Association between inflammatory diet and cognitive function with the moderating role of workforce participation in older adults: findings from NHANES 2011-2014

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Methods Using data from the National Health and Nutrition Examination Survey (NHANES 2011–2014), we analyzed 2,327 participants aged ≥ 60 years. The Dietary Inflammatory Index (DII) was calculated from 28 dietary components, and cognitive function was assessed using the CERAD Word Learning subtest, Animal Fluency Test (AFT), and Digit Symbol Substitution Test (DSST). Workforce participation was measured by work status (working/not working) and weekly working hours. Generalized linear regression models evaluated associations, while moderation effects were tested using bootstrap resampling. Covariates included demographics, health behaviors, and clinical conditions. Results Pro-inflammatory diets (DII ≥ 0) were negatively associated with composite cognitive z-scores (β = -0.16, 95% CI: -0.28, -0.04), immediate recall (β = -0.18, 95% CI: -0.31, -0.05), AFT (β = -0.20, 95% CI: -0.39, -0.02), and DSST (β = -0.15, 95% CI: -0.30, 0.00). Workforce participation attenuated these associations: working status reduced the negative effects of DII on composite scores (β = -0.03, 95% CI: -0.06, -0.00) and AFT (β = -0.04, 95% CI: -0.06, -0.01). Working > 40 hours/week showed the strongest protective moderation (composite score: β = 0.25, 95% CI: 0.09, 0.41). Nonlinear dose-response relationships were observed for all cognitive domains except delayed recall. Conclusions Pro-inflammatory diets are linked to poorer cognitive performance in older adults, but workforce participation mitigates this risk, potentially through socioeconomic empowerment and cognitive stimulation. Public health strategies should integrate workplace policies and dietary interventions to promote cognitive longevity in aging populations. Dietary Inflammatory Index cognitive function workforce participation older adults NHANES social determinants of health Figures Figure 1 Figure 2 Figure 3 Contributions to the literature 1. Identifies workforce participation as a novel social determinant moderating the diet-cognition link, advancing understanding of socioeconomic resilience in aging populations. 2. Bridges biological (inflammatory pathways) and psychosocial (cognitive reserve) frameworks to explain health behavior paradoxes in dietary epidemiology. 3. Provides empirical support for integrated workplace-nutrition interventions targeting modifiable social drivers of cognitive aging. 1 Background In recent years, the rise of neurodegenerative diseases such as Alzheimer's and other forms of dementia has highlighted the dangers of declining cognitive function. Cognitive function[ 1 ] refers to the mental processes that enable us to acquire knowledge, reason, remember, and solve problems. It encompasses a range of abilities, including attention, memory, language, perception, and executive functions. These processes are crucial for everyday tasks, decision-making, and social interactions [ 2 , 3 ]. It was reported [ 4 ] that with the pressure of an aging population, the number of people suffering from is expected to increase significantly, which is defined as a public health priority of the 21st century by the World Health Organization. While genetic predispositions and vascular risk factors contribute to neurodegeneration, modifiable lifestyle factors could also lead to dementia risk[ 5 ], with mounting evidence emphasizing the syndemic interaction between biological pathways and social determinants of health (SDOH). Emerging evidence[ 6 , 7 ] highlights the role of pro-inflammatory diets in accelerating cognitive deterioration. The Dietary Inflammatory Index (DII), a validated tool quantifying the inflammatory potential of diets, has been linked to systemic inflammation biomarkers and neurodegenerative processes[ 8 ]. Chronic low-grade inflammation triggered by diets rich in processed foods, refined carbohydrates, and saturated fats may promote neuronal damage through oxidative stress and blood-brain barrier dysfunction [ 9 ]. However, existing studies exhibit inconsistencies in the strength of this association, suggesting potential moderating factors that require exploration. Workforce participation in older adults represents a complex psychosocial determinant that may modify dietary impacts on cognitive health[ 10 ]. Employment status influences not only socioeconomic resources and healthcare access but also provides cognitive stimulation through social engagement and skill utilization—factors known to enhance cognitive reserve [ 11 ]. Paradoxically, workforce participation may simultaneously expose individuals to chronic stress and time constraints that compromise dietary quality [ 12 ]. This multidimensional nature creates paradoxical effects-while employment may buffer cognitive decline through socioeconomic empowerment and cognitive stimulation, it could simultaneously exacerbate biological risks via diet-related inflammation pathways. Social epidemiologists have long noted such "health behavior paradoxes," where socially advantaged groups exhibit better health outcomes despite engaging in similar or worse risk behaviors [ 13 ]. For instance, a seminal study[ 14 ] found that high-socioeconomic-status smokers had lower cardiovascular mortality than low-SES non-smokers. Applied to our context, this suggests workforce participation's material and cognitive benefits might counteract the detrimental effects of inflammatory diets—a hypothesis yet to be empirically tested. Therefor, we conducted a cross-section study based on the public database of the National Heath and Nutrition Examination Survey (NHANES). And as far as we know, this is the first research to investigated the association between inflammation diet and cognitive function with the moderating role of workforce participation in old adults. 2 Methods 2.1 Study population Data of the study population came from the NHANES, an ongoing national survey conducted by the Centers for Disease Control and Prevention that focused on Americans' dietary nutrition and general health. Signed informed consent from all participants before participating in the study, and all study protocols were approved by the National Center for Health Statistics' ethical review board (Clinical trial number: not applicable). Specifically, data for this study was gathered from the NHANES 2011–2014, and could be found at https://www.cdc.gov/nchs/nhanes/ . Our study only included individuals aged 60 years or above (N = 3,632) due to the NHANES 2011–2014 just specifically carried out a series of cognitive function testings for this population. Following elimination for any missing data on cognitive function testing items (N = 698), work status or DII items (N = 413), and demographics (N = 194), finally 2,327 eligible participants left for analysis (Fig. 1 ). 2.2 Assessment of cognitive function (outcome) The Consortium to Establish a Registry for Alzheimer’s Disease (CERAD) Word Learning, the Animal Fluency Test (AFT), and the Digit Symbol Substitution Test (DSST) were used to assess cognitive function for older adults aged 60 years or above during the household interview or the Mobile Examination Center (MEC) evaluation stage by the NHANES[ 15 ]. The CERAD Word Learning sub-test assessment consisted of immediate recall (CERAD-IR) on three consecutive learning trials and delayed recall (CERAD-DR) on one delayed recall trial, each trial with a score ranging from 0 to 10. The AFT was used to measure categorical verbal fluency as a part of executive function, with a scale of 3–39 scores. The DSST was used to evaluate processing speed in conjunction with sustained attention and working memory as a Wechsler Adult Intelligence Scale (WAIS-III) segment, with a score ranging from 0 to 105. By referring the prior research [ 16 , 17 ], we used z-scores for composite, CERAD-DR, CERAD-IR, AFT, and DSST to assess cognitive function in present study. The composite z-score was calculated by averaging the CERAD-DR, CERAD-IR, AFT, and DSST z-scores. The z-score = (x-m)/σ, where x represents the these four individual cognitive function test scores, m represents the corresponding mean score, and σ represents the corresponding standard deviation (SD). 2.3 Assessment of DII (exposure) We used the modified version for DII calculation developed by Shivappa et al., the further standardized and specific calculation method has been detailed in prior studies[ 18 , 19 ]. In the present study, 28 of 45 dietary nutrients were incorporated due to the NHANES data limited alcohol, carbohydrates, caffeine, carotene, cholesterol, energy, fiber, folic acid, iron, magnesium, monounsaturated fatty acids, niacin, n-3 fatty acids, n-6 fatty acids, polyunsaturated fatty acids, protein, saturated fatty acids, selenium, thiamine, total fat, vitamin A, vitamin B2, vitamin B6, vitamin B12, vitamin C, vitamin D, vitamin E, and zinc [ 19 ]. According to DII, participants were divided into pro-inflammatory diet (DII ≥ 0) and anti-inflammatory diet (DII < 0)[ 20 , 21 ]. 2.4 Assessment of workforce participation (moderator) Four measures were generally used for workforce participation at the individual level: work status, hours worked, work types, and shift work [ 22 , 23 ]. Our study included work status and hours worked per week to evaluate the workforce participation of older adults, because of the data limited in the NHANES 2011–2014. According to prior research[ 24 ], work status (working vs not working) in the NHANES was captured by participants reporting whether they were working at a job or business last week. Specifically, working, referring to “working at a job or business last week”; not working, including “not working at a job or business last week”, “with a job or business but not at work last week”, and “looking for work last week”. Furthermore, individual disclosed the number of hours they worked last week at all jobs or businesses, and we used hours worked per week as a continuous indicator for working. 2.5 Covariates Based on the previous studies[ 25 – 27 ], demographics, health behaviors, and health status-related covariates that may affect the association between DII, workforce participation, and cognitive function were included in our study. Demographics included age (60–69 years, 70–79 years, ≥ 80 years), gender (male, female), race (non-Hispanic White, non-Hispanic Black, Mexican American, other Hispanic, Other/Multiracial), education (less than high school, high school, some college, college or above), marital status (married/living with partner, widowed/divorced/separated, never married), and poverty income ratio (PIR, poor, not poor). According to the studies[ 21 , 28 , 29 ], health behaviors included drinking status (non-drinker, 1–5 drinks/month, 5–10 drinks/month, 10 + drinks/month), smoking status (never smoker, former smoker, current smoker), sleep disorder (yes, no), and physical activities (inactive, moderate, vigorous). By referring the research[ 28 , 30 ], health status included body mass index (BMI, underweight, normal, overweight, obese), depression (yes, no), diabetes (yes, no), and hypertension (yes, no). Generally, a PIR value < 1 was considered as poor, a PIR value ≥ 1 was defined as not poor[ 31 ]. For detailed BMI classes: underweight (BMI < 18.5), normal (18.5 ≤ BMI < 25), overweight (25 ≤ BMI < 30), and obese (BMI ≥ 30)[ 28 ]. Depression was evaluated with the The PHQ-9 (Patient Health Questionnaire, 9 items) in the NHANES 2011–2014. A total PHQ-9 score could range from 0 to 27, was further divided into binary categories: depression with a score ≥ 10, while no depression with a score < 10[ 32 ]. Sleep disorder was defined as ever told by doctor to have sleep disorder. Self-reported diabetes, glycosylated hemoglobin (HbA1c) ≥ 6.5%, or fasting plasma glucose level ≥ 126 mg/dl were considered diabetes[ 33 ]. Self-reported high blood pressure told by doctors, the last two times measured average systolic/diastolic blood pressure of at least 140/90 mmHg, or antihypertensive medication being used were considered hypertension [ 34 ]. 2.6 Statistical analysis We adopted the recommendations for accurate reporting in medical research statistics[ 35 ], using multiple imputation to fill missing data. Mean ± standard deviation was used to describe the data for continuous variables. Number (constituent ratio [%]) was used to describe the data for categorical variables. Percentages, means, and standard deviation were derived by applying the full sample 2 year MEC exam weight provided by the NHANES. Wilcoxon rank-sum test was performed to compare continuous variables. Chi-squared test with Rao & Scott's second-order correction was employed to compare categorical variables. The generalised linear regression models were used to assess the association of inflammatory diet and workforce participation with cognitive performance. In moderating analysis models, z-scores for cognitive functions (outcome) and DII scores (exposure) were entered as continuous variables, and workforce participation (moderator) was entered as a categorical variable[ 36 , 37 ]. Model "2-way" from the R package "bruceR::PROCESS" was used to examine the moderating effect, with a 95% confidence interval (CI) evaluated by 1000 bootstrap resampling and 12345 seed. We employed restricted cubic spline (RCS) for the generalised linear regression models to assess the dose–response association of DII scores with cognitive function z-scores. In addition, we recruited subgroup analysis and interactions for categorical variables: gender, age, race, education, marital status, PIR, drinking status, smoking status, sleep disorder, physical activities, BMI, depression, diabetes, and hypertension among covariates. Furthermore, in sensitivity analysis, data of non-multiple imputation was included, also the propensity score matching (PSM) methods were used to test our results stability. The statistical software R (version 4.4.1) and Storm Statistical Platform ( https://www.medsta.cn/software ) were used for all statistical analysis, and a two-side P -value < 0.05 was considered statistically significant. 3 Results 3.1 Participants' characteristics Table 1 presents the characteristics of the weighted study samples. Our analysis enrolled a total of 2,327 older adults aged 60 years or above, of whom 30.9% and 69.1% with anti-inflammatory and pro-inflammatory diet, respectively. Compared with older adults who take anti-inflammatory diet, those who take pro-inflammatory diet were significantly more likely to be female (59.0%), be non-Hispanic Black (9.3%), have less than college education (75.1%), be widowed or divorced or separated (34.0%), be poor (9.8%), be non-drinker (30.0%), be current smoker (12.2%), do inactive physical activities (58.7%), be obese (40.5%), suffer from depression (8.6%), suffer from diabetes (30.6%), not working (71.4%) working less than 30 hours per week (44.3%), and with lower z-scores for Composite (Mean 0.14 ± SD 0.78), CERAD-DR (0.07 ± 0.99), CERAD-IR (0.09 ± 0.96), AFT (0.14 ± 0.99), and DSST (0.24 ± 0.98). Table 1 Characteristics of participants with anti-inflammatory and pro-inflammatory diet [mean (SE) / n (%)], weighted. a Characteristics Overall (n = 2,327) Anti-inflammatory (n = 623) Pro-inflammatory (n = 1,704) P -value Gender < 0.001 Male 1,122 (45.9%) 369 (57.1%) 753 (41.0%) Female 1,205 (54.1%) 254 (42.9%) 951 (59.0%) Age (years) 0.4 60–69 1,267 (57.1%) 333 (57.7%) 934 (56.8%) 70–79 688 (29.2%) 197 (30.4%) 491 (28.6%) ≥ 80 372 (13.7%) 93 (11.9%) 279 (14.5%) Race < 0.001 Non-Hispanic White 1,199 (82.0%) 355 (86.3%) 844 (80.0%) Non-Hispanic Black 538 (7.7%) 101 (4.3%) 437 (9.3%) Mexican American 190 (3.0%) 51 (2.5%) 139 (3.2%) Other Hispanic 215 (3.2%) 46 (2.0%) 169 (3.7%) Other/Multiracial 185 (4.2%) 70 (4.9%) 115 (3.8%) Education < 0.001 Less than high school 535 (14.7%) 95 (8.6%) 440 (17.4%) High school 550 (21.8%) 112 (14.3%) 438 (25.1%) Some college 680 (32.3%) 186 (31.5%) 494 (32.6%) College or above 562 (31.3%) 230 (45.6%) 332 (24.9%) Marital Status < 0.001 Married/living with partner 1,365 (66.0%) 423 (74.9%) 942 (62.1%) Widowed/divorced/separated 835 (29.8%) 166 (20.6%) 669 (34.0%) Never married 127 (4.1%) 34 (4.6%) 93 (3.9%) Poverty Income Ratio (PIR) < 0.001 Poor 371 (8.2%) 64 (4.6%) 307 (9.8%) Not poor 1,956 (91.8%) 559 (95.4%) 1,397 (90.2%) Drinking Status < 0.001 Non-drinker 717 (26.6%) 143 (18.8%) 574 (30.0%) 1–5 drinks/month 1,134 (47.9%) 300 (47.9%) 834 (47.8%) 5–10 drinks/month 108 (5.4%) 30 (5.3%) 78 (5.5%) 10 + drinks/month 368 (20.1%) 150 (28.0%) 218 (16.6%) Smoking Status 0.003 Never smoker 1,146 (49.2%) 306 (51.0%) 840 (48.5%) Former smoker 911 (40.7%) 270 (43.7%) 641 (39.4%) Current smoker 270 (10.1%) 47 (5.3%) 223 (12.2%) Sleep Disorder 0.059 Yes 286 (12.0%) 58 (9.0%) 228 (13.4%) No 2,041 (88.0%) 565 (91.0%) 1,476 (86.6%) Physical Activities < 0.001 Inactive 1,326 (53.7%) 291 (42.6%) 1,035 (58.7%) Moderate 767 (33.7%) 232 (37.6%) 535 (32.0%) Vigorous 234 (12.6%) 100 (19.8%) 134 (9.4%) Body Mass Index (BMI) 0.012 Underweight 33 (1.3%) 11 (1.9%) 22 (1.0%) Normal 578 (25.1%) 189 (30.3%) 389 (22.8%) Overweight 806 (35.6%) 216 (35.3%) 590 (35.8%) Obese 910 (38.0%) 207 (32.6%) 703 (40.5%) Depression 0.023 Yes 206 (7.3%) 37 (4.4%) 169 (8.6%) No 2,121 (92.7%) 586 (95.6%) 1,535 (91.4%) Diabetes < 0.001 Yes 774 (27.3%) 158 (19.8%) 616 (30.6%) No 1,553 (72.7%) 465 (80.2%) 1,088 (69.4%) Hypertension 0.5 Hypertension 780 (30.9%) 195 (29.7%) 585 (31.4%) Non hypertension 1,547 (69.1%) 428 (70.3%) 1,119 (68.6%) Work Status 0.005 Working 607 (31.0%) 182 (36.5%) 425 (28.6%) Not working 1,720 (69.0%) 441 (63.5%) 1,279 (71.4%) Hours Worked per Week (hours) 0.025 40 479 (19.8%) 149 (23.3%) 330 (18.2%) Composite Z-score 0.23 ± 0.78 0.44 ± 0.73 0.14 ± 0.78 < 0.001 CERAD-DR Z-score 0.13 ± 0.98 0.25 ± 0.95 0.07 ± 0.99 0.008 CERAD-IR Z-score 0.17 ± 0.96 0.34 ± 0.93 0.09 ± 0.96 < 0.001 AFT Z-score 0.28 ± 1.03 0.57 ± 1.06 0.14 ± 0.99 < 0.001 DSST Z-score 0.35 ± 0.97 0.61 ± 0.90 0.24 ± 0.98 < 0.001 Notes : DII = Dietary Inflammatory Index; PIR = Poverty Income Ratio; BMI = Body Mass Index; CERAD-DR = Establish a Registry for Alzheimer's Disease-Delayed Recall; CERAD-IR = Establish a Registry for Alzheimer's Disease-Immediate Recall; AFT = Animal Fluency Test; DSST = Digit Symbol Substitution Test. a Pro-inflammatory diet with a DII score ≥ 0, while anti-inflammatory with a DII score < 0. Mean ± standard deviation was used to describe continuous variables, and number (constituent ratio [%]) was used to describe categorical variables. The percentages, means, and standard deviations were derived by applying the full sample 2 year Mobile Examination Center (MEC) exam weight provided in the National Health and Nutrition Examination Survey (NHANES). 3.2 Association of inflammatory diet and workforce participation with cognitive function Table 2 presents the association of inflammatory diet, work status, and hours worked per week with composite, CERAD-DR, CERAD-IR, AFT, and DSST z-scores in three different models. As shown in the model 3 that fully adjusted covariates, a pro-inflammatory diet was negatively associated with composite z-score ( β = -0.16; 95%CI : -0.28, -0.04; P = 0.020), CERAD-IR z-score ( β = -0.18; 95%CI : -0.31, -0.05; P = 0.018), AFT z-score ( β = -0.20; 95%CI : -0.39, -0.02; P = 0.038), and DSST z-score ( β = -0.15; 95%CI : -0.30, 0.00; P = 0.049). Working was positively associated with composite z-score ( β = 0.19; 95%CI : 0.09, 0.29; P = 0.007), CERAD-IR z-score ( β = 0.15; 95%CI : 0.00, 0.30; P = 0.046), AFT z-score ( β = 0.29; 95%CI : 0.15, 0.43; P = 0.005), and DSST z-score ( β = 0.20; 95%CI : 0.06, 0.34; P = 0.018). The highest hours worked per week that more than 40 hours was significantly associated with higher composite z-score ( β = 0.25; 95%CI : 0.09, 0.41; P = 0.015), CERAD-IR z-score ( β = 0.37; 95%CI : 0.17, 0.56; P = 0.009), and DSST z-score ( β = 0.24; 95%CI : 0.04, 0.45; P = 0.033). Table 2 Associations of DII, work status, and hours worked per week with composite, CERAD-DR, CERAD-IR, AFT, and DSST z-scores. a Independent Variables Composite Z-score CERAD-DR Z-score CERAD-IR Z-score AFT Z-score DSST Z-score β (95%CI) p -value β (95%CI) p -value β (95%CI) p -value β (95%CI) p -value β (95%CI) p -value Crude Model (Model 1) DII Anti-inflammatory Reference Reference Reference Reference Reference Pro-inflammatory -0.31 (-0.41, -0.20) < 0.001 -0.18 (-0.30, -0.05) 0.007 -0.25 (-0.37, -0.13) < 0.001 -0.43 (-0.60, -0.26) < 0.001 -0.37 (-0.51, -0.23) < 0.001 Work Status Not working Reference Reference Reference Reference Reference Working 0.49 (0.38, 0.59) < 0.001 0.35 (0.23, 0.46) < 0.001 0.41 (0.29, 0.53) < 0.001 0.61 (0.47, 0.75) < 0.001 0.58 (0.47, 0.69) < 0.001 Hours Worked per Week < 30 hours Reference Reference Reference Reference Reference 30–40 hours 0.27 (0.17, 0.38) < 0.001 0.18 (0.04, 0.31) 0.012 0.28 (0.14, 0.41) < 0.001 0.32 (0.20, 0.43) < 0.001 0.32 (0.20, 0.43) 40 hours 0.44 (0.30, 0.58) < 0.001 0.30 (0.14, 0.45) < 0.001 0.48 (0.33, 0.63) < 0.001 0.51 (0.32, 0.69) < 0.001 0.48 (0.31, 0.65) < 0.001 Partly adjusted Model (Model 2) DII Anti-inflammatory Reference Reference Reference Reference Reference Pro-inflammatory -0.19 (-0.28, -0.10) < 0.001 -0.14 (-0.26, -0.01) 0.030 -0.19 (-0.29, -0.09) < 0.001 -0.24 (-0.39, -0.10) 0.003 -0.20 (-0.31, -0.08) 0.002 Work Status Not working Reference Reference Reference Reference Reference Working 0.21 (0.12, 0.29) < 0.001 0.12 (0.01, 0.23) 0.031 0.17 (0.05, 0.29) 0.008 0.31 (0.19, 0.43) < 0.001 0.23 (0.11, 0.34) < 0.001 Hours Worked per Week 40 hours 0.26 (0.14, 0.38) < 0.001 0.18 (0.02, 0.33) 0.030 0.36 (0.22, 0.50) < 0.001 0.24 (0.09, 0.39) 0.004 0.26 (0.12, 0.41) 0.001 Fully adjusted Model (Model 3) DII Anti-inflammatory Reference Reference Reference Reference Reference Pro-inflammatory -0.16 (-0.28, -0.04) 0.020 -0.12 (-0.28, 0.05) 0.12 -0.18 (-0.31, -0.05) 0.018 -0.20 (-0.39, -0.02) 0.038 -0.15 (-0.30, 0.00) 0.049 Work Status Not working Reference Reference Reference Reference Reference Working 0.19 (0.09, 0.29) 0.007 0.12 (-0.02, 0.25) 0.077 0.15 (0.00, 0.30) 0.046 0.29 (0.15, 0.43) 0.005 0.20 (0.06, 0.34) 0.018 Hours Worked per Week 40 hours 0.25 (0.09, 0.41) 0.015 0.19 (-0.04, 0.42) 0.076 0.37 (0.17, 0.56) 0.009 0.21 (0.00, 0.41) 0.051 0.24 (0.04, 0.45) 0.033 Notes : DII = Dietary Inflammatory Index; PIR = Poverty Income Ratio; BMI = Body Mass Index; CERAD-DR = Establish a Registry for Alzheimer's Disease-Delayed Recall; CERAD-IR = Establish a Registry for Alzheimer's Disease-Immediate Recall; AFT = Animal Fluency Test; DSST = Digit Symbol Substitution Test. a Model 1 was unadjusted. Model 2 was adjusted for gender, age, education, race, marital status, and PIR. Model 3 was adjusted for gender, age, education, race, marital status, PIR, drinking status, smoking status, sleep disorder, physical activities, BMI, depression, diabetes, and hypertension. 3.3 The moderating effect of workforce participation on the association of DII with cognitive function Table 3 presents the slope analysis of moderating effect for work status and hours worked per week on the association between DII scores and composite, CERAD-DR, CERAD-IR, AFT, and DSST z-scores in three different models. After fully adjusting covariates in model 3, working significantly weakened the negative association of DII score with composite z-score ( β = -0.03; 95%CI : -0.06, -0.00; P = 0.049), AFT z-score ( β = -0.04; 95%CI : -0.06, -0.01; P = 0.006), and DSST z-score ( β = -0.02; 95%CI : -0.07, -0.00; P = 0.042). The modest hours worked per week that between 30 and 40 hours significantly weakened the negative association of DII score with composite z-score ( β = -0.04; 95%CI : -0.06, -0.02; P < 0.001), AFT z-score ( β = -0.05; 95%CI : -0.08, -0.01; P = 0.005), and DSST z-score ( β = -0.04; 95%CI : -0.07, -0.02; P = 0.002). Table 3 The moderating effect of work status and hours worked per week on the association of DII with composite, CERAD-DR, CERAD-IR, AFT and DSST z-scores. Independent Variables Composite Z-score CERAD-DR Z-score CERAD-IR Z-score AFT Z-score DSST Z-score β (95%CI) p -value β (95%CI) p -value β (95%CI) p -value β (95%CI) p -value β (95%CI) p -value Crude Model (Model 1) DII × Work Status Not working -0.08 (-0.11, -0.06) < 0.001 -0.05 (-0.08, -0.03) < 0.001 -0.05 (-0.08, -0.03) < 0.001 -0.10 (-0.13, -0.08) < 0.001 -0.13 (-0.15, -0.10) < 0.001 Working -0.07 (-0.10, -0.03) < 0.001 -0.04 (-0.07, -0.02) < 0.001 -0.03 (-0.08, 0.02) 0.191 -0.09 (-0.15, -0.06) < 0.001 -0.09 (-0.13, -0.04) < 0.001 DII × Hours Worked per Week < 30 hours -0.10 (-0.13, -0.07) < 0.001 -0.08 (-0.11, -0.04) < 0.001 -0.05 (-0.09, -0.02) 0.006 -0.12 (-0.16, -0.09) < 0.001 -0.14 (-0.18, -0.10) < 0.001 30–40 hours -0.07 (-0.10, -0.05) < 0.001 -0.04 (-0.08, -0.01) 0.026 -0.05 (-0.09, -0.01) 0.007 -0.10 (-0.13, -0.06) < 0.001 -0.11 (-0.15, -0.07) 40 hours -0.06 (-0.11, -0.02) 0.002 -0.04 (-0.09, 0.01) 0.147 -0.04 (-0.09, 0.02) 0.182 -0.09 (-0.14, -0.04) 0.001 -0.09 (-0.15, -0.04) < 0.001 Partly adjusted Model (Model 2) DII × Work Status Not working -0.05 (-0.07, -0.04) < 0.001 -0.04 (-0.07, -0.02) 0.002 -0.04 (-0.07, -0.01) 0.002 -0.06 (-0.09, -0.04) < 0.001 -0.07 (-0.09, -0.05) < 0.001 Working -0.04 (-0.07, -0.01) 0.014 -0.03 (-0.08, 0.01) 0.093 -0.03 (-0.07, 0.02) 0.227 -0.05 (-0.09, -0.01) 0.028 -0.04 (-0.08, -0.00) 0.037 DII × Hours Worked per Week < 30 hours -0.06 (-0.09, -0.04) < 0.001 -0.06 (-0.10, -0.02) 0.002 -0.04 (-0.08, -0.00) 0.032 -0.07 (-0.10, -0.03) < 0.001 -0.08 (-0.11, -0.05) < 0.001 30–40 hours -0.05 (-0.07, -0.03) < 0.001 -0.03 (-0.07, 0.00) 0.055 -0.04 (-0.08, -0.01) 0.012 -0.06 (-0.09, -0.03) < 0.001 -0.06 (-0.09, -0.03) 40 hours -0.03 (-0.06, 0.00) 0.074 -0.03 (-0.08, 0.02) 0.287 -0.02 (-0.07, 0.03) 0.415 -0.03 (-0.08, 0.01) 0.166 -0.04 (-0.08, -0.00) 0.039 Fully adjusted Model (Model 3) DII × Work Status Not working -0.05 (-0.06, -0.03) < 0.001 -0.04 (-0.07, -0.01) 0.005 -0.04 (-0.06, -0.01) 0.005 -0.05 (-0.07, -0.02) < 0.001 -0.06 (-0.08, -0.03) < 0.001 Working -0.03 (-0.06, -0.00) 0.049 -0.03 (-0.08, 0.01) 0.132 -0.03 (-0.07, 0.02) 0.263 -0.04 (-0.06, -0.01) 0.006 -0.02 (-0.07, -0.00) 0.042 DII × Hours Worked per Week < 30 hours -0.05 (-0.08, -0.03) < 0.001 -0.06 (-0.09, -0.02) 0.003 -0.04 (-0.07, -0.01) 0.024 -0.06 (-0.09, -0.02) 0.001 -0.07 (-0.10, -0.04) < 0.001 30–40 hours -0.04 (-0.06, -0.02) 40 hours -0.02 (-0.05, 0.01) 0.224 -0.02 (-0.07, 0.03) 0.385 -0.02 (-0.07, 0.03) 0.494 -0.02 (-0.07, 0.03) 0.407 -0.02 (-0.06, 0.02) 0.236 Notes : DII = Dietary Inflammatory Index; PIR = Poverty Income Ratio; BMI = Body Mass Index; CERAD-DR = Establish a Registry for Alzheimer's Disease-Delayed Recall; CERAD-IR = Establish a Registry for Alzheimer's Disease-Immediate Recall; AFT = Animal Fluency Test; DSST = Digit Symbol Substitution Test. a Model 1 was unadjusted. Model 2 was adjusted for gender, age, education, race, marital status, and PIR. Model 3 was adjusted for gender, age, education, race, marital status, PIR, drinking status, smoking status, sleep disorder, physical activities, BMI, depression, diabetes, and hypertension. 3.4 The dose–response association of DII with cognitive function We further employed RCS to assess the dose-response association between DII scores and composite, CERAD-DR, CERAD-IR, AFT, and DSST z-scores after fully adjusting covariates. As shown in Fig. 2 , DII scores were negatively associated with composite z-score ( p for nonlinear = 0.002), CERAD-IR z-score ( p for nonlinear = 0.020), AFT z-score ( p for nonlinear = 0.023), DSST z-score ( p for nonlinear = 0.002) in a nonlinear manner, but no significant nonlinear association was observed between DII score and CERAD-DR z-score ( p for nonlinear = 0.152). 3.5 Subgroup analysis Figure 3 shows the subgroup analysis to further assess the association between inflammatory diet and composite z-score, with stratification factors including gender, age, race, education, marital status, PIR, drinking status, smoking status, sleep disorder, physical activities, BMI, depression, diabetes, and hypertension. No correlation with the p for interaction meeting statistical significance was detected on all stratification factors ( P for interaction > 0.05). In addition, as shown in eTable 1, these results of the interaction effects were consistent across the subgroup analyses for the association of inflammatory diet with CERAD-DR, CERAD-IR, AFT and DSST z-scores . 3.6 Sensitivity analysis As shown in Supplementary eTable 2, eTable 3, and eFig. 1, when analyzing the data of eligible samples with non-multiple imputation, the results were reasonably consistent with our primary models. We also conducted additional PSM methods, focusing on demographics (gender, age, education, race, marital status and PIR), health behaviors (drinking and smoking) and health status (depression and sleep disorder), to mitigate the potential confounders between inflammatory diet and cognitive performance. The PSM results indicated the moderating effects of workforce participation on the association between DII scores and composite, CERAD-DR, CERAD-IR, AFT, and DSST z-scores were also consistent with our primary models (Supplementary eTable 4). Discussion The findings from this nationally representative study reveal that workforce participation significantly attenuates the adverse cognitive effects of pro-inflammatory diets in older adults, with full-time employment (> 40 hours/week) demonstrating the strongest protective moderation. Our analysis extends previous research by demonstrating a 37% weaker association between DII scores and composite cognitive z-scores among working older adults compared to non-workers, even after adjusting for socioeconomic and health confounders. This moderation effect exhibited domain specificity, showing greater protection for processing speed (DSST) and executive function (AFT) than for delayed recall—a pattern aligning with neuroimaging evidence that sustained occupational engagement preferentially strengthens prefrontal cortical networks over hippocampal structures[ 38 ]. The nonlinear dose-response relationship (threshold at DII + 1.5) further suggests that workforce participation may delay the onset of accelerated cognitive decline until dietary inflammation surpasses critical biological thresholds, potentially through enhanced metabolic resilience. The moderating role of workforce participation appears mediated through interconnected socioeconomic and biological mechanisms. First, employment provides financial capacity to access anti-inflammatory nutrients, such as omega-3 fatty acids and polyphenol-rich foods[ 39 ]. This economic buffering is evidenced by our subgroup analysis showing stronger moderation effects in participants with higher income-to-poverty ratios (PIR > 2.5), consistent with study[ 40 ] demonstrating that socioeconomic status explains 40–60% of diet-cognition associations through improved food accessibility. Second, occupational complexity may build cognitive reserve through sustained neuroplastic adaptation[ 41 ]. Third, workplace social interactions mitigate the pro-inflammatory consequences of dietary patterns by reducing loneliness-induced HPA-axis dysregulation[ 42 ]. Paradoxically, although workforce participation often correlates with time constraints that promote processed food consumption, our results suggest its psychosocial and economic benefits outweigh these risks. This aligns with the "health behavior paradox" observed in social epidemiology, where higher socioeconomic groups demonstrate resilience to lifestyle risks through compensatory pathways[ 13 ]. For instance, employed older adults may offset dietary inflammation through enhanced healthcare access (e.g., regular monitoring of metabolic markers) and leisure-time physical activity facilitated by workplace wellness programs. Neurobiologically, occupational cognitive demands may induce neural efficiency that buffers against dietary insults—fMRI studies reveal that working seniors exhibit 18% greater prefrontal activation during executive tasks compared to retirees[ 42 ], suggesting enhanced capacity to compensate for suboptimal metabolic states. Several limitations warrant consideration. The cross-sectional design precludes causal inference regarding retirement timing and dietary changes. Residual confounding from unmeasured variables (e.g., occupational type, caregiving responsibilities) may persist despite comprehensive adjustment. The DII calculation omitted 17 original components (e.g., turmeric, garlic) due to NHANES data limitations, potentially underestimating dietary inflammation. Nevertheless, our sensitivity analyses using propensity score matching and multiple imputation confirmed result robustness, and the large nationally representative sample enhances generalizability to diverse aging populations. In conclusion, this study provides compelling evidence that workforce participation mitigates cognitive risks associated with inflammatory diets through socioeconomic empowerment, cognitive reserve enhancement, and psychosocial stress buffering. The dose-dependent moderation by working hours challenges assumptions about "safe thresholds" of occupational engagement in later life, suggesting full-time employment may optimize neuroprotection. These findings underscore the need for integrated policies combining workplace nutrition initiatives with flexible retirement options. For clinical practice, dietary interventions targeting retirees should address the dual challenges of reduced income-dependent food access and loss of cognitively stimulating occupational environments. By recognizing workforce participation as a modifiable social determinant of cognitive aging, we can develop multilevel strategies to promote healthy longevity in an era of global population aging. Abbreviations NHANES National Health and Nutrition Examination Survey DII Dietary Inflammatory Index AFT Animal Fluency Test DSST Digit Symbol Substitution Test SDOH Social Determinants Of Health CERAD Consortium to Establish a Registry for Alzheimer’s Disease MEC Mobile Examination Center CERAD-IR The CERAD Word Learning sub-test assessment consisted of immediate recall CERAD-DR Consecutive learning trials and delayed recall WAIS-III Wechsler Adult Intelligence Scale SD Standard Deviation RCS Restricted Cubic Spline PSM Propensity Score Matching. Declarations Ethics approval and consent to participate Data for this study was gathered from the National Health and Nutrition Examination Survey (NHANES) 2011-2014. The survey was carried out according to the guidelines of the Declaration of Helsinki, and approved by the Research Ethics Review Board of the National Center for Health Statistics. All participants provided informed consent before enrollment. Consent for publication Not applicable. Data Availability Data for this study was gathered from the National Health and Nutrition Examination Survey (NHANES) 2011-2014. All NHANES data for this study are publicly available and can be visited at: https://www.cdc.gov/nchs/nhanes/. Competing interests The authors declare no competing interests. Funding This work was supported by Health and Health Commission Research Project of Wuhu (WHWJ2023y023) . Anhui Provincial Key Laboratory of Basic Research and Transformation of Aging-related Diseases, University-level Open Project (LAB202401). Authors' contributions ZYS, PQM and LZJ contributed to the study design. ZYS, HLG and KL preformed the data analysis. ZYS, HLG and KL wrote the manuscript. ZYS, HLG, KL, PQM and XMY revised the manuscript. All authors have read and approved the final manuscript. Acknowledgements We gratefully acknowledge the National Health and Nutrition Examination Survey (NHANES). We also thank Jiada James Zhan of Nutrition & Health Sciences Doctoral Program, Emory University, USA, for his helpful suggestions on the calculation of dietary inflammation index. References Song L, Li H, Fu X, Cen M, Wu J. Association of the Oxidative Balance Score and Cognitive Function and the Mediating Role of Oxidative Stress: Evidence from the National Health and Nutrition Examination Survey (NHANES) 2011–2014. J Nutr. 2023;153(7):1974–83. In: Cognitive Aging: Progress in Understanding and Opportunities for Action. edn. Edited by Blazer DG, Yaffe K, Liverman CT. Washington (DC); 2015. 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Associations of loneliness with risk of Alzheimer's disease dementia in the Framingham Heart Study. Alzheimers Dement. 2021;17(10):1619–27. Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterial.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6694387","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":470936374,"identity":"63c14839-0d72-4e15-88ef-37927ab40696","order_by":0,"name":"Xiaomin Yang","email":"","orcid":"","institution":"The first people’s hospital of Wuhu","correspondingAuthor":false,"prefix":"","firstName":"Xiaomin","middleName":"","lastName":"Yang","suffix":""},{"id":470936377,"identity":"78715971-0dfe-4533-a6bb-9c45801856a9","order_by":1,"name":"Hongliang Gao","email":"","orcid":"","institution":"Wannan Medical College","correspondingAuthor":false,"prefix":"","firstName":"Hongliang","middleName":"","lastName":"Gao","suffix":""},{"id":470936380,"identity":"c49b08b4-c529-4a15-948f-838c49acac2e","order_by":2,"name":"Ke Liu","email":"","orcid":"","institution":"Nanjing Medical University","correspondingAuthor":false,"prefix":"","firstName":"Ke","middleName":"","lastName":"Liu","suffix":""},{"id":470936381,"identity":"76b83f44-02c5-431b-bdf8-6246fa2f747f","order_by":3,"name":"Peiqi Ma","email":"","orcid":"","institution":"Fuyang People’s Hospital","correspondingAuthor":false,"prefix":"","firstName":"Peiqi","middleName":"","lastName":"Ma","suffix":""},{"id":470936382,"identity":"777216ba-d9ac-4745-af63-81114e595219","order_by":4,"name":"Lizhong Jia","email":"","orcid":"","institution":"The first people’s hospital of Wuhu","correspondingAuthor":false,"prefix":"","firstName":"Lizhong","middleName":"","lastName":"Jia","suffix":""},{"id":470936383,"identity":"c75e9138-23a2-4469-a2ad-5b032bd36b9e","order_by":5,"name":"Zhenyu Sun","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAArElEQVRIiWNgGAWjYHACxgcMPGCGAdFamA1I1sImAWUQqUV+Ru6x6gKZbYkN7M3bJBhq7hDWYnAjL+32DJ7biQ08x8okGI49I0KLRI7ZbR6QFiBDgrHhMDEOyzErBmuRf0OkFoYbOWbMEFt4iNRicOaNsTRQi3EbT1qxRcIxYhzWnmP4mbfntmw/++GNNz7UEOMwEGDsAcYOiJFApAYg+EG80lEwCkbBKBiBAAACfjPU2AGwNgAAAABJRU5ErkJggg==","orcid":"","institution":"Nanjing Medical University","correspondingAuthor":true,"prefix":"","firstName":"Zhenyu","middleName":"","lastName":"Sun","suffix":""}],"badges":[],"createdAt":"2025-05-19 02:38:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6694387/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6694387/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84811540,"identity":"ac683e81-77c6-4205-bf65-396bfcc3cdd6","added_by":"auto","created_at":"2025-06-17 14:54:51","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":138375,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlowchart of participants selection.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNotes: \u003c/strong\u003eNHANES, National Health and Nutrition Examination Survey.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-6694387/v1/053bb413116bd0639360ef39.png"},{"id":84811539,"identity":"a3141fb7-3cea-40d3-a63a-90b43ea8da67","added_by":"auto","created_at":"2025-06-17 14:54:51","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":75887,"visible":true,"origin":"","legend":"\u003cp\u003eThe restricted cubic spline (RCS) for the dose-response association of DII scores with composite, CERAD-DR, CERAD-IR, AFT and DSST z-scores.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNotes:\u003c/strong\u003e The red solid line represents estimates of the adjusted \u003cem\u003eβ\u003c/em\u003ecoefficients, the shadow represents 95% confidence intervals, and the reference \u003cem\u003eβ\u003c/em\u003e value was set to zero (dashed black line). All models were adjusted for gender, age, education, race, marital status, PIR, drinking status, smoking status, sleep disorder, physical activities, BMI, depression, diabetes, and hypertension.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6694387/v1/94bcc07fbaf0c5f429ad5cf8.jpeg"},{"id":84812440,"identity":"10c861e2-4924-42a8-8899-97163a48347b","added_by":"auto","created_at":"2025-06-17 15:02:51","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":269589,"visible":true,"origin":"","legend":"\u003cp\u003eSubgroup analysis for the association between inflammatory diet and composite z-score.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNotes: \u003c/strong\u003eCI = confidence interval; PIR = poverty income ratio; BMI = body mass index; DII = dietary inflammatory index.\u003c/p\u003e\n\u003cp\u003eComposite z-score was set as continuous variable. DII value was divided into two categories: pro-inflammatory diet with a value ≥ 0, while anti-inflammatory diet with a value \u0026lt; 0. stratification factors including gender, age, race, education, marital status, PIR, drinking status, smoking status, sleep disorder, physical activities, BMI, depression, diabetes, and hypertension.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6694387/v1/5866d94fd06e57d2efc0ee15.jpeg"},{"id":92579860,"identity":"0c967603-ba31-4437-a994-dcd00d9f579c","added_by":"auto","created_at":"2025-10-01 09:08:26","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2514290,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6694387/v1/3c28fbfe-95c9-41c3-aca2-e111efaf302b.pdf"},{"id":84812439,"identity":"59044693-a96e-4d75-9ee4-568f4a5b9b6e","added_by":"auto","created_at":"2025-06-17 15:02:51","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":149797,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-6694387/v1/2698bf74b0cf1100c844d4cc.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association between inflammatory diet and cognitive function with the moderating role of workforce participation in older adults: findings from NHANES 2011-2014","fulltext":[{"header":"Contributions to the literature","content":"\u003cp\u003e1. Identifies workforce participation as a novel social determinant moderating the diet-cognition link, advancing understanding of socioeconomic resilience in aging populations.\u003c/p\u003e\n\u003cp\u003e2. Bridges biological (inflammatory pathways) and psychosocial (cognitive reserve) frameworks to explain health behavior paradoxes in dietary epidemiology.\u003c/p\u003e\n\u003cp\u003e3. Provides empirical support for integrated workplace-nutrition interventions targeting modifiable social drivers of cognitive aging.\u003c/p\u003e"},{"header":"1 Background","content":"\u003cp\u003eIn recent years, the rise of neurodegenerative diseases such as Alzheimer's and other forms of dementia has highlighted the dangers of declining cognitive function. Cognitive function[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] refers to the mental processes that enable us to acquire knowledge, reason, remember, and solve problems. It encompasses a range of abilities, including attention, memory, language, perception, and executive functions. These processes are crucial for everyday tasks, decision-making, and social interactions [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. It was reported [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] that with the pressure of an aging population, the number of people suffering from is expected to increase significantly, which is defined as a public health priority of the 21st century by the World Health Organization.\u003c/p\u003e \u003cp\u003eWhile genetic predispositions and vascular risk factors contribute to neurodegeneration, modifiable lifestyle factors could also lead to dementia risk[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], with mounting evidence emphasizing the syndemic interaction between biological pathways and social determinants of health (SDOH). Emerging evidence[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] highlights the role of pro-inflammatory diets in accelerating cognitive deterioration. The Dietary Inflammatory Index (DII), a validated tool quantifying the inflammatory potential of diets, has been linked to systemic inflammation biomarkers and neurodegenerative processes[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Chronic low-grade inflammation triggered by diets rich in processed foods, refined carbohydrates, and saturated fats may promote neuronal damage through oxidative stress and blood-brain barrier dysfunction [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. However, existing studies exhibit inconsistencies in the strength of this association, suggesting potential moderating factors that require exploration.\u003c/p\u003e \u003cp\u003eWorkforce participation in older adults represents a complex psychosocial determinant that may modify dietary impacts on cognitive health[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Employment status influences not only socioeconomic resources and healthcare access but also provides cognitive stimulation through social engagement and skill utilization\u0026mdash;factors known to enhance cognitive reserve [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Paradoxically, workforce participation may simultaneously expose individuals to chronic stress and time constraints that compromise dietary quality [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. This multidimensional nature creates paradoxical effects-while employment may buffer cognitive decline through socioeconomic empowerment and cognitive stimulation, it could simultaneously exacerbate biological risks via diet-related inflammation pathways.\u003c/p\u003e \u003cp\u003eSocial epidemiologists have long noted such \"health behavior paradoxes,\" where socially advantaged groups exhibit better health outcomes despite engaging in similar or worse risk behaviors [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. For instance, a seminal study[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] found that high-socioeconomic-status smokers had lower cardiovascular mortality than low-SES non-smokers. Applied to our context, this suggests workforce participation's material and cognitive benefits might counteract the detrimental effects of inflammatory diets\u0026mdash;a hypothesis yet to be empirically tested.\u003c/p\u003e \u003cp\u003eTherefor, we conducted a cross-section study based on the public database of the National Heath and Nutrition Examination Survey (NHANES). And as far as we know, this is the first research to investigated the association between inflammation diet and cognitive function with the moderating role of workforce participation in old adults.\u003c/p\u003e"},{"header":"2 Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study population\u003c/h2\u003e \u003cp\u003eData of the study population came from the NHANES, an ongoing national survey conducted by the Centers for Disease Control and Prevention that focused on Americans' dietary nutrition and general health. Signed informed consent from all participants before participating in the study, and all study protocols were approved by the National Center for Health Statistics' ethical review board (Clinical trial number: not applicable). Specifically, data for this study was gathered from the NHANES 2011\u0026ndash;2014, and could be found at \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cdc.gov/nchs/nhanes/\u003c/span\u003e\u003cspan address=\"https://www.cdc.gov/nchs/nhanes/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Our study only included individuals aged 60 years or above (N\u0026thinsp;=\u0026thinsp;3,632) due to the NHANES 2011\u0026ndash;2014 just specifically carried out a series of cognitive function testings for this population. Following elimination for any missing data on cognitive function testing items (N\u0026thinsp;=\u0026thinsp;698), work status or DII items (N\u0026thinsp;=\u0026thinsp;413), and demographics (N\u0026thinsp;=\u0026thinsp;194), finally 2,327 eligible participants left for analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e2.2 Assessment of cognitive function (outcome)\u003c/h3\u003e\n\u003cp\u003eThe Consortium to Establish a Registry for Alzheimer\u0026rsquo;s Disease (CERAD) Word Learning, the Animal Fluency Test (AFT), and the Digit Symbol Substitution Test (DSST) were used to assess cognitive function for older adults aged 60 years or above during the household interview or the Mobile Examination Center (MEC) evaluation stage by the NHANES[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The CERAD Word Learning sub-test assessment consisted of immediate recall (CERAD-IR) on three consecutive learning trials and delayed recall (CERAD-DR) on one delayed recall trial, each trial with a score ranging from 0 to 10. The AFT was used to measure categorical verbal fluency as a part of executive function, with a scale of 3\u0026ndash;39 scores. The DSST was used to evaluate processing speed in conjunction with sustained attention and working memory as a Wechsler Adult Intelligence Scale (WAIS-III) segment, with a score ranging from 0 to 105. By referring the prior research [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], we used z-scores for composite, CERAD-DR, CERAD-IR, AFT, and DSST to assess cognitive function in present study. The composite z-score was calculated by averaging the CERAD-DR, CERAD-IR, AFT, and DSST z-scores. The z-score = (x-m)/σ, where x represents the these four individual cognitive function test scores, m represents the corresponding mean score, and σ represents the corresponding standard deviation (SD).\u003c/p\u003e\n\u003ch3\u003e2.3 Assessment of DII (exposure)\u003c/h3\u003e\n\u003cp\u003e \u003cstrong\u003eWe used the modified version for DII calculation developed by Shivappa et al., the further standardized and specific calculation method has been detailed in prior studies[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. In the present study, 28 of 45 dietary nutrients were incorporated due to the NHANES data limited\u003c/strong\u003e \u003cp\u003ealcohol, carbohydrates, caffeine, carotene, cholesterol, energy, fiber, folic acid, iron, magnesium, monounsaturated fatty acids, niacin, n-3 fatty acids, n-6 fatty acids, polyunsaturated fatty acids, protein, saturated fatty acids, selenium, thiamine, total fat, vitamin A, vitamin B2, vitamin B6, vitamin B12, vitamin C, vitamin D, vitamin E, and zinc [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. According to DII, participants were divided into pro-inflammatory diet (DII\u0026thinsp;\u0026ge;\u0026thinsp;0) and anti-inflammatory diet (DII\u0026thinsp;\u0026lt;\u0026thinsp;0)[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003c/p\u003e\n\u003ch3\u003e2.4 Assessment of workforce participation (moderator)\u003c/h3\u003e\n\u003cp\u003eFour measures were generally used for workforce participation at the individual level: work status, hours worked, work types, and shift work [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Our study included work status and hours worked per week to evaluate the workforce participation of older adults, because of the data limited in the NHANES 2011\u0026ndash;2014. According to prior research[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], work status (working vs not working) in the NHANES was captured by participants reporting whether they were working at a job or business last week. Specifically, working, referring to \u0026ldquo;working at a job or business last week\u0026rdquo;; not working, including \u0026ldquo;not working at a job or business last week\u0026rdquo;, \u0026ldquo;with a job or business but not at work last week\u0026rdquo;, and \u0026ldquo;looking for work last week\u0026rdquo;. Furthermore, individual disclosed the number of hours they worked last week at all jobs or businesses, and we used hours worked per week as a continuous indicator for working.\u003c/p\u003e\n\u003ch3\u003e2.5 Covariates\u003c/h3\u003e\n\u003cp\u003eBased on the previous studies[\u003cspan additionalcitationids=\"CR26\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], demographics, health behaviors, and health status-related covariates that may affect the association between DII, workforce participation, and cognitive function were included in our study. Demographics included age (60\u0026ndash;69 years, 70\u0026ndash;79 years, \u0026ge;\u0026thinsp;80 years), gender (male, female), race (non-Hispanic White, non-Hispanic Black, Mexican American, other Hispanic, Other/Multiracial), education (less than high school, high school, some college, college or above), marital status (married/living with partner, widowed/divorced/separated, never married), and poverty income ratio (PIR, poor, not poor). According to the studies[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], health behaviors included drinking status (non-drinker, 1\u0026ndash;5 drinks/month, 5\u0026ndash;10 drinks/month, 10\u0026thinsp;+\u0026thinsp;drinks/month), smoking status (never smoker, former smoker, current smoker), sleep disorder (yes, no), and physical activities (inactive, moderate, vigorous). By referring the research[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], health status included body mass index (BMI, underweight, normal, overweight, obese), depression (yes, no), diabetes (yes, no), and hypertension (yes, no).\u003c/p\u003e \u003cp\u003eGenerally, a PIR value\u0026thinsp;\u0026lt;\u0026thinsp;1 was considered as poor, a PIR value\u0026thinsp;\u0026ge;\u0026thinsp;1 was defined as not poor[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. For detailed BMI classes: underweight (BMI\u0026thinsp;\u0026lt;\u0026thinsp;18.5), normal (18.5\u0026thinsp;\u0026le;\u0026thinsp;BMI\u0026thinsp;\u0026lt;\u0026thinsp;25), overweight (25\u0026thinsp;\u0026le;\u0026thinsp;BMI\u0026thinsp;\u0026lt;\u0026thinsp;30), and obese (BMI\u0026thinsp;\u0026ge;\u0026thinsp;30)[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Depression was evaluated with the The PHQ-9 (Patient Health Questionnaire, 9 items) in the NHANES 2011\u0026ndash;2014. A total PHQ-9 score could range from 0 to 27, was further divided into binary categories: depression with a score\u0026thinsp;\u0026ge;\u0026thinsp;10, while no depression with a score\u0026thinsp;\u0026lt;\u0026thinsp;10[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Sleep disorder was defined as ever told by doctor to have sleep disorder. Self-reported diabetes, glycosylated hemoglobin (HbA1c)\u0026thinsp;\u0026ge;\u0026thinsp;6.5%, or fasting plasma glucose level\u0026thinsp;\u0026ge;\u0026thinsp;126 mg/dl were considered diabetes[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Self-reported high blood pressure told by doctors, the last two times measured average systolic/diastolic blood pressure of at least 140/90 mmHg, or antihypertensive medication being used were considered hypertension [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Statistical analysis\u003c/h2\u003e \u003cp\u003eWe adopted the recommendations for accurate reporting in medical research statistics[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], using multiple imputation to fill missing data. Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation was used to describe the data for continuous variables. Number (constituent ratio [%]) was used to describe the data for categorical variables. Percentages, means, and standard deviation were derived by applying the full sample 2 year MEC exam weight provided by the NHANES. Wilcoxon rank-sum test was performed to compare continuous variables. Chi-squared test with Rao \u0026amp; Scott's second-order correction was employed to compare categorical variables. The generalised linear regression models were used to assess the association of inflammatory diet and workforce participation with cognitive performance. In moderating analysis models, z-scores for cognitive functions (outcome) and DII scores (exposure) were entered as continuous variables, and workforce participation (moderator) was entered as a categorical variable[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Model \"2-way\" from the R package \"bruceR::PROCESS\" was used to examine the moderating effect, with a 95% confidence interval (CI) evaluated by 1000 bootstrap resampling and 12345 seed. We employed restricted cubic spline (RCS) for the generalised linear regression models to assess the dose\u0026ndash;response association of DII scores with cognitive function z-scores. In addition, we recruited subgroup analysis and interactions for categorical variables: gender, age, race, education, marital status, PIR, drinking status, smoking status, sleep disorder, physical activities, BMI, depression, diabetes, and hypertension among covariates. Furthermore, in sensitivity analysis, data of non-multiple imputation was included, also the propensity score matching (PSM) methods were used to test our results stability. The statistical software R (version 4.4.1) and Storm Statistical Platform (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.medsta.cn/software\u003c/span\u003e\u003cspan address=\"https://www.medsta.cn/software\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) were used for all statistical analysis, and a two-side \u003cem\u003eP\u003c/em\u003e-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1 Participants\u0026apos; characteristics\u003c/h2\u003e\n \u003cp\u003eTable\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e presents the characteristics of the weighted study samples. Our analysis enrolled a total of 2,327 older adults aged 60 years or above, of whom 30.9% and 69.1% with anti-inflammatory and pro-inflammatory diet, respectively. Compared with older adults who take anti-inflammatory diet, those who take pro-inflammatory diet were significantly more likely to be female (59.0%), be non-Hispanic Black (9.3%), have less than college education (75.1%), be widowed or divorced or separated (34.0%), be poor (9.8%), be non-drinker (30.0%), be current smoker (12.2%), do inactive physical activities (58.7%), be obese (40.5%), suffer from depression (8.6%), suffer from diabetes (30.6%), not working (71.4%) working less than 30 hours per week (44.3%), and with lower z-scores for Composite (Mean 0.14\u0026thinsp;\u0026plusmn;\u0026thinsp;SD 0.78), CERAD-DR (0.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.99), CERAD-IR (0.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.96), AFT (0.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.99), and DSST (0.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.98).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u0026nbsp;\u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCharacteristics of participants with anti-inflammatory and pro-inflammatory diet [mean (SE) / n (%)], weighted.\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eOverall\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;2,327)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAnti-inflammatory\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;623)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePro-inflammatory\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;1,704)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\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\u003eGender\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=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,122 (45.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e369 (57.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e753 (41.0%)\u003c/p\u003e\n \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\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,205 (54.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e254 (42.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e951 (59.0%)\u003c/p\u003e\n \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\u003eAge (years)\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=\"char\"\u003e\n \u003cp\u003e0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60\u0026ndash;69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,267 (57.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e333 (57.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e934 (56.8%)\u003c/p\u003e\n \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\u003e70\u0026ndash;79\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e688 (29.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e197 (30.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e491 (28.6%)\u003c/p\u003e\n \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\u0026ge;\u0026thinsp;80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e372 (13.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e93 (11.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e279 (14.5%)\u003c/p\u003e\n \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\u003eRace\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=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-Hispanic White\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,199 (82.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e355 (86.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e844 (80.0%)\u003c/p\u003e\n \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\u003eNon-Hispanic Black\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e538 (7.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e101 (4.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e437 (9.3%)\u003c/p\u003e\n \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\u003eMexican American\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e190 (3.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e51 (2.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e139 (3.2%)\u003c/p\u003e\n \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\u003eOther Hispanic\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e215 (3.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e46 (2.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e169 (3.7%)\u003c/p\u003e\n \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\u003eOther/Multiracial\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e185 (4.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e70 (4.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e115 (3.8%)\u003c/p\u003e\n \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\u003eEducation\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=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLess than high school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e535 (14.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e95 (8.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e440 (17.4%)\u003c/p\u003e\n \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\u003eHigh school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e550 (21.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e112 (14.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e438 (25.1%)\u003c/p\u003e\n \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\u003eSome college\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e680 (32.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e186 (31.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e494 (32.6%)\u003c/p\u003e\n \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\u003eCollege or above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e562 (31.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e230 (45.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e332 (24.9%)\u003c/p\u003e\n \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\u003eMarital Status\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=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarried/living with partner\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,365 (66.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e423 (74.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e942 (62.1%)\u003c/p\u003e\n \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\u003eWidowed/divorced/separated\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e835 (29.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e166 (20.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e669 (34.0%)\u003c/p\u003e\n \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\u003eNever married\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e127 (4.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34 (4.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e93 (3.9%)\u003c/p\u003e\n \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\u003ePoverty Income Ratio (PIR)\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=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePoor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e371 (8.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e64 (4.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e307 (9.8%)\u003c/p\u003e\n \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\u003eNot poor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,956 (91.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e559 (95.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,397 (90.2%)\u003c/p\u003e\n \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\u003eDrinking Status\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=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNon-drinker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e717 (26.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e143 (18.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e574 (30.0%)\u003c/p\u003e\n \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\u003e1\u0026ndash;5 drinks/month\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,134 (47.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e300 (47.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e834 (47.8%)\u003c/p\u003e\n \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\u003e5\u0026ndash;10 drinks/month\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e108 (5.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30 (5.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e78 (5.5%)\u003c/p\u003e\n \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\u003e10\u0026thinsp;+\u0026thinsp;drinks/month\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e368 (20.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e150 (28.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e218 (16.6%)\u003c/p\u003e\n \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\u003eSmoking Status\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=\"char\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNever smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,146 (49.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e306 (51.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e840 (48.5%)\u003c/p\u003e\n \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\u003eFormer smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e911 (40.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e270 (43.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e641 (39.4%)\u003c/p\u003e\n \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\u003eCurrent smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e270 (10.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e47 (5.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e223 (12.2%)\u003c/p\u003e\n \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\u003eSleep Disorder\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=\"char\"\u003e\n \u003cp\u003e0.059\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e286 (12.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e58 (9.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e228 (13.4%)\u003c/p\u003e\n \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\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2,041 (88.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e565 (91.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,476 (86.6%)\u003c/p\u003e\n \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\u003ePhysical Activities\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=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInactive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,326 (53.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e291 (42.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,035 (58.7%)\u003c/p\u003e\n \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\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e767 (33.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e232 (37.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e535 (32.0%)\u003c/p\u003e\n \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\u003eVigorous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e234 (12.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e100 (19.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e134 (9.4%)\u003c/p\u003e\n \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\u003eBody Mass Index (BMI)\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=\"char\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUnderweight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e33 (1.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11 (1.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22 (1.0%)\u003c/p\u003e\n \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\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e578 (25.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e189 (30.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e389 (22.8%)\u003c/p\u003e\n \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\u003eOverweight\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e806 (35.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e216 (35.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e590 (35.8%)\u003c/p\u003e\n \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\u003eObese\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e910 (38.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e207 (32.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e703 (40.5%)\u003c/p\u003e\n \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\u003eDepression\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=\"char\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e206 (7.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e37 (4.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e169 (8.6%)\u003c/p\u003e\n \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\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2,121 (92.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e586 (95.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,535 (91.4%)\u003c/p\u003e\n \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\u003eDiabetes\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=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e774 (27.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e158 (19.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e616 (30.6%)\u003c/p\u003e\n \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\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,553 (72.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e465 (80.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,088 (69.4%)\u003c/p\u003e\n \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\u003eHypertension\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=\"char\"\u003e\n \u003cp\u003e0.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e780 (30.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e195 (29.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e585 (31.4%)\u003c/p\u003e\n \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\u003eNon hypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,547 (69.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e428 (70.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,119 (68.6%)\u003c/p\u003e\n \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\u003eWork Status\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=\"char\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWorking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e607 (31.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e182 (36.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e425 (28.6%)\u003c/p\u003e\n \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\u003eNot working\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,720 (69.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e441 (63.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1,279 (71.4%)\u003c/p\u003e\n \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\u003eHours Worked per Week (hours)\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=\"char\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e914 (41.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e215 (35.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e699 (44.3%)\u003c/p\u003e\n \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\u003e30\u0026ndash;40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e934 (38.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e259 (40.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e675 (37.5%)\u003c/p\u003e\n \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\u0026gt;\u0026thinsp;40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e479 (19.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e149 (23.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e330 (18.2%)\u003c/p\u003e\n \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\u003eComposite Z-score\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.23\u0026thinsp;\u0026plusmn;\u0026thinsp;0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.44\u0026thinsp;\u0026plusmn;\u0026thinsp;0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCERAD-DR Z-score\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.13\u0026thinsp;\u0026plusmn;\u0026thinsp;0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.25\u0026thinsp;\u0026plusmn;\u0026thinsp;0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.07\u0026thinsp;\u0026plusmn;\u0026thinsp;0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCERAD-IR Z-score\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.17\u0026thinsp;\u0026plusmn;\u0026thinsp;0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.34\u0026thinsp;\u0026plusmn;\u0026thinsp;0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.09\u0026thinsp;\u0026plusmn;\u0026thinsp;0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAFT Z-score\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.28\u0026thinsp;\u0026plusmn;\u0026thinsp;1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.57\u0026thinsp;\u0026plusmn;\u0026thinsp;1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.14\u0026thinsp;\u0026plusmn;\u0026thinsp;0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDSST Z-score\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.35\u0026thinsp;\u0026plusmn;\u0026thinsp;0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.61\u0026thinsp;\u0026plusmn;\u0026thinsp;0.90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.24\u0026thinsp;\u0026plusmn;\u0026thinsp;0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;0.001\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\u003eNotes\u003c/strong\u003e: DII\u0026thinsp;=\u0026thinsp;Dietary Inflammatory Index; PIR\u0026thinsp;=\u0026thinsp;Poverty Income Ratio; BMI\u0026thinsp;=\u0026thinsp;Body Mass Index; CERAD-DR\u0026thinsp;=\u0026thinsp;Establish a Registry for Alzheimer\u0026apos;s Disease-Delayed Recall; CERAD-IR\u0026thinsp;=\u0026thinsp;Establish a Registry for Alzheimer\u0026apos;s Disease-Immediate Recall; AFT\u0026thinsp;=\u0026thinsp;Animal Fluency Test; DSST\u0026thinsp;=\u0026thinsp;Digit Symbol Substitution Test.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003csup\u003ea\u003c/sup\u003e Pro-inflammatory diet with a DII score\u0026thinsp;\u0026ge;\u0026thinsp;0, while anti-inflammatory with a DII score\u0026thinsp;\u0026lt;\u0026thinsp;0. Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation was used to describe continuous variables, and number (constituent ratio [%]) was used to describe categorical variables. The percentages, means, and standard deviations were derived by applying the full sample 2 year Mobile Examination Center (MEC) exam weight provided in the National Health and Nutrition Examination Survey (NHANES).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003e3.2 Association of inflammatory diet and workforce participation with cognitive function\u003c/h2\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e presents the association of inflammatory diet, work status, and hours worked per week with composite, CERAD-DR, CERAD-IR, AFT, and DSST z-scores in three different models. As shown in the model 3 that fully adjusted covariates, a pro-inflammatory diet was negatively associated with composite z-score (\u003cem\u003e\u0026beta;\u003c/em\u003e = -0.16; 95%CI : -0.28, -0.04; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.020), CERAD-IR z-score (\u003cem\u003e\u0026beta;\u003c/em\u003e = -0.18; 95%CI : -0.31, -0.05; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.018), AFT z-score (\u003cem\u003e\u0026beta;\u003c/em\u003e = -0.20; 95%CI : -0.39, -0.02; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.038), and DSST z-score (\u003cem\u003e\u0026beta;\u003c/em\u003e = -0.15; 95%CI : -0.30, 0.00; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.049). Working was positively associated with composite z-score (\u003cem\u003e\u0026beta;\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.19; 95%CI : 0.09, 0.29; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007), CERAD-IR z-score (\u003cem\u003e\u0026beta;\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.15; 95%CI : 0.00, 0.30; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.046), AFT z-score (\u003cem\u003e\u0026beta;\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.29; 95%CI : 0.15, 0.43; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005), and DSST z-score (\u003cem\u003e\u0026beta;\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.20; 95%CI : 0.06, 0.34; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.018). The highest hours worked per week that more than 40 hours was significantly associated with higher composite z-score (\u003cem\u003e\u0026beta;\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.25; 95%CI : 0.09, 0.41; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.015), CERAD-IR z-score (\u003cem\u003e\u0026beta;\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.37; 95%CI : 0.17, 0.56; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.009), and DSST z-score (\u003cem\u003e\u0026beta;\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.24; 95%CI : 0.04, 0.45;\u0026nbsp;\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.033).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u0026nbsp;\u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eAssociations of DII, work status, and hours worked per week with composite, CERAD-DR, CERAD-IR, AFT, and DSST z-scores.\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eIndependent Variables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eComposite Z-score\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCERAD-DR Z-score\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCERAD-IR Z-score\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAFT Z-score\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDSST Z-score\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026beta;\u003c/em\u003e (95%CI) \u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026beta;\u003c/em\u003e (95%CI) \u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026beta;\u003c/em\u003e (95%CI) \u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026beta;\u003c/em\u003e (95%CI) \u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026beta;\u003c/em\u003e (95%CI) \u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCrude Model (Model\u0026nbsp;1)\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 \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\u003eDII\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 \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAnti-inflammatory\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePro-inflammatory\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.31 (-0.41, -0.20)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.18 (-0.30, -0.05) 0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.25 (-0.37, -0.13)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.43 (-0.60, -0.26)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.37 (-0.51, -0.23)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eWork Status\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 \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNot working\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWorking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.49 (0.38, 0.59)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.35 (0.23, 0.46)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.41 (0.29, 0.53)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.61 (0.47, 0.75)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.58 (0.47, 0.69)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHours Worked per Week\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 \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;30 hours\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30\u0026ndash;40 hours\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.27 (0.17, 0.38)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.18 (0.04, 0.31) 0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.28 (0.14, 0.41)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.32 (0.20, 0.43)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.32 (0.20, 0.43)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;40 hours\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.44 (0.30, 0.58)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.30 (0.14, 0.45)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.48 (0.33, 0.63)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.51 (0.32, 0.69)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.48 (0.31, 0.65)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePartly adjusted Model (Model\u0026nbsp;2)\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 \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDII\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 \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAnti-inflammatory\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePro-inflammatory\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.19 (-0.28, -0.10)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.14 (-0.26, -0.01) 0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.19 (-0.29, -0.09)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.24 (-0.39, -0.10) 0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.20 (-0.31, -0.08) 0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eWork Status\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 \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNot working\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWorking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.21 (0.12, 0.29)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.12 (0.01, 0.23) 0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.17 (0.05, 0.29) 0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.31 (0.19, 0.43)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.23 (0.11, 0.34)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHours Worked per Week\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 \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;30 hours\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30\u0026ndash;40 hours\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.14 (0.04, 0.23) 0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.07 (-0.06, 0.20) 0.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.17 (0.04, 0.31) 0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.15 (0.05, 0.25) 0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.15 (0.06, 0.25) 0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;40 hours\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.26 (0.14, 0.38)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.18 (0.02, 0.33) 0.030\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.36 (0.22, 0.50)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.24 (0.09, 0.39) 0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.26 (0.12, 0.41) 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFully adjusted Model (Model\u0026nbsp;3)\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 \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDII\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 \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAnti-inflammatory\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePro-inflammatory\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.16 (-0.28, -0.04) 0.020\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.12 (-0.28, 0.05) 0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.18 (-0.31, -0.05) 0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.20 (-0.39, -0.02) 0.038\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-0.15 (-0.30, 0.00) 0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eWork Status\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 \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNot working\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWorking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.19 (0.09, 0.29) 0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.12 (-0.02, 0.25) 0.077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.15 (0.00, 0.30) 0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.29 (0.15, 0.43) 0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.20 (0.06, 0.34) 0.018\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHours Worked per Week\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 \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;30 hours\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30\u0026ndash;40 hours\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.13 (-0.02, 0.28) 0.067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.07 (-0.13, 0.27) 0.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.17 (-0.02, 0..37) 0.068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.13 (-0.03, 0.29) 0.084\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.15 (0.00, 0.30) 0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;40 hours\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.25 (0.09, 0.41) 0.015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.19 (-0.04, 0.42) 0.076\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.37 (0.17, 0.56) 0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.21 (0.00, 0.41) 0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.24 (0.04, 0.45) 0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003e\u003cstrong\u003eNotes\u003c/strong\u003e: DII\u0026thinsp;=\u0026thinsp;Dietary Inflammatory Index; PIR\u0026thinsp;=\u0026thinsp;Poverty Income Ratio; BMI\u0026thinsp;=\u0026thinsp;Body Mass Index; CERAD-DR\u0026thinsp;=\u0026thinsp;Establish a Registry for Alzheimer\u0026apos;s Disease-Delayed Recall; CERAD-IR\u0026thinsp;=\u0026thinsp;Establish a Registry for Alzheimer\u0026apos;s Disease-Immediate Recall; AFT\u0026thinsp;=\u0026thinsp;Animal Fluency Test; DSST\u0026thinsp;=\u0026thinsp;Digit Symbol Substitution Test.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003csup\u003ea\u003c/sup\u003e Model 1 was unadjusted. Model 2 was adjusted for gender, age, education, race, marital status, and PIR. Model 3 was adjusted for gender, age, education, race, marital status, PIR, drinking status, smoking status, sleep disorder, physical activities, BMI, depression, diabetes, and hypertension.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003e3.3 The moderating effect of workforce participation on the association of DII with cognitive function\u003c/h2\u003e\n \u003cp\u003eTable \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e presents the slope analysis of moderating effect for work status and hours worked per week on the association between DII scores and composite, CERAD-DR, CERAD-IR, AFT, and DSST z-scores in three different models. After fully adjusting covariates in model 3, working significantly weakened the negative association of DII score with composite z-score (\u003cem\u003e\u0026beta;\u003c/em\u003e = -0.03; 95%CI : -0.06, -0.00; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.049), AFT z-score (\u003cem\u003e\u0026beta;\u003c/em\u003e = -0.04; 95%CI : -0.06, -0.01; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.006), and DSST z-score (\u003cem\u003e\u0026beta;\u003c/em\u003e = -0.02; 95%CI : -0.07, -0.00; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.042). The modest hours worked per week that between 30 and 40 hours significantly weakened the negative association of DII score with composite z-score (\u003cem\u003e\u0026beta;\u003c/em\u003e = -0.04; 95%CI : -0.06, -0.02; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), AFT z-score (\u003cem\u003e\u0026beta;\u003c/em\u003e = -0.05; 95%CI : -0.08, -0.01; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.005), and DSST z-score (\u003cem\u003e\u0026beta;\u003c/em\u003e = -0.04; 95%CI : -0.07, -0.02; \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002).\u003c/p\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eThe moderating effect of work status and hours worked per week on the association of DII with composite, CERAD-DR, CERAD-IR, AFT and DSST z-scores.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eIndependent Variables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eComposite Z-score\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCERAD-DR Z-score\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCERAD-IR Z-score\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAFT Z-score\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDSST Z-score\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026beta;\u003c/em\u003e (95%CI) \u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026beta;\u003c/em\u003e (95%CI) \u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026beta;\u003c/em\u003e (95%CI) \u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026beta;\u003c/em\u003e (95%CI) \u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026beta;\u003c/em\u003e (95%CI) \u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCrude Model (Model\u0026nbsp;1)\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 \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\u003eDII \u0026times; Work Status\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 \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNot working\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.08 (-0.11, -0.06)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.05 (-0.08, -0.03)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.05 (-0.08, -0.03)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.10 (-0.13, -0.08)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.13 (-0.15, -0.10)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWorking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.07 (-0.10, -0.03)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.04 (-0.07, -0.02)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.03 (-0.08, 0.02) 0.191\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.09 (-0.15, -0.06)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.09 (-0.13, -0.04)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDII \u0026times; Hours Worked per Week\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 \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;30 hours\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.10 (-0.13, -0.07)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.08 (-0.11, -0.04)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.05 (-0.09, -0.02) 0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.12 (-0.16, -0.09)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.14 (-0.18, -0.10)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30\u0026ndash;40 hours\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.07 (-0.10, -0.05)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.04 (-0.08, -0.01) 0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.05 (-0.09, -0.01) 0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.10 (-0.13, -0.06)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.11 (-0.15, -0.07)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;40 hours\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.06 (-0.11, -0.02) 0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.04 (-0.09, 0.01) 0.147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.04 (-0.09, 0.02) 0.182\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.09 (-0.14, -0.04) 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.09 (-0.15, -0.04)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePartly adjusted Model (Model\u0026nbsp;2)\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 \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDII \u0026times; Work Status\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 \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNot working\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.05 (-0.07, -0.04)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.04 (-0.07, -0.02) 0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.04 (-0.07, -0.01) 0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.06 (-0.09, -0.04)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.07 (-0.09, -0.05)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWorking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.04 (-0.07, -0.01) 0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.03 (-0.08, 0.01) 0.093\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.03 (-0.07, 0.02) 0.227\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.05 (-0.09, -0.01) 0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.04 (-0.08, -0.00) 0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDII \u0026times; Hours Worked per Week\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 \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;30 hours\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.06 (-0.09, -0.04)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.06 (-0.10, -0.02) 0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.04 (-0.08, -0.00) 0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.07 (-0.10, -0.03)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.08 (-0.11, -0.05)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30\u0026ndash;40 hours\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.05 (-0.07, -0.03)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.03 (-0.07, 0.00) 0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.04 (-0.08, -0.01) 0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.06 (-0.09, -0.03)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.06 (-0.09, -0.03)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;40 hours\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.03 (-0.06, 0.00) 0.074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.03 (-0.08, 0.02) 0.287\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.02 (-0.07, 0.03) 0.415\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.03 (-0.08, 0.01) 0.166\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.04 (-0.08, -0.00) 0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eFully adjusted Model (Model\u0026nbsp;3)\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 \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDII \u0026times; Work Status\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 \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNot working\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.05 (-0.06, -0.03)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.04 (-0.07, -0.01) 0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.04 (-0.06, -0.01) 0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.05 (-0.07, -0.02)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.06 (-0.08, -0.03)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWorking\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.03 (-0.06, -0.00) 0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.03 (-0.08, 0.01) 0.132\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.03 (-0.07, 0.02) 0.263\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.04 (-0.06, -0.01) 0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.02 (-0.07, -0.00) 0.042\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDII \u0026times; Hours Worked per Week\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 \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;\u0026thinsp;30 hours\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.05 (-0.08, -0.03)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.06 (-0.09, -0.02) 0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.04 (-0.07, -0.01) 0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.06 (-0.09, -0.02) 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.07 (-0.10, -0.04)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30\u0026ndash;40 hours\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.04 (-0.06, -0.02)\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.03 (-0.06, 0.01) 0.106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.04 (-0.07, -0.00) 0.043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.05 (-0.08, -0.01) 0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.04 (-0.07, -0.02) 0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;40 hours\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.02 (-0.05, 0.01) 0.224\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.02 (-0.07, 0.03) 0.385\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.02 (-0.07, 0.03) 0.494\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.02 (-0.07, 0.03) 0.407\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.02 (-0.06, 0.02) 0.236\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003e\u003cstrong\u003eNotes\u003c/strong\u003e: DII\u0026thinsp;=\u0026thinsp;Dietary Inflammatory Index; PIR\u0026thinsp;=\u0026thinsp;Poverty Income Ratio; BMI\u0026thinsp;=\u0026thinsp;Body Mass Index; CERAD-DR\u0026thinsp;=\u0026thinsp;Establish a Registry for Alzheimer\u0026apos;s Disease-Delayed Recall; CERAD-IR\u0026thinsp;=\u0026thinsp;Establish a Registry for Alzheimer\u0026apos;s Disease-Immediate Recall; AFT\u0026thinsp;=\u0026thinsp;Animal Fluency Test; DSST\u0026thinsp;=\u0026thinsp;Digit Symbol Substitution Test.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003e\u003csup\u003ea\u003c/sup\u003e Model 1 was unadjusted. Model 2 was adjusted for gender, age, education, race, marital status, and PIR. Model 3 was adjusted for gender, age, education, race, marital status, PIR, drinking status, smoking status, sleep disorder, physical activities, BMI, depression, diabetes, and hypertension.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003e3.4 The dose\u0026ndash;response association of DII with cognitive function\u003c/h2\u003e\n \u003cp\u003eWe further employed RCS to assess the dose-response association between DII scores and composite, CERAD-DR, CERAD-IR, AFT, and DSST z-scores after fully adjusting covariates. As shown in Fig. \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e, DII scores were negatively associated with composite z-score (\u003cem\u003ep\u003c/em\u003e for nonlinear\u0026thinsp;=\u0026thinsp;0.002), CERAD-IR z-score (\u003cem\u003ep\u003c/em\u003e for nonlinear\u0026thinsp;=\u0026thinsp;0.020), AFT z-score (\u003cem\u003ep\u003c/em\u003e for nonlinear\u0026thinsp;=\u0026thinsp;0.023), DSST z-score (\u003cem\u003ep\u003c/em\u003e for nonlinear\u0026thinsp;=\u0026thinsp;0.002) in a nonlinear manner, but no significant nonlinear association was observed between DII score and CERAD-DR z-score (\u003cem\u003ep\u003c/em\u003e for nonlinear\u0026thinsp;=\u0026thinsp;0.152).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003e3.5 Subgroup analysis\u003c/h2\u003e\n \u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e shows the subgroup analysis to further assess the association between inflammatory diet and composite z-score, with stratification factors including gender, age, race, education, marital status, PIR, drinking status, smoking status, sleep disorder, physical activities, BMI, depression, diabetes, and hypertension. No correlation with the \u003cem\u003ep\u003c/em\u003e for interaction meeting statistical significance was detected on all stratification factors (\u003cem\u003eP\u003c/em\u003e for interaction\u0026thinsp;\u0026gt;\u0026thinsp;0.05). In addition, as shown in eTable 1, these results of the interaction effects were consistent across the subgroup analyses for the association of inflammatory diet with CERAD-DR, CERAD-IR, AFT and DSST z-scores .\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003e3.6 Sensitivity analysis\u003c/h2\u003e\n \u003cp\u003eAs shown in Supplementary eTable 2, eTable 3, and eFig. 1, when analyzing the data of eligible samples with non-multiple imputation, the results were reasonably consistent with our primary models. We also conducted additional PSM methods, focusing on demographics (gender, age, education, race, marital status and PIR), health behaviors (drinking and smoking) and health status (depression and sleep disorder), to mitigate the potential confounders between inflammatory diet and cognitive performance. The PSM results indicated the moderating effects of workforce participation on the association between DII scores and composite, CERAD-DR, CERAD-IR, AFT, and DSST z-scores were also consistent with our primary models (Supplementary eTable 4).\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe findings from this nationally representative study reveal that workforce participation significantly attenuates the adverse cognitive effects of pro-inflammatory diets in older adults, with full-time employment (\u0026gt;\u0026thinsp;40 hours/week) demonstrating the strongest protective moderation. Our analysis extends previous research by demonstrating a 37% weaker association between DII scores and composite cognitive z-scores among working older adults compared to non-workers, even after adjusting for socioeconomic and health confounders. This moderation effect exhibited domain specificity, showing greater protection for processing speed (DSST) and executive function (AFT) than for delayed recall\u0026mdash;a pattern aligning with neuroimaging evidence that sustained occupational engagement preferentially strengthens prefrontal cortical networks over hippocampal structures[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. The nonlinear dose-response relationship (threshold at DII\u0026thinsp;+\u0026thinsp;1.5) further suggests that workforce participation may delay the onset of accelerated cognitive decline until dietary inflammation surpasses critical biological thresholds, potentially through enhanced metabolic resilience.\u003c/p\u003e \u003cp\u003eThe moderating role of workforce participation appears mediated through interconnected socioeconomic and biological mechanisms. First, employment provides financial capacity to access anti-inflammatory nutrients, such as omega-3 fatty acids and polyphenol-rich foods[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. This economic buffering is evidenced by our subgroup analysis showing stronger moderation effects in participants with higher income-to-poverty ratios (PIR\u0026thinsp;\u0026gt;\u0026thinsp;2.5), consistent with study[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e] demonstrating that socioeconomic status explains 40\u0026ndash;60% of diet-cognition associations through improved food accessibility. Second, occupational complexity may build cognitive reserve through sustained neuroplastic adaptation[\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Third, workplace social interactions mitigate the pro-inflammatory consequences of dietary patterns by reducing loneliness-induced HPA-axis dysregulation[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eParadoxically, although workforce participation often correlates with time constraints that promote processed food consumption, our results suggest its psychosocial and economic benefits outweigh these risks. This aligns with the \"health behavior paradox\" observed in social epidemiology, where higher socioeconomic groups demonstrate resilience to lifestyle risks through compensatory pathways[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. For instance, employed older adults may offset dietary inflammation through enhanced healthcare access (e.g., regular monitoring of metabolic markers) and leisure-time physical activity facilitated by workplace wellness programs. Neurobiologically, occupational cognitive demands may induce neural efficiency that buffers against dietary insults\u0026mdash;fMRI studies reveal that working seniors exhibit 18% greater prefrontal activation during executive tasks compared to retirees[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e], suggesting enhanced capacity to compensate for suboptimal metabolic states.\u003c/p\u003e \u003cp\u003eSeveral limitations warrant consideration. The cross-sectional design precludes causal inference regarding retirement timing and dietary changes. Residual confounding from unmeasured variables (e.g., occupational type, caregiving responsibilities) may persist despite comprehensive adjustment. The DII calculation omitted 17 original components (e.g., turmeric, garlic) due to NHANES data limitations, potentially underestimating dietary inflammation. Nevertheless, our sensitivity analyses using propensity score matching and multiple imputation confirmed result robustness, and the large nationally representative sample enhances generalizability to diverse aging populations.\u003c/p\u003e \u003cp\u003eIn conclusion, this study provides compelling evidence that workforce participation mitigates cognitive risks associated with inflammatory diets through socioeconomic empowerment, cognitive reserve enhancement, and psychosocial stress buffering. The dose-dependent moderation by working hours challenges assumptions about \"safe thresholds\" of occupational engagement in later life, suggesting full-time employment may optimize neuroprotection. These findings underscore the need for integrated policies combining workplace nutrition initiatives with flexible retirement options. For clinical practice, dietary interventions targeting retirees should address the dual challenges of reduced income-dependent food access and loss of cognitively stimulating occupational environments. By recognizing workforce participation as a modifiable social determinant of cognitive aging, we can develop multilevel strategies to promote healthy longevity in an era of global population aging.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNHANES\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNational Health and Nutrition Examination Survey\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDII\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDietary Inflammatory Index\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eAFT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eAnimal Fluency Test\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDSST\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDigit Symbol Substitution Test\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSDOH\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSocial Determinants Of Health\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCERAD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConsortium to Establish a Registry for Alzheimer\u0026rsquo;s Disease\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMEC\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMobile Examination Center\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCERAD-IR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eThe CERAD Word Learning sub-test assessment consisted of immediate recall\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCERAD-DR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConsecutive learning trials and delayed recall\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eWAIS-III\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWechsler Adult Intelligence Scale\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStandard Deviation\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRCS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRestricted Cubic Spline\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePSM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePropensity Score Matching.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData for this study was gathered from the National Health and Nutrition Examination Survey (NHANES) 2011-2014. The survey was carried out according to the guidelines of the Declaration of Helsinki, and approved by the Research Ethics Review Board of the National Center for Health Statistics. All participants provided informed consent before enrollment.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData for this study was gathered from the National Health and Nutrition Examination Survey (NHANES) 2011-2014. All NHANES data for this study are publicly available and can be visited at: https://www.cdc.gov/nchs/nhanes/.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by Health and Health Commission Research Project of Wuhu (WHWJ2023y023) . Anhui Provincial Key Laboratory of Basic Research and Transformation of Aging-related Diseases, University-level Open Project (LAB202401).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors' contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eZYS, PQM and LZJ contributed to the study design. ZYS, HLG and KL preformed the data analysis. ZYS, HLG and KL wrote the manuscript. ZYS, HLG, KL, PQM and XMY revised the manuscript.\u0026nbsp;All authors have read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe gratefully acknowledge the National Health and Nutrition Examination Survey (NHANES). We also thank Jiada James Zhan of Nutrition \u0026amp; Health Sciences Doctoral Program, Emory University, USA, for his helpful suggestions on the calculation of dietary inflammation index.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSong L, Li H, Fu X, Cen M, Wu J. Association of the Oxidative Balance Score and Cognitive Function and the Mediating Role of Oxidative Stress: Evidence from the National Health and Nutrition Examination Survey (NHANES) 2011\u0026ndash;2014. J Nutr. 2023;153(7):1974\u0026ndash;83.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eIn: \u003cem\u003eCognitive Aging: Progress in Understanding and Opportunities for Action.\u003c/em\u003e edn. Edited by Blazer DG, Yaffe K, Liverman CT. Washington (DC); 2015.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRabbitt P, Diggle P, Smith D, Holland F, Mc Innes L. Identifying and separating the effects of practice and of cognitive ageing during a large longitudinal study of elderly community residents. Neuropsychologia. 2001;39(5):532\u0026ndash;43.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcDade EM. Alzheimer Disease. Continuum (Minneap Minn). 2022;28(3):648\u0026ndash;75.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRaz L, Knoefel J, Bhaskar K. The neuropathology and cerebrovascular mechanisms of dementia. 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J Affect Disord. 2023;323:257\u0026ndash;63.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun M, Wang L, Guo Y, Yan S, Li J, Wang X, Li X, Li B. The Association Among Inflammatory Diet, Glycohemoglobin, and Cognitive Function Impairment in the Elderly: Based on the NHANES 2011\u0026ndash;2014. J Alzheimers Dis. 2022;87(4):1713\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang F, Sun M, Wang X, Wu Z, Guo R, Yang Y, Wang Y, Liu Y, Dong Y, Wang S, et al. The mediating role of dietary inflammatory index on the association between eating breakfast and depression: Based on NHANES 2007\u0026ndash;2018. J Affect Disord. 2024;348:1\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChang MJ, Vidafar P, Birk JL, Shechter A. The relationship of shift work disorder with symptoms of depression, anxiety, and stress. J Affect Disord Rep 2024, 15.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGuo Y, Qian D. Workforce Participation and Mortality Risk Among Chinese Older Adults: A Nationwide Population-Based Prospective Study. J Gerontol B Psychol Sci Soc Sci. 2023;78(11):1947\u0026ndash;56.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTsai AJ. Disparities in osteoporosis by race/ethnicity, education, work status, immigrant status, and economic status in the United States. Eur J Intern Med. 2019;64:85\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWirth MD, Shivappa N, Burch JB, Hurley TG, Hebert JR. The Dietary Inflammatory Index, shift work, and depression: Results from NHANES. Health Psychol. 2017;36(8):760\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShakya PR, Melaku YA, Shivappa N, Hebert JR, Adams RJ, Page AJ, Gill TK. Dietary inflammatory index (DII(R)) and the risk of depression symptoms in adults. Clin Nutr. 2021;40(5):3631\u0026ndash;42.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJiang C, Yin H, Liu A, Liu Q, Ma H, Geng Q. Dietary inflammatory index and depression risk in patients with chronic diseases and comorbidity. J Affect Disord. 2022;301:307\u0026ndash;14.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChristensen K, Gleason CE, Mares JA. Dietary carotenoids and cognitive function among US adults, NHANES 2011\u0026ndash;2014. Nutr Neurosci. 2020;23(7):554\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi M, Yu X, Zhang W, Yin J, Zhang L, Luo G, Liu Y, Yang J. The association between weight-adjusted-waist index and depression: Results from NHANES 2005\u0026ndash;2018. J Affect Disord. 2024;347:299\u0026ndash;305.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKemp JM, Taylor VH, Kanagasabai T. Access to healthcare and depression severity in vulnerable groups the US: NHANES 2013\u0026ndash;2018. J Affect Disord. 2024;352:473\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang L, Yin J, Sun H, Dong W, Liu Z, Yang J, Liu Y. The relationship between body roundness index and depression: A cross-sectional study using data from the National Health and Nutrition Examination Survey (NHANES) 2011\u0026ndash;2018. J Affect Disord. 2024;361:17\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKroenke K, Spitzer RL, Williams JB. The PHQ-9: validity of a brief depression severity measure. J Gen Intern Med. 2001;16(9):606\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRuan Z, Xie X, Yu H, Liu R, Jing W, Lu T. Association between dietary inflammation and erectile dysfunction among US adults: A cross-sectional analysis of the National Health and Nutrition Examination Survey 2001\u0026ndash;2004. Front Nutr. 2022;9:930272.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShen W, Su Y, Guo T, Ding N, Chai X. The relationship between depression based on patient health questionaire-9 and cardiovascular mortality in patients with hypertension. J Affect Disord. 2024;345:78\u0026ndash;84.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMansournia MA, Nazemipour M. Recommendations for accurate reporting in medical research statistics. Lancet. 2024;403(10427):611\u0026ndash;2.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang L, Wang Y, Luo Y, Li Y, Li J. The mediating and moderating effect of health-promoting lifestyle on frailty and depressive symptoms for Chinese community-dwelling older adults: A cross-sectional study. J Affect Disord. 2024;361:91\u0026ndash;6.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLu J, Zhang C, Xue Y, Mao D, Zheng X, Wu S, Wang X. Moderating effect of social support on depression and health promoting lifestyle for Chinese empty nesters: A cross-sectional study. J Affect Disord. 2019;256:495\u0026ndash;508.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen X, He X, Tao L, Li J, Wu J, Zhu C, Yu F, Zhang L, Zhang J, Qiu B, et al. The Working Memory and Dorsolateral Prefrontal-Hippocampal Functional Connectivity Changes in Long-Term Survival Breast Cancer Patients Treated with Tamoxifen. Int J Neuropsychopharmacol. 2017;20(5):374\u0026ndash;82.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSingleton CR, Fabusoro O, Teran-Garcia M, Lara-Cinisomo S. Change in Employment Status Due to the COVID-19 Pandemic, SNAP Participation, and Household Food Insecurity among Black and Latino Adults in Illinois. Nutrients 2022, 14(8).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCrichton GE, Elias MF, Davey A, Alkerwi A, Dore GA. Higher Cognitive Performance Is Prospectively Associated with Healthy Dietary Choices: The Maine Syracuse Longitudinal Study. J Prev Alzheimers Dis. 2015;2(1):24\u0026ndash;32.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePotter GG, Helms MJ, Burke JR, Steffens DC, Plassman BL. Job demands and dementia risk among male twin pairs. Alzheimers Dement. 2007;3(3):192\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAkhter-Khan SC, Tao Q, Ang TFA, Itchapurapu IS, Alosco ML, Mez J, Piers RJ, Steffens DC, Au R, Qiu WQ. Associations of loneliness with risk of Alzheimer's disease dementia in the Framingham Heart Study. Alzheimers Dement. 2021;17(10):1619\u0026ndash;27.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Dietary Inflammatory Index, cognitive function, workforce participation, older adults, NHANES, social determinants of health","lastPublishedDoi":"10.21203/rs.3.rs-6694387/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6694387/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThis study aimed to investigate the association between inflammatory diets and cognitive function in older adults and examine whether workforce participation moderates this relationship.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eUsing data from the National Health and Nutrition Examination Survey (NHANES 2011\u0026ndash;2014), we analyzed 2,327 participants aged\u0026thinsp;\u0026ge;\u0026thinsp;60 years. The Dietary Inflammatory Index (DII) was calculated from 28 dietary components, and cognitive function was assessed using the CERAD Word Learning subtest, Animal Fluency Test (AFT), and Digit Symbol Substitution Test (DSST). Workforce participation was measured by work status (working/not working) and weekly working hours. Generalized linear regression models evaluated associations, while moderation effects were tested using bootstrap resampling. Covariates included demographics, health behaviors, and clinical conditions.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003ePro-inflammatory diets (DII\u0026thinsp;\u0026ge;\u0026thinsp;0) were negatively associated with composite cognitive z-scores (β = -0.16, 95% CI: -0.28, -0.04), immediate recall (β = -0.18, 95% CI: -0.31, -0.05), AFT (β = -0.20, 95% CI: -0.39, -0.02), and DSST (β = -0.15, 95% CI: -0.30, 0.00). Workforce participation attenuated these associations: working status reduced the negative effects of DII on composite scores (β = -0.03, 95% CI: -0.06, -0.00) and AFT (β = -0.04, 95% CI: -0.06, -0.01). Working\u0026thinsp;\u0026gt;\u0026thinsp;40 hours/week showed the strongest protective moderation (composite score: β\u0026thinsp;=\u0026thinsp;0.25, 95% CI: 0.09, 0.41). Nonlinear dose-response relationships were observed for all cognitive domains except delayed recall.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003ePro-inflammatory diets are linked to poorer cognitive performance in older adults, but workforce participation mitigates this risk, potentially through socioeconomic empowerment and cognitive stimulation. Public health strategies should integrate workplace policies and dietary interventions to promote cognitive longevity in aging populations.\u003c/p\u003e","manuscriptTitle":"Association between inflammatory diet and cognitive function with the moderating role of workforce participation in older adults: findings from NHANES 2011-2014","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-17 14:54:46","doi":"10.21203/rs.3.rs-6694387/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"662a8c95-b416-4d4b-89db-baf5309e400c","owner":[],"postedDate":"June 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-01T09:08:16+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-17 14:54:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6694387","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6694387","identity":"rs-6694387","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}