Dairy consumption and cardiometabolic diseases: systematic review and updated meta-analyses of prospective cohort studies

Purpose of Review Dairy products contain both beneficial and harmful nutrients in relation to cardiometabolic diseases. Here, we provide the latest scientific evidence regarding the relationship between dairy products and cardiometabolic diseases by reviewing the literature and updating meta-analyses of observational studies. Recent Findings We updated our previous meta-analyses of cohort studies on type 2 diabetes, coronary heart disease (CHD), and stroke with nine studies and confirmed previous results. Total dairy and low-fat dairy (per 200 g/d) were inversely associated with a 3–4% lower risk of diabetes. Yogurt was non-linearly inversely associatedwith diabetes (RR = 0.86, 95%CI: 0.83–0.90 at 80 g/ d). Total dairy and milk were not associated with CHD (RR~1.0). An increment of 200 g of daily milk intake was associated with an 8% lower risk of stroke. Summary The latest scientific evidence confirmed neutral or beneficial associations between dairy products and risk of cardiometabolic diseases

Current quark mass dependence of nucleon magnetic moments and radii

A calculation of the current-quark-mass-dependence of nucleon static electromagnetic properties is necessary in order to use observational data as a means to place constraints on the variation of Nature's fundamental parameters. A Poincare' covariant Faddeev equation, which describes baryons as composites of confined-quarks and -nonpointlike-diquarks, is used to calculate this dependence The results indicate that, like observables dependent on the nucleons' magnetic moments, quantities sensitive to their magnetic and charge radii, such as the energy levels and transition frequencies in Hydrogen and Deuterium, might also provide a tool with which to place limits on the allowed variation in Nature's constants.Comment: 23 pages, 2 figures, 4 tables, 4 appendice

The association of depression and all-cause and cause-specific mortality: an umbrella review of systematic reviews and meta-analyses

Background: Depression is a prevalent and disabling mental disorder that frequently co-occurs with a wide range of chronic conditions. Evidence has suggested that depression could be associated with excess all-cause mortality across different settings and populations, although the causality of these associations remains unclear. Methods: We conducted an umbrella review of systematic reviews and meta-analyses of observational studies. PubMed, PsycINFO, and Embase electronic databases were searched through January 20, 2018. Systematic reviews and meta-analyses that investigated associations of depression and all-cause and cause-specific mortality were selected for the review. The evidence was graded as convincing, highly suggestive, suggestive, or weak based on quantitative criteria that included an assessment of heterogeneity, 95% prediction intervals, small-study effects, and excess significance bias. Results: A total of 26 references providing 2 systematic reviews and data for 17 meta-analytic estimates met inclusion criteria (19 of them on all-cause mortality); data from 246 unique studies (N = 3,825,380) were synthesized. All 17 associations had P < 0.05 per random effects summary effects, but none of them met criteria for convincing evidence. Associations of depression and all-cause mortality in patients after acute myocardial infarction, in individuals with heart failure, in cancer patients as well as in samples from mixed settings met criteria for highly suggestive evidence. However, none of the associations remained supported by highly suggestive evidence in sensitivity analyses that considered studies employing structured diagnostic interviews. In addition, associations of depression and all-cause mortality in cancer and post-acute myocardial infarction samples were supported only by suggestive evidence when studies that tried to adjust for potential confounders were considered. Conclusions: Even though associations between depression and mortality have nominally significant results in all assessed settings and populations, the evidence becomes weaker when focusing on studies that used structured interviews and those that tried to adjust for potential confounders. A causal effect of depression on all-cause and cause-specific mortality remains unproven, and thus interventions targeting depression are not expected to result in lower mortality rates at least based on current evidence from observational studies

A multivariate logistic regression equation to screen for dysglycaemia: development and validation

Aims  To develop and validate an empirical equation to screen for dysglycaemia [impaired fasting glucose (IFG), impaired glucose tolerance (IGT) and undiagnosed diabetes]. Methods  A predictive equation was developed using multiple logistic regression analysis and data collected from 1032 Egyptian subjects with no history of diabetes. The equation incorporated age, sex, body mass index (BMI), post-prandial time (self-reported number of hours since last food or drink other than water), systolic blood pressure, high-density lipoprotein (HDL) cholesterol and random capillary plasma glucose as independent covariates for prediction of dysglycaemia based on fasting plasma glucose (FPG) ≥ 6.1 mmol/l and/or plasma glucose 2 h after a 75-g oral glucose load (2-h PG) ≥ 7.8 mmol/l. The equation was validated using a cross-validation procedure. Its performance was also compared with static plasma glucose cut-points for dysglycaemia screening. Results  The predictive equation was calculated with the following logistic regression parameters: P  = 1 + 1/(1 + e −X ) = where X = −8.3390 + 0.0214 (age in years) + 0.6764 (if female) + 0.0335 (BMI in kg/m 2 ) + 0.0934 (post-prandial time in hours) + 0.0141 (systolic blood pressure in mmHg) − 0.0110 (HDL in mmol/l) + 0.0243 (random capillary plasma glucose in mmol/l). The cut-point for the prediction of dysglycaemia was defined as a probability ≥ 0.38. The equation's sensitivity was 55%, specificity 90% and positive predictive value (PPV) 65%. When applied to a new sample, the equation's sensitivity was 53%, specificity 89% and PPV 63%. Conclusions  This multivariate logistic equation improves on currently recommended methods of screening for dysglycaemia and can be easily implemented in a clinical setting using readily available clinical and non-fasting laboratory data and an inexpensive hand-held programmable calculator. Diabet. Med. 22, 599–605 (2005)Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/75603/1/j.1464-5491.2005.01467.x.pd

Bisphosphonate prescribing, persistence and cumulative exposure in Ontario, Canada

Summary: We studied new users of oral bisphosphonates and found that less than half persisted with therapy for 2 years, and interruptions in use were common. During a median observation period of 4.7 years, 10% of patients filled only a single prescription, 37% switched therapies and median cumulative exposure was 2.2 years. Introduction: We sought to describe bisphosphonate prescribing, persistence and cumulative exposure among seniors in Ontario, Canada. Methods: We used Ontario Drug Benefit pharmacy claims to identify residents aged $\geq$ 66 years who initiated oral bisphosphonate therapy between April 1996 and March 2009. The first date of bisphosphonate dispensing was considered the index date. Persistence with therapy was defined as continuous treatment with no interruption exceeding 60 days. We examined persistence with therapy and the number of extended gaps (>60 days) between prescriptions over time periods ranging from 1 to 9 years. We also identified the proportion of patients filling only a single prescription and switching to a different bisphosphonate, and calculated the median days of exposure irrespective of gaps in therapy. Results: A total of 451,113 eligible new bisphosphonate users were identified: mean age = 75.6 years (SD = 6.9), 84% female, and median follow-up length = 4.7 years. Persistence with therapy declined from 63% at 1 year to 46% at 2 years and 12% at 9 years. Among those with at least 5 years of follow-up (n = 213,029), 61% had one or more extended gaps in bisphosphonate therapy. Overall, 10% of patients filled only a single prescription, 37% switched to a different bisphosphonate and the median exposure was 2.2 years. Conclusion: Less than half of patients persisted with bisphosphonate therapy for 2 years and interruptions in therapy were common, with most patients experiencing two or more >60-day gaps in therapy. Interventions are needed to improve persistence with bisphosphonate therapy and reduce the frequency of gaps in treatment

The Heme Biosynthetic Pathway of the Obligate Wolbachia Endosymbiont of Brugia malayi as a Potential Anti-filarial Drug Target

Human filarial nematodes are causative agents of elephantiasis and African river blindness, which are among the most debilitating tropical diseases. Currently used drugs mainly affect microfilariae (mf) and have less effect on adult filarial nematodes, which can live in the human host for more than a decade. Filariasis drug control strategy relies on recurrent mass drug administration for many years. Development of novel drugs is also urgently needed due to the threat of drug resistance occurrence. Most filarial worms harbor an obligate endosymbiotic bacterium, Wolbachia, whose presence has been identified as a potential drug target. Comparative genomics had suggested Wolbachia heme biosynthesis as a potential drug target, and we present an analysis of selected enzymes alongside their human homologues from several different aspects—gene phylogenetic analyses, in vitro enzyme kinetic and inhibition assays and heme-deficient E. coli complementation assays. We also conducted ex vivo Brugia malayi viability assays using heme pathway inhibitors. These experiments demonstrate that heme biosynthesis could be critical for filarial worm survival and thus is a potential anti-filarial drug target set

Predicting Class II MHC-Peptide binding: a kernel based approach using similarity scores

BACKGROUND: Modelling the interaction between potentially antigenic peptides and Major Histocompatibility Complex (MHC) molecules is a key step in identifying potential T-cell epitopes. For Class II MHC alleles, the binding groove is open at both ends, causing ambiguity in the positional alignment between the groove and peptide, as well as creating uncertainty as to what parts of the peptide interact with the MHC. Moreover, the antigenic peptides have variable lengths, making naive modelling methods difficult to apply. This paper introduces a kernel method that can handle variable length peptides effectively by quantifying similarities between peptide sequences and integrating these into the kernel. RESULTS: The kernel approach presented here shows increased prediction accuracy with a significantly higher number of true positives and negatives on multiple MHC class II alleles, when testing data sets from MHCPEP [1], MCHBN [2], and MHCBench [3]. Evaluation by cross validation, when segregating binders and non-binders, produced an average of 0.824 A(ROC )for the MHCBench data sets (up from 0.756), and an average of 0.96 A(ROC )for multiple alleles of the MHCPEP database. CONCLUSION: The method improves performance over existing state-of-the-art methods of MHC class II peptide binding predictions by using a custom, knowledge-based representation of peptides. Similarity scores, in contrast to a fixed-length, pocket-specific representation of amino acids, provide a flexible and powerful way of modelling MHC binding, and can easily be applied to other dynamic sequence problems