Routine Blood Biomarkers Research Articles

Application of machine learning algorithms in predicting carotid artery plaques using routine health assessments.

Cardiovascular diseases (CVD) constitute a grave global health challenge, engendering significant socio-economic repercussions. Carotid artery plaques (CAP) are critical determinants of CVD risk, and proactive screening can substantially mitigate the frequency of cardiovascular incidents. However, the unequal distribution of medical resources precludes many patients from accessing carotid ultrasound diagnostics. Machine learning (ML) offers an effective screening alternative, delivering accurate predictions without the need for advanced diagnostic equipment. This study aimed to construct ML models that utilize routine health assessments and blood biomarkers to forecast the onset of CAP. In this study, seven ML models, including LightGBM, LR, multi-layer perceptron (MLP), NBM, RF, SVM, and XGBoost, were used to construct the prediction model, and their performance in predicting the risk of CAP was compared. Data on health checkups and biochemical indicators were collected from 19,751 participants at the Beijing MJ Health Screening Center for model training and validation. Of these, 6,381 were diagnosed with CAP using carotid ultrasonography. In this study, 21 indicators were selected. The performance of the models was evaluated using the accuracy, sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV), F1 score, and area under the curve (AUC) value. Among the seven ML models, the light gradient boosting machine (LightGBM) had the highest AUC value (85.4%). Moreover, age, systolic blood pressure (SBP), gender, low-density lipoprotein cholesterol (LDL-C), and total cholesterol (CHOL) were the top five predictors of carotid plaque formation. This study demonstrated the feasibility of predicting carotid plaque risk using ML algorithms. ML offers effective tools for improving public health monitoring and risk assessment, with the potential to improve primary care and community health by identifying high-risk individuals and enabling proactive healthcare measures and resource optimization.

Integrating routine blood biomarkers and artificial intelligence for supporting diagnosis of silicosis in engineered stone workers

AbstractEngineered stone silicosis (ESS), primarily caused by inhaling respirable crystalline silica, poses a significant occupational health risk globally. ESS has no effective treatment and presents a rapid progression from simple silicosis (SS) to progressive massive fibrosis (PMF), with respiratory failure and death. Despite the use of diagnostic methods like chest x‐rays and high‐resolution computed tomography, early detection of silicosis remains challenging. Since routine blood tests have shown promise in detecting inflammatory markers associated with the disease, this study aims to assess whether routine blood biomarkers, coupled with machine learning techniques, can effectively differentiate between healthy individuals, subjects with SS, and PMF. To this end, 107 men diagnosed with silicosis, ex‐workers in the engineered stone (ES) sector, and 22 healthy male volunteers as controls not exposed to ES dust were recruited. Twenty‐one primary biochemical markers derived from peripheral blood extraction were obtained retrospectively from clinical hospital records. Relief‐F features selection technique was applied, and the resulting subset of 11 biomarkers was used to build five machine learning models, demonstrating high performance with sensitivities and specificities in the best case greater than 82% and 89%, respectively. The percentage of lymphocytes, the angiotensin‐converting enzyme, and lactate dehydrogenase indexes were revealed, among others, as blood biomarkers with significant cumulative importance for the machine learning models. Our study reveals that these biomarkers could detect a chronic inflammatory status and potentially serve as a supportive tool for the diagnosis, monitoring, and early detection of the progression of silicosis.

Blood Count and Renal Functionality Assessments in the Emergency Section Disclose Morbidity and Mortality in Omicron COVID-19 Patients: A Retrospective Study.

Background: SARS-CoV-2 is the coronavirus responsible for the COVID-19 pandemic. Even though we are no longer in a pandemic situation, people are still getting infected, some of them need hospitalization and a few of them die. Methods: We conducted a retrospective study including 445 patients who accessed the Emergency Section of Policlinico Umberto I, Rome, Italy, where they had routine blood exams. In this study, we focused on the complete blood count, serum creatinine and azotemia. The data were analyzed using ANOVA, Spearman correlation and ROC analyses. They were divided into four groups based on their clinical outcomes: (1) the emergency group (patients who had mild forms and were quickly discharged); (2) the hospital ward group (patients who were admitted to the emergency section and were then hospitalized in a COVID-19 ward); (3) the intensive care unit (ICU) group (patients who required intensive assistance after the admission in the emergency section); (4) the deceased group (patients who had a fatal outcome after admission to the emergency section). Results: We found significant changes for creatinine, azotemia, hematocrit, mean corpuscular hemoglobin concentration, basophils, monocytes, red blood cell distribution width, hemoglobin, hematocrit and red blood cell numbers using ANOVA according to their clinical outcomes, particularly for the deceased group. Also, we found linear correlations of clinical outcomes with eosinophils, hemoglobin, hematocrit, mean corpuscular hemoglobin concentration, lymphocyte, neutrophil, platelet and red blood cell number and red blood cell distribution width. Conclusions: This study discloses an early association between "classical" routine blood biomarkers and the severity of clinical outcomes in Omicron patients.

Beyond diagnosis: Leveraging routine blood and urine biomarkers to predict severity and functional outcome in acute ischemic stroke

BackgroundThe initial severity of acute ischemic stroke (AIS) is a crucial predictor of the disease outcome. In this study, blood and urine biomarkers from patients with AIS were measured to estimate stroke severity and predict long-term stroke outcomes. MethodsThe medical records of patients with AIS between October 2016 and May 2020 were retrospectively analyzed. The relationships of blood and urine biomarkers with stroke severity at admission were evaluated in patients with AIS. Predictive models for initial stroke severity and long-term prognosis were then developed using a panel of identified biomarkers. ResultsA total of 2229 patients were enrolled. Univariate analysis revealed 12 biomarkers associated with the National Institutes of Health Stroke Scale scores at admission. The area under the curve values for predicting initial stroke severity and long-term prognosis on the basis of these biomarkers were 0.7465, 0.7470, and 0.8061, respectively. Among multiple tested machine-learning, eXtreme gradient boosting exhibited the highest effectiveness in predicting 90-day modified Rankin Scale scores. SHapley Additive exPlanations revealed fasting glucose, albumin, hemoglobin, prothrombin time, and urine-specific gravity to be the top five most crucial biomarkers. ConclusionThese findings demonstrate that clinically available blood and urine biomarkers can effectively estimate initial stroke severity and predict long-term prognosis in patients with AIS. Our results provide a scientific basis for developing tailored clinical treatment and management strategies for AIS, through incorporating liquid biomarkers into stroke risk assessment and patient care protocols for patients with AIS.

Asthma prediction via affinity graph enhanced classifier: a machine learning approach based on routine blood biomarkers

BackgroundAsthma is a chronic respiratory disease affecting millions of people worldwide, but early detection can be challenging due to the time-consuming nature of the traditional technique. Machine learning has shown great potential in the prompt prediction of asthma. However, because of the inherent complexity of asthma-related patterns, current models often fail to capture the correlation between data samples, limiting their accuracy. Our objective was to use our novel model to address the above problem via an Affinity Graph Enhanced Classifier (AGEC) to improve predictive accuracy.MethodsThe clinical dataset used in this study consisted of 152 samples, where 24 routine blood markers were extracted as features to participate in the classification due to their ease of sourcing and relevance to asthma. Specifically, our model begins by constructing a projection matrix to reduce the dimensionality of the feature space while preserving the most discriminative features. Simultaneously, an affinity graph is learned through the resulting subspace to capture the internal relationship between samples better. Leveraging domain knowledge from the affinity graph, a new classifier (AGEC) is introduced for asthma prediction. AGEC’s performance was compared with five state-of-the-art predictive models.ResultsExperimental findings reveal the superior predictive capabilities of AGEC in asthma prediction. AGEC achieved an accuracy of 72.50%, surpassing FWAdaBoost (61.02%), MLFE (60.98%), SVR (64.01%), SVM (69.80%) and ERM (68.40%). These results provide evidence that capturing the correlation between samples can enhance the accuracy of asthma prediction. Moreover, the obtained p\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$p$$\\end{document} values also suggest that the differences between our model and other models are statistically significant, and the effect of our model does not exist by chance.ConclusionAs observed from the experimental results, advanced statistical machine learning approaches such as AGEC can enable accurate diagnosis of asthma. This finding holds promising implications for improving asthma management.

Assessment of Globulin Levels and Albumin-to-globulin Ratio in Patients with Type 2 Diabetes and Retinopathy: A Retrospective Single-center Study

Background: Diabetes is a global health burden, with diabetic retinopathy (DR) repeatedly arising as an inflammatory complication. This study aims to evaluate routine blood measures as inflammatory markers in DR. Methods: A cross-sectional study was conducted on patients with type 2 diabetes (T2D) attending an outpatient clinic at a tertiary care hospital. Data on glycated hemoglobin (HbA1c), C-reactive protein (CRP), total protein, albumin, and globulin were retrospectively collected from medical records. Data analysis involved independent t-tests, Mann-Whitney, and Pearson's correlation. Results: Encrypted data were collected and analyzed for 139 diabetic patients (70 DR, 69 non-DR). The mean globulin levels were significantly higher in the DR group compared to the non-DR group (30.1g/L±5.04 and 18g/L±9.14, respectively, p<0.001). Moreover, the DR group had a lower mean albumin-to-globulin ratio than the non-DR group (1.3±0.33 and 2.8±2.06, respectively, p<0.001) and a higher mean HbA1c level (8±1.49 and 7.4±1.58, respectively, p=0.020). A weak negative correlation between globulin and albumin levels was detected, with a Pearson's correlation coefficient of -0.085 (p=0.482). Mean values of total protein, albumin, and CRP differed between groups but were not statistically significant (p=0.133, 0.763, 0.396 respectively). Conclusion: The study highlights the potential use of routine blood biomarkers as useful indicators for DR in T2D. The observed increase in serum globulin levels and the negative correlation with albumin provide important insights into the pathophysiology of DR. However, further research is necessary to elucidate the mechanisms behind these associations and evaluate the clinical usefulness of these biomarkers.

Vitamin D supplementation reduced blood inflammatory cytokines expression and improved graft function in kidney transplant recipients.

Chronic allograft dysfunction(CAD) is the leading cause of graft loss in kidney transplant recipients (KTRs). Inflammatory process is believed to be one of the major contributors to CAD. The aim of this study is to explore the anti-inflammatory effect of vitamin D (VD) supplementation in KTRs and its role in the graft function improvement(protection). A retrospective cohort of 39 KTRs with chronic antibody mediated rejection(CAMR)or stable renal function and a prospective cohort of 42 KTRs treated or untreated with VD were enrolled. Serum levels of vitamin D metabolism and serum inflammatory cytokines, renal graft function, and routine blood biomarkers were tested and dynamically tracked within 12 months post-transplant. Compared with the stable group, the CAMR group exhibited significantly elevated serum levels of inflammatory cytokines IL-1β, IFN-γ, IL-2, IL-10, IP-10, and HMGB1 (P <0.05). The supplementation of vitamin D effectively increased the serum concentration of vitamin D in kidney transplant recipients (KTRs) in the treated group. During the course of treatment, the treated group exhibited a gradual increase in eGFR levels, which were significantly higher than those observed in the untreated group at 12 months post-transplant (p<0.05). Notably, as eGFR improved, there was a significant decrease in levels of IL-1β, IFN-γ, IL-2, IL-10, IP-10 and HMGB1 in the treated group compared to the untreated group (P<0.05). This study confirmed that immune-inflammation is a crucial factor in the development of CAD in KTRs.VD deficiency impairs its anti-inflammatory activity. By assisting in the regulation of excessive immune inflammation and restoration of immune homeostasis, effective VD supplementation contributes to protection and maintenance of graft function in KTRs.

Blood Biomarkers from the Emergency Department Disclose Severe Omicron COVID-19-Associated Outcomes.

Since its outbreak, Coronavirus disease 2019 (COVID-19), a life-threatening respiratory illness, has rapidly become a public health emergency with a devastating social impact. Lately, the Omicron strain is considered the main variant of concern. Routine blood biomarkers are, indeed, essential for stratifying patients at risk of severe outcomes, and a huge amount of data is available in the literature, mainly for the previous variants. However, only a few studies are available on early routine biochemical blood biomarkers for Omicron-afflicted patients. Thus, the aim and novelty of this study were to identify routine blood biomarkers detected at the emergency room for the early prediction of severe morbidity and/or mortality. 449 COVID-19 patients from Sapienza University Hospital of Rome were divided into four groups: (1) the emergency group (patients with mild forms who were quickly discharged); (2) the hospital ward group (patients that after the admission in the emergency department were hospitalized in a COVID-19 ward); (3) the intensive care unit (ICU) group (patients that after the admission in the emergency department required intensive assistance); (4) the deceased group (patients that after the admission in the emergency department had a fatal outcome). ANOVA and ROC data showed that high-sensitivity troponin-T (TnT), fibrinogen, glycemia, C-reactive protein, lactate dehydrogenase, albumin, D-dimer myoglobin, and ferritin for both men and women may predict lethal outcomes already at the level of the emergency department. Compared to previous Delta COVID-19 parallel emergency patterns of prediction, Omicron-induced changes in TnT may be considered other early predictors of severe outcomes.

The value of clinical routine blood biomarkers in predicting long-term mortality after stroke.

Several blood biomarkers have been identified as predictors for poor outcome after ischemic stroke. However, recent studies mainly focused on single or experimental biomarkers and considered rather short follow-up intervals limiting their value for daily clinical practice. We, therefore, aimed to compare various clinical routine blood biomarkers for their predictive value on post-stroke mortality over a 5-year follow-up period. This data analysis of a prospective single-center study included all consecutive ischemic stroke patients admitted to the stroke unit of our university hospital over a 1-year period. Various blood biomarkers of inflammation, heart failure, metabolic disorders, and coagulation were analyzed from standardized routine blood samples collected within 24 h of hospital admission. All patients underwent a thorough diagnostic workup and were followed for 5 years post-stroke. Of 405 patients (mean age: 70.3 years), 72 deceased (17.8%) during the follow-up period. While various routine blood biomarkers were associated with post-stroke mortality in univariable analyses, only NT-proBNP remained an independent predictor (adjusted odds ratio 5.1; 95% CI 2.0-13.1; p < 0.001) for death after stroke. NT-proBNP levels ⩾794 pg/mL (n = 169, 42%) had a sensitivity of 90% for post-stroke mortality with a negative predictive value of 97% and was additionally associated with cardioembolic stroke and heart failure (each p ⩽ 0.05). NT-proBNP represents the most relevant routine blood-based biomarker for the prediction of long-term mortality after ischemic stroke. Increased NT-proBNP levels indicate a vulnerable subgroup of stroke patients in which early and thorough cardiovascular assessment and consistent follow-ups could improve outcome after stroke.

Neutrophil-to-leukocyte ratio and admission glycemia as predictors of short-term death in very old elderlies with lobar intracerebral hemorrhage.

The incidence of spontaneous intracerebral hemorrhage (SICH) is highest in very old elderlies (≥75 years). The increasing use of antithrombotic drugs is shifting the epidemiology of SICH towards predominance of lobar subtype, suggesting an incremented propensity of bleeding associated with underlying cerebral amyloid angiopathy. With population aging and antithrombotic use, a parallel raise of proportion of lobar SICH is occurring. Improvement of prognostication in this specific age group and SICH type is needed. Routine blood biomarkers can contribute to prediction of short-term mortality after SICH. Our aim was to investigate the contribution of routine blood biomarkers for short-term mortality (30-days) in elderly patients with lobar SICH. Retrospective analysis of consecutive 130 patients with ≥ 75 years and lobar SICH. The outcome was 30-day mortality. Logistic regression analysis was used to investigate whether admission routine biomarkers can be used as predictors. The case fatality was 40.8%. Admission glycaemia level, neutrophil to lymphocyte ratio and mean platelet volume were significantly different between groups (p = 0.001, p = 0.024, p = 0.038, respectively). There was no significant difference in all other routine biomarkers. On multivariate analysis, admission higher mean BG level (odds ratio [OR]: 1.010, 95% confidence interval [CI]: 1.001-1.019, p = 0.026) and neutrophil to lymphocyte ratio (OR: 1.070, 95% CI: 1.008-1.136, p = 0.027) emerged as predictors. In very old patients with lobar SICH, higher BG level and neutrophil to lymphocyte ratio are associated with increased risk of short-term death.

Routine blood biomarkers for the detection of multiple myeloma using machine learning.

Primary laboratory tests performed in the diagnosis of multiple myeloma (MM) include bone marrow examination and free light chain assay; however, these may only be ordered after clinical suspicion of disease. In contrast, routine blood test results are readily available. Machine learning algorithms (ML) combined with routine blood tests were used to detect MM. Feature selection was performed to achieve improved classification performance. The robustness of the classification models was assessed in an internal and external validation data set. To minimize the divergence, the training and validation data sets were combined and used to assess the performance of the ML algorithms. The AdaBoost-DecisionTable produced the best performance (accuracy =94.75%, sensitivity =87.70%, positive predictive value (PPV) =92.50%, F-measure =90.00%, and areas under the receiver operating characteristic curves (AUC) =97.50%) in the training data set using a 10-fold cross-validation. Performance in the validation data sets was affected by the divergence of the data sets, with accuracy greater than 85% and AUC greater than 90% in the validation data sets. The ML algorithm achieved a high accuracy of 92.61%, high AUC (96.80%), a sensitivity value of 85.20%, a PPV value of 88.50%, and an F-measure of 86.80% in a test set that was randomly selected from the combined data set. Combining ML and routine serum biomarkers hold a potential benefit in MM diagnosis.

The AST/ALT Ratio (De Ritis Ratio) Represents an Unfavorable Prognosis in Patients in Early-Stage SFTS: An Observational Cohort Study.

BackgroundSevere fever with thrombocytopenia syndrome (SFTS), a widely prevalent infectious disease caused by severe fever with thrombocytopenia syndrome virus (SFTSV) that carries with it a high mortality rate, has emerged to be a public health concern. This study aimed to investigate the epidemiological and clinical characteristics of patients infected with SFTSV, seeking novel prognostic risk factors for SFTS.MethodsIn this retrospective and cross-sectional study, confirmed SFTS patients from the First Affiliated Hospital of Anhui Medical University were enrolled from September 1, 2019, to December 12, 2020. Cases were analyzed for epidemiological, demographic, clinical, and laboratory data. Logistic regression models were used to assess the association between predictors and outcome variables. A generalized additive mixed model (GAMM) was conducted to analyze the trending shift of aspartate aminotransferase/alanine transaminase-ratio (AST/ALT-ratio) and platelet (PLT) in SFTS patients treated with ribavirin. p values ≤ 0.05 were considered statistically significant.ResultsClinical and laboratory results of 107 hospitalized patients with SFTSV infection were retrospectively described. The mean age at onset of disease was 60.38 ± 11.29 years old and the ratio between male and female was 1:1.2. Fever and thrombocytopenia are hallmark features of SFTS. Furthermore, multiple cases also experienced neurological complications, gastrointestinal/skeletal muscle symptoms together with other non-specific clinical manifestations; laboratory dataset outcomes reported dysregulated levels for routine blood biomarkers, coagulation function, and biochemistry. Overall, 107 patients were segregated into two groups according to patient condition at the clinical endpoint (survivors/non-survivors). SFTS survivors had a higher level of PLT- counts, total protein (TP), and estimated glomerular filtration rate (eGFR), while levels of activated partial thromboplastin time (APTT), thrombin time (TT), D-dimer (D-D), fibrinogen degradation products (FDP), ALT, AST, AST/ALT-ratio, creatinine (Cr), creatine phosphokinase (CK) and procalcitonin (PCT) was higher in non-survivors. Results from univariate Cox regression revealed that elevated levels of FDP, TT, AST/ALT-ratio, PCT, as well as decreased eGFR level and presence of central nervous system symptoms (CNS), were significant predictors for SFTS prognostic, results from multivariate logistic regression analysis in three adjusted models showed AST/ALT-ratio and PCT were independent risk factors for the prognosis of SFTS patients. Kaplan–Meier survival analysis showed that SFTS patients with AST/ALT-ratio >2.683 were associated with a shorter futime (means survival time), therefore indicating an unfavorable prognosis. Treatment with ribavirin could increase PLT count while decreasing AST/ALT-ratio within SFTS patients.ConclusionSFTS is an emerging infectious disease, possibly leading to multiple-organ injury; AST/ALT-ratio was an independent risk factor for the prognosis of SFTS patients. Further investigation should be performed in order to gain more knowledge on this disease and guide clinical management.

Early Routine Biomarkers of SARS-CoV-2 Morbidity and Mortality: Outcomes from an Emergency Section.

Background. COVID-19 is a severe acute respiratory disease caused by SARS-CoV-2, a virus belonging to the Coronaviridae family. This disease has spread rapidly around the world and soon became an international public health emergency leading to an unpredicted pressure on the hospital emergency units. Early routine blood biomarkers could be key predicting factors of COVID-19 morbidity and mortality as suggested for C-reactive protein (CRP), IL-6, prothrombin and D-dimer. This study aims to identify other early routine blood biomarkers for COVID-19 severity prediction disclosed directly into the emergency section. Methods. Our research was conducted on 156 COVID-19 patients hospitalized at the Sapienza University Hospital “Policlinico Umberto I” of Rome, Italy, between March 2020 and April 2020 during the paroxysm’s initial phase of the pandemic. In this retrospective study, patients were divided into three groups according to their outcome: (1) emergency group (patients who entered the emergency room and were discharged shortly after because they did not show severe symptoms); (2) intensive care unit (ICU) group (patients who attended the ICU after admission to the emergency unit); (3) the deceased group (patients with a fatal outcome who attended the emergency and, afterward, the ICU units). Routine laboratory tests from medical records were collected when patients were admitted to the emergency unit. We focused on Aspartate transaminase (AST), Alanine transaminase (ALT), Lactate dehydrogenase (LDH), Creatine kinase (CK), Myoglobin (MGB), Ferritin, CRP, and D-dimer. Results. As expected, ANOVA data show an age morbidity increase in both ICU and deceased groups compared with the emergency group. A main effect of morbidity was revealed by ANOVA for all the analyzed parameters with an elevation between the emergency group and the deceased group. Furthermore, a significant increase in LDH, Ferritin, CRP, and D-dimer was also observed between the ICU group and the emergency group and between the deceased group and ICU group. Receiver operating characteristic (ROC) analyses confirmed and extended these findings. Conclusions. This study suggests that the contemporaneous presence of high levels of LDH, Ferritin, and as expected, CRP, and D-dimer could be considered as potential predictors of COVID-19 severity and death.

Preoperative blood testing for glioblastoma, brain metastases, and primary central nervous system lymphoma differentiation.

BackgroundDifferentiating glioblastoma (GBM), brain metastases, and primary central nervous system lymphoma (PCNSL) in clinical practice is difficult. This study aimed to evaluate the diagnostic value of routine blood biomarkers in patients with GBM, brain metastases, and PCNSL and find a preoperative differential diagnostic tool for these tumors.MethodsThe perioperative medical records of 70 GBM, 41 PCNSL, and 81 brain metastases patients and their preoperative blood test results were compared and analyzed, and a diagnostic model to differentiate among them established.ResultsPatient age, plateletcrit, international normalized ratio (INR), and thrombin time (TT) were independently associated with differential diagnosis by multinomial logistic regression. Compared with GBM patients, brain metastases patients were significantly older (OR =1.055, 95% CI: 1.016–1.094, P=0.005) and had lower plateletcrit levels (OR =0.008, 95% CI: 0.004–0.017, P=0.027). In addition, patients with GBM had lower INR and higher TT than patients with the other two tumor types. A diagnostic model including these parameters, had an accuracy of 88.2% and 76.1% for brain metastases and GBM, respectively.ConclusionsPreoperative plateletcrit, INR, and TT may be used as inexpensive blood diagnostic biomarkers for differentiating brain metastases from other intracranial malignant tumors.

Rapid and Effective Vitamin D Supplementation May Present Better Clinical Outcomes in COVID-19 (SARS-CoV-2) Patients by Altering Serum INOS1, IL1B, IFNg, Cathelicidin-LL37, and ICAM1.

Background: We aimed to establish an acute treatment protocol to increase serum vitamin D, evaluate the effectiveness of vitamin D3 supplementation, and reveal the potential mechanisms in COVID-19. Methods: We retrospectively analyzed the data of 867 COVID-19 cases. Then, a prospective study was conducted, including 23 healthy individuals and 210 cases. A total of 163 cases had vitamin D supplementation, and 95 were followed for 14 days. Clinical outcomes, routine blood biomarkers, serum levels of vitamin D metabolism, and action mechanism-related parameters were evaluated. Results: Our treatment protocol increased the serum 25OHD levels significantly to above 30 ng/mL within two weeks. COVID-19 cases (no comorbidities, no vitamin D treatment, 25OHD <30 ng/mL) had 1.9-fold increased risk of having hospitalization longer than 8 days compared with the cases with comorbidities and vitamin D treatment. Having vitamin D treatment decreased the mortality rate by 2.14 times. The correlation analysis of specific serum biomarkers with 25OHD indicated that the vitamin D action in COVID-19 might involve regulation of INOS1, IL1B, IFNg, cathelicidin-LL37, and ICAM1. Conclusions: Vitamin D treatment shortened hospital stay and decreased mortality in COVID-19 cases, even in the existence of comorbidities. Vitamin D supplementation is effective on various target parameters; therefore, it is essential for COVID-19 treatment.

COVID-19 is more dangerous for older people and its severity is increasing: a case-control study.

Coronavirus disease 2019 (COVID-19) triggers important changes in routine blood tests. In this retrospective case-control study, biochemical, hematological and inflammatory biomarkers between March 10, 2020, and November 30, 2020 from 3969 COVID-19 patients (3746 in the non-intensive care unit (non-ICU) group and 223 in the ICU group) were analyzed by dividing into three groups as spring, summer and autumn. In the non-ICU group, lymphocyte to monocyte ratio was lower in autumn than the other two seasons and neutrophil to lymphocyte ratio was higher in autumn than the other two seasons. Also, monocyte and platelet were higher in spring than autumn; and eosinophil, hematocrit, hemoglobin, lymphocyte, and red blood cells decreased from spring to autumn. In the non-ICU group, alanine aminotransferase and gamma-glutamyltransferase gradually increased from spring to autumn, while albumin, alkaline phosphatase, calcium, total bilirubin and total protein gradually decreased. Additionally, C-reactive protein was higher in autumn than the other seasons, erythrocyte sedimentation rate was higher in autumn than summer. The changes in routine blood biomarkers in COVID-19 varied from the emergence of the disease until now. Also, the timely changes of blood biomarkers were mostly more negative, indicating that the disease progresses severely. The study was approved by the Erzincan Binali Yildirim University Non-interventional Clinical Trials Ethic Committee (approval No. 86041) on June 21, 2021.

Analysis of Effectiveness of a Supplement Combining Harpagophytum procumbens, Zingiber officinale and Bixa orellana in Healthy Recreational Runners with Self-Reported Knee Pain: A Pilot, Randomized, Triple-Blind, Placebo-Controlled Trial.

Recreational running (RR) is becoming a popular way to increase physical activity for improving health, together with a higher incidence of knee injuries. The aim was to analyze the effect of a four-week supplementation with a mixture of Harpagophytum procumbens, Zingiber officinale and Bixa orellana on males, middle-aged, RR with an undiagnosed knee discomfort. A randomized triple-blind placebo-control trial was conducted among male RR aged 40–60 years suffering from self-declared knee discomfort after training. Participants were assigned to supplementation (2 g/day in 6 doses; n = 13; intervention group (IG)) or matched placebo (n = 15; control group (CG)) for 4 weeks. At pre- and post-intervention, assessment of routine blood biomarkers, body composition, running biomechanics and body temperature was performed using standardized procedures. Machine learning (ML) techniques were used to classify whether subjects belonged to IG or CG. ML model was able to correctly classify individuals as IG or CG with a median accuracy of 0.857. Leg fat mass decreased significantly (p = 0.037) and a deeper reduction in knee thermograms was observed in IG (p < 0.05). Safety evaluation revealed no significant differences in the rest of parameters studied. Subjects belonging to IG or CG are clearly differentiated, pointing into an effect of the supplement of ameliorating inflammation.

Addition of routine blood biomarkers to TIMI risk score improves predictive performance of 1-year mortality in patients with ST-segment elevation myocardial infarction

BackgroundSeveral biomarkers have been proposed as independent predictors of poor outcomes in ST-segment elevation myocardial infarction (STEMI). We investigated whether adding information obtained from routine blood tests including hypoxic liver injury (HLI), dysglycemia, anemia, and high neutrophil to lymphocyte ratio (NLR) could improve the prognostic performance of the TIMI risk score for the prediction of 1-year mortality.MethodsA total of 1057 patients with STEMI undergoing primary percutaneous coronary intervention (PCI) between 2007 and 2014 were retrospectively enrolled from 4-regional hospitals. HLI and dysglycemia were defined as serum transaminase > twice the normal upper limit and glucose < 90 or > 250 mg/dL, respectively. The effect of adding biomarkers to the TIMI risk score on its discriminative ability was assessed using c-statistic, net reclassification improvement (NRI), and integrated discrimination improvement (IDI).ResultsThe 1-year mortality rate was 7.1%. The best cutoff value of NLR for the prediction of 1-year mortality was 4.3 (sensitivity, 67%; specificity, 65%). HLI (HR 2.019; 95% CI 1.104–3.695), dysglycemia (HR 2.535; 95% CI 1.324–3.923), anemia (HR 2.071; 95% CI 1.093–3.923), and high NLR (HR 3.651; 95% CI 1.927–6.918) were independent predictors of 1-year mortality. When these 4 parameters were added to the TIMI risk score, the c-statistic significantly improved from 0.841 to 0.876 (p < 0.001), and the NRI and IDI were estimated at 0.203 (95% CI 0.130–0.275; p < 0.001) and 0.089 (95% CI 0.060–0.119; p < 0.001), respectively.ConclusionsThe addition of HLI, dysglycemia, anemia, and high NLR to the TIMI risk score may be useful for very early risk stratification in patients with STEMI receiving primary PCI.

Clinical and spirometric variables are better predictors of COPD exacerbations than routine blood biomarkers

BackgroundUnderstanding the risk factors for exacerbations of COPD may help provide a more personalised approach to exacerbation prevention. MethodObservational, prospective, international, multicentre study aimed at identifying risk factors for exacerbations of COPD. Clinical variables, lung function and CAT scores were collected at baseline. In addition, routine blood biomarkers were also obtained, and patients were followed for 12 months. ResultsA total of 326 patients were included. Of these, 155 (47.5%) presented at least one exacerbation. The median time to the first exacerbation was 147 days. Exacerbators had more respiratory symptoms, more impairment in FEV1(%), FVC(%) and a worse CAT score. Regarding biomarkers, only C-reactive protein was significantly higher in exacerbators (2.8 (standard deviation (SD):3.8) mg/dL vs. 1.9 (SD:2.6) mg/dL; p = 0.037). In multivariate analysis, only CAT scores, FEV1(%) and previous exacerbations were significantly associated with having an exacerbation during follow-up. In the equation of risk, patients with a CAT score ≥15, FEV1(%) <55% and at least one exacerbation the previous year had a probability of 76% of having an exacerbation during the next year, compared with 17% in patients who had none of the previous variables. No biomarkers showed a significant association in multivariate analysis. ConclusionsLess than half of the patients presented an exacerbation during the one-year follow-up. CAT scores, FEV1(%) and previous exacerbations were the only variables associated with increased risk of exacerbations. Routine biomarkers did not provide additional information to evaluate the risk of exacerbations.

Classical blood biomarkers identify patients with higher risk for relapse 6\xa0months after alcohol withdrawal treatment

This naturalistic study among patients with alcohol dependence examined whether routine blood biomarkers could help to identify patients with high risk for relapse after withdrawal treatment. In a longitudinal study with 6-month follow-up among 133 patients with alcohol dependence who received inpatient alcohol withdrawal treatment, we investigated the usefulness of routine blood biomarkers and clinical and sociodemographic factors for potential outcome prediction and risk stratification. Baseline routine blood biomarkers (gamma-glutamyl transferase [GGT], alanine aminotransferase [ALT/GPT], aspartate aminotransferase [AST/GOT], mean cell volume of erythrocytes [MCV]), and clinical and sociodemographic characteristics were recorded at admission. Standardized 6 months’ follow-up assessed outcome variables continuous abstinence, days of continuous abstinence, daily alcohol consumption and current abstinence. The combined threshold criterion of an AST:ALT ratio > 1.00 and MCV > 90.0 fl helped to identify high-risk patients. They had lower abstinence rates (P = 0.001), higher rates of daily alcohol consumption (P < 0.001) and shorter periods of continuous abstinence (P = 0.027) compared with low-risk patients who did not meet the threshold criterion. Regression analysis confirmed our hypothesis that the combination criterion is an individual baseline variable that significantly predicted parts of the respective outcome variances. Routinely assessed indirect alcohol biomarkers help to identify patients with high risk for relapse after alcohol withdrawal treatment. Clinical decision algorithms to identify patients with high risk for relapse after alcohol withdrawal treatment could include classical blood biomarkers in addition to clinical and sociodemographic items.