Relationship Between Cognitive Function, Oral Conditions and Systemic Metabolic Function in the Elderly.

Systemic metabolic disorders, including lifestyle-related diseases, are known risk factors for dementia. Furthermore, oral diseases such as periodontal disease and tooth decay are also associated with systemic metabolic disorders such as lifestyle-related diseases, and have also been reported to be indicators of risk factors for developing dementia. In this study, we investigated the relationship between cognitive function, oral conditions and systemic metabolic function in the elderly. We investigated the number of healthy teeth, the number of prosthetic teeth fitted, the number of missing prosthetic teeth, etc., in 41 elderly patients (69.7 ± 5.6 years old). Cognitive function was evaluated by the Mini Mental State Examination (MMSE). We also estimated MMSE scores for each subject using deep learning-based assessment of MMSE scores. This deep learning method enables the estimation of the MMSE score based on basic blood test data from medical examinations and reflects the systemic metabolic state including lifestyle-related diseases. The estimated MMSE score correlated negatively with age (r = -0.381), correlated positively with the number of healthy teeth (r = 0.37), and correlated negatively with the number of missing prosthetic teeth (r = -0.39). This relationship was not found in the measured MMSE scores. A negative correlation (r = -0.36) was found between age and the current number of teeth and a positive correlation (r = 0.37) was found between age and the number of missing prosthetic teeth. A positive correlation was found between the number of teeth requiring prosthesis and lifestyle-related diseases. The deep learning-based estimation method of cognitive function clearly demonstrated the close relationship between oral health condition, systemic metabolic function and the risk of cognitive impairment. It was determined that the smaller the number of existing teeth and the larger the number of missing prosthetic teeth, the higher is the risk of cognitive impairment. Systemic metabolic function is presumed to affect oral health and cognitive function. Interestingly, no such relationship was found in the measured MMSE scores. There are two possible reasons for this. The first is that MMSE is a subjective test and is less accurate in assessing cognitive function. The second is that because the MMSE estimated based on blood data using deep learning is calculated based on the metabolic function, it has a stronger correlation with the oral health condition affected by the metabolic function. In conclusion, oral health condition may predict cognitive impairment in the elderly.

Identification of Nutritional Factors to Evaluate Periodontal Clinical Parameters in Patients with Systemic Diseases.

Nutritional factors reflect the periodontal parameters accompanying periodontal status. In this study, the associations between nutritional factors, blood biochemical items, and clinical parameters were examined in patients with systemic diseases. The study participants were 94 patients with heart disease, dyslipidemia, kidney disease, or diabetes mellitus. Weak negative correlation coefficients were found between nine clinical parameters and ten nutritional factors. Stage, grade, mean probing depth (PD), rate of PD 4−5 mm, rate of PD ≥ 6 mm, mean clinical attachment level (CAL), and the bleeding on probing (BOP) rate were weakly correlated with various nutritional factors. The clinical parameters with coefficients of determinations (R2) > 0.1 were grade, number of teeth, PD, rate of PD 4−5 mm, CAL, and BOP rate. PD was explained by yogurt and cabbage with statistically significant standardized partial regression coefficients (yogurt: −0.2143; cabbage and napa cabbage: −0.2724). The mean CAL was explained by pork, beef, mutton, and dark green vegetables with statistically significant standardized partial regression coefficients (−0.2237 for pork, beef, and mutton; −0.2667 for dark green vegetables). These results raise the possibility that the frequency of intake of various vegetables can be used to evaluate periodontal stabilization in patients with systemic diseases.

Estimation of Human Cerebral Atrophy Based on Systemic Metabolic Status Using Machine Learning.

Background: Based on the assumption that systemic metabolic disorders affect cognitive function, we have developed a deep neural network (DNN) model that can estimate cognitive function based on basic blood test data that do not contain dementia-specific biomarkers. In this study, we used the same DNN model to assess whether basic blood data can be used to estimate cerebral atrophy. Methods: We used data from 1,310 subjects (58.32 ± 12.91years old) enrolled in the Brain Doc Bank. The average Mini Mental State Examination score was 28.6 ± 1.9. The degree of cerebral atrophy was determined using the MRI-based index (GM-BHQ). First, we evaluated the correlations between the subjects' age, blood data, and GM-BHQ. Next, we developed DNN models to assess the GM-BHQ: one used subjects' age and blood data, while the other used only blood data for input items. Results: There was a negative correlation between age and GM-BHQ scores (r = -0.71). The subjects' age was positively correlated with blood urea nitrogen (BUN) (r = 0.40), alkaline phosphatase (ALP) (r = 0.22), glucose (GLU) (r = 0.22), and negative correlations with red blood cell counts (RBC) (r = -0.29) and platelet counts (PLT) (r = -0.26). GM-BHQ correlated with BUN (r = -0.30), GLU (r = -0.26), PLT (r = 0.26), and ALP (r = 0.22). The GM-BHQ estimated by the DNN model with subject age exhibited a positive correlation with the ground truth GM-BHQ (r = 0.70). Furthermore, even if the DNN model without subject age was used, the estimated GM-BHQ showed a significant positive correlation with ground truth GM-BHQ (r = 0.58). Age was the most important variable for estimating GM-BHQ. Discussion: Aging had the greatest effect on cerebral atrophy. Aging also affects various organs, such as the kidney, and causes changes in systemic metabolic status, which may contribute to cerebral atrophy and cognitive impairment. The DNN model may serve as a new screening test for dementia using basic blood tests for health examinations. Finally, the blood data reflect systemic metabolic disorders in each subject-this method may thus contribute to personalized care.

Event-Related NIRS and EEG Analysis for Mental Stress Monitoring.

Mental disorders caused by chronic stress are difficult to identify, and colleagues in the work environment may suddenly report symptoms. Social barriers exist including the financial cost of medical services and the lack of a perceived need for treatment even if potential patients have a desire to receive mental healthcare. Self-report inventories such as the Beck Depression Inventory (BDI-II) and State-Trait Anxiety Inventory (STAI) can assess the emotional valence for mental health assessment, but medical expertise may be required for interpretation of the results. Contingency plans for clinical supervision and referral sources are necessary for sufficient mental healthcare using self-report inventories. On the other hand, the laterality index at rest (LIR) has been proposed for evaluation of the mental stress level from near-infrared spectroscopy (NIRS) data in the prefrontal cortex in the resting state. However, the potential for long-term monitoring has not been investigated with sufficient evaluation results. In this study, feature values were extracted from both NIRS and EEG signals each week for 10 weeks in four young participants with an average BDI-II score of 17.7, i.e., indicative of mild depression. Temporal changes in LIR and heart rate (HR) were compared with STAI-Y1 and BDI-II scores. We found cross-correlations between the time series of LIR and STAI-Y1 within one-week delay. In addition, the time series of LIR was also correlated with BDI-II with one-week delay. Importantly, by annotating the larger changes in LIR and HR on daily life events, the changes in LIR and HR were different depending on the type of life event that affected these moods.

Relationship Between Decrease of Oxygenation During Incremental Exercise and Partial Pressure End-Tidal Carbon Dioxide: Near-Infrared Spectroscopy Vector Analysis.

A previous study considered that a decrease in cerebral oxyhemoglobin (O2Hb) immediately before maximal exercise during incremental exercise is related to cerebral blood flow (CBF) and partial pressure end-tidal carbon dioxide (PETCO2). This study aimed to investigate the relationship between O2Hb, PETCO2, and the estimated value of cerebral blood volume (CBV) with cerebral oxygen exchange (COE) by using vector analysis. Twenty-four healthy young men participated in this study. They performed the incremental exercise (20 W/min) after a 4-min rest and warm-up. The O2Hb and deoxyhemoglobin (HHb) in the prefrontal cortex (PFC) were measured using near-infrared spectroscopy (NIRS). The PETCO2 was measured using a gas analyzer. The O2Hb, HHb, and PETCO2 were calculated as the amount of change (ΔO2Hb, ΔHHb, and ΔPETCO2) from an average 4-min rest. Changes in the CBV (ΔCBV) and COE (ΔCOE) were estimated using NIRS vector analysis. Moreover, the respiratory compensation point (RCP), which relates to the O2Hb decline, was detected. The Pearson correlation coefficient was used to establish the relationships among ΔO2Hb, ΔPETCO2, ΔCBV, and ΔCOE from the RCP to maximal exercise. The ΔPETCO2 did not significantly correlate with the ΔO2Hb (r = 0.03, p = 0.88), ΔCOE (r = -0.19, p = 0.36), and ΔCBV (r = -0.21, p = 0.31). These results showed that changes in the ΔPETCO2 from the RCP to maximal exercise were not related to changes in the ΔO2Hb, ΔCOE, and ΔCBV. Therefore, we suggested that the decrease of O2Hb immediately before maximal exercise during incremental exercise may be related to cerebral oxygen metabolism by neural activity increase, not decrease of CBF by the PETCO2.

Machine Learning-Based Assessment of Cognitive Impairment Using Time-Resolved Near-Infrared Spectroscopy and Basic Blood Test.

We have demonstrated that machine learning allows us to predict cognitive function in aged people using near-infrared spectroscopy (NIRS) data or basic blood test data. However, the following points are not yet clear: first, whether there are differences in prediction accuracy between NIRS and blood test data; second, whether there are differences in prediction accuracy for cognitive function in linear models and non-linear models; and third, whether there are changes in prediction accuracy when both NIRS and blood test data are added to the input layer. We used a linear regression model (LR) for the linear model and random forest (RF) and deep neural network (DNN) for the non-linear model. We studied 250 participants (mean age = 73.3 ± 12.6 years) and assessed cognitive function using the Mini Mental State Examination (MMSE) (mean MMSE scores = 22.9 ± 6.1). We used time-resolved NIRS (TNIRS) to measure absolute concentrations of hemoglobin and optical pathlength at rest in the bilateral prefrontal cortices. A basic blood test was performed on the same day. We compared predicted MMSE scores and grand truth MMSE scores; prediction accuracies were evaluated using mean absolute error (MAE) and mean absolute percentage error (MAPE). We found that (1) the DNN-based prediction using TNIRS data exhibited lower MAE and MAPE compared with those using blood test data, (2) the difference in MAPE between TNIRS and blood test data was only 0.3%, (3) adding TNIRS data to the blood test data of the input layer only improved MAPE by 1.0% compared to the use of blood test data alone, whereas the use of the blood test data alone exhibited the prediction accuracy with 81.8% sensitivity and 91.3% specificity (N = 202, repeated five-fold cross validation). Given these findings and the benefits of using blood test data (low cost and large-scale screening possible), we concluded that the DNN model using blood test data is still the most suitable for mass screening.

Deep Learning-Based Screening Test for Cognitive Impairment Using Basic Blood Test Data for Health Examination.

Background: In order to develop a new screening test of cognitive impairment, we studied whether cognitive function can be estimated from basic blood test data by applying deep learning models. This model was constructed based on the effects of systemic metabolic disorders on cognitive function. Methods: We employed a deep neural network (DNN) to predict cognitive function based on subject's age and blood test items (23 items). We included 202 patients (73.48 ± 13.1 years) with various systemic metabolic disorders for training of the DNN model, and the following groups for validation of the model: (1) Patient group, 65 patients (73.6 ± 11.0 years) who were hospitalized for rehabilitation after stroke; (2) Healthy group, 37 subjects (62.0 ± 8.6 years); (3) Health examination group, 165 subjects (54.0 ± 8.6 years) admitted for a health examination. The subjects underwent the Mini-Mental State Examination (MMSE). Results: There were significant positive correlations between the predicted MMSE scores and ground truth scores in the Patient and Healthy groups (r = 0.66, p < 0.001). There were no significant differences between the predicted MMSE scores and ground truth scores in the Patient group (p > 0.05); however, in the Healthy group, the predicted MMSE scores were slightly, but significantly, lower than the ground truth scores (p < 0.05). In the Health examination group, the DNN model classified 94 subjects as normal (MMSE = 27-30), 67 subjects as having mild cognitive impairment (24-26), and four subjects as having dementia (≤ 23). In 37 subjects in the Health examination group, the predicted MMSE scores were slightly lower than the ground truth MMSE (p < 0.05). In contrast, in the subjects with neurological disorders, such as subarachnoid hemorrhage, the ground truth MMSE scores were lower than the predicted scores. Conclusions: The DNN model could predict cognitive function accurately. The predicted MMSE scores were significantly lower than the ground truth scores in the Healthy and Health examination groups, while there was no significant difference in the Patient group. We suggest that the difference between the predicted and ground truth MMSE scores was caused by changes in atherosclerosis with aging, and that applying the DNN model to younger subjects may predict future cognitive impairment after the onset of atherosclerosis.

Prediction of MMSE Score Using Time-Resolved Near-Infrared Spectroscopy.

Time-resolved near-infrared spectroscopy (TRS) enables assessment of baseline concentrations of hemoglobin (Hb) in the prefrontal cortex, which reflects regional cerebral blood flow and neuronal activity at rest. In a previous study, we demonstrated that baseline concentrations of oxy-Hb, deoxy-Hb, total-Hb, and oxygen saturation (SO2) measured by TRS were correlated with mini mental state examination (MMSE) scores. In the present study, we investigated whether Hb concentrations measured with TRS at rest can predict MMSE scores in aged people with various cognitive functions. A total of 202 subjects (87 males, 115 females, age 73.4 ± 13 years) participated. First, MMSE was conducted to assess cognitive function, and then baseline concentrations of oxy-Hb, deoxy-Hb, total-Hb, and SO2 in the bilateral prefrontal cortex were measured by TRS. Then, we employed the deep neural network (DNN) to predict the MMSE score. From the comparison results, the DNN showed 91.5% accuracy by leave-one-out cross validation. We found that not only the baseline concentration of SO2 but also optical path lengths contributed to prediction of the MMSE score. These results suggest that TRS with the DNN is useful as a screening test for cognitive impairment.

Temporal Comparison Between NIRS and EEG Signals During a Mental Arithmetic Task Evaluated with Self-Organizing Maps.

Simultaneous monitoring of brain activity with near-infrared spectroscopy and electroencephalography allows spatiotemporal reconstruction of the hemodynamic response regarding the concentration changes in oxyhemoglobin and deoxyhemoglobin that are associated with recorded brain activity such as cognitive functions. However, the accuracy of state estimation during mental arithmetic tasks is often different depending on the length of the segment for sampling of NIRS and EEG signals. This study compared the results of a self-organizing map and ANOVA, which were both used to assess the accuracy of state estimation. We conducted an experiment with a mental arithmetic task performed by 10 participants. The lengths of the segment in each time frame for observation of NIRS and EEG signals were compared with the 30-s, 1-min, and 2-min segment lengths. The optimal segment lengths were different for NIRS and EEG signals in the case of classification of feature vectors into the states of performing a mental arithmetic task and being at rest.