Patricia Ancel

Diabetic retinopathy (DR) is a microvascular complication of diabetes mellitus (DM) which is the main cause of vision loss in the working-age population. Currently known risk factors such as age, disease duration, and hemoglobin A1c lack sufficient efficiency to distinguish patients with early stages of DR. A total of 194 plasma samples were collected from patients with type 2 DM and DR (moderate to proliferative (PDR) or control (no or mild DR) matched for age, gender, diabetes duration, HbA1c, and hypertension. Untargeted lipidomic and metabolomic approaches were performed. Partial-least square methods were used to analyze the datasets. Levels of 69 metabolites and 85 lipid species were found to be significantly different in the plasma of DR patients versus controls. Metabolite set enrichment analysis indicated that pathways such as metabolism of branched-chain amino acids (methylglutaryl carnitine p = 0.004), the kynurenine pathway (tryptophan p < 0.001), and microbiota metabolism (p-Cresol sulfate p = 0.004) were among the most enriched deregulated pathways in the DR group. Moreover, Glucose-6-phosphate (p = 0.001) and N-methyl-glutamate (p < 0.001) were upregulated in DR. Subgroup analyses identified a specific signature associated with PDR, macular oedema, and DR associated with chronic kidney disease. Phosphatidylcholines (PCs) were dysregulated, with an increase of alkyl-PCs (PC O-42:5 p < 0.001) in DR, while non-ether PCs (PC 14:0–16:1, p < 0.001; PC 18:2–14:0, p < 0.001) were decreased in the DR group. Through an unbiased multiomics approach, we identified metabolites and lipid species that interestingly discriminate patients with or without DR. These features could be a research basis to identify new potential plasma biomarkers to promote 3P medicine.

The epicardial adipose tissue (EAT) is the visceral fat depot of the heart which is highly plastic and in direct contact with myocardium and coronary arteries. Because of its singular proximity with the myocardium, the adipokines and pro-inflammatory molecules secreted by this tissue may directly affect the metabolism of the heart and coronary arteries. Its accumulation, measured by recent new non-invasive imaging modalities, has been prospectively associated with the onset and progression of coronary artery disease (CAD) and atrial fibrillation in humans. Recent studies have shown that EAT exhibits beige fat-like features, and express uncoupling protein 1 (UCP-1) at both mRNA and protein levels. However, this thermogenic potential could be lost with age, obesity and CAD. Here we provide an overview of the physiological and pathophysiological relevance of EAT and further discuss whether its thermogenic properties may serve as a target for obesity therapeutic management with a specific focus on the role of immune cells in this beiging phenomenon.

In magnetic resonance imaging (MRI), epicardial adipose tissue (EAT) overload remains often overlooked due to tedious manual contouring in images. Automated four-chamber EAT area quantification was proposed, leveraging deep-learning segmentation using multi-frame fully convolutional networks (FCN). The investigation involved 100 subjects—comprising healthy, obese, and diabetic patients—who underwent 3T cardiac cine MRI, optimized U-Net and FCN (noted FCNB) were trained on three consecutive cine frames for segmentation of central frame using dice loss. Networks were trained using 4-fold cross-validation (n = 80) and evaluated on an independent dataset (n = 20). Segmentation performances were compared to inter-intra observer bias with dice (DSC) and relative surface error (RSE). Both systole and diastole four-chamber area were correlated with total EAT volume (r = 0.77 and 0.74 respectively). Networks’ performances were equivalent to inter-observers’ bias (EAT: DSC_Inter = 0.76, DSC_U-Net = 0.77, DSC_FCNB = 0.76). U-net outperformed (p < 0.0001) FCNB on all metrics. Eventually, proposed multi-frame U-Net provided automated EAT area quantification with a 14.2% precision for the clinically relevant upper three quarters of EAT area range, scaling patients’ risk of EAT overload with 70% accuracy. Exploiting multi-frame U-Net in standard cine provided automated EAT quantification over a wide range of EAT quantities. The method is made available to the community through a FSLeyes plugin.

Diabetes mellitus (DM) has been identified as a risk factor for severe COVID-19. DM is highly prevalent in the general population. Defining strategies to reduce the health care system burden and the late arrival of some patients thus seems crucial. The study aim was to compare phenotypic characteristics between in and outpatients with diabetes and infected by COVID-19, and to build an easy-to-use hospitalization prediction risk score. This was a retrospective observational study. Patients with DM and laboratory- or CT-confirmed COVID-19, who did (n = 185) and did not (n = 159) require hospitalization between 10 March and 10 April 2020, were compared. Data on diabetes duration, treatments, glycemic control, complications, anthropometrics and peripheral oxygen saturation (SpO₂) were collected from medical records. Stepwise multivariate logistic regressions and ROC analyses were performed to build the DIAB score, a score using no more than five easy-to-collect clinical parameters predicting the risk of hospitalization. The DIAB score was then validated in two external cohorts (n = 132 and n = 2036). Hospitalized patients were older (68.0 ± 12.6 vs. 55.2 ± 12.6 years, p < 0.001), with more class III obesity (BMI ≥ 40 kg/m², 9.7 vs. 3.5%, p = 0.03), hypertension (81.6 vs. 44.3%, p < 0.0001), insulin therapy (37% vs. 23.7%, p = 0.009), and lower SpO₂ (91.6 vs. 97.3%, p < 0.0001) than outpatients. Type 2 DM (T2D) was found in 94% of all patients, with 10 times more type 1 DM in the outpatient group (11.3 vs. 1.1%, p < 0.0001). A DIAB score > 27 points predicted hospitalization (sensitivity 77.7%, specificity 89.2%, AUC = 0.895), and death within 28 days. Its performance was validated in the two external cohorts. Outpatients with diabetes were found to be younger, with fewer diabetic complications and less severe obesity than inpatients. DIAB score is an easy-to-use score integrating five variables to help clinicians better manage patients with DM and avert the saturation of emergency care units.

This study details the clinical and cellular phenotypes associated with two missense heterozygous mutations in LMNA, c.1745G>T p.(Arg582Leu), and c.1892G>A p.(Gly631Asp), in two patients with early onset of diabetes mellitus, hypertriglyceridemia and non-alcoholic fatty liver disease. In these two patients, subcutaneous adipose tissue was persistent, at least on the abdomen, and the serum leptin level remained in the normal range. Cellular studies showed elevated nuclear anomalies, an accelerated senescence rate and a decrease of replication capacity in patient cells. In cellular models, the overexpression of mutated prelamin A phenocopied misshapen nuclei, while the partial reduction of lamin A expression in patient cells significantly improved nuclear morphology. Altogether, these results suggest a link between lamin A mutant expression and senescence associated phenotypes. Transcriptome analysis of the whole subcutaneous adipose tissue from the two patients and three controls, paired for age and sex using RNA sequencing, showed the up regulation of genes implicated in immunity and the down regulation of genes involved in development and cell differentiation in patient adipose tissue. Therefore, our results suggest that some mutations in LMNA are associated with severe metabolic phenotypes without subcutaneous lipoatrophy, and are associated with nuclear misshaping.