Genetic analysis of low BMI phenotype in the Utah Population Database

Association between body mass index and respiratory symptoms in US adults: a national cross-sectional study

The correlation between body mass index (BMI) and the development of cough, shortness of breath, and dyspnea is unclear. Therefore, this study aimed to investigate the association between these parameters. Data from individuals who participated in the National Health and Nutrition Examination Survey between 2003 and 2012 were analyzed. Weighted logistic regression analysis and smoothed curve fitting were used to examine the correlation between BMI and respiratory symptoms. In addition, the relationship between BMI, chronic obstructive pulmonary disease (COPD), and bronchial asthma was examined. Stratified analysis was used to discover inflection points and specific groups. Weighted logistic regression and smoothed curve fitting revealed a U-shaped relationship between BMI and respiratory symptoms. The U-shaped relationship in BMI was also observed in patients with bronchial asthma and COPD. Stratified analysis showed that the correlation between BMI and wheezing and dyspnea was influenced by race. In addition, non-Hispanic black individuals had a higher risk of developing cough than individuals of the other three races [OR 1.040 (1.021, 1.060), p < 0.0001], and they also exhibited an inverted U-shaped relationship between BMI and bronchial asthma. However, the association of BMI with cough, wheezing, dyspnea, COPD, and asthma was not affected by sex. High or low BMI was associated with cough, shortness of breath, and dyspnea, and has been linked to bronchial asthma and COPD. These findings provide new insights into the management of respiratory symptoms and respiratory diseases.

Genetics of Obesity: What We Have Learned Over Decades of Research

There is a genetic component to human obesity that accounts for 40% to 50% of the variability in body weight status but that is lower among normal weight individuals (about 30%) and substantially higher in the subpopulation of individuals with obesity and severe obesity (about 60%-80%). The appreciation that heritability varies across classes of BMI represents an important advance. After controlling for BMI, ectopic fat and fat distribution traits are characterized by heritability levels ranging from 30% to 55%. Defects in at least 15 genes are the cause of monogenic obesity cases, resulting mostly from deficiencies in the leptin-melanocortin signaling pathway. Approximately two-thirds of the BMI heritability can be imputed to common DNA variants, whereas low-frequency and rare variants explain the remaining fraction. Diminishing allele effect size is observed as the number of obesity-associated variants expands, with most BMI-increasing or -decreasing alleles contributing only a few grams or less to body weight. Obesity-promoting alleles exert minimal effects in normal weight individuals but have larger effects in individuals with a proneness to obesity, suggesting a higher penetrance; however, it is not known whether these larger effect sizes precede obesity or are caused by an obese state. The obesity genetic risk is conditioned by thousands of DNA variants that make genetically based obesity prevention and treatment a major challenge.

Colorectal cancer risk based on extended family history and body mass index

Family history and body mass index (BMI) are well-known risk factors for colorectal cancer (CRC), however, their joint effects are not well described. Using linked data for genealogy, self-reported height and weight from driver's licenses, and the Utah Surveillance, Epidemiology, and End-Results cancer registry, we found that an increasing number of first-degree relatives (FDR) with CRC is associated with higher standardized incidence ratio (SIR) for overweight/obese probands but not for under/normal weight probands. For probands with two CRC-affected FDRs, the SIR = 1.91 (95% CI [0.52, 4.89]) for under/normal weight probands and SIR = 4.31 (95% CI [2.46, 7.00]) for overweight/obese probands. In the absence of CRC-affected FDRs, any number of CRC-affected SDRs did not significantly increase CRC risk for under/normal weight probands, but for overweight/obese probands with at least three CRC-affected SDRs the SIR = 2.68 (95% CI [1.29, 4.93]). In the absence of CRC-affected FDRs and SDRs, any number of CRC-affected third-degree relatives (TDRs) did not increase risk in under/normal weight probands, but significantly elevated risk for overweight/obese probands with at least two CRC-affected TDRs was observed; SIR = 1.32 (95% CI [1.04, 1.65]). For nonsyndromic CRC, maximum midlife BMI affects risk based on family history and should be taken into account for CRC risk communication when possible.

A population-based description of familial clustering of Hirschsprung disease

BACKGROUND

Familial recurrence of Hirschsprung disease (HSCR) is well documented, and risk estimates for relatives have been reported from various populations. We describe the familial clustering of HSCR cases using well-established unbiased familial aggregation techniques within the context of a population genealogy.

METHODS

Patients included 264 HSCR cases identified using ICD-9 diagnosis coding from the two largest healthcare providers in Utah who also had linked genealogy data. The GIF statistic was used to identify excess familial clustering by comparing average relatedness of cases to matched controls. In addition, relative risks (RRs) of HSCR in relatives of cases were estimated using age-, sex- and birthplace-matched disease rates, and for several diseases frequently associated with HSCR (Down syndrome, multiple endocrine neoplasia IIa, central hypoventilation syndrome, Bardet-Biedl syndrome, ventricular and atrial septal defect).

RESULTS

Significant excess relatedness was observed for all HSCRs (p<1e^-3). Significant RRs for HSCR were observed for first-, second-, and fourth-degree relatives of cases (RR=12.0, 10.0, and 4.6, respectively). Significant elevated risks of Down syndrome, Bardet-Biedl syndrome, and atrial and ventricular septal defects were observed for HSCR cases.

CONCLUSION

This population-based survey of HSCR provides confirmation of a genetic contribution to HSCR disease and presents unbiased risk estimates that may have clinical value in predicting recurrence.

LEVEL OF EVIDENCE RATING

Prognosis study, level II.

Recent progress in genetics, epigenetics and metagenomics unveils the pathophysiology of human obesity

In high-, middle- and low-income countries, the rising prevalence of obesity is the underlying cause of numerous health complications and increased mortality. Being a complex and heritable disorder, obesity results from the interplay between genetic susceptibility, epigenetics, metagenomics and the environment. Attempts at understanding the genetic basis of obesity have identified numerous genes associated with syndromic monogenic, non-syndromic monogenic, oligogenic and polygenic obesity. The genetics of leanness are also considered relevant as it mirrors some of obesity's aetiologies. In this report, we summarize ten genetically elucidated obesity syndromes, some of which are involved in ciliary functioning. We comprehensively review 11 monogenic obesity genes identified to date and their role in energy maintenance as part of the leptin-melanocortin pathway. With the emergence of genome-wide association studies over the last decade, 227 genetic variants involved in different biological pathways (central nervous system, food sensing and digestion, adipocyte differentiation, insulin signalling, lipid metabolism, muscle and liver biology, gut microbiota) have been associated with polygenic obesity. Advances in obligatory and facilitated epigenetic variation, and gene-environment interaction studies have partly accounted for the missing heritability of obesity and provided additional insight into its aetiology. The role of gut microbiota in obesity pathophysiology, as well as the 12 genes associated with lipodystrophies is discussed. Furthermore, in an attempt to improve future studies and merge the gap between research and clinical practice, we provide suggestions on how high-throughput '-omic' data can be integrated in order to get closer to the new age of personalized medicine.