Introduction

Metabolic syndrome is an escalating global health issue, largely driven by rising obesity rates and sedentary lifestyles.1 The primary characteristics of MetS encompass several medical conditions, including glucose intolerance, hypertriglyceridemia, abdominal obesity, low HDL levels and hypertension.2 This syndrome represents a collective variables that significantly elevate the likelihood of evolving T2DM and cardiovascular diseases.3

Adipose tissue significantly contributes to the initiation and advancement of metabolic disorders by synthesizing numerous adipocytokines. Among these, hyperleptinemia is particularly plays significant involvement in the onset of diabetes, hypertension, coronary heart disease and stroke. 4, 5 Circulating leptin, an adipokine primarily released by white adipocytes, serves as a multifaceted molecule involved in various physiological processes. These include appetite suppression, glucose utilization, increased energy expenditure, regulation of adipogenesis, and the progression toward insulin resistance.6

Emerging research is shedding light on various theoretical mechanisms that contribute pivotal roles in patients with Metabolic Syndrome. For instance, some researchers have demonstrated a significant association between body adiposity and leptin levels, indicating a correlation with obesity and contributing to insulin resistance, thereby increasing the risk of developing type 2 diabetes (T2DM). Moreover, hyperleptinemia has been identified as an autonomous risk factor for the onset of heart diseases and MetS.7, 8 Notably, research has revealed that women with Cardiac Syndrome X exhibit higher circulating leptin levels compared to healthy controls, even after controlling for body mass index.9 Furthermore, studies have observed reduced circulating adiponectin levels but elevated leptin levels, alongside an increased leptin/adiponectin ratio in CSX subjects compared to controls. In obese individuals, increased circulating leptin levels often correlate with greater adipose tissue accumulation. Hyperleptinemia is speculated to contribute significantly to insulin resistance.10 Moreover, females have higher leptin levels compared to males when these levels are adjusted for body fat percentage.11, 12

The hypotheses collectively suggest that circulating leptin plays a significant role in Metabolic Syndrome. A meta-analysis was performed to quantify the strength of the relationship between circulating leptin levels and MetS in adults. Furthermore,meta-regression analysis was performed to investigate other influencing factors in MetS. This research could help in early identification and intervention to reduce the incidence of MetS-related diseases.

Materials and Methods

Statistical analysis

In these meta-analyses, 14 selected studies were considered depending on sample size, study method, and year of publication shown in Figure 1. The analysis of statistical data was performed using the software MedCalc® version 22.00. The study compared the mean leptin levels and standard deviations between cases and controls. The proportional weight of each study was assigned according to its sample size. An independent sample t-test was applied to each study, and a 95% CI was drawn. The combined estimates of mean and standard deviation for cases and controls were calculated using a weighted average method, where weights were based on the sample size of each study. The standardized mean difference (SMD) and their respective 95% confidence intervals were also computed for each study. A combined estimation for all 14 studies was performed.

Figure 1

Study selection flow chart using (PRISMA) diagram

Table 1

The table is prepared with mean and standard deviation (SD) of variables contributing factors forMetS from selected studies.

Study

Material

Country

No. of Samples

BMI

Mean Age

FPG

SBP

DBP

HDL

TG

WC

Methods

Goguet-Rubio et al., 2017 13

Serum

US

16

48.5 ±3.73

38.5 ±2.2

111.6 ±7.2

136.3 ±4.9

85.75 ±1.5

43.4 ±1.8

141.86 ±9.4

NR

ELISA

Surpriya et al., 2018 14

Serum

China

19

NR

65 ± 11

122.4 ± 23.4

159 ± 17.2

83.9 ± 12.5

38.6 ± 7.7

221.4 ± 40.0

92.6 ± 10.2

ELISA

Gasier et al.,2016 15

Serum

US

33

30 ± 3

22 ±39

89 ±8

133 ± 12

82 ± 8

44 ± 9

160 ± 120

95.9 ±13.2

ELISA

Anna Szczepańska-Szerej et al.,2017 16

Serum

Poland

59

27.5 ±5.3

67.8 ±10.2

125.1 ±34.8

NR

NR

47.2 ±16.2

154.6 ±67.3

NR

ELISA

Yun-Jung Bae et al., 201317

Serum

Korea

198

28.0 ± 3.4

46.5 ± 11.5

108.5 ± 12.5

135.5 ± 15.7

78.7 ±13.3

42.6 ± 12.1

212.2 ± 114

95.8 ± 7.6

ELISA

Tseng PW et al., 2017 18

Serum

Taiwan

45

27.61 ±2.85

67 ±4

138.0 ± 13

151.5 ±19.7

85.0 ±10.7

44.0 ±10.9

141.7 ±60.40

93.53 ±6.60

ELISA

Chia-Chu Liu et al., 2017 19

Serum

Taiwan

234

27.6 ± 9.2

56.9 ± 6.0

109.8 ± 25.5

131.2 ± 12.1

82.6 ± 8.6

42.2 ± 8.5

170.9 ± 86.4

90.6 ± 7.0

ELISA

Somaye Yosaee et al., 2017 20

Plasma

Iran

51

31.4 ± 3.7

37.4 ± 6.5

116.2 ± 37.3

121.2 ± 11.7

80.8 ± 9.1

50.3 ± 8.3

266 ± 203

105 ± 8

ELISA

Li Hsuan Wang et al.., 2017 21

Serum

Taiwan

30

27.02 ±3.60

72.97 ±4.78

122.5 ± 58

134.7 ±21.9

71.63±9.50

45.07±9.55

157.30±61.52

98.1 ±9.98

ELISA

Olesia M. Bochar et al. ,2021 22

Serum

Ukraine

90

32.24 ±0.52

57.49±1.07

NR

168.2 ±2.28

107.2 ±0.66

44.08 ± 1.93

188.6 ± 11.5

95.87 ±1.33

ELISA

Arthur SC et al., 2017 23

Plasma

Brazil

157

30.9 ± 1.3

63 ± 2.6

107.4 ± 6.6

142 ± 3.0

82 ± 1.1

43 ± 2.2

142.4 ± 19.2

100 ± 13

ELISA

Abhishek G et al. ,2010 12

Serum

India

186

27.22 ± 5.07

28.32 ± 5.94

101.1 ± 17.3

132.4 ± 11.4

88.46 ± 7.8

41.98 ± 4.66

142.6 ± 36.0

86.35 ± 14.0

ELISA

Vinod W et al. ,2016 24

Serum

India

63

27.22 ± 4.16

49.7 ±5.32

106.6 ±32.4

129.2 ±5.19

84.11±5.14

39.78±7.22

151.56±56.23

NR

ELISA

KW Liang et al.,2015 25

Serum

Tiawan

15

28.57 ± 1.72

53.8 ± 4.31

111.4 ± 6.9

NR

NR

41.31 ± 2.30

155.3 ± 21.8

NR

ELISA

Figure 2

Forest Plot illustrating Leptin Levels between MetS Patients and Controls. Each datum represents a selected study, with the marker size corresponding to the count of participants in a particular study. The diamond indicates the overall effect size ofleptin levels from the selected studies.

Table 2

Assessment of Heterogeneity. The figure illustrates the leptin concentrations between Metabolic Syndrome (MetS) as N2 and controls as N1, combining studies to produce an estimate with a significant p-value less than 0.001, 95% CI for each.

Study	N1	N2	Total	SMD	SE	95% CI	T	p	Weight (%)
									Fixed	Random
Goguet-Rubio et al., 2017 13	16	24	40	16.754	1.900	12.908 to 20.599			0.084	2.27
Surpriya et al., 2018 14	19	20	39	0.814	0.327	0.151 to 1.476			2.83	8.01
Gasier et al.,2016 15	33	20	53	1.856	0.319	0.946 to 2.226			2.98	8.04
Anna Szczepańska-Szerej et al.,2017 16	59	99	158	0.678	0.168	0.346 to 1.010			10.74	8.50
Yun-Jung Bae et al., 201317	108	91	199	0.236	0.142	-0.0443 to 0.517			14.99	8.55
Tseng PW et al., 2017 18	45	19	64	0.811	0.280	0.252 to 1.370			3.88	8.18
Somaye Yosaee et al., 2017 20	51	51	102	0.688	0.202	0.286 to 1.089			7.41	8.41
Li Hsuan Wang et al.., 2017 21	30	32	62	2.965	0.366	2.233 to 3.697			2.27	7.86
Olesia M. Bochar et al. ,2021 22	90	50	140	9.721	0.607	8.521 to 10.921			0.82	6.74
Arthur SC et al., 2017 23	157	79	236	1.011	0.145	0.725 to 1.297			14.39	8.54
Abhishek G et al. ,2010 12	186	204	390	0.676	0.104	0.471 to 0.880			28.02	8.61
Vinod W et al. ,2016 24	63	63	126	0.431	0.179	0.0759 to 0.785			9.45	8.47
KW Liang et al.,2015 25	15	39	54	2.363	0.376	1.608 to 3.117			2.15	7.82
Total (Fixed effects)	872	791	1663	0.848	0.0551	0.740 to 0.956	15.407	<0.001	100.00	100.00
Total (Random effects)	872	791	1663	2.028	0.333	1.376 to 2.681	6.096	<0.001	100.00	100.00

Because of differences in populations and outcome criteria, a random-effects model (REM) was employed to detect combined estimates from selected studies.13 Figure 2 and Table 2 show SMDs and 95% CIs for individual studies and combined estimates. It pooled 95% CIs and ORs to assess internal exposure impact on outcomes. SMDs and their 95% CIs were combined using Cohen’s d to evaluate MetS and leptin concentration relationships.

Heterogeneity across meta-analyses was estimated by employing both the Cochrane Q test and the I² statistic. High heterogeneity was defined as I² greater than 75%, moderate as 25% to 75%, and low as less than 25%. 14 A sensitivity analysis was performed to evaluate the impact of excluding specific studies on total outcomes. Additionally, publication bias was assessed using the Egger funnel plot and linear regression test for studies with ten or more reporting meta-analysis results. 15 All statistical analyses were two-tailed, and significance was evaluated at a p-value threshold of less than 0.05   

Observation and Results

Comparative analysis

Figure 2 presents the forest plot depicting SMDs derived from the meta-analysis using a random-effects model. Table 2 illustrates significantly elevated circulating leptin levels in cases compared to controls with SMD = 0.84, 95% CI: 0.74 to 0.96, Z = 3.10, p < 0.001. Significant heterogeneity was observed among the studies (I² = 96.74%).

Table 3

Meta-regression analysis of studies on circulating leptin and MetS parameters

	Estimate	Standard error	p value	95% CI
BMI	0.1955	0.27	0.2815	0.7157	2.0657
Age	0.0835	0.05	0.0470	0.9881	1.196
FPG	0.2530	0.17	0.0092	0.9191	1.8044
SBP	0.0811	0.07	0.1655	0.9417	1.2489
DBP	0.0386	0.08	0.6322	0.8796	1.2281
HDL	0.2227	0.24	0.3131	0.7805	2.0003
TG	0.0046	0.01	0.7720	0.9732	1.0371

Table 3 summarizes the findings of the meta-regression analysis. No significant differences were observed in BMI, SBP, DBP, HDL, and triglyceride levels. However, inclusion of Age and Fasting Plasma Glucose (FPG) in the meta-regression model revealed that FPG significantly contributed to the sources of heterogeneity (95% CI: 0.91 to 1.8, p = 0.009), along with Age (95% CI: 0.98 to 1.19, p = 0.047).

Figure 3

Non-linear regression analysis illustrates the relationship between FPG values and SMD results derived from data in each study.

Figure 3 displays the findings from non-linear regression analysis between Fasting Plasma Glucose (FPG) and Leptin Standard Mean Difference (SMD). A positive relationship was observed between FPG and Leptin, with a significance level of p-value less than 0.001.

Figure 4

The logistic regression analysis predicts the likelihood of Metabolic Syndrome (MetS) using rising FPG levels from the studies as independent variables.

The sensitivity analysis demonstrated that the diagnostic ability of leptin in MetS was excellent (AUC = 1.000, p < 0.001), and for FPG it was also high (AUC = 0.852, p = 0.004). The Egger funnel plot for circulating leptin levels appeared symmetrical, while Egger's test did not indicate publication bias (p value = 0.0019, 95% CI: 3.92 to 13.17).(Figure 4)

Discussion

Metabolic Syndrome (MetS) is a multifactorial disease with complex pathogenic mechanisms, but central obesity and insulin resistance (IR) play crucial roles in its development. 1 This meta-analysis shows significantly higher circulating leptin levels than control with positive correlation, suggesting that elevated leptin levels may increase the risk of developing MetS. Furthermore, the study observed a slight trend towards a higher risk of MetS with increasing leptin levels. However, the meta-regression showed significant heterogeneity, largely explained by fasting plasma glucose (FPG). A positive linear relationship was observed amongst SMD of leptin and FPG, indicating that FPG significantly influences the regulation between circulating leptin and MetS. The study also examined data from different age groups to assess whether age variations impact circulating leptin levels.

Numerous studies have indicated that circulating leptin could potentially impact the risk of diabetes and cardiovascular disease. The link between T2DM and circulating leptin reveals significantly higher levels in T2DM individuals compared to controls. This study proposes that elevated circulating leptin levels may contribute to disruptions in the metabolism of glucose and lipids in individuals diagnosed with MetS. 11 Although it shares similarities with T2DM and CVDs, the circulating leptin in MetS is plays more complicated role and requires further investigation. Moreover, among women with MetS, serum leptin levels exhibited a significant positive correlation with HOMA-IR (p Value < 0.001) along with metabolic risk factors, alongside negative correlations with FBS, TG, and HDL-c. In adult women from North India, circulating leptin showed significant associations with insulin resistance, hyperlipidemia and alteration of metabolic risk factors. 11

Studies by Supriya et al.14 suggest that individuals with central obesity may experience more severe inflammatory conditions in Metabolic Syndrome (MetS), while research by Goguet-Rubio et al. 13 indicates that adipokines can act as biomarkers for detecting pathophysiological changes in the body, facilitating early diagnosis. Gasier et al.'s study on obese volunteers found that 30% of those meeting MetS criteria face a 3 to 7 times greater risk of developing diabetes and heart diseases. 15 Additionally, leptin levels were notably associated (p value < 0.001) with ischemic stroke, suggesting its as a potential biomarker for assessing the risk of cardioembolic stroke in patients with Metabolic Syndrome. 16

The results indicate that circulating leptin levels were elevated in Metabolic Syndrome (MetS) patients compared to controls across all age brackets.26 However, meta-analysis in adults showed significantly higher heterogeneity, suggesting that circulating leptin activity or bioconversion mechanisms could be compromised in adult patients diagnosed with Metabolic Syndrome (MetS). Additional research is required to elucidate the molecular mechanisms that underlie these variations in leptin levels among the patients of different age with Metabolic Syndrome (MetS).

Multiple studies have demonstrated a positive relationship between MetS scores or the presence of MetS and leptin levels (p < 0.05).17, 18, 19, 21, 20 Leptin levels are markedly elevated in overweight children compared to controls, indicating potential impairment in metabolic processes.22 The leptin/adiponectin ratio (LAR) examined by Catharina et al. suggests it may act as a dependable biomarker for hypertensive individuals at risk of developing MetS, with an odds ratio of 4.13 (p = 0.01).27 The circulating levels of the adipokine leptin also demonstrate significant associations with insulin resistance indices in individuals, regardless of whether they have cardiac or metabolic syndrome X. 25

Leptin abnormalities, such as hyperleptinemia or leptin resistance, are pivotal in Metabolic Syndrome (MetS). Diagnosis depends on glucose and lipids levels, blood pressure, BMI and waist measurements, indicating pathological conditions. Early MetS detection is critical due to its association with heightened disability and mortality risks.13 Asians face increased T2DM and obesity-related disease risks at lower BMIs than Europeans,5 likely linked to elevated blood glucose levels seen in MetS, contributing to heightened circulating leptin. Biomarker use in early MetS diagnosis can aid disease management and prevent irreversible complications.

Conclusions

The concentration of circulating leptin was notably elevated in Metabolic Syndrome patients compared to controls, and there was a suggestive trend indicating a potential increase in MetS risk with higher leptin levels. Furthermore, fasting plasma glucose (FPG) appears to exert a regulatory influence regarding the association between circulating leptin and MetS. These findings underscore the importance of assessing circulating leptin levels to understand their role in the pathophysiology of MetS. The findings from this meta-analysis may facilitate early identification, the exploration of new biomarkers, and the uncovering of potential therapeutic targets for addressing Metabolic Syndrome (MetS).

Source of Funding

None.

Conflict of Interest

None.