Phase angle and body composition in patients with overweight and obesity

doi:10.21203/rs.3.rs-1431094/v1

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Phase angle and body composition in patients with overweight and obesity

https://doi.org/10.21203/rs.3.rs-1431094/v1

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Background Phase angle(PHA) unveils a new insight to evaluate nutrition change in patients , but no researches verify the relationship between PHA and body composition in patients with overweight and obesity stratified by body mass index(BMI).Our study aimed to investigate the link between PHA and body composition stratified according to BMI, and to predict how PHA variability can be explained by body composition in this population.

Methods This was a cross-sectional study including 257 patients that was conducted in a single hospital. The population was divided into four groups by BMI. Pearson correlation coefficients and Spearman correlation coefficients were used to analyze the relationship between PHA and body composition. Stepwise linear regression analysis was used to determine the influence of body composition on PHA.

Results Total body water(TBW), intracellular water(ICW), ratio of extracellular water and intracellular water(ECW/ICW), fat mass(FM), fat-free mass(FFM), skeletal muscle mass(SMM), body cell volume(BCV) and protein had correlations with PHA in all of the four groups. The volume of minerals was positively correlated with PHA in three obese groups but not the overweight group. Only 42% of PHA variability could be explained by body composition in the overweight group whereas 85.8% in the obese graded I group,82.3% in the obese graded II group, and 87.6% in the obese graded III group.

Conclusions The determinants of phase angle differ between different BMI range. The body composition of patients with obesity is more closely linked to PHA.

bioelectrical impedance analysis

phase angle

body composition

overweight

obesity

Overweight and obese refers to the accumulation of excessive fat related to lifestyle modification(1, 2). Over the past few decades, the prevalence of overweight and obesity has reached epidemic proportions and become a major public health problem worldwide(1, 3). Concurrent with the raised body mass index(BMI) increases the risk of numerous obese-associated chronic diseases including hypertension(4), heart diseases(5), renal diseases(6), gastrointestinal diseases(7), certain types of cancer(8), and diabetes(9). According to the relevant data, every 5-unit increase in BMI above 25kg/m2 is associated with about 30% higher overall mortality, vascular mortality increases by 40%, diabetes-related, renal, and hepatic mortality increases by 60–120%, and neoplastic mortality increases by 10%(10). Besides the raised mortality, patients with obesity have less work attendance, reduced ability to create wealth, and the rising cost of healthcare may impose an economic burden on society(1). With the increasing prevalence of obesity and lifestyle diseases, it is crucial to find an effective way to monitor the nutritional changes associated with growth and disease conditions(11). BMI is one of the most commonly and traditionally used indicators to assess overweight and obesity, however, it cannot distinguish variations in body composition(2, 12).

When the mismatch is taken place between nutrient intake and requirement, changes in body composition will occur(12). The assessment of body composition contributes to the insights into several terms in the human body like nutritional status and body function(13, 14). This helps to describe growth and development in different life stages and learn about health and disease from the perspective of nutrition, setting individualized nutrition goals(12, 14). With the prevalence of obesity and lifestyle-related diseases become higher, more sensitive and accurate measurements of body composition are essential(11).

Bioelectrical impedance analysis(BIA) is widely used in clinical practice and for research purposes to assess body composition(15). Nowadays BIA technologies have been developed to measure multiple parts of the human body with multiple frequencies(16). Recent studies had shown multi-frequency devices might be a superior method to estimate total body composition for fewer errors which were resulted from the redistribution of extracellular water and intracellular water(13). Compared with other measurement methods, BIA is relatively non-invasive, quick, and easy to operate way(17–19). The human body consists of conductive elements such as water, and non-conductive elements such as body fat, in BIA measurements, multiple frequencies device applies alternating electrical current(20–22). Then the human body presents opposition to this current which is defined as bioelectrical impedance, consisting of two elements: the reactance and the resistance(22). The reactance reflects the capacitance of cellular membranes and tissue interfaces while the resistance refers to the resistive behavior to the electric current due to the volume of water in tissues(23, 24). PHA is a BIA-derived electrical parameter that is calculated by arctangent of reactance to resistance ratio then multiple by the ratio of number 180 to π(23, 24).PHA can be used to indicate the function of the cell membrane, and the distribution of total body water(25, 26).

Clinical trials have now demonstrated phase angle to be effective in the prognosis of diseases such as liver cirrhosis(27, 28),some types of cancer(29, 30), HIV infection(31, 32), renal diseases(33, 34),cardiovascular diseases(35, 36) and pulmonary diseases(37, 38). Therefore, PHA has been considered a useful indicator of general health or nutritional status. There exist articles that have studied the relationship between phase angle and BMI in patients with obesity(39), the influence of metabolic parameters(40), but no research verifies the relationship between phase angle and body composition stratified by BMI. Considering the lack of evidence correlating body composition with PHA in overweight-to-obese patients, the aim of this study was: 1)to investigate their possible link in a group of patients with obesity stratified according to body mass index (BMI) categories; 2)predict how PHA variability can be explained by body composition in patients with overweight and obesity.

2.1. Design and Setting

This was a cross-sectional observational study conducted in the Bariatric Surgery Department of a hospital in Changchun from August 2018 to December 2020.

2.2. Population

Patients were eligible for the study if they met the following inclusion criteria: i)BMI ≥ 25 kg/m²; ii)patients without any cardiovascular risk factors and physical limitations to complete the procedure; iii)patients understand the research purpose and voluntary participation in research. Specific exclusion criteria included: i)patients complicated with other serious medical conditions that may affect the result of measurement; ii)patients are reluctant to cooperate; iii)patients with pacemaker implantation and a contraindication for BIA methods; iv)the data was incomplete. According to BMI, we classified patients into four groups: overweight(BMI 25.0-29.9kg/m²),obese grade I(BMI 30.0–34.9kg/m2),obese grade II(BMI 35.0–39.9kg/m2),obese grade III(BMI ≥ 40.0kg/m2)(41).

2.3 Measurement

All study measures and data collection were obtained by trained study personnel. Anthropometric measures were conducted in the condition that patients wore a hospital gown without shoes. Height and weight were measured by an electronic height-weight meter which can be accurate to 0.1cm and 0.1kg.BMI(Body Mass Index) was calculated as weight in kilograms divided by the square of height in meters. Waist circumference was measured at the umbilical level and hip circumference was measured at the maximum level over light clothing, using a tape. Each participant was measured twice and took an average to reduce the error. WHR(Waist -Hip-Ratio) was calculated as waist circumference in centimeters divided by hip circumference in centimeters.

Body compositions measurements were made by using a multi-frequency bioelectrical impedance analyzer InBody 770, the machine sends very weak alternating current through the body, and body tissues against this current. All the patients were informed of the procedure of measurement. Patients should be tested on an empty stomach or without food for 2 hours and without water for 1 hour. If they engaged in vigorous activities, bathing or other activities, they needed to rest for 3 hours to participate in the test. Before the measurement began, patients were asked to empty their bladders and refrain from drinking excessive amounts of water. Patients removed shoes and socks and wiped hands and feet with wet paper towels to enhance electrical conductivity. Then patients were required to stand barefoot on the platform of the device with the soles of their feet positioned on the electrodes. Meanwhile, patients grasped the handles with their thumbs and palms to make direct contact with the corresponding electrodes. During the whole process, the patient couldn’t speak or move, and keeping the arms straight and away from the torso throughout the test. They stood still for some time and the output was printed.

2.4. Statistical analysis

All analyses were performed separately for each BMI group. Kolmogorov-Smirnov test was used to check normality and Levene test was used to check homogeneity of variance. If data satisfied normality and homogeneity of variance, descriptive statistics summarised participant anthropometric characteristics using mean(SD). If data didn’t meet the above conditions, median(IQR)was used to describe participant anthropometric characteristics. To analyze the differences in anthropometric characteristics between groups, the independent sample t-test, Chi-squared tests and Kruskal-Wallis were used. Pearson correlation coefficients and Spearman correlation coefficients were used to analyze the relationship between phase angle and body composition. Stepwise linear regression analysis was used to determine the influence of body composition on PHA and explore the effects of imbalanced baseline values added as covariates on outcomes. All tests were two-tailed, a p-value less than 0.05 was regarded as a significant level. R² is the model fit coefficient; the closer this value is to 1, the higher the relative accuracy of the model. Statistical analysis was conducted on SPSS Statistic 24.0.

2.5 Ethical Considerations

Participation in the study was voluntary and without compensation. We explained the purpose of the study to all patients and promised that all the information was used for research purposes only, then asked them to sign an informed consent. In the whole process of the study, through anonymous and ensuring the confidentiality of the information to make sure that patients can be real and effective provide answers. The study was approved by the ethical committee of Nursing College, Jilin University with the number 2020121602.All methods were carried out in accordance with relevant guidelines and regulations of the ethical committee of Nursing College, Jilin University.

The present study was conducted on 257 overweight-to-obese patients whose residential districts were concentrated in the north of China. Baseline characteristics of patients were summarized in Table 1. In this study,21.4% were overweight,30.0% were obese grade I,19.5% were obese grade II and 29.2% were obese grade III. In both of the four groups, more than one-half of the patients were females. The mean sample age varied from 29.21 to 36.82.Apart from age and gender, the other demographics characteristics included marital history, familial obese, smoking history, drinking history and residential district had no difference(P>0.05).

Table 1

Baseline demographics characteristics of obese patients and differentiated by BMI¹.
Variables	Overweight	Obese grade I	Obese grade II	Obese grade III	P value
N(%)	55	77	50	75
Age	36.82 ± 11.23	35.91 ± 11.10	33.04 ± 10.59	29.21 ± 7.06	<0.001*
Gender					0.009*
Female(n,%)	47(85.5)	66(85.7)	44(88)	51(68)
Male(n,%)	8(14.5)	11(14.3)	6(12)	24(32)
Marital history					0.184
Married(n,%)	37(67.3)	54(70.1)	32(64)	41(54.7)
Unmarried(n,%)	16(29.1)	23(29.9)	18(36)	33(44)
Divorced(n,%)	2(3.6)	0	0	1(1.3)
Familial obese					0.810
Yes(n,%)	5(9.1)	7(9.1)	6(12)	10(13.3)
No(n,%)	50(90.9)	70(90.9)	44(88)	65(86.7)
Smoking history					0.544
Yes(n,%)	9(16.4)	11(14.3)	4(8)	8(10.7)
No(n,%)	46(83.6)	66(85.7)	46(92)	67(89.3)
Drinking history					0.493
Yes(n,%)	10(18.2)	12(15.6)	10(20)	8(10.7)
No(n,%)	45(81.8)	65(84.4)	40(80)	67(89.3)
Residential district					0.076
East of China(n,%)	6(10.9)	6(7.8)	4(8)	3(4)
South of China(n,%)	4(7.3)	2(2.6)	0	0
West of China(n,%)	0	2(2.6)	0	2(2.7)
North of China(n,%)	41(74.5)	65(84.4)	46(92)	68(90.7)
Middle of China(n,%)	4(7.3)	2(2.6)	0	2(2.7)
¹ P values were determined with the use of the Chi-squared tests.

The anthropometric characteristics and body-composition characteristics were shown in Table 2. All the other variables except for ECW/ICW and PHA had significant differences among these four groups(P<0.05). The values of all statistically significant variables rose with the increase of BMI, the values were highest in the obese graded III group.The trends were shown in Supplemental Fig. 1.The mean level of PHA and ECW/ICW increased with the rise of BMI although there was no statistically significant difference,the trends were shown in Supplemental Fig. 2.

Table 2

Anthropometric characteristics, Body-composition characteristics of obese patients and differentiated by BMI.
Variables	Overweight	Obese grade I	Obese grade II	Obese grade III	P value
Weight(kg)¹	75.41 ± 6.35	89.2 ± 9.04	102.74 ± 10.46	127.6(117.1-141.7)	<0.001*
Height(cm)¹	164.28 ± 6.56	165(160.5–170)	165.76 ± 7.93	169.71 ± 8.77	0.002**
BMI(kg/m²)²	27.92 ± 1.25	32.46 ± 1.45	37.32 ± 1.46	43.91(41.67–48.41)	<0.001*
WHR¹	0.94 ± 0.05	0.99 ± 0.06	1.02(0.99–1.07)	1.05(1.00-1.09)	<0.001*
TBW(kg)¹	34.3(32.1–37)	37.5(34.2–40)	39.55 ± 6.21	48.77 ± 8.84	<0.001*
ECW(kg)¹	13.1(12-14.2)	14.4(13-15.2)	15.1(13.88–16.3)	18.4(16–21)	<0.001*
ICW(kg)¹	21.3(19.9–22.8)	23.2(21.2–24.8)	24.98 ± 3.69	28.6(26–34)	<0.001*
ECW/ICW¹	0.62(0.60–0.63)	0.61(0.60–0.63)	0.62(0.61–0.63)	0.62(0.61–0.63)	0.328
FM(kg)²	27.78 ± 4.10	37.54 ± 5.29	47.93 ± 5.75	64.67 ± 10.61	<0.001*
FFM(kg)¹	46.7(43.7–50.4)	51(46.6-54.25)	53.4(49.48–58.90)	66.13 ± 12.04	<0.001*
SMM(kg)¹	25.8(23.9–27.8)	28.3(25.7–30.3)	30.58 ± 4.82	35.3(32-42.4)	<0.001*
Mineral(kg)¹	3.27(3.13–3.59)	3.5(3.16–3.77)	3.69 ± 0.64	4.15(3.67–4.99)	<0.001*
Protein(kg)¹	9.2(8.60–9.90)	10(9.2–10.7)	10.55(9.68–11.65)	12.4(11.3–14.7)	<0.001*
BCV(kg)¹	30.5(28.5–33.3)	33.2(30.4-35.45)	35.98 ± 5.27	41(37.3–48.7)	<0.001*
PHA(°)²	5.41 ± 0.70	5.44 ± 0.57	5.58 ± 0.55	5.64 ± 0.70	0.120
P <0.05; *P <0.001
¹P values were determined with the use of the Kruskal-Wallis test; ²P values were determined with the use of the t-test.

Table 3 presented Pearson and Spearman correlation values between the PHA and the other anthropometric and body composition variables. According to this table, TBW, ICW, ECW/ICW, FM, FFM, SMM, protein, and BCV had correlations with PHA in all of the four groups. In these variables, ECW/ICW and FM were negatively correlated with PHA. Of each group of statistically significant variables, FM had the largest negative correlation with PHA except for the obese graded III group. The largest negative correlation between the PHA and BMI was observed only in the obese graded III group. The variables with the greatest positive correlation were different in each group, with BCV in both the overweight group(R = 0.418) and the obese graded II group(R = 0.508), SMM in the obese graded I group(R = 0.420), and ICW in the obese graded III group(R = 0.363). We found the volume of the mineral was positively correlated with PHA in three obese groups. A positive correlation between the PHA and ECW was observed in the obese graded I group and obese graded II(R varied from 0.260 to 0.356 ), whereas the two variables didn’t correlate in the overweight and obese graded III group.

Table 3

Correlation between PHA and body composition of patients with overweight and obesity differentiated by BMI.
Variables	Overweight		Obese grade I		Obese grade II		Obese grade III
	R	P	R	P	R	P	R	P
Weight(kg)	-0.018	0.894	0.041	0.724	0.11	0.447	-0.03	0.80
Height(cm)	0.002	0.989	0.099	0.392	0.083	0.567	0.218	0.06
BMI(kg/m²)	-0.031	0.823	-0.062	0.595	0.111	0.444	-0.268	0.02*
WHR	0.148	0.28	0.046	0.691	-0.142	0.326	0.165	0.156
TBW(kg)	0.334	0.013*	0.378	0.001*	0.364	0.009*	0.3	0.009*
ECW(kg)	0.183	0.18	0.260	0.022*	0.356	0.011*	0.191	0.101
ICW(kg)	0.409	0.002*	0.418	<0.001*	0.449	0.001*	0.363	0.001*
ECW/ICW	-0.904	<0.001*	-0.826	<0.001*	-0.749	<0.001*	-0.803	<0.001**
FM(kg)	-0.412	0.002*	-0.374	0.001*	-0.322	0.023*	-0.417	<0.001**
FFM(kg)	0.344	0.01*	0.360	0.001*	0.419	0.002*	0.311	0.007*
SMM(kg)	0.407	0.002*	0.420	<0.001*	0.450	0.001*	0.354	0.002*
Mineral(kg)	0.123	0.372	0.324	0.004*	0.338	0.016*	0.265	0.021*
Protein(kg)	0.404	0.002*	0.412	<0.001*	0.448	0.001*	0.360	0.002*
BCV(kg)	0.418	0.002*	0.417	<0.001*	0.508	<0.001*	0.358	0.002*
P <0.05; *P <0.001

Stepwise linear regression analyses were performed separately according to BMI to verify the determinant factors for PHA. The variables which presented the best correlation with PHA and imbalanced at baseline value were tested as predictors. Table 4 presented this analysis’ results for overweight-to-obese patients.

Table 4

Stepwise linear regression between PHA and determinant factors differentiated by BMI.
Variables	Overweight^a			Obese grade I^b			Obese grade II^c			Obese grade III^d
	B	β	P	B	β	P	B	β	P	B	β	P
Gender	-0.785	-0.397	0.002	-	-	-	-	-	-	-	-	-
Height(cm)	-	-	-	-0.052	-0.632	< 0.001	-	-	-	-	-	-
BMI(kg/m²)	0.164	0.291	0.024	-	-	-	0.095	0.250	0.005	-	-	-
WHR	-	-	-	-	-	-	-	-	-	-0.860	-0.098	0.038
ECW(kg)	-	-	-	-1.006	-3.63	< 0.001	-	-	-	-	-	-
ICW(kg)	-	-	-	-	-	-	-	-	-	0.087	0.685	0.020
ECW/ICW	-	-	-	-	-	-	-21.885	-0.590	< 0.001	-	-	-
FM(kg)	-0.076	-0.444	0.002	-	-	-	-0.052	-0.543	< 0.001	-0.012	-0.176	0.005
FFM(kg)	-	-	-	-	-	-	-	-	-	-0.865	-14.906	< 0.001
BCV(kg)	-	-	-	0.531	4.565	< 0.001	0.032	0.308	0.005	1.318	14.693	< 0.001
^aR²=0.42;^bR²=0.858;^cR²=0.823;^dR²=0.876

We found that gender was not associated with PHA in the three obese groups. However, BCV was a predictor of variance in PHA among the three groups, in the obese grade I group and obese grade III group this variance was the strongest factor that had a positive association with PHA. In the overweight group, the results were different. Gender had a negative association with PHA and it had a negative β value (β=-0.397).BCV was not associated with PHA.

Except for gender, BMI and FM were associated with PHA in the overweight group while other variables were not statistically associated with it. The only variable that was positively associated was BMI(β = 0.291). In the obese graded II group, BMI was also positively associated with PHA(β = 0.250), but we didn’t find this variable was a predictor of PHA in the other two groups.FM was negatively associated with PHA in both obese graded II and obese III groups. Among the three groups, FM was the strongest negative predictive variable, with β values were the largest of all the negative values.

Height and ECW were associated with PHA only in the obese grade I group, both of them had negative β values. ECW/ICW ratio was also only a negative predictor associated with PHA in the obese graded II group. While ECW was associated with PHA in the obese graded I group, ICW was found positive β value in the obese III group(β = 0.685).In addition to FM, WHR and FFM were the other variables that were negatively associated in the obese III group, and the largest negative β value was for the WHR (β = −0.098).

After controlled for the previous variables, above eighty percent of the PHA variability was explained in the obese groups. However, the three explained variances were even contributed less in the overweight group ( R2 = 0.421). The variables contributed most to the total variability in the obese graded III group by the stepwise linear regression ( R2 = 0.876).

We reported the association between PHA and body compositions in a sample of overweight-to-obese patients stratified according to BMI categories in this cross-sectional observational study. The study confirmed that a higher mean level of PHA was observed in the obese graded III group, but in this group, there was a negative correlation between PHA and BMI. Similar results were found in a study based on a large German database with a 230,337 sample population(39). As we mentioned above, PHA was calculated by reactance and resistance values which prompted changes in both hydrations of soft tissue and cell membrane permeability(23–26). Thus, results could be explained as follows: First, an increase in soft tissue hydration led to a decrease in PHA. Compared with non-obese people, obese people were often accompanied by an increase in fluid volumes(42). Pathological fluid overload occurred in patients with severe obesity(43, 44). Second, the abnormal membrane permeability also resulted in a decrease in PHA value. An increased PHA value indicated a greater number of intact cell membranes, and a smaller PhA value indicated cell death or a decrease in cell integrity(45). Inflammation is strongly associated with cellular health(46), obesity contributes to meta-inflammation that the levels of inflammatory mediators will increase, which could induce cellular damage and death(47, 48). Besides, obese people often had higher BCV, according to the current flow through physiological fluids, the higher BCV was, the lower resistance was(25, 39). This also led to a reduction in PHA values. BCV was an independent predictor of PHA in three obese groups.

PHA could reflect the water distribution between the intracellular and extracellular water compartments(42). Previous studies had shown an inverse relationship between BMI and water balance(49), due to greater hydration and fluids in the extracellular compartments, obese people had a higher ECW/ICW ratio than non-obese people(42, 50), and in healthy people, ECW/ICW ratio was an important determinant of the PHA(26). However, in our study, we didn’t find a significant change in the value of ECW/ICW with BMI increased. This ratio was influenced by two factors, ECW and ICW. The two factors increased with the rise of BMI, when the two factors rose by a similar degree, the ratio would stay similar. In previous studies, obese patients didn’t distinguish further according to BMI(51). However, this ratio had negative correlations with PHA in all four groups, this result was consistent with a previous study in other study populations(26). Similar to the above reasons, higher ECW/ICW ratio of the adipose tissue in obese patients reflected that the expansion was relatively greater for the extracellular compartment of which had greater hydration and fluids, and the overload fluids had negative effects on PHA(44, 50). There exists a study that confirmed that for obese people, even after weight reduction or weight maintenance, the value of ECW/ICW didn’t reverse(51). Obesity may be associated with primary (irreversible) changes in hemodynamics and humoral regulation(51–54). Mazariegos et al. found after weight loss elevated exchangeable Na + still reminded, this might be a reason to explain irreversible effects on this phenomenon(53). On the other hand, obesity is a state of chronic inflammation with raised circulating levels of inflammatory markers(55–57). White adipose tissue is a major endocrine organ that releases a range of protein signals and adipokines(55), this may be an influencing factor.

FM and FFM were often used to determine nutrient requirements and energy expenditure(58, 59). In our study, FFM had positive sides, the results were similar in previous studies conducted in other study samples(32, 60). The association between PHA and FFM can be explained by the percentage of water volume in FFM, that was to say the muscle tissue(60). Adipose tissue consisted of ~ 14% of water whereas the FFM consists of ~ 72% of water(61). Consider the nature of resistance and reactance mentioned above, the reactance is due to the electrostatic storage of cellular membranes, tissue interfaces, and nonionic tissues whereas the resistance refer to the pure proportional to the number of electrolytes due to extracellular and intracellular water(23, 24). Resistance was inversely proportional to the fluid volume of the human body(21). Therefore, when the integrity of the cell membrane is better, the value of reactance value will be higher. Because of the higher hydration of FFM, the amount of intracellular and extracellular water will also be higher(61). Then the resistance value of the body will be smaller. According to the calculation formula(23), PHA will be higher. In addition, FM had negative effects with PHA among all groups. Low hydration of FM results in high resistance, with the volume of FM increased, the reactance will decrease(21). Thus, the higher FM was, the lower PHA was.

Although all the body composition variables were included in this study, only 42% of PHA variability could be explained in the overweight group whereas 85.8% in the obese graded I group,82.3% in the obese graded II group, and 87.6% in the obese graded III group. Previously published studies suggested PHA was associated with metabolic parameters like uric acid levels(40), vitamin D(62), transthyretin levels(63), hs-CRP(64, 65). All the metabolic parameters had different correlations with PHA. Besides, PHA might be related to both muscle mass and muscle strength(66). Didn’t conform to the consistent results, we verified that skeletal muscle mass had a positive correlation with PHA. Thus, metabolic parameters and muscle function are also important aspects to explain PHA variability.

To our best knowledge, till now, this was the first study to investigate the association between body compositions and PHA in patients with obesity. Because of the increasing incidence of obesity and its adverse consequences, it was necessary to conduct research in this population. The main strength of the present study was that patients in different BMI categories were stratified to make comparisons. According to the stratification of BMI, we could intuitively find out the relationship between each body composition variable and PHA in different BMI levels. Meanwhile, the predictors of PHA by body composition in different BMI levels could be found out. When it came to the measurement of body composition, we chose a non-invasive, fast, and relatively cost-effective way. Inbody 770 uses multi-frequency measurement to improve the accuracy of the results.

Nevertheless, the current study has several limitations that warrant considerations. First, as we could see from the baseline, females made up a large proportion of our sample population. From previous studies, gender is the main predictor of PHA in adults(26, 42). Because of males’ and females’ uneven distribution in this study, the results might be biased. Second, only the data of preoperative overweight-to-obese patients were collected, and it is not clear whether the results of this study can be extended to patients after bariatric surgery. Third, the size of our sample was 257 patients and the study was conducted in a single-center hospital. As we mentioned before, obese is related to lifestyle modification, most of the patients lived in the same geographical area and had similar lifestyle patterns. The homogeneity of the sample population increases the selection bias. Further studies with larger samples and multi-centers are needed to verify the reliability of the results. Finally, our study was conducted in overweight-to-obese adults, whether the results are applicable in childhood obesity needs further exploration.

Our study provided initial insights into the relationship between PHA and body composition in overweight-to-obese patients. This association uncovered new potential usefulness: body compositions as predictors of PHA in overweight-to-obese patients. Future studies using variables could further increase our understanding of the biological importance of PHA in overweight-to-obese patients.

Acknowledgements

We thank the staff of Changchun Jiahe Surgical Hospital for their help of date collection and all of the participants and their families for their contributions and support.

Author contributions

The authors’ responsibilities were as follows—CL and BL: designed and guided the study; HX: supervised the data collection; LL, SC and NJ: conducted the study and performed statistical analysis; JZ and CL: interpreted results, wrote manuscript and revised. All authors read and approved the final manuscript and promise no conflict of interest.

Data Availability

Data described in our manuscript will not be made available because this study presents part of the data in our project, and our data will be applied in other aspects of the project.

Competing interests

All the authors have no conflicts of interest to declare

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Phase angle and body composition in patients with overweight and obesity

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