Frailty and the role of inflammation, immunosenescence and cellular ageing in the very old: Cross-sectional findings from the Newcastle 85+ Study
Highlights
► We confirm the importance of inflammation in frailty in a very old population. ► Limited evidence was found for role of immunosenescence in frailty in the very old. ► No frailty association was found for CMV or cellular ageing markers in the very old. ► Biomarker associations were generally consistent between Fried and Rockwood models. ► Difficulties in operationalizing the Fried model limit its utility in the very old.
Introduction
Age-related frailty is an increasing challenge for societies worldwide, with growing emphasis on identifying its underlying pathophysiology and prospects for intervention. It is generally agreed that frailty is characterised by increased vulnerability to stress due to decline in homeostatic reserve secondary to dysregulation in multiple inter-related systems (Bortz, 2002, Fried et al., 2001, Lipsitz, 2002, Walston et al., 2006). This vulnerability results in an increased risk of adverse health outcomes including disability, hospitalisation, institutionalisation and death (Bandeen-Roche et al., 2006, Fried et al., 2001, Kulminski et al., 2007, Mitnitski et al., 2005, Rockwood et al., 2011, Romero-Ortuno et al., 2011). Despite concerted efforts (Bergman et al., 2007, Rodríguez-Mañas et al., 2012, Walston et al., 2006), there is as yet no universally accepted definition of frailty or agreed method for its diagnosis. Existing approaches differ widely in how frailty is conceptualised and defined (Abellan van Kan et al., 2008, Hogan et al., 2003, Sternberg et al., 2011), with the approaches of Fried and Rockwood currently leading the field. Fried views frailty as a clinical syndrome – a cluster of specific symptoms and signs including weight loss, exhaustion, low physical activity, muscle weakness and slow walking speed (Fried et al., 2001). Rockwood considers frailty as a cumulative index of health deficits; it is regarded as a clinical state variable and proposed to act as an indicator of biological rather than chronological age (Rockwood and Mitnitski, 2007, Searle et al., 2008). The individual deficits can include diseases, symptoms and signs, function tests and laboratory tests. Provided enough deficits are included in the index, their exact nature seems unimportant (Rockwood et al., 2006).
Knowledge of the pathophysiological mechanisms underlying frailty remains limited. It is unclear to what extent frailty has its own specific causative mechanisms, as distinct from the more general deterioration of cellular and physiological functions which comprise the ageing process, which might be amenable to targeted interventions. Whilst Fried frailty has been associated with dysregulation in several systems, (Bandeen-Roche et al., 2009, Barzilay et al., 2007, Blaum et al., 2009, Cappola et al., 2009, Leng et al., 2007, Shardell et al., 2009, Travison et al., 2011, Walston et al., 2002), there are few investigations of underlying mechanisms for the Rockwood frailty index. Studies exploring the role of cellular and molecular mechanisms of ageing are scarce for either model, and biomarker investigations using both frailty models within the same sample are rare.
People aged 85 years and over are now the most rapidly expanding age group in the population with current numbers predicted to double over the next 20 years (United Nations, 2002). Biomarker associations with health outcomes may differ between very old and younger old populations; for example telomere length predicts mortality in younger old (Cawthon et al., 2003) but not in very old populations (Bischoff et al., 2006, Houben et al., 2011, Martin-Ruiz et al., 2005). The applicability of the Rockwood and Fried frailty measures in very old people has not been addressed.
The Newcastle 85+ Study, a population-based study of a large representative cohort of 85 year olds, collected comprehensive measures of health across multiple biological, clinical and psychosocial domains (Collerton et al., 2009). We examine the applicability and inter-relations of the Fried and Rockwood frailty measures within this cohort, and their associations with a range of biomarkers of inflammation, immunosenescence and cellular ageing.
Section snippets
Study population
The methodology for the Newcastle 85+ Study has been reported (Collerton et al., 2007, Collerton et al., 2009, Davies et al., 2010). In brief, members of the 1921 birth cohort living in Newcastle or North Tyneside (North-East England) were recruited at around age 85 through general practice patient lists. People living in institutions and those with cognitive impairment were included. At baseline the Newcastle 85+ cohort was socio-demographically representative of the local population and of
Sample selection
Details of sample selection are shown in Supplementary Fig. A1 (Appendix A). Data from both health assessment and review of general practice records were available for 845 participants (Collerton et al., 2009). Of these, RFI could be calculated for 811 participants (96.0%); the remaining 34 had more than the allowed 8 missing values. FFS could be assigned for 552 participants (65.3%); applying the Fried exclusion criteria resulted in loss of 252 participants and an additional 41 could not be
Overview of findings
This is the most comprehensive assessment to date of biomarker associations with objectively determined frailty in a very old population. We confirmed the importance of inflammatory markers in frailty in a very old population, previously established only in the younger old. CRP, IL-6, TNF-alpha and neutrophil count showed positive associations with both frailty measures and albumin inverse associations. Limited evidence was found to support the role of immunosenescence. Although total
Funding
This work was supported by the UK Medical Research Council and the Biotechnology and UK Biological Sciences Research Council (G0500997), and the UK Medical Research Council and Unilever Discover Colworth (G0601333). The funders had no role in the study design; in the collection, analysis and interpretation of data; in the writing of the paper; and in the decision to submit the paper for publication.
Conflict of interest statement
None declared.
Acknowledgements
Thanks are especially due to the 85 year olds of Newcastle and North Tyneside for the generous donation of their time and personal information to make the study possible. In addition we thank: the research nurses (Brenda Balderson, Sally Barker, Julie Burrows, June Edwards, Julie Ferguson, Gill Hedley, Joan Hughes, Judith Hunt and Victoria Raynor); biomarker technicians (Sam Jameson, and Anna Tang); data manager (Pauline Potts); project statistician (Andrew Kingston); project secretary (Lucy
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