Aims. To provide a yardstick for physicians/patients to efficiently communicate/measure incident diabetes risk. Methods. We included data on 5,960 (3,438 women) diabetes-free adults, aged ≥20 years at baseline who either developed diabetes during two consecutive examinations or completed the followup. Age, systolic blood pressure, family history of diabetes, waist-to-height ratio (WHtR), triglyceride-to-high-density lipoprotein cholesterol ratio (TG/HDLD-C), and fasting plasma glucose (FPG) were introduced into an accelerated failure time regression model. Results. Annual diabetes incidence rate was 0.85/1000-person (95% CIs 0.77-0.94). Point-score-system incorporated age (1 point for >65 years), family history of diabetes (4 points), systolic blood pressure (-1 to 3 points), WHtR (-4 to 6 points), TG/HDL-C (1 point for ≥1.5), and FPG (0 to 27 points). Harrell's C statistic = 0.830 (95% CIs 0.808-0.852) and Hosmer-Lemeshow χ (2) = 9.7 (P for lack of fitness = 0.462) indicated good discrimination and calibration. We defined beta-cell age as chronological age of a person with the same predicted risk but all risk factors at the normal levels (i.e., WHtR 0.50, no family history of diabetes, Ln (TG/HDL-C) = 0.531, and FPG = 4.9 (mmol·L(-1))). Conclusion. Hereby, we have made it also possible to estimate wide ranges of "beta-cell age" for most chronological ages to assist clinician with risk communication.
Keyphrases
- type diabetes
- cardiovascular disease
- blood pressure
- glycemic control
- risk factors
- blood glucose
- end stage renal disease
- ejection fraction
- single cell
- heart failure
- newly diagnosed
- body mass index
- left ventricular
- stem cells
- cell therapy
- machine learning
- patient reported outcomes
- body composition
- hypertensive patients
- peritoneal dialysis
- physical activity
- adipose tissue
- electronic health record
- fatty acid
- bone marrow
- deep learning
- atrial fibrillation