Minimum Pulse Rate During Sleep: A Potential Non-Invasive Biomarker for Subtle Abnormalities in Mini-Mental State Examination from an Exploratory Cross-Sectional Multifaceted Survey in Active Older Adults

Minimum Pulse Rate During Sleep: A Potential Non-Invasive Biomarker for Subtle Abnormalities in Mini-Mental State Examination from an Exploratory Cross-Sectional Multifaceted Survey in Active Older Adults | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Minimum Pulse Rate During Sleep: A Potential Non-Invasive Biomarker for Subtle Abnormalities in Mini-Mental State Examination from an Exploratory Cross-Sectional Multifaceted Survey in Active Older Adults Yuji Tanaka, Kozo Saito, Kyoichiro Tsuchiya, Yusuke Iwata, Takashi Ando, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4665921/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Purpose Developing quantitative indicators of daily life that can detect subtle cognitive decline is a significant challenge in the growing population of older adults worldwide. In this multifaceted survey conducted on active older adults, we aimed to explore novel indicators associated with subtle abnormalities in brief dementia screening tests. Methods Data were collected from 35 older adults who were not certified for long-term care or diagnosed with cognitive impairment using questionnaires, the Mini-Mental State Examination (MMSE), body composition measurements, sleep apnoea testing, activity monitoring, motor function assessments, blood tests, and nutrient analyses. Of the 89 factors examined in this study, several less invasive indicators for cognitive impairment were identified using Spearman’s correlation analysis, two-group comparison, and multiple linear regression model analysis. Results An elevated minimum pulse rate during sleep emerged as the most significant non-invasive marker correlated with both MMSE scores and the classification of cognitive impairment risk (mild cognitive impairment or dementia). Conclusion These findings could expedite further research into early cognitive decline detection among older adults and facilitate early intervention. Geriatrics & Gerontology Epidemiology Health Economics & Outcomes Research Cognitive Neuroscience Nursing Healthy longevity cognitive function MCI MMSE pulse rate biomarker geriatrics Figures Figure 1 Figure 2 Figure 3 Introduction Dementia is becoming a major problem, and its prevalence is expected to increase worldwide [ 1 ]. Various studies have been conducted to maintain cognitive function, including effective lifestyle management, treatment of related diseases [ 2 , 3 ], and provision of appropriate social support for older adults with cognitive impairment [ 4 ]. Anti-amyloid-β antibody drugs have recently been approved for patients with Alzheimer’s disease and have been shown to inhibit cognitive decline [ 5 ]. Improvements in cognitive function have also been reported with treatments such as Vitamin E and acetylcholinesterase administration, and lifestyle guidance [ 6 , 7 ]. However, as these anti-dementia drugs and other interventional treatments are effective only in the mild cognitive impairment (MCI) stage, it is crucial to diagnose dementia at this stage. Dementia is typically diagnosed by a physician based on several medical tests, such as the Mini-Mental State Examination (MMSE) [ 1 ], brain imaging for atrophy detection, and the detection of molecular markers (e.g., amyloid-β levels). Among these tests, the MMSE is the most widely used international screening tool for dementia. It evaluates orientation to time and place, memory, attention and calculation, language comprehension, and constructional ability in approximately 10 min, playing a crucial role in the early detection and monitoring of cognitive change. However, these medical tests and the diagnostic process are not conducted unless patients or their caregivers recognise a decline in cognitive abilities and seek medical attention. The early symptoms of dementia are mild, making it difficult for both patients and their families to recognise them, thus hindering early diagnosis [ 9 ]. Therefore, it is necessary to develop much simpler testing methods, such as prediction using data from general health examinations, simple questionnaires [ 10 ], and applications that are less burdensome and can be administered at home. While developing methods for easily assessing the risk of subtle cognitive dysfunctions at home is imperative, this goal has not yet been realised, making it a highly promising field for future research and development. Numerous studies have investigated factors associated with cognitive decline, such as increased haemoglobin A1c (HbA1c) levels [ 11 ]. However, many of these studies have been limited to evaluating only a few parameters through specific methods, such as blood tests or questionnaires. Consequently, it has not been possible to compare the efficacy of different parameters from various measurement methods in assessing the risk of cognitive decline. It is essential to have a dataset that includes a variety of parameters from different tests that can be measured in daily life, along with actual cognitive function assessments, like the MMSE, conducted on the same older individuals. However, such research requires substantial effort as it involves coordinating numerous testing devices simultaneously, making it a challenging task, and the older the study participants, the more difficult it becomes to conduct multifaceted measurements. Recently, we conducted a multifaceted survey of active older adults, with an average age of 87 years, who had not been certified for long-term care [ 12 , 13 ]. This survey included a variety of measurements, such as MMSE scores, body composition measurements, blood test results, sleep apnoea testing, activity monitoring, and other quantitative assessments of daily life. We analysed these results to explore indicators potentially associated with sleep apnoea syndrome [ 12 , 13 ]. In this study, we further analysed this multifaceted dataset to identify candidate medical indicators associated with a decline in MMSE scores. Materials and Methods Study design, setting, and participants The Yamanashi Healthy Active Life cohort study, which began in 2003, included 587 participants [ 14 ]. Among them, 104 older adults who were not certified for long-term care in 2020 were asked to participate in the multifaceted survey [ 15 ], various physical measurements, and Yamanashi Healthy active long-living older people Biobank for healthy ageing biosciences (YHAB) study [ 12 , 13 ]. We included participants who (i) did not require long-term care, (ii) provided informed consent for the survey and sleep apnoea syndrome (SAS) measurements, and (iii) were deemed by the researchers to be able to participate without problems. The researchers assessed the eligibility of each participant for participation on a per measurement basis. For example, if there was a possibility of falling based on walking conditions, motor function measurement for leg strength assessment was omitted. All measurements, including the sleep apnoea test, were performed between January and December 2020. Patients were excluded if the SAS test could not be adequately conducted (e.g., where measurement errors occurred). Ethical considerations The research plan for this study was formulated in accordance with the Declaration of Helsinki and the Japanese Ethical Guidelines for Medical Research Involving Human Subjects and was approved by the ethics committee of the University of Yamanashi School of Medicine (approval number: 2096; approval date: December 2019). The contents of the study were explained in writing and orally to the participants, and written informed consent was obtained. Measurements Questionnaire A health-related questionnaire, including a medical history, was completed by all participants. Basic physical measurements Data on the weight and body fat, muscle, and water percentages of the participants were evaluated using a multi-frequency segmental body composition analyser (Tanita MC-780A-N; Tanita Corp., Tokyo, Japan) [ 16 ]. Accurate weight values were measured by removing as much clothing as possible and subtracting the estimated weight of the remaining clothing from the actual measurement (assuming 1.0 kg for January and February and 0.5 kg for March through December). Height was measured using a stadiometer (Height Measurement HM 200P, Charder Electronic Co. Ltd.; Taichung City, Taiwan). The systolic blood pressure, diastolic blood pressure, and pulse rate were measured using a sphygmomanometer (Terumo ES-W300ZZ; Terumo Corp., Tokyo, Japan). MMSE Cognitive function was assessed using the Japanese version of the MMSE [ 17 ]. Sleep apnoea test (Apnoea Hypopnea Index [AHI] measurement) AHI was measured using a portable monitoring device (WatchPAT 200; Itamar Medical, Caesarea, Israel) that recorded peripheral arterial tonometry signals, heart rate, oxygen saturation, and actigraphy [ 18 ]. WatchPAT calculates clinical parameters, such as respiratory events and 4% oxygen desaturation indices, using an automated and proprietary algorithm. This is less burdensome for patients than full polysomnography and is recommended by the American Academy of Sleep Medicine guidelines for obstructive SAS [ 19 ]. The resulting data were automatically analysed to estimate respiratory events, such as AHI, respiratory disturbance index, and sleep states. This analysis has been described in detail elsewhere [ 20 ]. Three-axial activity metre measurement Daily activity was measured using a three-axial activity metre (ActiGraph wGT3X-BT; ActiGraph Corp., Pensacola, FL, USA) [ 21 ]. The participants wore the device on the wrist opposite that of the listener for seven days during wakefulness and sleep, except during bathing or feeling discomfort. The analysis used the average values of several parameters derived from dedicated software, including the number of steps per day, total sleep time, sleep efficiency (total sleep time/total sleep time), number of awakenings, wake time (min), wake after sleep onset, number of activities, activity index, fragmentation index, and sleep fragmentation index. Grip strength measurement Two grip strength measurements were obtained using a grip strength measuring device comprising a digital force gauge (product no. ZP-500N; IMADA, Toyohashi, Japan) and computer/display system [ 22 ]. The mean peak grip strength was analysed. Locomotive syndrome risk test The locomotive syndrome risk test (locomotive test) was used to detect locomotive syndrome [ 23 , 24 ]. This test comprises three parts: (1) stand up test, which evaluates the muscle strength required to stand up from seats of different heights; (2) two-step test, which evaluates the length of two strides; and (3) a questionnaire with 25 questions regarding physical movement correlated with the European QOL Scale-5 Dimensions (EQ-5D) [ 25 ]. The risk level for locomotive syndrome (locomotive score) was determined as previously described [ 22 , 25 ]. Blood tests Blood samples were collected into appropriate tubes (for plasma, clot formation, and whole blood). The samples were stored separately after centrifugation and subsequent processing. The following laboratory values were evaluated by SRL (Tokyo, Japan), or Japan Medical (Yamanashi, Japan): blood urea nitrogen, creatinine, total cholesterol, high-density lipoprotein cholesterol, HbA1c, haematocrit, and cystatin C levels, as well as the white blood cell and red blood cell counts. Dental examination including occlusal force measurement The occlusal force was measured using a bite force measurement system (Dentalplescale, GC, Tokyo, Japan) [ 26 ]. Evaluation of symptoms and impact of urinary incontinence The Incontinence Questionnaire-Urinary Incontinence Short Form (ICIQ-UI SF) was used to evaluate the symptoms and impact of urinary incontinence. The ICIQ-UI SF is a questionnaire used to assess and measure urinary incontinence symptoms and their impact on an individual's quality of life [ 27 ]. Statistical analyses Statistical analyses were performed using JMP®ฎPro 15.1.0 (SAS Institute Inc., Cary, NC, USA). Spearman's correlation analysis was conducted to calculate the correlation coefficients (ρ) and p-values between the MMSE and other measured data. A pooled t- test was performed to compare cognitively healthy participants (MMSE score ≥ 28 points) and patients (MMSE score < 28 points). Following these analyses, variables that exhibited significant correlations with cognitive function were identified using a threshold p-value of < 0.05. Subsequently, the relationships between these selected variables and the MMSE were re-evaluated using a simple linear regression model to calculate the estimation value (β) and p-values. Stepwise multiple linear regression analysis was performed to remove variables with a high variance inflation factor (VIF). Briefly, initially, the MMSE was estimated using a multiple linear multiple regression model. This model included the first selected MMSE-related variables along with typical MMSE-related variables, which were not selected in the first screening. The analysis computed metrics, such as R 2 , adjusted R 2 , and p-values for the entire model. Additionally, it determined the coefficient (β), p-value, and VIF for each variable. Then, the variable with the highest VIF of over 3.0 was removed. Consequently, the same estimation was performed using the remaining variables. This process of removing variables with a high VIF was repeated stepwise until multicollinearity was eliminated (the VIF of all variables was < 3.0). Results Participants and data collection Overall, 37 of 104 older adults with no certification for care requirements (35.6%) agreed to participate in the 2020 multifaceted YHAB survey. The MMSE was administered to 35 (20 male and 15 female) participants (94.6%). Thee obtained data are summarised in Fig. 1, Table 1 , and S1 Table. In a preliminary health survey using questionnaires, all participants indicated that they had not been diagnosed with MCI or dementia before the MMSE. In this study, the data were analysed together with 89 other factors obtained from the participants (Fig. 2, top). The basic statistics of all obtained and representative data are summarised in S1 Table and Table 1 , respectively. Measurements were attempted for all 35 participants and successfully obtained for 28–35 participants for each of the 90 tested parameters. Factors that contributed to the lack of data included participants declining to participate in certain measurements, items withdrawn by the researcher owing to physical risks, and parameters that did not reach the detection thresholds in assessments using electronic devices. As representative results of the characteristics of the population, the mean age and body mass index were 86.86 years (95% confidence interval [CI]: 85.76–87.96) and 23.29 kg/m 2 (95% CI: 22.26–24.33), respectively. Figure 1. MMSE Results of the Study Participants. (A) Normal quantile plot and (B) basic statistics according to severity and sex. MMSE, Mini-Mental State Examination; SD, standard deviation; SE, standard error. Figure 2. Process of Narrowing Down the Factors Associated with MMSE score from the Multifaceted Survey of Older Adults in this Study. MMSE, Mini-Mental State Examination. MMSE score and sex differences The results of the MMSE measurements are summarised in Fig. 1. The approximately straight lines of the normal quantile plots for all male and female participants were nearly identical (Fig. 1A), indicating that this population was normally distributed. The mean MMSE scores of all participants, male, and female were 26.34, 26.29, and 26.43 points, respectively (Fig. 1B). The 95% CI values also generally overlap for male (24.88–27.69) and female (24.52–28.33) participants. The distribution of older adults with robust MMSE scores (28–30 points) and abnormalities (19–27 points) showed consistency across sexes, with 42.9% and 57.1% in male and female participants, respectively. The mean MMSE scores and 95% CIs of the robust and abnormal cognitive function groups exhibited general consistency across sexes. Screening of MMSE-related factors using correlation analysis, comparison between two groups with robust and abnormal cognitive function, and simple and multiple linear regression analysis Table 1 lists the parameters refined in each of the three stages of the screening process conducted in this study. As a first step of the initial screening, the results of Spearman’s correlation analysis between the MMSE score and all the other 89 parameters (S1 Table) are presented in S2 Table. Ten MMSE-related parameters with an absolute value of Spearman’s correlation coefficient (| ρ| ) greater than 0.400 were identified. Subsequently, the participants were divided into two groups: those with robust cognitive function (MMSE score ≥ 28 points) and those with abnormal cognitive function (MMSE score < 28 points). In the initial screening, the difference in means and corresponding p-values (using the t- test) between the two groups were evaluated for each parameter. Six parameters with statistically significant differences (p < 0.05) (selected factors) were identified as selected factors (Table 2 ). All six selected factors showed larger values | ρ| and smaller p-values in both Spearman’s correlation analysis and two-group comparison analysis compared to all four unselected factors. Table 1 An Overview of Parameters Narrowed Down in the Process of Narrowing Down MMSE-Related Factors from Supra-multidimensional Surveys of Japanese Older Adults as Part of the Yamanashi Healthy Active Long-living Older People Biobank for Healthy Ageing Biosciences (YHAB). Method of Measurements Variable After Spearman's correlation analysis: |ρ| >0.400 After two groups comparisons: p-value < 0.050 (Parameters selected by initial screening) Multiple linear regression analysis: VIF < 3 (Pre-final factors selected in 2nd screening) Multiple linear regression analysis: VIF < 3, p-value < 0.050 (Final factors selected in 2nd screening) Basic parameter Age, Height, Body mass index, AGE value, Systolic blood pressure, Diastolic Blood pressure, Resting pulse rate - - - - Body composition analyser Body weight, Body Fat Percentage, Fat Mass, Muscle Mass, Estimated Bone Mass, Body Water Content - - - - SAS test AHI, Deep Sleep percentage, Light Sleep percentage, REM sleep percentage, REM-AHI, NREM-AHI, Mean Saturation, Max Saturation, Min Saturation, Mean Pulse Rate, Max Pulse Rate, Min Pulse Rate, Number of Sleep Time Min Pulse Rate Min Pulse Rate Min Pulse Rate Min Pulse Rate Three-axial activity metre Steps per Day, Average Sleep Efficiency, Average Onset, Average Total Sleep Time, Average wake after sleep onset, Average Number of Awakenings, Average Length of Awakenings in Minutes, Average Activity Counts, Average Movement Index, Average Sleep Fragmentation Index Average Sleep Efficiency, Average wake after sleep onset, Average Number of Awakenings, Average Length of Awakenings in Minutes Average Number of Awakenings - - Motor test Grip Strength Ave, Stand Up Test Locomo Score, Two-Step Test Value, Two-Step Test Locomo Score, Locomo 25 Question, Locomo 25 Question Score Two-Step Test Value Two-Step Test Value Two-Step Test Value - - Blood test Urea Nitrogen (BUN), Urea Acid, Creatinine, Total Cholesterol, HDL Cholesterol, Neutral fat (T-G), Aspartate aminotransferase (glutamic-oxaloacetic transaminase), Alanine Aminotransferase (glutamic-pyruvic transaminase), γ-GT (γ-GTP), Glucose (blood sugar), Haemoglobin A1c (NGSP), White Blood Cell Count, Red Blood Cell Count, Haemoglobin level, Haematocrit, Platelet Count - - - Oral cavity Number of Teeth, Occlusal Contact Area, Average Occlusal Force, Maximum Occlusal Force, Bite Force Occlusal Contact Area, Bite Force Occlusal Contact Area, Bite Force Occlusal Contact Area, Bite Force Urinary incontinence evaluation ICIQ-SF Frequency, ICIQ-SF Quantity, ICIQ-SF QOL, ICIQ-SF Total Score - - - Nutritional assessment Quantity Average, Average of Energy, Protein Average, Average of Fat, Average of Carbohydrates, Average of Total Dietary Fibre, Average of Sodium, Average Potassium, Average Calcium, Average of Iron, Average of Retinol Equivalent, Average of α-Carotene, Average of β-Carotene, Average of β-Cryptoxanthin, Average of Vitamin D, Average of α-Tocopherol, Average of Vitamin B1, Average of Vitamin B6, Average of Vitamin B12, Average of Folic Acid, Average of Vitamin C Average of Retinol Average of Retinol Average of Retinol AGE, Advanced Glycation End Products; AHI, Apnoea Hypopnea Index; ICIQ-UI SF, Incontinence Questionnaire-Urinary Incontinence Short Form; MMSE, Mini-Mental State Examination; NGSP, National Glycohemoglobin Standardisation Programme; NREM, non-REM sleep; QOL, quality of life; REM, Rapid Eye Movement sleep; SAS, sleep apnoea syndrome; VIF, variance inflation factor Table 2 Basic Statistics on both Six MMSE-related Factors Selected in initial screening and Four Typical Factors Reported to be related to MMSE but not Selected in initial screening process in this study. Description Variable Method of Measurement Mean 95% CI N Spearman ρ Spearman p-value Group characteristics of robust cognitive function and abnormal cognitive function, and their comparisons Grouping by MMSE score N Mean 95% CI Difference t-test p-value Cognitive test MMSE Cognitive function 26.34 25.27–27.42 35 - - ≥28 15 29.07 28.00–30.14 -4.767 < 0.001 (-) < 28 20 24.30 23.37–25.23 MMSE-related factors that are selected in initial screening Average number of awakenings Three-axial activity metre 6.52 5.39–7.64 30 0.636 < 0.001 ≥28 13 8.11 6.57–9.65 -2.82 0.009 (number) < 28 17 5.30 3.95–6.64 Occlusal contact area Oral cavity 13.53 6.08–20.98 34 0.630 < 0.001 ≥28 14 24.29 13.62–34.95 -18.29 0.012 (mm 2 ) < 28 20 6.00 -2.93–14.93 Two-step test value Motor test 1.13 1.03–1.23 31 0.523 0.003 ≥28 12 1.27 1.12–1.42 -0.22 0.022 (-) < 28 19 1.04 0.92–1.16 Bite force Oral cavity 281.3 209.01–353.58 34 0.457 0.007 ≥28 14 373.64 267.63–479.65 -156.99 0.027 (M) < 28 20 216.65 127.96–305.34 Average of retinol Nutrient intake 12.37 5.84–18.89 32 0.436 0.013 ≥28 13 21.67 12.27–31.07 -15.668 0.014 (µg) < 28 19 6.00 -1.77–13.78 Min pulse rate SAS test 47.96 45.64–50.29 28 -0.450 0.016 ≥28 13 45.31 42.15–48.47 4.96 0.026 (beats/min) < 28 15 50.27 47.33–53.21 Factors that are reported to be related to MMSE but were not selected in the initial screening Number of teeth Oral cavity 11.43 7.95–14.91 35 0.377 0.025 ≥28 15 15.20 10.09–20.31 -6.600 0.055 (-) < 28 20 8.60 4.18–13.02 Apnoea hypopnea index SAS test 22.13 16.67–27.59 34 0.151 0.395 ≥28 14 21.53 12.88–30.17 1.016 0.855 (events/h) < 28 20 22.55 15.31–29.78 Grip strength ave Motor test 24.87 21.37–28.38 35 0.063 0.720 ≥28 15 25.53 20.09–30.97 -1.148 0.748 (kg/m 2 ) < 28 20 24.38 19.67–29.09 Haemoglobin A1c (NGSP) Blood test 6.04 5.79–6.3 35 -0.160 0.358 ≥28 15 5.84 5.46–6.22 0.355 0.165 (%) < 28 20 6.20 5.86–6.53 Age Basic parameter 86.86 85.76–87.96 35 -0.209 0.229 ≥28 15 86.73 85.03–88.44 0.217 0.846 (years) < 28 20 154.62 151.35–157.89 CI, confidence interval; MMSE, Mini-Mental State Examination; NGSP, National Glycohemoglobin Standardisation Programme; SAS, sleep apnoea syndrome Bold: p-value < 0.05. The results of the simple linear regression analysis with MMSE scores for each of the six selected factors are shown in S3 Table. The three lowest p-values were observed for the average number of awakenings during sleep (p = 0.001), two-step test value (p = 0.001), and minimum pulse rate (p = 0.002). Subsequently, the six MMSE-related factors selected in the initial screening and four factors reported to be related to MMSE but not selected initial screening (Table 2 ) were evaluated using a multiple linear regression model (initial model) (S4 Table), and the factors with the highest VIF (Italics) were removed. Next, the remaining five selected and four unselected typical MMSE-related parameters were evaluated using a multiple linear regression model (final model), and the VIF of all parameters fell below 3.0 (Table 3 ). The R 2 value for the initial model, considering all 10 parameters, was 0.831, and it remained above 0.779 when the number of parameters was reduced to nine in the final model. Similarly, the adjusted R 2 for the initial model was 0.625, and it maintained a comparable level of 0.609 when the number of parameters was reduced to nine in the final model. Among the nine remaining parameters in the final model, only the minimum pulse rate had a p-value of < 0.05 (p = 0.048) (Table 3 ). Table 3 Final Model of Multiple Linear Regression Analysis with Pre-final factors selected after initial Multiple Linear Regression Analysis in 2nd screening process of this study. Whole Model Individual Parameters in Each Model R 2 Adjusted R 2 p-value Parameter β SE p-value VIF 0.779 0.609 0.0062 Intercept 68.156 19.647 0.004 Occlusal Contact Area 0.032 0.034 0.369 1.861 Two-Step Test Value 2.643 2.182 0.247 2.616 Bite Force -0.00076 0.00263 0.778 1.973 Average of Retinol 0.0423 0.0253 0.119 1.355 Min Pulse Rate -0.1855 0.0851 0.048 1.661 Number of Teeth -0.0113 0.0492 0.822 1.560 AHI 0.0065 0.0378 0.867 1.831 Grip Strength Ave -0.0315 0.0583 0.598 2.492 Haemoglobin A1c (NGSP) -0.8029 0.7246 0.288 1.356 Age -0.3583 0.1882 0.079 2.053 AHI, Apnoea Hypopnea Index; MMSE, Mini-Mental State Examination; NGSP, National Glycohemoglobin Standardisation Programme; SE, standard error; VIF, Variance Inflation Factor; Bold: p-value < 0.05. Bold and underlined: p < 0.01. The parameter with the largest VIF was removed from each model and repeated until the VIF of all parameters was < 3.0. Comparison of the final factor: minimum during sleep and three other pulse rates: mean at rest during wakefulness, mean during sleep, and maximum during sleep Figure 3 shows the detailed statistical information (Fig. 3A) and boxplot (Fig. 3B) of the comparison of the relationship among MMSE scores for all four different pulse rates examined in this study. The mean pulse rate decreased in the following order: maximum pulse rate during sleep (92.44 beats/min), resting pulse rate during wakefulness (71.14 beats/min), mean pulse rate during sleep (62.29 beats/min), and minimum pulse rate during sleep (47.96 beats/min). The difference in pulse rate between the groups with MMSE scores ≥ 28 and < 28 points increased in the following order: mean pulse rate during sleep, resting pulse rate during wakefulness, maximum pulse rate during sleep, and minimum pulse rate during sleep, and reached 4.96 beats/min. Three of the four parameters, other than the minimum pulse rate during sleep, showed no statistical correlation with the MMSE score or any statistical difference between the groups with MMSE scores of ≥ 28 and < 28 points. Figure 3. Comparison of the Relationship between MMSE Scores at Four Different Pulse Rates: Resting Pulse Rate during Awaking, Average Pulse Rate during Sleep, Minimum Pulse Rate during Sleep, and Maximum Pulse Rate During Sleep . Boxplots of pulse rate of the two groups: MMSE score ≥ 28 and < 28. *: p < 0.050 . MMSE, Mini-Mental State Examination; SD, standard deviation. Discussion This study conducted a multifaceted survey of the physical characteristics and lifestyle habits associated with cognitive impairment in older adults who maintained relatively good health. Remarkably, nearly half of the participants had lower MMSE scores indicative of MCI or dementia despite having never been diagnosed with cognitive impairment. In accordance with previous research [ 9 ], the present study elucidates the challenges associated with recognising, diagnosing, and administering appropriate treatment for cognitive impairment, both from an individual’s perspective and from the standpoint of surrounding family members. Interestingly, although some parameters that are often reported to be strongly associated with cognitive impairment, such as elevated HbA1c [ 11 ] and decreased number of teeth [ 28 – 30 ], were detected during the screening process of this study, the findings suggest that sleep, physical activity, oral status, and nutrient parameters are more important than municipal and medical physical examinations and blood tests in measuring previously reported indicators of cognitive decline. Particularly, less invasive parameters, such as increased minimum pulse rate during sleep and decreased two-step value in locomotive syndrome examination, were found to be most strongly associated with both MMSE scores and categorisation of the cognitive impairment group (MCI or dementia). The minimum pulse rate during sleep was the most important factor among all parameters examined in this study, including various well-known MMSE-related factors. It was associated with both MMSE scores and categorisation of the cognitive impairment group (MCI or dementia) in this study. To the best of our knowledge, there have been no reports of its relationship with cognitive function, although it has been associated with sleep disorders. Recent studies have shown that a higher resting heart rate (RHR), a well-established marker of cardiovascular disease [ 31 ], is associated with cognitive impairment [ 31 , 32 ]. However, these findings are controversial, with null associations reported in some studies [ 33 ]. Yamada et al. [ 24 ] recently demonstrated that RHR ≥ 80 bpm was associated with an increased risk of all-cause and vascular dementia (except for Alzheimer's dementia) using data from the UK Biobank. The RHR is also associated with cognitive impairment, decreased hippocampal subfield volume, and poor white matter integrity. Interestingly, the RHR is measured during wakefulness, which differs from the minimum pulse rate during sleep and represents a more restful state. This study found no relationship between the RHR, maximal or mean pulse rate during sleep, and cognitive function. However, we believe that these previous findings on the RHR and cognitive function support the validity of this study. The pulse rate during wakefulness can be significantly elevated even if the participants are at rest, because the measured values can be influenced by the measurement environment and other factors. The pulse rate during sleep is less likely to be affected by external environmental stimuli than that during wakefulness. The minimum pulse rate during sleep had the lowest diurnal variation in pulse rate. One explanation for the statistically significant findings observed in this study despite the limited sample size is that the lowest resting value in the diurnal variation of pulse rate, including both waking and sleeping, in our investigation closely resembled the waking measurements reported by Deng et al. [ 34 ]. Among other parameters selected as pre-final factors in the screening process, motor function, which was evaluated using the two-step value, was also included as an important parameter selected in the initial screening and remained after the final model of the multiple regression model analysis. As a factor related to motor and cognitive function [ 35 – 37 ], the reported cases are substantial, with grip strength being the most common factor [ 35 , 37 ]. In this study, grip strength, leg strength, and number of steps taken, which reflect exercise habits, were related to cognitive function. Two-step values were positively correlated with MMSE scores, which is consistent with the recent examples reported by Ikegami et al. [ 38 ]. Achieving a high score on the two-step measurement task necessitates leg strength, a good sense of balance, and correct understanding of the rules. This correlation with MMSE scores could be attributed to these factors. Next, the occlusal contact area and bite force were selected as the pre-final factors. Better oral-related factors, such as the number of remaining teeth [ 27 – 30 ] and tongue pressure [ 39 ], indicate better cognitive function. Among these participants, the number of remaining teeth was the easiest to assess and provided the most reliable evidence. The number of remaining teeth and MMSE scores showed a statistically significant positive correlation in our study, in consistency with previous reports [ 27 – 30 ]. In addition, the remaining occlusal area in the final model was positively correlated with the number of teeth, thereby guaranteeing its validity. Participants with better MMSE scores had more remaining back teeth with larger areas and retained upper and lower teeth in sets. This finding suggests a potential association between better cognitive function and enhanced masticatory ability. Moreover, ingested retinol equivalents were the only pre-final factors among the nutrient groups. Retinol equivalents are calculated in foods consumed by retinol itself, which include animal foods such as meat and fish, and pro-Vitamin A carotenoids, such as α-carotene, β-carotene, and γ-carotene. Several reports have shown that higher blood retinol or Vitamin A levels are associated with higher cognitive function, in consistency with previous reports [ 40 , 41 ]. While large cohort studies, such as the Hisayama study [ 42 ] and others [ 43 , 44 ], have reported the potential anti-dementia effects of specific foods, such as vegetables and fruits, as well as cognitive function-related factors (e.g., Vitamin B12 and Vitamin C) and individual carotenes, it is noteworthy that these factors were intentionally excluded during the initial screening phase of this study. These considerations highlight the importance of consuming both animal foods and carotenoid-rich vegetables. Additionally, through the inclusion of factors identified during the initial screening, such as minimum pulse rate during sleep, two-step values, occlusal contact area, along with established cognitive function-related factors like age, we successfully constructed a multiple regression model with an adjusted coefficient of determination of 0.6 or higher. This comprehensive approach allowed us to account for a broad spectrum of potential confounding variables in our analysis. Therefore, this result confirms the validity of the finding that the minimum pulse rate during sleep stands out as the most important MMSE-associated factor. In addition, this result suggests that a multiple regression model with these factors may be able to predict MMSE decline with high accuracy. Nevertheless, future studies are needed to determine whether similar predictions can be made by changing the number and areas that include more individuals with lower MMSE scores. One limitation of this study was the absence of a comparative analysis with biomarker data that have been extensively studied in large-scale investigations, such as dementia-related proteins, including amyloid-β and τ, and genetic information, such as apolipoprotein E [ 1 ]. The omics information, while valuable, may not align directly with the purpose of this study, which is to develop simple tests for cognitive function assessment. However, comparison with omics information can contribute to medical understanding and individualisation through simplified testing. In addition, this study was conducted in the Yamanashi Prefecture, a rural area in Japan with a particularly long healthy life expectancy [ 12 , 13 ]. There is no guarantee that similar results will be replicated in regions or urban areas with shorter healthy life expectancies. Therefore, it is necessary to verify whether the selected factors related to cognitive impairment are similar across other regions and ethnic groups. However, the population of older adults above life expectancy without certified long-term care, which was the target of this study, is very small compared to the corresponding other age groups. This makes it challenging to validate our findings in other countries with shorter life expectancies. Although the final factor and the five pre-final factors were generally consistent with previous studies of younger generations, validation studies of the MMSE-associated factors identified in this study are expected. Conclusions As the indicators narrowed down in this study do not require invasive clinical tests, blood tests, or expensive medical equipment, and can be used in daily life, we believe that they can be used for the early detection of cognitive decline in older adults who maintain relatively good health and have few opportunities to visit clinics or other social care facilities. The minimum pulse rate during sleep is easily detectable owing to recent developments and widespread use of wearable devices. Among the remaining indicators in the final model, the two-step test can be performed at home and the occlusal contact area can be quantified at the dentist's office and easily estimated by counting the number of teeth. Retinol inoculation can be evaluated simply by reviewing eating habits. The results of this study will not only provide opportunities for older adults and their families to recognise slight cognitive decline but will also contribute to the early detection of cognitive decline, as a guide for the development of research evaluating the detection sensitivity and specificity of these indicators. Declarations Competing Interest The authors declare that this study was conducted in the absence of any commercial or financial relationships that could be construed as potential conflict of interest. Funding This work was supported by the Japan Society for the Promotion of Science (JSPS) KAKENHI Grant-in-Aid for Scientific Research (B) (grant number 21H03351) and the Interdisciplinary Research Project of the University of Yamanashi to YT. The sponsor of the study, JSPS, and University of Yamanashi had the opportunity to review the entire project, including this paper. Author Contributions Methodology: Y.T., K.S., T.A., K.M., T.T., and T.M. Communication with participants: Y.T., K.S., and K.T. Data Curation: Y.T. Formal analysis: Y.T. and K.M. Investigation: Y.T., K.S., T.A., and K.T. Project administration: Y.T. Resources: Y.T. and YHAB research group. Supervision: Y.T., T.A., K.M., T.T., and T.M. Validation: Y.T. and K.M. Visualisation: Y.T. Writing – Original draft: Y.T., and K.M. Writing – review & editing: Y.T., K.S., T.A., K.M., K.T., T.T., and. T.M. All authors take responsibility for the integrity of the data analysis, reviewed the results, and approved the final version of the manuscript. Acknowledgements The authors thank all participants and their families for their willingness to participate in this study. The authors also thank Dr. Satoshi Igarashi, Dr. Yasumi Ito, Zentaro Yamagata, Dr. Masaru Iwasaki, Dr. Yusuke Iwata, Dr. Naana Baba-Mori, Dr. Yasumi Itoh, Dr. Daisuke Ando, Dr. Masaki Omata, Dr. Tomokazu Matsuoka, Dr. Katsue Suzuki-Inoue, Dr. Koichiro Ueki, Dr. Hirotaka Haro, Dr. Shuichi Koizumi, and Dr. Kenji Kashiwagi, who belong to the YHAB research group, for their effort regarding data collection. Dr. Kunio Miyake, Dr. Yoichi Shinozaki, Dr. Shinji Masui, Dr. Takashi Kohda, and Dr. Hiroshi Kurosawa, who also belong to the YHAB research group, for their scientific advice and assistance with the necessary equipment. The authors also thank Keiko Muramatsu, Megumi Nakata, Miho Saiki, Mai Fukasawa, Rika Tokoro, Yukiko Sakamoto, Mari Odagawa, and Naoko Hofuku for their assistance with the data management and secretarial work. The authors thank Asuka Iida for her assistance with blood sampling and Chisato Ogawa for her assistance with locomotive tests. The authors thank Dr. Shunji Mugikura, Dr. Nobuo Fuse, and Dr. Masayuki Yamamoto for their valuable advice based on their experience with the Tohoku Medical Megabank Project. The authors also thank Drs. Masatsugu Sasamoto and Misako Tanaka for their expertise and valuable advice. Data Availability Statement Qualified investigators will share anonymised data upon reasonable request to the corresponding author. All data requests will be submitted to the Ethics Committee and YHAB Foundation for approval. References Prince M, Bryce R, Ferri C (2011) World Alzheimer report The Benefits of Early Diagnosis and Intervention. 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Adv Nutr 10(suppl2):S105–S119. 10.1093/advances/nmy105 Zhou Y, Wang J, Cao L, Shi M, Liu H, Zhao Y et al (2022) Fruit and vegetable consumption and cognitive disorders in older adults: A meta-analysis of observational studies. Front Nutr 9:871061. 10.3389/fnut.2022.871061 Additional Declarations The authors declare no competing interests. Supplementary Files TableS1.xlsx S1 Table. Measured Parameters and their Basic Statistics. TableS1.xlsx S1 Table. Measured Parameters and their Basic Statistics. TableS3.xlsx S3 Table. Exploring the Statistically Significant Relationship between Cognitive Function and Selected Variables: A Simple Linear Regression Model Estimation of MMSE. TableS4.xlsx S4 Table. Initial Model of Multiple Regression for MMSE Estimation with Stepwise Parameter Reduction. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4665921","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":321036893,"identity":"35b07efc-cbcc-4919-8287-79e8a55d5565","order_by":0,"name":"Yuji Tanaka","email":"data:image/png;base64,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","orcid":"","institution":"Tohoku University","correspondingAuthor":true,"prefix":"","firstName":"Yuji","middleName":"","lastName":"Tanaka","suffix":""},{"id":321036894,"identity":"c2066b4e-e0b2-4d3d-ad0f-2d95392657ed","order_by":1,"name":"Kozo Saito","email":"","orcid":"","institution":"Yamanashi University","correspondingAuthor":false,"prefix":"","firstName":"Kozo","middleName":"","lastName":"Saito","suffix":""},{"id":321036895,"identity":"5468bd50-390f-4d49-bfe7-4288bfd091c5","order_by":2,"name":"Kyoichiro Tsuchiya","email":"","orcid":"","institution":"Yamanashi University","correspondingAuthor":false,"prefix":"","firstName":"Kyoichiro","middleName":"","lastName":"Tsuchiya","suffix":""},{"id":321036896,"identity":"42c10cf8-a035-496c-a84f-ee6c2aac14b3","order_by":3,"name":"Yusuke Iwata","email":"","orcid":"","institution":"Yamanashi University","correspondingAuthor":false,"prefix":"","firstName":"Yusuke","middleName":"","lastName":"Iwata","suffix":""},{"id":321036897,"identity":"47b63240-7248-4af9-9b43-8aa029438ccd","order_by":4,"name":"Takashi Ando","email":"","orcid":"","institution":"Yamanashi University","correspondingAuthor":false,"prefix":"","firstName":"Takashi","middleName":"","lastName":"Ando","suffix":""},{"id":321036898,"identity":"3b6375e5-3c77-4401-9853-9e1837256a28","order_by":5,"name":"Kazuki Mochizuki","email":"","orcid":"","institution":"Yamanashi University","correspondingAuthor":false,"prefix":"","firstName":"Kazuki","middleName":"","lastName":"Mochizuki","suffix":""},{"id":321036899,"identity":"0cbf38b3-8bb2-452b-927e-c4958437fdae","order_by":6,"name":"Tamami Taniguchi","email":"","orcid":"","institution":"Yamanashi University","correspondingAuthor":false,"prefix":"","firstName":"Tamami","middleName":"","lastName":"Taniguchi","suffix":""},{"id":321036900,"identity":"0b2cf5a6-99b7-4c7d-95cd-e263a66dc7e5","order_by":7,"name":"Takahiko Mitsui","email":"","orcid":"","institution":"Yamanashi University","correspondingAuthor":false,"prefix":"","firstName":"Takahiko","middleName":"","lastName":"Mitsui","suffix":""}],"badges":[],"createdAt":"2024-07-01 06:55:15","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-4665921/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4665921/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":59495896,"identity":"80828da9-6dd5-4d96-8e7b-18431acaab6d","added_by":"auto","created_at":"2024-07-02 13:13:27","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":192436,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eMMSE Results of the Study Participants.\u003c/strong\u003e \u003cstrong\u003e(A)\u003c/strong\u003e Normal quantile plot and \u003cstrong\u003e(B)\u003c/strong\u003e basic statistics according to severity and sex.\u003c/p\u003e\n\u003cp\u003eMMSE, Mini-Mental State Examination; 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Measured Parameters and their Basic Statistics.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"TableS1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4665921/v1/7f99f6bf18a9d307a9f6ab9b.xlsx"},{"id":59495310,"identity":"018e3ef8-a329-49d2-9d4a-218611968039","added_by":"auto","created_at":"2024-07-02 13:05:27","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":22901,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eS1 Table. Measured Parameters and their Basic Statistics.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"TableS1.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4665921/v1/5b771cb530ee87a85690939b.xlsx"},{"id":59495895,"identity":"a66f87f1-a792-4f45-91a6-430fcb45b792","added_by":"auto","created_at":"2024-07-02 13:13:26","extension":"xlsx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":14545,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eS3 Table. Exploring the Statistically Significant Relationship between Cognitive Function and Selected Variables: A Simple Linear Regression Model Estimation of MMSE.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"TableS3.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4665921/v1/f76d10785cef0742f4e9f8b6.xlsx"},{"id":59495313,"identity":"c9c27526-945c-4a57-b201-1afb005c5527","added_by":"auto","created_at":"2024-07-02 13:05:27","extension":"xlsx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":15383,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eS4 Table. Initial Model of Multiple Regression for MMSE Estimation with Stepwise Parameter Reduction.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"TableS4.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4665921/v1/c436e4b77950b557d7350d59.xlsx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eMinimum Pulse Rate During Sleep: A Potential Non-Invasive Biomarker for Subtle Abnormalities in Mini-Mental State Examination from an Exploratory Cross-Sectional Multifaceted Survey in Active Older Adults\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eDementia is becoming a major problem, and its prevalence is expected to increase worldwide [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Various studies have been conducted to maintain cognitive function, including effective lifestyle management, treatment of related diseases [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], and provision of appropriate social support for older adults with cognitive impairment [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Anti-amyloid-β antibody drugs have recently been approved for patients with Alzheimer\u0026rsquo;s disease and have been shown to inhibit cognitive decline [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Improvements in cognitive function have also been reported with treatments such as Vitamin E and acetylcholinesterase administration, and lifestyle guidance [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. However, as these anti-dementia drugs and other interventional treatments are effective only in the mild cognitive impairment (MCI) stage, it is crucial to diagnose dementia at this stage.\u003c/p\u003e \u003cp\u003eDementia is typically diagnosed by a physician based on several medical tests, such as the Mini-Mental State Examination (MMSE) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e], brain imaging for atrophy detection, and the detection of molecular markers (e.g., amyloid-β levels). Among these tests, the MMSE is the most widely used international screening tool for dementia. It evaluates orientation to time and place, memory, attention and calculation, language comprehension, and constructional ability in approximately 10 min, playing a crucial role in the early detection and monitoring of cognitive change. However, these medical tests and the diagnostic process are not conducted unless patients or their caregivers recognise a decline in cognitive abilities and seek medical attention. The early symptoms of dementia are mild, making it difficult for both patients and their families to recognise them, thus hindering early diagnosis [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Therefore, it is necessary to develop much simpler testing methods, such as prediction using data from general health examinations, simple questionnaires [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], and applications that are less burdensome and can be administered at home.\u003c/p\u003e \u003cp\u003eWhile developing methods for easily assessing the risk of subtle cognitive dysfunctions at home is imperative, this goal has not yet been realised, making it a highly promising field for future research and development. Numerous studies have investigated factors associated with cognitive decline, such as increased haemoglobin A1c (HbA1c) levels [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. However, many of these studies have been limited to evaluating only a few parameters through specific methods, such as blood tests or questionnaires. Consequently, it has not been possible to compare the efficacy of different parameters from various measurement methods in assessing the risk of cognitive decline. It is essential to have a dataset that includes a variety of parameters from different tests that can be measured in daily life, along with actual cognitive function assessments, like the MMSE, conducted on the same older individuals. However, such research requires substantial effort as it involves coordinating numerous testing devices simultaneously, making it a challenging task, and the older the study participants, the more difficult it becomes to conduct multifaceted measurements.\u003c/p\u003e \u003cp\u003eRecently, we conducted a multifaceted survey of active older adults, with an average age of 87 years, who had not been certified for long-term care [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. This survey included a variety of measurements, such as MMSE scores, body composition measurements, blood test results, sleep apnoea testing, activity monitoring, and other quantitative assessments of daily life. We analysed these results to explore indicators potentially associated with sleep apnoea syndrome [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. In this study, we further analysed this multifaceted dataset to identify candidate medical indicators associated with a decline in MMSE scores.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy design, setting, and participants\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe Yamanashi Healthy Active Life cohort study, which began in 2003, included 587 participants [\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e]. Among them, 104 older adults who were not certified for long-term care in 2020 were asked to participate in the multifaceted survey [\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e], various physical measurements, and Yamanashi Healthy active long-living older people Biobank for healthy ageing biosciences (YHAB) study [\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e]. We included participants who (i) did not require long-term care, (ii) provided informed consent for the survey and sleep apnoea syndrome (SAS) measurements, and (iii) were deemed by the researchers to be able to participate without problems. The researchers assessed the eligibility of each participant for participation on a per measurement basis. For example, if there was a possibility of falling based on walking conditions, motor function measurement for leg strength assessment was omitted. All measurements, including the sleep apnoea test, were performed between January and December 2020. Patients were excluded if the SAS test could not be adequately conducted (e.g., where measurement errors occurred).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical considerations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe research plan for this study was formulated in accordance with the Declaration of Helsinki and the Japanese Ethical Guidelines for Medical Research Involving Human Subjects and was approved by the ethics committee of the University of Yamanashi School of Medicine (approval number: 2096; approval date: December 2019). The contents of the study were explained in writing and orally to the participants, and written informed consent was obtained.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMeasurements\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eQuestionnaire\u003c/h2\u003e\n \u003cp\u003eA health-related questionnaire, including a medical history, was completed by all participants.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003eBasic physical measurements\u003c/h2\u003e\n \u003cp\u003eData on the weight and body fat, muscle, and water percentages of the participants were evaluated using a multi-frequency segmental body composition analyser (Tanita MC-780A-N; Tanita Corp., Tokyo, Japan) [\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e]. Accurate weight values were measured by removing as much clothing as possible and subtracting the estimated weight of the remaining clothing from the actual measurement (assuming 1.0 kg for January and February and 0.5 kg for March through December). Height was measured using a stadiometer (Height Measurement HM 200P, Charder Electronic Co. Ltd.; Taichung City, Taiwan). The systolic blood pressure, diastolic blood pressure, and pulse rate were measured using a sphygmomanometer (Terumo ES-W300ZZ; Terumo Corp., Tokyo, Japan).\u003c/p\u003e\n \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e\n \u003ch2\u003eMMSE\u003c/h2\u003e\n \u003cp\u003eCognitive function was assessed using the Japanese version of the MMSE [\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e\n \u003ch2\u003eSleep apnoea test (Apnoea Hypopnea Index [AHI] measurement)\u003c/h2\u003e\n \u003cp\u003eAHI was measured using a portable monitoring device (WatchPAT 200; Itamar Medical, Caesarea, Israel) that recorded peripheral arterial tonometry signals, heart rate, oxygen saturation, and actigraphy [\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e]. WatchPAT calculates clinical parameters, such as respiratory events and 4% oxygen desaturation indices, using an automated and proprietary algorithm. This is less burdensome for patients than full polysomnography and is recommended by the American Academy of Sleep Medicine guidelines for obstructive SAS [\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e]. The resulting data were automatically analysed to estimate respiratory events, such as AHI, respiratory disturbance index, and sleep states. This analysis has been described in detail elsewhere [\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e\n \u003c/div\u003e\n \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e\n \u003ch2\u003eThree-axial activity metre measurement\u003c/h2\u003e\n \u003cp\u003eDaily activity was measured using a three-axial activity metre (ActiGraph wGT3X-BT; ActiGraph Corp., Pensacola, FL, USA) [\u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e]. The participants wore the device on the wrist opposite that of the listener for seven days during wakefulness and sleep, except during bathing or feeling discomfort. The analysis used the average values of several parameters derived from dedicated software, including the number of steps per day, total sleep time, sleep efficiency (total sleep time/total sleep time), number of awakenings, wake time (min), wake after sleep onset, number of activities, activity index, fragmentation index, and sleep fragmentation index.\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eGrip strength measurement\u003c/h2\u003e\n \u003cp\u003eTwo grip strength measurements were obtained using a grip strength measuring device comprising a digital force gauge (product no. ZP-500N; IMADA, Toyohashi, Japan) and computer/display system [\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e]. The mean peak grip strength was analysed.\u003c/p\u003e\n \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e\n \u003ch2\u003eLocomotive syndrome risk test\u003c/h2\u003e\n \u003cp\u003eThe locomotive syndrome risk test (locomotive test) was used to detect locomotive syndrome [\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e]. This test comprises three parts: (1) stand up test, which evaluates the muscle strength required to stand up from seats of different heights; (2) two-step test, which evaluates the length of two strides; and (3) a questionnaire with 25 questions regarding physical movement correlated with the European QOL Scale-5 Dimensions (EQ-5D) [\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e]. The risk level for locomotive syndrome (locomotive score) was determined as previously described [\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003eBlood tests\u003c/h2\u003e\n \u003cp\u003eBlood samples were collected into appropriate tubes (for plasma, clot formation, and whole blood). The samples were stored separately after centrifugation and subsequent processing. The following laboratory values were evaluated by SRL (Tokyo, Japan), or Japan Medical (Yamanashi, Japan): blood urea nitrogen, creatinine, total cholesterol, high-density lipoprotein cholesterol, HbA1c, haematocrit, and cystatin C levels, as well as the white blood cell and red blood cell counts.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eDental examination including occlusal force measurement\u003c/h2\u003e\n \u003cp\u003eThe occlusal force was measured using a bite force measurement system (Dentalplescale, GC, Tokyo, Japan) [\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eEvaluation of symptoms and impact of urinary incontinence\u003c/h2\u003e\n \u003cp\u003eThe Incontinence Questionnaire-Urinary Incontinence Short Form (ICIQ-UI SF) was used to evaluate the symptoms and impact of urinary incontinence. The ICIQ-UI SF is a questionnaire used to assess and measure urinary incontinence symptoms and their impact on an individual\u0026apos;s quality of life [\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eStatistical analyses\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eStatistical analyses were performed using JMP\u0026reg;ฎPro 15.1.0 (SAS Institute Inc., Cary, NC, USA). Spearman\u0026apos;s correlation analysis was conducted to calculate the correlation coefficients (\u0026rho;) and p-values between the MMSE and other measured data. A pooled \u003cem\u003et-\u003c/em\u003etest was performed to compare cognitively healthy participants (MMSE score\u0026thinsp;\u0026ge;\u0026thinsp;28 points) and patients (MMSE score\u0026thinsp;\u0026lt;\u0026thinsp;28 points). Following these analyses, variables that exhibited significant correlations with cognitive function were identified using a threshold p-value of \u0026lt;\u0026thinsp;0.05. Subsequently, the relationships between these selected variables and the MMSE were re-evaluated using a simple linear regression model to calculate the estimation value (\u0026beta;) and p-values. Stepwise multiple linear regression analysis was performed to remove variables with a high variance inflation factor (VIF). Briefly, initially, the MMSE was estimated using a multiple linear multiple regression model. This model included the first selected MMSE-related variables along with typical MMSE-related variables, which were not selected in the first screening. The analysis computed metrics, such as R\u003csup\u003e2\u003c/sup\u003e, adjusted R\u003csup\u003e2\u003c/sup\u003e, and p-values for the entire model. Additionally, it determined the coefficient (\u0026beta;), p-value, and VIF for each variable. Then, the variable with the highest VIF of over 3.0 was removed. Consequently, the same estimation was performed using the remaining variables. This process of removing variables with a high VIF was repeated stepwise until multicollinearity was eliminated (the VIF of all variables was \u0026lt;\u0026thinsp;3.0).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eParticipants and data collection\u003c/p\u003e \u003cp\u003eOverall, 37 of 104 older adults with no certification for care requirements (35.6%) agreed to participate in the 2020 multifaceted YHAB survey. The MMSE was administered to 35 (20 male and 15 female) participants (94.6%). Thee obtained data are summarised in Fig.\u0026nbsp;1, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, and S1 Table. In a preliminary health survey using questionnaires, all participants indicated that they had not been diagnosed with MCI or dementia before the MMSE. In this study, the data were analysed together with 89 other factors obtained from the participants (Fig.\u0026nbsp;2, top). The basic statistics of all obtained and representative data are summarised in S1 Table and Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, respectively. Measurements were attempted for all 35 participants and successfully obtained for 28\u0026ndash;35 participants for each of the 90 tested parameters. Factors that contributed to the lack of data included participants declining to participate in certain measurements, items withdrawn by the researcher owing to physical risks, and parameters that did not reach the detection thresholds in assessments using electronic devices. As representative results of the characteristics of the population, the mean age and body mass index were 86.86 years (95% confidence interval [CI]: 85.76\u0026ndash;87.96) and 23.29 kg/m\u003csup\u003e2\u003c/sup\u003e (95% CI: 22.26\u0026ndash;24.33), respectively.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure\u0026nbsp;1. MMSE Results of the Study Participants. (A)\u003c/b\u003e Normal quantile plot and \u003cb\u003e(B)\u003c/b\u003e basic statistics according to severity and sex.\u003c/p\u003e \u003cp\u003eMMSE, Mini-Mental State Examination; SD, standard deviation; SE, standard error.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure\u0026nbsp;2. Process of Narrowing Down the Factors Associated with MMSE score from the Multifaceted Survey of Older Adults in this Study.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eMMSE, Mini-Mental State Examination.\u003c/p\u003e \u003cp\u003eMMSE score and sex differences\u003c/p\u003e\u003cp\u003eThe results of the MMSE measurements are summarised in Fig.\u0026nbsp;1. The approximately straight lines of the normal quantile plots for all male and female participants were nearly identical (Fig.\u0026nbsp;1A), indicating that this population was normally distributed. The mean MMSE scores of all participants, male, and female were 26.34, 26.29, and 26.43 points, respectively (Fig.\u0026nbsp;1B). The 95% CI values also generally overlap for male (24.88\u0026ndash;27.69) and female (24.52\u0026ndash;28.33) participants. The distribution of older adults with robust MMSE scores (28\u0026ndash;30 points) and abnormalities (19\u0026ndash;27 points) showed consistency across sexes, with 42.9% and 57.1% in male and female participants, respectively. The mean MMSE scores and 95% CIs of the robust and abnormal cognitive function groups exhibited general consistency across sexes.\u003c/p\u003e \u003cp\u003eScreening of MMSE-related factors using correlation analysis, comparison between two groups with robust and abnormal cognitive function, and simple and multiple linear regression analysis\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e lists the parameters refined in each of the three stages of the screening process conducted in this study. As a first step of the initial screening, the results of Spearman\u0026rsquo;s correlation analysis between the MMSE score and all the other 89 parameters (S1 Table) are presented in S2 Table. Ten MMSE-related parameters with an absolute value of Spearman\u0026rsquo;s correlation coefficient (|\u003cb\u003eρ|\u003c/b\u003e) greater than 0.400 were identified. Subsequently, the participants were divided into two groups: those with robust cognitive function (MMSE score\u0026thinsp;\u0026ge;\u0026thinsp;28 points) and those with abnormal cognitive function (MMSE score\u0026thinsp;\u0026lt;\u0026thinsp;28 points). In the initial screening, the difference in means and corresponding p-values (using the \u003cem\u003et-\u003c/em\u003etest) between the two groups were evaluated for each parameter. Six parameters with statistically significant differences (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (selected factors) were identified as selected factors (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). All six selected factors showed larger values |\u003cb\u003eρ|\u003c/b\u003e and smaller p-values in both Spearman\u0026rsquo;s correlation analysis and two-group comparison analysis compared to all four unselected factors.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAn Overview of Parameters Narrowed Down in the Process of Narrowing Down MMSE-Related Factors from Supra-multidimensional Surveys of Japanese Older Adults as Part of the Yamanashi Healthy Active Long-living Older People Biobank for Healthy Ageing Biosciences (YHAB).\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMethod of Measurements\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAfter Spearman's correlation analysis:\u003c/p\u003e \u003cp\u003e|ρ| \u0026gt;0.400\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAfter two groups comparisons: p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.050\u003c/p\u003e \u003cp\u003e(Parameters selected by initial screening)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMultiple linear regression analysis: \u003c/p\u003e \u003cp\u003eVIF\u0026thinsp;\u0026lt;\u0026thinsp;3\u003c/p\u003e \u003cp\u003e(Pre-final factors selected in 2nd screening)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMultiple linear regression analysis: \u003c/p\u003e \u003cp\u003eVIF\u0026thinsp;\u0026lt;\u0026thinsp;3, \u003c/p\u003e \u003cp\u003ep-value\u0026thinsp;\u0026lt;\u0026thinsp;0.050\u003c/p\u003e \u003cp\u003e(Final factors selected in 2nd screening)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBasic parameter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge, Height, Body mass index, AGE value, Systolic blood pressure, Diastolic Blood pressure, Resting pulse rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBody composition analyser\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBody weight, Body Fat Percentage, Fat Mass, Muscle Mass, Estimated Bone Mass, Body Water Content\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSAS test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAHI, Deep Sleep percentage, Light Sleep percentage, REM sleep percentage, REM-AHI, NREM-AHI, Mean Saturation, Max Saturation, Min Saturation, Mean Pulse Rate, Max Pulse Rate, Min Pulse Rate, Number of Sleep Time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMin Pulse Rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMin Pulse Rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMin Pulse Rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMin Pulse Rate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eThree-axial activity metre\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSteps per Day, Average Sleep Efficiency, Average Onset, Average Total Sleep Time, Average wake after sleep onset, Average Number of Awakenings, Average Length of Awakenings in Minutes, Average Activity Counts, Average Movement Index, Average Sleep Fragmentation Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAverage Sleep Efficiency, Average wake after sleep onset, Average Number of Awakenings, Average Length of Awakenings in Minutes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAverage Number of Awakenings\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMotor test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrip Strength Ave, Stand Up Test Locomo Score, Two-Step Test Value, Two-Step Test Locomo Score, Locomo 25 Question, Locomo 25 Question Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTwo-Step Test Value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTwo-Step Test Value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTwo-Step Test Value -\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlood test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrea Nitrogen (BUN), Urea Acid, Creatinine, Total Cholesterol, HDL Cholesterol, Neutral fat (T-G), Aspartate aminotransferase (glutamic-oxaloacetic transaminase), Alanine Aminotransferase (glutamic-pyruvic transaminase), γ-GT (γ-GTP), Glucose (blood sugar), Haemoglobin A1c (NGSP), White Blood Cell Count, Red Blood Cell Count, Haemoglobin level, Haematocrit, Platelet Count\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOral cavity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of Teeth, Occlusal Contact Area, Average Occlusal Force, Maximum Occlusal Force, Bite Force\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOcclusal Contact Area, Bite Force\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOcclusal Contact Area, Bite Force\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOcclusal Contact Area, Bite Force\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrinary incontinence evaluation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eICIQ-SF Frequency, ICIQ-SF Quantity, ICIQ-SF QOL, ICIQ-SF Total Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNutritional assessment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eQuantity Average, Average of Energy, Protein Average, Average of Fat, Average of Carbohydrates, Average of Total Dietary Fibre, Average of Sodium, Average Potassium, Average Calcium, Average of Iron, Average of Retinol Equivalent, Average of α-Carotene, Average of β-Carotene, Average of β-Cryptoxanthin, Average of Vitamin D, Average of α-Tocopherol, Average of Vitamin B1, Average of Vitamin B6, Average of Vitamin B12, Average of Folic Acid, Average of Vitamin C\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAverage of Retinol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAverage of Retinol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAverage of Retinol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAGE, Advanced Glycation End Products; AHI, Apnoea Hypopnea Index; ICIQ-UI SF, Incontinence Questionnaire-Urinary Incontinence Short Form; MMSE, Mini-Mental State Examination; NGSP, National Glycohemoglobin Standardisation Programme; NREM, non-REM sleep; QOL, quality of life; REM, Rapid Eye Movement sleep; SAS, sleep apnoea syndrome; VIF, variance inflation factor\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBasic Statistics on both Six MMSE-related Factors Selected in initial screening and Four Typical Factors Reported to be related to MMSE but not Selected in initial screening process in this study.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"14\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDescription\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMethod of Measurement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSpearman ρ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eSpearman\u003c/em\u003e\u003c/p\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c14\" namest=\"c9\"\u003e \u003cp\u003eGroup characteristics of robust cognitive function and abnormal cognitive function, and their comparisons\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eGrouping by MMSE score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\"\u003e \u003cp\u003eDifference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\"\u003e \u003cp\u003e\u003cem\u003et-test\u003c/em\u003e\u003c/p\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCognitive test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMMSE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCognitive function\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e26.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e25.27\u0026ndash;27.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026ge;28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e29.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e28.00\u0026ndash;30.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e-4.767\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e24.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e23.37\u0026ndash;25.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"11\" rowspan=\"12\"\u003e \u003cp\u003eMMSE-related factors that are \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eselected in initial screening\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAverage number of awakenings\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eThree-axial activity metre\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e6.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e5.39\u0026ndash;7.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.636\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026ge;28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e8.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e6.57\u0026ndash;9.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e-2.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003e0.009\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(number)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e5.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e3.95\u0026ndash;6.64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOcclusal contact area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOral cavity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e13.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e6.08\u0026ndash;20.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.630\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003e\u0026lt;\u0026thinsp;0.001\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026ge;28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e24.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e13.62\u0026ndash;34.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e-18.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003e0.012\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(mm\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e6.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-2.93\u0026ndash;14.93\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTwo-step test value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMotor test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1.03\u0026ndash;1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.523\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003e0.003\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026ge;28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e1.12\u0026ndash;1.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e-0.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003e0.022\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e0.92\u0026ndash;1.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBite force\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOral cavity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e281.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e209.01\u0026ndash;353.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.457\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003e0.007\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026ge;28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e373.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e267.63\u0026ndash;479.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e-156.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003e0.027\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(M)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e216.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e127.96\u0026ndash;305.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAverage of retinol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eNutrient intake\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e12.37\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e5.84\u0026ndash;18.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.436\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003e0.013\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026ge;28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e21.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e12.27\u0026ndash;31.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e-15.668\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003e0.014\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(\u0026micro;g)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e6.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e-1.77\u0026ndash;13.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMin pulse rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSAS test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e47.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e45.64\u0026ndash;50.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e-0.450\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003e0.016\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026ge;28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e45.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e42.15\u0026ndash;48.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e4.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003e0.026\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(beats/min)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e50.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e47.33\u0026ndash;53.21\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"9\" rowspan=\"10\"\u003e \u003cp\u003eFactors that are reported to be related to MMSE but were \u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003enot\u003c/span\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eselected\u003c/span\u003e in the initial screening\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber of teeth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOral cavity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e11.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e7.95\u0026ndash;14.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.377\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003e0.025\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026ge;28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e15.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e10.09\u0026ndash;20.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e-6.600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(-)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e8.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e4.18\u0026ndash;13.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eApnoea hypopnea index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSAS test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e22.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e16.67\u0026ndash;27.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.395\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026ge;28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e21.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e12.88\u0026ndash;30.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.855\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(events/h)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e22.55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e15.31\u0026ndash;29.78\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGrip strength ave\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMotor test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e24.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e21.37\u0026ndash;28.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.720\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026ge;28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e25.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e20.09\u0026ndash;30.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e-1.148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.748\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(kg/m\u003csup\u003e2\u003c/sup\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e24.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e19.67\u0026ndash;29.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHaemoglobin A1c (NGSP)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBlood test\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e6.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e5.79\u0026ndash;6.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e-0.160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026ge;28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e5.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e5.46\u0026ndash;6.22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.165\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e6.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e5.86\u0026ndash;6.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBasic parameter\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e86.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e85.76\u0026ndash;87.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e-0.209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.229\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026ge;28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e86.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e85.03\u0026ndash;88.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c13\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.217\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c14\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.846\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e(years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e154.62\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c12\"\u003e \u003cp\u003e151.35\u0026ndash;157.89\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eCI, confidence interval; MMSE, Mini-Mental State Examination; NGSP, National Glycohemoglobin Standardisation Programme; SAS, sleep apnoea syndrome\u003c/p\u003e \u003cp\u003eBold: p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003cp\u003eThe results of the simple linear regression analysis with MMSE scores for each of the six selected factors are shown in S3 Table. The three lowest p-values were observed for the average number of awakenings during sleep (p\u0026thinsp;=\u0026thinsp;0.001), two-step test value (p\u0026thinsp;=\u0026thinsp;0.001), and minimum pulse rate (p\u0026thinsp;=\u0026thinsp;0.002). Subsequently, the six MMSE-related factors selected in the initial screening and four factors reported to be related to MMSE but not selected initial screening (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) were evaluated using a multiple linear regression model (initial model) (S4 Table), and the factors with the highest VIF (Italics) were removed. Next, the remaining five selected and four unselected typical MMSE-related parameters were evaluated using a multiple linear regression model (final model), and the VIF of all parameters fell below 3.0 (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The R\u003csup\u003e2\u003c/sup\u003e value for the initial model, considering all 10 parameters, was 0.831, and it remained above 0.779 when the number of parameters was reduced to nine in the final model. Similarly, the adjusted R\u003csup\u003e2\u003c/sup\u003e for the initial model was 0.625, and it maintained a comparable level of 0.609 when the number of parameters was reduced to nine in the final model. Among the nine remaining parameters in the final model, only the minimum pulse rate had a p-value of \u0026lt;\u0026thinsp;0.05 (p\u0026thinsp;=\u0026thinsp;0.048) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eFinal Model of Multiple Linear Regression Analysis with Pre-final factors selected after initial Multiple Linear Regression Analysis in 2nd screening process of this study.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eWhole Model\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c9\" namest=\"c4\"\u003e \u003cp\u003eIndividual Parameters in Each Model\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAdjusted\u003c/p\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eParameter\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eVIF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"9\" rowspan=\"10\"\u003e \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003e0.779\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\" morerows=\"9\" rowspan=\"10\"\u003e \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003e0.609\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\" morerows=\"9\" rowspan=\"10\"\u003e \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003e0.0062\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eIntercept\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003e68.156\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003e19.647\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003e0.004\u003c/span\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eOcclusal Contact Area\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.369\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.861\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTwo-Step Test Value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.643\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.182\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.247\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.616\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBite Force\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.00076\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.00263\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.778\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.973\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAverage of Retinol\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0423\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eMin Pulse Rate\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e-0.1855\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0.0851\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0.048\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e1.661\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNumber of Teeth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.0113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0492\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.822\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.560\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAHI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.0065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0378\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.831\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGrip Strength Ave\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.0315\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.0583\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.598\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.492\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eHaemoglobin A1c (NGSP)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.8029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.7246\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.288\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.356\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.3583\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.1882\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"1\" nameend=\"c9\" namest=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAHI, Apnoea Hypopnea Index; MMSE, Mini-Mental State Examination; NGSP, National Glycohemoglobin Standardisation Programme; SE, standard error; VIF, Variance Inflation Factor;\u003c/p\u003e \u003cp\u003eBold: p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05. Bold and underlined: p\u0026thinsp;\u0026lt;\u0026thinsp;0.01.\u003c/p\u003e \u003cp\u003eThe parameter with the largest VIF was removed from each model and repeated until the VIF of all parameters was \u0026lt;\u0026thinsp;3.0.\u003c/p\u003e \u003cp\u003eComparison of the final factor: minimum during sleep and three other pulse rates: mean at rest during wakefulness, mean during sleep, and maximum during sleep\u003c/p\u003e\u003cp\u003eFigure 3 shows the detailed statistical information (Fig.\u0026nbsp;3A) and boxplot (Fig.\u0026nbsp;3B) of the comparison of the relationship among MMSE scores for all four different pulse rates examined in this study. The mean pulse rate decreased in the following order: maximum pulse rate during sleep (92.44 beats/min), resting pulse rate during wakefulness (71.14 beats/min), mean pulse rate during sleep (62.29 beats/min), and minimum pulse rate during sleep (47.96 beats/min). The difference in pulse rate between the groups with MMSE scores\u0026thinsp;\u0026ge;\u0026thinsp;28 and \u0026lt;\u0026thinsp;28 points increased in the following order: mean pulse rate during sleep, resting pulse rate during wakefulness, maximum pulse rate during sleep, and minimum pulse rate during sleep, and reached 4.96 beats/min. Three of the four parameters, other than the minimum pulse rate during sleep, showed no statistical correlation with the MMSE score or any statistical difference between the groups with MMSE scores of \u0026ge;\u0026thinsp;28 and \u0026lt;\u0026thinsp;28 points.\u003c/p\u003e \u003cp\u003e \u003cb\u003eFigure\u0026nbsp;3. Comparison of the Relationship between MMSE Scores at Four Different Pulse Rates: Resting Pulse Rate during Awaking, Average Pulse Rate during Sleep, Minimum Pulse Rate during Sleep, and Maximum Pulse Rate During Sleep\u003c/b\u003e. Boxplots of pulse rate of the two groups: MMSE score\u0026thinsp;\u0026ge;\u0026thinsp;28 and \u0026lt;\u0026thinsp;28. *: \u003cem\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.050\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eMMSE, Mini-Mental State Examination; SD, standard deviation.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study conducted a multifaceted survey of the physical characteristics and lifestyle habits associated with cognitive impairment in older adults who maintained relatively good health. Remarkably, nearly half of the participants had lower MMSE scores indicative of MCI or dementia despite having never been diagnosed with cognitive impairment. In accordance with previous research [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], the present study elucidates the challenges associated with recognising, diagnosing, and administering appropriate treatment for cognitive impairment, both from an individual\u0026rsquo;s perspective and from the standpoint of surrounding family members. Interestingly, although some parameters that are often reported to be strongly associated with cognitive impairment, such as elevated HbA1c [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] and decreased number of teeth [\u003cspan additionalcitationids=\"CR29\" citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], were detected during the screening process of this study, the findings suggest that sleep, physical activity, oral status, and nutrient parameters are more important than municipal and medical physical examinations and blood tests in measuring previously reported indicators of cognitive decline. Particularly, less invasive parameters, such as increased minimum pulse rate during sleep and decreased two-step value in locomotive syndrome examination, were found to be most strongly associated with both MMSE scores and categorisation of the cognitive impairment group (MCI or dementia).\u003c/p\u003e \u003cp\u003eThe minimum pulse rate during sleep was the most important factor among all parameters examined in this study, including various well-known MMSE-related factors. It was associated with both MMSE scores and categorisation of the cognitive impairment group (MCI or dementia) in this study. To the best of our knowledge, there have been no reports of its relationship with cognitive function, although it has been associated with sleep disorders. Recent studies have shown that a higher resting heart rate (RHR), a well-established marker of cardiovascular disease [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], is associated with cognitive impairment [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. However, these findings are controversial, with null associations reported in some studies [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Yamada et al. [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] recently demonstrated that RHR\u0026thinsp;\u0026ge;\u0026thinsp;80 bpm was associated with an increased risk of all-cause and vascular dementia (except for Alzheimer's dementia) using data from the UK Biobank. The RHR is also associated with cognitive impairment, decreased hippocampal subfield volume, and poor white matter integrity. Interestingly, the RHR is measured during wakefulness, which differs from the minimum pulse rate during sleep and represents a more restful state. This study found no relationship between the RHR, maximal or mean pulse rate during sleep, and cognitive function. However, we believe that these previous findings on the RHR and cognitive function support the validity of this study. The pulse rate during wakefulness can be significantly elevated even if the participants are at rest, because the measured values can be influenced by the measurement environment and other factors. The pulse rate during sleep is less likely to be affected by external environmental stimuli than that during wakefulness. The minimum pulse rate during sleep had the lowest diurnal variation in pulse rate. One explanation for the statistically significant findings observed in this study despite the limited sample size is that the lowest resting value in the diurnal variation of pulse rate, including both waking and sleeping, in our investigation closely resembled the waking measurements reported by Deng et al. [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAmong other parameters selected as pre-final factors in the screening process, motor function, which was evaluated using the two-step value, was also included as an important parameter selected in the initial screening and remained after the final model of the multiple regression model analysis. As a factor related to motor and cognitive function [\u003cspan additionalcitationids=\"CR36\" citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], the reported cases are substantial, with grip strength being the most common factor [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. In this study, grip strength, leg strength, and number of steps taken, which reflect exercise habits, were related to cognitive function. Two-step values were positively correlated with MMSE scores, which is consistent with the recent examples reported by Ikegami et al. [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Achieving a high score on the two-step measurement task necessitates leg strength, a good sense of balance, and correct understanding of the rules. This correlation with MMSE scores could be attributed to these factors.\u003c/p\u003e \u003cp\u003eNext, the occlusal contact area and bite force were selected as the pre-final factors. Better oral-related factors, such as the number of remaining teeth [\u003cspan additionalcitationids=\"CR28 CR29\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] and tongue pressure [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], indicate better cognitive function. Among these participants, the number of remaining teeth was the easiest to assess and provided the most reliable evidence. The number of remaining teeth and MMSE scores showed a statistically significant positive correlation in our study, in consistency with previous reports [\u003cspan additionalcitationids=\"CR28 CR29\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. In addition, the remaining occlusal area in the final model was positively correlated with the number of teeth, thereby guaranteeing its validity. Participants with better MMSE scores had more remaining back teeth with larger areas and retained upper and lower teeth in sets. This finding suggests a potential association between better cognitive function and enhanced masticatory ability.\u003c/p\u003e \u003cp\u003eMoreover, ingested retinol equivalents were the only pre-final factors among the nutrient groups. Retinol equivalents are calculated in foods consumed by retinol itself, which include animal foods such as meat and fish, and pro-Vitamin A carotenoids, such as α-carotene, β-carotene, and γ-carotene. Several reports have shown that higher blood retinol or Vitamin A levels are associated with higher cognitive function, in consistency with previous reports [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. While large cohort studies, such as the Hisayama study [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] and others [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], have reported the potential anti-dementia effects of specific foods, such as vegetables and fruits, as well as cognitive function-related factors (e.g., Vitamin B12 and Vitamin C) and individual carotenes, it is noteworthy that these factors were intentionally excluded during the initial screening phase of this study. These considerations highlight the importance of consuming both animal foods and carotenoid-rich vegetables.\u003c/p\u003e \u003cp\u003eAdditionally, through the inclusion of factors identified during the initial screening, such as minimum pulse rate during sleep, two-step values, occlusal contact area, along with established cognitive function-related factors like age, we successfully constructed a multiple regression model with an adjusted coefficient of determination of 0.6 or higher. This comprehensive approach allowed us to account for a broad spectrum of potential confounding variables in our analysis. Therefore, this result confirms the validity of the finding that the minimum pulse rate during sleep stands out as the most important MMSE-associated factor. In addition, this result suggests that a multiple regression model with these factors may be able to predict MMSE decline with high accuracy. Nevertheless, future studies are needed to determine whether similar predictions can be made by changing the number and areas that include more individuals with lower MMSE scores.\u003c/p\u003e \u003cp\u003eOne limitation of this study was the absence of a comparative analysis with biomarker data that have been extensively studied in large-scale investigations, such as dementia-related proteins, including amyloid-β and τ, and genetic information, such as apolipoprotein E [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The omics information, while valuable, may not align directly with the purpose of this study, which is to develop simple tests for cognitive function assessment. However, comparison with omics information can contribute to medical understanding and individualisation through simplified testing. In addition, this study was conducted in the Yamanashi Prefecture, a rural area in Japan with a particularly long healthy life expectancy [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. There is no guarantee that similar results will be replicated in regions or urban areas with shorter healthy life expectancies. Therefore, it is necessary to verify whether the selected factors related to cognitive impairment are similar across other regions and ethnic groups. However, the population of older adults above life expectancy without certified long-term care, which was the target of this study, is very small compared to the corresponding other age groups. This makes it challenging to validate our findings in other countries with shorter life expectancies. Although the final factor and the five pre-final factors were generally consistent with previous studies of younger generations, validation studies of the MMSE-associated factors identified in this study are expected.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eAs the indicators narrowed down in this study do not require invasive clinical tests, blood tests, or expensive medical equipment, and can be used in daily life, we believe that they can be used for the early detection of cognitive decline in older adults who maintain relatively good health and have few opportunities to visit clinics or other social care facilities. The minimum pulse rate during sleep is easily detectable owing to recent developments and widespread use of wearable devices. Among the remaining indicators in the final model, the two-step test can be performed at home and the occlusal contact area can be quantified at the dentist's office and easily estimated by counting the number of teeth. Retinol inoculation can be evaluated simply by reviewing eating habits. The results of this study will not only provide opportunities for older adults and their families to recognise slight cognitive decline but will also contribute to the early detection of cognitive decline, as a guide for the development of research evaluating the detection sensitivity and specificity of these indicators.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eCompeting Interest\u003c/h2\u003e \u003cp\u003eThe authors declare that this study was conducted in the absence of any commercial or financial relationships that could be construed as potential conflict of interest.\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eThis work was supported by the Japan Society for the Promotion of Science (JSPS) KAKENHI Grant-in-Aid for Scientific Research (B) (grant number 21H03351) and the Interdisciplinary Research Project of the University of Yamanashi to YT. The sponsor of the study, JSPS, and University of Yamanashi had the opportunity to review the entire project, including this paper.\u003c/p\u003e\u003ch2\u003eAuthor Contributions\u003c/h2\u003e \u003cp\u003eMethodology: Y.T., K.S., T.A., K.M., T.T., and T.M. Communication with participants: Y.T., K.S., and K.T. Data Curation: Y.T. Formal analysis: Y.T. and K.M. Investigation: Y.T., K.S., T.A., and K.T. Project administration: Y.T. Resources: Y.T. and YHAB research group. Supervision: Y.T., T.A., K.M., T.T., and T.M. Validation: Y.T. and K.M. Visualisation: Y.T. Writing \u0026ndash; Original draft: Y.T., and K.M. Writing \u0026ndash; review \u0026amp; editing: Y.T., K.S., T.A., K.M., K.T., T.T., and. T.M. All authors take responsibility for the integrity of the data analysis, reviewed the results, and approved the final version of the manuscript.\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003eThe authors thank all participants and their families for their willingness to participate in this study. The authors also thank Dr. Satoshi Igarashi, Dr. Yasumi Ito, Zentaro Yamagata, Dr. Masaru Iwasaki, Dr. Yusuke Iwata, Dr. Naana Baba-Mori, Dr. Yasumi Itoh, Dr. Daisuke Ando, Dr. Masaki Omata, Dr. Tomokazu Matsuoka, Dr. Katsue Suzuki-Inoue, Dr. Koichiro Ueki, Dr. Hirotaka Haro, Dr. Shuichi Koizumi, and Dr. Kenji Kashiwagi, who belong to the YHAB research group, for their effort regarding data collection. Dr. Kunio Miyake, Dr. Yoichi Shinozaki, Dr. Shinji Masui, Dr. Takashi Kohda, and Dr. Hiroshi Kurosawa, who also belong to the YHAB research group, for their scientific advice and assistance with the necessary equipment. The authors also thank Keiko Muramatsu, Megumi Nakata, Miho Saiki, Mai Fukasawa, Rika Tokoro, Yukiko Sakamoto, Mari Odagawa, and Naoko Hofuku for their assistance with the data management and secretarial work. The authors thank Asuka Iida for her assistance with blood sampling and Chisato Ogawa for her assistance with locomotive tests. The authors thank Dr. Shunji Mugikura, Dr. Nobuo Fuse, and Dr. Masayuki Yamamoto for their valuable advice based on their experience with the Tohoku Medical Megabank Project. The authors also thank Drs. Masatsugu Sasamoto and Misako Tanaka for their expertise and valuable advice.\u003c/p\u003e\u003ch2\u003eData Availability Statement\u003c/h2\u003e \u003cp\u003eQualified investigators will share anonymised data upon reasonable request to the corresponding author. All data requests will be submitted to the Ethics Committee and YHAB Foundation for approval.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003ePrince M, Bryce R, Ferri C (2011) World Alzheimer report The Benefits of Early Diagnosis and Intervention. London\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlty J, Farrow M, Lawler K (2020) Exercise and dementia prevention. 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Front Nutr 9:871061. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3389/fnut.2022.871061\u003c/span\u003e\u003cspan address=\"10.3389/fnut.2022.871061\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Healthy longevity, cognitive function, MCI, MMSE, pulse rate, biomarker, geriatrics ","lastPublishedDoi":"10.21203/rs.3.rs-4665921/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4665921/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003ePurpose\u003c/h2\u003e \u003cp\u003eDeveloping quantitative indicators of daily life that can detect subtle cognitive decline is a significant challenge in the growing population of older adults worldwide. In this multifaceted survey conducted on active older adults, we aimed to explore novel indicators associated with subtle abnormalities in brief dementia screening tests.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eData were collected from 35 older adults who were not certified for long-term care or diagnosed with cognitive impairment using questionnaires, the Mini-Mental State Examination (MMSE), body composition measurements, sleep apnoea testing, activity monitoring, motor function assessments, blood tests, and nutrient analyses. Of the 89 factors examined in this study, several less invasive indicators for cognitive impairment were identified using Spearman\u0026rsquo;s correlation analysis, two-group comparison, and multiple linear regression model analysis.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eAn elevated minimum pulse rate during sleep emerged as the most significant non-invasive marker correlated with both MMSE scores and the classification of cognitive impairment risk (mild cognitive impairment or dementia).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThese findings could expedite further research into early cognitive decline detection among older adults and facilitate early intervention.\u003c/p\u003e","manuscriptTitle":"Minimum Pulse Rate During Sleep: A Potential Non-Invasive Biomarker for Subtle Abnormalities in Mini-Mental State Examination from an Exploratory Cross-Sectional Multifaceted Survey in Active Older Adults","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-02 13:05:22","doi":"10.21203/rs.3.rs-4665921/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"edc06dc1-fbd8-4d1e-9e84-8d388fdae75d","owner":[],"postedDate":"July 2nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":33937929,"name":"Geriatrics \u0026 Gerontology"},{"id":33937930,"name":"Epidemiology"},{"id":33937931,"name":"Health Economics \u0026 Outcomes Research"},{"id":33937932,"name":"Cognitive Neuroscience"},{"id":33937933,"name":"Nursing"}],"tags":[],"updatedAt":"2024-07-02T13:05:22+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-02 13:05:22","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4665921","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4665921","identity":"rs-4665921","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}