Influence of Temperature Chronobiology on Stroke Outcome

Alonso-Alonso ML, Sampedro-Viana A, Rodríguez-Yáñez M et al. Int J Mol Sci. 2023 Feb 13;24(4):3746. doi: 10.3390/ijms24043746. PMID: 36835156

Abstract: : The circadian system regulates numerous physiological variables, including body temperature. Additionally, a circadian patter has been described in stroke onset. Considering this, we hypothesised that the chronobiology of temperature may have an impact on stroke onset and functional outcomes. We also studied the variation of blood biomarkers according to stroke onset time. This is a retrospective observational study. Of the patients included, 2763 had a stroke between midnight and 8:00 h; 1571 between 8:00–14:00 h; and 655 between 14:00 h and midnight. Axillary temperature was measured at admission. At this time, blood samples were collected for biomarker analysis (TNF-α, IL-1β, IL-6, IL-10, and glutamate). Temperature was higher in patients admitted from 8:00 h to midnight (p < 0.0001). However, the percentage of poor outcome at 3 months was highest in patients from midnight to 8:00 h (57.7%, p < 0.001). The association between temperature and mortality was highest during night time (OR: 2.79; CI 95%: 2.36–3.28; p < 0.001). These patients exhibited high glutamate (220.2 ± 140.2 µM), IL-6 (32.8 ± 14.3 pg/mL) and low IL-10 (9.7 ± 14.3 pg/mL) levels. Therefore, temperature chronobiology could have a significant impact on stroke onset and functional outcome. Superficial body hyperthermia during sleep seems to be more dangerous than during wakefulness. Further studies will be necessary to confirm our data. 

Funding: This research was funded by Spanish Ministry of Science and Innovation (SAF2017-84267- R), PDC2021-121455-I00, Xunta de Galicia (Consellería de Educación: IN607A2022-03), Instituto de Salud Carlos III (ISCIII) (PI17/00540, PI17/01103), ISCIII/PI21/01256/Co-financed by the European Union, Spanish Research Network on Cerebrovascular Diseases RETICS-INVICTUS PLUS (RD16/0019/0001), RICORS-ICTUS (Cerebrovascular diseases) D21/0006/0003. M. Bazarra-Barreiros is a PFIS Researcher (FI22/00200) of Instituto de Salud Carlos III. T. Sobrino (CPII17/00027), F. Campos (CPII19/00020) and R. Iglesias-Rey (CP22/00061) from the Miguel Servet Program of Instituto de Salud Carlos III and Co-financed by the EU. Sponsors did not participate in the study design, collection, analysis, or interpretation of the data, or in writing the report.

Direct Mechanical Thrombectomy vs. Bridging Therapy in Stroke Patients in A «Stroke Belt» Region of Southern Europe

Del Toro-Pérez C, Amaya-Pascasio L, Guevara-Sánchez E. et al. J Pers Med. 2023 Feb 28;13(3):440. doi: 10.3390/jpm13030440. PMID: 36983622

Abstract: The aim of this 4-year observational study is to analyze the outcomes of stroke patients treated with direct mechanical thrombectomy (dMT) compared to bridging therapy (BT) (intravenous thrombolysis [IVT] + BT) based on 3-month outcomes, in real clinical practice in the «Stroke Belt» of Southern Europe. In total, 300 patients were included (41.3% dMT and 58.6% BT). The frequency of direct referral to the stroke center was similar in the dMT and BT group, whereas the time from onset to groin was longer in the BT group (median 210 [IQR 160–303] vs. 399 [IQR 225–675], p = 0.001). Successful recanalization (TICI 2b-3) and hemorrhagic transformation were similar in both groups.
The BT group more frequently showed excellent outcomes at 3 months (32.4% vs. 15.4%, p = 0.004). Multivariate analysis showed that BT was independently associated with excellent outcomes (OR 2.7. 95% CI,1.2–5.9, p = 0.02) and lower mortality (OR 0.36. 95% CI 0.16–0.82, p = 015). Conclusions: Compared with dMT, BT was associated with excellent functional outcomes and lower 3-month mortality in this real-world clinical practice study conducted in a region belonging to the “Stroke Belt” of Southern Europe. Given the disparity of results on the benefit of BT in the current evidence, it is of vital importance to analyze the convenience of its use in each health area.

Funding: This study is part of the Spanish Health Outcomes-Oriented Cooperative Research Networks (RICORS-ICTUS), Instituto de Salud Carlos III (Carlos III Health Institute), Ministerio de Ciencia e Innovación (Ministry of Science and Innovation), RD21/0006/0010.

Factors associated with migraine aura mimicking stroke in code stroke

Macias-Gómez A, Suárez-Pérez A, Rodríguez-Campello A, Giralt-Steinhauer E, Moreira A, Guisado-Alonso D, Capellades J, Fernández-Pérez I, Jiménez-Conde J, Rey L, Jiménez-Balado J, Roquer J, Ois Á, Cuadrado-Godia E. Neurol Sci. 2023 Feb 7. doi: 10.1007/s10072-023-06641-y. Epub ahead of print. PMID: 36749530.

Conclusions: In code stroke, a model including age, sex, NIHSS, and fbrinogen showed a good discrimination capability to diferentiate between MA and Ischemic stroke. Whether these variables can be implemented in a diagnostic rule should be tested in future studies.

Funding: This work was supported in part by Spain’s Ministry ofHealth (Instituto de Salud Carlos III, Fondos FEDER, RICORS-ICTUS(RD21/0006/0021)).

Machine Learning Approximations to Predict Epigenetic Age Acceleration in Stroke Patients

Fernández-Pérez I, Jiménez-Balado J, Lazcano U, Giralt-Steinhauer E, et al. Int J Mol Sci. 2023 Feb 1;24(3):2759. doi: 10.3390/ijms24032759. PMID: 36769083; PMCID: PMC9917369

Abstract: Age acceleration (Age-A) is a useful tool that is able to predict a broad range of health outcomes. It is necessary to determine DNA methylation levels to estimate it, and it is known that Age-A is influenced by environmental, lifestyle, and vascular risk factors (VRF). The aim of this study is to estimate the contribution of these easily measurable factors to Age-A in patients with cerebrovascular disease (CVD), using different machine learning (ML) approximations, and try to find a more accessible model able to predict Age-A.We studied a CVD cohort of 952 patients with information about VRF, lifestyle habits, and target organ damage. We estimated Age-A using Hannum’s epigenetic clock, and trained six different models to predict Age-A: a conventional linear regression model, four ML models (elastic net regression (EN), K-Nearest neighbors, random forest, and support vector machine models), and one deep learning approximation (multilayer perceptron (MLP) model). The best-performing models were EN and MLP; although, the predictive capability was modest (R2 0.358 and 0.378, respectively). In conclusion, our results support the influence of these factors on Age-A; although, they were not enough to explain most of its variability.

Funding: This work was supported by grants from the Spanish Ministry of Science and Innovation, Instituto de Salud Carlos III with the grants “Registro BASICMAR” Funding for Research in Health (PI051737), Fondos de Investigación Sanitaria ISC III (PI12/01238), (PI15/00451), (PI18/00022), (PI21/00593); Sara Borrell program, funded by Instituto de Salud Carlos III (CD22/00001, J.J.-B.); and Fondos FEDER/EDRF Spanish stroke research network INVICTUS+ (RD16/0019/0002) and Grant “RICORS-ICTUS” (RD21/0006/0021) funded by Instituto de Salud Carlos III (ISCIII), and by Int. J. Mol. Sci. 2023, 24, 2759 13 of 15 the European Union NextGenerationEU, Mecanismo para la Recuperación y la Resiliencia (MRR). Additional support was provided by Recercaixa’13 (JJ086116).