Abstract
Objective
To identify the prevalence, lifestyle factors, chronic disease status, and assessing the metabolic profile, comparing key differences in a cohort of subjects with non-sarcopenia/non-obesity (H), sarcopenia/non-obesity (S), non-sarcopenia/obesity (O) and sarcopenia obesity (SO) in a multi-ethnic population in west China.
Design
A cross-sectional study.
Setting
The communities in Yunnan, Guizhou, Sichuan, and Xinjiang provinces.
Participants
We included 4,500 participants aged 50 years or older who did bioelectrical impedance in our analysis from West China Health and Aging Trend (WCHAT) study.
Measurements
We measured gait speed, handgrip strength and muscle mass by using bioelectrical impedance analysis (BIA) for all participants. We defined sarcopenia using the diagnostic algorithm recommended by the Asian Working Group for Sarcopenia (AWGS). Obesity was defined as the highest sex-specific quintile of the percentage body fat. Different variables like anthropometry measures, life styles, chronic disease and blood test were collected. Analysis of variance and a multinomial logistic regression analysis adjusting for covariates were used to assess the differences of metabolic profiles among different groups.
Results
Of 4500 participants aged 50 years old or older, the proportions of H, O, S, SO were 63.0%, 17.7%, 16.7% and 2.6%, respectively. And the prevalence of S subjects in men was 18.3% and 15.7% in women, while the prevalence of SO was 3.7% in men and 2.0% in women. Data showed that the prevalence of S and SO has an aging increase pattern which was opposite with O. Both S and SO tends to be older, lower educational level, without spouse, smoking, comorbidity of chronic disease, poor nutrition status, depression and cognitive decline compared to H and S seems to be worse than SO. Compared to H, S cohort showed a decrease in Vitamin D, triglyceride, albumin, fasting glucose, insulin, creatinine, ALT, nutrition scores and increase in HDL. SO cohort were observed for an increase in cholesterol, LDL, total protein and decrease in vitamin D. While O cohort showed an increase in triglyceride, cholesterol, LDL, total protein, glucose, insulin, WBC, uric acid, ALT and nutrition scores, but a decrease in HDL and vitamin D level.
Conclusions
Among individuals aged 50 years old or older in West China. S, O and SO participants demonstrate distinct differences in the life-styles, chronic disease profile, and metabolic profiles. The prevalence of S and SO has an aging increase pattern contrary to O. Both S and SO tend to be older, lower educational level, without spouse, smoking, comorbidity of chronic disease, poor nutrition status, depression and cognitive decline compared to H and S looks like to be worse than SO. Besides, the S subjects seem to have more metabolic index changes than SO compared to H. While O subjects have some contrary metabolic index to S subjects.
Key words: Sarcopenia, obesity, sarcopenia obesity, prevalence, West China, metabolic profile
Introduction
Sarcopenia is a condition that is characterized by a progressive and generalized loss of skeletal muscle mass and strength (1). It is associated with physical disability, poor health outcomes, low quality of life and premature death (2). The pathogenesis of sarcopenia involves many aspects, such as lack of exercise, hormonal imbalance, and undernutrition (3).
Obesity was a major health impact on the world and is defined as an excess of body fat. The body mass index (BMI) is the standard measure of overweight and greater or equal to 30 is regarded as obese. However, BMI does not differentiate muscle mass from fat in individuals. A recent study demonstrated that both low BMI and high body fat percentage are independently associated with increased mortality, indicating the importance of using direct measures of adiposity (4). In addition, BMI varies with age and sex. Thus, using BMI to define obesity is particularly problematic in elderly (5).
Sarcopenia obesity is an emerging syndrome that is observed in adults with obesity, which is a condition of reduced lean body mass in the context of excess adiposity (6). Sarcopenia obesity is most often reported in older people, as both risk and prevalence increase with age (7). Even few studies found that obesity exacerbates sarcopenia, increases the infiltration of fat into muscle, lowers physical function and increases the risk of mortality (8, 9, 10), the metabolic profile and other characteristics of sarcopenia obesity were not well understood.
In this study, we got the cross-sectional data from the West-China Health and Aging Trend (WCHAT) study. We aimed to identify the prevalence, lifestyle factors, chronic disease prevalence, and assessing the metabolic profile, comparing key differences (versus non-sarcopenia/non-obesity (H)) in a cohort of subjects with sarcopenia/non-obesity (S), non-sarcopenia/obesity (O) and sarcopenia obesity (SO) in multi-ethnic West China.
Method
The current research is a cross-sectional study using baseline data of the WCHAT study, which was approved by the Ethical Review Committee (reference: 2017–445). Data were collected from 4 provinces in West China, including Yunnan, Guizhou, Sichuan, and Xinjiang.
Study participants
All participants aged 50 years old or older were enrolled. Participants were recruited by convenience and asked verbally by the researchers about their willingness to take part in the study. Before the investigation, informed consent was signed and obtained by each participant. In this study, we have muscle mass data for 4,500 participants. Thus, we included these participants in the current analysis.
Demographic and anthropometric data collection
The baseline demographic information included the followings: (1) General personal data: age, gender, marital status, educational level, ethnic background; (2) Lifestyle characteristics: sleep quality, tea-drinking, alcohol drinking, smoking, housework doing. Anthropometric measurements include height, weight, body mass index (BMI), waist circumference (WC), body fat percentage.
Sarcopenia assessment
Sarcopenia was measured by the recommended diagnostic algorithm of AWGS which included muscle mass, muscle strength, and physical performance. Muscle mass was measured by bioimpedance analysis using an Inbody 770 (BioSpace, Seoul, Korea), which was a convenient method for the measurement of muscle mass (11, 12, 13). This method was also validated in China (14). Low muscle mass was defined as the skeletal muscle mass index (ASMI, ASM/height2) of 7.0 kg/m2 in men and, 5.7 kg/m2 in women. Muscle strength was assessed with the dominant hand using a dynamometer (EH101; Camry, Zhongshan, China). Tests were performed on two independent occasions and the largest value was recorded. The threshold todefine low grip strength was 26kg and 18kg for men and women, respectively (15). Usual walking speed was measured over 4 m. For sarcopenia definition in terms of low gait speed, the cut-off value was 0.8 m/s (11).
Sarcopenia obesity assessment
Participants were classified as obese if their percentage body fat measured by BIA was in the highest quintile (cutoff values: 34.3% for men, 42.1% for women). Based on the presence/absence of sarcopenia and obesity, participants were categorized into four groups: S, O, SO, and H.
Assessment of cognitive decline and chronic diseases
Cognitive status was measured using a 10-item Short Portable Mental Status Questionnaire (SPMSQ). For SPMSQ scoring, 0∼2 indicated complete cognitive function, 3∼4 indicated mild cognitive functional impairment, 5∼7 indicated moderate cognitive function impairment, and 8∼10 indicated severe cognitive function impairment. This judgment was based on educational level (16). A medical history of chronic disease was self-reported. These disease conditions included hypertension, osteoarticular disease, lung disease and diabetes mellitus. Chronic diseases comorbidities were considered as having two or more chronic diseases.
Assessment of sleep quality and nutrition status
Sleep quality was assessed using the Pittsburgh sleep quality index (PSQI). Scores >5 were considered poor self-reported sleep quality which was previously used in the Chinese population (17). Nutrition status was graded using MNA-SF on a scale of 0–14, with 0∼7 indicate poor nutrition status, 8∼11 indicate mild poor nutrition status, 12∼14 indicate good nutrition status and this was already validated in the Chinese population (18).
Blood sample measurements
Fasting venous blood samples were drawn in the morning, with the participants in a sitting position. Blood handling and collection were carried out under strictly standardized conditions. Serum albumin, fasting glucose, cholesterol, hemoglobin, triglyceride levels and other metabolites were measured.
Statistical analysis
We test the normality of variables using R version 3.6.1. The measurement data is expressed by mean±SD. For the normal distribution variables, the difference between the groups is compared by the independent sample T-test and the count data is expressed in %, using the χ2 test. We used the χ2 test to determine whether the prevalence of chronic condition including depression and cognition impairment differed by sarcopenia status. A value of P<0.05 (two-side) was considered to be statistically significant.
Multinomial logistic regression was used to predict the probability of category membership for a dependent variable with two or greater classifications, based on multiple independent variables. The model was adjusted by the age, sex and ethnic groups.
Results
Characteristics of the study participants
Overall, we enrolled 4,500 participants aged 50 years or older in the study. The proportions of H, O, S, SO were 63.0%, 17.7%, 16.7% and 2.6%, respectively. Table 1 summarizes the anthropometric measures between participants in the four groups. One-way ANOVA analysis of variance detected statistically significant differences (p<0.001) among these four groups in the height, weight, BMI, waist circumference, body fat percentage and ASMI. Table 2 summarizes the demographic characteristics of participants in the four groups. It also shows significant differences (p<0.001) among groups in age, gender, ethnic groups, educational level, marriage status, tea-drinking, smoking and cognitive status. Specifically, both S and SO tend to be older, lower educational level, without a spouse, smoking, comorbidity of chronic disease, poor nutrition status, depression and cognitive decline compared to H.
Table 1.
Anthropometric measures of study participating (n=4500)
| Anthropometric measures | Healthy | Obesity | Sarcopenia | Sarcopenia obesity | P value |
|---|---|---|---|---|---|
| N=2833 (63.0) | N=798 (17.7) | N=750 (16.7) | N=119 (2.6) | ||
| Height(cm) | 157.5(7.8) | 155.9(8.3) | 152.9(7.9) | 151.3(9.4) | <.001 |
| Weight(kg) | 62.1(9.3) | 72.9(10.3) | 50.3(7.8) | 61.0(9.8) | <.001 |
| BMI (kg/m2) | 25.0(2.8) | 30.1(3.3) | 21.3(2.1) | 26.3(2.2) | <.001 |
| BMI<18.5 | 28(1.0) | 0(0.0) | 69(9.2) | 0(0.0) | <.001 |
| 18.5≤BMI<24 | 977(34.5) | 5(0.6) | 612(81.6) | 11(9.2) | |
| 24≤BMI<27 | 1170(41.3) | 97(12.2) | 68(9.1) | 69(58.0) | |
| BMI≥27 | 658(23.2) | 696(87.2) | 1(0.1) | 39(32.8) | |
| waist circumference(cm) | 86.6(8.8) | 98.1(10.6) | 77.8(7.9) | 89.4(9.9) | <.001 |
| Body fat percentage (%) | 32.1(6.5) | 42.8(4.8) | 29.4(7.0) | 41.9(4.9) | <.001 |
| ASMI (kg/m2) | 6.8(0.8) | 7.0(0.9) | 5.7(0.7) | 5.9(0.7) | <.001 |
Data are shown using % or mean (standard deviation). P values were calculated with chi-squared tests and one-way analysis of variance (ANOVA) for categorical and continuous variables, respectively. BMI, body mass index; ASMI, appendicular skeletal muscle mass index.
Table 2.
Demographic characteristics of study participants (n=4500)
| Demographic characteristics | Healthy N=2833 (63.0) | Obesity N=798 (17.7) | Sarcopenia N=750 (16.7) | Sarcopenia obesity N=119 (2.6) | P value | |
|---|---|---|---|---|---|---|
| Age, years | 60.8(7.5) | 61.9 (7.6) | 68.0(8.7) | 68.0(9.3) | <.001 | |
| Gender | Female | 1834(64.7) | 529(66.3) | 452(60.3) | 58(48.7) | <.001 |
| Male | 999(35.3) | 269(33.7) | 298(39.7) | 61(51.3) | ||
| Ethnics | <.001 | |||||
| Han | 1252(44.2) | 254(31.8) | 382(50.9) | 49(41.2) | ||
| Zang | 645(22.8) | 364(45.6) | 164(21.9) | 60(50.4) | ||
| Qiang | 782(27.6) | 144(18.0) | 118(15.7) | 6(5.0) | ||
| Yi | 115(4.1) | 24(3.0) | 71(9.5) | 3(2.5) | ||
| Others | 39(1.4) | 12(1.5) | 15(2.0) | 1(0.8) | ||
| Educational level | <.001 | |||||
| No formal education | 744(26.3) | 228(28.6) | 284(37.9) | 38(31.9) | ||
| Elementary school | 887(31.3) | 274(34.3) | 251(33.5) | 36(30.3) | ||
| Middle school | 652(23.0) | 135(16.9) | 108(14.4) | 26(21.8) | ||
| High school or higher | 550 (19.4) | 161(20.2) | 107(14.3) | 19(16.0) | ||
| Marital status | <.001 | |||||
| Single | 12 (0.4) | 11 (1.5) | 6(0.8) | 2(1.8) | ||
| Married | 2353(86.9) | 612(82.5) | 541(76.0) | 87(76.3) | ||
| Divorced | 37(1.4) | 15(2.0) | 14(2.0) | 1(0.9) | ||
| Widowed | 305(11.3) | 104(14.0) | 151(21.2) | 24(21.1) | ||
| Life-styles | ||||||
| Drink tea | ||||||
| history | No | 1436(53.3) | 326(44.2) | 402(56.9) | 45(39.5) | <.001 |
| Yes | 1260(46.7) | 412(55.8) | 304(43.1) | 69(60.5) | ||
| Drink alcohol | ||||||
| history | No | 1992(73.9) | 567(76.8) | 532(75.2) | 94(82.5) | .090 |
| Yes | 705(26.1) | 171(23.2) | 175(24.8) | 20(17.5) | ||
| Smoking history | No | 2239(83.0) | 646(87.5) | 538(76.2) | 95(83.3) | <.001 |
| Yes | 459(17.0) | 92(12.5) | 168(23.8) | 19(16.7) | ||
| Sleeping quality | Good | 1459 (54.0) | 389 (52.6) | 340(48.0) | 63(55.3) | .038 |
| Bad | 1245(46.0) | 350(47.4) | 369(52.0) | 51(44.7) | ||
| Comorbidity of chronic disease | No | 703(24.8) | 231(28.9) | 165(22.0) | 27(22.7) | .014 |
| Yes | 2130(75.2) | 567(71.1) | 585(78.0) | 92(77.3) | ||
| Hypertension | 613(21.6) | 278(34.8) | 143(19.1) | 33(27.7) | <.001 | |
| Lung disease | 93(3.3) | 50(6.3) | 52(6.9) | 8(6.7) | .001 | |
| Diabetes mellitus | 189(6.7) | 55(6.9) | 51(6.8) | 14(11.8) | .200 | |
| Osteoarticular disease | 224(19.6) | 78(19.3) | 83(26.3) | 11(19.0) | .055 | |
| Nutrition status | Good | 1197(46.3) | 389(54.9) | 92(14.4) | 52(51.5) | <.001 |
| Mild poor | 1351(52.2) | 317(44.7) | 495(77.7) | 49(48.5) | ||
| Poor | 38(1.5) | 3(0.4) | 50(7.8) | 0(0.0) | ||
| Cognitive status | Good | 2397(89.0) | 626(85.1) | 543(76.9) | 94(82.5) | <.001 |
| Mild decline | 237(8.8) | 94(12.8) | 103(14.6) | 12(10.5) | ||
| M/S decline | 60(2.2) | 16(2.2) | 60(8.5) | 8(7.0) | ||
Note. Data are shown using % or mean (standard deviation). P values were calculated with chi-squared tests and one-way analysis of variance (ANOVA) for categorical and continuous variables, respectively. Others=other ethnic groups including Zhuang, Manchu, Hui, Mongolia, Tujia. GDS-15=short form Geriatric Depression Scale M/S=moderate/severe.
Table 3 summarizes the metabolic profile of the four groups. It also shows significant differences (p<0.001) among groups in the level of insulin, fasting glucose, triglyceride, cholesterol, HDL, LDL, vitamin D, total protein, albumin, prealbumin, WBC, GPR, LPR, uric acid, ALT and PTC. Specifically, S showed a marked decrease in insulin, prealbumin and vitamin D level, but a higher level of PTC compared to H.
Table 3.
Metabolic characteristics of study participants (n=4500)
| Metabolic characteristics | Healthy | Obesity | Sarcopenia | Sarcopenia obesity | p value |
|---|---|---|---|---|---|
| Insulin 0 (uU/ml) | 8.1 (9.2) | 11.9 (11.7) | 5.6 (5.0) | 8.8 (6.9) | <.001 |
| Fasting glucose(mmol/L) | 5.5(1.7) | 5.8(1.8) | 5.4(1.8) | 5.8(2.4) | <.001 |
| Triglyceride(mmol/L) | 1.9(1.9) | 1.9(1.3) | 1.6(1.5) | 1.8(1.0) | <.001 |
| Cholesterol(mmol/L) | 4.8(0.9) | 4.9(0.9) | 4.7(1.0) | 5.0(0.9) | .001 |
| HDL (mmol/L) | 1.3(0.3) | 1.2(0.3) | 1.4(0.4) | 1.2(0.3) | <.001 |
| LDL (mmol/L) | 2.6(0.9) | 2.8(0.8) | 2.6(0.8) | 2.9(0.8) | <.001 |
| Vitamin D (ng/mL) | 19.7(6.2) | 17.5(5.8) | 18.6(6.4) | 18.0(7.1) | <.001 |
| Total protein(g/L) | 71.7 (6.2) | 73.0 (4.6) | 70.9 (5.3) | 72.6 (4.8) | <.001 |
| Albumin(g/L) | 44.4(3.0) | 44.4(3.0) | 43.2(3.4) | 43.8(3.2) | <.001 |
| Prealbumin(g/L) | 291.7 (169.2) | 282.6 (104.3) | 261.3 (112.7) | 269.8 (47.4) | <.001 |
| WBC (10?9/L) | 5.8(1.6) | 6.1(1.6) | 5.9(2.0) | 6.2(1.7) | <.001 |
| GPR (%) | 60.8 (8.4) | 61.3 (8.6) | 62.3 (9.4) | 63.0 (7.9) | <.001 |
| LPR (%) | 31.8 (7.8) | 31.3 (7.8) | 30.5 (8.7) | 29.8 (7.3) | <.001 |
| Uric Acid (umol/L) | 323.8 (79.2) | 353.6 (87.6) | 316.0 (83.7) | 356.6 (93.5) | <.001 |
| CREA (umol/L) | 80.8 (20.0) | 79.1 (14.0) | 80.0 (18.5) | 82.4 (16.0) | .080 |
| Urea (mmol/L) | 5.4 (1.6) | 5.3 (1.5) | 5.5 (1.7) | 5.3 (1.7) | .053 |
| ALT (U/L) | 27.9 (19.5) | 31.9 (20.1) | 23.0 (14.7) | 25.8 (14.6) | <.001 |
| AST (U/L) | 29.1 (13.4) | 29.6 (12.7) | 30.0 (18.2) | 28.4 (12.0) | .296 |
| TSH (mU/L) | 3.4 (2.7) | 3.6 (3.1) | 3.5 (4.4) | 3.5 (2.8) | .724 |
| PTC (nmol/L) | 341.7 (136.9) | 337.6 (138.5) | 373.0 (179.0) | 358.5 (133.3) | <.001 |
Data are shown mean (standard deviation). P values were calculated with one-way analysis of variance (ANOVA). Insulin 0, fasting plasma insulin. HDL, high-density lipoprotein; LDL, low-density lipoprotein. WBC, white blood cells. GPR, neutrophils ratio. LPR, lymphocyte ratio. CREA, creatinine. ALT, alanine transaminase. AST, aspartate aminotransferase. TSH, thyroid stimulation hormone. PTC, adrenal cortisol.
Prevalence rates of O, S and SO
The prevalence of S and SO both displayed an age-related increasing pattern in men and women. While in the O group, it showed an age-related decreasing pattern in men and women. In Han and Qiang ethnic groups, the prevalence of both S and SO in men was higher than in women. While in the Yi ethnic group, it was the opposite. And the prevalence of O is higher in women than men in Han, Qiang and Yi. Totally, the prevalence of S and SO was higher in men than in women, while the prevalence of O was higher in women than in men (Table 4).
Table 4.
Prevalence rates of non-sarcopenia/obesity, sarcopenia/non-obesity, sarcopenia obesity according to age, sex, and ethnics (n=4500)
| Groups | Category | Obesity | Sarcopenia | Sarcopenia obesity | |||
|---|---|---|---|---|---|---|---|
| Men | Women | Men | Women | Men | Women | ||
| Age | 50–59 | 18.6% | 17.3% | 8.2% | 7.7% | 2.4% | 1.1% |
| 60–69 | 16.5% | 20.7% | 17.0% | 16.6% | 3.7% | 1.5% | |
| 70–79 | 14.3% | 17.8% | 35.1% | 35.6% | 5.8% | 5.1% | |
| ≥80 | 2.7% | 9.0% | 64.9% | 56.7% | 8.1% | 11.9% | |
| Ethnics | Han | 10.9% | 14.2% | 23.7% | 17.8% | 3.3% | 2.1% |
| Zang | 29.8% | 29.3% | 12.3% | 14.0% | 6.8% | 3.4% | |
| Qiang | 9.3% | 16.1% | 15.6% | 8.9% | 1.1% | 0.3% | |
| Yi | 10.7% | 11.6% | 24.0% | 38.4% | 0.0% | 2.2% | |
| Total | ≥50 yr old | 16.5% | 18.4% | 18.3% | 15.7% | 3.7% | 2.0% |
Note. Values are presented as prevalence rate in same age-groups divided by gender and presented as prevalence rate in same ethnic group divided by gender.
The key differences of S, O and SO compared to H
Table 5 shows the results of multinomial logistic regression analysis adjusting for age, sex and ethnic groups for key differences among the four groups. For the grip strength and gait speed, compared to the H group, we observed a reduction in all the other three groups. Key differences in S cohort were highlighted by a significantly decrease in Vitamin D, triglyceride, albumin, fasting glucose, insulin, creatinine, ALT, nutrition scores and increase in HDL, GPR, AST and PTC.
Table 5.
Factors associated with non-sarcopenia/obesity, sarcopenia/non-obesity and sarcopenia obesity (n=4500)
| Independent variables | Healthy | Sarcopenia | Obesity | Sarcopenia obesity |
|---|---|---|---|---|
| β; OR; CI 95% | β; OR; CI 95% | β; OR; CI 95% | β; OR; CI 95% | |
| Handgrip (kg) | 1.0 | −0.02; 0.98(0.97–1.00) | -0.11; 0.90(0.88-0.91) | −0.13; 0.88(0.85–0.92) |
| Gait speed (m/s) | 1.0 | −0.65; 0.52(0.36–0.76) | -1.18; 0.31(0.20-0.48) | −1.48; 0.23(0.09–0.60) |
| Metabolic profile | ||||
| Vitamin D (ng/mL) | 1.0 | −0.05; 0.95(0.94–0.97) | -0.03; 0.97(0.96-0.99) | −0.04; 0.96(0.93–0.99) |
| TG (mmol/L) | 1.0 | 0.05; 1.06(1.01–1.10) | -0.20; 0.82(0.75-0.89) | NS |
| CHL (mmol/L) | 1.0 | 0.10; 1.11(1.02–1.21) | NS | 0.30; 1.35(1.12–1.63) |
| HDL (mmol/L) | 1.0 | −1.26; 0.28(0.21–0.39) | 1.12; 3.07(2.33–4.05) | NS |
| LDL (mmol/L) | 1.0 | 0.15; 1.16(1.05–1.28) | NS | 0.37; 1.45(1.15–1.83) |
| Total protein(g/L) | 1.0 | 0.03; 1.03(1.01–1.05) | NS | 0.03; 1.03(1.00–1.06) |
| Albumin(g/L) | 1.0 | NS | −0.05; 0.96(0.93–0.99) | NS |
| Prealbumin(g/L) | 1.0 | NS | −0.003; 1.00(0.995–0.998) | NS |
| Glucose(mmol/L) | 1.0 | 0.08; 1.08(1.04–1.13) | −0.06; 0.94(0.88–1.00) | NS |
| Insulin 0 (uU/ml) | 1.0 | 0.05; 1.05(1.03–1.06) | −0.16; 0.86(0.83–0.88) | NS |
| WBC (10^9/L) | 1.0 | 0.12; 1.12(1.07–1.18) | NS | NS |
| GPR (%) | 1.0 | NS | 0.01; 1.01(1.00–1.02) | NS |
| LPR (%) | 1.0 | NS | NS | NS |
| Uric Acid (umol/L) | 1.0 | 0.01; 1.01(1.005–1.007) | −0.003; 1.00(0.995–0.998) | 0.003; 1.00(1.001–1.005) |
| CREA (umol/L) | 1.0 | NS | −0.02; 0.98(0.97–0.99) | NS |
| Urea (mmol/L) | 1.0 | NS | NS | NS |
| ALT (U/L) | 1.0 | 0.01; 1.01(1.01–1.02) | −0.02; 0.98(0.98–0.99) | NS |
| AST (U/L) | 1.0 | NS | 0.01; 1.01(1.00–1.01) | NS |
| TSH (mU/L) | 1.0 | NS | NS | NS |
| PTC (nmol/L) | 1.0 | NS | 0.001;1.00(1.00–1.001) | NS |
| Nutrition scores | 1.0 | 0.15; 1.16(1.07–1.26) | −0.46; 0.63(0.59–0.68) | NS |
Note. A multinomial logistic regression analysis was used for estimating adjusted key differences. The model was adjusted by the covariates age, sex, ethnic groups. TG, Triglyceride. HDL, high-density lipoprotein; LDL, low-density lipoprotein. NS, not statistically significant. CHL, Cholesterol. Insulin 0; Fasting plasma insulin. WBC, white blood cells. GPR, neutrophils ratio. LPR, lymphocyte ratio. CREA, creatinine. ALT, alanine transaminase. AST, aspartate aminotransferase. TSH, thyroid stimulation hormone. PTC, adrenal cortisol. All shown data was significantly that P<0.05..
Conversely, key differences in SO cohort were observed only for an increase in cholesterol, LDL, total protein and uric acid and only decrease in vitamin D. Furthermore, key differences in O cohort were observed an increase in triglyceride, cholesterol, LDL, total protein, glucose, insulin, WBC, uric acid, ALT and nutrition scores, but a decrease in HDL and vitamin D level.
Discussion
In our study, we compared S, O, SO with H among multiethnic participants aged 50 years or older. We presented the prevalence, the lifestyles, the chronic disease profile, and the metabolic profile of the four groups, and we compared the key differences among the four groups in the metabolic profile.
This study showed the prevalence of both S and SO was age increased pattern. This is in accordance with many studies (3, 19). As the major age-related changes in body composition include an increase in body fat and a decline in skeletal muscle, leading to increased proinflammatory cytokines, oxidative stress, insulin resistance, and hormonal changes (20, 21, 22).
This study showed that vitamin D levels are decreased in the groups whether having sarcopenia or obesity, or both, compared to the healthy group. Research has shown that low vitamin D level was both associated with obesity and sarcopenia (23, 24, 25). For obesity, due to their sedentary lifestyle and less outdoor activity, low sun exposure got in obese individuals, leading to less vitamin D production (26). On the other hand, the low vitamin D status takes part in the development of obesity by modulating adipocyte differentiation and lipid metabolism (25). For sarcopenia, different biological mechanisms by vitamin D might regulate skeletal muscle function have been evaluated, such as the direct biological role of the active vitamin D form in the regulation of genes and signaling pathways affecting calcium and phosphate homeostasis, proliferation and differentiation of muscle cells (27, 28). Besides, Vitamin D could bind to the VDR receptor on muscle fibers and increases their size, improving muscle strength and physical performance (29). A cohort study found that a high serum vitamin D level in mid- and late-life was associated with reduced odds of multiple adverse body composition, especially osteosarcopenic obesity (30). All of these studies indicate the supplement of vitamin D in the elderly is reasonable.
Our study found the S group had a decrease in the level of albumin and prealbumin compared with H. Many studies found the low level of albumin was associated with muscle loss and was considered as a risk factor of sarcopenia (31, 32, 33). Low albumin was shown to lead to more antioxidant capacities, causing more oxidative damage and therefore muscle breakdown (34, 35). Moreover, albumin could activate the phosphatidyl-inositol 3-kinase pathway, leading to muscle hypertrophy (36). And we found the O and SO groups did not show significant differences in albumin compared with H. However, a study showed that obesity and morbid obesity were associated with higher odds of hypoalbuminemia in adults without liver disease or renal failure (37). Future research should be done to investigate the relationship between obesity, sarcopenia obesity and albumin.
Interestingly, we found the O group has a higher fasting plasma insulin and S group has lower fasting plasma insulin compared to H. The research found that a lack of insulin or insulin resistance leads to accelerated development of sarcopenia. The mechanism study was found that insulin could mediate accretion of muscle mass through activation of p38 mitogen-activated protein kinase (MAPK) and mammalian target of rapamycin (mTOR)/p70S6 kinase, and thus stimulating mRNA translation (38). A previous study found that elevated fasting insulin levels increase the risk of abdominal obesity in Korean men (38). And the elevated fasting insulin level was also reported to be positively associated with the visceral fat area in computed tomography scans among adults and children (39). However, in our study, we found that the level of fasting insulin level was not significantly different in SO group compared with H. A recent research also found that sarcopenic obesity does not appear to confer greater risk for incident metabolic syndrome or insulin resistance than obesity alone in community-dwelling older men (40). Whether there exists a relationship between sarcopenic obesity and fasting plasma insulin needs further research.
Specifically, we found that only S group has a higher ratio of adrenal cortisol than H. Cortisol takes part in various physiological mechanisms like metabolism, the immune response, and the body's response to stress. And cortisol is mostly triggered by stress-induced activation of the hypothalamic-pituitary-adrenal axis (HPA). It was found that a relative increase in cortisol may reflect the presence of stress and stimulate muscle catabolism through inducing atrogin-1 and MuRF-1 in the ubiquitin-proteasome system, thus causing protein degradation of skeletal muscle, leading to sarcopenia (41, 42, 43). However, we did not find a significant difference between O, SO and H in the level of adrenal cortisol. While the research found substantial evidence of differential HPA axis activity in both generalized and abdominal obesity (44). But this also remains controversial that another research found that cortisol level is lower in overweight or class I obese women than in lean women and that research also found that both extreme underweight and overweight states could activate the HPA axis, result of hypercortisolemia, contributing to increased adiposity in the setting of caloric excess (45). This might need deeper research to find out whether sarcopenia obesity and obesity were associated with adrenal cortisol.
Moreover, we found a reduced alanine transaminase (ALT) level in S compared to H. Study showed that reduced ALT levels in older individuals could be a marker of frailty, disability, and sarcopenia, and also an independent predictor of adverse outcomes (46). Besides, High serum aspartate aminotransferase (AST) was found to be highly prevalent in older underweight people and might reflect skeletal muscle pathology (47). But in a Chinese community-dwelling population, the prevalence of metabolic syndrome and its components (including central obesity and high TG) was found to increase with an elevation in serum ALT levels within normal range (48). While the prevalence of elevated ALT did not exhibit a linear change with elevated BMI between 1999 and 2014 in U.S. adolescents (49). Whether there exists some relationship between sarcopenic obesity and ALT or AST needs to be explored further.
Overall, we found that both S and SO had an age-related prevalence increasing pattern, lower educational level, without spouse, smoking, comorbidity of chronic disease, poor nutrition status, depression and cognitive decline compared to H, and S seems to be worse than SO. Specifically, we found the S group looks like in a worse metabolic profile than SO compared to H, while O group was opposite to S group in some blood index like triglyceride, high-density lipoprotein, fasting glucose, fasting insulin, alanine transaminase and nutrition scores. As a result of a less harmful metabolic profile in SO compared to H, it seems like SO group was benefited from obesity. Previous research has a similar result showing that S subjects appear more vulnerable than SO and sarcopenia is closely related with an increase in the risk of hip-femur fractures, inflammation, edema, and malnutrition[50]. More metabolic profile changes in comparing S, O and SO need to be investigated.
Limitations
The strengths of this study were its large study sample. However, careful adjustment should be made for important confounders of the association under study, including lifestyle variables and dietary intake. Besides, no repeated measures of metabolic testing were available and there might be some testing error. Moreover, our study was an observational cross-sectional study, and a causal relationship between metabolic change in the sarcopenia or sarcopenia obesity should be investigated in the follow-up. In the end, persons who came to participate in our study are relatively younger, healthier and there existed some kind of bias in the whole study.
Conclusions
This study demonstrated that S, O and SO particpants demonstrate distinct differences in life-styles, chronic disease profile, and metabolic profiles. The prevalence of S and SO has an aging increasing pattern but O has an aging decrease pattern. Both S and SO tend to be older, lower educational level, without a spouse, smoking, comorbidity of chronic disease, poor nutrition status, depression and cognitive decline compared to H, and S seems to be worse than SO. Besides, the metabolic profile showing that S subjects had the worst metabolic profile. However, O subjects have some contrary metabolic factors to S. While SO seemed to be profit from the “obesity,” with better nutritional status and metabolic profile than S.
Acknowledgments
We thank all the volunteers for the participation and personnel for their contribution to the WCHAT study.
Contributor Information
Ning Ge, Email: grace7733@163.com.
Birong Dong, Email: birongdong@163.com.
Conflicts of Interest
The authors report no conflicts of interest in this work.
Ethnical statements
The current research was approved by the Ethical Review Committee of West China Hospital of Sichuan University with the committee's reference number 2017(445) and the registration number is ChiCTR 1800018895.
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