South Asians (S.Asians) experience elevated risk of cardiovascular disease (CVD), which remains after adjusting for lifestyle and socioeconomic status, suggesting a genetic contribution. Yet, this population remains markedly underrepresented in genomic research (<2% of genomic studies), limiting the applicability of global precision medicine initiatives. In Australia, S.Asians are one of the fastest growing migrant groups and over 1.4 million individuals report a S.Asian country of birth, making them an important target population for CVD prevention.
The South Asian Genes and Health in Australia study (SAGHA) aims to address this gap by creating a community-engaged genomic and health study focused on cardiometabolic disease in Australians of S.Asian ancestry. Phase 1 of SAGHA involved the recruitment of 200 participants of S.Asian ancestry within Queensland, in order to capture data on cardiometabolic risk factors and disease prevalence in this population and build capacity for future genomic research. Participants provided cross-sectional health questionnaire data and blood samples, from which genomic data and blood biomarkers of cardiometabolic health were generated/measured.
Recruitment for phase 1 completed in late 2024. Of the 200 participants recruited, 191 completed the health questionnaire (male=68, female=123). Median age at questionnaire completion was 41 (male=44.5, female=39). Self-reported Type 2 diabetes was highly prevalent, with 14% of participants reporting a diagnosis of Type 2 diabetes and a further 11% reporting prediabetes (6% and 0.5% in the general Australian population, respectively). The median age of type 2 diabetes onset was 39, earlier than in the general Australian population (median=60). 25% of the cohort reported one or more cardiovascular diagnoses, with the most common being hypertension (13.6%) and hypercholesterolaemia (11.5%).
Future genetic analyses will focus on identifying clinically reportable variants for familial hypercholesterolaemia, and on clarifying whether current imputation methods can accurately capture variation specific to this population using matched genotype array and WGS data.