ArticleBiomedicines2024
The Influence of COVID-19 in Glycemic Control: Predictive Value of Inflammation and Metabolic Parameters.
Article in Biomedicines, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
6 citing papers in PubMed.
- Combined Exposure to Ragweed and House Dust Mite Exacerbates Airway Epithelial Barrier Dysfunction: A Multimodal Approach.Medicina (Kaunas, Lithuania) · 2026Article
- Article
- Evaluation of glucose metabolism, endocrine, and renal functions following vaccination in post-COVID-19 individuals: a longitudinal retrospective single-center cohort study.BMC infectious diseases · 2025Article
- Colorectal Cancer-One Disease, Two Fires: Distinct Inflammatory Landscapes in Colon and Rectal Cancer.Diagnostics (Basel, Switzerland) · 2025Article
- From Blood to Outcome: Inflammatory Biomarkers in Rectal Cancer Surgery at a Romanian Tertiary Hospital.Diseases (Basel, Switzerland) · 2025Article
- The Role of Glycemic Control in Inflammation Markers and Clinical Outcomes in Type 2 Diabetes Patients with Severe COVID-19.Biomedicines · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
BACKGROUND/
objectivesPredicting post-COVID-19 diabetes is crucial for enhancing patient care and public health. This study investigates the role of metabolic factors in predicting the glycemic outcomes in patients recovering from moderate to severe COVID-19.
methodsWe conducted a retrospective analysis of 135 patients without pre-existing diabetes, selected from a cohort of 1980 individuals hospitalized between January 2020 and December 2022. Metabolic parameters, including blood glucose, Homeostasis Model Assessment of Insulin Resistance (HOMA-IR), Triglyceride/Glucose (TyG) index, and high-sensitivity C-reactive protein (hs-CRP), were assessed at discharge and followed up after 4 months (T4) and 12 months (T12).
resultsStatistical analysis revealed significant correlations of initial glycemia, HOMA-IR, and hs-CRP with the subsequent glycemic levels at T4 and T12. Multiple regression analysis confirmed that initial glycemia, HOMA-IR, and hs-CRP were strong predictors of elevated glycemia, while the TyG index did not show a significant predictive value. Conventional diabetes risk factors, including body mass index (BMI) and lipid profiles, showed low predictive power for post-COVID-19 glycemia.
conclusionsThis research highlights the critical role of metabolic and inflammatory pathways in managing glycemic control in COVID-19 patients. Markers like blood glucose, HOMA-IR, and hs-CRP are significant predictors of blood glucose levels, while the TyG index appears less helpful in this context. Early, targeted interventions based on these markers can improve patient outcomes and reduce the risk of post-COVID-19 complications like diabetes.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.