Exploring the link between pancreatic cancer and key risk factors: insights from decade-long study in Eastern Algeria

Introduction

Background

Pancreatic cancer (PC) is one of the deadliest malignancies, marked by a high mortality-to-incidence ratio of approximately 94%, making it a significant global health challenge (1,2). In 2022, an estimated 510,000 new cases of PC were diagnosed worldwide, resulting in approximately 467,000 deaths (1,2). Despite progress in research and treatment, the overall 5-year survival rate remains alarmingly low at only 13% (3,4). These statistics highlight the urgent need for improved detection and therapeutic strategies to tackle this aggressive disease.

In Algeria, PC ranks as the 14th most prevalent cancer, with 1,168 new cases and 1,136 deaths recorded in 2022 (5). These high mortality rates are indicative of significant regional disparities in healthcare access, diagnostic capacity, and public awareness (5,6).

The multifactorial etiology of PC includes diabetes mellitus (DM), smoking, obesity, and genetic predispositions (7). Notably, DM has been identified as both a risk factor and a consequence of PC. Recent studies indicate that individuals with recent-onset diabetes have a significantly higher risk of developing PC, with reported hazard ratios ranging from 2.17 to 2.97 (8,9).

Carbohydrate antigen 19-9 (CA 19-9) is widely utilized as a biomarker for pancreatic ductal adenocarcinoma (PDAC); however, its sensitivity and specificity are insufficient for definitive diagnosis, especially in low-risk populations (10). Studies have reported that CA19-9’s sensitivity ranges from 70% to 95%, and its specificity varies between 72% and 90% (11). Additionally, approximately 10% of individuals with a negative Lewis blood group phenotype do not produce CA19-9, leading to potential false-negative results (10). These limitations underscore the necessity for complementary diagnostic approaches to enhance accuracy.

Recent advancements in diagnostic techniques, such as liquid biopsies analyzing microRNAs and exosome-based biomarkers, have shown promise in improving diagnostic accuracy when combined with conventional tumor markers like CA19-9. These emerging approaches have demonstrated potential in achieving diagnostic accuracies exceeding 90%, offering hope for earlier detection and better patient outcomes (12-14).

Despite advancements in PC research, its aggressive nature often leads in late-stage diagnoses. The absence of specific symptoms means most patients present with advanced disease, limiting curative surgical options to only 15% of cases (15,16). PDAC, representing over 92% of cases, remains particularly challenging due to its early metastatic potential and poor surgical outcomes (16,17).

Rationale and knowledge gap

Although progress has been made in understanding the genetic and molecular landscape of PC, early detection remains a major challenge. Identifying novel biomarkers is crucial for improving risk prediction and guiding personalized treatment strategies.

In Algeria, epidemiological studies on PC risk factors are limited, with scarce data on the interplay between metabolic, lifestyle, and clinical variables in affected populations. This study seeks to bridge this gap by providing a comprehensive analysis of risk factors, tumor characteristics, and biomarker profiles in an Algerian cohort. By exploring regional trends and unique associations, our findings offer new insights that contribute to a deeper understanding of PC in this underrepresented population.

Furthermore, the Algerian healthcare system offers free medical services to all citizens through a network of public hospitals and clinics. Despite this, the system faces challenges, including a shortage of general practitioners and specialists, particularly in public hospitals, which has led to disparities in healthcare access and quality. These issues are exacerbated by regional disparities, with rural areas often experiencing limited access to advanced diagnostic tools and specialized care. Such challenges contribute to delays in early detection and treatment of diseases like PC, resulting in poorer patient outcomes.

Objective

This study explores the epidemiological trends and risk factors of PC while examining the diagnostic value of biomarkers, including CA19-9 and other emerging indicators, with the goal of improving early detection and enhancing patient outcomes.

Methods

Study design

This retrospective case-control study was conducted at Constantine University Hospital in Eastern Algeria from 2013 to 2023.

Population

The study included 415 participants, comprising 188 confirmed cases of PC and 227 control subjects. The case group included patients with a confirmed histopathological diagnosis of PC, complete medical records, and a recent diagnosis within the study period. Histopathological confirmation was based on the World Health Organization (WHO) Classification of Tumors of the Digestive System and national pathology guidelines (18).

Controls were recruited from the general population and outpatient departments of Constantine University Hospital. Their selection was conducted concurrently with the case group (2013–2023) to maintain temporal consistency. Controls were selected based on a 1:1.2 matching ratio, considering age groups and municipality of residence to minimize selection bias.

Selection and assessment of risk factors

Age, gender, smoking history, body mass index (BMI), diabetes, and hypertension were selected based on their well-documented association with PC. These factors have been extensively studied and identified as significant contributors to PC risk due to their biological and epidemiological relevance (19,20).

Data collection method

Patient data were collected retrospectively through structured interviews documented in medical records and follow-up consultations. Information on demographics, medical history, and lifestyle factors (such as smoking history, BMI, diabetes, and hypertension) was obtained from patient files, ensuring consistency and reliability. Supplementary data were extracted from hospital databases to complete missing information.

Definitions of clinical parameters

• Hypertension: defined as systolic blood pressure ≥140 mmHg and/or diastolic blood pressure ≥90 mmHg (21);
• Jaundice: yellowish discoloration of the skin, mucous membranes, and sclerae due to elevated bilirubin levels (22);
• Pruritus: persistent itching sensation associated with dermatologic or systemic conditions (23);
• Abdominal pain: pain between the chest and pelvis, varying in duration and etiology (24);
• Weight loss: unintentional reduction in body mass due to fluid, fat, or lean mass loss, potentially indicating underlying medical conditions (25).

Sample size calculation

To ensure adequate power to detect associations between risk factors and PC, we performed a priori sample size calculation. Based on an expected odds ratio of 2.0, a case-to-control ratio of approximately 1:1.2, and a power of 80% at a significance level of 0.05, a minimum of 400 participants (180 cases and 220 controls) was required. Our study cohort, consisting of 188 cases and 227 controls, exceeds this requirement, ensuring sufficient statistical power for valid inference.

Statistical analysis

Data were analyzed using SPSS (IBM, version 25). Categorical variables were analysed using Pearson’s Chi-squared test and presented as frequencies and percentages. Distribution normality was assessed with the Shapiro-Wilk and Kolmogorov-Smirnov tests. Non-normally distributed variables were analyzed using the Mann-Whitney U test and presented as median [interquartile range (IQR)], whereas normally distributed variables were analyzed using the independent t-test and presented as mean ± standard deviation (SD). Associations between demographic and clinical factors were first screened using univariate logistic regression. Variables with P<0.05 were entered into multivariate logistic regression models. Multicollinearity was assessed using variance inflation factors (VIF), with values >5 indicating significant Collinearity. Results were reported as adjusted odds ratios (AORs) with 95% confidence intervals (CIs). A two-tailed P value <0.05 was considered statistically significant.

Participant selection flowchart

A flowchart (Figure 1) visually represents participant recruitment, inclusion, and exclusion criteria, ensuring transparency in the selection process.

Figure 1 Study participant selection flowchart.

Ethical statement

The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the institutional ethics committee of the Faculty of Nature and Life Sciences, Mentouri Brothers University Constantine 1, Algeria. As the institution does not assign formal registration numbers, approval was documented through an official signed letter. Informed consent was obtained from all individual participants included in the study.

Results

A subgroup analysis of PDAC cases was performed to assess risk factor variations by histology.

A univariate analysis was first conducted to assess the association between demographic and clinical risk factors and PC. The results of the univariate analysis are presented in Table 1. Subsequently, a multivariate logistic regression analysis was performed, adjusting for potential confounders.

Regarding the univariate analysis, DM emerged as a significant risk factor for PC (P<0.001). In contrast, obesity (BMI ≥30 kg/m²) showed an inverse association with PC (P<0.001), with only 4.7% of the cases being classified as obese compared to 36.4% of the controls. Smoking, despite being more prevalent among cases, did not reach statistical significance (P=0.19).

The multivariate analysis confirmed these findings: DM remained an independent risk factor for PC (AOR =2.32; 95% CI: 1.52–3.53; P<0.001), whereas obesity remained inversely associated (AOR =0.28; 95% CI: 0.16–0.50; P<0.001).

Collinearity diagnostics between BMI and diabetes were conducted prior to the multivariate analysis. The VIF values obtained for BMI and diabetes were 2.05 and 2.08, respectively, suggesting the absence of significant multicollinearity (VIF <5).

It is important to note that we did not include “cancer-related weight loss” in the analysis. Given the retrospective nature of the study, reliable data regarding weight loss preceding cancer diagnosis were not available for most patients.

The mean age at diagnosis was 63.64±11.26 years for cases versus 36.30±11.25 years for controls. As matching was based on predefined age groups rather than exact age values, some variability persisted. Most PC cases were within the 60–69 years age group (36.7%), compared to 29.5% in the control group. However, this difference was not statistically significant (P=0.10).

A slight male predominance was observed among cases (54.8% vs. 49.8% in controls), yet the difference was not statistically significant (AOR =1.22, 95% CI: 0.83–1.80, P=0.30).

Smoking was exclusively reported among men in our cohort, with 32.7% of cases versus 20.7% of controls, but without statistical significance (AOR =0.53, 95% CI: 0.32–0.90, P=0.19).

Similarly, hypertension was comparable between cases (34.8%) and controls (37.0%), with no significant association (AOR =0.90, 95% CI: 0.60–1.35, P=0.63) (Table 1).

The analysis of tumor characteristics revealed a predominance of tumors located in the head of the pancreas (84.0%), followed by the body (11.2%) and tail (4.8%). The dominant histological type was adenocarcinoma (83.3%). Less frequent types included neuroendocrine tumors (6.9%), carcinoma (3.9%), pseudo papillary tumors (2.0%), papillary tumors (1.0%), and various rare types (2.9%).

Tumor staging was determined using computed tomography (CT) scans, magnetic resonance imaging (MRI), and histopathological analysis. The relatively lower proportion of metastatic cases at diagnosis may be attributed to referral bias or limitations in imaging sensitivity, which is discussed further in the limitations section. Regarding lymph node involvement, (60.3%) of patients were categorized as N0, (34.5%) as N1, and (5.2%) as N2. Notably, metastasis was detected in 33.9% of patients (M1), while the remaining 66.1% showed no signs of metastatic spread (M0) (Table 2).

Symptoms were analyzed according to primary tumor location. Patients with tumors in the pancreatic head frequently presented with jaundice (78.3%), while those with body and tail tumors more commonly reported abdominal pain (71.4%) and weight loss (55.2%). A detailed breakdown is provided in Table 3.

Concerning the biological findings, as summarized in Table 4, elevated levels of various biomarkers were observed in PC patients compared to controls. Comparisons between groups were performed using independent t-tests or Mann-Whitney U tests, as appropriate. Aspartate aminotransferase (ASAT) levels were notably higher in cases [88 (131.48) U/L] than in controls [17.85 (IQR, 8.57) U/L], with P<0.001. Similarly, alanine aminotransferase (ALAT) levels were markedly elevated in patients [85 (IQR, 164) U/L] versus controls [12.69 (IQR, 10.48) U/L], with P<0.001. Alkaline phosphatase (PAL) levels were also substantially higher in patients [385 (IQR, 629) U/L] versus controls (100.37 (IQR, 61.22) U/L), with P<0.001. Additionally, total bilirubin levels were similarly increased in patients [89.25 (IQR, 143.2) µmol/L] compared to controls [7.24 (IQR, 76.73) µmol/L], with P<0.001. Furthermore, blood glucose levels, which were normally distributed, showed a notable elevation among PC patients (1.61±0.8 mmol/L) compared to controls (0.99±0.37 mmol/L, P<0.001), suggesting a strong association between hyperglycemia and the presence of cancer. Lastly, the CA19-9 biomarker, often used for PC detection, was significantly higher in patients [257.35 (IQR, 819.65) U/mL] compared to controls [22.71 (IQR, 132.79) U/mL], with P=0.002. The relatively high CA19-9 levels in controls may be attributed to benign conditions such as biliary disease or subclinical inflammation, which should be considered when interpreting CA19-9 levels.

To examine the relationship between histopathology and sex, Table 5 indicates that adenocarcinoma was the predominant subtype for both sexes, accounting for 76.6% in females and 89.1% in males, with no significant difference between sexes (P=0.18). Other histopathological subtypes, such as carcinoma, papillary tumors, pseudo papillary tumors, neuroendocrine tumors, and additional types, appeared less frequently in both groups, with some subtypes not present in males.

In looking at the association between age and TNM classification, Table 6 shows that younger patients (<50 years) had lower frequencies of T3 and T4 stages compared to older groups, while T2 and T3 stages were more common in those aged 50–69 years. There were no significant associations between age and T stage (P=0.74), N stage (P=0.32), or M stage (P=0.19). However, older patients (≥60 years) tended to show metastasis (M1) more frequently than younger patients. Considering the tumor site and its relationship with diabetes status, Table 7 shows that pancreatic head tumors were the most common location in both diabetic and non-diabetic patients, with similar prevalence (84.81% vs. 83.49%). Tumors located in the body were less frequent in diabetic patients (7.59% vs. 13.76%), while tail tumors appeared more often in diabetics (7.59% vs. 2.75%). There was no significant association between diabetes status and tumor location (P=0.15).

Discussion

Key findings

PC remains a highly lethal malignancy due to its late-stage diagnosis and limited treatment options (26). Our study aimed to examine the association between demographic and clinical risk factors and the likelihood of developing PC.

The key findings include:

• A significantly higher prevalence of diabetes among PC patients, with an adjusted odds ratio (AOR) of 2.32, indicating a strong link between the two conditions;
• A slightly higher prevalence of PC in males (54.8%) compared to females (45.2%), though the difference was not statistically significant;
• The mean age at diagnosis was 63.64 years, with the majority of cases occurring in the 60–69 years age group, consistent with previous studies;
• The predominant histologic subtype was PDAC, accounting for 83.3% of cases, with 84.0% of tumors located in the pancreatic head;
• Elevated levels of key biomarkers (CA19-9, ASAT, ALAT, PAL and total bilirubin) in PC patients, suggesting potential diagnostic and prognostic significance.

Strengths and limitations

The main strength of this study lies in the robustness of the dataset, allowing for comprehensive analysis of multiple demographic and clinical variables. The use of validated biomarkers and clinical parameters enhances the reliability of the findings.

However, several limitations must be acknowledged. The retrospective design introduces potential biases, including selection bias and missing data. Moreover, while a strong association between diabetes and PC was observed, causality cannot be inferred. The absence of genetic profiling is another limitation, as it precluded the exploration of hereditary risk factors that could have enriched the analysis.

Comparison with similar research

Our results align with previous research demonstrating a strong association between diabetes and PC. Studies have shown that new-onset diabetes, particularly in individuals over the age of 50 years (27). Similarly, Ben et al. observed that new-onset diabetes is often a precursor to PC, highlighting the potential role of diabetes screening in early detection strategies (28).

Regarding gender distribution, our findings align with those of Rawla et al., who observed a slightly higher prevalence in males, potentially due to higher smoking rates, alcohol consumption, and occupational exposures (29). Several cohort studies have investigated the role of hormonal exposures in female pancreatic cancer risk, notably the Malmö Diet and Cancer Study (30). However, the rising incidence among younger women, as noted in our study, suggests evolving risk factors, such as changing dietary habits, obesity trends, and environmental exposures, that require further investigation.

The predominance of tumors in the pancreatic head corresponds with epidemiological data indicating that approximately 80% of PCs occurs in this region. This location often leads to earlier symptom onset, particularly obstructive jaundice, compared to tumors in the body or tail of the pancreas (31). A study by Yachida et al. suggested that tumors in the pancreatic head may have distinct molecular pathways and earlier progression compared to those in the tail, which are often diagnosed at more advanced stages due to delayed symptom presentation (32).

Our finding of an inverse association between obesity and PC contrasts with the well-established positive correlation reported in large-scale meta-analyses (33). One potential explanation is cancer-associated weight loss, where patients experience significant weight reduction before diagnosis, leading to a misleading inverse correlation. A similar observation was noted by Yuan et al., who found that weight loss often precedes a PC diagnosis, complicating the interpretation of BMI-related risk factors (34).

Explanations of findings

The inverse association between obesity and PC risk observed in our study may be attributed to cancer-associated cachexia, where significant weight loss precedes diagnosis (35). Additionally, prolonged obesity-related metabolic alterations, such as hypoinsulinemia resulting from beta-islet cell dysfunction, may influence cancer risk over time (36).

Furthermore, the elevated levels of biomarkers, including CA19-9, ASAT, and total bilirubin observed in our study, likely reflect the tumor’s impact on liver function and biliary obstruction. These findings reinforce the role of CA19-9 as a valuable diagnostic and prognostic biomarker, consistent with previous studies by Kang et al. and Humphris et al. (37,38).

Implications and actions needed

Our study underscores the importance of early detection strategies for PC, particularly in high-risk populations such as diabetic patients. Regular monitoring of blood glucose levels and pancreatic biomarkers in diabetic individuals may help identify cases at an earlier, more treatable stage.

Given the increasing incidence of PC in younger populations, lifestyle interventions targeting modifiable risk factors such as smoking, alcohol consumption, and obesity should be prioritized. Additionally, further research into sex-specific risk factors is necessary to explain the rising incidence among younger women.

Future investigations should incorporate genetic profiling to elucidate hereditary contributions to PC risk. Prospective cohort studies with long-term follow-up are essential to clarify the interplay between diabetes, obesity, and PC and to better understand the temporal sequence of risk factor development.

Conclusions

This study provides valuable insights into the epidemiological, clinical, and biological characteristics of PC in Eastern Algeria. Our findings reinforce DM as a significant risk factor and underscore the diagnostic relevance of biomarkers such as CA19-9 and liver enzymes. The observed inverse association between obesity and PC, challenges conventional understanding and calls for further investigation.

Given the aggressive nature of PC and the frequent late-stage diagnosis, enhancing early detection strategies and addressing modifiable risk factors are critical. Targeted prevention programs, improved healthcare accessibility, and the integration of emerging diagnostic tools could contribute to better patient outcomes in Algeria.

Acknowledgments

None.

Footnote

Data Sharing Statement: Available at https://apc.amegroups.com/article/view/10.21037/apc-24-33/dss

Peer Review File: Available at https://apc.amegroups.com/article/view/10.21037/apc-24-33/prf

Funding: None.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://apc.amegroups.com/article/view/10.21037/apc-24-33/coif). The authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of thee work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki and its subsequent amendments. Ethical approval was obtained from the Scientific Committee of the Faculty of Nature and Life Sciences, Mentouri Brothers University, Constantine 1, Algeria. As the institution does not assign formal registration numbers, approval was documented through an official signed letter. Informed consent was obtained from all individual participants included in the study.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

References

1. Sung H, Ferlay J, Siegel RL, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin 2021;71:209-49. [Crossref] [PubMed]
2. Rawla P, Sunkara T, Gaduputi V. Epidemiology of Pancreatic Cancer: Global Trends, Etiology and Risk Factors. World J Oncol 2019;10:10-27. [Crossref] [PubMed]
3. American Cancer Society. Key statistics for pancreatic cancer. (Accessed November 2024). Available online: https://www.cancer.org/cancer/types/pancreatic-cancer/about/key-statistics.html
4. Siegel RL, Miller KD, Fuchs HE, et al. Cancer statistics, 2022. CA Cancer J Clin 2022;72:7-33. [Crossref] [PubMed]
5. Ministry of Health, Population and Hospital Reform (Algeria), National Institute of Public Health (INSP). Algiers Tumor Registry - 2022. RNCEA Bulletin. (Accessed December 2024). Available online: https://ghdx.healthdata.org/organizations/ministry-health-population-and-hospital-reform-algeria
6. Ilic M, Ilic I. Epidemiology of pancreatic cancer. World J Gastroenterol 2016;22:9694-705. [Crossref] [PubMed]
7. Hu JX, Zhao CF, Chen WB, et al. Pancreatic cancer: A review of epidemiology, trend, and risk factors. World J Gastroenterol 2021;27:4298-321. [Crossref] [PubMed]
8. Yuan C, Babic A, Khalaf N, et al. Diabetes, Weight Change, and Pancreatic Cancer Risk. JAMA Oncol 2020;6:e202948. [Crossref] [PubMed]
9. Makhoul I, Yacoub A, Siegel E. Type 2 diabetes mellitus is associated with increased risk of pancreatic cancer: A veteran administration registry study. SAGE Open Med 2016;4:2050312116682257. [Crossref] [PubMed]
10. Lamerz R. CA 19-9, GICA (Gastrointestinal cancer antigen). In: Thomas L, editor. Labor und Diagnose. 5th ed. Frankfurt/Main: TH-Books Verlagsgesellschaft; 1998:966-79.
11. Boyd LNC, Ali M, Leeflang MMG, et al. Diagnostic accuracy and added value of blood-based protein biomarkers for pancreatic cancer: A meta-analysis of aggregate and individual participant data. EClinicalMedicine 2023;55:101747. [Crossref] [PubMed]
12. Zhou B, Xu JW, Cheng YG, et al. Early detection of pancreatic cancer: Where are we now and where are we going? Int J Cancer 2017;141:231-41. [Crossref] [PubMed]
13. Melo SA, Luecke LB, Kahlert C, et al. Glypican-1 identifies cancer exosomes and detects early pancreatic cancer. Nature 2015;523:177-82. [Crossref] [PubMed]
14. Rawat M, Kadian K, Gupta Y, et al. MicroRNA in Pancreatic Cancer: From Biology to Therapeutic Potential. Genes (Basel) 2019;10:752. [Crossref] [PubMed]
15. Hidalgo M. Pancreatic cancer. N Engl J Med 2010;362:1605-17. [Crossref] [PubMed]
16. Conroy T, Desseigne F, Ychou M, et al. FOLFIRINOX versus gemcitabine for metastatic pancreatic cancer. N Engl J Med 2011;364:1817-25. [Crossref] [PubMed]
17. Vincent A, Herman J, Schulick R, et al. Pancreatic cancer. Lancet 2011;378:607-20. [Crossref] [PubMed]
18. WHO Classification of Tumors Editorial Board. Digestive System Tumours. 5th ed. Lyon, France: International Agency for Research on Cancer; 2019.
19. Iodice S, Gandini S, Maisonneuve P, et al. Tobacco and the risk of pancreatic cancer: a review and meta-analysis. Langenbecks Arch Surg 2008;393:535-45. [Crossref] [PubMed]
20. Raimondi S, Lowenfels AB, Morselli-Labate AM, et al. Pancreatic cancer in chronic pancreatitis; aetiology, incidence, and early detection. Best Pract Res Clin Gastroenterol 2010;24:349-58. [Crossref] [PubMed]
21. Carretero OA, Oparil S. Essential hypertension. Part I: definition and etiology. Circulation 2000;101:329-35. [Crossref] [PubMed]
22. Kumar V, Abbas AK, Aster JC. Robbins and Cotran Pathologic Basis of Disease. 9th ed. Philadelphia: Elsevier Saunders; 2015:832-3.
23. Ständer S, Weisshaar E, Mettang T, et al. Clinical classification of itch: a position paper of the International Forum for the Study of Itch. Acta Derm Venereol 2007;87:291-4. [Crossref] [PubMed]
24. Longstreth GF, Thompson WG, Chey WD, et al. Functional bowel disorders. Gastroenterology 2006;130:1480-91. [Crossref] [PubMed]
25. Bosch X, Monclús E, Escoda O, et al. Unintentional weight loss: Clinical characteristics and outcomes in a prospective cohort of 2677 patients. PLoS One 2017;12:e0175125. [Crossref] [PubMed]
26. Siegel RL, Giaquinto AN, Jemal A. Cancer statistics, 2024. CA Cancer J Clin 2024;74:12-49. Erratum in: CA Cancer J Clin 2024;74:203. [Crossref] [PubMed]
27. Sharma A, Kandlakunta H, Nagpal SJS, et al. Model to Determine Risk of Pancreatic Cancer in Patients With New-Onset Diabetes. Gastroenterology 2018;155:730-739.e3. [Crossref] [PubMed]
28. Ben Q, Xu M, Ning X, et al. Diabetes mellitus and risk of pancreatic cancer: A meta-analysis of cohort studies. Eur J Cancer 2011;47:1928-37. [Crossref] [PubMed]
29. Rawla P, Sunkara T, Gaduputi V. Epidemiology of Pancreatic Cancer: Global Trends, Etiology and Risk Factors. World J Oncol 2019;10:10-27. [Crossref] [PubMed]
30. Andersson G, Borgquist S, Jirström K. Hormonal factors and pancreatic cancer risk in women: The Malmö Diet and Cancer Study. Int J Cancer 2018;143:52-62. [Crossref] [PubMed]
31. Ferlay J, Colombet M, Soerjomataram I, et al. Estimating the global cancer incidence and mortality in 2018: GLOBOCAN sources and methods. Int J Cancer 2019;144:1941-53. [Crossref] [PubMed]
32. Yachida S, Jones S, Bozic I, et al. Distant metastasis occurs late during the genetic evolution of pancreatic cancer. Nature 2010;467:1114-7. [Crossref] [PubMed]
33. Aune D, Greenwood DC, Chan DS, et al. Body mass index, abdominal fatness and pancreatic cancer risk: a systematic review and non-linear dose-response meta-analysis of prospective studies. Ann Oncol 2012;23:843-52. [Crossref] [PubMed]
34. Yuan C, Bao Y, Wu C, et al. Prediagnostic body mass index and pancreatic cancer survival. J Clin Oncol 2013;31:4229-34. [Crossref] [PubMed]
35. Genkinger JM, Spiegelman D, Anderson KE, et al. A pooled analysis of 14 cohort studies of anthropometric factors and pancreatic cancer risk. Int J Cancer 2011;129:1708-17. [Crossref] [PubMed]
36. Scully T, Ettela A, LeRoith D, et al. Obesity, Type 2 Diabetes, and Cancer Risk. Front Oncol 2021;10:615375. [Crossref] [PubMed]
37. Kang YM, Wang H, Li R, et al. Prognostic Role of Carbohydrate Antigen 19 to 9 in Predicting Survival of Patients With Pancreatic Cancer: A Meta-Analysis. Technol Cancer Res Treat 2021;20:15330338211043030. [Crossref] [PubMed]
38. Humphris JL, Chang DK, Johns AL, et al. The prognostic and predictive value of serum CA19.9 in pancreatic cancer. Ann Oncol 2012;23:1713-22. [Crossref] [PubMed]