Ryu, Euijung

Objectives Socioeconomic status (SES) is a well-established risk factor for many health outcomes. Recently, we developed an SES measure based on 4 housing-related characteristics (termed HOUSES) and demonstrated its ability to assess health disparities. In this study, we aimed to evaluate whether fewer housing-related characteristics could be used to provide a similar representation of SES. Study setting and participants We performed a cross-sectional study using parents/guardians of children aged 1–17 years from 2 US Midwestern counties (n=728 in Olmsted County, Minnesota, and n=701 in Jackson County, Missouri). Primary and secondary outcome measures For each participant, housing-related characteristics used in the formulation of HOUSES (assessed housing value, square footage, number of bedrooms and number of bathrooms) were obtained from the local government assessor's offices, and additional SES measures and health outcomes with known associations to SES (obesity, low birth weight and smoking exposure) were collected from a telephone survey. Housing characteristics with the greatest contribution for predicting the health outcomes were added to formulate a modified HOUSES index. Results Among the 4 housing characteristics used in the original HOUSES, the strongest contributions for predicting health outcomes were observed from assessed housing value and square footage (combined contribution ranged between 89% and 96%). Based on this observation, these 2 were used to calculate a modified HOUSES index. Correlation between modified HOUSES and other SES measures was comparable to the original HOUSES for both locations. Consistent with the original HOUSES formula, the strongest association with modified HOUSES was observed with smoking exposure (OR=0.24 with 95% CI 0.11 to 0.49 for comparing participants in highest HOUSES vs lowest group; overall p<0.001). Conclusions The modified HOUSES requires only 2 readily available housing characteristics thereby improving the feasibility of using this index as a proxy for SES in multiple communities, especially in the US Midwestern region.

ObjectivesBipolar disorder (BD) is a complex disease associated with various hereditary traits, including a higher body mass index (BMI). In a prior genome‐wide association study, we found that BMI modified the association of rs12772424 – a common variant in the gene encoding transcription factor 7‐like 2 (TCF7L2) – with risk for BD. TCF7L2 is a transcription factor in the canonical Wnt pathway, involved in multiple disorders, including diabetes, cancer and psychiatric conditions. Here, using an independent sample, we evaluated 26 TCF7L2 single nucleotide polymorphisms (SNPs) to explore further the association of BD with the TCF7L2–BMI interaction.MethodsUsing a sample of 662 BD cases and 616 controls, we conducted SNP‐level and gene‐level tests to assess the evidence for an association between BD and the interaction of BMI and genetic variation in TCF7L2. We also explored the potential mechanism behind the detected associations using human brain expression quantitative trait loci (eQTL) analysis.ResultsThe analysis provided independent evidence of an rs12772424–BMI interaction (p = 0.011). Furthermore, while overall there was no evidence for SNP marginal effects on BD, the TCF7L2–BMI interaction was significant at the gene level (p = 0.042), with seven of the 26 SNPs showing SNP–BMI interaction effects with p < 0.05. The strongest evidence of interaction was observed for rs7895307 (p = 0.006). TCF7L2 expression showed a significant enrichment of association with the expression of other genes in the Wnt canonical pathway.ConclusionsThe current study provides further evidence suggesting that TCF7L2 involvement in BD risk may be regulated by BMI. Detailed, prospective assessment of BMI, comorbidity, and other possible contributing factors is necessary to explain fully the mechanisms underlying this association.

The clinical utility of microbiome biomarkers depends on the reliable and reproducible nature of comparative results. Underappreciation of the variation associated with common demographic, health, and behavioral factors may confound associations of interest and generate false positives. Here, we present the Midwestern Reference Panel (MWRP), a resource for comparative gut microbiome studies conducted in the Midwestern United States. We analyzed the relationships between demographic and health behavior-related factors and the microbiota in this cohort, and estimated their effect sizes. Most variables investigated were associated with the gut microbiota. Specifically, body mass index (BMI), race, sex, and alcohol use were significantly associated with microbial β-diversity (P < 0.05, unweighted UniFrac). BMI, race and alcohol use were also significantly associated with microbial α-diversity (P < 0.05, species richness). Tobacco use showed a trend toward association with the microbiota (P < 0.1, unweighted UniFrac). The effect sizes of the associations, as quantified by adjusted R 2 values based on unweighted UniFrac distances, were small (< 1% for all variables), indicating that these factors explain only a small percentage of overall microbiota variability. Nevertheless, the significant associations between these variables and the gut microbiota suggest that they could still be potential confounders in comparative studies and that controlling for these variables in study design, which is the main objective of the MWRP, is important for increasing reproducibility in comparative microbiome studies.

Background Socioeconomic status (SES) is an important predictor for outcomes of chronic diseases. However, it is often unavailable in clinical data. We sought to determine whether an individual housing-based SES index termed HOUSES can influence the likelihood of multiple chronic conditions (MCC) and hospitalisation in a community population. Methods Participants were residents of Olmsted County, Minnesota, aged >18 years, who were enrolled in Mayo Clinic Biobank on 31 December 2010, with follow-up until 31 December 2011. Primary outcome was all-cause hospitalisation over 1 calendar-year. Secondary outcome was MCC determined through a Minnesota Medical Tiering score. A logistic regression model was used to assess the association of HOUSES with the Minnesota tiering score. With adjustment for age, sex and MCC, the association of HOUSES with hospitalisation risk was tested using the Cox proportional hazards model. Results Eligible patients totalled 6402 persons (median age, 57 years; 25th–75th quartiles, 45–68 years). The lowest quartile of HOUSES was associated with a higher Minnesota tiering score after adjustment for age and sex (OR (95% CI) 2.4 (2.0 to 3.1)) when compared with the highest HOUSES quartile. Patients in the lowest HOUSES quartile had higher risk of all-cause hospitalisation (age, sex, MCC-adjusted HR (95% CI) 1.53 (1.18 to 1.98)) compared with those in the highest quartile. Conclusions Low SES, as assessed by HOUSES, was associated with increased risk of hospitalisation and greater MCC health burden. HOUSES may be a clinically useful surrogate for SES to assess risk stratification for patient care and clinical research.

ObjectivesTo determine whether clinical features of bipolar disorder, such as history of psychosis, and cardiovascular disease (CVD) risk factors contribute to a higher risk of CVD among patients with bipolar disorder.MethodsThis cross‐sectional study included a sample of 988 patients with bipolar I or bipolar II disorder or schizoaffective bipolar type confirmed by the Structured Clinical Interview for DSM‐IV‐TR disorders (SCID). Medical comorbidity burden was quantified utilizing the Cumulative Illness Severity Rating Scale (CIRS). This 13‐item organ‐based scale includes cardiac disease severity quantification. Confirmed by medical record review, patients who scored 1 (current mild or past significant problem) or higher in the cardiac item were compared by logistic regression to patients who scored 0 (no impairment), adjusting for CVD risk factors that were selected using a backwards stepwise approach or were obtained from the literature.ResultsIn a multivariate model, age [odds ratio (OR) = 3.03, 95% confidence interval (CI): 1.66–5.54, p < 0.0001], hypertension (OR = 2.43, 95% CI: 1.69–3.55, p < 0.0001), and history of psychosis (OR = 1.48, 95% CI: 1.03–2.13, p = 0.03) were associated with CVD. When CVD risk factors from the literature were added to the analysis, age (OR = 3.19, 95% CI: 1.67–6.10, p = 0.0005) and hypertension (OR = 2.46, 95% CI: 1.61–3.76, p < 0.01) remained significant, with psychosis being at the trend level (OR = 1.43, 95% CI: 0.96–2.13, p = 0.08).ConclusionsThe phenotype of psychotic bipolar disorder may reflect higher illness severity with associated cardiac comorbidity. Further studies are encouraged to clarify the effect of the disease burden (i.e., depression), lifestyle, and treatment interventions (i.e., atypical antipsychotics) on this risk association.

Background/Aims: Depressive disorders (DEP) are often comorbid with other chronic conditions. Bidirectional associations between DEP and these conditions are poorly understood. In this study, we aimed to identify chronic conditions with the greatest influence on the DEP diagnosis. Methods: A validated electronic health record-based algorithm was applied to identify DEP patients receiving primary care at Mayo Clinic between 2000 and 2013. Cases were identified on the basis of having > 2 DEP-related ICD-9 diagnosis codes, > 1 antidepressant prescription, and > 3 mentions of in- or outpatient clinical notes for DEP. Controls were matched on birth year (± 2 years), sex and outpatient clinic visits at the same year. We ascertained 26 chronic condition categories, as defined by Chronic Conditions Data Warehouse, using the 5 years of medical records prior to the DEP diagnosis. For each age group at diagnosis (60 years), gradient boosting machine models were applied to estimate relative influence (RI) of the chronic conditions on the DEP diagnosis. Results: A total of 11,219 DEP cases were identified (median age at DEP diagnosis 44 years, 34% male and 89% white). The proportion of subjects with at least one co-occurring condition increased with older age and was higher in DEP cases compared to controls (9.5% vs. 6.9% for subjects 60 years). For subjects aged ≤ 45 years, diabetes mellitus (RI=19.6%) was identified as the most influential condition contributing to the risk of DEP, followed by asthma (RI=11.6%) and rheumatoid arthritis (RI=11.0%). For subjects aged 46–60 years, the most influential condition was rheumatoid arthritis (RI=13.2%) followed by diabetes mellitus (RI=12.9%). For subjects older than 60 years, dementia (RI=10.7%) and rheumatoid arthritis (RI=10.2%) showed similar relative contribution to the risk of DEP. Discussion: Among nonelderly adults who received primary care at Mayo Clinic (2000–2013), diabetes mellitus and rheumatoid arthritis were the strongest risk predictors for a diagnosis of depression among 26 chronic conditions considered. Our results suggest that certain chronic conditions may play a role in exacerbating, precipitating or increasing longitudinal risk of medically significant depression.

Abstract BACKGROUND: Biobanks collect and store biological samples and data from participants for use in research by multiple investigators. The Mayo Clinic Biobank was established by the Mayo Clinic Center for Individualized Medicine with a goal to support a wide array of health-related research studies at Mayo Clinic, including cancer studies. The Biobank is not focused on any particular disease, however, through annotation by questionnaires, the Mayo electronic medical record (EMR), and the Mayo Cancer Registry, it is a useful clinical research resource for cancer related studies. METHODS: Recruitment started in April 2009. Our goal is 50,000 enrollees. Eligible subjects are adult Mayo Clinic patients, U.S residents, and able to consent. Mailed invitations were sent to patients with appointments in certain Mayo Clinic departments. Each participant completed a health history questionnaire including a wide variety of health information as well as whether they or their 1st-degree relatives had ever been diagnosed with specific cancers. For self-reported cancers, subjects were asked to provide the age they were first diagnosed. Subjects also provided a blood sample and granted research access to clinically collected specimens and data from their Mayo Clinic record, at enrollment and into the future. All research projects utilizing samples from this resource are required to return to the Biobank the data generated from their projects. RESULTS: In the first three years, 21736 subjects consented to participate (30% response rate). Participants are 58% female, 95% White, and range in age from 18 to 97 with a median age of 62. At enrollment, 74% were residents in Minnesota, 6% in Iowa, 4% in Wisconsin, and 15% in the other states. Twelve percent reported being a current smoker, while 59% were never smokers. The most common self-reported cancers at baseline were non-melanoma skin cancer (n=2950), prostate cancer (n=1107), breast cancer (n=941), melanoma (n=692), colorectal cancer (n=296), cervical cancer (n=240), sarcoma (n=233), urinary/bladder (n=211), lymphoma (n=187), thyroid cancer (n=186), endometrial cancer (n=170), kidney cancer (n=163), lung cancer (n=145). Excluding non-melanoma skin cancer, 16188 (74%) (6516 male, 9672 female) self-reported being cancer free at baseline. Data will be presented on the frequency of incident cancers diagnosed since enrollment in the Biobank. To date, the Biobank has served as a source of controls for 17 cancer related studies on genetic susceptibility, including studies of the breast, colon, myeloma, brain, lung, pancreas, endometrium, and kidneys. CONCLUSIONS: The Mayo Clinic Biobank has quickly been established as a valuable resource for cancer researchers at Mayo Clinic - primarily as a source of controls for genetic studies. As the Biobank continues to grow in sample size and length of follow-up, it will also serve as a cohort study of cancer incidence embedded within an EMR of a large health organization. Citation Format: Janet E. Olson, Euijung Ryu, Kiley J. Johnson, Karen Maschke, Jody A. Morrisette, Mark Liebow, Paul Y. Takahashi, Ruchi G. Sharma, Kari S. Anderson, Matthew A. Hathcock, Jyotishman Pathak, Noralane M. Lindor, Timothy J. Beebe, Stephen N. Thibodeau, James R. Cerhan. The Mayo Clinic Biobank - A growing resource for cancer related studies. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 2515. doi:10.1158/1538-7445.AM2013-2515

AbstractWe provide a mini‐review of how biobanks can support clinical genetics in the era of personalized medicine. We discuss types of biobanks, including disease specific and general biobanks not focused on one disease. We present considerations in setting up a biobank, including consenting and governance, biospecimens, risk factor and related data, informatics, and linkage to electronic health records for phenotyping. We also discuss the uses of biobanks and ongoing considerations, including genotype‐driven recruitment, investigations of gene–environment associations, and the re‐use of data generated from studies. Finally, we present a brief discussion of some of the unresolved issues, such as return of research results and sustaining biobanks over time. In summary, carefully designed biobanks can provide critical research and infrastructure support for clinical genetics in the era of personalized medicine.