Article

Allostatic Load and Frailty in the Women's Health and Aging Studies

Johns Hopkins University School of Nursing, Baltimore, MD 21205, USA.
Biological Research for Nursing (Impact Factor: 1.34). 10/2008; 10(3):248-56. DOI: 10.1177/1099800408323452
Source: PubMed

ABSTRACT Frailty involves decrements in many physiologic systems, is prevalent in older ages, and is characterized by increased vulnerability to disability and mortality. It is yet unclear how this geriatric syndrome relates to a preclinical cumulative marker of multisystem dysregulation. The purpose of this study was to evaluate whether allostatic load (AL) was associated with the geriatric syndrome of frailty in older community-dwelling women.
We examined the cross-sectional relationship between AL and a validated measure of frailty in the baseline examination of two complementary population-based cohort studies, the Women's Health and Aging studies (WHAS) I and II. This sample of 728 women had an age range of 70-79. We used ordinal logistic regression to estimate the relationship between AL and frailty controlling for covariates.
About 10% of women were frail and 46% were prefrail. AL ranged from 0 to 8 with 91% of participants scoring between 0 and 4. Regression models showed that a unit increase in the AL score was associated with increasing levels of frailty (OR = 1.16, 95% CI = 1.04-1.28) controlling for race, age, education, smoking status, and comorbidities.
This study suggests that frailty is associated with AL. The observed relationship provides some support for the hypothesis that accumulation of physiological dysregulation may be related to the loss of reserve characterized by frailty.

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Available from: Sarah L Szanton, Dec 23, 2013
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    • "Under this scenario, aging is largely a system-level property caused by regulatory breakdown, not any lone biological mechanism such as an up-regulated gene or oxidative stress (Cohen et al., 2012). Multiple studies have shown associations between summary indices of allostatic load and aging outcomes (Crimmins et al., 2003; Glei et al., 2007; Seeman et al., 2001; Szanton et al., 2009). A small number of studies have applied sophisticated statistical approaches to measurement of the relationships among biomarkers in the context of dysregulation, generally with confirmatory but complex results (Arbeev et al., 2011; Gruenewald Contents lists available at SciVerse ScienceDirect "
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    ABSTRACT: Previous studies have identified many biomarkers that are associated with aging and related outcomes, but the relevance of these markers for underlying processes and their relationship to hypothesized systemic dysregulation is not clear. We address this gap by presenting a novel method for measuring dysregulation via the joint distribution of multiple biomarkers and assessing associations of dysregulation with age and mortality. Using longitudinal data from the Women's Health and Aging Study, we selected a 14-marker subset from 63 blood measures: those that diverged from the baseline population mean with age. For the 14 markers and all combinatorial sub-subsets we calculated a multivariate distance called the Mahalanobis distance (MHBD)(1) for all observations, indicating how "strange" each individual's biomarker profile was relative to the baseline population mean. In most models, MHBD correlated positively with age, MHBD increased within individuals over time, and higher MHBD predicted higher risk of subsequent mortality. Predictive power increased as more variables were incorporated into the calculation of MHBD. Biomarkers from multiple systems were implicated. These results support hypotheses of simultaneous dysregulation in multiple systems and confirm the need for longitudinal, multivariate approaches to understanding biomarkers in aging.
    Mechanisms of ageing and development 01/2013; 134(3). DOI:10.1016/j.mad.2013.01.004 · 3.51 Impact Factor
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    • "We have considered the relationship between frailty and biomarkers of a limited range of systems/processes, with each marker considered in isolation. Other systems are also implicated in frailty with dysregulation across multiple systems a key mechanism (Fried et al., 2009; Gruenewald et al., 2009; Sanders et al., 2011; Szanton et al., 2009). Complex interactions occur between markers and between systems (De Martinis et al., 2006; Derhovanessian et al., 2009; Fulop et al., 2010; Hummel and Abecassis, 2002; Kregel and Zhang, 2007; Larbi et al., 2007; Pawelec et al., 2009; Prosch et al., 1999; von Zglinicki and Martin-Ruiz, 2005). "
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    ABSTRACT: Age-related frailty is an increasing societal challenge with growing emphasis on identifying its underlying pathophysiology and prospects for intervention. We report findings from the first comprehensive study of frailty and biomarkers of inflammation, immunosenescence and cellular ageing in the very old. Using cross-sectional data from the Newcastle 85+ Study (n=845, aged 85), frailty was operationalized by the Fried and Rockwood models and biomarker associations explored using regression analysis. We confirmed the importance of inflammatory markers (IL-6, TNF-alpha, CRP, neutrophils) in frailty in the very old, previously established only in younger-old populations. Limited evidence was found for immunosenescence in frailty; although total lymphocyte count was inversely related, no association was found with the immune risk profile and the inverse associations observed with memory/naïve CD8 T and B cell ratios were in the opposite direction to that expected. We found no association with frailty in the very old for CMV sero-positivity, telomere length, markers of oxidative stress or DNA damage and repair. The Fried and Rockwood frailty models measure different albeit overlapping concepts yet biomarker associations were generally consistent between models. Difficulties in operationalizing the Fried model, due to high levels of co-morbidity, limit its utility in the very old.
    Mechanisms of ageing and development 06/2012; 133(6):456-66. DOI:10.1016/j.mad.2012.05.005 · 3.51 Impact Factor
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    • "In particular, Fried et al.'s frailty phenotype [21,22] has achieved international reputation. The method has been extensively validated in the research literature [23-25]; however, a criticism is that it is not readily applicable in routine primary care practice. "
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    ABSTRACT: A frailty paradigm would be useful in primary care to identify older people at risk, but appropriate metrics at that level are lacking. We created and validated a simple instrument for frailty screening in Europeans aged ≥50. Our study is based on the first wave of the Survey of Health, Ageing and Retirement in Europe (SHARE, http://www.share-project.org), a large population-based survey conducted in 2004-2005 in twelve European countries. Subjects: SHARE Wave 1 respondents (17,304 females and 13,811 males). Measures: five SHARE variables approximating Fried's frailty definition. Analyses (for each gender): 1) estimation of a discreet factor (DFactor) model based on the frailty variables using LatentGOLD. A single DFactor with three ordered levels or latent classes (i.e. non-frail, pre-frail and frail) was modelled; 2) the latent classes were characterised against a biopsychosocial range of Wave 1 variables; 3) the prospective mortality risk (unadjusted and age-adjusted) for each frailty class was established on those subjects with known mortality status at Wave 2 (2007-2008) (11,384 females and 9,163 males); 4) two web-based calculators were created for easy retrieval of a subject's frailty class given any five measurements. Females: the DFactor model included 15,578 cases (standard R2 = 0.61). All five frailty indicators discriminated well (p < 0.001) between the three classes: non-frail (N = 10,420; 66.9%), pre-frail (N = 4,025; 25.8%), and frail (N = 1,133; 7.3%). Relative to the non-frail class, the age-adjusted Odds Ratio (with 95% Confidence Interval) for mortality at Wave 2 was 2.1 (1.4 - 3.0) in the pre-frail and 4.8 (3.1 - 7.4) in the frail. Males: 12,783 cases (standard R2 = 0.61, all frailty indicators had p < 0.001): non-frail (N = 10,517; 82.3%), pre-frail (N = 1,871; 14.6%), and frail (N = 395; 3.1%); age-adjusted OR (95% CI) for mortality: 3.0 (2.3 - 4.0) in the pre-frail, 6.9 (4.7 - 10.2) in the frail. The SHARE Frailty Instrument has sufficient construct and predictive validity, and is readily and freely accessible via web calculators. To our knowledge, SHARE-FI represents the first European research effort towards a common frailty language at the community level.
    BMC Geriatrics 08/2010; 10:57. DOI:10.1186/1471-2318-10-57 · 2.00 Impact Factor
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