Laparoscopic longitudinal gastrectomy: long-term results and predictors of relapsing obesity
DOI: 10.32364/2587-6821-2024-8-4-1
V.A. Vetoshkin1,2, D.V. Gladyshev1,3,4, N.N. Sushentseva1, S.V. Apalko1,4, V.S. Shimansky1,4, M.E. Moiseev1, A.D. Gladyshev3, S.A. Kovalenko1, R.G. Askerkhanov5, S.G. Shcherbak1,4, B.N. Kotiv3
1St. Petersburg City Hospital No. 40, St. Petersburg, Russian Federation
2St. Petersburg Research Institute of Phthisiopulmonology, St. Petersburg, Russian Federation
3Military Medical Academy, St. Petersburg, Russian Federation
4St. Petersburg State University, St. Petersburg, Russian Federation
5A.S. Loginov Moscow Clinical Research Center, Moscow, Russian Federation
Background: morbid obesity is currently a serious medical and social problem. Bariatric surgery remains the most effective method of treating this disease. To ensure a more precise selection of patients for such interventions, it is necessary to determine the predictor factors of various treatment outcomes.
Aim: to evaluate the long-term results of laparoscopic longitudinal gastrectomy (LLG) and to determine the list of genes associated with the relapsing obesity in the long-term postoperative period.
Materials and Methods: the study included patients with obesity (n=81) (mean age 44.80±1.26 years, male patients — 27.16% (n=22)) hospitalized in the surgical department of St. Petersburg City Hospital No. 40. All patients underwent LLG. After hospital discharge, patients were monitored on an outpatient basis for 5 years. Then the patients were divided into two groups: 64 (79.0%) patients were included in the group of persistent weight loss, 17 (21.0%) — in the group of relapsing obesity. Besides, all patients included in the study underwent whole exome sequencing.
Results: based on the T-test results, 22 genes were identified (CARD8, FMC1, MUC16, MUC21, HTR3D, MAMDC4, IFNK, HLA-DQB1, LUC7L2, KIR3DL1, LILRB4, RBBP6, ANKRD30A, ABCC12, LRRIQ3, PDE6B, OR5H15, CCDC187, PMIS2, SAMD11, PTCHD3, AKR7L), mutations in which were presumably associated with the relapsing obesity after LLG (p<0.01).
Conclusion: the study results demonstrated the presence of variations in the genetic profiles of patients with different long-term LLG results.
Further detailed study of the obtained data, in particular, the analysis of the allele frequency between the two groups of patients, the ratio with clinical data and genetic risk score development, will potentially allow to create a model predicting the relapsing obesity after bariatric surgery.
Keywords: obesity, bariatrics, genes associated with obesity, surgery, laparoscopic longitudinal gastrectomy; body mass index.
For citation: Vetoshkin V.A., Gladyshev D.V., Sushentseva N.N., Apalko S.V., Shimansky V.S., Moiseev M.E., Gladyshev A.D., Kovalenko S.A., Askerkhanov R.G., Shcherbak S.G., Kotiv B.N. Laparoscopic longitudinal gastrectomy: long-term results and predictors of relapsing obesity. Russian Medical Inquiry. 2024;8(4):186–194 (in Russ.). DOI: 10.32364/2587-6821-2024-8-4-1.
Background
Obesity is a chronic disease that is defined by the excessive accumulation of body fat. This accumulation compromises health and is a major risk factor for a number of other chronic diseases, including type 2 diabetes (T2D) and cardiovascular disease.
Overweight and obesity contribute to public health issues in both developed and developing countries. As indicated by data fr om the World Health Organization (WHO) for 2016, approximately 39% of individuals aged 18 and above are classified as overweight, while 13% of the global population is affected by obesity. In the Russian Federation in 2016, the percentage of overweight individuals was 62.0%, while the percentage of obese individuals was 26.2% [3].
It is indisputable that the treatment of obesity necessitates a comprehensive and multifaceted approach. At present, bariatric surgery is regarded as the most efficacious technique for the treatment of obesity. However, the recurrence of obesity after these procedures is not as uncommon as patients and physicians would prefer.
The identification of predictive factors for various outcomes of bariatric surgery will facilitate a more precise selection of patients for surgery. The analysis of age, initial body mass index (BMI), and comorbidities may not always yield accurate predictions regarding outcomes. The identification of genetic factors associated with sustained weight loss after bariatric surgery may facilitate the development of pre- and postoperative weight management strategies, as well as indications for treatment selection and the elaboration of novel interventions.
The aim of this study was to evaluate the long-term outcomes of laparoscopic sleeve gastrectomy (LSG) and to identify genes associated with recurrence of obesity in the long-term postoperative period.
Patients and Methods
The study population consisted of 81 patients with a BM) classified as obese. The mean age of the patients was 44.80±1.26 years. Of the total number of patients, 72.84% (n=59) were women and 27.16% (n=22) were men.
The patients were admitted to the bariatric surgery unit. The study was conducted in accordance with the guidelines set forth by the research project, "Biobanking and Biomedical Research on Human Tissue Samples and Fluids," which was approved by the Ethics Committee.
Written informed consent was obtained from all study participants for the storage of their biomaterial in the biobank for future scientific purposes.
Inclusion criteria: men and women aged 20-70 years, with a BMI greater than 35, no history of bariatric surgery, no contraindications to surgery, no psychiatric diseases or cancer, and signed voluntary informed consent.
The examination of the patients included a series of laboratory tests (complete blood count and blood biochemistry, urinalysis, HIV, viral hepatitis, and syphilis), anthropometric measurements, abdominal and pelvic computed tomography, chest radiography, abdominal ultrasound, gastroscopy, echocardiography, spirometry, and consultations with a therapist, endocrinologist, and psychologist. Furthermore, blood samples were obtained from all patients prior to surgery for additional genetic analysis. It is noteworthy that many patients presented with a range of comorbidities, as detailed in Table 1.
All patients underwent LSG.
The patients were observed on an outpatient basis for a period of five years, at the conclusion of which they were divided into two groups based on the presence or absence of obesity recurrence. Of the subjects, 64 (79%) were included in the sustained weight loss group, while 17 (21%) were included in the obesity recurrence group. An increase in body weight of more than 25% of the minimum recorded weight in the postoperative period was considered indicative of obesity recurrence.
To identify potential genetic risk factors for obesity recurrence following LSG, whole exome sequencing was conducted on all patients.
DNA was isolated from blood samples using the QIACube automated station (QIAGEN, Germany) and QIAamp DNA Blood Mini reagents (QIAGEN, Germany). Gene libraries were prepared by hybridization with KAPA HyperExome full-exome sequencing probes (Roche, Switzerland). The High-Throughput Sequencing Primer Kit (App-C) and MGIEasy Universal Library Conversion Kit (App-A) (MGI, China) were used for conversion. The resulting libraries were sequenced on an MGISEQ-2000 sequencer using the 150 base pair length paired-end read method on a DNBSEQ-G400 high-throughput sequencing set (PE150, 540 GB) cell (MGI, China). Study quality requirements: average coverage >100×, read uniformity - at least 97% of 10× reads Sequencing results were processed on the ru.genomenal.com platform. Sample quality control was performed using Falco. Alignment to the hg38 version reference genome was performed using the BWA2 program. Deduplication, base quality recalibration and coverage counting were performed using the following GATK tools: MarkDuplicates, BaseRecalibrator and CollectHSMetrics, respectively. Genetic variant detection and filtering were also performed using GATK tools: HaplotypeCaller, VariantFiltration. Annotation was performed using the VEP program.
The statistical analysis was conducted using Microsoft Excel 365 and IBM SPSS Statistics Version 26. The quantitative data were summarized and presented as weighted arithmetic means with standard errors for normally distributed values, as interquartile range estimates for non-normally distributed values, and as relative indices (%). The normality of the distribution was evaluated using the Shapiro-Wilk test. The following statistical analysis methods were employed: the Mann-Whitney U test, the one-sided Fisher's exact test, Pearson's chi-squared test, the chi-squared test with Yates correction, and the t-test. A p-value of less than 0.05 was considered statistically significant. For the evaluation of single nucleotide polymorphisms (SNPs), a p-value of less than 0.01 was considered statistically significant.
Results and Discussion
In order to evaluate the efficacy of LSG one year after surgery, the %EBMIL index was calculated using the following formula:
Surgery was considered effective when %EBMIL was >50% [7]. LSG was considered effective in all cases included in this study.
A comparative analysis of the laboratory test results obtained prior to and following the surgical procedure, conducted at one-year and three-year intervals, revealed notable discrepancies (as determined by the Mann-Whitney U test) in the levels of the following parameters:
Glycated hemoglobin (Fig. 1);
Cholesterol (Fig. 2); -density lipoprotein (HDL) (Fig. 3);
Low-density lipoprotein (LDL) (Fig. 4);
Triiodothyronine (T3) (Fig. 5);
Thyroid stimulating hormone (TSH) (Fig. 6).
Five years following LSG, a number of diseases exhibited remission, and obesity-related conditions demonstrated regression (see Table 2).
Additionally, normalization of anthropometric measurements was observed.
The data obtained are consistent with and corroborate the findings of previous studies conducted by both domestic and foreign researchers. However, the recurrence of obesity was observed in every fifth patient, which is consistent with the global trends.
It is crucial to examine the effect of bariatric surgery on the thyroid gland as a distinct entity. There is a robust correlation between body weight and thyroid hormone levels. A deficiency of thyroid hormones can result in an increased body weight due to a reduction in the basal metabolic rate and thermogenesis. Additionally, there is a negative correlation between thyroid hormone levels and BMI. It is hypothesized that obesity may induce resistance to thyroid hormones through a mechanism similar to insulin resistance, resulting in elevated TSH levels.
As previously reported [11], the results of the preoperative examination did not allow for the construction of a mathematical model that could predict the probability of sustained weight loss following LSG. It is likely that the alterations in these variables are linked to the presence of obesity, which precludes their use in the construction of such a model. Accordingly, it is essential to utilize constant or gradually evolving indicators of obesity to identify predictive factors. The patient's genetic profile aligns with these criteria.
T-test identified 22 genes whose mutations were predicted to be associated with obesity recurrence after LSG (p<0.01). The following genes were identified as meeting the criteria: CARD8, FMC1, MUC16, MUC21, HTR3D, MAMDC4, IFNK, HLA-DQB1, LUC7L2, KIR3DL1, LILRB4, RBBP6, ANKRD30A, ABCC12, LRRIQ3, PDE6B, OR5H15, CCDC187, PMIS2, SAMD11, PTCHD3, and AKR7L.
A significant number of the identified genes and their products have been demonstrated to be directly or indirectly associated with obesity, energy metabolism disorders, gastrointestinal functions, and hormonal regulation of the body. This may elucidate their potential role in the recurrence of obesity in patients following LSG.
The CARD8 gene encodes a protein containing caspase recruitment domain family member 8, which is involved in the regulation of the NLRP3 inflammasome [12]. Prior research has indicated that NLRP3 plays a role in the development of obesity, T2D, and glucose resistance. It can thus be postulated that mutations in CARD8 may be a contributing factor in the development of obesity following LSG.
The MUC16 and MUC21 genes encode proteins belonging to the mucin family. Mucins are high molecular weight O-glycosylated proteins that play a pivotal role in the development of the protective mucosal barrier and are found on the apical surfaces of epithelial cells. Mucin 16 is also referred to as CA-125, an oncomarker utilized in the diagnosis of numerous malignancies, including ovarian cancer. It has been demonstrated that metabolic syndrome, elevated triglyceride levels, and low HDL levels are negatively correlated with CA-125 levels (13). This suggests that the presence of MUC16 variants and/or CA-125 levels may serve as predictors of recurrent obesity following bariatric surgery.
The FMC1 gene encodes a protein that is involved in the assembly of the proton-transporting ATP synthase complex, negative regulation of lipid catabolism, and regulation of insulin-producing pancreatic β-cell proliferation. It can be inferred that damage to this system may represent a potential mechanism underlying the recurrence of obesity following surgical interventions.
The HTR3D gene encodes the D-subunit of the 5-hydroxytryptamine (serotonin) type 3 receptor. Serotonin plays a role in the regulation of food intake. Two circuits of serotonin-mediated regulation of eating behavior have been identified: the homeostatic circuit, which adjusts energy intake to energy expenditure, and the hedonic circuit, which is involved in the rewarding and motivational aspects of energy intake. In the homeostatic circuit, serotoninergic signaling plays a role in integrating metabolic signals that convey the body's energy status and attenuate hunger when homeostatic needs are met. In the hedonic circuit, serotonergic signaling may inhibit reward-related motivational eating. Peripherally acting serotonin has been demonstrated to promote energy intake and storage. A significant association has been identified between impaired serotonergic signaling and obesity [15].
The KIR3DL1 gene encodes one of the killer cell immunoglobulin-like receptors (KIR), which are surface receptors of natural killer cells that bind to major histocompatibility complex type I (MHC I) ligands [16]. The rs78852323 and rs52788585640 variants have been demonstrated to be associated with a reduction in adipsin levels [17]. Adipsin is a cytokine secreted by adipocytes that plays a role in the activation of the alternative complement pathway (18). It catalyzes the release of complement factor C3a, which stimulates insulin production in pancreatic beta cells. A reduction in serum adipsin levels has been observed in individuals with T2D and beta cell failure. Moreover, adipsin plays a role in facilitating glucose uptake and enhancing triglyceride synthesis in adipocytes [19]. Mice with a hypothalamic model of obesity have been observed to exhibit deficiencies in adipsin, while in humans, adipsin levels have been demonstrated to be positively correlated with obesity [20].
The IFNK gene encodes interferon κ, a type I interferon (IFN-I) that plays a role in the immune response. The suppressor of cytokine signaling 3 (SOCS3) gene plays a pivotal role in regulating IFN-I and is also involved in the signaling pathways of leptin, ghrelin, and proinflammatory cytokines, whose levels are elevated in obesity [22]. It can thus be postulated that SOCS3 activation and altered systemic leptin levels may be responsible for the reduced response to IFN-I, as well as other immune dysfunctions, in obese individuals. In vitro studies have demonstrated a reduction in the production of IFN-I in obese individuals, which is associated with an increase in SOCS3 and elevated leptin levels. It can be postulated that disturbances in this signaling cascade may be a contributing factor to the recurrence of obesity after surgery.
The LILRB4 gene encodes a leukocyte immunoglobulin-like receptor that is expressed on immune cells, wh ere it binds to MHC I molecules on antigen-presenting cells, thereby suppressing the immune response [14]. Inhibition of FTO, a gene associated with obesity, has been demonstrated to suppress the expression of LILRB4. In a study of multiple populations, C. L. Saunders et al. [25] identified an association between FTO SNPs and elevated BMI.
The RBBP6 gene encodes the retinoblastoma tumor suppressor protein (rRB), which plays a role in regulating cell proliferation. Its expression is positively correlated with distant metastasis in numerous malignancies, including colorectal, ovarian, and breast cancer [26]. Furthermore, RBBP6 functions as a negative regulator of the transcription factor p53, which serves as an essential link in numerous signaling cascades within the cell. In particular, its role in the development of insulin resistance and T2D in obesity has been described in detail elsewhere [27, 28]. It is also noteworthy that p53 has been demonstrated to suppress glycolysis. Furthermore, the LUC7L2 gene has been identified as a regulator of glycolysis. It seems reasonable to hypothesize that RBBP6 and LUC7L2 may be associated with obesity and related conditions (T2D, glucose tolerance) through their effect on glucose metabolism in the cell.
The HLA-DQB1 gene encodes one of the MHC proteins whose variants have been associated with obesity. Currently, the associations with specific phenotypes and the clinical relevance of the genes listed below have not been described in the literature. Our study demonstrated that the following genes may be associated with the long-term outcome of LSG:
OR5H15 encodes one of the olfactory receptors [31];
CCDC187 encodes a protein putatively involved in the regulation of microtubule organization in the centrosome [14];
PMIS2 encodes a transmembrane protein [14];
SAMD11 encodes a protein involved in the negative regulation of DNA transcription [14];
PTCHD3 and AKR7L are pseudogenes [14];
LRRIQ3 is associated with schizophrenia [32];
PDE6B is associated with twilight blindness [33]; it is involved in the differentiation of primitive intestinal tube cells [34];
ABCC12 is associated with idiopathic chronic cholestasis [35];
ANKRD30 encodes the NY-BR-1 protein whose increased expression correlates with breast cancer progression [36];
MAMDC4 encodes one of the membrane proteins thought to be involved in intercellular trafficking, cell adhesion, gastrointestinal embryogenesis, and is associated with ankylosing spondylitis [37].
Further research is required to elucidate the function of these genes in the pathogenesis of postoperative obesity recurrence.
Conclusions
The question of identifying preoperative predictors of sustained weight loss after bariatric surgery remains unresolved. The results of preoperative examinations are inadequate for accurately predicting the outcome of surgical treatment for obesity. It is unlikely that a prognostic model can be constructed based on the results of laboratory and instrumental tests, anthropometry, objective examination data, and analysis of comorbidities, given that overweight affects the aforementioned parameters [11]. It is therefore imperative to employ consistent or minimally altered indicators of obesity, including the genetic profile, when searching for predictive factors.
About the authors:
Vyacheslav A. Vetoshkin — surgeon of the Department of Surgery, St. Petersburg City Hospital No. 40; 9, Borisov str., Sestroretsk, St. Petersburg, 197706, Russian Federation; Junior Researcher at the Research Laboratory of Interventional Pulmonology, St. Petersburg Research Institute of Phthisiopulmonology; 2-4, Ligovsky Avenue, St. Petersburg, 191036, Russian Federation; ORCID iD 0000-0001-5346-9257.
Dmitry V. Gladyshev — Dr. Sc. (Med.), Deputy Chief Medical Officer for Surgery, St. Petersburg City Hospital No. 40; 9, Borisov str., Sestroretsk, St. Petersburg, 197706, Russian Federation; Associate Professor of the Department of Hospital
Surgery, Military Medical Academy; 6, Academika Lebedeva
str., St. Petersburg, 194044, Russian Federation; Associate Professor of the Department of Postgraduate Education of the Medical Institute, St. Petersburg State University; 7-9, Universitetskaya emb., St. Petersburg, 199034, Russian Federation; ORCID iD 0000-0001-5318-2619.
Natalia N. Sushentseva — biologist of the Research Department, St. Petersburg City Hospital No. 40; 9, Borisov str., Sestroretsk, St. Petersburg, 197706, Russian Federation; ORCID iD 0000-0002-5100-5229.
Svetlana V. Apalko — C. Sc. (Bio.), Head of the Research Laboratory of Translational Biomedicine, Research Department of Innovative and Conversion Programs, St. Petersburg City Hospital No. 40; 9, Borisov str., Sestroretsk, St. Petersburg, 197706, Russian Federation; Senior Researcher of the Medical Institute, St. Petersburg State University; 7-9, Universitetskaya emb., St. Petersburg, 199034, Russian Federation; ORCID iD 0000-0002-3853-4185.
Valentin S. Shimansky — biologist of the Research Department, of St. Petersburg City Hospital No. 40; 9, Borisov str., Sestroretsk, St. Petersburg, 197706, Russian Federation; Junior Researcherof the Medical Institute, St. Petersburg State University; 7-9, Universitetskaya emb., St. Petersburg, 199034, Russian Federation; ORCID iD 0000-0001-5662-8663.
Mikhail E. Moiseev — surgeon of the Department of Surgery, St. Petersburg City Hospital No. 40; 9, Borisov str., Sestroretsk, St. Petersburg, 197706, Russian Federation; ORCID iD 0000-0002-6180-6695.
Andrey D. Gladyshev — clinical resident of the Second Department of Advanced Surgery, Military Medical
Academy; 6, Academika Lebedeva str., St. Petersburg, 194044, Russian Federation; ORCID iD 0009-0006-6562-5871.
Sergey A. Kovalenko — surgeon of the Department of Surgery, St. Petersburg City Hospital No. 40; 9, Borisov str., Sestroretsk, St. Petersburg, 197706, Russian Federation; ORCID iD 0000-0002-5850-0599.
Rashid H. Askerkhanov — C. Sc. (Med.), Head of the Center for Bariatric Surgery, A.S. Loginov Moscow Clinical Research Center; 86, build. 6, Entusiastov highroad, Moscow, 111123, Russian Federation; ORCID iD 0000-0002-6266-5855.
Sergey G. Shcherbak — Dr. Sc. (Med.), Professor, Chief Medical Officer of St. Petersburg City Hospital No. 40; 9, Borisov str., Sestroretsk, St. Petersburg, 197706, Russian Federation; Head of the Department of Postgraduate Education of the Medical Institute, St. Petersburg State University; 7-9, Universitetskaya emb., St. Petersburg, 199034, Russian Federation; ORCID iD 0000-0001-5036-1259.
Bogdan N. Kotiv — Dr. Sc. (Med.), Professor, Deputy Head for Educational and Research Work, Military Medical Academy; 6, Academika Lebedeva str., St. Petersburg, 194044, Russian Federation; ORCID iD 0000-0001-5609-0517.
Contact information: Vyacheslav A. Vetoshkin, e-mail: vetoshkinslava@gmail.com.
Financial Disclosure: no authors have a financial or property interest in any material or method mentioned.
There is no conflict of interest.
Received 05.05.2023.
Revised 01.06.2023.
Accepted 27.06.2023.
2. Obesity and overweight. (Electronic resource.) URL: https://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight (access date: 20.01.2024).
3. Дедов И.И., Шестакова М.В., Галстян Г.Р. Распространенность сахарного диабета 2 типа у взрослого населения России (исследование NATION). Сахарный диабет. 2016;19(2):104–112.Dedov I.I., Shestakova M.V., Galstyan G.R. The prevalence of type 2 diabetes mellitus in the adult population of Russia (NATION study). Diabetes mellitus. 2016;19(2):104–112 (in Russ.). DOI: 10.14341/DM2004116-17.
4. El Ansari W., Elhag W. Weight Regain and Insufficient Weight Loss After Bariatric Surgery: Definitions, Prevalence, Mechanisms, Predictors, Prevention and Management Strategies, and Knowledge Gaps-a Scoping Review. Obes Surg. 2021;31(4):1755–1766. DOI: 10.1007/s11695-020-05160-5.
5. Gupta S.R., Zhou Y., Wadden T.A. et al. A Systematic Review of Genetic Correlates of Weight Loss After Bariatric Surgery. Obes Surg. 2021;31(10):4612–4623. DOI: 10.1007/s11695-021-05585-6.
6. Yang P.J., Chen C.L., Chen C.N. et al. Early weight loss as a predictor of 3-year weight loss and weight regain in patients with good compliance after sleeve gastrectomy. Surg Obes Relat Dis. 2021;17(8):1418–1423. DOI: 10.1016/j.soard.2021.03.023.
7. Carbajo M.A., Jiménez J.M., Luque-de-León E. et al. Evaluation of Weight Loss Indicators and Laparoscopic One-Anastomosis Gastric Bypass Outcomes. Sci Rep. 2018;8(1):1961. DOI: 10.1038/s41598-018-20303-6.
8. Мирзорустамова А.М., Чернов А.Н., Ветошкин В.А. и др. Динамика антропометрических показателей у пациентов с ожирением при кор-рекционной терапии трофологического статуса после бариатрической хирургии. РМЖ. 2023;8:2–7.Mirzorustamova A.M., Chernov A.N., Vetoshkin V.A. et al. Changes in anthropometric parameters in obese patients during management of nutritional status after bariatric surgery. RMJ. 2023;8:2–7 (in Russ.).
9. Основы бариатрической медицины: практическое руководство. Под ред. Фишмана М.Б. СПб.: Издательско-полиграфическая ассоциация высших учебных заведений; 2019.Fundamentals of bariatric medicine: a practical guide. Fishman M.B., ed. St. Petersburg: Publishing and Printing Association of Higher Educational Institutions; 2019 (in Russ.).
10. Lei Y., Yang J., Li H. et al. Changes in glucose-lipid metabolism, insulin resistance, and inflammatory factors in patients with autoimmune thyroid disease. J Clin Lab Anal. 2019;33(7):e22929. DOI: 10.1002/jcla.22929.
11. Ветошкин В.А., Гладышев Д.В., Моисеев М.Е. и др. Предоперационные предикторы рецидива ожирения после лапароскопической продольной резекции желудка. Хирургия. Журнал им. Н.И. Пирогова. 2023;(9):40–44.Vetoshkin V.A., Gladyshev D.V., Moiseev M.E. et al. Preoperative predictors of obesity recurrence after laparoscopic gastrectomy. Pirogov Russian Journal of Surgery. 2023;(9):40–44 (in Russ.). DOI: 10.17116/hirurgia202309140. PMID: 37707330.
12. Tsetsos F., Roumeliotis A., Tsekmekidou X. et al. Genetic variation in CARD8, a gene coding for an NLRP3 inflammasome-associated protein, alters the genetic risk for diabetic nephropathy in the context of type 2 diabetes mellitus. Diab Vasc Dis Res. 2020;17(6):1479164120970892. DOI: 10.1177/1479164120970892.
13. Joo N.S., Kim K.N., Kim K.S. Serum CA125 concentration has inverse correlation with metabolic syndrome. J Korean Med Sci. 2011;26(10):1328–1332. DOI: 10.3346/jkms.2011.26.10.1328.
14. National Library of Medicine. Gene. (Electronic resource.) URL: https://www.ncbi.nlm.nih.gov/gene (access date: 13.03.2023).
15. Van Galen K.A., Ter Horst K.W., Serlie M.J. Serotonin, food intake, and obesity. Obes Rev. 2021;22(7):e13210. DOI: 10.1111/obr.13210.
16. Jiang Q., Sun L., Lu Y. et al. Identification of related long non-coding RNAs and mRNAs in subclinical hypothyroidism complicated with type 2 diabetes by transcriptome analysis — a preliminary study. Endokrynol Pol. 2020;71(3):213–226. DOI: 10.5603/EP.a2020.0025.
17. Meeks K.A.C., Bentley A.R., Gouveia M.H. et al. Genome-wide analyses of multiple obesity-related cytokines and hormones informs biology of cardiometabolic traits. Genome Med. 2021;13(1):156. DOI: 10.1186/s13073-021-00971-2.
18. Xu Y., Ma M., Ippolito G.C. et al. Complement activation in factor D-deficient mice. Proc Natl Acad Sci U S A. 2001;98(25):14577–14582. DOI: 10.1073/pnas.261428398.
19. Milek M., Moulla Y., Kern M. et al. Adipsin Serum Concentrations and Adipose Tissue Expression in People with Obesity and Type 2 Diabetes. Int J Mol Sci. 2022;23(4):2222. DOI: 10.3390/ijms23042222.
20. Napolitano A., Lowell B.B., Damm D. et al. Concentrations of adipsin in blood and rates of adipsin secretion by adipose tissue in humans with normal, elevated and diminished adipose tissue mass. Int J Obes Relat Metab Disord. 1994;18(4):213–218. PMID: 8044195.
21. Mesev E.V., LeDesma R.A., Ploss A. Decoding type I and III interferon signalling during viral infection. Nat Microbiol. 2019;4(6):914–924. DOI: 10.1038/s41564-019-0421-x.
22. Gao Y., Zhao H., Wang P. et al. The roles of SOCS3 and STAT3 in bacterial infection and inflammatory diseases. Scand J Immunol. 2018;88(6):e12727. DOI: 10.1111/sji.12727.
23. Terán-Cabanillas E., Hernández J. Role of Leptin and SOCS3 in Inhibiting the Type I Interferon Response During Obesity. Inflammation. 2017;40(1):58–67. DOI: 10.1007/s10753-016-0452-x.
24. Su R., Dong L., Li Y. et al. Targeting FTO Suppresses Cancer Stem Cell Maintenance and Immune Evasion. Cancer Cell. 2020;38(1):79–96.e11. DOI: 10.1016/j.ccell.2020.04.017.
25. Saunders C.L., Chiodini B.D., Sham P. et al. Meta-analysis of genome-wide linkage studies in BMI and obesity. Obesity (Silver Spring). 2007;15(9):2263–2275. DOI: 10.1038/oby.2007.269.
26. Xiao C., Wu G., Zhou Z. et al. RBBP6, a RING finger-domain E3 ubiquitin ligase, induces epithelial-mesenchymal transition and promotes metastasis of colorectal cancer. Cell Death Dis. 2019;10(11):833. DOI: 10.1038/s41419-019-2070-7.
27. Ntwasa M. Retinoblastoma Binding Protein 6, Another p53 Monitor. Trends Cancer. 2016;2(11):635–637. DOI: 10.1016/j.trecan.2016.10.003.
28. Itahana Y., Itahana K. Emerging Roles of p53 Family Members in Glucose Metabolism. Int J Mol Sci. 2018;19(3):776. DOI: 10.3390/ijms19030776.
29. Jourdain A.A., Begg B.E., Mick E. et al. Loss of LUC7L2 and U1 snRNP subunits shifts energy metabolism from glycolysis to OXPHOS. Mol Cell. 2021;81(9):1905–1919.e12. DOI: 10.1016/j.molcel.2021.02.033.
30. Prieto M., Krochik A.G., Chaler E. et al. Obesidad y factores de riesgo del síndrome metabólico en jóvenes con diabetes tipo 1 [Obesity and risk factors for metabolic syndrome in young people with type 1 diabetes]. Medicina (B Aires). 2012;72(4):291–297 (in Spanish). PMID: 22892080.
31. Barnes I.H.A., Ibarra-Soria X., Fitzgerald S. et al. Expert curation of the human and mouse olfactory receptor gene repertoires identifies conserved coding regions split across two exons. BMC Genomics. 2020;21(1):196. DOI: 10.1186/s12864-020-6583-3.
32. Curtis D., Vine A.E., McQuillin A. et al. Case-case genome-wide association analysis shows markers differentially associated with schizophrenia and bipolar disorder and implicates calcium channel genes. Psychiatr Genet. 2011;21(1):1–4. DOI: 10.1097/YPG.0b013e3283413382.
33. Sangermano R., Biswas P., Sullivan L.S. et al. Identification of a novel large multigene deletion and a frameshift indel in PDE6B as the underlying cause of early-onset recessive rod-cone degeneration. Cold Spring Harb Mol Case Stud. 2022;8(7):a006247. DOI: 10.1101/mcs.a006247.
34. Lee D.H., Ko J.J., Ji Y.G. et al. Proteomic identification of RREB1, PDE6B, and CD209 up-regulated in primitive gut tube differentiated from human embryonic stem cells. Pancreas. 2012;41(1):65–73. DOI: 10.1097/MPA.0b013e3182223e35.
35. Pham D.H., Kudira R., Xu L. et al. Deleterious Variants in ABCC12 are Detected in Idiopathic Chronic Cholestasis and Cause Intrahepatic Bile Duct Loss in Model Organisms. Gastroenterology. 2021;161(1):287–300.e16. DOI: 10.1053/j.gastro.2021.03.026.
36. Balafoutas D., zur Hausen A., Mayer S. et al. Cancer testis antigens and NY-BR-1 expression in primary breast cancer: prognostic and therapeutic implications. BMC Cancer. 2013;13:271. DOI: 10.1186/1471-2407-13-271.
37. Pasternak A.J., Hamonic G.M., Van Kessel A., Wilson H.L. Postnatal regulation of MAMDC4 in the porcine intestinal epithelium is influenced by bacterial colonization. Physiol Rep. 2016;4(21):e13018. DOI: 10.14814/phy2.13018.
This work is licensed under a Creative Commons «Attribution» 4.0 License.
