(2024) 22:302
Ottosson et al. BMC Medicine
https://doi.org/10.1186/s12916-024-03516-7

BMC Medicine

RESEARCH ARTICLE

Open Access

Unraveling the metabolomic architectureof autism in a large Danish population‑based
cohortFilip Ottosson1,2*† , Francesco Russo1,2†, Anna Abrahamsson1, Nadia MacSween1, Julie Courraud1,2,3,4,
Kristin Skogstrand1,2, Olle Melander5, Ulrika Ericson5, Marju Orho‑Melander5, Arieh S. Cohen1,2,6,
Jakob Grove2,7,8,9, Preben Bo Mortensen2,10,11, David M. Hougaard1,2 and Madeleine Ernst1,2*   

Abstract
Background The prevalence of autism in Denmark has been increasing, reaching 1.65% among 10-year-old children,and similar trends are seen elsewhere. Although there are several factors associated with autism, including genetic,
environmental, and prenatal factors, the molecular etiology of autism is largely unknown. Here, we use untargeted
metabolomics to characterize the neonatal metabolome from dried blood spots collected shortly after birth.
Methods We analyze the metabolomic profiles of a subset of a large Danish population-based cohort (iPSYCH2015)
consisting of over 1400 newborns, who later are diagnosed with autism and matching controls and in two Swed‑ish population-based cohorts comprising over 7000 adult participants. Mass spectrometry analysis was performed
by a timsTOF Pro operated in QTOF mode, using data-dependent acquisition. By applying an untargeted metabo‑
lomics approach, we could reproducibly measure over 800 metabolite features.
Results We detected underlying molecular perturbations across several metabolite classes that precede autism. In
particular, the cyclic dipeptide cyclo-leucine-proline (FDR-adjusted p = 0.003) and the carnitine-related 5-aminovalericacid betaine (5-AVAB) (FDR-adjusted p = 0.03), were associated with an increased probability for autism, indepen‑dently of known prenatal and genetic risk factors. Analysis of genetic and dietary data in adults revealed that 5-AVAB
was associated with increased habitual dietary intake of dairy (FDR-adjusted p < 0.05) and with variants near SLC22A4
and SLC22A5 (p < 5.0e − 8), coding for a transmembrane carnitine transporter protein involved in controlling intracel‑lular carnitine levels.
Conclusions Cyclo-leucine-proline and 5-AVAB are associated with future diagnosis of autism in Danish neonates,
both representing novel early biomarkers for autism. 5-AVAB is potentially modifiable and may influence carnitine
homeostasis.
Keywords Autism, Metabolomics, Neonatal, Dried blood spot, Metabolome, 5-Aminovaleric acid betaine, Cycloleucine-proline†

Filip Ottosson and Francesco Russo contributed equally to this work.

*Correspondence:
Filip Ottosson
faot@ssi.dk
Madeleine Ernst
maet@ssi.dk
Full list of author information is available at the end of the article© The Author(s) 2024. Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which
permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to theoriginal author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images orother third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line
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Ottosson et al. BMC Medicine(2024) 22:302

Background
Autism refers to a range of neurodevelopmental conditions characterized by impaired social ability, impaired
communication, and repetitive behavior. The prevalenceof autism in Denmark has been increasing, reaching
1.65% among 10-year-old children in 2017 [1]. Similar
trends are seen elsewhere [2, 3]. Although there are several factors associated with autism, including genetic[4] and prenatal factors [5], the molecular etiology ofautism is largely unknown. The gut microbiome has been
increasingly implicated in autism, as patients with autism
commonly experience gastrointestinal issues [6] and havean altered gut microbiota [7, 8]. Dysbiosis of the maternal gut microbiome could even contribute to altered fetal
neurodevelopment related to autism [9]. The circulating
metabolome reflects genetic [10], gut microbiome [11],and dietary intake variations [12, 13], potentially highlighting mediators between these exposures and autism
risk. Detecting autism-related metabolite alterations inearly life may facilitate an increased understanding of themolecular mechanisms behind autism, improve diagnosis, and contribute to preventive strategies.
Metabolomics has been used to characterize specific perturbations of the metabolome in children with
autism compared to neurotypical controls, including
amino acids [14], acylcarnitines [15], and various aromatic and phenolic compounds [15]. Moreover, a selection of metabolites in the folate-dependent one-carbon
metabolism have been used to classify autism from typically developed counterparts, indicating the potential for
metabolite-based methods to be used in autism diagnosis[16]. However, these studies reflect cross-sectional differences between autism cases and controls, which hampers
the attempts to identify early pathophysiological mechanisms of autism, due to the large risk of reverse causation. Metabolites that prospectively associate with autism
development have a higher chance of highlighting earlyprotective or detrimental mechanisms of autism that canbe targeted in novel preventive strategies.
To unravel underlying disease mechanisms and findtools for early diagnosis of autism, we analyzed themetabolomic profiles of a subset of a large Danish population-based cohort (iPSYCH2015). The subset is acase–control study consisting of over 1400 newborns,
including neonates who later were diagnosed with
autism, and matching controls. By using untargeted
metabolomics of neonatal dried blood spot samples(DBS), we aim at identifying metabolite features associated with autism, in order to better understand the disease etiology. Metabolites that were associated with
autism were further investigated in independent cohorts
comprising over 7000 adult individuals, in order todescribe the genetic and dietary determinants.

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Methods
Cohorts
iPSYCH cohorts

The neonatal cohort consisted of individuals who were
included in either the population-based cohort or as acase in iPSYCH2015 [17], which is a case-cohort study ofpsychiatric disorders, nested within the Danish population born between 1981 and 2008. The neonates includedin the current case–control study were born between
2003 and 2008. All individuals had available information
about gestational age, age at sampling, and neonatal DBS
available. Additionally, all samples passed quality control
for genotyping. Cases of autism were all male and definedas the first diagnosis of childhood autism (ICD-10: F84.0)
in the Danish Psychiatric Central Research Register in2015 or earlier, at age 1 or older. Controls were selectedamong males in the population-based cohort in iPSYCH
and with none of the following ICD-10 diagnosis codes inthe Danish Psychiatric Central Research Register in 2015
or earlier, at age 1 year or older: F84.0, F84.1, F84.2, F84.3,
F84.5, F84.8, F84.9, F90.0. Matching of controls was performed based on birth date + / − 14 days, gestationalage + / − 4 days, and age at sampling + / − 1 day (neonatal
age at time of DBS collection). For neonates born before
2006, autism cases were excluded if diagnosed with mental retardation (F70-F79) before the 10-year birthday in
the Danish Psychiatric Central Research Register as ofApril 2017. Family history of psychiatric disorders was
defined as any parent having registry entries (ICD-8:290–
315, ICD10:F00-F99) before the end of 2016. Seasonsare defined as starting from the 1st day of the month: 1st
Dec.–28th Feb. is winter, 1st March–31st May is spring,
1st June–31st August is summer, 1st Sept.–30th Nov. isautumn.
Malmö cohorts

The Malmö Diet and Cancer Study (MDC) is a population-based prospective cohort consisting of 28,449 individuals. The cardiovascular cohort of MDC was designed
to study the epidemiology of carotid artery disease.
Enrolling participants between 1991 and 1996 [18]. Citrate plasma was available for 3833 among the 5405 participants with fasted blood samples. Dietary intake data
was available for 3714 individuals and genome-wide genotyping for 3409 individuals.
Malmö Offspring Study (MOS) is an ongoing population-based cohort study where adult (> 18 years old)children and grandchildren from the MDC study arerecruited [19]. Participants were invited through letterand visited the research clinic where overnight fasting
EDTA plasma samples were collected and anthropometric measurements performed. Plasma samples wereOttosson et al. BMC Medicine(2024) 22:302

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available for 3430 participants. Dietary intake data wasavailable for 1539 individuals.

quality control procedures can be found in the Additional
file 1: supplementary information [20–23, 25–35].

Metabolomics profiling

Relative quantification of 5‑aminovaleric acid betaine
in Malmö cohorts

DBS samples (3.2-mm-diameter punches) were randomly distributed over nineteen 96-well plates (batches).
A batch of DBS consisting of adult blood was created
before sample preparation and stored at − 20 °C, referred
to as external control (EC) samples. Both EC samples
and plate-specific pools of neonatal samples were analyzed for quality control purposes. Sample preparation was performed by extraction in 80% methanol bybeing incubated for 45 min and subsequent centrifugation. The sample extract (supernatant) was evaporated
under nitrogen before being reconstituted in 95% solvent A (99.8% water and 0.2% formic acid) and 5% solvent B (49.9% methanol, 49.9% acetonitrile and 0.2%
formic acid). Mass spectrometry analysis was performed
using a timsTOF Pro mass spectrometer coupled to a
UHPLC Elute LC system, Bruker Daltonics (Billerica,
MA, US). The analytical separation was performed onan Acquity HSS T3 (100 Å, 2.1 mm × 100 mm, 1.8 µm)
column (Waters, Milford, MA, US). The analysis started
with 99% solvent A for 1.5 min, thereafter a linear gradient to 95% solvent B during 8.5 min followed by an isocratic condition at 95% mobile phase B for 2.5 min before
going back to 99% mobile phase A and equilibration for
2.4 min. Metabolomics preprocessing was done using the
Ion Identity Network workflow in MZmine [20, 21] (version 3.3.0). Before statistical analysis, metabolite features
present in less than 25% of the samples were removed
and features present in fewer than 75% were treated asbinary variables (present or absent). This resulted in a
final dataset with a total of 865 metabolite features measured, among which 452 features were continuous and413 were binary variables. Missing values for metabolitefeatures with continuous measurements were further
subjected to imputation using missForest [22] and subsequent batch correction in WaveICA [23]. Annotationof metabolite features was performed using mass spectral molecular networking through the GNPS Platform,
unsupervised substructure discovery using MS2LDA, insilico annotation through Network Annotation Propagation, Sirius + CSI:FingerID, MolNetEnhancer and deepneural networks in CANOPUS. Metabolite annotationlevels were defined according to the Metabolite Standards Initiative [24]. Metabolites were annotated at level 2(plausible 2D structure) and level 3 (plausible metaboliteclass). Detailed descriptions of sample preparation, mass
spectrometry analysis, preprocessing, annotation, andUntargeted metabolomics profiles of plasma samples
from MOS and MDC were acquired on a UPLC-QTOF(1290 LC, 6550 MS; Agilent Technologies, Santa Clara,
CA) as previously described [36]. Relative abundance of
5-aminovaleric acid betaine was retrieved by integrating the peak areas for the m/z putatively annotated as
5-AVAB (m/z = 160.13) using Agilent Profinder B.06.00(Agilent Technologies). Normalization was performed
using m/z 160.13 measurements in repeatedly injected
identical pooled quality control (QC) samples. A loworder nonlinear locally estimated smoothing (LOESS)
function was fitted to the m/z 160.13 areas in the
QC samples as a function of the injection order. The⍶-parameter (proportion of QC samples used for thecorrection curve) was set to 2/3. The resulting correction curve was used to normalize the analytical samples
as described previously [37]. MS/MS data was acquired
at 20 eV and an isolation width of 1.3 m/z. Annotation of m/z 160.13 was performed using a MASST [38]search of the fragmentation spectra of m/z 160.13,
where matching spectra where defined as cosine > 0.7,
minimum matched peaks = 4, parent mass tolerance < 0.05 Da and ion tolerance < 0.05 Da.
Genome‑wide association study and genetic risk score

Genome-wide genotyping of participants from the
MDC was conducted using the Illumina GSA Bead
Chip (Illumina Inc, San Diego, CA). Genome-wide
association studies (GWAS) for DBS and plasma levelsof metabolites were performed using a linear additive
genetic model adjusting for age, sex (adult cohort only),
genetic principal components, and autism status (neonatal cohort only), using PLINK v1.9 [39]. Manhattan
plots and locus zoom plots were generated in R, using
packages qqman and locuszoomr packages, respectively. The polygenic score (PRS) is a cross-validated
internally trained score that was generated by splitting
the iPSYCH sample in 50 random, non-overlapping
samples of roughly equal size. For each of the 50 subsamples, a GWAS was run on the complement and thesumstats of that was used to train a PRS in the subsample in question. We employed the SBayesR [40] algorithm as implemented in LDAK [41] following the bestpractices of LDAK. The score was subsequently standardized by subset strata. Principal components analysis
was performed in PLINK v1.9 [39].

Ottosson et al. BMC Medicine(2024) 22:302

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Dietary intake assessment

Dietary intakes in the MDC were assessed with amethod combining a 7-day menu book, a food frequency questionnaire, and a 45-min interview [42].
In the MOS dietary intake for participants was assessedwith a web-based 4-day food record, Riksmaten2010,
developed by the Swedish National Food Agency [43].
In both cohorts, six dietary intake groups were energyadjusted, by dividing intakes with non-alcoholic energy
intake.
Statistical analysis

Associations between metabolite features and autism
were analyzed using logistic regression models. Nominally significant associations were defined as P < 0.05,
and significant associations as false discovery rate (FDR)adjusted P < 0.05. The primary model was adjusted forgestational age, age at sampling, season of birth, and birthyear. Significant features (FDR-adjusted p < 0.05) wereanalyzed as quartiles (5-AVAB) or the secondary model
was additionally adjusted for family history of psychiatricdisorders, a genetic risk score for autism, and six genetic
principal components. The analyses were also stratified
for age of diagnosis (age of diagnosis < 6). Enrichment for
metabolite classes was analyzed using Fisher’s exact test.
Associations between metabolite features and family history of psychiatric disorders were analyzed using logistic
regression models. Corresponding analyses for metabolite features and genetic risk for autism were conductedusing linear regression. Analyses for family history of psychiatric disorders were adjusted for gestational age, age atsampling, season of birth, and birth year, while analyses
on genetic risk score for autism further were adjusted
for the first six genetic principal components. Inter-correlation analyses between metabolite features were conducted using Partial Pearson’s correlation tests, adjusted
for all metabolite features in each respective dataset.

Correlations between metabolites and habitual dietaryintakes were conducted using Partial Spearman’s correlation tests, adjusted for age, sex, and body-mass index.
Analyses using partial correlations were performed in the
ppcor [44] package. Phenome-wide analysis of 5-aminovaleric acid betaine was performed using least absoluteshrinkage selection operator (LASSO) regression. A fulldescription of the LASSO method can be found in Additional File 1:supplementary material. All statistical analysis was performed in R 4.2.1 and Jupyter Notebooks areaccessible at: https://​github.​com/​ssi-​dk/​CD-​MRG-​metab​olomi​cs_​autism.

Results
Study cohort

We utilized a cohort of 1478 male Danish neonates born
between 2003 and 2008, comprising 739 newborns who
were diagnosed with autism before the age of 10, and
739 typically developed individuals. Since the neonatal
DBS metabolome is influenced by the age at sampling(3 to 8 days), season of birth [45], gestational age [46](gestational week 34 to 42), and biobank storage time
(15–18 years) [47], these factors were used to match eachcase of incident autism with a control neonate. Cases
and controls were very similar in terms of gestationalage, age at sampling, season of birth, and age of mother(Table 1). Family history of mental disorders were significantly more common in incident autism cases compared
to controls (p < 0.001). The mean age of autism diagnosis
was 6.0 years. A total of 865 metabolites (mass spectralfeatures with unique MS/MS fragmentation patterns)
were measured and present in at least 25% of the samples in the discovery cohort. Putative annotation on themetabolite class level was conducted by combining mass
spectral molecular networking (GNPS) [26], unsupervised substructure discovery (MS2LDA) [27], in silico
annotation through Network Annotation Propagation

Table 1 Characteristics of the neonatal study cohort, presented as mean values for continuous variables (standard deviation
within parentheses) and percentage for categorical variables (numbers within parentheses). P-values are calculated using t-test forcontinuous variables and chi-square test for categorical variables
Cases (N = 739)

Controls (N = 739)

P

Age at sampling (years)

5.8 (± 1.2)

5.8 (± 1.1)

0.67

Gestational age (weeks)

40.0 (± 1.3)

40.0 (± 1.2)

0.85
0.99

Born in winter22.7% (168)

22.7% (168)

Born in spring

24.4% (180)

25.0% (185)

Born in summer

25.7% (190)

25.4% (188)

Born in autumn

27.2% (201)

26.8% (198)

Age of mother (years)

30.8 (± 5.1)

30.8 (± 4.7)

0.96

Age of autism onset (years)

6.0 (± 2.0)--

Family history of mental disorder

30.0% (222)

18.7% (138)< 0.001

Ottosson et al. BMC Medicine(2024) 22:302[30], Sirius + CSI:FingerID [48], MolNetEnhancer [32],and deep neural networks in CANOPUS [33]. This
resulted in level 2 annotations for 111 (12.8%) metabolitefeatures and level 3 annotations for 229 (26.5%) metabolite features [24].
Metabolites from a wide range of biochemical classes areassociated with autism

In total, 99 metabolites had nominally significant associations (p < 0.05) with development of autism, as evaluatedusing logistic regression models adjusted for gestationalage, age at sampling, season of birth, and birth year
(Fig. 1A) (Additional File 2: Table S1). These included 18
metabolites that could be assigned a structural annotation (level 2 annotation), additionally 15 metabolites witha putative metabolite class, and 66 unknown features.
The annotated features included six acylcarnitine-related
metabolites (5-aminovaleric acid betaine, C2:0-carnitine,

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C8:0-carnitine, C10:0-carnitine, C10:3-carnitine, andC16:0-carnitine), cyclic dipeptides (cyclo-leucine-proline
and cylo-proline-valine), tryptophan metabolites (indole3-acetic acid and indole-carboxaldehyde), methionine
cycle metabolites (methionine and betaine), alanine, uricacid, creatine, amino adipic acid, glycerophosphocholine
and pantothenic acid.
After adjusting for multiple testing, only two of the 99
metabolites remained significant, cyclo-Leucine-Proline(cLP) (FDR-adjusted p = 3.3e − 3) (Additional File 1: Fig.
S1) and 5-aminovaleric acid betaine (5-AVAB) (FDRadjusted p = 0.031) (Additional File 1: Fig. S2). Beingin the top quartile 5-AVAB was associated with a 69%
increased probability of future autism compared to being
in the lowest quartile (Fig. 1B). Since measurable levels of
cLP were detected in less than 75% of the neonatal cohort(33%), it was treated as a binary variable in the regression
analyses. Having measurable levels of cLP was associatedFig. 1 Associations between metabolites in neonatal DBS and future probability of autism spectrum disorder. Odds ratios are calculated using
logistic regression models, adjusted for age at sampling, gestational age, birth year, and season of birth. A Associations between all measured
metabolite features and autism, with significance threshold defined as false discovery rate adjusted p < 0.05. Odds ratios are expressed as increased
odds of autism per standard deviation increment of metabolite level. Significant features with an annotation confidence of level 2 or higher are
named in the figure. B Associations between 5-aminovaleric acid betaine (5-AVAB) (quartiles) and cyclo-leucine-proline (cLP) (binary) and autism.
Odds ratios are expressed as compared to the reference category (“Absent” for cLP and quartile 1 [Q1] for 5-AVAB). C Associations with autism
stratified for age of diagnosis (< 6 years of age) for 5-AVAB and cLP. Odds ratios are expressed per standard deviation increment of metabolite levelOttosson et al. BMC Medicine(2024) 22:302

with a 70% increased odds of developing autism in the
future (Fig. 1B).
Given that autism is partly dependent on genetic factors, we investigated whether the neonatal metabolome was associated with a genetic risk score for autism
and with a family history of psychiatric disorders. No
metabolites were significantly associated (FDR-adjusted
p < 0.05) with the genetic risk score of autism, but 24
nominally significant associations were found (Additional File 1: Fig. S3). The strength of the association
between the genetic risk score (AUC = 0.58) and autism
was similar as what was found for cLP (AUC = 0.55)
and 5-AVAB (AUC = 0.55). There were eight metabolites associated with family history of psychiatric disorders (FDR-adjusted p < 0.05), including 5-AVAB,
C8:1-carnitine, stachydrine, and trimethyllysine, while a
total of 127 metabolites had nominally significant associations (p < 0.05) (Additional File 1: Fig. S4). However,adjustments for family history of mental disorders andgenetic risk score for autism only had a minor impact on
the association between metabolites and future autism(Additional File 2: Table S2).
Speculating that age of diagnosis is related to the severity of autism traits and a higher likelihood of metabolic
markers of autism being present early in life, we performed analyses stratified on age of diagnosis. Overall,
the association between the neonatal metabolome andautism was stronger for individuals diagnosed before(N = 391), compared to after (N = 348) the age of six.

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There were 40 nominally significant associations forearly-diagnosed individuals and 13 nominally significant metabolites for individuals diagnosed late. Also,
two metabolites, 5-AVAB and an unknown feature (m/z247.13, 3.91 min) remained associated with early diagnosis of autism after adjustments for multiple testing(Additional File 2: Table S3). The association between
5-AVAB and autism was significantly stronger (p forinteraction = 0.032) in autism cases diagnosed at the ageof 6 years or earlier. For cLP the associations were similar for cases diagnosed before (p = 1.2e − 3) and after(p = 8.8e − 4) the age of six (Fig. 1C).
Cyclo(leucine‑proline) and autism

cLP and the structurally similar cyclo(pro-val) belong to
the metabolite class diketopiperazines (cyclic dipeptides),both associated with increased probability of autism inthe current study. Both metabolites were detected in lessthan 75% of the neonates and were thus treated as binary
variables. For cLP, cyclo(pro-val) was the most strongly
correlated metabolite (rho = 0.69), while the caffeine
metabolite paraxanthine was the most negatively correlated (rho =  − 0.10) (Fig. 2A). We did not find any significant correlation between cLP and caffeine in the neonatal
DBS (rho = 0.00 p = 0.97). Moreover, including both cLP
and cyclo(pro-val) as covariates in the regression model
on autism clearly attenuated the associations for both
metabolites (Additional File 2: Table S4), indicating that

Fig. 2 Correlations between the two metabolites significantly associated with autism and other detected metabolites. Correlation coefficients are
partial Pearson’s correlation coefficients adjusted for all metabolite features (N = 865). Significant correlations are indicated with an * at p < 0.05. A
Correlations between acylcarnitine-related metabolites. B Correlations between cyclo(leucine-proline) and other detected metabolites

Ottosson et al. BMC Medicine(2024) 22:302

the two diketopiperazines may have a shared pathophysiological relation to autism.
5‑Aminovaleric acid betaine and autism

5-AVAB is a key regulator of intracellular carnitine availability, since it influences both cellular uptake [49] andbiosynthesis of carnitine [50]. As aforementioned, we
also found that elevated levels of several acylcarnitines
were associated (p < 0.05) with the later development ofautism. Metabolite class enrichment analysis showedthat acylcarnitines were significantly enriched among
nominally significant associations with future autism(p = 0.015) and with family history of mental disorders(p = 0.013) (Additional File 2: Table S5). Partial correlation analysis revealed that 5-AVAB was correlated withseveral metabolites in the acylcarnitine class (Fig. 2B).
Notably, 5-AVAB showed positive correlations with C5:0carnitine and free carnitine but negative correlation withthe carnitine precursor 4-trimethylamoniumbutanoic
acid. Adjusting the model between 5-AVAB and autism
for the autism-associated acylcarnitines (C2:0-carnitine,
C8:0-carnitine, C10:0-carnitine, C10:3-carnitine, andC16:0-carnitine), the associations were attenuated but
remained significant (OR = 1.22, P = 1.5e − 3).
If a causal link between 5-AVAB and autism probability can be established, it is important to examine how
neonatal 5-AVAB levels can be modified. We have previously shown that 5-AVAB is vertically transferred frommother to infant, since the maternal levels, both during
gestational week 24 and 1 week postpartum, are strongly
correlated with the neonatal levels [51]. This suggeststhat the neonatal levels of 5-AVAB to a large extent are
a reflection of the prenatal exposure of 5-AVAB. Understanding the key determinants of 5-AVAB in adults,
should highlight potential strategies to modulate thematernal levels and thus the resulting prenatal exposure.
Dietary and genetic determinants of 5‑aminovaleric acidbetaineTo investigate determinants of 5-AVAB levels in adults,
we hypothesized that their levels could be influenced bydietary or genetic factors. We used plasma metabolomics,
genome-wide genotyping, and dietary intake data fromtwo Swedish population-based cohorts, MDC (N = 3833)
and MOS (N = 3430) (Additional File 2: Table S6). A
feature with correct MS1 match (m/z error < 5 ppm) to
5-AVAB (m/z 160.13) was putatively annotated in bothcohorts by matching acquired m/z 160.13 fragmentation spectra to online databases using MASST [38]. One
spectrum achieved a match in both MDC (cosine = 0.83,
mass difference < 0.01 and 5 shared peaks) and MOS(cosine = 0.81, mass difference < 0.01 and 4 shared peaks)
with the uploaded 5-AVAB spectrum (Additional File

Page 7 of 14

1: Fig. S5), and is thus referred to as 5-AVAB from here
onwards. In both MDC and MOS, 5-AVAB was correlated similarly with acylcarnitine-related metabolites asin the cohort of neonates. This includes overall positive
correlations with short-chain acylcarnitines and free carnitine along with negative correlations with 4-trimethylamoniumbutanoic acid for MOS only (Additional File 1:
Fig. S6–S7).
By investigating six dietary intake groups in MDC(N = 3714), 5-AVAB correlated significantly (FDRadjusted p < 0.05) with increased intake of dairy(rho = 0.18), decreased intake of meat (rho =  − 0.09),
and decreased intake fruit and vegetables (rho =  − 0.07).
Among specific dairy-related intakes, increased intakeof milk (rho = 0.22) and yogurt (rho = 0.09) were significantly correlated (Fig. 3A). Similar correlations wereseen for 5-AVAB in MOS (N = 1539), where dairy intake(rho = 0.15) was correlated with 5-AVAB (Fig. 3B). Intakeof milk (rho = 0.12), but not other specific dairy intakes,
was associated with 5-AVAB in both cohorts. In thestratified analysis, both total dairy and milk intake weresignificantly associated in men and women separately. In
MDC, stronger correlations were observed for dairy inmen compared to women, although no differences wereobserved in MOS (Additional File 2: Table S7).
By conducting a genome-wide association study(GWAS) in MDC (N = 3409), we found several genomewide significant (p < 5.e − 8) variants near the SLC22A5
and SLC22A4 genes (Fig. 4A), with rs272889 being themost strongly associated SNP (p = 3.6E − 20) (Fig. 4B)(Additional File 2: Table S8). Stronger associations wereseen in men (Additional File 1: Fig. S8) compared to
women (Additional File 1: Fig. S9) for variants near both
SLC22A4 and SLC22A5. Both dietary intake of dairy andthe top SNP was among the 10 most significantly correlated variables in a phenome-wide scan for 5-AVAB
determinants performed using 140 clinical, dietary,
genetic, and metabolomic variables in MDC (Additional
File 1: Fig. S10). In the neonatal cohort, there was no
genome-wide significant association between SNPs andeither 5-AVAB (Additional File 1: Fig. S11) (Additional
File 2: Table S10) or cLP (Additional File 1: Fig. S12)
(Additional File 2: Table S11).

Discussion
The main finding of our study is that levels of 5-AVAB
and cLP are higher in newborns who later develop autism
compared to neurotypical controls. We found additional
putative associations between metabolites previously
linked to autism in cross-sectional studies, indicatingthat these alterations may be present already at birth.
To our knowledge, this is the first large-scale study thatinvestigated the untargeted neonatal metabolome inOttosson et al. BMC Medicine(2024) 22:302

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Fig. 3 Associations between plasma levels of 5-AVAB and dietary intakes in the Malmö Diet and Cancer Study (N = 3714) and the Malmö Offspring
Study (N = 1539). The heatmap shows partial Spearman’s correlations (adjusted for age, sex, and body mass index) between eleven dietary intake
groups and 5-AVAB. Significance was defined as FDR-adjusted p < 0.05

relation to autism, paving the way for the identification ofearly metabolite biomarker candidates. We suggest thatneonatal 5-AVAB levels largely reflect the maternal levels, which may be modified by dietary interventions.
Diketopiperazines, such as cLP, have been proposed
to be involved in psychiatric disorders [52], have anobserved ability to cross the blood–brain barrier [53],and are present in a variety of food and beverages [54].
Diet is a potential determinant of cLP, given that its levelhas been associated with increased intake of coffee [55]and alcohol [56] and decreased intake of starchy vegetables [56] in previous studies. However, we were not able
to identify cLP in the Malmö adult cohorts, where dataon nutrition was available, and can therefore only speculate on potential sources of cLP in our study. It is unlikelythat maternal coffee consumption represents the major
contribution to neonatal cLP, since caffeine was not correlated with cLP in the neonates. Results from GWAS
show that genetic variants in CYP1A2 and CYP2C19 havebeen shown to be associated with cLP levels in adults[57]. Our finding that cLP was correlated with the caffeine metabolite paraxanthine, supports that neonatal
levels of cLP could be partly regulated by CYP1A2 activity, since it is the enzyme mainly responsible for the degradation of caffeine. CYP2C19 is an enzyme involvedin the metabolism of several drugs used for psychiatric disorders, including benzodiazepines and selective
serotonin receptor inhibitors (SSRI) [58] and elevatedexpression has been associated with lower hippocampal
volume in mice [59] and increased enzymatic capacity
is associated with depression in humans [60]. Prenatal
exposure to SSRIs is associated with an increased probability of autism [61], although meta-analyses indicatethat at least some of the probability increase can be
attributed to genetic confounding [62]. Furthermore, cLP
could potentially originate from the gut microbiome, as itpreviously has been proposed to function as a quorumsensing molecule for certain bacteria, while inhibiting the
growth of other bacteria and fungi. Thus, gut microbes
might produce cLP to induce growth suppression among
competing bacterial species [63].
The finding that neonatal levels of the metabolite
5-AVAB is associated with increased risk of future development of autism confirms the results from our previous pilot study in 37 neonates and matching controls[45]. Based on our finding that 5-AVAB is more strongly
associated with autism diagnosed before the age of six,
we speculate that neonatal levels are connected to theseverity of autism traits. Several studies in mice haveindicated that 5-AVAB is involved in brain development,
both in the fetus and adult mice. For instance, fetuses ofgerm-free (GF) mice lack several microbially produced
metabolites, including 5-AVAB, and simultaneously display a reduced thalamocortical axonogenesis compared
to conventionally raised mice. This effect was reversed byinjection of 5-AVAB in the GF mice, preserving normal

Ottosson et al. BMC Medicine(2024) 22:302

Page 9 of 14

Fig. 4 Associations between single nucleotide polymorphisms (SNPs) and plasma levels of 5-amino valeric acid betaine (5-AVAB) in the Malmö Diet
and Cancer Study (MDC) (N = 3409). A Locus zoom plot, indicating associations between SNPs near SLC22A5. B Manhattan plot of the genome-wide
association study of plasma levels of 5-AVAB. Genome-wide significance threshold is indicated at p < 5.0e − 8. Genome-wide significant SNPs arecolored in blue and are located in the SLC22A5 and SLC22A4 genes

Ottosson et al. BMC Medicine(2024) 22:302

thalamocortical axonogenesis [64]. Given that earlybrain growth is often faster in children who later develop
autism [65], one could speculate that high fetal brain levels of 5-AVAB may contribute to stimulating brain overgrowth. On the other hand, levels of 5-AVAB have beenshown to be increased in the blood and brain of older
adults and have negative effects on learning and memory[66]. Overall, these findings indicate that 5-AVAB may
be involved in brain development, although the detailed
functions are largely unknown.
Speculating that 5-AVAB is related to brain development, it is crucial to investigate how neonatal 5-AVAB
levels can be modulated. We have previously shown that
5-AVAB may be directly transferred from the mother tothe neonate, as the maternal plasma levels of 5-AVAB arestrongly correlated with the neonatal levels of DBS [51].
Thus, it is possible that the neonatal 5-AVAB levels could
be considered a proxy for the prenatal exposure to thematernal 5-AVAB.
The current and previous studies indicate that 5-AVAB
in adults originates from endogenous production, dietaryintake, and the gut microbiome, which is briefly discussed below. Endogenous production of 5-AVAB fromthe carnitine precursor trimethyllysine (TML) has beenreported [67, 68] and has been proposed to occur via
oxidative deamination followed by decarboxylation [69].
The exact production pathway and to what extent the
total pool of circulating 5-AVAB originates from endogenous production is however not known. More evidence
for an endogenous production of 5-AVAB comes from a
recent study investigating the effects of predicted loss-offunction variants in the X-chromosome gene TMLHE70.
TMLHE codes for the enzyme trimethyllysine dioxygenase, responsible for catalyzing the conversion of TML
to hydroxytrimethyllysine, the first step in the carnitine biosynthesis. Predicted loss-of-function variants
in TMLHE associate with increased levels of TML and
5-AVAB and with decreased levels of hydroxytrimethyllysine and 4-trimethylammoniobutanoic, both downstream of TMLHE in the carnitine biosynthesis pathway.
As expected, the effects of the loss-of-function variants
are significantly larger in men compared to women [70].
Although endogenous production is likely an important source of circulating 5-AVAB, there is mounting evidence that the gut microbiome also is a major contributor[49, 50]. These include several reports of lack of 5-AVAB
in germ-free mice [49, 64], strong associations between
microbial species in the human gut and levels of 5-AVAB
in the brain [66] and plasma [11], and correlations withgut microbial diversity [11]. Moreover, levels of 5-AVAB
in mice fetuses are distinctly diminished when maternal
microbiota is lacking [71]. When maternal microbiota ispresent, 5-AVAB appears to localize to a wide variety ofPage 10 of 14

tissues, including the brain [71]. The relative contribution of the endogenous and the gut microbial productionof 5-AVAB are not known and interspecies differences
may exist. For instance, substantial differences in thebiosynthesis of carnitine, where carnitine is produced
from TML much more efficiently in mice compared tohumans [72], may increase the bioavailability of TML
for the production of 5-AVAB in humans. Although thegut microbiota is likely an important source of human
circulating 5-AVAB, the exact microbial pathways to
produce 5-AVAB are unknown. In recent years, the gut
microbiome has increasingly been implicated in autism,
as children with autism experience an increased prevalence of gastrointestinal issues [6] and have an altered
gut microbiome [7, 8] compared to typically developingchildren. Maternal gut health has also been linked to theprobability of autism in the offspring [9], suggesting thatthe maternal gut microbiome may influence health outcomes for the offspring. In this context, we propose that
5-AVAB may be a mediator in the gut-brain axis.
Dietary intake could be an alternative source of
5-AVAB and previous studies have shown that plasma
5-AVAB is correlated with increased intake of wholegrain [73] and milk [74, 75], which we further supportby showing associations between habitual intake of total
dairy, milk, and yogurt with increased plasma 5-AVAB in
two large middle-age Swedish cohorts.
We found that plasma 5-AVAB in the adult was
genome-wide associated with variants near the SLC22A4
and SLC22A5 genes, coding for a transmembrane transporter for carnitines and 5-AVAB, supporting resultsfrom previous GWAS [76, 77]. Our findings also indicated that the associations with variants near SLC22A5
and SLC22A4 were stronger in men compared to women.
Similar associations were not seen in neonates, indicating that other factors are more important in determining
the neonatal level of 5-AVAB. It is however noteworthy
that the neonatal GWAS was performed in a significantly
smaller population, which limits the ability to rule out
genetic determinants. Overall, our findings indicate that
5-AVAB levels are influenced by both genetic and dietaryfactors, in addition to the well-established link with thegut microbiome. If any of the above causal links between
prenatal exposure to high 5-AVAB and autism development can be proven, 5-AVAB may represent an early andmodifiable biomarker of autism.
The most well-described function of 5-AVAB is its ability to influence lipid metabolism via regulating the cellular uptake of carnitine and acylcarnitines. Cellular uptakeof 5-AVAB is dependent on the carnitine transport protein SLC22A5, whereby it can compete with free carnitine and acylcarnitines for uptake into tissues [49, 67,78] and across the blood–brain barrier [79]. Moreover,Ottosson et al. BMC Medicine(2024) 22:302

5-AVAB has been shown to regulate the endogenous
synthesis of carnitine in mice by competitive inhibitionof ℽ-butyrobetaine hydroxylase, the enzyme catalyzing
the rate-limiting step in the carnitine biosynthesis pathway, the hydroxylation of 4-trimethylammoniobutanoic
acid (ℽ-butyrobetaine) to carnitine [50]. Thus, 5-AVAB
regulates carnitine homeostasis by decreasing carnitine
biosynthesis and lowering cellular uptake, which in concert may decrease fatty acid oxidation [49, 50] and leadto mitochondrial dysfunction [50], due to intracellularcarnitine deficiency. Our study also indicates an intricate relation between 5-AVAB and acylcarnitines, wherelevels of 5-AVAB were correlated with higher circulating
carnitine and short-chain acylcarnitines but lower levels of 4-trimethylammoniobutanoic acid. Deletions in
TMLHE are relatively common (1/350 males), cause carriers to be auxotrophic for carnitine, and have also been
linked to an increased probability of developing autism
in boys [80, 81]. Several previous cross-sectional metabolomics studies show that children with autism havealtered circulating levels of acylcarnitines [15, 82, 83] andfree carnitine [15]. Carnitine supplementation in children
with autism has resulted in improved behavioral scores[84], but larger studies are needed to confirm their efficacy. The directionality of the associations has not been
completely consistent and one study indicates that theacylcarnitines in children with autism differ dependingon the fatty acid side chain, where short-chain acylcarnitines generally were more abundant in autism compared
to controls and vice versa for long-chain acylcarnitines[15]. In our study, this is reflected in correlations between
acylcarnitines and 5-AVAB, with a gradient in correlations going from positive for short-chain to negative forlong-chain features. However, the distinction between
long- and short-chain features was not seen in the association with future autism. This leads us to propose twopotential mechanisms linking 5-AVAB to autism. The
results from the current studies are consistent with thetheory of brain carnitine deficiency as a causal factor for
autism and that 5-AVAB may act as a regulator of intracellular uptake of carnitine. An alternative hypothesis,
where there is a causal link between 5-AVAB and autism,
independent of carnitine, should also be further investigated. This could also explain the links between TMLHE
deletions in males and autism, since TMLHE deficiency
also results in increased TML and 5-AVAB levels.
Finally, several of the nominally significant metabolites
have previously been linked to autism. Altered productsof gut bacterial tryptophan metabolism have previously
been reported in children with autism [85]. In our study,
this was reflected by increased levels of indole-3-acetic
acid and indole-3-carboxaldehyde. Uric acid has beenreported to be elevated in the urine of children with

Page 11 of 14

autism [86]. The methionine cycle, including methionine
and betaine, has been suggested to be altered in autism,
but conflicting findings make its involvement in autism
etiology unclear [87].
We acknowledge several limitations with our study.
Although the described autism-associated alterationsin the neonatal metabolome are present shortly after
birth and precede autism diagnosis by several years, ourstudy is observational and should be interpreted withinthat context. It is thus not possible to infer causal relationships between metabolites and the development ofautism. Similarly, the associations shown by the GWAS
do not prove a causative link between the gene near associated variants and 5-AVAB. However, our findings generate hypotheses for mechanisms linking the neonatal
metabolites to autism, which should be further investigated in future studies, preferably using targeted metabolomics. Exclusion of co-occurring mental disorders was
performed for neonates born between 2003 and 2005,
but not for later births. A broader coverage of the metabolome could have been achieved by using a combinationof different extraction solvents, chromatographic separation, and ionization mode. Since the sample material was
limited, we aimed at using a single method with as broad
coverage of annotated semi-polar metabolites as possible.
We were unable to measure cLP in the independent middle-aged cohort, which limits our potential to investigate
its genetic and dietary determinants. Moreover, the neonatal cohort lacks information about socioeconomic status and maternal parity, which are potential confounders
in the association between the neonatal metabolome andautism. Finally, the neonatal cohort consisted of males
only, which hinders us from drawing conclusions about
the general population.

Conclusions
Cyclo-leucine-proline and 5-AVAB are associated with
future diagnosis of autism in Danish neonates, both representing novel early biomarkers for autism. 5-AVAB
is potentially modifiable and may influence carnitine
homeostasis.
Abbreviations
5-AVAB	5-Aminovaleric acid betaine
AUC​	Area under the curve
cLP	Cyclo-leucinepProline
DBS	Dried blood spot
EC	External control
FDR	False discovery-rate
GF	Germ free
GWAS	Genome-wide association study
ICD-10	International Statistical Classification of Diseases and Related Health
Problems—revision 10
LASSO	Least absolute shrinkage selection operator
LC	Liquid chromatography
LOESS	Locally estimated scatterplot smoothing
MDC	The Malmö Diet and Cancer Study

Ottosson et al. BMC Medicine(2024) 22:302

MOS	The Malmö Offspring Study
PRS	Polygenic risk score
QTOF	Quadrupole time-of-flight
SNP	Single nucleotide polymorphism
SSRI	Selective serotonin reuptake inhibitor
TML	Trimethyllysine
UHPLC	Ultra-high performance liquid chromatography

Supplementary Information
The online version contains supplementary material available at https://​doi.​
org/​10.​1186/​s12916-​024-​03516-7.
Additional file 1: Figure S1. Mirror plot showing the cyclo(leucine-proline)
fragmentation spectrum acquired in the neonatal cohort and corre‑
sponding library match. Mirror plot generated using the Metabolomics
Spectrum Resolver. Figure S2. Mirror plot showing the 5-aminovaleric acidbetaine fragmentation spectrum acquired in the neonatal cohort and cor‑
responding library match. Mirror plot generated using the Metabolomics
Spectrum Resolver. Figure S3. Associations between neonatal metabolites
and genetic risk score for autism. Associations were evaluated using linear
regression models adjusted for major factors causing variation in the
neonatal dried blood spot metabolome, including gestational age, age atsampling, season and year of birth. Nominally significant associations were
defined as P < 0.05, and significant associations as FDR-adjusted p < 0.05.
Figure S4. Associations between neonatal metabolite levels and family
history of psychiatric disorders. Nominally significant associations were
defined as P < 0.05, and significant associations as FDR-adjusted P < 0.05.
Figure S5. Mirror plot showing the m/z 160.13 fragmentation spectrum
acquired in the Malmö Offspring Study and corresponding library match
to 5-AVAB (cosine = 0.81, 4 matching fragments, mass difference < 0.01).
Figure S6. Partial Pearson’s correlation coefficients between carnitinerelated metabolites in the Malmö Diet and Cancer Study (N = 3833).
Figure S7. Partial Pearson’s correlation coefficients between carnitinerelated metabolites in the Malmö Offspring Study (N = 3430). Figure S8.
Associations between single nucleotide polymorphisms (SNP) locatednear SLC22A5 and 5-amino valeric acid betaine. Analysis is done in maleparticipants in the Malmö Diet and Cancer Study (N = 1382). Figure S9.
Associations between single nucleotide polymorphisms (SNP) locatednear SLC22A5 and 5-amino valeric acid betaine. Analysis is done in female
participants in the Malmö Diet and Cancer Study (N = 2026). Figure S10.
Phenome-wide scan for 5-amino valeric acid betaine (5-AVAB). Analysis
performed in the Malmö Diet and Cancer Study (N = 3077). (A) Correla‑tion between 5-AVAB and predicted 5-AVAB. 5-AVAB prediction was
performed using LASSO regression. R2 indicates the model’s explained
variance of 5-AVAB. (B) Top 30 variables in prediction of 5-AVAB. LASSO
coefficients indicate contribution to the model. Figure S11. Associations
between single nucleotide polymorphisms (SNPs) and blood levels of
5-amino valeric acid betaine (5-AVAB) in the Neonatal cohort (N = 1239).
The Manhattan plot of the genome-wide association study shows
genome-wide significance threshold is indicated at p < 5.0e-8. Figure S12.
Associations between single nucleotide polymorphisms (SNPs) and bloodlevels of cyclo-Leucine-Proline (cLP) in the Neonatal cohort (N = 1239). The
Manhattan plot of the genome-wide association study shows genomewide significance threshold is indicated at p < 5.0e-8. Figure S13. Relative
standard deviation (RSD) for metabolite features (N = 281) measured in all
external control samples
Additional file 2: Table S1. All metabolites associated with autism (p < 0.05)
in logistic regression model 1, adjusted for age at sampling, seasonof birth and gestational age. Proof of annotations from GNPS, SIRIUS,
MS2LDA and notes for manual annotations are presented. Table S2. Odds
ratios and p-values for logistic regression model 1, model 2 and model 3.
All significant (p < 0.05) metabolites in model 1 are shown. Table S3. Asso‑
ciations with autism stratified by age of diagnosis. Early indicates autism
diagnosis < 6 years old and late >  = 6 years old. Results are from logistic
regression model 1. Table S4. Autism associations for cyclic dipeptides
cyclo(Leu-Pro) and cyclo(Pro-Val) when included as covariates in the same
logistic regression model (model 1). Table S5. Metabolite class enrich‑ment analysis. P-values are calculated using Fisher’s exact test. Table S6.

Page 12 of 14

Baseline characteristics description of participants from the Malmö Diet
and Cancer Study (MDC) and the Malmö Offspring Study (MOS). Table S7.
Correlations between 5-AVAB and dietary intake in the Malmö Diet andCancer Study (MDC) and the Malmö Offspring Study (MOS) stratifed
by sex. Table S8. Top 500 SNPs from genomewide association study of
5-AVAB in the Malmö Diet and Cancer Study. Table S9. Relative standard
deviations (%) in repeated injections of quality control (QC) samples for 16
metabolites. QC samples consist of pooled adult blood spotted on filter
paper. Table S10. Top 500 SNPs from genomewide association study of
5-AVAB in the neonatal cohort. Table S11. Top 500 SNPs from genomewide
association study of cLP in the neonatal cohort.
Acknowledgements
This project was funded by the Lundbeck Foundation through The Lundbeck
Foundation Initiative for Integrative Psychiatric Research (iPSYCH), grant
number R248-2017-2003—Period III: 1 March 2018 to 28 February 2021;
R155-2014-1724: Period II: 1 March 2015 to 28 February 2018; R102-A9118:
Period I: 1 March 2012 to 28 February 2015. This research has been conductedusing the Danish National Biobank resource supported by the Novo Nordisk
Foundation. Some of the computing for this project was performed on the
GenomeDK cluster. We would like to thank GenomeDK and Aarhus University
for providing computational resources and support that contributed to theseresearch results.
Code availability
Source code and jupyter notebooks used for analysis are publicly available athttps://​github.​com/​ssi-​dk/​CD-​MRG-​metab​olomi​cs_​autism.
Authors’ contributions
F.O. performed the analysis and visualization. M.E. led the investigation. M.E.,
F.R., D.H., P.B.M., A.S.C., J.C., and K.S. contributed to neonatal study design. F.O.,
M.E., and F.R. drafted the manuscript and analyzed data. F.O., M.E., A.A., and
N.M. contributed to the mass spectrometry methodology of the neonatal
DBS. N.M and A.A acquired the neonatal DBS metabolomics data. O.M., M.O.M.,
and U.E. contributed to the design of the adult cohort study. F.O. contributed
to the mass spectrometry methodology, acquisition, and analyses of adultplasma samples. F.O. and U.E. contributed to dietary assessment analyses. J.G.
provided input to genetic analyses. All authors provided interpretation of theresults and critical feedback on the manuscript.
Availability of data and materials
The data underlying this study are not publicly available due to the Danish
Data Protection Act and European Regulation 2016/679 of the European
Parliament and of the Council (GDPR) that prohibit the distribution of personal
data. The data are available from the corresponding authors upon reasonable
request and under a data transfer and collaboration agreement.

Declarations
Ethics approval and consent to participate
The neonatal metabolomics study was conducted according to the principlesof the Declaration of Helsinki and approved by the Danish Ethics Committee(1–10-72–287-12) and the ethics committee of Lund University approved thestudy protocols for MOS (DNR 2012/594) and MDC (DNR2009/633).
Competing interests
The authors declare that they have no competing interests.
Author details
1
Section for Clinical Mass Spectrometry, Danish Center for Neonatal Screening,
Department of Congenital Disorders, Statens Serum Institut, Copenhagen,Denmark. 2 The Lundbeck Foundation Initiative for Integrative PsychiatricResearch, Copenhagen, Denmark. 3 Laboratory of Analytical Chemistry,
Department of Chemistry, National and Kapodistrian University of Athens,
15771 Panepistimiopolis, ZografouAthens, Greece. 4 Department of Clini‑
cal Therapeutics, School of Medicine, National and Kapodistrian Universityof Athens, Alexandra Hospital, 11528 Athens, Greece. 5 Department of Clinical
Sciences, Lund University, Malmö, Sweden. 6 Testcenter Denmark, StatensOttosson et al. BMC Medicine(2024) 22:302

Serum Institut, Copenhagen, Denmark. 7 Department of Biomedicine ‑ Human
Genetics, Aarhus University, Aarhus, Denmark. 8 Bioinformatics ResearchCenter, Aarhus University, Aarhus, Denmark. 9 Center for Genomics and Person‑
alized Medicine, Aarhus, Denmark. 10 NCRR - National Centre for Register-Based
Research, Aarhus University, Aarhus, Denmark. 11 CIRRAU - Centre for Inte‑grated Registerbased Research at Aarhus University, Aarhus, Denmark.
Received: 23 January 2024 Accepted: 2 July 2024

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