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. 2024 Jul 18;19(7):e0303835. doi: 10.1371/journal.pone.0303835

A case-control regression analysis of liver enzymes in obesity-induced metabolic disorders in South Asian females

Tamseela Mumtaz 1,*,#, Kainat Tariq 1,#, Khadija Kanwal 2, Zainab Tariq 1
Editor: Samiullah Khan3
PMCID: PMC11257360  PMID: 39024244

Abstract

Excessive body weight may disrupt hepatic enzymes that may be aggravated by obesity-related comorbidities. The current case-control study was designed to evaluate the extent of liver enzyme alteration in obesity-related metabolic disorders. Obese females with BMI ≥ 30 suffering from metabolic disorders were grouped according to existing co-morbidity and their hepatic enzymes were compared with non-obese healthy females. The resultant data was subjected to analysis of variance and mean difference in liver enzymes were calculated at P = 0.05. Analysis of variance indicated that obese diabetic and obese hypertensive females had almost 96% and 67% increase in the concentration of gamma-glutamyl transferase than control, respectively (P<0.0001). The obese females suffering from diabetes and hypertension exhibited nearly 54% enhancement in alanine transaminase level (P<0.0001) and a 17% increase in aspartate aminotransferase concentration (P = 0.0028). Obesity along with infertility decline liver enzyme production and a 31% significant decline in aspartate aminotransferase was observed while other enzyme concentrations were not significantly altered. Regression analysis was performed on the resultant data to understand the association between liver enzyme alteration and the development of metabolic diseases. Regression analysis indicated that obese diabetic and obese diabetic hypertensive women had 20% production of normal liver enzymes and 80% enzymes produced abnormally. Obese hypertensive and obese infertile females had only 5% and 6% normal production of liver enzymes, respectively. This research leads to the conclusion that the ability of the liver to function normally is reduced in obesity-related diabetes and hypertension. This may be due to inflamed and injured liver and poses a serious threat to developing fatty liver disease and ultimately liver cirrhosis.

Introduction

Metabolic syndrome (MetS) is a collection of cardiometabolic perils including abdominal obesity and overweightness, increased blood glucose levels and hypertension [1]. It is identified when three out of the following five conditions are present: increased waist circumference, high fasting glucose levels, high blood pressure, raised triglycerides and decreased high-density lipoproteins (HDL) [2]. Metabolic syndrome distresses approximately 30–40% of individuals till they reach 65, caused mainly by weight gain and fat accumulation within abdomen due to genetic or non-genetic factors [3].

Fatty liver and raised hepatic enzyme levels are frequently observed in obesity, serving as trademarks of non-alcoholic fatty liver disease (NAFLD), These enzymes are also raised in metabolic syndrome and insulin resistance (IR) [4]. Insulin resistance is the main factor associated with MetS and NAFLD which has been linked to accumulation of unnecessary fat in areas such as liver, promoting inflammation and stressing the endoplasmic reticulum. This stress and inflammation set up and exacerbate IR [5]. Increased body fat, raised BMI, high insulin level and insulin resistance elevate the levels of liver enzymes; alanine aminotransferase (ALT), aspartate aminotransferase (AST), and gamma-glutamyl transferase (GGT) [6–8]. Elevation of ALT and AST also occurs when there is hypertension and diabetes, which have adverse effects on the heart leading to coronary heart disease (CHD) [9]. Concentrations of GGT even in normal ranges are associated with hypertension in subjects with abdominal obesity, suggesting a role of fatty liver in obesity related hypertension [10] while on the other hand, elevated concentrations of GGT are associated with the increased probability of developing diabetes [11]. Previous studies showed greater circulating levels of hepatic enzymes in individuals with metabolic syndrome and IR and revealed a significant association of these liver enzymes with the likelihood of developing T2DM [12]. Obesity along with reduced liver functioning upsurges the risk of diabetes. By decreasing body weight, liver function can be better and the risk of diabetes development can be lower [13,14].

Considering the above literature, it is evident that the alteration of liver enzymes is associated with obesity. The current study, therefore, aimed to evaluate the correlation of liver enzymes with the obesity-related metabolic syndrome suffering from diabetes, hypertension, diabetes+hypertension, and infertility. The research also seeks to determine how much liver enzyme alterations may contribute to the onset of metabolic syndrome.

Materials and methods

Study design

A case-control study was designed to evaluate obesity-induced metabolic diseases and their association with liver enzymes. Obesity-induced diseases included in the study were hypertension, diabetes and infertility. The study design was placed before the Ethical Review Committee (ERC) of Government College Women University and got approved vide letter # GCWUF/IERC/21/132 dated 22-02-2021. The study started in August 2021 and ended in March 2023.

Study population and sample size

For this study, 162 females with BMI of ≥25 were interrogated. Among them, overweight and obese females were segregated for initial scrutiny and the women who were either overweight or obese without suffering from any comorbidity were excluded at this stage. The females who had intentionally lost their weight during the study to reduce the impact of disease were also excluded. All obese females with BMI≥30 (n = 120) who were suffering from diabetes, hypertension or infertility due to obesity were categorized into four subgroups based on obesity-related co-morbidities. Obese females suffering from diabetes were clustered as Group 1, whereas obese females with hypertension were categorized into Group 2. The obese women suffering from hypertension and diabetes were classified as Group 3. The obese infertile females were clustered into Group 4. All the groups had an equal number of subjects (n = 30). For comparison, 120 females with BMI≤25 and having no health issues were selected as control (n = 120). As this was a case-control study, the ratio of case-to-control was set as 4:1 which means 4 healthy females were recruited against 1 morbid obese female. This sample size was calculated by G* Power software by applying F family statistical test, “The ANOVA: Fixed effects, omnibus, one-way”. The applied analysis was post hoc: Compute achieved power. The input parameter included: Effect size f = 0.40, α err prob = 0.05. The output power of analysis (1-β err prob) was 0.9996582. The study plan was discussed with the participants, and it was made sure that all subjects participated voluntarily without any incentive. A written informed consent was taken and signed by the participants. A comprehensive questionnaire was designed to collect personal information, disease and demographic history. The personal information was kept confidential and each individual was assigned an anonymous identification number to maintain confidentiality (S1 File).

Inclusion and exclusion criteria

The age of the participants ranged from 23–60 years and special care was taken to ensure that no participant was over sixty years because this is the part of the age where the risk of getting diseases is high. It was made sure that all the selected females had BMI ≥30 kg/m2 and had developed comorbidity because of obesity. They must have one of the obesity-induced comorbidities such as diabetes, hypertension, and infertility. They should not suffer from any viral disease or any chronic illness like hepatitis, respiratory or gastrointestinal diseases, drug or alcohol usage, or hyperthyroidism that might lead to obesity. It was also made sure that females who were treated with antihypertensive or antidiabetic drugs that may affect liver function should not be part of the study. Women with less than a 30 BMI were not fit for participation in the study and were excluded. Those obese women who did not have any obesity-related diseasealso not considered (S2 File).

Blood sample collection and processing

Intravenous blood was collected from selected females through venipuncture and poured in 3 ml EDTA-coated vacutainers to prevent agglutination of blood. Blood was centrifuged at 3000 rpm for 15 minutes to separate the plasma. All personal and laboratory safety measures were ensured while sampling. Liver enzymes: ALT, AST, and GGT were determined from plasma using commercially available kits (ALT cat # GL732AL, AST cat # GL733AS and GGT cat # GL705GT) made by Bioactiva diagnostica GmbH with the help of a fully automated biochemistry analyzer (FA-200 Clindiag) and clinical chemistry analyzer (Erba Mannheim CHEM-7, Germany). All standard protocols were recommended by the International Federation of Clinical Chemistry (IFCC).

Statistical analysis

Obtained data were subjected to statistical analysis through ordinary one-way analysis of variance (Ordinary one-way ANOVA) using Graph-Pad Prism (V.6.0) and difference in mean values was observed at P = 0.05 by performing Dunnett’s multiple comparison test, with a single pooled variance. Further, regression analysis was performed through SPSS (V.21) to establish the relationships between liver enzyme variation and the metabolic ailments under study.

Results

One-way analysis of variance of anthropometric measurements in obese females

The waist-to-hip ratio was higher in obese diabetic and obese diabetic hypertensive women (1.00 ±0.01 and 1.02±0.01, respectively). Obese infertile females had the least rise in WHR (0.93±0.01) as compared to normal females (0.84±0.01). On the other hand, all the women in the study were moderately obese having a BMI of 30 < 34.9 (obese class 1). The BMI of females with metabolic disorders tremendously increased and the mean BMI of obese diabetic females reached up to 32.44±0.98. A similar trend of obesity was noticed in obese hypertensive females with a mean value of 33.26±1.20. When hypertension and diabetes co-existed, the body gained extra mass, and mean BMI increased by up to 36.6% than healthy females. Obese infertile females, however, had an average BMI of 30.07±0.80.

The random blood sugar (RBS) was significantly higher in the diabetic and diabetic hypertensive groups (P<0.0001). Obese hypertensive women also had a minor increase in their RBS level but the increase was within the normal limit (105.3±2.83). Contrary to this, the obese infertile females had a slight decrease in their RBS levels and again, the decrease was within the normal range (100.3±2.03). The systolic and diastolic blood pressure (SBP and DBP) were found to be significantly higher in the hypertensive and diabetic hypertensive groups as expected. Still, the diabetic group also showed an increase in SBP (124.0±1.76) implying stress on the heart by diabetes in association with the progression of obesity. Infertile obese females had normal blood pressure.

Surprisingly cholesterol level was found higher in obese diabetic women (176.3±4.55) than the obese hypertensive females (171.3±5.712). When both diseases were combined, the level shot up to 186.8±7.29 which was 30% higher than the healthy women’s cholesterol level (Tables 1 and 2).

Table 1. Anthropometric and biophysical measurements of participants in control and obese groups with metabolic disorders.

Values are mean±SEM.

Variables Groups
Control Ob DM Ob HTN Ob DM+HTN Ob Infertile P-value
WC (inches) 28.90±0.41 43.40±0.83 44.55±0.86 46.50±0.55 40.03±0.88 <0.0001
HC (inches) 34.50±0.45 43.67±0.78 46.14±0.82 45.63±0.74 43.10±0.81 <0.0001
WHR 0.84±0.01 1.00±0.01 0.97±0.01 1.02±0.01 0.93±0.01 <0.0001
BMI (kg/m2) 21.94±0.37 32.44±0.98 33.26±1.20 34.60±0.96 30.06±0.98 <0.0001
RBG (mg/dL) 104.5±1.69 232.6±13.30 105.3±2.83 239.1±13.50 100.3±2.03 <0.0001
SBP (mmHg) 113.7±1.31 124.0±1.76 147.7±3.02 144.7±3.41 117.7±1.90 <0.0001
DBP (mmHg) 74.33±1.24 81.33±0.92 92.67±1.51 90.67±1.51 75.33±1.33 <0.0001
Cholesterol (mg/dL) 143.5±1.36 176.3±4.55 171.3±5.712 186.8±7.295 141.3±3.387 <0.0001
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Significant at P<0.05 at 95% CI; Ob: Obese, DM diabetes mellitus, HTN: Hypertension, BMI: Body mass index; RBG; random blood glucose; SBP: Systolic blood pressure; DBP: Diastolic blood pressure; WC: Waist circumference; HC: Hip circumference; WHR: Waist-to-hip ratio.

Table 2. Percentage variation in biophysical measurements of healthy control and morbidly obese groups.

Groups Variables
WC% HC% WHR% BMI% Pulse rate % RBG% SBP% DBP% Cholesterol %
Control Vs Ob. DM 50.1****↑ 26.6****↑ 18.3↑ 47.9****↑ 5.6**↑ 122.6****↑ 9.0*↑ 9.3**↑ 22.7****↑
Control Vs Ob. HTN 54.1****↑ 33.7****↑ 14.9↑ 51.7****↑ 12.9****↑ 0.8↑ 29.8****↑ 24.5****↑ 19.2***↑
Control Vs Ob. DM+ HTN 60.9****↑ 32.3****↑ 21.3↑ 57.8****↑ 10.9****↑ 128.8****↑ 27.1****↑ 21.8****↑ 30.0****↑
Control Vs Ob. infertile 38.5****↑ 24.9****↑ 10.3*↑ 37.1****↑ 0.2↓ 4.0↓ 3.4↓ 1.2↓ 1.7↓
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↑ indicate an increase in value as compared to control.

* significant at P = 0.05

** significant at P = 0.01

*** significant at P = 0.001

**** significant at P = 0.0001.

One-way analysis of variance of biochemical measurements in obese females

Levels of liver enzymes in obese females suffering from allied diseases are greatly altered. Among the three liver enzymes studied, GGT was found to be significantly higher in obese diabetic and obese hypertensive females (P<0.0001), but its increase was non-significant in infertile obese females. Alanine aminotransferase also showed significant enhancement in obese diabetic and hypertensive females (P<0.0001) while, AST, on the other hand, showed negligible elevation in obese diabetic and obese diabetic hypertensive females. All hepatic enzymes except AST in infertile obese females remained within the normal range (Fig 1).

Fig 1. Individual GGT (U/L), ALT (U/L), and AST (U/L) concentrations in control (n = 120) and obese females with allied metabolic disorders (n = 30 in each metabolic disorder).

Fig 1

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The figure illustrates the graphical presentation of the three liver enzymes in each subject concerning the control.

The relationship of obesity with cholesterol is a key health indicator in women (Fig 2A–2E)

Fig 2. Line fit scatter plot for obese diabetic BMI vs cholesterol levels.

Fig 2

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A. The x-axis represents the BMI whereas the Y-axis represents the cholesterol values. The correlation between BMI and cholesterol levels in the obese hypertensive group revealed an r-value of -0.02927 which indicates a non-significant very small negative relationship between X and Y, p = 0.863 (Fig 2B). B. Line fit scatter plot for obese hypertensive BMI vs cholesterol levels. The x-axis represents the BMI whereas the Y-axis represents the cholesterol values.The correlation between BMI and cholesterol levels in the obese diabetic hypertensive group revealed an r-value of 0.1081 which indicates a non-significant small positive relationship between X and Y, p = 0.530 (Fig 2C). C. Line fit scatter plot for obese diabetic hypertensive BMI vs cholesterol levels. The x-axis represents the BMI whereas the Y-axis represents the cholesterol values. The correlation between BMI and cholesterol levels in the obese infertile group revealed an r-value of -0.03432 which indicates a non-significant very small negative relationship between X and Y, p = 0.821 (Fig 2D). D. Line fit scatter plot for obese infertile BMI vs cholesterol levels. The x-axis represents the BMI whereas the Y-axis represents the cholesterol values. The correlation between BMI and cholesterol levels in the control group revealed an r-value of 0.06306 which indicates a non-significant very small positive relationship between X and Y, p = 0.494 (Fig 2E). E. Line fit scatter plot for control BMI vs cholesterol levels. The x-axis represents the BMI whereas the Y-axis represents the cholesterol values.

The correlation between BMI and cholesterol levels in the obese diabetic group revealed an r-value of -0.07122 which indicates a non-significant very small negative relationship between X and Y, p = 0.654 (Fig 2A).

Production of liver enzymes altered in morbidly obese females

Liver function tests of obese diabetic females according to the regression line are given in Table 3.

Table 3. Regression model for liver function tests of obese diabetic females.


Coefficientsa
Model Unstandardized Coefficients Standardized Coefficients T Sig. 95% Confidence Interval for B
B Std. Error Beta Lower Bound Upper Bound
1 (Constant) 11.068 5.407 2.047 0.051 24.96 38.39
ALT 0.145 0.127 0.291 1.139 0.265 25.47 43.05
AST 0.059 0.092 0.157 0.639 0.528 32.76 48.21
GGT -0.058 0.087 -0.131 -0.661 0.515 31.08 39.87
Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 0.449a 0.202 0.110 8.3065
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In this data model, we take more interest in R Square which indicates how much variation covers, and we see it is only 20% of enzymes are made by the liver properly and 80% are deranged due to obese diabetes.

Liver function tests of obese hypertensive females according to the regression line are given in Table 4 (Fig 3B).

Table 4. Regression model for liver function tests of obese hypertensive females.

Coefficientsa
Model Unstandardized Coefficients Standardized Coefficients T Sig. 95.0% Confidence Interval for B
B Std. Error Beta Lower Bound Upper Bound
1 (Constant) 16.195 3.220 5.029 0.000 9.575 22.814
ALT -0.091 0.187 -0.154 -0.484 0.632 -.476 .294
AST 0.139 0.156 0.308 0.889 0.382 -.182 .460
GGT -0.060 0.055 -0.270 -1.089 0.286 -.173 .053
Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 0.235a 0.055 -0.054 9.0379
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Fig 3.

Fig 3

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A. Scatter plot regression model for liver function tests of obese-induced comorbid and control groups. The figure represents the data after converting all variables into one variable by adding it through SPSS transform.

When we look at R Square in this data model, which covered more variations, we found that only 5% of liver enzymes were produced correctly and 95% of enzyme production is abnormal due to obesity-induced hypertension. Liver function tests of obese diabetic hypertensive females according to the regression line are given in Table 5 (Fig 3C).

Table 5. Regression model for liver function tests of obese diabetic hypertensive females.

Coefficientsa
Model Unstandardized Coefficients Standardized Coefficients T Sig. 95.0% Confidence Interval for B
B Std. Error Beta Lower Bound Upper Bound
1 (Constant) 22.824 3.963 5.759 0.000 14.678 30.971
ALT -0.259 0.114 -0.469 -2.267 0.032 -.494 -.024
AST 0.002 0.077 0.005 0.026 0.979 -.157 .161
GGT 0.032 0.057 0.100 0.558 0.582 -.085 .148
Model summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 0.456a 0.208 0.117 8.2734
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According to this data model’s R Square, (which shows how much variation is covered), only 20% of the enzymes produced by the liver are produced appropriately, while the remaining 80% are out of whack due to obesity-induced hypertension and diabetes. Liver function tests of obese infertile females according to the regression line are given in Table 6 (Fig 3D).

Table 6. Regression model for liver function tests of obese infertile females.

Coefficientsa
Model Unstandardized Coefficients Standardized Coefficients T Sig. 95.0% Confidence Interval for B
B Std. Error Beta Lower Bound Upper Bound
1 (Constant) 9.428 5.597 1.684 0.104 -2.077 20.934
ALT 0.059 0.225 0.055 0.261 0.796 -.404 .521
AST 0.196 0.212 0.199 0.924 0.364 -.240 .632
GGT 0.029 0.060 0.094 0.480 0.635 -.095 .153
Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 0.233a 0.054 -0.055 9.0417
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According to this data model only 6% of enzymes are appropriately produced by the liver and 94% of production is deranged due to obesity-related infertility.

Liver function of obese healthy control females according to the regression line and R -Square are given in Table 7 (Fig 3E).

Table 7. Regression model summary for the control group.

Coefficicents
Model Unstandardized Coefficients t Sig. 95.0% Confidence Interval for B
B Std. Error Lower Bound Upper Bound
1 (Constant) 59.003 9.535 6.188 .000 40.119 77.888
ALT (U/L) .156 .400 .390 .697 -.637 .949
AST (U/L) -.030 .348 -.086 .931 -.720 .660
GGT (U/L) -.047 .283 -.164 .870 -.607 .514
Model Summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .916a .808 .657 12.284
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In this data model, we take more interest in R Square which indicates 20% variation covered and 80% enzymes made by the liver properly.

Liver enzymes are not equally altered in all obesity-induced metabolic disorders

According to the data sample in Table 8, we conclude that we cannot reject the H1 hypothesis (alternate hypothesis) and we are not able to accept the Ho hypothesis (null hypothesis) which states that all enzymes work equally based on the value of alpha α = 0.05. In obese diabetic, hypertensive, diabetic hypertensive, and infertile groups the observed p-values are greater than 0.05, indicating that all enzymes are not working equally in comorbid groups. On the contrary, the p-value in the control group is less than 0.05 implying that all enzymes are working equally in the control group.

Table 8. Significance of multiple variables by using ANOVA in regression for liver function tests of obesity-induced metabolic disorders groups.

Model Sum of Squares Df Mean Square F Sig.
Correlation model summary for liver function tests of the obese diabetic group
Regression 1270.731 2 635.366 1.408 0.250
Residual 40606.452 90 451.183
Total 41877.183 92
Correlation model summary for liver function tests of the obese hypertensive group
Regression 123.705 3 41.235 0.505 0.682
Residual 2123.795 26 81.684
Total 2247.500 29
Correlation model summary for liver function tests of the obese diabetic hypertensive group
Regression 467.816 3 155.939 2.278 0.103
Residual 1779.684 26 68.449
Total 2247.500 29
Correlation model summary for liver function tests of the obese infertile group
Regression 121.956 3 40.652 0.497 0.687
Residual 2125.544 26 81.752
Total 2247.500 29
Correlation model summary for liver function tests of the control group
Regression 372.829 3 124.276 1.724 0.0187
Residual 1874.671 26 72.103
Total 2247.500 29
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Further, if alpha changes or the sample data variates, results may vary.

ANOVA seeks to determine the difference in mean at each level of a factor. According to the results below, the results are not statistically significant in the comorbid groups. The whole factors are disturbed strongly and affect the liver enzymes. On the contrary, in the control group, the whole factors are not disturbed and do not affect the liver.

Discussion

The liver is a vital organ where the metabolism of glucose takes place along with its uptake, synthesis, and storage. Enhanced activities of liver enzymes; ALT, AST, and GGT serve as indicators for liver disorder and these elevations are observed mostly due to fatty penetration of the liver [12,15]. Elevated levels of liver enzymes can serve as biomarkers for assessing the severity of CHD especially GGT even in normal limits related to obesity allied hypertension [9,10].

The present study aimed to study the extent of liver enzymes involved in the severity of metabolic syndrome. Among the hepatic enzymes studied, GGT exhibited a strong impact on diabetes and hypertension, thereby, acting as a biomarker for obesity allied ailments. Concentrations of GGT in the liver not only indicate fatty liver volume but also serve as a marker for oxidative stress [16]. Levels of GGT are influenced majorly by obesity and other metabolic ailments like glucose imbalance and hypertension, hence validating the incidence of T2DM and hypertension with elevated GGT levels [17,18]. Hypertension was found to be linked with hepatic enzymes and noticeable elevation in anthropometric measurements, blood pressure, and GGT [19].

Obesity along with polycystic ovary syndrome (PCOS) is the main cause of infertility. In this study, infertile females chosen were obese class I, and obesity with PCOS was the reason for their infertility. In these females, GGT was found to be non-significantly high and linked with PCOS rather than obesity [20].

Aspartate aminotransferase is mainly widespread in the heart along with other parts like the liver, bone, lung, etc. Mitochondrial disturbance due to its oxidative stress or cell death in case of hepatic damage or myocardial infarction leads to enhanced leakage of AST into blood. Hence, its levels can be measured to evaluate the extent of heart and liver damage [21,22]. Negligible elevations in AST levels were noticed in females suffering from diabetes as well as hypertension and the one with diabetes only represented no vital heart and liver damage at this stage of diabetes and its allied issues onset. While it remained within the normal range in hypertension and infertile females, so AST seemed to be unaffected by obesity and have a weak association with it and these findings are consistent with [23].

Abnormal glucose metabolism and NAFLD are mainly responsible for increased levels of ALT apart from hepatitis and these deranged ALT values act as predictors of atherosclerosis and CVD [24,25]. The elevated levels of ALT have been associated with the prevalence of obesity and multiple ailments of MetS such as diabetes mellitus and CVD [26,27].

Outcomes of the present study showed a significant elevation in ALT levels in obese diabetic and obese diabetic hypertensive females, and this is similar to a study that narrated a positive linkage of ALT with increased risk of T2DM incidence [28]. However, a negligible increase was shown by obese infertile and obese diabetic and hypertensive females. High ALT levels expose fatty liver alterations, and these alterations further precede T2DM development [29]. The diabetes epidemic due to GGT and ALT is related to risk factors like BMI and waist-to-hip ratio (WHR) [30]. Thus, elevated ALT levels were found to be associated with hypertension, increased glucose levels, and abdominal obesity [31]. The findings of our study are in line with the findings of previous studies where ALT was found to be associated with general obesity and GGT was associated with both general and abdominal obesity [32]. ALT is also known to have positive associations with fasting blood glucose levels, IR, BMI, and WC along with GGT and these associations are more evident in females than males [23] whereas AST also exhibits positive associations with BMI and WC. The levels of liver enzymes aggravated further with an increase in the various metabolic abnormalities of metabolic syndrome [33]. Due to the presence of outliers in comorbid groups (Fig 3A–3E), the observed results are statistically significant indicating the rejection of the Ho hypothesis which implies that data is not variating equally.

Thus, the study concluded a positive relation between liver enzymes with body weight, especially of ALT and GGT. And since numerous serious health problems such as CVD, HTN, T2DM, and infertility are directly related to overweight and obesity, therefore, enhancement of liver enzymes is predictable in all these morbidities. This study sheds light on the possible role of liver enzymes particularly GGT in the indication of liver pathology accompanied by obesity.

Conclusions

Obesity paves the way for metabolic syndrome through complications like increased blood pressure, blood sugar levels, cholesterol, and fatty liver. An increase in body weight is notably linked with the levels of liver enzymes, especially GGT and ALT. GGT. When the two comorbidities; diabetes and hypertension occurred together they significantly doubled the levels of GGT. Moreover, the liver enzyme alteration was found to be maximum in obese hypertensive and obese infertile females with only 5 and 6% normal production of liver enzymes while the obese diabetic and obese diabetic hypertensive females had 80% of the liver enzymes deranged indicating that obesity along with multiple comorbidities severely impacts the liver functioning and can lead to liver complications and fatty liver diseases. This research concludes that liver enzymes, especially GGT and ALT might serve as biomarkers for identifying the probable presence of metabolic syndrome.

Supporting information

S1 File. Consent and questionnaire.

(PDF)

pone.0303835.s001.pdf (58.6KB, pdf)
S2 File. Inclusion and exclusion criteria.

(PDF)

pone.0303835.s002.pdf (94.8KB, pdf)
S3 File. Minimal data set.

(PDF)

pone.0303835.s003.pdf (668.2KB, pdf)
S4 File. Regression analysis equation.

(PDF)

pone.0303835.s004.pdf (62.3KB, pdf)
S5 File

(PDF)

pone.0303835.s005.pdf (791.1KB, pdf)
S6 File

(PDF)

pone.0303835.s006.pdf (769.3KB, pdf)
S7 File

(PDF)

pone.0303835.s007.pdf (668.2KB, pdf)
S8 File

(PDF)

pone.0303835.s008.pdf (62.3KB, pdf)

Acknowledgments

I would like to acknowledge the medical and para-medical staff at Faisal Hospital, Faisalabad for facilitating blood sample collection and laboratory tests.

Operational definitions

Ho hypothesis (Null hypothesis): All the variables show equal variations in all comparable groups.

H1 hypothesis (Alternate hypothesis): Each case group show different variations of liver enzymes depending on the status of comorbidities.

Abbreviations

ALT

Alanine Aminotransferase

ANOVA

Analysis of variance

AST

Aspartate Aminotransferase

BMI

Body Mass Index

CVD

Cardiovascular Disease

DBP

Diastolic Blood Pressure

ERC

Ethical Review Committee

GGT

Gamma-Glutamyl Transferase

HC

Hip Circumference

HDL

High Density Lipoproteins

IFCC

International Federation of Clinical Chemistry

IR

Insulin Resistance

MetS

Metabolic Syndrome

NAFLD

Non-Alcoholic Fatty Liver Disease

PCOS

Polycystic Ovary Syndrome

RBS

Random Blood Sugar

rpm

Revolutions per minute

SBP

Systolic Blood Pressure

SEM

Standard Error Mean

T2DM

Type 2 Diabetes Mellitus

WHR

Waist to Hip Ratio

WC

Waist Circumference

Data Availability

All relevant data are within the manuscript and its Supporting Information files.

Funding Statement

The author(s) received no specific funding for this work.

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Decision Letter 0

Samiullah Khan

7 Nov 2023

PONE-D-23-28668A case control regression analysis of altered liver enzymes in obesity-induced metabolic disordersPLOS ONE

Dear Dr. Mumtaz,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

 

Dear author,

Revise the whole manuscript as suggested by reviewers and submit for evaluation.

Thanks

Please submit your revised manuscript by Dec 22 2023 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

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We look forward to receiving your revised manuscript.

Kind regards,

Samiullah Khan, Ph. D

Academic Editor

PLOS ONE

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Additional Editor Comments:

Dear author,

Revise the whole manuscript carefully as suggested by both reviewers in their comments. Especially rephrase the title of study as advised by reviewer-2. Submit the revised version of manuscript for evaluation.

Thanks

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

**********

2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: I Don't Know

Reviewer #2: Yes

**********

3. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: No

Reviewer #2: Yes

**********

4. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: No

Reviewer #2: Yes

**********

5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: 1- To control diabetes or blood pressure, patients may have been treated with drugs. How did you separate their effects on liver enzymes from the effects of obesity on liver enzymes?

2- Not consuming alcohol should be an exclusion criteria. 3- Triglyceride, bilirubin and PT-INR - could provide more comprehensive information. Are these data available? 4- In daily practice, ultrasound of the upper abdomen and fibroscan of the liver provide valuable information about liver disease. It is even possible that the liver enzymes are normal and the fibroscan shows moderate degrees of fibrosis. Not using liver imaging is one of the limitations of the study and should be mentioned

Reviewer #2: The authors describe the relationship between obesity and alterations in liver enzymes potentially increasing the risk of liver diseases. The manuscript is well structured with a specific focus on South Asian females. The research sheds light on the unique health dynamics within this demographic.

With that said, I have some suggestions for further improvement:

Title:

The manuscript title is a critical element that sets the tone for the study. To enhance clarity, it may be beneficial to rephrase the title to specify that the study exclusively focuses on data obtained from female individuals. Additionally, consider including the demographic area of the population, for instance, "South Asian Female Population," to provide a more precise context.

Abstract:

I would like to kindly point out that the abstract in the manuscript appears to be incomplete.

Results:

1. Descriptive Titles: I recommend providing more descriptive titles for the ‘Results’ section that directly convey the key observations. This will help readers navigate the content more effectively.

2. Combining WHR and BMI Results: Given the brevity of the WHR and BMI sections and their shared focus on size-related factors, it could be advantageous to combine these into a single section to streamline the presentation.

3. Improving Table Legends: Enhance the clarity of table legends in terms of variable comparisons, particularly in Table 1, and ensure that the context of p-values is clearly defined. If applicable, consider conducting pairwise comparisons to improve data interpretation.

4. Visualization Enhancements: For Figure 1, I suggest utilizing a box plot format with overlaying individual scatter dots, which can provide a clearer representation of the data compared to dynamite plots.

5. Incorporate Regression Dot Plots: In addition to tables, it would be beneficial to present regression dot plots, displaying all data points alongside the main results. Additionally, consider including correlation plots with confidence intervals, and if authors find it suitable, these analyses can be integrated into a single plot.

6. Clear and Accessible Conclusions: Concluding each results section with a clear and less technical statement summarizing the main findings would greatly benefit the manuscript. This will facilitate a more accessible understanding of the research outcomes.

7. Supplementary Equations: To streamline the manuscript, it would be better to move the equations in line number 177/185/194/202/209 (e.g., yˆ=x+y+z) from the main text to a supplementary section, citing them where necessary for clarity.

8. Cholesterol Data and Correlations: To provide a comprehensive context, consider including correlation between cholesterol and obesity. Additionally, clarify whether the authors exclusively measured total cholesterol or if data is available for HDL, LDL, and triglycerides. Correlating this data with obesity would enhance the manuscript's depth.

**********

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Reviewer #1: Yes: Hafez Fakheri

Reviewer #2: Yes: Shrestha Mohapatra

**********

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PLoS One. 2024 Jul 18;19(7):e0303835. doi: 10.1371/journal.pone.0303835.r002

Author response to Decision Letter 0

7 Jan 2024

All the authors are very grateful for evaluating the manuscript through critical peer review. The reviewers indeed swotted the article with an eagle eye that depicts their competency. They raised such valuable points that greatly altered the study design and its outcome as well. We are much indebted for the reviewers' valuable comments and suggestions that improve the article's quality. The manuscript has been revised in light of reviewer’s recommendation. A detailed, point-to-point response to the reviewers’ comments is as follows

Reviewer Comments Response

Reviewer 1

1 To control diabetes or blood pressure, patients may have been treated with drugs. How did you separate their effects on liver enzymes from the effects of obesity on liver enzymes? It was made sure that patients either did not have any treatment for obesity-related co-morbidities or if treated, they must have shallow potency treatment that could not affect the efficacy of live function. See page 5 lines 107-109

2 Not consuming alcohol should be an exclusion criterion The study population is assumed to not be addicted to alcohol however to resolve the concern it has been mentioned in the manuscript. See page 5 line 106.

3 Triglyceride, bilirubin and PT-INR - could provide more comprehensive information. Are these data available Yes, these data are available but are part of another manuscript that is under process, hence cannot be provided.

4 In daily practice, ultrasound of the upper abdomen and fibro scan of the liver provide valuable information about liver disease. It is even possible that the liver enzymes are normal and the fibro scan shows moderate degrees of fibrosis. Not using liver imaging is one of the limitations of the study and should be mentioned The study was focused on serology and variations in liver enzyme production due to obesity, therefore, a fibro scan or ultrasound of the upper abdomen was not performed. A fibro scan with mild fibrosis may not fulfil the study objectives. This point could be valid if the study focused on liver echotexture rather than its function. However, the authors appreciated the logic, and they conceived the idea that obesity may cause fibrosis which may be asymptomatic with normal liver enzymes

Reviewer 2 Response

1 Title:

The manuscript title is a critical element that sets the tone for the study. To enhance clarity, it may be beneficial to rephrase the title to specify that the study exclusively focuses on data obtained from female individuals. Additionally, consider including the demographic area of the population, for instance, "South Asian Female Population," to provide a more precise context.

Thanks for bringing this point to our attention. Indeed, it is a very good suggestion. The title has been rephrased as suggested. See title page 1 lines 1-3

2 Abstract:

I would like to kindly point out that the abstract in the manuscript appears to be incomplete Acknowledged. The word cirrhosis was omitted from the abstract typographically. Issue resolved. see page 2 line 35

3 Results:

Descriptive Titles: I recommend providing more descriptive titles for the ‘Results’ section that directly convey the key observations. This will help readers navigate the content more effectively. Titles of results sections have been changed that indicate the key observations of results being discussed.

4 Combining WHR and BMI Results: Given the brevity of the WHR and BMI sections and their shared focus on size-related factors, it could be advantageous to combine these into a single section to streamline the presentation. The sections have been combined. See title page 6 lines 129-130

5 Improving Table Legends: Enhance the clarity of table legends in terms of variable comparisons, particularly in Table 1, and ensure that the context of p-values is clearly defined. If applicable, consider conducting pairwise comparisons to improve data interpretation. Table 1 legends are described more clearly indicating P=0.05 and pairwise comparisons are indicated in the form of percentage increase or decrease. Table 2 Page # 8

Visualization Enhancements: For Figure 1, I suggest utilizing a box plot format with overlaying individual scatter dots, which can provide a clearer representation of the data compared to dynamite plots. Thank you for your valuable suggestion scatter plot provides a better illustration of data. Figure 1 is updated with individual scatter dots. See Fig 1

Incorporate Regression Dot Plots: In addition to tables, it would be beneficial to present regression dot plots, displaying all data points alongside the main results. Additionally, consider including correlation plots with confidence intervals, and if authors find it suitable, these analyses can be integrated into a single plot. Regression dot plots incorporated as suggested. See Fig. 7.

The correlation plots with confidence intervals cannot be integrated into a single plot, thus presented in a table format. See page no.15 Table 8

Clear and Accessible Conclusions: Concluding each results section with a clear and less technical statement summarizing the main findings would greatly benefit the manuscript. This will facilitate a more accessible understanding of the research outcomes. Done as suggested.

Supplementary Equations: To streamline the manuscript, it would be better to move the equations in line number 177/185/194/202/209 (e.g., yˆ=x+y+z) from the main text to a supplementary section, citing them where necessary for clarity. The equations have been moved from the main file to the supplementary file (S8 File).

Cholesterol Data and Correlations: To provide a comprehensive context, consider including the correlation between cholesterol and obesity. Additionally, clarify whether the authors exclusively measured total cholesterol or if data is available for HDL, LDL, and triglycerides. Correlating this data with obesity would enhance the manuscript's depth. We appreciate your suggestion; indeed, it has given a new dimension to our manuscript as we have to go through the results once again thoroughly. Correlation between cholesterol and BMI is done as suggested in each respective comorbid group. See Fig. 2-6. Further data about HDL, LDL, and triglycerides cannot be provided now because it is used in another manuscript that is under process.

Hope the answer will satisfy the reviewers and make the understanding of the research better. We believe that manuscript is now suitable for publication in PLOS ONE

Attachment

Submitted filename: Response to Reviewers.docx

pone.0303835.s009.docx (20.6KB, docx)

Decision Letter 1

Samiullah Khan

8 Feb 2024

PONE-D-23-28668R1A case-control regression analysis of liver enzymes in obesity-induced metabolic disorders in South Asian femalesPLOS ONE

Dear Dr. Mumtaz,

Thank you for submitting your manuscript to PLOS ONE. After careful consideration, we feel that it has merit but does not fully meet PLOS ONE’s publication criteria as it currently stands. Therefore, we invite you to submit a revised version of the manuscript that addresses the points raised during the review process.

Please submit your revised manuscript by Mar 24 2024 11:59PM. If you will need more time than this to complete your revisions, please reply to this message or contact the journal office at plosone@plos.org. When you're ready to submit your revision, log on to https://www.editorialmanager.com/pone/ and select the 'Submissions Needing Revision' folder to locate your manuscript file.

Please include the following items when submitting your revised manuscript:

  • A rebuttal letter that responds to each point raised by the academic editor and reviewer(s). You should upload this letter as a separate file labeled 'Response to Reviewers'.

  • A marked-up copy of your manuscript that highlights changes made to the original version. You should upload this as a separate file labeled 'Revised Manuscript with Track Changes'.

  • An unmarked version of your revised paper without tracked changes. You should upload this as a separate file labeled 'Manuscript'.

If you would like to make changes to your financial disclosure, please include your updated statement in your cover letter. Guidelines for resubmitting your figure files are available below the reviewer comments at the end of this letter.

If applicable, we recommend that you deposit your laboratory protocols in protocols.io to enhance the reproducibility of your results. Protocols.io assigns your protocol its own identifier (DOI) so that it can be cited independently in the future. For instructions see: https://journals.plos.org/plosone/s/submission-guidelines#loc-laboratory-protocols. Additionally, PLOS ONE offers an option for publishing peer-reviewed Lab Protocol articles, which describe protocols hosted on protocols.io. Read more information on sharing protocols at https://plos.org/protocols?utm_medium=editorial-email&utm_source=authorletters&utm_campaign=protocols.

We look forward to receiving your revised manuscript.

Kind regards,

Samiullah Khan, Ph. D

Academic Editor

PLOS ONE

Journal Requirements:

Please review your reference list to ensure that it is complete and correct. If you have cited papers that have been retracted, please include the rationale for doing so in the manuscript text, or remove these references and replace them with relevant current references. Any changes to the reference list should be mentioned in the rebuttal letter that accompanies your revised manuscript. If you need to cite a retracted article, indicate the article’s retracted status in the References list and also include a citation and full reference for the retraction notice.

Additional Editor Comments:

Dear author,

Revise the manuscript by incorporating the all corrections suggested by reviewer#2 and submit the re-revised manuscript.

[Note: HTML markup is below. Please do not edit.]

Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: (No Response)

Reviewer #2: All comments have been addressed

**********

2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: (No Response)

Reviewer #2: Partly

**********

3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: I Don't Know

Reviewer #2: No

**********

4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

**********

5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

**********

6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: (No Response)

Reviewer #2: Dear Authors,

1. Please mention the post-hoc tests performed after ANNOVA in the methods and the figure legends.

2. Please mention the number of samples i.e., n=? at appropriate places including figure legends.

3. Please write the key observation as the title of each result instead of writing the method and the statistical test used. For example, result 1 shows the relationship of obesity, diabetes, and hypertension on key health indicators in women.

4. In Figure 2,3,4,5 & 6:

1. Please include Correlation coefficient (R2) and the P-value on the regression plots.

2. Please mention axis titles.

3. It would be visually appealing to put all these plots in a single page.

4. All the plots should have consistent Y axis limits for enhanced clarity and interpretability.

Thank you.

**********

7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: No

Reviewer #2: Yes: Shrestha Mohapatra

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PLoS One. 2024 Jul 18;19(7):e0303835. doi: 10.1371/journal.pone.0303835.r004

Author response to Decision Letter 1

23 Apr 2024

The authors are very much obliged for the suggestion of the reviewer 2 for valuable comments. The suggestion to express the results as a title rather than methods or statistical analysis changed the whole impact of the article. We have tried to address all the points raised by the reviewers except one that the Y-axis should have consistent limits. We are unable to make the Y-axis values consistent because every variable has a different unit value and can’t be set at uniformity. All regression dot plots are also incorporated on a single page to explain the results more obviously.

Attachment

Submitted filename: Response to Reviewers.docx

pone.0303835.s010.docx (19.1KB, docx)

Decision Letter 2

Samiullah Khan

2 May 2024

A case-control regression analysis of liver enzymes in obesity-induced metabolic disorders in South Asian females

PONE-D-23-28668R2

Dear Dr. Tamseela,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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Samiullah Khan, Ph. D

Academic Editor

PLOS ONE

Additional Editor Comments (optional):

Dear author, the manuscript is now suitble for publication in PLoS ONE because all the corections have been done and addressed all the queries raised by the reviewers.

Reviewers' comments:

Acceptance letter

Samiullah Khan

14 May 2024

PONE-D-23-28668R2

PLOS ONE

Dear Dr. Mumtaz,

I'm pleased to inform you that your manuscript has been deemed suitable for publication in PLOS ONE. Congratulations! Your manuscript is now being handed over to our production team.

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on behalf of

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Academic Editor

PLOS ONE

Associated Data

    This section collects any data citations, data availability statements, or supplementary materials included in this article.

    Supplementary Materials

    S1 File. Consent and questionnaire.

    (PDF)

    pone.0303835.s001.pdf (58.6KB, pdf)
    S2 File. Inclusion and exclusion criteria.

    (PDF)

    pone.0303835.s002.pdf (94.8KB, pdf)
    S3 File. Minimal data set.

    (PDF)

    pone.0303835.s003.pdf (668.2KB, pdf)
    S4 File. Regression analysis equation.

    (PDF)

    pone.0303835.s004.pdf (62.3KB, pdf)
    S5 File

    (PDF)

    pone.0303835.s005.pdf (791.1KB, pdf)
    S6 File

    (PDF)

    pone.0303835.s006.pdf (769.3KB, pdf)
    S7 File

    (PDF)

    pone.0303835.s007.pdf (668.2KB, pdf)
    S8 File

    (PDF)

    pone.0303835.s008.pdf (62.3KB, pdf)
    Attachment

    Submitted filename: Response to Reviewers.docx

    pone.0303835.s009.docx (20.6KB, docx)
    Attachment

    Submitted filename: Response to Reviewers.docx

    pone.0303835.s010.docx (19.1KB, docx)

    Data Availability Statement

    All relevant data are within the manuscript and its Supporting Information files.

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