Month: September 2020

Although the PHARMO RLS covers almost 24% of the Dutch population, still the numbers of several drug exposures and hospital admissions were relatively low leading to statistical power problems. Although cases and controls were matched on age and sex, other potential confounding factors like genetic related risk factors, autoimmune antibodies and family history of type 1 diabetes were not available. We cannot rule out that children in the preamble period of the clinical presentation of type 1 diabetes more often visit doctors with an increased chance of identification of diseases and drug prescriptions. Finally, there is the problem of multiple comparisons that increases the chance for type 1 errors. However, since this is an explorative study and the general picture is that most diseases and drugs are risk factors for type 1 diabetes we do not think it is necessary to control the family wise error rate . In conclusion, it appears that a substantial number of diseases and drugs or the underlying diseases for which these drugs were prescribed were significantly more prevalent among patients who eventually developed type 1 diabetes compared with diabetes-free controls. This knowledge may stimulate further research directed at the prevention of the occurrence and the optimal treatment of these conditions in children and young adults who are susceptible for type 1 diabetes. Few study limitations should be discussed at first. Firstly, our research was undertaken to analyze the role on ventilation behaviour during exercise of a respiratory comorbidity, COPD, in HF patients. We built a COPD model by adding an external dead space. Our model was over-simplistic also as regards lung mechanics because an artificial dead space increase does not generate air trapping which is one of the most characteristic features of COPD during exercise. Secondly, our model was short lasting, so that chronic ventilatory and chemoreceptor adaptations to increased DS were not evaluated as were not evaluated primitive chemoreceptor abnormalities as drivers of the alveolar hypoventilation observed in COPD patients. Thirdly, with the Yintercept we analyze an index of overall DS. However, in the present setting, we were able to change DS only by adding an external DS, so that we do not know if changes in physiological DS similarly Vismodegib influence the VEYint. Fourthly, VE changes during exercise are due to VCO2, VD/ VT and PaCO2 changes, and all may influence the VE vs. VCO2 relationship. In the present study, we added external DS, which at each step of exercise, was associated to an increase of VD/VT and PaCO2 resembling what happens during exercise in COPD patients. Therefore both PaCO2 and VD/VT changes have likely a role in the VE vs. VCO2 relationship changes we observed after adding DS. It is recognized that PaCO2 measurements were done only in HF patients and not in healthy subjects, but a different behaviour in healthy subjects is unlikely.

Among these gene variants, the glucokinase regulatory protein has been further confirmed as linked with steatosis in children, in obese patients, and in populations of different ethnicity. GCKR seems to interfere with glucose and lipid homeostasis by regulating glucose storage/disposal and by LY2109761 providing substrates for de novo lipogenesis via inhibition of glucokinase, but its potential association with the severity of liver damage has never been tested Having this in mind, the main outcome of this study was to assess whether GCKR rs780094 was associated with the histological features of liver damage in patients with biopsy-proven NAFLD, after correction for PNPLA3 genotype. In our study we also demonstrated that patient homozygous for the T allele of GCKR had higher serum triglycerides levels, in agreement with previous finding by Speliotes et al, and with the hypothesis that the GCKR rs780094 SNP could lead to a higher activity of liver glcokinase. In addition we also observed higher triglycerides levels in Sicilian compared with Center/ Northern Italy cohort, probably attributable to the more unhealthy lifestyle characterizing the southern population. By contrast we did not identify any association between severity of steatosis and GCKR genotype. Our data are not in contrast with the study of Speliotes et al and more recent studies showing a link between GCKR SNP and the presence of steatosis. We did not test subjects without steatosis of the same geographic area, and therefore we were not able to discriminate between presence/absence of fatty liver infiltration. Obviously, the lack of these data could partially affect the interpretation of our results, and in particular of the significance of the real effect of GCKR SNP on steatosis. Finally, we did not confirm the reported association between PNPLA3 genotype and severity of liver fibrosis. Differences in the characteristics of the individual study cohorts could explain the lack of association in our cohort. The main limitation of this study lies in its cross-sectional nature, making it impossible to dissect the temporal relation between genetic background and progression of liver disease over time. This issue should be tested in longitudinal analyses. A further methodological question is the potentially limited external validity of the results for different populations and settings. Our study included cohorts of Italian patients enrolled at tertiary care centers, who may be different, in terms of both metabolic features and severity of liver disease, from the majority of prevalent cases of NAFLD in the general population. Quantitative real-time reverse transcription polymerase chain reaction is one of the most effective and sensitive techniques for gene expression analysis. However, enabling comparisons across different samples, qRT-PCR data must be normalized to correct variations in pipetting, RNA concentration, reverse-transcription, and efficiency of PCR amplification.