Sex and population differences in the cardiometabolic continuum: a machine learning study using the UK Biobank and ELSA-Brasil cohorts

被引:0
|
作者
Paula, Daniela Polessa [1 ,2 ]
Camacho, Marina [3 ]
Barbosa, Odaleia [4 ]
Marques, Larissa [5 ]
Griep, Rosane Harter [8 ]
da Fonseca, Maria Jesus Mendes [7 ]
Barreto, Sandhi [6 ]
Lekadir, Karim [3 ]
机构
[1] Brazilian Inst Geog & Stat, Natl Sch Stat Sci, Rio De Janeiro, Brazil
[2] Univ Estado Rio De Janeiro, Inst Math & Stat, Rio De Janeiro, Brazil
[3] Univ Barcelona, Dept Matemat & Informat, Barcelona, Spain
[4] Univ Estado Rio De Janeiro, Inst Nutr, Rio De Janeiro, Brazil
[5] Fundacao Oswaldo Cruz, Coordinat Informat & Commun CINCO PEIC, Rio De Janeiro, RJ, Brazil
[6] Univ Fed Minas Gerais, Sch Med & Clin Hosp, Postgrad Program Publ Hlth, Belo Horizonte, Brazil
[7] Fundacao Oswaldo Cruz, Natl Sch Publ Hlth, Rio De Janeiro, Brazil
[8] Oswaldo Cruz Inst IOC, Hlth & Environm Educ Lab, Rio De Janeiro, RJ, Brazil
关键词
Cardiometabolic continuum; Cardiometabolic trajectories; Machine learning; SHAP; UK Biobank; ELSA-Brasil; IMPROVED PATIENT OUTCOMES; CLINICAL-EVIDENCE; RISK-FACTORS; DISEASE; SMOKING; MODEL;
D O I
10.1186/s12889-024-19395-9
中图分类号
R1 [预防医学、卫生学];
学科分类号
1004 ; 120402 ;
摘要
BackgroundThe temporal relationships across cardiometabolic diseases (CMDs) were recently conceptualized as the cardiometabolic continuum (CMC), sequence of cardiovascular events that stem from gene-environmental interactions, unhealthy lifestyle influences, and metabolic diseases such as diabetes, and hypertension. While the physiological pathways linking metabolic and cardiovascular diseases have been investigated, the study of the sex and population differences in the CMC have still not been described.MethodsWe present a machine learning approach to model the CMC and investigate sex and population differences in two distinct cohorts: the UK Biobank (17,700 participants) and the Brazilian Longitudinal Study of Adult Health (ELSA-Brasil) (7162 participants). We consider the following CMDs: hypertension (Hyp), diabetes (DM), heart diseases (HD: angina, myocardial infarction, or heart failure), and stroke (STK). For the identification of the CMC patterns, individual trajectories with the time of disease occurrence were clustered using k-means. Based on clinical, sociodemographic, and lifestyle characteristics, we built multiclass random forest classifiers and used the SHAP methodology to evaluate feature importance.ResultsFive CMC patterns were identified across both sexes and cohorts: EarlyHyp, FirstDM, FirstHD, Healthy, and LateHyp, named according to prevalence and disease occurrence time that depicted around 95%, 78%, 75%, 88% and 99% of individuals, respectively. Within the UK Biobank, more women were classified in the Healthy cluster and more men in all others. In the EarlyHyp and LateHyp clusters, isolated hypertension occurred earlier among women. Smoking habits and education had high importance and clear directionality for both sexes. For ELSA-Brasil, more men were classified in the Healthy cluster and more women in the FirstDM. The diabetes occurrence time when followed by hypertension was lower among women. Education and ethnicity had high importance and clear directionality for women, while for men these features were smoking, alcohol, and coffee consumption.ConclusionsThere are clear sex differences in the CMC that varied across the UK and Brazilian cohorts. In particular, disadvantages regarding incidence and the time to onset of diseases were more pronounced in Brazil, against woman. The results show the need to strengthen public health policies to prevent and control the time course of CMD, with an emphasis on women.
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页数:14
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