险研究数据库。结果我们将人群分为20个性别年龄组,分别从男性和女性亚人群中生成了2880万和3180万对成对的优势比。这些关联可在http://associations.phr.tmu.edu.tw在线获取。为了展示该数据库并验证所获得的关联估计值,我们使用相关分析来分析100种表型,这些表型被观察到与原发性高血压具有最强的正关联估计值。结果表明,关联模式在相邻年龄组之间往往具有很强的正相关性,而相关估计值往往随着年龄组距离的增加而下降,并且在跨性别组评估时会出现差异。
对不同年龄和性别组之间成对疾病关联模式的相关分析得出的结果在分析前大致可以预测,从而证实了所呈现数据库中信息的有效性。更多样化的个体疾病特异性分析将有助于更好地理解全表型关联,并使医生能够在预测、预防或启动伴随疾病的早期管理方面提供个性化护理。