Identification of plasma proteome signatures associated with ATN framework using SOMAscan
Authors: Shi Liu , Westwood Sarah , Baird Alison L. , Buckley Noel , Dobricic Valerija , Kilpert Fabian , Hong Shengjun , Franke Andreas , Hye Abdul , Ashton Nicholas J. , Morgan Angharad , Bos Isabelle , Vos Stephanie J.B. , ten Kate Mara , Scheltens Philip , Vandenberghe Rik , Gabel Silvy , Meersmans Karen , Engelborghs Sebastiaan , De Roeck Ellen Elisa , Sleegers Kristel , Frisoni Giovanni B. , Blin Olivier , Richardson Jill , Bordet Régis , Molinuevo Jose Luis , Rami Lorena , Wallin Anders , Kettunen Petronella , Tsolaki Magda , Verhey Frans R.J. , Lleó Alberto , Alcolea Daniel , Popp Julius , Peyratout Gwendoline , Martinez‐Lage Pablo , Tainta Mikel , Johannsen Peter , Teunissen Charlotte E , Frölich Lutz , Freund Yvonne , Legido‐Quigley Cristina , Barkhof Frederik , Blennow Kaj , Zetterberg Henrik , Morgan Paul , Streffer Johannes , Visser Pieter Jelle , Bertram Lars , Lovestone Simon , Nevado‐Holgado Alejo J , Winchester Laura 
Published: December 2020

Background

The National Institute on Aging‐Alzheimer’s Association (NIA‐AA) proposed the ATN framework as a classification system for Alzheimer’s disease. The ATN framework helps to inform participant inclusion and potentially trial outcomes as clinical trials are increasingly targeting a range of pathologies. However, it is limited by biomarkers that are either not yet fully qualified or are relatively invasive and where access can be difficult. A blood‐based version of the ATN framework would be of considerable value and recent progress suggests such an objective is realizable.

Method

To identify blood‐based biomarkers predicting different ATN profiles, we used SOMAscan assay platform to measure 4001 proteins in 785 subjects selected from the European Medical Information Framework for Alzheimer’s disease Multimodal Biomarker Discovery study (EMIF‐AD MBD) study, all of whom had measures of amyloid, CSF total tau (T‐tau) and phosphorylated tau (P‐tau). We firstly performed linear regression to identify single proteins associated with the ATN framework. Then we constructed protein co‐expression network to identify co‐expressed protein modules. We further rank‐ordered modules based upon their relevance to ATN. Using the proteins within the module with the highest relevance, we performed machine learning to differentiate different ATN profiles from non‐pathological controls (NPC).

Result

The proteins identified from linear regression were enriched with AD related pathways. Seven modules were identified from co‐expression analysis, among which blue module was highly associated with the ATN framework (Figure 1). Using machine learning, we identified a subset of proteins within the blue module, along with age and apolipoprotein E ε4, that discriminated NPC from amyloid pathology dementia including A+T‐N‐, A+T+N‐, A+T‐N+ and A+T+N+ profile with high area under the curve (AUC, 0.72, 0.80, 0.84, 0.84 respectively) (Figure 2). However, these proteins could not differentiate NPC from Suspected Non‐Alzheimer Pathology (SNAP), or non‐amyloid dementia (A‐T‐N+, A‐T+N‐ and A‐T+N+).

Conclusion

The results suggest that high‐dimensional plasma protein testing could be a useful and reproducible approach for discriminating NPC from amyloid pathology dementia. A minimally invasive and cost‐effective blood biomarker of the ATN framework could facilitate clinical trials by contributing to rapid and effective selection of participants.