Methylation profiles at birth linked to early childhood obesity.

Childhood obesity represents a significant global health concern and identifying its risk factors is crucial for developing intervention programs. Many "omics" factors associated with the risk of developing obesity have been identified, including genomic, microbiomic, and epigenomic factors. Here, using a sample of 48 infants, we investigated how the methylation profiles in cord blood and placenta at birth were associated with weight outcomes (specifically, conditional weight gain, body mass index, and weight-for-length ratio) at age six months. We characterized genome-wide DNA methylation profiles using the Illumina Infinium MethylationEpic chip, and incorporated information on child and maternal health, and various environmental factors into the analysis. We used regression analysis to identify genes with methylation profiles most predictive of infant weight outcomes, finding a total of 23 relevant genes in cord blood and 10 in placenta. Notably, in cord blood, the methylation profiles of three genes (PLIN4, UBE2F, and PPP1R16B) were associated with all three weight outcomes, which are also associated with weight outcomes in an independent cohort suggesting a strong relationship with weight trajectories in the first six months after birth. Additionally, we developed a Methylation Risk Score (MRS) that could be used to identify children most at risk for developing childhood obesity. While many of the genes identified by our analysis have been associated with weight-related traits (e.g., glucose metabolism, BMI, or hip-to-waist ratio) in previous genome-wide association and variant studies, our analysis implicated several others, whose involvement in the obesity phenotype should be evaluated in future functional investigations.

Methylation profiles at birth linked to early childhood obesity.

Childhood obesity represents a significant global health concern and identifying risk factors is crucial for developing intervention programs. Many 'omics' factors associated with the risk of developing obesity have been identified, including genomic, microbiomic, and epigenomic factors. Here, using a sample of 48 infants, we investigated how the methylation profiles in cord blood and placenta at birth were associated with weight outcomes (specifically, conditional weight gain, body mass index, and weight-for-length ratio) at age six months. We characterized genome-wide DNA methylation profiles using the Illumina Infinium MethylationEpic chip, and incorporated information on child and maternal health, and various environmental factors into the analysis. We used regression analysis to identify genes with methylation profiles most predictive of infant weight outcomes, finding a total of 23 relevant genes in cord blood and 10 in placenta. Notably, in cord blood, the methylation profiles of three genes (PLIN4, UBE2F, and PPP1R16B) were associated with all three weight outcomes, which are also associated with weight outcomes in an independent cohort suggesting a strong relationship with weight trajectories in the first six months after birth. Additionally, we developed a Methylation Risk Score (MRS) that could be used to identify children most at risk for developing childhood obesity. While many of the genes identified by our analysis have been associated with weight-related traits (e.g., glucose metabolism, BMI, or hip-to-waist ratio) in previous genome-wide association and variant studies, our analysis implicated several others, whose involvement in the obesity phenotype should be evaluated in future functional investigations.

Constructing a polygenic risk score for childhood obesity using functional data analysis.

Obesity is a highly heritable condition that affects increasing numbers of adults and, concerningly, of children. However, only a small fraction of its heritability has been attributed to specific genetic variants. These variants are traditionally ascertained from genome-wide association studies (GWAS), which utilize samples with tens or hundreds of thousands of individuals for whom a single summary measurement (e.g., BMI) is collected. An alternative approach is to focus on a smaller, more deeply characterized sample in conjunction with advanced statistical models that leverage longitudinal phenotypes. Novel functional data analysis (FDA) techniques are used to capitalize on longitudinal growth information from a cohort of children between birth and three years of age. In an ultra-high dimensional setting, hundreds of thousands of single nucleotide polymorphisms (SNPs) are screened, and selected SNPs are used to construct two polygenic risk scores (PRS) for childhood obesity using a weighting approach that incorporates the dynamic and joint nature of SNP effects. These scores are significantly higher in children with (vs. without) rapid infant weight gain-a predictor of obesity later in life. Using two independent cohorts, it is shown that the genetic variants identified in very young children are also informative in older children and in adults, consistent with early childhood obesity being predictive of obesity later in life. In contrast, PRSs based on SNPs identified by adult obesity GWAS are not predictive of weight gain in the cohort of young children. This provides an example of a successful application of FDA to GWAS. This application is complemented with simulations establishing that a deeply characterized sample can be just as, if not more, effective than a comparable study with a cross-sectional response. Overall, it is demonstrated that a deep, statistically sophisticated characterization of a longitudinal phenotype can provide increased statistical power to studies with relatively small sample sizes; and shows how FDA approaches can be used as an alternative to the traditional GWAS.

The Use of Brexpiprazole Combined With a Stimulant in Adults With Treatment-Resistant Attention-Deficit/Hyperactivity Disorder.

BACKGROUND: This is the first controlled pharmacologic study in either adults or children with uncomplicated, treatment-resistant attention-deficit/hyperactivity disorder (ADHD). This study augmented stimulant therapy with the atypical antipsychotic brexpiprazole. The Food and Drug Administration preapproved primary outcome measure (Conners' Adult ADHD Rating Scale [CAARS]) showed no drug-placebo differences. Often studies showing no efficacy on the prestudy, defined primary outcome variable go unpublished. While this is decried, publishing studies with equivocal results remains rare. This reanalysis highlights trends in secondary measures having implications for treatment and research regarding treatment resistant ADHD. METHODS: Initially, 559 stimulant-naive and 174 prior stimulant nonresponders received methylphenidate osmotic-release oral system, dexmethylphenidate hydrochloride, lisdexamfetamine, or mixed amphetamine salts. After 5 weeks, 168 stimulant-naive patients and 68 prior stimulant nonresponders who failed treatment were randomized to brexpiprazole or placebo in a 2:1 ratio while the remaining were on the stimulant. Outcome was measured with the CAARS, Montgomery-Asberg Depression Rating Scale, Beck Depression Inventory, Clinical Global Impression, and the Wender-Reimherr Adult Attention Deficit Disorder Scale (WRAADDS). The WRAADDS contains 2 factors: attention and emotional dysregulation. RESULTS: Stimulant-naive patients showed no improvement with adjunctive brexpiprazole. Prior stimulant nonresponders displayed no brexpiprazole effect on the CAARS, Montgomery-Asberg Depression Rating Scale, or Beck Depression Inventory. In contrast, the WRAADDS detected a trend in treatment benefit, primarily through emotional dysregulation symptoms. Adverse effects on brexpiprazole and placebo were equivalent. CONCLUSIONS: Brexpiprazole might be effective in ADHD adults who are nonresponders to 2 or more stimulants. Future trials in treatment-resistant ADHD should use a 1:1 randomization and use a measure of ADHD symptoms that includes emotional dysregulation.

Metabolomic profiling of stool of two-year old children from the INSIGHT study reveals links between butyrate and child weight outcomes.

BACKGROUND: Metabolomic analysis is commonly used to understand the biological underpinning of diseases such as obesity. However, our knowledge of gut metabolites related to weight outcomes in young children is currently limited. OBJECTIVES: To (1) explore the relationships between metabolites and child weight outcomes, (2) determine the potential effect of covariates (e.g., child's diet, maternal health/habits during pregnancy, etc.) in the relationship between metabolites and child weight outcomes, and (3) explore the relationship between selected gut metabolites and gut microbiota abundance. METHODS: Using 1 H-NMR, we quantified 30 metabolites from stool samples of 170 two-year-old children. To identify metabolites and covariates associated with children's weight outcomes (BMI [weight/height2 ], BMI z-score [BMI adjusted for age and sex], and growth index [weight/height]), we analysed the 1 H-NMR data, along with 20 covariates recorded on children and mothers, using LASSO and best subset selection regression techniques. Previously characterized microbiota community information from the same stool samples was used to determine associations between selected gut metabolites and gut microbiota. RESULTS: At age 2 years, stool butyrate concentration had a significant positive association with child BMI (p-value = 3.58 × 10-4 ), BMI z-score (p-value = 3.47 × 10-4 ), and growth index (p-value = 7.73 × 10-4 ). Covariates such as maternal smoking during pregnancy are important to consider. Butyrate concentration was positively associated with the abundance of the bacterial genus Faecalibacterium (p-value = 9.61 × 10-3 ). CONCLUSIONS: Stool butyrate concentration is positively associated with increased child weight outcomes and should be investigated further as a factor affecting childhood obesity.

Psychometric data and versions of the Wender Utah Rating Scale including the WURS-25 & WURS-45.

Our associated paper presented a psychometric evaluation of the Wender Utah Rating Scale (WURS) and its abbreviated version, the WURS-25. Instead of actual factors scores, we employed "item averages" calculated by the average score of each item comprising that factor. We did not present a factor analysis of the WURS-25. Herein we identify items of the full WURS that are redundant or not part of any of the scale's five factors. Removing these items produced a shortened version, the WURS-45. We performed a logistic regression using actual factor loadings as well as factors based on item averages, and compared major depressive disorder (MDD) to generalized anxiety disorder (GAD) patients in the same analysis. We performed exploratory factor analysis with the WURS-45 items. We then performed logistic regressions and Receiver Operating Characteristics (ROC) analyses with the WURS-45 and WURS-25 factors. No increase in specificity or sensitivity arose when actual factors scores were used as opposed to factor scores from item averages. MDD and GAD ROC curves were very similar, supporting combining MDD with GAD patients into a single group. WURS-45 factors paralleled those derived from the full WURS. ROC curves, logistic regression and confusion tables showed the WURS-45 preserved the excellent diagnostic separation produced by the full WURS. Similar analyses showed WURS-25 scoring using its three factors improved its diagnostic utility. The WURS-45 has reduced redundancy with minimal loss in discriminatory power. Analysis of the WURS-25 using factor scores boosts its performance. Both versions of the scale provide clinical information describing childhood ADHD and are useful in separating adult patients with ADHD from those with MDD or GAD.

Wender Utah Rating Scale: Psychometrics, clinical utility and implications regarding the elements of ADHD.

The Wender Utah Rating Scale (WURS) is a self-report instrument completed by adults assessing a range of childhood symptoms and behaviors consistent with ADHD persisting into adulthood. Many items reflect emotional dysregulation. Although over 30 publications have examined its psychometric properties, reliance on non-clinical samples has limited conclusions from these reports, as have sub-optimal statistical approaches in most previous publications. None compared the full WURS to the abbreviated WURS-25. We evaluated both versions with adults presenting for treatment: 137 with ADHD and 230 with GAD or MDD, along with 120 normal controls. Factor analysis was performed on the full WURS using the clinical cohorts. The WURS versions were compared using ANOVA, logistic regression, ROC and confusion matrices. Consistent with two previous reports, the full WURS generated five factors: Disruptive mood & behavior, ADHD, Anxiety/dysphoria, Social and Academic. The ADHD factor correlated r > 0.8 with the Disruptive mood/behavior and the Academic factor. ADHD patients scored higher than GAD/MDD subjects (p < .001) on the Disruptive mood & behavior, ADHD, and Academic factors. The WURS-25 produced good separation of ADHD subjects from normal controls with ROC (AUC = 0.974) and logistic regression (Sensitivity = 91%, Specificity = 92%). Conversely, the full WURS better separated ADHD subjects from psychiatric controls with both ROC (AUC = 0.995) and logistic regression (Sensitivity = 84%, Specificity = 94%). Use of the full WURS with its five factors proved more successful at distinguishing ADHD from MDD and GAD than did the WURS-25. Its factors identify symptoms, including those of emotional dysregulation, critical to understanding ADHD.

FEATURE SELECTION FOR GENERALIZED VARYING COEFFICIENT MIXED-EFFECT MODELS WITH APPLICATION TO OBESITY GWAS.

Motivated by an empirical analysis of data from a genome-wide association study on obesity, measured by the body mass index (BMI), we propose a two-step gene-detection procedure for generalized varying coefficient mixed-effects models with ultrahigh dimensional covariates. The proposed procedure selects significant single nucleotide polymorphisms (SNPs) impacting the mean BMI trend, some of which have already been biologically proven to be "fat genes." The method also discovers SNPs that significantly influence the age-dependent variability of BMI. The proposed procedure takes into account individual variations of genetic effects and can also be directly applied to longitudinal data with continuous, binary or count responses. We employ Monte Carlo simulation studies to assess the performance of the proposed method and further carry out causal inference for the selected SNPs.

Types of Adult Attention-Deficit/Hyperactivity Disorder: A Replication Analysis.

OBJECTIVE: Research supports the importance of emotional symptoms in adults with attention-deficit/hyperactivity disorder (ADHD), which are not reflected in the DSM-5 or ICD-10 criteria. The Wender-Reimherr Adult Attention Deficit Disorder Scale (WRAADDS) assesses these symptoms, plus inattention, hyperactivity, and impulsivity. This scale allowed us to divide adult ADHD into 2 subtypes in a 2015 publication: ADHD inattentive presentation and ADHD emotional dysregulation presentation. The present study refines this observation using a larger, more diverse sample. METHODS: Eight double-blind adult ADHD clinical trials (encompassing 1,490 subjects) were selected because they included assessment with the WRAADDS; a second, alternative ADHD measure; and the Clinical Global Impressions-Severity of Illness scale (CGI-S). These data were subjected to confirmatory factor analyses, and ADHD presentations were compared, including treatment response. RESULTS: The original factor structure fit poorly with these new data. However, an alternative 2-factor solution fit both the original and the new subjects. ADHD inattentive presentation (n = 774) was defined by the inattention factor, and ADHD emotional dysregulation presentation (n = 620) was defined by additional elevation of the emotional dysregulation factor. The proportion of ADHD emotional dysregulation presentation ranged from 25% to 73% across the 8 studies. The emotional dysregulation presentation was associated with both a greater severity as measured by the CGI-S (P < .001) and more manifestations of childhood ADHD as measured by the Wender Utah Rating Scale (P < .001). CONCLUSIONS: Factor analytic results supported the validity of 2 adult ADHD presentations based on levels of emotional dysregulation. This system offers a more clinically relevant approach to the diagnosis of ADHD in adults than does the DSM system.

Scalar-on-function regression for predicting distal outcomes from intensively gathered longitudinal data: Interpretability for applied scientists.

Researchers are sometimes interested in predicting a distal or external outcome (such as smoking cessation at follow-up) from the trajectory of an intensively recorded longitudinal variable (such as urge to smoke). This can be done in a semiparametric way via scalar-on-function regression. However, the resulting fitted coefficient regression function requires special care for correct interpretation, as it represents the joint relationship of time points to the outcome, rather than a marginal or cross-sectional relationship. We provide practical guidelines, based on experience with scientific applications, for helping practitioners interpret their results and illustrate these ideas using data from a smoking cessation study.

Functional data analysis for computational biology.

SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Child Weight Gain Trajectories Linked To Oral Microbiota Composition.

Gut and oral microbiota perturbations have been observed in obese adults and adolescents; less is known about their influence on weight gain in young children. Here we analyzed the gut and oral microbiota of 226 two-year-olds with 16S rRNA gene sequencing. Weight and length were measured at seven time points and used to identify children with rapid infant weight gain (a strong risk factor for childhood obesity), and to derive growth curves with innovative Functional Data Analysis (FDA) techniques. We showed that growth curves were associated negatively with diversity, and positively with the Firmicutes-to-Bacteroidetes ratio, of the oral microbiota. We also demonstrated an association between the gut microbiota and child growth, even after controlling for the effect of diet on the microbiota. Lastly, we identified several bacterial genera that were associated with child growth patterns. These results suggest that by the age of two, the oral microbiota of children with rapid infant weight gain may have already begun to establish patterns often seen in obese adults. They also suggest that the gut microbiota at age two, while strongly influenced by diet, does not harbor obesity signatures many researchers identified in later life stages.

Coherent synthesis of genomic associations with phenotypes and home environments.

Local adaptation is often studied via (i) multiple common garden experiments comparing performance of genotypes in different environments and (ii) sequencing genotypes from multiple locations and characterizing geographic patterns in allele frequency. Both approaches aim to characterize the same pattern (local adaptation), yet the complementary information from each has not yet been coherently integrated. Here, we develop a genome-wide association model of genotype interactions with continuous environmental gradients (G × E), that is reaction norms. We present an approach to impute relative fitness, allowing us to coherently synthesize evidence from common garden and genome-environment associations. Our approach identifies loci exhibiting environmental clines where alleles are associated with higher fitness in home environments. Simulations show our approach can increase power to detect loci causing local adaptation. In a case study on Arabidopsis thaliana, most identified SNPs exhibited home allele advantage and fitness trade-offs along climate gradients, suggesting selective gradients can maintain allelic clines. SNPs exhibiting G × E associations with fitness were enriched in genic regions, putative partial selective sweeps and associations with an adaptive phenotype (flowering time plasticity). We discuss extensions for situations where only adaptive phenotypes other than fitness are available. Many types of data may point towards the loci underlying G × E and local adaptation; coherent models of diverse data provide a principled basis for synthesis.

FEATURE SCREENING FOR TIME-VARYING COEFFICIENT MODELS WITH ULTRAHIGH DIMENSIONAL LONGITUDINAL DATA.

Motivated by an empirical analysis of the Childhood Asthma Management Project, CAMP, we introduce a new screening procedure for varying coefficient models with ultrahigh dimensional longitudinal predictor variables. The performance of the proposed procedure is investigated via Monte Carlo simulation. Numerical comparisons indicate that it outperforms existing ones substantially, resulting in significant improvements in explained variability and prediction error. Applying these methods to CAMP, we are able to find a number of potentially important genetic mutations related to lung function, several of which exhibit interesting nonlinear patterns around puberty.

You've gotta be lucky: Coverage and the elusive gene-gene interaction.

Genome-wide association studies (GWAS) have led to a large number of single-SNP association findings, but there has been, so far, no investigation resulting in the discovery of a replicable gene-gene interaction. In this paper, we examine some of the possible explanations for the lack of findings, and argue that coverage of causal variation not only has a large effect on the loss in power, but that the effect is larger than in the single-SNP analyses. We show that the product of linkage disequilibrium measures, r², between causal and tested SNPs offers a good approximation to the loss in efficiency as defined by the ratio of sample sizes that lead to similar power. We also demonstrate that, in addition to the huge search space, the loss in power due to coverage when using commercially available platforms makes the search for gene-gene interactions daunting.