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HbA1c as a predictor of pancreatic ductal adenocarcinoma

HbA1c as a predictor of pancreatic ductal adenocarcinoma
HbA1c as a predictor of pancreatic ductal adenocarcinoma
UK adults aged 60 or over with a new onset diabetes (NODM) diagnosis and recent weight loss are now being recommended for urgent investigations to rule out possible underlying pancreatic ductal adenocarcinoma (PDAC). Differently measured HbA1c concentration levels over time with repeat sampling may be a key approach to triggering referral pathways for further investigation. The aim of this study was to investigate the relationship between HbA1c interpreted as single values and as grouped values according to clinical criteria for pre-diabetes and diabetes, and occurrence of PDAC in individuals diagnosed with NODM. Methods In this matched case-control study, 502,459 UK Biobank dataset participants were screened for incidental PDAC and HbA1c measurements at study baseline. Cox proportional hazards regression modelling with univariate and multivariate analysis generated hazard ratios (HR) for each of our chosen variables. Log-rank tests were performed for statistical significance. Receiver-operating characteristic (ROC) analysis and Youden index calculations were used to evaluate the performance of HbA1c as a test for detecting PDAC. HbA1c was not a useful standalone predictive marker for PDAC. However, newly elevated HbA1c in participants without a history of diabetes suggestive of emerging new pre-diabetes or new diabetes diagnosis was associated with a significantly higher risk of PDAC. Interpretation of HbA1c measurements benefits from knowledge of prior diabetes status when predicting risk of PDAC. The limitations of our study with single HbA1c measurements suggest that to enhance our understanding of the relationship between HbA1c and PDAC, future studies may benefit from examining repeat HbA1c measurements over time.
University of Southampton
Cheang, Adrian Wei Ern
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Cheang, Adrian Wei Ern
f65318d6-9a11-4fb7-8813-e8b6a95d7951
Byrne, Chris
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Hamady, Zaed Z R
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Cheang, Adrian Wei Ern (2023) HbA1c as a predictor of pancreatic ductal adenocarcinoma. University of Southampton, Masters Thesis, 146pp.

Record type: Thesis (Masters)

Abstract

UK adults aged 60 or over with a new onset diabetes (NODM) diagnosis and recent weight loss are now being recommended for urgent investigations to rule out possible underlying pancreatic ductal adenocarcinoma (PDAC). Differently measured HbA1c concentration levels over time with repeat sampling may be a key approach to triggering referral pathways for further investigation. The aim of this study was to investigate the relationship between HbA1c interpreted as single values and as grouped values according to clinical criteria for pre-diabetes and diabetes, and occurrence of PDAC in individuals diagnosed with NODM. Methods In this matched case-control study, 502,459 UK Biobank dataset participants were screened for incidental PDAC and HbA1c measurements at study baseline. Cox proportional hazards regression modelling with univariate and multivariate analysis generated hazard ratios (HR) for each of our chosen variables. Log-rank tests were performed for statistical significance. Receiver-operating characteristic (ROC) analysis and Youden index calculations were used to evaluate the performance of HbA1c as a test for detecting PDAC. HbA1c was not a useful standalone predictive marker for PDAC. However, newly elevated HbA1c in participants without a history of diabetes suggestive of emerging new pre-diabetes or new diabetes diagnosis was associated with a significantly higher risk of PDAC. Interpretation of HbA1c measurements benefits from knowledge of prior diabetes status when predicting risk of PDAC. The limitations of our study with single HbA1c measurements suggest that to enhance our understanding of the relationship between HbA1c and PDAC, future studies may benefit from examining repeat HbA1c measurements over time.

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Published date: December 2023

Identifiers

Local EPrints ID: 485336
URI: http://eprints.soton.ac.uk/id/eprint/485336
PURE UUID: 6ac531a2-07ed-4d36-805a-72c9b48f0cad
ORCID for Chris Byrne: ORCID iD orcid.org/0000-0001-6322-7753
ORCID for Zaed Z R Hamady: ORCID iD orcid.org/0000-0002-4591-5226

Catalogue record

Date deposited: 04 Dec 2023 17:47
Last modified: 18 Mar 2024 04:05

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Contributors

Author: Adrian Wei Ern Cheang
Thesis advisor: Chris Byrne ORCID iD
Thesis advisor: Zaed Z R Hamady ORCID iD

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