This new paper from Boston, Maastricht, Gothenburg, and San Francisco finds 3 glycolysis enzymes to be AD biomarkers. In their conclusion they state: "These data suggest that a better understanding the cause-and-effect relationship of glycolysis with AD might be a key not only for diagnosing AD in living patients but also to development of alternatives to current therapeutic approaches, which primarily target amyloid β and/or tau."

Glycolysis is the metabolic pathway that converts glucose into pyruvate and forms the high-energy molecules adenosine triphosphate (ATP) and reduced nicotinamide adenine dinucleotide (NADH).

So this paper could give a totally new understanding of AD and perhaps also new treatment possibilities (???). This is a free paper. If PBT2 has any role in glycolysis is a question to be solved by Masters, but these enzymes need also metals to work.

Cell Rep Med

. 2023 Apr 18;4(4):101005.

doi: 10.1016/j.xcrm.2023.101005.

Meta-analysis of published cerebrospinal fluid proteomics data identifies and validates metabolic enzyme panel as Alzheimer's disease biomarkers

Patrick W van Zalm ¹, Saima Ahmed ², Benoit Fatou ², Rudy Schreiber ³, Omar Barnaby ², Adam Boxer ⁴, Henrik Zetterberg ⁵, Judith A Steen ⁶, Hanno Steen ⁷

Affiliations expand

• PMID: 37075703

DOI: 10.1016/j.xcrm.2023.101005

Abstract

To develop therapies for Alzheimer's disease, we need accurate in vivo diagnostics. Multiple proteomic studies mapping biomarker candidates in cerebrospinal fluid (CSF) resulted in little overlap. To overcome this shortcoming, we apply the rarely used concept of proteomics meta-analysis to identify an effective biomarker panel. We combine ten independent datasets for biomarker identification: seven datasets from 150 patients/controls for discovery, one dataset with 20 patients/controls for down-selection, and two datasets with 494 patients/controls for validation. The discovery results in 21 biomarker candidates and down-selection in three, to be validated in the two additional large-scale proteomics datasets with 228 diseased and 266 control samples. This resulting 3-protein biomarker panel differentiates Alzheimer's disease (AD) from controls in the two validation cohorts with areas under the receiver operating characteristic curve (AUROCs) of 0.83 and 0.87, respectively. This study highlights the value of systematically re-analyzing previously published proteomics data and the need for more stringent data deposition.