Type 2 diabetes mellitus (T2DM) is a common disorder worldwide. Impaired control of glucose levels predisposes to renal dysfunction, detected by a diagnosis of microalbuminuria. Several other risk factors have been identified in the development of microalbuminuria, such as hypertension, smoking, dyslipidemia, and obesity. Assessment of microalbuminuria and cardiovascular risk factors in type-II diabetic patients who attended the outpatient clinic for the internal medicine department at King Fahd University Hospital, Al-Khobar. A retrospective cross-sectional and an observational study included data from 2014 to 2022 collected from medical records. Patients with diabetes type-II and aged ≥18 years were included. The following were reviewed (age, sex, height, weight, body mass index, waist, hip, waist-hip ratio, systolic and diastolic blood pressure, smoking, sedentary lifestyle, diagnosis of dyslipidemia/hypertension, diabetes duration in years) and laboratory results (fasting blood glucose, HbA1C%, estimated glomerular filtration rate, serum creatinine, serum cholesterol, low-density lipoprotein, high-density lipoprotein, and triglycerides). Microalbuminuria was measured by the urine albumin to creatinine ratio and was diagnosed if levels were 30-300 mg/g. Among 301 studied patients, the prevalence of microalbuminuria was found at 36.8%. The mean age was 57.8 ± 12.6 years, and females were 45%. The mean ± SD fasting blood glucose was 165.9 ± 71.9 mg/dL, while HbA1C% was 8.8 ± 5.6. Microalbuminuria was significantly associated with age, diabetes duration, systolic blood pressure, HbA1C%, fasting blood glucose, and triglyceride levels (p≤0.05). Microalbuminuria in T2DM patients was high in this study, which emphasizes the need for early detection of microalbuminuria. The study suggests the need for effective diabetes control and the prevention of associated cardiovascular risk factors.

Diabetes mellitus is considered a major risk factor for cardiovascular diseases. Patients with diabetes mellitus type 2 (DM-II) are at twice as high risk for the development of cardiovascular diseases than the general population. Thus, we aimed to assess the most prevalent cardiovascular risk (CVR) factors among DM-II patients in the Eastern province of Saudi Arabia. This is a cross-sectional, retrospective, and observational study conducted on DM-II patients at King Fahad University Hospital (KFUH) Al Khobar, Saudi Arabia, from January 2016 to December 2021. The total number of participants was 373 who were patients with DM-II. The patients' demographic information (age, sex, marital status, height, weight, body mass index (BMI), waist, hip circumference, and waist-hip ratio were calculated or obtained from hospital electronic records as were the CVR factors, age, gender, smoking habits, physical activity, BMI, haemodynamic measurements, glycosylated haemoglobin (HbA1C) levels and lipid profile. The collected data were analyzed by using SPSS Statistics v.28 (IBM Corp., Armonk, NY). The descriptive statistics were reported using mean±SD for numerical data and relative frequencies (%) for categorical data. P < 0.05 were counted significant. Quantitative data were analyzed using the ANOVA test to compare the means of the three groups. Qualitative data were analyzed and compared using the chi-square test. Fisher’s exact test was also used to study the statistical significance of variables. Spearman rank correlation was used to study the relationship between HbA1C and other CV risk factors. The mean age was 58 (± 13) years; females were 57% of the sample. Around 92% were smokers, 84% had a sedentary lifestyle, 72% had dyslipidemia, 58% were obese, 30% were overweight, 58% reported poorly control of their diabetes, 50% had hypertension and 32% had pre-hypertension. Furthermore, 89% of participants had two or more CVR factors other than DM-II. We found a significant association between high body mass index, dyslipidemia, high systolic blood pressure and pulse pressure (p<0.05) with HbA1C. The majority of participants had two or more cardiovascular risk factors in addition to DM-II. Poor control of DM-II and cardiovascular risk factors cannot be ignored and primary to tertiary prevention must be the top priority when managing the diabetic population in order to prevent devastating outcomes and progression of reversible morbidity.

Background: Metabolic dysfunction-associated steatotic liver disease (MASLD) is highly prevalent in patients with type 2 diabetes (T2D), and advanced fibrosis is the strongest predictor of liver-related morbidity and mortality. Therefore, early noninvasive risk stratification is critical. While the Fibrosis-4 (FIB-4) index and vibration-controlled transient elastography (VCTE) are widely used, the newer FibroScan-AST (FAST) score has shown promise in detecting at-risk metabolic-associated steatohepatitis (MASH) with significant fibrosis. Evidence comparing the FAST and FIB-4 indices in Middle Eastern T2D populations remains limited. We compared the diagnostic performances of these models for advanced fibrosis in Saudi patients with T2D and MASLD. Methods: We conducted a retrospective analysis of 273 patients diagnosed with T2D and MASLD. All patients underwent VCTE. To identify advanced fibrosis, we used liver stiffness measurement (LSM) as a surrogate marker for liver biopsy. We calculated the FAST and FIB-4 indices for each patient. To assess the diagnostic performance of these scores, we evaluated their sensitivity, specificity, positive predictive value, negative predictive value, and area under the receiver operating characteristic curve (AUROC). Results: In this cohort study, 26.4% of participants had a high-risk FAST score (>0.35; median: 0.13). Patients with high-risk FAST scores (>0.35) were younger, had higher BMIs, elevated liver enzyme levels, and poorer glycemic control than those in the lower-risk groups. High-risk FAST scores were strongly correlated with elevated LSM, FIB-4, and controlled attenuation parameter values (p < 0.001). The FAST score demonstrated better performance than the FIB-4 index in detecting advanced fibrosis. It showed higher accuracy (85.4% vs. 77.3%), sensitivity (82.0% vs. 48.0%), and negative predictive value (95.5% vs. 87.8%) while maintaining a similar specificity. The AUROC values were 0.936 (95% CI: 0.901–0.971) for the FAST score compared to 0.711 (95% CI: 0.625–0.797) for the FIB-4 index. Conclusions: The FAST score demonstrated better diagnostic accuracy than the FIB-4 index and identified patients with poor metabolic control and obesity as being at the highest risk among Saudi patients with T2D and MASLD. These findings support the integration of other elastography-based tests into stepwise fibrosis screening pathways for diabetic populations, potentially improving the early detection of advanced fibrosis and patient outcomes.