Identification of novel alleles associated with insulin resistance in childhood obesity using pooled-DNA genome-wide association study approach

Unraveling the genetic and singaling landscapes of pediatric cancer

Pediatric cancer (PAEC) arises from gene mutations and their disrupted pathways, often driven by genetic instability affecting cell signaling. These pathways can help identify cancer triggers. Genomic studies have examined PAEC gene etiologies and disorders, but further analysis is needed to understand tumor progression mechanisms. We systematically analyzed PAEC datasets from cBioPortal, encompassing thirteen studies with 6568 samples. We identified 827 PAEC genes with mutation frequencies over fifteen across four tiers (I-IV). Tier I (mutation frequency ≥1 %) includes 40 genes, while Tier II(0.90-0.70 %), Tier III(0.60-0.50 %), and Tier IV(0.40-0.10 %) comprise 126, 336, and 325 genes, respectively. Key Tier I genes include TP53(5 %), NRAS(2.2 %), KRAS(1.8 %), CTNNB1(1.4 %), ATM(1.3 %), CREBBP(1.2 %), JAK2 (1.1 %), PIK3CA(1 %), PTEN(1 %), BRAF(0.9 %), EGFR(0.9 %), PIK3R1(0.8 %), and PTPN11(0.8 %). These genes participate in various signaling pathways (PI3K/AKT/mTOR, RAS/RAF/MAPK, JAK/STAT, and WNT/β-catenin), which are interconnected. We compared several PAEC panels with Tier I genes, and we found that the most shared across PAEC panels were TP53 (8), PTEN (7), and ATM (4). We further examined roles of TP53 in normal cells versus PEAC tumors using digital cellular and pathological imaging data supported by Human Protein Atlas. TP53 is expressed in cytosol, nucleosol, and vesicles and during cell-cycle TP53 protein in key regulator and it is present during all major cell-cycle events. Balancing of TP53WT and TP53MUT is the hallmark of the TP53 pathophysiology with severe functional implications. Notably, genes linked to insulin metabolism disorders may be PAEC risk factors, suggesting metabolic pathways as key research targets. This study highlights the therapeutic, prognostic, and diagnostic significance of these genes and pathways, emphasizing the need for ongoing PAEC research.

Multiomics and eXplainable artificial intelligence for decision support in insulin resistance early diagnosis: A pediatric population-based longitudinal study

Pediatric obesity can drastically heighten the risk of cardiometabolic alterations later in life, with insulin resistance standing as the cornerstone linking adiposity to the increased cardiovascular risk. Puberty has been pointed out as a critical stage after which obesity-associated insulin resistance is more difficult to revert. Timely prediction of insulin resistance in pediatric obesity is therefore vital for mitigating the risk of its associated comorbidities. The construction of effective and robust predictive systems for a complex health outcome like insulin resistance during the early stages of life demands the adoption of longitudinal designs for more causal inferences, and the integration of factors of varying nature involved in its onset. In this work, we propose an eXplainable Artificial Intelligence-based decision support pipeline for early diagnosis of insulin resistance in a longitudinal cohort of 90 children. For that, we leverage multi-omics (genomics and epigenomics) and clinical data from the pre-pubertal stage. Different data layers combinations, pre-processing techniques (missing values, feature selection, class imbalance, etc.), algorithms, training procedures were considered following good practices for Machine Learning. SHapley Additive exPlanations were provided for specialists to understand both the decision-making mechanisms of the system and the impact of the features on each automatic decision, an essential issue in high-risk areas such as this one where system decisions may affect people's lives. The system showed a relevant predictive ability (AUC and G-mean of 0.92). A deep exploration, both at the global and the local level, revealed promising biomarkers of insulin resistance in our population, highlighting classical markers, such as Body Mass Index z-score or leptin/adiponectin ratio, and novel ones such as methylation patterns of relevant genes, such as HDAC4, PTPRN2, MATN2, RASGRF1 and EBF1. Our findings highlight the importance of integrating multi-omics data and following eXplainable Artificial Intelligence trends when building decision support systems.

Brain-Type Glycogen Phosphorylase (PYGB) in the Pathologies of Diseases: A Systematic Review

Glycogen metabolism is a form of crucial metabolic reprogramming in cells. PYGB, the brain-type glycogen phosphorylase (GP), serves as the rate-limiting enzyme of glycogen catabolism. Evidence is mounting for the association of PYGB with diverse human diseases. This review covers the advancements in PYGB research across a range of diseases, including cancer, cardiovascular diseases, metabolic diseases, nervous system diseases, and other diseases, providing a succinct overview of how PYGB functions as a critical factor in both physiological and pathological processes. We present the latest progress in PYGB in the diagnosis and treatment of various diseases and discuss the current limitations and future prospects of this novel and promising target.

High-throughput functional dissection of noncoding SNPs with biased allelic enhancer activity for insulin resistance-relevant phenotypes

Most of the single-nucleotide polymorphisms (SNPs) associated with insulin resistance (IR)-relevant phenotypes by genome-wide association studies (GWASs) are located in noncoding regions, complicating their functional interpretation. Here, we utilized an adapted STARR-seq to evaluate the regulatory activities of 5,987 noncoding SNPs associated with IR-relevant phenotypes. We identified 876 SNPs with biased allelic enhancer activity effects (baaSNPs) across 133 loci in three IR-relevant cell lines (HepG2, preadipocyte, and A673), which showed pervasive cell specificity and significant enrichment for cell-specific open chromatin regions or enhancer-indicative markers (H3K4me1, H3K27ac). Further functional characterization suggested several transcription factors (TFs) with preferential allelic binding to baaSNPs. We also incorporated multi-omics data to prioritize 102 candidate regulatory target genes for baaSNPs and revealed prevalent long-range regulatory effects and cell-specific IR-relevant biological functional enrichment on them. Specifically, we experimentally verified the distal regulatory mechanism at IRS1 locus, in which rs952227-A reinforces IRS1 expression by long-range chromatin interaction and preferential binding to the transcription factor HOXC6 to augment the enhancer activity. Finally, based on our STARR-seq screening data, we predicted the enhancer activity of 227,343 noncoding SNPs associated with IR-relevant phenotypes (fasting insulin adjusted for BMI, HDL cholesterol, and triglycerides) from the largest available GWAS summary statistics. We further provided an open resource (http://www.bigc.online/fnSNP-IR) for better understanding genetic regulatory mechanisms of IR-relevant phenotypes.

Omics Data Preprocessing for Machine Learning: A Case Study in Childhood Obesity

The use of machine learning techniques for the construction of predictive models of disease outcomes (based on omics and other types of molecular data) has gained enormous relevance in the last few years in the biomedical field. Nonetheless, the virtuosity of omics studies and machine learning tools are subject to the proper application of algorithms as well as the appropriate pre-processing and management of input omics and molecular data. Currently, many of the available approaches that use machine learning on omics data for predictive purposes make mistakes in several of the following key steps: experimental design, feature selection, data pre-processing, and algorithm selection. For this reason, we propose the current work as a guideline on how to confront the main challenges inherent to multi-omics human data. As such, a series of best practices and recommendations are also presented for each of the steps defined. In particular, the main particularities of each omics data layer, the most suitable preprocessing approaches for each source, and a compilation of best practices and tips for the study of disease development prediction using machine learning are described. Using examples of real data, we show how to address the key problems mentioned in multi-omics research (e.g., biological heterogeneity, technical noise, high dimensionality, presence of missing values, and class imbalance). Finally, we define the proposals for model improvement based on the results found, which serve as the bases for future work.

Variants Tagging LGALS-3 Haplotype Block in Association with First Myocardial Infarction and Plasma Galectin-3 Six Months after the Acute Event

Galectin-3 is encoded by LGALS-3, located in a unique haplotype block in Caucasians. According to the Tagger server, rs4040064, rs11628437, and rs7159490 cover 82% (r² > 0.8) of the genetic variance of this HapBlock. Our aims were to examine the association of their haplotypes with first myocardial infarction (MI), changes in left ventricular echocardiographic parameters over time, and impact on plasma galectin-3 and LGALS-3 mRNA in peripheral blood mononuclear cells, both 6 months post-MI. The study group consisted of 546 MI patients and 323 controls. Gene expression was assessed in 92 patients and plasma galectin-3 in 189 patients. Rs4040064, rs11628437, rs7159490, and LGALS-3 mRNA expression were detected using TaqMan^® technology. Plasma galectin-3 concentrations were determined by the ELISA method. We found that the TGC haplotype could have a protective effect against MI (adjusted OR 0.19 [0.05-0.72], p = 0.015) and that the GAC haplotype had significantly higher galectin-3 concentrations (48.3 [37.3-59.4] ng/mL vs. 18.9 [14.5-23.4] ng/mL, p < 0.0001), both in males and compared to the referent haplotype GGC. Higher plasma Gal-3 was also associated with higher NYHA class and systolic dysfunction. Our results suggest that variants tagging LGALS-3 HapBlock could reflect plasma Gal-3 levels 6 months post-MI and may have a potential protective effect against MI in men. Further replication, validation, and functional studies are needed.

Testing the Utility of Polygenic Risk Scores for Type 2 Diabetes and Obesity in Predicting Metabolic Changes in a Prediabetic Population: An Observational Study

Prediabetes is an intermediate state of hyperglycemia during which glycemic parameters are above normal levels but below the T2D threshold. T2D and its precursor prediabetes affect 6.28% and 7.3% of the world’s population, respectively. The main objective of this paper was to create and compare two polygenic risk scores (PRSs) versus changes over time (Δ) in metabolic parameters related to prediabetes and metabolic complications. The genetics of 446 prediabetic patients from the Polish Registry of Diabetes cohort were investigated. Seventeen metabolic parameters were measured and compared at baseline and after five years using statistical analysis. Subsequently, genetic polymorphisms present in patients were determined to build a T2D PRS (68 SNPs) and an obesity PRS (21 SNPs). Finally, the association among the two PRSs and the Δ of the metabolic traits was assessed. After a multiple linear regression with adjustment for age, sex, and BMI at a nominal significance of (p < 0.05) and adjustment for multiple testing, the T2D PRS was found to be positively associated with Δ fat mass (FM) (p = 0.025). The obesity PRS was positively associated with Δ FM (p = 0.023) and Δ 2 h glucose (p = 0.034). The comparison of genotype frequencies showed that AA genotype carriers of rs10838738 were significantly higher in Δ 2 h glucose and in Δ 2 h insulin. Our findings suggest that prediabetic individuals with a higher risk of developing T2D experience increased Δ FM, and those with a higher risk of obesity experience increased Δ FM and Δ two-hour postprandial glucose. The associations found in this research could be a powerful tool for identifying prediabetic individuals with an increased risk of developing T2D and obesity.

Reduced serum calcium is associated with a higher risk of retinopathy in non-diabetic individuals: The Chinese Multi-provincial Cohort Study

AIMS

As a common micro-vascular disease, retinopathy can also present in non-diabetic individuals and increase the risk of clinical cardiovascular disease. Understanding the relationship between serum calcium and retinopathy would contribute to etiological study and disease prevention.

METHODS

A total of 1836 participants (aged 55-84 years and diabetes-free) from the Chinese Multi-Provincial Cohort Study-Beijing Project in 2012 were included for analyzing the relation between serum calcium level and retinopathy prevalence. Of these, 1407 non-diabetic participants with data on serum calcium in both the 2007 and 2012 surveys were included for analyzing the association of five-year changes in serum calcium with retinopathy risk. The retinopathy was determined from retinal images by ophthalmologists and a computer-aided system using convolutional neural network (CNN). The association between serum calcium and retinopathy risk was assessed by multivariate logistic regression.

RESULTS

Among the 1836 participants (male, 42.5%), 330 (18.0%) had retinopathy determined by CNN. After multivariate adjustment, the odds ratio (OR) comparing the lowest quartiles (serum calcium < 2.38 mmol/L) to the highest quartiles (serum calcium ≥ 2.50 mmol/L) for the prevalence of retinopathy determined by CNN was 1.58 (95% confidence interval [CI]: 1.10 - 2.27). The findings were consistent with the result discerned by ophthalmologists, and either by CNN or ophthalmologists. These relationships are preserved even in those without metabolic risk factors, including hypertension, high hemoglobin A1c, high fasting blood glucose, or high low-density lipoprotein cholesterol. Over 5 years, participants with the sustainably low levels of serum calcium (OR: 1.58; 95%CI: 1.05 - 2.39) and those who experienced a decrease in serum calcium (OR: 1.56; 95%CI: 1.04 - 2.35) had an increased risk of retinopathy than those with the sustainably high level of serum calcium.

CONCLUSIONS

Reduced serum calcium was independently associated with an increased risk of retinopathy in non-diabetic individuals. Moreover, reduction of serum calcium could further increase the risk of retinopathy even in the absence of hypertension, high glucose, or high cholesterol. This study suggested that maintaining a high level of serum calcium may be recommended for reducing the growing burden of retinopathy. Further large prospective studies will allow more detailed information.

Immune marker signature helps to predict survival in uveal melanoma

The detailed molecular function of tumor microenvironment (TEM) in uveal melanoma (UVM) remains unclear. This study generated the immune index and the stromal index scores by ESTIMATE algorithm based on RNA-sequencing data with 80 UVM patients. There was no correlation between the immune stromal index and clinical parameters. The differentially expressed genes related to the immune stromal index were calculated and were described by functional annotations and protein-protein interaction network diagrams. After univariate and multivariate Cox regression analyses, there were four genes (HLA-J, MMP12, HES6, and ADAMDEC1) with significant prognostic significance. The prognostic model was constructed using these four characteristic genes, and the KM curve and tROC curve were described to show that the model had a better ability to predict survival outcomes and prognosis. The verification results in GSE62075 showed that HLA-J and HES6 were expressed differently in the cancer group than in the non-cancer group. This study indicates that the risk signature based on the immune index can be used as an indicator to evaluate the prognosis of patients with UVM.

DNA methylation signature in blood mirrors successful weight-loss during lifestyle interventions: the CENTRAL trial

BACKGROUND

One of the major challenges in obesity treatment is to explain the high variability in the individual's response to specific dietary and physical activity interventions. With this study, we tested the hypothesis that specific DNA methylation changes reflect individual responsiveness to lifestyle intervention and may serve as epigenetic predictors for a successful weight-loss.

METHODS

We conducted an explorative genome-wide DNA methylation analysis in blood samples from 120 subjects (90% men, mean ± SD age = 49 ± 9 years, body mass-index (BMI) = 30.2 ± 3.3 kg/m2) from the 18-month CENTRAL randomized controlled trial who underwent either Mediterranean/low-carbohydrate or low-fat diet with or without physical activity.

RESULTS

Analyses comparing male subjects with the most prominent body weight-loss (responders, mean weight change - 16%) vs. non-responders (+ 2.4%) (N = 10 each) revealed significant variation in DNA methylation of several genes including LRRC27, CRISP2, and SLFN12 (all adj. P < 1 × 10-5). Gene ontology analysis indicated that biological processes such as cell adhesion and molecular functions such as calcium ion binding could have an important role in determining the success of interventional therapies in obesity. Epigenome-wide association for relative weight-loss (%) identified 15 CpGs being negatively correlated with weight change after intervention (all combined P < 1 × 10- 4) including new and also known obesity candidates such as NUDT3 and NCOR2. A baseline DNA methylation score better predicted successful weight-loss [area under the curve (AUC) receiver operating characteristic (ROC) = 0.95-1.0] than predictors such as age and BMI (AUC ROC = 0.56).

CONCLUSIONS

Body weight-loss following 18-month lifestyle intervention is associated with specific methylation signatures. Moreover, methylation differences in the identified genes could serve as prognostic biomarkers to predict a successful weight-loss therapy and thus contribute to advances in patient-tailored obesity treatment.

Genetic predisposition in type 2 diabetes: A promising approach toward a personalized management of diabetes

Diabetes mellitus, also known simply as diabetes, has been described as a chronic and complex endocrine metabolic disorder that is a leading cause of death across the globe. It is considered a key public health problem worldwide and one of four important non-communicable diseases prioritized for intervention through world health campaigns by various international foundations. Among its four categories, Type 2 diabetes (T2D) is the commonest form of diabetes accounting for over 90% of worldwide cases. Unlike monogenic inherited disorders that are passed on in a simple pattern, T2D is a multifactorial disease with a complex etiology, where a mixture of genetic and environmental factors are strong candidates for the development of the clinical condition and pathology. The genetic factors are believed to be key predisposing determinants in individual susceptibility to T2D. Therefore, identifying the predisposing genetic variants could be a crucial step in T2D management as it may ameliorate the clinical condition and preclude complications. Through an understanding the unique genetic and environmental factors that influence the development of this chronic disease individuals can benefit from personalized approaches to treatment. We searched the literature published in three electronic databases: PubMed, Scopus and ISI Web of Science for the current status of T2D and its associated genetic risk variants and discus promising approaches toward a personalized management of this chronic, non-communicable disorder.

Maternal high-fat diet exposure during gestation, lactation, or gestation and lactation differentially affects intestinal morphology and proteome of neonatal mice

Offspring nutrition depends on the mother during gestation and lactation; thus, maternal nutrition and metabolism can affect their development. We hypothesized that maternal exposure to high-fat (HF) diet affects neonate's gastrointestinal tract development. Our objective was to determine the effect of maternal HF diet during gestation and lactation on neonate's duodenum histomorphology and proteome. Female mice were fed either a control (C, 10% kcal fat) or an HF (60% kcal fat) diet for 4 weeks and bred. On postnatal day 2, half the pups were cross-fostered to dams fed on different diet, creating 4 treatments: C-C, C-HF, HF-C, and HF-HF, indicating maternal diet during gestation-lactation, respectively. On postnatal day 12, pups' duodenum was excised and prepared for histology and liquid chromatography-tandem mass spectrometry analysis of proteome. Villi were significantly longer in HF-HF pups, and crypt cell proliferation rate was not different among treatments. Between C-C and HF-HF, HF-C, or C-HF, 812, 601, or 894 proteins were differentially expressed (Tukey adjusted P < .05), respectively. Functional analysis clustered proteins upregulated in HF-HF vs C-C in fat digestion and absorption, extracellular matrix, cell adhesion, immune response, oxidation-reduction processes, phagocytosis, and transport categories. Proteins downregulated were classified as RNA splicing, translation, protein folding, endocytosis, and transport. There was evidence for a carryover effect of exposure to HF diet during gestation to the postnatal period. Alterations in proteome relative to HF exposure potentially reflect long-term changes in the functioning of the duodenum.

Trajectories of changes in glucose tolerance in a multiethnic cohort of obese youths: an observational prospective analysis

BACKGROUND

Type 2 diabetes is preceded by a prediabetic stage of impaired glucose tolerance that affects 10-23% of youth and is expected to double over the next decade. The natural history of impaired glucose tolerance and the determinants of β-cell dynamic response have never been investigated longitudinally in young people. We aimed to investigate the clinical and metabolic determinants of longitudinal glucose tolerance changes and β-cell function in a multiethnic cohort of obese youth.

METHODS

We followed up prospectively a multiethnic cohort of overweight and obese (body-mass index >85th percentile) adolescents with baseline normal glucose tolerance (plasma glucose <140 mg/dL) or impaired glucose tolerance (plasma glucose 140-199 mg/dL) at the Yale Pediatric Obesity Clinic (CT, USA). All participants underwent a 3-h oral glucose tolerance test at baseline and after 2 years to estimate insulin secretion (oral disposition index) in the context of body insulin sensitivity. As part of standard care at the clinic, all participants received dietary advice and underwent dietary assessment every 5-6 months. No structured lifestyle or pharmacological intervention was administered.

FINDINGS

Between January, 2010, and December, 2016, 526 adolescents (mean age 12·7 years, range 10·6-14·2) were enrolled to our study. At baseline, 364 had normal and 162 had impaired glucose tolerance. Median follow-up was 2·9 years (IQR 2·7-3·1). 105 (65%) of 162 with impaired glucose tolerance at baseline reverted to normal glucose tolerance at follow-up, 44 (27%) had persistent impaired glucose tolerance, and 13 (8%) progressed to type 2 diabetes. A feature of reversion to normal glucose tolerance was a roughly four-fold increase in the oral disposition index (from median 0·94 [IQR 0·68-1·35] at baseline to 3·90 [2·58-6·08] at follow-up; p<0·0001) and a significantly higher oral disposition index at follow-up compared with participants who maintained normal glucose tolerance across the study period (median 3·90 [IQR 2·58-6·08] vs 1·59 [1·12-2·23]; p<0·0001). By contrast, a decrease in insulin secretion was seen in participants who had persistent impaired glucose tolerance (median 1·31 [IQR 1·01-1·85]; p<0·0001) or who progressed to type 2 diabetes (0·20 [0·12-0·58]; p<0·0001), compared with participants who maintained normal glucose tolerance across the study period. Non-Hispanic white ethnic origin conferred five times the odds of reversion to normal glucose tolerance compared with non-Hispanic black ethnic origin (OR 5·06, 95% CI 1·86-13·76; p=0·001), with a two times greater annual increase in the oral disposition index (β 2·32, 95% CI 0·05-4·60; p=0·045).

INTERPRETATION

Impaired glucose tolerance is highly reversible in obese adolescents. Ethnic origin is the main clinical modifier of the dynamic β-cell response to prediabetic hyperglycaemia and, thus, determines the reversibility of impaired glucose tolerance, or its persistence. Therapeutic interventions for impaired glucose tolerance should target the specific mechanisms underpinning glucose tolerance changes in high-risk ethnic groups.

FUNDING

National Institutes of Health (National Institute of Child Health and Human Development, National Center for Research Resources, and National Institute of Diabetes and Digestive and Kidney Diseases), American Diabetes Association, International Society for Pediatric and Adolescent Diabetes, Robert Leet Patterson and Clara Guthrie Patterson Trust, European Society for Pediatric Endocrinology, American Heart Association, and the Allen Foundation.