TOSCCA: a framework for interpretation and testing of sparse canonical correlations.

Summary: In clinical and biomedical research, multiple high-dimensional datasets are nowadays routinely collected from omics and imaging devices. Multivariate methods, such as Canonical Correlation Analysis (CCA), integrate two (or more) datasets to discover and understand underlying biological mechanisms. For an explorative method like CCA, interpretation is key. We present a sparse CCA method based on soft-thresholding that produces near-orthogonal components, allows for browsing over various sparsity levels, and permutation-based hypothesis testing. Our soft-thresholding approach avoids tuning of a penalty parameter. Such tuning is computationally burdensome and may render unintelligible results. In addition, unlike alternative approaches, our method is less dependent on the initialization. We examined the performance of our approach with simulations and illustrated its use on real cancer genomics data from drug sensitivity screens. Moreover, we compared its performance to Penalized Matrix Analysis (PMA), which is a popular alternative of sparse CCA with a focus on yielding interpretable results. Compared to PMA, our method offers improved interpretability of the results, while not compromising, or even improving, signal discovery. Availability and implementation: The software and simulation framework are available at https://github.com/nuria-sv/toscca.

Adherence to statin treatment in patients with familial hypercholesterolemia: A dynamic prediction model.

BACKGROUND: Statins are the primary therapy in patient with heterozygous familial hypercholesterolemia (HeFH). Non-adherence to statin therapy is associated with increased cardiovascular risk. OBJECTIVE: We constructed a dynamic prediction model to predict statin adherence for an individual HeFH patient for each upcoming statin prescription. METHODS: All patients with HeFH, identified by the Dutch Familial Hypercholesterolemia screening program between 1994 and 2014, were eligible. National pharmacy records dated between 1995 and 2015 were linked. We developed a dynamic prediction model that estimates the probability of statin adherence (defined as proportion of days covered >80%) for an upcoming prescription using a mixed effect logistic regression model. Static and dynamic patient-specific predictors, as well as data on a patient's adherence to past prescriptions were included. The model with the lowest AIC (Akaike Information Criterion) value was selected. RESULTS: We included 1094 patients for whom 21,171 times a statin was prescribed. Based on the model with the lowest AIC, age at HeFH diagnosis, history of cardiovascular event, time since HeFH diagnosis and duration of the next statin prescription contributed to an increased adherence, while adherence decreased with higher untreated LDL-C levels and higher intensity of statin therapy. The dynamic prediction model showed an area under the curve of 0.63 at HeFH diagnosis, which increased to 0.85 after six years of treatment. CONCLUSION: This dynamic prediction model enables clinicians to identify HeFH patients at risk for non-adherence during statin treatment. These patients can be offered timely interventions to improve adherence and further reduce cardiovascular risk.

BAHAMA: A Bayesian Hierarchical Model for the Detection of MedDRA®-Coded Adverse Events in Randomized Controlled Trials.

INTRODUCTION: Patients participating in randomized controlled trials (RCTs) are susceptible to a wide range of different adverse events (AE) during the RCT. MedDRA® is a hierarchical standardization terminology to structure the AEs reported in an RCT. The lowest level in the MedDRA hierarchy is a single medical event, and every higher level is the aggregation of the lower levels. METHOD: We propose a multi-stage Bayesian hierarchical Poisson model for estimating MedDRA-coded AE rate ratios (RRs). To deal with rare AEs, we introduce data aggregation at a higher level within the MedDRA structure and based on thresholds on incidence and MedDRA structure. RESULTS: With simulations, we showed the effects of this data aggregation process and the method's performance. Furthermore, an application to a real example is provided and compared with other methods. CONCLUSION: We showed the benefit of using the full MedDRA structure and using aggregated data. The proposed model, as well as the pre-processing, is implemented in an R-package: BAHAMA.

Lipoprotein(a) levels from childhood to adulthood: Data in nearly 3,000 children who visited a pediatric lipid clinic.

BACKGROUND AND AIMS: Elevated lipoprotein(a) [Lp(a)] is an independent risk factor for cardiovascular disease. In clinical practice, Lp(a) is mostly measured only once assuming that it does not change with age nor vary within individuals. This is mainly based on adult data and data on Lp(a) levels during childhood is scarce. Therefore, we evaluated whether Lp(a) levels changed with age and determined the intra-individual variation of Lp(a) in a large cohort of children. METHODS: We collected all Lp(a) measurements of children referred to the pediatric lipid clinic of the Amsterdam UMC between 1989 and 2017. The association between Lp(a) and age, as well as the intra-individual variation of Lp(a), was assessed using mixed models. We stratified for lipid-lowering medication use. RESULTS: In total, we included 2740 children. From the age of 8 years onwards, mean Lp(a) increased with 22% in children that reached adulthood without lipid-lowering medication (n = 2254). In statin-users (n = 418) and children that used ezetimibe additionally (n = 65), Lp(a) increased with 43% and 9%, respectively. The intra-individual variation of Lp(a) was 70%. CONCLUSIONS: Lp(a) levels increase with age and exhibit considerable variation within children referred to a lipid clinic. Measuring Lp(a) only once during childhood might therefore lead to substantial over- or underestimation and possibly result in over- or under treatment in the future. Thus, to more accurately assess the Lp(a) level, we suggest measuring Lp(a) more than once during childhood and to repeat this in adulthood if a patient only has childhood assessment of Lp(a).

Development and Validation of a Dynamically Updated Prediction Model for Attrition From Marine Recruit Training.

ABSTRACT: Dijksma, I, Hof, MHP, Lucas, C, and Stuiver, MM. Development and validation of a dynamically updated prediction model for attrition from Marine recruit training. J Strength Cond Res 36(9): 2523-2529, 2022-Whether fresh Marine recruits thrive and complete military training programs, or fail to complete, is dependent on numerous interwoven variables. This study aimed to derive a prediction model for dynamically updated estimation of conditional dropout probabilities for Marine recruit training. We undertook a landmarking analysis in a Cox proportional hazard model using longitudinal data from 744 recruits from existing databases of the Marine Training Center in the Netherlands. The model provides personalized estimates of dropout from Marine recruit training given a recruit's baseline characteristics and time-varying mental and physical health status, using 21 predictors. We defined nonoverlapping landmarks at each week and developed a supermodel by stacking the landmark data sets. The final supermodel contained all but one a priori selected baseline variables and time-varying health status to predict the hazard of attrition from Marine recruit training for each landmark as comprehensive as possible. The discriminative ability (c-index) of the prediction model was 0.78, 0.75, and 0.73 in week one, week 4 and week 12, respectively. We used 10-fold cross-validation to train and evaluate the model. We conclude that this prediction model may help to identify recruits at an increased risk of attrition from training throughout the Marine recruit training and warrants further validation and updates for other military settings.

Improved selection of participants in genetic longevity studies: family scores revisited.

BACKGROUND: Although human longevity tends to cluster within families, genetic studies on longevity have had limited success in identifying longevity loci. One of the main causes of this limited success is the selection of participants. Studies generally include sporadically long-lived individuals, i.e. individuals with the longevity phenotype but without a genetic predisposition for longevity. The inclusion of these individuals causes phenotype heterogeneity which results in power reduction and bias. A way to avoid sporadically long-lived individuals and reduce sample heterogeneity is to include family history of longevity as selection criterion using a longevity family score. A main challenge when developing family scores are the large differences in family size, because of real differences in sibship sizes or because of missing data. METHODS: We discussed the statistical properties of two existing longevity family scores: the Family Longevity Selection Score (FLoSS) and the Longevity Relatives Count (LRC) score and we evaluated their performance dealing with differential family size. We proposed a new longevity family score, the mLRC score, an extension of the LRC based on random effects modeling, which is robust for family size and missing values. The performance of the new mLRC as selection tool was evaluated in an intensive simulation study and illustrated in a large real dataset, the Historical Sample of the Netherlands (HSN). RESULTS: Empirical scores such as the FLOSS and LRC cannot properly deal with differential family size and missing data. Our simulation study showed that mLRC is not affected by family size and provides more accurate selections of long-lived families. The analysis of 1105 sibships of the Historical Sample of the Netherlands showed that the selection of long-lived individuals based on the mLRC score predicts excess survival in the validation set better than the selection based on the LRC score . CONCLUSIONS: Model-based score systems such as the mLRC score help to reduce heterogeneity in the selection of long-lived families. The power of future studies into the genetics of longevity can likely be improved and their bias reduced, by selecting long-lived cases using the mLRC.

Feeding patterns and BMI trajectories during infancy: a multi-ethnic, prospective birth cohort.

BACKGROUND: Milk feeding type (exclusive breastfeeding [EBF], formula feeding or mixed feeding) and timing of complementary feeding (CF) have been associated with infant growth. However, studies evaluating their combined role, and the role of ethnicity, are scarce. We examined associations of feeding patterns (milk feeding type combined with timing of CF) with infant body mass index (BMI) trajectories and potential ethnic-specific associations. METHODS: Infant feeding and BMI data during the 1st year of life from 3524 children (Dutch n = 2880, Moroccan n = 404 and Turkish n = 240) from the Amsterdam Born Children and their Development (ABCD) cohort were used. Six feeding patterns were defined: EBF/earlyCF, EBF/lateCF (reference), formula/earlyCF, formula/lateCF, mixed/earlyCF and mixed/lateCF. A covariate adjusted latent class mixed model was applied to simultaneously model BMI trajectories and associations with feeding patterns. Potential ethnic differences in the associations were studied in a separate model where interactions between ethnicity and feeding patterns were included. RESULTS: Four distinct BMI trajectories (low, mid-low, mid-high and high) were identified. Feeding pattern of formula/earlyCF was associated with lower odds for low (OR: 0.43; 95% CI: 0.25, 0.76) or mid-high (0.28; 0.16, 0.51) (ref: high) trajectory compared with EBF/lateCF pattern (ref). An ethnic-specific model revealed that among Dutch infants, formula/earlyCF pattern was associated with lower odds for low trajectory (0.46; 0.24, 0.87), whereas among Turkish/Moroccan infants almost all feeding patterns were associated with lower odds for the low trajectory (ref: high). CONCLUSION: Infant feeding patterns are associated with early BMI trajectories with specific ethnic differences. Future studies should take the role of ethnicity into account in the associations between infant feeding and growth.

Long-term effectiveness and safety of treatment with dupilumab in patients with atopic dermatitis: Results of the TREAT NL (TREatment of ATopic eczema, the Netherlands) registry.

BACKGROUND: Evidence on long-term dupilumab treatment for atopic dermatitis in daily practice is lacking. OBJECTIVE: To investigate patient characteristics, treatment aspects, effectiveness, and safety of up to 84 weeks of dupilumab treatment. METHODS: An observational prospective cohort study was conducted of patients with atopic dermatitis starting dupilumab in routine clinical care. RESULTS: Of the 221 included patients, 103 used systemic therapy at baseline. At 84 weeks, we found a change of -15.2 (SE, 1.7) for the Eczema Area and Severity Index, -16.9 (SE, 1.4) for the Patient-Oriented Eczema Measure, and -17.2 (SE, 1.6) for the Dermatology Life Quality Index. We found a trend for improvement over time for the Investigator Global Assessment and Numerical Rating Scale for pruritus. Severe (n = 79) including serious (n = 11) adverse events were observed in 69 patients. Eye complaints were most frequently reported (n = 46). Twenty-one patients adjusted the regular dosing schedule, and 14 patients discontinued treatment, mainly due to ineffectiveness (n = 7). LIMITATIONS: Only adverse events of severe and serious nature were registered for feasibility reasons. CONCLUSION: Daily practice dupilumab treatment of up to 84 weeks is generally well-tolerated, apart from the reporting of eye complaints. It can be considered a long-term effective treatment for atopic dermatitis in combination with topical and initial concomitant systemic treatment, showing a sustained improvement of signs, symptoms, and quality of life.

Contributions of amino acid, acylcarnitine and sphingolipid profiles to type 2 diabetes risk among South-Asian Surinamese and Dutch adults.

INTRODUCTION: People of South Asian origin are at high risk of type 2 diabetes (T2D), but the underpinning mechanisms are not fully understood. We determined ethnic differences in acylcarnitine, amino acid and sphingolipid concentrations and determined the associations with T2D. RESEARCH DESIGN AND METHODS: Associations between these metabolites and incident T2D among Dutch and South-Asian Surinamese were determined in participants from the Healthy Life in an Urban Setting (HELIUS) study (Amsterdam, the Netherlands) using Prentice-weighted Cox regression. The HELIUS study includes 95 incident T2D cases and a representative subcohort of 700 people from a cohort of 5977 participants with a mean follow-up of 4 years. RESULTS: Concentrations of acylcarnitines were comparable between both ethnic groups. Amino acid and lactosylceramide concentrations were higher among South-Asian Surinamese than Dutch (eg, isoleucine 65.7 (SD 16.3) vs 60.7 (SD 15.6) µmol/L). Ceramide concentrations were lower among South-Asian Surinamese than Dutch (eg, Cer d18:1 8.48 (SD 2.04) vs 9.08 (SD 2.29) µmol/L). Metabolic dysregulation preceded T2D without evidence for a multiplicative interaction by ethnicity. Most amino acids and (dihydro)ceramides were associated with increased risk (eg, Cer d18:1 HR 2.38, 95% CI 1.81 to 3.12) while acylcarnitines, glycine, glutamine and lactosylceramides were associated with decreased risk for T2D (eg, LacCer d18:2 HR 0.56, 95% CI 0.42 to 0.77). CONCLUSIONS: Overall, these data suggest that the disturbances underlying amino acid and sphingolipid metabolism may be predictive of T2D risk in populations of both South Asian and European background. These observations may be used as starting point to unravel the underlying metabolic disturbances.

Growth patterns from birth to overweight at age 5-6 years of children with various backgrounds in socioeconomic status and country of origin: the ABCD study.

INTRODUCTION: Children from minority groups are at increased risk of overweight. This study compared BMI growth patterns from birth onwards of boys and girls with overweight at 5-6 years, according to socioeconomic status (SES) and country of origin, in order to gain more insight into the critical periods of growth to overweight. METHODS: A total of 3714 singletons of the multi-ethnic ABCD study were included. Within children with overweight at age 5-6 years (N = 487, prevalence boys: 11.6%, girls: 14.6%), BMI growth patterns from birth onwards (12.8 serial measurements; SD = 3.1) were compared between children from European (69.4%) and non-European mothers (30.6%), and between children from low (20.8%), mid (37.0%) or high SES (42.2%), based on maternal educational level. RESULTS: BMI growth to overweight did not differ between children of European or non-European mothers, but it did differ according to maternal SES. Children with overweight in the low and mid SES group had a lower BMI in the first 2 years of life, an earlier adiposity rebound and increased in BMI more rapidly after age 2, resulting in a higher BMI at age 7 years compared to children with overweight in the high SES group [∆BMI (kg/m2 ) between high and low SES: boys 1.43(95%CI:0.16;3.01) and girls 1.91(0.55;3.27)]. CONCLUSION: Children with overweight from low SES have an early adiposity rebound and accelerated growth to a higher BMI at age 5-6 years compared to children with overweight from the high SES group. These results imply that timing of critical periods for overweight development is earlier in children with a low socioeconomic background as compared to other children.

Multiset sparse partial least squares path modeling for high dimensional omics data analysis.

BACKGROUND: Recent technological developments have enabled the measurement of a plethora of biomolecular data from various omics domains, and research is ongoing on statistical methods to leverage these omics data to better model and understand biological pathways and genetic architectures of complex phenotypes. Current reviews report that the simultaneous analysis of multiple (i.e. three or more) high dimensional omics data sources is still challenging and suitable statistical methods are unavailable. Often mentioned challenges are the lack of accounting for the hierarchical structure between omics domains and the difficulty of interpretation of genomewide results. This study is motivated to address these challenges. We propose multiset sparse Partial Least Squares path modeling (msPLS), a generalized penalized form of Partial Least Squares path modeling, for the simultaneous modeling of biological pathways across multiple omics domains. msPLS simultaneously models the effect of multiple molecular markers, from multiple omics domains, on the variation of multiple phenotypic variables, while accounting for the relationships between data sources, and provides sparse results. The sparsity in the model helps to provide interpretable results from analyses of hundreds of thousands of biomolecular variables. RESULTS: With simulation studies, we quantified the ability of msPLS to discover associated variables among high dimensional data sources. Furthermore, we analysed high dimensional omics datasets to explore biological pathways associated with Marfan syndrome and with Chronic Lymphocytic Leukaemia. Additionally, we compared the results of msPLS to the results of Multi-Omics Factor Analysis (MOFA), which is an alternative method to analyse this type of data. CONCLUSIONS: msPLS is an multiset multivariate method for the integrative analysis of multiple high dimensional omics data sources. It accounts for the relationship between multiple high dimensional data sources while it provides interpretable results through its sparse solutions. The biomarkers found by msPLS in the omics datasets can be interpreted in terms of biological pathways associated with the pathophysiology of Marfan syndrome and of Chronic Lymphocytic Leukaemia. Additionally, msPLS outperforms MOFA in terms of variation explained in the chronic lymphocytic leukaemia dataset while it identifies the two most important clinical markers for Chronic Lymphocytic Leukaemia AVAILABILITY: http://uva.csala.me/mspls.https://github.com/acsala/2018_msPLS.

The high risk for type 2 diabetes among ethnic minority populations is not explained by low-grade inflammation.

Our aim was to identify whether low-grade inflammation, reflected by C-reactive protein (CRP), explains the higher risk for incident type 2 diabetes (T2D) among ethnic minorities. We included 837 Dutch, 712 South-Asian Surinamese, 797 African Surinamese, 804 Ghanaian, 817 Turkish and 778 Moroccan origin participants of the HELIUS study (Amsterdam, the Netherlands). We used multiple linear regression to assess ethnic differences in CRP levels. We determined the association of CRP with T2D and the modifying effect of ethnicity by cox regression, and compared hazard ratios for the association between ethnicity and T2D before and after adjustment for CRP. CRP levels were higher in ethnic minority groups than in Dutch origin participants. CRP was associated with a higher T2D incidence, similarly across ethnic groups (overall HR per SD 1.38 [95% CI 1.14; 1.68]). However, the association was attenuated and no longer statistically significant after adjustment for adiposity measures (HR 1.11 [95% CI 0.90; 1.37]). CRP accounted for a very small part of the ethnic differences in T2D, but only in models unadjusted for adiposity. Low-grade inflammation does not substantially contribute to the higher risk of T2D among ethnic minority populations compared to the Dutch.

20-Year Follow-up of Statins in Children with Familial Hypercholesterolemia.

BACKGROUND: Familial hypercholesterolemia is characterized by severely elevated low-density lipoprotein (LDL) cholesterol levels and premature cardiovascular disease. The short-term efficacy of statin therapy in children is well established, but longer follow-up studies evaluating changes in the risk of cardiovascular disease are scarce. METHODS: We report a 20-year follow-up study of statin therapy in children. A total of 214 patients with familial hypercholesterolemia (genetically confirmed in 98% of the patients), who were previously participants in a placebo-controlled trial evaluating the 2-year efficacy and safety of pravastatin, were invited for follow-up, together with their 95 unaffected siblings. Participants completed a questionnaire, provided blood samples, and underwent measurements of carotid intima-media thickness. The incidence of cardiovascular disease among the patients with familial hypercholesterolemia was compared with that among their 156 affected parents. RESULTS: Of the original cohort, 184 of 214 patients with familial hypercholesterolemia (86%) and 77 of 95 siblings (81%) were seen in follow-up; among the 214 patients, data on cardiovascular events and on death from cardiovascular causes were available for 203 (95%) and 214 (100%), respectively. The mean LDL cholesterol level in the patients had decreased from 237.3 to 160.7 mg per deciliter (from 6.13 to 4.16 mmol per liter) - a decrease of 32% from the baseline level; treatment goals (LDL cholesterol <100 mg per deciliter [2.59 mmol per liter]) were achieved in 37 patients (20%). Mean progression of carotid intima-media thickness over the entire follow-up period was 0.0056 mm per year in patients with familial hypercholesterolemia and 0.0057 mm per year in siblings (mean difference adjusted for sex, -0.0001 mm per year; 95% confidence interval, -0.0010 to 0.0008). The cumulative incidence of cardiovascular events and of death from cardiovascular causes at 39 years of age was lower among the patients with familial hypercholesterolemia than among their affected parents (1% vs. 26% and 0% vs. 7%, respectively). CONCLUSIONS: In this study, initiation of statin therapy during childhood in patients with familial hypercholesterolemia slowed the progression of carotid intima-media thickness and reduced the risk of cardiovascular disease in adulthood. (Funded by the AMC Foundation.).

From population reference to national standard: new and improved birthweight charts.

BACKGROUND: Antenatal detection of intrauterine growth restriction remains a major obstetrical challenge, with the majority of cases not detected before birth. In these infants with undetected intrauterine growth restriction, the diagnosis must be made after birth. Clinicians use birthweight charts to identify infants as small-for-gestational-age if their birthweights are below a predefined threshold for gestational age. The choice of birthweight chart strongly affects the classification of small-for-gestational-age infants and has an impact on both research findings and clinical practice. Despite extensive literature on pathological risk factors associated with small-for-gestational-age, controversy exists regarding the exclusion of affected infants from a reference population. OBJECTIVE: This study aims to identify pathological risk factors for abnormal fetal growth, to quantify their effects, and to use these findings to calculate prescriptive birthweight charts for the Dutch population. MATERIALS AND METHODS: We performed a retrospective cross-sectional study, using routinely collected data of 2,712,301 infants born in The Netherlands between 2000 and 2014. Risk factors for abnormal fetal growth were identified and categorized in 7 groups: multiple gestation, hypertensive disorders, diabetes, other pre-existing maternal medical conditions, maternal substance (ab)use, medical conditions related to the pregnancy, and congenital malformations. The effects of these risk factors on mean birthweight were assessed using linear regression. Prescriptive birthweight charts were derived from live-born singleton infants, born to ostensibly healthy mothers after uncomplicated pregnancies and spontaneous onset of labor. The Box-Cox-t distribution was used to model birthweight and to calculate sex-specific percentiles. The new charts were compared to various existing birthweight and fetal-weight charts. RESULTS: We excluded 111,621 infants because of missing data on birthweight, gestational age or sex, stillbirth, or a gestational age not between 23 and 42 weeks. Of the 2,599,640 potentially eligible infants, 969,552 (37.3%) had 1 or more risk factors for abnormal fetal growth and were subsequently excluded. Large absolute differences were observed between the mean birthweights of infants with and without these risk factors, with different patterns for term and preterm infants. The final low-risk population consisted of 1,629,776 live-born singleton infants (50.9% male), from which sex-specific percentiles were calculated. Median and 10th percentiles closely approximated fetal-weight charts but consistently exceeded existing birthweight charts. CONCLUSION: Excluding risk factors that cause lower birthweights results in prescriptive birthweight charts that are more akin to fetal-weight charts, enabling proper discrimination between normal and abnormal birthweight. This proof of concept can be applied to other populations.

Multiset sparse redundancy analysis for high-dimensional omics data.

Redundancy Analysis (RDA) is a well-known method used to describe the directional relationship between related data sets. Recently, we proposed sparse Redundancy Analysis (sRDA) for high-dimensional genomic data analysis to find explanatory variables that explain the most variance of the response variables. As more and more biomolecular data become available from different biological levels, such as genotypic and phenotypic data from different omics domains, a natural research direction is to apply an integrated analysis approach in order to explore the underlying biological mechanism of certain phenotypes of the given organism. We show that the multiset sparse Redundancy Analysis (multi-sRDA) framework is a prominent candidate for high-dimensional omics data analysis since it accounts for the directional information transfer between omics sets, and, through its sparse solutions, the interpretability of the result is improved. In this paper, we also describe a software implementation for multi-sRDA, based on the Partial Least Squares Path Modeling algorithm. We test our method through simulation and real omics data analysis with data sets of 364,134 methylation markers, 18,424 gene expression markers, and 47 cytokine markers measured on 37 patients with Marfan syndrome.

Socioeconomic inequalities in psychosocial problems of children: mediating role of maternal depressive symptoms.

Background: Socioeconomically disadvantaged children often have psychosocial problems. This study examined the mediating role of maternal depressive symptoms during pregnancy, infancy and early childhood in the association between maternal education, as indicator of socioeconomic status (SES), and child's psychosocial problems. Methods: Included were 3410 children from the Amsterdam Born Children and their Development (ABCD) study. To assess the child's psychosocial problems at age 5-6 years, mothers and teachers completed the Strengths and Difficulties Questionnaire (SDQ). Maternal depressive symptoms were assessed using the Center for Epidemiologic Studies Depression Scale and the Depressive Anxiety and Stress Scale 21. Mediation analysis was performed to calculate the direct effect of maternal education on SDQ score and indirect effects through maternal depressive symptoms. Results: The mean mother-reported SDQ total score was significantly higher (P < 0.001) for children of low-educated mothers (6.74 ± 4.41) compared with children of highly educated mothers (4.47 ± 3.73). Levels of maternal depressive symptoms were also higher in low-educated mothers during pregnancy, infancy and early childhood. Maternal depressive symptoms explained 27.5% of the association between maternal education and mother-reported SDQ scores and 22.9% for combined mother/teacher SDQ scores. Maternal depressive symptoms during pregnancy had the strongest indirect effect. Conclusion: Maternal depressive symptoms during pregnancy mediate the association between low maternal education and child's psychosocial problems. Early recognition and treatment of maternal depressive symptoms is important to prevent psychosocial problems in children, especially in those with low education.

Does maternal pre-pregnancy overweight or obesity influence offspring's growth patterns from birth up to 7years? The ABCD-study.

BACKGROUND: Maternal pre-pregnancy overweight is known to program offspring for adverse health outcomes later in life. AIMS: To investigate how growth patterns of weight, height and BMI from birth to 7years differ according to maternal pre-pregnancy weight (normal weight, overweight and obesity), with specific attention for sex differences. STUDY DESIGN: Prospective multi-ethnic ABCD-study. SUBJECTS: 3805 mother-child pairs were included. Self-reported maternal pre-pregnancy body mass index was categorized into: normal weight (18.5-25kg/m2; n=3354), overweight (25-30kg/m2; n=711) and obesity (≥30kg/m2; n=241). OUTCOME MEASURES: Population-specific growth patterns were used to compute SD-scores for weight, height and BMI (on average 12.7 (SD=2.6) measurements for each child) for term born boys and girls separately. Mixed effect models were fitted to these SD-scores to determine the effect of pre-pregnancy BMI category on postnatal growth, corrected for maternal characteristics. RESULTS: Compared to children of mothers with normal weight before pregnancy, children of overweight mothers grew faster in weight and BMI (boys and girls) and children of obese mothers grew faster in height (only girls), weight and BMI (boys and girls) during the first years of life. The differences seemed to increase with age and were in general larger in girls. CONCLUSION: Maternal overweight and obesity impact on offspring's weight, height and BMI growth pattern with increasing differences when children age. Effects were in general stronger for girls. These results suggest that a healthy weight before pregnancy may be beneficial for optimal weight, height and BMI growth in the offspring.

Sparse redundancy analysis of high-dimensional genetic and genomic data.

MOTIVATION: Recent technological developments have enabled the possibility of genetic and genomic integrated data analysis approaches, where multiple omics datasets from various biological levels are combined and used to describe (disease) phenotypic variations. The main goal is to explain and ultimately predict phenotypic variations by understanding their genetic basis and the interaction of the associated genetic factors. Therefore, understanding the underlying genetic mechanisms of phenotypic variations is an ever increasing research interest in biomedical sciences. In many situations, we have a set of variables that can be considered to be the outcome variables and a set that can be considered to be explanatory variables. Redundancy analysis (RDA) is an analytic method to deal with this type of directionality. Unfortunately, current implementations of RDA cannot deal optimally with the high dimensionality of omics data (p≫n). The existing theoretical framework, based on Ridge penalization, is suboptimal, since it includes all variables in the analysis. As a solution, we propose to use Elastic Net penalization in an iterative RDA framework to obtain a sparse solution. RESULTS: We proposed sparse redundancy analysis (sRDA) for high dimensional omics data analysis. We conducted simulation studies with our software implementation of sRDA to assess the reliability of sRDA. Both the analysis of simulated data, and the analysis of 485 512 methylation markers and 18,424 gene-expression values measured in a set of 55 patients with Marfan syndrome show that sRDA is able to deal with the usual high dimensionality of omics data. AVAILABILITY AND IMPLEMENTATION: http://uva.csala.me/rda. CONTACT: a.csala@amc.uva.nl. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

The Effect of Interpregnancy Interval on the Recurrence Rate of Spontaneous Preterm Birth: A Retrospective Cohort Study.

Objective We assessed, in women with a previous spontaneous preterm birth, the effect of interpregnancy interval on the subsequent preterm birth rate. Design Retrospective cohort study. Setting A nationwide longitudinal dataset of the the Netherlands Perinatal Registry. Population Women with three sequential singleton pregnancies between 1999 and 2009 and a spontaneous preterm birth <37 weeks in the first pregnancy. Methods We evaluated the impact of interpregnancy interval on the course of the next pregnancies. Antenatal death and/or congenital abnormalities were excluded. Conventional and conditional logistic regression analysis were applied. We adjusted for maternal age, ethnicity, socioeconomic status, artificial reproductive techniques, and year of birth. Main Outcome Measures Outcomes studied were preterm birth <37 weeks, <32 weeks, low birth weight <2500 g, and small for gestational age <10th percentile. Results Among 2,361 women with preterm birth in the first pregnancy, logistic regression analysis indicated a significant effect of a short interpregnancy interval (0-5 mo) on recurrent preterm birth <37 weeks (odds ratio [OR], 2.22; 95% confidence interval [CI], 1.62-3.05), <32 weeks (OR, 2.90; 95% CI, 1.43-5.87), and low birth weight (OR, 2.69; 95% CI, 1.79-4.03). In addition, a long interval (≥60 mo) had a significant effect on preterm birth <37 weeks (OR, 2.19; 95% CI, 1.29-3.74). Conditional logistic regression analysis confirmed the effect of a short interval on the recurrence of preterm birth rate <37 weeks and low birth weight. Conclusion In women with a previous spontaneous preterm birth, a short interpregnancy interval has a strong impact on the risk of preterm birth before 37 weeks and low birth weight in the next pregnancy, irrespective of the type of analysis performed.