Dataset Information

Age-dependent topic modeling of comorbidities in UK Biobank identifies disease subtypes with differential genetic risk.

ABSTRACT: The analysis of longitudinal data from electronic health records (EHRs) has the potential to improve clinical diagnoses and enable personalized medicine, motivating efforts to identify disease subtypes from patient comorbidity information. Here we introduce an age-dependent topic modeling (ATM) method that provides a low-rank representation of longitudinal records of hundreds of distinct diseases in large EHR datasets. We applied ATM to 282,957 UK Biobank samples, identifying 52 diseases with heterogeneous comorbidity profiles; analyses of 211,908 All of Us samples produced concordant results. We defined subtypes of the 52 heterogeneous diseases based on their comorbidity profiles and compared genetic risk across disease subtypes using polygenic risk scores (PRSs), identifying 18 disease subtypes whose PRS differed significantly from other subtypes of the same disease. We further identified specific genetic variants with subtype-dependent effects on disease risk. In conclusion, ATM identifies disease subtypes with differential genome-wide and locus-specific genetic risk profiles.

SUBMITTER: Jiang X

PROVIDER: S-EPMC10632146 | biostudies-literature | 2023 Nov

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Age-dependent topic modeling of comorbidities in UK Biobank identifies disease subtypes with differential genetic risk.

Jiang Xilin X Zhang Martin Jinye MJ Zhang Yidong Y Durvasula Arun A Inouye Michael M Holmes Chris C Price Alkes L AL McVean Gil G

Nature genetics 20231009 11

The analysis of longitudinal data from electronic health records (EHRs) has the potential to improve clinical diagnoses and enable personalized medicine, motivating efforts to identify disease subtypes from patient comorbidity information. Here we introduce an age-dependent topic modeling (ATM) method that provides a low-rank representation of longitudinal records of hundreds of distinct diseases in large EHR datasets. We applied ATM to 282,957 UK Biobank samples, identifying 52 diseases with he ...[more]

PMID: 37814053

Similar Datasets

Project description:BackgroundDisparities in COVID-19 outcomes exist on the basis of ethnicity and comorbidities. Minority ethnic groups in the UK are known to have poorer COVID-19 outcomes, but also an increased prevelance of certain comorbidities associated with severe outcomes. Additionally, despite the prevalence of certain psychiatric disorders there is a lack of research establishing their relationship with COVID-19 outcomes.MethodsWe used UK Biobank data, involving 472,182 participants, to test for an association between comorbidities and COVID-19 diagnosis (n = 30,901); and to test for an association between comorbidities and severe COVID-19 (n = 3182). This was done by performing univariable and multivariable logistic regression analysis, estimating odds ratios (ORs) and their 95% confidence intervals (95% CIs). The comorbidities studied were coronary heart disease (CHD), hypertension, type II diabetes mellitus (T2DM), obesity, chronic kidney disease (CKD), depression and anxiety. Multivariable models were adjusted for various socioeconomic, demographic and health-related confounders. We then performed sub-group analysis by common UK ethnic groups (White, South Asian, and Black).ResultsIncreased prevalence of all studied comorbidities was seen in both outcomes, compared to the rest of the cohort. All studied comorbidities were associated with an increased risk of COVID-19 infection and severity across all models. For example, the adjusted ORs (95% CI) for depression were 1.112 (1.083 - 1.161) for COVID-19 diagnosis and 2.398 (2.163 - 2.658) for severe COVID-19. Sub-group analysis revealed stronger associations of COVID-19 diagnosis and severe COVID-19 for South-Asian participants for CHD (OR 1.585 [95% CI 1.194-2.105] for COVID-19 diagnosis and 3.021 [1.683-5.390] for severe COVID-19), hypertension (1.488 [1.231-1.799]; 3.399 [1.862-6.206]) and T2DM (1.671 [1.346-2.076]; 5.412 [3.130-9.357]) compared to White participants (1.264 [1.195-1.336] and 1.627 [1.441-1.837] for CHD; 1.131 [1.097-1.116] and 2.075 [1.885-2.284] for hypertension; 1.402 [1.331-1.476] and 2.890 [2.596-3.216] for T2DM). Similar results were seen for Black participants with CKD and hypertension.ConclusionSpecific comorbidities are risk factors for poorer COVID-19 outcomes, supporting targeted interventions and policy aimed at individuals with these comorbidities. Although further research is required, there's also a need for targeted policies for ethnic minorities assessing the unique reasons they are at greater risk of poor COVID-19 outcomes.

Dataset Information

Age-dependent topic modeling of comorbidities in UK Biobank identifies disease subtypes with differential genetic risk.

Publications

Age-dependent topic modeling of comorbidities in UK Biobank identifies disease subtypes with differential genetic risk.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets