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Study of cardiovascular disease biomarkers among tobacco consumers, part 2: biomarkers of biological effect.

ABSTRACT: An age-stratified, cross-sectional study was conducted in the US among healthy adult male cigarette smokers, moist snuff consumers, and non-tobacco consumers to evaluate cardiovascular biomarkers of biological effect (BoBE). Physiological assessments included flow-mediated dilation, ankle-brachial index, carotid intima-media thickness and expired carbon monoxide. Approximately one-half of the measured serum BoBE showed statistically significant differences; IL-12(p70), sICAM-1 and IL-8 were the BoBE that best differentiated among the three groups. A significant difference in ABI was observed between the cigarette smokers and non-tobacco consumer groups. Significant group and age effect differences in select biomarkers were identified.

SUBMITTER: Nordskog BK

PROVIDER: S-EPMC4496813 | biostudies-literature | 2015 Feb

REPOSITORIES: biostudies-literature

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Study of cardiovascular disease biomarkers among tobacco consumers, part 2: biomarkers of biological effect.

Nordskog Brian K BK Brown Buddy G BG Marano Kristin M KM Campell Leanne R LR Jones Bobbette A BA Borgerding Michael F MF

Inhalation toxicology 20150201 3

An age-stratified, cross-sectional study was conducted in the US among healthy adult male cigarette smokers, moist snuff consumers, and non-tobacco consumers to evaluate cardiovascular biomarkers of biological effect (BoBE). Physiological assessments included flow-mediated dilation, ankle-brachial index, carotid intima-media thickness and expired carbon monoxide. Approximately one-half of the measured serum BoBE showed statistically significant differences; IL-12(p70), sICAM-1 and IL-8 were the ...[more]

PMID: 25787701

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Project description:IntroductionTobacco use is recognized as one of the most important causes of preventable death due to non-communicable diseases and disability worldwide. The present study was conducted with the aim of comparing social support and self-control between tobacco consumers and non-consumers in Hormozgan Province.MethodsThe present cross-sectional study was conducted on the adult population above the age of 15 years living in Hormozgan province. A total number of 1,631 subjects were selected using a convenient sampling method. An online questionnaire was used to collect the data, which consisted of three sections: demographic information, Zimet's perceived social support and Tangney's self-control questionnaires. In the present study, Cronbach's alpha coefficients of social support and self-control questionnaires were 0.886 and 0.721, respectively. Data were analyzed using chi-squared test, Mann-Whitney U-test, and logistic regression analysis with SPSS software (v. 25).ResultsAmong the participants, 842 (51.6%) reported to be tobacco non-consumers, and 789 (48.4%) reported to be consumers. The mean scores of perceived social support among the consumers and non-consumers were 4.6 ± 1.012 and 4.93 ± 0.518, respectively. The mean scores of self-control among the consumers and non-consumers were 2.74 ± 0.356 and 2.75 ± 0.354, respectively. There was a significant difference among tobacco consumers and non-consumers in gender, age, education level and job status (p < 0.001). The results showed that the mean scores of social support, support received from family and others were significantly higher in non-consumers than in consumers (p < 0.001). There was no statistically significant difference between the mean scores of self-control, self-discipline, and impulse control in consumers and non-consumers (p > 0.05).ConclusionAccording to our findings, tobacco consumers received more social support from family and others compared to non-consumers. Considering the important role of perceived support in tobacco consumption, this variable should receive copious attention in developing interventions and trainings, especially family education workshops.

Project description:(1) Background: Magnesium supplementation may be effective for the prevention of cardiometabolic diseases, but the mechanisms are unclear. Proteomic approaches can assist in identifying the underlying mechanisms. (2) Methods: We collected repeated blood samples from 52 individuals enrolled in a double-blind trial which randomized participants 1:1 to oral magnesium supplementation (400 mg magnesium/day in the form of magnesium oxide) or a matching placebo for 10 weeks. Plasma levels of 91 proteins were measured at baseline with follow-up samples using the Olink Cardiovascular Disease III proximity extension assay panel and were modeled as arbitrary units in a log2 scale. We evaluated the effect of oral magnesium supplementation for changes in protein levels and the baseline association between serum magnesium and protein levels. The Holm procedure was used to adjust for multiple comparisons. (3) Results: Participants were 73% women, 94% white, and had a mean age of 62. Changes in proteins did not significantly differ between the two intervention groups after correction for multiple comparisons. The most statistically significant effects were on myoglobin [difference -0.319 log2 units, 95% confidence interval (CI) (-0.550, -0.088), p = 0.008], tartrate-resistant acid phosphatase type 5 (-0.187, (-0.328, -0.045), p = 0.011), tumor necrosis factor ligand superfamily member 13B (-0.181, (-0.332, -0.031), p = 0.019), ST2 protein (-0.198, (-0.363, -0.032), p = 0.020), and interleukin-1 receptor type 1 (-0.144, (-0.273, -0.015), p = 0.029). Similarly, none of the associations of baseline serum magnesium with protein levels were significant after correction for multiple comparisons. (4) Conclusions: Although we did not identify statistically significant effects of oral magnesium supplementation in this relatively small study, this study demonstrates the value of proteomic approaches for the investigation of mechanisms underlying the beneficial effects of magnesium supplementation. Clinical Trials Registration: ClinicalTrials.gov NCT02837328.

Project description:BackgroundA growing number of cohort studies revealed an inverse association between cheese intake and cardiovascular diseases, yet the causal relationship is unclear.ObjectiveTo assess the causal relationship between cheese intake, and cardiovascular diseases and cardiovascular biomarkers.MethodsA two-sample Mendelian randomization (MR) analysis based on publicly available genome-wide association studies was employed to infer the causal relationship. The effect estimates were calculated using the random-effects inverse-variance-weighted method.ResultsCheese intake per standard deviation increase causally reduced the risks of type 2 diabetes (odds ratio (OR) = 0.46; 95% confidence interval (CI), 0.34-0.63; p = 1.02 × 10-6), heart failure (OR = 0.62; 95% CI, 0.49-0.79; p = 0.0001), coronary heart disease (OR = 0.65; 95% CI, 0.53-0.79; p = 2.01 × 10-5), hypertension (OR = 0.67; 95% CI, 0.53-0.84; p = 0.001), and ischemic stroke (OR = 0.76; 95% CI, 0.63-0.91; p = 0.003). Suggestive evidence of an inverse association between cheese intake and peripheral artery disease was also observed. No associations were observed for atrial fibrillation, cardiac death, pulmonary embolism, or transient ischemic attack. The better prognosis associated with cheese intake may be explained by lower body mass index (BMI; effect estimate = -0.58; 95% CI, from -0.88 to -0.27; p = 0.0002), waist circumference (effect estimate = -0.49; 95% CI, from -0.76 to -0.23; p = 0.0003), triglycerides (effect estimate = -0.33; 95% CI, from -0.50 to -0.17; p = 4.91 × 10-5), and fasting glucose (effect estimate = -0.20; 95% CI, from -0.33 to -0.07; p = 0.0003). There was suggestive evidence of a positive association between cheese intake and high-density lipoprotein. No influences were observed for blood pressure or inflammation biomarkers.ConclusionsThis two-sample MR analysis found causally inverse associations between cheese intake and type 2 diabetes, heart failure, coronary heart disease, hypertension, and ischemic stroke.

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Study of cardiovascular disease biomarkers among tobacco consumers, part 2: biomarkers of biological effect.

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Study of cardiovascular disease biomarkers among tobacco consumers, part 2: biomarkers of biological effect.

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