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Ann Geriatr Med Res > Volume 28(4); 2024 > Article
Moriyama, Tokunaga, Hori, Hachisuka, Itoh, Ochi, Matsushima, and Saeki: Correlation between Phase Angle and the Number of Medications in Older Inpatients: A Cross-Sectional Study

Abstract

Background

Muscle weakness in older adults elevates mortality risk and impairs quality of life, with the phase angle (PhA) indicating cellular health. Polypharmacy, common in geriatric care, could influence PhA. This investigates whether the number of medications and polypharmacy with PhA as a biomarker of muscle quality in older inpatients aged ≥65 and determines the extent to which multiple medications contribute to the risk of reduced muscle quality.

Methods

This retrospective cross-sectional study analyzed data from older inpatients requiring rehabilitation. PhA was measured using bioelectrical impedance analysis. The number of medications taken by each patient was recorded at admission. Polypharmacy was defined as the concurrent use of five or more medications at admission.

Results

In this study of 517 hospitalized older adults (median age 75 years; 47.4% men), 178 patients (34.4%) were diagnosed with sarcopenia. Polypharmacy was present in 66% of patients. The median PhA was 4.9° in men and 4.3° in women. Multivariate linear regression analysis was performed separately for men and women. In men, PhA was negatively correlated with the number of medications (β=–0.104, p=0.041) and polypharmacy (β=–0.045, p=0.383). In women, PhA was negatively correlated with the number of medications (β=–0.119, p=0.026) and polypharmacy (β=–0.098, p=0.063). Analyses were adjusted for age, body mass index, sarcopenia, C-reactive protein, and hemoglobin levels.

Conclusion

The number of medications at admission negatively impacted PhA in older inpatients, highlighting the importance of reviewing prescribed drugs and their interactions.

INTRODUCTION

Muscle weakness in older adults impacts their quality of life and independence and increases mortality.1-4) Muscle function is often assessed quantitatively through declines in strength, mass, and functionality. With aging, diminution in neural expression frequency,5,6) myofiber metamorphosis,7) and increased myosteatosis8) result in alterations in muscle quality, concurring with muscle weakness.9,10) Consequently, a growing imperative exists to evaluate muscle quality.11)
Phase angle (PhA), measured through bioelectrical impedance analysis (BIA), has been associated with muscle dysfunction in older individuals, such as reduced muscle strength, loss of skeletal muscle mass, a decline in physical performance, frailty, and sarcopenia.12-15) PhA reflects the cell membrane’s physiological functionality and structural robustness, with an elevated PhA indicating superior physiological efficiency and structural strength,16,17) manifesting muscle quality.15,18)
Polypharmacy is a common challenge in geriatric drug management due to multimorbidity,19) multiple physicians,20) and the prescribing cascade.21) The multifaceted repercussions of polypharmacy include decreased medication adherence, heightened susceptibility to adverse drug interactions, and increased healthcare expenditure.22,23) Polypharmacy and a higher number of medications further magnify sarcopenia risk.24,25) These factors can indirectly affect muscle mass and function, potentially influencing PhA measurements. These aspects are critical in maintaining muscle quality in older individuals. Thus, elucidating the relationship between the number of medications, polypharmacy, and PhA is critical.
Therefore, this study investigated the correlation between the number of medications, polypharmacy, and PhA in older adults. This investigation is pivotal for understanding how much medication burden can impact the physiological aspects of PhA. We posit that understanding the influence of multiple drugs on muscle quality is imperative for effective pharmacotherapy in geriatrics.

MATERIALS AND METHODS

This retrospective cross-sectional study was conducted at the hospital of the University of Occupational and Environmental Health, a tertiary care acute general hospital in Japan, between February 2022 and March 2024. This study included patients aged ≥65 who underwent physical rehabilitation and body composition assessment within one week after hospitalization. Patients with missing data, Brunnstrom stage 4 or lower in the upper limb and finger,26) and unhealed upper limb fracture were excluded.

Phase Angle Calculation

Patients used the body composition device (InBody S10; InBody Japan, Tokyo, Japan), which employs the BIA method, to simultaneously measure PhA and appendicular skeletal muscle mass in the supine position after resting for 10 minutes and at least 2 hours after eating. The appendicular skeletal muscle mass divided by height was defined as skeletal muscle index (SMI).
PhA was calculated from the impedance values of the right half of the body at 50 kHz and 200 μA of alternating current using the following equation:
PhA = arctangent (X/R) × (180/π),
where X is the reactance and R is the resistance. SMI was calculated by dividing the measured appendicular skeletal muscle mass by the squared height in meters. PhA and SMI were assessed within a few days from the physical rehabilitation start day.

Data Collection

All data were collected from the medical charts. Primary patient data included age, sex, body mass index (BMI), medical conditions, handgrip strength (HGS), C-reactive protein (CRP), hemoglobin, and pre-admission orientation. HGS was measured using a handgrip dynamometer (Grip-D; Takei Scientific Instruments, Tokyo, Japan) on the physical rehabilitation start day. CRP and hemoglobin levels were assessed at admission. Sarcopenia was defined as having HGS <28 kg for men and <18 kg for women and SMI <7 kg/m2 for men and <5.7 kg/m2 for women according to the cut-off values specific for Asian adults in the Asia Working Group for Sarcopenia 2019.27)

Number of Medications and Polypharmacy

Pharmacists identify and register the currently prescribed medications in the patient’s medical charts upon hospital admission as part of their routine medical duties. This is based on the prescribed record and information from the previous physician, patient, and family. At admission, the drug information of patients was investigated based on their medical charts. Polypharmacy was defined as five or more medications.28) The number of medications and polypharmacy was determined when assessing prescribed medications daily at admission based on patients’ medical charts, except for antibiotics, medications as needed, cold remedies, over-the-counter medicines, ocular preparations, and external medicines.

Statistical Analyses

All statistical analyses were performed using the EZR software for Windows.29) The level of statistical significance was set at p-value <0.05. Categorical variables were presented as numbers and percentages. Ordinal variables were represented as median and interquartile range (IQR; 25–75th percentiles). Continuous variables were expressed as mean and standard deviation or median and IQR based on the Shapiro–Wilk test results. In comparing patients with and without polypharmacy in each sex, categorical variables were analyzed using chi-squared and Fisher exact tests. In contrast, quantitative variables, including ordinal and continuous variables, were analyzed using the t-test and Mann–Whitney U tests after assessing the normality using the Shapiro–Wilk test. Multivariate linear regression analyses were performed for the PhA for each sex, with two models for each analysis. Model 1 included age, BMI, sarcopenia, CRP, hemoglobin, and the number of medications. Model 2 included age, BMI, sarcopenia, CRP, hemoglobin, and polypharmacy. CRP and hemoglobin were factors correlated with PhA.30,31) Multicollinearity was evaluated using the variance inflation factor (VIF), with a VIF value of 1–3 indicating the absence of multicollinearity.

Ethics Approval

This study was conducted following the Declaration of Helsinki and approved by the Ethics Committee of the University Hospital of Occupational and Environmental Health (No. CR23-110). Due to its retrospective observational nature, the requirement for written informed consent was waived. Patients had the option to withdraw from the study at any point. This study complied the ethical guidelines for authorship and publishing in the Annals of Geriatric Medicine and Research.32)

RESULTS

Overall, 517 inpatients were evaluated in the study (median age 75 years; 47.4% men). A total of 341 patients (66%) were in a state of polypharmacy, and the median number of medications at admission was six. The number of patients admitted for surgery was 234, including those with lung cancer (n=158), cardiovascular conditions (n=34), and knee osteoarthritis (n=27). All patients underwent preoperative rehabilitation assessments. The main reasons for hospital admission other than surgery were treatment for connective tissue disease (n=186), acute stroke (n=44), and either chemotherapy or radiotherapy for cancer (n=10). One hundred seventy-eight patients (34.4%) were diagnosed with sarcopenia.
Tables 1 and 2 show the characteristics and comparisons of patients with and without polypharmacy among men and women. The analysis included 245 older male inpatients (66.9% with polypharmacy) and 272 older female inpatients (65.1% with polypharmacy). Patients with polypharmacy were older in both men (p=0.006) and women (p=0.008). Hypertension prevalence and diabetes mellitus were higher in the polypharmacy group for both men (p<0.001, p=0.009) and women (p<0.001, p=0.003). Hemoglobin levels were lower in the polypharmacy group for both men (p=0.002) and women (p=0.046). PPI and benzodiazepine use were higher in the polypharmacy group for both men (p<0.001) and women (p<0.001). Men with polypharmacy had a higher prevalence of sarcopenia (p=0.036) and osteoporosis (p=0.009) compared to those without polypharmacy, whereas women did not show significant differences in these conditions. Chronic heart disease prevalence was higher in the polypharmacy group for both men (p=0.098) and women (p=0.003), with a more pronounced difference in women. Men with polypharmacy had a significantly lower PhA (4.7° vs. 5.1°, p=0.009), whereas women showed no significant difference in PhA between groups (4.2° vs. 4.4°, p=0.115).
In hospitalized older men (Table 3), PhA was negatively correlated with the number of medications (β=–0.104, p=0.041), indicating that an increase in the number of medications was linked to a decrease in PhA after adjusting for age, BMI, sarcopenia, CRP, and hemoglobin. However, polypharmacy did not significantly correlate with PhA (β=–0.045, p=0.383). In contrast, in hospitalized older women (Table 4), PhA was also negatively correlated with the number of medications (β=–0.119, p=0.026). However, polypharmacy approached significance (β=–0.098, p=0.063) but did not significantly correlate with PhA. Multicollinearity was not observed in all models.

DISCUSSION

We observed that a greater number of medications were independently correlated with decreased PhA in hospitalized older patients aged ≥65 years. However, polypharmacy did not relate to PhA. This suggests a possible synergistic effect when multiple drugs are used concurrently, potentially resulting in a cumulative burden on muscle quality. To the best of our knowledge, this is the first study to demonstrate the correlation between the number of medications and PhA, providing novel insights into the complex impact of multiple drugs on muscle quality for effective pharmacotherapy in geriatric patients.
Our findings suggest that the impact of multiple drugs, rather than a single drug, may influence muscle quality in older adults. Hence, it is critical to consider the number of medications prescribed and their pharmacological properties and interactions when administering effective pharmacotherapy in older adults. Prudent consideration should be directed toward the appropriate utilization of drug interventions and combinations within older people.
Measuring muscle function is essential, as sarcopenia is associated with poor clinical outcomes, such as increased risk of falls, fractures, or hospitalization. Sarcopenia is a medical condition characterized by low muscle mass, strength, and physical performance.11,27) Dual-energy-X-ray-absorptiometry (DEXA) and BIA are recommended to measure skeletal muscle mass based on the international consensus for sarcopenia diagnosis.11,27) However, DEXA equipment requires a dedicated room due to the relatively small risks of radiation exposure and high medical costs due to routine maintenance. The assessment of muscle mass by BIA overestimates muscle mass in excess water or edema.33) Furthermore, muscle strength and physical performance are affected by both pain and motivation. In contrast, PhA, which indicates muscle status, is an objective indicator that is more minimally invasive, safe, inexpensive, and accurate for assessing muscle function compared to previous muscle strength and skeletal muscle measurement methods and sarcopenia assessments.14) Wu et al.34) reviewed reports that sarcopenia and PhA are associated with various conditions such as community-dwelling older adults, stroke, cancer, and chronic kidney disease. Additionally, Norman et al.35) research supports using PhA to predict clinical outcomes, such as frailty, incident disability, falls, and mortality in older patients. However, there are some limitations to consider. PhA measurements may vary depending on the device used. Additionally, PhA is highly sensitive to body fluid balance, so measurements can be affected by food intake, urination, and the time of day the measurement is taken. Since a low PhA is associated with many poor clinical outcomes, it is unsuitable for indicating specific pathological conditions. Therefore, a low PhA should indicate an increased health risk for patients.
The definition of polypharmacy in this study may have been too broad, potentially diluting the specific effects of the number and types of medications on PhA. It is conceivable that the threshold of five medications was insufficient to impact PhA. Kojima et al.36) have reported that an increased number of medications is associated with increased toxicity, suggesting that specific drug combinations could be influencing PhA.
While this study identified the link between the number of medications and diminished muscle quality, the underlying mechanisms were not fully explored. We have expanded the discussion to include drug interactions, nutritional status changes, side effects of the medications themselves, and chronic diseases’ impact, which suggest potential mechanisms by which polypharmacy could negatively affect muscle quality.
Particular attention is given to the myotoxic effects of specific drug classes, such as non-steroidal anti-inflammatory drugs (NSAIDs), which might cause detrimental metabolic effects, such as mitochondrial dysfunction, diminished blood flow and electrolyte, hormonal or acid-base alterations.37) Studies have shown that withdrawal from long-term use of benzodiazepines can improve muscle strength and balance function.38) In this study, NSAIDs were prescribed to 5%, and benzodiazepines were prescribed to 8.9% of the participants. Additionally, this study found that many patients were taking PPIs. PPIs have been associated with decreased muscle function in outpatients aged 65 years and older39) and in patients with heart failure.40) PPIs can cause malabsorption of vitamin D and vitamin B12, both of which are essential for muscle synthesis, and may lead to sarcopenia by insufficiency.41,42) Long-term PPI use may have affected muscle quality. PPI use was present in 39.5% of this study. The accumulation of these medications may have contributed significantly to the relationship between the number of medications and PhA. Many medications can cause side effects such as fatigue and muscle pain. This can limit patients’ physical activity, decrease muscle strength, and reduce muscle quality. Polypharmacy is often conducted as part of the drug management of chronic diseases. The extent of polypharmacy and the number of medications taken may indicate the presence of multiple chronic diseases, which themselves could impact muscle quality. Therefore, accumulating these effects in older adults may reduce muscle quality, demonstrating the association between PhA and the number of medications.
In our study, the residuals of both Model 1 and Model 2 for each sex did not follow a normal distribution (Shapiro–Wilk test, p<0.001, respectively). This suggests that the normality assumption for the residuals was violated, potentially affecting the interpretation of the regression coefficients. Despite this issue, the number of medications was significantly correlated with PhA, with β values of –0.104 for men and –0.119 for women. These findings indicate that, even after adjusting for potential confounders, the number of medications remains an essential factor influencing PhA. Moreover, in the multivariate analysis, the number of medications had approximately half the impact on PhA compared to age, sex, and BMI.
However, the number of medications was consistently a significant variable in both men and women, unlike CRP and hemoglobin, which showed differing results between genders. This underscores the clinical significance of medication management in older patients for maintaining muscle quality and overall health. This study found that 66% of the patients were experiencing polypharmacy, consistent with previous research proportions.43)
This study has several limitations. First, generalizing the results may be challenging because this study was conducted at a single university hospital. Second, due to the study’s cross-sectional nature, a causal negative relationship between the number of medications and PhA cannot be proven. Third, the PhA reflects nutritional status,44) and this study did not include a comprehensive nutritional assessment index in the multiple regression equation. However, including nutrition-related indicators, such as BMI, hemoglobin, and CRP, would aid in understanding the reliability of the association between polypharmacy and PhA in hospitalized older patients.
In conclusion, the number of medications at admission, rather than the presence of polypharmacy, negatively impacted PhA in hospitalized older patients aged ≥65 years. Our findings indicate that the number of medications significantly impacts PhA measurements more than polypharmacy itself. This underscores the importance of considering the cumulative effects of multiple medications and suggests that medication burden may be a modifiable risk factor for impaired muscle quality in older adults.

ACKNOWLEDGMENTS

CONFLICT OF INTEREST

The researchers claim no conflicts of interest.

FUNDING

Health Labour Science Research Grants of the Ministry of Health Labour and Welfare supported this study (Grant No. 230301-1). The funding body was not involved in the design and conduct of the study, including the decision to submit the manuscript for publication.

AUTHOR CONTRIBUTIONS

Conceptualization, TM, MT, RH, AH, HI, MO, YM, SS; Data curation, TM, MT, RH, AH, HI, MO; Formal analysis, TM; Writing–original draft, TM; Supervision, SS; Writing–review & editing, TM, MT, RH, AH, HI, MO, YM, SS; Funding acquisition, SS.

Table 1.
Comparing older inpatients with and without polypharmacy in hospitalized older men
Overall (n=245) Polypharmacy
p-value
With (n=164) Without (n=81)
Age (y) 75 (71–80) 76 (72–80) 73 (70–78) 0.006
BMI (kg/m2) 22.7 (20.7–24.8) 24.7 (20.7–24.7) 22.7 (20.7–24.8) 0.971
Comorbid condition
 Hypertension 152 (62) 117 (71.3) 35 (43.2) <0.001
 Diabetes mellitus 65 (26.5) 52 (31.7) 13 (16) 0.009
 Connective tissue disease 62 (25.3) 44 (26.8) 18 (22.2) 0.532
 Cancer 124 (50.6) 85 (51.8) 39 (48.1) 0.684
 COPD 27 (11) 17 (10.4) 10 (12.3) 0.668
 Chronic heart disease 16 (6.5) 14 (8.5) 2 (2.5) 0.098
 Acute stroke 29 (11.8) 18 (11) 11 (13.6) 0.537
 Osteoporosis 28 (11.4) 25 (15.2) 3 (3.7) 0.009
 Dementia 11 (4.5) 8 (4.9) 3 (3.7) 1
Preoperative condition 142 (58) 98 (59.8) 44 (54.8) 0.492
Sarcopenia 93 (38) 70 (42.7) 23 (28.4) 0.036
Phase angle (°) 4.9 (4.2–5.5) 4.7 (4–5.5) 5.1 (4.6–5.5) 0.009
CRP (mg/dL) 0.18 (0.07–0.92) 0.21 (0.08–1.21) 0.16 (0.07–0.51) 0.187
Hemoglobin (g/dL) 13.1 (11.6–14.2) 12.8 (11.3–13.9) 13.4 (12.4–14.8) 0.002
Number of medications 6 (3–9) 8 (6–10) 2 (1–3) <0.001
 PPIs uses 94 (38.4) 84 (51.2) 10 (12.3) <0.001
 NSAIDs uses 11 (4.5) 10 (6.1) 1 (1.2) 0.107
 Benzodiazepine uses 20 (8.2) 20 (12.2) 0 (0) <0.001
Preadmission orientation 0.618
 Home 237 (96.7) 157 (95.7) 80 (98.8)
 Another hospital 7 (2.9) 6 (3.7) 1 (1.2)
 Nursing home 1 (0.4) 1 (0.6) 0 (0)

Values are presented as median (interquartile range) or number (%).

BMI, body mass index; COPD, chronic obstructive pulmonary disease; CRP, C-reactive protein; PPIs, proton pump inhibitors; NSAIDs, non-steroidal anti-inflammatory drugs.

Table 2.
Comparing older inpatients with and without polypharmacy in hospitalized older women
Overall (n=272) Polypharmacy
p-value
With (n=177) Without (n=95)
Age (y) 76 (71–80) 76 (72–81) 74 (69–79) 0.008
BMI (kg/m) 22.6 (20–25.5) 22.9 (20.1–25.9) 22.2 (19.9–24.9) 0.08
Comorbid condition
 Hypertension 150 (55.2) 115 (65) 35 (36.8) <0.001
 Diabetes mellitus 56 (20.6) 46 (26) 10 (10.5) 0.003
 Connective tissue disease 145 (53.3) 91 (51.4) 54 (56.8) 0.445
 Cancer 66 (24.3) 44 (24.9) 22 (23.2) 0.882
 COPD 8 (2.9) 5 (2.8) 3 (3.2) 1
 Chronic heart disease 20 (7.4) 19 (10.7) 1 (1.1) 0.003
 Acute stroke 15 (5.5) 8 (4.5) 7 (7.4) 0.405
 Osteoporosis 109 (40.1) 76 (42.9) 33 (34.7) 0.197
 Dementia 6 (2.2) 5 (2.8) 1 (1.1) 0.668
Preoperative condition 92 (33.8) 66 (37.3) 26 (27.4) 0.108
Sarcopenia 129 (47.4) 84 (47.5) 45 (47.4) 1
Phase angle (°) 4.3 (4.2–5.5) 4.2 (3.5–4.8) 4.4 (3.7–4.8) 0.115
CRP (mg/dL) 0.24 (0.06–1.05) 0.25 (0.07–1.03) 0.22 (0.06–1.05) 0.742
Hemoglobin (g/dL) 11.9 (10.7–13.1) 11.8 (10.7–12.8) 12.4 (11.1–13.5) 0.046
Number of medications 6 (4–9) 8 (6–10) 3 (2–4) <0.001
 PPIs uses 110 (40.4) 97 (54.8) 13 (13.7) <0.001
 NSAIDs uses 15 (5.5) 13 (7.3) 2 (2.1) 0.095
 Benzodiazepine uses 24 (8.8) 24 (13.6) 0 (0) <0.001
Preadmission orientation 0.922
 Home 250 (91.9) 163 (92.1) 87 (91.6)
 Another hospital 18 (6.6) 11 (6.2) 7 (7.4)
 Nursing home 4 (1.5) 3 (1.7) 1 (1.1)

Values are presented as median (interquartile range) or number (%).

BMI, body mass index; COPD, chronic obstructive pulmonary disease; CRP, C-reactive protein; PPIs, proton pump inhibitors; NSAIDs, non-steroidal anti-inflammatory drugs.

Table 3.
Multivariate linear regression analysis with phase angle in hospitalized older men
Variable Model 1
Model 2
β B 95% CI p-value β B 95% CI p-value
Age –0.274 –0.045 –0.061 to –0.029 <0.001 –0.275 –0.045 –0.062 to –0.029 <0.001
BMI 0.231 0.072 0.036 to 0.108 <0.001 0.222 0.07 0.033 to 0.106 <0.001
Sarcopenia –0.071 –0.147 –0.387 to –0.093 0.228 –0.079 –0.164 –0.406 to –0.077 0.181
CRP –0.002 –0.001 –0.034 to 0.033 0.965 0.002 0.001 –0.033 to 0.035 0.97
Hemoglobin 0.357 0.017 0.116 to 0.221 <0.001 0.373 0.176 0.124 to 0.228 <0.001
Number of medications –0.104 –0.027 –0.053 to –0.001 0.041 - - -
Polypharmacy - - - –0.045 –0.096 –0.411 to 0.011 0.383

Adjusted R2=0.419 in Model 1 and 0.411 in Model 2.

BMI, body mass index; CRP, C-reactive protein; CI, confidence interval.

Table 4.
Multivariate linear regression analysis with phase angle in hospitalized older women
Variable Model 1
Model 2
β B 95% CI p-value β B 95% CI p-value
Age –0.207 –0.032 –0.049 to –0.025 <0.001 –0.205 –0.032 –0.048 to –0.016 <0.001
BMI 0.249 0.059 0.03 to 0.088 <0.001 0.237 0.056 0.028 to 0.085 <0.001
Sarcopenia –0.192 –0.372 –0.608 to –0.136 0.002 –0.204 –0.395 –0.63 to –0.159 0.001
CRP –0.218 –0.05 –0.074 to –0.027 <0.001 –0.221 –0.051 –0.075 to –0.028 <0.001
Hemoglobin 0.082 0.016 –0.004 to 0.036 0.119 0.084 0.017 –0.009 to 0.006 0.107
Number of medications –0.119 –0.03 –0.056 to –0.004 0.026 - - -
Polypharmacy - - - –0.098 –0.2 –0.411 to 0.011 0.063

Adjusted R2=0.286 in Model 1 and 0.282 in Model 2.

BMI, body mass index; CRP, C-reactive protein; CI, confidence interval.

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