APOE and FABP2 Polymorphisms and History of Myocardial Infarction, Stroke, Diabetes, and Gallbladder Disease

Dysfunctional lipid metabolism plays a central role in pathogenesis of major chronic diseases, and genetic factors are important determinants of individual lipid profiles. We analyzed the associations of two well-established functional polymorphisms (FABP2 A54T and APOE isoforms) with past and family histories of 1492 population samples. FABP2-T54 allele was associated with an increased risk of past history of myocardial infarction (odds ratio (OR) = 1.51). Likewise, the subjects with APOE4, compared with E2 and E3, had a significantly increased risk of past history myocardial infarction (OR = 1.89). The OR associated with APOE4 was specifically increased in women for past history of myocardial infarction but decreased for gallstone disease. Interactions between gender and APOE isoforms were also significant or marginally significant for these two conditions. FABP2-T54 allele may be a potential genetic marker for myocardial infarction, and APOE4 may exert sex-dependent effects on myocardial infarction and gallbladder disease

Cancer Among Arab Americans in the Metropolitan Detroit Area

Detroit is home to one of the largest populations of Arab Americans outside of the Middle East, yet little is known about the cancer distribution in this ethnic group. The authors of this study created an Arab/Chaldean surname list and matched it with the Detroit SEER Registry to identify cancer cases of probable Arabic descent. We then determined proportional incidence ratios (PIR) for specific cancer sites among metropolitan Detroit Arab Americans as compared to non-Arab Whites, and contrasted the results with Middle Eastern data. Arab/Chaldean men had greater proportions of leukemia (29%), multiple myeloma (46%), liver (64%), kidney (33%), and urinary bladder (26%) cancers. Arab/Chaldean women had greater proportions of leukemia (23%), thyroid (57%), and brain (35%) cancers as compared with non-Arab White men and women. The cancers with significantly increased PIRs in the Detroit Arab/Chaldean population also are frequently diagnosed in Middle Eastern countries

Closed-loop optimization of fast-charging protocols for batteries with machine learning.

Simultaneously optimizing many design parameters in time-consuming experiments causes bottlenecks in a broad range of scientific and engineering disciplines1,2. One such example is process and control optimization for lithium-ion batteries during materials selection, cell manufacturing and operation. A typical objective is to maximize battery lifetime; however, conducting even a single experiment to evaluate lifetime can take months to years3-5. Furthermore, both large parameter spaces and high sampling variability3,6,7 necessitate a large number of experiments. Hence, the key challenge is to reduce both the number and the duration of the experiments required. Here we develop and demonstrate a machine learning methodology  to efficiently optimize a parameter space specifying the current and voltage profiles of six-step, ten-minute fast-charging protocols for maximizing battery cycle life, which can alleviate range anxiety for electric-vehicle users8,9. We combine two key elements to reduce the optimization cost: an early-prediction model5, which reduces the time per experiment by predicting the final cycle life using data from the first few cycles, and a Bayesian optimization algorithm10,11, which reduces the number of experiments by balancing exploration and exploitation to efficiently probe the parameter space of charging protocols. Using this methodology, we rapidly identify high-cycle-life charging protocols among 224 candidates in 16 days (compared with over 500 days using exhaustive search without early prediction), and subsequently validate the accuracy and efficiency of our optimization approach. Our closed-loop methodology automatically incorporates feedback from past experiments to inform future decisions and can be generalized to other applications in battery design and, more broadly, other scientific domains that involve time-intensive experiments and multi-dimensional design spaces

Epidemiologic Evaluation of Measurement Data in the Presence of Detection Limits

Quantitative measurements of environmental factors greatly improve the quality of epidemiologic studies but can pose challenges because of the presence of upper or lower detection limits or interfering compounds, which do not allow for precise measured values. We consider the regression of an environmental measurement (dependent variable) on several covariates (independent variables). Various strategies are commonly employed to impute values for interval-measured data, including assignment of one-half the detection limit to nondetected values or of “fill-in” values randomly selected from an appropriate distribution. On the basis of a limited simulation study, we found that the former approach can be biased unless the percentage of measurements below detection limits is small (5–10%). The fill-in approach generally produces unbiased parameter estimates but may produce biased variance estimates and thereby distort inference when 30% or more of the data are below detection limits. Truncated data methods (e.g., Tobit regression) and multiple imputation offer two unbiased approaches for analyzing measurement data with detection limits. If interest resides solely on regression parameters, then Tobit regression can be used. If individualized values for measurements below detection limits are needed for additional analysis, such as relative risk regression or graphical display, then multiple imputation produces unbiased estimates and nominal confidence intervals unless the proportion of missing data is extreme. We illustrate various approaches using measurements of pesticide residues in carpet dust in control subjects from a case–control study of non-Hodgkin lymphoma

Pregnancy outcomes in female childhood and adolescent cancer survivors: a linked cancer-birth registry analysis

Objective: To compare birth outcomes among childhood and adolescent female cancer survivors who subsequently bear children, relative to those of women without cancer history. Design: Retrospective cohort study. Setting: 4 U.S. regions. Participants: Cancer registries identified girls <20 years, diagnosed with cancer 1973-2000. Linked birth records identified first live births after diagnosis (n=1898). Comparison subjects were selected from birth records (n=14278). Cervical/genital tract cancer cases were analyzed separately. Main Exposure: Cancer diagnosis <20 years. Outcome Measures: Infant low birth weight, preterm delivery, sex ratio, malformations, mortality, delivery method; maternal diabetes, anemia, preeclampsia. Results: Childhood cancer survivors’ infants were more likely to be preterm (relative risk [RR] 1.54, 95% CI 1.30-1.83) and weigh <2500 g (RR 1.31, 95% CI 1.10-1.57). For cervical/genital cancer patients’ offspring, estimates were 1.33 (95% CI 1.13, 1.56), and 1.29 (95% CI 1.10-1.53), respectively. There were no increased risks of malformations, infant death, or altered sex ratio, suggesting no increased germ cell mutagenicity. In exploratory analysis, bone cancer survivors had an increased risk of diabetes (RR 4.92, 95% CI 1.60-15.13), and anemia was more common among brain tumor survivors (RR 3.05, 95% CI 1.16-7.98) and childhood cancer survivors with initial treatment of chemotherapy only (RR 2.45, 95% CI 1.16-5.17). Conclusions: Infants of female childhood and adolescent cancer patients were not at increased risk of malformations or death. Increased occurrence of preterm delivery and low birth weight suggest close monitoring is warranted. Increased diabetes and anemia among sub-groups have not been reported, suggesting areas for study

Reproductive outcomes in male childhood cancer survivors: a linked cancer-birth registry analysis

OBJECTIVE: Compare the risk of reproductive and infant outcomes between male childhood cancer survivors and a population-based comparison group. DESIGN: Retrospective cohort study. SETTING: 4 U.S. regions. PARTICIPANTS: Cancer registries identified males <20 years old diagnosed with cancer 1973-2000. Linked birth certificates identified first subsequent live offspring (n=470). Comparison subjects were identified from remaining birth certificates, frequency-matched on year and age at fatherhood, and race/ethnicity (n=4150). MAIN EXPOSURE: Cancer diagnosis prior to age 20. OUTCOME MEASURES: Pregnancy and infant outcomes identified from birth certificates. RESULTS: Compared with infants born to unaffected males, offspring of cancer survivors had a borderline risk of birth weight <2500 g (RR 1.43, 95% CI 0.99-2.05), with risk associated most strongly with younger age of cancer diagnosis and exposure to any chemotherapy (RR 1.96, 95% CI 1.22-3.17) or radiotherapy (RR 1.95, 95% CI 1.14-3.35). However, they were not at risk of being born prematurely, small for gestational age, having malformations or an altered male:female sex ratio. Overall, female partners of male survivors were not more likely to have maternal complications recorded on birth records versus the comparison group. However, preeclampsia was associated with some cancers, especially central nervous system tumors (RR 3.36, 95% CI 1.63-6.90). CONCLUSIONS: Most pregnancies resulting in live births among partners of male childhood cancer survivors were not at significantly greater risk of complications versus comparison subjects. The possibility of a paternal component affected by prior cancer history influencing predisposition towards some adverse perinatal outcomes merits further investigation

Human Leukocyte Antigen Class I and II Alleles and Overall Survival in Diffuse Large B-Cell Lymphoma and Follicular Lymphoma

Genetic variation in the 6p21 chromosomal region, including human leukocyte antigen (HLA) genes and tumor necrosis factor (TNF), has been linked to both etiology and clinical outcomes of lymphomas. We estimated the effects of HLA class I (A, B, and C), class II DRB1 alleles, and the ancestral haplotype (AH) 8.1 (HLAA*01-B*08-DRB1*03-TNF-308A) on overall survival (OS) among patients with diffuse large B-cell lymphoma (DLBCL) and follicular lymphoma (FL) in a population-based study of non-Hodgkin lymphoma. During a median followup of 89 months, 31% (52 of 166) DLBCL and 28% (46 of 165) FL patients died. Using multivariate Cox regression models, we observed statistically significant associations between genetic variants and survival: HLA-Cw*07:01 was associated with poorer OS among DLBCL patients (Hazard ratio [HR] = 1.76, 95% confidence interval [CI] = 1.01–3.05); HLA-A*01:01 was associated with poorer OS (HR = 2.23, 95% CI = 1.24–4.01), and HLA-DRB1*13 (HR = 0.12, 95% CI = 0.02–0.90) and HLA-B Bw4 (HR = 0.36, 95% CI = 0.20–0.63) with better OS among FL patients. These results support a role for HLA in the prognosis of DLBCL and FL and represent a promising class of prognostic factors that warrants further evaluation

Sexually transmitted diseases and other urogenital conditions as risk factors for prostate cancer: a case–control study in Wayne County, Michigan

Objective To investigate associations between prostate cancer and sexually transmitted diseases (STDs), prostatitis, benign prostatic hyperplasia (BPH), and vasectomy in a population-based case–control study in Wayne County, Michigan, among African American and white men aged 50–74 years.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/42533/1/10552_2004_Article_3486.pd

Analysis of Environmental Chemical Mixtures and Non-Hodgkin Lymphoma Risk in the NCI-SEER NHL Study

Background: There are several suspected environmental risk factors for non-Hodgkin lymphoma (NHL). The associations between NHL and environmental chemical exposures have typically been evaluated for individual chemicals (i.e., one-by-one). Objectives: We determined the association between a mixture of 27 correlated chemicals measured in house dust and NHL risk. Methods: We conducted a population-based case–control study of NHL in four National Cancer Institute–Surveillance, Epidemiology, and End Results centers—Detroit, Michigan; Iowa; Los Angeles County, California; and Seattle, Washington—from 1998 to 2000. We used weighted quantile sum (WQS) regression to model the association of a mixture of chemicals and risk of NHL. The WQS index was a sum of weighted quartiles for 5 polychlorinated biphenyls (PCBs), 7 polycyclic aromatic hydrocarbons (PAHs), and 15 pesticides. We estimated chemical mixture weights and effects for study sites combined and for each site individually, and also for histologic subtypes of NHL. Results: The WQS index was statistically significantly associated with NHL overall [odds ratio (OR) = 1.30; 95% CI: 1.08, 1.56; p = 0.006; for one quartile increase] and in the study sites of Detroit (OR = 1.71; 95% CI: 1.02, 2.92; p = 0.045), Los Angeles (OR = 1.44; 95% CI: 1.00, 2.08; p = 0.049), and Iowa (OR = 1.76; 95% CI: 1.23, 2.53; p = 0.002). The index was marginally statistically significant in Seattle (OR = 1.39; 95% CI: 0.97, 1.99; p = 0.071). The most highly weighted chemicals for predicting risk overall were PCB congener 180 and propoxur. Highly weighted chemicals varied by study site; PCBs were more highly weighted in Detroit, and pesticides were more highly weighted in Iowa. Conclusions: An index of chemical mixtures was significantly associated with NHL. Our results show the importance of evaluating chemical mixtures when studying cancer risk