Factors of geriatric syndrome associated with low physical function in older patients with chronic kidney disease: a cross-sectional study

Abstract

Background

Poor physical function in older patients with pre-dialysis chronic kidney disease (CKD) is a significant concern, as it affects their prognosis. This study investigated factors associated with poor physical function in this population, including geriatric syndrome, renal function, and anemia.

Methods

This cross-sectional study included 142 patients aged ≥ 65 years with stage 3–5 pre-dialysis CKD who were admitted for CKD education. Physical function was assessed using the Short Physical Performance Battery (SPPB), with scores of 12 points defined as normal group and scores ≤ 11 points as low physical function. Furthermore, the number of medications, Geriatric Depression Scale-5 (GDS-5) and Lubben Social Network Scale-6 (LSNS-6) scores, nutritional indices, renal function, and anemia were assessed.

Results

A total of 62 (43.7%) patients exhibited low physical function. GDS-5 and LSNS-6 scores, number of medications, exercise habits, and age significantly differed between the normal group and low physical function group (p < 0.05). Multivariate analysis revealed that GDS-5 score (odds ratio [OR]: 1.65, 95% confidence interval [CI]: 1.11–2.45) and number of medications (OR: 1.27, 95% CI: 1.09–1.46) were significantly associated with low physical function after adjustment for age, sex, body mass index (BMI), diabetes mellitus, hemoglobin (Hb), estimated glomerular filtration rate (eGFR), and Geriatric Nutritional Risk Index (GNRI) (p < 0.05), while LSNS-6 did not demonstrate a significant association.

Conclusions

Poor mental health and a high number of medications may be key factors contributing to low physical function in older patients with pre-dialysis CKD.

Introduction

The prevalence of chronic kidney disease (CKD) is rising among older patients in Japan [1]. Older patients with pre-dialysis CKD often have multiple comorbidities, including diabetes, hypertension, cardiovascular disease, and musculoskeletal disorders [2], and impaired physical function compared with their healthy counterparts [3]. Since poor physical function in patients with CKD is associated with increased mortality [3], cardiovascular disease [4], and need for renal replacement therapy [4], developing programs to effectively maintain and improve physical function is crucial for older patients with pre-dialysis CKD.

Further investigations are needed to elucidate the factors associated with low physical function in this population. Previous studies have reported that the factors influencing pre-dialysis CKD among older patients are multifactorial, including patient background (e.g., age, sex, and frailty) [5], sarcopenia [6], diabetes mellitus [7], malnutrition [8], renal function [9], and anemia [9]. Notably, a prior study demonstrated the efficacy of multidisciplinary interventions, including physical therapy, in preserving renal function [10]. However, previous research on physical function in patients with pre-dialysis CKD has primarily focused on adults and middle-aged patients. As such, the factors affecting low physical function in older patients with pre-dialysis CKD remain poorly understood. A comprehensive investigation of these factors, with consideration for age and CKD pathogenesis, is necessary to identify which ones are significant for older patients and to establish effective interventions.

Geriatric syndromes significantly impact physical function in older patients with pre-dialysis CKD. Defined as “multifactorial health conditions that occur when the accumulated effects of impairments in multiple systems render an older person vulnerable to situational challenges” [11], geriatric syndromes encompass issues specific to older adults, such as social isolation, polypharmacy, and depression, which are associated with increased institutionalization and mortality [12]. Previous studies on community-dwelling older adults have reported associations between physical function and geriatric-specific problems, including polypharmacy [13], depression [14], and social isolation [15]. Although a previous study has shown that patients with pre-dialysis CKD have a higher risk of geriatric syndrome [16], no studies have examined how these syndromes, when combined with CKD development, contribute to physical function decline among older patients. Investigating the factors associated with low physical function in older patients with pre-dialysis CKD, including geriatric syndromes, is important to accumulate evidence for improved care and treatment strategies in this population.

To that end, this study aimed to investigate the factors associated with low physical function in older patients with pre-dialysis CKD, including polypharmacy, mental health, social isolation, nutritional indices, renal function, and anemia. Identifying these factors can provide valuable data to support interventions aimed at improving physical function.

Patients and methods

Study participants

This single-center, cross-sectional study was conducted at Seirei Sakura Citizen Hospital, Japan between July 2020 and March 2023. Patients aged ≥ 65 years with non-dialyzed stage 3–5 CKD who were admitted for CKD education were enrolled in this study. Exclusion criteria included: (1) uncontrolled hypertension and cardiac failure, (2) leg amputation, (3) motor paralysis due to central nervous system disease, (4) dementia, and (5) history of kidney biopsy. All procedures performed in this study have been approved by the Ethics Committee of Seirei Sakura Citizen Hospital (approval no. 2024019).

Data collection

Data on patient characteristics were collected from medical records during admission. Three trained physical therapists assessed all measurements for each participant.

(1) Patient characteristics

Demographic data, including age, sex, body mass index (BMI), alcohol consumption, smoking status, living situation, exercise habits, and comorbidities (e.g., diabetes, hypertension, dyslipidemia, musculoskeletal disorders, angina pectoris, myocardial infarction, heart failure, and other cardiac diseases), were collected. In addition, pre-assessment laboratory data were obtained for total protein, serum albumin, blood urea nitrogen, creatinine, estimated glomerular filtration rate (eGFR), sodium, potassium, calcium, phosphorus, hemoglobin (Hb), hematocrit, and C-reactive protein. Nutritional status was assessed using the Geriatric Nutritional Risk Index (GNRI) calculated with the following formula [17]: GNRI = [14.89 × albumin (g/dL)] + [41.7 × (body weight/ideal body weight)].

For patients exceeding their ideal body weight, the body weight/ideal body weight was set to 1.

Exercise habits were evaluated using the transtheoretical model (TTM) [18]. Regular exercise was defined as engaging in physical activities at least 30 min, at least twice a week, and outside of work or housework. In this study, patients in the “maintenance” and “action” stages were assigned to the “exercise habits group,” whereas patients in the “preparation,” “contemplation,” and “pre-contemplation” stages were assigned to the “without exercise habits group.”

(2) Physical function

Physical function was assessed using the Short Physical Performance Battery (SPPB) test, consisting of three sub-items scored from 0 to 4 points each: balance, 4-m walk, and repeat chair stand tests [19]. The balance test involved the assessment of standing balance in three positions (side-by-side, semi-tandem, and tandem) for 10 s each. The 4-m walk test measured the time it took the patient to comfortably walk 4 m. Lastly, the chair stand test, performed five times, assessed the time it took the patient to rise from sitting position with arms crossed from a 40-cm armless chair, fully extending the knees and hips with each attempt. The total SPPB score ranged from 0 to 12 points, with lower scores indicating poorer physical function.

Following previous studies [20], 12 points was determined as the cutoff value for low physical function on the SPPB. Accordingly, patients scoring 12 points were assigned to the normal group, while those scoring ≤ 11 points were assigned to the low physical function group. As a subgroup analysis, a comparison was also conducted using the cutoff value of ≤ 9 points for sarcopenia [21].

(3) Number of medications

This study defined “personal medications” as medications taken daily prior to admission. Loop diuretics, renin–angiotensin system inhibitors (RASi), statins, sleeping pills, and other medications were investigated. The number of medications was investigated by pharmacists who reviewed the medications that patients brought with them during educational hospitalization and verified this information using medical records.

(4) Depressive symptoms

Depressive symptoms were assessed using the Geriatric Depression Scale-5 (GDS-5), which is the 5-item version of the 30-item Geriatric Depression Scale [22]. This scale includes five questions with yes/no responses, and the total score is calculated by summing all responses.

(5) Social isolation

Social isolation was defined as the lack of direct weekly contact with family, friends, or neighbors [23]. The Lubben Social Network Scale-6 (LSNS-6), a 6-point (0–5 points) questionnaire that evaluates family and friend networks with three questions each, was utilized to assess social isolation [24]. Family network questions included: “How many relatives do you see or hear from at least once a month?”, “How many relatives do you feel at ease with whom you can talk about private matters?”, and “How many relatives do you feel close to such that you can call on them for help?” Friend network questions included: “How many of your friends do you see or hear from at least once a month?”, “How many friends do you feel at ease with whom you can talk about private matters?”, and “How many friends do you feel close to such that you can call on them for help?” The total score ranged from 0 to 30 points, with higher scores indicating a larger social network.

Statistical analysis

To minimize bias, missing data for variables were complemented using the multiple assignment methods on the basis of the missing-at-random assumption with the IBM SPSS Missing Values 28 software (IBM Corp.; Armonk, NY, USA). Continuous variables were expressed as means and standard deviations (SDs), whereas categorical variables were expressed as percentages. The χ² and independent t-tests were used to compare patient characteristics between the low and normal physical function groups. Univariate and multivariate logistic regression analyses were used to calculate the odds ratios (ORs) and 95% confidence intervals (CIs) to assess factors associated with low physical function. Different explanatory variables were analyzed in models 1 and 2. Model 1 included explanatory variables with significant differences on univariate analysis, including age categorized into three groups (65–69, 70–79, and ≥ 80 years). Model 2 included variables that were used in previous studies, mainly sex, BMI, eGFR, Hb, and GNRI [7, 8–9]. Moreover, model 3 was refined by adding diabetes mellitus, hypertension, musculoskeletal disorders, and cardiovascular diseases as explanatory variables, alongside those included in model 1. To explore potential age-specific differences in the factors associated with low physical function, we additionally conducted subgroup analyses using multivariate logistic regression stratified by age groups (65–79 years and ≥ 80 years). In each age group, multivariate models were constructed to examine the association between low physical function and the GDS-5 score and number of medications. These models also included sex, BMI, eGFR, Hb, and GNRI as covariates. All analyses were performed using the SPSS software (version 28.0, IBM), and statistical significance was set at p < 0.05.

Results

Initially, 162 patients were enrolled in this study. Of these, 20 patients were excluded owing to the following reasons: three owing to uncontrolled blood pressure, four owing to dementia, two owing to musculoskeletal disorders (one patient with upper limb fracture and another with a leg amputation), three owing to central nervous system disease, three owing to prior renal biopsy, and five owing to refusal to participate. A total of 142 patients were included in the final analysis.

Table 1 summarizes the patient characteristics categorized by physical function level. From the 142 participants, 62 (43.7%) exhibited poor physical function and were assigned to the low physical function group. Comparisons between the two groups demonstrated significant differences in GDS-5 and LSNS-6 scores, number of medications, exercise habits, and age (p < 0.05).

Table 1. Patient characteristics

	Normal groups	Low physical function groups	p-value
Number of patients, n (%)	80 (56.3)	62 (43.7)
Age (year)	74.3 ± 5.6	78.9 ± 6.5	0.00
Sex (male/female)	52/28	36/26	0.40
BMI (kg/m2)	23.9 ± 3.2	24.0 ± 3.6	0.92
Alcohol consumption habit, yes n (%)	42 (52.5)	29 (46.8)	0.50
Current smoker, yes n (%)	36 (45.0)	32 (40.0)	0.43
Lives alone, n (%)	14 (17.5)	11 (17.7)	0.97
Exercise habits, n (%)	45 (56.3)	22 (35.5)	0.01
Loop diuretics use, n (%)	9 (11.3)	14 (22.6)	0.07
RASi use, n (%)	43 (53.8)	30 (48.4)	0.53
Statin use, n (%)	26 (32.5)	22 (35.5)	0.71
Sleeping pill use, n (%)	2 (2.5)	2 (3.2)	0.80
Number of medications	5.2 ± 2.6	7.6 ± 3.6	0.00
Comorbidity, n (%)
Diabetes mellitus	27 (33.8)	31 (50.0)	0.05
Hypertension	69 (86.3)	46 (74.2)	0.07
Dyslipidemia	32 (40.0)	24 (38.7)	0.87
Musculoskeletal disorders	20 (25.0)	21 (33.9)	0.24
Cardiovascular disease	9 (11.3)	10 (16.1)	0.40
Primary renal disease, n (%)
Chronic glomerulonephritis	6 (7.5)	4 (0)	0.89
Nephrosclerosis	39 (30)	23 (50)	0.17
Diabetic nephrology	18 (32.5)	23 (36.4)	0.06
Unclear, others	17 (30)	12 (13.6)	0.78
CKD stage, n (%)
Stage 3	31 (38.8)	17 (27.4)	0.16
Stage 4	23 (28.8)	30 (48.4)	0.02
Stage 5	26 (32.5)	15 (24.2)	0.28
Laboratory data
TP (g/dL)	6.8 ± 0.6	6.8 ± 0.7	0.85
Alb (g/dL)	3.8 ± 0.5	3.7 ± 0.5	0.10
BUN (mg/dL)	39.0 ± 19.6	36.5 ± 15.9	0.41
Cr (mg/dL)	2.5 ± 1.4	2.3 ± 1.1	0.34
eGFR (mL/min/1.73 m2)	24.4 ± 12.7	23.6 ± 10.6	0.71
Na (mEq/L)	139.0 ± 2.7	138.9 ± 2.6	0.13
K (mEq/L)	4.8 ± 0.6	4.6 ± 0.7	0.06
Ca (mEq/L)	8.9 ± 0.6	8.9 ± 0.8	0.81
P (mg/dlL)	3.9 ± 0.8	3.8 ± 0.7	0.46
Hb (g/dL)	11.9 ± 1.8	11.6 ± 1.6	0.28
Ht (%)	35.8 ± 5.3	35.5 ± 4.7	0.70
CRP (mg/dL)	0.2 ± 0.5	0.3 ± 0.5	0.61
GNRI	101.9 ± 11.0	100.3 ± 10.7	0.40
Social isolation
LSNS-6 (score)	15.0 ± 5.5	12.4 ± 5.9	0.01
Mental health
GDS-5 (score)	0.6 ± 1.1	1.7 ± 1.6	0.00

BMI, body mass index; RASi, renim-angiotensin system inhibitors; TP, total protein; Alb, albumin; BUN, blood urea nitrogen; Cr, creatinine; eGFR, estimated glomerular filtration rate; Na, sodium; K, potassium; Ca, calcium; P, phosphorus; Hb, hemoglobin; Ht, hematocrit; CRP, C-reactive protein; GNRI, Geriatric Nutrition Risk Index; LSNS-6, Lubben Social Network Scale-6; GDS-5, Geriatric Depression Scale-5

Note: Values are expressed as means ± SDs or numbers (%)

Table 2 presents the results of the logistic regression analysis. Model 1 showed that GDS-5 score (OR: 1.73, 95% CI: 1.18–2.55), number of medications (OR: 1.23, 95% CI: 1.09–1.49), and age ≥ 80 years (OR: 4.87, 95% CI: 1.08–21.87) were significantly associated with low physical function (p < 0.05). Model 2 indicated that GDS-5 score (OR: 1.65, 95% CI: 1.11–2.45) and number of medications (OR: 1.27, 95% CI: 1.09–1.46) remained significantly associated with low physical function after adjustment for age (categorized as 65–69, 70–79, and ≥ 80 years), sex, BMI, Hb, eGFR, and GNRI (p < 0.05). Model 3, adjusted for diabetes mellitus, hypertension, musculoskeletal disorders, and cardiovascular diseases, similarly showed that GDS-5 score (OR: 1.66, 95% CI: 1.12–2.45) and number of medications (OR: 1.22, 95% CI: 1.05–1.42) were significantly associated with low physical function (p < 0.05), while age ≥ 80 years remained a significant factor (OR: 5.58, 95% CI: 1.16–26.86, p = 0.03). However, LSNS-6 and exercise habits were significantly associated with low physical function in univariate analysis; these associations were no longer significant after adjusting for confounding factors.

Table 2. Multiple logistic regression analysis

	Univariate regression			Multiple logistic model 1			Multiple logistic model 2			Multiple logistic model 3
	OR	95% CI	p-value	OR	95% CI	p-value	OR	95% CI	p-value	OR	95% CI	p-value
GDS-5	1.85	1.355–2.538	0.00	1.61	1.105–2.346	0.01	1.65	1.111–2.449	0.01	1.66	1.121–2.453	0.01
Number of medications	1.17	1.045–1.322	0.01	1.24	1.083–1.421	0.00	1.27	1.094–1.464	0.00	1.22	1.049–1.422	0.01
LSNS-6	0.93	0.869–0.985	0.02	0.93	0.862–1.018	0.12	0.94	0.855–1.030	0.24	0.95	0.863–1.036	0.23
Exercise habits	0.45	0.232–0.865	0.02	0.64	0.269–1.523	0.31	0.58	0.236–1.438	0.24	0.62	0.250–1.553	0.31
Age (years)
65–69				Ref.			Ref.			Ref.
70–79				1.47	0.363–5.950	0.59	1.69	0.403–7.080	0.47	1.59	0.372–6.803	0.53
≥ 80				4.87	1.083–21.869	0.04	6.05	1.244–29.430	0.03	5.58	1.160–26.864	0.03
Sex	0.73	0.352–1.520	0.40				1.51	0.585–3.911	0.39
BMI	0.99	0.889–1.096	0.81				1.02	0.889–1.166	0.80
eGFR	0.99	0.965–1.021	0.62				1.01	0.967–1.057	0.62
Hb	0.89	0.713–1.112	0.30				1.07	0.785–1.463	0.66
GNRI	0.98	0.956–1.004	0.10				0.99	0.928–1.049	0.67
Diabetes mellitus	1.79	0.943–3.400	0.08							1.80	0.692–4.662	0.23
Hypertension	0.46	0.195–1.076	0.07							0.32	0.091–1.126	0.08
Musculoskeletal disorders	1.54	0.745–3.221	0.24							0.88	0.318–2.437	0.81
Cardiovascular disease	1.51	0.576–3.998	0.399							0.67	0.154–2.889	0.59

Model 1: Significant differences in the GDS-5 and LSNS-6 scores, number of medications, exercise habits, and age were observed between groups. Model 2: Sex, BMI, eGFR, Hb, and GNRI were used as adjustment variables. Model 3: Diabetes mellitus, hypertension, musculoskeletal disorders, and cardiovascular disease were used as adjustment variables. Odds ratios for all explanatory variables were calculated per unit change

CI, confidence interval; OR, odds ratio; GDS-5, Geriatric Depression Scale-5; LSNS-6, Lubben Social Network Scale-6; BMI, body mass index; eGFR, estimated glomerular filtration rate; Hb, hemoglobin; GNRI, Geriatric Nutritional Risk Index

Table 3 presents the subgroup analysis comparing patients grouped by a cutoff value of SPPB ≤ 9. Significant differences were observed in age, exercise habits, number of medications, LSNS-6, and GDS-5 (p < 0.05).

Table 3. Comparison between two groups using the SPPB cutoff value of ≤ 9

	SPPB ≥ 10	SPPB ≤ 9	p-value
Number of patients, n (%)	112 (78.9)	30 (21.1)
Age (years)	80.0 ± 5.8	75.3 ± 6.2	0.00
Sex (male/female)	75/37	13/17	0.02
BMI (kg/m2)	23.9 ± 3.1	24.9 ± 4.3	0.16
Alcohol consumption habit, yes n (%)	60 (53.6)	11 (36.7)	0.10
Current smoker, yes n (%)	56 (50.0)	12 (40)	0.50
Lives alone, n (%)	18 (16.1)	7 (23.3)	0.35
Exercise habits, n (%)	60 (53.6)	7 (23.3)	0.00
Number of medications	5.3 ± 2.8	7.2 ± 3.3	0.00
Comorbidity, n (%)
Diabetes mellitus	43 (38.4)	15 (50)	0.25
Hypertension	95 (84.8)	20 (66.7)	0.07
Dyslipidemia	45 (40.2)	11 (36.7)	0.72
Musculoskeletal disorders	33 (29.5)	8 (26.7)	0.82
Cardiovascular disease	12 (10.7)	7 (23.3)	0.07
Laboratory data
TP (g/dL)	6.8 ± 0.6	6.8 ± 0.5	0.86
Alb (g/dL)	3.8 ± 0.4	3.6 ± 0.6	0.09
BUN (mg/dL)	38.5 ± 18.6	36.2 ± 15.8	0.56
Cr (mg/dL)	2.5 ± 1.4	2.4 ± 1.0	0.81
eGFR (mL/min/1.73 m2)	24.7 ± 12.2	21.8 ± 9.7	0.25
Na (mEq/L)	138.9 ± 2.6	138.7 ± 3.1	0.73
K (mEq/L)	4.8 ± 0.7	4.6 ± 0.6	0.36
Ca (mEq/L)	8.9 ± 0.7	8.9 ± 0.6	0.93
P (mg/dlL)	3.8 ± 0.8	3.9 ± 0.7	0.50
Hb (g/dL)	11.8 ± 1.8	11.4 ± 1.5	0.23
Ht (%)	35.8 ± 5.1	34.8 ± 4.6	0.38
CRP (mg/dL)	0.2 ± 0.4	0.4 ± 0.6	0.09
GNRI	101.2 ± 10.2	101.2 ± 13.4	0.39
Social isolation
LSNS-6 (score)	14.6 ± 5.6	10.6 ± 5.2	0.00
Mental health
GDS-5 (score)	0.8 ± 1.1	2.2 ± 1.5	0.00

SPPB, Short Physical Performance Battery; BMI, body mass index; TP, total protein; Alb, albumin; BUN, blood urea nitrogen; Cr, creatinine; eGFR, estimated glomerular filtration rate; Na, sodium; K, potassium; Ca, calcium; P, phosphorus; Hb, hemoglobin; Ht, hematocrit; CRP, C-reactive protein; GNRI, Geriatric Nutrition Risk Index; LSNS-6, Lubben Social Network Scale-6; GDS-5, Geriatric Depression Scale-5

Note: Values are expressed as means ± SDs or numbers (%)

The results of the logistic regression analysis by age group are shown in Supplementary Tables 1 and 2. In patients aged 65–79 years (n = 100), multivariate logistic regression analysis revealed that the number of medications was significantly associated with low physical function (model 1: OR: 1.42, 95% CI: 1.18–1.72, p < 0.001; model 2: OR: 1.43, 95% CI: 1.18–1.72, p < 0.001; model 3: OR: 1.44, 95% CI: 1.18–1.75, p < 0.001). In contrast, GDS-5, LSNS-6, and exercise habits were not significantly associated with low physical function in any model. In patients aged ≥ 80 years (n = 42), both GDS-5 score (model 1: OR: 2.27, 95% CI: 1.08–4.78, p = 0.03; model 2: OR: 2.25, 95% CI: 1.06–4.79, p = 0.03; model 3: OR: 3.00, 95% CI: 1.18–7.62, p = 0.02) and number of medications (model 1: OR: 1.63, 95% CI: 1.05–2.52, p = 0.03; model 2: OR: 1.63, 95% CI: 1.05–2.53, p = 0.03; model 3: OR: 2.01, 95% CI: 1.09–3.68, p = 0.03) were significantly associated with low physical function.

Discussion

To the best of our knowledge, this is the first study to investigate factors affecting low physical function in older patients with pre-dialysis CKD, considering both CKD markers and geriatric syndromes. Our findings indicate that a higher number of medications and poorer mental health, in addition to age, were more strongly associated with low physical function, even after adjusting for the effects of exercise habits, nutritional status, renal function, anemia, and social isolation. These results provide valuable insights for developing interventions to prevent physical function decline in this population.

Physical dysfunction is considered a significant concern in older adults with pre-dialysis CKD. Previous studies have demonstrated a clear association between physical function and worsening renal function in pre-dialysis CKD [25], with affected individuals exhibiting approximately 30% lower function than that in healthy individuals [3]. In addition, this physical impairment can further impact mortality rates [3], cardiovascular disease [4], need for renal replacement therapy [4], social participation [26], and difficulties in medical care and self-management [27]. Therefore, interventions aimed at maintaining and improving physical function in older patients with pre-dialysis CKD are crucial for improving patient outcomes and preventing disease progression.

Our study identified depression, measured using the GDS-5, as a significant factor for low physical function. Studies show that approximately 13% and 70% of older patients with pre-dialysis CKD experience depression and anxiety about the future, respectively [28, 29]. These psychological and socioeconomic factors, including dialysis anxiety and treatment difficulties, can negatively impact mental health in this population. Moreover, physical factors, such as general malaise, dyspnea, nausea, itching, and sleep disturbances, can exacerbate mental health concerns. Existing research supports a relationship between depression and chronic inflammation in CKD [30], wherein inflammation promotes protein catabolism and low nutrition. Other studies have also shown associations between depression in CKD and fatigue [31], sleep quality [31], and physical activity [32]. Collectively, these findings indicate that chronic inflammation, reduced physical activity owing to fatigue, and poor sleep quality may contribute to the link between mental health and low physical function in older patients with pre-dialysis CKD. Thus, a holistic approach that considers both physical and mental health is paramount.

In the present study, the number of medications was significantly associated with poor physical function. Owing to the presence of multiple comorbidities and complications, patients with CKD are highly predisposed to polypharmacy, with a reported rate of 41% [33]. A systematic review involving community-dwelling older adults showed that polypharmacy was a recognized risk factor for decreased physical function [34]. Several mechanisms may underlie the association between polypharmacy and physical function decline. Loop diuretics, commonly used for fluid management, have been associated with sarcopenia and impaired skeletal muscle anabolism [35, 36]. Statins, commonly used to treat dyslipidemia, may also have negative effects on skeletal muscles [37]. Furthermore, side effects from sleeping pills, anxiety drugs, and antihypertensive drugs may increase fall risk among older patients [38]. While our study did not observe differences in medication types between the low physical function and normal groups, the sheer number of medications these patients take may contribute to physical limitations through skeletal muscle impairment and reduced physical activity. Several strategies have been proposed to address polypharmacy, such as deprescribing, reducing the use of unnecessary drugs, managing medication underuse, and performing medication reviews by pharmacists and other healthcare professionals [39, 40]. Targeting polypharmacy through these interventions may be a valuable approach to preventing physical function decline in this population.

Our findings indicate that LSNS-6, which evaluates social networks, was not significantly associated with low physical function in the multivariate analysis. This result may reflect certain limitations of LSNS-6 as a predictor of physical function. While LSNS-6 assesses the size and frequency of social connections, it does not capture qualitative aspects of social interactions, such as emotional support, which may more directly influence physical function. In addition, the unique characteristics of our study population—older adults with pre-dialysis CKD—may have contributed to this result. A previous study reported that CKD is associated with both mobility limitations and social isolation in older adults. However, mobility limitations alone did not fully account for reduced social engagement [41], indicating the influence of other factors. Physical function in this population may be more strongly affected by intrinsic disease factors, such as anemia and renal impairment, than by social network size. Furthermore, variables such as mental health and medication burden showed significant associations with low physical function, potentially diminishing the apparent influence of LSNS-6. These findings highlight the complexity of factors influencing physical function in older patients with CKD and suggest that future studies should consider more comprehensive measures of social support and their interactions with other predictors.

Residual renal function, nutritional indices, and anemia were not associated with low physical function in older patients with pre-dialysis CKD. Prior research suggested links between these factors and low physical function [8, 9]; however, the results of this study did not support these findings. One reason for this may be attributed to the relatively good medical management in our patients, resulting in adequate anemia and nutritional levels. The mean Hb values in the high (11.9 ± 1.8 g/dL) and low physical function groups (11.6 ± 1.6 g/dL) were higher than the standard anemia cutoff of 11.0 g/dL [42]. Similarly, the mean GNRI scores in the high (101.9 ± 11.0) and low physical function groups (100.3 ± 10.7) exceeded the nutritional index cutoff value of 91 [43]. Another reason for this may be owing to differences in the definition of low physical function. This study adopted a cutoff value of 12 points on the basis of a previous study [20], whereas other previous studies employed different cutoff values for SPPB, ranging from 7 to 9 points [8]. These differences in cutoffs likely resulted in the inclusion of patients with more severe presentations in physical function, anemia, and nutritional status.

Effective interventions to improve physical function in older patients with pre-dialysis CKD have yet to be established. While exercise therapy has been explored in these patients, no significant improvements were reported in leg muscle strength [44]. In addition, studies show that exercise interventions during hemodialysis may be influenced by age [45]. As older patients with CKD tend to have more complex medical conditions, exercise therapy may be less effective in improving physical function in this age group. Similarly, the effects of interventions for geriatric syndromes to improve physical function remain elusive. The significant associations identified with mental health and number of medications potentially indicate the need for a comprehensive approach that incorporates medications, psychological support, and exercise therapy. These findings further emphasize the importance of addressing depressive symptoms and medication burden in clinical practice. Routine screening for depression using tools such as GDS-5, combined with timely interventions, including psychological support and structured exercise programs, may help improve physical function. In addition, regular medication reviews to minimize polypharmacy and optimize essential treatments are critical. Integrating these strategies into care plans could provide a more comprehensive approach to managing physical function in older patients with CKD. Future research should explore the effectiveness of these interventions and their interactions with other contributing factors.

Despite the valuable insights provided by this study, several limitations must be acknowledged. First, the cross-sectional design and relatively small sample size limit our ability to establish causal relationships between low physical function, number of medications, and mental health. Second, renal function, which was measured on the basis of eGFR, may have been influenced by skeletal muscle mass and dietary intake. Third, the use of the SPPB as a tool for evaluating physical function inherently includes a ceiling effect, limiting its ability to differentiate among individuals with higher physical function. While our primary focus was to identify low-functioning patients, the SPPB may not adequately capture the nuances of physical performance among highly active individuals or those engaged in labor-intensive occupations. Fourth, we employed a cutoff value of ≤ 11 points as suggested by previous studies. Although this approach aligns with the study’s objective of detecting physical function decline, the validity of this cutoff remains a subject for further investigation. Finally, the absence of control groups, such as community-dwelling older adults or younger patients with CKD, hinders the generalizability of our results. Further research is needed to determine whether the results of this study are specific to older patients with pre-dialysis CKD.

Conclusions

This study demonstrated that poor mental health and a high number of medications were significantly associated with low physical function in older patients with pre-dialysis CKD. These findings underscore the necessity of developing multifaceted interventions that address poor mental health, high medication burden, and other contributing factors to prevent and improve low physical function in these patients.

Acknowledgements

We would like to thank the patients who participated in this study and the hospital staff for their tremendous cooperation during the study period.

Author contributions

Material preparation, data collection, and analysis were performed by A.T., T.K., and S.O.; A.T. and H.Y. were major contributors in writing the manuscript; T.Y., Y.M., T.S., and T.F. read and approved the final manuscript.

Funding

No funding was received for this study.

Availability of data and material

All data in this study are available upon request.

Declarations

Ethics approval and consent to participate

The Ethics Committee of Seirei Sakura Citizen Hospital approved all the procedures performed in this study (approval no. 2024019). Informed consent was obtained from all participants included in the study.

Consent for publication

Not applicable.

Conflicts of interests

All the authors have declared that no conflicts of interest exist.

Abbreviations

CKD

Chronic kidney disease

SPPB

Short Physical Performance Battery

GDS-5

Geriatric Depression Scale-5

LSNS-6

Lubben Social Network Scale-6

BMI

Body mass index

TTM

Transtheoretical model

eGFR

Estimated glomerular filtration rate

Hb

Hemoglobin

GNRI

Geriatric Nutritional Risk Index

TTM

Transtheoretical model

RASi

Renin–angiotensin system inhibitors

SD

Standard deviation

ORs

Odds ratios

CIs

Confidence intervals

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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