Dietary Intake,Menopausal Symptoms,and Body Composition Associated with Possible Sarcopenia Among Vietnamese Middle-Aged Women: A Cross-Sectional Study

Abstract

Background:

This study aims to investigate the prevalence of possible sarcopenia and its associated factors among middle-aged Vietnamese women.

Methods:

A cross-sectional study was conducted on 205 women aged 40–55 years who were admitted to the Can Tho Obstetrics and Gynecology Hospital between February and December 2023. The presence of possible sarcopenia was determined according to the AWGS 2019 criteria. Associated factors were dietary intake (total energy, protein, lipid, and carbohydrate intake), the severity of menopausal symptoms by using the Kupperman index, and body composition by using the bioelectrical impedance analysis device, Inbody S10. Logistic regressions were built to analyze the association between possible sarcopenia and its associated factors.

Results:

The prevalence of possible sarcopenia was 29.8%, with a mean age of 47.2. Possible sarcopenia was detected in 23% of the participants based on the criterion of low handgrip strength, whereas 83.6% of the participants when considered low performance in the chair stand test. Adjusted logistic regression analysis showed that living in a rural area (adjusted odds ratio [AOR]: 2.16, 95% confidence interval [95% CI]: 1.22–4.72), energy intake <25 kcal/body weight, (AOR: 1.94, 95% CI: 1.75–5.06), protein intake <0.91 g/body weight (AOR: 2.42, 95% CI: 1.51–5.76), skipping breakfast (AOR: 2.03, 95% CI: 0.91–4.54), mild menopausal symptoms (AOR: 2.68, 95% CI: 1.61–5.36), and obesity (AOR: 1.59, 95% CI: 1.29–3.67) were significantly associated with higher risk of possible sarcopenia. Conversely, higher muscle mass and higher upper limb mass were associated with a decreased risk of possible sarcopenia (total muscle mass AOR: 0.20, 95% CI: 0.07–0.59).

Conclusions:

These findings would provide a basis for enhancing management and prevention strategies to reduce the risk of sarcopenia in Vietnam. In particular, attention to nutrient intake and the management of menopausal symptoms may reduce the risk of sarcopenia.

Introduction

Recently, sarcopenia has emerged as a significant global healthcare concern that induces metabolic and endocrine abnormalities,¹ potentially resulting in long-term consequences such as impaired function, frailty, increased mortality, and higher medical care expenses.² Because of its complications, it is important to prevent sarcopenia to enhance the quality of life of older adults. Evidence indicates that the Asian population is aging at the fastest rate globally and that this population is experiencing the impact of sarcopenia more significantly than other continents.³ In 2019, the AWGS updated the term “possible sarcopenia” as a simple assessment criterion for early identification of sarcopenia in health clinics and communities.⁴ Early detection of sarcopenia allows for appropriate intervention, thereby postponing the onset of sarcopenia.⁵ According to the AWGS 2019 criteria, the assessment of possible sarcopenia is simple, starting with the calf circumference, SARC-F, or SARC-Calf score. An abnormal score in the aforementioned assessment suggests the presence of early indications of sarcopenia, prompting the need for evaluation of muscle strength and physical function. Lower grip strength, prolonged chair stand test, or both can define possible sarcopenia.⁴

Exploration of the potential risk factors for sarcopenia is necessary. It is widely known that sarcopenia is associated with various risk factors, typically physical inactivity,⁶ inadequate calorie or protein intake,⁷ certain medical conditions such as osteoporosis and obesity,⁸ and hormonal imbalance.⁹ Sarcopenia may manifest in the middle-aged population, especially in women undergoing menopausal transition, and involves alterations in body composition and hormone levels.¹⁰ Despite various studies focusing on the prevalence and risk factors of sarcopenia, limited research has been conducted to explore possible sarcopenia. Studies have reported that possible sarcopenia is associated with menopausal symptom severity, body composition, and nutrient intake. Indeed, an unbalanced diet and poor nutrition may result in malnutrition and a decrease in physical activity, leading to a high risk of sarcopenia.¹¹ A study examined that there was a negative association of carbohydrate intake with grip strength, key criteria of possible sarcopenia.¹² A study by Dhara revealed that there was no correlation between fat intake and grip strength.¹³ Among macronutrients, the role of protein in maintaining and improving muscle mass and strength cannot be underestimated. Some researchers determined that a high protein diet and protein supplementation can improve handgrip strength.^14,15 Additionally, during the menopausal transition, there are several body composition modifications, including a reduction in muscle mass, muscle function, and an increase in fat mass.¹⁶ Another study indicated that there was a negative correlation between handgrip strength and the severity of menopausal symptoms, although no similar association was found with muscle mass.⁹

Vietnam officially entered the “population aging” phase in 2017. According to United Nations Population Fund (UNFPA) data, the proportion of elderly people (60 years and older) is estimated to increase from 11.78% in 2019% to 26% in 2049.¹⁷ Therefore, sarcopenia in Vietnam is likely to be of greater concern. A study by Nguyen et al. claimed that over 50% of the elderly in Vietnam have problems with sarcopenia, with 60% of them being women.¹⁸ Despite the high prevalence of sarcopenia among Vietnamese patients, few studies have explored this phenomenon in the context of Vietnam. The early detection of sarcopenia enables early intervention, improves the condition, delays onset, and reduces treatment costs.⁵ Therefore, this study aimed to assess the prevalence of possible sarcopenia in Vietnamese women aged 40–55 years and to examine its associated factors, including dietary intake, menopausal symptoms, and body composition, in order to provide the basis for early prevention and management of sarcopenia.

Methods

Participants

All women admitted to the Gynecology Department at Can Tho Obstetrics and Gynecology Hospital between March and December 2023 were recruited and screened using inclusion and exclusion criteria. The inclusion criteria were (1) women aged 40–55 years and (2) willingness to provide a consent form. The exclusion criteria were (1) severe dementia or delirium, (2) blindness or deafness, (3) physical disabilities such as loss of hand, foot, or limbs, and (4) stage III or IV malignancies with cachexia. The sample size was calculated by using the G*Power 3.1 Program. With a significance level of 0.05 for regression analysis, medium effect size of 0.3, and power of 0.95, it was established that a minimum sample size of 134 participants was necessary. In this study, a total of 216 patients were initially recruited, but nine participants lacked body composition assessments, and two patients who were unable to recall their diets were subsequently excluded. Finally, 205 participants were enrolled in the study.

On the first day of hospital admission, patients were interviewed about their medical history as well as the severity of menopausal symptoms if available. Additionally, the dietary intake on the day before hospital admission was recorded. Demographic information, such as the year of birth, educational level, and area of residence, was extracted from the reviewed medical records. During their hospital stay, patients were instructed to perform handgrip strength and chair stand tests before receiving treatment for their primary condition, such as medication or surgery. Body composition was also monitored before the initiation of treatment. Written informed consent was obtained from all participants prior to the study. This study was approved by the Can Tho University of Medicine and Pharmacy Institutional Review Board (23.032.HV-IRBCTUMP).

Sociodemographic characteristics and anthropometric measurements

Sociodemographic information included age, education, area of residence, career, and history of diseases. Education was categorized as no education, primary school, secondary school, high school, university, or higher. Low education level was defined as having completed primary school or below. The area of residence was categorized as rural or urban. The presence of current diseases, namely hypertension and diabetes, was determined based on the previous diagnosis of the participants’ medical records or diagnosis by clinicians during hospital admission. Hypertension was defined as a blood pressure ≥140/90 mmHg.¹⁹ Diabetes was confirmed when the fasting plasma glucose >7.0 mmol/L or occasional plasma glucose >11.1 mmol/L or HbA1c >6.5%.²⁰

Anthropometric measurements, including height, weight, and calf circumference, were collected. Body height, weight, and calf circumference were recorded to the nearest 0.1 cm or 0.1 kg. Using these measurements, Body mass index (BMI) was calculated as weight in kilograms divided by squared height in meters and categorized according to the Asia-Pacific cut-off points.²¹ The patients were classified into four groups: underweight (<18.5 kg/m²), normal weight (18.5–22.9 kg/m²), overweight (23–24.9 kg/m²), and obese (≥25 kg/m²).

Body composition evaluation

Appendicular skeletal muscle mass, extremities lean mass, fat mass, and visceral fat were assessed by bioelectrical impedance analysis method via Inbody S10 (InBody Co. Ltd., Seoul, Korea). The measurement is conducted in a sitting position and electrodes are attached to subject at 4 points, typically on left and right dorsal foot, on both right and left middle fingers and thumb.

Possible sarcopenia assessment

Possible sarcopenia was determined based on the AWGS 2019 consensus criteria,⁴ using low muscle strength or low physical performance as indicators. Muscle strength was assessed using a handgrip strength Baseline Hydraulic Hand dynamometer (model number: W54652; Manufacturer: Baseline Company, USA). Participants were instructed to hold their shoulders adducted and neutrally rotated in a seated position. The participants flexed their elbows at a 90° angle, firmly grasped the dynamometer at maximum force, and held it for a few seconds. The test was repeated twice for each hand, and the highest value was recorded in kilograms. According to the AWGS 2019, grip strength was defined as low when it was <28 kg in men and <18 kg in women.⁴ Physical performance was assessed using the chair-stand test.²² Initially, the participants were instructed to rise from a seated position without using their arms. After completing the task, they were instructed to perform a series of five consecutive stand-up and sit-down cycles at their highest pace with their arms folded across their chests. The time required to complete the entire set of five stands was recorded. Low physical performance was defined as taking ≥12 sec to complete the test or the subject could not complete the test.²²

Dietary assessment

Dietary assessment was performed using the 24-hr recall method on the first day of hospital admission. Respondents were requested to detail the foods or beverages they had consumed within the previous 24 hr, including the time they ate, portion sizes, how the food was prepared, the source of the food, whether to eat snacks, at what place they ate, brand of food, and nutrition label (if present). The interviewers were dietetic students who had received professional training from hospital dietitians before the study commenced. They were carefully trained to ask and enter data, assess portion sizes, and use food models, pictures, and aids while asking questions.

The nutrient intake of all participants will be evaluated in terms of total energy (kcal/day), carbohydrates (g/day), protein (g/day), and total fat (g/day). To estimate energy and macronutrient intake (protein, fat, and carbohydrates), a data program developed and modified for the Vietnamese population, known as Eiyokun, was utilized.²³ The Excel Eiyokun program was specifically designed to calculate the total energy, macronutrient, and micronutrient energy by inputting the name and weight of the meal ingredient.²³ The Eiyokun program relies on comprehensive data from Vietnamese Food Composition Tables, which provide information on the energy and nutrient values of various food items.²⁴

Kupperman index

The severity of the menopausal symptoms was evaluated using the Kupperman index. It contained the following 11 items: vasomotor symptoms (weighting factor ×4), paresthesia, insomnia, nervousness (weighting factor ×2), melancholia, vertigo, weakness, arthralgia and myalgia, headaches, palpitations, and formication (weighting factor ×1). The scores for each item range from 0 to 3 (0 = no symptoms, 1 = mild, 2 = moderate, and 3 = severe). The total score was calculated by multiplying the level of symptom severity with the corresponding weighting factors. The severity of menopausal symptoms was categorized into 4 groups: 0–14 points signifying no symptoms, 15–20 points indicating mild symptoms, 21–34 points suggesting moderate symptoms, and >34 points indicating severe symptoms.²⁵

Statistical analysis

Data were analyzed using SPSS Statistics version 25.0. The data were presented as mean ± SD or percentages. Continuous variables, including age, BMI, and energy intake, were presented as mean ± SD and compared using an independent t-test. Categorical variables, such as education and area of residence, were expressed as frequencies (percentages) and analyzed using the chi-square test. Unadjusted and adjusted logistics regression analyses were conducted to investigate the association between possible sarcopenia and its related factors. Drawing from the literature review, we examined age group, educational level, area of residence, BMI, total energy intake per body weight, protein intake per body weight, breakfast skipping, severity of menopausal symptoms, and body composition as factors potentially associated with possible sarcopenia. For the adjusted regression model, all factors were adjusted for age, BMI, area of residence, educational level, and energy intake. Statistical significance was set at p < 0.05.

Results

Clinical characteristics of participants

The clinical characteristics of the participants according to their possible sarcopenia status are summarized in Table 1. The prevalence of possible sarcopenia was 29.8% (61/205). Among sociodemographic characteristics, age, BMI, skipping breakfast, and area of residence were significantly different between the two groups (p < 0.05). The prevalence of possible sarcopenia was highest among those aged ≥45 years (81.9%) and lowest among those aged 40–44 years (18.0%). Individuals who skipped breakfast or had a BMI ≥25 kg/m² showed a higher prevalence of possible sarcopenia. Individuals with possible sarcopenia exhibited decreased muscle and limb masses (p < 0.001). In contrast, the percentages of fat mass were higher in this group, although the difference was not statistically significant.

Table 1.

Clinical Characteristics of Subjects with or without Possible Sarcopenia

Variables	Possible sarcopenia n = 61^a	No sarcopenia n = 144	p-value
Percentage	61 (29.8)	144 (70.2)
Age group (years)			0.003
40–44	11 (18.0)^b	46 (31.9)
45–50	26 (42.6)	72 (50.0)
51–55	24 (39.3)	26 (18.1)
Job			0.636
Employee	5 (8.2)	13 (9.0)
Owner	14 (23.0)	46 (31.9)
Farmer	16 (26.2)	46 (31.9)
Housewife	20 (32.8)	37 (25.7)
Others	6 (9.8)	2 (1.4)
Education level			0.678
None	4 (6.6)	4 (2.8)
Primary school	20 (32.8)	41 (28.5)
Secondary school	23 (37.7)	60 (41.7)
High school	9 (14.8)	24 (16.7)
University or more	5 (8.2)	15 (10.4)
Area of residence			0.021
Rural	40 (65.6)	69 (47.9)
Urban	21 (34.4)	75 (52.1)
Current diseases			0.063
None	31 (50.8)	86 (59.7)
Hypertension	15 (24.6)	17 (11.8)
Diabetes	5 (8.2)	5 (3.5)
Hypertension+diabetes	4 (6.6)	10 (6.9)
Others	6 (9.8)	26 (18.1)
BMI group (kg/m²)			0.049
<18,5	1 (1.6)	0 (0.0)
18,5–22,9	23 (37.7)	42 (29.2)
23–24,9	8 (13.1)	41 (28.5)
≥25	29 (47.5)	61 (42.4)
Skipping breakfast			0.002
Yes	23 (37.7)	25 (17.4)
No	38 (62.3)	119 (82.6)
Hand grip strength (kg)			<0.001
≥ 18	47 (77.0)	144 (100)
< 18	14 (23.0)	0 (0)
Chair stand test (s)			<0.001
≥12	10 (16.4)	144 (100)
< 12	51 (83.6)	0 (0)
Total muscle mass (kg)	20.66 ± 2.22 ^c	23.35 ± 2.68	<0.001
Total fat mass (kg)	19.32 ± 4.97	19.21 ± 5.24	0.884
Percentage of fat mass (%)	33.77 ± 6.60	31.54 ± 5.65	0.023
Right hand muscle mass (kg)	1.94 ± 0.21	2.13 ± 0.33	<0.001
Left hand muscle mass (kg)	1.95 ± 0.22	2.11 ± 0.30	<0.001
Right leg muscle mass (kg)	6.45 ± 0.82	7.35 ± 1.05	<0.001
Left leg muscle mass (kg)	6.46 ± 0.75	7.38 ± 0.82	<0.001
Visceral fat (cm²)	68.00 ± 34.23	54.75 ± 32.77	0.01

Total number of people in group.

Number of cases and percentage in group.

Mean ± standard deviation. The p-value was calculated based on χ2 test for categorical variables or t-test for continuous variables; and considered significant at p < 0.05.

Handgrip strength and the five-chair stand test are two criteria for determining possible sarcopenia. The findings indicated that only 23% of the participants had low handgrip strength that was suggestive of possible sarcopenia, whereas 83.6% of the participants were identified when considering low performance in the chair stand test.

Association between possible sarcopenia and dietary intake

The dietary intakes of the participants according to their possible sarcopenia status are shown in Table 2. The nutrient intake of individuals in the possible sarcopenia group was lower than that of the no sarcopenia group for all nutrients except lipids. The mean energy intake in the robust group was 1387.41 kcal, which was significantly higher than that of individuals with possible sarcopenia (1186.20 kcal). The results also showed that patients with possible sarcopenia had more protein and carbohydrate deficiencies than those without sarcopenia (p < 0.001). In contrast, only the lipid intake was higher in the possible sarcopenia groups at 37.94 g comparedwith the no sarcopenia group at 35.95 g; however, the difference between these two groups was not statistically significant.

Table 2.

Dietary Intake of Participants according to Possible Sarcopenia Status

Variables	Possible sarcopenia (n = 61)	No sarcopenia (n = 144)	p-value
Energy (Kcal)	1186.20 ± 244.27	1387.41 ± 289.38	<0.001
Protein (g)	47.63 ± 11.62	58.49 ± 11.33	<0.001
Lipid (g)	37.94 ± 9.77	35.95 ± 11.48	0.236
Carbohydrate (g)	164.49 ± 47.14	298.37 ± 63.02	<0.001

All variables are expressed as mean ± standard deviation. The p-value was calculated based on the t-test and was considered significant at p < 0.05.

Association between possible sarcopenia and menopausal symptoms

Table 3 shows the association between the severity of menopausal symptoms and possible sarcopenia. The average Kupperman index score was 9.38 (data not shown), with a higher score observed in individuals with possible sarcopenia (14.98) compared with those in no sarcopenia group (7.19). Among the 11 symptoms of menopause assessed using the Kupperman index, possible sarcopenia was strongly correlated with vasomotor, vertigo (all p < 0.001), as well as paresthesia, nervousness, palpitations, arthralgia, and myalgia (all p = 0.001). There was a small but significant difference in insomnia and headaches. In addition, melancholia, weakness, and formication did not show any difference between the two groups.

Table 3.

Severity Level of Menopausal Symptoms according to Possible Sarcopenia Status

Variables	Possible sarcopenia (n = 61)	No sarcopenia (n = 144)	p-value
Total score	14.98 ± 8.74	7.19 ± 5.58	<0.001
Symptom 1: Vasomotor			<0.001
	1.25 ± 0.91	0.39 ± 0.59
Symptom 2: Paresthesia			0.001
	0.57 ± 0.69	0.23 ± 0.45
Symptom 3: Nervousness			0.001
	0.62 ± 0.80	0.25 ± 0.54
Symptom 4: Insomnia			0.005
	1.16 ± 1.03	0.75 ± 0.91
Symptom 5: Melancholia			0.385
	0.25 ± 0.57	0.17 ± 0.53
Symptom 6: Vertigo			<0.001
	0.98 ± 0.88	0.41 ± 0.68
Symptom 7: Palpitation			0.001
	0.67 ± 0.85	0.28 ± 0.52
Symptom 8: Weakness			0.136
	0.87 ± 0.72	0.70 ± 0.73
Symptom 9: Headaches			0.004
	0.98 ± 0.67	0.67 ± 0.72
Symptom 10: Arthralgia and myalgia			0.001
	1.18 ± 0.96	0.69 ± 0.82
Symptom 11: Formication			0.400
	0.34 ± 0.79	0.25 ± 0.54

All variables are expressed as mean ± standard deviation. The p-value was calculated based on the t-test and was considered significant at p < 0.05.

Associated factors on univariate and multivariate logistic regression

Unadjusted and adjusted logistic regression analyses of potential factors associated with possible sarcopenia are presented in Table 4. In unadjusted logistics regression, advanced age, living in a rural area, daily protein consumption <0.91 g/kg, skipping breakfast, and mild and moderate menopausal symptoms were associated with a higher risk of sarcopenia, whereas overweight, higher total muscle mass, and higher upper limb muscle mass were protective factors. After adjusting for age, BMI, education level, area of residence, and energy intake, we found that individuals living in rural areas who consumed <25 kcal/kg total energy, <0.91 g/kg protein, had a BMI of ≥25 kg/m² and had mild menopausal symptoms were associated with a higher risk of sarcopenia. In contrast, individuals with higher muscle mass and upper limb muscle mass exhibited a lower risk of sarcopenia.

Table 4.

Factors Associated with Possible Sarcopenia: unadjusted and Adjusted Logistic Regression Analyses

Variables	Univariate odds ratio (95% CI)^a	p ^a	Adjusted model odd ratio (95% CI)^b	p ^b
Age group (years)
40–44	1		1
45–49	1.51 (0.68–3.35)	0.31	1.35 (0.08–1.48)	0.154
50–55	2.86 (1.63–6.13)	0.002	2.21 (0.95–2.70)	0.207
Education level
Low education	1.43 (0.77–2.66)	0.263	1.81 (0.35–2.84)	0.622
Higher education	1		1
Area of residence
Urban	1		1
Rural	2.07 (1.11–3.85)	0.022	2.16 (1.22–4.72)	0.014
BMI group (kg/m²)
≤22,9	1		1
23–24,9	0.34 (0.14–0.85)	0.02	0.39 (0.18–1.11)	0.625
≥25	0.83 (0.43–1.96)	0.59	1.59 (1.29–3.67)	0.001
Energy intake (kcal/kg)
≥25	1		1
<25	1.95 (0.76–4.66)	0.089	1.94 (1.75–5.06)	0.004
Protein intake (g/kg)
<0,91	2.17 (1.70–5.90)	0.001	2.42 (1.51–5.76)	0.003
≥0,91	1		1
Skipping breakfast
Non-skipping	1		1
Skipping	2.88 (1.47–5.65)	0.002	2.03 (0.91–4.54)	0.085
Menopause severity
Minor	1		1
Mild	3.14 (2.28–5.74)	<0.001	2.68 (1.61–5.36)	0.005
Moderate	5.52 (4.08–5.91)	<0.001	5.07 (3.51–8.29)	0.065
Severe	7.68 (0.38–8.43)	0.090	4.92 (0.019–9.62)	0.667
Total muscle mass
	0.62 (0.52–0.73)	<0.001	0.20 (0.07–0.59)	0.004
Right hand muscle mass
	0.09 (0.03–0.32)	<0.001	0.35 (0.01–0.99)	0.010
Left hand muscle mass
	0.11 (0.03–0.39)	0.001	0.57 (0.27–0.93)	0.024

All values are described as odds ratios (95% CI). The p-values were expressed based on binary logistic regression for each model.

Unadjusted model.

Adjusted model for age, BMI, area of residence, educational level, and energy intake, and considered significant at p < 0.05.

Discussion

In this study, we examined the prevalence of possible sarcopenia and its associated factors in middle-aged women. We evaluated both handgrip strength and the five-chair stand test according to the AWGS 2019 criteria. The prevalence of possible sarcopenia was 29.8%. In other Asian countries, the prevalence of possible sarcopenia ranges from 2.9% in Japan to 98.1% in Taiwan, depending on the age of the group^20,21. In particular, the prevalence of possible sarcopenia in China was 46% in the group aged ≥55 years,²⁶ 14% in Singapore,²⁷ and 23.7% in Korea.²⁸ The variance in the rate may be attributed to the differing criteria used to determine possible sarcopenia and the different age groups in each study. In this study, 23% of the patients had a handgrip strength of <18 kg, whereas 83.6% of the patients had a chair stand test time of ≥12 sec. This indicates that it might be more effective to detect possible sarcopenia using the chair stand test rather than handgrip strength. This finding was also shown in the conclusion of another study, which indicated that physical performance (i.e., chair stand test) had a sensitivity of 94% and specificity of 80%, whereas the hand grip strength test had a lower sensitivity and specificity of 67% and 77%, respectively.²⁹ Another study conducted by Wu et al. in 11 rural community daycare stations in Taiwan found a similar finding that 86.8% of possible sarcopenia cases could be identified by low chair stand test performance.³⁰

The present study assessed the association of group age, level of education, area of residence, and skipping breakfast with possible sarcopenia. The older age group showed a higher prevalent risk of sarcopenia, varying from 18% in the 40–44 age group to 39.3% in the group the 50–55 age group, highlighting aging as a significant contributing factor to decreased muscle function.³¹ We found that 65.6% of patients with possible sarcopenia were living in rural areas, which suggested that individuals living in rural areas were likely to have a higher risk of possible sarcopenia (AOR: 2.16, 95% CI: 1.22–4.72). In Taiwan, Wu et al. reported a significant prevalence of possible sarcopenia, affecting 68.7% of adults attending rural community daycare stations.³⁰ One plausible explanation for the observed disparities in possible sarcopenia prevalence between rural and urban areas could be attributed to a combination of factors, including relatively modest healthcare services, poor socioeconomic conditions,³¹ and an insufficiently nutritious diet,³² potentially resulting in poorer health outcomes. Education level were not associated with an increased risk of sarcopenia. Compared with previous reports, researchers investigated higher educational level may promote a healthier lifestyle, including better nutrition and more physical activities throughout life span, which can lead to better muscle function and preserve good health in later life.²⁶

In this study, there was no significant difference between two groups in hypertension, diabetes. A longitudinal cohort study by Luo et al. revealed that possible sarcopenia was linked to an elevated risk of developing type 2 diabetes during a 7-year follow-up,³³ with individuals displaying possible sarcopenia having a 17.3% likelihood of developing type 2 diabetes. Low muscle strength is associated with an increase in metabolic syndrome and type 2 diabetes.³⁴ Surprisingly, the incidence of hypertension was lower in the pre-sarcopenia group than in the robust group.³⁵ The relationship between noncommunicable diseases and possible sarcopenia requires further research.

Regarding BMI, our findings indicated that specific groups exhibited higher possible sarcopenia rates in individuals with BMI <23 kg/m² and in those with obesity, with prevalence rates of 39.3% and 47.5%, respectively. A lower BMI is a risk factor for sarcopenia.³⁶

This finding is consistent with that of Wang et al., indicating that BMI serves as a protective factor against sarcopenia.³⁷ Individuals with a BMI >21 kg/m² exhibited a reduced risk, approximately 0.76 times lower, compared to those with a BMI <21 kg/m².³⁷ Similarly, BMI is U-shaped and associated with all-cause mortality and other adverse outcomes;³⁸ we found that people with a BMI of 23–24.9 have the lowest risk of sarcopenia compared to those with a BMI of 18.5–22.9 kg/m² or those with obese (i.e., BMI ≥25 kg/m²).

BMI is one of the criteria for evaluating nutritional status; however, it does not distinguish between different body components, such as fat and muscle mass. This study evaluated partial muscle mass (e.g., right arm, left arm, right leg, and left leg), total muscle mass, fat mass, and visceral fat. Individuals with possible sarcopenia exhibit reduced muscle mass and a higher percentage of fat mass. This finding aligns with evidence from the Hertfordshire cohort study, indicating that both sarcopenia and possible sarcopenia may present as conditions characterized by low muscle mass³⁹ along with increased fat mass. Evidence suggests that physiological changes in body composition are characterized by increased body weight and visceral fat and decreased lean muscle mass during mid-life.⁴⁰ During the premenopausal period, the visceral fat depot is around 5–8% of the total body fat, whereas during the postmenopausal period, it substantially increases to 15–20%.^36,37 In this study, patients with possible sarcopenia had higher visceral fat depot compared to those without sarcopenia. Other studies reported that patients with visceral fat accumulation have weak handgrip strength⁴¹and low physical performance.⁴²

In this study, one of the potential reasons for the higher prevalence of sarcopenia among women aged 50–55 years could be a decline in nutrient intake. Compared to the group aged 45–50 years, women of advanced age had lower energy and protein intakes (data not shown; see the Supplementary Table S1 ). The protein intake of the participants fell below the recommended daily protein consumption for Vietnamese women in the corresponding age range of 62g/day.⁴³ Food intake is reduced by approximately 25% between the ages of 40 and 70 years of age.¹¹ The findings of this study showed that 37.7% of patients with possible sarcopenia skipped breakfast. Skipping breakfast leads to reduced energy intake. In some regions of Vietnam, people commonly merge breakfast and lunch with late breakfast as their combined midday meal, or skip it due to lack of time, leading to various metabolic syndromes.⁴⁴ Adequate nutrition is essential to preserve the overall health of the musculoskeletal system. Total energy intake of ≥25 kcal/kg and protein intake of ≥0.91 g/kg were recommended to maintain the current muscle mass.^45,46 Unsurprisingly, in our study, the total energy intake and protein consumption were notably lower in patients with possible sarcopenia than in those without. Despite our population being middle-aged, our study supports evidence from several previous studies that lower calorie and protein intake are associated with a higher prevalence of sarcopenia risk.

Another important finding of this study is the positive association between the severity of menopausal symptoms and sarcopenia. The severity of symptoms was diverse among women, and we found that those with possible sarcopenia experienced a significant higher prevalence of vasomotor, paresthesia, nervousness, insomnia, vertigo, palpitations, headaches, and arthralgia/myalgia symptoms, based on the Kupperman index. The total score of menopausal symptom severity indicated that individuals with mild symptoms were approximately 2.68 times more likely to be at risk of sarcopenia in an adjusted model odds ratio. Another study found that sleep disorders, hot flashes, and physical and mental exhaustion are associated with sarcopenia.⁴⁷ A study by Lee and Lee (2013) revealed that handgrip strength decreased gradually according to menopausal symptom severity.⁹ Possible underlying mechanisms explaining the link between menopausal symptom severity and poor muscle strength could involve a reduction in sex hormone levels during the menopausal phase and oxidative stress.⁹ It is essential to assess for possible sarcopenia in middle-aged women and thereby advise for improving muscle mass and strength.

This study had some limitations that should be considered. First, it was a cross-sectional study. Therefore, a causal relationship between variables cannot be conclusively established. Second, dietary intake was estimated with one day 24-hr recall, which may not have reflected the estimates of usual intake. Third, our results were attributed to the relatively small sample size, indicating the need for further research. We suggest that further studies should be conducted to explore more associated factors in other different instructions with a larger sample size. Despite these limitations, this study had several strengths. To the best of our knowledge, this is the pioneering study to examine the novelty of the association between possible sarcopenia and various factors, including dietary intake, severity of menopausal symptoms, and body composition in middle-aged women.

Conclusions

In conclusion, women during the middle ages experienced various changes during menopausal transition. After adjusting for covariates, possible sarcopenia was significantly associated with reduced energy and protein intake, severity of menopausal symptoms, and low muscle mass.

Footnotes

Acknowledgement

The authors would like to give special thanks to Ms. Phan Thi Be Hang and Ms. Vo Gam Sieu for supporting the data collection process.

Authors’ Contributions

A.K.L., T.N.N., and K.Y.K. designed research. T.T.N. conducted research. A.K.L. and J.W.L. analyzed data. A.K.L. and K.Y.K. wrote the paper. A.K.L. had primary responsibility for final content. All authors read and approved the final article.

Author Disclosure Statement

The authors declare no conflict of interest

Funding Information

No funding was received for this article.

Supplementary Material

Supplementary Table S1

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