Next Article in Journal
Long-Term Outcome of HBV-Infected Patients with Clinically Significant Portal Hypertension Achieving Viral Suppression
Next Article in Special Issue
Sex Differences in Response to Treatment with Glucagon-like Peptide 1 Receptor Agonists: Opportunities for a Tailored Approach to Diabetes and Obesity Care
Previous Article in Journal
Intra- and Early Postoperative Evaluation of Malperfused Areas in an Irradiated Random Pattern Skin Flap Model Using Indocyanine Green Angiography and Near-Infrared Reflectance-Based Imaging and Infrared Thermography
Previous Article in Special Issue
Evidence for Cardiorenal Protection with SGLT-2 Inhibitors and GLP-1 Receptor Agonists in Patients with Diabetic Kidney Disease
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
JPM
Volume 12
Issue 2
10.3390/jpm12020238
jpm-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Immune Response and Lipid Metabolism Gene Polymorphisms Are Associated with the Risk of Obesity in Middle-Aged and Elderly Patients

by
Anastasia Ponasenko
1,*,
Maxim Sinitsky
1,
Varvara Minina
2,
Anna Vesnina
3,
Maria Khutornaya
1,
Alexander Prosekov
3 and
Olga Barbarash
4
1
Laboratory of Genome Medicine, Research Institute for Complex Issues of Cardiovascular Diseases, 650002 Kemerovo, Russia
2
Department of Genetic and Fundamental Medicine, Kemerovo State University, 650000 Kemerovo, Russia
3
Department of Bionanotechnology, Kemerovo State University, 650000 Kemerovo, Russia
4
Research Institute for Complex Issues of Cardiovascular Diseases, 650002 Kemerovo, Russia
*
Author to whom correspondence should be addressed.
J. Pers. Med. 2022, 12(2), 238; https://doi.org/10.3390/jpm12020238
Submission received: 30 November 2021 / Revised: 4 February 2022 / Accepted: 7 February 2022 / Published: 8 February 2022
(This article belongs to the Special Issue Obesity and Diabetes: Genetic Markers and Pharmacological Interventions)
Versions Notes

Abstract

:
More than two billion people around the world are overweight or obese. Even in apparently healthy people, obesity has a potent effect on their quality of life. Experimental data indicate the role of infectious agents in systemic inflammation, revealing a correlation between the dietary habits of people with obesity and the level of systemic inflammation mediators, serum lipid concentration, and hormonal and immune status. This study aimed to determine the association of immune response and lipid metabolism gene polymorphisms with the risk of obesity. This study included 560 Caucasian participants living in Western Siberia (Russian Federation). A total of 52 polymorphic sites in 20 genes were analyzed using the 5′ TaqMan nuclease assay. Four risk-associated polymorphic variants were discovered—two variants in immune response genes (IL6R rs2229238, OR = 1.92, 95% CI = 1.36–2.7, p = 0.0002 in the dominant model; IL18 rs1946518, OR = 1.45, 95% CI = 1.03–2.04, p = 0.033 in the over-dominant model) and two variants in lipid metabolism genes (LPA rs10455872, OR = 1.86, 95% CI = 1.07–3.21, p = 0.026 in the log-additive model; LEPR rs1137100, OR = 2.88, 95% CI = 1.52–5.46, p = 0.001 in the recessive model). Thus, polymorphisms in immune response and lipid metabolism genes are potentially associated with the modification of obesity risk in the Caucasian population.

1. Introduction

Despite specific public health policies targeting the obesity epidemic, more than two billion people around the world are overweight or obese [1]. It is predicted that one-fifth of the working-age population will be obese by 2025; the increase in the number of obese patients is accompanied by significant socio-economic losses [2], which determine the improvement of treatment and diagnosis of this pathology, as well as the assessment of individual susceptibility to its development.
Obesity is a multifactorial disease characterized by excessive accumulation of adipose tissue, accompanied by a low-grade chronic inflammation. The triggers of this inflammation are poorly studied, but it is known that the degree of inflammation correlates with the severity of obesity-associated pathologies, which suggests that understanding the inflammatory response may improve the treatment strategies of such diseases [3,4]. In addition to inflammation, impaired lipid metabolism can be a trigger of obesity [5,6].
It is known that the activity of molecules involved in inflammation and lipid metabolism is genetically determined. Genome-wide association studies (GWAS) allow identifying genetic variants associated with susceptibility to overweight and obesity [7]. In adults, the strongest associations with the risk of obesity were discovered for single-nucleotide polymorphisms (SNPs) in the FTO, MC4R, TMEM18, TNNI3K, SEC16B, GNPDA2, POMC, RPGRIP1L, IRX3, and IRX5 genes [8,9,10,11]. Moreover, it was shown that the genetic susceptibility to obesity is modified in a population-related manner [12,13,14]. Despite the previously obtained results, some issues related to the genetics of obesity, including the role of SNPs in genes involved in the inflammation and lipid metabolism pathways mediating obesity, are still poorly investigated, and the available results are contradictory. Therefore, understanding the role of genetic factors controlling the different pathways underlying the pathogenesis of obesity, particularly inflammation and lipid metabolism, plays a very important role in the development of personalized prevention strategies, especially in at-risk groups.
This study aimed to determine risk-associated polymorphic variants in immune response and lipid metabolism genes in obese middle-aged and elderly Caucasian patients.

2. Materials and Methods

2.1. Group Description

The present study included 560 Caucasian individuals aged 44 to 75 years (mean age of 59 years) that were long-term residents (at least three generations) in Western Siberia (Russian Federation) and undergoing a screening examination at the Research Institute for Complex Issues of Cardiovascular Diseases (Kemerovo, Russian Federation). Patients with cancer, autoimmune and mental diseases, and acute or exacerbated chronic infections associated with the inflammatory process were excluded from the study to avoid confounding effects. According to the World Health Organization age standards (2015), the patients included in this study were classified into two age groups: middle-aged (age ≤60 years) and elderly (age >60 years).
Obesity was defined as a body mass index (BMI) of 30 kg/m2 or greater. In the studied group, the mean BMI was 28 kg/m2, ranging from 17 kg/m2 to 41 kg/m2. According to this stratification, 220 individuals (39%) were obese, and 340 individuals (61%) had a normal BMI. The complete characteristics of patients included in this study are presented in Table 1.
The design of this study was approved by the Local Ethical Committee of the Research Institute for Complex Issues of Cardiovascular Diseases (Kemerovo, Russian Federation). All individuals included in this study provided written informed consent to participate in the examination. This study was performed in accordance with the World Medical Association Declaration of Helsinki (ethical principles for medical research involving human participants with amendments in 2000) and Good Clinical Practice.

2.2. Molecular Genetic Testing

Genomic DNA was isolated using the routine phenol–chloroform extraction method from whole blood collected from the cubital vein in vacuum tubes with K3EDTA and stored at −80 °C until the next stage of the experiment.
SNPs were selected according to the following criteria: (i) location within immune response and lipid metabolism genes; (ii) minor allele frequency >5% in Caucasian populations; (iii) functional consequences and related studies on their role in obesity pathogenesis. Accordingly, we selected 52 SNPs in 20 genes. The complete characteristics of the selected SNPs are presented in Table 2.
Molecular genetic testing was performed by allele-specific real-time polymerase chain reaction (real-time PCR) with fluorescently labeled TaqMan probes (Applied Biosystems, Waltham, MA, USA). Per each analyzed sample, 10 μL of reaction mixture containing 1.25 μL of appropriate TaqMan probe (Applied Biosystems, Waltham, MA, USA), 5 μL of TaqMan™ Universal PCR Master Mix (Applied Biosystems, Waltham, MA, USA), 1.75 μL of DNase-free water, and 2 μL of 100 ng genome DNA template was prepared. The amplification was performed using the ViiA 7 Real-Time PCR System (Applied Biosystems, Waltham, MA, USA) in 96-well PCR plates as follows: 10 min at 95 °C (one cycle), 15 s at 95 °C (one cycle), and 60 s at 60 °C (40 cycles). As a negative control, a reaction mixture without the genomic DNA template was used. Results of genotyping were analyzed using the QuantStudio™ Real-Time PCR Software v.1.3 (Applied Biosystems, Waltham, MA, USA). The quality of the PCR was evaluated by repeated genotyping of 10% of the samples.

2.3. Statistical Analysis

Statistical analysis was performed using STATISTICA 10.0 Software (StatSoft, Tulsa, OK, USA). Quantitative data were tested using the Yates’ chi-square test or the Fisher exact test. The genotyping results were analyzed using the SNPStats web tool. The most likely inheritance model for each specific gene polymorphism was determined using Akaike’s information criterion (AIC). The results are presented as the odds ratio (OR) and the 95% confidence interval (CI) calculated using five inheritance models (codominant, dominant, recessive, over-dominant, and log-additive). The differences were considered statistically significant at p < 0.05.

3. Results

Four SNPs associated with an increased risk of obesity were discovered—two variants in immune response genes (IL6R rs2229238, OR = 1.92, 95% CI = 1.36–2.7, p = 0.0002 in the dominant model; IL18 rs1946518, OR = 1.45, 95% CI = 1.03–2.04, p = 0.033 in the over-dominant model) and two variants in lipid metabolism genes (LPA rs10455872, OR = 1.86, 95% CI = 1.07–3.21, p = 0.026 in the log-additive model; LEPR rs1137100, OR = 2.88, 95% CI = 1.52–5.46, p = 0.001 in the recessive model). It was inferred that the A/A genotype (recessive model) of the TNF gene (rs1800629) was associated with a high risk of presenting an obesity phenotype (OR = 10.29, 95% CI 1.22–86.59, p = 0.0081). However, reliable conclusions concerning the pathogenetic effect of this SNP could not be drawn, since this genotype was discovered in only 0.3% of nonobese patients. Moreover, the recessive models of the CXCL8 gene (rs4073 and rs2227306) were characterized by a protective effect (OR = 0.56, 95% CI = 0.37–0.86, p = 0.0065 and OR = 0.49, 95% CI = 0.31–0.79, p = 0.0025, respectively (Table 3).
After stratification by gender, we found that the T/C genotype in the IL6R gene (rs2229238) and the G/G genotype in the LEPR gene (rs1137100) were associated with an increased risk of obesity only in males (OR = 2.27, 95% CI = 1.40–3.70, p = 0.0003 and OR = 2.80, 95% CI = 1.27–6.17, p = 0.028, respectively), while the T/G genotype in the IL18 gene (rs1946518) was associated with an increased risk of obesity only in females (OR = 2.02, 95% CI = 1.07–3.83, p = 0.03). A protective effect was shown for the T/T genotype in the CXCL8 gene (rs2227306) in females (OR = 0.44, 95% CI = 0.20–0.95, p = 0.04) and the G/G genotype in the IL1RL1 gene (rs11685424) in males (OR = 0.46, 95% CI = 0.23–0.94, p = 0.023) (Table 4).
In the group of middle-aged patients (age ≤60 years), the G/G genotype in the LEPR gene (rs1137100) was associated with a fourfold increased risk of obesity (OR = 4.23, 95% CI = 1.74–10.28, p = 0.03). The same tendency was shown for the T allele in the IL6R gene (rs2229238). Among elderly patients (age >60 years), a risk association was shown for the G/A genotype in the CRP gene (rs1130864) (OR = 1.98, 95% CI = 1.03–3.83, p = 0.01) and for the C allele in the TLR2 gene (rs3804099). Furthermore, the middle-aged patients with the A/A and T/T genotypes in the CXCL8 gene (rs4073 and rs2227306, respectively) had a twofold decreased risk of obesity (Table 5). In contrast, the C allele in the IL6R gene (rs2228145) was associated with a decreased risk of obesity in middle-aged patients, whereas this allele acquired was associated with an increased risk of obesity development in elderly patients (Table 5).

4. Discussion

Chronic inflammation is involved in the pathogenesis of many diseases including obesity, type 2 diabetes mellitus, and atherosclerosis [15]. Danger signals caused by molecular patterns of microbial agents and endogenous damage factors (PAMPs and DAMPs) trigger the assembly of innate immunity intracellular sensors, which leads to the activation of caspase-1 and the production of proinflammatory cytokines IL1β and IL18 [16]. Interleukin-18 (IL18) is an important proinflammatory cytokine involved in the pathogenesis of acute coronary events and type 2 diabetes mellitus [17], and it is associated with the modification of obesity and metabolic syndrome risk, although the underlying mechanisms remain unclear [18]. It is known that IL18R and IL18 expression in adipose tissue is enhanced in nondiabetic obesity, and it is associated with a proinflammatory gene signature and insulin resistance in such patients [19]. Polymorphic variant rs1946518 in the IL18 gene is located in the promoter region and is associated with type 1 and 2 diabetes mellitus [20,21]. In was shown that the NLRP3 inflammasomes regulate adipose tissue metabolism via promoting IL18 secretion [22]. Despite the fact that this SNP was not associated with a metabolic syndrome in a northern Iranian population [23], we found an association of this polymorphic variant with a risk of obesity in females. We suppose that the T/G genotype of the IL18 gene (rs1946518) is associated with increased activity of proinflammatory IL18, which interacts with the IL18Rα/β heterodimer receptor complex expressed mainly by immune cells (e.g., macrophages, dendritic cells, T and B lymphocytes), in addition to endothelial and smooth muscle cells, thus stimulating these cells in an autocrine/paracrine manner [19,24]. An increased number of these cells, especially macrophages, can be found in the expanding adipose tissue in obese patients [25].
Interleukin-6 (IL6) is a pleiotropic cytokine involved in both immune and nonimmune events in numerous cells and tissues outside of the immune system [26]. IL6 activates an intracellular signaling cascade leading to inflammation via binding to its receptor IL6R [27]. It was reported that IL6R gene polymorphism is associated with BMI and obesity [28,29,30]. The results herein describing an increased risk of obesity in middle-aged males carrying the C/T genotype of the IL6R gene (rs2229238) are consistent with the findings of an association between the C/T genotype of this gene and an increased risk of obesity in schoolboys from Taiwan [31]. We suppose that the C/T genotype is associated with an elevated serum IL6R concentration and an increased level of the IL6/IL6R complex, resulting in greater IL6 signal transduction and IL6 production, with an effect on adipocytes and immune cells in adipose tissue, as well as on insulin-targeting cells in peripheral tissues [7,31].
Lipoprotein(a), encoded by the LPA gene, is a serine protease with inhibition activity toward tissue plasminogen activator I. The encoded protein is proteolytically cleaved, resulting in fragments that can attach to atherosclerotic lesions and promote thrombogenesis. Elevated plasma levels of this protein are linked to atherosclerosis [32]. LPA genetic polymorphism is associated with different cardiovascular pathologies, e.g., coronary artery disease, aortic valve stenosis, and valvular calcification [33,34,35,36]. In the presented research, we determined for the first time an association between LPA gene polymorphism (rs10455872) and obesity risk according to the log-additive inheritance model, regardless of gender and age. We hypothesize that the log-additive inheritance model was characterized by some defects in the expression of lipoprotein(a) linked to apoprotein B-100, thus leading to an increase in its synthetic rate and, consequently, an elevated obesity risk.
The protein encoded by the LEPR gene is a receptor for leptin (an adipocyte-specific hormone regulating lipid metabolism). Mutations in this gene are associated with obesity and pituitary dysfunction. LEPR gene polymorphism is associated with early onset of severe obesity and hyperphagic eating behavior [37]. The G/G genotype of the LEPR gene (rs1137100) is potentially associated with increased expression of the leptin receptor located in hypothalamic tissue, which has a significant role in controlling energy homeostasis and lipid metabolism. The increased expression of the leptin receptor results in more active binding to leptin, whose elevated secretion by adipocytes is associated with the increased obesity risk. Our results are consistent with the literature data showing that the minor allele of the LEPR gene (rs1137100) is more frequent in obese patients from different populations [38,39].
An increased serum level of inflammatory markers and acute phase proteins, including C-reactive protein (CRP), is observed in obese patients [40]. It has been suggested that CRP has a direct role in the regulation of adiposity via affecting the action of adipokines [41]. Human CRP can dissociate into a physiologically active and proinflammatory monomeric form, which can bind to cell surface receptors [42] and is potentially involved in the pathogenesis of inflammatory diseases [43]. An association was revealed between CRP and leptin level [44], and a direct effect of leptin on CRP production by hepatocytes was discovered. Therefore, CRP is potentially involved in lipid metabolism via an adipo-hepato axis (leptin produced by adipocytes enhances CRP expression, which in turn may antagonize leptin action by limiting its tissue availability) [45]. Our results demonstrate that genetically determined changes in CRP production can affect the adipo-hepato axis, leading to the defects in lipid metabolism and an increased risk of obesity, but only in elderly individuals.
Defects in the relationship between adipocytes and macrophages play an important role in the initiation of adipose tissue inflammation, thereby triggering obesity [46,47,48]. Metabolic disorders lead to disbalance between pro- and anti-inflammatory regulators of macrophages toward the formation of proinflammatory M1-macrophages, which is linked to adipocyte dysfunction and the development of chronic inflammation in adipose tissue [48]. Toll-like receptors (TLRs) represent a possible pathophysiological link between obesity and inflammation. TLRs are widely represented on the surface of immune cells (macrophages, dendritic cells, neutrophils, basophils, B and T lymphocytes, natural killer cells) and nonimmune cells (fibroblasts, epithelial cells, keratinocytes) [49]. Moreover, adipocytes also express TLRs that actively participate not only in antibacterial defense, but also in the initiation of chronic inflammation of adipose tissue [50]. Enhanced lipolysis in adipocytes leads to an increase in the level of unsaturated fatty acids, which, through TLRs, promote the differentiation of macrophages into an M1 phenotype [51]. We found that the C allele of the TLR2 gene (rs3804099) was associated with a threefold increase in risk of obesity in elderly patients due to more active TLR-promoted inflammation.
Our most interesting results were the sex- and age-specific associations of immune response and lipid metabolism gene polymorphisms with the risk of obesity in the studied cohort. The gender dimorphism of biological and physiological functions is caused by gender-based chromosomal differences in gonadal hormone secretion. In humans, the level of gonadal hormones not only varies between males and females, but also changes depending on age, and this physiological alteration can influence the function of gonadal. hormone-sensitive genes [52]. Gonadal hormones can significantly modulate cell signaling pathways and control gene regulation and expression. It was shown that gonadal hormones may modify the immune response via regulating the production of pro- and anti-inflammatory cytokines and TLR expression [53,54,55,56]. Moreover, lipid metabolism also varies according to gender and age. Recently, a gender-specific association of FTO gene polymorphism with risk of obesity was revealed [57]. Therefore, the gender- and age-modulated associations of SNPs in the inflammatory response and lipid metabolism genes with obesity risk identified in the present study suggest the role of gene–gender interactions in the development of this pathology. It should be noted that our results need to be replicated in different populations with a larger sample size.

5. Conclusions

Genetic polymorphisms in the immune response and lipid metabolism genes are associated with increased obesity risk in middle-aged and elderly Caucasian patients in a gender- and age-depended manner. The obtained results can be used to assess the personalized risk of obesity in healthy donors during medical examination or screening, as well as to develop appropriate early prevention strategies targeting obesity in at-risk groups.

Author Contributions

Conceptualization, A.P. (Anastasia Ponasenko) and V.M.; methodology, A.P. (Anastasia Ponasenko); validation, A.P. (Alexander Prosekov) and O.B.; formal analysis, O.B.; investigation, A.V. and M.K.; resources, O.B.; data curation, A.P. (Anastasia Ponasenko); writing—original draft preparation, M.S.; writing—review and editing, A.P. (Anastasia Ponasenko) and V.M.; visualization, A.V.; supervision, A.P. (Anastasia Ponasenko); project administration, V.M.; funding acquisition, A.P. (Alexander Prosekov) and V.M. All authors read and agreed to the published version of the manuscript.

Funding

This study was supported by the Russian State Task No. N0352-2019-0011 and the Complex Program of Fundamental Research of the Siberian Branch of the Russian Academy of Sciences within the framework of the fundamental research project of Research Institute for Complex Issues of Cardiovascular Diseases No. 0419-2022-0001.

Institutional Review Board Statement

The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Ethics Committee of Research Institute for Complex Issues of Cardiovascular Diseases.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data presented in this study are available on request from the corresponding author. The data are not publicly available due to privacy statements.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Piché, M.E.; Tchernof, A.; Després, J.P. Obesity Phenotypes, Diabetes, and Cardiovascular Diseases. Circ. Res. 2020, 126, 1477–1500. [Google Scholar] [CrossRef] [PubMed]
  2. NCD Risk Factor Collaboration (NCD-RisC). Trends in adult body-mass index in 200 countries from 1975 to 2014: A pooled analysis of 1698 population-based measurement studies with 19·2 million participants. Lancet 2016, 387, 1377–1396. [Google Scholar] [CrossRef] [Green Version]
  3. Kawai, T.; Autieri, M.V.; Scalia, R. Adipose tissue inflammation and metabolic dysfunction in obesity. Am. J. Physiol. Cell Physiol. 2021, 320, 375–391. [Google Scholar] [CrossRef]
  4. Ying, W.; Fu, W.; Lee, Y.S.; Olefsky, J.M. The role of macrophages in obesity-associated islet inflammation and β-cell abnormalities. Nat. Rev. Endocrinol. 2020, 16, 81–90. [Google Scholar] [CrossRef] [Green Version]
  5. Lu, Q.; Guo, P.; Liu, A.; Ares, I.; Martínez-Larrañaga, M.R.; Wang, X.; Anadón, A.; Martínez, M.A. The role of long noncoding RNA in lipid, cholesterol, and glucose metabolism and treatment of obesity syndrome. Med. Res. Rev. 2021, 41, 1751–1774. [Google Scholar] [CrossRef]
  6. Kojta, I.; Chacińska, M.; Błachnio-Zabielska, A. Obesity, Bioactive Lipids, and Adipose Tissue Inflammation in Insulin Resistance. Nutrients 2020, 12, 1305. [Google Scholar] [CrossRef]
  7. Locke, A.E.; Kahali, B.; Berndt, S.I.; Justice, A.E.; Pers, T.H.; Day, F.R.; Powell, C.; Vedantam, S.; Buchkovich, M.L.; Yang, J.; et al. Genetic studies of body mass index yield new insights for obesity biology. Nature 2015, 518, 197–206. [Google Scholar] [CrossRef] [Green Version]
  8. Yu, K.; Li, L.; Zhang, L.; Guo, L.; Wang, C. Association between MC4R rs17782313 genotype and obesity: A meta-analysis. Gene 2020, 733, 144372. [Google Scholar] [CrossRef]
  9. Adamska-Patruno, E.; Bauer, W.; Bielska, D.; Fiedorczuk, J.; Moroz, M.; Krasowska, U.; Czajkowski, P.; Wielogorska, M.; Maliszewska, K.; Puckowska, S.; et al. An Association between Diet and MC4R Genetic Polymorphism, in Relation to Obesity and Metabolic Parameters—A Cross Sectional Population-Based Study. Int. J. Mol. Sci. 2021, 22, 12044. [Google Scholar] [CrossRef] [PubMed]
  10. Chauhdary, Z.; Rehman, K.; Akash, M.S.H. The composite alliance of FTO locus with obesity-related genetic variants. Clin. Exp. Pharmacol. Physiol. 2021, 48, 954–965. [Google Scholar] [CrossRef]
  11. Yılmaz, B.; Gezmen Karadağ, M. The current review of adolescent obesity: The role of genetic factors. J. Pediatr. Endocrinol. Metab. 2020, 34, 151–162. [Google Scholar] [CrossRef] [PubMed]
  12. Sinitskiy, M.Y.; Ponasenko, A.V.; Gruzdeva, O.V. Genetic profile and secretome of adipocytes from visceral and subcutaneous adipose tissue in patients with cardiovascular diseases. Complex Issues Cardiovasc. Dis. 2017, 3, 155–165. [Google Scholar] [CrossRef] [Green Version]
  13. Nakayama, K.; Inaba, Y. Genetic variants influencing obesity-related traits in Japanese population. Ann. Hum. Biol. 2019, 46, 298–304. [Google Scholar] [CrossRef]
  14. Younes, S.; Ibrahim, A.; Al-Jurf, R.; Zayed, H. Genetic polymorphisms associated with obesity in the Arab world: A systematic review. Int. J. Obes. 2021, 45, 1899–1913. [Google Scholar] [CrossRef]
  15. Marques-Rocha, J.L.; Samblas, M.; Milagro, F.I.; Bressan, J.; Martínez, J.A.; Marti, A. Noncoding RNAs, cytokines, and inflammation-related diseases. FASEB J. 2015, 29, 3595–3611. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  16. Stienstra, R.; van Diepen, J.A.; Tack, C.J.; Zaki, M.H.; van de Veerdonk, F.L.; Perera, D.; Neale, G.A.; Hooiveld, G.J.; Hijmans, A.; Vroegrijk, I.; et al. Inflammasome is a central player in the induction of obesity and insulin resistance. Proc. Natl. Acad. Sci. USA 2011, 108, 15324–15329. [Google Scholar] [CrossRef] [Green Version]
  17. Li, J.; Wu, S.; Wang, M.R.; Wang, T.T.; Li, B.K.; Zhu, J.M. Association of the interleukin-18 -137 C/G, -607 A/C polymorphisms with type 1 diabetes: A meta-analysis. Biomed. Rep. 2014, 2, 57–62. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  18. Netea, M.G.; Joosten, L.A. The NLRP1-IL18 Connection: A Stab in the Back of Obesity-Induced Inflammation. Cell Metab. 2016, 23, 6–7. [Google Scholar] [CrossRef] [PubMed]
  19. Ahmad, R.; Thomas, R.; Kochumon, S.; Sindhu, S. Increased adipose tissue expression of IL-18R and its ligand IL-18 associates with inflammation and insulin resistance in obesity. Immun. Inflamm. Dis. 2017, 5, 318–335. [Google Scholar] [CrossRef]
  20. Mojtahedi, Z.; Naeimi, S.; Farjadian, S.; Omrani, G.R.; Ghaderi, A. Association of IL-18 promoter polymorphisms with predisposition to Type 1 diabetes. Diabet. Med. 2006, 23, 235–239. [Google Scholar] [CrossRef]
  21. Szeszko, J.S.; Howson, J.M.; Cooper, J.D.; Walker, N.M.; Twells, R.C.; Stevens, H.E.; Nutland, S.L.; Todd, J.A. Analysis of polymorphisms of the interleukin-18 gene in type 1 diabetes and Hardy-Weinberg equilibrium testing. Diabetes 2006, 55, 559–562. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  22. Barra, G.N.; Henriksbo, D.B.; Anhê, F.F.; Schertzer, D.J. The NLRP3 inflammasome regulates adipose tissue metabolism. Biochem. J. 2020, 477, 1089–1107. [Google Scholar] [CrossRef] [Green Version]
  23. Aghajani, R.; Saeidi, M.; Amiriani, T.; Marjani, M.; Amiriani, A.H.; Akhavan Tabib, A.; Marjani, A. Genetic polymorphisms -137 (G > C) (rs187238) and -607 (C > A) (rs1946518) and serum level of interleukin 18 in Fars ethnic groups with metabolic syndrome in Northern Iran [published online ahead of print]. Arch. Physiol. Biochem. 2020. [Google Scholar] [CrossRef] [PubMed]
  24. Gerdes, N.; Sukhova, G.K.; Libby, P.; Reynolds, R.S.; Young, J.L.; Schönbeck, U. Expression of interleukin (IL)-18 and functional IL-18 receptor on human vascular endothelial cells, smooth muscle cells, and macrophages: Implications for atherogenesis. J. Exp. Med. 2002, 195, 245–257. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  25. Lumeng, C.N.; Bodzin, J.L.; Saltiel, A.R. Obesity induces a phenotypic switch in adipose tissue macrophage polarization. J. Clin. Invest. 2007, 117, 175–184. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  26. Kamimura, D.; Ishihara, K.; Hirano, T. IL-6 signal transduction and its physiological roles: The signal orchestration model. Rev. Physiol. Biochem. Pharmacol. 2003, 149, 1–38. [Google Scholar]
  27. Heinrich, P.C.; Behrmann, I.; Haan, S.; Hermanns, H.M.; Müller-Newen, G.; Schaper, F. Principles of interleukin (IL)-6-type cytokine signalling and its regulation. Biochem. J. 2003, 374, 1–20. [Google Scholar] [CrossRef] [Green Version]
  28. Wolford, J.K.; Colligan, P.B.; Gruber, J.D.; Bogardus, C. Variants in the interleukin 6 receptor gene are associated with obesity in Pima Indians. Mol. Genet. Metab. 2003, 80, 338–343. [Google Scholar] [CrossRef]
  29. Esteve, E.; Villuendas, G.; Mallolas, J.; Vendrell, J.; López-Bermejo, A.; Rodríguez, M.; Recasens, M.; Ricart, W.; Millán, J.L.S.; Escobar-Morreale, H.; et al. Polymorphisms in the interleukin-6 receptor gene are associated with body mass index and with characteristics of the metabolic syndrome. Clin. Endocrinol. 2006, 65, 88–91. [Google Scholar] [CrossRef]
  30. Bustamante, M.; Nogués, X.; Mellibovsky, L.; Agueda, L.; Jurado, S.; Cáceres, E.; Blanch, J.; Carreras, R.; Díez-Pérez, A.; Grinberg, D.; et al. Polymorphisms in the interleukin-6 receptor gene are associated with bone mineral density and body mass index in Spanish postmenopausal women. Eur. J. Endocrinol. 2007, 157, 677–684. [Google Scholar] [CrossRef] [Green Version]
  31. Lin, F.H.; Chu, N.F.; Lee, C.H.; Hung, Y.J.; Wu, D.M. Combined effect of C-reactive protein gene SNP +2147 A/G and interleukin-6 receptor gene SNP rs2229238 C/T on anthropometric characteristics among school children in Taiwan. Int. J. Obes. 2011, 35, 587–594. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  32. Wang, H.; Hong, C.E.; Lewis, J.P.; Zhu, Y.; Wang, X.; Chu, X.; Backman, J.; Hu, Z.; Yang, P.; Still, C.D.; et al. Effect of Two Lipoprotein (a)-Associated Genetic Variants on Plasminogen Levels and Fibrinolysis. G3 2016, 6, 3525–3532. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  33. Clarke, R.; Peden, J.F.; Hopewell, J.C.; Kyriakou, T.; Goel, A.; Heath, S.C.; Parish, S.; Barlera, S.; Franzosi, M.G.; Rust, S.; et al. Genetic variants associated with Lp(a) lipoprotein level and coronary disease. N. Engl. J. Med. 2009, 361, 2518–2528. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  34. Thanassoulis, G.; Campbell, C.Y.; Owens, D.S.; Smith, J.G.; Smith, A.V.; Peloso, G.M.; Kerr, K.F.; Pechlivanis, S.; Budoff, M.; Harris, T.; et al. Genetic associations with valvular calcification and aortic stenosis. N. Engl. J. Med. 2013, 368, 503–512. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  35. Arsenault, B.J.; Boekholdt, S.M.; Dubé, M.P.; Rhéaume, E.; Wareham, N.J.; Khaw, K.T.; Sandhu, M.S.; Tardif, J.-C. Lipoprotein(a) levels, genotype, and incident aortic valve stenosis: A prospective Mendelian randomization study and replication in a case-control cohort. Circ. Cardiovasc. Genet. 2014, 7, 304–310. [Google Scholar] [CrossRef] [Green Version]
  36. Kutikhin, A.G.; Yuzhalin, A.E.; Brusina, E.B.; Ponasenko, A.V.; Golovkin, A.S.; Barbarash, O.L. Genetic predisposition to calcific aortic stenosis and mitral annular calcification. Mol. Biol. Rep. 2014, 41, 5645–5663. [Google Scholar] [CrossRef]
  37. Rojano-Rodriguez, M.E.; Beristain-Hernandez, J.L.; Zavaleta-Villa, B.; Maravilla, P.; Romero-Valdovinos, M.; Olivo-Diaz, A. Leptin receptor gene polymorphisms and morbid obesity in Mexican patients. Hereditas 2016, 153, 2. [Google Scholar] [CrossRef] [Green Version]
  38. De Oliveira, R.; Cerda, A.; Genvigir, F.D.; Sampaio, M.F.; Armaganijan, D.; Bernik, M.; Dorea, E.L.; Hirata, M.H.; Hinuy, H.M.; Hirata, R.D.C. Leptin receptor gene polymorphisms are associated with adiposity and metabolic alterations in Brazilian individuals. Arq. Bras. Endocrinol. Metabol. 2013, 57, 677–684. [Google Scholar] [CrossRef] [Green Version]
  39. Ali, E.M.M.; Diab, T.; Elsaid, A.; Abd, E.; Daim, H.A.; Elshazli, R.M.; Settin, A. Fat mass and obesity-associated (FTO) and leptin receptor (LEPR) gene polymorphisms in Egyptian obese subjects. Arch. Physiol. Biochem. 2021, 127, 28–36. [Google Scholar] [CrossRef]
  40. Berg, A.H.; Scherer, P.E. Adipose tissue, inflammation, and cardiovascular disease. Circ. Res. 2005, 96, 939–949. [Google Scholar] [CrossRef] [Green Version]
  41. Sudhakar, M.; Silambanan, S.; Chandran, A.S.; Prabhakaran, A.A.; Ramakrishnan, R. C-Reactive Protein (CRP) and Leptin Receptor in Obesity: Binding of Monomeric CRP to Leptin Receptor. Front Immunol. 2018, 9, 1167. [Google Scholar] [CrossRef] [Green Version]
  42. Fujita, M.; Takada, Y.K.; Izumiya, Y.; Takada, Y. The binding of monomeric C-reactive protein (mCRP) to Integrins αvβ3 and α4β1 is related to its pro-inflammatory action. PLoS ONE 2014, 9, e93738. [Google Scholar] [CrossRef]
  43. Eisenhardt, S.U.; Thiele, J.R.; Bannasch, H.; Stark, G.B.; Peter, K. C-reactive protein: How conformational changes influence inflammatory properties. Cell Cycle 2009, 8, 3885–3892. [Google Scholar] [CrossRef]
  44. Shamsuzzaman, A.S.; Winnicki, M.; Wolk, R.; Svatikova, A.; Phillips, B.G.; Davison, D.E.; Berger, P.B.; Somers, V.K. Independent association between plasma leptin and C-reactive protein in healthy humans. Circulation 2004, 109, 2181–2185. [Google Scholar] [CrossRef] [Green Version]
  45. Hribal, M.L.; Fiorentino, T.V.; Sesti, G. Role of C reactive protein (CRP) in leptin resistance. Curr. Pharm. Des. 2014, 20, 609–615. [Google Scholar] [CrossRef] [Green Version]
  46. Huh, J.Y.; Park, Y.J.; Ham, M.; Kim, J.B. Crosstalk between adipocytes and immune cells in adipose tissue inflammation and metabolic dysregulation in obesity. Mol. Cells 2014, 37, 365–371. [Google Scholar] [CrossRef]
  47. Revelo, X.S.; Tsai, S.; Lei, H.; Luck, H.; Ghazarian, M.; Tsui, H.; Shi, S.Y.; Schroer, S.; Luk, C.T.; Lin, G.H.Y.; et al. Perforin is a novel immune regulator of obesity-related insulin resistance. Diabetes 2015, 64, 90–103. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  48. Thomas, D.; Apovian, C. Macrophage functions in lean and obese adipose tissue. Metabolism 2017, 72, 120–143. [Google Scholar] [CrossRef]
  49. Zakeri, A.; Russo, M. Dual Role of Toll-like Receptors in Human and Experimental Asthma Models. Front Immunol. 2018, 9, 1027. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  50. Akhter, N.; Madhoun, A.; Arefanian, H.; Wilson, A.; Kochumon, S.; Thomas, R.; Shenouda, S.; Al-Mulla, F.; Ahmad, R.; Sindhu, S. Oxidative Stress Induces Expression of the Toll-Like Receptors (TLRs) 2 and 4 in the Human Peripheral Blood Mononuclear Cells: Implications for Metabolic Inflammation. Cell Physiol. Biochem. 2019, 53, 1–18. [Google Scholar] [PubMed]
  51. Liu, Y.C.; Zou, X.B.; Chai, Y.F.; Yao, Y.M. Macrophage polarization in inflammatory diseases. Int. J. Biol. Sci. 2014, 10, 520–529. [Google Scholar] [CrossRef] [PubMed]
  52. Horstman, A.M.; Dillon, E.L.; Urban, R.J.; Sheffield-Moore, M. The role of androgens and estrogens on healthy aging and longevity. J. Gerontol. A Biol. Sci. Med. Sci. 2012, 67, 1140–1152. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  53. Iyer, S.S.; Cheng, G. Role of interleukin 10 transcriptional regulation in inflammation and autoimmune disease. Crit. Rev. Immunol. 2012, 32, 23–63. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  54. Jukkola-Vuorinen, A.; Rahko, E.; Vuopala, K.S.; Desmond, R.; Lehenkari, P.P.; Harris, K.W.; Selander, K.S. Toll-like receptor-9 expression is inversely correlated with estrogen receptor status in breast cancer. J. Innate Immun. 2009, 1, 59–68. [Google Scholar] [CrossRef] [PubMed]
  55. Liva, S.M.; Voskuhl, R.R. Testosterone acts directly on CD4+ T lymphocytes to increase IL-10 production. J. Immunol. 2001, 167, 2060–2067. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  56. Torcia, M.G.; Nencioni, L.; Clemente, A.M.; Civitelli, L.; Celestino, I.; Limongi, D.; Fadigati, G.; Perissi, E.; Cozzolino, F.; Garaci, E.; et al. Sex differences in the response to viral infections: TLR8 and TLR9 ligand stimulation induce higher IL10 production in males. PLoS ONE 2012, 7, e39853. [Google Scholar] [CrossRef]
  57. Zdrojowy-Wełna, A.; Bednarek-Tupikowska, G.; Zatońska, K.; Kolačkov, K.; Jokiel-Rokita, A.; Bolanowski, M. The association between FTO gene polymorphism rs9939609 and obesity is sex-specific in the population of PURE study in Poland. Adv. Clin. Exp. Med. 2020, 29, 25–32. [Google Scholar] [CrossRef] [Green Version]
Table
Table 1. Characteristics of patients included in the study.
Table 1. Characteristics of patients included in the study.
IndexNumber (%)
Male319 (57)
Female241 (43)
Age ≤60 years (middle-aged patients)382 (68)
Age >60 years (elderly patients)178 (32)
BMI ≥30 kg/m2220 (39)
BMI ≥30 kg/m2 in middle-aged patients156 (41)
BMI ≥30 kg/m2 in elderly patients64 (36)
Table
Table 2. Characteristics of the studied polymorphic variants.
Table 2. Characteristics of the studied polymorphic variants.
GeneReference SNP NumberChromosomal PositionNucleotide ChangeVariant Type
TLR1rs5743611chr4:38798593C > GMissense variant
rs5743551chr4:38806033T > A, C, G5’ UTR variant
TLR2rs5743708chr4:153705165G > AMissense variant
TLR4rs4986791chr9:117713324C > TMissense variant
rs4986790chr9:117713024A > G, TMissense variant
TLR6rs5743810chr4:38828729A > C, G, TMissense variant
rs3775073chr4:38828211T > C, GMissense variant
IL1RL1rs4988956chr2:102351547G > AMissense variant
rs11685424chr2:102310521G > AUpstream transcript variant
IL1Brs1143634chr2:112832813G > ASynonymous variant
rs169442:112837290A > GUpstream transcript variant
IL6Rrs2228145chr1:154454494A > C, TMissense variant
rs2229238chr1:154465420T > A, C3’ UTR variant
IL6rs1800796chr7:22726627G > A, CIntron variant
rs1554606chr7:22729088T > A, GIntron variant
rs2069827chr7:22725837G > C, TUpstream transcript variant
CXCL8rs2227306chr4:73741338C > TIntron variant
rs4073chr4:73740307A > C, G, TUpstream transcript variant
IL10rs1800871chr1:206773289A > GUpstream transcript variant
rs1800872chr1:206773062T > GUpstream transcript variant
rs1800896chr1:206773552T > CUpstream transcript variant
IL12RB1rs375947chr19:18069641A > GMissense variant
IL12Brs3212227chr5:159315942T > G3’ UTR variant
IL18RAPrs917997chr2:102454108T > A, C, GNot announced
rs2058659chr2:102438096G > AIntron variant
IL18R1rs13015714chr2:102355405G > A, TUpstream transcript variant
rs1974675chr2:102369915G > AIntron variant
rs6758936chr2:102374909G > AIntron variant
rs3755276chr2:102361999C > TIntron variant
IL18rs187238chr11:112164265C > A, GUpstream transcript variant
rs360719chr11:112165426A > GUpstream transcript variant
rs1946518chr11:112164735T > GUpstream transcript variant
IL33rs7025417chr9:6240084T > C, GIntron variant
TNFrs1799964chr6:31574531T > CUpstream transcript variant
rs361525chr6:31575324G > AUpstream transcript variant
rs1800629chr6:31575254G > AUpstream transcript variant
CRPrs3093077chr1:159709846A > C, G, TNot announced
rs1800947chr1:159713648C > A, G, TSynonymous variant
rs1130864chr1:159713301G > AIntron variant
rs1205chr1:159712443C > T3’ UTR variant
APOErs429358chr19: 44908684T > CMissense variant
rs769452chr19:44907853T > A, CMissense variant
rs7412chr19:44908822C > TMissense variant
APOBrs1042031chr2:21002881C > A, TMissense variant/Stop gained
rs6725189chr2:20996129G > TNot announced
LPArs10455872chr6:160589086A > GIntron variant
LIPCrs1800588chr15:58431476C > G, TIntron variant
CXCR1rs16858811chr2:218165120A > CMissense variant
CXCR2rs1126579chr2:218136011T > C3’ UTR variant
INSrs689chr11:2160994A > G, TIntron variant
IGF1Rrs2229765chr15:98934996G > A, TMissense variant
LEPrs7799039chr7:128238730G > A, CNot announced
LEPRrs1137101chr1:65592830A > G, TMissense variant
rs1137100chr1:65570758A > G, TMissense variant
IL1F9rs17659543chr2:112958729C > TNot announced
Table
Table 3. Association of SNPs with risk of obesity, adjusted by gender and age.
Table 3. Association of SNPs with risk of obesity, adjusted by gender and age.
GeneModelGenotypeNo Obesity, N (%)Obesity, N (%)OR (95% CI)pAIC
IL6R rs2229238CodominantC/C189 (55.9)88 (40)1.000.0009741.5
T/C123 (36.4)112 (50.9)1.97 (1.37–2.83)
T/T26 (7.7)20 (9.1)1.66 (0.88–3.14)
DominantC/C189 (55.9)88 (40)1.000.0002739.8
T/C-T/T149 (44.1)132 (60)1.92 (1.36–2.71)
RecessiveC/C-T/C312 (92.3)200 (90.9)1.000.56753.3
T/T26 (7.7)20 (9.1)1.20 (0.65–2.21)
Over-dominantC/C-T/T215 (63.6)108 (49.1)1.000.0006741.9
T/C123 (36.4)112 (50.9)1.83 (1.29–2.58)
Log-additive---1.53 (1.17–2.01)0.0016743.7
CXCL8 rs4073CodominantT/T91 (26.8)71 (32.3)1.000.022748.8
A/T154 (45.4)109 (49.5)0.90 (0.61–1.34)
A/A94 (27.7)40 (18.2)0.53 (0.33–0.86)
DominantT/T91 (26.8)71 (32.3)1.000.15752.4
A/T-A/A248 (73.2)149 (67.7)0.76 (0.53–1.11)
RecessiveT/T-A/T245 (72.3)180 (81.8)1.000.0065747
A/A94 (27.7)40 (18.2)0.56 (0.37–0.86)
Over-dominantT/T-A/A185 (54.6)111 (50.5)1.000.32753.5
A/T154 (45.4)109 (49.5)1.19 (0.85–1.67)
Log-additive---0.74 (0.58–0.94)0.013748.3
CXCL8 rs2227306CodominantC/C103 (30.3)77 (35)1.000.0098748
C/T160 (47.1)115 (52.3)0.94 (0.64–1.38)
T/T77 (22.6)28 (12.7)0.48 (0.28–0.81)
DominantC/C103 (30.3)77 (35)1.000.21753.7
C/T-T/T237 (69.7)143 (65)0.79 (0.55–1.14)
RecessiveC/C-C/T263 (77.3)192 (87.3)1.000.0025746.1
T/T77 (22.6)28 (12.7)0.49 (0.31–0.79)
Over-dominantC/C-T/T180 (52.9)105 (47.7)1.000.25754
C/T160 (47.1)115 (52.3)1.22 (0.87–1.71)
Log-additive---0.73 (0.57–0.94)0.012749
TNF rs1800629CodominantG/G261 (77)167 (75.9)1.000.028749.2
G/A77 (22.7)47 (21.4)0.93 (0.62–1.41)
A/A1 (0.3)6 (2.7)10.14 (1.20–85.48)
DominantG/G261 (77)167 (75.9)1.000.83754.3
G/A-A/A78 (23)53 (24.1)1.05 (0.70–1.56)
RecessiveG/G-G/A338 (99.7)214 (97.3)1.000.0081747.3
A/A1 (0.3)6 (2.7)10.29 (1.22–86.59)
Over-dominantG/G-A/A262 (77.3)173 (78.6)1.000.63754.1
G/A77 (22.7)47 (21.4)0.90 (0.60–1.37)
Log-additive---1.17 (0.81–1.69)0.41753.6
IL18 rs1946518CodominantG/G111 (32.6)63 (28.9)1.000.066748.3
T/G158 (46.5)122 (56)1.32 (0.89–1.95)
T/T71 (20.9)33 (15.1)0.78 (0.46–1.31)
DominantG/G111 (32.6)63 (28.9)1.000.45751.2
T/G-T/T229 (67.3)155 (71.1)1.15 (0.79–1.68)
RecessiveG/G-T/G269 (79.1)185 (84.9)1.000.062748.3
T/T71 (20.9)33 (15.1)0.65 (0.41–1.03)
Over-dominantG/G-T/T182 (53.5)96 (44)1.000.033747.2
T/G158 (46.5)122 (56)1.45 (1.03–2.04)
Log-additive---0.93 (0.73–1.20)0.59751.5
LPA rs10455872CodominantA/A315 (92.7)191 (87.6)1.000.032746.6
A/G25 (7.3)25 (11.5)1.64 (0.92–2.95)
G/G0 (0)2 (0.9)NA (0.00-NA)
DominantA/A315 (92.7)191 (87.6)1.000.049747.6
A/G-G/G25 (7.3)27 (12.4)1.79 (1.00–3.17)
RecessiveA/A-A/G340 (100)216 (99.1)1.000.042747.3
G/G0 (0)2 (0.9)N/A (0.00-N/A)
Over-dominantA/A-G/G315 (92.7)193 (88.5)1.000.1748.8
A/G25 (7.3)25 (11.5)1.62 (0.91–2.92)
Log-additive---1.86 (1.07–3.21)0.026746.5
LEPR rs1137100CodominantA/A181 (53.4)97 (44.5)1.000.0021740.4
A/G141 (41.6)94 (43.1)1.24 (0.87–1.78)
G/G17 (5)27 (12.4)3.19 (1.64–6.18)
DominantA/A181 (53.4)97 (44.5)1.000.036746.2
A/G-G/G158 (46.6)121 (55.5)1.44 (1.02–2.03)
RecessiveA/A-A/G322 (95)191 (87.6)1.000.001739.8
G/G17 (5)27 (12.4)2.88 (1.52–5.46)
Over-dominantA/A-G/G198 (58.4)124 (56.9)1.000.76750.6
A/G141 (41.6)94 (43.1)1.06 (0.75–1.49)
Log-additive---1.53 (1.16–2.00)0.0021741.2
Note: Statistically significant results after applying Akaike’s information criterion (AIC) are highlighted in bold.
Table
Table 4. Association of SNPs with risk of obesity in groups stratified by gender.
Table 4. Association of SNPs with risk of obesity in groups stratified by gender.
GeneGenderGenotypeNo Obesity, NObesity, NOR (95%CI)p
IL6R rs2229238MaleC/C112431.000.002
T/C73642.27 (1.40–3.70)
T/T15101.74 (0.73–4.17)
FemaleC/C77451.000.08
T/C50481.65 (0.96–2.83)
T/T11101.57 (0.62–4.00)
CXCL8 rs2227306MaleC/C66421.000.5
C/T92611.04 (0.63–1.72)
T/T44140.51 (0.25–1.04)
FemaleC/C37351.000.04
C/T68540.83 (0.46–1.49)
T/T33140.44 (0.20–0.95)
IL1RL1 rs11685424MaleA/A58371.000.023
G/A91651.12 (0.67–1.89)
G/G51150.46 (0.23–0.94)
FemaleA/A45301.000.43
G/A67491.11 (0.61–2.01)
G/G26241.39 (0.68–2.87)
IL18 rs1946518MaleG/G70431.000.48
T/G94591.01 (0.61–1.66)
T/T38150.63 (0.31–1.28)
FemaleG/G41201.000.03
T/G64632.02 (1.07–3.83)
T/T33181.10 (0.50–2.41)
LEPR rs1137100MaleA/A105531.000.028
A/G83451.07 (0.66–1.75)
G/G13182.80 (1.27–6.17)
FemaleA/A76441.000.004
A/G58491.48 (0.87–2.53)
G/G494.04 (1.17–13.94)
Note: Statistically significant results are highlighted in bold.
Table
Table 5. Association of SNPs with obesity risk in groups stratified by age.
Table 5. Association of SNPs with obesity risk in groups stratified by age.
GeneAgeGenotypeNo Obesity, NObesity, NOR (95%CI)p
IL6R rs22281454≤60 yearsA/A94881.000.004
A/C107590.58 (0.38–0.90)
C/C2490.40 (0.18–0.90)
>60 yearsA/A63251.000.027
A/C40322.03 (1.05–3.91)
C/C1071.70 (0.58–4.98)
IL6R rs2229238 ≤60 yearsC/C130591.000.015
T/C82812.21 (1.43–3.41)
T/T14162.53 (1.16–5.53)
>60 yearsC/C59291.000.49
T/C41311.54 (0.80–2.93)
T/T1240.67 (0.20–2.25)
CXCL8 rs4073 ≤60 yearsT/T59501.000.027
A/T100790.93 (0.58–1.51)
A/A67270.46 (0.26–0.83)
>60 yearsT/T32211.000.07
A/T54300.84 (0.41–1.71)
A/A27130.74 (0.31–1.75)
CXCL8 rs2227306 ≤60 yearsC/C65541.000.04
C/T110820.89 (0.56–1.40)
T/T51200.45 (0.24–0.86)
>60 yearsC/C38231.000.05
C/T50331.08 (0.55–2.14)
T/T2680.52 (0.20–1.36)
CRP rs1130864 ≤60 yearsG/G115791.000.19
G/A98620.91 (0.59–1.39)
A/A13151.70 (0.77–3.78)
>60 yearsG/G55221.000.01
G/A47371.98 (1.03–3.83)
A/A1251.04 (0.33–3.29)
LEPR rs1137100 ≤60 yearsA/A125701.000,03
A/G93671.29 (0.84–1.98)
G/G8184.23 (1.74–10.28)
>60 yearsA/A56271.000.15
A/G48271.14 (0.59–2.20)
G/G992.12 (0.75–5.98)
TLR2 rs3804099 ≤60 yearsT/T86641.000.36
T/C106680.86 (0.55–1.34)
C/C34240.95 (0.52–1.77)
>60 yearsT/T56161.000.01
T/C41302.55 (1.23–5.28)
C/C17173.35 (1.39–8.04)
Note: Statistically significant results are highlighted in bold.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Ponasenko, A.; Sinitsky, M.; Minina, V.; Vesnina, A.; Khutornaya, M.; Prosekov, A.; Barbarash, O. Immune Response and Lipid Metabolism Gene Polymorphisms Are Associated with the Risk of Obesity in Middle-Aged and Elderly Patients. J. Pers. Med. 2022, 12, 238. https://doi.org/10.3390/jpm12020238

AMA Style

Ponasenko A, Sinitsky M, Minina V, Vesnina A, Khutornaya M, Prosekov A, Barbarash O. Immune Response and Lipid Metabolism Gene Polymorphisms Are Associated with the Risk of Obesity in Middle-Aged and Elderly Patients. Journal of Personalized Medicine. 2022; 12(2):238. https://doi.org/10.3390/jpm12020238

Chicago/Turabian Style

Ponasenko, Anastasia, Maxim Sinitsky, Varvara Minina, Anna Vesnina, Maria Khutornaya, Alexander Prosekov, and Olga Barbarash. 2022. "Immune Response and Lipid Metabolism Gene Polymorphisms Are Associated with the Risk of Obesity in Middle-Aged and Elderly Patients" Journal of Personalized Medicine 12, no. 2: 238. https://doi.org/10.3390/jpm12020238

APA Style

Ponasenko, A., Sinitsky, M., Minina, V., Vesnina, A., Khutornaya, M., Prosekov, A., & Barbarash, O. (2022). Immune Response and Lipid Metabolism Gene Polymorphisms Are Associated with the Risk of Obesity in Middle-Aged and Elderly Patients. Journal of Personalized Medicine, 12(2), 238. https://doi.org/10.3390/jpm12020238

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop