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Record W4407353708 · doi:10.3390/healthcare13040376

Comparison Between the Impact of Diabetes Mellitus on Liver Diseases and Vice Versa Among Saudi and Egyptian Patients

2025· article· en· W4407353708 on OpenAlexaff
M. R. Mahmoud, Somia Ibrahim, Mona M. Shahien, Amal Daher Alshammari, Fahaad Alenazi, Fayez Saud Alreshidi, Ahmed Aljadani, Ashraf Abdel Khalik, Abeer Hassan Elhaj, A M Khalifa, Hend Faleh Hamad Alreshidi, Hemat El‐Sayed El‐Horany, Kamaleldin B. Said, Marwa H. Abdallah, Amna Metwaly

Bibliographic record

VenueHealthcare · 2025
Typearticle
Languageen
FieldMedicine
TopicLiver Disease Diagnosis and Treatment
Canadian institutionsCarleton University
FundersTheodor Bilharz Research Institute
KeywordsVersaDiabetes mellitusMedicineEnvironmental healthInternal medicineGerontologyEndocrinologyComputer scienceWorld Wide Web

Abstract

fetched live from OpenAlex

Background: The risk of dying from chronic liver diseases (CLDs) is two to three times higher for patients with diabetes (DM). Nonalcoholic fatty liver disease (NAFLD) is the primary cause of this increased risk, which has an etiology unrelated to alcohol or viruses. Previous research reported that diabetes and CLD are related, since they influence each other. Aim: Estimation of the impact of diabetes (DM) on liver diseases (LD), and of the impact of liver diseases on DM among Egyptian and Saudi patients. It is a descriptive and prospective analytical study design. The investigation was carried out in Saudi Arabia and Egypt at gastroenterology outpatient clinics. Methods: Prospective data were collected through face-to-face patient interviews during clinic visits between June 2021 and June 2023. The interviews covered the patients’ basic characteristics and information on DM and LD. Certain laboratory tests were conducted on these patients, such as liver function, glucose level, lipid profile, INR, and prothrombin time. Results: The total of 2748 participants in this study included 1242 diabetic patients of both genders from Saudi Arabia and 1506 from Egypt. Most Saudis had between 10 and 20 years’ duration of DM (35.5%), with HbA1c (7–10%) values of 47.8%, while the Egyptian patients had >20 years’ duration of DM (39.8%), with HbA1c (7–10%) values of 49.8%. Regarding the impact of DM on the development of liver diseases, about 35.5% (Saudis) vs. 23.5% (Egyptians) had liver diseases due to DM, a significant difference (p-value = 0.011). Liver enzymes were increased in many of the Egyptian and Saudi patients (41.4% vs. 33%), while the presence of fatty liver (28.2% vs. 35.7%) and hepatocellular carcinoma (13.7% vs. 6.1%) were also significantly different (p-value = 0.047). While the impact of liver diseases on DM was observed more among Egyptian (59%) than among Saudi (46.4%) patients because of liver cirrhosis (HCV or HBV), known to be a reason for diabetes in Egyptians (27.9%) vs. Saudis (8.0%), a higher incidence of fatty liver leading to DM was observed in Saudis than in Egyptians (15.9% vs. 11.6%) (p-value = 0.000. Obesity was more prevalent among Saudi patients (63.8%) than among Egyptian patients (48.6%) (p-value = 0.019). Fewer Egyptians (about 65%) suffered from dyslipidemia than Saudis (about 80%). Higher INR and longer prothrombin times were observed in Egyptians (29.9% and 29.1%, respectively) than in Saudis (20.3% and 18.8%, respectively), with a significant difference between the two nations (p-value < 0.050). Conclusions: We may conclude that diabetes in most patients has a negative impact on the development of liver diseases (particularly fatty liver in Saudi patients). In addition, most liver diseases (liver cirrhosis) have a negative influence on the development of DM (more so in Egyptian patients). There is a link between DM and liver disease. In particular, liver cirrhosis and diabetes were found to influence each other. Therefore, correct medication, adherence to treatment, lifestyle modifications, successful cirrhosis control (in patients with liver diseases), and diabetic control (in diabetic patients) could lead to effective management of both diseases. The negative fallouts in the two cases were prompted by obesity, morbid eating, and poor quality of life.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score0.309

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.022
GPT teacher head0.338
Teacher spread0.316 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations3
Published2025
Admission routes1
Has abstractyes

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