👤 Jan Wolski

Recently, the knowledge of the genetic basis of fertility disorders has expanded enormously, mainly thanks to the use of next-generation sequencing (NGS). However, the genetic cause of infertility, in the majority of patients, is still undefined. The aim was to identify novel and recurrent pathogenic/likely pathogenic variants in patients with isolated infertility or puberty delay using a targeted NGS technique. We have enrolled 41 patients (36 males and 5 females) with infertility problems or delayed puberty. We included the patients with hypogonadotropic hypogonadism (n = 12), hypergonadotropic hypogonadism (n = 15), abnormal sperm parameters (n = 10), androgen insensitivity syndrome (n = 3) and 46,XY gonadal dysgenesis (n = 1). Genetic tests were performed using targeted NGS panel of 35 genes implicated in fertility. Pathogenic or likely pathogenic variants potentially explaining the clinical phenotype were identified in 12 of 41 patients (29%). These included 9 of 12 patients (75%) with hypogonadotropic hypogonadism, 2 of 3 patients (66%) with androgen insensitivity syndrome, and the single patient with 46,XY gonadal dysgenesis. Among the 18 identified variants, 4 were novel (FGF8:p.Ala147Thr; SEMA3A:p.Arg544Cys; FGFR1:p.Thr141IlefsTer10; NSMF: p.Tyr242Cys), while 14 were recurrent. Our study expands the knowledge of the genetic basis of the infertility disorders and highlights the importance of genetic testing for proper diagnosis making and genetic counselling. Show less

Gestational weight gain (GWG) involves health consequences for both mother and offspring. Genetic factors seem to play a role in the GWG trait. For small effect sizes of a single genetic polymorphism (SNP), a genetic risk score (GRS) summarizing risk-associated variation from multiple SNPs can serve as an effective approach to genetic association analysis. The aim of the study was to analyze the association between genetic risk score (GRS) and gestational weight gain (GWG). GWG was calculated for a total of 342 healthy Polish women of Caucasian origin, aged 19 to 45 years. The SNPs rs9939609 (FTO), rs6548238 (TMEM18), rs17782313 (MC4R), rs10938397 (GNPDA2), rs10913469 (SEC16B), rs1137101 (LEPR), rs7799039 (LEP), and rs5443 (GNB3) were genotyped using commercial TaqMan SNP assays. A simple genetic risk score was calculated into two ways: GRS1 based on the sum of risk alleles from each of the SNPs, while GRS2 based on the sum of risk alleles of FTO, LEPR, LEP, and GNB3. Positive association between GRS2 and GWG (β = 0.12, p = 0.029) was observed. Genetic risk variants of TMEM18 (p = 0.006, OR = 2.6) and GNB3 (p < 0.001, OR = 3.3) are more frequent in women with increased GWG, but a risk variant of GNPDA2 (p < 0.001, OR = 2.7) is more frequent in women with adequate GWG, and a risk variant of LEPR (p = 0.011, OR = 3.1) in women with decreased GWG. GRS2 and genetic variants of TMEM18, GNB3, GNPDA2, and LEPR are associated with weight gain during pregnancy. Show less

High-density lipoprotein (HDL) levels are inversely associated with cardiovascular risk. Cholesteryl ester transfer protein inhibition with evacetrapib results in a marked increase in HDL and reduction in low-density lipoprotein (LDL) levels. We evaluated the impact of treatment with evacetrapib versus placebo in the subset of 8236 patients with diabetes mellitus (DM) enrolled in the Assessment of Clinical Effects of Cholesteryl Ester Transfer Protein Inhibition with Evacetrapib in Patients at a High Risk for Vascular Outcomes trial. Time to first occurrence of any component of the primary composite endpoint of cardiovascular death, myocardial infarction, stroke, revascularization, and hospitalization for unstable angina was compared among patients with DM randomized to treatment with evacetrapib (n=4127) or placebo (n=4109) over a median of 26 months of follow-up. The mean baseline LDL at initiation was 80 mg/dL with a mean baseline HDL of 44 mg/dL. In patients with DM, evacetrapib resulted in a 131% mean increase in HDL levels and a 32% mean decrease in LDL at 3 months that was sustained during the course of the trial. At 6 months, hemoglobin A1c (HbA1c) levels were lower with evacetrapib than placebo (7.08% vs 7.15%, p=0.023). Composite event rates were higher in patients with DM than without DM (Kaplan-Meier estimates: 15.2% vs 10.6%, HR 1.46, 95% CI 1.30 to 1.64, p<0.001). In the DM group, event rates for the composite endpoint (14.5% evacetrapib vs 16% placebo, HR 0.95, 95% CI 0.85 to 1.07, p=0.38) and individual components of the composite were similar for both evacetrapib and placebo groups. No significant treatment interaction between treatment assignment and diabetes status was noted. Despite a favorable increase in HDL, and decreases in LDL and HbA1c levels in patients with DM, we observed no benefits of treatment with evacetrapib on prespecified clinical outcomes in this high-risk population. Show less

Although the degradome, which comprises proteolytic fragments of blood proteins, presents a potential source of diagnostic biomarkers, studies on cancer peptide biomarkers have provided inconsistent conclusions. In the present study, we reevaluated the usefulness of serum degradome analyses for searching peptide cancer biomarker candidates. Particular attention was paid to pre-analytical factors influencing the variability of determined peptide levels, including clotting time and control group selection. Studies were conducted on 44 and 86 serum samples collected from cancer patients and healthy individuals, respectively, using liquid chromatography electrospray ionization mass spectrometry (LC-ESI-MS)-based analyses. We identified 1373 unique peptides, nearly 40% of which originated from five blood proteins: fibrinogen alpha chain, apolipoprotein A-IV (APOA4), complement C3, apolipoprotein A-I, and alpha-1-antitrypsin. A set of 118 and 88 peptides exhibited highly significant differences (adjusted p-value ≤ 0.01 and fold change ≥ 2) in pair-wise comparisons of control vs. prostate cancer and control vs. colorectal cancer, respectively, with 37 peptides displaying a consistent direction of change for these pair-wise comparisons. The levels of 67 peptides differed significantly in serum samples collected from healthy individuals immediately prior to colonoscopy and those who underwent colonoscopic examination at least four weeks earlier. Of them, 49 peptides originated from APOA4. Whereas earlier studies, including ours, have utilized fragments of fibrinopeptide A (FPA) to distinguish cancer from healthy cases, here we show that their absolute abundance is a sensitive indicator of clotting time. These observations may have implications for future serum peptidome studies since these issues have not previously been recognized. Show less