Evidence map›Paper›PMID 39147101›Full record

ArticleDiabetes research and clinical practice2024

Glycated haemoglobin is a major predictor of disease severity in patients with NAFLD.

Santo Colosimo, Hamish Miller, Dimitrios A Koutoukidis, Thomas Marjot, Garry D Tan, David J Harman, Guruprasad P Aithal, Pinelopi Manousou, Roberta Forlano, Richard Parker and 5 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Diabetes research and clinical practice, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

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.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

  1. Observational
  2. Article
  3. Article
  4. Article
  5. Article
  6. Article
  7. Article
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

15 authors.

Santo ColosimoOxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford and NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, UK.
Hamish MillerOxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford and NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, UK; Barts Liver Centre, Queen Mary University London and Barts Health NHS Trust, London, UK.
Dimitrios A KoutoukidisNuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK; NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, UK.
Thomas MarjotOxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford and NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, UK; Oxford Liver Unit, Oxford University Hospitals NHS Foundation Trust, Oxford, UK; UK NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, UK.
Garry D TanOxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford and NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, UK.
David J HarmanRoyal Berkshire Hospital NHS Foundation Trust, Reading, UK.
Guruprasad P AithalNIHR Nottingham Biomedical Research Centre, Nottingham University Hospitals NHS Trust and University of Nottingham, Nottingham, UK.
Pinelopi ManousouDepartment of Metabolism, Digestion and Reproduction, Imperial College London, London, UK.
Roberta ForlanoDepartment of Metabolism, Digestion and Reproduction, Imperial College London, London, UK.
Richard ParkerLeeds Liver Unit, St James's University Hospital Leeds, Leeds, UK.
David A SheridanInstitute of Translational and Stratified Medicine, University of Plymouth, Plymouth, UK.
Philip N NewsomeNational Institute for Health Research Birmingham Biomedical Research Centre at University Hospitals Birmingham NHS Foundation Trust and the University of Birmingham, Birmingham, UK.
William AlazawiBarts Liver Centre, Queen Mary University London and Barts Health NHS Trust, London, UK; Centre for Endocrinology, William Harvey Research Institute, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London, UK.
Jeremy F CobboldOxford Liver Unit, Oxford University Hospitals NHS Foundation Trust, Oxford, UK; UK NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, UK.
Jeremy W TomlinsonOxford Centre for Diabetes, Endocrinology and Metabolism, University of Oxford and NIHR Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, UK. Electronic address: Jeremy.tomlinson@ocdem.ox.ac.uk.

Funding

Wellcome Trust
6 · The paper itself

Abstract

objectivesCurrently, non-invasive scoring systems to stage the severity of non-alcoholic fatty liver disease (NAFLD) do not consider markers of glucose control (glycated haemoglobin, HbA1c); this study aimed to define the relationship between HbA1c and NAFLD severity in patients with and without type 2 diabetes. RESEARCH DESIGN AND

methodsData were obtained from 857 patients with liver biopsy staged NAFLD. Generalized-linear models and binomial regression analysis were used to define the relationships between histological NAFLD severity, age, HbA1c, and BMI. Paired biopsies from interventional studies (n = 421) were used to assess the impact of change in weight, HbA1c and active vs. placebo treatment on improvements in steatosis, non-alcoholic steatohepatitis (NASH), and fibrosis.

resultsIn the discovery cohort (n = 687), risk of severe steatosis, NASH and advanced fibrosis correlated positively with HbA1c, after adjustment for obesity and age. These data were endorsed in a separate validation cohort (n = 170). Predictive modelling using HbA1c and age was non-inferior to the established non-invasive biomarker, Fib-4, and allowed the generation of HbA1c, age, and BMI adjusted risk charts to predict NAFLD severity. Following intervention, reduction in HbA1c was associated with improvements in steatosis and NASH after adjustment for weight change and treatment, whilst fibrosis change was only associated with weight change and treatment.

conclusionsHbA1c is highly informative in predicting NAFLD severity and contributes more than BMI. Assessments of HbA1c must be a fundamental part of the holistic assessment of patients with NAFLD and, alongside age, can be used to identify patients with highest risk of advanced disease.

Indexed as

Diabetes Mellitus, Type 2Glycated HemoglobinNon-alcoholic Fatty Liver DiseaseSeverity of Illness IndexAdultAgedBiomarkersBody Mass IndexFemaleHumansMaleMiddle AgedBiomarkersGlycated Hemoglobinhemoglobin A1c protein, humanGlucose controlLiver fibrosisLiver histologyLiver steatosis

Identifiers

PMID39147101
PMCPMC7619438

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.