The Mutual Contribution of 3-NT, IL-18, Albumin, and Phosphate Foreshadows Death of Hemodialyzed Patients in a 2-Year Follow-Up

Association between Geriatric Nutritional Risk Index and all-cause mortality in individuals with osteoporotic fractures: a retrospective cohort study

BACKGROUND

The number of patients with osteoporotic fractures (OPFs) is on the rise because of global aging. However, few studies have examined the connection between Geriatric Nutritional Risk Index (GNRI) and overall mortality among inpatients with OPFs. Thus, our research seeks to investigate the link between GNRI and overall mortality in inpatients with OPFs.

METHODS

A retrospective cohort study was investigated on 3143 Kunshan OPFs residents aged ≥ 50 years. Participants were stratified into malnutrition (GNRI ≤ 98) and no malnutrition groups (GNRI > 98). Multivariate Cox regression analyses were utilized to evaluate the connection between GNRI and overall mortality. No non-linear association was detected through smoothed curve fitting and threshold analysis. Kaplan-Meier curves were employed to compare the cumulative risk of mortality across varying nutritional conditions. Subgroup analyses were conducted to further investigate the effects of age, sex and other clinical and laboratory factors on the link between GNRI and mortality.

RESULTS

3,143 qualified inpatients with OPFs were involved in the final evaluation. Kaplan-Meier curves revealed that the cumulative risk of mortality was markedly elevated in malnutrition group compared to no malnutrition group. In complete adjustments model, malnutrition group showed an adjusted hazard ratio (aHR) of 1.42 [95% CI 1.05, 1.90; P-value = 0.021]. Furthermore, subgroup analyses revealed that no substantial interactions were detected among all variables. (P-interaction > 0.05).

CONCLUSIONS

Reduced GNRI scores are linked to higher mortality in inpatients with OPFs. The GNRI potentially serve as a predictor for overall mortality risk in this population.

The Interplay Between Carotid Intima-Media Thickness and Selected Serum Biomarkers in Various Stages of Chronic Kidney Disease

Background/Objectives: Chronic kidney disease (CKD), the most common cause of which is hypertension and diabetes, is a recognized risk factor for cardiovascular disease (CVD). This study investigated the association between selected serum biomarkers in the context of intima-media thickness (IMT) changes, a common predictor of subsequent cardiovascular (CV) events. Methods: A total of 251 individuals were enrolled in the study, divided into groups based on the severity of CKD, the presence of CVD, and healthy controls. For this purpose, the data from the following groups of participants were analyzed: (1) end-stage renal disease (ESRD) (n = 106), (2) pre-dialyzed (PRE) (n = 48), (3) patients at stages 1 and 2 of CKD (CKD1-2) (n = 37), (4) patients with CVD and no kidney disease (CARD) (n = 28), and (5) healthy controls (HV) (n = 31). To find markers associated with elevated IMT, the each group with CVD (ESRD, PRE and CARD) was separated into two subgroups with normal and elevated IMT and compared in the relation of the studied serum biomarkers. Results: The findings identified glucose as the only marker exclusively associated with CVD. Markers uniquely linked to CKD included urea, creatinine, eGFR, total protein, CEL, neopterin, total calcium, phosphates, iPTH, sodium, iron, ferritin, and AST. All other markers reflected a combined influence of both CKD and CVD. By comparing patients with normal and elevated IMT, distinct types of CKD-CVD interactions were observed, i.e., independent (additive effects of CKD and CVD) for MPO, ALP, MMP-9, and MMP-9/TIMP-1; combined (enhanced effect due to interactions) for AOPPs and TIMP-1; and conditional (CVD impact specific to CKD patients) for AGEs, 3-NT, magnesium, UIBC, TIBC, ALT, and TIMP-1/MMP-9. However, certain markers, i.e., CML, sRAGEs, carbamylated protein groups, protein carbamylation, hsCRP, TC, HDL-C, LDL-C, TG, IL-18, klotho, FGF-23, klotho/FGF-23 ratio, potassium, NT-proBNP, and AIP were associated with both CKD and CVD, though the exact nature of their interaction could not be determined using IMT as a distinguishing factor. Conclusions: The results showed that relations between IMT and the remaining studied factors were not trivial, and most of the analyzed parameters were altered in CKD patients, especially if compared to patients with CVD but without CKD. IMT cannot be used as a universal CVD marker.

Pathomechanisms of Disturbances Underlying Chronic Disorders

Chronic disorders' complexity poses enormous challenges to our understanding of such disorders [...].

Chronic Kidney Disease as a Cardiovascular Disorder-Tonometry Data Analyses

Tonometry is commonly used to provide efficient and good diagnostics for cardiovascular disease (CVD). There are many advantages of this method, including low cost, non-invasiveness and little time to perform. In this study, the effort was undertaken to check whether tonometry data hides valuable information associated with different stages of chronic kidney disease (CKD) and end-stage renal disease (ESRD) treatment. For this purpose, six groups containing patients at different stages of CKD following different ways of dialysis treatment, as well as patients without CKD but with CVD and healthy volunteers were assessed. It was revealed that each of the studied groups had a unique profile. Only the type of dialysis was indistinguishable a from tonometric perspective (hemodialysis vs. peritoneal dialysis). Several techniques were used to build profiles that independently gave the same outcome: analysis of variance, network correlation structure analysis, multinomial logistic regression, and discrimination analysis. Moreover, to evaluate the classification potential of the discriminatory model, all mentioned techniques were later compared and treated as feature selection methods. Although the results are promising, it could be difficult to express differences as simple mathematical relations. This study shows that artificial intelligence can differentiate between different stages of CKD and patients without CKD. Potential future machine learning models will be able to determine kidney health with high accuracy and thereby classify patients. ClinicalTrials.gov Identifier: NCT05214872.