Summary of the evidence on modifiable risk factors for cognitive decline and dementia: A population-based perspective

Letter to the editor regarding: Summary of the evidence on modifiable risk factors for cognitive decline and dementia: A population-based perspective

ObjectiveTo systematically review the literature relating to the impact of multiple co-occurring modifiable risk factors for cognitive decline and dementia.DesignA systematic review and meta-analysis of the literature relating to the...

BackgroundGiven the projected increase in the number of people with dementia and the lack of curative treatment, there is increasing attention to the contribution of modifiable risk and protective factors to dementia risk. In 2015, the “LIfestyle for BRAin health" (LIBRA) index was developed based on the results of a systematic literature review and Delphi expert study (Deckers et al., 2015). LIBRA consists of twelve modifiable risk factors for cognitive decline and dementia. Although this risk index has been well‐validated for cognitive decline and dementia risk in numerous population‐based cohorts and intervention studies, newly emerged risk factors might ask for an update (Deckers et al., 2019; Deckers et al., 2020).MethodTo update LIBRA, we conducted an umbrella review to assess all systematic reviews (SR) and meta‐analyses (MA) on modifiable dementia risk factors published between January 2015 and June 2021 in four databases (Pubmed, Embase, Web of Science, and PsychINFO). In parallel, a two‐round Delphi expert study was conducted. In the first round (May‐June 2022), eighteen dementia experts were asked to evaluate the factors currently included in LIBRA. Additionally, we asked to list new modifiable factors not previously included in LIBRA. In the second round (December 2022‐January 2023), the experts ranked the most important risk factors identified in the umbrella review and first Delphi round.ResultThe search returned 6,540 hits, of which 147 SR/MA were included in our umbrella review. Next to the existing LIBRA factors, new candidate risk factors that were consistently associated with increased risk for cognitive decline and dementia included: low social engagement, hearing impairment, vision impairment, atrial fibrillation, and anxiety. After triangulation of the results of the umbrella review and the first Delphi round, the experts identified hearing impairment, lifecourse inequalities, social contact, atrial fibrillation, and sleep as the most important new modifiable risk factors in the second Delphi round.ConclusionSeveral new modifiable risk factors for cognitive decline and dementia have been identified in this umbrella review and Delphi consensus study. These factors will be used for updating the existing LIBRA index, which will subsequently be externally validated in population‐based cohort studies.

Given the fear and stigma surrounding dementia

Depression is highly prevalent among individuals with type 2 diabetes mellitus (T2DM) and has been increasingly recognized as a potential contributor to adverse cognitive outcomes. While both depression and diabetes independently increase the risk of cognitive decline and dementia, their combined impact on cognitive health remains an area of growing clinical interest. This systematic review aimed to evaluate the association between depression and cognitive outcomes in individuals with T2DM.A systematic literature search was conducted in PubMed to identify relevant studies published between January 1, 2015, and December 31, 2025. The final search was performed on February 15, 2026. The search strategy combined Medical Subject Headings (MeSH) and free-text terms related to type 2 diabetes, depression, dementia, mild cognitive impairment, and cognitive decline. Studies were eligible if depression was assessed at baseline and cognitive outcomes were evaluated longitudinally through incident dementia diagnosis or repeated cognitive testing, enabling assessment of cognitive decline trajectories.Six studies met the predefined eligibility criteria and were included in the qualitative synthesis. Across the included studies, depression was consistently associated with adverse cognitive outcomes in individuals with T2DM. Longitudinal cohort studies demonstrated increased risk of incident dementia, with reported hazard ratios ranging approximately from 2.17 to 2.59. Other studies reported accelerated cognitive decline or increased risk of mild cognitive impairment among individuals with coexisting depression and diabetes.Depression appears to be an important and potentially modifiable risk factor for cognitive decline and dementia among individuals with type 2 diabetes. Early identification and effective management of depressive symptoms in patients with diabetes may help reduce the risk of adverse cognitive outcomes and support preservation of cognitive health in this high-risk population.

BackgroundStudies have identified modifiable risk factors for both prevalence and incidence of cognitive impairment in lower educated older adults. However, patterns of cognitive change, and risk and protective factors for declining versus stable cognitive trajectories have rarely been described in this population. Our goal was to explore the long‐term trajectories of cognitive function and identify modifiable risk and protective factors for cognitive decline beyond education.MethodWe applied group‐based trajectory modelling and multinomial logistic regressions to explore possible trajectories and associated baseline factors on data from a population‐based Health, Welfare and Aging (SABE) survey collected in 2000, 2006, 2010, and 2015 in Sao Paulo, Brazil. Cognitive functioning was assessed using the abbreviated Mini‐Mental State Exam. A total of 354 respondents aged 60 + participating in all follow‐ups were included in the statistical analyses.ResultGroup‐based trajectory modelling identified three different cognitive trajectories, specifically groups with stable (37.4%), declining (53.8%), and strongly declining (8.8%) trajectories, with the last one reaching the threshold for cognitive impairment (Figure 1). Socioeconomic status, specifically living in rural areas during childhood and no schooling or primary education, as well as self‐identified race was systematically different between the groups (Table 1). Moreover, respondents in the stable trajectory were more likely to report living in urban areas, more than primary education, to be white, to earn more than four times the minimum wage/month, to practice manual work, craft or artistic activity at least once a week, and less likely to have had stroke or be widowed. On the other hand, those in the strongly declining trajectory were more likely to be black or mixed and smoke at baseline.ConclusionOur findings suggest substantial potential for modification of risk of cognitive decline even in lower educated older individuals at risk for cognitive impairment. Risk factors related to self‐identified race/ethnicity and socioeconomic status suggest systematic inequalities in brain health potential, with risk for later‐life cognitive impairment. Sample attrition suggests conservative estimates of the magnitude of the risk factors for cognitive decline. Health and social policies should address inequalities to improve later‐life cognitive function of at‐risk individuals.

Alzheimer's disease (AD) pathology, particularly amyloid-β (Aβ) deposition, occurs years before clinical symptoms. Modifiable risk factors may influence cognitive trajectories during this preclinical stage, but whether amyloid status alters their effects remains unclear. To investigate interactions between amyloid pathology and modifiable risk factors in predicting longitudinal cognitive decline among cognitively unimpaired older adults. This study was a secondary analysis of data derived from two large multicenter longitudinal cohort studies, the Anti-Amyloid Treatment in Asymptomatic Alzheimer Disease (A4) Study and the Longitudinal Evaluation of Amyloid Risk and Neurodegeneration (LEARN) Study. A total of 1707 cognitively unimpaired adults aged 65-85 years were included, comprising 1169 amyloid-positive participants from the A4 Study (Aβ+) and 538 amyloid-negative participants from the LEARN Study (Aβ-). Cognitive function was assessed every six months using the Preclinical Alzheimer's Cognitive Composite (PACC) over a mean follow-up of 4.9 years. Eight established modifiable risk factors-low education, alcohol use, diabetes, high cholesterol, high blood pressure, obesity, depressive symptoms, and physical inactivity-were evaluated. Linear mixed-effects models were applied to examine associations between each risk factor and longitudinal PACC decline, and to test interactions with amyloid status, adjusting for demographic and genetic covariates. Significant interactions between amyloid status and modifiable risk factors were observed for diabetes (adjusted β = -0.206, p = 0.032), high cholesterol (adjusted β = -0.155, p < 0.001), and physical inactivity (adjusted β = -0.161, p = 0.046), indicating combined effects rather than additive effects on cognitive decline among Aβ+ individuals. In the A4 study (Aβ+), low education, diabetes, high cholesterol, and physical inactivity were independently associated with accelerated cognitive decline, whereas obesity was linked to slower decline. In contrast, in the LEARN study (Aβ-), these associations were not statistically significant. In conclusion, the significant interactions with amyloid status were observed for diabetes, high cholesterol, and physical inactivity, indicating that these risk factors were associated with faster cognitive decline specifically in Aβ+ individuals. The results suggest that consideration of amyloid status may be important when evaluating the potential role of metabolic and lifestyle risk factors in preclinical cognitive decline. In Aβ+ individuals, obesity was associated with slower cognitive decline, while low education was linked to lower baseline cognition or a reduced symptom threshold, without a significant interaction with amyloid status. Future studies should incorporate amyloid status and longitudinal biomarkers to assess whether modifying these factors can slow preclinical cognitive decline.

BackgroundMany studies have proposed important risk and protective factors for dementia, although most are from Western samples in high‐income settings, including those summarized in the 2024 Lancet Commission on dementia. Our goal was to characterize modifiable risk factors of cognitive decline in a nationally representative study of older adults in India.MethodWe used a detailed neuropsychological battery administered to N = 6,166 community‐living adults aged 60+ years across two waves of the nationally‐representative Harmonized Diagnostic Assessment of Dementia for the Longitudinal Aging Study in India (LASI‐DAD). We established the factor structure of the cognitive battery, derived co‐calibrated measures of cognitive functioning across waves, and evaluated associations of modifiable and non‐modifiable risk factors for late‐life cognitive decline with up to 6.4 years of follow‐up. Risk factors included demographic characteristics, self‐reported and objective health characteristics (i.e., markers of cardiovascular disease), health behaviors, and sensory function.ResultConfirmatory factor analyses to co‐calibrate measures of general cognitive functioning, memory, executive functioning/attention, language/fluency, visuospatial ability, and orientation domains across LASI‐DAD waves 1 and 2 fit well to the data. Most risk factors, particularly demographics and cardiovascular characteristics, were associated with steeper cognitive decline.ConclusionMost risk factors we evaluated were associated with decline in expected directions, highlighting the potential generalizability of previously identified risk factors for dementia in India. Summary measures of cognitive domains derived in this study can be used in future longitudinal research on cognitive aging in India.

Background:Several modifiable risk factors for cognitive decline have been identified, but whether differences by gender and educational level exist is unclear.Objective:The present study aims to clarify this by prospectively investigating the relationship between health and lifestyle factors and cognitive functioning in different subgroups defined by gender and educational level.Methods:2,347 cognitive healthy individuals (mean age = 54.8, SD = 6.8, range: 41–71; 51.8% female; 26.2% low education) from the Doetinchem Cohort Study were examined for cognitive function at baseline, and at 5- and 10-year follow-up. Health- and lifestyle factors were captured by a poly-environmental risk score labelled ‘LIfestyle for BRAin Health’ (LIBRA). This score consists of 12 modifiable risk and protective factors for cognitive decline and dementia, with higher scores indicating greater risk (range: –2.7 to +12.7). Heterogeneity in associations between LIBRA and decline in verbal memory, cognitive flexibility, and mental speed between males and females and individuals with different levels of education were assessed in linear mixed models.Results:Overall, higher LIBRA scores predicted faster decline in verbal memory, cognitive flexibility, and mental speed over 10 years. Higher LIBRA scores were further associated with increased risk for incident cognitive impairment (one-point increase in LIBRA: HR = 1.09, 1.04–1.14, p = 0.001). In general, these effects were similar across gender and educational level.Conclusion:A composite risk score comprising unhealthy lifestyle and relatively poor health in midlife is significantly associated with a worse course of cognition 10 years later. These associations were for the most part unrelated to gender or educational differences.

Age-related hearing loss (ARHL), also known as presbycusis, is one of the most prevalent long-term sensory difficulties in older people. It affects more than two-thirds of people over 70. In addition to communication challenges, ARHL has recently been revealed as a possible modifiable risk factor for cognitive decline and dementia. Comprehending this link is crucial for creating preventative interventions and maintaining healthy cognitive aging. This narrative review intended to analyze the evidence comprehensively relating age-related hearing loss (ARHL) with cognitive decline, define the possible pathophysiological mechanisms that may explain this association and assess the plausibility of hearing rehabilitation as a preventative therapy. A full literature search was done in PubMed, MEDLINE, Google Scholar and Frontiers databases employing the phrases "hearing loss" AND ("cognitive decline" OR "aging"). We only looked at articles that were published in English between 2014 and 2024 and were either systematic reviews, meta-analyses, or original research. We did not include any papers that were not peer-reviewed, not about people or not written in English. A total of 37 publications satisfied the inclusion criteria and underwent extensive review. There is strong evidence that ARHL is associated with rapid cognitive decline and an increased risk of dementia. Epidemiological studies suggest that hearing loss contributes to roughly 8-9% of worldwide dementia cases, which represents one of the primary modifiable risk factors. Some of the suggested ways that ARHL and cognitive decline are connected include through increased cognitive load, neuroplastic rearrangement, vascular dysfunction, oxidative stress and social isolation. Neuroimaging studies have revealed a reduction in gray matter and cortical atrophy in the auditory and associative areas of the brain in individuals with hearing loss. Hearing rehabilitation with hearing aids and cochlear implants has been connected to increased communication, higher social engagement and decreased cognitive decline; nevertheless, findings are rather inconsistent due to methodological errors and limited follow-up periods. Age-related hearing loss is a moderately widespread risk factor for cognitive decline and dementia that can be reduced. Early examination and effective auditory therapy can slow down cognitive decline and make life better for older people. Future longitudinal, multicenter and interventional studies are important to explain causal pathways, enhance intervention timing and assess cost-effective public health techniques for sustaining cognitive health in aging populations.

BackgroundThe evidence on the relationship between sleep disorders and the risk of cognitive decline or dementia remains inconsistent.ObjectivesThis systematic review and meta-analysis aimed to provide updated evidence on the association between sleep disturbances and cognitive decline.MethodsPubMed, EMBASE, and Web of Science were systematically searched from their respective inceptions to 18 February 2025. Cohort studies investigating longitudinal associations between sleep disorders and cognitive decline or dementia were included. Pooled relative risks (RRs) with 95% confidence intervals were calculated. Sensitivity analyses were conducted to evaluate the robustness of the pooled estimates. Publication bias was assessed using Egger’s and Begg’s tests, and meta-regression analysis was performed to explore potential sources of heterogeneity across studies.ResultsSeventy-six eligible cohort studies with eight types of sleep disturbances were included in the meta-analysis. Insomnia was associated with an increased risk of dementia (RR = 1.13). Both short sleep duration (7 h; RR = 1.27) and long sleep duration (8 h; RR = 1.23 for cognitive decline, RR = 1.43 for all-cause dementia, and RR = 1.66 for Alzheimer's disease (AD) were significant risk factors for cognitive decline and dementia. Excessive daytime sleepiness significantly increased the risks of vascular dementia (VD) (RR = 1.85), all-cause dementia (RR = 1.41), and cognitive decline (RR = 1.37). Sleep-related movement disorders indicated the strongest association, markedly increasing the risk of VD (RR = 2.53). Poor sleep quality was also a significant risk factor for AD (RR = 1.24), all-cause dementia (RR = 1.17), and cognitive decline (RR = 1.18).ConclusionThis meta-analysis highlights sleep management as a pivotal modifiable factor in reducing the risk of all-cause cognitive decline. Systematic screening and early intervention for sleep disturbances should be prioritized as essential preventive strategies in clinical populations.Supplementary InformationThe online version contains supplementary material available at 10.1007/s00415-025-13372-x.

Introduction Subjective cognitive decline (SCD) is an early marker of neurodegenerative disease and a target for preventative interventions. With advances in smartphone-based clinical interventions and understanding of modifiable risk factors for cognitive decline, this simulation study aimed to estimate the potential benefits of a prescription digital therapeutic (PDT) with multi-risk-factor modification on the cognitive trajectory of individuals with SCD. We constructed a Monte Carlo simulation to model progression to mild cognitive impairment (MCI) over a five-year period. Methods A virtual cohort of 10,000 patients with SCD was simulated over five years. Baseline annual risk of progression to MCI was set at 10%. A PDT was assumed to yield a 30% relative risk reduction, modulated by adherence levels (70% full, 20% partial, 10% none). Additionally, 14 modifiable dementia risk factors were modeled based on the 2024 Lancet Commission update, with each risk factor assigned a prevalence, modifiability flag, relative risk reduction, and effectiveness rate. Risk adjustments were applied multiplicatively, and outcomes were tracked annually. Results After five years, 6,487 (65%) of patients remained cognitively stable, 2,496 (25%) progressed to MCI, and 1,017 (10%) dropped out. These results represent a significant improvement over the expected 41% progression rate in untreated SCD populations. Reductions in physical inactivity, hypertension, hearing loss, and social isolation contributed substantially to outcome improvements, particularly when multiple risk factors were addressed concurrently. Conclusion The incorporation of a PDT with systematic modification of multiple modifiable dementia risk factors demonstrates a meaningful reduction in cognitive decline risk among individuals with SCD. These findings highlight the potential of integrated digital-first strategies to meaningfully delay cognitive decline and may inform future PDT trials and dementia prevention programs.Validation in prospective clinical trials is warranted to confirm these simulation-based findings.

Introduction Meta-analytic studies support that insomnia is a risk factor for cognitive decline and dementia, but limited accounting of confounders reduces their validity. Further, it is unclear whether these associations are driven by specific insomnia phenotypes. In this study, we aimed to assess the relationship between insomnia and longitudinal global cognitive decline in community-dwelling older adults after considering self-reported sleep duration changes and other important characteristics. Methods From the Mayo Clinic Study of Aging (MCSA) cohort, we identified all (age&amp;gt;=50yo) cognitively unimpaired participants at baseline without comorbid neurological disease with at least two previous comprehensive neuropsychological evaluations. Participants with at least two occurrences of insomnia ICD diagnosis (by EMR and Rochester Epidemiological Project data) at least 30 days apart were classified as having insomnia, and those without any instance of diagnosis were considered negative for insomnia. Changes in sleep duration were assessed using the question #16 of BDI-2 and categorized in reduced sleep (yes/no). We fit mixed-effect regression models to assess whether insomnia was associated with standardized global cognitive scores, after adjusting for age, sex, education, APOEe4, composite cardiovascular and metabolic conditions scores, anxiety/depression, reduced sleep, OSA (ICD diagnosis), alcoholism (CAGE≥2), and pain (NSAIDs use). Number of cognitive assessments and time from baseline were included as both fixed and random effects. Multiple interactions (e.g. insomnia*reduced sleep) were included. A backward stepwise procedure maintaining the hierarchical principal for interactions was utilized to reach the most parsimonious models. Results 3063 participants (50.24% males, aged 70±9.73 at baseline) were included. Insomnia alone was not associated with a decrease in global cognition, but when combined with subjective reduction in sleep duration at baseline it was associated with a -0.19 (95% CI: -0.32, -0.07; interaction p=0.01) reduction in global cognitive scores. This was equivalent to approximately 3 additional years of age or 6 cardiometabolic comorbidities at baseline. The effect size was greater than those associated with baseline cognition for having any APOE e4 allele (-0.12) and alcoholism (-0.15). Conclusion Insomnia with reduced sleep duration phenotype is a potentially modifiable risk factor for cognitive decline in older adults. Further studies with objective sleep measures are necessary for objective confirmation. Support (if any) NIA/NIH

Association of Sickle Cell Trait with Measures of Cognitive Function and Dementia in African Americans

BackgroundDementia incidence is projected to significantly increase, posing unique challenges to healthcare systems. Identifying non‐modifiable and modifiable risk factors (RF) is crucial, including sex‐specific factors, given the higher prevalence among females (60%). Here, we employed a network analysis to examine prominent RF in healthy controls compared to those with cognitive decline (CD), as well as the interrelationships and interactions of RF on CD. Additionally, sex‐specific networks were compared to identify unique RF and interactions present among sex.MethodHealthy controls and CD individuals (mild cognitive impairment and Alzheimer’s dementia) were included from the Ontario Neurodegenerative Initiative and Canadian Consortium for Neurodegeneration in Aging (n = 339 total; 52% female; 72% CD). Non modifiable RF (e.g., age), modifiable RF (e.g., Framingham RF) and cognitive outcomes (e.g., executive functioning) were included in network modeling. Sex‐specific networks were created within the CD group and compared, as was between CD and healthy controls. Relationships among RF present in CD were identified and the strength. Nodes represented RF and edges are the pairwise dependency between RF, node centrality was investigated for the relative importance of each RF in the network.ResultHealthy controls and CD had statistically different networks (M = 0.536; p = 0.02), and the CD network had greater connectivity (S = 2.69; p = 0.005)[Figure 1]. Male and female networks were statistically different within CD (M = 0.432; p = 0.027), and the male’s network had statistically greater connectivity than the females with CD (S = 1.24; p = 0.049)[Figure 2]. Within females, the CD had significantly greater connectivity (S = 0.90; p = 0.03)[Figure 3] than healthy controls and no difference in males (p &gt; 0.05).ConclusionOur findings reveal unique sex‐specific network patterns of RF for CD, which further underscores the need for sex‐disaggregated analyses. The observed differences in heightened connectivity of typically studied RF in males, highlights a potential gap in the understanding of sex‐specific RF for Alzheimer’s. Future work should incorporate biomarkers, such as neuroimaging, to further comprehend sex‐specific RF for CD and to create the framework for precision medicine in targeting sex‐specific RF for CD.