The Association Between Non-Alcoholic Fatty Liver Disease (NAFLD) And Type 2 Diabetes Mellitus In Saudi Arabia: A Systematic Review
Bibliographic record
Abstract
Background: Non-alcoholic fatty liver disease (NAFLD) and Type 2 Diabetes Mellitus (T2DM) are closely linked metabolic disorders that share common pathophysiological pathways. The high prevalence of obesity and T2DM in Saudi Arabia creates a significant risk for NAFLD, yet a consolidated understanding of their association within this specific population is needed. This systematic review aims to determine the prevalence and association between NAFLD and T2DM in Saudi Arabia. Methods: A systematic literature search was conducted using PubMed, Scopus, ScienceDirect, Google Scholar, and the Saudi Digital Library for studies published between January 2000 and June 2025. The review adhered to PRISMA 2020 guidelines. Eligible studies were observational (cross-sectional, cohort, case-control) conducted on Saudi adults with T2DM, investigating NAFLD. Two independent reviewers performed study selection, data extraction, and quality assessment using the Newcastle-Ottawa Scale (NOS). Results: Six high-quality studies (NOS score ≥7/9), encompassing 1,387 diabetic patients, were included. The prevalence of NAFLD among individuals with T2DM in Saudi Arabia was consistently high, ranging from 47.8% to 80.8%, with a pooled average of 64.2%. Key predictors identified were elevated body mass index (BMI), waist circumference, alanine aminotransferase (ALT) levels, and poor glycemic control. One study using transient elastography (FibroScan) reported a higher prevalence (80.8%) and identified significant liver fibrosis in 8.8% of patients, suggesting that ultrasound may underestimate the true disease burden. Conclusion: There is a high prevalence of NAFLD among Saudi patients with T2DM, driven largely by shared metabolic risk factors like obesity and insulin resistance. These findings underscore the urgent need for integrated care models that include routine liver screening, utilizing sensitive non-invasive tools like FibroScan, within diabetes management protocols in Saudi Arabia to enable early detection and prevent progression to advanced liver disease.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".