More Like this

1. High‐throughput proteomic analysis of candidate biomarker changes in gingival crevicular fluid after treatment of chronic periodontitis

   https://doi.org/10.1111/jre.12575  

   Guzman, Y. A. ; Sakellari, D. ; Papadimitriou, K. ; Floudas, C. A. ( June 2018 , Journal of Periodontal Research)

   Untargeted, high‐throughput proteomics methodologies have great potential to aid in identifying biomarkers for the diagnosis of periodontal disease. The application of such methods to the discovery of candidate biomarkers for the resolution of periodontal inflammation after periodontal therapy has been investigated.

   Gingival crevicular fluid samples were collected from 10 patients diagnosed with chronic periodontitis at baseline and 1, 5, 9 and 13 weeks after completion of mechanical periodontal treatment. Clinical indices of periodontal disease, including probing depth, recession, clinical attachment level and bleeding on probing, were recorded at baseline and 13 weeks. Samples were analyzed using an online liquid chromatography‐nanoelectrospray‐hybrid ion trap‐Orbitrap mass spectrometer. Spectra were processed with thePILOT_PROTEINproteomics software suite.

   Clinical parameters were significantly improved 13 weeks after treatment (Wilcoxon signed ranks test,P < .05). From the substantial number of identified proteins, a small subset was extracted by filter methods that included temporal pattern matching, logistic function fitting and mixed‐integer linear optimization. This subset includes azurocidin, lysozyme C and myosin‐9 as candidate biomarkers prominent at baseline and alpha‐smooth muscle actin as prominent 13 weeks after treatment. Cross‐validation studies yielded average predictive accuracy and area under the curve of 0.900 and 0.930, respectively.

   High‐throughput proteomic analysis can contribute to identifying endpoints of periodontal therapy. These candidate biomarkers should be evaluated for clinical efficacy.

    

   more » « less
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.

    

   more » « less
3. 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.

    

   more » « less
4. Systematic evaluation of Copper(II)-loaded immobilized metal affinity chromatography for selective enrichment of copper-binding species in human serum and plasma

   https://doi.org/10.1093/mtomcs/mfac059  

   Janisse, Samuel E. ; Sharma, Vibha A. ; Caceres, Amanda ; Medici, Valentina ; Heffern, Marie C. ( August 2022 , Metallomics)

   Copper is essential in a host of biological processes, and disruption of its homeostasis is associated with diseases including neurodegeneration and metabolic disorders. Extracellular copper shifts in its speciation between healthy and disease states, and identifying molecular components involved in these perturbations could widen the panel of biomarkers for copper status. While there have been exciting advances in approaches for studying the extracellular proteome with mass spectrometry–based methods, the typical workflows disrupt metal–protein interactions due to the lability of these bonds either during sample preparation or in gas-phase environments. We sought to develop and apply a workflow to enrich for and identify protein populations with copper-binding propensities in extracellular fluids using an immobilized metal affinity chromatography (IMAC) resin. The strategy was optimized using human serum to allow for maximum quantity and diversity of protein enrichment. Protein populations could be differentiated based on protein load on the resin, likely on account of differences in abundance and affinity. The enrichment workflow was applied to plasma samples from patients with Wilson’s disease and protein IDs and differential abundancies relative to healthy subjects were compared to those yielded from a traditional proteomic workflow. While the IMAC workflow preserved differential abundance and protein ID information from the traditional workflow, it identified several additional proteins being differentially abundant including those involved in lipid metabolism, immune system, and antioxidant pathways. Our results suggest the potential for this IMAC workflow to identify new proteins as potential biomarkers in copper-associated disease states.

    

   more » « less
5. 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. 

   more » « less