BioMed Research International
Volume 2021 (2021), Article ID 2365399, 6 pages
https://doi.org/10.1155/2021/2365399

Research Article

The Association between Anthropometry Indices and Serum Concentrations of Gamma-Glutamyl Transferase, Alkaline Phosphatase, Alanine Aminotransferase, and Aspartate Aminotransferase

Sahar Sobhani,¹ Reihaneh Aryan,² Mina AkbariRad,³ Elahe Ebrahimi Miandehi,⁴ Maryam Alinezhad-Namaghi,⁵ Seyyed Reza Sobhani,⁵ and Sara Raji¹

¹Persian Cohort Research Center, Mashhad University of Medical Sciences, Mashhad, Iran
²Clinical Research Development Unit, Imam Reza Hospital, Mashhad University of Medical Sciences, Mashhad, Iran
³Department of Internal Medicine, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
⁴Department of Medical Informatics, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran
⁵Department of Nutrition, Faculty of Medicine, Mashhad University of Medical Sciences, Iran

Correspondence should be addressed to Sahar Sobhani; sobhanis3@mums.ac.ir

Received 10 September 2021; Accepted 9 November 2021; Published 22 November 2021

Academic Editor: Ali Abbara

Copyright © 2021 Sahar Sobhani et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract.

Background. Nowadays, metabolic syndrome (MetS) has become a great public health crisis that threatens too many lives worldwide. Many previous studies have been investigated the association between MetS and anthropometric indices. This study is aimed at investigating the association between anthropometric indices with gamma-glutamyl transferase (GGT), alkaline phosphatase (ALP), alanine aminotransferase (ALT), and aspartate aminotransferase (AST). We were using data from a large population-based cohort of seemingly healthy women and men. Methods. A total of 7216 participants were included in this study. The serum levels of GGT, ALP, ALT, and AST with bioimpedance measures were obtained at the time of enrollment. Multiple regression analysis was performed to assess the relationship between anthropometric indices and serum levels of the aforementioned laboratory tests. Results. Serum levels of GGT, ALP, ALT, and AST significantly correlated with body mass index (BMI). Only ALP had a significant association with visceral fat area (VFA). AST, ALT, and ALP levels had a positive correlation with 50 kHz whole-body phase. Conclusion. From the findings of this study, body mass index consistently appeared a good predictor of elevated hepatic enzymes and triglycerides. Thus, it can be helpful in clinical settings to identify patients at risk of nonalcoholic fatty liver disease, which is closely related to metabolic syndrome.

1. Introduction

Nowadays, metabolic syndrome (MetS) has become a great public health crisis that threatens too many lives worldwide [1]. This metabolic disorder occurs when at least three of the following medical conditions are being clustered: abdominal obesity, hypertriglyceridemia, low serum high-density lipoprotein (HDL) level, hyperglycemia, and hypertension [2]. Therefore, these conditions can potentially facilitate type 2 diabetes mellitus, chronic kidney disease, and cardiovascular disease and finally lead to increased mortality risk [3]. Many previous studies have investigated the association between MetS and anthropometric indices, but the best anthropometric indicator is not known to predict MetS incidence [4]. However, extensive epidemiological studies on the Iranian, Chinese, and Qatari adult populations demonstrated that waist circumference (WC) was highly associated with the MetS as its best indicator, while waist-to-height ratio (WHtR) was considered the best anthropometric index for MetS prediction in Japanese and Italian adults [5, 6].

The fact that the liver is known as the vital organ in metabolic regulation is not a secret [7]. According to recent studies, liver function tests (LFTs), including serum concentration of aspartate transaminase (AST), alanine transaminase (ALT), alkaline phosphatase (ALP), and gamma-glutamyl transferase (GGT), can be valuable parameters in evaluating one’s metabolic status, especially investigating cardio-metabolic disorders [8]. Interestingly, several studies have indicated a strong direct association between LFTs and MetS [9, 10]. Having attention to the liver metabolic pathways demonstrates that this multifunctional organ has a prominent role in regulating serum lipid profiles such as HDL, low-density lipoprotein (LDL), and triglyceride [11]. Obviously, each of these fatty components can eventually make the MetS incidence more probable [12]. Therefore, LFTs and liver enzymes can be associated with the state of lipid metabolism and play a role in metabolic disorders, including MetS [13, 14].

According to what was stated, it can be deduced that anthropometric indices, liver function, and metabolic status would be related to each other; in a detailed view, we hypothesized that anthropometric indices could be associated with the serum concentration of GGT, ALP, and liver enzymes and as key components of the metabolic disorders and MetS. Therefore, we investigated this hypothesis in a large population in Iran to demonstrate whether this association is established or not.

2. Material and Method

2.1. Setting and Participants

The PERSIAN Organizational Cohort Study in Mashhad (POCM) is a study of lifestyle and risk factors for noncommunicable diseases. Details of POCM have been reported previously [15]. This study was planned to recruit 12,000 employees (aged 30-70 years) in Mashhad, northeast of Iran. The detailed national protocol has been published elsewhere [16]. Baseline assessments were obtained by face-to-face interviews or examinations. The questionnaires consist of demographic characteristics, physical activity, personal and familial medical history, medication profile, and smoking, and alcohol use was collected by a standardized questionnaire [16]. Basic consent was obtained from all participants before entering the study. The study protocol was in accordance with the principles laid down in the Helsinki declaration and has been approved by the national and regional ethics committee (IR.MUMS.REC.1395.526; January 2017).

2.2. Study Measurements and Clinical Assessments

Bioimpedance and anthropometric measurements: the bioimpedance test was performed using the InBody 770 body analyzer (InBody Corporation, Seoul, South Korea). The participations were asked to abstain from caffeine consumption for at least 24 hours and drinks and foods for at least four hours prior to the test. The participants were required to stand motionless, keeping the standard position (standing barefoot on the footplates while holding hand electrodes with straight arms and no touches in the armpits area). The height and body circumferences (waist and hip) were measured to the nearest one decimal point in centimeters, while the participant was shoeless and had light underclothes. The pregnant women and the patients who had implanted pacemakers were excluded from the body analysis.

2.2.1. Laboratory Measures

All of the laboratories finding, such as liver enzymes, FBS, TG, LDL, HDL, and total cholesterol in POCM, were measured after 10-12 hours fasting in the morning (BT1500 auto analyzer, Biotechnical Instruments, Rome, Italy).

2.3. Statistical Analysis

Baseline characteristics of the study population were dichotomized based on men and women. Continuous variables were expressed as , and discrete variables were expressed as percentages. Comparisons of categorical variables were performed by chi-square test, and analyses of continuous variables were performed by independent-samples -test and Mann–Whitney. Multivariate linear regression analysis was used to determine the relationship between biochemical factors as dependent variables and measures of sex, age, WHR, BMI, TBW, VFA, and 50 kHz whole-body phase angle and measure circumferences of hip, BFM, TG, LDL, and HDL as independent variables. All analyses were performed using SPSS software version 22.

3. Results

A total of 7216 (3855 women and 3361 men) participants of the POCM from October 2017 to April 2021 were included in the present study. As shown Tables 1 and 2, men were older and had higher education level. Also, women had higher BMI, HDL, VFA, BFM, and measure circumference of chest and were less likely to be alcohol drinkers or smokers. Women and men had similar physical activity, but men had higher WC, whole-body phase angle, TBW, and FFM and higher GGT, ALT, AST, FBS, and LDL.

Table 1: Demographic characteristics of the participants.

Characteristics
Women ()
Men ()
value

Age, years, mean (SD)
44.1 (9.2)
46.3 (8.3)
<0.001

Education, %

<0.001

Primary or below
3.2
1.8

Middle school
21.6
14.1

College or above
75.2
84.1

Physical activity, %

0.958

Inactive
0.8
0.8

Moderate
95.9
95.9

Active
3.3
3.3

Alcohol use, %

<0.001

Never
98.1
95.2

Former
0.4
3.5

Current
1.3
1.5

Smoking, %

<0.001

Never
98.6
83.1

Former
0.3
6.6

Current
1.1
10.3

Table 2: Biochemical and anthropometry parameters.

Characteristics
Women ()
Men ()
value

Body mass index, kg/m², mean (SD)
27.2 (4.2)
26.8 (5.1)
<0.001

Waist circumference, cm, mean (SD)
94.8 (10.7)
98.4 (8.9)
<0.001

Waist hip ratio, cm, mean (SD)
0.94 (0.067)
0.92 (0.054)
<0.001

Visceral fat area, cm, mean (SD)
137.8 (42)
102 (39)
<0.001

50 kHz whole-body phase angle, degree, mean (SD)
5.08 (0.5)
5.89 (0.6)
<0.001

Measure circumference of neck cm, mean (SD)
35.4 (3.1)
38.5 (2.4)
<0.001

Measure circumference of chest cm, mean (SD)
101.3 (6.8)
93.8 (6.9)
<0.001

Measure circumference of abdomen cm, mean (SD)
90.2 (10.1)
95.3 (11)
<0.001

Measure circumference of hip cm, mean (SD)
98.4 (3.7)
101.1 (6.2)
<0.001

Measure circumference of right arm cm, mean (SD)
31.9 (3.3)
33.5 (2.9)
<0.001

Measure circumference of left arm cm, mean (SD)
31.8 (3.2)
33.4 (2.9)
<0.001

Measure circumference of right thigh cm, mean (SD)
53.1 (4.1)
54.7 (4.3)
<0.001

Measure circumference of left thigh cm, mean (SD)
52.6 (3.9)
54.3 (4.1)
<0.001

Total body water, mean (SD)
29.9 (3.6)
42.7 (5.6)
<0.001

Body fat mass, mean (SD)
27.1 (7.8)
22.6 (7.7)
<0.001

Fat free mass, mean (SD)
40.7 (4.9)
57.2 (7.7)
<0.001

Triglycerides, mg/dl, mean (95% CI)
105.65 (55.40)
141.96 (80.18)
<0.001

GGT, U/L, mean (SD)
23 (20.06)
32.10 (21.35)
<0.001

AST, U/L, mean (SD)
20.21 (7.17)
23.98 (8.58)
<0.001

ALT, U/L, mean (SD)
20.90 (10.68)
30.55 (14.98)
<0.001

Fasting blood sugar, mg/dl, mean (SD)
94.62 (21.2)
102.53 (28.4)
<0.001

LDL cholesterol, mg/dl, mean (SD)
98.98 (30.524)
101.57 (31.59)
0.007

HDL cholesterol, mg/dl, mean (SD)
60.45 (13.40)
51.10 (10.62)
<0.001

Total cholesterol, mg/dl, mean (SD)
180.08 (36.9)
180.20 (36.8)
<0.001

ALP IU/L, mean (SD)
168 (59.8)
183 (51.5)
<0.001

As shown in Table 3, multivariate regression analysis using sex, age, WHR, BMI, TBW, VFA, LDL, HDL, TG, 50 kHz whole-body phase angle, and measure circumferences of hip and BFM as independent variables demonstrated that ALP levels were determined predominantly by sex, age, BMI, TG, LDL, and HDL but not with TBW and BFM (). The main determinants of GGT levels found were sex, age, BMI, LDL, HDL, and TG. ALT and AST levels had an inverse association with measure circumferences of hip (, ; , ).

Table 3: Multiple regression analyses.

Model No.
Dependent variable
Independent variables
β-coefficient ()
Adjusted ()

ALP
Sex
-0.31 (0.001)
0.08 (0.001)

Age
0.06 (0.001)

WHR
0.12 (0.03)

BMI
-0.11 (0.02)

TBW
-0.02 (0.86)

VFA
0.61 (0.001)

50 kHz whole-body phase angle
0.16 (0.001)

Measure circumferences of hip
0.45 (0.27)

BFM
-0.09 (0.50)

HDL
0.03 (0.02)

LDL
0.07 (0.001)

TG
0.09 (0.001)

GGT
Sex
-0.19 (0.001)
0.12 (0.001)

Age
0.01 (0.001)

WHR
0.02 (0.95)

BMI
0.06 (0.04)

TBW
-0.30 (0.12)

VFA
0.14 (0.105)

50 kHz whole-body phase angle
-0.01 (0.48)

Measure circumferences of hip
-0.38 (0.32)

BFM
-0.13 (0.29)

TG
0.19 (0.001)

LDL
0.08 (0.001)

HDL
0.05 (0.001)

AST
Sex
-0.18 (0.001)
0.12 (0.001)

Age
0.01 (0.50)

WHR
-0.01 (0.82)

BMI
0.03 (0.03)

TBW
0.04 (0.66)

VFA
0.04 (0.62)

50 kHz whole-body phase angle
0.16 (0.02)

Measure circumferences of hip
-0.10 (0.02)

BFM
-0.12 (0.33)

TG
0.16 (0.001)

LDL
0.13 (0.001)

HDL
0.07 (0.001)

ALT
Sex
-0.24 (0.001)
0.23 (0.001)

Age
-0.05 (0.001)

WHR
-0.19 (0.3)

TBW
-0.23 (0.53)

VFA
-0.08 (0.30)

BMI
0.19 (0.004)

50 kHz whole-body phase angle
0.15 (0.001)

Measure circumferences of hip
-1.23 (0.001)

BFM
-0.56 (0.67)

TG
0.16 (0.001)

LDL
0.05 (0.001)

HDL
0.05 (0.001)

Abbreviations: WHR: waist-hip ratio; BMI: body mass index; TBW: total body water; VFA: visceral fat area; BFM: body fat mass.

AST, ALT, and ALP levels were a positive association with 50 kHz whole-body phase angle (, ; , ; , ), respectively, but GGT had no association with 50 kHz whole-body phase angle. AST, ALT, and GGT levels showed no significant relationship with VFA (). However, ALP had a significant association with VFA (). AST, ALT, ALP, and GGT levels had significantly positive correlations with TG, LDL, and HDL () (Table 3).

4. Discussion

In our studied population of more than 7000 men and women, BMI was significantly correlated with ALT, AST, ALP, GGT, and TG.

Obesity has been a growing concern in recent years due to the close correlation between this condition and chronic diseases of lifestyle [17]. The worldwide prevalence of obesity nearly tripled between 1975 and 2016. The WHO adult BMI database in 2016 indicates that 39% of men and 40% of women were overweight, and about 11% of men and 15% of women were obese [18]. As shown in our study, obesity has also been identified as a significant contributing factor to increased serum levels of liver enzymes [19–21]. The “portal hypothesis” proposes this relationship is due to the high release of free fatty acids from the visceral fat depot into the portal circulation, leading to hepatic damage and nonalcoholic fatty liver disease (NAFLD) [22].

We found that only serum levels of ALP had a significant correlation with VFA, and the other laboratory test showed no significant correlation with this index. Ali AT et al. suggested that ALP is involved in controlling of intracellular lipid accumulation in human preadipocytes [23]. In a study on middle-aged Korean men regarding the association between serum ALP levels and development of the MetS over four years, ALP showed a significant association with abdominal obesity and risk of developing MetS [24].

A study performed by Ghandehari et al. showed that waist circumference had the most accuracy in predicting liver injury in adults [25] which contrasts with our study as we found BMI to be a better consistent indicator for liver injury. This difference can be due to the difference in the method of diagnosing fatty liver in the two studies. In the previous study, a fibro scan was used, and the present study considered the toxic levels of liver enzymes. These variables may be associated with a low-grade fatty liver that has not yet increased liver enzymes.

Bioelectrical impedance analysis such as 50 kHz whole-body phase angle has been gaining attention because they are considered indexes of extra cellular (ECW) and intracellular water (ICW) distribution, body cell mass (BCM), and cellular integrity [26].

A Spanish study showed that low tertile of phase angle is a cardiovascular risk factor in obese patients [27]. Controversially, in our study, 50 kHz whole-body phase angle had a positive correlation with ALT, AST, ALP, and TG, as supported by a study done by Moreto et al. It can be deduced that 50 kHz whole-body angle is an indicator of good nutritional status in a healthy population and is not a good predictor of MetS [28].

ALP and GGT levels were positively correlated with age, while ALT was negatively correlated with age, and AST showed no such significance. A study performed by Dong et al. also showed that ALT levels, independent of metabolic syndrome components and other commonly used hepatic enzymes, decreased with age in both genders, while age did not affect AST levels [29]. The same results were reported in another study by Oliveira et al. in 2016 [30].

Sex differences in anthropometric indices seem to be inconsistent among different communities. In our studied population, women had higher BMI, HDL, WHR ratio, VFA, BFM, and chest circumference, while men had higher WC, LDL, TBW, FFM, neck circumference, and 50 kHz whole-body angle. In a study of 12,514 adults from Isfahan, Iran, women tended to have higher BMI, WHR, and also WC [31]. Controversially, in a study of 27,257 adults in China [32], men had significantly higher BMI.

Our male population had significantly higher serum levels of liver enzymes, TG, LDL cholesterol, and FBS along with lower serum levels of HDL cholesterol. These gender-based differences can be likely explained by gender disparities in gonad corticoids, body fat distribution, and lifestyle [33]. For instance, men were more likely to be smokers () and drinkers () in our study.

In this investigation, a large number of participants were included, and we implemented a population-based cohort design, which has never been done before on the population of Mashhad, the second largest city of Iran, regarding this subject. Although these findings may not translate to patients of other ethnicities, the results might still be widely applicable as they were mostly consistent with other studies around the world.

Our study was not without limitations. We did not exclude risk factors other than obesity and central adiposity for liver diseases, such as taking certain medications, old blood transfusions, and family history of liver disease. The presence of participants with these risk factors, who may have had elevated liver enzymes for reasons above, may have affected the results.

5. Conclusion

From the findings of anthropometry indices, body mass index consistently appeared a good predictor of elevated hepatic enzymes and fatty liver disease. Other incidences such as waist circumference could not be a good predictor of fatty liver. Thus, it can be useful in clinical settings to identify patients at risk of nonalcoholic fatty liver disease, which is closely related to metabolic syndrome. BMI is an adequate and efficacious measuring tool to identify patients at risk of NAFLD and MetS. We recommend BMI as a screening tool to identify patients at high risk of MetS in Khorasan Razavi province.

Data Availability

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

Ethical Approval

All procedures involving human participants were in accordance with the ethical standards of the national research committee and the 1964 Helsinki declaration and its later amendments or comparable ethical standards.

Consent

All participants voluntarily participated in Persian Cohort study. They signed an informed consent regarding publishing their data.

Conflicts of Interest

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Acknowledgments

This research was supported by the Vice Chancellor for Research of Mashhad University of Medical Sciences under grant number 980237. The authors would like to express their gratitude to the staff of PERSIAN Cohort Epidemiologic Studies, Mashhad site.

References

M. G. Saklayen, “The global epidemic of the metabolic syndrome,” Current hypertension reports, vol. 20, no. 2, pp. 1–8, 2018.

B. Bhavadharini, M. Dehghan, A. Mente et al., “Association of dairy consumption with metabolic syndrome, hypertension and diabetes in 147 812 individuals from 21 countries,” BMJ Open Diabetes Research and Care., vol. 8, no. 1, article e000826, 2020.

L. Jahangiry, L. Khosravi-Far, P. Sarbakhsh, A. Kousha, R. EntezarMahdi, and K. Ponnet, “Prevalence of metabolic syndrome and its determinants among Iranian adults: evidence of IraPEN survey on a bi-ethnic population,” Scientific Reports, vol. 9, no. 1, pp. 1–7, 2019.

L. Nasreddine, N. Bachir, S. Kharroubi et al., “Anthropometric cutoffs for increased cardiometabolic risk among Lebanese adults: a cross-sectional study,” Metabolic syndrome and related disorders, vol. 17, no. 10, pp. 486–493, 2019.

M. Gharipour, N. Sarrafzadegan, M. Sadeghi et al., “Predictors of metabolic syndrome in the Iranian population: waist circumference, body mass index, or waist to hip ratio?” Cholesterol, vol. 2013, 6 pages, 2013.

B. J. Lee and J. Y. Kim, “Identification of metabolic syndrome using phenotypes consisting of triglyceride levels with anthropometric indices in Korean adults,” BMC endocrine disorders, vol. 20, no. 1, pp. 1–12, 2020.

B. Tramunt, S. Smati, N. Grandgeorge et al., “Sex differences in metabolic regulation and diabetes susceptibility,” Diabetologia, vol. 63, no. 3, pp. 453–461, 2020.

H. R. Kim and M. A. Han, “Association between serum liver enzymes and metabolic syndrome in Korean adults,” International journal of environmental research and public health, vol. 15, no. 8, p. 1658, 2018.

S. Wang, J. Zhang, L. Zhu et al., “Association between liver function and metabolic syndrome in Chinese men and women,” Scientific Reports, vol. 7, no. 1, pp. 1–9, 2017.

L. Zhang, X. Ma, Z. Jiang et al., “Liver enzymes and metabolic syndrome: a large-scale case-control study,” Oncotarget, vol. 6, no. 29, pp. 26782–26788, 2015.

C. M. Madsen, A. Varbo, and B. G. Nordestgaard, “Novel insights from human studies on the role of high-density lipoprotein in mortality and noncardiovascular disease,” Arteriosclerosis, Thrombosis, and Vascular Biology, vol. 41, no. 1, pp. 128–140, 2021.

A. Misra, M. J. Soares, V. Mohan et al., “Body fat, metabolic syndrome and hyperglycemia in South Asians,” Journal of diabetes and its complications, vol. 32, no. 11, pp. 1068–1075, 2018.

Z. Belay, S. Daniel, K. Tedla, and N. Gnanasekaran, “Impairment of liver function tests and lipid profiles in type 2 diabetic patients treated at the diabetic center in Tikur Anbessa specialized teaching hospital (Tasth), Addis Ababa, Ethiopia,” Journal of Diabetes & Metabolism, vol. 5, no. 454, p. 2, 2014.

Z. Gaeini, Z. Bahadoran, P. Mirmiran, and F. Azizi, “The association between liver function tests and some metabolic outcomes: Tehran lipid and glucose study,” Hepatitis Monthly, vol. 20, no. 5, 2020.

F. Tohidinezhad, A. Khorsand, S. R. Zakavi et al., “The burden and predisposing factors of non-communicable diseases in Mashhad University of Medical Sciences personnel: a prospective 15-year organizational cohort study protocol and baseline assessment,” BMC Public Health, vol. 20, no. 1, pp. 1–15, 2020.

H. Poustchi, S. Eghtesad, F. Kamangar et al., “Prospective epidemiological research studies in Iran (the PERSIAN Cohort Study): rationale, objectives, and design,” American journal of epidemiology, vol. 187, no. 4, pp. 647–655, 2018.

P. Kopelman, “Health risks associated with overweight and obesity,” Obesity Reviews, vol. 8, pp. 13–17, 2007.

WHO, “Obesity and overweight,” 2021, https://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight.

A. S. K. E. Yu, “Nonalcoholic fatty liver disease,” Reviews in Gastroenterological Disorders, vol. 2, no. 1, pp. 11–19, 2002.

J. M. B. F. Clark and A. M. Diehl, “The prevalence and etiology of elevated aminotransferase levels in the United States,” The American Journal of Gastroenterology, vol. 98, no. 5, pp. 960–967, 2003.

N. S. A. Ali, A. H. Sumon, K. A. Fariha et al., “Assessment of the relationship of serum liver enzymes activity with general and abdominal obesity in an urban Bangladeshi population,” Scientific Reports, vol. 11, no. 1, p. 6640, 2021.

J. M. W. S. Rytka, E. J. Schoenle, and D. Konrad, “The portal theory supported by venous drainage-selective fat transplantation,” Diabetes, vol. 60, no. 1, pp. 56–63, 2011.

A. T. F. W. Ali, C. B. Penny, M. T. Van der Merwe, B. F. Jacobson, J. E. Paiker, and N. J. Crowther, “Lipid accumulation and alkaline phosphatase activity in human preadipocytes isolated from different body fat depots,” Journal of Endocrinology, Metabolism and Diabetes of South Africa, vol. 18, no. 1, pp. 58–64, 2013.

M. K. Kim, K. H. Baek, M. I. Kang et al., “Serum alkaline phosphatase, body composition, and risk of metabolic syndrome in middle-aged Korean,” Endocrine Journal, vol. 60, no. 3, pp. 321–328, 2013, PubMed PMID: 23149655. Epub 2012/11/15. eng.

M. Ghandehari, Z. Dehnavi, F. Razmpour et al., “The associations of anthropometric parameters and hepatic steatosis,” Journal of Nutrition, Fasting and Health, vol. 8, no. 1, pp. 23–27, 2020.

U. G. Kyle, I. Bosaeus, A. D. De Lorenzo et al., “Bioelectrical impedance analysis--part I: review of principles and methods,” Clinical Nutrition, vol. 23, no. 5, pp. 1226–1243, 2004.

D. A. de Luis, R. Aller, E. Romero, A. Dueñas, and J. L. Perez Castrillon, “Relation of phase angle tertiles with blood adipocytokines levels, insulin resistance and cardiovascular risk factors in obese women patients,” European Review for Medical and Pharmacological Sciences, vol. 14, no. 6, pp. 521–526, 2010.

F. Moreto, N. A. G. de França, F. F. Gondo et al., “High C-reactive protein instead of metabolic syndrome is associated with lower bioimpedance phase angle in individuals clinically screened for a lifestyle modification program,” Nutrire, vol. 42, no. 1, 2017.

M. H. Dong, R. Bettencourt, E. Barrett-Connor, and R. Loomba, “Alanine aminotransferase decreases with age: the Rancho Bernardo Study,” PLoS One, vol. 5, no. 12, article e14254, 2010.

S. McPherson, T. Hardy, J. F. Dufour et al., “Age as a confounding factor for the accurate non-invasive diagnosis of advanced NAFLD fibrosis,” The American journal of gastroenterology, vol. 112, no. 5, pp. 740–751, 2017.

N. Mohammadifard, M. Nazem, N. Sarrafzadegan et al., “Body mass index, waist-circumference and cardiovascular disease risk factors in Iranian adults: Isfahan healthy heart program,” Journal of health, population, and nutrition, vol. 31, no. 3, pp. 388–397, 2013.

J. Zhang, L. Xu, J. Li et al., “Gender differences in the association between body mass index and health-related quality of life among adults:a cross-sectional study in Shandong, China,” China. BMC public health, vol. 19, no. 1, pp. 1–9, 2019.

E. B. Geer and W. Shen, “Gender differences in insulin resistance, body composition, and energy balance,” Gender Medicine, vol. 6, pp. 60–75, 2009.