New-onset diabetes and pancreatic cancer: Current evidence on absolute risk and screening feasibility

Table 1 Contextualizing pancreatic cancer risk across screening-relevant populations

Population	Approximate risk context	Implication for screening
General population	Very low background incidence; population-level screening is not justified	No routine screening
Adults with glycemically defined NOD	Relative risk is increased, but absolute PDAC risk remains low, approximately around 1% over 3 years in older adults with NOD	Indiscriminate imaging of all NOD patients is inefficient
Enriched NOD subgroup	Risk increases with older age, weight loss, rapid glycemic worsening, or high END-PAC score	Candidate group for selective imaging or biomarker-based enrichment
Very high-risk clinical subgroup	NOD plus symptoms, abnormal biomarkers/imaging, hereditary risk, or strong clinical concern	Diagnostic evaluation rather than screening

END-PAC: Enriching new-onset diabetes for pancreatic cancer; NOD: New-onset diabetes; PDAC: Pancreatic ductal adenocarcinoma.

Table 2 Key studies informing the clinical interpretation of new-onset diabetes in pancreatic cancer detection

Ref.	Design and population	Key numerical findings	Main implication
Sharma et al[6]	Development and validation study of adults older than 50 years with glycemically defined NOD	Approximately 1% developed PDAC within 3 years; END-PAC score ≥ 3 yielded 78% sensitivity, 85% specificity, and 3.6% PDAC prevalence in the high-risk subgroup	NOD enriches risk, and simple clinical variables can improve enrichment
Chari et al[12]	Prospective cohort of 18838 adults aged 50 years or older with glycemically defined NOD	Eighty-two PDACs diagnosed; race-adjusted 3-year incidence 0.62%; mean lead time 8 months	Prospective validation confirms enrichment but also underscores low absolute risk
Mellenthin et al[16]	United Kingdom primary care cohort of 197092 patients with NOD	END-PAC AUC 0.69 after imputation and 0.71 after recalibration; stand-alone use judged insufficient for diagnostic workup selection	Real-world implementation attenuates performance of clinical scores
Cichosz et al[17]	Danish registry-based machine-learning model using routine biochemical trajectories	AUC 0.78; top 1% risk stratum had 12% 3-year PDAC risk, with 20% sensitivity	Longitudinal routine data can identify a very high-risk subgroup, but sensitivity remains limited
Khan and Bhushan[18]	Multisystem United States cohort using XGBoost	AUC 0.80; positive predictive value 12% at the Youden cutoff and ≥ 2.5% when sensitivity fell to 38%	Model discrimination can improve, but clinically acceptable positive predictive value remains difficult

NOD: New-onset diabetes; PDAC: Pancreatic ductal adenocarcinoma; END-PAC: Enriching new-onset diabetes for pancreatic cancer; AUC: Area under the curve.