scholarly journals Blood levels of adiponectin and IL-1Ra distinguish type 3c from type 2 diabetes: Implications for earlier pancreatic cancer detection in new-onset diabetes

EBioMedicine ◽  
2022 ◽  
Vol 75 ◽  
pp. 103802
Author(s):  
Lucy Oldfield ◽  
Anthony Evans ◽  
Rohith Gopala Rao ◽  
Claire Jenkinson ◽  
Tejpal Purewal ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Pancreatic Cancer ◽  
Cancer Detection ◽  
Blood Levels ◽  
Onset Diabetes ◽  
New Onset

scholarly journals Type 2 Diabetes Mellitus Intersects With Pancreatic Cancer Diagnosis and Development

2021 ◽  
Vol 11 ◽  
Author(s):  
Xiaoye Duan ◽  
Weihao Wang ◽  
Qi Pan ◽  
Lixin Guo
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Pancreatic Cancer ◽  
Type 2 Diabetes Mellitus ◽  
Cancer Diagnosis ◽  
Complex Diabetes ◽  
The Relationship ◽  
Early Manifestation ◽  
New Onset

The relationship between type 2 diabetes mellitus (T2DM) and pancreatic cancer (PC) is complex. Diabetes is a known risk factor for PC, and new-onset diabetes (NOD) could be an early manifestation of PC that may be facilitate the early diagnosis of PC. Metformin offers a clear benefit of inhibiting PC, whereas insulin therapy may increase the risk of PC development. No evidence has shown that novel hypoglycemic drugs help or prevent PC. In this review, the effects of T2DM on PC development are summarized, and novel strategies for the prevention and treatment of T2DM and PC are discussed.

Pancreatic cancer and diabetes: Is diabetes a risk factor or the outcome?

2007 ◽  
Vol 25 (18_suppl) ◽  
pp. 15123-15123
Author(s):  
P. Garg ◽  
R. Gupta
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Pancreatic Cancer ◽  
Risk Factor ◽  
Odds Ratio ◽  
Diabetes Mellitus Type 2 ◽  
Case Control ◽  
Onset Type ◽  
New Onset

15123 Background: Though pancreatic cancer ranks eighth among all major forms of cancer related deaths, it accounts for only 3% of all cancers worldwide. Diabetes mellitus type 2 and pancreatic cancer are known to be associated but this relationship decreases inversely with an increasing duration of diabetes. The aim of the present study is to assess whether new onset type 2 diabetes is a risk factor for pancreatic cancer or it results as an outcome of pancreatic cancer. Methods: We conducted a meta-analysis of all available studies to examine this association. Using PUBMED, EMBASE & MEDLINE we searched the literature for studies from 1980 to present. All published studies with quantitative estimates and standard errors, or confidence limits, of the association between type 2 diabetes and pancreatic cancer were included. Studies having patients with an un-defined period of diabetes or diabetes for greater than 5 years before the development of pancreatic cancer were excluded. 14 case control and 11 cohort trials with information on 5807 individuals with pancreatic cancer met the above criteria. Each study was independently reviewed by 2 reviewers. Results: The pooled odds ratio for case control studies for patients with diabetes and pancreatic cancer was 1.98 (95% CI - 1.58–2.42) while that for cohort studies was 1.88 (95% CI - 1.72–2.16). The combined odds ratio was 1.92 (95% CI - 1.62 - 2.38). However in patients who developed pancreatic cancer with in 2 years of diagnosis of diabetes the odd’s ratio was significantly higher (OR- 3.2, 95% CI - 2.24- 4.02). Conclusions: On the basis of above data pancreatic cancer is more likely to occur in new onset diabetes mellitus type 2 than the general population. However, the evidence above is insufficient to conclude that diabetes mellitus is indeed a risk factor for pancreatic cancer. Since the relative risk of developing pancreatic cancer was higher in patients with new onset type 2 diabetes in the first two years, it further supports the hypothesis that the development of diabetes may be a manifestation of early but yet undiagnosed pancreatic cancer. Large population based studies are needed to truly determine this relationship. No significant financial relationships to disclose.

scholarly journals New-onset type 2 diabetes mellitus – A high-risk group suitable for the screening of pancreatic cancer?

Pancreatology ◽  
2016 ◽  
Vol 16 (2) ◽  
pp. 266-271 ◽  
Author(s):  
Dóra Illés ◽  
Viktória Terzin ◽  
Gábor Holzinger ◽  
Klára Kosár ◽  
Richárd Róka ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Type 2 Diabetes ◽  
Pancreatic Cancer ◽  
Type 2 Diabetes Mellitus ◽  
High Risk ◽  
Risk Group ◽  
High Risk Group ◽  
Onset Type ◽  
New Onset

The predictive value of endothelin-1 and type 2 diabetes

2020 ◽  
Vol 41 (Supplement_2) ◽  
Author(s):  
T Youssri ◽  
M Ohlsson ◽  
V Hamrefors ◽  
O Mellander
Keyword(s):  
Risk Factors ◽  
Type 2 Diabetes ◽  
Predictive Value ◽  
Fasting Glucose ◽  
Hazard Ratio ◽  
Funding Source ◽  
Onset Diabetes ◽  
New Onset

Abstract Introduction Endothelin 1 is a potent vasoconstrictor released from mainly vascular endothelial cells and to a lesser extent adipose, muscle and renal tissues. Its involvement in cardiovascular disease is well documented, with increasing ET-1 levels correlated to cardiovascular events. Less is however, known about its role in insulin resistance and type 2 diabetes. Purpose To test if ET-1 plasma levels predict the risk of developing type 2 diabetes independently of known risk factors. Method The Malmo Preventive project is a prospective single centre population-based study which recruited 33 346 inhabitants in Malmo, Sweden between 1974–1992. A follow up study was conducted between 2002 and 2006 on willing participants of which 18 240 accepted. Cardiovascular risk factors were documented along with blood plasma samples frozen to −80°C available for further analysis among approximately 5000 subjects. Record linkage with national and regional diagnoses and drug prescription registries was performed to identify all new onset type 2 diabetes cases in this cohort during a mean follow-up period of nine years. C-terminal proendothelin-1 (proET-1), a stable precursor to ET-1, levels were analysed by a double sandwich immunoassay (ThermoFisher) among 4536 individuals with complete data and without diabetes at baseline. The subjects were divided into quartiles based on proET-1 levels and hazard ratios (HR) for new onset diabetes were calculated by Cox Proportional Hazards Model adjusting for age, gender, smoking, hypertension, body mass index (BMI) and fasting glucose. Results There was a positive relationship between increasing proET-1 quartiles and age (p<0.001), hypertension (p<0.001), BMI (p<0.001) and smoking (p<0.001). There was no significant relationship between ET-1 quartiles and fasting glucose (p=0.08) and gender (p=0.21). In models adjusted for age, gender, smoking, hypertension, fasting glucose and BMI among non-diabetic subjects each 1 standard deviation increment of proET-1 conferred a hazard ratio (95% confidence interval) for new onset diabetes during follow up period of 1.12 (1.00–1.26) (p=0.05). The hazard ratio for incident diabetes in quartile 4 (Q4) vs quartile 1 (Q1) was 1.40 (1.03–1.92) (p=0.03). Of note, the predictive value of proET-1 was markedly higher among individuals without pre-diabetes (fasting glucose <6.1) with a hazard ratio of 1.27 per standard deviation proET-1 (CI 1.09–1.49, p=0.02) and 2.18 (CI 1.41–3.36, p<0.001) when comparing proET-1 Q4 vs Q1. There was no significant relationship between the risk of new onset diabetes and proET-1 levels among pre-diabetic individuals. Conclusion Raised proET-1 levels among non-diabetic individuals independently predict risk of new onset type 2 diabetes. The predictive value is driven by the part of the population without prediabetes, suggesting that proET-1 might identify individuals at “hidden high risk”, i.e. indivduals who do not get medical attention by having prediabetes. Funding Acknowledgement Type of funding source: Private grant(s) and/or Sponsorship. Main funding source(s): Knut & Alice Wallenberg Foundation Clinical Scholars and Göran Gustafsson Foundation

scholarly journals Predicting risk of pancreatic cancer in individuals with new-onset type-2 diabetes in primary care: protocol for the development and validation of a clinical prediction model (QPancreasD)

2021 ◽  
Author(s):  
Pui San Tan ◽  
Ashley Clift ◽  
Weiqi Liao ◽  
Martina Patone ◽  
Carol Coupland ◽  
...  
Keyword(s):  
Type 2 Diabetes ◽  
Primary Care ◽  
Pancreatic Cancer ◽  
Prediction Model ◽  
Individual Risk ◽  
Diabetes Diagnosis ◽  
Clinical Prediction ◽  
Clinical Prediction Model ◽  
New Onset

Background Pancreatic cancer continues to have an extremely poor prognosis in part due to late diagnosis. 25% of pancreatic cancer patients have a prior diagnosis of diabetes, and hence identifying individuals at risk of pancreatic cancer in those with recently diagnosed type 2 diabetes may be a useful opportunity to identify candidates for screening and early detection. In this study, we will comparatively evaluate regression and machine learning-based clinical prediction models for estimating individual risk of developing pancreatic cancer two years after type 2 diabetes diagnosis. Methods In the development dataset, we will include adults aged 30-84 years with incident type-2 diabetes registered with QResearch primary care database. Patients will be followed up from type-2 diabetes diagnosis to first diagnosis of pancreatic cancer as recorded in any one of primary care records, hospital episode statistics, cancer registry data, or death records. Cox-proportional hazards models will be used to develop a risk prediction model for estimating individual risk of developing pancreatic cancer during up to 2 years of follow-up. We will perform variable selection using a combination of clinical and statistical significance approach i.e. HR <0.9 or >1.1 and p<0.01. Linear predictors and baseline survivor function at 2 years will be used to compute absolute risk predictions. Internal-external cross-validation (IECV) framework across geographical regions within England will be used to assess performance and pooled using random effects meta-analysis using: (i) model fit in terms of variation explained by the model Royston & Sauerbrei's R2D, (ii) calibration slope and calibration-in-the-large, and (iii) discrimination measured in terms of Harrell's C and Royston & Sauerbrei's D-statistic. Further, we will evaluate machine learning (ML) approaches for the clinical prediction model using neural networks (NN) and XGBoost. The model predictors and performance of these will be compared with the results of those derived from the regression-based strategy. Discussion The proposed study will develop and validate a novel risk prediction model to aid early diagnosis of pancreatic cancer in patients with new-onset diabetes in primary care. With an enhanced decision-risk tool for use at point-of care by general practitioners to assess pancreatic cancer risk, it may improve decision-making so that at-risk patients are rapidly prioritised to aid early diagnosis of pancreatic cancer in patients with newly diagnosed diabetes.

scholarly journals Validation of a Pancreatic Cancer Detection Test in New-Onset Diabetes Using Cell-Free DNA 5-Hydroxymethylation Signatures

2021 ◽  
Author(s):  
David Haan ◽  
Anna Bergamaschi ◽  
Gulfem Guler ◽  
Verena Friedl ◽  
Yuhong Ning ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Sensitivity And Specificity ◽  
Cancer Detection ◽  
Validation Dataset ◽  
Cell Free Dna ◽  
Independent Validation ◽  
Free Dna ◽  
Onset Diabetes ◽  
Increased Risk ◽  
New Onset

BACKGROUND Pancreatic cancer (PaC) has poor (10%) 5–year overall survival, largely due to predominant late-stage diagnosis. Patients with new-onset diabetes (NOD) are at a six– to eightfold increased risk for PaC. We developed a pancreatic cancer detection test for the use in a clinical setting that employs a logistic regression model based on 5–hydroxymethylcytosine (5hmC) profiling of cell-free DNA (cfDNA). METHODS: cfDNA was isolated from plasma from 89 subjects with PaC and 596 case–control non–cancer subjects, and 5hmC libraries were generated and sequenced. These data coupled with machine–learning, were used to generate a predictive model for PaC detection, which was independently validated on 79 subjects with PaC, 163 non–cancer subjects, and 506 patients with non–PaC cancers. RESULTS: The area under the receiver operating characteristic curve for PaC classification was 0.93 across the training data. Training sensitivity was 58.4% (95% confidence interval [CI]: 47.5–68.6) after setting a classification probability threshold that resulted in 98% (95% CI: 96.5–99) specificity. The independent validation dataset sensitivity and specificity were 51.9% (95% CI: 40.4–63.3) and 100.0% (95% CI: 97.8–100.0), respectively. Early–stage (stage I and II) PaC detection was 47.6% (95% CI: 23%–58%) and 39.4% (95% CI: 32%–64%) in the training and independent validation datasets, respectively. Sensitivity and specificity in NOD patients were 55.2% [95% CI: 35.7–73.6] and 98.4% [95% CI: 91.3–100.0], respectively. The PaC signal was identified in intraductal papillary mucinous neoplasm (64%), pancreatitis (56%), and non-PaC cancers (17%). CONCLUSIONS: The pancreatic cancer detection assay showed robust performance in the tested cohorts and carries the promise of becoming an essential clinical tool to enable early detection in high–risk NOD patients.

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