Background/aims: Pre-existing diabetes mellitus (DM) has been identified as an adverse prognostic variable associated with increased mortality in various cancers. Although DM and hyperglycemia are considered risk factors for pancreatic cancer (PC), antidiabetic treatments for patients with advanced PC have been overlooked. This study aimed to evaluate the impact of hemoglobin A1c (HbA1c) levels on PC survival.
Methods: We retrospectively reviewed the medical records of first-diagnosed patients with advanced PC who were admitted to Konkuk University Medical Center from 2005 to 2011.
Results: A total of 127 patients were enrolled, and there were 111 deaths (87.4%) within the 7-year observational period. The most common etiology was disease progression (n=108). DM before PC diagnosis was observed in 65 patients (51.1%), including 28 patients with new-onset DM. The overall median survival times in patients with and without DM were 198 and 263 days, respectively (p=0.091). Survival time according to HbA1c was significantly different between the <7.0% and ≥7.0% groups (362 and 144 days, respectively; p=0.038). In the HbA1c ≥7.0% group, the median overall survival time was 273 days for the metformin group and 145 days for the nonmetformin oral agent group; however, there was no significant difference between the two groups (p=0.058).
Conclusions: A high HbA1c level may be associated with worse survival in patients with advanced PC with DM. Antidiabetic treatment, metformin in particular, was associated with an improved outcome.
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http://dx.doi.org/10.5009/gnl.2014.8.2.205 | DOI Listing |
Exp Ther Med
February 2025
Department of Biochemistry, Faculty of Science, Beni-Suef University, Beni-Suef 62511, Egypt.
Inefficient control of elevated blood sugar levels can lead to certain health complications such as diabetic nephropathy (DN) and cardiovascular disease (CVD). The identification of effective biomarkers for monitoring diabetes was performed in the present study. The present study aimed to investigate the implications of long non-coding RNA megacluster (lnc-MGC), microRNA (miR)-132 and miR-133a, and their correlation with lactate dehydrogenase (LDH) activity and glycated hemoglobin (HbA1C) levels to identify biomarkers for the early diagnosis of diabetes mellitus, induced DN and CVD.
View Article and Find Full Text PDFFront Public Health
December 2024
AstraZeneca SpA, Milano Innovation District (MIND), Milano, Italy.
Background: Software as a Medical Device (SaMD) and mobile health (mHealth) applications have revolutionized the healthcare landscape in the areas of remote patient monitoring (RPM) and digital therapeutics (DTx). These technological advancements offer a range of benefits, from improved patient engagement and real-time monitoring, to evidence-based personalized treatment plans, risk prediction, and enhanced clinical outcomes.
Objective: The systematic literature review aims to provide a comprehensive overview of the status of SaMD and mHealth apps, highlight the promising results, and discuss what is the potential of these technologies for improving health outcomes.
Arq Gastroenterol
December 2024
Escola Bahiana de Medicina e Saúde Pública, Salvador, BA, Brasil.
Background: To investigate the association between metabolic dysfunction-associated steatotic pancreas disease (MASPD) and insulin resistance (IR).
Methods: This cross-sectional study involved 157 participants diagnosed with MASPD based on ultrasonography criteria. Baseline demographic data were collected, including age, gender, and body mass index.
Geroscience
January 2025
Guangdong Eye Institute, Department of Ophthalmology, Guangdong Provincial People's Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, 510080, China.
With the development of deep learning (DL) techniques, there has been a successful application of this approach to determine biological age from latent information contained in retinal images. Retinal age gap (RAG) defined as the difference between chronological age and predicted retinal age has been established previously to predict the age-related disease. In this study, we performed discovery genome-wide association analysis (GWAS) on the RAG using the 31,271 UK Biobank participants and replicated our findings in 8034 GoDARTS participants.
View Article and Find Full Text PDFJ Diabetes Sci Technol
January 2025
Division of Endocrinology and Metabolism, Department of Medicine, Samsung Medical Center, Sungkyunkwan University, School of Medicine, Seoul, Republic of Korea.
Background: The glycemia risk index (GRI) is a novel composite continuous glucose monitoring (CGM) metric composed of hypoglycemia and hyperglycemia components and is weighted toward extremes. This study aimed to investigate the association between GRI and the risk of albuminuria in type 1 diabetes.
Methods: The 90-day CGM tracings of 330 individuals with type 1 diabetes were included in the analysis.
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