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. 2025 Dec 21;20:76–83. doi: 10.1016/j.ibneur.2025.12.007

Cardiovascular risk factors, aging, and incidence of dementia (CAIDE) risk score and its association with cognitive performance and volumetric brain measures in mild cognitive impairment

Yasaman Abtahi a, Marjan Falahati b, Sheyda Sharabiani c, Fatemeh Gila d, Saeed Omidi e, Farshad Goharmanesh f, Yousef Nourbakhsh g, Marziye Jalalian Chaleshtari h, Mohammad Hossein Nourbakhshi i, Parham Mahmoudi j, Raziyeh Zamiri k, Laya Dalir l, Zahra Gharehdaghi m, Hamide Nasiri n,⁎, Mahsa Mayeli o, Hannaneh Azimizonuzi p,⁎; for the Alzheimer’s Disease Neuroimaging Initiative1
PMCID: PMC12809402  PMID: 41550977

Abstract

Background

The Cardiovascular Risk Factors, Aging, and Dementia (CAIDE) composite score is a promising measure connecting vascular health to cognitive decline; However, its association with brain imaging findings remains underexplored. This study aimed to evaluate the predictive value of the associations between CAIDE and structural brain measures in individuals with mild cognitive impairment (MCI).

Methods

Participants (n = 226) aged 55–90 years with available CAIDE scores and white matter hyperintensity (WMH) measurements and a diagnosis of amnestic MCI were included. Regression models were used to evaluate the association between CAIDE score and neuropsychiatric and imaging findings.

Results

Higher CAIDE scores were significantly correlated with lower MMSE scores (r = -0.22, p = 0.001) and higher CDR-SB (r = 0.34, p < 0.001) and ADAS-Cog 11 scores (r = 0.38, p < 0.001). CAIDE scores were significantly positively related to total cerebrum (β = 0.319), gray matter (β = 0.337), and hippocampal volumes (β = 0.250, all p < 0.001). ROC analysis demonstrated that total gray matter (AUC = 0.70) and total brain (AUC = 0.69) were more accurate predictors of dementia risk compared to WMH volume (AUC = 0.52).

Conclusion

Higher CAIDE dementia risk scores were linked to poorer cognitive performance but showed limited association with WMH burden in individuals with MCI. Gray matter and hippocampal volumes were stronger correlates of dementia risk than WMH volume and CAIDE-related risk may be better captured by cortical and hippocampal structural changes rather than white matter disease.

Keywords: White Matter Hyperintensity, Risk Score, Vascular Risk Factors, Dementia, Mild Cognitive Impairment

1. Introduction

The Cardiovascular Risk Factors, Aging, and Incidence of Dementia (CAIDE) Dementia Risk Score is a well-established tool for estimating an individual’s long-term risk of developing dementia, particularly in older adults. This score integrates several modifiable and non-modifiable risk factors including age, education, obesity, hypertension, physical inactivity, and smoking capturing the contribution of midlife vascular and lifestyle factors to later cognitive decline. Higher CAIDE scores have been consistently associated with an increased likelihood of progression to dementia among individuals with Mild Cognitive Impairment (MCI), and its predictive value has been replicated across diverse populations and clinical settings (Kivipelto et al., 2006a; Livingston et al., 2017; Kivipelto et al., 2001; Tolppanen et al., 2012).

White matter hyperintensities (WMHs) appear as bright areas on T2-weighted MRI scans and are common in both cognitively healthy older adults and individuals with cognitive impairment, including those with MCI and dementia such as Alzheimer’s disease (AD) (Appel et al., 2009). WMH volume, typically of vascular origin, has been consistently associated with cognitive decline, particularly in executive function, memory, and processing speed (Hu et al., 2021, Alber et al., 2019). WMHs are highly prevalent in the elderly, especially among individuals with hypertension or prior stroke, with reported rates ranging from 5.3 % to 100 %, depending on imaging techniques and the diagnostic criteria (Basile et al., 2006, Hopkins et al., 2006). Neuropathological studies link WMHs to cerebrovascular and tau pathology (Alosco et al., 2018) and to age, sex, ethnicity, smoking, education level, and cardiovascular risk factors (Liao et al., 1997). In people with MCI, a greater WMH burden was associated with faster cognitive deterioration (Bangen et al., 2018, Hirao et al., 2021), likely mediated by chronic hypoperfusion, ischemic injury, and blood–brain barrier dysfunction that contribute to axonal and myelin damage (Moroni et al., 2018, Wardlaw et al., 2013).

Although previous studies have examined the relationship between the CAIDE dementia score and some imaging markers (Enache et al., 2016, Liu et al., 2021a, Stephen et al., 2021), relatively few investigations have specifically explored the associations between CAIDE and WMH. Few studies reported a significant link between higher CAIDE scores and increased global WMH load (Salvadó et al., 2019; Low et al., 2022, Vuorinen et al., 2015a). However, none of these studies have specifically addressed individuals with MCI. Given that those affected by MCI are at increased risk of developing dementia (Hou et al., 2023), it is imperative to thoroughly examine WMH within this population. This study aims to evaluate the prognostic value of the CAIDE Dementia Risk Score in predicting WMH volume among people with MCI, thereby exploring its potential as a tool for identifying individuals with greater WMH burden. Additionally, the study contributes to understanding the relationship between dementia risk indices and neuroimaging biomarkers in MCI populations.

2. Methods

2.1. Participants

The data were obtained from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database (http://adni.loni.usc.edu), a public-private partnership initiated in 2003 and led by Principal Investigator Dr. Michael W. Weiner. ADNI's primary objective is to evaluate whether a combination of serial MRI, Positron Emission Tomography (PET), various biological markers, and clinical and neuropsychological assessments can effectively track the progression of MCI and early AD. Participants, aged 55–90, were required to undergo neuroimaging, lumbar punctures, and longitudinal follow-ups. Detailed inclusion and exclusion criteria are described elsewhere. Key exclusion criteria included a Hachinski Ischemic Score greater than 4, the use of unapproved medications, recent changes in permitted medications, a Geriatric Depression Scale score of 6 or higher, and less than six years of education or equivalent work experience. All individuals with available data for WMH measures and CAIDE dementia risk scores at baseline were included in this cross-sectional investigation. After removing the outliers with CAIDE scores (± 3 Standard Deviations), a final sample of 226 individuals was obtained from the initial enrollment of 243 people. The diagnosis of amnestic MCI was determined based on self-reported memory complaints, objective memory impairments, intact functional abilities, a Clinical Dementia Rating (CDR) global score of 0.5 (Morris, 1993), and a mini–mental state examination (MMSE) score of 24 or higher (Mckhann et al., 1984).

2.2. Cognitive assessment

The MMSE questionnaire has 11 questions that evaluates orientation, registration, attention and calculation, recollection, and language. Normal cognition is indicated by scores of 24 or above, whereas mild (19–23), moderate (10–18), and severe (9 or lower) cognitive impairment are suggested by scores below 24 (Tombaugh and Mcintyre, 1992, Mitchell, 2017).

The original Alzheimer's Disease Assessment Scale-Cognitive Subscale (ADAS-Cog) (Rosen et al., 1984) comprises 11 items that assess cognitive function across multiple domains, including memory, language, praxis, and orientation, with a total possible score of 70 points. Forty-eight points are allocated to the first nine items, and 22 points to the final two, which evaluate word recall and recognition. Performance is assessed based on errors in tasks such as following sequential commands, object and finger naming, constructional praxis (copying geometric figures), ideational praxis (preparing a letter for mailing), orientation, a 10-item word recall task, and a 12-item word recognition task with foils. Greater cognitive impairment is indicated by higher scores. By including a number cancellation job and a delayed free recall work, the modified ADAS-Cog (mohs et al., 1997) expands the original scale to 13 items, with a maximum possible score of 85 points. Without significantly lengthening the administration duration, the extra questions expand the cognitive domains evaluated and capture a greater range of symptom intensity. Higher scores on the modified ADAS-Cog indicate more cognitive impairment, just like the original scale did.

2.3. CAIDE dementia risk score

The CAIDE dementia risk score was calculated for each participant at baseline. This score is derived from weighted values of age, sex, education, systolic blood pressure, BMI, total cholesterol, physical activity, and APOE ε4 genotype, all measured at baseline (Kivipelto et al., 2006b). To simplify the scoring method, certain variables were dichotomized (sex, BMI, systolic blood pressure, total cholesterol, physical activity levels, and APOE ε4 genotype), while others (age and education) were categorized into three groups based on tertiles. The CAIDE score, which goes from 0 to 18, indicates a higher risk of dementia in the future, which means higher scores on the CAIDE are associated with higher risk of AD. The cut-off values for classification were 30 kg/m² for BMI, 140 mm Hg for systolic blood pressure, and 6.5 mmol/L for total cholesterol.

2.4. Neuroimaging

The T1-weighted (T1w), T2-weighted/Proton Density-weighted (T2w/PDw), and Fluid Attenuated Inversion Recovery (FLAIR) scans underwent the following pre-processing steps: (1) image denoising (Manjón et al., 2010), (2) intensity inhomogeneity correction, and (3) intensity scaling to a 0–100 range. Each subject's T2w, PDw, or FLAIR scans were co-registered to the corresponding T1w structural scan for the same timepoints using a six-parameter rigid registration with a mutual information objective function (Dadar et al., 2018a). Additionally, the T1w scans were linearly (Dadar et al., 2018a) and nonlinearly (Avants et al., 2008) aligned to the MNI-ICBM152 unbiased average template (Fonov et al., 2011). FLAIR inhomogeneity corrections after co-registration to the 3D T1 image were performed using a previously published local histogram normalization method (Decarli et al., 1996). White matter hyperintensity (WMH) volumes were normalized for intracranial volume to account for individual differences in head size, as is standard in such analyses. WMHs were automatically measured at both baseline and 1-year follow-up time points using a previously validated WMH segmentation technique, which was developed based on a library of manual segmentations from 100 ADNI subjects (independent from the 720 participants in this study) (Dadar et al., 2018b, Dadar et al., 2017a, Dadar et al., 2017b). This method uses a random forest classifier in conjunction with position and intensity characteristics to identify WMHs in T1w+FLAIR or T1w+T2w/PDw pictures. Calculated as the volume (in mm³) of all voxels recognized as WMH in standard space, standardized for head size, WMH burden is a marker for cerebrovascular disease. The WMH volumes were log-transformed to produce a normal distribution.

2.5. Statistical analysis

Categorical variables were summarized as frequencies and percentages, while continuous variables were described using means and standard deviations (SD). Separate linear regression models were used to examine the association between each WMH-derived measure and the CAIDE dementia risk score, with CAIDE as the independent variable and each WMH metric as the dependent variable. Age, sex, and education were not included as covariates to avoid multicollinearity and overadjustment, as these factors are already incorporated into the CAIDE score. Pearson correlation analyses were conducted to assess the associations between CAIDE scores and WMH measures.

To determine optimal CAIDE thresholds for predicting brain volume outcomes, logistic regression models were fitted with CAIDE as the independent variable and each brain volume dichotomized at its median as the dependent variable. Receiver operating characteristic (ROC) curves were generated, and optimal cut points were identified using Youden’s J statistic. Multiple comparisons were controlled using the Benjamini–Hochberg False Discovery Rate (FDR) procedure, with FDR-adjusted p < 0.05 considered statistically significant. All analyses were performed in Python.

3. Results

Table 1 summarizes the baseline characteristics of the study population (n = 226), all of whom had been diagnosed with MCI. Participants were 71.71 ± 7.65 years old on average. The sex distribution of the cohort was about equal, with 107 individuals (47.3 %) identifying as female and 119 (52.7 %) as male.

Table 1.

Baseline characteristics of participants.

Total number categories 226
sex [n (%)] Female 107 (47.3)
Male 119 (52.7)
Age, year [mean ± SD] 71.71 ± 7.65
Education level (years) 16.05 ± 2.61
APOE ε4 allele carrier [n (%)] Non-Carrier 122 (54)
1 allele 81 (35.8)
2 alleles 23 (10.2)
CDR-SB [mean ± SD] 1.47 ± 0.87
ADAS-Cog 11 [mean ± SD] 9.34 ± 4.33
ADAS-Cog 13 [mean ± SD] 14.78 ± 6.64
MMSE [mean ± SD] 28.09 ± 1.65
Total cholesterol, mmol/l [mean ± SD] 4.78 ± 0.91
Systolic Blood Pressure, mm/Hg [mean ± SD] 129.36 ± 17.47
Body Mass Index, Kg/m2 [mean ± SD] 29.59 ± 14.27
Daily activity [n (%)] Active 224 (99.1)
Inactive 2 (0.9)
CIADE [mean ± SD] 7.83 ± 1.74
Total Cerebral Volume (supratentorial portion) [mean ± SD] 1200.19 ± 120.09
Total Cerebrum Brain Volume [mean ± SD] 918.51 ± 92.1
Total Cerebrum Gray Matter Volume [mean ± SD] 492.21 ± 46.11
Total Cerebrum White Matter Volume [mean ± SD] 420.79 ± 53.21
Left Hippocampus Volume [mean ± SD] 3.09 ± 0.46
Right Hippocampus Volume [mean ± SD] 3.2 ± 0.46
Total Hippocampi Volume [mean ± SD] 6.29 ± 0.89
Total Brain Gray Matter Volume [mean ± SD] 587.54 ± 53.16
Total Brain White Matter Volume [mean ± SD] 471.54 ± 53.16
Total WMH Volume [mean ± SD] 5.52 ± 5.9
Total Brain Volume (includes both cerebrum and infratentorial region) [mean ± SD] 1401.86 ± 135.01
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All participants were in the MCI stage. Abbreviations: CDR-SB, Clinical Dementia Rating Scale-Sum of Boxes; ADAS-Cog, Alzheimer's Disease Assessment Scale-Cognitive Subscale; MMSE, Mini-Mental State Examination; CAIDE, Cardiovascular Risk Factors.

In the MCI group, there were significant correlations between the CAIDE score and imaging findings (Table 2). Both Total Brain Volume (adjusted R² = 0.109, β = 0.337, p < 0.001) and Total Cerebrum Cranial Volume (adjusted R² = 0.098, β = 0.319, p < 0.001) were positively correlated with higher CAIDE scores. The total brain gray matter volume (adjusted R2 = 0.110, β = 0.337, p < 0.001) and total cerebrum gray matter volume (adjusted R2 = 0.093, β = 0.312, p < 0.001) also showed significant positive relationships. Significant correlations were also found between hippocampal measurements; the CAIDE score was positively correlated with both the left and right hippocampal volumes (adjusted R2 = 0.059, β = 0.250, p < 0.001, and adjusted R2 = 0.049, β = 0.231, p = 0.001, respectively). Total Hippocampi Volume exhibited similar associations (adjusted R² = 0.058, β = 0.249, p < 0.001). No significant association was found between the CAIDE score and Total WMH With an ideal cutoff of 8.

Table 2.

Associations between CAIDE Score and Neuroimaging Measures in the MCI Group.

Imaging Measure Adjusted R² Standardized Beta FDR Adjusted P-Value
Total Cerebrum Cranial Volume (supratentorial portion) 0.098 0.319 < 0.001
Total Cerebrum Brain Volume 0.085 0.299 < 0.001
Total Cerebrum Gray Matter Volume 0.093 0.312 < 0.001
Total Cerebrum White Matter Volume 0.058 0.249 < 0.001
Left Hippocampus Volume 0.059 0.250 < 0.001
Right Hippocampus Volume 0.049 0.231 0.001
Total Hippocampi Volume 0.058 0.249 < 0.001
Total Brain Gray Matter Volume 0.110 0.337 < 0.001
Total Brain White Matter Volume 0.058 0.249 < 0.001
Total WMH -0.004 -0.021 0.752
Total Brain Volume (includes both cerebrum and infratentorial region) 0.109 0.337 < 0.001
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Higher CAIDE scores were associated with larger total and regional brain volumes, including Total Cerebrum Cranial Volume (r = 0.32, p < 0.001), Total Cerebrum Brain Volume (r = 0.30, p < 0.001), Total Cerebrum Gray Matter Volume (r = 0.22, p < 0.001), Total Cerebrum White Matter Volume (r = 0.31, p < 0.001), Left Hippocampus Volume (r = 0.25, p < 0.001), Right Hippocampus Volume (r = 0.25, p < 0.001), and Total Hippocampi Volume (r = 0.23, p < 0.001). Similarly, Total Brain Gray Matter Volume (r = 0.34, p = 0.04) and Total Brain White Matter Volume (r = 0.25, p = 0.04) were positively correlated with CAIDE. CAIDE also correlated with cognitive performance measures indicative of impairment, including the CDR-SB (r = 0.15, p = 0.04) and ADAS-Cog 11 (r = 0.15, p = 0.04), reflecting worse cognitive function with higher CAIDE scores. Correlations with MMSE were negative but did not reach statistical significance (r = -0.22, p = 0.01).

Strong inter-correlations were observed among the structural measures, particularly between Total Cerebrum Cranial and Brain Volumes (r = 0.92, p < 0.001) and between bilateral hippocampal volumes (Left vs Right Hippocampus, r = 0.97, p < 0.001), indicating coherent volumetric relationships across brain regions. In contrast, Total White Matter Hyperintensity Volume showed only modest correlations with other brain measures (r = 0.16, p = 0.02 with Total Brain Volume), suggesting its relative independence from overall brain size in this cohort (Fig. 1.).

Fig. 1.

Fig. 1

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Correlation Matrix of CAIDE Score with Cognitive and Neuroimaging Measures The heatmap illustrates the strength and significance of correlations between the CAIDE riskscore, various brain volume measures, and cognitive scores. Positive correlations are shown in red, negative correlations in blue, and the intensity represents the strength of the correlation. Significant correlations (p < 0.05) are marked with an asterisk.

Fig. 2. and Table 3 Shows how well different brain volume measures perform diagnostically in predicting a high CAIDE dementia risk score. Right Hippocampus Volume (AUC = 0.66, p < 0.001) and total Hippocampal Volume (AUC = 0.64, p < 0.001) were the two hippocampus measures that showed strong correlations, with respective sensitivities of 0.74 and 0.71 and accuracies of 0.64 and 0.62. The left hippocampus volume performed moderately (p = 0.002, accuracy = 0.659, sensitivity = 0.68, and AUC = 0.62). The highest predictive metric among gray matter measurements was total Brain Gray Matter Volume (AUC = 0.70, sensitivity = 0.78, accuracy = 0.68, p < 0.001). Moderate predictive value was also demonstrated by total cerebrum gray matter volume (AUC = 0.65, sensitivity = 0.74, accuracy = 0.64, p = 0.001). With total Brain White Matter Volume (AUC = 0.65, sensitivity = 0.74, accuracy = 0.64, p = 0.001) and total Cerebrum White Matter Volume (AUC = 0.66, sensitivity = 0.73, accuracy = 0.63, p = 0.001) performing similarly, white matter measures also showed strong correlations. The poorest correlation with dementia risk was seen for total WMH volume (AUC = 0.52, sensitivity = 0.44, accuracy = 0.53).

Fig. 2.

Fig. 2

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Diagnostic Performance of Brain Volume Metrics in Predicting High CAIDE ScoreReceiver Operating Characteristic (ROC) curves show the ability of different brain volumemeasures to predict high dementia risk (CAIDE ≥ 8) in individuals with MCI.

Table 3.

Diagnostic Performance of White Matter Hyperintensity Metrics in Predicting High CAIDE Score.

Feature AUC Optimal Probability Threshold Predictor Cutoff Sensitivity Accuracy
Total Cerebrum Cranial Volume (supratentorial portion) 0.66 0.51 8 0.74 0.65
Total Cerebrum Brain Volume 0.65 0.51 8 0.73 0.63
Total Cerebrum Cerebrospinal Fluid Volume 0.58 0.50 8 0.66 0.57
Total Cerebrum Gray Matter Volume 0.65 0.51 8 0.74 0.64
Total Cerebrum White Matter Volume 0.66 0.51 8 0.73 0.63
Left Hippocampus Volume 0.62 0.50 8 0.68 0.59
Right Hippocampus Volume 0.66 0.51 8 0.71 0.62
Total Hippocampi Volume 0.64 0.51 8 0.71 0.62
Total Brain Cerebrospinal Fluid Volume 0.57 0.50 8 0.67 0.58
Total Brain Gray Matter Volume 0.70 0.52 8 0.78 0.68
Total Brain White Matter Volume 0.65 0.51 8 0.74 0.64
Total WMH 0.52 0.50 7 0.44 0.53
Total Brain Volume (includes both cerebrum and infratentorial region) 0.69 0.51 8 0.77 0.67
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4. Discussion

This study examined the relationship between volumetric brain imaging measures, particularly WMH, and midlife vascular risk, as measured by the CAIDE score, in individuals with MCI. We found significant associations between vascular risk and brain structural changes, as well as significant correlations between CAIDE scores and several measures of brain volume, such as total brain volume, total cerebrum cranial volume, gray matter volumes, and hippocampal volumes. Specifically, the CAIDE score demonstrated a negative correlation with the MMSE scores and a positive correlation with clinical measures of cognitive impairment, such as the CDR-SB and ADAS-Cog 11, reinforcing its clinical relevance in MCI populations.

In contrast to previous studies, we found no significant correlation between CAIDE scores and total WMH volume. Prior research, including large-scale studies such as the Framingham Heart Study, has consistently demonstrated that higher WMH burden is associated with increased vascular risk (Jeerakathil et al., 2004, Salvadó et al., 2019). The discrepancy in our findings may arise from the heterogeneity of MCI pathology in our cohort, potential selection bias favoring individuals with predominantly neurodegenerative rather than vascular MCI subtypes, or the complex etiology of WMH beyond traditional vascular risk factors assessed by the CAIDE score (Garnier-Crussard and Chételat, 2024). Several vascular and related determinants not included in the CAIDE model, such as atrial fibrillation, sleep-disordered breathing, chronic kidney disease, elevated homocysteine levels, and systemic inflammation, have been independently associated with increased WMH burden in older adults (Shao et al., 2019, Wang et al., 2021, Wei et al., 2022, Zacharias et al., 2021, Walker et al., 2018). These findings suggest that while CAIDE effectively captures midlife vascular risk, it may not encompass all later-life vascular and inflammatory pathways contributing to WMH accumulation, particularly in individuals with MCI.

We evaluated the predictive ability of volumetric brain metrics for identifying individuals with high vascular risk (CAIDE ≥ 8) using ROC curve analysis. Total gray matter volume emerged as the most informative marker (AUC = 0.70, sensitivity = 0.76, accuracy = 0.68, p < 0.001), consistent with prior evidence elucidating that gray matter atrophy is a hallmark of neurodegeneration and cognitive decline (Liu et al., 2021a, Low et al., 2022). The findings in Table 2 further supports the hippocampus involvement in the progression from MCI to dementia. Specifically, both right hippocampal volume (AUC = 0.67, p < 0.001) and total hippocampal volume (AUC = 0.65, p < 0.001) demonstrated strong predictive value.

Gray matter and hippocampus measures outperformed WMH volumes (AUC = 0.53, p = 0.649), indicating that vascular risk factors may affect cortical atrophy through processes that are partially independent of apparent white matter lesions (Vipin et al., 2018). While WMH is widely recognized as a radiological indicator of cerebrovascular disease, our results point to the contribution of other or additional pathological pathways. According to a recent research, the APOE ε genotypes, a component of the CAIDE score, is associated with both age-related cognitive decline and reduced hippocampal volume (Rosenich et al., 2022).

We found significant correlations between CAIDE scores and hippocampal volumes (Left: adjusted R² = 0.059, β = 0.250, p < 0.001; Right: adjusted R² = 0.049, β = 0.231, p = 0.001), suggesting that vascular risk may have a stronger influence on hippocampal integrity in individuals with genetic predisposition. Our results are in line with recent longitudinal studies reporting an association between retained hippocampal volume and cognitive performance, as well as a decrease in CAIDE scores following multidomain lifestyle interventions (Perosa et al., 2024, Rosenich et al., 2022). The predictive strength of hippocampal measurements in our ROC analysis (AUCs ranging from 0.63 to 0.67) highlights the potential usefulness of therapies aimed at preserving hippocampal integrity in individuals with high CAIDE scores.

In contrast to findings in cognitively normal populations, where WMH burden is frequently linked to dementia risk elevation due to cardiovascular factors (Wardlaw et al., 2013), our MCI cohort demonstrated a relatively weak correlation between WMH burden and CAIDE score (Low et al., 2022, Wang et al., 2023). This suggests that, once MCI is diagnosed, conventional WMH measurements may be less effective in reflecting dementia risk, potentially due to other neurodegenerative processes that interact intricately with cerebrovascular disease. Considering recent developments in neuroimaging, WMH heterogeneity extends beyond volumetric measurements. Notably, factors such as oligodendrocyte dysfunction, reduced cerebrovascular reactivity, and impaired blood–brain barrier function may all contribute to WMH development (Voorter et al., 2025, Dadar and Parent, 2024). The idea that vascular risk factors in midlife induce later-life alterations in brain structure is supported by the observed correlations between CAIDE scores and total brain volume (adjusted R2 = 0.109, β = 0.337, p < 0.001). According to the emerging concept of "brain reserve," which aligns with this association, larger initial brain volumes may provide resilience against cognitive decline despite underlying pathology (Liu et al., 2021b, T O′brien et al., 2020).

Our statistical method provides strong evidence for these associations, employing ROC curve analysis and linear regression with appropriate adjustments. These approaches have been validated by recent meta-analyses and extensive neuroimaging investigations (Vuorinen et al., 2015b). These findings have significant clinical implications, suggesting that vascular risk classification tools such as the CAIDE score may help identify which MCI patients are most likely to benefit from targeted therapies. The varying predictive value of multiple brain measures highlights the potential for personalized approaches to risk assessment and disease management in MCI populations. Future studies should examine whether CAIDE-guided therapies may alter the course of MCI and whether various CAIDE score components have distinct impacts on the structure and function of the brain. Several longitudinal investigations employing multimodal imaging and a more detailed evaluation of vascular and neurodegenerative biomarkers are needed to disentangle the intricate interactions between vascular risk factors and brain structural alterations in the MCI-to-dementia continuum (Tolea et al., 2021, Stephen et al., 2021).

This study has several important limitations. First, the absence of cognitively normal controls alongside MCI patients limits comparisons across the cognitive spectrum, and the cross-sectional design precludes establishing a causal relationship between CAIDE scores and brain volume changes. Second, the CAIDE score, initially created for midlife evaluation, may not adequately reflect lifetime vascular risk profiles when applied to the aging population. Third, it is possible that modest associations were missed due to our exclusive reliance on volumetric WMH measures, without considering lesion location, signal intensity, or microstructural characteristics. Fourth, residual confounding from unmeasured variables may persist, despite adjustment for key demographic factors. Notably, in exploratory analyses, adjustment for total brain volume attenuated most associations between the CAIDE score and regional brain volumetric measures, suggesting that these relationships may be largely driven by global brain size rather than region-specific effects. Fifth, the absence of fluid biomarkers and molecular imaging impaired our ability to differentiate MCI subtypes and address underlying Alzheimer’s disease pathology. Finally, statistical power and generalizability to individuals with MCI may have been constrained by our sample size and clinic-based recruitment.

5. Conclusions

Higher CAIDE dementia risk scores were associated with poorer cognitive performance and gray matter and hippocampal volumes in patients with amnestic MCI, but demonstrated limited utility in predicting WMH burden. Although WMH volume showed only weak discriminative ability for identifying elevated dementia risk, gray matter and hippocampal volumes emerged as stronger neuroimaging correlates of the CAIDE score. These findings suggest that vascular-related dementia risk in MCI may be more strongly reflected in cortical and medial temporal lobe structural integrity than in white matter disease burden. Further longitudinal studies are needed to determine whether integrating CAIDE scores with targeted neuroimaging markers improves early identification of individuals at heightened risk for progression to dementia.

Author contributions

Yasaman Abtahi and Marjan Falahati contributed equally to this work and share first authorship. They were involved in manuscript drafting, and editing. Sheyda Sharabiani, Hannaneh Azimizonuzi, Saeed Omidi, Farshad Goharmanesh, Yousef Nourbakhsh, Marziye Jalalian Chaleshtari, Mohammad Hossein Nourbakhshi, Parham Mahmoudi, Raziyeh Zamiri, Laya Dalir, and Zahra Gharehdaghi contributed to manuscript drafting and editing. Fatemeh Gila contributed to writing the draft, revising, and validating the manuscript. Mahsa Mayeli contributed to conceptualization, supervision, project administration, and revising the final draft. Hamide Nasiri contributed to the data curation, formal analysis. All authors read and approved the final version of the manuscript.

CRediT authorship contribution statement

Yousef Nourbakhsh: Writing – review & editing. Farshad Goharmanesh: Writing – review & editing. Mohammad Hossein Nourbakhshi: Writing – review & editing. Marziye Jalalian Chaleshtari: Writing – review & editing. Raziyeh Zamiri: Writing – review & editing. Parham Mahmoudi: Writing – review & editing. Marjan Falahati: Writing – review & editing, Writing – original draft. Laya Dalir: Writing – review & editing. Yasaman Abtahi: Writing – review & editing, Writing – original draft. Hamide Nasiri: Formal analysis, Data curation. Sheyda Sharabiani: Writing – review & editing. Zahra Gharehdaghi: Writing – review & editing. Saeed Omidi: Writing – review & editing. Mahsa Mayeli: Supervision, Project administration, Conceptualization. Fatemeh Gila: Writing – review & editing, Writing – original draft, Validation. Hannaneh Azimizonuzi: Writing – review & editing.

Statement of ethics

This research utilized data from the ADNI study. The ADNI project is conducted in compliance with the Declaration of Helsinki.

Funding

None.

Declaration of Competing Interest

The authors have no conflicts of interest related to this work.

Acknowledgment

Data collection and sharing for this project was funded by the ADNI (National Institutes of Health Grant U01 AG024904) and DOD ADNI (Department of Defense award number W81XWH-12–2–0012). ADNI is funded by the National Institute on Aging, the National Institute of Biomedical Imaging and Bioengineering, and through generous contributions from the following: AbbVie, Alzheimer’s Association; Alzheimer’s Drug Discovery Foundation; Araclon Biotech; BioClinica, Inc.; Biogen; Bristol-Myers Squibb Company; CereSpir, Inc.; Cogstate; Eisai Inc.; Elan Pharmaceuticals, Inc.; Eli Lilly and Company; EuroImmun; F. Hoffmann-La Roche Ltd and its affiliated company Genentech, Inc.; Fujirebio; GE Healthcare; IXICO Ltd.; Janssen Alzheimer Immunotherapy Research & Development, LLC.; Johnson & Johnson Pharmaceutical Research & Development LLC.; Lumosity; Lundbeck; Merck & Co., Inc.; Meso Scale Diagnostics, LLC.; NeuroRx Research; Neurotrack Technologies; Novartis Pharmaceuticals Corporation; Pfizer Inc.; Piramal Imaging; Servier; Takeda Pharmaceutical Company; and Transition Therapeutics. The Canadian Institutes of Health Research is providing funds to support ADNI clinical sites in Canada. Private sector contributions are facilitated by the Foundation for the National Institutes of Health (www.fnih.org). The grantee organization is the Northern California Institute for Research and Education, and the study is coordinated by the Alzheimer’s Therapeutic Research Institute at the University of Southern California. ADNI data are disseminated by the Laboratory for Neuro Imaging at the University of Southern California.

Contributor Information

Yasaman Abtahi, Email: yasaman.abtahi.elt@gmail.com.

Marjan Falahati, Email: marjanfar96@gmail.com.

Sheyda Sharabiani, Email: sheyda.sharabiani@iau.ir.

Fatemeh Gila, Email: Fatemehgila99@gmail.com.

Saeed Omidi, Email: sao221@lehigh.edu.

Farshad Goharmanesh, Email: f.goharmanesh@gmail.com.

Yousef Nourbakhsh, Email: Nourbakhsh.yousef@gmail.com.

Marziye Jalalian Chaleshtari, Email: Mjalalian2910@gmail.com.

Mohammad Hossein Nourbakhshi, Email: mohammadnourbakhsh0@gmail.com.

Parham Mahmoudi, Email: parham.mahmoudi100@gmail.com.

Raziyeh Zamiri, Email: zamiriraziyeh@gmail.com.

Laya Dalir, Email: lad521@lehigh.edu.

Zahra Gharehdaghi, Email: Zahra.gharehdaghi0@gmail.com.

Hamide Nasiri, Email: hnasiri@sina.zums.ac.ir.

Mahsa Mayeli, Email: M-Mayeli@alumnus.tums.ac.ir.

Hannaneh Azimizonuzi, Email: Hannaneh.azimi97@gmail.com.

Data availability

The data were extracted from the Alzheimer's Disease Neuroimaging Initiative (ADNI). ADNI data are available to qualified researchers upon application and approval through the ADNI data access process.

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Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Data Availability Statement

The data were extracted from the Alzheimer's Disease Neuroimaging Initiative (ADNI). ADNI data are available to qualified researchers upon application and approval through the ADNI data access process.

Articles from IBRO Neuroscience Reports are provided here courtesy of Elsevier

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