Shengir M, Krishnamurthy S, Ghali P, Deschenes M, Wong P, Chen T, Sebastiani G. Prevalence and predictors of nonalcoholic fatty liver disease in South Asian women with polycystic ovary syndrome. World J Gastroenterol 2020; 26(44): 7046-7060
Corresponding Author of This Article
Giada Sebastiani, MD, Associate Professor, Department of Medicine, McGill University Health Centre, 1001 Blvd. Decarie, Montreal H4A3J1, Canada. email@example.com
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This article is an open-access article which was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/
Author contributions: Shengir M contributed to conception, study design, data, interpretation of the data; Krishnamurthy S and Chen T contributed to study design, data, interpretation of the data; Ghali P, Deschenes M and Wong P contributed to data and interpretation of data; Sebastiani G contributed to conception, study design, data and interpretation of the data, statistical analysis and first draft of the manuscript; all approved the final manuscript.
Supported byLibyan Ministry of Higher Education and Scientific Research sponsored through Canadian Bureau for International Education, No. 2979.
Institutional review board statement: The study was approved by the Research Ethics Board of the Research Institute of the MUHC (study code 2019-4584).
Informed consent statement: All study participants provided informed written consent prior to study enrollment.
Conflict-of-interest statement: Ghali P has acted as consultant for Merck and Gilead. Deschenes M has served as an advisory board member for Merck, Janssen, Gilead; Wong P has acted as consultant for BMS, Gilead, Merck, Novartis; Sebastiani G has acted as speaker for Merck, Gilead, Abbvie, Novonordisk, Novartis, Pfizer, served as an advisory board member for Merck, Intercept, Novartis, Gilead, Allergan and has received research funding from Merck and Theratec Inc. Shengir M, Krishnamurthy S and Chen T have no conflicts of interest to declare.
Data sharing statement: According to stipulations of the patient consent form signed by all study participants, ethical restrictions imposed by our Institutional Ethics review boards (Institutional Ethics Review Board Biomedical B Research Ethics Board of the McGill University Health Centre), and legal restrictions imposed by Canadian law regarding clinical trials, anonymized data are available upon reasonable request. Please send data access requests to Sheldon Levy, Biomedical B (BMB) Research Ethics Board (REB) Coordinator Centre for Applied Ethics, 5100, boul. de Maisonneuve Ouest, 5th floor, Office 576, Montréal, Québec, H4A 3T2, Canada.
STROBE statement: The authors have read the STROBE Statement-checklist of items, and the manuscript was prepared and revised according to the STROBE Statement-checklist of items.
Open-Access: This article is an open-access article that was selected by an in-house editor and fully peer-reviewed by external reviewers. It is distributed in accordance with the Creative Commons Attribution NonCommercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited and the use is non-commercial. See: http://creativecommons.org/Licenses/by-nc/4.0/
Corresponding author: Giada Sebastiani, MD, Associate Professor, Department of Medicine, McGill University Health Centre, 1001 Blvd. Decarie, Montreal H4A3J1, Canada. firstname.lastname@example.org
Received: September 20, 2020 Peer-review started: September 20, 2020 First decision: October 17, 2020 Revised: October 30, 2020 Accepted: November 13, 2020 Article in press: November 13, 2020 Published online: November 28, 2020
Polycystic ovary disease (PCOS) may be a risk factor for nonalcoholic fatty liver disease (NAFLD) due to common pathogenetic pathways, including insulin resistance and obesity. Both PCOS and NAFLD are more severe in South Asian women. Data on NAFLD in South Asian women with PCOS are lacking.
To investigate prevalence and predictors of NAFLD and liver fibrosis in PCOS patients from South Asia.
We conducted an observational routine screening program by means of transient elastography (TE) with associated controlled attenuation parameter (CAP). NAFLD was defined as CAP ≥ 288 decibels per meter. Significant liver fibrosis (stage 2 and higher out of 4) was defined as TE measurement ≥ 8.0 kilopascals. Elevated alanine aminotransferase (ALT) was defined as ALT > 24 IU/L, as per upper limit of normal reported in South Asian women. Biochemical hyperandrogenism was defined as free androgen index > 5. Predictors of NAFLD were determined by logistic regression analysis.
101 PCOS patients (mean age 36.3 years) with no significant alcohol intake or viral hepatitis were included. Prevalence of NAFLD and significant liver fibrosis was 39.6% and 6.9%, respectively. Elevated ALT was observed in 40% and 11.5% of patients with and without NAFLD, respectively. After adjusting for duration of PCOS and insulin resistance measured by homeostasis model for assessment of insulin resistance, independent predictors of NAFLD were higher body mass index [adjusted odds ratio (aOR) 1.30, 95% confidence interval (CI): 1.13-1.52], hyperandrogenism (aOR: 5.32, 95%CI: 1.56-18.17) and elevated ALT (aOR: 3.54, 95%CI: 1.10-11.47). Lifetime cardiovascular risk was higher in patients with NAFLD compared to those without NAFLD (0.31 ± 0.11 vs 0.26 ± 0.13).
Despite their young age, NAFLD diagnosed by TE with CAP is a frequent comorbidity in South Asian women with PCOS and is strongly associated with higher body mass index and hyperandrogenism. Non-invasive screening strategies could help early diagnosis and initiation of interventions, including counselling on weight loss, cardiovascular risk stratification and linkage to hepatology care where appropriate.
Core Tip: This is the first cohort study using transient elastography with controlled association parameter to investigate nonalcoholic fatty liver disease in patients with polycystic ovary syndrome. Despite their young age, South Asian women with polycystic ovary disease have high frequency of nonalcoholic fatty liver disease at 39.6%, which could also result in liver fibrosis. Non-invasive screening strategies could help early diagnosis and initiation of interventions, including weight loss, correction of dyslipidemia and cardiovascular risk stratification to initiate statin.
Citation: Shengir M, Krishnamurthy S, Ghali P, Deschenes M, Wong P, Chen T, Sebastiani G. Prevalence and predictors of nonalcoholic fatty liver disease in South Asian women with polycystic ovary syndrome. World J Gastroenterol 2020; 26(44): 7046-7060
Nonalcoholic fatty liver disease (NAFLD) is the most prevalent chronic liver disease, affecting 25% of the general adult population globally[1,2]. Nonalcoholic steatohepatitis (NASH), the progressive form of NAFLD leading to liver fibrosis and cirrhosis, currently represents the second indication for liver transplantation, with projections to become the leading indication in the next 10 years. Importantly, NASH is already the leading indication for liver transplantation in women, with ethnic differences. This alarming ascent would call for identification of higher risk groups, where screening strategies could be targeted more effectively, as recommended by several guidelines[2,5,6]. NAFLD is often associated with common extra-hepatic conditions, particularly cardiovascular disease which drives most of the mortality.
The prevalence of NAFLD may be higher in women with polycystic ovary syndrome (PCOS). PCOS represents the most frequent endocrinopathy in women of reproductive age. PCOS seems more frequent and severe in South Asian women. Moreover, NAFLD is a major health issue in South Asian women, which is even more frequent if they emigrate to Western countries. Some studies have observed an overlap between NAFLD and PCOS: In both conditions, metabolic comorbidities are relevant pathogenetic drivers. In the context of PCOS, a more complex pathogenesis may account for a relationship between the two diseases, particularly hyperan-drogenism. Despite these considerations, the prevalence of NAFLD in PCOS varies largely between 5.5% and 73.3% across studies. This discrepancy may be attributed to retrospective study design leading to selection bias and to varying diagnostic methods and definitions adopted for NAFLD. The majority of studies employed ultrasonography as diagnostic tool for NAFLD, which presents with intrinsic limitations including relatively low accuracy, inter-observer variation and inability of detecting hepatic steatosis involving less than 20%-30% of liver parenchyma. Furthermore, there are limited data on the prevalence of significant liver fibrosis, which mirrors the spectrum of liver disease severity and provides a proxy for NASH prevalence.
Liver biopsy is still considered the gold standard for the diagnosis of NAFLD and associated liver fibrosis, but it is costly, invasive and with an intrinsic risk of sampling error, making it impracticable as a screening tool. Transient elastography (TE) is an ultrasonography-based non-invasive method using liver stiffness as a surrogate for histologic liver fibrosis. The controlled attenuation parameter (CAP) measures the degree of hepatic attenuation by hepatic fat and is measured simultaneously with liver stiffness measurement (LSM). As such, CAP measurement is a surrogate for hepatic steatosis. In various clinical settings, TE with CAP presents with a good performance compared to liver histology for the detection of hepatic fibrosis and steatosis[16-19]. Thus far, there has been no study employing TE with CAP to screen for NAFLD and associated liver fibrosis in a PCOS population.
We employed TE with CAP in consecutive PCOS patients from South Asia as a part of a routine screening program with the following aims: (1) To assess prevalence and associated predictors of NAFLD; (2) To determine prevalence of significant liver fibrosis. Secondary aims included evaluation of lifetime cardiovascular risk and of other comorbidities associated with NAFLD.
MATERIALS AND METHODS
Study design and population
We performed a cross-sectional cohort study at the Department of Obstetrics and Gynecology of McGill University Health Centre (MUHC), which follows about 1000 active PCOS patients. At MUHC, there is a large population of South Asian women with PCOS. Between October 2018 and July 2019, consecutive South Asian adult patients with PCOS were invited to participate in the study by undergoing a TE examination with CAP as part of a screening program for liver disease. We included patients with PCOS defined by the modified Rotterdam criteria, after excluding other endocrine disorders. All patients met at least two criteria among clinical (hirsutism and/or other signs and symptoms of hyperandrogenism, i.e., acne/seborrhea and alopecia) and/or biochemical hyperandrogenism, ovulatory dysfunction and polycystic ovarian morphology. Exclusion criteria were the following: (1) Positivity for hepatitis C virus antibody or hepatitis B virus (HBV) surface antigen; (2) History of pre-existing liver disease or new diagnosis at the screening visit (auto-immune hepatitis, primary biliary cholangitis, primary sclerosing cholangitis, hemo-chromatosis, Wilson’s disease, alpha-1 anti-trypsin); (3) History of hepatocellular carcinoma, liver transplantation or decompensated liver disease (ascites, hepatorenal syndrome, spontaneous bacterial peritonitis, hepatic encephalopathy, variceal hemorrhage); (4) Hazardous alcohol intake, as estimated by an Alcohol Use Disorders Identification Test (AUDIT-C) score ≥ 7; (5) Pregnancy at time of recruitment; and (6) Failure of TE examination or unreliable measurement. All patients provided written informed consent for participation into the study. In order to validate the TE examination with CAP measurement in our cohort, we also reported the CAP values from another routine screening program for liver fibrosis running at MUHC. As part of routine assessment at our centre, patients with chronic HBV undergo CAP quantification during TE examination for LSM. We included only female patients aged < 50 years old with chronic HBV, as an appropriate comparator to our PCOS population. We chose this validation group as young patients with chronic HBV have been reported to have low prevalence of NAFLD[22,23]. The Research Ethics Board of the Research Institute of the MUHC approved the study (study code 2019-4584), which was conducted according to the Declaration of Helsinki.
The primary outcomes of the study were: (1) Prevalence and associated predictors of NAFLD; and (2) Prevalence of significant liver fibrosis. Any grade NAFLD (> 5% of hepatocytes) was defined as CAP ≥ 288 decibels per meter (dB/m), and significant liver fibrosis (stage ≥ F2 out of 4) as TE measurement ≥ 8.0 kilopascals[24-26]. We also explored the use of the recently proposed cut-off of 302 dB/m to diagnose any grade NAFLD.
Secondary outcomes were evaluation of the lifetime cardiovascular risk through the atherosclerotic cardiovascular risk equation, according to American College of Cardiology/American Heart Association guidelines and extra-hepatic diseases linked to NAFLD. Sleep apnea and hypothyroidism were diagnosed on the basis of clinical history. Chronic kidney disease (CKD) was defined as estimated glomerular filtration rate < 60 mL/min/1.73 m2, calculated using the CKD-Epi formula, as per KDIGO guidelines[28,29].
TE examination was performed on a 4-h fasting patient by two experienced operators. The standard M probe was first used in all patients. The XL probe was used in case of failure with M probe. Examinations were considered valid if the operator was able to obtain at least 10 validated measures and the interquartile range of those measures was < 30% of the median[17,30]. Given recent data on the lack of effect of probe type and steatosis on LSM, we did not use adjusted cut-off values.
The simple biomarker hepatic steatosis index (HSI) was calculated and the standard cut-off value of 36 was used to diagnose NAFLD[31,32]. The simple fibrosis biomarkers fibrosis-4 (FIB-4), aspartate aminotransferase (AST)-to-Platelets Ratio Index (APRI) and NAFLD fibrosis score were computed, as previously described[33-35].
Clinical and biological parameters
Anthropometric, clinical, and biochemical measurements and data were collected at recruitment. Family history of liver and cardiovascular diseases was also recorded. Regular physical exercise was defined as at least 150 min of moderate aerobic exercise. The diagnosis of diabetes was based on treatment with antidiabetic drugs or the International Diabetes Federation definition. Any alcohol intake was defined as a score ≥ 5 by the AUDIT-C questionnaire. Biological parameters, collected at time of recruitment, included: AST, alanine aminotransferase (ALT), gamma-glutamyl transferase, platelets, bilirubin, albumin, total cholesterol, high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, triglycerides, insulin and glycosylated hemoglobin, C-reactive protein. All patients were screened for pre-existing liver disease with the following: HBV and hepatitis C virus serologies, anti-nuclear antibody, anti-mitochondrial antibody, anti-smooth muscle antibody, ferritin, ceruloplasmin, alpha-1-antitrypsin. Elevated ALT was defined as ALT > upper limit of normal (ULN) of 24 IU/L, as previously described for South Asian women. Patients were classified into three groups according to their measured body mass index (BMI) values, and cut-off values from Asian guidelines were used for this categorisation; lean < 23 kg/m2, overweight 23-25 kg/m2, obese > 25 kg/m2. Waist circumference values exceeding 80 cm was used as the cut-off value for central obesity. Insulin was used to compute the homeostasis model for assessment of insulin resistance (HOMA-IR) index (fasting insulin (mIU/L) X fasting glucose (mmol/L)/22.5). HOMA-IR > 1.9 was considered indicative of insulin resistance. A patient was defined as metabolically abnormal in presence of any among diabetes, hypertension or hyperlipidemia (triglycerides ≥ 1.7 mmol/L and/or high-density lipoprotein < 1.3 mmol/L), while the absence of all three conditions defined a metabolically normal patient. The following hormonal parameters were evaluated for the diagnosis of biochemical hyperan-drogenism: Total testosterone, bioavailable testosterone and sex hormone-binding globulin. Free androgen index (FAI) was calculated as the ratio of total testosterone levels in nmol/L to sex hormone-binding globulin levels in nmol/L × 100 (%). A FAI > 5 was considered indicative of hyperandrogenism.
We compared characteristics of study subjects by NAFLD status using Student’s t-test for continuous variables and Pearson’s χ² or Fisher's exact test for categorical variables. Multivariate logistic regression modelling was employed to identify factors predictive of NAFLD. Results were reported as adjusted odds ratio (aOR) with 95% confidence interval (CI). Covariates were included a priori based on their clinical relevance or on their significance in univariate analysis (P < 0.10). Final models were adjusted for duration of PCOS, BMI, HOMA-IR, FAI > 5 and ALT > 24 IU/L. The corrected Akaike information criteria (AIC) and the Bayesian information criteria (BIC) were calculated and compared among the models to determine which one had the best goodness-of-fit measure. A lower AIC and/or BIC was indicative of a better fit. The performance of BMI, ALT and FAI to predict NAFLD was measured as area under the receiver operating characteristic curve (AUC). Standard errors of AUC were calculated by DeLong method. A concordance analysis between CAP and HSI was carried out using the kappa score, with results interpreted as follows: Less than 0, less than chance agreement; 0.01–0.20, slight agreement; 0.21–0.40, fair agreement; 0.41–0.60, moderate agreement; 0.61–0.80, substantial agreement and 0.81–0.99, almost perfect agreement. Pairwise correlation was employed to test the association of serum fibrosis biomarkers (FIB-4, APRI and NAFLD fibrosis score) with LSM. All tests were two-tailed and with a significance level of α = 0.05. Statistical analyses were performed using STATA 13.1 (STATA Corp. LP, College Station, TX, United States).
After applying exclusion criteria (Figure 1), 101 patients were included into the present study. The XL probe was employed in 19 (18.8%) cases, while the standard M probe was used in all other patients. The failure rate of TE examination (1%) was similar to previous studies. The characteristics of the study population are reported in Table 1. Only 2 out of 101 included patients reported any alcohol intake. Twelve (11.9%) patients were overweight (BMI: 23–25 kg/m2), and 72 (71.3%) were obese (BMI > 25 kg/m2). Central obesity was present in 97 (96%) cases. Elevated ALT was observed in 23 (22.8%) patients.
Figure 1 Flow chart displaying the selection of participants in the study cohort.
PCOS: Polycystic ovary syndrome; TE: Transient elastography.
Table 1 Demographic, clinical, biochemical, and pharmacological characteristics of the study population (n = 101) and univariate analyses by outcome status, that is presence of nonalcoholic fatty liver disease.
Continuous variables are expressed as mean (standard deviation) and categorical variables as numbers (%).
aP < 0.05.
bP < 0.001.
The P values refer to t test or χ2 test between patients with the outcome (nonalcoholic fatty liver disease or significant liver fibrosis) and those without the outcome. ALT: Alanine aminotransferase; APRI: Aspartate aminotransferase-to-platelet ratio index; ASCVD: Atherosclerotic cardiovascular disease; AST: Aspartate aminotransferase; BMI: Body mass index; dB/m: Decibels per meter; CAP: Controlled association parameter; CRP: C-reactive protein; FAI: Free androgen index; FIB-4: Fibrosis-4 score; GGT: Gamma-glutamyl transpeptidase; HbA1c: Glycosylated hemoglobin; HDL: High-density lipoprotein; HOMA-IR: Homeostasis model for assessment of insulin resistance; HSI: Hepatic steatosis index; IU: International unit; LDL: Low-density lipoprotein; LSM: Liver stiffness measurement; NAFLD: Nonalcoholic fatty liver disease; TSH: Thyroid-stimulating hormone.
Prevalence of NAFLD and significant liver fibrosis
The mean CAP value in the study population was 266.9 dB/m (standard deviation 63.0). In our validation group of 125 female patients with chronic HBV aged < 50 years, we found a much lower mean CAP value of 214 dB/m (standard deviation 55.5). Prevalence of NAFLD was 39.6% in the study population of PCOS women, compared to only 8% in the validation group of female patients with chronic HBV. By employing the cut-off of 302 dB/m, the prevalence of NAFLD in PCOS women was 29.7%. Table 1 depicts the characteristics of patients with and without NAFLD, with relative univariate analysis. All patients with NAFLD were metabolically abnormal (Figure 2). By HSI, prevalence of NAFLD was 39.6%. The number of observed agreements between HSI and CAP was 66 (65.3%) for the 288 dB/m and 60 (59.4%) for the 302 dB/m cut-off, respectively. The kappa-value was 0.34 (standard error: 0.08; 95%CI: 0.17-0.50) and 0.25 (standard error: 0.08; 95%CI: 0.10–0.40), compatible with a “fair” strength of agreement. Prevalence of significant liver fibrosis in the cohort was 6.9%. In patients with NAFLD, the prevalence of significant liver fibrosis was 15%, compared to only 1.6% among those without NAFLD. Table 2 depicts the main characteristics of patients with significant liver fibrosis. The prevalence of NAFLD and significant liver fibrosis was higher in obese patients compared to those overweight or lean (Figure 3A). As showed in Figure 3B, the prevalence of NAFLD was significantly higher in patients with hyperandrogenism (P = 0.007), insulin resistance (P < 0.001) and elevated ALT (P = 0.001). Given the known association between false positive results of LSM and elevated ALT, we conducted a sensitivity analysis by excluding patients with elevated ALT. First, no patient had ALT > 10 times the ULN. Second, among the 9 patients with ALT > 2 times the ULN, 3 had significant liver fibrosis. If we would exclude these patients from the analysis, the prevalence of significant liver fibrosis would be 4.3%. Among the serum fibrosis biomarkers, APRI was the only one showing a significant correlation with LSM (Figure 4).
Figure 2 Distribution of metabolically normal and abnormal patients by nonalcoholic fatty liver disease category.NAFLD: Nonalcoholic fatty liver disease.
Figure 3 Prevalence of nonalcoholic fatty liver disease and significant liver fibrosis.A: Prevalence of nonalcoholic fatty liver disease (NAFLD), severe NAFLD and significant liver fibrosis according to body mass index category; and B: Prevalence of NAFLD according to patients’ characteristics. NAFLD: Nonalcoholic fatty liver disease; ALT: Alanine aminotransferase; HOMA-IR: Homeostasis model for assessment of insulin resistance.
Figure 4 Scatterplot depicting the correlation between liver stiffness measurement.A: Aspartate aminotransferase-to-Platelets Ratio Index; B: Fibrosis-4; and C: Nonalcoholic fatty liver disease fibrosis score. NAFLD: Nonalcoholic fatty liver disease; APRI: aspartate aminotransferase-to-Platelets Ratio Index; FIB-4: Fibrosis-4.
Table 2 Demographic, clinical and biochemical characteristics of patients with significant liver fibrosis (n = 7).
PCOS duration (yr)
ALT: Alanine aminotransferase; BMI: Body mass index; dB/m: Decibels per meter; CAP: Controlled association parameter; FAI: Free androgen index; HOMA-IR: Homeostasis model for assessment of insulin resistance; PCOS: Polycystic ovary syndrome.
Predictors of NAFLD by multivariate analysis
Table 3 illustrates the multivariate analyses for predictors of NAFLD by CAP cut-offs of 288 and 302 dB/m. After adjustments, independent predictors of NAFLD were higher BMI (aOR: 1.30, 95%CI: 1.13-1.52; P < 0.001), hyperandrogenism (aOR: 5.32, 95%CI: 1.56-18.17; P = 0.008) and elevated ALT (aOR: 3.54, 95%CI: 1.10-11.47; P = 0.035). When the cut-off of 302 dB/m was applied, higher BMI (aOR: 1.33, 95%CI: 1.14-1.55; P < 0.001) and hyperandrogenism (aOR: 3.54, 95%CI: 1.00-12.57; P = 0.049) were independently associated with NAFLD. These models had lower AIC and BIC values than others, hence providing support for their use. The performance of BMI, FAI and ALT to predict NAFLD is reported in Figure 5. There was no difference in performance among the three predictors: AUC was 0.808 (standard error: 0.045; 95%CI: 0.719-0.897) for BMI, 0.761 (standard error: 0.049; 95%CI: 0.665-0.858) for FAI, and 0.722 (standard error: 0.054; 95%CI: 0.615-0.828) for ALT.
Figure 5 Area under the curve of body mass index, free androgen index and alanine aminotransferase for prediction of nonalcoholic fatty liver disease.BMI: Body mass index; ALT: Alanine aminotransferase; AUC: Area under the curve; FAI: Free androgen index.
Table 3 Multivariate analysis of factors associated with nonalcoholic fatty liver disease.
Odds ratios and 95% confidence intervals are shown for each variable analyzed in univariate and multivariate logistic regression analysis.
aP < 0.05.
bP < 0.001.
CAP: Controlled attenuation parameter; FAI: Free androgen index; HOMA-IR: Homeostasis model for assessment of insulin resistance; IU: International unit; aOR: Adjusted odds ratio; NAFLD: Nonalcoholic fatty liver disease; PCOS: Polycystic ovary syndrome.
Cardiovascular risk and other extra-hepatic complications
The atherosclerotic cardiovascular risk was higher in patients with NAFLD (Table 1). Only 12.5% of patients with NAFLD were on statin treatment. There was no difference in the prevalence of hypothyroidism among patients with NAFLD (25.0%) and those without NAFLD (31.1%). There was one case of sleep apnea (1.6%) and one case of CKD (1.6%) among patients with NAFLD.
This study, performed in a cohort of consecutive South Asian women with PCOS undergoing a routine screening program for liver disease, showed that NAFLD is a frequent comorbidity. To our knowledge, this is the first study to adopt TE with CAP to investigate NAFLD in PCOS women. TE with CAP is already commonly used in other at-risk populations[43-45]. We also showed that, despite their young age, women with PCOS and NAFLD could have significant liver fibrosis, possibly indicating the coexistence of NASH, the progressive counterpart of NAFLD. Finally, PCOS patients with NAFLD had higher cardiovascular risk score, which should be taken into account for overall risk stratification.
NAFLD affects one quarter of the general population globally[1,2]. NASH is now the second indication for liver transplantation in North America, predicted to become the leading indication within the next 10 years. This will soon impact on the physiognomy of liver transplant waiting lists and on organ supply. As such, there is an urgent need for diagnostic and treatment strategies. The prevalence of NAFLD increases in populations at risk, including those with type 2 diabetes and obesity. NAFLD is often a clinically silent disease until end-stage complications arise. Early identification and risk stratification for those at higher risk for fibrosis progression could help institute interventions to prevent NAFLD progression, and ultimately reduce liver-related morbidity and mortality.
NAFLD is frequent in women with PCOS. Patients with PCOS may be at higher risk for NAFLD due shared pathophysiological features with NAFLD, including insulin resistance, chronic inflammation, dyslipidemia. Moreover, hyperandrogenism likely represents a unique and independent risk factor for NAFLD in this population. Finally, alteration in gut microbiota has been linked to disease severity in both PCOS and NAFLD, thus acting as an additional potential pathogenic bridge between the two conditions[49,50]. In our routine screening program for liver disease, we reported a prevalence of NAFLD at 39.6% and such diagnosis was confirmed in many cases by another non-invasive method, namely the biomarker HSI. This figure is higher than that reported for the general population, where the prevalence of NAFLD is 25%[51,52]. Previous estimates of NAFLD prevalence among PCOS patients ranged widely, between 5.5% and 73.3% across studies. In the present study, we have included a homogeneous population of South Asian women, as both PCOS and NAFLD prevalence vary across ethnicities. South Asian women have been reported to have more severe PCOS symptoms at younger age, with greater insulin resistance than Caucasians. Moreover, NAFLD seems a major health issue in South Asian women, with high rates of advanced liver fibrosis, particularly if they emigrate to Western countries. Previous studies were either of retrospective nature or have employed less accurate diagnostic tools, such as ultrasound or simple serum biomarkers[13,32,55]. In the present study, we employed TE with CAP to investigate the prevalence of both NAFLD and significant liver fibrosis. We have adopted a cut-off value reported as optimal to detect any grade steatosis and we have also applied a recently reported higher cut-off. Significant liver fibrosis affected 6.9% of our cohort, which suggests the co-existence of a progressive disease, namely NASH. Of note, there was a poor correlation between LSM and NAFLD fibrosis score or FIB-4, likely because these two biomarkers incorporate age in their formula, while our study population was young. Conversely, APRI, which does not include age in its formula, had a significant correlation with LSM. Our data suggest that the simple fibrosis biomarker APRI may be preferable to FIB-4 and NAFLD fibrosis score in young PCOS patients.
We found that BMI, hyperandrogenism and ALT were independent predictors of NAFLD. Among them, BMI had the highest AUC to predict NAFLD. This finding underlines the relevance of obesity and associated metabolic conditions. Indeed, in our study population all patients with NAFLD were metabolically abnormal. South Asians have a higher proportion of visceral fat distribution and are more likely to have dyslipidemia than Western patients. However, South Asian patients with NAFLD have an overall lower BMI compared to Caucasians. Other factors contributing to NAFLD in this ethnic group may include genetic variants of the patatin-like phospholipase domain-containing 3 protein, physical inactivity, reduced disease awareness, late diagnosis, as well as sociocultural factors in comparison with Western patient populations. Indeed, in our cohort of young women, only 19.8% were practicing regular physical exercise. Hyperandrogenism measured by FAI was also an independent predictor of NAFLD. Our finding confirms previous data that high FAI correlates with liver disease markers and is a PCOS-specific feature that further increases the risk of NAFLD. Elevated ALT was also an independent predictor of NAFLD on multivariate analysis. Although only 22.8% of patients had elevated ALT, this finding indicates that liver enzyme abnormalities in patients with PCOS and no known pre-existing liver disease should prompt further investigations, including tests for etiologies of chronic liver disease and subsequent referral for TE examination to evaluate the degree of liver fibrosis. Indeed, in our cohort 21.7% of patients with elevated ALT had significant liver fibrosis on TE examination, compared to only 2.6% of patients with normal ALT. On the other hand, 60% of the patients with NAFLD had normal ALT. These figures are in line with data from the general population and suggest the development of NAFLD may be occult[58,59]. This finding emphasizes the need for sensitive diagnostic tools in this at-risk population. Currently, guidelines recommend routine screening strategies for NAFLD in at-risk individuals, such as those with type 2 diabetes and metabolic comorbidities, particularly in case of elevated ALT[5,6]. It is further recommended that at-risk populations should be looked for liver fibrosis using non-invasive markers (serology-based or TE) to quantify the risk of progression to liver cirrhosis. A similar strategy may be applicable in patients with PCOS, whereby those with obesity, elevated ALT or hyperandrogenism should undergo liver fibrosis assessment.
We found that young South Asian patients with PCOS and NAFLD have an increased lifetime risk of atherosclerotic cardiovascular risk. Emerging data support the concept that NAFLD is a multisystem disease affecting a variety of extra-hepatic organ systems. Recent evidences indicate an increased risk of all-cause mortality and a strong link between NAFLD and extra-hepatic diseases, such as cardiovascular disease, hypothyroidism and sleep apnea. Cardiovascular disease risk prediction in younger female patients has been more challenging than in older or male patients. Decisions to implement primary prevention measures are often consequently hindered in this patient population. Our study sheds new insights in the understanding of cardiovascular risk profile in young female population from NAFLD perspective. Our findings should be taken into consideration for risk stratification, especially after transition of women with PCOS to menopause, and for consideration of statin therapy.
Our study presents with several strengths, including the well-characterized homogeneous population and the use of a validated and accurate diagnostic method. The enrollment of consecutive patients minimizes the risk of selection bias. Some limitations of our study should be acknowledged. First, the cross-sectional study design did not allow us to capture the dynamics and associated factors of the disease in a longitudinal fashion. Second, the unavailability of genetic variants of the patatin-like phospholipase domain-containing 3 and other polymorphisms linked to hepatic steatosis prevented us from understanding their contribution to the pathogenesis of NAFLD in PCOS. Third, we included only South Asian women, so we cannot speculate on applicability of our findings to other ethnicities. Fourth, we did not include a group of age-matched patients without PCOS to act as control group. Finally, our study was carried out at a tertiary care centre, which may limit generalizability of our findings.
In conclusion, NAFLD diagnosed by TE with CAP is a frequent comorbidity in young South Asian women with PCOS without known liver disease. Obesity and hyperandrogenism seem the main associated factors. NAFLD can also progress to significant liver fibrosis, pointing towards the coexistence of NASH. Considering the young age of this population, these data suggest that monitoring for liver disease should be proposed in South Asian women with PCOS in case of obesity, elevated ALT, or hyperandrogenism. Early diagnosis of NAFLD via non-invasive screening tools may help prompt initiation of interventions, including life-style modification, hepatology specialized care and cardiovascular risk stratification. Future longitudinal studies should assess the effect of early diagnosis and interventions on long-term outcomes.
Nonalcoholic fatty liver disease (NAFLD) is the most frequent liver disease worldwide. It is essential to identify higher risk groups, where screening strategies could be targeted. Women with polycystic ovary syndrome (PCOS) may be at higher risk for NAFLD.
To date, no study has employed transient elastography (TE) with associated controlled attenuation parameter (CAP) to screen women with PCOS for NAFLD.
This work aims to determine prevalence and associated predictors of NAFLD and prevalence of significant liver fibrosis in South Asian women with PCOS.
A routine screening program through TE with CAP was conducted at a single centre. NAFLD was defined as CAP ≥ 288 decibels per meter. Significant liver fibrosis was defined as TE measurement ≥ 8.0 kilopascals. Predictors of NAFLD were determined by logistic regression analysis.
Prevalence of NAFLD and significant liver fibrosis was 39.6% and 6.9%, respectively. Independent predictors of NAFLD were higher body mass index, hyperandrogenism and elevated alanine aminotransferase.
NAFLD diagnosed by TE with CAP is a frequent comorbidity in South Asian women with PCOS, who can also develop liver fibrosis despite their young age.
To reduce the burden and complications of NAFLD, non-invasive screening strategies should be considered in South Asian women with PCOS.
Part of this work has been presented at the Liver Meeting of the American Association for the Study of Liver Diseases 2019 (Boston, United States). Giada Sebastiani is supported by a Junior 1 and 2 Salary Award from Fonds de la Recherche en Santé du Québec (n 27127 and 267806).
Manuscript source: Unsolicited manuscript
Corresponding Author's Membership in Professional Societies: American Association for the Study of Liver Diseases.
P-Reviewer: Fan JG, Malnick SD S-Editor: Zhang L L-Editor: A P-Editor: Ma YJ
Younossi ZM. Non-alcoholic fatty liver disease - A global public health perspective.J Hepatol. 2019;70:531-544.
Chalasani N, Younossi Z, Lavine JE, Charlton M, Cusi K, Rinella M, Harrison SA, Brunt EM, Sanyal AJ. The diagnosis and management of nonalcoholic fatty liver disease: Practice guidance from the American Association for the Study of Liver Diseases.Hepatology. 2018;67:328-357.
Charlton MR, Burns JM, Pedersen RA, Watt KD, Heimbach JK, Dierkhising RA. Frequency and outcomes of liver transplantation for nonalcoholic steatohepatitis in the United States.Gastroenterology. 2011;141:1249-1253.
Noureddin M, Vipani A, Bresee C, Todo T, Kim IK, Alkhouri N, Setiawan VW, Tran T, Ayoub WS, Lu SC, Klein AS, Sundaram V, Nissen NN. NASH Leading Cause of Liver Transplant in Women: Updated Analysis of Indications For Liver Transplant and Ethnic and Gender Variances.Am J Gastroenterol. 2018;113:1649-1659.
European Association for the Study of the Liver (EASL). EASL-EASD-EASO Clinical Practice Guidelines for the management of non-alcoholic fatty liver disease.J Hepatol. 2016;64:1388-1402.
Introduction: Standards of Medical Care in Diabetes-2019.Diabetes Care. 2019;42:S1-S2.
Taylor RS, Taylor RJ, Bayliss S, Hagström H, Nasr P, Schattenberg JM, Ishigami M, Toyoda H, Wai-Sun Wong V, Peleg N, Shlomai A, Sebastiani G, Seko Y, Bhala N, Younossi ZM, Anstee QM, McPherson S, Newsome PN. Association between Fibrosis Stage and Outcomes of Patients With Nonalcoholic Fatty Liver Disease: A Systematic Review and Meta-Analysis. Gastroenterology 2020; 158: 1611-1625.
Gutierrez-Grobe Y, Ponciano-Rodríguez G, Ramos MH, Uribe M, Méndez-Sánchez N. Prevalence of non-alcoholic fatty liver disease in premenopausal, posmenopausal and polycystic ovary syndrome women. The role of estrogens.Ann Hepatol. 2010;9:402-409.
Macut D, Božić-Antić I, Bjekić-Macut J, Tziomalos K. MANAGEMENT OF ENDOCRINE DISEASE: Polycystic ovary syndrome and nonalcoholic fatty liver disease.Eur J Endocrinol. 2017;177:R145-R158.
Neukam K, Bhagani S, Rodger A, Oben J, Nirmal D, Jain A, Nair DR. High prevalence of non-alcoholic fatty liver disease (NAFLD) among Gujarati Indians in North London: a population-based study.Clin Lipidiol . 2017;12:33-39.
Kim JJ, Kim D, Yim JY, Kang JH, Han KH, Kim SM, Hwang KR, Ku SY, Suh CS, Kim SH, Choi YM. Polycystic ovary syndrome with hyperandrogenism as a risk factor for non-obese non-alcoholic fatty liver disease.Aliment Pharmacol Ther. 2017;45:1403-1412.
Rocha ALL, Faria LC, Guimarães TCM, Moreira GV, Cândido AL, Couto CA, Reis FM. Non-alcoholic fatty liver disease in women with polycystic ovary syndrome: systematic review and meta-analysis.J Endocrinol Invest. 2017;40:1279-1288.
Dowman JK, Tomlinson JW, Newsome PN. Systematic review: the diagnosis and staging of non-alcoholic fatty liver disease and non-alcoholic steatohepatitis.Aliment Pharmacol Ther. 2011;33:525-540.
Lai M, Afdhal NH. Liver Fibrosis Determination.Gastroenterol Clin North Am. 2019;48:281-289.
Castera L, Friedrich-Rust M, Loomba R. Noninvasive Assessment of Liver Disease in Patients with Nonalcoholic Fatty Liver Disease. Gastroenterology 2019; 156: 1264-1281.
Karlas T, Petroff D, Sasso M, Fan JG, Mi YQ, de Lédinghen V, Kumar M, Lupsor-Platon M, Han KH, Cardoso AC, Ferraioli G, Chan WK, Wong VW, Myers RP, Chayama K, Friedrich-Rust M, Beaugrand M, Shen F, Hiriart JB, Sarin SK, Badea R, Jung KS, Marcellin P, Filice C, Mahadeva S, Wong GL, Crotty P, Masaki K, Bojunga J, Bedossa P, Keim V, Wiegand J. Individual patient data meta-analysis of controlled attenuation parameter (CAP) technology for assessing steatosis.J Hepatol. 2017;66:1022-1030.
European Association for Study of Liver. EASL-ALEH Clinical Practice Guidelines: Non-invasive tests for evaluation of liver disease severity and prognosis.J Hepatol. 2015;63:237-264.
Eddowes PJ, Sasso M, Allison M, Tsochatzis E, Anstee QM, Sheridan D, Guha IN, Cobbold JF, Deeks JJ, Paradis V, Bedossa P, Newsome PN. Accuracy of FibroScan Controlled Attenuation Parameter and Liver Stiffness Measurement in Assessing Steatosis and Fibrosis in Patients With Nonalcoholic Fatty Liver Disease.Gastroenterology. 2019;156:1717-1730.
Caussy C, Alquiraish MH, Nguyen P, Hernandez C, Cepin S, Fortney LE, Ajmera V, Bettencourt R, Collier S, Hooker J, Sy E, Rizo E, Richards L, Sirlin CB, Loomba R. Optimal threshold of controlled attenuation parameter with MRI-PDFF as the gold standard for the detection of hepatic steatosis.Hepatology. 2018;67:1348-1359.
Rotterdam ESHRE/ASRM-Sponsored PCOS consensus workshop group. Revised 2003 consensus on diagnostic criteria and long-term health risks related to polycystic ovary syndrome (PCOS).Hum Reprod. 2004;19:41-47.
Bradley KA, DeBenedetti AF, Volk RJ, Williams EC, Frank D, Kivlahan DR. AUDIT-C as a brief screen for alcohol misuse in primary care.Alcohol Clin Exp Res. 2007;31:1208-1217.
Joo EJ, Chang Y, Yeom JS, Ryu S. Hepatitis B virus infection and decreased risk of nonalcoholic fatty liver disease: A cohort study.Hepatology. 2017;65:828-835.
Wang B, Li W, Fang H, Zhou H. Hepatitis B virus infection is not associated with fatty liver disease: Evidence from a cohort study and functional analysis.Mol Med Rep. 2019;19:320-326.
Roulot D, Costes JL, Buyck JF, Warzocha U, Gambier N, Czernichow S, Le Clesiau H, Beaugrand M. Transient elastography as a screening tool for liver fibrosis and cirrhosis in a community-based population aged over 45 years.Gut. 2011;60:977-984.
Wong VW, Vergniol J, Wong GL, Foucher J, Chan HL, Le Bail B, Choi PC, Kowo M, Chan AW, Merrouche W, Sung JJ, de Lédinghen V. Diagnosis of fibrosis and cirrhosis using liver stiffness measurement in nonalcoholic fatty liver disease.Hepatology. 2010;51:454-462.
Wong VW, Vergniol J, Wong GL, Foucher J, Chan AW, Chermak F, Choi PC, Merrouche W, Chu SH, Pesque S, Chan HL, de Lédinghen V. Liver stiffness measurement using XL probe in patients with nonalcoholic fatty liver disease.Am J Gastroenterol. 2012;107:1862-1871.
Goff DC Jr, Lloyd-Jones DM, Bennett G, Coady S, D'Agostino RB, Gibbons R, Greenland P, Lackland DT, Levy D, O'Donnell CJ, Robinson JG, Schwartz JS, Shero ST, Smith SC Jr, Sorlie P, Stone NJ, Wilson PW, Jordan HS, Nevo L, Wnek J, Anderson JL, Halperin JL, Albert NM, Bozkurt B, Brindis RG, Curtis LH, DeMets D, Hochman JS, Kovacs RJ, Ohman EM, Pressler SJ, Sellke FW, Shen WK, Smith SC Jr, Tomaselli GF; American College of Cardiology/American Heart Association Task Force on Practice Guidelines. 2013 ACC/AHA guideline on the assessment of cardiovascular risk: a report of the American College of Cardiology/American Heart Association Task Force on Practice Guidelines.Circulation. 2014;129:S49-S73.
Stevens PE, Levin A; Kidney Disease: Improving Global Outcomes Chronic Kidney Disease Guideline Development Work Group Members. Evaluation and management of chronic kidney disease: synopsis of the kidney disease: improving global outcomes 2012 clinical practice guideline.Ann Intern Med. 2013;158:825-830.
Lopez-Giacoman S, Madero M. Biomarkers in chronic kidney disease, from kidney function to kidney damage.World J Nephrol. 2015;4:57-73.
Sandrin L, Fourquet B, Hasquenoph JM, Yon S, Fournier C, Mal F, Christidis C, Ziol M, Poulet B, Kazemi F, Beaugrand M, Palau R. Transient elastography: a new noninvasive method for assessment of hepatic fibrosis.Ultrasound Med Biol. 2003;29:1705-1713.
Lee JH, Kim D, Kim HJ, Lee CH, Yang JI, Kim W, Kim YJ, Yoon JH, Cho SH, Sung MW, Lee HS. Hepatic steatosis index: a simple screening tool reflecting nonalcoholic fatty liver disease.Dig Liver Dis. 2010;42:503-508.
Petta S, Ciresi A, Bianco J, Geraci V, Boemi R, Galvano L, Magliozzo F, Merlino G, Craxì A, Giordano C. Insulin resistance and hyperandrogenism drive steatosis and fibrosis risk in young females with PCOS.PLoS One. 2017;12:e0186136.
Vallet-Pichard A, Mallet V, Nalpas B, Verkarre V, Nalpas A, Dhalluin-Venier V, Fontaine H, Pol S. FIB-4: an inexpensive and accurate marker of fibrosis in HCV infection. Comparison with liver biopsy and fibrotest.Hepatology. 2007;46:32-36.
Angulo P, Hui JM, Marchesini G, Bugianesi E, George J, Farrell GC, Enders F, Saksena S, Burt AD, Bida JP, Lindor K, Sanderson SO, Lenzi M, Adams LA, Kench J, Therneau TM, Day CP. The NAFLD fibrosis score: a noninvasive system that identifies liver fibrosis in patients with NAFLD.Hepatology. 2007;45:846-854.
Wai CT, Greenson JK, Fontana RJ, Kalbfleisch JD, Marrero JA, Conjeevaram HS, Lok AS. A simple noninvasive index can predict both significant fibrosis and cirrhosis in patients with chronic hepatitis C.Hepatology. 2003;38:518-526.
International Diabetes Federation Guideline Development Group. Global guideline for type 2 diabetes.Diabetes Res Clin Pract. 2014;104:1-52.
Mohan P, Sundar V, Bhaskar E, Anthony S. Estimation of Upper Limit of Normal for Serum Alanine Transaminase in Healthy South Indian Population.Indian J Clin Biochem. 2017;32:337-342.
Aziz N, Kallur SD, Nirmalan PK. Implications of the revised consensus body mass indices for asian indians on clinical obstetric practice.J Clin Diagn Res. 2014;8:OC01-OC03.
Matthews DR, Hosker JP, Rudenski AS, Naylor BA, Treacher DF, Turner RC. Homeostasis model assessment: insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man.Diabetologia. 1985;28:412-419.
Vermeulen A, Verdonck L, Kaufman JM. A critical evaluation of simple methods for the estimation of free testosterone in serum.J Clin Endocrinol Metab. 1999;84:3666-3672.
Cohen J. Weighted kappa: nominal scale agreement with provision for scaled disagreement or partial credit.Psychol Bull. 1968;70:213-220.
Perazzo H, Veloso VG, Grinsztejn B, Hyde C, Castro R. Factors That Could Impact on Liver Fibrosis Staging by Transient Elastography.Int J Hepatol. 2015;2015:624596.
Benmassaoud A, Ghali P, Cox J, Wong P, Szabo J, Deschenes M, Osikowicz M, Lebouche B, Klein MB, Sebastiani G. Screening for nonalcoholic steatohepatitis by using cytokeratin 18 and transient elastography in HIV mono-infection.PLoS One. 2018;13:e0191985.
Saroli Palumbo C, Restellini S, Chao CY, Aruljothy A, Lemieux C, Wild G, Afif W, Lakatos PL, Bitton A, Cocciolillo S, Ghali P, Bessissow T, Sebastiani G. Screening for Nonalcoholic Fatty Liver Disease in Inflammatory Bowel Diseases: A Cohort Study Using Transient Elastography.Inflamm Bowel Dis. 2019;25:124-133.
Kwok R, Choi KC, Wong GL, Zhang Y, Chan HL, Luk AO, Shu SS, Chan AW, Yeung MW, Chan JC, Kong AP, Wong VW. Screening diabetic patients for non-alcoholic fatty liver disease with controlled attenuation parameter and liver stiffness measurements: a prospective cohort study.Gut. 2016;65:1359-1368.
Wong RJ, Aguilar M, Cheung R, Perumpail RB, Harrison SA, Younossi ZM, Ahmed A. Nonalcoholic steatohepatitis is the second leading etiology of liver disease among adults awaiting liver transplantation in the United States.Gastroenterology. 2015;148:547-555.
Heimbach J. Debate: A bridge too far--liver transplantation for nonalcoholic steatohepatitis will overwhelm the organ supply.Liver Transpl. 2014;20 Suppl 2:S32-S37.
Vassilatou E. Nonalcoholic fatty liver disease and polycystic ovary syndrome.World J Gastroenterol. 2014;20:8351-8363.
Boursier J, Mueller O, Barret M, Machado M, Fizanne L, Araujo-Perez F, Guy CD, Seed PC, Rawls JF, David LA, Hunault G, Oberti F, Calès P, Diehl AM. The severity of nonalcoholic fatty liver disease is associated with gut dysbiosis and shift in the metabolic function of the gut microbiota.Hepatology. 2016;63:764-775.
Yurtdaş G, Akdevelioğlu Y. A New Approach to Polycystic Ovary Syndrome: The Gut Microbiota.J Am Coll Nutr. 2020;39:371-382.
Obesity in Canada: a joint report from the public health agency of Canada and the Canadian Institute for Health Information. Available from: https://securecihica/free_products/Obesity_in_canada_2011_enpdf 2011.
Rinella M, Charlton M. The globalization of nonalcoholic fatty liver disease: Prevalence and impact on world health.Hepatology. 2016;64:19-22.
Browning JD, Szczepaniak LS, Dobbins R, Nuremberg P, Horton JD, Cohen JC, Grundy SM, Hobbs HH. Prevalence of hepatic steatosis in an urban population in the United States: impact of ethnicity.Hepatology. 2004;40:1387-1395.
Wijeyaratne CN, Seneviratne Rde A, Dahanayake S, Kumarapeli V, Palipane E, Kuruppu N, Yapa C, Seneviratne Rde A, Balen AH. Phenotype and metabolic profile of South Asian women with polycystic ovary syndrome (PCOS): results of a large database from a specialist Endocrine Clinic.Hum Reprod. 2011;26:202-213.
Kumarendran B, O'Reilly MW, Manolopoulos KN, Toulis KA, Gokhale KM, Sitch AJ, Wijeyaratne CN, Coomarasamy A, Arlt W, Nirantharakumar K. Polycystic ovary syndrome, androgen excess, and the risk of nonalcoholic fatty liver disease in women: A longitudinal study based on a United Kingdom primary care database.PLoS Med. 2018;15:e1002542.
Szanto KB, Li J, Cordero P, Oben JA. Ethnic differences and heterogeneity in genetic and metabolic makeup contributing to nonalcoholic fatty liver disease.Diabetes Metab Syndr Obes. 2019;12:357-367.
Singh S, Kuftinec GN, Sarkar S. Non-alcoholic Fatty Liver Disease in South Asians: A Review of the Literature.J Clin Transl Hepatol. 2017;5:76-81.
Mofrad P, Contos MJ, Haque M, Sargeant C, Fisher RA, Luketic VA, Sterling RK, Shiffman ML, Stravitz RT, Sanyal AJ. Clinical and histologic spectrum of nonalcoholic fatty liver disease associated with normal ALT values.Hepatology. 2003;37:1286-1292.
Ma X, Liu S, Zhang J, Dong M, Wang Y, Wang M, Xin Y. Proportion of NAFLD patients with normal ALT value in overall NAFLD patients: a systematic review and meta-analysis.BMC Gastroenterol. 2020;20:10.