Polygenic risk score in comparison with C-reactive protein for predicting incident coronary heart disease

Mapping prenatal predictors and neurobehavioral outcomes of an epigenetic marker of neonatal inflammation - A longitudinal population-based study

BACKGROUND

DNA methylation levels at specific sites can be used to proxy C-reactive protein (CRP) levels, providing a potentially more stable and accurate indicator of sustained inflammation and associated health risk. However, its use has been primarily limited to adults or preterm infants, and little is known about determinants for - or offspring outcomes of - elevated levels of this epigenetic proxy in cord blood. The aim of this study was to comprehensively map prenatal predictors and long-term neurobehavioral outcomes of neonatal inflammation, as assessed with an epigenetic proxy of inflammation in cord blood, in the general pediatric population.

METHODS

Our study was embedded in the prospective population-based Generation R Study (n = 2,394). We created a methylation profile score of CRP (MPS-CRP) in cord blood as a marker of neonatal inflammation and validated it against serum CRP levels in mothers during pregnancy, as well as offspring at birth and in childhood. We then examined (i) which factors (previously associated with sustained inflammation) explain variability in MPS-CRP at birth, including a wide range of prenatal lifestyle and clinical conditions, pro-inflammatory exposures, as well as child genetic liability to elevated CRP levels; and (ii) whether MPS-CRP at birth associates with child neurobehavioral outcomes, including global structural MRI and DTI measures (child mean age 10 and 14 years) as well as psychiatric symptoms over time (Child Behavioral Checklist, at mean age 1.5, 3, 6, 10 and 14 years).

RESULTS

MPS-CRP at birth was validated with serum CRP in cord blood (cut-off > 1 mg/L) (AUC = 0.72). Prenatal lifestyle pro-inflammatory factors explained a small part (i.e., < 5%) of the variance in the MPS-CRP at birth. No other prenatal predictor or the polygenic score of CRP in the child explained significant variance in the MPS-CRP at birth. The MPS-CRP at birth prospectively associated with a reduction in global fractional anisotropy over time on mainly a nominal threshold (β = -0.014, SE = 0.007, p = 0.032), as well as showing nominal associations with structural differences (amygdala [(β = 0.016, SE = 0.006, p = 0.010], cerebellum [(β = -0.007, SE = 0.003, p = 0.036]). However, no associations with child psychiatric symptoms were observed.

CONCLUSION

Prenatal exposure to lifestyle-related pro-inflammatory factors was the only prenatal predictor that accounted for some of the individual variability in MPS-CRP levels at birth. Further, while the MPS-CRP prospectively associated with white matter alterations over time, no associations were observed at the behavioral level. Thus, the relevance and potential utility of using epigenetic data as a marker of neonatal inflammation in the general population remain unclear. In the future, the use of epigenetic proxies for a wider range of immune markers may lend further insights into the relationship between neonatal inflammation and adverse neurodevelopment within the general pediatric population.

Analysis of risk factors for PCI no-reflow in coronary heart disease and construction of related prediction models

OBJECTIVE

To analyze the risk factors of percutaneous coronary intervention (PCI) no-reflow in patients with coronary heart disease (CHD) and construct a predictive nomogram model.

METHODS

This retrospective study included 260 patients with CHD who underwent PCI in the Third Affiliated Hospital of Chongqing Medical University from January 2022 to December 2023. The subjects were divided into a PCI no-reflow group (n = 86) and normal reflow group (n = 174) based on thrombolysis in myocardial infarction (TIMI) blood flow grading. General data, PCI related data and laboratory indexes of patients were collected. Logistic regression was used to analyze the risk factors of no-reflow after PCI in CHD patients. Based on the significant variables from regression analysis, a nomogram prediction model was constructed by using R language. The accuracy of the model was evaluated by receiver operating characteristic (ROC) curve and calibration curve, and the decision curve was drawn to clarify the clinical utility of the model. Model performance metrics included area under the curve (AUC), accuracy, sensitivity and specificity.

RESULTS

Multivariate logistic regression analysis showed that hypertension, cystatin C (Cys-C), hypersensitive c-reactive protein (hs-CRP) and platelet-to-lymphocyte ratio (PLR) were risk factors for no-reflow after PCI in CHD patients (OR > 1, P < 0.001), while ADAM metallopeptidase with thrombospondin type 1 motif 13 (ADAMTS-13) and lymphocyte (LYM) were protective factors (OR < 1, P < 0.001). The nomogram prediction model based on the above risk factors showed good predictive value. The AUC of the nomogram prediction model in the training set was 0.967 (95% CI: 0.946-0.989), with a specificity of 0.923 and a sensitivity of 0.908. In the validation set, the AUC was 0.894 (95% CI: 0.817-0.971), with a specificity of 0.807 and a sensitivity of 0.857. The calibration curve indicated good agreement between the predicted and actual probabilities, and the decision curve showed clinical benefit across a range of threshold probabilities in both the training and validation sets (0.0-0.99).

CONCLUSION

The risk factors affecting the occurrence of no-reflow after PCI in patients with CHD include hypertension, serum Cys-C, hs-CRP, PLR, ADAMTS-13 and LYM levels. The nomogram risk prediction model based on the above factors is valuable for identifying patients with high risk of no-reflow after PCI.

Inflammatory markers in pregnancy - surprisingly stable. Mapping trajectories and drivers in four large cohorts

Adaptations of the immune system throughout gestation have been proposed as important mechanisms regulating successful pregnancy. Dysregulation of the maternal immune system has been associated with adverse maternal and fetal outcomes. To translate findings from mechanistic preclinical studies to human pregnancies, studies of serum immune markers are the mainstay. The design and interpretation of human biomarker studies require additional insights in the trajectories and drivers of peripheral immune markers. The current study mapped maternal inflammatory markers (C-reactive protein (CRP), interleukin (IL)-1β, IL-6, IL-17A, IL-23, interferon- γ ) during pregnancy and investigated the impact of demographic, environmental and genetic drivers on maternal inflammatory marker levels in four multi-ethnic and socio-economically diverse population-based cohorts with more than 12,000 pregnant participants. Additionally, pregnancy inflammatory markers were compared to pre-pregnancy levels. Cytokines showed a high correlation with each other, but not with CRP. Inflammatory marker levels showed high variability between individuals, yet high concordance within an individual over time during and pre-pregnancy. Pre-pregnancy body mass index (BMI) explained more than 9.6% of the variance in CRP, but less than 1% of the variance in cytokines. The polygenic score of CRP was the best predictor of variance in CRP (>14.1%). Gestational age and previously identified inflammation drivers, including tobacco use and parity, explained less than 1% of variance in both cytokines and CRP. Our findings corroborate differential underlying regulatory mechanisms of CRP and cytokines and are suggestive of an individual inflammatory marker baseline which is, in part, genetically driven. While prior research has mainly focused on immune marker changes throughout pregnancy, our study suggests that this field could benefit from a focus on intra-individual factors, including metabolic and genetic components.

A Prediction Model Based on Systemic Immune-Inflammatory Index Combined with Other Predictors for Major Adverse Cardiovascular Events in Acute Myocardial Infarction Patients

Objective

To evaluate the prognostic value of the systemic immune-inflammatory index (SII) for predicting in-hospital major adverse cardiovascular events (MACEs) in patients with acute myocardial infarction (AMI) and establish a relevant nomogram.

Methods

This study included 954 AMI patients. We examined three inflammatory factors (SII, platelet to lymphocyte ratio (PLR) and neutrophil to lymphocyte ratio (NLR)) to see which one predicts in-hospital MACEs better. The predictors were subsequently screened using bidirectional stepwise regression method, and a MACE nomogram was constructed via logistic regression analysis. The predictive value of the model was evaluated using the area under the curve (AUC), sensitivity and specificity. In addition, the clinical utility of the nomogram was evaluated using decision curve analysis. We also compared the nomogram with the Global Registry of Acute Coronary Events (GRACE) scoring system.

Results

334 (35.0%) patients had MACEs. The SII (AUC =0.684) had a greater predictive value for in-hospital MACEs in AMI patients than the PLR (AUC =0.597, P<0.001) or NLR (AUC=0.654, P=0.01). The area under the curve (AUC) of the SII-based multivariable model for predicting MACEs, which was based on the SII, Killip classification, left ventricular ejection fraction, age, urea nitrogen (BUN) concentration and electrocardiogram-based diagnosis, was 0.862 (95% CI: 0.833-0.891). Decision curve and calibration curve analysis revealed that SII-based multivariable model demonstrated a good fit and calibration and provided positive net benefits than the model without SII. The predictive value of the SII-based multivariable model was greater than that of the GRACE scoring system (P<0.001).

Conclusion

SII is a promising, reliable biomarker for identifying AMI patients at high risk of in-hospital MACEs, and SII-based multivariable model may serve as a quick and easy tool to identify these patients.