👤 Johanna Mäkelä

Hydroxysteroid (17β) dehydrogenase (HSD17B) enzymes convert 17-ketosteroids to 17beta-hydroxysteroids, an essential step in testosterone biosynthesis. Human XY individuals with inactivating HSD17B3 mutations are born with female-appearing external genitalia due to testosterone deficiency. However, at puberty their testosterone production reactivates, indicating HSD17B3-independent testosterone synthesis. We have recently shown that Hsd17b3 knockout (3-KO) male mice display a similar endocrine imbalance, with high serum androstenedione and testosterone in adulthood, but milder undermasculinization than humans. Here, we studied whether HSD17B1 is responsible for the remaining HSD17B activity in the 3-KO male mice by generating a Ser134Ala point mutation that disrupted the enzymatic activity of HSD17B1 (1-KO) followed by breeding Hsd17b1/Hsd17b3 double-KO (DKO) mice. In contrast to 3-KO, inactivation of both HSD17B3 and HSD17B1 in mice results in a dramatic drop in testosterone synthesis during the fetal period. This resulted in a female-like anogenital distance at birth, and adult DKO males displayed more severe undermasculinization than 3-KO, including more strongly reduced weight of seminal vesicles, levator ani, epididymis, and testis. However, qualitatively normal spermatogenesis was detected in adult DKO males. Furthermore, similar to 3-KO mice, high serum testosterone was still detected in adult DKO mice, accompanied by upregulation of various steroidogenic enzymes. The data show that HSD17B1 compensates for HSD17B3 deficiency in fetal mouse testis but is not the enzyme responsible for testosterone synthesis in adult mice with inactivated HSD17B3. Therefore, other enzymes are able to convert androstenedione to testosterone in the adult mouse testis and presumably also in the human testis. Show less

Obesity is a known risk factor for cardiovascular disease. Early prediction of obesity is essential for prevention. The aim of this study is to assess the use of childhood clinical factors and the genetic risk factors in predicting adulthood obesity using machine learning methods. A total of 2262 participants from the Cardiovascular Risk in YFS (Young Finns Study) were followed up from childhood (age 3-18 years) to adulthood for 31 years. The data were divided into training (n=1625) and validation (n=637) set. The effect of known genetic risk factors (97 single-nucleotide polymorphisms) was investigated as a weighted genetic risk score of all 97 single-nucleotide polymorphisms (WGRS97) or a subset of 19 most significant single-nucleotide polymorphisms (WGRS19) using boosting machine learning technique. WGRS97 and WGRS19 were validated using external data (n=369) from BHS (Bogalusa Heart Study). WGRS19 improved the accuracy of predicting adulthood obesity in training (area under the curve [AUC=0.787 versus AUC=0.744, WGRS19 improves prediction of adulthood obesity. Predictive accuracy is highest among young children (3-6 years), whereas among older children (9-18 years) the risk can be identified using childhood clinical factors. The model is helpful in screening children with high risk of developing obesity. Show less