Global population ageing is accelerating, and for the first time, most people can expect to live to 60 years and beyond^[1]. The ageing process involves cumulative molecular and cellular damage that gradually reduces physiological reserve, increasing vulnerability to chronic diseases and disability^[2]. Traditional disease-focused models often overlook the broader functional capacities that enable independence in older adults. To address this, the World Health Organization introduced the concept of intrinsic capacity (IC), which includes cognition, psychological health, vitality, sensory function, and locomotion, as a central measure of healthy ageing^[3,4]. Declines in IC have been shown to predict frailty, dependence, and mortality more effectively than specific disease diagnoses^[5].

Cancer is primarily a disease of older adults, with over 60% of new diagnoses occurring in individuals aged 60 years or older^[6]. In this population, the presence of cancer and existing comorbidities, often leads to rapid declines in functional ability^[7]. Although comprehensive geriatric assessment has been shown to improve outcomes in older cancer patients, few studies have assessed IC across all five domains in this group. Research in low- and middle-income countries, where healthcare systems face additional challenges, is especially limited^[8,9].

This study was conducted to assess intrinsic capacity among older cancer patients attending a geriatric oncology clinic in Kolkata. The objectives were to determine the proportion of impairment in each IC domain, to explore patterns of multidomain impairment, and to identify socio-demographic and clinical factors associated with IC decline. By improving understanding of IC in this vulnerable population, the findings aim to support function-centered care strategies that maintain independence and improve quality of life.

This analytic cross‑sectional study was conducted in the geriatric oncology clinic of Medical College, Kolkata, from February 1, 2024, through January 31, 2025. The protocol was approved by the institutional ethics committee and written informed consent was obtained from all participants. Consecutive clinic attendees were screened; eligible patients were recruited. Eligibility criteria included the age of at least 60 years and cancer. Patients were excluded if they had a Mini–Mental State Examination (MMSE) score below 12, were bed‑bound, or had an intercurrent acute illness. The required sample size was calculated with Cochran’s formula based on an anticipated prevalence of intrinsic‑capacity impairment of 84.3% reported in older Indian adults, a 95‑percent confidence level, and a margin of error of 5%; 206 patients were needed, and an additional 15% were added for attrition, yielding a target of 236, among them 231 completed interview. A structured case‑record form captured sociodemographic data, body‑mass index (BMI; Asian cutoffs), comorbidities, and cancer characteristics. Socioeconomic status was graded with the BG Prasad 2023 scale. Blood pressure and pulse rate were measured. Intrinsic capacity was evaluated in six domains with validated instruments. Cognition was measured with the MMSE; a score below 24 indicated impairment. Psychological health was assessed with the 15‑item Geriatric Depression Scale; a score of 5 or more denoted depression. Vitality was classified with the Mini Nutritional Assessment–Short Form as normal (12–14), at risk of malnutrition (8–11), or malnourished (0–7). Locomotion was tested with the Timed Up and Go; a time of at least 13 seconds signified mobility impairment. Vision impairment was recorded when participants answered yes to a screening question regarding difficulty in distance or near tasks, and hearing impairment was recorded when any of three whispered words could not be repeated in either ear. A deficit in any domain constitutes impaired intrinsic capacity. Comorbid burden was quantified with the Charlson Comorbidity Index and fall-risk with the Morse scale. Data was entered in Excel and analyzed with SPSS version 26. Categorical variables are expressed as counts and percentages, and continuous variables as means with standard deviations. Group differences were examined with the chi‑square or Fisher exact test for categorical data and the independent‑samples t‑test or one‑way analysis of variance for continuous data. Associations between continuous variables were explored with Pearson correlation. All tests were two‑tailed, and a p value below 0.05 was considered to indicate statistical significance. Large language model (GPT-5) was used only to improve the language and structure of the manuscript. The authors thoroughly reviewed the manuscript and take full responsibility for the content.

In total, 231 patients completed all assessments. Among the 231 participants who completed assessment the mean (±SD) age was 64.0±4.8 years (range, 60 to 94) and 147 were men (63.6%). 61 patients (26.4%) were illiterate and only 24 (10.4%) had studied beyond the twelfth class. Most were engaged in unskilled work (63.0%) and only 46 patients (20.0%) reported professional occupations is shown in table 1. Socioeconomic status skewed toward higher strata, with 97 persons (42.0%) in the upper and 105 (45.5%) in the upper‑middle category. Gastrointestinal or hepatobiliary cancers were most common (28.1%), followed by haematolymphoid (20.8%), genitourinary (19.9%), and lung (13.9%) malignancies. Hypertension was present in 81 patients (35.1%) and diabetes in 50 (21.6%); 184 participants (79.7%) had a severe Charlson Comorbidity Index score. Symptoms of sleep disturbance were reported by 156 patients (67.5%).

Impairment in at least one domain of intrinsic capacity was identified in 220 participants, giving an overall prevalence of 95.2% (95% confidence interval 91.7 to 97.6). Depression was the most frequent deficit, affecting 182 patients (78.8%). Mobility impairment on the Timed Up and Go was observed in 118 (51.1%) and cognitive impairment on the Mini–Mental State Examination in 101 (43.7%). Nutritional compromise was widespread; 88 patients (38.1%) were malnourished and an additional 104 (45.0%) were at nutritional risk. Hearing impairment was detected in 74 patients (32.0%) and vision impairment in 68 (29.4%). Impairment in different domains of intrinsic capacities is shown in table 2. More than half the cohort (56.3%) displayed abnormalities in three or more domains; only 11 participants (4.8%) had fully preserved intrinsic capacity. Number of domains of intrinsic capacities affected is shown in table 3. High or low fall risk on the Morse scale was present in 109 patients (47.2%), including 42 (18.2%) at high risk.

Older age correlated with worse cognitive scores (r = –0.31, p<0.001) and slower Timed Up and Go performance (r = 0.30, p<0.001); it also correlated positively with the Charlson Comorbidity Index (r = 0.17, p = 0.01). Women were more likely than men to have cognitive impairment (52.4% vs. 38.8%, p = 0.045) but did not differ with respect to depression, mobility, sensory loss, or fall risk. Educational attainment showed strong graded associations: cognitive impairment was present in 68.9% of illiterate participants and in 16.7% of those educated beyond the twelfth class (p<0.001); vision impairment followed a similar trend (39.3% vs. 16.7%, p = 0.009). Higher body‑mass index correlated with better nutritional status (r = 0.46, p<0.001), better cognition (r = 0.20, p = 0.003), and lower depression scores (r = –0.14, p = 0.033).

Specific comorbid conditions exerted domain‑specific effects. Lung disease was associated with malnutrition (p<0.001), cognitive impairment (p = 0.036), and elevated fall risk (p = 0.025). Coronary artery disease, present in only three patients, nonetheless showed a relationship with fall risk (p = 0.024). Sleep disturbance was linked to poorer nutritional status (p = 0.016), depression (p = 0.005), mobility impairment (p = 0.037), and higher fall risk (p = 0.003). Finally, depression itself was strongly associated with fall risk; 31.3% of those with depressive symptoms were in the high‑risk category compared with 6.1% of those without depression (p<0.001).

These findings demonstrate a substantial burden of multidomain intrinsic‑capacity impairment in older adults with cancer and highlight modifiable clinical and sociodemographic factors that may inform targeted interventions.

Table 1: sociodemographic determinants

Table 2: Impairment in Different Domains of Intrinsic Capacities

Table 3: Number of Domains of Intrinsic Capacities Affected

The present study shows that intrinsic‑capacity impairment is almost ubiquitous among older adults with cancer who attend a tertiary clinic in eastern India, with 95% of participants having at least one affected domain and more than half showing deficits in three or more domains. This prevalence exceeds the 84% reported in community‑dwelling Indian elders[10] and the 79% described in gastrointestinal oncology patients in North America[11], indicating the substantial functional vulnerability of a hospital‑based cancer cohort. Psychological ill‑health was the most common deficit, affecting nearly four of five patients, a figure higher than the 57.3% prevalence of depressive symptoms observed in a Chinese inpatient study[12]. Cancer‑related uncertaintyand financial stress may amplify psychological distress in this setting. Mobility and cognitive impairments also affected roughly one half and two fifths of the cohort, respectively, proportions comparable to earlier observations from geriatric oncology clinics in high‑income countries[9] and from mixed medical wards in China[12,13]. Consistent with prior work, advancing age correlated with slower Timed Up and Go performance and lower Mini–Mental State Examination scores[14], while educational attainment showed a graded protective association with cognition and vision[3,4]. The strong link between nutritional compromise and both depression and fall risk supports the interconnectedness of vitality, psychological health, and locomotion that underlies the intrinsic‑capacity model[3]. Lung disease, sleep disturbance, and coronary artery disease demonstrated domain‑specific associations that align with other reports of multimorbidity accelerating functional decline.[15,16] These data strengthen calls for routine intrinsic‑capacity screening in geriatric oncology. Simple instruments such as the Mini Nutritional Assessment and Timed Up and Go can be administered in minutes and flag high‑risk patients for multidisciplinary care. Integrating mental‑health consultation, tailored exercise, and protein‑rich supplementation into cancer pathways could mitigate the observed burden of depression, mobility limitation, and malnutrition. Because depression doubled fall risk, antidepressant therapy and balance training should be prioritized in comprehensive plans. Future randomized trials are needed to test whether such interventions preserve capacity, reduce hospitalizations, and improve survival, as suggested by multidomain programs in other elder cohorts[17]. The study has limitations. Its cross‑sectional design precludes causal inference, intrinsic‑capacity impairment may be over‑represented in a referral center, and vision and hearing were assessed by screening tools rather than detailed examination. Residual confounding from cancer stage is possible. Strengths include the use of standardized instruments across all five capacity domains and analysis of a broad range of social and clinical covariates. Intrinsic‑capacity deficits are pervasive and multidimensional in Indian geriatric oncology patients. Socio‑demographic disadvantage, specific comorbidities, and modifiable lifestyle factors contribute meaningfully to this burden. Embedding intrinsic‑capacity assessment in routine cancer care offers a pragmatic route to person‑centered interventions that may help older patients maintain independence and quality of life. In this cross‑sectional analysis of older adults with cancer in a tertiary clinic in eastern India, impairment of intrinsic capacity was nearly universal, affecting 95% of patients, and more than half exhibited limitations in three or more domains. Depression, mobility restriction, cognitive decline, and malnutrition dominated the functional profile, while age, low education, sleep disturbance, lung disease, and low body‑mass index emerged as consistent correlates. These results confirm and extend prior work that links deficits in intrinsic capacity to frailty, disability, and adverse outcomes in community and hospital settings. Systematic screening of all five capacity domains during routine oncology visits appears feasible and would permit timely referral to nutrition, physiotherapy, and mental‑health services. Targeted multidomain interventions have the potential to slow functional erosion and maintain independence in this rapidly growing patient population. Longitudinal and interventional studies are now required to clarify causal pathways and to test whether preserving intrinsic capacity improves survival and quality of life. Source of support:This research received no external funding. Acknowledgement: The authors thank the patients who participated in the study and Mr Prasanta Pal, Ms Upasana Ghosh. Mr Abhik Bhowmick of the Department of Geriatric Medicine, Medical College Kolkata, for their support during data collection. Large language model (GPT-5) was used only to improve the language and structure of the manuscript. The authors thoroughly reviewed the manuscript and take full responsibility for the content. Conflicts of interest:The authors have no conflict of interest regarding this study All authors take responsibility for the integrity of the work as a whole from inception to published article and are to be designated as 'guarantor' The manuscript has been read and approved by all the authors, the requirements for authorship as stated earlier in this document have been met, and each author believes that the manuscript represents honest work.

1. Augusto Lopez-Claros. The Global Competitiveness Report 2006–2007 [Internet]. 2006 [cited 2025 Feb 3];Available from: https://www3.weforum.org/docs/WEF_GlobalCompetitivenessReport_2006-07.pdf
2. Kirkwood TBL. A systematic look at an old problem. Nature 2008;451(7179):644–7.
3. World Health Organisation. World Report On Ageing And Health. First Edition. Geneva: World Health Organisation; 2015.
4. Cesari M, Araujo De Carvalho I, Amuthavalli Thiyagarajan J, Cooper C, Martin FC, Reginster JY, et al. Evidence for the Domains Supporting the Construct of Intrinsic Capacity. The Journals of Gerontology: Series A 2018;73(12):1653–60.
5. van der Vorst A, Zijlstra GAR, Witte ND, Duppen D, Stuck AE, Kempen GIJM, et al. Limitations in Activities of Daily Living in Community-Dwelling People Aged 75 and Over: A Systematic Literature Review of Risk and Protective Factors. PLoS One 2016;11(10):e0165127.
6. Ferlay J, Colombet M, Soerjomataram I, Parkin DM, Piñeros M, Znaor A, et al. Cancer statistics for the year 2020: An overview. Int J Cancer 2021;
7. Parikh P, Chaitanya K, Boppana M, Kumar Ms, Shankar K. Geriatric oncology landscape in India – Current scenario and future projections. Cancer Res Stat Treat 2020;3(2):296.
8. Wildiers H, Heeren P, Puts M, Topinkova E, Janssen-Heijnen MLG, Extermann M, et al. International Society of Geriatric Oncology consensus on geriatric assessment in older patients with cancer. J Clin Oncol 2014;32(24):2595–603.
9. Puts MTE, Hardt J, Monette J, Girre V, Springall E, Alibhai SMH. Use of geriatric assessment for older adults in the oncology setting: a systematic review. J Natl Cancer Inst 2012;104(15):1133–63.
10. Rarajam Rao A, Waris M, Saini M, Thakral M, Hegde K, Bhagwasia M, et al. Prevalence and Factors Associated with Impairment in Intrinsic Capacity among Community-Dwelling Older Adults: An Observational Study from South India. Current Gerontology and Geriatrics Research 2023;2023:1–9.
11. Maheshwari S, Dai C, Giri S, Harmon C, Tucker A, Fowler ME, et al. Intrinsic capacity and survival among older adults with gastrointestinal malignancies: The Cancer and Aging Resilience Evaluation registry. Cancer 2024;130(20):3530–9.
12. Wu W, Sun L, Li H, Zhang J, Shen J, Li J, et al. Approaching person-centered clinical practice: A cluster analysis of older inpatients utilizing the measurements of intrinsic capacity. Front Public Health 2022;10:1045421.
13. Xuekelati S, Maimaitiwusiman Z, Xiang H, Wumaer A, Bai X, Wang H. Handgrip strength: A simple and effective tool to recognize decreased intrinsic capacity in Chinese older adults. Exp Gerontol 2024;196:112567.
14. Heuninckx S, Wenderoth N, Debaere F, Peeters R, Swinnen SP. Neural basis of aging: the penetration of cognition into action control. J Neurosci 2005;25(29):6787–96.
15. Song F, Chhetri JK, Hu M, Li B, Xia J, Yin C. Mapping multimorbidity cluster-specific intrinsic capacity impairment patterns and mortality risks in a community-dwelling aging population cohort. J Nutr Health Aging 2025;29(11):100687.
16. Konte HC, Dervis E, Alyan O, Aras D. Sleep Quality as a Predictor of Coronary Artery Disease Severity in Geriatric Acute Coronary Syndrome. Medicina 2026;62(1):101.
17. Liang CK, Lee WJ, Chou MY, Hwang AC, Lin CS, Peng LN, et al. Roles of Baseline Intrinsic Capacity and its Subdomains on the Overall Efficacy of Multidomain Intervention in Promoting Healthy Aging among Community-Dwelling Older Adults: Analysis from a Nationwide Cluster-Randomized Controlled Trial. J Prev Alzheimers Dis 2024;11(2):356–65.