Identification of Pre-frailty Sub-Phenotypes in Elderly Using Metabolomics

Estelle Pujos-Guillot, Mélanie Pétéra, Jérémie Jacquemin, Delphine Centeno, Bernard Lyan, Ivan Montoliu, Dawid Madej, Barbara Pietruszka, Cristina Fabbri, Aurelia Santoro, Anna Brzozowska, Claudio Franceschi, Blandine Comte, Estelle Pujos-Guillot, Mélanie Pétéra, Jérémie Jacquemin, Delphine Centeno, Bernard Lyan, Ivan Montoliu, Dawid Madej, Barbara Pietruszka, Cristina Fabbri, Aurelia Santoro, Anna Brzozowska, Claudio Franceschi, Blandine Comte

Abstract

Aging is a dynamic process depending on intrinsic and extrinsic factors and its evolution is a continuum of transitions, involving multifaceted processes at multiple levels. It is recognized that frailty and sarcopenia are shared by the major age-related diseases thus contributing to elderly morbidity and mortality. Pre-frailty is still not well understood but it has been associated with global imbalance in several physiological systems, including inflammation, and in nutrition. Due to the complex phenotypes and underlying pathophysiology, the need for robust and multidimensional biomarkers is essential to move toward more personalized care. The objective of the present study was to better characterize the complexity of pre-frailty phenotype using untargeted metabolomics, in order to identify specific biomarkers, and study their stability over time. The approach was based on the NU-AGE project (clinicaltrials.gov, NCT01754012) that regrouped 1,250 free-living elderly people (65-79 y.o., men and women), free of major diseases, recruited within five European centers. Half of the volunteers were randomly assigned to an intervention group (1-year Mediterranean type diet). Presence of frailty was assessed by the criteria proposed by Fried et al. (2001). In this study, a sub-cohort consisting in 212 subjects (pre-frail and non-frail) from the Italian and Polish centers were selected for untargeted serum metabolomics at T0 (baseline) and T1 (follow-up). Univariate statistical analyses were performed to identify discriminant metabolites regarding pre-frailty status. Predictive models were then built using linear logistic regression and ROC curve analyses were used to evaluate multivariate models. Metabolomics enabled to discriminate sub-phenotypes of pre-frailty both at the gender level and depending on the pre-frailty progression and reversibility. The best resulting models included four different metabolites for each gender. They showed very good prediction capacity with AUCs of 0.93 (95% CI = 0.87-1) and 0.94 (95% CI = 0.87-1) for men and women, respectively. Additionally, early and/or predictive markers of pre-frailty were identified for both genders and the gender specific models showed also good performance (three metabolites; AUC = 0.82; 95% CI = 0.72-0.93) for men and very good for women (three metabolites; AUC = 0.92; 95% CI = 0.86-0.99). These results open the door, through multivariate strategies, to a possibility of monitoring the disease progression over time at a very early stage.

Keywords: biomarkers; elderly; gender differences; pre-frailty; sub-phenotypes; untargeted metabolomics.

Figures

FIGURE 1
FIGURE 1
Study design and frailty status evolution of the subjects over the 1-year period. One hundred and twenty subjects (60 pre-frail and 60 non-frail) were first randomly selected at baseline among the intervention group from the Italian and Polish centers. In addition, all the remaining incident subjects shifting their frailty status during the follow-up (n = 92; n = 67 non-frail and n = 25 pre-frail) were also selected from the same centers, regardless of their intervention groups. Three sub-groups were defined: Stable subjects, who did not change their frailty status over time [Stable 0 (n = 57; 56 of them followed the NU-AGE diet); Stable 1 (n = 30, all of them followed the diet)], those who changed their status from pre-frail to non-frail [Improvement (n = 52, 29 of them were in the NU-AGE diet group)] and those who changed from non-frail to pre-frail [Degradation (n = 70, 34 of them followed the NU-AGE diet)] over 12 months.
FIGURE 2
FIGURE 2
Volcano plots of significant ions for pre-frailty status at baseline in the stable population (A: males and B: females) and in subjects who improved their frailty status (C: males and D: females). ANOVA with country as cofactor; p-values corrected for multiple testing (Benjamini-Hochberg) < 0.05. The dot lines represent the p-value cut-off used for the logistic regression analyses (p-values < 10-2).
FIGURE 3
FIGURE 3
ROC curve of the best multivariate prediction models of pre-frailty status at baseline (A: males and B: females) and box-plots of the error rate corresponding to the 100 cross-validated models (C: males and D: females). Data from serum metabolomics at baseline, of subjects who improved their pre-frailty status (n = 52) versus stable non-frail individuals (n = 57).
FIGURE 4
FIGURE 4
Boxplots of the ‘light’ pre-frailty biomarkers discovered at baseline in subjects who will improve their status (Improvement; n = 52) and valid at follow-up in subjects who just worsened it (Degradation; n = 70). Blue color: men’s data; red color: women’s data (a.u, arbitrary units).
FIGURE 5
FIGURE 5
Volcano plots of early pre-frailty markers. Significant ions at baseline in subjects (A: males and B: females) who worsened their frailty status over time (Degradation) versus stable non-frail subjects. ANOVA with country as cofactor; p-values corrected for multiple testing (Benjamini-Hochberg) < 0.05. The dot lines represent the p-value cut-off used for the logistic regression analyses (p-values < 10-2).
FIGURE 6
FIGURE 6
ROC curve of the best multivariate prediction models of evolution toward pre-frailty (A: males and B: females) and box-plots of the error rate corresponding to the 100 cross-validated models (C: males and D: females).

References

    1. Beard J. R., Officer A., de Carvalho I. A., Sadana R., Pot A. M., Michel J. P., et al. (2016). The World report on ageing and health: a policy framework for healthy ageing. Lancet 387 2145–2154. 10.1016/S0140-6736(15)00516-4
    1. Berendsen A., Santoro A., Pini E., Cevenini E., Ostan R., Pietruszka B., et al. (2014). Reprint of: a parallel randomized trial on the effect of a healthful diet on inflammageing and its consequences in European elderly people: design of the NU-AGE dietary intervention study. Mech. Ageing Dev. 13 14–21. 10.1016/j.mad.2014.03.001
    1. Berendsen A., van de Rest O., Feskens E. J. M., Santoro A., Ostan R., Pietruszka B., et al. (2018). Changes in dietary intake and adherence to the NU-AGE diet following a one-year dietary intervention among European older adults-Results of the NU-AGE randomized trial. Nutrients 10:E1905. 10.3390/nu10121905
    1. Calvani R., Marini F., Cesari M., Tosato M., Picca A., Anker S. D., et al. (2017). Biomarkers for physical frailty and sarcopenia. Aging Clin. Exp. Res. 29 29–34. 10.1007/s40520-016-0708-1
    1. Carcaillon L., Blanco C., Alonso-Bouzon C., Alfaro-Acha A., Garcia-Garcia F. J., Rodriguez-Manas L. (2012). Sex differences in the association between serum levels of testosterone and frailty in an elderly population: the Toledo study for healthy aging. PLoS One 7:e32401. 10.1371/journal.pone.0032401
    1. Cevenini E., Monti D., Franceschi C. (2013). Inflamm-ageing. Curr. Opin. Clin. Nutr. Metab. Care 16 14–20. 10.1097/MCO.0b013e32835ada13
    1. Clegg A., Rogers L., Young J. (2015). Diagnostic test accuracy of simple instruments for identifying frailty in community-dwelling older people: a systematic review. Age Ageing 44 148–152. 10.1093/ageing/afu157
    1. Collino S., Comte B., Pujos-Guillot E., Franceschi C., Nuñez Galindo A., Dayon L., et al. (2017). “Healthy ageing phenotypes and trajectories,” in Oxford Textbook of Geriatric Medicine eds Michel J.-P., Beattie B. L., Martin F. C., Walston J. D. (Oxford: Oxford University Press; ).
    1. Collino S., Martin F. P., Karagounis L. G., Horcajada M. N., Moco S., Franceschi C., et al. (2013). Musculoskeletal system in the old age and the demand for healthy ageing biomarkers. Mech. Ageing Dev. 134 541–547. 10.1016/j.mad.2013.11.003
    1. Collino S., Martin F. P., Karagounis L. G., Horcajada M. N., Moco S., Franceschi C., et al. (2014). Reprint of: musculoskeletal system in the old age and the demand for healthy ageing biomarkers. Mech. Ageing Dev. 136-137 94–100. 10.1016/j.mad.2014.03.002
    1. Collino S., Martin F. P., Kochhar S., Rezzi S. (2011). Nutritional metabonomics: an approach to promote personalized health and wellness. Chimia 65 396–399.
    1. DeLong E. R., DeLong D. M., Clarke-Pearson D. L. (1988). Comparing the areas under two or more correlated receiver operating characteristic curves: a nonparametric approach. Biometrics 44 837–845.
    1. Drey M., Wehr H., Wehr G., Uter W., Lang F., Rupprecht R., et al. (2011). The frailty syndrome in general practitioner care: a pilot study. Z. Gerontol. Geriatr. 44 48–54. 10.1007/s00391-010-0136-3
    1. Dumas M. E., Kinross J., Nicholson J. K. (2014). Metabolic phenotyping and systems biology approaches to understanding metabolic syndrome and fatty liver disease. Gastroenterology 146 46–62. 10.1053/j.gastro.2013.11.001
    1. Eurostat (2011). EUROPOP2010 – Convergence Scenario, National Level – Assumptions. Brussels: Eurostat.
    1. Fazelzadeh P., Hangelbroek R. W., Tieland M., de Groot L. C., Verdijk L. B., van Loon L. J., et al. (2016). The muscle metabolome differs between healthy and frail older adults. J. Proteome Res. 15 499–509. 10.1021/acs.jproteome.5b00840
    1. Fernandez-Garrido J., Ruiz-Ros V., Buigues C., Navarro-Martinez R., Cauli O. (2014). Clinical features of prefrail older individuals and emerging peripheral biomarkers: a systematic review. Arch. Gerontol. Geriatr. 59 7–17. 10.1016/j.archger.2014.02.008
    1. Franceschi C., Bonafe M., Valensin S. (2000a). Human immunosenescence: the prevailing of innate immunity, the failing of clonotypic immunity, and the filling of immunological space. Vaccine 18 1717–1720.
    1. Franceschi C., Bonafe M., Valensin S., Olivieri F., De Luca M., Ottaviani E., et al. (2000b). Inflamm-aging. An evolutionary perspective on immunosenescence. Ann. N. Y. Acad. Sci. 908 244–254.
    1. Franceschi C., Capri M., Monti D., Giunta S., Olivieri F., Sevini F., et al. (2007). Inflammaging and anti-inflammaging: a systemic perspective on aging and longevity emerged from studies in humans. Mech. Ageing Dev. 128 92–105. 10.1016/j.mad.2006.11.016
    1. Fried L. P., Tangen C. M., Walston J., Newman A. B., Hirsch C., Gottdiener J., et al. (2001). Frailty in older adults: evidence for a phenotype. J. Gerontol. A Biol. Sci. Med. Sci. 56 M146–M156.
    1. Gale C. R., Baylis D., Cooper C., Sayer A. A. (2013). Inflammatory markers and incident frailty in men and women: the English longitudinal study of ageing. Age 35 2493–2501. 10.1007/s11357-013-9528-9
    1. Giacomoni F., Le Corguille G., Monsoor M., Landi M., Pericard P., Petera M., et al. (2015). Workflow4Metabolomics: a collaborative research infrastructure for computational metabolomics. Bioinformatics 31 1493–1495. 10.1093/bioinformatics/btu813
    1. Gibellini F., Smith T. K. (2010). The Kennedy pathway–De novo synthesis of phosphatidylethanolamine and phosphatidylcholine. IUBMB Life 62 414–428. 10.1002/iub.337
    1. Gonzalez-Covarrubias V. (2013). Lipidomics in longevity and healthy aging. Biogerontology 14 663–672. 10.1007/s10522-013-9450-7
    1. Kenny L. C., Broadhurst D. I., Dunn W., Brown M., North R. A., McCowan L., et al. (2010). Robust early pregnancy prediction of later preeclampsia using metabolomic biomarkers. Hypertension 56 741–749. 10.1161/HYPERTENSIONAHA.110.157297
    1. Lawton K. A., Berger A., Mitchell M., Milgram K. E., Evans A. M., Guo L., et al. (2008). Analysis of the adult human plasma metabolome. Pharmacogenomics 9 383–397. 10.2217/14622416.9.4.383
    1. Lindon J. C., Nicholson J. K. (2014). The emergent role of metabolic phenotyping in dynamic patient stratification. Expert Opin. Drug Metab. Toxicol. 10 915–919. 10.1517/17425255.2014.922954
    1. Miura Y., Endo T. (2016). Glycomics and glycoproteomics focused on aging and age-related diseases–Glycans as a potential biomarker for physiological alterations. Biochim. Biophys. Acta 1860 1608–1614. 10.1016/j.bbagen.2016.01.013
    1. Morley J. E., Vellas B., van Kan G. A., Anker S. D., Bauer J. M., Bernabei R., et al. (2013). Frailty consensus: a call to action. J. Am. Med. Dir. Assoc. 14 392–397. 10.1016/j.jamda.2013.03.022
    1. Pujos-Guillot E., Brandolini M., Petera M., Grissa D., Joly C., Lyan B., et al. (2017). Systems metabolomics for prediction of metabolic syndrome. J. Proteome Res. 16 2262–2272. 10.1021/acs.jproteome.7b00116
    1. Ramautar R., Berger R., van der Greef J., Hankemeier T. (2013). Human metabolomics: strategies to understand biology. Curr. Opin. Chem. Biol. 17 841–846. 10.1016/j.cbpa.2013.06.015
    1. Robin X., Turck N., Hainard A., Tiberti N., Lisacek F., Sanchez J. C., et al. (2011). pROC: an open-source package for R and S+ to analyze and compare ROC curves. BMC Bioinformatics 12:77. 10.1186/1471-2105-12-77
    1. Santoro A., Pini E., Scurti M., Palmas G., Berendsen A., Brzozowska A., et al. (2014). Combating inflammaging through a Mediterranean whole diet approach: the NU-AGE project’s conceptual framework and design. Mech. Ageing Dev. 13 3–13. 10.1016/j.mad.2013.12.001
    1. Sebastiani P., Thyagarajan B., Sun F., Schupf N., Newman A. B., Montano M., et al. (2017). Biomarker signatures of aging. Aging Cell 16 329–338. 10.1111/acel.12557
    1. Shah S. H., Kraus W. E., Newgard C. B. (2012). Metabolomic profiling for the identification of novel biomarkers and mechanisms related to common cardiovascular diseases: form and function. Circulation 126 1110–1120. 10.1161/CIRCULATIONAHA.111.060368
    1. Shamsi K. S., Pierce A., Ashton A. S., Halade D. G., Richardson A., Espinoza S. E. (2012). Proteomic screening of glycoproteins in human plasma for frailty biomarkers. J. Gerontol. A Biol. Sci. Med. Sci. 67 853–864. 10.1093/gerona/glr224
    1. Siscovick D. S., Fried L., Mittelmark M., Rutan G., Bild D., O’Leary D. H. (1997). Exercise intensity and subclinical cardiovascular disease in the elderly. The Cardiovascular Health Study. Am. J. Epidemiol. 145 977–986.
    1. Smit E., Winters-Stone K. M., Loprinzi P. D., Tang A. M., Crespo C. J. (2013). Lower nutritional status and higher food insufficiency in frail older US adults. Br. J. Nutr. 110 172–178. 10.1017/S000711451200459X
    1. Sumner L. W., Amberg A., Barrett D., Beale M. H., Beger R., Daykin C. A., et al. (2007). Proposed minimum reporting standards for chemical analysis Chemical Analysis Working Group (CAWG) Metabolomics Standards Initiative (MSI). Metabolomics 3 211–221. 10.1007/s11306-007-0082-2
    1. Taylor H. L., Jacobs D. R., Jr., Schucker B., Knudsen J., Leon A. S., Debacker G., et al. (1978). A questionnaire for the assessment of leisure time physical activities. J. Chronic Dis. 31 741–755.
    1. van der Kloet F. M., Bobeldijk I., Verheij E. R., Jellema R. H. (2009). Analytical error reduction using single point calibration for accurate and precise metabolomic phenotyping. J. Proteome Res. 8 5132–5141. 10.1021/pr900499r
    1. Walston J., Fried L. P. (1999). Frailty and the older man. Med. Clin. North Am. 83 1173–1194.
    1. Wang-Sattler R., Yu Z., Herder C., Messias A. C., Floegel A., He Y., et al. (2012). Novel biomarkers for pre-diabetes identified by metabolomics. Mol. Syst. Biol. 8:615. 10.1038/msb.2012.43
    1. Wishart D. S., Tzur D., Knox C., Eisner R., Guo A. C., Young N., et al. (2007). HMDB: the human metabolome database. Nucleic Acids Res. 35 D521–D526. 10.1093/nar/gkl923
    1. Xia J., Broadhurst D. I., Wilson M., Wishart D. S. (2013). Translational biomarker discovery in clinical metabolomics: an introductory tutorial. Metabolomics 9 280–299. 10.1007/s11306-012-0482-9
    1. Xue Q. L., Bandeen-Roche K., Varadhan R., Zhou J., Fried L. P. (2008). Initial manifestations of frailty criteria and the development of frailty phenotype in the Women’s Health and Aging Study II. J. Gerontol. A Biol. Sci. Med. Sci. 63 984–990.

Source: PubMed

스폰서 및 공동 작업자

건강 상태

약물 개입

3
구독하다