Common obesity risk alleles in childhood attention-deficit/hyperactivity disorder

Children with attention-deficit/hyperactivity disorder (ADHD) have a higher rate of obesity than children without ADHD. Obesity risk alleles may overlap with those relevant for ADHD. We examined whether risk alleles for an increased body mass index (BMI) are associated with ADHD and related quantitative traits (inattention and hyperactivity/impulsivity). We screened 32 obesity risk alleles of single nucleotide polymorphisms (SNPs) in a genome-wide association study (GWAS) for ADHD based on 495 patients and 1,300 population-based controls and performed in silico analyses of the SNPs in an ADHD meta-analysis comprising 2,064 trios, 896 independent cases, and 2,455 controls. In the German sample rs206936 in the NUDT3 gene (nudix; nucleoside diphosphate linked moiety X-type motif 3) was associated with ADHD risk (OR: 1.39; P = 3.4 × 10(-4) ; Pcorr  = 0.01). In the meta-analysis data we found rs6497416 in the intronic region of the GPRC5B gene (G protein-coupled receptor, family C, group 5, member B; P = 7.2 × 10(-4) ; Pcorr  = 0.02) as a risk allele for ADHD. GPRC5B belongs to the metabotropic glutamate receptor family, which has been implicated in the etiology of ADHD. In the German sample rs206936 (NUDT3) and rs10938397 in the glucosamine-6-phosphate deaminase 2 gene (GNPDA2) were associated with inattention, whereas markers in the mitogen-activated protein kinase 5 gene (MAP2K5) and in the cell adhesion molecule 2 gene (CADM2) were associated with hyperactivity. In the meta-analysis data, MAP2K5 was associated with inattention, GPRC5B with hyperactivity/impulsivity and inattention and CADM2 with hyperactivity/impulsivity. Our results justify further research on the elucidation of the common genetic background of ADHD and obesity.

Association between inflammatory and obesity markers in a Swiss population-based sample (CoLaus Study)

To assess the associations between obesity markers (BMI, waist circumference and %body fat) and inflammatory markers (interleukin-1β (IL-1β); interleukin-6 (IL-6); tumor necrosis factor-α (TNF-α) and high-sensitivity C-reactive protein (hs-CRP)).

Obesity lowers the threshold of allergic sensitization and augments airway eosinophilia in a mouse model of asthma

Clinical and epidemiological studies show a close association between obesity and the risk of asthma development. The underlying cause-effect relationship between metabolism, innate and adaptive immunity, and inflammation remains to be elucidated.

Rates Of Obesity And Incidence Of Diabetes In Hispanics In The United States: An Examination Of The Epidemiological Paradox

Using pooled data from the 2008-2011 National Health Interview Survey and employing multinomial and binomial logistic regression methods, this research examines disparities in rates of obesity and incidence of diabetes between individual Hispanic subgroups in comparison to non-Hispanic whites and blacks. Immigration status(including nativity, duration in the United States, and citizenship status) is hypothesized to play a central role in rates and obesity and incidence of diabetes. Unlike Cuban-Americans, Mexican-Americans, Puerto Ricans, and other Hispanics were more likely to be overweight as well as obese when compared to non-Hispanic whites. Mexican-Americans had the only significance in prevalence of type 2 diabetes in comparison to non-Hispanic whites. Both of these health outcomes are strongly associated with the various immigration variables.

Trends in the Association Between Socioeconomic Status and Obesity in U.S. Adults: 2002-2012

Since the 1980s, the prevalence of obesity has more than doubled to over 30 percent of the adult population (Thorpe, 2004). Obesity is a key contributing factor to continually rising national healthcare costs. Addressing its negative implications is essential not only from a cost perspective, but also for the betterment of our nation¿s general health and wellbeing. Obesity is reportedly associated with a 35% increase in inpatient and outpatient spending, as well as a 77% increase in related necessary medications (Sturm, 2002). Obesity, which some have argued should be classified as a disease in itself, has roughly the same association with the development of chronic health conditions as does 20 years of aging (Sturm, 2002). Defined as ambulatory care-sensitive conditions, these obesity-related chronic health diagnoses ¿ like diabetes, cardiovascular disease, and hypertension ¿ are in turn the primary drivers of current healthcare spending, as well as future predicted health expenditures. It is well established that lower socioeconomic status (SES) is associated with higher rates of obesity and the subsequent development of aforementioned obesity-related conditions. Socioeconomic status has traditionally been defined by education, income, and occupation (Adler, 2002); however, this study found empirical evidence for education being the most fundamental of these three SES indicators in determining obesity outcomes. For both men and women, as education levels increased, the likelihood of an individual being obese decreased. However, with less education, there was increased disparity between the obesity rates for men and women. Women consistently saw higher rates of obesity and were more impacted in terms of obesity onset by belonging to a lower SES category than men. In addition, this study assessed whether the impact of one¿s socioeconomic status on obesity-related health outcomes (specifically the negative impact low-SES as measured by education level) has changed over time. Results deriving from annual data from the National Health Interview Survey (NHIS) for all years from 2002 to 2012 indicate that the association between low-socioeconomic status and negative health outcomes has not increased in magnitude over the past decade. Instead, obesity rates have increased across the overall U.S. adult population, most likely due to a number of larger external societal factors resulting in increased caloric intake and decreased energy expenditure across every SES group. In addition, while the association between low-SES and obesity has not worsened, a consequence of the Great Recession has been a larger percentage of the U.S. population in lower-SES, which is still consistently subject to the same worse health outcomes.

Content comparison of health status measures for obesity based on the international classification of functioning, disability and health

OBJECTIVE: To compare the content covered by twelve obesity-specific health status measures using the International Classification of Functioning, Disability and Health (ICF). DESIGN: Obesity-specific health status measures were identified and then linked to the ICF separately by two trained health professionals according to standardized guidelines. The degree of agreement between health professionals was calculated by means of the kappa (kappa) statistic. Bootstrapped confidence intervals (CI) were calculated. The obesity-specific health-status measures were compared on the component and category level of the ICF. MEASUREMENTS: welve condition-specific health-status measures were identified and included in this study, namely the obesity-related problem scale, the obesity eating problems scale, the obesity-related coping and obesity-related distress questionnaire, the impact of weight on quality of life questionnaire (short version), the health-related quality of life questionnaire, the obesity adjustment survey (short form), the short specific quality of life scale, the obesity-related well-being questionnaire, the bariatric analysis and reporting outcome system, the bariatric quality of life index, the obesity and weight loss quality of life questionnaire and the weight-related symptom measure. RESULTS: In the 280 items of the eight measures, a total of 413 concepts were identified and linked to the 87 different ICF categories. The measures varied strongly in the number of concepts contained and the number of ICF categories used to map these concepts. Items on body functions varied form 12% in the obesity-related problem scale to 95% in the weight-related symptom measure. The estimated kappa coefficients ranged between 0.79 (CI: 0.72, 0.86) at the component ICFs level and 0.97 (CI: 0.93, 1.0) at the third ICF's level. CONCLUSION: The ICF proved highly useful for the content comparison of obesity-specific health-status measures. The results may provide clinicians and researchers with new insights when selecting health-status measures for clinical studies in obesity.