Dataset related to the articles: "A machine learning based approach to identify carotid subclinical atherosclerosis endotypes", "Causal analysis of plasma IL-8 on carotid intima media thickness, a measure of subclinical atherosclerosis","Cross-Sectional Gene-Smoking Interaction Analysis in Relation to Subclinical Atherosclerosis-Results From the IMPROVE Study"

This record contains raw data related to the following articles:

1) "A machine learning based approach to identify carotid subclinical atherosclerosis endotypes"

2) "Causal analysis of plasma IL-8 on carotid intima media thickness, a measure of subclinical atherosclerosis"

3) "Cross-Sectional Gene-Smoking Interaction Analysis in Relation to Subclinical Atherosclerosis-Results From the IMPROVE Study"The uploaded data cover only those collected at the Milan site (Centro Cardiologico Monzino) of the IMPROVE study

1) Abstract

Aims: To define endotypes of carotid subclinical atherosclerosis.

Methods and results: We integrated demographic, clinical, and molecular data (n = 124) with ultrasonographic carotid measurements from study participants in the IMPROVE cohort (n = 3340). We applied a neural network algorithm and hierarchical clustering to identify carotid atherosclerosis endotypes. A measure of carotid subclinical atherosclerosis, the c-IMTmean-max, was used to extract atherosclerosis-related features and SHapley Additive exPlanations (SHAP) to reveal endotypes. The association of endotypes with carotid ultrasonographic measurements at baseline, after 30 months, and with the 3-year atherosclerotic cardiovascular disease (ASCVD) risk was estimated by linear (β, SE) and Cox [hazard ratio (HR), 95% confidence interval (CI)] regression models. Crude estimates were adjusted by common cardiovascular risk factors, and baseline ultrasonographic measures. Improvement in ASCVD risk prediction was evaluated by C-statistic and by net reclassification improvement with reference to SCORE2, c-IMTmean-max, and presence of carotid plaques. An ensemble stacking model was used to predict endotypes in an independent validation cohort, the PIVUS (n = 1061). We identified four endotypes able to differentiate carotid atherosclerosis risk profiles from mild (endotype 1) to severe (endotype 4). SHAP identified endotype-shared variables (age, biological sex, and systolic blood pressure) and endotype-specific biomarkers. In the IMPROVE, as compared to endotype 1, endotype 4 associated with the thickest c-IMT at baseline (β, SE) 0.36 (0.014), the highest number of plaques 1.65 (0.075), the fastest c-IMT progression 0.06 (0.013), and the highest ASCVD risk (HR, 95% CI) (1.95, 1.18-3.23). Baseline and progression measures of carotid subclinical atherosclerosis and ASCVD risk were associated with the predicted endotypes in the PIVUS. Endotypes consistently improved measures of ASCVD risk discrimination and reclassification in both study populations.

Conclusions: We report four replicable subclinical carotid atherosclerosis-endotypes associated with progression of atherosclerosis and ASCVD risk in two independent populations. Our approach based on endotypes can be applied for precision medicine in ASCVD prevention.

Keywords: ASCVD; Artificial intelligence; Atherosclerosis; Biological markers; Endotype; Progression of atherosclerosis.

2) Abstract

Background: We investigated the causality of IL-8 on carotid intima-media thickness (c-IMT), a measure of sub-clinical atherosclerosis.

Methods: The IMPROVE is a multicenter European study (n = 3,711). The association of plasma IL-8 with c-IMT (mm) was estimated by quantile regression. Genotyping was performed using the Illumina CardioMetabo and Immuno chips. Replication was attempted in three independent studies and a meta-analysis was performed using a random model.

Results: In IMPROVE, each unit increase in plasma IL-8 was associated with an increase in median c-IMT measures (all p<0·03) in multivariable analyses. Linear regression identified rs117518778 and rs8057084 as associated with IL-8 levels and with measures of c-IMT. The two SNPs were combined in an IL-8-increasing genetic risk that showed causality of IL-8 on c-IMT in IMPROVE and in the UK Biobank (n = 22,179). The effect of IL-8 on c-IMT measures was confirmed in PIVUS (n = 1,016) and MDCCC (n = 6,103). The association of rs8057084 with c-IMT was confirmed in PIVUS and UK Biobank with a pooled estimate effect (β) of -0·006 with 95%CI (-0·008- -0·003).

Conclusion: Our results indicate that genetic variants associated with plasma IL-8 also associate with c-IMT. However, we cannot infer causality of this association, as these variants lie outside of the IL8 locus.

Keywords: Carotid intima media thickness; Chemokines; Cohort studies; Genetic association studies; Subclinical atherosclerosis.

3) Abstract

Background: Smoking is associated with carotid intima-media thickness (C-IMT). However, knowledge about how genetics may influence this association is limited. We aimed to perform nonhypothesis driven gene-smoking interaction analyses to identify potential genetic variants, among those included in immune and metabolic platforms, that may modify the effect of smoking on carotid intima-media thickness.

Methods: We used baseline data from 1551 men and 1700 women, aged 55 to 79, included in a European multi-center study. Carotid intima-media thickness maximum, the maximum of values measured at different locations of the carotid tree, was dichotomized with cut point values ≥75, respectively. Genetic data were retrieved through use of the Illumina Cardio-Metabo- and Immuno- Chips. Gene-smoking interactions were evaluated through calculations of Synergy index (S). After adjustments for multiple testing, P values of <2.4×10-7 for S were considered significant. The models were adjusted for age, sex, education, physical activity, type of diet, and population stratification.

Results: Our screening of 207 586 SNPs available for analysis, resulted in the identification of 47 significant gene-smoking synergistic interactions in relation to carotid intima-media thickness maximum. Among the significant SNPs, 28 were in protein coding genes, 2 in noncoding RNA and the remaining 17 in intergenic regions.

Conclusions: Through nonhypothesis-driven analyses of gene-smoking interactions, several significant results were observed. These may stimulate further research on the role of specific genes in the process that determines the effect of smoking habits on the development of carotid atherosclerosis.

Keywords: carotid intima-media thickness; epidemiologic studies; gene-environment interaction; polymorphism, single nucleotide; smoking.