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

• Published in: BMC public health Vol. 24; no. 1; pp. 2131 - 14
• Main Authors: Paula, Daniela Polessa, Camacho, Marina, Barbosa, Odaleia, Marques, Larissa, Harter Griep, Rosane, da Fonseca, Maria Jesus Mendes, Barreto, Sandhi, Lekadir, Karim
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 06-08-2024; BioMed Central; BMC
• Subjects: Adult; Aged; Angina; Angina pectoris; Biobanks; Blood pressure; Brazil - epidemiology; Cardiometabolic continuum; Cardiometabolic Risk Factors; Cardiometabolic trajectories; Cardiovascular disease; Cardiovascular diseases; Cardiovascular Diseases - epidemiology; Cerebral infarction; Cholesterol; Clusters; Coffee; Cohort Studies; Congestive heart failure; Demographic aspects; Diabetes; Diabetes mellitus; Disease prevention; Education; ELSA-Brasil; Female; Gender differences; Glucose; Health policy; Heart attacks; Heart diseases; Heart failure; Hemoglobin; High density lipoprotein; Humans; Hypertension; Learning algorithms; Longitudinal Studies; Machine Learning; Male; Men; Metabolic disorders; Middle Aged; Myocardial infarction; Population studies; Public health; Research ethics; Risk factors; Sex differences; Sex Factors; Sexes; SHAP; Smoking; Stroke; UK Biobank; United Kingdom - epidemiology; Women; Brazil; United Kingdom; United Kingdom > UK; Cardiometabolic trajectories; SHAP; Cardiometabolic continuum; UK Biobank; ELSA-Brasil; Machine learning
• ISSN: 1471-2458; 1471-2458
• DOI: 10.1186/s12889-024-19395-9

The 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.
We 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.
Five 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.
There 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.