To save content items to your account,
please confirm that you agree to abide by our usage policies.
If this is the first time you use this feature, you will be asked to authorise Cambridge Core to connect with your account.
Find out more about saving content to .
To save content items to your Kindle, first ensure email@example.com
is added to your Approved Personal Document E-mail List under your Personal Document Settings
on the Manage Your Content and Devices page of your Amazon account. Then enter the ‘name’ part
of your Kindle email address below.
Find out more about saving to your Kindle.
Note you can select to save to either the @free.kindle.com or @kindle.com variations.
‘@free.kindle.com’ emails are free but can only be saved to your device when it is connected to wi-fi.
‘@kindle.com’ emails can be delivered even when you are not connected to wi-fi, but note that service fees apply.
It is crucial to understand the genetic mechanisms and biological pathways underlying the relationship between obesity and serum lipid levels. Structural equation models (SEMs) were constructed to calculate heritability for body mass index (BMI), total cholesterol (TC), triglyceride (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), and the genetic connections between BMI and the four classes of lipids using 1197 pairs of twins from the Chinese National Twin Registry (CNTR). Bivariate genomewide association studies (GWAS) were performed to identify genetic variants associated with BMI and lipids using the records of 457 individuals, and the results were further validated in 289 individuals. The genetic background affecting BMI may differ by gender, and the heritability of males and females was 71% (95% CI [.66, .75]) and 39% (95% CI [.15, .71]) respectively. BMI was positively correlated with TC, TG and LDL-C in phenotypic and genetic correlation, while negatively correlated with HDL-C. There were gender differences in the correlation between BMI and lipids. Bivariate GWAS analysis and validation stage found 7 genes (LOC105378740, LINC02506, CSMD1, MELK, FAM81A, ERAL1 and MIR144) that were possibly related to BMI and lipid levels. The significant biological pathways were the regulation of cholesterol reverse transport and the regulation of high-density lipoprotein particle clearance (p < .001). BMI and blood lipid levels were affected by genetic factors, and they were genetically correlated. There might be gender differences in their genetic correlation. Bivariate GWAS analysis found MIR144 gene and its related biological pathways may influence obesity and lipid levels.
The objective of this study was to investigate how different obesity measures link to circulating metabolites, and whether the connections are due to genetic or environmental factors. A cross-sectional analysis was performed on follow-up survey data at the Chinese National Twin Registry (CNTR), which was conducted in four areas of China (Shandong, Jiangsu, Zhejiang and Sichuan) in 2013. The survey collected detailed questionnaire information and conducted physical examinations, fasting blood sampling and untargeted metabolomic measurements among 439 adult twins. Linear regression models and bioinformatics analysis were used to examine the relation of obesity measures, including body mass index (BMI), waist circumference (WC) and waist-to-hip ratio (WHR) with serum metabolite levels and related pathways. A co-twin control study was additionally conducted among 15 obesity-discordant monozygotic (MZ) pairs (intrapair BMI difference >3 kg/m2) to examine any differences in metabolites controlling for genetic factors. Eleven metabolites were associated with BMI, WC and WHR after controlling for genetic and shared environmental factors. Pathway analysis identified pathways such as phenylalanine metabolism, purine metabolism, valine, leucine and isoleucine biosynthesis that were associated with obesity. A wide range of unfavorable alterations in the serum metabolome was associated with obesity. Obesity-discordant twin analysis suggests that these associations are independent of genetic liability.
The genetic contribution of blood pressure and heart rate (HR) varied widely between studies. Demographic factors such as ethnicity, age and/or sex might explain some of the heterogeneity. We performed a systematic review focusing on four phenotypes: systolic blood pressure (SBP), diastolic blood pressure (DBP), HR and pulse pressure (PP). Meta-regression was conducted to analyze potential factors in relation to SBP and DBP heritability. A total of 10,613 independent twins that came from 17 studies were included in the analysis. The weighted mean value of heritability for SBP and DBP was 0.54 (95% CIs: 0.48–0.60) and 0.49 (95% CIs: 0.42–0.56). Comparatively, three studies of HR and four studies of PP heritability were limited for the heterogeneity test. Meta-regression showed that, on average, SBP heritability with additive genes/unique environment (AE) model tend to have a higher heritability than additive genes/shared environment/unique environment (ACE) model (coefficient = 0.0947, p = .0142). A similar result was found for DBP as well. No other factors such as sex, age, ethnicity, publication year were significantly associated with heritability variance. Our study shows heritability estimates based on twin studies of both SBP and DBP are around 50%, using an AE rather than an ACE model; the variance due to C ended up in A, suggesting that the AE model may overestimate heritability if a small contribution of shared environment exists.
Email your librarian or administrator to recommend adding this to your organisation's collection.