Citations

AbstractIntroductionThis study combined data across four independent cohorts to examine the positive and negative predictive values of an Alzheimer's disease (AD) blood test if implemented in primary care.MethodsBlood samples from 1329 subjects from multiple independent, multiethnic, community‐based, and clinic‐based cohorts were analyzed. A “locked‐down” referent group of 1128 samples was generated with 201 samples randomly selected for validation purposes. Random forest analyses were used to create the AD blood screen. Positive (PPV) and negative (NPV) predictive values were calculated.ResultsIn detecting AD, PPV was 0.81, and NPV was 0.95 while using the full AD blood test. When detecting mild cognitive impairment, PPV and NPV were 0.74 and 0.93, respectively. Preliminary analyses were conducted to detect any “neurodegenerative disease”. The full 21‐protein AD blood test yielded a PPV of 0.85 and NPV of 0.94.DiscussionThe present study creates the first‐ever multiethnic referent sample that spans community‐based and clinic‐based populations for implementation of an AD blood screen.

Abstract Background: Despite a large body of literature evaluating the association between recreational physical activity and epithelial ovarian cancer (EOC) risk, the extant evidence is inconclusive, and little is known about the independent association between recreational physical inactivity and EOC risk. We conducted a pooled analysis of nine studies from the Ovarian Cancer Association Consortium to investigate the association between chronic recreational physical inactivity and EOC risk. Methods: In accordance with the 2008 Physical Activity Guidelines for Americans, women reporting no regular, weekly recreational physical activity were classified as inactive. Multivariable logistic regression was utilized to estimate the ORs and 95% confidence intervals (CI) for the association between inactivity and EOC risk overall and by subgroups based upon histotype, menopausal status, race, and body mass index. Results: The current analysis included data from 8,309 EOC patients and 12,612 controls. We observed a significant positive association between inactivity and EOC risk (OR = 1.34; 95% CI, 1.14–1.57), and similar associations were observed for each histotype. Conclusions: In this large pooled analysis examining the association between recreational physical inactivity and EOC risk, we observed consistent evidence of an association between chronic inactivity and all EOC histotypes. Impact: These data add to the growing body of evidence suggesting that inactivity is an independent risk factor for cancer. If the apparent association between inactivity and EOC risk is substantiated, additional work via targeted interventions should be pursued to characterize the dose of activity required to mitigate the risk of this highly fatal disease. Cancer Epidemiol Biomarkers Prev; 25(7); 1114–24. ©2016 AACR.

Abstract Background: Many epithelial ovarian cancer (EOC) risk factors relate to hormone exposure and elevated estrogen levels are associated with obesity in postmenopausal women. Therefore, we hypothesized that gene–environment interactions related to hormone-related risk factors could differ between obese and non-obese women. Methods: We considered interactions between 11,441 SNPs within 80 candidate genes related to hormone biosynthesis and metabolism and insulin-like growth factors with six hormone-related factors (oral contraceptive use, parity, endometriosis, tubal ligation, hormone replacement therapy, and estrogen use) and assessed whether these interactions differed between obese and non-obese women. Interactions were assessed using logistic regression models and data from 14 case–control studies (6,247 cases; 10,379 controls). Histotype-specific analyses were also completed. Results: SNPs in the following candidate genes showed notable interaction: IGF1R (rs41497346, estrogen plus progesterone hormone therapy, histology = all, P = 4.9 × 10−6) and ESR1 (rs12661437, endometriosis, histology = all, P = 1.5 × 10−5). The most notable obesity–gene–hormone risk factor interaction was within INSR (rs113759408, parity, histology = endometrioid, P = 8.8 × 10−6). Conclusions: We have demonstrated the feasibility of assessing multifactor interactions in large genetic epidemiology studies. Follow-up studies are necessary to assess the robustness of our findings for ESR1, CYP11A1, IGF1R, CYP11B1, INSR, and IGFBP2. Future work is needed to develop powerful statistical methods able to detect these complex interactions. Impact: Assessment of multifactor interaction is feasible, and, here, suggests that the relationship between genetic variants within candidate genes and hormone-related risk factors may vary EOC susceptibility. Cancer Epidemiol Biomarkers Prev; 25(5); 780–90. ©2016 AACR.

Treatment of polycystic liver disease (PLD) focuses on symptom improvement. Generic questionnaires lack sensitivity to capture PLD‐related symptoms, a prerequisite to determine effectiveness of therapy. We developed and validated a disease‐specific questionnaire that assesses symptoms in PLD (PLD‐Q). We identified 16 PLD‐related symptoms (total score 0‐100 points) by literature review and interviews with patients and clinicians. The developed PLD‐Q was validated in Dutch (n = 200) and United States (US; n = 203) PLD patients. We assessed the correlation of PLD‐Q total score with European Organization for Research and Treatment of Cancer (EORTC) symptom scale, global health visual analogue scale (VAS) of EQ‐5D, and liver volume. To test discriminative validity, we compared PLD‐Q total scores of patients with different PLD severity stages (Gigot classification) and PLD‐Q total scores of PLD patients with general controls and polycystic kidney disease patients without PLD. Reproducibility was tested by comparing original test scores with 2‐week retest scores. In total, 167 Dutch and 124 US patients returned the questionnaire. Correlation between PLD‐Q total score and EORTC symptom scale (The Netherlands [NL], r = 0.788; US, r = 0.811) and global health VAS (NL, r = −0.517; US, r = −0.593) was good. There was no correlation of PLD‐Q total score with liver volume (NL, r = 0.138; P = 0.236; US, r = 0.254; P = 0.052). Gigot type III individuals scored numerically higher than type II patients (NL, 46 vs. 40; P = 0.089; US, 48 vs. 36; P = 0.055). PLD patients scored higher on the PLD‐Q total score than general controls (NL, 42 vs. 17; US, 40 vs. 13 points) and polycystic kidney disease patients without PLD (22 points). Reproducibility of PLD‐Q was excellent (NL, r = 0.94; US, 0.96). Conclusion: PLD‐Q is a valid, reproducible, and sensitive disease‐specific questionnaire that can be used to assess PLD‐related symptoms in clinical care and future research. (Hepatology 2016;64:151–160)

In April 2012, the Organ Procurement and Transplantation Network (OPTN) implemented an online explant pathology form for recipients of liver transplantation who received additional wait‐list priority for their diagnosis of hepatocellular carcinoma (HCC). The purpose of the form was to standardize the data being reported to the OPTN, which had been required since 2002 but were submitted to the OPTN in a variety of formats via facsimile. From April 2012 to December 2014, over 4500 explant forms were submitted, allowing for detailed analysis of the characteristics of the explanted livers. Data from the explant pathology forms were used to assess agreement with pretransplant imaging. Explant data were also used to assess the risk of recurrence. Of those with T2 priority, 55.7% were found to be stage T2 on explant. Extrahepatic spread (odds ratio [OR] = 6.8; P < 0.01), poor tumor differentiation (OR = 2.8; P < 0.01), microvascular invasion (OR = 2.6; P < 0.01), macrovascular invasion (OR = 3.2; P < 0.01), and whether the Milan stage based on the number and size of tumors on the explant form was T4 (OR = 2.4; P < 0.01) were the strongest predictors of recurrence. In conclusion, this analysis confirms earlier findings that showed an incomplete agreement between pretransplant imaging and posttransplant pathology in terms of HCC staging, though the number of patients with both no pretransplant treatment and no tumor in the explant was reduced from 20% to <1%. In addition, several factors were identified (eg, tumor burden, age, sex, region, ablative therapy, alpha‐fetoprotein, Milan stage, vascular invasion, satellite lesions, etc.) that were predictive of HCC recurrence, allowing for more targeted surveillance of high‐risk recipients. Continued evaluation of these data will help shape future guidelines or policy recommendations. Liver Transplantation 22 757–764 2016 AASLD.