Search

Aims: To investigate, in a large population in primary care, the relationship between fasting plasma glucose and HbA1c measurements, as well as the clinical implications of anaemia or chronic kidney disease for the interpretation of HbA1c values. Methods: From a primary care resource, we examined HbA1c and fasting plasma glucose as well as haemoglobin and estimated GFR. We stratified observations by chronic kidney disease stage and anaemia level. The estimation of the mean fasting plasma glucose level from HbA1c alone, and from HbA1c, haemoglobin and estimated GFR, respectively, was evaluated. Results: In 198 346 individuals, the fasting plasma glucose–HbA1c relationship mimicked the regression described in the A1c‐Derived Average Glucose (ADAG) study, which was based on average capillary and interstitial glucose. The fasting plasma glucose–HbA1c relationship was unaffected in mild to moderate chronic kidney disease and in mild to moderate anaemia. The correlation changed only in severe hyperglycaemia and concurrent severe anaemia or when estimated GFR was <45 ml/min/1.73m², so that glucose concentration was underestimated by HbA1c in anaemia and overestimated in chronic kidney disease. The prevalence of estimated GFR <30 ml/min/1.73m² was 0.82%, while the prevalence of haemoglobin <81 g/l (5.0 mmol/l) was 0.11%. Conclusions: The relationship between fasting plasma glucose and HbA1c mimics that of the people with diabetes included in the ADAG study. Mild to moderate anaemia and CKD do not have a significant impact on the interpretation of HbA1c as a marker of retrograde glycaemia. Hence, it seems justified to use HbA1c without adjustment in primary care. What's new?: The clinical implications of anaemia and chronic kidney disease with regard to the interpretation of HbA1c are not well described.In a large primary care population, mild to moderate anaemia or mild to moderate chronic kidney disease do not have a significant impact on the relationship between glycaemia and HbA1c.HbA1c can be used to assess glycaemic control in primary care. [ABSTRACT FROM AUTHOR]

Background: Myeloproliferative neoplasms, such as polycythemia vera, essential thrombocythemia, and myelofibrosis, are chronic hematologic cancers with varied progression rates. The genomic characterization of patients with myeloproliferative neoplasms offers the potential for personalized diagnosis, risk stratification, and treatment.Methods: We sequenced coding exons from 69 myeloid cancer genes in patients with myeloproliferative neoplasms, comprehensively annotating driver mutations and copy-number changes. We developed a genomic classification for myeloproliferative neoplasms and multistage prognostic models for predicting outcomes in individual patients. Classification and prognostic models were validated in an external cohort.Results: A total of 2035 patients were included in the analysis. A total of 33 genes had driver mutations in at least 5 patients, with mutations in JAK2, CALR, or MPL being the sole abnormality in 45% of the patients. The numbers of driver mutations increased with age and advanced disease. Driver mutations, germline polymorphisms, and demographic variables independently predicted whether patients received a diagnosis of essential thrombocythemia as compared with polycythemia vera or a diagnosis of chronic-phase disease as compared with myelofibrosis. We defined eight genomic subgroups that showed distinct clinical phenotypes, including blood counts, risk of leukemic transformation, and event-free survival. Integrating 63 clinical and genomic variables, we created prognostic models capable of generating personally tailored predictions of clinical outcomes in patients with chronic-phase myeloproliferative neoplasms and myelofibrosis. The predicted and observed outcomes correlated well in internal cross-validation of a training cohort and in an independent external cohort. Even within individual categories of existing prognostic schemas, our models substantially improved predictive accuracy.Conclusions: Comprehensive genomic characterization identified distinct genetic subgroups and provided a classification of myeloproliferative neoplasms on the basis of causal biologic mechanisms. Integration of genomic data with clinical variables enabled the personalized predictions of patients' outcomes and may support the treatment of patients with myeloproliferative neoplasms. (Funded by the Wellcome Trust and others.). [ABSTRACT FROM AUTHOR]

Background: Increasingly, circulating tumor DNA (ctDNA) is proposed as a tool for minimal residual disease (MRD) assessment. Digital PCR (dPCR) offers low analysis costs and turnaround times of less than a day, making it ripe for clinical implementation. Here, we used tumor-informed dPCR for ctDNA detection in a large colorectal cancer (CRC) cohort to evaluate the potential for post-operative risk assessment and serial monitoring, and how the metastatic site may impact ctDNA detection. Additionally, we assessed how altering the ctDNA-calling algorithm could customize performance for different clinical settings., Patients and Methods: Stage II-III CRC patients (N = 851) treated with a curative intent were recruited. Based on whole-exome sequencing on matched tumor and germline DNA, a mutational target was selected for dPCR analysis. Plasma samples (8 ml) were collected within 60 days after operation and-for a patient subset (n = 246)-every 3-4 months for up to 36 months. Single-target dPCR was used for ctDNA detection., Results: Both post-operative and serial ctDNA detection were prognostic of recurrence [hazard ratio (HR) = 11.3, 95% confidence interval (CI) 7.8-16.4, P < 0.001; HR = 30.7, 95% CI 20.2-46.7, P < 0.001], with a cumulative ctDNA detection rate of 87% at the end of sample collection in recurrence patients. The ctDNA growth rate was prognostic of survival (HR = 2.6, 95% CI 1.5-4.4, P = 0.001). In recurrence patients, post-operative ctDNA detection was challenging for lung metastases (4/21 detected) and peritoneal metastases (2/10 detected). By modifying the cut-off for calling a sample ctDNA positive, we were able to adjust the sensitivity and specificity of our test for different clinical contexts., Conclusions: The presented results from 851 stage II-III CRC patients demonstrate that our personalized dPCR approach effectively detects MRD after operation and shows promise for serial ctDNA detection for recurrence surveillance. The ability to adjust sensitivity and specificity shows exciting potential to customize the ctDNA caller for specific clinical settings., (Copyright © 2023 The Author(s). Published by Elsevier Ltd.. All rights reserved.)