Correlation analysis between body composition, serological indexes and the risk of falls in the elderly

doi:10.21203/rs.3.rs-2245322/v1

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Correlation analysis between body composition, serological indexes and the risk of falls in the elderly

https://doi.org/10.21203/rs.3.rs-2245322/v1

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Objective: To study the risk factors of falls and explore the correlation between body composition, serological indexes and the risk of falls in the elderly.

Method: This is a cross-sectional study. The subjects were inpatients ≥ 60 years old in the cadre ward of the first hospital of Jilin University. The following information was obtained from the past comprehensive geriatric assessment database of the cadre ward. The body composition of the patients was measured by Bioelectrical Impedience Analysis (BIA) using the Inbody S10 device (Biospace, Seoul, Korea). We assessed according to the internationally used fall risk assessment tool (FRA), ≤ 2 points are divided into low-risk group, 3 - 9 points are divided into medium-risk group, and ≥ 10 points are divided into high-risk group.

Results: There were significant differences among the three groups in age, educational background, weight, cognitive impairment, malnutrition, ability of daily living, depression, systolic blood pressure, lower limb moisture, fat free weight, arm girth, body cell mass, skeletal muscle mass, limb muscle, red blood cell (RBC), hemoglobin (HGB), hematocrit (HCT), aspartate aminotransferase (AST), alanine aminotransferase (ALT), albumin (ALB), prealbumin (PA), anemia and hypoproteinemia (P< 0.05). Ordinal logistic regression analysis showed that, for each unit of decrease in educational background and hemoglobin, the probability of the fall risk increasing by one level is 7.64 times and 1.094 times higher than the original (OR= 7.64, 95%CI: 1.38 - 225.19, P= 0.027); (OR= 1.094, 95%CI: 0.102-11.693, P< 0.001), for each additional unit of age, cognitive impairment, malnutrition and right lower limb moisture, the probability of the fall risk increasing by one level is 13.531, 10.186, 2.936, 5.755 times higher than the original (OR= 13.531, 95%CI: 6.080-30.114, P< 0.001); (OR= 10.186, 95%CI: 3.854-26.924, P< 0.001); (OR= 2.936, 95%CI: 1.514-5.686, P= 0.001); (OR= 5.755, 95%CI: 1.775-18.672, P= 0.004).

Conclusion: Educational background, hemoglobin are significant protective factors, and age, cognitive impairment, malnutrition, right lower limb moisture are significant risk factors for falls in the elderly.

Health sciences/Health care/Disease prevention

Health sciences/Health care/Geriatrics

Health sciences/Health care/Nutrition

elderly

body composition

serological indexes

the risk of falls

At present, the aging situation of the global population is becoming more and more serious. By the end of 2019, China's elderly population aged 65 and over had reached 176 million, an increase of 5.67% over 2018^[1]. Research shows that about 30% of the people over the age of 65 living in the community fall every year. The injury caused by falling is more serious than that caused by other reasons^[2,3] .

Fall is one of the important causes of disability and death of the elderly, which refers to sudden, involuntary and unintentional body position changes of individuals, resulting in other body parts except feet contacting the ground or lower than the ground^[4]. Fall is the most common health problem of the elderly. Fall can lead to soft tissue injury and fracture of the elderly, and even death in severe cases ^[5]. The incidence rate of falls in the elderly is very high. According to epidemiological data, the incidence of falls in Chinese residents over 60 years old is at a high level, and the incidence of falls increases with age^[6]. In case of fracture and soft tissue contusion after falling, the patient needs to stay in bed for a long time, which will increase the risk of bedsore, deep vein thrombosis and accumulated pneumonia, and further affect the quality of life of elderly patients.

Therefore, it is very necessary to clarify the possible risk factors of falls in the middle-aged and elderly people and formulate targeted fall prevention programs. This paper aims to study the risk factors of falls in the elderly and explore the correlation between body composition, serological indexes and the risk of falls in the elderly.

2.1 Participants and eligibility criteria:

The subjects were inpatients ≥ 60 years old in the cadre ward of the first hospital of Jilin University from May 1, 2019 to December 31, 2020. Inclusion criteria: (1) age ≥ 60 years; (2) completed the comprehensive assessment questionnaire and test for the elderly. Exclusion criteria: (1) lake of basic data and indicators of comprehensive assessment questionnaire for the elderly; (2) suffering from malignant tumors, immune system diseases, serious liver and kidney dysfunction and other serious diseases.

2.2 Measurement:

From the electronic case system of the first hospital of Jilin University, the medical record information of all inpatients in line with this study from May 1, 2019 to December 31, 2020, including the measurement of patient height and weight and basic information, such as gender, age, education, smoking history, drinking history, serological indexes: white blood cell, percentage of neutrophils, absolute value of neutrophils, red blood cell, hemoglobin, hematocrit, mean erythrocyte volume, platelet, aspartate aminotransferase, alanine aminotransferase, glutamyl transpeptidase, alkaline phosphatase, cholinesterase, total protein, albumin, globulin, prealbumin, cholesterol, triglyceride, high density lipoprotein cholesterol, low density lipoprotein cholesterol, fasting blood glucose, etc. The following information was obtained from the past comprehensive geriatric assessment database of the cadre ward: the 4 meter pace measured by the stopwatch. The body composition of the patients was measured by Bioelectrical Impedience Analysis (BIA) using the Inbody S10 device (Biospace, Seoul, Korea), Activities of daily living scale (ADL & IADL), mini intelligence assessment scale (MMSE), Geriatric Depression Scale (GDS), the risk of falls assessment (FRA), mini nutrition assessment scale (MNA) (Part I, part I + Part II).

2.3 Body Composition Analysis:

The body composition of the patients was measured by BIA using the Inbody S10 device (Biospace, Seoul, Korea). All patients were supine for 10 minutes before analysis. In the analysis, the patients were supine with arms abduction of 15°, starting from the torso and legs separated shoulder-width apart. Eight electrodes were placed between the hands (thumbs and middle fingers) and between the ankles and heels of the patients. Alcohol was applied to clean the skin before placing the electrodes to reduce skin contact resistance. The patient's age, gender, height and weight were input to measure the patient's body composition. The measurement process in this study was completed by the same physician in the cadre ward department of the First Hospital of Jilin University.

2.4 Fall risk assessment:

We assessed according to the internationally used fall risk assessment tool (FRA), with a total of 35 items, each item has 1-3 points, the total score of the scale is 53 points, ≤ 2 points are divided into low-risk group, 3 - 9 points are divided into medium-risk group, and ≥ 10 points are divided into high-risk group.

2.5 Other Assessment:

Assessment of nutritional status: The MNA was used in most studies to assess the nutritional status of older adults and as a tool for evidence-based screening of older adults. MNA score < 17 indicates the presence of malnutrition.

Depression assessment: GDS screening questionnaire was designed specifically for older adults. FRA has a total of 30 items, each item has 1 point, the total score of the scale is 30 points, ≤ 10 points are normal, 11 - 20 points are mild depression, and 21 - 30 points are moderate to severe depression. In this study, > 10 points were defined as abnormal.

Cognitive Function: The Min-Mental State Examination (MMSE) is the psychometric screening tool most frequently administered to assess cognitive function. In this study, we classified the boundary of cognitive normality according to the level of education into primary school education > 20 points, junior high school or above > 24 points, below which was defined as cognitive dysfunction.

2.6 Statistical Methods:

SPSS 26.0 statistical software was used for statistical analysis (IBM Corp., Armonk, NY, USA). Continuous variables were presented as mean ± standard deviation (SD) or median (interquartile range, IQR) based on the distribution characteristics of the data. Classified variables were analyzed using percentage. T-test was used for the comparison between two groups of quantitative data, and one way ANOVA test was used for the comparison between multiple groups; chi-square(χ2) test was used to compare the two kinds of indicators such as gender. If it does not conform to the normal distribution, use the rank sum test. Ordinal logistic regression analysis was used to determine the correlation between body composition, serological indexes and the risk of falls. All statistical tests were conducted by two-sided test, and the difference was considered to be statistically significant if P < 0.05.

A total of 306 patients were included, including 193 males (63.1%) and 113 females (36.9%). According to FRA, there were 66 cases in the low-risk group (21.6%), 138 cases in the medium-risk group (45.1%) and 102 cases in the high-risk group (33.3%).

3.1 Baseline Characteristics：

As shown in Table 1, it is the comparison of general indicators among low-risk group, medium-risk group and high-risk group. There were significant differences among the three groups in age, educational background, weight, cognitive impairment, malnutrition, ability of daily living, depression, systolic blood pressure (P < 0.05).

Compared with the low-risk group, the high-risk group had higher age, ADL score, depression score, systolic blood pressure, more cognitive impairment, malnutrition, and lower height, weight, evaluation background and Mini-mental State Examination score; Compared with the middle risk group, the high-risk group had higher age, ADL score, depression score, more cognitive impairment, malnutrition, and lower evaluation background and Mini-mental State Examination score; Compared with the low-risk group, the middle-risk group had higher age, ADL score, depression score, systolic blood pressure, more cognitive impairment, malnutrition, and lower evaluation background, weight and Mini-mental State Examination score. The above differences were statistically significant (P < 0.05).

3.2 Comparison of body composition among low-risk group, medium-risk group and high-risk group:

Table 2 shows the comparison of body composition in the three groups. There were significant differences among the three groups in intracellular water, total body moisture, water ratio, trunk moisture, right lower limb moisture, left lower limb moisture, fat free weight, arm girth, body cell mass, skeletal muscle mass, right upper limb muscle, left upper limb muscle, right lower limb muscle, left lower limb muscle (P < 0.05).

Compared with the low-risk group, the high-risk group had higher water ratio, trunk moisture, right lower limb moisture, left lower limb moisture, and lower intracellular water, total body moisture, fat free weight, arm girth, body cell mass, skeletal muscle mass, right upper limb muscle, left upper limb muscle, right lower limb muscle, left lower limb muscle; Compared with the middle risk group, the high-risk group had higher water ratio, trunk moisture, right lower limb moisture, left lower limb moisture, and lower intracellular water, total body moisture, fat free weight, body cell mass; Compared with the low-risk group, the middle-risk group had higher water ratio, trunk moisture, right lower limb moisture, left lower limb moisture, and lower intracellular water, arm girth, skeletal muscle mass. The above differences were statistically significant (P < 0.05).

3.3 Comparison of serological indexes among low-risk group, medium-risk group and high-risk group:

As shown in Table 3 the comparison of serological indexes in three groups. There were significant differences among the three groups in RBC, HGB, HCT, AST, ALT, ALB, PA, anemia, hypoproteinemia (P < 0.05).

Compared with the low-risk group, the high-risk group had lower RBC, HGB, HCT, ALT, ALB, PA, cholinesterase(ChE), total protein(TP), and anemia and hypoproteinemia in the high-risk group were more serious; Compared with the middle risk group, the high-risk group had lower RBC, HCT, ALB, and anemia in the high-risk group were more serious; Compared with the low-risk group, the middle-risk group had lower HGB, AST, ALT, ALB, PA, and anemia and hypoproteinemia in the middle-risk group were more serious. The above differences were statistically significant (P < 0.05).

3.4 Ordinal Logistic regression analysis of body composition, serological indexes to predict the risk of falls among elderly patients in low-risk group, medium-risk group and high-risk group:

Table 4 summarize a logistic regression analysis of risk factors for falls in the elderly. This analysis showed that, Ordinal logistic regression analysis showed that, for each unit of decrease in educational background and hemoglobin, the probability of the fall risk increasing by one level is 7.64 times and 1.094 times higher than the original (OR= 7.64, 95%CI: 1.38 - 225.19, P= 0.027); (OR= 1.094, 95%CI: 0.102-11.693, P< 0.001), for each additional unit of age, cognitive impairment, malnutrition and right lower limb moisture, the probability of the fall risk increasing by one level is 13.531, 10.186, 2.936, 5.755 times higher than the original (OR= 13.531, 95%CI: 6.080-30.114, P< 0.001); (OR= 10.186, 95%CI: 3.854-26.924, P< 0.001); (OR= 2.936, 95%CI: 1.514-5.686, P= 0.001); (OR= 5.755, 95%CI: 1.775-18.672, P= 0.004).

Falls in the elderly are not only an urgent medical and health problem, but also a serious social problem. In this study, we found that lower limb moisture, fat free weight, arm girth, skeletal muscle mass, limb muscle, HGB and hypoproteinemia are closely related to the risk of falls in the elderly, educational background, hemoglobin are significant protective factors, and age, cognitive impairment, malnutrition, right lower limb moisture are significant risk factors for falls in the elderly. According to the results obtained in this paper, we can provide appropriate fall prevention programs and ensure the safety of life and property of the elderly.

The increase of age is characterized by the decline of multiple system reserve functions of the body, including the decline of muscle mass, muscle strength, basic metabolism, activity ability, self balance function and other changes, which increases the risk of falls. Although falls can occur in any age group, the fall risk of the elderly is always higher than that of the younger group ^[7]. This study found that the age group over 80 has the highest risk of falling, accounting for 73.5%, and the fall risk gradually increases with the increase of age, which is basically consistent with the research view of Timsina et al^[8].

Many studies have found that elderly people with cognitive impairment are more likely to fall ^{[9, 10]}, which may be because when cognitive ability decreases, the speed and executive ability of obtaining and processing external information will be reduced to varying degrees, especially the influence on the judgment and behavioral ability of the elderly is obvious, which increases the possibility of falls. At the same time, poor ability of daily living will also lead to a decline in adaptability to the environment, further increasing the possibility of falls. The results of this paper found that cognitive impairment and ability of daily living are related risk factors for falls in the elderly, and the overall cognitive function of the high-risk group is lower, the ability of daily activities is slightly worse, the difference is statistically significant, which is consistent with the above view. Moreover, the depression of the elderly is also one of the factors leading to falls. Because the depressive state of the elderly can not only lead to loneliness and depression, but also reduce the attention to the external environment. This decline in executive ability will affect the ability to complete dual tasks at the same time, such as walking and speaking, and fully process information, which will further cause gait and balance problems, leading to falls or affecting the recovery after falls ^[11]. This research data shows that educational background is the protective factor of fall risk. In the research and observation, it is found that the incidence of falls is higher in people with low education level, which suggests that the occurrence of falls is related to the patient's education level, which may be due to the strong health awareness of patients with high education.

The decrease of hemoglobin increases the risk of anemia, fatigue and falls. Hemoglobin has the function of carrying oxygen. Hemoglobin decreases, so that oxygenated hemoglobin decreases. The oxygen supply of tissue is insufficient, and the oxygen supply of brain is insufficient. When changing body position, it increases the risk of falls ^[12]. In a previous multicenter cohort of elderly men in the United States, the decrease of albumin level indicates that the possibility of improving asthenia is reduced. Albumin level is an important factor affecting the prognosis of the elderly. In the model of death of hospitalized elderly people one year after discharge proposed by the University of California, San Francisco, albumin decline is an important predictor ^[13]. Albumin level reflects the nutritional status to some extent. Systematic evaluation and meta-analysis show that malnutrition and frailty are related in the elderly ^[14]. A large number of studies have shown that frailty is related to falls, disability and increased risk of death ^[15-17]. Therefore, this paper analyzes the correlation between serum indicators and nutritional status and falls. The results show that hemoglobin, albumin levels, malnutrition and fall risk are also closely related. Malnutrition is one of the risk factors for falls in the elderly, and a higher level of hemoglobin is the protective factor for falls in the elderly.

The body of human and other animals is mainly composed of five components: water, fat, sugar, protein and minerals ^[18], and the mass of human body is basically the sum of the above five parts. Based on the comparison of body composition among the three groups of elderly fall risk, lower limb moisture, fat free weight, arm girth, skeletal muscle mass, limb muscle are risk factors for falls, and the difference is statistically significant, and lower limb moisture is positively correlated with fall risk, while fat free weight, arm girth, skeletal muscle mass, limb muscle is negatively correlated with fall risk. The reduction of muscle mass will reduce the types of motor neurons, which will have a certain impact on muscle performance, muscle quality and strength, which will affect the walking speed, walking morphology and balance ability of the elderly ^[19], affect balance ability and cause falls^{[20, 21]}. Therefore, the decrease of muscle mass, especially the decrease of skeletal muscle mass of limbs, is a risk factor for falls. Many studies^{[22, 23]}have shown that patients with heart failure are often accompanied by frailty. In order to understand the occurrence of debility in patients with cardiovascular disease, scholars Afilolo et al. ^[24]summarized and analyzed the data of 9 studies covering 54250 community residents, and concluded that the incidence of debility in elderly patients with cardiovascular disease was 25% - 50%. Nadruz and other ^[25]scholars have shown that the elderly are usually prone to frailty after suffering from heart failure. It is well known that lower limb moisture often indicates the occurrence and severity of heart failure, and there is a close relationship between frailty and fall risk. In the study of the correlation between body composition and fall risk, this paper concludes that lower limb moisture is positively correlated with the increased risk of falls in the elderly, and it is one of the risk factors of falls in the elderly, and the difference is statistically significant.

Based on the relevant results of this study, this study believes that we should actively publicize the importance of preventing falls in the elderly population, actively prevent falls, conduct regular examinations, reasonably plan meals, and increase protein intake, which is conducive to strengthening muscle mass, protecting body balance, and reducing the risk of falls in the elderly.

This study still has some limitations. First of all, the fall itself is a complex outcome caused by many factors, and there are many factors related to the fall, such as the subjects' hearing, vision, gait, lower limb muscle strength, etc. these indicators were not included in this study, so that the status of the subjects could not be comprehensively evaluated. This study will continue to improve the relevant information in the follow-up. We may have lacked statistical power for the interaction analyses, because the sample size was limited. According to the calculation of statistical sample size, the sample size of this study is sufficient. However, further expanding the sample size should help us conduct more comprehensive analysis and reduce class II errors.Besides，our results may not be generalizable to all populations; While we cannot exclude that we may have missed some events that occurred outside, in our experience this is quite uncommon.

Educational background, hemoglobin are significant protective factors, and age, cognitive impairment, malnutrition, right lower limb moisture are significant risk factors for falls in the elderly.

Ethics approval and consent to participate: In this study, the population is hospitalized patients aged ≥ 60 years in the cadre ward of the first hospital of Jilin University. All data subjects' relevant examination results are collected from the electronic case system of the first hospital of Jilin University, and there is no need to collect additional samples for this clinical work; In the whole process of clinical evaluation and trial, all clinical data will hide personal privacy information and will not be disclosed to any third party. The clinical test results are only used for experimental research and do not involve the subject's personal privacy and genetic information. The arrangement of subjects during the experiment complies with the relevant requirements of the declaration of Helsinki. Written informed consent was obtained from all subjects, and the study protocol was approved by the ethics committee of the first hospital of Jilin University.

Informed consent from LAR (Legally authorized representative): Not applicable.

Consent for publication: Informed consent was obtained from all individual participants included in the study.

Availability of data and materials: The data and materials that support the findings of this study are available from the corresponding author upon reasonable request.

Acknowledgements: The contributions of the clinical coordinators are duly acknowledged. The authors thank the staff and participants of this study for their important contributions. We thank all nursing home patients and healthcare workers who participated in this research study.

Competing interests: The authors declare that there are no financial conflicts of interest in regard to this manuscript.

Authors' contributions: All authors were involved in the study concept and design, analysis and interpretation of the data, and preparation of manuscript. Authors KZ, YJ, DY, MC, HL were involved in the acquisition of subjects and/or data.

Sponsor’s Role: The authors’ funding sources did not participate in the planning, collection, analysis, or interpretation of data or in the decision to submit for publication. The investigators had full access to the data and were responsible for the study protocol, progress of the study, analysis, reporting of the study, and the decision to publish.

Funding: The science and technology development plan project "Intelligent evaluation and management platform for elderly health status" (Project No.: 20200404207yy) of the Department of science and technology of Jilin Province. "Epidemiological investigation of senile syndrome and predictive value of machine learning" (Project No.: 2022C043-7) of Jilin Provincial Development and Reform Commission. "Study on related factors of sarcopenia in elderly patients with heart failure" (Project No.: JLSWSRCZX2021-046) of Jilin Provincial Department of Finance.

Impact statement: We certify that this work is confirmatory of recent novel clinical research. This is a novel and original study. We first explored the relationship between body composition, serological indexes and the risk of falls in the elderly. Our research suggested that the elderly should pay attention to the measurement of body composition and serological indexes, detect and prevent falls as soon as possible, and improve the health status of the elderly.

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Table1 Comparison of demographic information of the patients

	Low Risk Group（N=66）	Medium Risk Group（N=138）	High risk Risk Group（N=102）	P
Age（years old）				<0.001^*
60-69	47（71.2%）	34（24.6%）	9（8.8%）
70-79	17（25.8%）	39（28.3%）	18（17.6%）
≥80	2（3.0%）	65（47.1%）	75（73.5%）
Gender				0.753
Male	40（60.6%）	88（63.7%）	65（63.7%）
Female	26（39.4%）	50（36.2%）	37（36.3%）
Education				0.043^*
Illiteracy	0(0.0%)	3(2.2%)	1(1.0%)
Primary School	1(1.5%)	10(7.2%)	7(7.3%)
Middle School	10(15.2%)	30(21.7%)	31(32.3%)
College degree or above	55(83.3%)	95(68.8%)	57(59.4%)
Height（cm）	167.0±7.3	165.5±7.9	164.4±7.8^a	0.102
Weight（Kg）	70.6±10.7	66.3±11.6^a	65.8±11.7^a	0.018^*
Smoking				0.249
Yes	59(89.4%)	125(90.5%)	79(82.3%)
No	7(10.6%)	13(9.4%)	17(17.7%)
Alcohol				0.188
Yes	59(89.4%)	123(89.1%)	79(82.3%)
No	7(10.6%)	15(10.9%)	17(17.7%)
Cognitive Impairment				<0.001^*
Yes	0（0.0%）	11（8.0%）	40（39.2%）
No	66（100.0%）	127（92.0%）	62（60.8%）
Malnutrition				<0.001^*
Yes	3（4.5%）	32（23.2%）	46（45.1%）
No	63（95.5%）	106（76.8%）	56（54.9%）
Ability of Daily Living（score）	14.4±1.0	15.9±4.0	23.5±8.7^ab	<0.001^*
Depressive State（score）	25.0±6.5	27.5±7.3^a	33.4±9.4^ab	<0.001^*
Mini-mental State Examination（score）	28.7±1.4	27.3±2.8^a	25.3±4.3^ab	<0.001^*
FS (%)	28.8±13.1	31.6±9.7	31.8±9.5	0.599
EF (%)	48.7±26.5	57.0±18.9	58.8±16.5	0.208
Systolic Pressure（mmHg）	134.3±15.6	141.9±21.3^a	143.8±19.6^a	0.035^*
Diastolic Pressure（mmHg）	79.7±10.1	78.0±11.8	76.0±11.8	0.256
Heart Rate（cpm）	72.5±11.0	70.5±12.2	71.2±12.3	0.632

^a indicates that the difference is statistically significant compared with the low-risk group;

^b indicates that the difference is statistically significant compared with the medium-risk group;

EF: ejection fraction; FS: Left ventricular fractional shortening rate;

^* P< 0.05.

Table2 Comparison of body composition among low-risk group, medium-risk group and high-risk group

	All	Low Risk Group	Medium Risk Group	High risk Risk Group	P
Intracellular Water(L)	20.1±3.8	21.4±3.3	20.2±4.1^a	19.1±3.6^ab	<0.001^*
Extracellular Water(L)	13.0±2.3	13.4±1.9	13.1±2.5	12.7±2.3	0.166
Total Body Moisture(L)	33.2±6.1	34.8±5.2	33.4±6.5	31.8±5.7^ab	0.008^*
Water Ratio	0.392±0.012	0.385±0.009	0.392±0.013^a	0.399±0.010^ab	<0.001^*
Right Upper Limb Moisture(L)	0.382±0.024	0.376±0.012	0.380±0.007	0.408±0.039	0.349
Left Upper Limb Moisture(L)	0.384±0.022	0.377±0.011	0.381±0.008	0.405±0.039	0.348
Trunk Moisture(L)	0.393±0.014	0.385±0.015	0.392±0.013^a	0.400±0.012^ab	<0.001^*
Right Lower Limb Moisture(L)	0.391±0.014	0.383±0.011	0.391±0.015^a	0.399±0.012^ab	<0.001^*
Left Lower Limb Moisture(L)	0.396±0.015	0.388±0.016	0.395±0.014^a	0.403±0.013^ab	<0.001^*
Fat Free Weight(kg)	45.4±8.3	47.3±7.1	45.7±9.0	43.5±7.8^ab	0.015^*
Body Fat(kg)	21.8±7.9	23.1±7.5	20.6±7.5^a	22.6±8.5	0.062
Waistline(cm)	74.0±9.5	75.4±11.5	73.5±7.9	73.8±10.1	0.438
Arm Girth(cm)	27.7±3.6	28.9±4.3	27.3±3.1^a	27.3±3.5^a	0.011^*
Body Cell Mass(kg)	29.0±5.5	30.6±4.7	29.3±5.9	27.7±5.1^ab	0.004^*
BMI(kg/m²)	25.2±12.7	25.3±3.0	25.7±18.3	24.3±3.9	0.719
Skeletal Muscle Mass(kg)	24.7±6.6	27.1±10.1	24.7±5.3^a	23.1±4.6^a	0.001^*
Body Fat Percentage(%)	31.9±9.3	32.3±7.3	30.9±9.6	33.1±10.0	0.193
Visceral Fat Area(cm²)	88.7±54.4	93.7±90.3	80.9±37.6	96.4±39.2^b	0.075
Arm Muscle Dimension(cm)	22.2±5.8	22.3±3.9	22.1±5.4	22.2±7.2	0.974
Bone Mineral Content(kg)	2.9±1.8	2.8±0.5	3.0±2.5	2.7±0.8	0.482
Right Upper Limb Muscle(kg)	1.86±0.54	2.00±0.57	1.85±0.54	1.77±0.52^a	0.032^*
Left Upper Limb Muscle(kg)	1.82±0.54	1.98±0.56	1.83±0.54	1.70±0.50^a	0.007^*
Trunk Muscle(kg)	17.6±5.0	18.3±4.1	17.4±3.6	17.5±6.9	0.441
Right Lower Limb Muscle(kg)	8.65±1.87	9.16±1.82	8.65±1.93	8.28±1.76^a	0.015^*
Left Lower Limb Muscle(kg)	8.67±2.00	9.21±1.77	8.64±2.05	8.33±1.98^a	0.022^*

^a indicates that the difference is statistically significant compared with the low-risk group;

^b indicates that the difference is statistically significant compared with the medium-risk group;

^* P< 0.05.

Table3 Comparison of serological indexes among low-risk group, medium-risk group and high-risk group

	All	Low Risk Group	Medium Risk Group	High Risk Group	P
WBC(10^9/L)	6.19±1.97	6.05±1.97	6.03±1.87	6.47±3.09	0.376
NE%	0.65±0.48	0.61±0.10	0.69±0.68	0.63±0.12	0.686
NE#(10^9/L)	3.97±1.94	3.53±1.54	3.88±1.94	4.25±2.08	0.259
RBC(10^12/L)	4.25±0.62	4.56±0.74	4.30±0.59	4.06±0.55^ab	0.002^*
HGB(g/L)	138.9±101.9	193.8±17.9	132.6±16.8^a	125.0±17.8^a	0.012^*
HCT(L/L)	0.39±0.05	0.41±0.05	0.39±0.05	0.37±0.05^ab	0.001^*
MCV(fL)	92.0±11.1	91.7±5.9	92.2±15.1	91.8±5.1	0.970
PLT(10^9/L)	187.1±63.8	188.0±72.8	183.8±71.5	191.3±48.2	0.784
AST(U/L)	21.6±8.8	24.8±10.0	20.1±5.2^a	22.2±11.5	0.047^*
ALT(U/L)	17.2±11.4	22.8±11.8	17.2±7.8^a	17.2±11.4^a	0.031^*
γ-GT(U/L)	30.1±43.4	31.0±19.4	30.4±16.1	29.4±18.1	0.983
ALP(U/L)	60.8±17.4	62.6±16.5	60.7±18.4	60.3±16.5	0.852
ChE(U/L)	6725.4±1738.5	7429.9±2128.6	6654.3±1538.6	6533.1±1768.6^a	0.082
TP(g/L)	65.9±5.5	67.6±5.7	66.0±4.9	65.0±6.0^a	0.112
ALB(g/L)	37.0±4.1	39.4±3.4	37.3±4.0^a	35.6±4.0^ab	<0.001^*
GLB(g/L)	29.0±4.5	27.9±3.2	28.7±3.6	30.0±5.7	0.108
PA(g/L)	0.273±0.048	0.499±0.119	0.232±0.059^a	0.227±0.100^a	0.038^*
TC(mmol/L)	4.28±0.99	4.43±0.98	4.24±0.93	4.28±1.09	0.715
TG(mmol/L)	1.47±1.30	1.58±0.75	1.54±0.68	1.33±0.72	0.596
HDL-C(mmol/L)	1.16±0.29	1.17±0.29	1.16±0.31	1.16±0.26	0.965
LDL-C(mmol/L)	2.41±0.67	2.57±0.68	2.39±0.61	2.35±0.75	0.378
FBG(mmol/L)	5.56±1.55	5.53±1.01	5.49±1.67	5.68±1.57	0.772
Anemia					0.003^*
Normal	179（58.5%）	51（77.3%）	78（56.5%）	50（49.0%）
Mild	123（40.2%）	15（22.7%）	59（42.8%）	49（48.0%）
Moderate	4（1.3%）	0（0.0%）	1（0.7%）	3（2.9%）
Severe	0（0.0%）	0（0.0%）	0（0.0%）	0（0.0%）
Extremely Severe	0（0.0%）	0（0.0%）	0（0.0%）	0（0.0%）
Hypoproteinemia					0.011^*
Yes	142（46.4%）	41（62.1%）	55（39.9%）	46（45.1%）
No	164（53.6%）	25（37.9%）	83（60.1%）	56（54.9%）

^a indicates that the difference is statistically significant compared with the low-risk group;

^b indicates that the difference is statistically significant compared with the medium-risk group;

WBC: white blood cell, NE%: percentage of neutrophils, NE#: absolute value of neutrophils, RBC: red blood cell, HGB: Hemoglobin, HCT: hematocrit, MCV: mean erythrocyte volume, PLT: platelet, AST: aspartate aminotransferase, ALT: alanine aminotransferase, γ-GT: glutamyl transpeptidase, ALP: alkaline phosphatase, ChE: cholinesterase, TP: total protein, ALB: Albumin, GLB: globulin, PA: prealbumin, TC: cholesterol, TG: triglyceride, HDL-C: high density lipoprotein cholesterol, LDL-C: Low density lipoprotein cholesterol, FBG: fasting blood glucose.

^*P< 0.05.

Table4 Ordinal logistic regression analysis of body composition, serological indexes to predict the risk of falls among elderly patients in low-risk group, medium-risk group and high-risk group

Indicators	B	SE	Wald	OR	95%CI	P
Age(year)	2.605	0.408	40.700	13.531	6.080-30.114	<0.001^*
Education	2.87	1.30	4.88	17.64	1.38-225.19	0.027^*
Cognitive Impairment（score）	2.321	0.496	21.913	10.186	3.854-26.924	<0.001^*
Malnutrition（score）	1.077	0.338	10.165	2.936	1.514-5.686	0.001^*
Water Ratio	-0.700	0.603	1.351	0.497	0.152-1.618	0.245
Right Lower Limb Moisture(L)	1.750	0.600	8.504	5.755	1.775-18.672	0.004^*
Left Lower Limb Moisture(L)	-0.078	0.432	0.033	0.925	0.397-2.158	0.857
Skeletal Muscle Mass(kg)	-0.582	0.304	3.664	0.559	0.308-1.014	0.056
Skeletal Muscle Mass Index	0.299	0.368	0.660	1.349	0.656-2.770	0.417
Hemoglobin(g/L)	0.090	1.209	0.006	1.094	0.102-11.693	<0.001^*
Albumin(g/L)	-0.117	0.292	0.162	0.890	0.502-1.575	0.689

Dependent Variable: the risk of falls.

Independent Variable: Age, education, cognitive impairment, malnutrition, water ratio, right lower limb moisture, left lower limb moisture, skeletal muscle, skeletal muscle mass index, hemoglobin, albumin.

^*P< 0.05.

No competing interests reported.

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Correlation analysis between body composition, serological indexes and the risk of falls in the elderly

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