Adipose tissue n-3/n-6 fatty acids ratios versus n-3 fatty acids fractions as predictors of myocardial infarction.

BACKGROUND: Tissue levels of n-3 polyunsaturated fatty acids (PUFAs) have been inversely related with risk of myocardial infarction (MI). Whether ratios of n-3 to n-6 PUFAs, reflecting both dietary intake of n-3 PUFAs and competing n-6 PUFAs, are better predictors of future MI than n-3 PUFA fractions is unclear. We aimed at investigating whether such ratios in adipose tissue better predict MI than n-3 PUFA fractions. METHODS: Subcutaneous adipose tissue biopsies were obtained in a random sample (n = 3,500) of the Diet, Cancer and Health cohort (n = 57,053). Adipose tissue content of eicosapentaenoic acid (EPA), docosapentaenoic acid (DPA), docosahexaenoic acid (DHA), alpha-linolenic acid (ALA), arachidonic acid (AA) and linoleic acid was determined using gas chromatography. Fractions of selected n-3 PUFAs and n-3/n-6 PUFA ratios were correlated to the 15-year occurrence of MI in a case-cohort design. RESULTS: A total of 2,406 participants experienced an MI during follow-up. Adipose tissue total marine n-3 PUFAs, EPA+DHA, EPA, EPA/AA, DHA/AA and (EPA + DPA + DHA)/AA were all inversely associated with risk of incident MI. Evaluating the predictive power (Harrel's C-index) of the selected metrics, fractions of marine n-3 PUFAs and ratios of EPA/AA, DHA/AA, (EPA + DHA)/AA and (EPA + DPA + DHA)/AA all refined risk prediction over age and sex alone. At multivariable analyses, however, the above ratios were the only metrics providing additional risk prediction. Differences in ratios were related to differences in food intake. CONCLUSIONS: Both adipose tissue n-3 PUFAs fractions and ratios of n-3 PUFAs/AA were associated with a lower occurrence of MI, but ratios provided superior risk prediction. Dietary strategies affecting n-3/n-6 PUFA ratios should be further investigated for prediction of MI with dietary interventions at the population level and in intervention studies.

Quantification of Myocardial Contraction Fraction with Three-Dimensional Automated, Machine-Learning-Based Left-Heart-Chamber Metrics: Diagnostic Utility in Hypertrophic Phenotypes and Normal Ejection Fraction.

Aims: The differentiation of left ventricular (LV) hypertrophic phenotypes is challenging in patients with normal ejection fraction (EF). The myocardial contraction fraction (MCF) is a simple dimensionless index useful for specifically identifying cardiac amyloidosis (CA) and hypertrophic cardiomyopathy (HCM) when calculated by cardiac magnetic resonance. The purpose of this study was to evaluate the value of MCF measured by three-dimensional automated, machine-learning-based LV chamber metrics (dynamic heart model [DHM]) for the discrimination of different forms of hypertrophic phenotypes. Methods and Results: We analyzed the DHM LV metrics of patients with CA (n = 10), hypertrophic cardiomyopathy (HCM, n = 36), isolated hypertension (IH, n = 87), and 54 healthy controls. MCF was calculated by dividing LV stroke volume by LV myocardial volume. Compared with controls (median 61.95%, interquartile range 55.43-67.79%), mean values for MCF were significantly reduced in HCM-48.55% (43.46-54.86% p < 0.001)-and CA-40.92% (36.68-46.84% p < 0.002)-but not in IH-59.35% (53.22-64.93% p < 0.7). MCF showed a weak correlation with EF in the overall cohort (R2 = 0.136) and the four study subgroups (healthy adults, R2 = 0.039 IH, R2 = 0.089; HCM, R2 = 0.225; CA, R2 = 0.102). ROC analyses showed that MCF could differentiate between healthy adults and HCM (sensitivity 75.9%, specificity 77.8%, AUC 0.814) and between healthy adults and CA (sensitivity 87.0%, specificity 100%, AUC 0.959). The best cut-off values were 55.3% and 52.8%. Conclusions: The easily derived quantification of MCF by DHM can refine our echocardiographic discrimination capacity in patients with hypertrophic phenotype and normal EF. It should be added to the diagnostic workup of these patients.

Three-Dimensional Automated, Machine-Learning-Based Left Heart Chamber Metrics: Associations with Prevalent Vascular Risk Factors and Cardiovascular Diseases.

Background. Three-dimensional transthoracic echocardiography (3DE) powered by artificial intelligence provides accurate left chamber quantification in good accordance with cardiac magnetic resonance and has the potential to revolutionize our clinical practice. Aims. To evaluate the association and the independent value of dynamic heart model (DHM)-derived left atrial (LA) and left ventricular (LV) metrics with prevalent vascular risk factors (VRFs) and cardiovascular diseases (CVDs) in a large, unselected population. Materials and Methods. We estimated the association of DHM metrics with VRFs (hypertension, diabetes) and CVDs (atrial fibrillation, stroke, ischemic heart disease, cardiomyopathies, >moderate valvular heart disease/prosthesis), stratified by prevalent disease status: participants without VRFs or CVDs (healthy), with at least one VRFs but without CVDs, and with at least one CVDs. Results. We retrospectively included 1069 subjects (median age 62 [IQR 49−74]; 50.6% women). When comparing VRFs with the healthy, significant difference in maximum and minimum indexed atrial volume (LAVi max and LAVi min), left atrial ejection fraction (LAEF), left ventricular mass/left ventricular end-diastolic volume ratio, and left ventricular global function index (LVGFI) were recorded (p < 0.05). In the adjusted logistic regression, LAVi min, LAEF, LV ejection fraction, and LVGFI showed the most robust association (OR 3.03 [95% CI 2.48−3.70], 0.45 [95% CI 0.39−0.51], 0.28 [95% CI 0.22−0.35], and 0.22 [95% CI 0.16−0.28], respectively, with CVDs. Conclusions. The present data suggested that novel 3DE left heart chamber metrics by DHM such as LAEF, LAVi min, and LVGFI can refine our echocardiographic disease discrimination capacity.