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. Author manuscript; available in PMC: 2021 Feb 12.
Published in final edited form as: J Am Geriatr Soc. 2020 May 13;68(8):1834–1841. doi: 10.1111/jgs.16468

Patterns of Association between Depressive Symptoms and Chronic Medical Morbidities in Older Adults

Bruno Agustini *, Mojtaba Lotfaliany †, Robyn L Woods ‡, John J McNeil ‡, Mark R Nelson §, Raj C Shah , Anne M Murray ∥, Michael E Ernst **, Christopher M Reid ‡,††, Andrew Tonkin ‡, Jessica E Lockery ‡, Lana J Williams *, Michael Berk *,‡,‡‡, Mohammadreza Mohebbi *,†; ASPREE Investigator Group
PMCID: PMC7879564  NIHMSID: NIHMS1595520  PMID: 32402115

Abstract

OBJECTIVES:

To investigate the association between depressive symptoms and several medical morbidities, and their combination, in a large older population.

DESIGN:

Cross-sectional study of baseline data from the ASPirin in Reducing Events in the Elderly (ASPREE) trial.

SETTING:

Multicentric study conducted in Australia and the United States.

PARTICIPANTS:

A total of 19,110 older adults (mean age = 75 years [standard deviation = ±4.5]).

MEASUREMENTS:

Depressive symptoms were measured using the Center for Epidemiological Studies Depression (CES-D 10) scale. Medical morbidities were defined according to condition-specific methods. Logistic regression was used to calculate odds ratios (ORs) with 95% confidence intervals (CIs) to test associations before and after accounting for possible confounders.

RESULTS:

Depressive symptoms were significantly associated with obesity (OR = 1.19; 95% CI = 1.07–1.32), diabetes (OR = 1.22; 95% CI = 1.05–1.42), gastroesophageal reflux disease (GERD) (OR = 1.41; 95% CI = 1.28–1.57), metabolic syndrome (OR = 1.16; 95% CI = 1.03–1.29), osteoarthritis (OR = 1.41; 95% CI = 1.27–1.57), respiratory conditions (OR = 1.25; 95% CI = 1.10–1.42), history of cancer (OR = 1.19; 95% CI = 1.05–1.34), Parkinson’s disease (OR = 2.56; 95% CI = 1.83–3.56), polypharmacy (OR = 1.60; 95% CI = 1.44–1.79), and multimorbidity (OR = 1.29; 95% CI = 1.12–1.49). No significant association was observed between depressive symptoms and hypertension, chronic kidney disease, dyslipidemia, and gout (P > .05). A significant dose-response relationship was evident between the number of medical comorbidities and the prevalence of depression (OR = 1.18; 95% CI = 1.13–1.22).

CONCLUSION:

Late-life depressive symptoms are significantly associated with several medical morbidities, and there appears to be a cumulative effect of the number of somatic diseases on the prevalence of depression. These findings augment the evidence for a complex relationship between mental and physical health in an otherwise healthy older population and might guide clinicians toward early recognition of high-risk individuals.

Keywords: depression, late-life depression, medical comorbidity, somatic conditions, polypharmacy

Understanding the complex interactions between mental and physical health in older individuals is needed to guide public health efforts and to extend healthy life span and quality of life in a progressively older global population. Data from high-income countries suggest people aged 65 years and older will spend almost half of their remaining years with at least one limiting chronic physical and/or mental condition, with this figure expected to rise following the world’s demographic shift.1 Depression in older adults is a similarly highly debilitating and prevalent disorder and has been bidirectionally associated with several somatic consequences, further increasing its burden of disease. The presence of late-life depression in the context of physical comorbidities is associated with poorer recognition, increased health costs, and increased mortality, emphasizing the pressing need for a deeper understanding of this relationship.2–4

Depression has been associated with a myriad of chronic conditions such as diabetes mellitus,5 hypertension,6 heart disease,7 stroke,8 cancer,9 obesity,10 osteoarthritis,11 gout,12 gastroesophageal reflux disease (GERD),13 chronic kidney disease,14 and neurodegenerative conditions including Parkinson’s disease and dementia.15 This is at least in part mediated by common environmental risks and shared biological pathways for these disorders.16 Furthermore, chronic conditions frequently cluster in older adults, and multimorbidity (the presence of two or more chronic medical disorders) is a pressing issue for global healthcare systems, mainly organized to address single conditions. In this framework, older individuals often have to face multiple healthcare providers, increasing the chances of inappropriate polypharmacy, adverse medication effects, and poor adherence, resulting in risks for iatrogenic complications and suboptimal care.17 Several drugs may have adverse effects on mood, and evidence indicates that polypharmacy is a particular issue in older individuals with depression.18,19 The combination of multimorbidity, polypharmacy, and late-life depression potentiates their individual consequences, resulting in a compound effect that may be bigger than the sum of its parts.

Using a large and very well-characterized sample of community-dwelling older adults free of disability and/or known atherosclerotic cardiovascular disease, we aimed to investigate the association between specific medical comorbidities, as well as their combined effects (multimorbidity), and the presence of depressive symptoms. We hypothesized that participants with medical morbidities will have an increased co-occurrence of depressive symptoms and this association will vary according to type and number of concomitant conditions.

METHODS

Study Population

This cross-sectional study uses baseline data from a large community-based randomized controlled trial that recruited a total of 19,114 older individuals from Australia and the United States. The ASPirin in Reducing Events in the Elderly (ASPREE) study included community-dwelling men and women aged 70 years and older (≥65 years of age for US minorities, due to higher burden of disease and survival disadvantage seen in these groups) who were willing and able to provide informed consent. Participants were excluded if they had a current indication for or contraindication to the use of aspirin, the trial intervention, or had any component of the composite primary outcomes. We excluded those with a previous cardiovascular event or established cardiovascular disease or atrial fibrillation; dementia or a score lower than 78 on the Modified Mini-Mental State test; presence of significant disability (defined by severe difficulty or inability to perform any one of the Katz activities of daily living); a condition with a high current or recurrent risk of bleeding; anemia; a condition likely to cause death within 5 years; and current use of other antiplatelet or antithrombotic medication, current use of aspirin for secondary prevention, or severe uncontrolled hypertension (ie, systolic blood pressure ≥180 mm Hg or diastolic blood pressure ≥105 mm Hg). Recruitment was from March 2010 to December 2014. ASPREE study design and recruitment strategies were published in detail elsewhere.20

Instruments and Measures

Sociodemographic questionnaires were administered at baseline, with information including age, education, sex, race, smoking status, alcohol use, living status, number and type of current medication use, and self-reported presence and/or history of medical conditions. Participants had their height, weight (used to calculate body mass index [BMI], kg/m2), abdominal circumference, blood pressure, and heart rate measured by trained study staff. Blood samples were also collected, and biochemical analyses included lipid profile, glucose, and creatinine and hemoglobin levels.21

Medical Comorbidities

The presence of medical comorbidities was defined according to condition-specific methods using a combination of direct physical and laboratory measures (with thresholds defined in medical guidelines) and/or self-report and/or medication use. Supplementary Table S1 lists all the medical comorbidities investigated and their definitions. Using these methods, we were able to estimate the prevalence of several individual medical conditions such as hypertension, diabetes, obesity, dyslipidemia, metabolic syndrome, GERD, respiratory disorders, chronic kidney disease, osteoarthritis, gout, and Parkinson’s disease. History of cancer was defined by self-report. Multimorbidity was defined as the co-occurrence of two or more medical conditions and polypharmacy as the simultaneous use of five or more medications. Because the ASPREE study design excludes individuals with a history of or established cardiovascular disease and dementia, these subgroups could not be analyzed, resulting in a sample overall probably healthier than the general population.22

Depression Assessment

The Center for Epidemiological Studies Depression (CES-D 10) scale was used to quantify the presence of depressive symptoms at baseline.23 The CES-D 10 is a self-completed questionnaire that rates the frequency of mood symptoms “during the past week” on a 4-point scale. This instrument previously showed comparable accuracy with the full version of the CES-D (κ = .97) in classifying participants with depressive symptoms.23 Specifically, in the context of this population, construct validity study of the CES-D 10 showed that a single score was a reliable and valid measure of depression.24 When compared with a formal psychiatric diagnosis of late-life depression, the scale had a sensitivity of 97% and a specificity of 84%.25 Based on previous research and the recognized impact of even minor depressive symptoms in this population, a cutoff of 8 or above was defined as the optimal positive screening cutoff for depression.26

Statistical Analyses

Baseline sample characteristics of participants according to those with and without depressive symptoms were compared using χ2 tests for categorical measures and independent sample t tests for continuous measures. The association between individual medical comorbidities and depressive symptoms was determined using logistic regression models with odds ratios (ORs) and 95% confidence intervals (CIs) reported. For each medical condition, we fitted four logistic regression models: (1) univariate unadjusted; (2) age and sex adjusted; (3) age, sex, race, education level, smoking, alcohol use, and living status adjusted (each previously found to be associated with depression in this population26); and (4) model 3 plus BMI, because that most chronic conditions investigated are associated with obesity. To prevent multicollinearity, obesity and waist circumferences were excluded from model 4. To mitigate a false discovery rate, the association between depressive symptoms and physical comorbidities was only tested for model 4, and ORs and CIs only are presented for models 1 to 3. In addition, ORs were converted into Cohen’s D effect sizes,27 with effects interpreted as small (.20-.49), medium (.50-.79), or large (≥.80). The association between depressive symptoms and polypharmacy (defined as concomitant use of five or more medications) and multimorbidity was also estimated. The difference in the mean number of concomitant medications between groups was estimated using a multivariable linear model and displayed graphically. The relationship between depressive symptoms and the number of medical comorbidities was determined using multivariable logistic regression and presented in a plot of marginal prevalence. In exploratory analyses, we applied the same methods stratified by sex. All statistical tests were two tailed, with P <. 05 considered to indicate statistical significance. All analyses were performed using STATA software, v.15.0. (StataCorp, 2017, College Station, TX).

RESULTS

Characteristics of all participants and according to those with and without depressive symptoms are shown in Table 1. The mean age was 75 years (standard deviation [SD] = ±4.5), and the oldest participant was 98 years of age. Overall, 1,879 (9.8%) participants met the threshold criteria (CES-D ≥ 8) and were classified as having depressive symptoms. Only four (.02%) individuals did not complete the screening questionnaire and thus were excluded from the analyses. Participants with depressive symptoms were significantly more likely to be female, less educated, obese or overweight, current smokers, and former alcohol drinkers, compared with those without depressive symptoms (Table 1). They also had significantly lower scores on quality-of-life assessment in both the physical and mental health components of the Short Form Health Survey (SF-12) scale (Table 1). Baseline characteristics and demographic and socioeconomic factors associated with depressive symptoms in this population were described in detail previously.21,26

Table 1.

Characteristics of Participants Overall and According to Depression Status (Defined as CES-D score ≥8)

Overall (%) (N = 19,110) CES-D ≥ 8 (%) (N = 1,879) CES-D < 8 (%) (N = 17,231)
Sex
  Male 8,331 (44) 631 (34) 7,700 (45)
  Female 10,783 (56) 1,248 (66) 9,531 (55)
Age, y
  ≤74 11,163 (58) 903 (48) 8,665 (50)
  75–84 7,219 (38) 875 (47) 7,677 (45)
  ≥85 732 (4) 101 (5) 889 (5)
Living status
  At home with someone 12,777 (67) 1,063 (57) 11,714 (68)
  At home alone/In a residential home 6,333 (33) 816 (43) 5,517 (32)
Race
  White 17,450 (91) 1,687 (91) 16,007 (94)
  Other 1,664 (9) 174 (9) 1,045 (6)
Education
  ≤12 y 10,955 (57) 1,151 (61) 9,800 (57)
  >12 y 8,158 (43) 727 (39) 7,431 (43)
Smoking history
  Current 735 (4) 110 (6) 624 (4)
  Former 7,797 (41) 756 (40) 7,041 (41)
  Never 10,579 (55) 1,013 (54) 9,566 (55)
Alcohol use
  Current 14,641 (77) 1,365 (73) 13,273 (77)
  Former 1,136 (6) 155 (8) 981 (6)
  Never 3,336 (17) 359 (19) 2,977 (17)
Body mass index, kg/m2
  ≤25 5,050 (27) 458 (24) 4,518 (26)
  25–30 8,452 (44) 777 (42) 7,662 (44)
  30–35 4,009 (21) 421 (23) 3,641 (21)
  >35 1,524 (8) 213 (11) 1,331 (8)
No. of medical comorbidities
  0–1 3,214 (17) 248 (13) 2,966 (17)
  2–3 12,179 (64) 1,125 (60) 11,054 (64)
  ≥4 3,717 (19) 506 (27) 3,211 (19)
  Multimorbiditya 14,351 (75) 1,520 (81) 12,831 (74)
Quality of life (SF-12 scores)b
  Mental component score 55.67 (7.13) 47.97 (9.39) 56.51 (6.29)
(55.57–55.77) (47.54–48.39) (56.42–56.61)
  Physical component score 48.34 (8.76) 45.24 (10.47) 48.67 (8.49)
(48.21–48.46) (44.76–45.71) (48.55–48.80)
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Abbreviations: CES-D, Center for Epidemiologic Studies Depression; SF-12, Short Form Health Survey.

a

Multimorbidity was defined as the co-occurrence of two or more medical conditions excluding depression.

b

Reported as mean (standard deviation) and 95% confidence intervals.

Depressive symptoms were significantly associated with increased prevalence of several individual chronic conditions including diabetes, obesity, high waist circumference, GERD, metabolic syndrome, respiratory conditions, osteoarthritis, history of cancer, and Parkinson’s disease (Table 2). Multimorbidity and polypharmacy were also more prevalent in this group. The association between depressive symptoms and gout, hypertension, and chronic kidney disease was not significant (all P > .05) (Table 2). There was no evidence of collinearity in the multivariate models. Table 3 reports the OR for the association between depressive symptoms and chronic conditions. After adjusting for covariates, a significant association was found between depressive symptoms and obesity (OR = 1.19; 95% CI = 1.07–1.32), diabetes (OR = 1.22; 95% CI = 1.05–1.42), GERD (OR = 1.41; 95% CI = 1.28–1.57), metabolic syndrome (OR = 1.16; 95% CI = 1.03–1.29), osteoarthritis (OR = 1.41; 95% CI = 1.27–1.57), respiratory conditions (OR = 1.25; 95% CI = 1.10–1.42), history of cancer (OR = 1.19; 95% CI = 1.05–1.34), Parkinson’s disease (OR = 2.56; 95% CI = 1.83–3.56), and polypharmacy (OR = 1.60; 95% CI = 1.44–1.79) as well as the presence of multimorbidity (OR = 1.29; 95% CI = 1.12–1.49). Table 3 also presents Cohen’s D equivalences of the model-adjusted ORs.

Table 2.

Prevalence of Specific Medical Comorbidities Overall and According to Depression Status (Defined as CES-D Score ≥8)

Exposure Overall (%) (N = 19,110) CES-D ≥ 8 (%) (N = 1,879) CES-D < 8 (%) (N = 17,231)
Hypertension 14,294 (75) 1,435 (76) 12,859 (75)
Diabetes mellitusa 2,044 (11) 250 (13) 1,794 (10)
Obesitya 5,533 (29) 634 (34) 4,972 (29)
High waist circumferencea 10,733 (53) 1,158 (62) 9,575 (56)
Dyslipidemia 12,190 (64) 1,236 (66) 10,954 (64)
Metabolic syndromea 6,834 (36) 761 (41) 6,073 (36)
Gastroesophageal reflux diseasea 5,537 (29) 681 (36) 4,856 (28)
Respiratory conditiona 2,781 (14) 339 (18) 2,442 (14)
Chronic kidney disease 2,137 (11) 217 (12) 1,920 (11)
Osteoarthritisa,c 4,633 (55) 597 (64) 4,036 (54)
Gout 1,189 (6) 125 (7) 1,064 (6)
Parkinson’s diseasea 226 (1.2) 47 (2.5) 179 (1)
History of depressiona,c 1,666 (24) 408 (46) 1,258 (21)
Cancer historyb 3,660 (19) 393 (21) 3,267 (19)
Antidepressant usea 2,144 (11) 456 (24) 1,688 (10)
Mean no. of concomitant medicationsb 2.7 (2.6) 3.3 (2.6) 2.6 (2.3)
Polypharmacya (≥5 medications) 3,841 (20) 553 (29) 3,288 (19)
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Abbreviation: CES-D, Center for Epidemiologic Studies Depression scale.

Note: Prevalence of medical conditions was defined according to Supplementary Table S1. Participant numbers (N) in each categorical variable are >19,000 unless stated otherwise. High waist circumference was defined as ≥88 cm for women and ≥102 cm for men (data available for N = 18,901). Obesity was defined as a body mass index >30 kg/m2.

a

P value <.001.

b

P value <.05.

c

A specific question about osteoarthritis and history of depression was included after June 2013; hence the data for these conditions are available for N = 8,392 and N = 6,862 participants, respectively.

Table 3.

Examining the Association between Medical Comorbidities and Depressive Symptoms (Defined as CES-D Score ≥8) in Multivariable Logistic Regression Modelsa

Condition/Comorbidity Model-based OR (95% CI) P value Cohen’s D (SE)
Hypertension 1.02 (.91–1.15) .68 .00 (.01)
Diabetes mellitus 1.22 (1.05–1.42) .009 .05 (.02)
Obesityb 1.19 (1.07–1.32) .001 .04 (.01)
High waist circumference 1.21 (1.09–1.34) <.001 .04 (.01)
Dyslipidemia 1.03 (.92–1.14) .64 .007 (.01)
Metabolic syndrome 1.16 (1.03–1.29) .01 .03 (.01)
Respiratory condition 1.25 (1.10–1.42) <.001 .05 (.01)
Chronic kidney disease 1.12 (.96–1.32) .16 .03 (.02)
Parkinson’s disease 2.56 (1.83–3.56) <.001 .22 (.04)
Osteoarthritis (N = 8,392) 1.41 (1.27–1.57) <.001 .08 (.01)
Gastroesophageal reflux disease 1.41 (1.28–1.57) <.001 .08 (.01)
Gout 1.22 (1.00–1.49) .052 .05 (.02)
Cancer history 1.19 (1.05–1.34) .005 .04 (.01)
Polypharmacy 1.60 (1.44–1.79) <.001 .11 (.01)
Multimorbidity 1.29 (1.12–1.49) <.001 .06 (.02)
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Abbreviations: CES-D, Center for Epidemiologic Studies Depression scale; CI, confidence interval; OR, odds ratio; SE, standard error.

a

Modified odds ratio using a multivariate model adjusted for age, sex, race, education, smoking, alcohol use, living status, and body mass index. The prevalence of medical conditions was defined according to Supplementary Table S1. Participant numbers (N) in each categorical variable are more than 18,000 unless stated otherwise. ORs and CIs were transformed into Cohen’s D effect sizes.

b

High waist circumference was defined as ≥88 cm for women and ≥102 cm for men (data available for N = 18,901). Obesity was defined as a body mass index (BMI) >30 kg/m2. Obesity and waist circumference models exclude BMI due to multicollinearity.

A significant increase is observed in the prevalence of depressive symptoms with each increase in the number of medical morbidities (OR = 1.18; 95% CI = 1.13–1.22; P < .001) (Figure 1). Participants with depressive symptoms also reported greater medication use than those without for the same number of medical comorbidities (model-adjusted mean difference = .33; 95% CI = .23–.43; P < .001), after exclusion of antidepressant use (Figure 2).

Figure 1.

Figure 1.

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Association between number of medical comorbidities and prevalence of depression (defined as CES-D score ≥8). The figure shows the cure prevalence of depression stratified by number of medical comorbidities, using a multivariable-adjusted logistic regression accounting for age, sex, race, education, smoking, alcohol use, living status, and body mass index. A statistically significant dose-response relationship was found between depression and the number of medical comorbidities (P < .001).

Figure 2.

Figure 2.

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Relationship between depression, number of medical comorbidities, and mean number of concomitant medications. The figure shows the mean number of concomitant drug use stratified by depression status and number of medical comorbidities excluding depression. A linear model was fitted to assess the effect of depression on the association between the number of medical comorbidities and number of concomitant drugs, with the number of drugs as the outcome variable and the number of comorbidities, depression status, and their interaction term as the main predictors. The model was adjusted for age, sex, race, education, smoking, alcohol use, living status, and body mass index. Based on this model, there is significant evidence of an association between the number of medical comorbidities and increased number of concomitant drugs in those with depression as compared with those without depression (P = .009). Antidepressants were excluded from this analysis.

In exploratory analyses stratified by sex, metabolic syndrome (OR = 1.26; 95% CI = 1.10–1.44), gout (OR = 1.84; 95% CI = 1.35–2.52), and a history of cancer (OR = 1.18; 95% = CI 1.02–1.38) were only significantly associated with depressive symptoms in women, whereas diabetes (OR = 1.28; 95% CI = 1.02–1.61) was only significantly associated with depressive symptoms in men (Supplementary Table S2).

DISCUSSION

In this very large cross-sectional study of a well-characterized sample of 19,110 community-dwelling older adults without a history of heart disease and/or disability, we found that depressive symptoms were significantly associated with several individual somatic conditions including diabetes, obesity, metabolic syndrome, GERD, osteoarthritis, and Parkinson’s disease. Furthermore, we found a dose-response association between depressive symptoms and a number of medical comorbidities. Our findings provide further evidence for a strong relationship between depressive symptoms and physical health, even among older adults selected on the basis of better general health than their counterparts.22 It is also noteworthy that this was not true of all medical comorbidities. Depressive symptoms were not associated with hypertension, chronic kidney disease, dyslipidemia, and gout.

Our results concur with recent epidemiological data on the association of depression and chronic morbidities in older populations.28,29 It is known that depressed individuals tend to have unhealthier lifestyle habits, are less adherent with medication regimens, and have poorer self-care. However, after controlling for environmental factors, significant longitudinal and genetic data suggest an independent effect of depression in the incidence of many somatic disorders.30,31

Alternatively, the constantly seen bidirectional interactions between depression and medical comorbidities in older age suggests a common underlying biology. Depression shares multiple physiologic mechanisms with most of the senescence-associated medical morbidities found in this study, most of them characterized by persistent low-grade inflammation. These pathways driving the progression of psychiatric and medical comorbidities are referred to as neuroprogression and somatoprogression, respectively.16 Depression in later life frequently presents with an inflammatory pattern similar to the one seen in senescence,32 and it is likely that at least one subtype of late-life depression overlaps with the senescence-associated phenotype linked to most age-related diseases, emphasizing the importance of integrating mental and physical healthcare and including depression in the discussion about healthy aging.

We also found a significant difference in the number of medications used by participants with depressive symptoms compared with their counterparts. This finding might reflect an increased number of help-seeking visits to primary care services, potentially leading to a greater number of interventions (necessary or not). However, it can also imply a distinct risk for adverse medication effects and interactions in the mood of older adults. Genetic data suggest that individuals with depression are more sensitive to medication-adverse events, possibly compounding the risks of multiple drug use in a depressed older population.31 When prescribing for older adults, clinicians must balance the risks of treatment vs the risks of not treating an underlying condition with possible serious consequences.33 Although it is not clear if antidepressant treatment might mitigate some of these effects, the overall deleterious effects of late-life depression on health behaviors, quality of life, medication adherence, and probably somatic progression suggest that appropriate recognition and treatment is warranted in this age group. Furthermore, it was shown that collaborative care strategies for depression treatment are more effective than usual care and reduce rates of mortality, suicide, and suicidal ideation in this population.34–36

We did not find any significant association between depressive symptoms and hypertension, dyslipidemia, chronic kidney disease, and gout in this population, and the link between these entities is controversial. Depression has been bidirectionally associated with hypertension and metabolic disturbances, but evidence is conflicting.37 Furthermore, in older adults, the chronicity of these processes, leading to endothelial and vascular damage, as well as distinct mood-related effects of individual classes of medication used in their treatment (ie, different types of anti-inflammatories, cholesterol-lowering and antihypertensive drugs), can further complicate the picture in this population.38–40

In exploratory analyses, we found some sex differences in the relationship between depressive symptoms and metabolic syndrome, gout, and a history of cancer, with statistical significance only seen in women. Conversely, diabetes was only significantly associated with depressive symptoms in men. Although these conditions have been associated with depression in both sexes, this finding agrees with epidemiological and biological data suggesting sex-specific mechanisms in the relationship between metabolic disturbances and depressive symptoms.41,42 However, due to the exploratory nature of these findings and the loss of statistical power, particularly in the male analyses, these results should be interpreted with considerable caution.

The strengths of this study include a much larger, otherwise healthy, and well-characterized sample of community-dwelling older adults compared with previous studies, a well-validated instrument for depression screening, and a comprehensive assessment of participants including direct physical and laboratory measures, allowing accurate diagnosis of some common and possibly undiagnosed medical comorbidities. Recruiting a relatively healthy older sample mainly from primary care (where most are treated) also provided a much more naturalistic setting than previous studies, mostly focused on disease-specific populations. Our large sample size also allowed us to investigate potential associations in well-powered multivariable models accounting for a variety of possible confounders.

This study also has some limitations. First, due to its cross-sectional design, only association, and not causation, can be inferred. Also, notwithstanding the fact that the CES-D 10 is a validated tool for depression screening, it is not akin to a formal diagnosis of depression. Nevertheless, the impact of subthreshold depressive symptoms in older age makes this a valuable and reliable instrument for the purpose of this study.43 The duration and severity of specific diseases might also affect their relationship with depression, which we were not able to assess. Furthermore, number, quality, and type of treatment might also confound the relationship between chronic diseases and mood. Unfortunately, we do not have data on all of those. The generalizability of our findings must also be considered. Because the ASPREE study excluded subjects with severe diseases, disability, dementia, uncontrolled hypertension, and individuals with a history of heart disease, these subgroups were not addressed in this study. Because our sample is composed mainly of white older adults who may be generally healthier than their counterparts, our results might underestimate the associations found in the actual older population, as reflected by the small effect sizes found in this study.

In conclusion, late-life depressive symptoms are associated with several medical comorbidities. A dose-response relationship exists between the number of somatic diseases and the prevalence of depressive symptoms in this population. Participants with depressive symptoms also have an increased prevalence of polypharmacy and tend to be more medicated than their counterparts for the same number of co-occurring diseases. Evidence suggests integrated care and adequate screening and treatment of depression in the context of medical comorbidities might lead to a better prognosis and reduced mortality.44 The realization of this complex integration and its operative drivers might improve healthcare policies and increase early recognition of individuals with a high risk of depression by general practitioners and other professionals in the front line of care for older adults. This is a fundamental step in building momentum toward a healthier and happier older society.

Supplementary Material

Supplementary

Supplementary Table S1: Definitions for the Prevalence of Chronic Medical Comorbidities

Supplementary Table S2: Association between Medical Comorbidities and Depression (Defined as CES-D score ≥8) in Multivariable Logistic Regression Models Stratified by Sex.*

ACKNOWLEDGMENTS

We would like to acknowledge the efforts of research personnel and the long-term involvement of participants of the ASPREE Study.

Financial Disclosure: Lana J. Williams is supported by a National Health and Medical Research Council (NHMRC) Career Development Fellowship (1064272) and NHMRC Investigator grant (1174060). Michael Berk is supported by NHMRC Senior Principal Research Fellowships 1059660 and 1156072. Christopher M. Reid is supported by an NHMRC Senior Research Fellowship (1045862).

Sponsor’s Role: The ASPREE study is supported by the National Institute on Aging and the National Cancer Institute at the National Institutes of Health (grant number U01AG029824); the National Health and Medical Research Council of Australia (grant numbers 334047 and, 1127060); Monash University (Australia); the Victorian Cancer Agency (Australia). There was no additional funding for this study.

Footnotes

Conflict of Interest: The authors have declared no conflicts of interest for this article.

SUPPORTING INFORMATION

Additional Supporting Information may be found in the online version of this article.

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Associated Data

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Supplementary Materials

Supplementary

Supplementary Table S1: Definitions for the Prevalence of Chronic Medical Comorbidities

Supplementary Table S2: Association between Medical Comorbidities and Depression (Defined as CES-D score ≥8) in Multivariable Logistic Regression Models Stratified by Sex.*

NIHMS1595520-supplement-Supplementary.pdf (168.5KB, pdf)

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