Profiles of lifestyle health behaviors and cognitive decline in older adults.

INTRODUCTION: We aimed to identify profiles of modifiable, late-life lifestyle health behaviors related to subsequent maintenance of cognition and explore sociodemographics and health characteristics as effect modifiers. METHODS: Analyses used data from 715 older adults without baseline dementia from the Rush Memory and Aging Project and with lifestyle health behaviors (physical activity, cognitive activity, healthy diet, social activity) at baseline and ≥ 2 annual assessments of cognition. We used latent profile analysis to group participants based on behavior patterns and assessed change in cognition by group. RESULTS: Three latent profiles were identified: high (n = 183), moderate (n = 441), and low (n = 91) engagement in health behaviors. Compared to high engagement, the moderate (mean difference [MD] = -0.02, 95% CI = [-0.03;-0.0002], p = 0.048) and low (MD = -0.06, 95% CI = [-0.08;-0.03], p < 0.0001) groups had faster annual rates of decline in global cognition, with no significant effects modifiers (vascular risk factors, apolipoprotein E [APOE] ε4, motor function). DISCUSSION: Avoiding low levels of lifestyle health behaviors may help maintain cognition. HIGHLIGHTS: Latent profile analysis (LPA) captures lifestyle health behaviors associated with cognitive function. Such behavior include physical activity, cognitive activity, healthy diet, social activity. We used LPA to examine associations of behaviors and cognitive function over time. Older adults with low lifestyle health behaviors showed more rapid decline. To a lesser degree, so did those with moderate lifestyle health behaviors. Vascular conditions and risks, APOEε4, or motor function did not modify the effect.

Association of LIfestyle for BRAin health risk score (LIBRA) and genetic susceptibility with incident dementia and cognitive decline.

INTRODUCTION: Evaluating whether genetic susceptibility modifies the impact of lifestyle-related factors on dementia is critical for prevention. METHODS: We studied 5170 participants from a French cohort of older persons free of dementia at baseline and followed for up to 17 years. The LIfestyle for BRAin health risk score (LIBRA) including 12 modifiable factors was constructed at baseline (higher score indicating greater risk) and was related to both subsequent cognitive decline and dementia incidence, according to genetic susceptibility to dementia (reflected by the apolipoprotein E [APOE] ε4 allele and a genetic risk score [GRS]). RESULTS: The LIBRA was associated with higher dementia incidence, with no significant effect modification by genetics (hazard ratio for one point score = 1.09 [95% confidence interval, 1.05; 1.13]) in APOE ε4 non-carriers and = 1.15 [1.08; 1.22] in carriers; P = 0.15 for interaction). Similar findings were obtained with the GRS and with cognitive decline. DISCUSSION: Lifestyle-based prevention may be effective whatever the genetic susceptibility to dementia.

Sex and gender differences in cognitive resilience to aging and Alzheimer's disease.

Sex and gender-biological and social constructs-significantly impact the prevalence of protective and risk factors, influencing the burden of Alzheimer's disease (AD; amyloid beta and tau) and other pathologies (e.g., cerebrovascular disease) which ultimately shape cognitive trajectories. Understanding the interplay of these factors is central to understanding resilience and resistance mechanisms explaining maintained cognitive function and reduced pathology accumulation in aging and AD. In this narrative review, the ADDRESS! Special Interest Group (Alzheimer's Association) adopted a multidisciplinary approach to provide the foundations and recommendations for future research into sex- and gender-specific drivers of resilience, including a sex/gender-oriented review of risk factors, genetics, AD and non-AD pathologies, brain structure and function, and animal research. We urge the field to adopt a sex/gender-aware approach to resilience to advance our understanding of the intricate interplay of biological and social determinants and consider sex/gender-specific resilience throughout disease stages. HIGHLIGHTS: Sex differences in resilience to cognitive decline vary by age and cognitive status. Initial evidence supports sex-specific distinctions in brain pathology. Findings suggest sex differences in the impact of pathology on cognition. There is a sex-specific change in resilience in the transition to clinical stages. Gender and sex factors warrant study: modifiable, immune, inflammatory, and vascular.

nlive: an R package to facilitate the application of the sigmoidal and random changepoint mixed models.

BACKGROUND: The use of mixed effect models with a specific functional form such as the Sigmoidal Mixed Model and the Piecewise Mixed Model (or Changepoint Mixed Model) with abrupt or smooth random change allows the interpretation of the defined parameters to understand longitudinal trajectories. Currently, there are no interface R packages that can easily fit the Sigmoidal Mixed Model allowing the inclusion of covariates or incorporating recent developments to fit the Piecewise Mixed Model with random change. RESULTS: To facilitate the modeling of the Sigmoidal Mixed Model, and Piecewise Mixed Model with abrupt or smooth random change, we have created an R package called nlive. All needed pieces such as functions, covariance matrices, and initials generation were programmed. The package was implemented with recent developments such as the polynomial smooth transition of the piecewise mixed model with improved properties over Bacon-Watts, and the stochastic approximation expectation-maximization (SAEM) for efficient estimation. It was designed to help interpretation of the output by providing features such as annotated output, warnings, and graphs. Functionality, including time and convergence, was tested using simulations. We provided a data example to illustrate the package use and output features and interpretation. The package implemented in the R software is available from the Comprehensive R Archive Network (CRAN) at https://CRAN.R-project.org/package=nlive . CONCLUSIONS: The nlive package for R fits the Sigmoidal Mixed Model and the Piecewise Mixed: abrupt and smooth. The nlive allows fitting these models with only five mandatory arguments that are intuitive enough to the less sophisticated users.

The association of MIND diet with cognitive resilience to neuropathologies.

INTRODUCTION: Cognitive resilience (CR) can be defined as the continuum of better through worse than expected cognition, given the degree of neuropathology. The relation of healthy diet patterns to CR remains to be elucidated. METHODS: Using longitudinal cognitive data and post mortem neuropathology from 578 deceased older adults, we examined associations between the Mediterranean-DASH Intervention for Neurodegenerative Delay (MIND) diet at baseline and two standardized CR measures reflecting higher cognitive levels over time (CR Level ¯ $_{\overline {{\rm{Level}}}} $ ), and slower decline (CRSlope ), than expected given neuropathology. RESULTS: Compared to individuals in the lowest tertile of MIND score, those in the top tertile had higher CR Level ¯ $_{\overline {{\rm{Level}}}} $ (mean difference [MD] = 0.34; 95% confidence interval [CI] = 0.14, 0.55) and CRSlope (MD = 0.27; 95% CI = 0.05, 0.48), after multivariable adjustment. Overall MIND score was more strongly related to CR than the individual food components. DISCUSSION: The MIND diet is associated with both higher cognition and slower rates of cognitive decline, after controlling for neuropathology, indicating the MIND diet may be important to cognitive resilience.

Patterns of lifestyle behaviours from mid- through later-life in relation to exceptional episodic memory performance in older women: the Nurses' Health Study.

OBJECTIVES: to model and compare patterns from mid- to late-life of body mass index (BMI), alternate Mediterranean diet (A-MeDi) and physical activity, between women with exceptional episodic memory over age 80 and cognitively average controls. Our goal was to examine if lifestyle risk factors in early adulthood may be identified which promote exceptional memory status later in life. METHODS: a case-control sample nested within the Nurses' Health Study (initiated in 1976), including 7,557 cognitively healthy participants who survived to age 80 and had a cognitive assessment at 80-87 years. We defined women with exceptional memory (n = 455) as those with a composite score of episodic memory ≥1.5 standard deviation above the mean. Then, we selected 2,275 cognitively average controls with a score within 1 standard deviation of the mean, matched by age and education. Patterns of BMI, A-MeDi and physical activity at 52-62 through age 82 years were estimated between groups using latent process mixed models. RESULTS: In midlife, women with exceptional episodic memory had similar BMI (mean difference [MD] = -0.07 kg/m2 [95% confidence intervals {CI}:-0.41; 0.26]) but better adherence to A-MeDi (MD = +0.25 points [0.08; 0.43]) and more physical activity (MD = +3.50 metabolic-equivalent h/week [1.97; 5.09]) than controls. However, with ageing, both groups had similar patterns; both initially gained and later lost weight, had less activity and declining diet quality (all group-by-time interactions P > 0.07). CONCLUSIONS: our findings suggest that lifestyle factors differ primarily at earlier ages for those with exceptional versus average episodic memory, thus lifestyle may be most important in earlier life to preserve high levels of memory.

Trajectories of sleep duration and timing before dementia: a 14-year follow-up study.

BACKGROUND: given the complex relationship between sleep and neurodegenerative processes, it is important to examine whether changes in sleep patterns occur prior or close to dementia onset. OBJECTIVE: to examine the relationship between sleep parameters and dementia incidence and, to characterize trajectories of sleep patterns before dementia diagnosis. DESIGN: a 14-year longitudinal study including a nested case-control study. SETTING: the French Three-City Study. SUBJECTS: overall, 1,749 cognitively healthy participants (≥65 years) for the longitudinal study and, 182 incident dementia cases and 719 controls matched by sex, age and educational level for the case-control study. METHODS: dementia cases were assessed at each visit and self-reported sleep parameters at baseline, 2, 8, 10, 12 and 14 years. Cox models were used to estimate the risk of dementia associated with baseline sleep parameters (sleep duration, time in bed (TIB), sleep timing, sleepiness and insomnia). Latent-process mixed models were performed to compare sleep trajectories according to the case-control status. RESULTS: long baseline nighttime and 24-h sleep durations (≥9 h) as well as being persistent or becoming long sleepers during follow-up were associated with dementia incidence. Trajectories of sleep durations and TIB showed faster increases in cases compared with controls up to 12 years before dementia. The mean differences [95%CI] for 24-h sleep duration between cases and controls were: 0.27 h [0.01;0.52], 0.34 [0.09;0.58] and 0.67 [0.44;0.90] at -12, -8 and -2 years, respectively. Bedtime trajectories showed an earlier bedtime in cases up to -8 years. CONCLUSION: long sleep duration and earlier bedtime may impact dementia incidence.

Quantifying longitudinal cognitive resilience to Alzheimer's disease and other neuropathologies.

INTRODUCTION: Cognitive resilience (CR) has been defined as the continuum of better (or worse) than expected cognition, given the degree of neuropathology. To quantify this concept, existing approaches focus on either cognitive level at a single time point or slopes of cognitive decline. METHODS: In a prospective study of 1215 participants, we created a continuous measure of CR defined as the mean of differences between estimated person-specific and marginal cognitive levels over time, after accounting for neuropathologies. RESULTS: Neuroticism and depressive symptoms were associated with all CR measures (P-values < .012); as expected, cognitive activity and education were only associated with the cognitive-level approaches (P-values < .0002). However, compared with the existing CR measures focusing on a single measure or slopes of cognition, our new measure yielded stronger relations with risk factors. DISCUSSION: Defining CR based on the longitudinal differences between person-specific and marginal cognitive levels is a novel and complementary way to quantify CR.

Time-varying associations between an exposure history and a subsequent health outcome: a landmark approach to identify critical windows.

BACKGROUND: Long-term behavioral and health risk factors constitute a primary focus of research on the etiology of chronic diseases. Yet, identifying critical time-windows during which risk factors have the strongest impact on disease risk is challenging. To assess the trajectory of association of an exposure history with an outcome, the weighted cumulative exposure index (WCIE) has been proposed, with weights reflecting the relative importance of exposures at different times. However, WCIE is restricted to a complete observed error-free exposure whereas exposures are often measured with intermittent missingness and error. Moreover, it rarely explores exposure history that is very distant from the outcome as usually sought in life-course epidemiology. METHODS: We extend the WCIE methodology to (i) exposures that are intermittently measured with error, and (ii) contexts where the exposure time-window precedes the outcome time-window using a landmark approach. First, the individual exposure history up to the landmark time is estimated using a mixed model that handles missing data and error in exposure measurement, and the predicted complete error-free exposure history is derived. Then the WCIE methodology is applied to assess the trajectory of association between the predicted exposure history and the health outcome collected after the landmark time. In our context, the health outcome is a longitudinal marker analyzed using a mixed model. RESULTS: A simulation study first demonstrates the correct inference obtained with this approach. Then, applied to the Nurses' Health Study (19,415 women) to investigate the association between body mass index history (collected from midlife) and subsequent cognitive decline (evaluated after age 70), the method identified two major critical windows of association: long before the first cognitive evaluation (roughly 24 to 12 years), higher levels of BMI were associated with poorer cognition. In contrast, adjusted for the whole history, higher levels of BMI became associated with better cognition in the last years prior to the first cognitive interview, thus reflecting reverse causation (changes in exposure due to underlying disease). CONCLUSIONS: This approach, easy to implement, provides a flexible tool for studying complex dynamic relationships and identifying critical time windows while accounting for exposure measurement errors.

Long-Term Trajectories of Body Weight, Diet, and Physical Activity From Midlife Through Late Life and Subsequent Cognitive Decline in Women.

Healthy lifestyles are promising targets for prevention of cognitive aging, yet the optimal time windows for interventions remain unclear. We selected a case-control sample nested within the Nurses' Health Study (starting year 1976, mean age = 51 years), including 14,956 women aged ≥70 years who were free of both stroke and cognitive impairment at enrollment in a cognitive substudy (1995-2001). Cases (n = 1,496) were women with the 10% worst slopes of cognitive decline, and controls (n = 7,478) were those with slopes better than the median. We compared the trajectories of body mass index (weight (kg)/height (m)2), alternate Mediterranean diet (A-MeDi) score, and physical activity between groups, from midlife through 1 year preceding the cognitive substudy. In midlife, cases had higher body mass index than controls (mean difference (MD) = 0.59 units, 95% confidence interval (CI): 0.39, 0.80), lower physical activity (MD = -1.41 metabolic equivalent of task-hours/week, 95% CI: -2.07, -0.71), and worse A-MeDi scores (MD = -0.16 points, 95% CI: -0.26, -0.06). From midlife through later life, compared with controls, cases had consistently lower A-MeDi scores but a deceleration of weight gain and a faster decrease in physical activity. In conclusion, maintaining a healthy lifestyle since midlife may help reduce cognitive decline in aging. At older ages, both deceleration of weight gain and a decrease in physical activity may reflect early signs of cognitive impairment.

Modeling Risk-Factor Trajectories When Measurement Tools Change Sequentially During Follow-up in Cohort Studies: Application to Dietary Habits in Prodromal Dementia.

Modeling risk-factor trajectories is critical to understanding the natural history of diseases, yet the measurement tools used to assess risk factors often evolve during follow-up in cohorts, and such change prevents longitudinal analyses using standard models. We addressed this issue with a latent process model. Trajectories of average intakes of 5 food families (fish, meat, fruits, vegetables, and carbohydrate-rich foods) were described in prodromal dementia during the 10 years prior to diagnosis of cases and compared with those of controls, using a case-control sample nested within the Three-City Study, Bordeaux, France (1999-2012). Food intakes were measured by 2 or 3 different subquestionnaires across 5 repeated food frequency questionnaires. The sample comprised 205 incident cases and 410 controls matched for age, sex, education, and number of repeated food frequency questionnaires. Intakes of fish, fruits, and vegetables decreased at the approach of diagnosis among cases, suggesting reverse causation. This study demonstrated that the latent process model approach constitutes a powerful framework for modeling risk-factor trajectories, even when measurement tools change sequentially over time. Coupled with a case-control approach to contrast trajectories in prodromal disease versus healthy status, it can help us to understand the dynamic, causal relationships between risk factors and diseases.

Profiles of Lifestyle Health Behaviors and Postmortem Dementia-Related Neuropathology.

High engagement in lifestyle health behaviors appears to be protective against cognitive decline in aging. We investigated the association between patterns of modifiable lifestyle health behaviors and common brain neuropathologies of dementia as a possible mechanism. We examined 555 decedents from the Rush Memory and Aging Project, free of dementia at their initial concurrent report of lifestyle health behaviors of interest (physical, social, and cognitive activities, and healthy diet), and who underwent a postmortem neuropathology evaluation. First, we used latent profile analysis to group participants based on baseline behavior patterns. Second, we assessed the associations of profile membership with each neurodegenerative (global Alzheimer's disease [AD] pathology, amyloid-beta load, density of neurofibrillary tangles, and presence of cortical Lewy bodies and TAR DNA-binding protein 43 cytoplasmic inclusions) and neurovascular pathologies (presence of chronic gross or microscopic infarcts, arteriolosclerosis, atherosclerosis, and cerebral amyloid angiopathy), using separate linear or logistic regression models, adjusted for age at death, sex (core model), vascular disease risk factors, and vascular conditions (fully adjusted model). Participants had either consistently lower (N = 224) or consistently higher (N = 331) engagement across 4 lifestyle health behaviors. We generally found no differences in neuropathologies between higher and lower engagement groups in core or fully adjusted models; for example, higher engagement in lifestyle health behaviors was not associated with global AD pathology after core or full adjustment (both p > .8). In conclusion, we found no evidence of associations between patterns of lifestyle health behaviors and neuropathology. Other mechanisms may underlie protective effects of health behaviors against dementia.

nlive: an R Package to facilitate the application of the sigmoidal and random changepoint mixed models.

Background: The use of mixed effect models with a specific functional form such as the Sigmoidal Mixed Model and the Piecewise Mixed Model (or Changepoint Mixed Model) with abrupt or smooth random change allow the interpretation of the defined parameters to understand longitudinal trajectories. Currently, there are no interface R packages that can easily fit the Sigmoidal Mixed Model allowing the inclusion of covariates or incorporate recent developments to fit the Piecewise Mixed Model with random change. Results: To facilitate the modeling of the Sigmoidal Mixed Model, and Piecewise Mixed Model with abrupt or smooth random change, we have created an R package called nlive. All needed pieces such as functions, covariance matrices, and initials generation were programmed. The package was implemented with recent developments such as the polynomial smooth transition of piecewise mixed model with improved properties over Bacon-Watts, and the stochastic approximation expectation-maximization (SAEM) for efficient estimation. It was designed to help interpretation of the output by providing features such as annotated output, warnings, and graphs. Functionality, including time and convergence, was tested using simulations. We provided a data example to illustrate the package use and output features and interpretation. The package implemented in the R software is available from the Comprehensive R Archive Network (CRAN) at https://CRAN.R-project.org/package=nlive. Conclusions: The nlive package for R fits the Sigmoidal Mixed Model and the Piecewise Mixed: abrupt and smooth. The nlive allows fitting these models with only five mandatory arguments that are intuitive enough to the less sophisticated users.

Applications of artificial intelligence in dementia research.

More than 50 million older people worldwide are suffering from dementia, and this number is estimated to increase to 150 million by 2050. Greater caregiver burdens and financial impacts on the healthcare system are expected as we wait for an effective treatment for dementia. Researchers are constantly exploring new therapies and screening approaches for the early detection of dementia. Artificial intelligence (AI) is widely applied in dementia research, including machine learning and deep learning methods for dementia diagnosis and progression detection. Computerized apps are also convenient tools for patients and caregivers to monitor cognitive function changes. Furthermore, social robots can potentially provide daily life support or guidance for the elderly who live alone. This review aims to provide an overview of AI applications in dementia research. We divided the applications into three categories according to different stages of cognitive impairment: (1) cognitive screening and training, (2) diagnosis and prognosis for dementia, and (3) dementia care and interventions. There are numerous studies on AI applications for dementia research. However, one challenge that remains is comparing the effectiveness of different AI methods in real clinical settings.

Negative and Positive Psychosocial Factors in Relation to Cognitive Health in Older African Americans.

Background and Objectives: Identifying potential intervention strategies to reduce cognitive decline, particularly among older African Americans at high risk for Alzheimer's dementia, is critical. This study aimed to investigate whether depressive symptoms, purpose in life, and their interrelations are associated with cognitive decline in older African Americans. Research Design and Methods: We included older African Americans from the Minority Aging Research Study (n = 748) and Rush Memory and Aging Project (n = 109), without dementia at baseline. We assessed associations of depressive symptoms, purpose in life, and their interrelations, with baseline levels and change in global cognition using linear mixed-effects models. Results: At baseline, each unit increment in depressive symptoms was related to worse initial global cognition (mean difference = -0.03 standard unit; p = .003), while higher purpose in life was related to better cognition (mean difference = 0.12; p = .002). Further, participants with ≥1 depressive symptom who had a purpose in life score above the median appeared to have better global cognition (mean difference = 0.10; p = .01), compared to those with depressive symptoms but lower levels of purpose in life. However, we did not find relations of depressive symptoms or purpose in life with rates of cognitive decline over time, likely due to the modest follow-up. Discussion and Implications: In older African Americans, we found that lower depressive symptoms and greater purpose in life were independently related to higher initial levels of global cognition, but not cognitive decline. Preliminary findings of higher global cognition in individuals with depressive symptoms but greater purpose in life merit further investigation if purpose may eventually be considered as an intervention.

Nutrition state of science and dementia prevention: recommendations of the Nutrition for Dementia Prevention Working Group.

Observational studies suggest that nutritional factors have a potential cognitive benefit. However, systematic reviews of randomised trials of dietary and nutritional supplements have reported largely null effects on cognitive outcomes and have highlighted study inconsistencies and other limitations. In this Personal View, the Nutrition for Dementia Prevention Working Group presents what we consider to be limitations in the existing nutrition clinical trials for dementia prevention. On the basis of this evidence, we propose recommendations for incorporating dietary patterns and the use of genetic, and nutrition assessment tools, biomarkers, and novel clinical trial designs to guide future trial developments. Nutrition-based research has unique challenges that could require testing both more personalised interventions in targeted risk subgroups, identified by nutritional and other biomarkers, and large-scale and pragmatic study designs for more generalisable public health interventions across diverse populations.

Consumption of Nuts at Midlife and Healthy Aging in Women.

BACKGROUND: Nut consumption may reduce age-related diseases and lead to better health and well-being in aging. Many conditions of aging develop over decades, and thus earlier lifestyle factors may particularly influence later health. METHODS: In 1998 and 2002, we administered food frequency questionnaires to assess nut consumption (peanuts, walnuts, and other nuts and peanut butter) in women in the Nurses' Health Study in their 50 s/early 60 s. In 2012, those who survived beyond 65 years with no chronic diseases, no reported memory impairment, no physical disabilities, and intact mental health were considered "healthy agers." We used multivariable logistic regression to estimate odds ratios for healthy versus usual aging, controlled for sociodemographic, behavioral, dietary, and other potential confounding factors. RESULTS: Of 33,931 participants at midlife, 16% became "healthy agers." After age adjustment, we observed a significant association between total nut consumption at midlife and higher odds of healthy aging, with strongest associations observed excluding peanut butter (odds ratio (OR) = 1.46, 95% confidence interval (CI) 1.32-1.62, ≥3 servings/week versus none). Findings were attenuated after further control for covariates, including overall diet quality (OR = 1.14, 95% CI 1.02-1.28, P trend = 0.05). For nut types, we found statistically significantly higher odds of healthy aging across peanuts, walnuts, and other nuts after age adjustment. After full control for confounders, only walnut consumption remained associated with healthy aging (P trend = 0.0001); for example, the OR was 1.20 (95% CI 1.00-1.44) for ≥2 servings/week versus none. CONCLUSIONS: Women consuming nuts at midlife have a greater likelihood of overall health and well-being at older ages. Nut consumption may represent a simple intervention to explore and promote healthy aging.

Nutrition and Metabolic Profiles in the Natural History of Dementia: Recent Insights from Systems Biology and Life Course Epidemiology.

PURPOSE OF REVIEW: Worldwide, approximately 50 million people have dementia (mostly Alzheimer's disease). Dementia results from a multicomponent pathophysiology that follows complex dynamics over many years before symptoms become apparent. Nutrition may represent a target of choice for the primary prevention of dementia; however, there is still no firm answer on how to prevent dementia efficiently. We provide a broad overview of recent studies leveraging system biology and life-long epidemiology to address the multidimensionality and dynamical patterns underlying dementia and improve knowledge on the link between nutrition, cardiometabolic health and dementia risk. RECENT FINDINGS: The aging of reference population-based cohort studies, the increasing availability of cutting-edge biomarkers (e.g., brain imaging, metabolomics) and the refinement of statistical tools to model complex exposures and dynamical health outcomes have yielded substantial progress in the understanding of dementia. Systems biology coupled with life-course epidemiology will pave the way toward novel precision nutrition approaches for prevention and management of dementia.

Evaluation of the Concurrent Trajectories of Cardiometabolic Risk Factors in the 14 Years Before Dementia.

Importance: Cardiometabolic risk factors have been associated with an increased risk of dementia; yet, the optimal targets and time window for the management of cardiometabolic health to prevent dementia remain unknown. Objectives: To model concurrently and compare the trajectories of cardiometabolic risk factors up to 14 years preceding diagnosis in individuals with dementia and matched controls free of dementia. Design, Setting, and Participants: A case-control study nested within the Three-City study, a French population-based cohort of older persons (≥65 years), included 6 home visits with neuropsychological testing between 1999 and 2014. Data analysis was performed in September 2017. A total of 785 incident dementia cases and 3140 controls matched by sex, age, educational level, and cohort center at the time of diagnosis were evaluated. Exposures: Repeated measures of body mass index (BMI) and systolic (SBP) and diastolic (DBP) blood pressure, high-density lipoprotein (HDL-C) and low-density lipoprotein cholesterol (LDL-C), triglycerides, and glycemia levels between 1999 and 2014. Main Outcomes and Measures: Incidence of dementia based on systematic detection and validated diagnosis. Results: A total of 785 cases and 3140 controls (2530 [65%] women; mean [SD] age, 76 [5] years) were included in the study. Cases presented a faster decline in BMI, slower increase of SBP and constantly lower DBP. Mean values (95% CI) 14 years before diagnosis (-14 years) and at diagnosis (year 0) for the most common profile were BMI, 26.1 (25.6-26.5) and 24.8 (24.5-25.1) for a case, and 25.7 (25.4-26.1) and 25.3 (25.0-25.5) for a control; for SBP, 135.2 (131.8-138.7) and 142.1 (140.3-143.9) mm Hg for a case, and 135.8 (132.9-138.6) and 144.9 (143.7-146.1) mm Hg for a control; for DBP, 76.5 (74.7-78.5) and 74.0 (73.1-74.9) mm Hg for a case, and 76.7 (75.1-78.3) and 75.0 (74.2-75.7) mm Hg for a control. In contrast, glycemia was higher among cases (mean fasting glucose values [95% CI] at -14 years and year 0: 89.4 [86.9-92.1] and 96.4 [93.7-99.3] mg/dL for a case, and 87.1 [85.1-89.2] and 95.3 [93.5-97.1] mg/dL for a control), with a significant case-control difference from -1.6 to -14 years prior to diagnosis. There were no significant case-control differences in trajectories of blood lipid levels (mean values [95% CI] at -14 years and year 0: for HDL-C, 70.6 [67.6-73.9] and 61.3 [58.9-63.8] mg/dL for a case, and 70.4 [67.5-73.3] and 62.3 [60.2-64.3] mg/dL for a control; for LDL-C: 147.2 [140.5-154.5] and 141.6 [136.6-146.7] mg/dL for a case, and 144.3 [138.7-150.4] and 141.2 [137.5-145.2] mg/dL for a control; for triglycerides: 115.5 [103.6-149.1] and 112.6 [104.8-120.9] mg/dL for a case, and 112.5 [103.8-144.4] and 109.7 [105.0-114.8] mg/dL for a control). Conclusions and Relevance: In this large cohort of older persons, BMI declined in prodromal dementia, possibly reflecting early preclinical changes. Lower BP prior to dementia may reflect both a consequence and a contributing factor for the disease, whereas higher blood glucose levels may constitute a risk factor for dementia in the older age range. Overall, these findings suggest that elevated glycemia, low BP, and weight loss may be primary targets for the management of cardiometabolic health for primary and secondary prevention of dementia in the older age range.