A Comparison of Bone Mineral Density and Its Predictors in HIV-Infected and HIV-Uninfected Older Men

ObjectiveBone health in older individuals with HIV infection has not been well studied. This study aimed to compare bone mineral density (BMD), trabecular bone score (TBS), and bone markers between HIV-infected men and age- and body mass index (BMI)-matched HIV-uninfected men aged ≥60 years. We investigated the associations of risk factors related to fracture with BMD, TBS, and bone markers in HIV-infected men.MethodsThis cross-sectional study included 45 HIV-infected men receiving antiretroviral therapy and 42 HIV-uninfected men. Medical history, BMD and TBS measurements, and laboratory tests related to bone health were assessed in all the participants. HIV-related factors known to be associated with bone loss were assessed in the HIV-infected men.ResultsThe mean BMD, TBS, and osteopenia or osteoporosis prevalence were similar among the cases and controls. The HIV-infected men had significantly higher mean N-terminal propeptide of type 1 procollagen and C-terminal cross-linking telopeptide of type I collagen levels. Stepwise multiple linear regression analysis demonstrated that low BMI (lumbar spine, P = .015; femoral neck, P = .018; and total hip, P = .005), high C-terminal cross-linking telopeptide of type I collagen concentration (total hip, P = .042; and TBS, P = .010), and low vitamin D supplementation (TBS, P = .035) were independently associated with low BMD and TBS.ConclusionIn older HIV-infected men with a low fracture risk, the mean BMD and TBS were similar to those of the age- and BMI-matched controls. The mean bone marker levels were higher in the HIV group. Traditional risk factors for fracture, including low BMI, high C-terminal cross-linking telopeptide of type I collagen level, and low vitamin D supplementation, were significant predictors of low BMD and TBS.     

Plasmodium berghei: a mouse model for the "sudden death" and "malarial lung" syndromes

A mouse model for the "sudden death" and "malarial lung" syndromes is described. Mice of the C3H/z strain succumb suddenly approximately 7 days after an infection with Plasmodium berghei becomes patent, at a time when parasitemia is still moderate (6 to 8%). Death could be shown to be due to anaphylactoid shock, probably induced by soluble immune complexes. Increased vascular permeability caused transudation and leakage of serum proteins into the interstitium and the alveoli. The lungs were found to be edematous, with a fine granular precipitate in the alveoli and adherent to the vascular walls. The precipitates reacted with antiglobulins G and M, and could be shown to also contain malaria antigens and C3/4. A dramatic drop in hematocrit was recorded several hours before death, indicating the sudden release of malaria antigens. The myocardium of animals that had died very suddenly showed a patchy loss of phosphorylase activity. This loss of activity was much more extensive, and sometimes almost total, when there had been an agonal period of several (1 to 3) hours before death. In these cases the irreversibility of the myocardial damage was also indicated by the loss of activity of the dehydrogenases, as well as by typical inflammatory reactions of granulocytic and histiocytic infiltrations. The hearts thus presented a typical picture of the acute and peracute shock syndromes. In acute shock cardiac insufficiency develops so suddenly that death ensues before irreversible damage has occurred, and cardiac insufficiency can only be demonstrated by the most sensitive of enzyme histochemical means. In the present case shock was induced by the anaphylactoid activity of immune complexes with the lung as target organ. The described syndrome appears analogous to human "malarial lung."     

ATP-Activated Nonselective Cation Current in NG108-15 Cells

Abstract: ATP (1 mM) induced a biphasic increase in intracellular Ca²⁺ concentration ([Ca²⁺]_i), i.e., an initial transient increase decayed to a level of sustained increase, in NG108-15 cells. The transient increase was inhibited by a phospholipase C inhibitor, 1-[6-[[17β-3-methoxyestra-1,3,5(10)-trien-17-yl]amino]hexyl]-1H-pyrrole-2,5-dione (U73122), whereas the sustained increase was abolished by removal of external Ca²⁺. We examined the mechanism of the ATP-elicited sustained [Ca²⁺]_i increase using the fura-2 fluorescent method and the whole-cell patch clamp technique. ATP (1 mM) induced a membrane current with the reversal potential of 12.5 ± 0.8 mV (n = 10) in Tyrode external solution. The EC₅₀ of ATP was ～0.75 mM. The permeability ratio of various cations carrying this current was Na⁺ (defined as 1) > Li⁺ (0.92 ± 0.01; n = 5) > K⁺ (0.89 ± 0.03; n = 6) > Rb⁺ (0.55 ± 0.02; n = 6) > Cs⁺ (0.51 ± 0.01; n = 5) > Ca²⁺ (0.22 ± 0.03; n = 3) > N-methyl-d -glucamine (0.13 ± 0.01; n = 5), suggesting that ATP activated a nonselective cation current. The ATP-induced current was larger at lower concentrations of external Mg²⁺. ATP analogues that induced the current were 2-methylthio-ATP (2MeSATP), benzoylbenzoic-ATP, adenosine 5′-thiotriphosphate (ATPγS), and adenosine 5′-O-(2-thiodiphosphate), but not adenosine, ADP, α,β-methylene-ATP (AMPCPP), β,γ-methylene-ATP (AMPPCP), or UTP. Concomitant with the current data, 2MeSATP and ATPγS, but not AMPCPP or AMPPCP, increased the sustained [Ca²⁺]_i increase. We conclude that ATP activates a class of Ca²⁺-permeable nonselective cation channels via the P_2z receptor in NG108-15 cells.     

Mapping economic trait loci for somatic cell score in Holstein cattle using microsatellite markers and selective genotyping

Marker-assisted selection (MAS) uses genetic marker genotypes to predict an animal's production potential and will provide additional selection information for progeny testing. With the discovery of highly polymorphic microsatellite markers, the tools now exist to begin the search for economic trait loci (ETL), which is the first step toward MAS. The objective of this study was to identify ETL for somatic cell score in an existing Holstein population. Using the granddaughter design, sons from seven grandsire families were genotyped with 20 autosomal microsatellites from five chromosomes (4, 8, 13, 17, 23), with an emphasis on chromosome 23, which is the location of the bovine major histocompatibility complex (BoLA). Selective genotyping was used to reduce the number of genotypes required, in which the 10 highest and 10 lowest sons from the phenotypic distribution curve were tested (140 sons in seven families). One marker (513), located near BoLA, showed evidence of an ETL in three of five polymorphic families. Additional sons were genotyped from the five families to estimate the effect and to compare selective and ‘complete’ genotyping. Both methods detected an ETL at marker 513, but in different families. This study provides evidence of the usefulness of microsatellite markers and the granddaughter design in the detection of ETL; however, additional markers need to be evaluated to determine the usefulness of selective genotyping. Based on the results from the 20 studied markers, the most likely position of a somatic cell score ETL lies near marker 513, located on chromosome 23.     

Cross-trait prediction accuracy of summary statistics in genome-wide association studies

In the era of big data, univariate models have widely been used as a workhorse tool for quickly producing marginal estimators; and this is true even when in a high-dimensional dense setting, in which many features are “true,” but weak signals. Genome-wide association studies (GWAS) epitomize this type of setting. Although the GWAS marginal estimator is popular, it has long been criticized for ignoring the correlation structure of genetic variants (i.e., the linkage disequilibrium [LD] pattern). In this paper, we study the effects of LD pattern on the GWAS marginal estimator and investigate whether or not additionally accounting for the LD can improve the prediction accuracy of complex traits. We consider a general high-dimensional dense setting for GWAS and study a class of ridge-type estimators, including the popular marginal estimator and the best linear unbiased prediction (BLUP) estimator as two special cases. We show that the performance of GWAS marginal estimator depends on the LD pattern through the first three moments of its eigenvalue distribution. Furthermore, we uncover that the relative performance of GWAS marginal and BLUP estimators highly depends on the ratio of GWAS sample size over the number of genetic variants. Particularly, our finding reveals that the marginal estimator can easily become near-optimal within this class when the sample size is relatively small, even though it ignores the LD pattern. On the other hand, BLUP estimator has substantially better performance than the marginal estimator as the sample size increases toward the number of genetic variants, which is typically in millions. Therefore, adjusting for the LD (such as in the BLUP) is most needed when GWAS sample size is large. We illustrate the importance of our results by using the simulated data and real GWAS.     

Multikink quantile regression for longitudinal data with application to progesterone data analysis

Motivated by investigating the relationship between progesterone and the days in a menstrual cycle in a longitudinal study, we propose a multikink quantile regression model for longitudinal data analysis. It relaxes the linearity condition and assumes different regression forms in different regions of the domain of the threshold covariate. In this paper, we first propose a multikink quantile regression for longitudinal data. Two estimation procedures are proposed to estimate the regression coefficients and the kink points locations: one is a computationally efficient profile estimator under the working independence framework while the other one considers the within-subject correlations by using the unbiased generalized estimation equation approach. The selection consistency of the number of kink points and the asymptotic normality of two proposed estimators are established. Second, we construct a rank score test based on partial subgradients for the existence of the kink effect in longitudinal studies. Both the null distribution and the local alternative distribution of the test statistic have been derived. Simulation studies show that the proposed methods have excellent finite sample performance. In the application to the longitudinal progesterone data, we identify two kink points in the progesterone curves over different quantiles and observe that the progesterone level remains stable before the day of ovulation, then increases quickly in 5 to 6 days after ovulation and then changes to stable again or drops slightly.     

Enhancing estimation methods for integrating probability and nonprobability survey samples with machine-learning techniques. An application to a Survey on the impact of the COVID-19 pandemic in Spain

Web surveys have replaced Face-to-Face and computer assisted telephone interviewing (CATI) as the main mode of data collection in most countries. This trend was reinforced as a consequence of COVID-19 pandemic-related restrictions. However, this mode still faces significant limitations in obtaining probability-based samples of the general population. For this reason, most web surveys rely on nonprobability survey designs. Whereas probability-based designs continue to be the gold standard in survey sampling, nonprobability web surveys may still prove useful in some situations. For instance, when small subpopulations are the group under study and probability sampling is unlikely to meet sample size requirements, complementing a small probability sample with a larger nonprobability one may improve the efficiency of the estimates. Nonprobability samples may also be designed as a mean for compensating for known biases in probability-based web survey samples by purposely targeting respondent profiles that tend to be underrepresented in these surveys. This is the case in the Survey on the impact of the COVID-19 pandemic in Spain (ESPACOV) that motivates this paper. In this paper, we propose a methodology for combining probability and nonprobability web-based survey samples with the help of machine-learning techniques. We then assess the efficiency of the resulting estimates by comparing them with other strategies that have been used before. Our simulation study and the application of the proposed estimation method to the second wave of the ESPACOV Survey allow us to conclude that this is the best option for reducing the biases observed in our data.     

Model misspecification and bias for inverse probability weighting estimators of average causal effects

Commonly used semiparametric estimators of causal effects specify parametric models for the propensity score (PS) and the conditional outcome. An example is an augmented inverse probability weighting (IPW) estimator, frequently referred to as a doubly robust estimator, because it is consistent if at least one of the two models is correctly specified. However, in many observational studies, the role of the parametric models is often not to provide a representation of the data-generating process but rather to facilitate the adjustment for confounding, making the assumption of at least one true model unlikely to hold. In this paper, we propose a crude analytical approach to study the large-sample bias of estimators when the models are assumed to be approximations of the data-generating process, namely, when all models are misspecified. We apply our approach to three prototypical estimators of the average causal effect, two IPW estimators, using a misspecified PS model, and an augmented IPW (AIPW) estimator, using misspecified models for the outcome regression (OR) and the PS. For the two IPW estimators, we show that normalization, in addition to having a smaller variance, also offers some protection against bias due to model misspecification. To analyze the question of when the use of two misspecified models is better than one we derive necessary and sufficient conditions for when the AIPW estimator has a smaller bias than a simple IPW estimator and when it has a smaller bias than an IPW estimator with normalized weights. If the misspecification of the outcome model is moderate, the comparisons of the biases of the IPW and AIPW estimators show that the AIPW estimator has a smaller bias than the IPW estimators. However, all biases include a scaling with the PS-model error and we suggest caution in modeling the PS whenever such a model is involved. For numerical and finite sample illustrations, we include three simulation studies and corresponding approximations of the large-sample biases. In a dataset from the National Health and Nutrition Examination Survey, we estimate the effect of smoking on blood lead levels.