Introduction: Low educational attainment is a potential risk factor for Alzheimer’s disease (AD) development. Alpha-secretase ADAM10 plays a central role in AD pathology, attenuating the formation of beta-amyloid peptides and, therefore, their aggregation into senile plaques. This study seeks to investigate ADAM10 as a blood-based biomarker in mild cognitive impairment (MCI) and AD in a diverse group of community-dwelling older adults, focusing on those with limited educational attainment. Methods: Participants were recruited from public health services. Cognition was evaluated using Mini-Mental State Examination (MMSE) and Addenbrooke’s Cognitive Examination – Revised (ACE-R) batteries. Blood samples were collected to analyze plasma ADAM10 levels. A logistic regression was conducted to verify the influence of plasma ADAM10 on the AD diagnosis. Results: Significant differences in age, years of education, prescribed medications, and cognitive test scores were found between the MCI and AD groups. Regarding cognitive performance, both ACE-R and MMSE scores displayed significant differences between groups, with post hoc analyses highlighting these distinctions, particularly between AD and cognitively unimpaired individuals. Elevated plasma ADAM10 levels were associated with a 4.5-fold increase in the likelihood of a diagnosis of MCI and a 5.9-fold increase in the likelihood of a diagnosis of AD. These findings suggest ADAM10 levels in plasma as a valuable biomarker for assessing cognitive status in older individuals with low education attainment. Conclusion: This study underscores the potential utility of plasma ADAM10 levels as a blood-based biomarker for cognitive status, especially in individuals with low educational backgrounds, shedding light on their relevance in AD development and diagnosis.

Among several risk factors of Alzheimer’s disease (AD), such as age, metabolic disorders, and lifestyle habits, education plays an important role [1]. Fewer years of education are associated with cognitive deficits and conversion to dementia [2, 3]. On the other hand, educational attainment is frequently employed as an indicator of cognitive reserve, indicating that an advanced level of education is linked to a reduced risk of dementia [4‒6].

Since the recent approval of the first disease-modifying treatments [7, 8], there has been an emphasis on the pivotal role of AD biomarkers. However, considering that a significant portion of research on AD biomarkers primarily focuses on populations with elevated levels of education and socioeconomic status, there is a conspicuous gap in the current body of research, particularly concerning more diverse populations [9]. This study stands out by placing a crucial emphasis on individuals with lower educational attainment, thus effectively addressing this research gap.

Besides contributing to the AD clinical diagnosis, AD biomarkers are important monitoring tools for evaluating treatment responses [10]. Nonetheless, the most commonly employed biomarkers in clinical practice either rely on costly imaging or involve invasive analysis of the cerebrospinal fluid [11, 12]. In this context, blood-based AD biomarkers have recently gained much attention due to their cost-effectiveness and less invasive nature [13]. Nowadays, different isoforms of hyperphosphorylated tau and amyloid-beta isoforms ratio (Aβ42/Aβ40), both pathological hallmarks of AD, have been used as blood biomarkers, but they show a test-retest variability between trials. Therefore, using a combined biomarker panel is advised to increase the accuracy of the results [12, 14]. Hence, the search for novel blood-based markers is still needed.

ADAM10, the major alpha-secretase responsible for the nonamyloidogenic cleavage of amyloid protein precursor (APP) [15, 16], has been investigated as a blood-based AD biomarker. ADAM10 cleaves APP inside the Aβ sequence in physiological conditions, preventing its production [17]. However, in AD, decreased APP processing by ADAM10 is reported, leading to an increase in Aβ levels and aggregation into senile plaques [18]. Although prevalent in the central nervous system, ADAM10 can also be found peripherally in platelets [13]. It was previously reported that, in persons living with AD, ADAM10 levels are decreased in platelets when compared to cognitively unimpaired (CU) persons [19, 20]. Accordingly, higher levels of plasma ADAM10 were found in mild cognitive impairment (MCI) and AD [21, 22]. Remarkably, the elevation in ADAM10 plasma levels was associated with a subsequent decline in Mini-Mental State Examination (MMSE) scores during follow-up in persons without dementia, providing evidence that this biomarker can predict cognitive decline even in CU persons [23].

Given the potential risk factor of low education for AD development and the central role of ADAM10 in AD pathology, we seek to expand our understanding of its utility as a blood-based biomarker. Thus, this study compares cognitive performance and plasma ADAM10 levels in community-dwelling older adults with reduced years of formal education, who were divided into groups with unimpaired cognition, MCI, and AD.

This study analyzed data from community-dwelling older adults (n = 88) aged 60 or more recruited from primary (e.g., family health units) and secondary (e.g., specialty centers) care services in a municipality of São Carlos/Brazil. This is a secondary analysis of a previously investigated population whose characteristics were described elsewhere [23‒25]. Participants were divided into three different groups according to their neurocognitive status and classified as group 1: CU (n = 31), group 2: MCI (n = 27), and group 3: AD (n = 30).

The inclusion criteria varied according to the group. Group 1 participants were primary healthcare system users with no cognitive alteration. For the MCI group, participants needed to be registered and receive care from a doctor in the secondary care service. Also, they were required to have a diagnosis of MCI according to the Clinical Dementia Rating (CDR). Finally, for the AD group, in addition to fulfilling criteria from the MCI group, participants also needed to have a diagnosis of probable Alzheimer’s dementia [26] according to the CDR and be in use of AD medication (e.g., donepezil, rivastigmine, memantine, and/or galantamine). Participants were excluded if they had been diagnosed with bipolar disorder, major depression, schizophrenia, substance use disorder, hydrocephalus, a significant cerebrovascular disease that participated as an etiology of dementia, clinically significant changes in vitamin B12 and syphilis serology, previous traumatic brain injury, clinically significant and uncorrected hearing, and visual deficits, clinically significant or untreated systemic diseases (e.g., diabetes, high blood pressure, hypothyroidism, cancer, liver diseases, kidney diseases, heart diseases, lung diseases), they were also excluded if they were in current use of antiplatelet agents, anticoagulants, and corticosteroids medication.

Measurements

Sociodemographic questionnaire: this questionnaire was developed by the research group and contained questions about gender (male/female), age (in years), education (in years), income (quantified in minimum wages), morbidities (number of diagnosed health problems), and medication (number of prescribed medications in current use).

Addenbrooke’s Cognitive Examination – Revised (ACE-R): This examination is a cognitive screening battery that evaluates participants’ global cognitive functioning [27]. The final score ranges from 0 to 100, and the higher the score, the better the cognitive functioning of the participant [28]. Considering the heterogeneous and frequently low education attainment of the Brazilian population, the cutoff score for the test is 64 points to differentiate CU from AD and 69 to distinguish CU from MCI [29].

MMSE: This examination consists of a cognitive assessment instrument [30] widely used worldwide. In this study, scores referring to the MMSE were obtained from ACE-R. Its scores vary from 0 to 30, and the higher scores indicate better cognitive performance. MMSE has also been adapted and validated for the Brazilian population, and the cutoff scores are adjusted according to the participants’ schooling [31].

Blood Collection and Processing

Blood samples were collected in the local healthcare services as previously described [22]. Briefly, tubes containing sodium citrate solution (3.8%) and glucose (136 mM) were used to collect the blood (8.5 mL). Then, the tubes were inverted 10 times and maintained at a temperature of 4°C during the storage and transport phase. The collected material was centrifuged at 2,400 rpm for 10 min to obtain the plasma that was subsequently stored in an ultra-freezer (−80°C).

SDS-PAGE and Western Blotting

Following a previously standardized protocol, ADAM10 levels were evaluated in plasma [22]. Before the experiments, the BCA Kit (Thermo Scientific) determined each sample's protein concentration. The protein content was then separated through Mini-PROTEAN® TGX 4–20% gel (Bio-Rad) by SDS-PAGE and transferred onto Trans-Blot® Turbo Mini Nitrocellulose Transfer Packs (Bio-Rad) using the Mini Trans-Blot Cell transfer system (Bio-Rad) at 25 mA for 3 min. After protein transfer to the membranes, they were incubated in a 3% blocking solution (Blocker 3% - Bio-Rad) for 1 h and then incubated with the primary antibodies used in this study, namely anti-ADAM10 (Ab39180), for 15 h (overnight), followed by incubation in 3 mL of Goat Anti-Rabbit IgG-HRP secondary antibody (ab97051). The antibodies were diluted in a blocking solution (3% Blocker - Bio-Rad) with slight agitation on a shaking platform. For band visualization, the membranes were incubated following the Clarity Western ECL Substrate kit protocol and then revealed using a chemiluminescence imaging system (Chemidoc, Bio-Rad) and quantified using Image Lab 6.1 software (Bio-Rad). Human serum albumin protein was used as an endogenous control. A “young control” obtained from samples of young, healthy volunteers without cognitive and metabolic alterations was also used for band measurement. This control consists of a suspension prepared from a pool of plasma from a young donor, which was added to each gel to control analytical differences between blotted membranes prepared on different days (inter-assay variation). The measure of this young control sample was normalized according to the formula (band intensity of ADAM10/band intensity of endogen control)/band intensity (ADAM10/endogen) of young control sample.

Statistical Analyses

First, participants’ characteristics are described by percentages for categorical variables or by means, standard deviation, and median for continuous variables. Then, normality was tested using Kolmogorov-Smirnov and Shapiro-Wilk tests. To compare the differences between the groups, Kruskal-Wallis H and Pearson’s χ2 tests were performed for continuous and categorical variables, respectively. The Mann-Whitney U test, adjusted by the Bonferroni correction for multiple tests, was used for post hoc analysis for the Kruskal-Wallis H test. Spearman’s correlation was employed for correlation analysis. Finally, logistic regression was conducted to verify the influence of levels of plasma ADAM10 on diagnosing AD.

All analyses were conducted in IBM SPSS Statistics, version 29, and statistical significance was assumed when p < 0.05. Figures were created in GraphPad Prism, version 9.2.

The study involved a predominantly female participant group (62.1%), with a mean age of 74.55 (±8.19) and an average education of 4.64 years (±4.26). Table 1 provides detailed sociodemographic and clinical characteristics for the total sample and by group.

Table 1.

Participants’ sociodemographic and clinical characteristics

VariableTotal sample (n = 88)CU (n = 31)MCI (n = 27)AD (n = 30)Statistics
mean (SD)medianmean (SD)medianmean (SD)medianmean (SD)medianHp value
Gender, % female 62.10 60 66.70 60 0.84 
Age, years 74.55 (8.19) 74 71.35 (7.44) 70 72.81 (7.68) 74 79.40 (7.28) 80 15.89 <0.001a 
Education, years 4.64 (4.26) 4.52 (4.26) 3 (2.60) 6.23 (4.95) 9.16 0.01b 
Income* 0.53 
 Less than 2 MW, % 50.70 42.3 57.7 52.9 
 More than 2 MW, % 49.30 57.7 42.3 47.1 
Morbidities 1.74 (1.15) 1.24 (0.88) 1.93 (1.47) 2.04 (0.84) 7.65 0.02c 
Medication 4.16 (2.60) 2.75 (2.21) 3.96 (2.94) 5.47 (1.85) 5.50 17.88 <0.001d 
MMSE 19.88 (8.13) 22 25.42 (3.53) 26 22.52 (3.49) 23 11.77 (8.20) 11.50 44.26 <0.001e 
ACE-R 56.47 (15.95) 58 63.74 (14.16) 68 57.93 (10.22) 59 40.36 (20) 45 10.74 0.005f 
ADAM10, ng/mL 1.39 (0.56) 1.36 1.16 (0.39) 1.16 1.44 (0.52) 1.52 1.57 (0.67) 1.44 7.85 0.02g 
VariableTotal sample (n = 88)CU (n = 31)MCI (n = 27)AD (n = 30)Statistics
mean (SD)medianmean (SD)medianmean (SD)medianmean (SD)medianHp value
Gender, % female 62.10 60 66.70 60 0.84 
Age, years 74.55 (8.19) 74 71.35 (7.44) 70 72.81 (7.68) 74 79.40 (7.28) 80 15.89 <0.001a 
Education, years 4.64 (4.26) 4.52 (4.26) 3 (2.60) 6.23 (4.95) 9.16 0.01b 
Income* 0.53 
 Less than 2 MW, % 50.70 42.3 57.7 52.9 
 More than 2 MW, % 49.30 57.7 42.3 47.1 
Morbidities 1.74 (1.15) 1.24 (0.88) 1.93 (1.47) 2.04 (0.84) 7.65 0.02c 
Medication 4.16 (2.60) 2.75 (2.21) 3.96 (2.94) 5.47 (1.85) 5.50 17.88 <0.001d 
MMSE 19.88 (8.13) 22 25.42 (3.53) 26 22.52 (3.49) 23 11.77 (8.20) 11.50 44.26 <0.001e 
ACE-R 56.47 (15.95) 58 63.74 (14.16) 68 57.93 (10.22) 59 40.36 (20) 45 10.74 0.005f 
ADAM10, ng/mL 1.39 (0.56) 1.36 1.16 (0.39) 1.16 1.44 (0.52) 1.52 1.57 (0.67) 1.44 7.85 0.02g 

CU, cognitively unimpaired; MCI, mild cognitive impairment; AD, Alzheimer’s disease; p, p-value; SD, standard deviation; %, percentage; H, Kruskal-Wallis H; MW, minimum wage; MMSE, mini-mental state examination; ACE-R, Addenbrooke’s Cognitive Examination – Revised.

aCU versus AD (0.00), MCI versus AD (0.01); bMCI versus AD (0.00); cCU versus AD (0.02); dCU versus AD (0.00), MCI versus AD (0.02); eCU versus MCI (0.01), CU versus AD (<0.001), MCI versus AD (<0.001); fCU versus AD (0.00), MCI versus AD (0.02); gCU versus MCI (0.02), CU versus AD (0.01).

*In Brazilian reais, the amount is equivalent to Reais 1,412, which corresponds to USD 250.00.

As expected, CU participants had a better overall performance on MMSE (p = 0.015) and lower levels of ADAM10 in plasma compared to the MCI (p = 0.023) and AD (p = 0.01) groups. Compared to the CU group, AD patients were older (p = 0.00), had more comorbidities (p = 0.02), were prescribed more medications (p = 0.00), had worse performance both on MMSE (p < 0.001) and on ACE-R (p = 0.001), and had higher levels of plasma ADAM10 (p = 0.011). Finally, there was a significant difference between MCI and AD groups for age (p = 0.01), years of education (p = 0.008), prescribed medications (p = 0.02), MMSE score (p < 0.001), and ACE-R score (p = 0.024).

The post hoc analyses for ACE-R scores indicated a significant difference between AD and MCI (U= 13.4; p = 0.024) and between AD and CU (U = 20.6; p = 0.001) groups. For the MMSE scores, Mann-Whitney analyses suggested robust differences between all groups (AD/MCI -U = 4.0; p < 0.001; AD/CU -U = 6.6; p < 0.001; MCI/CU -U = 2.4; p = 0.015). Figure 1 shows participants’ cognitive performance on ACE-R and MMSE across groups.

Fig. 1.

Participants’ cognitive performance on MMSE (a) and ACE-R (b) according to the group.

Fig. 1.

Participants’ cognitive performance on MMSE (a) and ACE-R (b) according to the group.

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Specifically, the Kruskal-Wallis H test revealed a significant difference in ADAM10 levels between groups (H = 7.85; p = 0.02). Post hoc analyses indicated a significant difference between CU and MCI (U = −15.2; p = 0.023) and between CU and AD (U= −16.6; p = 0.011) groups. Figure 2 illustrates the levels of ADAM10 in plasma across groups.

Fig. 2.

Representative Western blotting membrane showing participants’ levels of ADAM10 in plasma according to group (a) and the respective graph representation (b). Plasma samples from cognitively unimpaired (CU), mild cognitive impairment (MCI), and Alzheimer’s disease (AD) participants were probed against anti-N-terminal ADAM10 antibodies that recognize a 50-kDa isoform of the protein. Human serum albumin protein (66 kDa) was used as endogenous control for the quantifications. A “young” control represented by samples collected from young persons without cognitive alterations was also used for normalization between experiments.

Fig. 2.

Representative Western blotting membrane showing participants’ levels of ADAM10 in plasma according to group (a) and the respective graph representation (b). Plasma samples from cognitively unimpaired (CU), mild cognitive impairment (MCI), and Alzheimer’s disease (AD) participants were probed against anti-N-terminal ADAM10 antibodies that recognize a 50-kDa isoform of the protein. Human serum albumin protein (66 kDa) was used as endogenous control for the quantifications. A “young” control represented by samples collected from young persons without cognitive alterations was also used for normalization between experiments.

Close modal

Spearman’s correlation analysis was performed to verify the relationship between the variables. ADAM10 levels were correlated with ACE-R total scores (ρ = −0.243; p = 0.036; 95% CI: [−1.0 to −0.015]). Participant’s age correlated with cognitive performance (MMSE - ρ = −0.378; p < 0.001; 95% CI: [−1.0 to −0.211]/ACE-R - ρ = −0.468; p < 0.001; 95% CI: [−1.0 to −0.270]), number of diagnosed health problems (ρ = 0.282; p = 0.006; 95% CI: [0.093–1.0]), and number of medications in current use (ρ = 0.345; p < 0.001; 95% CI: [0.167–1.0]). Also, in addition to their relationship with ADAM10 levels, ACE-R total scores correlated with MMSE scores (ρ = 0.802; p < 0.001; 95% CI: [0.703–1.0]), participant’s years of education (ρ = 0.275; p = 0.019; 95% CI: [0.052–1.0]), and medications in current use (ρ = −0.224; p = 0.047; 95% CI: [−1.0 to −0.002]). No significant correlation was observed between ADAM10 levels and participants’ age, years of education, MMSE total scores, number of diagnosed health problems, or number of medications in current use.

Finally, a logistic regression analysis was performed to investigate whether levels of ADAM10 in plasma impacted the diagnosis of AD status (Table 2). The proposed model was significant (χ2 [2] = 10.005; p < 0.007; R2Nagelkerke = 0.122). Considering the CU group, elevated plasma ADAM10 levels were associated with a 3.9-fold increase in the likelihood of a diagnosis of MCI (Exp(B) = 3.926; p = 0.026; 95% CI: [1.174–13.124]) and a 5.7-fold increase in the likelihood of a diagnosis of AD (Exp(B) = 5.662; p = 0.005; 95% CI: [1.687–19.005]).

Table 2.

Multinomial logistic regression of levels of ADAM10 on participants’ groups

ModelGroupVariableBSE.p valueExp(B)95% CI
MCI ADAM10 levels 1.368 0.616 0.026 3.926 1.174–13.124 
 AD ADAM10 levels 1.734 0.618 0.005 5.662 1.687–19.005 
MCI ADAM10 levels 1.504 0.652 0.021 4.498 1.254–16.139 
Age 0.023 0.039 0.546 1.024 0.949–1.105 
Years of education −0.173 0.107 0.105 0.841 0.682–1.037 
 Gender 0.522 0.651 0.423 1.685 0.470–6.040 
AD ADAM10 levels 1.784 0.697 0.010 5.951 1.519–23.312 
Age 0.154 0.044 <0.001 1.166 1.069–1.273 
Years of education 0.105 0.076 0.165 1.111 0.958–1.288 
Gender 0.545 0.701 0.437 1.724 0.436–6.811 
ModelGroupVariableBSE.p valueExp(B)95% CI
MCI ADAM10 levels 1.368 0.616 0.026 3.926 1.174–13.124 
 AD ADAM10 levels 1.734 0.618 0.005 5.662 1.687–19.005 
MCI ADAM10 levels 1.504 0.652 0.021 4.498 1.254–16.139 
Age 0.023 0.039 0.546 1.024 0.949–1.105 
Years of education −0.173 0.107 0.105 0.841 0.682–1.037 
 Gender 0.522 0.651 0.423 1.685 0.470–6.040 
AD ADAM10 levels 1.784 0.697 0.010 5.951 1.519–23.312 
Age 0.154 0.044 <0.001 1.166 1.069–1.273 
Years of education 0.105 0.076 0.165 1.111 0.958–1.288 
Gender 0.545 0.701 0.437 1.724 0.436–6.811 

MCI, mild cognitive impairment; AD, Alzheimer’s disease; B, Beta; SE, standard error; Exp(B), odds ratio; 95% CI, 95% confidence interval.

The same pattern was observed when controlling the analysis for participant’s age, years of education, and gender. The final model was significant (χ2(8) = 37.115; p < 0.001; R2Nagelkerke = 0.391). Considering the CU group, elevated plasma ADAM10 levels were associated with a 4.5-fold increase in the likelihood of a diagnosis of MCI (Exp(B) = 4.498; p = 0.021; 95% CI: [1.254–16.139]) and a 5.9-fold increase in the likelihood of a diagnosis of AD (Exp(B) = 5.951; p = 0.010; 95% CI: [1.519–23.312]). This suggests a significant association between higher ADAM10 levels and the risk of both MCI and AD.

This study explored the association between ADAM10 plasma levels and AD diagnosis in older adults, with a particular emphasis on individuals with limited years of formal education. In doing so, we introduced a unique perspective by investigating the association between plasma ADAM10 levels and AD diagnosis within this demographic.

As expected, cognitive assessments using MMSE and ACE-R revealed significant differences between groups with CU individuals outperforming those with MCI in MMSE. Conversely, individuals living with AD displayed significantly lower scores on both the MMSE and ACE-R. These lower scores indicate more pronounced cognitive impairment, reaffirming the effectiveness of these assessment tools in conducting cognitive screenings, even within populations with limited educational backgrounds [32]. Accordingly, we found that higher plasma ADAM10 levels were significantly associated with an increased likelihood of MCI/AD diagnosis, which aligns with prior studies, reinforcing the consistency of our results with earlier research [19, 20, 22].

One explanation for the association between lower educational attainment and an increased risk of dementia can be attributed to the role of cognitive reserve. Cognitive reserve is a theoretical concept suggesting that prolonged engagement in intellectually stimulating activities shapes the brain’s structure and function, potentially reducing the adverse impact of brain pathology on cognitive abilities [4]. Early-life factors, including lower educational attainment, influence cognitive reserve development and can help explain our results. On the other hand, it is well documented that elevated childhood educational achievements and lifelong pursuit of higher education are associated with a reduced risk of dementia [2, 33].

The sociodemographic and clinical characteristics of our study participants are essential to consider. The predominance of females, older age, limited education, health problems, and extensive medication use underscores the diverse profile of the studied population. In addition, many participants had limited income, highlighting the socioeconomic diversity within our sample. Our findings align with a meta-analysis that indicates a high global prevalence of dementia in Latin American countries and highlights an unequal distribution of the disease, with a more significant burden on women, individuals with lower educational attainment, and those residing in rural areas [34].

Females and individuals with limited educational attainment predominated in all groups within this study, a trend consistent with global [35] and Brazilian [36] epidemiological data. This observation further underscores the feminization of aging, a phenomenon in which women constitute a growing majority of the older adult population due to their longer life expectancy, resulting in unique challenges and needs related to aging [37].

Low educational levels among older women in our study can be attributed to limited career opportunities, often stemming from traditional gender roles that have historically directed many women to prioritize household and caregiving responsibilities for children and grandchildren [38]. These societal dynamics also substantially impact the income disparities within this population [39, 40]. Notably, the neuropsychological tests administered, MMSE and ACE-R, consistently yielded lower scores. These results, in conjunction with the low educational levels, point to significant risk factors associated with the onset of cognitive decline and the potential development of dementia [41]. This highlights the critical need for tailored interventions and support systems to address this demographic’s cognitive health.

Considering that dementia will become more prevalent in low- and middle-income countries in the coming years [42], identifying reliable biomarkers for AD in a diverse population, particularly among individuals with unfavorable socioeconomic statuses, is paramount in neurodegenerative research [43]. Of note, AD presents unique challenges in its diagnosis and management. While research has made considerable progress in uncovering potential biomarkers for early detection, it is crucial to ensure these markers are applicable and practical across diverse demographic groups [44]. Diverse populations encompass various genetic, cultural, and socioeconomic backgrounds. Within this diversity, there may be variations in the prevalence, presentation, and progression of AD. This diversity underscores the necessity of conducting research across different socioeconomic strata to ensure that diagnostic and predictive tools are applicable and equitable for all [44].

By focusing on individuals with unfavorable socioeconomic statuses, we addressed a particularly vulnerable and underserved group, as they may face barriers to access healthcare services and timely diagnosis. Developing biomarkers sensitive to this population’s unique challenges can facilitate earlier diagnosis, enabling interventions to be deployed when most effective, ultimately improving patient outcomes and reducing healthcare disparities. Moreover, understanding the intricate relationship between socioeconomic factors and AD can provide insights into the disease’s complex etiology and potentially lead to targeted interventions and preventive strategies.

Our results reinforce that ADAM10 may have the potential to be a blood-based biomarker for early AD detection. The study aligns with several previous studies linking ADAM10 to amyloid processing pathways, a crucial aspect of AD pathogenesis in mouse models [45‒48] and human studies [18, 21, 45, 46].

Specifically, our findings indicate that elevated levels of ADAM10 in plasma are associated with an increased likelihood of individuals being diagnosed with MCI or AD. This aligns with existing evidence in the literature [19, 20, 22]. Studies suggest that detachment from the cell membrane diminishes ADAM10 activity, leading to an elevated extracellular content [47]. This could explain the higher levels observed in the plasma of individuals diagnosed with MCI and AD. We hypothesize that different ADAM10 isoforms, such as those in platelets and plasma [22], may play distinct roles in AD pathology. However, a more thorough investigation is imperative to interpret these findings comprehensively.

Our study has some limitations, including the absence of a comparison involving individuals with a more extensive formal education background. Furthermore, another limitation lies in the exclusive focus on assessing the duration of education rather than considering the quality of education as a variable. In the context of older adults, the quality of education encompasses a broad spectrum of factors, which extends beyond the classroom and includes lifelong experiences. These factors may encompass cultural context, environmental influences, and various life experiences that continue to shape an individual’s cognitive abilities and overall cognitive health as they age [48]. Hence, it is essential to recognize that not all years of education are equivalent in terms of their impact on cognitive development and the ability to perform well on cognitive tests. By not considering the quality of education, our study might overlook potential variations in cognitive abilities among individuals with the same number of years of schooling. A more comprehensive approach would involve assessing both the quantity (years) and quality of education to understand better education’s impact on our specific sample’s cognitive functioning. This aspect is particularly significant when studying populations like ours, characterized by diverse educational backgrounds, as it enables researchers to address the disparities in cognitive abilities and potential biases in their research findings more effectively. In addition, individuals with limited education raise the issue of potential confounding by educational levels, which should be addressed in future research.

Despite its limitations, the study’s findings suggest that ADAM10 levels are a promising avenue for further exploration in AD research. These include conducting longitudinal studies to understand the trajectory of ADAM10 levels in terms of cognitive decline and AD progression. Additionally, investigating interventions to modulate ADAM10 levels and exploring the impact of sociodemographic factors on AD risk and outcomes are promising avenues for further study.

Our results offer valuable insights and raise important questions for future research. These findings enhance our comprehension of AD pathogenesis and propose opportunities for strengthening early detection and intervention strategies for individuals with limited education and unfavorable socioeconomic conditions.

This study was reviewed and approved by the Ethics Committee of the Federal University of São Carlos, approval numbers (02760312.0.0000.5504; 51995615.3.1001.5504; and 78602515.5.0000.5504). This research was conducted according to the CNS 466/2012 and CNS 510/2016 Brazilian resolutions.

Written informed consent was obtained from any adults participating in this study. Data acquisition and blood sample collection only started after participants and/or their representatives had consented. Written informed consent was obtained from ALL vulnerable patients’ legal guardians or healthcare proxies for participation in this study.

The authors have no conflicts of interest to declare.

This work was supported by grants 2021/01863-9, 2022/15314-0, and 2023/08952-2 from the São Paulo Research Foundation (FAPESP, Brazil). This work was also supported by the Coordination of Superior Level Staff Improvement (CAPES, finance code 001) and the National Council for Scientific and Technological Development (CNPq). The funder had no role in this study's design, data collection, analysis, or reporting.

L.N.C.P. conceptualized and designed the study, contributed to data analysis and interpretation, and drafted and revised the manuscript. V.A.S. drafted and revised the manuscript. M.M.G. contributed to data collection, drafted, and revised the manuscript. I.P.V. contributed to data collection, drafted, and revised the manuscript. P.R.M. contributed to data collection, analysis, and interpretation and drafted and revised the manuscript. M.R.C. secured funding and supervised the research process, contributed to data analysis and interpretation and drafted and revised the manuscript.

All data generated or analyzed during this study are included in this article. Further enquiries can be directed to the corresponding author.

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