Summary
Background
Metabolic-dysfunction-associated steatotic liver disease (MASLD) is rising globally, including in India, yet community-based data remain scarce. We address this critical knowledge gap by assessing the prevalence, distribution, and characteristics of MASLD subgroups and fibrosis, leveraging the Phenome India cohort.
Methods
In this prospective study, we recruited 10,267 adults across 37 laboratories of the Council of Scientific and Industrial Research (CSIR) from 27 Indian cities. All permanent staff members of the CSIR, including current employees, retirees, and their spouses who responded to the recruitment campaign and provided voluntary consent, were considered for participation in the Phenome India Cohort. Steatosis and fibrosis were assessed using Transient Elastography, along with associated clinical, biochemical, cytokine and anthropometric data. Overall, crude and age-adjusted prevalence rates were estimated in the study population and various subgroups.
Findings
Of 10,267 individuals screened, 7764 were included, 3712 (47.8%) fulfilled MASLD criteria, corresponding to an age-adjusted prevalence of 38.9% (95% CI 37.2–40.6). Significant fibrosis, defined as liver stiffness measurement (LSM) ≥8.2 kPa (≥F2), was more frequent in MASLD (6.3% [234 of 3688]) than in cases without-MASLD (1.7% [69 of 4027]), corresponding to an age-adjusted prevalence of 4.1% in MASLD. Overall age-adjusted prevalence of significant fibrosis was 2.4%, which clustered in older adults (>60 years) and in those with diabetes or obesity class II, with evidence of possible regional variation.
Interpretation
MASLD affected over one-third of participants. Site-specific disparities were observed, which suggest the need for large-scale longitudinal studies to elucidate region-specific risk factors and temporal trends. Community-based awareness and targeted public health interventions across diverse geographical and socio-cultural settings in India may help curb the rising burden.
Funding
The work was funded by CSIR, India grant HCP47.
Keywords: Prevalence, MASLD, Liver fibrosis, Phenome India, Cardio-metabolic, Cohort
Research in context.
Evidence before this study
PubMed and Google Scholar were searched systematically for articles, reviews, comments till September 2025 and in continuous manner with revision of article published in English language in and/or manner with search terms “NAFLD”, “MAFLD”, “MASLD”, “Prevalence”, “India”, “Global”, “Community” “Diabetes”, “Hypertension”, “Obesity”, “CAP”, “LSM”. A couple of recent large-scale, systematic, and well-planned studies that reported MASLD prevalence from India were limited to specific high-risk populations. Prior studies have not been conducted on the general population, nor have community-based studies comprehensively assessed the prevalence and characteristics of MASLD and associated liver fibrosis in India. Existing data are largely limited to hospital-based cohorts, small regional samples, or estimates for high-risk groups exclusively. A previous systematic review has been conducted on NAFLD from the South Asian region, including India.
Added value of this study
This community-based study, conducted among the urban and semi-urban cohorts of working and retired employees and their spouses, estimated the prevalence of MASLD and Cardiometabolic risk factors (CMRF) in India. The data comprehensively assesses over 7500 participants from a nationwide employee cohort. Our prospective study, leveraging the Phenome India cohort with 7764 adults from 27 cities, reveals an age-adjusted MASLD prevalence of 38.9% (95% CI: 37.2–40.6) and significant fibrosis [≥8.2 kPa LSM (≥F2)] in 2.4% overall, highlighting its epidemic scale in a diverse Indian population and regional variations. The prevalence of age-adjusted MASLD among adults in India was similar to that reported in previous systematic reviews for the pooled prevalence of NAFLD. This study additionally provides evidence of site-specific variations of age-adjusted prevalence and differences in CMRF in various regions of the country.
Implications of all the available evidence
Our findings highlight hotspots (e.g., higher fibrosis in Assam) and demographic vulnerabilities (e.g., elderly, diabetic, or obese individuals), with an increase of CMRFs in certain geographies, providing novel actionable evidence. Currently available data, combined with previously published literature, advocates targeted screening of fatty liver disease and liver fibrosis in both general and high-risk populations, along with community-based awareness and involvement for region-specific hotspots and public health interventions.
Introduction
Metabolic-dysfunction-associated steatotic liver disease (MASLD), previously known as non-alcoholic fatty liver disease (NAFLD), characterised by excessive intrahepatic lipid accumulation, is a major global health concern, given its association with substantial morbidity and mortality.1 Globally, MASLD affects approximately 25–50% of the adult population, with prevalence rates influenced by ethnicity, dietary patterns, socioeconomic status and genetic factors.2 Recent projections suggest that the prevalence of obesity is expected to rise among men and women from 342 million (95% uncertainty interval 337–346) and 496 million (490–503) in 2021 to 838 million (692–921) and 1.11 billion (0.94–1.21), by the year 2050 respectively.3 In India, while there are limited studies available for community-wide prevalence of MASLD in the general population, the prevalence of NAFLD is alarmingly high, ranging from 9 to 32% in urban areas, with certain regions reporting a prevalence of nearly 60%, attributed to rapid urbanisation, sedentary lifestyles, and dietary shifts toward processed, calorie-dense foods.4, 5, 6 The South Asian population's propensity for central obesity combined with genetic factors such as polymorphisms in the PNPLA3 gene, further increases susceptibility to obesity and, consequently, NAFLD.7,8 Fibrosis is a strong predictor of adverse outcomes in MASLD, determining risk of cirrhosis, hepatocellular carcinoma, liver-related mortality, and extrahepatic complications.9 Recent studies indicate that 10–15% of individuals with NAFLD or MASLD may already have advanced fibrosis, yet most remain undiagnosed until decompensation.9,10 Importantly, significant fibrosis is also observed in individuals without hepatic steatosis, indicating that liver fat alone may be an insufficient marker for screening.11 Thus, population-level screening for fibrosis irrespective of fatty liver status may be essential for risk stratification and timely intervention.
The asymptomatic nature of early-stage MASLD often leads to delayed diagnosis, with patients being detected only at advanced stages of liver disease. This not only limits the therapeutic measures but is also associated with higher non-liver-related mortality (cardiac disease, extrahepatic malignancies).12 The delay also reduces opportunities for preventive interventions in individuals at risk of developing fibrosis in MASLD. An integral component of developing and implementing such preventive measures is the availability of nationwide data that provide reliable estimates of MASLD, prevalence of cardiometabolic risk factors (CMRF), and stage of disease at the community level. However, most studies on the prevalence of MASLD in India are hospital-based, drawn from endocrinology clinics, and gastroenterology or hepatology units at tertiary care centres, and may be subject to referral bias.4,13,14 Heterogeneity in study populations, diagnostic criteria, and methodologies further complicates comparisons across studies.
The Phenome India cohort offers an opportunity to address these gaps. This nationwide, community-based urban and semi-urban cohort of working and retired employees, along with their spouses, is relatively homogeneous in terms of socioeconomic and educational background, yet geographically diverse across 27 cities. It may have the potential to enable a robust estimate for MASLD and fibrosis throughout India.15 Thus, in this study, we determine the prevalence and distribution of MASLD and liver fibrosis among participants from the Phenome India cohort.
Methods
We utilized the Phenome India-CSIR Health Cohort Knowledgebase (PI-CHeCK), a multi-center longitudinal cohort established by the Council of Scientific and Industrial Research (CSIR), to estimate MASLD prevalence, grade community-level disease severity, and profile associated cardiometabolic risk factors (CMRFs).15,16 Phenome India includes 37 CSIR laboratories and affiliated centres across 17 states and 2 Union Territories in India. The data collection was carried out between December 2023 and June 2024. All permanent staff members of the CSIR, including current employees, retirees, and their spouses who responded to the recruitment campaign and provided voluntary consent, were considered for participation in the Phenome India Cohort. Exclusion criteria included age <18 years, pregnancy and unwillingness to participate.
Participants with controlled attenuation parameter (CAP) ≥248 dB/m or with CAP <248 dB/m but liver stiffness measurement (LSM) ≥6.5 kPa, were screened for hepatitis B virus (HBV) and hepatitis C virus (HCV) using HBsAg and anti-HCV serological tests, respectively. Participants with evidence of HBV or HCV infection, defined by positive serology, were excluded from all analyses.
After overnight fasting, hepatic steatosis and fibrosis were measured using FibroScan FS Mini 430 Plus™ (Echosens, Paris, France). Probe choice (M or XL) was determined using the device's automated probe-recommendation system based on the skin-to-liver capsule distance.17,18 LSM and CAP measurements were performed by trained personnel who were blinded to the participant's clinical data and study outcomes. For each participant, we obtained ten consecutive valid readings for LSM with an Interquartile range/median (IQR/M) ratio <0.3. Failure of transient elastography was defined as the inability to obtain a valid LSM or CAP measurement.17,18 Contraindications for doing Fibroscan have been mentioned in previously published work.15
Anthropometry was performed to measure height, weight, chest circumference (CC), waist circumference (WC), abdominal circumference (AC), and hip circumference (HC) (Supplementary Methods). Details of anthropometry, biochemistry, and other tests done are available in the protocol paper of Phenome India, published previously.15
A Bio-Rad Human Cytokine 48-Plex screening assay (Cat. No. 12007283, Bio-Rad Laboratories, USA) was used for cytokine profiling, stratifying patients into subgroups: MASLD with/without fibrosis and without-MASLD for comparison, following the manufacturer's recommended protocol (Supplementary Methods). The assay was performed at two Institutes; hence, data were normalized (Supplementary Methods).19
MASLD was defined as hepatic steatosis detected on transient elastography (CAP value > 248 dB/m5,20) with any one or more of five CMRFs: obesity, dysglycaemia, elevated blood pressure or HDL levels or triglyceride levels.20,21 Hepatic steatosis was defined as CAP value > 248 dB/m, with severity graded as: S0 (no steatosis)- CAP <249 dB/m; S1 (mild steatosis)- CAP 249–267 dB/m; S2 (moderate steatosis)- CAP 268–279 dB/m; S3 (severe steatosis)- CAP ≥280 dB/m.22 Liver fibrosis was assessed non-invasively using transient elastography, and stiffness values (kPa) were categorised into standard TE-based fibrosis stages (F0–F4) using validated cut-offs: LSM <8.2 kPa (F0/F1 or no fibrosis/mild fibrosis); LSM 8.2 to <9.7 kPa (F2 or moderate fibrosis); LSM 9.7 to <13.6 kPa (F3 or severe fibrosis); LSM ≥13.6 kPa (F4 or cirrhosis).23 Dysglycaemia, Overweight and Obesity, Elevated Blood Pressure, Triglyceride levels and HDL levels were taken as CMRF for assessing MASLD status as published previously.5,20,21 The definition of each risk factor used as a criterion for assessing MASLD is provided in the Supplementary Text.
BMI was used to categorize weight status according to the Asia–Pacific threshold values as follows: underweight: BMI <18.5 kg/m2, normal weight: BMI ≥18.5 kg/m2 (lean was considered when BMI <23 kg/m2), overweight: BMI ≥23 and < 25 kg/m2, obesity class I: BMI ≥25 and < 30 kg/m2, obesity class II: BMI ≥30 kg/m2.5,20,24 Type 2 diabetes mellitus (T2DM) was labelled based on one or more of the following criteria: fasting blood glucose (FBG) ≥126 mg/dL, glycated haemoglobin (HbA1c) ≥6.5%, a self-reported history of Type 2 diabetes, or self-reported use of medication for Type 2 diabetes control.5 Prediabetes was labelled based on one or more of the following criteria: fasting blood glucose (FBG) ≥100 and ≤ 125 mg/dL, glycated haemoglobin (HbA1c) ≥5.7 and ≤ 6.4%. Elevated Blood Pressure was defined as SBP ≥130 or DBP ≥85 mmHg, or a self-reported diagnosis or use of antihypertensive drugs.20 Dyslipidemia was defined based on total cholesterol ≥200 mg/dL or Serum triglycerides levels ≥150 mg/dL5 or lipid-lowering treatment.
Statistical analysis
For continuous variables, we present the mean (SD) along with mean differences and their 95% confidence intervals. For cytokine data, median (IQR) and median differences and their 95% confidence intervals have been presented and for categorical variables, associations were expressed as odds ratios with their 95% confidence intervals. Regression analyses were conducted using complete-case analysis, with no imputation performed because the level of missingness was minimal (<3%). Age-adjusted values were calculated using the urban population data from the 2011 Census of India, categorized into 10-year age bins.25 We did not use null hypothesis significance testing or report p-values; however, we report effect sizes with their 95% confidence intervals. Univariate and multivariable logistic regression analyses were undertaken to identify risk factors associated with MASLD and liver fibrosis. Odds Ratio (OR) values represent crude associations, while Adjusted Odds Ratio (AOR) values represent adjusted estimates that account for age, gender, BMI category, waist–hip ratio, diabetes, elevated blood pressure, and dyslipidemia. Python Program was utilised with the required libraries for large-scale data analysis and statistical computation. Python 3.10.6 was utilised for data visualisation. Logistic regression analysis was done using Stata 19.5.
Ethics statement
The study was approved by the Institutional Human Ethics Committee (IHEC) at CSIR-IGIB [Institute of Genomics and Integrative Biology] (reference number: CSIR-IGIB/IHEC/2023-24/16) and registered with the Clinical Trials Registry of India (CTRI/2024/01/061,807). The study adheres to the Declaration of Helsinki, and written informed consent was obtained from all participants.
Role of the funding source
This study was supported through the HCP47 grant, awarded by the Council of Scientific and Industrial Research, New Delhi (CSIR). The funder of the study had no role in study design, data collection, data analysis, data interpretation, or writing of the report. The corresponding author/s had full access to all data in the study and had final responsibility for the decision to submit for publication in concurrence with all authors.
Results
The Phenome India cohort included 10,267 participants recruited from 27 cities across India between December 2023 and June 2024. Of these, 7792 were eligible for analysis. 3712 was categorized as MASLD, 4052 without-MASLD and 28 had SLD without identifiable CMRF's (Fig. 1). Hence, data from 7764 participants were included. Various anthropometric and biochemical parameters of the participants in the overall cohort, along with gender wise distribution, are shown in Table 1.
Fig. 1.
Flowchart for participant recruitment in Phenome India cohort: Abbreviations: CAP, Controlled Attenuation Parameter; SLD, Steatotic Liver Disease.
Table 1.
Baseline characteristics of the study population–Phenome India study.
| Variable | Total (n = 7764) Mean (SD) |
Male [n = 3671 (47.3%)] Mean (SD) |
Female [n = 4093 (52.7%)] Mean (SD) |
|---|---|---|---|
| Demographics and biochemical parameters | |||
| Age (years) | 51 (13) | 52 (14) | 50 (13) |
| Aspartate Aminotransferase (U/L) | 26.8 (15.1) | 28.8 (18.5) | 25 (11.1) |
| Alanine Aminotransferase (U/L) | 26.7 (18.5) | 31.3 (20.8) | 22.6 (14.9) |
| Glucose Fasting (mg/dL) | 99.2 (28.8) | 100.9 (30.4) | 97.7 (27.2) |
| Glycated Haemoglobin (%) | 5.9 (1.1) | 6 (1.1) | 5.8 (1) |
| HOMA-IR | 2.5 (4.4) | 2.6 (5.2) | 2.4 (3.7) |
| Total Cholesterol (mg/dL) | 178 (38.5) | 175.1 (38.6) | 180.5 (38.1) |
| Serum HDL Cholesterol (mg/dL) | 49.1 (10.1) | 45.7 (8.6) | 52.2 (10.4) |
| Serum LDL Cholesterol (mg/dL) | 116.1 (29.9) | 115.9 (30.4) | 116.3 (29.4) |
| Serum Triglycerides (mg/dL) | 125.4 (62.1) | 134.6 (68.8) | 117.2 (54.1) |
| Haemoglobin (g/dL) | 13 (1.6) | 14.1 (1.3) | 12.1 (1.2) |
| Anthropometry | |||
| Weight (kg) | 68.2 (11.8) | 71.6 (11.6) | 65.1 (11.2) |
| Height (cm) | 160.3 (9.1) | 167.2 (6) | 154.2 (6.1) |
| Waist circumference (cm) | 88 (10.7) | 89.6 (9.4) | 86.6 (11.6) |
| Waist-Hip ratio | 0.9 (0.1) | 0.9 (0.1) | 0.9 (0.1) |
| Comorbidities | |||
| Diabetes | 1927 (24.8%) | 1005 (27.4%) | 922 (22.5%) |
| (23.9–25.8) | (25.9–28.8) | (21.2–23.8) | |
| Elevated blood pressure | 4272 (55%) | 2160 (58.8%) | 2112 (51.6%) |
| (53.9–56.1) | (57.2–60.4) | (50–53.1) | |
| BMI categories | |||
| Lean | 1405 (18.1%) | 814 (22.2%) | 591 (14.4%) |
| (17.2–19) | (20.8–23.5) | (13.4–15.5) | |
| Overweight | 1446 (18.6%) | 852 (23.2%) | 594 (14.5%) |
| (17.8–19.5) | (21.8–24.6) | (13.4–15.6) | |
| Obese I | 3487 (44.9%) | 1622 (44.2%) | 1865 (45.6%) |
| (43.8–46) | (42.6–45.8) | (44.1–47.1) | |
| Obese II | 1424 (18.3%) | 383 (10.4%) | 1041 (25.4%) |
| (17.5–19.2) | (9.4–11.4) | (24.1–26.8) | |
Continuous variables are summarized using mean (SD). Categorical variables are summarized using n (%), with 95% CI (in italics); missing data for each variable were excluded when producing these summaries. Lean includes underweight: BMI <18.5 kg/m2 and normal weight: BMI 18.5–22.9 kg/m2.
Missing n = 2 for Total and n = 2 for Females in BMI.
Abbreviations: HOMA-IR, Homeostatic Model Assessment of Insulin Resistance; HDL, High-density lipoprotein; LDL.
Low-density lipoprotein; CAP, Controlled Attenuation Parameter.
Not all percentages may add up to 100% owing to rounding.
Prevalence of MASLD
We report a crude prevalence of 47.8% (3712 of 7764, 95% CI: 46.7–48.9) (Fig. 1), (Table 2) and an age-adjusted prevalence of 38.9% (95% CI: 37.2–40.6) in this study group. The age-adjusted prevalence in females and males was 33% and 45.9%, respectively. Given the variability in validated CAP thresholds across populations,17,18,26,27 we also evaluated steatosis prevalence using two commonly referenced cut-offs: ≥263 and ≥275 dB/m. The age-adjusted prevalence of MASLD was 30% (28.4–31.5) using ≥263 dB/m and 22.8% (21.5–24.1) using ≥275 dB/m (data not shown).
Table 2.
Comparison of biochemical, clinical, anthropometric and Transient Elastography parameters between MASLD and without-MASLD population subgroups.
| Variable | MASLD mean (SD) |
Without-MASLD mean (SD) |
Mean difference∗ OR odds ratio#/(95% CI) |
|---|---|---|---|
| n (%) | 3712 (47.8%) | 4052 (52.2%) | |
| (46.7–48.9) | (51.1–53.3) | ||
| Gender (Male)a | 1835 (50%) | 1836 (50%) | 1 |
| (48.4–51.6) | (48.4–51.6) | ||
| Gender (Female)a | 1877 (45.9%) | 2216 (54.1%) | 0.8# (0.8–0.9) |
| (44.3–47.4) | (52.6–55.6) | ||
| Age (years) | 53 (12) | 49 (14) | 4∗ (3–4) |
| Aspartate Aminotransferase (U/L) | 28.5 (14) | 25.2 (15.9) | 3.4∗ (2.7–4.1) |
| Alanine Aminotransferase (U/L) | 30.8 (21.2) | 22.9 (14.6) | 7.8∗ (7.1–8.7) |
| Glucose Fasting (mg/dL) | 105.3 (33) | 93.7 (23) | 11.6∗ (10.3–12.9) |
| Glycated Haemoglobin (%) | 6.2 (1.2) | 5.7 (0.9) | 0.5∗ (0.5–0.5) |
| Total Cholesterol (mg/dL) | 181.3 (39.3) | 175 (37.5) | 6.3∗ (4.6–8) |
| Serum HDL Cholesterol (mg/dL) | 48.3 (9.6) | 50 (10.6) | −1.7∗ (−2.1 to −1.3) |
| Serum LDL Cholesterol (mg/dL) | 119.2 (30.6) | 113.5 (29) | 5.7∗ (4.4–7) |
| Serum Triglycerides (mg/dL) | 140.6 (67.4) | 111.6 (53.1) | 29∗ (26.3–31.7) |
| Haemoglobin (g/dL) | 13.2 (1.6) | 13 (1.7) | 0.2∗ (0.1–0.3) |
| HOMA-IR | 3.2 (5.7) | 1.9 (2.9) | 1.3∗ (1.1–1.5) |
| Dyslipidemia | 2132 (57.4%) | 1672 (41.3%) | 1.9# (1.8–2.1) |
| (55.8–59) | (39.7–42.8) | ||
| Diabetes | 1280 (34.5%) | 647 (16%) | 2.8# (2.5–3.1) |
| (33–36) | (14.8–17.1) | ||
| Elevated blood pressure | 2386 (64.3%) | 1886 (46.5%) | 2.1# (1.9–2.3) |
| (62.7–65.8) | (45–48.1) | ||
| Female with menopause | 1118 (59.9%) | 806 (36.5%) | 2.6# (2.3–3) |
| (57.7–62.1) | (34.5–38.5) | ||
| Weight (kg) | 72.6 (11.8) | 64.2 (10.5) | 8.4∗ (7.9–8.9) |
| Height (cm) | 160.6 (9.4) | 160.2 (8.9) | 0.4∗ (−0 to 0.8) |
| Body Mass Index (kg/m2) | 28.1 (4) | 25 (3.7) | 3.1∗ (2.9–3.3) |
| Waist-Hip ratio | 0.91 (0.08) | 0.87 (0.8) | 0.03∗ (0.03–0.04) |
| Body Mass Index Category | |||
| Normal | 229 (6.2%) | 1072 (26.5%) | 1 |
| (5.4–6.9) | (25.1–27.8) | ||
| Underweight | 4 (0.1%) | 100 (2.5%) | 0.2# (0.1–0.5) |
| (0–0.2) | (2–2.9) | ||
| Overweight | 530 (14.3%) | 916 (22.6%) | 2.7# (2.3–3.2) |
| (13.2–15.4) | (21.3–23.9) | ||
| Obese I | 1913 (51.5%) | 1574 (38.9%) | 5.7# (4.9–6.7) |
| (49.9–53.1) | (37.4–40.4) | ||
| Obese II | 1036 (27.9%) | 388 (9.6%) | 12.5# (10.4–15) |
| (26.5–29.4) | (8.7–10.5) | ||
| FibroScan® LSM (in kPa) | |||
| <8.2 (F0–F1) | 3454 (93.7%) | 3958 (98.3%) | 1 |
| (92.9–94.4) | (97.9–98.7) | ||
| 8.2–<9.7 (F2) | 106 (2.9%) | 31 (0.8%) | 3.9# (2.6–5.9) |
| (2.3–3.4) | (0.5–1) | ||
| 9.7–<13.6 (F3) | 90 (2.4%) | 20 (0.5%) | 5.2# (3.2–8.4) |
| (1.9–2.9) | (0.3–0.7) | ||
| ≥13.6 (F4) | 38 (1%) | 18 (0.4%) | 2.4# (1.4–4.2) |
| (0.7–1.4) | (0.2–0.7) | ||
| CAP enhanced mean | 292.9 (32.5) | 208.5 (27.6) | 84.5∗ (83.2–85.8) |
Continuous variables are summarized using mean (SD) and categorical variables are summarized using n (%), with 95% CI (in italics); missing data for each variable were excluded when producing these summaries and are indicated in the tables.
Abbreviations: HDL, High-density lipoprotein; LDL, Low-density lipoprotein; HOMA-IR, Homeostatic Model Assessment of Insulin Resistance, LSM, Liver stiffness measurement; CAP-Controlled Attenuation Parameter.
The percentage is within the group (male/female affected by MASLD and without-MASLD out of all males/females). Missing n = 24 for MASLD and n = 25 in without-MASLD for Fibroscan. Missing n = 0 for MASLD and n = 2 in without-MASLD for BMI. Not all percentages may add up to 100% owing to rounding. ∗ Mean Difference, # Odds Ratio (95% CI).
Factors associated with MASLD
The MASLD group had higher proportion of overweight and obese (93.7% vs 71.1%), significant liver fibrosis (6.3% vs 1.7%), higher mean HbA1c values: 6.2 (1.2) vs 5.7 (0.9), greater insulin resistance (IR) as measured by homeostatic model for insulin resistance (HOMA-IR): 3.2 (5.7) vs 1.9 (2.9) and higher prevalence of dyslipidemia (57.4% vs 41.3%) compared to those without-MASLD (Table 2). Multivariable logistic regression identified obesity as the strongest risk factor for MASLD, with AOR rising from overweight (AOR 2.6, 95% CI 2.2–3.1) to Obese I (AOR 5.6, 95% CI 4.6–6.9) and Obese II (AOR 13.8, 95% CI 11.4–16.6). Diabetes (AOR 2.2, 95% CI 2–2.6), dyslipidemia (AOR 1.7, 95% CI 1.5–1.8) and elevated blood pressure (AOR 1.3, 95% CI 1.1–1.5) further increased MASLD risk (Supplementary Table S1).
Among 3712 MASLD participants, when stratified by BMI (lean 6.3%, overweight 14.3%, obese 79.4%), marked demographic and biochemical differences were observed (Supplementary Table S2).
The age-adjusted prevalence of MASLD demonstrated wide variation, ranging from 27% in Thiruvananthapuram to nearly 50% in Roorkee and Bhopal (Fig. 2 and Supplementary Table S3). Major metropolitan centers such as Delhi, Bengaluru, Pune, Hyderabad and Chennai showed intermediate prevalence (37–42%).
Fig. 2.
Prevalence of age-adjusted MASLD in different cities against the total population and gender: Male and Female. The red dotted line indicates the age-adjusted prevalence for the overall group, with the population group in parentheses. The panel below shows similar data in a map of India, with the size of the circle indicating the population screened at that city (Data in Supplementary Table S3). The city is the location of the Sample Collection Institute. Abbreviations: TRV, Thiruvananthapuram; KOL, Kolkata; DGR, Durgapur; DDN, Dehradun; PLMR, Palampur; KKDI, Karaikudi; CH, Chandigarh; PUNE, Pune; JMSD, Jamshedpur; BLR, Bengaluru; JRH, Jorhat; DL, Delhi; LKOW, Lucknow; MYS, Mysore; NGP, Nagpur; PIL, Pilani; RK, Roorkee; JK, Jammu; GOA, Goa; DHN, Dhanbad; BBS, Bhubaneswar; HYD, Hyderabad; CHE, Chennai; BHV, Bhavnagar; BHO, Bhopal; GZ, Ghaziabad; SNGR, Srinagar.
Map of India boundaries from-Runfola, D. et al. (2020) geoBoundaries: A global database of political administrative boundaries. PLoS ONE 15(4): e0231866. https://doi.org/10.1371/journal.pone.0231866.
The burden of CMRFs in MASLD was substantial, with 31.7% of individuals carrying three CMRFs, 23.6% carrying four, and 14.4% carrying all five (Supplementary Figure S2). Geographic variation was also evident (Supplementary Figure S3).
Liver fibrosis and associated factors
Crude prevalence of liver fibrosis [LSM ≥8.2 kPa (≥F2)] was 3.9% overall (Supplementary Figure S4A), while among those with MASLD, the rate was 6.3% with consistently higher rates in men across all stages. Fibrosis prevalence increased progressively with age, particularly for the LSM 8.2 to <9.7 kPa (F2) category, while cirrhosis [LSM ≥13.6 kPa (F4)] was largely confined to individuals over 60 years (Supplementary Figure S4B–D).
The crude prevalence of fibrosis was substantially higher among people with diabetes (9.1%) and among individuals with obesity (class I [3.7%] and class II [8.1%]), indicating high-risk metabolic phenotypes (Fig. 3A and B). The prevalence of F2 fibrosis was notably elevated among individuals with diabetes (4%) and individuals in obesity class I and II (class I [2%] and class II [2.9%]). Multivariable regression identified age, diabetes, obesity, and central adiposity as key determinants of liver fibrosis. Compared with individuals aged 20–30 years, the risk of fibrosis increased progressively with age (AOR 4.8, 95% CI 1.1–20.7 for 60–70 years; AOR 5, 95% CI 1.2–20 for ≥70 years) (Supplementary Table S4).
Fig. 3.
Prevalence of liver fibrosis among different BMI subgroups and diabetic status. A) Fibrosis as per different BMI sub-groups (n = 7713). B) Fibrosis with diabetes and pre-diabetics (n = 7715). Abbreviations: UW, Underweight; N, Normal weight; OW, Overweight; OB I, Obese I; OB II, Obese II.
MASLD and Fibrosis Distribution among different regional sites:
Site specific variations in patterns of MASLD and fibrosis demonstrated both overlap and divergence (Fig. 4A and B, Data in Supplementary Tables S5 and S6). Age-adjusted MASLD prevalence was highest in men from the sites belonging to central and northern regions and women showed a higher prevalence in the sites from Southern region, reflecting a clustering of gender-based metabolic risk in these regions in our cohort. Sites from North-East region exhibited a lower overall age-adjusted MASLD prevalence (Fig. 4A).
Fig. 4.
Region-wise age-adjusted prevalence of MASLD and Fibrosis. (A) Region-wise distribution of MASLD and (B) Distribution of Fibrosis in Phenome India Cohort. The dotted line represents the age-adjusted prevalence for the overall group, with the value in parentheses indicating the prevalence of fibrosis (Data in Supplementary Tables S5 and S6).
Across the cohort, 40 of 48 cytokines were higher in the MASLD group vs the without-MASLD group, irrespective of fibrosis status (Supplementary Table S7).
Comparison of MASLD participants with and without fibrosis
Majority of the cytokines and chemokines elevated in MASLD alone were further increased in the fibrosis sub-group. Molecules associated with type 1 immune responses, such as IL-7, IL-8 and CXCL10 (IP-10), were approximately two-fold higher in the fibrosis sub-group, suggesting that the transition to fibrosis may be accompanied by an intensified Th1-skewed inflammatory milieu (Supplementary Table S8).
Discussion
Our analysis reveals a substantial MASLD burden, with an age-adjusted prevalence of 38.9%. CAP thresholds for defining hepatic steatosis are known to vary across populations and, in part, may reflect demographic characteristics.17,18,26,27 We examined prevalence estimates using multiple established CAP cut-offs and as age-adjusted prevalence (30% and 22.8% with 263 and 275 dB/m as cut off for CAP score; data not shown) differed as expected with higher thresholds, the demographic and clinical characteristics of individuals identified as having steatosis remained consistent. It has also been previously shown that the association of independent variables with MAFLD remained consistent with cut-offs of 248 and 275 for the diagnosis of hepatic steatosis in an urban population based in Delhi.5
A previous systematic review for NAFLD from India showed a pooled prevalence of 38.6% (95% CI 32–45.5) in adults with community-based data showing 28.2% (95% CI 16.9–41%),4 and globally was shown to be 38%.28 Our study showed that prevalence had site specific variance, being higher in the Central, Northern and Southern regions of India.
NAFLD is the most prevalent chronic liver disease worldwide,29 with the highest prevalence reported in Latin America (44%), the Middle East and North Africa (36.5%), South Asia (33.8%), followed by North America (31%) and Western Europe (25%).29 While there are no major countrywide studies in India for the community-based prevalence of MASLD in the general population, India's pooled NAFLD prevalence is 38.6% (95% CI 32–45.5),4 and high urban estimates (56.4% MAFLD in Delhi and 61.5% NAFLD in Chennai cohorts) mirror this trend.5,30 Importantly, 6.3% (n = 234) individuals with MASLD were found to have significant liver fibrosis. These figures highlight the subclinical nature of early MASLD and its silent progression, which poses a potential burden to the public health systems if left unaddressed.
The strong association of MASLD and fibrosis with metabolic comorbidities, IR and hypertension emphasizes the need for integrated management of these comorbidities to halt disease progression and prevent both liver-related and non-liver-related events in these patients. Obesity was observed to be the predominant driver of MASLD in India, with additional contributions from diabetes, dyslipidemia, elevated blood pressure, and central adiposity, underscoring the need for aggressive metabolic risk.
Notably, in our study, 66.4% of participants with Diabetes had MASLD (1280 of 1927), while 9.1% (175 of 1915) had fibrosis. Recently, a clinic-based retrospective cohort study reported any degree of steatosis and fibrosis in 75.6% and 28.6% of 1070 patients with T2DM.31 In the “MAP” study, a clinic-based retrospective study, the prevalence of MASLD and fibrosis was reported to be 68.2% and 33.7% respectively. However, it is worth noting that the “MAP” study employed a lower cutoff for both CAP (238 dB/m) and F2 (7–10 kPa).13 Among overweight and obese MASLD individuals, fibrosis prevalence was high (6.5%).
In a recently published review, Hagström et al. reported that over the last 2 years, they have observed a prevalence of MASLD in the general population of 38%, while among adults living with diabetes, it is 65%.12 We also observed 38.9% age-adjusted prevalence of MASLD and 66.4% crude prevalence of MASLD in people living with diabetes, which are similar to what has been reported for properly conducted prevalence studies.12 While we observed relatively lower numbers of fibrosis in our study, this could be due to the fact that ours was a community-based rather than a clinic-based study, unlike the MAP study.13
As reported previously from an urban population of 6146 individuals from Delhi by Prabhakar T et al., males and females appear to be equally affected by MAFLD with an overall prevalence of approximately 56%, while in our study, we observed the prevalence of MASLD to be 45.9% in females and 50% in males.5 We also observed a higher prevalence of MASLD with increasing age (maximum at the 50–59 age group), similar to the study by Prabhakar T et al.5
Among various BMI subgroups, we observed a similar prevalence of lean MASLD as reported in other published literature for the Asian population.32,33 Our data also confirm the presence of lean MASLD, particularly associated with T2DM, likely reflecting greater visceral rather than subcutaneous adiposity. This suggests poorer glycaemic and lipemic control in individuals with lean MASLD, likely attributed to a greater quantity of visceral fat (rather than subcutaneous fat) and warrants further exploration. A higher proportion of postmenopausal females had MASLD, which is likely due to changes in adiposity distribution patterns postmenopause.34,35
The higher prevalence of fibrosis in individuals with MASLD as compared to those without-MASLD, underscores the need for targeted screening and preventive interventions. Fibrosis in MASLD is predominantly driven by IR and diabetes. This metabolic milieu promotes lipotoxicity and oxidative stress, accelerating hepatic stellate cell activation and extracellular matrix deposition, which are central to fibrosis progression.36,37
Our findings highlight that fibrosis risk in this cohort is primarily associated with advancing age, diabetes, and central obesity, with the highest risk concentrated in those with severe obesity and abdominal adiposity. While MASLD seems predominantly obesity-driven with contributions from metabolic comorbidities, progression to fibrosis largely seems to be associated more with age, diabetes, and central adiposity.
Our data suggests site specific disparities, with higher prevalence rate of fibrosis concentrated in Jorhat [8.3%] and Delhi [4.8%], Jammu [4.3%]) and Bhopal [4.4%] (data not shown)), suggesting possible contributions from specific regional factors such as dietary, genetic, or environmental influences. Lower prevalence of fibrosis in southern and eastern regions may reflect differences in lifestyle, healthcare access, or screening practices. The regional disparity was also reported in the “MAP” study.13 Similar to our observations, the “MAP” study also reported a lower prevalence of fibrosis in eastern and southern cities; however, unlike our study, they found a comparatively lower prevalence of fibrosis among people living in north eastern part of the country. This could potentially be due to selection bias in hospital-based studies and the need for further community-based studies to understand the true prevalence of MASLD and liver fibrosis in our country. This geographic variation underscores the importance of developing tailored public health strategies to screen for and identify liver fibrosis early, particularly in high-prevalence areas, and highlights the need for further investigation into region-specific etiological factors.13
Cytokines are critical drivers of liver fibrosis in mediating inflammation and fibrogenesis. In MASLD, pro-inflammatory cytokines (TNF-α, IL-6, and IL-1β) fuel chronic inflammation, progressing from simple steatosis to MASH and fibrosis.38, 39, 40, 41 Our study revealed elevated levels of both pro- (IFN-γ, IL-1β, IL-8, IL-18, IP-10, MCP-1, MIP-1α), and anti-inflammatory cytokines in MASLD, suggesting a complex interplay of inflammatory and regulatory responses. Without biopsy data, tissue-specific insights were limited. The strong upregulation of Th1-associated cytokines such as IL-7, IL-8, and CXCL10 underscores the role of type 1 immune responses in fibrotic progression. From a translational perspective, these results imply that cytokine signatures may serve as biomarkers to aid in the identification of fibrosis.
There are certain limitations to our study, including reliance on TE rather than histology, the exclusion of individuals with any alcohol intake, and the urban/semi-urban bias of the cohort. Exclusion of all individuals reporting any alcohol consumption ensured high internal validity for MASLD classification by removing potential confounding from alcohol-related liver injury; however, this strict criterion may have also excluded individuals who would still qualify for MASLD under standard guidelines, which may affect the generalizability of our findings. We further acknowledge that the Phenome cohort comprises predominantly urban, educated, and health-aware individuals, largely drawn from CSIR employees and their families and is fairly homogenous from a perspective of socio-economic status, with most of its staff belonging to urban and semi-urban middle- and higher-middle-income groups, hence may not be able to fully capture the socio-economic, educational, and rural diversity of the broader Indian population and truly be able to generalize the observations to the general population of India.
It is to be further noted that not testing for HBV/HCV in participants with normal CAP and LSM could leave a very small number of undiagnosed infections. Although this does not affect MASLD prevalence, the potential impact on clinical or cytokine comparisons between the groups is acknowledged; however, given the prevalence of asymptomatic HBV/HCV in our population, any such effect is expected to be minimal. This large community-based study in India, and its integration of epidemiology, metabolic risk factors, and immunological profiling provides a comprehensive understanding of disease burden and biology. Our findings underscore the need for early detection, integration of liver health into metabolic disease programs, and translational research into cytokine-driven pathways as therapeutic targets.
The high prevalence of MASLD and liver fibrosis in India represents an urgent public health challenge. Our findings advocate for policy shifts to include TE-based screening, especially for urban and high-risk populations. Training healthcare workers to use TE and integrating it with existing metabolic disease programs could enhance scalability. Future longitudinal studies are needed to track trends in MASLD and liver fibrosis and evaluate the impact of screening interventions.
Contributors
VS, SSG, KC, PC, GRC, SR, KBT, MJK, DG, and US conceptualized the study. KC, VS, SSG, APS, AS, MA, SP, VSK, PHL, AV, SRK and YK curated data. MA, AV, SRK, VS, SSG, KC, SP, AA, S and PC did formal data analysis. MA, AV, VSK, MAU, AN, MR, NR, AS, YK, PHL, MS, RS, DS, ANM, IJN, AH, BR, MT, NK, VPS, SC, DG, VKY, AM, SR, KBT, MJK, MSD, RW, KC, SSG, PC and VS were responsible for investigations presented within the study. SC, DG, VKY, AM, SR, KBT, MJK, MSD, RW, AA, KC, SSG, PC and VS designed the methodology. SC, DG, AM, SR, KBT, MJK, MSD, RW, KC, SSG, PC, and VS were responsible for project administration and resource allocation. KC, VS, APS, SSG, SP, VSK and AV supervised software and data services. VS, SSG, KC, PC, GRC, SR, KBT, MJK, DG, US, MA, AS and AM were responsible for overall supervision. AV, MA, VS, SSG, KC, SRK, AA, S and PC did data validation. VS wrote the original draft. VS, SSG, KC, AV, MA, S, SB, PC, SRK and AA reviewed and edited the manuscript. VS, SSG and KC had final responsibility for the decision to submit for publication. VS, AA, SSG, KC, PC, AV, MA, and SRK have accessed and verified the data. All authors approved the final draft of the manuscript.
Data sharing statement
Anonymized data for public use may be made available after 3 years from completion of the baseline phase of the study or as per advisory from the Monitoring Committee of the project if any revisions are required.
Editor's note
The Lancet Group takes a neutral position with respect to territorial claims in published maps and institutional affiliations.
Statement on AI use
While preparing the revised manuscript, we utilized Grammarly AI to refine the language and enhance comprehension. After using this tool, we reviewed and edited the content as needed and take full responsibility for the content of the publication.
Declaration of interests
I/We declare that I/we have no competing interests.
Acknowledgements
We acknowledge funding support from Council of Scientific and Industrial Research (CSIR), New Delhi, India. We acknowledge CSIR-IGIB as the nodal lab for logistics and administrative support. Support from assisting zonal labs, directors/heads of all CSIR labs and centers and all coordinators is acknowledged. The monitoring committee is acknowledged for its timely suggestions and course corrections. We acknowledge participants and volunteers. We further acknowledge the personnel listed in Appendix A and Phenome India Consortium Study Group (tabulated in Appendix B).
Footnotes
Supplementary data related to this article can be found at https://doi.org/10.1016/j.lansea.2026.100723.
Contributor Information
Kumardeep Chaudhary, Email: kumardeep.igib@csir.res.in.
Shantanu Sengupta, Email: shantanus.igib@csir.res.in.
Partha Chakrabarti, Email: pchakrabarti@iicb.res.in.
Viren Sardana, Email: viren.igib@csir.res.in.
Phenome India Consortium:
Abhishek Kumar, Ankit Basnal, Ankur Halder, Anshul Bhardwaj, Ansuman Sahu, Bharti Sharma, Debasis Dash, Deeksha Yadav, Deepak, Kalyani Verma, Komal Jindal, Md. Intyaz Ali, Mohit, Pankaj Pandey, Pranjal Tewari, Pratik Pathade, Praveena Mishra, Rohit Kumar, Ruchi, Safeer Khan, Shail Kumari, Shilpa Ray, Shivani Chitkara, Shubham Kumar, Shyam Singh Bisht, Sumant Kumar, Swarnendu Bag, Swati, Tanmay Pawaskar, Tarani Mathur, Vivek Junghare, Dipamoy Dutta, Jahangir Alam, Pratitusti Basu, Saheli Chowdhury, Saikat Majumder, Dibya Rana Saha Roy, Jukanti Akshitha, M.K. Kanakavalli, Rakhesh KV, Ajit A. Sutar, Ameya A. Pawar, Ankita Namdeo, Apurva Balge, Ashok P. Giri, Chiranjit Chowdhury, Dhanasekaran Shanmugam, Milind Kale, Narendra Y. Kadoo, Nikhilesh Yadav, Rashdajabeen Q. Shaikh, Sagar Baulia, Shivani V. Palkar, Shrutika M. Shewale, Shyam K. Gawari, Syed G. Dastager, Vaishnavi N. Mahajan, Bhabani S. Jena, Boopathy Ramasamy, Sai Adarsh Sahu, Sk Rameej Raja, T Pavan Kumar, Trupti Das, Jagadeshwar Reddy Thota, Prabhakar Sripadi, Ramakrishna Sistla, Ramesh Ummanni, Sai Balaji Andugulapati, Srinivasa Rao M, Adrija Rakshit, Amit Kumar Shahravat, Amit Lahiri, Deepanshu Sindhwani, Kabita Sarkar, Kajal KM, Lakra Promila, Mrigank Srivastava, Rahul Roy, Shail Singh, Shikha Yadav, Smita Pandey, Vivek Bhosale, Gopal Krishna Patra, Iranna Gogeri, Narendra Singh, Raju Khan, Neeraj Jain, Rajesh Kumar Verma, Ganesh Venkatachalam, Murugan Veerapandian, Amit Kumar, Deepak Bansal, Dheeraj Kumar Kharbanda, Dinesh Gupta, Sk. Masiul Islam, Vipul Sharma, Prakash M. Halami, S.P. Muthukumar, Anil Kumar Maurya, Anirban Pal, Daneshvar Prasad, A.K. Raman, Bhanu Pandey, Dikchha Singh, Jai Krishna Pandey, Parimala Karupannan, Suresh Kumar Anandasadagopan, Vandhana Anumaiya, Swati Saha, Vishal Anand, Mukti Advani, Rina Singh, Anamika Kothari, Suman Singh, Avinash Mishra, Pooja Aggarwal, Shreedhar Kanagarjan, Ankita Kumari, Ravi Raj, Vikram Patial, Yogendra Padwad, Fayaz Malik, Kaneez Fatima, Nancy Sharma, Sahaurti Sharma, Sakshi Nagial, Sumit G. Gandhi, Debashish Ghosh, Jyoti Porwal, Pramod Chauhan, Suchismita Benjwal, Neha Mehrotra, Prabhanshu Tripathi, Vikas Srivastava, Amit Tuli, Anshu Bhardwaj, Bhupender Singh, Deepak Sharma, Kuldeep Singh, Lalit Kumar, Parvez Ahmad, Pradip Sen, Pranavathiyani G, Pravin Kumar, Priyadarshan Kinatukara, Priyanshu Singh Raikwar, Rakesh Kumar, Rashmi Kumar, Ritu Jatav, Shiva Sundharam S, Siddhakam Palmal, Simran Gambhir, Srinivasan Krishnamurthi, Abbani Rakesh, Prakash L, Satisha Shri, Indrani Ghosh, Brahma Nanda Singh, Chandana Venkateswara Rao, Madan Mohan Pandey, Sanjeev Kumar Ojha, Vijayanandraj Selvaraj, Prashanti Niwant, Shilpa Paranjape, Manuj Kr Das, Pankaj Bharali, Sukanya Borkakoti, Tridip Phukan, Biswajit Mandal, E.V.S.S.K. Babu, T Vijaya Kumar, Rajeev K. Sukumaran, Rameshkumar N, Bhumika Shirodkar, Kalpana Sandesh Chodankar, Samir Ravikant Damare, Akshika, Arun Uniyal, Arvind Meena, Ansu J. Kailath, K Sudhakara Rao, Krishna Kumar, Kuldeep Singh Gour, Navneet Singh Randhawa, Nikhil Kumar, Priyanka Singh, Roshan Kumar, Arun Kant Singh, Ved Varun Agrawal, Maheswaran Srinivasan, Vasudevan Pandurangan, Manisha Sakpal, and Rashmi Arya
Appendix B. Supplementary data
References
- 1.Carr R.M., Oranu A., Khungar V. Nonalcoholic fatty liver disease: pathophysiology and management. Gastroenterol Clin North Am. 2016;45(4):639–652. doi: 10.1016/j.gtc.2016.07.003. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 2.Younossi Z.M., Golabi P., Paik J.M., Henry A., Van Dongen C., Henry L. The global epidemiology of nonalcoholic fatty liver disease (NAFLD) and nonalcoholic steatohepatitis (NASH): a systematic review. Hepatology. 2023;77(4):1335–1347. doi: 10.1097/HEP.0000000000000004. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 3.Sørensen T.I.A. Forecasting the global obesity epidemic through 2050. Lancet. 2025;405(10481):756–757. doi: 10.1016/S0140-6736(25)00260-0. [DOI] [PubMed] [Google Scholar]
- 4.Shalimar, Elhence A., Bansal B., et al. Prevalence of non-alcoholic fatty liver disease in India: a systematic review and meta-analysis. J Clin Exp Hepatol. 2022;12(3):818–829. doi: 10.1016/j.jceh.2021.11.010. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Prabhakar T., Prasad M., Kumar G., et al. High prevalence of MAFLD in general population: a large cross-sectional study calls for concerted public health action. Aliment Pharmacol Ther. 2024;59(7):843–851. doi: 10.1111/apt.17892. [DOI] [PubMed] [Google Scholar]
- 6.Asadullah M., Shivashankar R., Shalimar, et al. Rural-urban differentials in prevalence, spectrum and determinants of Non-alcoholic Fatty Liver Disease in North Indian population. PLoS One. 2022;17(2) doi: 10.1371/journal.pone.0263768. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 7.Vernekar M., Amarapurkar D.N. PNPLA3 gene polymorphisms associated with NAFLD patients in India. J Clin Exp Hepatol. 2015;5:S24–S25. [Google Scholar]
- 8.Sniderman A.D., Bhopal R., Prabhakaran D., Sarrafzadegan N., Tchernof A. Why might South Asians be so susceptible to central obesity and its atherogenic consequences? The adipose tissue overflow hypothesis. Int J Epidemiol. 2007;36(1):220–225. doi: 10.1093/ije/dyl245. [DOI] [PubMed] [Google Scholar]
- 9.Feng Q., Izzi-Engbeaya C.N., Manousou P., Woodward M. Fibrosis status, extrahepatic multimorbidity and all-cause mortality in 53,093 women and 74,377 men with metabolic dysfunction associated steatotic liver disease (MASLD) in UK biobank. BMC Gastroenterol. 2025;25(1):546. doi: 10.1186/s12876-025-04079-4. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Zhang L., You W., Zhang H., et al. PNPLA3 polymorphisms (rs738409) and non-alcoholic fatty liver disease risk and related phenotypes: a meta-analysis. J Gastroenterol Hepatol. 2015;30(5):821–829. doi: 10.1111/jgh.12889. [DOI] [PubMed] [Google Scholar]
- 11.Gangireddy V.G.R., Pilkerton C., Xiang J., Tinajero R., Ashcraft A.M. Hepatic fibrosis and steatosis in metabolic syndrome. J Obes Metab Syndr. 2022;31(1):61–69. doi: 10.7570/jomes21062. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 12.Hagström H., Shang Y., Hegmar H., Nasr P. Natural history and progression of metabolic dysfunction-associated steatotic liver disease. Lancet Gastroenterol Hepatol. 2024;9(10):944–956. doi: 10.1016/S2468-1253(24)00193-6. [DOI] [PubMed] [Google Scholar]
- 13.Mohan V., Joshi S., Kant S., et al. Prevalence of metabolic dysfunction-associated steatotic liver disease: mapping across different Indian populations (MAP study) Diabetes Ther. 2025;16(7):1435–1450. doi: 10.1007/s13300-025-01748-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Duseja A., Singh S.P., Mehta M., et al. Clinicopathological profile and outcome of a large cohort of patients with nonalcoholic fatty liver disease from South Asia: interim results of the Indian Consortium on Nonalcoholic Fatty Liver Disease. Metab Syndr Relat Disord. 2022;20(3):166–173. doi: 10.1089/met.2021.0104. [DOI] [PubMed] [Google Scholar]
- 15.Sengupta S, Phenome India Consortium Study research protocol for Phenome India-CSIR Health Cohort Knowledgebase: a prospective multi-modal follow-up study on a nationwide employee cohort. Biol Methods Protoc. 2025;10(1) doi: 10.1093/biomethods/bpaf061. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 16.Naushin S., Sardana V., Ujjainiya R., et al. Insights from a pan India sero-epidemiological survey (Phenome-India cohort) for SARS-CoV2. eLife. 2021;10 doi: 10.7554/eLife.66537. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Shalimar, Kumar R., Rout G., et al. Body mass index-based controlled attenuation parameter cut-offs for assessment of hepatic steatosis in non-alcoholic fatty liver disease. Indian J Gastroenterol. 2020;39(1):32–41. doi: 10.1007/s12664-019-00991-2. [DOI] [PubMed] [Google Scholar]
- 18.Rout G., Kedia S., Nayak B., et al. Controlled attenuation parameter for assessment of hepatic steatosis in Indian patients. J Clin Exp Hepatol. 2019;9(1):13–21. doi: 10.1016/j.jceh.2018.02.010. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 19.Behdenna A., Colange M., Haziza J., et al. pyComBat, a python tool for batch effects correction in high-throughput molecular data using empirical Bayes methods. BMC Bioinformatics. 2023;24(1):459. doi: 10.1186/s12859-023-05578-5. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 20.Tacke F., Horn P., Wai-Sun Wong V., et al. EASL–EASD–EASO Clinical Practice Guidelines on the management of metabolic dysfunction-associated steatotic liver disease (MASLD) J Hepatol. 2024;81(3):492–542. doi: 10.1016/j.jhep.2024.04.031. [DOI] [PubMed] [Google Scholar]
- 21.Rinella M.E., Lazarus J.V., Ratziu V., et al. A multisociety Delphi consensus statement on new fatty liver disease nomenclature. Hepatology. 2023;78(6):1966–1986. doi: 10.1097/HEP.0000000000000520. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 22.Karlas T., Petroff D., Sasso M., et al. Individual patient data meta-analysis of controlled attenuation parameter (CAP) technology for assessing steatosis. J Hepatol. 2017;66(5):1022–1030. doi: 10.1016/j.jhep.2016.12.022. [DOI] [PubMed] [Google Scholar]
- 23.Eddowes P.J., Sasso M., Allison M., et al. 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(6):1717–1730. doi: 10.1053/j.gastro.2019.01.042. [DOI] [PubMed] [Google Scholar]
- 24.Misra A., Chowbey P., Makkar B.M., et al. Consensus statement for diagnosis of obesity, abdominal obesity and the metabolic syndrome for Asian Indians and recommendations for physical activity, medical and surgical management. J Assoc Physicians India. 2009;57:163–170. [PubMed] [Google Scholar]
- 25.Census of India Website Office of the registrar general & census commissioner, India. http://censusindia.gov.in/2011census/population_enumeration.html Available from:
- 26.Cao Y.T., Xiang L.L., Qi F., Zhang Y.J., Chen Y., Zhou X.Q. Accuracy of controlled attenuation parameter (CAP) and liver stiffness measurement (LSM) for assessing steatosis and fibrosis in non-alcoholic fatty liver disease: a systematic review and meta-analysis. eClinicalMedicine. 2022;51 doi: 10.1016/j.eclinm.2022.101547. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 27.Berzigotti A., Tsochatzis E., Boursier J., et al. EASL Clinical Practice Guidelines on non-invasive tests for evaluation of liver disease severity and prognosis – 2021 update. J Hepatol. 2021;75(3):659–689. doi: 10.1016/j.jhep.2021.05.025. [DOI] [PubMed] [Google Scholar]
- 28.Younossi Z.M., Kalligeros M., Henry L. Epidemiology of metabolic dysfunction-associated steatotic liver disease. Clin Mol Hepatol. 2025;31(Suppl):S32–S50. doi: 10.3350/cmh.2024.0431. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 29.Miao L., Targher G., Byrne C.D., Cao Y.Y., Zheng M.H. Current status and future trends of the global burden of MASLD. Trends Endocrinol Metab. 2024;35(8):697–707. doi: 10.1016/j.tem.2024.02.007. [DOI] [PubMed] [Google Scholar]
- 30.Anton M.C., Shanthi B., Sridevi C. Prevalence of non-alcoholic fatty liver disease in urban adult population in a Tertiary Care Center, Chennai. Indian J Community Med. 2023;48(4):601–604. doi: 10.4103/ijcm.ijcm_437_22. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 31.Bhuvanesswar K.C., Srivastava B.K., Amutha A., et al. 2025. Prevalence of Hepatic Steatosis and Fibrosis in Asian Indian Individuals with Type 2 Diabetes. Diabetes Therapy: Research, Treatment and Education of Diabetes and Related Disorders. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 32.Younossi Z., Anstee Q.M., Marietti M., et al. Global burden of NAFLD and NASH: trends, predictions, risk factors and prevention. Nat Rev Gastroenterol Hepatol. 2018;15(1):11–20. doi: 10.1038/nrgastro.2017.109. [DOI] [PubMed] [Google Scholar]
- 33.Feng R.N., Du S.S., Wang C., et al. Lean-non-alcoholic fatty liver disease increases risk for metabolic disorders in a normal weight Chinese population. World J Gastroenterol. 2014;20(47):17932–17940. doi: 10.3748/wjg.v20.i47.17932. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 34.Opoku A.A., Abushama M., Konje J.C. Obesity and menopause. Best Pract Res Clin Obstet Gynaecol. 2023;88 doi: 10.1016/j.bpobgyn.2023.102348. [DOI] [PubMed] [Google Scholar]
- 35.Kodoth V., Scaccia S., Aggarwal B. Adverse changes in body composition during the menopausal transition and relation to cardiovascular risk: a contemporary review. Womens Health Rep. 2022;3(1):573–581. doi: 10.1089/whr.2021.0119. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 36.Horn P., Tacke F. Metabolic reprogramming in liver fibrosis. Cell Metab. 2024;36(7):1439–1455. doi: 10.1016/j.cmet.2024.05.003. [DOI] [PubMed] [Google Scholar]
- 37.Roh Y.J., Kim H., Choi D.W. Metabolic sparks in the liver: metabolic and epigenetic reprogramming in hepatic stellate cells activation and its implications for human metabolic diseases. Diabetes Metab J. 2025;49(3):368–385. doi: 10.4093/dmj.2025.0195. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 38.Rafaqat S., Gluscevic S., Mercantepe F., Rafaqat S., Klisic A. Interleukins: pathogenesis in non-alcoholic fatty liver disease. Metabolites. 2024;14(3) doi: 10.3390/metabo14030153. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 39.Sandireddy R., Sakthivel S., Gupta P., Behari J., Tripathi M., Singh B.K. Systemic impacts of metabolic dysfunction-associated steatotic liver disease (MASLD) and metabolic dysfunction-associated steatohepatitis (MASH) on heart, muscle, and kidney related diseases. Front Cell Dev Biol. 2024;12:2024. doi: 10.3389/fcell.2024.1433857. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 40.Burger K., Jung F., Baumann A., et al. TNFα is a key trigger of inflammation in diet-induced non-obese MASLD in mice. Redox Biol. 2023;66 doi: 10.1016/j.redox.2023.102870. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 41.Jee Y.M., Lee J.Y., Ryu T. Chronic inflammation and immune dysregulation in metabolic-dysfunction-associated steatotic liver disease progression: from steatosis to hepatocellular carcinoma. Biomedicines. 2025;13(5) doi: 10.3390/biomedicines13051260. [DOI] [PMC free article] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.