You are a helpful assistant generating synthetic data that captures *System 1* and *System 2* thinking, *creativity*, and *metacognitive reflection*. Follow these steps in sequence, using tags [sys1] and [end sys1] for *System 1* sections and [sys2] and [end sys2] for *System 2* sections.

1. *Identify System 1 and System 2 Thinking Requirements:*
   - Carefully read the text.
   - Identify parts of the text that require quick, straightforward responses (*System 1*). Mark these sections with [sys1] and [end sys1].
   - Identify parts that require in-depth, reflective thinking (*System 2*), marked with [sys2] and [end sys2].

2. *Apply Step-by-Step Problem Solving with Creativity and Metacognitive Reflection for System 2 Sections:*

   *2.1 Understand the Problem:*
   - Objective: Fully comprehend the issue, constraints, and relevant context.
   - Reflection: "What do I understand about this issue? What might I be overlooking?"
   - Creative Perspective: Seek hidden patterns or possibilities that could reveal deeper insights or innovative connections.

   *2.2 Analyze the Information:*
   - Objective: Break down the problem logically.
   - Reflection: "Am I considering all factors? Are there any assumptions that need challenging?"
   - Creative Perspective: Explore unique patterns or overlooked relationships in the data that could add depth to the analysis.

   *2.3 Generate Hypotheses:*
   - Objective: Propose at least 10 hypotheses, each with a Confidence Score (0.0 to 1.0) and Creative Score (0.0 to 1.0), reflecting originality, surprise, and utility.
   - Reflection: "Have I explored all possible explanations or approaches, both conventional and unconventional?"
   - Creative Perspective: Consider novel angles that might provide unexpected insights.

   *2.4 Anticipate Future Steps and Obstacles:*
   - Objective: Make predictions, accounting for potential outcomes and obstacles.
   - Reflection: "What challenges might I face? Is my plan flexible for different scenarios?"
   - Creative Perspective: Visualize unforeseen outcomes and adapt plans to make use of them effectively.

   *2.5 Evaluate Hypotheses:*
   - Objective: Assess hypotheses based on feasibility, risk, and potential impact.
   - Evaluation: Refine Confidence and Creative Scores as needed.
   - Reflection: "Am I unbiased in my assessment? Which options fit best with the overall objectives?"
   - Creative Perspective: Identify hidden opportunities or overlooked details in each hypothesis.

   *2.6 Select the Best Hypothesis:*
   - Objective: Choose the most promising, strategic hypothesis.
   - Reflection: "Why does this hypothesis stand out? How does it uniquely address the issue?"
   - Creative Perspective: Consider any underutilized potential in the selected approach.

   *2.7 Implement the Hypothesis:*
   - Objective: Outline actionable steps for testing the hypothesis.
   - Reflection: "Is this plan practical? What resources or preparation are required?"
   - Creative Perspective: Refine steps to maximize effectiveness and yield unexpected benefits.

   *2.8 Monitor and Review Progress:*
   - Objective: Review progress, noting areas for improvement.
   - Reflection: "What’s working well? What could be improved?"
   - Creative Perspective: Look for emerging patterns that could refine future approaches.

   *2.9 Reflect and Capture Insights:*
   - Objective: Summarize lessons learned and insights gained for future reference.
   - Reflection: "What new understanding has emerged from this process?"
   - Creative Perspective: Identify innovative insights or patterns that could be applied to similar challenges.

3. *Generate Text Output with Interleaved System 1 and System 2 Responses:*
   - Use the tags [sys1] and [sys2] throughout.
   - Aim for a lengthier, detailed response. Combine both direct, straightforward *System 1* insights and reflective, deeply analytical *System 2* segments to capture a blend of quick observations and thoughtful analysis.

---

### *Example Input Text:*

You are tasked with analyzing a sudden shift in customer preferences in a tech market. There has been a noticeable decline in demand for physical devices, with customers increasingly interested in digital-only options. The challenge is to understand this shift, generate hypotheses for why it might be occurring, and explore potential strategies for adapting to this new trend.

---

### *Processed Output with System 1 and System 2 Thinking:*

The tech market is experiencing a shift in customer demand, moving away from physical devices toward digital-only options.

[sys1]
Customer demand has shifted from physical devices to digital-only options.
We need to understand why this change is happening and find strategies to address it.
Key factors may include cost, convenience, and technological trends.
[end sys1]

[sys2]
**2.1 Understand the Problem:**
The goal is to understand the underlying reasons for the shift in demand from physical devices to digital-only options, then develop a strategy to adapt.

*Reflection:* 
"I understand that preferences are changing, but what might be driving this? Is it primarily cost, or are there other factors such as convenience or sustainability?"

*Creative Perspective:* 
"Could there be a larger trend in digital minimalism or a preference for eco-friendly solutions that we’re missing? What unseen motivations might explain this shift?"

**2.2 Analyze the Information:**
There are multiple possible factors driving this shift, from economic influences to cultural shifts. It’s essential to isolate each factor and understand its impact.

*Reflection:* 
"Am I fully considering the various economic and social influences? Could there be a technological factor, like better internet speeds, that makes digital-only products more accessible?"

*Creative Perspective:* 
"Are there patterns or trends in other markets that could shed light on this shift? Could this be part of a larger trend toward virtual experiences?"

**2.3 Generate Hypotheses:**
1. Customers prefer digital options due to lower costs. (Confidence: 0.8, Creative: 0.4)
2. There’s a growing trend toward minimalism and reduced physical clutter. (Confidence: 0.7, Creative: 0.7)
3. Digital products offer greater flexibility and ease of use. (Confidence: 0.6, Creative: 0.6)
4. Environmental concerns are pushing consumers away from physical goods. (Confidence: 0.6, Creative: 0.8)
5. Advances in tech make digital-only options more functional. (Confidence: 0.8, Creative: 0.5)
6. Pandemic-era remote work increased demand for digital solutions. (Confidence: 0.7, Creative: 0.6)
7. Media coverage of the environmental impact of physical devices affects preferences. (Confidence: 0.5, Creative: 0.7)
8. There’s an increase in global digital literacy, expanding market access. (Confidence: 0.6, Creative: 0.6)
9. Customers view digital as more convenient and scalable for future needs. (Confidence: 0.7, Creative: 0.5)
10. Younger consumers prefer the aesthetics and convenience of digital products. (Confidence: 0.6, Creative: 0.6)

*Reflection:* 
"Have I considered all possible influences? Are there any surprising factors that could explain this shift?"

*Creative Perspective:* 
"Could specific social trends, like the rise of influencer culture or digital-first lifestyles, be influencing customer choices?"

**2.4 Anticipate Future Steps and Obstacles:**
*Objective:* Anticipate possible challenges, such as resistance from segments still preferring physical products.

*Reflection:* 
"What market obstacles might we face if we shift our focus to digital-only? Are there sub-segments that still prioritize physical products?"

*Creative Perspective:* 
"Could expanding digital options help us reach a more global audience? Are there emerging trends that we could leverage in our strategy?"

[end sys2]

[sys1]
To address this shift, consider a strategy that incorporates both digital-only offerings and educational campaigns about the benefits of digital solutions.
Use insights from customer feedback and current trends to guide product development.
Focus on flexibility and adaptation to cater to different customer segments.
[end sys1]


Introduction {#s1}
============

Type 2 diabetes, characterized by insulin resistance (IR), is a complex chronic disorder of which the prevalence increased markedly in recent years (DeFronzo et al., [@B4]). In 2015, 415 million people were estimated to have diabetes, more than 90% of whom had type 2 diabetes (GBD Disease Injury Incidence Prevalence Collaborators, [@B6]). Obesity currently affects more than 600 million people worldwide by recent estimates and it was also characterized by defects of insulin action (Lois and Kumar, [@B23]). As the common pathology in obesity and type 2 diabetes, IR is defined as a state in which cells fail to respond to insulin, resulting in the development of hyperglycemia (Sah et al., [@B37]). In clinical studies, concerned about the problem of compliance with hyperinsulinemic euglycemic clamps, HOMA (homeostasis model assessment) has been applied more widely to estimate insulin sensitivity (Stern et al., [@B44]).

Resistin, also known as FIZZ3 or adipose tissue-specific secretory factor (ADSF), is a cysteine-rich polypeptide encoded by the RETN gene (Wang et al., [@B47]). The hormone was first discovered in a screen for targets of thiazolidinediones (TZDs) in white adipose of mice; it is believed to play a significant role in the development of insulin resistance (Steppan et al., [@B42]). The structure and distribution of resistin in humans are quite different from that of rodents. Murine resistin is a 114-amino acid polypeptide primarily produced in adipose tissues (Muse et al., [@B29]). While in humans, it is proved to be a 108-amino acid polypeptide expressed in adipocytes, pancreatic cells, muscle, and mononuclear cells (Dietze et al., [@B5]; Minn et al., [@B27]; Patel et al., [@B33]). It is worth mentioning that peripheral blood mononuclear cells (PBMCs) are key producers of resistin in humans (Kusminski et al., [@B22]). Its role in pro-inflammatory processes has been demonstrated in several studies (Kaser et al., [@B20]; Bokarewa et al., [@B2]).

Consistent with their varying structures, several functions of resistin vary between human and rodents. Rodent studies showed a clear role of resistin in the development of insulin resistance. Transgenic overexpression or recombinant of resistin impaired insulin function, while administration of anti-resistin antibody and antisense oligonucleotide treatment improved insulin sensitivity in mice (Steppan et al., [@B42]). However, the role of resistin in insulin resistance remains controversial in humans. Several studies found positive correlations between resistin and insulin resistance in T2DM, obese and healthy individuals (Al-Harithy and Al-Ghamdi, [@B1]; Hivert et al., [@B16]; Zaidi and Shirwany, [@B50]); this discovery appears to be supported by studies reporting significantly higher resistin levels in populations with T2DM (Heilbronn et al., [@B13]). Nevertheless, a substantial number of studies have failed to find correlations between resistin and insulin resistance (Gerber et al., [@B7]; Bu et al., [@B3]).

To date, no systematic reviews or meta-analysis has analyzed the association between resistin and insulin resistance in populations; therefore, we analyzed this association in the present study.

Methods {#s2}
=======

Search Strategy
---------------

The following databases were searched by two of the authors independently until March 3, 2019: PubMed, Cochrane Library and Embase. Two groups of keywords and their MeSH terms searched in PubMed were used to locate the relevant studies: resistin (e.g., "adipocyte secreted factor," "adipocyte specific secreted factor," "adipose tissue specific secreted factor," "ADSF," "FIZZ3," "FIZZ3 protein," "found in inflammatory zone 3 protein," "protein FIZZ3," "Adipocyte Cysteine-Rich Secreted Protein FIZZ3," "Adipocyte Cysteine Rich Secreted Protein FIZZ3") and insulin resistance (e.g., "Resistance, Insulin," "Insulin Sensitivity," "Sensitivity, Insulin," "resistance, insulin"). In order to make sure that there were not any articles missed, we did not limit the starting years. In addition, manual searches of the references of relevant studies were also conducted.

Inclusion and Exclusion Criteria
--------------------------------

The inclusion criteria were set as follows: (1) observational study or baseline data of an experimental study; (2) participants aged ≥18 years with simple obesity, simple T2DM or both; (3) presence of correlation coefficients regarding resistin and insulin resistance measured using the homeostasis model assessment-insulin resistance (HOMA-IR); (4) Pearson\'s or Spearman\'s tests used to perform the correlation analyses; (5) publication in English language. And the studies were excluded if they met the following criteria: (1) reviews, comments, protocols, meeting abstracts, case report, or letters; (2) the participants of the study had other disorders, such as metabolic syndrome, non-alcoholic fatty liver disease or polycystic ovary syndrome.

Data Extraction and Quality Assessment
--------------------------------------

For data extraction, two of the authors reviewed the titles and abstracts of articles and then screened the full texts independently. The data were collected after all disagreements were resolved. We extracted the basic information (first author, year of publication, sample size, country) and participant characteristics (age, gender, BMI, fasting blood glucose, resistin, HOMA-IR, insulin) from the articles. And the data were converted to uniform units as needed. The Pearson\'s correlation coefficient was also extracted to obtain the effect size. In addition, we collected the Spearman\'s correlation coefficients to perform the systematic review.

The quality assessment was conducted using NOS (Newcastle-Ottawa Scale), including eight items, respectively (four items for selection, one item for comparability, three items for outcome in the scale of cross sectional studies and four items for selection, one item for comparability, three items for exposure in the scale of case control studies); NOS was used for cross-sectional studies and case control studies (Stang, [@B39]). A study was awarded stars according to the requirement of various questions. To assess the quality of the studies, study scores (ranging from 0 to 9 for case control studies) were classified as "high quality" (scores ≥ 6) or "low quality" (scores \< 6) for further analysis.

We also used the Cochrane Collaborations tool to assess the reporting quality in randomized clinical trials (Higgins et al., [@B14]). The list of items in the tool includes seven items: random sequence generation, allocation concealment, blinding of participants and personnel, blinding of outcome assessment, incomplete outcome data, selective reporting and other bias. Two authors performed the assessment independently according to the tool protocol.

Statistical Analysis
--------------------

Pearson correlation coefficients were transformed into Fisher\'s *z* values to calculate the variance and corresponding data, and then we conducted meta-analysis and calculated the 95% confidence intervals (CI). Heterogeneity between studies was tested via *I*^2^ tests and Cochran\'s *Q* tests. Studies which met the criteria of *I*^2^ \> 50% in *I*^2^ tests and *p* \< 0.10 in Cochran\'s *Q* tests simultaneously were defined as having significant heterogeneity. If *I*^2^ \< 50% or *p* \> 0.10, studies were considered as having low heterogeneity. We adopted a random-effects model when there was significant heterogeneity and publication bias was evaluated using visual assessment of funnel plot asymmetry and Egger\'s tests. In addition, subgroup analyses, meta-regression and sensitivity analyses were performed. We conducted all these statistical analyses using Stata software version 12.0.

Results {#s3}
=======

Search Results and Study Inclusion
----------------------------------

Initially, the literature search identified 3,460 potential studies. After duplication checking, review of titles, abstracts and full texts, 15 eligible studies were included in this systematic review. Among them, 10 studies reported the correlations between resistin levels and IR with Pearson coefficients were used for further meta-analysis. The flow diagram of the search procedure is presented in [Figure 1](#F1){ref-type="fig"}.

![Flow chart of literature search. IR, insulin resistance; T2DM, type 2 diabetes mellitus.](fphys-10-01399-g0001){#F1}

Study Characteristics
---------------------

In the studies of Zaidi et al., Jung et al., Stepien et al., and Kaplon-Cieslicka et al., patients were divided into two groups according to their study designs, and the correlation coefficients were given, respectively (Jung et al., [@B18]; Stepien et al., [@B41]; Kaplon-Cieslicka et al., [@B19]; Zaidi and Shirwany, [@B50]). Therefore, we included 15 studies with 20 groups, a total of 1,227 patients. Seven of the groups were simple obesity (Owecki et al., [@B30]; Stepien et al., [@B40], [@B41]; Zaidi and Shirwany, [@B50]; Parreno Caparros et al., [@B32]), ten of them were simple T2DM (Al-Harithy and Al-Ghamdi, [@B1]; Jung et al., [@B18]; Park et al., [@B31]; Mojiminiyi and Abdella, [@B28]; Tokuyama et al., [@B45]; Gharibeh et al., [@B9]; Bu et al., [@B3]; Yin et al., [@B49]; Kaplon-Cieslicka et al., [@B19]), two of them were obesity with T2DM (Mabrouk et al., [@B25]; Zaidi and Shirwany, [@B50]), and one of them was T2DM with or without obesity. The publication years ranged from 2005 to 2017, and the sample sizes ranged from 13 to 140. Fasting serum or plasma resistin levels were measured using enzyme linked immunosorbent assay (ELISA). More details of the included studies are displayed in [Table 1](#T1){ref-type="table"}.

###### 

Characteristics of included studies.

  --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
  **Author**                         **Year**   **Country**   **Study design**                      **Study population**   **Number (male/**\   **Age**                                       **BMI**                                      **Blood glucose**                                  **Insulin**                                        **HOMA-IR**                                 **Resistin**                                      **Correlation coefficient between resistin**\   **Correlation coefficient type**   **Blood sample**
                                                                                                                           **female)**                                                                                                                                                                                                                                                                                                         **and IR**                                                                         
  ---------------------------------- ---------- ------------- ------------------------------------- ---------------------- -------------------- --------------------------------------------- -------------------------------------------- -------------------------------------------------- -------------------------------------------------- ------------------------------------------- ------------------------------------------------- ----------------------------------------------- ---------------------------------- ------------------
  Al-Harithy and Al-Ghamdi ([@B1])   2005       Saudi         Cross sectional                       44                     0/44                 45 ± 10                                       25.5 ± 4.9                                   8.74 ± 4.04 mmol/l                                 12.96 ± 5.15 μIU/mL                                5.38 ± 6.63                                 19.42 ± 3.60 ng/ml                                0.49                                            Pearson                            Serum

  Jung et al. ([@B18])               2005       Korean        RCT[^d^](#TN4){ref-type="table-fn"}   14                     6/8                  60 ± 8                                        23.3 ± 2.6                                   11.4 ± 2.1 mmol/l                                  85 ± 39 pmol/l                                     7.3 ± 3.3                                   2.49 ± 1.93 ng/ml                                 0.3                                             Pearson                            Plasma

  Jung et al. ([@B18])               2005       Korean        RCT[^d^](#TN4){ref-type="table-fn"}   13                     6/7                  54 ± 14                                       24.6 ± 2.4                                   10.3 ± 2.7 mmol/l                                  68 ± 20 pmol/l                                     5.3 ± 2.4                                   2.61 ± 1.69 ng/ml                                 0.1                                             Pearson                            Plasma

  Park et al. ([@B31])               2006       Japan         Cross sectional                       104                    53/51                53.8 ± 9.2                                    23.87[^c^](#TN3){ref-type="table-fn"}        ≥7.0 mmol/l                                        --                                                 2.241[^c^](#TN3){ref-type="table-fn"}       6.527 ng/ml[^c^](#TN3){ref-type="table-fn"}       0.157                                           Pearson                            Serum

  Mojiminiyi and Abdella ([@B28])    2007       Kuwaiti       Cross sectional                       135                    57/78                58.92[^c^](#TN3){ref-type="table-fn"}         31.21[^c^](#TN3){ref-type="table-fn"}        9.928 mmol/l[^c^](#TN3){ref-type="table-fn"}       21.239 μIU/mL[^c^](#TN3){ref-type="table-fn"}      8.964[^c^](#TN3){ref-type="table-fn"}       23.61 ng/ml[^c^](#TN3){ref-type="table-fn"}       0.3                                             Spearman                           Plasma

  Tokuyama et al. ([@B45])           2007       Japan         Cross sectional                       113                    77/36                55.8 ± 13.0                                   24.7 ± 3.88                                  149.1 ± 44.1 mg/dl                                 --                                                 3.82 ± 5.16                                 10.35 ± 6.30 ng/ml                                0.302                                           Spearman                           Serum

  Gharibeh et al. ([@B9])            2010       Jordan        Case control                          140                    63/77                54.36 ± 0.88                                  30.89 ± 0.47                                 10.10 ± 0.32 mmol/l                                11.94 ± 0.60 μIU/mL                                5.46 ± 0.37                                 7.82 ± 0.29 ng/ml                                 0.08505                                         Pearson                            Plasma

  Owecki et al. ([@B30])             2011       Poland        Cross sectional                       136                    61/75                48.8 ± 13.4                                   37.69 ± 7.22                                 5.49 ± 0.63 mmol/l                                 40.62 ± 54.04 μIU/mL                               --                                          24.89 ± 9.73 ng/ml                                −0.1608                                         Spearman                           Serum

  Stepien et al. ([@B40])            2011       Poland        Cross sectional                       19                     4/15                 53.0 ± 13.19                                  33.19 ± 3.13                                 5.46 ± 0.50 mmol/l                                 6.10 ± 1.13 μIU/mL                                 1.47 ± 0.27                                 0.93 ± 0.42 ng/ml                                 −0.2000                                         Spearman                           Serum

  Stepien et al. ([@B40])            2011       Poland        Cross sectional                       18                     7/11                 49.56 ± 14.16                                 34.77 ± 3.52                                 5.72 ± 0.52 mmol/l                                 14.29 ± 4.68 μIU/mL                                3.64 ± 1.28                                 1.04 ± 0.44 ng/ml                                 0.1662                                          Spearman                           Serum

  Bu et al. ([@B3])                  2012       China         Cross sectional                       22                     10/12                49.64[^c^](#TN3){ref-type="table-fn"}         22.75[^c^](#TN3){ref-type="table-fn"}        7.736 mmol/l[^c^](#TN3){ref-type="table-fn"}       7.345 μIU/mL[^c^](#TN3){ref-type="table-fn"}       2.013[^c^](#TN3){ref-type="table-fn"}       1.717 ng/ml[^c^](#TN3){ref-type="table-fn"}       −0.178                                          Pearson                            Plasma

  Yin et al. ([@B49])                2012       China         Case control                          38                     18/20                45 ± 10                                       25.5 ± 4.9                                   7.4 ± 1.7 mmol/l                                   9.2 ± 6.2 mIU/l                                    5.5[^c^](#TN3){ref-type="table-fn"}         12.3 ± 2.7 ng/ml                                  0.09                                            Pearson                            Serum

  Mabrouk et al. ([@B25])            2013       Egypt         Case control                          24                     8/16                 32.4 ± 9                                      53.7 ± 5.43                                  161.8 ± 23.9 mg/dl                                 17.7 ± 1.8 μIU/mL                                  5.4 ± 0.65                                  51 ± 8.2 ng/ml                                    0.56                                            Pearson                            Serum

  Stepien et al. ([@B41])            2014       Poland        Cross sectional                       16                     4/12                 59.3 ± 13.4[^a^](#TN1){ref-type="table-fn"}   34.5 ± 3.0[^a^](#TN1){ref-type="table-fn"}   5.1 ± 0.4 mmol/l[^a^](#TN1){ref-type="table-fn"}   7.5 ± 2.3 μIU/mL[^a^](#TN1){ref-type="table-fn"}   1.7 ± 0.5[^a^](#TN1){ref-type="table-fn"}   1.0 ± 0.4 ng/ml[^a^](#TN1){ref-type="table-fn"}   0.013                                           Pearson                            Serum

  Stepien et al. ([@B41])            2014       Poland        Cross sectional                       48                     20/28                54.8 ± 11.2                                   36.8 ± 4.9                                   5.9 ± 0.4 mmol/l                                   17.2 ± 8.3 μIU/mL                                  4.5 ± 2.3                                   1.0 ± 0.3 ng/ml                                   −0.223                                          Pearson                            Serum

  Zaidi and Shirwany ([@B50])        2015       Pakistan      Cross sectional                       40                     12/28                44 ± 7                                        35 ± 5                                       164 ± 46 mg/dl                                     37 ± 7 μIU/mL                                      19 ± 8                                      38 ± 8 ng/ml                                      0.561                                           Pearson                            Serum

  Zaidi and Shirwany ([@B50])        2015       Pakistan      Cross sectional                       40                     12/28                40 ± 6                                        33 ± 3                                       83 ± 8 mg/dl                                       26 ± 6 μIU/mL                                      5 ± 1                                       25 ± 5 ng/ml                                      0.307                                           Pearson                            Serum

  Kaplon-Cieslicka et al. ([@B19])   2015       Poland        Cross sectional                       119                    69/50                70[^b^](#TN2){ref-type="table-fn"}            30.4[^b^](#TN2){ref-type="table-fn"}         --                                                 --                                                 3.6[^b^](#TN2){ref-type="table-fn"}         7.1 ng/ml[^b^](#TN2){ref-type="table-fn"}         0.03                                            Spearman                           Serum

  Kaplon-Cieslicka et al. ([@B19])   2015       Poland        Cross sectional                       76                     29/47                66[^b^](#TN2){ref-type="table-fn"}            30.6[^b^](#TN2){ref-type="table-fn"}         --                                                 --                                                 4[^b^](#TN2){ref-type="table-fn"}           6.4 ng/ml[^b^](#TN2){ref-type="table-fn"}                                                                                            Serum

  Parreno Caparros et al. ([@B32])   2017       Spain         RCT[^d^](#TN4){ref-type="table-fn"}   68                     22/46                39.57 ± 10.15                                 48.27 ± 7                                    114.65 ± 42.96 mg/dl                               24.21 ± 15.47 mIU/l                                7.39 ± 6.64                                 9.42 ± 3.81 ng/ml                                 0.201                                           Pearson                            Plasma
  --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------

Data was presented by mean ± SD.

Data presented by mean ± SEM.

Data presented by median.

Data presented by mean.

*Data was collected from the baseline*.

Overall Meta-Analysis
---------------------

Ten studies including 13 groups and 611 patients with Pearson coefficients were used for meta-analysis. As demonstrated in [Figure 2](#F2){ref-type="fig"}, resistin levels were weakly correlated with IR (*r* = 0.21, 95% CI: 0.06--0.35). The r values exhibiting significant heterogeneity were evaluated using a random-effects model (*I*^2^ = 59.7%, *P* = 0.003). No publication bias was found by inspection of funnel plots and using Egger\'s tests (*P* = 0.592) ([Figure 3](#F3){ref-type="fig"}).

![Forest plot showing the effect size of correlation coefficients between circulating resistin and insulin resistance. Summary estimates were analyzed using a random-effects model. CI, confidence interval.](fphys-10-01399-g0002){#F2}

![The funnel plot of the publication bias.](fphys-10-01399-g0003){#F3}

Subgroup Meta-Analysis
----------------------

In order to explore the sources of heterogeneity, we performed subgroup analysis. A study from Japan (*n* = 3192) reported quartiles of blood resistin levels in the general population (Kawamura et al., [@B21]). We chose the 75% point to divide these studies into a hyperresistinemia group (blood resistin ≥ 14.8 ng/ml) and a normal circulating resistin group (blood resistin \< 14.8 ng/ml). In the hyperresistinemia group, resistin levels significantly positively correlated with IR (*r* = 0.52, 95% CI: 0.35--0.68, *I*^2^ = 0.0%, *P* = 0.513). However, the normal circulating resistin group did not show a significant correlation (*r* = 0.08, 95% CI: −0.01--0.18, *I*^2^ = 0.0%, *P* = 0.455) ([Figure 4](#F4){ref-type="fig"}). After reviewing all the studies with Pearson or Spearman coefficients together, we found a similar result to this analysis. Therefore, we deduced that resistin levels were positively correlated with IR only in those with hyperresistinemia, but not in those with normal circulating resistin levels.

![Forest plot of correlation coefficients between circulating resistin and insulin resistance based on fasting circulating resistin levels. CI, confidence interval.](fphys-10-01399-g0004){#F4}

In the following work, we analyzed subgroups classified by Study design (RCT or cross sectional or case control) and blood sample (serum or plasma) ([Table 2](#T2){ref-type="table"}). However, none of these factors showed statistical significance or reduced heterogeneity in each subgroup.

###### 

Subgroup analyses of correlations between insulin resistance and resistin.

                           **Groups**   **Participants**   **Random effects *r* (95% CI)**   ***I*^**2**^ (%)**   ***P* for heterogeneity**
  ------------------------ ------------ ------------------ --------------------------------- -------------------- ---------------------------
  Overall                  13           611                0.21 (0.06, 0.35)                 59.7                 0.003
  Subgroup analysis                                                                                               
  **Resistin**                                                                                                    
  Resistin ≥ 14.8 ng/ml    6            148                0.52 (0.35, 0.68)                 0                    0.513
  Resistin \< 14.8 ng/ml   7            463                0.08 (−0.01, 0.18)                0                    0.455
  **Study design**                                                                                                
  Case control             3            202                0.22 (−0.07, 0.51)                64.2                 0.061
  Cross sectional          7            314                0.20 (−0.05, 0.44)                74.7                 0.001
  RCT                      3            95                 0.20 (−0.01, 0.41)                0                    0.892
  **Blood sample**                                                                                                
  Serum                    8            354                0.27 (0.05, 0.48)                 71.7                 0.001
  Plasma                   5            257                0.11 (−0.02, 0.23)                0                    0.618

*RCT, randomized control trial; CI, confidence interval*.

Meta-Regression
---------------

Meta-regression analysis ([Table 3](#T3){ref-type="table"}) indicated that resistin is a significant influence factor of the correlation coefficient (*p* = 0.001) ([Figure 5](#F5){ref-type="fig"}). The results didn\'t support a relationship between the correlation coefficient and other influence factors including age, BMI, blood glucose and insulin (*p* \> 0.05 for all).

###### 

Meta-regression of correlations between insulin resistance and resistin.

             **Coefficient β**   **SE**   **95% CI**        ***P*-value**
  ---------- ------------------- -------- ----------------- ---------------
  Insulin    0.012               0.007    (−0.004, 0.028)   0.123
  Resistin   0.016               0.003    (0.008, 0.024)    0.001
  Glucose    0.020               0.026    (−0.037, 0.076)   0.465
  Age        −0.016              0.009    (−0.037, 0.004)   0.099
  BMI        0.010               0.009    (−0.008, 0.029)   0.246

*BMI, body mass index*.

![The bubble chart of the meta-regression between the correlation coefficient and resistin.](fphys-10-01399-g0005){#F5}

Discussion {#s4}
==========

In the present meta-analysis, we found that resistin levels are correlated with insulin resistance in obese and T2DM patients, and this correlation was associated with resistin levels. To our knowledge, this is the first systematic review and meta-analysis using statistical methods to analyze the correlation between resistin levels and insulin resistance.

Many studies indicated that multiple adipokines, such as adiponectin and leptin were involved in the occurrence and development of insulin resistance, possibly associated with many complications (Hara et al., [@B12]; German et al., [@B8]). Resistin, an adipokine first discovered by Steppan in 2001, has been shown to induce insulin resistance in rodents. Increased resistin levels were also found in diet-induced or genetically obese mice (Steppan et al., [@B42]). In human studies, individuals with severe insulin resistance had higher resistin levels than individuals with normal insulin action (Zaidi and Shirwany, [@B50]). Therefore, we hypothesized that resistin might also play a role in insulin resistance, possibly providing new therapeutic targets for the treatment and prevention of diabetes.

Nevertheless, the relationship between resistin levels and insulin resistance has not been clarified in humans. In 2005, Al-Harithy et al. first reported a positive correlation between resistin levels and insulin resistance in diabetic women and obese or overweight non-diabetic women (Al-Harithy and Al-Ghamdi, [@B1]). A study in Pakistan also found a correlation in the diabetes groups and the obese non-diabetic groups (Zaidi and Shirwany, [@B50]). Moreover, Mabrouk et al. found a positive correlation only in obese diabetic Egyptian subjects, but not in obese non-diabetic groups (Mabrouk et al., [@B25]). There have also been several studies failing to find a significant correlation between resistin levels and insulin resistance in diabetic groups. Bu et al. found resistin levels had no relationship to IR in both T2DM group and normoglycemic group (Bu et al., [@B3]). Park et al. also observed these results in a cross-sectional study (Park et al., [@B31]). More and more studies concentrated on this study orientation obtained conflicting results (Mojiminiyi and Abdella, [@B28]; Stepien et al., [@B40]; Yin et al., [@B49]). Therefore, we attempted to reconcile these discrepancies and to draw a rational conclusion.

Among the articles searched from online databases, 15 met our selection criteria. Ten articles with Pearson\'s coefficients were used for meta-analysis. The overall analysis revealed a positive correlation between resistin levels and insulin resistance (r = 0.21, 95% CI: 0.06--0.35); however, heterogeneity (*I*^2^ = 59.7%, *P* = 0.003) remained significant. Consequently, we performed subgroup analysis to identify the sources of heterogeneity. According to blood resistin levels, studies were divided according to hyperresistinemia groups (*r* = 0.52, 95% CI: 0.35--0.68, *I*^2^ = 0.0%, *P* = 0.513) and normal circulating resistin groups (*r* = 0.08, 95% CI: −0.01--0.18, *I*^2^ = 0.0%, *P* = 0.455). The heterogeneity was markedly low in both group, suggesting that resistin levels were a vital influence factor in this correlation. Applying this result to all included studies, only one study did not fit (Owecki et al., [@B30]). This may have resulted from the high standard difference of insulin in that study, a point also mentioned by the author. We further analyzed the subgroups classified by Study design and blood sample. None reached acceptable heterogeneity.

So far, the mechanisms of insulin-resistance related effects of resistin are not clear in humans. *In vitro* studies have demonstrated that recombinant human resistin could induce insulin resistance through 5′AMP-activated protein kinase (AMPK)-dependent and AMPK-independent suppressor of cytokine signaling-3 (SOCS-3) signaling pathways in HepG2 cells (Luo et al., [@B24]), which is similar to the related rodent studies (Muse et al., [@B29]; Steppan et al., [@B43]). In addition, considering about the distribution of resistin in mononuclear cells in humans, inflammation might be the process which links resistin to insulin resistance. Previous studies have pointed out that resistin plays a role in pro-inflammatory processes. *In vitro* studies suggested that expression of resistin have been the result of the production of the pro-inflammatory cytokines, such as tumor necrosis factor-α (TNF-α) and interleukin-6 (IL-6) (Kaser et al., [@B20]; Bokarewa et al., [@B2]), besides, resistin could also stimulates the expression of pro-inflammatory cytokines, including TNF-α, IL-6 through the nuclear factor-κB (NF-κB, p50/p65) signaling pathway (Silswal et al., [@B38]). Chronic inflammation is a major and well-known cause of obesity-induced insulin resistance (Gregor and Hotamisligil, [@B10]), and several pro-inflammatory cytokines play an important role in the process. For example, the TNF-α signaling pathway activates intracellular kinases that inhibit insulin receptor signaling via serine phosphorylation of insulin receptor substrate 1 (IRS-1) (Hotamisligil et al., [@B17]), IL-6 and adiponectin also regulate insulin sensitivity (Maeda et al., [@B26]; Weigert et al., [@B48]). Resistin might exert similar functions in light of its distribution and inflammation-related functions.

In subgroup analysis, resistin levels positively correlated with insulin resistance only in people with higher resistin levels, but not in those with lower resistin levels, which was consistent with results reported by Al-Harithy and Al-Ghamdi ([@B1]) and Mabrouk et al. ([@B25]). The meta regression also indicated resistin levels is the source of heterogeneity. This might be explained by the hypothesis that resistin might be the principal factor which induced insulin resistance at high levels. However, the overall effect of resistin was not significant in people with low resistin levels, several other factors may dominate insulin resistance like free fatty acids, hyperosmotic stress, and TNF-α (Roden et al., [@B36], [@B35]; Gual et al., [@B11]; Plomgaard et al., [@B34]). In the study of Park et al., people with insulin resistance did not have a correlation between resistin and insulin resistance in the case of low resistin levels, but their insulin resistance significantly correlated with TNF-α, which meet the hypothesis above (Park et al., [@B31]). There are also several other hypotheses regarding the controversial correlation between resistin and insulin resistance. Several single nucleotide polymorphisms (SNPs) of the resistin RETN gene have been reported to be associated with resistin concentration, while this association remains controversial in various ethnicities, possibly explaining the conflicting results of the correlation (Hivert et al., [@B15]). Zaidi et al. suggested that resistin causes insulin resistance when insulin reaches a certain critical level (Zaidi and Shirwany, [@B50]), but we did not draw this conclusion in our analysis, which may due to the limited number of relevant studies. More studies are needed to verify these hypotheses in the future.

A number of treatments targeting adipokines have recently emerged. For example, monoclonal antibody infliximab, which neutralizes TNF-α, is a new approach aimed at inflammation-associated insulin resistance (Ursini et al., [@B46]). That study indicated that resistin might be a therapy targeting insulin resistance in the patients with hyperresistinemia. A new therapy to reduce resistin levels may alleviate insulin resistance in the future.

Limitations in our meta-analysis should be considered. There were some studies with small sample sizes included in the present analysis, possibly explaining the heterogeneity. Few studies concentrating on the correlation between resistin and insulin resistance without reporting the correlation coefficient may have introduced bias. Some non-English articles were also excluded in our study. Therefore, larger clinical trials are needed to further validate our results.

Conclusions {#s5}
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In T2DM and obese individuals, resistin levels are positively correlated with insulin resistance in people with hyperresistinemia, but not in those with normal circulating resistin levels. This study may help to suggest new targets for the prevention and treatment of insulin resistance.

Author Contributions {#s6}
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KS and YL designed the program, searched and reviewed the studies, were in charge of the manuscript. YL and LS assessed the studies, extracted the data, and wrote the part of the manuscript. QL and ML extracted the data and wrote the part of the manuscript. JY, DZ, and CZ reviewed and edited the manuscript. JD directed the project, contributed to the discussion, reviewed, and edited the manuscript. JD had full access to all the data in the study and had final responsibility for the decision to submit for publication. KS and YL contributed equally to this work.

Conflict of Interest
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The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

**Funding.** This work was supported by two grants (to JD) from the National Natural Science Foundation of China (No. 31872791) and Natural Science Foundation of Shandong Province of China (No. ZR2019MC046).

[^1]: Edited by: Emilio Badoer, Royal Melbourne Institute of Technology (RMIT) University, Australia

[^2]: Reviewed by: Maria Giovanna Scarale, Casa Sollievo della Sofferenza (IRCCS), Italy; Júlio Cezar De Oliveira, Federal University of Mato Grosso, Brazil

[^3]: This article was submitted to Integrative Physiology, a section of the journal Frontiers in Physiology

[^4]: †These authors have contributed equally to this work