Causal validation between 179 lipids and hyperparathyroidism: A bidirectional Mendelian randomization combined meta-analysis with mediation factors

Abstract

Hyperparathyroidism, an endocrine disorder linked to hypercalcemia, increases with age, particularly in those over 60. Abnormal lipid metabolism may be closely related to its occurrence and progression. The study used Mendelian randomization (MR) analysis on 179 lipid traits against hyperparathyroidism in the Finngen and UK Biobank (UKB) databases. Meta-analysis of inverse variance weighted results followed, with significance P-values corrected for multiple comparisons. Causal validation was performed between positive lipids and renal failure, and MR analysis examined the link between renal failure and hyperparathyroidism. Reverse causal validation was also conducted between lipids and hyperparathyroidism, positive lipids and renal failure, and renal failure and hyperparathyroidism. The study conducted causal validation between 179 lipid traits and hyperparathyroidism, also exploring intermediary factors. Ultimately, MR analysis was performed on triacylglycerol (52:3) levels with hyperparathyroidism in both Finngen and UKB databases, followed by meta-analysis and multiple corrections. The results showed an odds ratio (OR) of 1.147 (95% confidence interval [CI]: 1.065–1.235, P = .040). The MR results for triacylglycerol (52:3) levels and renal failure indicated an OR of 1.054 (95% CI: 1.004–1.106, P = .032). For the intermediary factor renal failure, MR analysis with hyperparathyroidism in both Finngen and UKB databases followed by meta-analysis showed an OR of 1.336 (95% CI: 1.193–1.495, P = 4.78 × 10⁻⁷). Notably, no significant associations were found in the reverse validation of each analysis process. Furthermore, the mediation effect was β₁₂ = 0.0153, and the direct effect was β₃ = 0.1207 (0.1183, 0.1230). The mediation effect accounted for 11.25%, while the direct effect accounted for 88.75% of the total effect, Z = 12.5, indicating that the direct effect predominantly influences the overall impact. The research shows that triacylglycerol (52:3) levels can directly increase the risk of hyperparathyroidism. It also raises the risk indirectly by increasing the likelihood of renal failure, an intermediary factor. About one-tenth of the lipid’s effect on hyperparathyroidism is mediated through renal failure, while the direct effect constitutes roughly nine-tenths of the total effect.

1. Introduction

Hyperparathyroidism (HPT) is an endocrine disorder caused by hyperfunction of the parathyroid glands, characterized by primary and secondary parathyroid hyperplasia, parathyroid adenomas, and genetic factors.^[1] Its clinical manifestations are mainly associated with hypercalcemia, including osteoporosis, dyspepsia, increased urinary calcium, neurological symptoms, and cardiovascular symptoms.^[2,3] Early symptoms are often mild but can worsen gradually with disease progression. Early diagnosis and treatment of HPT are crucial to prevent severe complications it may cause.

The epidemiological characteristics of HPT include a global incidence of approximately 5 to 10 cases per million population annually, with a female-to-male ratio of 2 to 3:1.^[4,5] The onset age is mainly concentrated in the elderly population aged 60 and above, with approximately 10% to 15% of patients having a family history. HPT often coexists with other diseases such as osteoporosis, hypertension, and cardiovascular diseases, with about 80% of patients having osteoporosis as a comorbidity. Although there are differences in incidence rates among different regions and populations, HPT as an endocrine disorder is prevalent globally.^[6,7]

Furthermore, the incidence of HPT increases with age, with individuals aged 60 and above being the high-risk population. In addition, some studies suggest that chronic kidney disease is one of the main causes of HPT, especially in end-stage renal disease patients, where the incidence of HPT significantly increases. Moreover, HPT is also associated with factors such as vitamin D deficiency, inadequate calcium intake, and obesity.^[8–10] Therefore, assessing and preventing the risk of HPT considering factors such as age, gender, renal function, and nutritional status are significant.

Research on lipids holds significant importance in the biomedical field. Researchers focus on lipid metabolism, the association between lipids and diseases, lipid drug therapy, lipid biomarkers, and the relationship between lipids and nutrition.^[11,12] Studies have shown that lipid abnormalities are closely related to various diseases such as cardiovascular diseases, obesity, and diabetes. In response to these associations, scientists continue to develop and improve lipid-modulating drugs and explore the application of lipid biomarkers in early diagnosis and prognosis assessment. Furthermore, the importance of lipids in nutrition is highly regarded, with research involving lipid absorption and utilization in the body and the effects of different lipids on health.^[13–15] Overall, lipid research provides important scientific evidence for the prevention and treatment of various diseases, and its development is essential for promoting human health.

Regarding the relationship between lipids and HPT, some studies suggest that lipid metabolism abnormalities may be closely related to the occurrence and development of HPT, especially diseases associated with lipid metabolism abnormalities such as hyperlipidemia and obesity, which may increase the risk of developing HPT and its complications such as osteoporosis. Recent research has also begun to focus on the relationship between adipose tissue and HPT, as well as the interaction between lipids and vitamin D metabolism.^[16,17] It is noteworthy that lipids are risk factors for renal failure, and renal failure is a risk factor for HPT, laying the foundation for studying whether renal failure is an intermediary factor in the relationship between lipids and HPT.

However, existing studies on the relationship between lipids and renal failure indicate that lipid metabolism abnormalities are closely related to kidney disease. Abnormal levels of high cholesterol, high triglycerides, low-density lipoproteins (LDLs), etc are associated with an increased risk of declining kidney function and chronic kidney disease. Kidney disease may also affect lipid metabolism, as a reduced glomerular filtration rate can lead to elevated blood lipid levels. In addition, patients with renal failure often have increased inflammation and oxidative stress, which can also affect lipid metabolism. Studies have also found that lipid abnormalities are related to the severity and progression rate of kidney disease; thus, lipid abnormalities may not only result from kidney disease but also one of the driving factors accelerating its progression.^[18–20]

Most patients with renal failure often have abnormal parathyroid function. Research on how renal failure exacerbates HPT mainly focuses on exploring the relationship between the two and possible pathophysiological mechanisms. Impaired kidney function can affect the balance of calcium, phosphorus, and vitamin D in the body, thereby affecting parathyroid function. The kidneys play an important role in maintaining calcium and phosphorus balance, and renal failure may lead to disorders of calcium and phosphorus metabolism, stimulating parathyroid hyperfunction.^[21,22] In addition, patients with chronic kidney disease often have metabolic bone diseases such as osteoporosis, which are closely related to HPT. Furthermore, renal failure may lead to increased inflammation and oxidative stress in the body, affecting parathyroid function. These studies provide important clues for understanding the impact of renal failure on HPT.

Therefore, current research mainly focuses on further exploring how renal failure exacerbates HPT and finding possible treatment methods. Some studies focus on regulating calcium and phosphorus metabolism, controlling the use of vitamin D, and improving kidney function to slow down or prevent the impact of renal failure on HPT.^[23,24] In addition, research explores pharmacological interventions or other treatment strategies to manage the complications of both diseases, aiming to improve patients’ quality of life and prognosis.

Mendelian randomization (MR) studies based on genome-wide association study (GWAS) data are a method that utilizes genetic variants as causal inference. This method uses genetic variants discovered in GWAS as “natural randomized experiments” to assess the impact of certain factors on specific diseases or physiological characteristics. By analyzing the relationship between genetic variants and exposure factors, as well as the relationship between genetic variants and outcomes, MR studies can more accurately assess the impact of exposure factors on outcomes, thus inferring causality. The core advantage of MR lies in its use of genetic variants as instrumental variables (IVs), mimicking the causal inference logic of randomized controlled trials to effectively overcome the limitations of traditional observational studies: By leveraging the random allocation of genetic variants during fertilization, it inherently avoids confounding biases from postnatal environmental factors or lifestyle choices. Simultaneously, since genotypes are fixed at birth and precede disease onset, this method establishes a clear temporal and causal direction, eliminating reverse causation. In addition, it enables low-cost analyses using publicly available GWAS data, bypassing the need for long-term follow-up or interventional experiments, making it particularly suitable for studying long-term effects of exposures or rare disease outcomes. Furthermore, by linking genetic variants to specific biological mechanisms (e.g., lipid metabolism genes and cardiovascular disease), it provides molecular-level explanations for causal relationships, positioning MR as a critical alternative to randomized trials in ethically or practically constrained scenarios. Although MR studies can reduce confounding and reverse causality effects and provide reliable causal inferences, overall, MR studies based on GWAS data provide an effective method for evaluating the impact of exposure factors on disease risk and physiological characteristics, contributing to a better understanding of disease mechanisms and the development of corresponding prevention and treatment strategies.^[25,26]

The main focus of this study was to explore the causal validation between 179 lipids and HPT in different databases, mainly using MR analysis, and to verify the accuracy of the results from different databases through meta-analysis. In addition, the intermediary factors between lipids and HPT was explored. Finally, reverse causal validation was performed at each step to ensure the directionality between the two factors.

2. Methods and materials

2.1. Study design

Our study proceeded through three main steps: first, we conducted MR analysis on 179 lipids separately with the Finngen and UK Biobank (UKB) databases to assess their association with primary HPT. Next, we performed meta-analysis on the primary MR analysis results using the most significant result, inverse variance weighted (IVW), and applied multiple corrections to the significance P-values to enhance the accuracy of the results. Second, we further investigated the selected positive lipids’ MR analysis with the intermediary factor (renal failure). Finally, we analyzed the relationship between the intermediary factor and primary HPT, and similarly, the IVW results of this process were subjected to meta-analysis. In addition, we conducted a bidirectional MR analysis between the identified positive lipid and primary HPT using data from the publicly available GWAS catalog, to verify the causal relationship between the positive lipid and primary HPT. Throughout the entire study, we conducted reverse causal validation at each step to ensure the accuracy and directionality of the results.^[27]

This section aims to explore the potential causal relationship between lipid metabolism and HPT. Through systematic data collection, we screened HPT-related datasets from the GWAS catalog, Open GWAS, UK Biobank, and Finngen databases. Ultimately, we identified a dataset related to primary hyperparathyroidism (PHPT) in the GWAS catalog, which included 933 cases and 112,967 controls, comprising a total of 393,742 single-nucleotide polymorphisms (SNPs).

We initially performed a two-sample MR analysis on 179 lipid exposure traits and the PHPT dataset. After SNP matching, the results showed that only two SNPs (rs34372369 and rs7412) were present in both the exposure and PHPT datasets. Since MR analysis requires at least three independent SNPs as valid IVs, further validation of the causal relationship between lipid metabolism and PHPT was not feasible. Based on the existing data and analysis results, we conclude that there is no significant causal association between lipid metabolism and PHPT. However, we have confirmed a causal relationship between lipid metabolism and HPT. After ruling out a causal association with PHPT, we further hypothesize that lipid metabolism may exert its effects through secondary hyperparathyroidism (SHPT). This hypothesis aligns with the pathological characteristics of SHPT, which is often closely associated with chronic kidney disease, calcium-phosphorus metabolism disorders, and other metabolic abnormalities.

For instance, a recent study employing MR methodology systematically investigated the causal relationship between circulating antioxidant levels and coronary heart disease (CHD) risk. Researchers utilized genetic IVs combined with CHD outcome data from three independent databases, conducting separate MR analyses for circulating antioxidants to assess their potential association with CHD risk. Throughout the analytical process, association effect sizes from each database were calculated individually using the IVW method, with rigorous validation of IVs’ reliability and independence to ensure the robustness and scientific validity of inferential results.

Subsequently, the research team performed a meta-analysis of IVW results from the three databases, enhancing statistical power and reducing uncertainty in the findings through data integration. Comprehensive analysis demonstrated no significant evidence of protective effects from circulating antioxidants against CHD, either in the individual database analyses or in the meta-analysis of pooled results. This finding contradicts conventional perspectives suggesting antioxidants might reduce cardiovascular disease risk while indicating that circulating antioxidants may not constitute effective protective factors against CHD.^[27]

In summary, this study starts by investigating the causal relationship between lipid metabolism and HPT, preliminarily excludes a causal association with PHPT, and proposes the hypothesis that lipid metabolism may be related to SHPT. This finding provides a new direction for further elucidating the impact of lipid metabolism on parathyroid dysfunction.

All participants behind the data in the study obtained approval from relevant institutions and associations and signed informed consent forms. The STROBE-MR checklist was also completed to better understand the study (STROBE-MR checklist). To illustrate the research process more clearly, relevant flowcharts were drawn (Fig. 1).

Figure 1.

The process flowchart of the research methodology. MR = Mendelian randomization, UKB = UK Biobank.

2.2. The Genome-wide association study data sources for the 179 lipids

The data on 179 lipids are sourced from the publicly available GWAS catalog, where the original study explored the genetic associations of 179 plasma lipid species in 7174 Finnish individuals. The study identified 495 genetic associations covering relationships between multiple genes and plasma lipid levels. These findings provide crucial insights into the genetic basis of lipid metabolism and related diseases, offering potential support for developing personalized medicine and treatment approaches. The GWAS identifiers for these lipids range from GCST90277318 to GCST90277416. According to recent research, SNPs included in the study were selected based on the following criteria: P < 1 × 10⁻⁵, F > 10, minor allele frequency (MAF) > 0.01, and linkage disequilibrium processing criteria were clump_kb = 10,000, clump_r² = 0.001.^[28,29] The final number of SNPs meeting these criteria was 3390.

2.3. The GWAS data for hyperparathyroidism and renal failure

The primary outcome data for HPT in the study were obtained from two different databases. The first database is Finngen, with a sample size of 367,578, including 5590 cases and 361,988 controls, covering 20,170,045 SNPs.^[30] The second database is the UKB (Pan-UKB team. https://pan.ukbb.broadinstitute.org. 2020.), with a sample size of 416,915, including 1038 cases and 415,877 controls, containing 13,984,700 SNPs. In addition, the primary HPT data includes 933 cases and 112,967 controls, with 393,742 SNPs.

The intermediary factor data for renal failure in the study were sourced from the Finngen database, which is superior compared with other databases. It has a sample size of 412,181, including 15,475 cases and 396,706 controls, covering 21,306,349 SNPs.^[30] These data are notably superior to the HPT and renal failure data in other databases, mainly due to the superior sample size, adequate SNP coverage, and reliability of data sources.

2.4. Criteria for Selection of Instrumental Variables

In the omics MR study, selecting effective IVs is crucial. First, this study adopted a selection threshold with a P-value less than 1 × 10⁻⁵, to ensure that only SNPs strongly correlated with various lipids were retained. For most lipids, the number of related SNPs exceeded 3, ensuring the representativeness and relevance of the data.

Next, to further screen for robust IVs, this study calculated the F-statistic value for each SNP using the formula F = (beta/se)² (Supplementary Table 1, Supplemental Digital Content, https://links.lww.com/MD/P88). Only SNPs with an F-value >10 were retained, helping to remove weak IVs and enhancing the reliability of the study results. In addition, the MAF was calculated based on the effect allele frequency (eaf). If eaf was <0.5, MAF was set to eaf; otherwise, it was set to 1-eaf. Only SNPs with an MAF >0.01 were retained to exclude the influence of rare variants on the study results.^[31,32]

Finally, the selected data were formatted in the MR format, and the data underwent linkage disequilibrium processing to avoid the influence of linkage disequilibrium on the accuracy of the results. Specifically, the distance threshold was set to 10,000 kilobase pairs (kb), and the linkage disequilibrium threshold was set to 0.001.^[28] These steps ensured the independence of the IVs and the accuracy of the results.

3. Statistical analysis

3.1. Causal validation between 179 types of lipids and hyperparathyroidism

All data analysis in this study was conducted using the R 4.2.1 version (https://www.r-project.org/). First, SNPs of HPT from the two outcome databases were filtered, specifically by retaining only the data corresponding to the exposure data in HPT, and then removing palindromic SNPs from the data with the criterion action = 2. SNPs with mr_keep set to false were then excluded from the data to ensure more precise data processing.

Before conducting MR-PRESSO processing, a test for horizontal pleiotropy was performed on the processed data. If the P-value of an SNP was <.05, it was considered to have positive horizontal pleiotropy, indicating the presence of outliers. Subsequently, the MR-PRESSO method was used to remove these outliers to ensure data accuracy. The specific parameters for MR-PRESSO were set as NbDistribution = 3000 and SignifThreshold = 0.05. For SNPs with P-values >.05, it was assumed that there were no outliers.^[33,34]

After fine-tuning the data, heterogeneity testing was performed before conducting MR analysis. Although the impact of data heterogeneity on results is minimal, to optimize processing, MR analysis was performed using the random-effects model of IVW for SNPs with heterogeneity (Q_pval < .05); for SNPs without significant heterogeneity, the fixed-effects model of IVW was used. In addition to IVW, MR-Egger and weighted median methods were applied to the data, and odds ratios (ORs) were calculated. To enhance the reliability of the results, a meta-analysis was conducted on the analysis results of lipids and HPT from the two databases, specifically by performing a meta-analysis on the IVW results of MR. After analysis, multiple corrections were applied to the meta-analysis results to reduce the likelihood of Type I errors, using the Bonferroni method.^[35,36] Ultimately, only one group of lipids showed positive results after meta-analysis and multiple corrections.

3.2. Causal validation between hyperparathyroidism and positive lipid

Using the same thresholds and criteria as before, with positive lipids as the outcome and HPT as the exposure, we conducted data processing and analysis consistent with the forward approach.^[37,38] After processing and analysis, there was no evidence to support a reverse causal relationship between positive lipids and HPT.

3.3. Causal validation between positive lipid and renal failure

Similarly, using positive lipids as exposure data and renal failure as intermediary data, performing MR analysis between the two shows evidence of a causal relationship. This process aims to explore the mediating factors between lipids and HPT, thus identifying potential pathways of influence more accurately and providing more precise therapeutic targets and intervention measures for HPT treatment.

3.4. Causal validation between renal failure and positive lipid

After conducting MR analysis with renal failure as the exposure data and positive lipids as the outcome data, we did not find evidence of a reverse causal relationship between the two. This suggests that positive lipids may have a positive impact on renal failure, while renal failure does not significantly influence the change in positive lipids.

3.5. Causal validation between renal failure and hyperparathyroidism

Similarly, using renal failure as exposure data and HPT as outcome data, following the same data processing procedures as before, after conducting MR analysis, we also performed a meta-analysis on the IVW results from the MR analysis. Furthermore, we conducted multiple corrections on the significance P-values after meta-analysis. Ultimately, evidence suggests a causal relationship between renal failure and HPT, indicating that renal failure may influence HPT.

3.6. Causal validation between hyperparathyroidism and renal failure

Here, when HPT is taken as the exposure variable and renal failure as the outcome variable, no evidence of the reverse causal association between the two was found after MR analysis.

In summary, evidence suggests that out of the 179 lipid types, only one lipid type showed a significant causal association with HPT. In addition, there was a significant causal association between this positive lipid type and renal failure, and renal failure was significantly associated with HPT. Therefore, it can be inferred that this positive lipid type may influence HPT through its effect on renal failure, thereby considering renal failure as an intermediate factor between lipids and HPT. Interventions targeting the intermediate factor may slow down the progression of the disease.

3.7. Sensitivity analysis

Horizontal pleiotropy refers to the possibility that different treatment methods or interventions may have different effects on individuals or situations, and these effects may be mistakenly attributed to differences between experimental and control groups rather than actual treatment effects. To minimize the impact of horizontal pleiotropy on experimental results, we conducted a horizontal pleiotropy test on the GWAS data. For SNPs exhibiting horizontal pleiotropy (P < .05), we used MR-PRESSO to remove outliers from the data, with specific exclusion criteria set as NbDistribution = 3000 and SignifThreshold = 0.05 (Supplementary Table 2, Supplemental Digital Content, https://links.lww.com/MD/P88).

Heterogeneity refers to the existence of diversity or differences in study subjects, observed values, or experimental conditions. In statistics and research methodology, heterogeneity typically refers to differences between samples or individuals, which may arise from individual characteristics, environmental conditions, or other factors. Heterogeneity is common in research and can manifest in many aspects, including physiological and psychological differences between individuals, differences in socioeconomic status, and the influence of environmental factors, among others. This diversity and variability make research results more generalizable and representative but also increase the complexity and difficulty of interpretation. In the analysis process, we similarly conducted a heterogeneity test on the data. For SNPs exhibiting heterogeneity (Q_pval < .05), the IVW method in MR analysis was performed using a random-effects model; otherwise, a fixed-effects model was used to ensure the accuracy and reliability of the results (Supplementary Table 3, Supplemental Digital Content, https://links.lww.com/MD/P88).

In addition, we conducted a bidirectional MR analysis between the identified positive lipid and primary HPT using data from the publicly available GWAS catalog. In the primary HPT dataset, there are 933 cases and 112,967 controls. However, when performing the MR analysis, no matching SNPs were found between the two, and no significant association was observed. Therefore, it can be inferred that the pathway indicated by the study results is that the positive lipid directly influences the occurrence and development of secondary HPT, and may also indirectly affect the occurrence and development of secondary HPT through the progression of kidney failure.

4. Results

4.1. Causal validation between 179 types of lipids and hyperparathyroidism

After careful analysis and processing, we conducted MR analysis on 179 lipids with two sets of HPT. Through meta-analysis of the IVW results and multiple corrections of the significance P values, only one lipid (triacylglycerol [TAG] [52:3] levels, GCST90277397) showed a strong significant association. Specifically, this lipid showed an OR value of 1.141 (95% confidence interval [CI]: 1.055–1.233, P = .00098) and a β value of 0.132 in the MR analysis of HPT in the Finngen database. The β value obtained using the MR-Egger method was 0.164, and using the weighted median method, it was 0.124. The results were visualized using an MR combination plot (Fig. 2), and include 28 SNPs. Meanwhile, the analysis results of HPT in the UKB database showed an OR value of 1.206 (95% CI: 0.958–1.519, P = .111) and a β value of 0.188. The β value obtained using the MR-Egger method was 0.181, and using the weighted median method, it was 0.330 (Supplementary Table 4, Supplemental Digital Content, https://links.lww.com/MD/P88). Similarly, the results were visualized using an MR combination plot (Fig. 3), and include 18 SNPs.

Figure 2.

Combined MR plots of triacylglycerol (52:3) levels on hyperparathyroidism (finngen). MR = Mendelian randomization, SE = standard error, SNP = single-nucleotide polymorphism.

Figure 3.

Combined MR plots of triacylglycerol (52:3) levels on hyperparathyroidism (UKB). MR = Mendelian randomization, SE = standard error, SNP = single-nucleotide polymorphism.

Subsequently, meta-analysis was conducted on the analysis results of these two outcome databases (Supplementary Table 5, Supplemental Digital Content, https://links.lww.com/MD/P88), and the P values after the meta-analysis were subjected to multiple corrections (Supplementary Table 6, Supplemental Digital Content, https://links.lww.com/MD/P88). The results showed an OR value of 1.147 (95% CI: 1.065–1.235, P = .040). A forest plot was created for the meta-analysis results to provide a clearer presentation of the results (Fig. 4). It is noteworthy that the MR analysis results of this lipid in both outcome databases indicated positive β values for several MR analysis methods.

Figure 4.

Forest plot of the meta-analysis after MR analysis of triacylglycerol (52:3) levels and hyperparathyroidism. CI = confidence interval, IVW = inverse variance weighted, MR = Mendelian randomization, OR = odds ratio, SNP = single-nucleotide polymorphism, UKB = UK Biobank.

In summary, these results indicate that after meta-analysis in different databases, this lipid may act as an aggravating factor for HPT, increasing the risk of disease and potentially accelerating disease progression.

4.2. Causal validation between hyperparathyroidism and positive lipid

When the positive lipid was considered as the outcome and HPT as the exposure, the MR analysis results showed no significant association between this positive lipid and HPT. Specifically, the MR analysis results of HPT in the Finngen database with the positive lipid TAG (52:3) levels showed an OR value of 1.017 (95% CI: 0.958–1.078, P = .583); the MR analysis results of HPT in the UK database with the lipid TAG (52:3) levels showed an OR value of 1.028 (95% CI: 0.991–1.068, P = .138) (Supplementary Table 7, Supplemental Digital Content, https://links.lww.com/MD/P88).

4.3. Causal validation between positive lipid and renal failure

The MR analysis results between the positive lipid and the intermediate factor, kidney failure, showed an OR value of 1.054 (95% CI: 1.004–1.106, P = .032), with a β value of 0.052. The β value obtained using the MR-Egger method was 0.079, and under the weighted median method, the β value was 0.047. Visualization of these results was also conducted using an MR combination plot (Fig. 5), and include 28 SNPs. The finding suggests a P-value <.05, and β values were positive under all three MR methods (Supplementary Table 8, Supplemental Digital Content, https://links.lww.com/MD/P88). This indicates that the lipid may act as an exacerbating factor for kidney failure, potentially increasing the risk of the disease.

Figure 5.

Combined MR plots of triacylglycerol (52:3) levels on renal failure. MR = Mendelian randomization, SE = standard error, SNP = single-nucleotide polymorphism.

4.4. Causal validation between renal failure and positive lipid

The reverse MR validation between kidney failure and positive lipid indicates that there is no reverse causal association between the two. Specifically, the OR value is 1.005 (95% CI: 0.907–1.113, P = .924) (Supplementary Table 9, Supplemental Digital Content, https://links.lww.com/MD/P88).

4.5. Causal validation between renal failure and hyperparathyroidism

The mediation analysis between kidney failure and HPT from both the Finngen and UK Biobank databases via MR reveals significant associations. Specifically, in the MR analysis between kidney failure and HPT in the Finngen database, the IVW result shows an OR value of 1.322 (95% CI: 1.173–1.489, P = 4.48 × 10⁻⁶), with a β value of 0.279. The MR-Egger method yields a β value of 0.333, and the weighted median method yields a β value of 0.300. Visualization of the results is presented through an MR combination plot (Fig. 6), and include 25 SNPs. Meanwhile, the analysis between kidney failure and HPT in the UKB database indicates an OR value of 1.459 (95% CI: 1.032–2.063, P = .032), with a β value of 0.378. The MR-Egger method yields a β value of 0.074, and the weighted median method yields a β value of 0.368 (Supplementary Table 10, Supplemental Digital Content, https://links.lww.com/MD/P88). These results are also visualized through an MR combination plot (Fig. 7), and include 14 SNPs.

Figure 6.

Combined MR plots of renal failure on hyperparathyroidism (finngen). MR = Mendelian randomization, SE = standard error, SNP = single-nucleotide polymorphism.

Figure 7.

Combined MR plots of renal failure on hyperparathyroidism (UKB). MR = Mendelian randomization, SE = standard error, SNP = single-nucleotide polymorphism.

Subsequently, meta-analysis is conducted on the IVW results of kidney failure and HPT from both databases, yielding an OR value of 1.336 (95% CI: 1.193–1.495, P = 4.78 × 10⁻⁷). The postmeta-analysis results show a significant association (Supplementary Table 11, Supplemental Digital Content, https://links.lww.com/MD/P88). A forest map of the results was also drawn (Fig. 8). Moreover, the direction of β values from various MR analyses across both databases remains consistent, all being positive. This indicates that kidney failure acts as a risk factor for HPT, potentially increasing the risk of the disease and hastening its progression.

Figure 8.

Forest plot of the meta-analysis after MR analysis of renal failure and hyperparathyroidism. CI = confidence interval, IVW = inverse variance weighted, MR = Mendelian randomization, OR = odds ratio, SNP = single-nucleotide polymorphism, UKB = UK Biobank.

4.6. Causal validation between hyperparathyroidism and renal failure

Upon conducting MR analysis with HPT as the exposure data and kidney failure as the outcome data once again, the results indicate no significant reverse causal relationship between the two. Specifically, in the Finngen database, the MR analysis between HPT and kidney failure yields an OR value of 1.023 (95% CI: 0.982–1.066, P = .277), while in the UK Biobank database, the MR analysis between HPT and kidney failure shows an OR value of 0.977 (95% CI: 0.974–1.020, P = .765). These results do not provide evidence of a significant correlation (Supplementary Table 12, Supplemental Digital Content, https://links.lww.com/MD/P88).

4.7. The calculation of direct effect and mediating effect

Evidence suggests that the lipid TAG (52:3) levels directly increase the risk of HPT, serving as a risk factor for HPT. In addition, this lipid can indirectly affect HPT through an indirect pathway. Specifically, TAG (52:3) levels can indirectly influence renal failure, resulting in an increased risk of renal failure, which subsequently affects HPT, also increasing the risk of HPT. Renal failure is an intermediate factor in the metabolism process between TAG (52:3) levels and HPT.

The total effect (β) between lipid and HPT is 0.1360, the β₁ value between the positive lipid and renal failure is 0.0525, the β₂ value between renal failure and HPT is 0.2909, and the mediating effect is β₁₂ = β₁×β₂ = 0.0153. Therefore, the direct effect is β₃ = β–β₁₂ = 0.1207 (0.1183, 0.1230). The proportion of the mediating effect is 11.25%, and the proportion of the direct effect is 88.75%. Furthermore, we calculate Se = β₁² × se₁² + β₂² × se₂² = 0.0012. Based on this, Z = β₁₂/Se = 12.5. These results indicate that there is an intermediate factor, renal failure, between lipid and HPT, with the indirect effect of this intermediate factor on the pathway between lipid and HPT being approximately one-tenth of the direct pathway.

5. Discussion

Primary HPT (PHPT) is typically diagnosed through biochemical tests, imaging studies, and pathological confirmation. Biochemical tests first reveal elevated serum calcium levels (hypercalcemia) and increased parathyroid hormone (PTH) levels. Imaging studies, such as parathyroid ultrasound and radionuclide scans, can help locate the lesion. Finally, pathological examination is a crucial step in confirming the presence of parathyroid adenoma, hyperplasia, or cancer by analyzing surgically removed tissue to determine the nature of the disease.

Regarding the relationship between tumor size and vitamin D levels in PHPT patients, some studies have explored this topic. Research suggests that vitamin D deficiency is common among PHPT patients, and there is some evidence indicating that patients with lower vitamin D levels may have larger parathyroid adenomas. This association may be related to chronic stimulation of the parathyroid gland due to vitamin D deficiency, leading to increased PTH secretion. However, results from different studies vary, and the exact mechanism underlying this relationship remains unclear. Therefore, although there may be a potential association between vitamin D levels and tumor size in PHPT patients, further research is needed to confirm and clarify this relationship.^[39,40]

The findings of this study suggest that positive lipids may influence the disease in two ways. First, they act as a direct factor in the occurrence and development of secondary HPT and serve as a risk factor for the condition. Second, they influence kidney failure indirectly, which, in turn, affects the occurrence and development of secondary HPT, making this an additional risk factor.

The potential mechanisms of lipids in diseases involve various aspects, including atherosclerosis, diabetes, lipid deposition disorders, and fatty liver disease. In these conditions, factors such as abnormal lipid deposition, inflammatory responses, vascular damage, and metabolic disturbances often interact, collectively promoting disease development. For instance, elevated levels of cholesterol and triglycerides can lead to the formation of atherosclerotic plaques, increasing the risk of atherosclerosis and cardiovascular diseases. Abnormal lipid metabolism is closely associated with the occurrence and progression of diabetes, insulin resistance, and metabolic syndrome. In addition, genetic diseases such as familial hypercholesterolemia and familial hypertriglyceridemia, as well as metabolic disorders like fatty liver disease, are also linked to lipid metabolism disturbances.^[41–43] In summary, the potential mechanisms of lipids in diseases are multifaceted, including abnormal lipid deposition, inflammatory responses, vascular damage, and metabolic disturbances, which collectively contribute to the occurrence and development of various diseases.

Lipids play a significant role in the mechanisms underlying HPT. First, lipid metabolism abnormalities directly affect the parathyroid gland’s function and hormone secretion. Under high-fat diet conditions, excessive lipids may interfere with the signaling pathways within parathyroid cells, leading to an imbalance in the synthesis and secretion of PTH, thereby promoting the occurrence and development of HPT. Secondly, excessive lipids may disrupt the regulation of intracellular calcium ions, including affecting the function of calcium ion channels or the homeostasis of intracellular calcium ions, thereby influencing the regulation of PTH synthesis and secretion.^[3,44] Furthermore, abnormal lipid metabolism may also affect cell signaling pathways, such as the Wnt/β-catenin signaling pathway, impacting the growth and function of parathyroid cells. These changes result in abnormal perception and response of the parathyroid gland to calcium ions, thereby promoting excessive secretion of PTH, ultimately leading to the development of HPT.^[45,46] Although these mechanisms help understand the association between lipids and HPT, further research is needed to elucidate their exact relationship.

Lipid TAG is one of the most common lipid substances found intracellularly, playing a crucial role in storing and providing energy. It primarily exists in both animal and plant cells, formed by the esterification of three fatty acid molecules with one glycerol molecule. Lipid TAG is the primary form of lipids in adipose tissue and is also stored in the liver and muscles.^[47] Lipids ingested through food are hydrolyzed into lipid TAG in the intestine, absorbed into the bloodstream, and utilized by cells or stored in adipocytes when needed. The “52:3” notation represents the length and unsaturation level of fatty acid chains in TAG molecules. In this example, “52” indicates that each fatty acid chain consists of 52 carbon atoms. The length of fatty acid chains can affect the properties and functions of TAG; for instance, longer fatty acid chains may influence the storage of TAG inside cells and the rate of transport in the bloodstream. The “3” in “52:3” indicates the number of carbon-carbon double bonds in the fatty acid chain, further indicating the degree of unsaturation of fatty acids. In “52:3,” “3” denotes that each fatty acid chain contains three carbon-carbon double bonds.^[48] The degree of unsaturation of fatty acids can affect the physical properties of TAG; for example, unsaturated fatty acids can make TAG molecules more fluid, while saturated fatty acids may make them less metabolizable in the body.^[49,50]

Lipid TAG levels are influenced by various factors, including dietary components, metabolic status, and individual genetic characteristics. Elevated or reduced levels of lipid TAG may indicate health issues such as cardiovascular diseases, obesity, and metabolic-related disorders. Therefore, monitoring and assessing lipid TAG levels are crucial for the prevention and treatment of these chronic diseases.^[13,51,52]

The mechanisms and development process of lipid TAG (52:3) levels in HPT may involve: (1) Abnormal lipid metabolism: Patients with HPT often exhibit disturbances in lipid metabolism, including elevated triglyceride levels. This may be related to excessive secretion of PTH, leading to increased metabolism and breakdown of fat tissue, consequently resulting in elevated TAG levels in the blood.^[53,54] (2) Increased fat breakdown: HPT leads to elevated PTH levels, stimulating the breakdown of fat tissue. This results in the release of lipids, including TAG, from adipose tissue into the bloodstream, thereby increasing the concentration of TAG in the blood.^[55] (3) Postprandial metabolic abnormalities: Patients with HPT often exhibit metabolic abnormalities, including postprandial lipid metabolism abnormalities. This may include increased absorption of lipids by the digestive system and changes in lipid metabolism by the liver, leading to elevated TAG levels in the blood.^[56] (4) Influence on cell signaling pathways: PTH can influence various cell signaling pathways by acting on cell surface receptors. Some studies suggest that these signaling pathways may be associated with lipid metabolism, including regulating key enzymes involved in lipid synthesis and breakdown. Therefore, excessive secretion of PTH may affect the metabolism and levels of TAG by influencing intracellular signaling pathways.^[57] (5) Chronic inflammatory response: HPT may exacerbate chronic inflammatory responses, and there is a close connection between chronic inflammatory responses and abnormal lipid metabolism. This inflammation may affect lipid synthesis and metabolism in the liver, thereby influencing TAG levels.^[58]

The core driving factor of HPT is the excessive secretion of PTH, which disrupts lipid metabolism homeostasis through multiple pathways. PTH directly activates the calcium-sensing receptor (CaSR) on adipocyte surfaces, enhancing the activity of lipases such as hormone-sensitive lipase, leading to the release of large amounts of free fatty acids and specific triglyceride molecules (e.g., 52:3) into the circulation. At the same time, PTH induces insulin resistance by interfering with the insulin signaling pathway, inhibiting adipocyte uptake of free fatty acids, and promoting the expression of key enzymes involved in de novo lipogenesis (e.g., ACC and FAS) in the liver, further increasing the synthesis of triglycerides (e.g., 52:3). In addition, hypercalcemia exacerbates oxidative stress by disrupting mitochondrial function, promoting lipid peroxidation, and forming oxidized TAGs (e.g., 52:3), which have a reduced metabolic clearance rate, leading to a sustained elevation in their concentration in the blood.

The lipid metabolism abnormalities in patients with HPT exhibit significant individual variability, partly due to genetic and epigenetic regulation. Polymorphisms in the lipoprotein lipase gene may weaken the ability of peripheral tissues to hydrolyze triglycerides (TAGs), while differences in the sensitivity of the parathyroid hormone receptor (PTH1R) can amplify lipolytic signaling. At the epigenetic level, prolonged exposure to PTH may inhibit lipid oxidation-related genes (e.g., PPARα) through DNA methylation, or activate lipid synthesis regulators (e.g., SREBP-1c) via histone modifications, creating a metabolic memory effect. In addition, vitamin D deficiency, commonly associated with HPT, disrupts farnesoid × receptor signaling, exacerbates gut-liver axis dysfunction, promotes intestinal lipid absorption, and increases hepatic VLDL secretion, further elevating triglyceride (e.g., 52:3) levels.^[59,60]

From a therapeutic perspective, correcting excessive PTH is key to improving lipid metabolism disorders. Parathyroidectomy or CaSR agonists (such as cinacalcet) can rapidly lower circulating PTH levels, indirectly reducing lipolysis and hepatic lipid synthesis. Metformin or GLP-1 receptor agonists, which target insulin resistance, can synergistically improve glucose and lipid metabolism, while antioxidants (such as vitamin E) may alleviate the toxicity of oxidized TAGs (e.g., 52:3). It is worth noting that the dynamic changes in specific TAG molecules (e.g., 52:3) could serve as early biomarkers of metabolic risk in HPT. Future research should combine lipidomics and single-cell sequencing techniques to elucidate the role of TAGs (e.g., 52:3) in interorgan communication and develop novel therapies targeting CaSR or farnesoid × receptor signaling to achieve precise regulation of HPT-associated dyslipidemia.

In addition, a recent study on the interaction between vitamin D and PTH in bone, glucose, and lipid metabolism analyzed clinical data from 1240 blood donors to explore the effects of vitamin D and PTH on these metabolic processes. The study found that low levels of vitamin D (i.e., vitamin D deficiency) led to poor lipid metabolism, such as elevated triglycerides and decreased levels of high-density lipoprotein (HDL) cholesterol. Moreover, there was a negative correlation between vitamin D and PTH, meaning that the lower the vitamin D levels, the higher the PTH levels. These hormonal changes may further exacerbate metabolic disorders. Elevated PTH levels were closely associated with higher total cholesterol, LDL cholesterol, triglycerides, and glucose levels, suggesting that PTH plays an important role in metabolic disorders.

The study also demonstrated that vitamin D deficiency affects lipid metabolism through the action of PTH, leading to worse lipid profiles, such as higher triglycerides and lower HDL levels. In addition, elevated PTH levels were directly related to higher glucose and LDL cholesterol levels, indicating that PTH not only impacts bone metabolism but also has a significant influence on glucose and lipid metabolism.^[61]

The relationship between vitamin D and lipid metabolism is complex. Many studies have shown that vitamin D not only affects bone health but also plays a role in regulating lipid metabolism. Vitamin D deficiency is often associated with unfavorable lipid markers, such as elevated LDL cholesterol, decreased HDL cholesterol, and increased triglyceride levels. Obese individuals tend to have insufficient vitamin D levels, partly because vitamin D is excessively stored in adipose tissue, reducing its availability in the bloodstream. This is linked to adipose tissue inflammation and metabolic syndrome, which may affect insulin sensitivity and lipid metabolism balance. Vitamin D, through its receptor (VDR), regulates fatty acid metabolism, cholesterol synthesis and breakdown in the liver and adipose tissue, and inhibits inflammatory responses in adipose tissue. Most studies support the positive role of vitamin D in lipid metabolism.

In summary, lipid TAG (52:3) levels in HPT may be influenced by multiple factors, including abnormal lipid metabolism, increased fat breakdown, postprandial metabolic abnormalities, influence on cell signaling pathways, and chronic inflammatory responses. Vitamin D influences lipid metabolism, which may affect HPT’s occurrence and progression. These combined factors could lead to elevated TAG (triglyceride) levels in the blood, further contributing to the development and progression of HPT.

The study indicates that an increase in lipids elevates the risk of HPT, with renal failure identified as an intermediate factor, collectively augmenting the risk of HPT. In recent years, research on the relationship between lipids and renal failure has progressively deepened, revealing a complex and close association between them. Fundamental studies have confirmed the intimate connection between lipid metabolism abnormalities and the occurrence and progression of kidney diseases, suggesting a potentially significant role of lipids in renal function impairment.^[62,63] In addition, clinical research has demonstrated a close correlation between abnormal lipid levels and the risk and progression of kidney diseases. These findings provide crucial insight into the relationship between lipids and renal health. Future research should further explore the role of lipids in the occurrence and progression of kidney diseases to provide more effective strategies for their prevention and treatment. Therefore, the relationship between lipids and renal failure encompasses a comprehensive field involving basic science, clinical research, and translational medicine, necessitating interdisciplinary collaboration and in-depth exploration.^[64]

However, the elevation of lipid TAG (52:3) levels in renal failure can be attributed to various complex physiological and pathological mechanisms. First, patients with renal failure often exhibit chronic inflammatory states and oxidative stress, which can disrupt lipid metabolism, including increased lipid peroxidation and lipolysis, leading to elevated blood triglyceride levels, which may include lipid TAG (52:3).^[65] Second, due to impaired kidney function, patients with renal failure often face issues such as dietary restrictions, malabsorption, and metabolic disturbances, resulting in abnormal energy metabolism, increased lipid storage, thereby promoting the elevation of blood TAG levels. In addition, renal failure may also cause insulin resistance and metabolic syndrome, resulting in decreased responsiveness of adipose tissue to insulin, increased lipolysis, and lipid synthesis, further exacerbating the increase in blood TAG levels.^[66] It is worth noting that patients with renal failure often require treatment with various medications, some of which may directly or indirectly affect lipid metabolism, such as glucocorticoids, phosphate binders, and certain immunosuppressants, which may also contribute to the elevation of blood TAG levels.^[67] Therefore, the elevation of lipid TAG (52:3) levels in renal failure is the result of the combined action of multiple factors, and this lipid elevation may further enhance the direct effect of lipids on HPT. A deeper understanding of these mechanisms would facilitate better management and treatment of patients with renal failure. The treatment of renal failure mainly includes pharmacotherapy, dialysis therapy, nutritional support, kidney transplantation, and lifestyle management.^[64,68] These existing treatment strategies for renal failure may indirectly reduce the risk of occurrence and progression of HPT, slowing down the progression of the disease.

Regarding the intermediate factor of renal failure increasing the risk of HPT, existing research on the relationship between renal failure and HPT indicates a close association between lipid metabolism abnormalities and renal function. Excessive lipids may be associated with chronic kidney disease, and chronic kidney disease itself may also affect the normal regulation of lipid metabolism. Although some studies have delved into this relationship, more research is needed to further understand the interaction between the two. In addition, lipids may play a significant role in renal function impairment, as lipid deposition and metabolic abnormalities may lead to changes in tubular and glomerular structure and function, affecting normal kidney function. However, research on the specific mechanisms of lipid action in renal function impairment is still in its infancy and requires more experimental and clinical support. Furthermore, abnormal blood lipid levels are closely related to clinical outcomes of kidney disease, such as the progression of kidney disease and an increased risk of kidney function failure associated with elevated levels of cholesterol and triglycerides. However, further prospective studies are needed to determine the causal relationship between lipid metabolism abnormalities and the progression of kidney disease. In summary, a thorough investigation of the role of lipid metabolism in kidney function impairment and disease progression is of great significance for formulating more effective treatment and prevention strategies for kidney diseases.

The direct increase in lipid TAG (52:3) levels may elevate the risk of HPT through various mechanisms. First, lipid metabolism abnormalities resulting in lipid excess may directly affect signaling pathways within parathyroid cells, thereby disrupting the synthesis and secretion balance of PTH, promoting the occurrence of HPT. Second, lipid excess may interfere with the regulation of intracellular calcium ions, affecting the regulation of PTH synthesis and secretion. In addition, abnormal lipid metabolism may also affect cell signaling pathways, such as the Wnt/β-catenin signaling pathway, influencing the growth and function of parathyroid cells, further promoting excessive secretion of PTH. Renal failure, as an intermediate factor, indirectly increases the risk of HPT. The close association between lipid metabolism abnormalities and renal function implies a potential mutual influence between the two. On one hand, excessive lipids may be closely associated with chronic kidney disease, as the kidneys themselves are involved in the regulation of lipid metabolism. On the other hand, renal failure may trigger chronic inflammatory responses and lipid metabolism disorders, leading to increased blood triglyceride levels. This disrupted lipid metabolism may directly affect parathyroid function, exacerbating the risk of HPT. Therefore, elevated lipid TAG (52:3) levels may directly increase the risk of HPT and indirectly increase the risk of HPT through the intermediate factor of renal failure. A comprehensive understanding of these mechanisms would facilitate a better understanding of the relationship between lipids and HPT and renal failure, providing more effective strategies for the prevention and treatment of related diseases.

The connection and disparity between lipid TAG (52:3) levels in terms of mechanism and treatment with existing research lie in exploring their biological mechanisms with specific physiological states or diseases and evaluating the effects of different treatment modalities on their levels.^[69] Research may reveal the association of TAG (52:3) levels with metabolic diseases such as obesity and diabetes, and explore changes in cell signaling pathways or lipid metabolism pathways associated with them. In terms of treatment, research may assess the regulatory effects of drug therapy or lifestyle interventions on TAG (52:3) levels.^[70] However, differences in study design, sample sources, experimental techniques, and treatment regimens among different studies may lead to disparities in research findings, necessitating a comprehensive consideration of multiple factors to fully understand their connection and disparity in terms of mechanism and treatment.

The evidence from this study suggests that an increase in lipid TAG (52:3) levels may be associated with an elevated risk of HPT. This lipid elevation may reflect abnormalities in lipid metabolism within the body, thereby affecting the function of other systems, including the endocrine system. To mitigate this risk, interventions beyond lifestyle modifications and pharmacotherapy can be considered, such as weight control, reducing saturated fat intake, and increasing consumption of foods rich in omega-3 fatty acids. In addition, treatments targeting lipid TAG (52:3) levels may involve measures to regulate insulin sensitivity and improve insulin resistance. However, further research and clinical trials are needed to validate the effectiveness and safety of these treatment strategies. Therefore, in formulating personalized treatment plans, the overall health status of patients and other potential risk factors should be comprehensively considered.

6. Conclusions

The level of lipid TAG (52:3) may directly affect the occurrence and development of HPT through various mechanisms, including abnormalities in lipid metabolism, increased fat breakdown, postprandial metabolic abnormalities, effects on cell signaling pathways, and chronic inflammatory responses. In addition, the association between lipid TAG (52:3) and renal failure may affect lipid changes through factors such as chronic inflammation, oxidative stress, energy metabolism abnormalities, and medication therapy, and increase the impact of renal failure on HPT. Therefore, intervening in any step of this pathway holds the potential to reduce the risk of HPT and slow disease progression, providing more personalized treatment options for clinical practice.

Acknowledgments

First, we express our profound thanks to all individuals and researchers who participated in the GWAS data for this research. In addition, we extend our sincere gratitude and respect to the personnel involved with the associated public databases. Finally, our heartfelt appreciation goes out to every author who played a role in contributing to this study.

Author contributions

Conceptualization: Chongyang Zhu, Wanxian Xu, Jingze Yang.

Data curation: Chongyang Zhu, Wanxian Xu.

Formal analysis: Chongyang Zhu, Jingze Yang.

Methodology: Chongyang Zhu, Wanxian Xu, Jingze Yang.

Project administration: Chongyang Zhu, Wanxian Xu, Jingze Yang.

Software: Chongyang Zhu.

Validation: Chongyang Zhu, Wanxian Xu, Jingze Yang.

Visualization: Chongyang Zhu.

Writing – original draft: Chongyang Zhu, Wanxian Xu.

Investigation: Wanxian Xu.

Supervision: Jingze Yang.

Writing – review & editing: Jingze Yang.