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      A Potential Protective Effect of Alcohol Consumption in Male Genital Lichen Sclerosus: A Case-Control Study


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          Materials and Methods

          A nested case-control study design was chosen. Subjects enrolled were adult male patients who had a circumcision between January 2010 and December 2020 at our university hospital, with a confirmed LSc diagnosis on pathology. Cases were matched with controls by age with a ratio of 1 : 1, all of whom were circumcised and had a negative pathology report. Data collection consisted of sociodemographic, behavioral, and past medical and familial history characteristics.


          A total of 94 patients were enrolled. The mean age was 49.81 (±22.92) in the group of men with LSc. No significant differences in sociodemographic characteristics (age and BMI) were found between the two compared groups. Smoking cannot predict LSc as opposed to alcohol consumption, which we found to be a protective factor against the appearance of LSc ( p=0.027). Men with LSc had significantly higher rates of diabetes ( p=0.021) and hypertension ( p=0.004). No associations were found between LSc and the presenting chief complaints, family history of LSc, and past penile trauma.


          In this study, we were able to compare multiple variables between 47 circumcised patients diagnosed with LSc and a control group. We found that LSc patients showed higher rates of diabetes and hypertension. A potential protective effect of alcohol consumption is to be explored in future projects with bigger sample sizes and higher statistical powers.

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          Statistical notes for clinical researchers: Chi-squared test and Fisher's exact test

          When we try to compare proportions of a categorical outcome according to different independent groups, we can consider several statistical tests such as chi-squared test, Fisher's exact test, or z-test. The chi-squared test and Fisher's exact test can assess for independence between two variables when the comparing groups are independent and not correlated. The chi-squared test applies an approximation assuming the sample is large, while the Fisher's exact test runs an exact procedure especially for small-sized samples. Chi-squared test 1. Independency test The chi-squared test is used to compare the distribution of a categorical variable in a sample or a group with the distribution in another one. If the distribution of the categorical variable is not much different over different groups, we can conclude the distribution of the categorical variable is not related to the variable of groups. Or we can say the categorical variable and groups are independent. For example, if men have a specific condition more than women, there is bigger chance to find a person with the condition among men than among women. We don't think gender is independent from the condition. If there is equal chance of having the condition among men and women, we will find the chance of observing the condition is the same regardless of gender and can conclude their relationship as independent. Examples 1 and 2 in Table 1 show perfect independent relationship between condition (A and B) and gender (male and female), while example 3 represents a strong association between them. In example 3, women had a greater chance to have the condition A (p = 0.7) compared to men (p = 0.3). The chi-squared test performs an independency test under following null and alternative hypotheses, H0 and H1, respectively. H0: Independent (no association) H1: Not independent (association) The test statistic of chi-squared test: χ 2 = ∑ ( 0 - E ) 2 E ~ χ 2 with degrees of freedom (r - 1)(c - 1), Where O and E represent observed and expected frequency, and r and c is the number of rows and columns of the contingency table. The first step of the chi-squared test is calculation of expected frequencies (E). E is calculated under the assumption of independent relation or, in other words, no association. Under independent relationship, the cell frequencies are determined only by marginal proportions, i.e., proportion of A (60/200 = 0.3) and B (1400/200 = 0.7) in example 2. In example 2, the expected frequency of the male and A cell is calculated as 30 that is the proportion of 0.3 (proportion of A) in 100 Males. Similarly, the expected frequency of the male and A cell is 50 that is the proportion of 0.5 (proportion of A = 100/200 = 0.5) in 100 Males in example 3 (Table 1). Expected frequency (E) of Male & A = Number of A * Number of Male Total number = p A * p male * total number The second step is obtaining (O - E)2/E for each cell and summing up the values over each cell. The final summed value follows chi-squared distribution. For the ‘male and A’ cell in example 3, (O - E)2/E = (30 - 50)2/50 = 8. Chi-squared statistic calculated = ∑ ( 0 - E ) 2 E = 8 + 8 + 8 + 8 = 32 in example 3. For examples 1 and 2, the chi-squared statistics equal zero. A big difference between observed value and expected value or a large chi-squared statistic implies that the assumption of independency applied in calculation of expected value is irrelevant to the observed data that is being tested. The degrees of freedom is one as the data has two rows and two columns: (r - 1) * (c - 1) = (2 - 1) * (2 - 1) = 1. The final step is making conclusion referring to the chi-squared distribution. We reject the null hypothesis of independence if the calculated chi-squared statistic is larger than the critical value from the chi-squared distribution. In the chi-squared distribution, the critical values are 3.84, 5.99, 7.82, and 9.49, with corresponding degrees of freedom of 1, 2, 3, and 4, respectively, at an alpha level of 0.5. Larger chi-square statistics than these critical values of specific corresponding degrees of freedom lead to the rejection of null hypothesis of independence. In examples 1 and 2, the chi-squared statistic is zero which is smaller than the critical value of 3.84, concluding independent relationship between gender and condition. However, data in example 3 have a large chi-squared statistic of 32 which is larger than 3.84; it is large enough to reject the null hypothesis of independence, concluding a significant association between two variables. The chi-squared test needs an adequate large sample size because it is based on an approximation approach. The result is relevant only when no more than 20% of cells with expected frequencies < 5 and no cell have expected frequency < 1.1 2. Effect size As the significant test does not tell us the degree of effect, displaying effect size is helpful to show the magnitude of effect. There are three different measures of effect size for chi-squared test, Phi (φ), Cramer's V (V), and odds ratio (OR). Among them φ and OR can be used as the effect size only in 2 × 2 contingency tables, but not for bigger tables. φ = χ 2 n V = χ 2 n · d f , where n is total number of observation, and df is degrees of freedom calculated by (r - 1) * (c - 1). Here, r and c are the numbers of rows and columns of the contingency table. In example 3, we can calculate them as φ = χ 2 n = 32 200 = 0.4 , V = χ 2 n · d f = 32 200 · 1 = 0.4 , and O R = 70 · 70 30 · 30 = 5.44 . Referring to Table 2, the effect size V = 0.4 is interpreted medium to large. If number of rows and/or columns are larger than 2, only Cramer's V is available. 3. Post-hoc pairwise comparison of chi-squared test The chi-squared test assesses a global question whether relation between two variables is independent or associated. If there are three or more levels in either variable, a post-hoc pairwise comparison is required to compare the levels of each other. Let's say that there are three comparative groups like control, experiment 1, and experiment 2 and we try to compare the prevalence of a certain disease. If the chi-squared test concludes that there is significant association, we may want to know if there is any significant difference in three compared pairs, between control and experiment 1, between control and experiment 2, and between experiment 1 and experiment 2. We can reduce the table into multiple 2 × 2 contingency tables and perform the chi-squared test with applying the Bonferroni corrected alpha level (corrected α = 0.05/3 compared pairs = 0.017). Fisher's exact test Fisher's exact test is practically applied only in analysis of small samples but actually it is valid for all sample sizes. While the chi-squared test relies on an approximation, Fisher's exact test is one of exact tests. Especially when more than 20% of cells have expected frequencies < 5, we need to use Fisher's exact test because applying approximation method is inadequate. Fisher's exact test assesses the null hypothesis of independence applying hypergeometric distribution of the numbers in the cells of the table. Many packages provide the results of Fisher's exact test for 2 × 2 contingency tables but not for bigger contingency tables with more rows or columns. For example, the SPSS statistical package automatically provides an analytical result of Fisher's exact test as well as chi-squared test only for 2 × 2 contingency tables. For Fisher's exact test of bigger contingency tables, we can use web pages providing such analyses. For example, the web page ‘Social Science Statistics’ (http://www.socscistatistics.com/tests/chisquare2/Default2.aspx) permits performance of Fisher exact test for up to 5 × 5 contingency tables. The procedure of chi-squared test and Fisher's exact test using IBM SPSS Statistics for Windows Version 23.0 (IBM Corp., Armonk, NY, USA) is as follows:
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            Alcohol’s Effects on the Cardiovascular System

            Alcohol use has complex effects on cardiovascular (CV) health. The associations between drinking and CV diseases such as hypertension, coronary heart disease, stroke, peripheral arterial disease, and cardiomyopathy have been studied extensively and are outlined in this review. Although many behavioral, genetic, and biologic variants influence the interconnection between alcohol use and CV disease, dose and pattern of alcohol consumption seem to modulate this most. Low-to-moderate alcohol use may mitigate certain mechanisms such as risk and hemostatic factors affecting atherosclerosis and inflammation, pathophysiologic processes integral to most CV disease. But any positive aspects of drinking must be weighed against serious physiological effects, including mitochondrial dysfunction and changes in circulation, inflammatory response, oxidative stress, and programmed cell death, as well as anatomical damage to the CV system, especially the heart itself. Both the negative and positive effects of alcohol use on particular CV conditions are presented here. The review concludes by suggesting several promising avenues for future research related to alcohol use and CV disease. These include using direct biomarkers of alcohol to confirm self-report of alcohol consumption levels; studying potential mediation of various genetic, socioeconomic, and racial and ethnic factors that may affect alcohol use and CV disease; reviewing alcohol–medication interactions in cardiac patients; and examining CV effects of alcohol use in young adults and in older adults.
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              Pathogenesis of alcoholic liver disease: role of oxidative metabolism.

              Alcohol consumption is a predominant etiological factor in the pathogenesis of chronic liver diseases, resulting in fatty liver, alcoholic hepatitis, fibrosis/cirrhosis, and hepatocellular carcinoma (HCC). Although the pathogenesis of alcoholic liver disease (ALD) involves complex and still unclear biological processes, the oxidative metabolites of ethanol such as acetaldehyde and reactive oxygen species (ROS) play a preeminent role in the clinical and pathological spectrum of ALD. Ethanol oxidative metabolism influences intracellular signaling pathways and deranges the transcriptional control of several genes, leading to fat accumulation, fibrogenesis and activation of innate and adaptive immunity. Acetaldehyde is known to be toxic to the liver and alters lipid homeostasis, decreasing peroxisome proliferator-activated receptors and increasing sterol regulatory element binding protein activity via an AMP-activated protein kinase (AMPK)-dependent mechanism. AMPK activation by ROS modulates autophagy, which has an important role in removing lipid droplets. Acetaldehyde and aldehydes generated from lipid peroxidation induce collagen synthesis by their ability to form protein adducts that activate transforming-growth-factor-β-dependent and independent profibrogenic pathways in activated hepatic stellate cells (HSCs). Furthermore, activation of innate and adaptive immunity in response to ethanol metabolism plays a key role in the development and progression of ALD. Acetaldehyde alters the intestinal barrier and promote lipopolysaccharide (LPS) translocation by disrupting tight and adherent junctions in human colonic mucosa. Acetaldehyde and LPS induce Kupffer cells to release ROS and proinflammatory cytokines and chemokines that contribute to neutrophils infiltration. In addition, alcohol consumption inhibits natural killer cells that are cytotoxic to HSCs and thus have an important antifibrotic function in the liver. Ethanol metabolism may also interfere with cell-mediated adaptive immunity by impairing proteasome function in macrophages and dendritic cells, and consequently alters allogenic antigen presentation. Finally, acetaldehyde and ROS have a role in alcohol-related carcinogenesis because they can form DNA adducts that are prone to mutagenesis, and they interfere with methylation, synthesis and repair of DNA, thereby increasing HCC susceptibility.

                Author and article information

                Adv Urol
                Adv Urol
                Advances in Urology
                15 March 2023
                : 2023
                : 7208312
                1Notre Dame des Secours University Hospital Center, Byblos, Lebanon
                2School of Medicine and Medical Sciences, Holy Spirit University of Kaslik, Jounieh, Lebanon
                Author notes

                Academic Editor: Kostis Gyftopoulos

                Author information
                Copyright © 2023 Joey El Khoury et al.

                This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

                : 9 October 2022
                : 5 March 2023
                : 8 March 2023
                Research Article



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