Association Between Depressive Symptoms and Cognitive Impairment in Patients Over 75 years of Age in the High Andean Communities of Peru

Abstract

Objective:

To determine the association between depressive symptoms and cognitive impairment in patients over 75 years of age in the High Andean communities of Peru.

Patients and methods:

A retrospective cross-sectional study was conducted. The sample consisted of 181 older adults. The Yesavage Test was used for depression screening (5-item version), and the Pfeiffer questionnaire was used for cognitive impairment screening. Poisson regression with robust variance was used to determine the association between variables with a 95% confidence level. The analyses were performed using STATA 16.

Results:

About 63.54% were female, with an average age of 79.52 ± 4.21 years. About 43.6% showed cognitive impairment, of which 29.8% were mild and 13.26% were moderate. An association was found between depression and cognitive impairment (p < .001; aPR = 4.70, 95% CI [2.25, 9.79]).

Conclusion:

There is an association between depressive symptoms and cognitive impairment in older adults from high Andean communities in Peru.

Keywords

aged altitude cognitive dysfunction depression

Introduction

In the last decades the dementia syndrome has become a public health problem worldwide because of the high rates of dependence and disability caused in the elderly population (GBD 2019 Dementia Forecasting Collaborators, 2022; World Health Organization [WHO], 2023). According to the World Health Organization, 55 million people suffer from this disease, with 10 million new cases being added each year, and it is estimated that by 2030, 82 million people will have dementia and 152 million by the year 2050 (WHO, 2023). In Latin America, the global prevalence of dementia is 11%, with a higher prevalence in females and rural areas (Zurique-Sánchez et al., 2019). In Peru, the prevalence of dementia ranges from 3.8% to 13.5% in older adults (Contreras et al., 2019; Zegarra-Valdivia et al., 2023), and in Lima and Callao 6.9% of residents over 65 years of age start this disease due to Alzheimer’s, doubling their risk every 4 years thereafter (Carcelén & Cárdenas, 2019).

Cognitive impairment and depressive symptoms are often the most common neuropsychiatric disorders in older adults, and different studies indicate that affective symptoms could behave as an early sign of dementia (Calderón, 2018). Additionally, possible risk factors for presenting severe depressive symptoms may include cognitive impairment (Chan et al., 2019) and advanced age, and low academic training, low cognitive and physical activity, as well as a diet high in trans fats and lack of sleep can further increase the risk (James & Bennett, 2019).

On the other hand, some studies performed in high-altitude areas have described the mental and psychiatric status of these populations reporting statistically significant differences between presenting affective disorders and living in rural areas (Saenz-Miguel et al., 2019).

Living at high altitude can exacerbate the effects of depression on cognitive decline due to several environmental and physiological factors. One study highlights those older adults residing in high-altitude areas, such as the Andean communities in Peru, are exposed to chronic hypoxia which can affect brain function. This hypoxic environment may lead to alterations in neurotransmitter systems, particularly serotonin, which is crucial for mood regulation. These biochemical changes can increase the risk of depressive symptoms, which, when combined with altitude-related stressors like lower temperatures, higher radiation levels, and reduced humidity, can worsen cognitive functions (Valenzuela-Iglesias et al., 2021).

Similarly, according to a systematic review and meta-analysis that analyzed six studies (five of which were from Latin America) to determine the prevalence of cognitive impairment in older adults in high-altitude communities, the prevalence of cognitive impairment was higher compared to other parts of the world, which could possibly be explained by the characteristics, comorbidities, socioeconomic status and access to health care services in this population (Urrunaga-Pastor et al., 2021). However, the factors associated with cognitive problems in the Andean population have not been fully explored, and some educational and sociodemographic factors may influence these populations differently.

The results of this study will be useful for understanding the characteristics of patients living in high Andean communities to enhance awareness of the negative effects of suffering a depressive syndrome and allow timely action to avoid the loss of cognitive abilities. The purpose of this study was to determine the association between depressive symptoms and cognitive impairment in high Andean communities in Peru.

Materials and Methods

Study Design

The design of the present study was retrospective and analytical with a secondary analysis of the database from a study of older adults conducted in high Andean communities in Peru (≥1,500 m above sea level [masl]). The study included participants 60 years of age or older who were residents of different areas of Peru: La Jalca, Leimebamba, Llupa, San Pedro de Chaná, Atipayán, Pampamarca Ayahuanco, Paucarcolla, Vilca, Viñac, and Chacapampa.

In the primary study, non-probabilistic census-type sampling was used in each urban/rural community, achieving an approximate inclusion of 95% of older adult inhabitants in most of these communities (Urrunaga-Pastor et al., 2021).

This original research study was approved by the Ethics Committee of the Naval Medical Center located in Callao – Peru and the secondary analysis was approved by the Ethics Committee of the Universidad Científica del Sur (registration code: No. 278-2019-PRE15).

The exclusion criteria were severe cognitive impairment, that is, a score greater than or equal to 8 points in the Pfeiffer questionnaire; physical inability to perform physical and functional performance tests; visual or hearing impairment hindering the ability to carry out the survey; lack of understanding or communication in Spanish, in addition to not having a translator; or not having data related to the variables of interest for the present study.

Setting and Participants

The database had an original population of 415 older adults and 234 geriatric patients were excluded for being under 75 years of age. A total of 181 participants met the eligibility criteria. The data were collected from the years 2013 to 2016 and included sociodemographic characteristics, functional status (Barthel index), mental assessment (Pfeiffer Questionnaires, Yesavage), medical history (diabetes mellitus, hypertension, obesity), and anthropometric measurements (weight and height), in addition to geriatric syndrome evaluation (Casahuaman-Orellana et al., 2019; Castro-Benites et al., 2019; Urrunaga-Pastor et al., 2021).

In the secondary analysis, the sample size was not calculated since we used all the data from the database. However, the statistical power was calculated, assuming a mean difference of 2.5 points in the Yesavage Questionnaire score between patients with and without cognitive impairment, and a population size of 181 participants, we calculated a statistical power of 84.1%. OpenEpi version 3.0 software was used for this calculation (Urrunaga-Pastor et al., 2018).

Data Collection Process

The authors of this original study collected data based on self-reporting (corroborated by family members/companions) to evaluate the sociodemographic characteristics of each participant. Direct questions were asked, including age (≤70 years, 71–80 years, over 80 years), gender (male, female), educational level with information on degree of instruction (no education/incomplete primary school, completed primary school, completed secondary school), marital status (single, married, widowed/divorced), current employment (yes or no), current occupation (agriculture, commerce, others), and altitude of each town in masl and the type of rural-urban community according to the National Institute of Statistics and Informatics (INEI) of Peru. All this sociodemographic information obtained was confirmed with the participant’s national identity document (DNI).

Regarding the medical history, the following variables were evaluated based on self-reporting: the number of medications consumed by the participants, including frequent use, prescription medications and not considering nutritional supplements. Medical history of diseases such as hypertension and Type 2 diabetes mellitus was also evaluated with the questions “Do you have hypertension?” (yes or no) and “Do you have type 2 diabetes mellitus?” (yes or no). The information obtained was verified by a caregiver/family member at the time of data collection.

To assess variables such as cognitive impairment and depressive symptoms, different questionnaires were used. Cognitive impairment was evaluated using the Pfeiffer questionnaire, which consists of 10 questions, and scores were divided into: normal or without impairment (0–2 errors), mild cognitive impairment (3–4 errors), moderate cognitive impairment (5–7 errors), and severe impairment (8–10 errors). An additional point was assigned if participants had a higher technical education, and one point was subtracted if participants were illiterate or had incomplete schooling. This scale has previously been used in the Peruvian population (Oliva-Zapata et al., 2024).

The variable of depressive symptoms was evaluated using the five-item version of the Yesavage questionnaire, with a score ranging from 0 to 5. A score < 2 was considered normal, and a score ≥ 2 suggests depression. This variable was expressed as a continuous numerical value. This scale has previously been used in the Peruvian population (Vasquez-Goñi et al., 2022).

The Barthel Index was also used, which includes 10 basic activities of daily living (ADLs), with a total score ranging from 0 to 100. Those who did not achieve the maximum score (<100) were considered dependent for basic ADLs. This scale has been validated by another study in Peru (Cardenas et al., 2022).

Statistical Analysis

The statistical analysis was performed using STATA version 15.0. The variables of the descriptive analysis are expressed in percentages, means and standard deviation. For the bivariate analysis, the Fisher technique and ANOVA one way were used. Finally, for multivariate analysis, Poisson regression with robust variance was used to determine the association between depression and cognitive impairment. The prevalence ratio (PR) was calculated using a 95% confidence level (95% CI).

Results

The mean age of the older adult population studied was 79.52 ± 4.21 years, being predominantly female (63.54%). 83.4% had no education or incomplete schooling, and 52.5% were married. Furthermore, the average mean Yesavage questionnaire score was 2.07 ± 1.5, with a frequency of mild cognitive impairment of 29.8% and of moderate/severe cognitive impairment of 13.26% (Table 1).

Table 1.

Descriptive Analysis of the Study Variables (n = 181).

Variable	n	%	Mean	Standard deviation
Meters above sea level			3,260.06	501.53
Community type
Urban	70	38.67
Rural	111	61.33
Sex
Female	115	63.54
Male	66	36.46
Age in years			79.52	4.21
Education
Illiterate or incomplete school	151	83.43
Complete secondary school	29	16.02
Technical/superior	1	0.55
Marital status
Single	21	11.6
Married	95	52.49
Widowed	3	1.66
Divorced	62	34.25
Live alone
Yes	42	23.2
No	139	76.8
Drugs consumed			1.2	1.43
Hypertension
Yes	17	9.39
No	164	90.61
Diabetes mellitus
Yes	14	7.73
No	167	92.27
Weight in kg			64.4	12.71
Barthel index			90.03	11
Yesavage			2.07	1.49
Cognitive impairment
Normal	103	56.91
Mild cognitive impairment	54	29.83
Moderate cognitive impairment	24	13.26

Regarding the bivariate analysis between the dependent variable (cognitive impairment) and the covariates, the average mean Yesavage questionnaire score was significantly higher in patients with moderate cognitive impairment compared to those with lower degrees or absence of cognitive impairment (p = .02).

Likewise, the presence of cognitive impairment was significantly greater in participants living at higher elevations of masl (p = .05), The frequency of participants without cognitive impairment was significantly higher in those from rural communities compared to urban areas (p = .01). The mean medication consumption was higher in participants with greater cognitive impairment (p = .01), and a higher weight was related to cognitive impairment (p = .01; Table 2).

Table 2.

Bivariate Analysis Related to Cognitive Impairment (n = 181).

Variable	No cognitive impairment	Mild cognitive impairment	Moderate cognitive impairment	p-Value
Meters above sea level (mean and standard deviation)	3,186.91 (526.36)	3,328.63 (499.67)	3,419.67 (320.83)	.05
Community type (frequency and percentage)				.01
Urban	30 (29.13)	28 (51.85)	12 (50.0)
Rural	73 (70.87)	26 (48.15)	12 (50.0)
Sex (frequency and percentage)				.8
Female	66 (64.08)	35 (64.81)	14 (58.33)
Masculine	37 (35.92)	19 (35.19)	10 (41.67)
Age in years (mean and standard deviation)	79.35 (4.24)	79.94 (4.28)	79.29 (4.06)	.06
Marital status (frequency and percentage)				.43
Single	14 (13.59)	4 (7.41)	3 (12.50)
Married	55 (53.40)	25 (46.30)	15 (62.50)
Widowed	1 (0.97)	2 (3.70)	1 (0.01)
Divorced	33 (32.04)	23 (42.59)	6 (25.00)
Lives alone (frequency and percentage)				.72
No	78 (75.73)	79 (75.73)	80 (75.73)
Yes	25 (24.27)	26 (24.27)	27 (24.27)
Drugs (mean and standard deviation)	0.88 (1.44)	1.41 (0.90)	2.13 (1.85)	.01
Hypertension (frequency and percentage)				.06
No	91 (88.35)	53 (98.15)	20 (83.33)
Yes	12 (11.65)	1 (1.85)	4 (16.67)
Diabetes mellitus (frequency and percentage)				.14
No	92 (89.32)	53 (98.15)	22 (91.67)
Yes	11 (10.68)	1 (1.85)	2 (8.33)
Weight (mean and standard deviation)	63.43 (11.57)	64.14 (9.21)	68.67 (20.91)	.01
Barthel index (mean and standard deviation)	92.77 (10.00)	91.39 (3.28)	75.21 (14.56)	.01
Yesavage (mean and standard deviation)	1.41 (1.35)	2.72 (0.98)	3.42 (1.50)	.02

Finally, participants with moderate cognitive impairment presented a significantly lower Barthel Index compared to those with mild cognitive impairment (p = .01; Table 2).

The crude regression model showed an association between depression and cognitive impairment (p < .001; PR = 5.78, 95% CI [2.95, 11.29]). Likewise, this association was maintained in the model adjusted for gender, type of community (rural/urban), marital status, living alone, masl, medications, weight, and functional dependence (p < .001; aPR = 4.70, 95% CI [2.25, 9.79]).

Discussion

The present study found an association was between depressive symptoms and cognitive impairment. These results are similar to those of other studies (Guillén et al., 2022; Mirza et al., 2017) and could be attributed to certain neurobiological processes that occur in individuals with affective disorders, such as progressive deterioration of the locus coeruleus and substantia nigra (Vilchez-Cornejo et al., 2018).

Likewise, a previous meta-analysis evaluating patients with depression and mild cognitive impairment revealed an overlap in the reduction of volume in specific brain areas, especially in the insula, superior temporal gyrus, inferior frontal gyrus, amygdala, hippocampus, and thalamus. A decreased volume of the insula and superior temporal gyrus may reflect a lack of stimulation by social and mental activities, which have been described as risk factors for both depression and cognitive impairment (Zacková et al., 2021).

Regarding the association between higher average of masl with a higher degree of cognitive impairment, to our knowledge, few studies conducted have found a direct proportional correlation between these two variables. However, this association could be explained by the increased hypoxia at higher altitudes, which may have effects on the human brain, including hippocampal dendritic atrophy, alterations in serotonin synthesis, and disruptions in creatine kinase reaction, all of which can lead to neuronal dysfunction (Urrunaga-Pastor et al., 2021).

In addition, the present study found a high prevalence of cognitive impairment in the studied population similar to what has been observed in other investigations. The high rates of cognitive impairment in people living at high altitudes could also be attributed to socioeconomic factors such as the lack of education and limited access to health care services (Mejia et al., 2021; Urrunaga-Pastor et al., 2021). In fact, in the present study, 83.43% of the population was illiterate with incomplete schooling.

We observed that the frequency of cognitive impairment was higher in rural compared to urban areas, which is consistent with other studies and might be explained by the lower development, higher isolation levels, and lower health care access for older adults in rural areas (Zurique-Sánchez et al., 2019).

The association of higher medication consumption and higher weight with cognitive impairment in older adults could be explained by the use of polypharmacy being associated with a greater decline in quality of life, which could aggravate cognitive impairment (Antón Jiménez & González Guerrero, 2017; Maust et al., 2021). On the other hand, obesity can cause injuries to brain areas such as the hippocampus, causing inflammation, disrupting synaptic plasticity, neurodegeneration, and decreasing brain volume (Soriano-Ursua et al., 2022).

Lower Barthel Index scores in patients with cognitive impairment have also been reported in other studies. Functional dependence and cognitive impairment show similar patterns and increase as demographic, medical, or lifestyle factors change. Older adults with poorer cognitive performance tend to develop functional dependence in subsequent years. In addition, both cognitive impairment and functional dependence increase the risk of death and institutionalization (Valenzuela-Iglesias et al., 2021).

Although depression and cognitive impairment can appear independently in older adults, various studies show that the presence of depressive symptoms can affect cognitive function in older adults, even if in the absence of underlying neurocognitive disorders.

Thus, it is recommended that in order to correctly assess neurocognitive disorders in older adults, an affective evaluation should be taken into consideration because a history of depression can accelerate cognitive decline. Therefore, depression could be a potentially modifiable risk factor for cognitive impairment. This is especially important for older adults from high Andean communities, who often have limited access to health care services and do not receive proper comprehensive care.

This study provides local scientific evidence that describes the magnitude of depression and cognitive impairment in older adults and can help guide improvement of the mental health of this population based on multidisciplinary and multisectoral actions undertaken by health care professionals. Thus, early intervention should be implemented to avoid the progression of cognitive decline in older adults.

Regarding the limitations of this study, some variables were collected through self-reporting, and thus, there could be information bias. Likewise, since it is a cross-sectional design, it is not possible to confirm a cause-and-effect relationship, but rather only an association. Furthermore, the variables of cognitive impairment and depression were assessed using questionnaires that are not diagnostic tools; however, they are screening tests known for their simplicity in identifying these disorders and for their national validation.

Conclusion

A statistically significant association was found between depressive symptoms and mild cognitive impairment in older adults residing in high Andean communities in Peru.

Footnotes

ORCID iDs

Ericson L. Gutierrez

Fernando M. Runzer-Colmenares

Author Contributions

RMD: Conceptualization, Methodology, Investigation, Data management, Formal analysis, Interpretation, Writing – original draft, Final approval of the version to be published. CMC: Conceptualization, Methodology, Investigation, Data management, Formal analysis, Interpretation, Writing – original draft, Final approval of the version to be published. KMJ: Data management, Interpretation, Writing – original draft, Final approval of the version to be published. HLT: Data management, Interpretation, Writing – original draft, Final approval of the version to be published. AI: Data management, Interpretation, Writing – original draft, Final approval of the version to be published. ELG: Data management, Interpretation, Writing – review & editing, Final approval of the version to be published. JFP: Conceptualization, Design, Formal analysis, Supervision, Writing – review & editing, Final approval of the version to be published. FRC: Conceptualization, Methodology, Investigation, Data management, Formal analysis, Interpretation, Writing – original draft, Final approval of the version to be published.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

The data used to support the findings of this study are included within the article.

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