{
  "abstract": "Introduction Chronic inflammation predicts adverse cardiovascular outcomes, but mechanisms linking systemic inflammation to cardiac remodeling remain incompletely understood. We investigated associations between circulating inflammatory biomarkers and cardiac phenotypes in a population-based cohort and examined how environmental exposures and genetic susceptibility influence inflammatory responses.Methods We analyzed subsets of 488,079 UK Biobank participants with metabolomic and proteomic profiling, cardiac magnetic resonance (CMR) imaging, and longitudinal outcomes. Chronic inflammation was quantified using glycoprotein acetyls (GlycA) by nuclear magnetic resonance spectroscopy. Machine learning-based analysis extracted CMR phenotypes. Multivariable linear regression assessed GlycA-cardiac associations. Mediation analysis tested 80 inflammatory proteins as potential mediators. Cox models evaluated GlycA levels and major adverse cardiovascular events (MACE). An exposome-wide association study identified environmental determinants of inflammation, and gene-environment interactions were assessed using multi-ancestry polygenic risk scores. The study received approval from the National Research Ethics Service (11/NW/0382).Results Higher GlycA levels were associated with restrictive cardiac remodeling: reduced left ventricular indexed end-diastolic volume (β = –2.09) and stroke volume (β = –1.12) with compensatory increased heart rate (β = 1.38; all P < 10^-228). Interleukin (IL) -1 receptor antagonist mediated 27% of the GlycA effect on end-diastolic volume (average causal mediated effect –0.51 [95% CI, –0.53 to –0.64]; P < 10^-16). The highest GlycA quintile had 43% higher MACE risk versus the lowest (adjusted HR, 1.43 [95% CI, 1.38–1.49]). Trunk fat mass (β = 0.35), current smoking (β = 0.39), psychological distress, and low socioeconomic status were the strongest GlycA determinants (all P < 10^-50). Cardiovascular polygenic risk scores modified associations between environmental exposures, inflammation, and MACE.Conclusion Chronic systemic inflammation is associated with restrictive cardiac remodeling and increased cardiovascular risk mediated by circulating cytokines and growth factors. Individual inflammatory responses are shaped by gene-environment interactions, highlighting the complex interplay between genetic susceptibility, environmental exposures, and their cumulative impact on cardiovascular health.Abstract 166 Figure 1a) Study flowchart. Chronic inflammation was quantified using GlycA by nuclear magnetic resonance spectroscopy. The relationship between systemic inflammation and cardiac remodeling was assessed in 70,809 participants considering 33 CMR and 16 ECG traits. Mediation analysis tested 80 inflammatory proteins as mediators in 8,184 individuals. Outcome analysis investigated the association of GlycA on MACE (454,821), IDPs (72,541). An exposome-wide association study on 478,941 volunteers identified environmental determinants of inflammation. Gene-environment interactions were assessed using multi-ancestry polygenic risk scores available for 454,821 participants. b) Distribution of glycoprotein acetyls values. Top left: Ridge plots summarising the distribution densities of GlycA in men and women subgroups. Mid-bottom left: Scatter plots of GlycA with age and BSA, with linear model fit and marginal density plots. Right: Ridge plots summarising the distribution densities of GlycA across ethnicitiesAbstract 166 Table 1Abstract 166 Figure 2a) Effect of chronic inflammation on cardiac geometry. Multivariable linear regression coefficients for the effect of GlycA on IDPs and ECG variables in the pooled cohort. Each IDP or ECG variable is considered as predictor, and regressed against GlycA after adjusting for BSA, age at the date of CMR, sex, age:sex interaction, age2, and diabetes status. β coefficients are scaled. b) Proteins significantly mediating the effect of inflammation on survival and cardiac geometry. Red dots indicate proteins that replicated across all three IDPs and the survival analysis. Dots size is proportional to the absolute ACME value. c) Exposure-gene interactions. Manhattan plot showing the -log10(FDR-P value) of the interaction terms between exposures and PRS in predicting GlycA levels. Models were fitted with log-transformed GlycA as the dependent variable and exposures as independent variables. For each exposure, all PRS were tested in separate models by including a multiplicative interaction term, and models were adjusted for BSA, sex, age, age2, age–sex interaction, ethnicity, and Townsend deprivation index. Triangles indicate interactions with an FDR-adjusted P < 0.05. The top 20 interactions are labeled. Non-significant interactions are shown as gray dots. Upward-pointing triangles denote positive interaction effects (β > 0), whereas downward-pointing triangles denote negative interaction effects (β < 0)",
  "authors": [
    {
      "affiliations": [
        "MRC Laboratory of Medical Science, Imperial College London, London, United Kingdom"
      ],
      "name": "Mattia Corianò"
    },
    {
      "affiliations": [
        "MRC Laboratory of Medical Science, Imperial College London, London, United Kingdom"
      ],
      "name": "Shamin Tahasildar"
    },
    {
      "affiliations": [
        "MRC Laboratory of Medical Science, Imperial College London, London, United Kingdom"
      ],
      "name": "Ling Huang"
    },
    {
      "affiliations": [
        "MRC Laboratory of Medical Science, Imperial College London, London, United Kingdom"
      ],
      "name": "Khaled Rjoob"
    },
    {
      "affiliations": [
        "MRC Laboratory of Medical Science, Imperial College London, London, United Kingdom"
      ],
      "name": "Soodeh Kalaie"
    },
    {
      "affiliations": [
        "MRC Laboratory of Medical Science, Imperial College London, London, United Kingdom"
      ],
      "name": "Jin Zheng"
    },
    {
      "affiliations": [
        "MRC Laboratory of Medical Science, Imperial College London, London, United Kingdom"
      ],
      "name": "Lara Curran"
    },
    {
      "affiliations": [
        "MRC Laboratory of Medical Science, Imperial College London, London, United Kingdom"
      ],
      "name": "Parisa Gifani"
    },
    {
      "affiliations": [
        "MRC Laboratory of Medical Science, Imperial College London, London, United Kingdom"
      ],
      "name": "Marc-Emmauel Dumas"
    },
    {
      "affiliations": [
        "MRC Laboratory of Medical Science, Imperial College London, London, United Kingdom"
      ],
      "name": "Declan P O’Regan"
    }
  ],
  "title": "166 Gene-exposure interactions regulate cytokine-mediated chronic inflammation and cardiac remodeling",
  "uid": "c99e7c1f-552f-5aca-a2d1-47b047eb9da4"
}