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Role of human plasma metabolites in prediabetes and type 2 diabetes from the IMI-DIRECT study

September 2024
Diabetologia 67(12):2804-2818

DOI:10.1007/s00125-024-06282-6

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CC BY 4.0

Authors:

Sapna Sharma

Helmholtz Zentrum München Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH)

Mark Haid

Helmholtz Zentrum München Deutsches Forschungszentrum für Gesundheit und Umwelt (GmbH)

Jonathan Adam

Helmholtz Centre for Infection Research

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Aims/hypothesis Type 2 diabetes is a chronic condition that is caused by hyperglycaemia. Our aim was to characterise the metabolomics to find their association with the glycaemic spectrum and find a causal relationship between metabolites and type 2 diabetes. Methods As part of the Innovative Medicines Initiative - Diabetes Research on Patient Stratification (IMI-DIRECT) consortium, 3000 plasma samples were measured with the Biocrates AbsoluteIDQ p150 Kit and Metabolon analytics. A total of 911 metabolites (132 targeted metabolomics, 779 untargeted metabolomics) passed the quality control. Multivariable linear and logistic regression analysis estimates were calculated from the concentration/peak areas of each metabolite as an explanatory variable and the glycaemic status as a dependent variable. This analysis was adjusted for age, sex, BMI, study centre in the basic model, and additionally for alcohol, smoking, BP, fasting HDL-cholesterol and fasting triacylglycerol in the full model. Statistical significance was Bonferroni corrected throughout. Beyond associations, we investigated the mediation effect and causal effects for which causal mediation test and two-sample Mendelian randomisation (2SMR) methods were used, respectively. Results In the targeted metabolomics, we observed four (15), 34 (99) and 50 (108) metabolites (number of metabolites observed in untargeted metabolomics appear in parentheses) that were significantly different when comparing normal glucose regulation vs impaired glucose regulation/prediabetes, normal glucose regulation vs type 2 diabetes, and impaired glucose regulation vs type 2 diabetes, respectively. Significant metabolites were mainly branched-chain amino acids (BCAAs), with some derivatised BCAAs, lipids, xenobiotics and a few unknowns. Metabolites such as lysophosphatidylcholine a C17:0, sum of hexoses, amino acids from BCAA metabolism (including leucine, isoleucine, valine, N-lactoylvaline, N-lactoylleucine and formiminoglutamate) and lactate, as well as an unknown metabolite (X-24295), were associated with HbA1c progression rate and were significant mediators of type 2 diabetes from baseline to 18 and 48 months of follow-up. 2SMR was used to estimate the causal effect of an exposure on an outcome using summary statistics from UK Biobank genome-wide association studies. We found that type 2 diabetes had a causal effect on the levels of three metabolites (hexose, glutamate and caproate [fatty acid (FA) 6:0]), whereas lipids such as specific phosphatidylcholines (PCs) (namely PC aa C36:2, PC aa C36:5, PC ae C36:3 and PC ae C34:3) as well as the two n-3 fatty acids stearidonate (18:4n3) and docosapentaenoate (22:5n3) potentially had a causal role in the development of type 2 diabetes. Conclusions/interpretation Our findings identify known BCAAs and lipids, along with novel N-lactoyl-amino acid metabolites, significantly associated with prediabetes and diabetes, that mediate the effect of diabetes from baseline to follow-up (18 and 48 months). Causal inference using genetic variants shows the role of lipid metabolism and n-3 fatty acids as being causal for metabolite-to-type 2 diabetes whereas the sum of hexoses is causal for type 2 diabetes-to-metabolite. Identified metabolite markers are useful for stratifying individuals based on their risk progression and should enable targeted interventions. Graphical Abstract

Flag plots representing the results of the multivariable logistic regression models for NGR vs IGR (a), NGR vs type 2 diabetes (b) and IGR vs type 2 diabetes (c) as dependent variables and the metabolites as independent variables, adjusted for study centre, sex, age, BMI, BP, fasting HDL-cholesterol, fasting triacylglycerol, smoking status, alcohol status and health status. The x-axis shows OR (95% CI) and the y-axis shows each significant metabolite; metabolite classes are represented by different colours. SM, sphingomyelin

…

Schematic overview of mediation analysis with lysoPC a C17:0 and hexoses (a) or N-lactoylvaline, lactate, N-lactoylleucine, formiminoglutamate and X-24295 (b) as mediators. Numbers above the red arrows indicate the percentage and significance of mediation effects. T2D, type 2 diabetes

…

Forest plot representing causal estimates of type 2 diabetes on targeted and untargeted metabolites in the two-sample MR test. T2D, type 2 diabetes

…

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Vol:.(1234567890)

Diabetologia (2024) 67:2804–2818

https://doi.org/10.1007/s00125-024-06282-6

ARTICLE

Role ofhuman plasma metabolites inprediabetes andtype 2 diabetes

fromtheIMI‑DIRECT study

SapnaSharma1· QiulingDong1,2· MarkHaid3· JonathanAdam1,4· RobertoBizzotto5· JuanJ.Fernandez‑Tajes6·

AngusG.Jones7· AndreaTura5· AnnaArtati3· CorneliaPrehn3· GabiKastenmüller8· RobertW.Koivula9·

PaulW.Franks10· MarkWalker11· IanM.Forgie12· GiuseppeGiordano13· ImrePavo14· HartmutRuetten15·

ManolisDermitzakis16,17,18· MarkI.McCarthy6· OlufPedersen19,20· JochenM.Schwenk21·

KonstantinosD.Tsirigos22· FedericoDeMasi22· SorenBrunak20,23· AnaViñuela24· AndreaMari5·

TimothyJ.McDonald25· TarjaKokkola26· JerzyAdamski27,28,29· EwanR.Pearson12· HaraldGrallert1,4

Received: 28 February 2024 / Accepted: 29 July 2024 / Published online: 30 September 2024

Abstract

Aims/hypothesis Type 2 diabetes is a chronic condition that is caused by hyperglycaemia. Our aim was to characterise the

metabolomics to ﬁnd their association with the glycaemic spectrum and ﬁnd a causal relationship between metabolites and

type 2 diabetes.

Methods As part of the Innovative Medicines Initiative - Diabetes Research on Patient Stratiﬁcation (IMI-DIRECT) consor-

tium, 3000 plasma samples were measured with the Biocrates AbsoluteIDQ p150 Kit and Metabolon analytics. A total of 911

metabolites (132 targeted metabolomics, 779 untargeted metabolomics) passed the quality control. Multivariable linear and

logistic regression analysis estimates were calculated from the concentration/peak areas of each metabolite as an explanatory

variable and the glycaemic status as a dependent variable. This analysis was adjusted for age, sex, BMI, study centre in the basic

model, and additionally for alcohol, smoking, BP, fasting HDL-cholesterol and fasting triacylglycerol in the full model. Statis-

tical signiﬁcance was Bonferroni corrected throughout. Beyond associations, we investigated the mediation eﬀect and causal

eﬀects for which causal mediation test and two-sample Mendelian randomisation (2SMR) methods were used, respectively.

Results In the targeted metabolomics, we observed four (15), 34 (99) and 50 (108) metabolites (number of metabolites

observed in untargeted metabolomics appear in parentheses) that were signiﬁcantly diﬀerent when comparing normal glucose

regulation vs impaired glucose regulation/prediabetes, normal glucose regulation vs type 2 diabetes, and impaired glucose

regulation vs type 2 diabetes, respectively. Signiﬁcant metabolites were mainly branched-chain amino acids (BCAAs), with

some derivatised BCAAs, lipids, xenobiotics and a few unknowns. Metabolites such as lysophosphatidylcholine a C17:0,

sum of hexoses, amino acids from BCAA metabolism (including leucine, isoleucine, valine, N-lactoylvaline, N-lactoylleucine

and formiminoglutamate) and lactate, as well as an unknown metabolite (X-24295), were associated with HbA1c progression

rate and were signiﬁcant mediators of type 2 diabetes from baseline to 18 and 48 months of follow-up. 2SMR was used to

estimate the causal eﬀect of an exposure on an outcome using summary statistics from UK Biobank genome-wide association

studies. We found that type 2 diabetes had a causal eﬀect on the levels of three metabolites (hexose, glutamate and caproate

[fatty acid (FA) 6:0]), whereas lipids such as speciﬁc phosphatidylcholines (PCs) (namely PC aa C36:2, PC aa C36:5, PC ae

C36:3 and PC ae C34:3) as well as the two n-3 fatty acids stearidonate (18:4n3) and docosapentaenoate (22:5n3) potentially

had a causal role in the development of type 2 diabetes.

Conclusions/interpretation Our ﬁndings identify known BCAAs and lipids, along with novel N-lactoyl-amino acid metabo-

lites, signiﬁcantly associated with prediabetes and diabetes, that mediate the eﬀect of diabetes from baseline to follow-up (18

and 48 months). Causal inference using genetic variants shows the role of lipid metabolism and n-3 fatty acids as being causal

for metabolite-to-type 2 diabetes whereas the sum of hexoses is causal for type 2 diabetes-to-metabolite. Identiﬁed metabo-

lite markers are useful for stratifying individuals based on their risk progression and should enable targeted interventions.

Sapna Sharma, Qiuling Dong and Mark Haid contributed equally to

this study.

Extended author information available on the last page of the article

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2805Diabetologia (2024) 67:2804–2818

Keywords Causality· Glycaemic traits· HbA1c· IMI-DIRECT· Mediation· Metabolomics· N-lactoylaminoacids· Patient

stratiﬁcation· Targeted metabolomics· Type 2 diabetes· Untargeted metabolomics

Abbreviations

2SMR Two-sample MR

GWAS Genome-wide association study

H1 Hexoses

IGR Impaired glucose regulation

IGT Impaired glucose tolerance

IMI-DIRECT Innovative Medicines Initiative - Diabetes

Research on Patient Stratiﬁcation

Lac-Phe N-lactoyl-phenylalanine

LysoPC Lysophosphatidylcholine

MOVE Multi-omics variational autoencoders

MR Mendelian randomisation

NA Unidentiﬁed metabolite

NGR Normal glucose regulation

PC Phosphatidylcholine

Introduction

Type 2 diabetes is a complex and common metabolic disor-

der, resulting from the body’s ineﬀective use of insulin. It can

be characterised by hyperglycaemia (high blood sugar) due to

impaired insulin secretion and insulin resistance, with most

aﬀected people being overweight or obese [1]. Impaired glu-

cose tolerance (IGT) and impaired fasting glucose, together

known as impaired glucose regulation (IGR) or prediabetes,

characterise an intermediate condition before converging

towards diabetes. Recent studies show that a complex inter-

play of genetic susceptibility, environmental factors, lifestyle

(including diet, physical activity, smoking and alcohol con-

sumption), clinical heterogeneity, drugs and gut microbiome

orchestrates the development of type 2 diabetes [2]. Over

time, individuals with type 2 diabetes are more likely to have

a higher risk for heart attacks, strokes [3], neuropathy (nerve

damage), retinopathy (causing blindness) and kidney failure

as well as several infectious diseases including COVID-19,

reducing life quality and causing social burden [4, 5].

Metabolomics proﬁles involve a set of low-molecular-

weight biochemicals (metabolites) that includes sugars,

amino acids, organic acids, nucleotides, lipids, xenobiot-

ics and other compound classes. Identifying biochemi-

cal changes occurring between prediabetes and diabetes

improves risk prediction for better-targeted prevention [6,

7]. In addition, genetic composition can be used to make

predictions regarding disease susceptibility. Genome-wide

association studies (GWAS) show that more than 400 loci

inﬂuence the risk of type 2 diabetes [8] and that 900 genetic

variants have been associated with BMI [9]. Therefore,

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2806 Diabetologia (2024) 67:2804–2818

linking metabolites with genetics gives access to genetics’

inﬂuence on the metabolic compositions [10–13], provid-

ing comprehensive molecular understanding of the disease.

In the Innovative Medicines Initiative - Diabetes Research

on Patient Stratiﬁcation (IMI-DIRECT), we characterised

132 metabolites from targeted measurements and 779

metabolites from untargeted measurements proﬁled in 3000

individuals at baseline. The study population was stratiﬁed

by following ADA 2011 glycaemic categories as follows:

23.89% (n=692) had normal glucose regulation (NGR) with

fasting glucose 5.23 (SD=0.39) mmol/l; 48.91% (n=1418)

had IGR with fasting glucose 5.90 (SD=0.51) mmol/l; and

27.2% (n=890) had type 2 diabetes with fasting glucose 7.15

(SD=1.39) mmol/l [14]. For the integration of non-omics

data such as health status, lifestyle and medication with

metabolomics, advanced statistical techniques were applied

to analyse the data (see Methods). Beyond multivariate and

association analyses we performed causal mediation analysis

to evaluate potential causal roles of mediators on outcome

[15, 16]. A study on drug–omics associations in type 2 dia-

betes [17] used an unsupervised deep learning framework

of multi-omics variational autoencoders (MOVE) to extract

signiﬁcant drug response patterns from 789 individuals

newly diagnosed with type 2 diabetes in the IMI-DIRECT

cohort. We integrated the polypharmacy eﬀect on metab-

olomics knowledge from MOVE and compared with our

molecular ﬁndings in this study.

Our aims in this study were as follows: (1) to characterise

911 small molecular (132 targeted, 779 untargeted metabo-

lomics analysis approach) features associated with prediabetes/

IGR and type 2 diabetes; (2) to identify baseline metabolites

associated with progression rate estimated from cross-sectional

data; (3) to investigate potential mediation eﬀects of metabo-

lites from baseline glycaemic status to follow-up using media-

tion analysis; and (4) to identify causal relationships between

metabolites and type 2 diabetes using genetics drivers using

two-sample Mendelian randomisation (2SMR) tests.

Methods

DIRECT cohort

The Diabetes Research on Patient Stratiﬁcation (DIRECT)

cohort encompasses 24,682 European participants at varying

risk of glycaemic deterioration, identiﬁed and enrolled into

a prospective cohort (study 1) of prediabetes (n=2235) and

type 2 diabetes (n=830). Using ADA 2011 glycaemic catego-

ries in study 1, 33% (n=692) of cohort 1 (prediabetes risk)

had NGR, 67% (n=1418) had IGR and 108 were excluded.

In study 2, 789 samples were included and 41 samples were

excluded. From study 1, 101 excluded samples entered study

2 (n=890). The ratio of self-reported sex varied in each study.

Detailed characteristics on inclusion and exclusion criteria,

along with the protocol timeline for visits and tests for both

studies, have been described elsewhere [14, 18]. In summary,

venous blood fasting samples were obtained, followed by per-

formance of DNA extractions and additional biochemical

analyses. Metabolomics measurements for distinct samples

at the baseline is considered in this study.

Targeted metabolomics (AbsoluteIDQ p150 Kit)

Blood samples in the study were analysed with the Abso-

luteIDQ p150 Kit (BIOCRATES Life Sciences, Innsbruck,

Austria) (see electronic supplementary material [ESM]

Methods for details) [19]. After data export, lower and upper

outliers were deﬁned as samples with >33% of metabolite

concentrations below 25% quantile (±1.5 × IQR). Metabolite

traits with too many zero-concentration samples and uniden-

tiﬁed metabolites (NAs, >50%) were excluded (none). The

CV was calculated in reference samples for each metabolite

over all plates. Metabolite traits with CV>0.25 were excluded.

After quality control, 132 metabolites were included in

this study (ESM Table1). Metabolite concentrations were

loge-transformed and scaled (mean=0, SD=1) to ensure com-

parability between the metabolites.

Untargeted metabolomics (Metabolon platform)

Untargeted LC/MS-based techniques covers a broad spec-

trum of metabolites, in contrast to the targeted techniques

wherein metabolites are limited to a predefined set of

molecules. For details on sample preparation, measure-

ment and identiﬁcation of metabolites, see ESM Methods.

Incomplete databases and the presence of unknown or

novel metabolites have been reported with an asterisk (*)

against the metabolite name. The measured volume of the

datasets contained 12% missing values. We screened for

outlier remover (see ESM Fig.1 for an example), which

added 4% more missing values onto existing missing val-

ues (ESM Table2). Peaks were quantiﬁed using AUC.

For studies spanning multiple days, a data normalisation

step was performed to correct variation resulting from

instrument inter-day tuning diﬀerences. Essentially, each

compound was corrected in run-day blocks by register-

ing the medians to equal one and normalising each data

point proportionately (termed the ‘block correction’; ESM

Fig.2). Principal component analysis was performed on the

metabolite dataset and checked for technical eﬀects such as

centre and sex (see ESM Fig.3). The data missing pattern

was tested using logistic regression considering missing as

0 and non-missing as 1; there was no signiﬁcant association

between missing and regressors indicating the missing-at-

random pattern. The K-nearest neighbour (KNN)-based

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2807Diabetologia (2024) 67:2804–2818

imputation method was applied using K=10 as suggested

and optimised from German Cohort KORA F4 [20].

Statistics

Multivariable logistic regression and linear regression Identi-

fying metabolites speciﬁcally associated with the presence of

IGR and type 2 diabetes, we ran the logistic regression with

adjustment for age, sex, BMI and centre as the basic model,

and adjusted additionally for alcohol consumption, smok-

ing, BP, fasting HDL-cholesterol and fasting triacylglycerol

as the full model. The concentration of each metabolite was

loge-transformed and scaled to have a mean of zero and an

SD of 1. Each metabolite was taken as exposure and a binary

NGR-IGR, NGR-type 2 diabetes (NGR-T2D) or IGR-type

2 diabetes (IGR-T2D) variable as an outcome. The OR of

outcomes was calculated using the β coeﬃcient from logistic

regression, where OR>1 indicates higher odds of outcome

and OR<0 shows lower odds of outcome. To account for

multiple testing, the p values from regression analyses were

adjusted for multiple testing using the Bonferroni correction

(pfdr values). To stratify sex-dependent metabolites, men and

women were separated to test the associations by performing

the logistic regression full models.

For incidents of IGR and type 2 diabetes analysis, a

binary NGR-IGR, NGR-T2D or IGR-T2D variable at fol-

low-up times of 18 months and 48 months was taken as the

outcome; transformed metabolites and the same risk factors

in the full model were taken as exposure and covariates,

respectively. The same p correction method was adopted.

The linear regression model was used to explore the asso-

ciation between HbA1c progression rate and metabolites at

the baseline. HbA1c progression rate was computed with

a conditional linear mixed eﬀect model and adjusted for

changes in BMI and diabetes medications [21]. Each trans-

formed metabolite was taken as the independent variable and

HbA1c concentration as the dependent variable, with adjust-

ment for age and sex. Bonferroni correction was performed

for p correction.

Mediation analysis Mediation analysis followed the basic

steps suggested by Baron and Kenny [22], and the signiﬁ-

cance of the mediation eﬀect was tested with a non-para-

metric causal mediation analysis [22, 23]. Each identiﬁed

metabolite was taken as a mediator, glycaemic category sta-

tus at the baseline as the independent variable and glycaemic

category at the follow-up (18 months and 48 months) as

the dependent variable. R package ‘mediation (4.5.0)’ was

used to calculate the p value and proportion of the mediation

eﬀect by bootstrapping with 1000 resamples.

Mendelian randomisation We used 2SMR approaches from

the MRInstruments (0.3.2) and TwoSampleMR library

(v0.5.6) to check causal inference [24]. The 2SMR technique

enables the establishment of a causal relationship between

two observational studies (ESM Fig.4), solely relying on

summary statistics obtained from GWAS [24, 25]. To evalu-

ate the inﬂuence of type 2 diabetes on metabolite levels, we

conducted a 2SMR examination. Type 2 diabetes instruments

were obtained from the genome-wide genotyping study [26]

and the corresponding SNP estimates on metabolites were

extracted from the metabolite-GWAS [10, 27]. Prior to per-

forming Mendelian randomisation (MR) analysis, exposure

and outcome data were harmonised by aligning the SNPs

on the same eﬀect allele. We employed the inverse‐variance

weighting [10, 26, 27] to estimate the causal eﬀect.

Results

Study populations

After stringent quality control (see ESM Methods), we iden-

tiﬁed 132 (ESM Table1) and 779 (ESM Table2) metabolites

from targeted and untargeted metabolomics measurements,

respectively, that were proﬁled for 3000 samples (ESM

Table3) [28]. Baseline characteristics (Table1) revealed

that there were signiﬁcant diﬀerences in BMI, fasting vari-

ables and health status observed between NGR, IGR and

type 2 diabetes groups. No signiﬁcant diﬀerences in age and

smoking status were observed between these three groups.

In addition, the study was conducted across seven countries;

type 2 diabetes participants were recruited in all centres

while participants with NGR or IGR were only recruited

in the Amsterdam, Copenhagen, Kuopio and Lund centres.

Metabolites associated withprediabetes

anddiabetes fromtargeted metabolomics

measurements

A multivariable logistic regression model was used with

known diabetes-related variables as covariates to identify

signiﬁcant metabolites. Study centre, sex, age and BMI were

covariates in the basic model while the additional variables

systolic BP, fasting HDL-cholesterol, fasting triacylglycerol,

smoking status, alcohol status and health status were added in

the full model. Based on the full model, four metabolites dif-

fered signiﬁcantly between the NGR and IGR groups (Fig.1a).

Of these, hexoses (H1) showed the strongest association (OR

1.81 [95% CI 1.59, 2.06], pfdr=3.97×10−17) and served as a

positive control throughout our analysis. Thirty-four and 50

metabolites diﬀered signiﬁcantly between NGR and IGR vs

type 2 diabetes, respectively (Fig.1b,c). As a general pattern,

phosphatidylcholines (PCs) and lysophosphatidylcholine

(lysoPC) were negatively associated with progression to type

2 diabetes, while branched-chain and aromatic amino acids as

Content courtesy of Springer Nature, terms of use apply. Rights reserved.

2808 Diabetologia (2024) 67:2804–2818

well as valeryl/glutaryl-related acylcarnitines were positively

associated with type 2 diabetes.

H1 (OR 9.67 [95% CI 6.54, 14.32], pfdr=1.13×10−27) also

had the strongest associations in NGR-T2D while C5-M-DC

(OR=5.31 [95% CI 4.16, 6.77], pfdr=1.07×10−38) had the

strongest association in IGR-T2D. Three metabolites (H1,

lysoPC a C17:0, lysoPC a C18:0) were signiﬁcantly dif-

ferent in all comparisons (NGR-IGR, NGR-T2D and IGR-

T2D), suggesting their important roles in diabetes indica-

tion and severity. Detailed statistics for the basic model

and full model are shown in ESM Tables3–8. As there

were many more male participants than female participants

Table 1 Baseline characteristics

of the DIRECT participants

based on their glycaemic

Category

T2D to targeted metabolomics

T2D to untargeted metabolomics

Ta rgeted metabolomics to T2D

Untargeted metabolomics to T2D

Fig. 4 Forest plot representing causal estimates of type 2 diabetes on targeted and untargeted metabolites in the two-sample MR test. T2D, type

2 diabetes

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2816 Diabetologia (2024) 67:2804–2818

metabolites such as picolinoylglycine and N-lactoyl-amino

acids, as demonstrated by evidence from the DIRECT study.

N-lactoyl-amino acids are known to be exercise-induced

metabolites that suppress food intake and inﬂuence glucose

homeostasis. Additional functional research and quantiﬁca-

tion are needed to advance the identiﬁcation of early meta-

bolic biomarkers such as N-lactoyl-amino acids, which have

the potential to forecast the onset of type 2 diabetes. Collec-

tively, these ﬁndings direct attention towards novel metabolic

signatures associated with glycaemic deterioration.

Supplementary Information The online version contains peer-reviewed

but unedited supplementary material available at https:// doi. org/ 10.

1007/ s00125- 024- 06282-6.

Acknowledgements We extend our gratitude to the IMI-DIRECT study

participants who willingly participated in phenotyping as well as to the

clinical and technical staﬀ across European study centres for their con-

tributions to participant recruitment and clinical assessment. This pub-

lication’s development has been supported by the Innovative Medicines

Initiative Joint Undertaking under grant agreement 115317 (DIRECT),

with resources derived from the European Union’s Seventh Framework

Programme (FP7/2007–2013) and EFPIA companies’ in-kind contribu-

tions. Special thanks go to the study centre team, L. M. 't Hart, F. Rutters,

J. Vangipurapu and T. H. Hansen, for providing internal review on this

manuscript.

Data availability Access to the molecular and clinical raw data, as

well as the processed data, is restricted. This is in accordance with the

informed consent provided by study participants, the various national

ethical approvals obtained for the study, and compliance with the Euro-

pean General Data Protection Regulation (GDPR). Individual-level

clinical and molecular data cannot be transferred from the centralised

IMI-DIRECT repository. Requests for access will receive guidance on

accessing data through the DIRECT secure analysis platform after sub-

mitting an appropriate application. The IMI-DIRECT data access policy

and additional information about the IMI-DIRECT research consor-

tium’s initiatives and activities can be found at https:// direc tdiab etes. org.

Code used for MR in the study is included as ESM.

Funding Open Access funding enabled and organized by Projekt

DEAL. We would like to thank Helmholtz Munich, German Diabetes

Center (DZD) for their support in current research and China Research

Council (CRC) funding for a PhD student hosted by Helmholtz Munich.

Authors’ relationships and activities The authors declare that there are

no relationships or activities that might bias, or be perceived to bias,

their work.

Contribution statement SS, QD, MH, JA and HG conceptualised the

analysis plan. RWK, PWF, MW, IF, GG, IP, HR, MD, MM, OP, JS, KT,

FDM, SB, AV, AM, TM, TK, JA, EP and HG were involved in concep-

tion and design of the DIRECT study. SS, QD, MH, JA, GK, AA, CP,

RB, JFT, AJ and AT were involved in the data acquisition, pre-pro-

cessing and interpretation of data. SS organised inclusion of outlined

sections and, along with QD, wrote the original draft of the manuscript.

All authors contributed to drafting the article or critically revising it for

signiﬁcant intellectual content and have provided approval to the ﬁnal

version to be published. SS and HG are the guarantors of this work and,

as such, had full access to all the data in the study and take responsibil-

ity for the integrity of the data and the accuracy of the data analysis.

Open Access This article is licensed under a Creative Commons Attri-

bution 4.0 International License, which permits use, sharing, adapta-

tion, distribution and reproduction in any medium or format, as long

as you give appropriate credit to the original author(s) and the source,

provide a link to the Creative Commons licence, and indicate if changes

were made. The images or other third party material in this article are

included in the article’s Creative Commons licence, unless indicated

otherwise in a credit line to the material. If material is not included in

the article’s Creative Commons licence and your intended use is not

permitted by statutory regulation or exceeds the permitted use, you will

need to obtain permission directly from the copyright holder. To view a

copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

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Publisher's Note Springer Nature remains neutral with regard to

jurisdictional claims in published maps and institutional aﬃliations.

Authors and Aliations

SapnaSharma1· QiulingDong1,2· MarkHaid3· JonathanAdam1,4· RobertoBizzotto5· JuanJ.Fernandez‑Tajes6·

AngusG.Jones7· AndreaTura5· AnnaArtati3· CorneliaPrehn3· GabiKastenmüller8· RobertW.Koivula9·

PaulW.Franks10· MarkWalker11· IanM.Forgie12· GiuseppeGiordano13· ImrePavo14· HartmutRuetten15·

ManolisDermitzakis16,17,18· MarkI.McCarthy6· OlufPedersen19,20· JochenM.Schwenk21·

KonstantinosD.Tsirigos22· FedericoDeMasi22· SorenBrunak20,23· AnaViñuela24· AndreaMari5·

TimothyJ.McDonald25· TarjaKokkola26· JerzyAdamski27,28,29· EwanR.Pearson12· HaraldGrallert1,4

* Sapna Sharma

sapna.sharma@helmholtz-munich.de

* Harald Grallert

harald.grallert@helmholtz-munich.de

1 Research Unit ofMolecular Epidemiology, Institute

ofEpidemiology, German Research Center

forEnvironmental Health, Helmholtz Zentrum München,

Neuherberg, Germany

2 Faculty ofMedicine, Ludwig-Maximilians-University

München, Munich, Germany

3 Metabolomics andProteomics Core, German Research

Center forEnvironmental Health, Helmholtz Zentrum

München, Neuherberg, Germany

4 German Center forDiabetes Research (DZD),

MünchenNeuherberg, Germany

5 Institute ofNeuroscience, National Research Council,

Padova, Italy

6 Wellcome Trust Centre forHuman Genetics, University

ofOxford, Oxford, UK

7 Department ofClinical andBiomedical Sciences, University

ofExeter College ofMedicine & Health, Exeter, UK

8 Institute ofComputational Biology, Helmholtz Zentrum

München, Munich, Germany

9 Oxford Centre forDiabetes, Endocrinology andMetabolism,

University ofOxford, Oxford, UK

10 Department ofClinical Science, Genetic andMolecular

Epidemiology, Lund University Diabetes Centre, Malmö,

Sweden

11 Translational andClinical Research Institute, Faculty

ofMedical Sciences, University ofNewcastle,

NewcastleuponTyne, UK

12 Population Health andGenomics, Ninewells Hospital

andMedical School, University ofDundee, Dundee, UK

13 Department ofClinical Science, Genetic andMolecular

Epidemiology, Lund University Diabetes Centre, Malmö,

Sweden

14 Eli Lilly Regional Operations GmbH, Vienna, Austria

15 Sanoﬁ Partnering, Sanoﬁ-Aventis Deutschland GmbH,

FrankfurtamMain, Germany

16 Department ofGenetic Medicine andDevelopment,

University ofGeneva Medical School, Geneva, Switzerland

17 Institute forGenetics andGenomics inGeneva (iGE3),

University ofGeneva, Geneva, Switzerland

18 Swiss Institute ofBioinformatics, Geneva, Switzerland

19 Center forClinical Metabolic Research, Herlev andGentofte

University Hospital, Copenhagen, Denmark

20 Novo Nordisk Foundation Center forBasic Metabolic

Research, Faculty ofHealth andMedical Sciences,

University ofCopenhagen, Copenhagen, Denmark

21 Science forLife Laboratory, School ofBiotechnology, KTH

- Royal Institute ofTechnology, Solna, Sweden

22 Department ofHealth Technology, Technical University

ofDenmark, KongensLyngby, Denmark

23 Department ofHealth Technology, Technical University

ofDenmark, KongensLyngby, Denmark

24 Biosciences Institute, Faculty ofMedical Sciences,

University ofNewcastle, NewcastleuponTyne, UK

25 Blood Sciences, Royal Devon andExeter NHS Foundation

Trust, Exeter, UK

26 Internal Medicine, Institute ofClinical Medicine, University

ofEastern Finland, Kuopio, Finland

27 Department ofBiochemistry, Yong Loo Lin School

ofMedicine, National University ofSingapore, Singapore,

Singapore

28 Institute ofExperimental Genetics, German Research Center

forEnvironmental Health, Helmholtz Zentrum München,

Neuherberg, Germany

29 Institute ofBiochemistry, Faculty ofMedicine, University

ofLjubljana, Ljubljana, Slovenia

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The blood metabolome of cognitive function and brain health in middle-aged adults – influences of genes, gut microbiome, and exposome

Preprint

Full-text available

Dec 2024

Increasing evidence suggests the involvement of metabolic alterations in neurological disorders, including Alzheimer’s disease (AD), and highlights the significance of the peripheral metabolome, influenced by genetic factors and modifiable environmental exposures, for brain health. In this study, we examined 1,387 metabolites in plasma samples from 1,082 dementia-free middle-aged participants of the population-based Rotterdam Study. We assessed the relation of metabolites with general cognition (G-factor) and magnetic resonance imaging (MRI) markers using linear regression and estimated the variance of these metabolites explained by genes, gut microbiome, lifestyle factors, common clinical comorbidities, and medication using gradient boosting decision tree analysis. Twenty-one metabolites and one metabolite were significantly associated with total brain volume and total white matter lesions, respectively. Fourteen metabolites showed significant associations with G-factor, with ergothioneine exhibiting the largest effect (adjusted mean difference = 0.122, P = 4.65x10 ⁻⁷ ). Associations for nine of the 14 metabolites were replicated in an independent, older cohort. The metabolite signature of incident AD in the replication cohort resembled that of cognition in the discovery cohort, emphasizing the potential relevance of the identified metabolites to disease pathogenesis. Lifestyle, clinical variables, and medication were most important in determining these metabolites’ blood levels, with lifestyle, explaining up to 28.6% of the variance. Smoking was associated with ten metabolites linked to G-factor, while diabetes and antidiabetic medication were associated with 13 metabolites linked to MRI markers, including N-lactoyltyrosine. Antacid medication strongly affected ergothioneine levels. Mediation analysis revealed that lower ergothioneine levels may partially mediate negative effects of antacids on cognition (31.5%). Gut microbial factors were more important for the blood levels of metabolites that were more strongly associated with cognition and incident AD in the older replication cohort (beta-cryptoxanthin, imidazole propionate), suggesting they may be involved later in the disease process. The detailed results on how multiple modifiable factors affect blood levels of cognition- and brain imaging-related metabolites in dementia-free participants may help identify new AD prevention strategies.

Untargeted metabolome atlas for sleep-related phenotypes in the Hispanic community health study/study of Latinos

Article

Dec 2024

Background Sleep is essential to maintaining health and wellbeing of individuals, influencing a variety of outcomes from mental health to cardiometabolic disease. This study aims to assess the relationships between various sleep-related phenotypes and blood metabolites. Methods Utilising data from the Hispanic Community Health Study/Study of Latinos, we performed association analyses between 40 sleep-related phenotypes, grouped in several domains (sleep disordered breathing (SDB), sleep duration, sleep timing, self-reported insomnia symptoms, excessive daytime sleepiness (EDS), and heart rate during sleep), and 768 metabolites measured via untargeted metabolomics profiling. Network analysis was employed to visualise and interpret the associations between sleep phenotypes and metabolites. Findings The patterns of statistically significant associations between sleep phenotypes and metabolites differed by superpathways, and highlighted subpathways of interest for future studies. For example, primary bile acid metabolism showed the highest cumulative percentage of statistically significant associations across all sleep phenotype domains except for SDB and EDS phenotypes. Several metabolites were associated with multiple sleep phenotypes, from a few domains. Glycochenodeoxycholate, vanillyl mandelate (VMA) and 1-stearoyl-2-oleoyl-GPE (18:0/18:1) were associated with the highest number of sleep phenotypes, while pregnenolone sulfate was associated with all sleep phenotype domains except for sleep duration. N-lactoyl amino acids such as N-lactoyl phenylalanine (lac-Phe), were associated with sleep duration, SDB, sleep timing and heart rate during sleep. Interpretation This atlas of sleep–metabolite associations will facilitate hypothesis generation and further study of the metabolic underpinnings of sleep health. Funding R01HL161012, R35HL135818, R01AG80598.

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Mar 2024

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Mar 2024

Metformin, a widely used first-line treatment for type 2 diabetes (T2D), is known to reduce blood glucose levels and suppress appetite. Here we report a significant elevation of the appetite-suppressing metabolite N-lactoyl phenylalanine (Lac-Phe) in the blood of individuals treated with metformin across seven observational and interventional studies. Furthermore, Lac-Phe levels were found to rise in response to acute metformin administration and post-prandially in patients with T2D or in metabolically healthy volunteers.

Genetic analysis of blood molecular phenotypes reveals common properties in the regulatory networks affecting complex traits

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Aug 2023

We evaluate the shared genetic regulation of mRNA molecules, proteins and metabolites derived from whole blood from 3029 human donors. We find abundant allelic heterogeneity, where multiple variants regulate a particular molecular phenotype, and pleiotropy, where a single variant associates with multiple molecular phenotypes over multiple genomic regions. The highest proportion of share genetic regulation is detected between gene expression and proteins (66.6%), with a further median shared genetic associations across 49 different tissues of 78.3% and 62.4% between plasma proteins and gene expression. We represent the genetic and molecular associations in networks including 2828 known GWAS variants, showing that GWAS variants are more often connected to gene expression in trans than other molecular phenotypes in the network. Our work provides a roadmap to understanding molecular networks and deriving the underlying mechanism of action of GWAS variants using different molecular phenotypes in an accessible tissue.

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Full-text available

Feb 2023

Obesity plays an important role in the development of insulin resistance and diabetes, but the molecular mechanism that links obesity and diabetes is still not completely understood. Here, we used 146 targeted metabolomic profiles from the German KORA FF4 cohort consisting of 1715 participants and associated them with obesity and type 2 diabetes. In the basic model, 83 and 51 metabolites were significantly associated with body mass index (BMI) and T2D, respectively. Those metabolites are branched-chain amino acids, acylcarnitines, lysophospholipids, or phosphatidylcholines. In the full model, 42 and 3 metabolites were significantly associated with BMI and T2D, respectively, and replicate findings in the previous studies. Sobel mediation testing suggests that the effect of BMI on T2D might be mediated via lipids such as sphingomyelin (SM) C16:1, SM C18:1 and diacylphosphatidylcholine (PC aa) C38:3. Moreover, mendelian randomization suggests a causal relationship that BMI causes the change of SM C16:1 and PC aa C38:3, and the change of SM C16:1, SM C18:1, and PC aa C38:3 contribute to T2D incident. Biological pathway analysis in combination with genetics and mice experiments indicate that downregulation of sphingolipid or upregulation of phosphatidylcholine metabolism is a causal factor in early-stage T2D pathophysiology. Our findings indicate that metabolites like SM C16:1, SM C18:1, and PC aa C38:3 mediate the effect of BMI on T2D and elucidate their role in obesity related T2D pathologies.

Discovery of drug–omics associations in type 2 diabetes with generative deep-learning models

Article

Full-text available

Jan 2023

The application of multiple omics technologies in biomedical cohorts has the potential to reveal patient-level disease characteristics and individualized response to treatment. However, the scale and heterogeneous nature of multi-modal data makes integration and inference a non-trivial task. We developed a deep-learning-based framework, multi-omics variational autoencoders (MOVE), to integrate such data and applied it to a cohort of 789 people with newly diagnosed type 2 diabetes with deep multi-omics phenotyping from the DIRECT consortium. Using in silico perturbations, we identified drug–omics associations across the multi-modal datasets for the 20 most prevalent drugs given to people with type 2 diabetes with substantially higher sensitivity than univariate statistical tests. From these, we among others, identified novel associations between metformin and the gut microbiota as well as opposite molecular responses for the two statins, simvastatin and atorvastatin. We used the associations to quantify drug–drug similarities, assess the degree of polypharmacy and conclude that drug effects are distributed across the multi-omics modalities. Clinical multi-omics data are integrated and analyzed using a generative deep-learning model.

Quality assurance and quality control reporting in untargeted metabolic phenotyping: mQACC recommendations for analytical quality management

Article

Full-text available

Aug 2022
METABOLOMICS

Background Demonstrating that the data produced in metabolic phenotyping investigations (metabolomics/metabonomics) is of good quality is increasingly seen as a key factor in gaining acceptance for the results of such studies. The use of established quality control (QC) protocols, including appropriate QC samples, is an important and evolving aspect of this process. However, inadequate or incorrect reporting of the QA/QC procedures followed in the study may lead to misinterpretation or overemphasis of the findings and prevent future metanalysis of the body of work. Objective The aim of this guidance is to provide researchers with a framework that encourages them to describe quality assessment and quality control procedures and outcomes in mass spectrometry and nuclear magnetic resonance spectroscopy-based methods in untargeted metabolomics, with a focus on reporting on QC samples in sufficient detail for them to be understood, trusted and replicated. There is no intent to be proscriptive with regard to analytical best practices; rather, guidance for reporting QA/QC procedures is suggested. A template that can be completed as studies progress to ensure that relevant data is collected, and further documents, are provided as on-line resources. Key reporting practices Multiple topics should be considered when reporting QA/QC protocols and outcomes for metabolic phenotyping data. Coverage should include the role(s), sources, types, preparation and uses of the QC materials and samples generally employed in the generation of metabolomic data. Details such as sample matrices and sample preparation, the use of test mixtures and system suitability tests, blanks and technique-specific factors are considered and methods for reporting are discussed, including the importance of reporting the acceptance criteria for the QCs. To this end, the reporting of the QC samples and results are considered at two levels of detail: “minimal” and “best reporting practice” levels.

The Roles of Gut Microbiome and Plasma Metabolites in the Associations between ABO Blood Groups and Insulin Homeostasis: The Microbiome and Insulin Longitudinal Evaluation Study (MILES)

Article

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Aug 2022

Non-O blood groups are associated with decreased insulin sensitivity and risk of type 2 diabetes. A recent study pinpointed the associations between ABO blood groups and gut microbiome, which may serve as potential mediators for the observed increased disease risks. We aimed to characterize associations between ABO haplotypes and insulin-related traits as well as potential mediating pathways. We assessed insulin homeostasis in African Americans (AAs; n = 109) and non-Hispanic whites (n = 210) from the Microbiome and Insulin Longitudinal Evaluation Study. The ABO haplotype was determined by six SNPs located in the ABO gene. Based on prior knowledge, we included 21 gut bacteria and 13 plasma metabolites for mediation analysis. In the white study cohort (60 ± 9 years, 42% male), compared to the O1 haplotype, A1 was associated with a higher Matsuda insulin sensitivity index, while a lower relative abundance of Bacteroides massiliensis and lactate levels. Lactate was a likely mediator of this association but not Bacteroides massiliensis. In the AAs group (57 ± 8 years, 33% male), we found no association between any haplotype and insulin-related traits. In conclusion, the A1 haplotype may promote healthy insulin sensitivity in non-Hispanic whites and lactate likely play a role in this process but not selected gut bacteria.

An exercise-inducible metabolite that suppresses feeding and obesity

Article

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Jun 2022
NATURE

Exercise confers protection against obesity, type 2 diabetes and other cardiometabolic diseases1–5. However, the molecular and cellular mechanisms that mediate the metabolic benefits of physical activity remain unclear⁶. Here we show that exercise stimulates the production of N-lactoyl-phenylalanine (Lac-Phe), a blood-borne signalling metabolite that suppresses feeding and obesity. The biosynthesis of Lac-Phe from lactate and phenylalanine occurs in CNDP2⁺ cells, including macrophages, monocytes and other immune and epithelial cells localized to diverse organs. In diet-induced obese mice, pharmacological-mediated increases in Lac-Phe reduces food intake without affecting movement or energy expenditure. Chronic administration of Lac-Phe decreases adiposity and body weight and improves glucose homeostasis. Conversely, genetic ablation of Lac-Phe biosynthesis in mice increases food intake and obesity following exercise training. Last, large activity-inducible increases in circulating Lac-Phe are also observed in humans and racehorses, establishing this metabolite as a molecular effector associated with physical activity across multiple activity modalities and mammalian species. These data define a conserved exercise-inducible metabolite that controls food intake and influences systemic energy balance.

Novel Plasma Metabolomic Markers Associated with Diabetes Progression in Older Puerto Ricans

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Jun 2022

We assessed longitudinal associations between plasma metabolites, their network-derived clusters, and type 2 diabetes (T2D) progression in Puerto Rican adults, a high-risk Hispanic subgroup with established health disparities. We used data from 1221 participants free of T2D and aged 40–75 years at baseline in the Boston Puerto Rican Health and San Juan Overweight Adult Longitudinal Studies. We used multivariable Poisson regression models to examine associations between baseline concentrations of metabolites and incident T2D and prediabetes. Cohort-specific estimates were combined using inverse-variance weighted fixed-effects meta-analyses. A cluster of 13 metabolites of branched chain amino acids (BCAA), and aromatic amino acid metabolism (pooled IRR = 1.87, 95% CI: 1.28; 2.73), and a cell membrane component metabolite cluster (pooled IRR = 1.54, 95% CI: 1.04; 2.27) were associated with a higher risk of incident T2D. When the metabolites were tested individually, in combined analysis, 5 metabolites involved in BCAA metabolism were associated with incident T2D. These findings highlight potential prognostic biomarkers to identify Puerto Rican adults who may be at high risk for diabetes. Future studies should examine whether diet and lifestyle can modify the associations between these metabolites and progression to T2D.

Four groups of type 2 diabetes contribute to the etiological and clinical heterogeneity in newly diagnosed individuals: An IMI DIRECT study

Article

Full-text available

Jan 2022

The presentation and underlying pathophysiology of type 2 diabetes (T2D) is complex and heterogeneous. Recent studies attempted to stratify T2D into distinct subgroups using data-driven approaches, but their clinical utility may be limited if categorical representations of complex phenotypes are suboptimal. We apply a soft-clustering (archetype) method to characterize newly diagnosed T2D based on 32 clinical variables. We assign quantitative clustering scores for individuals and investigate the associations with glycemic deterioration, genetic risk scores, circulating omics biomarkers, and phenotypic stability over 36 months. Four archetype profiles represent dysfunction patterns across combinations of T2D etiological processes and correlate with multiple circulating biomarkers. One archetype associated with obesity, insulin resistance, dyslipidemia, and impaired β cell glucose sensitivity corresponds with the fastest disease progression and highest demand for anti-diabetic treatment. We demonstrate that clinical heterogeneity in T2D can be mapped to heterogeneity in individual etiological processes, providing a potential route to personalized treatments.

Role of human plasma metabolites in prediabetes and type 2 diabetes from the IMI-DIRECT study

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