Model to Determine Risk of Pancreatic Cancer in Patients With New-Onset Diabetes

Identification of biomarkers and potential therapeutic targets for pancreatic cancer by proteomic analysis in two prospective cohorts

Pancreatic cancer (PC) is the deadliest malignancy due to late diagnosis. Aberrant alterations in the blood proteome might serve as biomarkers to facilitate early detection of PC. We designed a nested case-control study of incident PC based on a prospective cohort of 38,295 elderly Chinese participants with ∼5.7 years' follow-up. Forty matched case-control pairs passed the quality controls for the proximity extension assay of 1,463 serum proteins. With a lenient threshold of p < 0.005, we discovered regenerating family member 1A (REG1A), REG1B, tumor necrosis factor (TNF), and phospholipase A2 group IB (PLA2G1B) in association with incident PC, among which the two REG1 proteins were replicated using the UK Biobank Pharma Proteomics Project, with effect sizes increasing steadily as diagnosis time approaches the baseline. Mendelian randomization analysis further supported the potential causal effects of REG1 proteins on PC. Taken together, circulating REG1A and REG1B are promising biomarkers and potential therapeutic targets for the early detection and prevention of PC.

Bidirectional relationship between pancreatic cancer and diabetes mellitus: a comprehensive literature review

Pancreatic cancer (PC) is a fatal malignant disease. It is well known that the relationship between PC and type 2 diabetes mellitus (T2DM) is a complicated bidirectional relationship. The most important factors causing increased risks of pancreatic cancer are hyperglycaemia, hyperinsulinemia, pancreatitis, and dyslipidemia. Genetics and the immune system also play an important role in the relationship between diabetes mellitus and pancreatic cancer. The primary contributors to this association involve insulin resistance and inflammatory processes within the tumour microenvironment. The combination of diabetes and obesity can contribute to PC by inducing hyperinsulinemia and influencing leptin and adiponectin levels. Given the heightened incidence of pancreatic cancer in diabetes patients compared to the general population, early screening for pancreatic cancer is recommended. Diabetes negatively impacts the survival of pancreatic cancer patients. Among patients receiving chemotherapy, it reduced their survival. The implementation of a healthy lifestyle, including weight management, serves as an initial preventive measure to mitigate the risk of disease development. The role of anti-diabetic drugs on survival is controversial; however, metformin may have a positive impact, especially in the early stages of cancer, while insulin therapy increases the risk of PC.

Imaging for Early Detection of Pancreatic Ductal Adenocarcinoma: Updates and Challenges in the Implementation of Screening and Surveillance Programs

Pancreatic ductal adenocarcinoma (PDA) is one of the most aggressive cancers. It has a poor 5-year survival rate of 12%, partly because most cases are diagnosed at advanced stages, precluding curative surgical resection. Early-stage PDA has significantly better prognoses due to increased potential for curative interventions, making early detection of PDA critically important to improved patient outcomes. We examine current and evolving early detection concepts, screening strategies, diagnostic yields among high-risk individuals, controversies, and limitations of standard-of-care imaging.

A Rigorous Multi-Laboratory Study of Known PDAC Biomarkers Identifies Increased Sensitivity and Specificity Over CA19-9 Alone

A blood test that enables surveillance for early-stage pancreatic ductal adenocarcinoma (PDAC) is an urgent need. Independent laboratories have reported PDAC biomarkers that could improve biomarker performance over CA19-9 alone, but the performance of the previously reported biomarkers in combination is not known. Therefore, we conducted a coordinated case/control study across multiple laboratories using common sets of blinded training and validation samples (132 and 295 plasma samples, respectively) from PDAC patients and non-PDAC control subjects representing conditions under which surveillance occurs. We analyzed the training set to identify candidate biomarker combination panels using biomarkers across laboratories, and we applied the fixed panels to the validation set. The panels identified in the training set, CA19-9 with CA199.STRA, LRG1, TIMP-1, TGM2, THSP2, ANG, and MUC16.STRA, achieved consistent performance in the validation set. The panel of CA19-9 with the glycan biomarker CA199.STRA improved sensitivity from 0.44 with 0.98 specificity for CA19-9 alone to 0.71 with 0.98 specificity (p < 0.001, 1000-fold bootstrap). Similarly, CA19-9 combined with the protein biomarker LRG1 and CA199.STRA improved specificity from 0.16 with 0.94 sensitivity for CA19-9 to 0.65 with 0.89 sensitivity (p < 0.001, 1000-fold bootstrap). We further validated significantly improved performance using biomarker panels that did not include CA19-9. This study establishes the effectiveness of a coordinated study of previously discovered biomarkers and identified panels of those biomarkers that significantly increased the sensitivity and specificity of early-stage PDAC detection in a rigorous validation trial.

Review of the cost-effectiveness of surveillance for hereditary pancreatic cancer

Individuals with hereditary pancreatic cancer risk include high risk individuals (HRIs) with germline genetic susceptibility to pancreatic cancer (PC) and/or a strong family history of PC. Previously, studies have shown that PC surveillance in HRIs can downstage PC diagnosis and extend survival leading to pancreatic surveillance being recommended for certain HRIs. However, the optimal surveillance strategy remains uncertain, including which modalities should be used for surveillance, how frequently should surveillance be performed, and which sub-groups of HRIs should undergo surveillance. Additionally, in the ideal world PC surveillance should also be cost-effective. Cost-effectiveness analysis is a valuable tool that can consider the costs, potential health benefits, and risks among various PC surveillance strategies. In this review, we summarize the cost-effectiveness of various PC surveillance strategies for HRIs for hereditary pancreatic cancer and provide potential avenues for future work in this field. Additionally, we include cost-effectiveness studies among individuals with new-onset diabetes (NoD), a high-risk group for sporadic PC, as a comparison.

Nanoformulation of dasatinib cannot overcome therapy resistance of pancreatic cancer cells with low LYN kinase expression

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) is one of the most difficult to treat tumors. The Src (sarcoma) inhibitor dasatinib (DASA) has shown promising efficacy in preclinical studies of PDAC. However, clinical confirmation could not be achieved. Overall, our aim was to deliver arguments for the possible reinitiating clinical testing of this compound in a biomarker-stratifying therapy trial for PDAC patients. We tested if the nanofunctionalization of DASA can increase the drug efficacy and whether certain Src members can function as clinical predictive biomarkers.

METHODS

Methods include manufacturing of poly(vinyl alcohol) stabilized gold nanoparticles and their drug loading, dynamic light scattering, transmission electron microscopy, thermogravimetric analysis, Zeta potential measurement, sterile human cell culture, cell growth quantification, accessing and evaluating transcriptome and clinical data from molecular tumor dataset TCGA, as well as various statistical analyses.

RESULTS

We generated homo-dispersed nanofunctionalized DASA as an AuNP@PVA-DASA conjugate. The composite did not enhance the anti-growth effect of DASA on PDAC cell lines. The cell model with high LYN expression showed the strongest response to the therapy. We confirm deregulated Src kinetome activity as a prevalent feature of PDAC by revealing mRNA levels associated with higher malignancy grade of tumors. BLK (B lymphocyte kinase) expression predicts shorter overall survival of diabetic PDAC patients.

CONCLUSIONS

Nanofunctionalization of DASA needs further improvement to overcome the therapy resistance of PDAC. LYN mRNA is augmented in tumors with higher malignancy and can serve as a predictive biomarker for the therapy resistance of PDAC cells against DASA. Studying the biological roles of BLK might help to identify underlying molecular mechanisms associated with PDAC in diabetic patients.

Temporal Trends in Body Composition and Metabolic Markers Prior to Diagnosis of Pancreatic Ductal Adenocarcinoma

BACKGROUND & AIMS

Changes in body composition and metabolic factors may serve as biomarkers for the early detection of pancreatic ductal adenocarcinoma (PDAC). The aim of this study was to capture the longitudinal changes in body composition and metabolic factors before diagnosis of PDAC.

METHODS

We performed a retrospective cohort study in which all patients (≥18 years) diagnosed with PDAC from 2002 to 2021 were identified. We collected all abdominal computed tomography scans and 10 different blood-based biomarkers up to 36 months before diagnosis. We applied a fully automated abdominal segmentation algorithm previously developed by our group for 3-dimensional quantification of body composition on computed tomography scans. Longitudinal trends of body composition and blood-based biomarkers before PDAC diagnosis were estimated using linear mixed models, compared across different time windows, and visualized using spline regression.

RESULTS

We included 1690 patients in body composition analysis, of whom 516 (30.5%) had ≥2 prediagnostic computed tomography scans. For analysis of longitudinal trends of blood-based biomarkers, 3332 individuals were included. As an early manifestation of PDAC, we observed a significant decrease in visceral and subcutaneous adipose tissue (β = -1.94 [95% confidence interval (CI), -2.39 to -1.48] and β = -2.59 [95% CI, -3.17 to -2.02]) in area (cm2)/height (m2) per 6 months closer to diagnosis, accompanied by a decrease in serum lipids (eg, low-density lipoprotein [β = -2.83; 95% CI, -3.31 to -2.34], total cholesterol [β = -2.69; 95% CI, -3.18 to -2.20], and triglycerides [β = -1.86; 95% CI, -2.61 to -1.11]), and an increase in blood glucose levels. Loss of muscle tissue and bone volume was predominantly observed in the last 6 months before diagnosis.

CONCLUSIONS

This study identified significant alterations in a variety of soft tissue and metabolic markers that occur in the development of PDAC. Early recognition of these metabolic changes may provide an opportunity for early detection.

Predicting the risk of pancreatic cancer in adults with new-onset diabetes: development and internal-external validation of a clinical risk prediction model

BACKGROUND

The National Institute for Health and Care Excellence (NICE) recommends that people aged 60+ years with newly diagnosed diabetes and weight loss undergo abdominal imaging to assess for pancreatic cancer. More nuanced stratification could lead to enrichment of these referral pathways.

METHODS

Population-based cohort study of adults aged 30-85 years at type 2 diabetes diagnosis (2010-2021) using the QResearch primary care database in England linked to secondary care data, the national cancer registry and mortality registers. Clinical prediction models were developed to estimate risks of pancreatic cancer diagnosis within 2 years and evaluated using internal-external cross-validation.

RESULTS

Seven hundred and sixty-seven of 253,766 individuals were diagnosed with pancreatic cancer within 2 years. Models included age, sex, BMI, prior venous thromboembolism, digoxin prescription, HbA1c, ALT, creatinine, haemoglobin, platelet count; and the presence of abdominal pain, weight loss, jaundice, heartburn, indigestion or nausea (previous 6 months). The Cox model had the highest discrimination (Harrell's C-index 0.802 (95% CI: 0.797-0.817)), the highest clinical utility, and was well calibrated. The model's highest 1% of predicted risks captured 12.51% of pancreatic cancer cases. NICE guidance had 3.95% sensitivity.

DISCUSSION

A new prediction model could have clinical utility in identifying individuals with recent onset diabetes suitable for fast-track abdominal imaging.

Associations between diabetes and cancer: A 10-year national population-based retrospective cohort study

AIMS

To investigate the risk of cancer in people with diabetes compared to the population without diabetes and to gain insight into the timely association between diabetes and cancer at national level.

METHODS

A retrospective cohort study was conducted to analyse the role of diabetes in the development of cancer, based on service utilisation and antidiabetic dispensing data of the population between 2010 and 2021. Univariate and multivariate Cox regression were used to examine how diabetes status, in relationship with age and sex are related to the time to cancer diagnosis.

RESULTS

Examining a population of 3 681 774 individuals, people with diabetes have a consistently higher risk for cancer diagnosis for each cancer site studied. Diabetes adds the highest risk for pancreatic cancer (HR = 2.294, 99 % CI: 2.099; 2.507) and for liver cancer (HR = 1.830, 99 % CI: 1.631; 2.054); it adds the lowest - but still significant - risk for breast cancer (HR = 1.137, 99 % CI: 1.055; 1.227) and prostate cancer (HR = 1.171, 99 % CI: 1.071; 1.280).The difference in cancer rate is driven by the younger age group (40-54 years: for patients with diabetes 5.4 % vs. controls 4.4 %; 70-89 years: for patients with diabetes 12.7 % vs. controls 12.4 %). There are no consistent results whether the presence of diabetes increases the risk of cancer diagnosis differently in males and females. The cancer incidence starts to increase before the diagnosis of diabetes and peaks in the year after. By the year after the start of the inclusion date, the incidence is 114/10,000 population in the control group, vs 195/10,000 population in the group with diabetes. Following this, the incidence drops close to the control group.

CONCLUSIONS

Screening activities should be revised and the guidelines on diabetes should be complemented with recommendations on cancer prevention also considering that the cancer incidence is highest around the time of the diagnosis of diabetes. For prostate cancer, our results contradict many previous studies, and further research is recommended to clarify this.

Five-year recurrence/survival after pancreatoduodenectomy for pancreatic adenocarcinoma: does pre-existing diabetes matter? Results from the Recurrence After Whipple's (RAW) study

BACKGROUND

Diabetes mellitus (DM) has a complex relationship with pancreatic cancer. This study examines the impact of preoperative DM, both recent-onset and pre-existing, on long-term outcomes following pancreatoduodenectomy (PD) for pancreatic ductal adenocarcinoma (PDAC).

METHODS

Data were extracted from the Recurrence After Whipple's (RAW) study, a multi-centre cohort of PD for pancreatic head malignancy (2012-2015). Recurrence and five-year survival rates of patients with DM were compared to those without, and subgroup analysis performed to compare patients with recent-onset DM (less than one year) to patients with established DM.

RESULTS

Out of 758 patients included, 187 (24.7%) had DM, of whom, 47 of the 187 (25.1%) had recent-onset DM. There was no difference in the rate of postoperative pancreatic fistula (DM: 5.9% vs no DM 9.8%; p = 0.11), five-year survival (DM: 24.1% vs no DM: 22.9%; p = 0.77) or five-year recurrence (DM: 71.7% vs no DM: 67.4%; p = 0.32). There was also no difference between patients with recent-onset DM and patients with established DM in postoperative outcomes, recurrence, or survival.

CONCLUSION

We found no difference in five-year recurrence and survival between diabetic patients and those without diabetes. Patients with pre-existing DM should be evaluated for PD on a comparable basis to non-diabetic patients.

The role of biomarkers in the early detection of pancreatic cancer

Pancreatic surveillance can detect early-stage pancreatic cancer and achieve long-term survival, but currently involves annual endoscopic ultrasound and MRI/MRCP, and is recommended only for individuals who meet familial/genetic risk criteria. To improve upon current approaches to pancreatic cancer early detection and to expand access, more accurate, inexpensive, and safe biomarkers are needed, but finding them has remained elusive. Newer approaches to early detection, such as using gene tests to personalize biomarker interpretation, and the increasing application of artificial intelligence approaches to integrate complex biomarker data, offer promise that clinically useful biomarkers for early pancreatic cancer detection are on the horizon.

Pancreatic surgery and tertiary pancreatitis services warrant provision for support from a specialist diabetes team

Pancreatic surgery units undertake several complex operations, albeit with considerable morbidity and mortality, as is the case for the management of complicated acute pancreatitis or chronic pancreatitis. The centralisation of pancreatic surgery services, with the development of designated large-volume centres, has contributed to significantly improved outcomes. In this editorial, we discuss the complex associations between diabetes mellitus (DM) and pancreatic/periampullary disease in the context of pancreatic surgery and overall management of complex pancreatitis, highlighting the consequential needs and the indispensable role of specialist diabetes teams in support of tertiary pancreatic services. Type 3c pancreatogenic DM, refers to DM developing in the setting of exocrine pancreatic disease, and its identification and management can be challenging, while the glycaemic control of such patients may affect their course of treatment and outcome. Adequate preoperative diabetes assessment is warranted to aid identification of patients who are likely to need commencement or escalation of glucose lowering therapy in the postoperative period. The incidence of new onset diabetes after pancreatic resection is widely variable in the literature, and depends on the type and extent of pancreatic resection, as is the case with pancreatic parenchymal loss in the context of severe pancreatitis. Early involvement of a specialist diabetes team is essential to ensure a holistic management. In the current era, large volume pancreatic surgery services commonly abide by the principles of enhanced recovery after surgery, with inclusion of provisions for optimisation of the perioperative glycaemic control, to improve outcomes. While various guidelines are available to aid perioperative management of DM, auditing and quality improvement platforms have highlighted deficiencies in the perioperative management of diabetic patients and areas of required improvement. The need for perioperative support of diabetic patients by specialist diabetes teams is uniformly underlined, a fact that becomes clearly more prominent at all different stages in the setting of pancreatic surgery and the management of complex pancreatitis. Therefore, pancreatic surgery and tertiary pancreatitis services must be designed with a provision for support from specialist diabetes teams. With the ongoing accumulation of evidence, it would be reasonable to consider the design of specific guidelines for the glycaemic management of these patients.

Genetic and other risk factors for pancreatic ductal adenocarcinoma (PDAC)

Pancreatic ductal adenocarcinoma (PDAC) is often diagnosed at an advanced stage, resulting in poor prognosis and low 5-year survival rates. While early evidence suggests increased long-term survival in those with screen-detected resectable cancers, surveillance imaging is currently only recommended for individuals with a lifetime risk of PDAC ≥ 5%. Identification of risk factors for PDAC provides opportunities for early detection, risk reducing interventions, and targeted therapies, thus potentially improving patient outcomes. Here, we summarize modifiable and non-modifiable risk factors for PDAC. We review hereditary cancer syndromes associated with risk for PDAC and their implications for patients and their relatives. In addition, other biologically relevant pathways and environmental and lifestyle risk factors are discussed. Future work may focus on elucidating additional genetic, environmental, and lifestyle risk factors that may modify PDAC risk to continue to identify individuals at increased risk for PDAC who may benefit from surveillance and risk reducing interventions.

Development and Validation of Case-Finding Algorithms to Identify Pancreatic Cancer in the Veterans Health Administration

BACKGROUND

Survival in pancreatic ductal adenocarcinoma (PDAC) remains poor due to late diagnosis. Electronic Health Records (EHRs) can be used to study this rare disease, but validated algorithms to identify PDAC in the United States EHRs do not currently exist.

AIMS

To develop and validate an algorithm using Veterans Health Administration (VHA) EHR data for the identification of patients with PDAC.

METHODS

We developed two algorithms to identify patients with PDAC in the VHA from 2002 to 2023. The algorithms required diagnosis of exocrine pancreatic cancer in either ≥ 1 or ≥ 2 of the following domains: (i) the VA national cancer registry, (ii) an inpatient encounter, or (iii) an outpatient encounter in an oncology setting. Among individuals identified with ≥ 1 of the above criteria, a random sample of 100 were reviewed by three gastroenterologists to adjudicate PDAC status. We also adjudicated fifty patients not qualifying for either algorithm. These patients died as inpatients and had alkaline phosphatase values within the interquartile range of patients who met ≥ 2 of the above criteria for PDAC. These expert adjudications allowed us to calculate the positive and negative predictive value of the algorithms.

RESULTS

Of 10.8 million individuals, 25,533 met ≥ 1 criteria (PPV 83.0%, kappa statistic 0.93) and 13,693 individuals met ≥ 2 criteria (PPV 95.2%, kappa statistic 1.00). The NPV for PDAC was 100%.

CONCLUSIONS

An algorithm incorporating readily available EHR data elements to identify patients with PDAC achieved excellent PPV and NPV. This algorithm is likely to enable future epidemiologic studies of PDAC.

Circulating tumor cells as prognostic marker in pancreatic cancer

In this editorial we comment on the article by Zhang et al published in the recent issue of the World Journal of Clinical Oncology. Pancreatic cancer is the fourth most common cause of cancer-related mortality and has the lowest survival rate among all solid cancers. It causes 227000 deaths annually worldwide, and the 5-year survival rate is very low due to early metastasis, which is 4.6%. Cancer survival increases with better knowledge of risk factors and early and accurate diagnosis. Circulating tumor cells (CTCs) are tumor cells that intravasate from the primary tumor or metastasis foci into the peripheral blood circulation system spontaneously or during surgical operations. Detection of CTC in blood is promising for early diagnosis. In addition, studies have associated high CTC levels with a more advanced stage, and more intensive treatments should be considered in cases with high CTC. In tumors that are considered radiologically resectable, it may be of critical importance in detecting occult metastases and preventing unnecessary surgeries.

Exploring the application and future outlook of Artificial intelligence in pancreatic cancer

Pancreatic cancer, an exceptionally malignant tumor of the digestive system, presents a challenge due to its lack of typical early symptoms and highly invasive nature. The majority of pancreatic cancer patients are diagnosed when curative surgical resection is no longer possible, resulting in a poor overall prognosis. In recent years, the rapid progress of Artificial intelligence (AI) in the medical field has led to the extensive utilization of machine learning and deep learning as the prevailing approaches. Various models based on AI technology have been employed in the early screening, diagnosis, treatment, and prognostic prediction of pancreatic cancer patients. Furthermore, the development and application of three-dimensional visualization and augmented reality navigation techniques have also found their way into pancreatic cancer surgery. This article provides a concise summary of the current state of AI technology in pancreatic cancer and offers a promising outlook for its future applications.

Prediction of pancreatic cancer risk in patients with new-onset diabetes using a machine learning approach based on routine biochemical parameters

OBJECTIVE

To develop a machine-learning model that can predict the risk of pancreatic ductal adenocarcinoma (PDAC) in people with new-onset diabetes (NOD).

METHODS

From a population-based sample of individuals with NOD aged >50 years, patients with pancreatic cancer-related diabetes (PCRD), defined as NOD followed by a PDAC diagnosis within 3 years, were included (n = 716). These PCRD patients were randomly matched in a 1:1 ratio with individuals having NOD. Data from Danish national health registries were used to develop a random forest model to distinguish PCRD from Type 2 diabetes. The model was based on age, gender, and parameters derived from feature engineering on trajectories of routine biochemical variables. Model performance was evaluated using receiver operating characteristic curves (ROC) and relative risk scores.

RESULTS

The most discriminative model included 20 features and achieved a ROC-AUC of 0.78 (CI:0.75-0.83). Compared to the general NOD population, the relative risk for PCRD was 20-fold increase for the 1 % of patients predicted by the model to have the highest cancer risk (3-year cancer risk of 12 % and sensitivity of 20 %). Age was the most discriminative single feature, followed by the rate of change in haemoglobin A1c and the latest plasma triglyceride level. When the prediction model was restricted to patients with PDAC diagnosed six months after diabetes diagnosis, the ROC-AUC was 0.74 (CI:0.69-0.79).

CONCLUSION

In a population-based setting, a machine-learning model utilising information on age, sex and trajectories of routine biochemical variables demonstrated good discriminative ability between PCRD and Type 2 diabetes.

Association between unstable diabetes mellitus and risk of pancreatic cancer

BACKGROUND

Deterioration of glycaemic control in people with long-standing diabetes mellitus (diabetes) may be a possible indicator of pancreatic cancer. However, the magnitude of the association between diabetes deterioration and pancreatic cancer has received little attention.

METHODS

We conducted a matched cohort study, nested within a population-based cohort of Australian women with diabetes. Women with unstable diabetes, defined as a change in medication after a 2-year period of stable medication use, were matched by birth year to those with stable diabetes, in a 1:4 ratio. We used flexible parametric survival models to estimate hazard ratios (HRs) and 95% confidence intervals (CI).

RESULTS

We included 134,954 and 539,789 women in the unstable and stable diabetes cohorts, respectively (mean age 68 years). In total, 1,315 pancreatic cancers were diagnosed. Deterioration of stable diabetes was associated with a 2.5-fold increased risk of pancreatic cancer (HR 2.55; 95% CI 2.29-2.85). The risk was particularly high within the first year after diabetes deteriorated. HRs at 3 months, 6 months and 1 year were: 5.76 (95% CI 4.72-7.04); 4.56 (95% CI 3.81-5.46); and 3.33 (95% CI 2.86-3.89), respectively. The risk was no longer significantly different after 7 years.

CONCLUSIONS

Deterioration in glycaemic control in people with previously stable diabetes may be an indicator of pancreatic cancer, suggesting investigations of the pancreas may be appropriate. The weaker longer-term (3-7 years) association between diabetes deterioration and pancreatic cancer may indicate that poor glycaemic control can be a risk factor for pancreatic cancer.

Determining the feasibility of calculating pancreatic cancer risk scores for people with new-onset diabetes in primary care (DEFEND PRIME): study protocol

INTRODUCTION

Worldwide, pancreatic cancer has a poor prognosis. Early diagnosis may improve survival by enabling curative treatment. Statistical and machine learning diagnostic prediction models using risk factors such as patient demographics and blood tests are being developed for clinical use to improve early diagnosis. One example is the Enriching New-onset Diabetes for Pancreatic Cancer (ENDPAC) model, which employs patients' age, blood glucose and weight changes to provide pancreatic cancer risk scores. These values are routinely collected in primary care in the UK. Primary care's central role in cancer diagnosis makes it an ideal setting to implement ENDPAC but it has yet to be used in clinical settings. This study aims to determine the feasibility of applying ENDPAC to data held by UK primary care practices.

METHODS AND ANALYSIS

This will be a multicentre observational study with a cohort design, determining the feasibility of applying ENDPAC in UK primary care. We will develop software to search, extract and process anonymised data from 20 primary care providers' electronic patient record management systems on participants aged 50+ years, with a glycated haemoglobin (HbA1c) test result of ≥48 mmol/mol (6.5%) and no previous abnormal HbA1c results. Software to calculate ENDPAC scores will be developed, and descriptive statistics used to summarise the cohort's demographics and assess data quality. Findings will inform the development of a future UK clinical trial to test ENDPAC's effectiveness for the early detection of pancreatic cancer.

ETHICS AND DISSEMINATION

This project has been reviewed by the University of Surrey University Ethics Committee and received a favourable ethical opinion (FHMS 22-23151 EGA). Study findings will be presented at scientific meetings and published in international peer-reviewed journals. Participating primary care practices, clinical leads and policy makers will be provided with summaries of the findings.

LASSO-derived prognostic model predicts cancer-specific survival in advanced pancreatic ductal adenocarcinoma over 50 years of age: a retrospective study of SEER database research

Background

This study aimed to develop a prognostic model for patients with advanced ductal adenocarcinoma aged ≥50 years.

Methods

Patient information was extracted from the Surveillance, Epidemiology, and End Results (SEER) database. Least absolute shrinkage and selection operator (LASSO) Cox regression analysis was performed to screen the model variables. Cases from Nanchang Central Hospital were collected for external validation. The new nomogram and the American Joint Committee on Cancer (AJCC) criteria were evaluated using integrated discrimination improvement (IDI) and net reclassification index (NRI) indicators. Survival curves presented the prognosis of the new classification system and AJCC criteria.

Results

In total, 17,621 eligible patients were included. Lasso Cox regression selected 4 variables including age, chemotherapy, radiotherapy and AJCC stage. The C-index of the training cohort was 0.721. The C-index value of the validation cohort was 0.729. The AUCs for the training cohorts at 1, 2, and 3 years were 0.749, 0.729, and 0.715, respectively. The calibration curves showed that the predicted and actual probabilities at 1, 2, and 3 years matched. External validation confirmed the model's outstanding predictive power. Decision curve analysis indicated that the clinical benefit of the nomogram was higher than that of the AJCC staging system. The model evaluation indices preceded the AJCC staging with NRI (1-year: 0.88, 2-year: 0.94, 3-year: 0.72) and IDI (1-year: 0.24, 2-year: 0.23, 3-year: 0.22). The Kaplan-Meier curves implied that the new classification system was more capable of distinguishing between patients at different risks.

Conclusions

This study established a prognostic nomogram and risk classification system for advanced pancreatic cancer in patients aged ≥50 years to provide a practical tool for the clinical management of patients with pancreatic ductal adenocarcinoma.

A machine learning prediction model for cancer risk in patients with type 2 diabetes based on clinical tests

BACKGROUND

The incidence of type 2 diabetes is rapidly increasing worldwide. Studies have shown that it is also associated with cancer-related morbidities. Early detection of cancer in patients with type 2 diabetes is crucial.

OBJECTIVE

This study aimed to construct a model to predict cancer risk in patients with type 2 diabetes.

METHODS

This study collected clinical data from a total of 5198 patients. A cancer risk prediction model was established by analyzing 261 items from routine laboratory tests. We screened 107 risk factors from 261 clinical tests based on the importance of the characteristic variables, significance of differences between groups (P< 0.05), and minimum description length algorithm.

RESULTS

Compared with 16 machine learning classifiers, five classifiers based on the decision tree algorithm (CatBoost, light gradient boosting, random forest, XGBoost, and gradient boosting) had an area under the receiver operating characteristic curve (AUC) of > 0.80. The AUC for CatBoost was 0.852 (sensitivity: 79.6%; specificity: 83.2%).

CONCLUSION

The constructed model can predict the risk of cancer in patients with type 2 diabetes based on tumor biomarkers and routine tests using machine learning algorithms. This is helpful for early cancer risk screening and prevention to improve patient outcomes.

Automated Artificial Intelligence Model Trained on a Large Data Set Can Detect Pancreas Cancer on Diagnostic Computed Tomography Scans As Well As Visually Occult Preinvasive Cancer on Prediagnostic Computed Tomography Scans

BACKGROUND & AIMS

The aims of our case-control study were (1) to develop an automated 3-dimensional (3D) Convolutional Neural Network (CNN) for detection of pancreatic ductal adenocarcinoma (PDA) on diagnostic computed tomography scans (CTs), (2) evaluate its generalizability on multi-institutional public data sets, (3) its utility as a potential screening tool using a simulated cohort with high pretest probability, and (4) its ability to detect visually occult preinvasive cancer on prediagnostic CTs.

METHODS

A 3D-CNN classification system was trained using algorithmically generated bounding boxes and pancreatic masks on a curated data set of 696 portal phase diagnostic CTs with PDA and 1080 control images with a nonneoplastic pancreas. The model was evaluated on (1) an intramural hold-out test subset (409 CTs with PDA, 829 controls); (2) a simulated cohort with a case-control distribution that matched the risk of PDA in glycemically defined new-onset diabetes, and Enriching New-Onset Diabetes for Pancreatic Cancer score ≥3; (3) multi-institutional public data sets (194 CTs with PDA, 80 controls), and (4) a cohort of 100 prediagnostic CTs (i.e., CTs incidentally acquired 3-36 months before clinical diagnosis of PDA) without a focal mass, and 134 controls.

RESULTS

Of the CTs in the intramural test subset, 798 (64%) were from other hospitals. The model correctly classified 360 CTs (88%) with PDA and 783 control CTs (94%), with a mean accuracy 0.92 (95% CI, 0.91-0.94), area under the receiver operating characteristic (AUROC) curve of 0.97 (95% CI, 0.96-0.98), sensitivity of 0.88 (95% CI, 0.85-0.91), and specificity of 0.95 (95% CI, 0.93-0.96). Activation areas on heat maps overlapped with the tumor in 350 of 360 CTs (97%). Performance was high across tumor stages (sensitivity of 0.80, 0.87, 0.95, and 1.0 on T1 through T4 stages, respectively), comparable for hypodense vs isodense tumors (sensitivity: 0.90 vs 0.82), different age, sex, CT slice thicknesses, and vendors (all P > .05), and generalizable on both the simulated cohort (accuracy, 0.95 [95% 0.94-0.95]; AUROC curve, 0.97 [95% CI, 0.94-0.99]) and public data sets (accuracy, 0.86 [95% CI, 0.82-0.90]; AUROC curve, 0.90 [95% CI, 0.86-0.95]). Despite being exclusively trained on diagnostic CTs with larger tumors, the model could detect occult PDA on prediagnostic CTs (accuracy, 0.84 [95% CI, 0.79-0.88]; AUROC curve, 0.91 [95% CI, 0.86-0.94]; sensitivity, 0.75 [95% CI, 0.67-0.84]; and specificity, 0.90 [95% CI, 0.85-0.95]) at a median 475 days (range, 93-1082 days) before clinical diagnosis.

CONCLUSIONS

This automated artificial intelligence model trained on a large and diverse data set shows high accuracy and generalizable performance for detection of PDA on diagnostic CTs as well as for visually occult PDA on prediagnostic CTs. Prospective validation with blood-based biomarkers is warranted to assess the potential for early detection of sporadic PDA in high-risk individuals.

[Pancreatic cancer-screening or surveillance?]

BACKGROUND

Despite continuous improvement of diagnostic and therapeutic procedures, the number of new pancreatic ductal adenocarcinoma (PDAC) cases diagnosed annually almost equals the number of PDAC-related deaths. Prerequisite for curative treatment is a resectable tumor at the time of diagnosis. Individuals with genetic and/or familial risk profiles should therefore be screened and included in structured surveillance programs.

OBJECTIVES

Description of the status quo and usefulness of current PDAC screening and surveillance concepts.

METHODS

A selective literature search of current national and international guidelines including underlying literature was performed.

RESULTS

Nearly half of pancreatic cancer cases are missed by currently available surveillance programs, even in high-risk cohorts. Magnetic resonance imaging and endoscopic ultrasound supplemented by CA19‑9 (± HbA1c) are not accurate enough to ensure robust earlier pancreatic cancer detection. Complementary biomarker panels will take on a crucial diagnostic role in the future.

An Integrative Pancreatic Cancer Risk Prediction Model in the UK Biobank

Pancreatic cancer (PaCa) is a lethal cancer with an increasing incidence, highlighting the need for early prevention strategies. There is a lack of a comprehensive PaCa predictive model derived from large prospective cohorts. Therefore, we have developed an integrated PaCa risk prediction model for PaCa using data from the UK Biobank, incorporating lifestyle-related, genetic-related, and medical history-related variables for application in healthcare settings. We used a machine learning-based random forest approach and a traditional multivariable logistic regression method to develop a PaCa predictive model for different purposes. Additionally, we employed dynamic nomograms to visualize the probability of PaCa risk in the prediction model. The top five influential features in the random forest model were age, PRS, pancreatitis, DM, and smoking. The significant risk variables in the logistic regression model included male gender (OR = 1.17), age (OR = 1.10), non-O blood type (OR = 1.29), higher polygenic score (PRS) (Q5 vs. Q1, OR = 2.03), smoking (OR = 1.82), alcohol consumption (OR = 1.27), pancreatitis (OR = 3.99), diabetes (DM) (OR = 2.57), and gallbladder-related disease (OR = 2.07). The area under the receiver operating curve (AUC) of the logistic regression model is 0.78. Internal validation and calibration performed well in both models. Our integrative PaCa risk prediction model with the PRS effectively stratifies individuals at future risk of PaCa, aiding targeted prevention efforts and supporting community-based cancer prevention initiatives.

Coupling magnetic levitation of graphene oxide–protein complexes with blood levels of glucose for early detection of pancreatic adenocarcinoma

Introduction

Pancreatic adenocarcinoma (PDAC) has a poor prognosis since often diagnosed too late. Dyslipidemia and hyperglycemia are considered risk factors, but the presence of the tumor itself can determine the onset of these disorders. Therefore, it is not easy to predict which subjects with diabetes or dyslipidemia will develop or have already developed PDAC. Over the past decade, tests based on the use of nanotechnology, alone or coupled with common laboratory tests (e.g., hemoglobin levels), have proven useful in aiding the diagnosis of PDAC. Tests based on magnetic levitation (MagLev) have demonstrated high diagnostic accuracy in compliance with the REASSURED criteria. Here, we aimed to assess the ability of the MagLev test in detecting PDAC when coupled with the blood levels of glycemia, cholesterol, and triglycerides.

Methods

Blood samples from 24 PDAC patients and 22 healthy controls were collected. Human plasma was let to interact with graphene oxide (GO) nanosheets and the emerging coronated systems were put in the MagLev device. Outcomes from Maglev experiments were coupled to glycemia, cholesterol, and triglycerides levels. Linear discriminant analysis (LDA) was carried out to evaluate the classification ability of the test in terms of specificity, sensitivity, and global accuracy. Statistical analysis was performed with Matlab (MathWorks, Natick, MA, USA, Version R2022a) software.

Results

The positions of the levitating bands were measured at the starting point (i.e., as soon as the cuvette containing the sample was subjected to the magnetic field). Significant variations in the starting position of levitating nanosystems in controls and PDACs were detected. The combination of the MagLev outcomes with the blood glycemic levels returned the best value of global accuracy (91%) if compared to the coupling with those of cholesterol and triglycerides (global accuracy of ~ 77% and 84%, respectively).

Conclusion

If confirmed by further studies on larger cohorts, a multiplexed Maglev-based nanotechnology-enabled blood test could be employed as a screening tool for PDAC in populations with hyperglycemia.

Accuracy of the END-PAC Model in Predicting the Risk of Developing Pancreatic Cancer in Patients with New-Onset Diabetes: A Systematic Review and Meta-Analysis

OBJECTIVES

To investigate the performance of the END-PAC model in predicting pancreatic cancer risk in individuals with new-onset diabetes (NOD).

METHODS

The PRISMA statement standards were followed to conduct a systematic review. All studies investigating the performance of the END-PAC model in predicting pancreatic cancer risk in individuals with NOD were included. Two-by-two tables, coupled forest plots and summary receiver operating characteristic plots were constructed using the number of true positives, false negatives, true negatives and false positives. Diagnostic random effects models were used to estimate summary sensitivity and specificity points.

RESULTS

A total of 26,752 individuals from four studies were included. The median follow-up was 3 years and the pooled risk of pancreatic cancer was 0.8% (95% CI 0.6-1.0%). END-PAC score ≥ 3, which classifies the patients as high risk, was associated with better predictive performance (sensitivity: 55.8% (43.9-67%); specificity: 82.0% (76.4-86.5%)) in comparison with END-PAC score 1-2 (sensitivity: 22.2% (16.6-29.2%); specificity: 69.9% (67.3-72.4%)) and END-PAC score < 1 (sensitivity: 18.0% (12.8-24.6%); specificity: 50.9% (48.6-53.2%)) which classify the patients as intermediate and low risk, respectively. The evidence quality was judged to be moderate to high.

CONCLUSIONS

END-PAC is a promising model for predicting pancreatic cancer risk in individuals with NOD. The score ≥3 should be considered as optimum cut-off value. More studies are needed to assess whether it could improve early pancreatic cancer detection rate, pancreatic cancer re-section rate, and pancreatic cancer treatment outcomes.

Therapeutic Strategies for Pancreatic-Cancer-Related Type 2 Diabetes Centered around Natural Products

Pancreatic ductal adenocarcinoma (PDAC), a highly malignant neoplasm, is classified as one of the most severe and devastating types of cancer. PDAC is a notable malignancy that exhibits a discouraging prognosis and a rising occurrence. The interplay between diabetes and pancreatic cancer exhibits a reciprocal causation. The identified metabolic disorder has been observed to possess noteworthy consequences on health outcomes, resulting in elevated rates of morbidity. The principal mechanisms involve the suppression of the immune system, the activation of pancreatic stellate cells (PSCs), and the onset of systemic metabolic disease caused by dysfunction of the islets. From this point forward, it is important to recognize that pancreatic-cancer-related diabetes (PCRD) has the ability to increase the likelihood of developing pancreatic cancer. This highlights the complex relationship that exists between these two physiological states. Therefore, we investigated into the complex domain of PSCs, elucidating their intricate signaling pathways and the profound influence of chemokines on their behavior and final outcome. In order to surmount the obstacle of drug resistance and eliminate PDAC, researchers have undertaken extensive efforts to explore and cultivate novel natural compounds of the next generation. Additional investigation is necessary in order to comprehensively comprehend the effect of PCRD-mediated apoptosis on the progression and onset of PDAC through the utilization of natural compounds. This study aims to examine the potential anticancer properties of natural compounds in individuals with diabetes who are undergoing chemotherapy, targeted therapy, or immunotherapy. It is anticipated that these compounds will exhibit increased potency and possess enhanced pharmacological benefits. According to our research findings, it is indicated that naturally derived chemical compounds hold potential in the development of PDAC therapies that are both safe and efficacious.

Blood-based Migration Signature Biomarker Panel Discriminates Early Stage New Onset Diabetes related Pancreatic Ductal Adenocarcinoma from Type 2 Diabetes

BACKGROUND AND AIMS

While type 2 diabetes is a well-known risk factor for pancreatic ductal adenocarcinoma (PDAC), PDAC-induced new-onset diabetes (PDAC-NOD) is a manifestation of underlying PDAC. In this study, we sought to identify potential blood-based biomarkers for distinguishing PDAC-NOD from type 2 diabetes (T2DM) without PDAC.

MATERIALS AND METHODS

By ELISA analysis, a migration signature biomarker panel comprising tissue factor pathway inhibitor (TFPI), tenascin C (TNC-FNIII-C) and CA 19-9 was analyzed in plasma samples from 50 PDAC-NOD and 50 T2DM controls.

RESULTS

Both TFPI (area under the curve (AUC) 0.71) and TNC-FNIII-C (AUC 0.69) outperformed CA 19-9 (AUC 0.60) in distinguishing all stages of PDAC-NOD from T2DM controls. The combined panel showed an AUC of 0.82 (95% CI = 0.73-0.90) (p = 0.002). In the PDAC-NOD early stage II samples, the three biomarkers had an AUC of 0.84 (95% CI = 0.73-0.93) vs CA 19-9, AUC = 0.60, (95% CI = 0.45-0.73), which also improved significance (p = 0.0123).

CONCLUSION

The migration signature panel adds significantly to CA 19-9 to discriminate PDAC-NOD from T2DM controls and warrants further validation for high-risk group stratification.

[Type 3 c diabetes: Approach from the first level doctor]

DM3c is diabetes (DM) of the exocrine pancreas that must be suspected whenever there is a history of chronic pancreatitis (CP), acute pancreatitis (AP) or recurrence (80% of cases) or new-onset DM in individuals from over 50 years of age without any other justification (negative autoimmunity tests, Glutamic Acid Decarboxylase antibodies). It is an entity misdiagnosed as type 2 diabetes (DM2) (90%) and therefore, if it is not suspected, it can go unnoticed. For its diagnosis, abdominal ultrasound, determination of the CA 19.9 tumor antigen (carbohydrate antigen 19-9), nuclear magnetic resonance (NMR) or computerized axial tomography (CT) are useful. The treatment is the same as DM2, although certain specifications depend on the type of drugs and with the particularity that in dealing with «fragile diabetes» greater caution must be taken with hypoglycemia (monitoring). Likewise, as it is a disease of the exocrine pancreas, it will have to be specifically treated to avoid metabolic, malabsorptive and/or nutritional alterations.

Do Polygenic Risk Scores Add to Clinical Data in Predicting Pancreatic Cancer? A Scoping Review

BACKGROUND

Polygenic risk scores (PRS) summarize an individual's germline genetic risk, but it is unclear whether PRS offer independent information for pancreatic cancer risk prediction beyond routine clinical data.

METHODS

We searched 8 databases from database inception to March 10, 2023 to identify studies evaluating the independent performance of pancreatic cancer-specific PRS for pancreatic cancer beyond clinical risk factors.

RESULTS

Twenty-one studies examined associations between a pancreatic cancer-specific PRS and pancreatic cancer. Seven studies evaluated risk factors beyond age and sex. Three studies evaluated the change in discrimination associated with the addition of PRS to routine risk factors and reported improvements (AUCs: 0.715 to 0.745; AUC 0.791 to 0.830; AUC from 0.694 to 0.711). Limitations to clinical applicability included using source populations younger/healthier than those at risk for pancreatic cancer (n = 10), exclusively of European ancestry (n = 13), or controls without relevant exposures (n = 1).

CONCLUSIONS

While most studies of pancreatic cancer-specific PRS did not evaluate the independent discrimination of PRS for pancreatic cancer beyond routine risk factors, three that did showed improvements in discrimination.

IMPACT

For pancreatic cancer PRS to be clinically useful, they must demonstrate substantial improvements in discrimination beyond established risk factors, apply to diverse ancestral populations representative of those at risk for pancreatic cancer, and use appropriate controls.

The Hong Kong consensus recommendations on the diagnosis and management of pancreatic cystic lesions

Background

The finding of pancreatic cystic lesions (PCL) on incidental imaging is becoming increasingly common. International studies report a prevalence of 2.2-44.7% depending on the population, imaging modality and indication for imaging, and the prevalence increases with age. Patients with PCL are at risk of developing pancreatic cancer, a disease with a poor prognosis. This publication summarizes recommendations for the diagnosis and management of PCL and post-operative pancreatic exocrine insufficiency (PEI) from a group of local specialists.

Methods

Clinical evidence was consolidated from narrative reviews and consensus statements formulated during two online meetings in March 2022. The expert panel included gastroenterologists, hepatobiliary surgeons, oncologists, radiologists, and endocrinologists.

Results

Patients with PCL require careful investigation and follow-up due to the risk of malignant transformation of these lesions. They should undergo clinical investigation and pancreas-specific imaging to classify lesions and understand the risk profile of the patient. Where indicated, patients should undergo pancreatectomy to excise PCL. Following pancreatectomy, patients are at risk of PEI, leading to gastrointestinal dysfunction and malnutrition. Therefore, such patients should be monitored for symptoms of PEI, and promptly treated with pancreatic enzyme replacement therapy (PERT). Patients with poor response to PERT may require increases in dose, addition of a proton pump inhibitor, and/or further investigation, including tests for pancreatic function. Patients are also at risk of new-onset diabetes mellitus after pancreatectomy; they should be screened and treated with insulin if indicated.

Conclusions

These statements are an accurate summary of our approach to the diagnosis and management of patients with PCL and will be of assistance to clinicians treating these patients in a similar clinical landscape.

Clinical trajectory of intraductal papillary mucinous neoplasms progressing to pancreatic carcinomas during long-term surveillance: a prospective series of 100 carcinoma cases

BACKGROUND

Trajectories of serological and morphological signatures have not been documented in pancreatic carcinogenesis related to intraductal papillary mucinous neoplasms (IPMNs).

METHODS

Using a prospective cohort of 3437 IPMN patients, we identified 100 IPMN patients who developed pancreatic carcinomas during long-term surveillance. We examined serial changes of blood markers (carbohydrate antigen 19-9 [CA19-9], hemoglobin A1c [HbA1c], and pancreatic enzymes) and morphological features (worrisome features and high-risk stigmata) during the prediagnostic period of pancreatic carcinomas, overall and by carcinoma types (IPMN-derived vs. concomitant pancreatic carcinomas).

RESULTS

CA19-9 elevation was observed in 39 patients and was associated with a metastatic stage. Compared to IPMN-derived carcinomas, concomitant carcinomas were more likely to represent CA19-9 elevation (60% vs. 30%, respectively; P = 0.005). HbA1c levels elevated only in 3 patients. Pancreatic enzyme elevation was observed in 18 patients with no differences in frequencies between the carcinoma types. All patients with elevated levels of blood markers had positive findings on cross-sectional imaging. High-risk stigmata or worrisome features were observed in all patients but one with concomitant carcinoma. The most common types of worrisome features were the main pancreatic duct dilatation and CA19-9 elevation in IPMN-derived and concomitant carcinomas, respectively. Compared to IPMN-derived carcinomas, concomitant carcinomas were less likely to harbor high-risk stigmata (16% vs. 86%, respectively; P < 0.001).

CONCLUSIONS

The usefulness of currently available blood biomarkers was limited in early detection of pancreatic carcinomas related to IPMNs. Morphological alterations were well correlated with long-term risk of IPMN-derived carcinomas, but not with that of concomitant carcinomas.

A blood-based metabolomic signature predictive of risk for pancreatic cancer

Emerging evidence implicates microbiome involvement in the development of pancreatic cancer (PaCa). Here, we investigate whether increases in circulating microbial-related metabolites associate with PaCa risk by applying metabolomics profiling to 172 sera collected within 5 years prior to PaCa diagnosis and 863 matched non-subject sera from participants in the Prostate, Lung, Colorectal, and Ovarian (PLCO) cohort. We develop a three-marker microbial-related metabolite panel to assess 5-year risk of PaCa. The addition of five non-microbial metabolites further improves 5-year risk prediction of PaCa. The combined metabolite panel complements CA19-9, and individuals with a combined metabolite panel + CA19-9 score in the top 2.5th percentile have absolute 5-year risk estimates of >13%. The risk prediction model based on circulating microbial and non-microbial metabolites provides a potential tool to identify individuals at high risk of PaCa that would benefit from surveillance and/or from potential cancer interception strategies.

Diabetes Status and Pancreatic Cancer Survival in the Nationwide Veterans Affairs Healthcare System

BACKGROUND

Long-standing type 2 diabetes is a known risk factor for developing pancreatic cancer, however, its influence on cancer-associated outcomes is understudied.

AIMS

To examine the associations between diabetes status and pancreatic cancer outcomes.

METHODS

We identified patients diagnosed with pancreatic adenocarcinoma in the national Veterans Administration System from 2010 to 2018. We classified each patient by pre-cancer diagnosis diabetes status: no diabetes, new-onset diabetes (NOD) of ≤ 3 years duration, or long-standing diabetes of > 3 years duration. We used Cox proportional hazards models to examine the association between diabetes status and survival. We adjusted the models for age, race, sex, body mass index, tobacco, and alcohol use, coronary artery disease, hypertension, chronic kidney disease, year of cancer diagnosis, and cancer stage and treatment.

RESULTS

We identified 6342 patients diagnosed with pancreatic adenocarcinoma. Most had long-standing diabetes (45.7%) prior to their cancer diagnosis, 14.5% had NOD, and 39.8% had no diabetes. Patients with long-standing diabetes had 10% higher mortality risk compared to patients without diabetes after adjusting for sociodemographic factors and medical comorbidities (adjusted HR 1.10; 95% CI 1.03-1.16). This difference in mortality remained statistically significant after additionally adjusting for cancer stage and receipt of potentially curative treatment (adjusted HR 1.09; 95% CI 1.02-1.15). There was no significant difference in mortality between patients with NOD compared to those without diabetes.

CONCLUSIONS

Long-standing but not new-onset diabetes is independently associated with increased mortality among patients with pancreatic cancer. This information has implication for prognostication and risk stratification among pancreatic cancer patients.

Risk of pancreatic cancer in people with new-onset diabetes: A Danish nationwide population-based cohort study

BACKGROUND

New onset diabetes (NOD) in people 50 years or older may indicate underlying pancreatic ductal adenocarcinoma (PDAC). The cumulative incidence of PDAC among people with NOD remains uncertain on a population-based level.

METHODS

This was a nationwide population-based retrospective cohort study based on the Danish national health registries. We investigated the 3-year cumulative incidence of PDAC in people 50 years or older with NOD. We further characterised people with pancreatic cancer-related diabetes (PCRD) in relation to demographic and clinical characteristics, including trajectories of routine biochemical parameters, using people with type 2 diabetes (T2D) as a comparator group.

RESULTS

During a 21-year observation period, we identified 353,970 people with NOD. Among them, 2105 people were subsequently diagnosed with pancreatic cancer within 3 years (0.59%, 95% CI [0.57-0.62%]). People with PCRD were older than people with T2D at diabetes diagnosis (median age 70.9 vs. 66.0 years (P < 0.001) and had a higher burden of comorbidities (P = 0.007) and more prescriptions of medications used to treat cardiovascular diseases (all P < 0.001). Distinct trajectories of HbA1c and plasma triglycerides were observed in PCRD vs. T2D, with group differences observed for up to three years prior to NOD diagnosis for HbA1c and up to two years for plasma triglyceride levels.

CONCLUSIONS

The 3-year cumulative incidence of PDAC is approximately 0.6% among people 50 years or older with NOD in a nationwide population-based setting. Compared to T2D, people with PCRD are characterised by distinct demographic and clinical profiles, including distinctive trajectories of plasma HbA1c and triglyceride levels.

A review of the last decade: pancreatic cancer and type 2 diabetes

Pancreatic cancer (PC) is a prevalent gastrointestinal tumour known for its high degree of malignancy, resulting in a mere 10% five-year survival rate for most patients. Over the past decade, a growing body of research has shed light on the intricate bidirectional association between PC and Type 2 diabetes (T2DM). The collection of PC- and T2DM-related articles is derived from two comprehensive databases, namely WOS (Web of Science Core Collection) and CNKI (China National Knowledge Infrastructure). This article discusses the last 10 years of research trends in PC and T2DM and explores their potential regulatory relationship as well as related medications.

Elevated Glycated Haemoglobin (HbA1c) Is Associated with an Increased Risk of Pancreatic Ductal Adenocarcinoma: A UK Biobank Cohort Study

BACKGROUND

The role of dysglycaemia as a risk marker for Pancreatic Ductal Adenocarcinoma (PDAC) is uncertain. We investigated the relationship between glycated haemoglobin (HbA1c) and incident PDAC using a retrospective cohort study within the UK Biobank.

METHODS

A study involving 499,804 participants from the UK Biobank study was undertaken. Participants were stratified by diabetes mellitus (DM) status, and then by HbA1c values < 42 mmol/mol, 42-47 mmol/mol, or ≥48 mmol/mol. Cox proportional hazard models were used to describe the association between HbA1c category (with time-varying interactions) and incident PDAC.

RESULTS

PDAC occurred in 1157 participants during 11.6 (10.9-12.3) years follow up [(median (interquartile range)]. In subjects without known DM at baseline, 12 months after recruitment, the adjusted hazard ratios (aHR, 95% CI) for incident PDAC for HbA1c 42-47 mmol/mol compared to HbA1c < 42 mmol/mol (reference group) was 2.10 (1.31-3.37, p = 0.002); and was 8.55 (4.58-15.99, p < 0.001) for HbA1c ≥ 48 mmol/mol. The association between baseline HbA1c and incident PDAC attenuated with increasing duration of time of follow-up to PDAC diagnosis.

CONCLUSIONS

Dysglycaemia detected by elevated HbA1c is associated with an increased risk of PDAC. The strength of the association between elevated HbA1c and incident PDAC is inversely proportional to the time from detecting dysglycaemia but remains significant for at least 60 months following HbA1c testing.

Machine Learning Predicts Patients With New-onset Diabetes at Risk of Pancreatic Cancer

BACKGROUND

New-onset diabetes represent a high-risk cohort to screen for pancreatic cancer.

GOALS

Develop a machine model to predict pancreatic cancer among patients with new-onset diabetes.

STUDY

A retrospective cohort of patients with new-onset diabetes was assembled from multiple health care networks in the United States. An XGBoost machine learning model was designed from a portion of this cohort (the training set) and tested on the remaining part of the cohort (the test set). Shapley values were used to explain the XGBoost's model features. Model performance was compared with 2 contemporary models designed to predict pancreatic cancer among patients with new-onset diabetes.

RESULTS

In the test set, the XGBoost model had an area under the curve of 0.80 (0.76 to 0.85) compared with 0.63 and 0.68 for other models. Using cutoffs based on the Youden index, the sensitivity of the XGBoost model was 75%, the specificity was 70%, the accuracy was 70%, the positive predictive value was 1.2%, and the negative predictive value was >99%. The XGBoost model obtained a positive predictive value of at least 2.5% with a sensitivity of 38%. The XGBoost model was the only model that detected at least 50% of patients with cancer one year after the onset of diabetes. All 3 models had similar features that predicted pancreatic cancer, including older age, weight loss, and the rapid destabilization of glucose homeostasis.

CONCLUSION

Machine learning models isolate a high-risk cohort from those with new-onset diabetes at risk for pancreatic cancer.

Diabetes Mellitus in Pancreatic Cancer: A Distinct Approach to Older Subjects with New-Onset Diabetes Mellitus

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) is associated with a very poor prognosis, with near-identical incidence and mortality. According to the World Health Organization Globocan Database, the estimated number of new cases worldwide will rise by 70% between 2020 and 2040. There are no effective screening methods available so far, even for high-risk individuals. The prognosis of PDAC, even at its early stages, is still mostly unsatisfactory. Impaired glucose metabolism is present in about 3/4 of PDAC cases.

METHODS

Available literature on pancreatic cancer and diabetes mellitus was reviewed using a PubMed database. Data from a national oncology registry (on PDAC) and information from a registry of healthcare providers (on diabetes mellitus and a number of abdominal ultrasound investigations) were obtained.

RESULTS

New-onset diabetes mellitus in subjects older than 60 years should be an incentive for a prompt and detailed investigation to exclude PDAC. Type 2 diabetes mellitus, diabetes mellitus associated with chronic non-malignant diseases of the exocrine pancreas, and PDAC-associated type 3c diabetes mellitus are the most frequent types. Proper differentiation of particular types of new-onset diabetes mellitus is a starting point for a population-based program. An algorithm for subsequent steps of the workup was proposed.

CONCLUSIONS

The structured, well-differentiated, and elaborately designed approach to the elderly with a new onset of diabetes mellitus could improve the current situation in diagnostics and subsequent poor outcomes of therapy of PDAC.

Hypoglycemic agents and incidence of pancreatic cancer in diabetic patients: a meta-analysis

Background and aims: Hypoglycemic agents are the primary therapeutic approach for the treatment of diabetes and have been postulated to impact pancreatic cancer (PC) incidence in diabetic patients. We conducted a meta-analysis to further evaluate and establish the associations between four common types of hypoglycemic agents [metformin, sulfonylureas, thiazolidinediones (TZDs), and insulin] and PC incidence in individuals with diabetes mellitus (DM). Methods: A comprehensive literature search of PubMed, Web of Science, Embase, and the Cochrane Library identified studies that analyzed the relationship between hypoglycemic agents and PC published between January 2012 and September 2022. Randomized control trials (RCTs), cohorts, and case-control studies were included if there was clear and evaluated defined exposure to the involved hypoglycemic agents and reported PC outcomes in patients with DM. Furthermore, reported relative risks or odds ratios (ORs) or other provided data were required for the calculation of odds ratios. Summary odds ratio estimates with a 95% confidence interval (CI) were estimated using the random-effects model. Additionally, subgroup analysis was performed to figure out the source of heterogeneity. Sensitivity analysis and publication bias detection were also performed. Results: A total of 11 studies were identified that evaluated one or more of the hypoglycemic agents, including three case-control studies and eight cohort studies. Among these, nine focused on metformin, six on sulfonylureas, seven on TZDs, and seven on insulin. Meta-analysis of the 11 observational studies reported no significant association between metformin (OR = 1.04, 95% CI 0.73-1.46) or TZDs (OR = 1.13, 95% CI 0.73-1.75) and PC incidence, while the risk of PC increased by 79% and 185% with sulfonylureas (OR = 1.79, 95% CI 1.29-2.49) and insulin (OR = 2.85, 95% CI 1.75-4.64), respectively. Considerable heterogeneity was observed among the studies and could not be fully accounted for by study design, region, or adjustment for other hypoglycemic agents. Conclusion: Sulfonylureas and insulin may increase the incidence of pancreatic cancer in diabetic patients, with varying effects observed among different ethnicities (Asian and Western). Due to significant heterogeneity across studies, further interpretation of the relationship between hypoglycemic agents and pancreatic cancer incidence in diabetic patients requires well-adjusted data and better-organized clinical trials.

The association of new-onset diabetes with subsequent diagnosis of pancreatic cancer-novel use of a large administrative database

BACKGROUND

Screening options for pancreatic ductal adenocarcinoma (PDAC) are limited. New-onset type 2 diabetes (NoD) is associated with subsequent diagnosis of PDAC in observational studies and may afford an opportunity for PDAC screening. We evaluated this association using a large administrative database.

METHODS

Patients were identified using claims data from the OptumLabs® Data Warehouse. Adult patients with NoD diagnosis were matched 1:3 with patients without NoD using age, sex and chronic obstructive pulmonary disease (COPD) status. The event of PDAC diagnosis was compared between cohorts using the Kaplan-Meier method. Factors associated with PDAC diagnosis were evaluated with Cox's proportional hazards modeling.

RESULTS

We identified 640 421 patients with NoD and included 1 921 263 controls. At 3 years, significantly more PDAC events were identified in the NoD group vs control group (579 vs 505; P < 0.001). When controlling for patient factors, NoD was significantly associated with elevated risk of PDAC (HR 3.474, 95% CI 3.082-3.920, P < 0.001). Other factors significantly associated with PDAC diagnosis were increasing age, increasing age among Black patients, and COPD diagnosis (P ≤ 0.05).

CONCLUSIONS

NoD was independently associated with subsequent diagnosis of PDAC within 3 years. Future studies should evaluate the feasibility and benefit of PDAC screening in patients with NoD.

S100A8, S100A9 and S100A8/A9 heterodimer as novel cachexigenic factors for pancreatic cancer-induced cachexia

BACKGROUND

Cancer cachexia, occurring in ~ 80% pancreatic cancer (PC) patients overall, is a paraneoplastic syndrome mediated by cancer-induced systemic inflammation and characterized by weight loss and skeletal muscle wasting. Identifying clinically relevant PC-derived pro-inflammatory factors with cachexigenic potential may provide novel insights and therapeutic strategies.

METHODS

Pro-inflammatory factors with cachexigenic potential in PC were identified by bioinformatic analysis. The abilities of selected candidate factors in inducing skeletal muscle atrophy were investigated. Expression levels of candidate factors in tumors and sera was compared between PC patients with and without cachexia. Associations between serum levels of the candidates and weight loss were assessed in PC patients.

RESULTS

S100A8, S100A9, and S100A8/A9 were identified and shown to induce C2C12 myotube atrophy. Tumors of PC patients with cachexia had markedly elevated expression of S100A8 (P = 0.003) and S100A9 (P < 0.001). PC patients with cachexia had significantly higher serum levels of S100A8, S100A9 and S100A8/A9. Serum levels of these factors positively correlated with percentage of weight loss [correlation coefficient: S100A8: 0.33 (P < 0.001); S100A9: 0.30 (P < 0.001); S100A8/A9: 0.24 (P = 0.004)] and independently predicted the occurrence of cachexia [adjusted odds ratio (95% confidence interval) per 1ng/ml increase: S100A8 1.11 (1.02-1.21), P = 0.014; S100A9 1.10 (1.04-1.16), P = 0.001; per 1 µg/ml increase: S100A8/A9 1.04 (1.01-1.06), P = 0.009].

CONCLUSIONS

Atrophic effects of S100A8, S100A9, and S100A8/A9 indicated them as potential pathogenic factors of PC-induced cachexia. In addition, the correlation with the degree of weight loss and prediction of cachexia in PC patients implicated their potential utility in the diagnosis of PC-induced cachexia.

Glycemic Abnormalities in Pancreatic Cystic Lesions—A Single-Center Retrospective Analysis

Background and Objectives: Glucose metabolism alterations are very common in solid pancreatic lesions, particularly in pancreatic cancer. Similarly, diabetes and especially new-onset diabetes (NOD) have been associated with the malignant transformation of pancreatic cysts. We aimed to assess the prevalence and relevant associations of glycemic abnormalities in pancreatic cystic lesions (PCLs) in a retrospective analysis. Materials and Methods: We retrospectively recruited all patients who underwent endoscopic ultrasound for a PCL over a period of 36 months (January 2018 to December 2021). Final diagnosis was set by means of tissue acquisition, surgery, follow-up, or board decision. Demographic and clinical data, laboratory workup, and imaging features were extracted from the patients’ charts according to a predefined protocol. We considered fasting blood glucose (FBG) and HbA1c values and stratified the patients as nondiabetic (FBG ≤ 99 mg/dL, HbA1c ≤ 5.6%, no history of glycemic abnormalities), prediabetic (FBG 100–125 mg/dL, HbA1c 5.7–6.4%), or diabetic (long-lasting diabetes or NOD). Results: Altogether, 81 patients were included, with a median age of 66 years, and 54.3% of them were male. The overall prevalence of fasting hyperglycemia was 54.3%, comprising 34.6% prediabetes and 22.2% diabetes, of which 16.7% had NOD. The mean FBG and HbA1c levels were higher in malignant and premalignant PCLs (intraductal papillary mucinous neoplasm (IPMN), mucinous cystic neoplasm (MCN), cystadenocarcinoma, and cystic neuroendocrine tumor) compared to the benign lesions (pseudocysts, walled-off necrosis, and serous cystadenoma): 117.0 mg/dL vs. 108.3 mg/dL and 6.1% vs. 5.5%, respectively. Conclusions: Hyperglycemia and diabetes are common in PCLs, with a high prevalence in premalignant and malignant cysts. Screening and follow-up for glycemic abnormalities should be routinely conducted for PCLs, as they can contribute to a tailored risk assessment of cysts.

Gene Expression Comparison between Alcohol-Exposed versus Not Exposed Pancreatic Ductal Adenocarcinoma Patients Reveals a Peculiar TGFβ-Related Phenotype: An Exploratory Analysis

Background: Over the past few decades, there has been much debate and research into the link between alcohol consumption and the development and progression of pancreatic ductal adenocarcinoma (PDAC). Objectives: To contribute to the ongoing discussion and gain further insights into this topic, our study analysed the gene expression differences in PDAC patients based on their alcohol consumption history. Methods: To this end, we interrogated a large publicly available dataset. We next validated our findings in vitro. Results: Our findings revealed that patients with a history of alcohol consumption showed significant enrichment in the TGFβ-pathway: a signaling pathway implicated in cancer development and tumor progression. Specifically, our bioinformatic dissection of gene expression differences in 171 patients with PDAC showed that those who had consumed alcohol had higher levels of TGFβ-related genes. Moreover, we validated the role of the TGFβ pathway as one of the molecular drivers in producing massive stroma, a hallmark feature of PDAC, in patients with a history of alcohol consumption. This suggests that inhibition of the TGFβ pathway could serve as a novel therapeutic target for PDAC patients with a history of alcohol consumption and lead to increased sensitivity to chemotherapy. Our study provides valuable insights into the molecular mechanisms underlying the link between alcohol consumption and PDAC progression. Conclusions: Our findings highlight the potential significance of the TGFβ pathway as a therapeutic target. The development of TGFβ-inhibitors may pave the way for developing more effective treatment strategies for PDAC patients with a history of alcohol consumption.

Pancreatic cancer is associated with medication changes prior to clinical diagnosis

Patients with pancreatic ductal adenocarcinoma (PDAC) commonly develop symptoms and signs in the 1-2 years before diagnosis that can result in changes to medications. We investigate recent medication changes and PDAC diagnosis in Nurses' Health Study (NHS; females) and Health Professionals Follow-up Study (HPFS; males), including up to 148,973 U.S. participants followed for 2,994,057 person-years and 991 incident PDAC cases. Here we show recent initiation of antidiabetic (NHS) or anticoagulant (NHS, HFS) medications and cessation of antihypertensive medications (NHS, HPFS) are associated with pancreatic cancer diagnosis in the next 2 years. Two-year PDAC risk increases as number of relevant medication changes increases (P-trend <1 × 10^-5), with participants who recently start antidiabetic and stop antihypertensive medications having multivariable-adjusted hazard ratio of 4.86 (95%CI, 1.74-13.6). These changes are not associated with diagnosis of other digestive system cancers. Recent medication changes should be considered as candidate features in multi-factor risk models for PDAC, though they are not causally implicated in development of PDAC.

Artificial Intelligence in the Diagnosis and Treatment of Pancreatic Cystic Lesions and Adenocarcinoma

Pancreatic cancer is projected to become the second leading cause of cancer-related mortality in the United States by 2030. This is in part due to the paucity of reliable screening and diagnostic options for early detection. Amongst known pre-malignant pancreatic lesions, pancreatic intraepithelial neoplasia (PanIN) and intraductal papillary mucinous neoplasms (IPMNs) are the most prevalent. The current standard of care for the diagnosis and classification of pancreatic cystic lesions (PCLs) involves cross-sectional imaging studies and endoscopic ultrasound (EUS) and, when indicated, EUS-guided fine needle aspiration and cyst fluid analysis. However, this is suboptimal for the identification and risk stratification of PCLs, with accuracy of only 65-75% for detecting mucinous PCLs. Artificial intelligence (AI) is a promising tool that has been applied to improve accuracy in screening for solid tumors, including breast, lung, cervical, and colon cancer. More recently, it has shown promise in diagnosing pancreatic cancer by identifying high-risk populations, risk-stratifying premalignant lesions, and predicting the progression of IPMNs to adenocarcinoma. This review summarizes the available literature on artificial intelligence in the screening and prognostication of precancerous lesions in the pancreas, and streamlining the diagnosis of pancreatic cancer.

A Reduced Pancreatic Polypeptide Response is Associated With New-onset Pancreatogenic Diabetes Versus Type 2 Diabetes

PURPOSE

Pancreatogenic diabetes refers to diabetes mellitus (DM) that develops in the setting of a disease of the exocrine pancreas, including pancreatic ductal adenocarcinoma (PDAC) and chronic pancreatitis (CP). We sought to evaluate whether a blunted nutrient response of pancreatic polypeptide (PP) can differentiate these DM subtypes from type 2 DM (T2DM).

METHODS

Subjects with new-onset DM (<3 years' duration) in the setting of PDAC (PDAC-DM, n = 28), CP (CP-DM, n = 38), or T2DM (n = 99) completed a standardized mixed meal tolerance test, then serum PP concentrations were subsequently measured at a central laboratory. Two-way comparisons of PP concentrations between groups were performed using Wilcoxon rank-sum test and analysis of covariance while adjusting for age, sex, and body mass index.

RESULTS

The fasting PP concentration was lower in both the PDAC-DM and CP-DM groups than in the T2DM group (P = 0.03 and <0.01, respectively). The fold change in PP at 15 minutes following meal stimulation was significantly lower in the PDAC-DM (median, 1.869) and CP-DM (1.813) groups compared with T2DM (3.283; P < 0.01 for both comparisons). The area under the curve of PP concentration was significantly lower in both the PDAC-DM and CP-DM groups than in T2DM regardless of the interval used for calculation and remained significant after adjustments.

CONCLUSIONS

Fasting PP concentrations and the response to meal stimulation are reduced in new-onset DM associated with PDAC or CP compared with T2DM. These findings support further investigations into the use of PP concentrations to characterize pancreatogenic DM and to understand the pathophysiological role in exocrine pancreatic diseases (NCT03460769).

Circulating Cell-Free Nucleic Acids as Biomarkers for Diagnosis and Prognosis of Pancreatic Cancer

A lack of reliable early diagnostic tools represents a major challenge in the management of pancreatic cancer (PCa), as the disease is often only identified after it reaches an advanced stage. This highlights the urgent need to identify biomarkers that can be used for the early detection, staging, treatment monitoring, and prognosis of PCa. A novel approach called liquid biopsy has emerged in recent years, which is a less- or non-invasive procedure since it focuses on plasmatic biomarkers such as DNA and RNA. In the blood of patients with cancer, circulating tumor cells (CTCs) and cell-free nucleic acids (cfNAs) have been identified such as DNA, mRNA, and non-coding RNA (miRNA and lncRNA). The presence of these molecules encouraged researchers to investigate their potential as biomarkers. In this article, we focused on circulating cfNAs as plasmatic biomarkers of PCa and analyzed their advantages compared to traditional biopsy methods.

The Role of Exosomes in Pancreatic Ductal Adenocarcinoma Progression and Their Potential as Biomarkers

Pancreatic ductal adenocarcinoma (PDAC), the most common pancreatic malignancy, is an aggressive and lethal cancer with a dismal five-year survival rate. Despite remarkable improvements in cancer therapeutics, the clinical outcome of PDAC patients remains poor due to late diagnosis of the disease. This highlights the importance of early detection, wherein biomarker evaluation including exosomes would be helpful. Exosomes, small extracellular vesicles (sEVs), are cell-secreted entities with diameters ranging from 50 to 150 nm that deliver cellular contents (e.g., proteins, lipids, and nucleic acids) from parent cells to regulate the cellular processes of targeted cells. Recently, an increasing number of studies have reported that exosomes serve as messengers to facilitate stromal-immune crosstalk within the PDAC tumor microenvironment (TME), and their contents are indicative of disease progression. Moreover, evidence suggests that exosomes with specific surface markers are capable of distinguishing patients with PDAC from healthy individuals. Detectable exosomes in bodily fluids (e.g., blood, urine, saliva, and pancreatic juice) are omnipresent and may serve as promising biomarkers for improving early detection and evaluating patient prognosis. In this review, we shed light on the involvement of exosomes and their cargos in processes related to disease progression, including chemoresistance, angiogenesis, invasion, metastasis, and immunomodulation, and their potential as prognostic markers. Furthermore, we highlight feasible clinical applications and the limitations of exosomes in liquid biopsies as tools for early diagnosis as well as disease monitoring. Taking advantage of exosomes to improve diagnostic capacity may provide hope for PDAC patients, although further investigation is urgently needed.