Aims/hypothesis: Our study aims to uncover glycaemic phenotype heterogeneity in type 1 diabetes. Methods: In the Study of the French-speaking Society of Type 1 Diabetes (SFDT1), we characterised glycaemic heterogeneity thanks to a set of complementary metrics: HbA_1c, time in range (TIR), time below range (TBR), CV, Gold score and glycaemia risk index (GRI). Applying the Discriminative Dimensionality Reduction with Trees (DDRTree) algorithm, we created a phenotypic tree, i.e. a 2D visual mapping. We also carried out a clustering analysis for comparison. Results: We included 618 participants with type 1 diabetes (52.9% men, mean age 40.6 years [SD 14.1]). Our phenotypic tree identified seven glycaemic phenotypes. The 2D phenotypic tree comprised a main branch in the proximal region and glycaemic phenotypes in the distal areas. Dimension 1, the horizontal dimension, was positively associated with GRI (coefficient [95% CI]) (0.54 [0.52, 0.57]), HbA_1c (0.39 [0.35, 0.42]), CV (0.24 [0.19, 0.28]) and TBR (0.11 [0.06, 0.15]), and negatively with TIR (−0.52 [−0.54, −0.49]). The vertical dimension was positively associated with TBR (0.41 [0.38, 0.44]), CV (0.40 [0.37, 0.43]), TIR (0.16 [0.12, 0.20]), Gold score (0.10 [0.06, 0.15]) and GRI (0.06 [0.02, 0.11]), and negatively with HbA_1c (−0.21 [−0.25, −0.17]). Notably, socioeconomic factors, cardiovascular risk indicators, retinopathy and treatment strategy were significant determinants of glycaemic phenotype diversity. The phenotypic tree enabled more granularity than traditional clustering in revealing clinically relevant subgroups of people with type 1 diabetes. Conclusions/interpretation: Our study advances the current understanding of the complex glycaemic profile in people with type 1 diabetes and suggests that strategies based on isolated glycaemic metrics might not capture the complexity of the glycaemic phenotypes in real life. Relying on these phenotypes could improve patient stratification in type 1 diabetes care and personalise disease management. Graphical

The increasing number of people living with Long COVID requires the development of more personalized care; currently, limited treatment options and rehabilitation programs adapted to the variety of Long COVID presentations are available. Our objective was to design an easy-to-use Long COVID classification to help stratify people with Long COVID. Individual characteristics and a detailed set of 62 self-reported persisting symptoms together with quality of life indexes 12 months after initial COVID-19 infection were collected in a cohort of SARS-CoV-2 infected people in Luxembourg. A hierarchical ascendant classification (HAC) was used to identify clusters of people. We identified three patterns of Long COVID symptoms with a gradient in disease severity. Cluster-Mild encompassed almost 50% of the study population and was composed of participants with less severe initial infection, fewer comorbidities, and fewer persisting symptoms (mean = 2.9). Cluster-Moderate was characterized by a mean of 11 persisting symptoms and poor sleep and respiratory quality of life. Compared to the other clusters, Cluster-Severe was characterized by a higher proportion of women and smokers with a higher number of Long COVID symptoms, in particular vascular, urinary, and skin symptoms. Our study evidenced that Long COVID can be stratified into three subcategories in terms of severity. If replicated in other populations, this simple classification will help clinicians improve the care of people with Long COVID.

The prognostic value of the hemoglobin/red blood cell distribution width ratio in primary hepatocellular carcinoma remains unknown. This study aimed to analyze the correlation between hemoglobin/red blood cell distribution width ratio and hepatocellular carcinoma prognosis. Medical records of hepatocellular carcinoma patients were analyzed retrospectively. The hemoglobin/red blood cell distribution width ratio cut-off value was determined as 0.987 by receiver operating characteristic curve analysis. Patients were divided into high- and low-level groups, and the clinical data were compared. The correlation among the ratio levels, progression-free survival, and overall survival was measured with univariate and multivariate Cox regression analyses. The prognostic utility of the ratio combined with alpha-fetoprotein was analyzed using Kaplan-Meier survival curves and log-rank detection, and the correlation between the ratio and tumor staging was studied using one-way analysis of variance. This study included 252 patients. Sex, smoking and alcohol consumption history, body mass index, surgery, staging, platelet/lymphocyte ratio, and hemoglobin/red blood cell distribution width ratio were associated with progression-free and overall survival (P<0.05). The ratio, alpha-fetoprotein, hemoglobin, staging, and surgery were independent risk factors for progression-free survival (P<0.05), and the ratio, alpha-fetoprotein, hemoglobin, body mass index, HBsAg, staging, and surgery were independent risk factors for overall survival (P<0.05). Patients with low ratio levels and high alpha-fetoprotein levels had the worst prognosis. Low hemoglobin/red blood cell distribution width ratio levels are associated with poor patient prognosis and are potential tumor prognosis markers.

Parkinson’s disease (PD) is a complex and heterogeneous disorder involving multiple genetic and environmental influences. Although a wide range of PD risk factors and clinical markers for the symptomatic motor stage of the disease have been identified, there are still no reliable biomarkers available for the early pre-motor phase of PD and for predicting disease progression. High-throughput RNA-based biomarker profiling and modeling may provide a means to exploit the joint information content from a multitude of markers to derive diagnostic and prognostic signatures. In the field of PD biomarker research, currently, no clinically validated RNA-based biomarker models are available, but previous studies reported several significantly disease-associated changes in RNA abundances and activities in multiple human tissues and body fluids. Here, we review the current knowledge of the regulation and function of non-coding RNAs in PD, focusing on microRNAs, long non-coding RNAs, and circular RNAs. Since there is growing evidence for functional interactions between the heart and the brain, we discuss the benefits of studying the role of non-coding RNAs in organ interactions when deciphering the complex regulatory networks involved in PD progression. We finally review important concepts of harmonization and curation of high throughput datasets, and we discuss the potential of systems biomedicine to derive and evaluate RNA biomarker signatures from high-throughput expression data.

MicroRNAs (miRNAs) play important roles in several human cancers. Although miR-188 has been suggested to function as a tumor repressor in cancers, its precise role in glioma and the molecular mechanism remain unknown. In the present study, we investigated the effect of miR-188 on glioma and explored its relevant mechanisms. We found that the expression of miR-188 is dramatically downregulated in glioma tissues and cell lines. Subsequent investigation revealed that miR-188 expression was inversely correlated with β-catenin expression in glioma tissue samples. Using a luciferase reporter assay, β-catenin was determined to be a direct target of miR-188. Overexpression of miR-188 reduced β-catenin expression at both the mRNA and protein levels, and inhibition of miR-188 increased β-catenin expression. Moreover, we found that overexpression of miR-188 suppressed glioma cell proliferation and cell cycle G₁‐S transition, whereas inhibition of miR-188 promoted glioma cell proliferation. Importantly, silencing β-catenin recapitulated the cellular and molecular effects seen upon miR-188 overexpression, which included inhibiting glioma cell proliferation and G₁‐S transition. Taken together, our results demonstrated that miR-188 inhibits glioma cell proliferation by targeting β-catenin, representing an effective therapeutic strategy for glioma.

Thoracic aortic aneurysm (TAA) can lead to fatal complications such as aortic dissection. Since aneurysm dimension poorly predicts dissection risk, microRNAs (miRNAs) may be useful to diagnose or risk stratify TAA patients. We aim to identify miRNAs associated with TAA pathogenesis and that are possibly able to improve TAA diagnosis. MiRNA microarray experiments of aortic media tissue samples from 19 TAA patients and 19 controls allowed identifying 232 differentially expressed miRNAs. Using interaction networks between these miRNAs and 690 genes associated with TAA, we identified miR-574-5p as a potential contributor of TAA pathogenesis. Interestingly, miR-574-5p was significantly down-regulated in the TAA tissue compared to the controls, but was up-regulated in serum samples from a separate group of 28 TAA patients compared to 20 controls (p < 0.001). MiR-574-5p serum levels discriminated TAA patients from controls with an area under the receiver operating characteristic curve of 0.87. In the Fbn1^C1041G/+ mouse model, miR-574-5p was down-regulated in aortic tissue compared to wild-type (p < 0.05), and up-regulated in plasma extracellular vesicles from Fbn1^C1041G/+ mice compared to wild-type mice (p < 0.05). Furthermore, in vascular smooth muscle cells, angiotensin II appears to induce miR-574-5p secretion in extracellular vesicles. In conclusion, miR-574-5p is associated with TAA pathogenesis and may help in diagnosing this disease.