The association between multidimensional obesity and cognitive function in the older adults: a cross-sectional study based on NHANES 2011-2014

doi:10.21203/rs.3.rs-4956846/v1

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The association between multidimensional obesity and cognitive function in the older adults: a cross-sectional study based on NHANES 2011-2014

https://doi.org/10.21203/rs.3.rs-4956846/v1

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Objective The eight indicators of Waist Circumference (WC), Body Mass Index (BMI), Body Roundness Index (BRI), A Body Shape Index (ABSI), Centrality Index (CI), Visceral Adiposity Index (VAI), Waist-to-Height Ratio (WHtR), and Waist-to-Weight Ratio (WWI) were used to assess the multidimensional relationship between obesity and cognitive function.

Methods By using data from The National Health and Nutrition Examination Survey database, researchers selected eight different obesity indices as obesity indicators and used a linear regression model to analyze the relationship between obesity and cognitive function.

Results This cross-sectional study analyzed cognitive function in 736 individuals aged 60 and older. Upon controlling for all potential confounding variables, there exists a substantial negative correlation between BRI, the WHtR and Animal fluency test. There exist statistically noteworthy negative correlations between the ABSI and the Digit Symbol Substitution Test (DSST) score. There were statistically significant negative associations between the 4st quartile WWI and the DSST score.

Conclusions In addition to focusing on traditional types of obesity such as BMI and WC, more attention should be paid to the risks of cognitive function decline brought about by obesity indicators such as BRI, ABSI, WHtR, and WWI.

Biological sciences/Neuroscience

Biological sciences/Psychology

Health sciences/Medical research

obesity indices

cognitive function

Older adults

NHANES

Cognitive function is an important part of human intellectual activities, covering multiple aspects such as memory, attention, thinking, and language^1,2. Good cognitive function is of great significance to individuals' learning, work, and life. There are approximately 5 million people worldwide who suffer from dementia, and this number is expected to increase to 152 million by 2050³. Cognitive impairment is mainly manifested as a decline in memory, attention, language skills, executive function, and other aspects, which can affect patients' daily lives and bring a heavy burden to their families⁴.

Age is one of the main risk factors for cognitive impairment. With age, the structure and function of the brain gradually undergo degenerative changes, leading to a decline in cognitive function⁵. For example, the incidence of dementia increases from 6% for people over 65 to one-third for people over 85³. Additionally, some genetic diseases, such as Alzheimer's disease, are closely related to the occurrence of cognitive impairment^6,7. These diseases have a familial clustering, indicating that genetic factors play an important role in the development of cognitive impairment. Poor lifestyle habits such as staying up late, alcohol abuse, smoking, etc., may have a negative impact on the brain and accelerate the decline in cognitive function⁸. Long-term exposure to environmental pollutants, such as harmful substances and heavy metals, and other environmental factors such as lack of social interaction may also increase the risk of cognitive impairment⁹.

With the improvement of living standards and changes in dietary structure, the number of obese people continues to increase¹⁰. The issue of obesity has become a major challenge in the field of global public health. Visceral obesity is an important indicator of obesity, which can be accurately quantified through computer tomography (CT) or magnetic resonance imaging (MRI)¹¹. However, this method has the disadvantages of high cost and exposure to electromagnetic radiation. BMI and WC are more commonly used indicators for assessing visceral obesity^{12 13}. In recent years, however, there have been indicators that combine anthropometric measurements with blood lipid values, such as VAI, which demonstrate higher accuracy in identifying visceral obesity¹⁴. In summary, there are various obesity indicators that can be used to describe the condition of body fat accumulation, such as WC, BMI, BRI, ABSI, CI, VAI, WHtR, and WWI.

Obesity is not only closely related to physical health issues such as cardiovascular disease and diabetes¹⁵, but in recent years, an increasing number of studies have shown that obesity may also have a negative impact on individual cognitive function^{15 16}. Obesity may damage cognitive function by affecting the normal operation of the nervous system. For example, obesity may lead to physiological changes such as abnormal blood lipids and insulin resistance, which may affect the blood supply and oxygen supply to the brain, thereby affecting cognitive function^17,18. In exploring the relationship between obesity and cognitive function, we must note that although a large number of studies^{19 20} have explored this topic, most of them use a single obesity index and lack a comparative analysis of influencing factors of different obesity indicators. This limitation may make it difficult for us to accurately grasp the complexity and diversity of obesity's impact on cognitive function. Therefore, to fully understand this area, we urgently need more detailed and systematic research to reveal the specific relationship between different obesity indicators and cognitive function.

We adopt a multi-dimensional evaluation method and select eight obesity indexes to comprehensively evaluate the characteristics of obesity. These indicators help us more accurately judge the degree of obesity of individuals. We use multiple linear regression methods to conduct a study on the correlation between multi-dimensional obesity and cognitive function.

2.1 Study population

The National Health and Nutrition Examination Survey (NHANES) is a comprehensive program of epidemiological studies conducted by the National Center for Health Statistics (NCHS) to assess the health and nutritional status of adults and children in the United States. The NHANES website provides detailed information on the survey's methodology, data collection processes, and the latest findings from the program's ongoing research (https://www.cdc.gov/nchs/nhanes/index.htm). All individuals involved in the survey gave their informed consent, and the survey protocol was granted approval by the National Health Statistics Research Ethics Review Board, ensuring adherence to ethical standards and participant rights.

The research employed two cycles of NHANES data, including 2011–2012, 2013–2014. Out of 19931 participants, 14059 filled out the obesity indices, 4548 participants excluded without cognitive information, and the survey of cognitive function questionnaire is aimed at people over 60 years old. At the same time, 588 were excluded due to missing covariate. Finally, 736 participants aged 60 and older

were included for the analysis. The flow gram of screening out eligible participants from NHANES program as shown in Fig. 1.

2.2 multidimensional obesity

To investigate and compare the correlations between various obesity indices and cognitive function, we utilized data from the NHANES database and selected eight distinct obesity indicators: WC, BMI, BRI, ABSI, CI, VAI, WHtR, WWI. Each employ the 25th, 50th,75th and 100th percentiles (p25, p50, p75, p100) as threshold values. Except for WC, the remaining anthropometric indices were calculated using the following Eqs. 2^1–25:

WWI is computed as WC in centimeters divided by the square root of weight in kilograms.

2.3 cognitive function

In the National Health and Nutrition Examination Survey (NHANES) cycles spanning from 2011 to 2014, a series of cognitive performance assessments were introduced to gain insights into cognitive functioning. These NHANES 2011–2014 modules encompass a comprehensive range of tests, including the Consortium to Establish a Registry for Alzheimer's Disease (CERAD)²⁶, the Animal Fluency Test (AFT)²⁷, and the Digit Symbol Substitution Test (DSST)²⁸. Each of these tools serves as a valuable metric in assessing various aspects of cognitive performance, thus contributing significantly to the overall understanding of cognitive health in the general population.

The CERAD test, comprising a rigorous suite of three learning assessments along with an additional delayed recall exercise, was administered to assess distinct cognitive abilities, encompassing the proficiency in acquiring novel words, delayed recollection, and recognition memories. In each phase of the examination, participants were tasked with reading and subsequently recollecting ten words provided by the NHANES staff. It is imperative to highlight that the CERAD test serves as a paramount instrument for evaluating episodic or declarative memory. The scoring system for each individual test is designed to range from 0 to 10, with the cumulative score for the entire battery (CERAD-WL and CERAD-DR) capping at 40 points, the higher score means stronger cognitive function. The current analysis encompasses both the CERAD-WL cognitive assessment and the CERAD-DR cognitive examination, providing a comprehensive evaluation of cognitive capabilities.

The AFT endeavors to thoroughly assess categorical verbal proficiency. Within the span of one minute, participants will be tasked with enumerating various animals, facilitated by the NHANES staff. For every animal successfully named in response to each inquiry, a point will be awarded, thereby providing a quantitative measure of the individual's categorical verbal fluency.

The DSST, stemming from the Wechsler Adult Intelligence Scale, serves as a comprehensive cognitive performance evaluation. It relies heavily on an individual's processing speed, their proficiency in maintaining sustained attention, and their working memory capabilities. Furthermore, this test serves as a pivotal experimental instrument in understanding the complexities of associative learning among humans ²⁹. During the examination, participants are tasked with reproducing the corresponding graphic symbol in precisely one hundred and thirty-three boxes within a stringent two-minute time frame. Each accurate match earns a point, culminating in a potential total of 133 points. However, it is noteworthy that, to date, there exists no formalized benchmark for determining a cutoff point in the CERAD test, Animal Fluency test, and DSST, for identifying cognitive deficiencies in participants. Nevertheless, this study adopts a well-established approach, aligning with prior research, where in the 25th quantile of each cognitive test's score serves as the designated cutoff point for identification^30,31.

2.4 Covariates

We took into account the following variables as covariates: gender, age, race, education level, marital status, family income to poverty ratio, smoking habits, alcohol consumption, diabetes status, stroke, coronary heart disease, and blood pressure.

There were five race categories were: Mexican American; Other Hispanic; Non-Hispanic White;Non-Hispanic Black; Other Race. There were five education level categories: Less than 9th grade; 9-11th grade; High school graduate/GED or equivalent; Some college or AA degree; College graduate or above. Ratio of family income to poverty: <1.3; 1.3–3.5; >3.5. Marital status was separated into 2 states: Married Living with partner; Widowed/Divorced/Separated/Never married. The diabetes diagnosis was identified by the questionnaire “Doctor told you have diabetes”, which were divided into three outcomes: Yes; No; Borderline.

Stroke was identified by the questionnaire answers “Has a doctor or other health professional ever told you that you had a stroke?”Coronary was identified by the questionnaire answers “ Has a doctor or other health professional ever told you that you had coronary heart disease?” Hypertension was identified by the questionnaire answers “Were you told on 2 or more different visits that you had hypertension, also called high blood pressure?”. The alcohol drinking states could be identified by the questionnaire answers “ Had at least 12 alcohol drinks/1 yr?”. The smoking status was in state “Smoked at least 100 cigarettes in life”.

2.5 Statistical Analysis

Weighted analyses were used in our analysis. participants were divided into obesity indices quartiles, and both Kruskal-Wallis test and Wilcoxon test were taken to evaluate demographic disparities. The lowest quartile (Q1) was used as the reference group for each obesity indices, then we fitted the multivariate linear regression to evaluate the effect of a obesity indice on cognitive function by comparing Q2, Q3, Q4 to Q1. Obesity indices were further used to analyze both in continuous variables and quartiles. Take the logarithm (ln) transformation of the data that do not belong to the normal distribution. In Multivariate linear regression analysis, Model 1 was constructed to directly analyze the relationship between obesity indices and cognitive impairment respectively, and then two additional models with different covariates were constructed for further analysis. Model 2 has further adjustment, incorporating factors such as age, gender, race, education, marital status and income-to-poverty ratio. Model 3 underwent further adjustments, incorporating variables such as smoking habits, alcohol consumption, diabetes status, stroke, coronary heart disease, and blood pressure. All statistical analyses were meticulously performed using Stata 17.0 software (Stata Corporation, College Station, TX, USA), with a two-sided significance level of p < 0.05 indicating statistical significance.

The population characteristics are shown in Table 1.736 individuals were contained in this study, 56.55% were females and 43.45% were male. The participants were 60 years old, 72.83% were 60–75 years and 27.17% were 75–80 years old. We observed a significant age difference among CERAD test, Animal Fluency test and DSST scores (p<0.01). Race, Education level and Ratio of family income to poverty were all significant in CERAD test, Animal Fluency test and DSST scores (p<0.01). While there is a statistically significant difference in Gender only for CERAD test and DSST scores. Diabetes is only statistically significant in Animal Fluency test and DSST scores.

The numerical range of various obesity index is divided into four quartiles, with the first quartile Q1 as the reference score. The values of various obesity indicators of different quartiles are summarized in Table 2.

Table 1

Basic characteristics of participants by obesity indices tertiles among US older adults
Characteristics	N	Weighted(%)	CERAD test		Animal fluency test		Digit symbol test
			Cognitive performance	p	Cognitive performance	p	Cognitive performance	p
Age(years)				<0.01		<0.01		<0.01
60–75	529	72.83	27.117(26.039, 28.194)		18.556(18.028, 19.083)		54.452(51.983, 56.92)
75–80	207	27.17	22.496(21.164, 23.827)		15.892(15.333, 16.451)		41.089(39.077, 43.1)
Gender				<0.01		0.385		<0.01
Male	338	43.45	25.293(24.226,26.359)		18.231(17.495,18.966)		49.654(47.694,51.613)
Female	398	56.55	26.298(25.017,27.579)		17.526(16.845,18.207)		51.718(49.128,54.308)
Race				<0.01		<0.01		<0.01
Mexican American	60	3.15	25.099(23.654,26.545)		16.226(14.595,17.856)		41.416(36.001,46.832)
Other Hispanic	61	3.04	22.653(21.238,24.069)		15.081(13.968,16.194)		33.602(29.473,37.73)
Non-Hispanic White	375	79.91	26.102(24.867,27.338)		18.436(17.737,19.135)		52.98(50.273,55.686)
Non-Hispanic Black	178	8.73	24.969(23.841,26.098)		14.626(13.956,15.296)		40.391(37.19,43.592)
Other Race	62	5.16	25.995(23.919,28.072)		16.51(13.634,19.386)		50.954(45.563,56.345)
Education level				<0.01		<0.01		<0.01
Less than 9th grade	74	6.07	22.371(19.465,25.278)		13.988(12.18,15.796)		28.919(25.646,32.192)
9-11th grade	107	11.31	22.795(21.787,23.802)		14.974(14.111,15.837)		38.787(34.791,42.782)
High school graduate/GED or equivalent	181	22.14	25.145(23.52,26.77)		17.35(16.614,18.085)		47.452(44.612,50.292)
Some college or AA degree	225	36.42	27.071(25.834,28.309)		18.242(17.346,19.139)		53.745(51.204,56.286)
College graduate or above	149	24.06	27.011(25.558,28.465)		19.969(18.867,21.07)		60.679(57.2,64.158)
Ratio of family income to poverty				<0.01		<0.01		<0.01
<1.3	215	18.38	24.211(23.119,25.303)		15.701(14.7,16.702)		39.514(36.394,42.634)
1.3–3.5	298	40.47	25.083(24.003,26.163)		16.973(16.344,17.601)		47.702(45.77,49.633)
>3.5	223	41.15	27.364(25.914,28.813)		19.629(18.922,20.336)		58.938(55.699,62.177)
Marital status				0.339		0.099		0.058
MarriedLiving with partner	446	66.05	26.285(24.931,27.639)		18.184(17.572,18.796)		52.097(49.723,54.471)
Widowed/Divorced/Separated/Never married	290	33.95	25.036(23.846,26.227)		17.147(16.151,18.143)		48.339(45.456,51.222)
Diabetes				0.161		<0.01		<0.01
Yes	194	22.72	24.97(23.385,26.555)		16.672(15.484,17.861)		45.479(42.806,48.152)
No	501	71.96	26.145(24.932,27.359)		18.188(17.508,18.868)		52.411(49.72,55.102)
Borderline	41	5.32	25.825(24.066,27.585)		17.97(16.707,19.234)		52.13(45.481,58.78)
Stroke				0.305		0.267		0.137
Yes	63	8.04	25.459(24.011,26.907)		17.385(15.13,19.641)		46.53(41.311,51.749)
No	673	91.96	25.896(24.766,27.027)		17.871(17.336,18.406)		51.196(48.917,53.476)
Coronary				<0.01		0.484		0.023
Yes	94	14.41	24.532(23.105,25.959)		17.274(16.216,18.332)		45.633(42.144,49.122)
No	642	85.51	26.086(24.891,27.282)		17.927(17.384,18.469)		51.7(49.428,53.972)
Hypertension				0.339		0.082		0.543
Yes	620	84.37	26.001(24.995,27.007)		17.701(17.242,18.16)		50.909(49.165,52.652)
No	116	15.63	25.109(23.062,27.157)		18.538(17.022,20.053)		50.349(44.97,55.727)
Alcohol use				0.141		0.095		0.093
Yes	500	72.4	26.221(24.972,27.469)		18.212(17.624,18.801)		52.729(50.092,55.365)
No	236	27.6	24.919(23.957,25.881)		16.835(16.002,17.667)		45.818(43.104,48.532)
Smoking status				0.415		0.189		0.77
Yes	500	55.44	25.857(24.496,27.218)		17.989(17.351,18.628)		50.667(47.973,53.361)
No	236	44.56	25.866(24.795,26.938)		17.637(16.856,18.418)		51.013(48.237,53.79)

Table 2

The numerical distribution of various obesity indexes
obesity indices	Q1	Q2	Q3	Q4
WC	4.241–4.543	4.543–4.635	4.635–4.729	4.729–5.092
BMI	2.754–3.245	3.245–3.371	3.371–3.512	3.512–4.143
BRI	0.856–1.567	1.567–1.778	1.778–2.014	2.014–2.736
ABSI	-2.726–2.507	-2.507–2.469	-2.469–2.434	-2.434- -2.316
CI	5.283–5.780	5.780–5.927	5.927–6.093	6.093–6.710
VAI	-1.615–0.041	-0.041- 0.498	0.498–0.961	0.961–2.730
WHtR	-0.814–0.560	-0.560–0.476	-0.476–0.378	-0.378- -0.624
WWI	9.274–11.112	11.112–11.567	11.567–12.018	12.018–14.196
Notes: WC, BMI, BRI,ABSI, CI ,VAI and WHtR have been converted through natural logarithm (ln).

The relationships between obesity indices and cognitive function are illustrated in Table 3. WC had no correlation with CERAD test, Animal fluency test and the DSST test in all three linear regression models. In the fully adjusted model (Model 3), a significant negative correlation was observed between BRI and Animal fluency test. Comparisons between individuals in the 2st quartile of the of BRI revealed declines in Animal fluency test scores of 0.098. There is a negative correlation between continuous ABSI and DSST in the three models, which are [β= -49.125, (-86.585,-11.665)], [β= -36.911, (-64.800,-9.021)], and [β= -31.132, (-59.081,-3.184)] respectively, and both ABSI and DSST are negatively correlated in the fourth layer of the three models. In the examination of the three models, it has been observed that there exists no discernible correlation between the variable of VAI and cognitive. This finding indicates that the influence of VAI on cognitive abilities remains undetected or negligible within the given frameworks.

In Model 3, a discernible negative correlation emerged between the WHtR and the Animal fluency test, indicating that with every incremental unit of WHtR, the Animal fluency test score declined by 0.097 points [β=-0.097, (-0.193,-0.001)], highlighting a potential health indicator. Furthermore, the statistical significance of the correlation between continuous WHtR and DSST scores remained consistent across all three models, providing a robust foundation for our analysis. Notably, the WHtR in each of these models revealed profound relationships with the DSST score. Specifically, in Model 1, an increment of one unit in WWI was associated with a corresponding decrease of 4.890 points in the DSST scores, demonstrating a moderate but significant impact [β=-4.890, 95% CI=(-8.849,-0.932)]. Similarly, Model 2 showed a decrement of 3.134 points in DSST scores for every unit increase in WWI [β=-3.134, 95% CI=(-5.559,-0.710)], comparisons between individuals in the 4st quartile of the of WWI revealed declines in the DSST scores. There is a negative correlation between the 4st quartile of the of WWI and DSST in the three models, which are [β= -8.973, 95% CI= (-15.521,-2.426)], [β= -5.778, 95% CI=(-9.788,-1.767)], and [β= -4.525, 95% CI= (-8.614,-0.436)] respectively.

The goal of this study is to deeply explore the potential interactions between obesity index and cognitive function. Through the analysis of the data set, we have found significant correlations between various obesity indexes and cognitive function parameters, including memory, attention, executive function and information processing speed.

Although BMI and WC are widely recognized as indicators for measuring obesity, they often fail to distinguish between adipose tissue and muscle tissue³². In our study, we not only included traditional obesity indices, but also new indicators such as BRI, ABSI, CI, VAI, WHtR, WWI, which consider changes in body composition, including muscle and fat mass. These indices provide a more comprehensive assessment of central obesity^33,34.

So far, there is still a lack of literature on the relationship between BRI and cognitive function. We found that the relationship between BRI and cognitive function is relatively complex, and only the second quartile shows a negative correlation with animal fluency tests. Our research results reveal a statistically significant negative correlation between WHtR and DSST scores, which is consistent with previous research results³⁵. In addition, ABSI is negatively correlated with cognitive function in three different models, which is in agreement with previous research findings²¹.

VAI not only comprehensively reflects the degree of visceral fat deposition but also more accurately reflects the individual's metabolic health status. Surprisingly, our research did not find a clear link between VAI and cognitive performance. Previous studies have shown a significant association between VAI and the slow decline in cognitive ability in Chinese people of middle age and the elderly, especially in terms of episodic memory³⁶. The differences in empirical results may be attributed to various factors, including cultural differences, social backgrounds, or individual characteristics of research participants, which may all affect research results. To further elucidate the complex relationship between VAI and cognition, more research work is urgently needed.

There may be three explanations for the impact of obesity on cognitive function. One possible mechanism is that metabolic disorders caused by obesity, such as insulin resistance and hyperglycemia, may have a direct impact on brain function^37,38. Obesity may also trigger a chronic inflammatory state³⁹, which may interfere with the normal function of neuronal pathways. At the same time, obesity is closely related to cardiovascular health. Cardiovascular disease may lead to a decrease in cerebral blood flow ¹⁶, thereby impairing cognitive function.

Although our research provides a new perspective and valuable insights into the impact of multi-dimensional obesity on cognitive impairment, it is not without limitations. Our research is based on cross-sectional data and cannot determine the causal relationship between obesity and cognitive function. Future longitudinal studies can more accurately describe the relationship between the two.

This new conclusion not only expands our understanding of the interaction between the two, but also provides a solid theoretical foundation for future research.

Our thorough research has revealed a profound correlation between elevated indices of ABSI, BRI, WHtR, and WWI, and a conspicuous decrement in cognitive functionality. This inverse relationship highlights the deleterious impact that these indicators, which often serve as proxies for unhealthy body composition, have on cognitive processes.

Data availability statement: This study analyzed datasets that are publicly accessible. The data utilized can be accessed through the NHANES repository. Please refer to the following URL: https://www.cdc.gov/nchs/nhanes/index.htm.

Author Contributions:Conceptualization, N.L; methodology, N.L.and J.L.; data curation, G.Q. and X.F; original draft preparation, N.L.; review and editing N.L., Y.L, L.L; funding acquisition. All authors have read and agreed to the published version of the manuscript.

Funding: The research project of Nanjing Youan Hospital in 2023 (grant number: 2023YAKY01).

Declaration of Competing Interest: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Conflicts of Interest: The authors declare no conflicts of interest.

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Table 3 is available in the Supplementary Files section.

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The association between multidimensional obesity and cognitive function in the older adults: a cross-sectional study based on NHANES 2011-2014

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Abstract

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1 Introduction

2 Materials and Methods

2.1 Study population

2.2 multidimensional obesity

2.3 cognitive function

2.4 Covariates

2.5 Statistical Analysis

3 Results

4 Discussion

5 Conclusions

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References

Tables

Additional Declarations

Supplementary Files

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