OMICs Signatures Linking Persistent Organic Pollutants to Cardiovascular Disease in the Swedish Mammography Cohort.
Tessa SchillemansYingxiao YanAnton RibbenstedtCarolina Donat-VargasChristian H LindhHannu KivirantaPanu RantakokkoAlicja WolkRikard LandbergAgneta ÅkessonCarl BruniusPublished in: Environmental science & technology (2024)
Cardiovascular disease (CVD) development may be linked to persistent organic pollutants (POPs), including organochlorine compounds (OCs) and perfluoroalkyl and polyfluoroalkyl substances (PFAS). To explore underlying mechanisms, we investigated metabolites, proteins, and genes linking POPs with CVD risk. We used data from a nested case-control study on myocardial infarction (MI) and stroke from the Swedish Mammography Cohort - Clinical ( n = 657 subjects). OCs, PFAS, and multiomics (9511 liquid chromatography-mass spectrometry (LC-MS) metabolite features; 248 proteins; 8110 gene variants) were measured in baseline plasma. POP-related omics features were selected using random forest followed by Spearman correlation adjusted for confounders. From these, CVD-related omics features were selected using conditional logistic regression. Finally, 29 (for OCs) and 12 (for PFAS) unique features associated with POPs and CVD. One omics subpattern, driven by lipids and inflammatory proteins, associated with MI (OR = 2.03; 95% CI = 1.47; 2.79), OCs, age, and BMI, and correlated negatively with PFAS. Another subpattern, driven by carnitines, associated with stroke (OR = 1.55; 95% CI = 1.16; 2.09), OCs, and age, but not with PFAS. This may imply that OCs and PFAS associate with different omics patterns with opposite effects on CVD risk, but more research is needed to disentangle potential modifications by other factors.
Keyphrases
- cardiovascular disease
- single cell
- mass spectrometry
- liquid chromatography
- genome wide
- atrial fibrillation
- heart failure
- type diabetes
- body mass index
- high resolution mass spectrometry
- magnetic resonance imaging
- ms ms
- electronic health record
- genome wide identification
- dna methylation
- transcription factor
- left ventricular
- magnetic resonance
- high resolution
- metabolic syndrome
- tandem mass spectrometry
- machine learning
- physical activity
- drinking water
- cardiovascular risk factors
- subarachnoid hemorrhage
- human health
- coronary artery disease
- atomic force microscopy
- deep learning
- brain injury
- high performance liquid chromatography
- solid phase extraction
- dual energy