Correlation Between Mild Behavioral Impairment and Peripheral Blood Biomarkers in Patients with Mild Cognitive Impairment

Correlation Between Mild Behavioral Impairment and Peripheral Blood Biomarkers in Patients with Mild Cognitive Impairment | 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 Article Correlation Between Mild Behavioral Impairment and Peripheral Blood Biomarkers in Patients with Mild Cognitive Impairment Wei Liang, Lan Wang, Mei Song, Hao Geng MM, Xinyang Jing MM, Wei Li, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4578874/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 Objective We examined the prevalence of Mild behavioral impairment (MBI) in healthy older adults (HOA) and individuals with mild cognitive impairment (MCI), and the association between MBI and plasma biomarkers of Alzheimer's disease(AD). Methods A total of 241 subjects (136 HOA and 105 MCI) were enrolled in the Yuhua District of Shijiazhuang City in China. The MBI Symptom Checklist (MBI-C) was employed for assessment and diagnosis of MBI (MBI-C ≥ 6.5). Fasting venous blood was collected from 70 patients (32 HOA, 38 MCI), and Aβ40, Aβ42, and P-Tau217 levels were measured using enzyme-linked immunosorbent assay. Results The prevalence of MBI in HOA and MCI groups was 4.4% and 15.3%, respectively (χ 2 = 7.262, P = 0.007), especially in terms of decreased motivation, impulse dyscontrol (highest detection rate), and social inappropriateness ( P < 0.05). MBI total score was correlated with Aβ42 and P-Tau217 ( r =-0.385, P = 0.019; r =-0.330, P = 0.041), but not with Aβ40 or Aβ42/40 ratio. Among the subdomains, impulse dyscontrol submains was correlated with Aβ42 (r=-0.401, P = 0.025). Conclusion Both MCI and HOA demonstrated a higher prevalence of MBI, with change in impulse control behavior being the most common. MBI not only serves as an independent risk factor for cognitive decline but is also associated with AD-related peripheral biomarkers. Health sciences/Medical research Health sciences/Risk factors Mild cognitive impairment Mild behavioral impairment Alzheimer's disease Neuropsychiatric symptom Aβ42 P-tau Figures Figure 1 Figure 2 Introduction The mild cognitive impairment (MCI) 1 and dementia are clinically characterized not only by cognitive dysfunction but also by a decreased motivation, anxiety, depression, and disinhibited behavior. These non-cognitive symptoms are referred to as neuropsychiatric symptoms (NPS). NPS span the entire course of cognitive disorders and may even manifest as psychiatric and behavioral abnormalities before the appearance of cognitive deficits 2 . In recent years, mild behavioral impairment (MBI) has received increasing attention from researcher, which was first defined by Taragano and Allegri (Eleventh Congress of the International Psychogeriatric Association), and it was introduced as a concept that includes neurobehavioral symptoms seen in older adults for at least 6 months and that do not meet the diagnostic criteria of any other psychiatric syndrome. MBI is observed in advance of cognitive impairment for some people, suggesting maybe the index manifestation of neurodegeneration. Longitudinal and cross-sectional studies consistently suggest that MBI is associated with a decline in cognitive ability and an accelerated progression to dementia 3,4 , indicating that MBI may be a potential risk factor for neurodegenerative diseases 5–8 . The Alzheimer's Association International Society to Advance Alzheimer's Research and Treatment (ISTAART) working group proposed MBI research diagnostic criteria in 2016 9 . The Mild Behavioral Impairment-Checklist (MBI-C) 10 was developed to quantify the severity of behavioral symptoms in multiple domains. Currently, the overall and subdomains of the MBI-C for clinical diagnosis and prognosis are being validated. Xu L et al. 11 evaluated the reliability and validity of the Chinese version of the MBI-C, finding that it has consistent internal validity and can be used to assess patients' psychological and behavioral changes. Although MBI represents non-cognitive symptoms and is considered an early manifestation of AD, the relationship between MBI and the preclinical pathophysiology of AD remains unclear. Previous studies on the etiology and pathology of MBI have made preliminary explorations, finding associations between MBI and biological markers of AD, including Aβ-positron emission tomography (PET) 12 , tau-PET 13 , cerebrospinal fluid (CSF)-tau, plasma neurofilament light 14 , and AD risk gene loci 15,16 . In terms of imaging, MBI patients exhibit AD-like brain imaging changes. A cross-sectional study by Shu J et al. 17 found atrophy in the left frontal cortex and right thalamus of MBI patients, which were associated with NPS in dementia, suggesting indicating that MBI may be an early sign for cognitive decline and dementia. In addition, these plasma biomarkers show an association with the future development of AD, however, the vast majority of studies are based on Western populations, and since race and ethnicity may influence the assessment of these markers, it is necessary to verify these manifestations in the context of the Chinese population. Although many studies have investigated the association between CSF and MBI, few studies have examined plasma biomarkers and MBI, and this study in the Chinese population reinforces previous European and American findings that plasma biomarkers are already altered during the MBI phase based on the Chinese population. Therefore, this study aims to explore the prevalence of MBI in healthy older adults (HOA) and MCI populations and investigate the relationship between MBI and AD biomarkers in MCI populations, particularly Aβ40, Aβ42, and p-tau217 levels. Materials and Methods 1. Study population This research was designed as a cross-sectional study conducted from April to October 2021. The participant pool was drawn from the older adult community in the Yuhua District of Shijiazhuang City, Hebei Province, China. Inclusion criteria were as follows: individuals aged 55 years and above; willingness to participate and complete both the questionnaire survey and cognitive function assessment; and consent to provide blood samples. The study population comprised 241 participants, with an age range of 55 to 93 years (mean age: 74.2 years). Of these, 85 were male and 161 were female. The average educational attainment of the cohort was 10.5 years. Exclusion criteria were as follows: potential alternate causes of cognitive decline, such as a clear history of stroke (cerebral infarction, cerebral hemorrhage), brain tumors, Parkinson's disease, epilepsy, severe head and nasal fractures; psychiatric disorders such as schizophrenia, bipolar disorder, and depression; inability to cooperate with neurological and psychiatric scale examinations; and severe physical diseases or significant visual or hearing impairments that would prevent cooperation with cognitive function examinations. All participants had signed informed consent forms, and the study was conducted with the approval of the Ethics Committee of the First Hospital of Hebei Medical University (ethical approval number: 20190416). It had been confirmed that the research was performed in accordance with the relevant guidelines/regulations, particularly Declaration of Helsinki. The population was divided into two groups: HOA and MCI. Diagnostic Criteria HOA were defined as follows: ( 1 ) no self-reported cognitive decline; ( 2 ) objective evidence of normal cognitive function, with Mini-Mental State Examination (MMSE) scores of ≥ 19 for illiterate individuals, ≥ 22 for those with primary school education, and ≥ 24 for those with junior high school education or higher 18 , and Montreal Cognitive Assessment (MoCA) scores of ≥ 13 for illiterate individuals, ≥ 19 for those with primary school education, and ≥ 24 for those with junior high school education or higher 19 ; ( 3 ) a Clinical Dementia Rating (CDR) of 0 20 ; ( 4 ) normal daily functioning, as indicated by an Activity of Daily Living Scale 21 (ADL) score of ≤ 26; ( 5 ) minimal anxiety or depression, as indicated by a Hamilton Anxiety Scale (HAMA) score of < 7 and a Hamilton Depression Scale (HAMD) score of < 7. MCI was diagnosed based on the 2006 consensus of the Chinese expert group on MCI diagnosis 22 and included: ( 1 ) self-perceived or family-reported significant memory impairment; ( 2 ) relatively intact or mildly impaired other cognitive functions, with MMSE scores similar to those for the HOA group, but with MoCA scores of < 13 for illiterate individuals, < 19 for those with primary school education, and < 24 for those with junior high school education or higher; ( 3 ) basic normal daily life, with an ADL score of ≤ 26; ( 4 ) a CDR of 0.5; ( 5 ) exclusion of the possibility of dementia, not yet reaching the diagnosis of dementia. Data Collection Procedures The researchers in this study received formal training to ensure the unified management of questionnaires and standardized guidance for the subjects. A one-on-one approach was adopted by the investigators to conduct neuropsychological assessments and fill out questionnaires for each subject, with each subject taking approximately 45 minutes to complete the survey. A self-made general information survey was used to collect basic information such as age, gender, years of education, marital status, smoking and drinking habits, and past medical history, including diabetes, coronary heart disease, hypertension, and hyperlipidemia. Neuropsychological Testing The MMSE 18 and MoCA 19 were used to assess overall cognitive function. The MMSE is currently the most widely used scale, covering orientation, memory, calculation, language, visual-spatial ability, application, and attention, with a total of 30 points. MoCA has a higher screening sensitivity than MMSE 23 ; Clock Drawing Task (CDT) 24 was used to assess visual-spatial ability; Boston Naming Test (BNT) 25 was used to evaluate language naming ability; Digit Span Test (DST) 26 was used to test instant memory function, attention, and information processing speed. Functional Activities Questionnaire (FAQ) was used to assess the difficulties in activities of daily living 27 , with higher scores indicating more difficulty. Behavioral assessment was performed using MBI-C 10 , consisting of 34 items divided into five domains: ( 1 ) decreased motivation: 6 items, including evaluation of cognitive, behavioral, and emotional apathy; ( 2 ) emotional dysregulation: 6 items, including one item each for depression, anhedonia, despair, guilt, anxiety, and panic; ( 3 ) impulse dyscontrol: 12 items, including agitation, aggression, impulsiveness, recklessness, etc.; ( 4 ) social inappropriateness: 5 items, including sensitivity, empathy, and disinhibited behavior; ( 5 ) abnormal perception or thought content: 5 questions, including suspicion, exaggeration, auditory hallucinations, and visual hallucinations. For each item, participants answered "yes" or "no," and for each item answered "yes," the behavior must have persisted for at least six months, indicating a significant change from baseline behavior, and the severity level (1-mild, 2-moderate, or 3-severe) was assessed. Individual scores for each subdomain of the MBI-C could be calculated by equally adding the severity scores for each category. MBI status (MBI+/-) classification was based on the MBI-C validation study for MCI patients, with a cutoff point of 6.5 points 28 for diagnosing MBI; MBI-C scores ≥ 6.5 were considered MBI+, while < 6.5 were considered MBI-. Blood Sample Collection and Biochemical Detection Seventy cases fasting blood samples were drawn into coagulation tubes at a fixed time (8–9 am), and whole blood samples were placed at room temperature for 2 hours and centrifuged at 3000 rpm for 10 minutes. Serum was collected, aliquoted, and stored at -80℃ for further analysis. Human amyloid-β (Aβ40), amyloid-β42 (Aβ42), and hyperphosphorylated tau (p-tau217) enzyme-linked immunosorbent assay (ELISA) kits were purchased from Shanghai Zhuocai Biotechnology Co., Ltd. The kit and samples were equilibrated to room temperature (18–25°C) before use. The kit provided six standards with known concentrations at 10 pg/mL, 20 pg/mL, 40 pg/mL, 80 pg/mL, 160 pg/mL, and 320 pg/mL, and a standard curve was plotted with six points. Standard and sample holes were set up, with 50 µL of different concentration standards added to each standard hole, and 40 µL of sample diluent added to the sample holes on the enzyme-labeled coated plate, followed by 10 µL of the sample (final dilution factor of 5). Sample addition was performed by adding the sample to the bottom of the enzyme-labeled plate hole without touching the hole walls, and gently shaking to mix. 100 µL of enzyme-labeled reagent was added to each hole, except for the blank hole. The plate was then incubated at 37°C for 60 minutes after sealing with a sealing film. The 20x concentrated washing solution was diluted 20 times with distilled water and set aside. The sealing film was carefully removed, the liquid discarded, and the plate dried by tapping. The washing solution was added to each hole, left for 30 seconds, and then discarded. This process was repeated five times, and then the plate was tapped dry. 50 µL of color developer A and 50 µL of color developer B were added to each hole, gently shaken to mix, and then incubated in the dark at 37°C for 15 minutes. 50 µL of stop solution was added to each hole to stop the reaction (the color changed from blue to yellow). The absorbance (OD value) of each hole was measured sequentially at 450 nm wavelength, with the blank hole used for zero adjustment, and the measurement was performed within 15 minutes after adding the stop solution. Statistical Analysis SPSS 24.0 was used for statistical analysis. Independent sample t -tests were used for comparisons of approximately normally distributed quantitative data between two groups, with means and standard deviations (SD) used for description. For severely skewed continuous variables, medians (P 25 , P 75 ) were calculated for description, and Mann-Whitney U non-parametric tests were used for comparison. Frequency and percentage were established for categorical variables, and χ 2 tests were used for contingency tables. In this sample, the prevalence of abnormal perception or thought content symptom was low, so it was not discussed in the analysis. Partial correlation analysis was used to explore the relationship between MBI-C scores and their subdomains and AD biomarkers, controlling for age, gender, years of education, and cognitive factors. The correlation coefficient (r) was used for the correlation between MBI and biomarker levels. Multivariable linear regression was performed to explore the effects of various factors on Aβ42 levels. GraphPad Prism 9 was used for graphing software. P < 0.05 was considered statistically significant. Results 1. Demographic, cognitive, and behavioral characteristics of HOA and MCI populations The flow diagram of the study and the specific number of participants is reported in Fig. 1 . The sample was composed of 136 HOA (45 males/91 females) and 105 patients with MCI (38 males/67 females). All subjects were divided to non-MBI group (n = 89) and MBI group (n = 16) according to MBI-C ≥ 6.5. As shown in Table 1 , there were significant differences between the two groups in age ( t = 5.160, P < 0.000), diabetes (χ 2 = 5.275, P = 0.022), cognitive assessment, and FAQ score ( P 0.05). Table 1 Demographic, cognitive, and behavioral characteristics of HOA and MCI populations HOA (n = 136) MCI (n = 105) t/χ 2 /K-W P Age (years, Mean ± SD) 71.8 ± 8.1 77.1 ± 7.9 5.160 <0.001 Male (n, %) 45 (33.1%) 38 (36.2%) 0.253 0.615 Years of education M (P 25 , P 75 ) 12 ( 9 , 14 ) 10 (7, 13.5) 1.305 0.192 Bereavement (n, %) 29 (21.3%) 25 (23.8%) 0.211 0.646 Smoking history (n, %) 6(4.4%) 7(6.7%) 0.606 0.738 Drinking history (n, %) 16(11.8%) 15(14.3%) 1.987 0.370 Hypertension(n, %) 48(35.3%) 41(39.0%) 0.358 0.549 Coronary heart disease(n, %) 26(19.1%) 31(29.5%) 3.553 0.059 Diabetes(n, %) 18(13.2%) 26(24.8%) 5.275 0.022 Hyperlipidemia(n, %) 40(29.4%) 29(27.6%) 0.093 0.760 SCD (yes, cases) 109 (80.1%) 89 (84.8%) 0.861 0.354 MMSE M (P 25 , P 75 ) 28 ( 28 , 29 ) 25 ( 24 , 27 ) 11.048 <0.001 MoCA M (P 25 , P 75 ) 27 ( 26 , 28 ) 23 ( 19 , 24 ) 11.862 <0.001 CDT M (P 25 , P 75 ) 4 ( 4 , 4 ) 3 ( 2 , 4 ) 5.780 <0.001 BNT M (P 25 , P 75 ) 26 ( 23 , 27 ) 24 ( 21 , 26 ) 4.129 <0.001 DST M (P 25 , P 75 ) 10 ( 8 , 11 ) 8 ( 6 , 10 ) 4.271 1 (n, %) 50 (36.8%) 50 (47.6%) 2.876 0.090 MBI>6.5 (n, %) 6 (4.4%) 16 (15.3%) 7.262 0.007 Decreased motivation (n, %) 14 (10.3%) 21 (20.2%) 4.635 0.031 Emotional dysregulation (n, %) 17 (12.5%) 20 (19.2%) 2.047 0.152 Impulse dyscontrol (n, %) 39 (28.7%) 51 (49.4%) 5.063 0.025 Social inappropriateness (n, %) 1 (0.7%) 5 (4.8%) 4.010 0.045 Abnormal perception or thought content (n, %) - - - - FAQ M (P 25 , P 75 ) 0 (0, 0) 0 (0, 2) 3.372 0.001 Note: HOA: Healthy Old Adults; MCI: Mild Cognitive Impairment; SCD: Subjective Cognitive Impairment; MMSE: Mini-Mental State Examination; MoCA: Montreal Cognitive Assessment; CDT: Clock Drawing Test; BNT: Boston Naming Test; DST: Digit Span Test; MBI: Mild Behavioral Impairment; SD: Standard Deviation; M (P 25 , P 75 ): median (lower quartile, upper quartile). Independent sample t -test for variable of normal distribution, Mann-Whitney U for variable of non-normal distribution, chi-square test for categorical variables. The prevalence of MBI-C > 1 in the HOA and MCI groups was 36.8% and 47.6%, respectively, and the prevalence of MBI+(MBI-C ≥ 6.5) was 4.4% and 15.3%, respectively, with a statistically significant difference (χ 2 = 7.262, P = 0.007). In the analysis of MBI-C subdomains, there were statistically significant differences between the two groups in decreased motivation (χ 2 = 4.635, P = 0.031), impulse dyscontrol (χ 2 = 5.063, P = 0.025), and emotional dysregulation (χ 2 = 4.010, P = 0.045). There were no significant differences in emotional dysregulation and abnormal perception or thought content between the groups. In the HOA group, the most common MBI symptoms were impulse dyscontrol (28.7%) and emotional dysregulation (12.5%), with less common symptoms being emotional dysregulation (0.7%). In the MCI group, the most common MBI symptoms were impulse dyscontrol (33.8%) and decreased motivation (20.2%), with less common symptoms being emotional dysregulation (4.8%). Both groups did not meet the criteria for abnormal perception or thought content. 2. Comparison of MBI-C scores between HOA and MCI groups with MBI symptoms The median MBI-C total scores for HOA and MCI groups were 7 and 9.5, respectively, with a statistically significant difference between the two groups ( Z = 2.459, P 0.05, Supplementary Table 1). 3. Comparison of MBI-C and subdomains scores between different genders There was no significant difference in the total score of MBI-C and its subdomains scores between genders ( P > 0.05, Supplementary Table 2 ) . 4. Demographic, cognitive, and behavioral characteristics of non-MBI and MBI groups in MCI The average age of patients in the MCI group was 77.1 ± 7.9 years, with 67 females (63.8%). The study found no significant differences in baseline demographic characteristics and cognitive assessments between the MBI and non-MBI groups (all P > 0.05, Table 2 ). The prevalence of hypertension in the non-MBI and MBI groups was 33.7% and 68.8%, respectively, with a statistically significant difference (χ2 = 6.997, P < 0.05), while no statistical differences were found for diabetes, coronary heart disease, and hyperlipidemia. There was a statistically significant difference in MBI-C total scores between the non-MBI and MBI groups ( Z = 6.322, P < 0.001), with median scores of 0 and 9.5 for the non-MBI and MBI groups, respectively. There was a difference in FAQ score between the non-MBI and MBI groups ( Z = 2.042, P < 0.05), with the MBI group having worse social function than the non-MBI group. Table 2 Demographic Characteristics and Cognitive and Behavioral Features of Non-MBI and MBI Groups in MCI MCI(n = 105) t/χ 2 /K-W P MBI-(n = 89) MBI+(n = 16) Age (years, Mean ± SD) 77.5 ± 8.1 75.3 ± 6.1 0.997 0.321 Male (n, %) 31 (34.8%) 7 (43.8%) 0.467 0.494 Education years M (P 25 , P 75 ) 10 ( 6 , 14 ) 12 (9.5, 12) 0.901 0.367 Bereavement (n, %) 22 (24.7%) 3 (13.8%) 0.266 0.606 Current smoking (n, %) 4 (4.5%) 3 (18.8%) 5.328 0.058 Current drinking (n, %) 11 (12.4%) 4 (25.0%) 3.413 0.171 Hypertension (n, %) 30 (33.7%) 11 (68.8%) 6.997 0.008 Coronary heart disease (n, %) 24 (27.0%) 7 (43.8%) 1.836 0.175 Diabetes (n, %) 20 (22.5%) 6 (37.5%) 1.644 0.200 Hyperlipidemia (n, %) 23 (25.8%) 6 (37.5%) 0.922 0.337 SCD (n, %) 73 (82.0%) 16 (100%) 3.394 0.065 MMSE M (P 25 , P 75 ) 25 ( 24 , 27 ) 25 (23.3, 27) 0.250 0.803 MoCA M (P 25 , P 75 ) 22 ( 19 , 24 ) 23 (19.5, 24) 0.405 0.686 CDT M (P 25 , P 75 ) 3 ( 2 , 4 ) 3.5 ( 2 , 4 ) 0.195 0.845 BNT (points, Mean ± SD) 22.8 ± 4.5 23.8 ± 3.2 0.839 0.403 DST (points, Mean ± SD) 8.5 ± 2.7 8.3 ± 2.0 0.227 0.821 MBI total score M (P 25 , P 75 ) 0 (0, 2) 9.5 (7.5, 12.8) 6.322 < 0.001 FAQ M (P 25 , P 75 ) 0 (0, 2) 1 (0, 4.8) 2.042 0.041 Note: MCI: Mild Cognitive Impairment; SCD: Subjective Cognitive Impairment; MMSE: Mini-Mental State Examination; MoCA: Montreal Cognitive Assessment; CDT: Clock Drawing Test; BNT: Boston Naming Test; DST: Digit Span Test; MBI: Mild Behavioral Impairment; FAQ: Functional Activities Questionnaire; M (P 25 , P 75 ): median (lower quartile, upper quartile). Independent sample t -test for variable of normal distribution, Mann-Whitney U for variable of non-normal distribution, chi-square test for categorical variables. 5. MBI-C total and subdomain scores of non-MBI and MBI groups in MCI There were significant differences ( P < 0.05) in all five MBI dimensions between the non-MBI and MBI groups. The median scores for each subdomain were: 2 for decreased motivation, 2 for emotional dysregulation, 4 for impulse dyscontrol, and 0 for social inappropriateness. The impulse dyscontrol subdomain score was the highest (Table 3 ). Table 3 MBI-C subdomain scores of non-MBI and MBI groups in MCI MCI (n = 105) MBI- (n = 89) MBI+ (n = 16) K-W P Decreased motivation M (P 25 , P 75 ) 0 (0, 0) 2 (0, 3) 4.561 <0.001 Emotional dysregulation M (P 25 , P 75 ) 0 (0, 0) 2 (0, 5) 5.143 <0.001 Impulse dyscontrol M (P 25 , P 75 ) 0 (0, 1) 4 (1.3, 7.5) 5.233 <0.001 Social inappropriateness M (P 25 , P 75 ) 0 (0, 0) 0 (0, 1) 5.349 <0.001 Abnormal perception or thought content M (P 25 , P 75 ) 0 (0, 0) 0 (0, 0) - - Note: MBI-C: Mild Behavioral Impairment-Checklist; MBI: Mild Behavioral Impairment; MCI: Mild Cognitive Impairment. P values were calculated using Mann Whitney U Test. 6. Factors related to MBI MBI-C total score was negatively correlated with diabetes ( r =-0.234, P < 0.05) and significantly positively correlated with FAQ ( r = 0.402, P 0.05) (see Supplementary Table 3). 7. Comparison of plasma biomarkers between non-MBI and MBI groups in MCI In the MCI group, Aβ42 in MBI + group was significantly lower than that in MBI- group, with statistically significant difference ( t = 2.40, P = 0.021), but there were no statistically remarked differences in Aβ40, Aβ42/Aβ40, and p-tau217 between MBI+/-group (all P > 0.05, Fig. 2 ). 8. Correlation analysis between MBI-C total score, subdomains, and AD plasma biomarkers MBI total score was negatively correlated with Aβ42 and p-tau217 ( r =-0.385, P = 0.019; r =-0.330, P = 0.041), while there was no correlation with Aβ40 and Aβ42/40. In the subdomains, it was found that the impulse dyscontrol score had a significant negative correlation with Aβ42 ( r =-0.401, P = 0.025, see Supplementary Table 4). 9. Factors influencing Aβ42 Aβ42 levels were used as dependent variables, age, education level, MMSE and MBI-C scores were used as independent variables to construct a multivariable linear regression equation. The results showed that MBI-C score had a statistically significant effect on Aβ42 (B=-5.277, t =-2.638, P = 0.0113), and MBI-C score negatively predicted Aβ42 level, while age and cognitive status had no statistically significant effect on Aβ42 level (Table 4 ). Table 4 Analysis of multivariable linear regression for factors influencing Aβ42 Unstandardized Coefficients Standardized Coefficients t P Variable B SE Beta Constant 288.851 93.373 3.094 0.004 Age 0.864 0.940 0.155 0.919 0.366 Years of education -0.641 1.807 -0.077 -0.355 0.725 MMSE total score -2.984 5.140 -0.177 -0.581 0.566 MBI-C total score -5.277 2.000 -0.445 -2.638 0.013 Note: MMSE: Mini-Mental State Examination; MBI: Mild Behavioral Impairment. P values were calculated using multivariable linear regression. Discussion In this study, we found that the prevalence of MBI in healthy older adults and MCI was 4.4% and 15.3%, respectively, with the MCI group being higher. Consistent with previous research findings, Mortby et al. 29 conducted a large-scale survey of 1377 community-dwelling older adults and found that the prevalence of MBI in MCI was higher than that in cognitively healthy (48.9% vs. 27.6%). Similar results were found in a study in Iran 30 , where the prevalence of MBI in 96 MCI patients in memory clinics was 50%. The results of this study and previous research suggest that behavioral impairment symptoms are very common in the pre-dementia stage and have a higher prevalence in MCI. In fact, previous research has shown that NPS is a risk factor for MCI and AD dementia 31,32 . This suggests that attention should be paid to the relationship between MBI and MCI, even AD. In this cross-sectional study, we found that the MBI symptoms was almost unrelated to cognitive symptoms, but individuals with MBI symptoms had poorer social functioning, consistent with previous research conclusions 33 . We did not find any relationship between the MBI and age, years of education. In addition, we did not find any differences in the prevalence of MBI and its subdomains between genders. However, previous research results were inconsistent. Some has reported that males are more likely to experience decreased motivation and impulse dyscontrol than females 29 , and abnormal perception or thought content domains are more common in females 33 , while other studies have found no significant differences between males and females 31 . In recent years, a large sample study in the UK has confirmed that gender differences in correlation between MBI and cognition 34 . In the future, larger sample Chinese studies are needed to clarify the differences in MBI prevalence among different genders. Although this study did not find a correlation between MBI and clinical symptoms of cognitive decline, we found that MBI + patients had significantly lower plasma Aβ42 levels, not Aβ40. Aβ42 is the main component of senile plaques 35 and has greater neurotoxicity than Aβ40, playing a significant role in brain amyloid angiopathy 36 . The relationship between Aβ42 levels and cognitive impairment may be closer than that between Aβ40 and cognitive impairment 37 . Sun Y et al. 38 confirmed the predictive relationship between baseline MBI and amyloid pathology progression in dementia-free people, revealing that the relationship between MBI and cognitive impairment may be related to amyloid pathology changes. Moreover, a cross-sectional study from the Mayo Clinic found that MCI patients with cerebral Aβ deposition had a higher risk of developing NPS 39 . But the mechanism by which MBI is involved in regulating amyloid changes is currently unclear. Furthermore, we also found a connection between MBI and late-stage AD tau-217 pathology. At present, there is some controversy in this area of research. Firoza Z. Lussier et al. 12 included 96 cognitively normal older adults and performed 18F Aβ-PET and 18F tau-PET scans, finding that increased MBI-C scores were most strongly associated with Aβ-PET uptake, especially in early-stage AD brain regions such as the neocortex, including the frontal neocortex, followed by the striatum, but MBI was not related to increased tau protein PET uptake, suggesting MBI is related to early-stage AD pathophysiology 2 in cognitively healthy older populations but not to late-stage pathophysiology. However, a study from the Swedish BioFINDER 40 included Aβ-positive cognitively normal older adults and found that MBI was related to cortical tau deposition in the entorhinal cortex, and olfactory system-related pathological changes occur early in AD 41,42 , which was demonstrated that Aβ-positive cognitively normal older adults are related to both early and late-stage AD. Since AD-related pathological deposition follows a certain temporal sequence, in early-stage AD, Aβ-related pathophysiological abnormalities will occur, followed by downstream neuronal biomarker damage such as tau pathology and neurodegenerative change markers 43 . Although late-stage tau protein changes can also lead to cognitive decline, significant tau protein aggregation is rarely observed in cognitively intact individuals 12 . Hence, in this study, we included MCI patients and found that higher MBI total scores were related to lower Aβ42 and tau-217 levels. The reasons for the different conclusions in these studies may be due to differences in the inclusion criteria, small sample, the method for blood-biomarkers quantification, and obtained information (PET vs. fluid biomarkers), etc . When observing MBI subdomains, it was showed that the decrease in plasma Aβ42 was significantly related to the impulse dyscontrol subdomain, but not to the decreased motivation or emotional dysregulation subdomain. Impulse dyscontrol include agitation, aggression, irritability, and abnormal motor behaviors 29 . Gill S et al. 44 found that the impulse dyscontrol was related to gray matter atrophy, particularly in the parahippocampal gyrus cortical thickness, suggesting a close relationship between the impulse dyscontrol and typical early AD-related brain structural changes. Other studies have found associations between AD biomarkers such as CSF Aβ42, tau protein, and agitation and aggression, but not with other subdomains 45 , and emotional dysregulation subdomain is significantly related to decreased plasma Aβ42/Aβ40 46 . In longitudinal follow-up studies, impulse dyscontrol have been found to be associated with sudden cognitive decline 47,48 , suggesting that the key to the possible relationship between MBI and AD-related pathological changes may be the impulse dyscontrol subdomain. Therefore, the impulse dyscontrol subdomain in MBI may be particularly important in predicting cognitive decline and dementia risk, and further exploration of the relationship between MBI structural domains and AD biomarkers is needed. In fact, NPS has a high rate of consultation in memory clinics 10 , and the occurrence of impulse dyscontrol is the most common in the population, consistent with previous research findings 8,49,50 . Therefore, when older adults exhibit impulsive and uncontrolled behaviors, it can have an impact on their families and society, making it easier for family members to notice and seek professional medical help earlier. Hence, further exploration of impulse dyscontrol is necessary, as it may be related to a higher risk of sudden cognitive decline and dementia. The strength of this study is that it verifies these manifestations in the context of the Chinese population and has good sensitivity and specificity for the diagnosis of MBI status with an MBI-C cut-off of 6.5 points. Second, the patient was not taking any dementia medication or psychotropic drugs and could not interfere with the behavioral assessment in any way. However, there are some limitations in this study. First, the sample size was small and no differences were found in MBI patients by sex, and larger sample sizes are needed in the future to validate these findings. Second, MBI is a concept that has just been proposed in recent years, and there are few long-term follow-up studies that directly study MBI. This study is cross-sectional, and we did not follow patients to determine which patients will convert to dementia and which type of dementia is more common in MBI patients. Finally, due to focusing on evaluating markers for AD, we were unable to estimate the specificity of the test for AD detection when other neurodegenerative diseases may be present. In conclusion, this study further validates the relationship between behavioral impairment and AD biomarkers in the context of the Chinese population, suggesting that plasma Aβ42 levels may be useful for predicting populations with MBI. Prior to cognitive decline, significant changes in behavioral impairment occurred that may help identify the MBI-C scale as a test tool for use in the preclinical stages of dementia. Declarations Author Contribution Wei Liang: Investigation, Formal analysis, Data curation, Writing-original draft. Lan Wang: Investigation, Writing-original draft. Mei Song: Investigation, Funding acquisition. Hao Geng: Investigation. Xinyang Jing: Investigation. Wei Li: Investigation. Yaxin Huo: Investigation. Anqi Huang: Investigation. Xueyi Wang: Conceptualization. Cuixia An: Conceptualization, Funding acquisition, Writing –review & editing. The author(s) read and approved the final manuscript. Data Availability The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request. Declarations of competing interest The funder had no role in study design, data collection, analysis, or writing of the manuscript. The authors have no conflict of interest to report. Acknowledgements The support of this study was obtained by the government funded clinical medicine excellent talents training project of Hebei Province [grant numbers ZF2024136], National Science Foundation of Hebei Province [grant numbers H2022206544], Science and Technology Program of Hebei Province [grant numbers SG2021189]. 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A Quantitative Meta-analysis of Olfactory Dysfunction in Epilepsy. Neuropsychol Rev 29 , 328-337, doi:10.1007/s11065-019-09406-7 (2019). Wirth, M. et al. The effect of amyloid beta on cognitive decline is modulated by neural integrity in cognitively normal elderly. Alzheimers Dement 9 , 687-698 e681, doi:10.1016/j.jalz.2012.10.012 (2013). Gill, S. et al. Neural correlates of the impulse dyscontrol domain of mild behavioral impairment. Int J Geriatr Psychiatry 36 , 1398-1406, doi:10.1002/gps.5540 (2021). Showraki, A. et al. Cerebrospinal Fluid Correlates of Neuropsychiatric Symptoms in Patients with Alzheimer's Disease/Mild Cognitive Impairment: A Systematic Review. J Alzheimers Dis 71 , 477-501, doi:10.3233/JAD-190365 (2019). Miao, R. et al. Plasma beta-Amyloid in Mild Behavioural Impairment - Neuropsychiatric Symptoms on the Alzheimer's Continuum. J Geriatr Psychiatry Neurol 35 , 434-441, doi:10.1177/08919887211016068 (2022). Wise, E. A., Rosenberg, P. B., Lyketsos, C. G. & Leoutsakos, J. M. Time course of neuropsychiatric symptoms and cognitive diagnosis in National Alzheimer's Coordinating Centers volunteers. Alzheimers Dement (Amst) 11 , 333-339, doi:10.1016/j.dadm.2019.02.006 (2019). Masters, M. C., Morris, J. C. & Roe, C. M. "Noncognitive" symptoms of early Alzheimer disease: a longitudinal analysis. Neurology 84 , 617-622, doi:10.1212/WNL.0000000000001238 (2015). Creese, B. et al. Profile of mild behavioral impairment and factor structure of the Mild Behavioral Impairment Checklist in cognitively normal older adults. Int Psychogeriatr 32 , 705-717, doi:10.1017/S1041610219001200 (2020). Fan, S. et al. Mild behavioral impairment is related to frailty in non-dementia older adults: a cross-sectional study. BMC Geriatr 20 , 510, doi:10.1186/s12877-020-01903-2 (2020) Additional Declarations No competing interests reported. Supplementary Files Supplementarytable1.docx Supplementarytable2.docx Supplementarytable3.docx Supplementarytable4.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-4578874","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":321393142,"identity":"a9c96285-b73d-40b5-a37c-35fc5abf97c3","order_by":0,"name":"Wei Liang","email":"","orcid":"","institution":"The First Hospital of Hebei Medical University, Hebei Technical Innovation Center for Mental Health Assessment and Intervention","correspondingAuthor":false,"prefix":"","firstName":"Wei","middleName":"","lastName":"Liang","suffix":""},{"id":321393144,"identity":"78830ed9-912e-484e-8b69-c06e64c3736d","order_by":1,"name":"Lan 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02:10:50","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4578874/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4578874/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":60339098,"identity":"885f4694-81c4-4ab3-9e75-7a8c38c19a87","added_by":"auto","created_at":"2024-07-15 17:52:30","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":65855,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlow diagram of the trial.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAbbreviations: HOA: Healthy Old Adults; MCI: Mild Cognitive Impairment; MBI: Mild Behavioral Impairment; M: Male; F: Female.\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-4578874/v1/d6934056c014bd57dde355f4.png"},{"id":60339103,"identity":"b73b0237-6259-4a7f-a7fb-26faab055aa5","added_by":"auto","created_at":"2024-07-15 17:52:30","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":930842,"visible":true,"origin":"","legend":"\u003cp\u003eComparison of plasma biomarkers between MCI groups with or without MBI.\u003c/p\u003e\n\u003cp\u003eNote: MBI: Mild Behavioral Impairment; MCI: Mild Cognitive Impairment; The sample size for MCI+MBI- was 89, and 16 for MCI+MBI+. Significance star for independent sample t-test, \u003csup\u003e*\u003c/sup\u003e represent \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-4578874/v1/c7868101a5dd381c5ddc85dc.png"},{"id":64719808,"identity":"d24f1efe-bccb-4d6a-b16a-87e4d7f0fbca","added_by":"auto","created_at":"2024-09-18 03:47:51","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2163797,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4578874/v1/b81f664e-58ec-4c32-9d8e-5dd7bdc0bcfe.pdf"},{"id":60339099,"identity":"2d97491a-bddf-4ba4-8d51-ca31ef70ccbc","added_by":"auto","created_at":"2024-07-15 17:52:30","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":18137,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-4578874/v1/2dd6f3a42c91be34826536fe.docx"},{"id":60339100,"identity":"a29f0e8f-ea14-42fe-a2ac-60d45533c67d","added_by":"auto","created_at":"2024-07-15 17:52:30","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":18570,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytable2.docx","url":"https://assets-eu.researchsquare.com/files/rs-4578874/v1/db56a1ad60c6c7a5be79145e.docx"},{"id":60339104,"identity":"8d0c5810-e1c5-489d-ad31-841beec8ae3f","added_by":"auto","created_at":"2024-07-15 17:52:30","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":19677,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytable3.docx","url":"https://assets-eu.researchsquare.com/files/rs-4578874/v1/2386031302c5162d79a29406.docx"},{"id":60339102,"identity":"97c45f2e-5622-442a-9f89-0cecfd8942fb","added_by":"auto","created_at":"2024-07-15 17:52:30","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":19073,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarytable4.docx","url":"https://assets-eu.researchsquare.com/files/rs-4578874/v1/1017c6c09042cc68ebdbc35e.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Correlation Between Mild Behavioral Impairment and Peripheral Blood Biomarkers in Patients with Mild Cognitive Impairment","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe mild cognitive impairment (MCI) \u003csup\u003e1\u003c/sup\u003eand dementia are clinically characterized not only by cognitive dysfunction but also by a decreased motivation, anxiety, depression, and disinhibited behavior. These non-cognitive symptoms are referred to as neuropsychiatric symptoms (NPS). NPS span the entire course of cognitive disorders and may even manifest as psychiatric and behavioral abnormalities before the appearance of cognitive deficits\u003csup\u003e2\u003c/sup\u003e. In recent years, mild behavioral impairment (MBI) has received increasing attention from researcher, which was first defined by Taragano and Allegri (Eleventh Congress of the International Psychogeriatric Association), and it was introduced as a concept that includes neurobehavioral symptoms seen in older adults for at least 6 months and that do not meet the diagnostic criteria of any other psychiatric syndrome. MBI is observed in advance of cognitive impairment for some people, suggesting maybe the index manifestation of neurodegeneration. Longitudinal and cross-sectional studies consistently suggest that MBI is associated with a decline in cognitive ability and an accelerated progression to dementia\u003csup\u003e3,4\u003c/sup\u003e, indicating that MBI may be a potential risk factor for neurodegenerative diseases\u003csup\u003e5\u0026ndash;8\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe Alzheimer's Association International Society to Advance Alzheimer's Research and Treatment (ISTAART) working group proposed MBI research diagnostic criteria in 2016\u003csup\u003e9\u003c/sup\u003e. The Mild Behavioral Impairment-Checklist (MBI-C)\u003csup\u003e10\u003c/sup\u003e was developed to quantify the severity of behavioral symptoms in multiple domains. Currently, the overall and subdomains of the MBI-C for clinical diagnosis and prognosis are being validated. Xu L et al. \u003csup\u003e11\u003c/sup\u003eevaluated the reliability and validity of the Chinese version of the MBI-C, finding that it has consistent internal validity and can be used to assess patients' psychological and behavioral changes. Although MBI represents non-cognitive symptoms and is considered an early manifestation of AD, the relationship between MBI and the preclinical pathophysiology of AD remains unclear. Previous studies on the etiology and pathology of MBI have made preliminary explorations, finding associations between MBI and biological markers of AD, including Aβ-positron emission tomography (PET)\u003csup\u003e12\u003c/sup\u003e, tau-PET\u003csup\u003e13\u003c/sup\u003e, cerebrospinal fluid (CSF)-tau, plasma neurofilament light\u003csup\u003e14\u003c/sup\u003e, and AD risk gene loci\u003csup\u003e15,16\u003c/sup\u003e. In terms of imaging, MBI patients exhibit AD-like brain imaging changes. A cross-sectional study by Shu J et al. \u003csup\u003e17\u003c/sup\u003efound atrophy in the left frontal cortex and right thalamus of MBI patients, which were associated with NPS in dementia, suggesting indicating that MBI may be an early sign for cognitive decline and dementia.\u003c/p\u003e \u003cp\u003eIn addition, these plasma biomarkers show an association with the future development of AD, however, the vast majority of studies are based on Western populations, and since race and ethnicity may influence the assessment of these markers, it is necessary to verify these manifestations in the context of the Chinese population. Although many studies have investigated the association between CSF and MBI, few studies have examined plasma biomarkers and MBI, and this study in the Chinese population reinforces previous European and American findings that plasma biomarkers are already altered during the MBI phase based on the Chinese population.\u003c/p\u003e \u003cp\u003eTherefore, this study aims to explore the prevalence of MBI in healthy older adults (HOA) and MCI populations and investigate the relationship between MBI and AD biomarkers in MCI populations, particularly Aβ40, Aβ42, and p-tau217 levels.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e1. Study population\u003c/h2\u003e \u003cp\u003eThis research was designed as a cross-sectional study conducted from April to October 2021. The participant pool was drawn from the older adult community in the Yuhua District of Shijiazhuang City, Hebei Province, China. Inclusion criteria were as follows: individuals aged 55 years and above; willingness to participate and complete both the questionnaire survey and cognitive function assessment; and consent to provide blood samples.\u003c/p\u003e \u003cp\u003eThe study population comprised 241 participants, with an age range of 55 to 93 years (mean age: 74.2 years). Of these, 85 were male and 161 were female. The average educational attainment of the cohort was 10.5 years.\u003c/p\u003e \u003cp\u003eExclusion criteria were as follows: potential alternate causes of cognitive decline, such as a clear history of stroke (cerebral infarction, cerebral hemorrhage), brain tumors, Parkinson's disease, epilepsy, severe head and nasal fractures; psychiatric disorders such as schizophrenia, bipolar disorder, and depression; inability to cooperate with neurological and psychiatric scale examinations; and severe physical diseases or significant visual or hearing impairments that would prevent cooperation with cognitive function examinations. All participants had signed informed consent forms, and the study was conducted with the approval of the Ethics Committee of the First Hospital of Hebei Medical University (ethical approval number: 20190416). It had been confirmed that the research was performed in accordance with the relevant guidelines/regulations, particularly Declaration of Helsinki. The population was divided into two groups: HOA and MCI.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eDiagnostic Criteria\u003c/h2\u003e \u003cp\u003eHOA were defined as follows: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) no self-reported cognitive decline; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) objective evidence of normal cognitive function, with Mini-Mental State Examination (MMSE) scores of \u0026ge;\u0026thinsp;19 for illiterate individuals, \u0026ge;\u0026thinsp;22 for those with primary school education, and \u0026ge;\u0026thinsp;24 for those with junior high school education or higher\u003csup\u003e18\u003c/sup\u003e, and Montreal Cognitive Assessment (MoCA) scores of \u0026ge;\u0026thinsp;13 for illiterate individuals, \u0026ge;\u0026thinsp;19 for those with primary school education, and \u0026ge;\u0026thinsp;24 for those with junior high school education or higher\u003csup\u003e19\u003c/sup\u003e; (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) a Clinical Dementia Rating (CDR) of 0\u003csup\u003e20\u003c/sup\u003e; (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) normal daily functioning, as indicated by an Activity of Daily Living Scale\u003csup\u003e21\u003c/sup\u003e (ADL) score of \u0026le;\u0026thinsp;26; (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) minimal anxiety or depression, as indicated by a Hamilton Anxiety Scale (HAMA) score of \u0026lt;\u0026thinsp;7 and a Hamilton Depression Scale (HAMD) score of \u0026lt;\u0026thinsp;7.\u003c/p\u003e \u003cp\u003eMCI was diagnosed based on the 2006 consensus of the Chinese expert group on MCI diagnosis\u003csup\u003e22\u003c/sup\u003e and included: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) self-perceived or family-reported significant memory impairment; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) relatively intact or mildly impaired other cognitive functions, with MMSE scores similar to those for the HOA group, but with MoCA scores of \u0026lt;\u0026thinsp;13 for illiterate individuals, \u0026lt;\u0026thinsp;19 for those with primary school education, and \u0026lt;\u0026thinsp;24 for those with junior high school education or higher; (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) basic normal daily life, with an ADL score of \u0026le;\u0026thinsp;26; (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) a CDR of 0.5; (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) exclusion of the possibility of dementia, not yet reaching the diagnosis of dementia.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eData Collection Procedures\u003c/h2\u003e \u003cp\u003eThe researchers in this study received formal training to ensure the unified management of questionnaires and standardized guidance for the subjects. A one-on-one approach was adopted by the investigators to conduct neuropsychological assessments and fill out questionnaires for each subject, with each subject taking approximately 45 minutes to complete the survey.\u003c/p\u003e \u003cp\u003eA self-made general information survey was used to collect basic information such as age, gender, years of education, marital status, smoking and drinking habits, and past medical history, including diabetes, coronary heart disease, hypertension, and hyperlipidemia.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eNeuropsychological Testing\u003c/h2\u003e \u003cp\u003eThe MMSE\u003csup\u003e18\u003c/sup\u003e and MoCA\u003csup\u003e19\u003c/sup\u003e were used to assess overall cognitive function. The MMSE is currently the most widely used scale, covering orientation, memory, calculation, language, visual-spatial ability, application, and attention, with a total of 30 points. MoCA has a higher screening sensitivity than MMSE\u003csup\u003e23\u003c/sup\u003e; Clock Drawing Task (CDT)\u003csup\u003e24\u003c/sup\u003e was used to assess visual-spatial ability; Boston Naming Test (BNT)\u003csup\u003e25\u003c/sup\u003e was used to evaluate language naming ability; Digit Span Test (DST)\u003csup\u003e26\u003c/sup\u003e was used to test instant memory function, attention, and information processing speed. Functional Activities Questionnaire (FAQ) was used to assess the difficulties in activities of daily living\u003csup\u003e27\u003c/sup\u003e, with higher scores indicating more difficulty.\u003c/p\u003e \u003cp\u003eBehavioral assessment was performed using MBI-C\u003csup\u003e10\u003c/sup\u003e, consisting of 34 items divided into five domains: (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e) decreased motivation: 6 items, including evaluation of cognitive, behavioral, and emotional apathy; (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e) emotional dysregulation: 6 items, including one item each for depression, anhedonia, despair, guilt, anxiety, and panic; (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e) impulse dyscontrol: 12 items, including agitation, aggression, impulsiveness, recklessness, etc.; (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e) social inappropriateness: 5 items, including sensitivity, empathy, and disinhibited behavior; (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e) abnormal perception or thought content: 5 questions, including suspicion, exaggeration, auditory hallucinations, and visual hallucinations. For each item, participants answered \"yes\" or \"no,\" and for each item answered \"yes,\" the behavior must have persisted for at least six months, indicating a significant change from baseline behavior, and the severity level (1-mild, 2-moderate, or 3-severe) was assessed. Individual scores for each subdomain of the MBI-C could be calculated by equally adding the severity scores for each category. MBI status (MBI+/-) classification was based on the MBI-C validation study for MCI patients, with a cutoff point of 6.5 points\u003csup\u003e28\u003c/sup\u003e for diagnosing MBI; MBI-C scores\u0026thinsp;\u0026ge;\u0026thinsp;6.5 were considered MBI+, while\u0026thinsp;\u0026lt;\u0026thinsp;6.5 were considered MBI-.\u003c/p\u003e \u003cp\u003eBlood Sample Collection and Biochemical Detection\u003c/p\u003e \u003cp\u003eSeventy cases fasting blood samples were drawn into coagulation tubes at a fixed time (8\u0026ndash;9 am), and whole blood samples were placed at room temperature for 2 hours and centrifuged at 3000 rpm for 10 minutes. Serum was collected, aliquoted, and stored at -80℃ for further analysis.\u003c/p\u003e \u003cp\u003eHuman amyloid-β (Aβ40), amyloid-β42 (Aβ42), and hyperphosphorylated tau (p-tau217) enzyme-linked immunosorbent assay (ELISA) kits were purchased from Shanghai Zhuocai Biotechnology Co., Ltd. The kit and samples were equilibrated to room temperature (18\u0026ndash;25\u0026deg;C) before use. The kit provided six standards with known concentrations at 10 pg/mL, 20 pg/mL, 40 pg/mL, 80 pg/mL, 160 pg/mL, and 320 pg/mL, and a standard curve was plotted with six points.\u003c/p\u003e \u003cp\u003e Standard and sample holes were set up, with 50 \u0026micro;L of different concentration standards added to each standard hole, and 40 \u0026micro;L of sample diluent added to the sample holes on the enzyme-labeled coated plate, followed by 10 \u0026micro;L of the sample (final dilution factor of 5). Sample addition was performed by adding the sample to the bottom of the enzyme-labeled plate hole without touching the hole walls, and gently shaking to mix. 100 \u0026micro;L of enzyme-labeled reagent was added to each hole, except for the blank hole. The plate was then incubated at 37\u0026deg;C for 60 minutes after sealing with a sealing film. The 20x concentrated washing solution was diluted 20 times with distilled water and set aside. The sealing film was carefully removed, the liquid discarded, and the plate dried by tapping. The washing solution was added to each hole, left for 30 seconds, and then discarded. This process was repeated five times, and then the plate was tapped dry. 50 \u0026micro;L of color developer A and 50 \u0026micro;L of color developer B were added to each hole, gently shaken to mix, and then incubated in the dark at 37\u0026deg;C for 15 minutes. 50 \u0026micro;L of stop solution was added to each hole to stop the reaction (the color changed from blue to yellow). The absorbance (OD value) of each hole was measured sequentially at 450 nm wavelength, with the blank hole used for zero adjustment, and the measurement was performed within 15 minutes after adding the stop solution.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eSPSS 24.0 was used for statistical analysis. Independent sample \u003cem\u003et\u003c/em\u003e-tests were used for comparisons of approximately normally distributed quantitative data between two groups, with means and standard deviations (SD) used for description. For severely skewed continuous variables, medians (P\u003csub\u003e25\u003c/sub\u003e, P\u003csub\u003e75\u003c/sub\u003e) were calculated for description, and Mann-Whitney U non-parametric tests were used for comparison. Frequency and percentage were established for categorical variables, and χ\u003csup\u003e2\u003c/sup\u003e tests were used for contingency tables. In this sample, the prevalence of abnormal perception or thought content symptom was low, so it was not discussed in the analysis. Partial correlation analysis was used to explore the relationship between MBI-C scores and their subdomains and AD biomarkers, controlling for age, gender, years of education, and cognitive factors. The correlation coefficient (r) was used for the correlation between MBI and biomarker levels. Multivariable linear regression was performed to explore the effects of various factors on Aβ42 levels. GraphPad Prism 9 was used for graphing software. \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e1. Demographic, cognitive, and behavioral characteristics of HOA and MCI populations\u003c/h2\u003e \u003cp\u003eThe flow diagram of the study and the specific number of participants is reported in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The sample was composed of 136 HOA (45 males/91 females) and 105 patients with MCI (38 males/67 females). All subjects were divided to non-MBI group (n\u0026thinsp;=\u0026thinsp;89) and MBI group (n\u0026thinsp;=\u0026thinsp;16) according to MBI-C\u0026thinsp;\u0026ge;\u0026thinsp;6.5.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, there were significant differences between the two groups in age (\u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5.160, \u003cem\u003eP\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.000), diabetes (χ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;5.275, \u003cem\u003eP\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.022), cognitive assessment, and FAQ score (\u003cem\u003eP\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.05). There were no significant differences in gender, years of education, lifestyle habits, and medical history (\u003cem\u003eP\u0026thinsp;\u0026gt;\u003c/em\u003e\u0026thinsp;0.05).\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\u003eDemographic, cognitive, and behavioral characteristics of HOA and MCI populations\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHOA\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;136)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMCI\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;105)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003et/χ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e/K-W\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e71.8\u0026thinsp;\u0026plusmn;\u0026thinsp;8.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e77.1\u0026thinsp;\u0026plusmn;\u0026thinsp;7.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale (n, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e45 (33.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e38 (36.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.615\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYears of education M (P\u003csub\u003e25\u003c/sub\u003e, P\u003csub\u003e75\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12 (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10 (7, 13.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.305\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.192\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBereavement (n, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29 (21.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25 (23.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.211\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.646\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking history (n, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6(4.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7(6.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.606\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.738\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDrinking history (n, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16(11.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15(14.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.987\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.370\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension(n, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48(35.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e41(39.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.549\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoronary heart disease(n, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26(19.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31(29.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.553\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes(n, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18(13.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26(24.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHyperlipidemia(n, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40(29.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29(27.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.760\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSCD (yes, cases)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e109 (80.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e89 (84.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.861\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.354\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMMSE M (P\u003csub\u003e25\u003c/sub\u003e, P\u003csub\u003e75\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28 (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25 (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMoCA M (P\u003csub\u003e25\u003c/sub\u003e, P\u003csub\u003e75\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27 (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23 (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.862\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCDT M (P\u003csub\u003e25\u003c/sub\u003e, P\u003csub\u003e75\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.780\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBNT M (P\u003csub\u003e25\u003c/sub\u003e, P\u003csub\u003e75\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26 (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24 (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDST M (P\u003csub\u003e25\u003c/sub\u003e, P\u003csub\u003e75\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8 (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMBI total score M (P\u003csub\u003e25\u003c/sub\u003e, P\u003csub\u003e75\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0, 2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0, 3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMBI\u0026thinsp;\u0026gt;\u0026thinsp;1 (n, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50 (36.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e50 (47.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.876\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.090\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMBI\u0026gt;6.5 (n, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6 (4.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 (15.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.262\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDecreased motivation (n, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14 (10.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21 (20.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.635\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmotional dysregulation (n, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17 (12.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20 (19.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.152\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImpulse dyscontrol (n, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39 (28.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51 (49.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial inappropriateness\u003c/p\u003e \u003cp\u003e(n, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1 (0.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5 (4.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbnormal perception or thought content\u003c/p\u003e \u003cp\u003e(n, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFAQ M (P\u003csub\u003e25\u003c/sub\u003e, P\u003csub\u003e75\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0, 0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0, 2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.372\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: HOA: Healthy Old Adults; MCI: Mild Cognitive Impairment; SCD: Subjective Cognitive Impairment; MMSE: Mini-Mental State Examination; MoCA: Montreal Cognitive Assessment; CDT: Clock Drawing Test; BNT: Boston Naming Test; DST: Digit Span Test; MBI: Mild Behavioral Impairment; SD: Standard Deviation; M (P\u003csub\u003e25\u003c/sub\u003e, P\u003csub\u003e75\u003c/sub\u003e): median (lower quartile, upper quartile). Independent sample \u003cem\u003et\u003c/em\u003e-test for variable of normal distribution, Mann-Whitney U for variable of non-normal distribution, chi-square test for categorical variables.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe prevalence of MBI-C\u0026thinsp;\u0026gt;\u0026thinsp;1 in the HOA and MCI groups was 36.8% and 47.6%, respectively, and the prevalence of MBI+(MBI-C\u0026thinsp;\u0026ge;\u0026thinsp;6.5) was 4.4% and 15.3%, respectively, with a statistically significant difference (χ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;7.262, \u003cem\u003eP\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.007). In the analysis of MBI-C subdomains, there were statistically significant differences between the two groups in decreased motivation (χ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;4.635, \u003cem\u003eP\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.031), impulse dyscontrol (χ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;5.063, \u003cem\u003eP\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.025), and emotional dysregulation (χ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;4.010, \u003cem\u003eP\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.045). There were no significant differences in emotional dysregulation and abnormal perception or thought content between the groups. In the HOA group, the most common MBI symptoms were impulse dyscontrol (28.7%) and emotional dysregulation (12.5%), with less common symptoms being emotional dysregulation (0.7%). In the MCI group, the most common MBI symptoms were impulse dyscontrol (33.8%) and decreased motivation (20.2%), with less common symptoms being emotional dysregulation (4.8%). Both groups did not meet the criteria for abnormal perception or thought content.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2. Comparison of MBI-C scores between HOA and MCI groups with MBI symptoms\u003c/h2\u003e \u003cp\u003eThe median MBI-C total scores for HOA and MCI groups were 7 and 9.5, respectively, with a statistically significant difference between the two groups (\u003cem\u003eZ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.459, \u003cem\u003eP\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.05). However, there was no statistical significance in the MBI-C subdomains scores (\u003cem\u003eP\u0026thinsp;\u0026gt;\u003c/em\u003e\u0026thinsp;0.05, Supplementary Table\u0026nbsp;1).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3. Comparison of MBI-C and subdomains scores between different genders\u003c/h2\u003e \u003cp\u003eThere was no significant difference in the total score of MBI-C and its subdomains scores between genders (\u003cem\u003eP\u0026thinsp;\u0026gt;\u003c/em\u003e\u0026thinsp;0.05, Supplementary Table\u0026nbsp;2\u003cb\u003e)\u003c/b\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4. Demographic, cognitive, and behavioral characteristics of non-MBI and MBI groups in MCI\u003c/h2\u003e \u003cp\u003eThe average age of patients in the MCI group was 77.1\u0026thinsp;\u0026plusmn;\u0026thinsp;7.9 years, with 67 females (63.8%). The study found no significant differences in baseline demographic characteristics and cognitive assessments between the MBI and non-MBI groups (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05, Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). The prevalence of hypertension in the non-MBI and MBI groups was 33.7% and 68.8%, respectively, with a statistically significant difference (χ2\u0026thinsp;=\u0026thinsp;6.997, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), while no statistical differences were found for diabetes, coronary heart disease, and hyperlipidemia. There was a statistically significant difference in MBI-C total scores between the non-MBI and MBI groups (\u003cem\u003eZ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6.322, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with median scores of 0 and 9.5 for the non-MBI and MBI groups, respectively. There was a difference in FAQ score between the non-MBI and MBI groups (\u003cem\u003eZ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.042, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05), with the MBI group having worse social function than the non-MBI group.\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\u003eDemographic Characteristics and Cognitive and Behavioral Features of Non-MBI and MBI Groups in MCI\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eMCI(n\u0026thinsp;=\u0026thinsp;105)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003et/χ\u003c/em\u003e\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u003cem\u003e/K-W\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMBI-(n\u0026thinsp;=\u0026thinsp;89)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMBI+(n\u0026thinsp;=\u0026thinsp;16)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e77.5\u0026thinsp;\u0026plusmn;\u0026thinsp;8.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e75.3\u0026thinsp;\u0026plusmn;\u0026thinsp;6.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.997\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.321\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale (n, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31 (34.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (43.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.467\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.494\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation years M (P\u003csub\u003e25\u003c/sub\u003e, P\u003csub\u003e75\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10 (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e12 (9.5, 12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.901\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.367\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBereavement (n, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22 (24.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (13.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.266\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.606\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent smoking (n, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4 (4.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3 (18.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.328\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent drinking (n, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11 (12.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (25.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.413\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.171\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension (n, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30 (33.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (68.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.997\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoronary heart disease (n, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24 (27.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7 (43.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.836\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.175\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes (n, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20 (22.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (37.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.644\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.200\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHyperlipidemia (n, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23 (25.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6 (37.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.922\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.337\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSCD (n, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e73 (82.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16 (100%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.394\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMMSE M (P\u003csub\u003e25\u003c/sub\u003e, P\u003csub\u003e75\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25 (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25 (23.3, 27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.803\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMoCA M (P\u003csub\u003e25\u003c/sub\u003e, P\u003csub\u003e75\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22 (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23 (19.5, 24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.405\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.686\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCDT M (P\u003csub\u003e25\u003c/sub\u003e, P\u003csub\u003e75\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.5 (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.195\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.845\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBNT (points, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22.8\u0026thinsp;\u0026plusmn;\u0026thinsp;4.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.8\u0026thinsp;\u0026plusmn;\u0026thinsp;3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.839\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.403\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDST (points, Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.5\u0026thinsp;\u0026plusmn;\u0026thinsp;2.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.3\u0026thinsp;\u0026plusmn;\u0026thinsp;2.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.227\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.821\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMBI total score M (P\u003csub\u003e25\u003c/sub\u003e, P\u003csub\u003e75\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0, 2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.5 (7.5, 12.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.322\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFAQ M (P\u003csub\u003e25\u003c/sub\u003e, P\u003csub\u003e75\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0, 2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1 (0, 4.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: MCI: Mild Cognitive Impairment; SCD: Subjective Cognitive Impairment; MMSE: Mini-Mental State Examination; MoCA: Montreal Cognitive Assessment; CDT: Clock Drawing Test; BNT: Boston Naming Test; DST: Digit Span Test; MBI: Mild Behavioral Impairment; FAQ: Functional Activities Questionnaire; M (P\u003csub\u003e25\u003c/sub\u003e, P\u003csub\u003e75\u003c/sub\u003e): median (lower quartile, upper quartile). Independent sample \u003cem\u003et\u003c/em\u003e-test for variable of normal distribution, Mann-Whitney U for variable of non-normal distribution, chi-square test for categorical variables.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e5. MBI-C total and subdomain scores of non-MBI and MBI groups in MCI\u003c/h2\u003e \u003cp\u003eThere were significant differences (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in all five MBI dimensions between the non-MBI and MBI groups. The median scores for each subdomain were: 2 for decreased motivation, 2 for emotional dysregulation, 4 for impulse dyscontrol, and 0 for social inappropriateness. The impulse dyscontrol subdomain score was the highest (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\u003eMBI-C subdomain scores of non-MBI and MBI groups in MCI\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eMCI (n\u0026thinsp;=\u0026thinsp;105)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMBI-\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMBI+\u003c/p\u003e \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eK-W\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDecreased motivation M (P\u003csub\u003e25\u003c/sub\u003e, P\u003csub\u003e75\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0, 0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (0, 3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.561\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmotional dysregulation M (P\u003csub\u003e25\u003c/sub\u003e, P\u003csub\u003e75\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0, 0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2 (0, 5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eImpulse dyscontrol M (P\u003csub\u003e25\u003c/sub\u003e, P\u003csub\u003e75\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0, 1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4 (1.3, 7.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial inappropriateness M (P\u003csub\u003e25\u003c/sub\u003e, P\u003csub\u003e75\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0, 0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0, 1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.349\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAbnormal perception or thought content\u003c/p\u003e \u003cp\u003eM (P\u003csub\u003e25\u003c/sub\u003e, P\u003csub\u003e75\u003c/sub\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (0, 0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0 (0, 0)\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 \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: MBI-C: Mild Behavioral Impairment-Checklist; MBI: Mild Behavioral Impairment; MCI: Mild Cognitive Impairment. \u003cem\u003eP\u003c/em\u003e values were calculated using Mann Whitney \u003cem\u003eU\u003c/em\u003e Test.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e6. Factors related to MBI\u003c/h2\u003e \u003cp\u003eMBI-C total score was negatively correlated with diabetes (\u003cem\u003er\u003c/em\u003e=-0.234, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and significantly positively correlated with FAQ (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.402, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). However, there was no correlation between MBI-C total score and age, years of education, hypertension, and cognitive function (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05) (see Supplementary Table\u0026nbsp;3).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e7. Comparison of plasma biomarkers between non-MBI and MBI groups in MCI\u003c/h2\u003e \u003cp\u003eIn the MCI group, Aβ42 in MBI\u0026thinsp;+\u0026thinsp;group was significantly lower than that in MBI- group, with statistically significant difference (\u003cem\u003et\u003c/em\u003e\u0026thinsp;=\u0026thinsp;2.40, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.021), but there were no statistically remarked differences in Aβ40, Aβ42/Aβ40, and p-tau217 between MBI+/-group (all \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05, Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e8. Correlation analysis between MBI-C total score, subdomains, and AD plasma biomarkers\u003c/h2\u003e \u003cp\u003eMBI total score was negatively correlated with Aβ42 and p-tau217 (\u003cem\u003er\u003c/em\u003e=-0.385, \u003cem\u003eP\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.019; \u003cem\u003er\u003c/em\u003e=-0.330, \u003cem\u003eP\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.041), while there was no correlation with Aβ40 and Aβ42/40. In the subdomains, it was found that the impulse dyscontrol score had a significant negative correlation with Aβ42 (\u003cem\u003er\u003c/em\u003e=-0.401, \u003cem\u003eP\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.025, see Supplementary Table\u0026nbsp;4).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e9. Factors influencing Aβ42\u003c/h2\u003e \u003cp\u003eAβ42 levels were used as dependent variables, age, education level, MMSE and MBI-C scores were used as independent variables to construct a multivariable linear regression equation. The results showed that MBI-C score had a statistically significant effect on Aβ42 (B=-5.277, \u003cem\u003et\u003c/em\u003e=-2.638, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.0113), and MBI-C score negatively predicted Aβ42 level, while age and cognitive status had no statistically significant effect on Aβ42 level (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAnalysis of multivariable linear regression for factors influencing Aβ42\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=\"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=\"char\" char=\".\" 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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eUnstandardized Coefficients\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStandardized Coefficients\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003et\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBeta\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e288.851\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e93.373\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.864\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.940\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.919\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.366\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYears of education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.641\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.807\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.725\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMMSE total score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-2.984\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.581\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.566\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMBI-C total score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-5.277\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.445\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-2.638\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eNote: MMSE: Mini-Mental State Examination; MBI: Mild Behavioral Impairment. \u003cem\u003eP\u003c/em\u003e values were calculated using multivariable linear regression.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we found that the prevalence of MBI in healthy older adults and MCI was 4.4% and 15.3%, respectively, with the MCI group being higher. Consistent with previous research findings, Mortby et al. \u003csup\u003e29\u003c/sup\u003econducted a large-scale survey of 1377 community-dwelling older adults and found that the prevalence of MBI in MCI was higher than that in cognitively healthy (48.9% vs. 27.6%). Similar results were found in a study in Iran\u003csup\u003e30\u003c/sup\u003e, where the prevalence of MBI in 96 MCI patients in memory clinics was 50%. The results of this study and previous research suggest that behavioral impairment symptoms are very common in the pre-dementia stage and have a higher prevalence in MCI. In fact, previous research has shown that NPS is a risk factor for MCI and AD dementia\u003csup\u003e31,32\u003c/sup\u003e. This suggests that attention should be paid to the relationship between MBI and MCI, even AD.\u003c/p\u003e \u003cp\u003eIn this cross-sectional study, we found that the MBI symptoms was almost unrelated to cognitive symptoms, but individuals with MBI symptoms had poorer social functioning, consistent with previous research conclusions\u003csup\u003e33\u003c/sup\u003e. We did not find any relationship between the MBI and age, years of education. In addition, we did not find any differences in the prevalence of MBI and its subdomains between genders. However, previous research results were inconsistent. Some has reported that males are more likely to experience decreased motivation and impulse dyscontrol than females\u003csup\u003e29\u003c/sup\u003e, and abnormal perception or thought content domains are more common in females\u003csup\u003e33\u003c/sup\u003e, while other studies have found no significant differences between males and females\u003csup\u003e31\u003c/sup\u003e. In recent years, a large sample study in the UK has confirmed that gender differences in correlation between MBI and cognition \u003csup\u003e34\u003c/sup\u003e. In the future, larger sample Chinese studies are needed to clarify the differences in MBI prevalence among different genders.\u003c/p\u003e \u003cp\u003eAlthough this study did not find a correlation between MBI and clinical symptoms of cognitive decline, we found that MBI\u0026thinsp;+\u0026thinsp;patients had significantly lower plasma Aβ42 levels, not Aβ40. Aβ42 is the main component of senile plaques\u003csup\u003e35\u003c/sup\u003e and has greater neurotoxicity than Aβ40, playing a significant role in brain amyloid angiopathy\u003csup\u003e36\u003c/sup\u003e. The relationship between Aβ42 levels and cognitive impairment may be closer than that between Aβ40 and cognitive impairment \u003csup\u003e37\u003c/sup\u003e. Sun Y et al. \u003csup\u003e38\u003c/sup\u003econfirmed the predictive relationship between baseline MBI and amyloid pathology progression in dementia-free people, revealing that the relationship between MBI and cognitive impairment may be related to amyloid pathology changes. Moreover, a cross-sectional study from the Mayo Clinic found that MCI patients with cerebral Aβ deposition had a higher risk of developing NPS\u003csup\u003e39\u003c/sup\u003e. But the mechanism by which MBI is involved in regulating amyloid changes is currently unclear.\u003c/p\u003e \u003cp\u003eFurthermore, we also found a connection between MBI and late-stage AD tau-217 pathology. At present, there is some controversy in this area of research. Firoza Z. Lussier et al.\u003csup\u003e12\u003c/sup\u003e included 96 cognitively normal older adults and performed 18F Aβ-PET and 18F tau-PET scans, finding that increased MBI-C scores were most strongly associated with Aβ-PET uptake, especially in early-stage AD brain regions such as the neocortex, including the frontal neocortex, followed by the striatum, but MBI was not related to increased tau protein PET uptake, suggesting MBI is related to early-stage AD pathophysiology\u003csup\u003e2\u003c/sup\u003e in cognitively healthy older populations but not to late-stage pathophysiology. However, a study from the Swedish BioFINDER\u003csup\u003e40\u003c/sup\u003e included Aβ-positive cognitively normal older adults and found that MBI was related to cortical tau deposition in the entorhinal cortex, and olfactory system-related pathological changes occur early in AD\u003csup\u003e41,42\u003c/sup\u003e, which was demonstrated that Aβ-positive cognitively normal older adults are related to both early and late-stage AD. Since AD-related pathological deposition follows a certain temporal sequence, in early-stage AD, Aβ-related pathophysiological abnormalities will occur, followed by downstream neuronal biomarker damage such as tau pathology and neurodegenerative change markers\u003csup\u003e43\u003c/sup\u003e. Although late-stage tau protein changes can also lead to cognitive decline, significant tau protein aggregation is rarely observed in cognitively intact individuals\u003csup\u003e12\u003c/sup\u003e. Hence, in this study, we included MCI patients and found that higher MBI total scores were related to lower Aβ42 and tau-217 levels. The reasons for the different conclusions in these studies may be due to differences in the inclusion criteria, small sample, the method for blood-biomarkers quantification, and obtained information (PET vs. fluid biomarkers), \u003cem\u003eetc\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eWhen observing MBI subdomains, it was showed that the decrease in plasma Aβ42 was significantly related to the impulse dyscontrol subdomain, but not to the decreased motivation or emotional dysregulation subdomain. Impulse dyscontrol include agitation, aggression, irritability, and abnormal motor behaviors\u003csup\u003e29\u003c/sup\u003e. Gill S et al.\u003csup\u003e44\u003c/sup\u003e found that the impulse dyscontrol was related to gray matter atrophy, particularly in the parahippocampal gyrus cortical thickness, suggesting a close relationship between the impulse dyscontrol and typical early AD-related brain structural changes. Other studies have found associations between AD biomarkers such as CSF Aβ42, tau protein, and agitation and aggression, but not with other subdomains\u003csup\u003e45\u003c/sup\u003e, and emotional dysregulation subdomain is significantly related to decreased plasma Aβ42/Aβ40\u003csup\u003e46\u003c/sup\u003e. In longitudinal follow-up studies, impulse dyscontrol have been found to be associated with sudden cognitive decline\u003csup\u003e47,48\u003c/sup\u003e, suggesting that the key to the possible relationship between MBI and AD-related pathological changes may be the impulse dyscontrol subdomain. Therefore, the impulse dyscontrol subdomain in MBI may be particularly important in predicting cognitive decline and dementia risk, and further exploration of the relationship between MBI structural domains and AD biomarkers is needed. In fact, NPS has a high rate of consultation in memory clinics\u003csup\u003e10\u003c/sup\u003e, and the occurrence of impulse dyscontrol is the most common in the population, consistent with previous research findings\u003csup\u003e8,49,50\u003c/sup\u003e. Therefore, when older adults exhibit impulsive and uncontrolled behaviors, it can have an impact on their families and society, making it easier for family members to notice and seek professional medical help earlier. Hence, further exploration of impulse dyscontrol is necessary, as it may be related to a higher risk of sudden cognitive decline and dementia.\u003c/p\u003e \u003cp\u003eThe strength of this study is that it verifies these manifestations in the context of the Chinese population and has good sensitivity and specificity for the diagnosis of MBI status with an MBI-C cut-off of 6.5 points. Second, the patient was not taking any dementia medication or psychotropic drugs and could not interfere with the behavioral assessment in any way. However, there are some limitations in this study. First, the sample size was small and no differences were found in MBI patients by sex, and larger sample sizes are needed in the future to validate these findings. Second, MBI is a concept that has just been proposed in recent years, and there are few long-term follow-up studies that directly study MBI. This study is cross-sectional, and we did not follow patients to determine which patients will convert to dementia and which type of dementia is more common in MBI patients. Finally, due to focusing on evaluating markers for AD, we were unable to estimate the specificity of the test for AD detection when other neurodegenerative diseases may be present.\u003c/p\u003e \u003cp\u003eIn conclusion, this study further validates the relationship between behavioral impairment and AD biomarkers in the context of the Chinese population, suggesting that plasma Aβ42 levels may be useful for predicting populations with MBI. Prior to cognitive decline, significant changes in behavioral impairment occurred that may help identify the MBI-C scale as a test tool for use in the preclinical stages of dementia.\u003c/p\u003e "},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eWei Liang: Investigation, Formal analysis, Data curation, Writing-original draft. Lan Wang: Investigation, Writing-original draft. Mei Song: Investigation, Funding acquisition. Hao Geng: Investigation. Xinyang Jing: Investigation. Wei Li: Investigation. Yaxin Huo: Investigation. Anqi Huang: Investigation. Xueyi Wang: Conceptualization. Cuixia An: Conceptualization, Funding acquisition, Writing \u0026ndash;review \u0026amp; editing. The author(s) read and approved the final manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclarations of competing interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe funder had no role in study design, data collection, analysis, or writing of the manuscript. The authors have no conflict of interest to report.\u003c/p\u003e\n\u003cp\u003eAcknowledgements\u003c/p\u003e\n\u003cp\u003eThe support of this study was obtained by the government funded clinical medicine excellent talents training project of Hebei Province [grant numbers ZF2024136], National Science Foundation of Hebei Province [grant numbers H2022206544], Science and Technology Program of Hebei Province [grant numbers SG2021189]. The authors declared no conflicts of interest. W. Liang and L. Wang contributed equally to this work.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAlbert, M. 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M. \u0026quot;Noncognitive\u0026quot; symptoms of early Alzheimer disease: a longitudinal analysis. \u003cem\u003eNeurology\u003c/em\u003e \u003cstrong\u003e84\u003c/strong\u003e, 617-622, doi:10.1212/WNL.0000000000001238 (2015).\u003c/li\u003e\n\u003cli\u003eCreese, B.\u003cem\u003e et al.\u003c/em\u003e Profile of mild behavioral impairment and factor structure of the Mild Behavioral Impairment Checklist in cognitively normal older adults. \u003cem\u003eInt Psychogeriatr\u003c/em\u003e \u003cstrong\u003e32\u003c/strong\u003e, 705-717, doi:10.1017/S1041610219001200 (2020).\u003c/li\u003e\n\u003cli\u003eFan, S.\u003cem\u003e et al.\u003c/em\u003e Mild behavioral impairment is related to frailty in non-dementia older adults: a cross-sectional study. \u003cem\u003eBMC Geriatr\u003c/em\u003e \u003cstrong\u003e20\u003c/strong\u003e, 510, doi:10.1186/s12877-020-01903-2 (2020)\u003cbr\u003e \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"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":"Mild cognitive impairment, Mild behavioral impairment, Alzheimer's disease, Neuropsychiatric symptom, Aβ42, P-tau","lastPublishedDoi":"10.21203/rs.3.rs-4578874/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4578874/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eWe examined the prevalence of Mild behavioral impairment (MBI) in healthy older adults (HOA) and individuals with mild cognitive impairment (MCI), and the association between MBI and plasma biomarkers of Alzheimer's disease(AD).\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA total of 241 subjects (136 HOA and 105 MCI) were enrolled in the Yuhua District of Shijiazhuang City in China. The MBI Symptom Checklist (MBI-C) was employed for assessment and diagnosis of MBI (MBI-C\u0026thinsp;\u0026ge;\u0026thinsp;6.5). Fasting venous blood was collected from 70 patients (32 HOA, 38 MCI), and Aβ40, Aβ42, and P-Tau217 levels were measured using enzyme-linked immunosorbent assay.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe prevalence of MBI in HOA and MCI groups was 4.4% and 15.3%, respectively (χ\u003csup\u003e\u003cem\u003e2\u003c/em\u003e\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;7.262, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007), especially in terms of decreased motivation, impulse dyscontrol (highest detection rate), and social inappropriateness (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). MBI total score was correlated with Aβ42 and P-Tau217 (\u003cem\u003er\u003c/em\u003e=-0.385, \u003cem\u003eP\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.019; \u003cem\u003er\u003c/em\u003e=-0.330, \u003cem\u003eP\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.041), but not with Aβ40 or Aβ42/40 ratio. Among the subdomains, impulse dyscontrol submains was correlated with Aβ42 (r=-0.401, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.025).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eBoth MCI and HOA demonstrated a higher prevalence of MBI, with change in impulse control behavior being the most common. MBI not only serves as an independent risk factor for cognitive decline but is also associated with AD-related peripheral biomarkers.\u003c/p\u003e","manuscriptTitle":"Correlation Between Mild Behavioral Impairment and Peripheral Blood Biomarkers in Patients with Mild Cognitive Impairment","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-15 17:52:25","doi":"10.21203/rs.3.rs-4578874/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":"c9d9fea2-09be-47bc-a320-78bc8b8a86be","owner":[],"postedDate":"July 15th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":33979288,"name":"Health sciences/Medical research"},{"id":33979289,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2024-09-18T03:39:43+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-15 17:52:25","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4578874","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4578874","identity":"rs-4578874","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}