The Effect of Lipoprotein Cholesterol Levels and Particle Sizes on HIV Cell Entry via gp41 C34: Insights from Mendelian Randomization Analysis

The Effect of Lipoprotein Cholesterol Levels and Particle Sizes on HIV Cell Entry via gp41 C34: Insights from Mendelian Randomization Analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Effect of Lipoprotein Cholesterol Levels and Particle Sizes on HIV Cell Entry via gp41 C34: Insights from Mendelian Randomization Analysis Liu Qing, Zhang Yanzhao This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4825185/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: The gp41 C34 peptide, which is part of the HIV envelope glycoprotein, is crucial for HIV entry into host cells because it facilitates membrane fusion and serves as a biomarker for viral replication. Lipoproteins, including HDL, LDL, IDL, VLDL, and chylomicrons, affect HIV infection via their cholesterol levels and particle sizes, but their causal relationships with HIV remain unclear. Methods: Utilizing the Mendelian randomization (MR) approach to infer causality, this study leverages genetic data from the UK Biobank (115,082 individuals) and the KORA cohort (997 individuals) to explore the causal relationships between 39 lipoprotein traits (cholesterol levels and subtype concentrations of different particle sizes) and gp41 C34 expression. The primary MR method employed was the random-effect inverse variance weighted (IVW) approach. To ensure robust and reliable causal inference, multiple sensitivity analyses, including weighted median, MR‒Egger regression, simple mode, weighted mode, and leave-one-out analyses, were conducted. Results : Elevated HDL cholesterol levels were significantly associated with reduced gp41 C34 expression (IVW: β = -0.61, SE = 0.186, p = 1.25e-4, FDR = 0.004), suggesting a protective role of HDL cholesterol in HIV infection. Higher HDL particle concentrations were also inversely associated with gp41 C34 expression (IVW: β = -0.549, SE = 0.202, p = 0.007, FDR = 0.032). Increased cholesterol levels in large HDL particles were significantly inversely related to gp41 C34 expression (IVW: β = -0.46, SE = 0.16, p = 0.004, FDR = 0.03). Similarly, higher concentrations of medium HDL particles were linked to lower gp41 C34 expression (IVW: β = -0.473, SE = 0.166, p = 0.005, FDR = 0.028). No significant causal relationships were found between gp41 C34 expression and the cholesterol levels or sizes of IDL, LDL, or VLDL particles or chylomicrons. Consequently, these lipoprotein particles are unlikely to influence gp41 C34 expression and HIV cell entry. Conclusion : HDL cholesterol and HDL particle concentrations, particularly large and medium HDL particles, play a protective role against HIV cell entry by reducing gp41 C34 expression. Other lipoprotein particles do not show significant causal relationships, indicating that specific lipid traits modulate HIV entry mechanisms. These findings enhance our understanding of the influence of lipoprotein traits on HIV infection and persistence. gp41 C34 HIV cell entry Lipoprotein cholesterol Particle sizes Mendelian randomization Figures Figure 1 Figure 2 1 Introduction The gp41 C34 peptide, which is part of the HIV envelope glycoprotein (Env), plays a critical role in the initial step of viral entry into host cells, facilitating the establishment of infection. Initially, gp41 is covered by gp120. Upon gp120 binding to CD4 and the chemokine coreceptor on the target cell, gp41 is exposed, initiating the fusion of the viral and target cell membranes. The C34 peptide is a conserved segment within the C-terminal heptad repeat (CHR) region of gp41, corresponding to amino acid residues 628 to 661 [1] . During viral membrane fusion, the C34 peptide binds to the N-terminal heptad repeat (NHR) region, forming a six-helix bundle structure [2] . This refolding process brings the viral and host cell membranes closer together, facilitating membrane fusion and viral entry [3] . An increase in gp41 C34 peptide levels reflects heightened viral fusion activity, making it a biomarker of active viral replication and HIV persistence despite host immune responses and antiretroviral therapy [4] [5] . The success of HIV in establishing infection and persisting within the host is influenced not only by viral proteins but also by host factors, including lipid metabolism. Lipoproteins, such as high-density lipoprotein (HDL), low-density lipoprotein (LDL), and very-low-density lipoprotein (VLDL), are critical for cholesterol transport and metabolism in the human body. In addition to their well-established roles in cardiovascular health, lipoproteins also significantly impact immune function and susceptibility to infections. The cholesterol levels and sizes of these lipoproteins can influence the body’s inflammatory response and immune activation, which are crucial in the context of HIV infection [6] . Cholesterol in lipoproteins has varying effects on HIV fusion. In an observational study, HIV-infected patients, especially those with lower CD4 + cell counts, had reduced HDL cholesterol levels [7] . This reduction is significant because a recent study revealed a negative correlation between HDL-C levels and the HIV viral load, underscoring the role of HDL in viral replication and disease progression [8] . HDL-C may interact with receptors such as SR-BI, influencing the internalization and recycling of the coreceptors CCR5 and CXCR4 and thereby inhibiting HIV entry. This modulation reduces the availability of these coreceptors on the cell surface, creating an environment that lowers HIV infectivity and supports immune function. Conversely, another study indicated that decreased serum total cholesterol (TC) levels are predictive of increased susceptibility to HIV infection in high-risk groups, suggesting a protective role of TC against HIV acquisition [9] . Additionally, HIV-infected individuals frequently exhibit reduced LDL cholesterol levels, a trend accentuated in those coinfected with hepatitis C [10] . Low LDL levels are linked to elevated PCSK9 and increased inflammation, whereas high oxidized LDL (ox-LDL) levels contribute to immune activation and atherosclerosis, highlighting the complex role of LDL in immune regulation and T-cell maturation in HIV patients [11, 12] . On the other hand, significantly increased VLDL cholesterol levels are observed in HIV-infected individuals receiving antiretroviral therapy (ART) involving protease inhibitors, which are linked to altered lipid metabolism and contribute to immune dysregulation [13] . Extensive research on gene polymorphisms related to lipoprotein metabolism, including lipoprotein lipase (LPL) and peroxisome proliferator-activated receptor alpha (PPARα), has revealed direct connections to fluctuations in VLDL cholesterol levels and increased susceptibility to HIV infection [14] . The size of lipid particles is also important. Recent evidence shows that not all HDL particles provide the same level of protection against infections. A lower number of small and medium HDL particles is linked to higher infection risk and mortality related to infectious diseases [15] . Interestingly, a very high number of these particles is also correlated with an elevated risk of infections, although not with mortality from infectious diseases. In contrast, the number of large and very large HDL particles is not significantly associated with infection risk or related mortality [16] . Small LDL particles can increase immune activation by increasing the production of reactive oxygen species (ROS) and promoting the release of proinflammatory cytokines. This can lead to persistent inflammation, which is detrimental in chronic infections and contributes to disease progression [17] . VLDL particles derived from periodontitis patients facilitate macrophage activation through lipopolysaccharide (LPS) activity. Larger VLDL particles have greater LPS activity, leading to increased cellular cholesterol accumulation and heightened expression of proinflammatory genes such as CD14, TNF-α, MCP-1, and IL-6 in macrophages [18] . Increased levels of large VLDL particles are associated with increased levels of inflammatory markers such as C-reactive protein (CRP) and interleukin-6 (IL-6). In states of metabolic syndrome, larger VLDL particles exacerbate inflammation and contribute to cardiovascular complications [19] . Harris et al. (1993) demonstrated that chylomicrons can mitigate endotoxin-induced lethality by shunting endotoxins to hepatocytes and away from macrophages, reducing the inflammatory response [20] . Mendelian randomization (MR) is a powerful epidemiological tool that leverages genetic variants as instrumental variables to infer causal relationships between modifiable risk factors and health outcomes. Unlike observational studies that can identify only associations, MR can provide evidence for causal relationships between exposures and health outcomes, helping to distinguish correlation from causation [21] . Genetic variants are randomly assorted during gamete formation, mimicking the randomization process in controlled trials, which reduces the influence of confounding variables that often plague observational studies [22] . MR further reduces bias from confounders that might be associated with both the exposure and the outcome, leading to more reliable causal inferences [23] . Since genetic variants are determined at conception and generally precede the development of disease, MR analysis helps to avoid reverse causation, where the disease might influence the risk factor rather than the other way around. The intricate interplay between lipid metabolism and viral infections adds complexity to their relationship with host immunity and disease progression. The current genetic studies regarding the relationship between lipoproteins and HIV viral infection progression are all observational, with insufficient cases. Despite extensive research, it remains unclear whether these associations are causal. Furthermore, the specific impacts of different lipoprotein levels and subclass particles of different sizes on viral infections and immune responses have only been partially explored. This study aims to address these gaps by conducting a comprehensive statistical analysis with two-sample MR to investigate the causal relationships between lipoprotein cholesterol levels, particle sizes, and the expression of the gp41 C34 fragment. By leveraging genetic variants as instrumental variables, we aim to disentangle the causal effects of lipid traits on HIV cell entry and provide a robust assessment of how lipid metabolism influences HIV infection and persistence. 2 Methods 2.1 Study design We designed a two-sample Mendelian randomization (MR) study to investigate the relationships among cholesterol levels, lipoprotein particle sizes, and gp41 C34 expression (HIV cell entry marker). Genetic IVs were selected on the basis of stringent criteria, and the analysis was conducted via MR methods (random-effect IVW) with various sensitivity analyses (MR‒Egger, weighted median, simple mode, weighted mode and leave-one-out analysis). The results were filtered on the basis of significance thresholds and outlier correction and reverse causation deletion. Fig. 1The flowchart of the study is described below. 2.2 Data Sources C34 gp41 exposure (marker of HIV viral entry) genetic data were acquired from the Cooperative Health Research in the Region of Augsburg (KORA) cohort in Germany, which comprises 997 individuals. Within this cohort, 509,946 common autosomal single nucleotide polymorphisms (SNPs) were analyzed for their association with gp41 c34 peptide levels in blood plasma. Linear additive genetic regression models were used for the analysis, adjusting for relevant covariates to identify significant associations [24] . The genetic instruments for 39 lipoprotein particle traits, encompassing various cholesterol levels and different sizes of lipid particles such as HDL, IDL, LDL, VLDL, and chylomicrons, were derived from genome-wide association studies (GWASs) conducted within the UK Biobank (UKB). These trait genotyping quality controls excluded individuals with sex mismatch, sex chromosome aneuploidy, and non-European descent, resulting in a sample size of 115,082. [25] . Measurements were taken by nonfasting EDTA plasma samples. All analyses were conducted under UKB application #15825, ensuring a robust and comprehensive approach to selecting genetic instruments for studying lipid levels [26] . The exposure and outcome data show different cohorts and different countries, and we might reasonably assume that no direct participant overlap was identified. 2.2. Selection of Genetic Instrumental Variables 2.2.1 Filter exposure genetic variables SNPs were selected on the basis of a stringent p value threshold of P < 5 × 10⁻⁸ to ensure strong associations with lipid traits. This ensures that only robust genetic associations are included in the analysis. 2.2.2 Linkage Disequilibrium (LD) Considerations Linkage disequilibrium (LD) measures the nonrandom association of alleles at two or more loci. High LD means that a few tag SNPs can represent a larger genomic region, simplifying genetic analyses. In this study, LD analysis was conducted on a European population. We used the r 2 metric to describe allele correlation at different loci. An r 2 value close to 1 indicates high correlation, whereas a value close to 0 indicates low correlation. To ensure SNP independence, we implemented a clumping procedure, excluding SNPs within a 10,000 kb radius that exceeded an LD threshold of r 2 > 0.001. This minimizes redundancy and ensures the independence of IVs, enhancing the validity of our analysis by reducing bias and improving precision. 2.2.3 Outcome Data Retrieval We retrieved outcome data associated with the identified SNPs, ensuring that these SNPs were not significantly related to the outcome. Relevant instrumental variables were selected without using proxy SNPs when direct SNP data were unavailable. 2.2.4 Eliminating SNPs with intermediate allele frequencies and palindromic sequences An MAF > 0.03 was set to exclude extremely low-frequency variants, reducing false positives and sequencing noise [27] . Alignment of allele coding between the exposure and outcome datasets was performed to ensure consistency in the interpretation of genetic effects. Palindromic SNPs, which can be misinterpreted owing to their reverse-complement nature, were excluded to avoid potential biases. This alignment process ensures that the effect estimates are comparable and accurately reflect the genetic associations. 2.2.5 The strength of the IVs is assessed via the F statistic, which is calculated as F =( N − k −1)/k ⋅ R 2 /(1- R 2 ) , where N is the sample size, k is the number of IVs, and R 2 represents the proportion of variance explained by the SNPs in the exposure database. R 2 is computed as R 2=∑{SE²⋅N/2⋅(1− MAF )⋅ MAF ⋅ β 2}, where MAF denotes the minor allele frequency, β is the genetic effect size, and SE is the standard error. An F statistic greater than 10 is considered sufficient to provide strong evidence of the influence of a genetic variant on exposure. 2.3 Mendelian randomization analysis 2.3.1 Random-effect inverse variance weighting (random-effect IVW) The inverse variance weighted (IVW) method estimates causal relationships in MR studies. However, in our comprehensive analysis, we observed significant heterogeneity (IVW Q p value < 0.05) in traits such as cholesterol levels in IDL, the concentration of small HDL particles, and cholesterol levels in small HDL particles. This indicates that the assumption of homogeneity is violated, making the random-effect IVW model preferable [28] . The causal effect is calculated similarly to ordinary IVW, but it includes an additional variance component that accounts for the heterogeneity among SNPs. More robust to heterogeneity and potential pleiotropy because it allows for variation in the causal effect across SNPs. 2.3.2 Weighted Median The weighted median method provides a robust causal estimate even when up to 50% of the instruments are invalid. The robustness of the weighted median method complements the sensitivity of the IVW method, ensuring that the final estimate is less likely to be biased by a few invalid instruments. Applying both random-effect IVW and weighted median methods provides a comprehensive view of the potential impact of pleiotropy and invalid instruments. If the estimates of both are similar, they can conclude that the findings are robust. This holistic approach enhances the credibility and reliability of causal inference and serves as a form of cross-validation [29] . 2.3.3 MR‒Egger MR‒Egger regression detects and adjusts for pleiotropy, providing unbiased estimates. In MR‒Egger regression, the null hypothesis states that there is no directional pleiotropy. It regresses SNP-outcome effects on SNP-exposure effects, including an intercept. The slope gives the causal estimate. If the p value associated with the intercept term is less than the chosen significance level (typically 0.05), the null hypothesis of no directional pleiotropy is rejected, which means that the genetic instruments have a direct effect on the outcome independent of the exposure. [30] . 2.3.4 Simple mode The simple mode method identifies the most frequent causal estimate (mode) among the genetic variants. It involves calculating the causal estimate for each SNP and then determining the mode of these estimates. This method assumes that most of the genetic instruments are valid and that the mode represents the true causal effect. This approach is beneficial when the majority of the instruments cluster around the true effect, providing a simple yet effective estimate [31] . 2.3.5 Weighted mode The weighted mode method is similar to the simple mode method but incorporates weights to increase the influence on more precise estimates. It involves calculating the causal estimate for each SNP, assigning weights on the basis of the inverse of the variance, and determining the mode of the weighted estimates. This method assumes that the valid instruments are clustered around the true causal effect, and it provides a robust estimate even when some instruments are invalid. By weighting the estimates, this method improves the precision of the causal estimate [31] . 2.3.6Leave-One-Out Analysis To assess the robustness of the MR findings, a LOO sensitivity analysis was conducted. LOO analysis systematically excludes one SNP at a time from the set of instrumental variables and recalculates the causal estimate to evaluate the influence of each individual SNP on the overall MR estimate. The TwoSampleMR package in R was used to perform the MR analysis and the LOO sensitivity analysis. Forest plots were generated to visualize the LOO results, showing the causal estimates and confidence intervals with each SNP excluded. The consistency of the causal estimate across different SNP exclusions indicates robustness. Significant deviations in the causal estimate upon exclusion of a specific SNP suggest potential issues such as pleiotropy or violations of MR assumptions [31] . [32] 2.4 Application of the False Discovery Rate (FDR) in Multiple Testing To account for multiple testing analyses, we applied Benjamini‒Hochberg (BH) correction to p values from the IVW method in our MR analysis to estimate causal relationships between the C34 gp41 HIV fragment and lipid traits, identifying significant and suggestive associations while controlling the false discovery rate (FDR) . This method balances the risk of Type I errors (false positives) and Type II errors (false negatives). This makes it more powerful, particularly for large datasets with many comparisons. The steps include ranking p values from MR analysis, calculating critical values (k/m * α, where k is the rank, m is the total number of tests, and α is 0.05), and comparing each p value to its critical value. P < FDR < 0.05 indicates a significant association, P < 0.05 < FDR indicates a suggestive association, and 0.05 < P < FDR denotes no association. 3 Results Figure 2 The forest plot illustrates the influence of lipoprotein traits, specifically cholesterol levels and particle sizes, on gp41 C34 expression using different Mendelian randomization methods. Protective factors, which are causally associated with a decrease in gp41 C34 expression, are indicated to the left of the red dashed line at zero, while risk factors are to the right. Lipoprotein particles are highly heterogeneous in structure, composition, metabolism, and function. Therefore, distinct lipoprotein subpopulations might exert differential effects on virus infection. To examine how cholesterol levels and different sizes of different lipoprotein particles impact HIV cell entry ability, our primary analysis utilized the Mendelian randomization (MR) approach. Significant causal relationships (p < 0.05 and FDR < 0.05): 3.1 Elevated HDL cholesterol levels are significantly associated with reduced gp41 C34 expression Our primary MR analysis demonstrated that elevated HDL cholesterol levels are significantly associated with reduced gp41 C34 expression (random-effect IVW: β = -0.61, SE = 0.186, p = 1.25–4e, FDR = 0.004), suggesting a protective role of HDL cholesterol in HIV infection. This finding was corroborated by the weighted median analysis, which indicated a consistent causal effect (β = -0.565, 95% CI: -0.976 to -0.154, p = 0.007). However, the MR‒Egger analysis did not reach statistical significance in that there was no evidence of directional pleiotropy. The simple mode estimate was not significant (β = -0.554, 95% CI: -1.721–0.614, p = 0.372), but the weighted mode analysis confirmed the protective effect (β = -0.554, 95% CI: -0.979–0.128, p = 0.027). The Q statistic was 8.026 with 11 degrees of freedom (p = 0.711), revealing no statistically significant heterogeneity. MR-PRESSO tests did not find outliers, further supporting the robustness of these findings. The absence of significant shifts in causal estimates upon the exclusion of individual SNPs suggests that heterogeneity and pleiotropy were minimal in our analysis. 3.2 HDL Particle Concentration and gp41 C34 Expression An inverse association between HDL particle concentration and gp41 C34 expression was also observed. The IVW method indicated that higher concentrations of HDL particles are associated with lower gp41 C34 expression (β = -0.549, SE = 0.202, p = 0.007, FDR = 0.032199). This was corroborated by the weighted median analysis (β = -0.699, 95% CI: -1.212 to -0.186, p = 0.008). MR‒Egger regression provided a causal estimate (β = -0.341, 95% CI: -1.103–0.421, p = 0.399), indicating that pleiotropy lacks statistical significance. The simple mode estimate was not significant (β = -0.346, 95% CI: -1.642–0.949, p = 0.583), whereas the weighted mode confirmed the protective effect (β = -0.687, 95% CI: -1.275–0.099, p = 0.041). The Q statistic was 10.741, with 11 degrees of freedom (p = 0.465), indicating that heterogeneity was not statistically significant. No SNP was found to have a significant effect on the results of the leave-one-out analysis. 3.3 Increased cholesterol levels in large HDL particles were significantly associated with reduced gp41 C34 expression. The IVW method revealed a significant inverse relationship (β = -0.477, SE = 0.143, p = 0.001, FDR = 0.032). This relationship was further supported by the weighted median (β = -0.505, 95% CI: -0.901–0.109, p = 0.012) and weighted mode (β = -0.501, 95% CI: -0.938–0.064, p = 0.026) analyses. The MR‒Egger regression (β = -0.345, 95% CI: -0.886 to 0.196, p = 0.208) and simple mode (β = -0.716, 95% CI: -2.111 to 0.679, p = 0.311) results were not significant, implying that pleiotropy does not achieve statistical significance. The Q statistic was 8.143, with 11 degrees of freedom (p = 0.700), implying a lack of heterogeneity. Leave-one-out analysis revealed no SNPs that may disproportionately impact the results. 3.4 Significant Inverse Causal Relationships between High HDL Particle Concentrations and gp41 C34 Expression Our Mendelian randomization analysis revealed a significant inverse causal relationship between the concentration of large HDL particles and gp41 C34 expression. The random-effect IVW method demonstrated that higher concentrations of large HDL particles are associated with reduced expression of gp41 C34 (β = -0.46, SE = 0.16, p = 0.004, FDR = 0.03). This finding suggests that specific sizes of HDL particles may influence mechanisms of HIV viral entry. The weighted median method supported the IVW findings, showing a consistent causal effect (β = -0.409, SE = 0.188, p = 0.03). MR Egger regression, which adjusts for directional pleiotropy, provided a causal estimate of β = -0.327 (SE = 0.233, p = 0.194), suggesting that the effects of pleiotropy are not statistically relevant. Although the simple mode estimate was not significant (β = -0.655, SE = 0.501, p = 0.221), the weighted mode estimate confirmed the protective effect of large HDL particles on gp41 C34 expression (β = -0.404, SE = 0.189, p = 0.059). The Q statistic for IVW was 9.62 with 10 degrees of freedom (p = 0.474), indicating that there was no significant heterogeneity across the analyses. These findings consistently suggest that large HDL particles may play a protective role by reducing gp41 C34 expression, potentially influencing HIV entry mechanisms. The results remained consistent across the leave‒one-out analysis, with no single SNP exerting an undue influence. 3.5 Medium HDL Particles and gp41 C34 Expression Higher cholesterol levels in medium HDL particles were linked to lower gp41 C34 expression. The IVW method revealed a significant inverse relationship (random-effect IVW:β = -0.42, SE = 0.159, p = 0.009, FDR = 0.035), which was confirmed by weighted median analysis (β = -0.409, 95% CI: -0.778– -0.039, p = 0.03). MR Egger regression provided a nonsignificant causal estimate (β = -0.519, 95% CI: -1.132 to 0.094, p = 0.098), indicating no pleiotropy. The simple mode estimate was not significant (β = -0.624, 95% CI: -1.708 to 0.461, p = 0.279), whereas the weighted mode confirmed the protective effect (β = -0.602, 95% CI: -1.054 to -0.149, p = 0.009). The Q statistic was 10.549 with 13 degrees of freedom (p = 0.649), indicating that no significant heterogeneity was detected. The leave-one-out analysis confirmed that the findings were robust and not driven by any specific SNP. 3.6 Higher concentrations of medium HDL particles were also significantly associated with reduced gp41 C34 expression (random-effect IVW: β = -0.473, SE = 0.166, p = 0.005, FDR = 0.028). This was supported by the weighted median (β = -0.617, 95% CI: -1.039 to -0.195, p = 0.004) and weighted mode analyses (β = -0.683, 95% CI: -1.159 to -0.207, p = 0.006). The results of MR Egger regression (β = -0.517, 95% CI: -1.399–0.366, p = 0.254) and simple mode (β = -0.521, 95% CI: -1.433–0.392, p = 0.254) were not significant, indicating minimal pleiotropy. The Q statistic was 8.678 with 10 degrees of freedom (p = 0.563), suggesting no significant evidence of heterogeneity. The leave-one-out analysis revealed that the exclusion of any individual SNP did not significantly alter the overall results. However, owing to their bidirectional causal relationships, the concentration of VLDL particles, concentration of very large HDL particles, and total concentration of lipoprotein particles were excluded from further consideration. VLDL cholesterol levels were excluded because the 95% CI of the weighted median was not consistent with the random-effects IVW results. 3.7 Lipoprotein particle traits have no causal relationship with C34 GP41 The cholesterol level and concentration of small HDL particles have no causal relationship with C34 gp41 expression. In addition, the results also indicate that cholesterol levels or sizes of IDL, LDL, and VLDL do not exhibit a significant causal association with gp41 C34 expression. Consequently, these specific lipoprotein particles do not genetically influence the expression of gp41 C34 and are unlikely to play a direct role in modulating the HIV viral entry process through the gp41 C34 pathway. 4 Discussion 4.1 Main results Our study utilized Mendelian randomization (MR) to investigate the causal relationships between lipoprotein cholesterol levels and their subtype concentrations and the expression of gp41 C34, a protein critical for HIV cell entry. These results strongly indicate that elevated HDL cholesterol levels, as well as higher concentrations of medium and large HDL particles, are significantly associated with reduced gp41 C34 expression, suggesting a protective role of HDL against HIV infection. In contrast, cholesterol levels or the sizes of IDL, LDL, and VLDL do not clearly or significantly affect gp41 C34 expression, underscoring the unique protective function of HDL cholesterol and its specific subpopulations in modulating HIV cell entry mechanisms. These results align with those of previous studies that demonstrated the inhibitory effects of HDL particles on HIV-1 fusion and entry, supporting the hypothesis that HDL particles play a crucial role in controlling HIV infection. For example, Kelesidis et al. (2016) reported that oxidized HDL particles are associated with reduced inflammation and immune activation in HIV-1 infection, suggesting a protective role of HDL [33] . Recent research has highlighted the connection between lower HDL cholesterol levels and increased inflammatory markers as well as disease progression in people living with HIV [34] . Unexpectedly, this study suggests that insufficient levels of small HDL particles may also play a role in the elevated HIV viral load in infected individuals. This hypothesis is supported by other research [34–36] , although it is somewhat divergent from our own results. Further investigation is needed to elucidate this potential association. 4.2Possible mechanism HDL-C is integral for reversing cholesterol transport, moving cholesterol from peripheral tissues back to the liver, which underpins several protective mechanisms against HIV infection. One key mechanism involves the role of HDL-C in cholesterol efflux from cells via transporters such as ABCA1 and ABCG1, lowering cholesterol levels within plasma membranes and disrupting lipid rafts. This disruption impairs the structural integrity and function of lipid rafts, which are essential for HIV entry [37] . Lipid rafts are cholesterol-rich microdomains essential for HIV entry, as they support the structural integrity and function of membrane proteins, including gp41, an HIV-1 envelope protein crucial for membrane fusion [38] . By disrupting lipid rafts, HDL-C reduces the efficiency of HIV binding and fusion with host cell membranes, thereby impeding viral entry [39] . In addition to disrupting lipid rafts, HDL-C possesses significant anti-inflammatory and antioxidant properties. HDL-C inhibits the expression of adhesion molecules such as V-CAM, I-CAM, and E-selectin and reduces the activation of the inflammasome pathway, which involves caspase-1 and the release of IL-1β, a cytokine linked to the death of CD4 + T cells, which are primary targets in HIV infection [40, 41] . Furthermore, HDL-C prevents the oxidation of low-density lipoprotein (LDL), which, when oxidized (oxLDL), can induce the production of proinflammatory cytokines such as IL-1β via inflammasomes [42] . HDL-C also plays a crucial role in modulating immune responses. It enhances the activity of antiviral proteins such as APOBEC3G, a cytidine deaminase, and induces hypermutations in HIV-1 DNA, which leads to defective viral particles [43] . Higher APOBEC3G activity is correlated with higher CD4 counts and slower disease progression in HIV-infected individuals. Additionally, HDL-C inhibits the inflammatory response triggered by the complement system in response to cholesterol crystals, contributing to the regulation of inflammation in HIV-infected individuals. Moreover, HDL-C modulates cholesterol distribution in T-cell membranes, which is vital for the organization and function of T-cell receptors (TCRs) [44] . Proper functioning of TCRs is essential for T-cell activation and proliferation in the immune response against HIV. By regulating cholesterol transport and reducing the membrane cholesterol content, HDL-C impairs the ability of HIV to fuse with host cells and creates an unfavorable environment for HIV replication. Medium and large HDL particles are likely more effective than small HDL particles in lowering C34 gp41 expression and reducing HIV cell entry through several mechanisms. First, medium- and large HDL particles are rich in cholesterol and have a greater capacity for cholesterol efflux. This capacity is crucial for removing cholesterol from the plasma membrane and disrupting lipid rafts, which are cholesterol-rich microdomains on host cell membranes that facilitate HIV entry. By disrupting these lipid rafts, medium and large HDL particles inhibit the formation of HIV entry points, thereby decreasing the efficiency of viral entry. Additionally, medium and large HDL particles have a lipid composition rich in phospholipids and sphingolipids, making them more stable than small HDL particles in vivo. Phospholipids, with their flexible bilayer structure, provide membrane fluidity, enabling membrane proteins to function properly and perform various cellular tasks [45] . Sphingolipids have a more rigid structure that stabilizes the membrane, especially in lipid rafts, which are crucial for cell signaling [44] . The combination of fluidity from phospholipids and rigidity from sphingolipids ensures membrane integrity and functionality, allowing medium and large HDL particles to withstand physical and chemical stresses. This stabilization is crucial in preventing HIV fusion with host cell membranes, thereby reducing the efficiency of viral entry [46] . These particles also carry a diverse array of apolipoproteins and other functional proteins, such as apolipoprotein A-I (ApoA-I), which have anti-inflammatory, antioxidant, and immune-modulatory properties [47] . This allows medium and large HDL particles to bind effectively and sequester C34 gp41 HIV, preventing the conformational changes necessary for viral fusion and entry into host cells [48] . In addition, this high apolipoprotein content also contributes to the structural stability of these particles, making them more stable and less susceptible to dissociation than smaller HDL particles are, ensuring prolonged circulation and effective protective functions in the bloodstream [49] . Large HDL particles are especially critical in this protective mechanism because they transport anti-inflammatory molecules such as sphingosine-1-phosphate (S1P), which possesses significant anti-inflammatory and endothelial-protective properties [50] . Low HDL-C and decreased numbers of large HDL particles are related to increased mitochondrial oxidative stress, as measured by PBMC 8-oxo-dG [35] . Furthermore, large HDL particles play a critical role in mitigating oxidative stress. They are equipped with antioxidant enzymes such as paraoxonase 1 (PON1) and glutathione peroxidase, which neutralize reactive oxygen species (ROS). This antioxidative function protects LDL particles and endothelial cells from oxidative stress, thereby reducing the risk of oxidative damage and preserving endothelial cell function and integrity. This preservation is crucial in preventing HIV from exploiting weakened cellular defenses to gain entry [50] . In contrast, small HDL particles, with lower cholesterol content, reduced efflux capacity, less optimal lipid composition, and reduced functional protein cargo, are less effective at disrupting lipid rafts, stabilizing cellular membranes, and inhibiting viral entry mechanisms. These deficiencies collectively render small HDL particles less effective at reducing HIV cell entry and lowering gp41 expression than medium and large HDL particles. In the context of HIV infection, our findings indicate that cholesterol levels or sizes of LDL, IDL, and VLDL do not exhibit a significant causal association with gp41 C34 expression. This lack of association can be explained by several underlying factors. HDL plays a unique role in maintaining cell membrane fluidity and microdomain structures, which can influence the integration and expression of gp41 C34. In contrast, LDL, IDL, and VLDL lack these properties. Additionally, HDL is involved in reverse cholesterol transport, a specific metabolic process that might impact gp41 C34 expression. LDL, IDL, and VLDL do not participate in this process, which may explain their lack of significant association with gp41 C34 expression.4.6 Limitations Our study's strengths include the application of the Mendelian randomization (MR) approach, which mitigates confounding and reverse causation, and comprehensive sensitivity analyses that reinforce the robustness of our findings [51] . However, certain limitations must be acknowledged. The genetic instruments used in MR studies may not capture all the variability in lipid traits, and potential pleiotropic effects, although minimal, cannot be entirely ruled out. Additionally, our findings are based on genetic data predominantly from European populations, which may limit their generalizability to other ethnic groups [52] . Values and Future Directions Future research should focus on elucidating the specific biological mechanisms by which HDL cholesterol and its particles influence HIV infection. Experimental studies could further investigate these interactions, particularly how HDL particle subtypes modulate the function of HIV envelope proteins such as gp41 and their impact on viral entry [53, 54] . Additionally, exploring the therapeutic potential of HDL-raising interventions or treatments that increase HDL particle size could provide valuable clinical insights into reducing the HIV viral load and improving immune function. Further studies should also examine whether these findings can be replicated in diverse populations and investigate the potential role of other lipid fractions in modulating HIV infection. These directions could pave the way for innovative therapeutic strategies to increase host resistance to HIV, leveraging the multifaceted roles of HDL and other lipoproteins in immune modulation and viral inhibition. By integrating genetic, molecular, and clinical research, we can develop a more comprehensive understanding of the impact of lipid metabolism on HIV infection and progression, ultimately contributing to the global effort to combat this persistent threat. Abbreviations HIV: Human immunodeficiency virus SNP: Single nucleotide polymorphism IVs: Instrumental variables MAF: Minor allele frequency β: Genetic effect size SE: Standard error CI: Confidence interval IVW: Inverse variance weighted FDR: False discovery rate HDL: High-density lipoprotein LDL: Low-density lipoprotein VLDL: Very low-density lipoprotein IDL: Intermediate-density lipoprotein LOO: leave-one-out Declarations Acknowledgments We express our gratitude to the participants and investigators of the UK Biobank and The KORA-Study Group for providing publicly available GWAS results. Thanks to the IEU OpenGWAS platform for summary-level providing the sorted summary-level genetic data. Funding Not applicable. Ethics declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Competing interests The authors declare that they have no competing interests. Availability of data and materials All the datasets analyzed in this study are publicly available summary statistics from IEU OpenGWAS (https://gwas.mrcieu.ac.uk/). References LOUIS J M, BABER J L, CLORE G M. The C34 Peptide Fusion Inhibitor Binds to the Six-Helix Bundle Core Domain of HIV-1 gp41 by Displacement of the C-Terminal Helical Repeat Region [J]. Biochemistry, 2015, 54(45): 6796-805. HE Y, LIU S, LI J, et al. Conserved Salt Bridge between the N- and C-Terminal Heptad Repeat Regions of the Human Immunodeficiency Virus Type 1 gp41 Core Structure Is Critical for Virus Entry and Inhibition [J]. Journal of Virology, 2008, 82(22): 11129-39. MARKOSYAN R M, MA X, LU M, et al. The Mechanism of Inhibition of HIV-1 Env-Mediated Cell–Cell Fusion by Recombinant Cores of gp41 Ectodomain [J]. Virology, 2002, 302(1): 174-84. MASLENNIKOVA A, KOMKOV D, ZOTOVA A, et al. Cell Surface-Expressed GPI-Anchored Peptides from the CHR Domain of gp41 Are Potent Inhibitors of HIV-1 Fusion, F 2020]. MDPI. TANG X, JIN H, CHEN Y, et al. A Membrane-Anchored Short-Peptide Fusion Inhibitor Fully Protects Target Cells from Infections of Human Immunodeficiency Virus Type 1 (HIV-1), HIV-2, and Simian Immunodeficiency Virus [J]. Journal of Virology, 2019, 93(22). FEINGOLD K R, GRUNFELD C. Lipids: a key player in the battle between the host and microorganisms [J]. Journal of Lipid Research, 2012, 53(12): 2487-9. SRIDEVI K, MALATHI S, KV C, et al. CD4 Cell Counts, Lipid Profile, and Oral Manifestations in HIV-Infected and AIDS Patients [J]. Frontiers in Dentistry, 2020. PICONI S, BOTTANELLI M, MARCHETTI G, et al. Is HDL-c plasma concentration a possible marker of HIV replication? A cross-sectional analysis in untreated HIV-infected individuals accessing HIV care in Italy [Z]. 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Characterizing metabolomic signatures of lipid-modifying therapies through drug target mendelian randomization [J]. PLoS biology, 2022, 20(2): e3001547. PANAGIOTOU O A, EVANGELOU E, IOANNIDIS J P A. Genome-wide Significant Associations for Variants With Minor Allele Frequency of 5% or Less—An Overview: A HuGE Review [J]. American Journal of Epidemiology, 2010, 172(8): 869-89. BURGESS S, FOLEY C N, ALLARA E, et al. A robust and efficient method for Mendelian randomization with hundreds of genetic variants [J]. Nature Communications, 2020, 11(1). BOWDEN J, DAVEY SMITH G, HAYCOCK P C, et al. Consistent Estimation in Mendelian Randomization with Some Invalid Instruments Using a Weighted Median Estimator [J]. Genetic Epidemiology, 2016, 40(4): 304-14. BURGESS S, THOMPSON S G. Interpreting findings from Mendelian randomization using the MR‒Egger method [J]. European Journal of Epidemiology, 2017, 32(5): 377-89. HARTWIG F P, DAVEY SMITH G, BOWDEN J. Robust inference in summary data Mendelian randomization via the zero modal pleiotropy assumption [J]. International Journal of Epidemiology, 2017, 46(6): 1985-98. STONE M. Cross-Validatory Choice and Assessment of Statistical Predictions [J]. Journal of the Royal Statistical Society Series B: Statistical Methodology, 1974, 36(2): 111-33. KELESIDIS T, JACKSON N, MCCOMSEY G A, et al. Oxidized lipoproteins are associated with markers of inflammation and immune activation in HIV-1 infection [J]. AIDS, 2016, 30(17): 2625-33. HARSLøF M, PEDERSEN K M, AFZAL S, et al. Lower levels of small HDL particles associated with increased infectious disease morbidity and mortality: a population-based cohort study of 30 195 individuals [J]. Cardiovascular Research, 2023, 119(4): 957-68. PARIKH N I, GERSCHENSON M, BENNETT K, et al. Lipoprotein concentration, particle number, size and cholesterol efflux capacity are associated with mitochondrial oxidative stress and function in an HIV positive cohort [J]. Atherosclerosis, 2015, 239(1): 50-4. BAKER J, AYENEW W, QUICK H, et al. High‐Density Lipoprotein Particles and Markers of Inflammation and Thrombotic Activity in Patients with Untreated HIV Infection [J]. The Journal of Infectious Diseases, 2010, 201(2): 285-92. SALEHEEN D, KHANUM S, HAIDER S R, et al. A novel haplotype in ABCA1 gene effects plasma HDL-C concentration [J]. International Journal of Cardiology, 2007, 115(1): 7-13. MUJAWAR Z, ROSE H, MORROW M P, et al. Human Immunodeficiency Virus Impairs Reverse Cholesterol Transport from Macrophages [J]. PLoS biology, 2006, 4(11): e365. TORIBIO M, PARK M H, ZANNI M V, et al. HDL Cholesterol Efflux Capacity in Newly Diagnosed HIV and Effects of Antiretroviral Therapy [J]. The Journal of Clinical Endocrinology & Metabolism, 2017, 102(11): 4250-9. CALABRESI L, GOMARASCHI M, VILLA B, et al. Elevated Soluble Cellular Adhesion Molecules in Subjects With Low HDL-Cholesterol [J]. Arteriosclerosis, Thrombosis, and Vascular Biology, 2002, 22(4): 656-61. JUREK A, TURYNA B, KUBIT P, et al. The ability of HDL to inhibit VCAM-1 expression and oxidized LDL uptake is impaired in renal patients [J]. Clinical Biochemistry, 2008, 41(12): 1015-8. ROBERTSON S, GONZALO, CLOE, et al. Colchicine therapy in acute coronary syndrome patients acts on caspase-1 to suppress NLRP3 inflammasome monocyte activation [J]. Clinical Science, 2016, 130(14): 1237-46. AN P, BLEIBER G, DUGGAL P, et al. APOBEC3G Genetic Variants and Their Influence on the Progression to AIDS [J]. Journal of Virology, 2004, 78(20): 11070-6. ABLAN S, RAWAT S S, VIARD M, et al. The role of cholesterol and sphingolipids in chemokine receptor function and HIV-1 envelope glycoprotein-mediated fusion [J]. Virology Journal, 2006, 3(1): 104. VAN MEER G, VOELKER D R, FEIGENSON G W. Membrane lipids: where they are and how they behave [J]. Nature Reviews Molecular Cell Biology, 2008, 9(2): 112-24. MIYAZAKI M, TAJIMA Y, ISHIHAMA Y, et al. Effect of phospholipid composition on discoidal HDL formation [J]. Biochimica et Biophysica Acta (BBA) - Biomembranes, 2013, 1828(5): 1340-6. DAVIDSON W S, SILVA R A G D, CHANTEPIE S, et al. Proteomic Analysis of Defined HDL Subpopulations Reveals Particle-Specific Protein Clusters [J]. Arteriosclerosis, Thrombosis, and Vascular Biology, 2009, 29(6): 870-6. MARTIN I, DUBOIS M C, SAERMARK T, et al. Apolipoprotein A-1 interacts with the N-terminal fusogenic domains of SIV (simian immunodeficiency virus) GP32 and HIV (human immunodeficiency virus) GP41: Implications in viral entry [J]. Biochemical and Biophysical Research Communications, 1992, 186(1): 95-101. HUANG R, SILVA R A G D, JEROME W G, et al. Apolipoprotein A-I structural organization in high-density lipoproteins isolated from human plasma [J]. Nature Structural & Molecular Biology, 2011, 18(4): 416-22. ZIMETTI F, ADORNI M P, MARSILLACH J, et al. Connection between the Altered HDL Antioxidant and Anti-Inflammatory Properties and the Risk to Develop Alzheimer’s Disease: A Narrative Review [J]. Oxidative Medicine and Cellular Longevity, 2021, 2021: 1-13. ZHAO Q, CHEN Y, WANG J, et al. Powerful three-sample genome-wide design and robust statistical inference in summary-data Mendelian randomization [J]. International Journal of Epidemiology, 2019, 48(5): 1478-92. PIRIM D, RADWAN Z H, WANG X, et al. Apolipoprotein E-C1-C4-C2 gene cluster region and interindividual variation in plasma lipoprotein levels: a comprehensive genetic association study in two ethnic groups [J]. PLOS ONE, 2019, 14(3): e0214060. NIETO‐GARAI J A, ARBOLEYA A, OTAEGI S, et al. Cholesterol in the Viral Membrane is a Molecular Switch Governing HIV‐1 Env Clustering [J]. Advanced Science, 2021, 8(3): 2003468. BRYAN A M, DEL POETA M. Sphingosine-1-phosphate receptors and innate immunity [J]. Cellular Microbiology, 2018, 20(5): e12836. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4825185","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":336188963,"identity":"36c9e7c8-c1ed-4e54-8824-d2ad2182c83a","order_by":0,"name":"Liu Qing","email":"","orcid":"","institution":"China Medical University","correspondingAuthor":false,"prefix":"","firstName":"Liu","middleName":"","lastName":"Qing","suffix":""},{"id":336188964,"identity":"b8910fd4-2c79-4a84-a071-e21ac121c445","order_by":1,"name":"Zhang Yanzhao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA9ElEQVRIiWNgGAWjYJACZhBhACI+QAQMiNfCOINkLcw8xGgxOH728OeCGht7c/azh1/b1GxLbGBv3ibBUHMHt5YzeQnGM46lMVv25KVZ5xy7ndjAc6xMguHYM9xaDuQYJPOwHWYDMsyMcxuAWiRyzCQYGw7j1nL+jcFhnn+HeYAMM2NLkBb5NwS03MgxbOZtOywBZBg/ZgTbwoNfi+SNN8bMvH1pBgY33pgx9hy7bdzGk1ZskXAMtxa+8znGn3m+2dgbABkfftTclu1nP7zxxoca3FoUDiDYbBJgEkQk4NTAwCDfgGAzf8CjcBSMglEwCkYwAAAHglazwuPK7gAAAABJRU5ErkJggg==","orcid":"","institution":"China Medical University","correspondingAuthor":true,"prefix":"","firstName":"Zhang","middleName":"","lastName":"Yanzhao","suffix":""}],"badges":[],"createdAt":"2024-07-30 02:53:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4825185/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4825185/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":63525296,"identity":"aad7b933-a106-4873-9463-c41e7ad1e36a","added_by":"auto","created_at":"2024-08-29 06:53:39","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":117190,"visible":true,"origin":"","legend":"\u003cp\u003eFlow chart for the Mendelian randomized analysis.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4825185/v1/a966f776f7b9305bea1b6744.png"},{"id":63525297,"identity":"26ba872a-2f9a-42ed-ba0a-c407fca6afb0","added_by":"auto","created_at":"2024-08-29 06:53:39","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":216741,"visible":true,"origin":"","legend":"\u003cp\u003eCausal relationship between lipoprotein traits and gp41 C34 expression\u003c/p\u003e\n\u003cp\u003eThe forest plot illustrates the influence of lipoprotein traits, specifically cholesterol levels and particle sizes, on gp41 C34 expression using different Mendelian randomization methods. Protective factors, which are causally associated with a decrease in gp41 C34 expression, are indicated to the left of the red dashed line at zero, while risk factors are to the right.\u003c/p\u003e\n\u003cp\u003ensnp, number of SNPs (single nucleotide polymorphisms) used; beta, estimated causal effect size; se, standard error; pval, p value; beta (95% CI), 95% confidence interval of beta; FDR, False Discovery Rate.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4825185/v1/b6a43bccde7e39914116a832.png"},{"id":72196192,"identity":"ce499776-eee4-445e-a18b-54ff30e0ebfd","added_by":"auto","created_at":"2024-12-23 14:54:25","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":886357,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4825185/v1/0de0b01a-f408-400d-a968-c4a9a5223532.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"The Effect of Lipoprotein Cholesterol Levels and Particle Sizes on HIV Cell Entry via gp41 C34: Insights from Mendelian Randomization Analysis","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eThe gp41 C34 peptide, which is part of the HIV envelope glycoprotein (Env), plays a critical role in the initial step of viral entry into host cells, facilitating the establishment of infection. Initially, gp41 is covered by gp120. Upon gp120 binding to CD4 and the chemokine coreceptor on the target cell, gp41 is exposed, initiating the fusion of the viral and target cell membranes. The C34 peptide is a conserved segment within the C-terminal heptad repeat (CHR) region of gp41, corresponding to amino acid residues 628 to 661\u003csup\u003e[1]\u003c/sup\u003e. During viral membrane fusion, the C34 peptide binds to the N-terminal heptad repeat (NHR) region, forming a six-helix bundle structure\u003csup\u003e[2]\u003c/sup\u003e. This refolding process brings the viral and host cell membranes closer together, facilitating membrane fusion and viral entry\u003csup\u003e[3]\u003c/sup\u003e. An increase in gp41 C34 peptide levels reflects heightened viral fusion activity, making it a biomarker of active viral replication and HIV persistence despite host immune responses and antiretroviral therapy \u003csup\u003e[4] [5]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe success of HIV in establishing infection and persisting within the host is influenced not only by viral proteins but also by host factors, including lipid metabolism. Lipoproteins, such as high-density lipoprotein (HDL), low-density lipoprotein (LDL), and very-low-density lipoprotein (VLDL), are critical for cholesterol transport and metabolism in the human body. In addition to their well-established roles in cardiovascular health, lipoproteins also significantly impact immune function and susceptibility to infections. The cholesterol levels and sizes of these lipoproteins can influence the body\u0026rsquo;s inflammatory response and immune activation, which are crucial in the context of HIV infection\u003csup\u003e[6]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eCholesterol in lipoproteins has varying effects on HIV fusion. In an observational study, HIV-infected patients, especially those with lower CD4\u0026thinsp;+\u0026thinsp;cell counts, had reduced HDL cholesterol levels\u003csup\u003e[7]\u003c/sup\u003e. This reduction is significant because a recent study revealed a negative correlation between HDL-C levels and the HIV viral load, underscoring the role of HDL in viral replication and disease progression\u003csup\u003e[8]\u003c/sup\u003e. HDL-C may interact with receptors such as SR-BI, influencing the internalization and recycling of the coreceptors CCR5 and CXCR4 and thereby inhibiting HIV entry. This modulation reduces the availability of these coreceptors on the cell surface, creating an environment that lowers HIV infectivity and supports immune function. Conversely, another study indicated that decreased serum total cholesterol (TC) levels are predictive of increased susceptibility to HIV infection in high-risk groups, suggesting a protective role of TC against HIV acquisition\u003csup\u003e[9]\u003c/sup\u003e. Additionally, HIV-infected individuals frequently exhibit reduced LDL cholesterol levels, a trend accentuated in those coinfected with hepatitis C\u003csup\u003e[10]\u003c/sup\u003e. Low LDL levels are linked to elevated PCSK9 and increased inflammation, whereas high oxidized LDL (ox-LDL) levels contribute to immune activation and atherosclerosis, highlighting the complex role of LDL in immune regulation and T-cell maturation in HIV patients\u003csup\u003e[11, 12]\u003c/sup\u003e. On the other hand, significantly increased VLDL cholesterol levels are observed in HIV-infected individuals receiving antiretroviral therapy (ART) involving protease inhibitors, which are linked to altered lipid metabolism and contribute to immune dysregulation\u003csup\u003e[13]\u003c/sup\u003e. Extensive research on gene polymorphisms related to lipoprotein metabolism, including lipoprotein lipase (LPL) and peroxisome proliferator-activated receptor alpha (PPARα), has revealed direct connections to fluctuations in VLDL cholesterol levels and increased susceptibility to HIV infection\u003csup\u003e[14]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe size of lipid particles is also important. Recent evidence shows that not all HDL particles provide the same level of protection against infections. A lower number of small and medium HDL particles is linked to higher infection risk and mortality related to infectious diseases \u003csup\u003e[15]\u003c/sup\u003e. Interestingly, a very high number of these particles is also correlated with an elevated risk of infections, although not with mortality from infectious diseases. In contrast, the number of large and very large HDL particles is not significantly associated with infection risk or related mortality\u003csup\u003e[16]\u003c/sup\u003e. Small LDL particles can increase immune activation by increasing the production of reactive oxygen species (ROS) and promoting the release of proinflammatory cytokines. This can lead to persistent inflammation, which is detrimental in chronic infections and contributes to disease progression\u003csup\u003e[17]\u003c/sup\u003e. VLDL particles derived from periodontitis patients facilitate macrophage activation through lipopolysaccharide (LPS) activity. Larger VLDL particles have greater LPS activity, leading to increased cellular cholesterol accumulation and heightened expression of proinflammatory genes such as CD14, TNF-α, MCP-1, and IL-6 in macrophages\u003csup\u003e[18]\u003c/sup\u003e. Increased levels of large VLDL particles are associated with increased levels of inflammatory markers such as C-reactive protein (CRP) and interleukin-6 (IL-6). In states of metabolic syndrome, larger VLDL particles exacerbate inflammation and contribute to cardiovascular complications\u003csup\u003e[19]\u003c/sup\u003e. Harris et al. (1993) demonstrated that chylomicrons can mitigate endotoxin-induced lethality by shunting endotoxins to hepatocytes and away from macrophages, reducing the inflammatory response\u003csup\u003e[20]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eMendelian randomization (MR) is a powerful epidemiological tool that leverages genetic variants as instrumental variables to infer causal relationships between modifiable risk factors and health outcomes. Unlike observational studies that can identify only associations, MR can provide evidence for causal relationships between exposures and health outcomes, helping to distinguish correlation from causation\u003csup\u003e[21]\u003c/sup\u003e. Genetic variants are randomly assorted during gamete formation, mimicking the randomization process in controlled trials, which reduces the influence of confounding variables that often plague observational studies\u003csup\u003e[22]\u003c/sup\u003e. MR further reduces bias from confounders that might be associated with both the exposure and the outcome, leading to more reliable causal inferences\u003csup\u003e[23]\u003c/sup\u003e. Since genetic variants are determined at conception and generally precede the development of disease, MR analysis helps to avoid reverse causation, where the disease might influence the risk factor rather than the other way around.\u003c/p\u003e \u003cp\u003eThe intricate interplay between lipid metabolism and viral infections adds complexity to their relationship with host immunity and disease progression. The current genetic studies regarding the relationship between lipoproteins and HIV viral infection progression are all observational, with insufficient cases. Despite extensive research, it remains unclear whether these associations are causal. Furthermore, the specific impacts of different lipoprotein levels and subclass particles of different sizes on viral infections and immune responses have only been partially explored. This study aims to address these gaps by conducting a comprehensive statistical analysis with two-sample MR to investigate the causal relationships between lipoprotein cholesterol levels, particle sizes, and the expression of the gp41 C34 fragment. By leveraging genetic variants as instrumental variables, we aim to disentangle the causal effects of lipid traits on HIV cell entry and provide a robust assessment of how lipid metabolism influences HIV infection and persistence.\u003c/p\u003e"},{"header":"2 Methods","content":"\u003cp\u003e2.1 Study design\u003c/p\u003e\n\u003cp\u003eWe designed a two-sample Mendelian randomization (MR) study to investigate the relationships among cholesterol levels, lipoprotein particle sizes, and gp41 C34 expression (HIV cell entry marker). Genetic IVs were selected on the basis of stringent criteria, and the analysis was conducted via MR methods (random-effect IVW) with various sensitivity analyses (MR‒Egger, weighted median, simple mode, weighted mode and leave-one-out analysis). The results were filtered on the basis of significance thresholds and outlier correction and reverse causation deletion.\u003c/p\u003e\n\u003cp\u003eFig. 1The flowchart of the study is described below.\u003c/p\u003e\n\u003cp\u003e2.2 Data Sources\u003c/p\u003e\n\u003cp\u003eC34 gp41 exposure (marker of HIV viral entry) genetic data were acquired from the Cooperative Health Research in the Region of Augsburg (KORA) cohort in Germany, which comprises 997 individuals. Within this cohort, 509,946 common autosomal single nucleotide polymorphisms (SNPs) were analyzed for their association with gp41 c34 peptide levels in blood plasma. Linear additive genetic regression models were used for the analysis, adjusting for relevant covariates to identify significant associations\u003csup\u003e[24]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe genetic instruments for 39 lipoprotein particle traits, encompassing various cholesterol levels and different sizes of lipid particles such as HDL, IDL, LDL, VLDL, and chylomicrons, were derived from genome-wide association studies (GWASs) conducted within the UK Biobank (UKB). These trait genotyping quality controls excluded individuals with sex mismatch, sex chromosome aneuploidy, and non-European descent, resulting in a sample size of 115,082. \u003csup\u003e[25]\u003c/sup\u003e.\u0026nbsp;Measurements were taken by nonfasting EDTA plasma samples. All analyses were conducted under UKB application #15825, ensuring a robust and comprehensive approach to selecting genetic instruments for studying lipid levels\u003csup\u003e[26]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003eThe exposure and outcome data show different cohorts and different countries, and we might reasonably assume that no direct participant overlap was identified.\u003c/p\u003e\n\u003cp\u003e2.2. Selection of Genetic Instrumental Variables\u003c/p\u003e\n\u003cp\u003e2.2.1 Filter exposure genetic variables\u003c/p\u003e\n\u003cp\u003eSNPs were selected on the basis of a stringent p value threshold of \u003cem\u003eP \u0026lt; 5 \u0026times; 10⁻⁸\u0026nbsp;\u003c/em\u003e to ensure strong associations with lipid traits. This ensures that only robust genetic associations are included in the analysis.\u003c/p\u003e\n\u003cp\u003e2.2.2 Linkage Disequilibrium (LD) Considerations\u003c/p\u003e\n\u003cp\u003eLinkage disequilibrium (LD) measures the nonrandom association of alleles at two or more loci. High LD means that a few tag SNPs can represent a larger genomic region, simplifying genetic analyses. In this study, LD analysis was conducted on a European population. We used the r\u003csup\u003e2\u003c/sup\u003e metric to describe allele correlation at different loci. An r\u003csup\u003e2\u003c/sup\u003e value close to 1 indicates high correlation, whereas a value close to 0 indicates low correlation. To ensure SNP independence, we implemented a clumping procedure, excluding SNPs within a 10,000 kb radius that exceeded an LD threshold of r\u003csup\u003e2\u003c/sup\u003e \u0026gt; 0.001. This minimizes redundancy and ensures the independence of IVs, enhancing the validity of our analysis by reducing bias and improving precision.\u003c/p\u003e\n\u003cp\u003e2.2.3 Outcome Data Retrieval\u003c/p\u003e\n\u003cp\u003eWe retrieved outcome data associated with the identified SNPs, ensuring that these SNPs were not significantly related to the outcome. Relevant instrumental variables were selected without using proxy SNPs when direct SNP data were unavailable.\u003c/p\u003e\n\u003cp\u003e2.2.4 Eliminating SNPs with intermediate allele frequencies and palindromic sequences\u003c/p\u003e\n\u003cp\u003eAn MAF \u0026gt; 0.03 was set to exclude extremely low-frequency variants, reducing false positives and sequencing noise\u003csup\u003e[27]\u003c/sup\u003e. Alignment of allele coding between the exposure and outcome datasets was performed to ensure consistency in the interpretation of genetic effects. Palindromic SNPs, which can be misinterpreted owing to their reverse-complement nature, were excluded to avoid potential biases. This alignment process ensures that the effect estimates are comparable and accurately reflect the genetic associations.\u003c/p\u003e\n\u003cp\u003e2.2.5 The strength of the IVs is assessed via the F statistic, which is calculated as \u003cem\u003eF\u003c/em\u003e=(\u003cem\u003eN\u003c/em\u003e\u0026minus;\u003cem\u003ek\u003c/em\u003e\u0026minus;1)/k\u0026nbsp;\u0026sdot;\u0026nbsp;R\u003csup\u003e2\u003c/sup\u003e/(1- R\u003csup\u003e2\u003c/sup\u003e) \u0026nbsp;, where \u003cem\u003eN\u003c/em\u003e is the sample size, \u003cem\u003ek\u003c/em\u003e is the number of IVs, and \u003cem\u003eR\u003c/em\u003e2 represents the proportion of variance explained by the SNPs in the exposure database. R\u003csup\u003e2\u003c/sup\u003e is computed as \u003cem\u003eR\u003c/em\u003e2=\u0026sum;{SE\u0026sup2;\u0026sdot;N/2\u0026sdot;(1\u0026minus;\u003cem\u003eMAF\u003c/em\u003e)\u0026sdot;\u003cem\u003eMAF\u003c/em\u003e\u0026sdot;\u003cem\u003e\u0026beta;\u003c/em\u003e2}, where MAF denotes the minor allele frequency, \u003cem\u003e\u0026beta;\u003c/em\u003e is the genetic effect size, and SE is the standard error. An F statistic greater than 10 is considered sufficient to provide strong evidence of the influence of a genetic variant on exposure.\u003c/p\u003e\n\u003cp\u003e2.3 Mendelian randomization analysis\u003c/p\u003e\n\u003cp\u003e2.3.1 Random-effect inverse variance weighting (random-effect IVW)\u003c/p\u003e\n\u003cp\u003eThe\u0026nbsp;inverse variance weighted (IVW) method estimates causal relationships in MR studies.\u0026nbsp;However, in our comprehensive analysis, we observed significant heterogeneity (IVW Q p value \u0026lt; 0.05) in traits such as cholesterol levels in IDL, the concentration of small HDL particles, and cholesterol levels in small HDL particles. This indicates that the assumption of homogeneity is violated, making the random-effect IVW model preferable\u003csup\u003e[28]\u003c/sup\u003e. The causal effect is calculated similarly to ordinary IVW, but it includes an additional variance component that accounts for the heterogeneity among SNPs. More robust to heterogeneity and potential pleiotropy because it allows for variation in the causal effect across SNPs.\u003c/p\u003e\n\u003cp\u003e2.3.2 Weighted Median\u003c/p\u003e\n\u003cp\u003eThe\u0026nbsp;weighted median\u0026nbsp;method provides a robust causal estimate even when up to 50% of the instruments are invalid.\u0026nbsp;The robustness of the weighted median method complements the sensitivity of the IVW method, ensuring that the final estimate is less likely to be biased by a few invalid instruments. Applying both random-effect IVW and weighted median methods provides a comprehensive view of the potential impact of pleiotropy and invalid instruments. If the estimates of both are similar, they can conclude that the findings are robust. This holistic approach enhances the credibility and reliability of causal inference and serves as a form of cross-validation\u003csup\u003e[29]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e2.3.3 MR‒Egger\u003c/p\u003e\n\u003cp\u003eMR‒Egger regression detects and adjusts for pleiotropy, providing unbiased estimates. In MR‒Egger regression, the null hypothesis states that there is no directional pleiotropy. It regresses SNP-outcome effects on SNP-exposure effects, including an intercept. The slope gives the causal estimate. If the p value associated with the intercept term is less than the chosen significance level (typically 0.05), the null hypothesis of no directional pleiotropy is rejected, which means that the genetic instruments have a direct effect on the outcome independent of the exposure.\u003csup\u003e[30]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e2.3.4 Simple mode\u003c/p\u003e\n\u003cp\u003eThe simple mode method identifies the most frequent causal estimate (mode) among the genetic variants. It involves calculating the causal estimate for each SNP and then determining the mode of these estimates. This method assumes that most of the genetic instruments are valid and that the mode represents the true causal effect. This approach is beneficial when the majority of the instruments cluster around the true effect, providing a simple yet effective estimate\u003csup\u003e[31]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e2.3.5 Weighted mode\u003c/p\u003e\n\u003cp\u003eThe weighted mode method is similar to the simple mode method but incorporates weights to increase the influence on more precise estimates. It involves calculating the causal estimate for each SNP, assigning weights on the basis of the inverse of the variance, and determining the mode of the weighted estimates. This method assumes that the valid instruments are clustered around the true causal effect, and it provides a robust estimate even when some instruments are invalid. By weighting the estimates, this method improves the precision of the causal estimate \u003csup\u003e[31]\u003c/sup\u003e.\u003c/p\u003e\n\u003cp\u003e2.3.6Leave-One-Out Analysis\u003c/p\u003e\n\u003cp\u003eTo assess the robustness of the MR findings, a LOO sensitivity analysis was conducted. LOO analysis systematically excludes one SNP at a time from the set of instrumental variables and recalculates the causal estimate to evaluate the influence of each individual SNP on the overall MR estimate. The TwoSampleMR package in R was used to perform the MR analysis and the LOO sensitivity analysis. Forest plots were generated to visualize the LOO results, showing the causal estimates and confidence intervals with each SNP excluded. The consistency of the causal estimate across different SNP exclusions indicates robustness. Significant deviations in the causal estimate upon exclusion of a specific SNP suggest potential issues such as pleiotropy or violations of MR assumptions\u003csup\u003e[31]\u003c/sup\u003e.\u003csup\u003e[32]\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.4 Application of the False Discovery Rate (FDR) in Multiple Testing\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo account for multiple testing analyses, we applied Benjamini‒Hochberg (BH) correction to p values from the IVW method in our MR analysis to estimate causal relationships between the C34 gp41 HIV fragment and lipid traits, identifying significant and suggestive associations while controlling the false discovery rate (FDR) . This method balances the risk of Type I errors (false positives) and Type II errors (false negatives). This makes it more powerful, particularly for large datasets with many comparisons. The steps include ranking p values from MR analysis, calculating critical values (k/m * \u0026alpha;, where k is the rank, m is the total number of tests, and \u0026alpha; is 0.05), and comparing each p value to its critical value. P \u0026lt; FDR \u0026lt; 0.05 indicates a significant association, P \u0026lt; 0.05 \u0026lt; FDR indicates a suggestive association, and 0.05 \u0026lt; P \u0026lt; FDR denotes no association.\u003c/p\u003e"},{"header":"3 Results","content":" \u003cp\u003eFigure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e The forest plot illustrates the influence of lipoprotein traits, specifically cholesterol levels and particle sizes, on gp41 C34 expression using different Mendelian randomization methods. Protective factors, which are causally associated with a decrease in gp41 C34 expression, are indicated to the left of the red dashed line at zero, while risk factors are to the right.\u003c/p\u003e \u003cp\u003eLipoprotein particles are highly heterogeneous in structure, composition, metabolism, and function. Therefore, distinct lipoprotein subpopulations might exert differential effects on virus infection. To examine how cholesterol levels and different sizes of different lipoprotein particles impact HIV cell entry ability, our primary analysis utilized the Mendelian randomization (MR) approach.\u003c/p\u003e \u003cp\u003eSignificant causal relationships (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 and FDR\u0026thinsp;\u0026lt;\u0026thinsp;0.05):\u003c/p\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Elevated HDL cholesterol levels are significantly associated with reduced gp41 C34 expression\u003c/h2\u003e \u003cp\u003eOur primary MR analysis demonstrated that elevated HDL cholesterol levels are significantly associated with reduced gp41 C34 expression (random-effect IVW: β = -0.61, SE\u0026thinsp;=\u0026thinsp;0.186, p\u0026thinsp;=\u0026thinsp;1.25\u0026ndash;4e, FDR\u0026thinsp;=\u0026thinsp;0.004), suggesting a protective role of HDL cholesterol in HIV infection. This finding was corroborated by the weighted median analysis, which indicated a consistent causal effect (β = -0.565, 95% CI: -0.976 to -0.154, p\u0026thinsp;=\u0026thinsp;0.007). However, the MR‒Egger analysis did not reach statistical significance in that there was no evidence of directional pleiotropy. The simple mode estimate was not significant (β = -0.554, 95% CI: -1.721\u0026ndash;0.614, p\u0026thinsp;=\u0026thinsp;0.372), but the weighted mode analysis confirmed the protective effect (β = -0.554, 95% CI: -0.979\u0026ndash;0.128, p\u0026thinsp;=\u0026thinsp;0.027).\u003c/p\u003e \u003cp\u003eThe Q statistic was 8.026 with 11 degrees of freedom (p\u0026thinsp;=\u0026thinsp;0.711), revealing no statistically significant heterogeneity. MR-PRESSO tests did not find outliers, further supporting the robustness of these findings. The absence of significant shifts in causal estimates upon the exclusion of individual SNPs suggests that heterogeneity and pleiotropy were minimal in our analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.2 HDL Particle Concentration and gp41 C34 Expression\u003c/h2\u003e \u003cp\u003eAn inverse association between HDL particle concentration and gp41 C34 expression was also observed. The IVW method indicated that higher concentrations of HDL particles are associated with lower gp41 C34 expression (β = -0.549, SE\u0026thinsp;=\u0026thinsp;0.202, p\u0026thinsp;=\u0026thinsp;0.007, FDR\u0026thinsp;=\u0026thinsp;0.032199). This was corroborated by the weighted median analysis (β = -0.699, 95% CI: -1.212 to -0.186, p\u0026thinsp;=\u0026thinsp;0.008). MR‒Egger regression provided a causal estimate (β = -0.341, 95% CI: -1.103\u0026ndash;0.421, p\u0026thinsp;=\u0026thinsp;0.399), indicating that pleiotropy lacks statistical significance. The simple mode estimate was not significant (β = -0.346, 95% CI: -1.642\u0026ndash;0.949, p\u0026thinsp;=\u0026thinsp;0.583), whereas the weighted mode confirmed the protective effect (β = -0.687, 95% CI: -1.275\u0026ndash;0.099, p\u0026thinsp;=\u0026thinsp;0.041). The Q statistic was 10.741, with 11 degrees of freedom (p\u0026thinsp;=\u0026thinsp;0.465), indicating that heterogeneity was not statistically significant. No SNP was found to have a significant effect on the results of the leave-one-out analysis.\u003c/p\u003e \u003cp\u003e3.3 Increased cholesterol levels in large HDL particles were significantly associated with reduced gp41 C34 expression. The IVW method revealed a significant inverse relationship (β = -0.477, SE\u0026thinsp;=\u0026thinsp;0.143, p\u0026thinsp;=\u0026thinsp;0.001, FDR\u0026thinsp;=\u0026thinsp;0.032). This relationship was further supported by the weighted median (β = -0.505, 95% CI: -0.901\u0026ndash;0.109, p\u0026thinsp;=\u0026thinsp;0.012) and weighted mode (β = -0.501, 95% CI: -0.938\u0026ndash;0.064, p\u0026thinsp;=\u0026thinsp;0.026) analyses. The MR‒Egger regression (β = -0.345, 95% CI: -0.886 to 0.196, p\u0026thinsp;=\u0026thinsp;0.208) and simple mode (β = -0.716, 95% CI: -2.111 to 0.679, p\u0026thinsp;=\u0026thinsp;0.311) results were not significant, implying that pleiotropy does not achieve statistical significance. The Q statistic was 8.143, with 11 degrees of freedom (p\u0026thinsp;=\u0026thinsp;0.700), implying a lack of heterogeneity. Leave-one-out analysis revealed no SNPs that may disproportionately impact the results.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Significant Inverse Causal Relationships between High HDL Particle Concentrations and gp41 C34 Expression\u003c/h2\u003e \u003cp\u003eOur Mendelian randomization analysis revealed a significant inverse causal relationship between the concentration of large HDL particles and gp41 C34 expression. The random-effect IVW method demonstrated that higher concentrations of large HDL particles are associated with reduced expression of gp41 C34 (β = -0.46, SE\u0026thinsp;=\u0026thinsp;0.16, p\u0026thinsp;=\u0026thinsp;0.004, FDR\u0026thinsp;=\u0026thinsp;0.03). This finding suggests that specific sizes of HDL particles may influence mechanisms of HIV viral entry.\u003c/p\u003e \u003cp\u003eThe weighted median method supported the IVW findings, showing a consistent causal effect (β = -0.409, SE\u0026thinsp;=\u0026thinsp;0.188, p\u0026thinsp;=\u0026thinsp;0.03). MR Egger regression, which adjusts for directional pleiotropy, provided a causal estimate of β = -0.327 (SE\u0026thinsp;=\u0026thinsp;0.233, p\u0026thinsp;=\u0026thinsp;0.194), suggesting that the effects of pleiotropy are not statistically relevant. Although the simple mode estimate was not significant (β = -0.655, SE\u0026thinsp;=\u0026thinsp;0.501, p\u0026thinsp;=\u0026thinsp;0.221), the weighted mode estimate confirmed the protective effect of large HDL particles on gp41 C34 expression (β = -0.404, SE\u0026thinsp;=\u0026thinsp;0.189, p\u0026thinsp;=\u0026thinsp;0.059).\u003c/p\u003e \u003cp\u003eThe Q statistic for IVW was 9.62 with 10 degrees of freedom (p\u0026thinsp;=\u0026thinsp;0.474), indicating that there was no significant heterogeneity across the analyses. These findings consistently suggest that large HDL particles may play a protective role by reducing gp41 C34 expression, potentially influencing HIV entry mechanisms. The results remained consistent across the leave‒one-out analysis, with no single SNP exerting an undue influence.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e3.5 \u003cb\u003eMedium HDL Particles and gp41 C34 Expression\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eHigher cholesterol levels in medium HDL particles were linked to lower gp41 C34 expression. The IVW method revealed a significant inverse relationship (random-effect IVW:β = -0.42, SE\u0026thinsp;=\u0026thinsp;0.159, p\u0026thinsp;=\u0026thinsp;0.009, FDR\u0026thinsp;=\u0026thinsp;0.035), which was confirmed by weighted median analysis (β = -0.409, 95% CI: -0.778\u0026ndash; -0.039, p\u0026thinsp;=\u0026thinsp;0.03). MR Egger regression provided a nonsignificant causal estimate (β = -0.519, 95% CI: -1.132 to 0.094, p\u0026thinsp;=\u0026thinsp;0.098), indicating no pleiotropy. The simple mode estimate was not significant (β = -0.624, 95% CI: -1.708 to 0.461, p\u0026thinsp;=\u0026thinsp;0.279), whereas the weighted mode confirmed the protective effect (β = -0.602, 95% CI: -1.054 to -0.149, p\u0026thinsp;=\u0026thinsp;0.009). The Q statistic was 10.549 with 13 degrees of freedom (p\u0026thinsp;=\u0026thinsp;0.649), indicating that no significant heterogeneity was detected. The leave-one-out analysis confirmed that the findings were robust and not driven by any specific SNP.\u003c/p\u003e \u003cp\u003e3.6 Higher concentrations of medium HDL particles were also significantly associated with reduced gp41 C34 expression (random-effect IVW: β = -0.473, SE\u0026thinsp;=\u0026thinsp;0.166, p\u0026thinsp;=\u0026thinsp;0.005, FDR\u0026thinsp;=\u0026thinsp;0.028). This was supported by the weighted median (β = -0.617, 95% CI: -1.039 to -0.195, p\u0026thinsp;=\u0026thinsp;0.004) and weighted mode analyses (β = -0.683, 95% CI: -1.159 to -0.207, p\u0026thinsp;=\u0026thinsp;0.006). The results of MR Egger regression (β = -0.517, 95% CI: -1.399\u0026ndash;0.366, p\u0026thinsp;=\u0026thinsp;0.254) and simple mode (β = -0.521, 95% CI: -1.433\u0026ndash;0.392, p\u0026thinsp;=\u0026thinsp;0.254) were not significant, indicating minimal pleiotropy. The Q statistic was 8.678 with 10 degrees of freedom (p\u0026thinsp;=\u0026thinsp;0.563), suggesting no significant evidence of heterogeneity. The leave-one-out analysis revealed that the exclusion of any individual SNP did not significantly alter the overall results.\u003c/p\u003e \u003cp\u003eHowever, owing to their bidirectional causal relationships, the concentration of VLDL particles, concentration of very large HDL particles, and total concentration of lipoprotein particles were excluded from further consideration. VLDL cholesterol levels were excluded because the 95% CI of the weighted median was not consistent with the random-effects IVW results.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e3.7 \u003cb\u003eLipoprotein particle traits have no causal relationship with C34 GP41\u003c/b\u003e\u003c/h2\u003e \u003cp\u003eThe cholesterol level and concentration of small HDL particles have no causal relationship with C34 gp41 expression. In addition, the results also indicate that cholesterol levels or sizes of IDL, LDL, and VLDL do not exhibit a significant causal association with gp41 C34 expression. Consequently, these specific lipoprotein particles do not genetically influence the expression of gp41 C34 and are unlikely to play a direct role in modulating the HIV viral entry process through the gp41 C34 pathway.\u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Main results\u003c/h2\u003e \u003cp\u003eOur study utilized Mendelian randomization (MR) to investigate the causal relationships between lipoprotein cholesterol levels and their subtype concentrations and the expression of gp41 C34, a protein critical for HIV cell entry. These results strongly indicate that elevated HDL cholesterol levels, as well as higher concentrations of medium and large HDL particles, are significantly associated with reduced gp41 C34 expression, suggesting a protective role of HDL against HIV infection. In contrast, cholesterol levels or the sizes of IDL, LDL, and VLDL do not clearly or significantly affect gp41 C34 expression, underscoring the unique protective function of HDL cholesterol and its specific subpopulations in modulating HIV cell entry mechanisms.\u003c/p\u003e \u003cp\u003eThese results align with those of previous studies that demonstrated the inhibitory effects of HDL particles on HIV-1 fusion and entry, supporting the hypothesis that HDL particles play a crucial role in controlling HIV infection. For example, Kelesidis et al. (2016) reported that oxidized HDL particles are associated with reduced inflammation and immune activation in HIV-1 infection, suggesting a protective role of HDL \u003csup\u003e[33]\u003c/sup\u003e. Recent research has highlighted the connection between lower HDL cholesterol levels and increased inflammatory markers as well as disease progression in people living with HIV\u003csup\u003e[34]\u003c/sup\u003e. Unexpectedly, this study suggests that insufficient levels of small HDL particles may also play a role in the elevated HIV viral load in infected individuals. This hypothesis is supported by other research \u003csup\u003e[34\u0026ndash;36]\u003c/sup\u003e, although it is somewhat divergent from our own results. Further investigation is needed to elucidate this potential association.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003e4.2Possible mechanism\u003c/h2\u003e \u003cp\u003eHDL-C is integral for reversing cholesterol transport, moving cholesterol from peripheral tissues back to the liver, which underpins several protective mechanisms against HIV infection. One key mechanism involves the role of HDL-C in cholesterol efflux from cells via transporters such as ABCA1 and ABCG1, lowering cholesterol levels within plasma membranes and disrupting lipid rafts. This disruption impairs the structural integrity and function of lipid rafts, which are essential for HIV entry\u003csup\u003e[37]\u003c/sup\u003e. Lipid rafts are cholesterol-rich microdomains essential for HIV entry, as they support the structural integrity and function of membrane proteins, including gp41, an HIV-1 envelope protein crucial for membrane fusion \u003csup\u003e[38]\u003c/sup\u003e. By disrupting lipid rafts, HDL-C reduces the efficiency of HIV binding and fusion with host cell membranes, thereby impeding viral entry \u003csup\u003e[39]\u003c/sup\u003e. In addition to disrupting lipid rafts, HDL-C possesses significant anti-inflammatory and antioxidant properties. HDL-C inhibits the expression of adhesion molecules such as V-CAM, I-CAM, and E-selectin and reduces the activation of the inflammasome pathway, which involves caspase-1 and the release of IL-1β, a cytokine linked to the death of CD4\u0026thinsp;+\u0026thinsp;T cells, which are primary targets in HIV infection \u003csup\u003e[40, 41]\u003c/sup\u003e. Furthermore, HDL-C prevents the oxidation of low-density lipoprotein (LDL), which, when oxidized (oxLDL), can induce the production of proinflammatory cytokines such as IL-1β via inflammasomes\u003csup\u003e[42]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eHDL-C also plays a crucial role in modulating immune responses. It enhances the activity of antiviral proteins such as APOBEC3G, a cytidine deaminase, and induces hypermutations in HIV-1 DNA, which leads to defective viral particles \u003csup\u003e[43]\u003c/sup\u003e. Higher APOBEC3G activity is correlated with higher CD4 counts and slower disease progression in HIV-infected individuals. Additionally, HDL-C inhibits the inflammatory response triggered by the complement system in response to cholesterol crystals, contributing to the regulation of inflammation in HIV-infected individuals. Moreover, HDL-C modulates cholesterol distribution in T-cell membranes, which is vital for the organization and function of T-cell receptors (TCRs) \u003csup\u003e[44]\u003c/sup\u003e. Proper functioning of TCRs is essential for T-cell activation and proliferation in the immune response against HIV. By regulating cholesterol transport and reducing the membrane cholesterol content, HDL-C impairs the ability of HIV to fuse with host cells and creates an unfavorable environment for HIV replication.\u003c/p\u003e \u003cp\u003eMedium and large HDL particles are likely more effective than small HDL particles in lowering C34 gp41 expression and reducing HIV cell entry through several mechanisms. First, medium- and large HDL particles are rich in cholesterol and have a greater capacity for cholesterol efflux. This capacity is crucial for removing cholesterol from the plasma membrane and disrupting lipid rafts, which are cholesterol-rich microdomains on host cell membranes that facilitate HIV entry. By disrupting these lipid rafts, medium and large HDL particles inhibit the formation of HIV entry points, thereby decreasing the efficiency of viral entry.\u003c/p\u003e \u003cp\u003eAdditionally, medium and large HDL particles have a lipid composition rich in phospholipids and sphingolipids, making them more stable than small HDL particles in vivo. Phospholipids, with their flexible bilayer structure, provide membrane fluidity, enabling membrane proteins to function properly and perform various cellular tasks\u003csup\u003e[45]\u003c/sup\u003e. Sphingolipids have a more rigid structure that stabilizes the membrane, especially in lipid rafts, which are crucial for cell signaling\u003csup\u003e[44]\u003c/sup\u003e. The combination of fluidity from phospholipids and rigidity from sphingolipids ensures membrane integrity and functionality, allowing medium and large HDL particles to withstand physical and chemical stresses. This stabilization is crucial in preventing HIV fusion with host cell membranes, thereby reducing the efficiency of viral entry \u003csup\u003e[46]\u003c/sup\u003e. These particles also carry a diverse array of apolipoproteins and other functional proteins, such as apolipoprotein A-I (ApoA-I), which have anti-inflammatory, antioxidant, and immune-modulatory properties\u003csup\u003e[47]\u003c/sup\u003e. This allows medium and large HDL particles to bind effectively and sequester C34 gp41 HIV, preventing the conformational changes necessary for viral fusion and entry into host cells\u003csup\u003e[48]\u003c/sup\u003e. In addition, this high apolipoprotein content also contributes to the structural stability of these particles, making them more stable and less susceptible to dissociation than smaller HDL particles are, ensuring prolonged circulation and effective protective functions in the bloodstream\u003csup\u003e[49]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eLarge HDL particles are especially critical in this protective mechanism because they transport anti-inflammatory molecules such as sphingosine-1-phosphate (S1P), which possesses significant anti-inflammatory and endothelial-protective properties\u003csup\u003e[50]\u003c/sup\u003e. Low HDL-C and decreased numbers of large HDL particles are related to increased mitochondrial oxidative stress, as measured by PBMC 8-oxo-dG\u003csup\u003e[35]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eFurthermore, large HDL particles play a critical role in mitigating oxidative stress. They are equipped with antioxidant enzymes such as paraoxonase 1 (PON1) and glutathione peroxidase, which neutralize reactive oxygen species (ROS). This antioxidative function protects LDL particles and endothelial cells from oxidative stress, thereby reducing the risk of oxidative damage and preserving endothelial cell function and integrity. This preservation is crucial in preventing HIV from exploiting weakened cellular defenses to gain entry \u003csup\u003e[50]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn contrast, small HDL particles, with lower cholesterol content, reduced efflux capacity, less optimal lipid composition, and reduced functional protein cargo, are less effective at disrupting lipid rafts, stabilizing cellular membranes, and inhibiting viral entry mechanisms. These deficiencies collectively render small HDL particles less effective at reducing HIV cell entry and lowering gp41 expression than medium and large HDL particles.\u003c/p\u003e \u003cp\u003eIn the context of HIV infection, our findings indicate that cholesterol levels or sizes of LDL, IDL, and VLDL do not exhibit a significant causal association with gp41 C34 expression. This lack of association can be explained by several underlying factors. HDL plays a unique role in maintaining cell membrane fluidity and microdomain structures, which can influence the integration and expression of gp41 C34. In contrast, LDL, IDL, and VLDL lack these properties. Additionally, HDL is involved in reverse cholesterol transport, a specific metabolic process that might impact gp41 C34 expression. LDL, IDL, and VLDL do not participate in this process, which may explain their lack of significant association with gp41 C34 expression.4.6 Limitations\u003c/p\u003e \u003cp\u003eOur study's strengths include the application of the Mendelian randomization (MR) approach, which mitigates confounding and reverse causation, and comprehensive sensitivity analyses that reinforce the robustness of our findings\u003csup\u003e[51]\u003c/sup\u003e. However, certain limitations must be acknowledged. The genetic instruments used in MR studies may not capture all the variability in lipid traits, and potential pleiotropic effects, although minimal, cannot be entirely ruled out. Additionally, our findings are based on genetic data predominantly from European populations, which may limit their generalizability to other ethnic groups\u003csup\u003e[52]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003e \u003cb\u003eValues and Future Directions\u003c/b\u003e \u003c/p\u003e \u003cp\u003eFuture research should focus on elucidating the specific biological mechanisms by which HDL cholesterol and its particles influence HIV infection. Experimental studies could further investigate these interactions, particularly how HDL particle subtypes modulate the function of HIV envelope proteins such as gp41 and their impact on viral entry\u003csup\u003e[53, 54]\u003c/sup\u003e. Additionally, exploring the therapeutic potential of HDL-raising interventions or treatments that increase HDL particle size could provide valuable clinical insights into reducing the HIV viral load and improving immune function. Further studies should also examine whether these findings can be replicated in diverse populations and investigate the potential role of other lipid fractions in modulating HIV infection.\u003c/p\u003e \u003cp\u003eThese directions could pave the way for innovative therapeutic strategies to increase host resistance to HIV, leveraging the multifaceted roles of HDL and other lipoproteins in immune modulation and viral inhibition. By integrating genetic, molecular, and clinical research, we can develop a more comprehensive understanding of the impact of lipid metabolism on HIV infection and progression, ultimately contributing to the global effort to combat this persistent threat.\u003c/p\u003e \u003c/div\u003e"},{"header":"Abbreviations","content":"\u003cp\u003e\u003cstrong\u003eHIV: Human immunodeficiency virus\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSNP: Single nucleotide polymorphism\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIVs: Instrumental variables\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMAF: Minor allele frequency\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026beta;: Genetic effect size\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSE: Standard error\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCI: Confidence interval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIVW: Inverse variance weighted\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFDR: False discovery rate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHDL: High-density lipoprotein\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLDL: Low-density lipoprotein\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVLDL: Very low-density lipoprotein\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIDL: Intermediate-density lipoprotein\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLOO: leave-one-out\u003c/strong\u003e\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe express our gratitude to the participants and investigators of the UK Biobank and The KORA-Study Group for providing publicly \u0026nbsp;available \u0026nbsp;GWAS \u0026nbsp;results. Thanks to the IEU OpenGWAS platform for summary-level providing the sorted summary-level genetic data.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics declarations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthics approval and consent to participate\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNot applicable.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eConsent for publication\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the datasets analyzed in this study are publicly available summary statistics from IEU OpenGWAS (https://gwas.mrcieu.ac.uk/).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLOUIS J M, BABER J L, CLORE G M. 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Oxidative Medicine and Cellular Longevity, 2021, 2021: 1-13.\u003c/li\u003e\n\u003cli\u003eZHAO Q, CHEN Y, WANG J, et al. Powerful three-sample genome-wide design and robust statistical inference in summary-data Mendelian randomization [J]. International Journal of Epidemiology, 2019, 48(5): 1478-92.\u003c/li\u003e\n\u003cli\u003ePIRIM D, RADWAN Z H, WANG X, et al. Apolipoprotein E-C1-C4-C2 gene cluster region and interindividual variation in plasma lipoprotein levels: a comprehensive genetic association study in two ethnic groups [J]. PLOS ONE, 2019, 14(3): e0214060.\u003c/li\u003e\n\u003cli\u003eNIETO‐GARAI J A, ARBOLEYA A, OTAEGI S, et al. Cholesterol in the Viral Membrane is a Molecular Switch Governing HIV‐1 Env Clustering [J]. Advanced Science, 2021, 8(3): 2003468.\u003c/li\u003e\n\u003cli\u003eBRYAN A M, DEL POETA M. Sphingosine-1-phosphate receptors and innate immunity [J]. Cellular Microbiology, 2018, 20(5): e12836.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"gp41 C34, HIV cell entry, Lipoprotein cholesterol, Particle sizes, Mendelian randomization","lastPublishedDoi":"10.21203/rs.3.rs-4825185/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4825185/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e The gp41 C34 peptide, which is part of the HIV envelope glycoprotein, is crucial for HIV entry into host cells because it facilitates membrane fusion and serves as a biomarker for viral replication. Lipoproteins, including HDL, LDL, IDL, VLDL, and chylomicrons, affect HIV infection via their cholesterol levels and particle sizes, but their causal relationships with HIV remain unclear.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e Utilizing the Mendelian randomization (MR) approach to infer causality, this study leverages genetic data from the UK Biobank (115,082 individuals) and the KORA cohort (997 individuals) to explore the causal relationships between 39 lipoprotein traits (cholesterol levels and subtype concentrations of different particle sizes) and gp41 C34 expression. The primary MR method employed was the random-effect inverse variance weighted (IVW) approach. To ensure robust and reliable causal inference, multiple sensitivity analyses, including weighted median, MR‒Egger regression, simple mode, weighted mode, and leave-one-out analyses, were conducted.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e: Elevated HDL cholesterol levels were significantly associated with reduced gp41 C34 expression (IVW: β = -0.61, SE = 0.186, p = 1.25e-4, FDR = 0.004), suggesting a protective role of HDL cholesterol in HIV infection. Higher HDL particle concentrations were also inversely associated with gp41 C34 expression (IVW: β = -0.549, SE = 0.202, p = 0.007, FDR = 0.032). Increased cholesterol levels in large HDL particles were significantly inversely related to gp41 C34 expression (IVW: β = -0.46, SE = 0.16, p = 0.004, FDR = 0.03). Similarly, higher concentrations of medium HDL particles were linked to lower gp41 C34 expression (IVW: β = -0.473, SE = 0.166, p = 0.005, FDR = 0.028).\u003c/p\u003e\n\u003cp\u003eNo significant causal relationships were found between gp41 C34 expression and the cholesterol levels or sizes of IDL, LDL, or VLDL particles or chylomicrons. Consequently, these lipoprotein particles are unlikely to influence gp41 C34 expression and HIV cell entry.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e: HDL cholesterol and HDL particle concentrations, particularly large and medium HDL particles, play a protective role against HIV cell entry by reducing gp41 C34 expression. Other lipoprotein particles do not show significant causal relationships, indicating that specific lipid traits modulate HIV entry mechanisms. These findings enhance our understanding of the influence of lipoprotein traits on HIV infection and persistence.\u003c/p\u003e","manuscriptTitle":"The Effect of Lipoprotein Cholesterol Levels and Particle Sizes on HIV Cell Entry via gp41 C34: Insights from Mendelian Randomization Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-29 06:53:34","doi":"10.21203/rs.3.rs-4825185/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e8f3467e-d9e9-44dd-838a-53f4fc2aa763","owner":[],"postedDate":"August 29th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-12-23T14:54:12+00:00","versionOfRecord":[],"versionCreatedAt":"2024-08-29 06:53:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4825185","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4825185","identity":"rs-4825185","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}