Introduction
Obesity is closely associated with cardiometabolic disorders. However, people with obesity are at heterogeneous risk of cardiometabolic diseases. Metabolically healthy obesity (MHO) refers to the concept of obesity without cardiometabolic disorders [
1]. It is still a subject of debate whether individuals with MHO are truly at equivalent cardiovascular risk compared to metabolically healthy normal weight. The inconsistent evidence can be partially attributed to a lack of consensus on metabolic health that causes 30 varying definitions applied in previous studies [
2]. Although previous standards of MHO are mainly based on the absence of metabolic syndromes, the criteria and cutoff values used varied considerably [
2]. A large prospective study on the third National Health and Nutrition Examination Survey and the UK biobank cohort systematically evaluated various metabolic risk factors and proposed a new definition of metabolic health derived from mortality risk [
3]. This new definition based on systolic blood pressure, use of antihypertensives, waist-to-hip ratio, and self-reported diabetes could stratify mortality risk for individuals with and without obesity [
3]. Furthermore, metabolic health status evolved over time for most people, including individuals with normal weight [
4,
5]. One-time classification might be inadequate, whereas complicated criteria might burden the dynamic evaluations. Therefore, this simple definition may be more feasible in clinical contexts. It is unclear, however, whether the new definition can be generalized to cardiovascular events in the general population.
Comprehensive metabolomic profiling may provide molecular pathophysiological insight into the heterogeneity of obesity. Previous cross-sectional metabolomics studies have reported clusters of metabolites associated with MHO [
6‐
9]. Nonetheless, simultaneous investigation of the association between obese phenotypes, cardiovascular events, and a large scale of metabolites could further underpin the metabolic pathways significant to obesity. Therefore, our primary hypothesis was that circulating metabolites associated with cardiovascular risk would identify subtle metabolism disorders for individuals with MHO, prior to evident cardiovascular risk. We first studied the cardiovascular risk for individuals with MHO to examine whether the new definition can be generalized for risk stratification of cardiovascular events in 2339 Europeans. Subsequently, with metabolomic profiling in 2218 participants, we identified metabolites distinct between different phenotypes classified by the new definition and assessed its association with cardiovascular events.
Discussion
In this population-based prospective study, our findings suggested that the new definition of metabolically healthy obesity could be extended to stratify cardiovascular risk for individuals with obesity. Individuals classified as MHO were not at increased risk of cardiovascular events, whereas those with metabolically unhealthy obesity did present higher risk. In participants with metabolomic profiling, we identified a circulating metabolomic factor associated with cardiovascular events, independent of cohort indicator, sex, age, current smoking, alcohol assumption, and physical activity. The metabolomic factor mainly consisted of glucose, glutamine, phosphoglycerate that were found to be individually associated with cardiovascular events as well. The metabolomic factor score was higher in individuals with metabolic unhealth. However, individuals with obesity consistently had higher proportion of the unfavorable metabolomic factor than those with normal weight or overweight, irrespective of metabolic healthy status. It is tempting to speculate that the cardiovascular risk of people with obesity can be stratified using the simple classification of metabolic health. However, our metabolomic profiling suggested that MHO classified by the new definition was not completely healthy, in terms of more evident unfavorable metabolic alterations in individuals with MHO than those with metabolic healthy normal weight. Meanwhile, the unfavorable metabolic alterations underlying the seemingly healthy phenotype of MHO could provide insights into the heterogeneity of obesity.
To our knowledge, no recent studies have evaluated whether the new definition can be extended from stratifying all-cause mortality and cardiovascular mortality to the risk assessment of fatal and nonfatal cardiovascular events, which may pave the way for achieving a uniform definition of metabolic health. This improved the interpretation of metabolic alterations and outcomes behind different obesity phenotypes. As an alternative approach to using the criteria for metabolic syndrome, Zembic et al. resolved this issue by deriving a new definition according to mortality risk and validating it in large cohorts [
3]. The new definition of metabolic health was based on the systematic investigation of anthropometric and metabolic factors and their associations with all-cause mortality and cardiovascular mortality [
3]. Compared with prior definitions based on the absence of MetS, the performance of the new definition was the most robust [
3]. We also observed more consistent association between obese phenotypes and cardiovascular events for the new definition. This new definition underscored waist-to-hip ratio. Although both waist-to-hip ratio and waist circumference are indicators of abdominal obesity, waist-to-hip ratio is less correlated with BMI than waist circumference and is more indicative of fat distribution. The lower collinearity might explain why the addition of waist-to-hip ratio to BMI was more likely to improve the C statistics and risk reclassification for cardiovascular diseases and mortality than the addition of waist circumference [
22,
23]. More importantly, metabolic health may be a transient status in people with obesity. The Whitehall II cohort study reported that around 50% of individuals who had healthy obesity at baseline converted to unhealthy obesity after 20 years of follow-up [
24]. In a period of 5–10 years, 30–65% of people with MHO at baseline converted to unhealthy status [
5,
25,
26]. The transient nature of metabolic health requires dynamic evaluations to reasonably assess cardiovascular risk and mortality. By including these easily obtained non-laboratory markers, this new definition can be feasibly implemented in clinical settings and research.
The unfavorable metabolomic factor existed in some individuals with MHO included metabolites associated with glycogenesis and insulin regulations. Previous studies have shown that insulin resistance is an independent risk factor for mortality, even in the absence of diabetes [
27]. Long-term exposure to metabolic disorders would eventually increase the risk for mortality and cardiovascular diseases. This indicated that the phenotype of MHO is not completely healthy and requires active intervention, such as adopting healthy lifestyle. The metabolomic factor associated with cardiovascular outcomes provided molecular insights for metabolic alterations of obesity that is relevant to future studies on mechanisms, biomarkers, and potential treatment targets. Phosphoglycerate is a glycolysis-related intermediate and is suggested to be associated with an increased risk of heart failure [
28]. Consisting with previous studies, glutamine is negatively associated with diabetes risk and was found lower in participants with MHO in this study [
29‐
31]. Other metabolites that significantly differed across metabolic subgroups were reported to be associated with cardiovascular risk, although they did not show independent association in our study. Acetate, obtained either from diets or produced by gut microbes based on indigestible foods, can beneficially influence glucose hemostasis and insulin sensitivity [
32]. Valerate, a short chain fatty acid, was elevated in individuals with unhealthy metabolic status. Higher levels of valerate were observed to be associated with an increased risk of cardiovascular disease. Although the mechanism is not fully understood, gut microbiota composition and diversity may link the production of valerate to cardiovascular risk [
33]. However, the evidence regarding this association is currently limited. Lactate, a waste of anaerobic metabolism and exercising skeletal muscle, was found to be lower in individuals with MHO compared to those with metabolically unhealthy obesity, but it was relatively higher than in those with metabolically healthy normal weight. One possible explanation is that lactate levels may reflect the status of aerobic metabolism and physical fitness. Individuals with MHO seemed to have a higher level of physical activity than those with metabolically unhealthy obesity (1566 vs. 1484 kcal/day). Further study on direct measurement of aerobic metabolism levels would be more informative. Leucine, a branched-chain amino acid, can induce insulin secretion by stimulating pancreatic β cells, improve insulin signaling, and regulate glucagon-like peptide-1 [
34]. Elevated circulating leucine has been repeatedly suggested to be associated with insulin resistance and diabetes risk [
29,
35‐
37]. We observed lower leucine levels in individuals with MHO. Alanine is used as a precursor for gluconeogenesis and induces glucagon secretion, leading to hyperglycemia [
38]. A meta-analysis suggested that high alanine is associated with a higher risk of diabetes [
39]. However, the effects of alanine on the complications of diabetes are complex, as circulating alanine was found to be inversely associated with microvascular disease in individuals with diabetes [
40]. Moreover, increasing attention has been recently committed to a metabolite, trimethylamine N-oxide, due to the association with adverse cardiovascular outcomes [
41]. Trimethylamine N-oxide is metabolized from trimethylamine, generated by the gut bacteria from dietary precursors, such as carnitine from red meat consumption. Despite the experimental evidence and cross-sectional analysis, a large prospective study failed to observe the association between plasma trimethylamine N-oxide with diabetes risk [
42]. The trimethylamine levels were observed lower in MHO but higher in metabolic unhealthy obesity. These intangible metabolomic alterations might have profoundly influence on the transition to unhealthy metabolism and subsequently change long-term outcomes.
Previous studies in metabolomics have demonstrated that individuals with MHO may exhibit undesirable alterations in their metabolism, regardless of the discrepancy between studies in the definition of MHO, the scope of investigated metabolites, statistical methods, and the clinical profiles of participants [
6‐
9]. For instance, compared to metabolically unhealthy obese individuals or those with normal weight, people with MHO tend to have an intermediated atherogenic lipoprotein profile characterized by elevated levels of VLDL and LDL and reduced levels of HDL [
6]. Several case-control studies have also reported similarities between the amino acid patterns of MHO and metabolically unhealthy obesity, including increased levels of alanine and leucine, compared with metabolically healthy normal weight [
7,
8]. However, findings on the metabolomic differences between MHO and metabolically unhealthy obesity have been inconsistent [
7‐
9]. Nonetheless, other metabolomics studies have illustrated the adverse impact of obesity on metabolism and outcomes [
43,
44]. A metabolomic study identified 49 metabolites that were associated with BMI and contributed to cardiometabolic risks, including glucose, branched-chain and aromatic amino acids, and phospholipids [
43]. A large multi-center study involving 7663 individuals used 108 metabolites to develop a metabolomic pattern that could predict BMI and obesity and had prognostic value for type 2 diabetes and mortality [
44]. The metabolites contributing to the predictive model were mainly amino acids (such as glutamate, valerate, leucine, glutamine, and valine) and nucleotides, providing insight into the heterogeneity of obesity [
44]. Based on the present study and the previous findings, combining metabolites and clinical criteria may benefit the characterization of MHO and improve cardiovascular risk stratification among individuals with obesity.
Strengths and limitations
The strengths of this present study included the prospective study design and relatively long-term follow-up, well-characterized population-based cohorts from two Europe centers, a large-scale investigation of metabolomics at baseline to illustrate intangible metabolic alterations underlying metabolic health phenotypes, and the inclusion of covariates to eliminate the potential confounding. This study has several limitations. First, the course of metabolic health status was not thoroughly investigated because the second classification did not perform due to lack of necessary information, such as waist-to-hip ratios during the follow-up. Second, the metabolomic profiling focused on small metabolites and did not measure the large metabolites, such as different fatty acids to reflect oxidation and saturated structure. Moreover, the coverage of metabolites was limited in terms of the entire metabolomics of thousands of metabolites involved in systematic metabolism [
17]. There is a need for future studies to use more comprehensive metabolomics approaches to fully understand the complex interplay between metabolism, MHO, and cardiovascular health. Third, NMR spectroscopy can quantitatively capture a comprehensive metabolomic pattern for blood samples. The metabolites measured by NMR are highly correlated with the results from clinical chemistry assays, and the association of metabolites measured by different platforms with disease outcomes was consistent [
19]. However, the complexity of the samples, such as molecular binding, and the concentration of metabolites can cause overlapped signals [
16,
17]. For example, the correlation between the NMR-measured glucose and clinically measured glucose was 0.37. This observed discrepancy could be due to several factors, including spectral crowding and overlap causing multiple glucose signals to be measured as glucose and glucose-composited metabolites, as well as variations in sample preparation and measurement procedures. To validate the reproducibility of metabolite identification and qualification, we applied a high-field NMR spectrometry operating at field strengths of ≥ 600 MHz and used reference compounds and spectral deconvolution. Additionally, we plan to perform additional validation studies in future research, such as high-resolution mass spectrometric techniques and developing advanced spectral fitting algorithms, to better understand the source of variation and improve the accuracy of NMR measurements. Last, new definition was only validated in European populations, and caution should be given when generalize to other populations.
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