Skip to main content
PMC home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2017 May 1.
Published in final edited form as: Int J Geriatr Psychiatry. 2015 Aug 27;31(5):466–474. doi: 10.1002/gps.4351

Physical frailty in late-life depression is associated with deficits in speed-dependent executive functions

Guy G Potter 1, Douglas R McQuoid 1, Heather E Whitson 2,3, David C Steffens 4
PMCID: PMC4769698  NIHMSID: NIHMS748284  PMID: 26313370

Abstract

Objective

To examine the association between physical frailty and neurocognitive performance in late-life depression (LLD).

Methods

Cross-sectional design using baseline data from a treatment study of late-life depression. Individuals aged 60 and older diagnosed with Major Depressive Disorder at time of assessment (N = 173). All participants received clinical assessment of depression and completed neuropsychological testing during a depressive episode. Physical frailty was assessed using an adaptation of the FRAIL scale. Neuropsychological domains were derived from a factor analysis that yielded three factors: 1) Speeded Executive and Fluency, Episodic Memory, and Working Memory. Associations were examined with bivariate tests and multivariate models.

Results

Depressed individuals with a FRAIL score >1 had worse performance than nonfrail depressed across all three factors; however, Speeded Executive and Fluency was the only factor that remained significant after controlling for depression symptom severity and demographic characteristics.

Conclusions

Although physical frailty is associated with broad neurocognitive deficits in LLD, it is most robustly associated with deficits in speeded executive functions and verbal fluency. Causal inferences are limited by the cross-sectional design, and future research would benefit from a comparison group of nondepressed older adults with similar levels of frailty. Research is needed to understand the mechanisms underlying associations among depression symptoms, physical frailty, and executive dysfunction, and how they are related to the cognitive and symptomatic course of LLD.

Keywords: geriatric depression, late-life depression, frailty, executive dysfunction, neuropsychological

INTRODUCTION

Major Depressive Disorder (MDD) is estimated to be the 3rd highest contributor to global disease burden, and the highest among middle- and high-income countries (World Health Organization, 2008). When depression occurs roughly after age 60, described as late-life depression (LLD), it is often accompanied by neurocognitive deficits, and this co-occurrence is associated with worse treatment response (Potter et al., 2004, Story et al., 2008, Alexopoulos et al., 2000), greater disability (Potter et al., 2012a, Kiosses et al., 2001), and increased risk of dementia (Potter et al., 2012b). Neurocognitive deficits in LLD also appear more intractable to treatment than other depression features, as remitted individuals demonstrate persistently worse neurocognitive performance compared to nondepressed individuals (Bhalla et al., 2006, Lee et al., 2007, Kohler et al., 2010). Thus, a critical strategy to reduce the health burden of LLD involves identifying factors associated with its neurocognitive deficits.

One potential contributor to neurocognitive deficits in LLD is frailty. The clinical syndrome of frailty characterizes older individuals at increased risk for adverse health outcomes including disability and morbidity. Frailty is believed to occur from declines across multiple physiological systems that fail to respond adequately to stressors (Abellan Van Kan et al., 2008a). There is currently no consensus definition of frailty. Leading definitions characterize frailty as a physical phenotype for underlying biological decline (Fried et al., 2001), an outcome of accumulated deficits, (Rockwood and Mitnitski, 2007), or a loss of capability across functional domains (Strawbridge et al., 1998), Despite a lack of consensus, these definitions of frailty are correlated and predict similar outcomes of disability and mortality (Cigolle et al., 2009, Rockwood et al., 2007, Malmstrom et al., 2014).

Research has shown that frailty and depression often co-occur. Increasing odds of frailty have been associated with increased depression symptoms in older adults (St John et al., 2013), and frailty has been associated with incidence and persistence of depression symptoms (Makizako et al., 2014, Feng et al., 2014). It has been argued that the combination of high depression symptoms and cerebrovascular burden is a prodrome for frailty (Paulson and Lichtenberg, 2013), while a recent review found empirical support for a bidirectional association between depression and frailty in later life (Mezuk et al., 2012). Nonetheless, frailty and LLD appear to be distinct syndromes despite overlapping elements (Mezuk et al., 2013).

There is growing attention in the research literature to the association between frailty and cognitive deficits, as many of the age-associated processes that lead to frailty also contribute to unhealthy brain aging. Frailty has been associated with cognitive decline using both biological and deficit accumulation models (Mitnitski et al., 2011), including outcomes of mild cognitive impairment and dementia (Boyle et al., 2010, Song et al., 2011, Solfrizzi et al., 2013). While there are consistent associations between frailty and lower global cognitive function with screening-type measures (Avila-Funes et al., 2009, Rockwood et al., 2007), studies using more extensive neurocognitive testing have tended to find that frailty is most strongly associated with information processing speed and speed-depended executive functions (e.g., timed outcomes), and is less frequently associated with episodic memory, working memory, and visuospatial skills (Boyle et al., 2010, Langlois et al., 2012, Patrick et al., 2002, Rolfson et al., 2013, Lin et al., 2014). The associations between frailty and cognition have led to the inclusion of cognitive impairment in some definitions of frailty (Sternberg et al., 2011); however, we share the position of Robertson and colleagues (Robertson et al., 2013) that while physical frailty and age-related cognitive impairment have bidirectional influences and shared risk factors, they are distinct and nonoverlapping constructs.

There is limited research examining frailty, depression, and neurocognition together, which is an important gap in the research literature because of the prevalence of neurocognitive deficits in LLD, and because these deficits lead to adverse morbidity and disability outcomes similar to frailty. While broad neurocognitive deficits have been found in LLD, several studies suggest that performances on neuropsychological measures of processing speed mediate the differences between depressed and nondepressed older adults on other neuropsychological domains (Butters et al., 2004, Nebes et al., 2000, Sheline et al., 2006). It is also potentially important to consider age of first depression onset, based on findings from some studies that neurocognitive deficits may be greater among individuals with first onset of MDD occurring later in life (Mackin et al., 2014, Sachs-Ericsson et al., 2013). To the best of our knowledge, no prior studies have examined the association between frailty and neurocognitive function in LLD. A neurocognitive phenotype for frailty in LLD would be a useful marker for detecting individuals at greater risk of adverse outcomes of depression, and better understanding the links between frailty, cognitive impairment, and LLD may lead to better approaches to preventing and treating all three conditions.

The objective of the current study was to identify whether there is a unique association of physical frailty to neurocognitive performance in active LLD, and to better determine the neurocognitive domains that are associated with it. Our hypothesis was that actively depressed individuals who exhibit characteristics of physical frailty would demonstrate worse neurocognitive performance overall compared to nonfrail individuals with LLD, and that the strongest association would exist in the domain of speed-dependent executive functions. The test of our hypothesis assumes the association between frailty and neurocognitive performance will remain significant after controlling for related clinical and demographic variables.

Methods

Participants

All participants were enrolled in the Neurocognitive Outcomes of Depression in the Elderly study (NCODE; Steffens et al., 2004). All individuals were 60 years or older with a diagnosis of major depressive disorder at the time of enrollment. Diagnoses were assigned by study-trained psychiatrists using standardized assessment instruments and diagnostic algorithms. Assessment included the Duke Depression Evaluation Schedule (DDES; Landerman et al., 1989), which includes psychiatric assessments, as well as self report of functional performance and medical conditions used in the current operational definition of frailty. Exclusion criteria at the time of enrollment included: 1) another major psychiatric illness; 2) alcohol or drug abuse or dependence; and 3) primary neurologic illness, including clinical stroke and dementia. Individuals were screened for dementia at time of enrollment based on an established protocol that included review of a comprehensive clinical evaluation, consultation with referring physicians, and cognitive screening with the Mini-Mental State Examination (MMSE; Folstein et al., 1975). Individuals whose baseline MMSE scores remained below 25 after an acute eight-week phase of treatment were not followed longitudinally.

Research with participants was conducted in accordance with the Helsinki Declaration (World Medical Association, 2013), and this study was approved by the Duke University Institutional Review Board.

Frailty Assessment

Our frailty variable was adapted from the FRAIL scale (Abellan Van Kan et al., 2008b, Morley et al., 2012). The FRAIL scale was developed by the Geriatric Advisory Panel of the International Academy of Nutrition and Aging for use as a case-finding tool, and as a measure that could be readily used with clinical patients. It combines elements of biological, deficit accumulation, and functional domain definitions of frailty (Malmstrom et al., 2014, Abellan Van Kan et al., 2008b), and has demonstrated construct and predictive validity (Woo et al., 2012, Morley et al., 2012, Ravindrarajah et al., 2013). The original FRAIL scale includes domains of: 1) Fatigue (“Are you fatigued?”), 2) Resistance (Cannot walk up 1 flight of stairs?”), 3) Aerobic (“Cannot walk 1 block?”), 4) Illnesses (“Do you have more than 5 illnesses?” [as read from list]), and 5) Loss of weight: (“Have you lost more than 5% of your weight in the past 6 months?”). The presence or absence of each item is defined dichotomously, and the total score is the sum of items present. For the purposes of the current study, we used analogs of the FRAIL items from our clinical assessment data. Fatigue was adapted from CES-D items 7 and 20, which are the same items used in Fried et al.’s biological definition (“I felt that everything I did was an effort”; “I could not ‘get’ going”). Resistance was based on DDES self-reported ability to walk up and down a flight of stairs without resting. Aerobic was based on DDES self-reported ability to walk a quarter of a mile, or approximately 3 city blocks. Illnesses was based on self-reported endorsement of 5 out of 10 illnesses assessed by the DDES that are comparable to the FRAIL scale (asthma, diabetes, heart trouble, hypertension, arthritis/rheumatism, cancer, emphysema, ulcers, hardening of the arteries, other chronic physical health problem). Loss of weight was defined by CES-D item 2 pertaining to appetite loss. Because we had no reliable measure of weight loss in the current study, we used appetite loss as a proxy indicator of nutritional compromise or cachexia, similar to the approach taken in the SHARE study (Santos-Eggimann et al., 2009).

Neuropsychological Assessment

Participants completed a neuropsychological assessment battery at the time of study enrollment. Participants were tested by a trained psychometrician using standardized administration and scoring procedures for each test. There were 13 tests included: 1) CERAD Word List Learning (Welsh et al., 1994), 2) CERAD Word List Delayed Recall (total correct), 3) Logical Memory I and 4) Logical Memory II (Wechsler, 1987), 5) Benton Visual Retention Test (Sivan, 1992), 6) Symbol-Digit Modalities Test (Smith, 1982), 7) Part A and 8) Part B of the Trail Making Test (Reitan, 1992), 9) Animal Naming, 10) Controlled Oral Word Association (Benton and Hamsher, 1988), 11) Forward, and 12) Backward subtests of Digit Span (Wechsler, 1997), and 13) Ascending Digit Span (Sair et al., 2006). Scores for each measure were based on total correct responses, with the exception of Trail Making, which was time to complete (reverse scored).

We used factor analysis to reduce the 13 neuropsychological measures to a smaller set of variables. Common factor analysis was performed with SAS Proc Factor (SAS Institute, 2012), using principal component extraction, varimax rotation to produce orthogonal factors, and factor scores from SAS Proc Score. To optimize factor stability, we used the full cohort of NCODE depressed participants (n = 327). The additional participants were those who did not have all frailty-related data required for the current study, but who otherwise met the same study entry criteria. The individuals only included for factor analysis (n = 154) differed from the current study sample with respect to a higher proportion of women in the total cohort compared to the current sample, χ2 (1) = 4.40, p = 0.04. A variable for gender (“sex”) was included subsequent regression models as a covariate. Eigenvalues greater than 1.0 and a scree plot were used to determine the number of factors. To facilitate interpretation, we weighted our consideration toward items with factor loadings ≥0.50 (Table 1): Factor 1 was interpreted as reflecting Speeded Executive and Fluency (SEF), Factor 2 was interpreted as Episodic Memory (EM), Factor 3 was interpreted as reflecting Working Memory (WM).

Table 1.

Results of factor analysis for neurocognitive measures

Factor
SEF EM WM
SDMT 0.7640 0.3108 0.2362
TMT-A (reversed) 0.7395 0.3510 0.2122
TMT-B (reversed) 0.7326 0.1974 0.0550
Animal Naming 0.7092 0.2653 0.1492
COWA 0.7053 0.0237 0.2476
BVRT 0.5891 0.4694 0.2046
LM I 0.2160 0.8978 0.0856
LM II 0.2318 0.8932 0.1128
WLM-D 0.2587 0.6407 0.2883
WLM-I 0.2718 0.5905 0.3828
DS-F 0.1226 0.1535 0.8313
DS-B 0.2646 0.1958 0.7851
DS-A 0.4759 0.2348 0.4759
Eigenvalue 6.2947 1.2569 1.0647
Variance explained (%) 27.44 23.36 15.48
Open in a new tab

Note. Factor loadings >0.50 indicated in bold. SEF = Speeded Executive and Fluency. EM = Episodic Memory. WM = Working Memory. TMT = Trail Making Test (reverse scored for factor analysis). SDMT = Symbol-Digit Modalities Test. COWA = Controlled Oral Word Association. BVRT = Benton Visual Retention Test. WLM-I = CERAD Word List Memory-Immediate. WLM-D = CERAD Word List Memory –Delayed. LM = Logical Memory. DS-F = Digit Span Forward. DS-B = Digit Span Backward. DS-A = Digit Span Ascending.

Statistical Methods

Statistical analysis included examination of descriptive statistics, and testing of group differences using chi-square tests, Student’s t-tests, and Satterthwaite’s approximate test. We planned multivariate regression models to formally test the relationship between frailty and neurocognition while controlling for demographics, depression severity, and onset of depression. Age of depression onset was obtained from the DDES as a self-report of age of first depression onset. As there may be some variability recalling a specific chronological age of first depression onset (Blazer, 2003), we dichotomized the variable with a cutpoint of age 60. We estimated one model for each neurocognitive domain score, in which the neurocognitive domain score was the dependent variable, and a dichotomous variable for frailty was entered as an independent variable in the model with the covariates. Prior research on the FRAIL in nondepressed samples defined prefrail status as FRAIL = 1–2 (Morley et al., 2012, Woo et al., 2012), but this may overdiagnose frailty in LLD due to the high prevalence of Fatigue (82%) in actively depressed older adults. To account for this, we this examined the distribution of frailty scores in the sample, and designated the nonfrail group to include adapted FRAIL scores of 0–1, and the frail group to subsume prefrailty and include adapted Frail scores of 2–5. A priori covariates included age, years of education, sex, race (Caucasian/non-Caucasian), age of depression onset, and depression severity. Depression severity was based on the sum of the CES-D minus items 2, 7, and 20 that were used in the operational definition of frailty. A p-value < 0.05 indicated statistical significance.

RESULTS

Frail individuals were older, had fewer years of education, were more depressed, were more often women, and were more often non-Caucasian (Table 2). Age of first depression onset did not differ between groups. Bivariate tests for group differences between frailty and neurocognitive performance indicated that individuals with frailty performed worse on all three neurocognitive factors, and on 10 out of 13 individual neurocognitive tests (Table 2).

Table 2.

Demographic and clinical characteristics associated with frailty

Nonfrail (n = 89)
M (SD)		 Frail (n = 84)
M (SD)		 Statistic
t (df), or χ2 (df)
Age 67.47 (5.75) 69.07 (6.85) t (171) = 1.67
Years education 14.75 (2.21) 13.69 (2.85) t (156.63)= 2.73 b
% female 47.19% 69.05% χ2 (1 ) = 8.46b
% Caucasian 87.64% 75.00% χ2 (1) = 4.58a
Age of first depression onset (% > 60) 20.22% 26.19% χ2 (1) = 0.87ns
CES-D 26.75 (11.51) 33.85 (8.47) t(161.55)=4.63b
CES-D, adj. 22.93 (10.24) 27.35 (7.43) t (160.57 )=3.26b
MMSE 28.29 (1.79) 27.58 (1.96) t (171) =2.48a
Modified FRAIL items
Fatigue 71.91% (64) 96.43% (81) χ2 (1) = 19.15b
Resistance 3.37% (3) 54.76% (46) χ2 (1) = 56.22b
Aerobic 1.12% (1) 26.19% (22) χ2 (1) = 23.56b
Illness 0.00% (0) 15.48% (13) χ2 (1) = 14.89b
Loss 3.37% (3) 65.48% (55) χ2 (1) = 74.79b
Neurocognitive factor scores
SEF 0.29 (0.88) −0.28(0.97) t (171) = 4.04b
EM 0.15 (0.88) 0.10 (0.92) t (171) = 0.37 ns
WM 0.10 (0.92) −0.14 (1.03) t (171) =1.58 ns
Neurocognitive raw scores (highest factor loading)
TMT-A (SEF) 38.91 (18.59) 47.99 (23.29) t (158.71) = 2.82b
TMT-B (SEF) 99.54 (55.8) 141.9 (74.6) t (153.46) = 4.20b
SDMT (SEF) 41.38 (10.12) 34.70 (11.26) t (171) = 4.11b
Animal Naming (SEF) 18.17 (4.52) 15.82 (4.44) t (171) = 3.44
COWA (SEF) 36.71 (11.64) 32.02 (12.21) t (171) = 2.58a
BVRT (SEF) 6.28 (1.67) 5.54 (2.07) t (159.47) = 2.59a
WLM-I (EM) 19.60 (4.16) 18.71 (3.97) t (171) = 1.42 ns
WLM-D (EM) 6.82 (1.96) 6.33 (1.86) t (171)= 1.68 ns
LM I (EM) 25.70 (7.02) 24.05 (7.57) t (171) = 1.49 ns
LM II (EM) 21.97 (7.82) 20.12 (8.44) t (171) = 1.49 ns
DS-F (WM) 8.94 (2.24) 7.83 (2.41) t (171) = 3.14b
DS-B (WM) 7.10 (2.14) 6.65 (2.27) t (171) = 1.33 ns
DS-A (WM) 8.80 (2.40) 8.02 (2.68) t (171) = 2.00a
Open in a new tab
a

p < .05

b

p < .01.

ns

= not significant.

Note. CES-D = Center for Epidemiologic Studies Depression Scale. CES-D adj.= adjusted sum of CES-D items, excluding those that are used to define Frailty (items 2, 7, 20). TMT = Trail Making Test (higher values reflect slower performance). SDMT = Symbol-Digit Modalities Test. COWA = Controlled Oral Word Association. WLM-I = CERAD Word List Memory-Immediate. WLM-D = CERAD Word List Memory-Delayed. LM = Logical Memory. DS-F = Digit Span Forward. DS-B = Digit Span Backward. DS-A = Digit Span Ascending.

The results of the multivariate regression models found that SEF was the only neurocognitive factor significantly associated with frail status when controlling for covariates. Because age, race, gender, and years of education were also associated with frail status and may serve as possible moderators, we ran separate models testing for interactions between these variables and frailty. None of the interaction terms were statistically significant.

DISCUSSION

This study found that community-dwelling older adults with active MDD and physical frailty demonstrated worse neurocognitive performance than a comparison group of active MDD without frailty. Although the frail individuals with LLD had lower performance than the nonfrail LLD participants across all three neurocognitive factors assessed, the Speeded Executive and Fluency domain had the only unique association with frailty, based on multivariate models controlling for demographic characteristics and depression severity. Neurocognitive factors were associated with covariates of age (SEF, EM, WM), education (SEF, EM, WM), race (SEF, WM), sex (EM), and age of depression onset (EM), but not with overall depression severity, and there were no significant interactions between any of these covariates and frailty. To our knowledge, this is the first study to examine multi-domain neurocognitive performance and frailty in clinically diagnosed MDD, and includes the novel finding that frailty is associated with unique variance in speed-dependent executive function performance. Thus, even among older adults who have been robustly characterized as clinically depressed, a physical/somatic frailty scale is useful in distinguishing people with different neurocognitive profiles.

Our finding that frailty in LLD was more strongly associated with a speed-dependent executive factor relative to other neurocognitive factors is consistent with findings in non-clinical samples (Robertson et al., 2013), including research suggesting depression symptoms and frailty indicators (gait speed) may represent a phenotype of aging associated with cognitive slowing and/or executive dysfunction (Hajjar et al., 2009). It also highlights the importance of information processing speed to the profile of neurocognitive deficits in LLD (Nebes et al., 2000, Butters et al., 2004, Sheline et al., 2006). The inclusion of verbal fluency in the SEF factor is consistent with the clinical use of verbal fluency tests to assess aspects of executive function (Lezak et al., 2012), and with research showing that executive functions and cognitive speed contribute to verbal fluency performance in older adults (Shao et al., 2014). Much as motor slowing is a core feature of physical frailty (Fried et al., 2001), slowed thought may be core feature of cognitive frailty.

A major clinical implication of this study is that frailty in LLD is associated with broad neurocognitive deficits and psychomotor-executive function in particular, which is a marker for poor treatment response (Sheline et al., 2012, Potter et al., 2004, Alexopoulos et al., 2005) and functional limitations in LLD (Romero-Ortuno and Soraghan, 2014, Gobbens and Van Assen, 2012). Physical and cognitive impairments may challenge adherence, self-management, and access to care. Thus, persons with LLD who exhibit frailty characteristics may benefit from additional resources and services and merit more thorough cognitive evaluation. Frailty in LLD is also associated with increased mortality (Buchman et al., 2009); in fact, one study found that increased mortality associated with active depression was itself strongly influenced by frailty (Almeida et al., 2014). In light of research suggesting that frailty is associated with adverse outcomes in LLD, interventions to reduce or remediate frailty symptoms in older adults, such as addressing diet and physical inactivity, may be helpful in reducing incident depression as well as minimizing persistent cognitive and functional limitations among individuals who are experience an active episode of MDD. Based on our findings and those in the broader aging literature (Fried et al., 2001), clinical attention to frailty may be particularly important among women and African Americans. In particular, the FRAIL scale appears well-suited to clinical use (Morley et al., 2012).

The current study has limitations, but ones which may provide directions for future research. Because our study design was cross-sectional, we cannot identify any temporal associations among mood, frailty, and cognition, which limits insight into causal mechanisms. Causal mechanisms are important to address in future research, in light of evidence for predisposing and bidirectional influences of frailty in LLD (Mezuk et al., 2013). The mean age of our sample could be considered “young-old.” We do not know how our findings would generalize to an “old-old” sample, but given the increased prevalence of frailty and cognitive decline with age (Jacobs et al., 2011), we expect greater convergence of neurocognitive dysfunction and frailty in older LLD samples. Our study also did not include a nondepressed comparison sample. Though potentially challenging to identify, future research on frailty in LLD would benefit from a nondepressed comparison group with low depression symptoms and a representative range of frailty.

Another limitation to the current study may exist in our operational definition of frailty. To our knowledge, this is the first study to examine frailty and neurocognitive function in the context of active clinically diagnosed MDD, and the parent study was not designed to include specific measures of physical performance like grip strength or gait speed. We selected a scale based on clinical information and designed our operational definition of frailty to be as close to the FRAIL scale as possible using available data. As noted by King-Kallimanis and colleagues (King-Kallimanis et al., 2014), frailty definitions are frequently opportunistic, and they argue it is reasonable to assume overlap when commonalities exist in the definition. Given that different definitions of frailty have been shown to converge (Lohman et al., 2015, Malmstrom et al., 2014), we do not expect that our operational definition of frailty would produce results that are substantially different from alternative definitions. The clinical definition of frailty used in the current study limits our ability to draw conclusions with respect to specific theoretical models (e.g., biological vs. deficit accumulation) or common underlying mechanisms, such as cerebrovascular disease (Paulson and Lichtenberg, 2013). These are directions for future research.

We also recognize a potential confound to studying frailty in MDD when defining a criteria of the two conditions overlap. This overlap is inherent in many operational definitions of frailty, including the biological (CES-D items 7 and 20; Fried et al., 2001) and deficit accumulation models (mental/emotional problems; Rockwood and Mitnitski, 2007). Unlike most studies, our participants were actively depressed based on established clinical diagnostic criteria, so our question addressed whether frailty in the context of MDD was associated with greater neurocognitive deficits, above and beyond the effects of overall depression severity. Our results indicated that greater frailty in the context of active MDD was associated with worse SEF performance, and we believe this was not confounded by depression-frailty overlap. On the broader question of overlap between depression and frailty, we agree with the positon that depression and frailty are distinct conditions with overlapping components that suggest common underlying mechanisms and risk factors (Mezuk et al., 2013, Lohman et al., 2015). Further study of these mechanisms and risk factors is warranted.

Another possible limitation is that participants were enrolled in a naturalistic treatment study designed to optimize individual treatment response. Thus, as a group, individuals were treated with several different psychotropic medications and combinations thereof. In this respect, our sample is more similar to the general population of depressed older adults treated in their community than are samples from single-agent trials, but it does limit the ability to control for potential effects of type and quantity of medication, which may contribute to frailty (Lakey et al., 2012). We examined total number of medications and total number of antidepressants among our sample, and found no associations with neurocognitive performance. Thus, we believe it unlikely that medication differences would have a systematic effect on neurocognitive performance, which is consistent with previous studies (Siepmann et al., 2003, Podewils and Lyketsos, 2002).

CONCLUSION

In conclusion, the presence of frailty in LLD is uniquely associated with worse performance on a neurocognitive factor reflecting speeded executive function and verbal fluency. Additional research is needed to more fully understand the shared and distinct characteristics of frailty and LLD and how these are related to mechanisms underlying executive dysfunction.

Table 3.

Multivariate regression models of frailty status predicting neurocognitive factors, with covariates

Factors SEF EM WM
B η2 p B η2 p B η2 p
Frail −.33 0.02 0.02 0.08 0.00 0.57 −.13 0.00 0.39
Age −.03 0.03 <0.01 −.02 0.02 0.04 0.03 0.03 0.02
Sex 0.02 0.00 0.88 −.27 0.02 0.05 0.11 0.00 0.49
Race −.40 0.02 0.02 −.26 0.01 0.13 −0.52 0.04 <0.01
Onset −.00 0.00 0.70 −.01 0.03 0.02 −.01 0.01 0.17
Educ. 0.13 0.11 <0.01 0.08 0.05 <0.01 0.07 0.03 0.02
CES-D, adj. −0.00 0.00 0.98 −.01 0.01 <0.19 0.00 0.00 0.81
F 10.30 4.63 3.83
R2 0.30 0.16 0.14
Model p <0.01 <0.01 <0.01
Open in a new tab

Note. SEF = Speed Executive/Fluency. EM = Episodic Memory. WM = Working Memory. For Frailty, Frail = 1. For Sex, female = 1. For Race, Caucasian = 1. For Age of first depression onset, < 60 = 1. Educ. = Years of education. CES-D adj.= adjusted sum of CES-D items, excluding those that are used to define Frailty (items 2, 7, 20).

Key point.

Physical frailty is a unique contributor to executive dysfunction in late-life depression

Acknowledgements

This research was supported by the following grants from the National Institutes of Health: R01MH054846, P50MH060451, K23MH087741. Dr. Whitson is supported by R01AG043438, R24AG045050, and P30 AG028716.

Footnotes

The authors have no conflicting financial interests.

REFERENCES

  1. Abellan Van Kan G, Rolland Y, Bergman H, Morley JE, Kritchevsky SB, Vellas B. The I.A.N.A Task Force on frailty assessment of older people in clinical practice. J Nutr Health Aging. 2008a;12:29–37. doi: 10.1007/BF02982161. [DOI] [PubMed] [Google Scholar]
  2. Abellan Van Kan G, Rolland YM, Morley JE, Vellas B. Frailty: toward a clinical definition. J Am Med Dir Assoc. 2008b;9:71–72. doi: 10.1016/j.jamda.2007.11.005. [DOI] [PubMed] [Google Scholar]
  3. Alexopoulos GS, Katz IR, Bruce ML, Heo M, Ten Have T, Raue P, Bogner HR, Schulberg HC, Mulsant BH, Reynolds CF, 3rd, Group P. Remission in depressed geriatric primary care patients: a report from the PROSPECT study. Am J Psychiatry. 2005;162:718–724. doi: 10.1176/appi.ajp.162.4.718. [DOI] [PMC free article] [PubMed] [Google Scholar]
  4. Alexopoulos GS, Meyers BS, Young RC, Kalayam B, Kakuma T, Gabrielle M, Sirey JA, Hull J. Executive dysfunction and long-term outcomes of geriatric depression. Archives of General Psychiatry. 2000;57:285–290. doi: 10.1001/archpsyc.57.3.285. [DOI] [PubMed] [Google Scholar]
  5. Almeida OP, Hankey GJ, Yeap BB, Golledge J, Norman PE, Flicker L. Depression, Frailty, and All-Cause Mortality: A Cohort Study of Men Older than 75 Years. J Am Med Dir Assoc. 2014 doi: 10.1016/j.jamda.2014.10.023. [DOI] [PubMed] [Google Scholar]
  6. Avila-Funes JA, Amieva H, Barberger-Gateau P, Le Goff M, Raoux N, Ritchie K, Carriere I, Tavernier B, Tzourio C, Gutierrez-Robledo LM, Dartigues JF. Cognitive impairment improves the predictive validity of the phenotype of frailty for adverse health outcomes: the three-city study. J Am Geriatr Soc. 2009;57:453–461. doi: 10.1111/j.1532-5415.2008.02136.x. [DOI] [PubMed] [Google Scholar]
  7. Benton A, Hamsher K. Multilingual Aphasia Examination. Iowa City: University of Iowa Press; 1988. [Google Scholar]
  8. Bhalla RK, Butters MA, Mulsant BH, Begley AE, Zmuda MD, Schoderbek B, Pollock BG, Reynolds CF, 3rd, Becker JT. Persistence of neuropsychologic deficits in the remitted state of late-life depression. Am J Geriatr Psychiatry. 2006;14:419–427. doi: 10.1097/01.JGP.0000203130.45421.69. 14/5/419 [pii] [DOI] [PubMed] [Google Scholar]
  9. Blazer DG. Depression in late life: review and commentary. J Gerontol A Biol Sci Med Sci. 2003;58:249–265. doi: 10.1093/gerona/58.3.m249. [DOI] [PubMed] [Google Scholar]
  10. Boyle PA, Buchman AS, Wilson RS, Leurgans SE, Bennett DA. Physical frailty is associated with incident mild cognitive impairment in community-based older persons. J Am Geriatr Soc. 2010;58:248–255. doi: 10.1111/j.1532-5415.2009.02671.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  11. Buchman AS, Boyle PA, Leurgans SE, Evans DA, Bennett DA. Pulmonary function, muscle strength, and incident mobility disability in elders. Proc Am Thorac Soc. 2009;6:581–587. doi: 10.1513/pats.200905-030RM. [DOI] [PMC free article] [PubMed] [Google Scholar]
  12. Butters MA, Whyte EM, Nebes RD, Begley AE, Dew MA, Mulsant BH, Zmuda MD, Bhalla R, Meltzer CC, Pollock BG, Reynolds CF, Becker JT. The nature and determinants of neuropsychological functioning in late-life depression. Archives of General Psychiatry. 2004;61:587–598. doi: 10.1001/archpsyc.61.6.587. [DOI] [PubMed] [Google Scholar]
  13. Cigolle CT, Ofstedal MB, Tian Z, Blaum CS. Comparing models of frailty: the Health and Retirement Study. J Am Geriatr Soc. 2009;57:830–839. doi: 10.1111/j.1532-5415.2009.02225.x. [DOI] [PubMed] [Google Scholar]
  14. Feng L, Nyunt MS, Feng L, Yap KB, Ng TP. Frailty predicts new and persistent depressive symptoms among community-dwelling older adults: findings from Singapore longitudinal aging study. J Am Med Dir Assoc. 2014;15:76 e7–76 e12. doi: 10.1016/j.jamda.2013.10.001. [DOI] [PubMed] [Google Scholar]
  15. Folstein MF, Folstein SE, Mchugh PR. “Mini-mental state”. A practical method for grading the cognitive state of patients for the clinician. J Psychiatr Res. 1975;12:189–198. doi: 10.1016/0022-3956(75)90026-6. [DOI] [PubMed] [Google Scholar]
  16. Fried LP, Tangen CM, Walston J, Newman AB, Hirsch C, Gottdiener J, Seeman T, Tracy R, Kop WJ, Burke G, Mcburnie MA, Cardiovascular Health Study Collaborative Research G. Frailty in older adults: evidence for a phenotype. J Gerontol A Biol Sci Med Sci. 2001;56:M146–M156. doi: 10.1093/gerona/56.3.m146. [DOI] [PubMed] [Google Scholar]
  17. Gobbens RJ, Van Assen MA. Frailty and its prediction of disability and health care utilization: the added value of interviews and physical measures following a self-report questionnaire. Arch Gerontol Geriatr. 2012;55:369–379. doi: 10.1016/j.archger.2012.04.008. [DOI] [PubMed] [Google Scholar]
  18. Hajjar I, Yang F, Sorond F, Jones RN, Milberg W, Cupples LA, Lipsitz LA. A novel aging phenotype of slow gait, impaired executive function, and depressive symptoms: relationship to blood pressure and other cardiovascular risks. J Gerontol A Biol Sci Med Sci. 2009;64:994–1001. doi: 10.1093/gerona/glp075. [DOI] [PMC free article] [PubMed] [Google Scholar]
  19. Jacobs JM, Cohen A, Ein-Mor E, Maaravi Y, Stessman J. Frailty, Cognitive Impairment and Mortality among the Oldest Old. Journal of Nutrition Health & Aging. 2011;15:678–682. doi: 10.1007/s12603-011-0096-3. [DOI] [PubMed] [Google Scholar]
  20. King-Kallimanis BL, Kenny RA, Savva GM. Factor structure for the frailty syndrome was consistent across Europe. J Clin Epidemiol. 2014;67:1008–1015. doi: 10.1016/j.jclinepi.2014.05.002. [DOI] [PubMed] [Google Scholar]
  21. Kiosses DN, Klimstra S, Murphy C, Alexopoulos GS. Executive dysfunction and disability in elderly patients with major depression. American Journal of Geriatric Psychiatry. 2001;9:369–374. [PubMed] [Google Scholar]
  22. Kohler S, Thomas AJ, Barnett NA, O’brien JT. The pattern and course of cognitive impairment in late-life depression. Psychol Med. 2010;40:591–602. doi: 10.1017/S0033291709990833. S0033291709990833 [pii] [DOI] [PubMed] [Google Scholar]
  23. Lakey SL, Lacroix AZ, Gray SL, Borson S, Williams CD, Calhoun D, Goveas JS, Smoller JW, Ockene JK, Masaki KH, Coday M, Rosal MC, Woods NF. Antidepressant use, depressive symptoms, and incident frailty in women aged 65 and older from the Women’s Health Initiative Observational Study. J Am Geriatr Soc. 2012;60:854–861. doi: 10.1111/j.1532-5415.2012.03940.x. [DOI] [PMC free article] [PubMed] [Google Scholar]
  24. Landerman R, George LK, Campbell RT, Blazer DG. Alternative models of the stress buffering hypothesis. American Journal of Community Psychology. 1989;17:626–642. doi: 10.1007/BF00922639. [DOI] [PubMed] [Google Scholar]
  25. Langlois F, Vu TT, Kergoat MJ, Chasse K, Dupuis G, Bherer L. The multiple dimensions of frailty: physical capacity, cognition, and quality of life. Int Psychogeriatr. 2012;24:1429–1436. doi: 10.1017/S1041610212000634. [DOI] [PubMed] [Google Scholar]
  26. Lee JS, Potter GG, Wagner HR, Welsh-Bohmer KA, Steffens DC. Persistent mild cognitive impairment in geriatric depression. Int Psychogeriatr. 2007;19:125–135. doi: 10.1017/S1041610206003607. [DOI] [PubMed] [Google Scholar]
  27. Lezak MD, Howieson DB, Bigler ED, Tranel D. Neuropsychological Assessment. 5th. New York: Oxford Univeristy Press; 2012. [Google Scholar]
  28. Lin F, Roiland R, Chen DG, Qiu C. Linking cognition and frailty in middle and old age: metabolic syndrome matters. Int J Geriatr Psychiatry. 2014 doi: 10.1002/gps.4115. [DOI] [PMC free article] [PubMed] [Google Scholar]
  29. Lohman M, Dumenci L, Mezuk B. Depression and Frailty in Late Life: Evidence for a Common Vulnerability. J Gerontol B Psychol Sci Soc Sci. 2015 doi: 10.1093/geronb/gbu180. [DOI] [PMC free article] [PubMed] [Google Scholar]
  30. Mackin RS, Nelson JC, Delucchi KL, Raue PJ, Satre DD, Kiosses DN, Alexopoulos GS, Arean PA. Association of Age at Depression Onset with Cognitive Functioning in Individuals with Late-Life Depression and Executive Dysfunction. American Journal of Geriatric Psychiatry. 2014;22:1633–1641. doi: 10.1016/j.jagp.2014.02.006. [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Makizako H, Shimada H, Doi T, Yoshida D, Anan Y, Tsutsumimoto K, Uemura K, Liu-Ambrose T, Park H, Lee S, Suzuki T. Physical Frailty Predicts Incident Depressive Symptoms in Elderly People: Prospective Findings From the Obu Study of Health Promotion for the Elderly. J Am Med Dir Assoc. 2014 doi: 10.1016/j.jamda.2014.08.017. [DOI] [PubMed] [Google Scholar]
  32. Malmstrom TK, Miller DK, Morley JE. A comparison of four frailty models. J Am Geriatr Soc. 2014;62:721–726. doi: 10.1111/jgs.12735. [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Mezuk B, Edwards L, Lohman M, Choi M, Lapane K. Depression and frailty in later life: a synthetic review. Int J Geriatr Psychiatry. 2012;27:879–892. doi: 10.1002/gps.2807. [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Mezuk B, Lohman M, Dumenci L, Lapane KL. Are depression and frailty overlapping syndromes in mid- and late-life? A latent variable analysis. Am J Geriatr Psychiatry. 2013;21:560–569. doi: 10.1016/j.jagp.2012.12.019. [DOI] [PMC free article] [PubMed] [Google Scholar]
  35. Mitnitski A, Fallah N, Rockwood MR, Rockwood K. Transitions in cognitive status in relation to frailty in older adults: a comparison of three frailty measures. J Nutr Health Aging. 2011;15:863–867. doi: 10.1007/s12603-011-0066-9. [DOI] [PubMed] [Google Scholar]
  36. Morley JE, Malmstrom TK, Miller DK. A simple frailty questionnaire (FRAIL) predicts outcomes in middle aged African Americans. J Nutr Health Aging. 2012;16:601–608. doi: 10.1007/s12603-012-0084-2. [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Nebes RD, Butters MA, Mulsant BH, Pollock BG, Zmuda MD, Houck PR, Reynolds CF. Decreased working memory and processing speed mediate cognitive impairment in geriatric depression. Psychological Medicine. 2000;30:679–691. doi: 10.1017/s0033291799001968. [DOI] [PubMed] [Google Scholar]
  38. Patrick L, Gaskovski P, Rexroth D. Cumulative illness and neuropsychological decline in hospitalized geriatric patients. Clin Neuropsychol. 2002;16:145–156. doi: 10.1076/clin.16.2.145.13239. [DOI] [PubMed] [Google Scholar]
  39. Paulson D, Lichtenberg PA. Vascular depression: an early warning sign of frailty. Aging Ment Health. 2013;17:85–93. doi: 10.1080/13607863.2012.692767. [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Podewils LJ, Lyketsos CG. Tricyclic antidepressants and cognitive decline. Psychosomatics. 2002;43:31–35. doi: 10.1176/appi.psy.43.1.31. [DOI] [PubMed] [Google Scholar]
  41. Potter GG, Kittinger JD, Wagner HR, Steffens DC, Krishnan KR. Prefrontal neuropsychological predictors of treatment remission in late-life depression. Neuropsychopharmacology. 2004;29:2266–2271. doi: 10.1038/sj.npp.1300551. [DOI] [PubMed] [Google Scholar]
  42. Potter GG, Mcquoid DR, Payne ME, Taylor WD, Steffens DC. Association of attentional shift and reversal learning to functional deficits in geriatric depression. International Journal of Geriatric Psychiatry. 2012a;27:1172–1179. doi: 10.1002/gps.3764. [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Potter GG, Wagner HR, Burke JR, Plassman BL, Welsh-Bohmer KA, Steffens DC. Neuropsychological Predictors of Dementia in Late-Life Major Depressive Disorder. Am J Geriatr Psychiatry. 2012b doi: 10.1016/j.jagp.2012.12.009. [DOI] [PMC free article] [PubMed] [Google Scholar]
  44. Ravindrarajah R, Lee DM, Pye SR, Gielen E, Boonen S, Vanderschueren D, Pendleton N, Finn JD, Tajar A, O’connell MD, Rockwood K, Bartfai G, Casanueva FF, Forti G, Giwercman A, Han TS, Huhtaniemi IT, Kula K, Lean ME, Punab M, Wu FC, O’neill TW, European Male Aging Study G. The ability of three different models of frailty to predict all-cause mortality: results from the European Male Aging Study (EMAS) Arch Gerontol Geriatr. 2013;57:360–368. doi: 10.1016/j.archger.2013.06.010. [DOI] [PubMed] [Google Scholar]
  45. Reitan RM. Trail Making Test: Manual for Administration and Scoring. Tuscon, AZ: Reitan Neuropsychological Laboratory; 1992. [Google Scholar]
  46. Robertson DA, Savva GM, Kenny RA. Frailty and cognitive impairment--a review of the evidence and causal mechanisms. Ageing Res Rev. 2013;12:840–851. doi: 10.1016/j.arr.2013.06.004. [DOI] [PubMed] [Google Scholar]
  47. Rockwood K, Andrew M, Mitnitski A. A comparison of two approaches to measuring frailty in elderly people. J Gerontol A Biol Sci Med Sci. 2007;62:738–743. doi: 10.1093/gerona/62.7.738. [DOI] [PubMed] [Google Scholar]
  48. Rockwood K, Mitnitski A. Frailty in relation to the accumulation of deficits. J Gerontol A Biol Sci Med Sci. 2007;62:722–727. doi: 10.1093/gerona/62.7.722. [DOI] [PubMed] [Google Scholar]
  49. Rolfson DB, Wilcock G, Mitnitski A, King E, De Jager CA, Rockwood K, Fallah N, Searle SD. An assessment of neurocognitive speed in relation to frailty. Age Ageing. 2013;42:191–196. doi: 10.1093/ageing/afs185. [DOI] [PubMed] [Google Scholar]
  50. Romero-Ortuno R, Soraghan C. A Frailty Instrument for primary care for those aged 75 years or more: findings from the Survey of Health, Ageing and Retirement in Europe, a longitudinal population-based cohort study (SHARE-FI75+) BMJ Open. 2014;4:e006645. doi: 10.1136/bmjopen-2014-006645. [DOI] [PMC free article] [PubMed] [Google Scholar]
  51. Sachs-Ericsson N, Corsentino E, Moxley J, Hames JL, Rushing NC, Sawyer K, Joiner T, Selby EA, Zarit S, Gotlib IH, Steffens DC. A longitudinal study of differences in late- and early-onset geriatric depression: depressive symptoms and psychosocial, cognitive, and neurological functioning. Aging Ment Health. 2013;17:1–11. doi: 10.1080/13607863.2012.717253. [DOI] [PMC free article] [PubMed] [Google Scholar]
  52. Sair HI, Welsh-Bohmer KA, Wagner HR, Steffens DC. Ascending digits task as a measure of executive function in geriatric depression. J Neuropsychiatry Clin Neurosci. 2006;18:117–120. doi: 10.1176/jnp.18.1.117. [DOI] [PubMed] [Google Scholar]
  53. Santos-Eggimann B, Cuenoud P, Spagnoli J, Junod J. Prevalence of frailty in middle-aged and older community-dwelling Europeans living in 10 countries. J Gerontol A Biol Sci Med Sci. 2009;64:675–681. doi: 10.1093/gerona/glp012. [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. Shao Z, Janse E, Visser K, Meyer AS. What do verbal fluency tasks measure? Predictors of verbal fluency performance in older adults. Frontiers in Psychology. 2014;5 doi: 10.3389/fpsyg.2014.00772. Artn 772. [DOI] [PMC free article] [PubMed] [Google Scholar]
  55. Sheline YI, Barch DM, Garcia K, Gersing K, Pieper C, Welsh-Bohmer K, Steffens DC, Doraiswamy PM. Cognitive function in late life depression: relationships to depression severity, cerebrovascular risk factors and processing speed. Biol Psychiatry. 2006;60:58–65. doi: 10.1016/j.biopsych.2005.09.019. [DOI] [PubMed] [Google Scholar]
  56. Sheline YI, Disabato BM, Hranilovich J, Morris C, D’angelo G, Pieper C, Toffanin T, Taylor WD, Macfall JR, Wilkins C, Barch DM, Welsh-Bohmer KA, Steffens DC, Krishnan RR, Doraiswamy PM. Treatment Course With Antidepressant Therapy in Late-Life Depression. Am J Psychiatry. 2012:1377136. doi: 10.1176/appi.ajp.2012.12010122. [pii] [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. Siepmann M, Grossmann J, Muck-Weymann M, Kirch W. Effects of sertraline on autonomic and cognitive functions in healthy volunteers. Psychopharmacology (Berl) 2003;168:293–298. doi: 10.1007/s00213-003-1448-4. [DOI] [PubMed] [Google Scholar]
  58. Sivan AB. Benton Visual Retention Test. Fifth. San Antonio: The Psychological Corporation; 1992. [Google Scholar]
  59. Smith A. Symbol Digit Modalities Test-Manual. Los Angeles: Western Psychological Services; 1982. [Google Scholar]
  60. Solfrizzi V, Scafato E, Frisardi V, Seripa D, Logroscino G, Maggi S, Imbimbo BP, Galluzzo L, Baldereschi M, Gandin C, Di Carlo A, Inzitari D, Crepaldi G, Pilotto A, Panza F, Italian Longitudinal Study on Aging Working G. Frailty syndrome and the risk of vascular dementia: the Italian Longitudinal Study on Aging. Alzheimers Dement. 2013;9:113–122. doi: 10.1016/j.jalz.2011.09.223. [DOI] [PubMed] [Google Scholar]
  61. Song X, Mitnitski A, Rockwood K. Nontraditional risk factors combine to predict Alzheimer disease and dementia. Neurology. 2011;77:227–234. doi: 10.1212/WNL.0b013e318225c6bc. [DOI] [PMC free article] [PubMed] [Google Scholar]
  62. St John PD, Tyas SL, Montgomery PR. Depressive symptoms and frailty. Int J Geriatr Psychiatry. 2013;28:607–614. doi: 10.1002/gps.3866. [DOI] [PubMed] [Google Scholar]
  63. Steffens DC, Welsh-Bohmer KA, Burke JR, Plassman BL, Beyer JL, Gersing KR, Potter GG. Methodology and preliminary results from the neurocognitive outcomes of depression in the elderly study. J Geriatr Psychiatry Neurol. 2004;17:202–211. doi: 10.1177/0891988704269819. 17/4/202 [pii] [DOI] [PubMed] [Google Scholar]
  64. Sternberg SA, Wershof Schwartz A, Karunananthan S, Bergman H, Mark Clarfield A. The identification of frailty: a systematic literature review. J Am Geriatr Soc. 2011;59:2129–2138. doi: 10.1111/j.1532-5415.2011.03597.x. [DOI] [PubMed] [Google Scholar]
  65. Story TJ, Potter GG, Attix DK, Welsh-Bohmer KA, Steffens DC. Neurocognitive correlates of response to treatment in late-life depression. Am J Geriatr Psychiatry. 2008;16:752–759. doi: 10.1097/JGP.0b013e31817e739a. [DOI] [PMC free article] [PubMed] [Google Scholar]
  66. Strawbridge WJ, Shema SJ, Balfour JL, Higby HR, Kaplan GA. Antecedents of frailty over three decades in an older cohort. J Gerontol B Psychol Sci Soc Sci. 1998;53:S9–S16. doi: 10.1093/geronb/53b.1.s9. [DOI] [PubMed] [Google Scholar]
  67. Wechsler D. Wechsler Memory Scale-Revised Manual. San Antonio: Psychological Corporation; 1987. [Google Scholar]
  68. Wechsler D. Wechsler Adult Intelligence Scale -Third Edition. Psychological Corporation: San Antonio, Texas; 1997. [Google Scholar]
  69. Welsh KA, Butters N, Mohs RC, Beekly D, Edland S, Fillenbaum G, Heyman A. The Consortium to Establish a Registry for Alzheimer’s Disease (CERAD). Part V. A normative study of the neuropsychological battery. Neurology. 1994;44:609–614. doi: 10.1212/wnl.44.4.609. [DOI] [PubMed] [Google Scholar]
  70. Woo J, Leung J, Morley JE. Comparison of frailty indicators based on clinical phenotype and the multiple deficit approach in predicting mortality and physical limitation. J Am Geriatr Soc. 2012;60:1478–1486. doi: 10.1111/j.1532-5415.2012.04074.x. [DOI] [PubMed] [Google Scholar]
  71. World Health Organization. Global Burden of Disease: 2004 Update. Geneva, Switzerland: WHO Press; 2008. [Google Scholar]
  72. World Medical Association. World Medical Association Declaration of Helsinki: ethical principles for medical research involving human subjects. Journal of the American Medical Association. 2013;310:2191–2194. doi: 10.1001/jama.2013.281053. 1760318 [pii] [DOI] [PubMed] [Google Scholar]

RESOURCES