Association between relative fat mass and cognitive function among US older men: NHANES 2011–2014

Association between relative fat mass and cognitive function among US older men: NHANES 2011–2014 | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Association between relative fat mass and cognitive function among US older men: NHANES 2011–2014 Linlin Liu, Anshi Wu, Shengnan Yang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6114375/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 08 May, 2025 Read the published version in Lipids in Health and Disease → Version 1 posted 8 You are reading this latest preprint version Abstract Background This study aimed to investigate the relationship between cognitive performance and relative fat mass (RFM) in older American males. Methods For the purpose of this cross-sectional research, a total of 1,321 individuals were selected from the National Health and Nutrition Examination Survey (NHANES), which was carried out between the years 2011 and 2014. Specifically, the Consortium to Establish a Registry for Alzheimer’s Disease Word Learning Test (CERAD-WL), the Animal Fluency Test (AFT), and the Digit Symbol Substitution Test (DSST) were used in order to achieve the objective of assessing cognitive function. Z-scores were calculated as a composite, generalized metric by taking the average of the standardized scores of the three previously mentioned assessments. RFM was ascertained by measuring waist circumference (WC) and height. Multivariate linear regression, threshold effect analyses, smooth curve fitting, and subgroup analyses were undertaken in order to investigate the connections that exist between RFM and cognitive function. Results The study included 1,321 male volunteers who were at least 60 years old, and complete data was provided for each individual. Fully adjusted models indicated a negative correlation between RFM and CERAD-WL scores[-0.17, (-0.32,-0.01)], DSST scores[-0.83, (-1.16,-0.50)] and Z-scores[-0.03, (-0.05, -0.01)]. It was observed that the negative correlation between RFM and Z-scores became more pronounced when RFM exceeded 35.78. Furthermore, subgroup analyses showed that the association between RFM and cognitive function was significantly impacted by education level, poverty-income ratio (PIR), smoking status, and drinking status. Conclusions In elderly men, a higher RFM was linked to lower cognitive function, suggesting that management of RFM may prove advantageous in mitigating cognitive decline among older male populations. Relative Fat Mass(RFM) cognitive function NHANES obesity older men Figures Figure 1 Figure 2 Introduction Cognitive impairment (CI) pertains to the reduction or weakening of cognitive capabilities. These capabilities cover functions like memory, language, attention, problem - solving, and executive functions. CI includes mild cognitive impairment (MCI) and different forms of dementia. As the global population ages, cognitive dysfunction in the elderly is becoming a significant challenge for global public health. Projections suggest that by 2050, over 130 million people globally will suffer from dementia[ 1 ]. Cognitive dysfunction impacts not just the well-being of older adults, but also creates significant challenges for their families and society[ 2 ].Research indicates that healthy older women exhibit superior verbal fluency, verbal memory and processing speed in comparison to their male counterparts[ 3 ]. These differences may stem from the positive effects of estrogen on the maintenance of neurocognitive integrity[ 4 , 5 ]. Identifying the risk factors affecting cognitive function, especially those that can be modified, and developing effective interventions can help alleviate the enormous public health pressures that lie ahead globally. Obesity constitutes a significant public health challenge on a global scale[ 6 , 7 ]. Compared to Body Mass Index (BMI), relative fat mass (RFM) provides a more accurate assessment of adults' total body fat percentage, making it a unique tool for assessing obesity[ 8 – 12 ].RFM is especially applicable in the context of clinical and epidemiological research[ 8 ]. Empirical evidence indicates that RFM is correlated with various health conditions, including hypertension, type 2 diabetes, coronary artery disease, and depression[ 13 – 16 ]. No research has been conducted on RFM and cognitive function to date. We investigated the connection between RFM and cognitive function in older males using the National Health and Nutrition Examination Survey (NHANES) database from 2011 to 2014 to assist clinical research. Materials and Methods Study population Health examinations and interviews from a countrywide representative sample of U.S. citizens and civilian populations living in communities was gathered from the NHANES sample using a complicated, multistage sampling design.This research analyzed a dataset from the NHANES conducted between 2011 and 2014. Informed permission in writing was acquired from all participants of NHANES. Our work was classified as exempt from ethical review due to its use of publicly accessible NHANES data. A comprehensive amount of information on cognitive function assessment and RFM was supplied by the participants.The study’s initial recruitment included 19,931 participants. However, after removing participants who were female (n = 10072), had incomplete data on cognitive function (n = 8431), missing data on RFM (n = 77) and covariates (n = 30), 1321 participants were remained in the analyses finally (Fig. 1). Variables Cognitive Function The cognitive function evaluations administered by NHANES during the period from 2011 to 2014 included Consortium to Establish a Registry for Alzheimer’s Disease Word Learning Test (CERAD-WL), the Animal Fluency Test (AFT), and the Digit Symbol Substitution Test (DSST) [ 17 – 19 ]. Cohort studies frequently use these three tests to evaluate language proficiency, memory, and general cognitive function[ 20 – 22 ]. The CERAD-WL is comprised of three Immediate Recall Tests (IRT) and one Delayed Recall Test (DRT)[ 23 ]. The IRT measures immediate recall ability, and the DRT measures delayed recall ability[ 24 , 25 ]. The cumulative sum of the scores received in both the IRT and DRT components of the CERAD-WL is used to calculate the overall score for the test. The AFT necessitates that participants identify and enumerate as many animal species as they can within a specified time frame.The scores of the AFT are related to the quality of language function[ 26 , 27 ]. Research showed that the AFT can distinguish between normal elderly and those with cognitive impairment[ 28 , 29 ]. The DSST involves digit - symbol pairs presented in a digit table. Within two minutes, participants are instructed to write the matching symbols in the boxes underneath the numbers[ 30 ]. The DSST was designed to evaluate attention, concentration, hand-eye coordination, information processing speed, and short - term memory. Cognitive function among the elderly population exhibits considerable variability, characterized by both ceiling and floor effects[ 31 ]. Therefore, we averaged the standardized scores [(subject's test score-average score) / standard deviation] of CERAD-WL, AFT, and DSST scores to create new metric called Z-score, providing a comprehensive indicator of cognitive function among the individuals in our research[ 32 , 33 ]. Z-score is calculated as Z-score = \(\:\frac{\frac{Score(CERAD-WL)-Mean(CERAD-WL)}{SD(CERAD-WL)}\:+\:\frac{Score\left(AFT\right)-Mean\left(AFT\right)}{SD\left(AFT\right)}\:+\:\frac{Score\left(DSST\right)-Mean\left(DSST\right)}{SD\left(DSST\right)}}{3}\) There is lack of cutoff point to determine different cognitive function statuses according to these tests. Referring to the published research,we used the lowest quartile value as the cutoff point, and divided the participants into two groups:normal cognitive performance and low cognitive performance. The cutoff point was 19 for CERAD-WL, 12 for AFT, 32 for DSST and − 0.55 for Z-score[ 34 – 36 ]. Relative Fat Mass(RFM) Professionals at the Mobile Examination Center (MEC) took height and waist circumference (WC) measurements. The equation utilized for determining the RFM in males is expressed as follows: RFM = 64 - (20 × Height/WC)[ 8 ]. Covariates This study incorporated the following covariates: age, race, education level, Poverty Income Ratio (PIR), BMI (kg/m²), smoking and drinking status, as well as the presence of hypertension, diabetes, coronary heart disease, and stroke. When it came to drinking status, the threshold for either drinking or not drinking was set at 12 drinks per year. On the other hand, smoking status was defined as smoking more than 100 cigarettes in lifetime. Hypertension, diabetes, coronary heart disease, and stroke were based on participants’ self-reported responses to the questions,“Have you ever been told by a doctor or other health professional that you had hypertension/diabetes/coronary heart disease/stroke”. Statistical analysis R software (version 4.2) and EmpowerStats (version 4.2) were utilized in order to carry out statistical studies. Categorical variables were represented as percentages (%), while continuous variables including were expressed as means ± standard deviations (SD). RFM was additionally employed to examine both continuous variables and quartile distributions. Multivariate linear regression was used to examine the relationship between RFM and cognitive test results. Three models were created to eliminate the influence of confounding factors on the study outcomes. A trend test was conducted to assess the linear progression of cognitive test scores across RFM quartiles. Smooth curve fitting was used in order to explore the nonlinear relationship that exists between RFM and cognitive test result.Threshold effect analyses were applied to identify the inflection point during the investigation process.In addition, subgroup analyses were carried out to evaluate the consistency of the association. Statistical significance was determined using a p -value < 0.05. Results Baseline Characteristics A total of 1,321 individuals took part in the research investigation, with a mean age of 69.23 ± 6.71 years and a mean RFM of 30.41 ± 4.41. The RFM levels from Q1 to Q4 were 24.82 ± 2.63, 29.63 ± 0.77, 31.89 ± 0.69, and 35.78 ± 1.98, respectively. The mean CERAD-WL score was 23.80 ± 6.13, and CERAD-WL levels from Q1 to Q4 were 25.66 ± 6.28, 24.40 ± 5.96, 25.21 ± 5.91, 25.12 ± 5.77, respectively. The mean AFT score was 16.90 ± 5.50, and AFT levels from Q1 to Q4 were19.15 ± 6.18, 18.16 ± 5.77, 18.23 ± 5.60, 18.86 ± 5.44, respectively. The mean DSST score was 43.88 ± 16.14, and DSST levels from Q1 to Q4 were 52.69 ± 16.57, 51.31 ± 15.29, 50.19 ± 14.07, 49.03 ± 15.06, respectively. The mean Z-scores was 0 ± 0.79, and Z-scores levels from Q1 to Q4 were 0.42 ± 0.87, 0.26 ± 0.79, 0.29 ± 0.74, 0.30 ± 0.76, respectively. Cognitive test scores were lower in the Q4 group compared to the Q1 group (Table 1 ). Table 1. Weighted baseline characteristics of participants in RFM quartiles Characteristic Relative Fat Mass(RFM) P -value Q1 11.93-27.90 Q2 27.90-30.66 Q3 30.66-33.20 Q4 33.20-43.21 N=330 N=329 N=331 N=331 Age(years) 67.49±6.23 69.11±6.99 69.84±6.21 68.20±6.27 ＜ 0.0001 Race(%) 0.0064 Mexican American 2.21 3.40 4.43 4.47 Other Hispanic 2.66 4.00 3.81 3.17 Non-Hispanic White 75.66 78.65 83.02 82.65 Non-Hispanic Black 10.78 6.94 5.79 6.76 Other Race 8.69 7.00 2.95 2.94 Education level(%) ＜ 0.0001 Less than 9 th grade 5.72 6.53 5.97 5.78 9-11 th grade 5.15 10.33 9.21 12.78 High school graduate /GED or equivalent 20.58 19.53 18.98 18.63 Some college or AA degree 23.90 21.48 32.02 35.30 College graduate or above 44.64 42.13 33.83 27.50 PIR(%) 0.2115 ＜ 1 6.94 6.83 4.34 8.26 ≥1 93.06 93.17 95.66 91.74 BMI(kg/m² ） ＜ 0.0001 ＜ 25 78.40 17.10 1.28 8.68 25-30 21.60 77.03 60.97 91.32 Continued Table 1. Weighted baseline characteristics of participants in RFM quartiles Characteristic Relative Fat Mass(RFM) P -value Q1 11.93-27.90 Q2 27.90-30.66 Q3 30.66-33.20 Q4 33.20-43.21 ≥30 0 5.87 37.75 0 RFM 24.82±2.63 29.36±0.77 31.89±0.69 35.78±1.98 ＜ 0.0001 Smoking (%) 0.0235 Yes 57.00 56.71 64.43 65.70 No 43.00 43.29 35.57 34.30 Drinking(%) 0.6463 Yes 83.93 86.86 86.84 84.96 No 16.07 13.14 13.16 15.04 Hypertension (%) ＜ 0.0001 Yes 31.75 57.11 59.53 72.25 No 68.25 42.89 40.47 27.75 Diabetes (%) ＜ 0.0001 Yes 8.99 13.21 22.26 38.31 No 91.01 86.79 77.74 61.69 Coronary heart disease (%) 0.0100 Yes 7.85 13.41 13.69 16.55 No 92.15 86.59 86.31 83.45 Stroke (%) 0.0062 Yes 5.54 5.11 3.28 9.37 No 94.46 94.89 96.72 90.63 CERAD-WL scores 25.66±6.28 24.40±5.96 25.21±5.91 25.12±5.77 0.0677 Continued Table 1. Weighted baseline characteristics of participants in RFM quartiles Characteristic Relative Fat Mass(RFM) P -value Q1 11.93-27.90 Q2 27.90-30.66 Q3 30.66-33.20 Q4 33.20-43.21 CERAD-WL Cognitive Function (%) 0.0198 Normal cognitive performance 87.24 78.45 82.87 85.26 Low cognitive performance 12.76 21.55 17.13 14.74 AFT scores 19.15±6.18 18.16±5.77 18.23±5.60 18.86±5.44 0.0799 AFT Cognitive Function (%) 0.4202 Normal cognitive performance 85.66 83.51 87.71 87.05 Low cognitive performance 14.34 16.49 12.29 12.95 DSST scores 52.69±16.57 51.31±15.29 50.19±14.07 49.03±15.06 0.0159 DSST Cognitive Function (%) 0.6897 Normal cognitive performance 89.11 87.93 88.18 86.12 Low cognitive performance 10.89 12.07 11.82 13.88 Z-scores 0.42±0.87 0.26±0.79 0.29±0.74 0.30±0.76 0.0596 Z-scores Cognitive Function (%) 0.9161 Normal cognitive performance 87.29 86.46 85.37 86.38 Low cognitive performance 12.71 13.54 14.63 13.62 Mean ± SD for continuous variables: the P value was calculated by the weighted linear regression model; (%) for categorical variables: the P value was calculated by the weighted chi-square test Abbreviations:RFM:Relative fat mass, CERAD-WL: Consortium to Establish a Registry for Alzheimer’s Disease Word Learning Test, AFT:Animal Fluency Test, DSST:Digit Symbol Substitution Test, Association of RFM with cognitive function RFM was negatively correlated with DSST scores and Z-scores in all three models. In Model 3, for every unit increase in RFM, there was a 0.17-point decrease in CERAD-WL scores ( p <0.05), a 0.83-point decrease in DSST scores ( p <0.05), and a 0.03-point decrease in Z-scores ( p 0.05) (Table 2 ). Table 2 Associations betweenRFM and cognitive function Cognitive function Model 1 β(95%CI) P -value Model 2 β(95%CI) P -value Model 3 β(95%CI) P -value CERAD-WL −0.06(−0.14,0.01)0.0995 −0.05(−0.12,0.02)0.1630 −0.17(−0.32,−0.01) 0.0422 Q1 Ref Ref Ref Q2 −1.26(−2.20,−0.32) 0.0086 −0.75(−1.63,0.12)0.0923 −0.54(−1.49,0.40)0.2563 Q3 −0.45(−1.36,0.47) 0.3414 0.25(−0.61,1.11) 0.5704 0.45(-0.60, 1.51) 0.3998 Q4 −0.54(−1.46,0.37) 0.2469 −0.38(−1.23,0.47)0.3827 0.13(−1.33, 1.60) 0.8578 P for trend 0.620 0.914 0.397 AFT −0.04(−0.11,0.03) 0.2633 −0.05(−0.12,0.02)0.1562 −0.08(−0.23,0.07) 0.2852 Q1 Ref Ref Ref Q2 −0.98(−1.88,−0.08) 0.0329 −0.70(−1.55,0.14)0.1043 −0.39(−1.27,0.49) 0.3863 Q3 −0.92(−1.80,−0.04) 0.0409 −0.61(−1.44,0.23)0.1574 −0.13(−1.11,0.86) 0.8020 Q4 −0.29(−1.17,0.59) 0.5205 −0.36(−1.19,0.47)0.3911 0.61 (−0.76,1.98) 0.3830 P for trend 0.624 0.499 0.458 DSST −0.30(−0.49,−0.11) 0.0019 −0.28(−0.44,−0.12) 0.0008 −0.83(−1.16,−0.50) < 0.0001 Q1 Ref Ref Ref Q2 −1.38(−3.78,1.01) 0.2568 −0.11(−2.14,1.93) 0.9174 −0.96 (−2.92, 0.99) 0.3340 Q3 −2.50(−4.84,−0.16) 0.0364 −0.70(−2.71,1.32)0.4982 −2.13 (−4.32, 0.06) 0.0573 Q4 −3.67(−6.00,−1.33) 0.0021 −3.33(−5.32,−1.34) 0.0011 −5.51 (−8.55,−2.47) 0.0004 P for trend 0.001 <0.001 <0.001 Z-scores −0.01(−0.02,−0.00) 0.0156 −0.01(−0.02,−0.00) 0.0084 −0.03(−0.05,−0.01) 0.0006 Q1 Ref Ref Ref Q2 −0.16(−0.28,−0.03) 0.0130 −0.09(−0.19,0.02) 0.1147 −0.07 (−0.18, 0.03) 0.1719 Q3 −0.13(−0.25,−0.01) 0.0328 −0.04(−0.14,0.07) 0.4857 −0.03 (−0.14, 0.09) 0.6539 Q4 −0.12(−0.24,−0.00) 0.0462 −0.11(−0.22,−0.01) 0.0359 −0.07 (−0.23, 0.09) 0.4041 P for trend 0.088 0.089 0.632 Model 1: variables were not adjusted. Model 2: adjustments were made to age and race. Model 3: Adjustments were made for age, race, education level, PIR, BMI, smoking status, drinking status, hypertension, diabetes, coronary heart disease, stroke. Abbreviations: RFM:Relative Fat Mass , CERAD-WL: Consortium to Establish a Registry for Alzheimer’s Disease Word Learning Test , AFT :Animal Fluency Test , DSST :Digit Symbol Substitution Test , PIR: Poverty Income Ratio , BMI: Body Measure Index. Comparisons between participants in the Q4 and Q1 of RFM showed a 5.51-point reduction in DSST scores ( p for trend0.05). The nonlinear association between RFM and cognitive test results was discovered via the use of smooth curve fitting (Fig. 2). In the CERAD-WL, the inflection point was identified at 35.51, indicating that the negative correlation between RFM and CERAD-WL scores became more noticeable when RFM>35.51. For every unit increase in RFM up to 35.51, CERAD-WL scores decreased by 0.25 points ( p <0.05); however, for every unit increase in RFM above 35.51, CERAD-WL scores decreased by 1.17 points ( p <0.05). In the AFT, the inflection point was identified at 37.16, indicating that the negative correlation between RFM and AFT scores is not significant when RFM ≤ 37.16; however, for every unit increase in RFM above 37.16, AFT scores decreased by 1.26 points ( p 33.01. For every unit increase in RFM up to 33.01, DSST scores decreased by 0.92 points ( p <0.05); however, for every unit increase in RFM above 33.01, DSST scores decreased by 2.45 points ( p 35.78. For every unit increase in RFM up to 35.78, Z-scores decreased by 0.04 points ( p <0.05); however, for every unit increase in RFM above 35.78, Z-scores decreased by 0.17 points ( p <0.05) (Table 3 ). Table 3 Threshold effect analyses of RFM on cognitive function Models CERAD-WL AFT DSST Z-scores Model I One line slope −0.17(−0.32,−0.01) 0.0422 −0.08(−0.23,0.07) 0.2852 −0.83(−1.16,−0.50) < 0.0001 −0.03(−0.05,−0.01) 0.0006 Model II Turning point(K) 35.51 37.16 33.01 35.78 <K −0.25(−0.41,−0.08) 0.0033 −0.16(−0.31,0.00) 0.0526 −0.92(−1.25,−0.59) < 0.0001 −0.04(−0.06,−0.02) K −1.17(−1.74,−0.59) < 0.0001 −1.26(−2.08,−0.43) 0.0028 −2.45(−3.24,−1.67) < 0.0001 −0.17(−0.24,−0.10) < 0.0001 OR betweenK −0.92(−1.42,−0.41) 0.0004 −1.10(−1.86,−0.34) 0.0045 −1.53(−2.20,−0.86) < 0.0001 −0.13(−0.19,−0.07) < 0.0001 Log-likelihood ratio < 0.001 0.004 < 0.001 < 0.001 Age, race, education level, PIR, BMI, smoking status, drinking status, hypertension, diabetes, coronary heart disease, stroke were adjusted. Abbreviations: RFM:Relative Fat Mass , CERAD-WL: Consortium to Establish a Registry for Alzheimer’s Disease Word Learning Test , AFT: Animal Fluency Test , DSST: Digit Symbol Substitution Test , PIR : Poverty Income Ratio , BMI: Body Measure Index. Subgroup analyses As shown in Table 4 , further subgroup analyses revealed variation in the association between RFM and cognitive function. Significant interactions emerged in the interaction analyses for education level, PIR, smoking and drinking status when Z-scores was used as an assessment of cognitive function ( p 0.05). Table 4. Subgroup analysis of the associations between RFM and cognitive function Subgroup CERAD-WL β （ 95%CI ） P for interaction AFT β （ 95%CI ） P for interaction DSST β （ 95%CI ） P for interaction Z-score β （ 95%CI ） P for interaction Age 0.2901 0.0130 0.7817 0.1176 ＜ 70 -0.25(-0.42,-0.08) 0.0033 -0.18(-0.34,-0.02) 0.0242 -0.98(-1.33,-0.63) ＜0.0001 -0.04（-0.06，-0.03）＜0.0001 70-79 -0.15(-0.35,0.04) 0.1220 0.04(-0.14,0.22) 0.6787 -1.07（-1.48，-0.66）＜0.0001 -0.03（-0.05，-0.01）0.0112 ≥80 -0.09(-0.37,0.19) 0.5321 -0.06(-0.32,0.21) 0.6695 -0.88（-1.47，-0.28）0.0038 -0.03（-0.06，0.01）0.0999 Race 0.5479 0.0160 0.8654 0.1193 Mexican American -0.16(-0.58,0.27) 0.4695 -0.20(-0.59,0.19) 0.3172 -0.81 (-1.68, 0.06) 0.0678 -0.04 (-0.08, 0.01) 0.1166 Other Hispanic 0.02(-0.51,0.54) 0.9538 -0.17(-0.65,0.32) 0.5067 -0.34 (-1.43, 0.74) 0.5346 -0.02 (-0.07, 0.04) 0.5827 Non-Hispanic White -0.19(-0.36,-0.03) 0.0234 -0.12(-0.28, 0.03) 0.1217 -0.87 (-1.21, -0.52) <0.0001 -0.04 (-0.05, -0.02) 0.0001 Non-Hispanic Black -0.16(-0.42,0.09) 0.2167 -0.08(-0.32, 0.15) 0.4887 -0.73 (-1.26, -0.20) 0.0070 -0.03 (-0.06, -0.00) 0.0455 Other Race 0.03(-0.27,0.32) 0.8539 0.32 (0.04, 0.59) 0.0230 -0.79 (-1.40, -0.18) 0.0113 0.00 (-0.03, 0.04) 0.7856 Education level 0.0919 0.8706 0.0089 0.0494 Less than 9 th grade -0.20 (-0.52, 0.12) 0.2278 0.02 (-0.28, 0.32) 0.8899 -0.83 (-1.50, -0.16) 0.0148 -0.03 (-0.06, 0.01) 0.1450 Continued Table 4. Subgroup analysis of the associations between RFM and cognitive function Subgroup CERAD-WL β （ 95%CI ） P for interaction AFT β （ 95%CI ） P for interaction DSST β （ 95%CI ） P for interaction Z-score β （ 95%CI ） P for interaction 9-11 th grade 0.12 (-0.16, 0.39) 0.4140 -0.04(-0.30, 0.22) 0.7549 -0.01 (-0.59, 0.56) 0.9620 0.00(-0.03,0.03) 0.8239 High school graduate /GED or equivalent -0.09(-0.31, 0.13) 0.4146 -0.07(-0.28, 0.14) 0.4976 -0.68 (-1.14, -0.22) 0.0036 -0.02(-0.05,0.00) 0.0620 Some college or AA degree -0.13(-0.33, 0.08) 0.2179 -0.05(-0.24, 0.14) 0.6281 -0.88 (-1.30, -0.46) <0.0001 -0.03(-0.05,-0.01) 0.0154 College graduate or above -0.25(-0.42,-0.07) 0.0055 -0.11 (-0.28, 0.05) 0.1715 -1.02 (-1.38, -0.66) <0.0001 -0.04(-0.06,-0.02) <0.0001 PIR 0.0759 0.1907 0.0074 0.0102 ＜ 1 0.01 (-0.24, 0.26) 0.9457 0.04 (-0.20, 0.27) 0.7462 -0.29 (-0.81, 0.23) 0.2697 -0.00(-0.03,0.02) 0.8214 ≥1 -0.19(-0.36,-0.03) 0.0197 -0.10(-0.25, 0.05) 0.1947 -0.92 (-1.26, -0.59) <0.0001 -0.04(-0.05,-0.02) 0.0001 BMI 0.0026 0.0797 0.0301 0.1313 ＜ 25 -0.34(-0.54, -0.14) 0.0010 -0.21(-0.40,-0.02) 0.0275 -0.42 (-0.84, -0.01) 0.0475 -0.04(-0.06,-0.02) 0.0005 25-30 0.18 (-0.05, 0.41) 0.1298 0.03 (-0.19, 0.24) 0.8183 -1.11 (-1.60, -0.63) <0.0001 -0.01(-0.04,0.01) 0.3783 Continued Table 4. Subgroup analysis of the associations between RFM and cognitive function Subgroup CERAD-WL β （ 95%CI ） P for interaction AFT β （ 95%CI ） P for interaction DSST β （ 95%CI ） P for interaction Z-score β （ 95%CI ） P for interaction ≥30 -0.18 (-0.37, 0.02) 0.0778 0.06 (-0.12, 0.25) 0.4905 -0.33 (-0.74, 0.07) 0.1077 -0.01(-0.03,0.01) 0.2594 Smoking 0.0013 0.0908 0.0127 0.0007 Yes -0.08 (-0.24, 0.09) 0.3704 -0.04(-0.19, 0.12) 0.6406 -0.69 (-1.04, -0.34) 0.0001 -0.02(-0.04,-0.00) 0.0293 No -0.30(-0.48, -0.12) 0.0010 -0.15(-0.32, 0.02) 0.0826 -1.06 (-1.43, -0.68) <0.0001 -0.05(-0.07,-0.03) <0.0001 Drinking 0.9107 0.0052 0.2296 0.0666 Yes -0.16(-0.33, -0.00) 0.0501 -0.13(-0.28, 0.02) 0.0998 -0.88 (-1.22, -0.54) <0.0001 -0.03(-0.05,-0.02) 0.0002 No -0.17 (-0.39, 0.04) 0.1189 0.12 (-0.09, 0.32) 0.2641 -0.65 (-1.10, -0.20) 0.0050 -0.02(-0.04,0.01) 0.2014 Hypertension 0.1266 0.0223 0.0025 0.4522 Yes -0.08 (-0.27, 0.11) 0.4084 0.04 (-0.14, 0.22) 0.6938 -1.18 (-1.58, -0.78) <0.0001 -0.03(-0.05,-0.01) 0.0151 No -0.20(-0.36, -0.03) 0.0195 -0.12(-0.28, 0.03) 0.1136 -0.71 (-1.05, -0.37) <0.0001 -0.03(-0.05,-0.01) 0.0004 Diabetes 0.1349 0.2991 0.3237 0.8595 Yes -0.32(-0.58, -0.06) 0.0155 0.02 (-0.22, 0.26) 0.8807 -0.62 (-1.16, -0.09) 0.0219 -0.03(-0.06,-0.00) 0.0464 Continued Table 4. Subgroup analysis of the associations between RFM and cognitive function Subgroup CERAD-WL β （ 95%CI ） P for interaction AFT β （ 95%CI ） P for interaction DSST β （ 95%CI ） P for interaction Z-score β （ 95%CI ） P for interaction No -0.17(-0.33, -0.01) 0.0358 -0.08(-0.23, 0.07) 0.3078 -0.83 (-1.16, -0.50) <0.0001 -0.03(-0.05,-0.01) 0.0006 Coronary heart disease 0.1665 0.0591 0.6971 0.1357 Yes -0.02 (-0.28, 0.23) 0.8592 0.10 (-0.14, 0.34) 0.4170 -0.92 (-1.45, -0.38) 0.0008 -0.01(-0.04,0.01) 0.3320 No -0.17(-0.33, -0.01) 0.0329 -0.09(-0.24, 0.06) 0.2252 -0.83 (-1.16, -0.50) <0.0001 -0.03(-0.05,-0.01) 0.0004 Stroke 0.5754 0.2258 0.9226 0.3522 Yes -0.08 (-0.41, 0.24) 0.6095 0.08 (-0.22, 0.39) 0.5999 -0.81 (-1.48, -0.13) 0.0192 -0.02(-0.05,0.02) 0.3761 No -0.17(-0.33, -0.01) 0.0400 -0.09(-0.23, 0.06) 0.2623 -0.84 (-1.17, -0.51) <0.0001 -0.03(-0.05,-0.01) 0.0005 Age, race, education level, PIR, BMI, smoking status, drinking status, hypertension, diabetes, coronary heart disease, stroke were adjusted. Abbreviations: RFM :Relative Fat Mass, CERAD-WL: Consortium to Establish a Registry for Alzheimer’s Disease Word Learning Test, AFT: Animal Fluency Test, DSST :Digit Symbol Substitution Test, PIR: Poverty Income Ratio, BMI :Body Measure Index. Discussion The results of this study showed a substantial negative correlation between RFM and CERAD-WL, DSST, and Z-scores. Comparisons between participants in the Q4 and Q1 of RFM revealed reduction in DSST scores. Smooth curve fitting revealed a nonlinear relationship between RFM and cognitive function (Log - likelihood ratio 35.51,33.01 and 35.78, respectively. The possible mechanisms that connect obesity and cognitive function are intricate and varied. Although the precise processes are still under investigation, several have been proposed[ 37 ]. The first mechanism is the inflammatory response.The consistent and ongoing presence of inflammation is one of the defining characteristics of obesity. Inflammation-related proteins and pro-inflammatory cytokines are secreted by adipose tissue, both of which play an essential role in the process of promoting systemic inflammation[ 38 ]. The resultant inflammatory condition may induce oxidative stress and subsequent neuronal damage, thereby impairing cognitive function[ 39 ]. The second mechanism is insulin resistance. Insulin resistance frequently occurs alongside obesity, resulting in cells becoming less sensitive to insulin's effects. Disruptions in insulin signaling may lead to diminished neuronal activity and cognitive impairments[ 39 ]. The third mechanism is vascular factors. As we know, obesity is associated with hypertension, dyslipidemia, and atherosclerosis[ 40 ]. It is possible that these disorders lead to cognitive impairment by lowering the amount of blood that flows through the cerebrovascular system and by causing the blood-brain barrier to become compromised[ 40 – 43 ]. The fourth mechanism is hormonal dysregulation. Obesity leads to changes in leptin and adiponectin levels, affecting brain function and synaptic plasticity[ 44 , 45 ]. These mechanisms are interrelated and have a combined impact. BMI is a prominent method that is used to evaluate obesity. Enhanced cognitive performance has been shown to be correlated with a decreased BMI, according to some study results[ 46 – 48 ]. Several other studies, on the other hand, have shown that a greater BMI is associated with superior cognitive function[ 49 – 51 ]. This phenomenon is called "obesity paradox"[ 52 – 54 ].One possible explanation for the paradox of obesity is that the BMI does not discriminate between fat mass and muscle mass. As a result, two individuals possessing identical BMI values may exhibit significantly divergent body compositions and experience varying health outcomes[ 55 – 58 ]. As a predictor of obesity, RFM is superior than BMI in terms of its accuracy, making it a more reliable instrument for evaluating body fat percentage[ 8 , 15 , 59 , 60 ]. Research indicates that the RFM metric demonstrates enhanced predictive accuracy for dyslipidemias and metabolic syndrome (MetS) in comparison to BMI[ 61 ]. Based on the results of a cohort research, elderly people who kept their weight at a reasonable level but had abdominal obesity were shown to have a greater chance of developing dementia in comparison to their counterparts who did not exhibit abdominal obesity[ 62 ]. The findings of this research indicate that early screening for negative RFM in older men could be a useful method for identifying at-risk groups, and that properly managing RFM might help postpone cognitive decline. Strengths and limitations This research used a dataset from NHANES comprising 1,321 older men, which improved the credibility and applicability of the results. To guarantee the study's rigor, a range of statistical techniques were used. The study findings indicated a substantial negative correlation between RFM and CERAD-WL scores, DSST scores, and Z-scores in older men. Early identification of adverse RFM is conducive to identifying high-risk groups for cognitive dysfunction. On the other hand, it is necessary to point out the constraints of this research. Firstly,as a cross-sectional survey, it could not demonstrate a causal link. Consequently, more investigation with an expanded sample size is required to confirm the causal relationship. Secondly, the study was limited to the years from 2011 to 2014. Therefore, cohort studies might be necessary to validate the current results. Furthermore,the study’s sample was restricted to older males, without considering the women; thus, it is required to do additional studies to ascertain the findings can be applied to other demographics. Future studies should include older women to enhance the generalizability of the findings and explore whether the management of RFM has an advantage in mitigating cognitive decline. Finally, although RFM is a new metric for obesity, further studies are needed to validate its advantage over conventional metric. Conclusion A substantial negative connection between RFM and cognitive function in elderly males in the United States is revealed in the present study. Elevated RFM levels correlated with diminished scores on cognitive assessments, particularly the CERAD and DSST. These results emphasize the potential benefit of preventing cognitive impairment by managing RFM levels. Abbreviations RFM relative fat mass U.S. United States NHANES National Health and Nutrition Examination Survey CERAD-WL Consortium to Establish a Registry for Alzheimer’s Disease Word Learning Test AFT Animal Fluency Test DSST Digit Symbol Substitution Test WC waist circumference CI cognitive impairment MCI mild cognitive impairment IRT Immediate Recall Test DRT Delayed Recall Test MEC Mobile Examination Center PIR Poverty Income Ratio BMI body mass index SD standard deviation NCHS National Center for Health Statistics MetS metabolic syndrome Declarations Ethics approval All participants submitted written informed consent and were approved by the National Ethics Board. Consent to participate Not applicable. Consent for publication Not applicable. Competing interests The authors declare no competing interests. Funding No financial support was received for the research, writing and/or publication of this paper. Author Contribution LL designed the study, collected and analyzed data, and completed the first draft of the paper. AW supervised the entire study and helped revise the paper. SY assisted with data analysis and paper writing. All authors reviewed the manuscript Acknowledgements We would like to thank the participants and staff of the National Health and Nutrition Examination Survey (NHANES) and all those who participated in this study. Data availability The datasets utilized in this investigation are accessible in online databases. Detailed information regarding the repositories and the relevant accession numbers can be located at: https://wwwn.cdc.gov/nchs/nhanes . References Prince M, Ali G, Guerchet M, Prina AM, Albanese E, Wu Y. Recent global trends in the prevalence and incidence of dementia, and survival with dementia. ALZHEIMERS RES THER. 2016;8(1):23. Matthews KA, Xu W, Gaglioti AH, Holt JB, Croft JB, Mack D, McGuire LC. Racial and ethnic estimates of Alzheimer's disease and related dementias in the United States (2015–2060) in adults aged >/=65 years. ALZHEIMERS DEMENT. 2019;15(1):17–24. Chen M, Tan C, Su H, Sung P, Chien C, Yu R. The Impact of Sex on the Neurocognitive Functions of Patients with Parkinson’s Disease. BRAIN SCI. 2021;11(10):1331. Chen S, Nilsen J, Brinton RD. Dose and temporal pattern of estrogen exposure determines neuroprotective outcome in hippocampal neurons: therapeutic implications. Endocrinology. 2006;147(11):5303–13. Ookubo M, Yokoyama H, Takagi S, Kato H, Araki T. Effects of estrogens on striatal damage after 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) neurotoxicity in male and female mice. MOL CELL ENDOCRINOL. 2008;296(1–2):87–93. Safaei M, Sundararajan EA, Driss M, Boulila W, Shapi'I A. A systematic literature review on obesity: Understanding the causes & consequences of obesity and reviewing various machine learning approaches used to predict obesity. COMPUT BIOL MED. 2021;136:104754. Hruby A, Hu FB. The Epidemiology of Obesity: A Big Picture. PharmacoEconomics. 2015;33(7):673–89. Woolcott OO, Bergman RN. Relative fat mass (RFM) as a new estimator of whole-body fat percentage horizontal line A cross-sectional study in American adult individuals. SCI REP-UK. 2018;8(1):10980. Guzmán-León AE, Velarde AG, Vidal-Salas M, Urquijo-Ruiz LG, Caraveo-Gutiérrez LA, Valencia ME. External validation of the relative fat mass (RFM) index in adults from north-west Mexico using different reference methods. PLoS ONE. 2019;14(12):e226767. Wang J, Guan J, Huang L, Li X, Huang B, Feng J, Zhang Y, Zhang J. Sex differences in the associations between relative fat mass and all-cause and cardiovascular mortality: A population-based prospective cohort study. Nutr Metabolism Cardiovasc Dis. 2024;34(3):738–54. Corrêa CR, Formolo NPS, Dezanetti T, Speretta GFF, Nunes EA. Relative fat mass is a better tool to diagnose high adiposity when compared to body mass index in young male adults: A cross-section study. CLIN NUTR ESPEN. 2021;41:225–33. Cichosz SL, Rasmussen NH, Vestergaard P, Hejlesen O. Is predicted body-composition and relative fat mass an alternative to body-mass index and waist circumference for disease risk estimation? DIABETES METAB SYND. 2022;16(9):102590. Yu P, Huang T, Hu S, Yu X. Predictive value of relative fat mass algorithm for incident hypertension: a 6-year prospective study in Chinese population. BMJ OPEN. 2020;10(10):e38420. Suthahar N, Wang K, Zwartkruis VW, Bakker SJL, Inzucchi SE, Meems LMG, Eijgenraam TR, Ahmadizar F, Sijbrands EG, Gansevoort RT, et al. Associations of relative fat mass, a new index of adiposity, with type-2 diabetes in the general population. EUR J INTERN MED. 2023;109:73–8. Efe SC, Karagoz A, Dogan C, Bayram Z, Kalkan S, Altintas MS, Yuksel Y, Karabag T, Ayca B, Ozdemir N. Relative Fat Mass Index can be solution for obesity paradox in coronary artery disease severity prediction calculated by SYNTAX Score. POSTGRAD MED J. 2021;97(1149):434–41. Zhu X, Yue Y, Li L, Zhu L, Cai Y, Shu Y. The relationship between depression and relative fat mass (RFM): A population-based study. J AFFECT DISORDERS. 2024;356:323–8. Morris JC, Heyman A, Mohs RC, Hughes JP, van Belle G, Fillenbaum G, Mellits ED, Clark C. The Consortium to Establish a Registry for Alzheimer's Disease (CERAD). Part I. Clinical and neuropsychological assessment of Alzheimer's disease. NEUROLOGY. 1989;39(9):1159–65. Selnes OA. A compendium of neuropsychological tests: Administration, norms, and commentary. NEUROLOGY. 1991;41(11):1856. Wechsler D. Wais-iii/wms-iii technical manual. San Antonio, TX: The Psychological Corporation; 1997. Lee S, Min J, Kim B, Ha S, Han JH, Min K. Serum sodium in relation to various domains of cognitive function in the elderly US population. BMC GERIATR. 2021;21(1):328. Botelho J, Leira Y, Viana J, Machado V, Mendes JJ. The Role of Inflammatory Diet and Vitamin D on the Link between Periodontitis and Cognitive Function: A Mediation Analysis in Older Adults. NUTRIENTS. 2021;13(3):924. Katzman EW, Nielsen SJ. The Association between Peanut and Peanut Butter Consumption and Cognitive Function among Community-Dwelling Older Adults. J Prev Alzheimer's disease. 2021;8(4):436–41. Farrer LA, Cupples LA, Haines JL, Hyman B, Kukull WA, Mayeux R, Myers RH, Pericak-Vance MA, Risch N, van Duijn CM. Effects of Age, Sex, and Ethnicity on the Association Between Apolipoprotein E Genotype and Alzheimer Disease: A Meta-analysis. JAMA: J Am Med Association. 1997;278(16):1349–56. Aka A, Phan TD, Kahana MJ, Benjamin AS. Predicting Recall of Words and Lists. J Exp Psychol Learn Mem Cognit. 2021;47(5):765–84. Santos-Galduróz RF, Oliveira FG, Galduróz JCF, Bueno OFA. Cognitive performance of young and elderly subjects on the free word recall memory test: effect of presentation order on recall order. BRAZ J MED BIOL RES. 2009;42(10):988–92. Lucas JA, Ivnik RJ, Smith GE, Ferman TJ, Willis FB, Petersen RC, Graff-Radford NR. Mayo's Older African Americans Normative Studies: Norms for Boston Naming Test, Controlled Oral Word Association, Category Fluency, Animal Naming, Token Test, Wrat-3 Reading, Trail Making Test, Stroop Test, and Judgment of Line Orientation. CLIN NEUROPSYCHOL. 2005;19(2):243–69. Rinehardt E, Eichstaedt K, Schinka JA, Loewenstein DA, Mattingly M, Fils J, Duara R, Schoenberg MR. Verbal fluency patterns in mild cognitive impairment and Alzheimer's disease. DEMENT GERIATR COGN. 2014;38(1–2):1–9. Sebaldt R, Dalziel W, Massoud F, Tanguay A, Ward R, Thabane L, Melnyk P, Landry P, Lescrauwaet B. Detection of cognitive impairment and dementia using the animal fluency test: the DECIDE study. CAN J NEUROL SCI. 2009;36(5):599–604. Garcia-Herranz S, Diaz-Mardomingo MC, Venero C, Peraita H. Accuracy of verbal fluency tests in the discrimination of mild cognitive impairment and probable Alzheimer's disease in older Spanish monolingual individuals. AGING NEUROPSYCHOL C. 2020;27(6):826–40. Brukner P, Khan KM, Kron J. The Encyclopedia of Exercise, Sport and Health. Choice Reviews Online. 2005;42:42–2530. Lim CR, Harris K, Dawson J, Beard DJ, Fitzpatrick R, Price AJ. Floor and ceiling effects in the OHS:an analysis of the NHS PROMs data set. BMJ OPEN. 2015;5(7):e7765. Li H, Wang Z, Fu Z, Yan M, Wu N, Wu H, Yin P. Associations between blood cadmium levels and cognitive function in a cross-sectional study of US adults aged 60 years or older. BMJ OPEN. 2018;8(4):e20533. Chu NM, Hong J, Harasemiw O, Chen X, Fowler KJ, Dasgupta I, Bohm C, Segev DL, McAdams-DeMarco MA. Chronic kidney disease, physical activity and cognitive function in older adults-results from the National Health and Nutrition Examination Survey (2011–2014). NEPHROL DIAL TRANSPL. 2022;37(11):2180–9. Zhang Y, Peng Y, Deng W, Xiang Q, Zhang W, Liu M. Association between dietary inflammatory index and cognitive impairment among American elderly: a cross-sectional study. FRONT AGING NEUROSCI 2024, 16. Li J, Sun J, Zhang Y, Zhang B, Zhou L. Association between weight-adjusted-waist index and cognitive decline in US elderly participants. FRONT NUTR. 2024;11:1390282. Gong H, Tang X, Chai Y, Qiao Y, Xu H, Patel I, Zhang J, Zhou J. Predicted lean body mass in relation to cognitive function in the older adults. FRONT ENDOCRINOL 2023, 14. Barone E, Di Domenico F, Perluigi M, Butterfield DA. The interplay among oxidative stress, brain insulin resistance and AMPK dysfunction contribute to neurodegeneration in type 2 diabetes and Alzheimer disease. FREE RADICAL BIO MED. 2021;176:16–33. Dermitzaki E, Liapakis G, Androulidaki A, Venihaki M, Melissas J, Tsatsanis C, Margioris AN. Corticotrophin-Releasing Factor (CRF) and the urocortins are potent regulators of the inflammatory phenotype of human and mouse white adipocytes and the differentiation of mouse 3T3L1 pre-adipocytes. PLoS ONE. 2014;9(5):e97060. Naomi R, Teoh SH, Embong H, Balan SS, Othman F, Bahari H, Yazid MD. The Role of Oxidative Stress and Inflammation in Obesity and Its Impact on Cognitive Impairments—A. Narrative Rev ANTIOXIDANTS-BASEL. 2023;12(5):1071. Friedman JI, Tang CY, de Haas HJ, Changchien L, Goliasch G, Dabas P, Wang V, Fayad ZA, Fuster V, Narula J. Brain imaging changes associated with risk factors for cardiovascular and cerebrovascular disease in asymptomatic patients. JACC-CARDIOVASC IMAG. 2014;7(10):1039–53. Ovsenik A, Podbregar M, Fabjan A. Cerebral blood flow impairment and cognitive decline in heart failure. BRAIN BEHAV. 2021;11(6):e2176. Nguyen JCD, Killcross AS, Jenkins TA. Obesity and cognitive decline: role of inflammation and vascular changes. FRONT NEUROSCI-SWITZ. 2014;8:375. Balasubramanian P, Kiss T, Tarantini S, Nyul-Toth A, Ahire C, Yabluchanskiy A, Csipo T, Lipecz A, Tabak A, Institoris A, et al. Obesity-induced cognitive impairment in older adults: a microvascular perspective. AM J PHYSIOL-HEART C. 2021;320(2):H740–61. Flores-Cordero JA, Pérez-Pérez A, Jiménez-Cortegana C, Alba G, Flores-Barragán A, Sánchez-Margalet V. Obesity as a Risk Factor for Dementia and Alzheimer's Disease: The Role of Leptin. Int J Mol Sci. 2022;23(9):5202. Forny-Germano L, De Felice FG, Vieira M. The Role of Leptin and Adiponectin in Obesity-Associated Cognitive Decline and Alzheimer's Disease. FRONT NEUROSCI-SWITZ. 2018;12:1027. Rubin LH, Gustafson D, Hawkins KL, Zhang L, Erlandson KM. Midlife adiposity predicts cognitive decline in the prospective Multicenter AIDS Cohort Study. NEUROLOGY. 2019;93(3):10–1212. Liu Z, Yang H, Chen S, Cai J, Huang Z. The association between body mass index, waist circumference, waist-hip ratio and cognitive disorder in older adults. J PUBLIC HEALTH-UK. 2019;41(2):305–12. Marioni RE, Yang J, Dykiert D, Mottus R, Campbell A, Davies G, Hayward C, Porteous DJ, Visscher PM, Deary IJ. Assessing the genetic overlap between BMI and cognitive function. MOL PSYCHIATR. 2016;21(10):1477–82. Aiken Morgan AT, Capuano AW, Arvanitakis Z, Barnes LL. Changes in Body Mass Index Are Related to Faster Cognitive Decline Among African American Older Adults. J Am Geriatr Soc (JAGS). 2020;68(11):2662–7. Kivimaki M, Luukkonen R, Batty GD, Ferrie JE, Pentti J, Nyberg ST, Shipley MJ, Alfredsson L, Fransson EI, Goldberg M, et al. Body mass index and risk of dementia: Analysis of individual-level data from 1.3 million individuals. ALZHEIMERS DEMENT. 2018;14(5):601–9. Kim S, Kim Y, Park SM. Body Mass Index and Decline of Cognitive Function. PLoS ONE. 2016;11(2):e148908. Yousung P, Nam H, Kim. Tae, Yeon, Kwon, Sin, Gon: A novel adiposity index as an integrated predictor of cardiometabolic disease morbidity and mortality. SCI REP-UK 2018(8):16753. Ding C, Shi Y, Li J, Li M, Hu L, Rao J, Liu L, Zhao P, Xie C, Zhan B et al. Association of weight-adjusted-waist index with all-cause and cardiovascular mortality in China: A prospective cohort study. Nutrition, metabolism, and cardiovascular diseases 2022, 32(5):1210–7. Elagizi A, Kachur S, Lavie CJ, Carbone S, Pandey A, Ortega FB, Milani RV. An Overview and Update on Obesity and the Obesity Paradox in Cardiovascular Diseases. PROG CARDIOVASC DIS. 2018;61(2):142–50. Yoon DH, Choi SH, Yu JH, Ha JH, Ryu SH, Park DH. The relationship between visceral adiposity and cognitive performance in older adults. AGE AGEING. 2012;41(4):456–61. Isaac V, Sim S, Zheng H, Zagorodnov V, Tai ES, Chee M. Adverse Associations between Visceral Adiposity, Brain Structure, and Cognitive Performance in Healthy Elderly. FRONT AGING NEUROSCI. 2011;3:12. Gallagher D, Visser M, Sepulveda D, Pierson RN, Harris T, Heymsfield SB. How useful is body mass index for comparison of body fatness across age, sex, and ethnic groups? AM J EPIDEMIOL. 1996;143(3):228–39. Okorodudu DO, Jumean MF, Montori VM, Romero-Corral A, Somers VK, Erwin PJ, Lopez-Jimenez F. Diagnostic performance of body mass index to identify obesity as defined by body adiposity: a systematic review and meta-analysis. INT J Obes. 2010;34(5):791–9. Gordon EH, Hubbard RE. Frailty: understanding the difference between age and ageing. AGE AGEING. 2022;51(8):1–6. Cha E, Akazawa MK, Kim KH, Dawkins CR, Lerner HM, Umpierrez G, Dunbar SB. Lifestyle habits and obesity progression in overweight and obese American young adults: Lessons for promoting cardiometabolic health. NURS HEALTH SCI. 2015;17(4):467–75. Zeng P, Li M, Cao JX, Zeng L, Jiang C, Lin F. Association of metabolic syndrome severity with frailty progression among Chinese middle and old-aged adults: a longitudinal study. CARDIOVASC DIABETOL. 2024;23(1):1–12. Cho GJ, Hwang SY, Lee KM, Choi KM, Hyun Baik S, Kim T, Han SW, Yoo HJ. Association between waist circumference and dementia in older persons: a nationwide population-based study. OBESITY. 2019;27(11):1883–91. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 08 May, 2025 Read the published version in Lipids in Health and Disease → Version 1 posted Editorial decision: Revision requested 17 Apr, 2025 Reviews received at journal 17 Apr, 2025 Reviews received at journal 04 Apr, 2025 Reviewers agreed at journal 03 Apr, 2025 Reviewers agreed at journal 01 Apr, 2025 Reviewers invited by journal 31 Mar, 2025 Submission checks completed at journal 31 Mar, 2025 First submitted to journal 31 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6114375","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":436704520,"identity":"ed57b680-4287-4f2a-8428-5924be3bf9de","order_by":0,"name":"Linlin Liu","email":"","orcid":"","institution":"Department of Anesthesiology, Beijing Chao-Yang Hospital","correspondingAuthor":false,"prefix":"","firstName":"Linlin","middleName":"","lastName":"Liu","suffix":""},{"id":436704521,"identity":"29ad56f0-7e38-4000-8287-5f5c11c49b8c","order_by":1,"name":"Anshi Wu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7ElEQVRIiWNgGAWjYBACNhBKbGPg4edvPgDkWzAw8BDQwg/VIiM541gCA0OCBGEtkg1ALYxtDDYGB3IMiNNicLv92YOHbYd5GA6c+fiY94eEHD/PAcYPH3PwaLlzxtwgcdthHsbm3s3GPAkSxpK9DcySM7fh0XIjh00CpIWZ4ew2aaCWxA3nGdiYefFosb+R/gyshY0h5xlxWgxuJJhJJP47zMPDkMMG0XK2gZCWHKCWc+k8EhLHjA3npAH90nOwmYBf0p9J/myztrc/3/zwwRsbG2CIJR/88BGPFmyAsYE09aNgFIyCUTAKMAAAJOdQpwBgni4AAAAASUVORK5CYII=","orcid":"","institution":"Department of Anesthesiology, Beijing Chao-Yang Hospital","correspondingAuthor":true,"prefix":"","firstName":"Anshi","middleName":"","lastName":"Wu","suffix":""},{"id":436704522,"identity":"5e2095dc-f294-4772-8b89-131c6516580d","order_by":2,"name":"Shengnan Yang","email":"","orcid":"","institution":"Department of Anesthesiology, Beijing Chao-Yang Hospital","correspondingAuthor":false,"prefix":"","firstName":"Shengnan","middleName":"","lastName":"Yang","suffix":""}],"badges":[],"createdAt":"2025-02-26 15:08:18","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6114375/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6114375/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12944-025-02593-8","type":"published","date":"2025-05-08T15:56:51+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":79761984,"identity":"9fe9d1f4-3ef0-4cf9-a45b-dd2db416cbbc","added_by":"auto","created_at":"2025-04-02 11:26:30","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":499063,"visible":true,"origin":"","legend":"\u003cp\u003eFlow diagram showing the participants’ selecting step\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-6114375/v1/4902a34f40c890059c619a8e.png"},{"id":79761983,"identity":"8d1adc71-343d-4238-8b83-689b491816eb","added_by":"auto","created_at":"2025-04-02 11:26:30","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":167811,"visible":true,"origin":"","legend":"\u003cp\u003eThe nonlinear negative relationship between RFM and cognitive function.\u003c/p\u003e\n\u003cp\u003eThe solid line represents the smooth curve fit between variables. Dotted line represents the 95% of confidence interval from the fit\u003c/p\u003e\n\u003cp\u003e(A) RFM and CERAD-WL score; (B)RFM and AFT score; (C)RFM and DSST score; (D) RFM and Z-score\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-6114375/v1/f503c3fd9a24089da4acbc87.png"},{"id":82537387,"identity":"64469b4b-a090-40ab-b1b1-e343e7cf5acd","added_by":"auto","created_at":"2025-05-12 15:59:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3451830,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6114375/v1/276df4ba-ea0a-4b39-8f5b-bbf25227b442.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association between relative fat mass and cognitive function among US older men: NHANES 2011–2014","fulltext":[{"header":"Introduction","content":"\u003cp\u003eCognitive impairment (CI) pertains to the reduction or weakening of cognitive capabilities. These capabilities cover functions like memory, language, attention, problem - solving, and executive functions. CI includes mild cognitive impairment (MCI) and different forms of dementia. As the global population ages, cognitive dysfunction in the elderly is becoming a significant challenge for global public health. Projections suggest that by 2050, over 130\u0026nbsp;million people globally will suffer from dementia[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Cognitive dysfunction impacts not just the well-being of older adults, but also creates significant challenges for their families and society[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].Research indicates that healthy older women exhibit superior verbal fluency, verbal memory and processing speed in comparison to their male counterparts[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. These differences may stem from the positive effects of estrogen on the maintenance of neurocognitive integrity[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Identifying the risk factors affecting cognitive function, especially those that can be modified, and developing effective interventions can help alleviate the enormous public health pressures that lie ahead globally. Obesity constitutes a significant public health challenge on a global scale[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Compared to Body Mass Index (BMI), relative fat mass (RFM) provides a more accurate assessment of adults' total body fat percentage, making it a unique tool for assessing obesity[\u003cspan additionalcitationids=\"CR9 CR10 CR11\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].RFM is especially applicable in the context of clinical and epidemiological research[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Empirical evidence indicates that RFM is correlated with various health conditions, including hypertension, type 2 diabetes, coronary artery disease, and depression[\u003cspan additionalcitationids=\"CR14 CR15\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. No research has been conducted on RFM and cognitive function to date. We investigated the connection between RFM and cognitive function in older males using the National Health and Nutrition Examination Survey (NHANES) database from 2011 to 2014 to assist clinical research.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003eStudy population\u003c/h2\u003e\n \u003cp\u003eHealth examinations and interviews from a countrywide representative sample of U.S. citizens and civilian populations living in communities was gathered from the NHANES sample using a complicated, multistage sampling design.This research analyzed a dataset from the NHANES conducted between 2011 and 2014. Informed permission in writing was acquired from all participants of NHANES. Our work was classified as exempt from ethical review due to its use of publicly accessible NHANES data. A comprehensive amount of information on cognitive function assessment and RFM was supplied by the participants.The study\u0026rsquo;s initial recruitment included 19,931 participants. However, after removing participants who were female (n\u0026thinsp;=\u0026thinsp;10072), had incomplete data on cognitive function (n\u0026thinsp;=\u0026thinsp;8431), missing data on RFM (n\u0026thinsp;=\u0026thinsp;77) and covariates (n\u0026thinsp;=\u0026thinsp;30), 1321 participants were remained in the analyses finally (Fig.\u0026nbsp;1).\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eVariables\u003c/h3\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003eCognitive Function\u003c/h2\u003e\n \u003cp\u003eThe cognitive function evaluations administered by NHANES during the period from 2011 to 2014 included Consortium to Establish a Registry for Alzheimer\u0026rsquo;s Disease Word Learning Test (CERAD-WL), the Animal Fluency Test (AFT), and the Digit Symbol Substitution Test (DSST) [\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e]. Cohort studies frequently use these three tests to evaluate language proficiency, memory, and general cognitive function[\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003eThe CERAD-WL is comprised of three Immediate Recall Tests (IRT) and one Delayed Recall Test (DRT)[\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e]. The IRT measures immediate recall ability, and the DRT measures delayed recall ability[\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e]. The cumulative sum of the scores received in both the IRT and DRT components of the CERAD-WL is used to calculate the overall score for the test.\u003c/p\u003e\n \u003cp\u003eThe AFT necessitates that participants identify and enumerate as many animal species as they can within a specified time frame.The scores of the AFT are related to the quality of language function[\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e]. Research showed that the AFT can distinguish between normal elderly and those with cognitive impairment[\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003eThe DSST involves digit - symbol pairs presented in a digit table. Within two minutes, participants are instructed to write the matching symbols in the boxes underneath the numbers[\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e]. The DSST was designed to evaluate attention, concentration, hand-eye coordination, information processing speed, and short - term memory.\u003c/p\u003e\n \u003cp\u003eCognitive function among the elderly population exhibits considerable variability, characterized by both ceiling and floor effects[\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e]. Therefore, we averaged the standardized scores [(subject\u0026apos;s test score-average score) / standard deviation] of CERAD-WL, AFT, and DSST scores to create new metric called Z-score, providing a comprehensive indicator of cognitive function among the individuals in our research[\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e]. Z-score is calculated as\u003c/p\u003e\n\u003c/div\u003e\n\u003cp\u003eZ-score = \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\frac{\\frac{Score(CERAD-WL)-Mean(CERAD-WL)}{SD(CERAD-WL)}\\:+\\:\\frac{Score\\left(AFT\\right)-Mean\\left(AFT\\right)}{SD\\left(AFT\\right)}\\:+\\:\\frac{Score\\left(DSST\\right)-Mean\\left(DSST\\right)}{SD\\left(DSST\\right)}}{3}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eThere is lack of cutoff point to determine different cognitive function statuses according to these tests. Referring to the published research,we used the lowest quartile value as the cutoff point, and divided the participants into two groups:normal cognitive performance and low cognitive performance. The cutoff point was 19 for CERAD-WL, 12 for AFT, 32 for DSST and \u0026minus;\u0026thinsp;0.55 for Z-score[\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e].\u003c/p\u003e\n\u003ch3\u003eRelative Fat Mass(RFM)\u003c/h3\u003e\n\u003cp\u003eProfessionals at the Mobile Examination Center (MEC) took height and waist circumference (WC) measurements. The equation utilized for determining the RFM in males is expressed as follows: RFM\u0026thinsp;=\u0026thinsp;64 - (20 \u0026times; Height/WC)[\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e\n\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\n \u003ch2\u003eCovariates\u003c/h2\u003e\n \u003cp\u003eThis study incorporated the following covariates: age, race, education level, Poverty Income Ratio (PIR), BMI (kg/m\u0026sup2;), smoking and drinking status, as well as the presence of hypertension, diabetes, coronary heart disease, and stroke. When it came to drinking status, the threshold for either drinking or not drinking was set at 12 drinks per year. On the other hand, smoking status was defined as smoking more than 100 cigarettes in lifetime. Hypertension, diabetes, coronary heart disease, and stroke were based on participants\u0026rsquo; self-reported responses to the questions,\u0026ldquo;Have you ever been told by a doctor or other health professional that you had hypertension/diabetes/coronary heart disease/stroke\u0026rdquo;.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003eStatistical analysis\u003c/h2\u003e\n \u003cp\u003eR software (version 4.2) and EmpowerStats (version 4.2) were utilized in order to carry out statistical studies. Categorical variables were represented as percentages (%), while continuous variables including were expressed as means\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviations (SD). RFM was additionally employed to examine both continuous variables and quartile distributions.\u003c/p\u003e\n \u003cp\u003eMultivariate linear regression was used to examine the relationship between RFM and cognitive test results. Three models were created to eliminate the influence of confounding factors on the study outcomes. A trend test was conducted to assess the linear progression of cognitive test scores across RFM quartiles. Smooth curve fitting was used in order to explore the nonlinear relationship that exists between RFM and cognitive test result.Threshold effect analyses were applied to identify the inflection point during the investigation process.In addition, subgroup analyses were carried out to evaluate the consistency of the association. Statistical significance was determined using a \u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eBaseline Characteristics\u003c/h2\u003e\n \u003cp\u003eA total of 1,321 individuals took part in the research investigation, with a mean age of 69.23\u0026thinsp;\u0026plusmn;\u0026thinsp;6.71 years and a mean RFM of 30.41\u0026thinsp;\u0026plusmn;\u0026thinsp;4.41. The RFM levels from Q1 to Q4 were 24.82\u0026thinsp;\u0026plusmn;\u0026thinsp;2.63, 29.63\u0026thinsp;\u0026plusmn;\u0026thinsp;0.77, 31.89\u0026thinsp;\u0026plusmn;\u0026thinsp;0.69, and 35.78\u0026thinsp;\u0026plusmn;\u0026thinsp;1.98, respectively. The mean CERAD-WL score was 23.80\u0026thinsp;\u0026plusmn;\u0026thinsp;6.13, and CERAD-WL levels from Q1 to Q4 were 25.66\u0026thinsp;\u0026plusmn;\u0026thinsp;6.28, 24.40\u0026thinsp;\u0026plusmn;\u0026thinsp;5.96, 25.21\u0026thinsp;\u0026plusmn;\u0026thinsp;5.91, 25.12\u0026thinsp;\u0026plusmn;\u0026thinsp;5.77, respectively. The mean AFT score was 16.90\u0026thinsp;\u0026plusmn;\u0026thinsp;5.50, and AFT levels from Q1 to Q4 were19.15\u0026thinsp;\u0026plusmn;\u0026thinsp;6.18, 18.16\u0026thinsp;\u0026plusmn;\u0026thinsp;5.77, 18.23\u0026thinsp;\u0026plusmn;\u0026thinsp;5.60, 18.86\u0026thinsp;\u0026plusmn;\u0026thinsp;5.44, respectively. The mean DSST score was 43.88\u0026thinsp;\u0026plusmn;\u0026thinsp;16.14, and DSST levels from Q1 to Q4 were 52.69\u0026thinsp;\u0026plusmn;\u0026thinsp;16.57, 51.31\u0026thinsp;\u0026plusmn;\u0026thinsp;15.29, 50.19\u0026thinsp;\u0026plusmn;\u0026thinsp;14.07, 49.03\u0026thinsp;\u0026plusmn;\u0026thinsp;15.06, respectively. The mean Z-scores was 0\u0026thinsp;\u0026plusmn;\u0026thinsp;0.79, and Z-scores levels from Q1 to Q4 were 0.42\u0026thinsp;\u0026plusmn;\u0026thinsp;0.87, 0.26\u0026thinsp;\u0026plusmn;\u0026thinsp;0.79, 0.29\u0026thinsp;\u0026plusmn;\u0026thinsp;0.74, 0.30\u0026thinsp;\u0026plusmn;\u0026thinsp;0.76, respectively. Cognitive test scores were lower in the Q4 group compared to the Q1 group (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cp\u003e\u003cstrong\u003eTable 1.\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eWeighted baseline characteristics of participants in RFM quartiles\u003c/strong\u003e\u003c/p\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"693\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.5079%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 51.8038%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRelative Fat Mass(RFM)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6883%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.5079%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5541%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ1\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e11.93-27.90\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2756%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ2\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e27.90-30.66\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4098%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ3\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e30.66-33.20\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5642%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ4\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e33.20-43.21\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6883%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.5079%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5541%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN=330\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2756%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN=329\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4098%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN=331\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5642%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eN=331\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6883%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.5079%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge(years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5541%;\"\u003e\n \u003cp\u003e67.49\u0026plusmn;6.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2756%;\"\u003e\n \u003cp\u003e69.11\u0026plusmn;6.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4098%;\"\u003e\n \u003cp\u003e69.84\u0026plusmn;6.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5642%;\"\u003e\n \u003cp\u003e68.20\u0026plusmn;6.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6883%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e＜\u003c/strong\u003e\u003cstrong\u003e0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.5079%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRace(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5541%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2756%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4098%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5642%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6883%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0064\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.5079%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMexican American\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5541%;\"\u003e\n \u003cp\u003e2.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2756%;\"\u003e\n \u003cp\u003e3.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4098%;\"\u003e\n \u003cp\u003e4.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5642%;\"\u003e\n \u003cp\u003e4.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6883%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.5079%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOther Hispanic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5541%;\"\u003e\n \u003cp\u003e2.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2756%;\"\u003e\n \u003cp\u003e4.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4098%;\"\u003e\n \u003cp\u003e3.81\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5642%;\"\u003e\n \u003cp\u003e3.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6883%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.5079%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-Hispanic White\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5541%;\"\u003e\n \u003cp\u003e75.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2756%;\"\u003e\n \u003cp\u003e78.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4098%;\"\u003e\n \u003cp\u003e83.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5642%;\"\u003e\n \u003cp\u003e82.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6883%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.5079%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-Hispanic Black\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5541%;\"\u003e\n \u003cp\u003e10.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2756%;\"\u003e\n \u003cp\u003e6.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4098%;\"\u003e\n \u003cp\u003e5.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5642%;\"\u003e\n \u003cp\u003e6.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6883%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.5079%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOther Race\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5541%;\"\u003e\n \u003cp\u003e8.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2756%;\"\u003e\n \u003cp\u003e7.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4098%;\"\u003e\n \u003cp\u003e2.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5642%;\"\u003e\n \u003cp\u003e2.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6883%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.5079%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation level(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5541%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2756%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4098%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5642%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6883%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e＜\u003c/strong\u003e\u003cstrong\u003e0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.5079%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLess than 9\u003csup\u003eth\u003c/sup\u003e grade\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5541%;\"\u003e\n \u003cp\u003e5.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2756%;\"\u003e\n \u003cp\u003e6.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4098%;\"\u003e\n \u003cp\u003e5.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5642%;\"\u003e\n \u003cp\u003e5.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6883%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.5079%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e9-11\u003csup\u003eth\u003c/sup\u003e grade\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5541%;\"\u003e\n \u003cp\u003e5.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2756%;\"\u003e\n \u003cp\u003e10.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4098%;\"\u003e\n \u003cp\u003e9.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5642%;\"\u003e\n \u003cp\u003e12.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6883%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.5079%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHigh school graduate\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e/GED or equivalent\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5541%;\"\u003e\n \u003cp\u003e20.58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2756%;\"\u003e\n \u003cp\u003e19.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4098%;\"\u003e\n \u003cp\u003e18.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5642%;\"\u003e\n \u003cp\u003e18.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6883%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.5079%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSome college or AA degree\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5541%;\"\u003e\n \u003cp\u003e23.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2756%;\"\u003e\n \u003cp\u003e21.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4098%;\"\u003e\n \u003cp\u003e32.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5642%;\"\u003e\n \u003cp\u003e35.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6883%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.5079%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCollege graduate or above\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5541%;\"\u003e\n \u003cp\u003e44.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2756%;\"\u003e\n \u003cp\u003e42.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4098%;\"\u003e\n \u003cp\u003e33.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5642%;\"\u003e\n \u003cp\u003e27.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6883%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.5079%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePIR(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5541%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2756%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4098%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5642%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6883%;\"\u003e\n \u003cp\u003e0.2115\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.5079%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e＜\u003c/strong\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5541%;\"\u003e\n \u003cp\u003e6.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2756%;\"\u003e\n \u003cp\u003e6.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4098%;\"\u003e\n \u003cp\u003e4.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5642%;\"\u003e\n \u003cp\u003e8.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6883%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.5079%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026ge;1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5541%;\"\u003e\n \u003cp\u003e93.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2756%;\"\u003e\n \u003cp\u003e93.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4098%;\"\u003e\n \u003cp\u003e95.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5642%;\"\u003e\n \u003cp\u003e91.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6883%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.5079%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI(kg/m\u0026sup2;\u003c/strong\u003e\u003cstrong\u003e）\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5541%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2756%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4098%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5642%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6883%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e＜\u003c/strong\u003e\u003cstrong\u003e0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.5079%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e＜\u003c/strong\u003e\u003cstrong\u003e25\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5541%;\"\u003e\n \u003cp\u003e78.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2756%;\"\u003e\n \u003cp\u003e17.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4098%;\"\u003e\n \u003cp\u003e1.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5642%;\"\u003e\n \u003cp\u003e8.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6883%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.5079%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e25-30\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5541%;\"\u003e\n \u003cp\u003e21.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.2756%;\"\u003e\n \u003cp\u003e77.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.4098%;\"\u003e\n \u003cp\u003e60.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.5642%;\"\u003e\n \u003cp\u003e91.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.6883%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003cstrong\u003eContinued Table 1.\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eWeighted baseline characteristics of participants in RFM quartiles\u003c/strong\u003e\u003c/p\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"693\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.4162%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 52.1676%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRelative Fat Mass(RFM)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4162%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.4162%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8613%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ1\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e11.93-27.90\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8613%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ2\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e27.90-30.66\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5723%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ3\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e30.66-33.20\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.8728%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ4\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e33.20-43.21\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4162%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.4162%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026ge;30\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8613%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8613%;\"\u003e\n \u003cp\u003e5.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5723%;\"\u003e\n \u003cp\u003e37.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.8728%;\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4162%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.4162%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRFM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8613%;\"\u003e\n \u003cp\u003e24.82\u0026plusmn;2.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8613%;\"\u003e\n \u003cp\u003e29.36\u0026plusmn;0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5723%;\"\u003e\n \u003cp\u003e31.89\u0026plusmn;0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.8728%;\"\u003e\n \u003cp\u003e35.78\u0026plusmn;1.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4162%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e＜\u003c/strong\u003e\u003cstrong\u003e0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.4162%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSmoking (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8613%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8613%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5723%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.8728%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4162%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0235\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.4162%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8613%;\"\u003e\n \u003cp\u003e57.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8613%;\"\u003e\n \u003cp\u003e56.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5723%;\"\u003e\n \u003cp\u003e64.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.8728%;\"\u003e\n \u003cp\u003e65.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4162%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.4162%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8613%;\"\u003e\n \u003cp\u003e43.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8613%;\"\u003e\n \u003cp\u003e43.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5723%;\"\u003e\n \u003cp\u003e35.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.8728%;\"\u003e\n \u003cp\u003e34.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4162%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.4162%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDrinking(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8613%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8613%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5723%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.8728%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4162%;\"\u003e\n \u003cp\u003e0.6463\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.4162%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8613%;\"\u003e\n \u003cp\u003e83.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8613%;\"\u003e\n \u003cp\u003e86.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5723%;\"\u003e\n \u003cp\u003e86.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.8728%;\"\u003e\n \u003cp\u003e84.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4162%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.4162%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8613%;\"\u003e\n \u003cp\u003e16.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8613%;\"\u003e\n \u003cp\u003e13.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5723%;\"\u003e\n \u003cp\u003e13.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.8728%;\"\u003e\n \u003cp\u003e15.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4162%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.4162%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHypertension (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8613%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8613%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5723%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.8728%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4162%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e＜\u003c/strong\u003e\u003cstrong\u003e0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.4162%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8613%;\"\u003e\n \u003cp\u003e31.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8613%;\"\u003e\n \u003cp\u003e57.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5723%;\"\u003e\n \u003cp\u003e59.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.8728%;\"\u003e\n \u003cp\u003e72.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4162%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.4162%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8613%;\"\u003e\n \u003cp\u003e68.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8613%;\"\u003e\n \u003cp\u003e42.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5723%;\"\u003e\n \u003cp\u003e40.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.8728%;\"\u003e\n \u003cp\u003e27.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4162%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.4162%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiabetes (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8613%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8613%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5723%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.8728%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4162%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e＜\u003c/strong\u003e\u003cstrong\u003e0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.4162%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8613%;\"\u003e\n \u003cp\u003e8.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8613%;\"\u003e\n \u003cp\u003e13.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5723%;\"\u003e\n \u003cp\u003e22.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.8728%;\"\u003e\n \u003cp\u003e38.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4162%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.4162%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8613%;\"\u003e\n \u003cp\u003e91.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8613%;\"\u003e\n \u003cp\u003e86.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5723%;\"\u003e\n \u003cp\u003e77.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.8728%;\"\u003e\n \u003cp\u003e61.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4162%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.4162%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCoronary heart disease (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8613%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8613%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5723%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.8728%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4162%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0100\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.4162%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8613%;\"\u003e\n \u003cp\u003e7.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8613%;\"\u003e\n \u003cp\u003e13.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5723%;\"\u003e\n \u003cp\u003e13.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.8728%;\"\u003e\n \u003cp\u003e16.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4162%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.4162%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8613%;\"\u003e\n \u003cp\u003e92.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8613%;\"\u003e\n \u003cp\u003e86.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5723%;\"\u003e\n \u003cp\u003e86.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.8728%;\"\u003e\n \u003cp\u003e83.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4162%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.4162%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStroke (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8613%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8613%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5723%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.8728%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4162%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0062\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.4162%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8613%;\"\u003e\n \u003cp\u003e5.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8613%;\"\u003e\n \u003cp\u003e5.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5723%;\"\u003e\n \u003cp\u003e3.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.8728%;\"\u003e\n \u003cp\u003e9.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4162%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.4162%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8613%;\"\u003e\n \u003cp\u003e94.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8613%;\"\u003e\n \u003cp\u003e94.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5723%;\"\u003e\n \u003cp\u003e96.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.8728%;\"\u003e\n \u003cp\u003e90.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4162%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.4162%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCERAD-WL scores\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8613%;\"\u003e\n \u003cp\u003e25.66\u0026plusmn;6.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8613%;\"\u003e\n \u003cp\u003e24.40\u0026plusmn;5.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5723%;\"\u003e\n \u003cp\u003e25.21\u0026plusmn;5.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.8728%;\"\u003e\n \u003cp\u003e25.12\u0026plusmn;5.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4162%;\"\u003e\n \u003cp\u003e0.0677\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003cstrong\u003eContinued Table 1.\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eWeighted baseline characteristics of participants in RFM quartiles\u003c/strong\u003e\u003c/p\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"693\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.4162%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" valign=\"top\" style=\"width: 52.1676%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRelative Fat Mass(RFM)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4162%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.4162%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8613%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ1\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e11.93-27.90\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8613%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ2\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e27.90-30.66\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5723%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ3\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e30.66-33.20\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.8728%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ4\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e33.20-43.21\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4162%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.4162%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCERAD-WL Cognitive Function (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8613%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8613%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5723%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.8728%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4162%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0198\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.4162%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNormal cognitive performance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8613%;\"\u003e\n \u003cp\u003e87.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8613%;\"\u003e\n \u003cp\u003e78.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5723%;\"\u003e\n \u003cp\u003e82.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.8728%;\"\u003e\n \u003cp\u003e85.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4162%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.4162%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; Low cognitive performance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8613%;\"\u003e\n \u003cp\u003e12.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8613%;\"\u003e\n \u003cp\u003e21.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5723%;\"\u003e\n \u003cp\u003e17.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.8728%;\"\u003e\n \u003cp\u003e14.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4162%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.4162%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAFT scores\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8613%;\"\u003e\n \u003cp\u003e19.15\u0026plusmn;6.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8613%;\"\u003e\n \u003cp\u003e18.16\u0026plusmn;5.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5723%;\"\u003e\n \u003cp\u003e18.23\u0026plusmn;5.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.8728%;\"\u003e\n \u003cp\u003e18.86\u0026plusmn;5.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4162%;\"\u003e\n \u003cp\u003e0.0799\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.4162%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAFT Cognitive Function (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8613%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8613%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5723%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.8728%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4162%;\"\u003e\n \u003cp\u003e0.4202\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.4162%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; Normal cognitive performance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8613%;\"\u003e\n \u003cp\u003e85.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8613%;\"\u003e\n \u003cp\u003e83.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5723%;\"\u003e\n \u003cp\u003e87.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.8728%;\"\u003e\n \u003cp\u003e87.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4162%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.4162%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; Low cognitive performance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8613%;\"\u003e\n \u003cp\u003e14.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8613%;\"\u003e\n \u003cp\u003e16.49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5723%;\"\u003e\n \u003cp\u003e12.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.8728%;\"\u003e\n \u003cp\u003e12.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4162%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.4162%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDSST scores\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8613%;\"\u003e\n \u003cp\u003e52.69\u0026plusmn;16.57\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8613%;\"\u003e\n \u003cp\u003e51.31\u0026plusmn;15.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5723%;\"\u003e\n \u003cp\u003e50.19\u0026plusmn;14.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.8728%;\"\u003e\n \u003cp\u003e49.03\u0026plusmn;15.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4162%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0159\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.4162%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDSST Cognitive Function (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8613%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8613%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5723%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.8728%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4162%;\"\u003e\n \u003cp\u003e0.6897\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.4162%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; Normal cognitive performance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8613%;\"\u003e\n \u003cp\u003e89.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8613%;\"\u003e\n \u003cp\u003e87.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5723%;\"\u003e\n \u003cp\u003e88.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.8728%;\"\u003e\n \u003cp\u003e86.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4162%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.4162%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; Low cognitive performance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8613%;\"\u003e\n \u003cp\u003e10.89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8613%;\"\u003e\n \u003cp\u003e12.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5723%;\"\u003e\n \u003cp\u003e11.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.8728%;\"\u003e\n \u003cp\u003e13.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4162%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.4162%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eZ-scores\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8613%;\"\u003e\n \u003cp\u003e0.42\u0026plusmn;0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8613%;\"\u003e\n \u003cp\u003e0.26\u0026plusmn;0.79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5723%;\"\u003e\n \u003cp\u003e0.29\u0026plusmn;0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.8728%;\"\u003e\n \u003cp\u003e0.30\u0026plusmn;0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4162%;\"\u003e\n \u003cp\u003e0.0596\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.4162%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eZ-scores Cognitive Function (%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8613%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8613%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5723%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.8728%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4162%;\"\u003e\n \u003cp\u003e0.9161\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.4162%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; Normal cognitive performance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8613%;\"\u003e\n \u003cp\u003e87.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8613%;\"\u003e\n \u003cp\u003e86.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5723%;\"\u003e\n \u003cp\u003e85.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.8728%;\"\u003e\n \u003cp\u003e86.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4162%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 36.4162%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp; Low cognitive performance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8613%;\"\u003e\n \u003cp\u003e12.71\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.8613%;\"\u003e\n \u003cp\u003e13.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.5723%;\"\u003e\n \u003cp\u003e14.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.8728%;\"\u003e\n \u003cp\u003e13.62\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.4162%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003cstrong\u003eMean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD for continuous variables: the \u003cem\u003eP\u0026nbsp;\u003c/em\u003evalue was calculated by the weighted linear regression model;\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(%) for categorical variables: the \u003cem\u003eP\u003c/em\u003e value was calculated by the weighted chi-square test\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eAbbreviations:RFM:Relative fat mass,\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eCERAD-WL: Consortium to Establish a Registry for Alzheimer\u0026rsquo;s Disease Word Learning Test,\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eAFT:Animal Fluency Test,\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eDSST:Digit Symbol Substitution Test,\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eAssociation of RFM with cognitive function\u003c/h2\u003e\n \u003cp\u003eRFM was negatively correlated with DSST scores and Z-scores in all three models. In Model 3, for every unit increase in RFM, there was a 0.17-point decrease in CERAD-WL scores (\u003cem\u003ep\u003c/em\u003e\u0026lt;0.05), a 0.83-point decrease in DSST scores (\u003cem\u003ep\u003c/em\u003e\u0026lt;0.05), and a 0.03-point decrease in Z-scores (\u003cem\u003ep\u003c/em\u003e\u0026lt;0.05). However, the negative relationship between RFM and AFT scores was not significant (\u003cem\u003ep\u003c/em\u003e\u0026gt;0.05) (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAssociations betweenRFM and cognitive function\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCognitive function\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003cp\u003e\u0026beta;(95%CI) \u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003cp\u003e\u0026beta;(95%CI) \u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModel 3\u003c/p\u003e\n \u003cp\u003e\u0026beta;(95%CI) \u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCERAD-WL\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;0.06(\u0026minus;0.14,0.01)0.0995\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;0.05(\u0026minus;0.12,0.02)0.1630\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;0.17(\u0026minus;0.32,\u0026minus;0.01)\u003cstrong\u003e0.0422\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;1.26(\u0026minus;2.20,\u0026minus;0.32)\u003cstrong\u003e0.0086\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;0.75(\u0026minus;1.63,0.12)0.0923\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;0.54(\u0026minus;1.49,0.40)0.2563\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;0.45(\u0026minus;1.36,0.47) 0.3414\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.25(\u0026minus;0.61,1.11) 0.5704\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.45(-0.60, 1.51) 0.3998\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;0.54(\u0026minus;1.46,0.37) 0.2469\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;0.38(\u0026minus;1.23,0.47)0.3827\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.13(\u0026minus;1.33, 1.60) 0.8578\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003c/strong\u003e \u003cstrong\u003efor trend\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.620\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.914\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.397\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAFT\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;0.04(\u0026minus;0.11,0.03) 0.2633\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;0.05(\u0026minus;0.12,0.02)0.1562\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;0.08(\u0026minus;0.23,0.07) 0.2852\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;0.98(\u0026minus;1.88,\u0026minus;0.08)\u003cstrong\u003e0.0329\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;0.70(\u0026minus;1.55,0.14)0.1043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;0.39(\u0026minus;1.27,0.49) 0.3863\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;0.92(\u0026minus;1.80,\u0026minus;0.04)\u003cstrong\u003e0.0409\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;0.61(\u0026minus;1.44,0.23)0.1574\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;0.13(\u0026minus;1.11,0.86) 0.8020\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;0.29(\u0026minus;1.17,0.59) 0.5205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;0.36(\u0026minus;1.19,0.47)0.3911\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.61 (\u0026minus;0.76,1.98) 0.3830\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003c/strong\u003e \u003cstrong\u003efor trend\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.624\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.499\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.458\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDSST\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;0.30(\u0026minus;0.49,\u0026minus;0.11)\u003cstrong\u003e0.0019\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;0.28(\u0026minus;0.44,\u0026minus;0.12)\u003cstrong\u003e0.0008\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;0.83(\u0026minus;1.16,\u0026minus;0.50)\u0026thinsp;\u003cstrong\u003e\u0026lt;\u0026thinsp;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;1.38(\u0026minus;3.78,1.01) 0.2568\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;0.11(\u0026minus;2.14,1.93) 0.9174\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;0.96 (\u0026minus;2.92, 0.99) 0.3340\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;2.50(\u0026minus;4.84,\u0026minus;0.16)\u003cstrong\u003e0.0364\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;0.70(\u0026minus;2.71,1.32)0.4982\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;2.13 (\u0026minus;4.32, 0.06) 0.0573\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;3.67(\u0026minus;6.00,\u0026minus;1.33)\u003cstrong\u003e0.0021\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;3.33(\u0026minus;5.32,\u0026minus;1.34)\u003cstrong\u003e0.0011\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;5.51 (\u0026minus;8.55,\u0026minus;2.47) \u003cstrong\u003e0.0004\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003c/strong\u003e \u003cstrong\u003efor trend\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eZ-scores\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;0.01(\u0026minus;0.02,\u0026minus;0.00)\u003cstrong\u003e0.0156\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;0.01(\u0026minus;0.02,\u0026minus;0.00)\u003cstrong\u003e0.0084\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;0.03(\u0026minus;0.05,\u0026minus;0.01) \u003cstrong\u003e0.0006\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRef\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;0.16(\u0026minus;0.28,\u0026minus;0.03)\u003cstrong\u003e0.0130\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;0.09(\u0026minus;0.19,0.02) 0.1147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;0.07 (\u0026minus;0.18, 0.03) 0.1719\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;0.13(\u0026minus;0.25,\u0026minus;0.01)\u003cstrong\u003e0.0328\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;0.04(\u0026minus;0.14,0.07) 0.4857\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;0.03 (\u0026minus;0.14, 0.09) 0.6539\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eQ4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;0.12(\u0026minus;0.24,\u0026minus;0.00)\u003cstrong\u003e0.0462\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;0.11(\u0026minus;0.22,\u0026minus;0.01)\u003cstrong\u003e0.0359\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;0.07 (\u0026minus;0.23, 0.09) 0.4041\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eP\u003c/strong\u003e \u003cstrong\u003efor trend\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.088\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.089\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.632\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003e\u003cstrong\u003eModel 1: variables were not adjusted.\u003c/strong\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003e\u003cstrong\u003eModel 2: adjustments were made to age and race.\u003c/strong\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003e\u003cstrong\u003eModel 3: Adjustments were made for age, race, education level, PIR, BMI, smoking status, drinking status, hypertension, diabetes, coronary heart disease, stroke.\u003c/strong\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003e\u003cstrong\u003eAbbreviations: RFM:Relative Fat Mass\u003c/strong\u003e,\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003e\u003cstrong\u003eCERAD-WL: Consortium to Establish a Registry for Alzheimer\u0026rsquo;s Disease Word Learning Test\u003c/strong\u003e,\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003e\u003cstrong\u003eAFT :Animal Fluency Test\u003c/strong\u003e,\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003e\u003cstrong\u003eDSST :Digit Symbol Substitution Test\u003c/strong\u003e,\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003e\u003cstrong\u003ePIR: Poverty Income Ratio\u003c/strong\u003e,\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003e\u003cstrong\u003eBMI: Body Measure Index.\u003c/strong\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eComparisons between participants in the Q4 and Q1 of RFM showed a 5.51-point reduction in DSST scores (\u003cem\u003ep\u003c/em\u003e for trend\u0026lt;0.05). Significant relationships between RFM quartiles and CERAD-WL, AFT, and Z-scores were not identified (\u003cem\u003ep\u003c/em\u003e for trend\u0026gt;0.05).\u003c/p\u003e\n \u003cp\u003eThe nonlinear association between RFM and cognitive test results was discovered via the use of smooth curve fitting (Fig.\u0026nbsp;2). In the CERAD-WL, the inflection point was identified at 35.51, indicating that the negative correlation between RFM and CERAD-WL scores became more noticeable when RFM\u0026gt;35.51. For every unit increase in RFM up to 35.51, CERAD-WL scores decreased by 0.25 points (\u003cem\u003ep\u003c/em\u003e\u0026lt;0.05); however, for every unit increase in RFM above 35.51, CERAD-WL scores decreased by 1.17 points (\u003cem\u003ep\u003c/em\u003e\u0026lt;0.05). In the AFT, the inflection point was identified at 37.16, indicating that the negative correlation between RFM and AFT scores is not significant when RFM\u0026thinsp;\u0026le;\u0026thinsp;37.16; however, for every unit increase in RFM above 37.16, AFT scores decreased by 1.26 points (\u003cem\u003ep\u003c/em\u003e\u0026lt;0.05). In the DSST, the inflection point was identified at 33.01, indicating that the negative correlation between RFM and DSST scores became more noticeable when RFM\u0026gt;33.01. For every unit increase in RFM up to 33.01, DSST scores decreased by 0.92 points (\u003cem\u003ep\u003c/em\u003e\u0026lt;0.05); however, for every unit increase in RFM above 33.01, DSST scores decreased by 2.45 points (\u003cem\u003ep\u003c/em\u003e\u0026lt;0.05). In Z-scores,the inflection point was identified at 35.78, indicating that the negative correlation between RFM and Z-scores became more noticeable when RFM\u0026gt;35.78. For every unit increase in RFM up to 35.78, Z-scores decreased by 0.04 points (\u003cem\u003ep\u003c/em\u003e\u0026lt;0.05); however, for every unit increase in RFM above 35.78, Z-scores decreased by 0.17 points (\u003cem\u003ep\u003c/em\u003e\u0026lt;0.05) (Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThreshold effect analyses of RFM on cognitive function\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModels\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCERAD-WL\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAFT\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDSST\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eZ-scores\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eModel I\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eOne line slope\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;0.17(\u0026minus;0.32,\u0026minus;0.01) 0.0422\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;0.08(\u0026minus;0.23,0.07) 0.2852\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;0.83(\u0026minus;1.16,\u0026minus;0.50)\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;0.03(\u0026minus;0.05,\u0026minus;0.01) 0.0006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eModel II\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTurning point(K)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e33.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35.78\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;K\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;0.25(\u0026minus;0.41,\u0026minus;0.08) \u003cstrong\u003e0.0033\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;0.16(\u0026minus;0.31,0.00) 0.0526\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;0.92(\u0026minus;1.25,\u0026minus;0.59)\u0026thinsp;\u003cstrong\u003e\u0026lt;\u0026thinsp;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;0.04(\u0026minus;0.06,\u0026minus;0.02)\u0026thinsp;\u003cstrong\u003e\u0026lt;\u0026thinsp;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026gt;K\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;1.17(\u0026minus;1.74,\u0026minus;0.59)\u0026thinsp;\u003cstrong\u003e\u0026lt;\u0026thinsp;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;1.26(\u0026minus;2.08,\u0026minus;0.43) \u003cstrong\u003e0.0028\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;2.45(\u0026minus;3.24,\u0026minus;1.67)\u0026thinsp;\u003cstrong\u003e\u0026lt;\u0026thinsp;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;0.17(\u0026minus;0.24,\u0026minus;0.10)\u0026thinsp;\u003cstrong\u003e\u0026lt;\u0026thinsp;0.0001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eOR between\u0026lt;K and \u0026gt;K\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;0.92(\u0026minus;1.42,\u0026minus;0.41) 0.0004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;1.10(\u0026minus;1.86,\u0026minus;0.34) 0.0045\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;1.53(\u0026minus;2.20,\u0026minus;0.86)\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026minus;0.13(\u0026minus;0.19,\u0026minus;0.07)\u0026thinsp;\u0026lt;\u0026thinsp;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eLog-likelihood ratio\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.004\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003e\u003cstrong\u003eAge, race, education level, PIR, BMI, smoking status, drinking status, hypertension, diabetes, coronary heart disease, stroke were adjusted.\u003c/strong\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003e\u003cstrong\u003eAbbreviations: RFM:Relative Fat Mass\u003c/strong\u003e,\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003e\u003cstrong\u003eCERAD-WL: Consortium to Establish a Registry for Alzheimer\u0026rsquo;s Disease Word Learning Test\u003c/strong\u003e,\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003e\u003cstrong\u003eAFT: Animal Fluency Test\u003c/strong\u003e,\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003e\u003cstrong\u003eDSST: Digit Symbol Substitution Test\u003c/strong\u003e,\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003e\u003cstrong\u003ePIR : Poverty Income Ratio\u003c/strong\u003e,\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003e\u003cstrong\u003eBMI: Body Measure Index.\u003c/strong\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003eSubgroup analyses\u003c/h2\u003e\n \u003cp\u003eAs shown in Table \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003e, further subgroup analyses revealed variation in the association between RFM and cognitive function. Significant interactions emerged in the interaction analyses for education level, PIR, smoking and drinking status when Z-scores was used as an assessment of cognitive function (\u003cem\u003ep\u003c/em\u003e\u0026lt;0.05). No significant interactive effects were detected in other variables (\u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv class=\"colspec\" align=\"left\"\u003e\u0026nbsp;\u003c/div\u003e\n \u003cp\u003e\u003cstrong\u003eTable 4.\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eSubgroup analysis of the associations between RFM and cognitive function\u003c/strong\u003e\u003c/p\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"968\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1198%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSubgroup\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.7066%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCERAD-WL\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u0026beta;\u003c/strong\u003e\u003cstrong\u003e（\u003c/strong\u003e\u003cstrong\u003e95%CI\u003c/strong\u003e\u003cstrong\u003e）\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.8843%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;for interaction\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.2934%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAFT\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026beta;\u003c/strong\u003e\u003cstrong\u003e（\u003c/strong\u003e\u003cstrong\u003e95%CI\u003c/strong\u003e\u003cstrong\u003e）\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.36777%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;for interaction\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4298%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDSST\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026beta;\u003c/strong\u003e\u003cstrong\u003e（\u003c/strong\u003e\u003cstrong\u003e95%CI\u003c/strong\u003e\u003cstrong\u003e）\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.9876%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;for interaction\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4298%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eZ-score\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026beta;\u003c/strong\u003e\u003cstrong\u003e（\u003c/strong\u003e\u003cstrong\u003e95%CI\u003c/strong\u003e\u003cstrong\u003e）\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.78099%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;for interaction\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1198%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.7066%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.8843%;\"\u003e\n \u003cp\u003e0.2901\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.2934%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.36777%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0130\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4298%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.9876%;\"\u003e\n \u003cp\u003e0.7817\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4298%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.78099%;\"\u003e\n \u003cp\u003e0.1176\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1198%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e＜\u003c/strong\u003e\u003cstrong\u003e70\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.7066%;\"\u003e\n \u003cp\u003e-0.25(-0.42,-0.08)\u003c/p\u003e\n \u003cp\u003e0.0033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.8843%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.2934%;\"\u003e\n \u003cp\u003e-0.18(-0.34,-0.02)\u003c/p\u003e\n \u003cp\u003e0.0242\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.36777%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4298%;\"\u003e\n \u003cp\u003e-0.98(-1.33,-0.63)\u003c/p\u003e\n \u003cp\u003e＜0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.9876%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4298%;\"\u003e\n \u003cp\u003e-0.04（-0.06，-0.03）＜0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.78099%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1198%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e70-79\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.7066%;\"\u003e\n \u003cp\u003e-0.15(-0.35,0.04)\u003c/p\u003e\n \u003cp\u003e0.1220\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.8843%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.2934%;\"\u003e\n \u003cp\u003e0.04(-0.14,0.22)\u003c/p\u003e\n \u003cp\u003e0.6787\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.36777%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4298%;\"\u003e\n \u003cp\u003e-1.07（-1.48，-0.66）＜0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.9876%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4298%;\"\u003e\n \u003cp\u003e-0.03（-0.05，-0.01）0.0112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.78099%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1198%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026ge;80\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.7066%;\"\u003e\n \u003cp\u003e-0.09(-0.37,0.19)\u003c/p\u003e\n \u003cp\u003e0.5321\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.8843%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.2934%;\"\u003e\n \u003cp\u003e-0.06(-0.32,0.21)\u003c/p\u003e\n \u003cp\u003e0.6695\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.36777%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4298%;\"\u003e\n \u003cp\u003e-0.88（-1.47，-0.28）0.0038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.9876%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4298%;\"\u003e\n \u003cp\u003e-0.03（-0.06，0.01）0.0999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.78099%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1198%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRace\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.7066%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.8843%;\"\u003e\n \u003cp\u003e0.5479\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.2934%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.36777%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0160\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4298%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.9876%;\"\u003e\n \u003cp\u003e0.8654\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4298%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.78099%;\"\u003e\n \u003cp\u003e0.1193\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1198%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMexican American\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.7066%;\"\u003e\n \u003cp\u003e-0.16(-0.58,0.27)\u003c/p\u003e\n \u003cp\u003e0.4695\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.8843%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.2934%;\"\u003e\n \u003cp\u003e-0.20(-0.59,0.19)\u003c/p\u003e\n \u003cp\u003e0.3172\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.36777%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4298%;\"\u003e\n \u003cp\u003e-0.81 (-1.68, 0.06) 0.0678\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.9876%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4298%;\"\u003e\n \u003cp\u003e-0.04 (-0.08, 0.01) 0.1166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.78099%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1198%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOther Hispanic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.7066%;\"\u003e\n \u003cp\u003e0.02(-0.51,0.54)\u003c/p\u003e\n \u003cp\u003e0.9538\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.8843%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.2934%;\"\u003e\n \u003cp\u003e-0.17(-0.65,0.32)\u003c/p\u003e\n \u003cp\u003e0.5067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.36777%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4298%;\"\u003e\n \u003cp\u003e-0.34 (-1.43, 0.74) 0.5346\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.9876%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4298%;\"\u003e\n \u003cp\u003e-0.02 (-0.07, 0.04) 0.5827\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.78099%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1198%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-Hispanic White\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.7066%;\"\u003e\n \u003cp\u003e-0.19(-0.36,-0.03)\u003c/p\u003e\n \u003cp\u003e0.0234\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.8843%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.2934%;\"\u003e\n \u003cp\u003e-0.12(-0.28, 0.03) 0.1217\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.36777%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4298%;\"\u003e\n \u003cp\u003e-0.87 (-1.21, -0.52) \u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.9876%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4298%;\"\u003e\n \u003cp\u003e-0.04 (-0.05, -0.02) 0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.78099%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1198%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNon-Hispanic Black\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.7066%;\"\u003e\n \u003cp\u003e-0.16(-0.42,0.09)\u003c/p\u003e\n \u003cp\u003e0.2167\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.8843%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.2934%;\"\u003e\n \u003cp\u003e-0.08(-0.32, 0.15) 0.4887\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.36777%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4298%;\"\u003e\n \u003cp\u003e-0.73 (-1.26, -0.20) 0.0070\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.9876%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4298%;\"\u003e\n \u003cp\u003e-0.03 (-0.06, -0.00) 0.0455\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.78099%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1198%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOther Race\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.7066%;\"\u003e\n \u003cp\u003e0.03(-0.27,0.32)\u003c/p\u003e\n \u003cp\u003e0.8539\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.8843%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.2934%;\"\u003e\n \u003cp\u003e0.32 (0.04, 0.59) 0.0230\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.36777%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4298%;\"\u003e\n \u003cp\u003e-0.79 (-1.40, -0.18) 0.0113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.9876%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4298%;\"\u003e\n \u003cp\u003e0.00 (-0.03, 0.04) 0.7856\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.78099%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1198%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation level\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.7066%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.8843%;\"\u003e\n \u003cp\u003e0.0919\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.2934%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.36777%;\"\u003e\n \u003cp\u003e0.8706\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4298%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.9876%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0089\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4298%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.78099%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0494\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1198%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLess than 9\u003csup\u003eth\u003c/sup\u003e grade\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.7066%;\"\u003e\n \u003cp\u003e-0.20 (-0.52, 0.12) 0.2278\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.8843%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.2934%;\"\u003e\n \u003cp\u003e0.02 (-0.28, 0.32) 0.8899\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.36777%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4298%;\"\u003e\n \u003cp\u003e-0.83 (-1.50, -0.16) 0.0148\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.9876%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4298%;\"\u003e\n \u003cp\u003e-0.03 (-0.06, 0.01) 0.1450\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.78099%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003cstrong\u003eContinued Table 4.\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eSubgroup analysis of the associations between RFM and cognitive function\u003c/strong\u003e\u003c/p\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"968\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1198%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSubgroup\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 12.7066%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCERAD-WL\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u0026beta;\u003c/strong\u003e\u003cstrong\u003e（\u003c/strong\u003e\u003cstrong\u003e95%CI\u003c/strong\u003e\u003cstrong\u003e）\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.8843%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;for interaction\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.2934%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAFT\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026beta;\u003c/strong\u003e\u003cstrong\u003e（\u003c/strong\u003e\u003cstrong\u003e95%CI\u003c/strong\u003e\u003cstrong\u003e）\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.36777%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;for interaction\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4298%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDSST\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026beta;\u003c/strong\u003e\u003cstrong\u003e（\u003c/strong\u003e\u003cstrong\u003e95%CI\u003c/strong\u003e\u003cstrong\u003e）\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.9876%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;for interaction\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4298%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eZ-score\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026beta;\u003c/strong\u003e\u003cstrong\u003e（\u003c/strong\u003e\u003cstrong\u003e95%CI\u003c/strong\u003e\u003cstrong\u003e）\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.78099%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;for interaction\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1198%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e9-11\u003csup\u003eth\u003c/sup\u003e grade\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.9835%;\"\u003e\n \u003cp\u003e0.12 (-0.16, 0.39) 0.4140\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 9.60744%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.2934%;\"\u003e\n \u003cp\u003e-0.04(-0.30, 0.22) 0.7549\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.36777%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4298%;\"\u003e\n \u003cp\u003e-0.01 (-0.59, 0.56) 0.9620\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.9876%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4298%;\"\u003e\n \u003cp\u003e0.00(-0.03,0.03) 0.8239\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.78099%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1198%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHigh school graduate\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e/GED or equivalent\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.9835%;\"\u003e\n \u003cp\u003e-0.09(-0.31, 0.13) 0.4146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 9.60744%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.2934%;\"\u003e\n \u003cp\u003e-0.07(-0.28, 0.14) 0.4976\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.36777%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4298%;\"\u003e\n \u003cp\u003e-0.68 (-1.14, -0.22) 0.0036\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.9876%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4298%;\"\u003e\n \u003cp\u003e-0.02(-0.05,0.00) 0.0620\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.78099%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1198%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSome college or\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eAA degree\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.9835%;\"\u003e\n \u003cp\u003e-0.13(-0.33, 0.08) 0.2179\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 9.60744%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.2934%;\"\u003e\n \u003cp\u003e-0.05(-0.24, 0.14) 0.6281\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.36777%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4298%;\"\u003e\n \u003cp\u003e-0.88 (-1.30, -0.46) \u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.9876%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4298%;\"\u003e\n \u003cp\u003e-0.03(-0.05,-0.01) 0.0154\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.78099%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1198%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCollege graduate or above\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.9835%;\"\u003e\n \u003cp\u003e-0.25(-0.42,-0.07) 0.0055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 9.60744%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.2934%;\"\u003e\n \u003cp\u003e-0.11 (-0.28, 0.05) 0.1715\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.36777%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4298%;\"\u003e\n \u003cp\u003e-1.02 (-1.38, -0.66) \u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.9876%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4298%;\"\u003e\n \u003cp\u003e-0.04(-0.06,-0.02) \u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.78099%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1198%;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePIR\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.9835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 9.60744%;\"\u003e\n \u003cp\u003e0.0759\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.2934%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.36777%;\"\u003e\n \u003cp\u003e0.1907\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4298%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.9876%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0074\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4298%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.78099%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0102\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1198%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e＜\u003c/strong\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.9835%;\"\u003e\n \u003cp\u003e0.01 (-0.24, 0.26) 0.9457\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 9.60744%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.2934%;\"\u003e\n \u003cp\u003e0.04 (-0.20, 0.27) 0.7462\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.36777%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4298%;\"\u003e\n \u003cp\u003e-0.29 (-0.81, 0.23) 0.2697\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.9876%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4298%;\"\u003e\n \u003cp\u003e-0.00(-0.03,0.02) 0.8214\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.78099%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1198%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026ge;1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.9835%;\"\u003e\n \u003cp\u003e-0.19(-0.36,-0.03) 0.0197\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 9.60744%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.2934%;\"\u003e\n \u003cp\u003e-0.10(-0.25, 0.05) 0.1947\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.36777%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4298%;\"\u003e\n \u003cp\u003e-0.92 (-1.26, -0.59) \u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.9876%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4298%;\"\u003e\n \u003cp\u003e-0.04(-0.05,-0.02) 0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.78099%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1198%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eBMI\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 11.9835%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 9.60744%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0026\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.2934%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.36777%;\"\u003e\n \u003cp\u003e0.0797\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4298%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.9876%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0301\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4298%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.78099%;\"\u003e\n \u003cp\u003e0.1313\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1198%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e＜\u003c/strong\u003e\u003cstrong\u003e25\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 12.7066%;\"\u003e\n \u003cp\u003e-0.34(-0.54, -0.14) 0.0010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.8843%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.2934%;\"\u003e\n \u003cp\u003e-0.21(-0.40,-0.02) 0.0275\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.36777%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4298%;\"\u003e\n \u003cp\u003e-0.42 (-0.84, -0.01) 0.0475\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.9876%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4298%;\"\u003e\n \u003cp\u003e-0.04(-0.06,-0.02) 0.0005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.78099%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1198%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e25-30\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 12.7066%;\"\u003e\n \u003cp\u003e0.18 (-0.05, 0.41) 0.1298\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.8843%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.2934%;\"\u003e\n \u003cp\u003e0.03 (-0.19, 0.24) 0.8183\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.36777%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4298%;\"\u003e\n \u003cp\u003e-1.11 (-1.60, -0.63) \u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.9876%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4298%;\"\u003e\n \u003cp\u003e-0.01(-0.04,0.01) 0.3783\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.78099%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 13.1198%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 11.9835%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 0.72314%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8.8843%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 12.2934%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8.36777%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 13.4298%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8.9876%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 13.4298%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd style=\"width: 8.78099%;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eContinued Table 4.\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eSubgroup analysis of the associations between RFM and cognitive function\u003c/strong\u003e\u003c/p\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"968\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1198%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSubgroup\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.7066%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCERAD-WL\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u0026beta;\u003c/strong\u003e\u003cstrong\u003e（\u003c/strong\u003e\u003cstrong\u003e95%CI\u003c/strong\u003e\u003cstrong\u003e）\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.8843%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;for interaction\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.2934%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAFT\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026beta;\u003c/strong\u003e\u003cstrong\u003e（\u003c/strong\u003e\u003cstrong\u003e95%CI\u003c/strong\u003e\u003cstrong\u003e）\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.36777%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;for interaction\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4298%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDSST\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026beta;\u003c/strong\u003e\u003cstrong\u003e（\u003c/strong\u003e\u003cstrong\u003e95%CI\u003c/strong\u003e\u003cstrong\u003e）\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.9876%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;for interaction\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4298%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eZ-score\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026beta;\u003c/strong\u003e\u003cstrong\u003e（\u003c/strong\u003e\u003cstrong\u003e95%CI\u003c/strong\u003e\u003cstrong\u003e）\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.78099%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;for interaction\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1198%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026ge;30\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.7066%;\"\u003e\n \u003cp\u003e-0.18 (-0.37, 0.02) 0.0778\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.8843%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.2934%;\"\u003e\n \u003cp\u003e0.06 (-0.12, 0.25) 0.4905\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.36777%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4298%;\"\u003e\n \u003cp\u003e-0.33 (-0.74, 0.07) 0.1077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.9876%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4298%;\"\u003e\n \u003cp\u003e-0.01(-0.03,0.01) 0.2594\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.78099%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1198%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSmoking\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.7066%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.8843%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0013\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.2934%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.36777%;\"\u003e\n \u003cp\u003e0.0908\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4298%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.9876%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0127\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4298%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.78099%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0007\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1198%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.7066%;\"\u003e\n \u003cp\u003e-0.08 (-0.24, 0.09) 0.3704\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.8843%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.2934%;\"\u003e\n \u003cp\u003e-0.04(-0.19, 0.12) 0.6406\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.36777%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4298%;\"\u003e\n \u003cp\u003e-0.69 (-1.04, -0.34) 0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.9876%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4298%;\"\u003e\n \u003cp\u003e-0.02(-0.04,-0.00) 0.0293\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.78099%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1198%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.7066%;\"\u003e\n \u003cp\u003e-0.30(-0.48, -0.12) 0.0010\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.8843%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.2934%;\"\u003e\n \u003cp\u003e-0.15(-0.32, 0.02) 0.0826\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.36777%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4298%;\"\u003e\n \u003cp\u003e-1.06 (-1.43, -0.68) \u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.9876%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4298%;\"\u003e\n \u003cp\u003e-0.05(-0.07,-0.03) \u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.78099%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1198%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDrinking\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.7066%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.8843%;\"\u003e\n \u003cp\u003e0.9107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.2934%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.36777%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0052\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4298%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.9876%;\"\u003e\n \u003cp\u003e0.2296\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4298%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.78099%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0666\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1198%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.7066%;\"\u003e\n \u003cp\u003e-0.16(-0.33, -0.00) 0.0501\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.8843%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.2934%;\"\u003e\n \u003cp\u003e-0.13(-0.28, 0.02) 0.0998\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.36777%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4298%;\"\u003e\n \u003cp\u003e-0.88 (-1.22, -0.54) \u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.9876%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4298%;\"\u003e\n \u003cp\u003e-0.03(-0.05,-0.02) 0.0002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.78099%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1198%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.7066%;\"\u003e\n \u003cp\u003e-0.17 (-0.39, 0.04) 0.1189\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.8843%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.2934%;\"\u003e\n \u003cp\u003e0.12 (-0.09, 0.32) 0.2641\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.36777%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4298%;\"\u003e\n \u003cp\u003e-0.65 (-1.10, -0.20) 0.0050\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.9876%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4298%;\"\u003e\n \u003cp\u003e-0.02(-0.04,0.01) 0.2014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.78099%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1198%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHypertension\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.7066%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.8843%;\"\u003e\n \u003cp\u003e0.1266\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.2934%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.36777%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0223\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4298%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.9876%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.0025\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4298%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.78099%;\"\u003e\n \u003cp\u003e0.4522\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1198%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.7066%;\"\u003e\n \u003cp\u003e-0.08 (-0.27, 0.11) 0.4084\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.8843%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.2934%;\"\u003e\n \u003cp\u003e0.04 (-0.14, 0.22) 0.6938\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.36777%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4298%;\"\u003e\n \u003cp\u003e-1.18 (-1.58, -0.78) \u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.9876%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4298%;\"\u003e\n \u003cp\u003e-0.03(-0.05,-0.01) 0.0151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.78099%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1198%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.7066%;\"\u003e\n \u003cp\u003e-0.20(-0.36, -0.03) 0.0195\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.8843%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.2934%;\"\u003e\n \u003cp\u003e-0.12(-0.28, 0.03) 0.1136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.36777%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4298%;\"\u003e\n \u003cp\u003e-0.71 (-1.05, -0.37) \u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.9876%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4298%;\"\u003e\n \u003cp\u003e-0.03(-0.05,-0.01) 0.0004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.78099%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1198%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiabetes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.7066%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.8843%;\"\u003e\n \u003cp\u003e0.1349\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.2934%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.36777%;\"\u003e\n \u003cp\u003e0.2991\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4298%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.9876%;\"\u003e\n \u003cp\u003e0.3237\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4298%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.78099%;\"\u003e\n \u003cp\u003e0.8595\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1198%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.7066%;\"\u003e\n \u003cp\u003e-0.32(-0.58, -0.06) 0.0155\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.8843%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.2934%;\"\u003e\n \u003cp\u003e0.02 (-0.22, 0.26) 0.8807\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.36777%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4298%;\"\u003e\n \u003cp\u003e-0.62 (-1.16, -0.09) 0.0219\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.9876%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4298%;\"\u003e\n \u003cp\u003e-0.03(-0.06,-0.00) 0.0464\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.78099%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003cstrong\u003eContinued Table 4.\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eSubgroup analysis of the associations between RFM and cognitive function\u003c/strong\u003e\u003c/p\u003e\n \u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"968\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1198%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSubgroup\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.7066%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCERAD-WL\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u0026beta;\u003c/strong\u003e\u003cstrong\u003e（\u003c/strong\u003e\u003cstrong\u003e95%CI\u003c/strong\u003e\u003cstrong\u003e）\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.8843%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;for interaction\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.2934%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAFT\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026beta;\u003c/strong\u003e\u003cstrong\u003e（\u003c/strong\u003e\u003cstrong\u003e95%CI\u003c/strong\u003e\u003cstrong\u003e）\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.36777%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;for interaction\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4298%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDSST\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026beta;\u003c/strong\u003e\u003cstrong\u003e（\u003c/strong\u003e\u003cstrong\u003e95%CI\u003c/strong\u003e\u003cstrong\u003e）\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.9876%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;for interaction\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4298%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eZ-score\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026beta;\u003c/strong\u003e\u003cstrong\u003e（\u003c/strong\u003e\u003cstrong\u003e95%CI\u003c/strong\u003e\u003cstrong\u003e）\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.78099%;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u003cem\u003eP\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;for interaction\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1198%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.7066%;\"\u003e\n \u003cp\u003e-0.17(-0.33, -0.01) 0.0358\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.8843%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.2934%;\"\u003e\n \u003cp\u003e-0.08(-0.23, 0.07) 0.3078\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.36777%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4298%;\"\u003e\n \u003cp\u003e-0.83 (-1.16, -0.50) \u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.9876%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4298%;\"\u003e\n \u003cp\u003e-0.03(-0.05,-0.01) 0.0006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.78099%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1198%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCoronary heart disease\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.7066%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.8843%;\"\u003e\n \u003cp\u003e0.1665\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.2934%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.36777%;\"\u003e\n \u003cp\u003e0.0591\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4298%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.9876%;\"\u003e\n \u003cp\u003e0.6971\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4298%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.78099%;\"\u003e\n \u003cp\u003e0.1357\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1198%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.7066%;\"\u003e\n \u003cp\u003e-0.02 (-0.28, 0.23) 0.8592\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.8843%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.2934%;\"\u003e\n \u003cp\u003e0.10 (-0.14, 0.34) 0.4170\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.36777%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4298%;\"\u003e\n \u003cp\u003e-0.92 (-1.45, -0.38) 0.0008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.9876%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4298%;\"\u003e\n \u003cp\u003e-0.01(-0.04,0.01) 0.3320\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.78099%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1198%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.7066%;\"\u003e\n \u003cp\u003e-0.17(-0.33, -0.01) 0.0329\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.8843%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.2934%;\"\u003e\n \u003cp\u003e-0.09(-0.24, 0.06) 0.2252\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.36777%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4298%;\"\u003e\n \u003cp\u003e-0.83 (-1.16, -0.50) \u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.9876%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4298%;\"\u003e\n \u003cp\u003e-0.03(-0.05,-0.01) 0.0004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.78099%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1198%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStroke\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.7066%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.8843%;\"\u003e\n \u003cp\u003e0.5754\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.2934%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.36777%;\"\u003e\n \u003cp\u003e0.2258\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4298%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.9876%;\"\u003e\n \u003cp\u003e0.9226\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4298%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.78099%;\"\u003e\n \u003cp\u003e0.3522\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1198%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.7066%;\"\u003e\n \u003cp\u003e-0.08 (-0.41, 0.24) 0.6095\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.8843%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.2934%;\"\u003e\n \u003cp\u003e0.08 (-0.22, 0.39) 0.5999\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.36777%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4298%;\"\u003e\n \u003cp\u003e-0.81 (-1.48, -0.13) 0.0192\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.9876%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4298%;\"\u003e\n \u003cp\u003e-0.02(-0.05,0.02) 0.3761\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.78099%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 13.1198%;\"\u003e\n \u003cp\u003e\u003cstrong\u003eNo\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.7066%;\"\u003e\n \u003cp\u003e-0.17(-0.33, -0.01) 0.0400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.8843%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 12.2934%;\"\u003e\n \u003cp\u003e-0.09(-0.23, 0.06) 0.2623\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.36777%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4298%;\"\u003e\n \u003cp\u003e-0.84 (-1.17, -0.51) \u0026lt;0.0001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.9876%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 13.4298%;\"\u003e\n \u003cp\u003e-0.03(-0.05,-0.01) 0.0005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 8.78099%;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003cstrong\u003eAge, race, education level, PIR, BMI, smoking status, drinking status, hypertension, diabetes, coronary heart disease, stroke were adjusted.\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eAbbreviations: RFM :Relative Fat Mass,\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eCERAD-WL: Consortium to Establish a Registry for Alzheimer\u0026rsquo;s Disease Word Learning Test,\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eAFT: Animal Fluency Test,\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eDSST :Digit Symbol Substitution Test, PIR:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ePoverty Income Ratio,\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eBMI :Body Measure Index.\u003c/strong\u003e\u003c/p\u003e\n \u003c/div\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe results of this study showed a substantial negative correlation between RFM and CERAD-WL, DSST, and Z-scores. Comparisons between participants in the Q4 and Q1 of RFM revealed reduction in DSST scores. Smooth curve fitting revealed a nonlinear relationship between RFM and cognitive function (Log - likelihood ratio\u0026thinsp;\u0026lt;\u0026thinsp;0.05). The negative correlation between RFM and CERAD-WL scores, DSST scores, and Z-scores became more pronounced when RFM\u0026gt;35.51,33.01 and 35.78, respectively.\u003c/p\u003e \u003cp\u003eThe possible mechanisms that connect obesity and cognitive function are intricate and varied. Although the precise processes are still under investigation, several have been proposed[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. The first mechanism is the inflammatory response.The consistent and ongoing presence of inflammation is one of the defining characteristics of obesity. Inflammation-related proteins and pro-inflammatory cytokines are secreted by adipose tissue, both of which play an essential role in the process of promoting systemic inflammation[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. The resultant inflammatory condition may induce oxidative stress and subsequent neuronal damage, thereby impairing cognitive function[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. The second mechanism is insulin resistance. Insulin resistance frequently occurs alongside obesity, resulting in cells becoming less sensitive to insulin's effects. Disruptions in insulin signaling may lead to diminished neuronal activity and cognitive impairments[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. The third mechanism is vascular factors. As we know, obesity is associated with hypertension, dyslipidemia, and atherosclerosis[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. It is possible that these disorders lead to cognitive impairment by lowering the amount of blood that flows through the cerebrovascular system and by causing the blood-brain barrier to become compromised[\u003cspan additionalcitationids=\"CR41 CR42\" citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. The fourth mechanism is hormonal dysregulation. Obesity leads to changes in leptin and adiponectin levels, affecting brain function and synaptic plasticity[\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. These mechanisms are interrelated and have a combined impact.\u003c/p\u003e \u003cp\u003eBMI is a prominent method that is used to evaluate obesity. Enhanced cognitive performance has been shown to be correlated with a decreased BMI, according to some study results[\u003cspan additionalcitationids=\"CR47\" citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Several other studies, on the other hand, have shown that a greater BMI is associated with superior cognitive function[\u003cspan additionalcitationids=\"CR50\" citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. This phenomenon is called \"obesity paradox\"[\u003cspan additionalcitationids=\"CR53\" citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e].One possible explanation for the paradox of obesity is that the BMI does not discriminate between fat mass and muscle mass. As a result, two individuals possessing identical BMI values may exhibit significantly divergent body compositions and experience varying health outcomes[\u003cspan additionalcitationids=\"CR56 CR57\" citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAs a predictor of obesity, RFM is superior than BMI in terms of its accuracy, making it a more reliable instrument for evaluating body fat percentage[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. Research indicates that the RFM metric demonstrates enhanced predictive accuracy for dyslipidemias and metabolic syndrome (MetS) in comparison to BMI[\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. Based on the results of a cohort research, elderly people who kept their weight at a reasonable level but had abdominal obesity were shown to have a greater chance of developing dementia in comparison to their counterparts who did not exhibit abdominal obesity[\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. The findings of this research indicate that early screening for negative RFM in older men could be a useful method for identifying at-risk groups, and that properly managing RFM might help postpone cognitive decline.\u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eStrengths and limitations\u003c/h2\u003e \u003cp\u003eThis research used a dataset from NHANES comprising 1,321 older men, which improved the credibility and applicability of the results. To guarantee the study's rigor, a range of statistical techniques were used. The study findings indicated a substantial negative correlation between RFM and CERAD-WL scores, DSST scores, and Z-scores in older men. Early identification of adverse RFM is conducive to identifying high-risk groups for cognitive dysfunction.\u003c/p\u003e \u003cp\u003eOn the other hand, it is necessary to point out the constraints of this research. Firstly,as a cross-sectional survey, it could not demonstrate a causal link. Consequently, more investigation with an expanded sample size is required to confirm the causal relationship. Secondly, the study was limited to the years from 2011 to 2014. Therefore, cohort studies might be necessary to validate the current results. Furthermore,the study\u0026rsquo;s sample was restricted to older males, without considering the women; thus, it is required to do additional studies to ascertain the findings can be applied to other demographics. Future studies should include older women to enhance the generalizability of the findings and explore whether the management of RFM has an advantage in mitigating cognitive decline. Finally, although RFM is a new metric for obesity, further studies are needed to validate its advantage over conventional metric.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusion","content":"\u003cp\u003eA substantial negative connection between RFM and cognitive function in elderly males in the United States is revealed in the present study. Elevated RFM levels correlated with diminished scores on cognitive assessments, particularly the CERAD and DSST. These results emphasize the potential benefit of preventing cognitive impairment by managing RFM levels.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eRFM relative fat mass\u003c/p\u003e\u003cp\u003eU.S. United States\u003c/p\u003e\u003cp\u003eNHANES National Health and Nutrition Examination Survey\u003c/p\u003e\u003cp\u003eCERAD-WL Consortium to Establish a Registry for Alzheimer’s Disease Word Learning Test\u003c/p\u003e\u003cp\u003eAFT Animal Fluency Test\u003c/p\u003e\u003cp\u003eDSST Digit Symbol Substitution Test\u003c/p\u003e\u003cp\u003eWC waist circumference\u003c/p\u003e\u003cp\u003eCI cognitive impairment\u003c/p\u003e\u003cp\u003eMCI mild cognitive impairment\u003c/p\u003e\u003cp\u003eIRT Immediate Recall Test\u003c/p\u003e\u003cp\u003eDRT Delayed Recall Test\u003c/p\u003e\u003cp\u003eMEC Mobile Examination Center\u003c/p\u003e\u003cp\u003ePIR Poverty Income Ratio\u003c/p\u003e\u003cp\u003eBMI body mass index\u003c/p\u003e\u003cp\u003eSD standard deviation\u003c/p\u003e\u003cp\u003eNCHS National Center for Health Statistics\u003c/p\u003e\u003cp\u003eMetS metabolic syndrome\u003c/p\u003e"},{"header":"Declarations","content":" \u003cp\u003e \u003cstrong\u003eEthics approval\u003c/strong\u003e \u003c/p\u003e\u003cp\u003e All participants submitted written informed consent and were approved by the National Ethics Board.\u003c/p\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent to participate\u003c/strong\u003e \u003c/p\u003e\u003cp\u003eNot applicable.\u003c/p\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent for publication\u003c/strong\u003e \u003c/p\u003e\u003cp\u003eNot applicable.\u003c/p\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eCompeting interests\u003c/strong\u003e \u003c/p\u003e\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e \u003cp\u003e\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eNo financial support was received for the research, writing and/or publication of this paper.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eLL designed the study, collected and analyzed data, and completed the first draft of the paper. AW supervised the entire study and helped revise the paper. SY assisted with data analysis and paper writing. All authors reviewed the manuscript\u003c/p\u003e\u003ch2\u003eAcknowledgements\u003c/h2\u003e \u003cp\u003e We would like to thank the participants and staff of the National Health and Nutrition Examination Survey (NHANES) and all those who participated in this study.\u003c/p\u003e\u003ch2\u003eData availability\u003c/h2\u003e \u003cp\u003eThe datasets utilized in this investigation are accessible in online databases. Detailed information regarding the repositories and the relevant accession numbers can be located at: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://wwwn.cdc.gov/nchs/nhanes\u003c/span\u003e\u003cspan address=\"https://wwwn.cdc.gov/nchs/nhanes\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003ePrince M, Ali G, Guerchet M, Prina AM, Albanese E, Wu Y. Recent global trends in the prevalence and incidence of dementia, and survival with dementia. ALZHEIMERS RES THER. 2016;8(1):23.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMatthews KA, Xu W, Gaglioti AH, Holt JB, Croft JB, Mack D, McGuire LC. Racial and ethnic estimates of Alzheimer's disease and related dementias in the United States (2015\u0026ndash;2060) in adults aged \u0026gt;/=65 years. ALZHEIMERS DEMENT. 2019;15(1):17\u0026ndash;24.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen M, Tan C, Su H, Sung P, Chien C, Yu R. The Impact of Sex on the Neurocognitive Functions of Patients with Parkinson\u0026rsquo;s Disease. BRAIN SCI. 2021;11(10):1331.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen S, Nilsen J, Brinton RD. Dose and temporal pattern of estrogen exposure determines neuroprotective outcome in hippocampal neurons: therapeutic implications. Endocrinology. 2006;147(11):5303\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOokubo M, Yokoyama H, Takagi S, Kato H, Araki T. Effects of estrogens on striatal damage after 1-methyl-4-phenyl-1,2,3,6-tetrahydropyridine (MPTP) neurotoxicity in male and female mice. MOL CELL ENDOCRINOL. 2008;296(1\u0026ndash;2):87\u0026ndash;93.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSafaei M, Sundararajan EA, Driss M, Boulila W, Shapi'I A. A systematic literature review on obesity: Understanding the causes \u0026amp; consequences of obesity and reviewing various machine learning approaches used to predict obesity. COMPUT BIOL MED. 2021;136:104754.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHruby A, Hu FB. The Epidemiology of Obesity: A Big Picture. PharmacoEconomics. 2015;33(7):673\u0026ndash;89.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWoolcott OO, Bergman RN. Relative fat mass (RFM) as a new estimator of whole-body fat percentage horizontal line A cross-sectional study in American adult individuals. SCI REP-UK. 2018;8(1):10980.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuzm\u0026aacute;n-Le\u0026oacute;n AE, Velarde AG, Vidal-Salas M, Urquijo-Ruiz LG, Caraveo-Guti\u0026eacute;rrez LA, Valencia ME. External validation of the relative fat mass (RFM) index in adults from north-west Mexico using different reference methods. PLoS ONE. 2019;14(12):e226767.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang J, Guan J, Huang L, Li X, Huang B, Feng J, Zhang Y, Zhang J. Sex differences in the associations between relative fat mass and all-cause and cardiovascular mortality: A population-based prospective cohort study. Nutr Metabolism Cardiovasc Dis. 2024;34(3):738\u0026ndash;54.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCorr\u0026ecirc;a CR, Formolo NPS, Dezanetti T, Speretta GFF, Nunes EA. Relative fat mass is a better tool to diagnose high adiposity when compared to body mass index in young male adults: A cross-section study. CLIN NUTR ESPEN. 2021;41:225\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCichosz SL, Rasmussen NH, Vestergaard P, Hejlesen O. Is predicted body-composition and relative fat mass an alternative to body-mass index and waist circumference for disease risk estimation? DIABETES METAB SYND. 2022;16(9):102590.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYu P, Huang T, Hu S, Yu X. Predictive value of relative fat mass algorithm for incident hypertension: a 6-year prospective study in Chinese population. BMJ OPEN. 2020;10(10):e38420.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSuthahar N, Wang K, Zwartkruis VW, Bakker SJL, Inzucchi SE, Meems LMG, Eijgenraam TR, Ahmadizar F, Sijbrands EG, Gansevoort RT, et al. Associations of relative fat mass, a new index of adiposity, with type-2 diabetes in the general population. EUR J INTERN MED. 2023;109:73\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEfe SC, Karagoz A, Dogan C, Bayram Z, Kalkan S, Altintas MS, Yuksel Y, Karabag T, Ayca B, Ozdemir N. Relative Fat Mass Index can be solution for obesity paradox in coronary artery disease severity prediction calculated by SYNTAX Score. POSTGRAD MED J. 2021;97(1149):434\u0026ndash;41.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhu X, Yue Y, Li L, Zhu L, Cai Y, Shu Y. The relationship between depression and relative fat mass (RFM): A population-based study. J AFFECT DISORDERS. 2024;356:323\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMorris JC, Heyman A, Mohs RC, Hughes JP, van Belle G, Fillenbaum G, Mellits ED, Clark C. The Consortium to Establish a Registry for Alzheimer's Disease (CERAD). Part I. Clinical and neuropsychological assessment of Alzheimer's disease. NEUROLOGY. 1989;39(9):1159\u0026ndash;65.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSelnes OA. A compendium of neuropsychological tests: Administration, norms, and commentary. NEUROLOGY. 1991;41(11):1856.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWechsler D. Wais-iii/wms-iii technical manual. San Antonio, TX: The Psychological Corporation; 1997.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee S, Min J, Kim B, Ha S, Han JH, Min K. Serum sodium in relation to various domains of cognitive function in the elderly US population. BMC GERIATR. 2021;21(1):328.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBotelho J, Leira Y, Viana J, Machado V, Mendes JJ. The Role of Inflammatory Diet and Vitamin D on the Link between Periodontitis and Cognitive Function: A Mediation Analysis in Older Adults. NUTRIENTS. 2021;13(3):924.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKatzman EW, Nielsen SJ. The Association between Peanut and Peanut Butter Consumption and Cognitive Function among Community-Dwelling Older Adults. J Prev Alzheimer's disease. 2021;8(4):436\u0026ndash;41.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFarrer LA, Cupples LA, Haines JL, Hyman B, Kukull WA, Mayeux R, Myers RH, Pericak-Vance MA, Risch N, van Duijn CM. Effects of Age, Sex, and Ethnicity on the Association Between Apolipoprotein E Genotype and Alzheimer Disease: A Meta-analysis. JAMA: J Am Med Association. 1997;278(16):1349\u0026ndash;56.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAka A, Phan TD, Kahana MJ, Benjamin AS. Predicting Recall of Words and Lists. J Exp Psychol Learn Mem Cognit. 2021;47(5):765\u0026ndash;84.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSantos-Galdur\u0026oacute;z RF, Oliveira FG, Galdur\u0026oacute;z JCF, Bueno OFA. Cognitive performance of young and elderly subjects on the free word recall memory test: effect of presentation order on recall order. BRAZ J MED BIOL RES. 2009;42(10):988\u0026ndash;92.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLucas JA, Ivnik RJ, Smith GE, Ferman TJ, Willis FB, Petersen RC, Graff-Radford NR. Mayo's Older African Americans Normative Studies: Norms for Boston Naming Test, Controlled Oral Word Association, Category Fluency, Animal Naming, Token Test, Wrat-3 Reading, Trail Making Test, Stroop Test, and Judgment of Line Orientation. CLIN NEUROPSYCHOL. 2005;19(2):243\u0026ndash;69.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRinehardt E, Eichstaedt K, Schinka JA, Loewenstein DA, Mattingly M, Fils J, Duara R, Schoenberg MR. Verbal fluency patterns in mild cognitive impairment and Alzheimer's disease. DEMENT GERIATR COGN. 2014;38(1\u0026ndash;2):1\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSebaldt R, Dalziel W, Massoud F, Tanguay A, Ward R, Thabane L, Melnyk P, Landry P, Lescrauwaet B. Detection of cognitive impairment and dementia using the animal fluency test: the DECIDE study. CAN J NEUROL SCI. 2009;36(5):599\u0026ndash;604.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGarcia-Herranz S, Diaz-Mardomingo MC, Venero C, Peraita H. Accuracy of verbal fluency tests in the discrimination of mild cognitive impairment and probable Alzheimer's disease in older Spanish monolingual individuals. AGING NEUROPSYCHOL C. 2020;27(6):826\u0026ndash;40.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBrukner P, Khan KM, Kron J. The Encyclopedia of Exercise, Sport and Health. Choice Reviews Online. 2005;42:42\u0026ndash;2530.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLim CR, Harris K, Dawson J, Beard DJ, Fitzpatrick R, Price AJ. Floor and ceiling effects in the OHS:an analysis of the NHS PROMs data set. BMJ OPEN. 2015;5(7):e7765.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi H, Wang Z, Fu Z, Yan M, Wu N, Wu H, Yin P. Associations between blood cadmium levels and cognitive function in a cross-sectional study of US adults aged 60 years or older. BMJ OPEN. 2018;8(4):e20533.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChu NM, Hong J, Harasemiw O, Chen X, Fowler KJ, Dasgupta I, Bohm C, Segev DL, McAdams-DeMarco MA. Chronic kidney disease, physical activity and cognitive function in older adults-results from the National Health and Nutrition Examination Survey (2011\u0026ndash;2014). NEPHROL DIAL TRANSPL. 2022;37(11):2180\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang Y, Peng Y, Deng W, Xiang Q, Zhang W, Liu M. Association between dietary inflammatory index and cognitive impairment among American elderly: a cross-sectional study. FRONT AGING NEUROSCI 2024, 16.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi J, Sun J, Zhang Y, Zhang B, Zhou L. Association between weight-adjusted-waist index and cognitive decline in US elderly participants. FRONT NUTR. 2024;11:1390282.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGong H, Tang X, Chai Y, Qiao Y, Xu H, Patel I, Zhang J, Zhou J. Predicted lean body mass in relation to cognitive function in the older adults. FRONT ENDOCRINOL 2023, 14.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarone E, Di Domenico F, Perluigi M, Butterfield DA. The interplay among oxidative stress, brain insulin resistance and AMPK dysfunction contribute to neurodegeneration in type 2 diabetes and Alzheimer disease. FREE RADICAL BIO MED. 2021;176:16\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDermitzaki E, Liapakis G, Androulidaki A, Venihaki M, Melissas J, Tsatsanis C, Margioris AN. Corticotrophin-Releasing Factor (CRF) and the urocortins are potent regulators of the inflammatory phenotype of human and mouse white adipocytes and the differentiation of mouse 3T3L1 pre-adipocytes. PLoS ONE. 2014;9(5):e97060.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNaomi R, Teoh SH, Embong H, Balan SS, Othman F, Bahari H, Yazid MD. The Role of Oxidative Stress and Inflammation in Obesity and Its Impact on Cognitive Impairments\u0026mdash;A. Narrative Rev ANTIOXIDANTS-BASEL. 2023;12(5):1071.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFriedman JI, Tang CY, de Haas HJ, Changchien L, Goliasch G, Dabas P, Wang V, Fayad ZA, Fuster V, Narula J. Brain imaging changes associated with risk factors for cardiovascular and cerebrovascular disease in asymptomatic patients. JACC-CARDIOVASC IMAG. 2014;7(10):1039\u0026ndash;53.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOvsenik A, Podbregar M, Fabjan A. Cerebral blood flow impairment and cognitive decline in heart failure. BRAIN BEHAV. 2021;11(6):e2176.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNguyen JCD, Killcross AS, Jenkins TA. Obesity and cognitive decline: role of inflammation and vascular changes. FRONT NEUROSCI-SWITZ. 2014;8:375.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBalasubramanian P, Kiss T, Tarantini S, Nyul-Toth A, Ahire C, Yabluchanskiy A, Csipo T, Lipecz A, Tabak A, Institoris A, et al. Obesity-induced cognitive impairment in older adults: a microvascular perspective. AM J PHYSIOL-HEART C. 2021;320(2):H740\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFlores-Cordero JA, P\u0026eacute;rez-P\u0026eacute;rez A, Jim\u0026eacute;nez-Cortegana C, Alba G, Flores-Barrag\u0026aacute;n A, S\u0026aacute;nchez-Margalet V. Obesity as a Risk Factor for Dementia and Alzheimer's Disease: The Role of Leptin. Int J Mol Sci. 2022;23(9):5202.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eForny-Germano L, De Felice FG, Vieira M. The Role of Leptin and Adiponectin in Obesity-Associated Cognitive Decline and Alzheimer's Disease. FRONT NEUROSCI-SWITZ. 2018;12:1027.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRubin LH, Gustafson D, Hawkins KL, Zhang L, Erlandson KM. Midlife adiposity predicts cognitive decline in the prospective Multicenter AIDS Cohort Study. NEUROLOGY. 2019;93(3):10\u0026ndash;1212.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLiu Z, Yang H, Chen S, Cai J, Huang Z. The association between body mass index, waist circumference, waist-hip ratio and cognitive disorder in older adults. J PUBLIC HEALTH-UK. 2019;41(2):305\u0026ndash;12.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMarioni RE, Yang J, Dykiert D, Mottus R, Campbell A, Davies G, Hayward C, Porteous DJ, Visscher PM, Deary IJ. Assessing the genetic overlap between BMI and cognitive function. MOL PSYCHIATR. 2016;21(10):1477\u0026ndash;82.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAiken Morgan AT, Capuano AW, Arvanitakis Z, Barnes LL. Changes in Body Mass Index Are Related to Faster Cognitive Decline Among African American Older Adults. J Am Geriatr Soc (JAGS). 2020;68(11):2662\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKivimaki M, Luukkonen R, Batty GD, Ferrie JE, Pentti J, Nyberg ST, Shipley MJ, Alfredsson L, Fransson EI, Goldberg M, et al. Body mass index and risk of dementia: Analysis of individual-level data from 1.3 million individuals. ALZHEIMERS DEMENT. 2018;14(5):601\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim S, Kim Y, Park SM. Body Mass Index and Decline of Cognitive Function. PLoS ONE. 2016;11(2):e148908.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYousung P, Nam H, Kim. Tae, Yeon, Kwon, Sin, Gon: A novel adiposity index as an integrated predictor of cardiometabolic disease morbidity and mortality. SCI REP-UK 2018(8):16753.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDing C, Shi Y, Li J, Li M, Hu L, Rao J, Liu L, Zhao P, Xie C, Zhan B et al. Association of weight-adjusted-waist index with all-cause and cardiovascular mortality in China: A prospective cohort study. Nutrition, metabolism, and cardiovascular diseases 2022, 32(5):1210\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eElagizi A, Kachur S, Lavie CJ, Carbone S, Pandey A, Ortega FB, Milani RV. An Overview and Update on Obesity and the Obesity Paradox in Cardiovascular Diseases. PROG CARDIOVASC DIS. 2018;61(2):142\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYoon DH, Choi SH, Yu JH, Ha JH, Ryu SH, Park DH. The relationship between visceral adiposity and cognitive performance in older adults. AGE AGEING. 2012;41(4):456\u0026ndash;61.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIsaac V, Sim S, Zheng H, Zagorodnov V, Tai ES, Chee M. Adverse Associations between Visceral Adiposity, Brain Structure, and Cognitive Performance in Healthy Elderly. FRONT AGING NEUROSCI. 2011;3:12.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGallagher D, Visser M, Sepulveda D, Pierson RN, Harris T, Heymsfield SB. How useful is body mass index for comparison of body fatness across age, sex, and ethnic groups? AM J EPIDEMIOL. 1996;143(3):228\u0026ndash;39.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOkorodudu DO, Jumean MF, Montori VM, Romero-Corral A, Somers VK, Erwin PJ, Lopez-Jimenez F. Diagnostic performance of body mass index to identify obesity as defined by body adiposity: a systematic review and meta-analysis. INT J Obes. 2010;34(5):791\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGordon EH, Hubbard RE. Frailty: understanding the difference between age and ageing. AGE AGEING. 2022;51(8):1\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCha E, Akazawa MK, Kim KH, Dawkins CR, Lerner HM, Umpierrez G, Dunbar SB. Lifestyle habits and obesity progression in overweight and obese American young adults: Lessons for promoting cardiometabolic health. NURS HEALTH SCI. 2015;17(4):467\u0026ndash;75.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZeng P, Li M, Cao JX, Zeng L, Jiang C, Lin F. Association of metabolic syndrome severity with frailty progression among Chinese middle and old-aged adults: a longitudinal study. CARDIOVASC DIABETOL. 2024;23(1):1\u0026ndash;12.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCho GJ, Hwang SY, Lee KM, Choi KM, Hyun Baik S, Kim T, Han SW, Yoo HJ. Association between waist circumference and dementia in older persons: a nationwide population-based study. OBESITY. 2019;27(11):1883\u0026ndash;91.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"lipids-in-health-and-disease","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"lhad","sideBox":"Learn more about [Lipids in Health and Disease](http://lipidworld.biomedcentral.com/)","snPcode":"12944","submissionUrl":"https://submission.nature.com/new-submission/12944/3","title":"Lipids in Health and Disease","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Relative Fat Mass(RFM), cognitive function, NHANES, obesity, older men","lastPublishedDoi":"10.21203/rs.3.rs-6114375/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6114375/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThis study aimed to investigate the relationship between cognitive performance and relative fat mass (RFM) in older American males.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e \u003cp\u003eFor the purpose of this cross-sectional research, a total of 1,321 individuals were selected from the National Health and Nutrition Examination Survey (NHANES), which was carried out between the years 2011 and 2014. Specifically, the Consortium to Establish a Registry for Alzheimer\u0026rsquo;s Disease Word Learning Test (CERAD-WL), the Animal Fluency Test (AFT), and the Digit Symbol Substitution Test (DSST) were used in order to achieve the objective of assessing cognitive function. Z-scores were calculated as a composite, generalized metric by taking the average of the standardized scores of the three previously mentioned assessments. RFM was ascertained by measuring waist circumference (WC) and height. Multivariate linear regression, threshold effect analyses, smooth curve fitting, and subgroup analyses were undertaken in order to investigate the connections that exist between RFM and cognitive function.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e \u003cp\u003eThe study included 1,321 male volunteers who were at least 60 years old, and complete data was provided for each individual. Fully adjusted models indicated a negative correlation between RFM and CERAD-WL scores[-0.17, (-0.32,-0.01)], DSST scores[-0.83, (-1.16,-0.50)] and Z-scores[-0.03, (-0.05, -0.01)]. It was observed that the negative correlation between RFM and Z-scores became more pronounced when RFM exceeded 35.78. Furthermore, subgroup analyses showed that the association between RFM and cognitive function was significantly impacted by education level, poverty-income ratio (PIR), smoking status, and drinking status.\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusions\u003c/b\u003e\u003c/p\u003e \u003cp\u003eIn elderly men, a higher RFM was linked to lower cognitive function, suggesting that management of RFM may prove advantageous in mitigating cognitive decline among older male populations.\u003c/p\u003e","manuscriptTitle":"Association between relative fat mass and cognitive function among US older men: NHANES 2011–2014","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-02 11:26:26","doi":"10.21203/rs.3.rs-6114375/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-04-17T10:36:46+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-17T09:45:08+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-04T09:52:10+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"70939565801878266522144164824935398601","date":"2025-04-03T09:19:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"285435791487267117033681844762746481930","date":"2025-04-01T06:57:13+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-01T02:51:27+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-01T01:39:15+00:00","index":"","fulltext":""},{"type":"submitted","content":"Lipids in Health and Disease","date":"2025-03-31T05:23:29+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"lipids-in-health-and-disease","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"lhad","sideBox":"Learn more about [Lipids in Health and Disease](http://lipidworld.biomedcentral.com/)","snPcode":"12944","submissionUrl":"https://submission.nature.com/new-submission/12944/3","title":"Lipids in Health and Disease","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"646740fb-c4b5-4ad3-a825-5d047cbf4780","owner":[],"postedDate":"April 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-05-12T15:58:00+00:00","versionOfRecord":{"articleIdentity":"rs-6114375","link":"https://doi.org/10.1186/s12944-025-02593-8","journal":{"identity":"lipids-in-health-and-disease","isVorOnly":false,"title":"Lipids in Health and Disease"},"publishedOn":"2025-05-08 15:56:51","publishedOnDateReadable":"May 8th, 2025"},"versionCreatedAt":"2025-04-02 11:26:26","video":"","vorDoi":"10.1186/s12944-025-02593-8","vorDoiUrl":"https://doi.org/10.1186/s12944-025-02593-8","workflowStages":[]},"version":"v1","identity":"rs-6114375","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6114375","identity":"rs-6114375","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}