# Effect of Adiponectin Variant on Lipid Profile and Plasma Adiponectin Levels: A Multicenter Systematic Review and Meta-Analysis

Authors: Guiqing Wang; Yufeng Wang; Zhi Luo
Journal: Cardiovascular Therapeutics
 (2022)

---

## Abstract

Background. Adiponectin is a recognized antiatherogenic molecule; this study was aimed at clarifying the effects of adiponectin variants on lipid and adiponectin levels. Methods. By searching PubMed and Cochrane databases for studies published before March 31, 2022, a total of 86,610 individuals were included in the analysis. Results. Variants of rs2241766 and rs266729 were associated with decreased adiponectin and high-density lipoprotein cholesterol (HDL-C), as well as increased triglycerides (TG), total cholesterol (TC), and low-density lipoprotein cholesterol (LDL-C) levels. In contrast, the rs1501299 variant was correlated with increased adiponectin and HDL-C, as well as decreased TG, TC, and LDL-C levels. Subgroup analysis indicated that the significant effect of the rs2241766 and rs266729 variants on lipid profile was predominant in Chinese, while the significant effect of the rs1501299 variant on lipid profile was primarily in Caucasians. Moreover, a stronger effect of the rs2241766 and rs1501299 variants on LDL-C levels was observed in males, while a considerable effect of the rs266729 variant on LDL-C levels was observed in children. Conclusions. The present study indicated that Chinese with the rs2241766 and rs266729 variants were at high risk of dyslipidemia, atherosclerosis, or coronary artery disease (CAD). Males with the rs2241766 variant were at high risk of CAD. Children with the rs266729 variant had a high risk to develop dyslipidemia, atherosclerosis, and even early onset of CAD in the future. These findings are beneficial to clinical physicians to choose different management strategies for cardiovascular disease (CVD) prevention.


---

## Body

## 1. Introduction

Adiponectin is a lipid regulator produced by white adipocytes [1]. The high and low levels of adiponectin may induce antiatherosclerotic [2] and atherogenic [3] lipid profiles, respectively. Consistent with this, the increase and decrease in adiponectin levels were proved to have antiatherosclerotic [4] and atherogenic [5] effects, respectively. Therefore, adiponectin may act as a key bridge to link lipid metabolism and atherosclerosis [6].Dyslipidemia is characterized by increased levels of plasma triglycerides (TG), total cholesterol (TC), and low-density lipoprotein cholesterol (LDL-C) and/or decreased levels of high-density lipoprotein cholesterol (HDL-C) in plasma. Dyslipidemia may result in a variety of severe diseases in humans. For instance, dyslipidemia involving coronary arteries may induce CAD or acute myocardial infarction [7]. Moreover, dyslipidemia involving cerebrovascular vessels may cause acute ischemic stroke [8]. Notably, emerging shreds of evidence have indicated that dyslipidemia may be related to pregnancy-induced hypertension (PIH) [9] and may play an important role in cancer proliferation and metastasis [10].The adiponectin genes (known as ADIPOQ, APM1, APN, ACDC, and ACRP30) are located in the long arm of human chromosome 3 at q27, composed of three exons and two introns. rs2241766 is located in the 2nd exon, generated by a nucleotide variation from thymine (T) to guanine (G); rs1501299 is located in the 2nd intron, generated by a nucleotide variation from guanine (G) to adenine (A); and rs266729 is located in the promoter region, generated by a nucleotide variation from cytosine (C) to guanine (G). Heid et al. [11] revealed that adiponectin levels are primarily determined by adiponectin expression. Therefore, variants of rs2241766, rs1501299, and rs266729 may affect circulating adiponectin levels by modulating adiponectin expression [12–14].Recently, a series of animal trials [15–17] showed that adiponectin knockout caused severe dyslipidemia. Moreover, several meta-analyses indicated that variants of rs2241766, rs266729, and rs1501299 impacted CAD risk [18, 19]. Since dyslipidemia accounts for more than 50% of the population-attributable risk for the onset of CAD, indicating the remodeled CAD risk induced by adiponectin variants may originate from a remodeled lipid profile. Therefore, we conducted this study to investigate the effects of adiponectin variants on lipid metabolism under evidence-based medicine.


## 2. Material and Methods

### 2.1. Literature Search

The search of the literature was executed using PubMed and the Cochrane databases from January 1, 2021, to March 31, 2022, by entering the following keywords: (“Adiponectin”, “ADIPOQ”, “APM1”, “APN”, “ACDC”, or “ACRP30”), (“rs2241766”, “rs1501299”, “rs266729”, “+45T>G”, “T45G”, “T94G”, “Gly15Gly”, “+276G>T”, “G276T”, or “-11377C>G”), (“variant”, “mutant”, or “polymorphism”) and (“lipid”, “lipids”, “lipid metabolism”, “lipoprotein”, “cholesterol”, “blood lipid”, “serum lipid”, or “circulating lipid”).


### 2.2. Inclusion Criteria

The procedure for obtaining literature was hierarchical. The titles were first assessed, and the abstracts and contents were then checked. The detailed inclusion criteria include the following: (1) the studies detected the effects of rs2241766, rs1501299, and rs266729 on adiponectin or lipid levels. (2) The studies at least offered one lipid parameter or adiponectin levels by the genotype of rs2241766, rs1501299, and rs266729. (3) The studies provided adiponectin or lipid levels by the mean and standard deviation (SD). (4) The studies provided the genotype frequencies of rs2241766, rs1501299, and rs266729. (5) The language was limited to English and Chinese.


### 2.3. Subgroup Analysis

Subgroup analysis was executed in ethnicity, gender, and disease status. The ethnicity was divided into Chinese, Japanese, Korean, Caucasian, Latino, Indian, Middle Eastern, and other ethnicities. Disease status was divided into CAD, T2DM, hypertension, obesity, polycystic ovarian syndrome (PCOS), metabolic syndrome (Mets), and nonalcoholic fatty liver disease (NAFLD). In addition, healthy subjects, pregnant subjects, and children subjects were also isolated for analysis.


### 2.4. Other Items

Data screening between the authors was compared by kappa statistics [20], since data extraction and analysis, heterogeneity processing, and publication bias tests were adopted from the previous methods, to avoid redundant descriptions (please see Liu et al. [21] publication for more details).


## 2.1. Literature Search

The search of the literature was executed using PubMed and the Cochrane databases from January 1, 2021, to March 31, 2022, by entering the following keywords: (“Adiponectin”, “ADIPOQ”, “APM1”, “APN”, “ACDC”, or “ACRP30”), (“rs2241766”, “rs1501299”, “rs266729”, “+45T>G”, “T45G”, “T94G”, “Gly15Gly”, “+276G>T”, “G276T”, or “-11377C>G”), (“variant”, “mutant”, or “polymorphism”) and (“lipid”, “lipids”, “lipid metabolism”, “lipoprotein”, “cholesterol”, “blood lipid”, “serum lipid”, or “circulating lipid”).


## 2.2. Inclusion Criteria

The procedure for obtaining literature was hierarchical. The titles were first assessed, and the abstracts and contents were then checked. The detailed inclusion criteria include the following: (1) the studies detected the effects of rs2241766, rs1501299, and rs266729 on adiponectin or lipid levels. (2) The studies at least offered one lipid parameter or adiponectin levels by the genotype of rs2241766, rs1501299, and rs266729. (3) The studies provided adiponectin or lipid levels by the mean and standard deviation (SD). (4) The studies provided the genotype frequencies of rs2241766, rs1501299, and rs266729. (5) The language was limited to English and Chinese.


## 2.3. Subgroup Analysis

Subgroup analysis was executed in ethnicity, gender, and disease status. The ethnicity was divided into Chinese, Japanese, Korean, Caucasian, Latino, Indian, Middle Eastern, and other ethnicities. Disease status was divided into CAD, T2DM, hypertension, obesity, polycystic ovarian syndrome (PCOS), metabolic syndrome (Mets), and nonalcoholic fatty liver disease (NAFLD). In addition, healthy subjects, pregnant subjects, and children subjects were also isolated for analysis.


## 2.4. Other Items

Data screening between the authors was compared by kappa statistics [20], since data extraction and analysis, heterogeneity processing, and publication bias tests were adopted from the previous methods, to avoid redundant descriptions (please see Liu et al. [21] publication for more details).


## 3. Results

### 3.1. Study Selection

The kappa value was 0.93 (>0.75) between the authors; the details of the study selection were summarized in Figure1 (please see Figure S1 for the full electronic search strategy).Figure 1


### 3.2. Effect of rs2241766 on Lipid Profile

All the results stated below were the data excluding heterogeneity. rs2241766 had a harmful effect on lipid profile (FigureS2–S4 and Figure 2). Subgroup analysis indicated that the significant effect of rs2241766 on lipid profile was primarily in Chinese, males, CAD patients, and T2DM patients (please see Table 1 for more details).Figure 2Table 1
Meta-analysis of adiponectin rs2241766 variant with lipid levels.
Groups or subgroupsComparisons (subjects)PHSMD (95% CI)PSMDOverall resultsTGAll120 (29 732)<0.0010.07 (0.02-0.12)<0.01EthnicityChinese56 (12 087)<0.0010.09 (0.01-0.16)0.03Japanese6 (575)0.100.07 (-0.18-0.31)0.58Korean9 (5 622)0.09-0.00 (-0.07-0.07)0.93Caucasian27 (6 099)0.380.02 (-0.05-0.08)0.65Latino6 (695)0.43-0.01 (-0.17-0.16)0.95Indian5 (2 762)<0.0010.42 (0.04-0.80)0.03Middle eastern10 (1 620)<0.010.06 (-0.13-0.25)0.53GenderMale5 (1 131)0.110.01 (-0.18-0.20)0.93Female15 (3 121)<0.0010.09 (-0.12-0.30)0.39Disease statusCAD4 (817)0.24-0.01 (-0.19-0.16)0.88T2DM25 (6 328)<0.0010.27 (0.12-0.42)<0.001Obesity13 (1 715)<0.0010.06 (-0.20-0.31)0.66Mets3 (357)0.17-0.05 (-0.39-0.30)0.80PCOS4 (504)0.31-0.09 (-0.34-0.16)0.48NAFLD3 (417)0.780.03 (-0.17-0.23)0.79Healthy subjects41 (10 421)<0.0010.03 (-0.03-0.10)0.34Children subjects7 (1 449)0.01-0.05 (-0.27-0.17)0.64TCAll118 (27 932)<0.0010.06 (0.01-0.10)0.02EthnicityChinese55 (10 894)<0.0010.09 (0.01-0.17)0.03Japanese5 (381)0.270.08 (-0.16-0.32)0.52Korean9 (5 672)0.05-0.03 (-0.10-0.05)0.51Caucasian24 (5 103)0.290.01 (-0.07-0.09)0.79Latino6 (695)<0.01-0.03 (-0.39-0.32)0.87Indian4 (1 763)0.190.07 (-0.09-0.23)0.40Middle eastern11 (2 741)0.090.03 (-0.09-0.15)0.59GenderMale5 (1 131)0.320.18 (0.04-0.33)0.01Female14 (2 269)0.020.08 (-0.06-0.23)0.27Disease statusCAD5 (895)0.470.22 (0.09-0.36)<0.001T2DM26 (6 272)<0.0010.14 (0.03-0.25)0.02Obesity15 (1 869)<0.0010.19 (0.02-0.37)0.03PCOS4 (504)0.33-0.12 (-0.36-0.13)0.35Healthy subjects41 (9 778)<0.0010.03 (-0.06-0.12)0.51Children subjects8 (1 616)<0.0010.12 (-0.14-0.38)0.36LDL-CAll94 (22 900)<0.0010.09 (0.04-0.14)<0.001EthnicityChinese43 (8 954)<0.0010.17 (0.08-0.26)<0.001Japanese3 (239)0.510.13 (-0.13-0.39)0.32Korean7 (4 220)0.53-0.03 (-0.09-0.03)0.29Caucasian19 (4 258)0.340.07 (-0.01-0.15)0.09Latino6 (695)0.60-0.12 (-0.29-0.05)0.16Indian3 (1 442)0.560.03 (-0.10-0.17)0.63Middle eastern10 (2 681)0.390.00 (-0.08-0.09)0.93Other ethnic3 (411)0.15-0.02 (-0.35-0.32)0.93GenderMale5 (1 068)0.580.18 (0.04-0.32)0.01Female10 (1 197)0.30-0.01 (-0.16-0.15)0.91Disease statusCAD3 (757)0.190.14 (-0.05-0.33)0.14T2DM23 (6 086)<0.0010.12 (0.00-0.24)0.05Obesity13 (1 571)<0.0010.19 (-0.05-0.43)0.12PCOS3 (451)0.88-0.10 (-0.34-0.14)0.43Healthy subjects32 (6 998)<0.0010.09 (0.00-0.17)0.04Children subjects8 (1 616)<0.0010.20 (-0.03-0.42)0.09HDL-CAll119 (30 380)<0.001-0.09 (-0.15--0.03)<0.01EthnicityChinese55 (12 479)<0.001-0.12 (-0.21--0.03)0.01Japanese5 (497)0.770.00 (-0.18-0.18)0.99Korean10 (5 762)0.01-0.03 (-0.12-0.06)0.53Caucasian24 (5 587)0.300.04 (-0.03-0.11)0.31Latino6 (695)0.44-0.13 (-0.29-0.04)0.14Indian4 (1 936)0.01-0.19 (-0.44-0.06)0.14Middle eastern11 (2 741)<0.001-0.29 (-0.73-0.15)0.19Other ethnic4 (683)0.090.02 (-0.27-0.32)0.88GenderMale5 (1 068)0.37-0.03 (-0.17-0.11)0.68Female11 (1 457)0.540.06 (-0.05-0.18)0.28Disease statusCAD4 (817)0.360.03 (-0.12-0.17)0.73T2DM25 (6 397)<0.001-0.16 (-0.28--0.04)0.01Obesity16 (1 959)<0.001-0.08 (-0.32-0.17)0.55PCOS3 (451)0.500.15 (-0.09-0.39)0.23Healthy subjects42 (10 304)<0.001-0.15 (-0.29--0.01)0.03Children subjects8 (1 616)0.060.02 (-0.15-0.18)0.86Recalculated results that eliminated heterogeneityTGAll108 (26 484)0.100.03 (0.01-0.06)0.01EthnicityChinese49 (10 726)0.050.04 (0.00-0.08)0.03Japanese6 (575)0.100.07 (-0.10-0.24)0.41Korean8 (4 597)0.840.03 (-0.03-0.08)0.40Caucasian26 (5 907)0.800.00 (-0.06-0.06)0.98Latino6 (695)0.43-0.01 (-0.17-0.16)0.95Indian3 (2 146)0.100.08 (-0.02-0.18)0.11Middle eastern9 (1 566)0.190.03 (-0.07-0.14)0.53GenderMale5 (1 131)0.11-0.01 (-0.13-0.12)0.94Female13 (3 020)0.81-0.05 (-0.13-0.03)0.20Disease statusCAD4 (817)0.240.00 (-0.14-0.14)0.99T2DM18 (4 777)0.580.06 (0.00-0.12)0.05Obesity10 (1 266)0.44-0.02 (-0.14-0.09)0.71Mets3 (357)0.170.01 (-0.21-0.24)0.91PCOS4 (504)0.31-0.10 (-0.33-0.12)0.37NAFLD3 (417)0.780.03 (-0.17-0.23)0.79Healthy subjects39 (9 173)0.200.04 (-0.01-0.08)0.11Children subjects6 (1 302)0.390.02 (-0.09-0.13)0.74TCAll103 (24 758)0.100.03 (0.00-0.05)0.04EthnicityChinese46 (9 093)0.140.03 (-0.01-0.07)0.17Japanese5 (381)0.270.10 (-0.11-0.31)0.34Korean8 (4 914)0.37-0.00 (-0.06-0.06)0.99Caucasian22 (4 651)0.720.04 (-0.03-0.11)0.25Latino4 (585)0.84-0.09 (-0.26-0.09)0.34Indian4 (1 763)0.190.06 (-0.06-0.18)0.36Middle eastern11 (2 741)0.090.01 (-0.07-0.09)0.85GenderMale5 (1 131)0.320.18 (0.05-0.31)0.01Female12 (2 152)0.500.05 (-0.05-0.15)0.32Disease statusCAD5 (895)0.470.22 (0.09-0.36)<0.001T2DM21 (4 937)0.200.08 (0.02-0.14)0.01Obesity14 (1 722)0.020.06 (-0.04-0.16)0.22PCOS3 (451)0.91-0.03 (-0.27-0.22)0.83Healthy subjects34 (8 203)0.810.03 (-0.02-0.08)0.22Children subjects6 (1 369)0.230.05 (-0.06-0.16)0.38LDL-CAll88 (21 117)0.130.03 (0.00-0.06)0.04EthnicityChinese38 (7 929)0.050.06 (0.01-0.10)0.02Japanese3 (239)0.510.13 (-0.13-0.39)0.32Korean6 (3 462)0.74-0.01 (-0.08-0.06)0.76Caucasian19 (4 258)0.340.07 (-0.01-0.14)0.08Latino6 (695)0.60-0.12 (-0.29-0.05)0.16Indian3 (1 442)0.560.03 (-0.10-0.17)0.63Middle eastern10 (2 681)0.390.00 (-0.08-0.08)0.92Other ethnic3 (411)0.15-0.03 (-0.22-0.17)0.79GenderMale5 (1 068)0.580.18 (0.04-0.32)0.01Female10 (1 197)0.30-0.02 (-0.15-0.12)0.82Disease statusCAD3 (757)0.190.16 (0.02-0.30)0.03T2DM20 (4 887)0.350.05 (-0.01-0.11)0.13Obesity11 (1 176)0.010.04 (-0.08-0.16)0.51PCOS3 (451)0.88-0.10 (-0.34-0.14)0.43Healthy subjects31 (6 809)0.560.04 (-0.01-0.09)0.16Children subjects7 (1 469)0.110.06 (-0.05-0.17)0.27HDL-CAll107 (27 703)0.12-0.03 (-0.06--0.00)0.04EthnicityChinese48 (11 165)0.18-0.05 (-0.09--0.01)0.03Japanese5 (497)0.770.00 (-0.18-0.18)0.99Korean9 (5 004)0.600.02 (-0.03-0.08)0.41Caucasian21 (5 280)0.750.01 (-0.06-0.07)0.80Latino6 (695)0.44-0.13 (-0.29-0.04)0.14Indian4 (1 936)0.01-0.03 (-0.22-0.16)0.75Middle eastern10 (2 443)0.51-0.09 (-0.17--0.01)0.03Other ethnic4 (683)0.090.02 (-0.27-0.32)0.88GenderMale5 (1 068)0.37-0.03 (-0.17-0.11)0.66Female10 (1 240)0.790.01 (-0.12-0.14)0.89Disease statusCAD4 (817)0.360.03 (-0.11-0.16)0.73T2DM21 (5 217)0.050.07 (-0.12--0.01)0.03Obesity15 (1 711)0.16-0.01 (-0.11-0.09)0.86PCOS3 (451)0.500.15 (-0.09-0.39)0.23Healthy subjects37 (9 342)0.42-0.02 (-0.06-0.03)0.48Children subjects7 (1 449)0.13-0.01 (-0.12-0.10)0.86SMD: standardized mean difference; 95% CI: 95% confidence interval;PH: PHeterogeneity; CAD: coronary artery disease; T2DM: type 2 diabetes mellitus; Mets: metabolic syndrome; PCOS: polycystic ovarian syndrome; NAFLD: nonalcoholic fatty liver disease; TG: triglycerides; TC: total cholesterol; LDL-C: low-density lipoprotein cholesterol; HDL-C: high-density lipoprotein cholesterol.


### 3.3. Effect of rs1501299 on Lipid Profile

The effects of rs1501299 on lipid profile were beneficial (FigureS5–S7 and Figure 3). Subgroup analysis indicated that the significant effect of rs1501299 on lipid profile was primarily in Chinese, Caucasians, and male subjects (please see Table 2 for more details).Figure 3Table 2
Meta-analysis of adiponectin rs1501299 variant with lipid levels.
Groups or subgroupsComparisons (subjects)PHSMD (95% CI)PSMDOverall resultsTGAll91 (23 853)<0.001-0.05 (-0.12-0.02)0.14EthnicityChinese24 (6 525)0.04-0.02 (-0.09-0.05)0.59Japanese8 (1 795)0.18-0.01 (-0.13-0.10)0.82Korean13 (5 889)<0.0010.11 (-0.06-0.28)0.20Caucasian31 (5 889)<0.001-0.09 (-0.17-0.00)0.05Latino4 (441)0.49-0.05 (-0.24-0.14)0.60Indian5 (2 278)<0.0010.27 (0.03-0.52)0.03Middle eastern6 (1 036)<0.001-1.19 (-2.02--0.36)0.01GenderMale5 (1 053)0.15-0.01 (-0.18-0.16)0.91Female10 (2 692)0.17-0.01 (-0.11-0.09)0.88Disease statusT2DM23 (5 327)<0.0010.03 (-0.17-0.23)0.76Hypertension2 (488)0.82-0.09 (-0.27-0.09)0.32Obesity16 (2 966)<0.001-0.37 (-0.62--0.12)<0.01PCOS2 (351)0.050.20 (-0.37-0.77)0.49Healthy subjects28 (7 279)0.11-0.03 (-0.09-0.03)0.26Children subjects8 (2 694)0.820.07 (-0.00-0.15)0.06TCAll86 (23 252)<0.001-0.03 (-0.11-0.06)0.52EthnicityChinese21 (5 108)<0.0010.10 (-0.17-0.36)0.49Japanese7 (1 601)0.56-0.04 (-0.14-0.06)0.41Korean13 (5 939)<0.001-0.08 (-0.18-0.01)0.08Caucasian27 (5 577)<0.001-0.12 (-0.23--0.00)0.04Latino4 (441)0.54-0.04 (-0.23-0.15)0.67Indian5 (2 278)<0.0010.23 (-0.08-0.54)0.14Middle eastern9 (2 308)<0.001-0.10 (-0.29-0.10)0.34GenderMale5 (1 053)0.48-0.19 (-0.31--0.07)<0.01Female9 (1 840)0.14-0.05 (-0.17-0.07)0.38Disease statusT2DM20 (4 923)0.16-0.03 (-0.10-0.04)0.40Obesity16 (2 966)<0.001-0.21 (-0.42--0.00)0.05PCOS2 (351)0.06-0.11 (-0.64-0.42)0.68NAFLD2 (145)0.44-0.14 (-0.49-0.21)0.44Healthy subjects27 (7 166)<0.001-0.06 (-0.14-0.02)0.15Children subjects9 (2 862)0.07-0.06 (-0.17-0.05)0.29LDL-CAll70 (18 731)<0.001-0.04 (-0.10-0.03)0.25EthnicityChinese13 (3 473)0.08-0.10 (-0.19--0.01)0.03Japanese4 (902)0.460.00 (-0.13-0.13)0.96Korean11 (4 487)0.25-0.01 (-0.08-0.06)0.84Caucasian26 (5 241)<0.01-0.10 (-0.19--0.02)0.02Latino4 (441)0.200.17 (-0.07-0.42)0.16Indian4 (1 957)<0.0010.32 (-0.00-0.65)0.05Middle eastern8 (2 230)0.01-0.05 (-0.21-0.12)0.58GenderMale6 (1 269)0.01-0.03 (-0.23-0.17)0.75Female9 (1 840)0.020.03 (-0.12-0.18)0.72Disease statusT2DM18 (4 665)0.07-0.06 (-0.14-0.01)0.10Obesity15 (2 904)<0.01-0.16 (-0.30--0.03)0.02PCOS2 (351)0.09-0.16 (-0.63-0.32)0.52NAFLD2 (145)0.82-0.18 (-0.53-0.18)0.33Healthy subjects21 (4 860)0.030.01 (-0.07-0.09)0.81Children subjects7 (2 110)0.06-0.08 (-0.21-0.06)0.25HDL-CAll90 (23 986)<0.0010.02 (-0.02-0.07)0.25EthnicityChinese21 (6 018)0.260.07 (0.01-0.13)0.02Japanese7 (1 717)0.03-0.05 (-0.20-0.10)0.54Korean14 (6 029)0.050.03 (-0.04-0.10)0.42Caucasian31 (6 136)<0.001-0.03 (-0.13-0.07)0.60Latino4 (441)0.51-0.03 (-0.22-0.15)0.72Indian4 (1 337)0.030.03 (-0.18-0.24)0.79Middle eastern9 (2 308)0.180.12 (-0.00-0.23)0.05GenderMale6 (1 269)0.09-0.15 (-0.30--0.00)0.05Female10 (1 930)0.37-0.03 (-0.12-0.07)0.59Disease statusT2DM21 (5 141)0.020.03 (-0.04-0.11)0.40Hypertension2 (488)0.72-0.17 (-0.35-0.01)0.07Obesity17 (3 056)<0.0010.07 (-0.08-0.22)0.38PCOS2 (351)0.360.00 (-0.21-0.21)0.99Healthy subjects29 (7 623)<0.01-0.02 (-0.09-0.05)0.60Children subjects8 (2 188)0.58-0.04 (-0.12-0.05)0.38Recalculated results that eliminated heterogeneityTGAll78 (19 776)0.18-0.04 (-0.07--0.01)<0.01EthnicityChinese23 (6 447)0.19-0.03 (-0.08-0.02)0.20Japanese8 (1 795)0.18-0.02 (-0.11-0.07)0.66Korean9 (4 116)0.06-0.05 (-0.11-0.01)0.09Caucasian28 (5 553)0.31-0.04 (-0.10-0.01)0.10Latino4 (441)0.49-0.05 (-0.24-0.14)0.60Indian3 (787)0.850.05 (-0.10-0.20)0.52Middle eastern3 (637)0.58-0.07 (-0.22-0.09)0.41GenderMale5 (1 053)0.15-0.00 (-0.12-0.12)0.96Female10 (2 692)0.17-0.03 (-0.10-0.05)0.50Disease statusT2DM19 (4 498)0.47-0.02 (-0.07-0.04)0.62Hypertension2 (488)0.82-0.09 (-0.27-0.09)0.32Obesity10 (2 234)0.22-0.03 (-0.11-0.06)0.57PCOS2 (351)0.050.05 (-0.16-0.26)0.65Healthy subjects27 (6 254)0.31-0.05 (-0.10-0.00)0.05Children subjects8 (2 694)0.820.07 (-0.00-0.15)0.06TCAll76 (20 042)0.26-0.05 (-0.07--0.02)<0.001EthnicityChinese19 (4 315)0.05-0.03 (-0.09-0.03)0.28Japanese7 (1 601)0.56-0.04 (-0.14-0.06)0.41Korean12 (5 512)0.45-0.04 (-0.09-0.01)0.15Caucasian24 (5 204)0.16-0.07 (-0.12--0.01)0.02Latino4 (441)0.54-0.04 (-0.23-0.15)0.67Indian3 (787)0.960.02 (-0.13-0.17)0.79Middle eastern7 (2 182)0.69-0.08 (-0.16-0.01)0.10GenderMale5 (1 053)0.48-0.19 (-0.31--0.07)<0.01Female9 (1 840)0.14-0.05 (-0.14-0.04)0.29Disease statusT2DM19 (4 790)0.32-0.05 (-0.11-0.01)0.08Obesity12 (2 543)0.25-0.02 (-0.09-0.06)0.71PCOS————NAFLD2 (145)0.44-0.14 (-0.49-0.21)0.44Healthy subjects25 (6 663)0.33-0.04 (-0.08-0.01)0.15Children subjects9 (2 862)0.07-0.04 (-0.12-0.03)0.27LDL-CAll63 (16 580)0.09-0.05 (-0.08--0.02)<0.01EthnicityChinese13 (3 473)0.08-0.09 (-0.16--0.03)0.01Japanese4 (902)0.460.00 (-0.13-0.13)0.96Korean11 (4 487)0.25-0.00 (-0.06-0.06)0.93Caucasian23 (4 824)0.29-0.06 (-0.12--0.01)0.03Latino3 (274)0.200.08 (-0.16-0.32)0.54Indian2 (466)0.950.02 (-0.17-0.20)0.88Middle eastern7 (2 154)0.31-0.09 (-0.18-0.00)0.04GenderMale5 (1 053)0.21-0.14 (-0.26--0.02)0.03Female8 (1 673)0.04-0.02 (-0.12-0.08)0.69Disease statusT2DM18 (4 665)0.07-0.05 (-0.10-0.01)0.12Obesity13 (2 703)0.14-0.03 (-0.11-0.05)0.46PCOS————NAFLD2 (145)0.82-0.18 (-0.53-0.18)0.33Healthy subjects18 (4 401)0.71-0.04 (-0.10-0.02)0.20Children subjects7 (2 110)0.06-0.05 (-0.14-0.03)0.21HDL-CAll80 (22 255)0.160.04 (0.01-0.06)0.01EthnicityChinese21 (6 018)0.260.07 (0.02-0.12)0.01Japanese6 (1 371)0.280.01 (-0.10-0.12)0.87Korean14 (6 029)0.050.02 (-0.04-0.07)0.53Caucasian24 (5 230)0.390.02 (-0.03-0.08)0.47Latino4 (441)0.51-0.03 (-0.22-0.15)0.72Indian3 (1 187)0.620.14 (0.02-0.26)0.02Middle eastern8 (1 979)0.580.04 (-0.06-0.13)0.45GenderMale5 (1 053)0.65-0.09 (-0.21-0.03)0.16Female10 (1 930)0.37-0.03 (-0.12-0.06)0.57Disease statusT2DM19 (4 645)0.480.06 (0.01-0.12)0.03Hypertension2 (488)0.72-0.17 (-0.35-0.01)0.07Obesity14 (2 595)0.430.10 (0.02-0.18)0.01PCOS2 (351)0.360.00 (-0.21-0.21)0.99Healthy subjects27 (7 337)0.14-0.00 (-0.05-0.05)0.98Children subjects8 (2 188)0.58-0.04 (-0.12-0.05)0.38SMD: standardized mean difference; 95% CI: 95% confidence interval;PH: PHeterogeneity; T2DM: type 2 diabetes mellitus; PCOS: polycystic ovarian syndrome; NAFLD: nonalcoholic fatty liver disease; TG: triglycerides; TC: total cholesterol; LDL-C: low-density lipoprotein cholesterol; HDL-C: high-density lipoprotein cholesterol.


### 3.4. Effect of rs266729 on Lipid Profile

rs2241766 had a harmful effect on lipid profile (FigureS8–S10 and Figure 4). Subgroup analysis indicated that the significant effect of rs1501299 on lipid profile was primarily in Chinese, children, T2DM patients, and CAD patients (please see Table 3 for more details).Figure 4Table 3
Meta-analysis of adiponectin rs266729 variant with lipid levels.
Groups or subgroupsComparisons (subjects)PHSMD (95% CI)PSMDOverall resultsTGAll62 (17 815)<0.0010.08 (0.01-0.16)0.03EthnicityChinese37 (8 612)<0.0010.11 (-0.01-0.23)0.08Japanese2 (1 919)0.250.03 (-0.26-0.31)0.87Korean3 (1 822)0.860.09 (-0.01-0.18)0.07Caucasian13 (2 926)<0.010.02 (-0.10-0.15)0.72Middle eastern3 (290)0.64-0.15 (-0.40-0.09)0.22GenderFemale6 (1 816)<0.0010.38 (-0.07-0.84)0.10Disease statusCAD3 (448)<0.010.48 (-0.05-1.01)0.08T2DM13 (2 454)<0.001-0.06 (-0.23-0.10)0.45Obesity10 (1 473)0.240.10 (-0.03-0.22)0.13Mets4 (3 334)0.18-0.09 (-0.20-0.02)0.11Healthy subjects15 (4 374)<0.010.06 (-0.05-0.17)0.32Children subjects4 (1 497)0.180.18 (0.04-0.31)0.01TCAll60 (15 635)<0.0010.09 (0.02-0.16)0.02EthnicityChinese36 (7 763)<0.0010.14 (0.02-0.26)0.02Japanese2 (1 919)0.840.06 (-0.03-0.15)0.17Korean2 (970)0.540.12 (-0.01-0.25)0.07Caucasian14 (3 653)0.10-0.00 (-0.09-0.09)0.98Middle eastern4 (445)0.06-0.07 (-0.40-0.25)0.65GenderFemale5 (964)<0.0010.51 (-0.25-1.27)0.19Disease statusCAD3 (448)0.500.26 (0.08-0.45)0.01T2DM13 (2 454)<0.0010.06 (-0.11-0.24)0.48Obesity11 (2 545)0.290.04 (-0.05-0.14)0.36Mets3 (2 485)0.260.01 (-0.14-0.15)0.94Healthy subjects13 (2 365)0.050.13 (0.00-0.25)0.04Children subjects4 (1 497)0.210.15 (0.02-0.28)0.03LDL-CAll53 (13 793)<0.0010.14 (0.05-0.23)<0.01EthnicityChinese29 (6 088)<0.0010.24 (0.08-0.41)<0.01Korean2 (970)0.460.10 (-0.03-0.23)0.13Caucasian14 (4 216)<0.010.02 (-0.09-0.13)0.70Middle eastern4 (445)0.30-0.12 (-0.35-0.10)0.27GenderFemale5 (964)<0.0010.48 (-0.38-1.34)0.27Disease statusCAD3 (448)<0.010.37 (-0.17-0.92)0.18T2DM10 (2 024)<0.0010.23 (0.01-0.45)0.05Obesity11 (2 545)0.140.07 (-0.04-0.18)0.21Mets2 (598)0.46-0.09 (-0.31-0.14)0.46Healthy subjects13 (3 421)0.160.12 (0.03-0.22)0.01Children subjects4 (1 497)0.800.15 (0.05-0.25)<0.01HDL-CAll57 (15 792)<0.001-0.08 (-0.14--0.03)<0.01EthnicityChinese32 (7 169)<0.001-0.08 (-0.16--0.01)0.03Japanese2 (1 919)0.93-0.03 (-0.12-0.06)0.51Korean2 (970)0.480.04 (-0.09-0.16)0.58Caucasian15 (3 818)0.24-0.07 (-0.15-0.02)0.11Middle eastern4 (445)<0.001-0.29 (-0.87-0.30)0.34GenderFemale5 (964)0.040.08 (-0.15-0.31)0.47Disease statusCAD3 (448)0.11-0.06 (-0.36-0.24)0.70T2DM12 (2 256)<0.001-0.13 (-0.33-0.08)0.22Obesity11 (2 545)0.35-0.03 (-0.12-0.06)0.57Mets4 (3 334)0.19-0.10 (-0.21-0.01)0.08Healthy subjects13 (3 421)0.31-0.06 (-0.14-0.03)0.18Children subjects4 (1 497)0.780.06 (-0.04-0.16)0.25Recalculated results that eliminated heterogeneityTGAll51 (13 937)0.060.04 (0.00-0.07)0.04EthnicityChinese28 (5 753)0.510.06 (0.00-0.11)0.04Japanese2 (1 919)0.25-0.03 (-0.12-0.06)0.54Korean3 (1 822)0.860.09 (-0.01-0.18)0.07Caucasian12 (2 478)0.010.03 (-0.05-0.10)0.52Middle eastern3 (290)0.64-0.15 (-0.40-0.09)0.22GenderFemale5 (1 560)0.420.11 (0.01-0.21)0.04Disease status—————T2DM12 (2 363)0.480.04 (-0.04-0.12)0.35Obesity10 (1 473)0.240.10 (0.00-0.21)0.05Mets2 (2 037)0.52-0.03 (-0.12-0.06)0.54Healthy subjects14 (4 070)0.060.07 (0.01-0.13)0.03Children subjects4 (1 497)0.180.16 (0.06-0.26)<0.01TCAll56 (14 415)0.070.05 (0.01-0.08)0.01EthnicityChinese32 (6 543)0.270.06 (0.01-0.11)0.02Japanese2 (1 919)0.840.06 (-0.03-0.15)0.17Korean2 (970)0.540.12 (-0.01-0.25)0.07Caucasian14 (3 653)0.10-0.00 (-0.07-0.07)0.96Middle eastern4 (445)0.06-0.10 (-0.30-0.09)0.30GenderFemale4 (708)0.970.12 (-0.03-0.27)0.12Disease statusCAD3 (448)0.500.26 (0.08-0.45)0.01T2DM11 (2 108)0.290.03 (-0.06-0.12)0.47Obesity11 (2 545)0.290.03 (-0.05-0.11)0.40Mets3 (2 485)0.260.04 (-0.05-0.12)0.38Healthy subjects13 (2 365)0.810.14 (0.06-0.23)<0.01Children subjects4 (1 497)0.210.15 (0.05-0.25)0.01LDL-CAll44 (11 297)0.140.07 (0.03-0.10)<0.001EthnicityChinese23 (4 648)0.150.08 (0.02-0.14)0.01Korean2 (970)0.460.10 (-0.03-0.23)0.13Caucasian12 (3 315)0.190.01 (-0.07-0.08)0.87Middle eastern3 (290)0.790.01 (-0.24-0.25)0.95GenderFemale4 (708)0.260.06 (-0.09-0.21)0.45Disease status—————T2DM9 (1 933)0.080.11 (0.02-0.20)0.02Obesity10 (2 411)0.950.02 (-0.06-0.10)0.57Mets2 (598)0.46-0.09 (-0.31-0.14)0.46Healthy subjects13 (3 421)0.160.11 (0.04-0.18)<0.01Children subjects4 (1 497)0.800.15 (0.05-0.25)<0.01HDL-CAll53 (15 033)0.12-0.08 (-0.11--0.05)<0.001EthnicityChinese29 (6 545)0.12-0.13 (-0.18--0.08)<0.001Japanese2 (1 919)0.93-0.03 (-0.12-0.06)0.51Korean2 (970)0.480.04 (-0.09-0.16)0.58Caucasian15 (3 818)0.24-0.06 (-0.13-0.01)0.08Middle eastern3 (310)0.56-0.05 (-0.29-0.18)0.66GenderFemale4 (708)0.69-0.04 (-0.19-0.11)0.59Disease statusCAD3 (448)0.11-0.01 (-0.20-0.18)0.92T2DM9 (1 753)0.12-0.15 (-0.25--0.05)<0.01Obesity11 (2 545)0.35-0.03 (-0.11-0.05)0.51Mets4 (3 334)0.19-0.08 (-0.15--0.00)0.04Healthy subjects13 (3 421)0.31-0.05 (-0.12-0.02)0.16Children subjects4 (1 497)0.780.06 (-0.04-0.16)0.25SMD: standardized mean difference; 95% CI: 95% confidence interval;PH: PHeterogeneity; CAD: coronary artery disease; T2DM: type 2 diabetes mellitus; Mets: metabolic syndrome; TG: triglycerides; TC: total cholesterol; LDL-C: low-density lipoprotein cholesterol; HDL-C: high-density lipoprotein cholesterol.


### 3.5. Effect of rs2241766, rs1501299, and rs266729 on Adiponectin Levels

rs2241766, rs1501299, and rs266729 had a significant effect on plasma adiponectin levels (FigureS11–S13). Subgroup analysis showed that the effect of rs2241766 and rs150129 on adiponectin levels was primarily in Chinese (please see Table S8 for more details), while the effect of rs266729 on adiponectin levels was primarily in Caucasians (please see Table S8 for more details).


### 3.6. Evaluation of Heterogeneity

Significant heterogeneity was detected in analyzing the effects of adiponectin variants on lipid and adiponectin levels (Tables1–3 and Table S8). However, the recalculated results did not change significantly after eliminating heterogeneity (see Tables 1–3 and Table S8 for more details), indicating that the analysis results were robust.


### 3.7. Publication Bias Test

No publication bias was detected (see FigureS14–S17 for more details), indicating that the synthetic results were reliable.


## 3.1. Study Selection

The kappa value was 0.93 (>0.75) between the authors; the details of the study selection were summarized in Figure1 (please see Figure S1 for the full electronic search strategy).Figure 1


## 3.2. Effect of rs2241766 on Lipid Profile

All the results stated below were the data excluding heterogeneity. rs2241766 had a harmful effect on lipid profile (FigureS2–S4 and Figure 2). Subgroup analysis indicated that the significant effect of rs2241766 on lipid profile was primarily in Chinese, males, CAD patients, and T2DM patients (please see Table 1 for more details).Figure 2Table 1
Meta-analysis of adiponectin rs2241766 variant with lipid levels.
Groups or subgroupsComparisons (subjects)PHSMD (95% CI)PSMDOverall resultsTGAll120 (29 732)<0.0010.07 (0.02-0.12)<0.01EthnicityChinese56 (12 087)<0.0010.09 (0.01-0.16)0.03Japanese6 (575)0.100.07 (-0.18-0.31)0.58Korean9 (5 622)0.09-0.00 (-0.07-0.07)0.93Caucasian27 (6 099)0.380.02 (-0.05-0.08)0.65Latino6 (695)0.43-0.01 (-0.17-0.16)0.95Indian5 (2 762)<0.0010.42 (0.04-0.80)0.03Middle eastern10 (1 620)<0.010.06 (-0.13-0.25)0.53GenderMale5 (1 131)0.110.01 (-0.18-0.20)0.93Female15 (3 121)<0.0010.09 (-0.12-0.30)0.39Disease statusCAD4 (817)0.24-0.01 (-0.19-0.16)0.88T2DM25 (6 328)<0.0010.27 (0.12-0.42)<0.001Obesity13 (1 715)<0.0010.06 (-0.20-0.31)0.66Mets3 (357)0.17-0.05 (-0.39-0.30)0.80PCOS4 (504)0.31-0.09 (-0.34-0.16)0.48NAFLD3 (417)0.780.03 (-0.17-0.23)0.79Healthy subjects41 (10 421)<0.0010.03 (-0.03-0.10)0.34Children subjects7 (1 449)0.01-0.05 (-0.27-0.17)0.64TCAll118 (27 932)<0.0010.06 (0.01-0.10)0.02EthnicityChinese55 (10 894)<0.0010.09 (0.01-0.17)0.03Japanese5 (381)0.270.08 (-0.16-0.32)0.52Korean9 (5 672)0.05-0.03 (-0.10-0.05)0.51Caucasian24 (5 103)0.290.01 (-0.07-0.09)0.79Latino6 (695)<0.01-0.03 (-0.39-0.32)0.87Indian4 (1 763)0.190.07 (-0.09-0.23)0.40Middle eastern11 (2 741)0.090.03 (-0.09-0.15)0.59GenderMale5 (1 131)0.320.18 (0.04-0.33)0.01Female14 (2 269)0.020.08 (-0.06-0.23)0.27Disease statusCAD5 (895)0.470.22 (0.09-0.36)<0.001T2DM26 (6 272)<0.0010.14 (0.03-0.25)0.02Obesity15 (1 869)<0.0010.19 (0.02-0.37)0.03PCOS4 (504)0.33-0.12 (-0.36-0.13)0.35Healthy subjects41 (9 778)<0.0010.03 (-0.06-0.12)0.51Children subjects8 (1 616)<0.0010.12 (-0.14-0.38)0.36LDL-CAll94 (22 900)<0.0010.09 (0.04-0.14)<0.001EthnicityChinese43 (8 954)<0.0010.17 (0.08-0.26)<0.001Japanese3 (239)0.510.13 (-0.13-0.39)0.32Korean7 (4 220)0.53-0.03 (-0.09-0.03)0.29Caucasian19 (4 258)0.340.07 (-0.01-0.15)0.09Latino6 (695)0.60-0.12 (-0.29-0.05)0.16Indian3 (1 442)0.560.03 (-0.10-0.17)0.63Middle eastern10 (2 681)0.390.00 (-0.08-0.09)0.93Other ethnic3 (411)0.15-0.02 (-0.35-0.32)0.93GenderMale5 (1 068)0.580.18 (0.04-0.32)0.01Female10 (1 197)0.30-0.01 (-0.16-0.15)0.91Disease statusCAD3 (757)0.190.14 (-0.05-0.33)0.14T2DM23 (6 086)<0.0010.12 (0.00-0.24)0.05Obesity13 (1 571)<0.0010.19 (-0.05-0.43)0.12PCOS3 (451)0.88-0.10 (-0.34-0.14)0.43Healthy subjects32 (6 998)<0.0010.09 (0.00-0.17)0.04Children subjects8 (1 616)<0.0010.20 (-0.03-0.42)0.09HDL-CAll119 (30 380)<0.001-0.09 (-0.15--0.03)<0.01EthnicityChinese55 (12 479)<0.001-0.12 (-0.21--0.03)0.01Japanese5 (497)0.770.00 (-0.18-0.18)0.99Korean10 (5 762)0.01-0.03 (-0.12-0.06)0.53Caucasian24 (5 587)0.300.04 (-0.03-0.11)0.31Latino6 (695)0.44-0.13 (-0.29-0.04)0.14Indian4 (1 936)0.01-0.19 (-0.44-0.06)0.14Middle eastern11 (2 741)<0.001-0.29 (-0.73-0.15)0.19Other ethnic4 (683)0.090.02 (-0.27-0.32)0.88GenderMale5 (1 068)0.37-0.03 (-0.17-0.11)0.68Female11 (1 457)0.540.06 (-0.05-0.18)0.28Disease statusCAD4 (817)0.360.03 (-0.12-0.17)0.73T2DM25 (6 397)<0.001-0.16 (-0.28--0.04)0.01Obesity16 (1 959)<0.001-0.08 (-0.32-0.17)0.55PCOS3 (451)0.500.15 (-0.09-0.39)0.23Healthy subjects42 (10 304)<0.001-0.15 (-0.29--0.01)0.03Children subjects8 (1 616)0.060.02 (-0.15-0.18)0.86Recalculated results that eliminated heterogeneityTGAll108 (26 484)0.100.03 (0.01-0.06)0.01EthnicityChinese49 (10 726)0.050.04 (0.00-0.08)0.03Japanese6 (575)0.100.07 (-0.10-0.24)0.41Korean8 (4 597)0.840.03 (-0.03-0.08)0.40Caucasian26 (5 907)0.800.00 (-0.06-0.06)0.98Latino6 (695)0.43-0.01 (-0.17-0.16)0.95Indian3 (2 146)0.100.08 (-0.02-0.18)0.11Middle eastern9 (1 566)0.190.03 (-0.07-0.14)0.53GenderMale5 (1 131)0.11-0.01 (-0.13-0.12)0.94Female13 (3 020)0.81-0.05 (-0.13-0.03)0.20Disease statusCAD4 (817)0.240.00 (-0.14-0.14)0.99T2DM18 (4 777)0.580.06 (0.00-0.12)0.05Obesity10 (1 266)0.44-0.02 (-0.14-0.09)0.71Mets3 (357)0.170.01 (-0.21-0.24)0.91PCOS4 (504)0.31-0.10 (-0.33-0.12)0.37NAFLD3 (417)0.780.03 (-0.17-0.23)0.79Healthy subjects39 (9 173)0.200.04 (-0.01-0.08)0.11Children subjects6 (1 302)0.390.02 (-0.09-0.13)0.74TCAll103 (24 758)0.100.03 (0.00-0.05)0.04EthnicityChinese46 (9 093)0.140.03 (-0.01-0.07)0.17Japanese5 (381)0.270.10 (-0.11-0.31)0.34Korean8 (4 914)0.37-0.00 (-0.06-0.06)0.99Caucasian22 (4 651)0.720.04 (-0.03-0.11)0.25Latino4 (585)0.84-0.09 (-0.26-0.09)0.34Indian4 (1 763)0.190.06 (-0.06-0.18)0.36Middle eastern11 (2 741)0.090.01 (-0.07-0.09)0.85GenderMale5 (1 131)0.320.18 (0.05-0.31)0.01Female12 (2 152)0.500.05 (-0.05-0.15)0.32Disease statusCAD5 (895)0.470.22 (0.09-0.36)<0.001T2DM21 (4 937)0.200.08 (0.02-0.14)0.01Obesity14 (1 722)0.020.06 (-0.04-0.16)0.22PCOS3 (451)0.91-0.03 (-0.27-0.22)0.83Healthy subjects34 (8 203)0.810.03 (-0.02-0.08)0.22Children subjects6 (1 369)0.230.05 (-0.06-0.16)0.38LDL-CAll88 (21 117)0.130.03 (0.00-0.06)0.04EthnicityChinese38 (7 929)0.050.06 (0.01-0.10)0.02Japanese3 (239)0.510.13 (-0.13-0.39)0.32Korean6 (3 462)0.74-0.01 (-0.08-0.06)0.76Caucasian19 (4 258)0.340.07 (-0.01-0.14)0.08Latino6 (695)0.60-0.12 (-0.29-0.05)0.16Indian3 (1 442)0.560.03 (-0.10-0.17)0.63Middle eastern10 (2 681)0.390.00 (-0.08-0.08)0.92Other ethnic3 (411)0.15-0.03 (-0.22-0.17)0.79GenderMale5 (1 068)0.580.18 (0.04-0.32)0.01Female10 (1 197)0.30-0.02 (-0.15-0.12)0.82Disease statusCAD3 (757)0.190.16 (0.02-0.30)0.03T2DM20 (4 887)0.350.05 (-0.01-0.11)0.13Obesity11 (1 176)0.010.04 (-0.08-0.16)0.51PCOS3 (451)0.88-0.10 (-0.34-0.14)0.43Healthy subjects31 (6 809)0.560.04 (-0.01-0.09)0.16Children subjects7 (1 469)0.110.06 (-0.05-0.17)0.27HDL-CAll107 (27 703)0.12-0.03 (-0.06--0.00)0.04EthnicityChinese48 (11 165)0.18-0.05 (-0.09--0.01)0.03Japanese5 (497)0.770.00 (-0.18-0.18)0.99Korean9 (5 004)0.600.02 (-0.03-0.08)0.41Caucasian21 (5 280)0.750.01 (-0.06-0.07)0.80Latino6 (695)0.44-0.13 (-0.29-0.04)0.14Indian4 (1 936)0.01-0.03 (-0.22-0.16)0.75Middle eastern10 (2 443)0.51-0.09 (-0.17--0.01)0.03Other ethnic4 (683)0.090.02 (-0.27-0.32)0.88GenderMale5 (1 068)0.37-0.03 (-0.17-0.11)0.66Female10 (1 240)0.790.01 (-0.12-0.14)0.89Disease statusCAD4 (817)0.360.03 (-0.11-0.16)0.73T2DM21 (5 217)0.050.07 (-0.12--0.01)0.03Obesity15 (1 711)0.16-0.01 (-0.11-0.09)0.86PCOS3 (451)0.500.15 (-0.09-0.39)0.23Healthy subjects37 (9 342)0.42-0.02 (-0.06-0.03)0.48Children subjects7 (1 449)0.13-0.01 (-0.12-0.10)0.86SMD: standardized mean difference; 95% CI: 95% confidence interval;PH: PHeterogeneity; CAD: coronary artery disease; T2DM: type 2 diabetes mellitus; Mets: metabolic syndrome; PCOS: polycystic ovarian syndrome; NAFLD: nonalcoholic fatty liver disease; TG: triglycerides; TC: total cholesterol; LDL-C: low-density lipoprotein cholesterol; HDL-C: high-density lipoprotein cholesterol.


## 3.3. Effect of rs1501299 on Lipid Profile

The effects of rs1501299 on lipid profile were beneficial (FigureS5–S7 and Figure 3). Subgroup analysis indicated that the significant effect of rs1501299 on lipid profile was primarily in Chinese, Caucasians, and male subjects (please see Table 2 for more details).Figure 3Table 2
Meta-analysis of adiponectin rs1501299 variant with lipid levels.
Groups or subgroupsComparisons (subjects)PHSMD (95% CI)PSMDOverall resultsTGAll91 (23 853)<0.001-0.05 (-0.12-0.02)0.14EthnicityChinese24 (6 525)0.04-0.02 (-0.09-0.05)0.59Japanese8 (1 795)0.18-0.01 (-0.13-0.10)0.82Korean13 (5 889)<0.0010.11 (-0.06-0.28)0.20Caucasian31 (5 889)<0.001-0.09 (-0.17-0.00)0.05Latino4 (441)0.49-0.05 (-0.24-0.14)0.60Indian5 (2 278)<0.0010.27 (0.03-0.52)0.03Middle eastern6 (1 036)<0.001-1.19 (-2.02--0.36)0.01GenderMale5 (1 053)0.15-0.01 (-0.18-0.16)0.91Female10 (2 692)0.17-0.01 (-0.11-0.09)0.88Disease statusT2DM23 (5 327)<0.0010.03 (-0.17-0.23)0.76Hypertension2 (488)0.82-0.09 (-0.27-0.09)0.32Obesity16 (2 966)<0.001-0.37 (-0.62--0.12)<0.01PCOS2 (351)0.050.20 (-0.37-0.77)0.49Healthy subjects28 (7 279)0.11-0.03 (-0.09-0.03)0.26Children subjects8 (2 694)0.820.07 (-0.00-0.15)0.06TCAll86 (23 252)<0.001-0.03 (-0.11-0.06)0.52EthnicityChinese21 (5 108)<0.0010.10 (-0.17-0.36)0.49Japanese7 (1 601)0.56-0.04 (-0.14-0.06)0.41Korean13 (5 939)<0.001-0.08 (-0.18-0.01)0.08Caucasian27 (5 577)<0.001-0.12 (-0.23--0.00)0.04Latino4 (441)0.54-0.04 (-0.23-0.15)0.67Indian5 (2 278)<0.0010.23 (-0.08-0.54)0.14Middle eastern9 (2 308)<0.001-0.10 (-0.29-0.10)0.34GenderMale5 (1 053)0.48-0.19 (-0.31--0.07)<0.01Female9 (1 840)0.14-0.05 (-0.17-0.07)0.38Disease statusT2DM20 (4 923)0.16-0.03 (-0.10-0.04)0.40Obesity16 (2 966)<0.001-0.21 (-0.42--0.00)0.05PCOS2 (351)0.06-0.11 (-0.64-0.42)0.68NAFLD2 (145)0.44-0.14 (-0.49-0.21)0.44Healthy subjects27 (7 166)<0.001-0.06 (-0.14-0.02)0.15Children subjects9 (2 862)0.07-0.06 (-0.17-0.05)0.29LDL-CAll70 (18 731)<0.001-0.04 (-0.10-0.03)0.25EthnicityChinese13 (3 473)0.08-0.10 (-0.19--0.01)0.03Japanese4 (902)0.460.00 (-0.13-0.13)0.96Korean11 (4 487)0.25-0.01 (-0.08-0.06)0.84Caucasian26 (5 241)<0.01-0.10 (-0.19--0.02)0.02Latino4 (441)0.200.17 (-0.07-0.42)0.16Indian4 (1 957)<0.0010.32 (-0.00-0.65)0.05Middle eastern8 (2 230)0.01-0.05 (-0.21-0.12)0.58GenderMale6 (1 269)0.01-0.03 (-0.23-0.17)0.75Female9 (1 840)0.020.03 (-0.12-0.18)0.72Disease statusT2DM18 (4 665)0.07-0.06 (-0.14-0.01)0.10Obesity15 (2 904)<0.01-0.16 (-0.30--0.03)0.02PCOS2 (351)0.09-0.16 (-0.63-0.32)0.52NAFLD2 (145)0.82-0.18 (-0.53-0.18)0.33Healthy subjects21 (4 860)0.030.01 (-0.07-0.09)0.81Children subjects7 (2 110)0.06-0.08 (-0.21-0.06)0.25HDL-CAll90 (23 986)<0.0010.02 (-0.02-0.07)0.25EthnicityChinese21 (6 018)0.260.07 (0.01-0.13)0.02Japanese7 (1 717)0.03-0.05 (-0.20-0.10)0.54Korean14 (6 029)0.050.03 (-0.04-0.10)0.42Caucasian31 (6 136)<0.001-0.03 (-0.13-0.07)0.60Latino4 (441)0.51-0.03 (-0.22-0.15)0.72Indian4 (1 337)0.030.03 (-0.18-0.24)0.79Middle eastern9 (2 308)0.180.12 (-0.00-0.23)0.05GenderMale6 (1 269)0.09-0.15 (-0.30--0.00)0.05Female10 (1 930)0.37-0.03 (-0.12-0.07)0.59Disease statusT2DM21 (5 141)0.020.03 (-0.04-0.11)0.40Hypertension2 (488)0.72-0.17 (-0.35-0.01)0.07Obesity17 (3 056)<0.0010.07 (-0.08-0.22)0.38PCOS2 (351)0.360.00 (-0.21-0.21)0.99Healthy subjects29 (7 623)<0.01-0.02 (-0.09-0.05)0.60Children subjects8 (2 188)0.58-0.04 (-0.12-0.05)0.38Recalculated results that eliminated heterogeneityTGAll78 (19 776)0.18-0.04 (-0.07--0.01)<0.01EthnicityChinese23 (6 447)0.19-0.03 (-0.08-0.02)0.20Japanese8 (1 795)0.18-0.02 (-0.11-0.07)0.66Korean9 (4 116)0.06-0.05 (-0.11-0.01)0.09Caucasian28 (5 553)0.31-0.04 (-0.10-0.01)0.10Latino4 (441)0.49-0.05 (-0.24-0.14)0.60Indian3 (787)0.850.05 (-0.10-0.20)0.52Middle eastern3 (637)0.58-0.07 (-0.22-0.09)0.41GenderMale5 (1 053)0.15-0.00 (-0.12-0.12)0.96Female10 (2 692)0.17-0.03 (-0.10-0.05)0.50Disease statusT2DM19 (4 498)0.47-0.02 (-0.07-0.04)0.62Hypertension2 (488)0.82-0.09 (-0.27-0.09)0.32Obesity10 (2 234)0.22-0.03 (-0.11-0.06)0.57PCOS2 (351)0.050.05 (-0.16-0.26)0.65Healthy subjects27 (6 254)0.31-0.05 (-0.10-0.00)0.05Children subjects8 (2 694)0.820.07 (-0.00-0.15)0.06TCAll76 (20 042)0.26-0.05 (-0.07--0.02)<0.001EthnicityChinese19 (4 315)0.05-0.03 (-0.09-0.03)0.28Japanese7 (1 601)0.56-0.04 (-0.14-0.06)0.41Korean12 (5 512)0.45-0.04 (-0.09-0.01)0.15Caucasian24 (5 204)0.16-0.07 (-0.12--0.01)0.02Latino4 (441)0.54-0.04 (-0.23-0.15)0.67Indian3 (787)0.960.02 (-0.13-0.17)0.79Middle eastern7 (2 182)0.69-0.08 (-0.16-0.01)0.10GenderMale5 (1 053)0.48-0.19 (-0.31--0.07)<0.01Female9 (1 840)0.14-0.05 (-0.14-0.04)0.29Disease statusT2DM19 (4 790)0.32-0.05 (-0.11-0.01)0.08Obesity12 (2 543)0.25-0.02 (-0.09-0.06)0.71PCOS————NAFLD2 (145)0.44-0.14 (-0.49-0.21)0.44Healthy subjects25 (6 663)0.33-0.04 (-0.08-0.01)0.15Children subjects9 (2 862)0.07-0.04 (-0.12-0.03)0.27LDL-CAll63 (16 580)0.09-0.05 (-0.08--0.02)<0.01EthnicityChinese13 (3 473)0.08-0.09 (-0.16--0.03)0.01Japanese4 (902)0.460.00 (-0.13-0.13)0.96Korean11 (4 487)0.25-0.00 (-0.06-0.06)0.93Caucasian23 (4 824)0.29-0.06 (-0.12--0.01)0.03Latino3 (274)0.200.08 (-0.16-0.32)0.54Indian2 (466)0.950.02 (-0.17-0.20)0.88Middle eastern7 (2 154)0.31-0.09 (-0.18-0.00)0.04GenderMale5 (1 053)0.21-0.14 (-0.26--0.02)0.03Female8 (1 673)0.04-0.02 (-0.12-0.08)0.69Disease statusT2DM18 (4 665)0.07-0.05 (-0.10-0.01)0.12Obesity13 (2 703)0.14-0.03 (-0.11-0.05)0.46PCOS————NAFLD2 (145)0.82-0.18 (-0.53-0.18)0.33Healthy subjects18 (4 401)0.71-0.04 (-0.10-0.02)0.20Children subjects7 (2 110)0.06-0.05 (-0.14-0.03)0.21HDL-CAll80 (22 255)0.160.04 (0.01-0.06)0.01EthnicityChinese21 (6 018)0.260.07 (0.02-0.12)0.01Japanese6 (1 371)0.280.01 (-0.10-0.12)0.87Korean14 (6 029)0.050.02 (-0.04-0.07)0.53Caucasian24 (5 230)0.390.02 (-0.03-0.08)0.47Latino4 (441)0.51-0.03 (-0.22-0.15)0.72Indian3 (1 187)0.620.14 (0.02-0.26)0.02Middle eastern8 (1 979)0.580.04 (-0.06-0.13)0.45GenderMale5 (1 053)0.65-0.09 (-0.21-0.03)0.16Female10 (1 930)0.37-0.03 (-0.12-0.06)0.57Disease statusT2DM19 (4 645)0.480.06 (0.01-0.12)0.03Hypertension2 (488)0.72-0.17 (-0.35-0.01)0.07Obesity14 (2 595)0.430.10 (0.02-0.18)0.01PCOS2 (351)0.360.00 (-0.21-0.21)0.99Healthy subjects27 (7 337)0.14-0.00 (-0.05-0.05)0.98Children subjects8 (2 188)0.58-0.04 (-0.12-0.05)0.38SMD: standardized mean difference; 95% CI: 95% confidence interval;PH: PHeterogeneity; T2DM: type 2 diabetes mellitus; PCOS: polycystic ovarian syndrome; NAFLD: nonalcoholic fatty liver disease; TG: triglycerides; TC: total cholesterol; LDL-C: low-density lipoprotein cholesterol; HDL-C: high-density lipoprotein cholesterol.


## 3.4. Effect of rs266729 on Lipid Profile

rs2241766 had a harmful effect on lipid profile (FigureS8–S10 and Figure 4). Subgroup analysis indicated that the significant effect of rs1501299 on lipid profile was primarily in Chinese, children, T2DM patients, and CAD patients (please see Table 3 for more details).Figure 4Table 3
Meta-analysis of adiponectin rs266729 variant with lipid levels.
Groups or subgroupsComparisons (subjects)PHSMD (95% CI)PSMDOverall resultsTGAll62 (17 815)<0.0010.08 (0.01-0.16)0.03EthnicityChinese37 (8 612)<0.0010.11 (-0.01-0.23)0.08Japanese2 (1 919)0.250.03 (-0.26-0.31)0.87Korean3 (1 822)0.860.09 (-0.01-0.18)0.07Caucasian13 (2 926)<0.010.02 (-0.10-0.15)0.72Middle eastern3 (290)0.64-0.15 (-0.40-0.09)0.22GenderFemale6 (1 816)<0.0010.38 (-0.07-0.84)0.10Disease statusCAD3 (448)<0.010.48 (-0.05-1.01)0.08T2DM13 (2 454)<0.001-0.06 (-0.23-0.10)0.45Obesity10 (1 473)0.240.10 (-0.03-0.22)0.13Mets4 (3 334)0.18-0.09 (-0.20-0.02)0.11Healthy subjects15 (4 374)<0.010.06 (-0.05-0.17)0.32Children subjects4 (1 497)0.180.18 (0.04-0.31)0.01TCAll60 (15 635)<0.0010.09 (0.02-0.16)0.02EthnicityChinese36 (7 763)<0.0010.14 (0.02-0.26)0.02Japanese2 (1 919)0.840.06 (-0.03-0.15)0.17Korean2 (970)0.540.12 (-0.01-0.25)0.07Caucasian14 (3 653)0.10-0.00 (-0.09-0.09)0.98Middle eastern4 (445)0.06-0.07 (-0.40-0.25)0.65GenderFemale5 (964)<0.0010.51 (-0.25-1.27)0.19Disease statusCAD3 (448)0.500.26 (0.08-0.45)0.01T2DM13 (2 454)<0.0010.06 (-0.11-0.24)0.48Obesity11 (2 545)0.290.04 (-0.05-0.14)0.36Mets3 (2 485)0.260.01 (-0.14-0.15)0.94Healthy subjects13 (2 365)0.050.13 (0.00-0.25)0.04Children subjects4 (1 497)0.210.15 (0.02-0.28)0.03LDL-CAll53 (13 793)<0.0010.14 (0.05-0.23)<0.01EthnicityChinese29 (6 088)<0.0010.24 (0.08-0.41)<0.01Korean2 (970)0.460.10 (-0.03-0.23)0.13Caucasian14 (4 216)<0.010.02 (-0.09-0.13)0.70Middle eastern4 (445)0.30-0.12 (-0.35-0.10)0.27GenderFemale5 (964)<0.0010.48 (-0.38-1.34)0.27Disease statusCAD3 (448)<0.010.37 (-0.17-0.92)0.18T2DM10 (2 024)<0.0010.23 (0.01-0.45)0.05Obesity11 (2 545)0.140.07 (-0.04-0.18)0.21Mets2 (598)0.46-0.09 (-0.31-0.14)0.46Healthy subjects13 (3 421)0.160.12 (0.03-0.22)0.01Children subjects4 (1 497)0.800.15 (0.05-0.25)<0.01HDL-CAll57 (15 792)<0.001-0.08 (-0.14--0.03)<0.01EthnicityChinese32 (7 169)<0.001-0.08 (-0.16--0.01)0.03Japanese2 (1 919)0.93-0.03 (-0.12-0.06)0.51Korean2 (970)0.480.04 (-0.09-0.16)0.58Caucasian15 (3 818)0.24-0.07 (-0.15-0.02)0.11Middle eastern4 (445)<0.001-0.29 (-0.87-0.30)0.34GenderFemale5 (964)0.040.08 (-0.15-0.31)0.47Disease statusCAD3 (448)0.11-0.06 (-0.36-0.24)0.70T2DM12 (2 256)<0.001-0.13 (-0.33-0.08)0.22Obesity11 (2 545)0.35-0.03 (-0.12-0.06)0.57Mets4 (3 334)0.19-0.10 (-0.21-0.01)0.08Healthy subjects13 (3 421)0.31-0.06 (-0.14-0.03)0.18Children subjects4 (1 497)0.780.06 (-0.04-0.16)0.25Recalculated results that eliminated heterogeneityTGAll51 (13 937)0.060.04 (0.00-0.07)0.04EthnicityChinese28 (5 753)0.510.06 (0.00-0.11)0.04Japanese2 (1 919)0.25-0.03 (-0.12-0.06)0.54Korean3 (1 822)0.860.09 (-0.01-0.18)0.07Caucasian12 (2 478)0.010.03 (-0.05-0.10)0.52Middle eastern3 (290)0.64-0.15 (-0.40-0.09)0.22GenderFemale5 (1 560)0.420.11 (0.01-0.21)0.04Disease status—————T2DM12 (2 363)0.480.04 (-0.04-0.12)0.35Obesity10 (1 473)0.240.10 (0.00-0.21)0.05Mets2 (2 037)0.52-0.03 (-0.12-0.06)0.54Healthy subjects14 (4 070)0.060.07 (0.01-0.13)0.03Children subjects4 (1 497)0.180.16 (0.06-0.26)<0.01TCAll56 (14 415)0.070.05 (0.01-0.08)0.01EthnicityChinese32 (6 543)0.270.06 (0.01-0.11)0.02Japanese2 (1 919)0.840.06 (-0.03-0.15)0.17Korean2 (970)0.540.12 (-0.01-0.25)0.07Caucasian14 (3 653)0.10-0.00 (-0.07-0.07)0.96Middle eastern4 (445)0.06-0.10 (-0.30-0.09)0.30GenderFemale4 (708)0.970.12 (-0.03-0.27)0.12Disease statusCAD3 (448)0.500.26 (0.08-0.45)0.01T2DM11 (2 108)0.290.03 (-0.06-0.12)0.47Obesity11 (2 545)0.290.03 (-0.05-0.11)0.40Mets3 (2 485)0.260.04 (-0.05-0.12)0.38Healthy subjects13 (2 365)0.810.14 (0.06-0.23)<0.01Children subjects4 (1 497)0.210.15 (0.05-0.25)0.01LDL-CAll44 (11 297)0.140.07 (0.03-0.10)<0.001EthnicityChinese23 (4 648)0.150.08 (0.02-0.14)0.01Korean2 (970)0.460.10 (-0.03-0.23)0.13Caucasian12 (3 315)0.190.01 (-0.07-0.08)0.87Middle eastern3 (290)0.790.01 (-0.24-0.25)0.95GenderFemale4 (708)0.260.06 (-0.09-0.21)0.45Disease status—————T2DM9 (1 933)0.080.11 (0.02-0.20)0.02Obesity10 (2 411)0.950.02 (-0.06-0.10)0.57Mets2 (598)0.46-0.09 (-0.31-0.14)0.46Healthy subjects13 (3 421)0.160.11 (0.04-0.18)<0.01Children subjects4 (1 497)0.800.15 (0.05-0.25)<0.01HDL-CAll53 (15 033)0.12-0.08 (-0.11--0.05)<0.001EthnicityChinese29 (6 545)0.12-0.13 (-0.18--0.08)<0.001Japanese2 (1 919)0.93-0.03 (-0.12-0.06)0.51Korean2 (970)0.480.04 (-0.09-0.16)0.58Caucasian15 (3 818)0.24-0.06 (-0.13-0.01)0.08Middle eastern3 (310)0.56-0.05 (-0.29-0.18)0.66GenderFemale4 (708)0.69-0.04 (-0.19-0.11)0.59Disease statusCAD3 (448)0.11-0.01 (-0.20-0.18)0.92T2DM9 (1 753)0.12-0.15 (-0.25--0.05)<0.01Obesity11 (2 545)0.35-0.03 (-0.11-0.05)0.51Mets4 (3 334)0.19-0.08 (-0.15--0.00)0.04Healthy subjects13 (3 421)0.31-0.05 (-0.12-0.02)0.16Children subjects4 (1 497)0.780.06 (-0.04-0.16)0.25SMD: standardized mean difference; 95% CI: 95% confidence interval;PH: PHeterogeneity; CAD: coronary artery disease; T2DM: type 2 diabetes mellitus; Mets: metabolic syndrome; TG: triglycerides; TC: total cholesterol; LDL-C: low-density lipoprotein cholesterol; HDL-C: high-density lipoprotein cholesterol.


## 3.5. Effect of rs2241766, rs1501299, and rs266729 on Adiponectin Levels

rs2241766, rs1501299, and rs266729 had a significant effect on plasma adiponectin levels (FigureS11–S13). Subgroup analysis showed that the effect of rs2241766 and rs150129 on adiponectin levels was primarily in Chinese (please see Table S8 for more details), while the effect of rs266729 on adiponectin levels was primarily in Caucasians (please see Table S8 for more details).


## 3.6. Evaluation of Heterogeneity

Significant heterogeneity was detected in analyzing the effects of adiponectin variants on lipid and adiponectin levels (Tables1–3 and Table S8). However, the recalculated results did not change significantly after eliminating heterogeneity (see Tables 1–3 and Table S8 for more details), indicating that the analysis results were robust.


## 3.7. Publication Bias Test

No publication bias was detected (see FigureS14–S17 for more details), indicating that the synthetic results were reliable.


## 4. Discussion

Our study indicated that variants of rs2241766, rs1501299, and rs266729 had significant effects on circulating adiponectin and lipid levels. Among them, variants of rs2241766 and rs266729 are atherogenic, while variant rs1501299 is antiatherogenic.Since variants of adiponectin are robustly related to lipid and adiponectin levels in specific populations, it can be helpful for physicians to choose different clinical management to intervention the onset of CVD.Previous studies showed that variants of rs2241766 [22], rs1501299 [14], and rs266729 [13] may affect adiponectin mRNA splicing, indicating that adiponectin variants may affect adiponectin levels by modulating adiponectin mRNA. The mechanisms underlying adiponectin variants impacted lipid profile have not been elucidated. However, emerging evidence indicated that the effects of adiponectin variants on lipid levels were possibly mediated by the circulating adiponectin levels [23–25].The present study showed that variants of rs2241766 and rs26672 were associated with higher TG, TC, and LDL-C, as well as lower HDL-C and adiponectin levels (Tables1 and 3 and Table S8), indicating that variants of rs2241766 and rs26672 decreased adiponectin and caused dyslipidemia. Therefore, rs2241766 and rs266729 should be considered the atherogenic genetic factors. In contrast, variant of rs1501299 was associated with lower TG, TC, and LDL-C, as well as higher HDL-C and adiponectin levels (Table 2 and Table S8), indicating that variant of rs1501299 elevated adiponectin and ameliorated lipid profile. Therefore, rs1501299 should be recognized as an antiatherogenic genetic factor. Intriguingly, the effects of these variants on lipid profile and adiponectin levels can explain, at least in part, the known correlations between the rs2241766, rs266729, and rs1501299 variants and the risk of CAD [18, 19].The decreased plasma adiponectin (TableS8) was associated with increased TG, TC, and LDL-C, as well as decreased HDL-C levels (Tables 1 and 3), indicating that low levels of adiponectin were linked to an atherogenic lipid profile. In contrast, the increased plasma adiponectin (Table S8) was correlated to decreased TG, TC, and LDL-C, as well as increased HDL-C levels (Table 2), indicating that high levels of adiponectin were linked to an antiatherogenic lipid profile. Taken together, indicating adiponectin was indeed an antiatherogenic molecule, and plasma levels of adiponectin should be recognized as a marker of dyslipidemia.According to the 2018 ACC/AHA [26], the 2019 ESC/EAS [27], and the adult treatment panel III (ATP III) cholesterol guidelines [28], LDL-C was considered the major cause of CAD and treated as the primary target for therapy, while other lipids were used as the secondary or supplementary therapeutic targets. In the present study, a considerable effect of rs2241766 on LDL-C (SMD=0.18, 95% CI=0.04−0.32, P=0.01) and TC (SMD=0.18, 95% CI=0.05−0.31, P=0.01) was observed in males (Table 1). Indicating the males with the rs2241766 variant had an increased risk of CAD. In sharp contrast to rs2241766, substantially decreased LDL-C (SMD=−0.14, 95% CI=−0.26−−0.02, P=0.03) and TC (SMD=−0.19, 95% CI=−0.31−−0.07, P<0.01) were observed in males with the rs1501299 variant (Table 2), indicating that males with the rs1501299 variant had reduced susceptibility to CAD. However, whether variant of rs266729 impacted the risk of CAD in males could not be determined due to the absence of data (Table 3). Further clinical trials in males are certainly needed.Subgroup analysis by ethnicity showed that significantly increased LDL-C, TC, and TG and decreased HDL-C were observed in Chinese with rs2241766 and rs266729 (Tables1 and 3), indicating that Chinese with variants of rs2241766 and rs266729 were at high risk of dyslipidemia, in other words, Chinese with the rs2241766 and rs266729 variants had an increased risk to develop atherosclerosis or CAD. However, decreased LDL-C and TC were observed in Caucasians with rs1501299 (Table 2), indicating that Caucasians with the rs1501299 variant had a reduced risk of CAD.Moreover, significant increases in TG and TC, as well as decreases in HDL-C, were detected in T2DM patients with rs2241766 (Table1), indicating that T2DM patients with the rs2241766 variant had an increased risk of dyslipidemia, but not CAD. Significant increases in HDL-C were detected in T2DM patients with rs1501299 (Table 2), indicating that T2DM patients with the rs1501299 variant were protected against dyslipidemia, whereas significant increases in LDL-C and decreases in HDL-C were detected in T2DM patients with rs266729 (Table 3), indicating that the T2DM patients with the rs266729 variant were at high risk of dyslipidemia and/or CAD.Notably, a significant increase in LDL-C (SMD=0.15, 95% CI=0.05−0.25, P<0.01), TC (SMD=0.15, 95% CI=0.05−0.25, P=0.01), and TG (SMD=0.16, 95% CI=0.06−0.26, P<0.01) was observed in the children with rs266729 (Table 3), indicating that children with the rs266729 variant were at high risk of dyslipidemia, atherosclerosis, and even early onset of CAD in the future; therefore, these children need our particular attention for early identification.


## 5. Strengths and Limitations

The present meta-analysis has several strengths. For instance, the clinical data of 86,610 individuals were included in the analysis, which increased the reliability of synthetic results due to high statistical power. Secondly, the synthetic results were recalculated after excluding the studies with heterogeneity, which further advanced the preciseness of conclusions drawn in this study and were not likely to be type I errors (false-positive results). However, several limitations of the present study should be noted. Firstly, dyslipidemia is involved in a large number of genes as well as some environmental factors. However, the interactions of the rs2241766, rs1501299, and rs266729 variants with other polymorphic loci or environmental factors on lipid profile have not been investigated in this study due to the lack of the original data from the included studies. In other words, more precise results could have been gained if more detailed individual data were available, or if the stratification analyses based on the environmental factors such as smoking, alcohol consumption, exercise, etc., were performed [29]. Secondly, this meta-analysis only included the studies published in English and Chinese as it was very difficult to get the full papers published in various languages [29]. Thirdly, a protocol (e.g., PROSPERO) had not been preregistered for this meta-analysis due to a huge workload and heavy analytical tasks, which may introduce potential bias to this study.


## 6. Conclusions

The present study indicated that Chinese with the rs2241766 and rs266729 variants were at high risk of dyslipidemia, atherosclerosis, or coronary artery disease (CAD). Males with the rs2241766 variant were at high risk of CAD. Children with the rs266729 variant had a high risk to develop dyslipidemia, atherosclerosis, and even early onset of CAD in the future. These findings are beneficial to clinical physicians to choose different management strategies for cardiovascular disease (CVD) prevention.



---

*Source: 4395266-2022-07-07.xml*