Development and validation of a trans-ancestry polygenic risk score for type 2 diabetes in diverse populations.

BACKGROUND: Type 2 diabetes (T2D) is a worldwide scourge caused by both genetic and environmental risk factors that disproportionately afflicts communities of color. Leveraging existing large-scale genome-wide association studies (GWAS), polygenic risk scores (PRS) have shown promise to complement established clinical risk factors and intervention paradigms, and improve early diagnosis and prevention of T2D. However, to date, T2D PRS have been most widely developed and validated in individuals of European descent. Comprehensive assessment of T2D PRS in non-European populations is critical for equitable deployment of PRS to clinical practice that benefits global populations. METHODS: We integrated T2D GWAS in European, African, and East Asian populations to construct a trans-ancestry T2D PRS using a newly developed Bayesian polygenic modeling method, and assessed the prediction accuracy of the PRS in the multi-ethnic Electronic Medical Records and Genomics (eMERGE) study (11,945 cases; 57,694 controls), four Black cohorts (5137 cases; 9657 controls), and the Taiwan Biobank (4570 cases; 84,996 controls). We additionally evaluated a post hoc ancestry adjustment method that can express the polygenic risk on the same scale across ancestrally diverse individuals and facilitate the clinical implementation of the PRS in prospective cohorts. RESULTS: The trans-ancestry PRS was significantly associated with T2D status across the ancestral groups examined. The top 2% of the PRS distribution can identify individuals with an approximately 2.5-4.5-fold of increase in T2D risk, which corresponds to the increased risk of T2D for first-degree relatives. The post hoc ancestry adjustment method eliminated major distributional differences in the PRS across ancestries without compromising its predictive performance. CONCLUSIONS: By integrating T2D GWAS from multiple populations, we developed and validated a trans-ancestry PRS, and demonstrated its potential as a meaningful index of risk among diverse patients in clinical settings. Our efforts represent the first step towards the implementation of the T2D PRS into routine healthcare.

Comparison of Static and Dynamic Baseline Creatinine Surrogates for Defining Acute Kidney Injury.

BACKGROUND: "Dynamic" baseline serum creatinine (sCr), based on a rolling 48-h window, and a static baseline sCr (previous outpatient sCr) were used to define acute kidney injury (AKI). METHODS: Retrospective cohort study of adult admissions to the University of Alabama (UAB) Health System hospitals for years 2016-2018. Included admissions had >1- and <180-day length of stay, >2 inpatient sCr measurements, and an averaged estimated glomerular filtration rate >15 mL/min/1.73 m2. The final cohort of 62,380 patients included 100,570 admissions, 3,509 inpatient deaths, and 1,916 admissions with inpatient dialysis. AKI was defined by Kidney Disease Improving Global Outcomes (KDIGO) criteria and a static or dynamic baseline sCr. Discrimination was evaluated with area under receiver operator curves (AUC), logistic regression, and net reclassification improvement (NRI). RESULTS: Preadmission outpatient "static" sCr values were available for 43,433 admissions. The lowest sCr value during a rolling 48-h window before each inpatient sCr defined a "dynamic" baseline sCr. Using point-wise comparisons, the dynamic baseline sCr performed better than static baseline sCr for inpatient mortality (AUC [0.819 vs. 0.741; p < 0.001] and NRI ≥0.306 [p < 0.001]) and inpatient dialysis (AUC [0.903 vs. 0.864; p < 0.001] and NRI ≥0.317 [p < 0.001]). CONCLUSIONS: The dynamic baseline sCr is available without reference to preadmission sCr values and avoids confounding associated with missing outpatient sCr values. AKI defined with the dynamic baseline sCr significantly improved discrimination of risk for inpatient mortality and dialysis compared to static baseline sCr.