MetSCORE: a molecular metric to evaluate the risk of metabolic syndrome based on serum NMR metabolomics

Abstract

Background: Metabolic syndrome (MetS) is a cluster of medical conditions and risk factors correlating with insulin resistance that increase the risk of developing cardiometabolic health problems. The specific criteria for diagnosing MetS vary among different medical organizations but are typically based on the evaluation of abdominal obesity, high blood pressure, hyperglycemia, and dyslipidemia. A unique, quantitative and independent estimation of the risk of MetS based only on quantitative biomarkers is highly desirable for the comparison between patients and to study the individual progression of the disease in a quantitative manner.

Methods: We used NMR-based metabolomics on a large cohort of donors (n = 21,323; 37.5% female) to investigate the diagnostic value of serum or serum combined with urine to estimate the MetS risk. Specifically, we have determined 41 circulating metabolites and 112 lipoprotein classes and subclasses in serum samples and this information has been integrated with metabolic profiles extracted from urine samples.

Results: We have developed MetSCORE, a metabolic model of MetS that combines serum lipoprotein and metabolite information. MetSCORE discriminate patients with MetS (independently identified using the WHO criterium) from general population, with an AUROC of 0.94 (95% CI 0.920-0.952, p < 0.001). MetSCORE is also able to discriminate the intermediate phenotypes, identifying the early risk of MetS in a quantitative way and ranking individuals according to their risk of undergoing MetS (for general population) or according to the severity of the syndrome (for MetS patients).

Conclusions: We believe that MetSCORE may be an insightful tool for early intervention and lifestyle modifications, potentially preventing the aggravation of metabolic syndrome.

Keywords: Diabetes; Dyslipidemia; Hypertension; Lipoproteins; Metabolic syndrome; NMR spectroscopy; Obesity; Precision medicine.

Fig. 1

Grouping of individuals based on serum metabolomic data using a Kohonen self-organizing maps. The wide white lines separating groups of cells are the result of hierarchical clustering of the representative vectors of each cell. a Cells colored by the percentage of individuals with metabolic syndrome according to the metadata. This shows how serum metabolism is affected by MetS, with individuals clustering in certain regions. b Cells coloured according to the average metabolic syndrome score obtained from the original urine model in the previous work [22]. This score is referred as ‘MetS score’. c Cells coloured according to MetSCORE obtained from the serum model of this work. The scale MetSCORE is centered around a threshold of 0.5 to optimise interpretability, so MetSCORE and ‘MetS score’ are not directly comparable

Fig. 2

Heatmap representing the univariate analysis for each metabolic syndrome profile compared to the asymptomatic profile. Panel a shows the analysis for the metabo/lipo_serum dataset and panel b for the combined_serum/urine dataset. The colors indicate the direction of change in metabolic levels compared to the asymptomatic profile. Red indicates an increase, and blue indicates a decrease. The intensity of the colour reflects the magnitude of change in standard deviation units. Statistically significant changes are marked with asterisks (*: adjusted p-value < 0.05; **: p-value < 0.01; ***: p-value < 0.001; ****: p-value < 0.0001). Both profiles and variables are organized into clusters based on their similarities, shown in the dendrogram. These heatmaps help visualize how different metabolic parameters change in patients with metabolic syndrome. Abbreviations: 4-DEA: 4-deoxythreonic acid; TMAO: trimethylamine-N-oxide

Fig. 3

Metabolic syndrome model for the metabo/lipo_serum dataset based on O-PLS-DA. a Scores plot with the predictive component on the X axis and the orthogonal component on the Y axis. The green dots represent individuals who do not have metabolic syndrome according to their metadata and WHO criteria, while the red triangles represent those who are classified as having metabolic syndrome. This plot helps visualize the separation between individuals with and without metabolic syndrome based on their metabolic profiles. b ROC curve showing the area under the curve for the final model along with its 95% confidence interval. It also indicates the sensitivity and specificity for the selected cut-off based on the Youden index. The dashed horizontal line shows the threshold selected using the Youden index from the ROC curve. This curve demonstrates the model’s ability to distinguish between individuals with and without metabolic syndrome. c Cartoon showing the most influential variables in the model; the size of the bar indicates their relative influence, while the colour indicates whether they are elevated or not in metabolic syndrome. This visualization highlights the key biomarkers that contribute to the model’s predictive power. d Distribution of the predictive component averages from the O-PLS-DA model across different metabolic risk profiles. The bar plot shows the average predictive component values for various combinations of metabolic risk factors from the O-PLS-DA model. Each bar represents a unique combination of risk factors, coded in binary format on the x-axis. The left y-axis indicates the average predictive component values, while the right y-axis provides the equivalent MetSCORE for reference. The dashed blue line represents the threshold for MetS diagnosis, with scores above the line indicating a higher risk of MetS. Error bars represent the standard error of the mean for each risk profile. This plot illustrates how different combinations of metabolic risk factors contribute to the overall risk of metabolic syndrome as predicted by the model

Fig. 4

Distribution of MetSCORE values based on different profiles. a Ridgeline plot for MetSCORE density values by profile. The ridgeline plot shows the distribution of MetSCORE values for each profile, illustrating the continuum of metabolic states and the overlaps in boundaries when using clinical data alone. b Graph with the different paths that an individual can follow from the asymptomatic profile to the metabolic syndrome profile with all risk factors, colored by average MetSCORE. This graph visualizes the potential progression paths from a healthy state (0000) to a fully developed metabolic syndrome state (1111), with colours indicating the average MetSCORE along each path. The MetSCORE is normalised between 0 and 1 and the average score values for the 0000 and 1111 profiles are also indicated in the legend. Shapes within the nodes define the criteria used in various definitions of MetS: squares and triangles represent the MetS definition according to the WHO, EGIR, and AACE; squares, triangles, and rhombus represent the definition from the NCEP and Harmonized; squares and rhombus represent the definition by the IDF. This helps to understand the relative risk of developing metabolic syndrome based on different combinations of risk factors