Dataset Information

Causal Relations between Exposome and Stroke: A Mendelian Randomization Study.

ABSTRACT:

Background and purpose

To explore the causal relationships of elements of the exposome with ischemic stroke and its subtypes at the omics level and to provide evidence for stroke prevention.

Methods

We conducted a Mendelian randomization study between exposure and any ischemic stroke (AIS) and its subtypes (large-artery atherosclerotic disease [LAD], cardioembolic stroke [CE], and small vessel disease [SVD]). The exposure dataset was the UK Biobank involving 361,194 subjects, and the outcome dataset was the MEGASTROKE consortium including 52,000 participants.

Results

We found that higher blood pressure (BP) (systolic BP: odds ratio [OR], 1.02; 95% confidence interval [CI], 1.01 to 1.04; diastolic BP: OR, 1.03; 95% CI, 1.01 to 1.05; pulse pressure: OR, 1.03; 95% CI, 1.00 to 1.06), atrial fibrillation (OR, 1.18; 95% CI, 1.13 to 1.25), and diabetes (OR, 1.13; 95% CI, 1.07 to 1.18) were significantly associated with ischemic stroke. Importantly, higher education (OR, 0.69; 95% CI, 0.60 to 0.79) decreased the risk of ischemic stroke. Higher systolic BP (OR, 1.06; 95% CI, 1.02 to 1.10), pulse pressure (OR, 1.08; 95% CI, 1.02 to 1.14), diabetes (OR, 1.28; 95% CI, 1.13 to 1.45), and coronary artery disease (OR, 1.58; 95% CI, 1.25 to 2.00) could cause LAD. Atrial fibrillation could cause CE (OR, 1.90; 95% CI, 1.71 to 2.11). For SVD, higher systolic BP (OR, 1.04; 95% CI, 1.00 to 1.07), diastolic BP (OR, 1.06; 95% CI, 1.01 to 1.12), and diabetes (OR, 1.22; 95% CI, 1.10 to 1.36) were causal factors.

Conclusions

The study revealed elements of the exposome causally linked to ischemic stroke and its subtypes, including conventional causal risk factors and novel protective factors such as higher education.

SUBMITTER: Li HQ

PROVIDER: S-EPMC9194538 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature

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Publications

Causal Relations between Exposome and Stroke: A Mendelian Randomization Study.

Li Hong-Qi HQ Feng Yi-Wei YW Yang Yu-Xiang YX Leng Xin-Yi XY Zhang Prof Can PC Chen Shi-Dong SD Kuo Kevin K Huang Shu-Yi SY Zhang Xue-Qing XQ Dong Yi Y Han Xiang X Cheng Xin X Cui Mei M Tan Lan L Dong Qiang Q Yu Jin-Tai JT

Journal of stroke 20220531 2

<h4>Background and purpose</h4>To explore the causal relationships of elements of the exposome with ischemic stroke and its subtypes at the omics level and to provide evidence for stroke prevention.<h4>Methods</h4>We conducted a Mendelian randomization study between exposure and any ischemic stroke (AIS) and its subtypes (large-artery atherosclerotic disease [LAD], cardioembolic stroke [CE], and small vessel disease [SVD]). The exposure dataset was the UK Biobank involving 361,194 subjects, and ...[more]

PMID: 35677978

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Project description:BackgroundEnvironmental factors are associated with human longevity, but their specificity and causality remain mostly unclear. By integrating the innovative "exposome" concept developed in the field of environmental epidemiology, this study aims to determine the components of exposome causally linked to longevity using Mendelian randomization (MR) approach.MethodsA total of 4587 environmental exposures extracting from 361,194 individuals from the UK biobank, in exogenous and endogenous domains of exposome were assessed. We examined the relationship between each environmental factor and two longevity outcomes (i.e., surviving to the 90th or 99th percentile age) from various cohorts of European ancestry. Significant results after false discovery rates correction underwent validation using an independent exposure dataset.ResultsOut of all the environmental exposures, eight age-related diseases and pathological conditions were causally associated with lower odds of longevity, including coronary atherosclerosis (odds ratio = 0.77, 95% confidence interval [0.70, 0.84], P = 4.2 × 10-8), ischemic heart disease (0.66, [0.51, 0.87], P = 0.0029), angina (0.73, [0.65, 0.83], P = 5.4 × 10-7), Alzheimer's disease (0.80, [0.72, 0.89], P = 3.0 × 10-5), hypertension (0.70, [0.64, 0.77], P = 4.5 × 10-14), type 2 diabetes (0.88 [0.80, 0.96], P = 0.004), high cholesterol (0.81, [0.72, 0.91], P = 0.0003), and venous thromboembolism (0.92, [0.87, 0.97], P = 0.0028). After adjusting for genetic correlation between different types of blood lipids, higher levels of low-density lipoprotein cholesterol (0.72 [0.64, 0.80], P = 2.3 × 10-9) was associated with lower odds of longevity, while high-density lipoprotein cholesterol (1.36 [1.13, 1.62], P = 0.001) showed the opposite. Genetically predicted sitting/standing height was unrelated to longevity, while higher comparative height size at 10 was negatively associated with longevity. Greater body fat, especially the trunk fat mass, and never eat sugar or foods/drinks containing sugar were adversely associated with longevity, while education attainment showed the opposite.ConclusionsThe present study supports that some age-related diseases as well as education are causally related to longevity and highlights several new targets for achieving longevity, including management of venous thromboembolism, appropriate intake of sugar, and control of body fat. Our results warrant further studies to elucidate the underlying mechanisms of these reported causal associations.

Project description:BackgroundUnderstanding the causal relations between dietary habits and stroke is crucial for prioritizing public health interventions and developing effective health strategies. This study utilized Mendelian randomization (MR) analysis to examine the causal associations between 20 dietary habits and various stroke subtypes, aiming to identify potential mediators and evaluate the proportions of mediation.MethodsA two-sample MR analysis was conducted to examine the causal relationships between dietary habits and stroke incidence. Mediation analysis, two-step MR (TSMR), and multivariable MR (MVMR) were employed to identify potential mediators. Genetic data pertaining to dietary habits and stroke were obtained from extensive genome-wide association study (GWAS) consortia. The inverse variance-weighted (IVW) method served as the primary analytical approach, with the additional scrutiny of significant correlations conducted through the Egger regression, MR-Pleiotropy Residual Sum and Outlier (MR-PRESSO), and weighted median techniques.ResultsOur analyses indicated that genetically predicted intakes of dried fruits, cheese, cereal, oily fish, and hot drink temperatures were protective against stroke, whereas higher intakes of lamb/mutton, poultry, and added salt significantly elevated stroke risk. Specifically, dried fruit consumption demonstrated a protective effect against total stroke (β = -0.009, p = 0.013), ischemic stroke (β = -0.475, p = 0.003), and small-vessel ischemic stroke (β = -0.682, p = 0.033) through reductions in BMI levels, accounting for mediated proportions of 3.2%, 17.1%, and 8.5%, respectively. Furthermore, cheese intake provided a protective effect against ischemic stroke (β = -0.275, p = 0.003) by decreasing BMI and increasing HDL-C levels, with mediated proportions of 30.5% and 6.5%. Together, BMI and HDL-C accounted for 34.9% of the beneficial effect of cheese intake on reducing the risk of ischemic stroke. In contrast, an increased salt intake exhibited a positive association with large-artery ischemic stroke (β = 0.432, p = 0.033) through BMI elevation, with a mediated proportion of 10.9%.ConclusionsOur findings provide compelling evidence supporting causal relationships between dietary habits and stroke subtypes, while identifying mediators and evaluating the proportions of mediation. Adhering to a low-calorie, nutrient-dense diet enriched with dried fruits, cheese, and cereal, along with reduced salt and poultry consumption, could potentially mitigate stroke risk.

Dataset Information

Causal Relations between Exposome and Stroke: A Mendelian Randomization Study.

Background and purpose

Methods

Results

Conclusions

Publications

Causal Relations between Exposome and Stroke: A Mendelian Randomization Study.

Similar Datasets

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