Beyond the Sum of Their Parts

Corresponding Author

In the context of extant demographic trends towards an aging population, our understanding of how age-related physiological changes interact with chronic disease states to influence risk of morbidity and mortality grows ever more important. Aging is accompanied by a gradual loss of functional reserve across multiple organ systems, rendering older adults more susceptible to adverse outcomes from stressors that would be well tolerated by younger individuals. To this end, the assessment and recognition of frailty in older adults—a multidimensional syndrome characterized by decreased strength, endurance, and physiologic function that increases an individual’s risk of dependency, hospitalization, and death—has emerged as a clinically sound method of prognostication and risk stratification.

Two predominant models are used to define and assess frailty, one which views frailty as a clinical phenotype of decreased physiological reserve and the other as an accumulation of various age-related deficits.¹ The phenotypic model, proposed by Fried et al,² operationalizes frailty as the presence of three or more of the following: unintentional weight loss, self-reported exhaustion, weakness (grip strength), slow walking speed, and low physical activity. In contrast, the cumulative deficit model, summarized as a frailty index (FI) and developed by Drs Rockwood and Minitski,³ conceptualizes frailty as the accumulation of age-related health deficits that may include medical comorbidities, laboratory abnormalities, cognitive and physical impairment, and poor nutritional status, among others. Both models have been validated and used across diverse populations, although there remains residual variation in identification of frail or prefrail subpopulations.¹

The prevalence of both cardiometabolic diseases (CMDs)—including ischemic heart disease, stroke, and diabetes mellitus—and cardiometabolic multimorbidity (CMM) increases in parallel with frailty in an aging population.⁴ The relationship between frailty and CMDs in older adults is postulated to be bidirectional, and the presence of frailty further complicates management of CMDs while compounding the risk of major adverse cardiac events and overall mortality.^4,5 Both frailty and prefrailty carry positive associations with CMM and myriad atherosclerotic risk factors; it is estimated that up to 72% of frail older adults have CMM⁶ whereas the presence of CMM confers twice the risk of frailty compared to older adults without CMM.⁷

In this issue of JACC: Asia, Zhu et al⁸ present a comprehensive analysis of the UK Biobank database on the joint associations of frailty and CMDs/CMM with all-cause and cardiovascular disease (CVD) mortality. The authors’ analysis of more than 460,000 adults 40 to 69 years of age with a median follow-up period of 13 years used both the frailty phenotype and FI and found frailty as assessed by both measures to produce a multiplicative effect with concomitant CMDs on the risk of all-cause and CVD mortality. Notably, individuals with both frailty (especially frailty phenotype) and CMM had significantly elevated risks of all-cause (HR: 4.91; 95% CI: 4.49-5.38) and CVD mortality (HR: 8.33; 95% CI: 7.13-9.72), with substantial portions of this excess risk (23% and 36%, respectively) attributable to additive interaction.⁸ The observed associations were particularly strong among females. This key insight underscores the importance of functional status as a prognostic tool. Importantly, the results add to growing evidence that frailty is independently associated with major adverse cardiac events and CVD mortality and may exacerbate the impact of coexisting cardiovascular conditions.^5,9,10 However, several limitations merit consideration. The studied cohort is generally healthier, less socioeconomically diverse, and predominantly White, which may limit generalizability. Frailty status was assessed only once at baseline, without accounting for potential changes over the long follow-up period, which could affect risk estimates. Additionally, the study would have been strengthened by disaggregating CMDs or CMM into specific disease combinations to better elucidate differential risk patterns.

The inclusion of both FI and frailty phenotype paradigms further allowed for comparison of the two tools in population screening and prognostication. Stronger effect sizes for mortality risk prediction with the frailty phenotype was partially attributed to the increased sensitivity of FI in distinguishing prefrail and mildly frail states.^8,11 Perhaps the most urgent implication of this work is the need to integrate frailty into existing models of care—not as an afterthought but as a core component of clinical decision-making. Risk prediction tools that fail to consider frailty may underestimate true vulnerability. Given the relative complexity of applying the FI and frailty phenotype tools in a general primary care setting, single-metric screening tools such as grip strength and the 4-/5-meter walk test should be considered as first-line screening in identifying at-risk individuals.

Beyond risk prediction, identifying frailty can inform clinical decision-making around intensive therapies, guide conversations around goals of care, and prompt timely referral to multidisciplinary interventions targeting strength, nutrition, and mobility. Frailty is not an immutable endpoint. Emerging evidence strongly suggests that improvements in lifestyle habits around physical activity, weight management, sleep, and diet alongside traditional cardiovascular risk factor optimization are associated with reduced risk of frailty regardless of age group.¹² Improved optimization appeared to be protective in the development of frailty even decades later.¹² Routine frailty screening could thus meaningfully enhance risk stratification and guide intensity of management. Yet such assessments remain underused in most clinical settings.

As demographic changes continue to sweep through Asia and the rest of the world, integrating frailty assessment into routine screening—especially among patients with cardiometabolic disease—is essential for identifying those at highest risk. Early recognition enables timely, targeted interventions including exercise, nutritional support, and risk factor optimization through a combination of lifestyle counseling and pharmacologic therapy. Rather than a passive marker of decline, frailty should be recognized as a modifiable risk state and a critical focus for reducing mortality and cardiovascular events in older adults. Future research should focus on exploring the biological pathways that drive the interaction between frailty and CMDs in relation to mortality, examining sex and racial differences in more diverse populations, assessing the dynamic progression of frailty and CMDs using longitudinal data, and evaluating whether interventions aimed at reducing frailty can help mitigate mortality risk among patients with CMDs.

Funding Support and Author Disclosures

The authors have reported that they have no relationships relevant to the contents of this paper to disclose.