Data availability
The data reported in this manuscript (text, tables, figures and the Supplementary Materials) will be shared in deidentified form owing to privacy protections. Requests for deidentified data and a data dictionary will be evaluated by the data-sharing committee of the different datasets used (ICOPE Care, INSPIRE-T and ICOPE-BR). The contacts for the different datasets are [email protected] for the ICOPE Care cohort, [email protected] for the INSPIRE-T cohort and [email protected] for the ICOPE-BR cohort. Data will be made available for investigators upon request for a preidentified scientific purpose developed in a research proposal with sound methodology, subject to the approval of the appropriate Committees according to the dataset requested. A Data Use Agreement must also to be signed. Data access requests are evaluated on a monthly basis. Data access is dependent on adherence to the INSPIRE-T Data Use Agreement and the publication policies outlined in this agreement (provided in the Supplementary Materials). Given the ongoing nature of the ICOPE Care program (real-life data from health care services), the INSPIRE-T program and the ICOPE-BR program, the authors are unable to make the dataset publicly accessible at this time. In addition, according to the policy of some of these research and care programs, all analyses using data from ICOPE Care, INSPIRE-T (including the present study) and ICOPE-BR must be evaluated and approved by the committee after submission of a comprehensive analysis proposal. Therefore, for researchers interested in accessing the data used for the current study, the same evaluation procedure must be followed.
Code availability
The code supporting the findings of this study is available upon reasonable request from the corresponding author. All analyses were performed using programming languages in SAS and R software packages.
References
World Health Organization. Integrated Care for Older People: Guidelines on Community-Level Interventions to Manage Declines in Intrinsic Capacity (World Health Organization, 2017).
de Souto Barreto, P. et al. Real-life intrinsic capacity screening data from the ICOPE-Care program. Nat. Aging 4, 1279–1289 (2024).
Tavassoli, N. et al. Implementation of the WHO integrated care for older people (ICOPE) programme in clinical practice: a prospective study. Lancet Healthy Longev. 3, e394–e404 (2022).
Gonzalez-Bautista, E., Andrieu, S., Gutiérrez-Robledo, L. M., García-Chanes, R. E. & de Souto Barreto, P. In the quest of a standard index of intrinsic capacity. A critical literature review. J. Nutr. Health Aging 24, 959–965 (2020).
Yong, K. et al. Predictive validity of intrinsic capacity composite scores for risk of frailty at 2 years: a comparison of 4 scales. J. Am. Med. Dir. Assoc. 25, 105146 (2024).
Kim, J. et al. Dietary patterns and intrinsic capacity in older adults: a 6-year prospective cohort study. J. Nutr. Health Aging 28, 100314 (2024).
Beyene, M. B. et al. Development and validation of an intrinsic capacity score in the UK Biobank study. Maturitas 185, 107976 (2024).
Sun, M. et al. Intrinsic capacity, polygenic risk score, APOE genotype, and risk of dementia: a prospective cohort study based on the UK Biobank. Neurology 102, e209452 (2024).
Bencivenga, L. et al. Visit-to-visit blood pressure variability is associated with intrinsic capacity decline: results from the MAPT Study. Eur. J. Intern. Med. 125, 82–88 (2024).
Aliberti, M. J. R. et al. Validating intrinsic capacity to measure healthy aging in an upper middle-income country: findings from the ELSI-Brazil. Lancet Reg. Health Am. 12, 100284 (2022).
Koivunen, K. et al. Development and validation of an intrinsic capacity composite score in the Longitudinal Aging Study Amsterdam: a formative approach. Aging Clin. Exp. Res. 35, 815–825 (2023).
Beard, J. R., Jotheeswaran, A. T., Cesari, M. & Araujo de Carvalho, I. The structure and predictive value of intrinsic capacity in a longitudinal study of ageing. BMJ Open 9, e026119 (2019).
Beard, J. R., Si, Y., Liu, Z., Chenoweth, L. & Hanewald, K. Intrinsic capacity: validation of a new WHO concept for healthy aging in a longitudinal Chinese study. J. Gerontol. A Biol. Sci. Med. Sci. 77, 94–100 (2022).
Lu, W.-H., Rolland, Y., Guyonnet, S., de Souto Barreto, P. & Vellas, B. Reference centiles for intrinsic capacity throughout adulthood and their association with clinical outcomes: a cross-sectional analysis from the INSPIRE-T cohort. Nat. Aging 3, 1521–1528 (2023).
Sousa-Santos, R. F., Miguelote, R. F., Cruz-Correia, R. J., Santos, C. C. & Bernardes, J. F. M. A. L. Development of a birthweight standard and comparison with currently used standards. What is a 10th centile? Eur. J. Obstet. Gynecol. Reprod. Biol. 206, 184–193 (2016).
Beard, J. R. et al. The world report on ageing and health: a policy framework for healthy ageing. Lancet 387, 2145–2154 (2016).
Hwang, A.-C. et al. Intrinsic capacity transitions predict overall and cause-specific mortality, incident disability, and healthcare utilization. J. Nutr. Health Aging 28, 100359 (2024).
Ramírez-Vélez, R. et al. Association of intrinsic capacity with incidence and mortality of cardiovascular disease: prospective study in UK Biobank. J. Cachexia Sarcopenia Muscle 14, 2054–2063 (2023).
Sánchez-Sánchez, J. L. et al. Association of intrinsic capacity with functional decline and mortality in older adults: a systematic review and meta-analysis of longitudinal studies. Lancet Healthy Longev. 5, e480–e492 (2024).
Zhao, Y. et al. Adverse health effects of declined intrinsic capacity in middle-aged and older adults: a systematic review and meta-analysis. Age Ageing 53, afae162 (2024).
Braun, T. et al. Association of clinical outcome assessments of mobility capacity and incident disability in community-dwelling older adults—a systematic review and meta-analysis. Ageing Res. Rev. 81, 101704 (2022).
Zhang, X. M., Wu, X. J., Cao, J., Jiao, J. & Chen, W. Association between cognitive frailty and adverse outcomes among older adults: a meta-analysis. J. Nutr. Health Aging 26, 817–825 (2022).
Ferriolli, E. et al. Project ICOPE Brazil: a study on the intrinsic capacity of Brazilian older adults and accuracy of the screening tool proposed by the World Health Organization. Geriatr. Gerontol. Aging 17, 1–3 (2023).
Su, H.-C. et al. Assessing intrinsic capacity in Taiwan: initial psychometric properties of the Integrated Care for Older People Screening Tool for Taiwanese (ICOPES-TW). BMC Geriatr. 24, 477 (2024).
Yan Wang, N. et al. Implementation and impact of the World Health Organization integrated care for older people (ICOPE) program in China: a randomised controlled trial. Age Ageing 53, afad249 (2024).
de Oliveira, V. P. et al. The sensitivity and specificity of the WHO’s ICOPE screening tool, and the prevalence of loss of intrinsic capacity in older adults: a scoping review. Maturitas 177, 107818 (2023).
Rodríguez-Laso, Á., García-García, F. J. & Rodríguez-Mañas, L. The ICOPE intrinsic capacity screening tool: measurement structure and predictive validity of dependence and hospitalization. J. Nutr. Health Aging 27, 808–816 (2023).
Rojano, I. et al. Identification of decreased intrinsic capacity: performance of diagnostic measures of the ICOPE screening tool in community dwelling older people in the VIMCI study. BMC Geriatr. 23, 106 (2023).
Meng, L.-C. et al. Intrinsic capacity impairment patterns and their associations with unfavorable medication utilization: a nationwide population-based study of 37,993 community-dwelling older adults. J. Nutr. Health Aging 26, 918–925 (2022).
Guyonnet, S. et al. The INSPIRE Bio-Resource research platform for healthy aging and geroscience: focus on the human translational research cohort (The INSPIRE-T Cohort). J. Frailty Aging 10, 110–120 (2021).
de Souto Barreto, P. et al. The INSPIRE Research Initiative: a program for geroscience and healthy aging research going from animal models to humans and the healthcare system. J. Frailty Aging 10, 86–93 (2021).
European Parliament, Council of the European Union. Regulation (EU) 2016/679 of the European Parliament and of the Council of 27 April 2016 on the protection of natural persons with regard to the processing of personal data and on the free movement of such data, and repealing Directive 95/46/EC. https://eur-lex.europa.eu/legal-content/EN/ALL/?uri=celex%3A32016R0679 (2016).
Tavassoli, N. et al. Framework implementation of the INSPIRE ICOPE-CARE program in collaboration with the World Health Organization (WHO) in the Occitania region. J. Frailty Aging 10, 103–109 (2020).
Ferriollia, E. et al. Assessment of intrinsic capacity in the Brazilian older population and the psychometric properties of the WHO/ICOPE screening tool: a multicenter cohort study protocol. Geriatr. Gerontol. Aging 18, 808–816 (2024).
World Health Organization. Integrated Care for Older People (ICOPE): Guidance for Person-Centred Assessment and Pathways in Primary Care (World Health Organization, 2019).
Moeller, J. A word on standardization in longitudinal studies: don’t. Front. Psychol. 6, 1389 (2015).
Cohen, P., Cohen, J., Aiken, L. & West, S. G. The problem of units and the circumstance for POMP. Multivariate Behav. Res. 34, 315–346 (1999).
Fried, L. P. et al. Frailty in older adults: evidence for a phenotype. J. Gerontol. A Biol. Sci. Med. Sci. 56, M146–M157 (2001).
Katz, S., Downs, T. D., Cash, H. R. & Grotz, R. C. Progress in development of the index of ADL. Gerontologist 10, 20–30 (1970).
Mahoney, F. I. & Barthel, D. W. Functional evaluation: the Barthel index. Md. State Med. J. 14, 61–65 (1965).
Lawton, M. P. & Brody, E. M. Assessment of older people: self-maintaining and instrumental activities of daily living. Gerontologist 9, 179–186 (1969).
Ferrucci, L. et al. Subsystems contributing to the decline in ability to walk: bridging the gap between epidemiology and geriatric practice in the InCHIANTI study. J. Am. Geriatr. Soc. 48, 1618–1625 (2000).
Ma, L. et al. Integrated care for older people screening tool for measuring intrinsic capacity: preliminary findings from ICOPE pilot in China. Front. Med. 7, 576079 (2020).
Leung, A. Y. M., Su, J. J., Lee, E. S. H., Fung, J. T. S. & Molassiotis, A. Intrinsic capacity of older people in the community using WHO Integrated Care for Older People (ICOPE) framework: a cross-sectional study. BMC Geriatr. 22, 304 (2022).
Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B Methodol. 57, 289–300 (1995).
Cole, T. J. Fitting smoothed centile curves to reference data. J. R. Stat. Soc. Ser. A Stat. Soc. 151, 385 (1988).
Cole, T. J. & Green, P. J. Smoothing reference centile curves: the LMS method and penalized likelihood. Stat. Med. 11, 1305–1319 (1992).
Rigby, R. A. & Stasinopoulos, D. M. Smooth centile curves for skew and kurtotic data modelled using the Box–Cox power exponential distribution. Stat. Med. 23, 3053–3076 (2004).
Rigby, R. A. & Stasinopoulos, D. M. Using the Box–Cox t distribution in GAMLSS to model skewness and kurtosis. Stat. Modelling 6, 209–229 (2006).
Rigby, R. A., Stasinopoulos, D. M. & Lane, P. W. Generalized additive models for location, scale and shape. J. R. Stat. Soc. Ser. C Appl. Stat. 54, 507–554 (2005).
Rigby, R. A. & Stasinopoulos, D. M. Automatic smoothing parameter selection in GAMLSS with an application to centile estimation. Stat. Methods Med. Res. 23, 318–332 (2014).
Vamvakas, G., Norbury, C. F., Vitoratou, S., Gooch, D. & Pickles, A. Standardizing test scores for a target population: the LMS method illustrated using language measures from the SCALES project. PLoS ONE 14, e0213492 (2019).
van Buuren, S. & Fredriks, M. Worm plot: a simple diagnostic device for modelling growth reference curves. Stat. Med. 20, 1259–1277 (2001).
Acknowledgements
ICOPE Care cohort. We thank all the health care professionals who participated in the ICOPE Care program; the members of the Gerontopole of Toulouse, especially those of the ‘Regional Team for Ageing and Prevention of Dependency’ (Isabelle Carrié, Justine de Kerimel, Christine Lafont, Céline Mathieu, Fanny Paris, Delphine Pennetier, Brune Rieunier and Alessia Robert-Millocco) and the ‘ICOPE remote monitoring platform’ (Laure Aldebert, Véronique Bezombes, Pascale Baby, Laure Bouchon, Marie Christine Cazes, Florence Da Costa, Magali Poly, Charlene Seguela, Lay-Nien Sephan and Catherine Takeda); all members of the Occitanie Territorial Teams of Ageing and Prevention of Dependency and the project leaders of National Experimentation, Article 51 (Mutualité Française PACA, DAC Sante landes, Inter-CPTS Haut-Rhin, CPTS Haute-Corrèze, Perigueux University Hospital, Filieris Sud, Civic Hospitals of Lyon, CPTS Grand Sud Réunion, InterURPS Pay de la Loire, CPTS Cerebellum, DAC 17, DAC 46, Clinique des Augustines, Brest University Hospital, Mutualité Française Bretagne). The ICOPE Care program was supported by grants from the Occitanie Regional Health Agency (Region Occitanie/Pyrénées-Méditerranée; reference number 1901175), ICOPE National Experimentation-Article 51 (Ministry of Solidarity and Health-Order of July 19, 2022-NOR: SPRS2221913A), the European Regional Development Fund (project number MP0022856) and The Interreg Program V-A Spain-France-Andorra (European Union) in the context of the APTITUDE (EFA232/16) and the APTITUDE-PROXI (EFA018/01) projects. This work was performed in the context of the IHU HealthAge, which has benefited from funding by the Agence Nationale de la Recherche under the France 2030 program (reference number: ANR-23-IAHU-0011). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.
INSPIRE program. This work was performed in the context of the IHU HealthAge, which has benefited from funding by the Agence Nationale de la Recherche under the France 2030 program (reference number ANR-23-IAHU-0011). The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. The INSPIRE platform was supported by grants from the Region Occitanie/Pyrénées-Méditerranée (reference number 1901175), the European Regional Development Fund (project number MP0022856) and the Inspire Chairs of Excellence funded by Alzheimer Prevention in Occitania and Catalonia, EDENIS, KORIAN, Pfizer and Pierre-Fabre. The IHU HealthAge Open Science initiative was supported by the French National Research Agency as part of the France 2030 program (reference number ANR-23 IAHU-0011) and builds on the work conducted in the Data Sharing Alzheimer project. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript.
Members of the IHU HealthAge INSPIRE/Open Science Group include the following. IHU HealthAge INSPIRE platform group. INSPIRE-T human cohort group: coordinators: Sophie Guyonnet and Bruno Vellas; project managers: Lauréane Brigitte and Agathe Milhet; clinical research assistants: Elodie Paez, Emeline Muller and Sabine Le Floch; investigators: Catherine Takeda, Catherine Faisant, Françoise Lala, Gabor Abellan Van Kan, Zara Steinmeyer, Antoine Piau, Tony Macaron, Davide Angioni and Pierre-Jean Ousset; nurses: Mélanie Comté, Nathalie Daniaud and Fanny Boissou-Parachaud; methodology, statistical analysis and data management subgroup: Sandrine Andrieu and Christelle Cantet; body composition, VO2 max, isocinetism subgroup: Yves Rolland, Philipe de Souto Barreto and Fabien Pillard; technician dual-energy X-ray absorptiometry: Bernard Teysseyre; MRI subgroup: Marie Faruch and Pierre Payoux; ICOPE subgroup: Catherine Takeda and Neda Tavassoli; biological sample collection subgroup: Marie Dorard, Bénédicte Razat, Camille Champigny and Sophie Guyonnet. INSPIRE animal cohort groups: Cédric Dray and Jean-Philippe Pradère (fish colony); Angelo Parini and Yohan Santin (mouse cohort). Associated research teams: Dominique Langin, Pierre Gourdy, Laurent O. Martinez, Anne Bouloumié and Angelo Parini (I2MC lab); Nicolas Fazilleau, Roland Liblau, Jean-Charles Guéry, Michel Simon, Nicolas Gaudenzio, Luciana Bostan, Hicham El Costa and Nabila Jabrane Ferrat (Infinity lab); Philippe Valet, Cedric Dray, Isabelle Ader, Valérie Planat and Louis Casteilla (Restore); Pierre Payoux and Patrice Peran (Tonic lab); Cyrille Delpierre and Sandrine Andrieu (CERPOP lab); Claire Rampon, Noelie Davezac and Bruno Guiard (CRCA/CBI lab); Nathalie Vergnolle, Jean-Paul Motta, Sara Djebali, Pauline Floch, Céline Deraison and Chrystelle Bonnart (IRSD lab); Jean-Emmanuel Sarry (CRCT lab). IHU HealthAge Open Science group: Nicola Coley, Sophie Guyonnet and Sandrine Andrieu. ICOPE-BR program: ICOPE-BR Steering Committee Members: Eduardo Ferrioli, Roberto A. Lourenço, Renato G. Bandeira de Mello, Renata Ferreti-Rebustini and Vitor Pelegrim de Oliveira. The ICOPE Brazil is funded by the Brazilian National Research Council (CNPq; grant number 406612/2021-8). Data from the ICOPE-BR feasibility study presented and shared for this study’s purposes were collected in three research centers: Hospital de Clínicas de Porto Alegre at Federal University of Rio Grande do Sul (HCPA-UFRGS; Renato G. Bandeira de Mello), Federal University of Ceará (UFC; Jarbas Roriz) and Federal University of Medical Sciences of Minas Gerais (FCMMG; Leani Pereira). Local research centers obtained additional funding as listed. Grants from the Fundação de Amparo à Pesquisa do Estado do Rio Grande do Sul (FAPERGS grant number 21/2551-0002084-9) and Fundo Institucional de Pesquisa of HCPA (FIPE HCPA) funded the ICOPE Porto Alegre Study Center; Unichristus University funded the ICOPE Ceará Study Center.
Author information
Author notes
These authors contributed equally: Philipe de Souto Barreto, Wan-Hsuan Lu.
Authors and Affiliations
IHU HealthAge, Toulouse, France
Philipe de Souto Barreto, Wan-Hsuan Lu, Neda Tavassoli, Fatemeh Nourhashémi, Sophie Guyonnet, Yves Rolland, Maria Eugenia Soto Martín & Bruno Vellas
CERPOP UMR 1295, University of Toulouse III, INSERM, UPS, Toulouse, France
Philipe de Souto Barreto, Fatemeh Nourhashémi, Sophie Guyonnet, Yves Rolland, Maria Eugenia Soto Martín & Bruno Vellas
Gerontopole of Toulouse, Institute on Aging, Toulouse University Hospital (CHU Toulouse), Toulouse, France
Philipe de Souto Barreto, Wan-Hsuan Lu, Fatemeh Nourhashémi, Yves Rolland & Bruno Vellas
Geriatric Unit of the Internal Medicine Division, Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil
Renato Gorga Bandeira de Mello
Department of Internal Medicine, Federal University of Rio Grande do Sul, Porto Alegre, Brazil
Renato Gorga Bandeira de Mello
Departamento de Clínica Médica, Faculdade de Medicina, Universidade de São Paulo, São Paulo, Brazil
Eduardo Ferriolli
Consortia
IHU HealthAge INSPIRE/Open Science study group
- Philipe de Souto Barreto
- , Neda Tavassoli
- , Sophie Guyonnet
- , Yves Rolland
- & Bruno Vellas
Contributions
P.S.B., W.-H.L. and B.V. contributed to the study design. P.S.B. and B.V. supervised the study. N.T., F.N., R.G.B.M., E.F., S.G., Y.R. and M.E.S.M. contributed to data collection and/or verification. W.-H.L. analyzed the data. All authors contributed to data interpretation. P.S.B. and W.-H.L. wrote the first version of the paper. All authors revised the paper with important intellectual content. All authors had full access to all the data in the study and had final responsibility for the decision to submit for publication.
Corresponding author
Correspondence to Philipe de Souto Barreto.
Ethics declarations
Competing interests
P.S.B. declares to have received research grant and consultancy fees from Pfizer. B.V. is the founder president of IHU HealthAge, Toulouse University Hospital, and an investigator in clinical trials sponsored by several industry partners (IHU, CRC and Inspire Geroscience platforms). All other authors declare no conflicts of interest.
Peer review
Peer review information
Nature Aging thanks Jean Woo and Parminder Raina for their contribution to the peer review of this work.
Additional information
Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Extended data
Extended Data Fig. 1 Diagram of IC CFA bi-factor models.
We performed confirmatory factor analysis (CFA) to examine the second-order and bi-factor IC models. Both second-order and bi-factor models were estimated using maximum likelihood estimation with robust standard errors to address the non-normality of the variables. Data from the training sample indicated that the bi-factor model fit better than the second-order model. The fit statistic of the bi-factor model in the training sample: robust model χ2 (87) = 897.7, Root Mean Square Error of Approximation (RMSEA [90% CI]) = 0.028 [0.027, 0.030], Comparative Fit Index (CFI) = 0.97, Tucker–Lewis Index (TLI) = 0.96, Standardized Root Mean Square Residual (SRMR) = 0.02.
Extended Data Fig. 2 Transition of IC centiles within one year.
The Sankey diagrams depict the transition of IC centiles between two visits used the ICOPE Step 1 score (left; n = 1511) and ICOPE-VAS (right; n = 935). These subjects had their second IC Step 1 assessments within 1 to 12 months of the baseline. Tables below the diagrams provide the number of individuals and percentage of distribution across centile groups (percentages add up to 100% in the rows).
Extended Data Fig. 3 Study design and participant selection.
a. Overview of the study framework using three study cohorts. b. Participant identification of the ICOPE Care cohort. c. Participant identification of the INSPIRE-T cohort.
Supplementary information
Rights and permissions
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
de Souto Barreto, P., Lu, WH., Tavassoli, N. et al. Reference centiles for intrinsic capacity to monitor clinical health outcomes in real-world primary care cohorts. Nat Aging (2025). https://doi.org/10.1038/s43587-025-00861-x
Received:
Accepted:
Published:
DOI: https://doi.org/10.1038/s43587-025-00861-x