👤 Piotr Adamski

To delineate organ-specific and systemic drivers of metabolic dysfunction-associated steatotic liver disease (MASLD), we applied integrative causal inference across clinical, imaging, and proteomic domains in individuals with and without type 2 diabetes (T2D). Bayesian network analyses and complementary two-sample Mendelian randomization were used to quantify causal pathways linking adipose distribution, glycemia, and insulin dynamics with liver fat in the IMI-DIRECT prospective cohort study. Data included frequently sampled metabolic challenge tests, MRI-derived abdominal and hepatic fat content, serological biomarkers, and Olink plasma proteomics from 331 adults with new-onset T2D and 964 adults without diabetes, with harmonized protocols enabling replication. High basal insulin secretion rate (BasalISR), estimated via C-peptide deconvolution, emerged as the primary potential causal driver of liver fat accumulation in both cohorts. BasalISR, a clearance-independent measure of β-cell insulin output distinct from peripheral insulin levels, was independently linked to hepatic steatosis. Visceral adipose tissue exhibited bidirectional associations with liver fat, suggesting a self-reinforcing metabolic loop. Of 446 analyzed proteins, 34 mapped to these metabolic networks (27 in the non-diabetes network, 18 in the T2D network, and 11 shared). Key proteins directly associated with liver fat included GUSB, ALDH1A1, LPL, IGFBP1/2, CTSD, HMOX1, FGF21, AGRP, and ACE2. Sex-stratified analyses identified GUSB in females and LEP in males as the strongest protein predictors of liver fat. BasalISR may better capture early β-cell-driven disturbances contributing to MASLD. These findings outline a multifactorial, sex- and disease stage-specific proteo-metabolic architecture of hepatic steatosis and identify potential biomarkers or therapeutic targets. Show less

Hyperlipidemia is the most prevalent cardiovascular (CV) risk factors. We aimed to analyze the distribution of lipid parameters and clinical variables associated with elevated and non-elevated selected lipid factors in a cohort of all consecutive patients whose lipid profile was assessed at a multi-specialist clinical center. This cross-sectional study analyzed electronic medical records of consecutive patients treated between March and November 2024. Lipid parameters measured included: total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), triglycerides (TG), apolipoprotein B (apoB), and lipoprotein (a) [Lp(a)]. Non-high-density lipoprotein cholesterol (non-HDL-C) was calculated as TC-HDL-C. We used multivariate analysis to identify factors associated with LDL-C, TG, and Lp(a) concentrations. A total of 10,597 patients were included in the analysis. The median lipid concentrations (mg/dL) were: TC 162 (IQR 132-198), LDL-C 94 (IQR 69-129), non-HDL-C 112 (IQR 88-146), apoB 78 (IQR 63-99), and Lp(a) 11 (IQR 5-29). Elevated LDL-C > 100 mg/dL was observed in 45.7% of patients, non-HDL-C > 130 mg/dL in 35.1%, and apoB > 100 mg/dL in 23.2%. A discordance between LDL-C and apoB concentrations was present in 23.7% of patients (p < 0.001), while LDL-C/non-HDL-C and apoB/non-HDL-C discordance rates were 13% and 12.6%, respectively (p < 0.001). Patients at very high CV risk had lower TC, LDL-C, non-HDL-C, and apoB concentrations compared to those with low-to-moderate and high CV risk (p < 0.001) and showed the highest median Lp(a) concentration of 13 mg/dL (IQR 5-31; p = 0.01). Goal achievements of LDL-C < 100 mg/dL, TG < 150 mg/dL, and Lp(a) < 30 mg/dL were associated with lipid-lowering treatment [OR 1.32 (95% CI 1.12-1.52)], atrial fibrillation [OR 1.31 (95% CI 1.11-1.54)], chronic coronary syndromes [OR 1.27 (95% CI 1.05-1.52)], smoking [OR 0.78 (95% CI 0.65-0.95)], BMI [OR 0.98 (95% CI 0.96-0.99)], and age [OR 1.006 (95% CI 1.002-1.009)]. The highest proportion of patients with results within the normal range was observed for apoB and the lowest for LDL-C. The highest discordance was observed between apoB/LDL-C, with similar discordance rates between LDL-C/non-HDL-C and apoB/non-HDL-C. Lipid profile control was associated with BMI, atrial fibrillation, age, chronic coronary syndrome, aortic stenosis, diabetes, male gender, lipid-lowering therapy, and smoking. These findings highlight the complexity of lipid management. Show less

This review explores the roles of interleukin-30 (IL-30) and interleukin-27 (IL-27) in inflammation and autoimmune diseases, with a focus on psoriasis. The two coexisting cytokines should be analysed in conjunction as their actions are antagonistic Show less

To delineate organ-specific and systemic drivers of metabolic dysfunction-associated steatotic liver disease (MASLD), we applied integrative causal inference across clinical, imaging, and proteomic domains in individuals with and without type 2 diabetes (T2D). We used Bayesian network analyses to quantify causal pathways linking adipose distribution, glycemia, and insulin dynamics with fatty liver using data from the IMI-DIRECT prospective cohort study. Measurements were made of glucose and insulin dynamics (using frequently-sampled metabolic challenge tests), MRI-derived abdominal and liver fat content, serological biomarkers, and Olink plasma proteomics from 331 adults with new-onset T2D and 964 adults free from diabetes at enrolment. The common protocols used in these two cohorts provided the opportunity for replication analyses to be performed. When the direction of the effect could not be determined with high probability through Bayesian networks, complementary two-sample Mendelian randomization (MR) was employed. High basal insulin secretion rate (BasalISR) was identified as the primary causal driver of liver fat accumulation in both diabetes and non-diabetes. Excess visceral adipose tissue (VAT) was bidirectionally associated with liver fat, indicating a self-reinforcing metabolic loop. Basal insulin clearance (Clinsb) worsened as a consequence of liver fat accumulation to a greater degree before the onset of T2D. Out of 446 analysed proteins, 34 mapped to these metabolic networks and 27 were identified in the non-diabetes network, 18 in the diabetes network, and 11 were common between the two networks. Key proteins directly associated with liver fat included GUSB, ALDH1A1, LPL, IGFBP1/2, CTSD, HMOX1, FGF21, AGRP, and ACE2. Sex-stratified analyses revealed distinct proteomic drivers: GUSB and LEP were most predictive of liver fat in females and males, respectively. Basal insulin hypersecretion is a modifiable, causal driver of MASLD, particularly prior to glycaemic decompensation. Our findings highlight a multifactorial, sex- and disease-stage-specific proteo-metabolic architecture of hepatic steatosis. Proteins such as GUSB, ALDH1A1, LPL, and IGFBPs warrant further investigation as potential biomarkers or therapeutic targets for MASLD prevention and treatment. Show less

Diabetes-associated metabolites may aid the identification of new risk variants for type 2 diabetes. Using targeted metabolomics within a subsample of the German EPIC-Potsdam study (n = 2500), we tested previously published SNPs for their association with diabetes-associated metabolites and conducted an additional exploratory analysis using data from the exome chip including replication within 2,692 individuals from the German KORA F4 study. We identified a total of 16 loci associated with diabetes-related metabolite traits, including one novel association between rs499974 (MOGAT2) and a diacyl-phosphatidylcholine ratio (PC aa C40:5/PC aa C38:5). Gene-based tests on all exome chip variants revealed associations between GFRAL and PC aa C42:1/PC aa C42:0, BIN1 and SM (OH) C22:2/SM C18:0 and TFRC and SM (OH) C22:2/SM C16:1). Selecting variants for gene-based tests based on functional annotation identified one additional association between OR51Q1 and hexoses. Among single genetic variants consistently associated with diabetes-related metabolites, two (rs174550 (FADS1), rs3204953 (REV3L)) were significantly associated with type 2 diabetes in large-scale meta-analysis for type 2 diabetes. In conclusion, we identified a novel metabolite locus in single variant analyses and four genes within gene-based tests and confirmed two previously known mGWAS loci which might be relevant for the risk of type 2 diabetes. Show less

The hydroxysteroid (17beta) dehydrogenase (HSD17B)12 gene belongs to the hydroxysteroid (17β) dehydrogenase superfamily, and it has been implicated in the conversion of estrone to estradiol as well as in the synthesis of arachidonic acid (AA). AA is a precursor of prostaglandins, which are involved in the regulation of female reproduction, prompting us to study the role of HSD17B12 enzyme in the ovarian function. We found a broad expression of HSD17B12 enzyme in both human and mouse ovaries. The enzyme was localized in the theca interna, corpus luteum, granulosa cells, oocytes, and surface epithelium. Interestingly, haploinsufficiency of the HSD17B12 gene in female mice resulted in subfertility, indicating an important role for HSD17B12 enzyme in the ovarian function. In line with significantly increased length of the diestrous phase, the HSD17B Show less

Metformin is used as a first-line oral treatment for type 2 diabetes (T2D). However, the underlying mechanism is not fully understood. Here, we aimed to comprehensively investigate the pleiotropic effects of metformin. We analyzed both metabolomic and genomic data of the population-based KORA cohort. To evaluate the effect of metformin treatment on metabolite concentrations, we quantified 131 metabolites in fasting serum samples and used multivariable linear regression models in three independent cross-sectional studies (n = 151 patients with T2D treated with metformin [mt-T2D]). Additionally, we used linear mixed-effect models to study the longitudinal KORA samples (n = 912) and performed mediation analyses to investigate the effects of metformin intake on blood lipid profiles. We combined genotyping data with the identified metformin-associated metabolites in KORA individuals (n = 1,809) and explored the underlying pathways. We found significantly lower (P < 5.0E-06) concentrations of three metabolites (acyl-alkyl phosphatidylcholines [PCs]) when comparing mt-T2D with four control groups who were not using glucose-lowering oral medication. These findings were controlled for conventional risk factors of T2D and replicated in two independent studies. Furthermore, we observed that the levels of these metabolites decreased significantly in patients after they started metformin treatment during 7 years' follow-up. The reduction of these metabolites was also associated with a lowered blood level of LDL cholesterol (LDL-C). Variations of these three metabolites were significantly associated with 17 genes (including FADS1 and FADS2) and controlled by AMPK, a metformin target. Our results indicate that metformin intake activates AMPK and consequently suppresses FADS, which leads to reduced levels of the three acyl-alkyl PCs and LDL-C. Our findings suggest potential beneficial effects of metformin in the prevention of cardiovascular disease. Show less

Emerging technologies based on mass spectrometry or nuclear magnetic resonance enable the monitoring of hundreds of small metabolites from tissues or body fluids. Profiling of metabolites can help elucidate causal pathways linking established genetic variants to known disease risk factors such as blood lipid traits. We applied statistical methodology to dissect causal relationships between single nucleotide polymorphisms, metabolite concentrations, and serum lipid traits, focusing on 95 genetic loci reproducibly associated with the four main serum lipids (total-, low-density lipoprotein-, and high-density lipoprotein- cholesterol and triglycerides). The dataset used included 2,973 individuals from two independent population-based cohorts with data for 151 small molecule metabolites and four main serum lipids. Three statistical approaches, namely conditional analysis, Mendelian randomization, and structural equation modeling, were compared to investigate causal relationship at sets of a single nucleotide polymorphism, a metabolite, and a lipid trait associated with one another. A subset of three lipid-associated loci (FADS1, GCKR, and LPA) have a statistically significant association with at least one main lipid and one metabolite concentration in our data, defining a total of 38 cross-associated sets of a single nucleotide polymorphism, a metabolite and a lipid trait. Structural equation modeling provided sufficient discrimination to indicate that the association of a single nucleotide polymorphism with a lipid trait was mediated through a metabolite at 15 of the 38 sets, and involving variants at the FADS1 and GCKR loci. These data provide a framework for evaluating the causal role of components of the metabolome (or other intermediate factors) in mediating the association between established genetic variants and diseases or traits. Show less

Nuclear magnetic resonance spectroscopy (NMR) provides robust readouts of many metabolic parameters in one experiment. However, identification of clinically relevant markers in (1)H NMR spectra is a major challenge. Association of NMR-derived quantities with genetic variants can uncover biologically relevant metabolic traits. Using NMR data of plasma samples from 1,757 individuals from the KORA study together with 655,658 genetic variants, we show that ratios between NMR intensities at two chemical shift positions can provide informative and robust biomarkers. We report seven loci of genetic association with NMR-derived traits (APOA1, CETP, CPS1, GCKR, FADS1, LIPC, PYROXD2) and characterize these traits biochemically using mass spectrometry. These ratios may now be used in clinical studies. Show less

Diabetes is generally diagnosed too late. Therefore, biomarkers indicating early stages of β-cell dysfunction and mass reduction would facilitate timely counteraction. Transgenic pigs expressing a dominant-negative glucose-dependent insulinotropic polypeptide receptor (GIPR(dn)) reveal progressive deterioration of glucose control and reduction of β-cell mass, providing a unique opportunity to study metabolic changes during the prediabetic period. Plasma samples from intravenous glucose tolerance tests of 2.5- and 5-month-old GIPR(dn) transgenic and control animals were analyzed for 163 metabolites by targeted mass spectrometry. Analysis of variance revealed that 26 of 163 parameters were influenced by the interaction Genotype × Age (P ≤ 0.0001) and thus are potential markers for progression within the prediabetic state. Among them, the concentrations of seven amino acids (Phe, Orn, Val, xLeu, His, Arg, and Tyr) were increased in 2.5-month-old but decreased in 5-month-old GIPR(dn) transgenic pigs versus controls. Furthermore, specific sphingomyelins, diacylglycerols, and ether phospholipids were decreased in plasma of 5-month-old GIPR(dn) transgenic pigs. Alterations in plasma metabolite concentrations were associated with liver transcriptome changes in relevant pathways. The concentrations of a number of plasma amino acids and lipids correlated significantly with β-cell mass of 5-month-old pigs. These metabolites represent candidate biomarkers of early phases of β-cell dysfunction and mass reduction. Show less

Genome-wide association studies (GWAS) analyze the genetic component of a phenotype or the etiology of a disease. Despite the success of many GWAS, little progress has been made in uncovering the underlying mechanisms for many diseases. The use of metabolomics as a readout of molecular phenotypes has enabled the discovery of previously undetected associations between diseases and signaling and metabolic pathways. In addition, combining GWAS and metabolomic information allows the simultaneous analysis of the genetic and environmental impacts on homeostasis. Most success has been seen in metabolic diseases such as diabetes, obesity and dyslipidemia. Recently, associations between loci such as FADS1, ELOVL2 or SLC16A9 and lipid concentrations have been explained by GWAS with metabolomics. Combining GWAS with metabolomics (mGWAS) provides the robust and quantitative information required for the development of specific diagnostics and targeted drugs. This review discusses the limitations of GWAS and presents examples of how metabolomics can overcome these limitations with the focus on metabolic diseases. Show less

Adverse levels of lipoproteins are highly heritable and constitute risk factors for cardiovascular outcomes. Hitherto, genome-wide association studies revealed 95 lipid-associated loci. However, due to the small effect sizes of these associations large sample numbers (>100 000 samples) were needed. Here we show that analyzing more refined lipid phenotypes, namely lipoprotein subfractions, can increase the number of significantly associated loci compared with bulk high-density lipoprotein and low-density lipoprotein analysis in a study with identical sample numbers. Moreover, lipoprotein subfractions provide novel insight into the human lipid metabolism. We measured 15 lipoprotein subfractions (L1-L15) in 1791 samples using (1)H-NMR (nuclear magnetic resonance) spectroscopy. Using cluster analyses, we quantified inter-relationships among lipoprotein subfractions. Additionally, we analyzed associations with subfractions at known lipid loci. We identified five distinct groups of subfractions: one (L1) was only marginally captured by serum lipids and therefore extends our knowledge of lipoprotein biochemistry. During a lipid-tolerance test, L1 lost its special position. In the association analysis, we found that eight loci (LIPC, CETP, PLTP, FADS1-2-3, SORT1, GCKR, APOB, APOA1) were associated with the subfractions, whereas only four loci (CETP, SORT1, GCKR, APOA1) were associated with serum lipids. For LIPC, we observed a 10-fold increase in the variance explained by our regression models. In conclusion, NMR-based fine mapping of lipoprotein subfractions provides novel information on their biological nature and strengthens the associations with genetic loci. Future clinical studies are now needed to investigate their biomedical relevance. Show less

Metabolomic profiling and the integration of whole-genome genetic association data has proven to be a powerful tool to comprehensively explore gene regulatory networks and to investigate the effects of genetic variation at the molecular level. Serum metabolite concentrations allow a direct readout of biological processes, and association of specific metabolomic signatures with complex diseases such as Alzheimer's disease and cardiovascular and metabolic disorders has been shown. There are well-known correlations between sex and the incidence, prevalence, age of onset, symptoms, and severity of a disease, as well as the reaction to drugs. However, most of the studies published so far did not consider the role of sexual dimorphism and did not analyse their data stratified by gender. This study investigated sex-specific differences of serum metabolite concentrations and their underlying genetic determination. For discovery and replication we used more than 3,300 independent individuals from KORA F3 and F4 with metabolite measurements of 131 metabolites, including amino acids, phosphatidylcholines, sphingomyelins, acylcarnitines, and C6-sugars. A linear regression approach revealed significant concentration differences between males and females for 102 out of 131 metabolites (p-values<3.8×10(-4); Bonferroni-corrected threshold). Sex-specific genome-wide association studies (GWAS) showed genome-wide significant differences in beta-estimates for SNPs in the CPS1 locus (carbamoyl-phosphate synthase 1, significance level: p<3.8×10(-10); Bonferroni-corrected threshold) for glycine. We showed that the metabolite profiles of males and females are significantly different and, furthermore, that specific genetic variants in metabolism-related genes depict sexual dimorphism. Our study provides new important insights into sex-specific differences of cell regulatory processes and underscores that studies should consider sex-specific effects in design and interpretation. Show less

17β-Hydroxysteroid dehydrogenase type 3 and 5 (17β-HSD3 and 17β-HSD5) catalyze testosterone biosynthesis and thereby constitute therapeutic targets for androgen-related diseases or endocrine-disrupting chemicals. As a fast and efficient tool to identify potential ligands for 17βHSD3/5, ligand- and structure-based pharmacophore models for both enzymes were developed. The models were evaluated first by in silico screening of commercial compound databases and further experimentally validated by enzymatic efficacy tests of selected virtual hits. Among the 35 tested compounds, 11 novel inhibitors with distinct chemical scaffolds, e.g. sulfonamides and triazoles, and with different selectivity properties were discovered. Thereby, we provide several potential starting points for further 17β-HSD3 and 17β-HSD5 inhibitor development. Article from the Special issue on Targeted Inhibitors. Show less

Serum metabolite concentrations provide a direct readout of biological processes in the human body, and they are associated with disorders such as cardiovascular and metabolic diseases. We present a genome-wide association study (GWAS) of 163 metabolic traits measured in human blood from 1,809 participants from the KORA population, with replication in 422 participants of the TwinsUK cohort. For eight out of nine replicated loci (FADS1, ELOVL2, ACADS, ACADM, ACADL, SPTLC3, ETFDH and SLC16A9), the genetic variant is located in or near genes encoding enzymes or solute carriers whose functions match the associating metabolic traits. In our study, the use of metabolite concentration ratios as proxies for enzymatic reaction rates reduced the variance and yielded robust statistical associations with P values ranging from 3 x 10(-24) to 6.5 x 10(-179). These loci explained 5.6%-36.3% of the observed variance in metabolite concentrations. For several loci, associations with clinically relevant parameters have been reported previously. Show less

The rapidly evolving field of metabolomics aims at a comprehensive measurement of ideally all endogenous metabolites in a cell or body fluid. It thereby provides a functional readout of the physiological state of the human body. Genetic variants that associate with changes in the homeostasis of key lipids, carbohydrates, or amino acids are not only expected to display much larger effect sizes due to their direct involvement in metabolite conversion modification, but should also provide access to the biochemical context of such variations, in particular when enzyme coding genes are concerned. To test this hypothesis, we conducted what is, to the best of our knowledge, the first GWA study with metabolomics based on the quantitative measurement of 363 metabolites in serum of 284 male participants of the KORA study. We found associations of frequent single nucleotide polymorphisms (SNPs) with considerable differences in the metabolic homeostasis of the human body, explaining up to 12% of the observed variance. Using ratios of certain metabolite concentrations as a proxy for enzymatic activity, up to 28% of the variance can be explained (p-values 10(-16) to 10(-21)). We identified four genetic variants in genes coding for enzymes (FADS1, LIPC, SCAD, MCAD) where the corresponding metabolic phenotype (metabotype) clearly matches the biochemical pathways in which these enzymes are active. Our results suggest that common genetic polymorphisms induce major differentiations in the metabolic make-up of the human population. This may lead to a novel approach to personalized health care based on a combination of genotyping and metabolic characterization. These genetically determined metabotypes may subscribe the risk for a certain medical phenotype, the response to a given drug treatment, or the reaction to a nutritional intervention or environmental challenge. Show less

Apolipoprotein A5 (APOA5) gene variants were reported to be associated with two components of metabolic syndrome (MetS): higher TG levels and lower HDL levels. Moreover, a recent Japanese case-control study found variant -1131T>C associated with MetS itself. Thus, our study systematically analyzed the APOA5 gene for association with lipid parameters, any other features of MetS, including waist circumference, glucose-related parameters, blood pressure, uric acid, and MetS itself in Caucasians. Ten polymorphisms were analyzed in a large fasting sample of the population-based Cooperative Health Research in the Region of Augsburg (KORA) survey S4 (n = 1,354; southern Germany) and in a second fasting sample, the Salzburg Atherosclerosis Prevention Program in Subjects at High Individual Risk (SAPHIR) study (n = 1,770; Austria). Minor alleles of variants -1131T>C, -3A>G, c.56C>G, 476G>A, and 1259T>C were significantly associated with higher TG levels in single polymorphism (P < 0.001) and haplotype (P G was associated with higher risk for MetS [odds ratio (95% confidence interval) = 1.43 (1.04, 1.99), P = 0.03 for KORA and 1.48 (1.10, 1.99), P = 0.009 for SAPHIR). Our study confirms the association of the APOA5 locus with TG and HDL levels in humans. Furthermore, the data suggest a different mechanism of APOA5 impact on MetS in Caucasians, as variant c.56C>G (not analyzed in the Japanese study) and not -1131T>C, as in the Japanese subjects, was associated with MetS. Show less

Formation and inactivation of testosterone is performed by various members of the 17beta-hydroxysteroid dehydrogenase (17beta-HSD) family. The main player in testosterone formation is considered to be 17beta-HSD type 3, which catalyzes the reduction of androstenedione to testosterone with high efficiency and is almost exclusively expressed in testis. So far, only the mammalian homologs have been characterized but nothing is known about the role of 17beta-HSD type 3 in other vertebrates. In this study, we describe the identification and characterization of the zebrafish homolog. We found zebrafish 17beta-HSD type 3 to be expressed in embryogenesis from sphere to 84 h post-fertilization. Expression was also detected in various tissues of both male and female adults, but displayed sexual dimorphism. Interestingly, expression was not highest in male testis but in male liver. In female adults, strongest expression was observed in ovaries. At the subcellular level, both human and zebrafish 17beta-HSD type 3 localize to the endoplasmic reticulum. The zebrafish enzyme in vitro effectively catalyzed the conversion of androstenedione to testosterone by use of NADPH as cofactor. Among further tested androgens epiandrosterone and dehydroepiandrosterone were accepted as substrates and reduced at C-17 by the human and the zebrafish enzyme. Androsterone and androstanedione though, were only substrates of human 17beta-HSD type 3, not the zebrafish enzyme. Furthermore, we found that both enzymes can reduce 11-ketoandrostenedione as well as 11beta-hydroxyandrostenedione at C-17 to the respective testosterone forms. Our results suggest that 17beta-HSD type 3 might play slightly different roles in zebrafish compared with human although testosterone itself is likely to have similar functions in both organisms. Show less

The 17 beta-hydroxysteroid dehydrogenases (17 beta-HSDs) are key enzymes in the final steps of steroid hormone synthesis. 17beta-HSD type 1 (HSD17B1) catalyzes the reduction of estrone to estradiol, while type 3 (HSD17B3) performs the conversion of androstenedione to testosterone. Here we present a functional genomics study of putative candidates of these enzymes in the zebrafish. By an in silico screen of zebrafish EST databases we identified three candidate homologs for both HSD17B1 and HSD17B3. Phylogenetic analysis, unique expression patterns (RT-PCR) during embryogenesis and adulthood, as well as activity measurements revealed that one of the HSD17B1 candidates is the zebrafish homolog, while the other two are paralogous photoreceptor-associated retinol dehydrogenases. All three HSD17B3 candidate genes showed nearly identical, ubiquitous expressions in embryogenesis and adult tissues and were identified to be paralogs of HSD17B12 and a yet uncharacterized putative steroid dehydrogenase. Phylogenetic analysis shows that HSD17B3 and HSD17B12 are descendants from a common ancestor. Show less