Stephen V. Faraone

Bio: Stephen V. Faraone is an academic researcher from State University of New York Upstate Medical University. The author has contributed to research in topics: Attention deficit hyperactivity disorder & Bipolar disorder. The author has an hindex of 188, co-authored 1427 publications receiving 140298 citations. Previous affiliations of Stephen V. Faraone include University of Bergen & National Institute for Health Research.

ADHD is a risk factor for overweight and obesity in children.

Abstract: OBJECTIVE: Although hyperactivity would seem to increase energy expenditure, attention-deficit hyperactivity disorder (ADHD) appears to increase the risk for being overweight. This study examined the body mass index (BMI) in children with ADHD and its relationship with age, gender, ADHD and comorbid symptom severity, inhibitory control, developmental coordination disorder, sleep duration, and methylphenidate use. METHOD: Participants were 372 Dutch children with ADHD combined type aged 5 to 17 years participating in the International Multicenter ADHD Genetics (IMAGE) study. We categorized BMI according to international age- and gender-specific reference values and calculated BMI-standard deviation scores (BMI-SDS). The control population was matched for age, gender, and ethnicity and originated from the same birth cohort as the ADHD group. Inhibitory control was measured by the computerized Stop-signal task. Prevalence differences of underweight, overweight, and obesity between groups were expressed in odds ratios. We used linear regression analyses with gender, age, parent- and teacher-rated ADHD and comorbid scores, inhibitory control, sleep duration, motor coordination, and methylphenidate use to predict BMI-SDS. RESULTS: Boys with ADHD aged 10 to 17 years and girls aged 10 to 12 years were more likely to be overweight than children in the general Dutch population. Younger girls and female teenagers, however, seemed to be at lower risk for being overweight. Higher oppositional behavior and social communication problems related to higher BMI-SDS scores, whereas more stereotyped behaviors related to lower BMI-SDS scores. We found no effects of the other examined associated risk factors on BMI-SDS. CONCLUSIONS: Attention-deficit hyperactivity disorder in boys is a risk factor for overweight. In girls with ADHD, the prevalence of overweight is age dependent and most pronounced in girls aged 10 to 12 years. They have a 4-fold risk of being obese. Higher oppositional and social communication problems pose an increased risk for overweight, whereas sleep duration, motor coordination problems, and methylphenidate use do not.

Co-transmission of conduct problems with attention-deficit= hyperactivity disorder: familial evidence for a distinct disorder

Abstract: Common disorders of childhood and adolescence are attention-deficit/hyperactivity disorder (ADHD), oppositional defiant disorder (ODD) and conduct disorder (CD). For one to two cases in three diagnosed with ADHD the disorders may be comorbid. However, whether comorbid conduct problems (CP) represents a separate disorder or a severe form of ADHD remains controversial. We investigated familial recurrence patterns of the pure or comorbid condition in families with at least two children and one definite case of DSM-IV ADHDct (combined-type) as part of the International Multicentre ADHD Genetics Study (IMAGE). Using case diagnoses (PACS, parental account) and symptom ratings (Parent/Teacher Strengths and Difficulties [SDQ], and Conners Questionnaires [CPTRS]) we studied 1009 cases (241 with ADHDonly and 768 with ADHD + CP), and their 1591 siblings. CP was defined as ≥4 on the SDQ conduct-subscale, and T ≥ 65, on Conners’ oppositional-score. Multinomial logistic regression was used to ascertain recurrence risks of the pure and comorbid conditions in the siblings as predicted by the status of the cases. There was a higher relative risk to develop ADHD + CP for siblings of cases with ADHD + CP (RRR = 4.9; 95%CI: 2.59–9.41); p < 0.001) than with ADHDonly. Rates of ADHDonly in siblings of cases with ADHD + CP were lower but significant (RRR = 2.9; 95%CI: 1.6–5.3, p < 0.001). Children with ADHD + CP scored higher on the Conners ADHDct symptom-scales than those with ADHDonly. Our finding that ADHD + CP can represent a familial distinct subtype possibly with a distinct genetic etiology is consistent with a high risk for cosegregation. Further, ADHD + CP can be a more severe disorder than ADHDonly with symptoms stable from childhood through adolescence. The findings provide partial support for the ICD-10 distinction between hyperkinetic disorder (F90.0) and hyperkinetic conduct disorder (F90.1).

Preliminary Examination of the Reliability and Concurrent Validity of the Attention-Deficit/Hyperactivity Disorder Self-Report Scale v1.1 Symptom Checklist to Rate Symptoms of Attention-Deficit/Hyperactivity Disorder in Adolescents

Abstract: Objective: To validate the attention-deficit/hyperactivity disorder (ADHD) Self-Report Scale (ASRS) v1.1 Symptom Checklist versus the clinician-administered ADHD Rating Scale (ADHD-RS) in adolescents with ADHD. Method: A total of 88 adolescents with ADHD aged 13–17 years participated in the study. The study was completed in one or two visits, 1–9 weeks apart. At each visit, participants completed the ASRS v1.1 Symptom Checklist, after which raters administered the ADHD-RS. Internal consistency of the ASRS v1.1 Symptom Checklist was assessed by Cronbach's alpha (Cronbach's α). Concurrent validity between the scales was assessed using Pearson's correlation coefficients. Item-by-item reliability between the scales was assessed by the Kappa coefficient of agreement. Results: The mean age of participants was 14.9±1.5 SD years. 76.1% (n=67) were male. 73.9% (n=65) were currently receiving medication for ADHD. Internal consistency of ASRS v1.1 Symptom Checklist items was high, with Cronbach's α coeffici...

The hierarchical factor model of ADHD: invariant across age and national groupings?

Abstract: Attention-Deficit/Hyperactivity Disorder (ADHD) is characterized by problems with attention, impulsivity, and hyperactivity. The diagnosis derives from 18 symptoms indexing these behavioural domains [American Psychiatric Association (APA), DSM-IV-TR, 2000]. There is substantial continuity in maintaining a diagnosis of ADHD from childhood to adolescence (Faraone, Biederman, & Mick, 2006); however the phenotypic expression is highly variable within the diagnosed group and across time (Barkley, 2006; Nigg, 2006). Current diagnostic formulations distinguish between symptoms of inattention and those of hyperactivity-impulsivity. Three ADHD subtypes are recognized in the DSM-IV: the predominantly inattentive type, the predominantly hyperactive-impulsive type, and the combined type (where patients meet criteria on both the inattention and the hyperactive/impulsivity domains). This formulation is currently under review as part of the deliberation of the DSM-5 panel. Indeed, this current characterization remains controversial (Barkley, 2001; Diamond, 2005; Hinshaw, 2001; Lahey, 2001; Milich, Balentine, & Lynam, 2001). Here we focus on factor models of co-occurrence among ADHD symptoms. Two major types of factor models, correlated factor models and hierarchical models, have been used to examine coherence and distinctness among ADHD symptom domains. Hierarchical models provide a way to simultaneously conceptualize both the coherence and separability of symptoms from separate domains. These models include a single general factor accounting for covariation among all symptoms along with separate, specific factors of inattention, hyperactivity, and impulsivity that vary orthogonally from the general factor. These models are also termed as bifactor models in the statistical literature. Hierarchical models are different from correlated factor models that only have factors for the symptom domains of inattention and hyperactivity and/or impulsivity (see Figure 1). Several studies have shown hierarchical models with a general factor as having a better fit than correlated models for reported symptoms of ADHD (e.g., Dumenci, McConaghy, & Achenbach, 2004; Gibbins et al., in press; Martel, Von Eye, & Nigg, 2010; Toplak et al., 2009). These papers span clinical and community samples, and child, adolescent, and adult samples with ADHD. A one-factor model has also been considered, but thus far it has no empirical support (Dumenci et al., 2004). Figure 1 Generic example of a correlated two-factor model for 10 observed symptoms Hierarchical models explicitly acknowledge the common covariation among all ADHD symptoms, which is consistent with the conceptualization of ADHD as a single disorder. There are several lines of evidence suggesting that there is substantial commonality between the domains of inattention and hyperactivity-impulsivity. Inattentive symptoms tend to be more highly correlated with hyperactivity and impulsivity than with other domains of psychopathology (Adams, Kelley, & McCarthy, 1997; Conners, 2008; Strickland et al., 2011), with the exception of oppositional defiant disorder in some studies (Lahey et al., 2008). Current models of ADHD also highlight how the symptom domains of inattention, hyperactivity, and impulsivity likely interact to give rise to the heterogeneous expression of ADHD (Nigg & Casey, 2005; Sagvolden, Johansen, Aase, & Russell, 2005; Sonuga-Barke, 2005; Sonuga-Barke, Sergeant, Nigg, & Willcutt, 2008). To replicate and extend these findings, the current study examined different factor models in a large sample of ADHD patients recruited from a broad age range and from diverse national groupings. We were thus able to test whether a hierarchical model held for the whole sample and whether it also was invariant across different age groups and nationalities. A developmental perspective is important to integrate into models of individual ADHD symptoms, such that a single set of factors could parsimoniously explain the changes that occur over development. Age differences in scores from ADHD measures may reflect true differences in the constructs being measured or may simply reflect measurement differences due to age. Therefore, establishing measurement invariance across age groups is important. The behavioural presentation of ADHD changes considerably from childhood to adolescence. For instance, the expression of hyperactivity seems to decrease from childhood to adolescence and inattention commonly appears later in development than hyperactivity and impulsivity (Biederman et al., 2000; Hart et al., 1995; Larsson et al., 2006; Nigg, 2006). This developmental change introduces complex issues with respect to diagnosis. Subtypes have been used to characterize these different symptom presentations, and the instability of ADHD subtypes in developmental samples has also been well demonstrated (Lahey, Pelham, Loney, Lee, & Willcutt, 2005; Todd et al., 2008). Some of this instability of subtypes may be attributable to measurement variability (Lahey et al., 2005; Valo & Tannock, 2010); however some of this variability would be expected from a developmental perspective, which would presume that children’s symptom presentations change over the course of development. What is needed is a coherent model that can represent these shifts and changes in symptoms. In addition to the question of developmental change and continuity in ADHD symptoms, the current sample also had the unique characteristic of having recruited participants from seven European countries and Israel by 12 different research centers. Most studies examining cross-national samples have been concerned with whether there are comparable rates of prevalence across different countries (Faraone, Sergeant, Gillberg, & Biederman, 2003; Polanczyk, de Lima, Horta, Biederman, & Rohde, 2007) rather than consistency in symptom patterns across countries. In addition to testing the five different factor models in the full sample, invariance analyses were also conducted to examine consistency of the best overall model across countries. Thus, in the current study we first estimated five different factor models to determine which model best accounted for ADHD symptoms pooling all ages and locations using a sample of children and adolescents with ADHD and their siblings. The five factor structures included: a) a one-factor model of inattention/ hyperactivity/impulsivity; b) a non-hierarchical two-factor model with correlated inattention and hyperactivity/impulsivity factors (the correlated 2-factor model); c) a non-hierarchical three-factor model with correlated inattention, hyperactivity, and impulsivity factors (the correlated 3-factor model); d) a hierarchical model of a general ADHD factor with two specific factors of inattention and hyperactivity/impulsivity (the hierarchical 2-factor model); and e) a hierarchical model of a general ADHD factor with three specific factors of inattention, hyperactivity, and impulsivity (the hierarchical 3-factor model). Based on previous research, we expected that a hierarchical model with a general ADHD factor would provide the best fit to observed ADHD symptoms in both the ADHD and sibling samples and across instruments and informants. We then examined whether these modeled relationships among symptoms are equivalent across different groups by formally assessing measurement invariance in the ADHD group. Group differences in observed scores on measurement instruments can be attributed to true differences on the constructs being measured only if measurement invariance or equivalence holds across groups (e.g., Widaman & Reise, 1997). Based on the best fitting model, we conducted invariance analyses to determine whether the measurement parameters relating the constructs implied by the model to the observed symptoms are equivalent across age groups and locations in the ADHD group.

疟原虫var基因转换速率变化导致抗原变异[英]／Paul H, Robert P, Christodoulou Z, et al//Proc Natl Acad Sci U S A

Abstract: 抗原变异可使得多种致病微生物易于逃避宿主免疫应答。表达在感染红细胞表面的恶性疟原虫红细胞表面蛋白1（PfPMP1）与感染红细胞、内皮细胞、树突状细胞以及胎盘的单个或多个受体作用，在黏附及免疫逃避中起关键的作用。每个单倍体基因组var基因家族编码约60种成员，通过启动转录不同的var基因变异体为抗原变异提供了分子基础。

Using Multivariate Statistics

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Human biochemical genetics

Abstract: So far in this course we have dealt entirely with the evolution of characters that are controlled by simple Mendelian inheritance at a single locus. There are notes on the course website about gametic disequilibrium and how allele frequencies change at two loci simultaneously, but we didn’t discuss them. In every example we’ve considered we’ve imagined that we could understand something about evolution by examining the evolution of a single gene. That’s the domain of classical population genetics. For the next few weeks we’re going to be exploring a field that’s actually older than classical population genetics, although the approach we’ll be taking to it involves the use of population genetic machinery. If you know a little about the history of evolutionary biology, you may know that after the rediscovery of Mendel’s work in 1900 there was a heated debate between the “biometricians” (e.g., Galton and Pearson) and the “Mendelians” (e.g., de Vries, Correns, Bateson, and Morgan). Biometricians asserted that the really important variation in evolution didn’t follow Mendelian rules. Height, weight, skin color, and similar traits seemed to

Genome-wide association study of 14,000 cases of seven common diseases and 3,000 shared controls

Abstract: There is increasing evidence that genome-wide association ( GWA) studies represent a powerful approach to the identification of genes involved in common human diseases. We describe a joint GWA study ( using the Affymetrix GeneChip 500K Mapping Array Set) undertaken in the British population, which has examined similar to 2,000 individuals for each of 7 major diseases and a shared set of similar to 3,000 controls. Case-control comparisons identified 24 independent association signals at P < 5 X 10(-7): 1 in bipolar disorder, 1 in coronary artery disease, 9 in Crohn's disease, 3 in rheumatoid arthritis, 7 in type 1 diabetes and 3 in type 2 diabetes. On the basis of prior findings and replication studies thus-far completed, almost all of these signals reflect genuine susceptibility effects. We observed association at many previously identified loci, and found compelling evidence that some loci confer risk for more than one of the diseases studied. Across all diseases, we identified a large number of further signals ( including 58 loci with single-point P values between 10(-5) and 5 X 10(-7)) likely to yield additional susceptibility loci. The importance of appropriately large samples was confirmed by the modest effect sizes observed at most loci identified. This study thus represents a thorough validation of the GWA approach. It has also demonstrated that careful use of a shared control group represents a safe and effective approach to GWA analyses of multiple disease phenotypes; has generated a genome-wide genotype database for future studies of common diseases in the British population; and shown that, provided individuals with non-European ancestry are excluded, the extent of population stratification in the British population is generally modest. Our findings offer new avenues for exploring the pathophysiology of these important disorders. We anticipate that our data, results and software, which will be widely available to other investigators, will provide a powerful resource for human genetics research.