Skip to main content

Sociodemographic and genetic determinants of nonalcoholic fatty liver disease in type 2 diabetes mellitus patients

Abstract

Background

Nonalcoholic fatty liver disease (NAFLD) showed significant association with PNPLA3 rs738409 polymorphism in unrelated individuals. However, it is still unknown whether the relationship of NAFLD with PNPLA3 variant exists or not among subjects with type 2 diabetes mellitus (T2DM). Therefore, the study aimed to evaluate sociodemographic and genetic determinants of NAFLD in type 2 diabetics.

Methods

The cross-sectional analytical study was conducted at the Department of Molecular Biology, Virtual University of Pakistan, Lahore, Pakistan, during 2019–2020. A total of 153 known cases of T2DM were enrolled using convenience sampling. After excluding patients (n = 24) with HCV, alcoholism, or missing information, data from 129 eligible diabetics with and without NAFLD were analyzed using SPSS. Odds ratios using crosstabs and adjusted odds ratios using binary and multinomial logistic regression were calculated to measure the risk of NAFLD.

Results

Adults 18–35 years were 7.0%, 36–55 years were 64.3%, ≥ 56 years were 28.7%, and females were 66.7%. A total of 41.1% of patients had obesity, 52.7% had NAFLD, and 29.05% carried mutant G allele of rs738409 polymorphism. Among overall diabetics, NAFLD showed association with female (OR = 2.998, p = 0.007), illiterate (OR = 3.067, p = 0.005), and obese (OR = 2.211, p = 0.046) but not with PNPLA3 genotype under any model (all p = > 0.05). Among obese diabetics, NAFLD showed association with female (AOR = 4.010, p = 0.029), illiterate (AOR = 3.506, p = 0.037), GG + CG/CC (AOR = 3.303, p = 0.033), and GG/CG + CC (AOR = 4.547, p = 0.034) using binary regression and with female (AOR = 3.411, p = 0.051), illiterate (AOR = 3.323, p = 0.048), GG + CG/CC (AOR = 3.270, p = 0.029), and GG/CG + CC (AOR = 4.534, p = 0.024) using multinomial regression.

Conclusions

NAFLD and obesity were the most common comorbid diseases of T2DM. Gender female, being illiterate, and PNPLA3 rs738409 polymorphism were significant risk factors of NAFLD among obese diabetic patients.

Background

Nonalcoholic fatty liver disease (NAFLD) is a polygenic and heritable disorder [1], characterized by excess accumulation of fat in the liver parenchyma without history of alcoholism and hepatitis [2]. Its prevalence is 25.0% in the general adult population [3] and 50.0 to 70.0% in patients with type 2 diabetes mellitus (T2DM) [4]. NAFLD increases the risk of developing T2DM, whereas diabetes increases the progression of NAFLD to nonalcoholic steatohepatitis (NASH) and the risk of cirrhosis and hepatocellular carcinoma (HCC). Hence, a two-way relationship is present between NAFLD and T2DM [5].

Various demographic and genetic factors demonstrated greater risk for NAFLD. Age [6, 7], gender [6,7,8], ethnicity [9], metabolic syndrome (MetS), and its components including dyslipidemia, obesity, hypertension (HTN), and T2DM [10] were associated with the risk of NAFLD. Genetic factors such as the patatin-like phospholipase domain containing 3 (PNPLA3), the transmembrane 6 superfamily member 2 (TM6SF2), the membrane-bound O-acyltransferase domain containing 7 (MBOAT7), and the glucokinase regulator (GCKR) [11] were also associated with the risk of NAFLD. The product of human PNPLA3 gene, i.e., triacylglycerol lipase enzyme mediates hydrolysis of triacylglycerol (TAG) in adipocytes. However, the substitution of isoleucine with methionine at position 148 (I148M) causes a loss of function [12]. Genome-wide association studies (GWAS) identified several genes such as the glucokinase (GCK), the glucokinase regulator (GCKR), the transcription factor 7-like 2 (TCF7L2), the hepatocyte nuclear factor-1A (HNF1A), and fat mass and obesity-associated (FTO) gene playing a role in developing T2DM [13]. In people with T2DM, PNPLA3 I148M or rs738409 polymorphism showed significant association with liver fibrosis independent of body mass index (BMI) [14] and with the risk of increased liver fat content (LFC) independent of serum lipids [15], but not with susceptibility of NAFLD [16]. However, it revealed association with the risk and severity of NAFLD in meta-analyses of case-control studies on unrelated individuals [17,18,19]. The variations across studies in terms of characteristics of study population, selection of controls, laboratory methods, and statistical approaches lead to the ambiguity, whether or not these demographic and genetic factors, particularly rs738409 polymorphism, are associated with NAFLD in T2DM cases. Therefore, the present study aimed to determine the sociodemographic and genetic determinants of NAFLD in T2DM patients.

Methods

Ethical approval

The study was approved by the subcommittee of Advanced Studies and Research Board (ASRB) of the Faculty of Science and Technology, Virtual University of Pakistan, Lahore, Pakistan, vide letter no.VU/ASRB/214-5 dated December 02, 2019. Written informed consent was sought from all volunteer patients.

Design, setting, and duration of study

The cross-sectional analytical study was conducted at the Department of Molecular Biology, Virtual University of Pakistan, Lahore, Pakistan, during 2019–2020.

Characteristics, size, and selection of sample

A total of 153 known T2DM patients, of age 18–90 years, both male and female patients, belonging to any income group, caste or area of Pakistan, were enrolled by non-probability convenience sampling technique. None of 153 patients was reactive to hepatitis B surface antigen (HBsAg); however, patients reactive to anti-HCV antibodies (6.5%), patients with γ-GT levels ≥ 55 IU/L or history of alcoholism (8.5%), and patients with incomplete data (0.7%) were excluded.

Data collection procedure

An interviewer-administered close-ended proforma was used to record age, sex, education, family income, cigarette smoking, co-illness, duration of diabetes, and family history of diabetes. Body weight (in kilograms) and height (in meters) were measured to calculate the BMI using the formula as follows: formula: BMI (Kg/m2) = [weight (in kilograms)] divided by [height (in meters)]2. Waist circumference (WC) in inches was measured to exclude central obesity, and abdominal ultrasonography (USG) was performed for diagnosing fatty liver. Random plasma glucose was estimated by glucose oxidase-phenol aminophenazone (GOD-PAP) method, hemoglobin A1c (HbA1c) by ion-exchange resin method, and liver enzymes by the International Federation of Clinical Chemistry (IFCC) method. The screening of hepatitis B and C infection was done by immuno-chromatographic technique (ICT). PNPLA3 genotype was done by polymerase chain reaction (PCR), and restriction fragment length polymorphism (RFLP) method and PNPLA3 allele frequencies were measured by Hardy-Weinberg equilibrium.

PCR-RFLP

The genomic deoxyribonucleic acid (DNA) was extracted by using the GeneJET Whole Blood Genomic DNA Purification Mini Kit. The amplification of PNPLA3 gene was performed by PCR using the forward primer (5′-TGGGCCTGAAGTCCGAGGGT-3′) and the reverse primer (5′-CCGACACCAGTGCCCTGCAG-3′) as reported by Dutta (2011) [20]. The composition of PCR reaction mix and the optimized conditions are shown in Table 1. The restriction of amplified PNPLA3 gene product (333 bp) was performed by using the BtsCI enzyme. The composition of the RFLP reaction mix is also shown in Table 1. The reaction mix was kept at 55 °C for overnight incubation. The digestion was stopped by keeping the reaction mix at 80 °C for 20 min. The restricted PNPLA3 gene product was evaluated by 3.0% (w/v) agarose gel electrophoresis. Comparing with 50 bp DNA ladder, two DNA fragments of length 200 bp and 133 bp were labeled as PNPLA3 genotype CC (wild-type homozygous), one DNA fragment of length 333 bp as genotype GG (mutant homozygous), and three fragments of length 333 bp, 200 bp, and 133 bp as genotype CG (mutant heterozygous) (Fig. 1).

Fig. 1
figure 1

PCR-RFLP results of PNPLA3 rs738409 polymorphism

Table 1 Composition of PCR-RFLP reaction mix and PCR conditions

Continuous variables

Each continuous variable was categorized into two groups to calculate the risk for NAFLD. Age categorized into ≤ 50 and > 50 years, family income into ≤ 20000 and > 20000 PKR, and duration of diabetes into < 10 and ≥ 10 years. Similarly, WC of male categorized into < 40 and ≥ 40 inch, WC of female into < 35 and ≥ 35 inch, BMI into < 30.0 and ≥ 30.0 Kg/m2, plasma glucose level into < 200 and ≥ 200 mg/dl, HbA1c level into ≤ 8.0 and ≥ 8.0 %, and alanine aminotransferase (ALT) level into < 40 and ≥ 40 IU/L.

Statistical analysis

Statistical Package for Social Sciences (SPSS) version 26 was used for data analysis. Continuous variables were reported by using mean ± standard deviation and categorical variables by number (percent). PNPLA3 genotype CC (wild-type homozygous), genotype GG (mutant homozygous), and genotype CG (mutant heterozygous) were categorized into dominant, recessive, and codominant model. The dominant model (GG + CG/CC) hypothesizes that the combination of mutant homozygous allele and mutant heterozygous allele can increase the risk of disease. The recessive model (GG/CG + CC) hypothesizes that mutant homozygous alleles can increase the risk of disease. The codominant models (GG/CC and CG/CC) hypothesize that mutant homozygous allele and mutant heterozygous allele can independently increase the risk of disease. The study population was categorized into NAFLD vs. non-NAFLD groups. Independent sample t-test and chi-square test were used to compare the means and frequencies between groups, respectively. Crosstabs analyses were performed to calculate the odds ratios (OR) with 95% confidence intervals for NAFLD. Then, the study population was categorized into obese-NAFLD, NAFLD alone, obese alone, and nonobese non-NAFLD groups. Binary and multinomial logistic regression analyses were performed under codominant, dominant, and recessive models. For each regression model, a total of 10 covariates were entered at step 1 with outcome variables obese NAFLD. The covariates were as follows: age, sex, income, education, smoking, comorbidity, duration of diabetes, family H/o diabetes, HbA1c level, and PNPLA3 genotype. p-value ≤ 0.05 was considered as significant.

Results

Population characteristics

The participation of middle-aged adults (36–55 y) was the highest 64.3%, followed by older adults (≥ 56 y) 28.7% and young adults (18–35 y) 7.0%. The participation of females was twice higher than of males (66.7% vs. 33.3%). The respective means of family income and duration of diabetes were 25542 ± 18766 PKR/month and 6.8 ± 5.7 years. Among diabetics with any comorbidity (48.1%), the frequency of HTN was 38.8%, HTN + CVD 7.0% and CVD 2.3%. The frequency of central obesity was almost 3 times higher in females than in males (87.2% vs. 30.2%). The overall frequencies of overweight and obesity were 37.2% and 41.1%, respectively. Only 12.4% diabetics had good glycemic control (HbA1c < 7.0%). Overall, 52.7% diabetics were diagnosed with NAFLD, while 20.9% diabetics were carriers of PNPLA3 genotype GG, 16.3% of CG, and 62.8% of CC. Consequently, the frequency of diabetics carrying mutant allele G was 29.05%. Other characteristics of the study population are shown in Table 2.

Table 2 Sociodemographic and clinical characteristics of study population (n = 129)

NAFLD in T2DM

Overall (n = 129), means of WC (39.5 ± 3.2 vs. 38.0 ± 3.9 inch; p = 0.022) and BMI (30.9 ± 5.8 vs. 28.1 ± 5.2 Kg/m2; p = 0.006) were significantly higher in NAFLD vs. non-NAFLD cases. In crosstabs analyses, females (OR = 2.998, 95% CI 1.398–3.430; p = 0.007), females with central obesity (OR = 5.333, 95% CI 1.301–21.869; p = 0.019), illiterates (OR = 3.067, 95% CI 1.446–6.505; p = 0.005), and obese (OR = 2.211, 95% CI 1.075–4.545; p = 0.046) showed significantly higher risk for NAFLD among overall diabetics. However, genotypes GG vs. CC (OR = 1.831, 95% CI 0.748–4.478; p = 0.266), CG vs. CC (OR = 1.436, 95% CI 0.545–3.780; p = 0.624), GG + CG vs. CC (OR = 1.644, 95% CI 0.797–3.391; p = 0.243), and GG vs. CG + CC (OR = 1.700, 95% CI 0.711–4.067; p = 0.326) had higher risk for NAFLD, but the association was not significant among overall diabetics (see Table 3).

Table 3 Factors associated with NAFLD in T2DM patients (n = 129)

NAFLD in obese T2DM

In binary logistic regression analyses, a total of 10 covariates were entered at step 1 with outcome variables obese NAFLD (n = 34) versus all others (n = 95). None out of ten covariates showed risk for NAFLD under codominant models; females (AOR = 4.010, 95% CI 1.156–13.912; p = 0.029) and PNPLA3 genotype GG + CG (AOR = 3.303, 95% CI 1.099–9.920; p = 0.033) revealed significantly higher risk for NAFLD under dominant model, and illiterates (AOR = 3.506, 95% CI 1.080–11.375; p = 0.037) and PNPLA3 genotype GG (AOR = 4.547, 95% CI 1.123–18.408; p = 0.034) revealed significantly higher risk for NAFLD under recessive model (see Table 4).

Table 4 Binary logistic regression for NAFLD in obese T2DM patients under dominant and recessive models (n = 129)

In multinomial logistic regression analyses, a total of 10 covariates were entered at step 1 with outcome variables obese NAFLD (n = 34) versus obese alone (n = 19) or NAFLD alone (n = 34) or nonobese non-NAFLD (n = 42). In obese NAFLD versus obese alone and NAFLD alone, none out of ten covariates showed risk for NAFLD under any of three PNPLA3 genotype models. Similarly, in obese NAFLD versus nonobese non-NAFLD, none showed risk for NAFLD under codominant model; however, females (AOR = 3.411, 95% CI 0.997–11.671; p = 0.051), and PNPLA3 genotype GG + CG (AOR = 3.270, 95% CI 1.131–9.455; p = 0.029) revealed significantly higher risk for NAFLD under dominant model, and illiterates (AOR = 3.323, 95% CI 1.010–10.937; p = 0.048) and PNPLA3 genotype GG (AOR = 4.534, 95% CI 1.221–16.826; p = 0.024) revealed significantly higher risk for NAFLD under recessive model (see Table 5).

Table 5 Multinomial regression analysis for NAFLD in obese T2DM patients under dominant and recessive models (n = 76)

Discussion

NAFLD is a multisystem disease that not only results in progressive liver disease but also affects extrahepatic organs [21]. Various demographic [6,7,8,9], clinical [10], and genetic factors [11] showed association with the risk of NAFLD. Among genetic risk factors, PNPLA3 rs738409 polymorphism demonstrated significant association with NAFLD. However, the characteristics of the study population varied across studies [17,18,19]. It is still unknown whether the association of PNPLA3 rs738409 polymorphism with NAFLD exists or not among type 2 diabetic patients. Therefore, the present study aimed to evaluate the sociodemographic and genetic determinants of NAFLD in T2DM patients. The results showed that PNPLA3 rs738409 polymorphism had higher risk for NAFLD under codominant, dominant, and recessive models, but was not associated with NAFLD in Pakistani adults with T2DM (all p > 0.05). It is in agreement with the results of Hsieh et al. (2015) who reported that rs738409 polymorphism had no association with NAFLD in Taiwanese patients with T2DM (p = 0.344) [16].

The prevalence of NAFLD is 50.0 to 70.0% in diabetic subjects [4]. An equivalent higher frequency of NAFLD 52.7% is obtained in the present study. Among sociodemographic factors, age, gender, obesity, and ethnicity are the most frequently reported risk factors for NAFLD. Hu et al. (2018) observed an increasing trend between advance age and occurrence of NAFLD (OR = 1.049; p = 0.607), but after adjustment, age had an inverse relation with NAFLD in adult Chinese (OR = 0.844; p = 0.157) [7]. Ferreira et al. (2010) also reported that age was not related with NAFLD in adults with T2DM (57.1 ± 10.9 vs. 57.6 ± 9.5 years; p = 0.818) [6]. In the same way, the present study found no significant relation between age and NAFLD (OR = 1.459; p = 0.382); however, an opposite trend was observed, where the highest frequency of NAFLD 57.1% was in age < 40 years and the lowest 42.9% in age > 60 years. Hu et al. (2018) reported that gender male was significantly associated with NAFLD in Chinese adults (OR = 3.484; p = < 0.001) [7]. Oppositely, Summart et al. (2017) reported that females had higher risk (OR = 1.3, 1.2–1.4) for NAFLD in Thai adults (> 40 years) [8]. Differently, Ferreira et al. (2010) reported that gender female was not associated with NAFLD in adults with T2DM (p = 0.939) [6], whereas gender female revealed significantly higher risk for NAFLD in the present study (OR = 2.998; p = 0.007). After adjustment, risk for NAFLD was further increased in obese females (AOR = 4.010; p = 0.029). Noteworthy, females with central obesity revealed significantly greater risk for NAFLD than of females without central obesity (OR = 5.333; p = 0.019). Education status also showed a significant relationship with the risk for NAFLD in the present study. Illiterates had significantly higher risk for NAFLD (OR = 3.067; p = 0.005); and after adjustment, risk for NAFLD was further increased in obese illiterates (AOR = 3.506; p = 0.037). Similar significant relation between low education level and FLD (OR = 0.704; p = 0.001) or NAFLD had been reported in other studies [22, 23]. The present study also showed association of elevated ALT levels with PNPLA3 genotype GG/CC (p = 0.024), GG + CG/CC (p = 0.054), and GG/CG + CC (p = 0.018), which is consistent with other studies, where rs738409 polymorphism was associated with elevated AST levels (p = 0.039) [24] and ALT levels (d = 0.47) [17].

The human PNPLA3 gene is expressed in various tissues of the body mainly in the liver. Its gene product, i.e., triacylglycerol lipase enzyme, mediates hydrolysis of TAG in adipocytes. However, PNPLA3 rs738409 polymorphism causes loss of enzyme function resulting in the accumulation of TAG in the liver [12]. PNPLA3 variants are the most common genetic risk factors leading to NAFLD in obese across different ethnic groups [25]. PNPLA3 rs738409 variant had been reported as a significant risk factor for NAFLD in all genetic models [19] and in codominant model [17, 24, 26] among different study populations. However, the present study found no relationship between rs738409 polymorphism and risk of NAFLD in the whole T2DM group, which is in agreement with the findings of Hsieh et al. (2015) [16]. Differently, both binary and multinomial logistic regression analyses in the present study revealed that rs738409 polymorphism was significantly associated with NAFLD in obese diabetics, under dominant and recessive models (all p < 0.05).

Conclusions

NAFLD and obesity were the most common comorbid diseases of T2DM in the setting. Gender female, being illiterate, and PNPLA3 rs738409 polymorphism were significant risk factors of NAFLD among obese diabetic patients. Further research studies are needed to evaluate the association of PNPLA3 rs738409 polymorphism and other genetic factors with the NAFLD particularly among obese diabetics.

Availability of data and materials

The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.

Abbreviations

ALT:

Alanine aminotransferase

BMI:

Body mass index

DNA:

Deoxyribonucleic acid

FTO:

Fat mass and obesity-associated

GCK:

Glucokinase

GCKR:

Glucokinase regulator

GOD-PAP:

Glucose oxidase-phenol amino phenazone

GWAS:

Genome-wide association studies

HbA1c:

Hemoglobin A1c

HCC:

Hepatocellular carcinoma

HBsAg:

Hepatitis B surface antigen

HCV:

Hepatitis C virus

HNF1A:

Hepatocyte nuclear factor-1A

HTN:

Hypertension

ICT:

Immuno-chromatographic technique

IFCC:

International Federation of Clinical Chemistry

LFC:

Liver fat content

MBOAT7:

Membrane-bound O-acyltransferase domain containing 7

MetS:

Metabolic syndrome

NASH:

Nonalcoholic steatohepatitis

NAFLD:

Nonalcoholic fatty liver disease

OR:

Odds ratios

PCR:

Polymerase chain reaction

PNPLA3:

Patatin-like phospholipase domain containing 3

RFLP:

Restriction fragment length polymorphism

SPSS:

Statistical Package for Social Sciences

T2DM:

Type 2 diabetes mellitus

TAG:

Triacylglycerol

TCF7L2:

Transcription factor 7-like 2

TM6SF2:

Transmembrane 6 superfamily member 2

USG:

Ultrasonography

WC:

Waist circumference

References

  1. Anstee QM, Seth D, Day CP (2016) Genetic factors that affect risk of alcoholic and nonalcoholic fatty liver disease. Gastroenterology 150:1728–1744. https://doi.org/10.1053/j.gastro.2016.01.037

    Article  Google Scholar 

  2. Stefan N, Haring HU, Cusi K (2019) Non-alcoholic fatty liver disease: causes, diagnosis, cardiometabolic consequences, and treatment strategies. Lancet Diabetes Endocrinol 7:313–324. https://doi.org/10.1016/S2213-8587(18)30154-2

    Article  Google Scholar 

  3. Araujo AR, Rosso N, Bedogni G, Tiribelli C, Bellentani S (2018) Global epidemiology of non-alcoholic fatty liver disease/non-alcoholic steatohepatitis: what we need in the future. Liver Int 38:47–51. https://doi.org/10.1111/liv.13643

    Article  Google Scholar 

  4. Lee YH, Cho Y, Lee BW, Park CY, Lee DH, Cha BS et al (2019) Nonalcoholic fatty liver disease in diabetes. Part I: epidemiology and diagnosis. Diabetes Metab J43:31–45. https://doi.org/10.4093/dmj.2019.0011

    Article  Google Scholar 

  5. Xia MF, Bian H, Gao X (2019) NAFLD and diabetes: two sides of the same coin? Rationale for gene-based personalized NAFLD treatment. Front Pharmacol 10:877. https://doi.org/10.3389/fphar.2019.00877

    Article  Google Scholar 

  6. Ferreira VS, Pernambuco RB, Lopes EP, Morais CN, Rodrigues MC, Arruda MJ et al (2010) Frequency and risk factors associated with non-alcoholic fatty liver disease in patients with type 2 diabetes mellitus. Arq Bras Endocrinol Metabol 54:362–368. https://doi.org/10.1590/s0004-27302010000400004

    Article  Google Scholar 

  7. Hu XY, Li Y, Li LQ, Zheng Y, Lv JH, Huang SC et al (2018) Risk factors and biomarkers of non-alcoholic fatty liver disease: an observational cross-sectional population survey. BMJ Open 8:e019974. https://doi.org/10.1136/bmjopen-2017-019974

    Article  Google Scholar 

  8. Summart U, Thinkhamrop B, Chamadol N, Khuntikeo N, Songthamwat M, Kim CS (2017) Gender differences in the prevalence of nonalcoholic fatty liver disease in the northeast of Thailand: a population-based cross-sectional study. F1000Res 6:1630. https://doi.org/10.12688/f1000research.12417.2

    Article  Google Scholar 

  9. Younossi ZM, Koenig AB, Abdelatif D, Fazel Y, Henry L, Wymer M (2016) Global epidemiology of nonalcoholic fatty liver disease-meta-analytic assessment of prevalence, incidence, and outcomes. Hepatology 64:73–84. https://doi.org/10.1002/hep.28431

    Article  Google Scholar 

  10. Sookoian S, Castano GO, Burgueno AL, Gianotti TF, Rosselli MS, Pirola CJ (2009) A nonsynonymous gene variant in the adiponutrin gene is associated with nonalcoholic fatty liver disease severity. J Lipid Res 50:2111–2116. https://doi.org/10.1194/jlr.P900013-JLR200

    Article  Google Scholar 

  11. Kim DY, Park JY (2020) Genetic risk factors associated with NAFLD. Hepatoma Res 6:85. https://doi.org/10.20517/2394-5079.2020.96

    Article  Google Scholar 

  12. Huang Y, Cohen JC, Hobbs HH (2011) Expression and characterization of a PNPLA3 protein isoform (I148M) associated with nonalcoholic fatty liver disease. J BiolChem 286:37085–37093. https://doi.org/10.1074/jbc.M111.290114

    Article  Google Scholar 

  13. Sun X, Yu W, Hu C (2014) Genetics of type 2 diabetes: insights into the pathogenesis and its clinical application. Biomed Res Int 2014:926713. https://doi.org/10.1155/2014/926713

    Article  Google Scholar 

  14. Petit JM, Guiu B, Masson D, Duvillard L, Jooste V, Buffier P et al (2011) PNPLA3 polymorphism influences liver fibrosis in unselected patients with type 2 diabetes. Liver Int 31:1332–1336. https://doi.org/10.1111/j.1478-3231.2011.02566.x

    Article  Google Scholar 

  15. Cox AJ, Wing MR, Carr JJ, Hightower RC, Smith SC, Xu J et al (2011) Association of PNPLA3 SNP rs738409 with liver density in African Americans with type 2 diabetes mellitus. Diabetes Metab 37:452–455. https://doi.org/10.1016/j.diabet.2011.05.001

    Article  Google Scholar 

  16. Hsieh CJ, Wang PW, Hu TH (2015) Association of adiponectin gene polymorphism with nonalcoholic fatty liver disease in Taiwanese patients with type 2 diabetes. PLoS One 10:e0127521. https://doi.org/10.1371/journal.pone.0127521

    Article  Google Scholar 

  17. Dai G, Liu P, Li X, Zhou X, He S (2019) Association between PNPLA3 rs738409 polymorphism and nonalcoholic fatty liver disease (NAFLD) susceptibility and severity: a meta-analysis. Medicine (Baltimore) 98:e14324. https://doi.org/10.1097/MD.0000000000014324

    Article  Google Scholar 

  18. Salameh H, Hanayneh MA, Masadeh M, Naseemuddin M, Matin T, Erwin A et al (2016) PNPLA3 as a genetic determinant of risk for and severity of non-alcoholic fatty liver disease spectrum. J Clin Transl Hepatol 4:175–191. https://doi.org/10.14218/JCTH.2016.00009

    Article  Google Scholar 

  19. Xu R, Tao A, Zhang S, Deng Y, Chen G (2015) Association between patatin-like phospholipase domain containing 3 gene (PNPLA3) polymorphisms and nonalcoholic fatty liver disease: a HuGE review and meta-analysis. Sci Rep 5:9284. https://doi.org/10.1038/srep09284

    Article  Google Scholar 

  20. Dutta AK (2011) A new PCR-RFLP method for diagnosing PNPLA3 RS738409 polymorphism. Webmedcentral Gen 2:WMC002401

    Google Scholar 

  21. Byrne CD, Targher G (2015) NAFLD: a multisystem disease. J Hepatol 62:S47–S64. https://doi.org/10.1016/j.jhep.2014.12.012

    Article  Google Scholar 

  22. Le MH, Devaki P, Ha NB, Jun DW, Te HS, Cheung RC et al (2017) Prevalence of non-alcoholic fatty liver disease and risk factors for advanced fibrosis and mortality in the United States. PLoS One 12:e0173499. https://doi.org/10.1371/journal.pone.0173499

    Article  Google Scholar 

  23. Zhou YJ, Li YY, Nie YQ, Ma JX, Lu LG, Shi SL et al (2007) Prevalence of fatty liver disease and its risk factors in the population of South China. World J Gastroenterol 13:6419–6424. https://doi.org/10.3748/wjg.v13.i47.6419

    Article  Google Scholar 

  24. Mazo DF, Malta FM, Stefano JT, Salles APM, Gomes-Gouvea MS, Nastri ACS et al (2019) Validation of PNPLA3 polymorphisms as risk factor for NAFLD and liver fibrosis in an admixed population. Ann Hepatol 18:466–471. https://doi.org/10.1016/j.aohep.2018.10.004

    Article  Google Scholar 

  25. Lin YC, Chang PF, Chang MH, Ni YH (2014) Genetic variants in GCKR and PNPLA3 confer susceptibility to nonalcoholic fatty liver disease in obese individuals. Am J Clin Nutr 99:869–874. https://doi.org/10.3945/ajcn.113.079749

    Article  Google Scholar 

  26. Peng XE, Wu YL, Lin SW, Lu QQ, Hu ZJ, Lin X (2012) Genetic variants in PNPLA3 and risk of non-alcoholic fatty liver disease in a Han Chinese population. PLoS One 7:e50256. https://doi.org/10.1371/journal.pone.0050256

    Article  Google Scholar 

Download references

Acknowledgements

We thankfully acknowledge Mr. Asif Javed for the collection of blood specimens and estimation of biochemical assays and Ms. Ayesha and Ms. Gohar for their help in molecular analysis involved in the study.

Strengths and limitations

To the best of our knowledge, this is the first study reporting the association of PNPLA3 rs738409 polymorphism with NAFLD among Pakistani adults with T2DM. The Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines are used for writing this manuscript. The smaller size of sample was further decreased during regression analysis.

Funding

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

Author information

Authors and Affiliations

Authors

Contributions

MA: conceived and designed the study; performed data collection, entry, analysis, and interpretation; AND wrote original draft, critically reviewed, and revised the manuscript; AND approved final version to be published; AND take responsibility for the content and similarity index of the manuscript. AW: performed supervision and data interpretation, AND critically reviewed the manuscript, AND approved final version to be published, AND take responsibility for the content and similarity index of the manuscript. WN: performed patient selection and data collection, AND critically reviewed the manuscript, AND approved final version to be published, AND take responsibility for the content and similarity index of the manuscript. AB: performed supervision and data interpretation, AND critically reviewed the manuscript, AND approved final version to be published, AND take responsibility for the content and similarity index of the manuscript. MA: performed data analysis and interpretation, AND critically reviewed the manuscript, AND approved final version to be published, AND take responsibility for the content and similarity index of the manuscript. KA: performed lab work, AND critically reviewed the manuscript, AND approved final version to be published, AND take responsibility for the content and similarity index of the manuscript. QA: performed lab work, AND critically reviewed the manuscript, AND approved final version to be published, AND take responsibility for the content and similarity index of the manuscript.

Corresponding author

Correspondence to Muhammad Adnan.

Ethics declarations

Ethics approval and consent to participate

The study was approved by the subcommittee of Advanced Studies and Research Board (ASRB) of the Faculty of Science and Technology, Virtual University of Pakistan, Lahore, Pakistan, via letter no.VU/ASRB/214-5 dated December 02, 2019. Written informed consent was sought from all volunteer patients.

Consent for publication

Not applicable

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Adnan, M., Wajid, A., Noor, W. et al. Sociodemographic and genetic determinants of nonalcoholic fatty liver disease in type 2 diabetes mellitus patients. J Genet Eng Biotechnol 20, 68 (2022). https://doi.org/10.1186/s43141-022-00349-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s43141-022-00349-w

Keywords

  • Diabetes mellitus type 2
  • Nonalcoholic fatty liver disease
  • Obesity
  • Polymorphism genetic
  • Risk factors