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Study Protocol

A study protocol for the exploration of metabolic syndrome in adults born with small weight for gestational age

[version 1; peer review: awaiting peer review]
PUBLISHED 03 Oct 2025
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Abstract

Objective

The rising prevalence of metabolic syndrome (MetS), a cluster of cardiometabolic risk factors, predictive of type 2 diabetes, relates largely to increasing obesity and sedentary lifestyle, but also to early metabolic life events. Considering the consequences on health of developing MetS in young adulthood, the objective of this study is to better characterize and understand MetS phenotypes in a young adult born with small weight for gestational age (SGA).

Methods

A nested case-control study is designed within the French community-based Haguenau cohort, including 92 male and female young SGA adults divided into cases (with MetS at follow-up, n=46) and controls (free of MetS, n=46), matched for age and sex, all free of MetS at baseline. Serum metabolic signatures will be first determined at the end of follow-up (30 years of age) to characterise phenotypes related to MetS using an untargeted mass spectrometry metabolomic approach. Results will be compared to those obtained in a population of adults born with adequate weight for gestational age (AGA), matched for age and sex, to reveal the specific part of phenotypes related to the imprint of the foetal environment. Lastly, the baseline signatures (20 y.o.) will be identified to explore early dysfunctions around 10 years before diagnosis and evaluate the capacities of serum metabolites to predict evolution towards MetS in SGA.

Ethics and dissemination

The study protocol was reviewed and approved by the ethical committee of the University of Paris-St Louis, and all subjects gave written informed consent. Research findings will be disseminated through peer reviewed publications and at national and international conferences with domain-specific societies, to researchers, clinicians, and policy markers sharing findings. This project was supported by the ‘Fondation Francophone de Recherche sur le Diabète’.

Keywords

Nested case control study, metabolic syndrome, small weight for gestational age, untargeted metabolomics, metabolic phenotype

Introduction

The rising prevalence of metabolic syndrome (MetS), a cluster of cardiometabolic risk factors, relates largely to increasing obesity and sedentary life style, but also to early metabolic life events. Prospective studies have shown that whatever definition of MetS used, it is a predictor of type 2 diabetes (T2D) in different populations.1 The origins of MetS are multiple, including genetics/epigenetics,2,3 dietary intake,4 education, sedentary lifestyle,5 physical inactivity,6 but also intrauterine environment.7 The first steps of metabolic dysfunctions are still difficult to detect despite being present for years before becoming clinically apparent. In addition, MetS does not progress in a continuous manner, and its development involves not only deterioration but also potential recovery.810 Therefore, a better characterization and understanding of MetS and its developmental mechanisms are essential to enable earlier management of the pathology and better prevention. Then, one of the challenges for clinicians is to identify as early as possible at-risk individuals, before irreversible metabolic and cardiovascular changes.

Intrauterine environment is recognized to be associated with later higher susceptibility for development of obesity, hypertension, insulin resistance, as well as T2D in adulthood. Hales and Barker’s hypothesis described the foetal adaptation of an adverse intra-uterine environment during the development with a thrifty phenotype leading to metabolic and cardiovascular outcomes later in life.11 Several studies have reported that small birth weight was also linked to the development of IR and other MetS components, with a significant increased risk of its development in early life.1217 Early impaired cardiometabolic parameters were also highlighted in the Haguenau French cohort, including young adults (20-30 y.o) born with either small (SGA) or adequate weight for gestational age (AGA): a first control-case study based on 20 years old young adults showed that SGA subjects had normal fasting glucose levels but a higher insulin response to an oral glucose tolerance test, in comparison to AGA.18 Then, Meas et al. showed that ten years later, these subjects were also more insulin resistant than their AGA counterparts, revealing already some metabolic dysfunctions.19 Moreover, the study of Matta et al. suggested that parameters of foetal programming are more associated with the development of MetS in adulthood rather than dietary pattern.20 Considering the consequences on health of developing MetS in young adulthood, we choose to focus the present study in this SGA, at-risk population, who is of particular interest for a better characterization of this early pathophysiological stage and the discovery of predictive biomarkers of its evolution. We thus hypothesized that serum metabolites involved in key pathogenic pathways of MetS are modulated in the early phases of its development and so potentially predict the evolution toward MetS.

The aim of this study is thus to better characterize and understand MetS phenotypes by identifying metabolic signatures in an SGA population. Therefore, a case-control study (subjects free of MetS at baseline) is designed within the French community-based Haguenau cohort. Serum metabolic signatures will be first determined at the end of follow-up (30 years of age) to characterise phenotypes related to MetS using an untargeted mass spectrometry metabolomic approach. Results will be compared to those obtained in an AGA population, matched for age and sex, to reveal the specific part of phenotypes related to the imprint of the foetal environment. Lastly, the baseline signatures (20 y.o.) will be identified to explore early dysfunctions around 10 years before diagnosis and evaluate the capacities of serum metabolites to predict evolution towards MetS in SGA.

Methods and analyses

Study population

The current case-control study was nested in the Haguenau cohort.

  • - Description of the Haguenau cohort

Individuals are identified from the Haguenau community-based cohort derived from a population-based registry of the metropolitan area of the city of Haguenau in France.21 Briefly, this registry included information about all pregnancies and deliveries occurring in the maternity unit of the hospital (1971-1985) with 80% degree of completeness. The SGA group (n=734) included singleton individuals born between 32 and 42 weeks of gestation with birth weight <10th percentile for sex and gestational age, whereas the AGA group (n=886) was made of similar individuals born at the same gestational age, with birth weight between the 25th and the 75th percentiles and who were the first babies in the registry born immediately after a baby born SGA. As previously described, the mean ages at the two observations (Baseline and Follow-up) were 22 and 30 years respectively, with a participation rate of 80.7%.18,22 Individuals were all born full-term and sex distribution did not differ between the AGA and SGA groups. Clinical characteristics of the study population at birth were previously described in details23: individuals born SGA were lighter (2.63±0.3 vs 3.37±0.27 kg), shorter (47.7±2 vs 50.3±1 cm) and thinner (11.6±1.1 vs 13.3±0.8 kg/m2) at birth than individuals born AGA. The study protocol was reviewed and approved by the ethical committee of the University of Paris-St Louis, and all subjects gave written informed consent.

  • - Subject selection

For this study, a sub-population is selected among those with available serum samples, anthropometric and clinical data at baseline and follow-up (around 10, years later) for a case-control study design. Incident cases of MetS were selected at the end of the follow-up (at 30 years of age), from subjects free of MetS at inclusion (at 20 years). For defining MetS, the International Diabetes Federation (IDF) criteria is used: abdominal obesity and at least two of the four following parameters: hypertension, fasting hypertriglyceridemia, hyperglycaemia and low high-density level (HDL)-cholesterol concentrations.24,25 The control group, free of MetS at follow-up and inclusion, is first matched for sex. Because of a large age heterogeneity among subjects, it is not possible to match controls on exact age values. Therefore, two criteria are used for matching controls: age class at baseline (age≤20 y.o.; age ≥21y.o.) and the time difference between the two observations (5, 6, 7, 11, 12 and 13 years).

These cases are selected independently from SGA and AGA subjects, and the controls are drawn by random matching within strata. Of the 1,227 eligible individuals (participants present at follow-up, free of MetS at baseline), 93 developed MetS at 30 years, 49 being SGAs and 44 AGAs. From the 186 consequently selected subjects, 9 pairs (Case/Controls) are excluded because of the serum sample availability. Figure 1 summarizes the final subject selection and sample size of the subgroups.

9ec39cac-ce3c-4eb4-9981-c9d4fe8145ae_figure1.gif

Figure 1. Study design.

A: Transversal study; B: Prospective study.

Phenotypic data

Beyond clinical parameters related to MetS and SGA status, other phenotypic data are available on selected subjects such as insulin area under the curve following an oral glucose tolerance test, homeostasis model assessment of insulin resistance, waist to hip ratio, physical activity, and characteristics of parents at birth (body mass index, smoking during pregnancy).

Untargeted metabolomics

Untargeted metabolomics will be performed following a slightly modified procedure described in Pereira et al.26 Briefly, serum samples (100 μL) will be deproteinized using 200 μL of cold methanol (ULC/MS grade, Biolsolve, catalogue number 136841); after centrifugation and evaporation under nitrogen, the dry residues were re-dissolved in 150 μL of 50/50 (v/v) acetonitrile (ULC/MS grade, Biolsolve, catalogue number 012041)/water containing 0.1% formic acid (ULC/MS grade, Biosolve, catalogue number 069141). Pooled quality control samples will be prepared by mixing 20 μL from each of the serum samples and prepared similarly. Metabolic profiles will be then determined using an ultra-high performance liquid chromatography (U3000 UHPLC system, Thermo Scientific) coupled with quadrupole-time of flight mass spectrometer (QToF, Impact HD2, Bruker), equipped with an electrospray source. Briefly, 5 μL of sample were injected into the UHPLC/QToF system and separations were carried out using an Acquity HSS T3 column (Waters) at 30 °C and a flow rate of 0.4 mL/min. Data will be acquired in positive and negative ion modes with a scan range from 50 to 1,000 mass-to-charge ratio (m/z). Samples will be randomized within the analytical sequence based on a Williams Latin Square strategy defined according to the main factors of the study. The stability of the analytical system will be monitored using pooled samples as quality control (QC), injected one time at the beginning of each sequence and then after each set of ten samples.

Data will be processed under the Galaxy web-based platform Worflow4Metabolomics,27 using first XCMS,28 followed by quality checks and signal drift correction according to the algorithm described by Van der Kloet et al.29 based on the use of pooled QC samples, to yield a data matrix containing retention times, masses and peak intensities that will be corrected for batch effects. These steps include noise filtering, automatic peak detection, and chromatographic alignment. In particular, all XCMS extractions will use a “minfrac” parameter of 0.2 to keep variables if present in at least 20% of the samples, since a large variability of profiles in the selected individuals is expected. After signal drift and batch effect correction, metabolite MS signals will be filtered using the following criteria: ratio of chromatographic peak areas of samples over blanks (above 3), repeatability of QC pool samples (coefficients of variation under 30%) and QC pool samples over biological samples (coefficients of variation ratio below 1.25). To reduce the analytical redundancy inside the obtained dataset, isotopes will finally be filtered.

Statistical analyses

Characteristics of the studied population will be analyzed using Mann-Whitney-Wilcoxon or Student’s t tests, depending on the variables’ distribution.

Serum metabolites modulated by MetS in young SGA adults

Extracted metabolomics data from follow-up will be first explored using principal component analysis (PCA) to assess main variations and show any trends or outlying data. Differential analysis will be performed using 2-way ANOVA tests to assess the effect of MetS with BMI as cofactor – obtained p-values will be corrected for multiple testing using the Benjamini-Hochberg correction. Calculation of a p-value for each feature will allow identifying significant differences (adjusted p-values ≤0.05). The log2 mean ratio of ion intensity of cases versus control subjects will be calculated to represent the fold change for selected features. Supervised multivariate analysis (Partial least-squares−discriminant analysis (PLS-DA, with prior Unit-Variance scaling) will also be used, to assess which metabolites among all detected features discriminate cases from controls. Variable importance on projection (VIP) values will be computed as indicators of importance of each metabolite in the PLS-DA model. Predictive discrimination ability by cumulative Q2 will be obtained according to a 7-fold cross validation method, with the validation of the reliability using permutation testing. ANOVA will be performed using the Galaxy web-service27 and PCA as well as PLS-DA will be performed using the SIMCA software (Umetrics, Umea, Sweden).

The same workflow will be applied to AGA metabolomics data at follow-up and modulated metabolites in both populations will be compared using Venn diagram.30

To explore the sub phenotypes, previously selected features will be analyzed by a hierarchical clustering analysis (HCA) using the R software.31

Serum metabolites predicting evolution towards MetS in young SGA adults

Extracted metabolomics data from baseline will be explored to evaluate the possibility to predict the MetS status at follow-up, using Logistic Regression modelling. First, to reduce the number of candidate metabolites to include in the modeling procedure, ANOVA will be performed and p-values will be used to exclude the metabolites the most unlikely to carry individual predictive capabilities. Then, the remaining candidates will be evaluated through the building of logistic regression models, first in an individual way (one model per candidate), and then by combining the best candidates in a single multivariate logistic model with complementary selection using the optimisation of the Akaike Information Criterion. The prediction model performance will be evaluated using a confusion matrix, error rates, and area under receiver operating characteristic (ROC) curves (AUC) (R package “pROC”32) with a confidence interval estimated with DeLong’s method.33 The increase in the AUC will be evaluated and tested for significance using the test also proposed by DeLong et al.33 All logistic regression models will be build using the R software.

A similar workflow will be performed on the available clinical variables, in order to be compared to the previously obtained metabolomics-based model. Complementary combined models will also be built by including metabolomic and clinical variables in common logistic regression models.

Annotation of modulated metabolites

Metabolites contributing to the discrimination of the different phenotypes will be first identified using an in-house database containing the reference spectra of more than 1,000 authentic standard compounds analysed in the same analytical conditions. All standard compounds were purchased from Sigma-Aldrich. All were analytical standard grade (≥98%). Then, the remaining unknown compounds will be annotated on the basis of their exact masses which will be compared to those registered in the Human Metabolome Database (HMDB; www.hmdb.ca),34 and/or in Lipidmaps.35 Database queries will be performed with a mass error of 0.005 Da and a retention time difference of 0.1 min (for the in-house database). Database results will be confirmed using appropriate standards when available, isotopic patterns, and mass fragmentation analyses. For unidentified ions, the number of plausible elemental compositions will be restricted to a small number (or uniquely identified) with the support of additional chemical information i.e., the molecular formula of the parent and knowledge of possible metabolic pathways. Metabolites will be classified accordingly to Sumner et al.36 concerning the levels of confidence in the annotation process: identified (confirmed by standard), putatively annotated (based upon physicochemical properties and/or spectral similarity with public/commercial spectral libraries), putatively characterized compound classes.

Ethics and dissemination

The Haguenau study protocol was reviewed and approved by the ethical committee of the University of Paris-St Louis, and all subjects gave written informed consent. Research findings will be disseminated through peer reviewed publications and at national and international conferences with domain-specific societies, to researchers, clinicians, and policy markers sharing findings.

Patient and public involvement

No patients were involved in the study design.

Discussion

Metabolic syndrome is now recognised a major public health concern, as one of the major clinical syndromes affecting human health. It is a progressive chronic pathophysiological state, with an overall prevalence of 25%, rising to 40% in those aged 65.37,38 MetS increases the risk of developing cardiovascular diseases, stroke, T2D, and diverse metabolic diseases. Its increasing global prevalence is largely linked to the increase in overall and abdominal obesity and sedentary lifestyle, but also to metabolic events occurring early in life. The genesis of this syndrome is complex, its installation mechanisms and the underlying pathophysiology are still far from being elucidated. Links between metabolism alteration and the immune system, involving low-grade inflammation have been established.17,3941 But it is also recognized that early exposure to adverse metabolic conditions, more specifically those leading to low birth weight for gestational age, is associated with an increased risk of MetS.

This project will contribute new evidence on early phenomena of evolution towards MetS, before the development of associated pathologies such as T2D, with the exploration of MetS phenotypes in young adults, and the identification of signatures specific to the footprint of the foetal environment. It will allow integrating intrinsic and extrinsic (nutrition, microbiota …) factors contributing to these early phenotypes associated with MetS, building new hypotheses on the mechanisms of its installation. Thanks to the integration of phenotypic data with metabolomics data, robust multidimensional predictive models will be built. One of the major impacts of the project will therefore be to provide potential biomarkers that, after validation in external cohorts, will contribute to detect at an early-stage subjects at risk of developing MetS. This new knowledge should make it possible to improve the prevention strategy, to implement early primary prevention trials with the discovered potential biomarkers and consequently to limit the progression of health status towards the syndrome and subsequent pathologies.

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Pétéra M, Centeno D, Czernichow S et al. A study protocol for the exploration of metabolic syndrome in adults born with small weight for gestational age [version 1; peer review: awaiting peer review]. F1000Research 2025, 14:1038 (https://doi.org/10.12688/f1000research.169049.1)
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Comments on this article Comments (0)

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VERSION 1 PUBLISHED 03 Oct 2025
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Alongside their report, reviewers assign a status to the article:
Approved - the paper is scientifically sound in its current form and only minor, if any, improvements are suggested
Approved with reservations - A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit.
Not approved - fundamental flaws in the paper seriously undermine the findings and conclusions
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