Current smoking is associated with a larger waist circumference and a more androgenic profile in young healthy women from high-risk breast cancer families

A Systematic Review and Meta-Analysis of Smoking and Circulating Sex Hormone Levels Among Premenopausal Women

It is established that higher pre-diagnostic circulating androgen and estrogen levels are associated with increased breast cancer risk in premenopausal and postmenopausal women. Pooled analyses in postmenopausal women report higher androgen and estrogen levels in current heavy cigarette smokers compared to nonsmokers. However, evidence among premenopausal women has been inconsistent. We conducted a systematic review and meta-analysis to estimate differences in standardized mean hormone levels among current premenopausal smokers compared to nonsmokers. We reviewed and collated publications with sex hormone levels by smoking status among healthy, premenopausal women who were nonusers of exogenous hormones, including oral contraceptives, using PubMed through December 2019. A random effects meta-analysis was conducted to combine the standardized mean differences (SMD) and 95% confidence intervals (CIs) for estradiol, progesterone, testosterone, dehydroepiandrosterone, dehydroepiandrosterone-sulfate, and sex hormone-binding globulin by smoking status. Findings were summarized by menstrual cycle phase and overall. Nineteen published peer-reviewed articles were included. Significantly increased testosterone levels among smokers compared to nonsmokers were identified from cross-sectional studies with varied menstrual phase timing (SMD 0.14; 95% CI 0.0005, 0.29) and significantly increased dehydroepiandrosterone-sulfate levels were found over all phases (SMD 0.12; 95% CI 0.01, 0.22). However, substantial heterogeneity existed in these studies. This meta-analysis suggests that smoking may increase blood androgen levels in healthy premenopausal women which may increase breast cancer risk; however, the differences were modest. Larger and covariate-adjusted studies with standardized collection over the menstrual cycle are needed to better understand this relationship and to reduce heterogeneity. Implications: Existing research has described associations between high pre-diagnostic estradiol and androgen levels with breast cancer risk among premenopausal women and has established active smoking as a breast cancer risk factor. However, the smoking and circulating sex hormone associations among premenopausal women remain inadequately studied. In this meta-analysis, we identified an association between smoking and higher mean testosterone and dehydroepiandrosterone-sulfate levels with consideration of menstrual phase, providing additional information on smoking's potential pathway to premenopausal breast cancer.

Negative Association between Acrylamide Exposure and Metabolic Syndrome Markers in Adult Population

Metabolic syndrome encompasses multiple conditions that increase the risk of cardiovascular disease, and exposure to environmental chemicals can cause metabolic syndrome. This cross-sectional study analyzed data from the US National Health and Nutrition Examination Survey (2003-2006) on 4318 adult participants to assess the association between acrylamide (AA) exposure and metabolic syndrome. Concentrations of hemoglobin-adducted AA (HbAA) and hemoglobin-adducted glycidamide (HbGA) were evaluated. Metabolic syndrome markers related to HbAA and HbGA and the effect of exposure to AA and GA on the prevalence of metabolic syndrome were studied by ANOVA and multivariate logistic regression analyses, respectively. HbAA concentration inversely correlated with the number of metabolic syndrome markers (p < 0.05). An increased HbAA concentration was noted with reduced high triglyceride and low high-density lipoprotein cholesterol levels in the adjusted model (p < 0.05). High fasting plasma glucose level significantly correlated with HbGA concentration in the adjusted model. In conclusion, AA exposure alters metabolic syndrome markers in adults. Additional clinical and animal studies will clarify the role of AA exposure at different stages in the progression of metabolic syndrome-related diseases.

Cigarette smoking and nicotine exposure contributes for aberrant insulin signaling and cardiometabolic disorders

Cigarette smoking- and nicotine-mediated dysregulation in insulin-signaling pathways are becoming leading health issues associated with morbidity and mortality worldwide. Many cardiometabolic disorders particularly insulin resistance, polycystic ovary syndrome (PCOS), central obesity and cardiovascular diseases are initiated from exposure of exogenous substances which augment by disturbances in insulin signaling cascade. Among these exogenous substances, nicotine and cigarette smoking are potential triggers for impairment of insulin-signaling pathways. Further, this aberrant insulin signaling is associated with many metabolic complications, which consequently give rise to initiation as well as progression of these metabolic syndromes. Hence, understanding the underlying molecular mechanisms responsible for cigarette smoking- and nicotine-induced altered insulin signaling pathways and subsequent participation in several health hazards are quite essential for prophylaxis and combating these complications. In this article, we have focused on the role of nicotine and cigarette smoking mediated pathological signaling; for instance, nicotine-mediated inhibition of nuclear factor erythroid 2-related factor 2 and oxidative damage, elevated cortisol that may promote central obesity, association PCOS and oxidative stress via diminished nitric oxide which may lead to endothelial dysfunction and vascular inflammation. Pathological underlying molecular mechanisms involved in mediating these metabolic syndromes via alteration of insulin signaling cascade and possible molecular mechanism responsible for these consequences on nicotine exposure have also been discussed.

Non-Alcoholic Fatty Liver Disease Is Strongly Associated with Smoking Status and Is Improved by Smoking Cessation in Japanese Males: A Retrospective Study

BACKGROUND

Cigarette smoking is known to be a significant risk factor associated with non-alcoholic fatty liver disease (NAFLD). We aimed to examine the association between smoking status and the severity of fatty liver with regard to sex and smoking cessation.

METHODS

In total, 13,466 subjects (6,642 males and 6,824 females) who had undergone abdominal ultrasonography for health check-up, multivariable logistic regression analysis was retrospectively conducted to assess the association between smoking status and the prevalence of NAFLD stratified by sex after adjusting for other potential confounders.

RESULTS

Male sex (odds ratio [OR] 3.27, 95% confidence interval [CI] 3.00-3.57) and smoking history (former smoker: OR 1.23, 95% CI 1.10-1.38, current smoker: OR 1.31, 95% CI 1.17-1.47) were significantly associated with NAFLD. In males with a smoking history, an increased pack-year was strongly associated with the prevalence and severity of NAFLD (prevalence of moderate to severe fatty liver: Pack-year from 0.01 to 9.99, 21.3%; Pack-year from 10.00 to 19.99, 27.2%; Pack-year ≥20.00, 33.7%; P<0.0001), although the prevalence of moderate to severe fatty liver was inversely associated with the duration of smoking cessation (more than 10 years vs. within 5 years, OR 0.71, 95% CI 0.53-0.96). In female subjects, light current smoking was negatively associated with NAFLD (current smoker with a pack-year from 0.01 to 9.99 vs. never smoker, OR 0.41, 95% CI 0.19-0.76).

CONCLUSIONS

Smoking status and pack-year were strongly associated with the prevalence and severity of NAFLD, especially in Japanese males. However, smoking cessation improved NAFLD in this population.

A Theoretical Approach for Correlating Proteins to Malignant Diseases

Malignant Tumors are developed over several years due to unknown biological factors. These biological factors induce changes in the body and consequently, they lead to Malignant Tumors. Some habits and behaviors initiate these biological factors. In effect, the immune system cannot recognize a Malignant Tumor as foreign tissue. In order to discover a fascinating pattern of these habits, behaviors, and diseases and to make effective decisions, different machine learning techniques should be used. This research attempts to find the association between normal proteins (environmental factors) and diseases that are difficult to diagnose and propose justifications for those diseases. This paper proposes a technique for medical data mining using association rules. The proposed technique overcomes some of the limitations in current association algorithms such as the Apriori algorithm and the Equivalence CLAss Transformation (ECLAT) algorithm. A modification to the Apriori algorithm has been proposed to mine Erythrocytes Dynamic Antigens Store (EDAS) data in a more efficient and tractable way. The experiments inferred that there is a relation between normal proteins as environment proteins, food proteins, commensal proteins, tissue proteins, and disease proteins. Also, the experiments show that habits and behaviors are associated with certain diseases. The presented tool can be used in clinical laboratories to discover the biological causes of malignant diseases.

The impact of body size changes on recurrence risk depends on age and estrogen receptor status in primary breast cancer

PURPOSE

To investigate the prognostic impact of body size changes during the first postoperative year in breast cancer.

METHODS

A cohort of 1,317 primary breast cancer patients included in Sweden (2002-2014) underwent body size measurements at the preoperative and 1-year visits (n = 1,178). Landmark survival analyses were used to investigate how postoperative weight gain or loss (> 5%) or change in waist-hip ratio (WHR) categories (≤ 0.85 or > 0.85) impact prognosis.

RESULTS

Median age at inclusion was 61 years and body mass index 25.1 kg/m². After a median follow-up of 5.0 years from inclusion, 165 recurrences and 77 deaths occurred. Weight gain (17.0%) conferred over twofold recurrence risk only in patients < 50 years (P_interaction = 0.033). Weight loss (8.6%) was only associated with a poor prognosis in patients ≥ 70 years, but not after restriction analysis. Weight change did not impact prognosis in patients 50 to < 70 years. Changes between WHR categories were associated with differential recurrence risk depending on estrogen receptor (ER) status (P_interaction = 0.007), with higher recurrence risk in patients with ER+ tumors and lower recurrence risk with ER- tumors.

CONCLUSION

Both changes in terms of weight and WHR category yielded independent prognostic information. Further research is imperative before recommending weight loss for all overweight breast cancer patients.

Applying Machine Learning of Erythrocytes Dynamic Antigens Store in Medicine

Erythrocytes Dynamic Antigens Store (EDAS) is a new discovery. EDAS consists of self-antigens and foreign (non-self) antigens. In patients with infectious diseases or malignancies, antigens of infection microorganism or malignant tumor exist in EDAS. Storing EDAS of normal individuals and patients in a database has, at least, two benefits. First, EDAS can be mined to determine biomarkers representing diseases which can enable researchers to develop a new line of laboratory diagnostic tests and vaccines. Second, EDAS can be queried, directly, to reach a precise diagnosis without the need to do many laboratory tests. The target is to find the minimum set of proteins that can be used as biomarkers for a particular disease. A hypothetical EDAS is created. Hundred-thousand records are randomly generated. The mathematical model of hypothetical EDAS together with the proposed techniques for biomarker discovery and direct diagnosis are described. The different possibilities that may occur in reality are experimented. Biomarkers' proteins are identified for pathogens and malignancies, which can be used to diagnose conditions that are difficult to diagnose. The presented tool can be used in clinical laboratories to diagnose disease disorders.