1. Introduction
According to the International Diabetes Federation, the global number of adults living with type 2 diabetes mellitus (T2DM) is predicted to rise to 643 million by 2030 and 783 million by 2045 [1]. Moreover, Taiwan has the highest prevalence of diabetes among Asian countries, and the prevalence is still rapidly rising by 25,000 people per year [2]. T2DM, with its occurrence mainly due to excessive body adiposity and insulin resistance, can lead to severe chronic complications, including cardiovascular, central and peripheral nervous, and immune disorders. Studies of twins have shown that genetics plays an essential role in the development of T2DM [3]. According to prior studies, several important genes have been indicated, including CAPN10 (calpain 10), PPARG (peroxisome proliferator-activated receptor gamma), KCNJ11 (potassium inwardly rectifying channel subfamily J member 11), TCF7L2 (transcription factor 7-like 2), KCNQ1 (potassium voltage-gated channel subfamily Q member 1), and MTNRB1 (melatonin receptor B1) [4].
Early age at menarche (AAM) is usually defined as menarche before the age of 12 years. Over the past 30 years, AAM has decreased in Taiwanese children, with a rate of decline of approximately 0.43 years per decade [5]. Early menarche has been associated with physical and psychosocial problems, including anxiety/depression, substance use, suicidal behaviors, early fusion of epiphyseal growth plates, cardiovascular diseases, and metabolic syndromes [6]. AAM is a hereditary trait, but its known genetic determinants only explain part of the variance [7].
Early AAM and T2DM are highly heritable traits with rising prevalence, and both may adversely affect healthy life outcomes [8,9]. Studies have revealed an association between AAM and the risk of T2DM [10], as women with early menarche have a higher risk of T2DM in adulthood. Although excessive adiposity is assumed to be a common driver, the two traits may share genetic loci, but their shared heritability remains unclear. Therefore, a genome-wide association study (GWAS) is required to investigate whether AAM and T2DM have common heritable origins and further determine the underlying molecular pathways.
In this study, we aimed to identify shared genetic variants of AAM and T2DM and investigate the connection between the two traits using a nationally representative database. However, GWAS has underlying shortcomings, such as low genetic coverage, missing phenotypes, and low allele frequency. Quality control (QC) of genotypic and phenotypic data was conducted to reduce biases in GWAS. We hypothesize that genetic variants may show differential associations with T2DM on the condition of their associations with AAM.
2. Methods
2.1. Participants
Genotypic and phenotypic data were available for 88,347 participants from the Taiwan Biobank (TWB,
2.2. Quality Control Procedures
QC procedures were implemented using PLINK (
2.3. Statistical Analysis
As population genetic structures differ across ethnicities and the Taiwanese population is less representative in international data sources, we randomly split our samples into two datasets for the exploration of genetic association with AAM (trait 1; N = 26,612) and T2DM and relevant phenotypes (trait 2; N = 26,612). This method is referred to as the Mendelian randomization approach, which is used to minimize confounding effects. We tested between-group differences to ensure similarities in these two subsets regarding waistline, overall health, body mass index (BMI), BFP, hypertension [16], and years of education [17]. Principal components analysis (PCA) was performed to identify the structure in the distribution of genetic variation across the genetics of all the participants.
Two separate GWASs were conducted for AAM and T2DM traits using two exclusive subsets of data. GWAS for the AAM trait was adjusted for age at enrollment, BMI, and the principal components; GWAS for T2DM traits was adjusted for education level, BMI, and the principal components. Initially, we depicted conditional quantile-quantile (Q-Q) plots to visualize SNP enrichment for T2DM and relevant phenotypes given the different levels of association with AAM. A greater and earlier departure from the reference line (leftward shift) with a higher association with AAM indicates a more significant genetic overlap between AAM and T2DM traits. Further, we applied the conditional false discovery rate (cFDR) method, an extension of the conventional FDR incorporating information from the GWAS summary statistics of a conditional phenotype, to identify the shared genetic variants between AAM and T2DM. In this study, the cFDR is defined as the probability that an SNP is not associated with T2DM traits, given that the p-values for both AAM and T2DM traits are below the significance threshold, and its value indicates the level of pleiotropic enrichment. The cFDR analysis was implemented using an R package (
3. Results
The average age of the entire study population was 50.9 years. Overall, the average AAM was 13.3 years, and a total of 4.57% reported being diagnosed with T2DM. The two randomly split datasets were similar in most baseline characteristics, age distribution, and risk of T2DM based on the ages of participants (see Supplementary Table S2 and Figure S2 for details).
We depicted conditional Q-Q plots showing SNP enrichment for T2DM traits given different levels of association with AAM (Figure 1). An earlier and greater departure from the reference line was well noted in the plots of BFP and FBG, indicating stronger pleiotropic enrichments being observed for these two traits conditioned on AAM. In contrast, the plots of T2DM diagnosis and HbA1C levels given AAM showed low levels of pleiotropic enrichment. Based on the cFDR analysis, the shared loci between AAM and T2DM and relevant traits were visualized in a Manhattan plot (Figure 2). Using a cutoff of a cFDR less than 0.05, a total of 39 independent shared genetic loci were identified for the four T2DM traits after the clumping procedure (Table 1). The most significant one is FN3KRP rs1046896, which has the highest significant level of enrichment (cFDR = 6.84 × 10−49). In addition, CDKAL1 rs2206734 (cFDR = 6.48 × 10−10), B3GNTL1 rs58431774 (cFDR = 2.95 × 10−10), G6PC2 rs1402837 (cFDR = 1.82 × 10−8), and KCNQ1 rs60808706 (cFDR = 9.49 × 10−8) were highlighted for their significant genetic enrichment.
Further, to discover the connectivity among the pleiotropic loci, we built a PPI network using the STRING database, and the network was significantly enriched by showing that the nodes were not random and the observed number of edges was significant (see Supplementary Figure S3 for details). The top 2 Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways were the insulin signaling pathway and glucagon signaling pathway (see Supplementary Table S3 for details).
4. Discussion
In this study, a genome-wide statistical approach was used to identify 39 shared loci between AAM and T2DM traits, with some reported in association with T2DM [19,20,21,22,23]. The conditional Q-Q plots showed pleiotropic enrichments for BFR and FBG conditioned on AAM, but the enrichment effect was less prominent for T2DM diagnosis and HbA1C levels. This discrepancy may result from data sources and availability. The diagnosis was self-reported, and the medication history was unavailable in TWB. There was a possibility that T2DM was undiagnosed or diabetic types were misclassified by participants. Moreover, HbA1C might reflect glycemic control corresponding to patients’ medication adherence rather than disease severity among patients with T2DM. Obtaining collateral reports from treating physicians or linked medication records may ameliorate data quality and accuracy.
Using the cFDR analysis, our study confirmed some novel enrichment loci in shared genetics between AAM and T2DM traits, such as rs1046896 (FN3KRP), rs2206734 (CDKAL1), rs58431774 (B3GNTL1), rs1402837 (G6PC2), and rs60808706 (KCNQ1). The SNP rs1046896 (FN3KRP) has the highest genetic enrichment level. FN3KRP (fructosamine-3-kinase-related protein) is involved in the deglycation of proteins modified via non-enzymatic glycation [24]. A high glucose concentration can result in the non-enzymatic oxidation of proteins via the reaction of glucose-6-phosphate and lysine residues. Proteins modified in this way become less active or functional. This reaction is referred to as the non-enzymatic glycation of proteins or the Maillard reaction. Hyperglycemia is a primary factor that promotes glycation, and the glycation end product forms on DNA, lipids, and proteins, where they represent pathophysiological modifications that precipitate dysfunction at a cellular and molecular level [25]. This further leads to several complications, such as blindness, heart disease, nerve damage, and kidney failure [26]. FN3KRP may result in the deglycation of proteins to restore their function.
CDKAL1 (cyclin-dependent kinase 5 regulatory subunit associated protein 1 like 1) has been reported as a major pathogenesis-related protein for T2DM [27]. The function of CDKAL1 has not been entirely determined. Research has shown that CDKAL1 may be involved in beta cell dysfunction in the pancreas and the regulation of mitochondrial function in adipose tissue [28,29].
B3GNTL1 (beta1,3-N-Acetylglucosaminyltransferase-Like Protein 1) protein is predicted to enable glycosyltransferase activity [30]. According to a recent study, analysis of the PPI network of the genes containing B3GNTL1 reveals that, at the molecular level, there seemed to be interconnected factors that affect the progression of renal impairment among diabetic patients [31].
G6PC2 (glucose-6-phosphatase catalytic subunit 2) catalyzes the hydrolysis of glucose-6-phosphate to produce glucose and inorganic phosphate. The gene is explicitly expressed in pancreatic islet beta cells. The mutation in G6PC2 was found to have a lead role in the modulation of FBG levels, and, thus, it might increase the risk of T2DM [32]. In addition, previous GWAS showed linked polymorphisms in G6PC2, with variations in FBG and BFP [33].
KCNQ1 was previously identified for its correlation with T2DM [34]. KCNQ1 encodes a voltage-gated potassium channel required for the repolarization phase of the action potential of cardiac muscles [35], and the gene is also expressed in other tissues, including the brain, adipose tissue, and pancreas, as well as in the insulin-secreting cell line [36]. In addition to its well-studied association with T2DM, research has revealed a significant association with menopausal age [37].
The PPI analysis revealed that the whole network was significantly enriched. The proteins at the center of the PPI network were insulin (encoded by INS) and AKT serine/threonine kinase 1 (encoded by AKT1). Both are involved in glucose homeostasis and metabolism [38]. Moreover, the top two KEGG pathways were the insulin and glucagon signaling pathways. Insulin plays a critical role in multiple physiological processes, such as promoting glucose uptake, glycogen synthesis, lipogenesis, and protein synthesis. Reduced production of insulin and insulin resistance are the main causes of diabetes. Insulin is an anabolic peptide hormone secreted by pancreatic β cells acting through a receptor located in the membrane of target cells. The receptor activates a complex intracellular signaling network. The two main pathways of insulin signaling emanating from the insulin receptor-IRS node are the PI3K (phosphatidylinositol 3-kinase)/AKT (AKT serine/threonine kinase 1) pathway and the Raf/Ras/MEK (mitogen-activated protein kinase kinase)/MAPK (mitogen-activated protein kinase) pathway [39]. Glucagon is also an important regulator of glucose homeostasis, and it is secreted by pancreatic α cells. Once glucagon binds to a transmembrane receptor on target cells, it leads to adenylate cyclase activation and cAMP formation. The increase in intracellular cAMP levels activates PKA (protein kinase A), which phosphorylates CREB (cAMP response element binding protein), PFK-2 (phospho-fructokinase 2)/FBPase2 (fructose 2,6-bisphosphatase), pyruvate kinase, phosphorylase kinase, and glycogen synthase. Overall, glucagon increases gluconeogenesis and glycogenolysis and decreases glycolysis [40]. Most of the novel-discovered genes were also found to be involved in these pathways. For example, CREB encodes the protein that regulates the gene expression of the enzymes of gluconeogenesis, and it has recently been regarded as a targeting factor in diabetic treatment [41].
Our findings may have implications for clinical practices and directions for future research. Firstly, genetic screening for the shared loci between AAM and T2DM may inform lifestyle modification and glycemic monitoring among early-maturing women. In this sense, further exploration of the gene–lifestyle interaction effects on T2DM may help to identify high-risk groups targeted for advanced pharmacotherapy. Moreover, enriched pathways involving shared genetics may shed light on experimental research into T2DM mechanisms associated with early AAM, a critical step toward personalized diabetic prevention and medication strategies.
Despite its population size and representativeness strength, this study has several limitations. Firstly, the genetic association analysis was derived from and, thus, may be limited to the Taiwanese population. Cross-ethnic validation may be needed, as genetic associations with AAM differ between Asian and European women [42]. Secondly, AAM and diagnosis of T2DM in TWB were based on self-reported questionnaires. Recall bias may exist. Thirdly, the use of hypoglycemic medications was not available in TWB. An augmented dataset linking the TWB and insurance claims of the National Health Insurance Research Database may help to clarify these issues. Fourthly, the GWAS results could be affected by selection bias, as individuals who volunteer to participate may not be representative of the underlying sampling population.
5. Conclusions
This study highlighted potential pleiotropic effects across AAM and T2DM that may explain the association between the two phenotypes. The result may suggest genetic screening for the enriched loci that may help to inform risks for T2DM in women with early AAM. The shared genetic variants discovered are targets for the future exploration of early AAM and T2DM mechanisms.
M.-C.T. conceived the study. Y.-F.C. conducted, C.-Y.Y. reviewed, and M.-C.T. supervised the statistical analysis. Y.-F.C. drafted and M.-C.T. critically reviewed the manuscript. All authors have read and agreed to the published version of the manuscript.
The study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Review Board of National Cheng Kung University (A-ER-108-331).
Informed consent was obtained from all subjects involved in the study.
The TWB dataset is available upon application for research purposes. A detailed description of TWB data availability and the application process could be found via the following link:
The authors have no conflicts of interest to declare.
Footnotes
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Enlarge this image.Figure 1. Conditional quantile–quantile plotsfor diabetic traits conditioned on p-values of AAM. The diagonal line indicates a null hypothesis. (Note: AAM, age at menarche; T2DM, type 2 diabetes mellitus; BFP, body fat percentage; FBG, fasting blood glucose; HbA1C, hemoglobin A1C).
Enlarge this image.Figure 2. Conditional false discovery rate (cFDR) Manhattan plot of −log10 values for T2DM traits. The dashed line indicates a statistical significance threshold of a cFDR of less than 0.05. (Note: BFP, body fat percentage; FBG, fasting blood glucose; HbA1C, hemoglobin A1C; T2DM, type 2 diabetes mellitus).
A total of 39 independent pleiotropic loci in AAM (trait 1) and T2DM (trait 2) with a conditional false discovery rate (cFDR) of less than 0.05. (Note: T2DM, type 2 diabetes mellitus; AAM, age at menarche; Chr, chromosome; hg38, Homo sapiens genome assembly GRCh38; BFP, body fat percentage; FBG, fasting blood glucose; HbA1C, glycated hemoglobin A1C).
| Chr | rs | Position (hg38) | Gene | p of Trait 1 | Trait 2 | p of Trait 2 | cFDR |
|---|---|---|---|---|---|---|---|
| 1 | rs148500298 | 1.02 × 108 | RP11-202K23.1 | 0.658659 | FBG | 5.25 × 10−6 | 0.040840737 |
| 1 | rs3138105 | 1.58 × 108 | CD1B, CD1C | 0.475425 | HbA1c | 9.86 × 10−6 | 0.045925272 |
| 1 | rs17042165 | 2.15 × 108 | LOC124904510 | 0.00217096 | HbA1c | 4.61 × 10−6 | 0.007599174 |
| 2 | rs780093 | 27,519,736 | GCKR | 0.320034 | FBG | 4.80 × 10−7 | 0.007515765 |
| 2 | rs895636 | 44,961,214 | AC012354.6 | 0.807344 | FBG | 1.67 × 10−7 | 0.003508871 |
| 2 | rs1402837 | 1.69 × 108 | G6PC2 | 0.280055 | HbA1c | 1.44 × 10−8 | 0.00028397 |
| FBG | 1.32 × 10−13 | 1.82 × 10−8 | |||||
| 2 | rs116933981 | 1.69 × 108 | LRP2 | 0.41804 | FBG | 8.63 × 10−7 | 0.012442801 |
| 3 | rs3804766 | 51,393,226 | RBM15B | 0.208714 | HbA1C | 3.20 × 10−6 | 0.033487115 |
| 4 | rs223461 | 1.03 × 108 | LOC102723704, LOC124900743 | 0.192597 | HbA1C | 5.45 × 10−6 | 0.048127897 |
| 4 | rs79196252 | 1.47 × 108 | SLC10A7 | 0.0439132 | FBG | 8.66 × 10−7 | 0.010702327 |
| 5 | rs248062 | 1.27 × 108 | CTB-1I21.1 | 0.0326756 | BFP | 4.10 × 10−7 | 0.007739594 |
| 5 | rs75170429 | 1.33 × 108 | FSTL4 | 0.419274 | HbA1C | 2.51 × 10−6 | 0.019516832 |
| 6 | rs2206734 | 20,694,653 | CDKAL1 | 0.712727 | HbA1C | 1.94 × 10−14 | 6.49 × 10−10 |
| FBG | 3.05 × 10−7 | 0.006010452 | |||||
| 6 | rs9376090 | 1.35 × 108 | HBS1L | 0.695327 | HbA1C | 7.17 × 10−6 | 0.041947507 |
| 7 | rs10244051 | 15,024,208 | GTF3AP5 | 0.776841 | FBG | 8.57 × 10−7 | 0.012993208 |
| 7 | rs16881016 | 44,171,808 | GCK, LOC105375257 | 0.480132 | HbA1C | 4.52 × 10−10 | 8.01 × 10−6 |
| FBG | 2.01 × 10−11 | 2.31 × 10−6 | |||||
| 7 | rs1799884 | 44,189,469 | GCK | 0.264629 | FBG | 3.12 × 10−6 | 0.029224933 |
| 7 | rs6975024 | 44,192,287 | GCK | 0.601593 | HbA1C | 2.22 × 10−7 | 0.002111339 |
| 7 | rs2233580 | 1.28 × 108 | PAX4 | 0.403983 | T2DM | 5.07 × 10−10 | 9.90 × 10−5 |
| HbA1C | 6.18 × 10−10 | 1.51 × 10−5 | |||||
| FBG | 2.70 × 10−7 | 0.005270602 | |||||
| 7 | rs6943771 | 1.4 × 108 | PARP12 | 0.995326 | FBG | 4.37 × 10−6 | 0.038811336 |
| 8 | rs3802177 | 1.17 × 108 | SLC30A8, LOC105375716 | 0.889634 | HbA1C | 8.07 × 10−12 | 1.67 × 10−7 |
| FBG | 7.81 × 10−12 | 1.62 × 10−6 | |||||
| 9 | rs10965250 | 22,133,285 | CDKN2B-AS1 | 0.236788 | HbA1C | 8.65 × 10−13 | 1.02 × 10−7 |
| FBG | 3.00 × 10−11 | 3.54 × 10−6 | |||||
| 9 | rs1050700 | 1.33 × 108 | TSC1 | 0.487359 | HbA1C | 1.16 × 10−5 | 0.049462307 |
| 10 | rs12221133 | 12,211,598 | CDC123 | 0.45323 | HbA1C | 1.44 × 10−6 | 0.010460355 |
| 10 | rs151268010 | 16,675,558 | RSU1 | 0.0122221 | BFP | 3.57 × 10−6 | 0.027178562 |
| 10 | rs75631171 | 92,449,649 | IDE | 0.190513 | T2DM | 2.55 × 10−7 | 0.024501519 |
| 10 | rs1111875 | 92,703,125 | Y_RNA | 0.664192 | T2DM | 2.59 × 10−7 | 0.027071426 |
| 10 | rs10786156 | 94,254,865 | PLCE1 | 0.332509 | BFP | 1.02 × 10−8 | 0.00165048 |
| 11 | rs60808706 | 2,836,003 | KCNQ1 | 0.589924 | HbA1C | 3.38 × 10−12 | 9.49 × 10−8 |
| FBG | 6.33 × 10−9 | 0.000221717 | |||||
| 11 | rs10466351 | 92,964,815 | LOC124902733 | 0.44799 | FBG | 1.25 × 10−10 | 6.76 × 10−6 |
| 11 | rs10830963 | 92,975,544 | MTNR1B | 0.526219 | HbA1C | 6.32 × 10−7 | 0.005298336 |
| 17 | rs761772 | 78,125,997 | TMC6 | 0.429945 | HbA1C | 4.79 × 10−12 | 1.42 × 10−7 |
| 17 | rs73357173 | 82,670,096 | RAB40B/MIR4525 | 0.0342794 | HbA1C | 2.89 × 10−6 | 0.019044672 |
| 17 | rs2250754 | 82,703,440 | LOC124904093 | 0.0102342 | HbA1C | 1.73 × 10−6 | 0.01124699 |
| 17 | rs1046896 | 82,727,657 | FN3KRP | 0.382707 | HbA1C | 3.68 × 10−54 | 6.84 × 10−49 |
| 17 | rs3785519 | 82,937,832 | TBCD | 0.177841 | HbA1C | 2.08 × 10−8 | 0.000467929 |
| 17 | rs58431774 | 83,000,587 | B3GNTL1 | 0.742557 | HbA1C | 7.61 × 10−15 | 2.95 × 10−10 |
| 21 | rs7510550 | 36,629,993 | AP000696.2 | 0.515658 | FBG | 7.92 × 10−7 | 0.01085386 |
| 22 | rs146847831 | 48,773,800 | FAM19A5 | 0.106661 | FBG | 1.42 × 10−6 | 0.019983018 |
Supplementary Materials
The following supporting information can be downloaded via this link:
References
1. International Diabetes Federation—Home. idf.org. Available online: https://idf.org/ (accessed on 25 March 2023).
2. Health Promotion Administration, Taiwan. 31 December 2016; Available online: https://www.hpa.gov.tw/Pages/List.aspx?nodeid=359 (accessed on 25 March 2023).
3. Hawkes, C.H. Twin Studies in Diabetes Mellitus. Diabet. Med.; 1997; 14, pp. 347-352. [DOI: https://dx.doi.org/10.1002/(SICI)1096-9136(199705)14:5<347::AID-DIA332>3.0.CO;2-7]
4. Ali, O. Genetics of type 2 diabetes. World J. Diabetes; 2013; 4, pp. 114-123. [DOI: https://dx.doi.org/10.4239/wjd.v4.i4.114] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/23961321]
5. Chow, J.C.; Chou, T.Y.; Tung, T.-H.; Yuh, Y.-S. Recent pubertal timing trends in Northern Taiwanese children: Comparison with skeletal maturity. J. Chin. Med Assoc.; 2020; 83, pp. 870-875. [DOI: https://dx.doi.org/10.1097/JCMA.0000000000000360] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/32902941]
6. Lee, H.S. Why should we be concerned about early menarche?. Clin. Exp. Pediatr.; 2021; 64, pp. 26-27. [DOI: https://dx.doi.org/10.3345/cep.2020.00521]
7. Karapanou, O.; Papadimitriou, A. Determinants of menarche. Reprod. Biol. Endocrinol.; 2010; 8, 115. [DOI: https://dx.doi.org/10.1186/1477-7827-8-115] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/20920296]
8. Shadyab, A.H.; Macera, C.A.; Shaffer, R.A.; Jain, S.; Gallo, L.C.; Gass, M.L.; Waring, M.E.; Stefanick, M.L.; LaCroix, A.Z. Ages at menarche and menopause and reproductive lifespan as predictors of exceptional longevity in women: The Women’s Health Initiative. Menopause; 2017; 24, pp. 35-44. [DOI: https://dx.doi.org/10.1097/GME.0000000000000710]
9. CDC. Learn How People with Type 2 Diabetes Can Live Longer; Centers for Disease Control and Prevention: Atlanta, GA, USA, 2022; Available online: https://www.cdc.gov/diabetes/resources-publications/research-summaries/reaching-treatment-goals.html (accessed on 25 March 2023).
10. Elks, C.E.; Ong, K.K.; Scott, R.A.; van der Schouw, Y.T.; Brand, J.S.; Wark, P.A.; Amiano, P.; Balkau, B.; Barricarte, A.; Boeing, H. et al. Age at Menarche and Type 2 Diabetes Risk. Diabetes Care; 2013; 36, pp. 3526-3534. [DOI: https://dx.doi.org/10.2337/dc13-0446]
11. Feng, Y.-C.A.; Chen, C.-Y.; Chen, T.-T.; Kuo, P.-H.; Hsu, Y.-H.; Yang, H.-I.; Chen, W.J.; Su, M.-W.; Chu, H.-W.; Shen, C.-Y. et al. Taiwan Biobank: A rich biomedical research database of the Taiwanese population. Cell Genom.; 2022; 2, 100197. [DOI: https://dx.doi.org/10.1016/j.xgen.2022.100197]
12. Fan, C.-T.; Hung, T.-H.; Yeh, C.-K. Taiwan Regulation of Biobanks. J. Law Med. Ethics; 2015; 43, pp. 816-826. [DOI: https://dx.doi.org/10.1111/jlme.12322]
13. Liao, S.-F.; Su, C.-Y.; Su, M.-H.; Chen, C.-Y.; Chen, C.-Y.; Lin, Y.-F.; Pan, Y.-J.; Hsiao, P.-C.; Chen, P.-C.; Huang, Y.-T. et al. Associations of polygenic risks, depression, and obesity-related traits in Taiwan Biobank. J. Affect. Disord.; 2023; 320, pp. 397-403. [DOI: https://dx.doi.org/10.1016/j.jad.2022.09.149]
14. Tsai, M.-C.; Hsu, C.-H.; Chu, S.-K.; Roy-Gagnon, M.-H.; Lin, S.-H. Genome-wide association study of age at menarche in the Taiwan Biobank suggests NOL4 as a novel associated gene. J. Hum. Genet.; 2023; 68, pp. 339-345. [DOI: https://dx.doi.org/10.1038/s10038-023-01124-6] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/36710296]
15. Lin, W.-Y.; Chan, C.-C.; Liu, Y.-L.; Yang, A.C.; Tsai, S.-J.; Kuo, P.-H. Performing different kinds of physical exercise differentially attenuates the genetic effects on obesity measures: Evidence from 18,424 Taiwan Biobank participants. PLoS Genet.; 2019; 15, e1008277. [DOI: https://dx.doi.org/10.1371/journal.pgen.1008277] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/31369549]
16. Vujkovic, M.; Keaton, J.M.; Lynch, J.A.; Miller, D.R.; Zhou, J.; Tcheandjieu, C.; Huffman, J.E.; Assimes, T.L.; Lorenz, K.; Zhu, X. et al. Discovery of 318 new risk loci for type 2 diabetes and related vascular outcomes among 1.4 million participants in a multi-ancestry meta-analysis. Nat. Genet.; 2020; 52, pp. 680-691. [DOI: https://dx.doi.org/10.1038/s41588-020-0637-y] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/32541925]
17. Borrell, L.N.; Dallo, F.J.; White, K. Education and Diabetes in a Racially and Ethnically Diverse Population. Am. J. Public Health; 2006; 96, pp. 1637-1642. [DOI: https://dx.doi.org/10.2105/AJPH.2005.072884] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/16873745]
18. Liley, J.; Wallace, C. A Pleiotropy-Informed Bayesian False Discovery Rate Adapted to a Shared Control Design Finds New Disease Associations from GWAS Summary Statistics. PLoS Genet.; 2015; 11, e1004926. [DOI: https://dx.doi.org/10.1371/journal.pgen.1004926]
19. Basile, K.J.; Johnson, M.E.; Xia, Q.; Grant, S.F.A. Genetic Susceptibility to Type 2 Diabetes and Obesity: Follow-Up of Findings from Genome-Wide Association Studies. Int. J. Endocrinol.; 2014; 2014, 769671. [DOI: https://dx.doi.org/10.1155/2014/769671] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/24719615]
20. Sanghera, D.K.; Blackett, P.R. Type 2 Diabetes Genetics: Beyond GWAS. J. Diabetes Metab.; 2012; 3, 6948. [DOI: https://dx.doi.org/10.4172/2155-6156.1000198]
21. Burillo, J.; Marqués, P.; Jiménez, B.; González-Blanco, C.; Benito, M.; Guillén, C. Insulin Resistance and Diabetes Mellitus in Alzheimer’s Disease. Cells; 2021; 10, 1236. [DOI: https://dx.doi.org/10.3390/cells10051236]
22. Demir, S.; Nawroth, P.P.; Herzig, S.; Üstünel, B.E. Emerging Targets in Type 2 Diabetes and Diabetic Complications. Adv. Sci.; 2021; 8, 2100275. [DOI: https://dx.doi.org/10.1002/advs.202100275]
23. Chen, C.; Han, M.; Zhang, W.; Cui, J.; Cui, N.; Cao, L.; Luo, G.; Sun, J. Identification of key genes and pathways in type 2 diabetes mellitus and vitamin C metabolism through bioinformatics analysis. Asia Pac. J. Clin. Nutr.; 2021; 30, pp. 715-729. [DOI: https://dx.doi.org/10.6133/apjcn.202112_30(4).0018] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/34967200]
24. FN3KRP Fructosamine 3 Kinase Related Protein [Homo Sapiens (Human)]—Gene—NCBI. Available online: https://www.ncbi.nlm.nih.gov/gene/79672#summary (accessed on 22 November 2023).
25. Szwergold, B.S. Fructosamine-6-phosphates are deglycated by phosphorylation to fructosamine-3,6-bisphosphates catalyzed by fructosamine-3-kinase (FN3K) and/or fructosamine-3-kinase-related-protein (FN3KRP). Med. Hypotheses; 2006; 68, pp. 37-45. [DOI: https://dx.doi.org/10.1016/j.mehy.2006.06.030] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/16920277]
26. Ansari, N.A.; Rasheed, Z. Non-enzymatic glycation of proteins: From diabetes to cancer. Biochem. (Moscow) Suppl. Ser. B Biomed. Chem.; 2009; 3, pp. 335-342. [DOI: https://dx.doi.org/10.1134/S1990750809040027]
27. Zeng, Q.; Zou, D.; Gu, S.; Han, F.; Cao, S.; Wei, Y.; Guo, R. Different Associations Between CDKAL1 Variants and Type 2 Diabetes Mellitus Susceptibility: A Meta-analysis. Front. Genet.; 2022; 12, 783078. [DOI: https://dx.doi.org/10.3389/fgene.2021.783078] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/35069684]
28. Xu, N.; Zhang, T.-T.; Han, W.-J.; Yin, L.-P.; Ma, N.-Z.; Shi, X.-Y.; Sun, J.-J. Association of CDKAL1 RS10946398 Gene Polymorphism with Susceptibility to Diabetes Mellitus Type 2: A Meta-Analysis. J. Diabetes Res.; 2021; 2021, 1254968. [DOI: https://dx.doi.org/10.1155/2021/1254968] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/34977253]
29. Palmer, C.J.; Bruckner, R.J.; Paulo, J.A.; Kazak, L.; Long, J.Z.; Mina, A.I.; Deng, Z.; LeClair, K.B.; Hall, J.A.; Hong, S. et al. Cdkal1, a type 2 diabetes susceptibility gene, regulates mitochondrial function in adipose tissue. Mol. Metab.; 2017; 6, pp. 1212-1225. [DOI: https://dx.doi.org/10.1016/j.molmet.2017.07.013] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/29031721]
30. B3GNTL1 UDP-GlcNAc:betaGal Beta-1,3-N-acetylglucosaminyltransferase Like 1 [Homo sapiens (human)]—Gene—NCBI. Available online: https://www.ncbi.nlm.nih.gov/gene/146712 (accessed on 22 November 2023).
31. Wu, I.-W.; Tsai, T.-H.; Lo, C.-J.; Chou, Y.-J.; Yeh, C.-H.; Chan, Y.-H.; Chen, J.-H.; Hsu, P.W.-C.; Pan, H.-C.; Hsu, H.-J. et al. Discovering a trans-omics biomarker signature that predisposes high risk diabetic patients to diabetic kidney disease. npj Digit. Med.; 2022; 5, 166. [DOI: https://dx.doi.org/10.1038/s41746-022-00713-7] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/36323795]
32. Udhaya Kumar, S.; Kamaraj, B.; Varghese, R.P.; Preethi, V.A.; Bithia, R.; George Priya Doss, C. Mutations in G6PC2 gene with increased risk for development of type 2 diabetes: Understanding via computational approach. Adv. Protein Chem. Struct. Biol.; 2022; 130, pp. 351-373. [DOI: https://dx.doi.org/10.1016/bs.apcsb.2022.02.005]
33. Bouatia-Naji, N.; Rocheleau, G.; Van Lommel, L.; Lemaire, K.; Schuit, F.; Cavalcanti-Proença, C.; Marchand, M.; Hartikainen, A.-L.; Sovio, U.; De Graeve, F. et al. A Polymorphism within the G6PC2 Gene Is Associated with Fasting Plasma Glucose Levels. Science; 2008; 320, pp. 1085-1088. [DOI: https://dx.doi.org/10.1126/science.1156849]
34. Kasuga, M. KCNQ1, a susceptibility gene for type 2 diabetes. J. Diabetes Investig.; 2011; 2, pp. 413-414. [DOI: https://dx.doi.org/10.1111/j.2040-1124.2011.00178.x]
35. KCNQ1 Potassium Voltage-Gated Channel Subfamily Q Member 1 [Homo Sapiens (Human)]—Gene—NCBI. Available online: https://www.ncbi.nlm.nih.gov/gene/3784 (accessed on 7 October 2023).
36. Zhang, W.; Wang, H.; Guan, X.; Niu, Q.; Li, W. Variant rs2237892 of KCNQ1 Is Potentially Associated with Hypertension and Macrovascular Complications in Type 2 Diabetes Mellitus in A Chinese Han Population. Genom. Proteom. Bioinform.; 2015; 13, pp. 364-370. [DOI: https://dx.doi.org/10.1016/j.gpb.2015.05.004]
37. Hardy, T.M.; de Mendoza, V.B.; Sun, Y.V.; Taylor, J.Y. Genomics of Reproductive Traits and Cardiometabolic Disease Risk in African American Women. Nurs. Res.; 2019; 68, pp. 135-144. [DOI: https://dx.doi.org/10.1097/NNR.0000000000000337]
38. Hay, N. Akt isoforms and glucose homeostasis—The leptin connection. Trends Endocrinol. Metab.; 2011; 22, pp. 66-73. [DOI: https://dx.doi.org/10.1016/j.tem.2010.09.003]
39. De Meyts, P.; Feingold, K.R.; Anawalt, B.; Boyce, A.; Chrousos, G.; de Herder, W.W.; Dhatariya, K.; Dungan, K.; Hershman, J.M.; Hofland, J. et al. The Insulin Receptor and Its Signal Transduction Network. Available online: https://www.ncbi.nlm.nih.gov/books/NBK378978/ (accessed on 6 March 2022).
40. Janah, L.; Kjeldsen, S.; Galsgaard, K.D.; Winther-Sørensen, M.; Stojanovska, E.; Pedersen, J.; Knop, F.K.; Holst, J.J.; Albrechtsen, N.J.W. Glucagon Receptor Signaling and Glucagon Resistance. Int. J. Mol. Sci.; 2019; 20, 3314. [DOI: https://dx.doi.org/10.3390/ijms20133314]
41. Benchoula, K.; Parhar, I.S.; Madhavan, P.; Hwa, W.E. CREB nuclear transcription activity as a targeting factor in the treatment of diabetes and diabetes complications. Biochem. Pharmacol.; 2021; 188, 114531. [DOI: https://dx.doi.org/10.1016/j.bcp.2021.114531] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/33773975]
42. Yermachenko, A.; Dvornyk, V. Nongenetic Determinants of Age at Menarche: A Systematic Review. BioMed Res. Int.; 2014; 2014, 371583. [DOI: https://dx.doi.org/10.1155/2014/371583] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/25050345]
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1 School of Medicine, College of Medicine, National Cheng Kung University, Tainan 70101, Taiwan
2 Department of Statistics, College of Management, National Cheng Kung University, Tainan 70101, Taiwan
3 Department of Pediatrics, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, 138 Shengli Road, Tainan 70403, Taiwan; Department of Genomic Medicine, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan 70101, Taiwan; Department of Medical Humanities and Social Medicine, College of Medicine, National Cheng Kung University, Tainan 70101, Taiwan