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1. Comparison of CSF neurofilament light chain, neurogranin, and tau to MRI markers

   https://doi.org/10.1002/alz.12239  

   Mielke, Michelle M. ; Przybelski, Scott A. ; Lesnick, Timothy G. ; Kern, Silke ; Zetterberg, Henrik ; Blennow, Kaj ; Knopman, David S. ; Graff‐Radford, Jonathan ; Petersen, Ronald C. ; Jack, Jr., Clifford R. ; et al ( March 2021 , Alzheimer's & Dementia)

   We determined whether cerebrospinal fluid (CSF) neurofilament light (NfL), neurogranin (Ng), and total‐tau (t‐tau) differentially mapped to magnetic resonance imaging (MRI) measures of cortical thickness, microstructural integrity (corpus callosum and cingulum fractional anisotropy [FA]), and white matter hyperintensities (WMH).

   Analyses included 536 non‐demented Mayo Clinic Study of Aging participants with CSF NfL, Ng, t‐tau, amyloid beta (Aβ)42 and longitudinal MRI scans. Linear mixed models assessed longitudinal associations between CSF markers and MRI changes.

   Higher CSF NfL was associated with decreasing microstructural integrity and WMH. Higher t‐tau was associated with decreasing temporal lobe and Alzheimer's disease (AD) meta region of interest (ROI) cortical thickness. There was no association between Ng and any MRI measure. CSF Aβ42 interacted with Ng for declines in temporal lobe and AD meta ROI cortical thickness and cingulum FA.

   CSF NfL predicts changes in white matter integrity, t‐tau reflects non‐specific changes in cortical thickness, and Ng reflects AD‐specific synaptic and neuronal degeneration.

    

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2. Use of blood pressure measurements extracted from the electronic health record in predicting Alzheimer's disease: A retrospective cohort study at two medical centers

   https://doi.org/10.1002/alz.12676  

   Tjandra, Donna ; Migrino, Raymond Q. ; Giordani, Bruno ; Wiens, Jenna ( April 2022 , Alzheimer's & Dementia)

   Studies investigating the relationship between blood pressure (BP) measurements from electronic health records (EHRs) and Alzheimer's disease (AD) rely on summary statistics, like BP variability, and have only been validated at a single institution. We hypothesize that leveraging BP trajectories can accurately estimate AD risk across different populations.

   In a retrospective cohort study, EHR data from Veterans Affairs (VA) patients were used to train and internally validate a machine learning model to predict AD onset within 5 years. External validation was conducted on patients from Michigan Medicine (MM).

   The VA and MM cohorts included 6860 and 1201 patients, respectively. Model performance using BP trajectories was modest but comparable (area under the receiver operating characteristic curve [AUROC] = 0.64 [95% confidence interval (CI) = 0.54–0.73] for VA vs. AUROC = 0.66 [95% CI = 0.55–0.76] for MM).

   Approaches that directly leverage BP trajectories from EHR data could aid in AD risk stratification across institutions.

    

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3. Learning Multi-Modal Biomarker Representations via Globally Aligned Longitudinal Enrichments

   https://doi.org/10.1609/aaai.v34i01.5426  

   Lu, Lyujian ; Elbeleidy, Saad ; Baker, Lauren Zoe ; Wang, Hua ( June 2020 , Proceedings of the AAAI Conference on Artificial Intelligence)

   Alzheimer's Disease (AD) is a chronic neurodegenerative disease that severely impacts patients' thinking, memory and behavior. To aid automatic AD diagnoses, many longitudinal learning models have been proposed to predict clinical outcomes and/or disease status, which, though, often fail to consider missing temporal phenotypic records of the patients that can convey valuable information of AD progressions. Another challenge in AD studies is how to integrate heterogeneous genotypic and phenotypic biomarkers to improve diagnosis prediction. To cope with these challenges, in this paper we propose a longitudinal multi-modal method to learn enriched genotypic and phenotypic biomarker representations in the format of fixed-length vectors that can simultaneously capture the baseline neuroimaging measurements of the entire dataset and progressive variations of the varied counts of follow-up measurements over time of every participant from different biomarker sources. The learned global and local projections are aligned by a soft constraint and the structured-sparsity norm is used to uncover the multi-modal structure of heterogeneous biomarker measurements. While the proposed objective is clearly motivated to characterize the progressive information of AD developments, it is a nonsmooth objective that is difficult to efficiently optimize in general. Thus, we derive an efficient iterative algorithm, whose convergence is rigorously guaranteed in mathematics. We have conducted extensive experiments on the Alzheimer's Disease Neuroimaging Initiative (ADNI) data using one genotypic and two phenotypic biomarkers. Empirical results have demonstrated that the learned enriched biomarker representations are more effective in predicting the outcomes of various cognitive assessments. Moreover, our model has successfully identified disease-relevant biomarkers supported by existing medical findings that additionally warrant the correctness of our method from the clinical perspective. 

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4. Brain structure and allelic associations in Alzheimer's disease

   https://doi.org/10.1111/cns.14073  

   Moon, Seok Woo ; Zhao, Lu ; Matloff, William ; Hobel, Sam ; Berger, Ryan ; Kwon, Daehong ; Kim, Jaebum ; Toga, Arthur W. ; Dinov, Ivo D. ; for the Alzheimer's Disease Neuroimaging Initiative ( December 2022 , CNS Neuroscience & Therapeutics)

   Alzheimer's disease (AD), the most prevalent form of dementia, affects 6.5 million Americans and over 50 million people globally. Clinical, genetic, and phenotypic studies of dementia provide some insights of the observed progressive neurodegenerative processes, however, the mechanisms underlying AD onset remain enigmatic.

   This paper examines late‐onset dementia‐related cognitive impairment utilizing neuroimaging‐genetics biomarker associations.

   The participants, ages 65–85, included 266 healthy controls (HC), 572 volunteers with mild cognitive impairment (MCI), and 188 Alzheimer's disease (AD) patients. Genotype dosage data for AD‐associated single nucleotide polymorphisms (SNPs) were extracted from the imputed ADNI genetics archive using sample‐major additive coding. Such 29 SNPs were selected, representing a subset of independent SNPs reported to be highly associated with AD in a recent AD meta‐GWAS study by Jansen and colleagues.

   We identified the significant correlations between the 29 genomic markers (GMs) and the 200 neuroimaging markers (NIMs). The odds ratios and relative risks for AD and MCI (relative to HC) were predicted using multinomial linear models.

   In the HC and MCI cohorts, mainly cortical thickness measures were associated with GMs, whereas the AD cohort exhibited different GM‐NIM relations. Network patterns within the HC and AD groups were distinct in cortical thickness, volume, and proportion of White to Gray Matter (pct), but not in the MCI cohort. Multinomial linear models of clinical diagnosis showed precisely the specific NIMs and GMs that were most impactful in discriminating between AD and HC, and between MCI and HC.

   This study suggests that advanced analytics provide mechanisms for exploring the interrelations between morphometric indicators and GMs. The findings may facilitate further clinical investigations of phenotypic associations that support deep systematic understanding of AD pathogenesis.

    

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5. Identifying precision AD biomarkers with varying prognosis effects in genetics driven subpopulations

   https://doi.org/10.1002/alz.053201  

   Lee, Brian N ; Wang, Junwen ; Shen, Li ( December 2021 , Alzheimer's & Dementia)

   Imaging, cognitive and fluid data have been widely studied to identify quantitative biomarkers that can help predict the status and progression of Alzheimer’s disease (AD). However, it is still an underexplored topic whether there exist subpopulations with different genetic profiles across which the biomarker‐based prediction models may vary. We propose to use the Chow test (Chow 1960 Econometrica 28(3)) to perform genetically stratified analyses for identifying SNP‐based subpopulations coupled with precision AD biomarkers with varying effects on future diagnosis in these subpopulations. The investigation of such SNPs and precision biomarkers may eventually pave the way for increased customization of AD care.

   Participants included 1,324 subjects from the ADNI cohort with both AD biomarker and genotyping data available (http://www.pi4cs.org/qt‐pad‐challenge). 30 significant (P < 1.5E‐278) AD SNPs were sourced from (Jansen 2019 NatGen). Chow tests were performed to determine whether each of baseline visit measures of 16 AD biomarkers predicted AD diagnosis at the three‐year visit with varying slopes when stratifying upon the allelic dosage of each of 30 chosen SNPs. Bonferroni correction (P < 1.04E‐4) was employed to correct for multiple comparisons.

   Multiple SNP‐biomarker pairs showed significant genetically driven deviations in the regression coefficients when predicting diagnosis in three years using baseline biomarkers (Figure 1). Top SNP hits involved rs769449 (Chr 19,APOE) and rs7561528 (Chr 2,LOC105373605), and almost all 16 studied biomarkers demonstrated differential slopes in different genotype groups to predict diagnosis in three years. To examine the details of these top findings, the regression coefficients calculated for each of the five most significant biomarkers of both SNPs were bootstrapped and plotted in Figure 2.

   Genetic analysis of AD candidate SNPs in conjunction with AD biomarker data via the Chow test identified several SNPs coupled with precision AD biomarkers with varying prognosis effects in the corresponding genotype groups. These findings provide valuable information to reveal disease heterogeneity and help facilitate precision medicine.

    

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