The Impact of Low Hemoglobin Levels on Cognitive Brain Functions

Relationship between hemoglobin and specific cognitive domain among older adults using network analysis

OBJECTIVES

Hemoglobin (Hgb) is associated with cognitive function, with low and high levels of Hgb leading to impaired cerebral oxygenation and perfusion. Yet, current studies focused on understanding the association between Hgb and cognitive function without consideration for each cognitive domain. Thus, this study aims to identify and visualize potentially interactive associations between Hgb and specific cognitive domains among older adults.

METHOD

This is a secondary data analysis using Wave II data from the National Social Life, Health, and Aging Project (NSHAP) and included 1022 older adults aged between 65 and 85 years. The network structure of three different models was estimated to understand the association between specific cognitive domains and Hgb in a mixed graphical model using the R-package 'mgm'. Model 1 did not adjust for any covariates, Model 2 adjusted for age and gender, and Model 3 adjusted for all covariates.

RESULTS

Among all cognitive domains, the visuospatial (edge weight = 0.06-0.10) and memory domains (0.04-0.07) were associated with Hgb in all three models Though not present in Model 3, the attention domain was associated with Hgb in Model 1 and Model 2 (0.08-0.11). In addition, the predictability of Hgb was the highest (8.1%) in Model 3.

CONCLUSION

Findings from this study suggest that cognition should be considered as a multidimensional construct, and its specific cognitive domain should be carefully assessed and managed in relation to Hgb among older adults.

Low Hemoglobin Level and Elevated Inflammatory Hematological Ratios Associated With Depression and Sleep Disturbance

BACKGROUND

The relationship between blood cell profiles, including hemoglobin (Hb) levels and inflammatory hematological ratios, and mental health problems currently remains unclear.

AIM

This study aimed to investigate the relationship between blood cell profiles and mental health issues, including depressive state and sleep disturbance, while adjusting for potential demographic confounders.

METHODOLOGY

This retrospective, cross-sectional, observational study used a population-based medical database from the Tohoku Medical Megabank Project with more than 60,000 volunteers. Data on age, sex, daily tobacco use, body mass index, and self-reported scores on the Kessler Psychological Distress Scale (K6), Athens Insomnia Scale (AIS), and the Center for Epidemiologic Studies Depression Scale (CES-D) were collected.

RESULTS

A total of 62,796 volunteers (23,663 males and 39,133 females), aged ≥20 years at the time of the blood test, agreed to participate in this study. Among the evaluated blood cell profiles, Hb, hematocrit, neutrophil-to-lymphocyte ratio (NLR), and platelet-to-lymphocyte ratio (PLR) were significantly correlated with the K6, AIS, and CES-D scores, with strong statistical significance (p<0.0001 for all) in bivariate correlation analyses. A significant adjusted odds ratio (aOR) of the Hb level for elevated CES-D scores (aOR=0.965 [95% CI: 0.949-0.981], p<0.0001) was confirmed after adjusting for demographic data and daily tobacco use using a logistic regression model. Sensitivity analyses revealed that these associations existed in both males and females but were more prominent in the former. In male participants, a low Hb level was significantly associated with an elevated AIS score. The evaluated inflammatory hematological ratios, including NLR, PLR, and monocyte-to-lymphocyte ratio (MLR), also showed significant aORs with the K6, AIS, and CES-D scores after adjusting for demographic background.

CONCLUSION

Low Hb levels and elevated inflammatory hematological ratios (NLR, MLR, and PLR) were associated with depressive state and sleep disturbances in the general population.

Predicting postoperative delirium after hip arthroplasty for elderly patients using machine learning

BACKGROUND

Postoperative delirium (POD) is a common and severe complication in elderly hip-arthroplasty patients.

AIM

This study aims to develop and validate a machine learning (ML) model that determines essential features related to POD and predicts POD for elderly hip-arthroplasty patients.

METHODS

The electronic record data of elderly patients who received hip-arthroplasty surgery between January 2017 and April 2021 were enrolled as the dataset. The Confusion Assessment Method (CAM) was administered to the patients during their perioperative period. The feature section method was employed as a filter to determine leading features. The classical machine learning algorithms were trained in cross-validation processing, and the model with the best performance was built in predicting the POD. Metrics of the area under the curve (AUC), accuracy (ACC), sensitivity, specificity, and F1-score were calculated to evaluate the predictive performance.

RESULTS

476 Arthroplasty elderly patients with general anesthesia were included in this study, and the final model combined feature selection method mutual information (MI) and linear binary classifier using logistic regression (LR) achieved an encouraging performance (AUC = 0.94, ACC = 0.88, sensitivity = 0.85, specificity = 0.90, F1-score = 0.87) on a balanced test dataset.

CONCLUSION

The model could predict POD with satisfying accuracy and reveal important features of suffering POD such as age, Cystatin C, GFR, CHE, CRP, LDH, monocyte count, history of mental illness or psychotropic drug use and intraoperative blood loss. Proper preoperative interventions for these factors could reduce the incidence of POD among elderly patients.

The Cause of Alzheimer's Disease: The Theory of Multipathology Convergence to Chronic Neuronal Stress

The field of Alzheimer's disease (AD) research critically lacks an all-inclusive etiology theory that would integrate existing hypotheses and explain the heterogeneity of disease trajectory and pathologies observed in each individual patient. Here, we propose a novel comprehensive theory that we named: the multipathology convergence to chronic neuronal stress. Our new theory reconsiders long-standing dogmas advanced by previous incomplete theories. Firstly, while it is undeniable that amyloid beta (Aβ) is involved in AD, in the seminal stage of the disease Aβ is unlikely pathogenic. Instead, we hypothesize that the root cause of AD is neuronal stress in the central nervous system (CNS), and Aβ is expressed as part of the physiological response to protect CNS neurons from stress. If there is no return to homeostasis, then Aβ becomes overexpressed, and this includes the generation of longer forms that are more toxic and prone to oligomerization. Secondly, AD etiology is plausibly not strictly compartmentalized within the CNS but may also result from the dysfunction of other physiological systems in the entire body. This view implies that AD may not have a single cause, but rather needs to be considered as a spectrum of multiple chronic pathological modalities converging to the persistent stressing of CNS neurons. These chronic pathological modalities, which include cardiovascular disease, metabolic disorders, and CNS structural changes, often start individually, and over time combine with other chronic modalities to incrementally escalate the amount of stress applied to CNS neurons. We present the case for considering Aβ as a marker of neuronal stress in response to hypoxic, toxic, and starvation events, rather than solely a marker of AD. We also detail numerous human chronic conditions that can lead to neuronal stress in the CNS, making the link with co-morbidities encountered in daily clinical AD practice. Finally, we explain how our theory could be leveraged to improve clinical care for AD and related dementia in personalized medicine paradigms in the near future.

Risk Factors and a Nomogram Model Establishment for Postoperative Delirium in Elderly Patients Undergoing Arthroplasty Surgery: A Single-Center Retrospective Study

Objective

To explore the related risk factors of postoperative delirium (POD) after hip or knee arthroplasty in elderly orthopedic patients and the predictive value of related risk factors. Material and Methods. In total, 309 patients (≥60 years) who received knee and hip arthroplasty between January 2017 and May 2020 were consecutively selected into the POD and nonpostoperative delirium (NPOD) groups. Group bias was eliminated through propensity score matching. Univariate and multivariable logistic analysis was used to determine the risk factors for POD. The nomogram was made by R.

Results

58 patients were included in each group after propensity score matching; multivariable analysis demonstrated that LDH (OR = 4.364, P = 0.017), CHE (OR = 4.640, P = 0.004), Cystatin C (OR = 5.283, P = 0.006), arrhythmia (OR = 5.253, P = 0.002), and operation duration (OR = 1.017, P = 0.050) were independent risk factors of POD. LDH, CHE, Cystatin C, and arrhythmia were used to construct a nomogram to predict the POD. The nomogram was well calibrated and had moderate discriminative ability (AUC = 0.821, 95% CI: 0.760~0.883). Decision curve analysis demonstrated that the nomogram was clinically useful.

Conclusions

Our study revealed that arrhythmia, operation duration, the increase of lactate dehydrogenase and Cystatin C, and the decrease of cholinesterase were reliable factors for predicting postoperative delirium after elderly hip and knee arthroplasty. Meanwhile, the nomogram we developed can assist the clinician to filtrate potential patients with postoperative delirium.