# Redefining ALS: Large-scale proteomic profiling reveals a prolonged pre-diagnostic phase with immune, muscular, metabolic, and brain involvement

Christina Lill, Jan Homann, Roxanna Korologou-Linden, Vivian Viallon, Sarah Morgan, Valerija Dobricic, Laura Deecke, Julia Schessner, Karl Smith-Byrne, Daniel Birtles, et al.

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Christina Lill, Jan Homann, Roxanna Korologou-Linden, Vivian Viallon, Sarah Morgan, et al. Re-defining ALS: Large-scale proteomic profiling reveals a prolonged pre-diagnostic phase with immune, muscular, metabolic, and brain involvement. 2025. hal-05299941

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# Redefining ALS: Large-scale proteomic profiling reveals a prolonged pre-diagnostic phase with immune, muscular, metabolic, and brain involvement

**Christina Lill**

christina.lill@uni-muenster.de

Institute of Epidemiology and Social Medicine, University of Münster, Münster, Germany
https://orcid.org/0000-0002-2805-1307

**Jan Homann**

University of Münster https://orcid.org/0000-0003-2791-7065

**Roxanna Korologou-Linden**

Imperial College London

**Vivian Viallon**

International Agency for Research on Cancer (IARC-WHO)

**Sarah Morgan**

Centre for Neuroscience, Surgery and Trauma, Blizard Institute, Queen Mary University of London, London, UK https://orcid.org/0000-0002-1734-4710

**Valerija Dobricic**

Lübeck Interdisciplinary Platform for Genome Analytics (LIGA), University of Lübeck, Lübeck, Germany

**Laura Deecke**

University of Lübeck

**Julia Schessner**

Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany

**Karl Smith-Byrne**

Cancer Epidemiology Unit, University of Oxford https://orcid.org/0000-0002-1932-7463

**Daniel Birtles**

Centre for Neuroscience, Surgery and Trauma, Blizard Institute, Queen Mary University of London, London, UK

**Yujia Zhao**

Institute for Risk Assessment Sciences, Utrecht University, Utrecht, The Netherlands

**Joanne Wuu**
Department of Neurology and ALS Center, Miller School of Medicine, University of Miami, Miami, FL,
USA https://orcid.org/0009-0005-9643-9855

**Fanny Artaud**

Université Paris-Saclay, UVSQ, Inserm, Gustave Roussy, CESP, Villejuif, France

**Fatema Hajizadah**

Centre for Neuroscience, Surgery and Trauma, Blizard Institute, Queen Mary University of London,
London, UK

**José Huerta**

Department of Epidemiology, Murcia Regional Health Council, Biomedical Research Institute of Murcia-
IMIB, Murcia, Spain

**Olena Ohlei**

Institute of Epidemiology and Social Medicine, University of Muenster, Muenster, Germany

**Mikhail Lebedev**

Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried,
Germany

**Pieter Kolijn**

Institute for Risk Assessment Sciences, Utrecht University, Utrecht, The Netherlands

**Marcela Guevara Eslava**

Navarra Institute for Health Research (IdiSNA)

**Ana Jiménez-Zabala**

Biogipuzkoa Health Research Institute, San Sebastian, Gipuzkoa, Spain

**Maria-Jose Sánchez**

Escuela Andaluza de Salud Publica

**Camino Trobajo-Sanmartín**

Instituto de Salud Publica y Laboral de Navarra, Pamplona, Spain

**Sandra Colorado-Yohar**

Murcia Regional Health Council

**Sonia Alonso-Martin**

Biogipuzkoa Health Research Institute https://orcid.org/0000-0002-3254-0365

**Dafina Petrova**

Escuela Andaluza de Salud Publica (EASP), Granada, Spain

**Sabina Sieri**

Epidemiology and Prevention Unit, Fondazione IRCCS Istituto Nazionale Dei Tumori Di Milano Via
Venezian, Milan, Italy

**Klaus Berger**

Institute of Epidemiology and Social Medicine, University of Münster

**Susan Peters**

Institute for Risk Assessment Sciences, Utrecht University, Utrecht, The Netherlands

**Nicholas Wareham**
University of Cambridge

**Rudolf Kaaks**

German Cancer Research Center (DKFZ)

**Ruth Travis**

University of Oxford

**Roel Vermeulen**

Utrecht University https://orcid.org/0000-0003-4082-8163

**Global Neurodegeneration Proteomics Consortium Global Neurodegeneration Proteomics Consortium**

Gates Ventures

**Ioanna Tzoulaki**

Imperial College London https://orcid.org/0000-0002-4275-9328

**Alexis Elbaz**

CESP, INSERM, Université Paris-Sud, UVSQ, Université Paris-Saclay, 94805, Villejuif, France

**Matthias Mann**

Max Planck Institute of Biochemistry https://orcid.org/0000-0003-1292-4799

**Carlotta Sacerdote**

Città della salute e della Scienza

**Giovanna Masala**

Institute for Cancer Research, Prevention and Clinical Network (ISPRO)

**Verena Katkze**

German Cancer Research Center (DKFZ)

**Michael Benatar**

Department of Neurology, Miller School of Medicine, University of Miami, Miami, FL, USA

**Lars Bertram**

University of Lübeck https://orcid.org/0000-0002-0108-124X

**Lefkos Middleton**

Imperial College London https://orcid.org/0000-0002-2176-403X

**Elio Riboli**

Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK

**Marc Gunter**

Nutrition and Metabolism Branch, International Agency for Research on Cancer, World Health Organization

**Oliver Robinson**

Imperial College London https://orcid.org/0000-0002-4735-0468

**Pietro Ferrari**

International Agency for Research on Cancer (IARC-WHO)

**Article**
**Keywords:**

**Posted Date:** September 1st, 2025

**DOI:** https://doi.org/10.21203/rs.3.rs-7452736/v1

**License:** © Ⓟ This work is licensed under a Creative Commons Attribution 4.0 International License.

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**Additional Declarations:** There is **NO** Competing Interest.
Redefining ALS: Large-scale proteomic profiling reveals a prolonged pre-diagnostic phase with immune, muscular, metabolic, and brain involvement

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Affiliations:

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1. Institute of Epidemiology and Social Medicine, University of Münster, Münster, Germany

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2. Ageing and Epidemiology Unit (AGE), School of Public Health, Imperial College London, London, UK

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3. International Agency for Research on Cancer (IARC/WHO), Nutrition and Metabolism Branch, Lyon, France

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4. Centre for Neuroscience, Surgery and Trauma, Blizard Institute, Queen Mary University of London, London, UK

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5. Lübeck Interdisciplinary Platform for Genome Analytics (LIGA), University of Lübeck, Lübeck, Germany

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6. Department of Proteomics and Signal Transduction, Max Planck Institute of Biochemistry, Martinsried, Germany

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7. Cancer Epidemiology Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK

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8. Institute for Risk Assessment Sciences, Utrecht University, Utrecht, The Netherlands

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9. Department of Neurology and the ALS Center, University of Miami, Miami, FL, USA

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10. Université Paris-Saclay, UVSQ, Inserm, Gustave Roussy, CESP, Villejuif, France

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11. Department of Epidemiology, Murcia Regional Health Council, Biomedical Research Institute of Murcia-IMIB, Murcia,

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Spain

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12. Centro de Investigación Biomédica en Red de Epidemiología y Salud Pública (CIBERESP), Madrid, Spain

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13. Instituto de Salud Pública y Laboral de Navarra, Pamplona, Spain

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14. Navarre Institute for Health Research (IdiSNA), Pamplona, Spain

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15. Biogipuzkoa Health Research Institute, San Sebastian, Gipuzkoa, Spain

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16. Ministry of Health of the Basque Government, Sub-Directorate for Public Health and Addictions of Gipuzkoa, San

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Sebastián, Gipuzkoa, Spain

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17. Escuela Andaluza de Salud Pública (EASP), Granada, Spain

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18. Instituto de Investigación Biosanitaria ibs.GRANADA, Granada, Spain

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19. Research Group On Demography and Health, National Faculty of Public Health, University of Antioquia, Medellín,

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Colombia

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20. CIBERNED, ISCIII (CIBER, Carlos III Institute, Spanish Ministry of Sciences and Innovation), Madrid, Spain

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21. Servicio de Oncología Médica. Hospital Universitario Virgen de las Nieves, Granada, Spain

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22. Epidemiology and Prevention Unit, Fondazione IRCCS Istituto Nazionale Dei Tumori Di Milano Via Venezian, Milan, Italy

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23. Medical Research Council Epidemiology Unit, University of Cambridge, Cambridge, UK

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24. Division of Cancer Epidemiology, German Cancer Research Center, Heidelberg, Germany

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25. University Medical Centre Utrecht, Utrecht, The Netherlands

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26. Department of Epidemiology and Biostatistics, School of Public Health, Imperial College London, London, UK

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27. Centre for Systems Biology, Biomedical Research Foundation, Academy of Athens, Athens, Greece

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28. NNF Center for Protein Research, Faculty of Health Sciences, University of Copenhagen, Copenhagen, Denmark

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29. Department of Health Sciences, University of Eastern Piedmont, Novara, Italy

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30. Clinical Epidemiology Unit, Institute for Cancer Research, Prevention and Clinical Network (ISPRO), Florence, Italy

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*These authors contributed equally as shared second authors.

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⁵For the index GNPC author list, see Supplementary Information.

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Corresponding Author:

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Dr. Christina Lill, MD, MSc, Translational Epidemiology Unit, Institute of Epidemiology and Social Medicine, University of

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Münster, Domagkstr. 3, 48149 Münster, Germany, telephone: +49 251 83 57101, email: christina.lill@uni-muenster.de and

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clill@ic.ac.uk

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58 **ABSTRACT**

59 **Background:** Amyotrophic lateral sclerosis (ALS) is a fatal neurodegenerative disorder with a
60 largely unknown duration and pathophysiology of the pre-diagnostic phase, especially for the
61 common non-monogenic form.

62 **Methods:** We leveraged the European Prospective Investigation into Cancer and Nutrition (EPIC) cohort with up to 30 years of follow-up to identify incident ALS cases across five European countries. Pre-diagnostic plasma samples from initially healthy participants underwent high-throughput proteomic profiling (7,285 protein markers, SomaScan). Cox proportional hazards models based on 4,567 participants (including 172 incident ALS cases) were used to identify protein biomarkers associated with future ALS diagnosis. Top results were indirectly validated in two independent case-control studies of prevalent ALS (n=417 ALS, 852 controls). Functional annotation included cross-disease comparisons, gene set and tissue enrichment testing, organ-specific proteomic clocks, and the application of large-language models (LLM).
76

72 **Findings:** Five proteins (SECTM1, CA3, THAP4, KLHL41, SLC26A7) were identified as significant
73 pre-diagnostic ALS biomarkers (FDR=0.05), detectable approximately two decades before
74 diagnosis. Of these, all except SECTM1 were indirectly validated in independent cohorts of
75 prevalent ALS cases, supporting their clinical significance. Additionally, 22 nominally
76 significant (p<0.05) pre-diagnostic biomarkers were FDR-significant in prevalent ALS with
77 consistent effect directions. Cross-disease comparisons with pre-diagnostic Parkinson's and
78 Alzheimer's disease suggested a largely specific pre-diagnostic ALS biomarker signature. Gene
79 ontology and tissue enrichment highlighted early involvement of immune, muscle, metabolic,
80 and digestive processes. Furthermore, analyses of proteomic clocks revealed accelerated
81 aging in brain-cognition, immune, and muscle tissues before clinical diagnosis. Druggability
82 and LLM analyses revealed possible therapeutic targets and novel strategies, emphasizing
83 translational relevance.

84 **Interpretation:** Our study provides first evidence of ultra-early molecular changes in common
85 ALS up to two decades prior to clinical onset, mainly affecting immune, muscle, metabolic,
86 digestive, and cognitive systems. Our study nominates several compelling candidates for risk
87 stratification studies and novel therapeutic targets for early intervention.
88 **Funding:** Clinical Research in ALS and Related Disorders for Therapeutic Development

89 (CREATE) Consortium, Cure Alzheimer's Fund, Michael J Fox Foundation, Interdisciplinary

90 Centre for Clinical Research, University Münster
91 INTRODUCTION

92 Amyotrophic lateral sclerosis (ALS) is a devastating neurodegenerative disease, characterized
93 by progressive loss of central and peripheral motor neurons. This neurodegeneration leads to
94 muscle atrophy, rapid functional decline, and usually death within a few years after symptom
95 onset. The disease places substantial physical, emotional, and financial burdens on patients,
96 families, and caregivers¹.

97 While 5-10% of patients carry highly penetrant mutations and often show familial aggregation
98 ("familial ALS"), the more common form is non-monogenic and usually does not show familial
99 clustering ("sporadic"). It is likely caused by a combination and interaction of genetic and
100 lifestyle/environmental risk factors. Although several genetic² and environmental/lifestyle¹
101 risk factors have been identified, the fundamental pathophysiological mechanisms and their
102 timing underlying ALS remain incompletely understood. This limited insight contributes to the
103 absence of effective disease-modifying therapies; current treatments are mostly symptomatic
104 offering only modest improvements in survival and quality of life¹.

105 Unlike Parkinson's (PD) and Alzheimer's disease (AD), where pre-diagnostic phases with initial
106 pathophysiological changes and unspecific symptoms may span decades, the timing of this
107 initial phase in ALS is largely unknown, particularly for the common non-monogenic form³.
108 This is largely due to the low incidence of ALS (~2-3 cases per 100,000 individuals annually¹)
109 and high short-term mortality⁴, which requires very large prospective cohorts with long-term
110 follow-up to investigate this phase. Moreover, the identification of pre-diagnostic markers —
111 such as blood-based biomarkers indicative of risk exposures and/or early pathophysiological
112 changes preceding clinical diagnosis — depends on the availability of carefully stored baseline
113 (i.e., pre-diagnostic) blood samples from such large cohorts. As a result, pre-diagnostic
114 biomarker signatures for ALS remain underexplored to date, especially for the common form:
115 One prospective study including 84 individuals who developed ALS and 161 matched controls
116 found plasma neurofilament light chain (NfL) levels to be elevated already within five years
117 before ALS diagnosis⁵. Furthermore, a study focusing on monogenic ALS found increases in
118 NfL levels in asymptomatic carriers about one year before symptom onset³,⁶⁻⁸.

119 At the same time, high-throughput proteomic technologies such as the aptamer-based
120 SomaScan® (SomaLogic, Inc.) technology offer promising opportunities for the discovery of
121 novel biomarker signatures by enabling simultaneous quantification of thousands of proteins.
122 In this context, blood-based proteomic biomarkers are attracting increasing interest because
they are minimally invasive, readily applicable in clinical practice, and have already
demonstrated strong predictive performance in other conditions⁹⁻¹¹, including
neurodegenerative diseases like AD¹²,¹³.

To advance insight into the pre-diagnostic phase of ALS, we leveraged data from the 'European Prospective Investigation into Cancer and Nutrition' (EPIC) cohort (n=521,000)¹⁴,¹⁵, which offers 30 years of follow-up, to identify incident ALS cases. This enabled us to establish a well-characterized, population-based case-cohort — hereafter referred to as EPIC4ALS — comprising 4,567 participants, including 172 incident ALS cases¹⁶. We utilized baseline (pre-diagnostic) plasma samples collected from all initially healthy EPIC4ALS participants to conduct a comprehensive high-resolution proteomic screen for biomarkers predictive of subsequent ALS development using the SomaScan 7K array (~7,000 protein markers). We tested the most promising pre-diagnostic biomarkers for 'indirect' validation in two independent studies of individuals with established ALS. To further contextualize these findings, we performed functional annotation analyses, including the assessment of organ-specific aging in pre-diagnostic ALS, and compared proteomic signatures from pre-diagnostic ALS with those from incident PD and AD. This multifaceted approach allowed us to characterize ultra-early molecular changes and disease-specific signatures in incident ALS, providing new perspectives on disease initiation.
141 **RESULTS**

142 **EPIC4ALS case-cohort**

143 The EPIC4ALS case-cohort comprises 4,737 participants including a randomly drawn subcohort
144 and 193 ALS cases identified by screening mortality records from 222,613 EPIC participants
145 across five European countries (Spain, Italy, the Netherlands, the UK, Germany) over 30 years
146 of follow-up. Age at death was used as a proxy for age at diagnosis in the Cox proportional
147 hazard regression analysis. In this context, to identify pre-diagnostic biomarkers, all primary
148 analyses were initiated five years after baseline, i.e., five years after blood collection, by
149 excluding 21 ALS cases and 149 subcohort members who died or were censored within this
150 initial window. This yielded 4,567 EPIC4ALS participants (including 172 incident ALS cases;
151 **Figure 1, Supplementary Tables 1-2**). These participants had a median age of 52 years at
152 baseline (range: 35-79), 66% were women (62% excluding Utrecht, which recruited only
153 women), and the median follow-up was 18 years. Among incident ALS cases, median time
154 from blood sampling to death was 14 years (range: 5-29). As expected, given our case-cohort
155 setting, incident ALS cases had a higher median age at baseline (58 vs. 52 years) and a greater
156 proportion of men (47% vs. 38%, excluding Utrecht) compared to non-cases (please note that
157 these differences are appropriately accounted for by stratification for age and sex in our Cox
158 models). Additionally, ALS cases were less likely to be overweight (BMI ≥25: 49% vs. 61%) and
159 less likely to be current smokers (17% vs. 24%; **Supplementary Table 1**).

160 **Association analyses in the EPIC4ALS discovery dataset**

161 To identify proteins associated with a future ALS diagnosis, we profiled 6,381 proteins (using
162 7,285 aptamers) in plasma samples of 4,567 EPIC4ALS participants, including 172 incident ALS
163 cases. Cox regression analyses, stratified by baseline age (in 5-year intervals), sex, and center,
164 identified only the SECTM1 protein as significantly associated with ALS risk over 5-30 years of
165 follow-up (hazard ratio [HR]=1.35 per standard deviation [SD] increase in log10 protein levels,
166 p=1.38×10⁻⁶) at a false-discovery rate (FDR) control of 0.05. Restricting analyses to 5-20 years
167 of follow-up, five proteins were FDR-significantly associated with ALS risk: SECTM1, CA3,
168 THAP4, KLHL41, and SLC26A7 (all HR>1.30, p≤2.77×10⁻⁵; **Table 1, Figure 2A, Supplementary
169 Figure 1**). Analysis of Schoenfeld residuals for all five biomarkers during 5-20 years follow-up
170 showed no deviation from the proportional hazards assumption (data not shown). Additional
171 adjustment for demographic and lifestyle factors had minimal impact, with effect size
172 estimates for the top biomarkers remaining stable (**Supplementary Figure 2, Supplementary**
**Table 3).** Sex-stratified analyses revealed stronger associations for CA3, THAP4, and SLC26A7 in men than in women (ratio of relative risks >1.30, all p<0.05 for sex interaction; **Figure 2B**, **Supplementary Table 4**). Sensitivity analyses excluding outliers instead of capping (see Methods) yielded similar effect size estimates, supporting the robustness of our top results (**Supplementary Figure 2**, **Supplementary Table 5**). Three biomarkers (KLHL41, CA3, SLC26A7) were moderately inter-correlated (r=0.30-0.54), suggesting shared regulation or pathways, whereas SECTM1 and THAP4 were largely uncorrelated with the others (**Supplementary Figure 3**, **Supplementary Table 6**).

## Analyses of time-varying effects

Next, in exploratory analyses, we examined whether and how the strength of the effect size estimates of the top 5 pre-diagnostic proteins varied across the entire follow-up time from the distal pre-diagnostic phase to the proximal phase prior to ALS-related death (prevalent ALS). To this end, we extended the dataset using 4,737 EPIC participants including all 193 ALS cases (i.e., re-including the 21 incident ALS patients and 149 non-cases who died or were censored within five years of baseline). HRs based on 10-year sliding windows suggested comparatively stable HRs for most biomarkers, demonstrating long-term risk associations 25-15 years or even earlier before death, i.e., ~20-10 years before diagnosis. We observed that HRs of all biomarkers except SECTM1 were slightly larger in time windows closer to ALS-related death (**Supplementary Figure 4**). Accordingly, interaction analyses with a 'protein level x log time-to-event' interaction term indicated nominally significant interactions (increasing risk with decreasing time-to-death) for all proteins except SECTM1 (**Supplementary Table 7**). The interaction analysis for SECTM1 showed an inverse (decreasing risk with decreasing time-to-death) but non-significant interaction estimate. Spline-based Cox models to assess potential non-linear relationships demonstrated statistical evidence for a time-varying effect for KLHL41 (p=0.0455), with larger effects in short-term risk settings (**Figure 2C**, **Supplementary Table 7**). For SECTM1, CA3, THAP4, and SLC26A7, the spline-based interaction terms were not statistically significant (p>0.11), suggesting that they showed relatively constant performance of predicting ALS risk over the 30-year follow-up period supporting their long-term predictive potential.

While it should be noted that these findings are based on relatively small sample sizes, they globally suggest that biomarker associations with ALS risk can be observed and are relatively stable starting in the time windows of 25-15 years before death (i.e., 20-10 years before death).
diagnosis). This underscores the importance of comprehensive baseline proteomic profiling
with longitudinal follow-up to reveal pre-diagnostic biomarker signatures in ALS.

## 207 Indirect validation in prevalent ALS

Due to the lack of sufficiently large pre-diagnostic ALS cohorts with SomaScan data — and
since strong effect estimates were seen near or after clinical diagnosis in EPIC4ALS — we used
two independent prevalent ALS case-control datasets (417 ALS cases, 852 controls) for indirect
validation. These datasets included SomaScan 7K data from the Global Neurodegeneration
Proteomics Consortium (GNPC)¹⁷, which we re-analyzed, and from the University of Miami,
with publicly available summary statistics¹⁸. Notably, all four FDR-significant pre-diagnostic
biomarkers except SECTM1 were also significantly associated with prevalent ALS status (with
FDR-significance on a proteome-wide scale), with effects in the same direction (Table 1, Figure
2D). Of the three biomarkers with sex-specific effect estimates pre-diagnostically, CA3 and
THAP4 showed similar differences in GNPC prevalent cases, although the interaction test was
not significant (Supplementary Table 4).

Of all biomarkers nominally significantly associated with pre-diagnostic ALS but not passing
FDR control (n=326), 22 proteins (23 aptamers) showed consistent (proteome-wide) FDR-
significant associations with prevalent ALS in both case-control datasets, suggesting genuine
— though potentially weaker— associations in pre-diagnostic ALS (“suggestive” biomarkers;
Table 1, Supplementary Table 8). Among nominally significant pre-diagnostic biomarkers, we
found significant enrichment of FDR-significant clinical ALS biomarkers (odds ratio [OR]=4.06,
p=3.63×10⁻⁶ [GNPC]; OR=2.27, p=8.35×10⁻⁵ [Miami], Fisher’s exact test) and strong
concordance of effect directions, which was greater than expected by chance (89%,
p=6.56×10⁻⁴ [GNPC]; 78%, p=1.05×10⁻³ [Miami], one-sided exact binomial test;
Supplementary Figure 5).

Collectively, these results demonstrate substantial overlap in dysregulated biomarkers at both
pre-diagnostic and clinical ALS stages and highlight the importance of comparing biomarkers
across disease stages.

## 232 Comprehensive assessment of previously described biomarker candidates

A total of 253 proteins linked to neurodegeneration and/or ALS from various sources (see
**Supplementary Methods**) were also measured in EPIC4ALS. Of these, 21 (8%) showed
nominally significant associations, including TNFRSF1B (ALS KEGG pathway), TEK, CST3, KDR,
236 TREM1, and ARSA (all on the NULISA CNS panel; **Supplementary Table 9**). Notably, none of
237 our top pre-diagnostic biomarkers appeared among these previously described candidates.
238 Conversely, 8 of the 27 at least suggestive pre-diagnostic ALS biomarkers overlapped with a
239 SomaScan-based Mendelian Randomization study¹⁹. Of these, CA3 showed a significant causal
240 association with ALS at FDR=0.05 (**Supplementary Table 10**).

241 **Cross-disease comparisons**

242 We evaluated the 27 pre-diagnostic ALS biomarkers in incident PD (n=4,519 including 555
243 cases) and AD (n=1,687 including 413 cases) using Cox regression analyses in the EPIC4PD and
244 EPIC4AD case-cohorts (**Supplementary Table 11**). None of the FDR-significant pre-diagnostic
245 ALS biomarkers were significantly associated with pre-diagnostic PD or AD. However, the
246 suggestive biomarkers MFAP5 and CD209 (seq.3029.52) were (proteome-wide) FDR-
247 significant in PD, with effect estimates pointing in the same direction. No ALS biomarkers were
248 FDR-significant for AD. Proteome-wide, 37 of 331 proteins (11%) nominally associated with
249 ALS over 5-20 years were also nominally associated with PD or AD; while 89% were ALS-
250 specific. No significant enrichment of PD or AD biomarkers among those identified for ALS was
251 found (p=0.133 and p=0.134). Among proteins nominally associated with both ALS and PD or
252 AD, 63% (PD) and 48% (AD) showed concordant directions, with a trend toward higher
253 concordance for PD but not AD (p=0.0717 and p=0.636; exact binomial test). In summary,
254 MFAP5 and CD209 are shared between pre-diagnostic ALS and PD, but most pre-diagnostic
255 ALS biomarkers are disease-specific.

256 **Functional characterization of pre-diagnostic ALS biomarkers**

257 Gene ontology (GO) analysis of nominally significant pre-diagnostic ALS biomarkers (p<0.05
258 for 5-20 year follow-up) identified nominally significant enrichment primarily in immune-
259 related processes (hemopoiesis, innate immune response, T cell-mediated immunity,
260 antibacterial humoral response). Additional terms included muscle structure development,
261 proteolysis, protein secretion, cell-cell adhesion, vascular permeability, and mitochondrial
262 catabolic processes. Triglyceride homeostasis and digestion were borderline significant. Key
263 molecular function enrichments were for calcium ion binding, serine-type endopeptidase
264 inhibitor activity, protein disulfide isomerase activity, and muscle-related functions. Nominally
265 significant enrichments were also found in the extracellular region, cell surface, endoplasmic
266 reticulum, and lysosomal lumen (**Supplementary Table 12**).
Heatmaps of RNA-seq expression data from the Human Protein Atlas [HPA] showed that several biomarkers, including CA3, KLHL41, and THAP4, were predominantly expressed in skeletal muscle (and to a lesser extent heart); other biomarkers were predominant in adipose tissue, brain (especially cerebral cortex), pancreas, and liver. Protein-level data based on immunohistochemistry (IHC) experiments available in the HPA were consistent (Supplementary Figures 6-7). Tissue enrichment analysis with UniProt annotations showed overrepresentation in lymphoid tissue (tonsil), pancreas, and skeletal muscle, and borderline presence in cerebrospinal fluid (Supplementary Table 12). Protein-protein interaction network (PPI) analysis of all FDR- and nominally significant pre-diagnostic biomarkers revealed nine functionally relevant clusters, enriching in immune-, muscle- and neuronal-related pathways (Figure 3, Supplementary Table 13). The convergence of both FDR- and nominally significant ALS biomarkers in the PPI suggests that a substantial number of the nominally significant biomarkers are relevant for the disease.

A semi-systematic, manual literature review found prior functional evidence for involvement in ALS or its models for 21 of the 27 proteins (Supplementary Table 14). Finally, comprehensive interpretation with the large-language model (LLM)-based “Proteome Interpreter” suggested increased cytoskeletal remodeling, energy buffering, oxidative/nitrosative stress defense, membrane transport/excitability, and growth-factor/immune signaling, but reduced fatty acid uptake, immune sensing, protein-folding stress resolution, and extracellular matrix maintenance (Supplementary Text).

## Organ-specific biological aging

Next, we investigated whether incident ALS patients show signs of accelerated organ-specific aging by calculating 11 organ-specific clocks, a 'cognition brain' clock, as well as a non-organ-specific conventional and 'organismal' clock, as recently described²⁰. Cox regressions on these 14 clocks revealed FDR-significant associations between incident ALS and the 'cognition brain' (HR=1.49, p=1.78E-03), muscle (HR=1.44, p=1.85E-03), and immune clocks (HR=1.39, p=2.51E-03), suggesting accelerated aging of incident ALS patients in these systems. Furthermore, conventional, organismal, brain, and liver clocks showed nominally significant associations (Figure 2E, Supplementary Table 15). Visualizing HRs in 10-year sliding windows across the complete follow-up time (0-30 years) suggested that organ-specific aging effects can be observed as early as 25-15 years before ALS-related death (20-10 years before clinical onset;
298 **Supplementary Figure 4).** Analyses using spline regression did not reveal any significant time-
299 varying effects (Figure 2E, Supplementary Table 7).

300 **Druggability assessment of pre-diagnostic biomarkers**

301 Given their potential relevance early in the disease process, we assessed whether any of the
302 27 top ALS biomarkers are druggable using the Open Targets database. Nearly half (44%)
303 showed evidence as potential drug targets, with CA3 being particularly prominent
304 **(Supplementary Table 16)**. The LLM model suggested therapies targeting
305 oxidative/nitrosative stress, sarcomere stabilization, calpain inhibition, enhanced unfolded
306 protein response, reduced fibrosis and TGF-β signaling, energy support, lipid handling, and
307 membrane trafficking. A combined regimen was considered to be most logical including a
308 mitochondria-targeted antioxidant, calpain inhibitor, UPR/HSP booster, with optional anti-
309 TGF-β agent to prevent fibrosis. Early testing was recommended to comprise ROS markers (8-
310 OHdG), calpain activity assays, ER-stress readouts (BiP, spliced XBP1), fibrosis indexes
311 (collagen I/III) and functional outputs (contractile force, echocardiography; **Supplementary**
312 **Text**).

313
314 DISCUSSION

315 In this population-based, prospective case-cohort study, we present the first comprehensive
316 characterization of ultra-early blood-based proteomic biomarker signatures for ALS.
317 Leveraging the large European EPIC cohort with extended follow-up, high-resolution
318 proteomic profiling, and independent indirect validation in prevalent ALS, we identified the
319 plasma protein biomarkers SECTM1, CA3, THAP4, KLHL41, and SLC26A7 as associated with ALS
320 up to 15-25 years before ALS-related death (or 10-20 years or more before diagnosis). All but
321 one (SECTM1) also replicated in independent prevalent ALS cases, confirming the robustness
322 of biomarker signals across disease stages. Overall, these findings provide important insights
323 into the timing and underlying biology of ALS in its pre-diagnostic phase, revealing very early
324 and sustained involvement of adaptive immune, muscle, and brain systems, all showing signs
325 of accelerated aging and having high biological plausibility. While some pre-diagnostic protein
326 biomarkers overlapped with PD, the ALS signature remains largely disease-specific. Thus, our
327 study sets a new foundation for biomarker research in ALS and provides new insights that
328 could potentially be leveraged for early detection, improved patient stratification in clinical
329 trials, and novel therapeutic approaches.

330 Our study provides several key findings: First, five specific plasma proteins emerge as
331 significant predictors of ALS many years before death/diagnosis. Our analyses of time-varying
332 associations suggest that these markers rise well before symptoms, with the earliest
333 associations detectable up to two decades or more years prior to diagnosis. This suggests that
334 ALS represents a slowly evolving gradual, multi-stage molecular process rather than a quickly-
335 evolving late-life event — mirroring the latent intervals described for PD and AD<sup>21,22</sup>.

336 Second, analysis of the temporal biomarker patterns has uncovered key pathophysiological
337 mechanisms. GO analyses indicate that immune and muscle systems are affected early in pre-
338 diagnostic ALS. Proteomic clocks further demonstrate that immune, muscle and
339 brain/cognitive aging processes are pronounced very early in pre-diagnostic ALS. While there
340 is a large body of literature supports a role for the immune system in ALS<sup>23-25</sup>, our data
341 suggests that immune alterations occur at the very beginning of the pathogenesis of ALS,
342 possibly indicating opportunities for early immune-modulating interventions. We further
343 observed more subtle early features of metabolic processes including lipid metabolism —
344 expanding on previous findings in presymptomatic mutation carriers<sup>26</sup> and prospective
studies<sup>27,28</sup> — and a previously unknown, potential involvement of the digestive system (liver and pancreas). 345 346

Third, prior studies have mainly addressed candidate biomarkers in prevalent or rare familial ALS (e.g., <sup>29,30</sup>). By contrast, our approach investigates the common pre-diagnostic form in a large, unselected population using large-scale proteomic profiling. Indirect validation in two independent ALS case-control datasets further supports the relevance and potential clinical utility of the identified biomarkers and illustrates that this indirect validation can help identify genuinely predictive markers. While we have not directly evaluated their predictive utility in risk modeling, the convergence of pre-diagnostic and prevalent biomarkers suggests that these markers may facilitate earlier diagnosis prior to current clinical assessment. This may also be valuable considering the substantial delays that currently often occur between initial symptom onset and clinical diagnosis. 347 348 349 350 351 352 353 354 355 356

Finally, for the first time, we applied the AI-based LLM “Proteome Interpreter” as a case study to complement our interpretation of molecular signatures and therapeutic opportunities. Notably, this tool reached conclusions consistent with our traditional functional annotation methods, efficiently mimicking expert analytical reasoning to enable rapid interpretation of high-dimensional molecular data and identification of actionable insights. Our biomarker associations — detectable up to two decades before diagnosis and stable until clinical diagnosis — open new avenues towards improvement of early diagnosis, stratification of patients in clinical trials, monitoring disease progression, and eventually timely interventions with novel disease-modifying therapies. Specifically, our findings suggest that addressing inflammation, muscle changes, and metabolic pathways at an early stage may help to favorably modify disease course and enhance outcomes for patients. 357 358 359 360 361 362 363 364 365 366 367

Strengths of this study include the large, population-based cohort, extensive follow-up, comprehensive pre-diagnostic proteomic profiling, and independent indirect validation in two external studies. Limitations include the relatively low number of ALS cases, single pre-diagnostic blood samples, using age at death as a proxy of age at diagnosis, and potential residual confounding. Future studies should leverage repeated sampling, diverse populations, and functional approaches to clarify causal roles and translational potential of identified biomarkers. 368 369 370 371 372 373 374

In summary, we redefine ALS as a condition with a prolonged molecular pre-diagnostic phase — characterized by immune, muscular, metabolic, and brain involvement detectable in blood 375 376
377 proteomes. Our results substantially advance the understanding of ALS pathophysiology and
378 underscore new prospects for biomarker-driven early detection and intervention. Strategic
379 application of blood-based proteomics could ultimately improve ALS diagnosis, care, and
380 prevention.
381 Acknowledgements

382 The authors thank all EPIC participants and the countless scientists who have contributed to
383 the EPIC cohort over the past 30 years. We thank the National Institute for Public Health and
384 the Environment (RIVM), Bilthoven, the Netherlands, for contributing cases and ongoing
385 support to the EPIC study. We sincerely thank Bertrand Hemon for his assistance with data
386 management, and Prof. Paolo Vineis, Prof. Martin Wolkewitz, and Dr. Jürgen Wellmann for
387 helpful discussions. This work was supported by NIHR Imperial Biomedical Research Centre at
388 Imperial.

389 Data access

390 Data can be accessed by external researchers after approval by the EPIC4ND working group
391 and the EPIC steering committee. Potential collaborators can contact the EPIC4ND working
392 group chair, Christina Lill (christina.lill@uni-muenster.de and clill@ic.ac.uk) and the EPIC
393 administrator Sherry Morris (epicadmin@imperial.ac.uk).

394 Funding

395 Updates of the ALS case ascertainment, generation and processing of proteomic data were
396 funded by the 'CREATe- Clinical Research in ALS and Related Disorders for Therapeutic
397 Development' Consortium (to C.M.L. and L.B.) and by intramural funds of University of
398 Münster, Germany. CReATe (U54 NS092091) is part of the Rare Diseases Clinical Research
399 Network (RDCRN), an initiative of the Office of Rare Diseases Research (ORDR), NCATS and
400 funded through collaborations between NCATS, NINDS, and the ALS Association. Part of the
401 proteomic data were also generated with funding from the Cure Alzheimer's Fund (to C.M.L.
402 and L.B.) and the Michael J Fox Foundation (#008994 to C.M.L. and E.R). Additional funding
403 was supplied by the Interdisciplinary Centre for Clinical Research, University Münster (to
404 C.M.L., Lil3/001/25). C.M. Lill was supported by the Heisenberg program of the DFG (DFG; LI
405 2654/4-1). OR was supported by UK Research and Innovation Future Leaders Fellowship
406 (MR/S03532X/1, MR/Y02012X/1).

407 The coordination of EPIC-Europe is financially supported by International Agency for Research
408 on Cancer (IARC) and also by the Department of Epidemiology and Biostatistics, School of
409 Public Health, Imperial College London which has additional infrastructure support provided
410 by the NIHR Imperial Biomedical Research Centre (BRC). The national cohorts are supported
411 by Associazione Italiana per la Ricerca sul Cancro-AIRC-Italy, Italian Ministry of Health, Italian
Ministry of University and Research (MUR), Compagnia di San Paolo (Italy); Dutch Ministry of Public Health, Welfare and Sports (VWS), the Netherlands Organisation for Health Research and Development (ZonMW), World Cancer Research Fund (WCRF), (The Netherlands); Instituto de Salud Carlos III (ISCIII), Regional Governments of Andalucía, Asturias, Basque Country, Murcia and Navarra, and the Catalan Institute of Oncology - ICO (Spain); Cancer Research UK (C864/A14136 to EPIC-Norfolk; C8221/A29017 to EPIC-Oxford), Medical Research Council (MR/N003284/1, MC-UU_12015/1 and MC_UU_00006/1 to EPIC-Norfolk; MR/Y013662/1 to EPIC-Oxford) (United Kingdom). Previous support has come from "Europe against Cancer" Programme of the European Commission (DG SANCO). SomaScan® data were generated under Master Research Agreement, 14th December 2021, between Imperial College London and SomaLogic Inc. SomaLogic were not involved in analyzing or interpreting the data; or in writing or submitting the manuscript for publication.

Disclaimer: Where authors are identified as personnel of the International Agency for Research on Cancer / World Health Organization, the authors alone are responsible for the views expressed in this article and they do not necessarily represent the decisions, policy or views of the International Agency for Research on Cancer / World Health Organization.

## Authors' contributions

Study concept and supervision: ER, MJG, PF, CML; acquisition/contribution of data: JMH, MG, AJZ, MJS, CTS, SMCY, SA, DP, SS, SP, TJK, NW, RK, RCT, RV, GNPC, CS, GM, VK, LM, ER, MJG, PF, CML, data analysis and interpretation: JH, RKL, VV, SM, VD, LD, JS, KSB, DB, YZ, JW, FA, FH, OO, ML, MK, IT, AE, MM, MB, LB, ER, MJG, PF, OR, CML; drafting the manuscript: JH, CML. Critical revision of the manuscript for content: all co-authors

## Competing interests

None of the authors reports any competing interest.
436 **Legends to figures**

437 **Figure 1. Visual summary of study design**

438 **Legend.** In this study, plasma proteomics from 4,567 participants (172 incident amyotrophic lateral sclerosis [ALS] cases) from the 'European Prospective Investigation into Cancer and Nutrition for amyotrophic lateral sclerosis' case-cohort (EPIC4ALS) were generated to identify pre-diagnostic protein biomarkers of ALS. Using the SomaScan 7K platform, 7,285 aptamers were analyzed across up to 30 years of follow up. Identified biomarkers were indirectly validated in two large datasets (Global Neurodegenerative Disease Consortium [GNPC] and the 'Miami' dataset by Dergai et al.³¹) including 417 prevalent ALS cases and 852 controls. Accelerated biological aging was investigated using organ-specific proteomic clocks²⁰. Further functional characterization included gene-set and cell-type enrichment, protein-protein network analyses, the application of large-language models (LLM) models, as well as cross-trait analyses with Parkinson's (PD) and Alzheimer's disease (AD).

446 **Figure 2. Association results of plasma proteins in the EPIC4ALS case-cohort**

447 **Legend.** This figure shows association results of protein biomarkers with amyotrophic lateral sclerosis (ALS) based on Prentice-weighted Cox proportional hazard regressions (stratified for age, sex, and center) in the EPIC4ALS cohort. (A) Volcano plot of Cox proportional hazard regression analyses across 4,567 EPIC4ALS participants (including 172 incident ALS cases) across 5-20 years of follow-up and stratified for center, sex, and age in 5-year categories. Protein names highlighted in bright blue indicate proteins showing significant association with incident ALS after false-discovery rate (FDR) control (FDR=0.05). (B) This figure shows hazard ratios (HR) with 95% confidence intervals for all FDR-significant pre-diagnostic ALS biomarkers upon stratification for sex across 5-20 years of follow-up. Protein names in bold indicate association results that showed significant interaction (p<0.05) by sex. (C) Time-varying hazard ratios of top proteins derived from spline regression across the full follow-up of 0-30 years. For these analyses, the 21 incident ALS patients and 149 non-cases who died or were censored within five years of baseline and were excluded from the main analyses were re-included. (D) Triangle plot showing an integrated overview of analysis results for proteins across incident and prevalent ALS, cross-disease comparisons, and gene expression and protein levels in relevant target tissues. Proteins showing FDR-significance in the pre-diagnostic EPIC4ALS analyses are listed in the upper tier, proteins showing nominally significant (p<0.05) pre-diagnostic associations and FDR-significance in the prevalent case-control datasets are listed in the lower tier. Triangles pointing upwards indicate a positive association of protein levels with disease risk or status, triangles pointing downwards indicate a corresponding inverse association. (E) This figure displays the EPIC4ALS association results of organ-specific proteomic clocks as a measure of organ-specific accelerated biological aging with incident amyotrophic lateral sclerosis (ALS) based on Cox proportional hazard regressions (stratified for age, sex, and center). Organ-specific clocks were calculated as described by Oh et al.²⁰. Left: Forest plot of results of pre-diagnostic clocks in EPIC4ALS. Pre-diagnostic organ clocks that surpassed FDR=0.05 are highlighted in color. Note that the 'organismal' clock refers to a model that uses the non-organ-specific proteins only, and that the 'conventional' clock refers to a model that uses all proteins regardless of organ specificity. Right: Time-varying hazard ratios of top proteins derived from spline regression across the full follow-up period (0-30 years).

469 **Figure 3. Protein-protein interaction networks of pre-diagnostic ALS biomarkers**

470 Protein-protein interaction network of all FDR-significant pre-diagnostic ALS biomarkers (larger nodes) and nominally significant pre-diagnostic ALS biomarkers (smaller nodes). The top Gene-Ontology biological process enrichment(s) for each cluster are summarised. Grey nodes clustered alone and lack enrichment.
471
472
473
<table><caption>Table 1. Plasma protein biomarkers significantly associated with future onset of amyotrophic lateral sclerosis</caption><thead><tr><th rowspan="2">Protein</th><th colspan="2">EPIC4ALS, incident</th><th colspan="2">GNPC, prevalent</th><th colspan="2">'Miami', prevalent</th></tr><tr><th>HR (95% CI)</th><th>p</th><th>OR (95% CI)</th><th>p</th><th>logFC</th><th>q</th></tr></thead><tbody><tr><td colspan="7"><strong>FDR-significant pre-diagnostic biomarkers</strong></td></tr><tr><td>SECTM1</td><td>1.42 (1.24-1.62)</td><td><strong>1.61E-07</strong></td><td>0.97 (0.75-1.24)</td><td>7.79E-01</td><td>-0.03</td><td>5.00E-01</td></tr><tr><td>CA3</td><td>1.43 (1.24-1.66)</td><td><strong>9.75E-07</strong></td><td>3.33 (2.48-4.45)</td><td><strong>6.75E-16</strong></td><td>1.21</td><td><strong>6.04E-35</strong></td></tr><tr><td>THAP4</td><td>1.41 (1.22-1.64)</td><td><strong>6.37E-06</strong></td><td>1.89 (1.39-2.56)</td><td><strong>4.92E-05</strong></td><td>0.13</td><td>6.80E-02</td></tr><tr><td>KLHL41</td><td>1.32 (1.16-1.49)</td><td><strong>1.44E-05</strong></td><td>3.06 (2.32-4.03)</td><td><strong>2.70E-15</strong></td><td>0.79</td><td><strong>6.90E-21</strong></td></tr><tr><td>SLC26A7</td><td>1.32 (1.16-1.51)</td><td><strong>2.77E-05</strong></td><td>3.27 (2.18-4.91)</td><td><strong>9.47E-09</strong></td><td>0.11</td><td><strong>1.20E-03</strong></td></tr><tr><td colspan="7"><strong>Suggestive pre-diagnostic biomarkers</strong></td></tr><tr><td>DUSP29</td><td>1.28 (1.12-1.45)</td><td>2.15E-04</td><td>4.46 (2.85-6.96)</td><td><strong>5.46E-11</strong></td><td>0.30</td><td><strong>1.27E-07</strong></td></tr><tr><td>ARL5B</td><td>1.26 (1.09-1.45)</td><td>1.34E-03</td><td>1.55 (1.17-2.05)</td><td>2.28E-03</td><td>0.11</td><td><strong>3.80E-02</strong></td></tr><tr><td>CD36</td><td>0.76 (0.64-0.90)</td><td>1.52E-03</td><td>0.71 (0.54-0.93)</td><td>1.29E-02</td><td>-0.28</td><td><strong>1.40E-03</strong></td></tr><tr><td>PDLIM3</td><td>1.26 (1.09-1.45)</td><td>1.68E-03</td><td>6.40 (4.25-9.62)</td><td><strong>5.65E-19</strong></td><td>1.57</td><td><strong>1.73E-44</strong></td></tr><tr><td>CST3</td><td>1.28 (1.09-1.51)</td><td>2.41E-03</td><td>1.31 (1.01-1.70)</td><td>4.36E-02</td><td>0.12</td><td><strong>7.40E-03</strong></td></tr><tr><td>DCT</td><td>0.72 (0.58-0.89)</td><td>2.68E-03</td><td>0.75 (0.56-1.00)</td><td>4.72E-02</td><td>-0.18</td><td><strong>3.00E-02</strong></td></tr><tr><td>ATF5</td><td>1.24 (1.08-1.43)</td><td>2.81E-03</td><td>2.66 (1.83-3.86)</td><td><strong>2.59E-07</strong></td><td>0.08</td><td>4.70E-01</td></tr><tr><td>MYL6B</td><td>1.23 (1.07-1.42)</td><td>4.70E-03</td><td>3.84 (2.58-5.72)</td><td><strong>3.64E-11</strong></td><td>0.35</td><td><strong>4.83E-11</strong></td></tr><tr><td>HSPB6</td><td>1.39 (1.10-1.74)</td><td>5.33E-03</td><td>3.91 (2.70-5.65)</td><td><strong>4.31E-13</strong></td><td>0.58</td><td><strong>6.90E-21</strong></td></tr><tr><td>MYL3</td><td>1.27 (1.07-1.50)</td><td>5.34E-03</td><td>2.22 (1.66-2.96)</td><td><strong>6.61E-08</strong></td><td>0.55</td><td><strong>3.36E-08</strong></td></tr><tr><td>ACYP2</td><td>1.43 (1.10-1.86)</td><td>8.47E-03</td><td>1.87 (1.35-2.60)</td><td><strong>1.92E-04</strong></td><td>0.06</td><td>4.10E-01</td></tr><tr><td>CAPN3</td><td>1.17 (1.03-1.33)</td><td>1.40E-02</td><td>4.38 (2.84-6.74)</td><td><strong>1.91E-11</strong></td><td>0.56</td><td><strong>8.19E-13</strong></td></tr><tr><td>DCTN2</td><td>1.24 (1.05-1.48)</td><td>1.40E-02</td><td>1.21 (0.94-1.56)</td><td>1.32E-01</td><td>0.08</td><td><strong>1.00E-02</strong></td></tr><tr><td>CD209_2</td><td>0.79 (0.64-0.96)</td><td>1.72E-02</td><td>0.73 (0.57-0.95)</td><td>1.77E-02</td><td>-0.17</td><td><strong>3.70E-02</strong></td></tr><tr><td>CD209_52</td><td>0.80 (0.67-0.96)</td><td>1.82E-02</td><td>0.73 (0.59-0.92)</td><td>6.24E-03</td><td>-0.31</td><td><strong>7.07E-05</strong></td></tr><tr><td>MYBPC1</td><td>1.19 (1.02-1.40)</td><td>2.80E-02</td><td>3.49 (2.56-4.74)</td><td><strong>1.83E-15</strong></td><td>0.85</td><td><strong>2.28E-14</strong></td></tr></tbody></table>
<table><thead><tr><th></th><th colspan="2">EPIC4ALS, incident</th><th colspan="2">GNPC, prevalent</th><th colspan="2">'Miami', prevalent</th></tr></thead><tbody><tr><td>MYOM2</td><td>1.20 (1.02-1.42)</td><td>2.83E-02</td><td>3.66 (2.64-5.07)</td><td><strong>5.53E-15</strong></td><td>1.19</td><td><strong>2.11E-20</strong></td></tr><tr><td>CKB | CKM</td><td>1.19 (1.02-1.39)</td><td>3.17E-02</td><td>2.43 (1.88-3.15)</td><td><strong>1.68E-11</strong></td><td>0.78</td><td><strong>1.76E-08</strong></td></tr><tr><td>SCN3B</td><td>1.18 (1.01-1.38)</td><td>3.48E-02</td><td>1.19 (0.82-1.73)</td><td>3.49E-01</td><td>0.26</td><td><strong>5.74E-04</strong></td></tr><tr><td>ATF6</td><td>0.81 (0.66-0.99)</td><td>3.63E-02</td><td>0.90 (0.70-1.15)</td><td>3.89E-01</td><td>-0.20</td><td><strong>7.07E-06</strong></td></tr><tr><td>EHD2</td><td>1.21 (1.01-1.46)</td><td>4.35E-02</td><td>1.28 (0.99-1.66)</td><td>5.88E-02</td><td>0.14</td><td><strong>2.20E-02</strong></td></tr><tr><td>PCDHGA10</td><td>1.17 (1.00-1.36)</td><td>4.59E-02</td><td>2.79 (1.94-4.01)</td><td><strong>2.71E-08</strong></td><td>0.36</td><td><strong>7.97E-07</strong></td></tr><tr><td>MFAP5</td><td>0.78 (0.61-1.00)</td><td>4.94E-02</td><td>0.58 (0.42-0.81)</td><td>1.17E-03</td><td>-0.24</td><td><strong>9.25E-08</strong></td></tr></tbody></table>

**Legend.** This table presents all plasma proteins showing significant (false discovery rate [FDR]=0.05) association with incident amyotrophic lateral sclerosis (ALS) in Cox proportional hazard regressions (upper tier) as well as "suggestive" proteins in EPIC4ALS. The latter are nominally significantly (p<0.05) associated with incident ALS in EPIC4ALS and show FDR-significance in prevalent ALS on a proteome-wide scale in at least one of the two prevalent case-control datasets and with consistent effect directions in all datasets (lower tier). Cox proportional hazard regression analyses were conducted on a follow-up of 5-20 years and stratified for center, sex and 5-year age categories. Note that DUSP29 is also known as DUPD1. GNPC=Global Neurodegenerative Disease Proteomics Consortium. 'Miami'=data pre-published in Dergai et al.³¹. HR=hazard ratio; OR=odds ratio, logFC=log fold change, CI=95% confidence interval; p=p value, q=FDR-adjusted p value (as provided in ³¹).

475

476

477

478

479

480

481
482 **METHODS**

483 A full description of the methods employed in this study is provided in the **Supplementary**

484 **Methods.**

485 **Discovery cohort and study design**

486 The EPIC4ALS case-cohort analyzed here is embedded within EPIC, a prospective study of
487 519,978 participants enrolled between 1991 and 2000 across 10 European countries, and aims
488 to identify molecular biomarkers prior to the emergence of clinically manifest <sup>16</sup>. ALS case
489 ascertainment was based on a source population of 222,613 subjects from 12 centers across
490 five countries (Spain, Italy, Netherlands, UK, and Germany), as described previously<sup>16</sup>. Given
491 the fatal nature and short survival after diagnosis<sup>4</sup>, incident cases were identified via mortality
492 records as previously described<sup>16</sup>. To identify pre-diagnostic ALS biomarkers, a 5-year wash-
493 out phase between blood draw and age at death/censoring was applied, excluding 21 ALS
494 patients and 149 subcohort members from most analyses.

495 At baseline, data on diet, lifestyle, medical history, and anthropometric measurements were
496 collected. Citrate plasma samples were processed using standardized protocols and stored in
497 liquid nitrogen at the EPIC biobank at IARC in Lyon, France. Participants are followed by regular
498 registry linkage and/or questionnaires. The study received ethics approval from IARC and all
499 relevant boards; all participants provided written informed consent.

500 **Generation and quality control of proteomic data in EPIC4ALS**

501 Protein abundances were quantified in plasma samples using the SomaScan 7K (v4.1) platform
502 (SomaLogic, Boulder, CO, USA), which employs chemically modified DNA aptamers for high-
503 throughput, high-specificity protein detection³². The 7K version quantifies 7,596 aptamers
504 targeting 6,432 unique proteins³³. Proteomic profiling was conducted for 17,841 EPIC
505 participants selected for various endpoints at SomaLogic, using additional control probes for
506 quality control (QC). Sample and data processing are detailed in the **Supplementary Methods**.
507 After normalization and QC, aptamer measurements were log10- and z-transformed to
508 mean=0, SD=1 in the subcohort. Values deviating more than 5 SD from the mean were
509 ‘capped’ at 5 SD. For all significant findings, log₁₀-transformed values were visually inspected
510 (Supplementary Figure 7).

511 **Cox proportional hazard regression analyses in EPIC4ALS**
The final effective EPIC4ALS case-cohort for Cox proportional hazard analyses included 4,567 participants (172 incident ALS cases) with 7,285 aptamers (6,381 proteins) for most analyses (i.e., those applying the 5-year wash-out period). We used Prentice-weighted³⁴ Cox regression models to examine associations between aptamers and risk of ALS-related death, which we used as a proxy indicator for ALS diagnosis, since clinical diagnosis data were not available for all participants. Age was used as a time scale³⁴. Age at death served as a proxy for diagnosis. The basic model was stratified by 5-year age intervals, sex, and center, and a 5-year wash-out was applied to exclude prevalent cases (and the corresponding non-cases) dying within the first 5 years. Analyses were performed for both full (5-30 years) and restricted (5-20 years) follow-up. Proteomic associations were considered significant at FDR 0.05. The proportional hazard assumption for the top biomarkers was tested by calculating Schoenfeld residuals (using the cox.zph() function). For the top biomarkers, we also performed sex-specific analyses and tested sex effects by interaction analyses³⁵.

In sensitivity analyses for the top biomarkers, we tested a full adjustment model including the baseline covariates 'education', 'smoking status', 'BMI', 'physical activity', 'postmenopausal status', and 'hormone use'. We also repeated analyses excluding protein outlier values (>5 SD from the mean), acknowledging that outliers may either signal true ALS biology or unrelated technical/biological variation.

## Assessment of time-varying effects of ALS-associated proteins

Time-varying effects of ALS-associated proteins were explored using different approaches across the entire EPIC4ALS dataset with 0-30 year follow-up, i.e., using 4,737 EPIC participants including 193 ALS cases (i.e., re-including the 21 incident ALS patients and 149 non-cases who died or were censored within five years of baseline). To visualize effect estimates of top results in different time windows, HRs were computed and plotted using 10-year sliding windows in one-year increments. Exploratory analyses of time-varying effects of ALS-associated proteins were tested analyzing Cox models (Prentice-weighted, adjusting for age and stratified for sex, and center) with a 'protein level × log time-to-event' interaction term, and by fitting Cox models incorporating natural splines to assess non-linear time-dependent effects. For all these analyses, we used time since blood collection instead of age as the time scale. For the splines, we split the follow-up time into fine intervals (using the survSplit() function) and applied a time-varying coefficient (TVC) approach for each protein using spline interactions (protein ×
spline(time-to-event)). The Prentice-weighted Cox models were used adjusting for age (as a time-varying variable) and stratifying by sex and center. The nsk() function from the R package splines was used with 2 degrees of freedom. We evaluated the statistical evidence for time-varying effects using Wald tests.

## Indirect validation and characterization of pre-diagnostic EPIC4ALS biomarkers in clinical datasets

FDR-significant pre-diagnostic biomarkers from EPIC4ALS were evaluated for association with prevalent ALS using a large SomaScan 7K profiled case-control dataset (260 ALS, 689 controls) from the GNPC initiative (https://www.neuroproteome.org/). Only datasets with both cases and controls were included. Control participants were those without a documented clinical diagnosis of ALS, mild cognitive impairment, dementia, or PD. Protein data were log10-transformed, standardized, and outliers (>5 SD) excluded. Residuals were obtained by removing age, sex, and center effects (removeBatchEffect, limma), on which the first two principal components (PC) from PC analysis were calculated. Logistic regression adjusted for age, sex, center, and PCs was performed, along with sex-stratified analyses (adjusted for age and PCs) for biomarkers with sex-specific effects in EPIC4ALS. Additionally, summary statistics from the Miami SomaScan 7K dataset (157 ALS cases, 163 controls)³¹ were used. To assess the enrichment of clinical biomarkers among pre-diagnostic biomarkers, we conducted a one-sided Fisher's exact test, testing whether FDR-significant clinical biomarkers were overrepresented among all pre-diagnostic biomarkers that reached nominal significance. Concordance of effect estimates in between pre-diagnostic and clinical datasets was tested using a one-sided binomial exact test.

## Comparison to previous studies

We compiled a list of 520 unique candidate biomarkers previously linked to neurodegeneration and/or ALS based on a range of different resources (see **Supplementary Methods**). The 253 proteins from this list measured in EPIC4ALS were tested for association and controlled using FDR=0.05.

## Cross-disease comparisons

For cross-disease comparisons, we analyzed FDR-significant pre-diagnostic ALS biomarkers in pre-diagnostic AD and PD using Prentice-weighted Cox regression of SomaScan 7K data in the
EPIC4AD case-cohort (1,687 participants, including 413 incident AD cases, 2-20 years follow-up) and EPIC4PD (4,519 participants, 555 PD cases, 2-20 years; for a description of the case-cohorts, see ref. ¹⁶). Analyses were stratified by 5-year age groups, sex, and center. Using a one-sided Fisher's exact test, we assessed whether significant associations in AD and PD were enriched among proteins nominally significant in ALS. A one-sided binomial test evaluated if overlapping nominally significant proteins had concordant effect directions more often than expected by chance.

## Expression and protein abundances of top biomarkers

Gene expression and protein abundance of top ALS biomarkers across tissues were assessed using bulk RNA-Seq and IHC data from the HPA (v24.0, Ensembl 109, accessed May 6th, 2025)³⁶. Pre-diagnostic proteins were annotated using 'Consensus RNA data' for 50 tissues and IHC data for 45 tissues. Heatmaps were generated using hierarchical clustering and gene-scaled expression values to highlight tissue-specific variation.

## Gene set and cell type enrichment analyses

Gene set and tissue enrichment was performed using DAVID (v2024q4; https://davidbioinformatics.nih.gov/home.jsp). All unique proteins nominally associated with incident ALS were tested for enrichment in 'biological process', 'molecular function', and 'cellular component' GO categories, as well as UniProt Tissue Annotations. The statistical overrepresentation tests were conducted with the background set to all analyzed SomaScan proteins. To minimize spurious results, only results based on at least three pre-diagnostic proteins were considered. Analyses were controlled at FDR=0.05.

## Protein-protein interaction network analysis

Protein-protein interaction networks were generated using the STRING database (v12.0) for all FDR-significant and nominally significant EPIC4ALS biomarkers and visualized with igraph (v2.1.4). Interactions with a minimum interaction score of 0.4 were based on the following sources: 'textmining', 'experiments', 'databases', 'co-expression', "neighborhood", 'gene fusion', and 'co-occurrence'. The network was clustered using a fast greedy algorithm, top GO enrichments for each cluster examined, and unconnected nodes removed.

## Organ-specific biological aging
Organ-specific biological age estimates were calculated using the organage package²⁰. In this context, organ age gaps represent organ-specific age acceleration measures by comparing chronological and estimated biological age based on specific sets of organ-specific proteins. Organ age gaps (z-scored) served as exposure variables in Cox proportional hazard models (with Prentice weights and stratified by 5-year age group, sex, and center) to assess associations with ALS incidence. While Oh et al. described the ‘cognition’ ages for all organs²⁰, we only analyzed the ‘cognition brain’ age. We applied FDR control to account for the 14 organ age estimates analyzed.

## Drug Repurposing

We used the Open Targets platform³⁷ to assess druggability and repurposing potential for all FDR-significant, putative pre-diagnostic ALS biomarkers. Focusing on small-molecule evidence only, we screened ~17,000 small-molecule drugs and 21,087 protein targets for interactions with the top 27 ALS biomarkers. Druggability was defined as reported direct binding or predicted high-quality pharmacological modulation, based on curated experimental, structural, or clinical Open Targets data.

## Large language model

The data was further analyzed using a proteomics-specific large language model (LLM)-based tool designed to expedite proteomic data interpretation (developed by the Mann laboratory, https://github.com/MannLabs/alphapeptstats/releases/tag/v0.7.1). This tool integrates the generalized abilities of LLMs with domain-specific resources and real-time knowledge retrieval. The primary input consisted of the list of all analyzed proteins including effect direction and p value (from the 5-20 year follow-up). For each protein, information such as name, gene symbol, and functional description was automatically obtained from UniProt to provide curated context to the LLM. This information was combined with a pre-defined prompt specifying persona, style, and output format, as well as a chain-of-thought (CoT) instruction, mirroring the reasoning process of a human scientist. The complete prompt was submitted to the OpenAI o3 API, which had demonstrated the best internal performance for this task, to achieve a comprehensive interpretation of the molecular ALS signature. The analysis was performed in three replicates to assess the consistency of responses. Additionally, a targeted follow-up query regarding potential therapeutic opportunities was included to identify initial avenues for exploring clinical interventions.
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# 1 Study population

<figure><img src="image_3.png" /><figcaption>Fig 1</figcaption></figure>

# 2 Identification of pre-diagnostic ALS biomarkers

<figure><img src="image_4.png" /></figure>

Cox proportional hazard models:

* 5-30-year follow-up

* 5-20-year follow-up

* basic vs fully adjusted

* sex-stratified

# 3 Indirect validation in prevalent ALS

<figure><img src="image_5.png" /></figure>

<figure><img src="image_6.png" /></figure>

* GNPC and 'Miami' case-control studies

* Logistic regression analyses

# 4 Temporal biomarker trajectories & organ specific aging

<figure><img src="image_7.png" /></figure>

* Cox models on organ-specific clocks (Oh et al., 2023)

* Cox models on sliding windows

* Interaction with time-to-event, spline regressions

# 5 Biological annotation & interpretation

<figure><img src="image_8.png" /></figure>

* Gene set and cell enrichment analyses

* Cross-disease comparisons

* Biological annotation & druggability assessment
A. Volcano plot on pre-diagnostic ALS biomarkers

<figure><img src="image_9.png" /></figure>

B. Sex-stratified results of top results

<figure><img src="image_10.png" /></figure>

C. Time-varying hazard ratios of top proteins

<figure><img src="image_11.png" /></figure>

E. Organ-based clocks and their time-varying hazard ratios

<figure><img src="image_12.png" /></figure>

D. Overview

<figure><img src="image_13.png" /><figcaption>Fig 2</figcaption></figure>
<figure><img src="image_14.png" /><figcaption>Fig 3</figcaption></figure>
Supplementary Files

This is a list of supplementary files associated with this preprint. Click to download.

* HomannALSproteomicsSupplementaryMaterialfinal.docx

• HomannALSproteomicsSupplementaryTablesfinal.xlsx