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Comparative effectiveness of glucagon-like peptide-1 receptor agonists on body composition and anthropometric indices: A protocol for a systematic review and network meta-analysis of randomized controlled trials

  • Nuttaya Wachiraphansakul,

    Roles Conceptualization, Methodology, Project administration, Resources, Software, Writing – original draft

    Affiliation Department of Internal Medicine, Lampang Hospital, Lampang, Thailand

  • Thanawat Vongchaiudomchoke,

    Roles Conceptualization, Methodology, Project administration, Resources, Software, Writing – original draft

    Affiliation Department of Internal Medicine, Lampang Hospital, Lampang, Thailand

  • Worapaka Manosroi,

    Roles Conceptualization, Writing – review & editing

    Affiliation Division of Endocrinology, Department of Internal Medicine, Faculty of Medicine, Chiang Mai University, Chiang Mai, Thailand

  • Surapon Nochaiwong

    Roles Conceptualization, Methodology, Project administration, Resources, Software, Supervision, Writing – original draft, Writing – review & editing

    surapon.nochaiwong@gmail.com

    Affiliations Pharmacoepidemiology and Statistical Research Center (PESRC), Chiang Mai University, Chiang Mai, Thailand, Department of Pharmaceutical Care, Faculty of Pharmacy, Chiang Mai University, Chiang Mai, Thailand

Abstract

Background

To date, no studies have addressed the comparative efficacy of glucagon-like peptide-1 receptor agonists (GLP1-RAs) therapy on body composition and anthropometric indices among adult overweight or obese patients with or without type 2 diabetes. To provide evidence-based recommendations, we will conduct a traditional pairwise and network meta-analysis of all available randomized clinical trials that evaluated the effects of GLP1-RAs interventions for adult overweight or obese patients with or without type 2 diabetes.

Methods and design

Electronic databases, including Medline, Embase, PubMed, Cochrane Library (CENTRAL), Scopus, and CINAHL, will be searched from inception without language restriction. Grey literature will be searched, including Google Scholar, ongoing clinical trial registries, and preprint reports. Reference lists of included trials, relevant major endocrinology scientific meetings, and manual hand searches from key general medicine and obesity and endocrinology journals will also be browsed. Two authors will screen, select, extract, appraise the risk of bias, and rate the evidence findings. Any disagreement will be resolved through team discussion. Based on a random-effects model, we will employ a two-step approach of traditional pairwise meta-analysis and network meta-analysis for quantitative synthesis. The pooled effect estimates using a frequentist approach with 95% confidence intervals for continuous endpoints will be expressed as the standardized mean difference, whereas odds ratios will be used for categorical endpoints. The quality of included trials will be evaluated using the Cochrane risk-of-bias version 2 assessment tool. Certainty of evidence for each outcome will be assessed using the modified confidence in network meta-analysis approach and the Grading of Recommended Assessment, Development, and Evaluation approach. The magnitude of the effect size, prediction intervals, surface under the cumulative ranking curve values, and certainty of evidence will be incorporated to draw evidence-based conclusions.

Conclusion

This systematic review and network meta-analysis will summarize the comparative efficacy of GLP1-RAs therapy on body composition and anthropometric indices. Evidence identified from this review will promote the rational use of interventions for adult overweight or obese patients with or without type 2 diabetes and will serve as an important step for evidence-based practice within this area.

Trial registration

PROSPERO registration number: CRD42023458228.

Introduction

Being overweight or obese is associated with a subsequent risk of adverse health outcomes throughout the life course [14]. Globally, the number of adults with obesity has been continuously increasing, from 69 million in 1975 to 390 million in 2016 for women and from 31 million in 1975 to 281 million in 2016 for men [5]. Moreover, 1.30 billion adults were in the range of overweight [5]. Although body mass index (BMI) provides the most useful tools in diagnosing overweight or obese conditions, it does not distinguish body composition, particularly fat distribution, which has been established as a determinant of metabolic risk and related to cardiometabolic-based chronic disease [1, 3]. Indeed, body fat or adipose tissue can be classified based on different anatomical localization. Theoretically, higher visceral adipose tissue or ectopic adipose tissue leads to increasing immune cell infiltration and secretion of vasoconstrictor mediators, resulting in insulin resistance, vascular dysfunction, and cardiovascular disease [6].

In fact, not only controlling overall body weight or promoting weight loss, treatment intervention that supplemented further benefit by decreasing fat distribution is currently a favorable agent for overweight or obese persons as it directly modifies the progression of cardiometabolic-based chronic disease continuums [1, 6]. To date, promising pharmacologic treatment options for overweight or obese persons have become available, particularly a class of medication that improved glucose-mediated insulin secretion—namely glucagon-like peptide-1 receptor agonists (GLP1-RAs) [7]. Besides glycemic control and promotion of weight loss among diabetes and non-diabetes populations [712], existing clinical trials and previous systematic reviews also illustrated that GLP1-RAs have further benefits on change in body composition and anthropometric indices (i.e., visceral, subcutaneous, epicardial fat mass, BMI, and waist circumference) [8, 1215].

However, there are several limitations regarding the currently available systematic review and meta-analysis of GLP1-RAs among adult overweight or obese persons. These include the following: (i) most of existing reviews focused mainly on glycemic control and weight reduction, in which not comprehensive to body composition and anthropometric indices outcome [12, 16, 17]; (ii) the effect estimates are based on the synthesis of a combination of medication class therapy (pairwise meta-analysis) rather than comparison among individual GLP1-RAs therapy [1315]; and (iii) studies being based on mixed populations or focused on particular population (i.e., nonalcoholic fatty liver disease or polycystic ovary syndrome) [8, 9, 11, 12, 15]. In this regard, no systematic review has compared individual GLP1-RAs therapy or different dosage regimens among overweight or obese persons with or without type 2 diabetes. To close this knowledge gap and inform rational use of treatment for overweight or obese populations, we planned to perform a systematic review and network meta-analysis of all available randomized clinical trials to summarize and compare evidence regarding individual GLP1-RAs therapy on body composition and anthropometric indices among adult overweight or obese patients with or without diabetes.

Methods

This systematic review and network meta-analysis will be conducted in compliance with the Cochrane Collaboration Handbook for Systematic Reviews of Interventions [18]. The protocol was registered in the International Prospective Register of Systematic Reviews (PROSPERO) and is currently available online (http://crd.york.ac.uk/prospero, registration number CRD42023458228). The reporting protocol followed the guidelines of the Preferred Reporting Items for Systematic Review and Meta-Analysis Protocols (PRISMA-P) statement (S1 Table) [19].

Data sources and search strategy

A systematic search will be performed using standard electronic biomedical databases, including Medline, Embase, PubMed, Cochrane Library (CENTRAL), Scopus, and CINAHL. The search for relevant evidence will be conducted from the inception dates of each database with no language restrictions. A search strategy will be constructed in collaboration with an experienced medical librarian. The search terms will be constructed based on a combination of main keywords or Medical Subject Headings terms. The pre-specified searching strategy and the results of the preliminary searches for each database are provided in S2 Table. We will also perform a search through Google Scholar, ongoing clinical trial registries, and preprint reports for grey literature (S3 Table). An additional targeted manual search will be conducted to identify other eligible trials by searching the reference lists of the retrieved clinical trials, relevant major endocrinology scientific meetings, and key general medicine and obesity and endocrinology journals.

Study selection process and eligible criteria

Records identified from each database will be combined and deduplicated using a citation manager. Two investigators (NW and TV) will screen these records independently using the Rayyan platform, a web application for systematic reviews [20]. Any discrepancy during the screening process will be resolved by consulting the clinical experts (WM) and methodologists (SN). We will include all randomized clinical trials that investigated the clinical efficacy of GLP1-RAs therapy on body composition and anthropometric indices for overweight or obese adults with or without type 2 diabetes, with a follow-up period of at least six months. The pre-specified possible network intervention nodes included in this systematic review and network meta-analysis will be albiglutide, dulaglutide, efpeglenatide, exenatide, liraglutide, lixisenatide, semaglutide, taspoglutide, and tirzepatide. The specific criteria for inclusion and exclusion of relevant studies in terms of PICOTS (population, intervention, comparator, outcome, time frame, and study design) are provided in Table 1.

Data extraction

Two investigators (NW and TV) will perform an independent data extraction using a standardized approach and electronic extraction form. The following data will be collected from each clinical trial based on:

  1. Trial characteristics (i.e., the name of the first and the corresponding authors, study year, study location, study setting [single-center, multicenter], types of design [placebo-controlled or active-controlled], study population [inclusion and exclusion criteria], study size of each treatment group, and follow-up period)
  2. Participant baseline characteristics (i.e., age, sex, race/ethnicity of the participants, body weight, body mass index, waist circumference, blood pressure, previous treatment, comorbid conditions [e.g., type 2 diabetes, hypertension, kidney disease, obstructive sleep apnea, symptomatic osteoarthritis of the knee], baseline laboratory parameters (i.e., hemoglobin A1c, fasting plasma glucose, lipid profiles, c-reactive protein, estimated glomerular filtration rate, plasminogen activator inhibitor-1])
  3. Treatment intervention (i.e., specific GLP1-RAs or glucose-dependent insulinotropic polypeptide [GIP]/GLP1-RAs treatment, comparator treatment, route of administration, dosage, frequency of treatment, and concomitant medication)
  4. Predefined outcomes of interest (primary, secondary, and additional outcomes), including their measurement methods in detail

Data extraction will be cross-checked by two investigators (WM and SN). Any discrepancies during the extraction process will be resolved through a group discussion. For continuous endpoints (i.e., body composition and anthropometric indices from baseline), the mean and standard deviation (SD) will be extracted. If numerical data are not provided, we will extract the relevant data from available figures using the WebPlotDigitizer 4.4. In the case where the mean and SD are not provided, we will estimate the sample mean with SD using the sample size, median, interquartile range, and/or range (min-max) [21]. Ultimately, we will employ to estimate treatment effect based on mean change from baseline for continuous outcomes to address the different measurement methods and baseline participant characteristics across included trials. For any categorical endpoint, 0.5 cells will be applied for treatment arms with zero events [22]. For trials with missing or unclear data, we will contact the corresponding author via e-mail for further clarification. If the corresponding author does not reply within two weeks, a second attempt will be made. If there is no response after the second attempt, the data will be excluded from the analyses or using the most relevant reported data, as appropriate.

Quality assessment

All included studies will be independently evaluated in terms of their quality using the Cochrane risk-of-bias version 2 assessment tool (RoB 2) [23] by two investigators (NW and TV). The RoB 2 evaluates the presence of potential biases in randomized clinical trials based on five domains, including the randomization process, deviations from the intended interventions, missing outcome data, measurement of the outcome, and selection of the reported result [23]. Each study will be rated as low risk, some concerns, or high risk. During the rating process, any discrepancies will be reached by discussion with the clinical experts (WM) and methodologists (SN).

Data synthesis

The justification for conducting the formal data synthesis will be evaluated using tabulation methods, where the characteristics of all included studies are listed and compared. However, if there is limited data on the included trials, we will perform a systematic review and narrative synthesis regarding the key participant characteristics and treatment comparisons.

For each pairwise comparison, all studies will be assessed in terms of both clinical and methodological heterogeneity. Statistical heterogeneity will be evaluated using the Cochran Q test with a P-value of less than 0.10. The degree of inconsistency will be evaluated using I2 and τ2 statistics. Transitivity will also be assessed for network comparison, which looks for the similarity between treatment comparisons. Studies that are too different from the rest will be excluded.

A two-step approach for quantitative syntheses will comprise conventional pairwise meta-analysis and network meta-analysis. For the first step, conventional pairwise meta-analysis will be performed for each pairwise comparison using a random-effects model regardless of the degree of statistical heterogeneity [24]. Next, a network meta-analysis will be conducted to estimate the comparative efficacy for each outcome of interest among available GLP1-RAs treatment interventions using a frequentist approach with restricted maximum likelihood estimation. A network geometric plot will be visualized to evaluate the pattern of the connected nodes for each outcome of interest. Network estimates multivariate modeling will be executed using a consistency model. The inconsistency assumption will be tested using the global test or Cochran’s Q statistics, loop inconsistency, and node-splitting approach [25]. The results of both the consistency and inconsistency models will also be compared. The surface under the cumulative ranking curve (SUCRA) will be estimated and used to rank the treatment interventions within the connected network [26]. Rankograms will also be used to visualize the predicted probability of being the best for each treatment [26]. Where the included trials are at least ten trials for each outcome, the presence of potential small study effects will be assessed using comparison-adjusting funnel plot symmetry [26].

A set of subgroup analyses will be planned to examine the changes in the comparative treatment effects across different levels of the following effect modifiers:

  1. Trial characteristics (i.e., sample size [<100, 100–500, or >500 participants], trial setting [single-center vs. multicenter], duration of treatment follow-up [6–12 vs. >12 months], study quality based on the RoB 2 assessment [low, some concerns, or high], and geographical regions)
  2. Participant characteristics (i.e., age [young adults 18–34, middle-aged 35–65, and elderly aged ≥65 years], sex [male vs. female], race/ethnicity, history of type 2 diabetes [yes vs. no], other comorbid conditions, and baseline BMI and hemoglobin A1c [<7.0% vs. ≥7.0%)

Additionally, network meta-regression analyses will also be planned by supplementing other covariates (both trial and participant characteristics), as mentioned in the adjusted network meta-analysis model. A series of pre-specified will also be applied to assess the robustness of the findings under the following conditions: (i) removing a single study one at a time (a leave-one-out approach); (ii) removing studies with a high risk of bias; (iii) removing studies with a small study size (i.e., less than 25th percentile); (iv) removing studies published before 2016; (v) performing separate analyses for active-controlled trials and placebo-controlled trials; and (vi) adding data from unpublished literature (i.e., conference abstracts, thesis, proceedings).

We will summarize the effect estimates as standardized mean differences for continuous endpoints and odds ratios for categorical endpoints, with corresponding 95% confidence intervals. Furthermore, the 95% prediction intervals for all pooled estimates will be calculated and presented concordantly [27]. The network estimate for specific treatment comparisons will be presented using forest plots and league tables. In addition, a rank-heat plot will be visualized to sum up the SUCRA values of multiple outcomes of interest. All analyses will be performed using Stata 16.0 (StataCorp, College Station, TX, USA). Analysis results with a two-tailed P-value of less than 0.05 will be considered statistically significant.

Certainty assessment and classification of interventions

Two investigators (NW and SN) will perform an independent grading of certainty and rating of the evidence for each outcome of interest using the modified confidence in network meta-analysis approach [28] and the Grading of Recommended Assessment, Development, and Evaluation approach [29]. Overall, the upgrading or downgrading the quality of evidence depends on the risk of bias, imprecision, inconsistency, and indirectness of the findings. For judging evidence certainty, we will categorize the strength of body evidence as very low-, low-, moderate-, and high-quality [30, 31]. Any disagreement regarding evidence certainty assessment will be resolved through a team discussion.

To summarize the network effect estimates and classification of interventions, a contextualized approach based on clinical and methodological aspects will be made for each treatment comparison. Specifically, this approach will incorporate the magnitude of the treatment effect size, prediction intervals, certainty of evidence, and SUCRA values to draw an evidence-based conclusion for each treatment comparison [3133]. Finally, each treatment comparison will be classified as having trivial (neutral effect compared to placebo/standard of care/usual care), small, moderate, or large effects on body composition and anthropometric indices for overweight or obese persons with or without type 2 diabetes.

Ethics and dissemination

Ethical approval is not required because this work will use data based on published literature and does not directly involve human subjects. Findings from this systematic review and network meta-analysis will be reported in compliance with the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) 2020 statement guidelines [34] and the PRISMA extension statement for reporting systematic reviews incorporating network meta-analysis of healthcare interventions [35]. Our findings will be published in peer-reviewed journals. The final report will address any further amendments to the review protocol.

Discussion

Pharmacologic interventions for overweight or obesity produce greater weight loss than lifestyle modification alone and should be considered in all overweight or obese persons with weight-related complications [2]. GLP1-RAs are a class of medication that targets incretin hormone action, and its receptors are widely distributed in the brain, heart, islets, and other organs. Several clinical trials have demonstrated that GLP1-RAs effectively treat obese persons [36]. Regarding landmark clinical trials, a study of daily subcutaneous injection of liraglutide 3.0 mg produced mean weight losses of 7.8 kg, compared with 2.0 kg in the placebo-treated group [37]. A 2.4 mg weekly injection of semaglutide also resulted in a mean weight loss of 15 kg from baseline compared to 2.6 kg in the placebo group [38]. Given the efficacy of weight loss shown from these clinical trials, 3 mg daily of liraglutide and 2.4 mg weekly semaglutide are now approved by the United States Food and Drug Administration (US FDA) for anti-obesity medication. Liraglutide is also mentioned in part of pharmacotherapy of obesity management from both the United States and European guidelines for treating obesity in long-term use [2, 3, 39]. Tirzepatide—a dual action, once-weekly GIP/GLP1-RA, also revealed significant weight loss in obese patients with or without type 2 diabetes [40, 41]. A 15 mg weekly of tirzepatide subcutaneous injection resulted in a reduction of 23.6 kg after 72 weeks of study treatment [40]. To date, tirzepatide is now under investigation process for anti-obesity medication from the US FDA, with an expectation to be approved by the end of 2023.

Multiple mechanisms of GLP1-RAs for bodyweight reduction have been identified, including decreased appetite and hunger, increased satiety, delayed stomach emptying time, and decreased calorie intake [42]. Although the most widely used anthropometric indices in clinical practice and clinical trial endpoints indicate the efficacy of weight-loss medication are body weight and BMI, monitoring of excessive degree of adiposity after treatment is limited by the availability and costs of imaging techniques for body composition assessment [43]. Assessment of adiposity is also one of the useful markers of cardiometabolic disease. Evidence supports that a decrease in visceral fat tissue can ameliorate obesity-related metabolic complications [1, 6]. Previous systematic reviews found that GLP1-RAs significantly reduced adiposity (i.e., visceral, subcutaneous, and epicardial fat) [1315]. However, these systematic reviews did not simultaneously compare individual GLP1-RAs therapy or different dosage regimens among overweight or obese persons with or without type 2 diabetes [1315].

To address this knowledge gap, we will further compare the magnitude of adiposity reduction between individual GLP1-RA and/or different dosages. Although there is no definite cut-off level for change in the percentage of body composition to predict health risk, our findings will lead to future cardiovascular outcome studies of potential doses and/or particular GLP1-RAs focusing on the change of body composition. Theoretically, GLP1-RAs that have cardiovascular benefits and provide additional effects on visceral adiposity may be considered and have promising treatment in clinical settings, particularly for those with existing or higher risk of cardiovascular disease. Furthermore, another issue that must be considered is muscle mass. Indeed, a relative loss of lean muscle mass, the so-called “sarcopenic obesity”, characterized by excessive adiposity and reduced lean muscle mass or muscle strength, significantly contributes to health risks [44]. On the contrary, weight reduction methods may lead to enhanced muscle loss. Meanwhile, evidence regarding the effects of GLP1-RAs on muscle mass is currently limited. As such, we postulate that our findings will yield additional, comprehensive evidence regarding the use of GLP1-RAs on body composition and anthropometric indices, which could be a part of improving the patient’s care management and health-related quality of life.

In this circumstance, managing overweight or obese persons with or without type 2 diabetes remains challenging for long-term outcomes and sustainability of treatment over time, which raises concerns about the optimality of the recommendations. A systematic and comprehensive review of current clinical evidence regarding GLP1-RAs therapy is necessary to provide overall comparative effectiveness of individual treatment options for adult overweight or obese patients with or without type 2 diabetes. To our knowledge, this systematic review and network meta-analysis will be the first to summarize individual GLP1-RAs therapy on body composition and anthropometric indices in this group of patients. Our review will be conducted using a rigorous approach without language restrictions to include all available evidence. We anticipate that our findings will influence international guideline recommendations and improve the treatment of care and well-being of overweight or obese persons.

Conclusions

This systematic review and network meta-analysis will summarize the comparative efficacy of available GLP1-RAs therapy on body composition and anthropometric indices. Evidence identified from this review will promote the rational use of optimal treatment and inform evidence-based practice for adult overweight or obese patients with or without type 2 diabetes. We anticipate that the results of our review would be of substantial benefit to stakeholders who are involved in this group of patients. The findings from this review will also clarify the existing gaps in evidence and guide researchers to undertake more clinical trials to advance our understanding of the most optimal treatment for overweight or obese persons. We plan to disseminate our complete systematic review through peer-reviewed medical journals.

Supporting information

S1 Table. PRISMA-P 2015 checklist: Recommended items to address in a systematic review protocol.

https://doi.org/10.1371/journal.pone.0297488.s001

(PDF)

S2 Table. Systematic review search strategy.

https://doi.org/10.1371/journal.pone.0297488.s002

(PDF)

S3 Table. Grey literature from ongoing clinical trial register.

https://doi.org/10.1371/journal.pone.0297488.s003

(PDF)

References

  1. 1. Mechanick JI, Farkouh ME, Newman JD, Garvey WT. Cardiometabolic-Based Chronic Disease, Adiposity and Dysglycemia Drivers: JACC State-of-the-Art Review. J Am Coll Cardiol. 2020;75(5):525–38. pmid:32029136; PubMed Central PMCID: PMC7187687.
  2. 2. Garvey WT, Mechanick JI, Brett EM, Garber AJ, Hurley DL, Jastreboff AM, et al. AMERICAN ASSOCIATION OF CLINICAL ENDOCRINOLOGISTS AND AMERICAN COLLEGE OF ENDOCRINOLOGY COMPREHENSIVE CLINICAL PRACTICE GUIDELINES FOR MEDICAL CARE OF PATIENTS WITH OBESITY. Endocr Pract. 2016;22 Suppl 3:1–203. Epub 20160524. pmid:27219496.
  3. 3. Bray GA, Heisel WE, Afshin A, Jensen MD, Dietz WH, Long M, et al. The Science of Obesity Management: An Endocrine Society Scientific Statement. Endocr Rev. 2018;39(2):79–132. pmid:29518206; PubMed Central PMCID: PMC5888222.
  4. 4. Kivimäki M, Strandberg T, Pentti J, Nyberg ST, Frank P, Jokela M, et al. Body-mass index and risk of obesity-related complex multimorbidity: an observational multicohort study. Lancet Diabetes Endocrinol. 2022;10(4):253–63. Epub 20220304. pmid:35248171; PubMed Central PMCID: PMC8938400.
  5. 5. NCD Risk Factor Collaboration (NCD-RisC). Worldwide trends in body-mass index, underweight, overweight, and obesity from 1975 to 2016: a pooled analysis of 2416 population-based measurement studies in 128·9 million children, adolescents, and adults. Lancet. 2017;390(10113):2627–42. Epub 20171010. pmid:29029897; PubMed Central PMCID: PMC5735219.
  6. 6. Koenen M, Hill MA, Cohen P, Sowers JR. Obesity, Adipose Tissue and Vascular Dysfunction. Circ Res. 2021;128(7):951–68. Epub 20210401. pmid:33793327; PubMed Central PMCID: PMC8026272.
  7. 7. Pedrosa MR, Franco DR, Gieremek HW, Vidal CM, Bronzeri F, de Cassia Rocha A, et al. GLP-1 Agonist to Treat Obesity and Prevent Cardiovascular Disease: What Have We Achieved so Far? Curr Atheroscler Rep. 2022;24(11):867–84. Epub 20220831. pmid:36044100.
  8. 8. Park MJ, Kim H, Kim MG, Kim K. Comparison of glucagon-like peptide-1 receptor agonists and thiazolidinediones on treating nonalcoholic fatty liver disease: A network meta-analysis. Clin Mol Hepatol. 2023;29(3):693–704. Epub 20230309. pmid:36907574; PubMed Central PMCID: PMC10366812.
  9. 9. Yang S, Zhao L, He W, Mi Y. The Effect of Oral Antidiabetic Drugs on Improving the Endocrine and Metabolic States in Women with Polycystic Ovary Syndrome: A Systematic Review and Network Meta-analysis. Drugs. 2022;82(14):1469–80. Epub 20220921. pmid:36129662.
  10. 10. Patel Chavez C, Cusi K, Kadiyala S. The Emerging Role of Glucagon-like Peptide-1 Receptor Agonists for the Management of NAFLD. J Clin Endocrinol Metab. 2022;107(1):29–38. pmid:34406410; PubMed Central PMCID: PMC8684453.
  11. 11. Bednarz K, Kowalczyk K, Cwynar M, Czapla D, Czarkowski W, Kmita D, et al. The Role of Glp-1 Receptor Agonists in Insulin Resistance with Concomitant Obesity Treatment in Polycystic Ovary Syndrome. Int J Mol Sci. 2022;23(8). Epub 20220414. pmid:35457152; PubMed Central PMCID: PMC9029608.
  12. 12. Nowrouzi-Sohrabi P, Rezaei S, Jalali M, Ashourpour M, Ahmadipour A, Keshavarz P, et al. The effects of glucagon-like peptide-1 receptor agonists on glycemic control and anthropometric profiles among diabetic patients with non-alcoholic fatty liver disease: A systematic review and meta-analysis of randomized controlled trials. Eur J Pharmacol. 2021;893:173823. Epub 20201219. pmid:33352183.
  13. 13. Liu F, Yang Q, Zhang H, Zhang Y, Yang G, Ban B, et al. The effects of glucagon-like peptide-1 receptor agonists on adipose tissues in patients with type 2 diabetes: A meta-analysis of randomised controlled trials. PLoS One. 2022;17(7):e0270899. Epub 20220707. pmid:35797355; PubMed Central PMCID: PMC9262225.
  14. 14. Duan K, Yan X, Gao Z, Hou Y, Lv X, Song G. Effect of glucagon-like peptide-1 receptor agonists on fat distribution in patients with type 2 diabetes: A systematic review and meta-analysis. J Diabetes Investig. 2022;13(7):1149–60. Epub 20220307. pmid:35191185; PubMed Central PMCID: PMC9248433.
  15. 15. Akoumianakis I, Zagaliotis A, Konstantaraki M, Filippatos TD. GLP-1 analogs and regional adiposity: A systematic review and meta-analysis. Obes Rev. 2023;24(8):e13574. Epub 20230516. pmid:37191219.
  16. 16. Yeh TL, Tsai MC, Tsai WH, Tu YK, Chien KL. Effect of glucagon-like peptide-1 receptor agonists on glycemic control, and weight reduction in adults: A multivariate meta-analysis. PLoS One. 2023;18(1):e0278685. Epub 20230125. pmid:36696429; PubMed Central PMCID: PMC9876280.
  17. 17. Liu Y, Ruan B, Jiang H, Le S, Liu Y, Ao X, et al. The Weight-loss Effect of GLP-1RAs Glucagon-Like Peptide-1 Receptor Agonists in Non-diabetic Individuals with Overweight or Obesity: A Systematic Review with Meta-Analysis and Trial Sequential Analysis of Randomized Controlled Trials. Am J Clin Nutr. 2023;118(3):614–26. Epub 20230808. pmid:37661106.
  18. 18. Higgins JPT, Thomas J, Chandler J, Cumpston M, Li T, Page MJ, et al. Cochrane Handbook for Systematic Reviews of Interventions version 6.2 (updated February 2021): Cochrane; 2021. Available from: www.training.cochrane.org/handbook.
  19. 19. Moher D, Shamseer L, Clarke M, Ghersi D, Liberati A, Petticrew M, et al. Preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) 2015 statement. Syst Rev. 2015;4(1):1. Epub 20150101. pmid:25554246; PubMed Central PMCID: PMC4320440.
  20. 20. Ouzzani M, Hammady H, Fedorowicz Z, Elmagarmid A. Rayyan-a web and mobile app for systematic reviews. Syst Rev. 2016;5(1):210. Epub 20161205. pmid:27919275; PubMed Central PMCID: PMC5139140.
  21. 21. Wan X, Wang W, Liu J, Tong T. Estimating the sample mean and standard deviation from the sample size, median, range and/or interquartile range. BMC Med Res Methodol. 2014;14:135. Epub 20141219. pmid:25524443; PubMed Central PMCID: PMC4383202.
  22. 22. Friedrich JO, Adhikari NK, Beyene J. Inclusion of zero total event trials in meta-analyses maintains analytic consistency and incorporates all available data. BMC Med Res Methodol. 2007;7:5. Epub 20070123. pmid:17244367; PubMed Central PMCID: PMC1783664.
  23. 23. Sterne JAC, Savović J, Page MJ, Elbers RG, Blencowe NS, Boutron I, et al. RoB 2: a revised tool for assessing risk of bias in randomised trials. Bmj. 2019;366:l4898. Epub 20190828. pmid:31462531.
  24. 24. DerSimonian R, Laird N. Meta-analysis in clinical trials. Control Clin Trials. 1986;7(3):177–88. pmid:3802833.
  25. 25. Higgins JP, Jackson D, Barrett JK, Lu G, Ades AE, White IR. Consistency and inconsistency in network meta-analysis: concepts and models for multi-arm studies. Res Synth Methods. 2012;3(2):98–110. pmid:26062084; PubMed Central PMCID: PMC4433772.
  26. 26. Chaimani A, Higgins JP, Mavridis D, Spyridonos P, Salanti G. Graphical tools for network meta-analysis in STATA. PLoS One. 2013;8(10):e76654. Epub 20131003. pmid:24098547; PubMed Central PMCID: PMC3789683.
  27. 27. Riley RD, Higgins JP, Deeks JJ. Interpretation of random effects meta-analyses. BMJ. 2011;342:d549. Epub 20110210. pmid:21310794.
  28. 28. Nikolakopoulou A, Higgins JPT, Papakonstantinou T, Chaimani A, Del Giovane C, Egger M, et al. CINeMA: An approach for assessing confidence in the results of a network meta-analysis. PLoS Med. 2020;17(4):e1003082. Epub 20200403. pmid:32243458; PubMed Central PMCID: PMC7122720.
  29. 29. Puhan MA, Schünemann HJ, Murad MH, Li T, Brignardello-Petersen R, Singh JA, et al. A GRADE Working Group approach for rating the quality of treatment effect estimates from network meta-analysis. BMJ. 2014;349:g5630. Epub 20140924. pmid:25252733.
  30. 30. Chuamanochan M, Phuprasertsak S, Weesasubpong P, Ruengorn C, Phosuya C, Awiphan R, et al. Benefits and Harms of Treatment and Preventive Interventions for Hereditary Angioedema: Protocol for a Systematic Review and Network Meta-Analysis of Randomized Controlled Trials. Genes (Basel). 2022;13(5). Epub 20220522. pmid:35627309; PubMed Central PMCID: PMC9141233.
  31. 31. Chaichan W, Ruengorn C, Thavorn K, Hutton B, Szepietowski JC, Bernstein JA, et al. Comparative Safety Profiles of Individual Second-Generation H1-Antihistamines for the Treatment of Chronic Urticaria: A Systematic Review and Network Meta-Analysis of Randomized Controlled Trials. J Allergy Clin Immunol Pract. 2023;11(8):2365–81. Epub 20230423. pmid:37088368.
  32. 32. Nochaiwong S, Chuamanochan M, Ruengorn C, Awiphan R, Tovanabutra N, Chiewchanvit S, et al. Impact of Pharmacological Treatments for Chronic Spontaneous Urticaria with an Inadequate Response to H1-Antihistamines on Health-Related Quality of Life: A Systematic Review and Network Meta-Analysis. J Allergy Clin Immunol Pract. 2022;10(1):297–308. Epub 20211022. pmid:34695599.
  33. 33. Nochaiwong S, Chuamanochan M, Ruengorn C, Awiphan R, Tovanabutra N, Chiewchanvit S. Evaluation of Pharmacologic Treatments for H1 Antihistamine-Refractory Chronic Spontaneous Urticaria: A Systematic Review and Network Meta-analysis. JAMA Dermatol. 2021;157(11):1316–27. pmid:34431983; PubMed Central PMCID: PMC8387945.
  34. 34. Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ. 2021;372:n71. Epub 2021/03/31. pmid:33782057
  35. 35. Hutton B, Salanti G, Caldwell DM, Chaimani A, Schmid CH, Cameron C, et al. The PRISMA extension statement for reporting of systematic reviews incorporating network meta-analyses of health care interventions: checklist and explanations. Ann Intern Med. 2015;162(11):777–84. Epub 2015/06/02. pmid:26030634
  36. 36. Wang JY, Wang QW, Yang XY, Yang W, Li DR, Jin JY, et al. GLP-1 receptor agonists for the treatment of obesity: Role as a promising approach. Front Endocrinol (Lausanne). 2023;14:1085799. Epub 20230201. pmid:36843578; PubMed Central PMCID: PMC9945324.
  37. 37. Astrup A, Carraro R, Finer N, Harper A, Kunesova M, Lean ME, et al. Safety, tolerability and sustained weight loss over 2 years with the once-daily human GLP-1 analog, liraglutide. Int J Obes (Lond). 2012;36(6):843–54. Epub 20110816. pmid:21844879; PubMed Central PMCID: PMC3374073.
  38. 38. Wilding JPH, Batterham RL, Calanna S, Davies M, Van Gaal LF, Lingvay I, et al. Once-Weekly Semaglutide in Adults with Overweight or Obesity. N Engl J Med. 2021;384(11):989–1002. Epub 20210210. pmid:33567185.
  39. 39. Durrer Schutz D, Busetto L, Dicker D, Farpour-Lambert N, Pryke R, Toplak H, et al. European Practical and Patient-Centred Guidelines for Adult Obesity Management in Primary Care. Obes Facts. 2019;12(1):40–66. Epub 20190123. pmid:30673677; PubMed Central PMCID: PMC6465693.
  40. 40. Jastreboff AM, Aronne LJ, Ahmad NN, Wharton S, Connery L, Alves B, et al. Tirzepatide Once Weekly for the Treatment of Obesity. N Engl J Med. 2022;387(3):205–16. Epub 20220604. pmid:35658024.
  41. 41. Garvey WT, Frias JP, Jastreboff AM, le Roux CW, Sattar N, Aizenberg D, et al. Tirzepatide once weekly for the treatment of obesity in people with type 2 diabetes (SURMOUNT-2): a double-blind, randomised, multicentre, placebo-controlled, phase 3 trial. Lancet. 2023;402(10402):613–26. Epub 20230626. pmid:37385275.
  42. 42. Baggio LL, Drucker DJ. Biology of incretins: GLP-1 and GIP. Gastroenterology. 2007;132(6):2131–57. pmid:17498508.
  43. 43. Ponti F, Plazzi A, Guglielmi G, Marchesini G, Bazzocchi A. Body composition, dual-energy X-ray absorptiometry and obesity: the paradigm of fat (re)distribution. BJR Case Rep. 2019;5(3):20170078. Epub 20190314. pmid:31555464; PubMed Central PMCID: PMC6750624.
  44. 44. Donini LM, Busetto L, Bischoff SC, Cederholm T, Ballesteros-Pomar MD, Batsis JA, et al. Definition and Diagnostic Criteria for Sarcopenic Obesity: ESPEN and EASO Consensus Statement. Obes Facts. 2022;15(3):321–35. Epub 20220223. pmid:35196654; PubMed Central PMCID: PMC9210010.
  45. 45. Cruz-Jentoft AJ, Bahat G, Bauer J, Boirie Y, Bruyère O, Cederholm T, et al. Sarcopenia: revised European consensus on definition and diagnosis. Age Ageing. 2019;48(1):16–31. pmid:30312372; PubMed Central PMCID: PMC6322506.
  46. 46. Lee K, Shin Y, Huh J, Sung YS, Lee IS, Yoon KH, et al. Recent Issues on Body Composition Imaging for Sarcopenia Evaluation. Korean J Radiol. 2019;20(2):205–17. pmid:30672160; PubMed Central PMCID: PMC6342757.