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Background:
Brief Report

Cognitive Performance Deficits Are Associated with Clinically Significant Depression Symptoms in Older US Adults

by
Orestis Delardas
1,† and
Panagiotis Giannos
1,2,*,†
1
Promotion of Emerging and Evaluative Research Society, London AL7 3XG, UK
2
Department of Life Sciences, Faculty of Natural Sciences, Imperial College London, London SW7 2AZ, UK
*
Author to whom correspondence should be addressed.
†
These authors have contributed equally to this work and share first authorship.
Int. J. Environ. Res. Public Health 2023, 20(7), 5290; https://doi.org/10.3390/ijerph20075290
Submission received: 23 December 2022 / Revised: 5 March 2023 / Accepted: 9 March 2023 / Published: 28 March 2023
Browse Figure
Versions Notes

Abstract

:
Accumulating research has described cognitive impairment in adults with depression, however, few studies have focused on this relationship during older adulthood. Our cross-sectional study investigated the association between cognitive function performance and clinically significant depression symptoms in older adults. We analysed the data from the 2011 to 2014 National Health and Nutrition Examination Survey on older (aged 60 years and above) US adults. Cognitive function was assessed as a composite score and on a test-by-test basis based on the Consortium to Establish a Registry for Alzheimer’s Disease Word List Learning Test, the Word List Recall Test, and Intrusion Word Count Test, the Animal Fluency Test, and the Digit Symbol Substitution Test (DSST). Depression was defined as clinically significant depression symptoms based on the standard cut-off point of the Patient Health Questionnaire-9 (PHQ-9) score of 10 or greater. Adjusted-logistic regression analysis was employed using survey weights to examine the former relationships. Sociodemographic factors, in addition to medical history and status in terms of self-reported chronic illness and the incidence of stroke or memory–cognitive function loss, were considered as covariates. Among 1622 participants of a survey-weighted 860,400 US older adults, cognitive performance was associated with clinically significant depression symptoms (p = 0.003) after adjustment. Most prominently, older adults with significant cognitive deficits had approximately two and a half times (OR: 2.457 [1.219–4.953]) higher odds for a PHQ-9 score above threshold compared to those with the highest performance. Particularly, those with lowest DSST score had increased odds of almost four times (OR: 3.824 [1.069–13.678]). Efforts to decipher the underlying aetiology of these negative disparities may help create opportunities and interventions that could alleviate the risks from depression, cognitive impairment, and associated consequences in older adults at a population level.

1. Introduction

Cognitive decline occurs as people age and poses significant repercussions on their ability to conduct daily activities and lead a quality life [1]. Late-life depression has been proposed to be an expression of cognitive impairment. A growing body of literature has highlighted a strong association between depression and cognitive impairments during adulthood, including deficits in word–list memory recall, working memory, and executive functioning [2,3,4,5,6]. Late-life depression has been linked to a more rapid decline in cognition, with older adults who report higher levels of depression performing worse on executive function tasks that require a greater mental workload compared to other cognitive domains [4,5]. The relationship between depression and cognition is complex, with ongoing debate as to whether bidirectionality exists [7]. Although it cannot be excluded, the majority of studies, particularly those with older populations, have consistently found that higher levels of depressive symptoms are a key risk factor for cognitive deficits and may serve as a precursor to cognitive impairment. To address the potential variations in and bidirectionality of cognitive function and late-life depression at a population level, we sought to examine the association between cognitive performance and clinically significant depression symptoms in older adults (aged 60 and above) in the US.

2. Methods

We extracted publicly available data from participants aged ≥60 years from the 2011 to 2014 survey cycles published in the National Health and Nutrition Examination Survey (NHANES).
Cognitive function was evaluated as a composite measure and a test-by-test basis based on the Consortium to Establish a Registry for Alzheimer’s Disease (CERAD) Word List Learning Test (WLLT), the Word List Recall Test (WLRT), and Intrusion Word Count Test (WLLT-IC and WLRT-IC), the Animal Fluency Test (AFT), and the Digit Symbol Substitution Test (DSST). The CERAD WLLT, WLLT-IC, WLRT, and WLRT-IC assess the immediate and delayed learning ability for novel verbal information and comprises three learning trials followed by a delayed recall challenge with a score ranging from 0 to 10. The AFT examines executive function by measuring categorical verbal fluency ranging from 3 to 39. The DSST consists of a performance challenge from the Wechsler Adult Intelligence Scale-III, which evaluates processing speed, sustained attention, and working memory, varying between 0 and 105. Higher test scores represent better cognitive performance.
Depression was measured using the Patient Health Questionnaire-9 (PHQ-9), a nine-item survey designed to diagnose the presence and frequency of depression symptoms over the previous 2 weeks. The score for each question ranges between 0 and 3 (0 = “not at all”, 1 = “several days”, 2 = “more than half the days”, 3 = “nearly every day”), reaching a total of up to 27. Clinically significant depression symptoms were defined using the standard cut-off point of the PHQ-9 score of 10 or greater.
Age, sex, ethnicity (race), family income status and size, education level, marital status, and US citizenship were treated as sociodemographic covariates. Medical history and status in terms of chronic illnesses based on the self-reported incidence of high blood pressure, diabetes, congestive heart failure, coronary heart disease, heart attack, stroke, memory–cognitive function loss, or cancer were also considered. Age was classified into groups between 60–69, 70–79 and ≥80 years of age. Ethnic groups comprised of Mexican American, other Hispanic, non-Hispanic White, non-Hispanic Black, non-Hispanic Asian, and other (multi) race. Family income status was categorised based on the family to income poverty ration (FIPR) as low–middle (FIPR < 1) and middle–high (FIPR ≥ 1). Family size of 6 or more individuals was defined as large and below 6 as small–medium. Education level was regarded as a college degree at minimum or below. US citizenship was assessed based on citizenship by birth or naturalization. Marital status comprised of four categories: never married, married, widowed/divorced/separated, and living with a partner. All covariates were treated as categorical variables.
Analyses were performed using survey weights to control for the multistage probability sampling design of NHANES. Participants without a response for any of the tests or questionnaires were excluded. A logistic regression model was used to assess and measure the association between cognitive performance and depression symptoms in older US adults after adjustment for all covariates. A sensitivity analysis treating cognitive performance and depression symptoms as continuous factors was also undertaken and the former relationship was reassessed with a linear regression model. The odds ratios (ORs) with 95% confidence intervals (CIs), and regression coefficients (β) were reported from the logistic and linear regression models. Statistical significance was established as p < 0.05. Statistical analysis was performed using the IBM SPSS statistics software (Version 28.0, IBM Corp., Armonk, NY, USA).

3. Results

Our study included 1622 participants (mean (SEM) age, 70 (0.2) years; 873 female participants (54%); 749 male participants (46%)) which represented a survey-weighted 860,400 US older adults, of whom 48% were non-Hispanic White (783) and 28% non-Hispanic Black (349). The majority of the participants held US citizenship (95% (1548)) and had a family income above the poverty threshold (82% (1335)). Most participants were married (54% (879)), of small family size (95% (1546)) and had a high school qualification or below at minimum (80% (1296)). A history of at least one chronic condition was reported in 91% of participants (1478), memory–cognitive function loss in 15% (248) and stroke in 9% (148). The average score for the CERAD WLLT was 18.8 (±0.1) out of 30, 5.8 (±0.1) of out 10 for the CERAD WLRT, 0.5 (±0.03) out of 12 for the CERAD WLLT-IC, 0.3 (±0.02) out of 10 for the CERAD WLRT-IC, 16.2 (±0.1) out of 40 for the AFT, 44.4 (±10.4) out of 100 for the DSST and 86.0 (±0.6) for their composite. Most participants (90% (1458)) had a score below the threshold for clinically significant depression symptoms. All baseline participant characteristics are described in Table 1.
Cognitive function performance was significantly and negatively associated with clinically significant depression symptoms (p = 0.03) in older US adults after controlling for multistage sampling, sociodemographic and medical history covariates (Figure 1). In particular, participants with significant deficits (Q1; below the 25th percentile) and above average (Q3; above the 50th percentile) cognitive performance had approximately 150% (OR: 2.457 [1.219–4.953]) and 40% (OR: 1.378 [0.741–2.559]) increased odds of reporting a PHQ-9 score above threshold, when compared to those with highest cognitive performance (Q4; above the 75th percentile). Participants with cognitive performance below average (Q2; between the 25th and 50th percentiles) had smaller difference in the likelihood (OR: 1.016 [0.427–2.414]). An overall similar association between cognitive function and depression symptoms (p = 0.00028, β: −0.03) was observed in the sensitivity analysis.
On a test-by-test basis, cognitive performance in terms of DSST was significantly associated with depression symptoms (p = 0.006) in older US adults. Specifically, participants with cognitive performance in Q1 and Q2 had increased odds of almost 300% (OR: 3.824 [1.069–13.678]) and 200% (OR: 2.087 [0.659–6.609]) for PHQ-9 score above threshold when compared to those in Q4. Participants in Q2 had less variation in the likelihood (OR: 1.172 [0.360–3.816]) of reporting depression symptoms. Similar association between DSST and depression (p < 0.001, β: −0.044) was seen in the sensitivity analysis. No associations were observed for WLLT (p = 0.311) and WLRT (p = 0.838). Analysis based on WLLT-IC, WLLR-IC and AFT were not possible due to incomplete representation among quartiles.

4. Discussion

Using a nationally representative sample from NHANES 2011 to 2014, our cross-sectional study showed that cognitive performance including DSST was negatively associated with clinically significant depression symptoms in older US adults after adjusting for sampling, sociodemographic and medical history indicators. In particular, individuals with significant cognitive deficits had two and half times higher odds and up to four times compared to those with highest cognitive performance.
Our results align with previous research that examined the relationship between cognitive function and depression. Numerous population-based studies have shed light on the intricate relationship between late-life depression and its symptoms with cognitive decline [4,5,8,9,10], mild cognitive impairment [11,12,13] and dementia [13,14,15,16]. In fact, the links between depression and poor performance in cognitive function have led to the belief that it may act as an early indicator of dementia. In support of this, several studies have reported the association between depressive symptoms in late-life and decreased performance on specific aspects of cognitive function [5,10,17,18,19,20,21].
The trend of negative associations between late-life depression and cognitive function is evident, however some discrepancies exist. While some studies report associations across all domains of cognitive function, others only report in specific aspects and some only finding connections during certain comorbidity periods. In the same way, the rate of cognitive decline among older adults also appears at variance [5,22]. Likely, this may be largely due to the varied methods used in defining late-life depression and depressive symptoms, as well as the different means of testing cognitive function among older adults. Nevertheless, these studies along with ours contribute to a comprehensive picture of the impact of late-life depression on cognitive function, while the existing variations highlight the need for further research in this field.
The underlying causes of normal aging-related cognitive impairment have been widely discussed and investigated, with multiple mechanisms being proposed. At the cellular level, it is believed that the aging process contributes to decreased neuronal plasticity, partly by suppressing prefrontal cortex activation while reducing hippocampal neurogenesis [23,24]. These regions are particularly vulnerable to age-related changes in neural systems [25] and their attenuated activation could have a negative impact on cognitive function [26]. Likewise, a decline in brain volume, especially of the prefrontal cortex, has been linked to impairments in the executive cognitive function [27]. On the molecular level, a reduction in neurotrophic factors such BDNF [28] and neurotransmitters such as dopamine [29], combined with increased levels of glucocorticoids including cortisol [30], may lead to neurostructural and neurofunctional deterioration during aging, which can then result in diminished cognitive functioning.
The relationship between depression and cognitive impairment is influenced by various neurobiological factors often described by genetic, neuroimaging, and neurotrophin alterations [31]. For example, the epsilon 4 allele of the apolipoprotein E epsilon 4 gene (APOE-e4) is a known genetic risk factor for Alzheimer’s disease (AD) and has been linked to depression, with studies suggesting an additive risk for cognitive impairment and a synergistic effect in increasing the incidence of dementia and mild cognitive impairment [32,33,34,35,36,37,38]. Additionally, dysregulation of the hypothalamic-pituitary-adrenal axis in individuals with depression can result in elevated cortisol levels and consequent hippocampal atrophy, which is also seen in AD [39,40,41]. Depression and cognitive impairment have also been associated with changes in white matter, such as increased atrophy in regions affected by AD and reduced activation in neural networks [42,43,44,45,46,47]. Furthermore, brain-derived neurotrophic factor (BDNF) and transforming growth factor-beta1 (TGF-β1) are implicated in the pathophysiology of depression and cognitive impairment, with the BDNF Val66Met allele linked to late-life depression and the BDNF 11757C allele associated with depression in AD, and the C/C phenotype of TGF-β1 linked to an increased risk of AD and depression in AD patients [48,49,50,51,52,53].
The connection between cognitive function and depression likely also represents the drastic changes in lifestyles and circumstances that these critical age groups experience, as individuals reach retirement age [54]. Previous research has provided evidence that retirement is associated with a higher risk of depression and a modest but negative effect on cognition [55,56,57]. Most prominently, the decline in cognitive abilities from reaching retirement age can result in disruptions in social networks and withdrawal from social activities [58,59,60,61]. This can lead to symptoms such as a loss of interest, changes in appetite, psychomotor retardation, and sleep disorders, which further complicates changes in bodily and mental functions that come with aging [62]. In this way, retired older adults become more vulnerable to loneliness, negative emotions and social isolation in their daily lives with significant consequences on overall health and well-being.
All in all, the inverse relationship between cognitive function and depression among older adults underscores the significance of proactive diagnostic evaluations and social support that may help counteract the negative impact of cognitive decline on the onset of late-life depression. Most prominently, it highlights the critical requirement for environments and healthcare interventions that address the unique needs and challenges of this age group.

5. Conclusions

The interplay between late-life cognitive impairment and depression may in part appear correctable. Our moderate findings reinforce the need for health policy makers to design adaptable health practices that could potentially alleviate the risks from depression, cognitive impairment, and the associated consequences. Efforts to decipher the mechanism from which this inverse association emerges will maximise the effectiveness of health initiatives and improve public health benefits for older US adults.

Author Contributions

P.G. conceptualized the idea. O.D. collected the data and performed the analysis. P.G. drafted the manuscript. O.D. and P.G. revised the manuscript. All authors have read and agreed to the published version of the manuscript.

Funding

This study was not supported in any part by grant or by a teaching or research scholarship.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data sets analysed in this study can be accessed in the 2011–2014 National Health and Nutrition Examination Survey (NHANES; https://www.cdc.gov/nchs/nhanes/index.htm, accessed on 23 December 2022).

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Brody, D.J.; Kramarow, E.A.; Taylor, C.A.; McGuire, L.C. Cognitive Performance in Adults Aged 60 and Over: National Health and Nutrition Examination Survey, 2011–2014; National Center for Health Statistics: Hyattsville, MD, USA, 2019.
  2. Rose, E.; Ebmeier, K. Pattern of impaired working memory during major depression. J. Affect. Disord. 2006, 90, 149–161. [Google Scholar] [CrossRef]
  3. Snyder, H.R. Major depressive disorder is associated with broad impairments on neuropsychological measures of executive function: A meta-analysis and review. Psychol. Bull. 2013, 139, 81. [Google Scholar] [CrossRef] [Green Version]
  4. Hu, L.; Smith, L.; Imm, K.R.; Jackson, S.E.; Yang, L. Physical activity modifies the association between depression and cognitive function in older adults. J. Affect. Disord. 2019, 246, 800–805. [Google Scholar] [CrossRef]
  5. Wei, J.; Ying, M.; Xie, L.; Chandrasekar, E.K.; Lu, H.; Wang, T.; Li, C. Late-life depression and cognitive function among older adults in the US: The National Health and Nutrition Examination Survey, 2011–2014. J. Psychiatr. Res. 2019, 111, 30–35. [Google Scholar] [CrossRef] [PubMed]
  6. Demakakos, P.; Muniz-Terrera, G.; Nouwen, A. Type 2 diabetes, depressive symptoms and trajectories of cognitive decline in a national sample of community-dwellers: A prospective cohort study. PLoS ONE 2017, 12, e0175827. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  7. Gale, C.; Allerhand, M.; Deary, I. Is there a bidirectional relationship between depressive symptoms and cognitive ability in older people? A prospective study using the English Longitudinal Study of Ageing. Psychol. Med. 2012, 42, 2057–2069. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  8. Paterniti, S.; Verdier-Taillefer, M.-H.; Dufouil, C.; Alpérovitch, A. Depressive symptoms and cognitive decline in elderly people: Longitudinal study. Br. J. Psychiatry 2002, 181, 406–410. [Google Scholar] [CrossRef] [Green Version]
  9. Wilson, R.; De Leon, C.M.; Bennett, D.; Bienias, J.; Evans, D. Depressive symptoms and cognitive decline in a community population of older persons. J. Neurol. Neurosurg. Psychiatry 2004, 75, 126–129. [Google Scholar]
  10. Yaffe, K.; Blackwell, T.; Gore, R.; Sands, L.; Reus, V.; Browner, W.S. Depressive symptoms and cognitive decline in nondemented elderly women: A prospective study. Arch. Gen. Psychiatry 1999, 56, 425–430. [Google Scholar] [CrossRef]
  11. Kopchak, O.; Pulyk, O. Association between depressive symptoms and cognitive impairment in patients with metabolic syndrome. Wiad. Lek. 2017, 70, 737–741. [Google Scholar]
  12. Ravaglia, G.; Forti, P.; Lucicesare, A.; Rietti, E.; Pisacane, N.; Mariani, E.; Dalmonte, E. Prevalent depressive symptoms as a risk factor for conversion to mild cognitive impairment in an elderly Italian cohort. Am. J. Geriatr. Psychiatry 2008, 16, 834–843. [Google Scholar] [CrossRef]
  13. Spira, A.P.; Rebok, G.W.; Stone, K.L.; Kramer, J.H.; Yaffe, K. Depressive symptoms in oldest-old women: Risk of mild cognitive impairment and dementia. Am. J. Geriatr. Psychiatry 2012, 20, 1006–1015. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  14. Mirza, S.S.; Wolters, F.J.; Swanson, S.A.; Koudstaal, P.J.; Hofman, A.; Tiemeier, H.; Ikram, M.A. 10-year trajectories of depressive symptoms and risk of dementia: A population-based study. Lancet Psychiatry 2016, 3, 628–635. [Google Scholar] [CrossRef] [PubMed]
  15. Saczynski, J.S.; Beiser, A.; Seshadri, S.; Auerbach, S.; Wolf, P.; Au, R. Depressive symptoms and risk of dementia: The Framingham Heart Study. Neurology 2010, 75, 35–41. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  16. van Uden, I.W.; van der Holst, H.M.; van Leijsen, E.M.; Tuladhar, A.M.; van Norden, A.G.; de Laat, K.F.; Claassen, J.A.; van Dijk, E.J.; Kessels, R.P.; Richard, E. Late-onset depressive symptoms increase the risk of dementia in small vessel disease. Neurology 2016, 87, 1102–1109. [Google Scholar] [CrossRef] [PubMed]
  17. da Costa Dias, F.L.; Teixeira, A.L.; Guimarães, H.C.; Barbosa, M.T.; Resende, E.d.P.F.; Beato, R.G.; Carmona, K.C.; Caramelli, P. Cognitive performance of community-dwelling oldest-old individuals with major depression: The Pietà study. Int. Psychogeriatr. 2017, 29, 1507–1513. [Google Scholar] [CrossRef] [PubMed]
  18. Ganguli, M.; Du, Y.; Dodge, H.H.; Ratcliff, G.G.; Chang, C.-C.H. Depressive symptoms and cognitive decline in late life: A prospective epidemiological study. Arch. Gen. Psychiatry 2006, 63, 153–160. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  19. Hamilton, J.L.; Brickman, A.M.; Lang, R.; Byrd, G.S.; Haines, J.L.; Pericak-Vance, M.A.; Manly, J.J. Relationship between depressive symptoms and cognition in older, non-demented African Americans. J. Int. Neuropsychol. Soc. 2014, 20, 756–763. [Google Scholar] [CrossRef] [Green Version]
  20. Morin, R.T.; Midlarsky, E. Depressive symptoms and cognitive functioning among older adults with cancer. Aging Ment. Health 2018, 22, 1465–1470. [Google Scholar] [CrossRef]
  21. Shimada, H.; Park, H.; Makizako, H.; Doi, T.; Lee, S.; Suzuki, T. Depressive symptoms and cognitive performance in older adults. J. Psychiatr. Res. 2014, 57, 149–156. [Google Scholar] [CrossRef]
  22. Hayden, K.M.; Reed, B.R.; Manly, J.J.; Tommet, D.; Pietrzak, R.H.; Chelune, G.J.; Yang, F.M.; Revell, A.J.; Bennett, D.A.; Jones, R.N. Cognitive decline in the elderly: An analysis of population heterogeneity. Age Ageing 2011, 40, 684–689. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  23. West, R.L. An application of prefrontal cortex function theory to cognitive aging. Psychol. Bull. 1996, 120, 272. [Google Scholar] [CrossRef] [PubMed]
  24. Van Praag, H.; Shubert, T.; Zhao, C.; Gage, F.H. Exercise enhances learning and hippocampal neurogenesis in aged mice. J. Neurosci. 2005, 25, 8680–8685. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  25. Grady, C.L.; Springer, M.V.; Hongwanishkul, D.; McIntosh, A.R.; Winocur, G. Age-related changes in brain activity across the adult lifespan. J. Cogn. Neurosci. 2006, 18, 227–241. [Google Scholar] [CrossRef]
  26. Villeda, S.A.; Luo, J.; Mosher, K.I.; Zou, B.; Britschgi, M.; Bieri, G.; Stan, T.M.; Fainberg, N.; Ding, Z.; Eggel, A. The ageing systemic milieu negatively regulates neurogenesis and cognitive function. Nature 2011, 477, 90–94. [Google Scholar] [CrossRef] [Green Version]
  27. Voelcker-Rehage, C.; Niemann, C.; Godde, B. The chronic exercise–cognition interaction in older adults. In Exercise-Cognition Interaction: Neuroscience Perspectives; Elsevier Academic Press: Cambridge, MA, USA, 2016. [Google Scholar]
  28. Lommatzsch, M.; Zingler, D.; Schuhbaeck, K.; Schloetcke, K.; Zingler, C.; Schuff-Werner, P.; Virchow, J.C. The impact of age, weight and gender on BDNF levels in human platelets and plasma. Neurobiol. Aging 2005, 26, 115–123. [Google Scholar] [CrossRef]
  29. Li, S.-C.; Lindenberger, U.; Bäckman, L. Dopaminergic modulation of cognition across the life span. Neurosci. Biobehav. Rev. 2010, 34, 625–630. [Google Scholar] [CrossRef]
  30. Lupien, S.; Lecours, A.; Schwartz, G.; Sharma, S.; Hauger, R.L.; Meaney, M.J.; Nair, N. Longitudinal study of basal cortisol levels in healthy elderly subjects: Evidence for subgroups. Neurobiol. Aging 1996, 17, 95–105. [Google Scholar] [CrossRef]
  31. Pellegrino, L.D.; Peters, M.E.; Lyketsos, C.G.; Marano, C.M. Depression in cognitive impairment. Curr. Psychiatry Rep. 2013, 15, 1–8. [Google Scholar] [CrossRef] [Green Version]
  32. Irie, F.; Masaki, K.H.; Petrovitch, H.; Abbott, R.D.; Ross, G.W.; Taaffe, D.R.; Launer, L.J.; White, L.R. Apolipoprotein E ε4 allele genotype and the effect of depressive symptoms on the risk of dementia in men: The Honolulu-Asia aging study. Arch. Gen. Psychiatry 2008, 65, 906–912. [Google Scholar] [CrossRef] [Green Version]
  33. Wang, W.-w.; Liu, X.-l.; Ruan, Y.; Wang, L.; Bao, T. Depression was associated with apolipoprotein E ε4 allele polymorphism: A meta-analysis. Iran. J. Basic Med. Sci. 2019, 22, 112–117. [Google Scholar] [PubMed]
  34. Bonk, S.; Kirchner, K.; Ameling, S.; Garvert, L.; Völzke, H.; Nauck, M.; Völker, U.; Grabe, H.J.; Van der Auwera, S. APOE ε4 in Depression-Associated Memory Impairment—Evidence from Genetic and MicroRNA Analyses. Biomedicines 2022, 10, 1560. [Google Scholar] [CrossRef] [PubMed]
  35. Supasitthumrong, T.; Tunvirachaisakul, C.; Aniwattanapong, D.; Tangwongchai, S.; Chuchuen, P.; Tawankanjanachot, I.; Snabboon, T.; Hemrungrojn, S.; Carvalho, A.F.; Maes, M. peripheral blood biomarkers coupled with the apolipoprotein e4 genotype are strongly associated with semantic and episodic memory impairments in elderly subjects with amnestic mild cognitive impairment and Alzheimer’s disease. J. Alzheimer’s Dis. 2019, 71, 797–811. [Google Scholar] [CrossRef] [PubMed]
  36. Fritze, F.; Ehrt, U.; Sønnesyn, H.; Kurz, M.; Hortobágyi, T.; Nore, S.P.; Ballard, C.; Aarsland, D. Depression in mild dementia: Associations with diagnosis, APOE genotype and clinical features. Int. J. Geriatr. Psychiatry 2011, 26, 1054–1061. [Google Scholar] [CrossRef]
  37. Kim, J.M.; Stewart, R.; Kim, S.Y.; Kim, S.W.; Bae, K.Y.; Yang, S.J.; Shin, I.S.; Yoon, J.S. Synergistic associations of depression and apolipoprotein E genotype with incidence of dementia. Int. J. Geriatr. Psychiatry 2011, 26, 893–898. [Google Scholar] [CrossRef]
  38. Caracciolo, B.; Bäckman, L.; Monastero, R.; Winblad, B.; Fratiglioni, L. The symptom of low mood in the prodromal stage of mild cognitive impairment and dementia: A cohort study of a community dwelling elderly population. J. Neurol. Neurosurg. Psychiatry 2011, 82, 788–793. [Google Scholar] [CrossRef] [PubMed]
  39. den Heijer, T.; Tiemeier, H.; Luijendijk, H.J.; van der Lijn, F.; Koudstaal, P.J.; Hofman, A.; Breteler, M.M. A study of the bidirectional association between hippocampal volume on magnetic resonance imaging and depression in the elderly. Biol. Psychiatry 2011, 70, 191–197. [Google Scholar] [CrossRef]
  40. Steffens, D.C.; McQuoid, D.R.; Payne, M.E.; Potter, G.G. Change in hippocampal volume on magnetic resonance imaging and cognitive decline among older depressed and nondepressed subjects in the neurocognitive outcomes of depression in the elderly study. Am. J. Geriatr. Psychiatry 2011, 19, 4–12. [Google Scholar] [CrossRef] [Green Version]
  41. Tsopelas, C.; Stewart, R.; Savva, G.M.; Brayne, C.; Ince, P.; Thomas, A.; Matthews, F.E. Neuropathological correlates of late-life depression in older people. Br. J. Psychiatry 2011, 198, 109–114. [Google Scholar] [CrossRef] [Green Version]
  42. Lee, G.J.; Lu, P.H.; Hua, X.; Lee, S.; Wu, S.; Nguyen, K.; Teng, E.; Leow, A.D.; Jack, C.R., Jr.; Toga, A.W. Depressive symptoms in mild cognitive impairment predict greater atrophy in Alzheimer’s disease-related regions. Biol. Psychiatry 2012, 71, 814–821. [Google Scholar] [CrossRef] [Green Version]
  43. O’brien, J.; Perry, R.; Barber, R.; Gholkar, A.; Thomas, A. The association between white matter lesions on magnetic resonance imaging and noncognitive symptoms. Ann. N. Y. Acad. Sci. 2000, 903, 482–489. [Google Scholar] [CrossRef] [PubMed]
  44. Mueller, S.G.; Mack, W.J.; Mungas, D.; Kramer, J.H.; Cardenas-Nicolson, V.; Lavretsky, H.; Greene, M.; Schuff, N.; Chui, H.C.; Weiner, M.W. Influences of lobar gray matter and white matter lesion load on cognition and mood. Psychiatry Res. Neuroimaging 2010, 181, 90–96. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  45. Wang, L.; Potter, G.G.; Krishnan, R.R.; Dolcos, F.; Smith, G.S.; Steffens, D.C. Neural correlates associated with cognitive decline in late-life depression. Am. J. Geriatr. Psychiatry 2012, 20, 653–663. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  46. Kang, J.Y.; Lee, J.S.; Kang, H.; Lee, H.-W.; Kim, Y.K.; Jeon, H.J.; Chung, J.-K.; Lee, M.C.; Cho, M.J.; Lee, D.S. Regional cerebral blood flow abnormalities associated with apathy and depression in Alzheimer disease. Alzheimer Dis. Assoc. Disord. 2012, 26, 217–224. [Google Scholar] [CrossRef] [PubMed]
  47. Kumar, A.; Kepe, V.; Barrio, J.R.; Siddarth, P.; Manoukian, V.; Elderkin-Thompson, V.; Small, G.W. Protein binding in patients with late-life depression. Arch. Gen. Psychiatry 2011, 68, 1143–1150. [Google Scholar] [CrossRef] [Green Version]
  48. Taylor, W.D.; Züchner, S.; McQuoid, D.R.; Steffens, D.C.; Speer, M.C.; Krishnan, K.R.R. Allelic differences in the brain-derived neurotrophic factor Val66Met polymorphism in late-life depression. Am. J. Geriatr. Psychiatry 2007, 15, 850–857. [Google Scholar] [CrossRef] [PubMed]
  49. Borroni, B.; Grassi, M.; Archetti, S.; Costanzi, C.; Bianchi, M.; Caimi, L.; Caltagirone, C.; Di Luca, M.; Padovani, A. BDNF genetic variations increase the risk of Alzheimer’s disease-related depression. J. Alzheimer’s Dis. 2009, 18, 867–875. [Google Scholar] [CrossRef] [PubMed]
  50. Borroni, B.; Archetti, S.; Costanzi, C.; Grassi, M.; Ferrari, M.; Radeghieri, A.; Caimi, L.; Caltagirone, C.; Di Luca, M.; Padovani, A. Role of BDNF Val66Met functional polymorphism in Alzheimer’s disease-related depression. Neurobiol. Aging 2009, 30, 1406–1412. [Google Scholar] [CrossRef]
  51. Zhang, L.; Fang, Y.; Zeng, Z.; Lian, Y.; Wei, J.; Zhu, H.; Jia, Y.; Zhao, X.; Xu, Y. BDNF gene polymorphisms are associated with Alzheimer’s disease-related depression and antidepressant response. J. Alzheimer’s Dis. 2011, 26, 523–530. [Google Scholar] [CrossRef] [Green Version]
  52. Caraci, F.; Copani, A.; Nicoletti, F.; Drago, F. Depression and Alzheimer’s disease: Neurobiological links and common pharmacological targets. Eur. J. Pharmacol. 2010, 626, 64–71. [Google Scholar] [CrossRef]
  53. Caraci, F.; Bosco, P.; Signorelli, M.; Spada, R.S.; Cosentino, F.I.; Toscano, G.; Bonforte, C.; Muratore, S.; Prestianni, G.; Panerai, S. The CC genotype of transforming growth factor-β1 increases the risk of late-onset Alzheimer’s disease and is associated with AD-related depression. Eur. Neuropsychopharmacol. 2012, 22, 281–289. [Google Scholar] [CrossRef] [PubMed]
  54. Jung, M.; Kim, H.; Loprinzi, P.D.; Ryu, S.; Kang, M. Age-varying association between depression and cognitive function among a national sample of older US immigrant adults: The potential moderating role of physical activity. Aging Ment. Health 2022, 1–10. [Google Scholar]
  55. Mukku, S.S.R.; Harbishettar, V.; Sivakumar, P. Psychological morbidity after job retirement: A review. Asian J. Psychiatry 2018, 37, 58–63. [Google Scholar] [CrossRef] [PubMed]
  56. Li, W.; Ye, X.; Zhu, D.; He, P. The longitudinal association between retirement and depression: A systematic review and meta-analysis. Am. J. Epidemiol. 2021, 190, 2220–2230. [Google Scholar] [CrossRef]
  57. Atalay, K.; Barrett, G.F.; Staneva, A. The effect of retirement on elderly cognitive functioning. J. Health Econ. 2019, 66, 37–53. [Google Scholar] [CrossRef]
  58. Shouse, J.N.; Rowe, S.V.; Mast, B.T. Depression and cognitive functioning as predictors of social network size. Clin. Gerontol. 2013, 36, 147–161. [Google Scholar] [CrossRef]
  59. Li, C.; Jiang, S.; Li, N.; Zhang, Q. Influence of social participation on life satisfaction and depression among Chinese elderly: Social support as a mediator. J. Community Psychol. 2018, 46, 345–355. [Google Scholar] [CrossRef]
  60. Noguchi, T.; Saito, M.; Aida, J.; Cable, N.; Tsuji, T.; Koyama, S.; Ikeda, T.; Osaka, K.; Kondo, K. Association between social isolation and depression onset among older adults: A cross-national longitudinal study in England and Japan. BMJ Open 2021, 11, e045834. [Google Scholar] [CrossRef]
  61. Zhou, S.; Li, K.; Ogihara, A.; Wang, X. Association between social capital and depression among older adults of different genders: Evidence from Hangzhou, China. Front. Public Health 2022, 10. [Google Scholar] [CrossRef]
  62. Sözeri-Varma, G. Depression in the elderly: Clinical features and risk factors. Aging Dis. 2012, 3, 465. [Google Scholar]
Ijerph 20 05290 g001 550
Figure 1. Logistic relationship between cognitive function and depression in older (≥60) US adults after adjustment for sociodemographic factors and medical history in terms of self-reported chronic condition and incidence of stroke or memory–cognitive function loss. Cognitive function was expressed as a composite score and on a test-by-test basis based on the Consortium to Establish a Registry for Alzheimer’s Disease Word List Learning Test, the Word List Recall Test, the Intrusion Word Count Test, the Animal Fluency Test, and the Digit Symbol Substitution Test. Depression was defined as clinically significant depression symptoms based on the standard cut-off point of Patient Health Questionnaire-9 (PHQ-9) score of 10 or greater. Significant associations between cognitive function including its components and depression symptoms are shown. Solid lines represent the estimated odds ratio and the area bound by the dashed lines represents the 95% confidence interval. Cubic splines with three knots were used for modelling. The highest quartile of cognitive performance and PHQ-9 score above threshold were used as the reference.
Figure 1. Logistic relationship between cognitive function and depression in older (≥60) US adults after adjustment for sociodemographic factors and medical history in terms of self-reported chronic condition and incidence of stroke or memory–cognitive function loss. Cognitive function was expressed as a composite score and on a test-by-test basis based on the Consortium to Establish a Registry for Alzheimer’s Disease Word List Learning Test, the Word List Recall Test, the Intrusion Word Count Test, the Animal Fluency Test, and the Digit Symbol Substitution Test. Depression was defined as clinically significant depression symptoms based on the standard cut-off point of Patient Health Questionnaire-9 (PHQ-9) score of 10 or greater. Significant associations between cognitive function including its components and depression symptoms are shown. Solid lines represent the estimated odds ratio and the area bound by the dashed lines represents the 95% confidence interval. Cubic splines with three knots were used for modelling. The highest quartile of cognitive performance and PHQ-9 score above threshold were used as the reference.
Ijerph 20 05290 g001
Table
Table 1. Socio-demographic and medical history characteristics of participants (n = 1622). Values are expressed as count (percentage) unless otherwise specified.
Table 1. Socio-demographic and medical history characteristics of participants (n = 1622). Values are expressed as count (percentage) unless otherwise specified.
CharacteristicsComposition
Age (year groups)
60–69829 (51.1)
70–79497 (30.6)
≥80296 (18.2)
Sex
Male749 (46.2)
Female873 (53.8)
Ethnicity
Mexican American123 (7.6)
Other Hispanic137 (8.4)
Non-Hispanic White783 (48.3)
Non-Hispanic Black449 (27.7)
Non-Hispanic Asian106 (6.5)
Other Race-including multiracial24 (1.5)
Family Income
Low–Middle287 (17.7)
Middle–High1335 (82.3)
Family Size
Small1546 (95.3)
Large76 (4.7)
Educational level
High School graduate or below1296 (79.9)
College degree or above326 (20.1)
Marital Status
Married879 (54.2)
Widowed356 (21.9)
Divorced214 (13.2)
Separated50 (3.1)
Never married91 (5.6)
Living with partner32 (2.0)
Citizenship
US Citizen1548 (95.4)
Non-US Citizen74 (4.6)
Chronic Condition
Yes144 (8.9)
No1478 (91.1)
Stroke
Yes248 (15.3)
No1374 (84.7)
Memory–Cognitive function loss
Yes148 (9.1)
No1474 (90.9)
CERAD WLLT (score)
Minimum0
Average *18.8 (0.1)
Maximum30
CERAD WLRT (score)
Minimum0
Average *5.8 (0.1)
Maximum10
CERAD WLLT-IC (score)
Minimum0
Average *0.5 (0.02)
Maximum15
CERAD WLRT-IC (score)
Minimum0
Average *0.3 (0.02)
Maximum8
AFT (score)
Minimum3
Average *16.2 (0.1)
Maximum39
DSST (score)
Minimum0
Average *44.4 (0.4)
Maximum105
Composite Cognition (Score)
Minimum15
Average *86.0 (0.6)
Maximum165
PHQ-9 thresold (Score)
Below 101458 (89.9)
10 or above164 (10.1)
* Values expressed as mean (standard error). AFT = Animal Fluency Test; CERAD = Consortium to Establish a Registry for Alzheimer’s Disease; DSST = Digit Symbol Substitution Test; PHQ-9 = Patient Health Questionnaire-9; WLLT = Word List Learning Test; WLRT = Word List Recall Test; WLLT-IC = Word List Learning Test–Intrusion Word Count; WLRT-IC = Word List Recall Test–Intrusion Word Count.
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Delardas, O.; Giannos, P. Cognitive Performance Deficits Are Associated with Clinically Significant Depression Symptoms in Older US Adults. Int. J. Environ. Res. Public Health 2023, 20, 5290. https://doi.org/10.3390/ijerph20075290

AMA Style

Delardas O, Giannos P. Cognitive Performance Deficits Are Associated with Clinically Significant Depression Symptoms in Older US Adults. International Journal of Environmental Research and Public Health. 2023; 20(7):5290. https://doi.org/10.3390/ijerph20075290

Chicago/Turabian Style

Delardas, Orestis, and Panagiotis Giannos. 2023. "Cognitive Performance Deficits Are Associated with Clinically Significant Depression Symptoms in Older US Adults" International Journal of Environmental Research and Public Health 20, no. 7: 5290. https://doi.org/10.3390/ijerph20075290

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