Association between sarcopenia and parity in American women in the National Health and Nutrition Examination Surveys (NHANES) 2011 to 2018

Association between sarcopenia and parity in American women in the National Health and Nutrition Examination Surveys (NHANES) 2011 to 2018 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Association between sarcopenia and parity in American women in the National Health and Nutrition Examination Surveys (NHANES) 2011 to 2018 Xuefeng Hou, Dong Chen, Yuchen Shen, Jian Jiang, Kangjie Xu, Bin Dai This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3890576/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Muscle atrophy is a condition characterized by a decrease in muscle mass, and it is more common in women compared to men. Currently, there is limited research on the relationship between parity (number of pregnancies) and muscle atrophy. This study aims to investigate the association between parity and muscle loss in a population of Americans. Materials and Methods We collected clinical data from 3,530 participants in the National Health and Nutrition Examination Survey (NHANES) conducted between 2011 and 2018. Dose-response analyses using restricted quadratic spline models were employed to assess the association between parity and muscle atrophy in the study sample. Propensity Score Matching (PSM) was used to balance confounding variables between the muscle atrophy group and the non-muscle atrophy group. Results Among the 3,530 participants, 330 (9.3%) were diagnosed with muscle atrophy. Our study revealed that factors such as older age, Mexican American, low education level, marital status, poverty, physical inactivity, and higher parity were associated with muscle loss. The dose-response analyses using restricted quadratic spline models showed a positive correlation between parity and muscle atrophy in all patients, with an increased risk of muscle atrophy with higher parity. Additionally, the Propensity Score Matching analysis still demonstrated a positive association between parity and muscle atrophy after adjusting for other confounding variables. Conclusion Our study suggests that higher parity is associated with an increased risk of muscle atrophy in postmenopausal American women. Regular exercise may be effective in reducing the risk of muscle atrophy. sarcopenia parity pregnancy skeletal muscle muscle mass BMI Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Sarcopenia is defined as the decline skeletal muscle mass that can lead decrease of physical abilities and functional even serious dependency and disability [ 1 ]. Although sarcopenia is thought to be closely age-related, the onset of sarcopenia is occurring at a younger age with changes in modern lifestyles [ 2 ]. The incidence of sarcopenia varies from 10–25% in various countries, but in some regions, the incidence can be as high as 50% in the elderly aged 80–89 years [ 3 , 4 ]. Currently, about 50 million people worldwide suffer from sarcopenia, and it is speculated that this number will reach 500 million by 2050 [ 5 , 6 ]. In addition, the prevalence of sarcopenia was different between male and female in different regions. A study from England showed that women have a higher risk of sarcopenia than men due to a range of sex-specific risk factors, but in China, South Korea and the United States, the results are reversed [ 7 – 11 ]. Sarcopenia is thought to be inherent to the process of body senescence, but it can be facilitated by multiple factors such as inflammation, inactivity, malnutrition, chronic illness and gut microbial dysbiosis [ 12 , 13 ]. It is worth noting that there is a bidirectional relationship between bone and muscle during human aging, and the decrease in muscle number will accelerate the loss of bone trabeculae, while the decrease in bone mass will also lead to the atrophy of muscle shape and decline in function [ 14 ]. Systemic hormonal endocrine networks play an important role in regulating bone and muscle growth and metabolism [ 15 ]. Pregnancy is a significant event in women that causes various physiological changes. A research based on a US population reported an increased risk of osteoporotic fracture of the lumbar spine in postmenopausal women with high parity [ 16 ]. Furthermore, an animal-based study has validated that the decline in trabecular bone mineral density becomes irreversible as litter size increases [ 17 ]. A growing body of evidence has verified the importance of sex hormones in muscle homeostasis [ 18 , 19 ]. Hormone deficiency can affect musculoskeletal quality in elderly women, which has become one of the risk factors for sarcopenia [ 20 ]. At present, the effects of parity on osteoporosis have been confirmed, but the specific association between parity and the risk for sarcopenia still needs to be elucidated in large samples from different regions and ethnic groups. Dual energy X-ray absorptiometry (DXA) is a relatively ideal measurement method for the diagnosis of sarcopenia. Therefore, this study analyzed public data from the NHANES database on DXA and number of pregnancies to determine the association between muscle mass and parity in the US population. 2. Participants and method 2.1 Study participants Our study population survey data were obtained from the US National Health and Nutrition Examination Surveys (NHANES) (2011–2018). The primary variables were examination data of appendicular skeletal muscle mass (ASM) measured by dual-energy X-ray absorptiometry (DXA) and pregnancy history questionnaire. We first extracted data from 31956 participants, and then we excluded partial participants according to the purpose of the study: (1) all male participants (n = 19308). (2) all nulliparous participants (n = 11489). (3) participants with incomplete appendicular skeletal muscle mass (ASM) information (n = 4432). (4) Participants with extremum of parity (n = 3). (5) Participants with incomplete data on covariates such as body mass index (BMI), education, poverty-income ratio (PIR), drug use, smoking, and waist circumference were finally excluded (n = 394). Finally, 3530 female participants were included this study. (Fig. 1) 2.2 Measurement and evaluation primary variables Figure 1 Flow chart of research sample selection Dual-energy x-ray absorptiometry (DXA) is the most widely accepted method of measuring body composition due in part to its speed, ease of use, and low radiation exposure [ 21 – 23 ]. The following people were excluded from the DXA examination who were pregnant, participant self-reported history of radiographic contrast material (barium) use in past 7 days and weight more than 450 pounds or height more than 6 feet 5 inches. According to the National Institutes of Health diagnostic criteria, the sarcopenia was defined as ASM/BMI (< 0.789 for men and < 0.512 for women). ASM was the sum of the skeletal lean mass of both arms and legs, and BMI was weight (kg) divided by the square of height (m) [ 24 ]. The number of pregnancies was assessed primarily based on data recorded by the questionnaire. In this study, Only the number of previous pregnancies was counted and not included currently pregnant who not participated in the DXA examination. 2.3 Other variables Based on the main variables of ASM, BMI and number of pregnancies, we found that the age range of participants included in the final data was 20–60 years old. To further analyze the risk of sarcopenia in different age groups, we divided them into two groups: 20–40 years old and 41–60 years old. Other covariates were selected according to previous literature [ 16 ]. Race (Non-Hispanic white, Non-Hispanic black, Mexican American and other), education level (less than high school, high school or equivalent, college or above and other), marital status (married, unmarried), PIR(low income < 1.3, middle income 1.3–3.5, and high income ≥ 3.5 ), vigorous recreational activities (yes, no), moderate recreational activities (yes, no), drug use(yes, no), smoking(current, former, never), number of pregnancies(1–2,3–4,≥5), hypertension (yes and no/unknown), diabetes (yes and no/unknown), body mass index (BMI) (< 25.0 kg/m 2 , and ≥ 50.0 kg/m 2 ). 2.4 Statistical analysis In our study, the distribution of continuous variables was described by mean ± standard deviation, and the distribution of classified variables was described by proportion. Chi-square analysis was used to evaluate the clinical characteristics of all participants. Sensitivity analysis excluded participant data with extremely high values (< 1%). Age was divided into two groups to study the characteristics of muscle atrophy in different age groups. Dose-response analysis using restricted quadratic spline models was used to evaluate the association between parity and muscle atrophy in the study sample. PSM was used to balance confounding variables between the muscle atrophy group and non-muscle atrophy group. For categorical variables, P-values were analyzed using chi-square tests and presented as percentages. For continuous variables, t-tests with slope were used in the generalized linear model and presented using interquartile range (IQR). In our study, all data were analyzed using R (v.4.2.2) software and SPSS (v.24.0). 3. Results Based on requirement of inclusion and exclusion criteria, we collected data from 3530 eligible participants in the 2011–2018 NHANES (Fig. 1). As the Table 1 , Demographic characteristics of female participants are shown by the presence or absence of sarcopenia. Statistical results show that 330 participants ill with sarcopenia and 3200 participants without sarcopenia. Chi-square test showed marked disparity among multiple variables which are age (p < 0.001), race (p < 0.001), education (p < 0.001), marital status (p < 0.012), PIR (p < 0.002), marijuana or hashish use (p < 0.012), vigorous and moderate recreational activities (p < 0.001), parity (p < 0.016), BMXWAIST (p < 0.012), BMI (p < 0.012), ASM/BMI (p < 0.012). We found that participants with sarcopenia were more concentrated among those aged 41 to 60 years (70%), Mexican American (37.9%), and more than high school (44.5%), married (59.7%), less moderate recreational activity (87.6%), less vigorous recreational activity (64.8%), low income (42.7%). It is strange that marijuana or hashish was significantly associated with sarcopenia, but cocaine/heroin/meth- amphetamine was not. Table 1 Demographic characteristics of female NHANES population a . Characteristic Total Sarcopenia Nonsarcopenia P value No. (%) No. (%) No. (%) Total patients 3530 330 (9.3) 3200 (90.7) Age, years < 0.001 20–40 1534 (43.5) 99 (30.0) 1436 (44.9) 41–60 1995 (56.5) 231 (70.0) 1764 (55.1) Race < 0.001 Non-Hispanic white 1266 (35.9) 93 (28.2) 1173 (36.7) Non-Hispanic black 808 (22.9) 23 (7.0) 785 (24.5) Mexican American 547 (15.5) 125 (37.9) 422 (13.2) Other race 909 (25.8) 89 (27.0) 820 (25.6) Education < 0.001 Less than high school 604 (17.1) 97 (29.4) 507 (15.8) High school 743 (21.0) 86 (26.1) 657 (20.5) More than high school 2183 (61.8) 147 (44.5) 2036 (63.6) Marital status 0.012 Married 1875 (53.1) 197 (59.7) 1678 (52.4) Unmarried 1655 (46.9) 133 (40.3) 1522 (47.6) PIR 0.002 ≤ 1.3 1240 (35.1) 141 (42.7) 1099 (34.3) 1.3–3.5 1255 (35.6) 116 (35.2) 1139 (35.6) ≥ 3.5 1035 (29.3) 73 (22.1) 962 (30.1) Vigorous recreational activities < 0.001 Yes 810 (22.9) 41 (12.4) 769 (24.0) No 2720 (77.1) 289 (87.6) 2431 (76.0) Moderate recreational activities 0.001 Yes 1547 (43.8) 116 (35.2) 1431 (44.7) No 1983 (56.2) 214 (64.8) 1769 (55.3) Ever used marijuana or hashish < 0.001 Yes 1709 (48.4) 119 (36.1) 1590 (49.7) No 1821 (51.6) 211 (63.9) 1610 (50.3) Ever used cocaine/heroin/meth- amphetamine 0.818 Yes 507 (14.4) 46 (13.9) 461 (14.4) No 3023 (85.6) 284 (86.1) 2739 (85.6) Smoking status 0.108 Current 762 (21.6) 58 (17.6) 704 (22.0) Former 540 (15.3) 47 (14.2) 493 (15.4) Never 2228 (63.1) 225 (68.2) 2003 (62.6) Number of pregnancies 0.016 1–2 1451 (41.1) 117 (35.5) 1334 (41.7) 3–4 1410 (39.9) 133 (40.3) 1277 (39.9) ≥ 5 669 (19.0) 80 (24.2) 589 (18.4) BMXWAIST (IQR) 85.0, 108.0 95.4, 116.0 84.0, 106.7 < 0.001 BMI (IQR) 24.2, 34.2 29.8, 39.4 23.8, 33.5 < 0.001 ASM/BMI (IQR) 0.57, 0.70 0.46, 0.50 0.58, 0.71 < 0.001 a For categorical variables, P values were analyzed by chi-square tests. For continuous variables, the t-test for slope was used in generalized linear models. PIR, Ratio of family income to poverty. BMXWAIST, waist circumference (cm). BMI, body mass index. ASM/BMI (IQR),appendicular skeletal muscle mass/ body mass index(interquartile range) To further analyze the association between the parity and the incidence of sarcopenia, we also studied the population distribution characteristics according to the number of pregnancies (Table 2 ). As anticipated, participants with ≥ 5 parities among those variables have a higher proportion which aged 41–60 years, unmarried, PIR ≤ 1.3, without vigorous and moderate recreational activities, never smoked, ever used marijuana or hashish and never cocaine/heroin/meth-amphetamine. Among different races, participants with ≥ 5 parities are concentrated in non-Hispanic white and black, while the number of pregnancies increases with the level of education. Importantly, we found that the prevalence of sarcopenia increased with the parity, suggesting an association between sarcopenia and parity. Table 2 Characteristics of the study population by number of pregnancies. Characteristic Number of pregnancies P value 1–2 3–4 ≥ 5 Total patients 1451 (41.1) 1410 (39.9) 669 (19.0) Age, years < 0.001 20–40 748 (51.6) 557 (39.5) 230 (34.4) 41–60 703 (48.4) 853 (60.5) 439 (65.6) Race < 0.001 Non-Hispanic white 568 (39.1) 505 (35.8) 193 (28.8) Non-Hispanic black 308 (21.2) 307 (21.8) 193 (28.8) Mexican American 172 (11.9) 245 (17.4) 130 (19.4) Other race 403 (27.8) 353 (25.0) 153 (22.9) Education < 0.001 Less than high school 153 (10.5) 276 (19.6) 175 (26.2) High school 287 (19.8) 310 (22.0) 146 (21.8) More than high school 1011 (69.7) 824 (58.4) 348 (52.0) Marital status 0.002 Married 775 (53.4) 783 (55.5) 317 (47.4) Unmarried 676 (46.6) 627 (44.5) 352 (52.6) PIR < 0.001 ≤ 1.3 391 (26.9) 508 (36.0) 341 (51.0) 1.3–3.5 517 (35.6) 517 (36.7) 221 (33.0) ≥ 3.5 543 (37.4) 385 (27.3) 107 (16.0) Vigorous recreational activities < 0.001 Yes 373 (25.7) 318 (22.6) 119 (17.8) No 1078 (74.3) 1092 (77.4) 550 (82.2) Moderate recreational activities < 0.001 Yes 671 (46.2) 634 (45.0) 242 (36.2) No 780 (53.8) 776 (55.0) 427 (63.8) Ever used marijuana or hashish 0.006 Yes 724 (49.9) 638 (45.2) 347 (51.9) No 727 (50.1) 772 (54.8) 322 (48.1) Ever used cocaine/heroin/meth- amphetamine < 0.001 Yes 176 (12.1) 195 (13.8) 136 (20.3) No 1275 (87.9) 1215 (86.2) 533 (79.7) Smoking status < 0.001 Current 265 (18.3) 311 (22.1) 186 (27.8) Former 207 (14.3) 234 (16.6) 99 (14.8) Never 979 (67.5) 865 (61.3) 384 (57.4) Sarcopenia 0.016 Yes 117 (8.1) 133 (9.4) 80 (12.0) No 1334 (91.9) 1277 (90.6) 589 (88.0) BMXWAIST (IQR) 83.0, 106.7 85.7, 107.9 88.0, 110.8 < 0.001 BMI (IQR) 23.4, 33.6 24.3, 34.4 25.7, 35.0 < 0.001 ASM/BMI (IQR) 0.58, 0.71 0.56, 0.69 0.56, 0.69 < 0.001 a For categorical variables, P values were analyzed by chi-square tests. For continuous variables, the t-test for slope was used in generalized linear models. PIR, Ratio of family income to poverty. BMXWAIST, waist circumference (cm). BMI, body mass index. ASM/BMI (IQR), appendicular skeletal muscle mass/ body mass index (interquartile range) In order to ascertain the independent effect of the number of pregnancies on the risk of sarcopenia, we conducted subgroup analysis. We found that individuals with ≤ 2 pregnancies had a lower risk of sarcopenia (OR 0.90; 95% CI 0.76–1.06), while those with > 4 pregnancies had a higher risk of sarcopenia (OR 1.07; 95% CI 0.93–1.23). Subgroup analysis by age revealed that among individuals aged 20–40, those with ≤ 2 pregnancies had a decreased risk of sarcopenia (OR 0.97; 95% CI 0.70–1.36), while those with > 4 pregnancies had an increased risk (OR 1.01; 95% CI 0.79–1.30). Similar patterns were observed in the 41–60 age group, where ≤ 2 pregnancies were associated with a lower risk of sarcopenia, and > 4 pregnancies were associated with a higher risk. Furthermore, when analyzing different marital statuses, among married individuals, those with ≤ 2 pregnancies had a lower risk of sarcopenia (OR 0.90; 95% CI 0.76–1.06), while those with 4 pregnancies (OR 1.17; 95% CI 0.99–1.40), 6 pregnancies (OR 1.61; 95% CI 1.19–2.18), and 8 pregnancies (OR 2.22; 95% CI 1.26–3.94) had an increased risk. The risk of sarcopenia significantly increased with a higher number of pregnancies. Among unmarried individuals, the number of pregnancies did not appear to have an association with the risk of sarcopenia (Table 3 ). The dose-response curve visually demonstrates that in different age groups, there is a direct positive relationship between the number of pregnancies and the risk or severity of sarcopenia when the number of pregnancies exceeds 4. In other words, as the number of pregnancies increases, the risk or severity of sarcopenia increases linearly, and this correlation is more pronounced in the 41–60 age group. Additionally, in the married population, there is a positive correlation between the number of pregnancies and sarcopenia, while no clear trend is observed in the unmarried population. Table 3 Adjusted odds ratios for associations between the number of pregnancies and the presence of sarcopenia in NHANES 2011–2018 a . Characteristic Sarcopenia Number of pregnancies 2 4 6 8 All 0.90 (0.76–1.06) 1.07 (0.93–1.23) 1.18 (0.94–1.49) 1.30 (0.85-2.00) 20–40 years 0.97 (0.70–1.36) 1.01 (0.79–1.30) 1.08 (0.69–1.68) 1.16 (0.49–2.72) 41–60 years 0.92 (0.79–1.07) 1.05 (0.85–1.28) 1.17 (0.84–1.61) 1.33 (0.82–2.16) Married 0.86 (0.68–1.09) 1.17 (0.99–1.40) 1.61(1.19–2.18) 2.22 (1.26–3.94) Unmarried 0.94 (0.78–1.14) 0.91 (0.70–1.20) 0.82 (0.53–1.26) 0.79 (0.40–1.54) a Adjusted covariates: Basic model: race, education levels, PIR, drug use, smoking status; Core model: basic model plus vigorous recreational activities, moderate recreational activities, BMXWAIST; Extended model: BMXBMI, ASM/BMI, Marital status, age. CI: confidence interval. aOR: adjusted odds ratio. T: tertile. To eliminate or minimize the influence of unrelated confounding factors on the study results, we conducted propensity score matching (PSM) correction. The results showed that the population with ≤ 2 pregnancies (OR 0.96; 95%CI 0.82–1.13) was less likely to develop sarcopenia, while the population with > 4 pregnancies (OR 1.03; 95%CI 0.81–1.31) was more likely to develop sarcopenia. Subsequently, we further conducted stratified analysis. In the population aged 20–40, it was found that those with ≤ 2 pregnancies were less likely to develop sarcopenia (OR 0.85; 95%CI 0.67–1.08), while those with > 4 pregnancies (OR 1.16; 95%CI 0.83–1.64) were more likely to develop sarcopenia. Consistent with the above results, in the population aged 41–60, it was found that those with ≤ 2 pregnancies were less likely to develop sarcopenia, while those with > 4 pregnancies were more likely to develop sarcopenia. Subsequently, we analyzed different marital status groups. In the married population, it was found that those with ≤ 2 pregnancies (OR 0.86; 95%CI 0.69–1.09) were less likely to develop sarcopenia. However, as the number of pregnancies increased, the risk of sarcopenia significantly increased, as observed in those with 4 pregnancies (OR 1.11; 95%CI 0.93–1.32), 6 pregnancies (OR 1.27; 95%CI 0.96–1.70), and 8 pregnancies (OR 1.45; 95%CI 0.85–2.47). For the unmarried population, the risk correlation was not significant. (Table 4 ). The dose-response curve after PSM correction still shows a certain risk correlation between the number of pregnancies and sarcopenia. Specifically, when the number of pregnancies exceeds 4, there is a positive relationship between the number of pregnancies and the risk or severity of sarcopenia in different age groups. In the married population, there is a positive correlation between the number of pregnancies and sarcopenia, while the risk correlation is lower in the unmarried population. Table 4 Adjusted odds ratios for associations between the sarcopenia and number of pregnancies in NHANES 2011–2018 a . Characteristic Sarcopenia Number of pregnancies 2 4 6 8 All 0.96 (0.82–1.13) 1.03 (0.81–1.31) 1.07 (0.74–1.55) 1.10 (0.61–1.96) 20–40 years 0.85 (0.67–1.08) 1.16 (0.83–1.64) 1.36 (0.80–2.31) 1.45 (0.68–3.07) 41–60 years 0.91 (0.74–1.12) 1.06 (0.90–1.23) 1.16 (0.88–1.53) 1.27 (0.76–2.14) Married 0.86 (0.69–1.09) 1.11 (0.93–1.32) 1.27 (0.96–1.70) 1.45 (0.85–2.47) Unmarried 0.91 (0.76–1.10) 1.06 (0.80–1.40) 1.09 (0.70–1.69) 1.07 (0.53–2.16) a Adjusted covariates: Basic model: race, education levels, PIR, drug use, smoking status; Core model: basic model plus vigorous recreational activities, moderate recreational activities, BMXWAIST; Extended model: BMXBMI, ASM/BMI, Marital status, age. CI: confidence interval. aOR: adjusted odds ratio. T: tertile. 4. Discussion Sarcopenia is a chronic progressive and generalized skeletal muscle disorder involving the accelerated loss of muscle mass and function that seriously affects the quality of life of patients [ 5 ]. In this large retrospective study, we used the NHANES database of Americans subjects to reveal the association between the number of pregnancies and sarcopenia in the US region. In our study, we divided number of pregnancies into three groups and analyzed the characteristics of the study population accordingly. The results showed that the risk of sarcopenia increased with the number of pregnancies, and sarcopenia was positively correlated with age and negatively correlated with activity, meanwhile, sarcopenia incidence rate was higher among married persons. Pregnancy history is an important indicator in women’s reproductive activity, which may affect the musculoskeletal system. In previous studies, the prevalence of osteoporosis and low bone mass in women was significantly higher than in men, whether it was in the femoral neck or lumbar spine. Research has found a certain association between parity and bone density, suggesting that multiple pregnancies may decrease women’s bone density and increase the risk of osteoporosis [ 25 ]. However, it is worth noting that several studies have shown a significant negative correlation between the number of pregnancies and spinal bone density, while having no significant impact on femoral neck bone density [ 26 , 27 ]. Another study reported that women who have given birth had significantly higher hip joint bone density compared to women who haven’t given birth, but no difference was found in femoral neck and lumbar spine [ 28 ]. Furthermore, some research suggests that there is no relationship between parity and bone density, and even high parity may have a protective effect against postmenopausal osteoporosis [ 29 , 30 ]. We speculate that this may be related to differences in race and lifestyle. Pregnancy and childbirth are factors that lead to hormonal changes during the postpartum period and throughout pregnancy. These hormonal changes can affect various systems in the body, including the musculoskeletal system. Additionally, parity can have non-hormonal effects on bone density due to changes in lifestyle and emotional factors such as nutrition, sleep, breastfeeding, and psychological activities [ 25 ]. Therefore, pregnancy and breastfeeding can alter the overall hormonal status and mineral levels in the body, which may have an impact on the musculoskeletal system. Research reports have shown that women aged 65 or older who have given birth to four or more children have a higher risk of disability compared to women with three or fewer children [ 31 , 32 ]. Another study in the United States found that postmenopausal women with three or more children had poorer physical functioning than women who had not given birth [ 33 ]. A study based on the Korean population showed that higher parity is independently associated with an increased risk of metabolic syndrome in postmenopausal women [ 34 ]. In our study, the fully adjusted subgroup analysis model, stratified by tertiles of parity, revealed that the proportion of individuals with sarcopenia was 8.1% among those with 1–2 pregnancies, while it increased to 12% among those with ≥ 5 pregnancies. Furthermore, we found that the proportion of individuals engaging in daily activities was lower in the high-parity group compared to the low-parity group. These findings strongly indicate a significant increase in the proportion of individuals with sarcopenia as parity increases, suggesting an association between parity and sarcopenia. To further elucidate the independent effect of the number of pregnancies on the risk of sarcopenia, we conducted separate analyses on the number of pregnancies and marital status in relation to sarcopenia. The results of the study showed a significant positive correlation between the number of pregnancies and sarcopenia, indicating that as the number of pregnancies increases, the risk of sarcopenia also increases. We further conducted age-stratified analyses and found that in both age groups, the risk of sarcopenia increased with an increase in the number of pregnancies. In terms of marital status, married individuals had a higher risk of sarcopenia compared to unmarried individuals. Due to the differences in many variables between the sarcopenia and non-sarcopenia groups, in order to eliminate or minimize the influence of unrelated confounding factors on the study results and make the comparison between the treatment and control groups more reliable and accurate, we applied PSM to adjust for all variables. Even after PSM correction, there was still a significant positive correlation between the number of pregnancies and sarcopenia in different subgroups. Worth noting is that, compared to before PSM, the correlation between the number of pregnancies and sarcopenia was even more pronounced in the 21–40 age group. While the unmarried group showed a certain level of risk correlation compared to before correction, there was no clear positive correlation trend observed. In summary, this study establishes an association between the number of pregnancies and sarcopenia in the American population. The risk of muscle wasting is positively correlated with parity in the American population, particularly among married women. However, the specific nature of the relationship between the number of pregnancies and sarcopenia has not been determined. Hormones may play a significant role, and nutritional status may also have an impact, especially in individuals who experience severe pregnancy reactions. Other factors that may influence sarcopenia include postpartum sleep, emotional changes, and alterations in vaginal microbiota. Further research is needed to investigate the specific association and underlying mechanisms between parity and muscle wasting. 5. Conclusion In this study indicated that higher parity is associated with an increased risk of muscle atrophy in postmenopausal American women. Regular exercise may be effective in reducing the risk of muscle atrophy. Abbreviations NHANES National Health and Nutrition Examination Survey PSM Propensity score matching DXA Dual energy X-ray absorptiometry ASM Appendicular skeletal muscle mass BMI Body mass index PIR Poverty-income ratio IQR interquartile range BMIWAIST Waist circumference Declarations Author contributions XH and DC wrote the main manuscript text，YS and KX analyzed data JJ and BD prepared fgures and tables. All authors reviewed the manuscript. Funding This study was supported and sponsored by the Jiangsu Commission of Health(Project number: 2022255). Availability of data and materials The data that support the findings of this study were available from opensource database. Ethics approval and consent to participate Not applicable. Competing interests The authors declare no competing interests. Author details 1 Binhai county people’s hospital, Yanchen, JiangSu，224500，People’s Republic of China References Beaudart C, Demonceau C, Reginster JY, Locquet M, Cesari M, Cruz Jentoft AJ, Bruyère O. Sarcopenia and health-related quality of life: A systematic review and meta‐analysis. Journal of Cachexia, Sarcopenia and Muscle. 2023. doi: 10.1002/jcsm.13243 . Bauer J, Morley JE, Schols A, Ferrucci L, Cruz-Jentoft AJ, Dent E, et al. Sarcopenia: A Time for Action. An SCWD Position Paper. J Cachexia Sarcopenia Muscle. 2019;10(5):956–61. doi: 10.1002/jcsm.12483 . Cruz-Jentoft AJ, Landi F, Schneider SM, Zuniga C, Arai H, Boirie Y, et al. Prevalence of and interventions for sarcopenia in ageing adults: a systematic review. 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Sarcopenia and mortality among a population-based sample of community-dwelling older adults. J Cachexia Sarcopenia Muscle. 2016;7(3):290–8. doi: 10.1002/jcsm.12073 . Hwang J, Park S. Sex Differences of Sarcopenia in an Elderly Asian Population: The Prevalence and Risk Factors. Int J Environ Res Public Health. 2022;19(19). doi: 10.3390/ijerph191911980 . Hai S, Wang H, Cao L, Liu P, Zhou J, Yang Y, Dong B. Association between sarcopenia with lifestyle and family function among community-dwelling Chinese aged 60 years and older. BMC Geriatr. 2017;17(1):187. doi: 10.1186/s12877-017-0587-0 . Liu X, Hao Q, Yue J, Hou L, Xia X, Zhao W, et al. Sarcopenia, Obesity and Sarcopenia Obesity in Comparison: Prevalence, Metabolic Profile, and Key Differences: Results from WCHAT Study. J Nutr Health Aging. 2020;24(4):429–37. doi: 10.1007/s12603-020-1332-5 . Therakomen V, Petchlorlian A, Lakananurak N. Prevalence and risk factors of primary sarcopenia in community-dwelling outpatient elderly: a cross-sectional study. Sci Rep. 2020;10(1):19551. doi: 10.1038/s41598-020-75250-y . Wang Z, Xu X, Deji Y, Gao S, Wu C, Song Q, et al. Bifidobacterium as a Potential Biomarker of Sarcopenia in Elderly Women. Nutrients. 2023;15(5). doi: 10.3390/nu15051266 . Yu C, Du Y, Peng Z, Ma C, Fang J, Ma L, et al. Research advances in crosstalk between muscle and bone in osteosarcopenia (Review). Exp Ther Med. 2023;25(4):189. doi: 10.3892/etm.2023.11888 . Huang J, Romero-Suarez S, Lara N, Mo C, Kaja S, Brotto L, et al. Crosstalk between MLO-Y4 osteocytes and C2C12 muscle cells is mediated by the Wnt/beta-catenin pathway. JBMR Plus. 2017;1(2):86–100. doi: 10.1002/jbm4.10015 . Yang Y, Wang S, Cong H. Association between parity and bone mineral density in postmenopausal women. BMC Womens Health. 2022;22(1):87. doi: 10.1186/s12905-022-01662-9 . de Bakker CM, Altman-Singles AR, Li Y, Tseng WJ, Li C, Liu XS. Adaptations in the Microarchitecture and Load Distribution of Maternal Cortical and Trabecular Bone in Response to Multiple Reproductive Cycles in Rats. J Bone Miner Res. 2017;32(5):1014–26. doi: 10.1002/jbmr.3084 . Ikeda K, Horie-Inoue K, Inoue S. Functions of estrogen and estrogen receptor signaling on skeletal muscle. J Steroid Biochem Mol Biol. 2019;191:105375. doi: 10.1016/j.jsbmb.2019.105375 . Hevener AL, Zhou Z, Drew BG, Ribas V. The Role of Skeletal Muscle Estrogen Receptors in Metabolic Homeostasis and Insulin Sensitivity. Adv Exp Med Biol. 2017;1043:257–84. doi: 10.1007/978-3-319-70178-3_13 . Mandelli A, Tacconi E, Levinger I, Duque G, Hayes A. The role of estrogens in osteosarcopenia: from biology to potential dual therapeutic effects. Climacteric. 2022;25(1):81–7. doi: 10.1080/13697137.2021.1965118 . Baran DT, Faulkner KG, Genant HK, Miller PD, Pacifici R. Diagnosis and management of osteoporosis: guidelines for the utilization of bone densitometry. Calcif Tissue Int. 1997;61(6):433–40. doi: 10.1007/s002239900362 . Genant HK, Engelke K, Fuerst T, Glüer CC, Grampp S, Harris ST, et al. Noninvasive assessment of bone mineral and structure: state of the art. J Bone Miner Res. 1996;11(6):707–30. doi: 10.1002/jbmr.5650110602 . Heymsfield SB, Wang J, Heshka S, Kehayias JJ, Pierson RN. Dual-photon absorptiometry: comparison of bone mineral and soft tissue mass measurements in vivo with established methods. Am J Clin Nutr. 1989;49(6):1283–9. doi: 10.1093/ajcn/49.6.1283 . Xu J, Han X, Chen Q, Cai M, Tian J, Yan Z, et al. Association between sarcopenia and prediabetes among non-elderly US adults. J Endocrinol Invest. 2023. doi: 10.1007/s40618-023-02038-y . Seo E, Lee Y, Kim HC. Association Between Parity and Low Bone Density Among Postmenopausal Korean Women. J Prev Med Public Health. 2021;54(4):284–92. doi: 10.3961/jpmph.21.162 . Allali F, Maaroufi H, Aichaoui SE, Khazani H, Saoud B, Benyahya B, et al. Influence of parity on bone mineral density and peripheral fracture risk in Moroccan postmenopausal women. Maturitas. 2007;57(4):392–8. doi: 10.1016/j.maturitas.2007.04.006 . Hassa H, Tanir HM, Senses T, Oge T, Sahin-Mutlu F. Related factors in bone mineral density of lumbal and femur in natural postmenopausal women. Arch Gynecol Obstet. 2005;273(2):86–9. doi: 10.1007/s00404-005-0015-0 . Song SY, Kim Y, Park H, Kim YJ, Kang W, Kim EY. Effect of parity on bone mineral density: A systematic review and meta-analysis. Bone. 2017;101:70–6. doi: 10.1016/j.bone.2017.04.013 . Heidari B, Hosseini R, Javadian Y, Bijani A, Sateri MH, Nouroddini HG. Factors affecting bone mineral density in postmenopausal women. Arch Osteoporos. 2015;10:15. doi: 10.1007/s11657-015-0217-4 . Okyay DO, Okyay E, Dogan E, Kurtulmus S, Acet F, Taner CE. Prolonged breast-feeding is an independent risk factor for postmenopausal osteoporosis. Maturitas. 2013;74(3):270–5. doi: 10.1016/j.maturitas.2012.12.014 . Akin B, Ege E, Koçoğlu D, Arslan SY, Bilgili N. Reproductive history, socioeconomic status and disability in the women aged 65 years or older in Turkey. Arch Gerontol Geriatr. 2010;50(1):11–5. doi: 10.1016/j.archger.2009.01.001 . Harville EW, Chen W, Guralnik J, Bazzano LA. Reproductive history and physical functioning in midlife: The Bogalusa Heart Study. Maturitas. 2018;109:26–31. doi: 10.1016/j.maturitas.2017.12.006 . Canonico M, Artaud F, Tzourio C, Elbaz A. Association of Reproductive History With Motor Function and Disability in Aging Women. Journal of the American Geriatrics Society. 2019;68(3):585–94. doi: 10.1111/jgs.16257 . Lee Y, Lee HN, Kim SJ, Koo J, Lee KE, Shin JE. Higher parity and risk of metabolic syndrome in Korean postmenopausal women: Korea National Health and Nutrition Examination Survey 2010–2012. J Obstet Gynaecol Res. 2018;44(11):2045–52. doi: 10.1111/jog.13766 . Additional Declarations No competing interests reported. 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hospital","correspondingAuthor":false,"prefix":"","firstName":"Dong","middleName":"","lastName":"Chen","suffix":""},{"id":268956669,"identity":"28de0692-81ea-45dc-9566-d53f3979aff3","order_by":2,"name":"Yuchen Shen","email":"","orcid":"","institution":"Binhai county people's hospital","correspondingAuthor":false,"prefix":"","firstName":"Yuchen","middleName":"","lastName":"Shen","suffix":""},{"id":268956670,"identity":"b477229f-8057-4382-a3b9-65ead62daccd","order_by":3,"name":"Jian Jiang","email":"","orcid":"","institution":"Binhai county people's hospital","correspondingAuthor":false,"prefix":"","firstName":"Jian","middleName":"","lastName":"Jiang","suffix":""},{"id":268956671,"identity":"7871a930-28c6-4a0c-b185-42fcec6f58b3","order_by":4,"name":"Kangjie Xu","email":"","orcid":"","institution":"Binhai county people's hospital","correspondingAuthor":false,"prefix":"","firstName":"Kangjie","middleName":"","lastName":"Xu","suffix":""},{"id":268956672,"identity":"dcaa4752-5bec-42a0-aa7f-6d5770cbc790","order_by":5,"name":"Bin Dai","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA00lEQVRIiWNgGAWjYBACAzDJxsBjwMzA+CChooY0LcwGD84cI14LiMEm+bCFmbAWc/azxx7zlNnJmLPzHqtIbGBj4G/vTsCrxbInL92Y51wyj2UzX9qNxB0yDBJnzm7A77ADOWbSvG3MPAaHecxuJJ5hYzCQyCWg5fwbkJZ6sJaCxDZmIrTcANtyGKyFgUgt79IN55w7DvQLj7FEwpljPIT9cj732IM3ZdX25vxnDD/+qKiR42/vxa+FgYGHjYkHmUtAOUQL4w8ilI2CUTAKRsEIBgCuAkOHhvtj+QAAAABJRU5ErkJggg==","orcid":"","institution":"Binhai county people's hospital","correspondingAuthor":true,"prefix":"","firstName":"Bin","middleName":"","lastName":"Dai","suffix":""}],"badges":[],"createdAt":"2024-01-23 09:14:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3890576/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3890576/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":50171810,"identity":"04aff561-a8b4-4644-b804-f20d74cf998b","added_by":"auto","created_at":"2024-01-25 15:48:58","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":196403,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of research sample selection\u003c/p\u003e","description":"","filename":"figure112.png","url":"https://assets-eu.researchsquare.com/files/rs-3890576/v1/f53919866f4bbab648b1c15b.png"},{"id":50171812,"identity":"efa41b31-1fa3-4045-ab84-cfd1c8514d83","added_by":"auto","created_at":"2024-01-25 15:48:58","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":196745,"visible":true,"origin":"","legend":"\u003cp\u003eThe restricted cubic spline for the association of number of parity and sarcopenia risk.\u003c/p\u003e","description":"","filename":"figure29.png","url":"https://assets-eu.researchsquare.com/files/rs-3890576/v1/a464ee064987329af5e00027.png"},{"id":50171811,"identity":"65ef52d0-5013-4adb-b95b-b386d99e9668","added_by":"auto","created_at":"2024-01-25 15:48:58","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":365992,"visible":true,"origin":"","legend":"\u003cp\u003ePropensity Score Matching.\u003c/p\u003e","description":"","filename":"figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3890576/v1/4b085e25276f51461ad67077.jpg"},{"id":50171813,"identity":"6f455b3e-a487-4be1-bf1f-200d4f2ccadf","added_by":"auto","created_at":"2024-01-25 15:48:58","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":426634,"visible":true,"origin":"","legend":"\u003cp\u003eRestricted cubic spline association between number of parity and sarcopenia risk after PSM correction.\u003c/p\u003e","description":"","filename":"figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-3890576/v1/5cac7f4910c2a52128fe61fd.png"},{"id":50530724,"identity":"407b7023-2bb0-4525-b186-a3db155a566e","added_by":"auto","created_at":"2024-02-02 02:24:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":886167,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3890576/v1/ecd2925f-e270-40f0-b103-2fa76b9bb4d8.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association between sarcopenia and parity in American women in the National Health and Nutrition Examination Surveys (NHANES) 2011 to 2018","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eSarcopenia is defined as the decline skeletal muscle mass that can lead decrease of physical abilities and functional even serious dependency and disability [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Although sarcopenia is thought to be closely age-related, the onset of sarcopenia is occurring at a younger age with changes in modern lifestyles [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. The incidence of sarcopenia varies from 10\u0026ndash;25% in various countries, but in some regions, the incidence can be as high as 50% in the elderly aged 80\u0026ndash;89 years [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Currently, about 50\u0026nbsp;million people worldwide suffer from sarcopenia, and it is speculated that this number will reach 500\u0026nbsp;million by 2050 [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. In addition, the prevalence of sarcopenia was different between male and female in different regions. A study from England showed that women have a higher risk of sarcopenia than men due to a range of sex-specific risk factors, but in China, South Korea and the United States, the results are reversed [\u003cspan additionalcitationids=\"CR8 CR9 CR10\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSarcopenia is thought to be inherent to the process of body senescence, but it can be facilitated by multiple factors such as inflammation, inactivity, malnutrition, chronic illness and gut microbial dysbiosis [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. It is worth noting that there is a bidirectional relationship between bone and muscle during human aging, and the decrease in muscle number will accelerate the loss of bone trabeculae, while the decrease in bone mass will also lead to the atrophy of muscle shape and decline in function [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Systemic hormonal endocrine networks play an important role in regulating bone and muscle growth and metabolism [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Pregnancy is a significant event in women that causes various physiological changes. A research based on a US population reported an increased risk of osteoporotic fracture of the lumbar spine in postmenopausal women with high parity [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Furthermore, an animal-based study has validated that the decline in trabecular bone mineral density becomes irreversible as litter size increases [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. A growing body of evidence has verified the importance of sex hormones in muscle homeostasis [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Hormone deficiency can affect musculoskeletal quality in elderly women, which has become one of the risk factors for sarcopenia [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAt present, the effects of parity on osteoporosis have been confirmed, but the specific association between parity and the risk for sarcopenia still needs to be elucidated in large samples from different regions and ethnic groups. Dual energy X-ray absorptiometry (DXA) is a relatively ideal measurement method for the diagnosis of sarcopenia. Therefore, this study analyzed public data from the NHANES database on DXA and number of pregnancies to determine the association between muscle mass and parity in the US population.\u003c/p\u003e"},{"header":"2. Participants and method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study participants\u003c/h2\u003e \u003cp\u003eOur study population survey data were obtained from the US National Health and Nutrition Examination Surveys (NHANES) (2011\u0026ndash;2018). The primary variables were examination data of appendicular skeletal muscle mass (ASM) measured by dual-energy X-ray absorptiometry (DXA) and pregnancy history questionnaire. We first extracted data from 31956 participants, and then we excluded partial participants according to the purpose of the study: (1) all male participants (n\u0026thinsp;=\u0026thinsp;19308). (2) all nulliparous participants (n\u0026thinsp;=\u0026thinsp;11489). (3) participants with incomplete appendicular skeletal muscle mass (ASM) information (n\u0026thinsp;=\u0026thinsp;4432). (4) Participants with extremum of parity (n\u0026thinsp;=\u0026thinsp;3). (5) Participants with incomplete data on covariates such as body mass index (BMI), education, poverty-income ratio (PIR), drug use, smoking, and waist circumference were finally excluded (n\u0026thinsp;=\u0026thinsp;394). Finally, 3530 female participants were included this study. (Fig.\u0026nbsp;1)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Measurement and evaluation primary variables\u003c/h2\u003e \u003cp\u003e \u003cb\u003eFigure\u0026nbsp;1\u003c/b\u003e Flow chart of research sample selection\u003c/p\u003e \u003cp\u003eDual-energy x-ray absorptiometry (DXA) is the most widely accepted method of measuring body composition due in part to its speed, ease of use, and low radiation exposure [\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The following people were excluded from the DXA examination who were pregnant, participant self-reported history of radiographic contrast material (barium) use in past 7 days and weight more than 450 pounds or height more than 6 feet 5 inches. According to the National Institutes of Health diagnostic criteria, the sarcopenia was defined as ASM/BMI (\u0026lt;\u0026thinsp;0.789 for men and \u0026lt;\u0026thinsp;0.512 for women). ASM was the sum of the skeletal lean mass of both arms and legs, and BMI was weight (kg) divided by the square of height (m) [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. The number of pregnancies was assessed primarily based on data recorded by the questionnaire. In this study, Only the number of previous pregnancies was counted and not included currently pregnant who not participated in the DXA examination.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Other variables\u003c/h2\u003e \u003cp\u003eBased on the main variables of ASM, BMI and number of pregnancies, we found that the age range of participants included in the final data was 20\u0026ndash;60 years old. To further analyze the risk of sarcopenia in different age groups, we divided them into two groups: 20\u0026ndash;40 years old and 41\u0026ndash;60 years old. Other covariates were selected according to previous literature [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Race (Non-Hispanic white, Non-Hispanic black, Mexican American and other), education level (less than high school, high school or equivalent, college or above and other), marital status (married, unmarried), PIR(low income\u0026thinsp;\u0026lt;\u0026thinsp;1.3, middle income 1.3\u0026ndash;3.5, and high income\u0026thinsp;\u0026ge;\u0026thinsp;3.5 ), vigorous recreational activities (yes, no), moderate recreational activities (yes, no), drug use(yes, no), smoking(current, former, never), number of pregnancies(1\u0026ndash;2,3\u0026ndash;4,\u0026ge;5), hypertension (yes and no/unknown), diabetes (yes and no/unknown), body mass index (BMI) (\u0026lt;\u0026thinsp;25.0 kg/m\u003csup\u003e2\u003c/sup\u003e, and \u0026ge;\u0026thinsp;50.0 kg/m\u003csup\u003e2\u003c/sup\u003e ).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Statistical analysis\u003c/h2\u003e \u003cp\u003eIn our study, the distribution of continuous variables was described by mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation, and the distribution of classified variables was described by proportion. Chi-square analysis was used to evaluate the clinical characteristics of all participants. Sensitivity analysis excluded participant data with extremely high values (\u0026lt;\u0026thinsp;1%). Age was divided into two groups to study the characteristics of muscle atrophy in different age groups. Dose-response analysis using restricted quadratic spline models was used to evaluate the association between parity and muscle atrophy in the study sample. PSM was used to balance confounding variables between the muscle atrophy group and non-muscle atrophy group. For categorical variables, P-values were analyzed using chi-square tests and presented as percentages. For continuous variables, t-tests with slope were used in the generalized linear model and presented using interquartile range (IQR). In our study, all data were analyzed using R (v.4.2.2) software and SPSS (v.24.0).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003eBased on requirement of inclusion and exclusion criteria, we collected data from 3530 eligible participants in the 2011\u0026ndash;2018 NHANES (Fig.\u0026nbsp;1). As the Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, Demographic characteristics of female participants are shown by the presence or absence of sarcopenia. Statistical results show that 330 participants ill with sarcopenia and 3200 participants without sarcopenia. Chi-square test showed marked disparity among multiple variables which are age (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), race (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), education (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), marital status (p\u0026thinsp;\u0026lt;\u0026thinsp;0.012), PIR (p\u0026thinsp;\u0026lt;\u0026thinsp;0.002), marijuana or hashish use (p\u0026thinsp;\u0026lt;\u0026thinsp;0.012), vigorous and moderate recreational activities (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), parity (p\u0026thinsp;\u0026lt;\u0026thinsp;0.016), BMXWAIST (p\u0026thinsp;\u0026lt;\u0026thinsp;0.012), BMI (p\u0026thinsp;\u0026lt;\u0026thinsp;0.012), ASM/BMI (p\u0026thinsp;\u0026lt;\u0026thinsp;0.012). We found that participants with sarcopenia were more concentrated among those aged 41 to 60 years (70%), Mexican American (37.9%), and more than high school (44.5%), married (59.7%), less moderate recreational activity (87.6%), less vigorous recreational activity (64.8%), low income (42.7%). It is strange that marijuana or hashish was significantly associated with sarcopenia, but cocaine/heroin/meth- amphetamine was not.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDemographic characteristics of female NHANES population \u003csup\u003ea\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSarcopenia\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNonsarcopenia\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eP value\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo. (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNo. (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo. (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal patients\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3530\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e330 (9.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3200 (90.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u0026ndash;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1534 (43.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e99 (30.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1436 (44.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e41\u0026ndash;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1995 (56.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e231 (70.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1764 (55.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic white\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1266 (35.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e93 (28.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1173 (36.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic black\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e808 (22.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23 (7.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e785 (24.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMexican American\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e547 (15.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e125 (37.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e422 (13.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther race\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e909 (25.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e89 (27.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e820 (25.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLess than high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e604 (17.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e97 (29.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e507 (15.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e743 (21.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e86 (26.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e657 (20.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMore than high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2183 (61.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e147 (44.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2036 (63.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1875 (53.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e197 (59.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1678 (52.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnmarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1655 (46.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e133 (40.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1522 (47.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePIR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1240 (35.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e141 (42.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1099 (34.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.3\u0026ndash;3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1255 (35.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e116 (35.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1139 (35.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1035 (29.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e73 (22.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e962 (30.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVigorous recreational activities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e810 (22.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e41 (12.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e769 (24.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2720 (77.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e289 (87.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2431 (76.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate recreational activities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1547 (43.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e116 (35.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1431 (44.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1983 (56.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e214 (64.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1769 (55.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEver used marijuana or hashish\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1709 (48.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e119 (36.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1590 (49.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1821 (51.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e211 (63.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1610 (50.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEver used cocaine/heroin/meth- amphetamine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.818\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e507 (14.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e46 (13.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e461 (14.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3023 (85.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e284 (86.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2739 (85.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.108\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e762 (21.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e58 (17.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e704 (22.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFormer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e540 (15.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47 (14.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e493 (15.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2228 (63.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e225 (68.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2003 (62.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of pregnancies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u0026ndash;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1451 (41.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e117 (35.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1334 (41.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u0026ndash;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1410 (39.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e133 (40.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1277 (39.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e669 (19.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e80 (24.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e589 (18.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMXWAIST (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e85.0, 108.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e95.4, 116.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e84.0, 106.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24.2, 34.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29.8, 39.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23.8, 33.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eASM/BMI (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.57, 0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.46, 0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.58, 0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003csup\u003ea\u003c/sup\u003eFor categorical variables, P values were analyzed by chi-square tests. For continuous variables, the t-test for slope was used in generalized linear models.\u003c/p\u003e \u003cp\u003ePIR, Ratio of family income to poverty. BMXWAIST, waist circumference (cm). BMI, body mass index. ASM/BMI (IQR),appendicular skeletal muscle mass/ body mass index(interquartile range)\u003c/p\u003e \u003cp\u003eTo further analyze the association between the parity and the incidence of sarcopenia, we also studied the population distribution characteristics according to the number of pregnancies (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). As anticipated, participants with \u0026ge;\u0026thinsp;5 parities among those variables have a higher proportion which aged 41\u0026ndash;60 years, unmarried, PIR\u0026thinsp;\u0026le;\u0026thinsp;1.3, without vigorous and moderate recreational activities, never smoked, ever used marijuana or hashish and never cocaine/heroin/meth-amphetamine. Among different races, participants with \u0026ge;\u0026thinsp;5 parities are concentrated in non-Hispanic white and black, while the number of pregnancies increases with the level of education. Importantly, we found that the prevalence of sarcopenia increased with the parity, suggesting an association between sarcopenia and parity.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of the study population by number of pregnancies.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eNumber of pregnancies\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eP value\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026ndash;2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u0026ndash;4\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;5\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal patients\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1451 (41.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1410 (39.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e669 (19.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge, years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u0026ndash;40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e748 (51.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e557 (39.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e230 (34.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e41\u0026ndash;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e703 (48.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e853 (60.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e439 (65.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic white\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e568 (39.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e505 (35.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e193 (28.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-Hispanic black\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e308 (21.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e307 (21.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e193 (28.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMexican American\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e172 (11.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e245 (17.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e130 (19.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther race\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e403 (27.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e353 (25.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e153 (22.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLess than high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e153 (10.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e276 (19.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e175 (26.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e287 (19.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e310 (22.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e146 (21.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMore than high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1011 (69.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e824 (58.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e348 (52.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e775 (53.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e783 (55.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e317 (47.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnmarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e676 (46.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e627 (44.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e352 (52.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePIR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e391 (26.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e508 (36.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e341 (51.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.3\u0026ndash;3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e517 (35.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e517 (36.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e221 (33.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e543 (37.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e385 (27.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e107 (16.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVigorous recreational activities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e373 (25.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e318 (22.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e119 (17.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1078 (74.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1092 (77.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e550 (82.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate recreational activities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e671 (46.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e634 (45.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e242 (36.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e780 (53.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e776 (55.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e427 (63.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEver used marijuana or hashish\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e724 (49.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e638 (45.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e347 (51.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e727 (50.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e772 (54.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e322 (48.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEver used cocaine/heroin/meth- amphetamine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e176 (12.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e195 (13.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e136 (20.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1275 (87.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1215 (86.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e533 (79.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e265 (18.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e311 (22.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e186 (27.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFormer\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e207 (14.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e234 (16.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e99 (14.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e979 (67.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e865 (61.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e384 (57.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSarcopenia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.016\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e117 (8.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e133 (9.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e80 (12.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1334 (91.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1277 (90.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e589 (88.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMXWAIST (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e83.0, 106.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e85.7, 107.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e88.0, 110.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23.4, 33.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24.3, 34.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25.7, 35.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eASM/BMI (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.58, 0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.56, 0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.56, 0.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003csup\u003ea\u003c/sup\u003eFor categorical variables, P values were analyzed by chi-square tests. For continuous variables, the t-test for slope was used in generalized linear models.\u003c/p\u003e \u003cp\u003ePIR, Ratio of family income to poverty. BMXWAIST, waist circumference (cm). BMI, body mass index. ASM/BMI (IQR), appendicular skeletal muscle mass/ body mass index (interquartile range)\u003c/p\u003e \u003cp\u003eIn order to ascertain the independent effect of the number of pregnancies on the risk of sarcopenia, we conducted subgroup analysis. We found that individuals with \u0026le;\u0026thinsp;2 pregnancies had a lower risk of sarcopenia (OR 0.90; 95% CI 0.76\u0026ndash;1.06), while those with \u0026gt;\u0026thinsp;4 pregnancies had a higher risk of sarcopenia (OR 1.07; 95% CI 0.93\u0026ndash;1.23). Subgroup analysis by age revealed that among individuals aged 20\u0026ndash;40, those with \u0026le;\u0026thinsp;2 pregnancies had a decreased risk of sarcopenia (OR 0.97; 95% CI 0.70\u0026ndash;1.36), while those with \u0026gt;\u0026thinsp;4 pregnancies had an increased risk (OR 1.01; 95% CI 0.79\u0026ndash;1.30). Similar patterns were observed in the 41\u0026ndash;60 age group, where \u0026le;\u0026thinsp;2 pregnancies were associated with a lower risk of sarcopenia, and \u0026gt;\u0026thinsp;4 pregnancies were associated with a higher risk. Furthermore, when analyzing different marital statuses, among married individuals, those with \u0026le;\u0026thinsp;2 pregnancies had a lower risk of sarcopenia (OR 0.90; 95% CI 0.76\u0026ndash;1.06), while those with 4 pregnancies (OR 1.17; 95% CI 0.99\u0026ndash;1.40), 6 pregnancies (OR 1.61; 95% CI 1.19\u0026ndash;2.18), and 8 pregnancies (OR 2.22; 95% CI 1.26\u0026ndash;3.94) had an increased risk. The risk of sarcopenia significantly increased with a higher number of pregnancies. Among unmarried individuals, the number of pregnancies did not appear to have an association with the risk of sarcopenia (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe dose-response curve visually demonstrates that in different age groups, there is a direct positive relationship between the number of pregnancies and the risk or severity of sarcopenia when the number of pregnancies exceeds 4. In other words, as the number of pregnancies increases, the risk or severity of sarcopenia increases linearly, and this correlation is more pronounced in the 41\u0026ndash;60 age group. Additionally, in the married population, there is a positive correlation between the number of pregnancies and sarcopenia, while no clear trend is observed in the unmarried population.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAdjusted odds ratios for associations between the number of pregnancies and the presence of sarcopenia in NHANES 2011\u0026ndash;2018 \u003csup\u003ea\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eSarcopenia\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of pregnancies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.90 (0.76\u0026ndash;1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.07 (0.93\u0026ndash;1.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.18 (0.94\u0026ndash;1.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.30 (0.85-2.00)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e20\u0026ndash;40 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.97 (0.70\u0026ndash;1.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.01 (0.79\u0026ndash;1.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.08 (0.69\u0026ndash;1.68)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.16 (0.49\u0026ndash;2.72)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e41\u0026ndash;60 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.92 (0.79\u0026ndash;1.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.05 (0.85\u0026ndash;1.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.17 (0.84\u0026ndash;1.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.33 (0.82\u0026ndash;2.16)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.86 (0.68\u0026ndash;1.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.17 (0.99\u0026ndash;1.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.61(1.19\u0026ndash;2.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.22 (1.26\u0026ndash;3.94)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnmarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.94 (0.78\u0026ndash;1.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.91 (0.70\u0026ndash;1.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.82 (0.53\u0026ndash;1.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.79 (0.40\u0026ndash;1.54)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003csup\u003ea\u003c/sup\u003eAdjusted covariates: Basic model: race, education levels, PIR, drug use, smoking status; Core model: basic model plus vigorous recreational activities, moderate recreational activities, BMXWAIST; Extended model: BMXBMI, ASM/BMI, Marital status, age. CI: confidence interval. aOR: adjusted odds ratio. T: tertile.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo eliminate or minimize the influence of unrelated confounding factors on the study results, we conducted propensity score matching (PSM) correction. The results showed that the population with \u0026le;\u0026thinsp;2 pregnancies (OR 0.96; 95%CI 0.82\u0026ndash;1.13) was less likely to develop sarcopenia, while the population with \u0026gt;\u0026thinsp;4 pregnancies (OR 1.03; 95%CI 0.81\u0026ndash;1.31) was more likely to develop sarcopenia. Subsequently, we further conducted stratified analysis. In the population aged 20\u0026ndash;40, it was found that those with \u0026le;\u0026thinsp;2 pregnancies were less likely to develop sarcopenia (OR 0.85; 95%CI 0.67\u0026ndash;1.08), while those with \u0026gt;\u0026thinsp;4 pregnancies (OR 1.16; 95%CI 0.83\u0026ndash;1.64) were more likely to develop sarcopenia. Consistent with the above results, in the population aged 41\u0026ndash;60, it was found that those with \u0026le;\u0026thinsp;2 pregnancies were less likely to develop sarcopenia, while those with \u0026gt;\u0026thinsp;4 pregnancies were more likely to develop sarcopenia. Subsequently, we analyzed different marital status groups. In the married population, it was found that those with \u0026le;\u0026thinsp;2 pregnancies (OR 0.86; 95%CI 0.69\u0026ndash;1.09) were less likely to develop sarcopenia. However, as the number of pregnancies increased, the risk of sarcopenia significantly increased, as observed in those with 4 pregnancies (OR 1.11; 95%CI 0.93\u0026ndash;1.32), 6 pregnancies (OR 1.27; 95%CI 0.96\u0026ndash;1.70), and 8 pregnancies (OR 1.45; 95%CI 0.85\u0026ndash;2.47). For the unmarried population, the risk correlation was not significant. (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe dose-response curve after PSM correction still shows a certain risk correlation between the number of pregnancies and sarcopenia. Specifically, when the number of pregnancies exceeds 4, there is a positive relationship between the number of pregnancies and the risk or severity of sarcopenia in different age groups. In the married population, there is a positive correlation between the number of pregnancies and sarcopenia, while the risk correlation is lower in the unmarried population.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAdjusted odds ratios for associations between the sarcopenia and number of pregnancies in NHANES 2011\u0026ndash;2018 \u003csup\u003ea\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c6\" namest=\"c2\"\u003e \u003cp\u003eSarcopenia\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eNumber of pregnancies\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAll\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.96 (0.82\u0026ndash;1.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.03 (0.81\u0026ndash;1.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.07 (0.74\u0026ndash;1.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.10 (0.61\u0026ndash;1.96)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e20\u0026ndash;40 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.85 (0.67\u0026ndash;1.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.16 (0.83\u0026ndash;1.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.36 (0.80\u0026ndash;2.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.45 (0.68\u0026ndash;3.07)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e41\u0026ndash;60 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.91 (0.74\u0026ndash;1.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.06 (0.90\u0026ndash;1.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.16 (0.88\u0026ndash;1.53)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.27 (0.76\u0026ndash;2.14)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.86 (0.69\u0026ndash;1.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.11 (0.93\u0026ndash;1.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.27 (0.96\u0026ndash;1.70)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.45 (0.85\u0026ndash;2.47)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eUnmarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.91 (0.76\u0026ndash;1.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.06 (0.80\u0026ndash;1.40)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.09 (0.70\u0026ndash;1.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.07 (0.53\u0026ndash;2.16)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003csup\u003ea\u003c/sup\u003eAdjusted covariates: Basic model: race, education levels, PIR, drug use, smoking status; Core model: basic model plus vigorous recreational activities, moderate recreational activities, BMXWAIST; Extended model: BMXBMI, ASM/BMI, Marital status, age. CI: confidence interval. aOR: adjusted odds ratio. T: tertile.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eSarcopenia is a chronic progressive and generalized skeletal muscle disorder involving the accelerated loss of muscle mass and function that seriously affects the quality of life of patients [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. In this large retrospective study, we used the NHANES database of Americans subjects to reveal the association between the number of pregnancies and sarcopenia in the US region. In our study, we divided number of pregnancies into three groups and analyzed the characteristics of the study population accordingly. The results showed that the risk of sarcopenia increased with the number of pregnancies, and sarcopenia was positively correlated with age and negatively correlated with activity, meanwhile, sarcopenia incidence rate was higher among married persons.\u003c/p\u003e \u003cp\u003ePregnancy history is an important indicator in women\u0026rsquo;s reproductive activity, which may affect the musculoskeletal system. In previous studies, the prevalence of osteoporosis and low bone mass in women was significantly higher than in men, whether it was in the femoral neck or lumbar spine. Research has found a certain association between parity and bone density, suggesting that multiple pregnancies may decrease women\u0026rsquo;s bone density and increase the risk of osteoporosis [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. However, it is worth noting that several studies have shown a significant negative correlation between the number of pregnancies and spinal bone density, while having no significant impact on femoral neck bone density [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Another study reported that women who have given birth had significantly higher hip joint bone density compared to women who haven\u0026rsquo;t given birth, but no difference was found in femoral neck and lumbar spine [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Furthermore, some research suggests that there is no relationship between parity and bone density, and even high parity may have a protective effect against postmenopausal osteoporosis [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. We speculate that this may be related to differences in race and lifestyle.\u003c/p\u003e \u003cp\u003ePregnancy and childbirth are factors that lead to hormonal changes during the postpartum period and throughout pregnancy. These hormonal changes can affect various systems in the body, including the musculoskeletal system. Additionally, parity can have non-hormonal effects on bone density due to changes in lifestyle and emotional factors such as nutrition, sleep, breastfeeding, and psychological activities [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Therefore, pregnancy and breastfeeding can alter the overall hormonal status and mineral levels in the body, which may have an impact on the musculoskeletal system. Research reports have shown that women aged 65 or older who have given birth to four or more children have a higher risk of disability compared to women with three or fewer children [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Another study in the United States found that postmenopausal women with three or more children had poorer physical functioning than women who had not given birth [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. A study based on the Korean population showed that higher parity is independently associated with an increased risk of metabolic syndrome in postmenopausal women [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. In our study, the fully adjusted subgroup analysis model, stratified by tertiles of parity, revealed that the proportion of individuals with sarcopenia was 8.1% among those with 1\u0026ndash;2 pregnancies, while it increased to 12% among those with \u0026ge;\u0026thinsp;5 pregnancies. Furthermore, we found that the proportion of individuals engaging in daily activities was lower in the high-parity group compared to the low-parity group. These findings strongly indicate a significant increase in the proportion of individuals with sarcopenia as parity increases, suggesting an association between parity and sarcopenia.\u003c/p\u003e \u003cp\u003eTo further elucidate the independent effect of the number of pregnancies on the risk of sarcopenia, we conducted separate analyses on the number of pregnancies and marital status in relation to sarcopenia. The results of the study showed a significant positive correlation between the number of pregnancies and sarcopenia, indicating that as the number of pregnancies increases, the risk of sarcopenia also increases. We further conducted age-stratified analyses and found that in both age groups, the risk of sarcopenia increased with an increase in the number of pregnancies. In terms of marital status, married individuals had a higher risk of sarcopenia compared to unmarried individuals.\u003c/p\u003e \u003cp\u003eDue to the differences in many variables between the sarcopenia and non-sarcopenia groups, in order to eliminate or minimize the influence of unrelated confounding factors on the study results and make the comparison between the treatment and control groups more reliable and accurate, we applied PSM to adjust for all variables. Even after PSM correction, there was still a significant positive correlation between the number of pregnancies and sarcopenia in different subgroups. Worth noting is that, compared to before PSM, the correlation between the number of pregnancies and sarcopenia was even more pronounced in the 21\u0026ndash;40 age group. While the unmarried group showed a certain level of risk correlation compared to before correction, there was no clear positive correlation trend observed.\u003c/p\u003e \u003cp\u003eIn summary, this study establishes an association between the number of pregnancies and sarcopenia in the American population. The risk of muscle wasting is positively correlated with parity in the American population, particularly among married women. However, the specific nature of the relationship between the number of pregnancies and sarcopenia has not been determined. Hormones may play a significant role, and nutritional status may also have an impact, especially in individuals who experience severe pregnancy reactions. Other factors that may influence sarcopenia include postpartum sleep, emotional changes, and alterations in vaginal microbiota. Further research is needed to investigate the specific association and underlying mechanisms between parity and muscle wasting.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn this study indicated that higher parity is associated with an increased risk of muscle atrophy in postmenopausal American women. Regular exercise may be effective in reducing the risk of muscle atrophy.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.869801084990957%\" valign=\"top\"\u003e\n \u003cp\u003eNHANES\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"76.13019891500905%\" valign=\"top\"\u003e\n \u003cp\u003eNational Health and Nutrition Examination Survey\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.869801084990957%\" valign=\"top\"\u003e\n \u003cp\u003ePSM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"76.13019891500905%\" valign=\"top\"\u003e\n \u003cp\u003ePropensity score matching\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.869801084990957%\" valign=\"top\"\u003e\n \u003cp\u003eDXA\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"76.13019891500905%\" valign=\"top\"\u003e\n \u003cp\u003eDual energy X-ray absorptiometry\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.869801084990957%\" valign=\"top\"\u003e\n \u003cp\u003eASM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"76.13019891500905%\" valign=\"top\"\u003e\n \u003cp\u003eAppendicular skeletal muscle mass\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.869801084990957%\" valign=\"top\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"76.13019891500905%\" valign=\"top\"\u003e\n \u003cp\u003eBody mass index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.869801084990957%\" valign=\"top\"\u003e\n \u003cp\u003ePIR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"76.13019891500905%\" valign=\"top\"\u003e\n \u003cp\u003ePoverty-income ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.869801084990957%\" valign=\"top\"\u003e\n \u003cp\u003eIQR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"76.13019891500905%\" valign=\"top\"\u003e\n \u003cp\u003einterquartile range\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.869801084990957%\" valign=\"top\"\u003e\n \u003cp\u003eBMIWAIST\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"76.13019891500905%\" valign=\"top\"\u003e\n \u003cp\u003eWaist circumference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXH and DC wrote the main manuscript text，YS and KX analyzed data JJ and BD prepared fgures and tables. All authors reviewed the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;This study was supported and sponsored by the Jiangsu Commission of Health(Project number: 2022255).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data that support the findings of this study were available from opensource database.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor details\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eBinhai county people\u0026rsquo;s hospital, Yanchen, JiangSu，224500，People\u0026rsquo;s Republic of China\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBeaudart C, Demonceau C, Reginster JY, Locquet M, Cesari M, Cruz Jentoft AJ, Bruy\u0026egrave;re O. 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J Obstet Gynaecol Res. 2018;44(11):2045\u0026ndash;52. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1111/jog.13766\u003c/span\u003e\u003cspan address=\"10.1111/jog.13766\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"sarcopenia, parity, pregnancy, skeletal muscle, muscle mass, BMI","lastPublishedDoi":"10.21203/rs.3.rs-3890576/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3890576/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eMuscle atrophy is a condition characterized by a decrease in muscle mass, and it is more common in women compared to men. Currently, there is limited research on the relationship between parity (number of pregnancies) and muscle atrophy. This study aims to investigate the association between parity and muscle loss in a population of Americans.\u003c/p\u003e\u003ch2\u003eMaterials and Methods\u003c/h2\u003e \u003cp\u003eWe collected clinical data from 3,530 participants in the National Health and Nutrition Examination Survey (NHANES) conducted between 2011 and 2018. Dose-response analyses using restricted quadratic spline models were employed to assess the association between parity and muscle atrophy in the study sample. Propensity Score Matching (PSM) was used to balance confounding variables between the muscle atrophy group and the non-muscle atrophy group.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAmong the 3,530 participants, 330 (9.3%) were diagnosed with muscle atrophy. Our study revealed that factors such as older age, Mexican American, low education level, marital status, poverty, physical inactivity, and higher parity were associated with muscle loss. The dose-response analyses using restricted quadratic spline models showed a positive correlation between parity and muscle atrophy in all patients, with an increased risk of muscle atrophy with higher parity. Additionally, the Propensity Score Matching analysis still demonstrated a positive association between parity and muscle atrophy after adjusting for other confounding variables.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eOur study suggests that higher parity is associated with an increased risk of muscle atrophy in postmenopausal American women. Regular exercise may be effective in reducing the risk of muscle atrophy.\u003c/p\u003e","manuscriptTitle":"Association between sarcopenia and parity in American women in the National Health and Nutrition Examination Surveys (NHANES) 2011 to 2018","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-25 15:48:53","doi":"10.21203/rs.3.rs-3890576/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"54122f6c-a034-49bb-9adc-819c388016d2","owner":[],"postedDate":"January 25th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-02-02T02:16:52+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-25 15:48:53","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3890576","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3890576","identity":"rs-3890576","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}