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Layman's Summary Stroke (brain attack) is a disease of the brain and circulation system and occurs when the blood supply to the brain is blocked. It is the 3rd most common cause of death and disability in the world. However, it is a preventable disease. Earlier studies have shown physical inactivity, unhealthy diet, smoking, high blood sugar and high blood pressure (unhealthy lifestyle factors) increase stroke risk. Therefore, in the last 20 years, health providers have focused on preventing stroke instead of treating it. In 2022, the American Heart Association (AHA) designed a scoring system that combines common heart disease risk factors (diet, physical activity, smoking, sleep health, body weight, blood fats, blood pressure, and blood sugar) to identify and track heart health. Thus, stroke and heart diseases can be prevented by improving these risk factors together. AHA called this scoring system Life's Essential 8 (LE8). A higher LE8 score means better heart health. In this study, we investigated the relationship between LE8 score and the risk of stroke. There were 37,358 participants in our research and we calculated LE8 scores for each participant and ran advanced analysis to identify the relationship. Our research showed that participants with better heart health (higher LE8 scores) had a lower risk of having a stroke. To conclude, LE8 is a new suitable scoring system to evaluate the heart health, and stroke can be prevented by having a high LE8 score. It is important to follow a healthy lifestyle to prevent stroke.

The planetary health diet (PHD) recommends to limit the intake of animal sourced foods and starchy vegetables and recommends high intake of plant-based foods. Adherence to the PHD has been associated with a lower risk of cardiovascular diseases (CVDs). However, the underlying biological mechanism of this relation is unknown. In our study we develop a metabolomic signature based on the PHD-score of participants, and investigate whether this metabolomic signature is associated with CVD. Data of 4088 participants (mean age 55.6 (9.90), 48.0% men (n= 1963)) from the Doetinchem Cohort study were used. The PHD-score was assessed using a validated food frequency questionnaire (FFQ). Metabolites were measured at the Nightingale platform, Finland. Least absolute shrinkage and selection operator (LASSO) was used to identify the metabolomic signature (round 4, R4) based on the PHD-score (R4). Cox regression analyses were used to examine association between the identified metabolomic signature and 10-year incidence CVD (R5- 6). We identified a metabolomic signature, comprised of 45 metabolites. The signature consisted mainly of relative lipoprotein concentrations, but also fatty acids, amino acids, ketone bodies, glycolysis related metabolites, metabolites involved in fluid balance, lipoprotein subclasses, other lipids, and metabolites involved in inflammation. In Cox regressions, the metabolomic signature showed a borderline significant association with CVD incidence after adjustment for age, sex, educational level, and lifestyle factors (smoking, alcohol consumption, and physical activity) [HR = 0.851 (95% CI: 0.704 – 1.029)]. We identified a metabolomic signature which reflects the adherence to the Planetary Health Diet. In Cox regression the metabolomic signature showed borderline significance after adjustment for age, sex, educational level, and lifestyle factors (smoking, alcohol consumption, and physical activity).

BACKGROUND: This study examined the association between Life’s Essential 8 (LE8) and the incidence of atrial fibrillation (AF). METHODS: In total, 37,807 participants from the European Prospective Investigation Into Cancer and Nutrition-Netherlands (EPIC-NL) cohort were included (mean age 49.4 ± 11.9 years, 74.7% women). The components diet, physical activity, sleep health, nicotine exposure, body mass index, blood glucose, blood lipids, blood pressure were all given a score between 0 and 100 and an overall LE8 score was created. The overall LE8 score was averaged and categorized into low cardiovascular health (CVH, 0-49), moderate CVH (50-79), and high CVH (80-100). AF cases were obtained through linkage with registries. RESULTS: During a median follow-up of 15.3 years (interquartile range: 14.1 – 26.5 years), 833 AF cases (2.3%) were identified. Compared to the high CVH score (19.5%), participants with a low CVH score (5%) had a 1.99 times higher hazard for incident AF (hazard ratio = 1.99, 95% confidence interval (CI) = 1.49 – 2.65) in a Cox proportional hazards model adjusted for age, sex, education level, heart failure, coronary heart disease, and alcohol consumption. Furthermore, an ideal component decrease was associated with a 1.11 times higher risk for incident AF (95% CI = 1.05 – 1.18). CONCLUSIONS: The results show that a worse CVH, indicated by a lower LE8 score, is associated with an increased risk for incident AF.

Background and aim: Healthy diet has been shown to have a positive impact on health status. However, underlying mechanisms are not completely understood. Metabolomics, in which metabolites in biofluids are analyzed and quantified, can be utilized to uncover metabolomic signatures associated with healthy and unhealthy dietary patterns. Utilizing metabolomics can improve understanding of the effects of healthy and unhealthy dietary patterns on metabolic processes and eventually provide a greater understanding of the underlying mechanisms in which healthy diet contributes to health status. This review aimed to conduct a systematic search to provide an overview of relevant literature and to gather and analyze available data on the metabolomic signatures associated with healthy and unhealthy dietary patterns. Methods: A systematic search was conducted to identify randomized controlled trials investigating endogenous metabolome in plasma, serum, or urine in subjects consuming a healthy dietary pattern compared to subjects consuming a relatively unhealthier dietary pattern. Metabolites reported as significant were extracted and used to compare metabolite class distributions between dietary patterns. Metabolites reported in multiple studies were highlighted. Pathway over-representation analyses of metabolites associated with a healthy or unhealthy dietary pattern were performed. Results: 20 relevant randomized controlled trials were identified and article characteristics were summarized. Differences in metabolite class distributions were found between dietary patterns and metabolite class compositions were dependent on sample specimen. Several metabolites were associated with either a healthy or unhealthy dietary pattern in multiple articles. Pathway over-representation analyses show several metabolic pathways significantly enriched in healthy or unhealthy dietary patterns. Conclusions: Differing metabolomic signatures were found to be associated with healthy and unhealthy