Evidence map›Paper›PMID 39901108›Full record

ArticleBMC cancer2025

Global, regional, and national burden of pancreatic cancer from 1990 to 2021, its attributable risk factors, and projections to 2050: a systematic analysis of the global burden of disease study 2021.

Tianyu Li, Chen Lin, Weibin Wang

Registry-linked trialAbstract read
In one paragraph

Article in BMC cancer, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07551388 (Optimal Weight-Based Indocyanine Green Dosage for Fluorescent Cholangiography in Laparoscopic Cholecystectomy Among Chinese Population:Construction and Validation), which is not on this map. Cited by 50 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
50citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT07551388 nacompletednot on this map

Optimal Weight-Based Indocyanine Green Dosage for Fluorescent Cholangiography in Laparoscopic Cholecystectomy Among Chinese Population:Construction and Validation

TypeinterventionalSponsorPeking Union Medical College HospitalRan2025 to 2025Enrolled21ConditionsAcute CholecystitisArmsIndocyanine Green (ICG) Fluorescence Cholangiography
3 · Its place in the literature

Who cites it

50 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  3. Unveiling the Diagnostic Value and Potential Therapeutic Targets of Phenylalanine Metabolism in Pancreatic Cancer via Integrated Multi-Omics and Machine Learning.FASEB journal : official publication of the Federation of American Societies for Experimental Biology · 2026
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Tianyu LiDepartment of General Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Chen LinDepartment of General Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China. linchen0818@sina.com.
Weibin WangDepartment of General Surgery, Peking Union Medical College Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China. wwb_xh@163.com.

Funding

Beijing Natural Science Foundation 7232127CAMS Innovation Fund for Medical Sciences 2021-I2M-1-002National High Level Hospital Clinical Research Funding No.2022-PUMCH-D-001, No. 2022-PUMCH-B-004National Natural Science Foundation of China 82173074Non-profit Central Research Institute Fund of Chinese Academy of Medical Science 2018PT32014
6 · The paper itself

Abstract

backgroundThe incidence and mortality rates of pancreatic cancer are rising globally. This study examines global and regional trends in pancreatic cancer incidence, Disability Adjusted Life Years (DALYs), and mortality from 1990 to 2021, utilizing data from the most recent Global Burden of Disease (GBD) 2021 database.

methodsData were sourced from the GBD database over the period from 1990 to 2021. Age-standardized rates for incidence, DALYs, and mortality were calculated per 100,000 population. We also calculated the proportion of DALYs and mortality attributable to risk factors. The Bayesian age-period-cohort model was applied to project future trends until 2050.

resultsBetween 1990 and 2021, the global incidence of pancreatic cancer increased significantly, with the number of cases rising from approximately 207,905 to 508,533 and the age-standardized incidence rate (ASIR) increasing from 5.47 to 5.96 per 100,000 population. The global burden of pancreatic cancer, measured in DALYs, rose from 5.21 million to 11.32 million. Mortality rates showed a similar upward trend, with the number of deaths increasing from around 211,613 to 505,752, and the age-standardized mortality rate (ASMR) rising from 5.655 to 5.948 per 100,000 population. Notable increases in ASIR and ASMR were observed in low-middle and low sociodemographic index regions with males experienced higher rates compared to females. Age-standardized DALYs rate (ASDR) and ASMR worldwide were attributable to tobacco smoking, high BMI, and high fasting plasma glucose. Furthermore, our projection model estimates that the ASIR and ASMR of pancreatic cancer will significantly decline, while the ASDR is anticipated to maintain a steady downward trend by 2050.

conclusionThis study offers a comprehensive analysis of pancreatic cancer trends, providing crucial insights for public health planning and policy-making. Addressing identified risk factors and targeting high-risk populations are essential for effective strategies to reduce the global burden of pancreatic cancer.

Indexed as

Global Burden of DiseasePancreatic NeoplasmsAdultAgedBayes TheoremDisability-Adjusted Life YearsFemaleGlobal HealthHumansIncidenceMaleMiddle AgedRisk FactorsGlobal burden of disease studyIncidenceMortalityPancreatic cancerProjectionsRisk factors

Identifiers

PMID39901108
PMCPMC11789343

What OpenQuestion holds

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Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.