Polypharmacy, Nutritional Risk, and Depressive Symptoms Among Older Outpatients at a Public Hospital: An Exploratory Cross-Sectional Study

Abstract

Purpose

Previous studies have linked polypharmacy with nutritional and psychological vulnerability in older adults, but their concurrent presentation in routine outpatient care remains incompletely characterized. This exploratory study examined nutritional status and depressive symptom burden according to polypharmacy status among older adults attending outpatient clinics at a public hospital.

Patients and Methods

This exploratory cross-sectional observational study included 28 community-dwelling adults aged 65 years or older attending rehabilitation medicine or family medicine outpatient clinics at a single public hospital in Seoul, Republic of Korea. Polypharmacy was defined as the regular use of five or more physician-prescribed medications. Nutritional status was assessed using the full Mini Nutritional Assessment (MNA), and depressive symptoms were assessed using the interviewer-administered Korean 15-item Short Form Geriatric Depression Scale (SGDS). Continuous variables were compared using Mann–Whitney U-tests and categorical variables using Fisher’s exact tests.

Results

Polypharmacy was present in 8 participants (28.6%). Compared with participants without polypharmacy, those with polypharmacy had lower MNA scores (median [IQR], 21.25 [20.25–22.13] vs 24.25 [23.25–26.00]; p=0.005), more frequent nutritional risk or malnutrition (100.0% vs 35.0%; p=0.002), higher SGDS scores (8.00 [4.75–9.25] vs 3.00 [2.00–6.00]; p=0.024), and smaller calf circumference (31.50 [30.38–33.90] vs 35.25 [33.45–36.63] cm; p=0.042). The prevalence of depressive symptoms defined by SGDS ≥8 did not differ significantly (62.5% vs 20.0%; p=0.068).

Conclusion

In this small exploratory sample, polypharmacy co-occurred with poorer nutritional status and greater depressive symptom burden. These unadjusted findings are hypothesis-generating and do not establish that polypharmacy causes poorer nutritional status or depressive symptoms. Larger prospective studies with adequate adjustment for multimorbidity and other potential confounders are needed.

Plain Language Summary

Older adults often take several medications for chronic diseases. This is called polypharmacy. Medication review is important, but older adults with polypharmacy may also have nutritional problems or depressive symptoms that deserve attention.

This exploratory study examined 28 older adults who visited rehabilitation medicine or family medicine outpatient clinics at a public hospital in Seoul, Republic of Korea. We compared nutritional status, depressive symptoms, and selected geriatric assessment variables between participants with and without polypharmacy. Polypharmacy was defined as taking five or more regularly prescribed medications.

Participants with polypharmacy had lower nutritional assessment scores, higher depressive symptom scores, and smaller calf circumference than those without polypharmacy. However, the study was small, and some frailty- and sarcopenia-related measures did not clearly differ between the groups.

These findings do not show that polypharmacy causes nutritional or psychological problems. Rather, they suggest that medication burden may be one clinical signal prompting consideration of broader nutritional and psychological assessment in older outpatients.

Introduction

Polypharmacy is common among older adults and often occurs in the context of multimorbidity and treatment complexity.^1–5 Its prevalence varies substantially by population, care setting, and operational definition. In some settings, more than half of older adults are exposed to polypharmacy.³ Evidence regarding associated adverse outcomes is heterogeneous. A systematic review of reviews found the most consistent associations with hospitalization and inappropriate prescribing, whereas evidence for adverse drug events was conflicting.⁴ In this context, medication burden may reflect not only the number of prescribed drugs but also the broader complexity of chronic disease management.

Previous studies have reported associations between polypharmacy and malnutrition or nutritional risk in older adults.^6–8 Nutritional vulnerability is common in later life and varies by care setting. A review based on the Mini Nutritional Assessment (MNA) reported malnutrition in approximately 9% and risk of malnutrition in approximately 45% of older outpatient and home-care populations.⁹ Nutritional vulnerability is also closely related to frailty, sarcopenia, functional decline, and adverse health outcomes.^9,10 A systematic review and meta-analysis of 48 studies reported a pooled global prevalence of depression of 28.4% among older adults, with substantial heterogeneity across geographic region, screening instruments, study populations, and study quality.¹¹ Prior studies have reported associations between polypharmacy and depressive symptoms in older adults.^12,13 These findings support the clinical relevance of concurrent assessment but do not establish that polypharmacy is a causal determinant of poor nutritional or psychological outcomes. Potential pathways include medication-related adverse effects and treatment complexity, but such mechanisms are difficult to separate from multimorbidity and underlying illness in observational data. Multimorbidity and other characteristics of medically complex older adults may contribute to both medication burden and these geriatric vulnerabilities.

Prior studies have evaluated polypharmacy in relation to individual geriatric outcomes, but the practical interpretation of medication burden alongside nutritional, depressive, anthropometric, sarcopenia-related, and frailty-related indicators remains context dependent. In routine outpatient care, comprehensive geriatric assessment may not be feasible at every encounter, and brief clinical signals may help identify patients who warrant broader assessment.^14,15 We focused on a public-hospital outpatient setting because it was the specific clinical context from which this cohort was recruited and in which medication burden and multiple geriatric domains were assessed, without assuming that public hospitals uniformly share the same populations, resources, or constraints.

Therefore, this exploratory cross-sectional study examined differences in nutritional status and depressive symptom burden according to polypharmacy status among older adults attending outpatient clinics at a public hospital. We additionally described selected anthropometric, sarcopenia-related, and frailty-related characteristics to provide a broader clinical profile. The study was intended to generate hypotheses and characterize co-occurring vulnerability rather than to establish causal relationships.

Material and Methods

Study Design and Setting

This exploratory cross-sectional observational study was conducted at the rehabilitation medicine and family medicine outpatient clinics of Seoul Medical Center, a single public hospital in Seoul, Republic of Korea. Participants were recruited between December 1, 2023, and February 28, 2025. Each participant underwent a single study interview and assessment. Thus, the 15-month period represents the calendar period of participant accrual rather than longitudinal follow-up.

Participants

Participants were community-dwelling adults aged 65 years or older attending the rehabilitation medicine or family medicine outpatient clinics. A convenience sample of patients who appeared to meet the eligibility criteria was approached during routine outpatient visits when the one-time interview and physical assessments could be completed. Eligible participants provided written informed consent, were able to communicate and complete the study procedures, could ambulate independently, and were able to undergo the planned assessments.

Individuals were excluded if they had conditions that could substantially affect muscle function or physical performance, including major neurological disorders, severe mobility limitations, recent fracture or trauma, difficulty in communication or questionnaire completion, or eating disorders. A formal screening log was not maintained; therefore, the exact numbers of patients screened, excluded before enrollment, or declining participation could not be retrospectively determined. Twenty-eight participants completed the study assessments and were included in the analysis.

Data Collection and Measurement Standardization

Data were collected through structured interviews and standardized physical assessments. Anthropometric and physical-performance measurements were performed by two trained research staff members at the Research Institute of Seoul Medical Center using a standardized measurement protocol established before data collection. The same protocol was used throughout the study to promote consistency across participants and assessors.

Sample Size

During planning of the original broader study, an a priori sample-size calculation was performed using G*Power version 3.1 for a planned cross-tabulation analysis. Assuming an effect size of 0.60, a significance level of 0.05, and statistical power of 0.95, the minimum sample size was estimated as 55 participants. The original study was designed more broadly to examine factors related to sarcopenia. Therefore, this calculation was not specifically based on the polypharmacy-related comparisons examined in the present exploratory analysis. Recruitment was discontinued in February 2025 before the estimated minimum sample size was reached, with 28 participants enrolled. Accordingly, the present analyses were considered exploratory and hypothesis-generating rather than confirmatory.

Medication Use and Comorbidity Assessment

Medication use was assessed at the study visit through a structured interview and was verified against available outpatient medical records. Polypharmacy was defined a priori as the regular use of five or more physician-prescribed medications per day.¹ Regular use referred to medications prescribed for ongoing use and being taken at the time of assessment. As-needed medications, over-the-counter medications, dietary supplements, and herbal medicines were excluded. Verification against medical records was used to reduce reliance on participant recall alone.

Information on physician-diagnosed chronic diseases was obtained through structured interviews and available clinical information. The number of chronic conditions was calculated for each participant. Selected comorbidities, including hypertension, diabetes, hyperlipidemia, cardiovascular disease, thyroid disease, arthritis, osteoporosis or chronic pain, physician-diagnosed depression, chronic obstructive pulmonary disease or asthma, cancer, and other chronic diseases, were assessed to characterize the clinical context of medication burden.

Primary and Secondary Outcomes

The primary outcome was nutritional status, assessed using the full MNA, including the screening and assessment sections, with a total possible score of 30 points.^9,10 The MNA score was analyzed as a continuous variable. Participants were classified as having normal nutritional status (24–30 points), being at risk of malnutrition (17–23.5 points), or being malnourished (<17 points). For categorical analyses, the latter two categories were combined as nutritional risk/malnutrition.

The secondary outcome was depressive symptom burden, assessed using the interviewer-administered Korean version of the 15-item Short Form Geriatric Depression Scale (SGDS).^16–18 Total scores range from 0 to 15, with higher scores indicating greater depressive symptom burden. The SGDS score was analyzed as a continuous variable. Depressive symptoms were additionally examined as a categorical variable using a cutoff of ≥8 based on validation studies of the Korean version in older adults.^17,18

Self-rated health was assessed using a prespecified single item adapted from the Korea National Health and Nutrition Examination Survey (KNHANES).¹⁹ Participants rated their usual health status on a 5-point scale, ordered in the study questionnaire from 1 (very poor) to 5 (very good), with higher scores indicating better perceived health.

Anthropometric, Sarcopenia-Related, and Frailty-Related Assessments

Anthropometric, sarcopenia-related, and frailty-related variables were assessed to characterize the clinical and functional profiles of the participants. Height was measured directly using a stadiometer in participants able to stand for measurement. Because participants were ambulatory and able to stand for the study assessment, direct standing height was feasible and was used consistently for body mass index (BMI) and appendicular skeletal muscle mass index (ASMI) calculations. Body weight and appendicular skeletal muscle mass were assessed using a multifrequency segmental bioelectrical impedance analyzer (InBody 720, Biospace, Seoul, Republic of Korea). Calf circumference (CC) and mid-upper arm circumference (MUAC) were measured using a non-elastic tape according to the standardized study protocol. MUAC was measured at the midpoint between the acromion and olecranon, and CC at the point of maximal girth. Both sides were measured, and the larger value was used for analysis. Low CC was defined using the Asian Working Group for Sarcopenia (AWGS) 2019 sex-specific cutoffs of <34 cm in men and <33 cm in women.²⁰

Handgrip strength was measured using a digital handheld dynamometer (TKK5401, Takei, Niigata, Japan). Participants were assessed in a standing position with the arm relaxed alongside the body and the elbow fully extended. Two measurements were obtained for each hand, and the highest value was used for analysis. Usual gait speed was assessed on a 6-m walkway, with timing recorded over the middle 4-m section to minimize acceleration and deceleration effects. Two trials were conducted, and the mean value was used for analysis.

Sarcopenia was defined according to the AWGS 2019 criteria.²⁰ Low muscle mass was defined as an ASMI <7.0 kg/m² in men and <5.7 kg/m² in women. Low handgrip strength was defined as <28 kg in men and <18 kg in women, and poor physical performance as usual gait speed <1.0 m/s. Sarcopenia was diagnosed when low muscle mass was present in combination with either low handgrip strength or poor physical performance.

Frailty-related status was assessed using the five domains of the Fried frailty phenotype: unintentional weight loss, exhaustion, weakness, slowness, and low physical activity.²¹ Unintentional weight loss was defined as an unintentional loss of ≥4.5 kg or ≥5% of body weight during the preceding year. Exhaustion was considered present when the participant reported that everything felt like an effort or that they could not get going on at least 3 days per week. For this study, weakness was operationalized using the AWGS 2019 low handgrip-strength cutoffs (<28 kg in men and <18 kg in women), and slowness as usual gait speed <1.0 m/s.²⁰ Low physical activity was defined as <600 MET-min/week according to the scoring protocol of the International Physical Activity Questionnaire short form.²² Participants meeting none, one or two, or three or more criteria were classified as robust, pre-frail, or frail, respectively. Non-robust status was defined as pre-frail or frail status.

Statistical Analysis

Continuous variables are presented as median [interquartile range] because of the small sample size and use of nonparametric group comparisons; categorical variables are presented as number and percentage. Participant characteristics, comorbidity burden, nutritional status, depressive symptom burden, and anthropometric, sarcopenia-related, and frailty-related variables were compared according to polypharmacy status. Mann–Whitney U-tests were used for continuous variables, and Fisher’s exact tests were used for categorical variables because of the small sample size and sparse cell counts. To complement p-values for the principal continuous findings, between-group median differences and percentile bootstrap 95% confidence intervals were estimated using stratified resampling within polypharmacy groups (10,000 resamples; random seed 2026). For sparse categorical outcomes, observed proportions and exact Fisher tests were retained rather than reporting unstable ratio estimates.

A supplementary analysis was performed according to nutritional risk status, categorized as normal nutritional status versus nutritional risk/malnutrition. All participants had complete data for the variables included in the analysis, and no imputation was performed.

Because of the small sample size and sparse outcome distribution, no multivariable models were fitted. Potential confounding, particularly by multimorbidity, was considered in the interpretation of the findings. No adjustment for multiple comparisons was applied because the analyses were exploratory and hypothesis-generating. Accordingly, individual p-values were interpreted cautiously, with recognition of the possibility of type I error. All statistical tests were two-sided, and p-values <0.05 were considered statistically significant. Exact p-values were reported unless p<0.001. All analyses were conducted using R software, version 4.5.1.

Results

Participant Characteristics According to Polypharmacy Status

A total of 28 older adults were included in the analysis, of whom 8 (28.6%) had polypharmacy. Baseline and clinical characteristics according to polypharmacy status are shown in Table 1.

Table 1.

Baseline and Clinical Characteristics According to Polypharmacy Status

Variable	Non-Polypharmacy (N = 20)	Polypharmacy (N = 8)	p-value
Age, years	77.5 [72.75–82.25]	75.5 [70.75–79.00]	0.430
Female sex, n (%)	13 (65.0)	5 (62.5)	1.000
BMI, kg/m2	24.65 [22.54–27.61]	23.44 [22.05–24.42]	0.258
Physician-diagnosed chronic disease, n (%)	18 (90.0)	8 (100.0)	1.000
Number of chronic conditions	2.5 [1.0–3.0]	3.0 [2.75–4.0]	0.083
Hypertension, n (%)	12 (60.0)	5 (62.5)	1.000
Hyperlipidemia, n (%)	11 (55.0)	2 (25.0)	0.221
Angina or MI, n (%)	1 (5.0)	2 (25.0)	0.188
Diabetes, n (%)	3 (15.0)	2 (25.0)	0.606
Arthritis, n (%)	1 (5.0)	3 (37.5)	0.058
Osteoporosis or chronic pain, n (%)	10 (50.0)	4 (50.0)	1.000
COPD or asthma, n (%)	0 (0.0)	3 (37.5)	0.017
Cancer, n (%)	0 (0.0)	1 (12.5)	0.286

Notes: Values are presented as median [interquartile range] or n (%). P-values were calculated using Mann–Whitney U-tests for continuous variables and Fisher’s exact tests for categorical variables. Polypharmacy was defined as the regular use of five or more physician-prescribed medications per day.

Abbreviations: BMI, body mass index; COPD, chronic obstructive pulmonary disease; MI, myocardial infarction.

Age, sex, and body mass index did not differ significantly between participants with and without polypharmacy. The number of chronic conditions was numerically higher in the polypharmacy group (median [IQR], 3.0 [2.75–4.0] vs 2.5 [1.0–3.0]; p=0.083). Chronic obstructive pulmonary disease or asthma was present in 3 of 8 participants with polypharmacy and none of the 20 participants without polypharmacy (p=0.017); given the sparse cell counts and multiple exploratory comparisons, this finding should be interpreted cautiously. Other individual comorbidities did not differ significantly according to polypharmacy status.

Nutritional Status, Depressive Symptom Burden, and Anthropometric Variables According to Polypharmacy Status

Nutritional status, depressive symptom burden, and anthropometric variables according to polypharmacy status are shown in Table 2. Participants with polypharmacy had lower MNA scores than those without polypharmacy (21.25 [20.25–22.13] vs 24.25 [23.25–26.00]; p=0.005), corresponding to a between-group median difference of −3.00 points (95% bootstrap CI, −5.75 to −1.50). Nutritional risk or malnutrition was present in all 8 participants with polypharmacy compared with 7 of 20 participants (35.0%) without polypharmacy (p=0.002). However, all participants with polypharmacy were in the at-risk range, and none were classified as malnourished.

Table 2.

Nutritional Status, Depressive Symptom Burden, and Anthropometric Variables According to Polypharmacy Status

Variable	Non-Polypharmacy (N = 20)	Polypharmacy (N = 8)	p-value
MNA score	24.25 [23.25–26.00]	21.25 [20.25–22.13]	0.005
Nutritional risk or malnutrition, n (%)	7 (35.0)	8 (100.0)	0.002
SGDS score	3.00 [2.00–6.00]	8.00 [4.75–9.25]	0.024
Depressive symptoms (SGDS ≥8), n (%)	4 (20.0)	5 (62.5)	0.068
Self-rated health	2.00 [1.75–3.00]	1.00 [1.00–2.00]	0.067
CC, cm	35.25 [33.45–36.63]	31.50 [30.38–33.90]	0.042
Low CC, n (%)	5 (25.0)	6 (75.0)	0.030
MUAC, cm	27.65 [26.00–29.00]	26.00 [25.63–26.68]	0.082

Notes: Values are presented as median [interquartile range] or n (%). P-values were calculated using Mann–Whitney U-tests for continuous variables and Fisher’s exact tests for categorical variables. Nutritional risk or malnutrition was defined using full MNA criteria. Depressive symptoms were defined as SGDS ≥8. Higher self-rated health scores indicated better perceived health. Low calf circumference was defined using sex-specific Asian Working Group for Sarcopenia 2019 cutoffs.

Abbreviations: CC, calf circumference; MNA, Mini Nutritional Assessment; MUAC, mid-upper arm circumference; SGDS, Short Form Geriatric Depression Scale.

Participants with polypharmacy also had higher SGDS scores (8.00 [4.75–9.25] vs 3.00 [2.00–6.00]; p=0.024), with a median difference of 5.00 points (95% bootstrap CI, 0.00 to 8.50). Depressive symptoms defined by SGDS ≥8 were present in 5 of 8 participants (62.5%) with polypharmacy and 4 of 20 (20.0%) without polypharmacy. This categorical difference did not reach statistical significance (p=0.068). Self-rated health also did not differ significantly. CC was smaller in the polypharmacy group (31.50 [30.38–33.90] vs 35.25 [33.45–36.63] cm; p=0.042), with a median difference of −3.75 cm (95% bootstrap CI, −5.65 to −0.20). Low CC was present in 75.0% versus 25.0% (p=0.030). MUAC did not differ significantly.

Figure 1 displays the distribution of MNA scores according to polypharmacy status.

Figure 1.

Mini Nutritional Assessment scores according to polypharmacy status. Boxes indicate the median and interquartile range. Dots indicate individual participants.

Sarcopenia-Related and Frailty-Related Variables

Sarcopenia-related and frailty-related variables according to polypharmacy status are summarized in Supplementary Table 1. Appendicular skeletal muscle mass index (6.01 [5.65–6.50] vs 6.59 [6.11–7.02] kg/m²; p=0.089), sarcopenia status (62.5% vs 25.0%; p=0.091), and handgrip strength (20.30 [18.60–28.03] vs 25.35 [23.13–30.75] kg; p=0.140) showed numerically less favorable values in the polypharmacy group, but none of these differences reached statistical significance. Gait speed, slow gait, frailty score, and non-robust status also did not differ significantly.

Supplementary Analysis According to Nutritional Risk Status

In the supplementary analysis according to nutritional risk status (Supplementary Table 2), polypharmacy was present in 8 of 15 participants (53.3%) with nutritional risk or malnutrition and in none of the 13 participants with normal nutritional status (p=0.002). Participants with nutritional risk or malnutrition had more chronic conditions (3.0 [2.5–4.0] vs 2.0 [1.0–3.0]; p=0.022), higher SGDS scores (6.0 [4.0–8.0] vs 2.0 [2.0–5.0]; p=0.026), and lower self-rated health scores (1.0 [1.0–2.0] vs 2.0 [2.0–3.0]; p=0.014). Sarcopenia-related and frailty-related variables did not differ significantly according to nutritional risk status.

Discussion

In this exploratory cross-sectional study of 28 older adults attending public hospital outpatient clinics, polypharmacy co-occurred with poorer nutritional status and greater depressive symptom burden. Participants with polypharmacy had lower MNA scores, more frequent nutritional risk, higher SGDS scores, and smaller CC. In contrast, several sarcopenia- and frailty-related measures did not differ significantly. Because all comparisons were unadjusted and the sample was small, these findings should be interpreted as a descriptive pattern of co-occurring vulnerability rather than evidence of an independent or causal effect of polypharmacy.

The clearest pattern involved nutritional status. Systematic and narrative reviews have previously described an association between polypharmacy and malnutrition or nutritional risk in older adults, while emphasizing that the relative contributions of medication burden, drug type, multimorbidity, and underlying illness remain difficult to separate.^6–8 A multicenter study of 675 older adults similarly evaluated polypharmacy together with nutritional status, depression, and physical function and reported less favorable nutritional and functional profiles among participants with polypharmacy.²³ Our study does not establish a causal relationship. Instead, it provides an exploratory characterization of medication burden, nutritional status, depressive symptoms, and selected geriatric indicators assessed concurrently in a Korean public-hospital outpatient setting.

Depressive symptom burden was also higher in participants with polypharmacy. Previous observational studies and meta-analyses have reported associations between polypharmacy and depression in older adults,^12,13,24 although the magnitude and independence of this relationship vary across populations and analytic adjustment. In the present study, the continuous SGDS score differed between groups, whereas the categorical comparison using SGDS ≥8 did not reach statistical significance. This discrepancy may partly reflect the loss of information from dichotomization and the imprecision associated with sparse cell counts in this very small sample. The result should therefore be interpreted as a hypothesis-generating difference in symptom burden, not as evidence that medication count independently determines depression risk.

Participants with polypharmacy had lower CC and were more often below the sex-specific cutoff, whereas appendicular skeletal muscle mass index, sarcopenia, gait speed, and frailty status did not differ significantly. CC is influenced by muscle mass, body size, and nutritional status and is used as a simple screening measure in older adults.^25–28 The present pattern therefore should not be interpreted as evidence that polypharmacy is broadly associated with sarcopenia or frailty. Instead, it suggests that the anthropometric signal observed in this sample may align more closely with nutritional vulnerability than with a consistent deficit across all physical-function domains. Frailty and sarcopenia are related but distinct constructs with partial overlap.²⁹ The absence of significant differences in several sarcopenia- and frailty-related measures, despite less favorable nutritional and depressive profiles, may reflect the small sample size, differences in clinical setting and participant characteristics, and variation in outcome definitions and analytic approaches across studies.

Several mechanisms could plausibly link medication burden with nutritional or psychological vulnerability, including anorexia, gastrointestinal symptoms, altered taste, drug-nutrient interactions, treatment complexity, and the cumulative burden of chronic disease.^2–8 These mechanisms were not measured in this study and should be regarded only as possible explanations. Conversely, poorer nutritional or psychological status may accompany greater multimorbidity and healthcare use, leading to more prescriptions. Recent studies of potentially inappropriate medications and prescribing criteria in older adult populations also underscore that medication-related risk is closely intertwined with comorbidity and overall prescribing complexity.^30,31 Thus, polypharmacy may be more appropriately viewed here as a clinical marker of complexity than as a demonstrated causal exposure.

This study has several limitations. First, the achieved sample of 28 participants, including only eight with polypharmacy, was smaller than the estimated minimum sample size of 55 for the original study, limiting statistical precision and precluding reliable multivariable adjustment. Residual confounding, particularly by multimorbidity and disease severity, therefore remains possible. Second, convenience sampling from two outpatient departments at a single public hospital and the absence of a formal screening log limit assessment of selection bias and generalizability. Third, the cross-sectional design precludes temporal or causal inference, and the 15-month accrual period may have introduced unmeasured temporal heterogeneity. Fourth, multiple exploratory comparisons were performed without multiplicity adjustment, increasing the possibility of type I error. Fifth, although medication use was verified against available outpatient records, medication classes, potentially inappropriate medications, anticholinergic burden, adherence, treatment duration, and specific adverse effects were not assessed. Finally, socioeconomic factors, social support, dietary intake, oral health, and other determinants of nutritional and depressive status were not comprehensively assessed.

Despite these limitations, the study combined contemporaneous medication verification with a full MNA, an interviewer-administered Korean SGDS, and standardized anthropometric and physical assessments performed by trained research staff. Its contribution is primarily descriptive, characterizing the co-occurrence of medication burden, nutritional vulnerability, and depressive symptoms in routine outpatient care. Polypharmacy status should not substitute for comprehensive geriatric assessment but may serve as a readily identifiable clinical signal prompting consideration of broader assessment. These findings may be relevant to multidisciplinary outpatient care involving physicians and pharmacists in medication review, together with nurses, dietitians, and mental health professionals in nutritional and psychological assessment.

Conclusion

In this small exploratory sample of older outpatients at a public hospital, polypharmacy co-occurred with poorer nutritional status and greater depressive symptom burden. These unadjusted findings are hypothesis-generating and do not establish that polypharmacy causes poorer nutritional status or depressive symptoms. Larger prospective studies with adequate adjustment for multimorbidity and other confounders are needed to determine whether medication burden may help identify older outpatients who warrant further nutritional and psychological assessment.

Funding Statement

This study was supported by Seoul Medical Center (Grant No. 2023-01). The funder had no role in the study design, data collection, data analysis, interpretation of data, manuscript preparation, or decision to submit the article for publication.

Abbreviations

ASMI, appendicular skeletal muscle mass index; AWGS, Asian Working Group for Sarcopenia; BMI, body mass index; CC, calf circumference; COPD, chronic obstructive pulmonary disease; MI, myocardial infarction; MNA, Mini Nutritional Assessment; MUAC, mid-upper arm circumference; SGDS, Short Form Geriatric Depression Scale.

Data Sharing Statement

The individual-level data underlying this study contain potentially identifying and sensitive clinical information from older adult outpatients. Because of ethical restrictions related to participant privacy and the small sample size, the data are not publicly available. De-identified data may be made available to qualified researchers upon reasonable request and after approval by the Institutional Review Board of Seoul Medical Center. Data access requests may be directed to the corresponding author and the Institutional Review Board of Seoul Medical Center.

Ethics Approval and Informed Consent

The study protocol was reviewed and approved by the Institutional Review Board of Seoul Medical Center (IRB No. 2023-09-008). All participants provided written informed consent before enrollment. All study procedures were conducted in accordance with the principles of the Declaration of Helsinki.

Consent for Publication

Not applicable. This manuscript does not contain any individual person’s identifiable data, images, videos, or other materials requiring consent for publication.

Author Contributions

The author made a significant contribution to the work reported, whether that is in the conception, study design, execution, acquisition of data, analysis and interpretation, or in all these areas; took part in drafting, revising or critically reviewing the article; gave final approval of the version to be published; has agreed on the journal to which the article has been submitted; and agrees to be accountable for all aspects of the work.

Disclosure

Eunjin Jeong reports leadership or fiduciary roles from Korean Society of Functional Medicine, outside the submitted work. The author declares no other competing interests in this work.