Association between sarcopenia and parity in American women based on data from the National Health and Nutrition Examination Surveys (NHANES) 2011–2018

doi:10.21203/rs.3.rs-5031624/v1

Background: Muscle atrophy is a condition characterized by a decrease in muscle mass, and it is more prevalent among females than males. Currently, there is limited research on the relationship between parity (number of pregnancies) and muscle atrophy. Therefore, this study aims to investigate the association between parity and muscle loss among Americans.

Materials and Methods: Clinical data from 3,530 participants of the National Health and Nutrition Examination Survey (NHANES) from 2011 to 2018 were analyzed. Restricted quadratic spline models were utilized in dose-response analyses to assess the relationship between parity and muscle atrophy. Propensity score matching (PSM) was used to balance confounding factors 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 descent, low educational level, married status, poverty, physical inactivity, and higher parity were associated with muscle loss. The dose–response analyses showed a positive correlation between increasing parity and muscle atrophy, signifying that a higher number of pregnancies is linked to an increased risk of muscle atrophy. The results of PSM analysis further supported the positive association between parity and muscle atrophy, even after adjusting for other confounding variables.

Conclusion: Expanding on our research, we observed a positive correlation between higher parity and an elevated likelihood of experiencing muscle atrophy in postmenopausal American women. Engaging in regular exercise might reduce this risk.

sarcopenila

parity

pregnancy

skeletal muscle

BMI

Sarcopenia is characterized by a decline in skeletal muscle mass, which can lead to reduced physical abilities, functional impairments, and even severe dependency and disability.[1] Although sarcopenia is thought to be closely related to aging, the onset of sarcopenia at younger ages is becoming more prevalent owing to changes in lifestyles.[2] The incidence of sarcopenia varies from 10–25% across different countries. However, in some regions, the incidence can be as high as 50% among individuals aged 80–89 years.[3, 4] Currently, an estimated 50 million individuals worldwide are afflicted by sarcopenia, and it is anticipated that this figure will surge to 500 million by the year 2050.[5, 6] In addition, the prevalence of sarcopenia differs between genders in different regions. Fory instance, research conducted in England revealed that women face a greater susceptibility to sarcopenia compared to men due to a variety of sex-specific risk factors. However, data from China, South Korea, and the United States indicate the opposite trend.[7–11]

Although sarcopenia is commonly associated with the process of body senescence, it can be exacerbated by multiple factors such as inflammation, inactivity, malnutrition, chronic illness, and gut microbial dysbiosis.[12, 13] It is important to highlight that there exists a mutually influential relationship between bone and muscle as individuals age: a decrease in muscle mass accelerates the loss of bone trabeculae, whereas a decrease in bone mass can also lead to muscle atrophy and functional decline.[14] Systemic hormonal endocrine networks play an important role in regulating bone and muscle growth and metabolism.[15]

Pregnancy is a significant event that causes various physiological changes in women. A study from the US reported an increased risk of osteoporotic fractures of the lumbar spine in postmenopausal women with a higher number of pregnancies.[16] Furthermore, an animal-based study demonstrated that the decline in trabecular bone mineral density becomes irreversible with increasing litter size.[17] A growing body of evidence has verified the importance of sex hormones in muscle homeostasis[18, 19]. Hormonal deficiencies in elderly women can affect musculoskeletal quality, making it a risk factor for sarcopenia.[20]

At present, while the effects of parity on osteoporosis have been confirmed, the specific association between parity and the risk of developing 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 diagnostic tool for sarcopenia. Therefore, this study analyzed public data from the NHANES database, focusing on DXA results and the number of pregnancies, to determine the association between muscle mass and parity in the American population.

2.1 Study participants

Our data were obtained from the US National Health and Nutrition Examination Surveys (NHANES, 2011–2018). The primary variables were examination data for appendicular skeletal muscle mass (ASM) measured by DXA and the pregnancy history questionnaire. Initially, data from 31,956 participants were extracted, and specific participants were excluded according to the exclusion criteria for this study, as follows: (1) all male participants (n = 19308); (2) all nulliparous participants (n = 11489); (3) participants with incomplete ASM information (n = 4432); (4) participants at the extremum of parity (n = 3); and (5) participants with incomplete data on covariates such as body mass index (BMI), education, poverty-income ratio (PIR), drug use, smoking habits, and waist circumference (n = 394). Finally, 3530 female participants were included in this study (Fig. 1).

2.2 Primary variables for measurement and evaluation

Due in part to its fast speed, user-friendly features, and minimal radiation exposure, DXA has become the most widely adopted technique for measuring body composition.[21–23] Individuals who met any of the following criteria were excluded from the DXA examination: pregnancy, recent exposure to radiographic contrast materials such as barium, weight exceeding 450 pounds or height exceeding 6 feet 5 inches. Sarcopenia was defined according to the National Institutes of Health diagnostic criteria, which were based on the ASM/BMI ratio, with a ratio of < 0.789 for men and < 0.512 for women. ASM was calculated as the sum of the skeletal lean mass of the arms and legs, while BMI was calculated by dividing weight (kg) by the square of an individual's height (m) .[24] The number of pregnancies was assessed primarily based on data recorded in the questionnaire. In this study, only the number of previous pregnancies was counted; ongoing pregnancies were excluded, as these individuals did not undergo DXA examinations.

2.3 Other variables

Using the main variables of ASM, BMI, and number of pregnancies, we found that the age range of the participants included in the final dataset was 20–60 years. To further analyze the risk of sarcopenia in different age groups, participants were divided into two groups: those aged 20–40 years old and those aged 41–60 years. Other covariates were selected for analysis based on previous literature.[16] These included race (Non-Hispanic White, Non-Hispanic Black, Mexican-American, and others), education level (less than high school, high school or equivalent, college or above, and other), marital status (married or unmarried), PIR (low income [PIR < 1.3], middle income [PIR 1.3–3.5], and high income [PIR ≥ 3.5], vigorous recreational activities (yes or no), moderate recreational activities (yes or no), drug use (yes or no), smoking status (current, former, or never), number of pregnancies (1–2, 3–4, or ≥ 5), hypertension (yes or no/unknown), diabetes (yes or no/unknown), BMI (< 25.0 kg/m² or ≥ 25.0 kg/m²).

2.4 Statistical analysis

In our study, continuous variables were described using mean ± standard deviation, whereas categorical variables were expressed as proportions. Chi-square analysis was used to evaluate the clinical characteristics of all participants. Sensitivity analysis excluded the data of participants with extremely high values (those below the first percentile). 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. Propensity score matching (PSM) was used to balance confounding variables between the muscle atrophy group and the non-muscle atrophy group. For categorical variables, P-values were analyzed using chi-square tests and presented as percentages. Continuous variables were analyzed using t-tests with a slope incorporated into the generalized linear model and presented using the interquartile range. All data were analyzed using R software (version 4.2.2) and SPSS version 24.0 (IBM, Armonk, NY, USA).

Adhering to the inclusion and exclusion criteria, we collected data from 3530 eligible participants from the NHANES 2011–2018 (Fig. 1). Table 1 shows the demographic characteristics of the participants, distinguishing those with and without sarcopenia. Out of the total participants, 330 were diagnosed with sarcopenia, whereas 3200 did not have sarcopenia. The chi-square test revealed significant differences among multiple variables, including 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), waist circumference (BMXWAIST, p < 0.012), BMI (p < 0.012), and ASM/BMI ratio (p < 0.012). We found that sarcopenia was more prevalent among participants aged 41–60 years (70%), Mexican-Americans (37.9%), those with an education level surpassing high school (44.5%), those who were married (59.7%), those reporting a lack of or less moderate (87.6%) and vigorous (64.8%) recreational activity, and those who fell in the low-income category (42.7%). Strangely, the consumption of marijuana or hashish was found to be significantly associated with sarcopenia, whereas that of cocaine, heroin, or methamphetamine showed no such correlation.

Table 1. Demographic characteristics of female NHANES population ^a.

Characteristic	Total	Sarcopenia	Nonsarcopenia	P value
Characteristic	No. (%)	No. (%)	No. (%)	P value
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

^aFor 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 parity and the incidence of sarcopenia, we studied population distribution characteristics according to the number of pregnancies (Table 2). As anticipated, participants with five or more parities were those aged 41–60 years, unmarried, those with a PIR ≤1.3, those who did not engage in vigorous or moderate recreational activities, non-smokers, past users of marijuana or hashish, and those who had never consumed cocaine, heroin, or methamphetamine. With regard to ethnicity, participants with five or more parities were predominantly non-Hispanic White and Black. In addition, the number of pregnancies increased with the level of education. Importantly, our findings indicate an increasing prevalence of sarcopenia with an increase in parity, suggesting an association between sarcopenia and parity.

Table 2. Characteristics of the study population by number of pregnancies ^a.

Characteristic	Number of pregnancies			P value
Characteristic	1-2	3-4	≥5	P value
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

^aFor 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 a subgroup analysis. We found that individuals with two or fewer pregnancies had a lower risk of sarcopenia (OR 0.90; 95% CI 0.76–1.06), whereas those with more than four 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 years, those with two or fewer pregnancies had a decreased risk of sarcopenia (OR 0.97; 95% CI 0.70–1.36), whereas those with more than four pregnancies had an increased risk (OR 1.01; 95% CI 0.79–1.30). Similar patterns were observed in the age group of 41–60 years, where two or fewer pregnancies were associated with a lower risk of sarcopenia, and more than more pregnancies were associated with a higher risk. Furthermore, when analyzing the influence of marital status, our results suggested that among married individuals, those with two or fewer pregnancies had a lower risk of sarcopenia (OR 0.90; 95% CI 0.76–1.06), whereas those with four (OR 1.17; 95% CI 0.99–1.40), six (OR 1.61; 95% CI 1.19–2.18), and eight pregnancies (OR 2.22; 95% CI 1.26–3.94) had an increased risk. Conversely, among unmarried women, the number of pregnancies did not appear to have an association with the risk of sarcopenia (Table 3).

Table 3. Adjusted odds ratios for sarcopenia and number of pregnancies (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)

^aAdjusted 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.

The dose–response curve clearly demonstrates a direct positive relationship between the number of pregnancies and the risk or severity of sarcopenia when the number of pregnancies exceeds four (Figure 2). In other words, as the number of pregnancies increased, the risk or severity of sarcopenia also increased, and this correlation was more pronounced among women aged 41–60 years. Additionally, in the married population, a positive correlation was observed between the number of pregnancies and sarcopenia risk. However, this correlation did not exist in the unmarried group.

Table 4. Adjusted ORs for 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)

^aAdjusted 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 PSM correction (Figure 3). The results showed that women with two or fewer pregnancies (OR 0.96; 95%CI 0.82–1.13) were less likely to develop sarcopenia, whereas those with more than four pregnancies (OR 1.03; 95%CI 0.81–1.31) were more likely to develop sarcopenia. On stratifying by age, among women aged 20–40 years, those with two or fewer pregnancies were less likely to develop sarcopenia (OR 0.85; 95%CI 0.67–1.08), whereas those with more than four pregnancies (OR 1.16; 95%CI 0.83–1.64) were more likely to develop sarcopenia. Consistent with the above results, in women aged 41–60 years, it was found that those with two or fewer pregnancies were less likely to develop sarcopenia, and those with more than four pregnancies were more likely to develop sarcopenia. After analyzing the participants based on their marital status, we found that married women with two or fewer 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 four (OR 1.11; 95%CI 0.93–1.32), six (OR 1.27; 95%CI 0.96–1.70), and eight (OR 1.45; 95%CI 0.85–2.47) pregnancies. However, among unmarried women, no significant correlation was observed (Table 4).

After applying PSM correction, the dose–response curve continued to show a certain risk association between the number of pregnancies and sarcopenia. Specifically, when the number of pregnancies exceeded four, a positive relationship was observed between the number of pregnancies and the risk or severity of sarcopenia in different age groups. Among married women, a positive correlation was observed between the number of pregnancies and sarcopenia, whereas this association was less pronounced among unmarried women (Figure 4).

Sarcopenia is a chronic progressive and generalized skeletal muscle disorder involving the accelerated loss of muscle mass and function, which seriously affects the quality of life of patients.[5] In this large retrospective study using the NHANES database, we evaluated the association between the number of pregnancies and the prevalence of sarcopenia in the American population. We divided the number of pregnancies into three groups, subsequently analyzing 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 levels. Notably, the incidence rate of sarcopenia was higher among married women.

The history of pregnancy serves as an important marker in understanding a woman’s reproductive journey and its potential impact on the musculoskeletal system. Numerous studies have established that the prevalence of osteoporosis and low bone mass in women is significantly higher than that in men, especially in the femoral neck and lumbar spine regions. Research has found a certain association between parity and bone density, suggesting that multiple pregnancies may reduce bone density and increase the risk of osteoporosis among women.[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 reporting no significant impact on femoral neck bone density.[26, 27] Another study reported that women who have given birth had a significantly higher hip joint bone density compared to those who had not given birth, although disparities in femoral neck and lumbar spine densities remained elusive.[28] Furthermore, some research suggests that there is no relationship between parity and bone density. Some studies even suggest that high parity may have a protective effect against postmenopausal osteoporosis.[29, 30] We speculate that this discrepancy in findings may be related to the differences in race and lifestyle habits among the different participants.

Pregnancy and childbirth are profound physiological events that lead to hormonal changes throughout pregnancy and during the postpartum period. These hormonal changes can affect various systems in the body, including the musculoskeletal system. Additionally, parity can exert non-hormonal effects on bone density, attributable 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 from the US found that postmenopausal women with three or more children had poorer physical functionality compared to women who had never 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 prevalence of sarcopenia was 8.1% among women with one or two pregnancies, whereas it increased to 12% among those with five or more pregnancies. Furthermore, we found that the proportion of women 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 with increasing parity, suggesting an association between parity and sarcopenia.

To further elucidate the independent effect of the number of pregnancies on the risk of sarcopenia, we separately analyzed the relationship between the number of pregnancies, marital status, and sarcopenia. The results revealed a significant positive correlation between the number of pregnancies and the risk of sarcopenia, indicating that an increase in the number of pregnancies is associated with an increased risk of sarcopenia. We further conducted age-stratified analyses and found that the risk of sarcopenia increased with an increase in the number of pregnancies across both age groups. In terms of marital status, married women had a higher risk of sarcopenia compared to their unmarried counterparts.

Owing to the substantial differences in many variables between the sarcopenia and non-sarcopenia groups, we used PSM 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. Even after PSM correction, a significant positive correlation persisted between the number of pregnancies and sarcopenia in different subgroups. Notably, after PSM adjustment, the correlation between the number of pregnancies and sarcopenia was even more pronounced in the age group of 21–40 years. While the unmarried group showed a certain level of risk correlation before the adjustment, no clear positive correlation trend was observed after correction.

In summary, this study establishes an association between the number of pregnancies and sarcopenia in the American population. A positive correlation was observed between muscle wasting and parity in the American population, particularly among married women. However, the specific nature of this relationship remains unclear. Hormonal fluctuations and nutritional status may also have an impact, especially in those with severe pregnancy symptoms. Other factors that may influence sarcopenia include postpartum sleep patterns, emotional changes, and alterations in vaginal microbiota. Further research is warranted to investigate the mechanisms underlying the association between parity and muscle wasting.

Ethics approval and consent to participate

All components of this research involving human subjects, materials, or data were conducted in accordance with the guidelines outlined in the Declaration of Helsinki and received approval from the NCHS Ethics Review Board. Written informed consent was obtained from all participants prior to their involvement in this study.

Consent for publish

Not applicable.

Author contributions

Writing manuscript: Xuefeng Hou and Bin Dai; data extraction and statistical analysis: Kangjie Xu and Jiang jian; conceptualization, project administration, and supervision: Dong Chen and Yucheng Shen. All authors have read and agreed to the published version of the manuscript.

Funding

This research was supported by the Jiangsu Provincial Health Commission (Grant No. 2022255) and the Yangzhou University Open Project (Grant No. 202103).

Data availability

The data utilized in this study can be accessed on the NHANES website: https://wwwn.cdc.gov/nchs/nhanes/.

Competing interests

The authors declared that there is no conflict of interest.

Acknowledgments

The authors gratefully acknowledge the NCHS for their efforts in collecting the data for the NHANES. The authors also acknowledge the Bullet Edits Limited for the linguistic editing and proofreading of the manuscript.

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No competing interests reported.

Association between sarcopenia and parity in American women based on data from the National Health and Nutrition Examination Surveys (NHANES) 2011–2018

Status:

Version 1

Abstract

Figures

1. Introduction

2. Participants and methods

2.1 Study participants

2.2 Primary variables for measurement and evaluation

2.3 Other variables

2.4 Statistical analysis

3. Results

4. Discussion

Declarations

References

Additional Declarations

Status:

Version 1