Body fat and metabolic age as indicators of inflammation and cardiovascular risk

Dear Editor Prevention of cardiovascular disease and its high mortality rates continues to be a challenge worldwide. Obesity is one of the main risk factors, but some data suggest it exerts a protective effect in some patients; this is called the obesity paradox.1,2 One of the problems is the tool used to measure obesity. Body mass index (BMI) is the most widely measured parameter, although it cannot be considered an independent predictor of mortality.3 A higher amount of body fat is related to a proinflammatory state. New data regarding the innate immune response establish the important role of the inflammasome, a multiprotein oligomer activated by Damage-Associated Molecular Patterns and Pathogen-Associated Molecular Patterns that induces production of one or more caspases, leading to the process and secretion of pro-inflammatory cytokines, mainly IL-1β. Obesity activates inflammation through NLRP3 inflammasome recognizing the lipotoxicity-associated ceramide and induces caspase-1 cleavage in macrophages and adipose tissue.4 Recently, its implication of inflammasome in the development of cardiovascular diseases has been highlighted.5,6 We performed a study with 69 male patients, without history of acute coronary episodes, and correlated anthropometric data with cardiovascular risk scores (Fuster BEWAT Score (FBS) and Framingham Risk Score (FRS)) and IL-1β levels. The study protocol and procedures were approved by the Seville University Ethics Committee. The participants gave written informed consent. Body fat and metabolic age were registered with bioimpedance (Tanita SC-330 Body Composition Analyzer, Tanita Corp., Tokyo, Japan). IL-1β was measured with the ELISA test (Biosource). Pearson correlation test was used to correlate body fat and metabolic age with FBS, FRS and IL-1β. For our analysis, we selected two groups of patients: one with a metabolic age more than 15 years above their real age (MMA; n = 28) and the other with metabolic age below their real age (BMA; n = 17). Comparisons were made with Snedecor F distribution and Mann–Whitney U test. Results showed a positive correlation between body fat and IL-1β levels and FRS, and negative correlation with FBS (Table 1). MMA had higher values of IL-1β and FRS and lower values of FBS, reaching statistical significance for IL-1β and FBS (Table 2). Relationship between body fat and risk factor (r = Pearson correlation coefficient). FBS: Fuster BEWAT Score; FRS: Framingham Risk Score Relationship between body fat and risk factor (r = Pearson correlation coefficient). FBS: Fuster BEWAT Score; FRS: Framingham Risk Score Results in both metabolic age groups. Figures express mean ± standard deviation. MMA: metabolic age above number of years; BMA: metabolic age below number of years; FBS: Fuster BEWAT Score; FRS: Framingham Risk Score Results in both metabolic age groups. Figures express mean ± standard deviation. MMA: metabolic age above number of years; BMA: metabolic age below number of years; FBS: Fuster BEWAT Score; FRS: Framingham Risk Score Our study demonstrates that two anthropometric measurements, body fat and metabolic age, other than BMI, and a new inflammatory parameter, the inflammasome, are related to two validated cardiovascular risk scores. These data suggest that obesity and inflammation could have synergistic effects as cardiovascular risk factors. Body fat and metabolic age are as easy to measure as blood pressure. The best way to evaluate fat as a risk factor is by measuring the epicardial fat and other visceral fat, but it requires sophisticated and expensive medical equipment used just by specialists and impractical to be used by the general population.7 We suggest that impedance could be incorporated into cardiovascular risk evaluation, especially in epidemiological studies and as regular patients’ control. Our results should be validated with more studies. JGR and PB contributed to conception and design. All the authors contributed in some the items: acquisition, analysis and interpretation. JGR, MDC and PB drafted the manuscript. All gave final approval and agree to be accountable for all aspects of work ensuring integrity and accuracy. The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This work was supported by Grupo de Investigacion Junta de Andalucia CTS113, Spain. Juan C Garcia-Rubira http://orcid.org/0000-0002-0407-1127 Francisco J Cano-Garcia http://orcid.org/0000-0002-7581-2455 Beatriz Bullon http://orcid.org/0000-0002-3996-3280 Tania Seoane http://orcid.org/0000-0002-1124-3281 Pablo V Villar http://orcid.org/0000-0001-7249-7667 Mario D Cordero http://orcid.org/0000-0003-0151-3644 Pedro Bullon http://orcid.org/0000-0003-4873-4196