Does mild cognitive impairment accelerate age-related changes in physical function and body composition? A three-year longitudinal follow-up study

Does mild cognitive impairment accelerate age-related changes in physical function and body composition? 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A three-year longitudinal follow-up study Hyuma Makizako, Shoma Akaida, Mana Tateishi, Daijo Shiratsuchi, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4246243/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 This prospective study examined the impact of mild cognitive impairment (MCI) on age-related changes in physical function and body composition among community-dwelling older adults. Older adults aged 70 years and above ( n = 180) completed at baseline and three-year follow-up assessments of physical function and body composition. Participants were divided into the MCI and non-MCI groups based on baseline status. A two-way repeated-measures analysis of covariance (ANCOVA), adjusting for age and gender, was used to analyze the group (MCI and non-MCI) by time (baseline and three-year follow-up) interaction. No variables had significant group by time interactions. Stratified analyses by gender confirmed a significant group by time interaction on BMI ( F = 5.63, p = 0.02) and ASMI ( F = 6.33, p = 0.01) among women (adjusted for age). The close associations of MCI with shrinking and muscle mass loss have important implications for targeting interventions among MCI women. muscle mass shrinking cognition longitudinal study aging Figures Figure 1 Figure 2 Introduction Age-related functional problems that are not severe diseases, such as mild cognitive impairment (MCI), frailty, and sarcopenia, may have greater impacts on healthcare systems serving an aging population. MCI is defined as a mental condition that lies between normal cognitive aging and early dementia [ 1 ]. The results of a recent mixed cohort study examining temporal trends indicated that older adults’ cognitive functioning is improving [ 2 ]. However, many previous longitudinal studies have indicated that MCI has high conversion rates to dementia, ranging from 10–15% [ 3 ], and not only cognitive disorders but also physical problems, including physical frailty and sarcopenia, may be negative effects of MCI among community-dwelling older adults [ 4 , 5 ]. In addition to age-related cognitive decline, age-related changes of physical function and body composition have future adverse effects on health such as disability [ 6 ], chronical diseases [ 7 ], and even death [ 8 ]. Grip strength and walking speed are simple and easy to assess for physical function in the community setting. Several longitudinal studies found that grip strength [ 9 ] and walking speed [ 10 ] were good predictors for the incidence of disability among community-dwelling older adults. Therefore, grip strength and walking speed are useful markers for predicting poor health and included in assessment items for frailty [ 11 ] and sarcopenia [ 12 , 13 ]. Body composition changes according to advancing age. Body mass index (BMI), calculated as weight divided by height (kg/m 2 ), has been used to assess obesity. In late life, BMI declines with advancing age and decreasing BMI is associated the higher mortality [ 14 ]. BMI is expressed using the full body weight; thus, there is a moderate decline compared with the appendicular skeletal muscle mass index (ASMI) [ 15 ], which is calculated by the appendicular skeletal muscle mass divided by height. The ASMI declines rapidly after age 65 years and is one of essential markers sarcopenia assessment [ 12 , 13 ]. Physical function indicators are also useful markers for early detection of cognitive decline among community-dwelling older adults. [ 16 ]. Although the association between cognitive status and physical function and body composition has been studied in community-dwelling older adults in cross-sectional studies, there have been few longitudinal studies. [ 17 ] Age-related declines in physical function, body composition, and cognitive function are interrelated. Additionally, there seem to be sex differences in functional declines of physical and cognitive status and changes in body composition [ 18 ]. This study aimed to investigate the effect of MCI on age-related changes in physical function and body composition among community-dwelling older adults. Methods Participants The study participants were community-dwelling older adults aged ≥ 70 years who participated into the XXXX study and completed a longitudinal assessment of physical function and body composition. The XXXX study is a health checkup survey of local residents, conducted in cooperation with XXXX XXXX Hospital, XXXX University (Faculty of XXXX), and XXXX City Hall. Participants in the XXXX study were selected from among citizens of XXXX City (a regional city in XXXX Prefecture) aged 40 years and older. The 2018 XXXX study included 667 older adults aged 70 years or older, 215 of whom participated in 2021 XXXX study. In the present study, the health-check in the 2018 XXXX study was used as a baseline assessment and a three-year follow-up assessment was held in 2021. Participants with a history of diagnosis of stroke (n = 10), dementia (n = 3), depression (n = 2), and other brain disorders (n = 2) in 2018 or 2021 were excluded. Participants with missing data for body composition measures (e.g., heart pace makers) (n = 5) and physical function measure (grip strength or walking speed) (n = 13) who had unsafe conditions (e.g., systolic blood pressure ≥ 180 mmHg) in 2018 or 2021 were also excluded. Finally, data from 180 community-dwelling older adults (aged ≥ 70 years, mean age = 75.3 years, 58.9% women) were analyzed (Fig. 1 ). The ethics committee (Ref. No. XXXX) approved this study. Informed consent was obtained from all participants before their enrollment in the study. Baseline assessment Multi-dimensional cognitive function was assessed using the National Center for Geriatrics and Gerontology Functional Assessment Tool (NCGG-FAT) [ 19 ] for the baseline. The NCGG-FAT includes subtests in the following four areas: (1) Memory (Word List Memory-I [immediate recognition] and Word List Memory-II [delayed recall]); (2) Attention (tablet version of TMT-part A); (3) Executive Function (tablet version of TMT-part B); and (4) Processing Speed (tablet version of Digit Symbol Substitution Test). In community-dwelling older adults, the NCGG-FAT has been shown to have high test-retest reliability, moderate to high criterion-related validity, and predictive validity[ 19 , 20 ]. MCI is defined as individuals who score below the criterion threshold (1.5 standard deviations), adjusted for age and education level on the four domain (memory, attention, executive function, and processing speed) subtests of the NCGG-FAT[ 21 ]. Participants were divided into the MCI and non-MCI groups based on their MCI status at baseline. Outcomes Physical function and body composition were assessed at baseline and the three-year follow-up assessments were used as outcomes. Physical function included grip strength and time taken to walk 10 m at normal and maximum pace. Body composition assessments calculated participants’ BMI and ASMI using bioelectrical impedance analysis at baseline and at the three-year follow-up assessments. Physical function Grip strength Grip strength was assessed by the maximum grip strength (in kilograms) of the participant's dominant hand. The instrument used to evaluate grip strength was a Smedley-type hand-held dynamometer (GRIP-D; Takei Corporation, Niigata, Japan) [ 22 ]. Normal and maximal walking time Walking time was measured in seconds using an infrared timing gate (YW; Yagami Corporation, Nagoya, Japan). Participants were asked to walk a straight, flat, 10-m-long walking path at normal and maximum walking speeds, and infrared timing gates were placed at the 2-m point and at the end of the path. Body composition Body mass index (BMI) BMI was calculated using data from a body measurement including height (cm) and body wight (kg). Body weight was measured by using the InBody 430 (InBody Japan, Tokyo, Japan). BMI (kg/m 2 ) was derived as the body weight in kilograms divided by the square of height in meters. Appendicular skeletal muscle mass index (ASMI) Appendicular skeletal muscle mass (ASM) was assessed by a multifrequency bioelectrical impedance analysis using the InBody 430, which employs a four-pole, eight-point tactile electrode system to measure the impedance of the trunk, legs, and arms separately for each segment at three different frequencies (5, 50, and 250 kHz). Each participant’s heel and forefoot were in contact with circular foot electrodes, and the surface of the hand electrodes were in contact with each of the five fingers. Participants were measured with their arms and legs in an extended position to avoid contact with other body parts during the measurement. ASM was derived as the sum of the muscle mass of the four limbs, and the ASM index (ASMI; kg/m 2 ) was calculated. Statistics Data have been presented as mean ± standard deviation (SD) for continuous variable and population (%) for categorical variables. The characteristics and baseline measurements of physical function and body composition were compared using the Student’s t-tests and chi-square tests between the MCI and non-MCI groups. A two-way repeated-measures analysis of covariance (ANCOVA), adjusted for age and gender, was used to analyze the groups (MCI and non-MCI) by time interaction (baseline and three-year follow-up). We also performed the two-way repeated-measures ANCOVA adjusted for age in men and women to test the main effects and group by time interaction. Data entry and analysis were performed using IBM SPSS Statistics for Windows (version 25.0). A p-value of < 0.05 was considered statistically significant. Results Thirty participants (16.7%) had MCI at baseline. Participants with MCI showed significant older age and poor physical performance (grip strength and normal walking speed) at the baseline assessment. There were no significant differences in body composition between the MCI and non-MCI groups (Table 1). Table 2 presents the results of the repeated-measures ANCOVA adjusted for age and gender and we conducted stratified analyses by gender. A significant group main effect was found with grip strength ( F = 13.63, p < 0.01) and significant time main effects were found with maximal walking time ( F = 13.51, p < 0.01) and ASMI ( F = 9.57, p = 0.01) in the repeated-measures ANCOVA using overall data. However, no variables had significant group by time interactions. The stratified analyses by gender (repeated-measures ANCOVA, adjusted for age) showed significant time main effects with normal ( F = 4.21, p = 0.04) and maximal ( F = 7.79, p < 0.01) walking time in men. There were no statistically significant group by time interactions in physical performance and body composition in men. A significant group main effect with grip strength ( F = 13.66, p < 0.01) and significant time main effects of grip strength ( F = 4.52, p = 0.04) and ASMI ( F = 13.81, p < 0.01) were observed in women. There were significant group by time interactions for BMI ( F = 5.63, p = 0.02) and ASMI ( F = 6.33, p = 0.01) among women. Figure 2 illustrates the changes and interactions in body composition between men and women. Discussion This longitudinal study with a three-year follow-up period indicated that there were no significant effects of MCI in age-related changes on physical function and body composition. However, the results of stratified analyses by gender suggested that older women with MCI may experience a greater impact of the acceleration of shrinking and age-related decline in muscle mass. Previous observational studies showed longitudinal changes of physical function in older adults with MCI. Having MCI or dementia is associated with greater physical decline compared to older people with normal cognition [ 23 ]. Older adults with MCI converted to dementia more rapidly than those without physical functional impairment [ 24 ]. Older adults with MCI may develop Alzheimer's disease (AD) in approximately 3.5 years, and both cognitive and physical function may decline gradually after AD onset [ 25 ]. In addition, physical functional limitations may suggest early signs of cognitive deficits [ 26 ]. Almost all community-dwelling older adults with MCI exhibit a change in the functional category each year [ 27 ]. Two-thirds of MCI were physically frail or pre-frail, mostly due to low lean muscle mass, slow gait speed, or balance and gait impairment [ 28 ]. There are close interactions between age-related declines in cognitive and physical function. Thus, targeting interventions for both functions will be needed to prevent dementia and disability among older individuals. This longitudinal study indicated that MCI leads to the acceleration of shrinking and age-related decline in muscle mass among older women. Not only physical function but also body composition changes with advancing age. There is a possibility of a difference in the age-related slope patterns among the parameters of body compositions. In particular, the age-related decrease in ASMI is more striking than that of BMI [ 15 ]. Those striking slopes are observed earlier in women than in men [ 15 ]. Cognitive status may be an important factor for acceleration of loss of muscle mass, and direct and indirect relationships between cognitive status and acceleration of loss of muscle mass should be considered. For instance, cognitive deficits are associated with physical inactivity in older adults [ 29 ], and physical inactivity may accelerate age-related decline in body composition [ 30 ]. Additionally, undesirable lifestyles, such as poor diet patterns [ 31 ] and sleep problems [ 32 ], coupled with cognitive deficits would affect shrinking among older adults. On the other hand, previous studies have reported negative impacts of shrinking among older adults future health outcomes, including cognitive function. Lower baseline BMI has been associated with a more rapid cognitive decline in MCI [ 33 ]. In addition, a higher BMI has been associated with a lower risk of dementia, and being underweight was associated with a higher risk of dementia [ 34 ]. BMI has been associated with increased risk for dementia; however, the “BMI paradox in dementia” requires further discussion [ 35 ]. Being overweight in mid-life has been associated with an increased risk of dementia in later life, whereas being overweight in later life has been linked to reduced dementia risk [ 36 ]. As our longitudinal study indicated, MCI accelerated shrinking and loss of muscle mass among older women. Thus, the effects of cognitive status should be considered to reduce the risk of adverse health impacts due to shrinking in late life. This study found no significant relationships between MCI and age-related changes on physical function and body composition in older men. Sex differences in the association between sarcopenia and MCI have been found in previous studies [ 37 ], with significant associations between sarcopenia and MCI in older women but no significant relationship in older men [ 37 ]. The impacts of cognitive status on muscle mass loss with advancing age may be greater in older women. The mediating factors between cognition and muscle mass loss will need to be clarified to understand these sex differences. For instance, cognitive status affects the levels of activities of daily living (ADL). Older adults with MCI, including during the prodromal stage, show steeper rates of decline in complex ADL and daily functioning than those with normal cognition [ 38 , 39 ]. Older adults’ interest in, participation in, and satisfaction with instrumental ADLs and leisure time activities may differ by sex [ 40 , 41 ]. Activities with physical and cognitive stimulation may mediate the relationships between cognitive status and age-related changes on physical function and body composition. Limitations Several limitations in this study should be noted. The three-year follow-up assessment conducting in the 2021 XXXX study 2021 had a limited sample due to the COVID-19 pandemic situation. Consequently, the follow-up rate in this study was low (approximately 30%). In addition, relatively healthy older adults may have participated in both the baseline and follow-up assessments. Age-related changes in this study may be underestimated due to the survival effects. In this study, associated factors with age-related changes of physical function and body composition, such as physical activity and nutrition, were not considered. Older adults decreased their physical activity [ 42 ] and might have a poorer nutritional status [ 43 ] under the COVID-19 pandemic situation. These temporary lifestyle changes may have led to accelerated declines in physical function and body composition. Although MCI affects age-related changes in body composition, shrinking, and muscle loss (e.g., sarcopenia) it may also accelerate cognitive deficits [ 44 , 45 ]. These interactive associations should be considered to assess the functional trajectory and plan preventive strategies. In conclusion, no significant effects of MCI in age-related changes on physical function and body composition were confirmed. However, older women with MCI may experience a greater impact of the acceleration of shrinking and age-related decline in muscle mass. The close associations of MCI with shrinking, including muscle mass loss, have important implications for targeting interventions among MCI women to prevent adverse health effects, such as sarcopenia and disability. 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Baseline characteristics Overall (n = 180) Non-MCI (n = 150) MCI (n = 30) p Age, years 75.3 ± 4.4 74.8 ± 4.3 77.4 ± 4.6 0.003 Women, n (%) 106 (58.9) 90 (60.0) 16 (53.3) 0.498 Education, years 11.7 ± 2.5 11.9 ± 2.5 11.1 ± 2.1 0.102 Medication, n/day 2.9 ± 2.8 2.9 ± 2.8 3.0 ± 2.5 0.828 Follow-up period, days 1140.1 ± 70.3 1141.3 ± 69.3 1134.3 ± 76.1 0.617 Grip strength, kg 25.8 ± 7.6 26.3 ± 7.5 23.2 ± 7.5 0.041 10-m walking time (normal), sec 7.8 ± 1.4 7.7 ± 1.3 8.0 ± 1.7 0.044 10-m walking time (maximum), sec 6.1 ± 1.0 6.0 ± 1.0 6.4 ± 1.3 0.054 BMI, kg/m 2 23.2 ± 3.1 23.1 ± 3.2 22.6 ± 2.7 0.429 ASMI, kg/m 2 6.4 ± 1.0 6.4 ± 1.0 6.2 ± 1.0 0.484 BMI, body mass index; ASMI, appendicular skeletal muscle mass index Table 2. Mild cognitive impairment and age-related changes in physical function and body composition (repeated-measures ANCOVA) Overall (n = 180) Men (n = 74) Women (n = 106) F value Group Time Group × Time interaction Group Time Group × Time interaction Group Time Group × Time interaction Physical function Grip strength 13.63 ** 1.41 1.00 2.36 0.84 0.70 13.66 ** 4.52 * 0.47 Normal walking time 2.15 3.66 0.43 1.15 4.21 * 0.56 0.93 0.13 2.20 Maximal walking time 3.87 13.51 ** 0.28 1.46 7.79 ** 0.73 2.47 2.87 0.002 Body composition BMI 0.64 0.73 0.84 0.58 0.14 0.88 0.14 2.77 5.63 * ASMI 1.92 9.57 * 0.89 0.02 0.01 0.81 2.44 13.81 ** 6.33 * BMI, body mass index; ASMI, appendicular skeletal muscle mass index Group: MCI / Non-MCI groups Time: Baseline / Three-year follow-up * p < 0.05, ** p < 0.01 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. 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Daigaku","correspondingAuthor":false,"prefix":"","firstName":"Daijo","middleName":"","lastName":"Shiratsuchi","suffix":""},{"id":291837748,"identity":"abcb3d52-4a32-4a59-a9bc-1f8ab8663013","order_by":4,"name":"Ryoji Kiyama","email":"","orcid":"","institution":"Kagoshima University: Kagoshima Daigaku","correspondingAuthor":false,"prefix":"","firstName":"Ryoji","middleName":"","lastName":"Kiyama","suffix":""},{"id":291837749,"identity":"166e2b3e-8f66-4657-80bf-8c82934c629b","order_by":5,"name":"Takuro Kubozono","email":"","orcid":"","institution":"Kagoshima University: Kagoshima Daigaku","correspondingAuthor":false,"prefix":"","firstName":"Takuro","middleName":"","lastName":"Kubozono","suffix":""},{"id":291837750,"identity":"a7736820-ff7b-4899-bc68-bebc22d2f33f","order_by":6,"name":"Toshihiro Takenaka","email":"","orcid":"","institution":"Tarumizu Chuo Hospital","correspondingAuthor":false,"prefix":"","firstName":"Toshihiro","middleName":"","lastName":"Takenaka","suffix":""},{"id":291837751,"identity":"4b2405c1-9e5e-465e-bdd6-1e8243d50166","order_by":7,"name":"Mitsuru Ohishi","email":"","orcid":"","institution":"Kagoshima University: Kagoshima Daigaku","correspondingAuthor":false,"prefix":"","firstName":"Mitsuru","middleName":"","lastName":"Ohishi","suffix":""}],"badges":[],"createdAt":"2024-04-10 08:45:38","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4246243/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4246243/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":55316619,"identity":"dd844465-df0f-4f18-8aa2-66d38ff07c03","added_by":"auto","created_at":"2024-04-25 15:47:12","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":273970,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"FiguresMCIEGEM1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4246243/v1/08e61cc51ff4bcb508cb2e20.jpg"},{"id":55316620,"identity":"da07e793-a4f8-4870-8a2c-ecd51b215799","added_by":"auto","created_at":"2024-04-25 15:47:12","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":251693,"visible":true,"origin":"","legend":"\u003cp\u003eSee image above for figure legend.\u003c/p\u003e","description":"","filename":"FiguresMCIEGEM2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4246243/v1/109383de621022c897f15595.jpg"},{"id":56475430,"identity":"3b9af40e-1e95-45de-8bbb-4d0e62bc28d4","added_by":"auto","created_at":"2024-05-14 17:25:12","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":510490,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4246243/v1/42dcb0ac-ce3c-49cc-8db5-d71643181443.pdf"}],"financialInterests":"","formattedTitle":"Does mild cognitive impairment accelerate age-related changes in physical function and body composition? A three-year longitudinal follow-up study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eAge-related functional problems that are not severe diseases, such as mild cognitive impairment (MCI), frailty, and sarcopenia, may have greater impacts on healthcare systems serving an aging population. MCI is defined as a mental condition that lies between normal cognitive aging and early dementia [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe results of a recent mixed cohort study examining temporal trends indicated that older adults\u0026rsquo; cognitive functioning is improving [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. However, many previous longitudinal studies have indicated that MCI has high conversion rates to dementia, ranging from 10\u0026ndash;15% [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], and not only cognitive disorders but also physical problems, including physical frailty and sarcopenia, may be negative effects of MCI among community-dwelling older adults [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn addition to age-related cognitive decline, age-related changes of physical function and body composition have future adverse effects on health such as disability [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], chronical diseases [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], and even death [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Grip strength and walking speed are simple and easy to assess for physical function in the community setting. Several longitudinal studies found that grip strength [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] and walking speed [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] were good predictors for the incidence of disability among community-dwelling older adults. Therefore, grip strength and walking speed are useful markers for predicting poor health and included in assessment items for frailty [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e] and sarcopenia [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBody composition changes according to advancing age. Body mass index (BMI), calculated as weight divided by height (kg/m\u003csup\u003e2\u003c/sup\u003e), has been used to assess obesity. In late life, BMI declines with advancing age and decreasing BMI is associated the higher mortality [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. BMI is expressed using the full body weight; thus, there is a moderate decline compared with the appendicular skeletal muscle mass index (ASMI) [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], which is calculated by the appendicular skeletal muscle mass divided by height. The ASMI declines rapidly after age 65 years and is one of essential markers sarcopenia assessment [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePhysical function indicators are also useful markers for early detection of cognitive decline among community-dwelling older adults. [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Although the association between cognitive status and physical function and body composition has been studied in community-dwelling older adults in cross-sectional studies, there have been few longitudinal studies. [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] Age-related declines in physical function, body composition, and cognitive function are interrelated. Additionally, there seem to be sex differences in functional declines of physical and cognitive status and changes in body composition [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. This study aimed to investigate the effect of MCI on age-related changes in physical function and body composition among community-dwelling older adults.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eThe study participants were community-dwelling older adults aged\u0026thinsp;\u0026ge;\u0026thinsp;70 years who participated into the XXXX study and completed a longitudinal assessment of physical function and body composition. The XXXX study is a health checkup survey of local residents, conducted in cooperation with XXXX XXXX Hospital, XXXX University (Faculty of XXXX), and XXXX City Hall. Participants in the XXXX study were selected from among citizens of XXXX City (a regional city in XXXX Prefecture) aged 40 years and older. The 2018 XXXX study included 667 older adults aged 70 years or older, 215 of whom participated in 2021 XXXX study. In the present study, the health-check in the 2018 XXXX study was used as a baseline assessment and a three-year follow-up assessment was held in 2021. Participants with a history of diagnosis of stroke (n\u0026thinsp;=\u0026thinsp;10), dementia (n\u0026thinsp;=\u0026thinsp;3), depression (n\u0026thinsp;=\u0026thinsp;2), and other brain disorders (n\u0026thinsp;=\u0026thinsp;2) in 2018 or 2021 were excluded. Participants with missing data for body composition measures (e.g., heart pace makers) (n\u0026thinsp;=\u0026thinsp;5) and physical function measure (grip strength or walking speed) (n\u0026thinsp;=\u0026thinsp;13) who had unsafe conditions (e.g., systolic blood pressure\u0026thinsp;\u0026ge;\u0026thinsp;180 mmHg) in 2018 or 2021 were also excluded. Finally, data from 180 community-dwelling older adults (aged\u0026thinsp;\u0026ge;\u0026thinsp;70 years, mean age\u0026thinsp;=\u0026thinsp;75.3 years, 58.9% women) were analyzed (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The ethics committee (Ref. No. XXXX) approved this study. Informed consent was obtained from all participants before their enrollment in the study.\u003c/p\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003eBaseline assessment\u003c/h2\u003e \u003cp\u003eMulti-dimensional cognitive function was assessed using the National Center for Geriatrics and Gerontology Functional Assessment Tool (NCGG-FAT) [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] for the baseline. The NCGG-FAT includes subtests in the following four areas: (1) Memory (Word List Memory-I [immediate recognition] and Word List Memory-II [delayed recall]); (2) Attention (tablet version of TMT-part A); (3) Executive Function (tablet version of TMT-part B); and (4) Processing Speed (tablet version of Digit Symbol Substitution Test). In community-dwelling older adults, the NCGG-FAT has been shown to have high test-retest reliability, moderate to high criterion-related validity, and predictive validity[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. MCI is defined as individuals who score below the criterion threshold (1.5 standard deviations), adjusted for age and education level on the four domain (memory, attention, executive function, and processing speed) subtests of the NCGG-FAT[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Participants were divided into the MCI and non-MCI groups based on their MCI status at baseline.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eOutcomes\u003c/h2\u003e \u003cp\u003ePhysical function and body composition were assessed at baseline and the three-year follow-up assessments were used as outcomes. Physical function included grip strength and time taken to walk 10 m at normal and maximum pace. Body composition assessments calculated participants\u0026rsquo; BMI and ASMI using bioelectrical impedance analysis at baseline and at the three-year follow-up assessments.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003ePhysical function\u003c/h2\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003eGrip strength\u003c/h2\u003e \u003cp\u003eGrip strength was assessed by the maximum grip strength (in kilograms) of the participant's dominant hand. The instrument used to evaluate grip strength was a Smedley-type hand-held dynamometer (GRIP-D; Takei Corporation, Niigata, Japan) [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eNormal and maximal walking time\u003c/h2\u003e \u003cp\u003eWalking time was measured in seconds using an infrared timing gate (YW; Yagami Corporation, Nagoya, Japan). Participants were asked to walk a straight, flat, 10-m-long walking path at normal and maximum walking speeds, and infrared timing gates were placed at the 2-m point and at the end of the path.\u003c/p\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003eBody composition\u003c/h2\u003e \u003cdiv id=\"Sec10\" class=\"Section4\"\u003e \u003ch2\u003eBody mass index (BMI)\u003c/h2\u003e \u003cp\u003eBMI was calculated using data from a body measurement including height (cm) and body wight (kg). Body weight was measured by using the InBody 430 (InBody Japan, Tokyo, Japan). BMI (kg/m\u003csup\u003e2\u003c/sup\u003e) was derived as the body weight in kilograms divided by the square of height in meters.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eAppendicular skeletal muscle mass index (ASMI)\u003c/h2\u003e \u003cp\u003eAppendicular skeletal muscle mass (ASM) was assessed by a multifrequency bioelectrical impedance analysis using the InBody 430, which employs a four-pole, eight-point tactile electrode system to measure the impedance of the trunk, legs, and arms separately for each segment at three different frequencies (5, 50, and 250 kHz). Each participant\u0026rsquo;s heel and forefoot were in contact with circular foot electrodes, and the surface of the hand electrodes were in contact with each of the five fingers. Participants were measured with their arms and legs in an extended position to avoid contact with other body parts during the measurement. ASM was derived as the sum of the muscle mass of the four limbs, and the ASM index (ASMI; kg/m\u003csup\u003e2\u003c/sup\u003e) was calculated.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eStatistics\u003c/h2\u003e \u003cp\u003eData have been presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (SD) for continuous variable and population (%) for categorical variables. The characteristics and baseline measurements of physical function and body composition were compared using the Student\u0026rsquo;s t-tests and chi-square tests between the MCI and non-MCI groups. A two-way repeated-measures analysis of covariance (ANCOVA), adjusted for age and gender, was used to analyze the groups (MCI and non-MCI) by time interaction (baseline and three-year follow-up). We also performed the two-way repeated-measures ANCOVA adjusted for age in men and women to test the main effects and group by time interaction. Data entry and analysis were performed using IBM SPSS Statistics for Windows (version 25.0). A p-value of \u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eThirty participants (16.7%) had MCI at baseline. Participants with MCI showed significant older age and poor physical performance (grip strength and normal walking speed) at the baseline assessment. There were no significant differences in body composition between the MCI and non-MCI groups (Table\u0026nbsp;1).\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;2 presents the results of the repeated-measures ANCOVA adjusted for age and gender and we conducted stratified analyses by gender. A significant group main effect was found with grip strength (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;13.63, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and significant time main effects were found with maximal walking time (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;13.51, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and ASMI (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;9.57, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01) in the repeated-measures ANCOVA using overall data. However, no variables had significant group by time interactions.\u003c/p\u003e \u003cp\u003eThe stratified analyses by gender (repeated-measures ANCOVA, adjusted for age) showed significant time main effects with normal (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.21, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.04) and maximal (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;7.79, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) walking time in men. There were no statistically significant group by time interactions in physical performance and body composition in men. A significant group main effect with grip strength (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;13.66, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and significant time main effects of grip strength (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;4.52, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.04) and ASMI (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;13.81, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.01) were observed in women. There were significant group by time interactions for BMI (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5.63, p\u0026thinsp;=\u0026thinsp;0.02) and ASMI (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6.33, p\u0026thinsp;=\u0026thinsp;0.01) among women. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e illustrates the changes and interactions in body composition between men and women.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis longitudinal study with a three-year follow-up period indicated that there were no significant effects of MCI in age-related changes on physical function and body composition. However, the results of stratified analyses by gender suggested that older women with MCI may experience a greater impact of the acceleration of shrinking and age-related decline in muscle mass.\u003c/p\u003e \u003cp\u003ePrevious observational studies showed longitudinal changes of physical function in older adults with MCI. Having MCI or dementia is associated with greater physical decline compared to older people with normal cognition [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Older adults with MCI converted to dementia more rapidly than those without physical functional impairment [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Older adults with MCI may develop Alzheimer's disease (AD) in approximately 3.5 years, and both cognitive and physical function may decline gradually after AD onset [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. In addition, physical functional limitations may suggest early signs of cognitive deficits [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Almost all community-dwelling older adults with MCI exhibit a change in the functional category each year [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Two-thirds of MCI were physically frail or pre-frail, mostly due to low lean muscle mass, slow gait speed, or balance and gait impairment [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. There are close interactions between age-related declines in cognitive and physical function. Thus, targeting interventions for both functions will be needed to prevent dementia and disability among older individuals.\u003c/p\u003e \u003cp\u003eThis longitudinal study indicated that MCI leads to the acceleration of shrinking and age-related decline in muscle mass among older women. Not only physical function but also body composition changes with advancing age. There is a possibility of a difference in the age-related slope patterns among the parameters of body compositions. In particular, the age-related decrease in ASMI is more striking than that of BMI [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Those striking slopes are observed earlier in women than in men [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Cognitive status may be an important factor for acceleration of loss of muscle mass, and direct and indirect relationships between cognitive status and acceleration of loss of muscle mass should be considered. For instance, cognitive deficits are associated with physical inactivity in older adults [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], and physical inactivity may accelerate age-related decline in body composition [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Additionally, undesirable lifestyles, such as poor diet patterns [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] and sleep problems [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e], coupled with cognitive deficits would affect shrinking among older adults.\u003c/p\u003e \u003cp\u003eOn the other hand, previous studies have reported negative impacts of shrinking among older adults future health outcomes, including cognitive function. Lower baseline BMI has been associated with a more rapid cognitive decline in MCI [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. In addition, a higher BMI has been associated with a lower risk of dementia, and being underweight was associated with a higher risk of dementia [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. BMI has been associated with increased risk for dementia; however, the \u0026ldquo;BMI paradox in dementia\u0026rdquo; requires further discussion [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Being overweight in mid-life has been associated with an increased risk of dementia in later life, whereas being overweight in later life has been linked to reduced dementia risk [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. As our longitudinal study indicated, MCI accelerated shrinking and loss of muscle mass among older women. Thus, the effects of cognitive status should be considered to reduce the risk of adverse health impacts due to shrinking in late life.\u003c/p\u003e \u003cp\u003eThis study found no significant relationships between MCI and age-related changes on physical function and body composition in older men. Sex differences in the association between sarcopenia and MCI have been found in previous studies [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e], with significant associations between sarcopenia and MCI in older women but no significant relationship in older men [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. The impacts of cognitive status on muscle mass loss with advancing age may be greater in older women. The mediating factors between cognition and muscle mass loss will need to be clarified to understand these sex differences. For instance, cognitive status affects the levels of activities of daily living (ADL). Older adults with MCI, including during the prodromal stage, show steeper rates of decline in complex ADL and daily functioning than those with normal cognition [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Older adults\u0026rsquo; interest in, participation in, and satisfaction with instrumental ADLs and leisure time activities may differ by sex [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Activities with physical and cognitive stimulation may mediate the relationships between cognitive status and age-related changes on physical function and body composition.\u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eSeveral limitations in this study should be noted. The three-year follow-up assessment conducting in the 2021 XXXX study 2021 had a limited sample due to the COVID-19 pandemic situation. Consequently, the follow-up rate in this study was low (approximately 30%). In addition, relatively healthy older adults may have participated in both the baseline and follow-up assessments. Age-related changes in this study may be underestimated due to the survival effects. In this study, associated factors with age-related changes of physical function and body composition, such as physical activity and nutrition, were not considered. Older adults decreased their physical activity [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] and might have a poorer nutritional status [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e] under the COVID-19 pandemic situation. These temporary lifestyle changes may have led to accelerated declines in physical function and body composition. Although MCI affects age-related changes in body composition, shrinking, and muscle loss (e.g., sarcopenia) it may also accelerate cognitive deficits [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. These interactive associations should be considered to assess the functional trajectory and plan preventive strategies.\u003c/p\u003e \u003cp\u003eIn conclusion, no significant effects of MCI in age-related changes on physical function and body composition were confirmed. However, older women with MCI may experience a greater impact of the acceleration of shrinking and age-related decline in muscle mass. The close associations of MCI with shrinking, including muscle mass loss, have important implications for targeting interventions among MCI women to prevent adverse health effects, such as sarcopenia and disability.\u003c/p\u003e \u003c/div\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003ePetersen RC, Caracciolo B, Brayne C, Gauthier S, Jelic V, Fratiglioni L (2014) Mild cognitive impairment: a concept in evolution. J Intern Med 275(3):214\u0026ndash;228\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNishita Y, Makizako H, Jeong S et al (2022) Temporal trends in cognitive function among community-dwelling older adults in Japan: Findings from the ILSA-J integrated cohort study. Arch Gerontol Geriatr 102:104718\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSatizabal C, Beiser AS, Seshadri S (2016) Incidence of Dementia over Three Decades in the Framingham Heart Study. 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Geriatr Gerontol Int 19(1):76\u0026ndash;80\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTaylor ME, Boripuntakul S, Toson B et al (2019) The role of cognitive function and physical activity in physical decline in older adults across the cognitive spectrum. Aging Ment Health 23(7):863\u0026ndash;871\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePellecchia A, Kritikos M, Guralnik J et al (2022) Physical Functional Impairment and the Risk of Incident Mild Cognitive Impairment in an Observational Study of World Trade Center Responders. Neurol Clin Pract 12(6):e162\u0026ndash;e171\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHandels RL, Xu W, Rizzuto D et al (2013) Natural progression model of cognition and physical functioning among people with mild cognitive impairment and alzheimer's disease. J Alzheimers Dis 37(2):357\u0026ndash;365\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBorges SM, Radanovic M, Forlenza OV (2018) Correlation between functional mobility and cognitive performance in older adults with cognitive impairment. Neuropsychol Dev Cogn B Aging Neuropsychol Cogn 25(1):23\u0026ndash;32\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRitchie K, Artero S, Touchon J (2001) Classification criteria for mild cognitive impairment: a population-based validation study. Neurology 56(1):37\u0026ndash;42\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNyunt MSZ, Soh CY, Gao Q et al (2017) Characterisation of Physical Frailty and Associated Physical and Functional Impairments in Mild Cognitive Impairment. 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J Gerontol B Psychol Sci Soc Sci 73(8):1491\u0026ndash;1500\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTomioka K, Kurumatani N, Saeki K (2019) Cross-Sectional Association Between Types of Leisure Activities and Self-rated Health According to Gender and Work Status Among Older Japanese Adults. J Epidemiol 29(11):424\u0026ndash;431\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSaid CM, Batchelor F, Duque G (2022) The Impact of the COVID-19 Pandemic on Physical Activity, Function, and Quality of Life. Clin Geriatr Med 38(3):519\u0026ndash;531\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNicklett EJ, Johnson KE, Troy LM, Vartak M, Reiter A (2021) Food Access, Diet Quality, and Nutritional Status of Older Adults During COVID-19: A Scoping Review. Front Public Health 9:763994\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHu Y, Peng W, Ren R, Wang Y, Wang G (2022) Sarcopenia and mild cognitive impairment among elderly adults: The first longitudinal evidence from CHARLS. J Cachexia Sarcopenia Muscle 13(6):2944\u0026ndash;2952\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePeng TC, Chen WL, Wu LW, Chang YW, Kao TW (2020) Sarcopenia and cognitive impairment: A systematic review and meta-analysis. Clin Nutr 39(9):2695\u0026ndash;2701\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Tables","content":"\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"612\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.97054009819967%\"\u003e\u003cbr\u003e\u0026nbsp; Table 1. Baseline characteristics\u003c/td\u003e\n \u003ctd width=\"18.985270049099835%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.875613747954173%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"15.875613747954173%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.292962356792144%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.97054009819967%\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.985270049099835%\"\u003e\n \u003cp\u003eOverall\u003cbr\u003e\u0026nbsp;(n = 180)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.875613747954173%\"\u003e\n \u003cp\u003eNon-MCI\u003cbr\u003e\u0026nbsp;(n = 150)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.875613747954173%\"\u003e\n \u003cp\u003eMCI\u003cbr\u003e\u0026nbsp;(n = 30)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.292962356792144%\"\u003e\n \u003cp\u003e\u003cem\u003ep\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.97054009819967%\"\u003e\n \u003cp\u003eAge, years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.985270049099835%\"\u003e\n \u003cp\u003e75.3 \u0026plusmn; 4.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.875613747954173%\"\u003e\n \u003cp\u003e74.8 \u0026plusmn; 4.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.875613747954173%\"\u003e\n \u003cp\u003e77.4 \u0026plusmn; 4.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.292962356792144%\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.97054009819967%\"\u003e\n \u003cp\u003eWomen, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.985270049099835%\"\u003e\n \u003cp\u003e106 (58.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.875613747954173%\"\u003e\n \u003cp\u003e90 (60.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.875613747954173%\"\u003e\n \u003cp\u003e16 (53.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.292962356792144%\"\u003e\n \u003cp\u003e0.498\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.97054009819967%\"\u003e\n \u003cp\u003eEducation, years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.985270049099835%\"\u003e\n \u003cp\u003e11.7 \u0026plusmn; 2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.875613747954173%\"\u003e\n \u003cp\u003e11.9 \u0026plusmn; 2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.875613747954173%\"\u003e\n \u003cp\u003e11.1 \u0026plusmn; 2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.292962356792144%\"\u003e\n \u003cp\u003e0.102\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.97054009819967%\"\u003e\n \u003cp\u003eMedication, n/day\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.985270049099835%\"\u003e\n \u003cp\u003e2.9 \u0026plusmn; 2.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.875613747954173%\"\u003e\n \u003cp\u003e2.9 \u0026plusmn; 2.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.875613747954173%\"\u003e\n \u003cp\u003e3.0 \u0026plusmn; 2.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.292962356792144%\"\u003e\n \u003cp\u003e0.828\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.97054009819967%\"\u003e\n \u003cp\u003eFollow-up period, days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.985270049099835%\"\u003e\n \u003cp\u003e1140.1 \u0026plusmn; 70.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.875613747954173%\"\u003e\n \u003cp\u003e1141.3 \u0026plusmn; 69.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.875613747954173%\"\u003e\n \u003cp\u003e1134.3 \u0026plusmn; 76.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.292962356792144%\"\u003e\n \u003cp\u003e0.617\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.97054009819967%\"\u003e\n \u003cp\u003eGrip strength, kg\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.985270049099835%\"\u003e\n \u003cp\u003e25.8 \u0026plusmn; 7.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.875613747954173%\"\u003e\n \u003cp\u003e26.3 \u0026plusmn; 7.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.875613747954173%\"\u003e\n \u003cp\u003e23.2 \u0026plusmn; 7.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.292962356792144%\"\u003e\n \u003cp\u003e0.041\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.97054009819967%\"\u003e\n \u003cp\u003e10-m walking time (normal), sec\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.985270049099835%\"\u003e\n \u003cp\u003e7.8 \u0026plusmn; 1.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.875613747954173%\"\u003e\n \u003cp\u003e7.7 \u0026plusmn; 1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.875613747954173%\"\u003e\n \u003cp\u003e8.0 \u0026plusmn; 1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.292962356792144%\"\u003e\n \u003cp\u003e0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.97054009819967%\"\u003e\n \u003cp\u003e10-m walking time (maximum), sec\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.985270049099835%\"\u003e\n \u003cp\u003e6.1 \u0026plusmn; 1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.875613747954173%\"\u003e\n \u003cp\u003e6.0 \u0026plusmn; 1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.875613747954173%\"\u003e\n \u003cp\u003e6.4 \u0026plusmn; 1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.292962356792144%\"\u003e\n \u003cp\u003e0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.97054009819967%\"\u003e\n \u003cp\u003eBMI, kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.985270049099835%\"\u003e\n \u003cp\u003e23.2 \u0026plusmn; 3.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.875613747954173%\"\u003e\n \u003cp\u003e23.1 \u0026plusmn; 3.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.875613747954173%\"\u003e\n \u003cp\u003e22.6 \u0026plusmn; 2.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.292962356792144%\"\u003e\n \u003cp\u003e0.429\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"37.97054009819967%\"\u003e\n \u003cp\u003eASMI, kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"18.985270049099835%\"\u003e\n \u003cp\u003e6.4 \u0026plusmn; 1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.875613747954173%\"\u003e\n \u003cp\u003e6.4 \u0026plusmn; 1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.875613747954173%\"\u003e\n \u003cp\u003e6.2 \u0026plusmn; 1.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.292962356792144%\"\u003e\n \u003cp\u003e0.484\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"72.83142389525368%\" colspan=\"3\"\u003e\n \u003cp\u003eBMI, body mass index; ASMI, appendicular skeletal muscle mass index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"15.875613747954173%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.292962356792144%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"973\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"89.20863309352518%\" colspan=\"11\"\u003e\u003cbr\u003e\u0026nbsp;Table 2. Mild cognitive impairment and age-related changes in physical function and body composition (repeated-measures ANCOVA)\u003c/td\u003e\n \u003ctd width=\"10.79136690647482%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.358974358974358%\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"27.17948717948718%\" colspan=\"3\"\u003e\n \u003cp\u003eOverall (n = 180)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5384615384615385%\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.71794871794872%\" colspan=\"3\"\u003e\n \u003cp\u003eMen (n = 74)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.5384615384615385%\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"26.666666666666668%\" colspan=\"3\"\u003e\n \u003cp\u003eWomen (n = 106)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.37782340862423%\"\u003e\n \u003cp\u003e\u003cem\u003eF\u003c/em\u003e value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.905544147843942%\"\u003e\n \u003cp\u003eGroup\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.905544147843942%\"\u003e\n \u003cp\u003eTime\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.396303901437372%\"\u003e\n \u003cp\u003eGroup \u0026times; Time\u003cbr\u003e\u0026nbsp;interaction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.540041067761807%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.776180698151951%\"\u003e\n \u003cp\u003eGroup\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.186858316221766%\"\u003e\n \u003cp\u003eTime\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.780287474332649%\"\u003e\n \u003cp\u003eGroup \u0026times; Time\u003cbr\u003e\u0026nbsp;interaction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.540041067761807%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.905544147843942%\"\u003e\n \u003cp\u003eGroup\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.905544147843942%\"\u003e\n \u003cp\u003eTime\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.780287474332649%\"\u003e\n \u003cp\u003eGroup \u0026times; Time\u003cbr\u003e\u0026nbsp;interaction\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.37782340862423%\"\u003e\n \u003cp\u003ePhysical function\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.905544147843942%\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.905544147843942%\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.396303901437372%\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.540041067761807%\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.776180698151951%\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.186858316221766%\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.780287474332649%\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.540041067761807%\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.905544147843942%\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.905544147843942%\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.780287474332649%\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.37782340862423%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Grip strength\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.905544147843942%\"\u003e\n \u003cp\u003e\u003cstrong\u003e13.63\u003csup\u003e\u0026nbsp;**\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.905544147843942%\"\u003e\n \u003cp\u003e1.41\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.396303901437372%\"\u003e\n \u003cp\u003e1.00\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.540041067761807%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.776180698151951%\"\u003e\n \u003cp\u003e2.36\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.186858316221766%\"\u003e\n \u003cp\u003e0.84\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.780287474332649%\"\u003e\n \u003cp\u003e0.70\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.540041067761807%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.905544147843942%\"\u003e\n \u003cp\u003e\u003cstrong\u003e13.66\u003csup\u003e\u0026nbsp;**\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.905544147843942%\"\u003e\n \u003cp\u003e\u003cstrong\u003e4.52\u003csup\u003e\u0026nbsp;*\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.780287474332649%\"\u003e\n \u003cp\u003e0.47\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.37782340862423%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Normal walking time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.905544147843942%\"\u003e\n \u003cp\u003e2.15\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.905544147843942%\"\u003e\n \u003cp\u003e3.66\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.396303901437372%\"\u003e\n \u003cp\u003e0.43\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.540041067761807%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.776180698151951%\"\u003e\n \u003cp\u003e1.15\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.186858316221766%\"\u003e\n \u003cp\u003e\u003cstrong\u003e4.21\u003csup\u003e\u0026nbsp;*\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.780287474332649%\"\u003e\n \u003cp\u003e0.56\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.540041067761807%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.905544147843942%\"\u003e\n \u003cp\u003e0.93\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.905544147843942%\"\u003e\n \u003cp\u003e0.13\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.780287474332649%\"\u003e\n \u003cp\u003e2.20\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.37782340862423%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;Maximal walking time\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.905544147843942%\"\u003e\n \u003cp\u003e3.87\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.905544147843942%\"\u003e\n \u003cp\u003e\u003cstrong\u003e13.51\u003csup\u003e\u0026nbsp;**\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.396303901437372%\"\u003e\n \u003cp\u003e0.28\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.540041067761807%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.776180698151951%\"\u003e\n \u003cp\u003e1.46\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.186858316221766%\"\u003e\n \u003cp\u003e\u003cstrong\u003e7.79\u003csup\u003e\u0026nbsp;**\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.780287474332649%\"\u003e\n \u003cp\u003e0.73\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.540041067761807%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.905544147843942%\"\u003e\n \u003cp\u003e2.47\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.905544147843942%\"\u003e\n \u003cp\u003e2.87\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.780287474332649%\"\u003e\n \u003cp\u003e0.002\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.37782340862423%\"\u003e\n \u003cp\u003eBody composition\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.905544147843942%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.905544147843942%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.396303901437372%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"1.540041067761807%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.776180698151951%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.186858316221766%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.780287474332649%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"1.540041067761807%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.905544147843942%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.905544147843942%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.780287474332649%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.37782340862423%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;BMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.905544147843942%\"\u003e\n \u003cp\u003e0.64\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.905544147843942%\"\u003e\n \u003cp\u003e0.73\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.396303901437372%\"\u003e\n \u003cp\u003e0.84\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.540041067761807%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.776180698151951%\"\u003e\n \u003cp\u003e0.58\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.186858316221766%\"\u003e\n \u003cp\u003e0.14\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.780287474332649%\"\u003e\n \u003cp\u003e0.88\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.540041067761807%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.905544147843942%\"\u003e\n \u003cp\u003e0.14\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.905544147843942%\"\u003e\n \u003cp\u003e2.77\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.780287474332649%\"\u003e\n \u003cp\u003e\u003cstrong\u003e5.63\u003csup\u003e\u0026nbsp;*\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.37782340862423%\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp;ASMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.905544147843942%\"\u003e\n \u003cp\u003e1.92\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.905544147843942%\"\u003e\n \u003cp\u003e\u003cstrong\u003e9.57\u003csup\u003e\u0026nbsp;*\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"11.396303901437372%\"\u003e\n \u003cp\u003e0.89\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.540041067761807%\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"6.776180698151951%\"\u003e\n \u003cp\u003e0.02\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.186858316221766%\"\u003e\n \u003cp\u003e0.01\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.780287474332649%\"\u003e\n \u003cp\u003e0.81\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.540041067761807%\"\u003e\n \u003cp\u003e \u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.905544147843942%\"\u003e\n \u003cp\u003e2.44\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.905544147843942%\"\u003e\n \u003cp\u003e\u003cstrong\u003e13.81\u003csup\u003e\u0026nbsp;**\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"10.780287474332649%\"\u003e\n \u003cp\u003e\u003cstrong\u003e6.33\u003csup\u003e\u0026nbsp;*\u003c/sup\u003e\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"45.58521560574949%\" colspan=\"4\"\u003e\n \u003cp\u003eBMI, body mass index; ASMI, appendicular skeletal muscle mass index\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"1.540041067761807%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.776180698151951%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.186858316221766%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.780287474332649%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"1.540041067761807%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.905544147843942%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.905544147843942%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.780287474332649%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.28336755646817%\" colspan=\"2\"\u003e\n \u003cp\u003eGroup: MCI / Non-MCI groups\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.905544147843942%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.396303901437372%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"1.540041067761807%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.776180698151951%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.186858316221766%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.780287474332649%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"1.540041067761807%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.905544147843942%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.905544147843942%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.780287474332649%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"26.28336755646817%\" colspan=\"2\"\u003e\n \u003cp\u003eTime: Baseline / Three-year follow-up\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.905544147843942%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.396303901437372%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"1.540041067761807%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.776180698151951%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.186858316221766%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.780287474332649%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"1.540041067761807%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.905544147843942%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.905544147843942%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.780287474332649%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"18.37782340862423%\"\u003e\n \u003cp\u003e* \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.05, ** \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"7.905544147843942%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.905544147843942%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"11.396303901437372%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"1.540041067761807%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"6.776180698151951%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.186858316221766%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.780287474332649%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"1.540041067761807%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.905544147843942%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"7.905544147843942%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"10.780287474332649%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\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":"muscle mass, shrinking, cognition, longitudinal study, aging","lastPublishedDoi":"10.21203/rs.3.rs-4246243/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4246243/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis prospective study examined the impact of mild cognitive impairment (MCI) on age-related changes in physical function and body composition among community-dwelling older adults. Older adults aged 70 years and above (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;180) completed at baseline and three-year follow-up assessments of physical function and body composition. Participants were divided into the MCI and non-MCI groups based on baseline status. A two-way repeated-measures analysis of covariance (ANCOVA), adjusting for age and gender, was used to analyze the group (MCI and non-MCI) by time (baseline and three-year follow-up) interaction. No variables had significant group by time interactions. Stratified analyses by gender confirmed a significant group by time interaction on BMI (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;5.63, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.02) and ASMI (\u003cem\u003eF\u003c/em\u003e\u0026thinsp;=\u0026thinsp;6.33, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01) among women (adjusted for age). The close associations of MCI with shrinking and muscle mass loss have important implications for targeting interventions among MCI women.\u003c/p\u003e","manuscriptTitle":"Does mild cognitive impairment accelerate age-related changes in physical function and body composition? A three-year longitudinal follow-up study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-04-25 15:47:07","doi":"10.21203/rs.3.rs-4246243/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":"90d30cad-89ea-421d-ae0c-8a2e782aaae0","owner":[],"postedDate":"April 25th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-05-14T17:17:05+00:00","versionOfRecord":[],"versionCreatedAt":"2024-04-25 15:47:07","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4246243","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4246243","identity":"rs-4246243","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}