OBJECTIVE

To determine whether adiposity depots modulate vaspin levels and whether vaspin predicts type 2 diabetes (T2D) risk, through epidemiological and genetic analyses.

RESEARCH DESIGN AND METHODS

We assessed the relationship of plasma vaspin concentration with incident and prevalent T2D and adiposity-related variables in 1) the Prospective Urban and Rural Epidemiology (PURE) biomarker substudy (N = 10,052) and 2) the Outcome Reduction with Initial Glargine Intervention (ORIGIN) trial (N = 7,840), using regression models. We then assessed whether vaspin is causally associated with T2D and whether genetic variants associated with MRI-measured adiposity depots modulate vaspin levels, using two-sample Mendelian randomization (MR).

RESULTS

A 1-SD increase in circulating vaspin levels was associated with a 16% increase in incident T2D in the PURE cohort (hazard ratio 1.16; 95% CI 1.09–1.23; P = 4.26 × 10−7) and prevalent T2D in the ORIGIN cohort (odds ratio [OR] 1.16; 95% CI 1.07–1.25; P = 2.17 × 10−4). A 1-unit increase in BMI and triglyceride levels was associated with a 0.08-SD (95% CI 0.06–0.10; P = 2.04 × 10−15) and 0.06-SD (95% CI 0.04–0.08; P = 4.08 × 10−13) increase, respectively, in vaspin in the PURE group. Consistent associations were observed in the ORIGIN cohort. MR results reinforced the association between vaspin and BMI-adjusted T2D risk (OR 1.01 per 1-SD increase in vaspin level; 95% CI 1.00–1.02; P = 2.86 × 10−2) and showed that vaspin was increased by 0.10 SD per 1-SD decrease in genetically determined gluteofemoral adiposity (95% CI 0.02–0.18; P = 2.01 × 10−2). No relationships were found between subcutaneous or visceral adiposity and vaspin.

CONCLUSIONS

These findings support that higher vaspin levels are related to increased T2D risk and reduced gluteofemoral adiposity, positioning vaspin as a promising clinical predictor for T2D.

The epidemiological link between type 2 diabetes (T2D) and both BMI and waist-to-hip ratio (WHR) has been well established (1). However, the biological mechanisms underlying the relationship between body distribution and T2D pathogenesis are not fully understood. Adipokines (cytokines produced by adipose tissue) are one of the biological links between adiposity and T2D. Increased lipid storage in adipose tissue alters adipokine secretion, leading to dysfunction of insulin target organs and tissues, including pancreatic β-cells (2). Primarily through in vivo and epidemiological studies, numerous circulating blood biomarkers have been suggested to link adipose dysregulation with T2D (3). However, few adipokines have been investigated for causal relationships with T2D. Given the potentially crucial role of adipokines in linking adipose dysfunction and T2D, pursuing investigations on their involvement in T2D pathogenesis may reveal novel circulating biomarkers with significant implications for T2D risk stratification.

Vaspin, also known as visceral adipose tissue–derived serine protease inhibitor, is an adipokine first discovered in the visceral adipose tissue of an animal model of abdominal obesity and T2D (4,5). In humans, vaspin expression levels have been reported in various tissues. Notably, differences in expression between subcutaneous and visceral adipose tissue have been described, making vaspin a promising factor to mediate the effect of adipose tissue on T2D risk (6). Evidence from prior epidemiological studies did not corroborate animal studies; to the contrary, they linked higher vaspin levels to higher risk of gestational diabetes (7), T2D (8), and atherosclerotic cardiovascular diseases (CVDs) (9). However, conclusions were limited by small sample size, lack of replication across different ethnicities, and lack of assessment of causal effect on T2D.

In this study, we conducted epidemiological analyses to shed light on the relationships of circulating vaspin levels with T2D risk (prevalent and incident), as well as with adiposity compartments. We used individual-level data in two expansive, longitudinal, multiethnic cohorts, in which vaspin levels were measured at baseline. We then investigated the causal role of vaspin on T2D risk using a two-sample Mendelian randomization (MR) approach. MR is a statistical method that uses genetic associations with both the putative causal factor (i.e., vaspin) and the outcome to infer causality while reducing bias due to confounding or reverse causation (10). We applied MR analyses to test the hypothesis that vaspin might increase the risk of T2D. We identified genetic variants within or near the vaspin locus influencing circulating vaspin concentrations and estimated whether those variants affected T2D risk, using publicly available summary statistics from large genetic consortia. We also tested whether genetically determined, MRI-measured adiposity compartments (i.e., subcutaneous, visceral, and gluteofemoral adiposity) modulated circulating vaspin levels, to better understand its mediating role between adiposity and T2D.

The Prospective Urban and Rural Epidemiology (PURE) study is an international prospective cohort study involving approximately 225,000 participants and conducted in 27 low-, middle-, and high-income countries from 2003 to 2009. The median follow-up time was 9.8 years (11). A subset of 12,066 participants in the PURE study was selected for a case-cohort analysis to include incident cardiometabolic events. This subset consisted of incident cases of T2D (n = 1,531) and participants randomly selected matched to case patients on the basis of ethnicity frequency (n = 8,521). Subsequently, 1,235 participants were excluded from the analyses due to missing values for BMI, WHR, baseline T2D status, age, and levels of triglycerides (TGs), HDL, and LDL. The participants’ characteristics are presented in Supplementary Table 1.

The Outcome Reduction with Initial Glargine Intervention (ORIGIN) study was conducted with 12,537 participants with T2D, impaired glucose tolerance, or impaired fasting glucose levels, and additional cardiovascular risk factors (12). Participants were recruited between 2003 and 2005 at 578 clinical sites in 40 countries, with a median follow-up time of 6.2 years. The study used a factorial design randomly allocating patients to two therapies (insulin glargine vs. standard care, n-3 fatty acid supplement vs. placebo). Of the 8,401 participants whose biomarker data were deemed suitable for analysis, individuals without specific biological ancestry information were removed, leaving 8,197 individuals. Subsequently, 357 individuals were excluded due to missing data for BMI, WHR, HDL, and LDL, resulting in a sample size of 7,840 individuals for biomarker analysis (Supplementary Table 1).

Circulating Vaspin Measurements

A 1.8-mL aliquot of plasma from each PURE participant was transported to the Clinical Research Laboratory and Biobank in Hamilton, Ontario, Canada. Blood concentration of vaspin was measured using an immunoassay based on proximity extension assay technology (Olink PEA CVD-II panel), for which the within-run precision coefficient of variation (CV) is 10%, and between-run precision CV is 22% (13). Data generated are expressed as relative quantification on the log2 scale of normalized protein expression values. Individual samples were excluded on the basis of quality controls for immunoassay and detection, as well as degree of hemolysis, as previously described (11). Normalized protein expression values were rank-based normal transformed for further analyses. A total of 779 samples were considered unsuitable after proteomic quality control exclusion, with the remaining 10,052 samples included for analysis.

After completion of the ORIGIN trial, 1 mL of serum from each participant was transported to Myriad RBM Inc. (Austin, Texas), to quantify 284 biomarkers, which were selected previously for their role in metabolic and CVDs, including vaspin, using a Luminex platform. Vaspin assay has a within-run precision CV of 4% and a between-run precision CV of 8% (Supplementary Material). Raw data of vaspin were expressed in picograms per milliliter (median, 239.0 pg/mL; interquartile range 145.0, 418.0 pg/mL). As detailed elsewhere (12), 238 biomarkers (including vaspin) from 8,401 participants were deemed suitable for analysis after quality control, with biomarkers that were not normally distributed being first log transformed and then standardized to a mean of 0 and an SD of 1.

Genotyping Analyses

PURE participants suitable for proteomics analyses were genotyped on the ThermoFisher Axiom Precision Medicine Research Array. Genotyping quality control has been described (11). Briefly, sample-level quality control checks included assessments of sample completeness (call rate > 0.95), potential sample mix-ups (discrepancies between reported vs. genetically determined sex and/or ethnicity), genetic duplicates, and sample contamination (excess heterozygosity). Samples exhibiting nonambiguous discrepancies between genetic and self-reported ancestry were removed. Variant-level quality control checks included assessments of variant completeness (call rate > 0.985), plate and batch effects, non-Mendelian segregation within families (Mendelian errors), Hardy-Weinberg equilibrium deviations (P < 1 × 10−5), and variant frequency (minor allele frequency > 0). After sample and variant quality control procedures, 11,112 samples and 749,783 variants remained. The average genotyping call rate among passing samples was >0.99.

Statistical Analyses

Epidemiological Analysis

Cox proportional hazard models were used to investigate the relationships of baseline vaspin level with incident T2D and other end points of the PURE cohort, including myocardial infarction, stroke, heart failure, and death. We used the Lin-Ying weighting method to account for the case-cohort design of the subset (14). The models were minimally adjusted for age, sex, ethnicity, and Olink reagent lot and further adjusted for seven CVD risk factors, including BMI, WHR, TGs, HDL, LDL, systolic blood pressure (SBP), and diastolic blood pressure (DBP), that capture components of metabolic syndrome at baseline.

In both PURE and ORIGIN, a logistic model was used to estimate the effect of vaspin on prevalent T2D, and linear models were used to estimate the effects of vaspin on fasting glucose, and HbA1c values. In the subset of ORIGIN participants with biomarker data who were allocated to receive insulin glargine (n = 3,832), we estimated the effects of vaspin on the median of the insulin glargine doses required to achieve normoglycemia (expressed in insulin units per kilogram of fat-free mass). This served as a proxy measure for insulin resistance, as previously described (15). All models were minimally adjusted for age, sex, ethnicity, and Olink reagent lot in PURE, and were further adjusted for baseline BMI, WHR, and the other CVD risk factors. To further dissect the relationships between CVD risk factors and vaspin levels, CVD risk factors were also regressed as independent variables against circulating vaspin levels, using a linear model. Associations with P < 7.14 × 10−3 (0.05/7) were deemed statistically significant after Bonferroni correction for multiple hypothesis testing. We then performed mediation analysis to estimate the mediation effect of the significant CVD risk factors on the relationships between vaspin and T2D risk, using a bootstrap method with 1,000 simulations (R package “Mediation”). Finally, as a sensitivity analysis, we calculated the interactions between the effect of the CVD risk factors on vaspin level with both BMI strata and diabetes status.

Mendelian Randomization Analysis

We conducted a genome-wide association study analysis testing the association between plasma vaspin concentration and each of the common genetic variants in 8,699 PURE participants, using a linear regression model adjusted for age, sex, and 20 ancestry-specific principal components. The associations between plasma vaspin concentration and genetic variants, also known as protein quantitative trait loci (pQTLs), were conducted separately in each ethnic group and then meta-analyzed using the METAL software (16). We selected genetic variants within 200 kilobases of the SERPINA12 locus, also known as cis-pQTLs. We kept genetic variants in common between the PURE and Diabetes Genetics Replication and Meta-Analysis (DIAGRAM) databases, and the PURE and FinnGen databases, and with minor allele frequencies >0.01. Furthermore, genetic variants that were a splicing-site variant or missense were removed, the latter given the potential for pleiotropy. Any cis variants associated with vaspin concentration (P < 5 × 10−5) were then pruned for linkage disequilibrium in the Latin, European, and Persian data sets at a threshold of r2 < 0.1. If two variants were in linkage disequilibrium in any of the ethnic groups that were included in the meta-analysis, the variant with the lowest P value was kept. A total of 16 single nucleotide polymorphisms (SNPs) were retained to perform the forward MR analyses (F = 245).

Two-sample forward MR analysis of vaspin concentration change on T2D risk used the effect of the vaspin concentration–increasing SNPs and the effect of those SNPs on T2D risk. We used summary-level data from the DIAGRAM consortium, which are from 898,130 individuals of European ancestry, including 53,524 T2D cases (defined as diabetes diagnosis by a physician) and 824,006 control cases (17); and from the FinnGen consortium data (release version 5), including 215,654 individuals with 32,469 cases of T2D (defined as ICD code E11 [“Non-insulin-dependent diabetes mellitus”]) (18). Notably, this includes all cases of T2D with and without complications. Control cases consisted of all individuals except those with type 1, type 2, and unspecified diabetes.

We then performed reverse MR of the effect of genetically determined body fat distribution on plasma vaspin concentration to assess if a specific fat depot modulates vaspin level. This was calculated by using SNPs associated with gluteofemoral, subcutaneous, and visceral adiposity quantified by body MRI in up to 38,965 UK Biobank participants (19); and the effect of those SNPs on plasma vaspin concentration in the PURE study. SNPs significantly associated with BMI-adjusted gluteofemoral adipose tissue, BMI-adjusted subcutaneous adipose tissue, and BMI-adjusted visceral adipose tissue (VAT) at P < 5 × 10−8 were pruned for linkage disequilibrium at a threshold of r2 < 0.1. A total of 57 SNPs (F = 54.47), 26 SNPs (F = 40.46), and 31 SNPs (F = 49.53) were respectively retained as genetic instruments for BMI-adjusted gluteofemoral adipose tissue, subcutaneous adipose tissue, and visceral adipose tissue to perform the reverse MR analyses.

For both forward and reverse MR, the inverse variance weighted (IVW) method was prioritized above the MR Egger approach to estimate the associations, unless the MR Egger intercept, which tests for pleiotropy, was substantially different from 0 (P v< 0.05). Other methods, such as the weighted median, robust adjusted profile score, and MR pleiotropy residual sum and outlier methods were also included. To avoid weak instrument bias, we used genetic instruments with an F statistic of >10 (20). Analyses were conducted using R, version 3.6.0 (MendelianRandomization R package).

Data and Resource Availability

No additional data are available beyond what is reported in this article.

Higher Vaspin Concentrations Are Associated With Increased Risk of T2D

Using adjusted and weighted Cox proportional hazards model in the PURE cohort, we found that higher circulating vaspin levels were associated with a 16% higher incidence of T2D adjusted for BMI (hazard ratio, 1.16 per 1-SD increase in vaspin; 95% CI 1.09–1.23; P = 4.26 × 10−7) for a median follow-up of 9.1 years. The association remained statistically significant in the fully adjusted model for other CVD risk factors (Fig. 1). There were no significant associations detected between vaspin and other outcomes including myocardial infarction, stroke, heart failure, and death (Supplementary Table 2). Additional analyses in people with dysglycemia from the ORIGIN cohort showed that a 1-SD increase in vaspin level was associated with a 15% increased risk in prevalent T2D adjusted for BMI (OR 1.15; 95% CI 1.07–1.24; P = 3.40 × 10−4), with significance and effect size remaining similar after adjusting for all other CVD risk factors (Supplementary Table 3).

Figure 1

Association of vaspin levels with incident T2D. A) Survival plot of incident diabetes in participants in the PURE study with vaspin level below vs. above the median, adjusted for age, sex, ethnicity, OLINK reagent lot number, and BMI. B) Cox proportional hazards models of effect of plasma vaspin concentration on incident clinical end points in the PURE study adjusted for age, sex, ethnicity, OLINK reagent lot number, BMI, WHR, and other CVD risk factors.

Figure 1

Association of vaspin levels with incident T2D. A) Survival plot of incident diabetes in participants in the PURE study with vaspin level below vs. above the median, adjusted for age, sex, ethnicity, OLINK reagent lot number, and BMI. B) Cox proportional hazards models of effect of plasma vaspin concentration on incident clinical end points in the PURE study adjusted for age, sex, ethnicity, OLINK reagent lot number, BMI, WHR, and other CVD risk factors.

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This observation was reinforced by associations between vaspin and diabetes-related variables, which showed that a 1-SD increase in vaspin level was associated with a 0.07-SD increase in fasting glucose concentration in the PURE cohort (95% CI 0.04–0.10; P = 3.88 × 10−6) and a 0.09-SD increase in fasting glucose concentration in the ORIGIN cohort (95% CI 0.05–0.13; P = 5.99 × 10−5), as well as with a 0.05-SD increase in HbA1c in the ORIGIN cohort (95% CI 0.02–0.07; P = 4.38 × 10−5). Higher vaspin level was also modestly associated with higher resistance to exogenous insulin action (i.e., increase by 0.02 insulin units per kilogram of fat-free mass to achieve normoglycemia, per 1-SD increase in vaspin level; 95% CI 0.00–0.04; P = 2.43 × 10−2) in the ORIGIN cohort.

Vaspin’s Associations With Cardiovascular Risk Factors

We next tested the effects of seven cardiovascular risk factors on circulating vaspin levels in both cohorts (Fig. 2). The strongest association was consistently seen with TG (mean vaspin change of 0.06 SD per 1 SD increase in TG level; 95% CI 0.04–0.08; P = 4.08 × 10−13), BMI (mean vaspin change of 0.08 SD per 1 SD increase in BMI; 95% CI 0.06–0.10; P = 2.04 × 10−15), SBP (mean vaspin change of 0.04 SD per 1 SD increase SBP; 95% CI 0.02–0.06; P = 2.48 × 10−4), and DBP (mean vaspin change of 0.04 SD per 1 SD increase DBP; 95% CI 0.02–0.06; P = 6.08 × 10−3). We found partial mediation effects of BMI (24%), TG (9.3%), SBP (3.5%), and DBP (2.8%) on the relationships of vaspin with T2D risk (Supplementary Table 4). Finally, there was no interaction between BMI or diabetes status with the effect of the risk factors on level of circulating vaspin.

Figure 2

Epidemiological associations of anthropometric and metabolic variables (given in SD) with circulating vaspin levels (given in SD).

Figure 2

Epidemiological associations of anthropometric and metabolic variables (given in SD) with circulating vaspin levels (given in SD).

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Assessing the Causality Between Vaspin and T2D Risk

A two-sample MR approach was then used to infer causality between vaspin and T2D risk to overcome the possible risk of confounding and reverse causation. Using the cis-pQTLs of vaspin determined in the PURE cohort as inputs, we estimated that a 1-SD increase in genetically determined vaspin level was associated with a 1% increased risk of BMI-adjusted incident T2D (IVW OR 1.01; 95% CI 1.00–1.02; P = 2.86 × 10−2) (Table 1). Other MR methods, including weighted median and MR Egger, were directionally consistent, though not significant.

Table 1

Two-sample MR of effect of genetically higher vaspin level on T2D risk

Outcome populationMethod (meta-analyzed)OR (95% CI)P valueP value of intercept
FinnGen IVW 1.01 (1.00–1.03) 1.17 × 10−2  
Weighted median 1.02 (1.00–1.03) 4.15 × 10−2  
MR Egger 1.02 (0.99–1.03) 0.313 0.716 
RAPS 1.02 (1.00–1.03) 1.04 × 10−2  
DIAGRAM IVW 1.01 (1.01–1.02) 9.08 × 10−4  
IVW adj. BMI 1.01 (1.00–1.02) 2.86 × 10−2  
Weighted median 1.01 (1.00–1.02) 5.60 × 10−3  
Weighted median adj. BMI 1.01 (1.00–1.03) 1.63 × 10−2  
MR Egger 1.01 (1.02–1.04) 3.09 × 10−3 0.167 
MR Egger adj. BMI 1.02 (1.00–1.04) 2.24 × 10−2  
RAPS 1.01 (1.01–1.02) 1.15 × 10−3  
RAPS adj. BMI 1.01 (1.00–1.02) 1.29 × 10−2  
MR-Presso 1.01 (1.01–1.02) 2.65 × 10−4  
MR Presso adj. BMI 1.01 (1.00–1.02) 1.58 × 10−2  
Outcome populationMethod (meta-analyzed)OR (95% CI)P valueP value of intercept
FinnGen IVW 1.01 (1.00–1.03) 1.17 × 10−2  
Weighted median 1.02 (1.00–1.03) 4.15 × 10−2  
MR Egger 1.02 (0.99–1.03) 0.313 0.716 
RAPS 1.02 (1.00–1.03) 1.04 × 10−2  
DIAGRAM IVW 1.01 (1.01–1.02) 9.08 × 10−4  
IVW adj. BMI 1.01 (1.00–1.02) 2.86 × 10−2  
Weighted median 1.01 (1.00–1.02) 5.60 × 10−3  
Weighted median adj. BMI 1.01 (1.00–1.03) 1.63 × 10−2  
MR Egger 1.01 (1.02–1.04) 3.09 × 10−3 0.167 
MR Egger adj. BMI 1.02 (1.00–1.04) 2.24 × 10−2  
RAPS 1.01 (1.01–1.02) 1.15 × 10−3  
RAPS adj. BMI 1.01 (1.00–1.02) 1.29 × 10−2  
MR-Presso 1.01 (1.01–1.02) 2.65 × 10−4  
MR Presso adj. BMI 1.01 (1.00–1.02) 1.58 × 10−2  

adj., adjusted; Presso, pleiotropy residual sum and outlier; RAPS, robust adjusted profile score.

Associations Between Body Fat Compartments and Vaspin

We applied reverse MR to assess the causal relationships between body fat compartments and vaspin levels (Table 2). A higher genetically determined gluteofemoral adiposity adjusted for BMI was associated with lower vaspin levels (IVW estimate, −0.10; 95% CI −0.18, −0.02; P = 2.01 × 10−2). No associations were found between genetically determined visceral or subcutaneous fat depots with vaspin. There was no evidence of pleiotropy indicated by the MR Egger intercept (Table 2).

Table 2

Reverse MR of the effect of genetically determined body fat distribution on vaspin level

ExposureNo. of SNPs in genetic instrumentF statistics of genetic instrumentChange in vaspin per 1-SD increase in genetically determined body fat distribution (95% CI)*P valueP value of MR Egger intercept
Gluteofemoral adipose tissue adj. BMI 57 54.47 −0.10 (−0.18, −0.02) 2.01 × 10−2 0.25 
Subcutaneous adipose tissue adj. BMI 26 40.46 −0.03 (−0.17, 0.11) 6.67 × 10−1 0.66 
Visceral adipose tissue adj. BMI 31 49.53 −0.06 (−0.19, 0.07) 3.48 × 10−1 0.72 
ExposureNo. of SNPs in genetic instrumentF statistics of genetic instrumentChange in vaspin per 1-SD increase in genetically determined body fat distribution (95% CI)*P valueP value of MR Egger intercept
Gluteofemoral adipose tissue adj. BMI 57 54.47 −0.10 (−0.18, −0.02) 2.01 × 10−2 0.25 
Subcutaneous adipose tissue adj. BMI 26 40.46 −0.03 (−0.17, 0.11) 6.67 × 10−1 0.66 
Visceral adipose tissue adj. BMI 31 49.53 −0.06 (−0.19, 0.07) 3.48 × 10−1 0.72 
*

Change in vaspin level was estimated using IVW method.

In this large-scale, multiethnic analysis of epidemiologic and genetic data, we show that higher circulating vaspin levels are associated with a 16% increased T2D risk, and reduced gluteofemoral adipose tissue.

The effect size of the association of vaspin with T2D is consistent with previously reported hazard ratios of other adipokines, ranging from 1.02 to 1.80 (21). Previous epidemiological studies identified positive correlations between plasma vaspin concentrations and T2D risk, BMI, and waist circumference (2224), and these were corroborated by our results. In a study in individuals with obesity who were enrolled in a weight management program, weight loss was associated with reduced circulating vaspin concentration (25). Interestingly, a decrease in circulating vaspin levels was also observed in individuals with severe obesity undergoing gastric bypass surgery, which is of particular interest given that most of these individuals experienced T2D remission after surgery (26,27). In prepubertal and pubertal children with obesity, studies also found that lower circulating vaspin levels were associated with improved insulin sensitivity (28,29). In smaller studies, no difference was observed in circulating vaspin levels between individuals with T2D and those with normal glucose tolerance (30), glucose tolerance, or insulin sensitivity (31).

Existing literature linking vaspin and other cardiometabolic risk factors and outcomes was also sparse and conflicting. A study of Chinese individuals with T2D showed that vaspin levels were increased in patients with T2D compared with healthy control participants, and further increased in individuals with T2D and coronary artery disease compared with individuals with T2D only (23). However, another study of Chinese individuals with acute myocardial infarction showed that lower vaspin levels were associated with higher incidence of major adverse cardiac events (32). Our analyses did not detect significant associations between circulating vaspin levels and incident cardiovascular outcomes, despite our sample size being much larger compared with these previous studies. Other studies have also reported positive associations between TG and vaspin levels (33). We demonstrate epidemiological associations between CVD risk factors and vaspin, including BMI, TGs, SBP, and DBP.

The exact mechanism by which vaspin acts is unknown. Authors of previous studies hypothesized vaspin to have insulin-sensitizing effects. Vaspin expression in adipocytes is regulated by peroxisome proliferator-activated receptor γ, an essential factor for adipocyte proliferation, differentiation, and gene expression of other important adipokines, such as adiponectin (3). Vaspin-deficient mice exhibit glucose intolerance, contrasting with vaspin-overexpressing mice, who seem to be protected from diet-induced obesity, glucose intolerance, and hepatic steatosis (34). The administration of recombinant vaspin in mice with diet-induced obesity showed an improvement in glucose tolerance and insulin sensitivity (5). Various mechanisms by which vaspin is involved in T2D have been proposed, including by acting as an inhibitory protease targeting human kallikrein 7, which cleaves insulin (35), and by activating the PI3K/AKT axis, which mediates glucose homeostasis (36); however, these mechanisms have not been yet fully elucidated. Moreover, higher vaspin mRNA expression was more frequently detected in patients with T2D and not detectable in lean participants (6). Visceral vaspin expression positively correlated with BMI and body fat percentage, whereas subcutaneous vaspin mRNA expression negatively correlated with WHR and fasting plasma insulin concentration (6). These findings suggest vaspin might be differently expressed across various body fat depots and that vaspin might have tissue-specific effects on overall metabolic status. Interestingly, we identified that vaspin levels might be modulated by BMI-adjusted gluteofemoral adiposity through MR analysis, with no significant associations found with visceral or subcutaneous adipose tissues. Gluteofemoral adipose tissue has been reported to be associated with a protective lipid and glucose profile (37), with independent associations with lower total cholesterol, LDL cholesterol, TGs, insulin resistance, glucose levels after oral glucose tolerance testing, and insulin levels (38,39). One mechanism by which gluteofemoral tissue might exert its protective effects is through the modulation of adipokines. Although leptin and adiponectin have both been described to be modulated by gluteofemoral adipose tissue (33), our study positions vaspin as a novel biomarker linking low gluteofemoral adipose tissue and increased risk of T2D.

Strengths of our analyses include the use of two expansive, multiethnic cohorts as compared with previous epidemiological studies, which were limited in sample size and ethnic diversity. Our analyses across the two cohorts use two different methods for measuring vaspin, decreasing the likelihood of measurement error. Our results are supported by our MR analyses, which are, to our knowledge, the first to strengthen the directionality of the effects of vaspin on T2D risk. We also present novel findings on the association between vaspin and adiposity compartments, identifying a potential pathophysiological mechanism by which vaspin may act to increase T2D risk. We acknowledge that our study has a few limitations. First, given that 80% of participants in the ORIGIN study had T2D at baseline, incident T2D could not be assessed in this cohort. Our MR findings show only modest causal effect of vaspin and T2D risk, but nonetheless reinforced the epidemiological results given the consistent directionality and significance. Circulating vaspin levels might not reflect the tissue-specific expression of vaspin in various adiposity depots, which might affect the interpretation of the causal effect of vaspin in T2D. Our MR analyses were limited to available summary statistics from previously published genome-wide association studies for T2D risk (16,17), for which there were no available genetic estimates adjusted for CVD risk factors, other than BMI. Finally, we were unable to directly assess the relationship between circulating vaspin and adiposity distribution along with the relationship between circulating vaspin and insulin resistance indexes such as the HOMA of insulin resistance, due to the absence of collection of such information in the PURE and ORIGIN studies. As a surrogate measure for insulin resistance, we used the median dose of insulin required to achieve normoglycemia, which we previously validated (14).

In conclusion, we have identified that a higher circulating level of vaspin is a predictor of higher risk of incident T2D, possibly mediating the effect of reduced gluteofemoral adiposity on T2D risk. We did not detect any relationships between circulating vaspin levels and cardiovascular events, and demonstrate associations between vaspin and BMI, TGs, SBP, and DBP. Our findings shed light on the overall deleterious effect of vaspin on T2D risk in humans, and further research is required to fully understand the underlying biological mechanisms involved in T2D pathogenesis.

This article contains supplementary material online at https://doi.org/10.2337/figshare.24545473.

G.P. and M.P. are co-senior authors for this article.

Acknowledgments. We are thankful to all the PURE and ORIGIN participants for contributing to this project and to the DIAGRAM consortia and the UK Biobank for making their summary statistics data available.

Funding. The ORIGIN trial and biomarker project were supported by Sanofi. The PURE study and biomarker project were supported by Bayer and CIHR. M.P. is supported by the Early Career Research Award from Hamilton Health Sciences. G.P. is supported by the Canada Research Chair in Genetic and Molecular Epidemiology and the CISCO Professorship in Integrated Health Biosystems.

Duality of Interest. All authors completed the International Committee of Medical Journal Editors uniform disclosure form (available on request from the corresponding author) and provided the following declarations. S.H. is an employee of Sanofi and may hold shares and/or stock options in the company. H.G. reports grants and personal fees Sanofi, grants and personal fees from Eli Lilly, grants and personal fees from Astra Zeneca, grants and personal fees from Boehringer Ingelheim, grants and personal fees from Abbott, grants and personal fees from Novo Nordisk, grants from Merck, personal fees from Kowa Research Institute, personal fees from Zuellig, personal fees from DKSH, grants from Hanmi, personal fees from Carbon Brand, and personal fees from Jiangsu Hanson Pharma, which are outside the submitted work. G.P. has received payment or honoraria for lectures, presentations, speakers bureaus, manuscript writing, or educational events from Amgen, Bayer, Novartis, and Sanofi, and has received support and grants or contracts from Bayer paid to his affiliated institution. No other potential conflicts of interest relevant to this article were reported.

Author Contributions. H.W., G.P., and M.P. designed the study, planned the analyses, interpreted the results, and wrote the manuscript. H.W., M.C., N.P., J.F., and M.P. contributed to the statistical and bioinformatics analyses. S.H. suggested including vaspin in the biomarker panel. H.W., M.C., N.P., J.F., S.H., S.Y., H.G., G.P., and M.P. contributed to the critical reading and revision of the manuscript and approved the submitted version of this manuscript. M.P. and G.P. are the guarantors of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Prior Presentation. Parts of this study were presented at the American Diabetes Association 82nd Scientific Sessions, New Orleans, LA, 3–7 June 2022.

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