Relationships between anthropometric adiposity indexes and bone mineral density in a cross-sectional Chinese study

Background ContextPrevious studies have reported conflicting results for the relationships between anthropometric adiposity indexes and bone mineral density, based on dual-energy X-ray absorptiometry (DXA). However, few studies were published based on quantitative computed tomography (QCT), especially for Chinese population.PurposeTo evaluate the associations of spine bone mineral density (BMD) with body mass index (BMI), waist circumstance (WC), waist-to-hip ratio (WHR), waist-to-height ratio (WHtR) and a body shape index (ABSI) using QCT.Study Design/SettingA Cross-sectional study.Patient SampleAround 3,457 participants in multiple communities across 7 administrative regions of China.Outcome MeasuresSpine BMD was measured using QCT, and the classification of osteoporosis was defined as follows: 1) osteoporosis if BMD <80mg/cm³, 2) osteopenia if BMD 80–119 mg/cm³, and 3) normal bone mass if BMD≥120 mg/cm³.MethodsThis study was conducted using convenient sampling between 2013 and 2017. Multivariable linear regression model and logistic regression models were used for the associations of continuous and categorical BMD, respectively.ResultsAround 3,405 participants were included in the final analyses, including 1,272 males and 2,133 females, with spine BMD of 111.00±35.47 mg/cm³ and 99.38±40.60 mg/cm³, respectively. Spine BMD decreased significantly with the increase of ABSI in females (adjusted β, ?5.74; 95% confidence interval [CI], ?8.50 to ?2.98), and this trend also was kept in females aged at less than 60 years (adjusted β, ?14.54; 95% CI, ?20.40 to ?8.68), and females with age ≥60 years (adjusted β, ?7.59; 95% CI, ?10.91 to ?4.28). However, this inverse association was observed only in males with age ≥ 60 years (adjusted β, ?5.19; 95% CI, ?10.08 to ?0.29). Except ABSI, negative associations of Spine BMD with WC (adjusted β, ?0.46; 95% CI, ?0.77 to ?0.15), WHR (adjusted β, ?6.25; 95% CI, ?10.63 to ?1.86), WHtR (adjusted β, ?6.80; 95% CI, ?11.63 to ?1.97) were shown in females aged at <60 years, and positive association with BMI in males with age ≥60 years (adjusted β, 0.92; 95% CI, 0.29–1.55).ConclusionsABSI had more remarkable association with spine BMD, compared with the other four indexes.     

Immuno-pharmacodynamics for evaluating mechanism of action and developing immunotherapy combinations

Immunotherapy has become a major modality of cancer treatment, with multiple new classes of immunotherapeutics recently entering the clinic and obtaining market approval from regulatory agencies. While the promise of these therapies is great, so is the number of possible combinations not only with each other but also with small molecule therapeutics. Furthermore, the observation of unusual dose-response relationships suggests a critical dependency of drug effectiveness on the dosage regimen (dose and schedule). Clinical pharmacodynamic (PD) biomarkers will be useful endpoints for confirming drug mechanism of action, evaluating combination therapies for synergy or antagonism, and identifying optimal dosage regimens. In contrast to conventional PD in which drug action occurs entirely within a single target cell (ie, is self-contained within the malignant cell), immunotherapy involves a complex mechanism of action with sequential steps that propagate through multiple cell types, both normal and malignant. Its intercellular pharmacology begins with molecular target engagement either on an immune effector cell or a malignant cell, followed by stimulatory biochemical and biological signals in immune effector cells, and then finally ends with activation of cell death mechanisms in malignant cells lying within a certain distance from the activated effector cells (immune cell–tumor cell proximity). Evaluating such “trans-cellular pharmacology,” in which different steps of drug action are distributed across multiple cell types, requires novel microscopy and image analysis tools capable of quantifying PD-biomarker responses, mapping the responses onto the cellular geography of the tumor using phenotypic biomarkers to identify specific cell types, and finally analyzing the spatial relationships between biomarkers in the context of each cell’s biological role. We have termed this form of nearest neighbor image analysis of drug action “proximity PD microscopy,” to indicate the importance of the location of the PD-biomarker response within the cellular landscape of a tumor specimen. We discuss herein the major modes of immunotherapy, and lay out a blueprint for using PD assessment to optimize dosage regimens of single agents and guide development of combination immunotherapy regimens, using PD1/PD-L1 immune checkpoint inhibition as a case study.