Background
Body fat percentage (BF%) is a proxy for adiposity that is genetically regulated through a leptin or melanocortin pathway in the central nervous system (CNS) [
1]. Recent studies found that high BF%, especially with excess visceral adipose tissue, is associated with increased risk of cardiovascular diseases (CVDs) [
2,
3], independent of body mass index (BMI) [
4,
5]. BF% is biologically important and distinct from other proxies of adiposity such as BMI and waist-to-hip ratio for its capacity to differentiate between fat-free mass (i.e., lean mass, bone mass and fluid mass) and fat mass [
6,
7]. One hypothesis to account for the link between high BF% and CVDs is shared genetic etiology. BF% and CVDs may share common genetic variants that influence metabolism or response to environmental risk factors [
8]. While the genetic basis for BF% and CVDs is poorly understood, large-scale genome-wide association studies (GWAS) may provide novel insight into specific biological processes underlying their comorbidity.
Genetic correlation analysis estimates the correlation of genetic effects between two clinically related traits and highlights the shared etiologies behind such an association. With methodological advances in molecular genetics and the increased number of available GWAS results, it is now feasible for us to investigate genome-wide genetic correlation and identify significant expression-trait associations for complex traits by using genomics resources (i.e., summary-level statistics from large GWAS [
8‐
14] and the Genotype-Tissue Expression (GTEx) project [
15]) and state-of-the-art statistical analysis methods (i.e., linkage disequilibrium score regression (LDSC) [
16,
17], multi-trait analysis of GWAS (MTAG) [
18], and transcriptome-wide association studies (TWAS) [
19]). Furthermore, previous twin and family studies have shown that BF% and CVDs are heritable traits, with heritability estimates ranging from 25 to 40% [
10,
20,
21]. Large-scale GWAS have enabled detection of more than 20 susceptibility loci for BF% [
8]. In particular, the identified BF%-related loci near
FTO predicts long-term CVD risk (OR = 1.895), suggesting that BF% and CVDs might share genetic architecture [
22]. Previous post-GWAS analyses conducted in a large European population found that genetically predicted BMI was strongly associated with several CVD outcomes, including heart failure (HF) and coronary artery disease (CAD) [
23]. Further, there was suggestive evidence of associations between genetically predicted fat mass index and some CVDs [
23]. However, these studies are limited because BMI is an imperfect measure of adiposity which does not directly measure body fat. To our knowledge, no large-scale genome-wide study has systematically reported the shared genetic loci between excess adiposity and CVDs, which is not fully accounted for through BMI evaluation.
Although observational studies have reported associations between BF% and adverse cardiovascular outcomes [
24‐
26], some of the findings have been inconsistent, which may be due to biases such as unmeasured confounding. Mendelian randomization (MR) is a form of instrumental variable analysis that can be used to estimate the causal association under certain assumptions [
27,
28], even in the presence of unmeasured confounders. HF is a catabolic state that can lead to weight reduction [
29], which indicates that the association between body weight and HF could be bidirectional. Given that inherited genetic variants are unlikely to be influenced by reverse causation or environmental confounders after accounting for population stratification, investigating the directions of these associations is crucially important.
Using genome-wide association study (GWAS) summary-level data from several international consortia (n = 6968-977,323), we investigated the genetic correlation and causality between BF% and 10 CVD-related traits with the overarching goal of characterizing the specific shared genetic loci and biological pathways. Further, we conducted a large-scale, genome-wide cross-trait analysis to explore novel genetic components among these diseases. The biological effects reflected by shared loci may play important roles in the co-occurrence of high BF% and CVDs.
Discussion
Using large-scale GWAS data from international consortia, we analyzed shared genetic etiology and causal associations between BF% and CVD-related traits. First, we found strong positive genetic correlations and further identified novel shared genetic loci between BF% and HF or CAD. Second, we found that the shared genetic loci were enriched mainly in blood vessels and brain tissues and glutathione-related metabolic pathway using functional analysis. Third, we found that the shared genes between BF% and HF or CAD are mostly from nervous, cardiovascular and exo-/endocrine system using TWAS analysis. Finally, we found that BF% was causally associated with HF using bidirectional MR analysis results. Taken together, evaluation of the genetic correlation and causality between BF% and CVDs furthers the understanding of the shared loci and biological mechanisms underlying this comorbidity.
BF% is becoming recognized as a more predictive measurement in cardiovascular risk assessment than BMI or waist circumference, and increased BF% was often related to higher CVD risks with the presence of the metabolic abnormalities [
25,
68]. Genetic studies have suggested that most of the BF% loci also affect cardiometabolic traits and comorbidities [
8]. Compared to GWAS results for single traits, MTAG discovered 6 novel loci for BF%, 8 novel loci for HF, and 13 novel loci for CAD. Importantly, we further conducted genome-wide cross-trait meta-analysis to improve our power to identify specific shared loci, including 3 novel loci between BF% and HF and 8 novel loci between BF% and CAD which did not reach genome-wide significance in previous GWAS. We highlight the potentially interesting functions of the novel associations for
NPC1,
PMAIP1, and
GNPDA2 between BF% and HF, along with
NEGR1,
HORMAD1,
GNPDA2,
DGKH,
CYP46A1,
FES,
POC1B, and
BCAS3 between BF% and CAD. Most of these loci have significant single-tissue expression quantitative trait loci (eQTL), mainly in the cardiovascular, nervous, and exo-/endocrine system (Table S
18-S
19).
The only top loci common to the BF%-HF and BF%-CAD meta-analysis was rs10938397 near
GNPDA2, which encodes an enzyme that catalyzes the deamination of the glucosamine-6-phosphate involved in the hexosamine signaling pathway [
59]. An animal study also suggested that
GNPDA2 was involved in the regulation of body weight, fat and energy metabolism in the development of cardiovascular disease [
69]. Other top associations for BF%-HF were
NPC1 gene variations, which might lead to metabolic diseases by modulating steroid hormone synthesis and lipid homeostasis [
70].
NPC1 is known to play a critical role in the atherosclerotic progression, and loss-of-function mutations in
NPC1 can cause adiposity in humans [
71,
72]. In particular, the
NPC1 gene was also confirmed to be shared between BF% and HF using TWAS (Table S
17). BCAS3 is a cytoskeletal protein that promotes directional cell migration and angiogenesis in vitro and is implicated in CAD [
73]. Additionally, other causal genes are highly expressed or known to act in human brain function (i.e.,
NEGR1,
GNPDA2,
CYP46A1,
DGKH) [
74‐
76] and various carcinomas (i.e.,
PMAIP1,
HORMAD1,
FES,
BCAS3) [
77‐
79], indicating the potential role of metabolic dysregulation in the pathogenesis of obesity and cardiac events.
We found that the shared loci for BF% with HF and CAD were highly expressed in blood vessel/heart tissues, indicating that these traits might be caused by dysfunction of the cardiovascular system. We found that the
FTO gene on chromosome 16 was associated with both BF%/HF and BF%/CAD.
FTO is an essential regulator in the development of obesity-induced metabolic and vascular changes, which is independent of its known function in regulation of obesity [
80]. Previous studies found that
FTO plays a critical role in cardiac contractile function during homeostasis, remodeling, and regeneration for HF and CAD [
81,
82]. Moreover, from the perspective of gene function, overlaps in genetic and molecular pathways advance our understanding of the potential role of brain/nerve tissue diseases in the association between BF% and CVDs. For example, the
NEGR1 gene, which is strongly expressed in the brain, has been reported to affect neuronal control of food intake and promote obesity [
83]. People who have mutations in the
NEGR1 gene may have abnormal fat deposition in various peripheral cells, suggesting a potential molecular target for regulating body fat [
84]. In the over-representation enrichment analysis, glutathione-related metabolic pathway was found to be common in shared genes of both BF%/HF and BF%/CAD meta-analyses. Experimental studies have reported that glutathione, which is the most abundant antioxidant in the heart, plays a key role in preventing the damage of redox homeostasis to cause obesity-related cardiovascular complications [
85,
86]. More importantly, the protective effect of glutathione on blood vessel and brain tissue against oxidative stress has been previously reported and partially explains our findings [
87,
88]. However, the nonsignificant genetic correlations between BF% and the other CVD-related traits suggested heterogeneous genetic architecture among CVD-related traits, which cannot be determined without further study.
In addition to the cardiovascular and nervous system, our TWAS also reported tissues enrichment from the exo-/endocrine and digestive system suggesting that the shared pathway between BF% and CVDs might have significant functions extending beyond brain and blood vessels. Besides a variety of adaptations/alterations in cardiac structure and function, body fat may affect the risk of HF with an altered metabolic profile in exo-/endocrine system [
89]. The incidence of CVDs are known to be closely related to endocrine factors such as hormone levels in vivo, which may be altered by body fat distribution and then affect the whole body organs such as ovary, thyroid, colon, and liver. For example, adolescent girls with obesity and polycystic ovary syndrome have increased fasting and postprandial plasma triglycerides and ApoB-lipoprotein remnants, and these indices are highly associated with early subclinical CVD risk [
90]. Indeed, an essential cause of CVDs is excessive body fat, which can be distributed in various organs of the body and cause pathological changes there. Thus, TWAS provided additional evidence of the enrichment of BF% and CVD genes expressed are not specific to a certain tissue; rather, it seems to be generalizable across metabolic organs of the whole body.
The bidirectional MR analysis showed a significant association between genetically predicted BF% and increased risk of HF (1.63-fold risk per 1-SD increase in BF%), which was stronger than that observed in a community-based cohort study (
n = 100,280) at 1.32-fold per 1-SD increase in BF% [
91]. Our findings were also consistent with a large cohort study which was conducted in 5520 community-based, elderly individuals, showing that higher BF% might contribute to the development of HF through increasing ventricular-arterial stiffness [
92]. However, clinically, the association between BF% and HF could be bidirectional. In addition, previous studies suggested that BMI or body weight was positively or negatively correlated with HF or clinical outcomes [
93,
94]. Although various associations were observed between BF% and CVDs, they were inconsistent possibly due to reverse causation and confounding. Therefore, this MR design is warranted to obtain causality which largely avoided bias such as reverse causation and confounding under MR key assumptions. Our MR findings provided reliable evidence of a causal role of BF% in HF based on large GWAS consortia regardless of unknown confounders existing in observational studies, highlighting the benefit of targeting BF% in the prevention of HF. The potential mechanisms underlying the causal association between BF% and HF require investigation, but the shared loci and related pathway could provide new directions in revealing shared etiologies.
In addition, we observed nominal positive correlation of BF% with intracerebral hemorrhage and negative correlation with HDL from genetic correlation analysis, but not with any other lipid profiles. Previous studies have suggested that low HDL may contribute directly to establishing or maintaining the obese condition due to the role of HDL in cellular lipid transport [
95]. HDL contribute to modulating body fat content by controlling the extent of lipolysis in mice model, which appears to be key components of lipid metabolism in adipose tissue and constitute new therapeutic targets in obesity [
96]. In cell culture studies, HDL specifically increased catecholamine-induced lipolysis possibly through modulating the adipocyte plasma membrane cholesterol content [
96]. In addition, the HERITAGE Family Study has shown that high HDL cholesterol levels are good correlates of the metabolic profile, representing a better index in CVD risk assessment [
97].
This study had limitations. First, the study is limited by the quality of data collected including the BF% measurement, CAD diagnosis, medication administration, and so on. However, each previous study has conducted the study-specific quality control to ensure the data quality. In addition, considering the natural and random assortment of genetic variants during meiosis yielding the random distribution of genetic variants in populations, the result and conclusion of this genetic analysis are less likely to be influenced by confounding factors. Second, while BF% accurately reflects the proportion of fat content in the human body, it does not give full insight to distinguish between visceral and subcutaneous fat or fat content in each anatomical part. Further studies are required to explore the genetic association of body fat distribution with CVD risk. Third, we used limited number of SNPs as instrumental variables in the MR analysis, so we cannot exclude the possibility that our findings might have been affected by weak instrument bias, although all genetic instruments were associated with the exposure (F-statistic> 10). Fourth, this study did not assess sex-specific genetic effects using LDSC and MR analysis. Since we conducted the analysis using summary-level GWASs without individual data, it is difficult for us to verify the findings of this study for any variation with sex-specific analysis. Fifth, we could not re-test our hypothesis using additional or alternative large cohort without individual information in the present study. However, the data we used are the largest and latest GWASs for these traits, and the sample sizes of other cohorts are small from which we cannot yield more reliable results. Finally, the current study was limited to assessing shared genetic factors between BF% and CVD-related traits, which could only explain a small proportion of these traits. The effect of gene-environment interaction, in which the genetic variants lead to the occurrence of diseases in high-risk environments, may explain part of the rest variance [
98]. Although CAD and MI or stroke vest in similar pathophysiologic mechanisms, the complex roles of interactions between genes and environmental factors may be different [
99]. More large-scaled, well-conducted studies on gene-environment interaction in the development of CVDs are warranted.
Publisher’s Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.