Multiple Sensitivity Analyses Research Articles (Page 1)

Previous studies have reported controversial associations between n-3 polyunsaturated fatty acids (n-3 PUFA) and bone mineral density (BMD). To clarify whether a causal relationship exists, this study employed a 2-sample Mendelian randomization analysis using single-nucleotide polymorphisms significantly associated with n-3 PUFA as instrumental variables; data for both exposure (n-3 PUFA) and outcome (BMD) were obtained from genome-wide association studies. The inverse variance weighting method served as the primary analytical approach, supplemented by multiple sensitivity analyses to assess the robustness of the findings. Results from the 2-sample Mendelian randomization analysis provided strong genomic evidence for a negative causal association between n-3 PUFA and BMD (inverse variance weighting: odds ratio = 0.94, 95% confidence interval: 0.90-0.98, P = .01). These findings indicate that higher n-3 PUFA is associated with an increased risk of reduced BMD, suggesting that elevated n-3 PUFA may be a risk factor for low BMD and potentially osteoporosis.

Integrative proteo-metabolomic analysis in rheumatoid arthritis reveals potential therapeutic targets: Harnessing Mendelian randomization.

Lifestyles' role in the relationship between socioeconomic deprivation and depression.

Mood instability and 22 gastrointestinal disorders: investigating the causal relationships and the mediating factors by Mendelian randomization analysis.

Heart failure is a major global health burden with increasing incidence and mortality. Emerging evidence suggests that gut microbiota (GM) and lipid metabolism may play key roles in heart failure, but their causal relationships remain unclear. We performed a 2-sample Mendelian randomization (MR) analysis using genome-wide association study data from European cohorts to investigate the causal effects of GM on heart failure. To assess mediation, a 2-step MR and multivariable MR were applied to quantify the role of 179 lipid metabolites. Robustness of findings was evaluated through multiple sensitivity analyses. Significant causal associations were observed between several GM taxa (e.g., Bifidobacterium catenulatum, Lawsonibacter sp000492175) and heart failure. Multiple lipid metabolites, particularly phosphatidylcholine (PC) subtypes, were identified as mediators, with mediation proportions ranging from 7% to 13%. Sensitivity analyses supported the stability of the results. This study provides evidence for a causal pathway linking GM to heart failure through lipid metabolism. The findings highlight potential microbiota-based and metabolic intervention targets, offering new insights into heart failure pathogenesis and informing precision prevention strategies.

Neurofilament light chain (NfL) is a biomarker of neuronal damage that has shown associations with various neurodegenerative conditions, but its relationship with cognitive function in diabetic populations remains understudied. This research investigated the association between serum NfL levels and cognitive performance in individuals with diabetes. Using data from 2013 to 2014 National Health and Nutrition Examination Survey, we conducted a cross-sectional analysis of 179 diabetic participants. Cognitive function was assessed using the digit symbol substitution test, with participants categorized into normal cognitive function (n = 135) and cognitive function decline groups (n = 44). Weighted logistic regression models were employed to examine the relationship between serum NfL and cognitive function. Significant differences in serum NfL levels were observed between the normal cognitive function group (25.84 pg/mL) and the cognitive function decline group (38.63 pg/mL; P = .004). After adjusting for sociodemographic factors, anthropometric measures, comorbidities, and medication use, each unit increase in serum NfL was associated with increased odds of cognitive impairment (odds ratio 1.04, 95% confidence interval: 1.010–1.071, P = .031). A linear association was observed between NfL concentration and the risk of cognitive impairment in diabetic individuals. Robustness of the observed associations was validated through multiple sensitivity analyses. These findings suggest that higher serum NfL levels may be associated with an increased risk of cognitive impairment in diabetic patients, highlighting the need for further research to establish causal relationships and underlying mechanisms.

Sepsis is a complex disease with limited therapeutics. We conducted a 2-sample Mendelian randomization (MR) analysis to identify circulating inflammatory proteins as potential drug targets for sepsis. Protein quantitative trait loci (pQTLs) at cis-region for 448 circulating inflammatory proteins were obtained from the UK biobank pharma proteomics project (UKB-PPP) (N = 54219), and summary-level genetic associations with sepsis were extracted from the FinnGen cohort (12,301 cases and 332,343 controls) for the primary MR analysis. The findings were further replicated using alternative cis-pQTL (Fenland, N = 10708) and sepsis genome-wide association study data (UK Biobank, 2811 cases and 406,150 controls). Multiple sensitivity analyses including summary-data-based MR (SMR), the HEIDI test, colocalization, and phenome-wide scanning were performed to verify the causality of the identified proteins. Moreover, protein–protein interaction analysis was performed to examine the interactions between identified proteins and current drug targets in clinical trials. With the adjusted P-value <.05, 1-SD increase in genetically determined APOE (apolipoprotein E) was associated with increased risk of sepsis (OR = 1.07, 95%CI = 1.04–1.11, P = 5.36 × 10−5). The findings were consistent in the validation analyses. Multi-SNPs SMR analysis (P = 1.42 × 10−3), HEIDI test (P = .53), and colocalization analysis (PP4 = 0.95) further supported the causality of APOE. Protein–protein interaction analysis suggested that APOE was interacted with multiple drug targets tested in the clinical trials for sepsis. This integrative analysis combing the pQTL and genome-wide association study data identified APOE as a promising drug target for sepsis. Further experimental studies were warranted to explore the details of the underlying biological mechanisms and feasibility of drug development.

Alopecia areata (AA) is a chronic autoimmune hair disorder with a substantial psychosocial impact, yet contemporary, population-scale estimates of incident psychiatric comorbidity are limited. To quantify the risk of incident psychiatric disorders in adults with newly diagnosed AA versus matched controls, we assessed sex differences, and explored pre- versus post-coronavirus disease 19 (COVID-19) patterns. We conducted a multinational, retrospective cohort study using de-identified electronic health records (2015-2025) from TriNetX. Adults with AA (ICD-10 L63) and at least 6months of prior records were matched 1:1 to general-examination controls without AA. Propensity score matching balanced age, sex, and major comorbidity domains. For the psychiatric outcomes, ICD-10 codes were used. Risk ratios (RRs), and time-to-event with hazard ratios (HRs) analyses were estimated overall, by sex, and by pre/post-pandemic epoch; multiple sensitivity analyses tested robustness. After matching, 57,389 patients with AA and 57,389 controls were analyzed. AA was associated with a markedly higher risk of any incident psychiatric disorder versus controls (RR = 4.49,95% CI 4.30-4.70). The largest relative increases were seen in depression and anxiety, with smaller but notable rises in insomnia/parasomnia and substance use disorders. Suicidal behaviors and psychotic disorders also increased but remained rare. Women experienced a higher overall psychiatric burden-particularly depression, anxiety, and eating disorders-while men showed relatively higher rates of substance use. After COVID-19, the most significant increases were in anxiety, insomnia/parasomnia, eating disorders, self-harm, and suicide. These results were consistent across various sensitivity analyses. Adults with AA have a significantly higher risk of experiencing various new psychiatric disorders, especially among women and following the COVID-19 pandemic. Regular mental health screening, prompt referrals, and combined dermatology-psychiatry care could enhance outcomes. Further prospective research, including assessments of disease severity and patient-reported data, is necessary.

Evidence that the calcium channel blocker (CCB) verapamil slows type 1 diabetes progression suggests possible preventive benefits among people at risk of developing type 2 diabetes. We compared type 2 diabetes incidence between users of verapamil versus other CCBs in a population-based cohort. From a random sample of Australians in national subsidized health care databases, we identified 90,026 individuals who initiated treatment with a CCB (at least two supplies) between July 2003 and December 2014. Incident diabetes was captured by subsequent receipt of glucose-lowering treatment or registration with the National Diabetes Services Scheme. Individuals were followed from first CCB supply until discontinuation, diabetes onset, death, or end of 2014. Multistate Poisson regression models characterized associations of CCB subclass with type 2 diabetes incidence and death (the competing event), after multivariable propensity score adjustment. The cohort comprised 4,485 verapamil users (5.0%) and 85,541 treated with other CCBs (mostly dihydropyridines). During a median 1.6-year follow-up, 101 individuals treated with verapamil developed type 2 diabetes (8.8 per 1,000 person-years) compared with 2,622 people treated with other CCBs (11.4 per 1,000 person-years). This translated to an incidence rate ratio of 0.77 (95% CI 0.63-0.94) in favor of verapamil (fully adjusted) and a lower probability of type 2 diabetes at 6 years (4.2% [95% CI 3.3-5.3] vs. 5.4% [4.7-6.3] for a typical clinical profile; absolute risk difference 1.3% [95% CI -0.1-2.4]). Results were robust across multiple sensitivity analyses. Verapamil use is associated with a lower incidence of type 2 diabetes compared with other CCBs.

BackgroundAccurate prediction of survival in oncology can guide targeted interventions. The traditional regression-based Cox proportional hazards (CPH) model has statistical assumptions and may have limited predictive accuracy. With the capability to model large datasets, machine learning (ML) holds the potential to improve the prediction of time-to-event outcomes, such as cancer survival outcomes. The present study aimed to systematically summarize the use of ML models for cancer survival outcomes in observational studies and to compare the performance of ML models with CPH models.MethodsWe systematically searched PubMed, MEDLINE (via EBSCO), and Embase for studies that evaluated ML models vs. CPH models for cancer survival outcomes. The use of ML algorithms was summarized, and either the area under the curve (AUC) or the concordance index (C-index) for the ML and CPH models were presented descriptively. Only studies that provided a measure of discrimination, i.e., AUC or C-index, and 95% confidence interval (CI) were included in the final meta-analysis. A random-effects model was used to compare the predictive performance in the pooled AUC or C-index estimates between ML and CPH models using R. The quality of the studies was evaluated using available checklists. Multiple sensitivity analyses were performed.ResultsA total of 21 studies were included for systematic review and 7 for meta-analysis. Across the 21 articles, diverse ML models were used, including random survival forest (N=16, 76.19%), gradient boosting (N=5, 23.81%), and deep learning (N=8, 38.09%). In predicting cancer survival outcomes, ML models showed no superior performance over CPH regression. The standardized mean difference in AUC or C-index was 0.01 (95% CI: -0.01 to 0.03). Results from the sensitivity analyses confirmed the robustness of the main findings.ConclusionsML models had similar performance compared with CPH models in predicting cancer survival outcomes. Although this systematic review highlights the promising use of ML to improve the quality of care in oncology, findings from this review also suggest opportunities to improve ML reporting transparency. Future systematic reviews should focus on the comparative performance between specific ML models and CPH regression in time-to-event outcomes in specific type of cancer or other disease areas.Supplementary InformationThe online version contains supplementary material available at 10.1186/s12874-025-02694-z.

Sodium-glucose cotransporter-2 inhibitors (SGLT-2i) have been suggested to exert anti-inflammatory effects in the gastrointestinal tract based on preclinical and observational studies. Whether these effects translate into a reduced risk of inflammatory bowel disease (IBD), compared with other antidiabetic agents of similar therapeutic rank, remains unclear. This study aims to evaluate the association between SGLT-2i use and the risk of IBD in patients with type 2 diabetes mellitus (T2DM), using dipeptidyl peptidase-4 inhibitors (DPP-4i) as an active comparator. A retrospective cohort study was conducted using Taiwan's National Health Insurance Research Database from 2016 to 2022. Patients with T2DM who initiated SGLT-2i or DPP-4i therapy were included. Propensity score matching (1:4) was performed based on key covariates. Adjusted hazard ratios (aHRs) and 95% confidence intervals (CIs) were calculated for incident IBD, including its subtypes ulcerative colitis (UC) and Crohn's disease (CD), using Cox proportional hazards models and Fine and Gray competing risk models (considering death as a competing event), with further adjustment for residual confounding. Sensitivity analyses were conducted using alternative induction periods and several on-treatment approaches that accounted for treatment discontinuation. Among 258,355 patients (51,671 SGLT-2i users and 206,684 DPP-4i users), the incidence of IBD was lower in the SGLT-2i group (12.51 vs. 35.83 per 100,000 person-years). SGLT-2i use was associated with a significantly reduced risk of IBD (aHR: 0.391; 95% CI 0.237-0.645; p < 0.001), with consistent results across multiple sensitivity analyses. In this nationwide real-world cohort, SGLT-2i use was associated with a lower risk of IBD compared with DPP-4i among patients with T2DM. These findings suggest potential gut-protective effects of SGLT-2i and support their consideration in clinical decision-making for patients at elevated risk of IBD. Further prospective studies are warranted to validate these observations and elucidate the underlying mechanisms.

Abstract Computational models are often used to explore the future of the global food system, including the implications for human nutrition, an essential aspect of sustainability. However, the confidence that can be placed in the outputs of these models is often poorly quantified. Here, a sensitivity analysis of the DELTA Model® - a linear mass balance model calculating global nutrient supply using global and regional food balance sheet, processing, waste, inedible portion, composition, and bioavailability datasets - is conducted. First, a one-at-a-time analysis, varying 4019 underpinning datapoints from the above datasets individually by ± 50% was conducted to identify those with the greatest impact on calculated global nutrient supply. The most influential values from this initial analysis were then carried forward into a multiple value sensitivity analysis, where all possible combinations of ± 50% variations were simulated. Values related to cereals supply, waste, and nutritional value were the most influential on model output, with selenium, cystine, and carbohydrate supply the most sensitive nutrients. When compared to global nutrient requirements, variations in the calculated supply of some nutrients led to qualitative changes from a sufficient global supply to an insufficient supply. These results, while indicative rather than precise estimates of uncertainty, emphasise the critical importance of accurate cereals data in food system models, provide insight on the degree of sensitivity of similar linear models, and should encourage broader application of sensitivity analysis in the field.

Background/Objectives: Atopic dermatitis (AD) is a common chronic inflammatory skin disease that may influence cancer risk through immune dysregulation and chronic inflammation. The association between AD and colorectal cancer (CRC) remains unclear, with previous studies reporting conflicting results. Evidence from East Asian populations, where CRC incidence is rapidly rising, is particularly limited. Methods: We conducted a nested case–control study using the Korean National Health Insurance Service–National Sample Cohort (2002–2019). A total of 9920 incident CRC cases were identified and matched 1:4 with 39,680 controls by age, sex, income, and residential region. AD was defined using diagnostic codes and prescription records. Overlap propensity score weighting was applied to minimize confounding, and weighted logistic regression was used to estimate odds ratios (ORs) with 95% confidence intervals (CIs). Results: AD was not significantly associated with CRC risk (adjusted OR = 0.97, 95% CI: 0.91–1.04). The null association was consistent across subgroups stratified by age, sex, comorbidity burden, and allergic comorbidities. Sensitivity analyses yielded similar results. Conclusions: In this large, nationwide, population-based study, AD did not exhibit a significant connection with the risk of CRC. This null association remained consistent across multiple subgroups and sensitivity analyses, suggesting that AD may not play a substantial role in colorectal carcinogenesis. However, the observational design and lack of detailed lifestyle information may limit causal interpretation.

Observational studies suggest an association between diet and periodontitis, but causality remains unclear. This study employed a Mendelian randomization analysis to investigate the causal effects of nutrients (UK Biobank, N = 64,979; 5 trace elements and 4 vitamins) and dietary patterns (Social Science Genetic Association Consortium, N = 2,68,922; relative carbohydrate intake [E%] (CHO), relative fat intake [E%], relative protein intake [E%]) on periodontitis (gene-lifestyle interactions in dental endpoints, N = 45,563).Using the inverse variance weighted method as the primary model, supplemented by multiple sensitivity analyses to validate robustness. Additionally, potential mechanisms were explored using multivariable Mendelian randomization and mediation analysis. The results indicate causal associations between specific nutrients and reduced periodontitis risk: serum magnesium levels (odds ratio [OR] = 0.900, 95% confidence interval [CI] = 0.829–0.977, P = .011), vitamin B12 levels (OR = 0.881, 95% CI = 0.780–0.994, P = .039), and CHO (OR = 0.728, 95% CI = 0.582–0.910, P = .005). The effects of other exposures were non-significant, and sensitivity analyses supported the robustness of these findings. Multivariable Mendelian randomization further demonstrated that CHO (OR = 0.868, 95% CI = 0.765–0.985, P = .029) remained significantly inversely associated with periodontitis risk after adjusting for other factors. Notably, mediation analysis revealed that body mass index may mediate approximately 23.1% of the effect between CHO and periodontitis. These findings provide evidence relevant to the development of dietary guidelines for the prevention of periodontitis.

BackgroundPrediabetes was a reversible process in the development of type 2 diabetes mellitus (T2DM). Traditional Chinese medicine (TCM) was used to regulate blood glucose for thousands of years. However, there was a lack of real-world evidence on the long-term impact of TCM in prediabetic populations. This study aimed to evaluate the association between the use of TCM and T2DM incidence among individuals with prediabetes.MethodsA long-term population-based cohort study of participants with prediabetes was conducted using the Yinzhou Regional Health Care Database (YRHCD). A cox model with propensity score (PS) matching was applied to estimate the hazard ratio (HR) of the association between the use of TCM and T2DM. Various subgroup analyses and multiple sensitivity analyses were also performed to demonstrate the robustness of results.ResultsA total of 14,164 patients with prediabetes were included from the YRHCD between 2009 and 2024, among whom 12,252 participants were TCM users and 1912 initiated western medications (WM). In the primary analysis, the incidence of T2DM was 33.95 and 94.85 per 1000 person-years in users of TCM and WM, respectively. TCM use was associated with a significantly lower risk of developing T2DM (HR 0.32 [95%CI 0.27, 0.38]) after controlling confounding using PS matching. The results were generally consistent in various subgroup analyses and sensitivity analyses.ConclusionUse of TCM was associated with a decreased T2DM incidence in patients with prediabetes in the study region.Supplementary InformationThe online version contains supplementary material available at 10.1186/s13020-025-01214-x.

Background:Adiposity has been associated with an increased risk of head and neck cancer (HNC). Although body mass index (BMI) has been inversely associated with HNC risk among smokers, this is likely due to confounding. Previous Mendelian randomization (MR) studies could not fully discount causality between adiposity and HNC. Hence, we aimed to revisit this using the largest genome-wide association study (GWAS) of HNC available, which has more granular data on HNC subsites.Methods:We assessed the genetically predicted effects of BMI (N=806,834), waist-to-hip ratio (WHR; N=697,734) and waist circumference (N=462,166) on the risk of HNC (N=12,264 cases) and its subsites using a two-sample MR framework. We used inverse variance weighted (IVW) MR and multiple sensitivity analyses, including multivariable MR (MVMR), to explore the direct effects of the adiposity measures on HNC, while accounting for smoking behaviour (a well-known HNC risk factor).Results:In univariable MR, higher genetically predicted BMI increased the risk of overall HNC (IVW OR = 1.17 per 1-SD higher BMI, 95% CI 1.02–1.34). However, the IVW effect was attenuated when smoking was included in the MVMR model (OR accounting for comprehensive smoking index = 0.96 per 1-SD higher BMI, 95% CI 0.80–1.15). Furthermore, we did not find a link between genetically predicted WHR (IVW OR = 1.05 per 1-SD higher WHR, 95% CI 0.89–1.24) or waist circumference and HNC risk (IVW OR = 1.01 per 1-SD higher waist circumference, 95% CI 0.85–1.21).Conclusions:Our findings suggest that adiposity does not play a major role in HNC risk.Funding:FMB was supported by a Wellcome Trust PhD studentship in Molecular, Genetic and Lifecourse Epidemiology (224982/Z/22/Z). RCR was supported by a Cancer Research UK grant (C18281/A29019). MCB is supported by a University of Bristol Vice Chancellor’s Fellowship, the British Heart Foundation (AA/18/1/34219) and the UK Medical Research Council (MC_UU_00032/5). GDS works within the MRC Integrative Epidemiology Unit at the University of Bristol, which is supported by the Medical Research Council (MC_UU_00032/1). CLR was supported by the Medical Research Council (MC_UU_00011/5) and by a Cancer Research UK (C18281/A29019) programme grant (the Integrative Cancer Epidemiology Programme). SV was funded by an EU Horizon 2020 grant (agreement number 825771) and NIDCR National Institutes of Dental and Craniofacial Health (R03DE030257). JK works in a unit that receives support from the University of Bristol, a Cancer Research UK grant (C18281/A29019) and the UK Medical Research Council (grant number: MC_UU_00032/7).

Adiposity has been associated with an increased risk of head and neck cancer (HNC). Although body mass index (BMI) has been inversely associated with HNC risk among smokers, this is likely due to confounding. Previous Mendelian randomization (MR) studies could not fully discount causality between adiposity and HNC. Hence, we aimed to revisit this using the largest genome-wide association study (GWAS) of HNC available, which has more granular data on HNC subsites. We assessed the genetically predicted effects of BMI (N=806,834), waist-to-hip ratio (WHR; N=697,734) and waist circumference (N=462,166) on the risk of HNC (N=12,264 cases) and its subsites using a two-sample MR framework. We used inverse variance weighted (IVW) MR and multiple sensitivity analyses, including multivariable MR (MVMR), to explore the direct effects of the adiposity measures on HNC, while accounting for smoking behaviour (a well-known HNC risk factor). In univariable MR, higher genetically predicted BMI increased the risk of overall HNC (IVW OR = 1.17 per 1-SD higher BMI, 95% CI 1.02-1.34). However, the IVW effect was attenuated when smoking was included in the MVMR model (OR accounting for comprehensive smoking index = 0.96 per 1-SD higher BMI, 95% CI 0.80-1.15). Furthermore, we did not find a link between genetically predicted WHR (IVW OR = 1.05 per 1-SD higher WHR, 95% CI 0.89-1.24) or waist circumference and HNC risk (IVW OR = 1.01 per 1-SD higher waist circumference, 95% CI 0.85-1.21). Our findings suggest that adiposity does not play a major role in HNC risk. FMB was supported by a Wellcome Trust PhD studentship in Molecular, Genetic and Lifecourse Epidemiology (224982/Z/22/Z). RCR was supported by a Cancer Research UK grant (C18281/A29019). MCB is supported by a University of Bristol Vice Chancellor's Fellowship, the British Heart Foundation (AA/18/1/34219) and the UK Medical Research Council (MC_UU_00032/5). GDS works within the MRC Integrative Epidemiology Unit at the University of Bristol, which is supported by the Medical Research Council (MC_UU_00032/1). CLR was supported by the Medical Research Council (MC_UU_00011/5) and by a Cancer Research UK (C18281/A29019) programme grant (the Integrative Cancer Epidemiology Programme). SV was funded by an EU Horizon 2020 grant (agreement number 825771) and NIDCR National Institutes of Dental and Craniofacial Health (R03DE030257). JK works in a unit that receives support from the University of Bristol, a Cancer Research UK grant (C18281/A29019) and the UK Medical Research Council (grant number: MC_UU_00032/7).

Background:Adiposity has been associated with an increased risk of head and neck cancer (HNC). Although body mass index (BMI) has been inversely associated with HNC risk among smokers, this is likely due to confounding. Previous Mendelian randomization (MR) studies could not fully discount causality between adiposity and HNC. Hence, we aimed to revisit this using the largest genome-wide association study (GWAS) of HNC available, which has more granular data on HNC subsites.Methods:We assessed the genetically predicted effects of BMI (N=806,834), waist-to-hip ratio (WHR; N=697,734) and waist circumference (N=462,166) on the risk of HNC (N=12,264 cases) and its subsites using a two-sample MR framework. We used inverse variance weighted (IVW) MR and multiple sensitivity analyses, including multivariable MR (MVMR), to explore the direct effects of the adiposity measures on HNC, while accounting for smoking behaviour (a well-known HNC risk factor).Results:In univariable MR, higher genetically predicted BMI increased the risk of overall HNC (IVW OR = 1.17 per 1-SD higher BMI, 95% CI 1.02–1.34). However, the IVW effect was attenuated when smoking was included in the MVMR model (OR accounting for comprehensive smoking index = 0.96 per 1-SD higher BMI, 95% CI 0.80–1.15). Furthermore, we did not find a link between genetically predicted WHR (IVW OR = 1.05 per 1-SD higher WHR, 95% CI 0.89–1.24) or waist circumference and HNC risk (IVW OR = 1.01 per 1-SD higher waist circumference, 95% CI 0.85–1.21).Conclusions:Our findings suggest that adiposity does not play a major role in HNC risk.Funding:FMB was supported by a Wellcome Trust PhD studentship in Molecular, Genetic and Lifecourse Epidemiology (224982/Z/22/Z). RCR was supported by a Cancer Research UK grant (C18281/A29019). MCB is supported by a University of Bristol Vice Chancellor’s Fellowship, the British Heart Foundation (AA/18/1/34219) and the UK Medical Research Council (MC_UU_00032/5). GDS works within the MRC Integrative Epidemiology Unit at the University of Bristol, which is supported by the Medical Research Council (MC_UU_00032/1). CLR was supported by the Medical Research Council (MC_UU_00011/5) and by a Cancer Research UK (C18281/A29019) programme grant (the Integrative Cancer Epidemiology Programme). SV was funded by an EU Horizon 2020 grant (agreement number 825771) and NIDCR National Institutes of Dental and Craniofacial Health (R03DE030257). JK works in a unit that receives support from the University of Bristol, a Cancer Research UK grant (C18281/A29019) and the UK Medical Research Council (grant number: MC_UU_00032/7).

Introduction Proton pump inhibitors (PPIs) are widely prescribed for gastrointestinal disorders and are often used empirically in patients with pancreatic disease, yet their long-term impact on pancreatic health remains unclear. We evaluated whether regular PPI use is associated with risks of acute pancreatitis (AP), chronic pancreatitis (CP), and pancreatic cancer (PC).MethodsWe analyzed 489,394 UK Biobank participants aged 38–73 years, comparing regular PPI users with non-users and with histamine-2 receptor antagonist (H2RA) users as an active comparator. Associations with incident pancreatic outcomes were estimated using Cox regression models, landmark analysis, and propensity score matching, supplemented by multiple sensitivity analyses, including stratified/interaction analyses, E-values, time-varying exposure models with immortal-time correction, dfbeta residuals correction, stricter follow-up with Firth penalization, full-cohort multivariable modeling, and alternative matching (disease risk score 1:1, entropy balancing). Complementary in vivo experiments used a cerulein-induced acute pancreatitis mouse model to examine the effects of short- and long-term PPI administration on pancreatic inflammation and histopathology.ResultsIn primary analyses, regular PPI use showed a time-dependent association with acute pancreatitis. However, this association was not robust: multiple sensitivity analyses indicated instability of the finding. Experimental validation in mice demonstrated that neither short-term nor long-term PPI administration altered pancreatic inflammation or histopathological damage in the cerulein-induced model.DiscussionIntegrating large-scale cohort data with experimental evidence, our findings suggest that regular PPI use does not meaningfully influence the risk of acute pancreatitis, chronic pancreatitis, or pancreatic cancer.

BackgroundDeveloping countries commonly face challenges regarding budget constraints and inadequate cost-accounting capabilities during the implementation of a Diagnosis-Related Group (DRG) payment system. China has initiated pilot reforms of the Diagnosis-Related Group point-based payment system (DRG-PBPS) in 40 cities. DRG-PBPS, using historical cost data, integrates global budgeting with case-based point-weighted payments. In this study, its impact on inpatient resource utilization is evaluated and potential strategic behaviors of healthcare providers are examined.MethodsUsing administrative data from 15,744 inpatient records of cerebral infarction cases from January 2018 to December 2022 in a Chinese city, this study uses a difference-in-differences (DID) approach to evaluate the effects of the DRG-PBPS reform on hospitalization costs and length of stay. Both the changes in the Charlson Comorbidity Index (CCI) score and 14-day, 30-day, and 90-day readmission rates between the reform and control groups were compared before and after implementation to assess whether providers were involved in patient selection and premature discharge.ResultsAfter DRG-PBPS implementation, hospitalization costs decreased by 9.7% (p < 0.01), and length of stay decreased by 6.5% (p < 0.05). No significant changes were observed in CCI or 14-day and 90-day readmission rates, whereas 30-day readmissions fell by 2.0% (p < 0.05). These findings were robust across multiple sensitivity analyses, and the estimated effects of DRG-PBPS were broadly consistent across hospitals of different levels.ConclusionThe implementation of DRG-PBPS significantly reduced inpatient resource utilization without inducing adverse provider behavior. China’s pilot practice illustrates that the DRG-PBPS serves as an effective alternative to the fee-for-service model. For developing countries with constrained budgets and underdeveloped cost-accounting systems, DRG-PBPS provides a feasible strategy for adopting DRG-based payment systems in inpatient care.