ABSTRACT
Obesity is a heterogeneous phenotype that is crudely measured by body mass index (BMI). More precise phenotyping and categorization of risk in large numbers of people with obesity is needed to advance clinical care and drug development. Here, we used non-targeted metabolome analysis and whole genome sequencing to identify metabolic and genetic signatures of obesity. We collected anthropomorphic and metabolic measurements at three timepoints over a median of 13 years in 1,969 adult twins of European ancestry and at a single timepoint in 427 unrelated volunteers. We observe that obesity results in a profound perturbation of the metabolome; nearly a third of the assayed metabolites are associated with changes in BMI. A metabolome signature identifies the healthy obese and also identifies lean individuals with abnormal metabolomes – these groups differ in health outcomes and underlying genetic risk. Because metabolome profiling identifies clinically meaningful heterogeneity in obesity, this approach could help select patients for clinical trials.
Introduction
Obesity is one of the most widespread problems facing our society’s health today. Excessive weight significantly increases risk for conditions like diabetes mellitus and cardiovascular disease1,2. Worldwide, the prevalence of obesity has nearly tripled since 1975, with 39% of the world’s adults being overweight and 13% being obese3. The high prevalence can partially be attributed to increasing consumption of hypercaloric foods and sedentary lifestyles3. Previous studies have identified metabolic signatures associated with obesity, including increased levels of branched-chain and aromatic amino acids as well as glycerol and glycerophosphocholines4,5,6,7,8,9. However, prior work has been limited by focusing on a relatively small numbers of metabolites, individuals, or obesity phenotypes.
The characterization of the metabolites that are associated with obesity can provide insights into the mechanisms that lead to this disease and associated consequences. Longitudinal assessment of weight gain and weight loss over time may indicate whether there are metabolomic changes that cause obesity—meaning that current metabolite levels could predict future weight changes—or whether all metabolite changes associated with obesity are a consequence of weight changes. Drawing genetics into this assessment allows the determination of whether genetic variation leads to metabolite changes that subsequently result in obesity, allowing the further delineation of the causal pathway to obesity. Finally, research in this area may identify biomarkers of obesity and of different types of obesity, for example biomarkers of so-called healthy obesity10.
There are recent calls to improve phenotyping in very large numbers of obese people with the goals of understanding factors that make people susceptible to (or protected from) obesity, accompanied by a better elucidation of the factors that account for variability in success of different obesity treatments11. Here, in an effort to understand the relationship between metabolic perturbations and the obese state, we analyzed 2,396 individuals with longitudinal measurements of body mass index (BMI), anthropomorphic data, whole body DEXA scans and metabolome, combined with baseline genetic risk. The metabolome assay covered up to 1,007 metabolites at up to three distinct time points for each individual over the course of study. We identified associations between nearly a third of the metabolome and BMI, and we show that metabolite levels can explain ~40% of the variation in BMI and can predict obesity status with ~80-90% specificity and sensitivity. The metabolome profile is a strong indicator of metabolic health compared to the polygenic risk assessment and anthropomorphic measurements of BMI.
Results
Profound perturbation of metabolome by obesity
Metabolites associated with BMI
We compared the levels of individual metabolites to the BMIs of 832, 882, and 861 unrelated individuals of European ancestry in the TwinsUK cohort12 at three timepoints spanning a total range of 8-18 years. We identified 284 metabolites that were significantly associated (p<5.5×10−5) with BMI at one or more timepoints (Table S1). We focused on metabolites that were significantly associated with BMI at all 3 timepoints and sought to replicate the associations in an independent sample of 427 unrelated individuals of European ancestry participating in the Health Nucleus cohort13. In total, our analyses identified 307 metabolites that were significantly associated with BMI in at least one cohort and timepoint (Table S1). We identified 83 metabolites that showed directions of effect that were consistent between the two cohorts, of which 49 were statistically significant replications (Figure 1 and Table 1).
The 49 metabolites that associated with BMI were primarily lipids (n=23, accounting for 7.5% of all lipids assayed across both cohorts) and amino acids (n=14, 9.3% of all amino acids) but also included nucleotides (n=3, 12.0% of all nucleotides), peptides (n=3, 12% of all peptides), and other categories (n=6, see Figure 1 and Table 1). The most significantly associated metabolite was urate (uric acid; p-value 1.2×10−40 for combined analysis of TwinsUK timepoint 1 and Health Nucleus data).
Patterns in metabolite change according to BMI
The majority of the 49 BMI-associated metabolites increased with increasing BMI (n=35) (Figure 1, Table S1). This included glucose, and, notably, mannose, which has recently been highlighted as playing a role in insulin resistance14. Most metabolites change linearly (both proportionally and inversely) with BMI, though some diverge at higher BMIs, especially 2-methylbutyrylcarnitine (see cofactors panel in Figure 1C). Branched-chain and aromatic amino acids as well as metabolites related to nucleotide metabolism like urate had the most rapid increases. Those that decreased (n=14) included phospholipids and lysolipids, as well as the amino acids asparagine and N-acetylglycine and the xenobiotic cinnamoylglycine, which has been identified as a product of the microbiome15. Of particular interest was the association with cortisone, a metabolite of the steroid hormone cortisol. We identified lower levels among the obese individuals, which is consistent with previous reports16–19. We examined the overall composition of the distributions of these metabolites via principal component analysis and found complex underlying correlations; in particular, the first principal component explained ~20% of the total variation in the levels of these 49 metabolites (Figure S1).
Modeling the metabolome of obesity
We used ridge regression to build a model that would predict BMI from the 49 BMI-associated metabolites (see Figure 2). We combined our data for the first visit of the TwinsUK cohort and the Health Nucleus cohort and trained with 10-fold cross-validation on a random half of the population. In our test set of the other half of the data, we found that the model could explain 39.1% of the variation in BMI (Figure 2A). In predicting whether participants were obese (BMI>=30) or normal weight (BMI 18.5-25), the model had an area under the curve (AUC) of 0.922, specificity of 89.1% and sensitivity of 80.2% (Figure S2). The model based on the metabolite signature was thereafter used as a tool to define mBMI, the predicted BMI on the basis of metabolome.
Identification and characterization of metabolic BMI outliers
Having established a model to predict BMI using the metabolome (mBMI), we split the participants into 5 groups (Figure 2A). Three groups included individuals whose metabolome accurately predicted their BMI (residual between -0.5 and 0.5): they were characterized as having a normal BMI (18.5-25), overweight (25-30), or obese (>30). Two groups were characterized as outliers: these included individuals whose metabolome predicted a substantially lower mBMI than the actual BMI (mBMI<<BMI, residual <-0.5) or a substantially higher mBMI than the actual BMI (mBMI>>BMI, residual >0.5). While these two outlier groups had the same weight range distribution (Figure 2B), they had very different values for many of the phenotypes of metabolic health collected from these cohorts (Figure 2B&C). Individuals with a mBMI prediction that was substantially lower than their actual BMI had levels of insulin resistance, blood pressure, waist/hip ratio, android/gynoid ratio, percent body fat, percent visceral fat, and percent subcutaneous fat that were similar to normal-weight individuals with healthy metabolomes. Individuals with a mBMI prediction that was substantially higher than their actual BMI had levels for these traits that were similar to those of obese individuals with obese metabolomes. Evaluating these data from a more clinical perspective, with individuals separated into clinical categories such as normal BMI with obese metabolome and obese BMI with healthy metabolome, generally confirmed these effects (Figure 3 and Figure S3). Our findings suggest that the metabolome can be used as a clinically meaningful instrument, where obesity is analyzed in the context of its metabolome perturbation rather than just on BMI alone. Thus, our results are important in the frame of the current debate on the metabolically “healthy” obese and also for the identification of individuals with normal BMI5 but poor metabolic health20,21.
Having characterized these outliers, we revisited their metabolome differences. As expected, those with mBMI<<BMI significantly differed in their metabolite levels from those with mBMI>>BMI for most of the 49 BMI-associated metabolites. However, two of the BMI-associated metabolites did not differ between these two groups: asparagine and cortisone. We additionally investigated the association between each of the BMI-associated metabolites and insulin resistance, as many previously reported markers of obesity have also been markers of diabetes4,7. We had quantitative insulin resistance measurements for 515 unrelated, European-ancestry participants. After controlling for BMI, we found that 12 of the 49 BMI-associated metabolites were also significantly associated (correcting for 49 tests requires p<0.001) with insulin resistance, all with positive directions of effect: tyrosine, alanine, kynurenate, gamma-glutamyltyrosine, 1-oleoyl-3-linoleoyl-glycerol (18:1/18:2), and six phospholipids, and as expected, glucose (see Table S1). Mannose, which recently underwent extensive study with regard to insulin resistance14, was nominally associated with insulin resistance after controlling for BMI in our study, p=0.004.
Evolution of obesity and metabolome clinical profiles
Given recent work suggesting that obese individuals who are metabolically healthy may remain at higher risk of negative health outcomes than are normal weight individuals who are metabolically healthy20, we next asked whether the outlier groups were more likely to become obese over time. Focusing on the 1,458 individuals from TwinsUK who had weight measurements at all three timepoints, we found that those who had a mBMI that was higher than their BMI were marginally more likely to gain weight and convert to an obese phenotype (BMI>30) over the 8-18 years of follow up. For example, 32.8% of those of normal weight but with an overweight or obese metabolome converted to being overweight or obese by timepoint 3 compared to 24.8% of those who were of normal weight and had a healthy metabolome (p=0.02, Figure 4 and Figure S4). Overall, the mBMI states of the individuals remained fairly stable with time and were a function of BMI changes (Figure 4 and Figure S4). For example, 68% of the individuals who began the study with an obese metabolome ended the study with an obese metabolome. When an individuals’ weight increased and then decreased, their mBMI followed suit, and no single metabolite was significantly predictive of subsequent BMI changes (Figure S4). In summary, our results are consistent with a favorable long-term health benefit for the overweight and obese individuals with a healthy metabolome.
Cardiovascular disease outcomes
Obesity is considered a risk factor for cardiovascular disease and ischemic stroke22. The longitudinal nature of the TwinsUK study allowed the collection of clinical endpoints in these unselected participants. The age of participants at the first visit ranged from 33 to 74 years old (median 51); and 42 to 88 years old (median 65) at the last visit. During the follow up (median 13 years), the study recorded 53 cardiovascular events (myocardial infarct, angina, angioplasty) or strokes for 1573 individuals. We calculated that our study had 80% power to identify effects with a hazard ratio of at least 1.5 for differences in cardiovascular event outcomes between the different mBMI/BMI groups. We found that participants with a healthy metabolome (normal BMI or obese) had 2 events per hundred individuals. Individuals with an obese metabolic profile, mBMI, had 3.7 (normal BMI) and 4.2 events (in obese individuals) per hundred individuals. Separated analysis of the various endpoints confirmed the trends, more accentuated for cardiovascular than for diagnosis of stroke (Figure S5). We then performed a formal survival analysis for participants to have any cardiovascular event after the first timepoint, and we found those with healthier metabolomes to have fewer/later cardiac events (p=0.02, Figure 4).
Correlations between twins
Because twin studies are important to analyze the heritability of traits, we reassessed the BMI model predictions and obesity status of 350 sets of twins where either both twins had normal BMI (n=244), both twins were obese (n=67), or one was obese and the other had normal BMI (n=39). To keep the categories clear, individuals with BMIs between 25 and 30 (overweight) and their twins were excluded. As asserted by the model’s high specificity and sensitivity, the metabolite-based obesity predictions reflected the actual obesity status of the individuals. This was even the case when only one twin was obese: the obese twin was generally predicted by their metabolome to be obese, while the normal weight twin was not (Figure S2). The correlations between the metabolite-based obesity predictions was also substantially higher between the monozygotic twins than the dizygotic twins, as expected. Interestingly, we identified 3 sets of twins where both twins were predicted from the metabolome to be of normal weight, but both were obese, and 8 sets of twins where the reverse was true. These outliers were thought to represent healthy obese and normal weight, metabolically unhealthy individuals described above.
Genetic analyses
Known genetics of obesity
We first investigated the known genetic factors contributing to high BMI. We calculated polygenic risk scores for BMI using known associations from the considerable literature of obesity and BMI GWAS23. As previously reported, we found that polygenic risk score only explained 2.2% of the variation in BMI at each of the three TwinsUK timepoints and in Health Nucleus for unrelated participants of European ancestry (Figure S6). We investigated whether unique individuals with the highest polygenic risk scores would have a significant perturbation of the metabolome and anthropomorphic, insulin resistance and DEXA measurements (Figure 5). While the data did not support a strong role for polygenic risk, there were trends for higher polygenic risk scores to be associated with a higher android/gynoid ratio (p=0.04) and waist/hip ratio (p=0.04). However, there was no statistical association between the polygenic score and mBMI (p=0.16).
Studies of rare variants in obesity have identified MC4R mutations as having effects large and clear enough to be appropriate for study in our dataset24. We therefore identified members of the study populations who were carrying rare (MAF<0.01%) coding variants in the known obesity gene melanocortin 4 receptor (MC4R). We identified 8 such carriers in the subset of unrelated participants (Table 2). Each variant was observed in one unrelated individual, and 5 of the 8 had already been annotated as causing obesity in clinical databases HGMD or ClinVar (Table 2). As a group, MC4R carriers had significantly higher BMI (p=0.02) than did non-carriers as well as non-significant trends toward a higher diastolic blood pressure, insulin resistance, and percent body fat (Figure 5). However, not all rare variants may be deleterious, and the metabolic impact could have been greater for the true subset of functional variants. The BMI data in the participants supported a pathogenic role for five of the variants (Met292fs, Arg236Cys, Ser180Pro, Ala175T, and Thr11Ala), but did not corroborate a role of Ile170V, which is defined in HGMD and ClinVar as pathogenic25,26. Importantly, of the five sets of twins who both carried the same MC4R variant, three sets included twins who were both overweight or obese. In the two cases where a carrier’s twin did not have the MC4R variant, their BMI was lower than their twin’s. We observed an enrichment of MC4R variant carriers among obese individuals with low polygenic risk scores (Figure S6). Out of 31 participants who were obese with polygenic risk scores in the lowest quartile, 6.1% were MC4R variant carriers, while the carrier frequency was just 0.3% in those of normal weight.
Genetics of the metabolically healthy obese
We obtained additional support of the decoupling of the genetics of high BMI versus the basis of obesity and predicted mBMI from the analysis of outliers. Individuals with a mBMI that was substantially lower than their actual BMI had a higher polygenic risk score for BMI than did other groups. In contrast, those whose mBMI was substantially higher than their actual BMI had low polygenic risk scores (Figure 2B; p=0.006 for a difference between these two groups). This result would support the notion that the polygenic risk score for BMI may capture an anthropomorphic phenotype (larger-framed individuals) rather than a unique association with obesity as a disease trait.
Genetics of metabolome differences
Last, we investigated whether obese individuals with different genetic backgrounds had different metabolomes from other obese individuals. We first searched for metabolites that could distinguish individuals with different BMI polygenic risk scores or MC4R variant carriers. Linear regression showed no significant associations between any single metabolites and polygenic risk or MC4R carrier status in either the entire population or in only the obese individuals. This result implies that metabolites are unlikely to be intermediate phenotypes that explain the underlying genetics of obesity. To check for more specific signals beyond the compiled polygenic risk score, we also performed separate analyses of each of the 97 variants that are used to calculate the polygenic risk score. We found no evidence for any of these known GWAS variants to be more strongly associated with a metabolite than with BMI itself, though our power for discovery was limited given the very small effect sizes of most individual GWAS variants. In summary, although it is known that there is a strong genetic component to metabolite levels27, most of the metabolic perturbations that occur in the obese state are a response to obesity as opposed to shared genetic mechanisms.
Discussion
The results of the present study highlight the profound disruption of the metabolome in obesity and identifies a metabolome signature that serves to examine metabolic health beyond anthropomorphic measurements. Nearly one third of the approximately 1000 metabolites measured in the study were associated with BMI, and 49 were selected as a strong signature for the study of the relationship between BMI, obesity, metabolic disease and the genetics of BMI.
Consistent with previous studies and earlier work in the TwinsUK cohort, branched-chain and aromatic amino acids, and metabolites involved in nucleotide metabolism, such as urate and pseudouridine, are strongly perturbed by obesity4,6,7,9. The underlying reason for the perturbation of branched-chain amino acid metabolism in obese individuals and those with insulin resistance is thought to be related to differences in the amino acid catabolism in adipose tissue28. The single metabolite with the most significant association with BMI was urate, as we previously reported9. It is well known that uric acid increases with obesity, due to insulin resistance reducing the kidneys’ ability to eliminate uric acid, but previous work has not emphasized the power of urate to predict BMI6,7,29. We also found a strong signal for lipids to be associated with BMI, with an enrichment of associations found for glycerol lipids. These results are consistent with previous studies showing that sphingomyelins and diacylglycerols increase with BMI while lysophosphocholines decrease with BMI, with other various phosphatidylcholines having effects in both directions6,7,4. A number of BMI-associated metabolites (12 of the 49-metabolite signature) were associated with insulin resistance after controlling for BMI. As previously observed20,30–32, the metabolome abnormalities associated with high BMI corrected with loss of weight. However, our study found that metabolite levels did not provide predictive power for future weight changes (Figure S4). Overall, the metabolome perturbations appear as a consequence of changes in weight as opposed to being a contributing factor.
The metabolome signature identified individuals whose predicted mBMI was either substantially lower or higher than their actual BMI. These individuals include the metabolically healthy obese, but we also emphasize the importance of the metabolome anomalies in identifying unhealthy individuals with a normal BMI. These profiles were generally stable over the prolonged follow-up. An abnormal metabolome signature, irrespective of BMI, was associated in the present study with three-fold increase in cardiovascular events (Figure 6). Thus, while our findings are in line with the known relationships between metabolically healthy obese status and health-related traits like metabolic syndrome and body fat20,33,34, we extend this relationship to the broader category of metabolically healthy and unhealthy individuals on the basis of the disparity between mBMI and BMI. For example, we observed differences in waist/hip ratio, percent visceral fat, and blood pressure between mBMI/BMI outliers despite having the same BMI distribution. The fact that the metabolically healthy obese have a high BMI polygenic risk score also supports the concept that some of the genetic studies may capture anthropomorphic associations – body size - rather than obesity sensu stricto. Overall, the health consequences observed across the various mBMI groups indicate that there is a durable benefit of maintaining a healthy metabolome signature and points to an ongoing risk for the individuals that have an unhealthy metabolome despite stability of BMI.
In contrast with metabolomics analyses, the present study does not support a strong association between metabolome changes and the genetics of BMI defined by a 97-variant polygenic risk score23. This may be explained by the fact that known BMI GWAS loci explain only a small fraction (∼3%) of BMI heritability23. Despite this overall lack of explanatory power, there was a clear signal for individuals with higher polygenic risk scores to have greater rates of obesity. Because the genetic risk score does not include rare variants, we also identified individuals who carried rare functional variants in the known obesity gene MC4R, which is the single best example of a gene where rare coding variants have a large effect on obesity24. The carriers of these variants were often obese individuals, but their metabolome was not categorically different from that of other obese individuals. The lack of metabolome differences for carriers of variants in this gene is not surprising given that MC4R variants cause obesity by increasing appetite. However, we did find that obese carriers of MC4R variants often had low polygenic risk scores for obesity; out of 31 participants who were obese with polygenic risk scores in the lowest quartile, 6.1% were MC4R variant carriers, while the carrier frequency was just 0.3% in those of normal weight. Thus, our study shows the interest of sequencing obese individuals with low polygenic risk scores because of the apparent enrichment for monogenic contributions. As we completed this study, a large consortium provided additional detail on the role of variants in pathways that implicate energy intake and expenditure in obesity24.
In summary, the present study highlights the health risks of the perturbed metabolome. The study also decouples the genetics of BMI from metabolic health and serves to prioritize a subset of individuals for genetic analysis. The assessment of the metabolome and genome of BMI lays groundwork for future studies of the heterogeneity of obesity and treatment of its endophenotypes. Specifically, the metabolome signature can act as a biomarker of response to the new therapeutics that target patients with MC4R mutations35. In the future, metabolic profiling could help select patients for clinical trials beyond genetic sequencing, thus expanding drug utility11.
STAR+ Methods
Samples and study design
Our study included 1,969 European ancestry twins enrolled in the TwinsUK registry, a British national register of adult twins12. We previously reported a detailed study of the genetic variants influencing the human metabolome in this cohort27. Serum samples were collected at three visits, 8-18 (median 13) years apart. The cohort is mainly composed of females (96.7%), and the sample set we used included 388 monozygotic twin pairs, 519 dizygotic twin pairs, and 155 unrelated individuals. The age of participants at the first timepoint ranged from 33 to 74 years old (median 51); 36 to 81 years old (median 59) at the second timepoint; and 42 to 88 years old (median 65) at the third timepoint. The BMI values measured at each metabolome timepoint were taken within two years of the blood draw date. The twins study was approved by St. Thomas’ Hospital Research Ethics Committee, and all participants provided informed written consent. BMI data were available for 1743 participants within two years of the timepoint for metabolome timepoint 1, 1834 for within two years of timepoint 2, and 1777 for up to 2 years before timepoint 3 or 4 years after this timepoint; 1,458 individuals had all three datapoints.
For independent validation and studies of phenotypes correlated with metabolic BMI outliers, we enrolled 617 unselected adults more than 18 years old who were able to come to the Health Nucleus in La Jolla, CA for a clinical research protocol13. Participants underwent a verbal review of the institutional review board-approved consent (Western Institutional Review Board). Participants ranged in age from 18-89 years old (median 53), were 32.9% female, and had BMI data measured at one timepoint.
Phenotyping
Individuals in the TwinsUK cohort and Health Nucleus both underwent DEXA imaging. The data from these scans were used to calculate android/gynoid ratio, percent body fat, visceral fat, and subcutaneous fat. TwinsUK cohort participants were additionally measured for circumference at the waist and hip using a measuring tip to calculate the waist/hip ratio. For a selected number of Health Nucleus participants, images of fat and water (imaging of muscle) were available from symmetrical chemical shift Magnetic Resonance Imaging (MRI) via the Dixon method. Quantitative insulin resistance (homeostatic model assessment, HOMA) was calculated as fasting insulin x fasting glucose / 405, and being insulin resistant was defined by HOMA score ≥3 (http://gihep.com/calculators/other/homa/)36.
Metabolite Profiling
The non-targeted metabolomics analysis of 901 metabolites in the TwinsUK cohort and 1,007 metabolites in the Health Nucleus cohort was performed at Metabolon, Inc. (Durham, North Carolina, USA) on a platform consisting of four independent ultra high performance liquid chromatography-tandem mass spectrometry (UPLC-MS/MS) methods. The detailed descriptions of the platform can be found in our previous publications27,37. For the TwinsUK cohort, blood serum was used for analysis, and the resulting raw values were transformed to z scores using the mean and standard deviation. For the Health Nucleus cohort, blood plasma was used for analysis, and values from multiple experimental batches were normalized into Z-scores based on a reference cohort of either 42 (n=457) or 300 (n=176) self-reported healthy individuals run with each batch. The 42 and 300-normalized batches were converted to the same scale using linear transformation based on the values obtained from 7 runs that included both the 42 and 300 controls. Samples with metabolite measurements that were below the detection threshold were imputed as the minimum value for that metabolite.
Genome sequencing and analysis
As previously described38, DNA samples were sequenced on an Illumina HiSeqX sequencer utilizing a 150 base paired-end single index read format. Reads were mapped to the human reference sequence build HG38. Variants were called using ISIS Analysis Software (v. 2.5.26.13; Illumina). A linear mixed model was applied to account for family structure in the cohort while testing for associations between genetic variants and the different phenotypes: BMI; BMI prediction model values and residuals after accounting for BMI, age, sex; and levels of the 49 BMI-associated metabolites. A genetic similarity matrix (GSM) was constructed from 301,556 variants that represented a random 20% of all common (MAF>5%) variants genome-wide after linkage-disequilibrium (LD) pruning (r2 less than 0.6, window size 200 kb) and was used to model the random effect in the linear mixed model via a “leave-out-one-chromosome” method for each tested variant. Each of 97 known BMI-associated variants was tested independently using customized Python scripts wrapping the FaST-LMM package39,23. Principal component axes were calculated to check ethnicity using plink, and the first principal component for those of European ancestry was used as a covariate in analyses of unrelated individuals in R described below. Polygenic risk scores were calculated using genotypes for 97 variants whose associations and betas had been published previously23. Rare variants in the gene MC4R were defined as coding and splice variants with MAF<0.1%.
Statistical analysis
R was used for the analysis and data manipulation. Bonferroni correction was used for all analyses. For each quantitative analysis of BMI or other traits, the subset of BMI values or other outcome variables used were rank-ordered and forced to a normal distribution. Analyses comparing metabolites to BMI were performed in R using the lm function, and age, sex, and the first genetic principal component were included as covariates. The obesity prediction model was built using ridge regression (alpha=0) with glmnet in R. The residuals used to separate participants into the five categories shown in Figure 2 were calculated using age, sex, and initial BMI. Heatmaps were generated in R using the pheatmap package. Survival analysis was performed using coxph in R with age at first visit included as a covariate. Power calculation was performed using the power.stratify command in powerSurvEpi.
Author contributions statement
A.T. and E.C. conceived of the experiment(s), E.C. conducted the experiment(s), L.G. supported metabolome analyses and interpretation, E.C., J.C.V. and A.T. analyzed the results. C.T.C. and B.T. provided detailed insight into metabolic diseases, C.S., N.S, L.H., E.K. and L.N. aided with data collection and interpretation, T.D.S. is responsible for the TwinUK study. All authors reviewed the manuscript.
Competing financial interests
E.C., C.S., N.S, L.H., E.K. L.N. and J.C.V are employees of Human Longevity, Inc. L.G. is employee of Metabolome Inc. A.T., B.T., and T.S. declare no conflict of interest.
Acknowledgements
We thank E. Muse for valuable comments. We acknowledge N. Schenker-Ahmed, L. Huang, M. Tyagi, and P. Sheth for aiding with data collection. Twins UK receives funding from the Wellcome Trust. European Community’s Seventh Framework Programme (FP7/2007-2013 and Horizon 2020 to TwinsUK); the National Institute for Health Research (NIHR) Clinical Research Facility at Guy’s & St Thomas’ NHS Foundation Trust and NIHR Biomedical Research Centre based at Guy’s and St Thomas’ NHS Foundation Trust and King’s College London.