Background:

Pancreatic ductal adenocarcinoma (PDAC) has a poor prognosis, and this is attributed to it being diagnosed at an advanced stage. Understanding the pathways involved in initial development may improve early detection strategies. This systematic review assessed the association between circulating protein and metabolite biomarkers and PDAC development.

Methods:

A literature search until August 2020 in MEDLINE, EMBASE, and Web of Science was performed. Studies were included if they assessed circulating blood, urine, or salivary biomarkers and their association with PDAC risk. Quality was assessed using the Newcastle-Ottawa scale for cohort studies. Random-effects meta-analyses were used to calculate pooled relative risk.

Results:

A total of 65 studies were included. Higher levels of glucose were found to be positively associated with risk of developing PDAC [n = 4 studies; pooled relative risk (RR): 1.61; 95% CI: 1.16–2.22]. Additionally, an inverse association was seen with pyridoxal 5′-phosphate (PLP) levels (n = 4 studies; RR: 0.62; 95% CI: 0.44–0.87). Meta-analyses showed no association between levels of C-peptide, members of the insulin growth factor signaling pathway, C-reactive protein, adiponectin, 25-hydroxyvitamin D, and folate/homocysteine and PDAC risk. Four individual studies also reported a suggestive positive association of branched-chain amino acids with PDAC risk, but due to differences in measures reported, a meta-analysis could not be performed.

Conclusions:

Our pooled analysis demonstrates that higher serum glucose levels and lower levels of PLP are associated with risk of PDAC.

Impact:

Glucose and PLP levels are associated with PDAC risk. More prospective studies are required to identify biomarkers for early detection.

Pancreatic cancer is considered to be one of the most lethal cancers with a very poor prognosis, and only 5% of patients survive for 10 or more years after diagnosis (1). Pancreatic ductal adenocarcinoma (PDAC) is the most commonly diagnosed pathologic subtype and is estimated to become the second leading cause of cancer-related deaths in the United States by 2030 (2, 3). This is often because PDAC is diagnosed at an advanced stage when surgical resection, the only potentially curative therapy, is not feasible (4, 5). Therefore, early detection is considered to be of utmost importance in order to improve survival rates.

The incidence of PDAC in the general population is low and accounts for only 3% of all new cancer cases diagnosed in the UK (6). This makes it very challenging for the development of early detection strategies and emphasizes the importance of identifying individuals who are at a higher than average risk of developing PDAC (7). Current strategies are limited to screening individuals with a genetic predisposition and involve the use of endoscopic ultrasound (EUS) in addition to other imaging modalities such as MRI and CT. However, there is an urgent need for the identification of less invasive biomarkers that can be used in combination with these approaches (8–11). Various circulating biomarkers present in blood, urine, or saliva are a less invasive alternative when compared with tissue-based markers.

A majority of the studies on circulating biomarkers associated with PDAC biology are case–control studies that have assessed the biomarker very close to diagnosis and therefore do not provide a lot of information on the initial development of the cancer (12–15). This requires prospective studies that evaluate the role of these biomarkers in prediagnostic samples collected in the years preceding diagnosis, in order to help identify high-risk individuals and aid in early detection. Several large-scale cohort studies looking at the association between various biomarkers and PDAC risk have been published in the recent years, and a thorough assessment of these studies will deepen our understanding of the molecular mechanisms of PDAC development. This systematic review, therefore, aims to collate data from all prospective studies on blood-, saliva-, and urine-based biomarkers, their association with PDAC risk and assess the quality of evidence presented by conducting a meta-analysis.

Search strategy

To assess the association between circulating biomarkers and risk of developing PDAC, Medline (1974–), Embase (1974–), and Web of Science (1970–) were searched systematically for eligible studies in humans using predefined search terms from date of inception to May 10, 2019. Medical Subject Headings and keywords for cancer, biomarkers, sample (blood/urine/saliva), and early detection/risk were used.

An updated search was performed in all three databases on August 20, 2020, to identify any new articles published before beginning the final analysis. The detailed search strategy for each database is included in Supplementary Table S1, and this protocol was registered in the PROSPERO database (CRD42019141149; ref. 16). Additionally, reference lists and manual searches were used to further identify any missed studies.

Eligibility criteria

Titles and abstracts of the studies identified by the search were screened for eligibility by two independent reviewers (SK reviewed all studies; RS, AMcG, AK, US, and PJ reviewed a subset). Any discrepancies were resolved by discussions with a third reviewer and a consensus was reached.

Eligibility criteria for inclusion in the review were as follows: observational and prospective studies assessing the association between prediagnostic nontissue (blood/urine/saliva) based circulating biomarkers and risk of subsequent development of PDAC. Included studies were required to have a follow-up period of at least six months after biomarker assessment and report on both the measure of association, in terms of odds ratios/hazard ratios and their corresponding 95% confidence intervals, or have enough data for these to be calculated. Case–control/retrospective studies, studies where the biomarker was measured at or close to the time of diagnosis (symptoms were already present) and those on biomarkers of cancer mortality were excluded. Participants/population included anyone with a diagnosis of pancreatic cancer or individuals who had no history of cancer at the time of biomarker assessment.

Because the main focus of the review was to look at protein/metabolite markers, studies assessing blood-based genetic markers, miRNA, and infectious agents were not included in the final analysis, and only abstracts were screened for the biomarker studied.

Full texts of the articles were assessed for eligibility, and data extraction was performed by one reviewer (SK). Extracted data from the individual studies included author names, date of publication, study characteristics, participant details, biomarker assessed, sample type, and outcomes of interest. The data extraction was checked by a second reviewer (RS, AM) to ensure accuracy. Study quality was assessed by using the Newcastle-Ottawa scale for cohort studies (17).

Statistical analysis

If at least three studies assessed the association between a particular circulating protein/metabolite biomarker and PDAC risk, a random-effects meta-analysis was performed. RevMan 5.4 (RRID:SCR_003581; ref. 18) software was used for data synthesis and calculating pooled relative risks and 95% confidence intervals from the eligible studies. A χ2 test was used to investigate heterogeneity and I2 statistic was calculated to report on variation between the study estimates. Heterogeneity was considered high if I2 statistic was above 75% (19). If individual studies reported results only stratified by sex, these estimates were first pooled together in an initial meta-analysis, and then this pooled estimate was used in the final meta-analysis.

The search identified 15,439 articles from the three databases. After removal of duplicates, the titles and abstracts of 13,437 articles were screened. A total of 145 studies were selected for full-text review, from which 62 studies were deemed eligible for inclusion in the review. An additional three studies were identified by manual searches and review of reference lists, bringing the total number of studies included in the review to 65 (Fig. 1). The characteristics of each of these 65 studies included in the review are summarized in Table 1.

Figure 1.

PRISMA flow diagram outlining study selection for the systematic review.

Figure 1.

PRISMA flow diagram outlining study selection for the systematic review.

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Table 1.

Summary of characteristics of the studies included in the review.

ReferenceCohortCountryNo. of casesAge rangeSexFollow-up periodBiomarkers measuredMethod of assessmentSpecimen type
Ahn et al. (80Alpha-Tocopherol, Beta-Carotene Cancer Prevention Study (ATBC) Finland 273 50–69 years Men Median: 14.9 years Total and high-density lipoprotein cholesterol  Serum 
Arendt et al. (35The Health Improvement Network (THIN) UK 844 18–99 years Both Median: 2.8 years Vitamin B12  Plasma 
Babic et al. (49Health Professionals Follow-Up Study (HPFS), Nurses' Health Study (NHS), Physicians' Health Study (PHS), Women's Health Initiative-Observational Study (WHI-OS), Women's Health Study (WHS) United States 470 30–84 years Both Median: 7.1 years Leptin  Plasma 
Banim et al. (36European Prospective Investigation into Cancer and Nutrition (EPIC)-Norfolk UK 76 40–74 years Both 17 years max Vitamin C  Serum 
Banim et al. (24European Prospective Investigation into Cancer and Nutrition (EPIC)-Norfolk UK 35 40–74 years Both 17 years max Glycosylated hemoglobin (HbA1c) HPLC Serum 
Bao et al. (102HPFS, NHS, PHS, WHI, WHS United States 470 30–84 years Both Median: 7.2 years CRP, IL6, and TNFαR2  Plasma 
Bao et al. (103HPFS, NHS, PHS, WHI, WHS United States 468 30–84 years Both Up to 26 years Adiponectin  Plasma 
Chatterjee et al. (57The Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial (PLCO) United States 303 55–70 years Both Median: 7 years Selenium  Serum 
Chen et al. (82THIN UK 1,241 60–80 years Both Mean: 7.2 years Cholesterol  Serum 
Chuang et al. (41EPIC Multiple 463 25–70 years Both Mean: 9.6 years One-carbon metabolites Mass spectrometry and microbiological methods Plasma 
Cui et al. (104Shanghai WHS, Shanghai Men's Health Study China 239 40–74 years Both Median 5.8 years Prostaglandin E2 metabolites (PGE-M) LC-MS/MS Urine 
De Gonzalez et al. (105Korean Cancer Prevention Study (KCPS) Korea 2,194 45+ years Both Median: 12 years Aspartate aminotransferase and alanine aminotransferase  Serum 
Douglas et al. (53Alpha-Tocopherol, Beta-Carotene (ATBC) Cancer Prevention Study cohort, The Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial (PLCO) Finland, United States 493 50–74 years Both ATBC: Median: 9.4 years C-reactive protein ELISA Serum 
      PLCO: Median: 5.4 years    
Douglas et al. (72PLCO United States 187 55–74 years Both Median: 5.4 years IGF-I, IGF-II, IGFBP-3, and IGF-I/IGFBP-3 molar ELISA Serum 
Gaur et al. (56Swedish Apolipoprotein Mortality Risk (AMORIS) Sweden 197 20 or older Both Mean: 10.57 years Iron, total iron binding capacity (TIBC)  Serum 
Grote et al. (47EPIC Multiple 452 35–70 years Both Mean: 5.3 years Adiponectin Multiplex immunoassay Serum 
Grote et al. (52EPIC Multiple 454 30–76 years Both Mean: 5.3 years Ne-(carboxymethyl)lysine (CML) and the endogenous secreted receptor for AGE (esRAGE) ELISA Serum, plasma 
Grote et al. (106EPIC Multiple 455 Mean: 58 years Both Mean: 5.3 years C-reactive protein (CRP), IL6, and soluble receptors of tumor necrosis factor-α (sTNFR1, R2) Immunoassays Serum 
Grote et al. (107EPIC Multiple 466 35–70 years Both Mean: 5.3 years C-peptide Radioimmunoassay Serum 
Huang et al. (42Shanghai Cohort Study Singapore Chinese Health Study Singapore, China 187 45–74 years Both Up to 23 years Methionine-related metabolites LC-MS/MS Serum 
Huang et al. (27Shanghai Cohort Study Singapore Chinese Health Study Singapore, China 187 45–74 years Both Shanghai: Mean 12.5 years B6 vitamers (pyridoxal 5′-phosphate, pyridoxal, and 4-pyridoxic acid) LC-MS/MS Serum 
      Singapore: Mean 6.8 years    
Jacobs et al. (108ATBC, The Cancer Prevention Study-II (CPS-II), PLCO Finland, United States 729 Median age: 62 years Both 23 years maximum TGF-β1 ELISA Serum 
Jeurnink et al. (37EPIC Multiple 466 Mean: 57.9 years Both Mean: 5.25 years Micronutrients HPLC/colorimetric assay Plasma 
Jiao et al. (51ATBC Finland 255 50–69 years Men Median: 15 years CML-AGE, sRAGE ELISA Serum 
Johansen et al. (62The Malmö Preventive Project Sweden 84 37–60 years Both Median: 25 years Forms of trypsinogen ELISA Serum 
Johansen et al. (66Metabolic Syndrome and Cancer Project (Me-Can) Multiple 862 30–59 years Both Mean: Men: 12.8 years; Women: 11.3 years Glucose, cholesterol, triglycerides Nonenzymatic/enzymatic method Serum 
Huang et al. (28Shanghai Cohort Study Singapore Chinese Health Study Singapore, China 187 45–74 years Both 10.7 years Tryptophan metabolism LC-MS/MS Serum 
Kabat et al. (81Women's Health Initiative United States 156 50–79 years Women Up to 23 years Total cholesterol, low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and triglycerides  Serum 
Katagiri et al. (44Japan Public Health Center Japan 170 Mean age: 57 years Both Median: 10.1 years Branched-chain amino acids LC/QqQMS Plasma 
Khalaf et al. (55WHI-Observational Study (WHI-OS), HPFS, NHS United States 396 Mean age: 64 years Both Median: 8 years Salicylurate LC/MS Plasma 
Kim et al. (109HPFS, NHS, PHS, WHI United States 500 30–84 years Both Maximum 26 years Proinsulin, adiponectin, IL6, and BCAAs  Plasma 
Kitahara et al. (46National Health Insurance Corporation Korea 2,575 30 to 95 years Both Mean: 12.7 years Cholesterol   
Laiyemo et al. (63ATBC Finland 227 50–69 years Men Mean 10.8 years Pepsinogen I (SPGI) Radioimmunoassay Serum 
Leenders et al. (54EPIC Multiple 146 25–70 years Both Mean: 8 years Cotinine MS Plasma 
Matejcic et al. (39EPIC Multiple 375  Both Median: 11.7 years Phospholipid fatty acids Gas chromatography Plasma 
Mayers et al. (43WHI-OS, HPFS, NHS, PHS United States 453 30–84 years Both Median: 8.7 years Branched-chain amino acids LC-MS Plasma 
Meinhold et al. (110ATBC Finland 305 50–69 years Men Median 16.1 years HDL-C  Serum 
Michaud et al. (25WHI-OS, HPFS, NHS, PHS United States 197 30–84 years Both Maximum 20 years C-peptide, insulin ELISA Plasma 
Mok et al. (65KCPS Korea  Mean: 41 years Both Up to 17 years Gamma-glutamyltransferase  Serum 
Nogueira et al. (26ATBC, The Cancer Prevention Study-II (CPS-II), PLCO Finland, United States 758 50–77 years Both Mean: 8.2 years C-peptide, total and high-molecular-weight adiponectin ELISA Serum 
Olson et al. (60PLCO United States 283 55–74 years Both Median: 7.8 years Immunoglobulin e Fluorescent enzyme immunoassay Serum 
Pang et al. (20China Kadoorie Biobank (CKB) China 512 Mean: 51.5 years Both 8 years Glucose SureStep Plus System Plasma 
Piper et al. (32PLCO United States 295 55–74 years Both Up to 15.1 years Vitamin D binding protein, 25(OH)D Immunoassay Serum 
Rohrmann et al. (73EPIC Multiple 422 30–76 years Both Mean: 5.4 years IGF-I and IGFBP3 Immunoassay Serum 
Schernhammer et al. (34WHI-OS, HPFS, NHS, PHS United States 208 30–84 years Both Median: 5.5 years Plasma folate, vitamin B6, vitamin B12, and homocysteine  Plasma 
Shu et al. (40Shanghai WHS, Shanghai Men's Health Study China 226 40–74 years Both Up to 13 years Lipids and other metabolites UPLC-QTOFMS, GC-TOFMS Plasma 
Sollie et al. (111AMORIS Sweden 286 20 or older Both Mean: 18.3 years CRP, albumin, haptoglobin, and leukocytes  Serum 
Sollie et al. (59AMORIS Sweden 689 20 or older Both Mean: 21.3 years Immunoglobulin G  Serum 
Stolzenberg-Solomon et al. (48ATBC, CPS-II, PLCO Finland, United States 731 Median: 71 years Both Mean: 8.3 years Leptin ELISA Serum 
Stolzenberg-Solomon et al. (112PLCO United States 184 55–74 years Both Median: 5.4 years 25-Hydroxyvitamin D  Serum 
Stolzenberg-Solomon et al. (22ATBC Finland 169 50–69 years Men Median: 13.8 years Glucose and insulin, insulin resistance (HOMA-IR) Immunoenzymatic assay Serum 
Stolzenberg-Solomon et al. (33ATBC Finland 126 50–69 years Men 7–10 years Homocysteine, vitamin B12, folate, PLP, and creatinine  Serum 
Stolzenberg-Solomon et al. (75ATBC Finland 93 50–69 years Men Up to 12.7 years IGF-1, IGF-binding protein-3 ELISA Serum 
Stolzenberg-Solomon et al. (38ATBC Finland 306 50–69 years Men Median: 16 years Alpha-tocopherol HPLC Serum 
Stolzenberg-Solomon et al. (29ATBC Finland 184 50–69 years Men Median: 11.8 years 25-Hydroxyvitamin D RIA Serum 
Stolzenberg-Solomon et al. (45ATBC, PLCO Finland, United States 479 50–79 years Both Up to 24 years Metabolites LC-MS/MS Serum 
Jee et al. (21KCPS Korea  30–95 years Both Up to 10 years Glucose Serum Blood, urine 
Sun et al. (61Southern Community Cohort Study (SCCS) United States 73  Both Median: 4 years Autoantibodies to Ezrin ELISA Plasma 
Tsuboya et al. (64Ohsaki Cohort Study Japan 67 40–79 years Both Up to 10 years Gamma-glutamyltransferase Szasz method Serum 
Weinstein et al. (31ATBC Finland 234 50–69 years Men At least 10 years Vitamin D binding protein RIA Serum 
White et al. (50Women's Health Initiative Study United States 472 50–79 years Women At least 10 years sRAGE, adipokines Immunoassay Serum 
Wolpin et al. (23WHI-OS, HPFS, NHS, PHS, WHS United States 449 30–84 years Both Up to 25 years HbA1c, insulin, proinsulin, and proinsulin-to-insulin ratio  Plasma 
Wolpin et al. (74WHI-OS, HPFS, NHS, PHS United States 212 30–84 years Both At least 8 years IGF-1, IGF-2, IGF-binding protein-3 ELISA Plasma 
Wolpin et al. (30HPFS, NHS, PHS, WHI, WHS United States 451 Median: 62.5 years Both Median: 14.3 years 25-Hydroxyvitamin D Immunoassay Plasma 
Wulaningsih et al. (58AMORIS Sweden 762 20 or older Both Mean: 12.75 years Inorganic phosphate  Serum 
ReferenceCohortCountryNo. of casesAge rangeSexFollow-up periodBiomarkers measuredMethod of assessmentSpecimen type
Ahn et al. (80Alpha-Tocopherol, Beta-Carotene Cancer Prevention Study (ATBC) Finland 273 50–69 years Men Median: 14.9 years Total and high-density lipoprotein cholesterol  Serum 
Arendt et al. (35The Health Improvement Network (THIN) UK 844 18–99 years Both Median: 2.8 years Vitamin B12  Plasma 
Babic et al. (49Health Professionals Follow-Up Study (HPFS), Nurses' Health Study (NHS), Physicians' Health Study (PHS), Women's Health Initiative-Observational Study (WHI-OS), Women's Health Study (WHS) United States 470 30–84 years Both Median: 7.1 years Leptin  Plasma 
Banim et al. (36European Prospective Investigation into Cancer and Nutrition (EPIC)-Norfolk UK 76 40–74 years Both 17 years max Vitamin C  Serum 
Banim et al. (24European Prospective Investigation into Cancer and Nutrition (EPIC)-Norfolk UK 35 40–74 years Both 17 years max Glycosylated hemoglobin (HbA1c) HPLC Serum 
Bao et al. (102HPFS, NHS, PHS, WHI, WHS United States 470 30–84 years Both Median: 7.2 years CRP, IL6, and TNFαR2  Plasma 
Bao et al. (103HPFS, NHS, PHS, WHI, WHS United States 468 30–84 years Both Up to 26 years Adiponectin  Plasma 
Chatterjee et al. (57The Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial (PLCO) United States 303 55–70 years Both Median: 7 years Selenium  Serum 
Chen et al. (82THIN UK 1,241 60–80 years Both Mean: 7.2 years Cholesterol  Serum 
Chuang et al. (41EPIC Multiple 463 25–70 years Both Mean: 9.6 years One-carbon metabolites Mass spectrometry and microbiological methods Plasma 
Cui et al. (104Shanghai WHS, Shanghai Men's Health Study China 239 40–74 years Both Median 5.8 years Prostaglandin E2 metabolites (PGE-M) LC-MS/MS Urine 
De Gonzalez et al. (105Korean Cancer Prevention Study (KCPS) Korea 2,194 45+ years Both Median: 12 years Aspartate aminotransferase and alanine aminotransferase  Serum 
Douglas et al. (53Alpha-Tocopherol, Beta-Carotene (ATBC) Cancer Prevention Study cohort, The Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial (PLCO) Finland, United States 493 50–74 years Both ATBC: Median: 9.4 years C-reactive protein ELISA Serum 
      PLCO: Median: 5.4 years    
Douglas et al. (72PLCO United States 187 55–74 years Both Median: 5.4 years IGF-I, IGF-II, IGFBP-3, and IGF-I/IGFBP-3 molar ELISA Serum 
Gaur et al. (56Swedish Apolipoprotein Mortality Risk (AMORIS) Sweden 197 20 or older Both Mean: 10.57 years Iron, total iron binding capacity (TIBC)  Serum 
Grote et al. (47EPIC Multiple 452 35–70 years Both Mean: 5.3 years Adiponectin Multiplex immunoassay Serum 
Grote et al. (52EPIC Multiple 454 30–76 years Both Mean: 5.3 years Ne-(carboxymethyl)lysine (CML) and the endogenous secreted receptor for AGE (esRAGE) ELISA Serum, plasma 
Grote et al. (106EPIC Multiple 455 Mean: 58 years Both Mean: 5.3 years C-reactive protein (CRP), IL6, and soluble receptors of tumor necrosis factor-α (sTNFR1, R2) Immunoassays Serum 
Grote et al. (107EPIC Multiple 466 35–70 years Both Mean: 5.3 years C-peptide Radioimmunoassay Serum 
Huang et al. (42Shanghai Cohort Study Singapore Chinese Health Study Singapore, China 187 45–74 years Both Up to 23 years Methionine-related metabolites LC-MS/MS Serum 
Huang et al. (27Shanghai Cohort Study Singapore Chinese Health Study Singapore, China 187 45–74 years Both Shanghai: Mean 12.5 years B6 vitamers (pyridoxal 5′-phosphate, pyridoxal, and 4-pyridoxic acid) LC-MS/MS Serum 
      Singapore: Mean 6.8 years    
Jacobs et al. (108ATBC, The Cancer Prevention Study-II (CPS-II), PLCO Finland, United States 729 Median age: 62 years Both 23 years maximum TGF-β1 ELISA Serum 
Jeurnink et al. (37EPIC Multiple 466 Mean: 57.9 years Both Mean: 5.25 years Micronutrients HPLC/colorimetric assay Plasma 
Jiao et al. (51ATBC Finland 255 50–69 years Men Median: 15 years CML-AGE, sRAGE ELISA Serum 
Johansen et al. (62The Malmö Preventive Project Sweden 84 37–60 years Both Median: 25 years Forms of trypsinogen ELISA Serum 
Johansen et al. (66Metabolic Syndrome and Cancer Project (Me-Can) Multiple 862 30–59 years Both Mean: Men: 12.8 years; Women: 11.3 years Glucose, cholesterol, triglycerides Nonenzymatic/enzymatic method Serum 
Huang et al. (28Shanghai Cohort Study Singapore Chinese Health Study Singapore, China 187 45–74 years Both 10.7 years Tryptophan metabolism LC-MS/MS Serum 
Kabat et al. (81Women's Health Initiative United States 156 50–79 years Women Up to 23 years Total cholesterol, low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and triglycerides  Serum 
Katagiri et al. (44Japan Public Health Center Japan 170 Mean age: 57 years Both Median: 10.1 years Branched-chain amino acids LC/QqQMS Plasma 
Khalaf et al. (55WHI-Observational Study (WHI-OS), HPFS, NHS United States 396 Mean age: 64 years Both Median: 8 years Salicylurate LC/MS Plasma 
Kim et al. (109HPFS, NHS, PHS, WHI United States 500 30–84 years Both Maximum 26 years Proinsulin, adiponectin, IL6, and BCAAs  Plasma 
Kitahara et al. (46National Health Insurance Corporation Korea 2,575 30 to 95 years Both Mean: 12.7 years Cholesterol   
Laiyemo et al. (63ATBC Finland 227 50–69 years Men Mean 10.8 years Pepsinogen I (SPGI) Radioimmunoassay Serum 
Leenders et al. (54EPIC Multiple 146 25–70 years Both Mean: 8 years Cotinine MS Plasma 
Matejcic et al. (39EPIC Multiple 375  Both Median: 11.7 years Phospholipid fatty acids Gas chromatography Plasma 
Mayers et al. (43WHI-OS, HPFS, NHS, PHS United States 453 30–84 years Both Median: 8.7 years Branched-chain amino acids LC-MS Plasma 
Meinhold et al. (110ATBC Finland 305 50–69 years Men Median 16.1 years HDL-C  Serum 
Michaud et al. (25WHI-OS, HPFS, NHS, PHS United States 197 30–84 years Both Maximum 20 years C-peptide, insulin ELISA Plasma 
Mok et al. (65KCPS Korea  Mean: 41 years Both Up to 17 years Gamma-glutamyltransferase  Serum 
Nogueira et al. (26ATBC, The Cancer Prevention Study-II (CPS-II), PLCO Finland, United States 758 50–77 years Both Mean: 8.2 years C-peptide, total and high-molecular-weight adiponectin ELISA Serum 
Olson et al. (60PLCO United States 283 55–74 years Both Median: 7.8 years Immunoglobulin e Fluorescent enzyme immunoassay Serum 
Pang et al. (20China Kadoorie Biobank (CKB) China 512 Mean: 51.5 years Both 8 years Glucose SureStep Plus System Plasma 
Piper et al. (32PLCO United States 295 55–74 years Both Up to 15.1 years Vitamin D binding protein, 25(OH)D Immunoassay Serum 
Rohrmann et al. (73EPIC Multiple 422 30–76 years Both Mean: 5.4 years IGF-I and IGFBP3 Immunoassay Serum 
Schernhammer et al. (34WHI-OS, HPFS, NHS, PHS United States 208 30–84 years Both Median: 5.5 years Plasma folate, vitamin B6, vitamin B12, and homocysteine  Plasma 
Shu et al. (40Shanghai WHS, Shanghai Men's Health Study China 226 40–74 years Both Up to 13 years Lipids and other metabolites UPLC-QTOFMS, GC-TOFMS Plasma 
Sollie et al. (111AMORIS Sweden 286 20 or older Both Mean: 18.3 years CRP, albumin, haptoglobin, and leukocytes  Serum 
Sollie et al. (59AMORIS Sweden 689 20 or older Both Mean: 21.3 years Immunoglobulin G  Serum 
Stolzenberg-Solomon et al. (48ATBC, CPS-II, PLCO Finland, United States 731 Median: 71 years Both Mean: 8.3 years Leptin ELISA Serum 
Stolzenberg-Solomon et al. (112PLCO United States 184 55–74 years Both Median: 5.4 years 25-Hydroxyvitamin D  Serum 
Stolzenberg-Solomon et al. (22ATBC Finland 169 50–69 years Men Median: 13.8 years Glucose and insulin, insulin resistance (HOMA-IR) Immunoenzymatic assay Serum 
Stolzenberg-Solomon et al. (33ATBC Finland 126 50–69 years Men 7–10 years Homocysteine, vitamin B12, folate, PLP, and creatinine  Serum 
Stolzenberg-Solomon et al. (75ATBC Finland 93 50–69 years Men Up to 12.7 years IGF-1, IGF-binding protein-3 ELISA Serum 
Stolzenberg-Solomon et al. (38ATBC Finland 306 50–69 years Men Median: 16 years Alpha-tocopherol HPLC Serum 
Stolzenberg-Solomon et al. (29ATBC Finland 184 50–69 years Men Median: 11.8 years 25-Hydroxyvitamin D RIA Serum 
Stolzenberg-Solomon et al. (45ATBC, PLCO Finland, United States 479 50–79 years Both Up to 24 years Metabolites LC-MS/MS Serum 
Jee et al. (21KCPS Korea  30–95 years Both Up to 10 years Glucose Serum Blood, urine 
Sun et al. (61Southern Community Cohort Study (SCCS) United States 73  Both Median: 4 years Autoantibodies to Ezrin ELISA Plasma 
Tsuboya et al. (64Ohsaki Cohort Study Japan 67 40–79 years Both Up to 10 years Gamma-glutamyltransferase Szasz method Serum 
Weinstein et al. (31ATBC Finland 234 50–69 years Men At least 10 years Vitamin D binding protein RIA Serum 
White et al. (50Women's Health Initiative Study United States 472 50–79 years Women At least 10 years sRAGE, adipokines Immunoassay Serum 
Wolpin et al. (23WHI-OS, HPFS, NHS, PHS, WHS United States 449 30–84 years Both Up to 25 years HbA1c, insulin, proinsulin, and proinsulin-to-insulin ratio  Plasma 
Wolpin et al. (74WHI-OS, HPFS, NHS, PHS United States 212 30–84 years Both At least 8 years IGF-1, IGF-2, IGF-binding protein-3 ELISA Plasma 
Wolpin et al. (30HPFS, NHS, PHS, WHI, WHS United States 451 Median: 62.5 years Both Median: 14.3 years 25-Hydroxyvitamin D Immunoassay Plasma 
Wulaningsih et al. (58AMORIS Sweden 762 20 or older Both Mean: 12.75 years Inorganic phosphate  Serum 

Abbreviations: AMORIS, Swedish Apolipoprotein Mortality Risk; ATBC, Alpha-Tocopherol, Beta-Carotene Cancer Prevention Study; CPS-II, The Cancer Prevention Study-II; EPIC, European Prospective Investigation into Cancer and Nutrition; HPFS, Health Professionals Follow-Up Study; KCPS, Korean Cancer Prevention Study; NHS, Nurses' Health Study; PHS, Physicians' Health Study; PLCO, The Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial; THIN, The Health Improvement Network; WHI, Women's Health Initiative Study; WHI-OS, Women's Health Initiative-Observational Study; WHS, Women's Health Study.

The Newcastle-Ottawa scale was used for assessing quality of the included studies, and 45 of these were considered to be of good quality and 20 were of fair quality (Supplementary Table S2).

Glucose metabolism–related biomarkers

We identified four studies that looked at the association between glucose levels and PDAC risk and the meta-analysis showed a positive association, with increased levels of glucose indicating an enhanced risk of early development of PDAC. However, a high degree of heterogeneity was seen [pooled relative risk (RR): 1.61; 95% CI: 1.16–2.22, I2 = 76%; Fig. 2A; Supplementary Table S3]. Sensitivity analysis revealed that exclusion of results from Pang and colleagues (ref. 20; pooled RR: 1.95; 95% CI: 1.64–2.31; I2 = 0%) or Jee and colleagues (ref. 21; pooled RR: 1.30; 95% CI: 1.08–1.55; I2 = 0%) lowered the heterogeneity observed and the positive association remained. Two studies reported consistent positive association between circulating levels of insulin (22, 23) and HbA1c (23, 24) with PDAC risk, but due to an insufficient number of studies, a meta-analysis could not be performed.

Figure 2.

Forest plots from random-effects meta-analysis of the association between glucose metabolism–related biomarkers and PDAC risk. A, Glucose; B, IGF-1; C, IGFBP-3; D, IGF-1/IGFBP-3 molar ratio; E, C-peptide.

Figure 2.

Forest plots from random-effects meta-analysis of the association between glucose metabolism–related biomarkers and PDAC risk. A, Glucose; B, IGF-1; C, IGFBP-3; D, IGF-1/IGFBP-3 molar ratio; E, C-peptide.

Close modal

We also identified four studies that looked at circulating levels of various components of the insulin growth factor (IGF) axis and their association with PDAC risk. The meta-analyses showed no significant association between levels of IGF-1 (RR: 1.04; 95% CI: 0.75–1.44, I2 = 36%; Fig. 2B), IGF-binding protein-3 (IGFBP-3; RR: 0.98; 95% CI: 0.76–1.27, I2 = 0%; Fig. 2C) and the IGF-1/IGFBP-3 molar ratio (RR: 1.07; 95% CI: 0.80–1.44, I2 = 26%; Fig. 2D).

Furthermore, three studies looked at the association between circulating C-peptide levels and PDAC risk. The meta-analysis showed no evidence of an association with PDAC risk (RR: 1.02; 95% CI: 0.62–1.68, I2 = 71%; Fig. 2E) with a certain degree of heterogeneity seen. Michaud and colleagues reported a positive association that was stronger among nonsmokers, and this was seen only in nonfasting blood samples (25). On the other hand, the study by Nogueira and colleagues found an inverse association between plasma C-peptide levels and PDAC risk in current smokers and no association in never or former-smokers (26). These results indicate that the association of C-peptide levels with PDAC risk is somewhat dependent on smoking status, but further investigation into its role in PDAC development is required.

Nutrition-related markers

We identified 17 studies that assessed the association between different nutrition-related biomarkers and PDAC risk, and these are listed in Supplementary Table S4. A meta-analysis of four studies found a significant inverse association between levels of circulating pyridoxal 5′-phosphate (PLP), which is the active form of vitamin B6 and risk of PDAC development (RR: 0.62; 95% CI: 0.44–0.87; I2 = 33%; Fig. 3A). Huang and colleagues also assessed the association between other forms of vitamin B6 vitamers (27) and the role of markers of the kynerurine pathway considered to be functional measures of PLP (28) but a meta-analysis could not be carried out due to insufficient studies (n < 3).

Figure 3.

Forest plots from random-effects meta-analysis of the association between nutrition-related biomarkers and PDAC risk. A, Pyridoxal 5′-phosphate (PLP) and (B) 25-hydroxyvitamin D [25-(OH)-D].

Figure 3.

Forest plots from random-effects meta-analysis of the association between nutrition-related biomarkers and PDAC risk. A, Pyridoxal 5′-phosphate (PLP) and (B) 25-hydroxyvitamin D [25-(OH)-D].

Close modal

We also conducted a meta-analysis of three studies and found that there was no association between levels of 25-hydroxyvitamin D (25-(OH)-D) and risk of developing PDAC (RR: 1.38; 95% CI: 0.53–3.61; I2 = 88%; Fig. 3B) with a high degree of heterogeneity seen. One of the studies (29) included in the meta-analysis reported a significant increased risk of PDAC with increasing 25-(OH)-D levels, whereas another found a decreased risk (30). Additionally, Weinstein and colleagues investigated the association between vitamin D binding protein (DBP), which is the primary carrier of various vitamin D forms and PDAC risk and found an inverse association, which was particularly evident in men with high 25-(OH)-D levels. This observation was accompanied by the reports of higher risk of PDAC in men with an elevated 25(OH)D:DBP molar ratio, which is a proxy for free 25(OH)D (31). Contrasting results were reported by another study that found a positive association between vitamin DBP levels and PDAC risk (32). Two studies reported no association between vitamin B12 levels and PDAC risk (33, 34). Another study found a positive association with PDAC risk, but a meta-analysis was not carried out as the reporting measures between the three studies were different (35).

Vitamin C levels and their association with PDAC risk were investigated by two studies, with one of them reporting an inverse association (36), while the other found no association (37). In addition, we also found two studies that reported inverse associations between a-tocopherol levels and PDAC risk (37, 38)

Metabolism-related biomarkers

We identified 17 studies that looked at the association between metabolism-related biomarkers and PDAC risk (Supplementary Table S5). A meta-analysis of five studies found a trend toward an inverse association between levels of total serum cholesterol and risk of PDAC [pooled relative risk (RR): 0.89 95% Confidence Interval (CI): 0.79–1.00, I2 = 0%); Fig. 4A]. Two of these studies also reported no association between levels of HDL-cholesterol (HDL-C) and triglycerides and PDAC risk. Additionally, Matejcic and colleagues (39) and Shu and colleagues (40) looked at levels of lipid-metabolism–related biomarkers and found inverse associations with PDAC risk for a number of glycerophospholipids and fatty acids.

Figure 4.

Forest plots from random-effects meta-analysis of the association between metabolism-related biomarkers and PDAC risk. A, Total cholesterol; B, folate; C, total homocysteine.

Figure 4.

Forest plots from random-effects meta-analysis of the association between metabolism-related biomarkers and PDAC risk. A, Total cholesterol; B, folate; C, total homocysteine.

Close modal

We also identified four studies that assessed the role of metabolites involved in the one-carbon metabolite pathway in the early development of PDAC. A meta-analysis of three studies found no association between the levels of folate (RR: 0.82; CI: 0.52–1.30; I2 = 61%; Fig. 4B) and homocysteine (RR: 0.90; CI: 0.58–1.39; I2 = 75%; Fig. 4C) with PDAC risk, and a high degree of heterogeneity was observed. Interestingly, Chuang and colleagues found a U-shaped dose–response relationship between plasma levels of folate and risk, with a significant inverse association observed for the fourth versus first quintile (OR = 0.5; 95% CI = 0.3–0.8), while there was no clear association for the fifth versus first quintile (OR = 0.8; 95% CI = 0.5–1.4; ref. 41). Two studies also assessed the association between methionine levels and PDAC risk but reported conflicting results with one study identifying a positive association in men (41), whereas a significant inverse association with PDAC risk was observed in the other study (42).

We also identified four studies that reported on the association between circulating branched-chain amino acids and PDAC risk. A prospective study of four large cohorts found that increased levels of the BCAAs leucine, isoleucine, and valine were associated with at least a 2-fold increased risk of developing PDAC. The levels of these markers were highly correlated and therefore showed a similar positive association for the sum total of the BCAAs as well (43). These interesting findings were supported by another study based in Japan, which also found that higher levels of the BCAAs were associated with an increased PDAC risk (44). The study by Shu and colleagues, which reported the association between several glycerophospholipids with PDAC risk, also identified BCAAs in their sample cohort, and this indicated a positive association that was stronger in subjects whose cancer was diagnosed early, but was not statistically significant (40). A similar positive association for the BCAAs was also reported in the large metabolomics study by Stolzenberg-Solomon and colleagues, but this did not pass their multiple comparison significance threshold (45). These are promising findings that provide evidence for the probable role of BCAAs in the early development of PDAC and should be investigated further. However, a meta-analysis could not be performed as the studies reported results/measures stratified differently, with Shu and colleagues (40) only reporting odds ratios stratified by follow-up time for the individual BCAAs, while Kitahara and colleagues (46) reported odds ratio stratified by follow-up time for the sum total of the BCAAs.

Inflammation-related markers

We identified 13 studies that looked at the association between levels of inflammation-related markers and the risk of developing PDAC (Supplementary Table S6). A meta-analysis of three studies looking at circulating adiponectin levels found no association with PDAC risk (RR: 0.90; 95% CI: 0.63–1.29; I2 = 61%; Fig. 5A) and also showed a certain degree of heterogeneity. Interestingly, like C-peptide levels, two studies reported an inverse association between circulating levels of adiponectin and PDAC risk, which was specific to never smokers (26, 47). We also identified two studies that looked at the association between another adipokine, leptin and PDAC risk and one of them reported no clear overall association (48), whereas the second study by Babic and colleagues found higher levels of leptin, indicating an increased risk in men but not in women (49). In addition, markers involved in the receptor for the advanced glycation end products (RAGE) pathway were assessed with baseline soluble RAGE (sRAGE) levels found to be inversely associated with PDAC risk in two studies (50, 51), and conflicting results were reported on the association between Nε-carboxymethyl-lysine (CML)-AGE, which is one of the best characterized AGEs (51, 52).

Figure 5.

Forest plots from random-effects meta-analysis of the association between inflammation-related biomarkers and PDAC risk. A, Adiponectin and (B) C-reactive protein.

Figure 5.

Forest plots from random-effects meta-analysis of the association between inflammation-related biomarkers and PDAC risk. A, Adiponectin and (B) C-reactive protein.

Close modal

We also identified four studies that looked at the association between the chronic inflammatory marker C-reactive protein (CRP) and PDAC risk. A meta-analysis found no association between the levels of this marker and pancreatic cancer risk (RR: 1.17; 95% CI: 0.96–1.42; I2 = 0%; Fig. 5B). However, in two nested case–control studies in the ATBC and PLCO cohorts, an inverse association was reported in younger participants (<66 years), which was not seen in the older group (66 or older; ref. 53).

Miscellaneous markers

We also identified 11 studies that assessed the association between biomarkers involved in tobacco metabolism (45, 54, 55), micronutrients (56–58), immunoglobulins (59–61), enzymes such as trypsinogen (62), pepsinogen (63), and gamma-glutamyl transferase (64, 65). These results are summarized in Supplementary Table S7.

The aim of this systematic review was to assess the association of various circulating biomarkers with PDAC risk, in order to understand their role in the early development of this cancer. We identified 65 eligible articles, and meta-analysis showed a positive association between glucose levels and PDAC risk (n = 4 studies). Additionally, an inverse association was found between levels of cholesterol (n = 5 studies) and PLP (n = 4 studies).

The positive association seen between levels of glucose and PDAC in the meta-analysis of four studies (20–22, 66) showed a large degree of heterogeneity. This association remained consistent despite geographical differences among the four studies, with two studies being conducted in Europe and the other two in Asia. However, sensitivity analysis showed that heterogeneity was lowered after exclusion of the results from Pang and colleagues (20) or Jee and colleagues (21), and the positive association remained. Three of these studies used fasting serum samples for the measurement of glucose levels and also excluded cases diagnosed early on in the follow-up period (lag ranged from within 1–5 years of follow-up), which could indicate that the associations seen between increasing glucose levels and PDAC risk are not a consequence of PDAC development or an early marker of disease (21, 22, 66). Pang and colleagues (20), on the other hand, measured glucose levels as random blood glucose in nonfasting samples. For the studies that used fasting blood samples, detailed information on fasting time was not provided, and this could influence the results seen in the meta-analysis as metabolite levels can be significantly affected by sampling conditions. Additionally, two of the studies included in the meta-analysis (22, 66) included adjustment for BMI, whereas both Pang and colleagues and Jee and colleagues did not, which could further explain the heterogeneity seen. These findings are in line with a systematic review of prospective observational studies published on the association between fasting blood glucose and risk of pancreatic cancer. This review included studies on PDAC mortality and those which estimated glucose levels from Hb1Ac values, which were not done in our study. They reported a 14% increase in PDAC risk with increasing glucose levels in their meta-analysis of nine studies (67). These results provide strong evidence of an association between glucose levels and PDAC risk; however, further understanding on the nature of this relationship is necessary to strengthen our knowledge on the molecular development of PDAC. Additionally, a number of studies identified in our review reported positive associations between levels of insulin, C-peptide as well as proinsulin and HOMA scores with PDAC risk (22, 23, 25, 26). As impaired β-cell function and insulin resistance have been reported to play a role in the glucose intolerance seen in pancreatic cancer, these findings suggest a close interaction between these pathways could play an important role in early development of PDAC (68, 69).

The IGF axis is another pathway that is closely related to insulin resistance. Insulin has been reported to increase the levels of biologically active IGF-1 and can also alter concentrations of its binding proteins (IGFBP; refs. 70, 71). In our review, results from four studies were consistent, and the meta-analyses showed no significant association between levels of IGF-1, IGFBP-3, and the IGF-1/IGFBP-3 molar ratio (72–75). Similar observations were made in a systematic review and meta-analysis on the association between the IGF-axis and PDAC risk; however, this included retrospective case–control studies as well (76). Although these findings suggest that the IGF axis plays a minimal role in the initial development of PDAC, other studies on genetic variants of different members of this axis have reported significant associations with PDAC risk as well as clinical outcomes, and therefore more studies are needed to make a proper conclusion on the role of this axis in PDAC development (77–79).

We also identified several studies that looked at different metabolism-related markers. A meta-analysis of five studies (46, 66, 80–82) showed a trend toward an inverse association between cholesterol levels and PDAC risk. However, only two of these studies included results that were independent of statin use, which could affect the overall association and could act as a confounding factor (81, 82). Moreover, four studies reported on overall cancer incidence, and only one of these studies was specific to pancreatic cancer and included follow-up data that suggested that the inverse association was attenuated as follow-up time increased. These results are in accordance with other cohort studies on cancer incidence, in which the association between cholesterol levels and cancer risk decreased as follow-up time increased or when cases diagnosed in the first few years after follow-up were excluded and could indicate that this association was likely a consequence of preclinical disease (80, 83, 84). These findings should however be interpreted with caution as other studies have also reported persistent associations with longer follow-up times and therefore more research is required on specific cancer sites in order to draw definitive conclusions (85–87). We also identified four studies that reported on the association between circulating branched-chain amino acids and PDAC risk. BCAAs have been reported to be elevated in individuals who are obese and with insulin resistance and also said to be associated with future development of diabetes (88–90). Because these are all considered to be risk factors for PDAC, BCAAs could play an important role in the initial development of PDAC and should be researched further.

Finally, our review explored the role of various biomarkers involved in the one-carbon metabolism pathway. No association was found in our study for folate or total homocysteine, but we found a significant inverse association between circulating levels of PLP (active form of vitamin B6) and PDAC risk in a meta-analysis of four studies (27, 33, 34, 41). All four studies reported no change in the inverse association seen on excluding cases diagnosed within two to four years of follow-up time, minimizing the bias contributed by reverse causality. Additionally, all studies except one (33) were able to adjust for risk factors of PDAC such as BMI and history of diabetes. However, only two studies (33, 34) had data on multivitamin supplement use, which could be a potential confounding factor. Dietary or circulating nutrients such as folate, methionine, vitamin B12, and B6 are considered to be potential risk factors for cancer and may have protective functions through their role in facilitating DNA methylation, nucleotide synthesis, DNA repair, and replication. Both vitamin B6 and B12 act as cofactors for various important reactions in this pathway (91, 92). Vitamin B6 has been reported to help protect against DNA damage and is also a cofactor involved in the production of the antioxidant glutathione. It may also serve as a scavenger of reactive oxygen species in addition to its role as a cofactor. It has also been shown that deficiency in PLP leads to accumulation of AGEs that increase genomic instability by elevating oxidative stress (93–95). Interestingly, levels of receptors for AGEs (RAGE) have been reported to be inversely associated with PDAC risk as identified in our review (50–52). These results are in line with two other systematic reviews assessing the association between dietary PLP intake (96, 97) and circulating PLP (97) with PDAC risk, and both report a significant inverse association suggestive of a protective role in PDAC development. The review on blood PLP levels also included one case–control study which did not meet our inclusion criteria (97). This protective function of vitamin B6 has also been reported for other cancers and further emphasizes the importance of studying the underlying role of these metabolites and their associated pathways in the early development of PDAC (98–101).

The main strength of this review is that, to the best of our knowledge, it is the first comprehensive systematic review and meta-analysis conducted on the association of circulating biomarkers as a whole and PDAC risk. The quality and risk of bias was assessed using the established tool NOS (17) and several studies earned a good score (<7). Additionally, the funnel plot of the included studies was symmetrical, indicating that there are fewer chances of introduction of publication bias (Supplementary Fig. S1). We also included only prospective cohort studies with a follow-up period of at least six months in order to ensure that the biomarkers are assessed not very close to diagnosis of PDAC so as to gain a better understanding on the biological pathways involved in early PDAC development. Despite the large number of studies identified in our review, very few studies have looked at the same biomarker in order to draw out definite conclusions. This is especially true in terms of the metabolite biomarkers studied and our review has helped discover this limitation, and therefore more studies focused on these biomarkers should be carried out. In terms of limitations, the studies included in the review measured the biomarker only once at baseline in prediagnostic samples, and longitudinal assessment of the biomarker in the same individual might provide greater insight into its role in cancer development. Additionally, a few studies assessing the same biomarker which were used in the meta-analyses had reported varying cutoff values for biomarker concentrations in terms of quartiles, tertiles, etc., and this could also potentially influence the associations observed.

In summary, our findings strengthened the evidence on the role of increasing glucose levels with PDAC development and also discovered an inverse association with PLP levels. We also identified a possible role of BCAAs and trend toward the role of low levels of cholesterol in the early development of PDAC. However, further research is required in order to draw definitive conclusions on the nature of these relationships and deepen our understanding of the role of these pathways in governing PDAC biology.

S. Kumar reports grants and personal fees from European Union Horizon 2020 Marie Sklodowska-Curie Actions COFUND Doctoral training program during the conduct of the study. A.J. McGuigan reports grants from Royal College of Surgeons and Ulster Society of Gastroenterology during the conduct of the study. R.C. Turkington reports personal fees from Almac Diagnostics outside the submitted work. No disclosures were reported by the other authors.

S. Kumar: Data curation, formal analysis, validation, investigation, visualization, methodology, writing–original draft, writing–review and editing. R.J. Santos: Data curation, formal analysis, validation, investigation, writing–review and editing. A.J. McGuigan: Data curation, formal analysis, validation, investigation, writing–review and editing. U. Singh: Data curation, formal analysis, validation, investigation, writing–review and editing. P. Johnson: Data curation, formal analysis, validation, investigation, writing–review and editing. A.T. Kunzmann: Conceptualization, supervision, validation, investigation, writing–original draft, writing–review and editing. R.C. Turkington: Conceptualization, supervision, funding acquisition, writing–original draft, writing–review and editing.

This work has been funded by the European Union Horizon 2020 Marie Sklodowska-Curie Actions COFUND Doctoral training program No: 754507 awarded to S. Kumar and Cancer Research UK grant no C50880/A29831 and Cancer Focus NI grant awarded to R.C. Turkington.

The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore be hereby marked advertisement in accordance with 18 U.S.C. Section 1734 solely to indicate this fact.

1.
Cancer Research UK
.
Pancreatic cancer survival statistics
.
2019
.
[cited 2021 Jan]. Available from
: https://www.cancerresearchuk.org/health-professional/cancer-statistics/statistics-by-cancer-type/pancreatic-cancer/survival#heading-Zero.
2.
Siegel
RL
,
Miller
KD
,
Jemal
A
.
Cancer statistics, 2019
.
CA Cancer J Clin
2019
;
69
:
7
34
.
3.
Rahib
L
,
Smith
BD
,
Aizenberg
R
,
Rosenzweig
AB
,
Fleshman
JM
,
Matrisian
LM
.
Projecting cancer incidence and deaths to 2030: the unexpected burden of thyroid, liver, and pancreas cancers in the united states
.
Cancer Res
2014
;
74
:
2913
21
.
4.
Bond-Smith
G
,
Banga
N
,
Hammond
TM
,
Imber
CJ
.
Pancreatic adenocarcinoma
.
BMJ
2012
;
344
:
1
10
.
5.
Witkowski
ER
,
Smith
JK
,
Tseng
JF
.
Outcomes following resection of pancreatic cancer
.
J Surg Oncol
2013
;
107
:
97
103
.
6.
Cancer Research UK
.
Pancreatic cancer incidence statistics
.
2019
.
[cited 2021 Jan]. Available from
: https://www.cancerresearchuk.org/health-professional/cancer-statistics/statistics-by-cancer-type/pancreatic-cancer/incidence#heading-Zero.
7.
Singhi
AD
,
Koay
EJ
,
Chari
ST
,
Maitra
A
.
Early detection of pancreatic cancer: opportunities and challenges
.
Gastroenterology
2019
;
156
:
2024
40
.
8.
Al-Sukhni
W
,
Borgida
A
,
Rothenmund
H
,
Holter
S
,
Semotiuk
K
,
Grant
R
, et al
.
Screening for pancreatic cancer in a high-risk cohort: an eight-year experience
.
J Gastrointest Surg
2012
;
16
:
771
83
.
9.
Canto
MI
,
Harinck
F
,
Hruban
RH
,
Offerhaus
GJ
,
Poley
JW
,
Kamel
I
, et al
.
International cancer of the pancreas screening (CAPS) consortium summit on the management of patients with increased risk for familial pancreatic cancer
.
Gut
2013
;
62
:
339
47
.
10.
Kato
S
,
Honda
K
.
Use of biomarkers and imaging for early detection of pancreatic cancer
.
Cancers
2020
;
12
:
1965
.
11.
Canto
MI
,
Goggins
M
,
Yeo
CJ
,
Griffin
C
,
Axilbund
JE
,
Brune
K
, et al
.
Screening for pancreatic neoplasia in high-risk individuals: an EUS-based approach
.
Clin Gastroenterol Hepatol
2004
;
2
:
606
21
.
12.
Wei
L
,
Yao
K
,
Gan
S
,
Suo
Z
.
Clinical utilization of serum- or plasma-based miRNAs as early detection biomarkers for pancreatic cancer: a meta-analysis up to now
.
Med
2018
;
97
:
e12132
.
13.
Dumstrei
K
,
Chen
H
,
Brenner
H
.
A systematic review of serum autoantibodies as biomarkers for pancreatic cancer detection
.
Oncotarget
2016
;
7
:
11151
64
.
14.
Litman-Zawadzka
A
,
Łukaszewicz-Zając
M
,
Mroczko
B
.
Novel potential biomarkers for pancreatic cancer–a systematic review
.
Adv Med Sci
2019
;
64
:
252
7
.
15.
Mehta
KY
,
Wu
H-J
,
Menon
SS
,
Fallah
Y
,
Zhong
X
,
Rizk
N
, et al
.
Metabolomic biomarkers of pancreatic cancer: a meta-analysis study
.
Oncotarget
2017
;
8
:
68899
915
.
16.
Kumar
S
,
Kunzmann
A
,
Turkington
RC
,
McGuigan
A
,
Santos
R
,
Singh
U
, et al
.
A systematic review and meta-analysis of non-tissue based biomarkers and pancreatic cancer risk
.
PROSPERO
2019
CRD42019141149. Available from
: https://www.crd.york.ac.uk/prospero/display_record.php?ID=CRD42019141149.
17.
Wells
GA
,
Shea
B
,
O'Connell
D
,
Peterson
J
,
Welch
V
,
Losos
M
, et al
.
The Newcastle-Ottawa scale (NOS) for assessing the quality of nonrandomised studies in meta-analyses
.
Ottawa
:
Ottawa Hosp Res Inst.
18.
Review Manager (RevMan) [Computer program]
.
Version 5.4.1
,
The Cochrane Collaboration
,
2020
.
19.
Higgins
JPT
,
Thompson
SG
,
Deeks
JJ
,
Altman
DG
.
Measuring inconsistency in meta-analyses
.
Br Med J
2003
;
327
:
557
60
.
20.
Pang
Y
,
Kartsonaki
C
,
Guo
Y
,
Bragg
F
,
Yang
L
,
Bian
Z
, et al
.
Diabetes, plasma glucose and incidence of pancreatic cancer: a prospective study of 0.5 million Chinese adults and a meta-analysis of 22 cohort studies
.
Int J Cancer
2017
;
140
:
1781
8
.
21.
Jee
SH
,
Ohrr
H
,
Sull
JW
,
Yun
JE
,
Ji
M
,
Samet
JM
.
Fasting serum glucose level and cancer risk in Korean men and women
.
JAMA
2005
;
293
:
194
.
22.
Stolzenberg-Solomon
RZ
,
BI G
SC
,
PR T
JV
,
Albanes
D
.
Insulin, glucose, insulin resistance, and pancreatic cancer in male smokers
.
JAMA
2005
;
294
:
2872
.
23.
Wolpin
BM
,
Bao
Y
,
Qian
ZR
,
Wu
C
,
Kraft
P
,
Ogino
S
, et al
.
Hyperglycemia, insulin resistance, impaired pancreatic β-cell function, and risk of pancreatic cancer
.
J Natl Cancer Inst
2013
;
105
:
1027
35
.
24.
Banim
PJ
,
Luben
R
,
Khaw
KT
,
Hart
AR
.
Dietary oleic acid is inversely associated with pancreatic cancer: data from food diaries in a cohort study
.
Pancreatology
2018
;
18
:
655
60
.
25.
Michaud
DS
,
Wolpin
B
,
Giovannucci
E
,
Liu
S
,
Cochrane
B
,
Manson
JE
, et al
.
Prediagnostic plasma C-peptide and pancreatic cancer risk in men and women
.
Cancer Epidemiol Biomarkers Prev
2007
;
16
:
2101
9
.
26.
Nogueira
LM
,
Newton
CC
,
Pollak
M
,
Silverman
DT
,
Albanes
D
,
Mannisto
S
, et al
.
Serum C-peptide, total and high molecular weight adiponectin, and pancreatic cancer: do associations differ by smoking?
Cancer Epidemiol Biomarkers Prev
2017
;
26
:
914
22
.
27.
Huang
JY
,
Butler
LM
,
Midttun
Ø
,
Koh
WP
,
Ueland
PM
,
Wang
R
, et al
.
Serum B6 vitamers (pyridoxal 5′-phosphate, pyridoxal, and 4-pyridoxic acid) and pancreatic cancer risk: two nested case–control studies in Asian populations
.
Cancer Causes Control
2016
;
27
:
1447
56
.
28.
Huang
JY
,
Butler
LM
,
Midttun
Ø
,
Ulvik
A
,
Wang
R
,
Jin
A
, et al
.
A prospective evaluation of serum kynurenine metabolites and risk of pancreatic cancer
.
PLoS One
2018
;
13
:
1
13
.
29.
Stolzenberg-Solomon
RZ
,
Vieth
R
,
Azad
A
,
Pietinen
P
,
Taylor
PR
,
Virtamo
J
, et al
.
A prospective nested case-control study of vitamin D status and pancreatic cancer risk in male smokers
.
Cancer Res
2006
;
66
:
10213
9
.
30.
Wolpin
BM
,
Ng
K
,
Bao
Y
,
Kraft
P
,
Stampfer
MJ
,
Michaud
DS
, et al
.
Plasma 25-hydroxyvitamin D and risk of pancreatic cancer
.
Cancer Epidemiol Biomarkers Prev
2012
;
21
:
82
91
.
31.
Weinstein
SJ
,
Stolzenberg-Solomon
RZ
,
Kopp
W
,
Rager
H
,
Virtamo
J
,
Albanes
D
.
Impact of circulating vitamin D binding protein levels on the association between 25-hydroxyvitamin d and pancreatic cancer risk: a nested case-control study
.
Cancer Res
2012
;
72
:
1190
8
.
32.
Piper
MR
,
Freedman
DM
,
Robien
K
,
Kopp
W
,
Rager
H
,
Horst
RL
, et al
.
Vitamin D-binding protein and pancreatic cancer: a nested case-control study
.
Am J Clin Nutr
2015
;
101
:
1206
15
.
33.
Stolzenberg-Solomon
RZ
,
Albanes
D
,
Nieto
FJ
,
Hartman
TJ
,
Tangrea
JA
,
Rautalahti
M
, et al
.
Pancreatic cancer risk and nutrition-related methyl-group availability indicators in male smokers
.
J Natl Cancer Inst
1999
;
91
:
535
41
.
34.
Schernhammer
E
,
Wolpin
B
,
Rifai
N
,
Cochrane
B
,
Manson
JA
,
Ma
J
, et al
.
Plasma folate, vitamin B6, vitamin B12, and homocysteine and pancreatic cancer risk in four large cohorts
.
Cancer Res
2007
;
67
:
5553
60
.
35.
Arendt
JFH
,
Sørensen
HT
,
Horsfall
LJ
,
Petersen
I
.
Elevated vitamin B12 levels and cancer risk in UK primary care: a THIN database cohort study
.
Cancer Epidemiol Biomarkers Prev
2019
;
28
:
814
21
.
36.
Banim
PJR
,
Luben
R
,
Mctaggart
A
,
Welch
A
,
Wareham
N
,
Khaw
K
, et al
.
Dietary antioxidants and the aetiology of pancreatic cancer: a cohort study using data from food diaries and biomarkers
.
Gut
2013
;
62
:
1489
96
.
37.
Jeurnink
SM
,
Ros
MM
,
Leenders
M
,
DFJB
Van
,
Siersema
PD
,
Jansen
EHJM
, et al
.
Plasma carotenoids, vitamin C, retinol and tocopherols levels and pancreatic cancer risk within the European Prospective Investigation into Cancer and Nutrition: a nested case–control study and pancreatic cancer risk.
Int J Cancer
2019
;
676
:
665
76
.
38.
Stolzenberg-solomon
RZ
,
Sheffler-collins
S
,
Weinstein
S
,
Garabrant
DH
,
Mannisto
S
,
Taylor
P
, et al
.
Vitamin E intake, a-tocopherol status, and pancreatic cancer in a cohort of male smokers
.
Am J Clin Nutr
2009
;
89
:
584
91
.
39.
Matejcic
M
,
Lesueur
F
,
Biessy
C
,
Renault
AL
,
Mebirouk
N
,
Yammine
S
, et al
.
Circulating plasma phospholipid fatty acids and risk of pancreatic cancer in a large European cohort
.
Int J Cancer
2018
;
143
:
2437
48
.
40.
Shu
X
,
Zheng
W
,
Yu
D
,
Li
HL
,
Lan
Q
,
Yang
G
, et al
.
Prospective metabolomics study identifies potential novel blood metabolites associated with pancreatic cancer risk
.
Int J Cancer
2018
;
143
:
2161
7
.
41.
Chuang
S-C
,
Stolzenberg-Solomon
R
,
Ueland
PM
,
Vollset
SE
,
Midttun
Ø
,
Olsen
A
, et al
.
A U-shaped relationship between plasma folate and pancreatic cancer risk in the European Prospective Investigation into Cancer and Nutrition
.
Eur J Cancer
2011
;
47
:
1808
16
.
42.
Huang
JY
,
Luu
HN
,
Butler
LM
,
Midttun
Ø
,
Ulvik
A
,
Wang
R
, et al
.
A prospective evaluation of serum methionine-related metabolites in relation to pancreatic cancer risk in two prospective cohort studies
.
Int J Cancer
2020
;
147
:
1917
27
.
43.
Mayers
JR
,
Wu
C
,
Clish
CB
,
Kraft
P
,
Torrence
ME
,
Fiske
BP
, et al
.
Elevation of circulating branched-chain amino acids is an early event in human pancreatic adenocarcinoma development
.
Nat Med
2014
;
20
:
1193
8
.
44.
Katagiri
R
,
Goto
A
,
Nakagawa
T
,
Nishiumi
S
,
Kobayashi
T
,
Hidaka
A
, et al
.
Increased levels of branched-chain amino acid associated with increased risk of pancreatic cancer in a prospective case–control study of a large cohort
.
Gastroenterology
2018
;
155
:
1474
82
.
45.
Stolzenberg-Solomon
R
,
Derkach
A
,
Moore
S
,
Weinstein
SJ
,
Albanes
D
,
Sampson
J
.
Associations between metabolites and pancreatic cancer risk in a large prospective epidemiological study
.
Gut
2020
;
69
:
2008
15
.
46.
Kitahara
CM
,
De González
AB
,
Freedman
ND
,
Huxley
R
,
Mok
Y
,
Jee
SH
, et al
.
Total cholesterol and cancer risk in a large prospective study in Korea
.
J Clin Oncol
2011
;
29
:
1592
8
.
47.
Grote
VA
,
Rohrmann
S
,
Dossus
L
,
Nieters
A
,
Halkjær
J
,
Tjønneland
A
, et al
.
The association of circulating adiponectin levels with pancreatic cancer risk: a study within the prospective EPIC cohort
.
Int J Cancer
2012
;
130
:
2428
37
.
48.
Stolzenberg-Solomon
RZ
,
Newton
CC
,
Silverman
DT
,
Pollak
M
,
Nogueira
LM
,
Weinstein
SJ
, et al
.
Circulating leptin and risk of pancreatic cancer: a pooled analysis from 3 cohorts
.
Am J Epidemiol
2015
;
182
:
187
97
.
49.
Babic
A
,
Bao
Y
,
Qian
ZR
,
Yuan
C
,
Giovannucci
EL
,
Aschard
H
, et al
.
Pancreatic cancer risk associated with prediagnostic plasma levels of leptin and leptin receptor genetic polymorphisms
.
Cancer Res
2016
;
76
:
7160
7
.
50.
White
DL
,
Hoogeveen
RC
,
Chen
L
,
Richardson
P
,
Ravishankar
M
,
Shah
P
, et al
.
A prospective study of soluble receptor for advanced glycation end products and adipokines in association with pancreatic cancer in postmenopausal women
.
Cancer Med
2018
;
7
:
2180
91
.
51.
Jiao
L
,
Weinstein
SJ
,
Albanes
D
,
Taylor
PR
,
Graubard
BI
,
Virtamo
J
, et al
.
Evidence that serum levels of the soluble receptor for advanced glycation end products are inversely associated with pancreatic cancer risk: a prospective study
.
Cancer Res
2011
;
71
:
3582
9
.
52.
Grote
VA
,
Nieters
A
,
Kaaks
R
,
Tjnøneland
A
,
Roswall
N
,
Overvad
K
, et al
.
The associations of advanced glycation end products and its soluble receptor with pancreatic cancer risk: a case-control study within the prospective EPIC cohort
.
Cancer Epidemiol Biomarkers Prev
2012
;
21
:
619
28
.
53.
Douglas
JB
,
Silverman
DT
,
Weinstein
SJ
,
Graubard
BI
,
Pollak
MN
,
Tao
Y
, et al
.
Serum C-reactive protein and risk of pancreatic cancer in two nested, case–control studies
.
Cancer Epidemiol Biomarkers Prev
2011
;
20
:
359
69
.
54.
Leenders
M
,
Chuang
SC
,
Dahm
CC
,
Overvad
K
,
Ueland
PM
,
Midttun
O
, et al
.
Plasma cotinine levels and pancreatic cancer in the EPIC cohort study
.
Int J Cancer
2012
;
131
:
997
1002
.
55.
Khalaf
N
,
Yuan
C
,
Hamada
T
,
Cao
Y
,
Babic
A
,
Morales-Oyarvide
V
, et al
.
Regular use of aspirin or non-aspirin nonsteroidal anti-inflammatory drugs is not associated with risk of incident pancreatic cancer in two large cohort studies
.
Gastroenterology
2018
;
154
:
1380
90
.
56.
Gaur
A
,
Collins
H
,
Wulaningsih
W
,
Holmberg
L
,
Garmo
H
,
Hammar
N
, et al
.
Iron metabolism and risk of cancer in the Swedish AMORIS study
.
Cancer Causes Control
2013
;
24
:
1393
402
.
57.
Chatterjee
S
,
Combs
GF
,
Chattopadhyay
A
,
Stolzenberg-Solomon
R
.
Serum selenium and pancreatic cancer: a prospective study in the Prostate, Lung, Colorectal and Ovarian Cancer Trial cohort
.
Cancer Causes Control
2019
;
30
:
457
64
.
58.
Wulaningsih
W
,
Michaelsson
K
,
Garmo
H
,
Hammar
N
,
Jungner
I
,
Walldius
G
, et al
.
Inorganic phosphate and the risk of cancer in the Swedish AMORIS study
.
BMC Cancer
2013
;
13
:
257
.
59.
Sollie
S
,
Santaolalla
A
,
Michaud
DS
,
Sarker
D
,
Karagiannis
SN
,
Josephs
DH
, et al
.
Serum immunoglobulin G is associated with decreased risk of pancreatic cancer in the Swedish AMORIS study
.
Front Oncol
2020
;
10
:
1
8
.
60.
Olson
SH
,
Hsu
M
,
Wiemels
JL
,
Bracci
PM
,
Zhou
M
,
Patoka
J
, et al
.
Serum immunoglobulin E and risk of pancreatic cancer in the prostate, lung, colorectal, and ovarian cancer screening trial
.
Cancer Epidemiol Biomarkers Prev
2014
;
23
:
1414
20
.
61.
Sun
Y
,
Wu
J
,
Cai
H
,
Wang
S
,
Liu
Q
,
Blot
WJ
, et al
.
A prospective study of autoantibodies to ezrin and pancreatic cancer risk
.
Cancer Causes Control
2016
;
27
:
831
5
.
62.
Johansen
D
,
Manjer
J
,
Regner
S
,
Lindkvist
B
.
Pre-diagnostic levels of anionic trypsinogen, cationic trypsinogen, and pancreatic secretory trypsin inhibitor in relation to pancreatic cancer risk
.
Pancreatology
2010
;
10
:
229
37
.
63.
Laiyemo
AO
,
Kamangar
F
,
Marcus
PM
,
Taylor
PR
,
Virtamo
J
,
Albanes
D
, et al
.
Serum pepsinogen level, atrophic gastritis and the risk of incident pancreatic cancer—a prospective cohort study
.
Cancer Epidemiol
2009
;
33
:
368
73
.
64.
Tsuboya
T
,
Kuriyama
S
,
Nagai
M
,
Hozawa
A
,
Sugawara
Y
,
Tomata
Y
, et al
.
Gamma-glutamyltransferase and cancer incidence: the Ohsaki cohort study
.
J Epidemiol
2012
;
22
:
144
50
.
65.
Mok
Y
,
Son
DK
,
Yun
YD
,
Jee
SH
,
Samet
JM
.
γ-Glutamyltransferase and cancer risk: the Korean cancer prevention study
.
Int J Cancer
2016
;
138
:
311
9
.
66.
Johansen
D
,
Stocks
T
,
Jonsson
H
,
Lindkvist
B
,
Björge
T
,
Concin
H
, et al
.
Metabolic factors and the risk of pancreatic cancer: a prospective analysis of almost 580,000 men and women in the metabolic syndrome and cancer project
.
Cancer Epidemiol Biomarkers Prev
2010
;
19
:
2307
17
.
67.
Liao
WC
,
Tu
YK
,
Wu
MS
,
Lin
JT
,
Wang
HP
,
Chien
KL
.
Blood glucose concentration and risk of pancreatic cancer: systematic review and dose-response meta-analysis
.
BMJ
2015
;
349
:
1
11
.
68.
Chari
ST
,
Zapiach
M
,
Yadav
D
,
Rizza
RA
.
Beta-cell function and insulin resistance evaluated by HOMA in pancreatic cancer subjects with varying degrees of glucose intolerance
.
Pancreatology
2005
;
5
:
229
33
.
69.
Cowey
S
,
Hardy
RW
.
The metabolic syndrome: a high-risk state for cancer?
Am J Pathol
2006
;
169
:
1505
22
.
70.
Arcidiacono
B
,
Iiritano
S
,
Nocera
A
,
Possidente
K
,
Nevolo
MT
,
Ventura
V
, et al
.
Insulin resistance and cancer risk: an overview of the pathogenetic mechanisms
.
Exp Diabetes Res
2012
;
2012
:
789174
.
71.
Giovannucci
E
.
Nutrition, insulin, insulin-like growth factors and cancer
.
Horm Metab Res
2003
;
35
:
694
704
.
72.
Douglas
JB
,
Silverman
DT
,
Pollak
MN
,
Tao
Y
,
Soliman
AS
,
Stolzenberg-Solomon
RZ
.
Serum IGF-I, IGF-II, IGFBP-3, and IGF-I/IGFBP-3 molar ratio and risk of pancreatic cancer in the prostate, lung, colorectal, and ovarian cancer screening trial
.
Cancer Epidemiol Biomarkers Prev
2010
;
19
:
2298
306
.
73.
Rohrmann
S
,
Grote
VA
,
Becker
S
,
Rinaldi
S
,
Tjønneland
A
,
Roswall
N
, et al
.
Concentrations of IGF-I and IGFBP-3 and pancreatic cancer risk in the European Prospective Investigation into Cancer and Nutrition
.
Br J Cancer
2012
;
106
:
1004
10
.
74.
Wolpin
BM
,
Michaud
DS
,
Giovannucci
EL
,
Schernhammer
ES
,
Stampfer
MJ
,
Manson
JE
, et al
.
Circulating insulin-like growth factor axis and the risk of pancreatic cancer in four prospective cohorts
.
Br J Cancer
2007
;
97
:
98
104
.
75.
Stolzenberg-Solomon
RZ
,
Limburg
P
,
Pollak
M
,
Taylor
PR
,
Virtamo
J
,
Albanes
D
.
Insulin-like growth factor (IGF)-1, IGF-binding protein-3, and pancreatic cancer in male smokers
.
Cancer Epidemiol Biomarkers Prev
2004
;
13
:
438
44
.
76.
Gong
Y
,
Zhang
B
,
Liao
Y
,
Tang
Y
,
Mai
C
,
Chen
T
, et al
.
Serum insulin-like growth factor axis and the risk of pancreatic cancer: systematic review and meta-analysis
.
Nutrients
2017
;
9
:
394
.
77.
Dong
X
,
Li
Y
,
Tang
H
,
Chang
P
,
Hess
KR
,
Abbruzzese
JL
, et al
.
Insulin-like growth factor axis gene polymorphisms modify risk of pancreatic cancer
.
Cancer Epidemiol
2012
;
36
:
206
11
.
78.
Dong
X
,
Javle
M
,
Hess
KR
,
Shroff
R
,
Abbruzzese
JL
,
Li
D
.
Insulin-like growth factor axis gene polymorphisms and clinical outcomes in pancreatic cancer
.
Gastroenterology
2010
;
139
:
464
73
.
79.
Suzuki
H
,
Li
Y
,
Dong
X
,
Hassan
MM
,
Abbruzzese
JL
,
Li
D
.
Effect of insulin-like growth factor gene polymorphisms alone or in interaction with diabetes on the risk of pancreatic cancer
.
Cancer Epidemiol Biomarkers Prev
2008
;
17
:
3467
73
.
80.
Ahn
J
,
Lim
U
,
Weinstein
SJ
,
Schatzkin
A
,
Hayes
RB
,
Virtamo
J
, et al
.
Prediagnostic total and high-density lipoprotein cholesterol and risk of cancer
.
Cancer Epidemiol Biomarkers Prev
2009
;
18
:
2814
21
.
81.
Kabat
GC
,
Kim
MY
,
Chlebowski
RT
,
Vitolins
MZ
,
Wassertheil-Smoller
S
,
Rohan
TE
.
Serum lipids and risk of obesity-related cancers in postmenopausal women
.
Cancer Causes Control
2018
;
29
:
13
24
.
82.
Chen
WCY
,
Boursi
B
,
Mamtani
R
,
Yang
YX
.
Total serum cholesterol and pancreatic cancer: a nested case-control study
.
Cancer Epidemiol Biomarkers Prev
2019
;
28
:
363
9
.
83.
Mamtani
R
,
Lewis
JD
,
Scott
FI
,
Ahmad
T
,
Goldberg
DS
,
Datta
J
, et al
.
Disentangling the association between statins, cholesterol, and colorectal cancer: a nested case-control study
.
PLoS Med
2016
;
13
:
1
15
.
84.
Strasak
AM
,
Pfeiffer
RM
,
Brant
LJ
,
Rapp
K
,
Hilbe
W
,
Oberaigner
W
, et al
.
Time-dependent association of total serum cholesterol and cancer incidence in a cohort of 172 210 men and women: a prospective 19-year follow-up study
.
Ann Oncol
2009
;
20
:
1113
20
.
85.
Strohmaier
S
,
Edlinger
M
,
Manjer
J
,
Stocks
T
,
Bjørge
T
,
Borena
W
, et al
.
Total serum cholesterol and cancer incidence in the metabolic syndrome and cancer project (Me-Can)
.
PLoS One
2013
;
8
:
e54242
.
86.
Schatzkin
A
,
Hoover
RN
,
Taylor
PR
,
Ziegler
R
,
Carter
CL
,
Albanes
D
, et al
.
Site-specific analysis of total serum cholesterol and incident cancer in the National Health and Nutrition Examination Survey I epidemiologic follow-up study
.
Cancer Res
1988
;
48
:
452
8
.
87.
Peto
R
,
Boreham
J
,
Chen
J
,
Li
J
,
Campbell
TC
,
Brun
T
.
Plasma cholesterol, coronary heart disease, and cancer
.
BMJ
1989
;
298
:
1249
.
88.
Bloomgarden
Z
.
Diabetes and branched-chain amino acids: what is the link?
J Diabetes
2018
;
10
:
350
2
.
89.
Adeva
MM
,
Calviño
J
,
Souto
G
,
Donapetry
C
.
Insulin resistance and the metabolism of branched-chain amino acids in humans
.
Amino Acids
2012
;
43
:
171
81
.
90.
O'Connell
T
.
The complex role of branched chain amino acids in diabetes and cancer
.
Metabolites
2013
;
3
:
931
45
.
91.
Mason
JB
,
Choi
SW
.
Folate and carcinogenesis: Developing a unifying hypothesis
.
Adv Enzyme Regul
2000
;
40
:
127
41
.
92.
Ames
BN
.
DNA damage from micronutrient deficiencies is likely to be a major cause of cancer
.
Mutat Res Mol Mech Mutagen
2001
;
475
:
7
20
.
93.
Kannan
K
,
Jain
SK
.
Effect of vitamin B6 on oxygen radicals, mitochondrial membrane potential, and lipid peroxidation in H2O2-treated U937 monocytes
.
Free Radic Biol Med
2004
;
36
:
423
8
.
94.
Marzio
A
,
Merigliano
C
,
Gatti
M
,
Vernì
F
.
Sugar and chromosome stability: clastogenic effects of sugars in vitamin B6-deficient cells
.
PLoS Genet
2014
;
10
:
e1004199
.
95.
Merigliano
C
,
Mascolo
E
,
La Torre
M
,
Saggio
I
,
Vernì
F
.
Protective role of vitamin B6 (PLP) against DNA damage in Drosophila models of type 2 diabetes
.
Sci Rep
2018
;
8
:
1
12
.
96.
Peng
YF
,
Han
MM
,
Huang
R
,
Dong
BB
,
Li
L
.
Vitamin B6 intake and pancreatic carcinoma risk: a meta-analysis
.
Nutr Cancer
2019
;
71
:
1061
6
.
97.
Wei
DH
,
Mao
QQ
.
Vitamin B6, vitamin B12 and methionine and risk of pancreatic cancer: a meta-analysis
.
Nutr J
2020
;
19
:
1
12
.
98.
Mocellin
S
,
Briarava
M
,
Pilati
P
.
Vitamin B6 and cancer risk: a field synopsis and meta-analysis
.
J Natl Cancer Inst
2017
;
109
:
1
9
.
99.
Yang
J
,
Li
H
,
Deng
H
,
Wang
Z
.
Association of one-carbon metabolism-related vitamins (folate, B6, B12), homocysteine and methionine with the risk of lung cancer: systematic review and meta-analysis
.
Front Oncol
2018
;
8
:
493
.
100.
Larsson
SC
,
Orsini
N
,
Wolk
A
.
Vitamin B 6 and risk of colorectal cancer
.
JAMA
2010
;
303
:
1077
.
101.
Wu
W
,
Kang
S
,
Zhang
D
.
Association of vitamin B 6, vitamin B 12 and methionine with risk of breast cancer: a dose-response meta-analysis
.
Br J Cancer
2013
;
109
:
1926
44
.
102.
Bao
Y
,
Giovannucci
EL
,
Kraft
P
,
Qian
ZR
,
Wu
C
,
Ogino
S
, et al
.
Inflammatory plasma markers and pancreatic cancer risk: a prospective study of five U.S. cohorts
.
Cancer Epidemiol Biomarkers Prev
2013
;
22
:
855
61
.
103.
Bao
Y
,
Giovannucci
EL
,
Kraft
P
,
Stampfer
MJ
,
Ogino
S
,
Ma
J
, et al
.
A prospective study of plasma adiponectin and pancreatic cancer risk in five US cohorts
.
J Natl Cancer Inst
2013
;
105
:
95
103
.
104.
Cui
Y
,
Shu
XO
,
Li
HL
,
Yang
G
,
Wen
W
,
Gao
YT
, et al
.
Prospective study of urinary prostaglandin E2 metabolite and pancreatic cancer risk
.
Int J Cancer
2017
;
141
:
2423
9
.
105.
De Gonzalez
AB
,
Ji
EY
,
Lee
SY
,
Klein
AP
,
Sun
HJ
.
Pancreatic cancer and factors associated with the insulin resistance syndrome in the Korean cancer prevention study
.
Cancer Epidemiol Biomarkers Prev
2008
;
17
:
359
64
.
106.
Grote
VA
,
Kaaks
R
,
Nieters
A
,
Tjønneland
A
,
Halkjær
J
,
Overvad
K
, et al
.
Inflammation marker and risk of pancreatic cancer: a nested case-control study within the EPIC cohort
.
Br J Cancer
2012
;
106
:
1866
74
.
107.
Grote
VA
,
Rohrmann
S
,
Nieters
A
,
Dossus
L
,
Tjønneland
A
,
Halkjær
J
, et al
.
Diabetes mellitus, glycated haemoglobin and C-peptide levels in relation to pancreatic cancer risk: a study within the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort
.
Diabetologia
2011
;
54
:
3037
46
.
108.
Jacobs
EJ
,
Newton
CC
,
Silverman
DT
,
Nogueira
LM
,
Albanes
D
,
Männistö
S
, et al
.
Serum transforming growth factor-β1 and risk of pancreatic cancer in three prospective cohort studies
.
Cancer Causes Control
2014
;
25
:
1083
91
.
109.
Kim
J
,
Yuan
C
,
Babic
A
,
Bao
Y
,
Clish
CB
,
Pollak
MN
, et al
.
Genetic and circulating biomarker data improve risk prediction for pancreatic cancer in the general population
.
Cancer Epidemiol Biomarkers Prev
2020
;
29
:
999
1008
.
110.
Meinhold
CL
,
de Gonzalez
AB
,
Albanes
D
,
Weinstein
SJ
,
Taylor
PR
,
Virtamo
J
, et al
.
Predictors of fasting serum insulin and glucose and the risk of pancreatic cancer in smokers
.
Cancer Causes Control
2009
;
20
:
681
90
.
111.
Sollie
S
,
Michaud
DS
,
Sarker
D
,
Karagiannis
SN
,
Josephs
DH
,
Hammar
N
, et al
.
Chronic inflammation markers are associated with risk of pancreatic cancer in the Swedish AMORIS cohort study
.
BMC Cancer
2019
;
19
:
5
10
.
112.
Stolzenberg-Solomon
RZ
,
Hayes
RB
,
Horst
RL
,
Anderson
KE
,
Hollis
BW
,
Silverman
DT
.
Serum vitamin D and risk of pancreatic cancer in the prostate, lung, colorectal, and ovarian screening trial
.
Cancer Res
2009
;
69
:
1439
47
.
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