The correlation between liver enzymes and lipid profile in T2D patients in the Yemeni population has been evaluated. This is a case-control study comprising 142 T2D patients and 142 healthy control subjects were carried out at the outpatient clinics of Ibn-Sina hospital, Mukalla, during the period from January to May 2020. Serum fasting blood glucose (FBG), total cholesterol, triglyceride, high-density lipoprotein cholesterol (HDL-C), alanine aminotransferase (ALT), aspartate aminotransferase (AST), and gamma-glutamyltransferase (GGT) were analyzed using the Cobas Integra Plus 400 autoanalyzer. Also, anthropometric and blood pressure measurements were taken from each participant. Independent sample T-test and Pearson correlation coefficient were used. T2D patients had significantly higher FBG (P ≤ 0.0001), total cholesterol (P ≤ 0.0001), LDL-C (P ≤ 0.0001), and GGT (P ≤ 0.0001) while HDL-C was significantly lower in T2D patients (P = 0.021). In correlation analysis, serum GGT was positively associated with FBG (r = 0.216; P ≤ 0.0001), total cholesterol (r = 0.196; P = 0.0001), triglyceride (r = 0.123; P = 0.038), and LDL-C (r = 0.209; P ≤ 0.0001). Also, serum ALT was positively associated with FBG (r = 0.145, P = 0.014) and triglyceride (r = 0.172, P = 0.004). In conclusion, higher levels of ALT and GGT are used as the predictive biomarkers for NAFLD in T2D patients with hyperlipidemia. Thus, routine screening of liver enzymes and lipid profile in T2D patients is recommended for the early detection of liver abnormalities and diminish diabetes complications.
Liver Enzymes Dyslipidemia Type 2 Diabetes Mellitus Yemeni Patients1. Introduction
Diabetes mellitus is a metabolic disorder characterized by chronic hyperglycemia which results from defective insulin action and secretion or both [1]. World Health Organization projects that the number of diabetic patients will exceed 350 million by 2030 [1]. Previous data have documented liver disease is a major cause of morbidity and mortality of type 2 diabetes patients [2] [3]. It is well known that the liver is a vital organ in the metabolism of carbohydrates and in maintaining glucose homeostasis during fasting and postprandial period [2] [4].
Non-alcoholic fatty liver disease (NAFLD) is the scope of chronic liver disease in T2D patients [5], which is characterized by excess deposition of fat in the liver and associated with hepatic insulin resistance [3] and T2D risk [5]. Serum alanine aminotransferase (ALT) and gamma-glutamyltransferase (GGT) are good biomarkers of NAFLD. ALT has been considered the specific marker of liver injury, as found in high concentrations in hepatocytes [6], while GGT is present on the surface of most cell types and highly active in the liver, kidneys, and pancreas [7]. Also, GGT is responsible for extracellular glutathione catabolism and may be linked to oxidative stress [8] and chronic inflammation [9]; both oxidative stress and chronic inflammation are important pathways for hepatic insulin resistance (IR) and subsequently T2D development [10].
Hyperinsulinemia and IR play an important role in lipid abnormalities for T2D patients [2] [11]. Also, altered lipoprotein patterns and liver enzymes have been identified as independent risk factors for the development of cardiovascular disease (CVD) [10] [11] [12]. Moreover, higher levels of triglycerides, LDL-C, total cholesterol, and lower levels of HDL-C were reported in T2D patients than normal healthy subjects [13]. However, few studies reported the correlation between liver enzymes and lipid profile in T2D patients; hence this case-control study was conducted to assess the association between liver enzymes and blood lipid profile in a sample of Yemeni patients with T2D.
2. Subjects and Methods2.1. Study Design and Subjects Selection
This is a case-control study was carried out at the College of Medicine and Health Sciences, Hadhramout University, and the subjects were selected from the diabetic outpatient clinic of Ibn-Sina hospital, Mukalla during the period from January to May 2020. A total of 284 Yemeni adult subjects, randomly selected, and recruited into this study. At recruitment, an in-person interview was conducted using a structured questionnaire to collect health-related information. The study group was subdivided into two groups: 142 healthy control subjects composed of 51 males and 91 females (age: 46.0 ± 7.94 yr.), and 142 T2D patients composed of 64 males and 78 females (age: 54.0 ± 8.29 yr.). T2D patients were those who reported being diagnosed with T2D. Healthy control subjects were selected from the remaining participants who were free of T2D and were matched for age, sex, and dialect group with cases on a 1:1 ratio. Moreover, the selected healthy control subjects were screened for the presence of undiagnosed T2D at the time of blood donation by measuring fasting blood glucose (FBG). Written consent was obtained from each participant entered into the study. The study was approved by the Ethics Committee of the Medicine and Health Sciences College, Hadhramout University, Mukalla, Yemen. Patients with co-morbidities such as chronic liver disease, chronic renal disease, cardiovascular disease, and malignancy were excluded.
2.2. Data Collection
A questionnaire form focusing on demographic information and diabetes history was given to all subjects. The patient’s demographic information, clinical presentation, medical history, and physical findings were taken from each subject. This included the patient’s age, sex, smoking status (never, current or past), hypertension status (yes or no), diabetes status (yes or no) diabetes duration (years), diabetes medication, and diabetes complications. Patients were diagnosed with diabetes based on medical history, present intake of diabetes medications, or according to the American Diabetes Association (ADA) criteria [14]. Patients with T2DM were defined as fasting blood glucose level ≥ 126 mg/dl (≥ 7.1 mmol/L), 2-hour postprandial plasma glucose level ≥ 200 mg/dl (≥11.1 mmol/L) or HbA1c ≥ 6.5% [14]. Classification of Body Mass Index (BMI) was based on the World Health Organization [15].
2.3. Anthropometric and Blood Pressure Measurements
Weight and height were measured following measured according to WHO guidelines [15]. Body mass index (BMI) was calculated as weight/height2 (Kg/m2). Obese subjects were defined as BMI ≥ 30 kg/m2 and normal-weight subjects having a BMI of 18-25 according to WHO guidelines [15]. Patients who had a blood pressure of ≥ 140/90mmHg or were taking antihypertensive medications were diagnosed with hypertension [16]. A true healthy normal ALT level ranges from 29 to 33 IU/l for males, and 19 to 25 IU/l for females and levels above this should be assessed as described by the American College of Gastroenterology (ACG) [17].
2.4. Biochemical Investigations
Ten milliliters of the venous blood sample was obtained from consenting subjects. The blood samples were collected by vein puncture in tubes without anticoagulant. The blood samples were then transported to the laboratory immediately. The serum was separated and stored at −20˚C freezers till analyses. The serum samples of matched case–control pairs were randomly placed next to each other with the case/control status blinded to the laboratory personnel and were processed, and tested in the same batch. All laboratory equipment was calibrated. Thawing freezing was avoided by dividing the samples into aliquots. Plasma fasting blood glucose (FBG), total cholesterol, triglycerides, and HDL-cholesterol (HDL-C) were determined enzymatically using a chemical autoanalyzer (Cobas Integra 400 Plus, Roche diagnostic GmbH, Mannheim, Switzerland), following the standard procedures as described by the manufacturer. Concentrations of LDL-cholesterol (LDL-C) were calculated using Friedwald’s formula [18]. All biochemical investigations were analyzed in the National Center for Public Health Labs-Mukalla, Yemen.
2.5. Statistical Analysis
Data were analyzed using the Statistical Package for the Social Sciences for Windows (version 24) and are expressed by mean ± standard deviation (SD) for continuous variables (normally distributed). Non-continuous variables are expressed by median (inter-quartile range) and n (percentage) for categorical variables. Independent Student’s t-test used for normally distributed continuous variables and Wilcoxon signed-rank test for skewed continuous variables. The Pearson correlation test was performed with ALT, AST, and GGT as the dependent variables. The statistical analysis was conducted at a 95% confidence level and a P -value < 0.05 was considered statistically significant.
3. Results
Descriptive statistics of anthropometric and biochemical data of the study population are presented in Table 1. T2D patients had significantly increased BMI (P = 0.008), systolic BP (P ≤ 0.0001), diastolic BP (P ≤ 0.0001), FBG (P ≤ 0.0001), total cholesterol (P ≤ 0.0001), LDL-C (P ≤ 0.0001), and GGT (P = 0.016) as compared to healthy control subjects. No significant difference was found in serum triglyceride (P = 0.097) and ALT (P = 0.07). Healthy control subjects had significantly increased HDL-C (P = 0.021) and AST (P = 0.001) as compared to T2D patients. On the other hand, 31.7% of T2D patients had hypertension, whereas, 6.3% of healthy control subjects had hypertension. Besides, in T2D patients, the current smokers were 4.2% and the former smokers were 3.5%. According to BMI criteria, 38.7% of T2D patients had overweight and 24.6% with obese as compared to healthy control subjects (40.1%, 14.1%) respectively.
Pearson correlation using ALT, AST, and GGT as dependent variables is presented in Table 2. Serum ALT was positively associated with FBG (r = 0.145, P = 0.014), triglyceride (r = 0.172, P = 0.004), AST (r = 590, P ≤ 0.001), and GGT (r = 0.507, P ≤ 0.001) respectively. Serum GGT was positively associated with systolic BP (r = 0.134, P = 0.024), diastolic BP (r = 0.218, P ≤ 0.001), FBG (r = 0.216, P ≤ 0.0001), total cholesterol (r = 0.196, P = 0.0001), triglyceride (r = 0.123, P = 0.038), LDL-cholesterol (r = 0.209, P ≤ 0.0001), and AST (r = 0.366, P ≤ 0.0001) across the combined group.
Using partial correlation analysis (Table 3), controlling for age and BMI, significant positive association between ALT with AST (r = 0.589, P ≤ 0.0001) and ALT (r = 0.514, P ≤ 0.0001) remained significant across the combined group, whilst, the association between ALT with FBG and triglyceride was no longer significant. Using the same analysis, the association between GGT with systolic BP (r = 0.124, P = 0.038), diastolic BP (r = 0.213, P ≤ 0.0001), FBG (r = 0.213, P ≤ 0.0001), total cholesterol (r = 0.199, P = 0.001), triglyceride (r = 0.127, P = 0.033), and LDL-C (r = 0.208, P ≤ 0.0001) remained significant before and after age and BMI as adjustment.
Anthropometric and biochemical data of healthy controls and T2D patients
Variables
Healthy controls
T2D patients
P-value
N
142
142
Age (years)
46.0 ± 7.94
54.0 ± 8.29
<0.0001
Sex: male/female
51 (35.9)/91 (64.1)
63 (44.4)/78 (54.9)
Weight (kg)
71.12 ± 10.67
69.61 ± 13.83
<0.0001
Height (cm)
164.57 ± 8.47
159.97 ± 10.04
<0.0001
BMI (kg/m2)
26.31 ± 3.95
27.21 ± 4.94
0.008
SBP (mmHg)
115.28 ± 13.11
128.80 ± 20.92
<0.0001
DBP (mmHg)
70.45 ± 9.02
79.47 ± 9.90
<0.0001
BMI classification: Normal weight Overweight Obese
65 (45.8) 57 (40.1) 20 (14.1)
52 (36.6) 55 (38.7) 35 (24.6)
History of hypertension: Yes/no
9 (6.3)/133 (93.7)
45 (31.7)/97 (68.3)
Smoking status: Never Smoker Current Smoker Former Smoker
142 (100) 0 (0) 0 (0)
131 (92.3) 6 (4.2) 5 (3.5)
FBG (mmol/L)
5.18 ± 0.91
8.91 ± 2.89
<0.0001
Total cholesterol (mmol/L)
4.70 ± 0.77
5.16 ± 1.20
<0.0001
Triglyceride (mmol/L)
1.24 ± 0.37
1.16 ± 0.42
0.097
HDL-C (mmol/L)
1.67 ± 0.42
1.57 ± 0.34
0.021
LDL-C (mmol/L)
2.77 ± 0.80
3.35 ± 1.17
0.001
ALT (IU/L)
13.1 (8.37 - 19.3)
11.6 (7.3 - 16.8)
0.07
AST (IU/L)
21.2 (17.8 - 28.7)
16.4 (13.3 - 21.7)
0.001
GGT (IU/L)
25.1 (16.8 - 34.7)
29.2 (18.4 - 49.7)
<0.0001
Data were presented as mean ± SD for normal continuous variables and median (interquartile range) for continuous non-normal variables. Independent sample T-test for normally distributed continuous variables and Mann-Whitney U test for skewed continuous variables. P-value < 0.05 was considered statistically significant. BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; FBG, fasting blood glucose; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; ALT, alanine aminotransferase; AST, aspartate aminotransferase; GGT, gamma-glutamyltransferase.
Pearson correlation using ALT, AST and GGT as dependent variables in the combined study group
N = 284
ALT
AST
GGT
r
P-value
r
P-value
r
P-value
Age (years)
−0.046
0.443
0.010
0.860
0.047
0.433
Sex (M/F)
0.119*
0.046
0.116
0.050
0.016
0.789
Weight (kg)
−0.001
0.989
−0.008
0.895
−0.004
0.945
Height (cm)
0.098
0.101
0.071
0.235
−0.058
0.334
BMI (kg/m2)
−0.070
0.241
−0.059
0.326
0.033
0.538
SBP (mmHg)
−0.037
0.533
−0.058
0.334
0.134*
0.024
DBP (mmHg)
0.013
0.830
−0.080
0.178
0.218**
<0.001
FBG (mmol/L)
0.145*
0.014
−0.067
0.260
0.216**
<0.0001
Total cholesterol (mmol/L)
0.027
0.653
0.081
0.176
0.196*
0.0001
Triglyceride (mmol/L)
0.172**
0.004
0.087
0.141
0.123*
0.038
HDL-C (mmol/L)
−0.091
0.124
−0.023
0.699
−0.064
0.285
LDL-C (mmol/L)
0.047
0.429
0.082
0.170
0.209**
<0.0001
ALT (IU/L)
0.590**
<0.0001
0.507**
<0.0001
AST (IU/L)
0.590**
<0.0001
0.366**
<0.0001
GGT (IU/L)
0.507**
<0.0001
0.366**
<0.0001
Pearson correlation coefficient with corresponding p-value (p < 0.05 is considered a significant). ** Correlation is significant at the 0.01 level (2-tailed). * Correlation is significant at the 0.05 level (2-tailed). BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; FBG, fasting blood glucose; TC, total cholesterol; TG, triglyceride; HDL-C, high-density lipoprotein cholesterol; LDL-C, low-density lipoprotein cholesterol; ALT, alanine aminotransferase; AST, aspartate aminotransferase; GGT, gamma-glutamyltransferase.
Pearson correlation using ALT, AST and GGT as dependent variables in the combined groups studied after Age and BMI adjustment as a covariance
N = 284
ALT
AST
GGT
r
P-value
r
P-value
r
P-value
Sex (M/F)
0.116
0.051
0.114
0.055
0.017
0.772
SBP (mmHg)
−0.018
0.764
−0.053
0.377
0.124*
0.038
DBP (mmHg)
0.024
0.686
−0.078
0.194
0.213**
0<0.0001
FBG (mmol/L)
0.161
0.007
−0.074
0.213
0.213**
<0.0001
Total cholesterol (mmol/L)
0.027
0.652
0.085
0.155
0.199
0.001
Triglyceride (mmol/L)
0.171
0.004
0.090
0.130
0.127
0.033
HDL-C (mmol/L)
−0.104
0.081
−0.026
0.668
−0.056
0.351
LDL-C (mmol/L)
0.052
0.388
0.087
0.147
0.208
<0.0001
ALT (IU/L)
0.589**
<0.0001
0.514**
<0.0001
AST (IU/L)
0.589**
<0.0001
0.368**
<0.0001
GGT (IU/L)
0.514**
<0.0001
0.368**
<0.0001
Pearson correlation coefficient with corresponding p-value (p < 0.05 is significant). ** Correlation is significant at the 0.01 level (2-tailed). * Correlation is significant at the 0.05 level (2-tailed). BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; FBG, fasting blood glucose; HDL-C, high-density lipoprotein cholesterol; LDL-cholesterol, low-density lipoprotein cholesterol; ALT, alanine aminotransferase; AST, aspartate aminotransferase; GGT, gamma-glutamyltransferase.
4. Discussion
Despite the incidence of diabetes is increasing worldwide and its prevalence is higher in developing countries, no studies have examined the relationship between elevated liver enzymes and T2D risk in Yemeni patients. Our study, therefore, was focused on the liver as the vital organ contributing to glucose homeostasis during fasting and postprandial stage. Serum ALT, AST, and GGT were taken from each participant and used for this work. Additionally, most people aged ≥ 45 years in developing countries suffer from diabetes [19]. These findings were convenient with our study showed that T2D patients had significantly higher mean age compared to healthy control subjects (Table 1).
Besides, our present findings also observed significantly increased BMI, systolic BP, and diastolic BP in T2D patients than healthy control subjects. Besides, the present study also showed that serum FBG, total cholesterol, and LDL-C were significantly higher in T2D patients than healthy control subjects, while, no significant difference was found among both groups for serum triglyceride. In contrast, HDL-C was significantly lower in T2D patients. Our study further revealed higher levels of GGT in T2D patients. While AST was significantly lower in T2D patients. Besides, no significant difference was found among both groups for ALT. Such a positive relationship between liver enzymes and blood lipids profile in T2D patients has been observed in previous studies [2] [20] [21] [22] [23].
This finding supports the role of hepatic insulin resistance in the pathogenesis of NAFLD in patients with T2D [24] [25]. Moreover, Cho et al. reported a correlation between ALT activity and increased fatty liver [26]. The impairment of the normal process of synthesis and elimination of triglycerides may progress to fibrosis, cirrhosis, and hepatocellular carcinoma [27] [28].
In addition to its effect on lipid metabolism, insulin also contributes a proinflammatory effect to liver abrasion [12]. Thus, inflammation contributes to IR. Pro-inflammatory cytokines and transcription factors are highly expressed in white adipose tissue and liver. Obesity, which is a state of chronic low-grade inflammation and a risk factor for IR and NAFLD, is induced by the overnutrition and is a primary cause of decreased insulin sensitivity. Obesity leads to lipid accumulation and activates the c-Jun N-terminal kinase (JNK) and nuclear factor-kappa B (NF-κB) signaling pathways, which consequently increase the production of pro-inflammatory cytokines, such as tumor necrosis factor-alpha (TNF-α) and interleukin-6 (IL-6) [29]. Besides, various adipose tissue-derived proteins, such as adiponectin and leptin, are considered to be major links between obesity, IR, and related inflammatory disorders [30].
GGT is known as a marker of hepatobiliary disorders and is associated with other pathological conditions like diabetes. Free radicals generated by diabetes consume glutathione which induces the increased expression of GGT in hepatocytes. Various studies have suggested the association of GGT concentrations with T2D [31] [32] [33] [34], and hyperlipidemia [35]. These findings are in agreement with our study; GGT was significantly associated with the hyperglycemic and hyperlipidemia profile. We observed ALT and GGT together were positively correlated. Moreover, some data also reported elevated GGT levels with ALT in T2D patients with dyslipidemia [32] [33] [36]. Although we did not confirm the presence of fatty liver by ultrasound techniques, we showed the relationship of ALT, AST, and GGT with the predictors of diabetes and lipid profile parameters, presenting hepatocellular injury.
A study of male Korean workers found that AST was independently associated with diabetes [37], while in a study of male Japanese office workers AST was not associated with T2D risk [33]. Some studies also reported that ALT is a significant predictor of diabetes while AST is not [38]. These findings are in agreement with our findings as AST does not show considerable relationship with the studied parameters. Besides, Clark et al. also suggested that mild or chronic elevations of these aminotransferases may be due to NAFLD [39] [40].
The strength of the present study included adjustment for well-established diabetes risk factors including BMI, blood lipids, and hypertension. However, there are some limitations. First, our sample size may be small and thus underpowered to detect the interaction with ALT and GGT. Second, we measured liver enzymes only once and may not represent a long-term profile. Third, we did not measure hepatitis B and C infection, which could result in elevated liver enzymes. Fourth, we did not measure insulin, CRP, leptin, and, adiponectin as the predictive biomarkers links between obesity, hepatic IR, and related inflammatory disorders in T2D patients. Thus, a further large sample size with measurement of insulin, CRP, leptin, adiponectin, and interleukins are required to confirm these correlations. We conclude that higher levels of ALT and GGT are used as the predictive biomarkers for NAFLD in T2D patients with hyperlipidemia.
5. Conclusion
Higher levels of ALT and GGT may be used as the predictive markers for NAFLD in T2D patients with hyperlipidemia. Thus, routine screening of liver enzymes and lipid profile in T2D patients is recommended for the early detection of liver abnormalities and diminish diabetes complications.
Acknowledgements
The authors are grateful to Al-Huda Medical Agency, Mukalla, Yemen, for funding the study and theIbn-Sina Hospital, Mukalla, Yemen for technical support. Also, we are thankful to the physicians and nurses who recruited and collected the data of the participants. Special thanks to National Center for the Public Health Laboratories, Mukalla for biochemical analysis. Special thanks to Students of Medical Laboratory Sciences Department (Ali Alqaaiti, Saleh Daiban, Sabri Barafah, Afaf Aldibani, Safa Basawaid, Noor Zahfan and Nasser Al-Mohamdi) for the performance of data and sample collection, data entry, and biochemical investigations. Special thanks to Ms. Nasiba Al-Aidros for the statistical analysis.
Conflicts of Interest
The authors declare no conflicts of interest regarding the publication of this paper.
Cite this paper
Bin Dahman, L.S., Humam, M.A., Barahim, O.H., Barahman, O.M. and Balfas, M.A. (2021) Association between Liver Enzymes and Dyslipidemia in Yemeni Patients with Type Two Diabetes Mellitus. Journal of Diabetes Mellitus, 11, 41-51. https://doi.org/10.4236/jdm.2021.112004
ReferencesClark, J.M., Brancati, F.L. and Diehl, A.M. (2003) The Prevalence and Etiology of Elevated Aminotransferase Levels in the United States. American Journal of Gastroenterology, 98, 960-967. https://doi.org/10.1111/j.1572-0241.2003.07486.xClark, J.M. and Diehl, A.M. (2003) Nonalcoholic Fatty Liver Disease: An Underrecognized Cause of Cryptogenic Cirrhosis. JAMA, 290, 3000-3004. https://doi.org/10.1001/jama.289.22.3000Vozarova, B., Stefan, N., Lindsay, R.S., Saremi, A., Pratley, R.E., Bogardus, C., et al. (2002) High Alanine Aminotransferase Is Associated with Decreased Hepatic Insulin Sensitivity and Predicts the Development of Type 2 Diabetes. Diabetes, 51, 1889-1895. https://doi.org/10.2337/diabetes.51.6.1889Ahn, H.R., Nam, H.S., Park, K.S., Park, K.-S., Lee, Y.-H., Jeong, S.-K., et al. (2014) The Association between Liver Enzymes and Risk of Type 2 Diabetes: The Namwon Study. Diabetology & Metabolic Syndrome, 6, Article No. 14. https://doi.org/10.1186/1758-5996-6-14Marchesini, G., Brizi, M., Bianchi, G., Tomassetti, S., Bugianesi, E., Lenzi, M., et al. (2001) Nonalcoholic Fatty Liver Disease: A Feature of the Metabolic Syndrome. Diabetes, 50, 1844-1850. https://doi.org/10.2337/diabetes.50.8.1844Sakuta, H., Suzuki, T., Yasuda, H. and Ito, T. (2005) Gamma-Glutamyl Transferase and Airflow Obstruction in Middle-Aged Men. European Journal of Internal Medicine, 16, 348-351. https://doi.org/10.1016/j.ejim.2005.06.005Lee, D.H., Jacobs, D.R., Gross, M., Kiefe, C.I., Roseman, J., Lewis, C.E., et al. (2003) Gamma-Glutamyltransferase Is a Predictor of Incident Diabetes and Hypertension: The Coronary Artery Risk Development in Young Adults (CARDIA) Study. Clinical Chemistry, 49, 1358-1366. https://doi.org/10.1373/49.8.1358Nakanishi, N., Suzuki, K. and Tatara, K. (2004) Serum Gamma-Glutamyltransferase and Risk of Metabolic Syndrome and Type 2 Diabetes in Middle Aged Japanese Men. Diabetes Care, 27, 1427-1432. https://doi.org/10.2337/diacare.27.6.1427Lee, D.H., Ha, M.H., Kim, J.H., Christiani, D.C., Gross, M.D., Steffes, M., et al. (2003) Gamma-Glutamyltransferase and Diabetes—A Four Year Follow up Study. Diabetologia, 46, 359-364. https://doi.org/10.1007/s00125-003-1036-5Lee, D.H., Silventonein, K., Jacobs, D.R., Jousilahti, P. and Tuomileto, J. (2014) Gamma-Glutamyltransferase, Obesity and the Risk of Type 2 Diabetes Observational Cohort Study among 20,158 Middle Aged Men and Women. The Journal of Clinical Endocrinology & Metabolism, 89, 5410-5414. https://doi.org/10.1210/jc.2004-0505Chen, Z., Yu, R., Xiong, Y., Du, F. and Zhu, S. (2017) A Vicious Circle between Insulin Resistance and inflammation in Nonalcoholic Fatty Liver Disease. Lipids in Health and Disease, 16, Article No. 203. https://doi.org/10.1186/s12944-017-0572-9Sharma, M., Vikram, N.K., Misra, A., Bhatt, S.P., Tarique, M., Parray, H.A., et al. (2013) Assessment of 11-Beta Hydroxysteroid Dehydrogenase (11-betaHSD1) 4478T>G and Tumor Necrosis Factor-Alpha (TNF-Alpha)-308>A Polymorphisms with Obesity and Insulin Resistance in Asian Indians in North India. Molecular Biology Reports, 40, 6261-6270. https://doi.org/10.1007/s11033-013-2738-5Chatila, R. and West, A.B. (1996) Hepatomegaly and Abnormal Liver Tests Due to Glycogenosis in Adults with Diabetes. Medicine, 75, 327-333. https://doi.org/10.1097/00005792-199611000-00003Tolman, K.G., Fonseca, V., Tan, M.H. and Dalpiaz, A. (2004) Narrative Review: Hepatobiliary Disease in Type 2 Diabetes Mellitus. Annals of Internal Medicine, 141, 946-956. https://doi.org/10.7326/0003-4819-141-12-200412210-00011Cho, N.H., Jang, H.C., Choi, S.H., Kim, H.R., Lee, H.K., Chan, J.C.N., et al. (2007) Abnormal Liver Function Test Predicts Type 2 Diabetes: A Community-Based Prospective Study. Diabetes Care, 30, 2566-2568. https://doi.org/10.2337/dc07-0106Nannipieri, M., Gonzales, C., Baldi, S., Posadas, R., Williams, K., Haffner, S.M., et al. (2005) Liver Enzymes, the Metabolic Syndrome, and Incident Diabetes: The Mexico City Diabetes Study. Diabetes Care, 28, 1757-1762. https://doi.org/10.2337/diacare.28.7.1757Tolman, K.G., Fonseca, V., Dalpiaz, A. and Tan, M.H. (2007) Spectrum of Liver Disease in Type 2 Diabetes and Management of Patients with Diabetes and Liver Disease. Diabetes Care, 30, 734-743. https://doi.org/10.2337/dc06-1539Adeniran, S.A., Dolapo, P.O., Oluwole, A.B., Adeola ‘Niran-Atiba, T., Jimoh, A.K. and Adepeju, A.A. (2013) Liver Enzymes and Lipid Profile among Type 2 Diabetic Patients in Osogbo, Nigeria. Greener Journal of Medical Sciences, 3, 174-178. https://doi.org/10.15580/GJMS.2013.5.011313373Belay, Z., Daniel, S., Tedla, K. and Gnanasekaran, N. (2014) 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 and Metabolism, 5, Article No. 11. https://doi.org/10.4172/2155-6156.1000454Han, N., Soe, H.K. and Htet, A. (2012) Determinants of Abnormal Liver Function Tests in Diabetes Patients in Myanmar. International Journal of Diabetes Research, 1, 36-41. https://doi.org/10.5923/j.diabetes.20120103.02Jain, H.R., Shetty, V., Singh, G.S. and Shetty, S. (2016) A Study of Lipid Profile in Diabetes Mellitus. International Journal of Scientific Study, 4, 56-61.Nwarfor, A. and Owhoji, A. (2001) The Prevalence of Diabetes Mellitus in Port-Harcourt Correlates with the Socio-Economic Status. Journal of Applied Sciences and Environmental Management, 5, 75-77.Friedewald, W.T., Levy, R.I. and Fredrickson, D.S. (1972) Estimation of the Concentration of Low-Density Lipoprotein Cholesterol in Plasma, without Use of the Preparative Ultracentrifuge. Clinical Chemistry, 18, 499-502. https://doi.org/10.1093/clinchem/18.6.499Kwo, P.Y., Cohen, S.M. and Lim, J.K. (2017) ACG Clinical Guideline: Evaluation of Abnormal Liver Chemistries. American Journal of Gastroenterology, 112, 18-35. https://doi.org/10.1038/ajg.2016.517Chobanian, A.V., Bakris, G.L., Black, H.R., Cushman, W.C., Green, L.A., Izzo Jr., J.L., et al. (2003) Seventh Report of the Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure. Hypertension, 42, 1206-1252. https://doi.org/10.1161/01.HYP.0000107251.49515.c2World Health Organization (1995) Physical Status: The Use and Interpretation of Anthropometry (1995) Report of WHO Expert Committee. WHO Technical Report Series, No. 854, World Health Organization, Geneva, 321-344. https://apps.who.int/iris/handle/10665/37003.American Diabetes Association (2016) Standards of Medical Care in Diabetes-2016. Diabetes Care, 39, S4-S5. https://doi.org/10.2337/dc16-S003Rajeswari, S., Kumar, A., Gandhi, M. and Swaminathan, S. (2014) Association between Lipid Profile and Liver Function Tests in Diabetic Patients. Indian Journal of Pure & Applied Biosciences, 2, 26-31.Balaji, A.S., Suhas, B.J., Ashok, M.A. and Mangesh, T. (2013) Serum Alanine Transaminases and Lipid Profile in Type 2 Diabetes Mellitus Indian Patient. Journal of Research in Diabetes, 2013, Article ID: 613176. https://doi.org/10.5171/2013.613176Ginsberg, H.N., Zhang, Y.L. and Hernandez-Ono, A. (2006) Metabolic Syndrome: Focus on Dyslipidemia. Obesity, 14, 41S-49S. https://doi.org/10.1038/oby.2006.281Wang, Y., Koh, W., Yuan, J. and Pan, A. (2016) Association between Liver Enzymes and Incident Type 2 Diabetes in Singapore Chinese Men and Women. BMJ Open Diabetes Research and Care, 4, e000296. https://doi.org/10.1136/bmjdrc-2016-000296Lee, D.H. and Jacobs Jr., D.R. (2005) Association between Serum Gamma-Glutamyl transferase and C-Reactive Protein. Atherosclerosis, 178, 327-330. https://doi.org/10.1016/j.atherosclerosis.2004.08.027Turgut, O. and Tandogan, I. (2011) Gamma-Glutamyltransferase to determine Cardiovascular Risk: Shifting the Paradigm Forward. Journal of Atherosclerosis and Thrombosis, 18, 177-181.Hanigan, M.H. and Frierson Jr., H.F. (1996) Immunohistochemical Detection of Gamma-Glutamyl Transpeptidase in Normal Human Tissue. Journal of Histochemistry & Cytochemistry, 44, 1101-11108. https://doi.org/10.1177/44.10.8813074Giannini, E.G., Testa, R. and Savarino, V. (2005) Liver Enzyme Alteration: A Guide for Clinicians. Canadian Medical Association Journal, 172, 367-379. https://doi.org/10.1503/cmaj.1040752Ballestri, S., Zona, S., Targher, G., Romagnoli, D., Baldelli, E., Nascimbeni, F., et al. (2016) Nonalcoholic Fatty Liver Disease Is Associated with an Almost Twofold Increased Risk of Incident Type 2 Diabetes and Metabolic Syndrome. Evidence from a Systematic Review and Meta-Analysis. Journal of Gastroenterology and Hepatology, 31, 936-944. https://doi.org/10.1111/jgh.13264Gavin, N. and Levinthal, A.S.T. (1999) Liver Disease and Diabetes Mellitus. Clinical Diabetes, 17, No. 2.Hanley, A.J., Williams K., Festa, A., Wagenknecht, L.E., D’Agostino, Jr., R.B., Kempf, J., et al. (2004) Elevations in Marker of Liver Injury and Risk of Type 2 Diabetes—The Insulin Resistance Atherosclerosis Study. Diabetes, 53, 2623-2632.https://doi.org/10.2337/diabetes.53.10.2623Al-Jameil, N., Khan, F. A., Arjumand, S., Khan, M.F. and Tabassum, H. (2014) Associated Liver Enzymes with Hyperlipidemic Profile in Type 2 Diabetes Patients. International Journal of Clinical and Experimental Pathology, 7, 4345-4349.World Health Organization (1999) Definition, Diagnosis and Classification of Diabetes Mellitus and Its Complications: Report of a WHO Consultation. Part 1, Diagnosis and Classification of Diabetes Mellitus. World Health Organization, Geneva. https://apps.who.int/iris/handle/10665/66040