Physical frailty, depression and multimorbidity among older adults in Lima, Peru: a cross-sectional study.

BACKGROUND

Multimorbidity, frailty, and depression frequently co-occur in older adults and contribute to adverse health outcomes. This study identified classes of physical multimorbidity and examined their association with depression, and whether this association differs by frailty status.

METHODS

We conducted a cross-sectional study in low-resource urban areas of Lima, Peru. We included adults aged ≥ 60 years, and assessed the presence of physical multimorbidity (COPD, asthma, chronic bronchitis, heart disease, diabetes, arthritis, and hypertension), physical frailty phenotype, and depression (Patient Health Questionnaire-9 score ≥ 7). We applied latent class analysis to categorize participants into physical multimorbidity classes. Subsequently, we conducted Poisson regression with robust variance to assess the relationship between somatic multimorbidity classes and depression. We studied the role of frailty by stratifying the analysis according to the presence or absence of frailty.

RESULTS

Among 1029 participants (mean age 68.9 years; 44.6% women), 201 (19.5%) had depression. Three multimorbidity classes were identified: cardiometabolic, multisystem, and apparently healthy. Only the multisystem class was associated with a higher prevalence of depressive symptoms compared to the apparently healthy class, after adjusting for sex, age, education, and socio-economic position (adjusted Prevalence Ratio, aPR = 1.80, 95% CI: 1.02-3.06). Among individuals with frailty, depression was more prevalent in the multisystem class (aPR 6.25, 95% CI 1.28-30.63), whereas no significant association was observed among non-frail participants (aPR 1.58, 95% CI 0.83-3.00).

CONCLUSIONS

The association between multisystem multimorbidity and depressive symptoms was stronger among individuals with frailty. Considering frailty alongside multimorbidity may improve identification of subgroups with greater depressive burden, particularly in resource-constrained settings.