Effectiveness of using simple measures as screening indicators for sarcopenic obesity to predict mortality risk in older male patients: a prospective study

Abstract

Objectives

This study was developed to determine whether sarcopenic obesity (SO), as identified using screening indices for sarcopenia and obesity, could predict mortality risk among older male individuals.

Methods

This prospective study enrolled male patients aged ≥ 60 years from 15 nursing homes in Zigong, China. Baseline data were collected from September 2021 to July 2022, with follow-up conducted until April 2024. Three parameters were used to identify SO, namely, handgrip strength (HGS), calf circumference (CC), and several obesity indices (waist-to-hip ratio [WHR], body mass index [BMI], waist circumference [WC], and WC_BMI). The HGS and CC cut-off values were selected based on the Asian Working Group on Sarcopenia (AWGS) 2019 consensus criteria. The WHR, BMI, and WC values were used to stratify patients by obesity status using previously published cut-off values. The cutoff value of WC_BMI was determined according to the third quartile. SO was defined by abnormalities in all three parameters, and the relationship between SO and mortality was examined using Cox proportional hazards models.

Results

A total of 491 subjects in nursing homes were enrolled in the study. 72 (14.66%) of the participants died between the baseline and end of follow-up. Of these, deceased males showed significantly lower CC, HGS, WC, and BMI but higher WC_BMI values compared to survivors (all P < 0.05). Mortality was significantly higher in subjects with low CC (18.13% vs. 5.8%, P < 0.001), low HGS (17.93% vs. 5.97%, P < 0.01), and high WC_BMI (25.83% vs. 11.05%, P < 0.001), and lower in those with high WC (17.37% vs. 8.92%, P < 0.05). Using low CC + low HGS+high WC_BMI as criteria for diagnosing SO, Cox regression analysis showed that the risk of death in the SO group was higher than that in the normal group (HR = 3.07, 95% CI: 1.846–5.108).

Conclusions

The findings indicate that low CC and HGS, as well as high WC_BMI, were significant independent predictors of mortality in older male nursing home residents. The findings demonstrate the clinical utility of incorporating CC and HGS measurements into routine geriatric assessments in long-term care facilities. Furthermore, the results highlight the need for sex-specific preventive strategies and indicate that WC_BMI may represent a more meaningful anthropometric indicator than traditional BMI for mortality risk stratification in older individuals. These evidence-based insights could significantly improve risk assessment and inform targeted interventions to reduce mortality in this vulnerable demographic.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12877-026-07083-9.

Introduction

Sarcopenic obesity (SO) is a functional and clinical disorder characterized by changes in body composition including low muscle mass/function together with high levels of adiposity [1, 2]. Reported SO prevalence rates range from 4.5% to 8.2% [3–6]. SO incidence is associated with a range of adverse patient outcomes that can include falls [3], disability [5], multiple comorbidities [5], polypharmacy [5], cognitive impairment [5], greater cardiovascular risk [2], and higher overall Survival (OS)risk [2]. The European Society for Clinical Nutrition and Metabolism (ESPEN) and the European Association for the Study of Obesity (EASO) guidelines are the common criteria employed for SO diagnosis [1]. Muscle mass and fat mass percentage values are often analyzed via dual-energy X-ray absorptiometry (DXA) or through the use of a bioimpedance analyzer (BIA), while handgrip strength (HGS) is quantified with a handgrip dynamometer [7]. However, these analyses can often be difficult to implement as not all institutions have access to a BIA and, even if one is available, screening for SO can be challenging owing to the attendant costs. There is thus a clear need to establish inexpensive, accessible approaches for widespread SO screening.

Calf circumference (CC) is recognized as an effective surrogate marker for muscle mass [8]. HGS is recommended by the European Working Group on Sarcopenia in Older People (EWGSOP) 2 and the Asian Working Group for Sarcopenia (AWGS) 2019 as a reliable indicator of muscle strength [9, 10]. De et al. reported associations of both HGS and CC with mortality in community-dwelling older adults with Alzheimer’s disease [11], while a study by Liao et al. demonstrated that using HGS and CC as parameters in sarcopenia screening was helpful in predicting mortality risk in older individuals in emergency department settings [12]. Commonly used parameters for obesity assessment include body mass index (BMI), waist circumference (WC), and the waist-to-hip ratio (WHR) [13]. These three obesity indicators have good prognostic value, as shown by Dai et al. who demonstrated an association between BMI and all-cause mortality risk among older individuals [14]. The WC value can also serve as a predictor of mortality risk in older patients with cancer [14], while Harris et al. noted that the WHR was more closely linked to all-cause mortality than BMI [15]. In addition, Koster et al. found that individuals with normal BMI but large WC values also faced a higher risk of death [16]. BMI is a reflection of overall obesity, while WC reflects central obesity; the essence of SO is the coexistence of “sarcopenia” and “obesity”, and central obesity is more closely related to metabolic risk. The WC_BMI ratio captures both these dimensions. Therefore, use of the WC_BMI may provide a more accurate identification of individuals with both muscle loss and excess fat, especially visceral fat. However, to date, there has been no investigation of the use of BMI-corrected WC (i.e., the WC_BMI) as an indicator for SO screening for prognostic prediction in older adults.

For frail older adults who suffer from various comorbid conditions and face a shorter life expectancy, nursing homes are frequently their last permanent residence [17]. Severe health issues and immobility are common among nursing home residents [17]. Almost half of all nursing home residents are impacted by Alzheimer’s disease or some other form of dementia, while 40% have heart disease, and 32% have diabetes [18]. After entering a nursing home, older individuals face a 3-year survival rate of 37% and a median survival interval of 25.8 months [19]. Mortality risk among nursing home residents is thus an important topic that warrants careful examination.

To date, no studies have explored the utility of CC and HGS as screening indices for sarcopenia in combination with obesity-related assessment indices as a means of detecting SO and screening for mortality risk among older male individuals in nursing homes. Therefore, the present study aimed to develop a simplified model for SO screening, specifically including sarcopenia-related indices (CC, HGS) and one of four different obesity-related indices (WHR, BMI, WC, WC_BMI), to evaluate mortality risk in independently living individuals aged 60 years and older in nursing homes males who were able to comply with the completion of questionnaires and physical tests.

Methods

Patient recruitment

This study specifically focused on male adults 60 + years of age residing in 15 different nursing homes in Zigong, China who were enrolled from September 2021 – July 2022. The exclusion criteria were consistent with those of previous publications [20], with the exclusion of individuals who (1) could not complete physical tests or questionnaires, (2) could complete tests and questionnaires but had conditions that would significantly affect the results of the body composition analyses, or (3) had unknown death dates or declined follow-up participation. In addition, individuals with (1) cognitive/behavioral barriers (severe cognitive impairment, delirium, or psychiatric disorders), (2) physical limitations (acute illness, recent fractures/amputation, musculoskeletal diseases, edema, or recent surgery within the previous three months), or (3) other factors (medical implants) were excluded. The Biomedical Ethics Review Committee of West China Hospital of Sichuan University approved this study (approval number: 2021 − 965), with all participants having provided written informed consent.

Measurements of body composition and muscle strength

A digital dynamometer (EH101, Xiangshan Company, Guangdong, China) was used to measure HGS while participants stood upright with their feet at shoulder width and their elbows fully extended. Three trials were performed by each subject using their dominant hand, and the maximum value from across these trials was recorded [20]. Low HGS threshold is 28 kg, that is, less than 28 kg is low HGS, vice versa is high HGS [10]. CC was measured as the circumference (in cm) of the calf at the thickest point, rounding the value to a single digit. CC measurements were taken for both sides of the body two times, with the maximum value from each side being averaged together. According to the AWGS 2019 diagnostic consensus cut-off threshold, subjects were divided into two groups, and if the CC measurement was < 34 cm, they were considered to have a low CC¹⁰. All subjects above these thresholds were considered to have a high CC. Height was measured to the nearest 0.5 cm using a portable stadiometer, while weight was measured to the nearest 0.1 kg using an electronic scale. BMI values (weight/height²; kg/m²) were used to separate patients into those with low (< 28 kg/m²) and high (≥ 28 kg/m²) BMI values [20]. WC was measured at the midpoint between the iliac crest and lower ribs following normal exhalation. WC was considered to be high when exceeding 90 cm, with individuals below these cut-offs having a low WC [21]. Hip circumference was measured at the widest point of the buttocks below the iliac crest. WHR is defined as the ratio of waist circumference to hip circumference. High WHR values were defined as ≥ 0.90, with all other individuals being considered to have a low WHR [22]. WC_BMI is defined as the ratio of WC to BMI. Since there is no generally accepted cutoff value, we referred to the cutoff values used in earlier publications, specifically, using three-quarters of the WC_BMI values as the cutoff value [23]. Trained staff collected all of these measurements at the medical center where this study was performed.

Covariates

Data collected through in-person interviews included age, sex, marital status, history of alcohol consumption, history of smoking, gait speed, activities of daily living (ADL) (as a measure of disability), and chronic diseases (osteoporosis, heart failure [HF], coronary heart disease [CHD], stroke history, diabetes, hypertension, chronic obstructive pulmonary disease (COPD), cancer, kidney disease, cognitive status, and polypharmacy). The Clock Drawing Test (CDT) was used for the assessment of cognitive performance, with one point each being awarded for drawing the outline of the clock, the hands, and the numbers [20, 24]. Total scores were between 0 and 3, with cognitive impairment being defined by a score of 0 or 1 [20]. Polypharmacy was defined by simultaneously utilizing ≥ 5 medications [25]. In this study, ADL scores < 100 represented disability [26]. If the subjects had a history of smoking and drinking, irrespective of current smoking or drinking, they were considered to have a history of smoking and drinking.

Outcome measurement

OS was measured from the baseline timepoint to death or survival at follow-up, which was performed in April 2024.

Statistical analysis

Statistical analyses were performed using SPSS 25.0 (IBM Corp., Armonk, NY, USA). All figures were generated using Python (version 3.9.12) with the Matplotlib (version 3.5.1) and Seaborn (version 0.11.2) libraries. A two-sided P-value < 0.05 was considered statistically significant. Normally distributed continuous data are presented as mean ± standard deviation (SD), while skewed data are presented as median (interquartile range, IQR). Comparisons of baseline characteristics were performed using Student’s t-tests, Mann-Whitney U tests, and Pearson chi-square tests, as appropriate. Differences in group mortality rates were assessed using Chi-squared tests. To account for multiple comparisons across the six nutritional metrics (CC, HGS, WC, BMI, WHR, WC_BMI), P-values were adjusted using the Benjamini-Hochberg procedure with a false discovery rate (FDR) of 5%. Survival analysis was conducted using Kaplan-Meier curves, and differences were assessed with the log-rank test. Three Cox proportional hazards models were used to assess the association between SO and mortality risk. Of these, Model 1 was unadjusted; Model 2 was adjusted for covariates with P-values less than 0.05 as shown in Table 1; and Model 3 was adjusted for variables with P < 0.1 in the univariate analysis and those previously reported in the literature to be associated with mortality.

Table 1.

Characteristics of the study population in males

General characteristics	Survival N = 419	Death N = 72	P
Age, year, median(iqr)	70(67, 77)	73.5(69.25, 83)	< 0.001
Educational level, n (%)			0.154
Primary and below	364(86.87)	58(80.56)
Junior high school and above	55(13.13)	14(19.44)
Marital status, n (%)			0.999
married	64(15.27)	11(15.28)
Divorced/widowed/ unmarried	355(84.73)	61(84.72)
Smoking history, n (%)			0.065
no	201(47.97)	43(59.72)
yes	218(52.03)	29(40.28)
Drinking history, n (%)			0.213
no	271(64.68)	52(72.22)
yes	148(35.32)	20(27.78)
Disability, n (%)			0.683
no	156(37.23)	25(34.72)
yes	263(62.77)	47(65.28)
COPD, n (%)			0.541
no	366(87.35)	61(84.72)
yes	53(12.65)	11(15.28)
Hypertension, n (%)			0.076
no	263(62.77)	53(73.61)
yes	156(37.23)	19(26.39)
Diabetes, n (%)			0.263
no	364(86.87)	59(81.94)
yes	55(13.13)	13(18.06)
CHD, n (%)			0.109
no	406(96.9)	67(93.06)
yes	13(3.1)	5(6.94)
HF, n (%)			0.407
no	407(97.14)	68(94.44)
yes	12(2.86)	4(5.56)
Cognitive impairment, n (%)			0.309
no	66(15.75)	8(11.11)
yes	353(84.25)	64(88.89)
Osteoporosis, n (%)			> 0.99
no	408(97.37)	70(97.22)
yes	11(2.63)	2(2.78)
Stroke history, n (%)			> 0.99
no	404(96.42)	70(97.22)
yes	15(3.58)	2(2.78)
Cancer, n (%)			0.083
no	418(99.76)	70(97.22)
yes	1(0.24)	2(2.78)
Kidney disease, n (%)			> 0.99
no	413(98.57)	71(98.61)
yes	6(1.43)	1(1.39)
Polymedication, n (%)			0.246
no	30(7.16)	8(11.11)
yes	389(92.84)	64(88.89)
Gait speed, seconds, median(iqr)	0.91(0.72,1.11)	0.75(0.58, 0.9)	< 0.001
Survival time, months, median(iqr)	28.4(28, 28.93)	11.78(6.53, 17.54)	< 0.001

COPD chronic obstructive pulmonary disease, CHD coronary heart disease, HF heart failure

Values presented in bold denote statistically significant differences in the comparison results

Results

In total, 491 subjects in nursing homes were enrolled in this study. From baseline through the follow-up period, 72 (14.66%) of these subjects dies. Low HGS and CC values were used as indices to screen for sarcopenia in this population, while high WHR, BMI, WC, and WC_BMI were utilized to assess obesity status.

In this study, the cutoff values of WC_BMI was 3.83, that is, WC_BMI ≥ 3.83 was considered obese. Otherwise, they were considered normal. Both surviving and deceased males showed significant differences in age, gait speed and survival time but not in terms of education level, marital status, smoking history, alcohol history, disability, COPD, hypertension, diabetes, CHD, HF, cognitive impairment, osteoporosis, history of stroke, cancer, kidney disease, or polymedication (Table 1).

Compared with the surviving group, patients in the deceased group had lower CC, HGS, WC, and BMI, and higher WC_BMI values (all P < 0.05) (Fig. 1). There was no difference in WHR between the two groups (Fig. 1). After grouping CC, HGS, WC, BMI, WHR, and WC_BMI according to the cutoff values, the mortality rates of patients in different groups were compared. The results showed that relative to the normal group, the mortality rates of patients in the low-CC group (18.13% vs. 5.8%, P < 0.001, Fig. 2), low-HGS group (17.93% vs. 5.97%, P < 0.01, Fig. 2), and high-WC_BMI group (25.83% vs. 11.05%, P < 0.001, Fig. 2) were higher. The mortality rate was lower in the high-WC group (17.37% vs. 8.92%, P < 0.05, Fig. 2). There was no difference in mortality between the two groups when grouped by BMI and WHR cutoff values.

Fig. 1.

Differences in CC, HGS, WC, BMI, WHR, and WC_BMI between the survival group and the death group. Note: Green represents the survival group, and orange represents the death group. CC, calf circumference; HGS, handgrip strength; WC, waist circumference; BMI, body mass index; WHR, waist-to-hip ratio

Fig. 2.

Mortality of patients in different CC, HGS, WC, BMI, WHR and WC_BMI groups. Note: CC, calf circumference; HGS, handgrip strength; WC, waist circumference; BMI, body mass index; WHR, waist-to-hip ratio. *: P<0.05, **: P<0.01, ***: P<0.001

Low CC + low HGS+high WC_BMI was used as the diagnostic criteria for male SO, and the mortality rate of patients in the SO group was higher than that in the survival group (37.5% vs. 12.17%, Table 2). In addition, the proportion of SO in the death group was higher than that of patients without SO (34.62% vs. 10.9%, Table 3). The KM curve showed that survival duration in patients in the SO group was shorter than that in the normal group (P < 0.001, Fig. 3). Cox regression analysis showed that in Model 1, the risk of death in the SO group was higher than that in the normal group (HR = 3.887, 95% CI: 2.401–6.293, Table 4). After adjustment for potential confounding factors, both Model 2 (HR = 3.239, 95% CI: 1.954–5.37, Table 4) and Model 3 (HR = 3.07, 95% CI: 1.846–5.108, Table 4) still showed a higher risk of death in the SO group.

Table 2.

Distribution of sarcopenic obesity (SO) status among survivors and non-survivors

Variable	Without SO N = 413	SO N = 78	P
Survival status, n (%)			< 0.001
Survival	368(87.83)	51(12.17)
Death	45(62.5)	27(37.5)

Without SO: Only abnormal CC/only abnormal HGS/abnormal WC_BMI/combination of any two of abnormal CC, abnormal HGS, and abnormal WC_BMI or none of abnormal CC or HGS or WC_BMI

SO: HGS + CC+ WC_BMI are all abnormal

Table 3.

Comparison of mortality between patients with and without SO

Variable	Survival N = 419	Death N = 72	P
Body composition (WC_BMI), n (%)			<0.001
Without SO	368(89.1)	45(10.9)
SO	51(65.38)	27(34.62)

Without SO: Only abnormal CC/only abnormal HGS/abnormal WC_BMI/combination of any two of abnormal CC, abnormal HGS, and abnormal WC_BMI or none of abnormal CC or HGS or WC_BMI

SO: HGS + CC+ WC_BMI are all abnormal

Fig. 3.

Survival curves of male patients in the without SO group and SO group. Note: The green line represents the SO group, and the blue line represents the without SO group. OS: without SO group: 29.26 months; SO group: 24.11 months. SO: sarcopenia obesity; OS: overall survival

Table 4.

Correlations between body composition and death

Variable	Model 1		Model 2		Model 3
	P-value	HR (95% CI)	P-value	HR (95% CI)	P-value	HR (95% CI)
Without SO	-	1	-	1	-	1
SO	< 0.001	3.887(2.401–6.293)	<0.001	3.239(1.954–5.37)	<0.001	3.07(1.846–5.108)

Without SO: Only abnormal CC/only abnormal HGS/abnormal WC_BMI/combination of any two of abnormal CC, abnormal HGS, and abnormal WC_BM or none of abnormal CC or HGS or WC_BMI

SO: HGS + CC+ WC_BMI are all abnormal

Model 1: a non-adjusted model

Model 2: adjusting for variables with p < 0.05 in Table 1. Specifically, they are variables such as age and gait speed

Model 3: adjusting for age, gait speed, smoking history, hypertension, cancer

Discussion

The present analysis showed that SO (defined by a combination of low HGS, low CC, and high WC_BMI) predicted mortality risk in male nursing-home residents aged 60 years and older. We did not find this link in women aged 60 and over (see supplementary material for details). This is the first prospective study to show that SO can predict the risk of death in this specific population. This study had several major strengths. First, it focused specifically on male nursing-home residents over the age of 60, a frequently overlooked population. Second, the definition of SO fully followed the EWGSOP2 and AWGS 2019 diagnostic framework for “sarcopenia” (strength + mass), as well as the ESPEN/EASO recommendations for the assessment of “obesity” (total fat + distribution) [1, 9, 10]. Our approach was the practical application of these consensus principles in specific research scenarios. Although the selection of indicators in this study differs from the consensus recommended “diagnostic gold standard”, the demonstrated feasibility and universality of the study findings indicate that this combination protocol is an efficient, low-cost, and suitable screening tool that can be applied in multiple scenarios.

The mortality rate facing the nursing home residents in this study was 14.66%, which is within the previously reported range extending from 5.7% to 70% [27, 28]. Nursing home residents generally face a higher mortality rate than community-dwelling individuals (3.6%) [28, 29], which may be related in part to the characteristics of nursing homes. For one, respiratory infections and related diseases are major causes of death among individuals living in nursing homes [30]. Several residents often share a room, allowing healthcare-related infections to spread more readily [31]. Falls are also particularly common among older individuals in nursing homes and can contribute to higher rates of mortality [32].

While SO could predict mortality in older men living in nursing homes, it was observed to lose its predictive power in women. Loss of muscle mass in men starts from a higher level, accompanied by decreased muscle strength and visceral fat deposits, triggering greater metabolic damage [33–35]. Women start with lower baseline muscle mass, and thus experience less absolute loss to reach SO thresholds, as well as carrying less inflammatory subcutaneous fat, resulting in relatively milder physiological stress that can easily be masked by comorbidities [34–38].

The findings demonstrate an obesity paradox, specifically, those in the high-WC group had a lower risk of death. The WC measurement itself reflects only the abdominal circumference and cannot distinguish the respective contributions of visceral and subcutaneous fat, nor can it provide a direct reflection of muscle mass. Males with high WC values may also have higher muscle mass, especially when their BMI is high. For example, a higher BMI in an athlete may be classified as “overweight” due to high muscle mass, although the metabolic risk is lower. Males with high WC values may have more visceral fat as an energy reserve, providing metabolic support during acute illness or trauma, reducing protein breakdown and muscle loss [37].

BMI is calculated only from weight and height, and is not able to distinguish between the ratio of fat and muscle, nor can it reflect the distribution of fat within the body. Although WC can provide an indication of abdominal fat accumulation, it does not take into account the overall weight and height of the individual. The WC_BMI value combines information on waist circumference and BMI, taking into account both the degree of abdominal fat accumulation and overall weight and height factors. It can thus provide a more accurate identification of older adults with normal BMI but large WC (i.e., abdominal obesity) values, who tend to be at higher risk of chronic diseases such as cardiovascular disease and diabetes [39–41]. The body fat composition and distribution alter during aging [42], seen in an increased fat ratio and a greater susceptibilty toward abdominal fat deposition. Traditional BMI may not provide an accurate reflection of the health risks posed by these changes, and WC_BMI represents a more comprehensive indicator of these changes in body composition in older adults. In addition, WC_BMI can be easily integrated into existing routine screening processes for geriatric patients. After routine determination of height, weight, and WC, the WC_BMI can be easily calculated without adding excessive screening time and costs.

This study has several limitations. First, although the participants were recruited from multiple nursing homes, all were from a single prefecture-level city in Sichuan Province, China. As variations in body composition, fat distribution, and muscle mass across ethnic groups are well-documented, such as the tendency for Asian populations to have lower muscle mass and higher visceral adiposity at a given BMI compared to Western populations [43, 44] the diagnostic and prognostic relevance of SO criteria may differ in other settings. Thus, these findings may thus not be generalizable to other populations without further validation. Second, the study considered only all-cause mortality, as information on specific causes of death was not available, preventing a more detailed analysis of cause-specific mortality. Disease information was self-reported, which also raises the possibility of underdiagnosis. Third, the cohort combined two distinct types of nursing homes: high-end urban facilities (primarily economically independent women who actively chose institutional care) and rural welfare institutions (mostly less-educated, socioeconomically disadvantaged men with limited care alternatives). Merging these groups may have obscured true sex and socioeconomic associations. Stratified analysis was not feasible due to sample size constraints. Fourth, the observed differences in marital status largely reflect unique sociodemographic patterns within the Chinese urban-rural system, and may not be applicable to other cultural contexts. Future studies should include region-specific stratifications and expanded sample sizes. Fifth, individuals with severe cognitive impairment, musculoskeletal disorders, or mental illness that prevented assessment were excluded. These residents represent a relatively large proportion of actual nursing home populations (fewer than 20% of all residents were included), limiting the ability of generalize the findings to very frail or impaired older adults. Sixth, as this study adopted a sex-specific model-building strategy, it did not identify obesity metrics that significantly predicted mortality in the female population; thus, it was not possible to conduct a traditional sex-interaction analysis for a direct comparison of the differences in the effect of a specific indicator between men and women. This methodological limitation implies that the observed sex differences, while clinically revealing, require further validation in future studies using uniformly standardized metrics and larger sample sizes. Finally, the lack of an external validation cohort restricts the broader applicability of the results. Future investigations should validate these findings in other populations (e.g., community-dwelling older adults or younger individuals) and establish ethnic and age-specific reference ranges through multicenter collaborations.

Conclusions

This observational study using a single-center cohort demonstrates that low CC, reduced HGS, and elevated WC_BMI are significant independent predictors of mortality in older male nursing home residents. These findings indicate the potential clinical utility of incorporating CC and HGS measurements into routine geriatric assessments in long-term care facilities. Furthermore, the results highlight the need for sex-specific preventive strategies and suggest that WC_BMI may represents a more meaningful anthropometric indicator than traditional BMI for mortality risk stratification in the older population.

Authors’ contributions

Study concept and design: Qian Yu, Sha Huang, Zecong Chen, Jiaxiu Zhu, Youguo Tan, Xiaoyan Chen.Acquisition of data: Qian Yu, Sha Huang, Zecong Chen, Jiaxiu Zhu, Youguo Tan, Xiaoyan Chen.Analysis and interpretation of data: Sha Huang; Xiaoyan Chen.Drafting of the manuscript: Qian Yu, Sha Huang.Critical revision of the manuscript for important intellectual content: Zecong Chen, Jiaxiu Zhu, Youguo Tan, Xiaoyan Chen.All authors of this manuscript have fully contributed to the manuscript, and all authors have approved the final manuscript. This manuscript has not been published before.

Funding

This work was funded by 2023 Zigong City Key Science and Technology Plan Project (Zigong Brain Science Research Institute Collaborative Innovation Category) (Project No. 2023-NKY-01-03, 2024-NKY-01-03), Zigong Psychiatric Research Center scientific research project (Project No. 2022ZD01, 2022ZD02, 2022ZD03), and the 2022 Key Science and Technology Plan of Zigong City (Project No. 2022ZCNKY14, 2022ZCNKY17). The sponsors did not participant in the design, methods, data collection, analysis, or in the preparation of this manuscript.

Data availability

The datasets generated and analyzed during the current study are not publicly available due to this is a database which has a lot of important information, and we are applying some important projects based on this. However, these datasets are now available from the corresponding author on reasonable request.

Declarations

Ethical approval and consent to participate

This study was conducted in accordance with the Declaration of Helsinki and the ethical approval was obtained from the Biomedical Ethics Review Committee of West China Hospital of Sichuan University (No. 2021 − 965). Written informed consent was obtained after an assessment of each participant’s capacity to consent; when decisional capacity was questionable, additional written consent was obtained from a legally authorized representative/guarantor. Individuals with severe dementia were excluded. All methods comply with relevant guidelines and regulations.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.