Matthew A. Hathcock

Preemptive genotyping for personalized medicine: design of the right drug, right dose, right time-using genomic data to individualize treatment protocol.

OBJECTIVE To report the design and implementation of the Right Drug, Right Dose, Right Time-Using Genomic Data to Individualize Treatment protocol that was developed to test the concept that prescribers can deliver genome-guided therapy at the point of care by using preemptive pharmacogenomics (PGx) data and clinical decision support (CDS) integrated into the electronic medical record (EMR). PATIENTS AND METHODS We used a multivariate prediction model to identify patients with a high risk of initiating statin therapy within 3 years. The model was used to target a study cohort most likely to benefit from preemptive PGx testing among the Mayo Clinic Biobank participants, with a recruitment goal of 1000 patients. We used a Cox proportional hazards model with variables selected through the Lasso shrinkage method. An operational CDS model was adapted to implement PGx rules within the EMR. RESULTS The prediction model included age, sex, race, and 6 chronic diseases categorized by the Clinical Classifications Software for International Classification of Diseases, Ninth Revision codes (dyslipidemia, diabetes, peripheral atherosclerosis, disease of the blood-forming organs, coronary atherosclerosis and other heart diseases, and hypertension). Of the 2000 Biobank participants invited, 1013 (51%) provided blood samples, 256 (13%) declined participation, 555 (28%) did not respond, and 176 (9%) consented but did not provide a blood sample within the recruitment window (October 4, 2012, through March 20, 2013). Preemptive PGx testing included CYP2D6 genotyping and targeted sequencing of 84 PGx genes. Synchronous real-time CDS was integrated into the EMR and flagged potential patient-specific drug-gene interactions and provided therapeutic guidance. CONCLUSION This translational project provides an opportunity to begin to evaluate the impact of preemptive sequencing and EMR-driven genome-guided therapy. These interventions will improve understanding and implementation of genomic data in clinical practice.

The Mayo Clinic Biobank: A Building Block for Individualized Medicine

OBJECTIVE To report the design and implementation of the first 3 years of enrollment of the Mayo Clinic Biobank. PATIENTS AND METHODS Preparations for this biobank began with a 4-day Deliberative Community Engagement with local residents to obtain community input into the design and governance of the biobank. Recruitment, which began in April 2009, is ongoing, with a target goal of 50,000. Any Mayo Clinic patient who is 18 years or older, able to consent, and a US resident is eligible to participate. Each participant completes a health history questionnaire, provides a blood sample, and allows access to existing tissue specimens and all data from their Mayo Clinic electronic medical record. A community advisory board provides ongoing advice and guidance on complex decisions. RESULTS After 3 years of recruitment, 21,736 individuals have enrolled. Fifty-eight percent (12,498) of participants are female and 95% (20,541) of European ancestry. Median participant age is 62 years. Seventy-four percent (16,171) live in Minnesota, with 42% (9157) from Olmsted County, where the Mayo Clinic in Rochester, Minnesota, is located. The 5 most commonly self-reported conditions are hyperlipidemia (8979, 41%), hypertension (8174, 38%), osteoarthritis (6448, 30%), any cancer (6224, 29%), and gastroesophageal reflux disease (5669, 26%). Among patients with self-reported cancer, the 5 most common types are nonmelanoma skin cancer (2950, 14%), prostate cancer (1107, 12% in men), breast cancer (941, 4%), melanoma (692, 3%), and cervical cancer (240, 2% in women). Fifty-six percent (12,115) of participants have at least 15 years of electronic medical record history. To date, more than 60 projects and more than 69,000 samples have been approved for use. CONCLUSION The Mayo Clinic Biobank has quickly been established as a valuable resource for researchers.

Secular trends in the incidence of primary hyperparathyroidism over five decades (1965-2010)☆

Introduction of automated serum calcium measurements in the 1970s resulted in a sharp rise in primary hyperparathyroidism (PHPT) incidence. However, recent investigations suggest a significant rise in PHPT incidence for unclear reasons. Our objective was to update our population-based secular trends in PHPT incidence, to determine if there has been a significant rise in PHPT incidence as suggested by others, and, if possible, to identify changes in clinical practice that might be responsible. Rochester, Minnesota, residents who met the criteria for PHPT from 2002 through 2010 were identified through the medical records-linkage system of the Rochester Epidemiology Project and added to the historical cohort beginning in 1965. Incidence rates were adjusted to the 2010 US white population. Altogether, 1142 Rochester residents have been diagnosed with PHPT since 1965, including 341 in 2002-2010. Over time, two periods of increased PHPT incidence occurred, one beginning in 1974 (121.7 per 100,000 person-years) and a second peak (86.2 per 100,000 person-years) starting in 1998. The median age of PHPT subjects has increased significantly from 55 years in 1985-1997 to 60 years of age in 1998-2010 and more patients (36%) had a parathyroidectomy in 1998-2010. Although serum calcium measurement has declined since 1996, there was a progressive increase in parathyroid hormone testing between 1994 and 2008. There was also a rise in orders for bone mineral density measurements in women since 1998, which peaked in 2003-2004. A second sharp rise in PHPT incidence occurred in our community in 1998, simultaneously with the introduction of national osteoporosis screening guidelines, Medicare coverage for bone density measurement, and new medications for the treatment of osteoporosis. Case ascertainment bias from targeted PHPT screening in patients being evaluated for osteoporosis is the most likely explanation.

Impact of demographics on human gut microbial diversity in a US Midwest population

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 R2 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.

Pre-transplant wasting (as measured by muscle index) is a novel prognostic indicator in lung transplantation

Frailty in non‐transplant populations increases morbidity and mortality. Muscle wasting is an important frailty characteristic. Low body mass index is used to measure wasting, but can over‐ or underestimate muscle mass. Computed tomography (CT) software can directly measure muscle mass. It is unknown if muscle wasting is important in lung transplantation.

Weight loss prior to lung transplantation is associated with improved survival

BACKGROUND Obesity is associated with increased mortality after lung transplantation and is a relative contraindication to transplant. It is unknown whether weight reduction prior to transplantation ameliorates this risk. Our objective was to determine whether weight loss prior to lung transplantation improves survival. METHODS Our investigation was a two-center, retrospective cohort study of lung transplant recipients between January 1, 2000 and November 5, 2010. Change in weight, demographics, transplant details, lung allocation score, length of intensive care and mechanical ventilator days and graft and patient survival were abstracted. Wilcoxons signed-rank test and the Cox proportional hazard model were used for analysis where appropriate. RESULTS Three hundred fifty-five patients (55% male, median age 59 years) satisfied inclusion and exclusion criteria. After adjusting for standard demographic and clinical measures, a 1-unit reduction in BMI pre-transplant was associated with a reduced risk of death with a hazard ratio 0.89 (95% confidence interval 0.82 to 0.96; p = 0.004). This survival benefit persisted in the group with baseline BMI ≥ 25 kg/m(2) (overweight and obese) and hazard ratio 0.85 (95% CI 0.77 to 0.95; p = 0.003), but not in those with a BMI ≤ 24.9 kg/m(2). The 1-unit reduction in BMI was also associated with a 6.1% decrease in median mechanical ventilator days (p = 0.02) and a trend toward decreased intensive care unit length of stay (p = 0.06). CONCLUSIONS A reduction in BMI prior to lung transplantation was associated with a reduction in the risk of death and mechanical ventilator days. A greater reduction in BMI was associated with a greater survival benefit.

Epidemiology of invasive fungal infections in lung transplant recipients on long-term azole antifungal prophylaxis

Lung transplant recipients (LTR) at our institution receive prolonged and mostly lifelong azole antifungal (AF) prophylaxis. The impact of this prophylactic strategy on the epidemiology and outcome of invasive fungal infections (IFI) is unknown. This was a single‐center, retrospective cohort study. We reviewed the medical records of all adult LTR from January 2002 to December 2011. Overall, 16.5% (15 of 91) of patients who underwent lung transplantation during this time period developed IFI. Nineteen IFI episodes were identified (eight proven, 11 probable), 89% (17 of 19) of which developed during AF prophylaxis. LTR with idiopathic pulmonary fibrosis were more likely to develop IFI (HR: 4.29; 95% CI: 1.15–15.91; p = 0.03). A higher hazard of mortality was observed among those who developed IFI, although this was not statistically significant (hazard ratio [HR]: 1.71; 95% confidence interval [CI] [0.58–4.05]; p = 0.27). Aspergillus fumigatus was the most common cause of IFI (45%), with pulmonary parenchyma being the most common site of infection. None of our patients developed disseminated invasive aspergillosis, cryptococcal or endemic fungal infections. IFI continue to occur in LTR, and the eradication of IFI appears to be challenging even with prolonged prophylaxis. Azole resistance is uncommon despite prolonged AF exposure.

The HOXB13 G84E Mutation is Associated with an Increased Risk for Prostate Cancer and other Malignancies

Background: A rare nonconservative substitution (G84E) in the HOXB13 gene has been shown to be associated with risk of prostate cancer. DNA samples from male patients included in the Mayo Clinic Biobank (MCB) were genotyped to determine the frequency of the G84E mutation and its association with various cancers. Methods: Subjects were genotyped using a custom TaqMan (Applied Biosystems) assay for G84E (rs138213197). In addition to donating a blood specimen, all MCB participants completed a baseline questionnaire to collect information on medical history and family history of cancer. Results: Forty-nine of 9,012 male patients were carriers of G84E (0.5%). Thirty-one percent (n = 2,595) of participants had been diagnosed with cancer, including 51.1% of G84E carriers compared with just 30.6% of noncarriers (P = 0.004). G84E was most frequently observed among men with prostate cancer compared with men without cancer (P < 0.0001). However, the mutation was also more commonly observed in men with bladder cancer (P = 0.06) and leukemia (P = 0.01). G84E carriers were more likely to have a positive family history of prostate cancer in a first-degree relative compared to noncarriers (36.2% vs. 16.0%, P = 0.0003). Conclusions: Our study confirms the association between the HOXB13 G84E variant and prostate cancer and suggests a novel association between G84E and leukemia and a suggestive association with bladder cancer. Future investigation is warranted to confirm these associations in order to improve our understanding of the role of germline HOXB13 mutations in human cancer. Impact: The associations between HOXB13 and prostate, leukemia, and bladder suggest that this gene is important in carcinogenesis. Cancer Epidemiol Biomarkers Prev; 24(9); 1366–72. ©2015 AACR.

Liver Transplantation after Share 35: Impact on Pre‐ and Post‐Transplant Costs and Mortality

Share 35 was implemented in 2013 to direct livers to the most urgent candidates by prioritizing Model for End‐Stage Liver Disease (MELD) ≥ 35 patients. We aim to evaluate this policys impact on costs and mortality. Our study includes 834 wait‐listed patients and 338 patients who received deceased donor, solitary liver transplants at Mayo Clinic between January 2010 and December 2014. Of these patients, 101 (30%) underwent transplantation after Share 35. After Share 35, 29 (28.7%) MELD ≥ 35 patients received transplants, as opposed to 46 (19.4%) in the pre–Share 35 era (P = 0.06). No significant difference in 90‐day wait‐list mortality (P = 0.29) nor 365‐day posttransplant mortality (P = 0.68) was found between patients transplanted before or after Share 35. Mean costs were

Liver transplantation after share 35: Impact on pretransplant and posttransplant costs and mortality.

3,049 (P = 0.30),