Effect of a Hop Extract Standardized in 8-Prenylnaringenin on Bone Health and Gut Microbiome in Postmenopausal Women with Osteopenia: A One-Year Randomized, Double-Blind, Placebo-Controlled Trial.

Estrogen deficiency increases the risk of osteoporosis and fracture. The aim of this study was to investigate whether a hop extract standardized in 8-prenylnaringenin (8-PN), a potent phytoestrogen, could improve bone status of osteopenic women and to explore the gut microbiome roles in this effect. In this double-blind, placebo-controlled, randomized trial, 100 postmenopausal, osteopenic women were supplemented with calcium and vitamin D3 (CaD) tablets and either a hop extract (HE) standardized in 8-PN (n = 50) or a placebo (n = 50) for 48 weeks. Bone mineral density (BMD) and bone metabolism were assessed by DXA measurements and plasma bone biomarkers, respectively. Participant's quality of life (SF-36), gut microbiome composition, and short-chain fatty acid (SCFA) levels were also investigated. In addition to the CaD supplements, 48 weeks of HE supplementation increased total body BMD (1.8 ± 0.4% vs. baseline, p < 0.0001; 1.0 ± 0.6% vs. placebo, p = 0.08), with a higher proportion of women experiencing an increase ≥1% compared to placebo (odds ratio: 2.41 ± 1.07, p < 0.05). An increase in the SF-36 physical functioning score was observed with HE versus placebo (p = 0.05). Gut microbiome α-diversity and SCFA levels did not differ between groups. However, a higher abundance of genera Turicibacter and Shigella was observed in the HE group; both genera have been previously identified as associated with total body BMD. These results suggest that an 8-PN standardized hop extract could beneficially impact bone health of postmenopausal women with osteopenia.

Sufficient dimension reduction for compositional data.

Recent efforts to characterize the human microbiome and its relation to chronic diseases have led to a surge in statistical development for compositional data. We develop likelihood-based sufficient dimension reduction methods (SDR) to find linear combinations that contain all the information in the compositional data on an outcome variable, i.e., are sufficient for modeling and prediction of the outcome. We consider several models for the inverse regression of the compositional vector or transformations of it, as a function of outcome. They include normal, multinomial, and Poisson graphical models that allow for complex dependencies among observed counts. These methods yield efficient estimators of the reduction and can be applied to continuous or categorical outcomes. We incorporate variable selection into the estimation via penalties and address important invariance issues arising from the compositional nature of the data. We illustrate and compare our methods and some established methods for analyzing microbiome data in simulations and using data from the Human Microbiome Project. Displaying the data in the coordinate system of the SDR linear combinations allows visual inspection and facilitates comparisons across studies.

Sufficient dimension reduction for censored predictors.

Motivated by a study conducted to evaluate the associations of 51 inflammatory markers and lung cancer risk, we propose several approaches of varying computational complexity for analyzing multiple correlated markers that are also censored due to lower and/or upper limits of detection, using likelihood-based sufficient dimension reduction (SDR) methods. We extend the theory and the likelihood-based SDR framework in two ways: (i) we accommodate censored predictors directly in the likelihood, and (ii) we incorporate variable selection. We find linear combinations that contain all the information that the correlated markers have on an outcome variable (i.e., are sufficient for modeling and prediction of the outcome) while accounting for censoring of the markers. These methods yield efficient estimators and can be applied to any type of outcome, including continuous and categorical. We illustrate and compare all methods using data from the motivating study and in simulations. We find that explicitly accounting for the censoring in the likelihood of the SDR methods can lead to appreciable gains in efficiency and prediction accuracy, and also outperformed multiple imputations combined with standard SDR.