Daily living activities as predictors of heart disease in elderly people: A population based case control study

Technical Summary: Daily Living Activities as Predictors of Heart Disease in Elderly People

Problem Statement
Heart disease remains a leading cause of morbidity and mortality among older adults, frequently exacerbated by comorbidities and functional impairments. While extensive literature establishes a link between cardiovascular disease (CVD) and a subsequent decline in Activities of Daily Living (ADL), the reverse relationship—whether difficulties in daily activities can serve as an independent predictor for the development of heart disease—remains less clear. Most previous studies have treated ADL decline as an outcome of chronic disease rather than a potential early warning sign. This study addresses this gap by investigating the association between ADL performance and the risk of heart disease in an elderly population, specifically within the context of routine geriatric care in Mashhad, Iran.

Methodology
The study employed a population-based case-control design utilizing data from the Sina Health Information System (SINAEHR) covering the period from March 1, 2017, to May 11, 2025.

• Participants: The final sample consisted of 7,572 elderly individuals (aged ≥60 years), divided equally into a case group (3,786 participants diagnosed with heart disease, ICD-10 codes I20–I25) and a control group (3,786 participants without heart disease). Controls were randomly selected and matched to cases based on age, sex, and place of residence.
• Data Sources: All variables, including ADL scores, demographics, and comorbidities, were extracted directly from electronic health records. Individuals with missing ADL data or incomplete demographic fields were excluded.
• Measurement: ADL was assessed using the Katz Index, which evaluates six core skills: bathing, dressing, transferring, toileting, feeding, and continence. Scores range from 0 (complete dependence) to 6 (full independence). Assessments were recorded at the time of first registration in the health system, prior to the documentation of a heart disease diagnosis.
• Statistical Analysis: Data were analyzed using Stata version 16. Chi-square tests were used for bivariate relationships. Both univariate and multivariable logistic regression models were employed to calculate adjusted odds ratios (aOR) and 95% confidence intervals (CI), controlling for potential confounders such as age, BMI, education, occupation, residence, and various comorbidities (e.g., hypertension, diabetes, cancer).

Key Results
The study identified significant associations between functional impairment and heart disease risk, even after adjusting for other factors:

• ADL Impairment: There was a dose-response relationship between ADL impairment and heart disease. Compared to participants with functional independence, those with moderate impairment had higher odds of heart disease (aOR 1.49; 95% CI 1.18–1.88), while those with severe impairment had substantially higher odds (aOR 2.78; 95% CI 2.22–3.49).
• Demographics and Comorbidities:
  • Age: Participants aged ≥80 years had higher odds of heart disease (aOR 1.43).
  • BMI: Obesity (BMI ≥30 kg/m²) was associated with increased risk (aOR 1.35).
  • Comorbidities: Hypertension (aOR 2.64) and cancer (aOR 2.08) showed the strongest associations with heart disease among the comorbidities studied. Diabetes, hyperlipidemia, thyroid disorders, and depressive episodes were also significant independent predictors.
  • Education and Residence: Contrary to some existing literature suggesting lower education correlates with higher CVD risk, this study found that higher education levels (specifically diploma and college degrees) were associated with increased odds of heart disease in the multivariate model. Similarly, living in metropolitan areas, urban fringes, and villages was associated with higher risk compared to small cities.
  • Gender and Marriage: While significant in univariate analysis, gender and marital status were not significant predictors in the multivariate model.

Key Contributions

• Reversal of Causality Focus: The primary contribution of this work is its investigation of ADL as a predictor of heart disease, rather than solely as a consequence. The authors posit that functional decline may precede and signal cardiovascular risk.
• Population-Based Evidence: This represents one of the first population-based case-control studies in Iran examining the specific link between ADL and heart disease among the elderly, utilizing a large dataset (N=7,572) from a comprehensive health information system.
• Multivariate Validation: The study demonstrates that the association between ADL impairment and heart disease persists even after controlling for major confounders like age, BMI, and specific comorbidities, suggesting ADL is an independent risk factor.

Significance and Claims
The authors conclude that limitations in ADL among the elderly are significantly associated with heart disease, independent of other known risk factors. They argue that ADL should be considered a potential predictive factor in future research and clinical practice. Specifically, the assessment of ADL could serve as an important criterion for informing healthcare and treatment decisions in older populations, potentially allowing for earlier identification of individuals at risk for cardiovascular events.

Limitations Acknowledged by the Authors
The paper explicitly notes several limitations:

1. Causality: The case-control design limits the ability to definitively establish causal relationships; longitudinal studies are needed to confirm causation.
2. Reverse Causality: There is a possibility that early, undetected cardiovascular disease influenced functional capacity and ADL performance prior to diagnosis.
3. Data Reliance: The study relied on data extracted from electronic records, which may include self-reported elements or reporting biases regarding functional abilities.
4. Scope of ADL: The Katz Index covers only six core activities and may not encompass the full spectrum of daily living activities.
5. Unmeasured Confounders: Factors such as detailed socioeconomic status and specific lifestyle behaviors (e.g., smoking, which was excluded due to data incompleteness) were not fully controlled for.