- Email: [email protected]
Association between body mass index and cognition function and all-cause mortality in Korean elderly people
Journal Pre-proof Association between Body Mass Index and Cognition function and all-cause mortality in Korean elderly people Jae-Hyun Kim PII:
S2451-8476(19)30094-6
DOI:
https://doi.org/10.1016/j.obmed.2019.100174
Reference:
OBMED 100174
To appear in:
Obesity Medicine
Received Date: 8 August 2019 Revised Date:
20 December 2019
Accepted Date: 22 December 2019
Please cite this article as: Kim, J.-H., Association between Body Mass Index and Cognition function and all-cause mortality in Korean elderly people, Obesity Medicine, https://doi.org/10.1016/ j.obmed.2019.100174. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier Ltd.
Authors statement Acquisition of data, conceived and designed the experiments: J. H. Kim. Performed the experiments: J.H. Kim,. Analyzed the data: J.H. Kim. Wrote the manuscript: J.H. Kim.
Association between Body Mass Index and Cognition function and all-cause mortality in Korean elderly people Running title: mortality in eldrly Jae-Hyun Kim1,2 1
Department of Health Administration, College of Health Science, Dankook University, Cheonan,
Republic of Korea 2
Institute of Health Promotion and Policy, Dankook University, Cheonan, Republic of Korea
Conflicts of interest: No author has any financial or other conflict of interest to declare.
*Corresponding Author: Jae-Hyun Kim, PhD Department of Health Administration, Dankook University 119, Dandae-ro, Dongnam-gu, Cheonan-si, Chungnam, 330-714, Korea Tel: 82-41-550-1472; E-mail: [email protected]
1
Abstract Objective According to previous research, lower cognitive function and body mass index have been related to increase in mortality in elderly people. However, little has been reported about compounding effects of BMI and cognitive function. This study explores the association between body mass index (BMI), cognitive function and mortality in the elderly population.
Methods Data from the Korean Longitudinal Study of Aging (KLoSA) from 2006 to 2014 was assessed using longitudinal data analysis and 3,121 research subjects were included at baseline 2006. Our modeling approach was based on Cox proportional hazard model for mortality.
Results The hazard ratio (HR) for mortality among individuals with thin and cognitive impairment, or normal/overweight and cognitive impairment was 2.666 and 2.470, respectively (p<0.0001), and that among individuals with thin and cognitive decline was 2.153 (p: 0.001). The likelihood of mortality increased within each level of BMI, as the cognitive function deteriorated.
Conclusion The presence of dementia does explain a protective effect of the association between low BMI and higher mortality. Therefore, because both BMI and cognitive function are modifiable, preventive approaches may be necessary.
Keywords: body mass index, cognition, elderly
2
Introduction
Korea has joined the ranks of developed countries due to the rapid economic growth and increase in the national income, which has led to advancement in medicine to a continuous increase in life-expectancy. The lengthened life-expectancy in addition to the low birth-rate has quickened the advent of aged-society and increased the prevalence of frailty. This is closely related to cognitive impairment, which has shown strong association with aging, and has shown negative outcomes, such as increased premature death [1]. Therefore, it is highly important to manage cognitive impairment in the elderly population in this rapidly aging Korean society. According to previous research, lower cognitive function has been related to increase in mortality [2]. The first study to propose the association between lowered cognitive functioning and increased mortality was published in 1962 by Kleemeier [3]. Recent studies included general population in longitudinal design to demonstrate the causal relation between cognitive impairment and increased mortality [4,5]. However, the relationship between mortality and cognitive function has not been thoroughly investigated in a middle aged and aged population, and little is known about the association between Mini-Mental State Examination (MMSE) and body mass index (BMI) that can affect risk of death. Likewise, being overweight is known to relate to chronic diseases such as hypertension, diabetes, dyslipidemia, and cancer. Also, some studies have shown that high BMI (body mass index) induce cardiovascular disorders and cancer, which result in death [68]. BMI is used worldwide as an indirect measure of nutritional status and has been shown to be associated with mortality. In some studies, low BMI was in fact associated with higher 3
mortality in the elderly population [9,10], and in another, a J- or a U-shaped association was apparent between BMI and mortality [11]. In addition, Stevens J et al [12] shows that greater body-mass index was associated with higher mortality from all causes and from cardiovascular disease in men and women up to 75 years of age. However, little is known about whether BMI by cognitive function should remain constant throughout adulthood or should be higher for older adults. Therefore, the aim of this study is to analyze the composite interaction between cognitive function and BMI, and its association to mortality rate using 2006-2014 data of in population of 65 years and older from the Korean Longitudinal Study of Aging (KLoSA).
Methods Data source The data used for the following analyses were derived from the Korean Longitudinal Study of Aging (KLoSA) in 2006, 2008, 2010, 2012 and 2014. KLoSA data was gathered for the purpose of preparing for the aged society in terms of system reform and policy decision. The data is composed of 7 categories such as population, family, health, employment, income, wealth, subjective expectation and life expectation. This biennial survey involves multistage stratified sampling based on geographical areas and housing types across Korea. Participants were selected randomly using a multistage, stratified probability sampling design to create a nationally representative sample of community-dwelling Koreans 45 years of age and older. Participant selection was performed by the Korea Labor Institute for these rapidly growing populations, including individuals from both urban and rural areas. In case of refusal to participate, another subject was selected from an additional, similar sample from the same
4
district. Out of the public data in Korea, KLoSA was considered as the most suitable data for the analysis involved in the current study. In this study, 3,121 participants were included in the analysis, excluding 64 years old or less and those with missing values for the variables of interest.
Independent variable The main independent variable was Body Mass Index (BMI) and cognitive functioning status. In the original KLoSA data, BMI was categorized into obese (BMI≥ ≥25), overweight (23≤BMI<25), normal (18.5≤BMI<23), and thin (BMI<18.5)[13]. In the current study,
obese
was
grouped
into
Obesity
group,
normal
and
overweight
into
Normal/Overweight group, thin into Thin group. Also, from the Korean Mini-Mental State Examination (K-MMSE) score, 17 and lower was categorized as Cognitive impairment, 1823 as Cognitive decline, 24 and above as Normal [14,15]. The K-MMSE, which is a modified version of MMSE to be used for the Korean population, included 11 items in 7 categories of cognitive functions, including orientation for time and place, registration, attention & calculation, recall, language, and visual construction [15,16]. The total score of the measure ranges from 0 to 30; the higher the score, the better the cognitive function. The validity of the K-MMSE was reported elsewhere [16,17].
Dependent variable During the follow-up period from 2006 to 2014, point of all-cause mortality was used to calculate the survival period. Death (all-cause mortality) over a maximum follow-up period of 8 years was determined by death certificate and coroner’s report. On the contrary, in 5
case of censored participants, because no point of mortality was observed, the point of loss was used to calculate the survival period. Control variable In terms of factors that affect mortality in middle-aged participants, age, gender, education level, economic activity status, smoking status, drinking status, self-rated health, diabetes, number of chronic diseases, and social engagement were considered. Specifically, in terms of chronic diseases, hypertension, degenerative arthritis, rheumatoid arthritis, cancer, chronic lung disease, liver disease, cardiovascular disease, cerebrovascular disease and mental illness were included. In terms of social engagement, frequency of meeting friends, participating in social gathering, leisure/culture activities, religious gatherings, and attending reunions. Refer to Kim JH et al., [18] for further detail.
Statistical analysis Using 2006 as the baseline year and considering the follow-up period until 2014, Cox Proportional Hazard Model was used to analyze the risk of all-cause mortality or being censored due to risk factors such as demographic and socio-economic, clinical, and health behaviors. Hazard Ratio (HR) as well as the 95% Confidence Interval (CI) were attained from the analysis. For all analyses, statistical significance was set to p ≤0.05, two-tailed. All analyses were conducted using the SAS statistical software package, version 9.2 (SAS Institute Inc., Cary, NC, USA).
Results The general characteristics of the participants are shown in Table 1. 3,121
6
participants were included in the analysis, and 790 (25.3%) died during the follow-up period. In terms of BMI, there were 244 (7.8%) participants in the Thin group, and of those 118 (48.4%) died. There were 2,251 (72.1%) participants in the Normal/Overweight group, and of those 568 (25.2%) died. Lastly, there were 626 (20.1%) participants in the Obesity group, and of those 104 (16.6%) died by the end of the follow-up period. In terms of cognitive function, there were 560 (17.9%) participants in the Cognitive impairment group, of which 256 (45.7%) participants died, and of the 897 (28.7%) participants in the Cognitive decline group, 215 (24.0%) died. In terms of the interaction between BMI and cognitive function, there were 72 (2.3%) in both Thin and Cognitive impairment group, of which 43 (59.7%) died. There were 401 (12.9%) participants in both Normal/Overweight and Cognitive impairment group, of which 189 (47.1%) died, and lastly of the 87 (2.8%) participants, 24 (27.6%) died by the end of the follow-up period (Table 1). Supplementary figure 1 illustrates the Kaplan-meier curve regarding mortality due to BMI and Cognitive function. The independent effects of BMI and cognitive function on mortality is shown in Table 2. After adjusting for the demographic and socio-economic, health behavior, clinical variables, the risk of mortality in the Thin group statistically increased by 218.9% (HR: 2.189, p<.0001) compared to the Obesity group. Also, compared to the risk of mortality for the Normal group, Cognitive impairment group’s risk (HR) was 1.703, and statistically higher (p<.0001). Table 3 illustrates the concurrent effects of BMI and cognitive function on mortality risk. Compared to the Obesity-Normal group, Thin-Cognitive impairment group’s HR was 2.666-fold (p<.0001) and had a protective effect, and Normal/Overweight-Cognitive impairment group’s HR was 2.470-fold (p<.0001). Obesity-Cognitive impairment group’s HR was 1.260 compared to the Obesity-Normal group, but not significant (p 0.364).
7
Meanwhile, compared to the Obesity-Normal group, Thin -Cognitive decline group’s HR was 2.153-fold (p 0.000), and although the HR’s for Obesity-Cognitive decline group were 1.051fold, they were not significant (Table 3). Table 4 illustrates the subgroup analysis according to gender. Regarding gender, the HR’s for the Thin -Cognitive impairment group were 2.697fold (p 0.005) for male, and 2.217-fold (p 0.010) for women, for the Normal/OverweightCognitive impairment, HR’s were 2.138-fold (p=0.002) for male, and 2.207-fold for women, compared to Obesity-Normal group.
Discussion
The purpose of this study was to analyze how BMI and cognitive functioning status effect mortality, and used the data of participants 65 years and older between 2006 and 2014 from the KLoSA data. To summarize the current findings, for elderly people, the risk of mortality for the Thin group increased by 218.9% compared to the Obesity group, more than 1.703 times higher for the Cognitive impairment group compared to Normal cognitive functioning group, and both BMI and cognitive function influence the risk of mortality considerably. By compounding the effects of BMI and cognitive function, the risk of mortality increased with worsened cognitive function in each BMI group, and the overall tendency showed increased risk of mortality due to decreased BMI and worsened cognitive function. There is robust evidence linking poor cognitive function to increased risk of mortality; low performance on tests of general cognition [19], processing speed [20], and memory [21] have also been related to future mortality. Our findings are consistent with
8
previous reports suggesting that all-cause mortality decreased with increases in MMSE subscale scores for time orientation, place orientation, delayed recall, naming objects, and listening and obeying.[22]. These findings were confirmed by our results of study (Table 2). In addition, as previously reported in other studies, the association between BMI and mortality differed between younger and older adults, and they observed that obese patients might have a survival benefit in older persons; this phenomenon was termed as the obesity paradox [23]. These findings were also confirmed by our results of study. From the present study, we showed that the obesity paradox is true in the 65 year or more Korean older population just as Yamazaki et al. had showed that in the Japanese older population [24]. The present study - the survivor effect of overweight or obesity representing a better nutritional status in the older population - confirmed that Korean older individuals also have a higher resistance to the adverse effects of obesity which is more likely to have high immune function. Furthermore, there is evidence that high cholesterol might be protective against infectious disease, cancer and cardiovascular disease in older adults [25]. Dementia may cause weight loss [26], lower BMI [27] and higher mortality [28] due to feeding difficulties [29]. García-Ptacek S et al., [30] stated that in younger adults, higher BMI are associated with impaired cognition. Overweight and obesity in middle age are lined to increased future dementia risk in old age. SaraGarcía-Ptacek et al [31] showed that higher BMI at the time of dementia diagnosis was associated with a reduction in mortality risk. According to Jang H et al., [32] study, relative to Alzheimer's Disease (AD) patients of normal weight, those who were underweight had an increased mortality rate, and overweight predicted decreased mortality in AD patients. The main contribution of our study to the literature on the association of BMI with
9
mortality in the elderly is the addition of cognitive function as a covariate and potential confounder. Therefore, our analysis suggests that the results of our study help to establish that presence of only poor cognitive function or BMI does not fully explain increased mortality, and support further investigation to elucidate the mechanisms that explain this association. Considering its predictive value, assessing both the BMI and cognitive function may help in the early identification of those at high risk of mortality. Moreover, because BMI and cognitive decline is modifiable[33], preventive approaches may be feasible. Given that cognitive function is modifiable factors, an effective management of both BMI and cognitive impairment might contribute to extending healthy life in later years. There are a number of strengths and limitations of this study. A major strength of the current study is that the participants were assessed and followed for nearly 8 years. In addition, the study obtained a large sample size, so the results can be generalized to adults aged 65 years and older within the South Korean population. Nevertheless, this study has several limitations. One is that respondents’ reports are subjective and are potentially affected by false consciousness and adaptation of resources. Second, we used broad categories of allcause mortality based on death certificate data, and more nuanced categorizations of mortality may need for further study. Another important limitation is that many of the demographic and lifestyle variables were significantly related to the mortality. Although we adjusted for them, it is possible that residual confounding remains and could further attenuate these associations, particularly as only baseline values were used. Despite these limitations, our study is predictive of future mortality in older adults. These findings demonstrate the need for more studies that examine the mechanisms of this association and explore the potential of BMI and cognitive function as a modifiable risk factor for healthy aging. 10
Conclusion This study shows that the presence of cognitive impairment does explain a protective effect of the association between low BMI and higher mortality in the elderly. Therefore, because both BMI and cognitive function are modifiable, preventive approaches may be necessary. An effective management of both BMI and cognitive function might contribute to extending healthy life in later years. Our study provides unique concrete evidence that effects of BMI and cognitive function for mortality in elderly people could have important health implications. Therefore, the results suggest both the possibility of and the need to encourage multi-dimensional measurement tools.
Acknowledgements: None
Conflict of interest: The authors declare no conflicts of interest.
Authors’ contributions Kim JH designed the study, researched data, performed statistical analyses and wrote the manuscript. Kim JH contributed to the discussion and reviewed and edited the manuscript. Kim JH is the guarantor of this work and as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.
11
Supplementary Figure 1 Kaplan Meier Curve for Combination between BMI and Cognitive function and all-cause mortality
12
Reference [1]
Shamliyan T, Talley KM, Ramakrishnan R, Kane RL. Association of frailty with survival: a systematic literature review. Ageing Res Rev 2013;12(2):719-36.
[2]
Shipley BA, Der G, Taylor MD, Deary IJ. Cognition and mortality from the major causes of death: the Health and Lifestyle Survey. J Psychosom Res 2008;65(2):143-52.
[3]
R. K. Intellectual changes in the senium. Proceedings of the Social Statistics Section of the American Statistical Association. Washington, DC: Statistical Association. 1962.
[4]
Bosworth HB, Schaie KW. Survival effects in cognitive function, cognitive style, and sociodemographic
variables
in
the
Seattle
Longitudinal
Study.
Exp
Aging
Res
1999;25(2):121-39. [5]
Johansson B, Berg S. The robustness of the terminal decline phenomenon: longitudinal data from the Digit-Span Memory Test. J Gerontol 1989;44(6):P184-6.
[6]
Taghizadeh N, Boezen HM, Schouten JP, Schroder CP, Elisabeth de Vries EG, Vonk JM. BMI and lifetime changes in BMI and cancer mortality risk. PLoS One 2015;10(4):e0125261.
[7]
Parr CL, Batty GD, Lam TH, Barzi F, Fang X, Ho SC, et al. Body-mass index and cancer mortality in the Asia-Pacific Cohort Studies Collaboration: pooled analyses of 424,519 participants. Lancet Oncol 2010;11(8):741-52.
[8]
Jee SH, Sull JW, Park J, Lee SY, Ohrr H, Guallar E, et al. Body-mass index and mortality in Korean men and women. N Engl J Med 2006;355(8):779-87.
[9]
Yu SY, Kim BS, Won CW, Choi H, Kim S, Kim HW, et al. Body Mass Index and Mortality according to Gender in a Community-Dwelling Elderly Population: The 3-Year Follow-up Findings from the Living Profiles of Older People Surveys in Korea. Korean J Fam Med 2016;37(6):317-22.
[10]
Willett WC, Dietz WH, Colditz GA. Guidelines for healthy weight. N Engl J Med 1999;341(6):427-34.
[11]
X. Z, Shu XO, Chow WH, Yang G, Li H, Gao J, et al. Body mass index at various ages and mortality in Chinese women: impact of potential methodological biases. Int J Obes 2008;32(7):1130-6.
[12]
Stevens J, Cai J, Pamuk ER, Williamson DF, Thun MJ, Wood JL. The effect of age on the association between body-mass index and mortality. N Engl J Med 1998;338(1):1-7.
[13]
Consultation WHOE. Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. Lancet 2004;363(9403):157-63.
[14]
Lee SH, Cho A, Min YK, Lee YK, Jung S. Comparison of the montreal cognitive assessment and the mini-mental state examination as screening tests in hemodialysis patients without symptoms. Ren Fail 2018;40(1):323-30.
[15]
Thomas F, Bean K, Pannier B, Oppert JM, Guize L, Benetos A. Cardiovascular mortality in overweight subjects: the key role of associated risk factors. Hypertension 2005;46(4):654-9. 13
[16]
Kang Y, Na DL, Hahn S. A validity study on the Korean Mini-Mental State Examination (KMMSE)
in
dementia
patients.
Journal
of
the
Korean
Neurological
Association
1997;15(2):300-8. [17]
Kim JH, Chon D. Association of solidarity between adult children and older parents with cognitive decline. Geriatr Gerontol Int 2018;18(10):1501-6.
[18]
Kim JH, Lee SG, Kim TH, Choi Y, Lee Y, Park EC. Influence of Social Engagement on Mortality in Korea: Analysis of the Korean Longitudinal Study of Aging (2006-2012). J Korean Med Sci 2016;31(7):1020-6.
[19]
Korten AE, Jorm AF, Jiao Zea. Health, cognitive, and psychosocial factors as predictors of mortality in an elderly community sample. J Epidemiol Community Health 1999;53:83-8.
[20]
Swan GE, Carmelli D, LaRue A. Performance on the digit symbol substitution test and 5year mortality in the Western Collaborative Group Study. Am J Epidemiol 1995;141(1):3240.
[21]
Shipley BA, Der G, Taylor MD, Deary IJ. Cognition and all-cause mortality across the entire adult age range: health and lifestyle survey. Psychosom Med 2006;68(1):17-24.
[22]
Takata Y, Ansai T, Soh I, Awano S, Nakamichi I, Akifusa S, et al. Cognitive function and 10 year mortality in an 85 year-old community-dwelling population. Clin Interv Aging 2014;9:1691-9.
[23]
Amundson DE, Djurkovic S, Matwiyoff GN. The obesity paradox. Crit Care Clin 2010;26(4):583-96.
[24]
Yamazaki K, Suzuki E, Yorifuji Tea. Is there an obesity paradox in the Japanese elderly population? A community-based cohort study of 13280 men and women. Geriatr Gerontol Int 2016;17:1257-64.
[25]
Ravnskov U, Diamond DM, Hama R, Hamazaki T, Hammarskjold B, Hynes N, et al. Lack of an association or an inverse association between low-density-lipoprotein cholesterol and mortality in the elderly: a systematic review. BMJ Open 2016;6(6):e010401.
[26]
Stewart R, Masaki K, Xue QL, Peila R, Petrovitch H, White LR, et al. A 32-year prospective study of change in body weight and incident dementia: the Honolulu-Asia Aging Study. Arch Neurol 2005;62(1):55-60.
[27]
Nourhashemi F, Deschamps V, Larrieu S, Letenneur L, Dartigues JF, Barberger-Gateau P, et al. Body mass index and incidence of dementia: the PAQUID study. Neurology 2003;60(1):117-9.
[28]
Guehne U, Riedel-Heller S, Angermeyer MC. Mortality in dementia. Neuroepidemiology 2005;25(3):153-62.
[29]
Wang SY. Weight loss and metabolic changes in dementia. J Nutr Health Aging 2002;6(3):201-5.
[30]
Garcia-Ptacek S, Faxen-Irving G, Cermakova P, Eriksdotter M, Religa D. Body mass index in 14
dementia. Eur J Clin Nutr 2014;68(11):1204-9. [31]
Garcia-Ptacek S, Kareholt I, Farahmand B, Cuadrado ML, Religa D, Eriksdotter M. Bodymass index and mortality in incident dementia: a cohort study on 11,398 patients from SveDem, the Swedish Dementia Registry. J Am Med Dir Assoc 2014;15(6):447 e1-7.
[32]
Jang H, Kim JH, Choi SH, Lee Y, Hong CH, Jeong JH, et al. Body Mass Index and Mortality Rate in Korean Patients with Alzheimer's Disease. J Alzheimers Dis 2015;46(2):399-406.
[33]
Ngandu T, Lehtisalo J, Solomon A, Levalahti E, Ahtiluoto S, Antikainen R, et al. A 2 year multidomain intervention of diet, exercise, cognitive training, and vascular risk monitoring versus control to prevent cognitive decline in at-risk elderly people (FINGER): a randomised controlled trial. Lancet 2015;385(9984):2255-63.
15
Table 1. General characteristics of subjects included for analysis at baseline Total N
%
Alive
All-cause Mortality % Dead
%
P-value <.0001
BMI 244 7.8 126 51.6 118 48.4 Thin 2,251 72.1 1,683 74.8 568 25.2 Normal/Overweight 626 20.1 522 83.4 104 16.6 Obesity Cognitive function 560 17.9 304 54.3 256 45.7 Cognitive impairment 897 28.7 682 76.0 215 24.0 Cognitive decline 1,664 53.3 1,345 80.8 319 19.2 Normal BMI-Cognitive function 72 2.3 29 40.3 43 59.7 Thin-Cognitive impairment 77 2.5 46 59.7 31 40.3 Thin-Cognitive decline 95 3.0 51 53.7 44 46.3 Thin-Normal 401 12.9 212 52.9 189 47.1 Normal/Overweight-Cognitive impairment 653 20.9 496 76.0 157 24.0 Normal/Overweight-Cognitive decline 1,197 38.4 975 81.5 222 18.6 Normal/Overweight-Normal 87 2.8 63 72.4 24 27.6 Obesity-Cognitive impairment 167 5.4 140 83.8 27 16.2 Obesity-Cognitive decline 372 11.9 319 85.8 53 14.3 Obesity-Normal Sex 1,387 44.4 967 69.7 420 30.3 Male 1,734 55.6 1,364 78.7 370 21.3 Female Age 1,194 38.3 1,040 87.1 154 12.9 65-69 901 28.9 717 79.6 184 20.4 70-74 600 19.2 403 67.2 197 32.8 75-79 426 13.7 171 40.1 255 59.9 ≥80 Education level 1,654 73.7 590 26.3 2,244 71.9 ≤ Elementary school 316 10.1 240 76.0 76 24.1 Middle school 393 12.6 305 77.6 88 22.4 High school 132 78.6 36 21.4 168 5.4 ≥ College Residential region 1,818 58.3 1,368 75.3 450 24.8 Urban 1,303 41.8 963 73.9 340 26.1 Rural Marital status 2,077 66.6 1,602 77.1 475 22.9 Married 1,044 33.5 729 69.8 315 30.2 Single Economic activity status 614 19.7 520 84.7 94 15.3 Yes 2,507 80.3 1,811 72.2 696 27.8 No Smoking status 2,235 71.6 1,733 77.5 502 22.5 Smoker 386 12.4 264 68.4 122 31.6 Former smoker 500 16.0 334 66.8 166 33.2 Never Alcohol use 916 29.4 686 74.9 230 25.1 Drinker 297 9.5 193 65.0 104 35.0 Former Drinker 1,908 61.1 1,452 76.1 456 23.9 Never Self-rated health 642 20.6 530 82.6 112 17.5 Good 2,149 68.9 1,621 75.4 528 24.6 Moderate 330 10.6 180 54.6 150 45.5 Bad Social Engagement 451 14.5 280 62.1 171 37.9 Ⅰ(Lowest) 958 30.7 680 71.0 278 29.0 Ⅱ 722 23.1 570 79.0 152 21.1 Ⅲ 347 11.1 280 80.7 67 19.3 Ⅳ 643 20.6 521 81.0 122 19.0 Ⅴ(Highest) Diabetes 2,627 84.2 1,988 75.7 639 24.3 No 494 15.8 343 69.4 151 30.6 Yes Number of chronic disease* 1,240 39.7 944 76.1 296 23.9 0 1,200 38.5 895 74.6 305 25.4 1 681 21.8 492 72.3 189 27.8 ≥2 3,121 100.0 2,331 74.7 790 25.3 Total *Hypertension, osteoarthritis, rheumatoid arthritis, cancer, chronic pulmonary disease, liver disease, cardiovascular disease, cerebrovascular disease, mental illness
16
<.0001
<.0001
<.0001
<.0001
0.211
0.395
<.0001
<.0001
<.0001
<.0001
<.0001
<.0001
0.003
0.172
Table 2. Adjusted effect between BMI and Cognitive function and mortality HR
All-cause mortality 95% CI
P-value
BMI Thin Normal/Overweight Obesity
2.189 1.445 1.000
1.658 1.167
2.891 1.790
<.0001 0.001
Cognitive impairment Cognitive decline Normal
1.703 1.150 1.000
1.386 0.954
2.092 1.386
<.0001 0.143
Male Female
2.336 1.000
1.869
2.918
<.0001
65-69 70-74 75-79 ≥80
1.000 1.434 2.240 4.249
1.154 1.796 3.382
1.781 2.794 5.337
0.001 <.0001 <.0001
≤ Elementary school Middle school High school ≥ College
1.043 1.203 1.171 1.000
0.728 0.805 0.792
1.495 1.797 1.733
0.818 0.368 0.429
Urban Rural
0.965 1.000
0.830
1.121
0.638
Married Single
0.775 1.000
0.646
0.931
0.006
Yes No
0.645 1.000
0.511
0.812
0.000
Smoker Former smoker Never
0.843 0.944 1.000
0.685 0.741
1.038 1.204
0.108 0.643
Drinker Former Drinker Never
0.925 1.011 1.000
0.761 0.790
1.125 1.295
0.435 0.930
Good Moderate Bad
1.000 1.149 1.868
0.925 1.413
1.426 2.470
0.209 <.0001
Ⅰ(Lowest) Ⅱ Ⅲ Ⅳ Ⅴ(Highest)
1.611 1.402 1.191 1.092 1.000
1.262 1.127 0.937 0.809
2.057 1.744 1.514 1.475
0.000 0.002 0.153 0.565
Yes No
1.397 1.000
1.161
1.682
0.000
Cognitive function
Sex
Age
Education level
Residential region
Marital status
Economic activity status
Smoking status
Alcohol use
Self-rated health
Social Engagement
Diabetes
Number of chronic disease* 1.000 0 1.051 0.889 1.242 0.561 1 1.131 0.926 1.382 0.228 ≥2 *Hypertension, osteoarthritis, rheumatoid arthritis, cancer, chronic pulmonary disease, liver disease, cardiovascular disease, cerebrovascular disease, mental illness
17
Table 3. association between BMI and Cognitive function and mortality HR
All-cause mortality 95% CI
P-value BMI-Cognitive function 2.666 1.728 4.113 <.0001 Thin-Cognitive impairment 2.153 1.358 3.413 0.001 Thin-Cognitive decline 2.927 1.936 4.428 <.0001 Thin-Normal 2.470 1.774 3.439 <.0001 Normal/Overweight-Cognitive impairment 1.533 1.113 2.111 0.009 Normal/Overweight-Cognitive decline 1.204 0.889 1.631 0.230 Normal/Overweight-Normal 1.260 0.765 2.078 0.364 Obesity-Cognitive impairment 1.051 0.657 1.681 0.837 Obesity-Cognitive decline 1.000 Obesity-Normal Sex 2.350 1.881 2.936 <.0001 Male 1.000 Female Age 1.000 65-69 1.446 1.163 1.796 0.001 70-74 2.238 1.794 2.794 <.0001 75-79 4.233 3.371 5.315 <.0001 ≥80 Education level 1.021 0.712 1.463 0.912 ≤ Elementary school 1.176 0.786 1.758 0.430 Middle school 1.169 0.789 1.731 0.436 High school 1.000 ≥ College Residential region 0.978 0.841 1.138 0.776 Urban 1.000 Rural Marital status 0.762 0.634 0.916 0.004 Married 1.000 Single Economic activity status 0.651 0.517 0.821 0.000 Yes 1.000 No Smoking status 0.855 0.694 1.053 0.141 Smoker 0.941 0.738 1.200 0.625 Former smoker 1.000 Never Alcohol use 0.940 0.773 1.143 0.533 Drinker 1.005 0.784 1.288 0.970 Former Drinker 1.000 Never Self-rated health 1.000 Good 1.130 0.909 1.403 0.270 Moderate 1.879 1.420 2.486 <.0001 Bad Social Engagement 1.626 1.274 2.076 <.0001 Ⅰ(Lowest) 1.423 1.144 1.770 0.002 Ⅱ 1.220 0.959 1.551 0.105 Ⅲ 1.129 0.835 1.526 0.432 Ⅳ 1.000 Ⅴ(Highest) Diabetes 1.418 1.178 1.707 0.000 Yes 1.000 No Number of chronic disease* 1.000 0 1.042 0.882 1.232 0.627 1 1.121 0.917 1.370 0.265 ≥2 *Hypertension, osteoarthritis, rheumatoid arthritis, cancer, chronic pulmonary disease, liver disease, cardiovascular disease, cerebrovascular disease, mental illness
18
Table 4. association between BMI and Cognitive function and all-cause mortality by gender HR BMI-Cognitive function Thin-Cognitive impairment Thin-Cognitive decline Thin-Normal Normal/Overweight-Cognitive impairment Normal/Overweight-Cognitive decline Normal/Overweight-Normal Obesity-Cognitive impairment Obesity-Cognitive decline Obesity-Normal *Adjusted for all variables
2.697 1.744 2.982 2.138 1.791 1.393 0.952 1.328 1.000
95% CI Male 1.347 0.834 1.799 1.333 1.183 0.955 0.353 0.692
19
5.403 3.650 4.945 3.429 2.712 2.032 2.569 2.550
All-cause mortality P-value HR
0.005 0.140 <.0001 0.002 0.006 0.086 0.923 0.394
2.217 2.190 2.877 2.207 1.202 0.838 1.182 0.848 1.000
95% CI Female 1.208 1.158 1.354 1.331 0.723 0.491 0.617 0.424
4.068 4.145 6.114 3.661 1.996 1.428 2.263 1.693
P-value
0.010 0.016 0.006 0.002 0.478 0.515 0.614 0.639
Supplementary 1. Combination between BMI and Cognitive function and all-cause mortality
Keypoints By compounding the effects of BMI and cognitive function, the risk of mortality increased with worsened cognitive function in each BMI group The overall tendency showed increased risk of mortality due to decreased BMI and worsened cognitive function The observed tendencies were more apparent in female participants
Conflict of interest: There is no inflict no interest
S2451-8476(19)30094-6
DOI:
https://doi.org/10.1016/j.obmed.2019.100174
Reference:
OBMED 100174
To appear in:
Obesity Medicine
Received Date: 8 August 2019 Revised Date:
20 December 2019
Accepted Date: 22 December 2019
Please cite this article as: Kim, J.-H., Association between Body Mass Index and Cognition function and all-cause mortality in Korean elderly people, Obesity Medicine, https://doi.org/10.1016/ j.obmed.2019.100174. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier Ltd.
Authors statement Acquisition of data, conceived and designed the experiments: J. H. Kim. Performed the experiments: J.H. Kim,. Analyzed the data: J.H. Kim. Wrote the manuscript: J.H. Kim.
Association between Body Mass Index and Cognition function and all-cause mortality in Korean elderly people Running title: mortality in eldrly Jae-Hyun Kim1,2 1
Department of Health Administration, College of Health Science, Dankook University, Cheonan,
Republic of Korea 2
Institute of Health Promotion and Policy, Dankook University, Cheonan, Republic of Korea
Conflicts of interest: No author has any financial or other conflict of interest to declare.
*Corresponding Author: Jae-Hyun Kim, PhD Department of Health Administration, Dankook University 119, Dandae-ro, Dongnam-gu, Cheonan-si, Chungnam, 330-714, Korea Tel: 82-41-550-1472; E-mail: [email protected]
1
Abstract Objective According to previous research, lower cognitive function and body mass index have been related to increase in mortality in elderly people. However, little has been reported about compounding effects of BMI and cognitive function. This study explores the association between body mass index (BMI), cognitive function and mortality in the elderly population.
Methods Data from the Korean Longitudinal Study of Aging (KLoSA) from 2006 to 2014 was assessed using longitudinal data analysis and 3,121 research subjects were included at baseline 2006. Our modeling approach was based on Cox proportional hazard model for mortality.
Results The hazard ratio (HR) for mortality among individuals with thin and cognitive impairment, or normal/overweight and cognitive impairment was 2.666 and 2.470, respectively (p<0.0001), and that among individuals with thin and cognitive decline was 2.153 (p: 0.001). The likelihood of mortality increased within each level of BMI, as the cognitive function deteriorated.
Conclusion The presence of dementia does explain a protective effect of the association between low BMI and higher mortality. Therefore, because both BMI and cognitive function are modifiable, preventive approaches may be necessary.
Keywords: body mass index, cognition, elderly
2
Introduction
Korea has joined the ranks of developed countries due to the rapid economic growth and increase in the national income, which has led to advancement in medicine to a continuous increase in life-expectancy. The lengthened life-expectancy in addition to the low birth-rate has quickened the advent of aged-society and increased the prevalence of frailty. This is closely related to cognitive impairment, which has shown strong association with aging, and has shown negative outcomes, such as increased premature death [1]. Therefore, it is highly important to manage cognitive impairment in the elderly population in this rapidly aging Korean society. According to previous research, lower cognitive function has been related to increase in mortality [2]. The first study to propose the association between lowered cognitive functioning and increased mortality was published in 1962 by Kleemeier [3]. Recent studies included general population in longitudinal design to demonstrate the causal relation between cognitive impairment and increased mortality [4,5]. However, the relationship between mortality and cognitive function has not been thoroughly investigated in a middle aged and aged population, and little is known about the association between Mini-Mental State Examination (MMSE) and body mass index (BMI) that can affect risk of death. Likewise, being overweight is known to relate to chronic diseases such as hypertension, diabetes, dyslipidemia, and cancer. Also, some studies have shown that high BMI (body mass index) induce cardiovascular disorders and cancer, which result in death [68]. BMI is used worldwide as an indirect measure of nutritional status and has been shown to be associated with mortality. In some studies, low BMI was in fact associated with higher 3
mortality in the elderly population [9,10], and in another, a J- or a U-shaped association was apparent between BMI and mortality [11]. In addition, Stevens J et al [12] shows that greater body-mass index was associated with higher mortality from all causes and from cardiovascular disease in men and women up to 75 years of age. However, little is known about whether BMI by cognitive function should remain constant throughout adulthood or should be higher for older adults. Therefore, the aim of this study is to analyze the composite interaction between cognitive function and BMI, and its association to mortality rate using 2006-2014 data of in population of 65 years and older from the Korean Longitudinal Study of Aging (KLoSA).
Methods Data source The data used for the following analyses were derived from the Korean Longitudinal Study of Aging (KLoSA) in 2006, 2008, 2010, 2012 and 2014. KLoSA data was gathered for the purpose of preparing for the aged society in terms of system reform and policy decision. The data is composed of 7 categories such as population, family, health, employment, income, wealth, subjective expectation and life expectation. This biennial survey involves multistage stratified sampling based on geographical areas and housing types across Korea. Participants were selected randomly using a multistage, stratified probability sampling design to create a nationally representative sample of community-dwelling Koreans 45 years of age and older. Participant selection was performed by the Korea Labor Institute for these rapidly growing populations, including individuals from both urban and rural areas. In case of refusal to participate, another subject was selected from an additional, similar sample from the same
4
district. Out of the public data in Korea, KLoSA was considered as the most suitable data for the analysis involved in the current study. In this study, 3,121 participants were included in the analysis, excluding 64 years old or less and those with missing values for the variables of interest.
Independent variable The main independent variable was Body Mass Index (BMI) and cognitive functioning status. In the original KLoSA data, BMI was categorized into obese (BMI≥ ≥25), overweight (23≤BMI<25), normal (18.5≤BMI<23), and thin (BMI<18.5)[13]. In the current study,
obese
was
grouped
into
Obesity
group,
normal
and
overweight
into
Normal/Overweight group, thin into Thin group. Also, from the Korean Mini-Mental State Examination (K-MMSE) score, 17 and lower was categorized as Cognitive impairment, 1823 as Cognitive decline, 24 and above as Normal [14,15]. The K-MMSE, which is a modified version of MMSE to be used for the Korean population, included 11 items in 7 categories of cognitive functions, including orientation for time and place, registration, attention & calculation, recall, language, and visual construction [15,16]. The total score of the measure ranges from 0 to 30; the higher the score, the better the cognitive function. The validity of the K-MMSE was reported elsewhere [16,17].
Dependent variable During the follow-up period from 2006 to 2014, point of all-cause mortality was used to calculate the survival period. Death (all-cause mortality) over a maximum follow-up period of 8 years was determined by death certificate and coroner’s report. On the contrary, in 5
case of censored participants, because no point of mortality was observed, the point of loss was used to calculate the survival period. Control variable In terms of factors that affect mortality in middle-aged participants, age, gender, education level, economic activity status, smoking status, drinking status, self-rated health, diabetes, number of chronic diseases, and social engagement were considered. Specifically, in terms of chronic diseases, hypertension, degenerative arthritis, rheumatoid arthritis, cancer, chronic lung disease, liver disease, cardiovascular disease, cerebrovascular disease and mental illness were included. In terms of social engagement, frequency of meeting friends, participating in social gathering, leisure/culture activities, religious gatherings, and attending reunions. Refer to Kim JH et al., [18] for further detail.
Statistical analysis Using 2006 as the baseline year and considering the follow-up period until 2014, Cox Proportional Hazard Model was used to analyze the risk of all-cause mortality or being censored due to risk factors such as demographic and socio-economic, clinical, and health behaviors. Hazard Ratio (HR) as well as the 95% Confidence Interval (CI) were attained from the analysis. For all analyses, statistical significance was set to p ≤0.05, two-tailed. All analyses were conducted using the SAS statistical software package, version 9.2 (SAS Institute Inc., Cary, NC, USA).
Results The general characteristics of the participants are shown in Table 1. 3,121
6
participants were included in the analysis, and 790 (25.3%) died during the follow-up period. In terms of BMI, there were 244 (7.8%) participants in the Thin group, and of those 118 (48.4%) died. There were 2,251 (72.1%) participants in the Normal/Overweight group, and of those 568 (25.2%) died. Lastly, there were 626 (20.1%) participants in the Obesity group, and of those 104 (16.6%) died by the end of the follow-up period. In terms of cognitive function, there were 560 (17.9%) participants in the Cognitive impairment group, of which 256 (45.7%) participants died, and of the 897 (28.7%) participants in the Cognitive decline group, 215 (24.0%) died. In terms of the interaction between BMI and cognitive function, there were 72 (2.3%) in both Thin and Cognitive impairment group, of which 43 (59.7%) died. There were 401 (12.9%) participants in both Normal/Overweight and Cognitive impairment group, of which 189 (47.1%) died, and lastly of the 87 (2.8%) participants, 24 (27.6%) died by the end of the follow-up period (Table 1). Supplementary figure 1 illustrates the Kaplan-meier curve regarding mortality due to BMI and Cognitive function. The independent effects of BMI and cognitive function on mortality is shown in Table 2. After adjusting for the demographic and socio-economic, health behavior, clinical variables, the risk of mortality in the Thin group statistically increased by 218.9% (HR: 2.189, p<.0001) compared to the Obesity group. Also, compared to the risk of mortality for the Normal group, Cognitive impairment group’s risk (HR) was 1.703, and statistically higher (p<.0001). Table 3 illustrates the concurrent effects of BMI and cognitive function on mortality risk. Compared to the Obesity-Normal group, Thin-Cognitive impairment group’s HR was 2.666-fold (p<.0001) and had a protective effect, and Normal/Overweight-Cognitive impairment group’s HR was 2.470-fold (p<.0001). Obesity-Cognitive impairment group’s HR was 1.260 compared to the Obesity-Normal group, but not significant (p 0.364).
7
Meanwhile, compared to the Obesity-Normal group, Thin -Cognitive decline group’s HR was 2.153-fold (p 0.000), and although the HR’s for Obesity-Cognitive decline group were 1.051fold, they were not significant (Table 3). Table 4 illustrates the subgroup analysis according to gender. Regarding gender, the HR’s for the Thin -Cognitive impairment group were 2.697fold (p 0.005) for male, and 2.217-fold (p 0.010) for women, for the Normal/OverweightCognitive impairment, HR’s were 2.138-fold (p=0.002) for male, and 2.207-fold for women, compared to Obesity-Normal group.
Discussion
The purpose of this study was to analyze how BMI and cognitive functioning status effect mortality, and used the data of participants 65 years and older between 2006 and 2014 from the KLoSA data. To summarize the current findings, for elderly people, the risk of mortality for the Thin group increased by 218.9% compared to the Obesity group, more than 1.703 times higher for the Cognitive impairment group compared to Normal cognitive functioning group, and both BMI and cognitive function influence the risk of mortality considerably. By compounding the effects of BMI and cognitive function, the risk of mortality increased with worsened cognitive function in each BMI group, and the overall tendency showed increased risk of mortality due to decreased BMI and worsened cognitive function. There is robust evidence linking poor cognitive function to increased risk of mortality; low performance on tests of general cognition [19], processing speed [20], and memory [21] have also been related to future mortality. Our findings are consistent with
8
previous reports suggesting that all-cause mortality decreased with increases in MMSE subscale scores for time orientation, place orientation, delayed recall, naming objects, and listening and obeying.[22]. These findings were confirmed by our results of study (Table 2). In addition, as previously reported in other studies, the association between BMI and mortality differed between younger and older adults, and they observed that obese patients might have a survival benefit in older persons; this phenomenon was termed as the obesity paradox [23]. These findings were also confirmed by our results of study. From the present study, we showed that the obesity paradox is true in the 65 year or more Korean older population just as Yamazaki et al. had showed that in the Japanese older population [24]. The present study - the survivor effect of overweight or obesity representing a better nutritional status in the older population - confirmed that Korean older individuals also have a higher resistance to the adverse effects of obesity which is more likely to have high immune function. Furthermore, there is evidence that high cholesterol might be protective against infectious disease, cancer and cardiovascular disease in older adults [25]. Dementia may cause weight loss [26], lower BMI [27] and higher mortality [28] due to feeding difficulties [29]. García-Ptacek S et al., [30] stated that in younger adults, higher BMI are associated with impaired cognition. Overweight and obesity in middle age are lined to increased future dementia risk in old age. SaraGarcía-Ptacek et al [31] showed that higher BMI at the time of dementia diagnosis was associated with a reduction in mortality risk. According to Jang H et al., [32] study, relative to Alzheimer's Disease (AD) patients of normal weight, those who were underweight had an increased mortality rate, and overweight predicted decreased mortality in AD patients. The main contribution of our study to the literature on the association of BMI with
9
mortality in the elderly is the addition of cognitive function as a covariate and potential confounder. Therefore, our analysis suggests that the results of our study help to establish that presence of only poor cognitive function or BMI does not fully explain increased mortality, and support further investigation to elucidate the mechanisms that explain this association. Considering its predictive value, assessing both the BMI and cognitive function may help in the early identification of those at high risk of mortality. Moreover, because BMI and cognitive decline is modifiable[33], preventive approaches may be feasible. Given that cognitive function is modifiable factors, an effective management of both BMI and cognitive impairment might contribute to extending healthy life in later years. There are a number of strengths and limitations of this study. A major strength of the current study is that the participants were assessed and followed for nearly 8 years. In addition, the study obtained a large sample size, so the results can be generalized to adults aged 65 years and older within the South Korean population. Nevertheless, this study has several limitations. One is that respondents’ reports are subjective and are potentially affected by false consciousness and adaptation of resources. Second, we used broad categories of allcause mortality based on death certificate data, and more nuanced categorizations of mortality may need for further study. Another important limitation is that many of the demographic and lifestyle variables were significantly related to the mortality. Although we adjusted for them, it is possible that residual confounding remains and could further attenuate these associations, particularly as only baseline values were used. Despite these limitations, our study is predictive of future mortality in older adults. These findings demonstrate the need for more studies that examine the mechanisms of this association and explore the potential of BMI and cognitive function as a modifiable risk factor for healthy aging. 10
Conclusion This study shows that the presence of cognitive impairment does explain a protective effect of the association between low BMI and higher mortality in the elderly. Therefore, because both BMI and cognitive function are modifiable, preventive approaches may be necessary. An effective management of both BMI and cognitive function might contribute to extending healthy life in later years. Our study provides unique concrete evidence that effects of BMI and cognitive function for mortality in elderly people could have important health implications. Therefore, the results suggest both the possibility of and the need to encourage multi-dimensional measurement tools.
Acknowledgements: None
Conflict of interest: The authors declare no conflicts of interest.
Authors’ contributions Kim JH designed the study, researched data, performed statistical analyses and wrote the manuscript. Kim JH contributed to the discussion and reviewed and edited the manuscript. Kim JH is the guarantor of this work and as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.
11
Supplementary Figure 1 Kaplan Meier Curve for Combination between BMI and Cognitive function and all-cause mortality
12
Reference [1]
Shamliyan T, Talley KM, Ramakrishnan R, Kane RL. Association of frailty with survival: a systematic literature review. Ageing Res Rev 2013;12(2):719-36.
[2]
Shipley BA, Der G, Taylor MD, Deary IJ. Cognition and mortality from the major causes of death: the Health and Lifestyle Survey. J Psychosom Res 2008;65(2):143-52.
[3]
R. K. Intellectual changes in the senium. Proceedings of the Social Statistics Section of the American Statistical Association. Washington, DC: Statistical Association. 1962.
[4]
Bosworth HB, Schaie KW. Survival effects in cognitive function, cognitive style, and sociodemographic
variables
in
the
Seattle
Longitudinal
Study.
Exp
Aging
Res
1999;25(2):121-39. [5]
Johansson B, Berg S. The robustness of the terminal decline phenomenon: longitudinal data from the Digit-Span Memory Test. J Gerontol 1989;44(6):P184-6.
[6]
Taghizadeh N, Boezen HM, Schouten JP, Schroder CP, Elisabeth de Vries EG, Vonk JM. BMI and lifetime changes in BMI and cancer mortality risk. PLoS One 2015;10(4):e0125261.
[7]
Parr CL, Batty GD, Lam TH, Barzi F, Fang X, Ho SC, et al. Body-mass index and cancer mortality in the Asia-Pacific Cohort Studies Collaboration: pooled analyses of 424,519 participants. Lancet Oncol 2010;11(8):741-52.
[8]
Jee SH, Sull JW, Park J, Lee SY, Ohrr H, Guallar E, et al. Body-mass index and mortality in Korean men and women. N Engl J Med 2006;355(8):779-87.
[9]
Yu SY, Kim BS, Won CW, Choi H, Kim S, Kim HW, et al. Body Mass Index and Mortality according to Gender in a Community-Dwelling Elderly Population: The 3-Year Follow-up Findings from the Living Profiles of Older People Surveys in Korea. Korean J Fam Med 2016;37(6):317-22.
[10]
Willett WC, Dietz WH, Colditz GA. Guidelines for healthy weight. N Engl J Med 1999;341(6):427-34.
[11]
X. Z, Shu XO, Chow WH, Yang G, Li H, Gao J, et al. Body mass index at various ages and mortality in Chinese women: impact of potential methodological biases. Int J Obes 2008;32(7):1130-6.
[12]
Stevens J, Cai J, Pamuk ER, Williamson DF, Thun MJ, Wood JL. The effect of age on the association between body-mass index and mortality. N Engl J Med 1998;338(1):1-7.
[13]
Consultation WHOE. Appropriate body-mass index for Asian populations and its implications for policy and intervention strategies. Lancet 2004;363(9403):157-63.
[14]
Lee SH, Cho A, Min YK, Lee YK, Jung S. Comparison of the montreal cognitive assessment and the mini-mental state examination as screening tests in hemodialysis patients without symptoms. Ren Fail 2018;40(1):323-30.
[15]
Thomas F, Bean K, Pannier B, Oppert JM, Guize L, Benetos A. Cardiovascular mortality in overweight subjects: the key role of associated risk factors. Hypertension 2005;46(4):654-9. 13
[16]
Kang Y, Na DL, Hahn S. A validity study on the Korean Mini-Mental State Examination (KMMSE)
in
dementia
patients.
Journal
of
the
Korean
Neurological
Association
1997;15(2):300-8. [17]
Kim JH, Chon D. Association of solidarity between adult children and older parents with cognitive decline. Geriatr Gerontol Int 2018;18(10):1501-6.
[18]
Kim JH, Lee SG, Kim TH, Choi Y, Lee Y, Park EC. Influence of Social Engagement on Mortality in Korea: Analysis of the Korean Longitudinal Study of Aging (2006-2012). J Korean Med Sci 2016;31(7):1020-6.
[19]
Korten AE, Jorm AF, Jiao Zea. Health, cognitive, and psychosocial factors as predictors of mortality in an elderly community sample. J Epidemiol Community Health 1999;53:83-8.
[20]
Swan GE, Carmelli D, LaRue A. Performance on the digit symbol substitution test and 5year mortality in the Western Collaborative Group Study. Am J Epidemiol 1995;141(1):3240.
[21]
Shipley BA, Der G, Taylor MD, Deary IJ. Cognition and all-cause mortality across the entire adult age range: health and lifestyle survey. Psychosom Med 2006;68(1):17-24.
[22]
Takata Y, Ansai T, Soh I, Awano S, Nakamichi I, Akifusa S, et al. Cognitive function and 10 year mortality in an 85 year-old community-dwelling population. Clin Interv Aging 2014;9:1691-9.
[23]
Amundson DE, Djurkovic S, Matwiyoff GN. The obesity paradox. Crit Care Clin 2010;26(4):583-96.
[24]
Yamazaki K, Suzuki E, Yorifuji Tea. Is there an obesity paradox in the Japanese elderly population? A community-based cohort study of 13280 men and women. Geriatr Gerontol Int 2016;17:1257-64.
[25]
Ravnskov U, Diamond DM, Hama R, Hamazaki T, Hammarskjold B, Hynes N, et al. Lack of an association or an inverse association between low-density-lipoprotein cholesterol and mortality in the elderly: a systematic review. BMJ Open 2016;6(6):e010401.
[26]
Stewart R, Masaki K, Xue QL, Peila R, Petrovitch H, White LR, et al. A 32-year prospective study of change in body weight and incident dementia: the Honolulu-Asia Aging Study. Arch Neurol 2005;62(1):55-60.
[27]
Nourhashemi F, Deschamps V, Larrieu S, Letenneur L, Dartigues JF, Barberger-Gateau P, et al. Body mass index and incidence of dementia: the PAQUID study. Neurology 2003;60(1):117-9.
[28]
Guehne U, Riedel-Heller S, Angermeyer MC. Mortality in dementia. Neuroepidemiology 2005;25(3):153-62.
[29]
Wang SY. Weight loss and metabolic changes in dementia. J Nutr Health Aging 2002;6(3):201-5.
[30]
Garcia-Ptacek S, Faxen-Irving G, Cermakova P, Eriksdotter M, Religa D. Body mass index in 14
dementia. Eur J Clin Nutr 2014;68(11):1204-9. [31]
Garcia-Ptacek S, Kareholt I, Farahmand B, Cuadrado ML, Religa D, Eriksdotter M. Bodymass index and mortality in incident dementia: a cohort study on 11,398 patients from SveDem, the Swedish Dementia Registry. J Am Med Dir Assoc 2014;15(6):447 e1-7.
[32]
Jang H, Kim JH, Choi SH, Lee Y, Hong CH, Jeong JH, et al. Body Mass Index and Mortality Rate in Korean Patients with Alzheimer's Disease. J Alzheimers Dis 2015;46(2):399-406.
[33]
Ngandu T, Lehtisalo J, Solomon A, Levalahti E, Ahtiluoto S, Antikainen R, et al. A 2 year multidomain intervention of diet, exercise, cognitive training, and vascular risk monitoring versus control to prevent cognitive decline in at-risk elderly people (FINGER): a randomised controlled trial. Lancet 2015;385(9984):2255-63.
15
Table 1. General characteristics of subjects included for analysis at baseline Total N
%
Alive
All-cause Mortality % Dead
%
P-value <.0001
BMI 244 7.8 126 51.6 118 48.4 Thin 2,251 72.1 1,683 74.8 568 25.2 Normal/Overweight 626 20.1 522 83.4 104 16.6 Obesity Cognitive function 560 17.9 304 54.3 256 45.7 Cognitive impairment 897 28.7 682 76.0 215 24.0 Cognitive decline 1,664 53.3 1,345 80.8 319 19.2 Normal BMI-Cognitive function 72 2.3 29 40.3 43 59.7 Thin-Cognitive impairment 77 2.5 46 59.7 31 40.3 Thin-Cognitive decline 95 3.0 51 53.7 44 46.3 Thin-Normal 401 12.9 212 52.9 189 47.1 Normal/Overweight-Cognitive impairment 653 20.9 496 76.0 157 24.0 Normal/Overweight-Cognitive decline 1,197 38.4 975 81.5 222 18.6 Normal/Overweight-Normal 87 2.8 63 72.4 24 27.6 Obesity-Cognitive impairment 167 5.4 140 83.8 27 16.2 Obesity-Cognitive decline 372 11.9 319 85.8 53 14.3 Obesity-Normal Sex 1,387 44.4 967 69.7 420 30.3 Male 1,734 55.6 1,364 78.7 370 21.3 Female Age 1,194 38.3 1,040 87.1 154 12.9 65-69 901 28.9 717 79.6 184 20.4 70-74 600 19.2 403 67.2 197 32.8 75-79 426 13.7 171 40.1 255 59.9 ≥80 Education level 1,654 73.7 590 26.3 2,244 71.9 ≤ Elementary school 316 10.1 240 76.0 76 24.1 Middle school 393 12.6 305 77.6 88 22.4 High school 132 78.6 36 21.4 168 5.4 ≥ College Residential region 1,818 58.3 1,368 75.3 450 24.8 Urban 1,303 41.8 963 73.9 340 26.1 Rural Marital status 2,077 66.6 1,602 77.1 475 22.9 Married 1,044 33.5 729 69.8 315 30.2 Single Economic activity status 614 19.7 520 84.7 94 15.3 Yes 2,507 80.3 1,811 72.2 696 27.8 No Smoking status 2,235 71.6 1,733 77.5 502 22.5 Smoker 386 12.4 264 68.4 122 31.6 Former smoker 500 16.0 334 66.8 166 33.2 Never Alcohol use 916 29.4 686 74.9 230 25.1 Drinker 297 9.5 193 65.0 104 35.0 Former Drinker 1,908 61.1 1,452 76.1 456 23.9 Never Self-rated health 642 20.6 530 82.6 112 17.5 Good 2,149 68.9 1,621 75.4 528 24.6 Moderate 330 10.6 180 54.6 150 45.5 Bad Social Engagement 451 14.5 280 62.1 171 37.9 Ⅰ(Lowest) 958 30.7 680 71.0 278 29.0 Ⅱ 722 23.1 570 79.0 152 21.1 Ⅲ 347 11.1 280 80.7 67 19.3 Ⅳ 643 20.6 521 81.0 122 19.0 Ⅴ(Highest) Diabetes 2,627 84.2 1,988 75.7 639 24.3 No 494 15.8 343 69.4 151 30.6 Yes Number of chronic disease* 1,240 39.7 944 76.1 296 23.9 0 1,200 38.5 895 74.6 305 25.4 1 681 21.8 492 72.3 189 27.8 ≥2 3,121 100.0 2,331 74.7 790 25.3 Total *Hypertension, osteoarthritis, rheumatoid arthritis, cancer, chronic pulmonary disease, liver disease, cardiovascular disease, cerebrovascular disease, mental illness
16
<.0001
<.0001
<.0001
<.0001
0.211
0.395
<.0001
<.0001
<.0001
<.0001
<.0001
<.0001
0.003
0.172
Table 2. Adjusted effect between BMI and Cognitive function and mortality HR
All-cause mortality 95% CI
P-value
BMI Thin Normal/Overweight Obesity
2.189 1.445 1.000
1.658 1.167
2.891 1.790
<.0001 0.001
Cognitive impairment Cognitive decline Normal
1.703 1.150 1.000
1.386 0.954
2.092 1.386
<.0001 0.143
Male Female
2.336 1.000
1.869
2.918
<.0001
65-69 70-74 75-79 ≥80
1.000 1.434 2.240 4.249
1.154 1.796 3.382
1.781 2.794 5.337
0.001 <.0001 <.0001
≤ Elementary school Middle school High school ≥ College
1.043 1.203 1.171 1.000
0.728 0.805 0.792
1.495 1.797 1.733
0.818 0.368 0.429
Urban Rural
0.965 1.000
0.830
1.121
0.638
Married Single
0.775 1.000
0.646
0.931
0.006
Yes No
0.645 1.000
0.511
0.812
0.000
Smoker Former smoker Never
0.843 0.944 1.000
0.685 0.741
1.038 1.204
0.108 0.643
Drinker Former Drinker Never
0.925 1.011 1.000
0.761 0.790
1.125 1.295
0.435 0.930
Good Moderate Bad
1.000 1.149 1.868
0.925 1.413
1.426 2.470
0.209 <.0001
Ⅰ(Lowest) Ⅱ Ⅲ Ⅳ Ⅴ(Highest)
1.611 1.402 1.191 1.092 1.000
1.262 1.127 0.937 0.809
2.057 1.744 1.514 1.475
0.000 0.002 0.153 0.565
Yes No
1.397 1.000
1.161
1.682
0.000
Cognitive function
Sex
Age
Education level
Residential region
Marital status
Economic activity status
Smoking status
Alcohol use
Self-rated health
Social Engagement
Diabetes
Number of chronic disease* 1.000 0 1.051 0.889 1.242 0.561 1 1.131 0.926 1.382 0.228 ≥2 *Hypertension, osteoarthritis, rheumatoid arthritis, cancer, chronic pulmonary disease, liver disease, cardiovascular disease, cerebrovascular disease, mental illness
17
Table 3. association between BMI and Cognitive function and mortality HR
All-cause mortality 95% CI
P-value BMI-Cognitive function 2.666 1.728 4.113 <.0001 Thin-Cognitive impairment 2.153 1.358 3.413 0.001 Thin-Cognitive decline 2.927 1.936 4.428 <.0001 Thin-Normal 2.470 1.774 3.439 <.0001 Normal/Overweight-Cognitive impairment 1.533 1.113 2.111 0.009 Normal/Overweight-Cognitive decline 1.204 0.889 1.631 0.230 Normal/Overweight-Normal 1.260 0.765 2.078 0.364 Obesity-Cognitive impairment 1.051 0.657 1.681 0.837 Obesity-Cognitive decline 1.000 Obesity-Normal Sex 2.350 1.881 2.936 <.0001 Male 1.000 Female Age 1.000 65-69 1.446 1.163 1.796 0.001 70-74 2.238 1.794 2.794 <.0001 75-79 4.233 3.371 5.315 <.0001 ≥80 Education level 1.021 0.712 1.463 0.912 ≤ Elementary school 1.176 0.786 1.758 0.430 Middle school 1.169 0.789 1.731 0.436 High school 1.000 ≥ College Residential region 0.978 0.841 1.138 0.776 Urban 1.000 Rural Marital status 0.762 0.634 0.916 0.004 Married 1.000 Single Economic activity status 0.651 0.517 0.821 0.000 Yes 1.000 No Smoking status 0.855 0.694 1.053 0.141 Smoker 0.941 0.738 1.200 0.625 Former smoker 1.000 Never Alcohol use 0.940 0.773 1.143 0.533 Drinker 1.005 0.784 1.288 0.970 Former Drinker 1.000 Never Self-rated health 1.000 Good 1.130 0.909 1.403 0.270 Moderate 1.879 1.420 2.486 <.0001 Bad Social Engagement 1.626 1.274 2.076 <.0001 Ⅰ(Lowest) 1.423 1.144 1.770 0.002 Ⅱ 1.220 0.959 1.551 0.105 Ⅲ 1.129 0.835 1.526 0.432 Ⅳ 1.000 Ⅴ(Highest) Diabetes 1.418 1.178 1.707 0.000 Yes 1.000 No Number of chronic disease* 1.000 0 1.042 0.882 1.232 0.627 1 1.121 0.917 1.370 0.265 ≥2 *Hypertension, osteoarthritis, rheumatoid arthritis, cancer, chronic pulmonary disease, liver disease, cardiovascular disease, cerebrovascular disease, mental illness
18
Table 4. association between BMI and Cognitive function and all-cause mortality by gender HR BMI-Cognitive function Thin-Cognitive impairment Thin-Cognitive decline Thin-Normal Normal/Overweight-Cognitive impairment Normal/Overweight-Cognitive decline Normal/Overweight-Normal Obesity-Cognitive impairment Obesity-Cognitive decline Obesity-Normal *Adjusted for all variables
2.697 1.744 2.982 2.138 1.791 1.393 0.952 1.328 1.000
95% CI Male 1.347 0.834 1.799 1.333 1.183 0.955 0.353 0.692
19
5.403 3.650 4.945 3.429 2.712 2.032 2.569 2.550
All-cause mortality P-value HR
0.005 0.140 <.0001 0.002 0.006 0.086 0.923 0.394
2.217 2.190 2.877 2.207 1.202 0.838 1.182 0.848 1.000
95% CI Female 1.208 1.158 1.354 1.331 0.723 0.491 0.617 0.424
4.068 4.145 6.114 3.661 1.996 1.428 2.263 1.693
P-value
0.010 0.016 0.006 0.002 0.478 0.515 0.614 0.639
Supplementary 1. Combination between BMI and Cognitive function and all-cause mortality
Keypoints By compounding the effects of BMI and cognitive function, the risk of mortality increased with worsened cognitive function in each BMI group The overall tendency showed increased risk of mortality due to decreased BMI and worsened cognitive function The observed tendencies were more apparent in female participants
Conflict of interest: There is no inflict no interest