Omega-3 Blood Levels and Stroke Risk: A Pooled and Harmonized Analysis of 183 291 Participants From 29 Prospective Studies.

BACKGROUND: The effect of marine omega-3 PUFAs on risk of stroke remains unclear. METHODS: We investigated the associations between circulating and tissue omega-3 PUFA levels and incident stroke (total, ischemic, and hemorrhagic) in 29 international prospective cohorts. Each site conducted a de novo individual-level analysis using a prespecified analytical protocol with defined exposures, covariates, analytical methods, and outcomes; the harmonized data from the studies were then centrally pooled. Multivariable-adjusted HRs and 95% CIs across omega-3 PUFA quintiles were computed for each stroke outcome. RESULTS: Among 183 291 study participants, there were 10 561 total strokes, 8220 ischemic strokes, and 1142 hemorrhagic strokes recorded over a median of 14.3 years follow-up. For eicosapentaenoic acid, comparing quintile 5 (Q5, highest) with quintile 1 (Q1, lowest), total stroke incidence was 17% lower (HR, 0.83 [CI, 0.76-0.91]; P<0.0001), and ischemic stroke was 18% lower (HR, 0.82 [CI, 0.74-0.91]; P<0.0001). For docosahexaenoic acid, comparing Q5 with Q1, there was a 12% lower incidence of total stroke (HR, 0.88 [CI, 0.81-0.96]; P=0.0001) and a 14% lower incidence of ischemic stroke (HR, 0.86 [CI, 0.78-0.95]; P=0.0001). Neither eicosapentaenoic acid nor docosahexaenoic acid was associated with a risk for hemorrhagic stroke. These associations were not modified by either baseline history of AF or prevalent CVD. CONCLUSIONS: Higher omega-3 PUFA levels are associated with lower risks of total and ischemic stroke but have no association with hemorrhagic stroke.

Prioritising and incentivising productivity within indicator-based approaches to Research Impact Assessment: a commentary.

Research Impact Assessment (RIA) represents one of a suite of policies intended to improve the impact generated from investment in health and medical research (HMR). Positivist indicator-based approaches to RIA are widely implemented but increasingly criticised as theoretically problematic, unfair, and burdensome. This commentary proposes there are useful outcomes that emerge from the process of applying an indicator-based RIA framework, separate from those encapsulated in the metrics themselves. The aim for this commentary is to demonstrate how the act of conducting an indicator-based approach to RIA can serve to optimise the productive gains from the investment in HMR. Prior research found that the issues regarding RIA are less about the choice of indicators/metrics, and more about the discussions prompted and activities incentivised by the process. This insight provides an opportunity to utilise indicator-based methods to purposely optimise the research impact. An indicator-based RIA framework specifically designed to optimise research impacts should: focus on researchers and the research process, rather than institution-level measures; utilise a project level unit of analysis that provides control to researchers and supports collaboration and accountability; provide for prospective implementation of RIA and the prospective orientation of research; establish a line of sight to the ultimate anticipated beneficiaries and impacts; Include process metrics/indicators to acknowledge interim steps on the pathway to final impacts; integrate 'next' users and prioritise the utilisation of research outputs as a critical measure; Integrate and align the incentives for researchers/research projects arising from RIA, with those existing within the prevailing research system; integrate with existing peer-review processes; and, adopt a system-wide approach where incremental improvements in the probability of translation from individual research projects, yields higher impact across the whole funding portfolio.Optimisation of the impacts from HMR investment represents the primary purpose of Research Impact policy. The process of conducting an indicator-based approach to RIA, which engages the researcher during the inception and planning phase, can directly contribute to this goal through improvements in the probability that an individual project will generate interim impacts. The research project funding process represents a promising forum to integrate this approach within the existing research system.