# Effect of Gender on Serum Leptin in Type 2 Diabetes Mellitus: A System Review and Meta-Analysis

Authors: Yushan Li; Xiao Chen; Xingji Gong; Jian Yao; Dongyong He; Wenjie Du
Journal: Computational and Mathematical Methods in Medicine
 (2022)

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## Abstract

Objective. To assess the effect of gender factors on serum leptin levels in patients with diabetes mellitus. Methods. To remove any studies that indicated a relationship between leptin-based inflammatory variables and the prevalence of type 2 diabetes in particular patient categories, a comprehensive search of all articles published between July 2019 and June 2021 was performed on PubMed/MEDLINE, Web of Science, Scopus, and EBSCO Host, including Academic Search Premier, Africa-Wide Information, and Cumulative Index to Nursing and Allied Health Literature. A summary description of the combined analysis across multiple centers, regions, and continents will help us better understand the effect of gender on serum leptin levels in patients with diabetes. The meta-analysis was performed using RevMan 5.2 software on the literature that satisfied the inclusion and exclusion criteria. Results. Plasma CRP levels in women with type 2 diabetes were found to be no different from those in males with type 2 diabetes, with an OR of 0.12, 95 percent confidence interval (CI) of 0.12 to 0.12, P=0.01. There was no statistically significant difference in the plasma level of interleukin-6 (IL-6) between women with type 2 diabetes and males with type 2 diabetes However, the “inverted funnel” diagram is asymmetrical, indicating a publication bias in the included studies, despite the fact that there was no statistically significant difference in abnormal leptin levels between men with type 2 diabetes and women patients (OR=−0.69, 95 percent CI (0.88, 1.00), P<0.05). Conclusion. Gender factors did not affect the level of inflammatory factors and leptin level in type 2 diabetes.


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## Body

## 1. Introduction

There are 167 amino acids in leptin. Adipocytes remove the 21-amino acid N-terminal signal peptide during the secretion process of leptin to produce active leptin [1]. The omentum, mesentery, retroperitoneum, and subcutaneous adipocytes were discovered to have significant leptin quantities. Additionally, skeletal muscle, the stomach’s epithelium, bone, and the heart synthesize [2]. Bursts of release punctuate the 24-hour cycle of leptin secretion. From 20:00 to 3:00 in the morning the following day, secretion is at its maximum and lowest around lunchtime [3]. An important insulin-regulatory hormone, leptin, has a role in glucose metabolism. One of leptin’s primary targets in our brain and nervous system is the hypothalamic arcuate nucleus (ARC). Neuropeptide Y (NPY) neurons and proopiomelanocortin (POMC)/cocaine amphetamine-regulated transcription factor (CART) neurons are present in ARC. Activating POMC/cart neurons and inhibiting AgRP/NPY neurons are only a few ways leptin works to lower blood glucose levels and boost energy expenditure while also decreasing hepatocyte glucose release [4]. Glucocorticoid and glucagon levels, insulin levels, and blood glucose stability are maintained via the hypothalamic-pituitary-adrenal axis (HPA). In the sympathetic nervous system, when hypoglycemia occurs, preinsulin mRNA and insulin gene activity may be reduced, and insulin production in the peripheral may be limited by leptin [5]. Leptin may also increase the activity of AMPK in islet B cells and activate the ATP-sensitive potassium channel (KATP), which results in potassium outflow. Leptin can also decrease insulin release after baseline and glucose stimulation and help regulate glucose homeostasis [6].In addition to insulin resistance, hyperglycemia and hyperlipidemia are all signs of T2DM, which are all associated with obesity. Leptin levels rise in obese people due to increased endogenous leptin resistance [7]. Long-term stimulation with high amounts of leptin reduces islet B cell responsiveness, inhibits insulin synthesis, and increases insulin release. This causes hyperinsulinemia and enhanced insulin resistance, which worsens obesity by increasing leptin secretion and exacerbating leptin resistance. Over the course of the study, leptin levels in men with type 2 diabetes mellitus (T2DM) were considerably greater than those in men without diabetes, indicating that leptin may be used to predict the development of T2DM. It has been shown that leptin levels are favorably related to blood insulin levels in obese and diabetic individuals. Some studies suggest that serum leptin may be an excellent predictor of insulin resistance in people with type 2 diabetes [8].Prospective epidemiological studies on the association of leptin with the risk of type 2 diabetes in the last decade have shown inconclusive results, with some studies suggesting that the association between leptin and type 2 diabetes risk may be sex-specific [9, 10]. We performed a meta-analysis of prospective studies for different genders to get a more comprehensive understanding of the effect of gender on leptin levels in diabetic patients.


## 2. Methods

The PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) declaration was used to perform this systematic review. Because all of the analyses were based on previously published research, no ethical approval or patient permission was required to carry out the study.


### 2.1. Literature Search

We searched PubMed/MEDLINE, Web of Science, Scopus, and EBSCO Host (including Academic Search Premier, Africa-Wide Information, and Cumulative Index to Nursing and Allied Health Literature) for all articles published between July 2019 and June 2021. A comprehensive analysis of literature from multiple centers, regions, and continents was conducted. A manual search of pertinent references in previously published works was also carried out. There were no linguistic limitations. Keywords for search include “factor” or “cytokine” or “leptin” or “diabetes” or “leptin + diabetes.” The research methods include cohort study, prospective study, follow-up study, and case control study. This search strategy was modified to allow it to be used in more databases if required.


### 2.2. Study Selection

Inclusion criteria: the selected literature and research must meet all the following criteria: (1) it is a randomized controlled trial, including male group and female group, which are comparable between the two groups, (2) the subjects were type 2 diabetic patients with no definite diagnosis, and (3) published English literature with complete data. Exclusion criteria: (1) patients with type 1 diabetes and impaired fasting glucose, (2) self-controlled test, (3) animal experiments, (4) overview/meeting summary/case study, etc., (5) unable to obtain complete data, (6) republished literature, (8) summary of the meeting, and (7) documents with incomplete content and unable to extract data. Due to the separate study of leptin gender, the number of papers is small. We also extracted and analyzed the data of other fat factors.


### 2.3. Data Extraction and Assessment of Quality

Endnote X7 was used to collect references (Clarivate Analytics, USA). Two distinct reviewers used search results to assess the acceptability of the papers, which a third independent reviewer subsequently evaluated. Two writers cooperated in creating a table of general research features (information on important outcome indicators and adverse events, e.g., study name, diagnosis, and participant number). Emails or phones were issued to the authors to get any missing information. When the same topic has been addressed, the data were chosen from the complete journal papers. Researchers compared two versions of the same article to identify which was the first. Blinding, sequence creation, allocation concealment, and other aspects were considered when calculating the risk of bias in each trial, which was assessed using the Cochrane Bias Scale. In addition, two reviewers independently conducted these assessments. During the previous rounds, one author was expected to arbitrate any disagreements among the reviewers. To identify the quality of the research, an in-depth methodological review was necessary; hence, unpublished articles and conference abstracts were omitted from the search.


### 2.4. Statistical Analysis

All statistical computations in this research were performed using RevMan 5.2. (USA). The relative risk (RR) and 95 percent confidence interval (CI) were used for binary outcomes, whereas the mean difference (MD) and confidence interval (CI) were used for continuous outcomes. Cochran’sQ test and the I2 statistic were employed in the study to examine statistical heterogeneity, and the I2 statistic was utilized to quantify variance. A fixed-effect model was utilized to account for the difference when I2 was less than 50%. Aside from that, we analyzed the data using a random-effects model. Subgroup analysis means that in the study, the research objects are divided into different subgroups according to some characteristics of the research objects (such as gender and disease severity), and then, the effect values of different groups are estimated and compared among subgroups. In meta-analysis, the research can be divided into subgroups for analysis according to certain research characteristics. Its purpose is to study the interaction or effect modification, that is, whether the effect value is different in different populations or conditions. Subgroup analysis is one of the important methods to analyze heterogeneous results or to answer questions about specific patients, intervention types, or research types. A subgroup analysis was undertaken on a range of treatments in control groups. The leave-one-out strategy was employed to evaluate sensitivity. To establish whether there is evidence of publication bias, a funnel plot or a metaregression analysis may be done, depending on the number of articles. In the research, the tests Harbord’s and Egger’s were used to uncover the results for binary and continuous outcomes, respectively. A statistically significant outcome was one that had a P value of 0.05 or less.


## 2.1. Literature Search

We searched PubMed/MEDLINE, Web of Science, Scopus, and EBSCO Host (including Academic Search Premier, Africa-Wide Information, and Cumulative Index to Nursing and Allied Health Literature) for all articles published between July 2019 and June 2021. A comprehensive analysis of literature from multiple centers, regions, and continents was conducted. A manual search of pertinent references in previously published works was also carried out. There were no linguistic limitations. Keywords for search include “factor” or “cytokine” or “leptin” or “diabetes” or “leptin + diabetes.” The research methods include cohort study, prospective study, follow-up study, and case control study. This search strategy was modified to allow it to be used in more databases if required.


## 2.2. Study Selection

Inclusion criteria: the selected literature and research must meet all the following criteria: (1) it is a randomized controlled trial, including male group and female group, which are comparable between the two groups, (2) the subjects were type 2 diabetic patients with no definite diagnosis, and (3) published English literature with complete data. Exclusion criteria: (1) patients with type 1 diabetes and impaired fasting glucose, (2) self-controlled test, (3) animal experiments, (4) overview/meeting summary/case study, etc., (5) unable to obtain complete data, (6) republished literature, (8) summary of the meeting, and (7) documents with incomplete content and unable to extract data. Due to the separate study of leptin gender, the number of papers is small. We also extracted and analyzed the data of other fat factors.


## 2.3. Data Extraction and Assessment of Quality

Endnote X7 was used to collect references (Clarivate Analytics, USA). Two distinct reviewers used search results to assess the acceptability of the papers, which a third independent reviewer subsequently evaluated. Two writers cooperated in creating a table of general research features (information on important outcome indicators and adverse events, e.g., study name, diagnosis, and participant number). Emails or phones were issued to the authors to get any missing information. When the same topic has been addressed, the data were chosen from the complete journal papers. Researchers compared two versions of the same article to identify which was the first. Blinding, sequence creation, allocation concealment, and other aspects were considered when calculating the risk of bias in each trial, which was assessed using the Cochrane Bias Scale. In addition, two reviewers independently conducted these assessments. During the previous rounds, one author was expected to arbitrate any disagreements among the reviewers. To identify the quality of the research, an in-depth methodological review was necessary; hence, unpublished articles and conference abstracts were omitted from the search.


## 2.4. Statistical Analysis

All statistical computations in this research were performed using RevMan 5.2. (USA). The relative risk (RR) and 95 percent confidence interval (CI) were used for binary outcomes, whereas the mean difference (MD) and confidence interval (CI) were used for continuous outcomes. Cochran’sQ test and the I2 statistic were employed in the study to examine statistical heterogeneity, and the I2 statistic was utilized to quantify variance. A fixed-effect model was utilized to account for the difference when I2 was less than 50%. Aside from that, we analyzed the data using a random-effects model. Subgroup analysis means that in the study, the research objects are divided into different subgroups according to some characteristics of the research objects (such as gender and disease severity), and then, the effect values of different groups are estimated and compared among subgroups. In meta-analysis, the research can be divided into subgroups for analysis according to certain research characteristics. Its purpose is to study the interaction or effect modification, that is, whether the effect value is different in different populations or conditions. Subgroup analysis is one of the important methods to analyze heterogeneous results or to answer questions about specific patients, intervention types, or research types. A subgroup analysis was undertaken on a range of treatments in control groups. The leave-one-out strategy was employed to evaluate sensitivity. To establish whether there is evidence of publication bias, a funnel plot or a metaregression analysis may be done, depending on the number of articles. In the research, the tests Harbord’s and Egger’s were used to uncover the results for binary and continuous outcomes, respectively. A statistically significant outcome was one that had a P value of 0.05 or less.


## 3. Results

### 3.1. Study Characteristics

This meta-analysis includes 10 articles, all of which have studied the relationship between leptin level and type 2 diabetes risk. Table1 provides an overview of each study’s characteristics. There were 31696 diabetics and 31696 nondiabetics who participated in the 10 studies, which were published between 1999 and 2021, according to the data. One study was carried out in China while the other eight were undertaken in the United States. It ranged from 3.2 years to 22 years in the follow-up period. Leptin levels and type 2 diabetes relationship have been studied in five of the ten studies using OR estimations. A radioimmunoassay or an enzyme-linked immunosorbent assay was often used to measure leptin levels, whereas self-report and blood glucose levels were frequently used to detect diabetes, as seen in Figure 1.Table 1
Features of each research.
Author (year)ParticipantsOutcomesLilja (2012) [9]640Leptin in men and adiponectin in both sexes were independent predictors of T2DM. The association was modified by the degree of insulin sensitivity. The leptin/adiponectin ratio may add predictive information beyond the separate hormones.McNeely (1999) [10]410Among Japanese Americans, increased baseline leptin levels are associated with increased risk of developing diabetes in men but not in women.Sans S; Padró T (2013) [29]1011In a population with relatively high diabetes incidence, BMI and glucose were strong risk factors, while adiponectin protected against diabetes, especially in men with high glycemic level.Schmidt (2006) [13]10275High leptin levels, probably reflecting leptin resistance, predict an increased risk of diabetes. Adjusting for factors purportedly related to leptin resistance unveils a protective association, independent of adiponectin and consistent with some of leptin’s described protective effects against diabetes.Sun (2010) [16]32826These data suggest a strong inverse association between plasma sOB-R levels and risk of type 2 diabetes, independent of BMI, leptin, and adiponectin levels.Thorand (2010) [17]7936Two adipokines leptin and adiponectin interact in modulating type 2 diabetes risk, but adiponectin is more strongly associated with type 2 diabetes risk than leptin.Welsh (2009) [18]5672Leptin, similar to other markers of adiposity in general, is more strongly related to risk of diabetes than CVD in the elderly.Söderberg (2007) [19]2330High leptin levels are associated with the future development of diabetes, and the association is independent of other factors in men, but not in women.Kouvari (2021) [20]2020Report an inverse association between Mediterranean diet and NAFLD. Mediterranean diet protected against diabetes and CVD prospectively among subjects with NAFLD.Peller (2020) [21]273In type 2 DM, patients with AF have higher resistin and adiponectin concentrations than patients with no AF. None of the studied adipokines proved a predictor of future AF development.Figure 1
Document screening process.


### 3.2. Quality Evaluation

10 literatures were included, of which 10 reported the randomization method and the rest did not mention the randomization method or the method had high risk. There is no literature to implement double-blind. Only one literature used blind method for clinical evaluation and laboratory analysis report. In the outcome data report, all literatures reported preset outcome indicators, which were rated as low risk. Other biases of 10 literatures were evaluated as uncertain risk, as shown in Figure2.Figure 2
Quality evaluation of 10 literatures.


### 3.3. Plasma CRP Results

A total of 10 research results were included for heterogeneity test. There was heterogeneity among the groups, and the heterogeneity was large (P<0.00001, I2=100%) using random-effects model for meta-analysis; the results showed that compared with male patients with type 2 diabetes, plasma CRP level in women with type 2 diabetes no significant difference (OR=−0.12, 95% CI (-0.13, -0.12), P<0.0001); see Figure 3.Figure 3
CRP level in women with type 2 diabetes with no significant difference.


### 3.4. TNF-α Test Results

A total of 5 cases were included and tested for heterogeneity. The heterogeneity between each group was small (P≤0.00001, I2=100%); a fixed-effect model was used for meta-analysis. The results showed that plasma TNF-α level in females with type 2 diabetes has no significant difference with that in men (OR=0.19, 95% CI (0.18, 0.19), P<0.00001); see Figure 4.Figure 4
TNF-α level in females with type 2 diabetes has no significant difference with that in men.


### 3.5. Results of IL-6 Test

A total of 10 cases were included and tested for heterogeneity. There was great heterogeneity among the groups (P<0.00001, I2=97%); a random-effects model was used to carry out the meta-analysis. The results of plasma IL-6 levels in women in type 2 diabetes areas compared with men are presented in Figure 5. The difference in plasma IL-6 levels between genders was not statistically significant (OR=−0.00, 95% CI (-0.00, -0.01), P<0.00001).Figure 5
IL-6 level among women with type 2 diabetes compared with men has no significant difference.


### 3.6. Leptin Test Results

A total of 10 cases were included and tested for heterogeneity. The heterogeneity between groups was small (P=1.00, I2=0%); a fixed-effect model was used for meta-analysis. The results showed that there was no statistically significant difference in abnormal leptin level between man patients with type 2 diabetes and woman patients (OR=−0.69, 95% CI (0.88, 1.00), P<0.05); see Figure 6.Figure 6
Leptin level between man patients with type 2 diabetes and woman patients has no significant difference.


### 3.7. Publication Bias

A funnel chart (Figure7) was made according to the leptin level test results, which showed that there may be publication bias.Figure 7
Publication bias funnel diagram.


## 3.1. Study Characteristics

This meta-analysis includes 10 articles, all of which have studied the relationship between leptin level and type 2 diabetes risk. Table1 provides an overview of each study’s characteristics. There were 31696 diabetics and 31696 nondiabetics who participated in the 10 studies, which were published between 1999 and 2021, according to the data. One study was carried out in China while the other eight were undertaken in the United States. It ranged from 3.2 years to 22 years in the follow-up period. Leptin levels and type 2 diabetes relationship have been studied in five of the ten studies using OR estimations. A radioimmunoassay or an enzyme-linked immunosorbent assay was often used to measure leptin levels, whereas self-report and blood glucose levels were frequently used to detect diabetes, as seen in Figure 1.Table 1
Features of each research.
Author (year)ParticipantsOutcomesLilja (2012) [9]640Leptin in men and adiponectin in both sexes were independent predictors of T2DM. The association was modified by the degree of insulin sensitivity. The leptin/adiponectin ratio may add predictive information beyond the separate hormones.McNeely (1999) [10]410Among Japanese Americans, increased baseline leptin levels are associated with increased risk of developing diabetes in men but not in women.Sans S; Padró T (2013) [29]1011In a population with relatively high diabetes incidence, BMI and glucose were strong risk factors, while adiponectin protected against diabetes, especially in men with high glycemic level.Schmidt (2006) [13]10275High leptin levels, probably reflecting leptin resistance, predict an increased risk of diabetes. Adjusting for factors purportedly related to leptin resistance unveils a protective association, independent of adiponectin and consistent with some of leptin’s described protective effects against diabetes.Sun (2010) [16]32826These data suggest a strong inverse association between plasma sOB-R levels and risk of type 2 diabetes, independent of BMI, leptin, and adiponectin levels.Thorand (2010) [17]7936Two adipokines leptin and adiponectin interact in modulating type 2 diabetes risk, but adiponectin is more strongly associated with type 2 diabetes risk than leptin.Welsh (2009) [18]5672Leptin, similar to other markers of adiposity in general, is more strongly related to risk of diabetes than CVD in the elderly.Söderberg (2007) [19]2330High leptin levels are associated with the future development of diabetes, and the association is independent of other factors in men, but not in women.Kouvari (2021) [20]2020Report an inverse association between Mediterranean diet and NAFLD. Mediterranean diet protected against diabetes and CVD prospectively among subjects with NAFLD.Peller (2020) [21]273In type 2 DM, patients with AF have higher resistin and adiponectin concentrations than patients with no AF. None of the studied adipokines proved a predictor of future AF development.Figure 1
Document screening process.


## 3.2. Quality Evaluation

10 literatures were included, of which 10 reported the randomization method and the rest did not mention the randomization method or the method had high risk. There is no literature to implement double-blind. Only one literature used blind method for clinical evaluation and laboratory analysis report. In the outcome data report, all literatures reported preset outcome indicators, which were rated as low risk. Other biases of 10 literatures were evaluated as uncertain risk, as shown in Figure2.Figure 2
Quality evaluation of 10 literatures.


## 3.3. Plasma CRP Results

A total of 10 research results were included for heterogeneity test. There was heterogeneity among the groups, and the heterogeneity was large (P<0.00001, I2=100%) using random-effects model for meta-analysis; the results showed that compared with male patients with type 2 diabetes, plasma CRP level in women with type 2 diabetes no significant difference (OR=−0.12, 95% CI (-0.13, -0.12), P<0.0001); see Figure 3.Figure 3
CRP level in women with type 2 diabetes with no significant difference.


## 3.4. TNF-α Test Results

A total of 5 cases were included and tested for heterogeneity. The heterogeneity between each group was small (P≤0.00001, I2=100%); a fixed-effect model was used for meta-analysis. The results showed that plasma TNF-α level in females with type 2 diabetes has no significant difference with that in men (OR=0.19, 95% CI (0.18, 0.19), P<0.00001); see Figure 4.Figure 4
TNF-α level in females with type 2 diabetes has no significant difference with that in men.


## 3.5. Results of IL-6 Test

A total of 10 cases were included and tested for heterogeneity. There was great heterogeneity among the groups (P<0.00001, I2=97%); a random-effects model was used to carry out the meta-analysis. The results of plasma IL-6 levels in women in type 2 diabetes areas compared with men are presented in Figure 5. The difference in plasma IL-6 levels between genders was not statistically significant (OR=−0.00, 95% CI (-0.00, -0.01), P<0.00001).Figure 5
IL-6 level among women with type 2 diabetes compared with men has no significant difference.


## 3.6. Leptin Test Results

A total of 10 cases were included and tested for heterogeneity. The heterogeneity between groups was small (P=1.00, I2=0%); a fixed-effect model was used for meta-analysis. The results showed that there was no statistically significant difference in abnormal leptin level between man patients with type 2 diabetes and woman patients (OR=−0.69, 95% CI (0.88, 1.00), P<0.05); see Figure 6.Figure 6
Leptin level between man patients with type 2 diabetes and woman patients has no significant difference.


## 3.7. Publication Bias

A funnel chart (Figure7) was made according to the leptin level test results, which showed that there may be publication bias.Figure 7
Publication bias funnel diagram.


## 4. Discussion

Leptin is considered to be a regulator of human food intake and energy consumption, because hypothalamic receptors easily bind to leptin, which can regulate appetite [11]. Several studies have shown that leptin decreases insulin sensitivity, which in turn decreases glucose tolerance [12]. Leptin has been shown to inhibit the expression of insulin precursor gene and finally affect the secretion of insulin. According to these results, higher leptin levels are associated with an increased risk of diabetes because of the role it plays in regulating insulin sensitivity and secretion [13]. According to the results of this prospective study, there were no gender differences in leptin levels and type 2 diabetes, which is not consistent with the findings of Chen et al. [14]. In the present study, we also considered the obesity of the patients and found that the relationship between obese leptin levels and diabetes was not strong, which is consistent with previous studies [14, 15]. Our assessment factors were broader than previous studies (including plasma CRP results, TNF-α test results, results of IL-6 test, leptin test results, and publication bias), so the comprehensiveness of the assessment results may be higher.Obesity and leptin have a strong correlation in humans, which may be due to a condition known as leptin resistance. So far, we have found some reasons for leptin resistance, such as the reduction of BBB transport and the lack of leptin signal transduction in neurons [16]. Chronically high levels of leptin may compromise receptor system responsiveness in insulin-producing cells, resulting in insulin resistance [17]. The inability to control insulin release is due to a reduction in the responsiveness of cells. Hyperinsulinemia may aggravate obesity by increasing leptin and gene expression in white adipose tissue (a major source of leptin production) [18]. Obesity and insulin resistance may occur from an overproduction of leptin, which in turn promotes the development of diabetes. In a prospective trial, males who were insulin sensitive had a higher risk of developing type 2 diabetes, whereas insulin resistance had a lower risk (RR=1.03 for a 1-log ng mL1 elevation in leptin, 95% CI 0.76–1.39), according to the researchers. This study reveals that leptin and insulin sensitivity may be linked [19].Gender differences in the association between leptin and diabetes may be caused by many different factors [11]. The fat distributions of males and women are vastly different. Type 2 diabetes may be linked to men’s visceral fat, whereas women’s subcutaneous fat is a key source of leptin, a hormone linked to weight gain and obesity [20]. When leptin levels rise, males may have a greater chance of developing diabetes than women. Leptin has a central catabolic effect on female rats’ brains, which are more sensitive than male rats [21]. It is also possible that there are gender-specific differences in leptin transport across the BBB or in leptin intracellular signaling cascades when estrogen deficiency occurs [22]. The nonlinear leptin–diabetes association found in men may suggest that the central effect of leptin, rather than its peripheral effect, contributes to the inhibitory effect of leptin on insulin secretion because free leptin levels in cerebrospinal fluid are already saturated at low circulating levels of leptin. The results were also inconclusive when looking at the data by gender [23].Despite variances in research design, time of follow-up, illness identification, and other technical difficulties [24], it is possible that ethnic discrepancies in the connection between leptin and diabetes led to study heterogeneity [25]. An analysis of the American Cohort Research found that blacks had greater leptin levels than whites after controlling for body mass index and other factors, according to the study [25]. In the Atherosclerosis Risk in Communities Study, it was shown that blacks had considerably higher leptin levels than whites even after controlling for age, gender, and geographic location [26]. Using a mixed ethnicity stratified research, we found that leptin had no influence on the incidence of type 2 diabetes in males and a minor protective effect on the incidence in women (all studies within the stratum had recruited blacks) [27]. As a result, further future research is required to investigate correlations that are specific to ethnicity [28].


## 5. Conclusions

In summary, gender factors did not affect the level of inflammatory factor leptin in type 2 diabetes.


## 6. Limitation

Our research also has several limitations. (1) No consideration is given to racial characteristics. We discovered that, although there is no difference between males and women, there are disparities across races. (2) The included literature is limited and outdated. (3) There are flaws in data statistical analysis. Efforts should be taken in the future to prevent making similar mistakes in the study.



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*Source: 4875799-2022-09-10.xml*