Detecting genetic and nutritional lung cancer risk factors related to folate metabolism using Bayesian generalized linear models

Complex diseases, such as cancer, are caused by various genetic and environmental factors, and their interactions. Joint analysis of these factors and their interactions would increase the power to detect risk factors but is statistically. Bayesian generalized linear models using student-t prior distributions on coefficients, is a novel method to simultaneously analyze genetic factors, environmental factors, and interactions. I performed simulation studies using three different disease models and demonstrated that the variable selection performance of Bayesian generalized linear models is comparable to that of Bayesian stochastic search variable selection, an improved method for variable selection when compared to standard methods. I further evaluated the variable selection performance of Bayesian generalized linear models using different numbers of candidate covariates and different sample sizes, and provided a guideline for required sample size to achieve a high power of variable selection using Bayesian generalize linear models, considering different scales of number of candidate covariates. ^ Polymorphisms in folate metabolism genes and nutritional factors have been previously associated with lung cancer risk. In this study, I simultaneously analyzed 115 tag SNPs in folate metabolism genes, 14 nutritional factors, and all possible genetic-nutritional interactions from 1239 lung cancer cases and 1692 controls using Bayesian generalized linear models stratified by never, former, and current smoking status. SNPs in MTRR were significantly associated with lung cancer risk across never, former, and current smokers. In never smokers, three SNPs in TYMS and three gene-nutrient interactions, including an interaction between SHMT1 and vitamin B12, an interaction between MTRR and total fat intake, and an interaction between MTR and alcohol use, were also identified as associated with lung cancer risk. These lung cancer risk factors are worthy of further investigation.^

Effect of patient accrual patterns on power and size of log-rank test for time-to-event outcome in clinical trials

The determination of size as well as power of a test is a vital part of a Clinical Trial Design. This research focuses on the simulation of clinical trial data with time-to-event as the primary outcome. It investigates the impact of different recruitment patterns, and time dependent hazard structures on size and power of the log-rank test. A non-homogeneous Poisson process is used to simulate entry times according to the different accrual patterns. A Weibull distribution is employed to simulate survival times according to the different hazard structures. The current study utilizes simulation methods to evaluate the effect of different recruitment patterns on size and power estimates of the log-rank test. The size of the log-rank test is estimated by simulating survival times with identical hazard rates between the treatment and the control arm of the study resulting in a hazard ratio of one. Powers of the log-rank test at specific values of hazard ratio (≠1) are estimated by simulating survival times with different, but proportional hazard rates for the two arms of the study. Different shapes (constant, decreasing, or increasing) of the hazard function of the Weibull distribution are also considered to assess the effect of hazard structure on the size and power of the log-rank test. ^

Frequencies and risk factors for bisphosphonate-related osteonecrosis of the jaw in cancer patients: A matched case-control study

Bisphosphonates represent a unique class of drugs that effectively treat and prevent a variety of bone-related disorders including metastatic bone disease and osteoporosis. High tolerance and high efficacy rates quickly ranked bisphosphonates as the standard of care for bone-related diseases. However, in the early 2000s, case reports began to surface that linked bisphosphonates with osteonecrosis of the jaw (ONJ). Since that time, studies conducted have corroborated the linkage. However, as with most disease states, many factors can contribute to the onset of disease. The aim of this study was to determine which comorbid factors presented an increased risk for developing ONJ in cancer patients.^ Using a case-control study design, investigators used a combination of ICD-9 codes and chart review to identify confirmed cases of ONJ at The University of Texas M. D. Anderson Cancer Center (MDACC). Each case was then matched to five controls based on age, gender, race/ethnicity, and primary cancer diagnosis. Data querying and chart review provided information on variables of interest. These variables included bisphosphonate exposure, glucocorticoids exposure, smoking history, obesity, and diabetes. Statistical analysis was conducted using PASW (Predictive Analytics Software) Statistics, Version 18 (SPSS Inc., Chicago, Illinois).^ One hundred twelve (112) cases were identified as confirmed cases of ONJ. Variables were run using univariate logistic regression to determine significance (p < .05); significant variables were included in the final conditional logistic regression model. Concurrent use of bisphosphonates and glucocorticoids (OR, 18.60; CI, 8.85 to 39.12; p < .001), current smokers (OR, 2.52; CI, 1.21 to 5.25; p = .014), and presence of diabetes (OR, 1.84; CI, 1.06 to 3.20; p = .030) were found to increase the risk for developing ONJ. Obesity was not associated significantly with ONJ development.^ In this study, cancer patients that received bisphosphonates as part of their therapeutic regimen were found to have an 18-fold increase in their risk of developing ONJ. Other factors included smoking and diabetes. More studies examining the concurrent use of glucocorticoids and bisphosphonates may be able to strengthen any correlations.^

Performance of HbA1c for classifying diabetes and diagnostic threshold for diabetes among the Mexican American border community

Objective. In 2009, the International Expert Committee recommended the use of HbA1c test for diagnosis of diabetes. Although it has been recommended for the diagnosis of diabetes, its precise test performance among Mexican Americans is uncertain. A strong “gold standard” would rely on repeated blood glucose measurement on different days, which is the recommended method for diagnosing diabetes in clinical practice. Our objective was to assess test performance of HbA1c in detecting diabetes and pre-diabetes against repeated fasting blood glucose measurement for the Mexican American population living in United States-Mexico border. Moreover, we wanted to find out a specific and precise threshold value of HbA1c for Diabetes Mellitus (DM) and pre-diabetes for this high-risk population which might assist in better diagnosis and better management of patient diabetes. ^ Research design and methods. We used CCHC dataset for our study. In 2004, the Cameron County Hispanic Cohort (CCHC), now numbering 2,574, was established drawn from randomly selected households on the basis of 2000 Census tract data. The CCHC study randomly selected a subset of people (aged 18-64 years) in CCHC cohort households to determine the influence of SES on diabetes and obesity. Among the participants in Cohort-2000, 67.15% are female; all are Hispanic. ^ Individuals were defined as having diabetes mellitus (Fasting plasma glucose [FPG] ≥ 126 mg/dL or pre-diabetes (100 ≤ FPG < 126 mg/dL). HbA1c test performance was evaluated using receiver operator characteristic (ROC) curves. Moreover, change-point models were used to determine HbA1c thresholds compatible with FPG thresholds for diabetes and pre-diabetes. ^ Results. When assessing Fasting Plasma Glucose (FPG) is used to detect diabetes, the sensitivity and specificity of HbA1c≥ 6.5% was 75% and 87% respectively (area under the curve 0.895). Additionally, when assessing FPG to detect pre-diabetes, the sensitivity and specificity of HbA1c≥ 6.0% (ADA recommended threshold) was 18% and 90% respectively. The sensitivity and specificity of HbA1c≥ 5.7% (International Expert Committee recommended threshold) for detecting pre-diabetes was 31% and 78% respectively. ROC analyses suggest HbA1c as a sound predictor of diabetes mellitus (area under the curve 0.895) but a poorer predictor for pre-diabetes (area under the curve 0.632). ^ Conclusions. Our data support the current recommendations for use of HbA1c in the diagnosis of diabetes for the Mexican American population as it has shown reasonable sensitivity, specificity and accuracy against repeated FPG measures. However, use of HbA1c may be premature for detecting pre-diabetes in this specific population because of the poor sensitivity with FPG. It might be the case that HbA1c is differentiating the cases more effectively who are at risk of developing diabetes. Following these pre-diabetic individuals for a longer-term for the detection of incident diabetes may lead to more confirmatory result.^

Genetic underpinnings of variation in brain ventricular volume: A genome-wide association study

The ventricular system is a critical component of the central nervous system (CNS) that is formed early in the developmental stages and remains functional through the lifetime. Changes in the ventricular system can be easily discerned via neuroimaging procedures and most of the time it reflects changes in the physiology of the CNS. In this study we attempted to identify specific genes associated with variation in ventricular volume in humans. Methods. We conducted a genome wide association (GWA) analysis of the volume of the lateral ventricles among 1605 individuals of European ancestry from two community based cohorts, the Genetics of Microangiopathic Brain Injury (GMBI; N=814) and Atherosclerosis Risk in Communities (ARIC; N=791). Significant findings from the analysis were tested for replication in both the cohorts and then meta-analyzed to get an estimate of overall significance. Results. In our GWA analyses, no single nucleotide polymorphism (SNP) reached a genome-wide significance of p<10−8. There were 25 SNPs in GMBI and 9 SNPs in ARIC that reached a threshold of p<10 −5. However, none of the top SNPs from each cohort were replicated in the other. In the meta-analysis, no SNP reached the genome-wide threshold of 5×10−8, but we identified five novel SNPs associated with variation in ventricular volume at the p<10 −5 level. Strongest association was for rs2112536 in an intergenic region on chromosome 5q33 (Pmeta= 8.46×10−7 ). The remaining four SNPs were located on chromosome 3q23 encompassing the gene for Calsyntenin-2 (CLSTN2). The SNPs with strongest association in this region were rs17338555 (Pmeta= 5.28×10 −6), rs9812091 (Pmeta= 5.89×10−6 ), rs9812283 (Pmeta= 5.97×10−6) and rs9833213 (Pmeta= 6.96×10−6). Conclusions. This GWA study of ventricular volumes in the community-based cohorts of European descent identifies potential locus on chromosomes 3 and 5. Further characterization of these loci may provide insights into pathophysiology of ventricular involvement in various neurological diseases.^

Regression with autocorrelated data: A study of time trend of global infant mortality rate

The infant mortality rate (IMR) is considered to be one of the most important indices of a country's well-being. Countries around the world and other health organizations like the World Health Organization are dedicating their resources, knowledge and energy to reduce the infant mortality rates. The well-known Millennium Development Goal 4 (MDG 4), whose aim is to archive a two thirds reduction of the under-five mortality rate between 1990 and 2015, is an example of the commitment. ^ In this study our goal is to model the trends of IMR between the 1950s to 2010s for selected countries. We would like to know how the IMR is changing overtime and how it differs across countries. ^ IMR data collected over time forms a time series. The repeated observations of IMR time series are not statistically independent. So in modeling the trend of IMR, it is necessary to account for these correlations. We proposed to use the generalized least squares method in general linear models setting to deal with the variance-covariance structure in our model. In order to estimate the variance-covariance matrix, we referred to the time-series models, especially the autoregressive and moving average models. Furthermore, we will compared results from general linear model with correlation structure to that from ordinary least squares method without taking into account the correlation structure to check how significantly the estimates change.^

Metabolic syndrome in Mexican American females---prevalence and impact of age, socioeconomic status, and family history of type II diabetes

This thesis presents an analysis of data from Molecular Epidemiology of Type II Diabetes Mellitus in Mexican Americans. The study included 294 families. Among the participating families were 500 Mexican American females aged 19 to 86 who provided information on characteristics such as height, weight, and a variety of biochemical indicators. The research questions for this thesis are: (1) How strong is the association between indicators of the metabolic syndrome in study participants and their family histories of type II diabetes; and (2) How is an individual's family history of type II diabetes, age and socioeconomic status associated with the metabolic syndrome? In this thesis education status of the participants is used as an indicator of socioeconomic status. Answers to these questions are provided through the analysis of women's responses to written questionnaires and biochemical data. ^

Analysis of life expectancy using linear and nonlinear models

Life expectancy has consistently increased over the last 150 years due to improvements in nutrition, medicine, and public health. Several studies found that in many developed countries, life expectancy continued to rise following a nearly linear trend, which was contrary to a common belief that the rate of improvement in life expectancy would decelerate and was fit with an S-shaped curve. Using samples of countries that exhibited a wide range of economic development levels, we explored the change in life expectancy over time by employing both nonlinear and linear models. We then observed if there were any significant differences in estimates between linear models, assuming an auto-correlated error structure. When data did not have a sigmoidal shape, nonlinear growth models sometimes failed to provide meaningful parameter estimates. The existence of an inflection point and asymptotes in the growth models made them inflexible with life expectancy data. In linear models, there was no significant difference in the life expectancy growth rate and future estimates between ordinary least squares (OLS) and generalized least squares (GLS). However, the generalized least squares model was more robust because the data involved time-series variables and residuals were positively correlated. ^

Association between length of stay in the U.S. and diabetes risk factors among Chinese Americans in Houston, Texas in 2004-2005

Studies have suggested that acculturation is related to diabetes prevalence and risk factors among immigrant groups in the United States (U.S.), however scant data are available to investigate this relationship among Asian Americans and Asian American subgroups. The objective of this cross-sectional study was to examine the association between length of stay in the U.S. and type 2 diabetes prevalence and its risk factors among Chinese Americans in Houston, Texas. Data were obtained from the 2004-2005 Asian-American Health Needs Assessment in Houston, Texas (N=409 Chinese Americans) for secondary analysis in this study. Diabetes prevalence and risk factors (overweight/obesity and access to medical care) were based on self-report. Descriptive statistics summarized demographic characteristics, diabetes prevalence, and reasons for not seeing a doctor. Logistic regression, using an incremental modeling approach, was used to measure the association between length of stay and diabetes prevalence and related risk factors, while adjusting for the potential confounding factors of age, gender, education level, and income level. Although the prevalence of type 2 diabetes was highest among those living in the U.S. for more than 20 years, there was no significant association between length of stay in the U.S. and diabetes prevalence among these Chinese Americans after adjustment for confounding factors. No association was found between length of stay in the U.S. and overweight/obese status among this population either, after adjusting for confounding factors, too. On the other hand, a longer length of stay was significantly associated with increased health insurance coverage in both unadjusted and adjusted models. The findings of this study suggest that length of stay in the U.S. alone may not be an indicator for diabetes risk among Chinese Americans. Future research should consider alternative models to measure acculturation (e.g., models that reflect acculturation as a multi-dimensional, not uni-dimensional process), which may more accurately depict its effect on diabetes prevalence and related risk factors.^

Correlation of blood type with thrombosis in pancreatic cancer patients

Objective: The study aimed to identify the risk factors involved in initiating thromboembolism (TE) in pancreatic cancer (PC) patients, with focus on ABO blood type. ^ Methods and Patients: There were 35.7% confirmed cases of TE and 64.3% cases remained free of TE (n=687). There were 12.7% only Pulmonary embolism (PE), 9% only Deep vein thrombosis (DVT), 53.5% only other sites, 3.3% combined PE and DVT, 8.6% combined PE and other sites, 9.8% combined DVT and other sites, and 3.3% all three combined cases. ^ Results: The risk factors for thrombosis identified by multivariate logistic regression were: history of previous anti-thrombotic treatment, tumor site in pancreatic body or tail, large tumor size, maximum glucose category more than 126 and 200 mg/dL. ^ The factors with worse overall survival by multivariate Cox regression and Kaplan Meier analyses were: locally advanced or metastatic stage, worsening performance status, high CA 19-9 levels, and HbA1C levels more than 6 %, at diagnosis. ^ There were 29.1% and 39.1% of the patients with thrombosis in the O and non-O blood type groups respectively. Both Non-O blood type (P=0.02) and the A, B and AB blood types (P= 0.007) were associated with thrombosis as compared to O type. The odds of thrombosis were nearly half in O blood type patients as compared to non-O blood type [OR-0.54 (95% C.I.- 0.37-0.79), P<0.001]. ^ Conclusion: A better understanding of the TE and PC relationship and involved risk factors may provide insights on tumor biology and patient response to prophylactic anticoagulation therapy.^

Data quality in trauma transfusion studies and the impact of missing data on predicting massive transfusion

Maximizing data quality may be especially difficult in trauma-related clinical research. Strategies are needed to improve data quality and assess the impact of data quality on clinical predictive models. This study had two objectives. The first was to compare missing data between two multi-center trauma transfusion studies: a retrospective study (RS) using medical chart data with minimal data quality review and the PRospective Observational Multi-center Major Trauma Transfusion (PROMMTT) study with standardized quality assurance. The second objective was to assess the impact of missing data on clinical prediction algorithms by evaluating blood transfusion prediction models using PROMMTT data. RS (2005-06) and PROMMTT (2009-10) investigated trauma patients receiving ≥ 1 unit of red blood cells (RBC) from ten Level I trauma centers. Missing data were compared for 33 variables collected in both studies using mixed effects logistic regression (including random intercepts for study site). Massive transfusion (MT) patients received ≥ 10 RBC units within 24h of admission. Correct classification percentages for three MT prediction models were evaluated using complete case analysis and multiple imputation based on the multivariate normal distribution. A sensitivity analysis for missing data was conducted to estimate the upper and lower bounds of correct classification using assumptions about missing data under best and worst case scenarios. Most variables (17/33=52%) had <1% missing data in RS and PROMMTT. Of the remaining variables, 50% demonstrated less missingness in PROMMTT, 25% had less missingness in RS, and 25% were similar between studies. Missing percentages for MT prediction variables in PROMMTT ranged from 2.2% (heart rate) to 45% (respiratory rate). For variables missing >1%, study site was associated with missingness (all p≤0.021). Survival time predicted missingness for 50% of RS and 60% of PROMMTT variables. MT models complete case proportions ranged from 41% to 88%. Complete case analysis and multiple imputation demonstrated similar correct classification results. Sensitivity analysis upper-lower bound ranges for the three MT models were 59-63%, 36-46%, and 46-58%. Prospective collection of ten-fold more variables with data quality assurance reduced overall missing data. Study site and patient survival were associated with missingness, suggesting that data were not missing completely at random, and complete case analysis may lead to biased results. Evaluating clinical prediction model accuracy may be misleading in the presence of missing data, especially with many predictor variables. The proposed sensitivity analysis estimating correct classification under upper (best case scenario)/lower (worst case scenario) bounds may be more informative than multiple imputation, which provided results similar to complete case analysis.^

Developing the follow-up schedules for women with breast cancer

Background: The follow-up care for women with breast cancer requires an understanding of disease recurrence patterns and the follow-up visit schedule should be determined according to the times when the recurrence are most likely to occur, so that preventive measure can be taken to avoid or minimize the recurrence. Objective: To model breast cancer recurrence through stochastic process with an aim to generate a hazard function for determining a follow-up schedule. Methods: We modeled the process of disease progression as the time transformed Weiner process and the first-hitting-time was used as an approximation of the true failure time. The women's "recurrence-free survival time" or a "not having the recurrence event" is modeled by the time it takes Weiner process to cross a threshold value which represents a woman experiences breast cancer recurrence event. We explored threshold regression model which takes account of covariates that contributed to the prognosis of breast cancer following development of the first-hitting time model. Using real data from SEER-Medicare, we proposed models of follow-up visits schedule on the basis of constant probability of disease recurrence between consecutive visits. Results: We demonstrated that the threshold regression based on first-hitting-time modeling approach can provide useful predictive information about breast cancer recurrence. Our results suggest the surveillance and follow-up schedule can be determined for women based on their prognostic factors such as tumor stage and others. Women with early stage of disease may be seen less frequently for follow-up visits than those women with locally advanced stages. Our results from SEER-Medicare data support the idea of risk-controlled follow-up strategies for groups of women. Conclusion: The methodology we proposed in this study allows one to determine individual follow-up scheduling based on a parametric hazard function that incorporates known prognostic factors.^

Retrospective analysis of Methicillin-Resistant Staphylococcus Aureus (MRSA) skin and Soft-Tissue Infections (SSTI) in a university hospital emergency department (ED)

Study Objective: Identify the most frequent risk factors of Community Acquired-MRSA (CA-MRSA) Skin and Soft-tissue Infections (SSTIs) using a case series of patients and characterize them by age, race/ethnicity, gender, abscess location, druguse and intravenous drug-user (IVDU), underlying medical conditions, homelessness, treatment resistance, sepsis, those whose last healthcare visit was within the last 12 months, and describe the susceptibility pattern from this central Texas population that have come into the University Medical Center Brackenridge (UMCB) Emergency Department (ED). ^ Methods: This study was a retrospective case-series medical record review involving a convenience sample of patients in 2007 from an urban public hospital's ED in Texas that had a SSTI that tested positive for MRSA. All positive MRSA cultures underwent susceptibility testing to determine antibiotic resistance. The demographic and clinical variables that were independently associated with MRSA were determined by univariate and multivariate analysis using logistic regression to calculate odds ratios (OR), 95% confidence intervals, and significance (p≤ 0.05). ^ Results: In 2007, there were 857 positive MRSA cultures. The demographics were: males 60% and females 40%, with the average age of 36.2 (std. dev. =13) the study population consisted of non-Hispanic white (42%), Hispanics (38%), and non-Hispanic black (18.8%). Possible risk factors addressed included using recreational drugs (not including IVDU) (27%) homelessness (13%), diabetes status (12.6%) or having an infectious disease, and IVDU (10%). The most frequent abscess location was the leg (26.6%), followed by the arm and torso (both 13.7%). Eighty-three percent of patients had one prominent susceptibility pattern that had a susceptibility rate for the following antibiotics: trimethoprim/sulfamethoxazole (TMP-SMX) and vancomycin had 100%, gentamicin 99%, clindamycin 96%, tetracycline 96%, and erythromycin 56%. ^ Conclusion: The ED is becoming an important area for disease transmission between the sterile hospital environment and the outside environment. As always, it is important to further research in the ED in an effort to better understand MRSA transmission and antibiotic resistance, as well as to keep surveillance for the introduction of new opportunistic pathogens into the population. ^

An investigation of pertussis cases in a Texas county from 2008 through 2012

Background: Once thought to be eradicated, pertussis is now making a steady comeback throughout Texas and the United States. Pertussis can have an effect on all demographics, but infants have the greatest health concern as they suffer the highest case-fatality rate. The objective of this study was to create and report a comprehensive summary of confirmed or probable pertussis cases in a Texas County during the 2008 through 2012 time period.^ Methods: A cross-sectional study design was used to show at risk populations in a Texas county using descriptive statistics of data from probable and confirmed pertussis cases in this Texas County from 2008-2012. Data was collected during routine pertussis investigations conducted by the local health department of this Texas County.^ Results: There was a sharp increase in pertussis cases seen in this county in 2012. Hispanics made up the majority of cases (74.9%) as compared to 12.8% of cases among Whites, 3.1% of cases among Blacks and 9.2% of cases among unknown/other. The population of Hispanics within this county was 58.9%. Almost a quarter of cases (24.2%) in this study were hospitalized. There was no difference identified in the proportion of male sources of exposure (48.9%) as compared to female (51.1%). Household contacts were the main sources of exposure: siblings (29.2%), fathers (14.5%), children (14.6%), and mothers (12.5%).^ Conclusion: Prevention intervention needs to be designed to target vulnerable populations and reduce the effect of this sometimes fatal disease. These results show pertussis proportionally has a greater effect on Hispanics. Additional research needs to be conducted on risk factors such as household crowding and immunization status among Hispanics to identify if ethnicity plays a role in risk of transmission of pertussis. The results were limited due to the large amount of missing data in vaccination history and identification of source of exposure.^

Risk factors for inflammatory breast cancer

Background: Inflammatory breast cancer (IBC) is rare and accounts for 2.5% of all invasive breast cancers. The 5-year survival rates are significantly lower than for other types of breast cancer, highlighting the significance of cancer prevention in IBC. The comprehensive multi-disciplinary team Morgan Welch Inflammatory Breast Cancer Research Program and Clinic at University of Texas MD Anderson Cancer Center treats the largest number of Inflammatory Breast patients in a single center. Because of this unique center, large patient resources, and good medical and epidemiological records, we were able to conduct the largest single center case-control and case-case study on IBC. Methods: We identified 246 patients diagnosed with IBC and 397 cancer free patients seen at the Dan L Duncan Cancer Prevention Clinic. Breast cancer reproductive risk factors and lifestyle risk factors were compared between tumor subtypes of IBC patients (Estrogen Receptor positive (ER+) and/or Progesterone Receptor positive (PR+), Human Epidermal Growth Factor 2 positive (HER2+)), and (ER -/PR-/HER2-)) and cancer free controls. Results: Breastfeeding was the only significant risk factor (p<0.01) between tumor subtypes in IBC patients. In the case-control study that included all IBC patients and cancer free patients the descriptive statistics indicate significant difference in BMI, history of smoking, number of children, age of first pregnancy, any breastfeeding and total time breastfeeding (p<0.05). No differences were found in the frequency of other breast cancer risk factors. Conclusion: The associations determined between cancer free controls and IBC patients have identified previously unknown risk factors for IBC. The risk factors identified by the case control study suggest BMI, history of smoking, and the protective effect of breastfeeding as potential modifiable risk factors that can be used to decrease the incidence of IBC. Impact: These results highlight the importance of evaluating epidemiologic risk factors of IBC, which could lead to the identification of distinct etiologic pathways that could be targeted for prevention.^