Association Between Skeletal Muscle Mass Indices and Cognitive Function Among Inpatients With Stable Schizophrenia

Article information

Psychiatry Investig. 2025;22(9):1048-1056

Publication date (electronic) : 2025 August 21

doi : https://doi.org/10.30773/pi.2025.0024

Dan Shuai ^,^*

, Binyou Wang ^,^*

, Duanfang Cai ^,^*

, Yan Guo

, Xiuping Lei

, Kezhi Liu

, Yilin Wang

Department of Psychiatry, Zigong Mental Health Center, the Zigong Affiliated Hospital, Southwest Medical University, Zigong, China

Correspondence: Kezhi Liu, PhD Department of Psychiatry, Zigong Mental Health Center, the Zigong Affiliated Hospital, Southwest Medical University, 666 Gongshu Road, Zigong 643020, China Tel: +86-08135156272, E-mail: liukezhi@swmu.edu.cn

Correspondence: Yilin Wang, MD Department of Psychiatry, Zigong Mental Health Center, the Zigong Affiliated Hospital, Southwest Medical University, 666 Gongshu Road, Zigong 643020, China Tel: +86-08135156272, E-mail: wangyilinzg0321@163.com

*These authors contributed equally to this work.

Received 2025 January 14; Revised 2025 June 10; Accepted 2025 July 14.

Abstract

Objective

To investigate the correlation between appendicular skeletal muscle mass (ASM)/height (ASMI^ht), ASM/body mass index (ASMI^BMI), ASM/weight (ASMI^wt), and ASM/waist circumference (ASMI^wc) and cognitive function among inpatients with stable schizophrenia.

Methods

This was a cross-sectional study of 235 stable schizophrenia inpatients, including 60% males (n=141). Patient demographic information and body composition data were collected. The Montreal Cognitive Assessment-Chinese version (MoCA-C) was used to measure cognitive function. To determine the association between the muscle mass indices and cognitive function, multiple linear regressions were established.

Results

The median age of males and females were 51 years (range 42–55) and 51 (range 39–58), respectively. Spearman’s correlation analysis revealed a significant association between ASMI^wc and the MoCA-C scores (r=0.323, false discovery rate [FDR]=0.004) in males, while ASMI^BMI, ASMI^wt, and ASMI^wc (r=0.268–0.421, all FDR <0.05) were significantly correlated with MoCA-C scores in females. Furthermore, covariate-adjusted multiple linear regression analysis further confirmed that only the ASMI^wc was related to MoCAC scores after controlling for relevant variables (males: β=0.565, 95% confidence interval [CI], 0.156–0.974, p=0.007; females: β=0.96, 95% CI, 0.394–1.526, p=0.001).

Conclusion

Our findings showed a substantial correlation between the ASMI^wc and cognitive function in schizophrenia inpatients. Further validation of these data in broader study populations is now necessary.

Keywords: Skeletal muscle; Schizophrenia; Body composition; Cognitive function

INTRODUCTION

Schizophrenia is a severe psychiatric disorder that impacts approximately 1% of the global population. The condition is a long-lasting and debilitating disorder characterized by distinct behavioral symptoms and disturbances in brain function [1-4]. Cognitive impairment is an essential characteristic of schizophrenia patients and is commonly associated with poor social interactions, work performance, and everyday activities [5-7]. These deficits contribute to the overall burden of disease [8].

The comprehensive MATRICS Consensus Cognitive Battery (MCCB) and the Montreal Cognitive Assessment (MoCA) are the primary tools used to test cognitive performance in patients with schizophrenia due to uncertain etiologic pathways and lack of recognized biomarkers [9,10]. Both scales are time-consuming and necessitate a professional’s clinical knowledge. Simpler and more efficient diagnostic tools for determining patients’ cognitive condition are required to improve the accuracy of diagnosis, as well as following care and therapy for schizophrenia patients.

Patients in China diagnosed with schizophrenia are typically admitted to a psychiatric hospital. In such a setting, monotonous diets, limited physical activity, and cramped living spaces are common, which contribute to obesity and a loss of muscle mass [11-13]. Furthermore, most antipsychotics have metabolic adverse effects, such as weight gain, insulin resistance, and dyslipidemia [14,15]. Previous research has revealed correlations between obesity, muscle loss, and impaired cognitive performance [16-18].

A reduction in muscle mass is defined by a decline in the size and quantity of skeletal muscle cells, leading to diminished muscle strength and increased connective tissue and fat levels. This is often evaluated using appendicular skeletal muscle mass (ASM) [19]. Several methods have been used to adjust for body size, including ASM/height (ASMI^ht), ASM/weight (ASMI^wt), and ASM/body mass index (ASMI^BMI) [20-22]. Moreover, patients with schizophrenia exhibit central obesity [23]. Comparing only height and weight is insufficient to determine fat distribution and visceral and abdominal fat accumulation. Waist circumference (WC) is a useful clinical indication of metabolic syndrome corresponding to visceral fat [24]. Therefore, on the basis of the above, we introduced WC to adjust the ASM.

In this study, the relationship between four body composition measures, namely ASMI^ht, ASMI^BMI, ASMI^wt, ASMI^wc, and cognitive function was explored amongst inpatients with stable schizophrenia. Since body composition varies according to gender, the study was stratified according to age [25,26]. This study aims to identify a simple and direct measure for assessing cognitive function in patients with schizophrenia by analyzing the relationship between body composition metrics and cognitive performance. It was hypothesized that this comprehensive evaluation would improve our understanding of patient health and establish a foundation for more personalized treatment strategies.

METHODS

Study design and participants

This cross-sectional analysis utilized data collected between August 1 and August 31, 2023, from an ongoing multi-cohort longitudinal study initiated by the Department of Psychiatry at Zigong Mental Health Center. The study was conducted in accordance with the principles of the Declaration of Helsinki and received ethical approval from the Zigong Mental Health Center Institutional Review Board (IRB approval number: 2023024). The diagnosis of schizophrenia was confirmed by two experienced psychiatrists using standards set by the International Classification of Diseases, Tenth Revision. Individuals with stable schizophrenia were those whose condition had been consistent for over 1 month. Inclusion criteria were: 1) patients over 18 years old, 2) diagnosis of stable schizophrenia by psychiatrists, and 3) willing to participate after providing informed consent. Exclusion criteria were: 1) lacking a diagnosis of stable schizophrenia, 2) no signed informed consent provided, 3) patients with significantly compromised liver or kidney function, 4) patients with autoimmune disease or receiving cancer treatment, and 5) inability to calculate any of the four body composition indices.

Assessment of cognition function

The MCCB is an essential tool for the assessment of cognitive performance in schizophrenia. Since the consistency of the MoCA score with the MCCB score has been demonstrated [27], expert psychiatrists assessed the patients’ cognitive function using the Montreal Cognitive Assessment-Chinese version (MoCA-C) [28]. The MoCA-C scale must be completed in 15 minutes with a maximum score of 30 points. Lower scores indicate decreased cognitive function.

Assessment of body composition

ASM was assessed using a validated formula for the Chinese population [29]. It has been confirmed that the Dual-energy X-ray Absorptiometry (DXA) and the ASM formula are consistent. The latter is the gold standard by which ASM is measured [30,31]. ASM was calculated using the following formula:

ASM=0.193×weight (kg)+0.107×height (cm)-4.157×sex-0.037×age (yr)-2.631.

Males were assigned a sex code of 1, and females a code of 2. ASMI^ht was calculated by dividing ASM by height (m) squared. ASMI^BMI was calculated by dividing ASM by BMI (kg/m²), and ASMI^wt was calculated by dividing ASM by weight (kg). ASMI^wc was calculated by dividing ASM by WC (m).

Covariates

Covariate information was collected through self-reporting or electronic medical records. The data contained the following: age, length of the disease, length of hospital stay, number of siblings, marital status (married, single, divorced, widowed), number of children, educational level (illiterate, high school and below, or university and above), first episode, number of chronic diseases, family history of mental sickness, vision issues, drinking and hearing issues, history of smoking, falls; history of COVID-19, and antipsychotics (typical, atypical, or combined).

After collecting all required covariates (height, weight, Patient Health Questionnaire-9 [PHQ-9] score, and Generalized Anxiety Disorder 7 scale [GAD-7] score excluded) from electronic medical records, the patient’s WC, height, and weight were measured. Patients were then evaluated for PHQ-9 and GAD-7 immediately. The entire process was overseen by trained researchers dedicated to ensuring accurate assessments and scores.

Statistical analyses

Sample size determination was not performed as this study used a census sampling approach. All 325 inpatients with schizophrenia hospitalized at Zigong Mental Health Center during the study period (August 1–31, 2023) were initially recruited. After implementation of the inclusion/exclusion criteria, 235 participants were enrolled in the final analysis.

Data were analyzed using SPSS 25.0 (IBM Corp.). Two-sided p-values<0.05 were considered statistically significant. Categorical variables were provided as numbers and percentages. All quantitative variables, including age, height, weight, WC, BMI, disease duration, hospitalization time, number of siblings, number of children, GAD-7 scores, and PHQ-9 scores, exhibit non-normal distributions and are therefore presented as medians (P₂₅, P₇₅).

The GAD-7 was used to measure the severity of anxiety symptoms, and the PHQ-9 assessed depression severity. A score below 5 on either scale indicated the absence of clinically significant anxiety or depression symptoms [32]. In addition, BMI values were categorized into three groups according to the Chinese standard of obesity: underweight (<18.5 kg/m²), normal weight (18.5–23.9 kg/m²), and obese (≥24 kg/m²) [33]. Due to the small number of participants (n=8) who had BMI values <18.5, this group was combined with the 18.5–23.9 group. Consequently, the final BMI classification comprised two groups, namely, the <24 and ≥24 groups.

Differences in MoCA-C scores across various patient characteristics were assessed using non-parametric rank-sum tests. Spearman’s correlation coefficients were used to evaluate associations between the four muscle mass indices and the Mo- CA-C scores. The Benjamini-Hochberg method was applied to control for the false discovery rate (FDR), with a FDR threshold set at 0.05 [34]. As the correlation analysis revealed significant associations between ASMI^wc and the MoCA-C scores in males, and between ASMI^BMI, ASMI^wt, and ASMI^wc and MoCA-C scores in females, multiple linear regression models were constructed for further investigation of these relationships. For each analysis, two models were developed: Model 1 (unadjusted) and Model 2 (adjusted for covariates exhibiting statistically significant associations with MoCA-C scores at p<0.05). Specifically, Model 2 for males included adjustments for age, educational level, hearing problems, and drinking history, while for females, adjustments were made for educational level and history of falls. The appropriateness of the multiple linear regression approach was confirmed for all models by verification of the normality of the residuals (Supplementary Figure 1).

RESULTS

Characteristics of schizophrenia inpatients

Table 1 provides a comprehensive overview of the demographic and clinical characteristics of the 235 participants with stable schizophrenia, classified in terms of sex (male: n=141, female: n=94). Sex-based disparities were evident, with a higher prevalence of family history of mental disorders among females (28.7% vs. 18.4%) and significantly higher marriage rates in females (33.0% vs. 10.6%). In contrast, males showed higher rates of smoking (63.8% vs. 8.5%) and alcohol consumption (40.4% vs. 3.2%). Both groups showed similar levels of education (≥high school: males 95.0%, females 90.4%) and used predominantly atypical or combined antipsychotic regimens (males: 97.2%, females: 98.9%). Continuous variables, such as age, anthropometry (height, weight, BMI, WC), disease duration, hospitalization length, family indices (siblings, offspring), and psychiatric scales (PHQ-9, GAD-7), are presented as medians with interquartile ranges in Table 1.

Table 1.

Descriptive statistics of participant demographics

Correlations between body composition indicators and MoCA-C scale scores

Table 2 summarizes the patient characteristics according to MoCA-C scores. In male patients with schizophrenia, the MoCA-C scores significantly differed in terms of patient age (p=0.018), educational level (p<0.001), hearing problems (p=0.026), and history of drinking (p=0.03). In female patients with schizophrenia, differences in MoCA-C scores were significant in terms of patient educational level (p<0.001) and history of falls (p=0.006).

Table 2.

Characteristics by MoCA-C scores

Table 3 presents the results of the Spearman correlation analysis between the four muscle mass indices and the MoCA-C scores. Among males, only ASMI^wc was found to be significantly associated with the MoCA-C score (r=0.323, FDR=0.004). In females, significant correlations were observed for ASMI^BMI, ASMI^wt, and ASMI^wc (r=0.268–0.421, all FDR <0.05). Multiple linear regression analyses were then performed for further investigation of these relationships, with the results summarized in Table 4. After adjustment for all relevant covariates, ASMI^wc remained positively and significantly associated with MoCA-C scores in both sexes (males: β=0.565, 95% confidence interval [CI], 0.156–0.974, p=0.007; females: β=0.960, 95% CI, 0.394–1.526, p=0.001) (Table 4).

Table 3.

Correlational analysis of body composition and MoCA-C scale scores in patients with stable schizophrenia

Table 4.

Correlations between body composition and MoCA-C scale scores in patients with stable schizophrenia

DISCUSSION

The relationship between four muscle mass indices, including ASMI^ht, ASMI^wt, ASMI^BMI, and ASMI^wc, and cognitive performance among inpatients with stable schizophrenia was investigated. Only ASMI^wc scores were significantly associated with MoCA-C scores, which emerged as a simple and effective tool for evaluating the cognitive function of hospitalized schizophrenia patients. The proposed approach provides a more comprehensive knowledge of the connection between body composition and cognitive health in this population and the identification of novel targets for intervention, care and treatment.

ASM quantifies muscle tissue in the limbs, encompassing arms and shoulders and the lower limbs, including the thighs and calves [35]. In addition to assisting the bones in maintaining a stable posture, muscle tissue provides movement and athletic ability [36]. Muscles also play a crucial role in maintaining metabolic homeostasis, regulating energy expenditure, storing fat, and regulating insulin and glucagon levels. Energy metabolism has been linked to cognitive function [37,38].. A reduction in muscle mass is strongly associated with cognitive decline. Individuals with schizophrenia frequently encounter muscle-related issues. Sedentary lifestyles, drug side effects, and nutrition deficiencies all increase symptoms. Reduced muscle mass contributes to obesity and raises cardiovascular and metabolic risk. It also limits the ability to do daily activities and starts an endless loop in which muscle loss further degrades physical performance [12,39].

Muscle mass is inherently related to body composition. Individuals with larger body sizes may possess greater muscle mass, while obese patients have a lower muscle mass [35,40]. To obtain a more accurate assessment of muscle mass, ASM must be adjusted according to body type. Several body contouring methods have been utilized, including ASMI^ht, ASMI^BMI, ASMI^wt, and ASM/body fat percentage (ASMI^BFP). These indexes vary in their assessment of physical health outcomes. The Asian Working Group for Sarcopenia (AWGS) advises calculating the skeletal muscle index as the ASMI^ht for diagnosing sarcopenia [41], while the Foundation for the National Institutes of Health (FNIH) Sarcopenia proposed ASMI^BMI as a muscle mass index [42]. In studies of middle-aged and older adults, the ability of the muscle mass index to predict adverse outcomes showed differing results. Hsu et al. [22] reported that ASMI^ht does not correlate with mobility in middle-aged and older adults compared to ASMI^wt and ASMI^BMI. Tan et al. [43] reported that ASMI^BMI is significantly associated with inflammation and handgrip strength (HGS) in obese pre-frail older adults. ASMI^BFP was also associated with HGS in all pre-frail older adults. In a nutshell, body composition may need to be adjusted for different populations and health conditions.

This study found that only ASMI^wc scores were substantially associated with MoCA-C scores in hospitalized patients with schizophrenia. This was anticipated as the WC measures abdominal fat compared to visceral fat in those with schizophrenia [44,45]. Indeed, the accumulation of ectopic fat is a crucial risk factor for several diseases, including cardiovascular disease, inflammatory responses, frailty, and type 2 diabetes [46-49]. Notably, all of these diseases are strongly associated with impaired cognitive function [50-52]. Height, weight, and BMI do not provide complete information on body shape and fat distribution, which may explain why ASMI^ht, ASMI^wt, and ASMI^BMI are unrelated to MoCA-C scale scores [53]. More appropriate methods for predicting the prognosis of patients with stable schizophrenia are ASMI^wc scores, which include ASM and WC. Furthermore, ASM modifications could vary for those who significantly acquire schizophrenia.

It is important to note a few limitations of the present study. Initially, a small sample size was used in the study, which was carried out at a single medical facility. This might make it less applicable to larger groups of people. Second, because testing was not conducted, baseline covariate data, including self-reported family history and COVID-19 infection status, were taken from medical records containing potentially biased information. Third, the cross-sectional study could not determine a causal relationship between muscle mass indices and cognitive function. Fourth, an anthropometric equation was used to estimate muscle instead of AWGS-recommended DXA or BIA methods. Lastly, despite their specific training, the two psychiatrists who conducted the study may have added subjective elements to the scoring system. Future research should expand the geographic breadth and employ larger sample numbers to understand these limitations. Furthermore, efforts should be undertaken to obtain more accurate data to reduce the impact of confounding variables.

In conclusion, the present study demonstrated a strong association between ASMI^wc scores and cognitive function among male and female inpatients with stable schizophrenia.

Supplementary Materials

The Supplement is available with this article at https://doi.org/10.30773/pi.2025.0024.

Supplementary Figure 1.

Linear regression models assessing the association between ASMI variables and MoCA-C score. Residual analyses were conducted to assess the assumptions of linear regression. A and B: ASMI^wc, model 1, male. C and D: ASMI^wc, model 2, male. E and F: ASMI^wc, model 1, female. G and H: ASMI^wc, model 2, female. I and J: ASMI^BMI, model 1, female. K and L: ASMI^BMI, model 2, female. M and N: ASMI^wt, model 1, female. O and P: ASMI^wt, model 2, female. MoCA-C, Montreal Cognitive Assessment-Chinese version; BMI, body mass index; ASM, appendicular skeletal muscle mass; ASMI, Appendicular Skeletal Muscle Index; ASMI^BMI, ASM/BMI; ASMI^wt, ASM/ weight; ASMI^wc, ASM/waist circumference.

pi-2025-0024-Supplementary-Fig-1.pdf

Notes

Availability of Data and Material

The datasets generated or analyzed during the study are available from the corresponding author on reasonable request.

Conflicts of Interest

The authors have no potential conflicts of interest to disclose.

Author Contributions

Conceptualization: Kezhi Liu, Yilin Wang. Data curation: Duanfang Cai, Yan Guo. Formal analysis: Dan Shuai. Investigation: Binyou Wang, Xiuping Lei. Methodology: Yan Guo, Kezhi Liu. Project administration: Yilin Wang. Supervision: Kezhi Liu, Yilin Wang. Writing—original draft: Dan Shuai, Binyou Wang, Duanfang Cai. Writing—review & editing: Dan Shuai, Binyou Wang, Duanfang Cai, Yilin Wang.

Funding Statement

This work was funded by the Zigong Key Science and Technology Plan (Collaborative Innovation Project of Zigong Institute of Brain Sciences) (No.2023-NKY-02-03, 2023-NKY-02-04 and 2022ZCNKY09) and the Key Science and Technology Plan of Zigong City (2023-YKY-12).

Acknowledgments

None

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Characteristics (N=235)	Male (N=141)	Female (N=94)
Age (yr)	51 (42, 55)	51 (39, 58)
Height (cm)	165 (160, 170)	156 (152, 161)
Weight (kg)	64.5 (58.6, 72.5)	61.8 (55.7, 72.1)
Waist circumference (cm)	89.0 (81.3, 97.0)	88.0 (80.0, 98.0)
Body mass index (kg/m²)	24.01 (21.80, 27.05)	25.45 (23.16, 28.83)
Disease duration (yr)	20 (12, 28)	18 (9, 29)
Hospitalized time (mon)	29.0 (9.5, 53.5)	20.5 (6.0, 44.0)
Number of siblings	3 (2, 4)	3 (2, 5)
Number of children	0 (0, 1)	1 (0, 2)
PHQ-9 score	2 (1, 6)	3 (1, 4)
GAD-7 score	0 (0, 3)	1 (0, 3)
Number of chronic diseases	0 (0, 1)	0 (0, 1)
Family history of mental disorder
No	115 (81.6)	67 (71.3)
Yes	26 (18.4)	27 (28.7)
First episode
No	135 (95.7)	93 (98.9)
Yes	6 (4.3)	1 (1.1)
Marital status
Married	15 (10.6)	31 (33.0)
Unmarried/divorced/widowed	126 (89.4)	63 (67.0)
Education
Illiterate	7 (5.0)	9 (9.6)
High school and below	123 (87.2)	74 (78.7)
University and above	11 (7.8)	11 (11.7)
Vision problems
No	126 (89.4)	78 (83.0)
Yes	15 (10.6)	16 (17.0)
Hearing problems
No	131 (92.9)	87 (92.6)
Yes	10 (7.1)	7 (7.4)
Smoking history
No	51 (36.2)	86 (91.5)
Yes	90 (63.8)	8 (8.5)
Drinking history
No	84 (59.6)	91 (96.8)
Yes	57 (40.4)	3 (3.2)
Falls history
No	136 (96.5)	85 (90.4)
Yes	5 (3.5)	9 (9.6)
COVID-19 history
No	86 (61.0)	55 (58.5)
Yes	55 (39.0)	39 (41.5)
Anti-psychotics
Typical	4 (2.8)	1 (1.1)
Atypical	131 (92.9)	83 (88.3)
Combined	6 (4.3)	10 (10.6)

Variable	Male		Female
Variable	MoCA-C score (median [P₂₅, P₇₅])	p	MoCA-C scores (median [P₂₅, P₇₅])	p
Age (yr)		0.018		0.316
<60	20.0 (15.0, 24.0)		16.0 (9.5, 21.5)
≥60	12.0 (6.3, 18.8)		15.0 (7.5, 19.0)
Body mass index (kg/m²)		0.666		0.062
<24	20.0 (14.0, 24.0)		19.0 (9.5, 22.0)
≥24	20.0 (13.0, 24.0)		14.0 (8.0, 20.0)
Disease duration (yr)		0.586		0.111
<5	21.5 (15.3, 23.5)		19.0 (13.0, 21.0)
5–10	21.0 (18.0, 23.0)		19.0 (13.0, 25.0)
>10	18.0 (13.0, 24.0)		14.5 (7.3, 20.0)
Hospitalized time (mon)		0.739		0.066
<6	20.0 (17.0, 23.0)		19.0 (10.3, 25.8)
≥6	19.5 (13.8, 24.0)		15.5 (8.0, 20.0)
Number of siblings		0.634		0.105
≤1	20.0 (17, 24.0)		22.0 (13, 24.0)
≥2	20.0 (13.0, 24.0)		16.0 (8.0, 20.0)
Number of children		0.952		0.336
0	20.0 (13.0, 24.0)		15.0 (8.8, 24.0)
≥1	18.0 (15.0, 24.0)		16.0 (9.3 20.0)
PHQ-9 score		0.171		0.176
<5	21.0 (14.0, 24.5)		14.5 (8.3, 20.8)
≥5	18.0 (15.0, 22.0)		18.5 (12.0, 23.3)
GAD-7 score		0.077		0.303
<5	20.0 (15.0, 24.0)		15.0 (8.5, 21.0)
≥5	17.0 (11.0, 22.0)		17.0 (12.5, 24.0)
Family history of mental disorder		0.293		0.099
No	20.0 (15.0, 24.0)		14.0 (8.0, 20.0)
Yes	17.5 (11.0, 24.0)		20.0 (10.0, 22.0)
First episode		0.751		-
No	20.0 (14.0, 24.0)		16.0 (9.0, 21.0)
Yes	21.5 (15.5, 23.3)		-
Marital status		0.323		0.075
Married	16.0 (13.0, 22.0)		14.0 (6.0, 19.0)
Unmarried/divorced/widowed	20.0 (14.0, 24.0)		17.0 (10.0, 22.0)
Education		<0.001		<0.001
Illiterate	12.0 (5.0, 13.0)		6.0 (4.0, 14.5)
High school and below	20.0 (14.0, 24.0)		15.0 (9.8, 20.0)
University and above	24.0 (22.0, 27.0)		22.0 (21.0, 27.0)
Vision problems		0.397		0.097
No	18.5 (13.0, 24.0)		15.0 (8.8, 20.3)
Yes	21.0 (18.0, 24.0)		20.0 (13.3, 24.8)
Hearing problems		0.026		0.173
No	20.0 (15.0, 24.0)		16.0 (10.0, 21.0)
Yes	11.0 (6.5, 21.3)		14.0 (7.0, 16.0)
Smoking history
No	21.0 (13.0, 24.0)	0.666	16.0 (8.8, 20.3)	0.193
Yes	18.5 (14.0, 24.0)		22.5 (10.8, 25.0)
Drinking history		0.030		0.643
No	21.0 (15.3, 24.8)		16.0 (9.0, 21.0)
Yes	17.0 (12.5, 23.0)		14.0 (11.0, 16.5)
Falls history		0.205		0.006
No	20.0 (14.3, 24.0)		17.0 (10.0, 21.5)
Yes	12.0 (7.5, 22.5)		9.0 (5.5, 13.0)
COVID-19 history		0.089		0.065
No	21.0 (15.8, 24.0)		17.0 (10.0, 23.0)
Yes	18.0 (12.0, 23.0)		14.0 (8.0, 20.0)
Number of chronic diseases		0.387		0.100
0	20.0 (15.0, 24.0)		18.0 (10.0, 23.0)
1	17.0 (11.0, 23.0)		14.0 (6.0, 17.0)
≥2	21.0 (14.0, 24.0)		13.5 (8.8, 19.3)
Anti-psychotics		0.311		0.200
Typical	24.5 (18.8, 25.8)		-
Atypical	20.0 (14.0, 24.0)		16.0 (9.0, 20.0)
Combined	19.0 (7.8, 22.3)		22.5 (12.0, 25.3)

Variable	Male			Female
Variable	Median (P₂₅, P₇₅)	r	FDR	Median (P₂₅, P₇₅)	r	FDR
ASMI^ht	7.96 (7.52, 8.52)	0.038	0.750	6.47 (6.02, 7.26)	-0.024	0.820
ASMI^BMI	0.89 (0.83, 0.98)	0.170	0.070	0.63 (0.58, 0.68)	0.268	0.020
ASMI^wt	0.33 (0.32, 0.35)	0.128	0.170	0.26 (0.25, 0.27)	0.299	0.008
ASMI^wc	24.39 (22.75, 26.30)	0.323	0.004	17.85 (16.81, 19.82)	0.421	0.004

Variable	Male				Female
	Model 1		Model 2		Model 1		Model 2
	β (95% CI)	p	β (95% CI)	p	β (95% CI)	p	β (95% CI)	p
ASMI^wc	0.827 (0.407–1.246)	<0.001	0.565 (0.156–0.974)	0.007	1.390 (0.795–1.985)	<0.001	0.960 (0.394–1.526)	0.001
ASMI^BMI	-	-	-	-	22.462 (6.012–38.911)	0.008	7.309 (-8.466–23.083)	0.360
ASMI^wt	-	-	-	-	134.420 (45.711–223.130)	0.003	67.326 (-15.672–150.324)	0.111