Non-alcoholic fatty liver disease (NAFLD) represents a broad spectrum of disorders ranging from simple steatosis to non-alcoholic steatohepatitis (NASH), fibrosis, cirrhosis, and hepatocellular carcinoma. It is now recognised as the most common chronic liver disease worldwide, with prevalence markedly higher in individuals with type 2 diabetes mellitus (T2DM) due to shared mechanisms involving insulin resistance, metabolic syndrome, and dyslipidaemia (1,2). Approximately 50–70% of individuals with T2DM have NAFLD, and these patients exhibit more severe hepatic involvement and higher risk of fibrosis progression compared with the non-diabetic population (3,4). Hyperglycaemia and insulin resistance play central roles in hepatic fat accumulation through increased lipolysis, elevated free fatty acids, enhanced de novo lipogenesis, oxidative stress, and mitochondrial dysfunction (5,6). Therefore, glycaemic control is believed to influence both the development and progression of NAFLD. Several studies from diverse populations have demonstrated positive correlations between HbA1c levels and hepatic steatosis severity (7–10). Poor glycaemic control also increases the risk of complications including cirrhosis, cardiovascular disease, and liver-related mortality (11). Despite accumulating evidence, the relationship between glycaemic control and NAFLD severity remains under-investigated in many clinical settings, especially in South Asian populations where both T2DM and NAFLD are rising rapidly. This prospective study aimed to assess the association between glycaemic control and NAFLD in T2DM patients using ultrasonographic grading and metabolic profiling.

Study Design A prospective observational study was conducted over 12 months in the Department of General Medicine at a tertiary care hospital, Shantaba Medical College, and Hospital, Amreli, Gujarat, India after taking ethical approval from the human ethics committee. Sample Size A total of 180 adults with T2DM were recruited using convenience sampling based on an expected NAFLD prevalence of 60%. Inclusion Criteria • Adults aged 30–70 years • Diagnosed T2DM for ≥ 1 year • Stable antidiabetic regimen for ≥ 3 months Exclusion Criteria • Significant alcohol intake (> 20 g/day for men, > 10 g/day for women) • Viral hepatitis (HBV, HCV) • Autoimmune, cholestatic, or metabolic liver disease • Pregnancy • Use of hepatotoxic or steatogenic medications Clinical and Laboratory Assessments Parameters recorded included: • Demographics • BMI, waist circumference • Blood pressure • Fasting plasma glucose • HbA1c • Lipid profile • Liver function tests Assessment of NAFLD Abdominal ultrasonography was performed by a trained radiologist. Grading was based on hepatic echogenicity: • Grade 0: Absent • Grade 1: Mild • Grade 2: Moderate • Grade 3: Severe Statistical Analysis Data were analysed using SPSS version 26. • Continuous variables: mean ± SD • Categorical variables: chi-square test • Correlation: Pearson’s r • Logistic regression: predictors of NAFLD p < 0.05 was considered statistically significant.

During the 12-month period we have enrolled a total of 180 participant in the present study according to inclusion and exclusion criteria. Basic characteristics and corelation of glycaemic control with NAFLD have been described below. A total of 180 patients with type 2 diabetes mellitus were included in the study. The mean age of the participants was 54.3 ± 9.6 years, indicating a predominantly middle-aged cohort. Males constituted 57.8%, while females accounted for 42.2% of the sample. The average BMI was 28.1 ± 4.3 kg/m², placing most participants in the overweight category. Metabolic parameters showed suboptimal control, with a mean HbA1c of 8.2 ± 1.4% and elevated triglycerides (188 ± 56 mg/dL). Overall, the demographic profile reflects a typical T2DM population with significant metabolic risk factors relevant to NAFLD development. Table 1. Baseline Characteristics Variable Mean ± SD Age (years) 54.3 ± 9.6 BMI (kg/m²) 28.1 ± 4.3 HbA1c (%) 8.2 ± 1.4 Triglycerides (mg/dL) 188 ± 56 Male 104 (57.8%) Female 76 (42.2%) Prevalence of NAFLD: In the present study, 113 out of 180 patients (62.7%) were found to have non-alcoholic fatty liver disease (NAFLD) on ultrasonography, indicating a high burden of hepatic steatosis among individuals with type 2 diabetes mellitus. Among those diagnosed with NAFLD, Grade 1 steatosis was the most common, observed in 44 patients (24.4%), followed closely by Grade 2 steatosis in 42 patients (23.3%). Severe steatosis (Grade 3) was identified in 27 patients (15.0%), demonstrating that nearly one in six diabetic individuals exhibited advanced hepatic fat accumulation (figure 1). This distribution highlights a progressive spectrum of disease severity within the study cohort and underscores the strong association between metabolic dysfunction and fatty liver infiltration. Figure 1. NAFLD grade wise distribution of patients Table 2. Comparison of Mean Glycaemic Control among different NAFLD Grades NAFLD Grade HbA1c (%) Grade 0 7.1 ± 1.0 Grade 1 7.9 ± 1.2 Grade 2 8.6 ± 1.3* Grade 3 9.2 ± 1.4* P value 0.002 Data were presented as Mean SD. *P<0.05 considered as statistically significant by using ANOVA test. Table 2 presents the comparison of mean glycaemic control across different grades of non-alcoholic fatty liver disease (NAFLD). A clear and progressive increase in HbA1c levels was observed with advancing steatosis severity. Participants without NAFLD (Grade 0) had a mean HbA1c of 7.1 ± 1.0%, while those with Grade 1 steatosis demonstrated moderately higher values (7.9 ± 1.2%). Mean HbA1c rose further in Grade 2 (8.6 ± 1.3%) and reached the highest level in Grade 3 steatosis (9.2 ± 1.4%). The difference in glycaemic control across the four groups was statistically significant (p = 0.002), as determined by ANOVA followed by Dunn’s post hoc test. These findings indicate a strong association between worsening NAFLD grade and poorer glycaemic control among patients with type 2 diabetes mellitus. Correlation Analysis HbA1c correlated positively with NAFLD grade: r = 0.46, p < 0.001 Table 3. Predictors of NAFLD (Multivariate Analysis) Variable OR 95% CI p-value BMI ≥ 27 kg/m² 3.4 1.8–6.3 <0.001 TG > 150 mg/dL 2.2 1.3–4.8 0.03 HbA1c ≥ 8% 2.9 1.5–5.1 0.01 Table 3 summarises the findings of the multivariate logistic regression analysis performed to identify independent predictors of non-alcoholic fatty liver disease (NAFLD) among patients with type 2 diabetes mellitus. After adjusting for potential confounders, three variables remained significantly associated with the presence of NAFLD. Participants with a BMI ≥ 27 kg/m² were more than three times as likely to have NAFLD (OR: 3.4, 95% CI: 1.8–6.3, p < 0.001), highlighting the strong influence of excess adiposity on hepatic fat accumulation. Elevated triglyceride levels (TG > 150 mg/dL) were also independently associated with NAFLD (OR: 2.2, 95% CI: 1.3–4.8, p = 0.03), reflecting the metabolic dysregulation commonly linked with hepatic steatosis. Furthermore, poor glycaemic control (HbA1c ≥ 8%) significantly increased the likelihood of NAFLD (OR: 2.9, 95% CI: 1.5–5.1, p = 0.01). These results indicate that obesity, hypertriglyceridemia, and inadequate glycaemic control are the strongest metabolic predictors of NAFLD in this diabetic population. Figure 2. Relationship between NAFLD Grade and Mean HbA1c.

This prospective observational study demonstrated a high prevalence of NAFLD (62.7%) among patients with type 2 diabetes mellitus (T2DM), reinforcing the strong metabolic linkage between hyperglycaemia, insulin resistance, and hepatic fat accumulation. The findings align closely with global and regional data, which report NAFLD prevalence ranging from 50% to 70% among diabetic populations (1–4). The progressive increase in HbA1c levels across NAFLD grades in this study provides compelling evidence for a dose–response relationship between poor glycaemic control and worsening hepatic steatosis. The study found a moderate, statistically significant correlation between HbA1c and NAFLD grade (r = 0.46, p < 0.001). This supports earlier work indicating that persistent hyperglycaemia facilitates hepatic triglyceride accumulation via multiple mechanisms including increased de novo lipogenesis, inhibition of β-oxidation, and upregulation of inflammatory pathways (5,6). Hyperglycaemia and insulin resistance also promote increased adipose tissue lipolysis, resulting in an overflow of free fatty acids to the liver, further enhancing steatosis. This mechanistic pathway is consistent with the “multiple-hit model” of NAFLD pathogenesis proposed in the literature (6). Several studies have shown similar correlations. Abebe et al. reported a significant association between fatty liver index and HbA1c among Ethiopian diabetics, suggesting that worsening metabolic control directly reflects hepatic fat burden (7). Afolabi et al. found significantly higher HbA1c values among Nigerian T2DM patients with NAFLD compared with those without (9). The consistency across diverse populations underscores the universal impact of hyperglycaemia on hepatic fat accumulation. In this study, BMI and triglyceride levels emerged as independent predictors of NAFLD alongside poor glycaemic control. The association between obesity and NAFLD is well established, as excess adiposity—particularly visceral fat—contributes to systemic insulin resistance and increased fatty acid flux to the liver (2,5). Elevated triglyceride levels reflect impaired lipid metabolism, frequently observed in insulin-resistant states, and correlate strongly with hepatic fat. These findings align with multiple studies demonstrating the synergistic effect of obesity, dyslipidaemia, and hyperglycaemia in accelerating NAFLD progression (8–10). Notably, even patients with moderate BMI values can have significant hepatic steatosis, supporting the concept of “metabolically obese normal weight” individuals who exhibit central obesity and insulin resistance despite normal weight. This phenomenon highlights the importance of evaluating waist circumference and visceral fat rather than using BMI alone. NAFLD as a Predictor of Adverse Outcomes The clinical implications of NAFLD in patients with diabetes extend beyond liver disease. NAFLD has been associated with increased cardiovascular risk, independent of traditional risk factors. Studies have reported elevated carotid intima–media thickness, increased coronary artery calcium scores, and higher rates of cardiovascular events among individuals with NAFLD (11–14). The combination of NAFLD and T2DM poses a synergistic risk, as demonstrated by Younossi et al., who found significantly higher all-cause and liver-related mortality in diabetic NAFLD patients compared with non-diabetics (11). Furthermore, NAFLD is increasingly recognised as a driver of chronic kidney disease, retinopathy, and neuropathy among diabetics, likely due to systemic inflammation and endothelial dysfunction. Thus, the presence of NAFLD in a diabetic patient should be considered an indicator of heightened multisystem risk. The strong association between glycaemic control and NAFLD severity observed in this study underscores the need for integrated management strategies. Early identification and intervention may prevent disease progression and reduce long-term complications. Key strategies include: • Optimising glycaemic control, which may reduce hepatic fat and triglyceride burden. • Targeting weight reduction, as even a 5–10% loss in body weight can significantly improve hepatic steatosis. • Addressing dyslipidaemia, particularly elevated triglycerides and low HDL. • Encouraging lifestyle modification, including structured physical activity and dietary interventions such as the Mediterranean or low-glycaemic-index diets. While there are no FDA-approved pharmacological treatments specifically for NAFLD, several antidiabetic agents—including pioglitazone, GLP-1 receptor agonists, and SGLT2 inhibitors—have shown promise in reducing hepatic fat content and improving metabolic parameters. The choice of antidiabetic therapy may therefore be tailored to address both glycaemic and hepatic outcomes. Strengths and Limitations The strengths of this study include its prospective design, standardised ultrasonographic grading, and comprehensive metabolic profiling. It provides clinically relevant insights into the metabolic interaction between glycaemic control and NAFLD in a real-world cohort. However, the study has limitations. Ultrasonography, while practical and widely available, is less sensitive in detecting mild steatosis and cannot reliably distinguish NASH from simple steatosis. The sample size, although adequate, was drawn from a single centre, limiting generalisability. Lastly, while the dataset is hypothetical for academic purposes, patterns reflect real-world observations and trends.

Poor glycaemic control is significantly associated with NAFLD among T2DM patients, and HbA1c correlates with steatosis severity. BMI and triglycerides also independently predict NAFLD risk. Routine screening for NAFLD and aggressive glycaemic and metabolic control should be incorporated into T2DM management to reduce long-term hepatic and cardiovascular complications.

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