Sex differences in modifiable risk profiles for cognitive decline: findings from DeCo Chair Project

Sex differences in modifiable risk profiles for cognitive decline: findings from DeCo Chair Project | 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 Article Sex differences in modifiable risk profiles for cognitive decline: findings from DeCo Chair Project Mar García-Zamora, María Gil-Peinado, Juan Pardo, Lucrecia Moreno Royo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9265483/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Dementia prevention strategies increasingly focus on modifiable risk factors, yet sex-specific risk profiles remain insufficiently characterized. In addition to biological mechanisms, social, behavioural, and environmental determinants may shape sex-specific trajectories of cognitive aging. Using the framework of the Lancet Commission on dementia prevention, intervention, and care modifiable risk factors, we investigated sex differences in cumulative risk burden and their association with cognitive decline in a community-based population. We conducted a cross-sectional study including 1162 adults recruited in community pharmacies in Valencia (Spain) between 2018 and 2024 as part of the DeCo Chair project. A cumulative risk score based on Lancet Commission modifiable risk factors was calculated using population-attributable fraction weighting. Logistic regression analyses evaluated associations between individual risk factors and cognitive decline. Men accumulated a higher burden of modifiable risk factors, including smoking, higher body mass index, alcohol consumption, diabetes, and hearing loss. Higher BMI and traumatic brain injury history were associated with increased odds of cognitive decline in men. In contrast, women showed higher prevalence of low cognitive reserve, physical inactivity, depression, and social isolation, which were all associated with increased likelihood of cognitive decline. Higher cognitive reserve was protective in both sexes. These findings reveal distinct sex-specific risk profiles, with cardiometabolic and lifestyle risk factors predominating in men and psychosocial and cognitive reserve–related vulnerabilities in women. Our results highlight the importance of sex-tailored prevention strategies to optimize dementia risk reduction. Health sciences/Diseases Health sciences/Health care Health sciences/Risk factors sex aging cognitive decline lifestyle exercise depression diabetes BMI Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1 Introduction Dementia is a growing global health challenge. According to the World Health Organization, more than 57 million people were living with dementia worldwide in 2021, making it a major public health priority [ 1 ]. Alzheimer's disease accounts for approximately 60–70% of dementia cases[ 1 – 3 ] and is characterized by the accumulation of β-amyloid plaques[ 4 ] and tau protein aggregates [ 5 ]. Despite advances in disease-modifying therapies [ 6 ], prevention remains a key strategy for reducing dementia burden [ 7 ]. The Lancet Commission on dementia prevention, intervention, and care has identified 14 modifiable risk factors that together may account for up to 45% of dementia cases. These factors include lower education, hearing loss, high low-density lipoprotein cholesterol, depression, traumatic brain injury, physical inactivity, diabetes, smoking, hypertension, obesity, excessive alcohol consumption, social isolation, air pollution, and visual impairment [ 8 ]. Understanding how these factors interact and accumulate across populations is critical for developing effective prevention strategies [ 7 ]. Emerging evidence suggests that dementia risk is not distributed equally between sexes. Women have been consistently reported to show a higher prevalence of cognitive decline and dementia than men [ 9 – 16 ]. However, the extent to which modifiable dementia risk factors accumulate and contribute differently to cognitive decline (CD) in men and women remains insufficiently characterized [ 3 , 14 , 17 ]. Identifying sex-specific risk profiles may help refine prevention strategies and improve the targeting of interventions. Previous studies have also identified female sex as a non-modifiable risk factor for CD, together with genetic susceptibility [ 15 ]. Importantly, sex differences in dementia risk cannot be explained solely by women’s longer life expectancy. Increasing evidence suggests that distinct biological [ 10 – 12 ] and aging trajectories [ 11 , 12 , 14 , 18 ] contribute to these disparities, including hormonal transitions across the lifespan [ 11 , 12 , 14 , 19 , 20 ], differences in brain structure [ 11 , 14 ] and connectivity [ 11 ], small-vessel disease burden [ 11 , 21 ], and patterns of tau accumulation [ 11 , 12 ]. In this context, the present cross-sectional study aimed to examine sex differences in the distribution and impact of modifiable dementia risk factors on cognitive decline in a community-based population in Valencia (Spain). Using a cumulative risk score based on the Lancet Commission on dementia prevention, intervention, and care framework, we evaluated the burden of modifiable risk factors and their association with cognitive decline in men and women. 2 Materials and methods 2.1.- Study design The present work is a cross-sectional study carried out in community pharmacies and associations in Valencia (Spain), in collaboration with the Muy Ilustre Colegio Oficial de Farmacéuticos de Valencia (MICOF, the Official College of Pharmacists of Valencia), which provided financial support for the project. Different clinical and demographic data were collected contemporaneously with the screenings carried out from 2018 to 2024 through interviews. These data are part of the DeCo Chair project [ 22 – 24 ]. This study was reviewed and approved by the Ethical Committee for Clinical Research with Medications of the Arnau de Vilanova Health Department (CEIm 7/2022). All participants signed informed consent to participate in the study. The study was conducted in accordance with the ethical standards of the institutional and/or national research committee and with the Declaration of Helsinki and its later amendments and ethical standards. All participants gave written informed consent. 2.2.- Participants selection Individuals included in the initial population (n = 1469) were those whose informed consent was accepted and signed, and who did not have a diagnosis of AD or other dementias, mental illness, or other sensory deficits. Of these 1469 participants, specifically for this study, those individuals ≥ 60 years (n = 1162) were included (Fig. 1 ). 2.3.- Assessment of cognitive status To detect people with possible CD, three neuropsychological tests were carried out, following the recommendations of the Valencian Regional Ministry of Health [ 25 ]. Thus, participants were assessed with the following tests: Memory Impairment Screening (MIS) [ 26 ], Verbal Semantic Fluency (VSF) [ 27 ], and Pfeiffer's Short Portable Mental State Questionnaire (SPMSQ) [ 28 ]. Sensitivity, specificity, and test duration are shown in Supplementary Table 1. Participants with at least one positive cognitive test were classified as individuals with CD, and those who did not fail any test were classified as participants without CD. 2.4.- Variables The operationalization of the variables, including the selection of instruments, cut-off points, and scoring procedures, followed the methodology described in detail in our previous publication [ 29 ]. For this study specifically, of the 14 modifiable risk factors identified by The Lancet Commission [ 8 ], 13 were included in the present study. Air pollution was not included because all the participants live in the same urban area. A cumulative risk SCORE was created, assigning 1 point per risk factor present (description at Supplementary Table 2). Cognitive reserve was assessed using the Cognitive Reserve Questionnaire (CRQ), where values less than or equal to 6 indicate low cognitive reserve, values between 7 and 9 indicate medium/low cognitive reserve, values between 10 and 14 indicate medium/high cognitive reserve, and values greater than or equal to 15 indicate high cognitive reserve. Low and medium/low were assigned 1 point in the cumulative risk SCORE, while medium/high and high were scored 0 points. Depression was assessed using the Yesavage Scale for Geriatric Depression (GDS5), in which values ≥ 2 are associated with risk of depression (1 point), and values < 2 are associated with no depression (0 points). Social isolation was evaluated utilising the Lubben Social Network Scale (LSNS6), where values of 12 or more are associated with risk of isolation (1 point). Values < 12 were assigned 0 points. Physical inactivity was measured using the International Physical Activity Questionnaire (IPAQ). Low and moderate levels were scored as 1 point, and high intensity physical activity as 0 points. Additionally, obesity was classified according to body mass index (BMI). Normal weight scored 0 points, and obesity and overweight scored 1 point. LDL levels ≥ 100 mg/dL were assigned 1 point, and LDL levels lower than 100 mg/dL were assigned 0 points. Alcohol consumption was defined as intake of ≥ 1 standard alcohol dose per day. Finally, hearing loss, Traumatic Brain Injury (TBI), diabetes, hypertension, and visual loss were dichotomic variables (Yes = 1 point /No = 0 points). 2.5.- Statistical analyses First, descriptive analyses were performed using the Chi-square test for categorical variables. For quantitative variables, the Shapiro–Wilk test was used to assess normality. Since the variables did not follow a normal distribution, Mann-Whitney U-test was applied to compare the variables between women and men. Second, a cumulative risk SCORE separating sexes was calculated. Each variable was assigned a value of 0 or 1. Furthermore, the percentages assigned for The Lancet Commission of each factor’s contribution to cognitive decline were considered [ 8 ]. Subsequently, the Mann-Whitney U-test was employed to compare the median scores between women and men. The prevalence of each risk factor was calculated, differentiating between women and men. Finally, to assess whether the modifiable risk factors included in the study were associated with CD in women and men, logistic regression models were developed. These regressions were adjusted for age. All statistical tests performed were two-tailed, and a p-value < 0.05 indicated statistical significance. Furthermore, it is important to note that, as this is an exploratory study which aims to uncover new working hypotheses, no multiplicity adjustments were made [ 30 , 31 ]. Analyses were performed using RStudio (R version 2026.01.0 + 392) [ 32 ]. 3 Results 3.1.- Study population The final number of participants included in the study was 1162, of which 782 were women and 380 men (Fig. 1 ). As shown in Table 1 , there were no differences in age between women and men (p-value = 0.452). However, the percentage of men with a high level of cognitive reserve was significantly higher than that of women. In contrast, low cognitive reserve was more common among women (p-value = 0.002). Figure 1 . Flow chart of the study Table 1 Characteristics of the participants that differentiate between women and men. Variables Women (n = 782) Men (n = 380) p-value Age 73.0 ± 12.0 74.0 ± 12.8 0.455 Cognitive reserve Low 92 (11.8%) 40 (10.5%) 0.002 Medium/low 84 (10.7%) 40 (10.5%) Medium/high 131 (16.8%) 71 (18.7%) High 105 (13.4%) 97 (25.5%) Cardiovascular comorbidities Diabetes 150 (19.2%) 98 (25.8%) 0.018 Hypertension 427 (54.6%) 219 (57.6%) 0.340 LDL 110.0 ± 42.0 102.0 ± 42.0 0.126 Obesity BMI 26.6 ± 5.4 26.9 ± 4.2 0.104 Muscle mass 39.8 ± 5.2 54.8 ± 9.7 < 0.001 Body fat 36.6 ± 8.5 26.4 ± 8.7 < 0.001 Physical activity Low 125 (16.0%) 48 (12.6%) < 0.001 Moderate 206 (26.3%) 116 (30.5%) High 79 (10.1%) 81 (21.3%) Depression GDS5 No risk 285 (36.4%) 202 (53.2%) 0.008 Risk 148 (18.9%) 47 (12.4%) Social isolation LSNS6 No risk 355 (45.4%) 211 (55.5%) 0.756 Risk 58 (7.4%) 37 (9.7%) Hearing loss 155 (19.8) 114 (30.0) 0.011 Visual loss 64 (8.2) 31 (8.2) 0.194 Traumatic brain injury 20 (2.6) 12 (3.2) 0.884 Tobacco Ex - smoker 152 (19.4%) 175 (46.1%) < 0.001 Smoker 50 (6.4%) 40 (10.5%) Passive smoker 34 (4.3%) 16 (4.2%) Non-smoker 545 (69.7%) 151 (39.7%) Cognitive variables SMQ 487 (62.3%) 172 (45.3%) < 0.001 Cognitive decline 227 (29.0%) 98 (25.8%) 0.249 Pffeifer No risk 643 (82.2%) 332 (87.4%) 0.038 Risk 140 (17.9%) 50 (13.2%) MIS No risk 680 (87.0%) 320 (84.2%) 0.225 Risk 103 (13.2%) 60 (15.8%) FVS No risk 687 (87.9%) 342 (90.0%) 0.230 Risk 95 (12.1%) 37 (9.7%) Note . Data are presented as number (proportion, %) and median (interquartile range). Abbreviations : BMI: body mass index; GDS5: Geriatric Depression Scale – 5 items; LSNS6: Lubben Social Network Scale – 6 items; SMQ: subjective memory complaint; MIS: Memory Impairment Screen; FVS: Verbal Fluency Score. P-value <0.05 Regarding cardiovascular comorbidities, diabetes was more prevalent among men (p-value = 0.018). Nevertheless, muscle mass was higher and body fat lower in male participants compared to women (p-value <0.001). In addition, men were more prone to practicing high-intensity physical activity (p-value <0.001). Smoking was also more prevalent among men compared to women (p-value <0.001). Finally, depression (p-value = 0.008) and subjective memory complaint (SMC) (p-value <0.001) were more common in women than in men. 3.2.- Cumulative risk SCORE and prevalence of the different risk factors As illustrated in Figure 2, the median cumulative risk SCORE in men compared to women is significantly higher (Women: 15.75 ± 11.3; Men: 17.06 ± 13.9; p-value < 0.001). Figure 2. Violin plot of the weighted risk SCORE in women and men. Test: Mann-U Whitney Women (median ± interquartile range): 16.9 ± 11.3 Men (median ± interquartile range): 18.3 ± 13.9 Using the scoring given for the cumulative risk SCORE calculation, the prevalence of each risk factor was calculated. Low cognitive reserve (p-value = 0.010), physical inactivity (p-value <0.001), and depression (p-value <0.001) were significantly more prevalent among women (Figure 3 and Supplementary Table 3). Nevertheless, diabetes (p-value = 0.023), high BMI (p-value = 0.002), hearing loss (p-value = 0.014), alcohol consumption (p-value <0.001), and tobacco (p-value <0.001) were more prevalent in men (Figure 3 and Supplementary Table 3). Figure 3. Prevalence of risk factors for cognitive impairment, stratified by sex Abbreviations : LDL: low-density lipoprotein; BMI: body mass index; TBI: traumatic brain injury. P-value < 0.001 ***, <0.01 **, <0.05 * 3.3.- Association between the 13 modifiable risk factors and CD Compared to having low level of cognitive reserve, medium/low (Women: [0.50 (0.26, 0.95); p-value = 0.035]; Men: [0.34 (0.13, 0.87); p-value = 0.028]), medium/high (Women: [0.27 (0.14, 0.50); p-value <0.001]; Men: [0.24 (0.09, 0.58); p-value = 0.002]) and high level of cognitive reserve (Women: [0.23 (0.11, 0.45); p-value <0.001]; Men: [0.17 (0.07, 0.41); p-value <0.001]) were associated with less CD in both, women and men (Figure 4 and Supplementary Table 4). Figure 4. Forest plot of the comparison of the odds ratio (ORs) and confidence intervals (CI) of the different risk factors for cognitive decline in women and men. Abbreviations : LDL, low density lipoprotein; BMI, body mass index; TBI, traumatic brain injury Reference categories : Cognitive reserve: low level; LDL: < 100; Physical activity: low intensity; Tobacco: non-smoker; all other variables: absence of the condition P-value < 0.001 ***, <0.01 **, <0.05 * Additionally, high BMI (Women: [1.03 (0.99, 1.07); p-value = 0.117]; Men: [1.06 (1.00, 1.14); p-value = 0.044]) and TBI (Women: [0.82 (0.23, 2.35); p-value = 0.729]; Men: [4.46 (1.27, 15.84); p-value = 0.018]) were associated with increased likelihood of developing CD, but only in men (Figure 4 and Supplementary Table 4). In women, social isolation [2.15 (1.17, 3.90); p-value = 0.012] and depression [1.68 (1.06, 2.67); p-value = 0.027] may increase the likelihood of CD. In contrast, high intensity physical activity compared to low intensity, may be beneficial [0.26 (0.10, 0.58); p-value = 0.002]. 4 Discussion In this cross-sectional study of 1162 participants, a cumulative risk score was calculated using 13 of the 14 modifiable risk factors for dementia established by the Lancet Commission, weighted by their respective population-attributable fractions. This score showed that men had higher values than women, suggesting that men accumulate more modifiable risk factors for CD than women. Moreover, in our study, we have also examined the prevalence of these factors by sex. As shown in Figure 5, smoking, higher BMI values, drinking more alcohol, diabetes, and hearing loss were more prevalent among men. Additionally, logistic regression analyses showed that higher BMI and a history of TBI were associated with increased odds of CD in men. In contrast, lower cognitive reserve, engaging in less physical activity, and reporting depressive symptoms were more prevalent among women. Furthermore, these factors, along with social isolation, were associated with a higher likelihood of CD in women. Figure 5. Overview of modifiable CD risk factors in women and men Abbreviations: TBI: traumatic brain injury Created in https://BioRender.com It has been hypothesized that women experience a slower rate of CD before menopause; however, after menopause, this decline accelerates, leading to faster cognitive deterioration compared to men [12,18,20]. This finding is consistent with a longitudinal study showing that, in midlife, women performed better cognitively than men, but most sex differences in cognitive function diminished with aging [9]. Oestrogen receptors are distributed throughout the brain and regulate various physiological processes [12,20,33]. Indeed, some studies suggest that oestrogen may reduce beta-amyloid levels [12] and exert anti-inflammatory and antioxidant effects [20]. During menopause, there is a marked decrease in oestrogen levels [20], which may partly explain the observed acceleration of cognitive decline in women after menopause [14]. Nevertheless, a study involving 66670 participants found no significant sex differences in cognitive decline across the studied outcomes [34]. A further explanation for the higher prevalence of CD among women has been the lower levels of education. Education level has been identified as a significant risk factor for both dementia and CD [8]. Limited access to education reduces cognitive reserve and heightens CD vulnerability [35]. Historically, access to education has been restricted to men [11–14,18,19,35,36], while women have been confined to occupational roles that often limit their autonomy and remuneration and provide minimal cognitive stimulation [3,35]. Although low cognitive reserve is more prevalent among women in our cohort, higher levels of cognitive reserve have been shown to consistently lower CD likelihood across both sexes. Recent longitudinal analyses have revealed dynamic sex differences in late-life cognition. Women from later birth cohorts have been shown to exhibit better memory, attenuated fluency deficits, and slower memory decline. This highlights the role of education in narrowing the historical cognitive gaps between the sexes [36]. Furthermore, previous research has reported that women tend to have more adverse psychosocial profiles [18,20,37,38], whereas men more often exhibit unfavourable cardiovascular profiles [37]. However, after menopause, cardiovascular diseases increasingly affect women, being the leading cause of death among women worldwide [20]. Oestrogens also play an important role in cardiovascular health [20], which may partly account for these sex-specific patterns in cardiovascular risk. This agrees with our outcomes, diabetes and higher BMI were more prevalent among men. On the contrary, depression increased the likelihood of CD by almost 70%, and social isolation doubled this likelihood in women. Additionally, our results show that engaging in physical activity decreases the likelihood of CD by almost 75% in women. This is consistent with previous research in which physical inactivity has been associated with an increased risk of dementia [13,20]. Physical activity has been socially framed as a predominantly male activity, which may have limited women’s participation [14,39]. Nonetheless, prior evidence suggests that women may derive greater benefits from physical activity in later life, particularly in domains such as cognitive function, emotional well-being, and neural structure [35]. Men, in contrast, are more likely to participate in high-intensity activities and a broad range of sports that entail a higher risk of head impacts and injuries [18]. These are major sources of TBI. This pattern of exposure is relevant because TBI is a potentially modifiable risk for AD and other dementias [8]. Indeed, existing studies indicate an association between participation in professional contact sports and an increased risk of neurological disorders and neurodegenerative diseases [40]. These findings align with our results, which show that men who have experienced TBI face four times higher odds of CD than those who have not experienced it. Finally, consistent with prior epidemiological evidence [19,20,35,41], our results confirm that men exhibit a higher prevalence of smoking and excessive alcohol consumption, two modifiable risk factors independently linked to increased dementia risk [8]. These behavioural patterns likely contribute to men's elevated cumulative risk scores observed in our cohort. These findings suggest that women’s excess risk may not be explained solely by historically lower educational opportunities, as cognitive reserve appears to have a similar impact on women and men. Rather, they support the idea that women and men differ in their physiological vulnerability and in their risk profiles. This highlights the need for personalised prevention strategies and for further studies that explicitly incorporate sex differences in the assessment and modelling of dementia risk. Strengths and limitations One of the main strengths of this study is the use of face-to-face interviews, with all participants assessed in person during approximately one-hour structured interviews. This direct approach allowed for a comprehensive and standardized collection of clinical, lifestyle, and psychosocial data, reducing the risk of misinterpretation, missing information, and reporting bias commonly associated with self-administered or online questionnaires. Moreover, in-person assessments facilitated clarification of responses, improved data accuracy, and ensured inclusion of participants who might otherwise be underrepresented in digital-based studies, thereby enhancing the overall quality and reliability of the dataset. However, this study, while providing relevant evidence on the relationship between CD risk factors and sex in older adults, has several limitations that should be considered. First, the cross-sectional design limits the ability to infer causality between sex, risk factor burden, and CD. Although significant associations were observed, it is not possible to draw definitive conclusions about causality. The cross-sectional nature of the data does not allow examination of how risk factors and cognition change over time or how their trajectories may differ between women and men. In addition, the sample was recruited from community pharmacies and associations in the Valencian Community, using the same database as our previous work. This recruitment strategy and geographical restrictions may limit the generalizability of the findings to other regions or populations. These limitations highlight the need for larger, longitudinal studies that address these questions and allow for a more complete understanding of sex-specific patterns in dementia risk factors and cognitive outcomes. Despite these limitations, the findings of the present study contribute significantly to knowledge about sex differences in CD risk profiles in older adults and may help inform tailored prevention strategies. 5 Conclusion Our findings reveal distinct sex-specific profiles of modifiable risk factors associated with cognitive decline. Men showed a higher burden of cardiometabolic and lifestyle-related risk factors, whereas women presented greater prevalence of psychosocial vulnerabilities and lower cognitive reserve. Importantly, higher cognitive reserve was associated with a reduced likelihood of cognitive decline in both sexes. These results suggest that differences in modifiable risk profiles may contribute to sex disparities in cognitive aging. Considering sex-specific patterns of risk accumulation may therefore help refine dementia prevention strategies and support the development of more targeted and effective interventions. Integrating sex as a biological and social variable in dementia risk assessment could improve the design of preventive strategies aimed at reducing the global burden of cognitive decline. Declarations Author contributions Conceptualization : MGZ, MGP and LM; Data curation : MGZ and JP; Formal analysis : MGZ and JP; Funding acquisition : LM; Investigation : MGZ; Methodology : MGZ; Project administration : JP and LM; Resources : MGZ; Software : MGZ; Supervision : JP and LM; Validation : MGP, JP and LM; Visualization : MGZ, MGP, JP and LM; Writing-original draft : MGZ; Writing-review and editing : MGP, JP and LM. Funding sources This work was supported by Cathedra DeCo MICOF-CEU and Ayudas a la Formación de Jóvenes Investigadores SANTANDER-CEU. Data availability Data will be available upon request Declaration of generative AI and AI-assisted technologies in the manuscript preparation process During the preparation of this work the author(s) used chat GPT in order to rectify grammatical errors and enhance the clarity of the language. 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Med Clin (Barc). 2001;117:129–34. https://doi.org/10.1016/S0025-7753(01)72040-4 García C, Moreno L, Alacreu M, Muñoz FJ, Martínez LA. Addressing Psychosocial Factors in Cognitive Impairment Screening from a Holistic Perspective: The DeCo-Booklet Methodology Design and Pilot Study. Int J Environ Res Public Health. 2022;19:12911. https://doi.org/10.3390/ijerph191912911 Bender R, Lange S. Adjusting for multiple testing—when and how? J Clin Epidemiol. 2001;54:343–9. https://doi.org/10.1016/S0895-4356(00)00314-0 Rosendale N, Wood AJ, Leung CW, Kim AS, Caceres BA. Differences in Cardiovascular Health at the Intersection of Race, Ethnicity, and Sexual Identity. JAMA Netw Open. 2024;7:e249060. https://doi.org/10.1001/jamanetworkopen.2024.9060 https://posit.co/download/rstudio-desktop/. Posit R studio. 2026. Szoeke C, Downie SJ, Parker AF, Phillips S. Sex hormones, vascular factors and cognition. Front Neuroendocrinol. 2021;62:100927. https://doi.org/10.1016/j.yfrne.2021.100927 Wolfova K, Frycova B, Seblova D, Tom S, Skirbekk VF, Brennan Kearns P. Sex differences in cognitive decline among middle-aged and older adults: a cohort study in Europe. Age Ageing. 2024;53. https://doi.org/10.1093/ageing/afae078 Dong Y, Shi L, Ma Y, Liu T, Sun Y, Jin Q. Gender Differences in the Effects of Exercise Interventions on Alzheimer’s Disease. Brain Sci. 2025;15:812. https://doi.org/10.3390/brainsci15080812 Bloomberg M, Dugravot A, Dumurgier J, Kivimaki M, Fayosse A, Steptoe A, et al. Sex differences and the role of education in cognitive ageing: analysis of two UK-based prospective cohort studies. Lancet Public Health. 2021;6:e106–15. https://doi.org/10.1016/S2468-2667(20)30258-9 MacAulay RK, Halpin A, Cohen AS, Calamia M, Boeve A, Zhang L, et al. Predictors of Heterogeneity in Cognitive Function: APOE-e4, Sex, Education, Depression, and Vascular Risk. Archives of Clinical Neuropsychology. 2020;35:660–70. https://doi.org/10.1093/arclin/acaa014 Ren Y, Savadlou A, Park S, Siska P, Epp JR, Sargin D. The impact of loneliness and social isolation on the development of cognitive decline and Alzheimer’s Disease. Front Neuroendocrinol. 2023;69:101061. https://doi.org/10.1016/j.yfrne.2023.101061 Anstey KJ, Peters R, Mortby ME, Kiely KM, Eramudugolla R, Cherbuin N, et al. Association of sex differences in dementia risk factors with sex differences in memory decline in a population-based cohort spanning 20–76 years. Sci Rep. 2021;11:7710. https://doi.org/10.1038/s41598-021-86397-7 Iverson GL, Castellani RJ, Cassidy JD, Schneider GM, Schneider KJ, Echemendia RJ, et al. Examining later-in-life health risks associated with sport-related concussion and repetitive head impacts: a systematic review of case-control and cohort studies. Br J Sports Med. 2023;57:810–24. https://doi.org/10.1136/bjsports-2023-106890 Nianogo RA, Rosenwohl-Mack A, Yaffe K, Carrasco A, Hoffmann CM, Barnes DE. Risk Factors Associated With Alzheimer Disease and Related Dementias by Sex and Race and Ethnicity in the US. JAMA Neurol. 2022;79:584. https://doi.org/10.1001/jamaneurol.2022.0976 Additional Declarations No competing interests reported. Supplementary Files Graphicalabstract.jpg SupplementarynpjAging.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers invited by journal 29 Apr, 2026 Editor assigned by journal 29 Apr, 2026 Editor invited by journal 07 Apr, 2026 Submission checks completed at journal 01 Apr, 2026 First submitted to journal 01 Apr, 2026 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. 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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-9265483","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":632945782,"identity":"69b3a8c3-5f08-4ca0-bbfa-58649ba5374d","order_by":0,"name":"Mar García-Zamora","email":"","orcid":"","institution":"Cathedra DeCo MICOF-CEU UCH","correspondingAuthor":false,"prefix":"","firstName":"Mar","middleName":"","lastName":"García-Zamora","suffix":""},{"id":632945783,"identity":"9de42de7-28db-42d4-80f1-88db76207c53","order_by":1,"name":"María Gil-Peinado","email":"","orcid":"","institution":"Muy Ilustre Colegio Oficial de Farmacéuticos de Valencia","correspondingAuthor":false,"prefix":"","firstName":"María","middleName":"","lastName":"Gil-Peinado","suffix":""},{"id":632945784,"identity":"a76f5729-25f3-499d-8836-6739942b6f9b","order_by":2,"name":"Juan Pardo","email":"","orcid":"","institution":"CEU Cardinal Herrera University","correspondingAuthor":false,"prefix":"","firstName":"Juan","middleName":"","lastName":"Pardo","suffix":""},{"id":632945785,"identity":"abb6f5e3-f2b9-4dfc-9ec2-c92f494de9d9","order_by":3,"name":"Lucrecia Moreno Royo","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAu0lEQVRIiWNgGAWjYBACPigtx8DAQ6QWNihtTLqWxAbitYgdPvbh4w6b9A3Hzx788LGNQU6+gZAW6bTkmTPPpOVuOJOXLDmzjcHY4ABBLTnGzLxth3M33OAxAzIYEjcQdBhIy9+2w+kGIC1/2xjq5xN2GFALY9vhBLAWxjaGBAbCDktLZuxtSzOceSbHWLLnnIThBkJa+KWTDzP8bLOR5zt+xvDDjzIbeYIhhg4kSFQ/CkbBKBgFowArAAAtOThFsu0HEwAAAABJRU5ErkJggg==","orcid":"","institution":"CEU Cardinal Herrera University","correspondingAuthor":true,"prefix":"","firstName":"Lucrecia","middleName":"Moreno","lastName":"Royo","suffix":""}],"badges":[],"createdAt":"2026-03-30 10:26:04","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9265483/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9265483/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":108718033,"identity":"5e9205d0-404f-4083-9540-173c7eb5745b","added_by":"auto","created_at":"2026-05-07 15:26:48","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":44794,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart of the study\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-9265483/v1/b6fde6596c80881008073534.png"},{"id":108718063,"identity":"2bc909db-496e-4cc7-9819-c5a8b46aecee","added_by":"auto","created_at":"2026-05-07 15:27:04","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":3143120,"visible":true,"origin":"","legend":"\u003cp\u003eViolin plot of the weighted risk SCORE in women and men. Test: Mann-U Whitney\u003c/p\u003e\n\u003cp\u003eWomen (median ± interquartile range): 16.9 ± 11.3\u003c/p\u003e\n\u003cp\u003eMen (median ± interquartile range): 18.3 ± 13.9\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-9265483/v1/fe4fff39df8047d79512aa43.png"},{"id":108718038,"identity":"822aca5f-0de0-44cc-810c-d5985b326340","added_by":"auto","created_at":"2026-05-07 15:26:49","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":142944,"visible":true,"origin":"","legend":"\u003cp\u003ePrevalence of risk factors for cognitive impairment, stratified by sex\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations\u003c/strong\u003e: LDL: low-density lipoprotein; BMI: body mass index; TBI: traumatic brain injury. \u003cem\u003eP-value \u0026lt; 0.001 ***, \u0026lt;0.01 **, \u0026lt;0.05 *\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-9265483/v1/81140098a24cf5481e174454.png"},{"id":108718073,"identity":"ebd4944c-280f-4f4f-b882-f01d4b78a27d","added_by":"auto","created_at":"2026-05-07 15:27:06","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":4834476,"visible":true,"origin":"","legend":"\u003cp\u003eForest plot of the comparison of the odds ratio (ORs) and confidence intervals (CI) of the different risk factors for cognitive decline in women and men.\u003c/p\u003e\n\u003cp\u003eAbbreviations: LDL, low density lipoprotein; BMI, body mass index; TBI, traumatic brain injury\u003c/p\u003e\n\u003cp\u003eReference categories: Cognitive reserve: low level; LDL: \u0026lt; 100; Physical activity: low intensity; Tobacco: non-smoker; all other variables: absence of the condition\u003c/p\u003e\n\u003cp\u003eP-value \u0026lt; 0.001 ***, \u0026lt;0.01 **, \u0026lt;0.05 *\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-9265483/v1/5d887433b459f788b9260d25.png"},{"id":108718006,"identity":"ffc33c15-fffa-498c-af18-de223550e435","added_by":"auto","created_at":"2026-05-07 15:26:46","extension":"jpeg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":881062,"visible":true,"origin":"","legend":"\u003cp\u003eOverview of modifiable CD risk factors in women and men\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations:\u003c/strong\u003e TBI: traumatic brain injury\u003c/p\u003e\n\u003cp\u003eCreated in https://BioRender.com\u003c/p\u003e","description":"","filename":"Figure5.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9265483/v1/23cbc246a8d490ba86f55e0a.jpeg"},{"id":108718151,"identity":"631c3ccc-7201-407f-a84d-5961ea9378c4","added_by":"auto","created_at":"2026-05-07 15:27:29","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7489750,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9265483/v1/93ec8164-a054-48e9-b16d-60d5e9b50fad.pdf"},{"id":108718005,"identity":"4c806aa4-80d0-4236-8da2-8528b53d93e7","added_by":"auto","created_at":"2026-05-07 15:26:44","extension":"jpg","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":212870,"visible":true,"origin":"","legend":"","description":"","filename":"Graphicalabstract.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9265483/v1/5ad6f5517ebf04f547ac130a.jpg"},{"id":108718035,"identity":"e2c048ad-e5d9-4a9e-b76f-abf2f670e7b3","added_by":"auto","created_at":"2026-05-07 15:26:48","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":34341,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementarynpjAging.docx","url":"https://assets-eu.researchsquare.com/files/rs-9265483/v1/d2e2b964c1ab924cd30afa3e.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Sex differences in modifiable risk profiles for cognitive decline: findings from DeCo Chair Project","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eDementia is a growing global health challenge. According to the World Health Organization, more than 57\u0026nbsp;million people were living with dementia worldwide in 2021, making it a major public health priority [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Alzheimer's disease accounts for approximately 60\u0026ndash;70% of dementia cases[\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e] and is characterized by the accumulation of β-amyloid plaques[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e] and tau protein aggregates [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDespite advances in disease-modifying therapies [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], prevention remains a key strategy for reducing dementia burden [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The Lancet Commission on dementia prevention, intervention, and care has identified 14 modifiable risk factors that together may account for up to 45% of dementia cases. These factors include lower education, hearing loss, high low-density lipoprotein cholesterol, depression, traumatic brain injury, physical inactivity, diabetes, smoking, hypertension, obesity, excessive alcohol consumption, social isolation, air pollution, and visual impairment [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Understanding how these factors interact and accumulate across populations is critical for developing effective prevention strategies [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eEmerging evidence suggests that dementia risk is not distributed equally between sexes. Women have been consistently reported to show a higher prevalence of cognitive decline and dementia than men [\u003cspan additionalcitationids=\"CR10 CR11 CR12 CR13 CR14 CR15\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. However, the extent to which modifiable dementia risk factors accumulate and contribute differently to cognitive decline (CD) in men and women remains insufficiently characterized [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Identifying sex-specific risk profiles may help refine prevention strategies and improve the targeting of interventions. Previous studies have also identified female sex as a non-modifiable risk factor for CD, together with genetic susceptibility [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Importantly, sex differences in dementia risk cannot be explained solely by women\u0026rsquo;s longer life expectancy. Increasing evidence suggests that distinct biological [\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] and aging trajectories [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] contribute to these disparities, including hormonal transitions across the lifespan [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], differences in brain structure [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] and connectivity [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], small-vessel disease burden [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], and patterns of tau accumulation [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this context, the present cross-sectional study aimed to examine sex differences in the distribution and impact of modifiable dementia risk factors on cognitive decline in a community-based population in Valencia (Spain). Using a cumulative risk score based on the Lancet Commission on dementia prevention, intervention, and care framework, we evaluated the burden of modifiable risk factors and their association with cognitive decline in men and women.\u003c/p\u003e"},{"header":"2 Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1.- Study design\u003c/h2\u003e \u003cp\u003e The present work is a cross-sectional study carried out in community pharmacies and associations in Valencia (Spain), in collaboration with the Muy Ilustre Colegio Oficial de Farmac\u0026eacute;uticos de Valencia (MICOF, the Official College of Pharmacists of Valencia), which provided financial support for the project.\u003c/p\u003e \u003cp\u003eDifferent clinical and demographic data were collected contemporaneously with the screenings carried out from 2018 to 2024 through interviews. These data are part of the DeCo Chair project [\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e This study was reviewed and approved by the Ethical Committee for Clinical Research with Medications of the Arnau de Vilanova Health Department (CEIm 7/2022). All participants signed informed consent to participate in the study.\u003c/p\u003e \u003cp\u003e The study was conducted in accordance with the ethical standards of the institutional and/or national research committee and with the Declaration of Helsinki and its later amendments and ethical standards. All participants gave written informed consent.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2.- Participants selection\u003c/h2\u003e \u003cp\u003e Individuals included in the initial population (n\u0026thinsp;=\u0026thinsp;1469) were those whose informed consent was accepted and signed, and who did not have a diagnosis of AD or other dementias, mental illness, or other sensory deficits.\u003c/p\u003e \u003cp\u003eOf these 1469 participants, specifically for this study, those individuals\u0026thinsp;\u0026ge;\u0026thinsp;60 years (n\u0026thinsp;=\u0026thinsp;1162) were included (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3.- Assessment of cognitive status\u003c/h2\u003e \u003cp\u003eTo detect people with possible CD, three neuropsychological tests were carried out, following the recommendations of the Valencian Regional Ministry of Health [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Thus, participants were assessed with the following tests: Memory Impairment Screening (MIS) [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], Verbal Semantic Fluency (VSF) [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e], and Pfeiffer's Short Portable Mental State Questionnaire (SPMSQ) [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Sensitivity, specificity, and test duration are shown in Supplementary Table\u0026nbsp;1.\u003c/p\u003e \u003cp\u003eParticipants with at least one positive cognitive test were classified as individuals with CD, and those who did not fail any test were classified as participants without CD.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4.- Variables\u003c/h2\u003e \u003cp\u003eThe operationalization of the variables, including the selection of instruments, cut-off points, and scoring procedures, followed the methodology described in detail in our previous publication [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. For this study specifically, of the 14 modifiable risk factors identified by The Lancet Commission [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], 13 were included in the present study. Air pollution was not included because all the participants live in the same urban area.\u003c/p\u003e \u003cp\u003eA cumulative risk SCORE was created, assigning 1 point per risk factor present (description at Supplementary Table\u0026nbsp;2).\u003c/p\u003e \u003cp\u003eCognitive reserve was assessed using the Cognitive Reserve Questionnaire (CRQ), where values less than or equal to 6 indicate low cognitive reserve, values between 7 and 9 indicate medium/low cognitive reserve, values between 10 and 14 indicate medium/high cognitive reserve, and values greater than or equal to 15 indicate high cognitive reserve. Low and medium/low were assigned 1 point in the cumulative risk SCORE, while medium/high and high were scored 0 points.\u003c/p\u003e \u003cp\u003eDepression was assessed using the Yesavage Scale for Geriatric Depression (GDS5), in which values\u0026thinsp;\u0026ge;\u0026thinsp;2 are associated with risk of depression (1 point), and values\u0026thinsp;\u0026lt;\u0026thinsp;2 are associated with no depression (0 points). Social isolation was evaluated utilising the Lubben Social Network Scale (LSNS6), where values of 12 or more are associated with risk of isolation (1 point). Values\u0026thinsp;\u0026lt;\u0026thinsp;12 were assigned 0 points.\u003c/p\u003e \u003cp\u003ePhysical inactivity was measured using the International Physical Activity Questionnaire (IPAQ). Low and moderate levels were scored as 1 point, and high intensity physical activity as 0 points. Additionally, obesity was classified according to body mass index (BMI). Normal weight scored 0 points, and obesity and overweight scored 1 point.\u003c/p\u003e \u003cp\u003eLDL levels\u0026thinsp;\u0026ge;\u0026thinsp;100 mg/dL were assigned 1 point, and LDL levels lower than 100 mg/dL were assigned 0 points. Alcohol consumption was defined as intake of \u0026ge;\u0026thinsp;1 standard alcohol dose per day.\u003c/p\u003e \u003cp\u003eFinally, hearing loss, Traumatic Brain Injury (TBI), diabetes, hypertension, and visual loss were dichotomic variables (Yes\u0026thinsp;=\u0026thinsp;1 point /No\u0026thinsp;=\u0026thinsp;0 points).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5.- Statistical analyses\u003c/h2\u003e \u003cp\u003eFirst, descriptive analyses were performed using the Chi-square test for categorical variables. For quantitative variables, the Shapiro\u0026ndash;Wilk test was used to assess normality. Since the variables did not follow a normal distribution, Mann-Whitney U-test was applied to compare the variables between women and men.\u003c/p\u003e \u003cp\u003eSecond, a cumulative risk SCORE separating sexes was calculated. Each variable was assigned a value of 0 or 1. Furthermore, the percentages assigned for The Lancet Commission of each factor\u0026rsquo;s contribution to cognitive decline were considered [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Subsequently, the Mann-Whitney U-test was employed to compare the median scores between women and men. The prevalence of each risk factor was calculated, differentiating between women and men.\u003c/p\u003e \u003cp\u003eFinally, to assess whether the modifiable risk factors included in the study were associated with CD in women and men, logistic regression models were developed. These regressions were adjusted for age.\u003c/p\u003e \u003cp\u003eAll statistical tests performed were two-tailed, and a p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 indicated statistical significance. Furthermore, it is important to note that, as this is an exploratory study which aims to uncover new working hypotheses, no multiplicity adjustments were made [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Analyses were performed using RStudio (R version 2026.01.0\u0026thinsp;+\u0026thinsp;392) [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003e3.1.- Study population\u003c/h2\u003e\n \u003cp\u003eThe final number of participants included in the study was 1162, of which 782 were women and 380 men (Fig. \u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eAs shown in Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, there were no differences in age between women and men (p-value\u0026thinsp;=\u0026thinsp;0.452). However, the percentage of men with a high level of cognitive reserve was significantly higher than that of women. In contrast, low cognitive reserve was more common among women (p-value\u0026thinsp;=\u0026thinsp;0.002).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eFigure\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e Flow chart of the study\u003c/p\u003e\n \u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCharacteristics of the participants that differentiate between women and men.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003eWomen\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;782)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003eMen\u003c/p\u003e\n \u003cp\u003e(n\u0026thinsp;=\u0026thinsp;380)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003ep-value\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e73.0\u0026thinsp;\u0026plusmn;\u0026thinsp;12.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e74.0\u0026thinsp;\u0026plusmn;\u0026thinsp;12.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.455\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eCognitive reserve\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e92 (11.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e40 (10.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.002\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eMedium/low\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e84 (10.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e40 (10.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eMedium/high\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e131 (16.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e71 (18.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e105 (13.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e97 (25.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colspan=\"4\" nameend=\"c4\" namest=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eCardiovascular comorbidities\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eDiabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e150 (19.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e98 (25.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.018\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eHypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e427 (54.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e219 (57.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.340\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eLDL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e110.0\u0026thinsp;\u0026plusmn;\u0026thinsp;42.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e102.0\u0026thinsp;\u0026plusmn;\u0026thinsp;42.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.126\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eObesity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e26.6\u0026thinsp;\u0026plusmn;\u0026thinsp;5.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e26.9\u0026thinsp;\u0026plusmn;\u0026thinsp;4.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.104\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eMuscle mass\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e39.8\u0026thinsp;\u0026plusmn;\u0026thinsp;5.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e54.8\u0026thinsp;\u0026plusmn;\u0026thinsp;9.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eBody fat\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e36.6\u0026thinsp;\u0026plusmn;\u0026thinsp;8.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e26.4\u0026thinsp;\u0026plusmn;\u0026thinsp;8.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003ePhysical activity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eLow\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e125 (16.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e48 (12.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eModerate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e206 (26.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e116 (30.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eHigh\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e79 (10.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e81 (21.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eDepression\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eGDS5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eNo risk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e285 (36.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e202 (53.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.008\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eRisk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e148 (18.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e47 (12.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eSocial isolation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eLSNS6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eNo risk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e355 (45.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e211 (55.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003e0.756\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eRisk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e58 (7.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e37 (9.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eHearing loss\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e155 (19.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e114 (30.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.011\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eVisual loss\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e64 (8.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e31 (8.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.194\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eTraumatic brain injury\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e20 (2.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e12 (3.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.884\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eTobacco\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eEx - smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e152 (19.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e175 (46.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eSmoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e50 (6.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e40 (10.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003ePassive smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e34 (4.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e16 (4.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eNon-smoker\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e545 (69.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e151 (39.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003e\u003cstrong\u003eCognitive variables\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eSMQ\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e487 (62.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e172 (45.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;\u0026thinsp;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eCognitive decline\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e227 (29.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e98 (25.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\n \u003cp\u003e0.249\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003ePffeifer\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eNo risk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e643 (82.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e332 (87.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.038\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eRisk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e140 (17.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e50 (13.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eMIS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eNo risk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e680 (87.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e320 (84.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003e0.225\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eRisk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e103 (13.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e60 (15.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eFVS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eNo risk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e687 (87.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e342 (90.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e\n \u003cp\u003e0.230\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" colname=\"c1\"\u003e\n \u003cp\u003eRisk\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c2\"\u003e\n \u003cp\u003e95 (12.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\" colname=\"c3\"\u003e\n \u003cp\u003e37 (9.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"4\"\u003e\u003cstrong\u003eNote\u003c/strong\u003e. Data are presented as number (proportion, %) and median (interquartile range).\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003cstrong\u003eAbbreviations\u003c/strong\u003e: BMI: body mass index; GDS5: Geriatric Depression Scale \u0026ndash; 5 items; LSNS6: Lubben Social Network Scale \u0026ndash; 6 items; SMQ: subjective memory complaint; MIS: Memory Impairment Screen; FVS: Verbal Fluency Score. \u003cstrong\u003eP-value\u003c/strong\u003e \u0026lt;0.05\u003c/p\u003e\n \u003cp\u003eRegarding cardiovascular comorbidities, diabetes was more prevalent among men (p-value = 0.018). Nevertheless, muscle mass was higher and body fat lower in male participants compared to women (p-value \u0026lt;0.001). In addition, men were more prone to practicing high-intensity physical activity (p-value \u0026lt;0.001). Smoking was also more prevalent among men compared to women (p-value \u0026lt;0.001).\u003c/p\u003e\n \u003cp\u003eFinally, depression (p-value = 0.008) and subjective memory complaint (SMC) (p-value \u0026lt;0.001) were more common in women than in men.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e3.2.- Cumulative risk SCORE and prevalence of the different risk factors\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eAs illustrated in Figure 2, the median cumulative risk SCORE in men compared to women is significantly higher (Women: 15.75 \u0026plusmn; 11.3; Men: 17.06 \u0026plusmn; 13.9; p-value \u0026lt; 0.001).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eFigure 2.\u003c/strong\u003e Violin plot of the weighted risk SCORE in women and men. Test: Mann-U Whitney\u003c/p\u003e\n \u003cp\u003eWomen (median \u0026plusmn; interquartile range): 16.9 \u0026plusmn; 11.3\u003c/p\u003e\n \u003cp\u003eMen (median \u0026plusmn; interquartile range): \u0026nbsp;18.3 \u0026plusmn; 13.9\u003c/p\u003e\n \u003cp\u003eUsing the scoring given for the cumulative risk SCORE calculation, the prevalence of each risk factor was calculated. Low cognitive reserve (p-value = 0.010), physical inactivity (p-value \u0026lt;0.001), and depression (p-value \u0026lt;0.001) were significantly more prevalent among women (Figure 3 and Supplementary Table 3). Nevertheless, diabetes (p-value = 0.023), high BMI (p-value = 0.002), hearing loss (p-value = 0.014), alcohol consumption (p-value \u0026lt;0.001), and tobacco (p-value \u0026lt;0.001) were more prevalent in men (Figure 3 and Supplementary Table 3).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eFigure 3.\u003c/strong\u003e Prevalence of risk factors for cognitive impairment, stratified by sex\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eAbbreviations\u003c/strong\u003e: LDL: low-density lipoprotein; BMI: body mass index; TBI: traumatic brain injury. \u003cem\u003eP-value \u0026lt; 0.001 ***, \u0026lt;0.01 **, \u0026lt;0.05 *\u003c/em\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e3.3.- Association between the 13 modifiable risk factors and CD\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eCompared to having low level of cognitive reserve, medium/low (Women: [0.50 (0.26, 0.95); p-value = 0.035]; Men: [0.34 (0.13, 0.87); p-value = 0.028]), medium/high \u0026nbsp;(Women: [0.27 (0.14, 0.50); p-value \u0026lt;0.001]; Men: [0.24 (0.09, 0.58); p-value = 0.002]) and high level of cognitive reserve (Women: [0.23 (0.11, 0.45); p-value \u0026lt;0.001]; Men: [0.17 (0.07, 0.41); p-value \u0026lt;0.001]) were associated with less CD in both, women and men (Figure 4 and Supplementary Table 4).\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eFigure 4.\u003c/strong\u003e Forest plot of the comparison of the odds ratio (ORs) and confidence intervals (CI) of the different risk factors for cognitive decline in women and men.\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eAbbreviations\u003c/strong\u003e: LDL, low density lipoprotein; BMI, body mass index; TBI, traumatic brain injury\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eReference\u003c/strong\u003e \u003cstrong\u003ecategories\u003c/strong\u003e: Cognitive reserve: low level; LDL: \u0026lt; 100; Physical activity: low intensity; Tobacco: non-smoker; all other variables: absence of the condition\u003c/p\u003e\n \u003cp\u003eP-value \u0026lt; 0.001 ***, \u0026lt;0.01 **, \u0026lt;0.05 *\u003c/p\u003e\n \u003cp\u003eAdditionally, high BMI (Women: [1.03 (0.99, 1.07); p-value = 0.117]; Men: [1.06 (1.00, 1.14); p-value = 0.044]) and TBI (Women: [0.82 (0.23, 2.35); p-value = 0.729]; Men: [4.46 (1.27, 15.84); p-value = 0.018]) were associated with increased likelihood of developing CD, but only in men (Figure 4 and Supplementary Table 4).\u003c/p\u003e\n \u003cp\u003eIn women, social isolation [2.15 (1.17, 3.90); p-value = 0.012] and depression [1.68 (1.06, 2.67); p-value = 0.027] may increase the likelihood of CD. In contrast, high intensity physical activity compared to low intensity, may be beneficial [0.26 (0.10, 0.58); p-value = 0.002].\u003c/p\u003e\n\u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eIn this cross-sectional study of 1162 participants, a cumulative risk score was calculated using 13 of the 14 modifiable risk factors for dementia established by the Lancet Commission, weighted by their respective population-attributable fractions. This score showed that men had higher values than women, suggesting that men accumulate more modifiable risk factors for CD than women.\u003c/p\u003e\n\u003cp\u003eMoreover, in our study, we have also examined the prevalence of these factors by sex. As shown in Figure 5, smoking, higher BMI values, drinking more alcohol, diabetes, and hearing loss were more prevalent among men. Additionally, logistic regression analyses showed that higher BMI and a history of TBI were associated with increased odds of CD in men. In contrast, lower cognitive reserve, engaging in less physical activity, and reporting depressive symptoms were more prevalent among women. Furthermore, these factors, along with social isolation, were associated with a higher likelihood of CD in women.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFigure 5.\u003c/strong\u003e Overview of modifiable CD risk factors in women and men\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAbbreviations:\u003c/strong\u003e TBI: traumatic brain injury\u003c/p\u003e\n\u003cp\u003eCreated in https://BioRender.com\u003c/p\u003e\n\u003cp\u003eIt has been hypothesized that women experience a slower rate of CD before menopause; however, after menopause, this decline accelerates, leading to faster cognitive deterioration compared to men [12,18,20]. This finding is consistent with a longitudinal study showing that, in midlife, women performed better cognitively than men, but most sex differences in cognitive function diminished with aging [9]. Oestrogen receptors are distributed throughout the brain and regulate various physiological processes [12,20,33]. Indeed, some studies suggest that oestrogen may reduce beta-amyloid levels [12] and exert anti-inflammatory and antioxidant effects [20]. During menopause, there is a marked decrease in oestrogen levels [20], which may partly explain the observed acceleration of cognitive decline in women after menopause [14]. Nevertheless, a study involving 66670 participants found no significant sex differences in cognitive decline across the studied outcomes [34].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA further explanation for the higher prevalence of CD among women has been the lower levels of education. Education level has been identified as a significant risk factor for both dementia and CD [8]. Limited access to education reduces cognitive reserve and heightens CD vulnerability [35]. Historically, access to education has been restricted to men [11\u0026ndash;14,18,19,35,36], while women have been confined to occupational roles that often limit their autonomy and remuneration and provide minimal cognitive stimulation [3,35]. Although low cognitive reserve is more prevalent among women in our cohort, higher levels of cognitive reserve have been shown to consistently lower CD likelihood across both sexes. Recent longitudinal analyses have revealed dynamic sex differences in late-life cognition. Women from later birth cohorts have been shown to exhibit better memory, attenuated fluency deficits, and slower memory decline. This highlights the role of education in narrowing the historical cognitive gaps between the sexes [36].\u003c/p\u003e\n\u003cp\u003eFurthermore, previous research has reported that women tend to have more adverse psychosocial profiles [18,20,37,38], whereas men more often exhibit unfavourable cardiovascular profiles [37]. However, after menopause, cardiovascular diseases increasingly affect women, being the leading cause of death among women worldwide [20]. Oestrogens also play an important role in cardiovascular health [20], which may partly account for these sex-specific patterns in cardiovascular risk. This agrees with our outcomes, diabetes and higher BMI were more prevalent among men. On the contrary, depression increased the likelihood of CD by almost 70%, and social isolation doubled this likelihood in women.\u003c/p\u003e\n\u003cp\u003eAdditionally, our results show that engaging in physical activity decreases the likelihood of CD by almost 75% in women. This is consistent with previous research in which physical inactivity has been associated with an increased risk of dementia [13,20].\u0026nbsp;Physical activity has been socially framed as a predominantly male activity, which may have limited women\u0026rsquo;s participation [14,39]. Nonetheless, prior evidence suggests that women may derive greater benefits from physical activity in later life, particularly in domains such as cognitive function, emotional well-being, and neural structure [35].\u003c/p\u003e\n\u003cp\u003eMen, in contrast, are more likely to participate in high-intensity activities and a broad range of sports that entail a higher risk of head impacts and injuries [18]. These are major sources of TBI. This pattern of exposure is relevant because TBI is a potentially modifiable risk for AD and other dementias [8]. Indeed, existing studies indicate an association between participation in professional contact sports and an increased risk of neurological disorders and neurodegenerative diseases [40]. These findings align with our results, which show that men who have experienced TBI face four times higher odds of CD than those who have not experienced it.\u003c/p\u003e\n\u003cp\u003eFinally, consistent with prior epidemiological evidence [19,20,35,41], our results confirm that men exhibit a higher prevalence of smoking and excessive alcohol consumption, two modifiable risk factors independently linked to increased dementia risk [8]. These behavioural patterns likely contribute to men\u0026apos;s elevated cumulative risk scores observed in our cohort.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThese findings suggest that women\u0026rsquo;s excess risk may not be explained solely by historically lower educational opportunities, as cognitive reserve appears to have a similar impact on women and men. Rather, they support the idea that women and men differ in their physiological vulnerability and in their risk profiles. This highlights the need for personalised prevention strategies and for further studies that explicitly incorporate sex differences in the assessment and modelling of dementia risk.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStrengths and limitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOne of the main strengths of this study is the use of face-to-face interviews, with all participants assessed in person during approximately one-hour structured interviews. This direct approach allowed for a comprehensive and standardized collection of clinical, lifestyle, and psychosocial data, reducing the risk of misinterpretation, missing information, and reporting bias commonly associated with self-administered or online questionnaires. Moreover, in-person assessments facilitated clarification of responses, improved data accuracy, and ensured inclusion of participants who might otherwise be underrepresented in digital-based studies, thereby enhancing the overall quality and reliability of the dataset.\u003c/p\u003e\n\u003cp\u003eHowever, this study, while providing relevant evidence on the relationship between CD risk factors and sex in older adults, has several limitations that should be considered.\u003c/p\u003e\n\u003cp\u003eFirst, the cross-sectional design limits the ability to infer causality between sex, risk factor burden, and CD. Although significant associations were observed, it is not possible to draw definitive conclusions about causality. The cross-sectional nature of the data does not allow examination of how risk factors and cognition change over time or how their trajectories may differ between women and men.\u003c/p\u003e\n\u003cp\u003eIn addition, the sample was recruited from\u0026nbsp;community pharmacies and associations in the Valencian Community, using the same database as our previous work. This recruitment strategy and geographical restrictions may limit the generalizability of the findings to other regions or populations.\u003c/p\u003e\n\u003cp\u003eThese limitations highlight the need for larger, longitudinal studies that address these questions and allow for a more complete understanding of sex-specific patterns in dementia risk factors and cognitive outcomes. Despite these limitations, the findings of the present study contribute significantly to knowledge about sex differences in CD risk profiles in older adults and may help inform tailored prevention strategies.\u003c/p\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eOur findings reveal distinct sex-specific profiles of modifiable risk factors associated with cognitive decline. Men showed a higher burden of cardiometabolic and lifestyle-related risk factors, whereas women presented greater prevalence of psychosocial vulnerabilities and lower cognitive reserve. Importantly, higher cognitive reserve was associated with a reduced likelihood of cognitive decline in both sexes.\u003c/p\u003e\n\u003cp\u003eThese results suggest that differences in modifiable risk profiles may contribute to sex disparities in cognitive aging. Considering sex-specific patterns of risk accumulation may therefore help refine dementia prevention strategies and support the development of more targeted and effective interventions.\u003c/p\u003e\n\u003cp\u003eIntegrating sex as a biological and social variable in dementia risk assessment could improve the design of preventive strategies aimed at reducing the global burden of cognitive decline.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConceptualization\u003c/strong\u003e: MGZ, MGP and LM;\u0026nbsp;\u003cstrong\u003eData curation\u003c/strong\u003e: MGZ and JP;\u0026nbsp;\u003cstrong\u003eFormal analysis\u003c/strong\u003e: MGZ and JP;\u0026nbsp;\u003cstrong\u003eFunding acquisition\u003c/strong\u003e: LM;\u0026nbsp;\u003cstrong\u003eInvestigation\u003c/strong\u003e: MGZ;\u0026nbsp;\u003cstrong\u003eMethodology\u003c/strong\u003e: MGZ;\u0026nbsp;\u003cstrong\u003eProject administration\u003c/strong\u003e: JP and LM;\u0026nbsp;\u003cstrong\u003eResources\u003c/strong\u003e: MGZ;\u0026nbsp;\u003cstrong\u003eSoftware\u003c/strong\u003e: MGZ;\u0026nbsp;\u003cstrong\u003eSupervision\u003c/strong\u003e: JP and LM;\u0026nbsp;\u003cstrong\u003eValidation\u003c/strong\u003e: MGP, JP and LM;\u0026nbsp;\u003cstrong\u003eVisualization\u003c/strong\u003e: MGZ, MGP, JP and LM;\u0026nbsp;\u003cstrong\u003eWriting-original draft\u003c/strong\u003e: MGZ;\u0026nbsp;\u003cstrong\u003eWriting-review and editing\u003c/strong\u003e: MGP, JP and LM.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding sources\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by Cathedra DeCo MICOF-CEU and Ayudas a la Formaci\u0026oacute;n de J\u0026oacute;venes Investigadores SANTANDER-CEU.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData will be available upon request\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration of generative AI and AI-assisted technologies in the manuscript preparation process\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDuring the preparation of this work the author(s) used chat GPT in order to rectify grammatical errors and enhance the clarity of the language. After using this tool/service, the author(s) reviewed and edited the content as needed and take(s) full responsibility for the content of the published article.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003ehttps://www.who.int/news-room/fact-sheets/detail/dementia. Dementia WHO. 2023. \u003c/li\u003e\n\u003cli\u003eAlzheimer\u0026rsquo;s Disease Puzzle: Delving into Pathogenesis Hypotheses. Aging Dis. 2024; https://doi.org/10.14336/AD.2023.0608\u003c/li\u003e\n\u003cli\u003eEmrani S, Sundermann EE. Sex/gender differences in the clinical trajectory of Alzheimer\u0026rsquo;s disease: Insights into diagnosis and cognitive reserve. Front Neuroendocrinol. 2025;77:101184. https://doi.org/10.1016/j.yfrne.2025.101184\u003c/li\u003e\n\u003cli\u003eMalfitano AM, Marasco G, Proto MC, Laezza C, Gazzerro P, Bifulco M. Statins in neurological disorders: An overview and update. 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Brain Sci. 2025;15:812. https://doi.org/10.3390/brainsci15080812\u003c/li\u003e\n\u003cli\u003eBloomberg M, Dugravot A, Dumurgier J, Kivimaki M, Fayosse A, Steptoe A, et al. Sex differences and the role of education in cognitive ageing: analysis of two UK-based prospective cohort studies. Lancet Public Health. 2021;6:e106\u0026ndash;15. https://doi.org/10.1016/S2468-2667(20)30258-9\u003c/li\u003e\n\u003cli\u003eMacAulay RK, Halpin A, Cohen AS, Calamia M, Boeve A, Zhang L, et al. Predictors of Heterogeneity in Cognitive Function: APOE-e4, Sex, Education, Depression, and Vascular Risk. Archives of Clinical Neuropsychology. 2020;35:660\u0026ndash;70. https://doi.org/10.1093/arclin/acaa014\u003c/li\u003e\n\u003cli\u003eRen Y, Savadlou A, Park S, Siska P, Epp JR, Sargin D. The impact of loneliness and social isolation on the development of cognitive decline and Alzheimer\u0026rsquo;s Disease. Front Neuroendocrinol. 2023;69:101061. https://doi.org/10.1016/j.yfrne.2023.101061\u003c/li\u003e\n\u003cli\u003eAnstey KJ, Peters R, Mortby ME, Kiely KM, Eramudugolla R, Cherbuin N, et al. Association of sex differences in dementia risk factors with sex differences in memory decline in a population-based cohort spanning 20\u0026ndash;76 years. Sci Rep. 2021;11:7710. https://doi.org/10.1038/s41598-021-86397-7\u003c/li\u003e\n\u003cli\u003eIverson GL, Castellani RJ, Cassidy JD, Schneider GM, Schneider KJ, Echemendia RJ, et al. Examining later-in-life health risks associated with sport-related concussion and repetitive head impacts: a systematic review of case-control and cohort studies. Br J Sports Med. 2023;57:810\u0026ndash;24. https://doi.org/10.1136/bjsports-2023-106890\u003c/li\u003e\n\u003cli\u003eNianogo RA, Rosenwohl-Mack A, Yaffe K, Carrasco A, Hoffmann CM, Barnes DE. Risk Factors Associated With Alzheimer Disease and Related Dementias by Sex and Race and Ethnicity in the US. JAMA Neurol. 2022;79:584. https://doi.org/10.1001/jamaneurol.2022.0976\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"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":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"sex, aging cognitive decline, lifestyle, exercise, depression, diabetes, BMI","lastPublishedDoi":"10.21203/rs.3.rs-9265483/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9265483/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eDementia prevention strategies increasingly focus on modifiable risk factors, yet sex-specific risk profiles remain insufficiently characterized. In addition to biological mechanisms, social, behavioural, and environmental determinants may shape sex-specific trajectories of cognitive aging. Using the framework of the Lancet Commission on dementia prevention, intervention, and care modifiable risk factors, we investigated sex differences in cumulative risk burden and their association with cognitive decline in a community-based population.\u003c/p\u003e \u003cp\u003eWe conducted a cross-sectional study including 1162 adults recruited in community pharmacies in Valencia (Spain) between 2018 and 2024 as part of the DeCo Chair project. A cumulative risk score based on Lancet Commission modifiable risk factors was calculated using population-attributable fraction weighting. Logistic regression analyses evaluated associations between individual risk factors and cognitive decline.\u003c/p\u003e \u003cp\u003eMen accumulated a higher burden of modifiable risk factors, including smoking, higher body mass index, alcohol consumption, diabetes, and hearing loss. Higher BMI and traumatic brain injury history were associated with increased odds of cognitive decline in men. In contrast, women showed higher prevalence of low cognitive reserve, physical inactivity, depression, and social isolation, which were all associated with increased likelihood of cognitive decline. Higher cognitive reserve was protective in both sexes.\u003c/p\u003e \u003cp\u003eThese findings reveal distinct sex-specific risk profiles, with cardiometabolic and lifestyle risk factors predominating in men and psychosocial and cognitive reserve\u0026ndash;related vulnerabilities in women. Our results highlight the importance of sex-tailored prevention strategies to optimize dementia risk reduction.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e","manuscriptTitle":"Sex differences in modifiable risk profiles for cognitive decline: findings from DeCo Chair Project","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-07 15:25:54","doi":"10.21203/rs.3.rs-9265483/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2026-04-29T12:44:25+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-29T11:24:55+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-04-07T12:00:49+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-01T15:45:50+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2026-04-01T13:33:18+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1af5e53e-becf-46d5-8a18-e0ec5fd97a50","owner":[],"postedDate":"May 7th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewersInvited","content":"5","date":"2026-04-29T12:44:25+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-29T11:24:55+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":67371503,"name":"Health sciences/Diseases"},{"id":67371504,"name":"Health sciences/Health care"},{"id":67371505,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2026-05-07T15:25:55+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-07 15:25:54","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9265483","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9265483","identity":"rs-9265483","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}