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We aimed to screen metabolomes of combined hypertension and obesity in a Spanish general population cohort to identify differential metabolic profiles for better risk estimation and patient stratification.

Design and method:

We measured blood serum high resolution NMR spectra from hypertensive subjects with (HT_OB, n = 356) or without (HT_noOB, n = 280) abdominal obesity and normotensive subjects with (noHT_OB, n = 291) or without (noHT_noOB, n = 555) abdominal obesity for detecting metabolic cores specifically affected in the different groups. We performed four different projection to latent structures for discriminant analysis (PLS-DA) for binary comparison of the different groups (HT_OB vs HT_noOB, noHT_OB vs noHT_noOB, HT_OB vs noHT_OB and nHT_noOB vs HT_noOB). The models were cross-validated using the Venetian Blinds approach (20 technical replicates). Statistical analysis was performed using SPSS, in-house MATLAB scripts and the PLS Toolbox statistical multivariate analysis library.


Our approach revealed a common set of metabolites associated to hypertension and obesity both in combination and separately. Total fatty acids, glucose and valine moieties contributed to all the PLS-DA models with a variable importance in projection (VIP) score greater than 1. The models for discrimination of hypertension included additional contribution of lactate, 3-hydroxyvalerate and LDL/VLDL regardless of abdominal obesity. Cholesterol contributed to all the models with the exception of hypertension in a non-obese context (HT_noOB vs noHT_noOB). The metabolic profiles also revealed associations with distinct short chain fatty acids (SCFAs) in all the models. Tryptophan was associated in a specific manner with the discrimination of hypertension in obese subjects. GlycA moieties, recently associated to systemic inflammation, only contributed to the discrimination of obesity in no hypertensive patients.


Our stratified metabolomic analysis of hypertension and obesity using chemometric techniques reveals common metabolic alterations previously associated to metabolic wellness. The specific models demonstrate that the discriminant value of the different potential biomarkers highly depend on previous health states. The involvement of SCFA and tryptophan in the models also suggests some role of the brain-gut-microbiome axis in the interaction between obesity and hypertension.

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