Left atrial reservoir strain in relation to metabolic and inflammatory biomarkers: a community-based study

Abstract

Abstract Background The pathophysiological mechanisms that underlie progressive left atrium (LA) remodelling and dysfunction are only partially understood. Metabolic disturbances and chronic inflammation might mediate LA dysfunction. To date, population data investigating the contribution of these processes to LA reservoir dysfunction remain scarce. Purpose In a large population sample, we investigated the association between LA reservoir function and a panel of 38 metabolic and inflammatory biomarkers. Methods In 1236 community-dwelling individuals (mean age, 51.0 years; 51.5% women), we echocardiographically assessed LA reservoir strain (LARS) using 2D speckle-tracking analysis. LA reservoir dysfunction was defined as having LARS <23%. We applied partial least squares-discriminant analysis (PLS-DA) to identify biomarkers associated with LA dysfunction. We further explored the associations between LARS and selected biomarkers that were the most influential in PLS-DA, while adjusting for important clinical correlates such as age, sex, body mass index (BMI), heart rate, systolic blood pressure (BP) and antihypertensive treatment. We applied stepwise regression to identify the clinical features and circulating biomarkers most valuable for prediction of abnormal LARS. Results The three latent factors constructed from the panel of 38 biomarkers during PLS-DA explained 16.9% of the variation between the normal and the impaired LA function group. The PLS-DA model discriminated between normal and abnormal LA reservoir strain with 79% accuracy (P<0.0001). In PLS-DA, serum uric acid, serum insulin, γ-glutamyl transferase, interleukin-6, D-dimer and triglycerides were the top biomarkers responsible for class discrimination. On average, these top biomarkers were higher in the LA dysfunction group as compared to their normal counterparts (P<0.0001 for all). In multivariable-adjusted continuous analyses, LARS decreased significantly with the level of serum insulin, serum uric acid and γ-glutamyl transferase (P≤0.0035 for all). Of the clinical correlates and the top biomarkers selected in PLS-DA, stepwise regression models highlighted age, BMI, systolic BP, serum insulin, serum uric acid and interleukin-6 as the main predictors of an impaired LA reservoir function (see figure). Conjointly, these clinical and biochemical features identified LA reservoir dysfunction with an overall accuracy of 85%. Conclusions Circulating markers of insulin resistance, hyperuricemia and chronic inflammation were independently associated with impaired LA reservoir function. These markers may help to further unravel the pathophysiological processes behind LA maladaptation and improve the management of early LA dysfunction in the community. Funding Acknowledgement Type of funding source: Public grant(s) – National budget only. Main funding source(s): Research Foundation Flanders