Obesity is a growing health problem worldwide. An increase in visceral adiposity is a considerable risk factor for many metabolic and cardiovascular disorders. Several studies provide evidence that obesity and weight gain are associated with an increased risk of developing diabetes mellitus, high blood pressure, and high cholesterol, while weight loss decreases this risk [1].
Fatty acid binding proteins (FABPs) are intracellular lipid chaperones that regulate lipid trafficking and responses in cells and facilitate lipolysis in adipocytes. At least nine different isoforms have been identified in mammals [2]. FABP4 (A-FABP) is mainly expressed in adipocytes and macrophages and have a significant role in the development of insulin resistance and atherosclerosis. FABP4 plays an important role as an adipokine, and its increased circulating levels were associated with metabolic syndrome, obesity, type 2 diabetes mellitus, insulin resistance, hypertension, cardiovascular disease, atherosclerosis, cardiovascular events, alcoholic steatohepatitis, adipose tissue inflammation, diabetic nephropathy, adverse renal outcomes, mortality, and elevated low-density lipoprotein cholesterol, and reduced high-density lipoprotein cholesterol [2 - 4].
One prospective study investigated the association between circulating FABP4 levels and the development of subclinical atherosclerosis in type 2 diabetes patients over 8 years. They concluded that FABP4 levels predict the development of subclinical atherosclerosis in type 2 diabetic patients [5]. Another prospective study over 10 years showed that high FABP4 levels at baseline independently predicted the development of type 2 diabetes [6]. Both of these studies were performed on Chinese subjects.
FABP4 levels are substantially increased by lipolytic stimulation. High amounts of circulating fatty acids contribute to the development of insulin resistance. Long-term elevation of free fatty acids predispose for inhibition of insulin-stimulated glucose uptake and glycogen synthesis and β-cell death [7]. FABP4 deficiency in diet-induced and genetic obesity mice models resulted in increasing sensitivity to insulin and reducing hyperinsulinemia. FABP4-deficient adipocytes have attenuated lipolysis and fatty acid mobilization both in vitro and in vivo [8]. FABP4 has been shown to bind to and inhibit insulin receptor signaling [7].
Hundreds of compounds were synthesized in the past years to serve as FABP4 inhibitors. The purpose was to find drugs effective in the treatment of atherosclerosis and diabetes [9]. In preclinical studies using genetic mouse models, a potent and selective human and murine FABP4 inhibitor (BMS309403) was among them. This inhibitor has been shown to reduce inflammation and atherosclerosis, to improve lipid profiles and glucose homeostasis, and inhibit tumor progression and metastasis [7, 10, 11]. Some studies provided evidence that FABP4 might be a potential target for some drugs, and inhibitors of FABP4 may serve as therapeutic agents to treat some components of the metabolic syndrome. Anagliptin, a DPP-4 inhibitor, was found to reduce FABP4 concentration in patients with type 2 diabetes and dyslipidemia treated with statins. The effect was not related to hemoglobin A1c or LDL-cholesterol levels [12]. Metformin was found to inhibit the intracellular accumulation of lipids in macrophages and to reduce the expression of FABP4 [13]. A number of structurally different angiotensin II receptor blockers given to hypertensive patients reduced circulating FABP4 levels. This effect was not due to blocking their receptors on adipocytes [13, 14]. Atorvastatin, sitagliptin, and omega-3 fatty acids were found to decrease circulating FABP4 concentrations [14].
The aP2 (FABP4) gene locus was mapped to chromosome 8q21. It consists of 4 exons and encodes a 132-amino acid protein [15]. A functionally significant genetic variation at the aP2 locus was found to be associated with decreased adipose tissue expression of the aP2 gene. Subjects having the T-87C polymorphism had lower serum triglycerides and reduced risk of coronary heart disease and type 2 diabetes than individuals with the wild-type allele [14, 16]. Genomic DNA sequence of the promoter and coding regions identified 5 distinct SNPs. Two of these variants (C2600T and G4356C) were previously identified as rs8192688 and rs1051252, respectively. All of the SNPs were outside the coding region except the G4356C, which has been described as a silent variant on exon 4 [16]. Another missense SNP (rs1054135) of FABP4 gene is also located on chromosome 8 [3]. One study genotyped 7 SNPs near the FABP4 gene and measured FABP4 levels in older adults aged 65 years and older [17]. The authors concluded that there is an association between FABP4 gene SNPs and fasting glucose levels, but not fasting insulin or body mass index (BMI). The SNPs rs1054135, rs16909196, and rs16909187 were among the genotyped SNPs. The rs16909187 polymorphism was found to have no effect on FAPB4 concentration in Sorbs from Germany [18].
Few publications are available regarding the association of FABP4 gene polymorphisms with the development of type 2 diabetes mellitus and obesity. Such information is not available among Jordanians. Therefore, we designed this study to investigate the association of 3 SNPs in FABP4 gene (rs1054135, rs16909196 and rs16909187) with type 2 diabetes and obesity in Jordanians.
A total of 397 Jordanians were enrolled in the study. They were recruited from the National Center for Diabetes, Endocrinology and Genetics (NCDEG), Amman, Jordan. The study protocol was approved by The NCDEG Institutional Review Board. A written and signed consent form had been obtained from each participant before blood sampling and data collection.
This study was a cross sectional study where the study population has been divided into four groups: Group 1 constituted type 2 adult diabetic patients who are either obese or over-weight (BMI>25 kg/m²). Group 2 patients were type 2 adult diabetic patients with normal body weight (BMI<25 kg/m²). Group 3 patients were overweight and obese adults (BMI>25 kg/m²) with normal serum glucose and HBA1c < 5.7. Group 4 patients were normal weight adults (BMI<25 kg/m²) with normal serum glucose and (HBA1c < 5.7) and served as the control group.
Every subject donated 3-5 mL of venous blood in EDTA tubes.
Genomic DNA was extracted in the same day of blood withdrawal using Promega-Wizard (USA) genomic purification kit, according to the manufacturer recommendations. The absorbance was measured at a 260 nm wavelength (A260) by using Nano Drop Spectrophotometer (Thermo Fisher, USA) in the NCDEG and the ratio of the absorbance at A260/A280 nm wavelengths was measured. DNA extraction was considered adequate when the ratio was between 1.6-1.8 [19]. The concentration of all DNA samples were also measured. The precise length of genomic DNA was determined by gel electrophoresis using 1% agarose gel.
The FABP4 sequence containing (rs1054135, rs16909196 and rs16909187) SNPs was amplified by PCR followed by sequencing of the PCR product. The primers sequences were: CACGAGAGTTTATGAGAGAGC for forward primer and GCAACGCACTAAGACAGAG for reverse primer. Primers were designed by DNA-MAN program (www.lynnon.com/dnaman.html) and both selectivity and specificity were checked.
The PCR protocol constituted initial denaturation at 95ºC for 5 minutes followed by 39 cycles of denaturation at 95ºC for 30 seconds, annealing at 55ºC for 30 seconds, extension at 72ºC for 45 seconds and final extension at 72ºC for 7 minutes.
PCR products were visualized using agarose gel run in a tris-borate-EDTA (TBE) buffer. The agarose gel was stained using RedSafe™ dye, and PCR products’ bands were detected by loading them in the stained agarose gel that was placed under UV light. A 100-1500 base pair (bp) ladder (New England Biolabs, USA) was used as a marker to estimate the size of the amplified product. PCR products were sent for DNA Sanger Sequencing Analysis to Macrogen, South Korea. PCR product size was 530 bp.
χ2 was calculated using an online calculator for 2x2 contingency tables at http://www.socscistatistics.com/tests/chisquare/Default2.aspx. The odds ratios were calculated using the online statistical software available at https://www.medcalc.org/calc/odds_ratio.php. The Hardy-Weinberg equilibrium was assessed using the online calculator at https://www.wolframalpha.com/widgets/view.jsp?id=2fefa8b126607e29fe2990c722ee6cae. This final site calculates the parameter X. When X is greater or equal to zero, then there are significant differences between the observed and expected genotype frequencies, and the genotypes are not in Hardy-Weinberg equilibrium. The linkage disequilibrium (LD) analysis and estimation of haplotype diversity among the various groups were performed using Haploview 4.2 population genetic analysis software at https://haploview.software.informer.com/4.2/. The LD between the genetic variants was measured using Dʹand correlation coefficient (r2).
The study was performed on 397 subjects who were divided into 4 groups. Males were more than females (252 subjects, 63.5%). The age range was 18-80 years. The number of subjects in each group, the demographics, and clinical laboratory findings of subjects enrolled in the study are presented in (Table 1). The control group (group 4) has the lowest values for triglycerides, total cholesterol, LDL-cholesterol, hemoglobin A1C and body mass index. HDL-cholesterol was lowest in group 3 (Non-diabetic but overweight or obese). Concerning age, group 4 subjects were the youngest.
Demographic and clinical laboratory characteristics of subjects enrolled in the study.
| Characteristic | Group 1 (Obese or overweight diabetics) (n=98) | Group 2 (Diabetics with normal weight) (n=103) | Group 3 (Nondiabetic but overweight or obese) (n=102) | Group 4 (Normal weight and nondiabetics) (n=94) | p-value | |
|---|---|---|---|---|---|---|
| Mean age ± SDa (years) | 57.6 ± 9.8 | 52.5 ±16.1 | 47.8 ± 12.4 | 34.5 ± 14.8 | 0.00b | |
| Gender | Male | 46 | 64 | 83 | 59 | 0.00c |
| Female | 52 | 39 | 19 | 35 | ||
| Mean TG ± SD (mg/dL) | 184.7 ± 158.9 | 174.5 ± 136.0 | 223.05 ± 184.1 | 110.1 ± 66.1 | 0.00b | |
| Mean TC ± SD (mg/dL) | 187.3 ± 43.3 | 189.1 ± 44.6 | 197.6 ± 36.8 | 181.9 ± 44.4 | 0.00b | |
| Mean LDL ± SD (mg/dL) | 123.3 ± 36.4 | 121.8 ± 37.8 | 128.5 ± 33.8 | 108.2 ± 36.2 | 0.00b | |
| Mean HDL ± SD (mg/dL) | 43.5 ± 12.3 | 45.6 ± 19.4 | 39.3 ± 13.8 | 44.6 ± 11.4 | 0.016b | |
| Mean HbA1C ± SD (%) | 8.8 ± 8.8 | 7.8 ± 2.1 | 5.07 ± 0.39 | 4.8 ± 0.44 | 0.00b | |
| Mean BMI ± SD (Kg/m2) | 33.8 ± 6.5 | 22.1 ± 2.1 | 31.3 ± 4.6 | 21.5 ± 2.09 | 0.00b | |
TG (triglycerides), TC (total cholesterol), LDL (low density lipoprotein), HDL (high density lipoprotein), HBA1C (hemoglobin A1C), BMI (body mass index), m (height in meters),
SDa (standard deviation),
(ANOVA test),
(Pearson Chi-square test).
The genotype and allele frequencies of the three SNPs (rs1054135, rs16909196 and rs16909187) in the whole sample studied are presented in (Table 2). The minor allele frequencies were 0.068, 0.202, and 0.199 for rs1054135, rs16909196 and rs16909187, respectively.
Genotypes and allele frequencies of rs1054135, rs16909196 and rs16909187 SNPs among all groups combined.
| rs1054135 | |||
| Genotype | Number of subjects | Frequency | |
| Homozygous major allele | GG | 348 | 0.877 |
| Heterozygous | GA | 44 | 0.111 |
| Homozygous minor allele | AA | 5 | 0.013 |
| Total | 397 | 1.0 | |
| Allele | Number of copies | Frequency | |
| G | 740 | 0.932 | |
| A | 54 | 0.068 | |
| Total | 794 | 1.0 | |
| rs16909196 | |||
| Genotype | Number of subjects | Frequency | |
| Homozygous major allele | AA | 259 | 0.652 |
| Heterozygous | AT | 116 | 0.292 |
| Homozygous minor allele | TT | 22 | 0.055 |
| Total | 397 | 1.0 | |
| Allele | Number of copies | Frequency | |
| A | 634 | 0.798 | |
| T | 160 | 0.202 | |
| Total | 794 | 1.0 | |
| rs16909187 | |||
| Genotype | Number of subjects | Frequency | |
| Homozygous major allele | CC | 261 | 0.657 |
| Heterozygous | CT | 114 | 0.287 |
| Homozygous minor allele | TT | 22 | 0.055 |
| Total | 397 | 1.0 | |
| Allele | Number of copies | Frequency | |
| C | 636 | 0.801 | |
| T | 158 | 0.199 | |
| Total | 794 | ||
The Hardy-Weinberg equilibrium was calculated in all the studied subjects together and in the control group alone for each of the 3 SNPs. The results showed that the 3 SNPs followed the Hardy-Weinberg equilibrium in the control group who were non-obese and non-diabetic, and that equilibrium was not affected by the disease states. The results are presented in (Table 3).
Assessment of the Hardy-Weinberg equilibrium of the three SNPs in the study sample as a whole and in the control group (group 4) alone.
| SNP | Genotypes | The whole studied group (n=397) | X* parameter | Control group (n=94) | X* parameter | ||
|---|---|---|---|---|---|---|---|
| Observed | Expected | Observed | Expected | ||||
| rs1054135 | GG | 0.8770 | 0.8687 | - 3.8245 | 0.8400 | 0.8263 | - 3.8140 |
| GA | 0.1110 | 0.1276 | 0.1380 | 0.1654 | |||
| AA | 0.0130 | 0.0047 | 0.0220 | 0.0083 | |||
| rs16909196 | AA | 0.6520 | 0.6374 | - 3.8333 | 0.5740 | 0.5700 | - 3.8410 |
| AT | 0.2920 | 0.3211 | 0.3620 | 0.3670 | |||
| TT | 0.0550 | 0.0404 | 0.0640 | 0.0600 | |||
| rs16909187 | CC | 0.6570 | 0.6414 | - 3.8319 | 0.5640 | 0.5625 | - 3.8414 |
| CT | 0.2870 | 0.3181 | 0.3720 | 0.3750 | |||
| TT | 0.0550 | 0.0394 | 0.064 | 0.0625 | |||
According to the online calculator used, when X is greater than or equal to zero, then there are significant changes between the observed and expected genotype frequencies, and the genotypes are not in Hardy-Weinberg equilibrium.
The distribution of the genotypes of the 3 SNPs among the 4 groups studied is presented in (Table 4). The genotype distribution was similar for the 3 SNPs in group 1 (Obese or overweight diabetics) compared to group 4 (normal weight non-diabetics) and in group 2 (diabetics with normal weight) compared to group 4. The distribution of rs1054135 genotypes were also similar in group 3 (Non-diabetic but overweight or obese) compared to group 4. The distribution of the genotypes of rs16909196 and rs16909187 was similar in group 2 compared to group 4. However, the distribution of the genotypes in group 3 was significantly different from that in group 4. Regarding rs16909196, the homozygous wild type genotype (AA), was significantly higher in group 3 than in group 4 (OD = 2.26, 95% CI 1.24-4.14, z score 2.649 and p-value 0.01). There was no difference in the homozygous minor allele genotype (TT) between the two groups (OD = 0.29, 95% CI 0.06-1.49, z score 1.479 and p-value 0.14). The heterozygous genotype (AT) was higher in the control group (OD = 0.52, 95% CI 0.28-0.96, z score 2.08 and p-value 0.04). This result may indicate that the wild type homozygous genotype AA of rs16909196 SNP may be a risk factor for obesity, while the heterozygous AT may be protective. With regard to rs16909187, the homozygous wild type genotype CC was significantly higher in group 3 than in group 4 (OD = 2.17, 95% confidence interval 1.18-3.96, z score 2.505 and p-value 0.01). There was no difference in the homozygous minor allele genotype (TT) between the two groups (OD = 0.29, 95% CI 0.06-1.49, z score 1.479 and p-value 0.14). Concerning the heterozygous genotype (CT), it was higher in the control group (OD = 0.54, 95% CI 0.29-1.01, z score 1.926 and p-value 0.05), but it did not each statistical significance. This result may indicate that the wild type homozygous genotype CC of rs16909187 SNP may be a risk factor for obesity, while the heterozygous may be protective. The association between (rs1054135, rs16909196 and rs16909187) haplotypes and obesity is presented in (Table 5). The haplotype of the wild type allele of the 3 SNPs (GAC) is significantly associated with obesity or overweight (p-value = 0.02). A higher proportion of the obese or overweight subjects had this haplotype than control group. While the haplotype composed of the wild type allele of rs1054135 and the recessive alleles of rs16909196 and rs16909187 (GTT) was found at higher proportion in the control group when compared to the obese or overweight group, but the difference did not reach statistical significance (p = 0.05). The haplotypes AAC was not significantly associated with obesity and overweight and diabetes (p = 0.33).
Genotype distribution of FABP4 SNPs rs1054135, rs16909196, and rs16909187 among the four study groups.
| SNPs | rs1054135 | rs16909196 | rs16909187 | |||||||
|---|---|---|---|---|---|---|---|---|---|---|
| Genotypes | GG | GA | AA | AA | AT | TT | CC | CT | TT | |
| Group 1 n=98 | 88 | 8 | 2 | 62 | 29 | 7 | 63 | 28 | 7 | |
| Group 2 n=103 | 90 | 12 | 1 | 68 | 28 | 7 | 68 | 28 | 7 | |
| Group 3 n=102 | 91 | 11 | 0 | 76 | 24 | 2 | 76 | 24 | 2 | |
| Group 4 n=94 | 79 | 13 | 2 | 53 | 35 | 6 | 54 | 34 | 6 | |
| Groups 1 vs 4 | Z score | 1.176 | 1.245 | 0.042 | 0.972 | 1.123 | 0.21 | 0.971 | 1.124 | 0.210 |
| P value | 0.24 | 0.21 | 0.97 | 0.33 | 0.26 | 0.83 | 0.33 | 0.2 | 0.83 | |
| Odds ratio | 1.67 | 0.55 | 0.96 | 1.33 | 0.71 | 1.13 | 1.33 | 0.71 | 1.13 | |
| Groups 2 vs 4 | Z score | 0.668 | 0.458 | 0.646 | 1.385 | 1.506 | 0.117 | 1.236 | 1.353 | 0.117 |
| P value | 0.50 | 0.65 | 0.52 | 0.17 | 0.13 | 0.91 | 0.22 | 0.18 | 0.91 | |
| Odds ratio | 1.31 | 0.82 | 0.45 | 1.5 | 0.63 | 1.07 | 1.44 | 0.66 | 1.07 | |
| Groups 3 vs 4 | Z score | 1.061 | 0.650 | 1.100 | 2.649 | 2.080 | 1.479 | 2.505 | 1.926 | 1.479 |
| P value | 0.29 | 0.52 | 0.27 | 0.01 | 0.04 | 0.14 | 0.01 | 0.05 | 0.14 | |
| Odds ratio | 1.57 | 0.75 | 0.18 | 2.26 | 0.52 | 0.29 | 2.17 | 0.54 | 0.29 | |
Comparison of (rs1054135, rs16909196, and rs16909187) haplotypes between control group and other groups.
| rs1054135, rs16909196, and rs16909187 haplotypes | GAC | GTT | AAC | |
|---|---|---|---|---|
| Group 1 (Obese or overweight diabetics) | 0.732 | 0.202 | 0.049 | |
| Group 2 (Diabetics with normal weight) | 0.723 | 0.199 | 0.068 | |
| Group 3 (Non-diabetic but overweight or obese) | 0.809 | 0.137 | 0.054 | |
| Group 4 (Control) (Normal weight nondiabetics) | 0.660 | 0.245 | 0.090 | |
| Groups 1 vs 4 | z score | 1.085 | -0.716 | -1.12 |
| p value | 0.28 | 0.47 | 0.26 | |
| Groups 2 vs 4 | z score | 0.958 | -0.777 | -0.574 |
| p value | 0.34 | 0.44 | 0.57 | |
| Groups 3 vs 4 | z score | 2.369 | -1.93 | -0.979 |
| p value | 0.02 | 0.05 | 0.33 | |
The linkage disequilibrium (LD) analysis, estimation of haplotype diversity among the four groups was performed using Haploview 4.2 population genetic analysis software (Table 6). The D′ and r2 values obtained were used to assess linkage disequilibrium. The results show that rs16909196, and rs16909187 SNPs have a strong linkage disequilibrium in the control group (D′ = 1, r2=0.97). These 2 SNPs remained in strong linkage disequilibrium in the other 3 groups as well. The SNP rs1054135 was not in linkage disequilibrium with either rs16909187 (D′ = 1, r2=0.03) or rs16909196 (D′ = 1, r2=0.03) SNPs.
Linkage disequilibrium of FABP4 genetic variants (rs1054135, rs16909187, and rs16909196) in the four study groups using the Haploview program.
| Study Group | FABP4 Gene Variants | D′ (Linkage disequilibrium parameter) | R2 (Correlation Coefficient) |
|---|---|---|---|
| Group 1: | rs1054135 and rs16909187 | 0.046 | 0.0 |
| rs1054135 and rs16909196 | 0.084 | 0.0 | |
| rs16909187 and rs16909196 | 1.0 | 0.97 | |
| Group 2: | rs1054135 and rs16909187 | 1.0 | 0.019 |
| rs1054135 and rs16909196 | 1.0 | 0.019 | |
| rs16909187 and rs16909196 | 0.97 | 0.94 | |
| Group 3: | rs1054135 and rs16909187 | 1.0 | 0.009 |
| rs1054135 and rs16909196 | 1.0 | 0.009 | |
| rs16909187 and rs16909196 | 1.0 | 1.0 | |
| Group 4: | rs1054135 and rs16909187 | 1.0 | 0.03 |
| rs1054135 and rs16909196 | 1.0 | 0.03 | |
| rs16909187 and rs16909196 | 1.0 | 0.97 |
Concerning the association of FABP4 gene polymorphisms (rs1054135, rs16909196 and rs16909187) with serum concentrations of LDL, HDL, TC and TG among the studied groups, only the rs1054135 SNP showed significant association with higher LDL levels in the third group subjects who are overweight or obese (p-value=0.03). However, there was no significant association between rs1054135 SNP and HDL, TC and TG levels. The rest of the results in this regard are not presented.
This study examined the association of (rs1054135, rs16909196 and rs16909187) polymorphisms in the FABP4 gene with the susceptibility to T2DM, obesity, and the relation of these variants with serum concentrations of total cholesterol, LDL, HDL, and triglycerides.
The observed minor allele frequency of rs1054135 of 0.09 in the control group is not statistically different from that of the whole studied sample (0.068).
The genotypes of the three SNPs are in Hardy-Weinberg equilibrium in our control group and remained so in the whole studied sample.
Our findings, showing no association between rs1054135 and both T2DM and obesity, are consistent with what was reported among postmenopausal women, 50-79 years old of multiple ethnicities (Whites, African Americans, Hispanic/Latino Americans, Asian, American-Asian Pacific Islanders) who were enrolled in the “Women’s Health Initiative” study [20]. The study examined 11 SNPs including rs1054135, and the results did not support the association of these SNPs with increased risk of type 2 diabetes mellitus. On the other hand, our results were not in agreement with those of two other studies. A study was performed on 309 children, between 5-7 years of age, in the United States. The children were divided into obese and non-obese groups. Most of the children were white, but the study also included African-Americans. Four SNPs were tested (rs1051231, rs2303519, rs16909233 and rs1054135) and fasting plasma glucose, lipids, insulin, hsCRP, and FABP4 levels were measured. HOMA (homeostasis model assessment) was used as a measure of insulin sensitivity. The frequency of rs1054135 allelic variant was increased in obese children (3). A second study, performed among Chinese teenagers in Shanghai, concluded that the rs1054135 minor allele frequency was significantly lower among obese females compared with normal weight females [21].
Concerning rs16909187 and rs16909196, we found that the homozygous wild type genotype (CC) of rs16909187 and the homozygous wild type genotype (AA) of rs16909196 were significantly associated with obesity. However, rs16909187 and rs16909196 minor allele (T) may be protective against obesity. The number of subjects with the homozygous minor allele genotypes is too small to draw conclusions in this regard. The heterozygous minor allele genotypes hint to such protection. The alleles of these two SNPs were not associated with type 2 diabetes mellitus.
The haplotypes’ frequencies of rs1054135, rs16909196, and rs16909187 were compared among the various studied groups. Only haplotypes GAC and GTT were related to obesity but not with T2DM. The frequency of the GAC haplotype was higher in the overweight and obese group, while that of GTT haplotype was higher in the control group.
The present study showed a significant association between homozygous (GG) and heterozygous (GA) wild type genotypes of rs1054135 and high level of LDL among group 3 (non-diabetic but obese) in comparison with controls (p-value = 0.03). However, there was no significant association between rs1054135 SNP and the serum levels of total cholesterol, HDL and triglycerides among all groups. The rs16909187 and rs16909196 SNPs were not found to be associated with total cholesterol, LDL, HDL, or triglycerides in any of the groups studied.
The rs16909187 and rs16909196 SNPs were in linkage disequilibrium among all studied groups. However, none of these two SNPs were in linkage disequilibrium with rs1054135. This agrees with what has been previously reported: rs1054135 has no linkage disequilibrium with other SNPs in FABP4 gene [20].
The limitations of our study are the small sample size studied (397 subjects in the four groups) and the reduced number of females compared to males in all of the groups studied.
In conclusion, the 3 SNPs studied followed Hardy-Weinberg equilibrium. There was no association between (rs1054135, rs16909196 and rs16909187) polymorphisms and T2DM. Both rs16909196 and rs16909187 SNPs homozygous wild type genotypes were associated with obesity, while the minor allele genotypes may be protective against obesity. The rs1054135 SNP was not associated with obesity. The rs1054135 SNP was significantly associated with an increase in LDL serum level among obese subjects, but the other two SNPs (rs16909196 and rs16909187) were not associated with any significant changes in lipid profile parameters. There was linkage disequilibrium between the rs16909187 and rs16909196 SNPs in the control and the other studied groups. The rs1054135 SNP was not in linkage disequilibrium with either of the two SNPs.