Predicting Pancreatic Cancer in the UK Biobank Cohort Using Polygenic Risk Scores and Diabetes Mellitus

From classical approaches to artificial intelligence, old and new tools for PDAC risk stratification and prediction

Pancreatic ductal adenocarcinoma (PDAC) is recognized as one of the most lethal malignancies, characterized by late-stage diagnosis and limited therapeutic options. Risk stratification has traditionally been performed using epidemiological studies and genetic analyses, through which key risk factors, including smoking, diabetes, chronic pancreatitis, and inherited predispositions, have been identified. However, the multifactorial nature of PDAC has often been insufficiently addressed by these methods, leading to limited precision in individualized risk assessments. Advances in artificial intelligence (AI) have been proposed as a transformative approach, allowing the integration of diverse datasets-spanning genetic, clinical, lifestyle, and imaging data into dynamic models capable of uncovering novel interactions and risk profiles. In this review, the evolution of PDAC risk stratification is explored, with classical epidemiological frameworks compared to AI-driven methodologies. Genetic insights, including genome-wide association studies and polygenic risk scores, are discussed, alongside AI models such as machine learning, radiomics, and deep learning. Strengths and limitations of these approaches are evaluated, with challenges in clinical translation, such as data scarcity, model interpretability, and external validation, addressed. Finally, future directions are proposed for combining classical and AI-driven methodologies to develop scalable, personalized predictive tools for PDAC, with the goal of improving early detection and patient outcomes.

The relationship of the microbiome, associated metabolites and the gut barrier with pancreatic cancer

Pancreatic cancers have high mortality and rising incidence rates which may be related to unhealthy western-type dietary and lifestyle patterns as well as increasing body weights and obesity rates. Recent data also suggest a role for the gut microbiome in the development of pancreatic cancer. Here, we review the experimental and observational evidence for the roles of the oral, gut and intratumoural microbiomes, impaired gut barrier function and exposure to inflammatory compounds as well as metabolic dysfunction as contributors to pancreatic disease with a focus on pancreatic ductal adenocarcinoma (PDAC) initiation and progression. We also highlight some emerging gut microbiome editing techniques currently being investigated in the context of pancreatic disease. Notably, while the gut microbiome is significantly altered in PDAC and its precursor diseases, its utility as a diagnostic and prognostic tool is hindered by a lack of reproducibility and the potential for reverse causality in case-control cohorts. Future research should emphasise longitudinal and mechanistic studies as well as integrating lifestyle exposure and multi-omics data to unravel complex host-microbiome interactions. This will allow for deeper aetiologic and mechanistic insights that can inform treatments and guide public health recommendations.

Biomarkers, omics and artificial intelligence for early detection of pancreatic cancer

Pancreatic ductal adenocarcinoma (PDAC) is frequently diagnosed in its late stages when treatment options are limited. Unlike other common cancers, there are no population-wide screening programmes for PDAC. Thus, early disease detection, although urgently needed, remains elusive. Individuals in certain high-risk groups are, however, offered screening or surveillance. Here we explore advances in understanding high-risk groups for PDAC and efforts to implement biomarker-driven detection of PDAC in these groups. We review current approaches to early detection biomarker development and the use of artificial intelligence as applied to electronic health records (EHRs) and social media. Finally, we address the cost-effectiveness of applying biomarker strategies for early detection of PDAC.

Targeting early diagnosis and treatment of pancreatic cancer among the diabetic population: a comprehensive review of biomarker screening strategies

Pancreatic ductal adenocarcinoma (PDAC) is an aggressive malignancy characterized by challenging early diagnosis, limited therapeutic options, and a poor prognosis. Diabetes mellitus, marked by altered glucose metabolism, has emerged as a significant risk factor for PDAC development, highlighting a complex, bidirectional pathogenic relationship. This review systematically examines the intricate interactions between diabetes and PDAC, emphasizing their shared pathophysiological mechanisms. A comprehensive understanding of these mechanisms can inform the development of targeted therapeutic strategies, potentially improving patient outcomes by concurrently managing diabetes and pancreatic cancer. We further evaluate current biomarker screening approaches for PDAC within diabetic subpopulations, assess the effectiveness of screening programs among high-risk groups, and propose practical strategies for the early identification and monitoring of PDAC. Early detection in diabetic individuals through targeted biomarker screening followed by timely therapeutic intervention may significantly reduce mortality, improve survival rates, and extend patient longevity. In conclusion, an integrated approach combining early diagnosis, targeted treatments, and a detailed understanding of the underlying pathogenesis represents the most promising strategy for enhancing clinical outcomes and survival among diabetic patients diagnosed with pancreatic cancer.

Combining genetic and non-genetic factors to predict the risk of pancreatic cancer in patients with new-onset diabetes mellitus

BACKGROUND

Recent research suggests that new-onset diabetes mellitus (NODM) often results from pancreatic cancer (PC) rather than causing it. Determining if NODM is type 2 diabetes mellitus (T2DM) or PC-related NODM (NODM-PC) aids in the early diagnosis of PC. We developed a NODM-PC risk prediction model to stratify PC risk in patients with NODM.

METHODS

This study utilized data from the UK Biobank, including 238 NODM-PC cases and 14,825 cancer-free T2DM controls. Polygenic risk scores (PRSs) for PC and T2DM were constructed using previously reported single nucleotide polymorphisms (SNPs) separately, while the NODM-PC PRS was developed by combining SNPs from both. Non-genetic factors were selected as candidate predictors based on prior NODM-PC prediction models. We developed three Cox models to estimate the risk of PC diagnosis within 3 years in the NODM population and evaluated them by internal-external cross-validation.

RESULTS

Elevated NODM-PC PRS and PC PRS scores positively correlated with NODM-PC risk, while T2DM PRS showed an inverse correlation. The NODM-PC PRS achieved the highest AUC at 0.719. Three Cox models were developed: Model 1 included age at T2DM diagnosis, smoking status, HbA1c, PC PRS, and T2DM PRS; Model 2 replaced PC and T2DM PRS with NODM-PC PRS; Model 3 included only non-genetic factors. Model 2 had the highest discrimination (Harrell's C-index 0.823 (95% CI: 0.806-0.840)), demonstrated the best clinical utility with good calibration, and showed significant classification improvement (continuous net reclassification index: 26.89% and 31.93% for cases, 28.51% and 30.90% for controls, compared to Models 1 and 3). The positive predictive value for the top 1% predicted risk in Model 2 was 13.25%.

CONCLUSIONS

This NODM-PC PRS enhances NODM-PC risk prediction, efficiently identifies individuals at high risk for PC screening, and improves PC screening efficiency at the population level among NODM individuals.

Potentially diagnostic and prognostic roles of piRNAs/PIWIs in pancreatic cancer: A review

Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive malignancy with limited early diagnostic methods and therapeutic options, contributing to its poor prognosis. Recent advances in high-throughput sequencing have highlighted the critical roles of noncoding RNAs (ncRNAs), particularly PIWI-interacting RNAs (piRNAs), in cancer biology. In this review, we systematically summarize the emerging roles of piRNAs and their associated PIWI proteins in PDAC pathogenesis, progression, and prognosis. We provide a comprehensive analysis of the molecular mechanisms by which piRNAs/PIWIs regulate gene expression and cellular signaling pathways in PDAC. Furthermore, we discuss their potential as novel biomarkers for early diagnosis and therapeutic targets. Importantly, this review identifies key piRNAs/PIWIs involved in PDAC and proposes innovative strategies for improving diagnosis and treatment outcomes. Our work not only consolidates current knowledge but also offers new perspectives for future research and clinical applications in PDAC management.

Can AI Be Useful in the Early Detection of Pancreatic Cancer in Patients with New-Onset Diabetes?

Pancreatic cancer is one of the most lethal neoplasms. Despite considerable research conducted in recent decades, not much has been achieved to improve its survival rate. That may stem from the lack of effective screening strategies in increased pancreatic cancer risk groups. One population that may be appropriate for screening is new-onset diabetes (NOD) patients. Such a conclusion stems from the fact that pancreatic cancer can cause diabetes several months before diagnosis. The most widely used screening tool for this population, the ENDPAC (Enriching New-Onset Diabetes for Pancreatic Cancer) model, has not achieved satisfactory results in validation trials. This provoked the first attempts at using artificial intelligence (AI) to create larger, multi-parameter models that could better identify the at-risk population, which would be suitable for screening. The results shown by the authors of these trials seem promising. Nonetheless, the number of publications is limited, and the downfalls of using AI are not well highlighted. This narrative review presents a summary of previous publications, recent advancements and feasible solutions for effective screening of patients with NOD for pancreatic cancer.

Cancer incidence inconsistency between UK Biobank participants and the population: a prospective cohort study

BACKGROUND

While the UK Biobank has been widely used for cancer research, its representativeness of the population in terms of cancer incidence has not been thoroughly investigated.

METHODS

We conducted a prospective cohort study of 466,163 UK Biobank participants who were cancer-free at recruitment. Standardised incidence ratios (SIRs) were calculated for all cancers combined and for 25 cancers, by comparing incidences for the participants with the UK national incidences. Variations in SIR by age, sex and deprivation measures were investigated.

RESULTS

Over a median follow-up period of 12 years, 47,535 participants had a cancer diagnosis. The SIR for all cancers combined was 0.90 (95% CI: 0.89, 0.91). The SIR increased with age and deprivation (P = 10-9). The SIRs of 17 cancers differed from 1 (Bonferroni-adjusted P < 0.05): for prostate cancer and melanoma the SIRs were 1.2 and for the other 15 cancers the SIRs ranged from 0.43 to 0.93. The SIRs of 13 cancers differed by deprivation: the greater the deprivation, the lower the SIRs for prostate cancer and melanoma, and the higher the SIRs for the other 11 cancers.

CONCLUSIONS

The overall cancer incidence was 10% lower for the UK Biobank participants compared with the population, with most cancers having a lower incidence that increased with deprivation. Irrespective of their causes, the inconsistencies could bias UK Biobank research results related to absolute cancer risks, such as the development and/or validation of cancer risk models and penetrance estimates for cancer susceptibility genes.

Pancreatic Cancer: Screening and Early Detection

Pancreatic cancer, often diagnosed at advanced stages, has poor survival rates. Effective screening aims to detect the disease early, improving outcomes. Current guidelines recommend screening high-risk groups, including those with a family history or genetic predispositions, using methods like endoscopic ultrasound and MRI. The American Gastroenterological Association and other organizations advise annual surveillance for high-risk individuals, typically starting at the age of 50 or 10 years younger than the youngest affected relative. For certain genetic syndromes, such as Peutz-Jeghers syndrome or hereditary pancreatitis, screening may begin as early as the age of 35 to 40 years.

Recommendations, evidence and sustainability of screening for pancreatic cancer in high-risk individuals

Pancreatic cancer is a highly lethal malignancy and is predicted to become the second leading cause of cancer-related deaths by 2030. Early detection significantly improves outcomes, but general population screening remains infeasible due to the low prevalence of the disease and lack of specific biomarkers. This review evaluates current recommendations for pancreatic cancer surveillance in high-risk individuals, synthesises evidence from recent studies and explores the sustainability of current imaging-based surveillance programmes. Challenges such as overdiagnosis, economic feasibility and disparities in access highlight the need for targeted, cost-effective strategies. Collaborative initiatives and consortia are needed to advance biomarker research and refine risk stratification. By integrating evidence-based recommendations with sustainable approaches, this review outlines pathways to improve early detection and reduce mortality from pancreatic cancer.

Genetic landscape for screening and early diagnosis of pancreatic ductal adenocarcinoma: is there a signature?

The last 15 years have seen unprecedent advancement in genomics techniques such as dense single nucleotide variants (SNVs) arrays or next generation Sequencing. In parallel, new analytical methodologies have been developed to streamline data understanding and integration. These advances have been instrumental in identifying common genetic variants associated with pancreatic ductal adenocarcinoma (PDAC) risk. The role of the individual variants is rather small, and they have no clinical utility for screening or early detection. However, their combined effect computed though polygenic risk scores (PGS) are showing promising potentiality in PDAC risk prediction. There still caveats, and limitations that need to be properly addressed however it is foreseeable that the genetic background will become a powerful tool in PDAC prediction, leveraging the advantage that it has compared to other biomarkers: germline genetics is invariable from birth to death.

Utility of Polygenic Risk Scores (PRSs) in Predicting Pancreatic Cancer: A Systematic Review and Meta-Analysis of Common-Variant and Mixed Scores with Insights into Rare Variant Analysis

Pancreatic adenocarcinoma is the most common histological subtype of pancreatic cancer, representing approximately 85% of all cases [...].

Sex-Related Differences in Pancreatic Ductal Adenocarcinoma Progression and Response to Therapy

Pancreatic ductal adenocarcinoma (PDAC) is one of the most deadly malignancies with an increasing incidence rate and limited therapeutic options. Biological sex has an impact on many aspects of PDAC development and response to therapy, yet it is highly unappreciated in both basic and translational research, and worryingly in PDAC clinical trials. In this review, we summarize how biological sex influences PDAC incidence and mortality, genetic and epigenetic landscapes, anti-tumor immunity, responses to hormones, cachexia, and the efficacy of therapy. We highlight the importance of sex as a variable and discuss how to implement it into preclinical and clinical research. These considerations should be of use to researchers aiming at improving understanding of PDAC biology and developing precision medicine therapeutic strategies.

Sorcin can trigger pancreatic cancer-associated new-onset diabetes through the secretion of inflammatory cytokines such as serpin E1 and CCL5

A rise in blood glucose is an early warning sign of underlying pancreatic cancer (PC) and may be an indicator of genetic events in PC progression. However, there is still a lack of mechanistic research on pancreatic cancer-associated new-onset diabetes (PCAND). In the present study, we identified a gene SRI, which possesses a SNP with the potential to distinguish PCAND and Type 2 diabetes mellitus (T2DM), by machine learning on the basis of the UK Biobank database. In vitro and in vivo, sorcin overexpression induced pancreatic β-cell dysfunction. Sorcin can form a positive feedback loop with STAT3 to increase the transcription of serpin E1 and CCL5, which may directly induce β-cell dysfunction. In 88 biopsies, the expression of sorcin was elevated in PC tissues, especially in PCAND samples. Furthermore, plasma serpin E1 levels are higher in peripheral blood samples from PCAND patients than in those from T2DM patients. In conclusion, sorcin may be the key driver in PCAND, and further study on the sorcin-STAT3-serpin E1/CCL5 signaling axis may help us better understand the pathogenesis of PCAND and identify potential biomarkers.

Association of healthy lifestyle factors and genetic liability with bipolar disorder: Findings from the UK Biobank

BACKGROUND

The interplay between genetic and lifestyle factors in the development of bipolar disorder (BD) remains unclear.

METHODS

A cohort study was carried out on 365,517 participants from the UK Biobank. Lifestyle scores, based on smoking, physical activity, diet, alcohol consumption, sedentary behavior, sleep duration, and social contact, were grouped as favorable (scores 6-7), intermediate (scores 4-5), or unfavorable (scores 0-3). The BD polygenic risk score (PRS) was also categorized into high, intermediate, and low-risk groups using PRS tertiles. Cox regression models determined hazard ratios (HRs) and 95 % confidence intervals (CIs) for BD.

RESULTS

During the 12.9-year follow-up, 529 individuals developed BD. Comparing those with favorable lifestyles to those with unfavorable participants, the HR of developing BD was 3.28 (95 % CI, 2.76-3.89). Similarly, individuals with a high PRS had a risk of 3.20 (95 % CI, 2.83-3.63) compared to those with a low PRS. Notably, individuals with both a high PRS and an unfavorable lifestyle had a significantly higher risk of BD (HR = 6.31, 95 % CI, 4.14-9.63) compared to those with a low PRS and a favorable lifestyle. Additionally, the interaction between PRS and lifestyle contributed an additional risk, with a relative excess risk of 1.74 (95 % CI, 0.40-3.07) and an attributable proportion due to the interaction of 0.37 (95 % CI, 0.16-0.58).

CONCLUSIONS

Our findings suggest that genetic liability for BD, measured as PRS, and lifestyle have an additive effect on the risk of developing BD. A favorable lifestyle was associated with a reduced risk of developing BD.

Predicting Pancreatic Cancer in New-Onset Diabetes Cohort Using a Novel Model With Integrated Clinical and Genetic Indicators: A Large-Scale Prospective Cohort Study

INTRODUCTION

Individuals who develop new-onset diabetes have been identified as a high-risk cohort for pancreatic cancer (PC), exhibiting an incidence rate nearly 8 times higher than the general population. Hence, the targeted screening of this specific cohort presents a promising opportunity for early pancreatic cancer detection. We aimed to develop and validate a novel model capable of identifying high-risk individuals among those with new-onset diabetes.

METHODS

Employing the UK Biobank cohort, we focused on those developing new-onset diabetes during follow-up. Genetic and clinical characteristics available at registration were considered as candidate predictors. We conducted univariate regression analysis to identify potential indicators and used a 5-fold cross-validation method to select optimal predictors for model development. Five machine learning algorithms were used for model development.

RESULTS

Among 12,735 patients with new-onset diabetes, 100 (0.8%) were diagnosed with PC within 2 years. The final model (area under the curve, 0.897; 95% confidence interval, 0.865-0.929) included 5 clinical predictors and 24 single nucleotide polymorphisms. Two threshold cut-offs were established: 1.28% and 5.26%. The recommended 1.28% cut-off, based on model performance, reduces definitive testing to 13% of the total population while capturing 76% of PC cases. The high-risk threshold is 5.26%. Utilizing this threshold, only 2% of the population needs definitive testing, capturing nearly half of PC cases.

CONCLUSIONS

We, for the first time, combined clinical and genetic data to develop and validate a model to determine the risk of pancreatic cancer in patients with new-onset diabetes using machine learning algorithms. By reducing the number of unnecessary tests while ensuring that a substantial proportion of high-risk patients are identified, this tool has the potential to improve patient outcomes and optimize healthcare sources.

Genetically predicted gut bacteria, circulating bacteria-associated metabolites and pancreatic ductal adenocarcinoma: a Mendelian randomisation study

Pancreatic ductal adenocarcinoma (PDAC) has high mortality and rising incidence rates. Recent data indicate that the gut microbiome and associated metabolites may play a role in the development of PDAC. To complement and inform observational studies, we investigated associations of genetically predicted abundances of individual gut bacteria and genetically predicted circulating concentrations of microbiome-associated metabolites with PDAC using Mendelian randomisation (MR). Gut microbiome-associated metabolites were identified through a comprehensive search of Pubmed, Exposome Explorer and Human Metabolome Database. Single Nucleotide Polymorphisms (SNPs) associated by Genome-Wide Association Studies (GWAS) with circulating levels of 109 of these metabolites were collated from Pubmed and the GWAS catalogue. SNPs for 119 taxonomically defined gut genera were selected from a meta-analysis performed by the MiBioGen consortium. Two-sample MR was conducted using GWAS summary statistics from the Pancreatic Cancer Cohort Consortium (PanScan) and the Pancreatic Cancer Case-Control Consortium (PanC4), including a total of 8,769 cases and 7,055 controls. Inverse variance-weighted MR analyses were performed along with sensitivity analyses to assess potential violations of MR assumptions. Nominally significant associations were noted for genetically predicted circulating concentrations of mannitol (odds ratio per standard deviation [ORSD] = 0.97; 95% confidence interval [CI]: 0.95-0.99, p = 0.006), methionine (ORSD= 0.97; 95%CI: 0.94-1.00, p = 0.031), stearic acid (ORSD= 0.93; 95%CI: 0.87-0.99, p = 0.027), carnitine = (ORSD=1.01; 95% CI: 1.00-1.03, p = 0.027), hippuric acid (ORSD= 1.02; 95%CI: 1.00-1.04, p = 0.038) and 3-methylhistidine (ORSD= 1.05; 95%CI: 1.01-1.10, p = 0.02). Two gut microbiome genera were associated with reduced PDAC risk; Clostridium sensu stricto 1 (OR: 0.88; 95%CI: 0.78-0.99, p = 0.027) and Romboutsia (OR: 0.87; 95%CI: 0.80-0.96, p = 0.004). These results, though based only on genetically predicted gut microbiome characteristics and circulating bacteria-related metabolite concentrations, provide evidence for causal associations with pancreatic carcinogenesis.

The role of aspirin in the prevention of pancreatic cancer: A nested case-control study in the UK Biobank

BACKGROUND

Aspirin and other non-steroidal anti-inflammatory drugs (NSAIDs) usage has been associated with pancreatic ductal adenocarcinoma (PDAC) prevention, though epidemiological data have not reliably demonstrated this. The aim of this study is to identify if aspirin and other NSAIDs are effective in the primary prevention of PDAC in a large UK prospective cohort.

METHODS

A nested case-control study was conducted using the UK Biobank cohort. Incident PDAC cases (n = 1129 of whom 239 (21.2 %) were using aspirin) were age and sex-matched with cancer-free controls (n = 8822 of whom 1752 (19.9 %) were using aspirin). Conditional logistic regression models were used to generate odds ratios (ORs) and 95 % confidence intervals (CI) for risk of PDAC with and without regular use of aspirin, non-aspirin NSAIDs and all NSAIDs respectively. Exploratory analyses were carried out assessing interactions with diabetes mellitus (DM) as a condition with increased pancreatic cancer risk.

RESULTS

Regular aspirin use at initial recruitment was independently associated with a decreased risk of PDAC (OR [95 % CI] = 0.80 [0.68-0.95] P = 0.01). Regular non-aspirin NSAID use was not associated with a risk reduction of PDAC (OR [95 % CI] = 1.01 [0.84-1.23] P = 0.88). Exploratory analyses showed that in those with DM; regular aspirin use reduced risk of PDAC (OR [95 % CI] = 0.60 [0.42-0.85] P = 0.004) compared to non-use.

DISCUSSION

Regular aspirin use is associated with a reduction in risk of PDAC. The reduced risk is more apparent in participants with DM.

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.

Remnant Pancreas Volume Affects New-Onset Impaired Glucose Homeostasis Secondary to Pancreatic Cancer

BACKGROUND

New-onset diabetes (NOD) has been identified as a high-risk factor for the early detection of pancreatic ductal adenocarcinoma (PDAC). The role of tumor volume and remnant pancreas volume (RPV) in the progression from normal to NOD in PDAC patients is not fully illustrated yet.

METHODS

In this cross-sectional study, glycemic metabolism traits of 95 PDAC patients before pancreatic surgery were described and compared with chronic pancreatitis and type 2 diabetes mellitus patients based on the oral glucose tolerance test. The remnant RPV and tumor volume, calculated by three-dimensional reconstruction of radiological images, were included in the ordinal logistic regression models.

RESULTS

The prevalence of NOD was high among PDAC patients (38.9%). However, normal glucose tolerance (NGT) or prediabetes mellitus status were present as more than half (24/44) of advanced tumor stage patients. Indexes reflecting beta-cell function but not insulin sensitivity gradually worsened from NGT to NOD patients (all p < 0.05). The remnant pancreas volume (RPV) was identified as a potential protective factor for diabetes secondary to PDAC (odds ratio 0.95, 95% CI [0.92, 0.97], p < 0.001).

CONCLUSIONS

Reduced RPV causing beta-cell dysfunction might be one of the mechanisms of NOD secondary to PDAC. Subjects with sufficient pancreas volume could not be detected earlier when regarding patients with NOD as the population at risk for PDAC.

Trace elements in pancreatic cancer

BACKGROUND

Pancreatic cancer (PCA) is an extremely aggressive malignant cancer with an increasing incidence and a low five-year survival rate. The main reason for this high mortality is that most patients are diagnosed with PCA at an advanced stage, missing early treatment options and opportunities. As important nutrients of the human body, trace elements play an important role in maintaining normal physiological functions. Moreover, trace elements are closely related to many diseases, including PCA.

REVIEW

This review systematically summarizes the latest research progress on selenium, copper, arsenic, and manganese in PCA, elucidates their application in PCA, and provides a new reference for the prevention, diagnosis and treatment of PCA.

CONCLUSION

Trace elements such as selenium, copper, arsenic and manganese are playing an important role in the risk, pathogenesis, diagnosis and treatment of PCA. Meanwhile, they have a certain inhibitory effect on PCA, the mechanism mainly includes: promoting ferroptosis, inducing apoptosis, inhibiting metastasis, and inhibiting excessive proliferation.

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.

Studying the impact of marital status on diagnosis and survival prediction in pancreatic ductal carcinoma using machine learning methods

Pancreatic cancer is a commonly occurring malignant tumor, with pancreatic ductal carcinoma (PDAC) accounting for approximately 95% of cases. According of its poor prognosis, identifying prognostic factors of pancreatic ductal carcinoma can provide physicians with a reliable theoretical foundation when predicting patient survival. This study aimed to analyze the impact of marital status on survival outcomes of PDAC patients using propensity score matching and machine learning. The goal was to develop a prognosis prediction model specific to married patients with PDAC. We extracted a total of 206,968 patient records of pancreatic cancer from the SEER database. To ensure the baseline characteristics of married and unmarried individuals were balanced, we used a 1:1 propensity matching score. We then conducted Kaplan-Meier analysis and Cox proportional-hazards regression to examine the impact of marital status on PDAC survival before and after matching. Additionally, we developed machine learning models to predict 5-year CSS and OS for married patients with PDAC specifically. In total, 24,044 PDAC patients were included in this study. After 1:1 propensity matching, 8043 married patients and 8,043 unmarried patients were successfully enrolled. Multivariate analysis and the Kaplan-Meier curves demonstrated that unmarried individuals had a poorer survival rate than their married counterparts. Among the algorithms tested, the random forest performed the best, with 0.734 5-year CSS and 0.795 5-year OS AUC. This study found a significant association between marital status and survival in PDAC patients. Married patients had the best prognosis, while widowed patients had the worst. The random forest is a reliable model for predicting survival in married patients with PDAC.

Identification of Pancreatic Cancer Germline Risk Variants With Effects That Are Modified by Smoking

PURPOSE

Pancreatic cancer (PC) is a deadly disease most often diagnosed in late stages. Identification of high-risk subjects could both contribute to preventative measures and help diagnose the disease at earlier timepoints. However, known risk factors, assessed independently, are currently insufficient for accurately stratifying patients. We use large-scale data from the UK Biobank (UKB) to identify genetic variant-smoking interaction effects and show their importance in risk assessment.

METHODS

We draw data from 15,086,830 genetic variants and 315,512 individuals in the UKB. There are 765 cases of PC. Crucially, robust resampling corrections are used to overcome well-known challenges in hypothesis testing for interactions. Replication analysis is conducted in two independent cohorts totaling 793 cases and 570 controls. Integration of functional annotation data and construction of polygenic risk scores (PRS) demonstrate the additional insight provided by interaction effects.

RESULTS

We identify the genome-wide significant variant rs77196339 on chromosome 2 (per minor allele odds ratio in never-smokers, 2.31 [95% CI, 1.69 to 3.15]; per minor allele odds ratio in ever-smokers, 0.53 [95% CI, 0.30 to 0.91]; P = 3.54 × 10-8) as well as eight other loci with suggestive evidence of interaction effects (P < 5 × 10-6). The rs77196339 region association is validated (P < .05) in the replication sample. PRS incorporating interaction effects show improved discriminatory ability over PRS of main effects alone.

CONCLUSION

This study of genome-wide germline variants identified smoking to modify the effect of rs77196339 on PC risk. Interactions between known risk factors can provide critical information for identifying high-risk subjects, given the relative inadequacy of models considering only main effects, as demonstrated in PRS. Further studies are necessary to advance toward comprehensive risk prediction approaches for PC.

[Early detection of sporadic pancreatic cancer]

The incidence of pancreatic cancer is rising. At present, pancreatic cancer is the third most common cancer-causing death in Germany, but it is expected to become the second in 2030 and finally the leading cause of cancer death in 2050. Pancreatic ductal adenocarcinoma (PC) is generally diagnosed at advanced stages, and 5-year-survival has remained poor. Early detection of sporadic PC at stage IA, however, can yield a 5-year-survival rate of about 80%. Early detection initiatives aim at identifying persons at high risk. People with new-onset diabetes at age 50 or older have attracted much interest. Novel strategies regarding how to detect sporadic PC at an early stage are being discussed.

Association of genetic risk and lifestyle with pancreatic cancer and their age dependency: a large prospective cohort study in the UK Biobank

BACKGROUND

Pancreatic cancer (PC) is influenced by both genetic and lifestyle factors. However, further research is still needed to comprehensively clarify the relationships among lifestyle, genetic factors, their combined effect on PC, and how these associations might be age-dependent.

METHODS

We included 340,631 participants from the UK Biobank. Three polygenic risk score (PRS) models for PC were applied, which were derived from the previous study and were categorized as low, intermediate, and high. Two healthy lifestyle scores (HLSs) were constructed using 9 lifestyle factors based on the World Cancer Research Fund/American Institute of Cancer Research (WCRF/AICR) lifestyle score and the American Cancer Society (ACS) guidelines and were categorized as unfavorable, intermediate, and favorable. Data were analyzed using Cox proportional hazards models.

RESULTS

There were 1,129 cases of incident PC during a median follow-up of 13.05 years. Higher PRS was significantly associated with an increased risk of PC (hazard ratio [HR], 1.58; 95% confidence intervals [CI], 1.47-1.71). Adhering to a favorable lifestyle was associated with a lower risk (HR, 0.48; 95% CI, 0.41-0.56). Participants with an unfavorable lifestyle and a high PRS had the highest risk of PC (HR, 2.84; 95% CI, 2.22-3.62). Additionally, when stratified by age, a favorable lifestyle was most pronounced associated with a lower risk of PC among participants aged ≤ 60 years (HR, 0.35; 95% CI, 0.23-0.54). However, the absolute risk reduction was more pronounced among those aged > 70 years (ARR, 0.19%, 95% CI, 0.13%-0.26%). A high PRS was more strongly associated with PC among participants aged ≤ 60 years (HR, 1.89; 95% CI, 1.30-2.73). Furthermore, we observed a significant multiplicative interaction and several significant additive interactions.

CONCLUSIONS

A healthy lifestyle was associated with a lower risk of PC, regardless of the participants' age, sex, or genetic risk. Importantly, our findings indicated the age-dependent association of lifestyle and genetic factors with PC, emphasizing the importance of early adoption for effective prevention and potentially providing invaluable guidance for setting the optimal age to start preventive measures.

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.

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.

Effects of potentilla discolor bunge extracts on oxidative stress and glycolipid metabolism in animal models of diabetes: a systematic review and meta-analysis

Background/aim: Potentilla discolor Bunge (PDB) is an ancient herb of traditional Chinese medicine. Studies have suggested that extracts of PDB may ameliorate diabetes mellitus (DM). This study aimed to systematically assess the efficacy of PDB extracts on glycolipid metabolism and oxidative stress in animal models of diabetes and to provide evidence-based references for the use of PDB extracts. Methods: This study followed the PRISMA 2020 guidelines. Studies were searched from eight databases until January 2023. Statistical analysis was performed using StataSE 15.0 and RevMan 5.3. The standard mean difference (SMD) and 95% confidence intervals (CI) were computed using the random-effects model. SYRCLE's risk of bias tool was used to assess the risk of bias. Results: In total, 32 studies with 574 animals were included. The findings demonstrated that PDB extracts considerably lowered fasting blood glucose (SMD: -3.56, 95%CI: -4.40 to -2.72, p < 0.00001); insulin resistance (SMD: -3.19, 95% CI: -5.46 to -0.92, p = 0.006), total cholesterol (SMD: -2.18, 95%CI: -2.89 to -1.46, p < 0.00001), triglyceride (SMD: -1.48, 95% CI: -2.01 to -0.96, p < 0.00001), low-density lipoprotein cholesterol (SMD: -1.80, 95% CI: -2.58 to -1.02], p < 0.00001), malondialdehyde (SMD: -3.46, 95% CI: -4.64 to -2.29, p < 0.00001) and free fatty acid levels (SMD: -3.25, 95%CI: -5.33 to -1.16, p = 0.002), meanwhile, increased insulin sensitivity index (SMD: 2.51 95% CI: 1.10 to 3.92, p = 0.0005), body weight (SMD:1.20, 95% CI: 0.38 to 2.01, p = 0.004), and the levels of high-density lipoprotein cholesterol (SMD: 1.04, 95% CI: 0.40 to 1.69, p = 0.001), superoxide dismutase (SMD:2.63, 95% CI: 1.53 to 3.73, p < 0.00001), glutathione peroxidase (SMD:1.13, 95%CI: 0.42 to1.83, p = 0.002), and catalase (SMD:0.75, 95% CI: 0.11 to 1.40], p = 0.02). Conclusion: These findings suggest that PDB extracts can ameliorate DM by improving glycolipid metabolism and oxidative stress. PDB may be a promising medication for DM; however, due to significant heterogeneity between studies, these findings should be interpreted with caution. In addition, future well-designed trials should determine which components of the PDB play a major role in ameliorating DM and whether these benefits persist in humans. Systematic Review Registration: https://www.crd.york.ac.uk/prospero, CRD42023379391.

Polygenic scores in cancer

Since the publication of the first genome-wide association study for cancer in 2007, thousands of common alleles that are associated with the risk of cancer have been identified. The relative risk associated with individual variants is small and of limited clinical significance. However, the combined effect of multiple risk variants as captured by polygenic scores (PGSs) may be much greater and therefore provide risk discrimination that is clinically useful. We review the considerable research efforts over the past 15 years for developing statistical methods for PGSs and their application in large-scale genome-wide association studies to develop PGSs for various cancers. We review the predictive performance of these PGSs and the multiple challenges currently limiting the clinical application of PGSs. Despite this, PGSs are beginning to be incorporated into clinical multifactorial risk prediction models to stratify risk in both clinical trials and clinical implementation studies.

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.

Increased risk of pancreatic, thyroid, prostate and breast cancers in men with a family history of breast cancer: A population-based study

The association between a family history of breast cancer (FHBC) in female first-degree relatives (FDRs) and cancer risk in men has not been evaluated. This study aimed to compare the risks of overall and site-specific cancers in men with and without FHBC. A population-based study was conducted with 3 329 106 men aged ≥40 years who underwent national cancer screening between 2013 and 2014. Men with and without FHBC in their female FDRs were age-matched in a 1:4 ratio. Men without FHBC were defined as those without a family history of any cancer type in their FDRs. Data from 69 124 men with FHBC and 276 496 men without FHBC were analyzed. The mean follow-up period was 4.7 ± 0.9 years. Men with an FHBC in any FDR (mother or sister) had a higher risk of pancreatic, thyroid, prostate and breast cancers than those without an FHBC (adjusted hazard ratios [aHRs] (95% confidence interval [CI]): 1.35 (1.07-1.70), 1.33 (1.12-1.56), 1.28 (1.13-1.44) and 3.03 (1.130-8.17), respectively). Although an FHBC in any one of the FDRs was not associated with overall cancer risk, FHBC in both mother and sibling was a significant risk factor for overall cancer (aHR: 1.69, 95% CI:1.11-2.57) and increased the risk of thyroid cancer by 3.41-fold (95% CI: 1.10-10.61). FHBC in the mother or sister was a significant risk factor for pancreatic, thyroid, prostate and breast cancers in men; therefore, men with FHBC may require more careful BRCA1/2 mutation-related cancer surveillance.

Emerging role of non-invasive and liquid biopsy biomarkers in pancreatic cancer

A global increase in the incidence of pancreatic cancer (PanCa) presents a major concern and health burden. The traditional tissue-based diagnostic techniques provided a major way forward for molecular diagnostics; however, they face limitations based on diagnosis-associated difficulties and concerns surrounding tissue availability in the clinical setting. Late disease development with asymptomatic behavior is a drawback in the case of existing diagnostic procedures. The capability of cell free markers in discriminating PanCa from autoimmune pancreatitis and chronic pancreatitis along with other precancerous lesions can be a boon to clinicians. Early-stage diagnosis of PanCa can be achieved only if these biomarkers specifically discriminate the non-carcinogenic disease stage from malignancy with respect to tumor stages. In this review, we comprehensively described the non-invasive disease detection approaches and why these approaches are gaining popularity for their early-stage diagnostic capability and associated clinical feasibility.

KRAS mutation: The booster of pancreatic ductal adenocarcinoma transformation and progression

Pancreatic ductal adenocarcinoma (PDAC) is the most common type of pancreatic cancer. It has a poor response to conventional therapy and has an extremely poor 5-year survival rate. PDAC is driven by multiple oncogene mutations, with the highest mutation frequency being observed in KRAS. The KRAS protein, which binds to GTP, has phosphokinase activity, which further activates downstream effectors. KRAS mutation contributes to cancer cell proliferation, metabolic reprogramming, immune escape, and therapy resistance in PDAC, acting as a critical driver of the disease. Thus, KRAS mutation is positively associated with poorer prognosis in pancreatic cancer patients. This review focus on the KRAS mutation patterns in PDAC, and further emphases its role in signal transduction, metabolic reprogramming, therapy resistance and prognosis, hoping to provide KRAS target therapy strategies for PDAC.

A Strategic Research Framework for Defeating Diabetes in India: A 21st-Century Agenda

Indian people are at high risk for type 2 diabetes (T2DM) even at younger ages and lower body weights. Already 74 million people in India have the disease, and the proportion of those with T2DM is increasing across all strata of society. Unique aspects, related to lower insulin secretion or function, and higher hepatic fat deposition, accompanied by the rise in overweight (related to lifestyle changes) may all be responsible for this unrelenting epidemic of T2DM. Yet, research to understand the causes, pathophysiology, phenotypes, prevention, treatment, and healthcare delivery of T2DM in India seriously lags behind. There are major opportunities for scientific discovery and technological innovation, which if tapped can generate solutions for T2DM relevant to the country's context and make leading contributions to global science. We analyze the situation of T2DM in India, and present a four-pillar (etiology, precision medicine, implementation research, and health policy) strategic research framework to tackle the challenge. We offer key research questions for each pillar, and identify infrastructure needs. India offers a fertile environment for shifting the paradigm from imprecise late-stage diabetes treatment toward early-stage precision prevention and care. Investing in and leveraging academic and technological infrastructures, across the disciplines of science, engineering, and medicine, can accelerate progress toward a diabetes-free nation.

[Prevention of pancreatic cancer]

The incidence of pancreatic cancer is rising in Germany. At present pancreatic cancer is the third commonest cause of cancer death but is expected to become the second in 2030 and finally the leading cause of cancer death in 2050. Pancreatic ductal adenocarcinoma (PC) is generally diagnosed at far advanced stages and 5-year-survival has remained poor. Modifiable risk factors of PC are tobacco smoking, excess body weight, alcohol use, type 2-diabetes and the metabolic syndrome. Smoking cessation and -in case of obesity- intentional weight loss can reduce PC risk by as much as 50 %. Early detection of asymptomatic sporadic PC at stage IA - stage IA-PC now has a 5-year-survival rate of about 80 %- has become a realistic chance for people older than 50 years with new-onset diabetes.

Pancreatic Cancer: Changing Epidemiology and New Approaches to Risk Assessment, Early Detection, and Prevention

Pancreatic cancer usually results in poor survival with limited options for treatment, as most affected individuals present with advanced disease. Early detection of preinvasive pancreatic neoplasia and identifying molecular therapeutic targets provide opportunities for extending survival. Although screening for pancreatic cancer is currently not recommended for the general population, emerging evidence indicates that pancreatic surveillance can improve outcomes for individuals in certain high-risk groups. Changes in the epidemiology of pancreatic cancer, experience from pancreatic surveillance, and discovery of novel biomarkers provide a roadmap for new strategies for pancreatic cancer risk assessment, early detection, and prevention.

Role of Up-Regulated Transmembrane Channel-Like Protein 5 in Pancreatic Adenocarcinoma

BACKGROUND

Pancreatic adenocarcinoma (PAAD) is a malignant tumor responsible for a heavy disease burden. Previously, only one pan-cancer study of Transmembrane channel-like protein 5 (TMC5) showed that TMC5 was highly expressed in PAAD, but the results lacked comprehensive verification, and the mechanism of TMC5 in PAAD was still unclear.

METHODS

For exploring the expression and clinical value of TMC5 in PAAD better, we adopted a comprehensive evaluation method, using internal immunohistochemistry (IHC) data combined with microarray and RNA-sequencing data collected from public databases. The single cell RNA-sequencing (scRNA-seq) data were exploited to explore the TMC5 expression in cell populations and intercellular communication. The potential mechanism of TMC5 in PAAD was analyzed from the aspects of immune infiltration, transcriptional regulation, function and pathway enrichment.

RESULTS

Our IHC data includes 148 PAAD samples and 19 non-PAAD samples, along with the available microarray and RNA-sequencing data (1166 PAAD samples, 704 non-PAAD samples). The comprehensive evaluation results showed that TMC5 was evidently up-regulated in PAAD (SMD = 1.17). Further analysis showed that TMC5 was over-expressed in cancerous epithelial cells. Furthermore, TMC5 was up-regulated in more advanced tumor T and N stages. Interestingly, we found that STAT3 as an immune marker of Th17 cells was not only positively correlated with TMC5 and up-regulated in PAAD tissues, but also the major predicted TMC5 transcription regulator. Moreover, STAT3 was involved in cancer pathway of PAAD.

CONCLUSION

Up-regulated TMC5 indicates advanced tumor stage in PAAD patients, and its role in promoting PAAD development may be regulated by STAT3.

Oxidatively Damaged Nucleic Acid: Linking Diabetes and Cancer

Significance: Our current knowledge of the mechanism between diabetes and cancer is limited. Oxidatively damaged nucleic acid is considered a critical factor to explore the connections between these two diseases. Recent Advances: The link between diabetes mellitus and cancer has attracted increasing attention in recent years. Emerging evidence supports that oxidatively damaged nucleic acid caused by an imbalance between reactive oxygen species generation and elimination is a bridge connecting diabetes and cancer. 8-Oxo-7,8-dihydro-2'-deoxyguanosine and 8-oxo-7,8-dihydroguanosine assume important roles as biomarkers in assessing the relationship between oxidatively damaged nucleic acid and cancer. Critical Issues: The consequences of diabetes are extensive and may lead to the occurrence of cancer by influencing a combination of factors. At present, there is no direct evidence that diabetes causes cancer by affecting a single factor. Furthermore, the difficulty in controlling variables and differences in detection methods lead to poor reliability and repeatability of results, and there are no clear cutoff values for biomarkers to indicate cancer risk. Future Directions: A better understanding of connections as well as mechanisms between diabetes and cancer is still needed. Both diabetes and cancer are currently intractable diseases. Further exploration of the specific mechanism of oxidatively damaged nucleic acid in the connection between diabetes and cancer is urgently needed. In the future, it is necessary to further take oxidatively damaged nucleic acid as an entry point to provide new ideas for the diagnosis and treatment of diabetes and cancer. Experimental drugs targeting the repair process of oxidatively generated damage require an extensive preclinical evaluation and could ultimately provide new treatment strategies for these diseases. Antioxid. Redox Signal. 37, 1153-1167.

Familial Pancreatic Cancer

Individuals at increased risk of developing pancreatic cancer, including those with a significant family history of the disease and those with pancreatic cancer susceptibility gene variants, can benefit from pancreas surveillance. Most pancreatic cancers diagnosed during surveillance are early-stage and such patients can achieve long-term survival. Determining who should undergo pancreas surveillance is still a work-in-progress, but the main tools clinicians use to estimate an individual's risk of pancreatic cancer are patient's age, the extent of their family history of pancreatic cancer, and whether or not they have a pancreatic cancer susceptibility gene mutation.