GCKR common functional polymorphisms are associated with metabolic syndrome and its components: a 10-year retrospective cohort study in Iranian adults

Genotypic variation in CYP2E1, GCKR, and PNPLA3 among nonalcoholic steatohepatitis patients of Turkish origin

BACKGROUND

This study examines genetic variations in CYP2E1 (rs6413432, rs3813867), GCKR (rs780094, rs1260326), and PNPLA3 (rs738409) among Turkish patients to assess their influence on nonalcoholic steatohepatitis.

METHODS

Allele and genotype frequencies were compared between 245 NASH patients and 120 healthy controls using SNP genotyping via polymerase chain reaction-restriction fragment length polymorphism. Additionally, the deviation of the observed genotype frequencies from Hardy-Weinberg proportion was examined.

RESULTS

No significant differences were found in the allelic and genotypic distributions of rs6413432, rs3813867, and rs780094 between NASH patients and healthy controls. However, significant disparities were noted for rs1260326 and rs738409. Gender and age-specific distributions showed no notable differences. The only observed deviation from Hardy-Weinberg proportion was in the genotype frequency of rs738409.

CONCLUSIONS

Variants in GCKR (rs1260326) and PNPLA3 (rs738409) are significantly associated with increased NASH risk in the Turkish population, with the rs738409 variant potentially playing a more prominent role in NASH development.

GlycA and CRP Are Genetically Correlated: Insight into the Genetic Architecture of Inflammageing

Inflammageing is a condition of perpetual low-grade inflammation induced by ageing. Inflammageing may be predicted by the C-reactive protein (CRP) or by a recently described biomarker which measures N-glycosylated side chains of the carbohydrate component of several acute-phase proteins known as GlycA. The objective of this study was to examine in depth the genetic relationships between CRP and GlycA as well as between each of them and other selected cytokines, which may shed light on the mechanisms of inflammageing. Using the Olink 96 Inflammation panel, data on inflammatory mediators for 1518 twins from the TwinsUK dataset were acquired. Summary statistics for genome-wide association studies for several cytokines as well as CRP and GlycA were collected from public sources. Extensive genetic correlation analyses, colocalization and genetic enrichment analyses were carried out to detect the shared genetic architecture between GlycA and CRP. Mendelian randomization was carried out to assess potential causal relationships. GlycA predicted examined cytokines with a magnitude twice as great as that of CRP. GlycA and CRP were significantly genetically correlated (Rg = 0.4397 ± 0.0854, p-value = 2.60 × 10-7). No evidence of a causal relationship between GlycA and CRP, or between these two biomarkers and the cytokines assessed was obtained. However, the aforementioned relationships were explained well by horizontal pleiotropy. Five exonic genetic variants annotated to five genes explain the shared genetic architecture observed between GlycA and CRP: IL6R, GCKR, MLXIPL, SERPINA1, and MAP1A. GlycA and CRP possess a shared genetic architecture, but the relationship between them appears to be modest, which may imply the promotion of differing inflammatory pathways. GlycA appears to be a more robust predictor of cytokines compared to CRP.

Should Glucokinase be Given a Chance in Diabetes Therapeutics? A Clinical-Pharmacological Review of Dorzagliatin and Lessons Learned So Far

Despite advances in the management of type 2 diabetes mellitus (T2DM), one-third of patients with diabetes do not achieve the desired glycemic goal. Considering this inadequacy, many agents that activate glucokinase have been investigated over the last two decades but were withdrawn before submission for marketing permission. Dorzagliatin is the first glucokinase activator that has been granted approval for T2DM, only in China. As overstimulation of glucokinase is linked with pathophysiological disturbances such as fatty liver and cardiovascular issues and a loss of therapeutic efficacy with time. This review aims to highlight the benefits of glucokinase activators vis-à-vis the risks associated with chronic enzymatic activation. We discuss the multisystem disturbances expected with chronic activation of the enzyme, the lessons learned with glucokinase activators of the past, the major efficacy and safety findings with dorzagliatin and its pharmacological properties, and the status of other glucokinase activators in the pipeline. The approval of dorzagliatin in China was based on the SEED and the DAWN trials, the major pivotal phase III trials that enrolled patients with T2DM with a mean glycosylated hemoglobin of 8.3-8.4%, and a mean age of 53-54.5 years from multiple sites in China. Patients with uncontrolled diabetes, cardiac diseases, organ dysfunction, and a history of severe hypoglycemia were excluded. Both trials had a randomized double-blind placebo-controlled phase of 24 weeks followed by an open-label phase of 28 weeks with dorzagliatin. Drug-naïve patients with T2DM with a disease duration of 11.7 months were enrolled in the SEED trial while the DAWN trial involved patients with T2DM with a mean duration of 71.5 months and receiving background metformin therapy. Compared with placebo, the decline in glycosylated hemoglobin at 24 weeks was more with dorzagliatin with an estimated treatment difference of - 0.57% in the SEED trial and - 0.66% in the DAWN trial. The desired glycosylated hemoglobin (< 7%) was also attained at more than two times higher rates with dorzagliatin. The glycemic improvement was sustained in the SEED trial but decreased over 52 weeks in the DAWN trial. Hyperlipidemia was observed in 12-14% of patients taking dorzagliatin versus 9-11% of patients receiving a placebo. Additional adverse effects noticed over 52 weeks with dorzagliatin included an elevation in liver enzymes, hyperuricemia, hyperlacticacidemia, renal dysfunction, and cardiovascular disturbances. Considering the statistically significant improvement in glycosylated hemoglobin with dorzagliatin in patients with T2DM, the drug may be given a chance in treatment-naïve patients with a shorter disease history. However, with the waning therapeutic efficacy witnessed in patients with long-standing diabetes, which was also one of the potential concerns with previously tested molecules, extended studies involving patients with chronic and uncontrolled diabetes are needed to comment upon the long-term therapeutic performance of dorzagliatin. Likewise, evidence needs to be generated from other countries, patients with organ dysfunction, a history of severe hypoglycemia, cardiac diseases, and elderly patients before extending the use of dorzagliatin. Apart from monitoring lipid profiles, long-term safety studies of dorzagliatin should involve the assessment of serum uric acid, lactate, renal function, liver function, and cardiovascular parameters.

Whole-exome sequencing reveals novel variants of monogenic diabetes in Tunisia: impact on diagnosis and healthcare management

Introduction: Monogenic diabetes (MD) accounts for 3%-6% of all cases of diabetes. This prevalence is underestimated due to its overlapping clinical features with type 1 and type 2 diabetes. Hence, genetic testing is the most appropriate tool for obtaining an accurate diagnosis. In Tunisia, few cohorts of MD have been investigated until now. The aim of this study is to search for pathogenic variants among 11 patients suspected of having MD in Tunisia using whole-exome sequencing (WES). Materials and methods: WES was performed in 11 diabetic patients recruited from a collaborating medical center. The pathogenicity of genetic variation was assessed using combined filtering and bioinformatics prediction tools. The online ORVAL tool was used to predict the likelihood of combinations of pathogenic variations. Then, Sanger sequencing was carried out to confirm likely pathogenic predicted variants among patients and to check for familial segregation. Finally, for some variants, we performed structural modeling to study their impact on protein function. Results: We identified novel variants related to MD in Tunisia. Pathogenic variants are located in several MODY and non-MODY genes. We highlighted the presence of syndromic forms of diabetes, including the Bardet-Biedl syndrome, Alström syndrome, and severe insulin resistance, as well as the presence of isolated diabetes with significantly reduced penetrance for Wolfram syndrome-related features. Idiopathic type 1 diabetes was also identified in one patient. Conclusion: In this study, we emphasized the importance of genetic screening for MD in patients with a familial history of diabetes, mainly among admixed and under-represented populations living in low- and middle-income countries. An accurate diagnosis with molecular investigation of MD may improve the therapeutic choice for better management of patients and their families. Additional research and rigorous investigations are required to better understand the physiopathological mechanisms of MD and implement efficient therapies that take into account genomic context and other related factors.

Regularized Machine Learning Models for Prediction of Metabolic Syndrome Using GCKR, APOA5, and BUD13 Gene Variants: Tehran Cardiometabolic Genetic Study

OBJECTIVE

Metabolic syndrome (MetS) is a complex multifactorial disorder that considerably burdens healthcare systems. We aim to classify MetS using regularized machine learning models in the presence of the risk variants of GCKR, BUD13 and APOA5, and environmental risk factors.

MATERIALS AND METHODS

A cohort study was conducted on 2,346 cases and 2,203 controls from eligible Tehran Cardiometabolic Genetic Study (TCGS) participants whose data were collected from 1999 to 2017. We used different regularization approaches [least absolute shrinkage and selection operator (LASSO), ridge regression (RR), elasticnet (ENET), adaptive LASSO (aLASSO), and adaptive ENET (aENET)] and a classical logistic regression (LR) model to classify MetS and select influential variables that predict MetS. Demographics, clinical features, and common polymorphisms in the GCKR, BUD13 and APOA5 genes of eligible participants were assessed to classify TCGS participant status in MetS development. The models' performance was evaluated by 10-repeated 10-fold crossvalidation. Various assessment measures of sensitivity, specificity, classification accuracy, and area under the receiver operating characteristic curve (AUC-ROC) and AUC-precision-recall (AUC-PR) curves were used to compare the models.

RESULTS

During the follow-up period, 50.38% of participants developed MetS. The groups were not similar in terms of baseline characteristics and risk variants. MetS was significantly associated with age, gender, schooling years, body mass index (BMI), and alternate alleles in all the risk variants, as indicated by LR. A comparison of accuracy, AUCROC, and AUC-PR metrics indicated that the regularization models outperformed LR. Regularized machine learning models provided comparable classification performances, whereas the aLASSO model was more parsimonious and selected fewer predictors.

CONCLUSION

Regularized machine learning models provided more accurate and parsimonious MetS classifying models. These high-performing diagnostic models can lay the foundation for clinical decision support tools that use genetic and demographical variables to locate individuals at high risk for MetS.

A wide range of missing imputation approaches in longitudinal data: a simulation study and real data analysis

BACKGROUND

Missing data is a pervasive problem in longitudinal data analysis. Several single-imputation (SI) and multiple-imputation (MI) approaches have been proposed to address this issue. In this study, for the first time, the function of the longitudinal regression tree algorithm as a non-parametric method after imputing missing data using SI and MI was investigated using simulated and real data.

METHOD

Using different simulation scenarios derived from a real data set, we compared the performance of cross, trajectory mean, interpolation, copy-mean, and MI methods (27 approaches) to impute missing longitudinal data using parametric and non-parametric longitudinal models and the performance of the methods was assessed in real data. The real data included 3,645 participants older than 18 years within six waves obtained from the longitudinal Tehran cardiometabolic genetic study (TCGS). The data modeling was conducted using systolic and diastolic blood pressure (SBP/DBP) as the outcome variables and included predictor variables such as age, gender, and BMI. The efficiency of imputation approaches was compared using mean squared error (MSE), root-mean-squared error (RMSE), median absolute deviation (MAD), deviance, and Akaike information criteria (AIC).

RESULTS

The longitudinal regression tree algorithm outperformed based on the criteria such as MSE, RMSE, and MAD than the linear mixed-effects model (LMM) for analyzing the TCGS and simulated data using the missing at random (MAR) mechanism. Overall, based on fitting the non-parametric model, the performance of the 27 imputation approaches was nearly similar. However, the SI traj-mean method improved performance compared with other imputation approaches.

CONCLUSION

Both SI and MI approaches performed better using the longitudinal regression tree algorithm compared with the parametric longitudinal models. Based on the results from both the real and simulated data, we recommend that researchers use the traj-mean method for imputing missing values of longitudinal data. Choosing the imputation method with the best performance is widely dependent on the models of interest and the data structure.

Waist circumference mediates the association between rs1260326 in GCKR gene and the odds of lean NAFLD

While non-alcoholic fatty liver disease (NAFLD) has been widely studied, the pathophysiology of lean NAFLD, the critical NAFLD subgroup, remains elusive. This study aimed to clarify the association between polymorphisms of GCKR, waist circumference, and the odds of lean NAFLD in the elderly Chinese Han population who live in the Zhangjiang community center of Shanghai, China. Three single nucleotide polymorphisms (SNPs), including rs1260326, rs780093, and rs780094, were genotyped in MassARRAY Analyzer. The association between SNPs with waist circumference in five genetic models was analyzed and rechecked by the logistic regression analysis. Mediation models were established to evaluate whether the waist circumstance can mediate the association between SNPs and lean NAFLD. In this study, the frequency of the C allele of rs1260326, rs780093, and rs780094 was significantly lower in lean NAFLD individuals than in lean non-NAFLD ones. The association between rs1260326 in GCKR and the odds of lean NAFLD was mediated via waist circumference after adjusting gender and age in the elderly Chinese Han population (β = 1.196, R² = 0.043, p = 0.020). For the first time, this study examined the mediating effect of waist circumference on the association between rs1260326 in GCKR and the odds of lean NAFLD (β = 0.0515, 95% CI 0.0107-0.0900, p = 0.004). It may contribute to illustrating the pathogenesis of lean NAFLD and indicate that waist circumference management might improve lean NAFLD control.

Unique genetic variants of lean nonalcoholic fatty liver disease: a retrospective cohort study

We investigated the prevalence and clinical metabolic characteristics of lean nonalcoholic fatty liver disease (NAFLD) in an elderly Chinese population and assessed the relevance of lipid markers and genetic variation. All 5,338 community subjects underwent detailed clinical and laboratory examinations and were divided into three groups: lean (Body mass index (BMI) < 23 kg/m², n = 2,012), overweight (BMI = 23-24.9 kg/m², n = 1,354), and obese (BMI ≥ 25 kg/m², n = 1,972). Single nucleotide polymorphisms were selected based on those reported in previous NAFLD or obesity genome-wide association studies. The frequencies of alleles and genotypes were calculated and statistically analyzed with Pearson's χ² tests. One-way ANCOVA was used to test the association between positive SNPs and metabolic parameters in lean NAFLD individuals. Our results showed that the C allele frequency of rs2279026, the G allele of rs2279028, the C allele of rs780093, and the C allele frequency of rs1260326 were higher in obese NAFLD than in lean NAFLD (P < 0.05). In addition, we observed an association between the CC of rs1421085, TT of rs3751812, AA of rs8050136, and AA of rs9939609 genotypes in the FTO gene and low-density lipoprotein levels (P < 0.05). In conclusion, our findings provide a unique perspective on the prevalence, genetic characteristics, and metabolic profile of NAFLD in older lean individuals in China. This is the first study to examine the association between genetic variants in the FTO, TFAP2B and GCKR genes and NAFLD in a cohort of lean individuals.

A haplotype-resolved genome assembly of the Nile rat facilitates exploration of the genetic basis of diabetes

Background

The Nile rat (Avicanthis niloticus) is an important animal model because of its robust diurnal rhythm, a cone-rich retina, and a propensity to develop diet-induced diabetes without chemical or genetic modifications. A closer similarity to humans in these aspects, compared to the widely used Mus musculus and Rattus norvegicus models, holds the promise of better translation of research findings to the clinic.

Results

We report a 2.5 Gb, chromosome-level reference genome assembly with fully resolved parental haplotypes, generated with the Vertebrate Genomes Project (VGP). The assembly is highly contiguous, with contig N50 of 11.1 Mb, scaffold N50 of 83 Mb, and 95.2% of the sequence assigned to chromosomes. We used a novel workflow to identify 3613 segmental duplications and quantify duplicated genes. Comparative analyses revealed unique genomic features of the Nile rat, including some that affect genes associated with type 2 diabetes and metabolic dysfunctions. We discuss 14 genes that are heterozygous in the Nile rat or highly diverged from the house mouse.

Conclusions

Our findings reflect the exceptional level of genomic resolution present in this assembly, which will greatly expand the potential of the Nile rat as a model organism.

The Association of Serum Uric Acid Level, Gout, and Alzheimer's Disease: A Bidirectional Mendelian Randomization Study

BACKGROUND

The relationship between serum uric acid (UA) and Alzheimer's disease (AD) risk still remained ambiguous despite extensive attempts.

OBJECTIVE

Via the two-sample Mendelian randomization (MR) design, we aimed to examine the bidirectional causal relationships of serum UA, gout, and the risk of AD.

METHODS

Genetic variants of UA, gout, and AD were extracted from published genome-wide association summary statistics. The inverse-variance weighted (IVW, the primary method), and several sensitivity methods (MR-Egger, weighted median, and weighted mode) were used to calculate the effect estimates. Egger regression, MR-PRESSO and leave-one-SNP-out analysis were performed to identify potential violations.

RESULTS

Genetic proxies for serum UA concentration [odds ratio (ORIVW) = 1.09, 95% confidence interval (CI) = 1.01-1.19, p = 0.031] were related with an increased risk of AD using 25 single nucleotide polymorphisms (SNPs). This causal effect was confirmed by sensitivity analyses including MR-Egger (1.22, 1.06-1.42, p = 0.014), weighted median (1.18, 1.05-1.33, p = 0.006), and weighted mode (1.20, 1.07-1.35, p = 0.005) methods. No evidence of notable directional pleiotropy and heterogeneity were identified (p >  0.05). Three SNPs (rs2078267, rs2231142, and rs11722228) significantly drove the observed causal effects. Supportive causal effect of genetically determined gout on AD risk was demonstrated using two SNPs (ORIVW = 1.05, 95% CI = 1.00-1.11, p = 0.057). No reverse causal effects of AD on serum UA levels and gout risk were found.

CONCLUSION

The findings revealed a causal relationship between elevated serum UA level and AD risk. However, further research is still warranted to investigate whether serum UA could be a reliable biomarker and therapeutic target for AD.

Early Prediction for Prediabetes and Type 2 Diabetes Using the Genetic Risk Score and Oxidative Stress Score

We aimed to use a genetic risk score (GRS) constructed with prediabetes and type 2 diabetes-related single nucleotide polymorphisms (SNPs) and an oxidative stress score (OSS) to construct an early-prediction model for prediabetes and type 2 diabetes (T2DM) incidence in a Korean population. The study population included 549 prediabetes and T2DM patients and 1036 normal subjects. The GRS was constructed using six prediabetes and T2DM-related SNPs, and the OSS was composed of three recognized oxidative stress biomarkers. Among the nine SNPs, six showed significant associations with the incidence of prediabetes and T2DM. The GRS was profoundly associated with increased prediabetes and T2DM (OR = 1.946) compared with individual SNPs after adjusting for age, sex, and BMI. Each of the three oxidative stress biomarkers was markedly higher in the prediabetes and T2DM group than in the normal group, and the OSS was significantly associated with increased prediabetes and T2DM (OR = 2.270). When BMI was introduced to the model with the OSS and GRS, the area under the ROC curve improved (from 69.3% to 70.5%). We found that the prediction model composed of the OSS, GRS, and BMI showed a significant prediction ability for the incidence of prediabetes and T2DM.

Evaluating machine learning-powered classification algorithms which utilize variants in the GCKR gene to predict metabolic syndrome: Tehran Cardio-metabolic Genetics Study

BACKGROUND

Metabolic syndrome (MetS) is a prevalent multifactorial disorder that can increase the risk of developing diabetes, cardiovascular diseases, and cancer. We aimed to compare different machine learning classification methods in predicting metabolic syndrome status as well as identifying influential genetic or environmental risk factors.

METHODS

This candidate gene study was conducted on 4756 eligible participants from the Tehran Cardio-metabolic Genetic study (TCGS). We compared predictive models using logistic regression (LR), Random Forest (RF), decision tree (DT), support vector machines (SVM), and discriminant analyses. Demographic and clinical features, as well as variables regarding common GCKR gene polymorphisms, were included in the models. We used a 10-repeated tenfold cross-validation to evaluate model performance.

RESULTS

50.6% of participants had MetS. MetS was significantly associated with age, gender, schooling years, BMI, physical activity, rs780094, and rs780093 (P < 0.05) as indicated by LR. RF showed the best performance overall (AUC-ROC = 0.804, AUC-PR = 0.776, and Accuracy = 0.743) and indicated BMI, physical activity, and age to be the most influential model features. According to the DT, a person with BMI < 24 and physical activity < 8.8 possesses a 4% chance for MetS. In contrast, a person with BMI ≥ 25, physical activity < 2.7, and age ≥ 33, has 77% probability of suffering from MetS.

CONCLUSION

Our findings indicated that, on average, machine learning models outperformed conventional statistical approaches for patient classification. These well-performing models may be used to develop future support systems that use a variety of data sources to identify persons at high risk of getting MetS.

Association between glucokinase regulator gene polymorphisms and serum uric acid levels in Taiwanese adolescents

The glucokinase regulator gene (GCKR) is located on chromosome 2p23. It plays a crucial role in maintaining plasma glucose homeostasis and metabolic traits. Recently, genome-wide association studies have revealed a positive association between hyperuricemia and GCKR variants in adults. This study investigated this genetic association in Taiwanese adolescents. Data were collected from our previous cross-sectional study (Taipei Children Heart Study). The frequencies of various genotypes (CC, CT, and TT) or alleles (C and T) of the GCKR intronic single-nucleotide polymorphism (SNP) rs780094 and the coding SNP rs1260326 (Pro446Leu, a common 1403C-T transition) were compared between a total of 968 Taiwanese adolescents (473 boys, 495 girls) with hyperuricemia or normal uric acid levels on the basis of gender differences. Logistic and linear regression analyses explored the role of GCKR in abnormal uric acid (UA) levels. Boys had higher UA levels than girls (6.68 ± 1.29 and 5.23 ± 0.95 mg/dl, respectively, p < 0.001). The analysis of both SNPs in girls revealed that the T allele was more likely to appear in patients with hyperuricemia than the C allele. After adjusting for confounders, the odds ratio (OR) for hyperuricemia incidence in the TT genotype was 1.75 (95% confidence interval [CI] 1.02–3.00), which was higher than that in the C allele carriers in rs1260326 in the girl population. Similarly, the TT genotypes had a higher risk of hyperuricemia, with an OR of 2.29 (95% CI 1.11–4.73) for rs1260326 and 2.28 (95% CI 1.09–4.75) for rs780094, than the CC genotype in girl adolescents. The T (Leu446) allele of GCKR rs1260326 polymorphism is associated with higher UA levels in Taiwanese adolescent girls.

Parental Transmission Plays the Major Role in High Aggregation of Type 2 Diabetes in Iranian Families: Tehran Lipid and Glucose Study

BACKGROUND

This study is the first to evaluate familial aggregation, heritability and inheritance mode of type 2 diabetes (T2D) in Tehran Lipid Glucose Study (TLGS) participants as a representative sample of the Iranian population.

METHODS

From the ongoing family-based TLGS cohort, 13,741 individuals at least 20 years of age (mean ± standard deviation, 39.71±16.56) were assessed. After correcting family structures using genomic information from the Tehran Cardiometabolic Genetic Study, 2,594 constituent pedigrees were constructed. Familial aggregation was assessed based on genealogic index testing, familial intraclass correlation and positive family history. Family-based heritability was checked with 2 linear mixed models, including 2 different random components: the kinship matrix and the genomic relationship matrix. The mode of inheritance of T2D was investigated by complex segregation analysis (CSA).

RESULTS

Familial aggregation of T2D was significant (p<0.05), and family-based heritability showed a high degree of genetic variation in T2D between individuals at 65% (standard error, 0.034). Within first-degree relatives (parent/offspring and siblings), the likelihood of a parental affect was higher than in siblings (odds ratio, 4.11 vs 1.65). Family history of T2D among first-degree relatives was more noteworthy than for second-degree relatives (odds ratio, 3.84 vs 0.59). CSA revealed that the polygenic model is best to illustrate the mode of inheritance of T2D for TLGS participants.

CONCLUSIONS

Our findings demonstrate that the heritability of T2D with polygenic mode in the Iranian population is higher than the global average. We also found that T2D is transmitted equally into siblings, with parental affect the leading risk factor. These data suggest that policymakers should change individual-level to family-level prevention.