The rarity of neoplastic cells in the biopsy imposes major technical hurdles that have so far limited genomic studies in classical Hodgkin lymphoma (cHL). By using a highly sensitive and robust deep next-generation sequencing approach for circulating tumor DNA (ctDNA), we aimed to identify the genetics of cHL in different clinical phases, as well as its modifications on treatment. The analysis was based on specimens collected from 80 newly diagnosed and 32 refractory patients with cHL, including longitudinal samples collected under ABVD (adriamycin, bleomycin, vinblastine, dacarbazine) chemotherapy and longitudinal samples from relapsing patients treated with chemotherapy and immunotherapy. ctDNA mirrored Hodgkin and Reed-Sternberg cell genetics, thus establishing ctDNA as an easily accessible source of tumor DNA for cHL genotyping. By identifying as the most frequently mutated gene in ∼40% of cases, we refined the current knowledge of cHL genetics. Longitudinal ctDNA profiling identified treatment-dependent patterns of clonal evolution in patients relapsing after chemotherapy and patients maintained in partial remission under immunotherapy. By measuring ctDNA changes during therapy, we propose ctDNA as a radiation-free tool to track residual disease that may integrate positron emission tomography imaging for the early identification of chemorefractory patients with cHL. Collectively, our results provide the proof of concept that ctDNA may serve as a novel precision medicine biomarker in cHL.

Objective: To examine the persistence of the original treatment effects 10 years after the Diabetes Control and Complications Trial (DCCT) in the follow-up Epidemiology of Diabetes Interventions and Complications (EDIC) study. In the DCCT, intensive therapy aimed at nearnormal glycemia reduced the risk of microvascular complications of type 1 diabetes mellitus compared with conventional therapy.Methods: Retinopathy was evaluated by fundus photography in 1211 subjects at EDIC year 10. Further 3-step progression on the Early Treatment Diabetic Retinopathy Study scale from DCCT closeout was the primary outcome.Results: After 10 years of EDIC follow-up, there was no significant difference in mean glycated hemoglobin levels (8.07% vs 7.98%) between the original treatment groups. Nevertheless, compared with the former conven-tional treatment group, the former intensive group had significantly lower incidences from DCCT close of further retinopathy progression and proliferative retinopathy or worse (hazard reductions, 53%-56%; PϽ.001). The risk (hazard) reductions at 10 years of EDIC were attenuated compared with the 70% to 71% over the first 4 years of EDIC (PϽ.001). The persistent beneficial effects of former intensive therapy were largely explained by the difference in glycated hemoglobin levels during DCCT. Conclusion:The persistent difference in diabetic retinopathy between former intensive and conventional therapy ("metabolic memory") continues for at least 10 years but may be waning.

Background: Many CpGs become hyper or hypo-methylated with age. Multiple methods have been developed by Horvath et al. to estimate DNA methylation (DNAm) age including Pan-tissue, Skin & Blood, PhenoAge, and GrimAge. Pan-tissue and Skin & Blood try to estimate chronological age in the normal population whereas PhenoAge and GrimAge use surrogate markers associated with mortality to estimate biological age and its departure from chronological age. Here, we applied Horvath's four methods to calculate and compare DNAm age in 499 subjects with type 1 diabetes (T1D) from the Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications (DCCT/EDIC) study using DNAm data measured by Illumina EPIC array in the whole blood. Association of the four DNAm ages with development of diabetic complications including cardiovascular diseases (CVD), nephropathy, retinopathy, and neuropathy, and their risk factors were investigated. Results: Pan-tissue and GrimAge were higher whereas Skin & Blood and PhenoAge were lower than chronological age (p < 0.0001). DNAm age was not associated with the risk of CVD or retinopathy over 18-20 years after DNAm measurement. However, higher PhenoAge (β = 0.023, p = 0.007) and GrimAge (β = 0.029, p = 0.002) were associated with higher albumin excretion rate (AER), an indicator of diabetic renal disease, measured over time. GrimAge was also associated with development of both diabetic peripheral neuropathy (OR = 1.07, p = 9.24E−3) and cardiovascular autonomic neuropathy (OR = 1.06, p = 0.011). Both HbA1c (β = 0.38, p = 0.026) and T1D duration (β = 0.01, p = 0.043) were associated with higher PhenoAge. Employment (β = − 1.99, p = 0.045) and leisure time (β = − 0.81, p = 0.022) physical activity were associated with lower Pan-tissue and Skin & Blood, respectively. BMI (β = 0.09, p = 0.048) and current smoking (β = 7.13, p = 9.03E−50) were positively associated with Skin & Blood and GrimAge, respectively. Blood pressure, lipid levels, pulse rate, and alcohol consumption were not associated with DNAm age regardless of the method used. Conclusions: Various methods of measuring DNAm age are sub-optimal in detecting people at higher risk of developing diabetic complications although some work better than the others.

Hodgkin lymphoma (HL) is among the neoplastic diseases that has the best long-term outcome after cytotoxic treatment. Cure rates approach 80–90%; however, 15–20% of patients will be resistant to therapy (primary refractory) or relapse after treatment. Prognostic factors should help to stratify treatment according to the risk profile and identify patients at risk for failure. Significance of prognostic factors partly depends on the efficacy of the treatments administered, since new effective therapies can variably counterbalance the adverse effects of some unfavorable clinical determinants. As a consequence, some prognostic factors thought to be important in the past may become meaningless when modern successful therapies are used. Therefore, the value of prognostic factors has to be updated periodically, and then adapted to new emerging biomarkers. Besides the prognostic role of PET imaging, tissue and circulating biomarkers, as the number of tumor-infiltrating macrophages, cytokine and chemokine levels and profiling of circulating nucleic acids (DNA and microRNAs) have shown promise.

Refractive error (RE) is a complex, multifactorial disorder characterized by a mismatch between the optical power of the eye and its axial length that causes object images to be focused off the retina. The two major subtypes of RE are myopia (nearsightedness) and hyperopia (farsightedness), which represent opposite ends of the distribution of the quantitative measure of spherical refraction. We performed a fixed effects meta-analysis of genome-wide association results of myopia and hyperopia from 9 studies of European-derived populations: AREDS, KORA, FES, OGP-Talana, MESA, RSI, RSII, RSIII and ERF. One genome-wide significant region was observed for myopia, corresponding to a previously identified myopia locus on 8q12 (p = 1.25×10−8), which has been reported by Kiefer et al. as significantly associated with myopia age at onset and Verhoeven et al. as significantly associated to mean spherical-equivalent (MSE) refractive error. We observed two genome-wide significant associations with hyperopia. These regions overlapped with loci on 15q14 (minimum p value = 9.11×10−11) and 8q12 (minimum p value 1.82×10−11) previously reported for MSE and myopia age at onset. We also used an intermarker linkage- disequilibrium-based method for calculating the effective number of tests in targeted regional replication analyses. We analyzed myopia (which represents the closest phenotype in our data to the one used by Kiefer et al.) and showed replication of 10 additional loci associated with myopia previously reported by Kiefer et al. This is the first replication of these loci using myopia as the trait under analysis. “Replication-level” association was also seen between hyperopia and 12 of Kiefer et al.'s published loci. For the loci that show evidence of association to both myopia and hyperopia, the estimated effect of the risk alleles were in opposite directions for the two traits. This suggests that these loci are important contributors to variation of refractive error across the distribution.