Bioelectrical impedance analysis-derived phase angle (PhA) in adults with normal weight obesity and correlation of PhA with body composition

doi:10.21203/rs.3.rs-4360279/v1

Background: Normal weight obesity (NWO) is defined as having a normal body mass index (BMI) accompany with a high percent body fat (PBF). Individuals with NWO have a higher risk for type 2 diabetes, cardiometabolic diseases and adverse health issues. Asian populations tend to have normal BMI with high PBF, which is concerning. The excessive adipose accumulation results in a significant reduction of health-related physical fitness and can affect the phase angle (PhA). Therefore, this study aimed to assess the body composition among Chinese adults with normal BMI, and to establish PhA reference data for NWO population.

Methods: A total of 4839 adult participants were enrolled for analysis. A four-group comparison of the PhA for participants categorized with NWO, NWO with low muscle mass, low muscle mass, and others were conducted. Pearson’s correlation coefficients were used to evaluate the correlation between PhA and age, PBF, waist circumference (WC), visceral fat area (VFA), appendicular skeletal muscle mass index (ASMI) and BMI. Multivariate regression analysis was applied to determine the relationship between PhA and NWO, NWO with low muscle mass, low muscle mass, and others. Receiver Operating Characteristic (ROC) curves were used to identify the optimal PhA cut-off values for detecting NWO with low muscle mass in males and females.

Results: A significantly difference in PhA among participants with four groups (P <0.001). Pearson correlation analysis showed a positive correlation between PhA and WC, ASMI, BMI; but a negative correlation between PhA and age, PBF, VFA (P <0.001). The results of multivariate linear regression analysis showed that age, gender, PBF, and ASMI were independent and significant determinants of PhA in NWO participants (P <0.001). NWO, NWO with low muscle mass, low muscle mass were significantly and negatively associated with the PhA in males and females (P <0.001). These results indicated that NWO with low muscle mass was more strongly associated with the PhA than NWO and low muscle mass. The area under the ROC curve (AUC) of PhA for NWO with low muscle mass was 0.786 (95% CI, 0.755-0.818, P<0.001) for males, and 0.746 (95% CI, 0.705-0.786, P <0.001) for females. The optimal cut-off value was ≤5.48° (specificity 0.660, sensitivity 0.782) for males, and ≤4.60° (specificity 0.788, sensitivity 0.565) for females.

Conclusion: A negative correlation was observed between PhA and PBF. PhA can be used as a predictor of NWO with low muscle mass, and the cut-off values were ≤5.55° for males and ≤4.79° for females. Simple and practical PhA-based prediction of pre-sarcopenic obesity in normal weight population would be useful in clinical practice.

Chinese adult

normal weight obesity

phase angle

percent body fat

low muscle mass

Obesity is a chronic disease with excessive or abnormal adipose accumulation, and it is an independent risk factor for cardiovascular diseases, metabolic syndrome, stroke, cancer, and mortality [1-4]. Although body mass index (BMI) is commonly and widely used for diagnosing obesity, it is not a perfect tool to separate fat mass from muscle mass [5]. It may even classify an individual with a muscular build as obese or identify an individual with excessive body fat as normal BMI [6-8].

Normal weight obesity (NWO) is defined as having a normal BMI accompany with a high percent body fat (PBF) [9]. Individuals with NWO have a higher risk for type 2 diabetes, cardiometabolic diseases and adverse health issues [10-13]. Asian populations tend to have normal BMI with high PBF, which is concerning.

Bioelectrical impedance analysis (BIA) is a rapid, noninvasive, and reproducible method often applied in daily clinical practice to assess body composition, particularly muscle mass and hydration [14]. The BIA-derived phase angle (PhA), a raw bioelectrical parameter, has been applied extensively recently. A higher PhA value reflects greater cellularity, membrane integrity, and cellular function [14,15]. As it is known, the association between PhA with muscle mass parameters and sarcopenia have been confirmed [16-18]. Besides, some studies have confirmed that PhA is lower in severe or morbidly obese patients [19,20]. In particular, a decline began with a BMI value of 35 kg/m², then decreased sharply for a BMI of 40 kg/m²[18]. In addition, some studies showed that the excessive adipose accumulation results in a significant reduction of health-related physical fitness and can affect the PhA value [13]. However, there is limited evidence on the association between PhA and NWO among normal BMI individuals, which warrants further study.

In this study, a cross-sectional survey with a large sample size was conducted with Chinese adults aged 40 to 89 years, and it aimed to assess the associations between PhA and age gender, PBF, NWO, and NWO with low muscle mass among Chinese population with normal BMI.

Participants

This retrospective cross-sectional study was conducted in the Physical Examination Center of Tongde Hospital of Zhejiang Province, and the participants in this study were limited to healthy people who visited for physical examination from August 1, 2017, to October 31, 2021. All participants enrolled in this study for analysis met the inclusion and exclusion criteria. The inclusion criteria were as follows: participants (1) aged 40–89 years, (2) BMI ≥18.5 kg/m²and <24.0 kg/m², (3) able to walk independently (including with a cane), and (4) had no cognitive disorder. The exclusion criteria were as follows: (1) musculoskeletal disorders; (2) comorbidities that could contribute to a higher risk of falls (e.g., frail elderly, parkinsonism, stroke, etc.); (3) long-term care in nursing homes or hospitals; (3) use of drugs that influence muscle function and body composition (e.g., glucocorticoids, diuretics, etc.); and (4) BIA device manufacturer-specified contraindications, for instance, the presence of an electronic implant (e.g., a pacemaker) and limb amputation.

The study was conducted in accordance with the guidelines of the Declaration of Helsinki, and the study protocol was approved by the Scientific Research Ethics Committee of Tongde Hospital of Zhejiang Providence (Approval NO.2022-11-JY).

Anthropometric measurements and body composition test

The height and weight of all participants were measured using an ultrasonic height-measuring machine (HCM-800, Sheng Yuan, China). The waist circumference (WC) was measured using a non-elastic measuring tape. All anthropometrics with the measurement value accurate to 0.1 cm and 0.1 kg.

Body composition was measured using a bioelectrical impedance data acquisition system (InBody 720, BIOSPACE, Seoul, Korea). This BIA device system uses electrical current at different frequencies (5, 50, 250,500, and 1000 kHz) to measure the fat-free mass and total water content (including intracellular water and extracellular water) in the body. Before the test, the participants were asked not to eat and drink, to avoid strenuous physical activity at least 3 h before the test, and to empty the bladder before the measurement. During the test, the subjects were required to wear light clothing, stand barefoot with their feet on the foot electrodes, hold the electrodes with both hands, and relax their bodies with their upper limbs hanging down naturally. The data results were automatically stored in the computer through body composition software as soon as the operator entered the subject’s height, age, and gender.

According to segmental body composition data, appendicular skeletal muscle mass (ASMM). The appendicular skeletal muscle mass index (ASMI) was calculated as the ASMM (kg) divided by the square of the height (m²) [21]. The BIA-derived PhA was calculated as the arctangent (Xc/R) multiplied by (180/π), where R is the resistance of the right half of the body and Xc is the reactance measured at 50 kHz [22].

Diagnostic criteria

First, NWO with low muscle mass was assessed. Second NWO was assessed in participants who were not diagnosed with NWO with low muscle mass. Finally, low muscle mass was assessed in the participants who were not diagnosed with NWO. Participants who did not meet the criteria for NWO with low muscle mass, NWO, or low muscle mass were classified as “others.”

NWO with low muscle mass was defined as the co-existence of NWO and low muscle mass, and diagnostic procedures were performed according to the ESPEN/EASO criteria [23]. First, we assessed NWO. For reference, we screened for normal weight, adopting a BMI ≥18.5 kg/m²and <24.0 kg/m²; then subsequently diagnosed obesity, adopting PBF cutoff values ≥ 25% in males and ≥ 35% in females. The standard diagnostic cutoff values for obesity that is widely used in clinical practice in China [24]. Second, low muscle mass was also assessed. For reference, we screened for low muscle mass, adopting ASMI cutoff values <7.0 kg/m² in males and <5.7 kg/m² in females. The diagnostic cutoff values for low muscle mass based on the 2019 Asian Working Group for Sarcopenia criteria [21].

Statistical analysis

Quantitative variables were expressed as mean ± SD or median (interquartile range). Qualitative variables are presented as numbers (percentages). A comparison of patient backgrounds among four groups: NWO with low muscle mass, NWO, low muscle mass, and others, was conducted using the Kruskal-Wallis test. Further, a four-group comparison (Kruskal-Wallis test) and post-hoc test (Steel-Dwass method) of the PhA for participants categorized with low muscle mass, NWO, NWO with low muscle mass, and others were conducted.

Pearson’s correlation coefficients were used to evaluate the correlation between PhA and age, PBF, WC, visceral fat area (VFA), ASMI and BMI. Univariate and multivariate linear regression analyses were performed to evaluate the association between PhA and age, gender, PBF and ASMI. Multivariate regression analysis was applied to determine the relationship between PhA and NWO with low muscle mass, NWO, low muscle mass, and others.

Receiver Operating Characteristic (ROC) curves were used to identify the optimal PhA cut-off values for detecting NWO with low muscle mass in males and females. Data processing and analysis were performed using R (version 3.6.3) and Zstats v0.90. The threshold of statistical significance for comparing differences was set at P value <0.05.

A total of 4839 individuals were incorporated into statistical analysis, and it classified into four groups: NWO with low muscle mass, NWO, low muscle mass, and others. The rates of NWO and NWO with low muscle mass were 7.79% and 7.03% separately among normal BMI population. The baseline characteristics of the enrolled participants are presented in Table 1. Comparison of participants background between the four groups showed differences in age, gender, PhA, BMI, PBF, high PBF, ASMI, low ASMI, WC, central obesity and VFA (P<0.001). Comparison of PhA among participants with four groups, the results displayed significantly differences (Figure 1). In each age group, the results showed that an age-related decrease in PhA, ASMI, WC, but an increase in PBF, VFA. The prevalence rates of low ASMI, NWO with low muscle mass, and high PBF were increased with aging in both sexes (Table 2).

Pearson correlation analysis showed a positive correlation between PhA and ASMI, BMI, WC; but a negative correlation between PhA and age, PBF, VFA (Figure 2). The results of multivariate linear regression analysis for PhA showed that age, gender, PBF, and ASMI were independent and significant determinants of PhA in NWO participants (Table 3 & 4). In addition, PhA was positively correlated with BMI and ASMI but negatively correlated with age, WC, and PBF. After multivariate regression analysis was applied to examine the association between PhA and NWO with low muscle mass, NWO, low muscle mass by gender (Table 5). The results showed that NWO with low muscle mass (β=-0.307, P<0.001), NWO (β=-0.190, P<0.001), low muscle mass (β=-0.030, P<0.001) were significantly and negatively associated with the PhA in males. Likewise, NWO with low muscle mass (β=-0.259, P<0.001), NWO (β=-0.166, P<0.001), low muscle mass (β=-0.142, P<0.001) were significantly and negatively associated with the PhA in female. These results indicated that NWO with low muscle mass was more strongly associated with the PhA than NWO and low muscle mass.

The ROC curves used to identify the optimal PhA cut-off values for detecting NWO with low muscle mass in males and females are shown in Figure 3. The area under the ROC curve (AUC) of PhA for NWO with low muscle mass was 0.786 (95% CI, 0.755-0.818, P<0.001) for males, and 0.746 (95% CI, 0.705-0.786, P <0.001) for females. The optimal cut-off value was ≤5.48° (specificity 0.660, sensitivity 0.782) for males, and ≤4.60° (specificity 0.788, sensitivity 0.565) for females.

NWO is defined as having a normal BMI accompany with a high PBF [9]. Individuals with NWO have a higher risk for type 2 diabetes, cardiometabolic diseases and adverse health issues [10-13]. As fairly common for Asian populations tend to have normal BMI with high PBF, which is concerning. Thus, it is necessary to compare the body composition in individuals with different body fat and muscle mass in normal weight. The aim of the present study was to examine the associations between PhA and age, gender, PBF, NWO and NWO with low muscle mass among Chinese population with normal BMI, and to establish 50kHz-PhA reference data for NWO population.

BIA often used in daily clinical practice to assess body composition, particularly muscle mass and hydration [14]. A higher PhA value reflects greater cellularity, membrane integrity, and cellular function [14,15]. Besides, some studies showed that the excessive adipose accumulation results in a significant reduction of health-related physical fitness and can affect the PhA value [13]. The classical approach to BIA is based on regression models obtained from studies using human subjects for certain profiles, and the body composition parameters from different BIA apparatuses may have differences. A recent study confirmed that the raw parameters at a frequency of 50 kHz obtained by single and multispectral BIA devices were highly associated and showed an acceptable agreement [25]. Thus, to evaluate the association between PhA and NWO and establish 50kHz-PhA reference data for NWO population, which was feasible and meaningful.

High PBF in an individual with normal BMI, usually even accompany with low muscle mass. The literature reviews illustrated that sarcopenia and obesity are closely associated with chronic inflammation [27,28]. The excessive or abnormal adipose tissue leads to systemic inflammation, which contributes to affecting the skeletal muscles and decreasing oxidative capacity [29]. Muscle mass reduction may occur in obese individuals of any age. Additionally, the growing problems of poor dietary structure and quality, physical inactivity, and chronic diseases have led to a trend of young and middle-aged adults suffering from NWO, NWO with low muscle mass, obesity, or even sarcopenic obesity [8,30]. The present study demonstrated that the rates of NWO and NWO with low muscle mass were 7.79% and 7.03% separately among normal BMI population. It has been suggested that early diagnosis and intervention for NWO are essential.

The results showed that an age-related decrease in PhA, ASMI, WC, but an increase in PBF, VFA. The prevalence rates of low ASMI, NWO with low muscle mass, and high PBF were increased with aging in both sexes. This may be due to sex-specific hormonal changes in the elderly. Aging is an essential risk factor for adipose accumulation and muscle loss. Loss of SMM and muscle function generally occurs with aging and is usually accompanied by a relative or absolute gain in body fat mass [31,32].

The present study verified the negative correlations between PhA and age, PBF, VFA, which is consistent with the results of previous studies [18,19,26]. Other studies have already pointed out that female sex was inversely correlated with PhA [13]. Same finding showed in the present study. Further analysis showed that NWO with low muscle mass, NWO, and low muscle mass were significantly and negatively associated with PhA in both genders. And these results indicated that NWO with low muscle mass was more strongly associated with PhA than NWO. Then, the ROC curves used to identify the optimal PhA cut-off values for detecting NWO with low muscle mass in males and females. The optimal cut-off value was ≤5.48°for males and ≤4.60° for females. This is the first study to examine the diagnostic value of PhA for NWO with low muscle mass individually in males and female among normal BMI Chinese adults aged 40-89 years. Compared with previous studies, the cutoff values were higher in our study [21,33]. This may due to the target groups of the previous studies were patients with chronic diseases, and the muscle function was included in the diagnostic criteria. In addition, nation and race can also influence the PhA.

In conclusion, a more detailed appraisal of BIA-derived PhA in NWO and NWO with low muscle mass population reported in this study. It displayed that PhA was negatively associated with PBF among normal weight population. PhA may serve as a promising biomarker for NWO with low muscle mass, with optimal cut-off values of ≤5.48° for males and ≤4.60° for females. Simple and practical PhA-based prediction of pre-sarcopenic obesity in normal weight population would be useful in clinical practice.

NWO: normal weight obesity; BIA: bioelectrical impedance analysis; PhA: phase angle; BMI: body mass index; PBF: percent body fat; WC: waist circumference; VFA: visceral fat area; ASMI: appendicular skeletal muscle mass index; CI: confidence interval; ROC: receiver operating characteristic; AUC: area under receiver operating characteristic curve

Ethics approval and consent to participate

The study was approved by the Medical Ethics Committee of Tongde Hospital of Zhejiang Province (NO.2022-11-JY). The need for written informed consent was waived by the Institutional Review Board of Tongde Hospital of Zhejiang Province due to retrospective nature of the study. All methods were performed in accordance with the relevant guidelines and regulations.

Consent for publication

Not applicable.

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Competing interests

The authors declare that they have no competing interests.

Funding

This research supported by Chinese Medicine Research Program of Zhejiang Province (Grant No.2024ZL 349) and Research Fund Project of Zhejiang Nutrition Society (Grant No.ZN-YCHP-2023-005).

Authors' contributions

Xi Luo designed the study and wrote the manuscript. Xi Luo and Weiwei Jin collected, analyzed, and interpreted the data. Xi Luo and Weiwei Jin critically reviewed, edited, and approved the manuscript. All authors read and approved the final manuscript.

Acknowledgements

This study was supported by Tongde Hospital of Zhejiang Province. We are grateful for the help of doctors and nurses at the Physical Examination Center of Tongde Hospital, Zhejiang Province, for facilitating recruitment and interviews.

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Table 1. Participant background for each participant with total, NOW with low muscle mass, NWO, low muscle mass and others.

Variables	Total	NWO with low muscle mass	NWO	Low muscle mass	Others	F	P-value
n	4839	340	377	759	3363
Age, years	53.18 ± 10.71	62.88 ± 13.47	54.48 ± 10.72	56.87 ± 11.68	51.21 ± 9.29	180.74	<0.001
Gender, n (%)	Male 1967 (40.65%)	Male 202 (59.41%)	Male 228 (60.48%)	Male 272 (35.84%)	1265 (37.62%)	44.90	<0.001
PhA, °	5.21 ± 0.65	4.77 ± 0.67	5.21 ± 0.64	4.89 ± 0.60	5.32 ± 0.62	158.80	<0.001
BMI, kg/m²	21.92 ± 1.39	22.38 ± 1.06	23.33 ± 0.56	20.47 ± 1.10	22.04 ± 1.27	581.72	<0.001
ASMI, kg/m²	6.59 ± 0.80	6.10 ± 0.68	6.77 ± 0.67	5.89 ± 0.66	6.78 ± 0.75	368.60	<0.001
PBF, %	26.19 ± 5.92	31.91 ± 4.76	30.50 ± 4.86	26.63 ± 5.33	25.03 ± 5.68	250.70	<0.001
WC, cm	79.80 ± 5.31	82.22 ± 4.90	86.04 ± 4.41	76.09 ± 4.23	79.69 ± 4.89	399.80	<0.001
VFA, cm²	61.63 ± 23.39	79.34 ± 30.77	82.48 ± 28.41	56.48 ± 20.07	58.67 ± 20.39	221.71	<0.001
ASMI Class, n (%)						inf.	<0.001
Normal	3740 (77.29%)	0 (0.00%)	377 (100.00%)	0 (0.00%)	3363 (100.00%)
low muscle mass	1099 (22.71%)	340 (100.00%)	0 (0.00%)	759 (100.00%)	0 (0.00%)
PBF Class, n (%)						inf.	<0.001
Normal	4122 (85.18%)	0 (0.00%)	0 (0.00%)	759 (100.00%)	3363 (100.00%)
Obese	717 (14.82%)	340 (100.00%)	377 (100.00%)	0 (0.00%)	0 (0.00%)
WC Class, n (%)						119.69	<0.001
Normal	4481 (92.60%)	305 (89.71%)	266 (70.56%)	757 (99.74%)	3153 (93.76%)
Central obesity	358 (7.40%)	35 (10.29%)	111 (29.44%)	2 (0.26%)	210 (6.24%)

Comparison of variables among participants with NOW and NWO with low ASMI: age, PhA, BMI, ASMI, ASMI (n, %), PBF (n, %), WC, VFA, P<0.001; PBF, P=0.004; gender, P=0.991. Comparison of variables among participants with low ASMI and NWO with low ASMI: age, gender, PhA, BMI, PBF, PBF (n, %), ASMI, ASMI (n, %), WC, WC (n, %), VFA, P<0.001. Comparison of variables among participants with others and NWO with low ASMI: age, gender, BMI, PBF, PBF (n, %), ASMI, ASMI (n, %), WC, VFA, P<0.001; PhA, P=0.019; WC (n, %), P=0.025. Comparison of variables among participants with NWO and low ASMI: age, gender, PhA, BMI, PBF, PBF (n, %), ASMI, ASMI (n, %), WC, WC (n, %), VFA, P<0.001. Comparison of variables among participants with NWO and others: gender, age, BMI, PBF, PBF (n, %), ASMI (n, %), WC, WC (n, %), VFA, P<0.001; age, P=0.001; PhA, P=0.006; ASMI, P=0.998. Comparison of variables among participants with low ASMI and others: PhA, age, BMI, PBF, ASMI, ASMI (n, %), WC, WC (n, %), VFA, P<0.001; PBF (n, %), P=0.001; gender, P=0.798. NWO, normal weight obesity; ASMI, appendicular skeletal muscle mass index; PhA, phase angle; BMI, body mass index; PBF, percent body fat; WC, waist circumference; VFA, visceral fat area. Low muscle mass was categorized as <7.0 kg/m² in males and <5.7 kg/m² in females. PBF based obese was categorized as ≥25% in male and ≥35% in female. Central obesity was categorized as WC ≥90 cm in male and ≥85 cm in female.
Table 2. Age-related changes in anthropometrics and body composition parameters of all participants.

Variables	40-49.9 years	50-59.9 years	60-69.9 years	70-79.9 years	80-89.9 years	P-value
Male
n	820	566	365	130	86
Age, years	44.04 ± 2.89	54.35 ± 2.75	64.12 ± 2.76	73.63 ± 2.88	83.31 ± 2.54	<0.001
PhA, °	5.92 ± 0.50	5.66 ± 0.51	5.38 ± 0.49	4.90 ± 0.48	4.20 ± 0.55	<0.001
BMI, kg/m²	22.32 ± 1.27	22.30 ± 1.27	22.08 ± 1.33	22.11 ± 1.29	21.99 ± 1.37	0.006
ASMI, kg/m²	7.47 ± 0.47	7.38 ± 0.50	7.24 ± 0.47	6.99 ± 0.54	6.85 ± 0.56	<0.001
PBF, %	20.95 ± 4.23	21.31 ± 4.42	21.66 ± 4.20	24.35 ± 4.53	25.02 ± 4.94	<0.001
WC, cm	82.53 ± 5.32	82.18 ± 5.21	80.86 ± 5.61	80.46 ± 5.00	79.71 ± 5.53	<0.001
VFA, cm²	53.83 ± 16.72	55.70 ± 17.87	55.67 ± 18.39	59.62 ± 20.84	65.55 ± 23.74	<0.001
ASMI Class, n (%)						<0.001
Normal	698 (85.12%)	442 (78.09%)	257 (70.41%)	62 (47.69%)	34 (39.53%)
low ASMI	122 (14.88%)	124 (21.91%)	108 (29.59%)	68 (52.31%)	52 (60.47%)
PBF Class, n (%)						<0.001
Normal	688 (83.90%)	460 (81.27%)	281 (76.99%)	69 (53.08%)	39 (45.35%)
Obese	132 (16.10%)	106 (18.73%)	84 (23.01%)	61 (46.92%)	47 (54.65%)
Group class, n (%)						<0.001
NWO with low muscle mass	41 (5.00%)	46 (8.13%)	36 (9.86%)	41 (31.54%)	38 (44.19%)
NWO	91 (11.10%)	60 (10.60%)	48 (13.15%)	20 (15.38%)	9 (10.47%)
low muscle mass	81 (9.88%)	78 (13.78%)	72 (19.73%)	27 (20.77%)	14 (16.28%)
Others	607 (74.02%)	382 (67.49%)	209 (57.26%)	42 (32.31%)	25 (29.07%)
Female						<0.001
n	1358	862	453	153	46
Age, years	43.98 ± 2.91	53.99 ± 2.76	64.01 ± 2.67	73.54 ± 3.05	82.22 ± 2.25	<0.001
PhA, °	5.09 ± 0.43	4.94 ± 0.45	4.73 ± 0.45	4.44 ± 0.45	3.95 ± 0.47	<0.001
BMI, kg/m²	21.61 ± 1.43	21.83 ± 1.35	21.73 ± 1.44	21.76 ± 1.45	21.64 ± 1.50	0.008
ASMI, kg/m²	6.17 ± 0.46	6.09 ± 0.50	5.92 ± 0.50	5.79 ± 0.57	5.43 ± 0.47	<0.001
PBF, %	28.50 ± 4.47	29.74 ± 4.53	30.32 ± 4.54	30.75 ± 4.23	32.20 ± 4.47	<0.001
WC, cm	78.20 ± 4.71	79.11 ± 4.55	78.22 ± 4.79	77.29 ± 5.51	75.16 ± 5.02	<0.001
VFA, cm²	62.38 ± 23.64	68.17 ± 25.68	69.22 ± 27.71	67.95 ± 29.05	79.30 ± 29.96	<0.001
ASMI Class, n (%)						<0.001
Normal	1171 (86.23%)	686 (79.58%)	302 (66.67%)	76 (49.67%)	12 (26.09%)
low ASMI	187 (13.77%)	176 (20.42%)	151 (33.33%)	77 (50.33%)	34 (73.91%)
PBF Class, n (%)						<0.001
Normal	1280 (94.26%)	762 (88.40%)	386 (85.21%)	126 (82.35%)	31 (67.39%)
Obese	78 (5.74%)	100 (11.60%)	67 (14.79%)	27 (17.65%)	15 (32.61%)
Group class, n (%)						<0.001
NWO with low muscle mass	65 (2.98%)	88 (6.16%)	72 (8.80%)	63 (22.26%)	52 (39.39%)
NWO	145 (6.66%)	118 (8.26%)	79 (9.66%)	25 (8.83%)	10 (7.58%)
low muscle mass	244 (11.20%)	212 (14.85%)	187 (22.86%)	82 (28.98%)	34 (25.76%)
Others	1724 (79.16%)	1010 (70.73%)	480 (58.68%)	113 (39.93%)	36 (27.27%)

NWO, normal weight obesity; ASMI, appendicular skeletal muscle mass index; PhA, phase angle; BMI, body mass index; PBF, percent body fat; WC, waist circumference; VFA, visceral fat area. Low muscle mass was categorized as <7.0 kg/m² in males and <5.7 kg/m² in females. PBF based obese was categorized as ≥25% in male and ≥35% in female.

Table 3. Univariate and multivariate linear regression analysis between PhA and age, ASMI, PBF as continues variables.

Variables	Univariate analysis					Multivariate analysis
Variables	β	S.E	b	P	95% CI	β	S.E	t	P	95% CI
Gender (ref: male)
Female	-0.665	0.016	-40.398	<0.001	-0.697 ~ -0.633	-0.246	0.022	-11.130	<0.001	-0.289 ~ -0.203
Age (years)	-0.025	0.001	-31.170	<0.001	-0.026 ~ -0.023	-0.023	0.001	-36.372	<0.001	-0.024 ~ -0.022
PBF (%)	-0.057	0.001	-42.053	<0.001	-0.060 ~ -0.054	-0.010	0.002	-6.515	<0.001	-0.013 ~ -0.007
ASMI (kg/m²)	0.507	0.009	56.016	<0.001	0.490 ~ 0.525	0.305	0.014	21.344	<0.001	0.277 ~ 0.333

PhA, phase angle; PBF, percent body fat; ASMI, appendicular skeletal muscle mass index; CI: Confidence Interval.

Table 4. Univariate and multivariate linear regression analysis between PhA and age, ASMI, PBF as categorical variables.

Variables	Univariate analysis					Multivariate analysis
Variables	β	S.E	b	P	95% CI	β	S.E	t	P	95% CI
Gender (ref: male)
Female	-0.665	0.016	-40.398	<0.001	-0.697 ~ -0.633	-0.744	0.014	-55.090	<0.001	-0.771 ~ -0.718
Age (ref: 40-49.9 years)
50-59.9 years	-0.170	0.020	-8.464	<0.001	-0.210 ~ -0.131	-0.159	0.016	-10.229	<0.001	-0.189 ~ -0.128
60-69.9 years	-0.383	0.024	-15.781	<0.001	-0.430 ~ -0.335	-0.373	0.019	-19.735	<0.001	-0.410 ~ -0.336
70-79.9 years	-0.748	0.037	-20.017	<0.001	-0.821 ~ -0.675	-0.674	0.029	-22.859	<0.001	-0.731 ~ -0.616
80-89.9 years	-1.284	0.053	-24.228	<0.001	-1.388 ~ -1.180	-1.288	0.042	-30.675	<0.001	-1.371 ~ -1.206
PBF (ref: non-obese)
obese	-0.239	0.026	-9.162	<0.001	-0.290 ~ -0.188	-0.174	0.019	-8.940	<0.001	-0.212 ~ -0.136
ASMI (ref: non-low ASMI)
low ASMI	-0.455	0.021	-21.344	<0.001	-0.497 ~ -0.414	-0.266	0.017	-16.053	<0.001	-0.299 ~ -0.234

PhA, phase angle; PBF, percent body fat; ASMI, appendicular skeletal muscle mass index; CI: Confidence Interval.

Table 5. Multivariate regression analysis to examine the association between PhA and NOW, NWO with low muscle mass, low muscle mass by gender.

Exposure	Total	Male	Female
Exposure	β (95% CI), P value	β (95% CI), P value	β (95% CI), P value
Non-adjusted
Others	Reference	Reference	Reference
Low muscle mass	-0.417 (-0.457, -0.378), <0.001	-0.450 (-0.524, -0.376), <0.001	-0.398 (-0.444, -0.352), <0.001
NWO	-0.273 (-0.330, -0.215), <0.001	-0.306 (-0.392, -0.220), <0.001	-0.241 (-0.308, -0.173), <0.001
NWO with low muscle mass	-0.702 (-0.772, -0.632), <0.001	-0.830 (-0.933, -0.728), <0.001	-0.533 (-0.616, -0.451), <0.001
Adjust I
Others	Reference	Reference	Reference
Low muscle mass	-0.282 (-0.317, -0.246), <0.001	-0.284 (-0.348, -0.220), <0.001	-0.294 (-0.336, -0.252), <0.001
NWO	-0.202 (-0.252, -0.153), <0.001	-0.218 (-0.285, -0.152), <0.001	-0.187 (-0.255, -0.119), <0.001
NWO with low muscle mass	-0.431 (-0.490, -0.372), <0.001	-0.471 (-0.556, -0.386), <0.001	-0.339 (-0.411, -0.268), <0.001
Adjust II
Others	Reference	Reference	Reference
Low muscle mass	-0.085 (-0.124, -0.047), <0.001	-0.030 (-0.097, 0.037), <0.001	-0.142 (-0.188, -0.097), <0.001
NWO	-0.193 (-0.242, -0.143), <0.001	-0.190 (-0.258, -0.121), <0.001	-0.166 (-0.238, -0.094), <0.001
NWO with low muscle mass	-0.323 (-0.375, -0.271), <0.001	-0.307 (-0.380, -0.235), <0.001	-0.259 (-0.333, -0.185), <0.001

Adjust I adjust for: Age. Adjust II adjust for: Age, BMI, WC, and VFA. PhA, phase angle; NWO, normal weight obesity; BMI, body mass index; WC, waist circumference; VFA, visceral fat area.

No competing interests reported.

Bioelectrical impedance analysis-derived phase angle (PhA) in adults with normal weight obesity and correlation of PhA with body composition

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