Open-Angle Glaucomatous Optic Neuropathy Is Related to Dips Rather Than Increases in the Mean Arterial Pressure Over 24-H

BACKGROUND

Mean arterial pressure (MAP) drives ocular perfusion. Excessive 24-h MAP variability relates to glaucoma, however, whether this is due to dips or increases in the blood pressure (BP) is undocumented. We investigated the association of open-angle glaucoma (OAG) in relation to the 5 largest MAP dips/increases over 24-h, henceforth called dips/blips.

METHODS

In the Maracaibo Aging Study (MAS), 93 participants aged ≥40 y (women, 87.1%; mean age, 61.9 y) underwent baseline ophthalmological and 24-h ambulatory BP monitoring assessments. OAG was the presence of optic nerve damage and visual field defects. Statistical methods included logistic regression and the generalized R2 statistic. For replication, 48 OAG cases at the Leuven Glaucoma Clinic were matched with 48 controls recruited from Flemish population.

RESULTS

In the MAS, 26 participants had OAG. OAG compared to non-OAG participants experienced longer and deeper dips (116.5 vs. 102.7 minutes; to 60.3 vs. 66.6 mm Hg; -21.0 vs. -18.0 mm Hg absolute or 0.79 vs. 0.81 relative dip compared to the preceding reading). The adjusted odds ratios associated with dip measures ranged from 2.25 (95% confidence interval [CI], 1.31-4.85; P = 0.009) to 3.39 (95% CI, 1.36-8.46; P = 0.008). On top of covariables and 24-MAP level/variability, the dip measures increased the model performance (P ≤ 0.025). Blips did not associate with OAG. The case-control study replicated the MAS observations.

CONCLUSIONS

Dips rather than increases in the 24-h MAP level were associated with increased risk for OAG. An ophthalmological examination combined with 24-h BP monitoring might be precautious steps required in normotensive and hypertensive patients at risk of OAG.

The novel proteomic signature for cardiac allograft vasculopathy

AIMS

Cardiac allograft vasculopathy (CAV) is the major long-term complication after heart transplantation, leading to mortality and re-transplantation. As available non-invasive biomarkers are scarce for CAV screening, we aimed to identify a proteomic signature for CAV.

METHODS AND RESULTS

We measured urinary proteome by capillary electrophoresis coupled with mass spectrometry in 217 heart transplantation recipients (mean age: 55.0 ± 14.4 years; women: 23.5%), including 76 (35.0%) patients with CAV diagnosed by coronary angiography. We randomly and evenly grouped participants into the derivation cohort (n = 108, mean age: 56.4 ± 13.8 years; women: 22.2%; CAV: n = 38) and the validation cohort (n = 109, mean age: 56.4 ± 13.8 years; women: 24.8%, CAV: n = 38), stratified by CAV. Using the decision tree-based machine learning methods (extreme gradient boost), we constructed a proteomic signature for CAV discrimination in the derivation cohort and verified its performance in the validation cohort. The proteomic signature that consisted of 27 peptides yielded areas under the curve of 0.83 [95% confidence interval (CI): 0.75-0.91, P < 0.001] and 0.71 (95% CI: 0.60-0.81, P = 0.001) for CAV discrimination in the derivation and validation cohort, respectively. With the optimized threshold of 0.484, the sensitivity, specificity, and accuracy for CAV differentiation in the validation cohort were 68.4%, 73.2%, and 71.6%, respectively. With adjustment of potential clinical confounders, the signature was significantly associated with CAV [adjusted odds ratio: 1.31 (95% CI: 1.07-1.64) for per 0.1% increment in the predicted probability, P = 0.012]. Diagnostic accuracy significantly improved by adding the signature to the logistic model that already included multiple clinical risk factors, suggested by the integrated discrimination improvement of 9.1% (95% CI: 2.5-15.3, P = 0.005) and net reclassification improvement of 83.3% (95% CI: 46.7-119.5, P < 0.001). Of the 27 peptides, the majority were the fragments of collagen I (44.4%), collagen III (18.5%), collagen II (3.7%), collagen XI (3.7%), mucin-1 (3.7%), xylosyltransferase 1 (3.7%), and protocadherin-12 (3.7%). Pathway analysis performed in Reactome Pathway Database revealed that the multiple pathways involved by the signature were related to the pathogenesis of CAV, such as collagen turnover, platelet aggregation and coagulation, cell adhesion, and motility.

CONCLUSIONS

This pilot study identified and validated a urinary proteomic signature that provided a potential approach for the surveillance of CAV. These proteins might provide insights into CAV pathological processes and call for further investigation into personalized treatment targets.