Meat intake frequency and its associations with incident dementia and cognitive performance: Findings from the UK Biobank

Meat intake frequency and its associations with incident dementia and cognitive performance: Findings from the UK Biobank | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Meat intake frequency and its associations with incident dementia and cognitive performance: Findings from the UK Biobank Johannes Volkmer, Lisa Reisinger, Nathan Weisz, Ya-Ping Chen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8887266/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Evidence linking meat intake to dementia and cognitive performance remains mixed, and the results may depend on how meat intake is defined and modelled. Our study aimed to examine the associations between baseline meat intake frequency and (1) incident all-cause dementia during follow-up and (2) baseline cognitive performance in the UK Biobank. Methods We analysed UK Biobank 493,644 participants without prevalent dementia at baseline. Meat intake frequency was derived from touchscreen dietary questions on five meat types (processed meat, poultry, beef, lamb/mutton, pork) and defined using the maximum reported frequency across meat types. The primary exposure contrasted high-frequency versus low-frequency intake. Incident all-cause dementia was assessed through follow-up and analysed via Cox proportional hazards models with age as the underlying time scale, excluding dementia cases occurring within the first year after baseline. Baseline cognitive performance (reaction time, numeric memory, fluid intelligence) was analysed via linear regression. Several sensitivity analyses were additionally conducted to test the robustness. Dose-response analyses used five categories of maximum meat frequency (never for all meat types; <1 times/week; ≥1 times/week; ≥2–4 times/week; ≥5–6 times/week). Results Over a mean follow-up of 14 ± 2.4 years, 10,952 incident dementia cases occurred. According to the fully adjusted model, high-frequency meat intake was associated with higher dementia risk (HR 1.21; 95% CI 1.13–1.31) and results were consistent across sensitivity analyses. For cognition, high-frequency meat intake was associated with slightly slower reaction time (n = 489,151; β = 0.036 SD, ~ 4 ms), whereas associations with numeric memory were small and attenuated in sensitivity analyses (n = 50,933; β = −0.068) and fluid intelligence was null after multivariable adjustment (n = 163,390). Conclusions Higher meat intake frequency—particularly ≥ 5–6 times/week—was associated with increased dementia risk, whereas associations with baseline cognitive performance were modest and outcome specific. Further analyses that incorporate repeated cognitive assessments may help clarify whether meat intake is associated with cognitive trajectories over time. dementia dietary patterns meat intake cognitive function Introduction In the context of rapidly aging societies, it is increasingly important to examine how lifestyle choices affect health. One such choice concerns eating habits, particularly meat intake. According to the Organisation for Economic Co-operation and Development (OECD), global meat production is projected to reach 406 million tonnes per year by 2034 [ 1 ]. Public discourse on meat intake has also intensified. The heightened awareness stems not only from ecological concerns related to the climate crisis [ 2 ]—including extreme temperature fluctuations that can be particularly harmful to vulnerable populations—and ethical issues surrounding factory farming [ 3 ] but also from growing attention to human health [ 4 ]. Recent studies have extensively investigated the associations between meat intake and various diseases. Studies have shown that meat intake, especially red meat and processed meat, may increase the risk of developing cancer [ 5 – 7 ], cardiovascular diseases [ 8 , 9 ] and diabetes [ 9 , 10 ]. Conversely, several studies have shown that a meatless diet has a protective effect on these conditions [ 11 – 13 ] including dementia [ 14 ], which is among the diseases associated with the highest caregiving burden [ 15 , 16 ]. Dementia is not a single disease but rather an umbrella term for a range of symptoms that occur in various neurodegenerative disorders. It is caused by abnormal changes in the brain that impair the function of nerve cells and their connections, leading to a progressive decline in cognitive ability. This decline severely limits patients’ daily functioning and independence [ 17 ]. Although symptoms vary between individuals and dementia types, common early signs include memory loss, communication problems, difficulty concentrating, and mood changes [ 18 ]. According to the World Health Organization (WHO), more than 55 million people worldwide currently live with dementia, and this figure is projected to rise to approximately 150 million by 2050 [ 19 ]. To counter this rapid increase, it is essential to identify risk factors that may influence the likelihood of developing dementia. These factors can be broadly divided into nonmodifiable factors (e.g., age, genetics, and sex) and modifiable factors (e.g., lifestyle and diet). Although the presence of risk factors does not guarantee that an individual will develop dementia, it increases the probability [ 20 , 21 ]. In this study, we focused on one modifiable risk factor: the frequency of meat intake. One diet that has attracted substantial attention in recent years is the Mediterranean diet, which is characterized by high intake of vegetables, fruits, whole grains, nuts, legumes, and extra virgin olive oil; moderate intake of eggs, poultry, dairy products, and fish; and low intake of sweets and red meat [ 22 ]. The Mediterranean diet has been linked to numerous health benefits, including reduced risks of cardiovascular disease [ 23 ], various cancers [ 24 ], and dementia [ 25 ]. Because this diet is largely plant-based, it raises important questions about differences between high- and low-meat-intake patterns. Comparing these groups may provide valuable insights into diet-related dementia risk. However, evidence on the association between meat intake and dementia remains limited and inconsistent. For example, Jiang et al. (2020) [ 26 ] reported that a high intake of red meat in midlife was associated with an increased risk of cognitive impairment later in life, whereas substituting red meat with poultry or fish reduced this risk. Tsai et al. (2022) [ 27 ] reported that vegetarians had a lower risk of dementia than nonvegetarians did. Zhang et al. (2021) [ 14 ], using UK Biobank data, reported that processed meat may increase dementia risk, whereas red meat (a combination of unprocessed beef, lamb, and pork) was associated with a reduced risk. No significant linear trend was observed for unprocessed poultry or total meat intake. However, their fully adjusted model included BMI and a history of stroke, which may have introduced overadjustment. In addition, age was treated as a covariate rather than being modelled nonlinearly or used as the time scale, which could lead to residual confounding by age. These inconsistencies highlight the need for further research to clarify how dietary patterns are related to dementia development. The evidence on the relationship between diet and cognitive function is also mixed. Zhang et al. (2020) [ 28 ] did not find clear associations between meat intake and cognitive disorders in their systematic review, although they noted that the evidence base was limited. Gatto et al. (2021) [ 29 ] also reported no differences in processing speed, memory, language, or executive function between vegetarians and non-vegetarians. However, they reported that individuals with more stable dietary patterns—characteristic of vegetarians—performed better in memory and language tasks. Because of this inconsistent evidence, we included an analysis of cognitive function via three measures: reaction time, numerical memory, and fluid intelligence [ 28 , 30 ]. Given that meat intake is a modifiable component of diet, examining its relationship with these cognitive measures may help clarify whether frequent meat intake is associated with subtle differences in cognitive performance. Moreover, assessing cognitive performance alongside incident dementia risk provides complementary evidence on whether any associations with dementia may be preceded by measurable differences in cognition. The UK Biobank provides a uniquely large, well-characterised cohort with linked health records and cognitive testing, enabling detailed investigations of lifestyle risk factors for dementia at scale. Building on prior UK Biobank evidence, including that of Zhang et al. (2021) [ 14 ], we extended the analysis in two key ways. First, we modelled incident dementia prospectively via Cox proportional hazards regression with age as the underlying time scale, which more directly accounts for the strong age dependence of dementia risk [ 31 ]. Second, within the same study framework, we examined the cross-sectional association between baseline meat intake frequency and baseline cognitive performance. This allows us to assess whether any observed dementia association is accompanied by contemporaneous differences in cognitive function across multiple domains. We examined two primary hypotheses: 1) High-frequency meat intake is associated with a greater incidence of dementia, and 2) high-frequency meat intake is associated with poorer baseline cognitive performance than low-frequency meat intake, after adjustment for age at baseline and other covariates. Methods This study is reported in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology – Nutritional Epidemiology (STROBE-nut) checklist [ 32 ]. Data source The UK Biobank is one of the world’s largest and most comprehensive databases of health and genetic information, encompassing data from over 500,000 participants across the United Kingdom. To support scientific efforts aimed at preventing, diagnosing, and treating diseases earlier, participants consented to long-term health monitoring. The project was conducted across 22 assessment centres in England, Scotland, and Wales. For this study, we obtained access to UK Biobank data under application number 158668. To date, four major data collection phases have been completed: Instance 0 (2006–2010), Instance 1 (2012–2013), Instance 2 (2014 onward), and Instance 3 (2019 onward). In the present study, we used data from Instance 0, which included 501,936 participants who were 37 to 73 years old at baseline. At the baseline visit, after providing written informed consent, participants completed a touchscreen questionnaire that assessed a range of sociodemographic, lifestyle, and health condition variables, including diet and cognition. Dementia assessment Different types of dementia, such as Alzheimer's disease or vascular dementia were recorded in the UK Biobank. This was accomplished through self-reports and data linkages of hospitalizations and death registries. In this study, we summarized all cases of dementia diseases as all cause dementia. Only dementia cases that occurred from one year after recruitment up to the most recent status of the UK Biobank were included in the analysis of the study to prevent disease- related bias. Diet assessment At the assessment centre, participants completed a food frequency questionnaire (FFQ) via touchscreen ( https://biobank.ndph.ox.ac.uk/ukb/label.cgi?id=100052 ). The questionnaire included 29 diet-related items, which were designed primarily to capture the frequency of intake of various food groups. In this study, we focused on items related to meat intake, specifically processed meat, beef, poultry, lamb/mutton, and pork. The intake of fish was not included here but as an independent covariate in the analysis. The participants reported their meat intake frequency using six response categories: never = 0, less than once a week = 1, once a week = 2, 2–4 times a week = 3, 5–6 times a week = 4, and once or more daily = 5. To enhance interpretability for the general population, we further classified participants into two broader categories: high-frequency meat intake, defined as consuming any type of meat five to six times per week or more; and low-frequency meat intake, defined as intake below this threshold. This definition captures the highest habitual meat consumption regardless of meat-type preference, reduces misclassification from substitution across meat types, and improves estimation stability in the primary models given the coarse frequency measurement. To assess potential regression dilution arising from a single baseline dietary assessment, we examined repeat meat-consumption data collected at the first repeat assessment visit (Instance 1: 2012–2013; n = 20,095) and quantified the stability of the high- vs low-frequency classification [ 33 , 34 ]. Cognitive function assessment During the recruitment period, some participants completed a touchscreen assessment designed to evaluate cognitive function. The questionnaire consisted of several subtests, three of which were analysed in greater detail in this dataset: reaction time, numeric memory, and fluid intelligence. These tests were selected because they provided the most comprehensive and complete data among the available cognitive measures. Reaction time The reaction time test was based on the card game Snap. The participants were shown pairs of cards and instructed to press a button as quickly as possible whenever the two cards matched. Data were collected across 12 rounds of the task, recording which cards were displayed, how often the button was pressed by each participant, and the time elapsed between the appearance of the matching cards and the participant’s response. The primary measure, the mean time to correctly identify matches, was used as an indicator of raw processing speed and reaction time and served as the focus of this analysis (Field ID: 20023). The first four rounds were excluded as practice trials. In addition, reaction times below 250 ms (classified as anticipatory) and above 2000 ms (when cards had already disappeared) were excluded. The final reaction time values were rounded to the nearest whole millisecond for analysis. Numeric memory During the numeric memory test, participants were shown a two-digit number on a touchscreen to memorize. After a brief delay, the number disappeared, and the participants were asked to re-enter it on the screen. In subsequent trials, the length of the number sequence increased by one digit—up to a maximum of 12 digits—each time the participant correctly recalled the previous sequence. For the present analysis, we included the variable “maximum digits correctly recalled during the numeric memory test” (Field ID: 4282) as a measure of short-term memory capacity. Fluid Intelligence The fluid intelligence test was designed to assess reasoning and problem-solving abilities that are independent of acquired knowledge. The participants were given two minutes to answer a series of 13 questions on a touchscreen. For the present analysis, we included the variable “sum of the number of correct answers” (Field ID: 20016) as a measure of fluid intelligence. Covariates The participants’ demographic and lifestyle data were collected via electronic questionnaires and onsite measurements at the assessment centre. We focused on factors that may increase dementia risk. Accordingly, we examined using sociodemographic factors (age, sex, ethnicity, university degree or equivalent, and the Townsend deprivation index), physical health factors (waist-to-height ratio, as an indicator of central adiposity, physical activity level, and sleep duration), and lifestyle factors (alcohol drinking status and smoking status). The selection and categorization of covariates were generally based on Zhang et al. (2021) [ 14 ], with some modifications. For example, we used the waist-to-height ratio instead of body mass index (BMI) to reflect adiposity. Further details on the covariates are provided in Supplemental Methods 2. Statistical analysis Meat intake frequency and dementia Participants with prevalent dementia and those with incomplete data on meat intake frequency were excluded before analysis. Given the possibility that prodromal dementia may lead to changes in dietary behaviours before diagnosis, we excluded incident dementia cases occurring within the first year after baseline dietary assessment to reduce the potential for reverse causality. In sensitivity analyses (see the flowchart in Supplementary Fig. 1), we applied a more stringent 5-year cut-off, restricted the sample to participants aged ≥ 60 years, included only those with complete covariate data, and additionally adjusted for histories of cardiometabolic conditions (including diabetes, heart attack, angina, stroke, and high-blood pressure) as well as the waist-to-height ratio as an indicator of adiposity to assess robustness to reverse causation and pathway attenuation [ 35 – 38 ]. We applied Cox proportional hazards regression to estimate the association between meat intake frequency and the risk of incident dementia. Age was used as the underlying time scale to account for its strong association with dementia risk. The time-to-event variable was defined as the interval between the participant’s age at baseline and age at dementia diagnosis or censoring. We fitted three progressively adjusted models via a set of a priori-defined potential confounders: Model 1, unadjusted; Model 2, a minimally adjusted model, adjusted for sociodemographic factors, including sex, ethnicity, educational level, and socioeconomic status (Townsend deprivation index), determined by a directed acyclic graph [ 39 ] (Supplementary Method 1); and Model 3, additionally adjusted for family history of dementia, dietary factors (intake of fruit, vegetables, and fish), physical health factors (physical activity level and sleep duration), and lifestyle factors (alcohol drinking status and smoking status). More details on the covariates can be found in Supplementary Method 2. For covariates where participants answered “do not know” or “prefer not to answer,” these responses were classified as missing. A “missing” category was created to replace missing values for each covariate; the impact of this approach was assessed in a sensitivity analysis restricted to participants with complete data on all covariates. As the primary analysis used a dichotomized exposure derived from the maximum frequency reported for any meat type, we conducted additional dose–response analyses using a five-level variable reflecting the maximum reported frequency across processed meat, poultry, beef, lamb/mutton, and pork. The participants were categorized as 0 (never for all meat types), 1 (< 1 time/week), 2 (1 time/week), 3 (2–4 times/week), or 4 (≥ 5–6 times/week, including once or more daily). This approach maintains consistency with the primary dichotomous exposure by capturing each participant’s highest habitual meat consumption frequency (mirroring the primary definition of high-frequency intake as ≥ 5–6 times/week for any meat type), reduces potential misclassification due to differences in meat-type preferences or substitution between meat types, and avoids overestimation that could arise if coarse, non-mutually exclusive frequency categories were summed across meat types. Cox proportional hazards models with age as the underlying time scale were fitted using the fully adjusted covariate set (Model 3). We estimated hazard ratios (HRs) and 95% confidence intervals (CIs) for each category (reference = category 1) and obtained a global p-value across categories via a likelihood ratio test. To assess the linear trend, the variable was additionally modelled as an ordinal term, and the Wald p-value was reported as the p-trend. Meat intake frequency and baseline cognitive performance Using procedures similar to those described for Hypothesis 1 (including the same exposure definition and covariate set), we tested the hypothesis that high-frequency meat intake is associated with poorer baseline cognitive performance. Meat intake frequency was categorized into high- and low-frequency meat intake, with the low-frequency group as the reference category. Cognitive performance was assessed at baseline using three measures: reaction time, numeric memory, and fluid intelligence. Reaction time was log-transformed to reduce skewness and standardized to facilitate interpretation; numeric memory was defined as the longest number sequence correctly recalled; and fluid intelligence was defined as the number of correct answers on a reasoning test. Each outcome was analysed separately via linear regression. Three progressively adjusted models were fitted for reaction time, numeric memory, and fluid intelligence. The model setups were similar to those used for examining the association between meat intake and dementia, as the factors that may affect dementia and cognitive performance largely overlap. Model 1, unadjusted; Model 2, a minimally adjusted model, adjusted for sociodemographic factors, including age, sex, ethnicity, educational level, and socioeconomic status (Townsend deprivation index), determined by a directed acyclic graph; and Model 3, additionally adjusted for family history of dementia, dietary factors (intake of fruit, vegetables, and fish), physical health factors (waist-to-height ratio, physical activity level, and sleep duration), and lifestyle factors (alcohol drinking status and smoking status). Regression coefficients (β) and 95% confidence intervals (CIs) were reported, representing the mean difference in cognitive performance between high- and low-frequency meat intake groups. In the sensitivity analyses, we again restricted the analyses to participants aged ≥ 60 years at baseline, repeated analyses in complete cases with no missing covariate data, and and additionally adjusted for histories of cardiometabolic conditions (diabetes, heart attack, angina, stroke, and high-blood pressure) as well as the waist-to-height ratio for adiposity. For the cognitive performance analyses, we did not apply a 5-year lag (i.e., excluding outcomes occurring within the first 5 years of follow-up) in sensitivity analyses of cognitive performance because cognitive performance was assessed at baseline. A lag analysis is intended to reduce reverse causation in incident outcome analyses by removing early events that may reflect preclinical disease influencing exposure. Excluding participants based on future dementia occurrence within 5 years would condition on a post-baseline event and could introduce selection bias rather than reverse causation for the baseline cognitive outcome. To complement the primary dichotomized exposure definition, we conducted additional dose-response analyses with a modelling strategy similar to that used for incident dementia analyses. Using a five-level frequency-based variable derived from the maximum reported intake frequency across processed meat, poultry, beef, lamb/mutton, and pork, participants were classified into five mutually exclusive categories: 0 (never for all meat types), 1 (< 1 time/week), 2 (1 time/week), 3 (2–4 times/week), and 4 (≥ 5 times/week, including once or more daily), on the basis of the highest frequency reported for any meat type. Associations between performance and baseline cognitive test performance were evaluated via linear regression models fitted with the fully adjusted covariate set as Model 3, and the results are reported as beta coefficients (β) and corresponding p-values. Evidence of a linear trend across categories was assessed by modelling the variable as an ordinal term. All analyses were performed with R version 4.5.2 with an α level set at 0.05 [ 40 ]. Results During a mean follow-up of 14 ± 2.4 y, excluding dementia cases arising in the first year of follow-up (n = 69), 10,952 incident cases of all-cause dementia occurred. Baseline characteristics stratified by dementia status are provided in Table 1 . The participants who developed dementia during follow-up were older and had less favourable socioeconomic and health profiles (lower education and income, higher central adiposity, more smoking, less current alcohol use, and longer sleep), and had poorer cognitive performance (slower reaction time, lower numeric memory, and lower fluid intelligence) than those who remained dementia-free. More men than women were diagnosed with dementia in the study population. Participant characteristics across 2 categories of reported intake frequency are shown in Supplementary Table 1. Compared with participants in the low-frequency meat intake group, those in the high-frequency group were generally more often male, consumed fewer fish, vegetables, and fruits, and had greater central adiposity, whereas differences in baseline cognitive measures were small. Among the participants with available meat-frequency data at both baseline and the first repeat assessment ( n = 20,095), the high-/low-frequency classification showed 92.6% overall agreement between baseline and repeat assessment. However, agreement differed by baseline category: among those classified as high-frequency at baseline ( n = 1,143), 34.7% remained high-frequency at repeat assessment ( n = 397) while 65.3% were reclassified to low-frequency ( n = 746). In contrast, among those classified as low-frequency at baseline ( n = 18,952), 96.1% remained low-frequency at repeat ( n = 18,215) and 3.9% were reclassified to high-frequency ( n = 737). Overall, these findings indicate substantial stability of the low-frequency group but notable reclassification among baseline high-frequency participants, consistent with potential attenuation of baseline-only estimates due to regression dilution. Table 1 Baseline characteristics of participants stratified by incident dementia status Characteristic All Participants 1 (N = 493,644) Incident Dementia 1 (N = 10,952) No Dementia 1 (N = 482,692) P-value 2 SMD 3 Age at baseline (year) 56.5 ± 8.1 64.3 ± 4.7 56.4 ± 8.1 < 0.001 1.20 Duration of follow-up (year) 14.7 ± 2.4 10.7 ± 3.2 14.8 ± 2.3 < 0.001 1.48 Frequency of meat intake 0.006 0.03 High frequency (5 times/week or more) 30270 (6.1%) 740 (6.8%) 29530 (6.1%) Low frequency (4 times/week or less) 463374 (93.9%) 10212 (93.2%) 453162 (93.9%) Sex < 0.001 0.13 Male 224646 (45.5%) 5665 (51.7%) 218981 (45.4%) Female 268998 (54.5%) 5287 (48.3%) 263711 (54.6%) Ethnicity < 0.001 0.08 White 466623 (94.5%) 10519 (96.0%) 456104 (94.5%) Asian or Asian British 10762 (2.2%) 152 (1.4%) 10610 (2.2%) Black or Black British 7463 (1.5%) 143 (1.3%) 7320 (1.5%) Mixed/Others 7138 (1.4%) 101 (0.9%) 7037 (1.5%) Missing 1658 (0.3%) 37 (0.3%) 1621 (0.3%) Education < 0.001 0.27 With college/university degree 159948 (32.4%) 2252 (20.6%) 157696 (32.7%) Without college/university degree 329994 (66.8%) 8540 (78.0%) 321454 (66.6%) Missing 3702 (0.7%) 160 (1.5%) 3542 (0.7%) Townsend deprivation index < 0.001 0.07 Low deprivation (-6.26 -3.15) 165490 (33.5%) 3452 (31.5%) 162038 (33.6%) Moderate deprivation (-3.14 -0.59) 165367 (33.5%) 3557 (32.5%) 161810 (33.5%) High deprivation (-0.58 11) 162180 (32.9%) 3932 (35.9%) 158248 (32.8%) Missing 607 (0.1%) 11 (0.1%) 596 (0.1%) Total Fish 3 times/wk 135025 (27.4%) 3671 (33.5%) 131354 (27.2%) missing 3162 (0.6%) 124 (1.1%) 3038 (0.6%) Vegetables and fruits < 0.001 0.13 0–2 servings/d 48803 (9.9%) 956 (8.7%) 47847 (9.9%) 2.1-4 servings/d 153564 (31.1%) 2949 (26.9%) 150615 (31.2%) 4.1-6 servings/d 150644 (30.5%) 3212 (29.3%) 147432 (30.5%) More than 6 servings/d 128399 (26.0%) 3354 (30.6%) 125045 (25.9%) Missing 12234 (2.5%) 481 (4.4%) 11753 (2.4%) Physical activity level 0.71 0.01 Low 70859 (14.4%) 1428 (13.0%) 69431 (14.4%) Moderate 155392 (31.5%) 3082 (28.1%) 152310 (31.6%) High 155858 (31.6%) 3060 (27.9%) 152798 (31.7%) Missing 111535 (22.6%) 3382 (30.9%) 108153 (22.4%) Smoking status < 0.001 0.17 Never 269413 (54.6%) 5126 (46.8%) 264287 (54.8%) Previous 170769 (34.6%) 4620 (42.2%) 166149 (34.4%) Current 51744 (10.5%) 1135 (10.4%) 50609 (10.5%) Missing 1718 (0.3%) 71 (0.6%) 1647 (0.3%) Alcohol drinker status < 0.001 0.17 Never 21391 (4.3%) 741 (6.8%) 20650 (4.3%) Previous 17605 (3.6%) 680 (6.2%) 16925 (3.5%) Current 454175 (92.0%) 9510 (86.8%) 444665 (92.1%) Missing 473 (0.1%) 21 (0.2%) 452 (0.1%) Sleep duration < 0.001 0.14 7–8 hour/day 332574 (67.4%) 6735 (61.5%) 325839 (67.5%) 8 hour/day 31230 (6.3%) 998 (9.1%) 30232 (6.3%) Family history of dementia < 0.001 0.35 Yes 57718 (11.7%) 2255 (20.6%) 55463 (11.5%) No 318990 (64.6%) 5471 (50.0%) 313519 (65.0%) Missing 116936 (23.7%) 3226 (29.5%) 113710 (23.6%) History of cardiometabolic diseases < 0.001 0.44 Yes 154521 (31.3%) 5672 (51.8%) 148849 (30.8%) No 337576 (68.4%) 5237 (47.8%) 332339 (68.9%) Missing 1547 (0.3%) 43 (0.4%) 1504 (0.3%) Waist-Height Ratio < 0.001 0.24 0.4 to 0.49 (healthy) 153694 (31.1%) 2479 (22.6%) 151215 (31.3%) 0.5 to 0.59 (increased risk) 240422 (48.7%) 5488 (50.1%) 234934 (48.7%) ≥ 0.6 (high risk) 89993 (18.2%) 2815 (25.7%) 87178 (18.1%) Missing 9535 (1.9%) 170 (1.6%) 9365 (1.9%) Cognitive Function (Incident Dementia/Total) Reaction Time (10,709/489,179) 558.9 ± 117.0 612.2 ± 140.2 557.7 ± 116.1 < 0.001 0.42 Numeric Memory (971/50,933) 8.13 ± 1.68 7.51 ± 1.83 8.14 ± 1.68 < 0.001 0.36 Fluid Intelligence (2,821/163,391) 5.42 ± 2.02 5.43 ± 2.02 4.72 ± 1.93 < 0.001 0.36 1 Mean ± SD; n (%) 2 P -values from t tests for continuous variables with a normal distribution, Wilcoxon tests for continuous variables with a nonnormal distribution, or chi-square tests for categorical variables. 3 Standardized mean differences (SMDs) of approximately 0.1 indicate a small difference, 0.2 a moderate difference, and values ≥ 0.5 a large difference. Meat intake frequency and dementia The associations between meat intake frequency and incident dementia were evaluated via Cox proportional hazards models with age as the underlying time scale (Table 2 ). According to the fully adjusted model (Model 3), high-frequency meat intake was associated with a greater risk of incident dementia than low-frequency meat intake was (HR = 1.21; 95% CI: 1.13, 1.31; p < 0.001). The association between high-frequency meat intake and incident dementia was consistent across sensitivity analyses (Supplementary Table 2), additional adjustments for histories of cardiometabolic conditions and waist-to-height ratios (n = 493,644), including a 5-year exclusion window (n = 492,819), restrictions to participants aged ≥ 60 years at baseline (n = 213,671), and complete-case analyses (n = 292,028). Dose–response analyses included n = 493,644 participants and 10,952 incident dementia cases (Table 3 ). The distribution of the maximum frequency of any meat type was as follows: category 0 (never for all meat types, n = 20,443), 1 (< 1 time/week, n = 20,886), 2 (≥ 1 time/week, n = 127,550), 3 (≥ 2–4 times/week, n = 294,495), and 4 (≥ 5–6 times/week, n = 30,270). Using participants who consumed meat < 1 time/week (category 1) as the reference, those who never consumed any of the five meat types had a lower risk of dementia (HR = 0.86; p = 0.046). Dementia risk did not differ across intermediate categories (category 2: HR = 0.94; p = 0.245; category 3: HR = 0.97; p = 0.467). In contrast, participants in the highest-frequency category (category 4; ≥5–6 times/week) had a greater risk of dementia (HR = 1.16; p = 0.011). There was strong evidence of heterogeneity across categories (p-overall < 0.001), and modelling the variable as an ordinal term suggested an overall increasing trend (HR per category increase = 1.03; p-trend = 0.008). Table 2 Hazard ratios (95% confidence intervals) for the association between meat intake frequency and incident dementia over 14.7 ± 2.4 years of follow-up maximum frequency of any meat type HR (95% CI) for high-frequency meat intake a P value Cases/Participants 10952/493644 Model 1 b 1.36 (1.26, 1.47) < 0.001 Model 2 c 1.27 (1.17, 1.36) < 0.001 Model 3 d 1.21 (1.13, 1.31) < 0.001 a Compared with low-frequency meat intake. All the models used age as the time scale. b Model 1: unadjusted. c Model 2: adjusted for sociodemographic factors, including sex, ethnicity, educational level, and socioeconomic status (Townsend deprivation index). d Model 3: Model 2 + adjusted for family history of dementia, dietary factors (intake of fruit, vegetables, and fish), physical health factors (physical activity level, and sleep duration), and lifestyle factors (alcohol drinking status and smoking status). Table 3 Dose–response association between maximum frequency of any meat intake and incident dementia risk (fully adjusted Cox proportional hazards model) Cases/Participants HR (95% CI) for high-frequency meat intake P -value Never 279/20443 0.86 (0.74, 0.99) 0.046 Less than once a week 480/20886 1.00 (Reference) Once a week 2,988/127550 0.94 (0.86, 1.04) 0.245 2–4 times a week 6,465/294495 0.97 (0.88, 1.06) 0.467 5 times a week or more 740/30270 1.16 (1.04, 1.31) 0.011 Ptrend < 0.001 Meat intake frequency and baseline cognitive performance The associations between meat intake frequency and baseline cognitive performance were evaluated via linear regression models. The complete results for reaction time, numeric memory, and fluid intelligence across the three models and sensitivity analyses are summarized in Table 4 and Supplementary Table 3. The results of the dose-response analyses are summarized in Table 5 . High-frequency meat intake was associated with slightly slower reaction times than low-frequency meat intake. In the fully adjusted model (Model 3, n = 489,151), participants in the high-frequency group had 0.036 standard deviations (SDs) greater reaction times than those in the low-frequency group did (i.e., slightly slower reaction time; approximately about 4 ms (SD = 116.9 ms). This pattern was consistent with the results of the sensitivity analyses via the fully adjusted model (Supplementary Table 3): additional adjustments for histories of cardiometabolic conditions and waist-to-height ratios (n = 489,151), restricted to participants aged ≥ 60 years (n = 211,359), and in complete-case analyses (n = 290,313), high-frequency meat intake remained associated with longer reaction times, with similar directions and magnitudes. In dose-response analyses using the maximum frequency across meat types, there was evidence of heterogeneity across categories (p-overall < 0.001). Using participants who consumed meat < 1 time/week (category 1) as the reference, the reaction time was similar to that of those who consumed meat ≥ 1 time/week (category 2; β = 0.001 SD; p = 0.90) and was slightly lower (faster) among those who consumed meat ≥ 2–4 times/week (category 3; β = −0.024 SD; p < 0.001), whereas participants in the highest-frequency category (≥ 5–6 times/week; category 4) had slightly higher (slower) reaction times (β = 0.020 SD; p = 0.020). Overall, these dose–response results suggest a nonlinear pattern, but the increased reaction time observed in the highest-frequency category is consistent with the primary dichotomized comparison. Numeric memory analyses were based on n = 50,933 participants (the smallest cognitive subsample, compared with reaction time n = 489,155 and fluid intelligence n = 163,390). According to the fully adjusted linear regression model (Model 3), high-frequency meat intake was associated with a slightly lower numeric memory score than low-frequency intake was (β = −0.068; p = 0.026). This association was attenuated and no longer statistically significant in sensitivity analyses, including additional adjustments for history of cardiometabolic conditions and waist-to-height ratio (β = −0.117; p = 0.058), restrictions to participants aged ≥ 60 years (n = 22,634; β = 0.021; p = 0.670), and complete-case analyses (n = 30,939; β = −0.034; p = 0.375). In dose-response analyses using the maximum frequency across meat types and < 1 time/week (category 1) as a reference: numeric memory scores were broadly similar across intake frequency categories. Compared with the reference group, scores were not different in those who never consumed any of the meat types (category 0; β = 0.083; p = 0.094), consumed meat ≥ 1 time/week (category 2; β = −0.058; p = 0.12), ≥ 2–4 times/week (category 3; β = −0.015; p = 0.69), or ≥ 5–6 times/week or more (category 4; β = −0.088; p = 0.055). Although there was evidence of overall differences across categories (p-overall < 0.001), modelling the variable as an ordinal variable showed no linear trend (β per category = − 0.011; p = 0.22). Overall, the dose–response findings suggest that the primary dichotomised association was small and not clearly graded across frequency categories. High-frequency meat intake was associated with lower fluid intelligence in the age-adjusted model (Model 1: β = −0.14; p < 0.001; n = 163,390); however, after partial and full adjustment (Model 2 & 3), there was no association between meat intake frequency and fluid intelligence. The results of the sensitivity analyses were consistent, with no associations with additional adjustments for history of cardiometabolic conditions and waist-to-height ratios (n = 163,390), age ≥ 60 restriction (n = 73,665), or complete-case analyses (n = 100,825). In dose-response analyses using the maximum frequency across meat types, there was evidence of overall differences across categories (p-overall < 0.001). Using participants who consumed meat < 1 time/week (category 1) as the reference, those who never consumed any of the meat types (category 0) had higher fluid intelligence scores (β = 0.233; p < 0.001), whereas participants who consumed meat ≥ 1 time/week had slightly lower scores (category 2; β = −0.083; p < 0.001). Differences for higher-frequency categories were not significant (category = 3; β = 0.039; p = 0.087; category 4; β = −0.012; p = 0.68). Overall, these dose–response findings align with the primary fully adjusted results, indicating no clear graded association between increasing meat intake frequency and fluid intelligence after multivariable adjustment, with observed differences largely driven by the “never” intake group. Table 4 Linear regression coefficients (95% confidence intervals) for the associations between meat intake frequency and cognitive performance Reaction Time β (95% CI) for log-transformed reaction time (z score) a P value n 489151 Model 1 b -0.014 (-0.026, -0.003) 0.02 Model 2 c 0.046 (0.035, 0.057) < 0.001 Model 3 d 0.036 (0.025, 0.047) < 0.001 Numeric Memory β (95% CI), difference in numeric memory score a P value n 50933 Model 1 b -0.053 (-0.114, 0.007) 0.08 Model 2 c -0.078 (-0.138, -0.018) 0.011 Model 3 d -0.067 (-0.128, -0.008) 0.026 Fluid Intelligence β (95% CI), difference in fluid intelligence score a P value n 163391 Model 1 b -0.118 (-0.159, -0.078) < 0.001 Model 2 c -0.052 (-0.090, -0.015) 0.006 Model 3 d -0.028 (-0.065, 0.009) 0.137 a β represents the adjusted mean difference for high-frequency vs low-frequency meat intake (reference) b Model 1: unadjusted. c Model 2: Model 1 + adjusted for sociodemographic factors (age, sex, ethnicity, educational level, and socioeconomic status (Townsend deprivation index). d Model 3: Model 2 + adjusted for family history of dementia, dietary factors (intake of fruit, vegetables, and fish), physical health factors (physical activity level and sleep duration), and lifestyle factors (alcohol drinking status and smoking status). Table 5 Dose-response associations between the maximum frequency of any meat intake and cognitive performance (fully adjusted linear regression model) Reaction Time β (95% CI) for log-transformed reaction time (z score) P value Never -0.016 (-0.034, 0.003) 0.093 Less than once a week 0 (Reference) Once a week 0.001 (-0.013, 0.015) 0.090 2–4 times a week -0.024 (-0.037, -0.011) < 0.001 5 times a week or more 0.020 (0.003, 0.037) 0.020 Ptrend < 0.001 Numeric Memory β (95% CI), difference in numeric memory score P value Never 0.083 (-0.014, 0.181) 0.094 Less than once a week 0 (Reference) Once a week -0.058 (-0.132, 0.016) 0.124 2–4 times a week -0.015 (-0.086, 0.057) 0.688 5 times a week or more -0.088 (-0.178, 0.002) 0.055 Ptrend < 0.001 Fluid Intelligence β (95% CI), difference in fluid intelligence score P value Never 0.233 (0.173, 0.293) < 0.001 Less than once a week 0 (Reference) Once a week -0.083 (-0.129, -0.036) < 0.001 2–4 times a week 0.039 (-0.006, 0.084) 0.086 5 times a week or more -0.012 (-0.068, 0.044) 0.676 Ptrend < 0.001 Discussion In this large prospective analysis of 493,644 UK Biobank participants followed for an average of 14 years, high-frequency meat intake—defined as eating any type of meat five to six times per week or more—was associated with a greater risk of incident all-cause dementia than low-frequency intake. The association remained robust after extensive adjustment for sociodemographic, lifestyle, and adiposity-related factors and across multiple sensitivity analyses. The participants with high-frequency meat intake also exhibited slightly slower reaction times but showed no robust differences in numeric memory or fluid intelligence scores. Baseline characteristics and risk profiles The baseline profiles of dementia cases in our study were consistent with established epidemiological evidence—older age, socioeconomic deprivation, lower education, a greater burden of cardiometabolic conditions, and greater adiposity are strong predictors of dementia onset. High-frequency meat consumers also exhibited lower intakes of fish, fruit, and vegetables and higher smoking prevalence and central adiposity, paralleling risk profiles reported in prior nutritional epidemiology studies [ 41 ]. Despite these overlapping factors, our results remained robust after full adjustment, suggesting that the observed association between meat intake and dementia reflects additional biological or lifestyle pathways beyond standard covariates. Association between meat intake and dementia risk The observed association between higher meat intake frequency and increased dementia risk may reflect a combination of nutritional, metabolic, and behavioural pathways. Frequent meat intake is often correlated with greater intake of saturated fats and sodium and lower intake of plant-based foods, which can contribute to vascular dysfunction, inflammation, and insulin resistance—mechanisms implicated in neurodegenerative disease development [ 42 , 43 ]. These mechanisms are consistent with findings from other large cohort and meta-analytic studies linking higher red and processed meat intake to greater dementia risk and cognitive decline [ 28 , 44 , 45 ]. Although individuals with high meat intake in this study tended to have less favourable health profiles—including higher central adiposity, smoking prevalence, and lower levels of fish, vegetables, and fruits intake—our fully adjusted models suggest that the positive association between meat intake and dementia risk is not entirely explained by these correlated factors. It is therefore plausible that frequent meat intake captures additional dietary or metabolic risk dimensions beyond standard lifestyle covariates. These findings highlight the importance of considering both nutritional quality and broader lifestyle clustering when interpreting diet–dementia associations. Dose-response analyses on the basis of the maximum frequency across meat types provided additional insight into the shape of the association. Compared with that of participants who consumed meat less than once per week, dementia risk was elevated only in the highest intake frequency category (≥ 5–6 times/week), whereas intermediate categories showed little evidence of increased risk. This pattern suggests that the association may be driven primarily by very frequent intake rather than a strictly monotonic increase across the full range of intake. Notably, participants who reported never consuming any of the assessed meat types had a lower dementia risk than the low-frequency reference group did, which may reflect differences in broader dietary patterns or health behaviours among nonmeat consumers and should be interpreted cautiously. Cognitive performance in relation to meat intake In analyses of baseline cognitive performance, we found that high-frequency meat intake was associated with slightly slower reaction time, whereas associations with numeric memory were small and attenuated in sensitivity analyses, and fluid intelligence showed no association after multivariable adjustment. Overall, these patterns suggest that any relationship between meat intake frequency and cognition in this cohort is modest, may vary by cognitive domain, and is not consistently graded across intake categories. These findings are in keeping with broader evidence that diet-cognition associations are often small and heterogeneous and that observed relationships can be sensitive to confounding by socioeconomic and lifestyle factors [ 46 ]. Our results also broadly align with prior literature indicating inconsistent associations between meat intake and cognitive outcomes [ 28 , 47 ]. In addition, our cognitive outcomes were assessed at baseline only, whereas long-term intake and longitudinal cognitive changes may better capture cumulative dietary effects [ 44 ]. Strengths and Limitations The strengths of this study include its large sample size, prospective design, long follow-up, and consistent modelling framework using age as the underlying time scale. These methodological features improve causal interpretation by reducing potential reverse causation (e.g., excluding early incident cases) and align with approaches commonly used in large prospective diet–dementia studies [ 44 , 45 ]. Important limitations should be considered. First, we relied on self-reported meat intake frequency to enhance interpretability for the general population and to harmonize reporting across meat types. Although this high-/low-frequency classification showed high overall agreement at repeat assessment (92.6%), reclassification was more common among participants initially classified as high-frequency, indicating potential cut-point misclassification and possible attenuation of baseline-only associations toward the null. In addition, this approach does not distinguish between meat subtypes (e.g., unprocessed poultry vs processed meat) or preparation methods (e.g., frying vs steaming). Second, cognitive measures were assessed cross-sectionally and are subject to measurement error and selection effects, particularly for numeric memory and fluid intelligence, which have smaller analytic samples. Third, the UK Biobank enables internally consistent analyses with age-as-time-scale modelling and extensive covariate adjustment; however, as a volunteer cohort it is not population-representative. The observed estimates should be interpreted primarily as within-cohort associations. Finally, despite extensive adjustment, residual confounding remains possible. For example, we did not adjust for genotype-level dementia risk (e.g., APOE ε4) or comprehensive diet-quality indices, so residual genetic and diet-quality confounding factors may remain. Conclusions Together, these findings suggest that reducing meat intake frequency may be an accessible behavioural target for dementia prevention. Although this study focused on dementia risk and cognitive outcomes, the broader implications extend to planetary health, as high meat production contributes significantly to greenhouse gas emissions and climate change. The simplified two-category classification of meat intake used here was designed to enhance interpretability and public relevance, facilitating translation of epidemiologic findings into actionable dietary guidance for the general population. Abbreviations CI Confidence interval HR Hazard ratio SD Standard deviation Declarations Ethics approval and consent to participate The UK biobank was approved by the Northwest Multi-centre Research Ethics Committee (11/NW/0382; https://www.ukbiobank.ac.uk/ethics/), and all participants agreed to their inclusion, and signed written informed consent forms. All procedures were conducted in accordance with the Declaration of Helsinki. Clinical trial number: not applicable. Consent for publication Not applicable; this study used de-identified data and did not include any identifiable individual-level information. Availability of data and materials The data are available from the UK Biobank (https://www.ukbiobank.ac.uk/), but access is subject to the UK Biobank’s rigorous application and approval process; therefore, the data are not publicly available. Competing interests The authors declare that there is no duality of interest associated with this manuscript. Funding This work was conducted without external grant funding and was supported by institutional resources. Authors’ contribution J. V.: conceptualization, data curation, formal analysis, writing – original draft. L. R.: conceptualization, data curation, writing – review & editing. N. W.: conceptualization, supervision, writing – review & editing. Y.-P. C.: data curation, formal analysis, writing – review & editing. All authors read and approved the final manuscript. Acknowledgements We would like to thank the UK Biobank staff and management team for their invaluable contribution to data collection. We also thank Dr. Anne Hauswald for her guidance in obtaining access to the UK Biobank dataset. References OECD FAO, OECD-FAO Agricultural F, Rome, FAO. Italy: OECD; ; 2025 [cited 2026 Jan 7]. https://openknowledge.fao.org/handle/20.500.14283/cd6043en . Accessed 7 Jan 2026. Sanchez-Sabate R, Sabaté J. Consumer Attitudes Towards Environmental Concerns of Meat Consumption: A Systematic Review. Int J Environ Res Public Health. 2019;16:1220. https://doi.org/10.3390/ijerph16071220 . Grethe H. The Economics of Farm Animal Welfare. Annu Rev Resour Econ Annual Reviews. 2017;9:75–94. https://doi.org/10.1146/annurev-resource-100516-053419 . Neff RA, Edwards D, Palmer A, Ramsing R, Righter A, Wolfson J. Reducing meat consumption in the USA: a nationally representative survey of attitudes and behaviours. Public Health Nutr. 2018;21:1835–44. https://doi.org/10.1017/S1368980017004190 . Farvid MS, Sidahmed E, Spence ND, Mante Angua K, Rosner BA, Barnett JB. Consumption of red meat and processed meat and cancer incidence: a systematic review and meta-analysis of prospective studies. Eur J Epidemiol. 2021;36:937–51. https://doi.org/10.1007/s10654-021-00741-9 . Huang Y, Cao D, Chen Z, Chen B, Li J, Guo J, et al. Red and processed meat consumption and cancer outcomes: Umbrella review. Food Chem. 2021;356:129697. https://doi.org/10.1016/j.foodchem.2021.129697 . Lippi G, Mattiuzzi C, Cervellin G. Meat consumption and cancer risk: a critical review of published meta-analyses. Crit Rev Oncol Hematol. 2016;97:1–14. https://doi.org/10.1016/j.critrevonc.2015.11.008 . Abete I, Romaguera D, Vieira AR, De Munain AL, Norat T. Association between total, processed, red and white meat consumption and all-cause, CVD and IHD mortality: a meta-analysis of cohort studies. Br J Nutr. 2014;112:762–75. https://doi.org/10.1017/s000711451400124x . Shi W, Huang X, Schooling CM, Zhao JV. Red meat consumption, cardiovascular diseases, and diabetes: a systematic review and meta-analysis. Eur Heart J. 2023;44:2626–35. https://doi.org/10.1093/eurheartj/ehad336 . Kim Y, Keogh J, Clifton P. A review of potential metabolic etiologies of the observed association between red meat consumption and development of type 2 diabetes mellitus. Metabolism. 2015;64:768–79. https://doi.org/10.1016/j.metabol.2015.03.008 . Dinu M, Abbate R, Gensini GF, Casini A, Sofi F. Vegetarian, vegan diets and multiple health outcomes: A systematic review with meta-analysis of observational studies. Crit Rev Food Sci Nutr. 2017;57:3640–9. https://doi.org/10.1080/10408398.2016.1138447 . Olfert MD, Wattick RA. Vegetarian Diets and the Risk of Diabetes. Curr Diab Rep. 2018;18:101. https://doi.org/10.1007/s11892-018-1070-9 . Salehin S, Rasmussen P, Mai S, Mushtaq M, Agarwal M, Hasan SM, et al. Plant Based Diet and Its Effect on Cardiovascular Disease. Int J Environ Res Public Health. 2023;20:3337. https://doi.org/10.3390/ijerph20043337 . Zhang H, Greenwood DC, Risch HA, Bunce D, Hardie LJ, Cade JE. Meat consumption and risk of incident dementia: cohort study of 493,888 UK Biobank participants. Am J Clin Nutr. 2021;114:175–84. https://doi.org/10.1093/ajcn/nqab028 . Demirbas M, Hahn-Pedersen JH, Jørgensen HL. Comparison Between Burden of Care Partners of Individuals with Alzheimer’s Disease Versus Individuals with Other Chronic Diseases. Neurol Ther. 2023;12:1051–68. https://doi.org/10.1007/s40120-023-00493-6 . Rezaei H, Niksima SH, Ghanei Gheshlagh R. Burden of Care in Caregivers of Iranian patients with chronic disorders: a systematic review and meta-analysis. Health Qual Life Outcomes. 2020;18:261. https://doi.org/10.1186/s12955-020-01503-z . Gale SA, Acar D, Daffner KR, Dementia. Am J Med. 2018;131:1161–9. https://doi.org/10.1016/j.amjmed.2018.01.022 . Cerejeira J, Lagarto L, Mukaetova-Ladinska EB. Behavioral and Psychological Symptoms of Dementia. Front Neurol [Internet]. 2012 [cited 2025 Dec 7];3. https://doi.org/10.3389/fneur.2012.00073 Nichols E, Steinmetz JD, Vollset SE, Fukutaki K, Chalek J, Abd-Allah F, et al. Estimation of the global prevalence of dementia in 2019 and forecasted prevalence in 2050: an analysis for the Global Burden of Disease Study 2019. Lancet Public Health. 2022;7:e105–25. https://doi.org/10.1016/S2468-2667(21)00249-8 . Licher S, Ahmad S, Karamujić-Čomić H, Voortman T, Leening MJG, Ikram MA, et al. Genetic predisposition, modifiable-risk-factor profile and long-term dementia risk in the general population. Nat Med. 2019;25:1364–9. https://doi.org/10.1038/s41591-019-0547-7 . Peters R, Booth A, Rockwood K, Peters J, D’Este C, Anstey KJ. Combining modifiable risk factors and risk of dementia: a systematic review and meta-analysis. BMJ Open. 2019;9:e022846. https://doi.org/10.1136/bmjopen-2018-022846 . Restivo J. Guide to the Mediterranean diet [Internet]. Harv. Health. 2023 [cited 2025 Dec 8]. https://www.health.harvard.edu/staying-healthy/guide-to-the-mediterranean-diet . Accessed 8 Dec 2025. Ros E, Martínez-González MA, Estruch R, Salas-Salvadó J, Fitó M, Martínez JA, et al. Mediterranean Diet and Cardiovascular Health: Teachings of the PREDIMED Study. Adv Nutr. 2014;5:S330–6. https://doi.org/10.3945/an.113.005389 . Mentella MC, Scaldaferri F, Ricci C, Gasbarrini A, Miggiano GAD. Cancer and Mediterranean Diet: A Review. Nutrients. 2019;11:2059. https://doi.org/10.3390/nu11092059 . Petersson SD, Philippou E, Mediterranean, Diet. Cognitive Function, and Dementia: A Systematic Review of the Evidence. Adv Nutr. 2016;7:889–904. https://doi.org/10.3945/an.116.012138 . Jiang Y-W, Sheng L-T, Pan X-F, Feng L, Yuan J-M, Pan A, et al. Meat consumption in midlife and risk of cognitive impairment in old age: the Singapore Chinese Health Study. Eur J Nutr. 2020;59:1729–38. https://doi.org/10.1007/s00394-019-02031-3 . Tsai J-H, Huang C-F, Lin M-N, Chang C-E, Chang C-C, Lin C-L. Taiwanese Vegetarians Are Associated with Lower Dementia Risk: A Prospective Cohort Study. Nutrients. 2022;14:588. https://doi.org/10.3390/nu14030588 . Zhang H, Hardie L, Bawajeeh AO, Cade J, Meat, Consumption. Cognitive Function and Disorders: A Systematic Review with Narrative Synthesis and Meta-Analysis. Nutrients. 2020;12:1528. https://doi.org/10.3390/nu12051528 . Gatto NM, Garcia-Cano J, Irani C, Jaceldo-Siegl K, Liu T, Chen Z, et al. Vegetarian Dietary Patterns and Cognitive Function among Older Adults: The Adventist Health Study-2. J Nutr Gerontol Geriatr. 2021;40:197–214. https://doi.org/10.1080/21551197.2021.1965939 . Hepsomali P, Groeger JA. Diet and general cognitive ability in the UK Biobank dataset. Sci Rep Nat Publishing Group. 2021;11:11786. https://doi.org/10.1038/s41598-021-91259-3 . Thiébaut ACM, Bénichou J. Choice of time-scale in Cox’s model analysis of epidemiologic cohort data: a simulation study. Stat Med. 2004;23:3803–20. https://doi.org/10.1002/sim.2098 . Lachat C, Hawwash D, Ocké MC, Berg C, Forsum E, Hörnell A, et al. Strengthening the Reporting of Observational Studies in Epidemiology—Nutritional Epidemiology (STROBE-nut): An Extension of the STROBE Statement. PLOS Med Public Libr Sci. 2016;13:e1002036. https://doi.org/10.1371/journal.pmed.1002036 . Hutcheon JA, Chiolero A, Hanley JA. Random measurement error and regression dilution bias. BMJ. Br Med J Publishing Group. 2010;340:c2289. https://doi.org/10.1136/bmj.c2289 . Bradbury KE, Young HJ, Guo W, Key TJ. Dietary assessment in UK Biobank: an evaluation of the performance of the touchscreen dietary questionnaire. J Nutr Sci. 2018;7:e6. https://doi.org/10.1017/jns.2017.66 . Frith E, Loprinzi PD. Fitness Fatness Index and Alzheimer-specific mortality. Eur J Intern Med. 2017;42:51–3. https://doi.org/10.1016/j.ejim.2017.04.015 . Han XD, Li YJ, Wang P, Han XL, Zhao MQ, Wang JF, et al. Insulin Resistance-Varying Associations of Adiposity Indices with Cerebral Perfusion in Older Adults: A Population-Based Study. J Nutr Health Aging. 2023;27:219–27. https://doi.org/10.1007/s12603-023-1894-2 . Zelicha H, Kloting N, Kaplan A, Yaskolka Meir A, Rinott E, Tsaban G, et al. The effect of high-polyphenol Mediterranean diet on visceral adiposity: the DIRECT PLUS randomized controlled trial. BMC Med. 2022;20:327. https://doi.org/10.1186/s12916-022-02525-8 . Zhang J, Huang X, Ling Y, Xie X, Zeng X, Lyu J, et al. Associations of cardiometabolic multimorbidity with all-cause dementia, alzheimer’s disease, and vascular dementia: a cohort study in the UK biobank. BMC Public Health. 2025;25:2397. https://doi.org/10.1186/s12889-025-23352-5 . van der Textor J, Gilthorpe MS, Liśkiewicz M, Ellison GT. Robust causal inference using directed acyclic graphs: the R package ‘dagitty’. Int J Epidemiol. 2016;45:1887–94. https://doi.org/10.1093/ije/dyw341 . Core Team R, Vienna R. Austria: R Foundation for Statistical Computing; 2025 [cited 2025 Oct 31]. http://www.R-project.org . Accessed 31 Oct 2025. Dominguez LJ, Barbagallo M. Nutritional prevention of cognitive decline and dementia. Acta Bio Med Atenei Parm. 2018;89:276–90. https://doi.org/10.23750/abm.v89i2.7401 . Grosso G, Laudisio D, Frias-Toral E, Barrea L, Muscogiuri G, Savastano S, et al. Anti-Inflammatory Nutrients and Obesity-Associated Metabolic-Inflammation: State of the Art and Future Direction. Nutrients. 2022;14:1137. https://doi.org/10.3390/nu14061137 . Misra R, Balagopal P, Raj S, Patel TG. Red Meat Consumption (Heme Iron Intake) and Risk for Diabetes and Comorbidities? Curr Diab Rep. 2018;18:100. https://doi.org/10.1007/s11892-018-1071-8 . Li Y, Li Y, Gu X, Liu Y, Dong D, Kang JH, et al. Long-Term Intake of Red Meat in Relation to Dementia Risk and Cognitive Function in US Adults. Neurol Wolters Kluwer. 2025;104:e210286. https://doi.org/10.1212/WNL.0000000000210286 . Quan W, Xu Y, Luo J, Zeng M, He Z, Shen Q, et al. Association of dietary meat consumption habits with neurodegenerative cognitive impairment: an updated systematic review and dose–response meta-analysis of 24 prospective cohort studies. Food Funct Royal Soc Chem. 2022;13:12590–601. https://doi.org/10.1039/D2FO03168J . Dalile B, Kim C, Challinor A, Geurts L, Gibney ER, Galdos MV, et al. The EAT-Lancet reference diet and cognitive function across the life course. Lancet Planet Health. 2022;6:e749–59. https://doi.org/10.1016/S2542-5196(22)00123-1 . Bigras C, Mazzoli R, Laurin D, Malavolti M, Barbolini G, Vinceti M, et al. Plant-Based Diets and Cognitive Outcomes: A Systematic Review and Meta-analysis. Adv Nutr. 2025;16:100537. https://doi.org/10.1016/j.advnut.2025.100537 . Additional Declarations No competing interests reported. Supplementary Files supplementarydietdementia0215.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-8887266","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":592655839,"identity":"f8644bd2-dd53-4635-92bd-362f6ff0d167","order_by":0,"name":"Johannes Volkmer","email":"","orcid":"","institution":"University of Salzburg","correspondingAuthor":false,"prefix":"","firstName":"Johannes","middleName":"","lastName":"Volkmer","suffix":""},{"id":592655840,"identity":"6d08ebaa-524a-4d53-82b9-5dea06a20053","order_by":1,"name":"Lisa Reisinger","email":"","orcid":"","institution":"University of Salzburg","correspondingAuthor":false,"prefix":"","firstName":"Lisa","middleName":"","lastName":"Reisinger","suffix":""},{"id":592655841,"identity":"4cb4729d-5814-4e90-b052-e0a4f2e6328b","order_by":2,"name":"Nathan Weisz","email":"","orcid":"","institution":"University of Salzburg","correspondingAuthor":false,"prefix":"","firstName":"Nathan","middleName":"","lastName":"Weisz","suffix":""},{"id":592655842,"identity":"35e4be59-47a2-4e7f-ba79-18ae7067d3bb","order_by":3,"name":"Ya-Ping Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBUlEQVRIiWNgGAWjYLCCBBAhAcQfGCxI1MI4A6wRDAyI0AdUzMxDjBaD4wfYHjzcUcvAP7v52GPbHRKJ8xuYH35gqPmDW8uZBHaDxDPHGSTuHEs3zj0jkbjhAJuxBMMxPLYcyP8mkdh2jMFAIsdMOrcNqIWBwYyBgQ2PlvMP2KBa8r9JW7aBHMb+jYHhHx4tNxJAWmpAtrBJMwK1NBzgMWNgbMOtRfLGA6Bf2g7wSNxIM5PsPSNhvOEwT7FEYp8xTi185xPYHv5sq5Pjn5H8TOLnDhvZ+e3tGz98+CaHU4vCAQY2IHWYB8xjbAASzAzQyMUB5BvAWuoYEFpGwSgYBaNgFKABAOJIT7EZR7KhAAAAAElFTkSuQmCC","orcid":"","institution":"University of Salzburg","correspondingAuthor":true,"prefix":"","firstName":"Ya-Ping","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2026-02-15 16:23:09","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8887266/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8887266/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108396962,"identity":"cb9a9234-ba5f-40d3-9dc8-befd5b3132e2","added_by":"auto","created_at":"2026-05-04 08:11:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":632067,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8887266/v1/a6ef0fc6-44c9-4c82-940e-79e97d2b60a5.pdf"},{"id":102905919,"identity":"606f998a-4431-4e5b-910a-b7950da85e91","added_by":"auto","created_at":"2026-02-18 09:12:02","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":809277,"visible":true,"origin":"","legend":"","description":"","filename":"supplementarydietdementia0215.docx","url":"https://assets-eu.researchsquare.com/files/rs-8887266/v1/a73df3e313c13b8118448d36.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Meat intake frequency and its associations with incident dementia and cognitive performance: Findings from the UK Biobank","fulltext":[{"header":"Introduction","content":"\u003cp\u003eIn the context of rapidly aging societies, it is increasingly important to examine how lifestyle choices affect health. One such choice concerns eating habits, particularly meat intake. According to the Organisation for Economic Co-operation and Development (OECD), global meat production is projected to reach 406\u0026nbsp;million tonnes per year by 2034 [\u003cspan class=\"CitationRef\"\u003e1\u003c/span\u003e]. Public discourse on meat intake has also intensified. The heightened awareness stems not only from ecological concerns related to the climate crisis [\u003cspan class=\"CitationRef\"\u003e2\u003c/span\u003e]—including extreme temperature fluctuations that can be particularly harmful to vulnerable populations—and ethical issues surrounding factory farming [\u003cspan class=\"CitationRef\"\u003e3\u003c/span\u003e] but also from growing attention to human health [\u003cspan class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eRecent studies have extensively investigated the associations between meat intake and various diseases. Studies have shown that meat intake, especially red meat and processed meat, may increase the risk of developing cancer [\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e–\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e], cardiovascular diseases [\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e] and diabetes [\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e]. Conversely, several studies have shown that a meatless diet has a protective effect on these conditions [\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e–\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e] including dementia [\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e], which is among the diseases associated with the highest caregiving burden [\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDementia is not a single disease but rather an umbrella term for a range of symptoms that occur in various neurodegenerative disorders. It is caused by abnormal changes in the brain that impair the function of nerve cells and their connections, leading to a progressive decline in cognitive ability. This decline severely limits patients’ daily functioning and independence [\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e]. Although symptoms vary between individuals and dementia types, common early signs include memory loss, communication problems, difficulty concentrating, and mood changes [\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e]. According to the World Health Organization (WHO), more than 55\u0026nbsp;million people worldwide currently live with dementia, and this figure is projected to rise to approximately 150\u0026nbsp;million by 2050 [\u003cspan class=\"CitationRef\"\u003e19\u003c/span\u003e]. To counter this rapid increase, it is essential to identify risk factors that may influence the likelihood of developing dementia. These factors can be broadly divided into nonmodifiable factors (e.g., age, genetics, and sex) and modifiable factors (e.g., lifestyle and diet). Although the presence of risk factors does not guarantee that an individual will develop dementia, it increases the probability [\u003cspan class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e21\u003c/span\u003e]. In this study, we focused on one modifiable risk factor: the frequency of meat intake.\u003c/p\u003e \u003cp\u003eOne diet that has attracted substantial attention in recent years is the Mediterranean diet, which is characterized by high intake of vegetables, fruits, whole grains, nuts, legumes, and extra virgin olive oil; moderate intake of eggs, poultry, dairy products, and fish; and low intake of sweets and red meat [\u003cspan class=\"CitationRef\"\u003e22\u003c/span\u003e]. The Mediterranean diet has been linked to numerous health benefits, including reduced risks of cardiovascular disease [\u003cspan class=\"CitationRef\"\u003e23\u003c/span\u003e], various cancers [\u003cspan class=\"CitationRef\"\u003e24\u003c/span\u003e], and dementia [\u003cspan class=\"CitationRef\"\u003e25\u003c/span\u003e]. Because this diet is largely plant-based, it raises important questions about differences between high- and low-meat-intake patterns. Comparing these groups may provide valuable insights into diet-related dementia risk.\u003c/p\u003e \u003cp\u003eHowever, evidence on the association between meat intake and dementia remains limited and inconsistent. For example, Jiang et al. (2020) [\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e] reported that a high intake of red meat in midlife was associated with an increased risk of cognitive impairment later in life, whereas substituting red meat with poultry or fish reduced this risk. Tsai et al. (2022) [\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e] reported that vegetarians had a lower risk of dementia than nonvegetarians did. Zhang et al. (2021) [\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e], using UK Biobank data, reported that processed meat may increase dementia risk, whereas red meat (a combination of unprocessed beef, lamb, and pork) was associated with a reduced risk. No significant linear trend was observed for unprocessed poultry or total meat intake. However, their fully adjusted model included BMI and a history of stroke, which may have introduced overadjustment. In addition, age was treated as a covariate rather than being modelled nonlinearly or used as the time scale, which could lead to residual confounding by age. These inconsistencies highlight the need for further research to clarify how dietary patterns are related to dementia development.\u003c/p\u003e \u003cp\u003eThe evidence on the relationship between diet and cognitive function is also mixed. Zhang et al. (2020) [\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e] did not find clear associations between meat intake and cognitive disorders in their systematic review, although they noted that the evidence base was limited. Gatto et al. (2021) [\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e] also reported no differences in processing speed, memory, language, or executive function between vegetarians and non-vegetarians. However, they reported that individuals with more stable dietary patterns—characteristic of vegetarians—performed better in memory and language tasks. Because of this inconsistent evidence, we included an analysis of cognitive function via three measures: reaction time, numerical memory, and fluid intelligence [\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e]. Given that meat intake is a modifiable component of diet, examining its relationship with these cognitive measures may help clarify whether frequent meat intake is associated with subtle differences in cognitive performance. Moreover, assessing cognitive performance alongside incident dementia risk provides complementary evidence on whether any associations with dementia may be preceded by measurable differences in cognition.\u003c/p\u003e \u003cp\u003eThe UK Biobank provides a uniquely large, well-characterised cohort with linked health records and cognitive testing, enabling detailed investigations of lifestyle risk factors for dementia at scale. Building on prior UK Biobank evidence, including that of Zhang et al. (2021) [\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e], we extended the analysis in two key ways. First, we modelled incident dementia prospectively via Cox proportional hazards regression with age as the underlying time scale, which more directly accounts for the strong age dependence of dementia risk [\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e]. Second, within the same study framework, we examined the cross-sectional association between baseline meat intake frequency and baseline cognitive performance. This allows us to assess whether any observed dementia association is accompanied by contemporaneous differences in cognitive function across multiple domains. We examined two primary hypotheses: 1) High-frequency meat intake is associated with a greater incidence of dementia, and 2) high-frequency meat intake is associated with poorer baseline cognitive performance than low-frequency meat intake, after adjustment for age at baseline and other covariates.\u003c/p\u003e "},{"header":"Methods","content":"\u003cp\u003eThis study is reported in accordance with the Strengthening the Reporting of Observational Studies in Epidemiology – Nutritional Epidemiology (STROBE-nut) checklist [\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eData source\u003c/p\u003e\u003cp\u003eThe UK Biobank is one of the world’s largest and most comprehensive databases of health and genetic information, encompassing data from over 500,000 participants across the United Kingdom. To support scientific efforts aimed at preventing, diagnosing, and treating diseases earlier, participants consented to long-term health monitoring. The project was conducted across 22 assessment centres in England, Scotland, and Wales. For this study, we obtained access to UK Biobank data under application number 158668. To date, four major data collection phases have been completed: Instance 0 (2006–2010), Instance 1 (2012–2013), Instance 2 (2014 onward), and Instance 3 (2019 onward). In the present study, we used data from Instance 0, which included 501,936 participants who were 37 to 73 years old at baseline. At the baseline visit, after providing written informed consent, participants completed a touchscreen questionnaire that assessed a range of sociodemographic, lifestyle, and health condition variables, including diet and cognition.\u003c/p\u003e\u003cp\u003eDementia assessment\u003c/p\u003e\u003cp\u003eDifferent types of dementia, such as Alzheimer's disease or vascular dementia were recorded in the UK Biobank. This was accomplished through self-reports and data linkages of hospitalizations and death registries. In this study, we summarized all cases of dementia diseases as all cause dementia. Only dementia cases that occurred from one year after recruitment up to the most recent status of the UK Biobank were included in the analysis of the study to prevent disease- related bias.\u003c/p\u003e\u003cp\u003eDiet assessment\u003c/p\u003e\u003cp\u003eAt the assessment centre, participants completed a food frequency questionnaire (FFQ) via touchscreen (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://biobank.ndph.ox.ac.uk/ukb/label.cgi?id=100052\u003c/span\u003e\u003cspan class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e).\u003c/span\u003e The questionnaire included 29 diet-related items, which were designed primarily to capture the frequency of intake of various food groups. In this study, we focused on items related to meat intake, specifically processed meat, beef, poultry, lamb/mutton, and pork. The intake of fish was not included here but as an independent covariate in the analysis.\u003c/p\u003e\u003cp\u003e The participants reported their meat intake frequency using six response categories: never = 0, less than once a week = 1, once a week = 2, 2–4 times a week = 3, 5–6 times a week = 4, and once or more daily = 5. To enhance interpretability for the general population, we further classified participants into two broader categories: high-frequency meat intake, defined as consuming any type of meat five to six times per week or more; and low-frequency meat intake, defined as intake below this threshold. This definition captures the highest habitual meat consumption regardless of meat-type preference, reduces misclassification from substitution across meat types, and improves estimation stability in the primary models given the coarse frequency measurement.\u003c/p\u003e\u003cp\u003eTo assess potential regression dilution arising from a single baseline dietary assessment, we examined repeat meat-consumption data collected at the first repeat assessment visit (Instance 1: 2012–2013; n = 20,095) and quantified the stability of the high- vs low-frequency classification [\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eCognitive function assessment\u003c/p\u003e\u003cp\u003eDuring the recruitment period, some participants completed a touchscreen assessment designed to evaluate cognitive function. The questionnaire consisted of several subtests, three of which were analysed in greater detail in this dataset: reaction time, numeric memory, and fluid intelligence. These tests were selected because they provided the most comprehensive and complete data among the available cognitive measures.\u003c/p\u003e\u003ch3\u003eReaction time\u003c/h3\u003e\u003cp\u003eThe reaction time test was based on the card game Snap. The participants were shown pairs of cards and instructed to press a button as quickly as possible whenever the two cards matched. Data were collected across 12 rounds of the task, recording which cards were displayed, how often the button was pressed by each participant, and the time elapsed between the appearance of the matching cards and the participant’s response.\u003c/p\u003e\u003cp\u003eThe primary measure, the mean time to correctly identify matches, was used as an indicator of raw processing speed and reaction time and served as the focus of this analysis (Field ID: 20023). The first four rounds were excluded as practice trials. In addition, reaction times below 250 ms (classified as anticipatory) and above 2000 ms (when cards had already disappeared) were excluded. The final reaction time values were rounded to the nearest whole millisecond for analysis.\u003c/p\u003e\u003ch2\u003eNumeric memory\u003c/h2\u003e\u003cp\u003eDuring the numeric memory test, participants were shown a two-digit number on a touchscreen to memorize. After a brief delay, the number disappeared, and the participants were asked to re-enter it on the screen. In subsequent trials, the length of the number sequence increased by one digit—up to a maximum of 12 digits—each time the participant correctly recalled the previous sequence. For the present analysis, we included the variable “maximum digits correctly recalled during the numeric memory test” (Field ID: 4282) as a measure of short-term memory capacity.\u003c/p\u003e\u003ch3\u003eFluid Intelligence\u003c/h3\u003e\u003cp\u003eThe fluid intelligence test was designed to assess reasoning and problem-solving abilities that are independent of acquired knowledge. The participants were given two minutes to answer a series of 13 questions on a touchscreen. For the present analysis, we included the variable “sum of the number of correct answers” (Field ID: 20016) as a measure of fluid intelligence.\u003c/p\u003e\u003cp\u003eCovariates\u003c/p\u003e\u003cp\u003eThe participants’ demographic and lifestyle data were collected via electronic questionnaires and onsite measurements at the assessment centre. We focused on factors that may increase dementia risk. Accordingly, we examined using sociodemographic factors (age, sex, ethnicity, university degree or equivalent, and the Townsend deprivation index), physical health factors (waist-to-height ratio, as an indicator of central adiposity, physical activity level, and sleep duration), and lifestyle factors (alcohol drinking status and smoking status). The selection and categorization of covariates were generally based on Zhang et al. (2021) [\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e], with some modifications. For example, we used the waist-to-height ratio instead of body mass index (BMI) to reflect adiposity. Further details on the covariates are provided in Supplemental Methods 2.\u003c/p\u003e\u003ch2\u003eStatistical analysis\u003c/h2\u003e\u003ch2\u003eMeat intake frequency and dementia\u003c/h2\u003e\u003cp\u003eParticipants with prevalent dementia and those with incomplete data on meat intake frequency were excluded before analysis. Given the possibility that prodromal dementia may lead to changes in dietary behaviours before diagnosis, we excluded incident dementia cases occurring within the first year after baseline dietary assessment to reduce the potential for reverse causality. In sensitivity analyses (see the flowchart in Supplementary Fig.\u0026nbsp;1), we applied a more stringent 5-year cut-off, restricted the sample to participants aged ≥ 60 years, included only those with complete covariate data, and additionally adjusted for histories of cardiometabolic conditions (including diabetes, heart attack, angina, stroke, and high-blood pressure) as well as the waist-to-height ratio as an indicator of adiposity to assess robustness to reverse causation and pathway attenuation [\u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e–\u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e]. We applied Cox proportional hazards regression to estimate the association between meat intake frequency and the risk of incident dementia. Age was used as the underlying time scale to account for its strong association with dementia risk. The time-to-event variable was defined as the interval between the participant’s age at baseline and age at dementia diagnosis or censoring. We fitted three progressively adjusted models via a set of a priori-defined potential confounders: Model 1, unadjusted; Model 2, a minimally adjusted model, adjusted for sociodemographic factors, including sex, ethnicity, educational level, and socioeconomic status (Townsend deprivation index), determined by a directed acyclic graph [\u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e] (Supplementary Method 1); and Model 3, additionally adjusted for family history of dementia, dietary factors (intake of fruit, vegetables, and fish), physical health factors (physical activity level and sleep duration), and lifestyle factors (alcohol drinking status and smoking status). More details on the covariates can be found in Supplementary Method 2. For covariates where participants answered “do not know” or “prefer not to answer,” these responses were classified as missing. A “missing” category was created to replace missing values for each covariate; the impact of this approach was assessed in a sensitivity analysis restricted to participants with complete data on all covariates.\u003c/p\u003e\u003cp\u003eAs the primary analysis used a dichotomized exposure derived from the maximum frequency reported for any meat type, we conducted additional dose–response analyses using a five-level variable reflecting the maximum reported frequency across processed meat, poultry, beef, lamb/mutton, and pork. The participants were categorized as 0 (never for all meat types), 1 (\u0026lt; 1 time/week), 2 (1 time/week), 3 (2–4 times/week), or 4 (≥ 5–6 times/week, including once or more daily). This approach maintains consistency with the primary dichotomous exposure by capturing each participant’s highest habitual meat consumption frequency (mirroring the primary definition of high-frequency intake as ≥ 5–6 times/week for any meat type), reduces potential misclassification due to differences in meat-type preferences or substitution between meat types, and avoids overestimation that could arise if coarse, non-mutually exclusive frequency categories were summed across meat types.\u003c/p\u003e\u003cp\u003eCox proportional hazards models with age as the underlying time scale were fitted using the fully adjusted covariate set (Model 3). We estimated hazard ratios (HRs) and 95% confidence intervals (CIs) for each category (reference = category 1) and obtained a global p-value across categories via a likelihood ratio test. To assess the linear trend, the variable was additionally modelled as an ordinal term, and the Wald p-value was reported as the p-trend.\u003c/p\u003e\u003ch3\u003eMeat intake frequency and baseline cognitive performance\u003c/h3\u003e\u003cp\u003eUsing procedures similar to those described for Hypothesis 1 (including the same exposure definition and covariate set), we tested the hypothesis that high-frequency meat intake is associated with poorer baseline cognitive performance. Meat intake frequency was categorized into high- and low-frequency meat intake, with the low-frequency group as the reference category. Cognitive performance was assessed at baseline using three measures: reaction time, numeric memory, and fluid intelligence. Reaction time was log-transformed to reduce skewness and standardized to facilitate interpretation; numeric memory was defined as the longest number sequence correctly recalled; and fluid intelligence was defined as the number of correct answers on a reasoning test. Each outcome was analysed separately via linear regression.\u003c/p\u003e\u003cp\u003eThree progressively adjusted models were fitted for reaction time, numeric memory, and fluid intelligence. The model setups were similar to those used for examining the association between meat intake and dementia, as the factors that may affect dementia and cognitive performance largely overlap. Model 1, unadjusted; Model 2, a minimally adjusted model, adjusted for sociodemographic factors, including age, sex, ethnicity, educational level, and socioeconomic status (Townsend deprivation index), determined by a directed acyclic graph; and Model 3, additionally adjusted for family history of dementia, dietary factors (intake of fruit, vegetables, and fish), physical health factors (waist-to-height ratio, physical activity level, and sleep duration), and lifestyle factors (alcohol drinking status and smoking status). Regression coefficients (β) and 95% confidence intervals (CIs) were reported, representing the mean difference in cognitive performance between high- and low-frequency meat intake groups. In the sensitivity analyses, we again restricted the analyses to participants aged ≥ 60 years at baseline, repeated analyses in complete cases with no missing covariate data, and and additionally adjusted for histories of cardiometabolic conditions (diabetes, heart attack, angina, stroke, and high-blood pressure) as well as the waist-to-height ratio for adiposity. For the cognitive performance analyses, we did not apply a 5-year lag (i.e., excluding outcomes occurring within the first 5 years of follow-up) in sensitivity analyses of cognitive performance because cognitive performance was assessed at baseline. A lag analysis is intended to reduce reverse causation in incident outcome analyses by removing early events that may reflect preclinical disease influencing exposure. Excluding participants based on future dementia occurrence within 5 years would condition on a post-baseline event and could introduce selection bias rather than reverse causation for the baseline cognitive outcome.\u003c/p\u003e\u003cp\u003eTo complement the primary dichotomized exposure definition, we conducted additional dose-response analyses with a modelling strategy similar to that used for incident dementia analyses. Using a five-level frequency-based variable derived from the maximum reported intake frequency across processed meat, poultry, beef, lamb/mutton, and pork, participants were classified into five mutually exclusive categories: 0 (never for all meat types), 1 (\u0026lt; 1 time/week), 2 (1 time/week), 3 (2–4 times/week), and 4 (≥ 5 times/week, including once or more daily), on the basis of the highest frequency reported for any meat type. Associations between performance and baseline cognitive test performance were evaluated via linear regression models fitted with the fully adjusted covariate set as Model 3, and the results are reported as beta coefficients (β) and corresponding p-values. Evidence of a linear trend across categories was assessed by modelling the variable as an ordinal term.\u003c/p\u003e\u003cp\u003eAll analyses were performed with R version 4.5.2 with an α level set at 0.05 [\u003cspan class=\"CitationRef\"\u003e40\u003c/span\u003e].\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eDuring a mean follow-up of 14\u0026thinsp;\u0026plusmn;\u0026thinsp;2.4 y, excluding dementia cases arising in the first year of follow-up (n\u0026thinsp;=\u0026thinsp;69), 10,952 incident cases of all-cause dementia occurred. Baseline characteristics stratified by dementia status are provided in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The participants who developed dementia during follow-up were older and had less favourable socioeconomic and health profiles (lower education and income, higher central adiposity, more smoking, less current alcohol use, and longer sleep), and had poorer cognitive performance (slower reaction time, lower numeric memory, and lower fluid intelligence) than those who remained dementia-free. More men than women were diagnosed with dementia in the study population. Participant characteristics across 2 categories of reported intake frequency are shown in Supplementary Table\u0026nbsp;1. Compared with participants in the low-frequency meat intake group, those in the high-frequency group were generally more often male, consumed fewer fish, vegetables, and fruits, and had greater central adiposity, whereas differences in baseline cognitive measures were small.\u003c/p\u003e \u003cp\u003eAmong the participants with available meat-frequency data at both baseline and the first repeat assessment (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;20,095), the high-/low-frequency classification showed 92.6% overall agreement between baseline and repeat assessment. However, agreement differed by baseline category: among those classified as high-frequency at baseline (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1,143), 34.7% remained high-frequency at repeat assessment (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;397) while 65.3% were reclassified to low-frequency (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;746). In contrast, among those classified as low-frequency at baseline (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;18,952), 96.1% remained low-frequency at repeat (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;18,215) and 3.9% were reclassified to high-frequency (\u003cem\u003en\u003c/em\u003e\u0026thinsp;=\u0026thinsp;737). Overall, these findings indicate substantial stability of the low-frequency group but notable reclassification among baseline high-frequency participants, consistent with potential attenuation of baseline-only estimates due to regression dilution.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline characteristics of participants stratified by incident dementia status\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAll Participants\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;493,644)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIncident Dementia\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;10,952)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo Dementia\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e(N\u0026thinsp;=\u0026thinsp;482,692)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSMD\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge at baseline (year)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e56.5\u0026thinsp;\u0026plusmn;\u0026thinsp;8.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64.3\u0026thinsp;\u0026plusmn;\u0026thinsp;4.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56.4\u0026thinsp;\u0026plusmn;\u0026thinsp;8.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.20\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDuration of follow-up (year)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14.7\u0026thinsp;\u0026plusmn;\u0026thinsp;2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.7\u0026thinsp;\u0026plusmn;\u0026thinsp;3.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14.8\u0026thinsp;\u0026plusmn;\u0026thinsp;2.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFrequency of meat intake\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh frequency \u003c/p\u003e \u003cp\u003e (5 times/week or more)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30270 (6.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e740 (6.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29530 (6.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow frequency\u003c/p\u003e \u003cp\u003e (4 times/week or less)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e463374 (93.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10212 (93.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e453162 (93.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e224646 (45.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5665 (51.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e218981 (45.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e268998 (54.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5287 (48.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e263711 (54.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEthnicity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e466623 (94.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10519 (96.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e456104 (94.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsian or Asian British\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10762 (2.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e152 (1.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10610 (2.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack or Black British\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7463 (1.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e143 (1.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7320 (1.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMixed/Others\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7138 (1.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e101 (0.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7037 (1.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1658 (0.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37 (0.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1621 (0.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWith college/university degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e159948 (32.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2252 (20.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e157696 (32.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWithout college/university degree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e329994 (66.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8540 (78.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e321454 (66.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3702 (0.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e160 (1.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3542 (0.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTownsend deprivation index\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow deprivation (-6.26 -3.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e165490 (33.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3452 (31.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e162038 (33.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate deprivation (-3.14 -0.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e165367 (33.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3557 (32.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e161810 (33.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh deprivation (-0.58 11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e162180 (32.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3932 (35.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e158248 (32.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e607 (0.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11 (0.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e596 (0.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTotal Fish\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;1 times/wk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e126906 (25.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2374 (21.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e124532 (25.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1.1-1-9 times/wk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e107186 (21.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2096 (19.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e105090 (21.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u0026ndash;3 times/wk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e121365 (24.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2687 (24.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e118678 (24.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt; 3 times/wk\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e135025 (27.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3671 (33.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e131354 (27.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003emissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3162 (0.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e124 (1.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3038 (0.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eVegetables and fruits\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u0026ndash;2 servings/d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e48803 (9.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e956 (8.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47847 (9.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2.1-4 servings/d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e153564 (31.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2949 (26.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e150615 (31.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4.1-6 servings/d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e150644 (30.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3212 (29.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e147432 (30.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMore than 6 servings/d\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e128399 (26.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3354 (30.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e125045 (25.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12234 (2.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e481 (4.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11753 (2.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePhysical activity level\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.01\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e70859 (14.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1428 (13.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e69431 (14.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e155392 (31.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3082 (28.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e152310 (31.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e155858 (31.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3060 (27.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e152798 (31.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e111535 (22.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3382 (30.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e108153 (22.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSmoking status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e269413 (54.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5126 (46.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e264287 (54.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrevious\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e170769 (34.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4620 (42.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e166149 (34.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51744 (10.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1135 (10.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50609 (10.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1718 (0.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e71 (0.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1647 (0.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAlcohol drinker status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21391 (4.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e741 (6.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20650 (4.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrevious\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17605 (3.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e680 (6.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16925 (3.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCurrent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e454175 (92.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9510 (86.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e444665 (92.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e473 (0.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21 (0.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e452 (0.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSleep duration\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.14\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u0026ndash;8 hour/day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e332574 (67.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6735 (61.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e325839 (67.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt; 7 hour/day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e129840 (26.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3219 (29.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e126621 (26.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt; 8 hour/day\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e31230 (6.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e998 (9.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30232 (6.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFamily history of dementia\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e57718 (11.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2255 (20.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e55463 (11.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e318990 (64.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5471 (50.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e313519 (65.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e116936 (23.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3226 (29.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e113710 (23.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHistory of cardiometabolic diseases\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.44\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e154521 (31.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5672 (51.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e148849 (30.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e337576 (68.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5237 (47.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e332339 (68.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1547 (0.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43 (0.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1504 (0.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWaist-Height Ratio\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0.4 to 0.49 (healthy)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e153694 (31.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2479 (22.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e151215 (31.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0.5 to 0.59 (increased risk)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e240422 (48.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5488 (50.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e234934 (48.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge; 0.6 (high risk)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e89993 (18.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2815 (25.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e87178 (18.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMissing\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9535 (1.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e170 (1.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9365 (1.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCognitive Function\u003c/b\u003e (Incident Dementia/Total)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReaction Time (10,709/489,179)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e558.9\u0026thinsp;\u0026plusmn;\u0026thinsp;117.0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e612.2\u0026thinsp;\u0026plusmn;\u0026thinsp;140.2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e557.7\u0026thinsp;\u0026plusmn;\u0026thinsp;116.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumeric Memory (971/50,933)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.13\u0026thinsp;\u0026plusmn;\u0026thinsp;1.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.51\u0026thinsp;\u0026plusmn;\u0026thinsp;1.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.14\u0026thinsp;\u0026plusmn;\u0026thinsp;1.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFluid Intelligence (2,821/163,391)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.42\u0026thinsp;\u0026plusmn;\u0026thinsp;2.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5.43\u0026thinsp;\u0026plusmn;\u0026thinsp;2.02\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.72\u0026thinsp;\u0026plusmn;\u0026thinsp;1.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003e1\u003c/sup\u003e Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD; n (%)\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003csup\u003e2\u003c/sup\u003e \u003cem\u003eP\u003c/em\u003e -values from t tests for continuous variables with a normal distribution, Wilcoxon tests for continuous variables with a nonnormal distribution, or chi-square tests for categorical variables.\u003c/p\u003e \u003cp\u003e \u003csup\u003e3\u003c/sup\u003e Standardized mean differences (SMDs) of approximately 0.1 indicate a small difference, 0.2 a moderate difference, and values\u0026thinsp;\u0026ge;\u0026thinsp;0.5 a large difference.\u003c/p\u003e \u003cp\u003eMeat intake frequency and dementia\u003c/p\u003e \u003cp\u003eThe associations between meat intake frequency and incident dementia were evaluated via Cox proportional hazards models with age as the underlying time scale (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). According to the fully adjusted model (Model 3), high-frequency meat intake was associated with a greater risk of incident dementia than low-frequency meat intake was (HR\u0026thinsp;=\u0026thinsp;1.21; 95% CI: 1.13, 1.31; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The association between high-frequency meat intake and incident dementia was consistent across sensitivity analyses (Supplementary Table\u0026nbsp;2), additional adjustments for histories of cardiometabolic conditions and waist-to-height ratios (n\u0026thinsp;=\u0026thinsp;493,644), including a 5-year exclusion window (n\u0026thinsp;=\u0026thinsp;492,819), restrictions to participants aged\u0026thinsp;\u0026ge;\u0026thinsp;60 years at baseline (n\u0026thinsp;=\u0026thinsp;213,671), and complete-case analyses (n\u0026thinsp;=\u0026thinsp;292,028).\u003c/p\u003e \u003cp\u003eDose\u0026ndash;response analyses included n\u0026thinsp;=\u0026thinsp;493,644 participants and 10,952 incident dementia cases (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The distribution of the maximum frequency of any meat type was as follows: category 0 (never for all meat types, n\u0026thinsp;=\u0026thinsp;20,443), 1 (\u0026lt;\u0026thinsp;1 time/week, n\u0026thinsp;=\u0026thinsp;20,886), 2 (\u0026ge;\u0026thinsp;1 time/week, n\u0026thinsp;=\u0026thinsp;127,550), 3 (\u0026ge;\u0026thinsp;2\u0026ndash;4 times/week, n\u0026thinsp;=\u0026thinsp;294,495), and 4 (\u0026ge;\u0026thinsp;5\u0026ndash;6 times/week, n\u0026thinsp;=\u0026thinsp;30,270). Using participants who consumed meat\u0026thinsp;\u0026lt;\u0026thinsp;1 time/week (category 1) as the reference, those who never consumed any of the five meat types had a lower risk of dementia (HR\u0026thinsp;=\u0026thinsp;0.86; p\u0026thinsp;=\u0026thinsp;0.046). Dementia risk did not differ across intermediate categories (category 2: HR\u0026thinsp;=\u0026thinsp;0.94; p\u0026thinsp;=\u0026thinsp;0.245; category 3: HR\u0026thinsp;=\u0026thinsp;0.97; p\u0026thinsp;=\u0026thinsp;0.467). In contrast, participants in the highest-frequency category (category 4; \u0026ge;5\u0026ndash;6 times/week) had a greater risk of dementia (HR\u0026thinsp;=\u0026thinsp;1.16; p\u0026thinsp;=\u0026thinsp;0.011). There was strong evidence of heterogeneity across categories (p-overall\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and modelling the variable as an ordinal term suggested an overall increasing trend (HR per category increase\u0026thinsp;=\u0026thinsp;1.03; p-trend\u0026thinsp;=\u0026thinsp;0.008).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eHazard ratios (95% confidence intervals) for the association between meat intake frequency and incident dementia over 14.7\u0026thinsp;\u0026plusmn;\u0026thinsp;2.4 years of follow-up\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003emaximum frequency of any meat type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHR (95% CI) for high-frequency meat intake\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCases/Participants\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10952/493644\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 1\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.36 (1.26, 1.47)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 2\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.27 (1.17, 1.36)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 3\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.21 (1.13, 1.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003csup\u003ea\u003c/sup\u003eCompared with low-frequency meat intake. All the models used age as the time scale. \u003csup\u003eb\u003c/sup\u003eModel 1: unadjusted. \u003csup\u003ec\u003c/sup\u003eModel 2: adjusted for sociodemographic factors, including sex, ethnicity, educational level, and socioeconomic status (Townsend deprivation index). \u003csup\u003ed\u003c/sup\u003eModel 3: Model 2\u0026thinsp;+\u0026thinsp;adjusted for family history of dementia, dietary factors (intake of fruit, vegetables, and fish), physical health factors (physical activity level, and sleep duration), and lifestyle factors (alcohol drinking status and smoking status).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDose\u0026ndash;response association between maximum frequency of any meat intake and incident dementia risk (fully adjusted Cox proportional hazards model)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCases/Participants\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHR (95% CI) for high-frequency meat intake\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e279/20443\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.86 (0.74, 0.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLess than once a week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e480/20886\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.00 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOnce a week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,988/127550\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.94 (0.86, 1.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.245\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u0026ndash;4 times a week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6,465/294495\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.97 (0.88, 1.06)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.467\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5 times a week or more\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e740/30270\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.16 (1.04, 1.31)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePtrend\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eMeat intake frequency and baseline cognitive performance\u003c/p\u003e \u003cp\u003eThe associations between meat intake frequency and baseline cognitive performance were evaluated via linear regression models. The complete results for reaction time, numeric memory, and fluid intelligence across the three models and sensitivity analyses are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Supplementary Table\u0026nbsp;3. The results of the dose-response analyses are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eHigh-frequency meat intake was associated with slightly slower reaction times than low-frequency meat intake. In the fully adjusted model (Model 3, n\u0026thinsp;=\u0026thinsp;489,151), participants in the high-frequency group had 0.036 standard deviations (SDs) greater reaction times than those in the low-frequency group did (i.e., slightly slower reaction time; approximately about 4 ms (SD\u0026thinsp;=\u0026thinsp;116.9 ms). This pattern was consistent with the results of the sensitivity analyses via the fully adjusted model (Supplementary Table\u0026nbsp;3): additional adjustments for histories of cardiometabolic conditions and waist-to-height ratios (n\u0026thinsp;=\u0026thinsp;489,151), restricted to participants aged\u0026thinsp;\u0026ge;\u0026thinsp;60 years (n\u0026thinsp;=\u0026thinsp;211,359), and in complete-case analyses (n\u0026thinsp;=\u0026thinsp;290,313), high-frequency meat intake remained associated with longer reaction times, with similar directions and magnitudes.\u003c/p\u003e \u003cp\u003eIn dose-response analyses using the maximum frequency across meat types, there was evidence of heterogeneity across categories (p-overall\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Using participants who consumed meat\u0026thinsp;\u0026lt;\u0026thinsp;1 time/week (category 1) as the reference, the reaction time was similar to that of those who consumed meat\u0026thinsp;\u0026ge;\u0026thinsp;1 time/week (category 2; β\u0026thinsp;=\u0026thinsp;0.001 SD; p\u0026thinsp;=\u0026thinsp;0.90) and was slightly lower (faster) among those who consumed meat\u0026thinsp;\u0026ge;\u0026thinsp;2\u0026ndash;4 times/week (category 3; β = \u0026minus;0.024 SD; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), whereas participants in the highest-frequency category (\u0026ge;\u0026thinsp;5\u0026ndash;6 times/week; category 4) had slightly higher (slower) reaction times (β\u0026thinsp;=\u0026thinsp;0.020 SD; p\u0026thinsp;=\u0026thinsp;0.020). Overall, these dose\u0026ndash;response results suggest a nonlinear pattern, but the increased reaction time observed in the highest-frequency category is consistent with the primary dichotomized comparison.\u003c/p\u003e \u003cp\u003eNumeric memory analyses were based on n\u0026thinsp;=\u0026thinsp;50,933 participants (the smallest cognitive subsample, compared with reaction time n\u0026thinsp;=\u0026thinsp;489,155 and fluid intelligence n\u0026thinsp;=\u0026thinsp;163,390). According to the fully adjusted linear regression model (Model 3), high-frequency meat intake was associated with a slightly lower numeric memory score than low-frequency intake was (β = \u0026minus;0.068; p\u0026thinsp;=\u0026thinsp;0.026). This association was attenuated and no longer statistically significant in sensitivity analyses, including additional adjustments for history of cardiometabolic conditions and waist-to-height ratio (β = \u0026minus;0.117; p\u0026thinsp;=\u0026thinsp;0.058), restrictions to participants aged\u0026thinsp;\u0026ge;\u0026thinsp;60 years (n\u0026thinsp;=\u0026thinsp;22,634; β\u0026thinsp;=\u0026thinsp;0.021; p\u0026thinsp;=\u0026thinsp;0.670), and complete-case analyses (n\u0026thinsp;=\u0026thinsp;30,939; β = \u0026minus;0.034; p\u0026thinsp;=\u0026thinsp;0.375).\u003c/p\u003e \u003cp\u003eIn dose-response analyses using the maximum frequency across meat types and \u0026lt;\u0026thinsp;1 time/week (category 1) as a reference: numeric memory scores were broadly similar across intake frequency categories. Compared with the reference group, scores were not different in those who never consumed any of the meat types (category 0; β\u0026thinsp;=\u0026thinsp;0.083; p\u0026thinsp;=\u0026thinsp;0.094), consumed meat\u0026thinsp;\u0026ge;\u0026thinsp;1 time/week (category 2; β = \u0026minus;0.058; p\u0026thinsp;=\u0026thinsp;0.12), \u0026ge;\u0026thinsp;2\u0026ndash;4 times/week (category 3; β = \u0026minus;0.015; p\u0026thinsp;=\u0026thinsp;0.69), or \u0026ge;\u0026thinsp;5\u0026ndash;6 times/week or more (category 4; β = \u0026minus;0.088; p\u0026thinsp;=\u0026thinsp;0.055). Although there was evidence of overall differences across categories (p-overall\u0026thinsp;\u0026lt;\u0026thinsp;0.001), modelling the variable as an ordinal variable showed no linear trend (β per category\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.011; p\u0026thinsp;=\u0026thinsp;0.22). Overall, the dose\u0026ndash;response findings suggest that the primary dichotomised association was small and not clearly graded across frequency categories.\u003c/p\u003e \u003cp\u003eHigh-frequency meat intake was associated with lower fluid intelligence in the age-adjusted model (Model 1: β = \u0026minus;0.14; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001; n\u0026thinsp;=\u0026thinsp;163,390); however, after partial and full adjustment (Model 2 \u0026amp; 3), there was no association between meat intake frequency and fluid intelligence. The results of the sensitivity analyses were consistent, with no associations with additional adjustments for history of cardiometabolic conditions and waist-to-height ratios (n\u0026thinsp;=\u0026thinsp;163,390), age\u0026thinsp;\u0026ge;\u0026thinsp;60 restriction (n\u0026thinsp;=\u0026thinsp;73,665), or complete-case analyses (n\u0026thinsp;=\u0026thinsp;100,825).\u003c/p\u003e \u003cp\u003eIn dose-response analyses using the maximum frequency across meat types, there was evidence of overall differences across categories (p-overall\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Using participants who consumed meat\u0026thinsp;\u0026lt;\u0026thinsp;1 time/week (category 1) as the reference, those who never consumed any of the meat types (category 0) had higher fluid intelligence scores (β\u0026thinsp;=\u0026thinsp;0.233; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), whereas participants who consumed meat\u0026thinsp;\u0026ge;\u0026thinsp;1 time/week had slightly lower scores (category 2; β = \u0026minus;0.083; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Differences for higher-frequency categories were not significant (category\u0026thinsp;=\u0026thinsp;3; β\u0026thinsp;=\u0026thinsp;0.039; p\u0026thinsp;=\u0026thinsp;0.087; category 4; β = \u0026minus;0.012; p\u0026thinsp;=\u0026thinsp;0.68). Overall, these dose\u0026ndash;response findings align with the primary fully adjusted results, indicating no clear graded association between increasing meat intake frequency and fluid intelligence after multivariable adjustment, with observed differences largely driven by the \u0026ldquo;never\u0026rdquo; intake group.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eLinear regression coefficients (95% confidence intervals) for the associations between meat intake frequency and cognitive performance\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReaction Time\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eβ (95% CI) for log-transformed reaction time (z score)\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e489151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 1\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.014 (-0.026, -0.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 2\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.046 (0.035, 0.057)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 3\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.036 (0.025, 0.047)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumeric Memory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eβ (95% CI), difference in numeric memory score\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e50933\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 1\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.053 (-0.114, 0.007)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 2\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.078 (-0.138, -0.018)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 3\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.067 (-0.128, -0.008)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFluid Intelligence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eβ (95% CI), difference in fluid intelligence score\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e163391\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 1\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.118 (-0.159, -0.078)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 2\u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.052 (-0.090, -0.015)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel 3\u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.028 (-0.065, 0.009)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.137\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003csup\u003ea\u003c/sup\u003eβ represents the adjusted mean difference for high-frequency vs low-frequency meat intake (reference) \u003csup\u003eb\u003c/sup\u003eModel 1: unadjusted. \u003csup\u003ec\u003c/sup\u003eModel 2: Model 1\u0026thinsp;+\u0026thinsp;adjusted for sociodemographic factors (age, sex, ethnicity, educational level, and socioeconomic status (Townsend deprivation index). \u003csup\u003ed\u003c/sup\u003eModel 3: Model 2\u0026thinsp;+\u0026thinsp;adjusted for family history of dementia, dietary factors (intake of fruit, vegetables, and fish), physical health factors (physical activity level and sleep duration), and lifestyle factors (alcohol drinking status and smoking status).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDose-response associations between the maximum frequency of any meat intake and cognitive performance (fully adjusted linear regression model)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReaction Time\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eβ (95% CI) for log-transformed reaction time (z score)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.016 (-0.034, 0.003)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.093\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLess than once a week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOnce a week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.001 (-0.013, 0.015)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.090\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u0026ndash;4 times a week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.024 (-0.037, -0.011)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5 times a week or more\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.020 (0.003, 0.037)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePtrend\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumeric Memory\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eβ (95% CI), difference in numeric memory score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.083 (-0.014, 0.181)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.094\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLess than once a week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOnce a week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.058 (-0.132, 0.016)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.124\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u0026ndash;4 times a week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.015 (-0.086, 0.057)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.688\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5 times a week or more\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.088 (-0.178, 0.002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePtrend\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFluid Intelligence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eβ (95% CI), difference in fluid intelligence score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.233 (0.173, 0.293)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLess than once a week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0 (Reference)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOnce a week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.083 (-0.129, -0.036)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u0026ndash;4 times a week\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.039 (-0.006, 0.084)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.086\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5 times a week or more\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.012 (-0.068, 0.044)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.676\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePtrend\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this large prospective analysis of 493,644 UK Biobank participants followed for an average of 14 years, high-frequency meat intake\u0026mdash;defined as eating any type of meat five to six times per week or more\u0026mdash;was associated with a greater risk of incident all-cause dementia than low-frequency intake. The association remained robust after extensive adjustment for sociodemographic, lifestyle, and adiposity-related factors and across multiple sensitivity analyses. The participants with high-frequency meat intake also exhibited slightly slower reaction times but showed no robust differences in numeric memory or fluid intelligence scores.\u003c/p\u003e \u003cp\u003eBaseline characteristics and risk profiles\u003c/p\u003e \u003cp\u003eThe baseline profiles of dementia cases in our study were consistent with established epidemiological evidence\u0026mdash;older age, socioeconomic deprivation, lower education, a greater burden of cardiometabolic conditions, and greater adiposity are strong predictors of dementia onset. High-frequency meat consumers also exhibited lower intakes of fish, fruit, and vegetables and higher smoking prevalence and central adiposity, paralleling risk profiles reported in prior nutritional epidemiology studies [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Despite these overlapping factors, our results remained robust after full adjustment, suggesting that the observed association between meat intake and dementia reflects additional biological or lifestyle pathways beyond standard covariates.\u003c/p\u003e \u003cp\u003eAssociation between meat intake and dementia risk\u003c/p\u003e \u003cp\u003eThe observed association between higher meat intake frequency and increased dementia risk may reflect a combination of nutritional, metabolic, and behavioural pathways. Frequent meat intake is often correlated with greater intake of saturated fats and sodium and lower intake of plant-based foods, which can contribute to vascular dysfunction, inflammation, and insulin resistance\u0026mdash;mechanisms implicated in neurodegenerative disease development [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. These mechanisms are consistent with findings from other large cohort and meta-analytic studies linking higher red and processed meat intake to greater dementia risk and cognitive decline [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Although individuals with high meat intake in this study tended to have less favourable health profiles\u0026mdash;including higher central adiposity, smoking prevalence, and lower levels of fish, vegetables, and fruits intake\u0026mdash;our fully adjusted models suggest that the positive association between meat intake and dementia risk is not entirely explained by these correlated factors. It is therefore plausible that frequent meat intake captures additional dietary or metabolic risk dimensions beyond standard lifestyle covariates. These findings highlight the importance of considering both nutritional quality and broader lifestyle clustering when interpreting diet\u0026ndash;dementia associations. Dose-response analyses on the basis of the maximum frequency across meat types provided additional insight into the shape of the association. Compared with that of participants who consumed meat less than once per week, dementia risk was elevated only in the highest intake frequency category (\u0026ge;\u0026thinsp;5\u0026ndash;6 times/week), whereas intermediate categories showed little evidence of increased risk. This pattern suggests that the association may be driven primarily by very frequent intake rather than a strictly monotonic increase across the full range of intake. Notably, participants who reported never consuming any of the assessed meat types had a lower dementia risk than the low-frequency reference group did, which may reflect differences in broader dietary patterns or health behaviours among nonmeat consumers and should be interpreted cautiously.\u003c/p\u003e \u003cp\u003eCognitive performance in relation to meat intake\u003c/p\u003e \u003cp\u003eIn analyses of baseline cognitive performance, we found that high-frequency meat intake was associated with slightly slower reaction time, whereas associations with numeric memory were small and attenuated in sensitivity analyses, and fluid intelligence showed no association after multivariable adjustment. Overall, these patterns suggest that any relationship between meat intake frequency and cognition in this cohort is modest, may vary by cognitive domain, and is not consistently graded across intake categories. These findings are in keeping with broader evidence that diet-cognition associations are often small and heterogeneous and that observed relationships can be sensitive to confounding by socioeconomic and lifestyle factors [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Our results also broadly align with prior literature indicating inconsistent associations between meat intake and cognitive outcomes [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. In addition, our cognitive outcomes were assessed at baseline only, whereas long-term intake and longitudinal cognitive changes may better capture cumulative dietary effects [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eStrengths and Limitations\u003c/p\u003e \u003cp\u003eThe strengths of this study include its large sample size, prospective design, long follow-up, and consistent modelling framework using age as the underlying time scale. These methodological features improve causal interpretation by reducing potential reverse causation (e.g., excluding early incident cases) and align with approaches commonly used in large prospective diet\u0026ndash;dementia studies [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eImportant limitations should be considered. First, we relied on self-reported meat intake frequency to enhance interpretability for the general population and to harmonize reporting across meat types. Although this high-/low-frequency classification showed high overall agreement at repeat assessment (92.6%), reclassification was more common among participants initially classified as high-frequency, indicating potential cut-point misclassification and possible attenuation of baseline-only associations toward the null. In addition, this approach does not distinguish between meat subtypes (e.g., unprocessed poultry vs processed meat) or preparation methods (e.g., frying vs steaming). Second, cognitive measures were assessed cross-sectionally and are subject to measurement error and selection effects, particularly for numeric memory and fluid intelligence, which have smaller analytic samples. Third, the UK Biobank enables internally consistent analyses with age-as-time-scale modelling and extensive covariate adjustment; however, as a volunteer cohort it is not population-representative. The observed estimates should be interpreted primarily as within-cohort associations. Finally, despite extensive adjustment, residual confounding remains possible. For example, we did not adjust for genotype-level dementia risk (e.g., APOE ε4) or comprehensive diet-quality indices, so residual genetic and diet-quality confounding factors may remain.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eTogether, these findings suggest that reducing meat intake frequency may be an accessible behavioural target for dementia prevention. Although this study focused on dementia risk and cognitive outcomes, the broader implications extend to planetary health, as high meat production contributes significantly to greenhouse gas emissions and climate change. The simplified two-category classification of meat intake used here was designed to enhance interpretability and public relevance, facilitating translation of epidemiologic findings into actionable dietary guidance for the general population.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eCI Confidence interval\u003c/p\u003e\u003cp\u003eHR Hazard ratio\u003c/p\u003e\u003cp\u003eSD Standard deviation\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eEthics approval and consent to participate\u003c/h2\u003e\n\u003cp\u003eThe UK biobank was approved by the Northwest Multi-centre Research Ethics Committee (11/NW/0382; https://www.ukbiobank.ac.uk/ethics/), and all participants agreed to their inclusion, and signed written informed consent forms. All procedures were conducted in accordance with the Declaration of Helsinki. Clinical trial number: not applicable.\u003c/p\u003e\n\u003ch2\u003eConsent for publication\u003c/h2\u003e\n\u003cp\u003eNot applicable; this study used de-identified data and did not include any identifiable individual-level information.\u003c/p\u003e\n\u003ch2\u003eAvailability of data and materials\u003c/h2\u003e\n\u003cp\u003eThe data are available from the UK Biobank (https://www.ukbiobank.ac.uk/), but access is subject to the UK Biobank\u0026rsquo;s rigorous application and approval process; therefore, the data are not publicly available.\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\n\u003cp\u003eThe authors declare that there is no duality of interest associated with this manuscript.\u003c/p\u003e\n\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis work was conducted without external grant funding and was supported by institutional resources.\u003c/p\u003e\n\u003ch2\u003eAuthors\u0026rsquo; contribution\u0026nbsp;\u003c/h2\u003e\n\u003cp\u003eJ. V.: conceptualization, data curation, formal analysis, writing \u0026ndash; original draft. L. R.: \u0026nbsp;conceptualization, data curation, writing \u0026ndash; review \u0026amp; editing. N. W.: conceptualization, supervision, writing \u0026ndash; review \u0026amp; editing. Y.-P. C.: data curation, formal analysis, writing \u0026ndash; review \u0026amp; editing. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003ch2\u003eAcknowledgements\u003c/h2\u003e\n\u003cp\u003eWe would like to thank the UK Biobank staff and management team for their invaluable contribution to data collection. We also thank Dr. Anne Hauswald for her guidance in obtaining access to the UK Biobank dataset.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eOECD FAO, OECD-FAO Agricultural F, Rome, FAO. Italy: OECD; ; 2025 [cited 2026 Jan 7]. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://openknowledge.fao.org/handle/20.500.14283/cd6043en\u003c/span\u003e\u003cspan address=\"https://openknowledge.fao.org/handle/20.500.14283/cd6043en\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 7 Jan 2026.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSanchez-Sabate R, Sabat\u0026eacute; J. Consumer Attitudes Towards Environmental Concerns of Meat Consumption: A Systematic Review. Int J Environ Res Public Health. 2019;16:1220. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/ijerph16071220\u003c/span\u003e\u003cspan address=\"10.3390/ijerph16071220\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGrethe H. The Economics of Farm Animal Welfare. Annu Rev Resour Econ Annual Reviews. 2017;9:75\u0026ndash;94. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1146/annurev-resource-100516-053419\u003c/span\u003e\u003cspan address=\"10.1146/annurev-resource-100516-053419\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNeff RA, Edwards D, Palmer A, Ramsing R, Righter A, Wolfson J. Reducing meat consumption in the USA: a nationally representative survey of attitudes and behaviours. Public Health Nutr. 2018;21:1835\u0026ndash;44. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1017/S1368980017004190\u003c/span\u003e\u003cspan address=\"10.1017/S1368980017004190\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFarvid MS, Sidahmed E, Spence ND, Mante Angua K, Rosner BA, Barnett JB. Consumption of red meat and processed meat and cancer incidence: a systematic review and meta-analysis of prospective studies. Eur J Epidemiol. 2021;36:937\u0026ndash;51. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s10654-021-00741-9\u003c/span\u003e\u003cspan address=\"10.1007/s10654-021-00741-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuang Y, Cao D, Chen Z, Chen B, Li J, Guo J, et al. Red and processed meat consumption and cancer outcomes: Umbrella review. Food Chem. 2021;356:129697. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.foodchem.2021.129697\u003c/span\u003e\u003cspan address=\"10.1016/j.foodchem.2021.129697\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLippi G, Mattiuzzi C, Cervellin G. Meat consumption and cancer risk: a critical review of published meta-analyses. Crit Rev Oncol Hematol. 2016;97:1\u0026ndash;14. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.critrevonc.2015.11.008\u003c/span\u003e\u003cspan address=\"10.1016/j.critrevonc.2015.11.008\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAbete I, Romaguera D, Vieira AR, De Munain AL, Norat T. Association between total, processed, red and white meat consumption and all-cause, CVD and IHD mortality: a meta-analysis of cohort studies. Br J Nutr. 2014;112:762\u0026ndash;75. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1017/s000711451400124x\u003c/span\u003e\u003cspan address=\"10.1017/s000711451400124x\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShi W, Huang X, Schooling CM, Zhao JV. Red meat consumption, cardiovascular diseases, and diabetes: a systematic review and meta-analysis. Eur Heart J. 2023;44:2626\u0026ndash;35. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/eurheartj/ehad336\u003c/span\u003e\u003cspan address=\"10.1093/eurheartj/ehad336\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKim Y, Keogh J, Clifton P. A review of potential metabolic etiologies of the observed association between red meat consumption and development of type 2 diabetes mellitus. Metabolism. 2015;64:768\u0026ndash;79. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.metabol.2015.03.008\u003c/span\u003e\u003cspan address=\"10.1016/j.metabol.2015.03.008\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDinu M, Abbate R, Gensini GF, Casini A, Sofi F. Vegetarian, vegan diets and multiple health outcomes: A systematic review with meta-analysis of observational studies. Crit Rev Food Sci Nutr. 2017;57:3640\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/10408398.2016.1138447\u003c/span\u003e\u003cspan address=\"10.1080/10408398.2016.1138447\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOlfert MD, Wattick RA. Vegetarian Diets and the Risk of Diabetes. Curr Diab Rep. 2018;18:101. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11892-018-1070-9\u003c/span\u003e\u003cspan address=\"10.1007/s11892-018-1070-9\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSalehin S, Rasmussen P, Mai S, Mushtaq M, Agarwal M, Hasan SM, et al. Plant Based Diet and Its Effect on Cardiovascular Disease. Int J Environ Res Public Health. 2023;20:3337. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/ijerph20043337\u003c/span\u003e\u003cspan address=\"10.3390/ijerph20043337\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang H, Greenwood DC, Risch HA, Bunce D, Hardie LJ, Cade JE. Meat consumption and risk of incident dementia: cohort study of 493,888 UK Biobank participants. Am J Clin Nutr. 2021;114:175\u0026ndash;84. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/ajcn/nqab028\u003c/span\u003e\u003cspan address=\"10.1093/ajcn/nqab028\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDemirbas M, Hahn-Pedersen JH, J\u0026oslash;rgensen HL. Comparison Between Burden of Care Partners of Individuals with Alzheimer\u0026rsquo;s Disease Versus Individuals with Other Chronic Diseases. Neurol Ther. 2023;12:1051\u0026ndash;68. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s40120-023-00493-6\u003c/span\u003e\u003cspan address=\"10.1007/s40120-023-00493-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRezaei H, Niksima SH, Ghanei Gheshlagh R. Burden of Care in Caregivers of Iranian patients with chronic disorders: a systematic review and meta-analysis. Health Qual Life Outcomes. 2020;18:261. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12955-020-01503-z\u003c/span\u003e\u003cspan address=\"10.1186/s12955-020-01503-z\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGale SA, Acar D, Daffner KR, Dementia. Am J Med. 2018;131:1161\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.amjmed.2018.01.022\u003c/span\u003e\u003cspan address=\"10.1016/j.amjmed.2018.01.022\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCerejeira J, Lagarto L, Mukaetova-Ladinska EB. Behavioral and Psychological Symptoms of Dementia. Front Neurol [Internet]. 2012 [cited 2025 Dec 7];3. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3389/fneur.2012.00073\u003c/span\u003e\u003cspan address=\"10.3389/fneur.2012.00073\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNichols E, Steinmetz JD, Vollset SE, Fukutaki K, Chalek J, Abd-Allah F, et al. Estimation of the global prevalence of dementia in 2019 and forecasted prevalence in 2050: an analysis for the Global Burden of Disease Study 2019. Lancet Public Health. 2022;7:e105\u0026ndash;25. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S2468-2667(21)00249-8\u003c/span\u003e\u003cspan address=\"10.1016/S2468-2667(21)00249-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLicher S, Ahmad S, Karamujić-Čomić H, Voortman T, Leening MJG, Ikram MA, et al. Genetic predisposition, modifiable-risk-factor profile and long-term dementia risk in the general population. Nat Med. 2019;25:1364\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41591-019-0547-7\u003c/span\u003e\u003cspan address=\"10.1038/s41591-019-0547-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePeters R, Booth A, Rockwood K, Peters J, D\u0026rsquo;Este C, Anstey KJ. Combining modifiable risk factors and risk of dementia: a systematic review and meta-analysis. BMJ Open. 2019;9:e022846. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1136/bmjopen-2018-022846\u003c/span\u003e\u003cspan address=\"10.1136/bmjopen-2018-022846\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRestivo J. Guide to the Mediterranean diet [Internet]. Harv. Health. 2023 [cited 2025 Dec 8]. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.health.harvard.edu/staying-healthy/guide-to-the-mediterranean-diet\u003c/span\u003e\u003cspan address=\"https://www.health.harvard.edu/staying-healthy/guide-to-the-mediterranean-diet\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 8 Dec 2025.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRos E, Mart\u0026iacute;nez-Gonz\u0026aacute;lez MA, Estruch R, Salas-Salvad\u0026oacute; J, Fit\u0026oacute; M, Mart\u0026iacute;nez JA, et al. Mediterranean Diet and Cardiovascular Health: Teachings of the PREDIMED Study. Adv Nutr. 2014;5:S330\u0026ndash;6. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3945/an.113.005389\u003c/span\u003e\u003cspan address=\"10.3945/an.113.005389\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMentella MC, Scaldaferri F, Ricci C, Gasbarrini A, Miggiano GAD. Cancer and Mediterranean Diet: A Review. Nutrients. 2019;11:2059. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/nu11092059\u003c/span\u003e\u003cspan address=\"10.3390/nu11092059\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePetersson SD, Philippou E, Mediterranean, Diet. Cognitive Function, and Dementia: A Systematic Review of the Evidence. Adv Nutr. 2016;7:889\u0026ndash;904. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3945/an.116.012138\u003c/span\u003e\u003cspan address=\"10.3945/an.116.012138\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJiang Y-W, Sheng L-T, Pan X-F, Feng L, Yuan J-M, Pan A, et al. Meat consumption in midlife and risk of cognitive impairment in old age: the Singapore Chinese Health Study. Eur J Nutr. 2020;59:1729\u0026ndash;38. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s00394-019-02031-3\u003c/span\u003e\u003cspan address=\"10.1007/s00394-019-02031-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTsai J-H, Huang C-F, Lin M-N, Chang C-E, Chang C-C, Lin C-L. Taiwanese Vegetarians Are Associated with Lower Dementia Risk: A Prospective Cohort Study. Nutrients. 2022;14:588. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/nu14030588\u003c/span\u003e\u003cspan address=\"10.3390/nu14030588\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang H, Hardie L, Bawajeeh AO, Cade J, Meat, Consumption. Cognitive Function and Disorders: A Systematic Review with Narrative Synthesis and Meta-Analysis. Nutrients. 2020;12:1528. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/nu12051528\u003c/span\u003e\u003cspan address=\"10.3390/nu12051528\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGatto NM, Garcia-Cano J, Irani C, Jaceldo-Siegl K, Liu T, Chen Z, et al. Vegetarian Dietary Patterns and Cognitive Function among Older Adults: The Adventist Health Study-2. J Nutr Gerontol Geriatr. 2021;40:197\u0026ndash;214. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/21551197.2021.1965939\u003c/span\u003e\u003cspan address=\"10.1080/21551197.2021.1965939\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHepsomali P, Groeger JA. Diet and general cognitive ability in the UK Biobank dataset. Sci Rep Nat Publishing Group. 2021;11:11786. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1038/s41598-021-91259-3\u003c/span\u003e\u003cspan address=\"10.1038/s41598-021-91259-3\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eThi\u0026eacute;baut ACM, B\u0026eacute;nichou J. Choice of time-scale in Cox\u0026rsquo;s model analysis of epidemiologic cohort data: a simulation study. Stat Med. 2004;23:3803\u0026ndash;20. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1002/sim.2098\u003c/span\u003e\u003cspan address=\"10.1002/sim.2098\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLachat C, Hawwash D, Ock\u0026eacute; MC, Berg C, Forsum E, H\u0026ouml;rnell A, et al. Strengthening the Reporting of Observational Studies in Epidemiology\u0026mdash;Nutritional Epidemiology (STROBE-nut): An Extension of the STROBE Statement. PLOS Med Public Libr Sci. 2016;13:e1002036. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pmed.1002036\u003c/span\u003e\u003cspan address=\"10.1371/journal.pmed.1002036\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHutcheon JA, Chiolero A, Hanley JA. Random measurement error and regression dilution bias. BMJ. Br Med J Publishing Group. 2010;340:c2289. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1136/bmj.c2289\u003c/span\u003e\u003cspan address=\"10.1136/bmj.c2289\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBradbury KE, Young HJ, Guo W, Key TJ. Dietary assessment in UK Biobank: an evaluation of the performance of the touchscreen dietary questionnaire. J Nutr Sci. 2018;7:e6. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1017/jns.2017.66\u003c/span\u003e\u003cspan address=\"10.1017/jns.2017.66\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFrith E, Loprinzi PD. Fitness Fatness Index and Alzheimer-specific mortality. Eur J Intern Med. 2017;42:51\u0026ndash;3. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.ejim.2017.04.015\u003c/span\u003e\u003cspan address=\"10.1016/j.ejim.2017.04.015\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHan XD, Li YJ, Wang P, Han XL, Zhao MQ, Wang JF, et al. Insulin Resistance-Varying Associations of Adiposity Indices with Cerebral Perfusion in Older Adults: A Population-Based Study. J Nutr Health Aging. 2023;27:219\u0026ndash;27. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s12603-023-1894-2\u003c/span\u003e\u003cspan address=\"10.1007/s12603-023-1894-2\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZelicha H, Kloting N, Kaplan A, Yaskolka Meir A, Rinott E, Tsaban G, et al. The effect of high-polyphenol Mediterranean diet on visceral adiposity: the DIRECT PLUS randomized controlled trial. BMC Med. 2022;20:327. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12916-022-02525-8\u003c/span\u003e\u003cspan address=\"10.1186/s12916-022-02525-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZhang J, Huang X, Ling Y, Xie X, Zeng X, Lyu J, et al. Associations of cardiometabolic multimorbidity with all-cause dementia, alzheimer\u0026rsquo;s disease, and vascular dementia: a cohort study in the UK biobank. BMC Public Health. 2025;25:2397. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1186/s12889-025-23352-5\u003c/span\u003e\u003cspan address=\"10.1186/s12889-025-23352-5\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003evan der Textor J, Gilthorpe MS, Liśkiewicz M, Ellison GT. Robust causal inference using directed acyclic graphs: the R package \u0026lsquo;dagitty\u0026rsquo;. Int J Epidemiol. 2016;45:1887\u0026ndash;94. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1093/ije/dyw341\u003c/span\u003e\u003cspan address=\"10.1093/ije/dyw341\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCore Team R, Vienna R. Austria: R Foundation for Statistical Computing; 2025 [cited 2025 Oct 31]. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.R-project.org\u003c/span\u003e\u003cspan address=\"http://www.R-project.org\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e. Accessed 31 Oct 2025.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDominguez LJ, Barbagallo M. Nutritional prevention of cognitive decline and dementia. Acta Bio Med Atenei Parm. 2018;89:276\u0026ndash;90. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.23750/abm.v89i2.7401\u003c/span\u003e\u003cspan address=\"10.23750/abm.v89i2.7401\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGrosso G, Laudisio D, Frias-Toral E, Barrea L, Muscogiuri G, Savastano S, et al. Anti-Inflammatory Nutrients and Obesity-Associated Metabolic-Inflammation: State of the Art and Future Direction. Nutrients. 2022;14:1137. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3390/nu14061137\u003c/span\u003e\u003cspan address=\"10.3390/nu14061137\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMisra R, Balagopal P, Raj S, Patel TG. Red Meat Consumption (Heme Iron Intake) and Risk for Diabetes and Comorbidities? Curr Diab Rep. 2018;18:100. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1007/s11892-018-1071-8\u003c/span\u003e\u003cspan address=\"10.1007/s11892-018-1071-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLi Y, Li Y, Gu X, Liu Y, Dong D, Kang JH, et al. Long-Term Intake of Red Meat in Relation to Dementia Risk and Cognitive Function in US Adults. Neurol Wolters Kluwer. 2025;104:e210286. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1212/WNL.0000000000210286\u003c/span\u003e\u003cspan address=\"10.1212/WNL.0000000000210286\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQuan W, Xu Y, Luo J, Zeng M, He Z, Shen Q, et al. Association of dietary meat consumption habits with neurodegenerative cognitive impairment: an updated systematic review and dose\u0026ndash;response meta-analysis of 24 prospective cohort studies. Food Funct Royal Soc Chem. 2022;13:12590\u0026ndash;601. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1039/D2FO03168J\u003c/span\u003e\u003cspan address=\"10.1039/D2FO03168J\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDalile B, Kim C, Challinor A, Geurts L, Gibney ER, Galdos MV, et al. The EAT-Lancet reference diet and cognitive function across the life course. Lancet Planet Health. 2022;6:e749\u0026ndash;59. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/S2542-5196(22)00123-1\u003c/span\u003e\u003cspan address=\"10.1016/S2542-5196(22)00123-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBigras C, Mazzoli R, Laurin D, Malavolti M, Barbolini G, Vinceti M, et al. Plant-Based Diets and Cognitive Outcomes: A Systematic Review and Meta-analysis. Adv Nutr. 2025;16:100537. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.advnut.2025.100537\u003c/span\u003e\u003cspan address=\"10.1016/j.advnut.2025.100537\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"dementia, dietary patterns, meat intake, cognitive function","lastPublishedDoi":"10.21203/rs.3.rs-8887266/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8887266/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eEvidence linking meat intake to dementia and cognitive performance remains mixed, and the results may depend on how meat intake is defined and modelled. Our study aimed to examine the associations between baseline meat intake frequency and (1) incident all-cause dementia during follow-up and (2) baseline cognitive performance in the UK Biobank.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe analysed UK Biobank 493,644 participants without prevalent dementia at baseline. Meat intake frequency was derived from touchscreen dietary questions on five meat types (processed meat, poultry, beef, lamb/mutton, pork) and defined using the maximum reported frequency across meat types. The primary exposure contrasted high-frequency versus low-frequency intake. Incident all-cause dementia was assessed through follow-up and analysed via Cox proportional hazards models with age as the underlying time scale, excluding dementia cases occurring within the first year after baseline. Baseline cognitive performance (reaction time, numeric memory, fluid intelligence) was analysed via linear regression. Several sensitivity analyses were additionally conducted to test the robustness. Dose-response analyses used five categories of maximum meat frequency (never for all meat types; \u0026lt;1 times/week; \u0026ge;1 times/week; \u0026ge;2\u0026ndash;4 times/week; \u0026ge;5\u0026ndash;6 times/week).\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eOver a mean follow-up of 14\u0026thinsp;\u0026plusmn;\u0026thinsp;2.4 years, 10,952 incident dementia cases occurred. According to the fully adjusted model, high-frequency meat intake was associated with higher dementia risk (HR 1.21; 95% CI 1.13\u0026ndash;1.31) and results were consistent across sensitivity analyses. For cognition, high-frequency meat intake was associated with slightly slower reaction time (n\u0026thinsp;=\u0026thinsp;489,151; β\u0026thinsp;=\u0026thinsp;0.036 SD, ~\u0026thinsp;4 ms), whereas associations with numeric memory were small and attenuated in sensitivity analyses (n\u0026thinsp;=\u0026thinsp;50,933; β = \u0026minus;0.068) and fluid intelligence was null after multivariable adjustment (n\u0026thinsp;=\u0026thinsp;163,390).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eHigher meat intake frequency\u0026mdash;particularly\u0026thinsp;\u0026ge;\u0026thinsp;5\u0026ndash;6 times/week\u0026mdash;was associated with increased dementia risk, whereas associations with baseline cognitive performance were modest and outcome specific. Further analyses that incorporate repeated cognitive assessments may help clarify whether meat intake is associated with cognitive trajectories over time.\u003c/p\u003e","manuscriptTitle":"Meat intake frequency and its associations with incident dementia and cognitive performance: Findings from the UK Biobank","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-18 09:10:32","doi":"10.21203/rs.3.rs-8887266/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":"3c979391-e313-4d71-ab3e-dd684ee6aa48","owner":[],"postedDate":"February 18th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Rejected","date":"2026-05-04T08:01:17+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-05-04T07:12:51+00:00","index":56,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-04T08:10:53+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-18 09:10:32","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8887266","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8887266","identity":"rs-8887266","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}