The angiotensin-converting enzyme insertion/deletion polymorphism and its associations with carotid artery wall thickness and coronary heart disease: The atherosclerosis risk in communities study

Coronary heart disease (CHD) is the leading cause of death in the United States. Recently, renin-angiotensin system (RAS) was found associated with atherosclerosis formation, with angiotensin II inducing vascular smooth muscle cell growth and migration, platelet activation and aggregation, and stimulation of plasminogen activator inhibitor-1. Angiotensin II is converted from angiotensin I by angiotensin I-converting enzyme (ACE) and this enzyme is mainly genetically determined. The ACE gene has been assigned to chromosome 17q23 and an insertion/deletion (I/D)polymorphism has been characterized by the presence/absence of a 287 bp fragment in intron 16 of the gene. The two alleles form three genotypes, namely, DD, ID and II and the DD genotype has been linked to higher plasma ACE levels and cell ACE activity.^ In this study, the association between the ACE I/D polymorphism and carotid artery wall thickness measured by B-mode ultrasound was investigated in a biracial sample, and the association between the gene and incident CHD was investigated in whites and if the gene-CHD association in whites, if any, was due to the gene effect on atherosclerosis. The study participants are from the prospective Atherosclerosis Risk in Communities (ARIC) Study, including adults aged 45 to 65 years. The present dissertation used a matched case-control design for studying the associations of the ACE gene with carotid artery atherosclerosis and an unmatched case-control design for the association of the gene with CHD. A significant recessive effect of the D allele on carotid artery thickness was found in blacks (OR = 3.06, 95% C.I: 1.11-8.47, DD vs. ID and II) adjusting for age, gender, cigarette smoking, LDL-cholesterol and diabetes. No similar associations were found in whites. The ACE I/D polymorphism is significantly associated with coronary heart disease in whites, and while stratifying data by carotid artery wall thickness, the significant associations were only observed in thin-walled subgroups. Assuming a recessive effect of the D allele, odds ratio was 2.84 (95% C.I:1.17-6.90, DD vs. ID and II) and it was 2.30 (95% C.I:1.22-4.35, DD vs. ID vs. II) assuming a codominant effect of the D allele. No significant associations were observed while comparing thick-walled CHD cases with thin-walled controls. Following conclusions could be drawn: (1) The ACE I/D polymorphism is unlikely to confer appreciable increase in the risk of carotid atherosclerosis in US whites, but may increases the risk of carotid atherosclerosis in blacks. (2) ACE I/D polymorphism is a genetic risk factor for incident CHD in US whites and this effect is separate from the chronic process of atherosclerosis development. Finally, the associations observed here are not causal, since the I/D polymorphism is in an intron, where no ACE proteins are encoded. ^

The role of workplace absence as an organizational mechanism in predicting employee alcohol and drug disorders

Background. EAP programs for airline pilots in companies with a well developed recovery management program are known to reduce pilot absenteeism following treatment. Given the costs and safety consequences to society, it is important to identify pilots who may be experiencing an AOD disorder to get them into treatment. ^ Hypotheses. This study investigated the predictive power of workplace absenteeism in identifying alcohol or drug disorders (AOD). The first hypothesis was that higher absenteeism in a 12-month period is associated with higher risk that an employee is experiencing AOD. The second hypothesis was that AOD treatment would reduce subsequent absence rates and the costs of replacing pilots on missed flights. ^ Methods. A case control design using eight years (time period) of monthly archival absence data (53,000 pay records) was conducted with a sample of (N = 76) employees having an AOD diagnosis (cases) matched 1:4 with (N = 304) non-diagnosed employees (controls) of the same profession and company (male commercial airline pilots). Cases and controls were matched on the variables age, rank and date of hire. Absence rate was defined as sick time hours used over the sum of the minimum guarantee pay hours annualized using the months the pilot worked for the year. Conditional logistic regression was used to determine if absence predicts employees experiencing an AOD disorder, starting 3 years prior to the cases receiving the AOD diagnosis. A repeated measures ANOVA, t tests and rate ratios (with 95% confidence intervals) were conducted to determine differences between cases and controls in absence usage for 3 years pre and 5 years post treatment. Mean replacement costs were calculated for sick leave usage 3 years pre and 5 years post treatment to estimate the cost of sick leave from the perspective of the company. ^ Results. Sick leave, as measured by absence rate, predicted the risk of being diagnosed with an AOD disorder (OR 1.10, 95% CI = 1.06, 1.15) during the 12 months prior to receiving the diagnosis. Mean absence rates for diagnosed employees increased over the three years before treatment, particularly in the year before treatment, whereas the controls’ did not (three years, x = 6.80 vs. 5.52; two years, x = 7.81 vs. 6.30, and one year, x = 11.00cases vs. 5.51controls. In the first year post treatment compared to the year prior to treatment, rate ratios indicated a significant (60%) post treatment reduction in absence rates (OR = 0.40, CI = 0.28, 0.57). Absence rates for cases remained lower than controls for the first three years after completion of treatment. Upon discharge from the FAA and company’s three year AOD monitoring program, case’s absence rates increased slightly during the fourth year (controls, x = 0.09, SD = 0.14, cases, x = 0.12, SD = 0.21). However, the following year, their mean absence rates were again below those of the controls (controls, x = 0.08, SD = 0.12, cases, x¯ = 0.06, SD = 0.07). Significant reductions in costs associated with replacing pilots calling in sick, were found to be 60% less, between the year of diagnosis for the cases and the first year after returning to work. A reduction in replacement costs continued over the next two years for the treated employees. ^ Conclusions. This research demonstrates the potential for workplace absences as an active organizational surveillance mechanism to assist managers and supervisors in identifying employees who may be experiencing or at risk of experiencing an alcohol/drug disorder. Currently, many workplaces use only performance problems and ignore the employee’s absence record. A referral to an EAP or alcohol/drug evaluation based on the employee’s absence/sick leave record as incorporated into company policy can provide another useful indicator that may also carry less stigma, thus reducing barriers to seeking help. This research also confirms two conclusions heretofore based only on cross-sectional studies: (1) higher absence rates are associated with employees experiencing an AOD disorder; (2) treatment is associated with lower costs for replacing absent pilots. Due to the uniqueness of the employee population studied (commercial airline pilots) and the organizational documentation of absence, the generalizability of this study to other professions and occupations should be considered limited. ^ Transition to Practice. The odds ratios for the relationship between absence rates and an AOD diagnosis are precise; the OR for year of diagnosis indicates the likelihood of being diagnosed increases 10% for every hour change in sick leave taken. In practice, however, a pilot uses approximately 20 hours of sick leave for one trip, because the replacement will have to be paid the guaranteed minimum of 20 hour. Thus, the rate based on hourly changes is precise but not practical. ^ To provide the organization with practical recommendations the yearly mean absence rates were used. A pilot flies on average, 90 hours a month, 1080 annually. Cases used almost twice the mean rate of sick time the year prior to diagnosis (T-1) compared to controls (cases, x = .11, controls, x = .06). Cases are expected to use on average 119 hours annually (total annual hours*mean annual absence rate), while controls will use 60 hours. The cases’ 60 hours could translate to 3 trips of 20 hours each. Management could use a standard of 80 hours or more of sick time claimed in a year as the threshold for unacceptable absence, a 25% increase over the controls (a cost to the company of approximately of $4000). At the 80-hour mark, the Chief Pilot would be able to call the pilot in for a routine check as to the nature of the pilot’s excessive absence. This management action would be based on a company standard, rather than a behavioral or performance issue. Using absence data in this fashion would make it an active surveillance mechanism. ^

Functional activation of p53 induces apoptosis in the absence of MDM2

The p53 tumor suppressor gene product is negatively regulated by the product of its downstream target, mdm2. The mdm2 oncogene abrogates p53 transactivation function. Amplification of mdm2 occurs in 36% of human sarcomas, which often retain p53 in wild type form, suggesting that overexpression of mdm2 in tumors results in p53 inactivation. Thus, the relationship of p53 to mdm2 is important in tumorigenesis. The deletion of mdm2 in the mouse results in embryonic lethality by 5.5 days post coitum. Embryonic lethality of the mdm2 null embryos was overcome by simultaneous loss of the p53 tumor suppressor, which substantiates the importance of the negative regulatory function of MDM2 on p53 function in vivo. These data suggest that the loss of MDM2 function allowed the constitutively active p53 protein to induce either a complete G1 arrest or the p53-dependent apoptotic pathway, resulting in the death of the mdm2−/− embryos.^ The present study examines the hypothesis that the absence of mdm2 induces apoptosis due to p53 activation. Viability of the p53−/−mdm2−/− mice has allowed establishment of mouse embryo fibroblasts (MEFs) and a detailed examination of the properties of these cells. To introduce p53 into this system, and essentially recreate a mdm2 null cell, a temperature sensitive p53 (tsp53) point mutant (A135V) was used, which exhibits a nonfunctional, mutant conformation at 39°C and wild type, functional conformation at 32°C. Infected pools of p53−/− and p53−/−mdm2−/− MEFs with the tsp53 gene were established and single-cell clonal populations expressing tsp53 were selected. Shifting the cells from 39°C to 32°C caused p53−/−mdm2 −/− lines expressing tsp53 to undergo up to 80% apoptosis, which did not occur in the p53−/− lines expressing tsp53 nor the parental lines lacking p53 expression. Furthermore, the amount of p53 present in the clonal population determined the extent of apoptosis. Tsp53 is transcriptionally active in this system, however, it discriminates among different target promoters and does not induce the apoptosis effector targets bax or Fas/Apo1. ^ In summary, this study indicates that the presence or absence of mdm2 is the determining factor for the ability of p53 to trigger apoptosis in this system. The loss of mdm2 promotes p53-dependent apoptosis in MEFs in a cell cycle and dose-dependent manner. p53 is differentially phosphorylated in the presence and absence of mdm2, but does not induce the apoptosis effectors, bax or Fas/ Apo1. ^

Left, right and interval bivariate censored data: Evaluating screening mammography in the presence of lead -time bias, length bias and over-detection

Of the large clinical trials evaluating screening mammography efficacy, none included women ages 75 and older. Recommendations on an upper age limit at which to discontinue screening are based on indirect evidence and are not consistent. Screening mammography is evaluated using observational data from the SEER-Medicare linked database. Measuring the benefit of screening mammography is difficult due to the impact of lead-time bias, length bias and over-detection. The underlying conceptual model divides the disease into two stages: pre-clinical (T0) and symptomatic (T1) breast cancer. Treating the time in these phases as a pair of dependent bivariate observations, (t0,t1), estimates are derived to describe the distribution of this random vector. To quantify the effect of screening mammography, statistical inference is made about the mammography parameters that correspond to the marginal distribution of the symptomatic phase duration (T1). This shows the hazard ratio of death from breast cancer comparing women with screen-detected tumors to those detected at their symptom onset is 0.36 (0.30, 0.42), indicating a benefit among the screen-detected cases. ^