Malondialdehyde, lipoprotein-a, lipoprotein ratios, comprehensive lipid tetrad index and atherogenic index as surrogate markers for cardiovascular disease in patients with psoriasis: a case-control study.
Dhaarna WadhwaVikram K MahajanKaraninder S MehtaPushpinder S ChauhanRajinder S YadavSatya BhushanVikas SharmaAnuj SharmaAditi SharmaShailja ChauhanPublished in: Archives of dermatological research (2019)
Psoriasis is now recognized as an immune-mediated inflammatory dermatosis with increased risk for metabolic syndrome, its individual components, and cardiovascular disease. We quantitatively estimated malondialdehyde (MDA), lipoprotein-a (LP-a), lipoprotein ratios, comprehensive lipid tetrad index (CLTI), and atherogenic index (AI), and evaluated cardiovascular risk in 132 (M:F 94:38) patients with psoriasis aged 20-79 years with chronic plaque psoriasis and equal number of age and gender-matched controls. Lipoprotein ratios, CLTI and AI were calculated using standard formulae. Cardiovascular 10-year risk was graded by Framingham risk score (FRS) as low, intermediate and severe. Mild-to-moderate and severe psoriasis was present in 125 (94.7%), and 7 (5.3%) patients, respectively, and 19 (14.39%) patients had psoriatic arthritis. Statistically significant differences were noted for LDL, LDL/HDL, non-HDL/HDL, MDA, LP-a, AI and CLTI. There was a significantly positive correlation between PASI with LP-a (p = 0.003, r = 0.25) and AI (p = 0.012, r = 0.22). Serum levels of MDA correlated positively with LP-a (p < 0.001, r = 0.55), AI (p < 0.001, r = 0.51) and CLTI (p = 0.006, r = 0.24). FRS was low, intermediate and severe in 78%, 18.9%, and 3% patients compared to 85.6%, 13.6%, and 0.8% controls, respectively, and the difference was not statistically significant. Psoriasis appears to be an independent risk factor for elevated serum MDA, LP-a, CLTI and AI. However, whether they can be used as surrogate markers for enhanced cardiovascular risk in patients with psoriasis, remains conjectural.
Keyphrases
- cardiovascular disease
- end stage renal disease
- metabolic syndrome
- artificial intelligence
- newly diagnosed
- chronic kidney disease
- ejection fraction
- low density lipoprotein
- prognostic factors
- early onset
- type diabetes
- coronary artery disease
- oxidative stress
- machine learning
- mental health
- skeletal muscle
- atopic dermatitis
- adipose tissue
- patient reported outcomes
- patient reported
- uric acid
- cell cycle arrest
- deep learning