Effects of high-intensity interval training on sleep disturbances associated with posttraumatic stress disorder.

Sleep disturbances are common in individuals with posttraumatic stress disorder. Exercise interventions are a promising approach in the treatment of sleep disorders, but little is known about the efficacy of exercise interventions for sleep disturbances associated with posttraumatic stress disorder. A total of 40 individuals with posttraumatic stress disorder were randomized to six sessions of either high-intensity interval training or low-to-moderate-intensity training, administered within 12 days. Sleep quality was assessed over 24 days from baseline to post with the Pittsburgh Sleep Quality Index, a sleep log, and a waist-worn actigraphy. Analyses revealed that, regardless of group allocation, Pittsburgh Sleep Quality Index score improved significantly by 2.28 points for high-intensity interval training and 1.70 points for low-to-moderate-intensity training (d = 0.56 for high-intensity interval training; 0.49 for low-to-moderate-intensity training) over time, while there were no significant changes in any sleep log or actigraphy measure. Analysis of a subsample of those affected by clinically significant sleep disturbances (n = 24) revealed a significant time effect with no difference between exercise interventions: Pittsburgh Sleep Quality Index improved significantly by 2.65 points for high-intensity interval training and 2.89 points for low-to-moderate-intensity training (d = 0.53 for high-intensity interval training; 0.88 for low-to-moderate-intensity training), and actigraphy measure of wake after sleep onset was reduced significantly by 14.39 minutes for high-intensity interval training and 6.96 minutes for low-to-moderate-intensity training (d = 0.47 for high-intensity interval training; 0.11 for low-to-moderate-intensity training) from baseline to post. In our pilot study, we found an improvement in sleep quality from pre- to post-assessment. There were no significant differences between exercise groups. Further studies are needed to investigate whether the found time effects reflect the exercise intervention or unrelated factors.

A longitudinal study of the influence of air pollutants on children: a robust multivariate approach.

This paper aims to evaluate the statistical association between exposure to air pollution and forced expiratory volume in the first second (FEV1) in both asthmatic and non-asthmatic children and teenagers, in which the response variable FEV1 was repeatedly measured on a monthly basis, characterizing a longitudinal experiment. Due to the nature of the data, an robust linear mixed model (RLMM), combined with a robust principal component analysis (RPCA), is proposed to handle the multicollinearity among the covariates and the impact of extreme observations (high levels of air contaminants) on the estimates. The Huber and Tukey loss functions are considered to obtain robust estimators of the parameters in the linear mixed model (LMM). A finite sample size investigation is conducted under the scenario where the covariates follow linear time series models with and without additive outliers (AO). The impact of the time-correlation and the outliers on the estimates of the fixed effect parameters in the LMM is investigated. In the real data analysis, the robust model strategy evidenced that RPCA exhibits three principal component (PC), mainly related to relative humidity (Hmd), particulate matter with a diameter smaller than 10 µm (PM10) and particulate matter with a diameter smaller than 2.5 µm (PM2.5).

Processing causal structure sentences in Mandarin Chinese: an eye movements study.

It remains uncertain whether causal structure prediction can improve comprehension in Chinese sentences and whether the position of the headword mediates the prediction effect. We conducted an experiment to explore the effect of causal prediction and headword position in Chinese sentence reading. Participants were asked to read sentences containing causal connectives with their eye movements recorded. In the experiment, we manipulated the causal structure of the sentence and the position of the headword. We found a promoting effect of causal structure on first-pass reading time and a hindering impact on total reading time. However, the effect was not mediated by the headword position. The results show that causal syntactic prediction facilitated early-stage processing and increased the integration cost in the late stage of Chinese sentence processing. These findings also support the constraint-based approach, which suggests an isolation between semantic and syntactic processing.

Asymptotically Exact Fit for Linear Mixed Model in Genetic Association Studies.

The linear mixed model (LMM) has become a standard in genetic association studies to account for population stratification and relatedness in the samples to reduce false positives. Much recent progresses in LMM focused on approximate computations. Exact methods remained computationally demanding and without theoretical assurance. The computation is particularly challenging for multiomics studies where tens of thousands of phenotypes are tested for association with millions of genetic markers. We present IDUL and IDUL† that use iterative dispersion updates to fit LMMs, where IDUL† is a modified version of IDUL that guarantees likelihood increase between updates. Practically, IDUL and IDUL† produced identical results, both are markedly more efficient than the state-of-the-art Newton-Raphson method, and in particular, both are highly efficient for additional phenotypes, making them ideal to study genetic determinants of multiomics phenotypes. Theoretically, the LMM likelihood is asymptotically unimodal, and therefore the gradient ascent algorithm IDUL† is asymptotically exact. A software package implementing IDUL and IDUL† for genetic association studies is freely available at https://github.com/haplotype/IDUL.

Predicting Longitudinal Cognitive Decline and Alzheimer's Conversion in Mild Cognitive Impairment Patients Based on Plasma Biomarkers.

The increasing burden of Alzheimer's disease (AD) emphasizes the need for effective diagnostic and therapeutic strategies. Despite available treatments targeting amyloid beta (Aß) plaques, disease-modifying therapies remain elusive. Early detection of mild cognitive impairment (MCI) patients at risk for AD conversion is crucial, especially with anti-Aß therapy. While plasma biomarkers hold promise in differentiating AD from MCI, evidence on predicting cognitive decline is lacking. This study's objectives were to evaluate whether plasma protein biomarkers could predict both cognitive decline in non-demented individuals and the conversion to AD in patients with MCI. This study was conducted as part of the Korean Longitudinal Study on Cognitive Aging and Dementia (KLOSCAD), a prospective, community-based cohort. Participants were based on plasma biomarker availability and clinical diagnosis at baseline. The study included MCI (n = 50), MCI-to-AD (n = 21), and cognitively unimpaired (CU, n = 40) participants. Baseline plasma concentrations of six proteins-total tau (tTau), phosphorylated tau at residue 181 (pTau181), amyloid beta 42 (Aß42), amyloid beta 40 (Aß40), neurofilament light chain (NFL), and glial fibrillary acidic protein (GFAP)-along with three derivative ratios (pTau181/tTau, Aß42/Aß40, pTau181/Aß42) were analyzed to predict cognitive decline over a six-year follow-up period. Baseline protein biomarkers were stratified into tertiles (low, intermediate, and high) and analyzed using a linear mixed model (LMM) to predict longitudinal cognitive changes. In addition, Kaplan-Meier analysis was performed to discern whether protein biomarkers could predict AD conversion in the MCI subgroup. This prospective cohort study revealed that plasma NFL may predict longitudinal declines in Mini-Mental State Examination (MMSE) scores. In participants categorized as amyloid positive, the NFL biomarker demonstrated predictive performance for both MMSE and total scores of the Korean version of the Consortium to Establish a Registry for Alzheimer's Disease Assessment Packet (CERAD-TS) longitudinally. Additionally, as a baseline predictor, GFAP exhibited a significant association with cross-sectional cognitive impairment in the CERAD-TS measure, particularly in amyloid positive participants. Kaplan-Meier curve analysis indicated predictive performance of NFL, GFAP, tTau, and Aß42/Aß40 on MCI-to-AD conversion. This study suggests that plasma GFAP in non-demented participants may reflect baseline cross-sectional CERAD-TS scores, a measure of global cognitive function. Conversely, plasma NFL may predict longitudinal decline in MMSE and CERAD-TS scores in participants categorized as amyloid positive. Kaplan-Meier curve analysis suggests that NFL, GFAP, tTau, and Aß42/Aß40 are potentially robust predictors of future AD conversion.

Media Use and Its Associations With Paranoia in Schizophrenia and Bipolar Disorder: Ecological Momentary Assessment.

Background: Paranoia is a spectrum of fear-related experiences that spans diagnostic categories and is influenced by social and cognitive factors. The extent to which social media and other types of media use are associated with paranoia remains unclear. Objective: We aimed to examine associations between media use and paranoia at the within- and between-person levels. Methods: Participants were 409 individuals diagnosed with schizophrenia spectrum or bipolar disorder. Measures included sociodemographic and clinical characteristics at baseline, followed by ecological momentary assessments (EMAs) collected 3 times daily over 30 days. EMA evaluated paranoia and 5 types of media use: social media, television, music, reading or writing, and other internet or computer use. Generalized linear mixed models were used to examine paranoia as a function of each type of media use and vice versa at the within- and between-person levels. Results: Of the 409 participants, the following subgroups reported at least 1 instance of media use: 261 (63.8%) for using social media, 385 (94.1%) for watching TV, 292 (71.4%) for listening to music, 191 (46.7%) for reading or writing, and 280 (68.5%) for other internet or computer use. Gender, ethnoracial groups, educational attainment, and diagnosis of schizophrenia versus bipolar disorder were differentially associated with the likelihood of media use. There was a within-person association between social media use and paranoia: using social media was associated with a subsequent decrease of 5.5% (fold-change 0.945, 95% CI 0.904-0.987) in paranoia. The reverse association, from paranoia to subsequent changes in social media use, was not statistically significant. Other types of media use were not significantly associated with paranoia. Conclusions: This study shows that social media use was associated with a modest decrease in paranoia, perhaps reflecting the clinical benefits of social connection. However, structural disadvantage and individual factors may hamper the accessibility of media activities, and the mental health correlates of media use may further vary as a function of contents and contexts of use.

Potency Assay Variability Estimation in Practice.

During the drug development process, testing potency plays an important role in the quality assessment required for the manufacturing and marketing of biologics. Due to multiple operational and biological factors, higher variability is usually observed in bioassays compared with physicochemical methods. In this paper, we discuss different sources of bioassay variability and how this variability can be statistically estimated. In addition, we propose an algorithm to estimate the variability of reportable results associated with different numbers of runs and their corresponding OOS rates under a given specification. Numerical experiments are conducted on multiple assay formats to elucidate the empirical distribution of bioassay variability.

JASPER: Fast, powerful, multitrait association testing in structured samples gives insight on pleiotropy in gene expression.

Joint association analysis of multiple traits with multiple genetic variants can provide insight into genetic architecture and pleiotropy, improve trait prediction, and increase power for detecting association. Furthermore, some traits are naturally high-dimensional, e.g., images, networks, or longitudinally measured traits. Assessing significance for multitrait genetic association can be challenging, especially when the sample has population sub-structure and/or related individuals. Failure to adequately adjust for sample structure can lead to power loss and inflated type 1 error, and commonly used methods for assessing significance can work poorly with a large number of traits or be computationally slow. We developed JASPER, a fast, powerful, robust method for assessing significance of multitrait association with a set of genetic variants, in samples that have population sub-structure, admixture, and/or relatedness. In simulations, JASPER has higher power, better type 1 error control, and faster computation than existing methods, with the power and speed advantage of JASPER increasing with the number of traits. JASPER is potentially applicable to a wide range of association testing applications, including for multiple disease traits, expression traits, image-derived traits, and microbiome abundances. It allows for covariates, ascertainment, and rare variants and is robust to phenotype model misspecification. We apply JASPER to analyze gene expression in the Framingham Heart Study, where, compared to alternative approaches, JASPER finds more significant associations, including several that indicate pleiotropic effects, most of which replicate previous results, while others have not previously been reported. Our results demonstrate the promise of JASPER for powerful multitrait analysis in structured samples.

A Statistical Analysis of the Impact of Gun Ownership on Mass Shootings in the USA Between 2013 and 2022.

Mass shootings (incidents with four or more people shot in a single event, not including the shooter) are becoming more frequent in the United States, posing a significant threat to public health and safety in the country. In the current study, we intended to analyze the impact of state-level prevalence of gun ownership on mass shootings-both the frequency and severity of these events. We applied the negative binomial generalized linear mixed model to investigate the association between gun ownership rate, as measured by a proxy (i.e., the proportion of suicides committed with firearms to total suicides), and population-adjusted rates of mass shooting incidents and fatalities at the state level from 2013 to 2022. Gun ownership was found to be significantly associated with the rate of mass shooting fatalities. Specifically, our model indicated that for every 1-SD increase-that is, for every 12.5% increase-in gun ownership, the rate of mass shooting fatalities increased by 34% (p value < 0.001). However, no significant association was found between gun ownership and rate of mass shooting incidents. These findings suggest that restricting gun ownership (and therefore reducing availability to guns) may not decrease the number of mass shooting events, but it may save lives when these events occur.

Serum-Derived Bovine Immunoglobulin Promotes Barrier Integrity and Lowers Inflammation for 24 Human Adults Ex Vivo.

Serum-derived bovine immunoglobulin (SBI) prevents translocation and inflammation via direct binding of microbial components. Recently, SBI also displayed potential benefits through gut microbiome modulation. To confirm and expand upon these preliminary findings, SBI digestion and colonic fermentation were investigated using the clinically predictive ex vivo SIFR® technology (for 24 human adults) that was, for the first time, combined with host cells (epithelial/immune (Caco-2/THP-1) cells). SBI (human equivalent dose (HED) = 2 and 5 g/day) and the reference prebiotic inulin (IN; HED = 2 g/day) significantly promoted gut barrier integrity and did so more profoundly than a dietary protein (DP), especially upon LPS-induced inflammation. SBI also specifically lowered inflammatory markers (TNF-α and CXCL10). SBI and IN both enhanced SCFA (acetate/propionate/butyrate) via specific gut microbes, while SBI specifically stimulated valerate/bCFA and indole-3-propionic acid (health-promoting tryptophan metabolite). Finally, owing to the high-powered cohort (n = 24), treatment effects could be stratified based on initial microbiota composition: IN exclusively stimulated (acetate/non-gas producing) Bifidobacteriaceae for subjects classifying as Bacteroides/Firmicutes-enterotype donors, coinciding with high acetate/low gas production and thus likely better tolerability of IN. Altogether, this study strongly suggests gut microbiome modulation as a mechanism by which SBI promotes health. Moreover, the SIFR® technology was shown to be a powerful tool to stratify treatment responses and support future personalized nutrition approaches.

Genome-wide association analysis of fleece traits in Northwest Xizang white cashmere goat.

Northwest Xizang White Cashmere Goat (NXWCG) is the first new breed of cashmere goat in the Xizang Autonomous Region. It has significant characteristics of extremely high fineness, gloss, and softness. Genome-wide association analysis is an effective biological method used to measure the consistency and correlation of genotype changes between two molecular markers in the genome. In addition, it can screen out the key genes affecting the complex traits of biological individuals. The aim of this study was to analyze the genetic mechanism of cashmere trait variation in NXWCG and to discover SNP locus and key genes closely related to traits such as superfine cashmere. Additionally, the key genes near the obtained significant SNPs were analyzed by gene function annotation and biological function mining. In this study, the phenotype data of the four traits (cashmere length, fiber length, cashmere diameter, and cashmere production) were collected. GGP_Goat_70K SNP chip was used for genotyping the ear tissue DNA of the experimental group. Subsequently, the association of phenotype data and genotype data was performed using Gemma-0.98.1 software. A linear mixed model was used for the association study. The results showed that four fleece traits were associated with 18 significant SNPs at the genome level and 232 SNPs at the chromosome level, through gene annotated from Capra hircus genome using assembly ARS1. A total of 107 candidate genes related to fleece traits were obtained. Combined with Gene Ontology and Kyoto Encyclopedia of Genes and Genomes enrichment analysis, we can find that CLNS1A, CCSER1, RPS6KC1, PRLR, KCNRG, KCNK9, and CLYBL can be used as important candidate genes for fleece traits of NXWCG. We used Sanger sequencing and suitability chi-square test to further verify the significant loci and candidate genes screened by GWAS, and the results show that the base mutations loci on the five candidate genes, CCSER1 (snp12579, 34,449,796, A → G), RPS6KC1 (snp41503, 69,173,527, A → G), KCNRG (snp41082, 67,134,820, G → A), KCNK9 (14:78472665, 78,472,665, G → A), and CLYBL (12: 9705753, 9,705,753, C → T), significantly affect the fleece traits of NXWCG. The results provide a valuable basis for future research and contribute to a better understanding of the genetic structure variation of the goat.

Exploring predictors of substance use disorder treatment engagement with machine learning: The impact of social determinants of health in the therapeutic landscape.

BACKGROUND: Improved knowledge of factors that influence treatment engagement could help treatment providers and systems better engage patients. The present study used machine learning to explore associations between individual- and neighborhood-level factors, and SUD treatment engagement. METHODS: This was a secondary analysis of the Global Appraisal of Individual Needs (GAIN) dataset and United States Census Bureau data utilizing random forest machine learning and generalized linear mixed modelling. Our sample (N = 15,873) included all people entering SUD treatment at GAIN sites from 2006 to 2012. Predictors included an array of demographic, psychosocial, treatment-specific, and clinical measures, as well as environment-level measures for the neighborhood in which patients received treatment. RESULTS: Greater odds of treatment engagement were predicted by adolescent age and psychiatric comorbidity, and at the neighborhood-level, by low unemployment and high population density. Lower odds of treatment engagement were predicted by Black/African American race, and at the neighborhood-level by high rate of public assistance and high income inequality. Regardless of the degree of treatment engagement, individuals receiving treatment in areas with high unemployment, alcohol sale outlet concentration, and poverty had greater substance use and related problems at baseline. Although these differences reduced with treatment and over time, disparities remained. CONCLUSIONS: Neighborhood-level factors appear to play an important role in SUD treatment engagement. Regardless of whether individuals engage with treatment, greater loading on social determinants of health such as unemployment, alcohol sale outlet density, and poverty in the therapeutic landscape are associated with worse SUD treatment outcomes.

The impact of incident stroke on cognitive trajectories in later life.

BACKGROUND: Cognitive impairment is common after stroke, and a large proportion of stroke patients will develop dementia. However, there have been few large prospective studies which have assessed cognition both prior to and after stroke. This study aims to determine the extent to which incident stroke impacts different domains of cognitive function in a longitudinal cohort of older community-dwelling individuals. METHODS: 19,114 older individuals without cardiovascular disease or major cognitive impairment were recruited and followed over a maximum 11 years. Stroke included ischaemic and haemorrhagic stroke and was adjudicated by experts. Cognitive function was assessed regularly using Modified Mini-Mental State Examination (3MS), Hopkins Verbal Learning Test-Revised (HVLT-R), Symbol Digit Modalities Test (SDMT), and Controlled Oral Word Association Test (COWAT). Linear mixed models were used to investigate the change in cognition at the time of stroke and decline in cognitive trajectories following incident stroke. RESULTS: During a median follow-up period of 8.4 [IQR: 7.2, 9.6] years, 815 (4.3%) participants experienced a stroke. Over this time, there was a general decline observed in 3MS, HVLT-R delayed recall, and SDMT scores across participants. However, for individuals who experienced a stroke, there was a significantly greater decline across all cognitive domains immediately after the event immediately after the event (3MS: -1.03 [95%CI: -1.45, -0.60]; HVLT-R: -0.47 [-0.70, -0.24]; SDMT: -2.82 [-3.57, -2.08]; COWAT: -0.67 [-1.04, -0.29]) and a steeper long-term decline for three of these domains (3MS -0.62 [-0.88, -0.35]; COWAT: -0.30 [-0.46, -0.14]); HVLT-R: -0.12 [95%CI, -0.70, -0.24]). However individuals with stroke experienced no longer-term decline in SDMT compared to the rest of the participants. CONCLUSIONS: These findings highlight the need for comprehensive neuropsychology assessments for ongoing monitoring of cognition following incident stroke; and potential early intervention.

Effects of Ambient Air Pollution on Brain Cortical Thickness and Subcortical Volume: A Longitudinal Neuroimaging Study.

INTRODUCTION: Several cross-sectional studies have shown that long-term exposures to air pollutants are associated with smaller brain cortical volume or thickness. Here, we investigated longitudinal associations of long-term air pollution exposures with cortical thickness and subcortical volume. METHODS: In this longitudinal study, we included a prospective cohort of 361 adults residing in four cities in the Republic of Korea. Long-term concentrations of particulate matter with aerodynamic diameters of ≤10 µm (PM10) and ≤2.5 µm (PM2.5) and nitrogen dioxide (NO2) at residential addresses were estimated. Neuroimaging markers (cortical thickness and subcortical volume) were obtained from brain magnetic resonance images at baseline (August 2014 to March 2017) and at the 3-year follow-up (until September 2020). Linear mixed-effects models were used, adjusting for covariates. RESULTS: A 10-µg/m3 increase in PM10 was associated with reduced whole-brain mean (ß = -0.45, standard error [SE] = 0.10; p < 0.001), frontal (ß = -0.53, SE = 0.11; p < 0.001) and temporal thicknesses (ß = -0.37, SE = 0.12; p = 0.002). A 10-ppb increase in NO2 was associated with a decline in the whole-brain mean cortical thickness (ß = -0.23, SE = 0.05; p < 0.001), frontal (ß = -0.25, SE = 0.05; p < 0.001), parietal (ß = -0.12, SE = 0.05; p = 0.025), and temporal thicknesses (ß = -0.19, SE = 0.06; p = 0.001). Subcortical structures associated with air pollutants included the thalamus. CONCLUSIONS: Long-term exposures to PM10 and NO2 may lead to cortical thinning in adults.

Assessment of biometrics and stress indicators of the adult Village Weaver birds (Ploceus cucullatus) during breeding and post-breeding seasons in Jos, Nigeria.

In this era of climate change, some biological conservationists' concerns are based on seasonal studies that highlight how wild birds' physiological fitness are interconnected with the immediate environment to avoid population decline. We investigated how seasonal biometrics correlated to stress parameters of the adult Village Weavers (Ploceus cucullatus) during breeding and post-breeding seasons of the Weaver birds in Amurum Forest Reserve. Specifically, we explored the following objectives: (i) the seasonal number of birds captured; (ii) whether seasonal baseline corticosterone (CORT), packed cell volume (PCV), and heterophil to lymphocytes ratio (H:L) were sex-dependent; (iii) whether H:L ratio varied with baseline (CORT); (iv) whether phenotypic condition (post-breeding moult) and brood patch varied with baseline (CORT) and H:L ratio; and (v) how body biometrics co-varied birds' seasonal baseline (CORT), (PCV) and (H:L) ratio. Trapping of birds (May-November) coincided with breeding and post-breeding seasons. The birds (n = 53 males, 39 females) were ringed, morphologically assessed (body mass, wing length, moult, brood patch) and blood collected from their brachial vein was used to assess CORT, PCV and H:L ratio. Although our results indicated more male birds trapped during breeding, the multiple analyses of variance (MANOVA) indicated that the seasonal temperature of the trapping sites correlated (P < 0.05) significantly to baseline (CORT). The general linear mixed model analyses (GLMMs) indicated that the baseline (CORT) also correlated significantly to H:L ratio of the male and female birds. However, PCV correlated significantly to body size of the birds (wing length) and not body mass. Haematological parameters such as the baseline CORT and the H:L ratio as indicators of stress in wild birds. Hence, there is the possibility that the Village Weaver birds suffered from seasonally induced stress under the constrained effect of environmental temperature. Hence, future studies should investigate whether the effect observed is also attributable to other passerine species.

Ecological and anthropogenic drivers of waterfowl productivity are synchronous across species, space, and time.

Knowledge of interspecific and spatiotemporal variation in demography-environment relationships is key for understanding the population dynamics of sympatric species and developing multispecies conservation strategies. We used hierarchical random-effects models to examine interspecific and spatial variation in annual productivity in six migratory ducks (i.e., American wigeon [Mareca americana], blue-winged teal [Spatula discors], gadwall [Mareca strepera], green-winged teal [Anas crecca], mallard [Anas platyrhynchos] and northern pintail [Anas acuta]) across six distinct ecostrata in the Prairie Pothole Region of North America. We tested whether breeding habitat conditions (seasonal pond counts, agricultural intensification, and grassland acreage) or cross-seasonal effects (indexed by flooded rice acreage in primary wintering areas) better explained variation in the proportion of juveniles captured during late summer banding. The proportion of juveniles (i.e., productivity) was highly variable within species and ecostrata throughout 1961-2019 and generally declined through time in blue-winged teal, gadwall, mallard, pintail, and wigeon, but there was no support for a trend in green-winged teal. Productivity in Canadian ecostrata declined with increasing agricultural intensification and increased with increasing pond counts. We also found a strong cross-seasonal effect, whereby more flooded rice hectares during winter resulted in higher subsequent productivity. Our results suggest highly consistent environmental and anthropogenic effects on waterfowl productivity across species and space. Our study advances our understanding of current year and cross-seasonal effects on duck productivity across a suite of species and at finer spatial scales, which could help managers better target working-lands conservation programs on both breeding and wintering areas. We encourage other researchers to evaluate environmental drivers of population dynamics among species in a single modeling framework for a deeper understanding of whether conservation plans should be generalized or customized given limited financial resources.

RETROSPECTIVE VARYING COEFFICIENT ASSOCIATION ANALYSIS OF LONGITUDINAL BINARY TRAITS: APPLICATION TO THE IDENTIFICATION OF GENETIC LOCI ASSOCIATED WITH HYPERTENSION.

Many genetic studies contain rich information on longitudinal phenotypes that require powerful analytical tools for optimal analysis. Genetic analysis of longitudinal data that incorporates temporal variation is important for understanding the genetic architecture and biological variation of complex diseases. Most of the existing methods assume that the contribution of genetic variants is constant over time and fail to capture the dynamic pattern of disease progression. However, the relative influence of genetic variants on complex traits fluctuates over time. In this study, we propose a retrospective varying coefficient mixed model association test, RVMMAT, to detect time-varying genetic effect on longitudinal binary traits. We model dynamic genetic effect using smoothing splines, estimate model parameters by maximizing a double penalized quasi-likelihood function, design a joint test using a Cauchy combination method, and evaluate statistical significance via a retrospective approach to achieve robustness to model misspecification. Through simulations we illustrated that the retrospective varying-coefficient test was robust to model misspecification under different ascertainment schemes and gained power over the association methods assuming constant genetic effect. We applied RVMMAT to a genome-wide association analysis of longitudinal measure of hypertension in the Multi-Ethnic Study of Atherosclerosis. Pathway analysis identified two important pathways related to G-protein signaling and DNA damage. Our results demonstrated that RVMMAT could detect biologically relevant loci and pathways in a genome scan and provided insight into the genetic architecture of hypertension.

The course of acute low back pain: a community-based inception cohort study.

Introduction: Acute low back pain (LBP) is increasingly recognized for its potential recurrent nature and long-term implications. Objectives: This community-based inception cohort study aimed to delineate trajectories of acute LBP over one year and investigate associated biopsychosocial variables. Methods: One hundred seventy-six participants with acute LBP were monitored at 5 follow-up time points over 52 weeks. Pain trajectories were identified using a latent class linear mixed model, and their associations with baseline biopsychosocial factors were evaluated through multinomial logistic regression. Results: Four distinct LBP trajectories were discerned: "mild/moderate fluctuating pain" (54.0%), "delayed recovery by week 52" (6.2%), "persistent moderate pain" (33.0%), and "moderate/severe fluctuating pain" (6.8%). Increased baseline pain intensity and history of LBP episodes were significantly linked with less favorable trajectories. Contrary to expectations, psychological variables like stress, anxiety, and depression did not significantly associate with unfavorable trajectories. Discussion: This study underscores the heterogeneity of acute LBP's course over a year, challenging the conventionally benign perception of the condition. Recognizing these distinct trajectories might enable more tailored, effective clinical interventions for LBP patients. The small sample size of certain trajectories may influence the generalizability of the results. Conclusion: Acute LBP can manifest in different trajectories, with nearly half of the participants experiencing less favorable trajectories. Baseline pain intensity and previous episodes of LBP emerged as key factors, whereas psychological variables had no discernible influence. Recognition of these trajectories may be necessary for improved patient management and targeted interventions.

Prediction of Junior High School Students' Problematic Internet Use: The Comparison of Neural Network Models and Linear Mixed Models in Longitudinal Study.

Purpose: With the rise of big data, deep learning neural networks have garnered attention from psychology researchers due to their ability to process vast amounts of data and achieve superior model fitting. We aim to explore the predictive accuracy of neural network models and linear mixed models in tracking data when subjective variables are predominant in the field of psychology. We separately analyzed the predictive accuracy of both models and conduct a comparative study to further investigate. Simultaneously, we utilized the neural network model to examine the influencing factors of problematic internet usage and its temporal changes, attempting to provide insights for early interventions in problematic internet use. Patients and Methods: This study compared longitudinal data of junior high school students using both a linear mixed model and a neural network model to ascertain the efficacy of these two methods in processing psychological longitudinal data. Results: The neural network model exhibited significantly smaller errors compared to the linear mixed model. Furthermore, the outcomes from the neural network model revealed that, when analyzing data from a single time point, the influences of seventh grade better predicted Problematic Internet Use in ninth grade. And when analyzing data from multiple time points, the influences of sixth, seventh, and eighth grades more accurately predicted Problematic Internet Use in ninth grade. Conclusion: Neural network models surpass linear mixed models in precision when predicting and analyzing longitudinal data. Furthermore, the influencing factors in lower grades provide more accurate predictions of Problematic Internet Use in higher grades. The highest prediction accuracy is attained through the utilization of data from multiple time points.