Multimorbidity

Health systems have adopted models of integrated care to better align services around the needs of aging populations. The results are encouraging, but inconsistent. Although they are untested, recent approaches — such as the WHO’s ‘Integrated Care for Older People’ — that are explicitly person-centered suggest that more-radical reform may be possible.

Multimorbidity is increasing globally, and addressing it requires a shift in the prevailing clinical, educational and scientific thinking. This Review discusses emerging mechanisms, research challenges and the implications for patients and healthcare systems.

Non-communicable diseases in low- and middle-income countries can be tackled with integrated health systems interventions that consider multimorbidity, supported by patient involvement and new technologies.

This Review discusses the effect of comorbidities and multimorbidity on the three mechanistically distinct phases of COVID-19, evaluating the evidence in the context of confounding factors and our evolving understanding of the disease.

Atrial fibrillation is associated with an increased risk of thromboembolic events and vascular dementia in patients with no previous history of stroke or any conventional risk factors, compared to patients without atrial fibrillation.

Analyses from the US Department of Veterans Affairs databases reported residual elevated risk and health burden of long COVID at 3 years in hospitalized individuals after SARS-CoV-2 infection.

Beaney et al. compare co-occurrence and sequence-based embedding methods and apply graph-based clustering to identify clusters of diseases at multiple resolutions, in over ten million people with multimorbidity. Interpretable clusters of diseases are found that correspond to both established and novel patterns.

Post hoc analysis of the DAPA-HF and DELIVER trials reports on the approach of win statistics to evaluate the effect of dapagliflozin on a hierarchical composite kidney outcome in patients with heart failure.

Dysmetabolism of triglyceride-rich lipoproteins is considered a shared risk factor for cardiometabolic diseases, but their associations with cardiometabolic multimorbidity have not been fully understood. Here, the authors show that elevated levels of remnant cholesterol and triglycerides were observationally and genetically associated with a higher risk of cardiometabolic multimorbidity.

Stitching together electronic health records with partial longitudinal coverage, Mendelson Cohen et al. use machine learning to untangle healthy aging from chronic disease, identifying markers of healthy aging and analyzing the heritability of longevity.

Multimorbidity—the occurrence of chronic diseases together—represents a major challenge for healthcare systems. Here, the authors characterise multimorbidity patterns in a large dataset of patients residing in southern Spain and show the unequal distribution of multimorbidity patterns along different socioeconomic areas at the local level.

There is scant evidence for how intrinsic capacity (IC), the combination of an individual’s physical and mental capacities, varies throughout adulthood. In this study, the authors demonstrated a method to establish IC reference centile curves using data of individuals aged 20–102 years from the French INSPIRE-T cohort.

Zhang, Zhou et al. correlate birth weight and childhood body size with later development of chronic diseases and multimorbidity. Using data from the UK Biobank they show low or high birth weight and a body size in childhood that differs from the average associate with higher risks of developing multimorbidity and many chronic conditions in late life.

Here, the authors show that longer duration and greater degree of overweight and obesity during early adulthood as well as younger age of onset of a high body mass index are associated with a higher risk of 18 cancer types.

In a study involving more than 100,000 individuals in the UK Biobank, a neural network model trained on metabolomic data can predict disease risk for over 20 conditions and adds predictive information over clinical variables for eight common diseases.