Daytime Hypercapnia Impairs Working Memory in Young and Middle-Aged Patients with Obstructive Sleep Apnea Hypopnea Syndrome.

Purpose: Obstructive sleep apnea hypopnea syndrome (OSAHS) can lead to cognitive impairment, though few studies have so far examined hypercapnia as its causal mechanism, due to the invasive nature of conventional arterial CO2 measurement. The study aims to investigate the effects of daytime hypercapnia on working memory in young and middle-aged patients with OSAHS. Patients and Methods: This prospective study screened 218 patients and eventually recruited 131 patients (aged 25-60 years) with polysomnography (PSG)-diagnosed OSAHS. Using a cut-off of 45mmHg daytime transcutaneous partial pressure of carbon dioxide (PtcCO2), 86 patients were assigned into the normocapnic group and 45 patients into the hypercapnic group. Working memory was evaluated using the Digit Span Backward Test (DSB) and the Cambridge Neuropsychological Test Automated Battery. Results: Compared with the normocapnic group, the hypercapnic group performed worse in verbal, visual, and spatial working memory tasks. PtcCO2≥45mmHg was an independent predictor for lower DSB scores (OR=4.057), lower accuracy in the immediate Pattern Recognition Memory (OR=2.600), delayed Pattern Recognition Memory (OR=2.766) and Spatial Recognition Memory (OR=2.722) tasks, lower Spatial Span scores (OR=4.795), and more between errors in the Spatial Working Memory task (OR=2.734 and 2.558, respectively). Notably, PSG indicators of hypoxia and sleep fragmentation did not predict task performance. Conclusion: Hypercapnia may be plays an important role in working memory impairment in patients with OSAHS, perhaps more so than hypoxia and sleep fragmentation. Routine CO2 monitoring in these patients could prove of utility in clinical practices.

Multi-state model in the evaluation of outcome on mild cognitive impairment to Alzheimer' s disease / 中华流行病学杂志

Objective The aim of this study was to introduce the multi-slate Markov model for the prediction of mild cognitive impairment (MCI) to Alzheimer' s disease (AD) and to find out the related factors for AD prevention and early intervention among the elderly.Methods MCI,moderate to severe cognitive impairment,and AD were defined as state 1,2 and 3,respectively.A three-state homogeneous model with discrete states and discrete times from data on six follow-up visits was constructed to explore factors for various progressive stages from MCI to AD.Transition probability and survival curve were made after the model fit assessment.Results At the level of 0.05,data from the multivariate analysis showed that gender (HR=I.23,95％CI:1.12-1.38),age (HR=I.37,95％ CI:1.07-1.72),hypertension (HR=l.54,95％ CI:1.31-2.19) were statistically significant for the transition from state 1 to state 2,while age (HR=0.78,95％ CI:0.69-0.98),education level (HR=1.35,95％ CI:1.09-1.86) and reading (HR=1.20,95％ CI:1.01-1.41 ) were statistically significant for transition from state 2 to state 1,and gender (HR=1.59,95％ CI:1.33-1.89),age (HR=1.33,95％ CI:1.02-1.64),hypertension (HR=l.22,95％ CI:1.11-1.43),diabetes (HR=1.52,95％CI:1.12-2.00),ApoEε4 (HR=1.44,95％CI:1.09-1.68) were statistically significant for transition from state 2 to state 3.Based on the fired model,the three-year transition probabilities during each state at average covariate level were estimated.Conclusion To delay the disease progression of MCI,phase by phase prevention measures could be adopted based on the main factors of each stage.Multi-state Markov model could imitate the natural history of disease and showed great advantage in dynamically evaluating the development of chronic diseases with multi-states and multi-faetors.

Multi-state Markov model in expressing the outcome of mild cognitive impairment among community-based elderly residents / 中华流行病学杂志

Objective To introduce the Multi-state Markov model in studying the outcome prediction of mild cognitive impairment (MCI). Methods Based on the intelligence quotient (IQ)changes that reflecting the trends in cognitive function in the patients under follow-up program, we constructed a four states model and used Multi-state Markov model to analyze the patients. Results Among 600 MCI patients, there were 174(29.0%) males and 426(71.0%) females, with age range of 65-90 years-old (average 69.7±6.6). For univariate analysis, gender, age, education level, marital status, smoking, household income, cerebral hemorrhage, hypertension, high cholesterol, diabetes,LDL-C, SBP and DBP were found to be associated with cognitive function. For multivariate analysis,female, older age, cerebral hemorrhage and higher SBP were shown to be the risk factors for transition from the state of cognitive stability to the state of severe deterioration, and their coefficients were 0.762,0.366,0.885, and 0.069, respectively. Age (0.038) could influence the transition from the state of cognitive stability to slight deterioration. Higher education level was shown to be the protective factor for these transitions (-0.219 and-0.297). Transition intensity from the state of cognitive stability to the state of slight and severe deterioration was 1.2 times that of transition to the state of improving. Transition intensity from state of slight deterioration to cognitive stability was 11.4times that of transition to severe deterioration. Conclusion Multi-state Markov model was an effective tool in dealing with longitudinal data.