Smoking status, snus use, and variation at the CHRNA5-CHRNA3-CHRNB4 locus in relation to obesity: the GLACIER study

The identification of key genes and pathways in polycystic ovary syndrome by bioinformatics analysis of next-generation sequencing data

Background

Polycystic ovary syndrome (PCOS) is a reproductive endocrine disorder. The specific molecular mechanism of PCOS remains unclear. The aim of this study was to apply a bioinformatics approach to reveal related pathways or genes involved in the development of PCOS.

Methods

The next-generation sequencing (NGS) dataset GSE199225 was downloaded from the gene expression omnibus (GEO) database and NGS dataset analyzed is obtained from in vitro culture of PCOS patients’ muscle cells and muscle cells of healthy lean control women. Differentially expressed gene (DEG) analysis was performed using DESeq2. The g:Profiler was utilized to analyze the gene ontology (GO) and REACTOME pathways of the differentially expressed genes. A protein–protein interaction (PPI) network was constructed and module analysis was performed using HiPPIE and cytoscape. The miRNA-hub gene regulatory network and TF-hub gene regulatory network were constructed. The hub genes were validated by using receiver operating characteristic (ROC) curve analysis.

Results

We have identified 957 DEG in total, including 478 upregulated genes and 479 downregulated gene. GO terms and REACTOME pathways illustrated that DEG were significantly enriched in regulation of molecular function, developmental process, interferon signaling and platelet activation, signaling, and aggregation. The top 5 upregulated hub genes including HSPA5, PLK1, RIN3, DBN1, and CCDC85B and top 5 downregulated hub genes including DISC1, AR, MTUS2, LYN, and TCF4 might be associated with PCOS. The hub gens of HSPA5 and KMT2A, together with corresponding predicted miRNAs (e.g., hsa-mir-34b-5p and hsa-mir-378a-5p), and HSPA5 and TCF4 together with corresponding predicted TF (e.g., RCOR3 and TEAD4) were found to be significantly correlated with PCOS.

Conclusions

These study uses of bioinformatics analysis of NGS data to obtain hub genes and key signaling pathways related to PCOS and its associated complications. Also provides novel ideas for finding biomarkers and treatment methods for PCOS and its associated complications.

Central and peripheral actions of nicotine that influence blood glucose homeostasis and the development of diabetes

Cigarette smoking has long been recognized as a risk factor for type 2 diabetes (T2D), although the precise causal mechanisms underlying this relationship remain poorly understood. Recent evidence suggests that nicotine, the primary reinforcing component in tobacco, may play a pivotal role in connecting cigarette smoking and T2D. Extensive research conducted in both humans and animals has demonstrated that nicotine can elevate blood glucose levels, disrupt glucose homeostasis, and induce insulin resistance. The review aims to elucidate the genetic variants of nicotinic acetylcholine receptors associated with diabetes risk and provide a comprehensive overview of the available data on the mechanisms through which nicotine influences blood glucose homeostasis and the development of diabetes. Here we emphasize the central and peripheral actions of nicotine on the release of glucoregulatory hormones, as well as its effects on glucose tolerance and insulin sensitivity. Notably, the central actions of nicotine within the brain, which encompass both insulin-dependent and independent mechanisms, are highlighted as potential targets for intervention strategies in diabetes management.

Association of Cardiovascular Disease Risk Factors with Exclusive Smokeless Tobacco Use among US Males: Cross-Sectional Analysis of NHANES Data 2003-2018

PURPOSE

To evaluate cardiovascular disease (CVD) risk factors among smokeless tobacco (ST) users. Exclusive ST users were compared to exclusive cigarette smokers and non-tobacco users.

DESIGN

Cross-sectional study.

SAMPLE

Data were used from 16,336 adult males who participated in one of the National Health and Nutrition Examination Surveys (NHANES) from 2003 to 2018.

MEASURES

Biochemically verified tobacco use, CVD risk factors (hypertension, cholesterol levels, BMI categories), physical activity, cotinine concentration, and sociodemographic variables.

ANALYSIS

Weighted analysis of the aggregate data was performed. ST users were compared with cigarette smokers and nontobacco users for their association with CVD risk factors. Associations were examined using univariate and multiple logistic regression with odds ratios (OR) and 95% confidence intervals (CI) reported.

RESULTS

Prevalence of exclusive ST use was 4.4% whereas, exclusive smoking was 22.2%. Among ST users, 36.2% were hypertensive, 24.5% had high cholesterol levels, and most of them were overweight (31.1%) or obese (52.6%). ST users were more likely to have hypertension compared to smokers (aOR = 1.48, 95%CI: 1.12, 1.95) and nontobacco users (aOR = 1.41, 95%CI: 1.09, 1.83) adjusted for other covariates. ST users were twice more likely to be obese than nontobacco users (aOR = 2.18, 95%CI: 1.52, 3.11). ST users had significantly higher cotinine concentration than smokers.

CONCLUSION

Study findings indicate substantial association of ST use among males with hypertension and obesity which are independent risk factors of CVD.

Metabolic Disorder in Chronic Obstructive Pulmonary Disease (COPD) Patients: Towards a Personalized Approach Using Marine Drug Derivatives

Metabolic disorder has been frequently observed in chronic obstructive pulmonary disease (COPD) patients. However, the exact correlation between obesity, which is a complex metabolic disorder, and COPD remains controversial. The current study summarizes a variety of drugs from marine sources that have anti-obesity effects and proposed potential mechanisms by which lung function can be modulated with the anti-obesity activity. Considering the similar mechanism, such as inflammation, shared between obesity and COPD, the study suggests that marine derivatives that act on the adipose tissues to reduce inflammation may provide beneficial therapeutic effects in COPD subjects with high body mass index (BMI).

The association of cigarette smoking and alcohol drinking with body mass index: a cross-sectional, population-based study among Chinese adult male twins

Background

Obesity is a multifactorial abnormality which has an underlying genetic control but requires environmental influences to trigger. Numerous epidemiological studies have examined the roles of physical inactivity and dietary factors in obesity development. Interactions between obesity-related genes and these lifestyles have also been confirmed. However, less attention has been paid to these complex relationship between cigarette smoking, alcohol drinking and obesity. The purpose of this study was to assess whether cigarette smoking and alcohol drinking were associated with body mass index (BMI), and whether these lifestyle factors modified the genetic variance of BMI.

Methods

Subjects were twins recruited through the Chinese National Twin Registry, aged 18 to 79 years, and the sample comprised 6121 complete male twin pairs. Information on height, weight, cigarette smoking and alcohol drinking status were assessed with self-report questionnaires. The associations of cigarette smoking and alcohol drinking with BMI were evaluated by linear regression models. Further, structure equation models were conducted to estimate whether cigarette smoking and alcohol drinking status modified the degree of genetic variance of BMI.

Results

After adjustment for a variety of socio-demographic and lifestyle factors, former smokers had higher BMI (β = 0.475; 95 % CI, 0.196 to 0.754) whereas moderate to heavy smokers had lower BMI (β = −0.115; 95 % CI, −0.223 to −0.007) when compared with nonsmokers. BMI decreased with increased cigarette pack-years (β = −0.008; 95 % CI, −0.013 to −0.003). These effects still existed substantially in within-MZ twin pair analyses. By contrast, current alcohol drinking had no significant influence on BMI when additionally controlled for shared factors in within-pair analyses. Genetic modification by alcohol drinking was statistically significant for BMI (β = −0.137; 95 % CI, −0.215 to −0.058), with the intake of alcohol decreasing the additive genetic component of BMI.

Conclusions

Cigarette smoking was negatively associated with BMI independent of genetic influences. The influence of genes on BMI was moderated by alcohol drinking, such that for individuals who were regular drinkers, genetic factors became less influential. Our findings highlight gene-alcohol interaction in finding candidate genes of BMI and elucidating the etiological factors of obesity.

High tobacco consumption lowers body weight: a Mendelian randomization study of the Copenhagen General Population Study

BACKGROUND

Conflicting evidence has been found regarding the association between high tobacco consumption and body weight among smokers. We tested the hypothesis that high tobacco consumption is causally associated with low body weight.

METHODS

We conducted a Mendelian randomization study with a genetic variant in CHRNA3 (rs1051730) as proxy for high tobacco consumption. The cohort consisted of 80,342 participants from the Copenhagen General Population Study, with details on body weight, smoking habits and CHRNA3 genotype, including 15,220 current smokers.

RESULTS

In observational analyses, high tobacco consumption was associated with high body weight, body mass index, waist circumference, hip circumference and waist-hip ratio. In multivariable adjusted models a 1-cigarette/day higher tobacco consumption was associated with 0.05 kg (95% confidence interval 0.02; 0.08) higher body weight, 0.02 kg/m(2) (0.01; 0.02) higher body mass index, 0.10 cm (0.07; 0.12) higher waist circumference and 0.02 cm (0.001; 0.03) higher hip circumference. In contrast, the per-allele increase inCHRNA3 rs1051730 associated with high tobacco consumption was associated with 0.59 kg (0.96; 0.22) lower body weight, 0.23 kg/m(2) (0.33; 0.13) lower body mass index, 0.32 cm (0.74; 0.003) lower waist circumference and 0.45 cm (0.66; 0.24) lower hip circumference. No association was found between tobacco consumption and waist-hip ratio in genetic analysis, or among CHRNA3 genotype and any of the outcome variables in former or never smokers.

CONCLUSIONS

High tobacco consumption causes lower body weight among current smokers. However, smoking does not seem to affect body shape or fat distribution causally. The lack of association between CHRNA3 genotype and body weight among former smokers and never smokers favours smoking as the causal factor for the observed associations.

Gene-Lifestyle Interactions in Complex Diseases: Design and Description of the GLACIER and VIKING Studies

Most complex diseases have well-established genetic and non-genetic risk factors. In some instances, these risk factors are likely to interact, whereby their joint effects convey a level of risk that is either significantly more or less than the sum of these risks. Characterizing these gene-environment interactions may help elucidate the biology of complex diseases, as well as to guide strategies for their targeted prevention. In most cases, the detection of gene-environment interactions will require sample sizes in excess of those needed to detect the marginal effects of the genetic and environmental risk factors. Although many consortia have been formed, comprising multiple diverse cohorts to detect gene-environment interactions, few robust examples of such interactions have been discovered. This may be because combining data across studies, usually through meta-analysis of summary data from the contributing cohorts, is often a statistically inefficient approach for the detection of gene-environment interactions. Ideally, single, very large and well-genotyped prospective cohorts, with validated measures of environmental risk factor and disease outcomes should be used to study interactions. The presence of strong founder effects within those cohorts might further strengthen the capacity to detect novel genetic effects and gene-environment interactions. Access to accurate genealogical data would also aid in studying the diploid nature of the human genome, such as genomic imprinting (parent-of-origin effects). Here we describe two studies from northern Sweden (the GLACIER and VIKING studies) that fulfill these characteristics.

Causal associations of tobacco smoking with cardiovascular risk factors: a Mendelian randomization analysis of the HUNT Study in Norway

BACKGROUND

Tobacco smoking has been associated with cardiovascular risk factors including adverse serum lipid levels, central obesity and higher resting heart rate, but lower blood pressure and body mass index (BMI). We used a Mendelian randomization approach to study whether these associations may be causal. If smoking affects cardiovascular risk factors then rs1051730 T alleles, predictors of increased smoking quantity, should be associated with cardiovascular risk factors among smokers, but not among never smokers.

METHODS

Among 56,625 participants of a population-based study, we estimated associations of rs1051730 T alleles with cardiovascular risk factors and examined whether the associations differed by smoking status.

RESULTS

Rs1051730 T alleles were associated with lower BMI and waist and hip circumferences and higher resting heart rate and estimated glomerular filtration rate (eGFR), and the associations were strongest among current smokers (P interaction 5×10(-9) to 0.01). Rs1051730 T alleles were associated with lower systolic blood pressure and pulse pressure and higher HDL cholesterol concentrations, but these associations did not robustly differ by smoking status. There were no convincing associations of rs1051730 T alleles with waist-hip ratio, diastolic blood pressure and non-fasting serum concentrations of non-HDL cholesterol, triglycerides, glucose and C-reactive protein.

CONCLUSIONS

This Mendelian randomization analysis provides evidence that smoking may cause lower BMI and waist and hip circumferences and higher resting heart rate and eGFR. The findings further suggest that smoking is not a major determinant of waist-hip ratio or adverse blood pressure, serum lipid or glucose levels.