Methods to predict heart failure in diabetes patients.
Alexander E BerezinTetiana A BerezinaUta C HoppeMichael LichtenauerAlexander A BerezinPublished in: Expert review of endocrinology & metabolism (2024)
Multiple diagnostic algorithms based on echocardiographic parameters of cardiac remodeling including global longitudinal strain/strain rate are likely to be promising approach to justify individuals at higher risk of incident HF. Signature of cardiometabolic status may justify HF risk among T2DM individuals with low levels of natriuretic peptides, which preserve their significance in HF with clinical presentation. However, diagnostic and predictive values of conventional guideline-directed biomarker HF strategy may be non-optimal in patients with obesity and T2DM. Alternative biomarkers affecting cardiac fibrosis, inflammation, myopathy, and adipose tissue dysfunction are plausible tools for improving accuracy natriuretic peptides among T2DM patients at higher HF risk. In summary, risk identification and management of the patients with T2DM with established HF require conventional biomarkers monitoring, while the role of alternative biomarker approach among patients with multiple CV and metabolic risk factors appears to be plausible tool for improving clinical outcomes.
Keyphrases
- acute heart failure
- heart failure
- ejection fraction
- adipose tissue
- left ventricular
- risk factors
- type diabetes
- glycemic control
- cardiovascular disease
- oxidative stress
- machine learning
- newly diagnosed
- weight loss
- pulmonary hypertension
- deep learning
- late onset
- early onset
- body mass index
- skeletal muscle
- left atrial
- weight gain
- peritoneal dialysis
- muscular dystrophy
- bioinformatics analysis