Plasma Biomarkers of Alzheimer's Disease Are Associated with Physical Functioning Outcomes Among Cognitively Normal Adults in the Multiethnic HABS-HD Cohort

Associations Between Blood-Based Biomarkers and Cognitive and Functional Trajectories Among Participants of the MEMENTO Cohort

BACKGROUND AND OBJECTIVES

Elevated levels of Alzheimer disease (AD) blood-based biomarkers are associated with accelerated cognitive decline. However, their distinct relationships with specific cognitive and functional domains require further investigation. We aimed at estimating the associations between AD blood-based biomarkers and the trajectories of distinct cognitive and functional domains over a 5-year follow-up period.

METHODS

We conducted a clinic-based prospective study using data from the MEMENTO study, a nationwide French cohort. We selected dementia-free individuals at baseline aged 60 years or older. Baseline measurements of β-amyloid (Aβ) 40 and 42, phosphorylated tau (p-tau181), and neurofilament light chain (NfL) concentrations were obtained using the Simoa HD-X analyzer. Mini-Mental State Examination (MMSE), Free and Cued Selective Reminding Test (FCSRT), animal fluency, Trail Making Tests A and B, Short Physical Performance Battery (SPPB), and Instrumental Activities of Daily Living were administered annually for up to 5 years. We used linear mixed models, adjusted for potential confounders, to model AD biomarkers' relation with cognitive and functional decline.

RESULTS

A total of 1,938 participants were included in this study, with a mean (SD) baseline age of 72.8 (6.6) years, and 62% were women. Higher baseline p-tau181 and NfL were associated with significantly faster decline in most cognitive, physical, and functional outcomes (+1 SD p-tau181: βMMSE = -0.055, 95% CI -0.067 to -0.043, βFCSRT = -0.034, 95% CI -0.043 to -0.025, βfluency = -0.029, 95% CI -0.038 to -0.020, βSPPB = -0.040, 95% CI -0.057 to -0.022, and β4IADL = -0.115, 95% CI 0.091-0.140. +1 SD NfL: βMMSE = -0.039, 95% CI -0.053 to -0.025, βFCSRT = -0.022, 95% CI -0.032 to -0.012, βfluency = -0.014, 95% CI -0.024 to -0.004, and β4IADL = 0.077, 95% CI 0.048-0.105). A multiplicative association of p-tau181 and NfL with worsening cognitive and functional trajectories was evidenced. Lower Aβ42/40 ratio was only associated with slightly faster cognitive decline in FCSRT and semantic fluency (+1 SD: β = 0.011, 95% CI 0.002-0.020, and β = 0.011, 95% CI 0.003-0.020, respectively). These associations were not modified by APOE ε4, sex, nor education level.

DISCUSSION

In a memory clinic sample, p-tau181 and NfL, both independently and jointly, are linked to more pronounced cognitive, physical and functional declines. Blood-based biomarker measurement in AD research may provide useful insights regarding biological processes underlying cognitive, physical, and functional declines in at-risk individuals.

Digging into the intrinsic capacity concept: Can it be applied to Alzheimer's disease?

Historically, aging research has largely centered on disease pathology rather than promoting healthy aging. The World Health Organization's (WHO) policy framework (2015-2030) underscores the significance of fostering the contributions of older individuals to their families, communities, and economies. The WHO has introduced the concept of intrinsic capacity (IC) as a key metric for healthy aging, encompassing five primary domains: locomotion, vitality, sensory, cognitive, and psychological. Past AD research, constrained by methodological limitations, has focused on single outcome measures, sidelining the complexity of the disease. Our current scientific milieu, however, is primed to adopt the IC concept. This is due to three critical considerations: (I) the decline in IC is linked to neurocognitive disorders, including AD, (II) cognition, a key component of IC, is deeply affected in AD, and (III) the cognitive decline associated with AD involves multiple factors and pathophysiological pathways. Our study explores the application of the IC concept to AD patients, offering a comprehensive model that could revolutionize the disease's diagnosis and prognosis. There is a dearth of information on the biological characteristics of IC, which are a result of complex interactions within biological systems. Employing a systems biology approach, integrating omics technologies, could aid in unraveling these interactions and understanding IC from a holistic viewpoint. This comprehensive analysis of IC could be leveraged in clinical settings, equipping healthcare providers to assess AD patients' health status more effectively and devise personalized therapeutic interventions in accordance with the precision medicine paradigm. We aimed to determine whether the IC concept could be extended from older individuals to patients with AD, thereby presenting a model that could significantly enhance the diagnosis and prognosis of this disease.

Demographic and Clinical Characteristics Associated With Serum GFAP Levels in an Ethnically Diverse Cohort

BACKGROUND AND OBJECTIVES

Elevations in circulating glial fibrillary acidic protein (GFAP), a putative marker of reactive astrocytosis, have been found to associate with cognitive decline and dementia status. Further validation in diverse cohorts and evaluation of potential health disparities are necessary for broader generalization. The goal of this study was to examine the associations between demographics, cardiovascular risk factors, and APOE ε4 status with serum GFAP levels among Mexican American and non-Hispanic White older adults across the continuum from cognitively unimpaired to Alzheimer disease dementia.

METHODS

Serum GFAP levels were assayed using a Simoa HD-1 analyzer in older adults enrolled in the observational Texas Alzheimer Research and Care Consortium. Associations between demographic and clinical characteristics with serum GFAP levels were evaluated using linear regression. The diagnostic accuracy of serum GFAP was further examined using area under the receiver operating characteristic curves (AUROC) in univariate and adjusted models, and optimal cut points were derived using the maximum Kolmogorov-Smirnov metric. All models were also stratified by ethnicity and disease stage.

RESULTS

A total of 1,156 Mexican American and 587 non-Hispanic White participants were included (mean age = 68 years, standard deviation = 10; 65% female). Older age (β = 0.562 (95% CI 0.515-0.609), p < 0.001), apolipoprotein ε4 status (β = 0.139 (95% CI 0.092-0.186), p < 0.001), and cognitive impairment (β = 0.150 (95% CI 0.103-0.197), p < 0.001) were positively associated with serum GFAP. By contrast, higher body mass index (β = -0.181 (95% CI -0.228 to -0.134), p < 0.001), diabetes (β = -0.065 (95% CI -0.112 to -0.018), p < 0.001), and tobacco use (β = -0.059 (95% CI -0.106 to -0.012), p < 0.001) were inversely associated with serum GFAP. AUROC values were generally comparable across ethnicities and model fit improved with inclusion of additional covariates. However, optimal cut-off values were consistently lower in Mexican Americans relative to non-Hispanic White participants.

DISCUSSION

The study results highlight the importance of understanding the role of broader demographic and clinical factors on circulating GFAP levels within diverse cohorts to enhance precision across clinical, research, and community settings.

Comorbidities Incorporated to Improve Prediction for Prevalent Mild Cognitive Impairment and Alzheimer's Disease in the HABS-HD Study

BACKGROUND

Blood biomarkers have the potential to transform Alzheimer's disease (AD) diagnosis and monitoring, yet their integration with common medical comorbidities remains insufficiently explored.

OBJECTIVE

This study aims to enhance blood biomarkers' sensitivity, specificity, and predictive performance by incorporating comorbidities. We assess this integration's efficacy in diagnostic classification using machine learning, hypothesizing that it can identify a confident set of predictive features.

METHODS

We analyzed data from 1,705 participants in the Health and Aging Brain Study-Health Disparities, including 116 AD patients, 261 with mild cognitive impairment, and 1,328 cognitively normal controls. Blood samples were assayed using electrochemiluminescence and single molecule array technology, alongside comorbidity data gathered through clinical interviews and medical records. We visually explored blood biomarker and comorbidity characteristics, developed a Feature Importance and SVM-based Leave-One-Out Recursive Feature Elimination (FI-SVM-RFE-LOO) method to optimize feature selection, and compared four models: Biomarker Only, Comorbidity Only, Biomarker and Comorbidity, and Feature-Selected Biomarker and Comorbidity.

RESULTS

The combination model incorporating 17 blood biomarkers and 12 comorbidity variables outperformed single-modal models, with NPV12 at 92.78%, AUC at 67.59%, and Sensitivity at 65.70%. Feature selection led to 22 chosen features, resulting in the highest performance, with NPV12 at 93.76%, AUC at 69.22%, and Sensitivity at 70.69%. Additionally, interpretative machine learning highlighted factors contributing to improved prediction performance.

CONCLUSIONS

In conclusion, combining feature-selected biomarkers and comorbidities enhances prediction performance, while feature selection optimizes their integration. These findings hold promise for understanding AD pathophysiology and advancing preventive treatments.

Combination of Serum and Plasma Biomarkers Could Improve Prediction Performance for Alzheimer’s Disease

Alzheimer’s disease (AD) can be predicted either by serum or plasma biomarkers, and a combination may increase predictive power, but due to the high complexity of machine learning, it may also incur overfitting problems. In this paper, we investigated whether combining serum and plasma biomarkers with feature selection could improve prediction performance for AD. 150 D patients and 150 normal controls (NCs) were enrolled for a serum test, and 100 patients and 100 NCs were enrolled for the plasma test. Among these, 79 ADs and 65 NCs had serum and plasma samples in common. A 10 times repeated 5-fold cross-validation model and a feature selection method were used to overcome the overfitting problem when serum and plasma biomarkers were combined. First, we tested to see if simply adding serum and plasma biomarkers improved prediction performance but also caused overfitting. Then we employed a feature selection algorithm we developed to overcome the overfitting problem. Lastly, we tested the prediction performance in a 10 times repeated 5-fold cross validation model for training and testing sets. We found that the combined biomarkers improved AD prediction but also caused overfitting. A further feature selection based on the combination of serum and plasma biomarkers solved the problem and produced an even higher prediction performance than either serum or plasma biomarkers on their own. The combined feature-selected serum–plasma biomarkers may have critical implications for understanding the pathophysiology of AD and for developing preventative treatments.