Urinary peptides provide information about the risk of mortality across a spectrum of diseases and scenarios

Background: There is evidence of pre-established vulnerability in individuals that increases the risk of their progression to severe disease or death, although the mechanisms causing this are still not fully understood. Previous research has demonstrated that a urinary peptide classifier (COV50) predicts disease progression and death from SARS-CoV-2 at an early stage, indicating that the outcome prediction may be partly due to vulnerabilities that are already present. The aim of this study is to examine the ability of COV50 to predict future non-COVID-19-related mortality, and evaluate whether the pre-established vulnerability can be generic and explained on a molecular level by urinary peptides. Methods: Urinary proteomic data from 9193 patients (1719 patients sampled at intensive care unit (ICU) admission and 7474 patients with other diseases (non-ICU)) were extracted from the Human Urinary Proteome Database. The previously developed COV50 classifier, a urinary proteomics biomarker panel consisting of 50 peptides, was applied to all datasets. The association of COV50 scoring with mortality was evaluated. Results: In the ICU group, an increase in the COV50 score of one unit resulted in a 20% higher relative risk of death [adjusted HR 1.2 (95% CI 1.17–1.24)]. The same increase in COV50 in non-ICU patients resulted in a higher relative risk of 61% [adjusted HR 1.61 (95% CI 1.47–1.76)], consistent with adjusted meta-analytic HR estimate of 1.55 [95% CI 1.39–1.73]. The most notable and significant changes associated with future fatal events were reductions of specific collagen fragments, most of collagen alpha I (I). Conclusion: The COV50 classifier is predictive of death in the absence of SARS-CoV-2 infection, suggesting that it detects pre-existing vulnerability. This prediction is mainly based on collagen fragments, possibly reflecting disturbances in the integrity of the extracellular matrix. These data may serve as a basis for proteomics-guided intervention aiming towards manipulating/ improving collagen turnover, thereby reducing the risk of death

Association of ATP2B1 common variants with asymptomatic intracranial and extracranial large artery stenosis in hypertension patients

Background and aims: Genetic factors play an important role in the cervico-cerebral large-artery atherosclerotic stenosis (LAS), and ATP2B1 gene has been associated with the process of atherosclerosis disorders, such as coronary artery disease and arterial stiffness. But there is little information about the relationship between ATP2B1 gene and atherosclerosis in the intracranial arteries. We hereby investigated the association of common variants in ATP2B1 gene with LAS in asymptomatic Chinese hypertension patients. Methods: The stenosis of intracranial and extracranial arteries were evaluated in 899 subjects through computerized tomography angiography from the aortic arch to the skull base. A total of 11 ATP2B1 common variants were genotyped. Multivariate logistic regression was carried out in a dominant model with confounding factors adjusted. Results: rs17249754-A (OR = 0.43, p = 0.0002) and rs1401982-G (OR = 0.47, p = 0.0007) were associated with decreased susceptibility of concurrent extra and intracranial stenosis even after Bonferroni correction. These two minor alleles were also significantly associated with less stenotic arteries and moderate-to-severe stenosis. Conclusion: rs17249754 and rs1401982 were associated with asymptomatic LAS in stroke-free Chinese hypertension patients and might benefit early recognition of LAS patients in clinical practice

Analysis Of The Association Between Sh2B1 chr16.28884655 And Type 2 Diabetes

Purpose: To determine correlation between genetic susceptibility of type 2 diabetes mellitus (T2DM) and Src homology 2 B adapter protein 1 (SH2B1) gene polymorphism in a diabetic population.  Methods: A total of 111 T2DM patients (DM group) and 34 healthy controls (NC group) from Shanxi Provincial People’s Hospital were included in this study. Exon 9 of the SH2B1 gene was detected using the Sanger sequencing method, and the relationship between SH2B1 gene polymorphism and diabetes was analyzed.  Results: Comparison of the data between the two groups showed that the values of TG, the updated HOMA of insulin resistance (HOMA2-IR), weight, body mass index, waist circumference, fasting blood glucose and fasting insulin levels of the DM group were higher than those of the NC group (P < 0.05). The HOMA2 insulin sensitivity (%S) of the DM group was lower than that of the NC group (P < 0.05). Sequencing analysis revealed that the following five single nucleotide polymorphisms in exon 9 of SH2B1 may be related to T2DM: rs181578610, rs550079240, chr16.28884655, chr16.28884659 and chr16.28884831. Among them, chr16.28884655 was found to be significantly related to diabetes; this site, located on the NM_015503 exon, was related to TG, LDL-C and waist circumference. Conclusion: The SH2B1 gene locus chr16.28884655 was found to be significantly related to genetic susceptibility to T2DM

Predictive value of attended automated office blood pressure and resting pulse rate for mortality in community‐dwelling octogenarians: Minhang study

Systolic blood pressure (SBP) and resting pulse rate (RPR) have been linked to mortality and cardiovascular events in younger population. Till now, no studies simultaneously investigate the non‐linear association of SBP and RPR with all‐cause and cardiovascular mortality among population aged 80 and older. Data of 2828 eligible participants were selected from electronic health records linked attended automated office blood pressure measurement system. The dose‐response relationship between the SBP, RPR, and the risk of all‐cause and cardiovascular mortality was analyzed by Cox model with restricted cubic splines. During the 3.6‐year follow‐up, 442 deaths occurred. Comparing with the optimal SBP (117‐145 mmHg), the lower (HR: 1.39, 95% CI: 1.07‐1.81) and higher SBP (HR: 1.34, 95% CI: 1.08‐1.65) were significantly associated with an increasing risk of all‐cause mortality. The higher SBP (>144 mmHg) was associated with cardiovascular mortality, with the HR (95% CI) as 1.51 (1.07‐2.12). The faster RPR showed the higher risk of all‐cause (HR: 1.36, 95% CI: 1.05‐1.76) and cardiovascular (HR: 1.51, 95% CI: 1.07‐2.13) mortality. We found both higher SBP and faster RPR were independently associated with all‐cause and cardiovascular mortality, and lower SBP was only associated with the increased risk of all‐cause mortality in oldest old community‐dwelling Chinese population. Our results demonstrate the prognostic importance of both SBP and RPR in the elderly

Urinary peptides provide information about the risk of mortality across a spectrum of diseases and scenarios

Abstract Background There is evidence of pre-established vulnerability in individuals that increases the risk of their progression to severe disease or death, although the mechanisms causing this are still not fully understood. Previous research has demonstrated that a urinary peptide classifier (COV50) predicts disease progression and death from SARS-CoV-2 at an early stage, indicating that the outcome prediction may be partly due to vulnerabilities that are already present. The aim of this study is to examine the ability of COV50 to predict future non-COVID-19-related mortality, and evaluate whether the pre-established vulnerability can be generic and explained on a molecular level by urinary peptides. Methods Urinary proteomic data from 9193 patients (1719 patients sampled at intensive care unit (ICU) admission and 7474 patients with other diseases (non-ICU)) were extracted from the Human Urinary Proteome Database. The previously developed COV50 classifier, a urinary proteomics biomarker panel consisting of 50 peptides, was applied to all datasets. The association of COV50 scoring with mortality was evaluated. Results In the ICU group, an increase in the COV50 score of one unit resulted in a 20% higher relative risk of death [adjusted HR 1.2 (95% CI 1.17–1.24)]. The same increase in COV50 in non-ICU patients resulted in a higher relative risk of 61% [adjusted HR 1.61 (95% CI 1.47–1.76)], consistent with adjusted meta-analytic HR estimate of 1.55 [95% CI 1.39–1.73]. The most notable and significant changes associated with future fatal events were reductions of specific collagen fragments, most of collagen alpha I (I). Conclusion The COV50 classifier is predictive of death in the absence of SARS-CoV-2 infection, suggesting that it detects pre-existing vulnerability. This prediction is mainly based on collagen fragments, possibly reflecting disturbances in the integrity of the extracellular matrix. These data may serve as a basis for proteomics-guided intervention aiming towards manipulating/ improving collagen turnover, thereby reducing the risk of death