@article {6220, title = {Predicting stroke through genetic risk functions: the CHARGE Risk Score Project.}, journal = {Stroke}, volume = {45}, year = {2014}, month = {2014 Feb}, pages = {403-12}, abstract = {

BACKGROUND AND PURPOSE: Beyond the Framingham Stroke Risk Score, prediction of future stroke may improve with a genetic risk score (GRS) based on single-nucleotide polymorphisms associated with stroke and its risk factors.

METHODS: The study includes 4 population-based cohorts with 2047 first incident strokes from 22,720 initially stroke-free European origin participants aged >=55 years, who were followed for up to 20 years. GRSs were constructed with 324 single-nucleotide polymorphisms implicated in stroke and 9 risk factors. The association of the GRS to first incident stroke was tested using Cox regression; the GRS predictive properties were assessed with area under the curve statistics comparing the GRS with age and sex, Framingham Stroke Risk Score models, and reclassification statistics. These analyses were performed per cohort and in a meta-analysis of pooled data. Replication was sought in a case-control study of ischemic stroke.

RESULTS: In the meta-analysis, adding the GRS to the Framingham Stroke Risk Score, age and sex model resulted in a significant improvement in discrimination (all stroke: Δjoint area under the curve=0.016, P=2.3{\texttimes}10(-6); ischemic stroke: Δjoint area under the curve=0.021, P=3.7{\texttimes}10(-7)), although the overall area under the curve remained low. In all the studies, there was a highly significantly improved net reclassification index (P<10(-4)).

CONCLUSIONS: The single-nucleotide polymorphisms associated with stroke and its risk factors result only in a small improvement in prediction of future stroke compared with the classical epidemiological risk factors for stroke.

}, keywords = {Age Factors, Aged, Aged, 80 and over, Area Under Curve, Case-Control Studies, Cohort Studies, European Continental Ancestry Group, Female, Genetic Predisposition to Disease, Genome-Wide Association Study, Genotype, Humans, Male, Middle Aged, Polymorphism, Single Nucleotide, Regression Analysis, Risk Factors, ROC Curve, Sex Factors, Stroke}, issn = {1524-4628}, doi = {10.1161/STROKEAHA.113.003044}, author = {Ibrahim-Verbaas, Carla A and Fornage, Myriam and Bis, Joshua C and Choi, Seung Hoan and Psaty, Bruce M and Meigs, James B and Rao, Madhu and Nalls, Mike and Fontes, Jo{\~a}o D and O{\textquoteright}Donnell, Christopher J and Kathiresan, Sekar and Ehret, Georg B and Fox, Caroline S and Malik, Rainer and Dichgans, Martin and Schmidt, Helena and Lahti, Jari and Heckbert, Susan R and Lumley, Thomas and Rice, Kenneth and Rotter, Jerome I and Taylor, Kent D and Folsom, Aaron R and Boerwinkle, Eric and Rosamond, Wayne D and Shahar, Eyal and Gottesman, Rebecca F and Koudstaal, Peter J and Amin, Najaf and Wieberdink, Renske G and Dehghan, Abbas and Hofman, Albert and Uitterlinden, Andr{\'e} G and DeStefano, Anita L and Debette, Stephanie and Xue, Luting and Beiser, Alexa and Wolf, Philip A and DeCarli, Charles and Ikram, M Arfan and Seshadri, Sudha and Mosley, Thomas H and Longstreth, W T and van Duijn, Cornelia M and Launer, Lenore J} } @article {8919, title = {Pre-Statistical Considerations for Harmonization of Cognitive Instruments: Harmonization of ARIC, CARDIA, CHS, FHS, MESA, and NOMAS.}, journal = {J Alzheimers Dis}, volume = {83}, year = {2021}, month = {2021}, pages = {1803-1813}, abstract = {

BACKGROUND: Meta-analyses of individuals{\textquoteright} cognitive data are increasing to investigate the biomedical, lifestyle, and sociocultural factors that influence cognitive decline and dementia risk. Pre-statistical harmonization of cognitive instruments is a critical methodological step for accurate cognitive data harmonization, yet specific approaches for this process are unclear.

OBJECTIVE: To describe pre-statistical harmonization of cognitive instruments for an individual-level meta-analysis in the blood pressure and cognition (BP COG) study.

METHODS: We identified cognitive instruments from six cohorts (the Atherosclerosis Risk in Communities Study, Cardiovascular Health Study, Coronary Artery Risk Development in Young Adults study, Framingham Offspring Study, Multi-Ethnic Study of Atherosclerosis, and Northern Manhattan Study) and conducted an extensive review of each item{\textquoteright}s administration and scoring procedures, and score distributions.

RESULTS: We included 153 cognitive instrument items from 34 instruments across the six cohorts. Of these items, 42\%were common across >=2 cohorts. 86\%of common items showed differences across cohorts. We found administration, scoring, and coding differences for seemingly equivalent items. These differences corresponded to variability across cohorts in score distributions and ranges. We performed data augmentation to adjust for differences.

CONCLUSION: Cross-cohort administration, scoring, and procedural differences for cognitive instruments are frequent and need to be assessed to address potential impact on meta-analyses and cognitive data interpretation. Detecting and accounting for these differences is critical for accurate attributions of cognitive health across cohort studies.

}, issn = {1875-8908}, doi = {10.3233/JAD-210459}, author = {Brice{\~n}o, Emily M and Gross, Alden L and Giordani, Bruno J and Manly, Jennifer J and Gottesman, Rebecca F and Elkind, Mitchell S V and Sidney, Stephen and Hingtgen, Stephanie and Sacco, Ralph L and Wright, Clinton B and Fitzpatrick, Annette and Fohner, Alison E and Mosley, Thomas H and Yaffe, Kristine and Levine, Deborah A} } @article {9437, title = {Prediction of Multiple Individual Primary Cardiovascular Events Using Pooled Cohorts.}, journal = {medRxiv}, year = {2023}, month = {2023 Aug 02}, abstract = {

INTRODUCTION: Most current clinical risk prediction scores for cardiovascular disease prevention use a composite outcome. Risk prediction scores for specific cardiovascular events could identify people who are at higher risk for some events than others informing personalized care and trial recruitment. We sought to predict risk for multiple different events, describe how those risks differ, and examine if these differences could improve treatment priorities.

METHODS: We used participant-level data from five cohort studies. We included participants between 40 and 79 years old who had no history of myocardial infarction (MI), stroke, or heart failure (HF). We made separate models to predict 10-year rates of first atherosclerotic cardiovascular disease (ASCVD), first fatal or nonfatal MI, first fatal or nonfatal stroke, new-onset HF, fatal ASCVD, fatal MI, fatal stroke, and all-cause mortality using established ASCVD risk factors. To limit overfitting, we used elastic net regularization with alpha = 0.75. We assessed the models for calibration, discrimination, and for correlations between predicted risks for different events. We also estimated the potential impact of varying treatment based on patients who are high risk for some ASCVD events, but not others.

RESULTS: Our study included 24,505 people; 55.6\% were women, and 20.7\% were non-Hispanic Black. Our models had C-statistics between 0.75 for MI and 0.85 for HF, good calibration, and minimal overfitting. The models were least similar for fatal stroke and all MI (0.58). In 1,840 participants whose risk of MI but not stroke or all-cause mortality was in the top quartile, we estimate one blood pressure-lowering medication would have a 2.4\% chance of preventing any ASCVD event per 10 years. A moderate-strength statin would have a 2.1\% chance. In 1,039 participants who had top quartile risk of stroke but not MI or mortality, a blood pressure-lowering medication would have a 2.5\% chance of preventing an event, but a moderate-strength statin, 1.6\%.

CONCLUSION: We developed risk scores for eight key clinical events and found that cardiovascular risk varies somewhat for different clinical events. Future work could determine if tailoring decisions by risk of separate events can improve care.

}, doi = {10.1101/2023.08.01.23293525}, author = {Sussman, Jeremy B and Whitney, Rachael T and Burke, James F and Hayward, Rodney A and Galecki, Andrzej and Sidney, Stephen and Allen, Norrina Bai and Gottesman, Rebecca F and Heckbert, Susan R and Longstreth, William T and Psaty, Bruce M and Elkind, Mitchell S V and Levine, Deborah A} } @article {9781, title = {Plasma proteome-wide analysis of cerebral small vessel disease identifies novel biomarkers and disease pathways.}, journal = {medRxiv}, year = {2024}, month = {2024 Oct 08}, abstract = {

Cerebral small vessel disease (SVD), as defined by neuroimaging characteristics such as white matter hyperintensities (WMHs), cerebral microhemorrhages (CMHs), and lacunar infarcts, is highly prevalent and has been associated with dementia risk and other clinical sequelae. Although conditions such as hypertension are known to contribute to SVD, little is known about the diverse set of subclinical biological processes and molecular mediators that may also influence the development and progression of SVD. To better understand the mechanisms underlying SVD and to identify novel SVD biomarkers, we used a large-scale proteomic platform to relate 4,877 plasma proteins to MRI-defined SVD characteristics within 1,508 participants of the Atherosclerosis Risk in Communities (ARIC) Study cohort. Our proteome-wide analysis of older adults (mean age: 76) identified 13 WMH-associated plasma proteins involved in synaptic function, endothelial integrity, and angiogenesis, two of which remained associated with late-life WMH volume when measured nearly 20 years earlier, during midlife. We replicated the relationship between 9 candidate proteins and WMH volume in one or more external cohorts; we found that 11 of the 13 proteins were associated with risk for future dementia; and we leveraged publicly available proteomic data from brain tissue to demonstrate that a subset of WMH-associated proteins was differentially expressed in the context of cerebral atherosclerosis, pathologically-defined Alzheimer{\textquoteright}s disease, and cognitive decline. Bidirectional two-sample Mendelian randomization analyses examined the causal relationships between candidate proteins and WMH volume, while pathway and network analyses identified discrete biological processes (lipid/cholesterol metabolism, NF-kB signaling, hemostasis) associated with distinct forms of SVD. Finally, we synthesized these findings to identify two plasma proteins, oligodendrocyte myelin glycoprotein (OMG) and neuronal pentraxin receptor (NPTXR), as top candidate biomarkers for elevated WMH volume and its clinical manifestations.

}, doi = {10.1101/2024.10.07.24314972}, author = {Gomez, Gabriela T and Shi, Liu and Fohner, Alison E and Chen, Jingsha and Yang, Yunju and Fornage, Myriam and Duggan, Michael R and Peng, Zhongsheng and Daya, Gulzar N and Tin, Adrienne and Schlosser, Pascal and Longstreth, W T and Kalani, Rizwan and Sharma, Malveeka and Psaty, Bruce M and Nevado-Holgado, Alejo J and Buckley, Noel J and Gottesman, Rebecca F and Lutsey, Pamela L and Jack, Clifford R and Sullivan, Kevin J and Mosley, Thomas and Hughes, Timothy M and Coresh, Josef and Walker, Keenan A} }