Mortality for chronic-degenerative diseases in Tuscany: Ecological study comparing neighboring areas with substantial differences in environmental pollution

Objectives: Environmental pollution is associated with morbidity and mortality for chronic-degenerative diseases. Recent data points out a relationship between proximity to industrial plants and mortality due to neoplasms. The aim of this study has been to compare mortality due to chronic-degenerative diseases in the area of Tuscany (Bassa Val di Cecina), Italy, characterized by the presence of 2 neighboring municipalities similar in terms of size but with substantial differences in industrial activities: Rosignano (the site of chemical, energy production and waste processing industries) and Cecina (with no polluting activity). Material and Methods: Standardized mortality rates for the 2001–2010 decade were calculated; the data of the whole Tuscany was assumed as reference. Environmental levels of pollutants were obtained by databases of the Environmental Protection Agency of Tuscany Region (Agenzia Regionale per la Protezione Ambientale della Toscana – ARPAT). Maximum tolerated pollutant levels set by national laws were assumed as reference. Results: In the whole Bassa Val di Cecina, significantly elevated standardized mortality rates due to mesothelioma, ischemic heart diseases, cerebrovascular diseases and Alzheimer and other degenerative diseases of nervous system were observed. In the municipality of Rosignano, a significant excess of mortality for all these groups of diseases was confirmed. On the contrary, the municipality of Cecina showed only significantly higher mortality rates for ischemic heart diseases. Elevated levels of heavy metals in sea water and of particulate matter which contains particles of diameter ≤ 10 mm (PM10) and ozone in air were detected in Rosignano. Conclusions: This study shows an excess of mortality for chronic-degenerative diseases in the area with elevated concentration of polluting factories. Proximity to industrial plants seems to represent a risk factor for those diseases. Int J Occup Med Environ Health 2017;30(4):641–65

Cerebellocerebral connectivity predicts body mass index: A new open-source Python-based framework for connectome-based predictive modeling

Background: The cerebellum is one of the major central nervous structures consistently altered in obesity. Its role in higher cognitive function, parts of which are affected by obesity, is mediated through projections to and from the cerebral cortex. We therefore investigated the relationship between body mass index (BMI) and cerebellocerebral connectivity.Methods: We utilized the Human Connectome Project's Young Adults dataset, including functional magnetic resonance imaging (fMRI) and behavioral data, to perform connectome-based predictive modeling (CPM) restricted to cerebellocerebral connectivity of resting-state fMRI and task-based fMRI. We developed a Python-based open-source framework to perform CPM, a data-driven technique with built-in cross-validation to establish brain-behavior relationships. Significance was assessed with permutation analysis.Results: We found that (i) cerebellocerebral connectivity predicted BMI, (ii) task-general cerebellocerebral connectivity predicted BMI more reliably than resting-state fMRI and individual task-based fMRI separately, (iii) predictive networks derived this way overlapped with established functional brain networks (namely, frontoparietal networks, the somatomotor network, the salience network, and the default mode network), and (iv) we found there was an inverse overlap between networks predictive of BMI and networks predictive of cognitive measures adversely affected by overweight/obesity.Conclusions: Our results suggest obesity-specific alterations in cerebellocerebral connectivity, specifically with regard to task execution. With brain areas and brain networks relevant to task performance implicated, these alterations seem to reflect a neurobiological substrate for task performance adversely affected by obesity

Collateral fattening in body composition autoregulation: its determinants and significance for obesity predisposition

Collateral fattening refers to the process whereby excess fat is deposited as a result of the body’s attempt to counter a deficit in lean mass through overeating. Its demonstration and significance to weight regulation and obesity can be traced to work on energy budget strategies in growing mammals and birds, and to men recovering from experimental starvation. The cardinal features of collateral fattening rests upon (i) the existence of a feedback system between lean tissue and appetite control, with lean tissue deficit driving hyperphagia, and (ii) upon the occurrence of a temporal desynchronization in the recovery of body composition, with complete recovery of fat mass preceeding that of lean mass. Under these conditions, persistent hyperphagia driven by the need to complete the recovery of lean tissue will result in the excess fat deposition (hence collateral fattening) and fat overshooting. After reviewing the main lines of evidence for the phenomenon of collateral fattening in body composition autoregulation, this article discusses the causes and determinants of the desynchronization in fat and lean tissue recovery leading to collateral fattening and fat overshooting, and points to their significance in the mechanisms by which dieting, developmental programming and sedentariness predispose to obesity

Use of a High-Density Protein Microarray to Identify Autoantibodies in Subjects with Type 2 Diabetes Mellitus and an HLA Background Associated with Reduced Insulin Secretion

New biomarkers for type 2 diabetes mellitus (T2DM) may aid diagnosis, drug development or clinical treatment. Evidence is increasing for the adaptive immune system's role in T2DM and suggests the presence of unidentified autoantibodies. While high-density protein microarrays have emerged as a useful technology to identify possible novel autoantigens in autoimmune diseases, its application in T2DM has lagged. In Pima Indians, the HLA haplotype (HLA-DRB1*02) is protective against T2DM and, when studied when they have normal glucose tolerance, subjects with this HLA haplotype have higher insulin secretion compared to those without the protective haplotype. Possible autoantibody biomarkers were identified using microarrays containing 9480 proteins in plasma from Pima Indians with T2DM without the protective haplotype (n = 7) compared with those with normal glucose regulation (NGR) with the protective haplotype (n = 11). A subsequent validation phase involving 45 cases and 45 controls, matched by age, sex and specimen storage time, evaluated 77 proteins. Eleven autoantigens had higher antibody signals among T2DM subjects with the lower insulin-secretion HLA background compared with NGR subjects with the higher insulin-secretion HLA background (p<0.05, adjusted for multiple comparisons). PPARG2 and UBE2M had lowest p-values (adjusted p = 0.023) while PPARG2 and RGS17 had highest case-to-control antibody signal ratios (1.7). A multi-protein classifier involving the 11 autoantigens had sensitivity, specificity, and area under the receiver operating characteristics curve of 0.73, 0.80, and 0.83 (95% CI 0.74-0.91, p = 3.4x10-8), respectively. This study identified 11 novel autoantigens which were associated with T2DM and an HLA background associated with reduced insulin secretion. While further studies are needed to distinguish whether these antibodies are associated with insulin secretion via the HLA background, T2DM more broadly, or a combination of the two, this study may aid the search for autoantibody biomarkers by narrowing the list of protein targets

Adaptive filtering for removing nonstationary physiological noise from resting state fMRI BOLD signals

fMRI is used to investigate brain functional connectivity after removing nonneural components by General Linear Model (GLM) approach with a reference ventricle-derived signal as covariate. Ventricle signals are related to low-frequency modulations of cardiac and respiratory rhythms, which are nonstationary activities. Herein, we employed an adaptive filtering approach to improve removing physiological noise from BOLD signals. Comparisons between filtering approaches were performed by evaluating the amount of removed signal variance and the connectivity between homologous contralateral regions of interest (ROIs). The global connectivity between ROIs was estimated with a generalized correlation named RV coefficient. The mean ROI decrease of variance was -52% and -11%, for adaptive filtering and GLM, respectively. Adaptive filtering led to higher connectivity between grey matter ROIs than that obtained with GLM. Thus, adaptive filtering is a feasible method for removing the physiological noise in the low frequency band and to highlight resting state functional networks

Cell death and impairment of glucose-stimulated insulin secretion induced by 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) in the beta-cell line INS-1E.

The aim of this research was to characterize 2,3,7,8-tetrachlorodibenzo-p-dioxin (TCDD) toxicity on the insulin-secreting beta-cell line INS-1E. A sharp decline of cell survival (below 20%) was observed after 1 h exposure to TCDD concentrations between 12.5 and 25 nM. Ultrastructurally, beta-cell death was characterized by extensive degranulation, appearance of autophagic vacuoles, and peripheral nuclear condensation. Cytotoxic concentrations of TCDD rapidly induced a dose-dependent increase in intracellular calcium concentration. Blocking calcium entry by EGTA significantly decreased TCDD cytotoxicity. TCDD was also able to rapidly induce mitochondrial depolarization. Interestingly, 1 h exposition of INS-1E cells to very low TCDD concentrations (0.05-1 nM) dramatically impaired glucose-stimulated but not KCl-stimulated insulin secretion. In conclusion, our results clearly show that TCDD exerts a direct beta-cell cytotoxic effect at concentrations of 15-25 nM, but also markedly impairs glucose-stimulated insulin secretion at concentrations 20 times lower than these. On the basis of this latter observation we suggest that pancreatic beta-cells could be considered a specific and sensitive target for dioxin toxicity

Sirolimus as a second-line treatment for Graves’ orbitopathy

Objectives: A beneficial effect of sirolimus in Graves’ orbitopathy (GO) was reported, suggesting a possible use in clinical practice. We conducted an observational, single-centre, no-profit, clinical study to investigate the efficacy of sirolimus as a second-line treatment for moderate-to-severe, active GO compared with methylprednisolone. Methods: Data from consecutive patients given sirolimus (2 mg orally on first day, followed by 0.5 mg/day for 12 weeks) or methylprednisolone [500 mg iv/weekly (6 weeks), 250 mg/weekly (6 weeks)] as a second-line treatment were collected and compared. Primary objective: overall GO outcome at 24 weeks, based on a composite evaluation. Secondary objectives at 24 weeks: (1) improvement in quality of life, evaluated using a specific uestionnaire (GO-QoL); (2) reduction in proptosis; (3) reduction in the clinical activity score (CAS); (4) improvement of eye ductions; and (5) reduction in eyelid aperture. Results: Data from 30 patients (15 per group) treated between January 15, 2020, and June 15, 2021, were analysed. Proportion of GO responders (primary outcome) at 24 weeks was significantly greater in sirolimus group compared with methylprednisolone group (86.6% vs 26.6%; OR: 17.8; 95% CI from 2.7 to 116.8; P = 0.0026). GO-quality of life (GO-QoL) score was greater in sirolimus group. Proportion of proptosis responders was greater in sirolimus group, as well as proportion of clinical activity score (CAS) responders. No serious adverse events were observed, with no differences between groups. Conclusions: Sirolimus seems to be an effective second-line treatment for GO. Further randomized clinical trials are needed to confirm our observations

A Genome-Wide Association Study Using a Custom Genotyping Array Identifies Variants in GPR158 Associated with Reduced Energy Expenditure in American Indians

Pima Indians living in Arizona have a high prevalence of obesity, and we have previously shown that a relatively lower energy expenditure (EE) predicts weight and fat mass gain in this population. EE is a familial trait (heritability = 0.52); therefore, in the current study, we aimed to identify genetic variants that affect EE and thereby influence BMI and body fatness in Pima Indians. Genotypic data from 491,265 variants were analyzed for association with resting metabolic rate (RMR) and 24-h EE assessed in a whole-room calorimeter in 507 and 419 Pima Indians, respectively. Variants associated with both measures of EE were analyzed for association with maximum BMI and percent body fat (PFAT) in 5,870 and 912 Pima Indians, respectively. rs11014566 nominally associated with both measures of EE and both measures of adiposity in Pima Indians, where the G allele (frequency: Pima Indians = 0.60, Europeans <0.01) associated with lower 24-h EE ( = -33 kcal/day per copy), lower RMR ( = -31 kcal/day), higher BMI ( = +0.6 kg/m(2)), and higher PFAT ( = +0.9%). However, the association of rs11014566 with BMI did not directionally replicate when assessed in other ethnic groups. rs11014566 tags rs144895904, which affected promoter function in an in vitro luciferase assay. These variants map to GPR158, which is highly expressed in the brain and interacts with two other genes (RGS7 and CACNA1B) known to affect obesity in knockout mice. Our results suggest that common ethnic-specific variation in GPR158 may influence EE; however, its role in weight gain remains controversial, as it either had no association with BMI or associated with BMI but in the opposite direction in other ethnic groups