Browsing by Subject "Clinical factors"

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    Adverse Health Outcomes in Relationship to Hypogonadism After Chemotherapy: A Multicenter Study of Testicular Cancer Survivors
    (National Comprehensive Cancer Network, 2019-05-01) Abu Zaid, Mohammad; Dinh, Paul C., Jr.; Monahan, Patrick O.; Fung, Chunkit; El-Charif, Omar; Feldman, Darren R.; Hamilton, Robert J.; Vaughn, David J.; Beard, Clair J.; Cook, Ryan; Althouse, Sandra; Ardeshir-Rouhani-Fard, Shirin; Sesso, Howard D.; Huddart, Robert; Mushiroda, Taisei; Kubo, Michiaki; Dolan, M. Eileen; Einhorn, Lawrence H.; Fossa, Sophie D.; Travis, Lois B.; Platinum Study Group; Medicine, School of Medicine
    Background: This study examined the prevalence of hypogonadism, its clinical and genetic risk factors, and its relationship to adverse health outcomes (AHOs) in North American testicular cancer survivors (TCS) after modern platinum-based chemotherapy. Patients and Methods: Eligible TCS were <55 years of age at diagnosis and treated with first-line platinum-based chemotherapy. Participants underwent physical examinations and completed questionnaires regarding 15 AHOs and health behaviors. Hypogonadism was defined as serum testosterone levels ≤3.0 ng/mL or use of testosterone replacement therapy. We investigated the role of 2 single nucleotide polymorphisms (rs6258 and rs12150660) in the sex hormone-binding globulin (SHBG) locus implicated in increased hypogonadism risk in the general population. Results: Of 491 TCS (median age at assessment, 38.2 years; range, 18.7–68.4 years), 38.5% had hypogonadism. Multivariable binary logistic regression analysis identified hypogonadism risk factors, including age at clinical evaluation (odds ratio [OR], 1.42 per 10-year increase; P=.006) and body mass index of 25 to <30 kg/m2 (OR, 2.08; P=.011) or ≥30 kg/m2 (OR, 2.36; P=.005) compared with <25 kg/m2. TCS with ≥2 risk alleles for the SHBG SNPs had a marginally significant increased hypogonadism risk (OR, 1.45; P=.09). Vigorous-intensity physical activity appeared protective (OR, 0.66; P=.07). Type of cisplatin-based chemotherapy regimen and socioeconomic factors did not correlate with hypogonadism. Compared with TCS without hypogonadism, those with hypogonadism were more likely to report ≥2 AHOs (65% vs 51%; P=.003), to take medications for hypercholesterolemia (20.1% vs 6.0%; P<.001) or hypertension (18.5% vs 10.6%; P=.013), and to report erectile dysfunction (19.6% vs 11.9%; P=.018) or peripheral neuropathy (30.7% vs 22.5%; P=.041). A marginally significant trend for increased use of prescription medications for either diabetes (5.8% vs 2.6%; P=.07) or anxiety/depression (14.8% vs 9.3%; P=.06) was observed. Conclusions: At a relatively young median age, more than one-third of TCS have hypogonadism, which is significantly associated with increased cardiovascular disease risk factors, and erectile dysfunction. Providers should screen TCS for hypogonadism and treat symptomatic patients.
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    Altered metabolite levels and correlations in patients with colorectal cancer and polyps detected using seemingly unrelated regression analysis
    (Springer Nature, 2017-11) Chen, Chen; Gowda, G. A. Nagana; Zhu, Jiangjiang; Deng, Lingli; Gu, Haiwei; Chiorean, E. Gabriela; Zaid, Mohammad Abu; Harrison, Marietta; Zhang, Dabao; Zhang, Min; Raftery, Daniel; Graduate Medical Education, IU School of Medicine
    Introduction: Metabolomics technologies enable the identification of putative biomarkers for numerous diseases; however, the influence of confounding factors on metabolite levels poses a major challenge in moving forward with such metabolites for pre-clinical or clinical applications. Objectives: To address this challenge, we analyzed metabolomics data from a colorectal cancer (CRC) study, and used seemingly unrelated regression (SUR) to account for the effects of confounding factors including gender, BMI, age, alcohol use, and smoking. Methods: A SUR model based on 113 serum metabolites quantified using targeted mass spectrometry, identified 20 metabolites that differentiated CRC patients (n = 36), patients with polyp (n = 39), and healthy subjects (n = 83). Models built using different groups of biologically related metabolites achieved improved differentiation and were significant for 26 out of 29 groups. Furthermore, the networks of correlated metabolites constructed for all groups of metabolites using the ParCorA algorithm, before or after application of the SUR model, showed significant alterations for CRC and polyp patients relative to healthy controls. Results: The results showed that demographic covariates, such as gender, BMI, BMI2, and smoking status, exhibit significant confounding effects on metabolite levels, which can be modeled effectively. Conclusion: These results not only provide new insights into addressing the major issue of confounding effects in metabolomics analysis, but also shed light on issues related to establishing reliable biomarkers and the biological connections between them in a complex disease.