Type 2 diabetes mellitus (T2DM) is a degenerative disease affecting morbidity and mortality. The prevalence of T2DM is increasing worldwide, and >60% of patients with T2DM are located in Asia. It is predicted that by 2035, the incidence of T2DM in Indonesia will be 2 times higher than it was in 2013. Indonesia has the second-highest ranking of T2DM prevalence among Western Pacific Region countries [1, 2]. Our previous study found that T2DM prevalence was higher in the Sleman population of Yogyakarta, a densely populated province on the island of Java in Indonesia [3].
It is widely known that T2DM is a disease with multi-factorial etiology, including environmental and multigenic factors that are involved in T2DM pathogenesis [4]. The racial, ethnic, social, economic, and cultural differences of Pacific Islanders, including in Indonesia, have created complex gene-environment interactions [1]. It is noteworthy that a study conducted in the United States population found that T2DM genetic risk also increased the risk of mortality [5].
Previous studies have investigated many genes that correlate with T2DM risk. Genome-wide association studies (GWASs) are some of the largest to explore the association of genetics with disease risk, including for T2DM. A GWAS meta-analysis found approximately 143 variants and risk alleles that could increase risk of T2DM [6]. While early GWASs were focused on Europe, a cohort study in Singapore has observed multiethnic populations in Southeast Asia, engaging Malay, Chinese, and Asian Indian patients. Despite the study's success in discovering variants that have an association with T2DM risk, it was limited in that a study of disease risk based exclusively on specific populations was still required [7]. Heritability of T2DM is reported to be about 20%–80% from progeny or twin studies [8, 9], but T2DM genetic risk is not always inherited, and is well-known as “missing heritability”. Gene–environment and gene–gene interaction might contribute to missing heritability of T2DM [10, 11]. Accordingly, detection in a specific population is better to reduce missing heritability risk, which in this present study has focused only on Indonesian patients newly diagnosed with T2DM in Yogyakarta.
Variation of the genes that contribute to glucose and fat metabolism may contribute to the increasing of T2DM risk [12].
An association between genetic variations of
Fasting plasma glucose (FPG) and glycated hemoglobin A1c (HbA1c) are the most widely used biomarkers to diagnose T2DM based on the American Diabetes Association (ADA) criteria. Our study implies that the diagnostic tool could be enhanced by merging these with analyses of genetic variation. Meanwhile, T2DM is a degenerative disease that could lead to complications, so it is essential to examine clinical characteristics as conventional risk factors. Body mass index (BMI), waist circumference, elevated blood pressure, and hyperglycemia could augment T2DM severity and increase the risk of T2DM complications [28]. Additionally, reduced renal function is a common T2DM complication marked by declining levels of the estimated glomerular filtration rate (eGFR) [29, 30]. Accordingly, those factors were observed in our study. The association of
In the present cross-sectional study, we recruited 190 patients with suspected T2DM from 10 primary health care (PHC) centers located in Yogyakarta, Indonesia, between June 2019 and July 2020. The study size was calculated using a 5% level of significance and power of 80%, while the expected prevalence of T2DM in rs2746342 of TG genotype was 49% and in rs2746342 of GG genotype was 26% [24], and we applied 2 equal groups. Therefore, using the Fleiss formula, we ascertained that a sample of 156 patients was required.
The inclusion criteria as in the previous study were patients with age 20–75 years, Indonesian, and a diagnosis by a physician of T2DM based on the ADA criteria, which are FPG ≥126 mg/dL or HbA1c ≥6.5%. We conducted the laboratory tests to determine concentrations of FPG, HbA1c, and creatinine serum for all participants. Any participant who did not have laboratory test results was excluded. A nurse obtained blood pressure by direct measurement. A nutritionist in the PHCs conducted anthropometric measurements, including height, weight, and waist circumference. We calculated BMI by dividing weight (kg) by height (m2) and obtained age and sex data from the patients’ medical records.
The study protocol was approved by the Medical and Health Research Ethics Committee (MHREC), Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada – Dr. Sardjito General Hospital in Yogyakarta, Indonesia (reference No. KE/FK/0633/EC/2019) as recognized by the FERCAP and complied with the ethical principles of the contemporary revision of the Declaration of Helsinki and other international and national guidelines on ethical standards and procedures for research on human beings. All participants signed an informed consent form to participate in this study. This study is reported according to STREGA reporting guidelines, extended from the STROBE statement [31].
Based on previous studies, we selected 3 SNPs that have minor allele frequency (MAF) > 10%: rs2796498, rs9803799, and rs2746342. These SNPs have been identified among Han Chinese and a U.S. population of various ancestries [24, 27, 32]. Dependent variables were clinical characteristics of patients newly diagnosed with T2DM including age, BMI, waist circumference, blood pressure, FPG, HbA1c, and renal function. Lifestyle, age, and sex might influence the results besides the effect of genetic variation as a potential bias. Therefore, for the present study we conducted further analysis adjusting for sex, age, and waist circumference.
After an overnight fast, an analyst at the PHC collected a venous blood sample into a tube containing ethylenediaminetetraacetic acid (EDTA). Blood sample parameters were measured on the same day as the sample was collected. All laboratory tests were measured by Prodia Laboratory Instruments (Yogyakarta, Indonesia). FPG was measured using a hexokinase method, and serum creatinine was measured using an enzymatic method. HbA1c was quantified by ion-exchange high-performance liquid chromatography D-10. eGFR was calculated using a Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) formula for non-Black populations and included serum creatinine (mg/dL).
Blood samples of participants were collected by venipuncture in 1.5 mL tubes containing EDTA and stored at −20 °C in a freezer. A genomic DNA sample was isolated from the whole blood–EDTA sample using a Genomic DNA Mini Kit (Blood) (RA501500; Genaid, Taiwan) according to the manufacturer's instructions and stored at −80 °C. The genetic variations were genotyped using TaqMan SNP genotyping assays and Applied Biosystems qPCR 7500 Fast Real-Time PCR System located at the Faculty of Medicine, Public Health and Nursing, Universitas Gadjah Mada. The total reaction volume was 10 μL. Details of all TaqMan primers and probes (catalog Nos. 4351379 and 4403311), and conditions for genotyping, are available upon request. Context sequences (VIC/FAM) for TaqMan assay are listed in
Context sequence (VIC/FAM) rs2796498, rs9803799, and rs2746342
rs2796498 | CTGTAACAGTGTTAGTGATTTAAAC |
rs9803799 | TAAATACAGGGTTTATATCCCCACA |
rs2746342 | AGAGAGGCTAAGATGCAGGCTGTAC |
TaqMan SNP Genotyping Assays by Applied Biosystems (Thermo Fisher Scientific).
SNP, single point mutation.
Descriptive analysis was conducted to analyze the baseline characteristics of the participants. Clinical characteristics of participants with different genotypes in each SNP were compared. First, we performed a test of homogeneity to determine whether to use a one-way ANOVA or Kruskal–Wallis test. The mean difference of eGFR in rs2796498 and serum creatinine in rs2796498 and rs9803799 was
We included 166 patients newly diagnosed with T2DM in the present study. We had excluded 20 participants who had FPG <126 mg/dL or HbA1c <6.5% from the initial 190 patients. We had also excluded 4 participants because of lysis of their blood sample. Genotypes of all participants were analyzed successfully. The baseline characteristics of the participants are presented in
Baseline characteristics of the patients with T2DM
Age (years) | 54.0 ± 9.7 |
Sex (female) | 117 (70.5) |
Systolic blood pressure (mmHg) | 130.4 ± 18.7 |
Diastolic blood pressure (mmHg) | 81.1 ± 8.7 |
BMI (kg/m2) | 25.0 ± 4.0 |
Waist circumference (cm) | 87.6 ± 9.2 |
FPG (mg/dL) | 189.0 ± 71.2 |
HbA1c (%) | 9.61 ± 2.32 |
CrSr (mg/dL) | 0.89 ± 0.80 |
eGFR (mL/min/1.73 m2) | 91.6 ± 26.7 |
Continuous variables are presented as mean ± standard deviation, sex is presented as n (%).
BMI, body mass index; CrSr, serum creatinine; eGFR, estimated glomerular filtration rate; FPG, fasting plasma glucose; HbA1c, hemoglobin A1c; T2DM, type 2 diabetes mellitus.
Genotype frequencies of the
The mean differences of clinical characteristics and genotype frequencies are listed in
Clinical characteristics of patients with T2DM patients based on
Age (years) | 53.3 ± 9.5 | 54.7 ± 10.2 | 55.7 ± 10.1 | 0.60 | 54.1 ± 9.6 | 53.7 ± 11.3 | 45.0 ± 2.8 | 0.42 | 54.3 ± 9.6 | 53.7 ± 9.8 | 54.0 ± 10.2 | 0.94 |
BMI (kg/m2) | 24.95 ± 3.78 | 24.83 ± 4.20 | 27.14 ± 5.40 | 0.35 | 25.06 ± 4.03 | 24.53 ± 4.09 | 24.50 ± 3.54 | 0.87 | 24.60 ± 4.20 | 25.23 ± 3.69 | 25.09 ± 4.73 | 0.66 |
WC (cm) | 87.4 ± 8.7 | 87.6 ± 10.1 | 91.0 ± 8.1 | 0.61 | 87.4 ± 9.5 | 90.1 ± 6.5 | 85.5 ± 3.5 | 0.49 | 86.6 ± 9.2 | 88.2 ± 9.6 | 88.0 ± 7.9 | 0.58 |
SBP (mmHg) | 129.5 ± 19.1 | 131.3 ± 18.5 | 136.4 ± 15.0 | 0.57 | 131.1 ± 18.9 | 125.7 ± 14.8 | 124.0 ± 33.9 | 0.47 | 127.4 ± 19.7 | 131.8 ± 18.2 | 132.9 ± 17.7 | 0.31 |
DBP (mmHg) | 81.2 ± 8.9 | 81.1 ± 8.5 | 80.4 ± 9.6 | 0.98 | 81.3 ± 8.2 | 79.6 ± 12.3 | 82.0 ± 17.0 | 0.74 | 80.0 ± 9.5 | 81.8 ± 8.6 | 81.3 ± 7.2 | 0.51 |
FPG (mg/dL) | 188.8 ± 72.2 | 191.8 ± 70.8 | 167.9 ± 68.1 | 0.70 | 188.5 ± 70.0 | 195.5 ± 83.1 | 196.0 ± 103.2 | 0.97 | 186.5 ± 75.2 | 189.6 ± 68.8 | 192.7 ± 73.1 | 0.93 |
HbA1c (%) | 9.65 ± 2.30 | 9.61 ± 2.35 | 8.97 ± 2.43 | 0.76 | 9.61 ± 2.25 | 9.66 ± 2.85 | 8.9 ± 3.40 | 0.91 | 9.42 ± 2.30 | 9.79 ± 2.32 | 9.39 ± 2.89 | 0.58 |
CrSr (mg/dL) | 0.81 ± 0.49 | 1.04 ± 1.13 | 0.66 ± 0.10 | 0.48 | 0.87 ± 0.62 | 1.13 ± 1.75 | 0.67 ± 0.13 | 0.37 | 0.83 ± 0.52 | 0.97 ± 1.01 | 0.80 ± 0.33 | 0.48 |
eGFR (mL/min) | 94.0 ± 24.7 | 87.5 ± 30.2 | 96.6 ± 13.1 | 0.66 | 91.5 ± 26.2 | 90.7 ± 32.6 | 111.5 ± 3.5 | 0.57 | 92.1 ± 24.6 | 90.9 ± 29.0 | 93.0 ± 23.7 | 0.93 |
Data were analyzed using a one-way ANOVA or Kruskal–Wallis test, as appropriate.
BMI, body mass index; CrSr, serum creatinine; DBP, diastolic blood pressure; eGFR, estimated glomerular filtration rate; FPG, fasting plasma glucose; HbA1c, hemoglobin A1c; HWE, Hardy–Weinburg equilibrium; SBP, systolic blood pressure; T2DM, type 2 diabetes mellitus; WC, waist circumference.
We also analyzed the association between
Association between
rs2796498 | ||||||
GG | 1 (Reference) | |||||
AG | 1.24 (0.66–2.34) | 0.90 (0.48–1.71) | 1.74 (0.86–3.51) | 2.51 (0.96–6.54) | 0.98 (0.51–1.92) | 1.18 (0.63–2.23) |
AA | 0.99 (0.21–4.69) | 0.46 (0.09–2.51) | <0.01 (<0.01–NA) | <0.01 (<0.01–NA) | 1.41 (0.30–6.68) | 1.48 (0.31–6.98) |
Dominant (GG vs. AG+AA) | 1.21 (0.65–1.26) | 0.85 (0.46–1.58) | 1.49 (0.75–2.98) | 2.21 (0.85–5.74) | 1.02 (0.54–1.95) | 1.21 (0.65–2.24) |
Recessive (GG+AG vs. AA) | 0.91 (0.20–4.18) | 0.48 (0.09–2.57) | <0.01 (<0.01–NA) | <0.01 (<0.00–NA) | 1.42 (0.31–6.56) | 1.39 (0.30–6.39) |
G allele | 1 (Reference) | |||||
A allele | 1.13 (0.68–1.87) | 0.83 (0.50–1.38) | 1.19 (0.64–1.97) | 1.47 (0.71–3.04) | 1.06 (0.62–1.80) | 1.18 (0.71–1.96) |
rs9803799 | ||||||
TT | 1 (Reference) | |||||
GT | 1.10 (0.40–2.98) | 0.86 (0.31–2.38) | 0.82 (0.25–2.67) | 1.64 (0.43–6.29) | 0.55 (0.17–1.76) | 0.90 (0.33–2.46) |
GG | 1.23 (0.08–19.99) | 1.23 (0.08–19.99) | <0.01 (<0.01–NA) | <0.01 (<0.01–NA) | 1.77 (0.11–28.94) | 1.01 (0.06–16.51) |
Dominant (TT vs. GT+GG) | 1.11 (0.42–2.88) | 0.89 (0.34–2.35) | 0.71 (0.22–2.28) | 1.43 (0.38–5.44) | 0.63 (0.22–1.86) | 0.91 (0.35–2.38) |
Recessive (TT+GT vs. GG) | 1.22 (0.08–19.78) | 1.25 (0.08–20.27) | <0.01 (<0.01–NA) | <0.01 (<0.01–NA) | 1.88 (0.12–30.57) | 1.03 (0.06–16.66) |
T allele | 1 (Reference) | |||||
G allele | 1.11 (0.46–2.69) | 0.93 (0.38–2.27) | 0.64 (0.21–1.94) | 1.23 (0.35–4.39) | 0.73 (0.28–1.94) | 0.93 (0.38–2.24) |
rs2746342 | ||||||
GG | 1 (Reference) | |||||
GT | 1.43 (0.72–2.84) | 1.55 (0.78–3.08) | 0.95 (0.43–2.06) | 2.48 (0.77–7.97) | 1.44 (0.70–2.99) | 1.90 (0.95–3.77) |
TT | 1.18 (0.45–3.07) | 1.23 (0.49–3.32) | 1.65 (0.60–4.56) | 1.11 (0.19–6.49) | 1.63 (0.60–4.37) | 1.39 (0.53–3.59) |
Dominant (GG vs. GT+TT) | 1.37 (0.71–2.64) | 1.48 (0.77–2.86) | 1.09 (0.52–2.27) | 2.15 (0.68–6.76) | 1.48 (0.74–2.98) | 1.77 (0.92–3.40) |
Recessive (GG+GT vs. TT) | 0.95 (0.40–2.23) | 0.97 (0.41–2.29) | 1.70 (0.70–4.20) | 0.59 (0.13–6.16) | 1.29 (0.54–3.09) | 1.94 (0.40–2.19) |
G allele | 1 (Reference) | |||||
T allele | 1.14 (0.73–1.76) | 1.19 (0.76–1.84) | 1.21 (0.74–1.97) | 1.21 (0.62–2.35) | 1.28 (0.81–2.02) | 1.27 (0.82–1.97) |
AMP, adenosine monophosphate; AMPKα2, AMP-activated protein kinase (EC 2.7.11.31) α2 catalytic subunit; CI, confidence interval; CrSr, serum creatinine; eGFR, estimated glomerular filtration rate; FPG, fasting plasma glucose; HbA1c, hemoglobin A1c; NA, not available; OR, odds ratio;
Even after adjusting for age and sex (
Multiple regression logistic analysis adjusted for age and sex
rs2796498 | ||||||
GG | 1 (Reference) | |||||
AG | 1.27 (0.67–2.41) | 0.96 (0.50–1.85) | 1.79 (0.81–3.94) | 2.38 (0.87–6.50) | 0.95 (0.48–1.86) | 1.20 (0.64–2.28) |
AA | 1.03 (0.22–4.89) | 0.44 (0.08–2.48) | <0.01 (<0.01–NA) | <0.01 (<0.01–NA) | 1.27 (0.26–6.14) | 1.49 (0.31–7.07) |
Dominant (GG vs. AG+AA) | 1.24 (0.67–2.32) | 0.89 (0.47–1.69) | 1.51 (0.70–3.29) | 2.08 (0.77–5.67) | 0.98 (0.51–1.88) | 1.23 (0.66–2.28) |
Recessive (GG+AG vs. AA) | 0.93 (0.20–4.34) | 0.45 (0.08–2.48) | <0.01 (<0.01–NA) | <0.01 (<0.01–NA) | 1.30 (0.28–6.13) | 1.38 (0.30–6.42) |
G allele | 1 (Reference) | |||||
A allele | 1.15 (0.69–1.92) | 0.85 (0.50–1.44) | 1.11 (0.59–2.09) | 1.34 (0.65–3.00) | 1.02 (0.59–1.74) | 1.19 (0.72–1.98) |
rs9803799 | ||||||
TT | 1 (Reference) | |||||
GT | 1.08 (0.39–2.98) | 0.79 (0.27–2.27) | 0.86 (0.22–3.33) | 1.67 (0.39–7.14) | 0.54 (0.17–1.74) | 0.89 (0.32–2.44) |
GG | 1.06 (0.06–17.61) | 1.07 (0.06–17.93) | <0.01 (<0.01–NA) | <0.01 (0.01–NA) | 2.52 (0.15–42.41) | 0.96 (0.06–15.95) |
Dominant (TT vs. GT+GG) | 1.08 (0.41–2.83) | 0.81 (0.30–2.21) | 0.72 (0.19–2.70) | 1.53 (0.37–6.43) | 0.65 (0.22–1.91) | 0.90 (0.34–2.34) |
Recessive (TT+GT vs. GG) | 1.05 (0.06–17.44) | 1.03 (0.06–18.30) | <0.01 (<0.01–NA) | <0.01 (0.01–NA) | 2.66 (0.16–44.71) | 0.97 (0.06–16.11) |
T allele | 1 (Reference) | |||||
G allele | 1.07 (0.44–2.61) | 0.71 (0.84–2.12) | 0.63 (0.18–2.22) | 1.38 (0.35–5.39) | 0.77 (0.29–2.06) | 0.91 (0.37–2.21) |
rs2746342 | ||||||
GG | 1 (Reference) | |||||
GT | 1.42 (0.71–2.83) | 1.55 (0.76–3.14) | 0.99 (0.42–2.37) | 2.81 (0.83–9.51) | 1.48 (0.71–3.09) | 1.89 (0.95–3.76) |
TT | 1.17 (0.45–3.06) | 1.30 (0.48–3.47) | 1.88 (0.59–5.95) | 1.04 (0.16–6.65) | 1.67 (0.61–4.54) | 1.39 (0.54–3.60) |
Dominant (GG vs. GT+TT) | 1.36 (0.71–2.63) | 1.49 (0.75–2.93) | 1.16 (0.51–2.63) | 2.34 (0.71–7.71) | 1.52 (0.75–3.07) | 1.76 (0.91–3.40) |
Recessive (GG+GT vs. TT) | 0.95 (0.40–2.23) | 0.99 (0.41–2.40) | 1.89 (0.68–5.26) | 0.52 (0.10–2.62) | 1.31 (0.54–3.16) | 0.94 (0.40–2.21) |
G allele | 1 (Reference) | |||||
T allele | 1.13 (0.73–1.76) | 1.20 (0.76–1.87) | 1.28 (0.73–2.21) | 1.22 (0.61–2.45) | 1.03 (0.82–2.06) | 1.27 (0.82–1.97) |
CI, confidence interval; CrSr, serum creatinine; eGFR, estimated glomerular filtration rate; FPG, fasting plasma glucose; HbA1c, hemoglobin A1c; NA, not available; OR, odds ratio.
Our third model is presented in
Multiple regression logistic analysis adjusted for age, sex, and waist circumference
rs2796498 | ||||||
GG | 1 (Reference) | |||||
AG | 1.29 (0.67–2.45) | 0.97 (0.50–1.87) | 1.79 (0.81–3.96) | 2.38 (0.87–6.49) | 0.92 (0.46–1.84) | 1.31 (0.60–2.87) |
AA | 1.14 (0.24–5.46) | 0.48 (0.09–2.71) | <0.01 (<0.01–NA) | <0.01 (<0.01–NA) | 1.08 (0.22–5.25) | 0.89 (0.14–5.49) |
Dominant (GG vs. AG+AA) | 1.27 (0.68–2.38) | 0.91 (0.48–1.72) | 1.51 (0.70–3.29) | 2.09 (0.77–5.68) | 0.94 (0.48–1.83) | 1.26 (0.59–2.67) |
Recessive (GG+AG vs. AA) | 1.03 (0.22–4.81) | 0.49 (0.09–2.69) | <0.01 (<0.01–NA) | <0.01 (<0.01–NA) | 1.11 (0.23–5.29) | 0.80 (0.13–4.84) |
G allele | 1 (Reference) | |||||
A allele | 1.18 (0.70–1.97) | 0.87 (0.51–1.47) | 1.11 (0.59–2.09) | 1.40 (0.65–3.03) | 0.97 (0.56–1.68) | 1.14 (0.61–2.13) |
rs9803799 | ||||||
TT | 1 (Reference) | |||||
GT | 1.17 (0.42–3.24) | 0.83 (0.29–2.43) | 0.84 (0.22–3.31) | 1.82 (0.42–7.92) | 0.45 (0.14–1.51) | 0.50 (0.15–1.62) |
GG | 0.99 (0.06–16.53) | 0.95 (0.05–16.76) | <0.01 (<0.01–NA) | <0.01 (<0.01–NA) | 3.06 (0.18–52.68) | 1.38 (0.07–26.18) |
Dominant (TT vs. GT+GG) | 1.15 (0.43–3.03) | 0.85 (0.31–2.33) | 0.71 (0.19–2.68) | 1.64 (0.39–6.99) | 0.57 (0.19–1.72) | 0.57 (0.19–1.72) |
Recessive (TT+GT vs. GG) | 0.98 (0.06–16.27) | 0.97 (0.06–17.00) | <0.01 (<0.01–NA) | <0.01 (<0.01–NA) | 3.24 (0.19–55.53) | 1.46 (0.08–27.28) |
T allele | 1 (Reference) | |||||
G allele | 1.12 (0.46–2.74) | 0.87 (0.34–2.19) | 0.62 (0.18–2.20) | 1.45 (0.37–5.68) | 0.71 (0.26–1.92) | 0.65 (0.24–1.78) |
rs2746342 | ||||||
GG | 1 (Reference) | |||||
GT | 1.49 (0.75–2.99) | 1.63 (0.79–3.34) | 0.99 (0.41–2.36) | 2.87 (0.85–9.72) | 1.39 (0.66–2.95) | 1.94 (0.84–4.49) |
TT | 1.23 (0.47–3.22) | 1.35 (0.50–3.66) | 1.87 (0.59–5.93) | 1.05 (0.16–6.77) | 1.60 (0.58–4.43) | 1.33 (0.43–4.11) |
Dominant (GG vs. GT+TT) | 1.43 (0.73–2.78) | 1.56 (0.79–3.11) | 1.15 (0.50–2.63) | 2.39 (0.72–7.87) | 1.44 (0.70–2.95) | 1.77 (0.80–3.92) |
Recessive (GG+GT vs. TT) | 0.96 (0.40–2.27) | 0.99 (0.41–2.42) | 1.89 (0.68–5.25) | 0.52 (0.10–2.63) | 1.30 (0.53–3.20) | 0.89 (0.33–2.44) |
G allele | 1 (Reference) | |||||
T allele | 1.16 (0.74–1.81) | 1.22 (0.77–1.93) | 1.27 (0.73–2.21) | 1.23 (0.61–2.48) | 1.26 (0.79–2.02) | 1.25 (0.74–2.13) |
CI, confidence interval; CrSr, serum creatinine; eGFR, estimated glomerular filtration rate; FPG, fasting plasma glucose; HbA1c, hemoglobin A1c; NA, not available; OR, odds ratio; WC, waist circumference.
Several studies have revealed the physiological functions of AMPK. AMPK comprises 3 groups, which are AMPKα, β, and γ, and has been studied as a target for T2DM therapy [19]. The role of AMPK in reducing T2DM risk has been discovered. AMPK has a role in glucose uptake in skeletal muscle, suppressing lipogenesis, protein synthesis, lipolysis, stimulating anti-inflammatory effects, and inhibition of gluconeo-genesis [15, 34, 35]. Therefore, a mutation in AMPK might induce susceptibility of T2DM.
To our knowledge, this is the first study to investigate the association of
Genotype frequencies of rs2796498, rs9803799, and rs2746342 in our findings were in Hardy–Weinberg equilibrium. Therefore, those genetic variations remain relatively constant in our participant population [40], although there were low frequencies in wild-type rs2796498 and rs980799.
Of note, our study failed to discover any significant associations of FPG, HbA1c, serum creatinine, eGFR, blood pressure, or obesity status with genetic variations in rs2796498, rs9803799, or rs2746342. Several studies have investigated an association between
Similarly, Li et al. proposed that rs2746342 is associated with T2DM risk in a haplotype model, especially with increasing nephropathy. In addition, they studied rs2796498 and suggested it was significantly associated with susceptibility to T2DM [25]. Previously, rs9803799 was found to be correlated with metformin effectiveness [27]. However, only Shen et al. reported the
Even though AMPKα2 is correlated with hyperglycemia, our study could not ascertain the association of this genetic variation with FPG and HbA1c as glycemic indicators. Therefore, our findings suggest that we still could not combine glycemic indicators and genetic variation analysis as a diagnostic tool for T2DM in our population. Most notably, we found that our study's major allele is a risk factor of T2DM as shown in our previous study. Therefore, our results confirmed previous findings related to the association of
We could not detect any association of these genetic variations with declining renal function (eGFR <60 mL/min/1.73 m2) nor elevated blood pressure as a common comorbidity in patients with T2DM. The absence of apparent association might be caused by our study's recruitment of patients newly diagnosed T2DM. Progressive declining renal function and elevated blood pressure among patients with T2DM depend on T2DM duration [41, 42]. It is possible that for patients newly diagnosed with T2DM, as in our patient population, renal function has not yet changed, and blood pressure remains controlled. AMPK has a unique role in diabetic nephropathy by influencing metabolic memory, podocytes, proximal tubule cells, and fibrosis [43]. The findings that rs2746342 had the highest OR for renal function after adjusting for sex and age warrants further investigation. AMPK has been well-studied in causing arterial dilatation by SERCA and BKCA channels in vascular smooth muscle [44].
Our study did not find any significant association of
We suggest that the lack of association between
The present study is limited, first, by our relatively small sample size, and the findings should be confirmed using a larger sample. Second, we did not examine other factors that could influence clinical characteristics, such as diet, physical activities, and medication adherence. Third, we recognize that there is genetic heterogeneity in the Indonesian population. Accordingly, to reduce this heterogeneity, we conducted the study only in Yogyakarta where the majority of the people are Javanese. Therefore, in light of our study's limitations, readers should be cautious when generalizing our findings.