Bone Microarchitecture Phenotypes Identified in Older Adults Are Associated With Different Levels of Osteoporotic Fracture Risk

Prevalence of osteoporosis is more than 50% in older adults, yet current clinical methods for diagnosis that rely on areal bone mineral density (aBMD) fail to detect most individuals who have a fragility fracture. Bone fragility can manifest in different forms, and a “one-size-fits-all” approach to diagnosis and management of osteoporosis may not be suitable. High-resolution peripheral quantitative computed tomography (HR-pQCT) provides additive information by capturing information about volumetric density and microarchitecture, but interpretation is challenging because of the complex interactions between the numerous properties measured. In this study, we propose that there are common combinations of bone properties, referred to as phenotypes, that are predisposed to different levels of fracture risk. Using HR-pQCT data from a multinational cohort (n = 5873, 71% female) between 40 and 96 years of age, we employed fuzzy c-means clustering, an unsupervised machine-learning method, to identify phenotypes of bone microarchitecture. Three clusters were identified, and using partial correlation analysis of HR-pQCT parameters, we characterized the clusters as low density, low volume, and healthy bone phenotypes. Most males were associated with the healthy bone phenotype, whereas females were more often associated with the low volume or low density bone phenotypes. Each phenotype had a significantly different cumulative hazard of major osteoporotic fracture (MOF) and of any incident osteoporotic fracture (p < 0.05). After adjustment for covariates (cohort, sex, and age), the low density followed by the low volume phenotype had the highest association with MOF (hazard ratio = 2.96 and 2.35, respectively), and significant associations were maintained when additionally adjusted for femoral neck aBMD (hazard ratio = 1.69 and 1.90, respectively). Further, within each phenotype, different imaging biomarkers of fracture were identified. These findings suggest that osteoporotic fracture risk is associated with bone phenotypes that capture key features of bone deterioration that are not distinguishable by aBMD. © 2021 American Society for Bone and Mineral Research (ASBMR).     

Molecular simplification of lipid A structure: TLR4-modulating cationic and anionic amphiphiles

A growing body of data suggests that therapies based on Toll-like receptors (TLR) targeting, in particular TLR4, holds promise in curing autoimmune and inflammatory pathologies still lacking specific treatment, included several rare diseases. While TLR4 activators (agonists) have already found successful clinical application as vaccine adjuvants, the use of TLR4 blockers (antagonists) as antisepsis agents or as agents against inflammatory diseases (including arthritis, multiple sclerosis, neuroinflammations) and cancer is still at a preclinical phase of development. This minireview focuses on recent achievements on the development of TLR4 modulators based on lipid A structure simplification, in particular on compounds having disaccharide or monosaccharide structures. As the TLR4 activity of natural TLR4 ligands (lipopolysaccharide, LPS and its biologically active part, the lipid A) depends on both the structure of endotoxin aggregates in solution and on single-molecule interaction with MD-2 and CD14 receptors, the rational design of TLR4 modulators should in principle take into account both these factors. In the light of the most recent advances in the field, in this minireview we discuss the structure–activity relationship in simplified lipid A analogs, with cationic or anionic amphiphilic structures.     

Patterns in metabolite profile are associated with risk of more aggressive prostate cancer: A prospective study of 3,057 matched case–control sets from EPIC

Metabolomics may reveal novel insights into the etiology of prostate cancer, for which few risk factors are established. We investigated the association between patterns in baseline plasma metabolite profile and subsequent prostate cancer risk, using data from 3,057 matched case–control sets from the European Prospective Investigation into Cancer and Nutrition (EPIC). We measured 119 metabolite concentrations in plasma samples, collected on average 9.4 years before diagnosis, by mass spectrometry (AbsoluteIDQ p180 Kit, Biocrates Life Sciences AG). Metabolite patterns were identified using treelet transform, a statistical method for identification of groups of correlated metabolites. Associations of metabolite patterns with prostate cancer risk (OR_1SD) were estimated by conditional logistic regression. Supplementary analyses were conducted for metabolite patterns derived using principal component analysis and for individual metabolites. Men with metabolite profiles characterized by higher concentrations of either phosphatidylcholines or hydroxysphingomyelins (OR_1SD = 0.77, 95% confidence interval 0.66–0.89), acylcarnitines C18:1 and C18:2, glutamate, ornithine and taurine (OR_1SD = 0.72, 0.57–0.90), or lysophosphatidylcholines (OR_1SD = 0.81, 0.69–0.95) had lower risk of advanced stage prostate cancer at diagnosis, with no evidence of heterogeneity by follow-up time. Similar associations were observed for the two former patterns with aggressive disease risk (the more aggressive subset of advanced stage), while the latter pattern was inversely related to risk of prostate cancer death (OR_1SD = 0.77, 0.61–0.96). No associations were observed for prostate cancer overall or less aggressive tumor subtypes. In conclusion, metabolite patterns may be related to lower risk of more aggressive prostate tumors and prostate cancer death, and might be relevant to etiology of advanced stage prostate cancer.     

The Well-being Model: A New Drug Interaction Model for Positive and Negative Effects

Background: Drugs are routinely combined in anesthesia and pain management to obtain an enhancement of the desired effects. However, a parallel enhancement of the undesired effects might take place as well, resulting in a limited therapeutic usefulness. Therefore, when addressing the question of optimal drug combinations, side effects must be taken into account.

Methods: By extension of a previously published interaction model, the authors propose a method to study drug interactions considering also their side effects. A general outcome parameter identified as patient's well-being is defined by superposition of positive and negative effects. Well-being response surfaces are computed and analyzed for varying drugs pharmacodynamics and interaction types. In particular, the existence of multiple maxima and of optimal drug combinations is investigated for the combination of two drugs.

Results: Both drug pharmacodynamics and interaction type affect the well-being surface and the deriving optimal combinations. The effect of the interaction parameters can be explained in terms of synergy and antagonism and remains unchanged for varying pharmacodynamics. For all simulations performed for the combination of two drugs, the presence of more than one maximum was never observed.     

Gonadotropin-releasing hormone antagonists increase follicular fluid insulin-like growth factor-I and vascular endothelial growth factor during ovarian stimulation cycles.

The aim of the present study was to investigate the effect of gonadotropin-releasing hormone (GnRH) antagonists (GnRH-ant) on follicular fluid (FF) insulin-like growth factor-I (IGF-I) and FF vascular endothelial growth factor (VEGF) levels. Sixty women undergoing assisted reproduction were randomized and assigned to two different GnRH analog regimens: GnRH agonist (GnRH-a) and GnRH-ant. FF VEGF and FF IGF-I concentrations were significantly increased in the patients treated with GnRH-ant (p < 0.001). In the same patients we observed a statistically significant reduction in serum luteinizing hormone (LH) and estradiol (E2) levels (p < 0.001 and p < 0.05, respectively), FF E2 and FF androstenedione levels (p < 0.05 and p < 0.001, respectively), as well as a reduction in the number of pregnancies although this was not statistically significant. In the GnRH-ant group, FF VEGF levels were positively correlated with FF IGF-I levels, and both were negatively correlated with serum LH levels. The increase in FF IGF-I and FF VEGF levels in women treated with GnRH-ant could be explained by a deleterious follicular environment in response to profound suppression of LH and E2 levels.