Metabolomics, Lipid Pathways, and Blood Pressure Change

Elevated blood pressure (BP) is the single biggest contributor to the global burden of disease.^{1, 2} The 2010 global prevalence of hypertension was approximately 1.4 billion and is projected to exceed 1.6 billion by 2025.³ Global mortality due to sequelae of hypertension was estimated at 10.5 million deaths annually.^{1, 2} In 2010, globally only 37.1% of those self-reporting antihypertensive treatment successfully lowered their BP to goal.³ Given the wide number of pharmacologic options to treat hypertension (11 different drug classes with ~70 different drugs or combination formulations⁴), these numbers are especially alarming. In addition to the large human burden, the financial costs related to hypertension are significant; in the United States alone, the direct and indirect cost of high BP for 2014 to 2015 was $55.9 billion.⁵

Our grasp of the pathophysiology of hypertension remains deficient. Novel genetic and genomic approaches to expand our understanding of hypertension have been used for nearly 50 years,⁶ and while some genomic studies have pointed to novel BP regulatory pathways, discoveries of common variants affecting BP account for <4% of the interindividual variation in the trait.⁷ Methodological developments have given rise to other -omic domains such as transcriptomics, proteomics, and metabolomics that could further elucidate the pathophysiology of hypertension. The latter entails the study of small molecules comprising the substrates and products of metabolic processes. The metabolome is the composite of all metabolites that reflect both endogenous and exogenous (e.g., xenobiotic) metabolic pathway activities and can provide a dynamic evaluation of hypertension pathophysiology.

Identifying biomarkers of essential hypertension has been a long-standing goal for both discovery of hypertension mechanisms but also in personalizing treatment. For example, renin profiling initially showed promise as a way to select for chlorthalidone or propranolol⁸ but its utility has been debated.⁹ Compared to genomics, metabolomics holds promise for providing insight into mechanisms underlying hypertension, prediction of disease progression, and/or novel treatments. While metabolomic approaches have been used to identify markers of hypertension, these studies have been few in number and of limited power.^{10, 11} No prior studies have reported metabolomics in relation to longitudinal changes in BP.

In this issue, Lin and colleagues present the findings of their global metabolomics study and longitudinal BP change in the Prospective Investigation of the Vasculature in Uppsala Seniors Study (PIVUS) cohort.¹² They sought to discover associations of circulating metabolites with longitudinal systolic and diastolic BP changes to identify pathways involved in hypertension pathophysiology. The PIVUS study (i.e., the discovery cohort) followed 504 men and women aged 70–80 years with up to three supine BP measurements and two plasma metabolomics measures over a five-year follow up period. Participants with heart failure, myocardial infarction, stroke, treated hypertension at baseline, or missing BP measurements were excluded. Eleven structurally similar species of lipids from this discovery cohort were validated in 222 males aged ~77–82 yr over 5 years of follow up in the Uppsala Longitudinal Study of Adult Men (ULSAM) study (i.e., the validation cohort). The mean change in systolic and diastolic BP over five years in PIVUS was +3.7and −0.05 mmHg, respectively. No metabolomic associations were detected with change in systolic BP or change in BP status. Five findings were identified in the PIVUS cohort in association with diastolic BP [glycerolipids, ceramide (d18:1, C24:0), triacylglycerol (C16:0, C16:1), oleic acid (C18:cis(9)1), and cholesterylester (C16:0)]. Of these, diacylglycerol (36:2) and monoacylglycerol (18:0), two glycerolipids, were associated with diastolic BP change in the validation cohort. Surprisingly, these metabolites all point to lipid pathways which could indicate that, in these older cohorts, dyslipidemia-induced atherosclerosis and its associated increased arterial stiffening lead to the well-known age-associated declines in diastolic BP observed in the PIVUS study.¹³

Metabolomic studies can be challenging to interpret. Major challenges in comparing datasets include the variability in methods for sample preparation, chromatographic technique employed to deliver analytes to the mass detector, ionization of the analytes, ion monitoring, and more. Each of these substantially alter the detectability and sensitivity of any given metabolite. Lin et al measured circulating metabolite levels using both gas and liquid chromatography followed by mass-spectrometry in the PIVUS cohort and ultra-performance liquid chromatography with mass-spectrometry in the ULSAM cohort. Ionization, mass detectors, ion monitoring, and data processing all differed in the analysis of the discovery cohort and the validation cohort. As a result, the “validation” consisted of assessing structurally similar metabolites rather than the same species. While structurally similar and sharing biochemical pathways, ceramide (d18:1, C24) in the discovery sample is not the same molecule as ceramide phosphoethanolamine in the validation sample (i.e., they have differing numbers of carbons, molecular weights, and the latter can be the product of the former through the activity of ceramide phosphoethanolamine synthases and the membrane phospholipid, phosphatidyl ethanolamine). Similarly, glycerolipids were assessed as a class in the discovery sample but “validated” by selected diacylglycerol and monoacylglyerol species, many of which were not statistically significantly associated with diastolic BP. Therefore, it is unclear if the different measurement methods constitute “validation.” This illustrates an important challenge of metabolomics research, namely, the variety of platforms used for testing impedes independent validation of findings across studies, and in the present case, within the same report. Nonetheless, that significant classes and species of structurally similar, circulating lipids were predictive of longitudinal changes in diastolic BP suggest that additional investigations in this emerging field and the harmonization of detection methods are warranted.

The metabolome can adapt quickly to the physiological, pathophysiological, and environmental conditions, thereby providing a snapshot of an individual’s status. Multiple factors, including age,¹⁴ are related to differences in levels of metabolites. Lin et al studied individuals who were normotensive at age 70 years with no history of cardiovascular disease or stroke. Since the cohort had survived well beyond the age of typical hypertension onset, it is possible that the causal metabolites for blood pressure change and/or hypertension were not captured in this analysis.

For discovery of novel biomarkers for blood pressure change and hypertension, the work of Lin and colleagues represents a proof-of-concept for future metabolomic investigations in younger, pre-hypertensive age groups from multiethnic populations. The challenge is to discover metabolites that are predictive of hypertension and that further our understanding of their physiological roles and their interdependencies in metabolic networks. Ultimately, the metabolome may yield novel biomarkers of hypertension risk, leading to more optimal prevention and treatment strategies.