Elucidating the association of obstructive sleep apnea with brain structure and cognitive performance.

BACKGROUND: Obstructive sleep apnea (OSA) is a pervasive, chronic sleep-related respiratory condition that causes brain structural alterations and cognitive impairments. However, the causal association of OSA with brain morphology and cognitive performance has not been determined. METHODS: We conducted a two-sample bidirectional Mendelian randomization (MR) analysis to investigate the causal relationship between OSA and a range of neurocognitive characteristics, including brain cortical structure, brain subcortical structure, brain structural change across the lifespan, and cognitive performance. Summary-level GWAS data for OSA from the FinnGen consortium was used to identify genetically predicted OSA. Data regarding neurocognitive characteristics were obtained from published meta-analysis studies. Linkage disequilibrium score regression analysis was employed to reveal genetic correlations between OSA and related traits. RESULTS: Our MR study provided evidence that OSA was found to significantly increase the volume of the hippocampus (IVW ß (95% CI) = 158.997 (76.768 to 241.227), P = 1.51e-04), with no heterogeneity and pleiotropy detected. Nominally causal effects of OSA on brain structures, such as the thickness of the temporal pole with or without global weighted, amygdala structure change, and cerebellum white matter change covering lifespan, were observed. Bidirectional causal links were also detected between brain cortical structure, brain subcortical, cognitive performance, and OSA risk. LDSC regression analysis showed no significant correlation between OSA and hippocampus volume. CONCLUSIONS: Overall, we observed a positive association between genetically predicted OSA and hippocampus volume. These findings may provide new insights into the bidirectional links between OSA and neurocognitive features, including brain morphology and cognitive performance.

Identification of cachexia in lung cancer patients with an ensemble learning approach.

Objective: Nutritional intervention prior to the occurrence of cachexia will significantly improve the survival rate of lung cancer patients. This study aimed to establish an ensemble learning model based on anthropometry and blood indicators without information on body weight loss to identify the risk factors of cachexia for early administration of nutritional support and for preventing the occurrence of cachexia in lung cancer patients. Methods: This multicenter study included 4,712 lung cancer patients. The least absolute shrinkage and selection operator (LASSO) method was used to obtain the key indexes. The characteristics excluded weight loss information, and the study data were randomly divided into a training set (70%) and a test set (30%). The training set was used to select the optimal model among 18 models and verify the model performance. A total of 18 machine learning models were evaluated to predict the occurrence of cachexia, and their performance was determined using area under the curve (AUC), accuracy, precision, recall, F1 score, and Matthews correlation coefficient (MCC). Results: Among 4,712 patients, 1,392 (29.5%) patients were diagnosed with cachexia based on the framework of Fearon et al. A 17-variable gradient boosting classifier (GBC) model including body mass index (BMI), feeding situation, tumor stage, neutrophil-to-lymphocyte ratio (NLR), and some gastrointestinal symptoms was selected among the 18 machine learning models. The GBC model showed good performance in predicting cachexia in the training set (AUC = 0.854, accuracy = 0.819, precision = 0.771, recall = 0.574, F1 score = 0.658, MCC = 0.549, and kappa = 0.538). The abovementioned indicator values were also confirmed in the test set (AUC = 0.859, accuracy = 0.818, precision = 0.801, recall = 0.550, F1 score = 0.652, and MCC = 0.552, and kappa = 0.535). The learning curve, decision boundary, precision recall (PR) curve, the receiver operating curve (ROC), the classification report, and the confusion matrix in the test sets demonstrated good performance. The feature importance diagram showed the contribution of each feature to the model. Conclusions: The GBC model established in this study could facilitate the identification of cancer cachexia in lung cancer patients without weight loss information, which would guide early implementation of nutritional interventions to decrease the occurrence of cachexia and improve the overall survival (OS).

The combination of handgrip strength and CONUT predicts overall survival in patients with gastrointestinal cancer: A multicenter cohort study.

BACKGROUND: The controlled nutritional status score (CONUT) and handgrip strength (HGS) were both predictive indexes for the prognosis of cancers. However, the combination of CONUT and HGS for predicting the prognosis of gastrointestinal cancer had not been developed. This study aimed to explore the combination of CONUT and HGS as the potential predictive prognosis in patients with gastric and colorectal cancer. METHODS: A cohort study was conducted with gastric and colorectal cancer patients in multicenter in China. Based on the optimal HGS cutoff value for different sex, the HGS cutoff value was determined. The patients were divided into high and low HGS groups based on their HGS scores. A CONUT score of 4 or less was defined as a low CONUT, whereas scores higher than 4 were defined as high CONUT. The Kaplan-Meier method was used to create survival curves, and the log-rank test was used to compare time-event relationships between groups. A Cox proportional hazard regression model was used to determine independent risk factors for overall survival (OS). RESULTS: A total 2177 gastric and colorectal patients were enrolled in this study, in which 1391 (63.9%) were men (mean [SD] age, 66.11 [11.60] years). Multivariate analysis revealed that patients with high HGS had a lower risk of death than those with low HGS (hazard ratio [HR],0.87; 95% confidence interval [CI], 0.753-1.006, P = 0.06), while high CONUT had a higher risk of death than those with low CONUT (HR, 1.476; 95% CI, 1.227-1.777, P < 0.001). Patients with both low HGS and high CONUT had 1.712 fold increased risk of death (HR, 1.712; 95% CI, 1.364-2.15, P < 0.001). Moreover, cancer type and sex were stratified and found that patients with high CONUT and low HGS had lower survival rate than those with low CONUT and high HGS in both gastric or colorectal cancer, and both male and female. CONCLUSION: A combination of low HGS and high CONUT was associated with poor prognosis in patients with gastrointestinal cancer, which could probably predict the prognosis of gastrointestinal cancer more accurate than HGS or CONUT alone.

Evaluation and validation of neutrophil to albumin ratio as a promising prognostic marker for all-cause mortality in patients with cancer: a multicenter cohort study.

OBJECTIVES: The practicality and effectiveness of using the prognostic value of the neutrophil-to-albumin ratio (NAR) in evaluating patients with cancer remain unclear, and research is needed to fully understand its potential application in the cancer population. METHODS: The Kaplan-Meier method was used for survival analysis, and the log-rank test was employed for comparison. Univariate and multivariate Cox proportional hazards models were used to determine the prognostic biomarkers, and Logistic regression analysis was conducted to investigate the relationship between NAR and 90-day outcomes and cachexia. RESULTS: The study included 14 682 patients with cancer, divided into discovery (6592 patients), internal validation (2820 patients), and external validation groups (5270 patients). Patients with high NAR had higher all-cause mortality than those with low NAR in the discovery (50.15% versus 69.29%, P < 0.001), internal validation (54.18% versus 70.91%, P < 0.001), and external validation cohorts (40.60% versus 66.68%, P < 0.001). In the discovery cohort, high NAR was observed to be independently associated with all-cause mortality in patients (HR 1.16, 95% CI 1.12-1.19; P < 0.001). Moreover, we validated the promising prognostic value of NAR as a predictor of survival in patients with cancer through internal validation (HR 1.21, 95% CI 1.16-1.27, P < 0.001) and external validation cohorts (HR 1.27, 95% CI 1.21-1.34, P < 0.001). Additionally, in the subgroup analysis by tumor type, high NAR was identified as a risk factor for most cancers, except for breast cancer. CONCLUSIONS: This study showed that NAR is a feasible and promising biomarker for predicting prognosis and cancer cachexia in cancer patients.

Association of cardiometabolic multimorbidity with all-cause and cardiovascular disease mortality among Chinese hypertensive patients.

BACKGROUND: Hypertension usually clusters with multiple comorbidities. However, the association between cardiometabolic multimorbidity (CMM) and mortality in hypertensive patients is unclear. This study aimed to investigate the association between CMM and all-cause and cardiovascular disease (CVD) mortality in Chinese patients with hypertension. METHODS: The data used in this study were from the China National Survey for Determinants of Detection and Treatment Status of Hypertensive Patients with Multiple Risk Factors (CONSIDER), which comprised 5006 participants aged 19-91 years. CMM was defined as the presence of one or more of the following morbidities: diabetes mellitus, dyslipidemia, chronic kidney disease, coronary heart disease, and stroke. Cox proportional hazard models were used to calculate the hazard ratios (HR) with 95% CI to determine the association between the number of CMMs and both all-cause and CVD mortality. RESULTS: Among 5006 participants [mean age: 58.6 ± 10.4 years, 50% women (2509 participants)], 76.4% of participants had at least one comorbidity. The mortality rate was 4.57, 4.76, 8.48, and 16.04 deaths per 1000 person-years in hypertensive patients without any comorbidity and with one, two, and three or more morbidities, respectively. In the fully adjusted model, hypertensive participants with two cardiometabolic diseases (HR = 1.52, 95% CI: 1.09-2.13) and those with three or more cardiometabolic diseases (HR = 2.44, 95% CI: 1.71-3.48) had a significantly elevated risk of all-cause mortality. The findings were similar for CVD mortality but with a greater increase in risk magnitude. CONCLUSIONS: In this study, three-fourths of hypertensive patients had CMM. Clustering with two or more comorbidities was associated with a significant increase in the risk of all-cause and cardiovascular mortality among hypertensive patients, suggesting more intensive treatment and control in this high-risk patient group.

Applications of artificial intelligence in dementia research.

More than 50 million older people worldwide are suffering from dementia, and this number is estimated to increase to 150 million by 2050. Greater caregiver burdens and financial impacts on the healthcare system are expected as we wait for an effective treatment for dementia. Researchers are constantly exploring new therapies and screening approaches for the early detection of dementia. Artificial intelligence (AI) is widely applied in dementia research, including machine learning and deep learning methods for dementia diagnosis and progression detection. Computerized apps are also convenient tools for patients and caregivers to monitor cognitive function changes. Furthermore, social robots can potentially provide daily life support or guidance for the elderly who live alone. This review aims to provide an overview of AI applications in dementia research. We divided the applications into three categories according to different stages of cognitive impairment: (1) cognitive screening and training, (2) diagnosis and prognosis for dementia, and (3) dementia care and interventions. There are numerous studies on AI applications for dementia research. However, one challenge that remains is comparing the effectiveness of different AI methods in real clinical settings.