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. 2025 Nov 14;104(46):e45826. doi: 10.1097/MD.0000000000045826

Genetic causal association between metabolic syndrome and idiopathic pulmonary fibrosis: A 2-sample Mendelian randomization

Qing Liu a, Qin Luo a, Wei Chen a, Xuemei Kuang a, Jianmei Wu a, Jianhua Tan a,*
PMCID: PMC12622708  PMID: 41239700

Abstract

Prior epidemiological investigations have indicated a linkage between metabolic syndrome (MetS) and idiopathic pulmonary fibrosis (IPF). The objective of the present research was to elucidate the inherited causal relationship of MetS with IPF. A 2-sample Mendelian randomization (MR) design was adopted to assess the genetic cause-effect link between MetS and IPF. Multiple analytical strategies, such as inverse-variance weighted (IVW), MR-Egger, weighted median, and weighted mode, were implemented. Outlier evaluation was carried out through a leave-one-out analysis. MR-PRESSO and the MR-Egger intercept test were undertaken to reduce the influence of horizontal pleiotropy. Heterogeneity was appraised using Cochran’s Q statistic. IVW analysis suggested a potential causal association between waist circumference and IPF (odds ratio: 1.0016, 95% confidence interval: 1.0006–1.0025, P = .001), although this association was not consistently observed in other MR models. By contrast, high-density lipoprotein cholesterol showed a more consistent positive association with IPF, supported by IVW, MR-Egger, and weighted mode analyses (odds ratio: 1.0006, 95% confidence interval: 1.0001–1.0012, P = .033) The heterogeneity test results indicated that our IVW analysis findings exhibited minimal presence of heterogeneity (P > .05). The results of the pleiotropy test showed that there was no pleiotropy in our results (P > .05). No genetic causal association was found for MetS as a whole, essential hypertension, fasting blood glucose, or triglycerides with IPF. This study indicates a potential causal link between metabolic traits such as waist circumference and high-density lipoprotein cholesterol with IPF. Future clinical research is warranted to further confirm these associations and to clarify their relevance in IPF prevention and treatment.

Keywords: causal association, genetic analyses, idiopathic pulmonary fibrosis, Mendelian randomization, metabolic syndrome

1. Introduction

Idiopathic pulmonary fibrosis (IPF) is a chronic and progressive fibrosing interstitial lung disorder of unknown etiology that predominantly occurs in middle aged and elderly individuals.[1,2] In recent times, there has been a noticeable increase in the occurrence of IPF. Based on incomplete data, the frequency among Asian populations varies between 1.2 and 4.2 cases/1,00,000 people annually, potentially resulting in a 5-year survival rate below 30%. The lung function of IPF patients gradually deteriorates, significantly impacting their quality of life and reducing their lifespan.[3–5] Due to the intricate development process involving various factors, the precise mechanisms behind IPF are not yet fully understood. There is a strong rationale for thorough exploration of the origins of IPF, considering substantial evidence that this condition correlates with prolonged mechanical ventilation and extended hospitalization,[6] a higher probability of readmission,[7] and increased illness burden and death rates among inpatients.[8]

Metabolic syndrome (MetS) represents a worldwide medical challenge characterized by diverse metabolic disturbances, such as central obesity, hyperglycemia, hypertension, and dyslipidemia.[9–14] MetS contributes to an increased vulnerability to chronic diseases like diabetes, CVD, stroke, NAFLD, certain cancers, and depression.[15–18] Some research suggests a possible connection between IPF and MetS.[19,20] One view proposes that MetS might contribute to IPF development by causing insulin resistance, inflammation, and oxidative stress, which harm alveolar epithelial cells’ function and repair capacity, leading to pulmonary fibrosis.[21] Epidemiological studies also show a link between MetS or its components and IPF incidence or mortality.[22] Another perspective suggests a 2-way interaction between IPF and MetS, where both conditions can worsen each other’s effects, creating a harmful cycle.[23] Experimental studies support this idea, showing that MetS can cause cell death and fibrosis in alveolar epithelial cells, while IPF can increase insulin resistance and contribute to fatty liver development.[24]

Mendelian randomization (MR) offers a more dependable strategy for inferring causal links, thereby overcoming key constraints of observational research.[25,26] By using genetic variants tightly correlated with the exposure as instrumental variables (IVs), MR effectively reduces the influence of confounders and reverse causation.[27,28] The broad discovery of numerous loci associated with complex traits through genome-wide association studies (GWAS) has greatly promoted the application of MR in scientific investigations.[29,30] This investigation applied a bidirectional 2-sample MR design to examine the bidirectional causal interplay between IPF and MetS.

2. Materials and methods

2.1. Study design overview

A concise depiction of the MR design is illustrated in Figure 1. The features of MetS were defined by 5 components according to the guidelines of the National Cholesterol Education Program Adult Treatment Panel III.[31] The causal association between IPF and MetS was evaluated. Summary-level statistics were drawn from large-scale GWAS meta-analyses for MetS, hypertension, serum triglycerides (TG), waist circumference (WC), fasting blood glucose (FBG), and serum high-density lipoprotein cholesterol (HDL-C). MR is based on 3 core premises: the genetic instruments show strong associations with the exposure of interest; they are independent of confounders affecting the exposure–outcome relationship; and they influence the outcome solely through the exposure.[32] To reduce possible bias from population heterogeneity, analyses were confined to participants predominantly of European ancestry. All datasets originated from publicly accessible GWAS summary statistics with prior institutional ethics approval and participant consent in their primary investigations. Thus, no further ethical authorization was necessary for the current analyses.

Figure 1.

Figure 1.

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Flowchart of the study. EPH = essential primary hypertension, FBG = fasting blood glucose, GWAS = genome-wide association study, HDL-C = high-density lipoprotein cholesterol, MetS = metabolic syndrome, MR = Mendelian randomization, SNP = single nucleotide polymorphism, TG = triglycerides, WC = waist circumference.

2.2. Exposure data of MetS

MetS summary data were obtained from the most extensive GWAS in the UK Biobank,[33] which included individuals with complete genotypic, phenotypic, and covariate information. Between 2006 and 2010, the UK Biobank enrolled about 5,00,000 participants aged 37 to 73 years throughout the UK, obtaining physical metrics, biological samples, and hospital-linked long-term follow-up data. All participants were of European ancestry, and MetS was diagnosed according to globally endorsed clinical and metabolic definitions involving abdominal obesity, fasting glucose, blood pressure, TG, and HDL-C.

WC data were drawn from the Medical Research Council Integrative Epidemiology Unit database of the UK Biobank, covering 4,62,166 participants of European ancestry.[34] Regarding hypertension, summary statistics were also derived from the Medical Research Council Integrative Epidemiology Unit UK Biobank pipeline, encompassing 4,63,010 participants (54,358 cases and 4,08,652 controls). FBG data were taken from the largest GWAS conducted by the meta-analyses of glucose and insulin-related traits consortium, which examined 2,00,622 diabetes-free participants.[35] For HDL-C and TG, summary statistics originated from large-scale GWAS conducted by the global lipids genetics consortium, which combined data from 45 studies, most of which comprised individuals of European ancestry. Lipid levels were typically measured after a minimum of 8 hours of fasting, and, where feasible, participants on lipid-lowering medication were excluded. Trait residuals were adjusted for age and sex, and in disease-based cohorts (e.g., CAD or T2D studies), cases and controls underwent separate analysis. Altogether, over 94,595 individuals contributed to the HDL-C and TG datasets from the UK Biobank and associated consortia[36] (Table 1).

Table 1.

Details of GWAS studies.

Phenotypes GWAS ID Sample size (case/control) nSNPs PMID
Metabolic syndrome https://ctg.cncr.nl/software/summary_statistics NA NA 35983957
Waist circumference ebi-a-GCST90014020 4,07,661 1,07,83,687 34017140
Essential (primary) hypertension ukb-b-12493 54,358/4,08,652 98,51,867 NA
Fasting blood glucose ebi-a-GCST90002232 2,00,622 3,10,08,728 34059833
HDL cholesterol ebi-a-GCST002223 94,595 24,18,527 24097068
Triglycerides ebi-a-GCST002216 94,595 24,10,057 24097068
Idiopathic pulmonary fibrosis ebi-a-GCST90018120 4,51,025 1,61,37,102 33197388
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GWAS = genome-wide association studies, HDL = high-density lipoprotein, SNP = single nucleotide polymorphism.

2.3. Outcome data of idiopathic pulmonary fibrosis

GWAS summary data for IPF-related genetic associations were derived from the most extensive meta-analysis organized by the Duckworth A Laboratory.[37] The study comprised 4,51,025 European-ancestry subjects (1369 cases and 4,35,866 controls), examining roughly 16.1 million variants after rigorous quality checks and imputation. IPF cases came from the UK Biobank, a forward-looking cohort of around 5,00,000 individuals aged 37 to 73 years enrolled between 2006 and 2010, identified through hospital episode data and ICD-10 code J84.1. All individuals included were of European descent, and diagnoses followed internationally recognized clinical guidelines.

2.4. Genetic instruments selection and harmonization

In this analysis, IVs were required to meet the following standards: SNPs showing genome-wide significance (P < 5 × 10−8) and robust association with MetS were initially identified. SNPs were screened to ensure a minor allele frequency exceeding 0.01. To eliminate linkage disequilibrium, variants were pruned by applying R² < 0.001 within a 10,000 kb window.[38] For IVs missing in the outcome dataset, surrogate SNPs exhibiting strong linkage disequilibrium (R² > 0.8) were located and applied as replacements. Each IV SNP’s strength was determined by computing its F statistic – F = R² × (N − 2)/(1 − R²) – to rule out weak-instrument bias, where R² denotes the fraction of exposure variance accounted for by the SNPs. Only IVs with an F statistic above 10 were retained.[39]

2.5. Statistical analyses

R² was calculated to estimate how much of the variance in each exposure was attributable to the IVs. The F statistic assessed the association strength between instruments and the exposure under investigation.[40] For binary exposures, we reported causal effects as odds ratios (ORs) with 95% confidence intervals (CIs), representing the risk change per log-odds unit of genetically inferred exposure. For continuous exposures, causal estimates were reported as ORs with 95% CIs per standard deviation increase. We used a random-effects inverse-variance weighted (IVW) method as the main MR approach to explore causal links between MetS and IPF.[41] This method provides a robust estimation even when there is no directional pleiotropy present. Complementary analyses were performed employing weighted median, simple mode, weighted mode, and MR-Egger methods. Directional horizontal pleiotropy was evaluated via MR-Egger intercept tests, while heterogeneity was examined using Cochran’s Q statistics and funnel plots.[42,43] Sensitivity was further assessed with a leave-one-out analysis. All computations were implemented using the TwoSampleMR package in R (version 4.0.5; www.r-project.org/) and Stata 16 (StataCorp, College Station). To adjust for multiple comparisons, P values were corrected by the false discovery rate method, setting statistical significance at PFDR < .05, and a threshold of P < .10 was adopted for both MR-Egger and heterogeneity tests.

3. Results

3.1. Selection of instrumental variables

In this study, an MR analysis was conducted using MetS, WC, primary hypertension, FBG, HDL-C, and TG as exposures. A total of 193, 337, 71, 66, 87, and 54 IVs were selected for each respective exposure (Tables 2 and S1, Supplemental Digital Content, https://links.lww.com/MD/Q606). The IV rs12783517 was used as a proxy for rs35587371 in primary hypertension; rs57086307 was used as a proxy for rs58925536 in FBG; rs247617 was used as a proxy for rs247616 in HDL-C and TG was represented by rs9297994 instead of rs4738684.

Table 2.

Details of instrumental variables.

Phenotypes nIV F F min F max R 2
Metabolic syndrome (MetS) 193 53.81 29.48 682.6 38.93
Waist circumference (WC) 337 56.69 29.58 888.78 43.98
Essential (primary) hypertension 71 46.86 28.91 164.68 27.56
Fasting blood glucose (FBG) 66 132.81 23.78 1600.9 33.95
HDL cholesterol (HDL-C) 87 145.22 29.26 3995.26 39.78
Triglycerides (TG) 54 154.49 28.76 1210.95 31.04
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FBG = fasting blood glucose, HDL = high-density lipoprotein cholesterol, IV = instrumental variable, MetS = metabolic syndrome, TG = triglycerides, WC = waist circumference.

3.2. The causal effect of idiopathic pulmonary fibrosis on MetS

Genetic predictions indicated potential associations of WC and HDL-C with the risk of IPF. The IVW analysis suggested a possible association between WC and IPF (OR: 1.0016, 95% CI: 1.0006–1.0025, P = .001), although this was not consistently replicated across other MR models (MR-Egger, weighted median, weighted mode). In contrast, HDL-C showed a more consistent positive association with IPF, supported not only by the IVW analysis (OR: 1.0006, 95% CI: 1.0001–1.0012, P = .033) but also by MR-Egger and weighted mode results (Table 3). However, no significant association was observed between other exposure factors and the disease. Scatter plots were used to visualize the effect sizes of individual SNPs on MetS and IPF, illustrating the relationship between genetic variants and the outcomes (Fig. 2). Forest plots based on the IVW method demonstrated the contribution of each variant to the overall causal estimate, with no single variant exerting undue influence (Fig. 3).

Table 3.

Association between genetic predictions of causal risk for metabolic syndrome and idiopathic pulmonary fibrosis.

Exposure Outcome nSNPs Methods OR (95% CI) P
Metabolic syndrome Idiopathic pulmonary fibrosis 188 IVW 1.0009 (0.9998–1.002) .125
MR-Egger 1.0011 (0.9981–1.0042) .471
Weighted median 1.0003 (0.9986–1.0021) .714
Weighted mode 1.0007 (0.9982–1.0032) .595
Waist circumference 322 IVW 1.0016 (1.0006–1.0025) .001
MR-Egger 1.0016 (0.9988–1.0044) .254
Weighted median 1.0012 (0.9996–1.0027) .134
Weighted mode 1.0005 (0.9978–1.0031) .742
Essential (primary) hypertension 69 IVW 1.0028 (0.9965–1.0092) .388
MR-Egger 0.9937 (0.9703–1.0176) .603
Weighted median 1 (0.9909–1.0091) .998
Weighted mode 0.9953 (0.9776–1.0134) .613
Fasting blood glucose 64 IVW 1.0001 (0.9985–1.0016) .921
MR-Egger 1 (0.9971–1.0028) .984
Weighted median 1.0006 (0.9984–1.0028) .572
Weighted mode 1.0004 (0.9984–1.0024) .712
HDL cholesterol 86 IVW 1.0006 (1.0001–1.0012) .033
MR-Egger 1.0014 (1.0004–1.0023) .006
Weighted median 1.0009 (0.9999–1.0018) .064
Weighted mode 1.0008 (1–1.0016) .047
Triglycerides 54 IVW 0.9994 (0.9986–1.0002) .129
MR-Egger 0.9995 (0.9982–1.0009) .492
Weighted median 0.999 (0.9979–1.0002) .095
Weighted mode 0.999 (0.9979–1.0001) .073
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CI = confidence interval, IVW = inverse-variance weighted, MR = Mendelian randomization, OR= odds ratio, SNP = single nucleotide polymorphism.

Figure 2.

Figure 2.

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The scatter plots of the association between genetically predicted MetS on IPF. FBG = fasting blood glucose, HDL-C = high-density lipoprotein cholesterol, MetS = metabolic syndrome, TG = triglycerides, WC = waist circumference.

Figure 3.

Figure 3.

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The forest plots of the association between genetically predicted MetS on IPF. FBG = fasting blood glucose, HDL-C = high-density lipoprotein cholesterol, MetS = metabolic syndrome, TG = triglycerides, WC = waist circumference.

3.3. Pleiotropy, heterogeneity, and sensitivity analysis

The bulk of causal links displayed no heterogeneity, as evidenced by IVW testing and MR-Egger regression, with Q statistics suggesting P > .05 (Table S2, Supplemental Digital Content, https://links.lww.com/MD/Q606). Furthermore, every MR-Egger intercept showed no significant deviation from 0 (intercept P > .05), implying no horizontal pleiotropy (Table S2, Supplemental Digital Content, https://links.lww.com/MD/Q606). Likewise, MR-PRESSO testing revealed no evidence of horizontal pleiotropy (P > .05; Table S3, Supplemental Digital Content, https://links.lww.com/MD/Q606). Funnel plots indicate that causal estimates are unlikely to be influenced by potential biases (Fig. 4). Furthermore, based on individual SNPs were found to have insignificant influence on the signals associated with causality during the leave-one-out analysis (Fig. 5).

Figure 4.

Figure 4.

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The funnel plots of the association between genetically predicted MetS on IPF. FBG = fasting blood glucose, HDL-C = high-density lipoprotein cholesterol, MetS = metabolic syndrome, TG = triglycerides, WC = waist circumference.

Figure 5.

Figure 5.

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The LOO plots of the association between genetically predicted MetS on IPF. FBG = fasting blood glucose, HDL-C = high-density lipoprotein cholesterol, MetS = metabolic syndrome, TG = triglycerides, WC = waist circumference.

4. Discussion

In this investigation employing a 2-sample MR framework, we explored the possible causal links between individual MetS components and IPF. Our findings provide insights into how factors such as WC, FBG, hypertension, TG, and HDL-C correlate with the risk of developing IPF.

Our analysis indicated associations of both WC and HDL-C with the risk of IPF, consistent with prior evidence linking abdominal obesity and lipid metabolism to pulmonary diseases.[44–46] Abdominal obesity, characterized by increased WC, contributes to metabolic disturbances, inflammatory responses, and fibrotic processes within the lung tissue,[47] similarly, low HDL-C are known to influence lung health through mechanisms involving pulmonary surfactant composition and inflammatory processes,[48,49] our findings suggest these relationships may be more complex and not as directly causal as hypothesized. These findings emphasize the need for clinical strategies that address both metabolic control and weight management as integral components of IPF risk mitigation. Interestingly, although no causal association was detected for MetS as a whole, individual components such as WC showed evidence of association with IPF. This highlights that different components of MetS may act through distinct biological pathways, and their effects can be masked when considered collectively, reinforcing the value of analyzing MetS at the level of its individual traits.

Conversely, the roles of hypertension and TG in IPF are less clear from our results, illustrating a departure from some previous studies. While hypertension has been discussed in prior research as a contributor to pulmonary arterial hypertension and IPF through mechanisms impacting lung oxygenation and hemodynamics,[50] our results did not conclusively support a direct causal relationship. Similarly, although high TGs are known to influence lung health through mechanisms involving pulmonary surfactant composition and inflammatory processes,[48,49] our findings suggest the relationship may be more complex and not as directly causal as hypothesized. This divergence potentially highlights the limitations of retrospective studies in adequately controlling for confounders or the variability in study populations and methodologies. It suggests that our understanding of how these metabolic factors influence IPF remains incomplete and possibly confounded by other unmeasured factors.

A recent MR investigation likewise evaluated the association between endocrine and metabolic factors and the susceptibility to IPF.[51] Nevertheless, methodological differences and contrasting outcomes exist between the investigation by Jiang et al and the present study. Our study corroborates the selection of FBG, HDL-C, WC, and TG as metabolic indicators, aligning with the aforementioned publication. However, we extended our analytical framework to incorporate essential hypertension and MetS additional clinical endpoints in MR analyses. Regarding data sources, Jiang et al’s research relied solely on UK Biobank data for exposure, whereas the present study derived exposure summary data from multiple independent cohort investigations. Likewise, outcome data in both studies employed distinct epidemiological datasets, potentially contributing to the observed discrepancy in association profiles. Additionally, Jiang et al’s research reported positive associations for body weight, body mass index, total body fat mass, WC, trunk fat mass, body fat percentage, and apolipoprotein B. In contrast, our investigation revealed statistically significant relationships only between WC and HDL-C and IPF. The analyses of based on different datasets consistently identified WC as a positive risk factor for IPF, further underscoring the robustness of this finding. Variability among different datasets could potentially lead to discrepancies in results. Nevertheless, through an extensive evaluation of data from multiple sources, our study strengthens the evidence that WC is a risk factor for IPF. The observation that WC serves as a positive causal factor was validated across multiple independent datasets, thereby enhancing the credibility of our conclusion. The agreement across datasets reinforces the credibility of our findings and stresses the need to account for WC when assessing IPF risk.

The mixed findings from our investigation underscore the imperative for future research to adopt more sophisticated methodologies that can dissect these complex interactions. Longitudinal studies with comprehensive metabolic profiling, advanced genomic technologies, and detailed phenotyping of IPF cases are necessary to unravel the underlying biological mechanisms. Such studies could provide a clearer picture of the causative pathways and help in identifying specific metabolic targets for intervention. In clinical practice, these insights call for a holistic approach to managing patients at risk of or suffering from IPF. Healthcare providers should consider the full spectrum of metabolic health, beyond traditional respiratory care, to include management of blood glucose levels, lipid profiles, and blood pressure, as part of an integrated treatment plan.

The study has several advantages. First, genetic variants were used as proxies for environmental exposures to clarify causal connections between exposures and disease occurrence. Genetic variants are assumed to be randomly assigned prior to birth, ensuring independence from environmental influences and long-term stability before disease manifestation. This property helps to minimize residual confounding and reverse causality frequently observed in traditional observational studies. Second, the use of publicly available datasets enables more precise effect estimates and enhances statistical power owing to the large sample sizes of GWAS. The findings are likewise resistant to horizontal pleiotropy and other confounding factors. Collectively, these features provide adequate statistical strength to detect a significant association between MetS and IPF. Nevertheless, several limitations deserve attention. To limit population stratification, we focused on participants of European origin, which may confine the generalizability of the results. In addition, the lack of demographic details like sex and ethnicity in the original data prevented subgroup analyses. These constraints may narrow the applicability of our conclusions and modestly affect their accuracy.

In brief, we have conducted thorough validation on the association between MetS and IPF. Notably, there is a direct correlation observed between WC and IPF. MetS hold potential as novel biomarkers, providing significant perspectives for the prevention and management of IPF.

Author contributions

Conceptualization: Qing Liu, Qin Luo.

Data curation: Qin Luo.

Formal analysis: Wei Chen.

Investigation: Wei Chen.

Methodology: Xuemei Kuang.

Project administration: Xuemei Kuang, Jianmei Wu.

Resources: Jianmei Wu.

Writing – original draft: Qing Liu.

Writing – review & editing: Jianhua Tan.

Supplementary Material

medi-104-e45826-s001.xlsx (65.1KB, xlsx)

Abbreviations:

CI
confidence interval
FBG
fasting blood glucose
GWAS
genome-wide association studies
HDL-C
high-density lipoprotein cholesterol
IPF
idiopathic pulmonary fibrosis
IV
instrumental variable
IVW
inverse-variance weighted
MetS
metabolic syndrome
MR
Mendelian randomization
OR
odds ratio
TG
triglycerides
WC
waist circumference

All authors have read, approved the final manuscript, and confirmed the accuracy and completeness of this declaration.

This study was supported by the Health Commission of Hunan Province, China (Grants: Z2023016, 20201922) and The Second Affiliated Hospital of Nanhua University (Grant: 2025JJ81037).

The authors have no conflicts of interest to disclose.

All data generated or analyzed during this study are included in this published article [and its supplementary information files].

Supplemental Digital Content is available for this article.

How to cite this article: Liu Q, Luo Q, Chen W, Kuang X, Wu J, Tan J. Genetic causal association between metabolic syndrome and idiopathic pulmonary fibrosis: A 2-sample Mendelian randomization. Medicine 2025;104:46(e45826).

Contributor Information

Qing Liu, Email: lq139741@163.com.

Qin Luo, Email: 1533711317@qq.com.

Wei Chen, Email: 454945725@qq.com.

Xuemei Kuang, Email: 1622492936@qq.com.

Jianmei Wu, Email: 812222386@qq.com.

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