Study design and population
This cross-sectional, observational study was based on data from the STOP ALZHEIMER – DEBA project, an epidemiological initiative conducted in 2015 in the municipality of Deba (Basque Country, Spain). The project aimed to assess the prevalence and clinical spectrum of cognitive impairment, including MCI and dementia, in individuals aged 60 years and older from a real-world, community-based population.
This screening involved the collection of sociodemographic data, cardiovascular risk assessment using the CAIDE score (Cardiovascular Risk Factors, Aging, and Incidence of Dementia)12, and brief cognitive evaluation through the Mini-Mental State Examination (MMSE), the Memory Alteration Test (M@T), the AD8 questionnaire, and the Fototest. These brief cognitive tests were conducted in a primary care setting by professionals who had received prior training from clinical neuropsychologists. The screening tests were administered in the participant’s preferred language—either Basque or Spanish—given that the study was conducted in a bilingual region.
Individuals screening positive on any test using traditional thresholds were invited to continue to the diagnostic phase, along with a matched sample of cognitively negative participants matched for age, sex, education, and CAIDE score. A total of 277 individuals completed a comprehensive diagnostic evaluation at the CITA-Alzheimer Foundation (Donostia-San Sebastián, Basque Country, Spain), including clinical syndromic diagnosis, full neuropsychological assessment, structural brain MRI, blood and plasma biomarker analysis, APOE genotyping, and optional lumbar puncture.
Of these 277 participants, 181 underwent lumbar puncture for CSF biomarker analysis. This subgroup was used for analyses involving CSF as a reference standard. Comparisons between participants with and without CSF data were performed to assess sample representativeness (see Table 1).
Clinical and neurological evaluation
Participants underwent a physical and neurological examination and a standardized clinical assessment that included documentation of vascular risk factors such as hypertension, diabetes, dyslipidemia, and smoking status, along with anthropometric measures including body mass index. Laboratory data included fasting glucose, HbA1c, and total cholesterol levels.
Neuropsychiatric symptoms were assessed using the Neuropsychiatric Inventory (NPI)35, while affective symptoms were measured with the Hospital Anxiety and Depression Scale (HADS)36, including separate anxiety and depression subscales. Global cognitive and functional status was evaluated using the Clinical Dementia Rating (CDR) and the CDR Sum of Boxes (CDR-SB)37. Subtle motor symptoms were evaluated using Part III (motor examination) of the Movement Disorder Society–sponsored revision of the Unified Parkinson’s Disease Rating Scale (MDS-UPDRS)38.
Neuropsychological assessment
A detailed neuropsychological battery was administered by trained professionals and covered major cognitive domains. Memory was assessed using the CERAD Word List (learning and delayed recall)39, Face-Name Associative Memory Exam (FNAME)40, and the Rey-Osterrieth Complex Figure recall41. Language abilities were measured using the Boston Naming Test (short form)42, semantic and phonemic verbal fluency tasks43. Visuospatial and visuoconstructive skills were evaluated with the VOSP Number Location test44, VOSP Object Decision test, Rey-Osterrieth Complex Figure copy, and the 15-Objects Test45. Executive functioning and attention were assessed with the Trail Making Test (Parts A and B)46, WAIS-III Digit Span (forward and backward). Results were interpreted using validated normative data adjusted for age, sex, and education level.
Syndromic diagnosis and cognitive screening
Diagnostic classification was established through multidisciplinary consensus meetings involving neurologists, neuropsychologists, and other trained clinicians. Final diagnoses were based on the integration of clinical history, functional status (assessed via the Clinical Dementia Rating and CDR Sum of Boxes), and performance on the neuropsychological battery described in Sect. 5.3, interpreted using age-, sex-, and education-adjusted norms. Participants were classified as cognitively unimpaired, MCI, or dementia according to internationally accepted clinical criteria. MCI was defined as objective cognitive decline in one or more domains without significant impairment of daily functioning, whereas dementia was diagnosed when cognitive deficits interfered with autonomy in daily life.All participants underwent brief cognitive testing using MMSE, T@M, AD8, and Fototest. Traditional cut-offs were defined as MMSE ≤ 24, T@M ≤ 37, Fototest ≤ 29, and AD8 ≥ 2. Optimized cut-offs derived from recent research included MMSE ≤ 28, T@M ≤ 40, Fototest ≤ 35, and AD8 ≥ 113. Composite variables were created to indicate positive screening using either traditional (Brief Cognitive Test traditional cutoff, BCTt) or optimized (Brief Cognitive Test optimized cutoff, BCTo) thresholds. These variables were used in logistic regression models and ROC curve analyses.
Plasma and CSF biomarker analysis
Venous blood was collected and processed according to standardized protocols. Samples were processed within two hours, centrifuged, aliquoted, and frozen at − 80 °C until analysis. Plasma levels of phosphorylated tau at threonine 181 (p-tau181), amyloid β1–42 and β1–40 (used to calculate the Aβ42/40 ratio), glial fibrillary acidic protein (GFAP), and neurofilament light chain (NfL) were quantified using the Single Molecule Array (SIMOA) HD-X platform (Quanterix, USA) at the Achucarro Basque Center for Neuroscience Foundation. The Neurology 4-Plex E kit was used for Aβ1–40, Aβ1–42, NfL, and GFAP, and the pTau181 V2 Advantage Kit was used for p-tau181. All laboratory personnel were blinded to clinical data.
CSF was collected via lumbar puncture under sterile conditions using atraumatic needles. Samples were processed within two hours, centrifuged, aliquoted, and frozen at − 80 °C until analysis. Biomarker quantification was performed using Elecsys® assays (Roche Diagnostics)47, which included Aβ42, total tau, and p-tau181. Amyloid positivity (A+) was defined by low Aβ42 levels (< 1000 pg/ml) and the p-tau/Aβ42 ratio, with values > 0.024 indicating abnormal amyloid pathology. Tau positivity (T+) was defined by elevated p-tau181 levels (> 27 pg/ml). These thresholds were based on reference values provided by the laboratory and applied consistently to classify CSF biomarker status.
MRI acquisition and interpretation
Structural MRI was acquired using a Siemens Magnetom Trio Tim 3 T scanner at the CITA-Alzheimer Foundation. Sequences included 3D T1-weighted, FLAIR, T2-weighted, diffusion-weighted imaging (DWI), and susceptibility-weighted imaging (SWI). All MRIs were interpreted by an experienced neuroradiologist blinded to clinical and biomarker data. Medial temporal atrophy (MTA)48 was visually rated and considered abnormal when ≥ 2. Cerebrovascular burden was defined as either a Fazekas score49 ≥ 2 or the presence of ≥ 4 cerebral microbleeds.
APOE genotyping
APOE genotyping was conducted using polymerase chain reaction (PCR) and restriction fragment length polymorphism (RFLP) analysis. Genotypes were categorized based on the presence of at least one ε4 allele, and participants were classified as APOE ε4 carriers or non-carriers. Genotyping was performed blinded to clinical outcomes and biomarker status.
Statistical analysis
Descriptive statistics were computed for demographic, clinical, cognitive, metabolic, and biomarker variables. Continuous variables were expressed as means and standard deviations and compared using independent samples t-tests. Categorical variables were described as percentages and compared using chi-square tests.
Diagnostic performance of brief cognitive tests and plasma biomarkers was assessed using ROC curves and area under the curve (AUC) calculations. Optimized cut-offs were applied for MMSE (≤ 28), M@T (≤ 40), Fototest (≤ 35), and AD8 (≥ 1), and a binary composite variable (BCTo) was defined as positive if any test met its threshold. AUCs were calculated globally and stratified by sex, age group, and educational attainment.
Plasma biomarker thresholds were estimated using the Youden index, defined as the maximum value of sensitivity + specificity − 1. Sensitivity, specificity, and cut-offs for p-tau181, Aβ42/40 ratio, GFAP, and NfL were reported. Given the lack of universally approved diagnostic cut-offs for plasma biomarkers measured on the SIMOA platform, and the variability in thresholds proposed across studies and cohorts, these cut-offs were derived in an exploratory fashion based on our cohort-specific distribution.
Multivariable logistic regression models were constructed to predict a range of binary outcomes: syndromic cognitive impairment, MCI, CSF amyloid positivity (A+), combined amyloid and tau positivity (A + T+), and hybrid outcomes (e.g., MCI with A + T + pathology). Predictors included BCTo, plasma biomarkers (p-tau181, Aβ42/40), APOE ε4 status, demographic variables (age, sex), and MRI features (medial temporal atrophy ≥ 2, vascular pathology). Models were progressively layered into three complexity levels: the first (interview-based) included BCTo, the total CAIDE score, and the total Neuropsychiatric Inventory (NPI) score (sum of frequency × severity across all domains); the second level added visual MRI features—medial temporal atrophy (dichotomized as ≥ 2) and Fazekas scale (dichotomized as ≥ 2); the third (full multimodal) level incorporated plasma biomarkers (p-tau181, Aβ42/40 ratio) and APOE ε4 status, all entered as binary variables based on internally derived cut-offs. AUCs were computed for each level and outcome, and subgroup analyses were conducted for sex, CAIDE score, and age.
Due to the limited number of participants with confirmed dementia and CSF biomarker data, predictive modeling in Sect. 2.6 and 2.7 focused on MCI combined with biological positivity (MCI + A+), which represents a clinically actionable pre-dementia state. This approach also aligns with our aim to support early identification strategies in primary care.
All analyses were performed using Python 3.10 and R 4.2.2 with appropriate packages (pandas, scikit-learn, pROC). AUC values were considered excellent if ≥ 0.80, good if ≥ 0.70, and acceptable if ≥ 0.60. A p-value < 0.05 was considered statistically significant. No corrections were made for multiple comparisons. To evaluate whether the stepwise increases in AUCs between model levels were statistically significant, DeLong tests for correlated ROC curves were performed using the subset of participants with complete data across all levels.
Ethical considerations
The protocol and informed consent procedure of the STOP ALZHEIMER – DEBA project were approved by the Ethics Committee of the Basque Country, under reference number PI2015153. All participants gave written informed consent prior to enrollment. The study was conducted in accordance with the Declaration of Helsinki and Spanish data protection legislation.