ASCVD remains the leading cause of death worldwide despite the effectiveness of statin therapy in reducing low-density lipoprotein cholesterol (LDL-C) levels (Baigent et al., 2005; Roth et al., 2020). Additional therapeutic targets and adjunctive treatments are needed to address the burden arising from residual risk (Rosenson and Goonewardena, 2021). TGs play vital roles in physiology, ranging from energy storage and mobilization to inflammation, thrombosis, and hormone-like signaling (Zewinger et al., 2020; Norata et al., 2007). However, a causal relationship between TGs and ASCVDs remains controversial (Miller et al., 2011; Albrink and Man, 1959), recently culminating in the conflicting reports of two double-blinded randomized controlled trials (RCT), STRENGTH, and REDUCE-IT (Nicholls et al., 2020; Bhatt et al., 2019; Doi et al., 2021). Nevertheless, drug development for reducing TG-rich lipoproteins (TRL) is an active area of research and several targets have now been validated, including angiopoietin-like 3 (ANGPTL3) (Graham et al., 2017; Dewey et al., 2017; Musunuru et al., 2010; Gaudet et al., 2017), angiopoietin-like 4 (ANGPTL4) (Dewey et al., 2016), and apolipoprotein C-III (APOC3) (Gaudet et al., 2015; Crosby et al., 2014). Clinical trials are currently evaluating these targets for dyslipidemias (Arrowhead, 2021b; Arrowhead, 2021a).

Whether TGs are causal risk factors or simply associative biomarkers remains uncertain not only for ASCVDs but also for other diseases of different organ systems. Understanding the causal effects of TGs across a broader range of human diseases could have significant implications for drug repurposing. TG-lowering agents, such as fibrates (Frick et al., 1987; Ginsberg et al., 2010) and omega-3 fatty acids Bhatt et al., 2019 have already been approved; however, not knowing which diseases are causally affected by TGs precludes their use for indications other than hypertriglyceridemia. Understanding the protective effects of TGs could also have implications for drug safety. With the recent approval of icosapent ethyl (Bhatt et al., 2019; Gaba et al., 2022) and the ongoing development of other TG-lowering agents (Shaik and Rosenson, 2021), such as ANGPTL3 (Graham et al., 2017; Dewey et al., 2017), ANGPTL4, and APOC3 inhibitors (Gaudet et al., 2015), long-term safety becomes a matter of concern. Post-market surveillance data are limited; therefore, identifying diseases with negative causal links to TGs could suggest adverse side effects, whereas protective causal links to TGs could suggest therapeutic avenues and indices informative for drug development. Furthermore, this could inform polypharmacy and drug titration in clinical practice.

Several methodological challenges have prohibited causal conclusions about TGs across human diseases. First, TGs are correlated with established ASCVD risk factors, such as obesity and insulin resistance (Eckel et al., 2005). They also correlate with LDL particles or apolipoprotein B (apoB) concentration (Cromwell et al., 2007) and inversely correlated with high-density lipoprotein cholesterol (HDL-C) (Phillips and Smith, 1991). Conventional observational studies have thus been limited in drawing causal inferences due to potential confounding. Second, available TG-lowering agents have pleiotropic effects on several major lipid fractions, including VLDL-C, LDL-C, HDL-C, and apolipoproteins, including APOC3 (Ginsberg et al., 2010; Keech et al., 2005; Frick et al., 1987). Thus, costly RCTs have had limited power to disentangle the effects of lowering TG specifically (Sarwar et al., 2010; Goldberg et al., 2011; Bhatt et al., 2019). Finally, MR studies have typically focused on individual diseases selected a priori (Davey Smith and Hemani, 2014), narrowing the scope of causal estimates to ASCVDs while overlooking non-ASCVD diseases (Harrison et al., 2018; Smith et al., 2014; Allara et al., 2019).

Phenome-wide MR is a high-throughput extension of MR that estimates the causal effects of an exposure on multiple outcomes simultaneously. As in conventional MR, this method uses genetic variants as instrumental variables (IV) to proxy modifiable exposures (Smith and Ebrahim, 2003), and importantly, it relies on three critical assumptions: (1) The genetic variant is directly associated with the exposure; (2) The genetic variant is unrelated to confounders between the exposure and outcome; and (3) The genetic variant has no effect on the outcome other than through the exposure (Smith and Ebrahim, 2003). A distinction, however, is that phenome-wide MR enables comprehensive scans of the phenotypic spectrum, limiting bias from prior assumptions and facilitating the discovery of unforeseen causal relationships. This has recently become feasible with the maturation of large-scale biobanks providing extensive genetic and phenotypic data, such as the UK Biobank (UKB) (Bycroft et al., 2018) and FinnGen project (FinnGen, 2020).

Here, we use phenome-wide MR to systematically estimate the causal effects of plasma TGs on 2600 disease outcomes in UKB, followed by replication testing in FinnGen. We then apply multiple MR methods for sensitivity analyses and MVMR to control for plasma LDL-C, HDL-C, and ApoB levels.

We performed phenome-wide, two-sample MR to estimate the causal effects of plasma TG levels on a spectrum of human disease traits (n=2600) using a discovery cohort from UKB and a replication cohort from FinnGen. We report seven disease traits reaching Bonferroni-corrected significance in both the discovery and replication analyses, which we categorized as tier 1 results. These traits were predominantly manifestations of ASCVD, such as ischemic heart disease, angina pectoris, and myocardial infarction. Using MVMR, we found that these associations remained even after controlling for LDL-C, HDL-C, and ApoB levels (Figure 4). We also identified three disease traits that are Bonferroni-significant in discovery (p<1.92 × 10^–5) and at least nominally significant in replication (p<0.05), categorized as tier 2 results. Lastly, we identified nine disease traits at least nominally significant in discovery and Bonferroni-significant in replication, categorized as tier 3 results. Several of these non-ASCVD disease traits have never been reported to be causally associated with TGs, potentially introducing new opportunities for drug repurposing and experimental study.

Our results are consistent with prior work suggesting that plasma TG levels are causally associated with ASCVD risk (Sarwar et al., 2010; Castañer et al., 2020; Do et al., 2013; Dewey et al., 2016; Holmes et al., 2015; White et al., 2016; Varbo et al., 2013; Ibi et al., 2021; Rosenson et al., 2021). We do not prove here that circulating TGs per se are directly atherogenic; rather, we show that plasma TG measurement, which comprises multiple classes of triglyceride-rich lipoproteins (TRL) and their remnants, captures a clinically significant mechanism that is causally associated with ASCVD (Rosenson et al., 2021). This interpretation is consistent with an emerging view that apolipoprotein B (ApoB) particle number is a more important determinant of atherogenesis than LDL-C and that TRL particles are as important a risk factor in ASCVD as LDL particles since they each carry a single ApoB molecule (Ference et al., 2019; Marston et al., 2022; Richardson et al., 2020). According to this paradigm, the causal association we observe between plasma TG levels and ASCVD risk may be mediated by the concentration of ApoB particles in TRLs, captured by plasma TG level measurements. We evaluated this possibility by accounting for ApoB levels in a MVMR framework and found that significant causal associations persist between plasma TG levels and all examined ASCVD traits, despite a modest decrease in effect size (Figure 4; Supplementary file 4). These data suggest that ApoB levels may explain at least a portion of the detected associations; however, plasma TG measurement appears to capture additional mechanisms of ASCVD risk involving cholesterol-enriched TRL remnants that remain unclear. Scientific interest in the atherogenic mechanisms of TRLs continues to grow, as recently reviewed elsewhere (Borén et al., 2022; Ginsberg et al., 2021).

Regarding non-ASCVDs, we present suggestive genetic evidence of potentially causal associations between plasma TG levels and uterine leiomyomas (uterine fibroids), diverticular disease of the intestine, paroxysmal tachycardia, hemorrhage from respiratory passages (hemoptysis), and calculus of kidney and ureter (kidney stones). Due to the weaker statistical evidence supporting these associations, special caution is encouraged when interpreting these results to infer causality, and further replication and validation studies are essential for all tier 2 and 3 results. Nevertheless, prior studies have reported correlational associations between many of these diseases and plasma TG levels or risk factors correlated to plasma TGs. For example, studies had documented positive correlations between leiomyoma risk and plasma TG levels (Uimari et al., 2016; Tonoyan et al., 2021; Peshkova et al., 2020). Others had shown diverticular disease risk is positively associated with BMI, body fat percentage, and visceral fat area (Shih et al., 2022; Freckelton et al., 2018; Böhm, 2021). Similarly, kidney stones have been associated with the triglyceride-glucose (TyG) index (Qin et al., 2021), and atrial tachycardias have been associated with VLDL levels in metabolic syndrome patients (Lee et al., 2017; Park and Lee, 2018). Our study is the first to suggest that these associations may be causally related and specific to plasma TG levels, as opposed to confounding risk factors, such as obesity and hormone replacement therapy. However, we note that these associations were not as robust as those in tier 1 associations related to ASCVDs, and we encourage caution in drawing causal conclusions from these estimates of a causal effect. Though, we present the rationale for potentially repurposing TG-lowering agents towards these non-ASCVD indications and for pursuing mechanistic studies interrogating TG biology, RCTs remain the gold standard for assessing causality and remain necessary for assessing clinical potential.

Our results also suggest a novel, negative causal association between plasma TG levels and alcoholic liver disease (ALD), suggesting that excessively reducing plasma TG levels may increase the risk of this disease trait. A mechanistic explanation for this association remains elusive as previous studies on dysregulated lipid metabolism in liver disease have generally focused on non-alcoholic fatty liver disease (NAFLD). However, one animal study suggests that medium-chain TGs may decrease lipid peroxidation and reverse established alcoholic liver injury in rat models of ALD (Nanji et al., 1996). Nevertheless, that elevated TG levels may protect against ALD is surprising and requires external validation. It remains unclear whether this association is only relevant to lifelong, chronic lowering of plasma TG levels rather than transient lowering by drug-based interventions. It is also unclear whether ALD could be prevented by increasing TG levels. This finding suggests the potential intolerability of TG-lowering agents and the importance of maintaining these drugs’ concentrations within therapeutic windows for patient safety; however, we reiterate that this tier 3 association was only nominally significant in discovery, while Bonferroni-significant in replication, and future studies are needed to validate the statistical evidence.

The study has several limitations. First, MR is a powerful but potentially fallible method that relies on several key assumptions, namely that genetic instruments are (i) associated with the exposure (the relevance assumption); (ii) have no common cause with the outcome (the independence assumption); and (iii) have effects on the outcome solely through the exposure (the exclusion restriction assumption) (Hartwig et al., 2016). In MR, (i) is relatively straightforward to test, while (ii) and (iii) are difficult to establish unequivocally. As a prominent example, horizontal or type I pleiotropy has been shown to be common in genetic variation, which can bias MR estimates (Verbanck et al., 2018; Jordan et al., 2019). This occurs when a genetic instrument is associated with multiple traits other than the outcome of interest. To detect and correct for this as best as possible, we used various MR tests as sensitivity analyses that each aims to adjust for or account for the presence of horizontal pleiotropy, including MR-PRESSO, as well as MR-Egger and weighted median methods. There is no universally accepted method that is perfectly robust to horizontal pleiotropy, but we take the best current approach by using multiple methods and examining the consistency of results. A second limitation of the study is that genetic variants confer exposures that are lifelong but small in effect size; thus, MR may over- or underestimate the effect sizes of pharmacological interventions. MR also cannot make comparisons between TG-lowering agents as it evaluates drug targets, not drug subclasses and unique pharmacodynamics (Gill et al., 2019; Sofat et al., 2010). However, the utility of MR in drug target validation is well-established as estimates still indicate the presence and direction of causality (Gill et al., 2021). Third, UKB and FinnGen have innate differences in participant demographics and medical coding systems, due in part to the former being based in the United Kingdom and the latter in Finland. As such, the potential misclassification of participants in the case-control assignment is a liability to this study. We exercised caution in mapping UKB traits to FinnGen traits, but we were unable to reliably map all ‘categorical’ traits from UKB to corresponding traits in FinnGen, testing for replication only 221 of the 598 associations that were nominally significant in the primary analysis. We note however that, despite geographical differences, both datasets largely involve White European participants of older age, with the mean age in UKB and FinnGen being 56.5 and 59.8, respectively. Fourth, discovery and replication analyses were not completely independent, since UKB data were used in both analyses. This could potentially exacerbate demographic and measurement biases inherent to UKB; however, we show that taking a traditional replication approach using GLGC instead of UKB for selecting exposure instruments in replication returns comparable tier 1 results (Supplementary file 5), while losing statistical power to highlight many of the tier 2 and 3 results. Fifth, the GLGC GWAS used to select instruments for plasma TG levels in discovery had accounted for lipid-lowering treatment, while the UKB GWAS used in replication had not. Last, we acknowledge that this study was restricted to populations of European ancestry, limiting the generalizability of our findings. A recent study observed comparable MR estimates for the causal effects of lipid traits on ischemic stroke risk between African and European ancestry individuals (Fatumo et al., 2021), suggesting the potential generalizability of MR results across ancestries in some specific cases. Nevertheless, trans-ancestry MR analyses are warranted to validate our study’s findings in diverse ancestry populations.

In conclusion, this study demonstrates a high-throughput application of two-sample MR to estimate the causal effects of plasma TG levels on phenome-wide disease risk. Future studies may consider the functional and qualitative attributes of TRL subtypes, and metabolomic data partitioning TRLs by size and composition may soon enable this to identify the specific component of a plasma TG measurement that drives disease risk in the detected associations (Holmes et al., 2015). With the proliferation of multi-omic data, this systematic MR approach could be generalized to study the causal effects of serum biomarkers on disease risk, at scale. However, caution is still warranted in inferring causality, as MR depends on specific assumptions and the validity of those assumptions must be carefully assessed. Thus, diverse study designs remain necessary to triangulate evidence on the causal effects of plasma TG levels.

All data generated in this study are included in the manuscript and supplementary tables. All analyses used publicly available data (UKB , FinnGen), including previously published GWAS (GLGC) (Willer et al., 2013). Obtaining access to UKB (Pan-UKB_Team, 2020) and FinnGen (FinnGen, 2020) GWAS summary statistics is detailed here (https://www.finngen.fi/en/access_results) and here (https://pan.ukbb.broadinstitute.org/downloads). Please note the summary statistics for FinnGen and Pan-UKB are made publicly available.

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Author details

1. Joshua K Park

   1. Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, United States
   2. Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, United States
   3. Medical Scientist Training Program, Icahn School of Medicine at Mount Sinai, New York, United States

   Contribution

   Data curation, Formal analysis, Investigation, Writing - original draft, Writing – review and editing

   Contributed equally with

   Shantanu Bafna

   Competing interests

   No competing interests declared

   "This ORCID iD identifies the author of this article:" 0000-0002-1719-3537

2. Shantanu Bafna

   1. Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, United States
   2. Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, United States

   Contribution

   Data curation, Formal analysis, Investigation, Writing - original draft, Writing – review and editing

   Contributed equally with

   Joshua K Park

   Competing interests

   No competing interests declared

3. Iain S Forrest

   1. Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, United States
   2. Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, United States
   3. Medical Scientist Training Program, Icahn School of Medicine at Mount Sinai, New York, United States

   Contribution

   Formal analysis, Validation, Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

4. Áine Duffy

   1. Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, United States
   2. Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, United States

   Contribution

   Resources, Validation, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

5. Carla Marquez-Luna

   1. Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, United States
   2. Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, United States

   Contribution

   Supervision, Validation, Investigation, Writing – review and editing

   Competing interests

   No competing interests declared

6. Ben O Petrazzini

   1. Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, United States
   2. Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, United States

   Contribution

   Resources, Supervision, Investigation, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

7. Ha My Vy

   1. Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, United States
   2. Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, United States

   Contribution

   Resources, Data curation, Supervision, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

8. Daniel M Jordan

   1. Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, United States
   2. Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, United States

   Contribution

   Supervision, Investigation, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

9. Marie Verbanck

   Université Paris Cité, Paris, France

   Contribution

   Investigation, Methodology, Writing – review and editing

   Competing interests

   No competing interests declared

10. Jagat Narula

    1. Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, United States
    2. Cardiovascular Imaging Program, Zena and Michael A. Wiener Cardiovascular Institute, Mount Sinai Heart, Icahn School of Medicine at Mount Sinai, New York, United States

    Contribution

    Resources, Data curation, Supervision, Writing – review and editing

    Competing interests

    No competing interests declared

11. Robert S Rosenson

    1. Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, United States
    2. Metabolism and Lipids Unit, Zena and Michael A. Wiener Cardiovascular Institute, Mount Sinai Heart, Icahn School of Medicine at Mount Sinai, New York, United States

    Contribution

    Supervision, Validation, Methodology, Writing – review and editing

    Competing interests

    reports receiving grants from Amgen, Arrowhead, Lilly, Novartis and Regeneron; consulting fees from Amgen, Arrowhead, Lilly, Novartis and Regeneron; honoraria for non-promotional lectures from Amgen, Kowa and Regeneron, royalties from Wolters Kluwer (UpToDate); and stock holdings in MediMergent, LLC

12. Ghislain Rocheleau

    1. Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, United States
    2. Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, United States

    Contribution

    Resources, Data curation, Supervision, Investigation, Methodology, Writing – review and editing

    Competing interests

    No competing interests declared

    "This ORCID iD identifies the author of this article:" 0000-0002-9989-7553

13. Ron Do

    1. Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, United States
    2. Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, United States

    Contribution

    Conceptualization, Data curation, Formal analysis, Supervision, Funding acquisition, Investigation, Methodology, Project administration, Writing – review and editing

    For correspondence

    ron.do@mssm.edu

    Competing interests

    reports receiving grants from AstraZeneca; grants and non-financial support from Goldfinch Bio; being a scientific co-founder, consultant, and equity holder (pending) for Pensieve Health; and a consultant for Variant Bio, all unrelated to this work

    "This ORCID iD identifies the author of this article:" 0000-0002-3144-3627

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