In anticipation of updates to cholesterol guidelines globally, evidence since the most recent iteration of recommendations across US and Europe for risk assessment and lipid management are reviewed. A Show more
In anticipation of updates to cholesterol guidelines globally, evidence since the most recent iteration of recommendations across US and Europe for risk assessment and lipid management are reviewed. ASCVD risk estimation is at the core of determining lipid lowering goals and consideration for therapies. In primary prevention, incorporation of the PREVENT equations will be featured in updated guidelines, which will likely demarcate new, lower risk thresholds compared to the prior Pooled Cohort Equations. Additionally, the use of coronary artery calcium (CAC) improves risk estimation to inform medication allocation and LDL-C goals beyond traditional risk factor risk estimation. To achieve lower LDL-C, many adults will need multiple lipid-lowering medications. For high-risk individuals, combination therapy with low/moderate intensity statin and ezetimibe or bempedoic acid should be considered. Additionally, proprotein convertase subtilisin/kexin type 9 inhibitor (PCSK9i) therapies can be used to attain lower LDL-C in high-risk individuals, including those with clinical ASCVD or a high CAC burden. In very-high risk patients, treatment to LDL-C values as low as <30 mg/dL further reduces ASCVD risk without significant adverse events. Among individuals treated with PSCK9i therapy, those with elevated Lp(a) may have greater ASCVD risk reduction and may be a patient population that is prioritized for PCSK9i until therapies directly targeting Lp(a) are available. An ASCVD risk-based approach should be the foundation for determining LDL-C goals with consideration that multiple lipid-lowering therapies are often necessary for high and very-high risk patients who were treated to very low LDL-C in more recent randomized controlled trials. Show less
Women are under-represented in research on suicidality to date. Although women have a lower rate of suicide completion than men, due in part to the less-violent methods used, they have a higher rate o Show more
Women are under-represented in research on suicidality to date. Although women have a lower rate of suicide completion than men, due in part to the less-violent methods used, they have a higher rate of suicide attempts. Our group has previously identified genomic (blood gene expression biomarkers) and clinical information (apps) predictors for suicidality in men. We now describe pilot studies in women. We used a powerful within-participant discovery approach to identify genes that change in expression between no suicidal ideation (no SI) and high suicidal ideation (high SI) states (n=12 participants out of a cohort of 51 women psychiatric participants followed longitudinally, with diagnoses of bipolar disorder, depression, schizoaffective disorder and schizophrenia). We then used a Convergent Functional Genomics (CFG) approach to prioritize the candidate biomarkers identified in the discovery step by using all the prior evidence in the field. Next, we validated for suicidal behavior the top-ranked biomarkers for SI, in a demographically matched cohort of women suicide completers from the coroner's office (n=6), by assessing which markers were stepwise changed from no SI to high SI to suicide completers. We then tested the 50 biomarkers that survived Bonferroni correction in the validation step, as well as top increased and decreased biomarkers from the discovery and prioritization steps, in a completely independent test cohort of women psychiatric disorder participants for prediction of SI (n=33) and in a future follow-up cohort of psychiatric disorder participants for prediction of psychiatric hospitalizations due to suicidality (n=24). Additionally, we examined how two clinical instruments in the form of apps, Convergent Functional Information for Suicidality (CFI-S) and Simplified Affective State Scale (SASS), previously tested in men, perform in women. The top CFI-S item distinguishing high SI from no SI states was the chronic stress of social isolation. We then showed how the clinical information apps combined with the 50 validated biomarkers into a broad predictor (UP-Suicide), our apriori primary end point, predicts suicidality in women. UP-Suicide had a receiver-operating characteristic (ROC) area under the curve (AUC) of 82% for predicting SI and an AUC of 78% for predicting future hospitalizations for suicidality. Some of the individual components of the UP-Suicide showed even better results. SASS had an AUC of 81% for predicting SI, CFI-S had an AUC of 84% and the combination of the two apps had an AUC of 87%. The top biomarker from our sequential discovery, prioritization and validation steps, BCL2, predicted future hospitalizations due to suicidality with an AUC of 89%, and the panel of 50 validated biomarkers (BioM-50) predicted future hospitalizations due to suicidality with an AUC of 94%. The best overall single blood biomarker for predictions was PIK3C3 with an AUC of 65% for SI and an AUC of 90% for future hospitalizations. Finally, we sought to understand the biology of the biomarkers. BCL2 and GSK3B, the top CFG scoring validated biomarkers, as well as PIK3C3, have anti-apoptotic and neurotrophic effects, are decreased in expression in suicidality and are known targets of the anti-suicidal mood stabilizer drug lithium, which increases their expression and/or activity. Circadian clock genes were overrepresented among the top markers. Notably, PER1, increased in expression in suicidality, had an AUC of 84% for predicting future hospitalizations, and CSNK1A1, decreased in expression, had an AUC of 96% for predicting future hospitalizations. Circadian clock abnormalities are related to mood disorder, and sleep abnormalities have been implicated in suicide. Docosahexaenoic acid signaling was one of the top biological pathways overrepresented in validated biomarkers, which is of interest given the potential therapeutic and prophylactic benefits of omega-3 fatty acids. Some of the top biomarkers from the current work in women showed co-directionality of change in expression with our previous work in men, whereas others had changes in opposite directions, underlying the issue of biological context and differences in suicidality between the two genders. With this study, we begin to shed much needed light in the area of female suicidality, identify useful objective predictors and help understand gender commonalities and differences. During the conduct of the study, one participant committed suicide. In retrospect, when the analyses were completed, her UP-Suicide risk prediction score was at the 100 percentile of all participants tested. Show less