What is the clinical evidence from this study?

Study design: Cohort. Population: Cardiometabolic multimorbidity (n=329908). Intervention: Metabolic subgroup with kidney stress, high adiposity and inflammation (Subgroup III) vs. Metabolically favorable subgroup (Subgroup IV). Primary outcome: Incident ischemic heart disease (IHD) (HR 2.11).

May 21, 2022Scientific Reports25 citationsOpen Access

Cross-sectional metabolic subgroups and 10-year follow-up of cardiometabolic multimorbidity in the UK Biobank

AMAnwar Mulugeta EHElina Hyppönen MAMika Ala‐Korpela

Key Result

A metabolic subgroup characterized by kidney stress, high adiposity, and inflammation was associated with a significantly increased risk of incident ischemic heart disease (HR 2.11) and mortality (HR 2.12) compared to a metabolically favorable reference group.

Study Design

Type

Cohort (n=329,908)

Multicenter

Yes

Structured PICO

Do distinct metabolic subgroups based on biochemical measures predict incident disease and multimorbidity in UK Biobank participants?

Population

329,908 adults aged 37-73 years of white British ancestry from the UK Biobank, followed for a mean of 10.8 years to assess cardiometabolic multimorbidity across data-driven metabolic subgroups.

Exposure

Assignment into six metabolic subgroups based on a self-organizing map of 51 biochemical measures

Comparator

The subgroup with the most favorable metabolic traits

Outcome

Incident disease (ischemic heart disease, cancer, dementia, mortality, diabetes) and incident multi-morbidityhard clinical

Distinct metabolic profiles identified via machine learning of biochemical measures strongly predict specific trajectories of incident disease and multimorbidity over 10 years.

Main Result

Hazard Ratio: 2.11

Limitations

Generalizability to other cohorts, ethnicities, or populations of different circumstances is limited
Results are dependent on the selection of available biomarkers
Statistical accuracy of population-based data is insufficient to develop a machine learning model for a clinically robust predictive test
UK Biobank recruited volunteers only, which may introduce healthy volunteer bias
Cross-sectional metabolic data cannot provide information on longitudinal metabolic trajectories

Abstract

We assigned 329,908 UK Biobank participants into six subgroups based on a self-organizing map of 51 biochemical measures (blinded for clinical outcomes). The subgroup with the most favorable metabolic traits was chosen as the reference. Hazard ratios (HR) for incident disease were modeled by Cox regression. Enrichment ratios (ER) of incident multi-morbidity versus randomly expected co-occurrence were evaluated by permutation tests; ER is like HR but captures co-occurrence rather than event frequency. The subgroup with high urinary excretion without kidney stress (HR = 1.24) and the subgroup with the highest apolipoprotein B and blood pressure (HR = 1.52) were associated with ischemic heart disease (IHD). The subgroup with kidney stress, high adiposity and inflammation was associated with IHD (HR = 2.11), cancer (HR = 1.29), dementia (HR = 1.70) and mortality (HR = 2.12). The subgroup with high liver enzymes and triglycerides was at risk of diabetes (HR = 15.6). Multimorbidity was enriched in metabolically favorable subgroups (3.4 ≤ ER ≤ 4.0) despite lower disease burden overall; the relative risk of co-occurring disease was higher in the absence of obvious metabolic dysfunction. These results provide synergistic insight into metabolic health and its associations with cardiovascular disease in a large population sample.

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