Comparing Vascular Brain Injury and Stroke by Cranial Magnetic Resonance Imaging, Physician-Adjudication, and Self-Report: Data from the Strong Heart Study

<b><i>Background:</i></b> Epidemiologic studies often use self-report as proxy for clinical history. However, whether self-report correctly identifies prevalence in minority populations with health disparities and poor health-care access is unknown. Furthermore, overlap of clinical vascular events with covert vascular brain injury (VBI), detected by imaging, is largely unexamined. <b><i>Methods:</i></b> The Strong Heart Study recruited American Indians from 3 regions, with surveillance and adjudication of stroke events from 1989 to 2013. In 2010–2013, all 817 survivors, aged 65–95 years, underwent brain imaging, neurological history interview, and cognitive testing. VBI was defined as imaged infarct or hemorrhage. <b><i>Results:</i></b> Adjudicated stroke was prevalent in 4% of participants and separately collected, self-reported stroke in 8%. Imaging-defined VBI was detected in 51% and not associated with any stroke event in 47%. Compared with adjudication, self-report had 76% sensitivity and 95% specificity. Participants with adjudicated or self-reported stroke had the poorest performance on cognitive testing; those with imaging-only (covert) VBI had intermediate performance. <b><i>Conclusion:</i></b> In this community-based cohort, self-report for prior stroke had good performance metrics. A majority of participants with VBI did not have overt, clinically recognized events but did have neurological or cognitive symptoms. Data collection methodology for studies in a resource-limited setting must balance practical limitations in costs, accuracy, feasibility, and research goals.

DNA methylation and cancer incidence: lymphatic–hematopoietic versus solid cancers in the Strong Heart Study

Background Epigenetic alterations may contribute to early detection of cancer. We evaluated the association of blood DNA methylation with lymphatic–hematopoietic cancers and, for comparison, with solid cancers. We also evaluated the predictive ability of DNA methylation for lymphatic–hematopoietic cancers. Methods Blood DNA methylation was measured using the Illumina Infinium methylationEPIC array in 2324 Strong Heart Study participants (41.4% men, mean age 56 years). 788,368 CpG sites were available for differential DNA methylation analysis for lymphatic–hematopoietic, solid and overall cancers using elastic-net and Cox regression models. We conducted replication in an independent population: the Framingham Heart Study. We also analyzed differential variability and conducted bioinformatic analyses to assess for potential biological mechanisms. Results Over a follow-up of up to 28 years (mean 15), we identified 41 lymphatic–hematopoietic and 394 solid cancer cases. A total of 126 CpGs for lymphatic–hematopoietic cancers, 396 for solid cancers, and 414 for overall cancers were selected as predictors by the elastic-net model. For lymphatic–hematopoietic cancers, the predictive ability (C index) increased from 0.58 to 0.87 when adding these 126 CpGs to the risk factor model in the discovery set. The association was replicated with hazard ratios in the same direction in 28 CpGs in the Framingham Heart Study. When considering the association of variability, rather than mean differences, we found 432 differentially variable regions for lymphatic–hematopoietic cancers. Conclusions This study suggests that differential methylation and differential variability in blood DNA methylation are associated with lymphatic–hematopoietic cancer risk. DNA methylation data may contribute to early detection of lymphatic–hematopoietic cancers.