Contemporaneous and temporal network analysis of complex Posttraumatic stress disorder among Chinese college students with Childhood adversity: A longitudinal study.

OBJECTIVE: To enhance understanding of the longitudinal progression of complex posttraumatic stress disorder (CPTSD) symptoms, this longitudinal study examined how CPTSD symptoms interact over time in Chinese college students with childhood trauma. METHODS: From 18,933 college students who took part in two surveys 12 months apart, 4,006 participants who reported adverse childhood experiences were screened. Cross-sectional network comparisons and cross-lagged panel network (CLPN) analysis characterized interactions among CPTSD symptoms. RESULTS: In the cross-sectional networks, feeling like a failure and avoid activities reminiscent of the trauma were the central symptoms. Takes long time to calm down and exaggerated startle are important bridge symptoms in the two networks respectively. The comparison of cross-sectional networks indicates that the global network strength was stable. The findings of the CLPN model reveal that feel worthless and feel like a failure had the highest "out" expected influence; exaggerated startle and avoid thoughts and feelings about the trauma had the highest "in" expected influence. CONCLUSIONS: By conducting cross-sectional network analyses, the study illuminated the attributes of CPTSD networks across various time points. Additionally, the CLPN analysis uncovered the longitudinal patterns of CPTSD symptoms.

Posttraumatic stress disorder symptoms and posttraumatic growth among college students with a history of childhood maltreatment in China: A latent profile analysis.

BACKGROUND: Childhood maltreatment is prevalent in China, and research indicates that trauma-exposed individuals may exhibit concurrent negative and positive posttraumatic psychological responses. OBJECTIVE: To examine the co-occurring patterns of posttraumatic stress disorder (PTSD) symptoms and posttraumatic growth (PTG) among college students in China having a history of childhood maltreatment. METHOD: Participants (N = 2,968) were college students from China who had experienced childhood maltreatment and had completed the PTSD Checklist, a PTG inventory, and a childhood maltreatment questionnaire. RESULTS: The results revealed four heterogeneous profiles characterized by low symptoms (n = 666, 22.4%), growth (n = 960, 32.3%), struggle (n = 1,285, 43.3%), and distress (n = 57, 1.9%). The three-step approach revealed emotional abuse and sexual abuse as crucial risk factors for the distress profile. In addition, age, sex, parental education, and forms of childhood maltreatment play distinctive roles in the four profiles of posttraumatic reactions. CONCLUSION: Posttraumatic reactions exhibit heterogeneous characteristics among Chinese college students exposed to childhood maltreatment, and professionals provide the service targeting their needs. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Likelihood ratio combination of multiple biomarkers via smoothing spline estimated densities.

The diagnostic accuracy of multiple biomarkers in medical research is crucial for detecting diseases and predicting patient outcomes. An optimal method for combining these biomarkers is essential to maximize the Area Under the Receiver Operating Characteristic (ROC) Curve (AUC). Although the optimality of the likelihood ratio has been proven by Neyman and Pearson, challenges persist in estimating the likelihood ratio, primarily due to the estimation of multivariate density functions. In this study, we propose a non-parametric approach for estimating multivariate density functions by utilizing Smoothing Spline density estimation to approximate the full likelihood function for both diseased and non-diseased groups, which compose the likelihood ratio. Simulation results demonstrate the efficiency of our method compared to other biomarker combination techniques under various settings for generated biomarker values. Additionally, we apply the proposed method to a real-world study aimed at detecting childhood autism spectrum disorder (ASD), showcasing its practical relevance and potential for future applications in medical research.

Disruption of lysosomal nutrient sensing scaffold contributes to pathogenesis of a fatal neurodegenerative lysosomal storage disease.

The ceroid lipofuscinosis neuronal 1 (CLN1) disease, formerly called infantile neuronal ceroid lipofuscinosis, is a fatal hereditary neurodegenerative lysosomal storage disorder. This disease is caused by loss-of-function mutations in the CLN1 gene, encoding palmitoyl-protein thioesterase-1 (PPT1). PPT1 catalyzes depalmitoylation of S-palmitoylated proteins for degradation and clearance by lysosomal hydrolases. Numerous proteins, especially in the brain, require dynamic S-palmitoylation (palmitoylation-depalmitoylation cycles) for endosomal trafficking to their destination. While 23 palmitoyl-acyl transferases in the mammalian genome catalyze S-palmitoylation, depalmitoylation is catalyzed by thioesterases such as PPT1. Despite these discoveries, the pathogenic mechanism of CLN1 disease has remained elusive. Here, we report that in the brain of Cln1-/- mice, which mimic CLN1 disease, the mechanistic target of rapamycin complex-1 (mTORC1) kinase is hyperactivated. The activation of mTORC1 by nutrients requires its anchorage to lysosomal limiting membrane by Rag GTPases and Ragulator complex. These proteins form the lysosomal nutrient sensing scaffold to which mTORC1 must attach to activate. We found that in Cln1-/- mice, two constituent proteins of the Ragulator complex (vacuolar (H+)-ATPase and Lamtor1) require dynamic S-palmitoylation for endosomal trafficking to the lysosomal limiting membrane. Intriguingly, Ppt1 deficiency in Cln1-/- mice misrouted these proteins to the plasma membrane disrupting the lysosomal nutrient sensing scaffold. Despite this defect, mTORC1 was hyperactivated via the IGF1/PI3K/Akt-signaling pathway, which suppressed autophagy contributing to neuropathology. Importantly, pharmacological inhibition of PI3K/Akt suppressed mTORC1 activation, restored autophagy, and ameliorated neurodegeneration in Cln1-/- mice. Our findings reveal a previously unrecognized role of Cln1/Ppt1 in regulating mTORC1 activation and suggest that IGF1/PI3K/Akt may be a targetable pathway for CLN1 disease.

Exploring gender differences in the relationships among childhood maltreatment, PTSD, and depression in young adults through network analysis.

BACKGROUND AND OBJECTIVES: College students who have experienced childhood maltreatment commonly exhibit adverse psychological consequences such as post-traumatic stress disorder (PTSD) and depression. The aim of this study is to use network analysis to investigate the gender differences in complex connections among various types of childhood maltreatment, PTSD, and depression. METHODS: This study involved 481 participants (M = 19.25 years, 54.5 % female) who were selected from a larger sample of 5231 college students. These participants had experienced childhood maltreatment and exhibited significant clinical symptoms of PTSD and depression. The participants completed validated measures that assessed childhood trauma, PTSD, and depression. RESULTS: The network analysis performed on the entire sample revealed robust connections among various types of childhood maltreatment, symptom clusters of PTSD, and depression. The most central symptom that emerged was negative alterations in cognitions and mood (NACM), with emotional abuse displaying the highest centrality measure among the various types of childhood maltreatment. Upon comparing the subnetworks, the centrality analysis identified significant gender differences in nodes such as sexual abuse, physical neglect, emotional neglect, and avoidance. CONCLUSIONS: The study's findings confirm that various forms of childhood abuse have intricate and multifaceted connections with depression and PTSD symptoms in adulthood. The study suggests that NACM could be the most significant symptom, and emotional abuse may play a vital role in adverse psychological outcomes. Furthermore, notable gender discrepancies were identified in the relationship between various forms of maltreatment and psychopathological symptoms.

Optimizing treatment allocation in randomized clinical trials by leveraging baseline covariates.

We consider the problem of optimizing treatment allocation for statistical efficiency in randomized clinical trials. Optimal allocation has been studied previously for simple treatment effect estimators such as the sample mean difference, which are not fully efficient in the presence of baseline covariates. More efficient estimators can be obtained by incorporating covariate information, and modern machine learning methods make it increasingly feasible to approach full efficiency. Accordingly, we derive the optimal allocation ratio by maximizing the design efficiency of a randomized trial, assuming that an efficient estimator will be used for analysis. We then expand the scope of optimization by considering covariate-dependent randomization (CDR), which has some flavor of an observational study but provides the same level of scientific rigor as a standard randomized trial. We describe treatment effect estimators that are consistent, asymptotically normal, and (nearly) efficient under CDR, and derive the optimal propensity score by maximizing the design efficiency of a CDR trial (under the assumption that an efficient estimator will be used for analysis). Our optimality results translate into optimal designs that improve upon standard practice. Real-world examples and simulation results demonstrate that the proposed designs can produce substantial efficiency improvements in realistic settings.

Loss of Cognitive Flexibility Practice Effects in Female Rats Exposed to Simulated Space Radiation.

During the planned missions to Mars, astronauts will be faced with many potential health hazards including prolonged exposure to space radiation. Ground-based studies have shown that exposure to space radiation impairs the performance of male rats in cognitive flexibility tasks which involve processes that are essential to rapidly and efficiently adapting to different situations. However, there is presently a paucity of information on the effects of space radiation on cognitive flexibility in female rodents. This study has determined the impact that exposure to a low (10 cGy) dose of ions from the simplified 5-ion galactic cosmic ray simulation [https://www.bnl.gov/nsrl/userguide/SimGCRSim.php (07/2023)] (GCRSim) beam or 250 MeV/n 4He ions has on the ability of female Wistar rats to perform in constrained [attentional set shifting (ATSET)] and unconstrained cognitive flexibility (UCFlex) tasks. Female rats exposed to GCRSim exhibited multiple decrements in ATSET performance. Firstly, GCRSim exposure impaired performance in the compound discrimination (CD) stage of the ATSET task. While the ability of rats to identify the rewarded cue was not compromised, the time the rats required to do so significantly increased. Secondly, both 4He and GCRSim exposure reduced the ability of rats to reach criterion in the compound discrimination reversal (CDR) stage. Approximately 20% of the irradiated rats were unable to complete the CDR task; furthermore, the irradiated rats that did reach criterion took more attempts to do so than did the sham-treated animals. Radiation exposure also altered the magnitude and/or nature of practice effects. A comparison of performance metrics from the pre-screen and post-exposure ATSET task revealed that while the sham-treated rats completed the post-exposure CD stage of the ATSET task in 30% less time than for completion of the pre-screen ATSET task, the irradiated rats took 30-50% longer to do so. Similarly, while sham-treated rats completed the CDR stage in ∼10% fewer attempts in the post-exposure task compared to the pre-screen task, in contrast, the 4He- and GCRSim-exposed cohorts took more (∼2-fold) attempts to reach criterion in the post-exposure task than in the pre-screen task. In conclusion, this study demonstrates that female rats are susceptible to radiation-induced loss of performance in the constrained ATSET cognitive flexibility task. Moreover, exposure to radiation leads to multiple performance decrements, including loss of practice effects, an increase in anterograde interference and reduced ability or unwillingness to switch attention. Should similar effects occur in humans, astronauts may have a compromised ability to perform complex tasks.

Significance tests for covariates in the diagnostic accuracy index of a biomarker against a continuous gold standard.

Receiver operating characteristic (ROC) curve is a popular tool to describe and compare the diagnostic accuracy of biomarkers when a binary-scale gold standard is available. However, there are many examples of diagnostic tests whose gold standards are continuous. Hence, Several extensions of receiver operating characteristic (ROC) curve are proposed to evaluate the diagnostic potential of biomarkers when the gold standard is continuous-scale. Moreover, in evaluating these biomarkers, it is often necessary to consider the effects of covariates on the diagnostic accuracy of the biomarker of interest. Covariates may include subject characteristics, expertise of the test operator, test procedures or aspects of specimen handling. Applying the covariate adjustment to the case that the gold standard is continuous is challenging and has not been addressed in the literature. To fill the gap, we propose two general testing frameworks to account for the covariates effect on diagnostic accuracy. Simulation studies are conducted to compare the proposed tests. Data from a study that assessed three types of imaging modalities with the purpose of detecting neoplastic colon polyps and cancers are used to illustrate the proposed methods.

Low-rank latent matrix-factor prediction modeling for generalized high-dimensional matrix-variate regression.

Motivated by diagnosing the COVID-19 disease using two-dimensional (2D) image biomarkers from computed tomography (CT) scans, we propose a novel latent matrix-factor regression model to predict responses that may come from an exponential distribution family, where covariates include high-dimensional matrix-variate biomarkers. A latent generalized matrix regression (LaGMaR) is formulated, where the latent predictor is a low-dimensional matrix factor score extracted from the low-rank signal of the matrix variate through a cutting-edge matrix factor model. Unlike the general spirit of penalizing vectorization plus the necessity of tuning parameters in the literature, instead, our prediction modeling in LaGMaR conducts dimension reduction that respects the geometric characteristic of intrinsic 2D structure of the matrix covariate and thus avoids iteration. This greatly relieves the computation burden, and meanwhile maintains structural information so that the latent matrix factor feature can perfectly replace the intractable matrix-variate owing to high-dimensionality. The estimation procedure of LaGMaR is subtly derived by transforming the bilinear form matrix factor model onto a high-dimensional vector factor model, so that the method of principle components can be applied. We establish bilinear-form consistency of the estimated matrix coefficient of the latent predictor and consistency of prediction. The proposed approach can be implemented conveniently. Through simulation experiments, the prediction capability of LaGMaR is shown to outperform some existing penalized methods under diverse scenarios of generalized matrix regressions. Through the application to a real COVID-19 dataset, the proposed approach is shown to predict efficiently the COVID-19.

Multiple decrements in switch task performance in female rats exposed to space radiation.

Astronauts on the Artemis missions to the Moon and Mars will be exposed to unavoidable Galactic Cosmic Radiation (GCR). Studies using male rats suggest that GCR exposure impairs several processes required for cognitive flexibility performance, including attention and task switching. Currently no comparable studies have been conducted with female rats. Given that both males and females will travel into deep space, this study determined whether simulated GCR (GCRsim) exposure impairs task switching performance in female rats. Female Wistar rats exposed to 10 cGy GCRsim (n = 12) and shams (n = 14) were trained to perform a touchscreen-based switch task that mimics a switch task used to evaluate pilots' response times. In comparison to sham rats, three-fold more GCRsim-exposed rats failed to complete the stimulus response stage of training, a high cognitive loading task. In the switch task, 50% of the GCRsim-exposed rats failed to consistently transition between the repeated and switch blocks of stimuli, which they completed during lower cognitive loading training stages. The GCRsim-exposed rats that completed the switch task only performed at 65% of the accuracy of shams. Female rats exposed to GCRsim thus exhibit multiple decrements in the switch task under high, but not low, cognitive loading conditions. While the operational significance of this performance decrement is unknown, if GCRSim exposure was to induce similar effects in astronauts, our data suggests there may be a reduced ability to execute task switching under high cognitive loading situations.

The reciprocal relations among self-compassion, posttraumatic stress disorder, and posttraumatic growth among adolescents after the Jiuzhaigou earthquake: a three-wave cross-lagged study.

BACKGROUND: Previous cross-sectional studies have examined the relationship between self-compassion, post-traumatic stress disorder (PTSD), and post-traumatic growth (PTG). But no study has tested whether self-compassion is a cause or a consequence of PTSD, PTG, or both. AIMS: The cross-lag analysis was used to examine the reciprocal effects among self-compassion, PTSD, and PTG. METHOD: We used data from 244 adolescents who had experienced earthquakes. We assessed self-compassion, PTSD, and PTG via self-report measures after the earthquake in Jiuzhaigou, as well as 6 and 12 months later. RESULTS: Cross-lagged analyses indicated that positive self-compassion could significantly predict subsequent PTSD and PTG. Meanwhile, PTSD and PTG also predicted later positive self-compassion. Negative self-compassion at T1 increased PTSD at T2, and neither PTSD nor PTG significantly predicted subsequent negative self-compassion. In addition, negative self-compassion at T1 significantly predicted positive self-compassion at T2, while positive self-compassion at T2 significantly predicted negative self-compassion at T3. CONCLUSIONS: Positive self-compassion is a protective factor of post-traumatic psychological response, and it is maintained for a long time, while negative self-compassion may aggravate the negative psychological outcomes of adolescents in the early stage of experiencing traumatic events. In addition, positive and negative self-compassion can influence each other over time.

The reciprocal relations among self-compassion, and depression among adolescents after the Jiuzhaigou earthquake: A three-wave cross-lagged study.

BACKGROUND: Previous cross-sectional studies have examined the relationship between self-compassion and depression. Although it is often implicitly assumed that self-compassion may increase the vulnerability of an individual to depression, only a few studies have assessed whether self-compassion is a cause or a consequence of depression or both. METHOD: To examine such reciprocal effects, we assessed self-compassion and depression via self-report measures. At the baseline assessment (Time 1, T1), 450 students (M = 13.72, SD = 0.83, 54.2% females) participated 10 months after the Jiuzhaigou earthquake. We reassessed the T1 sample after 6- and 12-month intervals. At Time 2 (T2) assessment, 398 (56.0% female participants) of the Wave 1 participants were retained, and at Time 3 (T3) assessment, 235 (52.5% female participants) of the T1 and T2 participants were retained. RESULTS: Cross-lagged analyses indicated that positive self-compassion could significantly reduce subsequent depression. However, depression did not significantly predict later positive self-compassion. Negative self-compassion at T1 increased depression at T2, but negative self-compassion at T2 did not significantly predict depression at T3. In addition, positive self-compassion significantly reduced subsequent negative self-compassion. CONCLUSIONS: Positive self-compassion appears to protect adolescents against depression and maintain this protection over time, whereas negative self-compassion may worsen depression in adolescents during the initial stages of traumatic events. Additionally, positive self-compassion may decrease the level of negative self-compassion.

Relationship between self-compassion and nonsuicidal self-injury in middle school students after earthquake: Gender differences in the mediating effects of gratitude and posttraumatic growth.

OBJECTIVE: The present study examined the mediating roles of gratitude and posttraumatic growth (PTG) in the association between self-compassion and nonsuicidal self-injury (NSSI). It also analyzed the gender differences in the mediating effect among Chinese adolescents after an earthquake. METHOD: This study assessed self-compassion, gratitude, PTG, and NSSI via self-report measures among 597 adolescents after earthquake. RESULTS: In the entire sample, positive self-compassion (PSC) had a negative effect on NSSI, whereas negative self-compassion (NSC) had a positive effect on it. In addition, gratitude and PTG played significant mediating roles between self-compassion and NSSI. In the male group, the mediating roles of gratitude and PTG in the correlation between self-compassion and NSSI were significant. In the female group, only gratitude had a significant mediating effect between PSC and NSSI. CONCLUSIONS: PSC was associated with low NSSI levels in adolescents, whereas NSC was associated with high NSSI levels. The mediating effects of gratitude and PTG on the relationship between self-compassion and NSSI were also different by gender. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Nested Group Testing Procedure.

We investigated the false-negative, true-negative, false-positive, and true-positive predictive values from a general group testing procedure for a heterogeneous population. We show that its false (true)-negative predictive value of a specimen is larger (smaller), and the false (true)-positive predictive value is smaller (larger) than that from individual testing procedure, where the former is in aversion. Then we propose a nested group testing procedure, and show that it can keep the sterling characteristics and also improve the false-negative predictive values for a specimen, not larger than that from individual testing. These characteristics are studied from both theoretical and numerical points of view. The nested group testing procedure is better than individual testing on both false-positive and false-negative predictive values, while retains the efficiency as a basic characteristic of a group testing procedure. Applications to Dorfman's, Halving and Sterrett procedures are discussed. Results from extensive simulation studies and an application to malaria infection in microscopy-negative Malawian women exemplify the findings.

Modifiable risk factors and long term risk of type 2 diabetes among individuals with a history of gestational diabetes mellitus: prospective cohort study.

OBJECTIVES: To evaluate the individual and combined associations of five modifiable risk factors with risk of type 2 diabetes among women with a history of gestational diabetes mellitus and examine whether these associations differ by obesity and genetic predisposition to type 2 diabetes. DESIGN: Prospective cohort study. SETTING: Nurses' Health Study II, US. PARTICIPANTS: 4275 women with a history of gestational diabetes mellitus, with repeated measurements of weight and lifestyle factors and followed up between 1991 and 2009. MAIN OUTCOME MEASURE: Self-reported, clinically diagnosed type 2 diabetes. Five modifiable risk factors were assessed, including not being overweight or obese (body mass index <25.0), high quality diet (top two fifthsof the modified Alternate Healthy Eating Index), regular exercise (≥150 min/week of moderate intensity or ≥75 min/week of vigorous intensity), moderate alcohol consumption (5.0-14.9 g/day), and no current smoking. Genetic susceptibility for type 2 diabetes was characterised by a genetic risk score based on 59 single nucleotide polymorphisms associated with type 2 diabetes in a subset of participants (n=1372). RESULTS: Over a median 27.9 years of follow-up, 924 women developed type 2 diabetes. Compared with participants who did not have optimal levels of any of the risk factors for the development of type 2 diabetes, those who had optimal levels of all five factors had >90% lower risk of the disorder. Hazard ratios of type 2 diabetes for those with one, two, three, four, and five optimal levels of modifiable factors compared with none was 0.94 (95% confidence interval 0.59 to 1.49), 0.61 (0.38 to 0.96), 0.32 (0.20 to 0.51), 0.15 (0.09 to 0.26), and 0.08 (0.03 to 0.23), respectively (Ptrend<0.001). The inverse association of the number of optimal modifiable factors with risk of type 2 diabetes was seen even in participants who were overweight/obese or with higher genetic susceptibility (Ptrend<0.001). Among women with body mass index ≥25 (n=2227), the hazard ratio for achieving optimal levels of all the other four risk factors was 0.40 (95% confidence interval 0.18 to 0.91). Among women with higher genetic susceptibility, the hazard ratio of developing type 2 diabetes for having four optimal factors was 0.11 (0.04 to 0.29); in the group with optimal levels of all five factors, no type 2 diabetes events were observed. CONCLUSIONS: Among women with a history of gestational diabetes mellitus, each additional optimal modifiable factor was associated with an incrementally lower risk of type 2 diabetes. These associations were seen even among individuals who were overweight/obese or were at greater genetic susceptibility.

Prospective relations of maternal reward-related eating, pregnancy ultra-processed food intake and weight indicators, and feeding mode with infant appetitive traits.

BACKGROUND: Infant appetitive traits including eating rate, satiety responsiveness, food responsiveness, and enjoyment of food predict weight gain in infancy and early childhood. Although studies show a strong genetic influence on infant appetitive traits, the association of parent and infant appetite is understudied. Furthermore, little research examines the influence of maternal pregnancy dietary intake, weight indicators, and feeding mode on infant appetite. The present study investigated relations of maternal reward-related eating, pregnancy ultra-processed food intake and weight indicators, and feeding mode with infant appetitive traits. METHODS: Mothers in the Pregnancy Eating Attributes Study (458 mothers enrolled, 367 retained through delivery) completed self-report measures of reward-related eating, and principal component analysis yielded two components: (1) food preoccupation and responsiveness and (2) reinforcing value of food. Mothers completed 24-h dietary recalls across pregnancy, and the standardized NOVA (not an acronym) system categorized recalled foods based on processing level. Maternal anthropometrics were measured across pregnancy. At infant age 6 months, mothers reported on feeding mode and infant appetitive traits. Linear regressions were conducted predicting infant appetitive traits from household income-poverty ratio (step 1); maternal reward-related eating components (step 2); pregnancy ultra-processed food intake (% of energy intake), early pregnancy body mass index, and gestational weight gain (step 3); and exclusive breastfeeding duration (step 4). RESULTS: A 1-SD greater maternal food preoccupation and responsiveness was associated with 0.20-SD greater infant satiety responsiveness (p = .005). A 1-SD greater % energy intake from ultra-processed foods during pregnancy was associated with 0.16-SD lower infant satiety responsiveness (p = .031). A 1-SD longer exclusive breastfeeding duration was associated with 0.18-SD less infant food responsiveness (p = .014). Other associations of maternal reward-related eating, pregnancy ultra-processed food intake and weight indicators, and feeding mode with infant appetitive traits were non-significant. CONCLUSIONS: Proximal early-life environmental factors including maternal pregnancy dietary intake and feeding mode may facilitate or protect against obesogenic infant appetitive traits, whereas infant appetite may not parallel maternal reward-related eating. Further investigation into the etiology of appetitive traits early in development, particularly during solid food introduction, may elucidate additional modifiable risk factors for child obesity. TRIAL REGISTRATION: Clinicaltrials.gov. Registration ID - NCT02217462 . Date of registration - August 13, 2014.

An adaptive direction-assisted test for microbiome compositional data.

MOTIVATION: Microbial communities have been shown to be associated with many complex diseases, such as cancers and cardiovascular diseases. The identification of differentially abundant taxa is clinically important. It can help understand the pathology of complex diseases, and potentially provide preventive and therapeutic strategies. Appropriate differential analyses for microbiome data are challenging due to its unique data characteristics including compositional constraint, excessive zeros and high dimensionality. Most existing approaches either ignore these data characteristics or only account for the compositional constraint by using log-ratio transformations with zero observations replaced by a pseudocount. However, there is no consensus on how to choose a pseudocount. More importantly, ignoring the characteristic of excessive zeros may result in poorly powered analyses and therefore yield misleading findings. RESULTS: We develop a novel microbiome-based direction-assisted test for the detection of overall difference in microbial relative abundances between two health conditions, which simultaneously incorporates the characteristics of relative abundance data. The proposed test (i) divides the taxa into two clusters by the directions of mean differences of relative abundances and then combines them at cluster level, in light of the compositional characteristic; and (ii) contains a burden type test, which collapses multiple taxa into a single one to account for excessive zeros. Moreover, the proposed test is an adaptive procedure, which can accommodate high-dimensional settings and yield high power against various alternative hypotheses. We perform extensive simulation studies across a wide range of scenarios to evaluate the proposed test and show its substantial power gain over some existing tests. The superiority of the proposed approach is further demonstrated with real datasets from two microbiome studies. AVAILABILITY AND IMPLEMENTATION: An R package for MiDAT is available at https://github.com/zhangwei0125/MiDAT. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Associations of ultra-processed food intake with maternal weight change and cardiometabolic health and infant growth.

BACKGROUND: Excessive intake of ultra-processed foods, formulated from substances extracted from foods or derived from food constituents, may be a modifiable behavioral risk factor for adverse maternal and infant health outcomes. Prior work has predominately examined health correlates of maternal ultra-processed food intake in populations with substantially lower ultra-processed food intake compared to the US population. This longitudinal study investigated relations of ultra-processed food intake with maternal weight change and cardiometabolic health and infant growth in a US cohort. METHODS: Mothers in the Pregnancy Eating Attributes Study were enrolled at ≤12 weeks gestation and completed multiple 24-Hour Dietary Recalls within six visit windows through one-year postpartum (458 mothers enrolled, 321 retained at one-year postpartum). The NOVA (not an acronym) system categorized food and underlying ingredient codes based on processing level. Maternal anthropometrics were measured throughout pregnancy and postpartum, and infant anthropometrics were measured at birth and ages 2 months, 6 months, and 1 year. Maternal cardiometabolic markers were analyzed from blood samples obtained during the second and third trimesters. RESULTS: Holding covariates and total energy intake constant, a 1-SD greater percent energy intake from ultra-processed foods during pregnancy was associated with 31% higher odds of excessive gestational weight gain (p = .045, 95% CI [1.01, 1.70]), 0.68±0.29 mg/L higher c-reactive protein during pregnancy (p = .021, 95% CI [0.10, 1.26]), 6.7±3.4% greater gestational weight gain retained (p = .049, 95% CI [0.03, 13.30]), and 1.09±0.36 kg greater postpartum weight retention (p = .003, 95% CI [0.38, 1.80]). No other significant associations emerged. CONCLUSIONS: Ultra-processed food intake during pregnancy may be a modifiable behavioral risk factor for adverse maternal weight outcomes and inflammation. Randomized controlled trials are needed to test whether targeting ultra-processed food intake during pregnancy may support optimal maternal health. TRIAL REGISTRATION: Clinicaltrials.gov. Registration ID - NCT02217462 . Date of registration - August 13, 2014.

Exosomal Carboxypeptidase E (CPE) and CPE-shRNA-Loaded Exosomes Regulate Metastatic Phenotype of Tumor Cells.

BACKGROUND: Exosomes promote tumor growth and metastasis through intercellular communication, although the mechanism remains elusive. Carboxypeptidase E (CPE) supports the progression of different cancers, including hepatocellular carcinoma (HCC). Here, we investigated whether CPE is the bioactive cargo within exosomes, and whether it contributes to tumorigenesis, using HCC cell lines as a cancer model. METHODS: Exosomes were isolated from supernatant media of cancer cells, or human sera. mRNA and protein expression were analyzed using PCR and Western blot. Low-metastatic HCC97L cells were incubated with exosomes derived from high-metastatic HCC97H cells. In other experiments, HCC97H cells were incubated with CPE-shRNA-loaded exosomes. Cell proliferation and invasion were assessed using MTT, colony formation, and matrigel invasion assays. RESULTS: Exosomes released from cancer cells contain CPE mRNA and protein. CPE mRNA levels are enriched in exosomes secreted from high- versus low-metastastic cells, across various cancer types. In a pilot study, significantly higher CPE copy numbers were found in serum exosomes from cancer patients compared to healthy subjects. HCC97L cells, treated with exosomes derived from HCC97H cells, displayed enhanced proliferation and invasion; however, exosomes from HCC97H cells pre-treated with CPE-shRNA failed to promote proliferation. When HEK293T exosomes loaded with CPE-shRNA were incubated with HCC97H cells, the expression of CPE, Cyclin D1, a cell-cycle regulatory protein and c-myc, a proto-oncogene, were suppressed, resulting in the diminished proliferation of HCC97H cells. CONCLUSIONS: We identified CPE as an exosomal bioactive molecule driving the growth and invasion of low-metastatic HCC cells. CPE-shRNA loaded exosomes can inhibit malignant tumor cell proliferation via Cyclin D1 and c-MYC suppression. Thus, CPE is a key player in the exosome transmission of tumorigenesis, and the exosome-based delivery of CPE-shRNA offers a potential treatment for tumor progression. Notably, measuring CPE transcript levels in serum exosomes from cancer patients could have potential liquid biopsy applications.

Marginal, conditional, and pseudo likelihood ratio approaches for biomarker combination to predict a binary disease outcome.

It is a common practice in public health research that multiple biomarkers are collected to diagnose or predict a disease outcome. A natural question is how to combine multiple biomarkers to improve the diagnostic accuracy. It has been shown by Neyman-Pearson lemma that the likelihood ratio statistic achieves the optimal AUC in theory. However, practical difficulty often lies in the estimation of the multivariate density functions. We propose three novel methods for the biomarker combination, with the idea of breaking down the joint densities to a series of univariate densities. The marginal likelihood ratio approach only assumes the marginal distribution of each biomarker. While the conditional likelihood ratio (CLR) and pseudo likelihood ratio (PLR) approaches assume the conditional distributions of a marker given others, and hence make use of the correlation structure to estimate the combination rules. The proposed methods make it much easier to assume and validate the univariate distributions of a biomarker than making multivariate distributional assumptions. Extensive simulation studies demonstrate that the CLR and the PLR approaches outperform many existing methods, and are therefore recommended for practical use. The proposed methods are motivated by and applied to a biomarker study to diagnose childhood autism/autism spectrum disorder.