Paraoxonase 1 (PON1) Q192R and L55M polymorphisms, lipid profile, lipid peroxidation and lipoprotein-a levels in Turkish patients with pregnancy-related disorders

The genetic risk factors for pregnancy-induced hypertension: Evidence from genetic polymorphisms

Pregnancy-induced hypertension (PIH) is a multifactorial and severe pregnancy complication including preeclampsia/eclampsia, gestational hypertension, chronic (pre-existing) hypertension, and preeclampsia/eclampsia variants superimposed on chronic hypertension. PIH-induced maternal mortality accounts for approximately 9% of all maternal deaths over the world. A large number of case-control studies have established the importance of various genetic factors in the occurrence and development of PIH. In this narrative review, we summarized the genetic risk factors involved in the renin-angiotensin system, endothelin system, inflammatory factors, oxidative stress, and other functional networks, with the aim of sorting out the genetic factors that may play a potential role in PIH and providing new ideas to elucidate the pathogenesis of PIH.

Reduced paraoxonase 1 activities may explain the comorbidities between temporal lobe epilepsy and depression, anxiety and psychosis

BACKGROUND

Temporal lobe epilepsy (TLE) is the most common focal epilepsy subtype in adults and is frequently accompanied by depression, anxiety and psychosis. Aberrations in total paraoxonase 1 (PON1) status may occur in TLE and these psychiatric conditions.

AIM

To examine PON1 status, namely Q192R PON1 genotypes and PON1 enzymatic activities, in TLE.

METHODS

We recruited 40 normal controls and 104 TLE patients, 27 without comorbidities and 77 with comorbidities including mood disorders (n = 25), anxiety disorders (n = 27) and psychosis (n = 25).

RESULTS

Four-(chloromethyl)phenyl acetate hydrolysis (CMPAase) and arylesterase activities were significantly lower in TLE and mesial temporal sclerosis (MTS) with and without psychiatric comorbidities than those in normal controls. The areas under the receiver operating characteristic curve of CMPAase were 0.893 (0.037) for TLE and 0.895 (± 0.037) for MTS. Partial least squares path analysis showed that there were specific indirect effects of PON1 genotype on TLE severity (P < 0.0001) and psychopathology (P < 0.0001), which were both mediated by lowered CMPAase activity, while arylesterase activity was not significant. The severity of TLE was significantly associated with psychopathology scores. Furthermore, PON1 CMPAase activity was inversely associated with Mini Mental State Examination score.

CONCLUSION

The severity of TLE and comorbidities are to a large extent explained by reduced PON1 enzyme activities and by effects of the Q192R genotype, which are mediated by reduced CMPAase activity. Total PON1 status plays a key role in the pathophysiology of TLE, MTS and psychiatric comorbidities by increasing the risk of oxidative toxicity. PON1 enzyme activities are new drug targets in TLE to treat seizure frequency and psychiatric comorbidities.

Effect of gestational diabetes mellitus on lipid profile: A systematic review and meta-analysis

Gestational diabetes mellitus (GDM) can have adverse effects on pregnancy. GDM is associated with changes in the lipid profile of pregnant women. Finding out the early ways to diagnose GDM can prevent the adverse outcomes. This meta-analysis study aimed to determine the effect of GDM on lipid profile. PubMed, ProQuest, Web of Science, Scopus, Science Direct, Google Scholar, and ClinicalTrial were systematically searched for published articles relating to GDM until 2021 according to PRISMA guidelines. Newcastle Ottawa scale was used to assess the quality of the studies. Thirty-three studies with a sample size of 23,792 met the criteria for entering the meta-analysis. Pooled standardized mean difference (SMD) for total cholesterol (TC) and triglyceride (TG) was 0.23 mg/dL (95% CI: 0.11–0.34) and 1.14 mg/dL (95% CI: 0.91–1.38), respectively. The mean of TC and TG in people with GDM was higher than that in normal pregnant women. A similar pattern was observed for the very low-density lipoprotein (VLDL) and TG/high-density lipoprotein (HDL) ratio, with pooled SMD of 0.99 mg (95% CI: 0.71–1.27) and 0.65 mg (95% CI: 0.36–0.94), respectively. Pooled SMD for HDL was −0.35 mg/dL (95% CI: −0.54 to −0.16), women with GDM had a mean HDL lower than normal pregnant women. Although pooled SMD was higher for low-density lipoprotein (LDL) in the GDM group, this difference was not significant (0.14 [95% CI: −0.04 to 0.32]). Of all the lipid profiles, the largest difference between the GDM and control groups was observed in TG (SMD: 1.14). Elevated serum TG had the strongest effect on GDM. Higher levels of TC, LDL, VLDL, and TG/HDL ratio, and lower level of HDL were exhibited in GDM group. So, these markers can be considered as a reliable marker in the diagnosis of GDM.

Gestational Diabetes Mellitus: The Genetic Susceptibility Behind the Disease

Gestational diabetes mellitus (GDM), a type of pregnancy-specific glucose intolerance or hyperglycemia, is one of the most common metabolic disorders in pregnant women with 16.9% of the global prevalence of gestational hyperglycemia. Not only are women with GDM likely to develop T2DM, but their children are also at risk for birth complications or metabolic disease in adulthood. Therefore, identifying the potential risk factors for GDM is very important in the prevention and treatment of GDM. Previous studies have shown that genetic predisposition is an essential component in the occurrence of GDM. In this narrative review, we describe the role of polymorphisms in different functional genes associated with increased risk for GDM, and available evidence on genetic factors in the risk of GDM is summarized and discussed.

Beyond Lipoprotein(a) plasma measurements: Lipoprotein(a) and inflammation

Genome wide association, epidemiological, and clinical studies have established high lipoprotein(a) [Lp(a)] as a causal risk factor for atherosclerotic cardiovascular disease (ASCVD). Lp(a) is an apoB100 containing lipoprotein covalently bound to apolipoprotein(a) [apo(a)], a glycoprotein. Plasma Lp(a) levels are to a large extent determined by genetics. Its link to cardiovascular disease (CVD) may be driven by its pro-inflammatory effects, of which its association with oxidized phospholipids (oxPL) bound to Lp(a) is the most studied. Various inflammatory conditions, such as rheumatoid arthritis (RA), systemic lupus erythematosus, acquired immunodeficiency syndrome, and chronic renal failure are associated with high Lp(a) levels. In cases of RA, high Lp(a) levels are reversed by interleukin-6 receptor (IL-6R) blockade by tocilizumab, suggesting a potential role for IL-6 in regulating Lp(a) plasma levels. Elevated levels of IL-6 and IL-6R polymorphisms are associated with CVD. Therapies aimed at lowering apo(a) and thereby reducing plasma Lp(a) levels are in clinical trials. Their results will determine if reductions in apo(a) and Lp(a) decrease cardiovascular outcomes. As we enter this new arena of available treatments, there is a need to improve our understanding of mechanisms. This review will focus on the role of Lp(a) in inflammation and CVD.

Early Prediction of Gestational Diabetes Mellitus in the Chinese Population via Advanced Machine Learning

CONTEXT

Accurate methods for early gestational diabetes mellitus (GDM) (during the first trimester of pregnancy) prediction in Chinese and other populations are lacking.

OBJECTIVES

This work aimed to establish effective models to predict early GDM.

METHODS

Pregnancy data for 73 variables during the first trimester were extracted from the electronic medical record system. Based on a machine learning (ML)-driven feature selection method, 17 variables were selected for early GDM prediction. To facilitate clinical application, 7 variables were selected from the 17-variable panel. Advanced ML approaches were then employed using the 7-variable data set and the 73-variable data set to build models predicting early GDM for different situations, respectively.

RESULTS

A total of 16 819 and 14 992 cases were included in the training and testing sets, respectively. Using 73 variables, the deep neural network model achieved high discriminative power, with area under the curve (AUC) values of 0.80. The 7-variable logistic regression (LR) model also achieved effective discriminate power (AUC = 0.77). Low body mass index (BMI) (≤ 17) was related to an increased risk of GDM, compared to a BMI in the range of 17 to 18 (minimum risk interval) (11.8% vs 8.7%, P = .09). Total 3,3,5'-triiodothyronine (T3) and total thyroxin (T4) were superior to free T3 and free T4 in predicting GDM. Lipoprotein(a) was demonstrated a promising predictive value (AUC = 0.66).

CONCLUSIONS

We employed ML models that achieved high accuracy in predicting GDM in early pregnancy. A clinically cost-effective 7-variable LR model was simultaneously developed. The relationship of GDM with thyroxine and BMI was investigated in the Chinese population.