Multimorbidity patterns and trajectories in young and middle-aged adults: a large-scale population-based cohort study

Abstract

Introduction: The presence of multiple chronic conditions, also referred to as multimorbidity, is a common finding in adults. Epidemiologic research can help identify groups of individuals with similar clinical profiles who could benefit from similar interventions. Many cross-sectional studies have revealed the existence of different multimorbidity patterns. Most of these studies were focused on the older population. However, multimorbidity patterns begin to form at a young age and can evolve over time following distinct multimorbidity trajectories with different impact on health. In this study, we aimed to identify multimorbidity patterns and trajectories in adults 18-65 years old.

Methods: We conducted a retrospective longitudinal epidemiologic study in the EpiChron Cohort, which includes all inhabitants of Aragón (Spain) registered as users of the Spanish National Health System, linking, at the patient level, information from electronic health records from both primary and specialised care. We included all 293,923 patients 18-65 years old with multimorbidity in 2011. We used cluster analysis at baseline (2011) and in 2015 and 2019 to identify multimorbidity patterns at four and eight years of follow-up, and we then created alluvial plots to visualise multimorbidity trajectories. We performed age- and sex-adjusted logistic regression analysis to study the association of each pattern with four- and eight-year mortality.

Results: We identified three multimorbidity patterns at baseline, named dyslipidaemia & endocrine-metabolic, hypertension & obesity, and unspecific. The hypertension & obesity pattern, found in one out of every four patients was associated with a higher likelihood of four- and eight-year mortality (age- and sex-adjusted odds ratio 1.11 and 1.16, respectively) compared to the unspecific pattern. Baseline patterns evolved into different patterns during the follow-up.

Discussion: Well-known preventable cardiovascular risk factors were key elements in most patterns, highlighting the role of hypertension and obesity as risk factors for higher mortality. Two out of every three patients had a cardiovascular profile with chronic conditions like diabetes and obesity that are linked to low-grade systemic chronic inflammation. More studies are encouraged to better characterise the relatively large portion of the population with an unspecific disease pattern and to help design and implement effective and comprehensive strategies towards healthier ageing.

Keywords: metabolic syndrome; multimorbidity development; multimorbidity evolution; multimorbidity patterns; multimorbidity trajectories; multiple chronic conditions; noncommunicable diseases; systemic chronic inflammation.

Figure 1

Clinical trajectories of individuals 18–65 years old in the EpiChron Cohort (Aragón, Spain) who had multimorbidity at baseline (2011). Multimorbidity was defined as the co-existence of ≥2 chronic conditions in the same individual. Clinical information is based on electronic health records from both primary and specialised care. The alluvial plot depicts the evolution of the multimorbidity patterns considering both four-year follow-up periods. The boxes represent the multimorbidity patterns identified through k-means non-hierarchical clustering at baseline, and then at the fourth (2015) and eighth (2019) year of follow-up. The stripes represent multimorbidity trajectories across the multimorbidity patterns during the follow-up (the number of individuals who evolved from one multimorbidity pattern to another). The size of the features (boxes and stripes) is proportional to the number of individuals in the corresponding pattern/trajectory.

Figure 2

Eight-year clinical trajectories of individuals 18–65 years old in the EpiChron Cohort (Aragón, Spain) who had multimorbidity at baseline (2011). Multimorbidity was defined as the co-existence of ≥2 chronic conditions in the same individual. Clinical information is based on electronic health records from both primary and specialised care. The alluvial plot depicts the evolution of the multimorbidity patterns at eight years from baseline. The boxes represent the multimorbidity patterns identified through k-means non-hierarchical clustering in 2011 and 2019. The stripes represent multimorbidity trajectories across the multimorbidity patterns during the follow-up (the number of individuals who evolved from one multimorbidity pattern to another). The size of the features (boxes and stripes) is proportional to the number of individuals in the corresponding pattern/trajectory.