Associations between Urinary Advanced Glycation End Products and Cardiometabolic Parameters in Metabolically Healthy Obese Women.
Estifanos BayeAlicja B MarkMalene W PoulsenJeanette M AndersenLars Ove DragstedSussane G BügelBarbora De CourtenPublished in: Journal of clinical medicine (2019)
Advanced glycation end products (AGEs) have been implicated in the pathophysiology of type 2 diabetes and cardiovascular disease. We aimed to determine the associations of urinary carboxymethyl-lysine (CML) and methylglyoxal-hydroimidazolone (MG-H1) levels with cardiometabolic parameters in metabolically healthy obese women. Anthropometric, glycemic, cardiovascular, and urinary AGE parameters were measured in 58 metabolically healthy obese women (age: 39.98 ± 8.72 years; body mass index (BMI): 32.29 ± 4.05 kg/m2). Urinary CML levels were positively associated with BMI (r = 0.29, p = 0.02). After adjustment for age and BMI, there was a trend for positive associations between urinary CML levels and fasting (p = 0.06) and 2 h insulin (p = 0.05) levels, and insulin resistance measured by homeostatic model assessment (HOMA-IR) (p = 0.06). Urinary MG-H1 levels were positively associated with systolic and diastolic blood pressure, pulse pressure, mean arterial pressure, and total and low-density lipoprotein cholesterol after adjustment for age, BMI, and HOMA-IR (all p ˂ 0.05). There were no associations between urinary CML levels and cardiovascular parameters, and between urinary MG-H1 levels and glycemic measurements. Our data support a role of urinary AGEs in the pathophysiology of insulin resistance and cardiovascular disease; however, future studies are highly warranted.
Keyphrases
- body mass index
- type diabetes
- blood pressure
- cardiovascular disease
- insulin resistance
- adipose tissue
- polycystic ovary syndrome
- metabolic syndrome
- weight loss
- weight gain
- heart failure
- physical activity
- pregnant women
- body composition
- bariatric surgery
- machine learning
- big data
- pregnancy outcomes
- hypertensive patients
- artificial intelligence
- obese patients
- breast cancer risk
- ejection fraction
- deep learning
- case control