Understanding multimorbidity requires sign-disease networks and higher-order interactions, a perspective.

ABSTRACT:

Background

Count scores, disease clustering, and pairwise associations between diseases remain ubiquitous in multimorbidity research despite two major shortcomings: they yield no insight into plausible mechanisms underlying multimorbidity, and they ignore higher-order interactions such as effect modification.

Objectives

We argue that two components are currently missing but vital to develop novel multimorbidity metrics. Firstly, networks should be constructed which consists simultaneously of signs, symptoms, and diseases, since only then could they yield insight into plausible shared biological mechanisms underlying diseases.Secondly, learning pairwise associations is insufficient to fully characterize the correlations in a system. That is, synergistic (e.g., cooperative or

SUBMITTER: Hourican C 

PROVIDER: S-EPMC10557993 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature