Regular paper

A single nucleotide polymorphism in BCAT1 gene is associated with type 2 diabetes mellitus

Mahmoud A. Alfaqih1✉, Zaina E. Abu-Khdair1, Omar Khabour2, Khalid A. Kheirallah3
and Mariam Khanfar1

1Department of Physiology and Biochemistry, Jordan University of Science and Technology, Irbid 22110, Jordan; 2Department of Medical
Laboratory Sciences, Jordan University of Science and Technology, Irbid 22110, Jordan; 3Department of Public Health and Community Medicine, Jordan University of Science and Technology, Irbid 22110, Jordan

The level of circulatory branched chain amino acids (BCAAs) is often increased in type 2 diabetes mellitus (T2DM). Catabolism of BCAAs involves a transamination reaction mediated by the branched chain amino acid aminotransferase (BCAT1) enzyme. Differences in the level of BCAT1 were found to be linked with hypertension, obesity, and cancer. Herein, using a case control design, we tested the association of rs9668920 and rs12321766 polymorphisms in BCAT1 gene with T2DM. Three hundred subjects were recruited in the study. Genotyping of the indicated polymorphisms was achieved using restriction fragment length polymorphism technique after amplification of the target sequences. The results showed that, under a recessive inheritance model, the GG genotype of rs9668920 increased the risk of T2DM (P=0.026; OR 2.60; 95% CI 1.119–6.048). This effect was independent of the age, body mass index, waist circumference, serum glucose, cholesterol, triglycerides, and BCAAs (P>0.05). In conclusion, The GG genotype of BCAT1 rs9668920 SNP might be a risk factor of T2DM. More studies are required to confirm this finding.

Keywords: BCAT1, Branched chain amino acids, Type 2 diabetes mellitus

Received: 05 September, 2020; revised: 28 December, 2020; accepted: 11 January, 2021; available on-line: 21 December, 2021

✉e-mail: maalfaqih@just.edu.jo

Acknowledgment of financial support: this work has been supported by Jordan University of Science and Technology, Irbid, Jordan (award number: 233/2016).

Abbreviations: branched chain amino acids, BCAAs; T2DM, type 2 diabetes mellitus; BCAT, branched chain amino acid aminotransferase; DM, diabetes mellitus; MENA, Middle East and North Africa; ADA, American diabetes association; JUST, Jordan University of Science and Technology; Kauh, King Abdullah University Hospital; FBG, fasting blood glucose; BMI, body mass index; WC, waist circumference; PCR, polymerase chain reaction; RFLP, restriction fragment length polymorphism; IR, insulin resistance

INTRODUCTION

Diabetes mellitus (DM) is currently considered a global health problem with a particularly high prevalence in low-middle income nations (Correia et al., 2019) such as countries of the Middle East and North Africa (MENA) regions. In 2019, 463 million adults (20–79 years) were living with DM worldwide (IDF diabetes atlas, 2019). The economic burden of DM is among the highest in the MENA region as it reached 17.1 billion USD in the year 2015 and is expected to double by the year 2040 (Ogurtsova et al., 2017). Thus, there is an urgent need for a better understanding of the factors that increase the risk of DM worldwide to search for interventions that can mitigate the effects DM has on health and the economy (Ali et al., 2020).

DM has long been recognized to be a disease caused by a dysregulation in the metabolism of carbohydrate, fat and protein (Goyal et al., 2020). For example, changes in hundreds of metabolites including amino acids were implicated in the etiology of DM (Hameed et al., 2020). Although differences in the levels of circulating amino acids in DM were indicated as early as 1970 (Felig, 1975), the role of these changes in determining disease risk or pathogenesis has not received attention until recently (Hameed et al., 2020). Of the many amino acids that may contribute to DM pathogenesis, the role of the branched chain amino acids (BCAAs) has been extensively studied over the last few years (Nawaz & Siddiqui, 2020; Siddik & Shin, 2019). A growing body of literature has shown elevated BCAAs levels in the serum of DM patients (Floegel et al., 2013; Isanejad et al., 2017). BCAAs (leucine, isoleucine, and valine) are grouped together because of similarities in their structure, absorption, metabolism, and function. For instance, BCAAs have a branch in their aliphatic side chain and are absorbed through a common carrier across the epithelial wall of the gut (Zhou et al., 2018). Moreover, BCAAs reach the systemic circulation without any regulation of their plasma levels by the liver (Nawaz & Siddiqui, 2020). An essential step in BCAA metabolism is catalysed by BCAT1 enzyme and involves an aminotransferase reaction where the amino group of the BCAA is transferred to α-ketoglutarate (Adeva-Andany et al., 2017). This is followed by an oxidative decarboxylation step which is catalysed by the branched chain α-keto acid dehydrogenase complex enzyme (Adeva-Andany et al., 2017). Following this step, the catabolic pathway of each BCAA diverges with each of the acids producing its corresponding acyl-CoA product which eventually enters the citric acid cycle (Yoon, 2016).

Noteworthy, mammalian tissues have two different BCAT genes located on two different chromosomes; a cytosolic BCAT isoform encoded by the BCAT1 gene locus on chromosome 12 (Duarte et al., 2007), and a mitochondrial BCAT isoform encoded by the BCAT2 gene locus on chromosome 19 (Bledsoe et al., 1997). A number of studies found that polymorphisms in the BCAT1 gene are associated with chronic diseases such as hypertension (Rhee et al., 2011), obesity (Chen et al., 2013) and cancer (Wang et al., 2015). However, the association between SNPs in BCAT1 and the risk of T2DM has not been investigated in any country of the MENA region before. Herein, two SNPs in BCAT1 (rs9668920 and rs12321766) were tested for their association with T2DM.

MATERIALS AND METHODS

Study design

The study is case-control in design. The study was conducted accordingly to the ethics committee (249/2016) of the Jordan University of Science and Technology (JUST). Study participants were interviewed and were asked to fill an informed consent prior to their enrolment. This included a full explanation of the study objectives and procedures. Enrolment of subjects was during the year 2017 at King Abdullah University Hospital (KAUH) located in Irbid, Jordan. The minimum sample size required for this study, based on an assumed odds ratio of 3, alpha level of 0.05, power of 0.8, and 1 to 1 case to control ratio, was a total of 166 cases and control (83 cases and 83 controls). For post hoc power calculation revealed that the power to detect a difference as that under investigation was 90%, assuming alpha of 0.05, 150 cases, 150 controls, with case to control ratio of 1:1, and an estimated odds ratio of 2.6.

Subject description

A total of three hundred subjects (age 35–70 years) were enrolled in the study. One hundred and fifty T2DM patients diagnosed according to the American Diabetes Association (ADA) guidelines were included as cases (HbA1c≥6.5%, fasting blood glucose (FBG) ≥126 mg/dL). The diabetic subjects were patients actively treated for T2DM at the Endocrinology clinics of KAUH and had no history of macro or microangiopathies at the time of recruitment. A total of 150 healthy and non-obese subjects, with no chronic illness at the time of the study, were recruited during their visit to other KAUH clinics. Following a short interview, it was determined that the non-diabetic subjects did not complain about any of the usual symptoms associated with T2DM at the time of their recruitment. Moreover, non-diabetic subjects were requested to give a venous blood sample collected in a plain tube to assess their FBG levels on two separate occasions to confirm the absence of T2DM. Exclusion criteria include pre-diabetic individuals (FBG: 100–125 mg/dL), Cushing’s syndrome, polycystic ovarian syndrome, thyroid dysfunction, or hyperprolactinemia.

Anthropometric measurements

During the subject’s visit to KAUH clinics, the height (cm) and the weight (Kgs) of the subjects were measured and recorded to calculate body mass index (BMI) as previously described (Alomari et al., 2020). During the same visit, the waist circumference (WC) was obtained in centimetres using a tape measure.

Blood sampling

Following a 12-hour fast, 3 mLs of blood were collected into an evacuated EDTA tube (AFCO, Amman, Jordan) and 5 mLs of blood were collected in a plain tube obtained from AFCO (Amman, Jordan). Plain blood samples were centrifuged at 4 500×g for 5 min and the resulting serum was pipetted into 1.5 mL tubes and stored at –80°C until its use for the measurement of FBG, cholesterol, triglycerides and BCAAs.

Biochemical measurements

Biochemical parameters were measured in the laboratories of KAUH using an automated biochemical analyser system obtained from Roche Diagnostics/Germany. BCAAs were measured using a commercially available colorimetric assay (catalogue number: ab83374; Abcam, Cambridge, UK) as previously described (Alfaqih et al., 2018).

Determination of BCAT1 genotypes

DNA was purified from whole blood samples using a kit obtained from Qiagen (Germany) as explained previously (Alfaqih et al., 2018). The quality and quantity of extracted DNA were assayed on a Nanodrop instrument (Thermo-Scientific, MA, USA). Genotyping of rs9668920 and rs12321766 was performed using polymerase chain reaction (PCR) followed by digestion with a restriction enzyme and then agarose gel electrophoresis. All amplification reactions were carried out in 25 µl reaction volume using ready to use master mix (Promega, Madison, USA), 10 Nano gram of DNA and 0.5 µM of each primer. The sequences of the primers, the corresponding fragment sizes and restriction enzymes are listed in Table 1. All restriction enzymes were purchased from NEB (New England, MA). The undigested PCR products and the fragments that resulted from the restriction enzyme digestion were electrophoresed on a 3% agarose gel stained with ethidium bromide. The products were then visualized under UV light. Genotyping strategy of each SNP is illustrated in Fig. 1. To validate the genotyping procedure, 10 samples of each genotype were randomly selected for sequencing at Princess Haya Centre for Biotechnology (Irbid, Jordan).

Statistical analysis

The student’s t-test was used for two group comparisons of means. The frequencies of the different genotype categories of each SNP were compared between the two groups using Pearson Chi-square. The SNPStats software tool (http://bioinfo.iconcologia.net/SNPstats) was used to test if allele and genotype frequencies deviated from Hardy Weinberg equilibrium and to test the association of each SNP with T2DM in different inheritance models. Binary logistic regression analysis was used to assess the adjusted effects of the independent variables on diabetes; a backward selection method was utilized. Variables identified in the model were presented in terms of adjusted odds ratio (OR), 95% confidence interval (CI) and P value. The null hypothesis was rejected if P-value<0.05.

RESULTS

Subject description and biochemical profile

One hundred and fifty diabetic and one hundred and fifty non-diabetic subjects agreed to enrol in the study. Diabetic subjects were different from non-diabetic subjects in age, WC, FBG, total cholesterol, triglycerides, and BCAAs (P<0.01, Table 2).

Association of rs9668920 with T2DM

Two SNPs in BCAT1 gene (rs9668920 and rs12321766) were tested for their association with T2DM. Both SNPs were in the Hardy-Weinberg equilibrium (P>0.05) and were genotyped using a PCR- restriction fragment length polymorphism (RFLP) based strategy (Fig. 1A and 1B). The genotype and allele frequencies of rs9668920 and rs12321766 are displayed in Table 3. Statistical analysis showed that rs9668920 is associated with T2DM under a recessive inheritance model (Table 4). In this model, the GG genotype of rs9668920 significantly increased the risk of T2DM by twofold (P=0.028; OR 2.20; 95% CI 1.09–4.49). On the other hand, rs12321766 was not associated with T2DM under any of the tested inheritance models (Table 5).

In binomial regression analysis, the GG genotype of rs9668920 remained associated with T2DM and increased its risk relative to the (AA-AG) genotype category (P=0.026; OR 2.60; 95% CI 1.119–6.048) (Table 6) after adjustment for gender, age, BMI, WC, BCAAs, cholesterol, and triglycerides.

DISCUSSION

In this investigation, two SNPs (rs9668920 and rs12321766) located in different regions of the BCAT1 gene were tested for their association with T2DM. The results demonstrated that rs9668920 SNP was associated with T2DM under a recessive model of inheritance. In this model, subjects with the GG genotype were at an increased risk of T2DM relative to individuals with the AA-AG genotypes. In multivariate analysis, the effect of rs9668920 SNP in modulating the risk of T2DM was independent of age, BMI, WC, serum glucose, cholesterol, triglycerides, and BCAAs levels.

BCAT1 gene encodes branched-chain amino acid transaminase enzyme located in the cytosol. The function of this enzyme is to catalyse the transamination of branched-chain α-keto acids to branched-chain L-amino acids. Such a reaction is essential for homeostasis within cells. Genetic variations in BCAT1 gene have been shown to be associated with several conditions. For example, The BCAT1 rs7961152 SNP has been shown to be associated with salt sensitivity after adjusting for confounding factors among the Korean population (Rhee et al., 2011). In addition, rs2242400 has been shown to be associated with T2DM (Rampersaud et al., 2007). Finally, genome wide association scans have identified BCAT1 as a candidate gene for T2DM and resting heart rate (Eijgelsheim et al., 2010; Zemunik et al., 2009; Zhou et al., 2018). In the present study, an association between rs9668920 SNP and T2DM was reported. Since this is the first study that examined rs9668920 and rs12321766 BCAT1 SNPs in T2DM, there is no literature available to compare the present findings.

The mechanism by which rs9668920 modifies T2DM risk remains to be determined. Rs9668920 is an upstream variant of the BCAT1 gene that could be part of an enhancer sequence element (Zerbino et al., 2018). Accordingly, rs9668920 might modulate the expression of the BCAT1 gene. BCAT1 gene codes for an enzyme that catalyses the first step in the catabolism of BCAAs (Suryawan et al., 1998). A study found that the skeletal muscle tissues of individuals with T2DM have lower levels of BCAAs catabolic products relative to those recovered from individuals with normal glucose tolerance (Lerin et al., 2016). Taken together, a model of BCAAs metabolism in T2DM patients can be deduced from the above data. In this model the expression/activity of enzymes involved in BCAAs catabolism in skeletal muscles or other tissues, including BCAT1, is lower in individuals with T2DM (Lerin et al., 2016). It is then proposed that downregulation of BCAAs catabolism in extrahepatic tissues results in an elevation in serum BCAAs levels as observed in a number of reports (Wang et al., 2011; Xu et al., 2013). An elevation in serum BCAAs could contribute to the development of insulin resistance (IR) via an impairment of the insulin signalling cascade (Yoon, 2016). In line with the above assumption, Newgard et al. reported a decrease in BCAAs catabolism in obese individuals (Newgard et al., 2009). However, it still remains to be determined whether rs9668920 indeed affects expression/activity levels of BCAT1 in the different tissues of T2DM patients.

Rs12321766 was not associated with T2DM under any of the tested models of inheritance. This might be explained by a lack of linkage disequilibrium between rs9668920 and rs12321766 or the presence of other SNPs in the vicinity of this chromosomal region which might be modifying the association of rs12321766 with T2DM. More investigations are required to confirm the current findings.

Among the limitations of the current study is that only two SNPs in BCAT1 gene were examined. Another SNP in this gene called rs2242400 has been shown to be associated with T2DM using genome-wide association approach (Rampersaud et al., 2007). Thus, inclusion of more SNPs in the BCAT1 gene is strongly recommended in future investigations to uncover the role of this gene in the etiology of diabetes.

CONCLUSION

In conclusion, rs9668920 in BCAT1 gene is associated with the risk of T2DM in a Jordanian population. Previous reports found that variations in BCAT1 gene were also found to be associated with obesity and hypertension. Taken together, our findings and those of others, highlight that BCAAs and/or their metabolites could be a common factor implicated in the etiology of several chronic diseases. The results presented in this report require further validation in other populations.

Conflicts of Interest

Authors have nothing to declare.

References

Adeva-Andany MM, Lopez-Maside, L, Donapetry-Garcia, C, Fernandez-Fernandez, C, Sixto-Leal, C (2017) Enzymes involved in branched-chain amino acid metabolism in humans. Amino Acids 49: 1005–1028. https://doi.org/10.1007/s00726-017-2412-7

Alfaqih MA, Abu-Khdair, Z, Saadeh, R, Saadeh, N, Al-Dwairi, AN, Al-Shboul, O (2018a) Serum branched chain amino acids are associated with type 2 diabetes mellitus in Jordan. Korean J Family Med 39: 313–317. https://doi.org/doi: 10.4082/kjfm.17.0122

Alfaqih MA, Khader, YS, Al-Dwairi, AN, Alzoubi, A, Al-Shboul, O, Hatim, A (2018b) Correction: lower levels of serum adiponectin and the T allele of rs1501299 of the ADIPOQ gene are protective against polycystic ovarian syndrome in Jordan. Korean J Fam Med 39: 207. https://doi.org/10.4082/kjfm.2018.39.3.207

Ali SH, Misra, S, Parekh, N, Murphy, B, DiClemente, RJ (2020) Preventing type 2 diabetes among South Asian Americans through community-based lifestyle interventions: a systematic review. Preventive Med Rep 101182. https://doi.org/10.1016/j.pmedr.2020.101182

Alomari MA, Khabour, OF, Alawneh, K, Alzoubi, KH, Maikano, AB (2020) The importance of physical fitness for the relationship of BDNF with obesity measures in young normal-weight adults. Heliyon 6: e03490. https://doi.org/10.1016/j.heliyon.2020.e03490

Bledsoe RK, Dawson, PA, Hutson, SM (1997) Cloning of the rat and human mitochondrial branched chain aminotransferases (BCATm) Biochim Biophys Acta 1339: 9–13. https://doi.org/10.1016/s0167-4838(97)00044-7

Chen J, Meng, Y, Zhou, J, Zhuo, M, Ling, F, Zhang, Y, Du, H, Wang, X (2013) Identifying candidate genes for Type 2 Diabetes Mellitus and obesity through gene expression profiling in multiple tissues or cells. J Diabetes Res 2013: 970435. https://doi.org/10.1155/2013/970435

Correia JC, Lachat, S, Lagger, G, Chappuis, F, Golay, A, Beran, D, Project, C (2019) Interventions targeting hypertension and diabetes mellitus at community and primary healthcare level in low- and middle-income countries:a scoping review. BMC Public Health 19: 1542. https://doi.org/10.1186/s12889-019-7842-6

Duarte N, Lundholm, M, Holmberg, D (2007) The Idd6.2 diabetes susceptibility region controls defective expression of the Lrmp gene in nonobese diabetic (NOD) mice. Immunogenetics 59: 407–416. https://doi.org/10.1007/s00251-007-0194-x

Eijgelsheim M, Newton-Cheh, C, Sotoodehnia, N, de Bakker, PI, Muller, M, Morrison, AC, Smith, AV, Isaacs, A, Sanna, S, Dörr, M, Navarro P, Fuchsberger C, Nolte IM, de Geus EJ, Estrada K, Hwang SJ, Bis JC, Rückert IM, Alonso A, Launer LJ, Hottenga JJ, Rivadeneira F, Noseworthy PA, Rice KM, Perz S, Arking DE, Spector TD, Kors JA, Aulchenko YS, Tarasov KV, Homuth G, Wild SH, Marroni F, Gieger C, Licht CM, Prineas RJ, Hofman A, Rotter JI, Hicks AA, Ernst F, Najjar SS, Wright AF, Peters A, Fox ER, Oostra BA, Kroemer HK, Couper D, Völzke H, Campbell H, Meitinger T, Uda M, Witteman JC, Psaty BM, Wichmann HE, Harris TB, Kääb S, Siscovick DS, Jamshidi Y, Uitterlinden AG, Folsom AR, Larson MG, Wilson JF, Penninx BW, Snieder H, Pramstaller PP, van Duijn CM, Lakatta EG, Felix SB, Gudnason V, Pfeufer A, Heckbert SR, Stricker BH, Boerwinkle E, O’Donnell CJ (2010) Genome-wide association analysis identifies multiple loci related to resting heart rate. Hum Mol Genet 19: 3885–3894. https://doi.org/10.1093/hmg/ddq303

Felig P (1975) Amino acid metabolism in man. Ann Rev Biochem 44: 933–955. https://doi.org/10.1146/annurev.bi.44.070175.004441

Floegel A, Stefan N, Yu Z, Mühlenbruch K, Drogan D, Joost HG, Fritsche A, Häring HU, Hrabě de Angelis M, Peters A, Roden M, Prehn C, Wang-Sattler R, Illig T, Schulze MB, Adamski J, Boeing H, Pischon T (2013) Identification of serum metabolites associated with risk of type 2 diabetes using a targeted metabolomic approach. Diabetes 62: 639–648. https://doi.org/10.2337/db12-0495

Goyal R, Nguyen M, Jialal I (2020) Glucose Intolerance. In StatPearls, Treasure Island, FL)

Hameed A, Mojsak, P, Buczynska, A, Suleria, HAR, Kretowski, A, Ciborowski, M (2020) Altered metabolome of lipids and amino acids species: a source of early signature biomarkers of T2DM. J Clin Med 9. https://doi.org/10.3390/jcm9072257

Isanejad M, LaCroix AZ, Thomson CA, Tinker L, Larson JC, Qi Q, Qi L, Cooper-DeHoff RM, Phillips LS, Prentice RL, Beasley JM (2017) Branched-chain amino acid, meat intake and risk of type 2 diabetes in the Women’s Health Initiative. Brit J Nutr 117: 1523–1530. https://doi.org/10.1017/S0007114517001568

Lerin C, Goldfine AB, Boes T, Liu M, Kasif S, Dreyfuss JM, De Sousa-Coelho AL, Daher G, Manoli I, Sysol JR, Isganaitis E, Jessen N, Goodyear LJ, Beebe K, Gall W, Venditti CP, Patti ME (2016) Defects in muscle branched-chain amino acid oxidation contribute to impaired lipid metabolism. Mol Metab 5: 926–936. https://doi.org/10.1016/j.molmet.2016.08.001

Nawaz SS, Siddiqui, K (2020) The emerging role of branch chain amino acids in the prediction of diabetes: A brief review. Curr Diabetes Rev 16: 532–537. https://doi.org/10.2174/1573399815666190502113632

Newgard CB, An J, Bain JR, Muehlbauer MJ, Stevens RD, Lien LF, Haqq AM, Shah SH, Arlotto M, Slentz CA, Rochon J, Gallup D, Ilkayeva O, Wenner BR, Yancy WS Jr, Eisenson H, Musante G, Surwit RS, Millington DS, Butler MD, Svetkey LP (2009) A branched-chain amino acid-related metabolic signature that differentiates obese and lean humans and contributes to insulin resistance. Cell Metabol 9: 311–326. https://doi.org/10.1016/j.cmet.2009.02.002

Ogurtsova K, da Rocha Fernandes, JD, Huang, Y, Linnenkamp, U, Guariguata, L, Cho, NH, Cavan, D, Shaw, JE, Makaroff, LE (2017) IDF Diabetes Atlas: Global estimates for the prevalence of diabetes for 2015 and 2040. Diabetes Res Clin Practice 128: 40–50. https://doi.org/10.1016/j.diabres.2017.03.024

Rampersaud E, Damcott CM, Fu M, Shen H, McArdle P, Shi X, Shelton J, Yin J, Chang YP, Ott SH, Zhang L, Zhao Y, Mitchell BD, O’Connell J, Shuldiner AR (2007) Identification of novel candidate genes for type 2 diabetes from a genome-wide association scan in the Old Order Amish: evidence for replication from diabetes-related quantitative traits and from independent populations. Diabetes 56: 3053–3062. https://doi.org/10.2337/db07-0457

Rhee MY, Yang, SJ, Oh, SW, Park, Y, Kim, CI, Park, HK, Park, SW, Park, CY (2011) Novel genetic variations associated with salt sensitivity in the Korean population. Hypertension Res 34: 606–611. https://doi.org/10.1038/hr.2010.278

Siddik MAB, Shin, AC (2019) Recent progress on branched-chain amino acids in obesity, diabetes, and beyond. Endocrinol Metabol 34: 234–246. https://doi.org/10.3803/EnM.2019.34.3.234

Suryawan A, Hawes, JW, Harris, RA, Shimomura, Y, Jenkins, AE, Hutson, SM (1998) A molecular model of human branched-chain amino acid metabolism. Am J Clin Nutr 68: 72–81. https://doi.org/10.1093/ajcn/68.1.72

Wang TJ, Larson MG, Vasan RS, Cheng S, Rhee EP, McCabe E, Lewis GD, Fox CS, Jacques PF, Fernandez C, O’Donnell CJ, Carr SA, Mootha VK, Florez JC, Souza A, Melander O, Clish CB, Gerszten RE (2011) Metabolite profiles and the risk of developing diabetes. Nat Med 17: 448–453. https://doi.org/10.1038/nm.2307

Wang ZQ, Faddaoui A, Bachvarova M, Plante M, Gregoire J, Renaud MC, Sebastianelli A, Guillemette C, Gobeil S, Macdonald E, Vanderhyden B, Bachvarov D (2015) BCAT1 expression associates with ovarian cancer progression: possible implications in altered disease metabolism. Oncotarget 6: 31522–31543. https://doi.org/10.18632/oncotarget.5159

Xu F, Tavintharan, S, Sum, CF, Woon, K, Lim, SC, Ong, CN (2013) Metabolic signature shift in type 2 diabetes mellitus revealed by mass spectrometry-based metabolomics. J Clin Endocrinol Metab 98: E1060–E1065. https://doi.org/10.1210/jc.2012-4132

Yoon MS (2016) The emerging role of branched-chain amino acids in insulin resistance and metabolism. Nutrients 8. https://doi.org/10.3390/nu8070405

Zemunik T, Boban M, Lauc G, Janković S, Rotim K, Vatavuk Z, Bencić G, Dogas Z, Boraska V, Torlak V, Susac J, Zobić I, Rudan D, Pulanić D, Modun D, Mudnić I, Gunjaca G, Budimir D, Hayward C, Vitart V, Wright AF, Campbell H, Rudan I (2009) Genome-wide association study of biochemical traits in Korcula Island, Croatia. Croatian Med J 50: 23–33. https://doi.org/10.3325/cmj.2009.50.23

Zerbino DR, Achuthan P, Akanni W, Amode MR, Barrell D, Bhai J, Billis K, Cummins C, Gall A, Girón CG, Gil L, Gordon L, Haggerty L, Haskell E, Hourlier T, Izuogu OG, Janacek SH, Juettemann T, To JK, Laird MR, Lavidas I, Liu Z, Loveland JE, Maurel T, McLaren W, Moore B, Mudge J, Murphy DN, Newman V, Nuhn M, Ogeh D, Ong CK, Parker A, Patricio M, Riat HS, Schuilenburg H, Sheppard D, Sparrow H, Taylor K, Thormann A, Vullo A, Walts B, Zadissa A, Frankish A, Hunt SE, Kostadima M, Langridge N, Martin FJ, Muffato M, Perry E, Ruffier M, Staines DM, Trevanion SJ, Aken BL, Cunningham F, Yates A, Flicek P (2018) Ensembl 2018. Nucleic Acids Res 46: D754–D761. https://doi.org/10.1093/nar/gkx1098

Zhou H, Yu, B, Gao, J, Htoo, JK, Chen, D (2018) Regulation of intestinal health by branched-chain amino acids. Animal Sci J = Nihon chikusan Gakkaiho 89: 3–11. https://doi.org/10.1111/asj.12937