Genetic determinants of glucose homeostasis

Mangiferin: An effective therapeutic agent against several disorders (Review)

Mangiferin (1,3,6,7‑tetrahydroxyxanthone‑C2‑β‑D‑glucoside) is a bioactive ingredient predominantly isolated from the mango tree, with potent antioxidant activity and multifactorial pharmacological effects, including antidiabetic, antitumor, lipometabolism regulating, cardioprotective, anti‑hyperuricemic, neuroprotective, antioxidant, anti‑inflammatory, antipyretic, analgesic, antibacterial, antiviral and immunomodulatory effects. Therefore, it possesses several health‑endorsing properties and is a promising candidate for further research and development. However, low solubility, mucosal permeability and bioavailability restrict the development of mangiferin as a clinical therapeutic, and chemical and physical modification is required to expand its application. This review comprehensively analyzed and collectively summarized the primary pharmacological actions of mangiferin that have been demonstrated in the literature, to support the potential future development of mangiferin as a novel therapeutic drug.

CRP-level-associated polymorphism rs1205 within the CRP gene is associated with 2-hour glucose level: The SAPPHIRe study

C-reactive protein (CRP) encoded by CRP gene is a reflection of systemic inflammation. Many studies associated CRP level with diabetes and glucose levels, but the association of CRP gene with these traits is unclear. We conducted a cross-sectional study consisting of 945 siblings from 330 families collected by the Stanford Asian Pacific Program in Hypertension and Insulin Resistance (SAPPHIRe) to investigate associations between CRP polymorphisms, circulating CRP, diabetes, and glucose levels. Five single-nucleotide polymorphisms were analyzed: rs3093059, rs2794521, rs1417938, rs1800947, and rs1205. The generalized estimating equation approach was used to deal with correlated data within families. CRP level was positively correlated with diabetes prevalence and levels of fasting and 2-hour glucose (each P < 0.008). Alleles C at rs3093059 and G at rs1205 were associated with elevated CRP level (each P < 1.2 × 10⁻⁶). Allele C at rs3093059 was associated with fasting glucose (β = 0.20, P = 0.045) and G at rs1205 was associated with 2-hour glucose (β = 0.46, P = 0.00090) post oral glucose tolerance test, but only the latter passed Bonferroni correction. No polymorphism was associated with diabetes. Since 2-hour glucose is an indicator of glucose tolerance, this study indicated CRP gene is associated with glucose intolerance.

Evaluating the transferability of 15 European-derived fasting plasma glucose SNPs in Mexican children and adolescents

Genome wide association studies (GWAS) have identified single-nucleotide polymorphisms (SNPs) that are associated with fasting plasma glucose (FPG) in adult European populations. The contribution of these SNPs to FPG in non-Europeans and children is unclear. We studied the association of 15 GWAS SNPs and a genotype score (GS) with FPG and 7 metabolic traits in 1,421 Mexican children and adolescents from Mexico City. Genotyping of the 15 SNPs was performed using TaqMan Open Array. We used multivariate linear regression models adjusted for age, sex, body mass index standard deviation score, and recruitment center. We identified significant associations between 3 SNPs (G6PC2 (rs560887), GCKR (rs1260326), MTNR1B (rs10830963)), the GS and FPG level. The FPG risk alleles of 11 out of the 15 SNPs (73.3%) displayed significant or non-significant beta values for FPG directionally consistent with those reported in adult European GWAS. The risk allele frequencies for 11 of 15 (73.3%) SNPs differed significantly in Mexican children and adolescents compared to European adults from the 1000G Project, but no significant enrichment in FPG risk alleles was observed in the Mexican population. Our data support a partial transferability of European GWAS FPG association signals in children and adolescents from the admixed Mexican population.

Caveolin 1 Modulates Aldosterone-Mediated Pathways of Glucose and Lipid Homeostasis

Background

Overactivation of the aldosterone and mineralocorticoid receptor (MR) pathway is associated with hyperglycemia and dyslipidemia. Caveolin 1 (cav‐1) is involved in glucose/lipid homeostasis and may modulate MR signaling. We investigated the interplay between cav‐1 and aldosterone signaling in modulating insulin resistance and dyslipidemia in cav‐1–null mice and humans with a prevalent variant in the CAV1 gene.

Methods and Results

In mouse studies, cav‐1 knockout mice exhibited higher levels of homeostatic model assessment of insulin resistance, cholesterol, and resistin and lower ratios of high‐ to low‐density lipoprotein (all P<0.001 versus wild type). Moreover, cav‐1 knockout mice displayed hypertriglyceridemia and higher mRNA levels for resistin, retinol binding protein 4, NADPH oxidase 4, and aldose reductase in liver and/or fat tissues. MR blockade with eplerenone significantly decreased glycemia (P<0.01), total cholesterol (P<0.05), resistin (P<0.05), and described enzymes, with no effect on insulin or triglycerides. In the human study, we analyzed the CAV1 gene polymorphism rs926198 in 556 white participants; 58% were minor allele carriers and displayed higher odds of insulin resistance (odds ratio 2.26 [95% CI 1.40–3.64]) and low high‐density lipoprotein (odds ratio 1.54 [95% CI 1.01–3.37]). Aldosterone levels correlated with higher homeostatic model assessment of insulin resistance and resistin and lower high‐density lipoprotein only in minor allele carriers. CAV1 gene expression quantitative trait loci data revealed lower cav‐1 expression in adipose tissues by the rs926198 minor allele.

Conclusions

Our findings in mice and humans suggested that decreased cav‐1 expression may activate the effect of aldosterone/MR signaling on several pathways of glycemia, dyslipidemia, and resistin. In contrast, hyperinsulinemia and hypertriglyceridemia are likely mediated by MR‐independent mechanisms. Future human studies will elucidate the clinical relevance of MR blockade in patients with genotype‐mediated cav‐1 deficiency.

Genome-wide association study identifies common loci influencing circulating glycated hemoglobin (HbA1c) levels in non-diabetic subjects: the Long Life Family Study (LLFS)

OBJECTIVE

Glycated hemoglobin (HbA1c) is a stable index of chronic glycemic status and hyperglycemia associated with progressive development of insulin resistance and frank diabetes. It is also associated with premature aging and increased mortality. To uncover novel loci for HbA1c that are associated with healthy aging, we conducted a genome-wide association study (GWAS) using non-diabetic participants in the Long Life Family Study (LLFS), a study with familial clustering of exceptional longevity in the US and Denmark.

METHODS

A total of 4088 non-diabetic subjects from the LLFS were used for GWAS discoveries, and a total of 8231 non-diabetic subjects from the Atherosclerosis Risk in Communities Study (ARIC, in the MAGIC Consortium) and the Health, Aging, and Body Composition Study (HABC) were used for GWAS replications. HbA1c was adjusted for age, sex, centers, 20 principal components, without and with BMI. A linear mixed effects model was used for association testing.

RESULTS

Two known loci at GCK rs730497 (or rs2908282) and HK1 rs17476364 were confirmed (p<5e-8). Of 25 suggestive (5e-8

CONCLUSIONS

The analysis reconfirmed two known GWAS loci (GCK, HK1) and identified 25 suggestive loci including one reconfirmed variant in G6PC2 and one replicated variant near OR10R3P/SPTA1. Future focused survey of sequence elements containing mainly functional and regulatory variants may yield additional findings.

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Key Words

• Genome-wide association study
• Glucose
• Insulin resistance and diabetes
• Non-enzymatic glycation
• Premature aging processes

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MESH Headings

• Aged
• Cohort Studies
• Denmark
• Diabetes Mellitus/blood
• Female
• Genome-Wide Association Study
• Genotype
• Glycated Hemoglobin/metabolism
• Humans
• Longevity
• Male
• Middle Aged
• Phenotype
• United States

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Grants

• R01 AG032319 NIA NIH HHS
• U01 AG023744 NIA NIH HHS
• R01 HL059367 NHLBI NIH HHS
• P30 AG034424 NIA NIH HHS
• HHSN268201100006C NHLBI NIH HHS
• U01AG023712 NIA NIH HHS
• HHSN268200782096C NHLBI NIH HHS
• K01DK076595 NIDDK NIH HHS
• R21 DK080294 NIDDK NIH HHS
• U19AG023122 NIA NIH HHS
• R01HL086694 NHLBI NIH HHS
• HHSN268201100012C NHLBI NIH HHS
• UL1RR025005 NCRR NIH HHS
• U01 AG023712 NIA NIH HHS
• HHSN268201100009I NHLBI NIH HHS
• P30 AG024827 NIA NIH HHS
• R01HL59367 NHLBI NIH HHS
• HHSN268201100010C NHLBI NIH HHS
• UL1 RR025005 NCRR NIH HHS
• N01 AG062101 NIA NIH HHS
• HHSN268201100008C NHLBI NIH HHS
• UL1 TR001079 NCATS NIH HHS
• HHSN268201100005G NHLBI NIH HHS
• U19 AG023122 NIA NIH HHS
• HHSN268201100008I NHLBI NIH HHS
• HHSN268201100007C NHLBI NIH HHS
• P50 AG008702 NIA NIH HHS
• K01067207 PHS HHS
• HHSN268201100011I NHLBI NIH HHS
• P30 DK079637 NIDDK NIH HHS
• HHSN268201100011C NHLBI NIH HHS
• R01 HL086694 NHLBI NIH HHS
• 1R01AG032098-01A1 NIA NIH HHS
• U01AG023746 NIA NIH HHS
• K24 AG025727 NIA NIH HHS
• N01 AG062106 NIA NIH HHS
• HHSN268200625226C PHS HHS
• U01 HG004402 NHGRI NIH HHS
• U01HG004402 NHGRI NIH HHS
• R01 AG032098 NIA NIH HHS
• U01AG023744 NIA NIH HHS
• K01 DK076595 NIDDK NIH HHS
• R01HL087641 NHLBI NIH HHS
• U01 AG023749 NIA NIH HHS
• R21 HL114237 NHLBI NIH HHS
• HHSN268201100005I NHLBI NIH HHS
• N01 AG062103 NIA NIH HHS
• U01AG023755 NIA NIH HHS
• R21HL114237 NHLBI NIH HHS
• U01 AG023755 NIA NIH HHS
• U01AG023749 NIA NIH HHS
• K24AG025727 NIA NIH HHS
• Intramural NIH HHS
• U01 AG023746 NIA NIH HHS
• HHSN268201100009C NHLBI NIH HHS
• HHSN268201100005C NHLBI NIH HHS
• HHSN268201100007I NHLBI NIH HHS
• R21DK080294 NIDDK NIH HHS
• R01AG032319 NIA NIH HHS
• R01 HL087641 NHLBI NIH HHS

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Affiliation(s)

Ping An Department of Genetics Division of Statistical Genomics, Washington University School of Medicine, St. Louis, MO, USA.
Iva Miljkovic University of Pittsburgh, Graduate School of Public Health, Department of Epidemiology, Center for Aging and Population Health, Pittsburgh, PA, USA
Bharat Thyagarajan Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN, USA
Aldi T Kraja Department of Genetics Division of Statistical Genomics, Washington University School of Medicine, St. Louis, MO, USA
E Warwick Daw Department of Genetics Division of Statistical Genomics, Washington University School of Medicine, St. Louis, MO, USA
James S Pankow Division of Epidemiology and Community Health, University of Minnesota, Minneapolis, MN, USA
Elizabeth Selvin Department of Epidemiology, Johns Hopkins University, Baltimore, MD, USA
W H Linda Kao Department of Epidemiology, Johns Hopkins University, Baltimore, MD, USA
Nisa M Maruthur Division of General Internal Medicine, Johns Hopkins University, Baltimore, MD, USA
Micahel A Nalls Laboratory of Neurogenetics, NIA/NIH, Bethesda, MD, USA
Yongmei Liu Department of Epidemiology and Prevention, Division of Public Health Sciences, Wake Forest University, Winston-Salem, NC, USA
Tamara B Harris Laboratory of Epidemiology, Demography and Biometry, NIA/NIH, Bethesda, MD, USA
Joseph H Lee Gertrude H. Sergievsky Center and the Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University, New York City, NY, USA
Ingrid B Borecki Department of Genetics Division of Statistical Genomics, Washington University School of Medicine, St. Louis, MO, USA
Kaare Christensen Danish Aging Research Center, Epidemiology, University of Southern Denmark, Odense, Denmark; Department of Clinical Biochemistry and Pharmacology and Department of Clinical Genetics, Odense University Hospital, Odense, Denmark
John H Eckfeldt Department of Laboratory Medicine and Pathology, University of Minnesota, Minneapolis, MN, USA
Richard Mayeux Gertrude H. Sergievsky Center and the Taub Institute for Research on Alzheimer's Disease and the Aging Brain, Columbia University, New York City, NY, USA
Thomas T Perls Division of Geriatrics, Department of Medicine, Boston University Medical Center, Boston, MA, USA
Anne B Newman University of Pittsburgh, Graduate School of Public Health, Department of Epidemiology, Center for Aging and Population Health, Pittsburgh, PA, USA
Michael A Province Department of Genetics Division of Statistical Genomics, Washington University School of Medicine, St. Louis, MO, USA

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Genetics, genomics and metabolomics: new insights into maternal metabolism during pregnancy

Maternal glucose metabolism during pregnancy differs from the non-gravid state to allow the mother to meet her own and the growing fetus's energy needs. New insights into the mechanisms underlying maternal metabolism during pregnancy are being gained through the use of new 'omics' technologies. This review focuses on the application of genetics/genomics and metabolomics to the study of maternal metabolism during pregnancy. Following the identification of susceptibility genes for Type 2 diabetes through genome-wide association studies, association has been demonstrated of some Type 2 diabetes susceptibility genes with gestational diabetes mellitus, suggesting that the genetic architecture of Type 2 diabetes and gestational diabetes are, in part, similar. More recent genome-wide association studies examining maternal metabolism during pregnancy have demonstrated overlap of genes associated with metabolic traits in the gravid and non-gravid population, as well as genes that appear to be relatively unique to pregnancy. Metabolomics has also been used to profile the metabolic state of women during pregnancy through the multiplexed measurement of many low molecular weight metabolites. Measurement of amino acids and conventional metabolites have demonstrated changes in mothers with higher insulin resistance and glucose similar to changes in non-gravid, insulin-resistant populations, suggesting similarities in the metabolic profile characteristic of insulin resistance and hyperglycaemia in pregnant and non-pregnant populations. Metabolomics and genomics are but a few of the now available high-throughput 'omics' technologies. Future studies that integrate data from multiple technologies will allow an integrated systems biology approach to maternal metabolism during pregnancy.

Maternal-fetal metabolic gene-gene interactions and risk of neural tube defects

Single-gene analyses indicate that maternal genes associated with metabolic conditions (e.g., obesity) may influence the risk of neural tube defects (NTDs). However, to our knowledge, there have been no assessments of maternal-fetal metabolic gene-gene interactions and NTDs. We investigated 23 single nucleotide polymorphisms among 7 maternal metabolic genes (ADRB3, ENPP1, FTO, LEP, PPARG, PPARGC1A, and TCF7L2) and 2 fetal metabolic genes (SLC2A2 and UCP2). Samples were obtained from 737 NTD case-parent triads included in the National Birth Defects Prevention Study for birth years 1999-2007. We used a 2-step approach to evaluate maternal-fetal gene-gene interactions. First, a case-only approach was applied to screen all potential maternal and fetal interactions (n = 76), as this design provides greater power in the assessment of gene-gene interactions compared to other approaches. Specifically, ordinal logistic regression was used to calculate the odds ratio (OR) and 95% confidence interval (CI) for each maternal-fetal gene-gene interaction, assuming a log-additive model of inheritance. Due to the number of comparisons, we calculated a corrected p-value (q-value) using the false discovery rate. Second, we confirmed all statistically significant interactions (q < 0.05) using a log-linear approach among case-parent triads. In step 1, there were 5 maternal-fetal gene-gene interactions with q < 0.05. The "top hit" was an interaction between maternal ENPP1 rs1044498 and fetal SLC2A2 rs6785233 (interaction OR = 3.65, 95% CI: 2.32-5.74, p = 2.09×10(-8), q=0.001), which was confirmed in step 2 (p = 0.00004). Our findings suggest that maternal metabolic genes associated with hyperglycemia and insulin resistance and fetal metabolic genes involved in glucose homeostasis may interact to increase the risk of NTDs.

New opportunities: harnessing induced pluripotency for discovery in diabetes and metabolism

The landmark discovery of induced pluripotent stem cells (iPSCs) by Shinya Yamanaka has transformed regenerative biology. Previously, insights into the pathogenesis of chronic human diseases have been hindered by the inaccessibility of patient samples. However, scientists are now able to convert patient fibroblasts into iPSCs and differentiate them into disease-relevant cell types. This ability opens new avenues for investigating disease pathogenesis and designing novel treatments. In this review, we highlight the uses of human iPSCs to uncover the underlying causes and pathological consequences of diabetes and metabolic syndromes, multifactorial diseases whose etiologies have been difficult to unravel using traditional methodologies.

MicroRNAs as pharmacological targets in diabetes

Diabetes is characterized by high levels of blood glucose due to either the loss of insulin-producing beta-cells in the pancreas, leading to a deficiency of insulin in type 1 diabetes, or due to increased insulin resistance, leading to reduced insulin sensitivity and productivity in type 2 diabetes. There is an increasing need for new options to treat diabetes, especially type 2 diabetes at its early stages due to an ineffective control of its development in patients. Recently, a novel class of small noncoding RNAs, termed microRNAs (miRNAs), is found to play a key role as important transcriptional and posttranscriptional inhibitors of gene expression in fine-tuning the target messenger RNAs (mRNAs). miRNAs are implicated in the pathogenesis of diabetes and have become an intriguing target for therapeutic intervention. This review focuses on the dysregulated miRNAs discovered in various diabetic models and addresses the potential for miRNAs to be therapeutic targets in the treatment of diabetes.