Analysis of prevalence and associated biomarkers of sarcopenia living in rural community older adults in Wuhan, China

doi:10.21203/rs.3.rs-4814100/v1

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Analysis of prevalence and associated biomarkers of sarcopenia living in rural community older adults in Wuhan, China

https://doi.org/10.21203/rs.3.rs-4814100/v1

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Participants and study design

This study is a single-center rural community-based cross-sectional study. The subjects of our study were residents of Huangling Community and Fenghuang Yuan Community in Wuhan, China. From September 26, 2020 to October 30, 2020, the elderly ≥ 65 years old participated in the routine health examination in Junshan Health Center of Huangling Community, Wuhan. Participants were excluded from the study if they were: (1) unable to communicate with the investigator or refused to sign the informed consent; (2) the participants were unable to complete a series of tests such as height, weight, body composition, grip strength and 6-meter walking speed test; (3) having a physical disability. The total number of people who underwent health examinations in 2020 was 571. Based on the exclusion criteria established, the final 236 participants（92 men and 144 women) met the inclusion criteria. For the sample size calculation of this cross-sectional study, the estimated prevalence of sarcopenia was 13 V体育官网入口. 5%, α=0. 05, precision 10%, the interval to be used will be a two-sided confidence interval, The sample size was calculated by PASS 11 software, N=198. Considering the 10% loss rate, at least 220 patients should be included in this study. According to our set into the exclusion criteria, this study finally included in the number of cases to 236. The protocol of this study was approved by the ethics committee of Union Hospital, Tongji Medical College, Huazhong University of Science and Technology (UHCT-IEC-SOP-016-03-01) and followed the tenets of the Helsinki Declaration.

Body composition

We use the 8- contact electrode bioelectrical impedance analysis (BIA) device (BCA-2A, TFHT, Beijing, China) to measure the body composition VSports在线直播. A multi-frequency electrical impedance body composition analysers, the BCA-2A uses a quadrupole 8-point haptic electrode system with five different measurement frequencies (5, 50, 100, 250 and 500 kHz). Skeletal muscle content (ASM) of extremities is the sum of skeletal muscle content of both upper and lower limbs[7]. Skeletal muscle mass normalized for height (RASM, ASM/Ht2) is defined as the ratio of ASM (kg) to height square (m2)[8]. The ASM/Ht2 < 7. 0kg/m2 in men or < 5. 7kg/m2 in women is diagnosed as skeletal muscle loss. .

Handgrip strength

Hand grip strength was measured using the standard Adjustable Digital Grip Strength Tester (CSTF-WL, TFHT, Beijing, China) V体育2025版. Participants were asked to perform two maximum grip strength tests with their favorable hand for data analysis with the best results[9,10]. The criteria for low grip strength is less than 28kg for men or less than 18kg for women. .

Gait speed

A digital walking speed tester (CSTF-6MBS, TFHT, Beijing, China) was used to measure walking speed (m/s). Participants were asked to perform the test at their usual walking speed on a 6-meter track. If participants preferred using a walker, they were allowed to use a walker during the test VSports. Slow walking speed is defined as a walking speed < 1. 0m/s[11]. .

Anthropometry

Height and weight were measured using a digital height and weight tester (CSTF-ST, TFHT, Beijing, China). Participants were asked to remove their shoes during measurement. Body mass index (BMI) was calculated by dividing their weight by the square of their height (kg/m²).

Definition of sarcopenia

We identified patients with sarcopenia by using the AWGS2019 sarcopenia consensus. The diagnosis of sarcopenia requires ASM/Ht² < 7.0 kg/m² in male and < 5.7 kg/m²in female; grip strength < 28kg in male and < 18kg in female and/or gait speed < 1.0m/s^[11].

Laboratory testing

Fasting blood samples were collected by venipuncture and centrifuged within 1 hour after sampling. 15 parameters including ALT, AST, DBIL, TBIL, ALB, ALP, γ-GT, CREA, UA, GLU, TG, TC, HDL, LDL and GSP were analyzed. 8 biomarkers including ADP, FGF19, GDF11, GDF15, IGF-1, TNF-α, IL-6 and DHEA were determined by human enzyme-related immunosorbent assay kit (ELISA) (ELK Biotechnology CO., LTD).

Statistical analysis

SPSS 25.0 software was used to analyze the data. Continuous variables and categorical variables were expressed as mean ± SD and percentage, respectively. Independent sample t-test was used for comparing continuous variables,Use tˊ-test when the variance of variables is Unequal or does not follow a normal distribution.,and Chi-square test was used for figuring out the difference among categorical variables. The independent association factors of sarcopenia were analyzed by multiple logistics regression analysis, and the statistical test was two-tail test. p<0.05 was considered to be statistically significant.

Clinical characteristics

The participant characteristics are shown in Table 1. During the data collection period from September 2019 to June 2020, a total of 236 elderly people (aged ≥65 years) from rural communities in Wuhan participated in the sarcopenia screening program, including 92 males and 144 females. The average baseline physical fitness measurements of participants were as follows: Age: 70.6 ± 4.4 years; BMI: 23.8 ± 3.4kg/m²; walking speed: 0.86 ± 0.19m/s; grip strength: 23.0 ± 8.3kg; upper limb fat content: 2.54 ± 0.98kg; lower limb fat content: 6.09 ± 1.85kg; upper limb muscle content: 4.22 ± 1.08kg; The lower extremity muscle content was 12.81 ± 3.37kg, body fat percentage was 28.8 ± 6.3%, and ASM/Ht² was 6.82 ± 1.39kg/m². A total of 60 patients were diagnosed with sarcopenia according to the 2019AWGS diagnostic criteria, including 19 males and 41 females. The prevalence of sarcopenia was 25.4%. The prevalence was 20.7% in elderly men and 28.5% in elderly women.

Variables Associated With Sarcopenia In Rural Community Older Adults

The mean BMI, grip Strength and body fat percentage of participants in sarcopenia group were significantly lower than those in non-sarcopenia group, (BMI, non-sarcopenia group: 25.1± 2.7, sarcopenia group: 20.1 ± 1.8, p < 0.001), (handgrip strength, non-sarcopenia group: 24.0 ± 8.8, sarcopenia group: 20.0 ± 6.0, p < 0.001) (body fat percentage, non-sarcopenia group:30.1 ± 5.9, sarcopenia group: 24.7 ± 5.8, p < 0.001), and the fat content and muscle content of limbs in sarcopenia group were significantly lower than those in non-sarcopenia group. There were no significant differences in age, sex and walking speed between sarcopenia group and non-sarcopenia groups (p > 0.05).

The circulating biomarker tests of the participants are shown in Table 2. t-test or tˊ-test analysis revealed differences in circulation markers between older subjects with and without sarcopenia. Compared with circulating factors in non-sarcopenia elderly subjects, FGF19 and GDF11 were significantly higher. The circulating factors in sarcopenia group were significantly decreased: ALT, AST, ALB, CREA, UA, GLU, TG, LDL, GDF15, TNF-α and IL6. There were no significant differences in other circulating factors (DBIL, TBIL, ALP, γ-GT, TC, HDL, GSP, ADP, IGF-1 and DHEA) between non-sarcopenia and sarcopenia groups (p ≥ 0.05).

Independent Factors Associated With Sarcopenia In Rural Community Older Adults

In order to further elaborate the related factors of sarcopenia, We included the 14 significantly variables BMI,FGF19 ,GDF11 ,ALT, AST, ALB, CREA, UA, GLU, TG, LDL, GDF15, TNF-α and IL6 from the above univariate analysis into the multivariate analysis,an analysis of multiple determinants of the sarcopenia was conducted using a stepwise conditional logistic regression with p < 0.05 (Table3). The results showed that GDF11 was an independent risk factor for sarcopenia. BMI, ALB, FGF19 and TNF-α were independent protective factors (Table3). In addition, LDL was not an independent factor in the development of sarcopenia (p > 0.05).

In this study, our main finding was that the prevalence of sarcopenia in rural elderly in China was 25.4%, in which 20.7% for men and 28.5% for women. The subjects with sarcopenia had a lower BMI than the non-sarcopenic older subjects. There were no significant differences in age, sex, or walking speed between non-sarcopenic and sarcopenic subjects. Compared with non-sarcopenic subjects, sarcopenic subjects had higher levels of GDF11 and lower levels of ALB, FGF19, and TNF-α. In addition, inflammatory cytokine IL-6 and β-superfamily cytokine GDF15 are not independent factors of sarcopenia.

In recent years, epidemiological survey results of sarcopenia in Chinese population show that the prevalence rate of sarcopenia in elderly people aged 60 and above is 3.1%-62.9%^[12]. A study in Taiwan showed that the prevalence of sarcopenia was 18.6% in 302 aged 65 and older men and 23.0% in women, which is similar to our results^[13]. A New Mexico senior health survey found that the prevalence of sarcopenia in older men and women was 28.5% and 33.9%, respectively^[14]. However, other studies have reported low prevalence of sarcopenia, and differences in prevalence results may be due to differences in the definition of sarcopenia, diagnostic cut-off values, or ethnic background of the population.

BMI, waist circumference, and/or waist-to-hip ratio are commonly used to define obesity, and BMI in older adults needs to be interpreted with caution because of reduced physiological height and lack of correlation between BMI and body fat percentage, distribution, or body composition in older adults.That is why we discussed here is just sarcopenia and non-sarcopenia groups of relatively high low BMI, and overweight and obese relations with less muscle disease needs further research.Our finding that a lower BMI is a risk factor for sarcopenia is consistent with the findings of multiple previous studies suggesting that the higher the BMI in older adults, the lower the risk of sarcopenia^{[15, 16]}.While obesity is considered a risk factor for many adverse outcomes, in older adults, being slightly overweight may be beneficial. Studies have also shown that older men are resistant to the dangers of overweight and obesity; Mild overweight, obesity, and even central obesity were beneficial for survival^[17].Similarly, a study of hospitalized older adults found that fat mass was associated with a lower risk of death or complications^[18].In addition, studies have shown that BMI is positively correlated with muscle mass and fat mass^[19–21].It has been pointed out that fat is an energy store for the elderly and helps individuals survive in disease or poor physical condition. The amount of fat can affect lean body mass for several ages, and people with high fat amount may have a higher protein intake, which is a protective mechanism against muscle loss^{[22, 23]}.Given this, we believe that high BMI may play a protective role against loss of muscle mass and strength in older adults. The results of this study further support the negative association between BMI and sarcopenia, so maintaining a healthy BMI in older adults may help maintain muscle mass and strength.

Studies have shown that the serum albumin of the elderly with sarcopenia is lower than that of the non-sarcopenia elderly participants^{[24, 25]}. A meta-study of sarcopenia involving 4,904 community-living and institutional older adults (68-87.6 years) from 9 countries and another meta-analysis of 4,071 participants from 5 countries showed that regardless of age and setting, Albumin is negatively associated with sarcopenia, and given that there is no conclusive evidence that serum albumin can be definitively used as a biomarker for sarcopenia, which is easier and less costly to implement than other approaches and ensures timely assessment and early intervention in elderly patients with sarcopenia, Therefore, serum albumin decline can be considered as a reference indicator for biomarkers of sarcopenia. However, albumin concentrations are also affected by processes such as inflammation and infection, so there is no consensus on optimal limits and reference ranges for serum values to assess nutritional status in older adults^[26–28]. Albumin is the most abundant plasma protein in the body, and its basic role is to regulate the passage of water and solutes through capillaries by maintaining colloidal pressure in the vascular system. Albumin has long been considered integral to the assessment of nutritional status, it is considered to be a very important plasma protein in the assessment of nutritional status, reducing it can alter wound healing, cause immune problems, and reduce lean body mass^[29]. Improving the nutritional status of the elderly can increase the serum albumin content, which may play a certain role in the treatment of sarcopenia in the elderly.

In elderly patients with sarcopenia, the level of GDF11 was higher in the sarcopenia group than in the non-sarcopenia group, while GDF15 was not statistically different between the two groups. GDF11 and GDF15 are members of the transforming growth factor β superfamily of cytokines, and GDF11 is closely related to myostatin (MSTN). GDF15 is also known as MIC-1^[30]. However, some scholars prefer to believe that GDF15 belongs to the glial cell derived neurotrophic factor (GDNF) -like growth factor or cytokine family, because GDF15 receptor GFRAL is a co-receptor of Ret tyrosine kinase^{[31, 32]}. Multiple studies have suggested that GDF11 inhibits skeletal muscle regeneration^[33–35]. In a recent study, Juli E. Jones found that elevated GDF11 could induce the expression of oxia in mice with decreased muscle mass, food intake and body weight, and that GDF11 could also up-regulate plasma GDF15. Blocking the GDF15 receptor alleviates anorexia but does not reduce muscle loss, whereas blocking the GDF11 receptor ActR II prevents muscle loss and reduces anorexia^[33]. Therefore, elevated GDF11 can be considered as an independent predictor of sarcopenia.

We also found that FGF19 was lower in the sarcopenia group than in the non-sarcopenia group. FGF19 is a member of the FGF family, a family of proteins involved in differentiation, development and metabolism^[36]. FGF19 is similar to endocrine hormone and is secreted by ileum intestinal epithelial cells. The pathogenesis of sarcopenia is related to skeletal muscle metabolism, and FGF19 has been found to play a role in muscle metabolism^[37]. Therefore, FGF19 may be an emerging factor for the treatment of sarcopenia, and can contribute to the diagnosis and prevention of sarcopenia. The negative association between FGF19 and sarcopenia was also confirmed by a recent cross-sectional study in Turkey of 88 elderly outpatient participants aged 65 years or older^[38].

A number of literatures have shown that sarcopenia is related to inflammatory cytokines, but the existing studies are controversial on whether inflammatory factors play a positive or negative role in sarcopenia. Studies have shown that chronic inflammation leads to a loss of muscle mass by affecting protein synthesis and catabolism. However, most of the people in these studies were obese or had other chronic conditions, so the role of inflammatory factors in sarcopenia remains controversial^[39]. In a recent study, 299 Japanese residents (127 males and 172 females) participated in urban health check-ups. It was found that IL-6 is not an independent factor in sarcopenia^[40]. This conclusion is similar to ours. A recent community sarcopenia study in Taiwan also found no significant correlation between serum IL-6 levels and ASMI, grip strength, or gait speed^[41]. IL-6 is an effective regulator of human fat metabolism, which can increase lipolysis and fat oxidation. IL-6 acts in an anti-inflammatory way during muscle contraction. Because IL-6 has pro-inflammatory and anti-inflammatory effects, and the IL-6 secreted into the blood cannot be determined whether it is derived from muscle tissue^[42].

The role of TNF-α played in sarcopenia remains controversial. Results in this study demonstrated that a low TNF-α level in sarcopenic old adults. In vitro studies have shown that TNF-α has a direct inhibitory effect on insulin signaling and can cause insulin resistance in skeletal muscle, thereby increasing free fatty acids and causing chronic inflammation. A clinical study showed the level of TNF-α in the elderly sarcopenia group was significantly lower than that in the non-sarcopenia group. In multiple logistic regression analysis, low level of TNF-α was identified as an independent risk factor for sarcopenia^{[40, 43]}. Further research is needed to better understand the role of TNF-α in sarcopenia.

There are some limitations to our study. First, because the participants came from rural communities, most of whom may have different occupations and living standards than older people in urban environments, the study has population limitations. Secondly, there are some unmeasured or unknown confounding factors that may affect the results of the study. Sarcopenia is an emerging and complex syndrome, and its influencing factors and pathogenesis are complex and diverse. Therefore, it is impossible to make a definitive diagnosis of sarcopenia through a single serum marker. Another, the lack of investigation of the clinical characteristics of the participants with noncommunicable diseases is also a limitation of this study, and the noncommunicable diseases of the participants may also affect the conclusions of the study. In addition, this is a cross-sectional, single-center study that cannot definitively establish a causal relationship between serum biomarkers and sarcopenia. Further longitudinal and multi-center collaborative studies are needed to confirm our results.

This study shows that the prevalence of sarcopenia in rural elderly in China is 25.4%, 20.7% for men and 28.5% for women. BMI, serum ALB, FGF19, and TNF-α levels were lower in the elderly population with sarcopenia, while GDF11 was increased in the serum of patients with sarcopenia. Although this study cannot draw a cause-and-effect relationship, our results may help to identify reliable biomarkers related to sarcopenia that could benefit the early detection, diagnosis, treatment, and prevention of sarcopenia.

Ethics approval and consent to participate

All participants signed a written informed consent prior to enrollment.It was approved by the Ethics committee of Union Hospital, Tongji Medical College, and Huazhong University of Science and Technology. It followed the tenets of the Helsinki Declaration in conducting this study.

Consent for publication

Not applicable.

Availability of data and materials

The datasets used and / or analyzed in the current study are available from the corresponding authors,without improper reservations.

Competing interests

The authors declare that they have no competing interest.

Funding

This research was supported by the National Key Research and Development Programs of China (No. 2018YFC2002100, 2018YFC2002102, and 2020YFC2006000).

Authors' contributions

ZHW and PH has contributed to the design of this study.YZ performed statistical analysis and wrote the first draft. YZ, PH, KMZ and YLZ participated in patient examination and data collection. YZ, ZHW and PH performed laboratory analysis. All authors approved the final version of the manuscript.

Acknowledgements

Not applicable.

Ethics Statement

Authors’ contributions

Funding

This research was supported by the National Key Research and Development Programs (No. 2018YFC2002100, 2018YFC2002102, and 2020YFC2006000).

Availability of data and materials

The datasets used and / or analyzed in the current study are available from the corresponding authors,without improper reservations.

Conflict of Interest

The authors declare that they have no competing interest.

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Table 1 Demographics characteristics and the index of body examination of 236 living in rural community older adults in Wuhan, China

Variable	Total （n=236）	Non-sarcopenia （n=176）	Sarcopenia （n=60）	t/χ²	P-value
Age (years)	70.6±4.4	70.5±4.3	70.7±4.9	-0.345	0.731
Male / Female	92/144	73/103	19/41	1.811	0.178
BMI (kg/m²)	23.8±3.4	25.1±2.7	20.1±1.8	16.021	＜0.001^*
GS (m/s)	0.86±0.19	0.87±0.20	0.82±0.17	1.694	0.092
HS (kg)	23.0±8.3	24.0±8.8	20.0±6.0	3.850	＜0.001^*
ASM/Ht² (kg/m²)	6.81±1.38	7.28±1.21	5.41±0.81	13.503	＜0.001^*
Upper limb fat ( kg)	2.54±0.98	2.79±0.80	1.84±1.12	7.152	＜0.001^*
Lower limb fat (kg)	6.09±1.85	6.72±1.60	4.24±1.19	11.032	＜0.001^*
Upper limb muscles (kg)	4.22±1.08	4.59±0.95	3.15±0.63	13.229	＜0.001^*
Lower limb muscles (kg)	12.81±3.37	13.77±3.17	10.01±2.17	10.205	＜0.001^*
PBF (%)	28.8±6.3	30.1±5.9	24.7±5.8	6.148	＜0.001^*
BMI: body mass index, HS: handgrip strength, GS: gait speed, ASM/Ht²: appendicular skeletal muscle mass, appendicular soft lean mass/(height)², PBF: percentage body fat.

Table 2 Biomarker characteristics of 236 participants living in rural community older adults in Wuhan,China

Variable	Non-sarcopenia	Sarcopenia	t	P-value
ALT (U/L)	18.84±11.72	12.57±6.69	5.074	＜0.001*
AST (U/L)	25.08±9.56	22.42±6.94	1.984	0.048*
DBIL (μmol/L)	9.82±2.72	9.75±2.67	0.185	0.853
TBIL (μmol/L)	11.80±3.50	11.83±3.41	-0.720	0.943
ALB (g/L)	36.60±3.68	35.45±4.00	2.047	0.042*
ALP (U/L)	80.82±26.66	79.43±28.67	0.342	0.732
γ-GT (U/L)	33.15±35.95	25.66±35.12	1.400	0.163
CREA(μmol/L)	64.81±25.57	57.94±14.38	1.976	0.049*
UA (μmol/L)	278.49±79.24	239.54±77.71	3.304	0.001*
GLU (mmol/L)	4.58±1.02	4.29±0.92	1.990	0.048*
TG (mmol)	0.99±0.52	0.76±0.40	3.111	0.002*
TC (mmol)	4.14±0.79	4.06±0.77	0.719	0.473
HDL (mmol)	1.04±0.29	1.10±0.35	-1.325	0.186
LDL (mmol)	2.65±0.71	2.44±0.72	2.005	0.046*
GSP (mmol/L)	1.81±0.30	1.85±0.30	-0.884	0.377
ADP (μg/mL)	9.46±2.70	9.34±1.76	0.402	0.688
FGF19 (pg/mL)	111.08±25.09	124.49±18.75	-4.367	＜0.001*
GDF11 (ng/mL)	191.97±142.53	417.33±214.55	-7.586	＜0.001*
GDF15 (ng/mL)	0.75±0.48	0.54±0.35	3.153	0.002*
IGF-1 (ng/mL)	383.80±96.33	376.55±62.43	0.668	0.505
TNFa (pg/mL)	25.00±13.06	16.18±7.22	6.508	＜0.001*
IL6 (pg/mL)	5.59±3.20	3.19±2.64	5.221	＜0.001*
DHEA (ng/mL)	574.74±193.38	550.28±246.49	0.699	0.487
ALT: alanine aminotransferase, AST: aspartate aminotransferase, DBIL: direct bilirubin, TBiL: total bilirubin, ALB: albumin, ALP: alkaline - phosphatase, γ-GT: gamma - glutamyl transferase, CREA: creatinine, UA: uric acid, GLU: glucose, TG: triglycerides, TC: total cholesterol, HDL: high-density lipoprotein, LDL: low-density lipoprotein, GSP: glycosylated serum protein, ADP: adiponectin, FGF19: fibroblast growth factor 19, GDF11: Growth differentiation factor 11, GDF15: growth differentiation factor 15, IGF - 1: insulin-like growth factor 1, TNF-α: tumour necrosis factor alpha, (IL-6): interleukin 6, DHEA: dehydroepiandrosterone.

Table 3 Models of logistic regression were used to identify factors associated with sarcopenia living in rural community older adults in Wuhan, China

	B	SE	Wald	df	p	Exp(B)	95%CI
BMI	-5.015	1.839	7.437	1	0.006	0.007	0.000-0.244
ALB	-0.714	0.283	6.350	1	0.012	0.490	0.281-0.853
LDL	1.709	0.889	3.699	1	0.054	5.522	0.968-31.508
FGF19	-0.218	0.083	6.875	1	0.009	0.804	0.683-0.946
GDF11	0.030	0.010	8.773	1	0.003	1.031	1.010-1.052
TNF-α	-0.970	0.343	8.024	1	0.005	0.379	0.194-0.742

No competing interests reported.

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Analysis of prevalence and associated biomarkers of sarcopenia living in rural community older adults in Wuhan, China

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