Evidence map›Paper›PMID 39949437›Full record

ArticleCureus2025

Impact of Diabetes Mellitus on Coronary Artery Disease Severity: A Comparative Analysis of Diabetic and Non-diabetic Patients.

Muhammad Muneeb Arshad, Muhammad Jalal-Ud-Din, Junaid Qayyum, Masooma Zainab, Ibrahim Shah, Saira Fayyaz, Muhammad Sajid

Abstract read
In one paragraph

Article in Cureus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Muhammad Muneeb ArshadInternal Medicine, University Hospitals Birmingham National Health Service (NHS) Foundation Trust, Birmingham, GBR.
Muhammad Jalal-Ud-DinInternal Medicine, University Hospitals Coventry and Warwickshire, Coventry, GBR.
Junaid QayyumMedicine, District Headquarter (DHQ) Hospital, Rawalpindi, PAK.
Masooma ZainabGeneral Internal Medicine, University Hospitals Coventry and Warwickshire, Coventry, GBR.
Ibrahim ShahCardiology, Gajju Khan Medical College, Swabi, PAK.
Saira FayyazCardiology, Sheikh Mohamed Bin Zayed Al Nahyan Institute of Cardiology, Quetta, PAK.
Muhammad SajidBiological Sciences, International Islamic University, Islamabad, PAK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background One of the main risk factors for coronary artery disease (CAD), which is a significant source of morbidity and death, is diabetes mellitus (DM), which also speeds up the disease's course and severity. This study aimed to compare the severity of CAD between diabetic and non-diabetic patients, providing insights to guide clinical decision-making and improve therapeutic outcomes. Methodology A prospective, observational study was conducted at the Sheikh Mohamed Bin Zayed Al Nahyan Institute of Cardiology, Quetta, from August 2018 to July 2019, involving 204 adult patients with CAD, consisting of 102 patients with diabetes and 102 without diabetes. Demographic, clinical, and laboratory data were collected, and coronary angiography was performed to assess the severity of CAD, which was defined as a binary outcome variable (1 = severe CAD, 0 = not severe CAD), based on criteria such as multi-vessel involvement and degree of stenosis. Statistical analysis was carried out using IBM SPSS Statistics for Windows, V. 25.0 (IBM Corp., Armonk, NY, USA), with chi-squared tests used for categorical variables (e.g., number of affected vessels, plaque distribution) and independent t-tests for continuous variables (e.g., fasting blood glucose, hemoglobin A1c(HbA1c) levels, lipid profiles). Logistic regression was employed to identify independent predictors of CAD severity, and odds ratios (ORs) with 95% confidence intervals (CI) were calculated, with statistical significance defined as a p-value of less than 0.05. Results Diabetic patients exhibited significantly higher rates of multi-vessel disease (70 (68.63%) vs. 52 (50.98%); p = 0.02), three-vessel involvement (37 (36.27%) vs. 25 (24.51%); p = 0.01), and severe stenosis (64 (62.75%) vs. 52 (50.98%); p = 0.019) compared to non-diabetic patients. Fasting blood glucose (158.21 ± 45.63 mg/dL vs. 98.31 ± 12.74 mg/dL; p < 0.001) and HbA1c (8.21 ± 1.34% vs. 5.62 ± 0.41%; p < 0.001) levels were significantly higher in diabetics. Multivariate regression analysis revealed that diabetes (OR = 2.12; 95% CI: 1.35-3.32; p = 0.001), hypertension (OR = 1.45; 95% CI: 1.01-2.08; p = 0.04), elevated glucose levels (fasting blood glucose OR = 1.04; 95% CI: 1.02-1.06; p < 0.001; HbA1c OR = 1.27; 95% CI: 1.15-1.41; p < 0.001), and age (OR = 1.03; 95% CI: 1.01-1.05; p = 0.02) were significant predictors of CAD severity, with each increase in fasting blood glucose, HbA1c, and age associated with higher odds of severe CAD. Other variables, such as gender, hyperlipidemia, smoking history, sedentary lifestyle, and regular exercise, did not show significant associations with CAD severity (p > 0.05). However, a family history of CAD was identified as a significant predictor (OR = 1.56; 95% CI: 1.06-2.31; p = 0.02), suggesting that those with a family history of CAD had 56% higher odds of experiencing severe CAD. Systolic blood pressure (OR = 1.18; 95% CI: 1.07-1.30; p = 0.002) was associated with CAD severity, with each 10 mmHg increase in systolic blood pressure increasing the odds of severe CAD.  Conclusion DM is linked to more severe CAD, with high blood pressure, HbA1c, and blood glucose levels contributing to increased severity, emphasizing the need for early detection and aggressive treatment to mitigate its impact on heart health, and further research, including long-term follow-up and therapeutic interventions, is needed to enhance management for diabetic CAD patients.

Indexed as

cad severitycoronary artery diseasediabetes mellitusfasting blood glucosehba1cmulti-vessel disease

Identifiers

PMID39949437
PMCPMC11821364

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.