Covariate Interactions Research Articles

Leveraging the variational Bayes autoencoder for survival analysis

Survival analysis in medical research has witnessed a growing interest in applying deep learning techniques to model complex, high-dimensional, heterogeneous, incomplete, and censored data. Current methods make assumptions about the relations between data that may not be valid in practice. Therefore, we introduce SAVAE (Survival Analysis Variational Autoencoder). SAVAE, based on Variational Autoencoders, contributes significantly to the field by introducing a tailored Evidence Lower BOund formulation, supporting various parametric distributions for covariates and survival time (if the log-likelihood is differentiable). It offers a general method that demonstrates robustness and stability through different experiments. Our proposal effectively estimates time-to-event, accounting for censoring, covariate interactions, and time-varying risk associations. We validate our model in diverse datasets, including genomic, clinical, and demographic tabular data, with varying levels of censoring. This approach demonstrates competitive performance compared to state-of-the-art techniques, as assessed by the Concordance Index and the Integrated Brier Score. SAVAE also offers an interpretable model that parametrically models covariates and time. Moreover, its generative architecture facilitates further applications such as clustering, data imputation, and synthetic patient data generation through latent space inference from survival data. This approach fosters data sharing and collaboration, improving medical research and personalized patient care.

Relativistic two-fluid hydrodynamics with quantized vorticity from the nonlinear Klein-Gordon equation

Abstract We consider a relativistic two-fluid model of superfluidity, in which the superfluid is described by an order parameter that is a complex scalar field satisfying the nonlinear Klein-Gordon equation (NLKG). The coupling to the normal fluid is introduced via a covariant current-current interaction, which results in the addition of an effective potential, whose imaginary part describes particle transfer between superfluid and normal fluid. Quantized vorticity arises in a class of singular solutions and the related vortex dynamics is incorporated in the modified NLKG, facilitating numerical analysis which is usually very complicated in the phenomenology of vortex filaments. The dual transformation to a string theory description (Kalb-Ramond) of quantum vorticity, the Magnus force and the mutual friction between quantized vortices and normal fluid are also studied.

Gut microbiota and intestinal rehabilitation: a prospective childhood cohort longitudinal study of short bowel syndrome (the MIRACLS study): study protocol

IntroductionShort bowel syndrome (SBS) is the predominant cause of paediatric intestinal failure. Although life-saving, parenteral nutrition (PN) is linked to complications and may impact quality of life (QoL). Most children...

Gestational diabetes mellitus is associated with greater incidence of dementia during long-term post-partum follow-up.

The impact of gestational diabetes mellitus (GDM) on incident dementia is unknown. Our aim was to evaluate the relationship between GDM and all-cause dementia and the mediating effects of chronic diseases on this relationship. This prospective cohort study included women from the UK Biobank who were grouped based on GDM history. Multivariate Cox proportional hazard models were used to explore the associations between GDM and dementia. We further analysed the mediating effects of chronic diseases on this relationship and the interactions of covariates. A total of 1292 women with and 204,171 women without a history of GDM were included. During a median follow-up period of 45 years after first birth, 2921 women were diagnosed with dementia. Women with a GDM history had a 67% increased risk of incident dementia (hazard ratio 1.67, 95% confidence interval: 1.03-2.69) compared with those without a GDM history. According to mediation analyses, type 2 diabetes, coronary heart disease, chronic kidney disease and comorbidities (diagnosed with any two of the three diseases) explained 34.5%, 8.4%, 5.2% and 18.8% of the mediating effect on the relationship. Subgroup analyses revealed that physical activity modified the association between GDM history and dementia (p for interaction=0.030). Among physically inactive women, GDM was significantly associated with incident dementia; however, this association was not observed among physically active women. A history of GDM was associated with a greater risk of incident dementia. Type 2 diabetes partially mediated this relationship. Strategies for dementia prevention might be considered for women with a history of GDM.

How Close to the Mark Might Published Heritability Estimates Be?

The behavioural scientist who requires an estimate of narrow heritability, h2, will conduct a twin study, and input the resulting estimated covariance matrices into a particular mode of estimation, the latter derived under supposition of the standard biometric model (SBM). It is known that the standard biometric model can be expected to misrepresent the phenotypic (genetic) architecture of human traits. The impact of this misrepresentation on the accuracy of h2 estimation is unknown. We aimed to shed some light on this general issue, by undertaking three simulation studies. In each, we investigated the parameter recovery performance of five modes- Falconer’s coefficient and the SEM models, ACDE, ADE, ACE, and AE- when they encountered a constructed, non-SBM, architecture, under a particular informational input. In study 1, the architecture was single-locus with dominance effects and genetic-environment covariance, and the input was a set of population covariance matrices yielded under the four twin designs, monozygotic-reared together, monozygotic-reared apart, dizygotic-reared together, and dizygotic-reared apart; in study 2, the architecture was identical to that of study 1, but the informational input was monozygotic-reared together and dizygotic-reared together; and in study 3, the architecture was multi-locus with dominance effects, genetic-environment covariance, and epistatic interactions. The informational input was the same as in study 1. The results suggest that conclusions regarding the coverage of h2 must be drawn conditional on a) the general class of generating architecture in play; b) specifics of the architecture’s parametric instantiations; c) the informational input into a mode of estimation; and d) the particular mode of estimationemployed. The results showed that the more complicated the generating architecture, the poorer a mode’s h2 recovery performance. Random forest analyses furthermore revealed that, depending on the genetic architecture, h2, the dominance and locus additive parameter, and proportions of alleles were involved in complex interaction effects impacting on h2 parameter recovery performance of a mode of estimation. Data and materials: https://osf.io/aq9sx/

Sex Differences in Left Ventricular Stroke Volume During Muscle Metaboreflex Activation

Introduction: Muscle metaboreflex activation (MMA) increases blood pressure, possibly by disproportionate increases in left ventricular (LV) stroke volume (SV) in males compared to females due to greater grip strength and metabolite production. The purpose of this study was to determine the effect of sex on SV during isolated MMA using cardiac MRI in young healthy adults. We hypothesized that SV would increase during MMA to a greater degree in males compared to females, and that this effect would be negated by accounting for grip strength. Methods: Ten males [23(2) yrs] and 10 females [26(5) yrs] underwent cardiac MRI during rest, 30% maximal voluntary contraction (MVC) isometric handgrip exercise (6 min), and isolation of MMA with brachial cuff circulatory occlusion (6 min). SV, end-systolic (ESV) and end-diastolic volumes (EDV) were measured by 2- and 4-chamber cine images and indexed (i) to body surface area to account for body size. Heart rate (HR, ECG) and mean arterial pressure (MAP, brachial cuff) were recorded. Cardiac output index (COi) and total peripheral resistance (TPR) were calculated. Analyses included ANOVA (sex × condition), ANCOVA [100% MVC (M=49(13) kg, F=26(7) kg) covariate], and linear regression. Results: SVi decreased from rest to MMA in males [67(8) to 61(5) mL/m2, p=0.0001] but not females [59(9) to 59(7) mL/m2, p=0.998; interaction p=0.002]. Notably, the reduction in SVi during MMA in males ( p=0.002) but not females ( p=0.957) remained with ANCOVA using MVC as the covariate (interaction p=0.021). ESVi increased during MMA in males [39(5) to 48(10) mL/m2, p<0.0001] but not females [32(5) to 36(6) mL/m2, p=0.092; interaction p=0.029]. MMA decreased both EDVi ( p=0.004) and COi ( p=0.045) and increased both MAP ( p<0.0001) and TPR ( p<0.0001) pooled across sex. HR did not change with MMA in either sex (all p≥0.093). Linear regression revealed significant relationships between ΔSVi and ΔESVi in males ( p=0.002, R2=0.709) but not females ( p=0.223, R2=0.179) and between ΔSVi and ΔTPR in males ( p=0.005, R2=0.649) but not females ( p=0.522, R2=0.053). Conclusions: MMA lowers SVi in males but not females, even when accounting for grip strength and despite similar pressor responses in both groups. These findings from gold-standard MRI suggest differential LV responses to MMA between sexes, potentially related to greater sensitivity to increased TPR in males than females. Thus, sex differences in blood pressure control during MMA may be impacted by differences in LV afterload sensitivity. Further, the blood pressure response to MMA may be primarily mediated through TPR and not CO in healthy young adults. Heart and Stroke Foundation of Canada. This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Whole-brain Functional Connectivity Correlates of Brain Structural Aging in Adult Schizophrenia Patients Compared to Healthy Controls

IntroductionAge-related changes of brain functional connectivity, in contrast to brain structure, are understudied in schizophrenia. Importantly, patients with schizophrenia demonstrate an increased difference between the brain-predicted age and chronological age indicating that brain structural aging may be accelerated in this mental disorder (Constantinides et al. Mol Psychiatry 2023; 28 1201-1209). Research on functional connectivity correlates of this process seems to be fruitful.ObjectivesWe aimed to search for the brain regions whose resting-state whole-brain functional connectivity is differently associated with brain-predicted age in schizophrenia patients compared to healthy controls.MethodsEighty-three male patients with schizophrenia (age range 17.3 – 52.3; mean age 32.1 ± 10.5) and eighty-seven male healthy individuals (age range 18.3 – 53.6; mean age 31.7 ± 10.0) underwent structural MRI and resting-state fMRI (Philips Ingenia 3T scanner). Brain-predicted age was individually estimated using a model trained on independent data based on 68 measures of cortical thickness and surface area, 7 subcortical volumes, lateral ventricular volumes, and total intracranial volume, all derived from T1-weighted MRI scans (Han et al. Mol Psychiatry 2021; 26 5124-39). The associations between the brain-predicted age and the whole-brain global correlation (GCOR) were compared between groups; t-contrasts were calculated (one-way ANCOVA covariate interaction via CONN; RRID:SCR_009550; www.nitrc.org/projects/conn). The chronological age was a covariate of no interest.ResultsSchizophrenia patients had a higher difference between brain-predicted age and chronological age (T(168) = 2.1; p = .036; Cohen’s d = 0.32; 95% CI 0.02-0.63). Greater brain-predicted age in schizophrenia patients, compared to controls, was associated with lower functional connectivity of a region in the right Heschl’s gyrus, planum temporale, as well as central opercular and insular cortex with the rest of the brain (p < .001 voxelwise, p[FDR] < .05 clusterwise; Figure 1).Image: ConclusionsOur results coincide with earlier findings on accelerated brain structural aging in schizophrenia. To the best of our knowledge, the present study is the first to indicate that this process is coupled with a decline of the whole-brain functional connectivity of a region located in the right temporal, insular, and parietal cortices, and this effect is not driven by chronological age. Further studies are needed to clarify the clinical and cognitive correlates of this decline of functional connectivity.The study was supported by RSF grant project 22-25-00706.Disclosure of InterestNone Declared

Bone-Modifying Agents in Patients With High-Risk Metastatic Castration-Sensitive Prostate Cancer Treated With Abiraterone Acetate

The association between the use of bone-modifying agents (BMAs) and the outcomes among patients with metastatic castration-sensitive prostate cancer (mCSPC) treated with abiraterone acetate plus prednisone (AAP) remains unclear. To investigate the association between BMA use and the outcomes of patients with mCSPC receiving AAP. In this cohort study, a post hoc analysis of individual participant data from the LATITUDE trial was performed. The LATITUDE trial, a phase 3 randomized clinical trial, aimed to assess the efficacy of AAP and androgen deprivation therapy (ADT) vs dual-placebo and ADT in patients with high-risk mCSPC (data cutoff, August 15, 2018). Eligible patients had newly diagnosed prostate cancer with metastases and at least 2 of 3 high-risk factors (Gleason score ≥8, presence of ≥3 lesions on bone scan, or presence of measurable visceral metastasis). The trial was conducted at 235 sites in 34 countries. Data for the present study were evaluated from July 18 to September 23, 2023. Use of BMAs was defined as the administration of bisphosphonates and denosumab within 90 days before and after randomization. The primary outcomes were time to skeletal-related events (SREs) and overall survival (OS). An SRE was defined as a clinical or pathological fracture, spinal cord compression, palliative radiation to bone, or surgery involving bone. Differences in these outcomes were examined using the restricted mean survival time from inverse probability of treatment weighting-adjusted Kaplan-Meier curves, estimated until the last event was observed (longest time observed, 63.9 months). Treatment × covariate interactions were analyzed using weighted Cox proportional hazards regression models for the total cohort. In the total cohort of 1199 patients (956 [79.7%] younger than 75 years), 597 (49.8%) received AAP and ADT, including 474 (79.4%) younger than 75 years and 384 (64.3%) with more than 10 bone metastases (AAP cohort); 602 (50.2%) were treated with dual placebo and ADT, including 482 (80.1%) younger than 75 years and 377 (62.6%) with more than 10 bone metastases (ADT cohort). In the AAP cohort, 132 patients (22.1%) received BMAs, while in the ADT cohort, 131 (21.8%) did. Zoledronic acid was the most frequently administered BMA in both the AAP (93 [70.5%]) and the ADT (88 [67.2%]) cohorts. During the median follow-up of 51.8 (IQR, 47.2-57.0) months in the AAP cohort, BMA use was associated with a longer time to SRE (difference, 7.8 [95% CI, 4.2-11.3] months) but not with OS (difference, 1.6 [95% CI, -2.5 to 5.8] months). In the ADT cohort, BMA use was associated with both time to SRE (difference, 9.3 [95% CI, 5.2-13.3] months) and OS (difference, 5.5 [95% CI, 3.2-9.8] months). No evidence was found that the outcomes of BMA varied by AAP or ADT (hazard ratio for time to SRE, 0.99 [95% CI, 0.48-2.08]; P = .99 for interaction; hazard ratio for OS, 1.31 [95% CI, 0.88-1.96]; P = .18 for interaction). The findings of this cohort study suggest that use of BMAs was associated with a longer time to SRE in patients with high-risk mCSPC treated with ADT, with or without AAP, suggesting that BMA use might provide benefits to this population.

Item-level heterogeneous treatment effects of selective serotonin reuptake inhibitors (SSRIs) on depression: implications for inference, generalizability, and identification

Abstract Objectives In analysis of randomized controlled trials (RCTs) with patient-reported outcome measures (PROMs), Item Response Theory (IRT) models that allow for heterogeneity in the treatment effect at the item level merit consideration. These models for “item-level heterogeneous treatment effects” (IL-HTE) can provide more accurate statistical inference, allow researchers to better generalize their results, and resolve critical identification problems in the estimation of interaction effects. In this study, we extend the IL-HTE model to polytomous data and apply the model to determine how the effect of selective serotonin reuptake inhibitors (SSRIs) on depression varies across the items on a depression rating scale. Methods We first conduct a Monte Carlo simulation study to assess the performance of the polytomous IL-HTE model under a range of conditions. We then apply the IL-HTE model to item-level data from 24 RCTs measuring the effect of SSRIs on depression using the 17-item Hamilton Depression Rating Scale (HDRS-17) and estimate heterogeneity by subscale (HDRS-6). Results Our simulation results show that ignoring IL-HTE can yield standard errors that are as much as 50 % too small and create significant bias in treatment by covariate interaction effects when item-specific treatment effects are correlated with item location, and that the application of the IL-HTE model resolves these issues. Our empirical application shows that while the average effect of SSRIs on depression is beneficial (i.e., negative) and statistically significant, there is substantial IL-HTE, with estimates of the standard deviation of item-level effects nearly as large as the average effect. We show that this substantial IL-HTE is driven primarily by systematically larger effects on the HDRS-6 subscale items. Conclusions The IL-HTE model has the potential to provide new insights for the inference, generalizability, and identification of treatment effects in clinical trials using PROMs.

Structure-primed embedding on the transcription factor manifold enables transparent model architectures for gene regulatory network and latent activity inference

BackgroundModeling of gene regulatory networks (GRNs) is limited due to a lack of direct measurements of genome-wide transcription factor activity (TFA) making it difficult to separate covariance and regulatory interactions. Inference of regulatory interactions and TFA requires aggregation of complementary evidence. Estimating TFA explicitly is problematic as it disconnects GRN inference and TFA estimation and is unable to account for, for example, contextual transcription factor-transcription factor interactions, and other higher order features. Deep-learning offers a potential solution, as it can model complex interactions and higher-order latent features, although does not provide interpretable models and latent features.ResultsWe propose a novel autoencoder-based framework, StrUcture Primed Inference of Regulation using latent Factor ACTivity (SupirFactor) for modeling, and a metric, explained relative variance (ERV), for interpretation of GRNs. We evaluate SupirFactor with ERV in a wide set of contexts. Compared to current state-of-the-art GRN inference methods, SupirFactor performs favorably. We evaluate latent feature activity as an estimate of TFA and biological function in S. cerevisiae as well as in peripheral blood mononuclear cells (PBMC).ConclusionHere we present a framework for structure-primed inference and interpretation of GRNs, SupirFactor, demonstrating interpretability using ERV in multiple biological and experimental settings. SupirFactor enables TFA estimation and pathway analysis using latent factor activity, demonstrated here on two large-scale single-cell datasets, modeling S. cerevisiae and PBMC. We find that the SupirFactor model facilitates biological analysis acquiring novel functional and regulatory insight.

Towards systematic large scale Quasiparticle Random-Phase Approximation calculations with covariant and chiral interactions

Towards systematic large scale Quasiparticle Random-Phase Approximation calculations with covariant and chiral interactions

The association of platelet to white blood cell ratio with diabetes: a nationwide survey in China.

Inflammation is integral to diabetes pathogenesis. The novel hematological inflammatory biomarker, platelet to white blood cell ratio (PWR), is linked with various conditions such as chronic kidney disease and stroke. However, the association of this novel clinical indicator with diabetes still remains unclear, which is investigated in this study. A total of 10,973 Chinese participants were included and grouped according to the tertiles of PWR (T1, T2, and T3 groups). Diagnosis of prediabetes and diabetes adhered to American Diabetes Association criteria. Binary logistic regression was adopted to assess the relationship between PWR and both diabetes and prediabetes. The dose-response relationship of PWR and diabetes was examined using restricted cubic spline regression. Subgroup and interaction analyses were conducted to investigate potential covariate interactions. Individuals with higher PWR had better lifestyles and lipid profiles (all P < 0.05). After adjusting for all the covariates, the T2 group had a 0.83-fold (95% CI: 0.73-0.93, P < 0.01) risk of diabetes and that for the T3 group was 0.68-fold (95% CI: 0.60-0.78. P < 0.001). Dose-response analysis identified non-linear PWR-diabetes associations in the general population and females (both P < 0.05), but absent in males. Participants with prediabetes in the T2 and T3 groups had lower risks of diabetes (OR = 0.80 for the T2 group, P < 0.001 and 0.68 for the T3 group, P < 0.001) in the full models. All the sensitivity analysis support consistent conclusions. An increase in PWR significantly correlates with reduced diabetes risks. A non-linear PWR-diabetes relationship exists in the general population and females, but not in males. The correlation between PWR and diabetes indicates that PWR holds potentials in early identification and prevention of diabetes.

Evaluating the heterogeneity of hippocampal avoidant whole brain radiotherapy treatment effect: A secondary analysis of NRG CC001.

Hippocampal avoidant whole brain radiotherapy (HA-WBRT) is the standard of care for patients needing WBRT for brain metastases. This study, using existing data from NRG Oncology CC001 including baseline tumor characteristics and patient-reported MD Anderson Symptom Inventory-Brain Tumor (MDASI-BT) scores, sought to identify subgroups of patients that demonstrate differential neuroprotective treatment response to HA-WBRT. An exploratory analysis of NRG CC001, a phase 3 trial in which 518 patients were randomly assigned to WBRT plus memantine or HA-WBRT plus memantine, was performed. Rates of neurocognitive function failure (NCFF) were estimated between subgroups and stratified by arm. Covariate and subgroup interaction with differential treatment response were calculated. The benefit of HA-WBRT on decreasing NCFF was seen in patients living ≥ 4 months (HR 0.75, 95% CI: 0.58-0.97, P = .03), whereas patients living < 4 months derived no significant neurocognitive benefit. A significant association between baseline MDASI-BT cognitive factor and treatment response (interaction P = .03) was identified. Patients with lower MDASI-BT scores (less patient-reported cognitive impairment) derived significantly greater benefit (HR = 0.64, 95% CI: 0.48-0.85, P = .002) compared to those with highest MDASI-BT scores (HR = 1.24, 95% CI: 0.76-2.04, P = .39). Tumor histology also had a significant interaction (P = .01) with treatment response. Primary lung histology patients derived cognitive failure risk reduction (HR = 0.58, 95% CI: 0.43-0.77, P = .0007) from HA-WBRT, in contrast to nonlung primary histology patients (HR = 1.15, 95% CI: 0.78-1.50, P = .48). Differential neuroprotective response to HA-WBRT was identified in this analysis. Patients surviving ≥ 4 months derived benefit from HA-WBRT. There is evidence of heterogeneity of treatment effect for patients with less severe patient-reported cognitive impairment at baseline and those with primary lung histology.

Covariant Cubic Interacting Vertices for Massless and Massive Integer Higher Spin Fields

We develop the BRST approach to construct the general off-shell local Lorentz covariant cubic interaction vertices for irreducible massless and massive higher spin fields on d-dimensional Minkowski space. We consider two different cases for interacting higher spin fields: with one massive and two massless; two massive, both with coinciding and with different masses and one massless field of spins s1,s2,s3. Unlike the previous results on cubic vertices we extend our earlier result in (Buchbinder, I.L.; et al. Phys. Lett. B 2021, 820, 136470) for massless fields and employ the complete BRST operator, including the trace constraints, which is used to formulate an irreducible representation with definite integer spin. We generalize the cubic vertices proposed for reducible higher spin fields in (Metsaev, R.R. Phys. Lett. B 2013, 720, 237) in the form of multiplicative and non-multiplicative BRST-closed constituents and calculate the new contributions to the vertex, which contains the additional terms with a smaller number of space-time derivatives. We prove that without traceless conditions for the cubic vertices in (Metsaev, R.R. Phys. Lett. B 2013, 720, 237) it is impossible to provide the noncontradictory Lagrangian dynamics and find explicit traceless solution for these vertices. As the examples, we explicitly construct the interacting Lagrangians for the massive spin of the s field and the massless scalars, both with and without auxiliary fields. The interacting models with different combinations of triples higher spin fields: massive spin s with massless scalar and vector fields and with two vector fields; massless helicity λ with massless scalar and massive vector fields; two massive fields of spins s, 0 and massless scalar is also considered.

Sample size and predictive performance of machine learning methods with survival data: A simulation study.

Prediction models are increasingly developed and used in diagnostic and prognostic studies, where the use of machine learning (ML) methods is becoming more and more popular over traditional regression techniques. For survival outcomes the Cox proportional hazards model is generally used and it has been proven to achieve good prediction performances with few strong covariates. The possibility to improve the model performance by including nonlinearities, covariate interactions and time-varying effects while controlling for overfitting must be carefully considered during the model building phase. On the other hand, ML techniques are able to learn complexities from data at the cost of hyper-parameter tuning and interpretability. One aspect of special interest is the sample size needed for developing a survival prediction model. While there is guidance when using traditional statistical models, the same does not apply when using ML techniques. This work develops a time-to-event simulation framework to evaluate performances of Cox regression compared, among others, to tuned random survival forest, gradient boosting, and neural networks at varying sample sizes. Simulations were based on replications of subjects from publicly available databases, where event times were simulated according to a Cox model with nonlinearities on continuous variables and time-varying effects and on the SEER registry data.

Incorporating machine learning into factor mixture modeling: Identification of covariate interactions to explain population heterogeneity

Factor mixture modeling (FMM) has been widely adopted in health and behavioral sciences to examine unobserved population heterogeneity. Covariates are often included in FMM as predictors of the latent class membership via multinomial logistic regression to help understand the formation and characterization of population heterogeneity. However, interaction effects among covariates have received considerably less attention, which might be attributable to the fact that interaction effects cannot be identified in a straightforward fashion. This study demonstrated the utility of structural equation model or SEM trees as an exploratory method to automatically search for covariate interactions that might explain heterogeneity in FMM. That is, following FMM analyses, SEM trees are conducted to identify covariate interactions. Next, latent class membership is regressed on the covariate interactions as well as all main effects of covariates. This approach was demonstrated using the Traumatic Brain Injury Model System National Database.

Reviewing and improving spatiotemporal modeling approaches for mackerel’s total annual egg production

Since the late 1970s the international ICES mackerel egg survey takes place in the Northeast Atlantic to obtain an estimate of total annual egg production (TAEP), to assess the spawning stock biomass and to support the sustainable management of the mackerel stock. However, its standard calculation of TAEP has some shortcomings: egg production is estimated from arithmetic averages of individual observations regardless whether they are extreme, rarely observed values, and has shown difficulties coping with the spatial expansion of mackerel spawning observed since 2007 (increasing the number of interpolated estimates). Various previous modeling efforts attempted to map the habitat of spawning mackerel to obtain more accurate egg production estimates by using generalized additive models (GAMs). In this study, we review and improve these models by introducing new features: We model both the western and southern components of the mackerel stock with a Tweedie distribution and test more than 400 model forms, including new predictors (like population size) and complex covariate interactions. We select the best model with various metrics, including the score of a tenfold cross validation. Our results show that environmental variables should be included in the model as simple functional, unimodal terms. This leads to a model that is more similar to a generalized linear mixed model than the typical GAM with smoothers. Models including only smoothers performed worse as they have a poor skill to extrapolate beyond the sampled region. Additionally, accounting for the effect of population size on the spatial distribution of eggs was shown to be fundamental to better model performance.Graphical abstract

Modeling behavioral economic strategies in social marketing messages to promote vegetable consumption to low-resource Louisiana residents: A conjoint analysis.

Behavioral economic strategies implemented within social marketing campaigns improve eating behavior. To identify which behavioral economic (BE) strategies in social marketing messages, i.e., messengers, norms, and commitments, will promote low-income Louisiana (LA) residents to add more vegetables to meals. Full profile traditional conjoint analysis survey with single concept ratings. Purposive sample of low-resource LA residents (N = 213) SETTING: Online survey INTERVENTION: Participants rated randomized concepts that featured a messenger and a message. Ratings (1-9) of likelihood to add vegetables to meals. A model of ratings was generated using mixed-effects multiple regression, which accounted for repeated measurement of participants. Interactions of variables and covariates were modeled. There was significant variation in the main effect of the messenger variable (P = 0.02) and the main effect of the message variable (P = 0.008). Pairwise comparisons demonstrated differences between friend (mean = 6.80) and mother (mean = 7.06, P = 0.03) as well as friend and normal weight doctor (mean = 7.03, P = 0.04). Pairwise comparisons demonstrated differences between descriptive norm (mean = 6.79) and grocery list commitment (mean = 7.02, P = 0.05) along with descriptive norm and injunctive norm (mean = 6.98, P = 0.04). Covariate models demonstrated main effects of race (P = 0.006) and gender (P = 0.0001). There was significant variation in the message variable and frequency of vegetable intake interaction (P = 0.01). Both message and messenger variables predicted the likelihood to add vegetables to meals. Race and gender influenced ratings to add vegetables. As reported vegetable consumption increased, behavioral economic messages improved the likelihood to add vegetables to meals. Behavioral economic approaches are well-suited to social marketing messages that aim to promote healthy eating behavior in low-income LA residents.

What is the most appropriate follow-up time for detecting the epidemiological relationship between coronary artery disease and its main risk factors: novel findings from a 35-year follow-up study.

The aim was to investigate the most appropriate follow-up time to detect the associations of coronary artery disease (CAD) with its traditional risk factors in a long-term prospective cohort study. The Kuopio Ischaemic Heart Disease Risk Factors Study provided the study material of 1958 middle-aged men free from CAD at baseline and followed up for 35 years. We performed Cox models adjusted for age, family history, diabetes, obesity, hypercholesterolemia, hypertension, smoking, and physical activity, investigated covariate interactions, and tested Schoenfeld residuals to detect time-dependent covariates. Moreover, we applied a sliding window procedure with a subarray of 5 years to better differentiate between risk factors manifested within years and those manifested within decades. The investigated manifestations were CAD and fatal acute myocardial infarction (AMI). Seven hundred seventeen (36.6%) men had CAD, and 109 (5.6%) men died from AMI. After 10 years of follow-up, diabetes became the strongest predictor of CAD with a fully adjusted hazard ratio (HR) of 2.5-2.8. During the first 5 years, smoking was the strongest predictor (HR 3.0-3.8). When the follow-up time was 8-19 years, hypercholesterolemia predicted CAD with a HR of >2. The associations of CAD with age and diabetes depended on time. Age hypertension was the only statistically significant covariate interaction. The sliding window procedure highlighted the significance of diabetes over the first 20 years and hypertension after that. Regarding AMI, smoking was associated with the highest fully adjusted HR (2.9-10.1) during the first 13 years. The associations of extreme and low physical activity with AMI peaked when the follow-up time was 3-8 years. Diabetes showed its highest HR (2.7-3.7) when the follow-up time was 10-20 years. During the last 16 years, hypertension was the strongest predictor of AMI (HR 3.1-6.4). The most appropriate follow-up time for most CAD risk factors was 10-20 years. Concerning smoking and hypertension shorter and longer follow-up times could be considered, respectively, particularly when studying fatal AMI. In general, prospective cohort studies of CAD would provide more comprehensive results by reporting point estimates in relation to more than one timepoint and concerning sliding windows.

Calcium supplementation to prevent pre-eclampsia: protocol for an individual participant data meta-analysis, network meta-analysis and health economic evaluation

IntroductionLow dietary calcium intake is a risk factor for pre-eclampsia, a major contributor to maternal and perinatal mortality and morbidity worldwide. Calcium supplementation can prevent pre-eclampsia in women with low...