Risk and protective factors associated with grey matter patterns in older adults

doi:10.21203/rs.3.rs-5137523/v1

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Risk and protective factors associated with grey matter patterns in older adults

https://doi.org/10.21203/rs.3.rs-5137523/v1

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INTRODUCTION

Early dementia detection in the general population is challenged by high cerebral grey matter (GM) heterogeneity preceding behavioural symptoms. Here, we identify distinct GM patterns and their associated factors in a population-based cohort to detect at-risk individuals.

METHODS

This cross-sectional study examined 746 dementia-free 70-years-old individuals from the Gothenburg H70 Study to identify GM patterns using random forest clustering on MRI measures and examine their associations with sociodemographic, cardiometabolic, cognitive, genetic, and biomarker characteristics.

RESULTS

Five GM clusters emerged, primarily differentiated by frontoparietal regions. Compared to Cluster 1 (reference), cortical thickness was greater in Clusters 3 and 4, while reduced in Cluster 2, and mixed in Cluster 5. Significant factors included education, cardiometabolic conditions, depression, neurodegeneration, small vessel disease, lipidic and inflammatory alterations. Interestingly, GM patterns reflected cognitive performance.

DISCUSSION

Cerebrovascular health and related processes could be crucial for GM heterogeneity in late life, with implications for preventive strategies.

Neurology

Geriatrics & Gerontology

Nuclear Medicine & Medical Imaging

Cognitive Neuroscience

aging

MRI

atrophy

clustering

heterogeneity

prevention

cerebrovascular

cardiometabolic

lifestyle

With the current 55 million people living with dementia expected to triple over the next 30 years, dementia has been declared a public health priority [1]. Alzheimer’s disease (AD) is the most common form of dementia (60–80%) followed by dementia due to cerebrovascular disorders (5–10%) [2]. Despite the recent development of disease modifying drugs for AD [3], the cognitive benefits are modest. Therefore, prevention is necessary and feasible by targeting modifiable risk and protective factors already in dementia-free individuals [4].

Early detection of people at risk of dementia is challenging, thus the implementation of successful intervention strategies might be delayed. This can in part be explained by the heterogeneity of accumulating brain changes, which long precede the clinical manifestation of dementia [5]. Based on the location and extent of neurodegeneration, it has been possible to identify distinct patterns of cortical and subcortical atrophy in AD [6–8]. Despite differences in their prevalence, the patterns are consistent at group level, including typical AD, limbic-predominant, hippocampal-sparing, and minimal atrophy [9, 10]. Distinct subtypes have also been identified in PD [11], DLB [12], and selected healthy controls [13]. However, investigating patterns of regional grey matter (GM) in the (non-clinical) general population and the factors potentially contributing to these GM patterns during the lifespan could help identify and target at-risk individuals. Genetic predisposition together with sociodemographic factors (increasing age, female sex, lower education), lifestyle (physical inactivity, smoking, risk alcohol consumption), and health conditions (hypertension, heart disease, diabetes, depression) have been shown to increase dementia risk [4, 14]. In fact, these can promote detrimental pathways including proteinopathies, culminating in accelerated cognitive/brain aging and neurodegeneration, as well as cerebrovascular disease in the brain’s large and small vessels, inflammation and/or metabolic alterations [14–18].

This study aimed to (i) identify specific cortical and subcortical grey matter patterns possibly preceding future neurodegenerative disorder and (ii) investigate how these patterns are related to sociodemographic, lifestyle, and health-related factors, cognitive function, and neuroimaging and fluid biomarkers, in dementia-free older individuals from the general population. Unravelling grey matter heterogeneity in dementia-free individuals may provide an optimal window to identify people at risk of cognitive disorders and for interventions to prevent or slow cognitive decline.

2.1. Study design

Among the 1203 70-year-olds born 1944 from the Swedish population-based Gothenburg H70 Birth Cohort Study (H70-1944) (for the full cohort description see Rydberg et al. [19]), 791 underwent brain MRI scan (response rate = 65.8%) at baseline (Jan 2014-Dec 2016) Figure 1. Of these 3 subjects due to failed FreeSurfer preprocessing, 20 individuals with poor image segmentation, 8 with dementia diagnosis, 2 with MMSE <24, 3 with PD, 2 with Multiple Sclerosis, 6 with epilepsy, and 1 with brain cancer were excluded. This yielded a final sample of 746 individuals (cerebrospinal fluid sampling available for a subset of 286 individuals), including 353 men and 393 women. The H70 study received ethical approval from the Regional Ethical Review Board in Gothenburg. Written informed consent in agreement with the Helsinki Declaration was obtained from participants or, when the person was unable to provide their consent, from their next-of-kin.

2.2. Brain MRI markers of neurodegeneration and cerebrovascular pathology

Participants were scanned on a 3.0T MRI scanner (Philips Medical Systems). The imaging protocol included a three-dimensional (3D) T1-weighted Turbo Field Echo (TFE) sequence to assess structural changes; T2-weighted images to exclude other pathologies (e.g., tumors) and to evaluate enlarged perivascular spaces; a fluid-attenuated inversion recovery (FLAIR) sequence for the detection of as white matter hyperintensities volume (WMHV) and lacunes; a diffusion tensor imaging (DTI) sequence to determine the white matter microstructural integrity; and venous bold sequence (VenoBOLD) for the detection of microbleeds. For detailed MRI acquisition parameters see Rydberg et al. [19]. All images were processed and stored through the-Hive database system at Karolinska Institute [20], and underwent quality control as described previously [21].

2.2.1. Structural imaging. Brain MRIs were automatically pre-processed using FreeSurfer 7.2. Cortical thickness measurements were extracted for a total of 34 cortical regions of interest (ROIs) using the Desikan atlas [22]. Additionally, volume measurements were extracted for 7 subcortical ROIs, including the hippocampus, thalamus, amygdala, putamen, globus pallidus, nucleus accumbens, and caudate nucleus. Left and right hemisphere measurements were averaged for all ROIs. Subcortical volumes were adjusted for total intracranial volume (TIV) to account for natural interindividual variability in head sizes using residuals of a least-squares-derived linear regression between each volume and TIV from FreeSurfer [23]. Mean cortical thickness was used as a marker of non-AD-specific neurodegeneration [24]. A cortical signature of AD-specific neurodegeneration was calculated by averaging bilateral entorhinal, inferior temporal, middle temporal, and fusiform thickness, adjusted by cortical surface areas [25]. A cortical signature of brain resilience was calculated by averaging anterior cingulate and temporal pole thickness, adjusted by cortical surface areas [26].

2.2.2. FLAIR. FLAIR-derived WMHV was automatically segmented using the Lesion Segmentation Toolbox (LST 2.0.15) in the SPM software (https://www.fil.ion.ucl.ac.uk/spm/) [27]. WMHV was adjusted for each participant’s TIV from SPM. The continuous WMHV measure was categorized intro three groups according to the distribution: T1 (<3.2), T2 (3.2 – 6.4), T3 (>6.4 mm³), with higher values indicating greater small vessel vasculopathy.

2.2.3. DTI. DTI-derived measure of fractional anisotropy (FA) was extracted using the FMRIB’s Diffusion Toolbox from FSL (https://fsl.fmrib.ox.ac.uk/fsl/fslwiki) [28]. The FA measure in the white matter used in our analyses is the mean FA of all voxels for each participant computed within the FA skeleton mask, as described in detail elsewhere [29]. The continuous FA measure was categorized into three groups according to the distribution: T1 (<0.3), T2 (0.3 - 0.4), T3 (>0.4), with lower values indicating WM degeneration.

2.2.4. Visual assessment of markers of small vessel disease (SVD). Markers cerebral SVD were visually assessed by a neuroradiologist using standard rating scales and according to the Standards for Reporting Vascular Changes on Neuroimaging (STRIVE) [30]. SVD markers included lacunes, identified as 3-15 mm hypointensities in T1 and FLAIR, or hyperintensities in T2; cerebral microbleeds (CMBs) count according to the microbleed anatomical rating scales (MARS) [31]; perivascular spaces count in the centrum semiovale (PVScs) and basal ganglia (PVSbg), according to the Mac Lullich’s rating scale (0-10 vs ≥11) [32]. Large infarctions were also visually assessed.

Mean thickness, AD and resilience signature thickness, and hippocampal volume were distributed normally, hence used as continuous measures. Conversely, WMHV and FA values were divided into tertiles based on their skewed distribution.

2.3. Non-neuroimaging data

We considered sociodemographic, lifestyle, health-related, and cognitive features as well as blood/CSF biomarkers to characterize the identified patterns. Information on sociodemographic, lifestyle factors, health conditions, and cognitive function were collected by trained research nurses or medical doctors. Detailed assessment procedures [19] and operationalization are published elsewhere [33], thus a brief description is provided below.

2.3.1. Sociodemographic factors. Participants self-reported their biological sex at birth and educational attainment, which was categorized into primary and lower secondary schooling (<9 years of formal education), higher secondary schooling (9 years of schooling or ≤2 years of vocational training), and higher education (>2 years of vocational training or university).

2.3.2. Lifestyle and cardiometabolic conditions. Smoking was dichotomized into never vs current/former smoking. At-risk alcohol consumption was identified if the person consumed ≥100-g alcohol/week (equating to heavy consumption by the National Institute on Alcohol Abuse and Alcoholism). Physical activity was categorized into inactivity (no activity/sedentary most of the day or irregular lighter walks) vs active (regular non-demanding physical exercise 2–4 times/week, demanding physical activities at least 1 h/week, or regular hard exercise). Body Mass Index (BMI) was dichotomized into normal vs overweight/obesity if ≥ 25 kg/m². Hypertension was defined as systolic blood pressure ≥ 140 or diastolic blood pressure ≥90 mm Hg or current antihypertensive treatment. Cardio- and cerebrovascular conditions (heart diseases such as myocardial infarction, angina pectoris, heart failure, atrial fibrillation, and stroke/TIA) were diagnosed based on examinations, self-report, medication use, or via linkage to the Swedish National Patient Register (NPR), using the International Classification of Diseases-10th edition (ICD-10) codes. Prediabetes and diabetes were identified based on self-reported medical history, use of glucose-lowering treatments (diet, oral hypoglycemic agents, or insulin), or fasting/nonfasting blood glucose of ≥7.0/11.1 mmol/L [33].

2.3.3. Neuro-psychiatric conditions. Depression (major and minor) was diagnosed according to the Diagnostic and Statistical Manual of Mental Disorders (DSM), 4th or 5th editions criteria [34]. Dementia was diagnosed according to the DSM 3rd revised criteria merging neuropsychiatric examination and key informant interviews and used only as exclusion criterion in the current study [35]. Medical history of multiple sclerosis, epilepsy, brain cancer, and traumatic brain injury was based on self-reported general health and close informant interviews.

2.3.4. Cognitive functioning. This was assessed with the a detailed cognitive test battery encompassing five composite domains: (1) episodic memory (Memory in Reality–free recall and 12-object delayed recall, and Thurstone’s picture memory); (2) executive function (Digit Span Backward and Figure Logic); (3) attention and perceptual speed (Figure Identification-PSIF and Digit Span Forward); (4) verbal fluency (Controlled Oral Word Association-FAS and semantic fluency from “animals” task); (5) visuospatial abilities (Koh’s Block Test) [33]. A composite score for global cognitive performance was generated by averaging the z-scores across the five domains.

2.3.5. Blood biomarkers and apolipoprotein (APOE) genotype. Blood sampling was available for all participants and analysed following the standard lab routines at the Sahlgrenska University Hospital. The following indicators of altered lipid metabolism were considered: high triglycerides (≥1.7 mmol/L or use of lipid-lowering medication [Anatomical Therapeutic Chemical code C10]), high-density lipoprotein (HDL) cholesterol (<1.03 mmol/L in men and <1.29 mmol/L in women), and low-density lipoprotein (LDL) cholesterol, which was divided into tertiles (T1 ≤3; T2=3.1-3.9; T3≥4). Additionally, altered homocysteine (>13.5 µmol/L) or C-reactive protein (CRP, ≥8 mg/L) was used as measures of vascular-related and systemic inflammation respectively [17]. Finally, APOE was genotyped, and participants categorized into ε4 allele carriers (one or both ε4 alleles) vs non-carriers.

2.3.6. CSF biomarkers. Lumbar puncture was available for a subset of 286 participants (see Arvidsson Rådestig et al. for detailed CSF sampling procedure [36]). Biomarkers included: β-amyloid 42 (Aβ₄₂) ≤530 pg/mL, total-tau (t-tau) ≥350 pg/mL, and phosphorylated tau at threonine 181 (p-tau) ≥80 pg/mL [36].

2.4 Statistical analysis

2.4.1. Identification of the GM patterns through clustering. For the cross-sectional clustering, we selected 41 brain ROIs, among which 34 measured cortical thickness and 7 measured subcortical volumes. We first applied a Linear Mixed Effect Regression (LMER), controlling for TIV as fixed effect in subcortical ROIs and subject as random effect, and then unsupervised hierarchical clustering using random forest (R software, version 4.0.3, randomForest package, version 4.7-1.1). This clustering method has been previously used to identify atrophy subtypes in AD, PD and DLB [11–13,37]. In our dataset, the optimal random forest parameters were: ntree to 6000, mtry to 6, and nodesize to 3. The optimal cluster solution was established based on a composite score of Dunn and Calinski-Harabasz (CH) indices [38]. For a detailed description of the clustering pipeline see Poulakis et al. [37]. To identify the most discriminating ROIs between the clusters, we then calculated Cohen's d statistics between cluster 1 (utilized as reference group) vs. clusters 2 to 5 based on independent two-samples t-test for each ROI with False Discovery Rate (FDR) correction for multiple comparisons (p value <0.05).

2.4.2. Characterization of the GM patterns. Sociodemographic, lifestyle, health-related factors, cognitive function, and fluid biomarkers between the clusters (1-5) were analyzed using Chi-square test for categorical variables, one-way ANOVA for continuous variables normally distributed and Kruskal-Wallis test for continuous variables not normally distributed. Multinomial logistic regression (MLR) was employed to assess Odds Ratios (ORs) and 95% confidence intervals (CIs) in examining the relationships between the outcome – defined by cluster allocation obtained through unsupervised clustering (with cluster 1 as the reference group) – and sociodemographic, lifestyle, health-related, and fluid biomarkers predictors (as characterizing features or exposures). We fitted the following four separate MLR models: (1) Health and sociodemographic model included sex (reference=men), educational attainment (reference=primary/lower schooling), any APOE-ε4 carriership, MMSE, former/current smoking, at-risk alcohol consumption, physical inactivity, overweigh/obesity, increased triglycerides, increased LDL, reduced HDL, hypertension, heart disease, stroke/TIA, prediabetes and diabetes, depression, and TBI; (2) Cerebrovascular model included mean FA, WMLV, PVScs, PVSbg, CMBs (absence vs present), lacunes (absence vs present), large infarctions (absence vs present); (3) Inflammation model included altered homocysteine and CRP levels; (4) CSF model was run only on the subset of participants with available CSF information, and included Aβ42, t-tau, and p-tau biomarkers. Additionally, we evaluated whether GM pattern predicted cognitive status through six separate Generalized Linear Models (GLMs) with the composite scores for global cognition and the five cognitive domains as outcomes, and cluster allocation as the predictor. To gauge the risk of multicollinearity bias, we assessed the variable inflation factor (VIF) for all the variables included. The VIF consistently remained below 1.7, allowing for the inclusion of all variables. A two-sided p value <0.05 indicated statistical significance. All statistical analyses were performed with R software version 4.0.3.

3.1. Identification of the GM patterns through clustering

The optimal cluster solution comprised 5 clusters, as illustrated in Supplementary Figure 1. Cluster 1 emerged as the largest including 278 (37.3%) participants, followed by Cluster 4 with 157 (21.1%) participants, Cluster 2 with 142 (19.0%) participants, Cluster 3 with 121 (16.2%) participants, and Cluster 5 with 48 (6.4%) participants. In the subsequent analysis, Cluster 1 was set as the reference group given that it was the largest cluster, with no/minimal GM differences from the sample mean. The identified clusters significantly differed in both GM thickness/volume and regional distribution. Figure 2 shows brain differences between clusters in Cohen’s d effect size that survived FDR correction p<0.05, Clusters 1-5 vs. the overall mean (2A) and Cluster 2-5 vs. Cluster 1 (2C). Figure 2B illustrates the three most discriminative ROIs between Cluster 2-5 vs. cluster 1 with Cohen’s d effect sizes > |1.2|: superior parietal, superior frontal, supramarginal ROIs (in the order). We choose this threshold as Cohen’s values drop after |1.2|, as highlighted in Supplementary Figure 2.

In comparison to cluster 1, Cluster 2 exhibited the most widespread and severe GM pattern, displaying Cohen’s d < -1.2 (reduced GM) in numerous ROIs encompassing superior frontal, inferior parietal, caudal middle frontal, supramarginal, middle temporal, postcentral, precuneus, lateral occipital, superior parietal, precentral, superior temporal, rostral middle frontal, parstriangularis, and parsopercularis. Cluster 3 vs. cluster 1 exceeded Cohen’s d > 1.2 (thicker cortex) in lateral orbitofrontal, parsopercularis, supramarginal, superior temporal, superior frontal, rostral middle frontal, parstriangularis, middle temporal, fusiform, inferior parietal, caudal middle frontal, precentral, precuneus, parsorbitalis, insula, paracentral and lingual ROIs, whereas Cluster 4 vs. cluster 1 reached the largest Cohen’s d > 1.04 (thicker cortex) in the supramarginal area. Cluster 5 vs. cluster 1 showed thinner posterior cortex in superior parietal, precuneus, inferior parietal, paracentral, and lateral occipital areas with Cohen’s d < -1.2, alongside thicker cingulate, insular, and orbitofrontal cortex with Cohen’s d > 0.4. Supplementary Figure 3 compares thickness and volume values (mm and mm³, respectively) across clusters for all the included ROIs.

3.2. Characterization of the GM patterns (descriptive statistics)

Table 1 summarizes sociodemographic, lifestyle, health-related and cognitive features, while Table 2 reports neuroimaging variables (GM and SVD). Finally, CSF biomarkers distribution across clusters 1-5 is reported in Table 3.

Variations among clusters were detected in elevated triglycerides levels, diabetes risk, mean and AD-specific cortical thickness, cortical signature of brain resilience, hippocampal volume, and mean FA, along with WMHV, and large infarctions. We found no significant differences in sociodemographic, lifestyle, health-related factors, cognitive function, and fluid biomarkers between participants with CSF information (n=286) vs. those without (n=460) through chi-square or one-way ANOVA/Kruskal-Wallis, confirming the representativeness of the CSF biomarkers results (Supplementary Table 1 and 2).

3.3. Characterization of the GM patterns (linear regression models)

3.3.1. Health and sociodemographic model. Multivariate MLR (reference=cluster 1) for health and sociodemographic features showed that Cluster 2 was associated with significantly increased risk alcohol consumption (OR=1.83, 95% CI [1.13, 2.97]) and diabetes (OR=2.54, 95% CI [1.27, 5.06]). Conversely, Cluster 3 showed decreased ORs of overweight or obesity (OR=0.57, 95% CI [0.35, 0.94]). Cluster 4 had increased ORs of higher education (OR=2.52, 95% CI [1.08, 5.87]) and decreased ORs of current/former smoking (OR=0.62, 95% CI [0.40, 0.95]), as well as decreased ORs of elevated triglycerides (OR=0.55, 95% CI [0.32, 0.95]) or depression (OR=0.17, 95% CI [0.05, 0.56]). Cluster 5 showed increased ORs of risk alcohol consumption (OR=2.09, 95% CI [1.05, 4.16]) and heart disease (OR=3.44, 95% CI [1.48, 8.01]) (see Figure 3A for significances, Supplementary table 3 for all the results).

3.3.2. Cerebrovascular model. MLR for cerebrovascular features (reference=cluster 1) indicated that Cluster 2 was less associated with the highest FA (OR=0.43, 95% CI [0.24, 0.78]). Cluster 3 was less associated with the second tertile of WMHV (OR=0.45, 95% CI [0.26, 0.77]) together with Cluster 4 (OR=0.54, 95% CI [0.33, 0.88]). Cluster 3 was also less associated with the highest tertile of WMHV (OR=0.50, 95% CI [0.27, 0.91]), together with Cluster 5 (OR=0.23, 95% CI [0.08, 0.64]). In addition, Cluster 4 was associated with less brain lacunes (OR=0.18, 95% CI [0.05, 0.62]), while Cluster 5 with more large infarctions (OR=10.80, 95% CI [1.42, 82.23]) (see Figure 3B for significances, Supplementary table 3 for all the results).

3.3.3. Inflammatory model. MLR models for inflammation biomarkers (reference=cluster 1) indicated that Cluster 5 was associated with lower homocysteine (OR=0.40, 95% CI [0.19, 0.84]) (see Figure 3C for significances, Supplementary table 3 for all the results).

3.3.4. CSF model. In the CSF sub-sample, Cluster 5 had increased ORs of elevated t-tau (OR=3.35, 95% CI [1.22, 9.14]) (see Figure 3E for significances, Supplementary table 3 for all the results).

3.3.5. Cognition model. The GLM on cognition revealed significantly poorer performance in Cluster 2 in episodic memory (β=-0.190, p=0.014) and visuospatial abilities (β=-0.209, p=0.044). Conversely, cognitive performance was significantly higher in Cluster 4 in global cognition (β=0.158, p=0.010), episodic memory (β=0.155, p=0.040), verbal fluency (β=0.237, p=0.006), and visuospatial abilities (β=0.222, p=0.026). Furthermore, borderline significances indicated a statistical trend of lower global cognition (β=-0.122, p=0.052) and executive functions (β=-0.157, p=0.057) in Cluster 2, and higher verbal fluency in Cluster 3 (β=0.161, p=0.087), however not significant (see Table 4 and Figure 4 for significances, Supplementary Table 4 for all the results).

In this study of dementia-free 70-year-old individuals, we identified five patterns of GM (cortical thickness/subcortical volumes) and their associated factors. One cluster showed minimal GM differences from the sample mean (Cluster 1), one was characterized by lower GM across multiple brain areas (Cluster 2), and two (Clusters 3 and 4) by higher GM, possibly suggesting neuroprotection. A fifth cluster emerged as a mix of higher and lower regional GM differences. Three ROIs mainly discriminated between the five clusters: superior frontal, superior parietal, and supramarginal gyrus. Each cluster was characterized by different combinations of dementia risk/protective factors, biological, and cognitive features. The clusters with less GM, 2 and 5, had higher prevalence of diabetes, alcohol consumption, heart disease, large infarcts, and higher CSF tau levels. Conversely, the more preserved clusters, 3 and 4, had less cardiometabolic disorders (lower triglycerides, less overweight/obesity, smoking), lower depression prevalence, higher education, lower WMHV, and lacunes. Importantly, the cluster with the least GM and the most risk factors (Cluster 2) also had lower cognition in two subdomains (episodic memory and visuospatial abilities), while the cluster with higher GM and the most protective factors (Cluster 4) had higher cognition in several subdomains (global cognition, episodic memory, verbal fluency, and visuospatial abilities). Altogether, these findings indicate that distinct patterns of GM (cortical thickness/subcortical volumes) exist also in the general population, as recently shown in selected healthy controls [13]. These GM patterns differ in magnitude and localization, with cardiometabolic risk factors and disorders playing a key role. To our knowledge, no prior studies have investigated heterogeneity in GM patterns in the general population to identify at-risk individuals.

Cluster 1 (37.3% of the participants) was chosen as the reference, exhibiting minimal differences in regional GM compared to the overall sample average. Specifically, Cluster 1 showed minimal differences in the insular cortex, consistent with previously reported age-related thinning of insular cortex in healthy adults [39]. Cluster 2 exhibited widespread and severe GM reduction, contrasting Clusters 3 and 4. In particular, brain preservation was more pronounced but more localized in Cluster 3, while less pronounced but more widespread in Cluster 4. Cluster 5 presented a mixed profile, with lower thickness in the inferior parietal, paracentral, and lateral occipital areas, involved in sensory-motor control, visuospatial attention, and object recognition [40, 41], but also relative preservation of cingulate, insular, and orbitofrontal cortices. When examining the most discriminant ROIs between the five clusters, we primarily identified frontoparietal differences, similar to the clustering in healthy controls [13], specifically in the superior frontal, superior parietal, and supramarginal areas. These ROIs were thinner in Clusters 2 and 5 and thicker in Clusters 3 and 4 compared to the reference. Previous cross-sectional studies have shown either frontal or parietal differences among dementia-free individuals, rarely the two together [42–44]. For example, three studies described similar thickness in the lateral frontoparietal cortex with the specific involvement of superior frontal [44] or supramarginal areas [43]. Our clustering study underscored the existence of both parietal and frontal differences in non-pathological aging [13, 42]. Frontal and parietal regions are crucial for higher cognitive processing and integration [45]. Thus, pathological changes in these regions can significantly impact cognitive performance [46, 47]. This was evident in Cluster 2, which exhibited less frontal and parietal GM and lower cognition, while cognitive performance was higher in Cluster 4, characterized by the preservation of fronto-parietal cortex [46, 47]. Also, although not reaching significance, Cluster 3 showed a trend of higher verbal fluency alongside GM preservation. Cluster 5, with lower cortical thickness in posterior regions, showed no differences in cognitive performance compared to the reference. However, this cluster also showed relative preservation of cingulate, insular, and orbitofrontal cortices. Metabolic and structural integrity in these regions have been linked to maintaining cognitive performance despite advanced age [26, 43, 48–51], being a plausible reason for cognition maintenance in Cluster 5.

Our results indicate that different combinations of risk/protective factors, alongside biological and cognitive features, are specific to each cluster, emphasizing the impact of cardiovascular and metabolic conditions on brain health, and in turn on cognitive health. Cluster 2 (widespread GM reduction) was characterized by increased presence of diabetes, alcohol risk consumption, lower FA suggesting less white-matter integrity, and poorer cognitive performance in global and specific cognitive domains. Cluster 5 (posterior GM reduction and partial anterior preservation) was characterized by increased alcohol risk consumption, heart disease, and higher CSF levels of t-tau, which suggest increased neurodegeneration. Cardiometabolic conditions such as heavy alcohol consumption, obesity, heart disease, and diabetes are well-established risk factors for dementia, with SVD and white matter integrity alterations as possible underlying mechanisms [14, 33, 52–57]. Moreover, previous research suggested that a shared genetic predisposition might be at play between diabetes, microstructural white-matter alterations, and overt grey matter atrophy [58, 59]. This shared predisposition may make individuals with diabetes or subclinical insulin-resistance particularly vulnerable to brain atrophy, even in the absence of AD pathology or cerebrovascular burden [58, 60, 61]. It is plausible to hypothesize that cardiometabolic conditions may contribute to widespread or localized patterns of less GM as observed in Clusters 2 and 5. However, the biological pathways underlying this contribution remain unclear. These may involve vascular damage and metabolic dysregulations affecting brain function and structure and, ultimately, cognitive outcomes [62–66]. Longitudinal studies are needed to elucidate the specific pathways involved.

Conversely from Clusters 2 and 5, Clusters 3 and 4 were characterized by cortical and subcortical preservation. In Cluster 3, brain preservation effect size was the strongest, yet not evenly widespread but rather localized to middle temporal, inferior parietal, and precentral areas. Cluster 3 was associated with lower prevalence of overweight/obesity, indicating a more favourable cardiometabolic profile, and lower WMHV. Brain structural preservation in Cluster 4 was less pronounced than Cluster 3, but more evenly widespread across the mantle. Similarly to Cluster 3, Cluster 4 was associated with more favourable cardiometabolic (i.e., lower current/former smoking or altered triglycerides) and cerebrovascular (lower WHMs and lacunes) profiles. Overweight/obesity and smoking are well-established modifiable vascular risk factors for dementia [4] and have been associated with an increased risk of small vessel disease, such as WMHs and lacunes [14], and accelerated brain atrophy [67]. Lipid alterations and WMHV also showed a strong association with brain atrophy [54–57, 68], possibly contributing to cognitive impairment and dementia [14, 69, 70]. Here, we show that a lower presence of these vascular risk factors in Clusters 3 and 4 is related to more preserved brain structure, possibly contributing to brain maintenance. These clusters are also characterized by lower WMHs and lacunes as well as less lipids dysregulation. Altogether, these findings suggest that SVD and lipids metabolism could play key roles in preserving brain structure into late life – a hypothesis that needs further testing. Similarly, the favourable cerebrovascular and inflammatory profile (lower WMHV and homocysteine, in line with previous findings linking systemic inflammation and cerebrovascular lesions [17, 71]) and anterior cingulate preservation in Cluster 5 might have counteracted the effects of reduced GM and risk factors, perhaps safeguarding cognition through brain maintenance (i.e., the preservation of cognitive function and neural resources) and/or cognitive reserve (i.e., the brain's ability to maintain cognitive function despite aging or pathology) [49, 50, 72]. While further research should elucidate the role of additional protective environmental, alongside their interactions with genetic influences, in explaining brain preservation, especially in Cluster 3, our findings suggest that lower cardiovascular and metabolic features are likely to be protective, thus constituting an optimal target for prevention of brain deterioration in the general population [4, 14]. Future intervention studies should test this hypothesis targeting these factors through pharmacological and/or lifestyle intervention. Finally, as seen in Cluster 4, higher education—a traditional proxy measure of cognitive resilience [73, 74]—might contribute to brain maintenance by driving individual differences in cognitive abilities [75]. However, longitudinal associations have been challenged [76] and future research needs to better understand the contribution of education to brain preservation.

This study has several strengths. First, the clustering approach used here has previously been applied to neurodegenerative diseases such as AD, PD, and DLB, revealing minimal, widespread, and localized atrophy patterns [11, 12, 37] as well as in selected healthy controls [13]. In the current study, we applied this clustering approach for the first time to a dementia-free cohort of 70-years-old from the general population, enabling comparisons between different GM patterns in aging. When atrophy was localized in previous studies, it was predominantly cortical or limbic in AD [37], fronto-occipital in DLB [12], and frontoparietal in PD [11]. As expected, the patterns of GM observed in our study are not directly comparable, some similarities are evident, particularly in clusters 2 (widespread) and 5 (localized). Second, investigating a cohort where all participants have the same chronological age (70 years) rules out age-related confounding effects. Third, this cohort offers a large well-characterized MRI sample from the general population with detailed clinical and lifestyle information. However, certain limitations need to be acknowledged. Firstly, the cross-sectional design of this descriptive study did not allow to establish temporal relationships between brain patterns and associated factors. Secondly, the potential for selection bias and residual confounders, such as self-reporting, cannot be completely ruled out in descriptive studies.

In conclusion, this study is the first identifying distinct patterns of GM in a dementia-free general population, as well as the specific vascular and lifestyle factors related to brain and cognitive health. Our findings indicate that cardiovascular, metabolic, and lifelong exposures are important contributors to different GM patterns in late life, potentially influencing the risk of future cognitive decline, as previously reported [14], but also the underlying cerebrovascular, neuropathological, and inflammatory mechanisms. Further research is needed to elucidate the specific interactions between lipidic, vascular, inflammatory, and structural brain mechanisms in relation to cognitive outcomes. Detecting early changes and targeting lifelong cardiovascular factors during symptom-free timeframes is crucial to reduce cerebrovascular burden, thus opening a window of opportunity for identifying at-risk individuals to be able to tailor more individualized preventive strategies, when many of the described factors are still modifiable.

Acknowledgements: We thank all the participants and staff involved in the data collection and management in the Gothenburg H70 Birth Cohort Studies.

Funding Sources: The H70 study was financed by grants from the Swedish state under the agreement between the Swedish government and the county councils, the ALF-agreement (grant number 716681); the Swedish Research Council (grant numbers 2012–5041, 2015–02830, 2019-01096, 2013–8717, 2017-00639); Swedish Research Council for Health, Working Life and Welfare (grant numbers 2013-1202, 2018-00471, AGECAP 2013–2300, 2013–2496); Konung Gustaf V:s och Drottning Victorias Frimurarestiftelse; Hjärnfonden (grant numbers FO2014-0207, FO2016-0214, FO2018-0214, FO2019-0163, FO2020-0235); Alzheimerfonden (grant numbers AF-554461, AF-647651, AF-743701, AF-844671, AF-930868, AF-940139); and Eivind och Elsa K:son Sylvans stiftelse.

DF receives funding from the Swedish Research Council (Vetenskapsrådet, grant 2022-00916), the Center for Innovative Medicine (CIMED, grants 20200505 and FoUI-988826), the regional agreement on medical training and clinical research of Stockholm Region (ALF Medicine, grants FoUI-962240 and FoUI-987534), the Swedish Brain Foundation (Hjärnfonden FO2023-0261, FO2022-0175, FO2021-0131), the Swedish Alzheimer Foundation (Alzheimerfonden AF-968032, AF-980580, AF-994058), the Swedish Dementia Foundation (Demensfonden), the Gamla Tjänarinnor Foundation, the Gun och Bertil Stohnes Foundation, the Åke Wiberg Foundation, Funding for Research from Karolinska Institutet, Neurofonden, and the Foundation for Geriatric Diseases at Karolinska Institutet, as well as contributions from private bequests.

SK was financed by grants from the Swedish state under the agreement between the Swedish government and the county councils, the ALF-agreement (ALFGBG-965923, ALFGBG-81392, ALF GBG-771071). The Alzheimerfonden (AF-842471, AF-737641, AF-929959, AF-939825). The Swedish Research Council (2019-02075, 2019-02075_15), Stiftelsen Psykiatriska Forskningsfonden, Stiftelsen Demensfonden, Stiftelsen Hjalmar Svenssons Forskningsfond, Stiftelsen Wilhelm och Martina Lundgrens vetenskapsfond. ME was funded by the Swedish research council grant (#2020-02014) and the regional agreement on medical training and clinical research between the Stockholm County council and the Karolinska Institutet (ALF, FoUI FoUI-975207), the KI foundations, the Swedish Innovation Agency (Vinnova, grant # 2021-02680) and the European Union’s Horizon Europe research Innovative Health Initiative Joint Undertaking (PROMINENT, grant #101112145).

RM was supported by Center for Innovative Medicine (CIMED) FoUI-987392, The Swedish Society for Medical Research (SSMF) PD21-0042, The Lars Hierta Memorial Foundation, KI Foundations.

EW was supported the Swedish Research Council (VR) 2016-02282, 2021-01861, the Center for Innovative Medicine (CIMED) FoUI-954459, FoUI-975174, the regional agreement on medical training and clinical research (ALF) between Stockholm County Council and Karolinska Institutet FoUI-952838, FoUI-954893, The Swedish Brain Foundation (Hjärnfonden) FO2021-0119, FO2022-0084, The Swedish Alzheimer's Foundation (Alzheimerfonden) AF-967495, AF-980387, The Swedish Parkinson's foundation (Parkinsonfonden) 1443/2022, 1521/23, King Gustaf V:s and Queen Victorias Foundation, and Olle Engkvists Foundation (Olle Engkvists Stiftelse) 186-0660, 224-0069 as well as Birgitta and Sten Westerberg for additional financial support.

The funding bodies had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

Conflicts: SK has served at scientific advisory boards, speaker and / or as consultant for Roche, Eli Lilly, Geras Solutions, Optoceutics, Biogen and Bioarctic.

ME has served as speaker and/ or consultant on dementia on sporadic advisory board meetings for Biogen, Bioarctic, Roche and Eli Lilly,

DF consults for BioArctic and has received honoraria from Esteve.

Consent Statement: All human subjects provided informed consent.

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Tables 1-4 are available in the Supplementary Files section

The authors declare no competing interests.

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Risk and protective factors associated with grey matter patterns in older adults

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