Dataset Information

Evaluating Approaches for Communication About Genomic Influences on Body Weight.

ABSTRACT:

Background

Developments in genomics may improve patient consultations about weight management; however, optimal approaches for these communications are unstudied.

Purpose

We assessed the influence of receiving generic, genomic information and of physician communication approach on overweight females in simulated clinical weight counseling interactions.

Methods

Two hundred participants were randomized to receive information about genomic or behavioral underpinnings of body weight from a virtual reality-based physician who used either a supportive or directive communication approach. Participants completed post-test self-report questionnaires.

Results

Genomic explanations for body weight led patients to perceive less blame [F (1,196) = 47.68, p < .0001] and weight stigma [F (1,196) = 5.75, p = .017] in the consultation. They did not lead to negative outcomes in physician-patient interaction or affect health behavior-related attitudes and beliefs. Physician's supportive or directive communication approach did not moderate these effects.

Conclusions

Integrating genomic concepts into health care has potential to positively influence the patient-provider relationship while addressing longstanding challenges in weight management. ClinicalTrials.gov number NCT01443910.

SUBMITTER: Persky S

PROVIDER: S-EPMC9295661 | biostudies-literature | 2015 Oct

REPOSITORIES: biostudies-literature

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Publications

Evaluating Approaches for Communication About Genomic Influences on Body Weight.

Persky Susan S Street Richard L RL

Annals of behavioral medicine : a publication of the Society of Behavioral Medicine 20151001 5

<h4>Background</h4>Developments in genomics may improve patient consultations about weight management; however, optimal approaches for these communications are unstudied.<h4>Purpose</h4>We assessed the influence of receiving generic, genomic information and of physician communication approach on overweight females in simulated clinical weight counseling interactions.<h4>Methods</h4>Two hundred participants were randomized to receive information about genomic or behavioral underpinnings of body w ...[more]

PMID: 25894275

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Project description:The objective of this study was to identify the SNPs and candidate genes related to body weight and seven body conformation traits at the age of 8 months in the Russian aboriginal Karachai goats (n = 269) by conducting genome-wide association studies (GWAS), using genotypes generated by Goat SNP BeadChip (Illumina Inc., USA). We identified 241 SNPs, which were significantly associated with the studied traits, including 47 genome-wide SNPs (p < 10-5) and 194 suggestive SNPs (p < 10-4), distributed among all goat autosomes except for autosome 23. Fifty-six SNPs were common for two and more traits (1 SNP for six traits, 2 SNPs for five traits, 12 SNPs for four traits, 20 SNPs for three traits, and 21 SNPs for two traits), while 185 SNPs were associated with single traits. Structural annotation within a window of 0.4 Mb (±0.2 Mb from causal SNPs) revealed 238 candidate genes. The largest number of candidate genes was identified at Chr13 (33 candidate genes for the five traits). The genes identified in our study were previously reported to be associated with growth-related traits in different livestock species. The most significant genes for body weight were CRADD, HMGA2, MSRB3, MAX, HACL1 and RAB15, which regulate growth processes, body sizes, fat deposition, and average daily gains. Among them, the HMGA2 gene is a well-known candidate for prenatal and early postnatal development, and the MSRB3 gene is proposed as a candidate gene affecting the growth performance. APOB, PTPRK, BCAR1, AOAH and ASAH1 genes associated with withers height, rump height and body length, are involved in various metabolic processes, including fatty acid metabolism and lipopolysaccharide catabolism. In addition, WDR70, ZBTB24, ADIPOQ, and SORCS3 genes were linked to chest width. KCNG4 was associated with rump height, body length and chest perimeter. The identified candidate genes can be proposed as molecular markers for growth trait selection for genetic improvement in Karachai goats.

Project description:OBJECTIVES:We applied three statistical approaches for evaluating associations between prenatal urinary concentrations of a mixture of phthalate metabolites and birth weight. METHODS:We included 300 women who provided 732 urine samples during pregnancy and delivered a singleton infant. We measured urinary concentrations of metabolites of di(2-ethylhexyl)-phthalate, di-isobutyl-, di-n-butyl-, butylbenzyl-, and diethyl phthalates. We applied 1) linear regressions; 2) classification methods [principal component analysis (PCA) and structural equation models (SEM)]; and 3) Bayesian kernel machine regression (BKMR), to evaluate associations between phthalate metabolite mixtures and birth weight adjusting for potential confounders. Data were presented as mean differences (95% CI) in birth weight (grams) as each phthalate increased from the 10th to the 90th percentile. RESULTS:When analyzing individual phthalate metabolites using linear regressions, each metabolite demonstrated a modest inverse association with birth weight [from -93 (-206, 21) to -49 (-164, 65)]. When simultaneously including all metabolites in a multivariable model, inflation of the estimates and standard errors were noted. PCA identified two principal components, both inversely associated with birth weight [-23 (-68, 22), -27 (-71, 17), respectively]. These inverse associations were confirmed when applying SEM. BKMR further identified that monoethyl and mono(2-ethylhexyl) phthalate and phthalate concentrations were linearly related to lower birth weight [-51(-164, 63) and -122 (-311, 67), respectively], and suggested no evidence of interaction between metabolites. CONCLUSIONS:While none of the methods produced significant results, we demonstrated the potential issues arising using linear regression models in the context of correlated exposures. Among the other selected approaches, classification techniques identified common sources of exposures with implications for interventions, while BKMR further identified specific contributions of individual metabolites.

Dataset Information

Evaluating Approaches for Communication About Genomic Influences on Body Weight.

Background

Purpose

Methods

Results

Conclusions

Publications

Evaluating Approaches for Communication About Genomic Influences on Body Weight.

Similar Datasets

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