Citations

Background: In individuals with a low diastolic blood pressure (DBP), the potential benefits or risks of intensive systolic blood pressure (SBP) lowering are unclear. Methods: SPRINT (Systolic Blood Pressure Intervention Trial) was a randomized controlled trial that compared the effects of intensive (target <120 mm Hg) and standard (target <140 mm Hg) SBP control in 9361 older adults with high blood pressure at increased risk of cardiovascular disease. The primary outcome was a composite of cardiovascular disease events. All-cause death and incident chronic kidney disease were secondary outcomes. This post hoc analysis examined whether the effects of the SBP intervention differed by baseline DBP. Results: Mean baseline SBP and DBP were 139.7±15.6 and 78.1±11.9 mm Hg, respectively. Regardless of the randomized treatment, baseline DBP had a U-shaped association with the hazard of the primary cardiovascular disease outcome. However, the effects of the intensive SBP intervention on the primary outcome were not influenced by baseline DBP level ( P for interaction=0.83). The primary outcome hazard ratio for intensive versus standard treatment was 0.78 (95% confidence interval, 0.57–1.07) in the lowest DBP quintile (mean baseline DBP, 61±5 mm Hg) and 0.74 (95% confidence interval, 0.61–0.90) in the upper 4 DBP quintiles (mean baseline DBP, 82±9 mm Hg), with an interaction P value of 0.78. Results were similar for all-cause death and kidney events. Conclusions: Low baseline DBP was associated with increased risk of cardiovascular disease events, but there was no evidence that the benefit of the intensive SBP lowering differed by baseline DBP. Clinical Trial Registration: URL: https://www.clinicaltrials.gov . Unique identifier: NCT01206062.

Obstructive sleep apnea (OSA) is associated with cardiometabolic diseases. Telomere shortening is linked to hypertension, diabetes mellitus, and cardiovascular diseases. Because these conditions are highly prevalent in OSA, we hypothesized that telomere length (TL) would be reduced in OSA patients. We identified 106 OSA and 104 non-OSA subjects who underwent polysomnography evaluation. Quantitative PCR was used to measure telomere length in genomic DNA isolated from peripheral blood samples. The association between OSA and TL was determined using unadjusted and adjusted linear models. There was no difference in TL between the OSA and non-OSA (control) group. However, we observed a J-shaped relationship between TL and OSA severity: the longest TL in moderate-to-severe OSA [4,918 ± 230 (SD) bp] and the shortest TL in mild OSA (4,735 ± 145 bp). Mean TL in moderate-to-severe OSA was significantly longer than in the control group after adjustment for age, sex, body mass index, hypertension, dyslipidemia, and depression (β = 96.0, 95% confidence interval: 15.4–176.6, P = 0.020). In conclusion, moderate-to-severe OSA is associated with telomere lengthening. Our findings support the idea that changes in TL are not unidirectional processes, such that telomere shortening occurs with age and disease but may be prolonged in moderate-to-severe OSA. NEW & NOTEWORTHY Here, we show that moderate-to-severe obstructive sleep apnea is associated with longer telomeres, independent of age and cardiovascular risk factors, challenging the hypothesis that telomere shortening is a unidirectional process related to age/disease. A better understanding of the mechanisms underlying telomere dynamics may identify targets for therapeutic intervention in cardiovascular aging/other chronic diseases.

Purpose: No previous population-based study has addressed the contribution of activation procedures to the yield of epileptiform abnormalities on serial EEGs. We assessed yield of activation-related epileptiform abnormalities and predictors of finding an activation-related abnormality with multiple EEGs in a population-based study of newly diagnosed epilepsy. Methods: We used the resources of the Rochester Epidemiology Project to identify 449 residents of Rochester, Minnesota with a diagnosis of newly diagnosed epilepsy at age 1 year or older, between 1960 and 1994, who had at least one EEG. Information on all activation procedures (i.e., sleep, hyperventilation, and photic activation) and seizure/epilepsy characteristics was obtained by comprehensive review of medical records. Results: At the first EEG, the yield of epileptiform abnormalities was greatest for individuals 1 to 19 years of age at diagnosis, for each activation procedure. The yield in patients aged 1 to 19 versus ≥20 years was 21.6% versus 10.3% for sleep, 6.5% versus 3.3% for photic stimulation, and 10.3% versus 5% for hyperventilation. Among young people (aged 1–19 years), sleep was associated with an increased likelihood of finding an activation-related abnormality on any EEG. The likelihood of finding an activation-related abnormality on any EEG was decreased for postnatal symptomatic and for unknown etiology. Conclusions: Among activation procedures, sleep showed the highest yield of epileptiform abnormalities. There was a low yield for photic stimulation and hyperventilation. Within each activation procedure, younger age at diagnosis had the greatest yield. Sleep is the most effective activation procedure, especially in younger patients, and should be performed when possible.

Abstract Background: High percent mammographic density (MD), which reflects the relative fibroglandular tissue content of the breast, is one of the strongest breast cancer risk factors; however, the pathologic mediators of this risk are unknown. We hypothesize that analysis of breast tissue sections using deep learning approaches may characterize histologic features that underpin risk associated with high MD. Methods: Non-targeted H&E stained breast tissue sections of diagnostic image-guided breast biopsies were evaluated among 588 women enrolled following an abnormal mammogram in the Breast Radiology Evaluation and Study of Tissues (BREAST) Stamp Project (2007-2010). Overall volumetric percent MD for the biopsied breast and localized volumetric percent MD surrounding the biopsy site were determined for each participant. A deep convolutional neural network (CNN) model was trained to identify and quantitatively assess breast epithelial, stroma and fat tissue and their organizational and spatial arrangements. Least absolute shrinkage and selection operator (Lasso) regression was used to determine relationships between MD measures and pathological features. To ensure reliability of the model, a cross-validation strategy was employed to build and assess the performance of the fitted model. Finally, Spearman correlation coefficients were estimated to test the association between the predicted density values by each model (predicting overall or localized MD) and the actual MD measurements. We report the average and standard deviation (SD) of the correlation coefficients. Results: In an independent validation set, the CNN model was 95.5% accurate in classifying epithelial, stromal and fat tissue. The mean (SD) correlations between the predicted model and the actual measurements for overall and localized MD were 0.70 (0.06) and 0.65 (0.06) respectively. The amount of stroma identified (normalized to tissue area) had the highest selection probability (P-value) by the Lasso model and thus the strongest positive relationship with MD (P-value &gt;0.9 for each MD measurement). In contrast, the amount of normalized epithelial tissue was not related to MD (P-value=0.01 for each MD measurement). No association was observed for the total normalized fat area with MD (P-value &lt;0.31 for each MD measurement). In addition, the number of distributed epithelial regions was positively associated, whereas the distance between epithelial regions was inversely associated with overall MD (P-value &lt;0.87 and 0.62, respectively). Conclusions: These results show that greater stromal tissue amount and spatial distribution patterns of epithelial regions, rather than total epithelial amounts, had the strongest relationships with elevated MD. Future work will determine the relationship of these MD features with biopsy diagnosis. Citation Format: Maeve Mullooly, Babak Ehteshami Bejnordi, Maya Palakal, Pamela M. Vacek, Donald L. Weaver, John A. Shepherd, Bo Fan, Amir Pasha Mahmoudzadeh, Jeff Wang, Jason M. Johnson, Sally D. Herschorn, Brian L. Sprague, Ruth M. Pfeiffer, Louise A. Brinton, Mark E. Sherman, Andrew Beck, Gretchen L. Gierach. Application of convolutional neural networks to breast biopsies to uncover tissue correlates of mammographic breast density [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 4235. doi:10.1158/1538-7445.AM2017-4235

Incidental imaging findings require an assessment of risk and clinical relevance, as well as consideration of further evaluation. Incidental findings are common on imaging obtained in the hospital, with pulmonary nodules being among the most frequent findings that may require additional evaluation. We conducted a retrospective study to determine the factors associated with documentation of incidental findings in the hospital discharge summary, using pulmonary nodules reported on abdominal computed tomography (CT) as an example of incidental findings with well‐defined follow‐up guidelines. Between January 1, 2012 and December 31, 2014, 7173 patients underwent in‐patient abdominal CT without concurrent chest CT; of these patients, 62.2% were ≥60 years old, 50.6% were men, and 45.5% were current or former smokers. Incidental pulmonary nodules were reported in 402 patients (5.6%; 95% confidence interval [CI], 5.1%‐6.2%). Based on nodule size, reported size stability, and patients' smoking status, 208 patients (2.9%; 95% CI, 2.5%‐3.3%) required follow‐up surveillance, per the 2005 Fleischner Society guidelines. Of these 208 patients, 48 (23%) received discharge summaries that included documentation of the incidental findings, with 34 summaries including a recommendation for nodule follow‐up and 19 summaries including a time frame for repeat CT. Three factors were positively associated with the inclusion of the pulmonary nodule in the discharge summary: mention of the pulmonary nodule in the summary headings of the radiology report (P ≤ 0.001), radiologist recommendations for further surveillance (P ≤ 0.001), and medical discharging service (P = 0.016). These findings highlight the need for a multidisciplinary systems‐based approach to incidental pulmonary nodule documentation and surveillance.