Figures
Abstract
Introduction
Insulin resistance (IR) could be regarded as a therapeutic target for metabolic diseases. Therefore, multiple therapeutic strategies that target IR should be applied to provide a more effective means of treatment. It aims to determine Tuina’s efficacy and safety for IR through this systematic review and meta-analysis.
Methods
From the inception to July 31, 2023, we will search four English databases (Pubmed, Embase, Cochrane Central Register of Controlled Trials, Web of Science) and two Chinese databases (China National Knowledge Infrastructure and the Chinese Science and Technology Periodical Database). We will search and include studies of both human and animal models that evaluate Tuina’s effects on insulin sensitivity or resistance. Data selection, data extraction, and risk of bias assessment will be made by two independent reviewers. We will evaluate the methodological quality of all included studies and conduct meta-analyses using Review Manager Software 5.4.1.
Citation: Zhao Z, Yan J, Ding Y, Wang Y, Li Y (2023) Tuina (Chinese massage) for insulin resistance and sensitivity: A protocol for systematic review and meta-analysis of animal and human studies. PLoS ONE 18(7): e0288414. https://doi.org/10.1371/journal.pone.0288414
Editor: Ricardo Ney Oliveira Cobucci, UFRN: Universidade Federal do Rio Grande do Norte, BRAZIL
Received: March 14, 2023; Accepted: June 21, 2023; Published: July 20, 2023
Copyright: © 2023 Zhao et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: No datasets were generated or analysed during the current study. All relevant data from this study will be made available upon study completion.
Funding: The study is supported in part by the State Administration of Traditional Chinese Medicine of People’s Republic of China (http://www.natcm.gov.cn/), grant number JDZX2015058 and Heilongjiang Provincial Administration of Traditional Chinese Medicine (http://tcm.hlj.gov.cn/), grant number ZHY2020-130 and ZHW2022-110 funded to YL. The funders did not and will not have a role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
Introduction
As a commonly seen pathological condition, insulin resistance (IR) is primarily associated with resistance to insulin-mediated glucose disposal [1]. In recent years, it has been demonstrated that IR is associated with a broad spectrum of chronic morbidities, including obesity, metabolic syndrome (MetS), type 2 diabetes mellitus (T2DM), polycystic ovarian syndrome (PCOS), and cardiovascular diseases [2,3].
There is a close connection between obesity and IR, a complex interplay of genetic and environmental factors seems to be involved in obesity-induced insulin resistance [4], and the pandemic of obesity caused by modern lifestyles has increased the prevalence of IR [5,6]. In the U.S., the incidence of IR increased by 35.1% in nondiabetic adult [7]; in obese children and adolescence, the incidence of IR reaches 47.1% to 52.1% [8,9]. In developing countries, notably in Asia, there is a rapid increase in diseases associated with IR [10].
MetS, also known as insulin resistance syndrome, consists of a group of metabolic abnormalities, including glucose metabolism disorder, hypertension, dyslipidaemia and obesity [11,12] and it is believed that IR is responsible for the occurrence of MetS components [13]. In the circumstances of IR, target tissues including skeletal muscle, liver, and adipose tissue cannot provide an expected response under normal insulin level; therefore, pancreatic beta-cells secrete more insulin to overcome hyperglycaemia [14]. When pancreatic beta-cells cannot secrete enough insulin to overcome IR, T2DM manifests [14].
Althoughthe exact etiology remains incompletely comprehended, IR is widely acknowledged as an important underlying mechanism associated with T2DM [15]; and in T2DM, IR disrupts glycaemic homeostasis, causing pancreatic beta cell failure [16]. Overproduction of cytokines and reactive oxygen species could contribute to IR, which could be associated with MetS [17].
Even though not included in the diagnostic criteria, IR is a very common condition in PCOS patients. It is reported that the incidence of IR is 59.3% in normal-weight subjects, 77.5% in overweight subjects, and 93.9% in obese subjects [18]. However, the molecular mechanisms of IR in PCOS seem to vary from other disorders such as obesity and T2DM [19].
Despite the fact that numerous researchers are studying IR, there is little underlying knowledge about its origins and development. Various mechanisms have been proposed, including peripheral IR and defective insulin secretion [20].
As one of the common causes of MetS, IR should be considered a therapeutic target for metabolic diseases, including diabetes [21,22]. Thus, attenuating IR has always been the foremost therapeutic goal. Therapeutic strategies for IR include changing dietary-lifestyle habits, applying pharmaceutical and surgical approaches, and complementary and alternative medicine therapies [23–27].
Chinese massage (also known as Tuina) is a traditional form of physical therapy in Chinese medicine that traces its origins to 220 BC. Tuina follows the theory of Chinese medicine: the practitioner may brush, knead, roll, press, and rub the areas [28]. From the perspective of modern medicine, Tuina can dilate blood vessels, promote blood flow, improve microcirculation [29], promote and improve insulin secretion [30–32], improve the regulation function of the central nervous system [33,34] and autonomic nervous system [35], boostimmunity [36,37], strengthen metabolism in the body [38], and ensure thatglucose in muscle tissue is fully utilized to achieve the purpose of lowering blood sugar and treating diabetes [39,40].
The potential effects of Tuina on IR have been studied previously in both human [41,42] and animal studies [39,40], and its benefits have been reported, but the efficacy of Tuina in treating IR arouses controversy. Therefore, the purpose of the present study is to evaluate the effectiveness and safety of Tuina in treating IR.
Methods
Study registration
This systematic review was prospectively registered on PROSPERO (CRD42022360128).This protocol is reported according to the guidelines of the preferred reporting items for systematic review and meta-analysis protocols (PRISMA-P) [43] and is followed the structure provided in the Systematic Review Protocol for Animal Intervention Studies [44].
Inclusion criteria
Types of studies.
This study will include both randomized controlled trial (RCT) and non-randomized controlled trial (NRCT) published in English and Chinese. There will be no restriction on dates.
Types of patients and animal models.
We will include studies of both human and animal models that evaluate Tuina’s effects on insulin sensitivity or resistance. No restrictions will be placed on the patients’ age, gender, location, race, disease, and the course of the disease. Models of all animals will be included, regardless of their species or size.
Types of interventions and comparator(s)/control.
This study will include Tuina, guided by Chinese medicine theory, without restrictions on treatment types or duration. The comparators will consist of no treatment, sham Tuina, placebo Tuina point control, and other active interventions, including medications, exercise, and other complementary and alternative medicines. We will also include studies comparing Tuina in combination with another intervention to the same other intervention alone.
Types of outcomes.
The primary outcome is the homeostasis model assessment of insulin resistance (HOMA-IR) since it is the most commonly used to assess IR in clinical practice [45].
Other outcomes include GDR (glucose disposal rate) evaluated by hyperinsulinemic-euglycemic clamp, fasting plasma glucose (FBG), fasting insulin (FINS), glucose tolerance test (GTT), insulin challenge test (ICT), body weight (BW), body mass index (BMI), other insulin resistance surrogate indices such as QUICKI (quantitative insulin sensitivity check index) and adverse events.
Collection and analysis of data
Search strategy.
Pubmed, Embase, Cochrane Central Register of Controlled Trials, Web of Science, China National Knowledge Infrastructure (CNKI), and the Chinese Science and Technology Periodical Database (VIP) will be searched from inception to July 31, 2023. A detailed search strategy is shown in Table 1.
Data selection.
We will enter the results of all searches into one single EndNote library, and there will be an identification and removal of duplicate studies. Two review authors (ZZ and JY) will review the titles and abstracts of each study independently, and the third review author (YD) will assist in resolving any disagreements. We will request additional information from the authors of the studies if necessary. PRISMA flow chart (Fig 1) illustrates the screening process.
Data extraction.
A standardized spreadsheet will be created before data extraction. Two qualified authors (YW and YL) will extract data from all included studies and fillout the data extraction form. We will extract the following information: (1) features of the study, such as location, setting, study design, sample size, duration, funding sources, and objectives study characteristics, including location, setting, study design, size, duration, and study objectives; (2) features of human subjects,including age, sex, BMI,condition, duration, activity and exercise status, and dietary habits; (3) features of animal models, including species, age, body mass, source, sex, genotype, typeof condition, and modeling method; (4) interventions for treatment and control, such as the type of Tuina method used, the type of control used, the duration, frequency, medication administration, and dosage; (5) outcome characteristics for IR, including the type of measurement, sample sizes, data at baseline and end-of-treatment, intervention adherence, dropout rates and reasons, and the number and nature of adverse events. We will extract measures of central tendency and dispersion from figures in the articles using Web Plot Digitizer 4.5 (https://apps.automeris.io/wpd/), if necessary. If there is insufficient or ambiguous data, an email requesting additional information or clarification will be sent to the authors of the original studies. A third reviewer (YD) will resolve any disagreements raised.
Risk of bias assessment.
In human trials, we will assess the risk of bias using the Cochrane Risk of Bias 2.0 (RoB 2.0) tool [46] for RCTs and the Risk Of Bias In Non-randomized Studies of Interventions (ROBINS-I) tool [47] for NRCTs. We will assess the risk of bias in animal studies using the Systematic Review Centre for Laboratory Animal Experimentation (SYRCLE) tool [48]. Biases will be classified as low risk, high risk, or some concerns as described in ROB 2.0.Accordingto the ROBINS-I, the overall risk of bias judgment for the outcome being assessed will be low risk, moderate risk, serious risk, critical risk, and no information. Based on SYRCLE statement, the studies will be classified as low bias, high bias, or unclear bias. Two authors (ZZ and YL) will assess the risk of bias independently, and any disagreements will be discussed further by all authors.
Statistical analysis
Assessment of heterogeneity.
To assess heterogeneity in the included studies, forest plots will be inspected visually. Cochran’s Q and I2 tests will be used to assess statistical heterogeneity across studies. I2> 50% indicates statistically significant heterogeneity exist, and the overall treatment effect will be determined by a meta-analysis using a random-effects model. If the I2 is<50%, a fixed-effects model will be used. In case of heterogeneity, a subgroup analysis will be conducted.
Synthesis of data.
Detailed information about the study subjects, interventions, and outcomes of the studies will be presented in the form of summary tables and descriptive text in the review.We will conduct meta-analyses using Review Manager Software 5.4.1. To determine treatment effects, we will calculate the risk ratio (RR) with 95% confidence intervals (CI) for dichotomous outcomes and the mean difference (MD) with a 95% CI for continuous data.A standardized mean differences (SMDs) analysis with a 95% CI will be performed if the outcomes are measured on different scales.
Sensitivity analysis.
Sensitivity analysis will be performed based on the overall risk of bias if there has been sufficient research for each intervention and outcome. We will recalculate the estimates, excluding studies with a high or unclear risk of bias in specific domains.
Subgroup analysis.
We will perform a subgroup analysis to identify reasons for heterogeneity according to variations in the treatment, disease/condition and controls.
Quality of evidence.
Each outcome will be evaluated using GRADE (Grading of Recommendations Assessment, Development and Evaluation) [49]. Rating the quality of the evidence will begin with study design, there are five domains can downgrade the quality and three domains can upgrade the quality (Fig 2) [50]. Two independent investigators (ZZ and YL) will independently evaluatethe quality of the evidence. The quality will be graded as “high,” “moderate,” “low,” or “very low”.
Discussion
In this study, we will comprehensively review the studies of Tuina on IR, including both human and animal studies. It will help guide future research and clinical decisions by generating evidence for the efficacy and safety of Tuina in treating IR.
Supporting information
S1 Checklist. PRISMA-P (Preferred Reporting Items for Systematic review and Meta-Analysis Protocols) 2015 checklist: Recommended items to address in a systematic review protocol*.
https://doi.org/10.1371/journal.pone.0288414.s001
(DOC)
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