Maternal educational attainment in pregnancy and epigenome-wide DNA methylation changes in the offspring from birth until adolescence.

Maternal educational attainment (MEA) shapes offspring health through multiple potential pathways. Differential DNA methylation may provide a mechanistic understanding of these long-term associations. We aimed to quantify the associations of MEA with offspring DNA methylation levels at birth, in childhood and in adolescence. Using 37 studies from high-income countries, we performed meta-analysis of epigenome-wide association studies (EWAS) to quantify the associations of completed years of MEA at the time of pregnancy with offspring DNA methylation levels at birth (n = 9 881), in childhood (n = 2 017), and adolescence (n = 2 740), adjusting for relevant covariates. MEA was found to be associated with DNA methylation at 473 cytosine-phosphate-guanine sites at birth, one in childhood, and four in adolescence. We observed enrichment for findings from previous EWAS on maternal folate, vitamin-B12 concentrations, maternal smoking, and pre-pregnancy BMI. The associations were directionally consistent with MEA being inversely associated with behaviours including smoking and BMI. Our findings form a bridge between socio-economic factors and biology and highlight potential pathways underlying effects of maternal education. The results broaden our understanding of bio-social associations linked to differential DNA methylation in multiple early stages of life. The data generated also offers an important resource to help a more precise understanding of the social determinants of health.

Measures of Early-life Behavior and Later Psychopathology in the LifeCycle Project - EU Child Cohort Network: A Cohort Description.

BACKGROUND: The EU LifeCycle Project was launched in 2017 to combine, harmonize, and analyze data from more than 250,000 participants across Europe and Australia, involving cohorts participating in the EU-funded LifeCycle Project. The purpose of this cohort description is to provide a detailed overview of the major measures within mental health domains that are available in 17 European and Australian cohorts participating in the LifeCycle Project. METHODS: Data on cognitive, behavioral, and psychological development has been collected on participants from birth until adulthood through questionnaire and medical data. We developed an inventory of the available data by mapping individual instruments, domain types, and age groups, providing the basis for statistical harmonization across mental health measures. RESULTS: The mental health data in LifeCycle contain longitudinal and cross-sectional data from birth throughout the life course, covering domains across a wide range of behavioral and psychopathology indicators and outcomes, including executive function, depression, ADHD, and cognition. These data span a unique combination of qualitative data collected through behavioral/cognitive/mental health questionnaires and examination, as well as data from biological samples and indices in the form of imaging (MRI, fetal ultrasound) and DNA methylation data. Harmonized variables on a subset of mental health domains have been developed, providing statistical equivalence of measures required for longitudinal meta-analyses across instruments and cohorts. CONCLUSION: Mental health data harmonized through the LifeCycle project can be used to study life-course trajectories and exposure-outcome models that examine early life risk factors for mental illness and develop predictive markers for later-life disease.

Functional validation of variants of unknown significance using CRISPR gene editing and transcriptomics: A Kleefstra syndrome case study.

There are an estimated > 400 million people living with a rare disease globally, with genetic variants the cause of approximately 80% of cases. Next Generation Sequencing (NGS) rapidly identifies genetic variants however they are often of unknown significance. Low throughput functional validation in specialist laboratories is the current ad hoc approach for functional validation of genetic variants, which creating major bottlenecks in patient diagnosis. This study investigates the application of CRISPR gene editing followed by genome wide transcriptomic profiling to facilitate patient diagnosis. As proof-of-concept, we introduced a variant in the Euchromatin histone methyl transferase (EHMT1) gene into HEK293T cells. We identified changes in the regulation of the cell cycle, neural gene expression and suppression of gene expression changes on chromosome 19 and chromosome X, that are in keeping with Kleefstra syndrome clinical phenotype and/or provide insight into disease mechanism. This study demonstrates the utility of genome editing followed by functional readouts to rapidly and systematically validating the function of variants of unknown significance in patients suffering from rare diseases.

CRISPR single base editing, neuronal disease modelling and functional genomics for genetic variant analysis: pipeline validation using Kleefstra syndrome EHMT1 haploinsufficiency.

BACKGROUND: Over 400 million people worldwide are living with a rare disease. Next Generation Sequencing (NGS) identifies potential disease causative genetic variants. However, many are identified as variants of uncertain significance (VUS) and require functional laboratory validation to determine pathogenicity, and this creates major diagnostic delays. METHODS: In this study we test a rapid genetic variant assessment pipeline using CRISPR homology directed repair to introduce single nucleotide variants into inducible pluripotent stem cells (iPSCs), followed by neuronal disease modelling, and functional genomics on amplicon and RNA sequencing, to determine cellular changes to support patient diagnosis and identify disease mechanism. RESULTS: As proof-of-principle, we investigated an EHMT1 (Euchromatin histone methyltransferase 1; EHMT1 c.3430C > T; p.Gln1144*) genetic variant pathogenic for Kleefstra syndrome and determined changes in gene expression during neuronal progenitor cell differentiation. This pipeline rapidly identified Kleefstra syndrome in genetic variant cells compared to healthy cells, and revealed novel findings potentially implicating the key transcription factors REST and SP1 in disease pathogenesis. CONCLUSION: The study pipeline is a rapid, robust method for genetic variant assessment that will support rare diseases patient diagnosis. The results also provide valuable information on genome wide perturbations key to disease mechanism that can be targeted for drug treatments.

Maternal haemoglobin levels in pregnancy and child DNA methylation: a study in the pregnancy and childhood epigenetics consortium.

Altered maternal haemoglobin levels during pregnancy are associated with pre-clinical and clinical conditions affecting the fetus. Evidence from animal models suggests that these associations may be partially explained by differential DNA methylation in the newborn with possible long-term consequences. To test this in humans, we meta-analyzed the epigenome-wide associations of maternal haemoglobin levels during pregnancy with offspring DNA methylation in 3,967 newborn cord blood and 1,534 children and 1,962 adolescent whole-blood samples derived from 10 cohorts. DNA methylation was measured using Illumina Infinium Methylation 450K or MethylationEPIC arrays covering 450,000 and 850,000 methylation sites, respectively. There was no statistical support for the association of maternal haemoglobin levels with offspring DNA methylation either at individual methylation sites or clustered in regions. For most participants, maternal haemoglobin levels were within the normal range in the current study, whereas adverse perinatal outcomes often arise at the extremes. Thus, this study does not rule out the possibility that associations with offspring DNA methylation might be seen in studies with more extreme maternal haemoglobin levels.

Identifying young adults at high risk of cardiometabolic disease using cluster analysis and the Framingham 30-yr risk score.

BACKGROUND AND AIMS: Current strategies to reduce cardiovascular disease (CVD) risk in young adults are largely limited to those at extremes of risk. In cohort studies we have shown cluster analysis identified a large sub-group of adolescents with multiple risk factors. This study examined if individuals classified at 'high-risk' by cluster analysis could also be identified by their Framingham risk scores. METHODS AND RESULTS: Raine Study data at 17- (n = 1048) and 20-years (n = 1120) identified high- and low-risk groups by cluster analysis using continuous measures of systolic BP, BMI, triglycerides and insulin resistance. We assessed:- CVD risk at 20-years using the Framingham 30 yr-risk-score in the high- and low-risk clusters, and cluster stability from adolescence to adulthood. Cluster analysis at 17- and 20-years identified a high-risk group comprising, 17.9% and 21.3%, respectively of the cohort. In contrast, only 1.2% and 3.4%, respectively, met the metabolic syndrome criteria, all of whom were within the high-risk cluster. Compared with the low-risk cluster, Framingham scores of the high-risk cluster were elevated in males (9.4%; 99%CI 8.3, 10.6 vs 6.0%; 99%CI 5.7, 6.2) and females (4.9%; 99%CI 4.4, 5.4 vs 3.2%; 99%CI 3.0, 3.3) (both P < 0.0001). A score >8 for males and >4 for females identified those at high CVD risk with 99% confidence. CONCLUSION: Cluster analysis using multiple risk factors identified ∼20% of young adults at high CVD risk. Application of our Framingham 30 yr-risk cut-offs to individuals allows identification of more young people with multiple risk factors for CVD than conventional metabolic syndrome criteria.

Machine Learning-Based DNA Methylation Score for Fetal Exposure to Maternal Smoking: Development and Validation in Samples Collected from Adolescents and Adults.

BACKGROUND: Fetal exposure to maternal smoking during pregnancy is associated with the development of noncommunicable diseases in the offspring. Maternal smoking may induce such long-term effects through persistent changes in the DNA methylome, which therefore hold the potential to be used as a biomarker of this early life exposure. With declining costs for measuring DNA methylation, we aimed to develop a DNA methylation score that can be used on adolescent DNA methylation data and thereby generate a score for in utero cigarette smoke exposure. METHODS: We used machine learning methods to create a score reflecting exposure to maternal smoking during pregnancy. This score is based on peripheral blood measurements of DNA methylation (Illumina's Infinium HumanMethylation450K BeadChip). The score was developed and tested in the Raine Study with data from 995 white 17-y-old participants using 10-fold cross-validation. The score was further tested and validated in independent data from the Northern Finland Birth Cohort 1986 (NFBC1986) (16-y-olds) and 1966 (NFBC1966) (31-y-olds). Further, three previously proposed DNA methylation scores were applied for comparison. The final score was developed with 204 CpGs using elastic net regression. RESULTS: Sensitivity and specificity values for the best performing previously developed classifier ("Reese Score") were 88% and 72% for Raine, 87% and 61% for NFBC1986 and 72% and 70% for NFBC1966, respectively; corresponding figures using the elastic net regression approach were 91% and 76% (Raine), 87% and 75% (NFBC1986), and 72% and 78% for NFBC1966. CONCLUSION: We have developed a DNA methylation score for exposure to maternal smoking during pregnancy, outperforming the three previously developed scores. One possible application of the current score could be for model adjustment purposes or to assess its association with distal health outcomes where part of the effect can be attributed to maternal smoking. Further, it may provide a biomarker for fetal exposure to maternal smoking. https://doi.org/10.1289/EHP6076.

Maternal Smoking During Pregnancy Induces Persistent Epigenetic Changes Into Adolescence, Independent of Postnatal Smoke Exposure and Is Associated With Cardiometabolic Risk.

Background: Several studies have shown effects of current and maternal smoking during pregnancy on DNA methylation of CpG sites in newborns and later in life. Here, we hypothesized that there are long-term and persistent epigenetic effects following maternal smoking during pregnancy on adolescent offspring DNA methylation, independent of paternal and postnatal smoke exposure. Furthermore, we explored the association between DNA methylation and cardiometabolic risk factors at 17 years of age. Materials and Methods: DNA methylation was measured using the Illumina HumanMethylation450K BeadChip in whole blood from 995 participants attending the 17-year follow-up of the Raine Study. Linear mixed effects models were used to identify differential methylated CpGs, adjusting for parental smoking during pregnancy, and paternal, passive, and adolescent smoke exposure. Additional models examined the association between DNA methylation and paternal, adolescent, and passive smoking over the life course. Offspring CpGs identified were analyzed against cardiometabolic risk factors (blood pressure, triacylglycerols (TG), high-density lipoproteins cholesterol (HDL-C), and body mass index). Results: We identified 23 CpGs (genome-wide p level: 1.06 × 10-7) that were associated with maternal smoking during pregnancy, including associated genes AHRR (cancer development), FTO (obesity), CNTNAP2 (developmental processes), CYP1A1 (detoxification), MYO1G (cell signalling), and FRMD4A (nicotine dependence). A sensitivity analysis showed a dose-dependent relationship between maternal smoking and offspring methylation. These results changed little following adjustment for paternal, passive, or offspring smoking, and there were no CpGs identified that associated with these variables. Two of the 23 identified CpGs [cg00253568 (FTO) and cg00213123 (CYP1A1)] were associated with either TG (male and female), diastolic blood pressure (female only), or HDL-C (male only), after Bonferroni correction. Discussion: This study demonstrates a critical timing of cigarette smoke exposure over the life course for establishing persistent changes in DNA methylation into adolescence in a dose-dependent manner. There were significant associations between offspring CpG methylation and adolescent cardiovascular risk factors, namely, TG, HDL-C, and diastolic blood pressure. Future studies on current smoking habits and DNA methylation should consider the importance of maternal smoking during pregnancy and explore how the persistent DNA methylation effects of in utero smoke exposure increase cardiometabolic risk.

In Epigenomic Studies, Including Cell-Type Adjustments in Regression Models Can Introduce Multicollinearity, Resulting in Apparent Reversal of Direction of Association.

Background: Association studies of epigenome-wide DNA methylation and disease can inform biological mechanisms. DNA methylation is often measured in peripheral blood, with heterogeneous cell types with different methylation profiles. Influences such as adiposity-associated inflammation can change cell-type proportions, altering measured blood methylation levels. To determine whether associations between loci-specific methylation and outcomes result from cellular heterogeneity, many studies adjust for estimated blood cell proportions, but high correlations between methylation and cell-type proportions could violate the statistical assumption of no multicollinearity. We examined these assumptions in a population-based study. Methods: CDKN2A promoter CpG methylation was measured in peripheral blood from 812 adolescents aged 17 years (Western Australian Pregnancy Cohort Study). Loge adolescent BMI was used as the outcome in a regression analysis with DNA methylation as predictor, adjusting for age/sex. Further regression analyses additionally adjusted for estimated cell-type proportions using the reference-based Houseman method, and simulations modeled the effects of varying levels of correlation between cell proportions and methylation. Correlations between estimated cell proportions and CpG methylation from Illumina 450K were measured. Results: Lower DNA methylation was associated with higher BMI when cell-type adjustment was not included; for CpG4, ß = -0.004 logeBMI/%methylation (95% CI -0.0065, -0.001; p = 0.003). The direction of association reversed when adjustment for six cell types was made; for CpG4, ß = 0.004 logeBMI/%methylation (-0.0002, 0.0089; p = 0.06). Correlations between CpG methylation and cell-type proportions were high, and variance inflation factors (VIFs) were extremely high (25 to 113.7). Granulocyte count was correlated with BMI, and removing granulocytes from the regression model reduced all VIFs to <3.1, with persistence of a positive association between methylation and BMI [CpG4 ß = 0.004 logeBMI/%methylation (-0.0002, 0.0088; p = 0.06)]. Simulations supported major effects of multicollinearity on regression results. Conclusions: Where cell types are highly correlated with other covariates in regression models, the statistical assumption of no multicollinearity may be violated. This can result in reversal of direction of association, particularly when examining associations with phenotypes related to inflammation, as CpG methylation may associate with changes in cell-type proportions. Removing predictors with high correlations from regression models may remove the multicollinearity. However, this might hinder biological interpretability.

Epigenetic Age Acceleration in Adolescence Associates With BMI, Inflammation, and Risk Score for Middle Age Cardiovascular Disease.

CONTEXT: "Accelerated aging," assessed by adult DNA methylation, predicts cardiovascular disease (CVD). Adolescent accelerated aging might predict CVD earlier. We investigated whether epigenetic age acceleration (assessed age, 17 years) was associated with adiposity/CVD risk measured (ages 17, 20, and 22 years) and projected CVD by middle age. DESIGN: DNA methylation measured in peripheral blood provided two estimates of epigenetic age acceleration: intrinsic (IEAA; preserved across cell types) and extrinsic (EEAA; dependent on cell admixture and methylation levels within each cell type). Adiposity was assessed by anthropometry, ultrasound, and dual-energy x-ray absorptiometry (ages 17, 20, and 22 years). CVD risk factors [lipids, homeostatic model assessment of insulin resistance (HOMA-IR), blood pressure, inflammatory markers] were assessed at age 17 years. CVD development by age 47 years was calculated by Framingham algorithms. Results are presented as regression coefficients per 5-year epigenetic age acceleration (IEAA/EEAA) for adiposity, CVD risk factors, and CVD development. RESULTS: In 995 participants (49.6% female; age, 17.3 ± 0.6 years), EEAA (per 5 years) was associated with increased body mass index (BMI) of 2.4% (95% CI, 1.2% to 3.6%) and 2.4% (0.8% to 3.9%) at 17 and 22 years, respectively. EEAA was associated with increases of 23% (3% to 33%) in high-sensitivity C-reactive protein, 10% (4% to 17%) in interferon-γ-inducible protein of 10 kDa, and 4% (2% to 6%) in soluble TNF receptor 2, adjusted for BMI and HOMA-IR. EEAA (per 5 years) results in a 4% increase in hard endpoints of CVD by 47 years of age and a 3% increase, after adjustment for conventional risk factors. CONCLUSIONS: Accelerated epigenetic age in adolescence was associated with inflammation, BMI measured 5 years later, and probability of middle age CVD. Irrespective of whether this is cause or effect, assessing epigenetic age might refine disease prediction.

Phospholipids in lipoproteins: compositional differences across VLDL, LDL, and HDL in pregnant women.

OBJECTIVE: The aim of this study was to analyse the differences in the phospholipid composition of very low density (VLDL), low density (LDL) and high density lipoprotein (HDL) monolayers in pregnant lean and obese women. METHODS: LDL, HDL, and VLDL were isolated from plasma samples of 10 lean and 10 obese pregnant women, and their species composition of phosphatidylcholines (PC) and sphingomyelins (SM) was analysed by liquid-chromatography tandem mass-spectrometry. Wilcoxon-Mann-Whitney U test and principal component analysis (PCA) were used to investigate if metabolite profiles differed between the lean/obese group and between lipoprotein species. RESULTS: No significant differences have been found in the metabolite levels between obese and non-obese pregnant women. The PCA components 1 and 2 separated between LDL, HDL, and VLDL but not between normal weight and obese women. Twelve SM and one PCae were more abundant in LDL than in VLDL. In contrast, four acyl-alkyl-PC and two diacyl-PC were significantly higher in HDL compared to LDL. VLDL and HDL differed in three SM, seven acyl-alkyl-PC and one diacyl-PC (higher values in HDL) and 13 SM (higher in VLDL). We also found associations of some phospholipid species with HDL and LDL cholesterol. CONCLUSION: In pregnant women phospholipid composition differs significantly in HDL, LDL and VLDL, similar to previous findings in men and non-pregnant women. Obese and lean pregnant women showed no significant differences in their lipoprotein associated metabolite profile.

Early Life Factors, Obesity Risk, and the Metabolome of Young Adults.

OBJECTIVE: Noncommunicable diseases such as obesity have become a serious global public health epidemic. This study aimed to examine whether there was an association between early life factors (with a special focus on breastfeeding) BMI, waist circumference, and the metabolome in offspring at 20 years. METHODS: Data from the Western Australian Pregnancy Cohort (Raine) Study were analyzed using 1,024 plasma samples from the 20-year follow-up. A liquid chromatography, tandem mass spectrometry metabolomics approach was used to measure metabolites. Multiple linear regression models were performed and adjusted for relevant confounders. Inverse probability weighting was used to adjust the 20-year data for differences in socioeconomic variables between participants and nonparticipants since the commencement of the study. RESULTS: An inverse association between breastfeeding and BMI or waist circumference at 20 years was lost after adjusting for parental prepregnancy BMI and maternal smoking during pregnancy. There was no significant effect of breastfeeding on metabolite concentrations at 20 years. CONCLUSIONS: Although other studies have shown associations between breastfeeding, obesity, and metabolite concentrations at younger ages, this was not evident in our study in young adults. We found no association of metabolites previously associated with waist circumference at 20 years and breastfeeding in early life.

Sex differences in the association of phospholipids with components of the metabolic syndrome in young adults.

BACKGROUND: There are differences in the prevalence and severity of diseases between males, females not taking hormonal contraceptives (non-HC females) and females taking hormonal contraceptives (HC females). The aim of this study was to identify sex-specific differences in the metabolome and its relation to components of the metabolic syndrome in a young adult population. METHODS: The subjects analysed are from the 20-year follow-up of the Western Australian Pregnancy Cohort (Raine) Study. Two hundred fifteen plasma metabolites were analysed in 1021 fasted plasma samples by a targeted liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS) metabolomics approach. Principal component analysis between males (n = 550), non-HC females (n = 199) and HC females (n = 269) was applied. Regression analysis with a sex × metabolite concentration interaction was performed on components of the MetS, namely waist circumference, systolic blood pressure, and plasma HDL-C, triglycerides and glucose concentration, as outcome to select the significant metabolites of the interaction. Those selected metabolites were used as predictors in a sex group stratified analysis to compare the different ß coefficients and therefore the sex group-dependent associations. RESULTS: Principal component analysis between males, non-HC females, and HC females showed a general discriminating trend between males and HC females. One hundred twenty-seven metabolites were significantly different between males and non-HC females, whereas 97 differed between non-HC females and HC females. Males and non-HC females mainly differed in sphingomyelin, lyso-phosphatidylcholine, acyl-carnitine and amino acid species, whilst non-HC females and HC females mainly differed in phosphatidylcholine, lyso-phosphatidylcholine and acyl-carnitine concentrations. Forty-one metabolites (phosphatidylcholines, sphingomyelines, lyso-phosphatidylcholine) were significantly differently associated with the MetS factors in the different groups. CONCLUSIONS: We have shown clear differences between plasma metabolite concentrations in males, and HC or non-HC females, especially in lyso-phosphatidylcholine, sphingomyelin and phosphatidylcholine, which have been shown to associate with obesity in other studies. The association of these metabolites differed between sexes with components of the metabolic syndrome, which means that development of diseases like obesity and diabetes may differ between the sexes. Our findings highlight the importance of considering sex differences when conducting a metabolomics study and the need to account for the effect of HC usage in females in future studies.

Early Programming of Obesity Throughout the Life Course: A Metabolomics Perspective.

BACKGROUND: Over the last decades, research on early life risk factors for obesity and its comorbidities in early life has gained attention within the field of developmental origins of health and diseases. Metabolomics studies that are trying to find early life biomarker and intervention targets for the early development of obesity and associated cardiovascular diseases could help break the inter-generational cycle of obesity. SUMMARY: Metabolomics studies in the field of early programming are scarce and causality is lacking at this stage, as most of the studies are cross-sectional. The main metabolites in the focus of obesity are branched-chain and aromatic amino acids, long-chain polyunsaturated fatty acids, lysophosphatidylcholines, and sphingomyelins. Sex and puberty have not been considered in most of the biomarker studies, but show differences in the metabolite associations to obesity. Key Messages: There is still a lot unknown about the associations between early programming exposures, metabolite concentrations, and the development of obesity. The few studies focusing on this topic find similar metabolite classes in the same age groups being associated with rapid early growth or obesity; but due to differences in the methodological and statistical approaches, the single species often differ. Therefore, more research, preferably with standardized approaches, is needed.

Lipidomics Reveals Associations of Phospholipids With Obesity and Insulin Resistance in Young Adults.

CONTEXT: Obesity and related diseases have become a global public health burden. Identifying biomarkers will lead to a better understanding of the underlying mechanisms associated with obesity and the pathways leading to insulin resistance (IR) and diabetes. OBJECTIVE: This study aimed to identify the lipidomic biomarkers associated with obesity and IR using plasma samples from a population-based cohort of young adults. DESIGN AND SETTING: The Western Australian Pregnancy Cohort (Raine) study enrolled 2900 pregnant women from 1989 to 1991. The 20-year follow-up was conducted between March 2010 and April 2012. Participants and Samples: Plasma samples from 1176 subjects aged 20 years were analyzed using mass spectrometry-based metabolomics. MAIN OUTCOME MEASURES: Associations of analytes with markers of obesity and IR including body mass index, waist circumference, homeostasis model assessment (HOMA-IR), and insulin were examined. Analyses were stratified by body mass index and adjusted for lifestyle and other factors. RESULTS: Waist circumference was positively associated with seven sphingomyelins and five diacylphosphatidylcholines and negatively associated with two lysophosphatidylcholines. HOMA-IR was negatively associated with two diacylphosphatidylcholines and positively with one lysophosphatidylcholine and one diacylphosphatidylcholine. No significant association was found in the obese/overweight group of the HOMA-IR model. In the normal-weight group, one lysophosphatidylcholine was increased. CONCLUSION: A possible discriminative effect of sphingomyelins, particularly those with two double bonds, and lysophosphatidylcholines was identified between subjects with normal weight and obesity independent of low-density lipoprotein cholesterol and high-density lipoprotein cholesterol concentrations. Our results suggest weight status-dependent mechanisms for the development of IR with lysophosphatidylcholine C14:0 as a key metabolite in nonobese IR.

Metabolomic biomarkers for obesity in humans: a short review.

BACKGROUND: The prevalence and incidence of obesity have become a major public health problem during the last decades, but the underlying biochemical and metabolic processes are not fully understood. Metabolomics, the science of small molecules of the metabolism, is helping to unravel these mechanisms via the identification of markers related to obesity. These biomarkers are used to prevent diseases in later life or for the early diagnosis of diseases. This review focuses on articles dealing with biomarkers for obesity. KEY MESSAGES: Branched-chain amino acids (BCAA), nonesterified fatty acids, organic acids, acylcarnitines, and phospholipids were identified as potential biomarkers for obesity. This indicates a relation between elevated BCAA, and other amino acids, and the obese state. Furthermore, deregulation of ß-oxidation is associated with the development of obesity. The results have several limitations, including the differing ages of the subjects in the studies, the fact that all of the studies had a case-control design and therefore no causal explanatory power, and that most looked for similar metabolites and reported almost equal results. CONCLUSION: The strength of this review is that it gives a comprehensive overview of the current status of the knowledge on metabolomics biomarkers for obesity, but further research is needed because the methods used in the studies to date are very homogenous, e.g. most used a targeted approach and therefore analyzed almost the same group of metabolites. Moreover, prospective studies are lacking since all of the studies are either case-control or cross-sectional studies.