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Article

Exploring the Association of FTO rs9939609 with Type 2 Diabetes, Fasting Glucose and HbA1c in a Southeastern Mexican Region of Predominant Mayan Genetic Background

by
Nicolas Fragoso-Bargas
1,*,†,
Litzy Naomi Toloza-Couoh
2,
Irma Quintal-Ortiz
2,
Guillermo Valencia-Pacheco
2 and
Nina Valadez-Gonzalez
2,†
1
Mohn Center for Diabetes Precision Medicine, Department of Clinical Science, University of Bergen, 5009 Bergen, Norway
2
Hematology Laboratory, Regional Research Center “Dr. Hideyo Noguchi”, Autonomous University of Yucatán, Yucatán 97225, Mexico
*
Author to whom correspondence should be addressed.
†
These authors contributed equally as senior authors.
Biomolecules 2025, 15(11), 1492; https://doi.org/10.3390/biom15111492
Submission received: 1 September 2025 / Revised: 19 October 2025 / Accepted: 20 October 2025 / Published: 23 October 2025
(This article belongs to the Special Issue Molecular Mechanisms and Therapeutic Strategies for Metabolic Dysfunction: From Molecules to Population Health)
Browse Figure
Review Reports Versions Notes

Abstract

Type 2 diabetes (T2D) is a multifactorial disease characterized by chronic hyperglycemia. The FTO variant rs9939609 has been widely associated with obesity, but emerging evidence suggests a broader role for T2D risk across diverse populations. However, Mayan ancestry individuals remain underrepresented in genetic studies. Thus, we evaluated the association of rs9939609 with T2D, fasting glucose, and HbA1c in a southeastern Mexican region with prevalent Mayan ancestry. Birthplace was used as a proxy for ancestry, although no formal ancestry assessment was conducted. The A allele was associated with increased risk for T2D in both additive (OR = 1.88 [1.08–3.40], p = 0.031) and dominant (OR = 2.09 [1.08–4.15], p = 0.032) models, even after adjusting for age, sex, BMI, waist circumference, and waist/hip ratio. The A allele was also associated with increased fasting glucose and HbA1c levels in the dominant model. Mediation analysis suggested that T2D may mediate the effect of rs9939609-A on glucose traits. rs9939609-A may be a risk factor for T2D and elevated glucose levels in this population. However, the results should be interpreted with caution due to the limited sample size, which may result in under- or overestimation of effect sizes.

1. Introduction

Type 2 diabetes (T2D) is a multifactorial disease characterized by the progressive dysfunction of beta-cells often accompanied by insulin resistance [1]. When beta-cells can no longer compensate for the increased insulin demand, disturbances in glucose homeostasis arise, ultimately leading to chronic hyperglycemia, a key criterion for the clinical diagnosis of T2D. Hyperglycemia is typically assessed via fasting glucose, oral glucose tolerance, or HbA1c levels [2]. In Mexico, the prevalence of T2D among individuals over 20 years of age is estimated at 18.3% [3], making it the third leading cause of death nationwide [4]. Several risk factors have been associated with T2D risk, such as obesity, family history, physical inactivity, age, and ancestry, particularly among Hispanics/Latinos and Native Americans [5].
The biggest GWAS meta-analysis has successfully identified ~1300 genetic variants associated with T2D risk [6]. While Hispanic individuals are represented in this study, the majority of participants are of European ancestry. Moreover, the Hispanic cohort from Mexico [7] mainly included admixed individuals from Mexico City, whose Amerindian ancestral background comprises mainly of Indigenous groups such as the Nahuatl, Mazahua and Otomi [8]. On the other hand, in the southeastern region of Mexico, specifically on the Yucatan Peninsula, Mayan ancestry is predominant [9]. Compared to other Mexican pre-Hispanic ancestries, individuals with Maya ancestry have a higher prevalence of T2D [9]. However, southeastern Mexican populations from the Yucatan peninsula, where Mayan ancestry is prevalent, remain underrepresented in genetic association studies.
Among the efforts to identify T2D-associated variants in populations in southeastern Mexico, a GWAS identified the rs1914711-G variant near AGTR2 [10], although the sample size was limited (n = 92), reducing the power for locus discovery. Similarly, an exome sequencing study (n = 24) found that rs1799999-A near PPP1R3A was associated with an increased risk of T2D [11]. Candidate gene studies have also reported associations between higher T2D risk and variants such as rs10811661-T (CDKN2A/2B) [12], rs9282541-T (ABCA1) [12], rs1799987-A (CCR5) [13], rs7896005-G (SIRT1) [14], and rs6446482-G (WFS1) [14], while rs1800795-C (IL6) has been suggested as a protective factor in males [15]. Given the limited number of SNPs currently identified as associated with T2D risk, further research is needed to identify the genetic factors contributing to T2D in the southeastern region of Mexico.
The fat mass and obesity (FTO) is a gene over 400kb long located in the long arm of chromosome 16 (16a12.2) that contains 9 exons and 8 introns [16]. FTO encodes a deacetylase involved in several metabolic processes and traits, including adipocyte differentiation, appetite regulation, lipid accumulation, satiety, and body mass index (BMI) [16]. FTO harbors the genetic variant rs9939609-A, which has been primarily associated with higher BMI and obesity [17,18,19,20]. However, several studies across different populations have shown that rs9939609-A is also associated with an increased risk of T2D [21,22,23].
In Central and Northern Mexican populations, the rs9939609-A allele has been associated with an increased risk of metabolic syndrome [24], as well as elevated serum insulin levels [25]. A study in Mayan children also examined its association with fasting glucose, insulin, and HOMA-IR, though no statistically significant findings were reported [26]. Additionally, evidence from Chiapas, a region with predominant Mayan ancestry, suggests a possible link between rs9939609-A and hyperglycemia, categorized as fasting glucose ≥100 mg/dL [27]. To date, no study has reported the association between rs9939609-A and diagnosed T2D in adult individuals from Southeastern Mexico. Therefore, the aims of this study were to: (1) evaluate if rs9939609-A is associated with T2D and (2) assess whether rs9939609-A is associated with fasting glucose and HbA1c levels in a southeastern region (Yucatan, México) where Mayan ancestry is predominant.

2. Materials and Methods

2.1. Study Design and Participants

The study population consisted of residents from the locality of San José Tecoh, Mérida, Yucatán, Mexico. All participants were recruited at the health center “Unidad Universitaria de Inserción Social (UUIS) San José Tecoh” between 2018 and 2019. For cases, the inclusion criteria were: (1) diagnosis of T2D according to the Mexican diagnostic criteria NOM-015-SSA2-2010, which include a fasting plasma glucose ≥126 mg/dL, a random plasma glucose ≥200 mg/dL, or a plasma glucose ≥200 mg/dL two hours after an oral load of 75 g of anhydrous glucose dissolved in water; (2) individuals of any sex; (3) age over 20 years; and (4) being born in Mérida, Yucatán. For controls, the inclusion criteria were: (1) no family history of T2D in at least two generations; (2) individuals of any sex; (3) age over 20 years; and (4) being born in Mérida, Yucatán. Among the general exclusion criteria were: having a relative already included in the study, being pregnant, and having a diagnosis of type 1 diabetes (for case participants). Although ancestry markers were not measured in all individuals, a previously analyzed subset of controls (n = 37) included in this study showed evidence of Mayan ancestry [10,28].
All participants signed an informed consent form, indicating their voluntary participation, in accordance with the recommendations of the Declaration of Helsinki and the General Health Law. The study was approved by the Ethics Committee of the Dr. Hideyo Noguchi Regional Research Center at the Autonomous University of Yucatán (UADY).

2.2. Anthropometrics and Biochemical Measurements

Age, height, weight, waist circumference and hip measurements were obtained at recruitment, and BMI and waist-hip-ratio were subsequently calculated. Fasting plasma glucose and whole blood HbA1c were measured in both cases and controls using the same Roche Cobas® c111 analyzer (Roche Diagnostics Ltd., Rotkreuz, Switzerland), following the manufacturer’s instructions. All individuals included in this study have complete data for these variables.

2.3. Genotyping

DNA was extracted using the Macherey-Nagel® kit (Macherey-Nagel GmbH & Co. KG, Düren, Germany) from 200 µL of peripheral blood, following the manufacturer’s instructions. Rs9939609 genotyping was performed using allele discrimination assays with the specific TaqMan® probe (Applied Biosystems, Foster City, CA, USA) C__30090620_10, following the manufacturer’s instructions. The amplification program began with an initial activation at 95 °C for 10 min, followed by 40 amplification cycles: each cycle included a denaturation step at 92 °C for 15 s and an annealing/extension step at 60 °C for 90 s. Each assay included both positive and negative controls.

2.4. Statistical Analyses

Hardy–Weinberg equilibrium was evaluated in cases, controls and all the study population using the R package “HardyWeinberg” (version 1.7.9) [29,30,31]. The Exact test for Hardy–Weinberg equilibrium described by Wigginton and collaborators [32] was used since it adequately controls for type I error in both large and small datasets.
For the association analyses, we used the additive (TT = 0, AT = 1, AA = 2), dominant (TT = 0, AT/AA = 1), and recessive (TT/AT = 0, AA = 1) models. The association between T2D and rs9939609 was evaluated using both univariable and multivariable logistic regression models. The multivariable models were adjusted for age and sex since both are well-established demographic factors that influence metabolic traits and disease risk. Additionally, given the strong association between FTO and adiposity, we adjusted for BMI, waist circumference, and waist/hip ratio. However, since these three variables are all proxies for adiposity and are highly correlated, we constructed separate models for each rather than including them simultaneously to avoid redundancy. For fasting glucose and HbA1c, we used three types of linear models: (1) univariable (i.e., unadjusted); (2) adjusted for age, sex, and one adiposity-related measure; and (3) the same as the previous model, with additional adjustment for T2D status. All logistic and linear models were constructed in R Studio (version 2025.09.1+401).
If a significant association was found between a glucose-related quantitative trait and rs9939609, we conducted a mediation analysis using the R package mediation (version 4.5.1) [33] to explore whether the association was mediated by T2D status. The mediation analysis estimates the following effects: (1) the Average Causal Mediation Effect (ACME), which quantifies the indirect effect, i.e., the portion of the association transmitted through the mediator (T2D); (2) the Average Direct Effect (ADE), which captures the direct effect of rs9939609 on the trait, adjusting for the mediator; (3) the Total Effect, which is the sum of the ACME and ADE; and (4) the Proportion Mediated, which represents the fraction of the total effect explained by the mediation pathway. To estimate the effects and their confidence intervals, we used the percentile method with 1000 simulations, setting a random seed of 100 to ensure reproducibility.
For the association and mediation analyses, we considered a p-value <0.05 as significant and a p-value <0.1 as a statistical trend.

3. Results

3.1. Population Characteristics

Clinical characteristics of the study population (n = 184) can be consulted in Table 1. Variables include age, sex distribution, fasting glucose, HbA1c, BMI, waist circumference and waist/hip ratio. There were 92 individuals with T2D (cases) and 92 without (controls), and 72% were females. There was no missing data.

3.2. Frequency Distribution and Associations with T2D

Genotype and allele frequencies can be consulted in Table 2. The minor allele frequency of the risk allele (A) was 15.2% for the entire population, 19.6% in cases and 10.9% in controls. We did not observe significant deviation from Hardy–Weinberg equilibrium in cases (p = 0.323), controls (p = 0.276), or the overall sample (p = 0.146). According to the univariable logistic regression model, rs9939609 conferred a 1.88-fold increased risk of T2D per copy of the A allele under the additive model (Table 3) and a 2.09-fold increased risk under the dominant model. The association with T2D remained significant in both the additive and dominant models, even after adjusting for covariates (Table 2). No significant association was identified in the recessive model.

3.3. Associations with Fasting Glucose and HbA1c

The univariable linear regression model showed that the A allele of rs9939609 was associated with increased fasting glucose levels under both the additive and dominant models, while for HbA1c, the association was observed only under the dominant model (Table 3). For both traits, associations under the dominant model remained significant after adjusting for age, sex, and an adiposity measurement, but were no longer significant in models further adjusted for T2D status (Table 3).

3.4. Mediation Analysis

Since we observed consistent significant associations between rs9939609-A and both fasting glucose and HbA1c levels under the dominant model, which disappeared after adjusting for T2D, we performed mediation analyses based on the dominant model to explore whether T2D mediates this effect. The results provide suggestive evidence (p < 0.1) that T2D may mediate the relationship between rs9939609-A and both glucose traits, given that the ACME was stronger than the ADE (Table 4 and Figure 1). Additionally, the analysis suggests (p < 0.1) that T2D may explain up to 66% of this relationship (Table 4) for fasting glucose and 62% for HbA1c; however, the confidence intervals were wide.

4. Discussion

In this study, we provide evidence that rs9939609-A may be a genetic risk factor for T2D in a southeastern Mexican population. This association remains after adjusting for adiposity traits under both the additive and dominant models. Furthermore, we also observed that rs9939609-A is associated with increased glucose and HbA1c levels mainly under the dominant model. Mediation analysis provided suggestive evidence that T2D may be a mediator between rs9939609 and fasting glucose levels.
In our population, the A allele conferred an increased risk for T2D in both univariable and multivariable logistic regression under both additive and dominant genetic models. This is in line with a multi-ancestry meta-analysis, which concluded that rs9939609-A is consistently associated with T2D risk [21]. One proposed biological mechanism by which intronic FTO SNPs may increase T2D risk is through their influence on the expression of adjacent genes, IRX3 and IRX5, which modulate brown adipocyte differentiation [34,35], as well as through appetite regulation via the hypothalamus [36,37]. Thus, rs9939609-A and other FTO polymorphisms may contribute to T2D risk by dysregulating adipocyte differentiation and appetite control. However, our results suggest that the association is independent of adiposity, as it persisted after adjusting for BMI, waist circumference, and waist/hip ratio. Consistent with this, previous studies in Norwegian [22] and Vietnamese populations [38] have also shown that the association with T2D remains significant after adjusting for BMI. This suggests that rs9939609 could act through pathways not solely related to adiposity, which remain to be elucidated. However, further verification in other Mayan or other indigenous populations are needed to verify the robustness of this relationship.
Regarding the positive association of rs9939609-A with both fasting glucose and HbA1c observed mainly under the dominant model, other populations have reported associations with glucose measurements. The A allele has been associated with increased glucose levels in Middle Eastern populations [39] and with both fasting glucose [40] and HbA1c [41] in South Asian populations. Moreover, a population from Chiapas, Mexico, who also has predominance of Mayan ancestry, suggested that rs9939609-A is associated with hyperglycemia, categorized as fasting glucose levels ≥ 126 mg/dL [27]. Taken together, these findings support the relevance of rs9939609-A in glycemic homeostasis regulation within populations with predominantly Mayan descent and highlight the transferability of rs9939609-A as a risk factor for both T2D and elevated glucose levels across diverse populations worldwide.
Our analyses suggest that the associations between rs9939609 and both fasting glucose and HbA1c may be influenced by T2D status. To explore this, we conducted a mediation analysis to evaluate whether T2D acts as a potential mediator between rs9939609 and these glycemic traits. The ACME model indicated that T2D may mediate the effect of rs9939609 on both outcomes. This suggests that the variant could influence fasting glucose and HbA1c levels indirectly by increasing the risk of T2D, rather than exerting a direct effect in non-diabetic individuals. While this finding is intriguing, it should be interpreted with caution. The significance of the ACME model was only a statistical trend (p > 0.05 and <0.1). Furthermore, the mediation analysis was not intended to establish causality but rather to serve as an exploratory, hypothesis-generating approach, given our limited sample size. It is also important to note that our study design includes a balanced number of T2D cases and controls, which may influence these observations. Larger studies in Mayan or other populations are needed to confirm this potential pathway.
An important limitation of this study is that ancestry markers have been not assessed in all the study population. Nevertheless, birthplace in Mérida was used as a proxy for regional ancestry, given that Mayan ancestry is the predominant Indigenous component in the Yucatán Peninsula [9]. Furthermore, a previous study that included 45 individuals from the municipality of Mérida, 37 of whom are also part of this study, compared their ancestry proportions with those of 47 individuals from another municipality in Yucatán (Sisal) [10,28]. Both populations, despite being from distinct localities, showed evidence of Mayan ancestry. Hence, although the ancestry markers were not assessed on all individuals, it is highly likely that most participants in this study have Mayan ancestry. Moreover, the same study identified a statistical trend between rs9939609-A and T2D, with a coefficient consistent with our direction of effect (OR = 1.32, p = 0.080). This suggests that the significant association observed in our study may be driven by the larger sample size, and that the lack of ancestry confirmation in all individuals likely does not affect the interpretation of this association, given the consistent effect direction observed in a previous analysis of confirmed Mayan individuals. Finally, the replication of the association between rs9939609-A and T2D in our cohort, along with previous evidence linking this variant to hyperglycemia in another region with a high prevalence of Mayan ancestry, supports the robustness of our findings.
Strengths of this study include a balanced number of cases and controls, which enhances statistical power and reduces bias. Carefully selected controls with no family history of diabetes for at least two generations, strengthening the validity of the control group, and the use of quantitative glucose measurements, allowing for the assessment of genetic associations beyond binary T2D status and mediation analysis. However, some limitations should be acknowledged. Since the sample size was limited, the presented associations should ideally be replicated in a larger study. Therefore, our results should be interpreted with caution because it is likely that our effect sizes reported are either under- or overestimated. Additionally, the lack of ancestry-informative marker measurements in all subjects limits our ability to precisely verify the presence and proportion of Mayan ancestry among participants.

5. Conclusions

To conclude, our findings support the role of rs9939609-A as a genetic risk factor for T2D in a population from southeastern México, a region where Mayan ancestry is predominant. This association is robust after adjusting for adiposity-related measurements. These results contribute to the growing evidence of FTO’s pleiotropic effects, as it may influence glycemic traits through mechanisms beyond adiposity. Future studies in similar populations using ancestry-informative markers or with increased statistical power are needed to validate and extend the presented findings.

Author Contributions

N.F.-B. and N.V.-G. conceptualized and designed the study. N.V.-G. collected the data. N.F.-B. performed the core statistical analyses and wrote the manuscript. L.N.T.-C. contributed to the study design and performed preliminary statistical analyses. I.Q.-O. and G.V.-P. contributed to sample processing and laboratory assessments. All authors have read and agreed to the published version of the manuscript.

Funding

This work is part of the project “Search for genetic factors that confer risk in the development of lack of response to oral hypoglycemic agents through the characterization of families with diabetes in a population from Yucatán, Mexico,” with financial support from CONACYT PROJECT-2010-02-151325. Project registration in SISTPROY: CIRB-2011-005. The University of Bergen covered the article processing charge (APC) for this manuscript through its publishing agreement.

Institutional Review Board Statement

The study was approved by Bioethics Committee of the Dr. Hideyo Noguchi Regional Research Center at the Autonomous University of Yucatán (UADY). The approval code: CEI-CIR-UADY-007-2011. The approval date: 13 May 2011.

Informed Consent Statement

All participants signed an informed consent form, indicating their voluntary participation in accordance with the recommendations of the Declaration of Helsinki and the General Health Law.

Data Availability Statement

To protect patient rights and ensure anonymity, the data are available upon reasonable request.

Acknowledgments

We thank all the participants who took part in this study. Special thanks to the social service and thesis students for their valuable contributions throughout the project.

Conflicts of Interest

The authors declare no conflicts of interest.

Abbreviations

The following abbreviations are used in this manuscript:
ADEAverage Direct Effect
ACMEAverage Causal Mediation Effect
BMIBody mass index
FTOFat mass and obesity-associated
SNVSingle-nucleotide variant
T2DType 2 diabetes 

References

  1. Lu, X.; Xie, Q.; Pan, X.; Zhang, R.; Zhang, X.; Peng, G.; Zhang, Y.; Shen, S.; Tong, N. Type 2 diabetes mellitus in adults: Pathogenesis, prevention and therapy. Signal Transduct. Target Ther. 2024, 9, 262. [Google Scholar] [CrossRef] [PubMed]
  2. Ikegami, H.; Babaya, N.; Noso, S. β-Cell failure in diabetes: Common susceptibility and mechanisms shared between type 1 and type 2 diabetes. J. Diabetes Investig. 2021, 12, 1526–1539. [Google Scholar] [CrossRef]
  3. Basto-Abreu, A.; López-Olmedo, N.; Rojas-Martínez, R.; Aguilar-Salinas, C.A.; Moreno-Banda, G.L.; Carnalla, M.; Rivera, J.A.; Romero-Martinez, M.; Barquera, S.; Barrientos-Gutierrez, T.; et al. Prevalencia de prediabetes y diabetes en México: Ensanut 2022. Salud Pública México 2023, 65, s163–s168. [Google Scholar] [CrossRef]
  4. Instituto Nacional de Estadística y Geografía. Comunicado de Prensa No.645/21. INEGI. 2021. Available online: https://www.inegi.org.mx/contenidos/saladeprensa/aproposito/2021/EAP_Diabetes2021.pdf (accessed on 28 August 2025).
  5. American Diabetes Association Professional Practice Committee. 2. Diagnosis and Classification of Diabetes: Standards of Care in Diabetes—2025. Diabetes Care 2024, 48 (Suppl. 1), S27–S49. [Google Scholar] [CrossRef]
  6. Suzuki, K.; Hatzikotoulas, K.; Southam, L.; Taylor, H.J.; Yin, X.; Lorenz, K.M.; Mandla, R.; Huerta-Chagoya, A.; Melloni, G.E.M.; Rayner, N.W.; et al. Genetic drivers of heterogeneity in type 2 diabetes pathophysiology. Nature 2024, 627, 347–357. [Google Scholar] [CrossRef]
  7. Diabetes Genetics Replication and Meta-Analysis (DIAGRAM) Consortium; Parra, E.J.; Below, J.E.; Krithika, S.; Valladares, A.; Barta, J.L.; Cox, N.J.; Hanis, C.L.; Wacher, N.; Garcia-Mena, J.; et al. Genome-wide association study of type 2 diabetes in a sample from Mexico City and a meta-analysis of a Mexican-American sample from Starr County, Texas. Diabetologia 2011, 54, 2038–2046. [Google Scholar]
  8. Guardado-Estrada, M.; Juarez-Torres, E.; Medina-Martinez, I.; Wegier, A.; Macías, A.; Gomez, G.; Cruz-Talonia, F.; Roman-Bassaure, E.; Piñero, D.; Kofman-Alfaro, S.; et al. A great diversity of Amerindian mitochondrial DNA ancestry is present in the Mexican mestizo population. J. Hum. Genet. 2009, 54, 695–705. [Google Scholar] [CrossRef]
  9. Lara-Riegos, J.; Azcorra-Pérez, H.S. Diabesity among the Yucatecan Maya. Metabolism, Genotype, Phenotype and Considerations of the Sociocultural Environment and Early Development. Rev. Bioméd. 2023, 34, 168–178. Available online: https://revistabiomedica.uady.mx/index.php/revbiomed/article/view/1048/1208 (accessed on 28 April 2023). [CrossRef]
  10. Domínguez-Cruz, M.G.; Muñoz, M.d.L.; Totomoch-Serra, A.; García-Escalante, M.G.; Burgueño, J.; Valadez-González, N.; Pinto-Escalantes, D.; Díaz-Badillo, Á. Pilot genome-wide association study identifying novel risk loci for type 2 diabetes in a Maya population. Gene 2018, 677, 324–331. [Google Scholar] [CrossRef] [PubMed]
  11. Sánchez-Pozos, K.; Ortíz-López, M.G.; Peña-Espinoza, B.I.; Granados-Silvestre, M.d.L.Á.; Jiménez-Jacinto, V.; Verleyen, J.; Tekola-Ayele, F.; Sanchez-Flores, A.; Menjivar, M. Whole-exome sequencing in maya indigenous families: Variant in PPP1R3A is associated with type 2 diabetes. Mol. Genet. Genom. 2018, 293, 1205–1216. [Google Scholar] [CrossRef]
  12. Lara-Riegos, J.; Ortiz-López, M.; Peña-Espinoza, B.; Montúfar-Robles, I.; Peña-Rico, M.; Sánchez-Pozos, K.; Granados-Silvestre, M.; Menjivar, M. Diabetes susceptibility in Mayas: Evidence for the involvement of polymorphisms in HHEX, HNF4α, KCNJ11, PPARγ, CDKN2A/2B, SLC30A8, CDC123/CAMK1D, TCF7L2, ABCA1 and SLC16A11 genes. Gene 2015, 565, 68–75. [Google Scholar] [CrossRef]
  13. Martínez-Aguilar, V.M.; Carrillo-Ávila, B.A.; García-Escalante, G.; Vera-Gamboa, L.; Escobar-García, D.M.; Pozos-Guillén, A.; Guzmán-Marin, E.; Valadez-González, N. Association of CCR5 G59029A and RANTES –28 C/G polymorphisms in patients with chronic periodontitis and/or Type 2 Diabetes Mellitus, in a Southeastern Mexican population. Investig. Clínica 2018, 59, 135. [Google Scholar] [CrossRef]
  14. Domínguez-Cruz, M.G.; Muñoz, M.d.L.; Totomoch-Serra, A.; García-Escalante, M.G.; Burgueño, J.; Valadez-González, N.; Pinto-Escalante, D.; Díaz-Badillo, A. Maya gene variants related to the risk of type 2 diabetes in a family-based association study. Gene 2020, 730, 144259. [Google Scholar] [CrossRef]
  15. Fragoso-Bargas, N.; Valadez González, N.; Ruíz-García, L.; Vera-Gamboa, L.; Martínez-Aguilar, V.M.; Nakazawa-Ueji, Y.E.; Valencia-Pacheco, G. Asociación del polimorfismo rs1800795 de la Interleucina-6 con la Diabetes tipo 2 y características clínicas relacionadas. Rev. Médica Univ. Costa Rica 2024, 18, 28–37. [Google Scholar]
  16. Popović, A.-M.; Turković, A.H.; Žuna, K.; Bačun-Družina, V.; Rubelj, I.; Matovinović, M. FTO Gene Polymorphisms at the Crossroads of Metabolic Pathways of Obesity and Epigenetic Influences. Food Technol. Biotechnol. 2023, 61, 14–26. [Google Scholar] [CrossRef] [PubMed]
  17. Frayling, T.M.; Timpson, N.J.; Weedon, M.N.; Zeggini, E.; Freathy, R.M.; Lindgren, C.M.; Perry, J.R.B.; Elliott, K.S.; Lango, H.; Rayner, N.W.; et al. A Common Variant in the FTO Gene Is Associated with Body Mass Index and Predisposes to Childhood and Adult Obesity. Science 2007, 316, 889–894. [Google Scholar] [CrossRef] [PubMed]
  18. Ali, A.H.A.; Shkurat, T.P.; Abbas, A.H. Association analysis of FTO gene polymorphisms rs9939609 and obesity risk among the adults: A systematic review and meta-analysis. Meta Gene 2021, 27, 100832. [Google Scholar] [CrossRef]
  19. Pratiwi, D.; Sidartha, M.; Wiyarta, E.; Harimawan, I.W.A.; Lestari, N.M.D.A.; Kim, B.; Taslim, N.A.; Tallei, T.E.; Nurkolis, F.; Syahputra, R.A. Comparison of the risk of obesity in the FTO rs9939609 genotype in a multiethnic group in Asia systematic review and meta-analysis. Front. Med. 2025, 12, 1522318. [Google Scholar] [CrossRef]
  20. Vázquez-Pérez, L.A.; Hattori-Hara, M.; Arankowsky-Sandoval, G.; Pérez-Mendoza, G.; Rubi-Castellanos, R.; Rangel-Méndez, J.A.; Pinto-Escalante, D.; Canto-Cetina, T.; González-Herrera, L.; P, G. Association between personality traits, eating behaviors, and the genetic polymorphisms FTO-rs9939609 and MAO-A 30 bp u-VNTR with obesity in Mexican Mayan children. Front. Genet. 2024, 15, 1421870. [Google Scholar] [CrossRef]
  21. Amine Ikhanjal, M.; Ali Elouarid, M.; Zouine, C.; Errafii, K.; Ghazal, H.; Alidrissi, N.; Bakkali, F.; Benmoussa, A.; Hamdi, S. FTO gene variants (rs9939609, rs8050136 and rs17817449) and type 2 diabetes mellitus risk: A Meta-Analysis. Gene 2023, 887, 147791. [Google Scholar] [CrossRef]
  22. Hertel, J.K.; Johansson, S.; Sonestedt, E.; Jonsson, A.; Lie, R.T.; Platou, C.G.P.; Nilsson, P.M.; Rukh, G.; Midthjell, K.; Hveem, K.; et al. FTO, Type 2 Diabetes, and Weight Gain Throughout Adult Life: A Meta-Analysis of 41,504 Subjects From the Scandinavian HUNT, MDC, and MPP Studies. Diabetes 2011, 60, 1637–1644. [Google Scholar] [CrossRef]
  23. Sabarneh, A.; Ereqat, S.; Cauchi, S.; AbuShamma, O.; Abdelhafez, M.; Ibrahim, M.; Nasereddin, A. Common FTO rs9939609 variant and risk of type 2 diabetes in Palestine. BMC Med. Genet. 2018, 19, 156. [Google Scholar] [CrossRef] [PubMed]
  24. Velazquez-Roman, J.; Angulo-Zamudio, U.A.; León-Sicairos, N.; Medina-Serrano, J.; DeLira-Bustillos, N.; Villamil-Ramírez, H.; Canizales-Quinteros, S.; Macías-Kauffer, L.; Campos-Romero, A.; Alcántar-Fernández, J.; et al. Association of FTO, ABCA1, ADRB3, and PPARG variants with obesity, type 2 diabetes, and metabolic syndrome in a Northwest Mexican adult population. J. Diabetes Complicat. 2021, 35, 108025. [Google Scholar] [CrossRef] [PubMed]
  25. Villalobos-Comparán, M.; Flores-Dorantes, M.T.; Villarreal-Molina, M.T.; Rodríguez-Cruz, M.; García-Ulloa, A.C.; Robles, L.; Huertas-Vázquez, A.; Saucedo-Villarreal, N.; López-Alarcón, M.; Sánchez-Muñoz, F.; et al. The FTO Gene Is Associated With Adulthood Obesity in the Mexican Population. Obesity 2008, 16, 2296–2301. [Google Scholar] [CrossRef] [PubMed]
  26. Peña-Espinoza, B.; Juárez-López, C.; Ortiz-López, G.; Granados-Silvestre, A.; Menjivar, M. High frequency of metabolic syndrome in non-obese Maya children from México: Implications of PPARG, KCNJ1, HHEX, HNF4A, ACE (I/D), FTO and ABCA1 genetics variants. Gac. Med. Mex. 2025, 161, 110–118. [Google Scholar] [CrossRef]
  27. Ortega, P.E.N.; Meneses, M.E.; Delgado-Enciso, I.; Irecta-Nájera, C.A.; Castro-Quezada, I.; Solís-Hernández, R.; Flores-Guillén, E.; García-Miranda, R.; Valladares-Salgado, A.; Locia-Morales, D.; et al. Association of rs9939609-FTO with metabolic syndrome components among women from Mayan communities of Chiapas, Mexico. J. Physiol. Anthropol. 2021, 40, 11. [Google Scholar] [CrossRef]
  28. Domínguez-Cruz, M.G. Búsqueda de SNPs en Genes de Susceptibilidad a Diabetes Tipo 2 Asociados a Origen Étnico en Una Población Mexicana (Yucatán); Centro de Investigación y de Estudios Avanzados del Instituto Politécnico Nacional (Cinvestav): México, México, 2018; Available online: https://repositorio.cinvestav.mx/handle/cinvestav/3277 (accessed on 20 August 2025).
  29. Graffelman, J.; Weir, B.S. Testing for Hardy–Weinberg equilibrium at biallelic genetic markers on the X chromosome. Heredity 2016, 116, 558–568. [Google Scholar] [CrossRef]
  30. Graffelman, J. Exploring Diallelic Genetic Markers: The HardyWeinberg Package. J. Stat. Softw. 2015, 64, 1–23. [Google Scholar] [CrossRef]
  31. Graffelman, J.; Camarena, J.M. Graphical Tests for Hardy-Weinberg Equilibrium Based on the Ternary Plot. Hum. Hered. 2007, 65, 77–84. [Google Scholar] [CrossRef]
  32. Wigginton, J.E.; Cutler, D.J.; Abecasis, G.R. Abecasis GR. A Note on Exact Tests of Hardy-Weinberg Equilibrium. Am. J. Hum. Genet. 2005, 76, 887–893. [Google Scholar] [CrossRef]
  33. Tingley, D.; Yamamoto, T.; Hirose, K.; Keele, L.; Imai, K. Mediation: R Package for Causal Mediation Analysis. J. Stat. Softw. 2014, 59, 1–38. [Google Scholar] [CrossRef]
  34. Guang, L.; Ma, S.; Yao, Z.; Song, D.; Chen, Y.; Liu, S.; Wang, P.; Su, J.; Wang, Y.; Luo, L.; et al. An obesogenic FTO allele causes accelerated development, growth and insulin resistance in human skeletal muscle cells. Nat. Commun. 2025, 16, 1645. [Google Scholar] [CrossRef]
  35. Claussnitzer, M.; Dankel, S.N.; Kim, K.H.; Quon, G.; Meuleman, W.; Haugen, C.; Glunk, V.; Sousa, I.S.; Beaudry, J.L.; Puviindran, V.; et al. FTO Obesity Variant Circuitry and Adipocyte Browning in Humans. N. Engl. J. Med. 2015, 373, 895–907. [Google Scholar] [CrossRef]
  36. Poosri, S.; Boonyuen, U.; Chupeerach, C.; Soonthornworasiri, N.; Kwanbunjan, K.; Prangthip, P. Association of FTO variants rs9939609 and rs1421085 with elevated sugar and fat consumption in adult obesity. Sci. Rep. 2024, 14, 25618. [Google Scholar] [CrossRef] [PubMed]
  37. Liu, S.; Song, S.; Wang, S.; Cai, T.; Qin, L.; Wang, X.; Zhu, G.; Wang, H.; Yang, W.; Fang, C.; et al. Hypothalamic FTO promotes high-fat diet-induced leptin resistance in mice through increasing CX3CL1 expression. J. Nutr. Biochem. 2024, 123, 109512. [Google Scholar] [CrossRef]
  38. Binh, T.Q.; Phuong, P.T.; Nhung, B.T.; Thoang, D.D.; Lien, H.T.; Van Thanh, D. Association of the common FTO-rs9939609 polymorphism with type 2 diabetes, independent of obesity-related traits in a Vietnamese population. Gene 2013, 513, 31–35. [Google Scholar] [CrossRef]
  39. Saber-Ayad, M.; Manzoor, S.; El Serafi, A.; Mahmoud, I.; Hammoudeh, S.; Rani, A.; Abusnana, S.; Sulaiman, N. The FTO rs9939609 “A” allele is associated with impaired fasting glucose and insulin resistance in Emirati population. Gene 2019, 681, 93–98. [Google Scholar] [CrossRef] [PubMed]
  40. Naghshband, Z.; Malini, S.S. Association of FTO gene variant rs9939609 with hyperandrogenemia and fasting glucose levels in South Indian women with polycystic ovarian syndrome. Egypt J. Med. Hum. Genet. 2022, 23, 79. [Google Scholar] [CrossRef]
  41. Hussain, M.; Waheed, A.; Elahi, A.; Mustafa, G. Fat Mass and Obesity-Related (FTO) Gene Variant Is a Predictor of CVD in T2DM Patients. J. Diabetes Res. 2024, 2024, 5914316. [Google Scholar] [CrossRef]
Biomolecules 15 01492 g001
Figure 1. Schematic representation of the potential mediation pathway between rs9939609 and fasting glucose (a), and HbA1c (b), with type 2 diabetes as a potential mediator. ACME: Average Causal Mediation Effect; ADE: Average Direct Effect.
Figure 1. Schematic representation of the potential mediation pathway between rs9939609 and fasting glucose (a), and HbA1c (b), with type 2 diabetes as a potential mediator. ACME: Average Causal Mediation Effect; ADE: Average Direct Effect.
Biomolecules 15 01492 g001
Table 1. Clinical characteristics of the study population. Data are mean (SD) for quantitative measures and n (%) for categorical variables).
Table 1. Clinical characteristics of the study population. Data are mean (SD) for quantitative measures and n (%) for categorical variables).
PhenotypeAll (n = 184)Cases (n = 92)Controls (n = 92)
Age58.21 (10.78)56.83 (10.20)59.60 (11.21)
Sex (female)133 (72)70 (76)63 (69)
Fasting glucose (mg/dL)121.52 (53.88)155.50 (57.36)87.53 (14.59)
HbA1c (%)6.69 (2.02)8.08 (1.94)5.29 (0.70)
BMI (kg/m2)29.60 (5.07)30.30 (5.55)28.89 (4.47)
Waist circumference (cm)95.66 (10.81)97.79 (10.97)93.54 (10.28)
Waist/hip ratio0.91 (0.06)0.91 (0.06)0.91 (0.06)
Table 2. Genotype and allele frequency distribution between cases and controls, and logistic regression analyses evaluating the association between rs9939609 and T2D risk. The model column indicates whether the analysis was unadjusted or adjusted for specific covariates. Data are n (%) for genotype and allele frequencies.
Table 2. Genotype and allele frequency distribution between cases and controls, and logistic regression analyses evaluating the association between rs9939609 and T2D risk. The model column indicates whether the analysis was unadjusted or adjusted for specific covariates. Data are n (%) for genotype and allele frequencies.
Genotype and Allele frequencies
GenotypeAllCasesControlsAlleleAllCasesControls
AA7 (3.8)5 (5.4)2 (2.2)A56 (15.2)36 (19.6)20 (10.9)
AT42 (22.8)26 (28.3)16 (17.4)T312 (84.8)148 (80.4)164 (89.1)
TT135 (73.4)61 (66.3)74 (80.4)
Association analyses with T2D
ModelAdditive
OR (IC)		pDominant
OR (IC)		pRecessive
OR (IC)		p
Unadjusted1.88
(1.08–3.40)		0.0312.09
(1.08–4.15)		0.0322.59
(0.54–18.39)		0.264
Age + Sex + BMI1.89
(1.07–3.47)		0.0322.14
(1.09–4.30)		0.0302.43
(0.51–17.36)		0.297
Age + Sex + Waist1.88
(1.06–3.47)		0.0362.11
(1.06–4.29)		0.0362.54
(0.52–18.46)		0.280
Age + Sex + WHR1.93
(1.09–3.55)		0.0272.18
(1.10–4.39)		0.0272.58
(0.52–18.75)		0.274
Abbreviations: Waist: Waist circumference, WHR: waist/hip ratio. Highlighted in bold are p < 0.05.
Table 3. Linear regression analyses of the association between rs9939609 and fasting glucose and HbA1c. The model column indicates whether the analysis was unadjusted or adjusted for specific covariates.
Table 3. Linear regression analyses of the association between rs9939609 and fasting glucose and HbA1c. The model column indicates whether the analysis was unadjusted or adjusted for specific covariates.
PhenotypeModelAdditivepDominantpRecessivep
Beta (SE) Beta (SE) Beta (SE)
Glucose (mg/dL)Unadjusted14.54 (7.34)0.04919.76 (8.89)0.0278.85 (20.81)0.671
Age + Sex + BMI7.26 (1.98)0.04919.88 (8.79)0.0256.94 (20.60)0.737
Age + Sex + BMI + T2D4.61 (5.86)0.4328.11 (7.09)0.254−6.88 (16.28)0.673
Age + Sex + Waist13.57 (7.09)0.05718.66 (8.59)0.0317.21 (20.08)0.720
Age + Sex + Waist + T2D4.52 (5.80)0.4367.88 (7.03)0.264−6.34 (16.13)0.694
Age + Sex + WHR14.61 (7.17)0.04320.15 (8.68)0.0217.43 (20.35)0.716
Age + Sex + WHR + T2D4.85 (5.79)0.4038.38 (7.00)0.233−6.49 (16.09)0.687
HbA1c (%) Unadjusted0.52 (0.28)0.0610.78 (0.33)0.020−0.08 (0.78)0.914
Age + Sex + BMI 0.52 (0.27)0.0580.79 (0.33)0.016−0.17 (0.77)0.829
Age + Sex + BMI + T2D0.11 (0.20)0.5850.31 (0.25)0.215−0.74 (0.56)0.186
Age + Sex + Waist0.49 (0.27)0.0670.76 (0.32)0.020−0.16 (0.76)0.833
Age + Sex + Waist + T2D0.11 (0.20)0.5890.30 (0.24)0.219−0.74 (0.56)0.190
Age + Sex + WHR0.52 (0.27)0.0530.80 (0.32)0.015−0.15 (0.76)0.841
Age + Sex + WHR + T2D0.12 (0.20)0.5640.31(0.24)0.202−0.73 (0.56)0.188
Abbreviations: Waist: Waist circumference, WHR: waist/hip ratio. Highlighted in bold are p < 0.05.
Table 4. Mediation analysis evaluating T2D as a mediator in the relationship between rs9939609 and fasting glucose and HbA1c.
Table 4. Mediation analysis evaluating T2D as a mediator in the relationship between rs9939609 and fasting glucose and HbA1c.
Glucose (mg/dL)
ModelEffect CI 95%p-Value
ACME (mg/dL)14.90−1.053, 25.000.066
ADE (mg/dL)7.67−8.56, 24.320.374
Total Effect (mg/dL)22.580.01, 39.920.050
Proportion Mediated 66−35, 2420.082
HbA1c (%)
ACME (%)0.46−0.02, 0.1000.056
ADE (%)0.28−0.26, 0.850.342
Total Effect (%)0.75−0.01, 1.560.056
Proportion Mediated (%) *62−11, 2260.060
ACME: Average causal mediation effect, ADE: Average direct effect. * Values represent mediation proportions expressed as percentages (0–100%), not HbA1c concentration percentages.
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Fragoso-Bargas, N.; Toloza-Couoh, L.N.; Quintal-Ortiz, I.; Valencia-Pacheco, G.; Valadez-Gonzalez, N. Exploring the Association of FTO rs9939609 with Type 2 Diabetes, Fasting Glucose and HbA1c in a Southeastern Mexican Region of Predominant Mayan Genetic Background. Biomolecules 2025, 15, 1492. https://doi.org/10.3390/biom15111492

AMA Style

Fragoso-Bargas N, Toloza-Couoh LN, Quintal-Ortiz I, Valencia-Pacheco G, Valadez-Gonzalez N. Exploring the Association of FTO rs9939609 with Type 2 Diabetes, Fasting Glucose and HbA1c in a Southeastern Mexican Region of Predominant Mayan Genetic Background. Biomolecules. 2025; 15(11):1492. https://doi.org/10.3390/biom15111492

Chicago/Turabian Style

Fragoso-Bargas, Nicolas, Litzy Naomi Toloza-Couoh, Irma Quintal-Ortiz, Guillermo Valencia-Pacheco, and Nina Valadez-Gonzalez. 2025. "Exploring the Association of FTO rs9939609 with Type 2 Diabetes, Fasting Glucose and HbA1c in a Southeastern Mexican Region of Predominant Mayan Genetic Background" Biomolecules 15, no. 11: 1492. https://doi.org/10.3390/biom15111492

APA Style

Fragoso-Bargas, N., Toloza-Couoh, L. N., Quintal-Ortiz, I., Valencia-Pacheco, G., & Valadez-Gonzalez, N. (2025). Exploring the Association of FTO rs9939609 with Type 2 Diabetes, Fasting Glucose and HbA1c in a Southeastern Mexican Region of Predominant Mayan Genetic Background. Biomolecules, 15(11), 1492. https://doi.org/10.3390/biom15111492

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