Proteins in the pathway from high red blood cell width distribution to all-cause mortality

Validation of candidate protein biomarkers previously identified by genetic instruments for prostate cancer risk: A prospective cohort analysis of directly measured protein levels in the ARIC study

BACKGROUND

Multiple novel protein biomarkers have been shown to be associated with prostate cancer risk using genetic instruments. This study aimed to externally validate the associations of 30 genetically predicted candidate proteins with prostate cancer risk using aptamer-based levels in US Black and White men in the Atherosclerosis Risk in Communities (ARIC) study. Plasma protein levels were previously measured by SomaScan® using the blood collected in 1990-1992.

METHODS

Among 4864 eligible participants, we ascertained 667 first primary prostate cancer cases through 2015. Hazard ratios (HRs) of prostate cancer and 95% confidence intervals (CIs) were estimated using Cox proportional hazards regression for tertiles of each protein. We adjusted for age, race, and other risk factors.

RESULTS

Of the 30 proteins and considering a nominal p trend < 0.05, two were positively associated with prostate cancer risk-RF1ML (tertile 3 vs. 1: HR = 1.23; 95% CI 1.02-1.48; p trend = 0.037) and TPST1 (1.28, 95% CI 1.06-1.55; p trend = 0.0087); two were inversely associated-ATF6A (HR = 0.80, 95% CI 0.65-0.98; p trend = 0.028) and SPINT2 (HR = 0.74, 95% CI 0.61-0.90; p trend = 0.0025). One protein, KDEL2, which was nonlinearly associated (test-for-linearity: p < 0.01) showed a statistically significant lower risk in the second tertile (HR = 0.79, 95% CI 0.65-0.95). Of these five, four proteins-ATF6A, KDEL2, RF1ML, and TPST1-were consistent in the direction of association with the discovery studies.

CONCLUSION

This study validated some pre-diagnostic protein biomarkers of the risk of prostate cancer.

Proteomic Analysis of the Senescence-Associated Secretory Phenotype: GDF-15, IGFBP-2, and Cystatin-C Are Associated With Multiple Aging Traits

Cellular senescence, a hallmark of aging, results in a senescence-associated secretory phenotype (SASP) with an increased production of proinflammatory cytokines, growth factors, and proteases. Evidence from nonhuman models demonstrates that SASP contributes to tissue dysfunction and pathological effects of aging. However, there are relatively few human studies on the relationship between SASP and aging-related health outcomes. Proteins from the SASP Atlas were measured in plasma using aptamer-based proteomics (SomaLogic). Regression models were used to identify SASP protein associations with aging-related traits representing multiple aspects of physiology in 1 201 participants from 2 human cohort studies (BLSA/GESTALT and InCHIANTI). Traits examined were fasting glucose, C-reactive protein, interleukin-6, alkaline phosphatase, blood urea nitrogen, albumin, red blood cell distribution width, waist circumference, systolic and diastolic blood pressure, gait speed, and grip strength. Study results were combined with a fixed-effect inverse-variance weighted meta-analysis. In the meta-analysis, 28 of 77 SASP proteins were significantly associated with age. Of the 28 age-associated SASP proteins, 18 were significantly associated with 1 or more clinical traits, and 7 SASP proteins were significantly associated with 3 or more traits. Growth/differentiation factor 15, Insulin-like growth factor-binding protein 2, and Cystatin-C showed significant associations with inflammatory markers and measures of physical function (grip strength or gait speed). These results support the relevance of SASP proteins to human aging, identify specific traits that are potentially affected by SASP, and prioritize specific SASP proteins for their utility as biomarkers of human aging.

Red blood cell distribution width and mortality risk in critically ill cardiovascular patients

Background

The association between red blood cell distribution width (RDW) and mortality risk in critically ill cardiovascular patients has not been well studied.

Objective

To examine the association between RDW and 30-day all-cause and cause-specific mortality in critically ill cardiovascular patients.

Methods

This cohort study included 47,266 patients from the eICU database. RDW was categorized as <13.0 %, 13.0-13.4 %, 13.5-13.9 %, 14.0-14.4 %, 14.5-14.9 %, ≥15.0 %. Logistic regression model was used to estimate adjusted odds ratios (ORs), and log-linear regression model was used to examine absolute rate differences (RDs) in mortality risk. Cubic spline curve was used to explore the nonlinear association between changes in RDW and mortality.

Results

A graded association between higher RDW and incremental risk of death was observed. Compared with RDW of <13.0 %, the adjusted odds ratios for all-cause mortality were 1.29 (95 % CI, 1.10 to 1.53) for RDW of 13.5-13.9 %, 1.57 (95 % CI, 1.33 to 1.85) for RDW of 14.0-14.4 %, 1.94 (95 % CI, 1.64 to 2.29) for RDW of 14.5-15.0 %, and 3.15 (95 % CI, 2.74 to 3.63) for RDW of ≥15.0 %. The absolute rate differences for RDW of 13.5-13.9 %, 14.0-14.4 %, 14.5-14.9 %, and ≥15.0 % indicated an additional 6, 13, 14, and 40 deaths per 1000 patients, respectively. RDW was associated with most, but not all, cause-specific deaths.

Conclusion

RDW was strongly associated with all-cause mortality and most cause-specific mortality in critically ill cardiovascular patients. These findings underscore the importance of this readily available hematologic indicator in mortality risk stratification.

Red Blood Cell Distribution Width as a Potential Valuable Survival Predictor in Hepatitis B Virus-related Hepatocellular Carcinoma

Objectives: Red blood cell distribution width (RDW) is a widely used clinical parameter recently deployed in predicting various cancers. This study aimed to evaluate the prognostic value of RDW in patients with hepatitis B virus (HBV)-related hepatocellular carcinoma (HCC). Methods: We conducted a retrospective study of 745 patients with HBV-related HCC, 253 patients with chronic hepatitis B (CHB), and 256 healthy individuals to compare their hematological parameters and analyze their RDW levels. Potential risk factors for long-term all-cause mortality in patients with HBV-related HCC were predicted using Multivariate Cox regression. A nomogram was generated, and its performance was evaluated. Results: The RDW of patients with HBV-related HCC was significantly higher than that of those with CHB and healthy controls. In the former, splenomegaly, liver cirrhosis, larger tumor diameter, multiple tumor number, portal vein tumor thrombus, and lymphatic or distant metastasis were significantly increased, and the later the Child-Pugh grade and Barcelona clinic liver cancer stage, the higher the RDW. Furthermore, multivariate Cox regression analysis identified RDW as an independent risk factor for predicting long-term all-cause mortality in patients with HBV-related HCC. Finally, we successfully generated a nomogram incorporating RDW and validated its predictive ability. Conclusions: RDW is a potentially valuable hematological marker for predicting the survival and prognosis of patients with HBV-related HCC. The nomogram incorporating RDW can be used as an effective tool to plan the individualized treatment of such patients.

Association between Red Blood Cell Distribution Width and Obstructive Sleep Apnea Syndrome: A Systematic Review and Meta-Analysis

Although polysomnography is the gold standard method to diagnose obstructive sleep apnea syndrome (OSAS), there is an ongoing quest for simpler and relatively inexpensive biomarkers of disease presence and severity. To address this issue, we conducted a systematic review of the potential diagnostic role of the red blood cell distribution width (RDW), a routine hematological parameter of red blood cell volume variability, in OSAS. A total of 1478 articles were initially identified in the databases PubMed, Web of Science, Scopus, Embase, and Google Scholar, from their inception to February 2023, and 20 were selected for final analysis. The RDW was significantly higher in OSAS than in non-OSAS subjects (SMD = 0.44, 95% CI 0.20 to 0.67, p < 0.001; low certainty of evidence). In univariate meta-regression, the mean oxygen saturation (SpO₂) was significantly associated with the effect size. No significant between-group differences were observed in subgroup analyses. Notably, in OSAS subjects, the RDW SMD progressively increased with disease severity. In conclusion, these results suggest that the RDW is a promising biomarker of OSAS (PROSPERO registration number: CRD42023398047).

A computational framework of routine test data for the cost-effective chronic disease prediction

Chronic diseases, because of insidious onset and long latent period, have become the major global disease burden. However, the current chronic disease diagnosis methods based on genetic markers or imaging analysis are challenging to promote completely due to high costs and cannot reach universality and popularization. This study analyzed massive data from routine blood and biochemical test of 32 448 patients and developed a novel framework for cost-effective chronic disease prediction with high accuracy (AUC 87.32%). Based on the best-performing XGBoost algorithm, 20 classification models were further constructed for 17 types of chronic diseases, including 9 types of cancers, 5 types of cardiovascular diseases and 3 types of mental illness. The highest accuracy of the model was 90.13% for cardia cancer, and the lowest was 76.38% for rectal cancer. The model interpretation with the SHAP algorithm showed that CREA, R-CV, GLU and NEUT% might be important indices to identify the most chronic diseases. PDW and R-CV are also discovered to be crucial indices in classifying the three types of chronic diseases (cardiovascular disease, cancer and mental illness). In addition, R-CV has a higher specificity for cancer, ALP for cardiovascular disease and GLU for mental illness. The association between chronic diseases was further revealed. At last, we build a user-friendly explainable machine-learning-based clinical decision support system (DisPioneer: http://bioinfor.imu.edu.cn/dispioneer) to assist in predicting, classifying and treating chronic diseases. This cost-effective work with simple blood tests will benefit more people and motivate clinical implementation and further investigation of chronic diseases prevention and surveillance program.

SARS-CoV-2 Altered Hemorheological and Hematological Parameters during One-Month Observation Period in Critically Ill COVID-19 Patients

Hematological and hemorheological parameters are known to be altered in COVID-19; however, the value of combined monitoring in order to deduce disease severity is only scarcely examined. A total of 44 acute SARS-CoV-2-infected patients (aCOV) and 44 age-matched healthy controls (Con) were included. Blood of aCOV was sampled at admission (T0), and at day 2 (T2), day 5 (T5), day 10 (T10), and day 30 (T30) while blood of Con was only sampled once. Inter- and intra-group differences were calculated for hematological and hemorheological parameters. Except for mean cellular volume and mean cellular hemoglobin, all blood cell parameters were significantly different between aCOV and Con. During the acute disease state (T0-T5), hematological and hemorheological parameters were highly altered in aCOV; in particular, anemic conditions and increased immune cell response/inflammation, oxidative/nitrosative stress, decreased deformability, as well as increased aggregation, were observed. During treatment and convalescence until T30, almost all abnormal values of aCOV improved towards Con values. During the acute state of the COVID-19 disease, the hematological, as well as the hemorheological system, show fast and potentially pathological changes that might contribute to the progression of the disease, but changes appear to be largely reversible after four weeks. Measuring RBC deformability and aggregation, as well as oxidative stress induction, may be helpful in monitoring critically ill COVID-19 patients.

The Emerging Clinical Significance of the Red Cell Distribution Width as a Biomarker in Chronic Obstructive Pulmonary Disease: A Systematic Review

There is an intense focus on the identification of novel biomarkers of chronic obstructive pulmonary disease (COPD) to enhance clinical decisions in patients with stable disease and acute exacerbations (AECOPD). Though several local (airway) and circulatory inflammatory biomarkers have been proposed, emerging evidence also suggests a potential role for routine haematological parameters, e.g., the red cell distribution width (RDW). We conducted a systematic literature search in PubMed, Web of Science, and Scopus, from inception to April 2022, for articles investigating the diagnostic and prognostic role of the RDW in stable COPD and AECOPD. The risk of bias was assessed using the Joanna Briggs Institute Critical Appraisal Checklist. Significant associations between the RDW and the presence and severity of disease, outcomes (mortality, hospital readmission), and other relevant clinical parameters (right heart failure, pulmonary arterial hypertension) were reported in 13 out of 16 studies in stable COPD (low risk of bias in 11 studies), and 17 out of 21 studies of AECOPD (low risk of bias in 11 studies). Pending further research, our systematic review suggests that the RDW might be useful, singly or in combination with other parameters, for the diagnosis and risk stratification of patients with stable COPD and AECOPD (PROSPERO registration number: CRD42022348304).

Prognostic significance of pretreatment red blood cell distribution width in primary diffuse large B-cell lymphoma of the central nervous system for 3P medical approaches in multiple cohorts

Background/aims

Predicting the clinical outcomes of primary diffuse large B-cell lymphoma of the central nervous system (PCNS-DLBCL) to methotrexate-based combination immunochemotherapy treatment in advance and therefore administering the tailored treatment to the individual is consistent with the principle of predictive, preventive, and personalized medicine (PPPM/3PM). The red blood cell distribution width (RDW) has been reported to be associated with the clinical outcomes of multiple cancer. However, its prognostic role in PCNS-DLBCL is yet to be evaluated. Therefore, we aimed to effectively stratify PCNS-DLBCL patients with different prognosis in advance and early identify the patients who were appropriate to methotrexate-based combination immunochemotherapy based on the pretreatment level of RDW and a clinical prognostic model.

Methods

A prospective-retrospective, multi-cohort study was conducted from 2010 to 2020. We evaluated RDW in 179 patients (retrospective discovery cohorts of Huashan Center and Renji Center and prospective validation cohort of Cancer Center) with PCNS-DLBCL treated with methotrexate-based combination immunochemotherapy. A generalized additive model with locally estimated scatterplot smoothing was used to identify the relationship between pretreatment RDW levels and clinical outcomes. The high vs low risk of RDW combined with MSKCC score was determined by a minimal P-value approach. The clinical outcomes in different groups were then investigated.

Results

The pretreatment RDW showed a U-shaped relationship with the risk of overall survival (OS, P = 0.047). The low RDW (< 12.6) and high RDW (> 13.4) groups showed significantly worse OS (P < 0.05) and progression-free survival (PFS; P < 0.05) than the median group (13.4 > RDW > 12.6) in the discovery and validation cohort, respectively. RDW could predict the clinical outcomes successfully. In the discovery cohort, RDW achieved the area under the receiver operating characteristic curve (AUC) of 0.9206 in predicting the clinical outcomes, and the predictive value (AUC = 0.7177) of RDW was verified in the validation cohort. In addition, RDW combined with MSKCC predictive model can distinguish clinical outcomes with the AUC of 0.8348 for OS and 0.8125 for PFS. Compared with the RDW and MSKCC prognosis variables, the RDW combined with MSKCC scores better identified a subgroup of patients with favorable long-term survival in the validation cohort (P < 0.001). RDW combined MSKCC score remained to be independently associated with clinical outcomes by multivariable analysis.

Conclusions

Based on the pretreatment RDW and MSKCC scores, a novel predictive tool was established to stratify PCNS-DLBCL patients with different prognosis effectively. The predictive model developed accordingly is promising to judge the response of PCNS-DLBCL to methotrexate-based combination immunochemotherapy treatment. Thus, hematologists and oncologists could tailor and adjust therapeutic modalities by monitoring RDW in a prospective rather than the reactive manner, which could save medical expenditures and is a key concept in 3PM. In brief, RDW combined with MSKCC model could serve as an important tool for predicting the response to different treatment and the clinical outcomes for PCNS-DLBCL, which could conform with the principles of predictive, preventive, and personalized medicine.

Supplementary Information

The online version contains supplementary material available at 10.1007/s13167-022-00290-5.

Red blood cell distribution width is associated with increased interactions of blood cells with vascular wall

The mechanism underlying the association between elevated red cell distribution width (RDW) and poor prognosis in variety of diseases is unknown although many researchers consider RDW a marker of inflammation. We hypothesized that RDW directly affects intravascular hemodynamics, interactions between circulating cells and vessel wall, inducing local changes predisposing to atherothrombosis. We applied different human and animal models to verify our hypothesis. Carotid plaques harvested from patients with high RDW had increased expression of genes and proteins associated with accelerated atherosclerosis as compared to subjects with low RDW. In microfluidic channels samples of blood from high RDW subjects showed flow pattern facilitating direct interaction with vessel wall. Flow pattern was also dependent on RDW value in mouse carotid arteries analyzed with Magnetic Resonance Imaging. In different mouse models of elevated RDW accelerated development of atherosclerotic lesions in aortas was observed. Therefore, comprehensive biological, fluid physics and optics studies showed that variation of red blood cells size measured by RDW results in increased interactions between vascular wall and circulating morphotic elements which contribute to vascular pathology.