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Depressive symptoms and cognitive decline in older adults

Published online by Cambridge University Press:  16 April 2024

Malcolm Forbes Open the ORCID record for Malcolm Forbes [Opens in a new window] ,
Mojtaba Lotfaliany ,
Mohammadreza Mohebbi ,
Charles F. Reynolds ,
Robyn L. Woods ,
Suzanne Orchard ,
Trevor Chong ,
Bruno Agustini ,
Adrienne O’Neil  and
Joanne Ryan
...Show all authors
Malcolm Forbes*
Affiliation:
The Institute for Mental and Physical Health and Clinical Translation (IMPACT), Deakin University, Geelong, VIC, Australia
Mojtaba Lotfaliany
Affiliation:
School of Medicine, Barwon Health, Deakin University, The Institute for Mental and Physical Health and Clinical Translation (IMPACT), Geelong, VC, Australia
Mohammadreza Mohebbi
Affiliation:
The Institute for Mental and Physical Health and Clinical Translation (IMPACT), Deakin University, Geelong, VIC, Australia
Charles F. Reynolds
Affiliation:
School of Medicine, University of Pittsburgh, Pittsburgh, PA, USA
Robyn L. Woods
Affiliation:
School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia
Suzanne Orchard
Affiliation:
School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia
Trevor Chong
Affiliation:
School of Psychological Sciences, Turner Institute for Brain and Mental Health, Monash University, Melbourne, VIC, Australia
Bruno Agustini
Affiliation:
The Institute for Mental and Physical Health and Clinical Translation (IMPACT), Deakin University, Geelong, VIC, Australia
Adrienne O’Neil
Affiliation:
The Institute for Mental and Physical Health and Clinical Translation (IMPACT), Deakin University, Geelong, VIC, Australia
Joanne Ryan
Affiliation:
School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC, Australia
Michael Berk
Affiliation:
The Institute for Mental and Physical Health and Clinical Translation (IMPACT), Deakin University, Geelong, VIC, Australia
*
Correspondence should be addressed to: Malcolm Forbes, The Institute for Mental and Physical Health and Clinical Translation (IMPACT), Deakin University, Geelong 3020, Victoria, Australia. E-mail: [email protected]

Abstract

Objectives:

Few studies have examined the impact of late-life depression trajectories on specific domains of cognitive function. This study aims to delineate how different depressive symptom trajectories specifically affect cognitive function in older adults.

Design:

Prospective longitudinal cohort study

Setting:

Australia and the United States of America

Participants:

In total, 11,035 community-dwelling older adults with a mean age of 75 years

Measurements:

Depressive trajectories were modelled from depressive symptoms according to annual Centre for Epidemiological Studies Depression Scale 10 (CES-D-10) surveys. Four trajectories of depressive symptoms were identified: low (“nondepressed”), consistently mild (“subthreshold depression”), consistently moderate (“persistent depression”), and initially low but increasing (“emerging depression”). Global cognition (Modified Mini-Mental State Examination [3MS]), verbal fluency (Controlled Oral Word Association Test [COWAT]), processing speed (Symbol Digit Modalities Test [SDMT]), episodic memory (Hopkins Verbal Learning Test – Revised [HVLT-R]), and a composite z-score were assessed over a subsequent median 2 years.

Results:

Subthreshold depression predicted impaired performance on the SDMT (Cohen’s d −0.04) and composite score (−0.03); emerging depression predicted impaired performance on the SDMT (−0.13), HVLT-R (−0.09), 3 MS (−0.08) and composite score (−0.09); and persistent depression predicted impaired performance on the SDMT (−0.08), 3 MS (−0.11), and composite score (−0.09).

Conclusions:

Depressive symptoms are associated with later impaired processing speed. These effects are small. Diverse depression trajectories have different impacts on cognitive function.

Keywords

major depressive disorderdepressive symptomscognitive impairment
Type
Original Research Article
Information
International Psychogeriatrics , Volume 36 , Special Issue 11: Issue Theme: Brain and Late-Life Depression , November 2024 , pp. 1039 - 1050
Creative Commons
Creative Common License - CCCreative Common License - BY
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.
Copyright
© The Author(s), 2024. Published by Cambridge University Press on behalf of International Psychogeriatric Association

Introduction

Late-life depression (LLD) and cognitive impairment share a complex and bidirectional link, each influencing and exacerbating the other (van den Kommer et al., Reference van den Kommer, Comijs, Aartsen, Huisman, Deeg and Beekman2013). Older individuals with depression often report cognitive difficulties, such as problems with memory, attention and executive function. Furthermore, negative attentional biases can slow information processing (Alexopoulos, Reference Alexopoulos2019). The risk of dementia is more than doubled for both men and women with diagnosed depression (Elser et al., Reference Elser, Horváth-Puhó, Gradus, Smith, Lash, Glymour, Toft Sørensen and Henderson2023). Conversely, cognitive impairment is a risk factor for the development of depression. The prevalence of depression in individuals with mild cognitive impairment (MCI) living in the community is 25% (Ismail et al., Reference Ismail, Elbayoumi, Fischer, Hogan, Millikin, Schweizer, Mortby, Smith, Patten and Fiest2017), almost double that of general older population estimates (Mohebbi et al., Reference Mohebbi, Agustini, Woods, McNeil, Nelson, Shah, Nguyen, Storey, Murray, Reid, Kirpach, Wolfe, Lockery and Berk2019). The prevalence of depression in individuals affected by dementia ranges from 37 to 41% (Leung et al., Reference Leung, Chan, Spector and Wong2021).

We previously reported that emerging depressive symptoms in an initially healthy community-dwelling population are associated with incident dementia (HR 1.42 [95% CI 1.02–1.97]) (Agustini et al., Reference Agustini, Lotfaliany, Mohebbi, Woods, McNeil, Nelson, Shah, Murray, Reid, Tonkin and Ryan2022). The current study builds upon a growing body of literature using trajectory modeling to identify the longitudinal relationship between depressive symptoms and cognitive function. This literature includes the Whitehall II prospective cohort study (n = 774) that found that those with emerging depressive symptoms later in life had impaired executive function (Demnitz et al., Reference Demnitz, Anatürk, Allan, Griffanti, Mackay, Mahmood, Sexton, Suri, Topiwala, Zsoldos, Kivimäki, Singh-Manoux and Ebmeier2020) and the English Longitudinal Study of Aging (n = 7,610) that found persistent depressive symptoms were associated with global cognitive impairment (Zheng et al., Reference Zheng, Zhong, Song and Xie2018). While most evidence suggests that elevated depressive symptoms are associated with greater incidence of cognitive impairment or dementia (Choi et al., Reference Choi, Han, Jeon, Jang, Kim and Park2019, Demnitz et al., Reference Demnitz, Anatürk, Allan, Griffanti, Mackay, Mahmood, Sexton, Suri, Topiwala, Zsoldos, Kivimäki, Singh-Manoux and Ebmeier2020, Formánek et al., Reference Formánek, Csajbók, Wolfová, Kučera, Tom, Aarsland and Cermakova2020, Zheng et al., Reference Zheng, Zhong, Song and Xie2018), there are conflicting findings about how different trajectories of depressive symptoms predict cognitive impairment (Graziane et al., Reference Graziane, Beer, Snitz, Chang and Ganguli2016) and which specific cognitive domains are affected. This is important as cognitive decline presents differently in clinical practice depending on the specific domains of cognition affected. This study sought to delineate the specific cognitive domains affected by different depression trajectories. Understanding these associations has implications for clinical practice given global population aging.

Methods

Study design and participants

This study involved secondary data analysis from the ASPREE/-XT study. ASPREE (ASPirin in Reducing Events in the Elderly) was a double-blind, randomized, placebo controlled primary prevention trial to assess whether daily 100 mg aspirin could extend the duration of disability- and dementia-free life in relatively healthy community-dwelling older adults. The trial enrolled 19,111 Australian and American men and women ≥ 70 years of age or older (or ≥ 65 years of age among Blacks and Hispanics in the United States) between 2010 and 2014, and randomly assigned 9,525 to receive aspirin and 9,589 to receive matched placebo. All participants were free from cardiovascular disease, dementia and independence-limiting physical disability on enrollment, and had a score on the Modified Mini-Mental State Examination (3 MS) of at least 78/100 for eligibility. Full details of the trial have been published elsewhere (ASPREE Investigator Group, 2013), including the secondary outcomes related to depression (Berk et al., Reference Berk, Woods, Nelson, Shah, Reid, Storey, Fitzgerald, Lockery, Wolfe, Mohebbi, Dodd, Murray, Stocks, Fitzgerald, Mazza, Agustini and McNeil2020) and cognitive decline (Ryan et al., Reference Ryan, Storey, Murray, Woods, Wolfe, Reid, Nelson, Chong, Williamson, Ward, Lockery, Orchard, Trevaks, Kirpach, Newman, Ernst, McNeil and Shah2020).

The ASPREE-eXTension (ASPREE-XT) study is a prospective cohort study following up ASPREE participants to investigate the long-term effects of low-dose aspirin as well as a range of factors that contribute to the maintenance of physical and cognitive health in older adults. All participants in the ASPREE trial were eligible to continue in the ASPREE-XT cohort. Those who did not consent into ASPREE-XT (7%) were slightly older and had lower cognitive scores and more age-related conditions than those who consented (Ernst et al., Reference Ernst, Broder, Wolfe, Woods, Nelson, Ryan, Shah, Orchard, Chan, Espinoza, Wilson, Kirpach, Reid, McNeil, Williamson and Murray2023). A novel analysis was established with a new ‘baseline’ set, such that depression trajectories were modeled during the clinical trial phase and prior to the new baseline, and cognitive outcomes explored thereafter, as illustrated in Figure 1. All participants who participated at wave 5 (considered ‘baseline’) and had at least one follow-up in ASPREE-XT were included in the analysis.

Figure 1. Timeline of when depressive symptoms (blue)* and cognitive outcomes (green)ϕ were measured.

xAssessment is relative to the time at which a participant enrolled is ASPREE.

*Depressive trajectories were calculated over a median 3.98 (IQR 3.93−4.02) years.

ϕCognitive outcomes were measured over a meadian 1.99 (IQR 1.37−2.17) years.

Assessment of depressive symptoms and cognitive function

Depressive symptoms were obtained annually using the Center for Epidemiologic Studies Depression Scale 10-item (CES-D-10), a reliable measure of depression with high internal consistency (Mohebbi et al., Reference Mohebbi, Nguyen, McNeil, Woods, Nelson, Shah, Storey, Murray, Reid, Kirpach, Wolfe, Lockery and Berk2018). A battery of cognitive tests was used to measure a range of cognitive processes in the ASPREE-XT cohort. These included the Controlled Oral Word Association Test (COWAT) letter F (Ross, Reference Ross2003), which measures verbal fluency; the Symbol Digit Modalities Test (SDMT) (Smith, Reference Smith1973), which measures processing speed; the Hopkins Verbal Learning Test – Revised (HVLT-R) (Benedict et al., Reference Benedict, Schretlen, Groninger and Brandt1998), which measures verbal delayed recall; and the Modified Mini-Mental State Examination (3 MS) (Teng & Chui, Reference Teng and Chui1987), which measures a broad range of cognitive functions, and was used as part of the ASPREE participant eligibility assessment. The composite measure was defined as a sum of the z-scores of the four cognitive tests. Impaired performance on each of these measures – 3 MS, COWAT, HVLT-R, and SMDT – has been associated with dementia and persistent physical disability (Wu et al., Reference Wu, Woods, Chong, Orchard, Shah, Wolfe, Storey, Sheets, Murray, McNeil and Ryan2022). Whilst these cognitive assessments were administered to participants at enrollment and then biennially during the trial phase, the novel analysis used to establish preceding depression trajectories required a new baseline to be set, as indicated in Figure 1. Data from cognitive assessments administered after the new baseline were used in this analysis.

Assessment of other covariates

Covariate information was collected annually. Details regarding ascertainment of demographic and clinical information are described elsewhere (ASPREE Investigator Group, 2013). A-priori covariates and potential confounders were selected based on the literature on depressive symptoms (Shin & Cho, Reference Shin and Cho2022, Xiang, Reference Xiang2020) and cognitive functioning (Wu et al., Reference Wu, Agustini, Woods, McNeil, Nelson, Shah, Nguyen, Storey, Murray, Reid, Kirpach, Wolfe, Lockery and Berk2021, Zheng et al., Reference Zheng, Cagney and Choi2023) in older individuals. These were age, gender, ethnicity, smoking status, alcohol consumption, education, polypharmacy, and living arrangements. Polypharmacy was defined by the use of five or more medications simultaneously. Information on medical comorbidities was included in a sensitivity analysis and included proxy measures of gastro-oesophageal reflux disease, hypertension, diabetes, chronic kidney disease, dyslipidaemia, respiratory disease, Parkinson’s disease, and cancer (see Table 1).

Table 1. Baseline characteristics for the nondepressed

* ND = nondepressed, STD = subthreshold depression, ED = emerging depression, PD = persistent depression (see Fig. 2 for trajectories).

a See detailed demographic information in McNeil et al.35

b Defined using the National Institute on Alcohol Abuse and Alcoholism guidelines.36

c Defined as taking≥5 prescription medications daily.

d Defined as the diagnosis of any cancer during the study period or a history of a cancer diagnosis.

e Defined an estimated glomerular filtration rate of less than 60mL/min/1.73m2.

f Defined as the use of any drug use for the treatment of diabetes, including insulin, or a fasting blood glucose level of greater than or equal to 7mmol/L.

g Defined as a total cholesterol level of greater than or equal to 200mg/dL or a low-density lipoprotein level of greater than or equal to 100mg/dL.

h Defined as the use of any proton pump inhibitor or H2-receptor antagonist.

i Defined as the use of ACE inhibitors or angiotensin II receptor blockers, centrally acting antiadrenergic agents, beta-blockers or calcium channel blockers or, in the absence of the use of these agents, a systolic blood pressure greater than or equal to 140 mmHg or a diastolic blood pressure greater than or equal to 90 mmHg.

j Defined as the use of any anti-parkinsonian drugs, including anticholinergic agents.

k Defined as the use of any drug for chronic obstructive pulmonary disease or asthma, including inhaled beta-adrenergic agents and anticholinergic agents.

Statistical analysis

The exposure in this study was depressive symptoms. Depressive symptom trajectories were modeled using linear and nonlinear latent class mixed models from annual measures across five annual data collection periods (“waves”), commencing from enrollment into the ASPREE trial (Fig. 1 – area shaded blue). Longitudinal trajectories were used to characterize dynamic change and identify unobserved heterogeneity. These trajectories were significantly different (χ 2 28.7, df 3, p < 0.0001). There was a median observation of 3.98 years (IQR 3.93–4.02 years). Four distinct trajectories identified corresponding to low (“nondepressed”), consistently mild (“subthreshold depression”), consistently moderate (“persistent depression”), and initially low but increasing (“emerging depression”) CES-D-10 depressive symptoms (Fig. 2). These data are based on the mean CES-D-10 score for each trajectory with statistical analysis details published elsewhere (Agustini et al., Reference Agustini, Lotfaliany, Mohebbi, Woods, McNeil, Nelson, Shah, Murray, Reid, Tonkin and Ryan2022). Data on cognitive outcomes were collected over the subsequent period post the new baseline reset (Fig. 1 – area shaded green). Our study sample pertained to those who participated in the final year of the ASPREE trial and had estimates of depression trajectories (n = 11,035).

Figure 2. Depressive symptom tragjectories showing subthreshold, emerging, persistent, and nondepressed groups.

Baseline (see Fig. 1) characteristics were analyzed using descriptive statistics, with measures of central tendency using Student’s t-test for continuous variables and chi-square tests for categorical variables (Table 1).

The association between depressive symptom trajectories and cognitive outcomes was assessed using generalized estimating equations (GEE) with robust variance estimation to handle repeated measures and clustered data. Group-based trajectory modeling enabled an estimation of the probabilities for multiple trajectories rather than only fitting the overall population mean. Full details of the trajectory modeling can be found elsewhere (Agustini et al., Reference Agustini, Lotfaliany, Mohebbi, Woods, McNeil, Nelson, Shah, Murray, Reid, Tonkin and Ryan2022).

For the five cognitive outcomes, three models were presented: model 1 was unadjusted, model 2 was adjusted for age and gender (see Supplementary Appendix), and model 3 was adjusted for age, gender, ethnicity, smoking status, alcohol consumption, education, polypharmacy, living arrangements, and baseline cognitive function (Table 2). To provide an estimation of magnitude and facilitate comparison across different cognitive domains, model adjusted Cohen’s d scores were calculated (Fig. 3). False discovery rate was controlled for by using the Benjamini-Hochberg correction for multiple comparisons with the FDR set at 5% (Benjamini & Yekutieli, Reference Benjamini and Yekutieli2001). Three sensitivity analyses were conducted (see Supplementary Appendix). The first excluded those in the study who developed dementia during the exposure period (n = 218). The second excluded those taking antidepressants during the exposure period (n = 1,326), given that antidepressants may have effects on cognition in LLD (Ainsworth et al., Reference Ainsworth, Marawi, Maslej, Perivolaris, Blumberger, Pat McAndrews, Pollock and Mulsant2023). The final sensitivity analysis excluded those without data on medical comorbidities (n = 4,914).

Figure 3. Forest plot of cognitive outcomes with effect size using Cohen’s d for subthreshold, emerging, and persistent depression groups (compared to the nondepressed group).

Table 2. Mean differences in cognitive measures for each depression trajectory in extended follow−up*

* Model 3: adjusted for age, gender, ethnicity, smoking status, alcohol consumption, education, polypharmacy, living arrangements, and baseline cognitive function (for COWAT, SDMT, HVLT-R, 3 MS and composite score), with p-values corrected via the Benjamini−Hochberg method.

Results

Baseline characteristics

At baseline (see Fig. 1), 11,035 participants (56.3% female, mean age at randomization 75.0 ± 4.2 years) were included in the study. Among all participants, 4,304 belonged to the subthreshold trajectory, 886 to the persistent depression trajectory, and 673 to the emerging depression trajectory (Table 1). There were statistically significant differences between depression trajectory groups and education level, ethnicity, body mass index, living situation, polypharmacy, diabetes, gastro-oesophageal reflux disease, and respiratory disease, as well as baseline cognitive measures.

Depression trajectories and risk of cognitive decline

In multiple adjusted GEE models, consistently mild (subthreshold depression), consistently moderate (persistent depression), and emerging depressive symptoms were all associated with composite cognitive impairment. Table 2 and Figure 3 provide results for model 3, with mean differences and effect sizes presented. The emerging depressive symptom trajectory predicted cognitive decline over time on the SDMT, HVLT-R, 3 MS and composite score with the largest effect size for SDMT (Cohen’s d −0.13). The persistent depressive symptom trajectory predicted cognitive impairment on SDMT, 3 MS and composite score. The subthreshold symptom trajectory predicted cognitive impairment on SDMT and composite score, albeit with smaller effect sizes. Sensitivity analyses revealed that the exclusion of individuals taking antidepressants was associated with small reductions in magnitude of impairment across all cognitive outcomes in the persistent depressive group only. The exclusion of individuals with dementia did not result in any statistically significant changes in the emerging or persistent depression groups.

Discussion

The evidence that early- and mid-life depression increases the likelihood of dementia is mixed (Elser et al., Reference Elser, Horváth-Puhó, Gradus, Smith, Lash, Glymour, Toft Sørensen and Henderson2023, Singh-Manoux et al., Reference Singh-Manoux, Dugravot, Fournier, Abell, Ebmeier, Kivimaki and Sabia2017) but there is robust evidence that indicates that late-life depression increases the likelihood of dementia (Han et al., Reference Han, Wang, Wu, Yao, Hao and Pei2021, Yang et al., Reference Yang, Deng, Leng, Ou, Li, Chen, He, Wu, Huang, Zhang, Kuo, Feng, Dong, Feng, Suckling, Smith, Li, Cheng and Yu2023). While the link between depression and dementia is established, it is important to note that cognitive decline, even without dementia, is an important concern for older adults.

This study, which to our knowledge is the largest study to date to comprehensively assess different trajectories of depressive symptoms and several cognitive outcomes in a cohort predominantly in their 8th decade, identifying the domains of cognition most affected by depressive symptoms over time. It demonstrates that both persistent (mild and moderate) and emerging depressive symptoms predict small but significant impairments in cognition, most consistently processing speed.

There are few studies that have examined the specific cognitive domains affected by depressive symptoms. In a study of 7,610 adults aged 50 years or older, depressive symptoms were significantly associated with subsequent cognitive decline over a 10-year follow-up period (Zheng et al., Reference Zheng, Zhong, Song and Xie2018). However, this study modeled trajectories on only two time points, limiting its ability to identify potentially clinically relevant depressive trajectories, and did not assess processing speed. An analysis of the Korean Longitudinal Study of Ageing which assessed global cognition using a mini-mental state examination (MMSE) but did not utilize any other cognitive measures, found a similar relationship (Choi et al., Reference Choi, Han, Jeon, Jang, Kim and Park2019). A much larger European study of 69,066 participants found that individuals with persistently high and emerging depressive symptoms experienced linear cognitive decline, but given the large study sample, only a modest battery of cognitive assessments were undertaken (Formánek et al., Reference Formánek, Csajbók, Wolfová, Kučera, Tom, Aarsland and Cermakova2020).

A key strength of our study is its size and the assessment of domain-specific cognitive outcomes. It demonstrates that, in a large sample, cognitive decline occurs across several domains of cognition. These findings are robust to sensitivity analyses excluding individuals taking antidepressants and those with dementia. Another strength is that this study examined a cohort enrolled from a trial, allowing estimation of the impact of depressive symptoms on cognitive outcomes in a group free from clinically apparent cardiovascular disease or dementia at baseline.

There are several possible explanations for the relationship we found between emerging depressive symptoms and persistent depressive symptoms and cognitive decline. Firstly, depressive symptoms may be a prodrome of a dementia. Neuropathological changes in the brain associated with various dementias occur years prior to cognitive symptom onset, thus depressive symptoms occurring de novo in older individuals may represent non-cognitive prodromal features of a dementia (Leoutsakos et al., Reference Leoutsakos, Wise, Lyketsos and Smith2019, Panza et al., Reference Panza, Frisardi, Capurso, D'Introno, Colacicco, Imbimbo, Santamato, Vendemiale, Seripa, Pilotto, Capurso and Solfrizzi2010). This concept, known as mild behavioral impairment (MBI), is characterized by changes in behavior or personality starting after the age of 50 and persisting, at least intermittently, for six months. The neuropsychiatric symptoms associated with MBI include decreased motivation and affective symptoms (Ismail et al., Reference Ismail, Smith, Geda, Sultzer, Brodaty, Smith, Agüera‐Ortiz, Sweet, Miller and Lyketsos2016) and there is considerable overlap with the diagnostic criteria for major depressive disorder (American Psychiatric Association, 2013). This finding is supported by other studies that show emerging depressive symptoms over time increases the risk of cognitive decline (Babulal et al., Reference Babulal, Chen, Murphy, Doherty, Johnson and Morris2023, Krell-Roesch et al., Reference Krell‐Roesch, Syrjanen, Machulda, Christianson, Kremers, Mielke, Knopman, Petersen, Vassilaki and Geda2021) and dementia (Kaup et al., Reference Kaup, Byers, Falvey, Simonsick, Satterfield, Ayonayon, Smagula, Rubin and Yaffe2016, Mirza et al., Reference Mirza, Wolters, Swanson, Koudstaal, Hofman, Tiemeier and Ikram2016). The timing of onset of depressive symptoms is important. Ly et al. (Reference Ly, Karim, Becker, Lopez, Anderson, Aizenstein, Reynolds, Zmuda and Butters2021) found that late-onset (first depressive episode ≥ 60 years) LLD was associated with a more rapid decline in verbal skills and delayed recall than individuals with early-onset (first depressive episode ≤ 60 years) LLD. These late-onset depressive symptoms may be related to cortical amyloid (Gatchel et al., Reference Gatchel, Rabin, Buckley, Locascio, Quiroz, Yang, Vannini, Amariglio, Rentz, Properzi, Donovan, Blacker, Johnson, Sperling and Marshall2019) and neurofilament light levels (Gatchel et al., Reference Gatchel, Yang, Mimmack, Arnold, Blacker, Liu, Selkoe, Johnson, Sperling, Marshall and Chhatwal2022). A recent study found two different types of LLD: one with relatively preserved brain anatomy and one characterized by widespread brain atrophy and white matter changes, with a higher rate of progression to Alzheimer’s disease (Wen et al., Reference Wen, Fu, Tosun, Veturi, Yang, Abdulkadir, Mamourian, Srinivasan, Skampardoni, Singh, Nawani, Bao, Erus, Shou, Habes, Doshi, Varol, Mackin, Sotiras, Fan, Saykin, Sheline, Shen, Ritchie, Wolk, Albert, Resnick and Davatzikos2022). Interestingly, our study found impairment in delayed recall in the emerging depression group only.

Secondly, it may be the case that the impairments in cognition found in this study relate to ongoing depressive symptoms, particularly given the short period of time between measurement of depressive symptoms and short duration of measurement of cognitive outcomes. It is known that depression is associated with moderate deficits within the domains of executive function, attention and memory (Rock et al., Reference Rock, Roiser, Riedel and Blackwell2014). A meta-analysis of 252 studies found that deficits in selective attention, working memory, and long-term memory persist in remission from a major depressive episode. Older age was associated with lower performance relative to controls on the mini-mental state examination (MMSE), word list learning and delayed recall, logical memory immediate and delayed recall, and the Wisconsin Card Sorting Test (Semkovska et al., Reference Semkovska, Quinlivan, O'Grady, Johnson, Collins, O'Connor, Knittle, Ahern and Gload2019). Furthermore, remitted depression is also associated with cognitive deficits in attention, working memory and long-term recall (Semkovska et al., Reference Semkovska, Quinlivan, O'Grady, Johnson, Collins, O'Connor, Knittle, Ahern and Gload2019). One caveat regarding this relationship is that Alzheimer’s disease could have been a confounder in several studies examining older individuals. Previous research has indicated that it may account for some of the cognitive impairment findings in LLD (Rhodes et al., Reference Rhodes, Insel, Butters, Morin, Bickford, Tosun, Gessert, Rosen, Aisen, Raman, Landau, Saykin, Toga, Jack, Weiner, Nelson and Mackin2021). Our sensitivity analysis did not find significant differences when those with dementia were excluded during the exposure timeframe.

Interestingly, while executive dysfunction and impaired verbal fluency are common in LLD (Szymkowicz et al., Reference Szymkowicz, Ryan, Elson, Kang and Taylor2023b), this study found no significant decline on the COWAT. This may be due in minor part to the age range of participants. The most pronounced deficits in COWAT are found in those with predominant frontal lobe pathology and the relatively young mean age of onset of frontotemporal dementia means these individuals would not have met inclusion criteria for the ASPREE study. Our study included a measure of verbal delayed recall (HVLT-R), which was impaired only in the emerging depression group, possibly heralding the emergence of Alzheimer’s disease, which is typically associated with impairments in encoding and storing new verbal information. However, this cannot be stated unequivocally based on the data and given that impairments in delayed recall also occur in those with recurrent depressive episodes (Gorwood et al., Reference Gorwood, Corruble, Falissard and Goodwin2008).

The mechanisms underlying the link between depression and cognitive impairment have not been fully elucidated. Neurobiological models of LLD have implicated the accumulation of allostatic load and neurotransmitter dysfunction secondary to senescence, inflammation and vascular disease destabilizing functional brain networks (Szymkowicz et al., Reference Szymkowicz, Gerlach, Homiack and Taylor2023a, Taylor et al., Reference Taylor, Zald, Felger, Christman, Claassen, Horga, Miller, Gifford, Rogers, Szymkowicz and Rutherford2022). Dopaminergic system dysfunction may be more common in LLD characterized by cerebrovascular damage (Taylor et al., Reference Taylor, Zald, Felger, Christman, Claassen, Horga, Miller, Gifford, Rogers, Szymkowicz and Rutherford2022). Depressive symptoms occurring in dementia may relate to neurodegeneration of monoamine pathways and loss of neuroplasticity, and a recent meta-analysis of voxel-based morphometry studies found shared volumetric reductions in the insula, superior temporal gyrus, inferior frontal gyrus, amygdala, hippocampus and thalamus in both individuals with depression and those with MCI (Zacková et al., Reference Zacková, Jáni, Brázdil, Nikolova and Marečková2021), suggesting either shared pathophysiology or phenotypic overlap between the two conditions. Similarly, a systematic review of structural magnetic resonance imaging studies found mild frontotemporal volume reduction and widespread white matter changes to be associated with impaired cognition in LLD (Marawi et al., Reference Marawi, Ainsworth, Zhukovsky, Rashidi-Ranjbar, Rajji, Tartaglia, Voineskos and Mulsant2023).

In our study, subthreshold depressive symptoms predicted cognitive impairment, albeit with lower effect sizes than the emerging and persistent depressive symptom groups. This finding accords with a pooled analysis of the results of the Health and Retirement Study and English Longitudinal Study of Ageing (Zhu et al., Reference Zhu, Li, Xie, Zhong, Wu and Blumenthal2022) and the Chinese Health and Retirement Longitudinal Study (Zhang et al., Reference Zhang, Lin, Hu, Sun, Xu, Hao and Zhu2022) and suggests a dose effect. Clinically, assessing individual depressive symptoms may be important, even if the symptom threshold for a diagnosis of a major depressive disorder is not met (Fried and Nesse, Reference Fried and Nesse2015).

Interestingly, there is evidence from two longitudinal studies suggesting that elevated depressive symptoms do not necessarily lead to accelerated cognitive decline provided that depressive symptoms are reduced (Mirza et al., Reference Mirza, Wolters, Swanson, Koudstaal, Hofman, Tiemeier and Ikram2016, Zhu et al., Reference Zhu, Li, Xie, Zhong, Wu and Blumenthal2022), suggesting a potential protective role for timely and effective treatment intervention (Yu et al., Reference Yu, Xu, Tan, Andrieu, Suckling, Evangelou, Pan, Zhang, Jia, Feng, Kua, Wang, Wang, Tan, Li, Hou, Wan, Tan, Mok, Tan, Dong, Touchon, Gauthier, Aisen and Vellas2020). This epidemiological finding has been shown in a clinical study, with improvement in depressive symptoms associated with improvements in verbal learning, memory and set shifting (Kassel et al., Reference Kassel, Rhodes, Insel, Woodworth, Garrison‐Diehn, Satre, Nelson, Tosun and Mackin2022). Clinical subtypes of LLD have recently been identified using a machine learning approach that have different prognoses and may benefit from streamlined interventions (Solomonov et al., Reference Solomonov, Lee, Banerjee, Chen, Sirey, Gunning, Liston, Raue, Areán and Alexopoulos2023). Since it is known that antidepressant medication has limited efficacy in treating depression that occurs in dementia (Costello et al., Reference Costello, Roiser and Howard2023, Taylor et al., Reference Taylor, Boyd, Elson, Andrews, Albert, Vega, Newhouse, Woodward, Kang and Shokouhi2021), incorporation of biomarkers to identify dementia processes, and considering information about depressive symptom trajectories, may be of clinical relevance for personalized treatments.

Our study was limited by its relatively short follow-up period relative to the clinical course of cognition, and its homogenous study sample. Furthermore, we did not include data on some possible mediating factors between depression and impaired cognitive outcomes, including social activities and mobility (Hung et al., Reference Hung, Lao, Yeh and Lee2023), which was only available for a subset of our study sample, and a broader range of medical comorbidities, which were not available for the entire cohort.

In conclusion, this study demonstrates that persistent and emerging depressive symptom trajectories are associated with a subsequent decline in processing speed in a large sample of initially healthy older community-dwelling adults. Further research should consider data-driven depression subtyping.

Conflicts of interest

None.

Funding

The ASPREE clinical trial was supported by the National Institute on Aging and the National Cancer Institute at the National Institutes of Health (U01AG029824 and U19AG062682); the National Health and Medical Research Council (NHMRC) of Australia (334047 and 1127060); Monash University (Australia) and the Victorian Cancer Agency (Australia). The authors express their gratitude to the participants and staff involved in data collection and management of ASPREE and ASPREE-XT. MF has received a scholarship from Deakin University and is supported by Dr Roth Trisno and family through the Trisno Family PhD Research Scholarship awarded by the RANZCP Foundation. He has received past funding from the National Health & Medical Research Council and Avant Mutual. MB is supported by a NHMRC Senior Principal Research Fellowship and Leadership 3 Investigator grant (1156072 and 2017131).

Description of author(s)’ roles

MF was involved in all aspects of the research and wrote the first draft. ML and MF completed the data analysis under the supervision of MM and AO. RW, SO, TC, BA, JR, and MB were all involved in the design of the cohort study. CR, JR, and MB provided supervision to MF.

Acknowledgements

Dr Forbes would like to acknowledge Dr Roth Trisno and family and the RANZCP Foundation for their support.

Consent statement

All human subjects provided informed consent.

Supplementary material

The supplementary material for this article can be found at https://doi.org/10.1017/S1041610224000541.

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Figure 0

Figure 1. Timeline of when depressive symptoms (blue)* and cognitive outcomes (green)ϕ were measured.xAssessment is relative to the time at which a participant enrolled is ASPREE.*Depressive trajectories were calculated over a median 3.98 (IQR 3.93−4.02) years.ϕCognitive outcomes were measured over a meadian 1.99 (IQR 1.37−2.17) years.

Figure 1

Table 1. Baseline characteristics for the nondepressed

Figure 2

Figure 2. Depressive symptom tragjectories showing subthreshold, emerging, persistent, and nondepressed groups.

Figure 3

Figure 3. Forest plot of cognitive outcomes with effect size using Cohen’s d for subthreshold, emerging, and persistent depression groups (compared to the nondepressed group).

Figure 4

Table 2. Mean differences in cognitive measures for each depression trajectory in extended follow−up*

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