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There is limited evidence for the effects of diet on cardiometabolic profiles during the pubertal transition. We collected repeated measures of diet quality and cardiometabolic risk factors among Mexican youth. This analysis included 574 offspring of the Early Life Exposure in Mexico to Environmental Toxicants (ELEMENT) birth cohort followed up to three time points. Dietary Approaches to Stop Hypertension (DASH), alternate Mediterranean Diet (aMedDiet), and Children’s Dietary Inflammatory Index (C-DIITM) scores were computed from food frequency questionnaires. Higher DASH and aMedDiet scores reflect a higher diet quality, and lower C-DII scores reflect an anti-inflammatory diet. Cardiometabolic risk factors were lipid profile, glucose homeostasis, blood pressure, and waist circumference. Linear mixed models were used between quartiles of each diet score and outcomes. Compared to the first quartile, the fourth DASH quartile was inversely associated with log serum insulin (μIU/mL) [β = −0.19, p = 0.0034] and log-Homeostatic Model Assessment of Insulin Resistance [β = −0.25, p = 0.0008]. Additionally, log serum triglycerides (mg/dL) was linearly associated with aMedDiet score [β = −0.03, p = 0.0022]. Boys in the highest aMedDiet quartile had higher serum high-density lipoprotein cholesterol (mg/dL) [β = 4.13, p = 0.0034] compared to the reference quartile. Higher diet quality was associated with a better cardiometabolic profile among Mexican youth.

Purpose The impact of ventriculomegaly (VM) on cortical development and brain functionality has been extensively explored in existing literature. VM has been associated with higher risks of attention‐deficit and hyperactivity disorders, as well as cognitive, language, and behavior deficits. Some studies have also shown a relationship between VM and cortical overgrowth, along with reduced cortical folding, both in fetuses and neonates. However, there is a lack of longitudinal studies that study this relationship from fetuses to neonates. Method We used a longitudinal dataset of 30 subjects (15 healthy controls and 15 subjects diagnosed with isolated non‐severe VM (INSVM)) with structural MRI acquired in and ex utero for each subject. We focused on the impact of fetal INSVM on cortical development from a longitudinal perspective, from the fetal to the neonatal stage. Particularly, we examined the relationship between ventricular enlargement and both volumetric features and a multifaceted set of cortical folding measures, including local gyrification, sulcal depth, curvature, and cortical thickness. Findings Our results show significant effects of isolated non‐severe VM (INSVM) compared to healthy controls, with reduced cortical thickness in specific brain regions such as the occipital, parietal, and frontal lobes. Conclusion These findings align with existing literature, confirming the presence of alterations in cortical growth and folding in subjects with isolated non‐severe VM (INSVM) from the fetal to neonatal stage compared to controls. This study investigates the longitudinal impact of isolated non‐severe ventriculomegaly (INSVM) on cortical development from fetal to neonatal stages using MRI data from 30 subjects (15 with VM and 15 healthy controls). The results indicate that VM subjects exhibit larger cortical volume, reduced cortical thickness and altered local gyrification over time, particularly in the occipital, parietal, and frontal lobes, confirming cortical overgrowth and delayed cortical folding observed in cross‐sectional studies.

The estimation of large functional and longitudinal data, which refers to the estimation of mean function, estimation of covariance function, and prediction of individual trajectory, is one of the most challenging problems in the field of high-dimensional statistics. Functional Principal Components Analysis (FPCA) and Functional Linear Mixed Model (FLMM) are two major statistical tools used to address the estimation of large functional and longitudinal data; however, the former suffers from a dramatically increasing computational burden while the latter does not have clear asymptotic properties. In this paper, we propose a computationally effective estimator of large functional and longitudinal data within the framework of FLMM, in which all the parameters can be automatically estimated. Under certain regularity assumptions, we prove that the mean function estimation and individual trajectory prediction reach the minimax lower bounds of all nonparametric estimations. Through numerous simulations and real data analysis, we show that our new estimator outperforms the traditional FPCA in terms of mean function estimation, individual trajectory prediction, variance estimation, covariance function estimation, and computational effectiveness.

Multimorbidity is a significant public health concern, characterized by the coexistence and interaction of multiple preexisting medical conditions. This complex condition has been associated with an increased risk of COVID-19. Individuals with multimorbidity who contract COVID-19 often face a significant reduction in life expectancy. The postpandemic period has also highlighted an increase in frailty, emphasizing the importance of integrating existing multimorbidity details into epidemiological risk assessments. Managing clinical data that include medical histories presents significant challenges, particularly due to the sparsity of data arising from the rarity of multimorbidity conditions. Also, the complex enumeration of combinatorial multimorbidity features introduces challenges associated with combinatorial explosions. This study aims to assess the severity of COVID-19 in individuals with multiple medical conditions, considering their demographic characteristics such as age and sex. We propose an evolutionary machine learning model designed to handle sparsity, analyzing preexisting multimorbidity profiles of patients hospitalized with COVID-19 based on their medical history. Our objective is to identify the optimal set of multimorbidity feature combinations strongly associated with COVID-19 severity. We also apply the Apriori algorithm to these evolutionarily derived predictive feature combinations to identify those with high support. We used data from 3 administrative sources in Piedmont, Italy, involving 12,793 individuals aged 45-74 years who tested positive for COVID-19 between February and May 2020. From their 5-year pre-COVID-19 medical histories, we extracted multimorbidity features, including drug prescriptions, disease diagnoses, sex, and age. Focusing on COVID-19 hospitalization, we segmented the data into 4 cohorts based on age and sex. Addressing data imbalance through random resampling, we compared various machine learning algorithms to identify the optimal classification model for our evolutionary approach. Using 5-fold cross-validation, we evaluated each model's performance. Our evolutionary algorithm, utilizing a deep learning classifier, generated prediction-based fitness scores to pinpoint multimorbidity combinations associated with COVID-19 hospitalization risk. Eventually, the Apriori algorithm was applied to identify frequent combinations with high support. We identified multimorbidity predictors associated with COVID-19 hospitalization, indicating more severe COVID-19 outcomes. Frequently occurring morbidity features in the final evolved combinations were age>53, R03BA (glucocorticoid inhalants), and N03AX (other antiepileptics) in cohort 1; A10BA (biguanide or metformin) and N02BE (anilides) in cohort 2; N02AX (other opioids) and M04AA (preparations inhibiting uric acid production) in cohort 3; and G04CA (Alpha-adrenoreceptor antagonists) in cohort 4. When combined with other multimorbidity features, even less prevalent medical conditions show associations with the outcome. This study provides insights beyond COVID-19, demonstrating how repurposed administrative data can be adapted and contribute to enhanced risk assessment for vulnerable populations.

In longitudinal studies comparing two treatments with a maximum follow-up time there may be interest in examining treatment effects for intermediate follow-up times. One motivation may be to identify the time period with greatest treatment difference when there is a non-monotone treatment effect over time; another motivation may be to make the trial more efficient in terms of time to reach a decision on whether a new treatment is efficacious or not. Here, we test the composite null hypothesis of no differene at any follow-up time versus the alternative that there is a difference at at least one follow-up time. The methods are applicable when a few measurements are taken over time, such as in early longitudinal trials or in ancillary studies. Suppose the test statistic $Z_{t_{k}}$ will be used to test the hypothesis of no treatment effect at a fixed follow-up time tk. In this context a common approach is to perform a pilot study on N1 subjects, and evaluate the treatment effect at the fixed time points t1,...,tK and choose t* as the value of tk for which $Z_{t_{k}}$ is maximized. Having chosen t* a second trial can be designed. In a setting with group sequential testing we consider several adaptive alternatives to this approach that treat the pilot and second trial as a seamless, combined entity and evaluate Type I error and power characteristics. The adaptive designs we consider typically have improved power over the common, separate trial approach.

We replicated and extended Shoda, Mischel, and Peake’s (1990) famous marshmallow study, which showed strong bivariate correlations between a child’s ability to delay gratification just before entering school and both adolescent achievement and socioemotional behaviors. Concentrating on children whose mothers had not completed college, we found that an additional minute waited at age 4 predicted a gain of approximately one tenth of a standard deviation in achievement at age 15. But this bivariate correlation was only half the size of those reported in the original studies and was reduced by two thirds in the presence of controls for family background, early cognitive ability, and the home environment. Most of the variation in adolescent achievement came from being able to wait at least 20 s. Associations between delay time and measures of behavioral outcomes at age 15 were much smaller and rarely statistically significant.

Background Non-pharmaceutical interventions (NPIs) are used to reduce transmission of SARS coronavirus 2 (SARS-CoV-2) that causes coronavirus disease 2019 (COVID-19). However, empirical evidence of the effectiveness of specific NPIs has been inconsistent. We assessed the effectiveness of NPIs around internal containment and closure, international travel restrictions, economic measures, and health system actions on SARS-CoV-2 transmission in 130 countries and territories. Methods We used panel (longitudinal) regression to estimate the effectiveness of 13 categories of NPIs in reducing SARS-CoV-2 transmission using data from January to June 2020. First, we examined the temporal association between NPIs using hierarchical cluster analyses. We then regressed the time-varying reproduction number ( R t ) of COVID-19 against different NPIs. We examined different model specifications to account for the temporal lag between NPIs and changes in R t , levels of NPI intensity, time-varying changes in NPI effect, and variable selection criteria. Results were interpreted taking into account both the range of model specifications and temporal clustering of NPIs. Results There was strong evidence for an association between two NPIs (school closure, internal movement restrictions) and reduced R t . Another three NPIs (workplace closure, income support, and debt/contract relief) had strong evidence of effectiveness when ignoring their level of intensity, while two NPIs (public events cancellation, restriction on gatherings) had strong evidence of their effectiveness only when evaluating their implementation at maximum capacity (e.g. restrictions on 1000+ people gathering were not effective, restrictions on < 10 people gathering were). Evidence about the effectiveness of the remaining NPIs (stay-at-home requirements, public information campaigns, public transport closure, international travel controls, testing, contact tracing) was inconsistent and inconclusive. We found temporal clustering between many of the NPIs. Effect sizes varied depending on whether or not we included data after peak NPI intensity. Conclusion Understanding the impact that specific NPIs have had on SARS-CoV-2 transmission is complicated by temporal clustering, time-dependent variation in effects, and differences in NPI intensity. However, the effectiveness of school closure and internal movement restrictions appears robust across different model specifications, with some evidence that other NPIs may also be effective under particular conditions. This provides empirical evidence for the potential effectiveness of many, although not all, actions policy-makers are taking to respond to the COVID-19 pandemic.

In 2013, Internet gaming disorder (IGD) was incorporated in the current version of the DSM-5. IGD refers to a problematic use of video games. Longitudinal studies on the etiology of IGD are lacking. Furthermore, it is currently unclear to which extent associated psychopathological problems are causes or consequences of IGD. In the present survey, longitudinal associations between IGD and adolescent and parental mental health were investigated for the first time, as well as the temporal stability of IGD. In a cross-lagged panel design study, family dyads (adolescent with a parent each) were examined in 2016 (t1) and again 1 year later (2017, t2). Overall, 1095 family dyads were assessed at t1 and 985 dyads were re-assessed at t2 with standardized measures of IGD and several aspects of adolescent and parental mental health. Data were analyzed with structural equation modeling (SEM). Male gender, a higher level of hyperactivity/inattention, self-esteem problems and IGD at t1 were predictors of IGD at t2. IGD at t1 was a predictor for adolescent emotional distress at t2. Overall, 357 out of the 985 adolescents received a diagnosis of IGD at t1 or t2: 142 (14.4%) at t1 and t2, 100 (10.2%) only at t1, and 115 (11.7%) only at t2. Hyperactivity/inattention and self-esteem problems seem to be important for the development of IGD. We found first empirical evidence that IGD could prospectively contribute to a deterioration of adolescent mental health. Only a subgroup of affected adolescents showed IGD consistently over 1 year.

Some cross-sectional studies suggest reduced cerebellar volumes with aging, but there have been few longitudinal studies of age changes in cerebellar subregions in cognitively healthy older adults. In this work, 2,023 magnetic resonance (MR) images of 822 cognitively normal participants from the Baltimore Longitudinal Study of Aging (BLSA) were analyzed. Participants ranged in age from 50 to 95 years (mean 70.7 years) at the baseline assessment. Follow-up intervals were 1–9 years (mean 3.7 years) for participants with two or more visits. We used a recently developed cerebellum parcellation algorithm based on convolutional neural networks to divide the cerebellum into 28 subregions. Linear mixed effects models were applied to the volume of each cerebellar subregion to investigate cross-sectional and longitudinal age effects, as well as effects of sex and their interactions, after adjusting for intracranial volume. Our findings suggest spatially varying atrophy patterns across the cerebellum with respect to age and sex both cross-sectionally and longitudinally.

Longitudinal sampling provides valuable information about temporal trends and subject/population heterogeneity. We describe q2-longitudinal, a software plugin for longitudinal analysis of microbiome data sets in QIIME 2. The availability of longitudinal statistics and visualizations in the QIIME 2 framework will make the analysis of longitudinal data more accessible to microbiome researchers. Studies of host-associated and environmental microbiomes often incorporate longitudinal sampling or paired samples in their experimental design. Longitudinal sampling provides valuable information about temporal trends and subject/population heterogeneity, offering advantages over cross-sectional and pre-post study designs. To support the needs of microbiome researchers performing longitudinal studies, we developed q2-longitudinal, a software plugin for the QIIME 2 microbiome analysis platform ( https://qiime2.org ). The q2-longitudinal plugin incorporates multiple methods for analysis of longitudinal and paired-sample data, including interactive plotting, linear mixed-effects models, paired differences and distances, microbial interdependence testing, first differencing, longitudinal feature selection, and volatility analyses. The q2-longitudinal package ( https://github.com/qiime2/q2-longitudinal ) is open-source software released under a 3-clause Berkeley Software Distribution (BSD) license and is freely available, including for commercial use. IMPORTANCE Longitudinal sampling provides valuable information about temporal trends and subject/population heterogeneity. We describe q2-longitudinal, a software plugin for longitudinal analysis of microbiome data sets in QIIME 2. The availability of longitudinal statistics and visualizations in the QIIME 2 framework will make the analysis of longitudinal data more accessible to microbiome researchers.