Abstract
Introduction:
Metabolic dysfunction-associated steatotic liver disease (MASLD) is highly prevalent in individuals with type 2 diabetes mellitus (T2DM). This study evaluated the effect of dapagliflozin on liver fat, liver stiffness, and visceral adiposity in patients with T2DM and MASLD.
Methods:
This was a single-arm, prospective, open-label interventional study with a paired design. Sixty-three patients with T2DM and moderate-to-severe hepatic steatosis on ultrasonography received dapagliflozin 10 mg daily for six months. Liver fat by dual-echo-magnetic resonance imaging (MRI), liver stiffness measurement (LSM) by fibroscan, and visceral adipose tissue (VAT) by dual X-ray absorptiometry were assessed at baseline and after six months. Response to therapy was defined as >10% decrease in liver fat and >15% decrease in LSM, respectively (Responders). Multivariable logistic regression analysis was performed to identify predictors of response with respect to change in liver fat and LSM.
Results:
After six months, mean weight decreased by 2.55 kg (3.23%) and HbA1c by 0.84%. MRI-liver fat percentage, LSM, and VAT volume declined by 2.7%, 0.85kPa, and 472 cm3, respectively (P < 0.001 for all). Change in VAT mass (r = 0.546) and volume (r = 0.437) correlated with change in liver fat, and change in aspartate aminotransferase (AST) correlated with change in LSM (r = 0.351). Sixteen patients (25.4%) and 33 patients (52.4%) met the criteria for responders for liver fat and LSM, respectively. Baseline liver fat and LSM were independent predictors of decrease in liver fat and LSM, respectively in responders.
Conclusion:
Dapagliflozin significantly reduced liver fat, liver stiffness, and visceral adiposity in patients with T2DM and MASLD.
Keywords: Dapagliflozin, dual echo MRI, LSM, MASLD, T2DM
INTRODUCTION
Metabolic dysfunction-associated steatotic liver disease (MASLD) is a global health concern, with an estimated prevalence of 23.4% among adults in 2019.[1,2] Systematic review and meta-analysis among adults in India showed a higher prevalence of 38.6%.[3] Approximately 20% of individuals with MASLD progress to metabolic dysfunction-associated steatohepatitis (MASH).[4] Patients with type 2 diabetes mellitus (T2DM) with obesity, have a 2.5-fold higher risk of fibrosis in high-risk individuals compared to obese people without T2DM.[5] Although resmetirom, a thyroid hormone receptor β-selective agonist, has been approved by FDA for treatment of MASH, its use is limited by cost and availability.[6]
Sodium-glucose cotransporter-2 inhibitors (SGLT2i) reduce hepatic fat by 2–5%,[7,8,9,10] and reduce total body fat and visceral fat.[11,12] However, conflicting evidence exists regarding their effects on skeletal muscle mass, with reports of both preservation and potential loss.[11,12] The effect of SGLT2i on liver fat is due to transcriptional downregulation of lipogenesis by carbohydrate-responsive element-binding protein (ChREBP) and sterol regulatory element-binding protein (SREBP) pathways.[13,14] Increased hepatic beta-oxidation mediated through increased glucagon secretion has been postulated as additional mechanism.[15] Few studies have evaluated effect of SGLT2i on liver stiffness measurement (LSM), and most found no significant decrease,[16,17] except one study.[18] Previous studies on effect of SGLT2i on liver fat were limited by small sample sizes, short treatment periods, and use of non-standardized hepatic fat quantification methods.[19,20]
The present study aims to evaluate effect of dapagliflozin over six months in patients with T2DM and MASLD on liver fat, LSM, and visceral adiposity using standardized, validated methods. Liver fat was assessed by dual-echo magnetic resonance imaging (MRI), LSM by vibration-controlled transient elastography (VCTE), and visceral adipose tissue (VAT) by dual-energy X-ray absorptiometry (DXA).
MATERIALS AND METHODS
Participants
This prospective, single-arm open-label interventional study was conducted from June 2023 to December 2024 at a tertiary care center. Inclusion criteria were patients with T2DM aged >18 years, with glycated hemoglobin (HbA1c) between 6.5% and 10%, on stable doses of metformin and/or sulfonylureas for at least eight weeks prior to enrolment, with grade II or higher hepatic steatosis on abdominal ultrasonography (USG). T2DM was defined as per the American Diabetes Association (ADA) criteria. Exclusion criteria included patients with a history of diabetic ketoacidosis, requirement for insulin therapy, or presence of any serious intercurrent illness during the study period. Patients receiving any other anti-diabetic agents apart from metformin and sulfonylureas, like SGLT2i, pioglitazone, glucagon-like peptide-1 receptor agonists, and dipeptidyl peptidase-4 inhibitors, were excluded. Additional exclusion criteria were pregnancy, lactation, gestational diabetes, estimated glomerular filtration rate (eGFR) below 20 mL/min/1.73 m², alcohol consumption exceeding 30 grams per day for men or 20 grams per day for women, positivity for hepatitis B surface antigen (HBsAg) or hepatitis C virus (HCV), serum ferritin above 300 ng/mL in men or 200 ng/mL in women, serum ceruloplasmin below 10 ng/mL, or an antinuclear antibody titer greater than 1:40. Patients using medications known to affect hepatic steatosis like vitamin E, or ursodeoxycholic acid were also excluded.
All participants underwent comprehensive assessments at baseline and after six months of dapagliflozin therapy at a dose of 10 mg daily. All patients received dapagliflozin molecule from the same pharmaceutical manufacturer to maintain uniformity. Anthropometric measurements included body weight, height, body mass index (BMI), waist circumference, and waist–hip ratio. Waist circumference was measured using a non-stretchable measuring tape at the midpoint between the lowest rib and the iliac crest at the end of normal expiration. Body weight was measured using a calibrated digital weighing scale with participants wearing light clothing and no footwear. Glycemic status was evaluated using fasting plasma glucose (FPG), 2-hour postprandial plasma glucose (PPG) and HbA1c, while insulin resistance was estimated using fasting serum insulin and the homeostatic model assessment of insulin resistance (HOMA-IR). Biochemical profiling included liver function tests, renal function tests, fasting lipid profile, and complete blood count. Plasma glucose, blood urea, serum creatinine, lipids and liver parameters were estimated using an automated biochemical analyzer (Siemens AUTOPAK 300APK). Plasma glucose was estimated using the hexokinase enzymatic method on the automated analyzer. HbA1c was estimated by the Bio-Rad D-10 machine using high-performance liquid chromatography (HPLC) method. Serum insulin was measured by chemiluminescent immunoassay on the ADVIA Centaur CP analyzer, and HOMA-IR was calculated using the formula (fasting insulin [µU/mL] × fasting glucose [mg/dL])/405. The Fibrosis-4 (FIB-4) index was obtained from age, serum aspartate transaminase (AST), serum alanine transaminase (ALT), and total platelet count (TPC) and was calculated as (age × AST)/(TPC × √ALT), where AST and ALT were expressed in IU/L and platelet count in 10⁹/L.[21]
Liver fat assessment by dual-echo MRI
Liver fat was assessed using dual-echo MRI protocol on a 1.5T MRI system. MRI T2-weighted images were acquired at times when proton signals from fat and water were either in phase (aligned in the same direction) or out of phase (aligned in opposite directions). In-phase (IP) imaging captures the combined signal from both fat (F) and water (W) protons (IP = F + W), while out-of-phase (OP) imaging reflects the water signal, without fat signal (OP = W – F). The fat signal fraction (η), which represents liver fat percentage, is calculated using the formula η = [(IP – OP)/(2 × IP)] ×100. Dual-echo MRI provides a non-invasive estimate of hepatic steatosis and has demonstrated sensitivity and specificity comparable to MRI–proton density fat fraction (MRI-PDFF), which is the non-invasive gold standard method for estimation of hepatic steatosis.[22]
Assessment of LSM and visceral fat
LSM was measured using a handheld VCTE device (FibroScan by Echosens) by a single trained operator. Measurements were taken from the right lobe of liver through the intercostal spaces, with the patient in supine position and the right arm maximally abducted, on an empty stomach. A median of 10 valid measurements was recorded, and results were expressed in kilopascals (kPa). Values with an interquartile range (IQR) ≤30% of the median were considered reliable. If LSM measurements were non-reliable (IQR >30%), the measurements were repeated. Only reliable measurements were included in the final analysis.
VAT mass, volume, and whole-body fat were assessed using whole-body DXA on a Hologic Discovery scanner (Hologic Inc., USA) with APEX software. Differentiation of VAT from subcutaneous fat was performed using the validated InnerCore™ algorithm. Daily quality control, incorporated within the APEX software, was performed each morning, and scans were conducted only after ensuring a QC pass. All scans were performed by a single trained operator to minimize variability.
Outcomes
The study assessed the effect of dapagliflozin on liver fat, LSM, VAT mass, and VAT volume after six months of therapy. Responders with regard to liver fat and LSM were defined as having >10%[23] and >15% reduction, respectively. The criterion for defining responders as having a LSM decrease >15% was chosen arbitrarily, as there were no previous studies defining LSM response in patients with T2DM and MASLD. Other variables assessed included changes in anthropometric, glycemic, and metabolic parameters, including HOMA-IR and the FIB-4 index.
Sample size calculation
The sample size was calculated using the formula: n = [2σ² (Zα/2 + Zβ) ²]/d², where n is the sample size, σ is the standard deviation, d is the expected mean difference, Zα/2 is the critical value for a two-tailed α of 0.05 (1.96), and Zβ is the value for 80% power (0.84). Based on a previous study by Ghosh et al.,[7] the sample size was calculated to be 39 for the present study. Allowing for a 15% dropout rate, the final target sample size was estimated to be 46. However, in the present study, we have enrolled 63 T2DM patients in the final statistical analysis to enhance statistical power and enable subgroup analysis.
Statistical analysis
Normality of distribution was checked using the Shapiro–Wilk test. For comparison of parametric data, paired t-test (intra-group) and independent t-test (inter-group) were used, whereas for non-parametric data, the Wilcoxon signed-rank test (intra-group) and the Mann–Whitney U test (inter-group) were used. Comparisons between qualitative variables were done using the Chi-square test. Effect sizes were reported as Cohen’s d for parametric analyses and rank-biserial correlation for non-parametric analyses. Spearman’s rank test was conducted for correlation analysis. Participants were classified as responders if they had a ≥10% relative reduction in liver fat or >15% relative reduction in LSM from baseline. Univariate analysis was performed to assess the association of baseline parameters with response to dapagliflozin therapy. Parameters showing a significant association on univariate analysis were entered into a multivariable logistic regression model to identify independent predictors of liver fat and LSM in responders. A P value <0.05 was considered statistically significant. All data were analyzed using IBM SPSS Statistics version 30 (2024).
Ethical aspects
IEC Approval Details: Ethics Committee name- IEC M.K.C.G MEDICAL COLLEGE, Institution name- M.K.C.G Medical College and Hospital, Berhampur-760004, Letter No- 1501, Date of issue- 05.04.2023. Informed Consent: Written informed consent was obtained from all the subjects for their participation in the study and use of patient data for research and educational purposes. Human Ethics Statement: The study was conducted in accordance with guidelines laid down in the Declaration of Helsinki 1964 and as revised later (2013). Clinical trial registry number: CTRI/2025/01/079702.
RESULTS
Baseline characteristics of study participants
Out of 200 T2DM patients, 81 patients were found to have grade II or higher hepatic steatosis on abdominal ultrasonography. One patient was excluded due to elevated serum ferritin (>300 ng/mL). The remaining 80 patients were started on dapagliflozin. During the intervention period, 17 patients were excluded, including three patients who developed recurrent urinary tract infections, three requiring insulin therapy, five requiring additional oral hypoglycemic agents other than protocol-defined and six patients were lost to follow-up. So, the final analysis included 63 T2DM patients [Figure 1].
Figure 1.
Flowchart for analysis of subjects. T2DM, type 2 diabetes mellitus; HbA1c, glycated hemoglobin; MF, metformin; SU, sulfonylurea; USG, ultrasonography; UTI, urinary tract infection; OHA, oral hypoglycemic agents
Table 1 shows the baseline characteristics and change in parameters after six months of therapy with dapagliflozin. Participants had a mean age of 46.06 ± 15.2 years, and 36 (57.14%) were male. The study cohort had a mean liver fat of 29.47 ± 2.18%, with a mean LSM of 5.99 ± 1.1 kPa. Other baseline parameters were as shown in Table 1. The mean weight, BMI, and waist circumference decreased by 2.55 Kg, 0.97 Kg/m2 and 1.46 cm, respectively. There was a significant reduction in FPG, PPG and HbA1c of 27 mg/dl, 40.7 mg/dl, and 0.84%, respectively. Mean HOMA-IR reduced significantly from 6.22 ± 1.82 to 4.18 ± 1.79 after six months of therapy (P < 0.001). Other significant changes included AST, ALT, and FIB 4 as shown in Table 1. The FIB-4 index decreased significantly from 1.32 ± 0.09 to 1.19 ± 0.08 (P < 0.001).
Table 1.
Baseline characteristics and changes in various parameters following dapagliflozin therapy
| Parameter (n=63) | Baseline | Post-Treatment | Mean Difference | % Change | P | Effect Size | Effect Size Interpretation |
|---|---|---|---|---|---|---|---|
| Age (years) | 46.06±15.2 | 46.56±15.2 | |||||
| Weight (Kg) | 80.42±13.09 | 77.87±13.09 | -2.55 | -3.17% | <0.001 | 0.95 | Large |
| BMI (kg/m2) | 29.94±3.10 | 28.98±3.07 | -0.97 | -3.23% | <0.001 | 0.95 | Large |
| WC (cm) | 103.95±10.21 | 102.49±9.75 | -1.46 | -1.40% | <0.001 | 0.90 | Large |
| WHR | 0.96±0.09 | 0.95±0.10 | -0.01 | -1.04% | <0.001 | 1.00 | Large |
| FPG (mg/dl) | 130.95±25.61 | 103.40±23.67 | -27.56 | -21.04% | <0.001 | 1.00 | Large |
| PPG (mg/dl) | 218.37±38.78 | 177.63±35.03 | -40.73 | -18.65% | <0.001 | 0.97 | Large |
| HbA1c (%) | 8.07±1.14 | 7.22±1.14 | -0.84 | -10.45% | <0.001 | 1.00 | Large |
| Insulin (mU/L) | 19.26±3.87 | 18.74±4.76 | -0.52 | -2.69% | 0.055 | 0.31 | Medium |
| HOMA IR | 6.22±1.82 | 4.81±1.79 | -1.41 | -22.71% | <0.001 | 1.00 | Large |
| AST (IU/L)* | 24.0 (20.0–29.0) | 22.0 (19.0–26.0) | -3.71 | -13.72% | <0.001 | 0.93 | Large |
| ALT (IU/L)* | 27.0 (22.5-31.0) | 25.0 (20.5–29.0) | -1.81 | -6.07% | <0.001 | 0.99 | Large |
| TC (mg/dl) | 160.86±20.56 | 156.32±11.32 | -4.54 | -2.82% | 0.141 | 0.35 | Medium |
| TG (mg/dl) | 151.51±74.39 | 140.71±50.16 | -10.79 | -7.12% | 0.001 | 0.55 | Large |
| HDL (mg/dl) | 53.65±10.56 | 56.97±7.90 | 3.32 | 6.19% | <0.001 | -0.90 | Large |
| LDL (mg/dl) | 76.90±14.60 | 71.16±12.16 | -5.75 | -7.47% | 0.006 | 0.62 | Large |
| Urea (mg/dl) | 24.63±9.92 | 24.76±9.47 | 0.13 | 0.52% | 0.345 | -0.05 | Negligible |
| Creatinine (mg/dl) | 0.84±0.32 | 0.83±0.31 | -0.00 | -0.36% | 0.688 | 0.05 | Negligible |
| FIB4* | 0.77 (0.54-0.96) | 0.71 (0.47–0.92) | -0.04 | -5.15% | <0.001 | 0.85 | Large |
| Body Fat% | 31.34±4.37 | 29.04±4.06 | -2.30 | -7.35% | <0.001 | 1.00 | Large |
| VAT mass (g) | 1249.21±108.35 | 926.52±94.52 | -322.68 | -25.83% | <0.001 | 2.84 | Large |
| VAT volume (cm3) | 1337.14±114.95 | 864.97±86.67 | -472.17 | -35.31% | <0.001 | 3.80 | Large |
| MRI Liver Fat% | 29.47±2.18 | 26.77±2.11 | -2.70 | -9.17% | <0.001 | 1.00 | Large |
| LSM (kPa) | 5.99±1.10 | 5.15±0.91 | -0.85 | -14.13% | <0.001 | 0.99 | Large |
*Data expressed as median (IQR), remaining data are expressed as mean±SD. BMI, body mass index; WC, waist circumference; WHR, waist-to-hip ratio; FPG, fasting plasma glucose; PPG, post-prandial plasma glucose; HbA1c, glycated hemoglobin; HOMA-IR, homeostatic model assessment of insulin resistance; AST, aspartate aminotransferase; ALT, alanine aminotransferase; TC, total cholesterol; TG, triglyceride; HDL, high-density lipoprotein; LDL, low-density lipoprotein; FIB-4, fibrosis-4 index; VAT, visceral adipose tissue; MRI, magnetic resonance imaging; LSM, liver stiffness measurement
Effect of dapagliflozin on liver fat
MRI-derived liver fat significantly decreased from 29.47% ± 2.18% to 26.77% ± 2.11%, with an absolute decrease of 2.7% (relative decrease of 9.17%, P < 0.001) and a large effect size (0.879) [Table 1]. Correlation analysis showed that change in liver fat (Δ Liver Fat %) was significantly correlated with the change in VAT mass (r = 0.546, P < 0.001) and VAT volume (r = 0.437, P <0.001) [Figure 2]. There was also a weak inverse correlation between change in liver fat and change in ALT (r = –0.279, P = 0.027). Change in liver fat showed no significant correlation with change in HbA1c (r = 0.002, P = 0.987), FPG (r = 0.016, P = 0.902) or PPG (r = -0.015, P = 0.910). No significant correlation was seen between change in body weight and change in liver fat (r = 0.009, P = 0.944). On multiple linear regression analysis, only the change in VAT mass was independently associated with the change in liver fat (β < 0.001, P < 0.001).
Figure 2.
Correlation between change (Δ) in liver fat and change (Δ) in visceral adipose tissue mass and volume following dapagliflozin therapy. VAT, visceral adipose tissue
The criterion for responders, as defined by ≥10% reduction in liver fat, was met by 16 (25.4%) patients and the remaining 47 (74.6%) were non-responders [Table 2]. Comparing responders vs non responders, responders were more likely to be men (P = 0.011), with lower BMI (P = 0.002), HbA1c (P = 0.015), body fat (P = 0.011), and higher liver fat (P = 0.034), VAT mass (P = 0.036), and VAT volume (P = 0.021). In multivariate logistic regression analysis, baseline liver fat was the only independent predictor of response to dapagliflozin therapy (β = 1.63, P < 0.001).
Table 2.
Comparison of baseline parameters between responders and non-responders for liver fat
| Parameter | Responders (n=16) | Non-Responders (n=47) | P |
|---|---|---|---|
| Age (years) | 51.44±14.24 | 44.23±15.22 | 0.097 |
| Gender (Male n, %) | 14, 87.5% | 22, 46.8% | 0.011 |
| Weight (kg) | 79.97±11.38 | 80.57±13.74 | 0.865 |
| BMI (kg/m2) | 28.00±2.68 | 30.61±2.97 | 0.002 |
| WC (cm) | 102.69±8.79 | 104.38±10.71 | 0.534 |
| WHR | 1.01±0.04 | 0.94±0.10 | 0.155 |
| FPG (mg/dl) | 126.00±28.96 | 132.64±24.47 | 0.105 |
| PPG (mg/dl) | 208.38±42.78 | 221.77±37.19 | 0.230 |
| HbA1c (%) | 7.43±1.14 | 8.28±1.06 | 0.015 |
| Insulin (mU/L) | 20.04±2.77 | 18.99±4.17 | 0.261 |
| HOMA IR | 6.25±1.76 | 6.21±1.86 | 0.868 |
| AST (IU/L)* | 24.5 (19.0–27.5) | 24.0 (20.5–29.0) | 0.607 |
| ALT (IU/L)* | 27.0 (22.0–31.3) | 27.0 (23.0–31.0) | 0.800 |
| TC (mg/dl) | 157.19±20.03 | 162.11±20.79 | 0.064 |
| TG (mg/dl) | 163.56±140.78 | 147.40±30.45 | 0.252 |
| HDL (mg/dl) | 49.06±10.69 | 55.21±10.16 | 0.055 |
| LDL (mg/dl) | 75.44±5.53 | 77.40±16.62 | 0.800 |
| Urea (mg/dl) | 23.81±10.02 | 24.91±9.97 | 0.706 |
| Creatinine (mg/dl) | 0.80±0.29 | 0.85±0.33 | 0.564 |
| FIB4* | 0.81 (0.67–0.95) | 0.76 (0.51–0.95) | 0.448 |
| Body Fat% | 29.14±2.58 | 32.10±4.62 | 0.011 |
| VAT mass (g) | 1303.75±74.64 | 1230.64±112.33 | 0.036 |
| VAT volume (cm3) | 1395.31±78.13 | 1266.02±277.45 | 0.021 |
| MRI Liver Fat% | 30.64±1.56 | 29.07±2.23 | 0.034 |
| LSM (kPa) | 6.03±0.61 | 5.98±1.22 | 0.224 |
*Data expressed as median (IQR), remaining data are expressed as mean±SD. BMI, body mass index; WC, waist circumference; WHR, waist-to-hip ratio; FPG, fasting plasma glucose; PPG, post-prandial plasma glucose; HbA1c, glycated hemoglobin; HOMA-IR, homeostatic model assessment of insulin resistance; AST, aspartate aminotransferase; ALT, alanine aminotransferase; TC, total cholesterol; TG, triglyceride; HDL, high-density lipoprotein; LDL, low-density lipoprotein; FIB-4, fibrosis-4 index; VAT, visceral adipose tissue; MRI, magnetic resonance imaging; LSM, liver stiffness measurement
Effect of dapagliflozin on LSM
LSM improved significantly from 5.99 ± 1.10 kPa to 5.15 ± 0.91 kPa (P < 0.001), with a mean decrease of 0.85 kPa [Table 1]. On correlation analysis, the change in LSM was significantly correlated with the change in AST (r = 0.351, P = 0.004). On multiple linear regression analysis, the change in AST independently predicted the change in LSM (β = 0.068, P = 0.040).
The criterion for responders, as defined by >15% reduction in LSM, was met by 33 patients (52.4%) [Table 3]. Comparing the responders vs non-responders, responders were older (P < 0.001), had higher baseline LSM (P < 0.001), ALT (P = 0.029), and FIB-4 (P < 0.001). In multivariable logistic regression analysis, age (β = 0.126, P < 0.001) and baseline LSM (β = 4.44, P = 0.027) independently predicted the response to dapagliflozin therapy.
Table 3.
Comparison of baseline parameters between responders and non-responders for LSM
| Parameter | Responder (n=33) | Non-responder (n=30) | P |
|---|---|---|---|
| Age (years) | 54.88±11.84 | 36.37±12.39 | <0.001 |
| Gender (Male n, %) | 22, 66.67 | 14, 46.67 | 0.114 |
| Weight (kg) | 82.50±13.96 | 78.13±11.88 | 0.185 |
| BMI (kg/m2) | 29.69±2.94 | 30.22±3.29 | 0.253 |
| WC (cm) | 104.45±9.02 | 103.40±11.51 | 0.689 |
| WHR | 0.97±0.09 | 0.94±0.10 | 0.322 |
| FPG (mg/dl) | 134.61±25.35 | 126.93±25.71 | 0.160 |
| PPG (mg/dl) | 225.12±31.17 | 210.93±45.09 | 0.156 |
| HbA1c (%) | 8.10±0.99 | 8.03±1.29 | 0.819 |
| Insulin (mU/L) | 19.55±3.39 | 18.94±4.37 | 0.367 |
| HOMA IR | 6.46±1.56 | 5.96±2.07 | 0.087 |
| AST (IU/L)* | 26.0 (22.0–29.0) | 22.0 (19.0–28.0) | 0.069 |
| ALT (IU/L)* | 29.0 (25.0–32.0) | 24.0 (22.0–30.0) | 0.029 |
| TC (mg/dl) | 162.45±18.66 | 159.10±22.65 | 0.610 |
| TG (mg/dl) | 153.67±98.18 | 149.13 ± 34.39 | 0.321 |
| HDL (mg/dl) | 54.27±9.74 | 52.96±11.53 | 0.628 |
| LDL (mg/dl) | 77.45±13.16 | 76.30±16.24 | 0.684 |
| Urea (mg/dl) | 24.39±9.80 | 24.90±10.20 | 0.842 |
| Creatinine (mg/dl) | 0.83±0.29 | 0.85±0.35 | 0.956 |
| FIB4* | 0.91 (0.79–1.01) | 0.53 (0.41–0.63) | <0.001 |
| Body Fat% | 0.31±0.04 | 0.32±0.05 | 0.133 |
| VAT mass (g) | 1272.88±87.10 | 1223.17±124.04 | 0.079 |
| VAT volume (cm3) | 1363.79±90.85 | 1307.83±132.09 | 0.066 |
| MRI Liver Fat% | 0.30±0.02 | 0.29±0.03 | 0.054 |
| LSM (kPa) | 6.30±0.55 | 5.66±1.42 | <0.001 |
*Data expressed as median (IQR), remaining data are expressed as mean±SD. BMI, body mass index; WC, waist circumference; WHR, waist-to-hip ratio; FPG, fasting plasma glucose; PPG, post-prandial plasma glucose; HbA1c, glycated hemoglobin; HOMA-IR, homeostatic model assessment of insulin resistance; AST, aspartate aminotransferase; ALT, alanine aminotransferase; TC, total cholesterol; TG, triglyceride; HDL, high-density lipoprotein; LDL, low-density lipoprotein; FIB-4, fibrosis-4 index; VAT, visceral adipose tissue; MRI, magnetic resonance imaging; LSM, liver stiffness measurement
Effect on visceral adipose tissue and body fat
VAT showed a significant reduction in both mass and volume. These reductions accounted for a decrease of 322.68 g (25%) in VAT mass and 472.17 cm3 (35%) in VAT volume, respectively. In contrast, body fat decreased from 31.3% to 29%, with an absolute decrease of 2.3% and a relative decrease of 7.35% [Table 1].
DISCUSSION
The present study showed that in patients with T2DM and MASLD, dapagliflozin 10 mg daily for six months resulted in significant reductions in liver fat, VAT, body fat, and FIB-4 index. The present study also showed a significant decrease in liver stiffness with a clinically meaningful large effect size.
By using dual echo MRI, the present study demonstrated a significant decrease in liver fat by 2.7% (relative decrease of 9.17%) after six months of dapagliflozin therapy in patients with T2DM who were on metformin and/or sulfonylureas. This decrease was also associated with a large effect size, indicating that the reduction in liver fat by dapagliflozin is not only statistically significant but also likely to have a clinically meaningful benefit in real-world settings. Previous studies by Shi et al.,[10] Eriksson et al.,[8] Latva-Rasku et al.,[9] and Ghosh et al.[7] reported reductions in liver fat of 4.18% over 24 weeks, 2.23% over 12 weeks, 3.74% over 8 weeks, and 5.10% over 17 weeks, respectively. The absolute reduction of 2.7% observed in the present study over six months is lower than previous studies. This difference may be attributed to variations in the study population, inclusion of baseline hepatic fat criteria by USG, and different methods of liver fat estimation, such as CT, MRI-PDFF or dual-echo MRI. Dapagliflozin reduces hepatic steatosis through multiple interrelated mechanisms. Daily glycosuria-induced caloric loss (~240–320 kcal) contributes to modest weight reduction (2–3 kg), which improves insulin resistance.[24] This glucose loss shifts fuel metabolism toward fatty acid utilization and enhances β-oxidation in liver, thus limiting triglyceride accumulation. In parallel, the reduction in glycemia downregulates ChREBP, while improved insulin sensitivity suppresses SREBP-1c—both critical transcription factors involved in hepatic lipogenesis.[13,14] Moreover, the insulin-independent glucose-lowering effect of dapagliflozin leads to a relative increase in glucagon, mimicking a fasting state that promotes hepatic β-oxidation, triglyceride clearance, and adipose tissue lipolysis.[15] In the present study, both correlation analysis and linear regression analysis showed that change in VAT mass was strongly associated with change in liver fat. This implies that the decrease in both hepatic fat and visceral adipose tissue mass with dapagliflozin therapy may be due to a similar mechanism. Besides, higher baseline liver fat was the sole predictor of a >10% decrease in hepatic fat in responders. This implies that patients with T2DM who have greater hepatic fat content at baseline are more likely to respond to dapagliflozin therapy.
Few previous studies have evaluated the effect of dapagliflozin on LSM.[16,17,18] The present study demonstrated a significant reduction in LSM of 0.85 kPa after six months of dapagliflozin therapy, along with a corresponding decrease in the FIB-4 index. Studies by Weng et al.,[16] and Das et al.[17] did not report any significant reduction in LSM. Only one recent study by Lin et al.[18] found a significant decrease in LSM of 3.23 kPa after 48 weeks of dapagliflozin therapy. These differences could be due to longer duration of dapagliflozin therapy and higher baseline LSM of 9.3 kPa in the study by Lin et al.[18] compared to the present study of 24 weeks and baseline LSM of 5.99 kPa. In our study, 33 responders who achieved a ≥15% reduction in LSM had significantly higher baseline LSM, ALT, and FIB-4 values, and were older in age. Multivariable regression confirmed that age and baseline LSM were independent predictors of >15% reduction in LSM. This implies that older T2DM patients with higher baseline LSM are more likely to respond to dapagliflozin therapy. Dapagliflozin exerts anti-inflammatory and antioxidative effects, both of which contribute to decreased hepatic stellate cell activation and reduced fibrogenesis.[25] It also modulates the gut–liver axis[26] and lowers serum uric acid levels,[27] which may further attenuate fibrosis through suppression of stellate cell activity.[28] Additionally, dapagliflozin increases ketone body levels, which have been shown to reduce hepatic CD8⁺ T-cell infiltration and cytotoxic activity, thereby mitigating hepatic fibrosis.[29]
Our study demonstrated substantial reductions in VAT mass and volume of 25.83% and 35.31%, respectively. Previous studies with dapagliflozin have reported reductions in VAT volume ranging from 6.72% to 24.9%.[8,9,20] The greater reduction in VAT mass and volume in the present study could be attributed to the use of DXA rather than other methods such as CT, MRI or bioelectrical impedance analysis and longer duration of treatment. Dapagliflozin reduces VAT by inducing a negative energy balance through glycosuria and improving insulin sensitivity.[13,30] It also modulates adipokines and reduces inflammation, leading to loss of metabolically active visceral fat.[31,32]
Dapagliflozin also significantly improved glycemic and anthropometric measures. Participants experienced a mean weight loss of 2.55 kg, accompanied by significant reductions in BMI, waist circumference, FPG, PPG, HbA1c, and HOMA-IR. These results highlight that improvements in hepatic parameters with dapagliflozin occur alongside broader metabolic improvements.
Limitations
The present study has certain limitations. First, it did not have a control group for comparison. Second, liver fibrosis was assessed using non-invasive techniques (LSM and FIB-4), rather than liver biopsy. Third, the criterion for defining LSM responders as >15% decrease was chosen arbitrarily, as no previous studies have addressed this area in patients with T2DM and MASLD.
CONCLUSION
The present study clearly shows that dapagliflozin significantly reduces liver fat, visceral adipose tissue, and liver stiffness in patients with type 2 diabetes mellitus and metabolic dysfunction-associated steatotic liver disease.
Author contributions
BD: Managed recruitment, performed statistical analysis and manuscript writing, PRK: Conceptualized and designed the study, managed recruitment, manuscript editing and proof reading, DKD: Contributed to recruitment, performed statistical analysis and proof reading, DP: Managed recruitment, PKS: Managed recruitment. MVKR: Contributed to radiological evaluation of study participants.
Conflicts of interest
There are no conflicts of interest.
Use of artificial intelligence
Artificial intelligence was not used for preparation of the manuscript.
Data availability statement
The data set used in the current study is available on request from corresponding author upon reasonable request.
Padala Ravi Kumar: padala797@gmail.com
Acknowledgment
The authors express their sincere gratitude to the Department of Radiology for their invaluable support in performing radiologic assessments. We are deeply grateful to the dedicated staff of the Multi-Disciplinary Research Unit (MRU) at our institute for their meticulous assistance with biochemical analyses and to the Endocrine Laboratory team for their unwavering support in conducting hormonal evaluations.
Funding Statement
The study received research grant from the Odisha Endocrine Society (OES).
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Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.
Data Availability Statement
The data set used in the current study is available on request from corresponding author upon reasonable request.
Padala Ravi Kumar: padala797@gmail.com