Blood-based omic profiling supports female susceptibility to tobacco smoke-induced cardiovascular diseases

Chatziioannou, Aristotelis; Georgiadis, Panagiotis; Hebels, Dennie G. A. J.; Liampa, Irene; Valavanis, Ioannis; Bergdahl, Ingvar A.; Johansson, Anders; Palli, Domenico; Chadeau‐Hyam, Marc; Siskos, Alexandros P.; Keun, Hector C.; Botsivali, Maria; Kok, Theo M. C. M. de; Pérez, Almudena Espín; Kleinjans, Jos; Víneis, Paolo; Kyrtopoulos, Soterios A.; Gottschalk, Ralph W.H.; Leeuwen, Danitsja van; Timmermans, L; Bendinelli, Benedetta; Kelly, Rachel S.; Vermeulen, Roel; Portengen, Lützen; Saberi-Hosnijeh, Fatemeh; Melin, Beatrice; Hallmans, Göran; Lenner, Per; Athersuch, Toby J.; Kogevinas, Manolis; Stephanou, Euripides G.; Myridakis, Antonis; Fazzo, Lucia; Santis, Marco De; Comba, Pietro; Kiviranta, Hannu; Rantakokko, Panu; Airaksinen, Riikka; Ruokojärvi, Päivi; Gilthorpe, Mark S.; Fleming, Sarah; Fleming, Thomas; Tu, Yu‐Kang; Jonsson, Bo; Lundh, Thomas; Chen, Wei-Jen; Lee, Wen‐Chung; Hsiao, Chuhsing Kate; Chien, Kuo‐Liong; Kuo, Po-Hsiu; Hung, Hung; Liao, Shu-Fen

doi:10.1038/srep42870

Cited by 22 publications

(22 citation statements)

References 68 publications

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“…For 224 case–control pairs (from both cohorts), full‐resolution DNA methylation data were also available from Illumina Infinium Human Methylation 450 platform using standard protocol and preprocessing/normalizing steps as described elsewhere 20. From these profiles, using an established deconvolution approach,21 we estimated the proportion of the following six blood components: CD4, CD8 and natural T‐cells, B‐cells, monocytes and granulocytes.…”

Section: Methodsmentioning

confidence: 99%

Pre‐diagnostic blood immune markers, incidence and progression of B‐cell lymphoma and multiple myeloma: Univariate and functionally informed multivariate analyses

Vermeulen¹,

Hosnijeh²,

Bodinier³

et al. 2018

Intl Journal of Cancer

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Recent prospective studies have shown that dysregulation of the immune system may precede the development of B‐cell lymphomas (BCL) in immunocompetent individuals. However, to date, the studies were restricted to a few immune markers, which were considered separately. Using a nested case–control study within two European prospective cohorts, we measured plasma levels of 28 immune markers in samples collected a median of 6 years before diagnosis (range 2.01–15.97) in 268 incident cases of BCL (including multiple myeloma [MM]) and matched controls. Linear mixed models and partial least square analyses were used to analyze the association between levels of immune marker and the incidence of BCL and its main histological subtypes and to investigate potential biomarkers predictive of the time to diagnosis. Linear mixed model analyses identified associations linking lower levels of fibroblast growth factor‐2 (FGF‐2 p = 7.2 × 10−4) and transforming growth factor alpha (TGF‐α, p = 6.5 × 10−5) and BCL incidence. Analyses stratified by histological subtypes identified inverse associations for MM subtype including FGF‐2 (p = 7.8 × 10−7), TGF‐α (p = 4.08 × 10−5), fractalkine (p = 1.12 × 10−3), monocyte chemotactic protein‐3 (p = 1.36 × 10−4), macrophage inflammatory protein 1‐alpha (p = 4.6 × 10−4) and vascular endothelial growth factor (p = 4.23 × 10−5). Our results also provided marginal support for already reported associations between chemokines and diffuse large BCL (DLBCL) and cytokines and chronic lymphocytic leukemia (CLL). Case‐only analyses showed that Granulocyte‐macrophage colony stimulating factor levels were consistently higher closer to diagnosis, which provides further evidence of its role in tumor progression. In conclusion, our study suggests a role of growth‐factors in the incidence of MM and of chemokine and cytokine regulation in DLBCL and CLL.

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Section: Methodsmentioning

confidence: 99%

Pre‐diagnostic blood immune markers, incidence and progression of B‐cell lymphoma and multiple myeloma: Univariate and functionally informed multivariate analyses

Vermeulen¹,

Hosnijeh²,

Bodinier³

et al. 2018

Intl Journal of Cancer

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“…Methylation at one of the probes included in the score (cg05575921, AHRR ), has consistently been strongly inversely associated with smoking status (42-45), with four further probes (cg25250132, cg17412005, cg25250132, cg20059928, cg04928129) showing similar, though weaker, associations (46, 47). Because of this, the association between the DNAm IL-6 score and log(IL-6) was re-run in a regression analysis, adjusting for a methylation-based score for smoking.…”

Section: Resultsmentioning

confidence: 97%

Creating and validating a DNA methylation-based proxy for Interleukin-6

Stevenson

Gadd

Hillary

et al. 2020

Preprint

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Chronic inflammation is a pervasive feature of ageing and may be linked to age-related cognitive decline. However, population studies evaluating its relationship with cognitive functioning have produced heterogeneous results. A potential reason for this is the variability of inflammatory mediators which could lead to misclassifications of individuals' persisting levels of inflammation. The epigenetic mechanism DNA methylation has shown utility in indexing environmental exposures and could potentially be leveraged to provide proxy signatures of chronic inflammation. We conducted an elastic net regression of interleukin-6 (IL-6) in a cohort of 895 older adults (mean age: 69 years) to develop a DNA methylation-based predictor. The predictor was tested in an independent cohort (n=7,028 [417 with measured IL-6], mean age: 51 years).We examined the association between the DNA methylation IL-6 score and serum IL-6, its association with age and established correlates of circulating IL-6, and with cognitive ability. A weighted score from 12 DNA methylation sites optimally predicted IL-6 (independent test set R2=5.1%). In the independent test cohort, both measured IL-6, and the DNA methylation proxy, increased as a function of age (serum IL-6: n=417, β=0.02, SE=0.004 p=1.3x10-7; DNAm IL-6 score: n=7,028, β=0.02, SE=0.0009, p<2x10-16). Serum IL-6 was not found to associate with cognitive ability (n=417, β=-0.06, SE=0.05, p=0.19); however, an inverse association was identified between the DNA methylation score and cognitive functioning (n=7,028, β=-0.14, SE=0.02, pFDR=1.5x10-14). These results suggest DNA methylation-based predictors can be used as proxies for inflammatory markers, potentially allowing for reliable insights into the relationship between chronic inflammation and pertinent health outcomes.

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“…Similarly, the proportion of MAIT cells are associated with DNAm levels in the 5'UTR of IL21R (P=8.3x10 -21 ), which is known to regulate MAIT cell numbers (25). Several cell sub-types, including leukocytes, lymphocytes, monocytes and ILC, are associated with DNAm sites within AHRR, F2RL3 and GATA3 genes, which are known to be strongly affected by cigarette consumption (26)(27)(28). We consistently showed recently that circulating levels of these different blood cell subsets are significantly impacted by smoking status (14).…”

Section: Elastic Netmentioning

confidence: 92%

Accurate prediction of cell composition, age, smoking consumption and infection serostatus based on blood DNA methylation profiles

Bergstedt

Urrutia

Duffy

et al. 2018

Preprint

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DNA methylation is a stable epigenetic alteration that plays a key role in cellular differentiation and gene regulation, and that has been proposed to mediate environmental effects on disease risk. Epigenome-wide association studies have identified and replicated associations between methylation sites and several disease conditions, which could serve as biomarkers in predictive medicine and forensics. Nevertheless, heterogeneity in cellular proportions between the compared groups could complicate interpretation. Reference-based cell-type deconvolution methods have proven useful in correcting epigenomic studies for cellular heterogeneity, but they rely on reference libraries of sorted cells and only predict a limited number of cell populations. Here we leverage >850,000 methylation sites included in the Methyla-tionEPIC array and use elastic net regularized and stability selected regression models to predict the circulating levels of 70 blood cell subsets, measured by standardized flow cytometry in 962 healthy donors of western European descent. We show that our predictions, based on a hundred of methylation sites or lower, are less error-prone than other existing methods, and extend the number of cell types that can be accurately predicted. Application of the same methods to age, smoking consumption and several serological responses to pathogen antigens also provide accurate estimations. Together, our study substantially improves predictions of blood cell composition based on methylation profiles, which will be critical in the emerging field of medical epigenomics. Methylation | Prediction | Elastic net | Cellular composition | EWAS | AgeCorrespondence: jacob.bergstedt @control.lth.se, epatin @pasteur.fr

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Blood-based omic profiling supports female susceptibility to tobacco smoke-induced cardiovascular diseases

Cited by 22 publications

References 68 publications

Pre‐diagnostic blood immune markers, incidence and progression of B‐cell lymphoma and multiple myeloma: Univariate and functionally informed multivariate analyses

Pre‐diagnostic blood immune markers, incidence and progression of B‐cell lymphoma and multiple myeloma: Univariate and functionally informed multivariate analyses

Creating and validating a DNA methylation-based proxy for Interleukin-6

Accurate prediction of cell composition, age, smoking consumption and infection serostatus based on blood DNA methylation profiles

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