Abstract
Diabetes mellitus is a complex and heterogeneous metabolic disorder which is often pre- or post-existent with complications such as cardiovascular disease, hypertension, inflammation, chronic kidney disease, diabetic retino- and nephropathies. However, the frequencies of these co-morbidities vary among individuals and across populations. It is, therefore, not unlikely that certain genetic variants might commonly contribute to these conditions. Here, we identified four single nucleotide polymorphisms (rs5186, rs1800795, rs1799983 and rs1800629 in AGTR1, IL6, NOS3 and TNFA genes, respectively) to be commonly associated with each of these conditions. We explored their possible interplay in diabetes and associated complications. The variant allele and haplotype frequencies at these polymorphic loci vary among different super-populations (African, European, admixed Americans, South and East Asians). The variant alleles are particularly highly prevalent in different European and admixed American populations. Differential distribution of these variants in different ethnic groups suggests that certain drugs might be more effective in selective populations rather than all. Therefore, population specific genetic architectures should be considered before considering a drug for these conditions.
Similar content being viewed by others
Introduction
Diabetes is a metabolic disorder characterized primarily by chronic hyperglycemia, which results from the inability of pancreas to produce and/or secrete enough insulin and/or resistance to insulin in the peripheral tissues1. Insulin is a master regulator of cellular metabolism and its reduced supply and/or action has pleiotropic effects on metabolic pathways1,2. According to International Diabetes Federation (IDF) Atlas (9th edition), in 2019 approximately 463 million adults between 20 and 79 years of age had diabetes and it caused 4.2 million deaths3. The number of individuals with this debilitating disease is expected to reach 700 million in 20453.
The leading cause of morbidity and mortality among diabetic individuals is long-term micro- and macro-vascular complications4. Microangiopathy is a major characteristic of diabetes, which is seen more prominently in the eyes (diabetic retinopathy) and in the kidneys (diabetic nephropathy)5. Even in the early stages of diabetes, microvascular damages might be demonstrable in the form of basement membrane thickening of small blood vessel5. The risk of vascular complications in diabetic individuals increases with aggregated comorbidities4. Hypertension or sustained high blood pressure (≥ 140/90 mmHg) is strongly associated with diabetic complications and known as a risk factor for atherosclerotic cardiovascular disease (ASCVD) and microvascular complications6. In people with diabetes, anti-hypertensive medications reduce microvascular complications as well as ASCVD events7. Hyperglycemia and hypertension are two prominent risk factors for chronic kidney disease (CKD), which is developed in approximately 40% of the diabetic patients8. In both developed and developing countries, the leading causes of end-stage renal disease (ESRD) are diabetic nephropathy (DN) and hypertensive nephropathy9. An important characteristic of diabetic kidney disease is albuminuria, which is accompanied with increased blood pressure in chronic kidney disease progression10. Hypertension not only contributes to CKD progression, but also causes damage to the kidney by arterial stiffness, endothelial dysfunction, increased renin–angiotensin–aldosterone system (RAAS) and increased salt retention11. RAAS is also associated with pathophysiology of diabetes and its micro- and macrovascular complications12. Additionally, increased amount of urea in blood in individuals with chronic kidney disease causes defect in insulin secretion from the pancreatic β cells13. Such interplay among diabetes and associated complications hint towards common contributing factors.
Single nucleotide polymorphisms (SNPs) can contribute to development and progression of diseases by influencing expression of gene, stability of messenger RNA (mRNA) and/or translational efficiency14. For example, diabetes associated risk alleles have been reported in genes that regulate pancreatic beta cell development and function, insulin gene expression, secretion and action15,16,17,18. Many SNPs have also been associated with hypertension19, inflammation20, chronic kidney disease21, cardiovascular disease22, diabetic retino-23 and nephropathies24. Prevalence of diabetes associated complications varies among different ethnic groups25,26,27. Disparities in the variant allele frequencies among different ethnic regions may contribute to varied disease susceptibilities28. Early detection of diabetes and risk measurement is of significant importance to minimize these complications3.
In this study, the common SNPs that are associated with diabetes, diabetic nephro- and retinopathies, cardiovascular disease, inflammation, hypertension and kidney diseases were identified and population specific variant allele and haplotype frequencies at these loci were discerned. In addition, the genes that harbor these variants and associated pathways were investigated to understand their roles in the etiology of diabetes associated complications.
Result
Common genetic variants in diabetes and associated complications
Only four SNPs (rs5186, rs1800629, rs1799983, and rs1800795) were found to have association with diabetes, cardiovascular diseases, diabetic nephropathy, diabetic retinopathy, hypertension, inflammation, and kidney diseases. These SNPs reside in four different genes- Angiotensin II receptor type 1 (AGTR1), Tumor necrosis factor alpha (TNFA), Nitric oxide synthase 3 (NOS3, also known as endothelial nitric oxide synthase or eNOS) and Interleukin-6 (IL6)) on three different chromosomes (Table 1). rs1800629 and rs1800795 reside in the upstream region of TNFA and IL6, while rs5186 and rs1799983 cause variations in the 3′ prime untranslated region (UTR) and coding region (missense) of AGTR1 and NOS3 genes, respectively. rs1799983 leads to transversion from guanine (G) to thymine (T) at nucleotide position 894 (G894T) resulting in the replacement of glutamic acid by aspartic acid at codon 298 (Glu298Asp).
Distribution of the common genetic variants in different populations
Minor (variant) allele frequencies (MAFs) of these SNPs in world populations are shown in Table 1. SNPs with MAFs ≥ 0.2 are shown as bold italic, while MAFs with values ≥ 0.3 are underlined in Table 1. Frequencies of these variants follow similar pattern among the constituent populations within a super-population, but vary among the super-populations. For example, compared to other populations, MAFs of all the variants are relatively low in the African populations with no value ≥ 0.2. In four African populations (Yoruba in Ibadan, Nigeria (YRI), Luhya in Webuye, Kenya (LWK), Mende in Sierra Leone (MSL), Esan in Nigeria (ESN)) the variant allele at rs1800795 is non-existent (MAF = 0.00). The MAFs at these loci in the East Asian populations are also very low. At the rs1800795 locus, the MAFs range from 0.00 to 0.005 in these populations. Frequency of the C_T haplotype, which is formed by two variant alleles at rs1800795 and rs1799983 loci, in the African and the East Asian populations is zero. In the South Asian populations, the variant allele frequencies at these loci are not as low as the African and the East Asian populations, but all these MAFs, except one, are smaller than 0.2.
In the admixed American (AMR) populations, the MAFs at these loci are much higher than the African, East- and South Asian populations. The MAFs are ≥ 0.2 at rs5186 in the four admixed American (AMR) populations. Puerto Rican (PUR) and Colombian (CLM) populations within AMR super-population have MAF > 0.2 at rs1800795 as well. The overall variant allele frequency of rs1799983 in AMR super-population is > 0.2, although the MAFs are greater than 0.2 in only two constituent populations (PUR and CLM).
The European sub-populations have high MAF values for rs5186, rs1799983 and rs1800795. The overall MAFs at rs5186, rs1799983 and rs1800795 in the European super-population are 0.272, 0.344 and 0.416, respectively. Particularly at the rs1800795 locus, the MAFs are much higher in the European populations (ranging from 0.351 to 0.485). Both IL6 (rs1800795) and NOS3 (rs1799983) reside on chromosome 7. The haplotype harboring the variant alleles (C_T) at both rs1800795 and rs1799983 loci are present at frequencies > 0.1 in the European populations only.
Interaction among the candidate genes
The protein–protein interaction (PPI) networks generated through IMEx29 and STRING30 indicate interactions among these gene encoded proteins (Fig. 1). However, the nature of interactions (direct and indirect) depicted by IMEx and STRING are somewhat different. These differences might have derived from the nature of these databases. IMEx consortium annotates experimental interaction evidences directly from the source publications and provide curated non-redundant set of physical and molecular interaction data29. The STRING database, on the other hand, collects and integrates all publicly available protein–protein interaction information as well as predicted interactions along with annotated pathway knowledge and text-mining results to provide direct (physical) as well as indirect (functional) interactions30.
Protein–protein interaction networks. (A) Network derived from IMEx interactome database (using NetworkAnalyst120 web-based visual analytics platform) shows interactions among AGTR1, IL6, NOS3 and TNFA. (B) Network derived through STRING database30 shows the nature of interactions among AGTR1, IL6, NOS3 and TNFA.
Based on the PPI network predicted using STRING, TNFA inhibits NOS3 and IL6. It has no known direct functional effect on AGTR1, which is also seen in the network derived with IMEx. IL6 activates TNFA and AGTR1 and regulates transcription of NOS3. In the IMEx-derived network, zinc finger and BTB domain-containing protein 16 (ZBTB16), a transcription factor with nine zinc fingers, connects AGTR1 and IL6, whereas alpha-2-macroglobulin (A2M) connects IL6, TNFA and NOS3.
Pathways that incorporate these proteins (AGTR1, IL6, NOS3 and TNFA) are listed in Table 2. These pathways were identified from the KEGG database based on the PPI networks derived through IMEx and SRTING. Among these IL6, NOS3, TNFA and AGTR1 plays role in 9, 8, 7, and 5 pathways, respectively. Only the AGE-RAGE signaling pathway in diabetic complications accommodates all four candidate genes.
Distribution of the other potentially deleterious genetic variants of AGTR1, IL6, NOS3 and TNFA in world populations
We used the Ensembl Allele Frequency Calculator, SIFT and PolyPhen-2 tools to retrieve frequencies of the other potentially deleterious variants in AGTR1, IL6, NOS3 and TNFA genes in worldwide populations based on the data in the 1000 Genomes Project database (Supplementary Table S1). Except a few, majority of these potentially deleterious variants are either absent or have very low frequencies in most populations. Among these variants, rs2069842 and rs2069849 are present at relatively high frequencies particularly in the African populations.
Drug-responsiveness of rs5186, rs1800629, rs1799983 and rs1800795 variants
As AGRT1, IL6, NOS3 and TNFA genes are common candidates for diabetes and associated complications, drugs that target these might mitigate multiple risk factors simultaneously. Table 3 provides the list of FDA approved drugs which target these gene products. Interactions of the SNPs with these drugs are shown in Table 4. Interestingly, rs1045642, a variant in the multi-drug transporter ABCB1 (also known as MDR1 and P-gp), is in strong linkage disequilibrium with rs1800795 (IL6) and rs1799983 (NOS3) (Supplementary Table S2). rs1045642 is associated with hypertension and modulates responses to anti-hypertensive drugs31,32. Haplotypes with ≥ 2 variant alleles at rs1800795, rs1045642 andrs1799983 have high frequencies in the European populations (Supplementary Table S2).
Discussion
Relation of rs5186, rs1800629, rs1799983 and rs1800795 variants to diabetes and associated complications
Diabetes is a multifactorial endocrine disorder which heightens the chances of developing other complex conditions33. It is, therefore, not impossible to have an underlying link among these conditions. Determining these underlying common genetic factors may pave ways for devising more effective diagnostic and prognostic strategies, improve treatment regimes and increase individualized drug efficacies. Interethnic differences in the frequencies of such common disease causing genetic factors may be accounted for variation in the prevalence of disease conditions in different regions. Here, we identified four SNPs (rs5186, rs1800629, rs1799983, rs1800795) that are associated with diabetes, cardiovascular disease, diabetic retino- and nephropathies, hypertension, inflammation and kidney diseases.
rs5186 is located at position 1166 in the 3′ untranslated region of AGTR1, which encodes the angiotensin II receptor type 134. Frequencies of the variant allele (C) at rs5186 are high in the European and the admixed American populations (Table 1). Complementary base pairing of microRNA-155 (miR-155) suppresses translation of AGTR1 mRNA when the wild type allele (A) is present at the rs5186 locus34. The C allele prevents this base pairing, which leads to increased AGTR1 protein levels34. In the South Asian population, A allele of rs5186 acts as a protective factor against renal disease development34. Compared to the AA genotype at this locus, the CC genotype is related to diabetic nephropathy35,36. Elevated AGTR1 protein lead to increased activation of renin–angiotensin–aldosterone system (RAAS) as angiotensin II binds primarily to AGTR1 to exert its actions37. Pancreatic RAAS plays an important role in the pathophysiology of diabetes38. Activation of AGTR1 can cause pancreatic β-cell death via NADPH oxidase induction and ROS generation39. Increased activation of RAAS is also associated with hypertension40, regulation of inflammatory cascade41, progression to chronic kidney disease (CKD) and coronary heart disease (CHD)42. The exact mechanism of RAAS in the development of diabetic retinopathy is still not fully elucidated, but its regulatory role in vascular hydrodynamics and upregulation of its components in diabetic retinopathy point towards its involvement in the onset and progression of this disease43. High prevalence of rs5186 variant allele may result in higher incidences of the above-mentioned complications in the European and admixed American populations through over-activation of RAAS.
TNFA is a pro-inflammatory cytokine produced by a variety of cell-types such as lymphocytes, macrophages and adipocytes44. This immunoregulatory cytokine stimulates many other cytokines leading to a cytokine cascade, which causes inflammation45. TNFA rs1800629 (-308G > A) variant is located upstream of the coding region of the gene and known to influence TNFA levels45. The -308A polymorphism affects transcription factor binding, which can lead to TNF gene transcription causing inappropriate and excessive TNFA production46. Higher levels of this cytokine have been found in diabetic patients compared to non-diabetic controls44. Moreover, serum level of TNFA is significantly higher in obese diabetic patients47. It plays an important pathophysiological role in the development of insulin resistance, especially in people with high Body Mass Index (BMI)48. Overweight and obesity are highly prevalent in Europe49. Interestingly, among the five super-populations in this study, European super-population has the largest MAF at rs1800629 locus (Table 1). TNFA also plays a key regulatory role in endothelial dysfunction in diabetes50. Local activation of this cytokine within fibrovascular membranes of eyes plays an important role in the development of the proliferation phase of diabetic retinopathy51. Its serum level is more elevated in diabetic nephropathy patients compared to diabetic patients52.
Among the five super-populations, the largest MAF at rs1800795 is present in the European populations. rs1800795 (-174 G > C) is a variant in the promoter region of IL6 gene, which is a classic inflammatory cytokine53. rs1800795 modulates transcription rate of IL653. Carriers of rs1800795 C allele have higher plasma level of IL654. This variant allele is more common in diabetic patients compared to healthy controls55. In addition, this allele increases the risk of obesity-related metabolic disorders, especially insulin resistance, in people with excessive body-weight56. High prevalence of overweight and obesity in the European populations49 may exacerbate the effect of this variant allele, which is highly prevalent at this locus. rs1870095 C allele can also lead to a greater predisposition to atherosclerosis and myocardial or vascular injury54. Intraocular accumulation of IL6 is observed in proliferative diabetic retinopathy, although its involvement might not occur until the later stage of this disease57. Renal expression of pro-inflammatory cytokines including both TNFA and IL6 are increased in diabetic nephropathy58.
The variant allele frequencies at the rs1799983 locus in NOS3 gene are high in the European and admixed American populations. This missense variant resides in the exon 7 of NOS3 gene. This gene is constitutively expressed to make of nitric oxide (NO) in the endothelial cells and plays role in vasodilation and blood pressure regulation59. The variant (T) allele at this position is associated with significantly reduced basal NO production60. Decreased NO level can lead to hypertension as it plays a role in maintaining normal blood pressure by dilating smooth muscles to allow relaxed blood flow61. Reduced level of NO is also associated with abnormal levels of blood lipids60. Additionally, without the antioxidative role of NO, the hydroxyl radicals increase, which contribute to endothelial dysfunction60. Dyslipidemia and endothelial dysfunction are associated with atherosclerosis60. The oxidative effects by low levels of NO might also play a role in insulin resistance and diabetes62. NOS knocked-out diabetic mice exhibit accelerated retinopathy63. Reduced level of NO has been reported in progressive diabetic nephropathy and chronic kidney disease (CKD)64.
The rs5186, rs1800629, rs1799983 and rs1800795 variants in the AGTR1, TNFA, NOS3 and IL6, respectively, are therefore relevant to diabetes and associated complications. These four genes are involved in AGE-RAGE signaling pathway (Table 2), which is known to cause diabetes associated complications65,66. Hyperglycemia accelerates the formation of advanced glycation end products (AGEs), which interact with their major cell surface signal transduction receptor for AGE (RAGE) on smooth muscle cells, endothelial cells and monocytes, thereby inducing a wide range of signaling pathways65,67. Ligand-engagement of RAGE triggers activation of NADPH oxidase, which leads to generation of reactive oxygen species (ROS), that in turn can activate renin-angiotensin system (RAS)68. Pathophysiological cross-talk between AGE-RAGE axis and RAS can contribute to the progression of diabetes-associated vascular damage69. RAS activation and subsequent angiotensin II production play an important role in hypertension as well as renal and cardiac fibrosis70. Additionally, angiotensin II stimulates the expression of transforming growth factor-β (TGF-β) in the kidney, which play significant roles in the development of diabetic nephropathy71.
ROS also triggers nuclear factor kappa-light-chain-enhancer of activated B cells (NF-κB), which is a key player in cytokine and inflammatory mediator expression that results in micro-vasculopathy65. The up-regulation of transcription factor NF-κB triggers the secretion of pro-inflammatory cytokines such as IL6 and TNFA72. ROS generated by AGE-RAGE interaction can activate protein kinase C (PKC), which upregulates TGF-β, VEGF, NF-κB, NADPH oxidase and down-regulates endothelial nitric oxide synthase (NOS3) causing reduced production of NO, and thus affect blood flow and capillary permeability73. Furthermore, interaction between TNFA and IL6 increases oxidative stress, reduce NOS3 phosphorylation and contribute to coronary endothelial dysfunction in diabetic mice50. Thus, up-regulation of AGTR1, IL6 and TNFA and reduced production of NO in the context of AGE-RAGE pathway contribute to diabetes and its complications. As mentioned earlier, the variant alleles at rs5186, rs1800629, rs1799983 and rs11800795 loci cause such up- and down-regulations in the expression of corresponding genes. Thereby, presence of these variant alleles may exacerbate diabetic complications.
IL6, TNFA and NOS3 are associated with insulin resistance (Table 2). Insulin resistance (IR) is characterized as a condition in which sensitivity to insulin and its downstream signaling pathways is decreased in three primary insulin sensitive metabolic tissues– skeletal muscle, liver, and white adipose under normal serum glucose concentrations74. By increasing STAT3 (signal transducer and activator of transcription 3) phosphorylation, IL6 enhances the expression of suppressor of cytokine signaling 3 (SOCS3), which directly interferes with insulin signaling by binding to insulin receptor (IR), preventing its interaction with insulin receptor substrate-1 (IRS-1) (primarily in skeletal muscle and adipose tissue) or IRS-2 (primarily in liver) and targeting IRS-1 and IRS-2 for degradation75. IL6 also contributes to insulin resistance by inducing TLR4 expression in skeletal muscle via STAT376. In addition, IL6 may impair insulin mediated IRS-1/PI3-kinase/Akt pathway by activating c-Jun NH2-terminal kinase (JNK) and extracellular signal-regulated kinase-1/2 (ERK1/2), which in turn induce serine phosphorylation of IRS-177. Serine phosphorylation of IRS-1 results in reduced insulin receptor kinase activity78. These disruptions in early events of insulin pathway ultimately leads to insulin resistance and attenuated GLUT-4 mediated glucose uptake75. TNFA impairs insulin signal transduction in skeletal muscle by causing increased serine phosphorylation and reduced tyrosine phosphorylation of IRS-1, which leads to interference with GLUT4 translocation due to impaired phosphorylation of Akt substrate of 160 kDa (AS160)79.
As depicted in the protein–protein interaction networks (Fig. 1B), TNFA inhibits NOS3 activity, whereas IL6 regulates of NOS3 and AGTR1. In cultured vascular smooth muscle cell, IL6 have been shown to enhance expression of AGTR180. So, the complications arising from increased level of AGTR1 may be mediated by IL6. Based on the PPI network (Fig. 1), TNFA and IL6 can influence each-others activation. In obesity, production of IL6 is stimulated by both IL-1β and TNFA81. This inductive role of IL-1β is outlined in the supplementary Fig. S1. This proinflammatory cytokine along with elevated IL6 inhibits insulin signaling pathways and contributes to the development of diabetes82.
As shown in Table 2, IL6 and NOS3 are part of the Hypoxia-inducible factor 1 (HIF-1) signaling pathway. HIF-1 binds DNA at the hypoxia response element (HRE) and activates expression of several hypoxia induced genes involved in glucose metabolism, angiogenesis, cell proliferation, survival and metastasis83. HIF-1 binds to the HRE in the NOS3 gene promoter causing its increased expression which in turn elevates NO levels in the endothelial cells, and thus contributes to vasodilation and vascular permeability84. HIF-1α (one of the constituent subunits of HIF-1) expression is elevated by activated IL6/STAT3 pathway85. So, increased IL6 expression in presence of the rs1800795 variant (C) allele (as previously discussed) may stimulate HIF-1 expression, which transcriptionally activates several angiogenic genes (such as VEGF) and their receptors86. VEGF level is significantly elevated in diabetic patients with different micro- and macrovascular complications and it is a principle mediator of diabetic retinopathy87.
HIF-1α contributes to the hypoxia induced regulation of apelin and the apelin receptor88 to stimulate cell proliferation and angiogenesis via PI3K-Akt and mTOR mediated processes89. Both IMEx and STRING predicted PPI networks found AGTR1 and NOS3 to be associated with the Apelin signaling pathway, cGMP-PKG signaling pathway and calcium signaling pathway (Table 2). Apelin signaling pathway is involved in normal vascular development and in regulation of NO-dependent vasodilatation88. Reduced production of NO resulting from NOS3 rs1799983 T allele might interfere with this apelin regulated vasodilation.
TNFA and IL6, the two cytokines are involved in IL-17 signaling pathway, Toll-like receptor signaling pathway and TNF signaling pathway (Table 2). IL-17 stimulates production of proinflammatory cytokines (IL-1β, IL6 and TNFA), which contributes to the induction of insulin resistance and development of diabetes90. IL6 activated STAT3 increases TLR4 gene expression76. TLR4 activation can contribute to insulin resistance by proinflammatory cytokines in diabetes91. Thus, rs180079 variant mediated increased IL6 expression may contribute to insulin resistance via TLR4 activation.
IL6 and TNFA are associated with NOD-like receptor signaling pathway (Table 2). NOD-like receptors (NLRs) contribute significantly to the pathogenesis of obesity mediated insulin resistance by inducing proinflammatory cytokines92. Inflammatory cytokines induced by NLRs play role in diabetic retinopathy93 and progression to chronic kidney disease94.
As shown in Table 2, NOS3 and TNFA participate in sphingolipid signaling pathway. TNFA can stimulate sphingolipid (ceramide) generation95 and insulin resistance is promoted by elevated sphingolipid loads in the diabetic pancreas96. More research approaches are needed to investigate the concurrence of increased TNFA and sphingolipids in insulin resistance pathways.
Based on the IMEx predicted network (Fig. 1A) ZBTB16 connects between AGTR1 and IL6. ZBTB16, also known as promyelocytic leukemia zinc finger (PLZF), is a transcription factor (TF) that is be related to cardiac hypertrophy and/or fibrosis associated with hypertension and connected to components of metabolic syndrome such as dyslipidemia and insulin resistance via TNFA and IL697. The level of serum alpha-2-macroglobulin (A2M), a protease inhibitor98, which is the connector between IL6, NOS3 and TNF in the network, is known to be elevated in diabetes99, retinopathy100 and nephrotic syndrome101. Further research focusing on whether there is any significant contribution of these two components (ZBTB16 and A2M) to diabetes and associated complications is needed. These connectors may help developing novel therapeutic strategies rather than targeting the individual nodes.
Distribution patterns of variant allele and haplotype frequencies in populations of different ethnic orogins
The 1000 Genomes Project reconstructed the genomes of 2504 individuals from 26 populations from five super-populations (African (AFR), East Asian (EAS), European (EUR), South Asian (SAS), and admixed Americans (AMR)) with the aim of providing a comprehensive description of the human genetic variants102. Earlier studies revealed genetic stratifications among populations of different ethnicities and geographic distributions103. Global human population has gone through a number of stratifications and divisions during the course of evolution and migration. Within these subdivisions, people remixed and settled in widely varying environments, which resulted in the shuffling of genes leading to genetic heterogeneity. That is why allele frequency and haplotype analyses are important to understand population specific risk alleles.
Table 1 shows the different patterns in the distribution of variant alleles at rs1800629, rs1800795, rs1799983 and rs5186 loci in populations of different ethnicities. As mentioned earlier, the C allele at the rs5186 locus affects translation of AGTR1 leading to increased activation of the renin–angiotensin–aldosterone system. The range of MAFs for this variant is 0.207–0.266 in the admixed American populations and 0.207–0.313 in the European populations, whereas in other populations (Africans, East and South Asians) the range is 0.005 to 0.118. In the European populations, the variant allele frequencies of rs1799983 and rs1800795 are higher than the other populations. This might result in low circulating level of vasodilator NO by the variant allele itself or its linkage with the causal variant(s). For Utah residents with Northern and Western European ancestry and Finnish in Finland of European super-population, the MAFs at rs1800795 are close to 0.5. This variant allele was reported to be associated with insulin resistance, type 2 diabetes and myocardial injury54,104. The haplotype with two variant alleles (C_T) at rs1800795 and rs1799983 (two variants of chromosome 7) has significantly higher frequencies in European populations. Frequencies of this particular haplotype range from 0.101 to 0.182 in the European populations, whereas in other populations this range is between 0.002 and 0.087. At these loci the MAFs are > 0.2 in two admixed American sub-populations (Puerto Ricans from Puerto Rico and Colombians from Medellin, Colombia). Latin American populations are characterized by high level of genetic admixture among the Africans, Europeans and native American ancestral populations105. As shown in Table 1, among the four Latin American populations in Colombia, Mexico, Peru, and Puerto Rico- the pattern of variant allele frequencies in Puerto Ricans and Colombians is similar to the European populations, which might be because these two population inherited more genetic content from European ancestry than the Mexicans and the Peruvians103.
rs1800629, rs1800795, rs1799983 and rs5186 variants have low allele and haplotype frequencies in the African populations (Table 1). The pattern of MAFs at rs5186 in the two populations with African ancestry, but now residing in America [Americans of African Ancestry (ASW) and African Caribbeans in Barbados (ACB)] are similar to the African populations. The reason could be that the African Americans inherited approximately 80% of their genome from African ancestor and 20% from the Europeans103. The ancestors of African Americans and the African Caribbeans in Barbados were mostly from West Africa103. It was reported earlier that the East Asian and the African populations along with the Americans of African ancestry in USA (ASW) and the African Caribbeans in Barbados form completely different cluster from rest of the world populations103. The African populations, however, have relatively higher frequencies of other potentially deleterious variants in AGTR1, IL6, NOS3 and TNFA (Supplementary Table S1).
Drug responses and distribution of variants in populations of different ethnicities
Approved drugs, that target these candidate genes, are listed in Table 3. Ethnic differences in allele frequencies at drug response-related SNP loci contribute to inter-population variability in drug response103. Three (rs5186, rs1800629 and rs1800795) of the four SNPs identified to be associated with diabetes and its complications can affect responses to drugs (Table 4). rs5186 is associated with the efficacies of losartan and angiotensin II (Table 4). Carriers of the variant allele (C) at rs5186 may have better humoral and renal hemodynamic responses to losartan treatment compared to individuals with the AA genotype106. High MAFs (≥ 0.2) at rs5186 in the admixed American and the European populations may make losartan a more effective drug for improving renal functions.
Individuals with the major allele G at rs1800629 show better response to anti-TNFA treatments in comparison to the carriers of the variant allele A107. Hence, individuals with increased TNFA level unsurprisingly show decreased response to anti-TNFA treatments. Efficacy of anti-TNFA treatment may also depend on the individual’s genotype at rs1800795- an SNP that affects IL6 expression level. Obese psoriasis patients with the major allele G at rs1800795 show decreased response to TNFA inhibitors108. Apart from the European populations and two admixed American populations (CLM and PUR), MAFs at rs1800795 in most populations, especially in the East Asians, is very low. Further studies are needed to fully elucidate how these differences in allele frequencies contribute to interpopulation differences in response to anti-TNFA therapeutics. Protein–protein interactions exist between TNFA and IL6 (Fig. 1). As previously discussed, obesity may influence plasma TNFA level. These complex interactions may explain why SNPs in IL6 may modulate response to TNFA inhibitors.
Since rs1799983 resides on the same chromosome with rs1800795 and rs1045642, all three were considered during haplotype frequency calculation (Supplementary Table S2). The haplotype with the major alleles at three SNP loci (G_G_G) is the most frequent in the African, admixed American and East Asian super-populations. The most common haplotype (G_A_G) in these the European and the South Asian super-populations contain the variant allele (A) at rs1045642. In these super-populations, the variant allele frequencies at this locus are ≥ 0.5. Hypertensive patients with rs1045642 A allele may have better response to losartan109. Again, efficacy of losartan might be more in treating diabetes associated complications in the European populations. Besides, rs1045642 is located in the ABCB1 (ATP‐binding cassette, subfamily B) gene, which encodes a drug efflux transporter110. Haplotype containing the variant alleles at all SNP loci (C_A_T) has a higher frequency (0.089) in the European super-population compared to the other populations. Thus, this haplotype calls for further investigation considering its association with diseases and drug-responses. Haplotype (C_G_T) with the variant alleles at rs1800795 (C) and rs1799983 (T) loci also has a higher frequency (0.062) in European super-population.
In addition to their contributions in diabetes and associated complications, TNFA, IL6, AGTR1 and NOS3 may play roles in other diseases111 (Supplementary Fig. S2). Drugs targeted to these genes might also have therapeutic benefits for other diseases with the same cause. For example, increased peripheral or central IL6 levels contributes to stress reaction and depressive disorder for which tocilizumab (antibody against IL6) mediated therapy can be a strategy to reduce the IL6 activity112.
In conclusion, the four genetic variants (rs5186, rs1800629, rs1799983, rs1800795) exert pleiotropic effects that can influence diabetes and associated complications and reside in genes that participate in pathways leading to these complications. These variants might also modulate other pathophysiological pathways. Based on the variant allele and haplotype frequencies, the European and admixed American populations have higher prevalence of the disease associated alleles at rs5186, rs1799983 and rs1800795 loci, which might make these populations more vulnerable to the diabetes and associated complications. These variants might modulate responses to approved drugs, and therefore, inter-population variability in response to drugs must be regarded. Early genetic characterization will give scope for more effective personalized treatment strategies. With prior knowledge and better understanding of risk factors and population specific haplotypes diagnostic tools and algorithms may be developed or improvised for different populations and preventive treatment strategies may be implicated to halt progression to severe complications.
Materials and methods
Identification of the common SNPs and associated genes
SNPCurator113 and SNP4Disease114 databases were searched with medical subject headings (MeSH)- “Diabetes mellitus”, “Cardiovascular diseases”, “Diabetic nephropathy”, “Diabetic retinopathy”, “Hypertension”, “Inflammation” and “Kidney diseases” to retrieve the list of SNPs associated with these conditions. SNPcurator is text mining system that extracts information from the genome wide and candidate genes studies113. The SNP4Disease database- developed by the Max Planck Institute for Heart and Lung Research, provides information on disease associated SNPs by literature-mining techniques from various sources114. With SNPCurator p < 0.05 was used to select SNPs. With SNP4Disease, it was not possible to filter based on p-values. There is wide variation in the sample sizes in different studies. It was not considered as a selection criterion during data retrieval. SNPs obtained for a particular MeSH term were merged and duplicates were removed. SNPs that are common to all MeSH terms were identified. Ultimately, four SNPs were identified to be commonly involved in the above mentioned conditions.
Allele and haplotype frequencies of the common variants
LDhap tool at the open source LDlink suite115 was used for retrieving minor (variant) allele frequencies (MAF) of the SNPs and haplotype frequencies (of two SNPs that reside on the same chromosome) in five super-populations (African, admixed Americans, European East Asian, and South Asian) along with 26 constituent sub-populations. LDlink is a web-based collection of bioinformatic modules that query SNPs in population groups of interest for generating haplotype tables and calculating pair-wise linkage disequilibrium (LD) using the Phase 3 haplotype data from the 1000 Genomes Project as a reference115. Other SNPs within the candidate genes, that harbor the common variants, in the above mentioned 26 populations were retrieved using the Ensembl allele frequency calculator116.
Determining phenotypic effects of the variants
PolyPhen-2117 and SIFT118 tools within the Ensembl variant effect predictor (VEP) were used to predict the effect of the genetic variants119. Only potentially deleterious/damaging variants were selected for further analysis. PolyPhen-2 predicts the functional significance of an allele replacement from its individual features by Naïve Bayes classifier trained using supervised machine-learning based on two pairs of datasets- HumDiv and HumVar117. In addition to calculating the probability whether a mutation is damaging by estimating the false positive rate (FPR, the chance that the mutation is classified as damaging when it is in fact non-damaging) and the true positive rate (TPR, the chance that the mutation is classified as damaging when it is indeed damaging), PolyPhen-2 also classifies a mutation qualitatively as- benign, possibly damaging, or probably damaging based on false positive rate (FPR) thresholds. On the other hand, SIFT is a multistep algorithm that predicts whether an amino acid substitution affects protein function based on sequence homology and the physical properties of amino acids118. SIFT scores ranges from 0 to 1. The amino acid substitution is predicted deleterious (affects protein function), if the score is < 0.05, and tolerated (neutral) if the score is ≥ 0.05. More details on the principle and procedure of calculating the PolyPhen-2 and SIFT scores are described in Adzhubei et al.117 and Vaser et al.118, respectively.
Protein–protein network analysis
NetworkAnalyst120 web-interface was used to visualize the interactions among the gene products based on the protein–protein interaction data in the International Molecular Exchange Consortium (IMEx) database (using the default parameters and first-order network), which is a curated database containing non-redundant set of interaction data from a broad taxonomic range of organisms29. The modes of regulation among these gene products were retrieved using the STRING database using the default parameters30. The pathways that incorporate these gene products (with false discovery rate (FDR) ≤ 0.05) were retrieved from the KEGG pathway database121.
Disease-gene interaction analysis
DisGeNET, one of the largest publicly accessible collections of genes and variants associated with human diseases111, was used via the NetworkAnalyst120 platform to visualize all possible associations of the candidate genes with human diseases.
Analysis of drug-response
DGIdb122 and DrugBank123 databases were searched for the drugs that act on the candidate genes. Only the approved drugs were selected. List of SNPs associated with responses to the approved drugs targeting the candidate genes was retrieved from the pharmacogenomics knowledgebase124. Chromosomal positions and functional consequences of the SNPs were collected from dbSNP125. Alleles and minor allele frequencies of SNPs present in the 1000 Genomes Project were collected via Ensembl116. Haplotype frequencies were calculated with LDhap module of LDlink115. Only haplotypes with frequency of ≥ 0.05 in at least one super-population were listed.
Statement of ethics
This study did not use human or animals. No ethical approval is required.
References
American Diabetes Association. Diagnosis and classification of diabetes mellitus. Diabetes Care 32, S62–S67. https://doi.org/10.2337/dc09-S062 (2009).
Kharroubi, A. T. & Darwish, H. M. Diabetes mellitus: The epidemic of the century. World J. Diabetes 6, 850–867. https://doi.org/10.4239/wjd.v6.i6.850 (2015).
IDF. IDF Diabetes Atlas 2019. 9th edn (International Diabetes Federation, 2019).
Matheus, A. S. et al. Impact of diabetes on cardiovascular disease: An update. Int. J. Hypertens. 2013, 653789. https://doi.org/10.1155/2013/653789 (2013).
Marble, A. The banting memorial lecture 1967: Angiopathy in diabetes: An unsolved problem. Diabetes 16, 825–838. https://doi.org/10.2337/diab.16.12.825 (1967).
de Boer, I. H. et al. Diabetes and hypertension: A position statement by the American Diabetes Association. Diabetes Care 40, 1273–1284. https://doi.org/10.2337/dci17-0026 (2017).
Brunström, M. & Carlberg, B. Effect of antihypertensive treatment at different blood pressure levels in patients with diabetes mellitus: Systematic review and meta-analyses. BMJ 352, i717. https://doi.org/10.1136/bmj.i717 (2016).
Alicic, R. Z., Rooney, M. T. & Tuttle, K. R. Diabetic kidney disease: Challenges, progress, and possibilities. Clin. J. Am. Soc. Nephrol. 12, 2032–2045. https://doi.org/10.2215/CJN.11491116 (2017).
Ghaderian, S. B., Hayati, F., Shayanpour, S. & Beladi Mousavi, S. S. Diabetes and end-stage renal disease; A review article on new concepts. J. Renal Injury Prevent. 4, 28–33, https://doi.org/10.12861/jrip.2015.07 (2015).
Lin, Y. C., Chang, Y. H., Yang, S. Y., Wu, K. D. & Chu, T. S. Update of pathophysiology and management of diabetic kidney disease. J. Formos. Med. Assoc. 117, 662–675. https://doi.org/10.1016/j.jfma.2018.02.007 (2018).
Pugh, D., Gallacher, P. J. & Dhaun, N. Management of hypertension in chronic kidney disease. Drugs 79, 365–379. https://doi.org/10.1007/s40265-019-1064-1 (2019).
Joseph, J. J. et al. Renin-angiotensin-aldosterone system, glucose metabolism and incident type 2 diabetes mellitus: MESA. J. Am. Heart Assoc. 7, e009890. https://doi.org/10.1161/JAHA.118.009890 (2018).
Koppe, L. et al. Urea impairs β cell glycolysis and insulin secretion in chronic kidney disease. J. Clin. Invest. 126, 3598–3612. https://doi.org/10.1172/JCI86181 (2016).
Shastry, B. S. SNPs: Impact on gene function and phenotype. Methods Mol. Biol. 578, 3–22. https://doi.org/10.1007/978-1-60327-411-1_1 (2009).
Xue, A. et al. Genome-wide association analyses identify 143 risk variants and putative regulatory mechanisms for type 2 diabetes. Nat. Commun. 9, 2941. https://doi.org/10.1038/s41467-018-04951-w (2018).
Hu, M., Cherkaoui, I., Misra, S. & Rutter, G. Functional genomics in pancreatic β cells: Recent advances in gene deletion and genome editing technologies for diabetes research. Front. Endocrinol. 11, 576632. https://doi.org/10.3389/fendo.2020.576632 (2020).
Thomsen, S. K. & Gloyn, A. L. The pancreatic β cell: Recent insights from human genetics. Trends Endocrinol. Metab. 25, 425–434. https://doi.org/10.1016/j.tem.2014.05.001 (2014).
Povel, C. M. et al. Single nucleotide polymorphisms (SNPs) involved in insulin resistance, weight regulation, lipid metabolism and inflammation in relation to metabolic syndrome: An epidemiological study. Cardiovasc. Diabetol. 11, 133. https://doi.org/10.1186/1475-2840-11-133 (2012).
Izawa, H. et al. Prediction of genetic risk for hypertension. Hypertension 41, 1035–1040. https://doi.org/10.1161/01.HYP.0000065618.56368.24 (2003).
Sode, J. et al. Genetically determined high activities of the TNF-alpha, IL23/IL17, and NFkB pathways were associated with increased risk of ankylosing spondylitis. BMC Med. Genet. 19, 165. https://doi.org/10.1186/s12881-018-0680-z (2018).
Corredor, Z. et al. Genetic variants associated with chronic kidney disease in a Spanish population. Sci. Rep. 10, 144. https://doi.org/10.1038/s41598-019-56695-2 (2020).
De Rosa, S. et al. Type 2 diabetes mellitus and cardiovascular disease: Genetic and epigenetic links. Front. Endocrinol. 9, 2. https://doi.org/10.3389/fendo.2018.00002 (2018).
Azzam, S. K. et al. Genetic associations with diabetic retinopathy and coronary artery disease in Emirati patients with type-2 diabetes mellitus. Front. Endocrinol. 10, 283. https://doi.org/10.3389/fendo.2019.00283 (2019).
Rizvi, S., Raza, S. T. & Mahdi, F. Association of genetic variants with diabetic nephropathy. World J. Diabetes 5, 809–816. https://doi.org/10.4239/wjd.v5.i6.809 (2014).
Lanting, L. C., Joung, I. M. A., Mackenbach, J. P., Lamberts, S. W. J. & Bootsma, A. H. Ethnic differences in mortality, end-stage complications, and quality of care among diabetic patients. Diabetes Care 28, 2280–2288. https://doi.org/10.2337/diacare.28.9.2280 (2005).
Spanakis, E. K. & Golden, S. H. Race/ethnic difference in diabetes and diabetic complications. Curr. Diab. Rep. 13, 814–823. https://doi.org/10.1007/s11892-013-0421-9 (2013).
Hamoudi, R. et al. Prediabetes and diabetes prevalence and risk factors comparison between ethnic groups in the United Arab Emirates. Sci. Rep. 9, 17437. https://doi.org/10.1038/s41598-019-53505-7 (2019).
Huang, T., Shu, Y. & Cai, Y.-D. Genetic differences among ethnic groups. BMC Genomics 16, 1093. https://doi.org/10.1186/s12864-015-2328-0 (2015).
Orchard, S. et al. Protein interaction data curation: The International Molecular Exchange (IMEx) consortium. Nat. Methods 9, 345–350. https://doi.org/10.1038/nmeth.1931 (2012).
Szklarczyk, D. et al. STRING v11: Protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 47, D607–D613. https://doi.org/10.1093/nar/gky1131 (2018).
Bouatou, Y. et al. Recipient rs1045642 polymorphism is associated with office blood pressure at 1-year post kidney transplantation: A single center pharmacogenetic cohort pilot study. Front. Pharmacol. 9, 184 (2018).
Sychev, D. et al. Effects of ABCB1 rs1045642 polymorphisms on the efficacy and safety of amlodipine therapy in Caucasian patients with stage I-II hypertension. Pharmacogenomics Pers. Med. 11, 157–165. https://doi.org/10.2147/PGPM.S158401 (2018).
Papatheodorou, K., Banach, M., Bekiari, E., Rizzo, M. & Edmonds, M. Complications of diabetes 2017. J. Diabetes Res. 2018, 3086167. https://doi.org/10.1155/2018/3086167 (2018).
Smyth, L. J., Cañadas-Garre, M., Cappa, R. C., Maxwell, A. P. & McKnight, A. J. Genetic associations between genes in the renin-angiotensin-aldosterone system and renal disease: A systematic review and meta-analysis. BMJ Open 9, e026777. https://doi.org/10.1136/bmjopen-2018-026777 (2019).
Ding, W. et al. Association between two genetic polymorphisms of the renin-angiotensin-aldosterone system and diabetic nephropathy: A meta-analysis. Mol. Biol. Rep. 39, 1293–1303. https://doi.org/10.1007/s11033-011-0862-7 (2012).
Shah, V. N. et al. ACACβ gene (rs2268388) and AGTR1 gene (rs5186) polymorphism and the risk of nephropathy in Asian Indian patients with type 2 diabetes. Mol. Cell. Biochem. 372, 191–198. https://doi.org/10.1007/s11010-012-1460-2 (2013).
Orenes-Piñero, E. et al. Impact of polymorphisms in the renin–angiotensin–aldosterone system on hypertrophic cardiomyopathy. J. Renin-Angiotensin-Aldosterone Syst. 12, 521–530, https://doi.org/10.1177/1470320311405247 (2011).
van der Zijl, N. J., Moors, C. C. M., Goossens, G. H., Blaak, E. E. & Diamant, M. Does interference with the renin–angiotensin system protect against diabetes? Evidence and mechanisms. Diabetes Obes. Metab. 14, 586–595. https://doi.org/10.1111/j.1463-1326.2012.01559.x (2012).
Kooptiwut, S. et al. Testosterone reduces AGTR1 expression to prevent β-cell and islet apoptosis from glucotoxicity. J. Endocrinol. 224, 215–224. https://doi.org/10.1530/JOE-14-0397 (2015).
Satou, R., Penrose, H. & Navar, L. G. Inflammation as a regulator of the renin-angiotensin system and blood pressure. Curr. Hypertens. Rep. 20, 100. https://doi.org/10.1007/s11906-018-0900-0 (2018).
Montecucco, F., Pende, A. & Mach, F. The renin-angiotensin system modulates inflammatory processes in atherosclerosis: Evidence from basic research and clinical studies. Mediators Inflamm. 2009, 752406. https://doi.org/10.1155/2009/752406 (2009).
Lin, J., Hu, F. B., Qi, L. & Curhan, G. C. Genetic polymorphisms of angiotensin-2 type 1 receptor and angiotensinogen and risk of renal dysfunction and coronary heart disease in type 2 diabetes mellitus. BMC Nephrol. 10, 9. https://doi.org/10.1186/1471-2369-10-9 (2009).
Whitehead, M., Wickremasinghe, S., Osborne, A., Van Wijngaarden, P. & Martin, K. R. Diabetic retinopathy: A complex pathophysiology requiring novel therapeutic strategies. Expert Opin. Biol. Ther. 18, 1257–1270. https://doi.org/10.1080/14712598.2018.1545836 (2018).
Rodrigues, K. F. et al. IL-6, TNF-alpha, and IL-10 levels/polymorphisms and their association with type 2 diabetes mellitus and obesity in Brazilian individuals. Arch. Endocrinol. Metab. 61, 438–446. https://doi.org/10.1590/2359-3997000000254 (2017).
Khan, S. et al. TNF-α -308 G > A (rs1800629) polymorphism is associated with celiac disease: A meta-analysis of 11 case-control studies. Sci. Rep. 6, 32677. https://doi.org/10.1038/srep32677 (2016).
Abraham, L. J. & Kroeger, K. M. Impact of the -308 TNF promoter polymorphism on the transcriptional regulation of the TNF gene: Relevance to disease. J. Leukoc. Biol. 66, 562–566. https://doi.org/10.1002/jlb.66.4.562 (1999).
Alzamil, H. Elevated serum TNF-α is related to obesity in type 2 diabetes mellitus and is associated with glycemic control and insulin resistance. J. Obes. 2020, 5076858. https://doi.org/10.1155/2020/5076858 (2020).
Swaroop, J. J., Rajarajeswari, D. & Naidu, J. N. Association of TNF-α with insulin resistance in type 2 diabetes mellitus. Indian J. Med. Res. 135, 127–130. https://doi.org/10.4103/0971-5916.93435 (2012).
Marques, A., Peralta, M., Naia, A., Loureiro, N. & de Matos, M. G. Prevalence of adult overweight and obesity in 20 European countries, 2014. Eur. J. Pub. Health 28, 295–300. https://doi.org/10.1093/eurpub/ckx143 (2017).
Lee, J. et al. Interaction of IL-6 and TNF-α contributes to endothelial dysfunction in type 2 diabetic mouse hearts. PLoS ONE 12, e0187189. https://doi.org/10.1371/journal.pone.0187189 (2017).
Limb, G. A., Chignell, A. H., Green, W., LeRoy, F. & Dumonde, D. C. Distribution of TNF alpha and its reactive vascular adhesion molecules in fibrovascular membranes of proliferative diabetic retinopathy. Br. J. Ophthalmol. 80, 168–173. https://doi.org/10.1136/bjo.80.2.168 (1996).
Chen, Y.-L. et al. Serum TNF-α concentrations in type 2 diabetes mellitus patients and diabetic nephropathy patients: a systematic review and meta-analysis. Immunol. Lett. 186, 52–58. https://doi.org/10.1016/j.imlet.2017.04.003 (2017).
Zakharyan, R. et al. Interleukin-6 promoter polymorphism and plasma levels in patients with schizophrenia. Tissue Antigens 80, 136–142. https://doi.org/10.1111/j.1399-0039.2012.01886.x (2012).
González-Castro, T. B. et al. Interleukin 6 (rs1800795) gene polymorphism is associated with cardiovascular diseases: A meta-analysis of 74 studies with 86,229 subjects. EXCLI J. 18, 331–355. https://doi.org/10.17179/excli2019-1248 (2019).
Plataki, M. N. et al. Association of the interleukin-6 rs1800795 polymorphism with type 2 diabetes mellitus in the population of the island of Crete, Greece. Genet. Test. Mol. Biomark. 22, 448–452. https://doi.org/10.1089/gtmb.2017.0220 (2018).
Goyenechea, E., Parra, D. & Martínez, J. A. Impact of interleukin 6–174G>C polymorphism on obesity-related metabolic disorders in people with excess in body weight. Metab. Clin. Exp. 56, 1643–1648. https://doi.org/10.1016/j.metabol.2007.07.005 (2007).
Gustavsson, C., Agardh, C.-D. & Agardh, E. Profile of intraocular tumour necrosis factor-α and interleukin-6 in diabetic subjects with different degrees of diabetic retinopathy. Acta Ophthalmol. 91, 445–452. https://doi.org/10.1111/j.1755-3768.2012.02430.x (2013).
Navarro, J. F., Milena, F. J., Mora, C., León, C. & García, J. Renal pro-inflammatory cytokine gene expression in diabetic nephropathy: Effect of angiotensin-converting enzyme inhibition and pentoxifylline administration. Am. J. Nephrol. 26, 562–570. https://doi.org/10.1159/000098004 (2006).
Förstermann, U. & Sessa, W. C. Nitric oxide synthases: Regulation and function. Eur. Heart J. 33, 829–837d. https://doi.org/10.1093/eurheartj/ehr304 (2012).
Luo, Z. et al. Associations of the NOS3 rs1799983 polymorphism with circulating nitric oxide and lipid levels: A systematic review and meta-analysis. Postgrad. Med. J. 95, 361–371. https://doi.org/10.1136/postgradmedj-2019-136396 (2019).
Li, Q., Youn, J.-Y. & Cai, H. Mechanisms and consequences of endothelial nitric oxide synthase dysfunction in hypertension. J. Hypertens. 33, 1128–1136. https://doi.org/10.1097/HJH.0000000000000587 (2015).
Piccoli, J. C. E. et al. Association between 894G>T endothelial nitric oxide synthase gene polymorphisms and metabolic syndrome. Arq. Brasil. Endocrinol. Metabol. 52, 1367–1373. https://doi.org/10.1590/s0004-27302008000800027 (2008).
Li, Q. et al. Diabetic eNOS-knockout mice develop accelerated retinopathy. Invest. Ophthalmol. Vis. Sci. 51, 5240–5246. https://doi.org/10.1167/iovs.09-5147 (2010).
Dellaméa, B. S., Leitão, C. B., Friedman, R. & Canani, L. H. Nitric oxide system and diabetic nephropathy. Diabetol. Metab. Syndr. 6, 17. https://doi.org/10.1186/1758-5996-6-17 (2014).
Senatus, L. M. & Schmidt, A. M. The AGE-RAGE axis: Implications for age-associated arterial diseases. Front. Genet. 8, 187. https://doi.org/10.3389/fgene.2017.00187 (2017).
Kay, A. M., Simpson, C. L. & Stewart, J. A. The role of AGE/RAGE signaling in diabetes-mediated vascular calcification. J. Diabetes Res. 2016, 6809703. https://doi.org/10.1155/2016/6809703 (2016).
Manigrasso, M. B., Juranek, J., Ramasamy, R. & Schmidt, A. M. Unlocking the biology of RAGE in diabetic microvascular complications. Trends Endocrinol. Metab. 25, 15–22. https://doi.org/10.1016/j.tem.2013.08.002 (2014).
Hsieh, T. et al. High glucose stimulates angiotensinogen gene expression via reactive oxygen species generation in rat kidney proximal tubular cells. Endocrinology 143, 2975–2985. https://doi.org/10.1210/endo.143.8.8931 (2002).
Yamagishi, S.-I. et al. Role of advanced glycation end products (AGEs) and oxidative stress in vascular complications in diabetes. Biochem. Biophys. Acta. 663–671, 2012. https://doi.org/10.1016/j.bbagen.2011.03.014 (1820).
Border, W. A. & Noble, N. A. Interactions of transforming growth factor-β and angiotensin II in renal fibrosis. Hypertension 31, 181–188. https://doi.org/10.1161/01.HYP.31.1.181 (1998).
Wolf, G. Renal injury due to renin–angiotensin–aldosterone system activation of the transforming growth factor-β pathway. Kidney Int. 70, 1914–1919. https://doi.org/10.1038/sj.ki.5001846 (2006).
Krishnasamy, S. et al. Association of advanced glycation end products (AGEs) with endothelial dysfunction, oxidative stress in gestational diabetes mellitus (GDM). Int. J. Diabetes Dev. Countries 40, 276–282. https://doi.org/10.1007/s13410-019-00766-7 (2019).
Nakagawa, T., Segal, M., Croker, B. & Johnson, R. J. A breakthrough in diabetic nephropathy: The role of endothelial dysfunction. Nephrol. Dial. Transplant. 22, 2775–2777. https://doi.org/10.1093/ndt/gfm380 (2007).
Chen, L., Chen, R., Wang, H. & Liang, F. Mechanisms linking inflammation to insulin resistance. Int. J. Endocrinol. 2015, 508409. https://doi.org/10.1155/2015/508409 (2015).
Sarvas, J. L., Khaper, N. & Lees, S. J. The IL-6 paradox: Context dependent interplay of SOCS3 and AMPK. J. Diabetes Metab. Suppl 13, https://doi.org/10.4172/2155-6156.S13-003 (2013).
Kim, T. H. et al. IL-6 induction of TLR-4 gene expression via STAT3 has an effect on insulin resistance in human skeletal muscle. Acta Diabetol. 50, 189–200. https://doi.org/10.1007/s00592-011-0259-z (2013).
Andreozzi, F. et al. Interleukin-6 impairs the insulin signaling pathway, promoting production of nitric oxide in human umbilical vein endothelial cells. Mol. Cell. Biol. 27, 2372–2383. https://doi.org/10.1128/MCB.01340-06 (2007).
Saltiel, A. R. & Kahn, C. R. Insulin signalling and the regulation of glucose and lipid metabolism. Nature 414, 799–806. https://doi.org/10.1038/414799a (2001).
Plomgaard, P. et al. Tumor necrosis factor-α induces skeletal muscle insulin resistance in healthy human subjects via inhibition of Akt substrate 160 phosphorylation. Diabetes 54, 2939–2945. https://doi.org/10.2337/diabetes.54.10.2939 (2005).
Wassmann, S. et al. Interleukin-6 induces oxidative stress and endothelial dysfunction by overexpression of the angiotensin II type 1 receptor. Circ. Res. 94, 534–541. https://doi.org/10.1161/01.RES.0000115557.25127.8D (2004).
Marseglia, L. et al. Oxidative stress in obesity: A critical component in human diseases. Int. J. Mol. Sci. 16, 378–400. https://doi.org/10.3390/ijms16010378 (2014).
Stienstra, R., Tack, Cees J., Kanneganti, T. D., Joosten, Leo A. B. & Netea, Mihai G. The inflammasome puts obesity in the danger zone. Cell Metab. 15, 10–18, https://doi.org/10.1016/j.cmet.2011.10.011 (2012).
Semenza, G. L. Targeting HIF-1 for cancer therapy. Nat. Rev. Cancer 3, 721–732. https://doi.org/10.1038/nrc1187 (2003).
Rodriguez-Miguelez, P. et al. Hypoxia-inducible factor-1 modulates the expression of vascular endothelial growth factor and endothelial nitric oxide synthase induced by eccentric exercise. J. Appl. Physiol. 118, 1075–1083. https://doi.org/10.1152/japplphysiol.00780.2014 (2015).
Gai, X. et al. Hyperactivation of IL-6/STAT3 pathway leaded to the poor prognosis of post-TACE HCCs by HIF-1α/SNAI1 axis-induced epithelial to mesenchymal transition. J. Cancer 11, 570–582. https://doi.org/10.7150/jca.35631 (2020).
Zimna, A. & Kurpisz, M. Hypoxia-inducible factor-1 in physiological and pathophysiological angiogenesis: Applications and therapies. Biomed. Res. Int. 2015, 549412. https://doi.org/10.1155/2015/549412 (2015).
Mahdy, R. A., Nada, W. M., Hadhoud, K. M. & El-Tarhony, S. A. The role of vascular endothelial growth factor in the progression of diabetic vascular complications. Eye 24, 1576–1584. https://doi.org/10.1038/eye.2010.86 (2010).
Andersen, C. U., Hilberg, O., Mellemkjær, S., Nielsen-Kudsk, J. E. & Simonsen, U. Apelin and pulmonary hypertension. Pulm. Circ. 1, 334–346. https://doi.org/10.4103/2045-8932.87299 (2011).
Eyries, M. et al. Hypoxia-induced apelin expression regulates endothelial cell proliferation and regenerative angiogenesis. Circ. Res. 103, 432–440. https://doi.org/10.1161/CIRCRESAHA.108.179333 (2008).
Abdel-Moneim, A., Bakery, H. H. & Allam, G. The potential pathogenic role of IL-17/Th17 cells in both type 1 and type 2 diabetes mellitus. Biomed. Pharmacother. 101, 287–292. https://doi.org/10.1016/j.biopha.2018.02.103 (2018).
Kim, J. J. & Sears, D. D. TLR4 and insulin resistance. Gastroenterol. Res. Pract. 2010, 212563. https://doi.org/10.1155/2010/212563 (2010).
Prajapati, B., Jena, P. K., Rajput, P. & Seshadri, S. Understanding and modulating the toll like receptors (TLRs) and NOD like receptors (NLRs) cross talk in type 2 diabetes. Curr. Diabetes Rev. 10, 190–200. https://doi.org/10.2174/1573399810666140515112609 (2014).
Lim, R. R. et al. NOD-like receptors in the eye: Uncovering its role in diabetic retinopathy. Int. J. Mol. Sci. 21, 899. https://doi.org/10.3390/ijms21030899 (2020).
Anders, H. J. & Lech, M. NOD-like and Toll-like receptors or inflammasomes contribute to kidney disease in a canonical and a non-canonical manner. Kidney Int. 84, 225–228. https://doi.org/10.1038/ki.2013.122 (2013).
Malagarie-Cazenave, S. et al. Sphingolipid signalling: Molecular basis and role in TNF-alpha-induced cell death. Expert Rev. Mol. Med. 4, 1–15. https://doi.org/10.1017/S146239940200546X (2002).
Russo, S. B., Ross, J. S. & Cowart, L. A. Sphingolipids in obesity, type 2 diabetes, and metabolic disease. Handb. Exp. Pharmacol. 373–401, https://doi.org/10.1007/978-3-7091-1511-4_19 (2013).
Šeda, O. et al. ZBTB16 and metabolic syndrome: A network perspective. Physiol. Res. 66, S357–S365. https://doi.org/10.33549/physiolres.933730 (2017).
Sottrup-Jensen, L. Alpha-macroglobulins: structure, shape, and mechanism of proteinase complex formation. J. Biol. Chem. 264, 11539–11542. https://doi.org/10.1016/S0021-9258(18)80094-1 (1989).
Takada, T. et al. Serum monomeric α2-macroglobulin as a clinical biomarker in diabetes. Atherosclerosis 228, 270–276. https://doi.org/10.1016/j.atherosclerosis.2013.02.035 (2013).
Gray, R. et al. Alpha2-macroglobulin and proliferative retinopathy in type 1 diabetes. Horm. Metab. Res. 14, 389–392 (1982).
de Sain-van der Velden, M. G. et al. Plasma alpha 2 macroglobulin is increased in nephrotic patients as a result of increased synthesis alone. Kidney Int. 54, 530–535, https://doi.org/10.1046/j.1523-1755.1998.00018.x (1998).
1021000 Genomes Project Consortium et al. A global reference for human genetic variation. Nature 526, 68–74, https://doi.org/10.1038/nature15393 (2015).
Ahsan, T., Urmi, N. J. & Sajib, A. A. Heterogeneity in the distribution of 159 drug-response related SNPs in world populations and their genetic relatedness. PLoS ONE 15, e0228000. https://doi.org/10.1371/journal.pone.0228000 (2020).
Xia, J. & Sun, R. Association between interleukin-6 rs 1800795 polymorphism and the decreased risk of type 2 diabetes mellitus: An updated meta-analysis. Int. J. Clin. Exp. Med. 12, 86–97 (2019).
Norris, E. T. et al. Genetic ancestry, admixture and health determinants in Latin America. BMC Genomics 19, 861. https://doi.org/10.1186/s12864-018-5195-7 (2018).
Miller, J. A., Thai, K. & Scholey, J. W. Angiotensin II type 1 receptor gene polymorphism predicts response to losartan and angiotensin II. Kidney Int. 56, 2173–2180. https://doi.org/10.1046/j.1523-1755.1999.00770.x (1999).
Tong, Q. et al. Association of TNF-α polymorphism with prediction of response to TNF blockers in spondyloarthritis and inflammatory bowel disease: a meta-analysis. Pharmacogenomics 14, 1691–1700. https://doi.org/10.2217/pgs.13.146 (2013).
Di Renzo, L. et al. −174G/C IL-6 gene promoter polymorphism predicts therapeutic response to TNF-α blockers. Pharmacogenet. Genomics 22, 134–142. https://doi.org/10.1097/FPC.0b013e32834e5e7b (2012).
Göktaş, M. T. et al. Relationship between genetic polymorphisms of drug efflux transporter MDR1 (ABCB1) and response to losartan in hypertension patients. Eur. Rev. Med. Pharmacol. Sci. 20, 2460–2467 (2016).
Marzolini, C., Paus, E., Buclin, T. & Kim, R. B. Polymorphisms in human MDR1 (P-glycoprotein): Recent advances and clinical relevance. Clin. Pharmacol. Ther. 75, 13–33. https://doi.org/10.1016/j.clpt.2003.09.012 (2004).
Piñero, J. et al. The DisGeNET knowledge platform for disease genomics: 2019 update. Nucleic Acids Res. 48, D845–D855. https://doi.org/10.1093/nar/gkz1021 (2019).
Ting, E.Y.-C., Yang, A. C. & Tsai, S.-J. Role of interleukin-6 in depressive disorder. Int. J. Mol. Sci. 21, 2194. https://doi.org/10.3390/ijms21062194 (2020).
Tawfik, N. S. & Spruit, M. R. The SNPcurator: literature mining of enriched SNP-disease associations. Database (Oxford) 2018, bay020, https://doi.org/10.1093/database/bay020 (2018).
Athanasiadis, E. I. et al. A web-based database of genetic association studies in cutaneous melanoma enhanced with network-driven data exploration tools. Database 2014, bau101, https://doi.org/10.1093/database/bau101 (2014).
Machiela, M. J. & Chanock, S. J. LDlink: A web-based application for exploring population-specific haplotype structure and linking correlated alleles of possible functional variants. Bioinformatics 31, 3555–3557. https://doi.org/10.1093/bioinformatics/btv402 (2015).
Hunt, S. E. et al. Ensembl variation resources. Database 2018, bay119, https://doi.org/10.1093/database/bay119 (2018).
Adzhubei, I., Jordan, D. M. & Sunyaev, S. R. Predicting functional effect of human missense mutations using PolyPhen-2. Curr. Protoc. Hum. Genet. 7(7.20), https://doi.org/10.1002/0471142905.hg0720s76 (2013).
Vaser, R., Adusumalli, S., Leng, S. N., Sikic, M. & Ng, P. C. SIFT missense predictions for genomes. Nat. Protoc. 11, 1–9. https://doi.org/10.1038/nprot.2015.123 (2016).
McLaren, W. et al. The Ensembl variant effect predictor. Genome Biol. 17, 122. https://doi.org/10.1186/s13059-016-0974-4 (2016).
Zhou, G. et al. NetworkAnalyst 3.0: A visual analytics platform for comprehensive gene expression profiling and meta-analysis. Nucleic Acids Res. 47, W234–W241, https://doi.org/10.1093/nar/gkz240 (2019).
Kanehisa, M., Furumichi, M., Tanabe, M., Sato, Y. & Morishima, K. KEGG: New perspectives on genomes, pathways, diseases and drugs. Nucleic Acids Res. 45, D353–D361. https://doi.org/10.1093/nar/gkw1092 (2017).
Cotto, K. C. et al. DGIdb 3.0: A redesign and expansion of the drug–gene interaction database. Nucleic Acids Res. 46, D1068–D1073, https://doi.org/10.1093/nar/gkx1143 (2018).
Wishart, D. S. et al. DrugBank 5.0: A major update to the DrugBank database for 2018. Nucleic Acids Res. 46, D1074–D1082, https://doi.org/10.1093/nar/gkx1037 (2018).
Whirl-Carrillo, M. et al. Pharmacogenomics knowledge for personalized medicine. Clin. Pharmacol. Ther. 92, 414–417. https://doi.org/10.1038/clpt.2012.96 (2012).
Sherry, S. T. et al. dbSNP: The NCBI database of genetic variation. Nucleic Acids Res. 29, 308–311. https://doi.org/10.1093/nar/29.1.308 (2001).
Acknowledgements
This study was supported by Grant on Advanced Research in Education (GARE) from the Ministry of Education, Bangladesh and the Biotechnology Research Centre, University of Dhaka, Bangladesh. The authors are thankful for the support.
Author information
Authors and Affiliations
Contributions
A.A.S.: Design of the study; S.S.S., T.A. and K.F.: Acquisition, analysis, and interpretation of data; S.S.S., T.A., and A.A.S.: Manuscript preparation.
Corresponding author
Ethics declarations
Competing interests
The authors declare no competing interests.
Additional information
Publisher's note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Supplementary Information
Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
About this article
Cite this article
Shoily, S.S., Ahsan, T., Fatema, K. et al. Common genetic variants and pathways in diabetes and associated complications and vulnerability of populations with different ethnic origins. Sci Rep 11, 7504 (2021). https://doi.org/10.1038/s41598-021-86801-2
Received:
Accepted:
Published:
DOI: https://doi.org/10.1038/s41598-021-86801-2
This article is cited by
-
Geospatial analysis of type 2 diabetes mellitus and hypertension in South Sulawesi, Indonesia
Scientific Reports (2023)
-
Genetic screening for pathogenic variants in type 2 diabetes of the Arab Gulf population: A systematic review and meta-analysis
International Journal of Diabetes in Developing Countries (2023)
-
Role of eNOS and TGFβ1 gene polymorphisms in the development of diabetic nephropathy in type 2 diabetic patients in South Indian population
Egyptian Journal of Medical Human Genetics (2022)
Comments
By submitting a comment you agree to abide by our Terms and Community Guidelines. If you find something abusive or that does not comply with our terms or guidelines please flag it as inappropriate.
