The Value of Genetic and Non-Genetic Factors in the Emergence and in the Development of Androgenetic Alopecia in Men: Multifactor Analysis

Abstract

Background: Among pathological hair loss conditions in men the androgenic alopecia (L64 according to ICD-10) has been the most common diagnosed. However, the reasons of the occurrence and development of the disease remain incompletely clarified, that determines the difficulties of personalized therapy. Aims: To analyze both genetic and non-genetic factors involved in the pathogenesis of androgenic alopecia in men, and to create personalized multifactorial model for description of individual causes of the disease. Materials and methods: The genetic predisposition to androgenic alopecia was estimated by the set of SNP rs929626, rs5919324, rs1998076, rs12565727 and rs756853, analyzed by mini-sequencing. The non-genetic factors included: hormones and metabolic markers, trace elements, and vitamins. Two-stage model creation of androgenic alopecia occurrence and development was carried out using a neural network (for genetic factors) followed by step-by-step linear discriminate analysis (for non-genetic factors). Results: The case-control study included 50 men revealing IIV stages of androgenic alopecia (according to Norwood-Hamilton classification) and 25 healthy volunteers relevant in their age and origin. The analysis of each SNP separately did not show significant differences between these groups, while SNP joint consideration in neural network model made it possible to assess the genetic predisposition to androgenic alopecia, as well as to divide the low and high genetic risk subgroups. A large number of significant non-genetic factors, including elevated levels of dihydrotestosterone, 17-OH-progesterone, insulin, and deficiency of Mg, Cu, Zn, Se, vitamins D, E, folic acid was shown in low genetic risk subgroup. In turn, in the high genetic risk subgroup the set of significant non-genetic factors was limited to metabolic and micronutrient disorders only. These data were used for the multifactorial model showing 81.285.1% accuracy being the most effective in early (III) stages of androgenic alopecia. Conclusions: The different influence of non-genetic factors in patients with low and high genetic risk of androgenic alopecia has been revealed. The integral factors consideration in the proposed two-stage multifactorial model identifies individual causes of the disease and gives the chance for the development of personalized therapy of androgenic alopecia in men.