Association between pan-immune-inflammation value and hyperlipidemia in the United States population

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Methods: The authors selected the relevant data from National Health and Nutrition Examination Survey (NHANES) for a detailed cross-sectional study. The independent variable used the logarithmic form of PIV-log10 (PIV). The definition of dependent variable-hyperlipidemiawas based on the National Cholesterol Education Program standards. Both variables were calculated from measured laboratory data. Weighted multivariate logistic regression analyses and restricted cubic splines (RCS) were conducted to analyze the association between PIV and hyperlipidemia. Stratified analyses were used to identify potential associations between PIV and hyperlipidemia with other covariates. The study also constructed the receiver operating characteristic (ROC) curve to assess the predictive value for hyperlipidemia of PIV compared to systemic immune-inflammation index (SII). Results: In the study, 7,715 participants from NHANES were included. After adjusting for all confounders, PIV and hyperglycemia had an significantly positive association (OR (95%CI): 1.55 (1.17-2.06); P = 0.002). Compared to participants with lowest quartile (Q1) of PIV, participants with the highest quartile (Q4) had a significantly higher risk of hyperlipidemia (OR (95%CI): 1.47 (1.21-1.79); P < 0.001). The RCS curve showed a linear relationship between PIV and hyperlipidemia ( P -nonlinear = 0.0633, P -overall < 0.001). The ROC curve found that compared with SII, PIV had a slightly higher predictive value (0.547 vs 0.542, P = 0.267). Conclusion: This national cross-sectional study discovered that PIV had a significantly positive relationship with hyperlipidemia, particularly in young overweight individuals. More prospective studies are needed to verify whether the PIV is a more reliable and effective index for assessing the risk of hyperlipidemia. pan-immune-inflammation value hyperlipidemia NHANES inflammation cross-sectional study Figures Figure 1 Figure 2 Figure 3 Figure 4 1 Introduction Hyperlipidemia was a condition that is characterized by abnormally elevated blood lipids, which could be attributed to a variety of genetic predispositions or acquired health conditions. In adults, hyperlipidemia was recognized to contribute to the development of cardiovascular disease (CVD) [ 1 ]. The available evidence suggested an association between hyperlipidemia which was characterized by the increase of triglycerides (TG) and low-density lipoprotein cholesterol (LDL-C) in plasma, and the prevalence of coronary artery disease [ 2 ]. Not only did it promote atherosclerosis of blood vessels, hyperlipidemia could also act directly on the heart leading to ischemia-reperfusion injury [ 3 ]. Moreover, the CVD accounted for the highest percentage of all causes of death among adults in the United States, and the risk of developing CVD in people with hyperlipidemia was about twice than those without dyslipidemia [ 4 ]. The link between atherosclerosis and hyperlipidemia, as well as the role of persistent low-grade inflammation and lipid abnormalities, has prompted research into the potential association between elevated lipid levels and inflammatory states [ 5 ]. The cholesterol has been proved to directly induce inflammation by the activation of the NLRP3 inflammasomes, possibly contributing to the initiation and exacerbation of local and systemic immune inflammatory responses [ 6 ]. The pan-immune-inflammation value (PIV) represented a novel biomarker for predicting inflammatory status, including four cell types in peripheral blood [ 7 ]. The definition of it relied on the counts of neutrophils, monocytes, lymphocytes, and platelets [ 8 ]. In 2020, it was initially used in metastatic colorectal cancer patients, showing a good ability to assess the prognosis of them and it even exceeded that of previously established markers related to inflammation [ 9 ]. PIV was a potent and independent predictor of coronary slow flow, superior to other inflammatory markers [ 10 ]. In a cross-sectional study, elevated PIV was associated with an increased risk of all-cause mortality (1.37 (1.20–1.55); P < 0.001) and cardiovascular mortality (1.62 (1.22–2.15); P < 0.001) in hypertensive patients [ 11 ]. The systemic immune-inflammation index (SII) was applied to the assessment of the risk of hyperlipidemia previously, but the correlation between them was relatively small (Model 2: OR (95%CI): 1.03 (1.01–1.05); Model 3: OR (95%CI): 1.02 (1.00-1.04)) [ 12 ]. The association between novel composite biomarker PIV and hyperlipidemia has not been reported. Therefore, the study is aimed to explore the association between PIV and hyperlipidemia and answer whether PIV can be used to assess the risk of hyperlipidemia more effectively. 2 Methods 2.1 Data source and participants The NHANES, administered by the National Center for Health Statistics (NCHS), represents a biennial nationwide survey dedicated to examining the health and nutritional status of citizens in the United States. The purpose of this program is to understand as fully as possible the contemporary disease patterns and better help the development of public health services and the optimization of public health policies. The NHANES data is open to the public and able to be freely downloaded via the official website: https://www.cdc.gov/nchs/nhanes/index.htm [ 13 ]. The authors initially included statistics of 59,842 participants of the NHANES (2007–2018) in this study. Data screening for inclusion and exclusion followed the prescribed procedures: (1) participants without PIV data (N = 10,705); (2) participants without lipid data (N = 30,664); (3) participants who had extreme data and missing covariate data (N = 10,758). A total of 7,715 participants were ultimately selected for subsequent analyses after manual data filtration. The complete flow chart of participant being included and excluded was presented in Fig. 1 . 2.2 Assessment of Hyperlipidemia Hyperlipidemia was defined in accordance with the guidelines developed by the National Cholesterol Education Program Adult Treatment Panel III (NCEP-ATP3). Specifically, hyperlipidemia was defined as a total cholesterol (TC) level of 200 mg/dL or higher, TG of 150 mg/dL or higher, high-density lipoprotein cholesterol (HDL-C) less than 40 mg/dL for males and 50 mg/dL for females, or LDL-C of 130 mg/dL or higher. Furthermore, subjects who reported taking lipid-lowering drugs were also defined as having hyperlipidemia [ 12 ]. 2.3 Assessment of PIV The formula for calculating PIV was as follows: neutrophil count × platelet count × monocyte count / lymphocyte count. The numeric expression of cell counts was indicated as ×1000 cells/µL [ 14 ]. Because it was a study based on data of multiple periods, the measurements of target cells in the blood were operated with different instruments in the NHANES mobile examination centers. 2.4 Covariates In order to reduce the influences of confounders on the final results, covariates with known or potential associations with hyperlipidemia were selected in the analysis. Demographic variables consisted of gender, race, age, education, body mass index (BMI), poverty-to-income ratio (PIR), smoking, and alcohol use. Hypertension and diabetes were included as medical conditions. Age was stratified into three groups as follows: 20–39, 40–59, and > 59 years. Five types of races were divided: Mexican American, non-Hispanic black, non-Hispanic white, other Hispanic and other races. Education levels were classified into three types: above high school, high school or GED, and less than high school. The economic situation was based on the PIR, which was categorized into three levels: 3.5. The 30 kg/㎡BMI degrees were also established. The definition of smoking was having smoked 100 or more cigarettes over the lifetime. The definition of alcohol use was the consumption of at least 12 cups of alcoholic beverages in the last 12 months or had a drink of any kind of alcohol over the lifetime. Hypertension was diagnosed as an average SBP ≥ 140 mmHg, or DBP ≥ 90 mmHg based on at least three standard consecutive seated measurements. The definition for diabetes consisted of fasting serum glucose ≥ 126 mg/dL, self-reported diabetes or any insulin use, and glycated hemoglobin ≥ 6.5%. 2.5 Statistical analyses All statistical analyses took into account the complex, multistage probabilistic sampling design of NHANES through the utilization of appropriate sampling weights. The R software version 4.3.3 (R foundation) was operated for statistical analyses of this study. In the baseline, weighted means and standard deviations (SD) or interquartile ranges (IQR) were presented for continuous variables, while categorical variables were presented as weighted proportions. The comparative analyses of weighted t-tests and Rao-Scott chi-square tests were carried out to assess the baseline clinical characteristics of each group of patients. The PIV performed a log10 transformation before regression analysis due to its right-skewed nature of the distribution. Weighted logistic regressions were carried out to explore the association between PIV and hyperlipidemia. The odd ratio (OR) and 95% confidence interval (CI) were calculated for each one-unit increase in PIV as well as for each PIV quartile. The study constructed three regression models: Model 1, adjusted for non-covariates; Model 2, adjusted for age, gender, race; and Model 3, adjusted for all covariates (added BMI, PIR, education, smoking, alcohol use, medical conditions to Model 2). Additionally, the restricted cubic splines (RCS) were performed to assess the nonlinear relationships between PIV and hyperlipidemia. Stratified analyses for age, gender, BMI, education level, PIR, hypertension, diabetes, smoking, and alcohol use, were conducted to explore the potential heterogeneity in different subgroups. The receiver operating characteristic (ROC) curve analysis was applied to compare the predictive value of PIV and SII for hyperlipidemia. Only bilateral P -value of < 0.05 for all statistical tests were considered significant. 3 Results 3.1 Statistics of baseline There were all baseline characteristics presented in Table 1 of 7,715 individuals categorized by whether they had hyperlipidemia or not, 48.25% of whom were male. And there were 70.2% of participants having hyperlipidemia. In all variables, age, race, education level, BMI, smoking, hypertension, diabetes, and PIV had significant associations with hyperlipidemia (with or without) ( P < 0.001). The characteristics of people who were susceptible to hyperlipidemia were as follows: non-Hispanic white, middle-aged (40–59 years), above high school, 25 ≤ BMI ≤ 30 kg/㎡, non-diabetic, non-hypertensive, nonsmokers, as well as having higher PIV. However, for the three covariates of gender, PIR, and alcohol use, their associations with lipid status were not statistically significant ( P > 0.05). Table 1 Clinical characteristics of participants by groups whether had hyperlipidemia or not. Characteristics Hyperlipidemia P value Level Overall No Yes n 129345805.2 (100.0) 38594494.9 (29.8) 90751310.3 (70.2) Gender (%) 0.676 Male 66336938.6 (51.3) 19983471.8 (51.8) 46353466.8 (51.1) Female 63008866.6 (48.7) 18611023.0 (48.2) 44397843.5 (48.9) Race (%) < 0.001 Mexican American 10801263.9 (8.4) 3218968.4 (8.3) 7582295.5 (8.4) Other Hispanic 7197721.0 (5.6) 2328087.0 (6.0) 4869634.0 (5.4) Non-Hispanic White 89706135.6 (69.4) 25542297.8 (66.2) 64163837.8 (70.7) Non-Hispanic Black 12610752.1 (9.7) 4776419.6 (12.4) 7834332.5 (8.6) Other Races 9029932.6 (7.0) 2728722.0 (7.1) 6301210.6 (6.9) Education level (%) < 0.001 Less than high school 19866317.4 (15.4) 4873646.3 (12.6) 14992671.1 (16.5) High school or GED 27684145.5 (21.4) 7710050.4 (20.0) 19974095.1 (22.0) Above high school 81795342.3 (63.2) 26010798.2 (67.4) 55784544.1 (61.5) Age (years, %) < 0.001 20–39 49827024.5 (38.5) 22632924.2 (58.6) 27194100.3 (30.0) 40–59 50414266.0 (39.0) 11055196.8 (28.6) 39359069.2 (43.4) ≥ 60 29104514.7 (22.5) 4906373.9 (12.7) 24198140.8 (26.7) PIR (%) 0.065 3.5 56977132.5 (44.1) 16451244.6 (42.6) 40525887.8 (44.7) BMI (kg/m2, %) < 0.001 30 42135139.1 (32.6) 8190656.0 (21.2) 33944483.2 (37.4) Diabetes (%) < 0.001 Yes 14769743.2 (11.4) 1887554.6 (4.9) 12882188.6 (14.2) No 114576062.0 (88.6) 36706940.2 (95.1) 77869121.8 (85.8) Hypertension (%) < 0.001 Yes 15411816.3 (11.9) 2440017.7 (6.3) 12971798.6 (14.3) No 113933988.9 (88.1) 36154477.2 (93.7) 77779511.7 (85.7) SBP (mmHg, mean (SD)) 120.15 (15.56) 116.14 (14.28) 121.85 (15.77) < 0.001 DBP (mmHg, mean (SD)) 70.09 (10.56) 68.03 (10.12) 70.97 (10.62) < 0.001 Smoking (%) < 0.001 Yes 58236737.2 (45.0) 15165170.0 (39.3) 43071567.2 (47.5) No 71109068.0 (55.0) 23429324.8 (60.7) 47679743.1 (52.5) Alcohol use (%) 0.175 Yes 103219245.2 (79.8) 31214542.0 (80.9) 72004703.3 (79.3) No 26126560.0 (20.2) 7379952.9 (19.1) 18746607.1 (20.7) PIV (median [IQR]) 221.54 [148.40, 328.22] 202.71 [135.85, 301.92] 230.00 [155.07, 342.83] < 0.001 Log10 (PIV) (mean (SD)) 2.35 (0.25) 2.31 (0.24) 2.36 (0.24) < 0.001 Baseline data shown above were weighted by Fasting Subsample 2 Year MEC Weight/6. PIR, poverty-to-income ratio; BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; PIV, pan-immune-inflammation value. 3.2 Association between PIV and Hyperlipidemia All results of the multivariable logistic regression analyses between PIV and hyperlipidemia were presented in Table 2 . The associations in three models were all statistically significant: model 1 (2.28 (1.75–2.98)), model 2 (2.00 (1.51–2.66)) and model 3 (1.55 (1.17–2.06)). In addition, when the independent variable-PIV was divided into four quartiles, Q1 (< 2.16), Q2 (2.16–2.33), Q3 (2.33–2.51) and Q4 (≥ 2.51), the multivariable logistic regression analysis adjusted for all covariates revealed that the ORs for the risk of hyperlipidemia compared to Q1 were 1.28, 1.38, and 1.47 respectively. Meanwhile, the RCS curve (included all variables adjusted in model 3) did not display a nonlinear relationship of PIV with hyperlipidemia ( P -nonlinear = 0.0633, P -overall < 0.001) in Fig. 2 . Table 2 Association between PIV and hyperlipidemia. PIV Hyperlipidemia OR (95%CI), P -value No. Model 1 Model 2 Model 3 Log10 (PIV) 7751 2.28 (1.75, 2.98) <0.001 2.00 (1.51, 2.66) <0.001 1.55 (1.17, 2.06) 0.002 Log10 (PIV) Tertile Q1 (< 2.16) 1929 Reference Reference Reference Q2 (2.16–2.33) 1928 1.38 (1.16, 1.65) <0.001 1.33 (1.10, 1.61) <0.010 1.28 (1.05, 1.57) <0.050 Q3 (2.33–2.51) 1929 1.49 (1.27, 1.75) <0.001 1.49 (1.26, 1.75) <0.001 1.38 (1.18, 1.62) <0.001 Q4 (≥ 2.51) 1929 1.88 (1.57, 2.24) <0.001 1.71 (1.42, 2.07) <0.001 1.47 (1.21, 1.79) <0.001 P for trend - < 0.001 < 0.001 < 0.001 Data are presented as OR (95%CI). OR, odds ratio; CI, confidence interval; PIV, pan-immune-inflammation value; Q1, 1st quartile; Q2, 2nd quartile; Q3, 3rd quartile; Q4, 4th quartile. Model 1: adjusted for non-covariates. Model 2: adjusted for adjusted for age, gender, race. Model 3: further adjusted for BMI, PIR, education, smoking, alcohol use, medical conditions (diabetes and hypertension). 3.3 Stratified analysis In order to investigate whether the positive relationship between PIV and hyperlipidemia was heterogeneous among all subgroups, stratified analysis were conducted within fully adjusted models. All results were shown in Fig. 3 . The findings revealed that most of the covariates-gender, education level, PIR, smoking, alcohol use, and hypertension did not affect the stable association between PIV and hyperlipidemia ( P for interaction for each group > 0.05), except for age, BMI and diabetes. For these three confounders, patients with hyperlipidemia were more likely to be overweight (BMI 25–30; OR (95%CI): 2.13 (1.40–3.26)), young (aged 20–39 years; OR (95%CI): 2.27 (1.59–3.23)), and non-diabetic (OR (95%CI): 1.66 (1.23–2.24)). 3.4 ROC analysis Table 3 and Fig. 4 showed the comparison of predictive value of PIV and SII for hyperlipidemia. The area under curve (AUC) of PIV (0.547 (0.532–0.561)) and SII (0.542 (0.528–0.556)) were almost equal. There was no significance in predictive value between the two indexes ( P = 0.267). Compared with SII, PIV had higher sensitivity (0.769 vs 0.631) but lower specificity (0.302 vs 0.441). Table 3 Comparison of the predictive value of PIV and SII for hyperlipidemia based on the ROC curve. AUC (95%CI) Sensitivity Specificity P value (PIV - SII) PIV 0.547 (0.532, 0.561) 0.769 0.302 0.267 SII 0.542 (0.528, 0.556) 0.631 0.441 PIV, pan-immune-inflammation value; SII, systemic immune-inflammation index; ROC, receiver operating characteristic; AUC, area under curve. 4 Discussion In the present study, the authors selected 7,715 adults in United States from the NHANES datasets. The important discovers of this cross-sectional study were as follows: (1) PIV index had an significantly positive association with the risk of hyperlipidemia. And this effect maintained stable in both continuous and quartile independent variables even after adjustment for all confounders. The prevalence of hyperlipidemia was relatively significant in female, 20–39 years old, 25-30kg/㎡, PIR > 3.5, above high school, non-diabetic, non-hypertensive, non-smoking, and non-drinking individuals. (2) In the stratified analysis, except for BMI, age, and diabetes condition, the rest were all not statistically significant. Patients with hyperlipidemia were concentrated in young (aged 20–39 years) and overweight (BMI 25–30). Therefore, it might be necessary to call on young overweight individuals to reduce their weight reasonably to avoid the risk of hyperlipidemia in the future. Meanwhile, this new index might be a reliable predictor of dyslipidemia indirectly. To our knowledge, the association between PIV and hyperlipidemia was first reported in this study based on a national population of the United States. The PIV was derived from the four important immune cells in plama, neutrophils, monocytes, lymphocytes, and platelets [ 14 ]. Acute inflammation was initially considered a compensatory mechanism for injury repair, but when it progressed to a chronic state, the direction of lipid synthesis altered, manifesting in a reduction of HDL and an elevation in very low-density lipoprotein (VLDL) levels [ 15 ]. The long-term existence of infection and inflammation could cause abnormal lipid metabolism [ 16 ]. In individuals with primary Sjogren’s syndrome, interleukin-2 and LDL-C were positively correlated (r = 0.7, P = 0.02) [ 17 ]. In addition, interleukin-6 (r = 0.39, P = 0.01) and tumor necrosis factor-alpha (TNF-α) levels had significant associations with TG (r = 0.4, P = 0.007) and HDL-C (r = -0.4, P < 0.001) [ 17 ]. Among individuals with heterozygous familial hypercholesterolemia, nuclear factor-kappa B (NF-kB) activity of mononuclear cells in blood was independently associated with apolipoprotein B (r = 0.287, P = 0.03) and oxidized LDL (r = 0.300, P = 0.02) [ 18 ]. Meanwhile, modified LDLs had the ability to activate the toll-like receptors, thereby priming the Nod-like receptor protein 3 inflammasomes and ultimately lead to the activation of interleukin-1β and secondary inflammatory responses [ 19 ]. In newly diagnosed patients with metabolic syndrome, there were associations between TNF-α and fasting blood glucose (r = 0.179, P = 0.021), LDL-C (r = 0.199, P = 0.01), atherogenic index (r = 0.219, P = 0.004), TG (r = 0.351, P < 0.001), and HDL-C (r = -0.244, P = 0.001) [ 20 ]. Among individuals without severe cardiovascular risks, there was a positive association between serum TG and high-sensitivity C-reactive protein (CRP) (r = 0.298, P < 0.001) [ 21 ]. In a Korean cross-sectional study, elevated CRP levels were positively associated with hypertriglyceridaemia (OR (95%CI): 1.157 (1.040–1.287); P = 0.007) [ 22 ]. In Inner Mongolia of China, individuals with the highest quartile of inflammatory biomarkers were more likely to have dyslipidemia (High-sensetivity CRP: OR (95%CI): 3.215 (2.551–4.116)) [ 23 ]. The administration of pro-atherogenic cytokines, such as TNF-α, interleukin-1β, and interleukin-6 in rats could result in an elevation of plasma VLDL-TG levels [ 24 ]. Excessive migration of LDL to the artery wall triggered an inflammatory cascade, which then accelerated the accumulation of cholesterol, further exacerbating the inflammatory response. This vicious cycle ultimately accelerated the formation of atherosclerotic plaque [ 25 ]. It was also reported that the lipoprotein-mediated enhancement of inflammation was mainly mediated by TG-rich lipoproteins, not LDL [ 26 ]. Hypertriglyceridemia enriched with apolipoproteins C-III, could activate NF-kB inflammatory signaling pathways, leading to development of atherosclerosis [ 27 ]. In mastitis mice, elevated LDL-C, TG, and TC in plasma were observed possibly due to decreased expression of lipoprotein lipase and increased expression of ANGPTL which was a liver-specific secretory protein with homology to angiopoietin [ 28 ]. In addition, the TG and cholesterol levels in plasma were elevated in mice with double-knockout genes (Tribbles homolog 1 and LDL receptor), leading to systemic inflammation and progression of atherosclerosis [ 29 ]. And the level of secretory phospholipase A2 increased during inflammation, potentially leading to an acceleration in HDL catabolism [ 30 ]. The acute-phase protein serum amyloid A1 in inflammation could remarkably alter the composition of HDL. Additionally, HDL at this stage had a decreased ability in mediating cholesterol transport and protecting LDL from oxidative stress [ 31 ]. The co-existence of inflammation and hyperlipidemia has been identified as important factors in the progression of atherosclerosis. Anti-inflammatory therapy and lipid-lowering interventions were not mutually exclusive, but had a synergistic effect [ 32 ]. Colchicine, a well-known anti-inflammatory drug, could reduce lipid levels and inflammatory markers in rats [ 33 ]. Compared with monotherapy, colchicine combined with atorvastatin further reduced inflammatory markers and lipoprotein associated phospholipase A2 in rats [ 34 ]. In individuals treated with statins, high-sensitivity CRP had a potent predictive ability of cardiovascular event (1.31 (1.20–1.43); P < 0.0001) than LDL-C [ 35 ]. In addition, women were more susceptible to hyperlipidemia in this study (1.71 (1.15–2.55) vs 1.53 (1.01–2.34); P for interaction = 0.4). Among patients aged 20 to 39 years with moderate to severe hypercholesterolemia in the United States, lipid control was worse in women than in men. It was possibly because reproductive-aged women were concerned about teratogenicity of drugs (such as statins) and underestimated the long-term cardiovascular risks [ 36 ]. Moreover, the overweight patients were more likely to have hyperlipidemia, which was similar to a cross-sectional study from the United States and Spain (The relationship between BMI and LDL had an inverted U shape) [ 37 ]. The imbalance between body weight and dyslipidemia might be related to the functional failure of adipose tissues in a state of obesity [ 38 ]. The majority of people in this study were non-diabetic, so this might explain why the proportion of non-diabetics was higher in patients with hyperlipidemia. 5 Strengths and limitations There were several strengths and limitations in the study. The authors performed statistical analyses using a large, nationally representative sample with adjustment for demographic, examination, questionnaire, and laboratory covariates to ensure that the findings are generalizable to practice. In addition, the study used a novel inflammatory biomarker PIV, which might be more reliable and effective in the assessment of inflammation. Lastly, the definitions of independent and dependent variables in the study were based on standardized laboratory tests, which largely ensured the objectivity and accuracy of the data and avoided recall bias. Some limitations were also considered. First, it was a cross-sectional study which had statistical significance between both variables, but could not verify their causal connection. Second, the study only considered the population in United States, and it was unclear whether the findings could be generalized to other races in the world. Third, although this study adjusted the relevant confounders, there might be other unknown confounders. Fourth, there was not a stratified analysis by race due to the lack of some data of racial sampling survey, the study could not explore the effect of PIV on different races. However, the overall interaction for all races was not significant ( P = 0.7). 6 Conclusion The study discovered a significantly positive association between PIV and hyperlipidemia in United States adults, particularly in young overweight population. Our findings might provide more solid evidence for the synergistic treatment of lipid-lowering and anti-inflammatory drugs in the future. Abbreviations BMI body mass index CRP C-reactive protein CVD cardiovascular disease HDL-C high-density lipoprotein cholesterol LDL-C low-density lipoprotein cholesterol NF-kB nuclear factor-kappa B NHANES National Health and Nutrition Examination Surveys PIR poverty-to-income ratio PIV pan-immune-inflammation value SII systemic immune-inflammation index TC total cholesterol TG triglyceride TNF-α tumor necrosis factor-alpha VLDL very low-density lipoprotein Declarations Data availability statement The current study involved the analysis of publicly available datasets. These datasets are accessible at the following URL: https://www.cdc.gov/nchs/nhanes/index.htm. Ethics statement The studies involving human subjects were approved by the Ethics Review Board of the National Center for Health Statistics. These studies complied with both local legislation and institutional regulations. Prior to participating in NHANES, all participants provided written informed consent. The data was anonymised and all participants' information was drawn from the publicly accessible NHANES datasets. Therefore, the study did not require further approval and followed ethical guidelines. Author contributions Yu Yan: Conceptualization, Data curation, Software, Methodology, Investigation, Writing - original draft, Writing - review & editing. Shanshan Jia: Conceptualization, Methodology, Investigation, Writing - review & editing. Xingwei Huo: Investigation, Methodology, Writing - review & editing. Lu Liu: Investigation, Writing - review & editing. Lirong Sun: Investigation, Writing - review & editing. Shuangliang Ma: Investigation, Writing - review & editing. Xiaoping Chen: Investigation, Funding acquisition, Supervision, Writing - review & editing. Acknowledgments The study thanks all NHANES staff and CDC for data collection and processing. Funding The study was funded by the National Natural Science Foundation of China (Grant No. 81970355). 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Yang, L., et al., Dysregulated serum lipid profile is associated with inflammation and disease activity in primary Sjögren's syndrome: a retrospective study in China. Immunol Lett, 2024. 267 : p. 106865. Real, J.T., et al., Circulating mononuclear cells nuclear factor-kappa B activity, plasma xanthine oxidase, and low grade inflammatory markers in adult patients with familial hypercholesterolaemia. Eur J Clin Invest, 2010. 40 (2): p. 89-94. Tuñón, J., et al., Interplay between hypercholesterolaemia and inflammation in atherosclerosis: Translating experimental targets into clinical practice. Eur J Prev Cardiol, 2018. 25 (9): p. 948-955. Mahdavi-Roshan, M., et al., Inflammatory Markers and Atherogenic Coefficient: Early Markers of Metabolic Syndrome. Int J Endocrinol Metab, 2022. 20 (4): p. e127445. Raposeiras-Roubin, S., et al., Triglycerides and Residual Atherosclerotic Risk. J Am Coll Cardiol, 2021. 77 (24): p. 3031-3041. Jeong, H., et al., C reactive protein level as a marker for dyslipidaemia, diabetes and metabolic syndrome: results from the Korea National Health and Nutrition Examination Survey. BMJ Open, 2019. 9 (8): p. e029861. Tang, L., et al., Association of biomarkers of inflammation with dyslipidemia and its components among Mongolians in China. PLoS One, 2014. 9 (2): p. e89023. van Diepen, J.A., et al., Interactions between inflammation and lipid metabolism: relevance for efficacy of anti-inflammatory drugs in the treatment of atherosclerosis. Atherosclerosis, 2013. 228 (2): p. 306-15. Manduteanu, I. and M. Simionescu, Inflammation in atherosclerosis: a cause or a result of vascular disorders? J Cell Mol Med, 2012. 16 (9): p. 1978-90. Wadström, B.N., et al., Inflammation compared to low-density lipoprotein cholesterol: two different causes of atherosclerotic cardiovascular disease. Curr Opin Lipidol, 2023. 34 (3): p. 96-104. Welty, F.K., How do elevated triglycerides and low HDL-cholesterol affect inflammation and atherothrombosis? Curr Cardiol Rep, 2013. 15 (9): p. 400. Xiao, H.B., J.Y. Wang, and Z.L. Sun, ANGPTL3 is part of the machinery causing dyslipidemia majorily via LPL inhibition in mastitis mice. Exp Mol Pathol, 2017. 103 (3): p. 242-248. Arndt, L., et al., Trib1 Deficiency Promotes Hyperlipidemia, Inflammation, and Atherosclerosis in LDL Receptor Knockout Mice. Arterioscler Thromb Vasc Biol, 2023. 43 (6): p. 979-994. de Beer, F.C., et al., Secretory non-pancreatic phospholipase A2: influence on lipoprotein metabolism. J Lipid Res, 1997. 38 (11): p. 2232-9. Marsche, G., et al., Inflammation alters HDL composition and function: implications for HDL-raising therapies. Pharmacol Ther, 2013. 137 (3): p. 341-51. Ridker, P.M., Targeting residual inflammatory risk: The next frontier for atherosclerosis treatment and prevention. Vascul Pharmacol, 2023. 153 : p. 107238. Zălar, D.M., et al., Effects of Colchicine in a Rat Model of Diet-Induced Hyperlipidemia. Antioxidants (Basel), 2022. 11 (2). Huang, C., et al., Synergistic effects of colchicine combined with atorvastatin in rats with hyperlipidemia. Lipids Health Dis, 2014. 13 : p. 67. Ridker, P.M., et al., Inflammation and cholesterol as predictors of cardiovascular events among patients receiving statin therapy: a collaborative analysis of three randomised trials. Lancet, 2023. 401 (10384): p. 1293-1301. Newton, S.L., et al., Management of Severe and Moderate Hypercholesterolemia in Young Women and Men. JAMA Cardiol, 2022. 7 (2): p. 227-230. Laclaustra, M., et al., LDL Cholesterol Rises With BMI Only in Lean Individuals: Cross-sectional U.S. and Spanish Representative Data. Diabetes Care, 2018. 41 (10): p. 2195-2201. Laclaustra, M., D. Corella, and J.M. Ordovas, Metabolic syndrome pathophysiology: the role of adipose tissue. Nutr Metab Cardiovasc Dis, 2007. 17 (2): p. 125-39. 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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-4662107","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":329511838,"identity":"e27b4197-5bfa-4e9a-9051-ad57d63e8680","order_by":0,"name":"Yu Yan","email":"","orcid":"","institution":"Department of Cardiology, West China Hospital, Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Yu","middleName":"","lastName":"Yan","suffix":""},{"id":329511839,"identity":"c318bd36-b437-4189-b53d-cf560efe1d65","order_by":1,"name":"Shanshan Jia","email":"","orcid":"","institution":"Department of Cardiology, West China Hospital, Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Shanshan","middleName":"","lastName":"Jia","suffix":""},{"id":329511840,"identity":"d858101e-b1d3-4d4b-940e-35d32840fd5d","order_by":2,"name":"Xingwei Huo","email":"","orcid":"","institution":"Department of Cardiology, West China Hospital, Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Xingwei","middleName":"","lastName":"Huo","suffix":""},{"id":329511841,"identity":"18dcdb0d-fa79-4de6-8f8d-98f693b5017c","order_by":3,"name":"Lu Liu","email":"","orcid":"","institution":"Department of Cardiology, West China Hospital, Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Lu","middleName":"","lastName":"Liu","suffix":""},{"id":329511842,"identity":"37840595-f636-4e0b-aab2-4c8498a14540","order_by":4,"name":"Shuangliang Ma","email":"","orcid":"","institution":"Department of Cardiology, West China Hospital, Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Shuangliang","middleName":"","lastName":"Ma","suffix":""},{"id":329511843,"identity":"f391c28a-3f25-4354-9a01-20ab2589f040","order_by":5,"name":"Xiaoping Chen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAyUlEQVRIiWNgGAWjYDACCTDJxsPY3tj48AMJWvhkmHsONxtLkKBFzoZ9RnqbAA8xOvhnNx97zLvDjId35sM2oH47Od0GQpbcOZZuOPNMGo/k7MS2BwUMycZmBwhoMZDIMZP42HaMx3B2YruBBMOBxG2EteR/k0hs+89jf/NgmwQPcVpy2IC2AAN5BiORWiRupJlJzjwD1NKTCAxkAyL8wj8j+Zk07w42e8b24w8ffqiwkyOoBQwYG+DuJEY5qpZRMApGwSgYBVgAANioPzj3i3rKAAAAAElFTkSuQmCC","orcid":"","institution":"Department of Cardiology, West China Hospital, Sichuan University","correspondingAuthor":true,"prefix":"","firstName":"Xiaoping","middleName":"","lastName":"Chen","suffix":""}],"badges":[],"createdAt":"2024-06-30 09:38:24","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4662107/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4662107/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":60930029,"identity":"a6d65721-a65c-4279-9d05-9c59e3c190c0","added_by":"auto","created_at":"2024-07-23 16:57:38","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":55242,"visible":true,"origin":"","legend":"\u003cp\u003eParticipant flowchart.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-4662107/v1/9cd17c6162ccff097aeb9c6f.png"},{"id":60930034,"identity":"1d0badf6-4725-4173-a1cb-20a440c1fd02","added_by":"auto","created_at":"2024-07-23 16:57:39","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":32768,"visible":true,"origin":"","legend":"\u003cp\u003eThe RCS curve of the association between PIV and hyperlipidemia odds ratio in the NHANES 2007–2018. RCS regression was adjusted for age, gender, race, PIR, BMI, education level, smoking, alcohol use, diabetes, and hypertension. RCS, restricted cubic spline; PIV, pan-immune-inflammation value.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-4662107/v1/d8011fa805725d71acc664c1.png"},{"id":60930976,"identity":"55dd15ca-4468-49ec-9687-31b715825e06","added_by":"auto","created_at":"2024-07-23 17:05:38","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":86725,"visible":true,"origin":"","legend":"\u003cp\u003eStratified analysis for the association between PIV and hyperlipidemia. BMI, body mass index; PIR, poverty-to-income ratio; PIV, pan-immune-inflammation value.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-4662107/v1/4164e27253dac9a435bf5b6c.png"},{"id":60930032,"identity":"82768a20-05d5-4245-9666-8e60a396b29b","added_by":"auto","created_at":"2024-07-23 16:57:38","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":26865,"visible":true,"origin":"","legend":"\u003cp\u003eROC analysis. ROC, receiver operating characteristic; PIV, pan-immune-inflammation value; SII, systemic immune-inflammation index.\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-4662107/v1/46260b5a7bd1e7812b15d9a6.png"},{"id":62326002,"identity":"52386b27-e0ee-4c88-8260-c5e7ef3a7b98","added_by":"auto","created_at":"2024-08-13 02:49:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":883062,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4662107/v1/dd7096b6-96f8-4f63-9d81-9178021a4710.pdf"},{"id":60930030,"identity":"9857a96c-36c4-41be-aeec-64e243cfadc9","added_by":"auto","created_at":"2024-07-23 16:57:38","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":2091584,"visible":true,"origin":"","legend":"","description":"","filename":"Plagiarismcheck.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4662107/v1/1d41a21c456c99f479411e1c.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Association between pan-immune-inflammation value and hyperlipidemia in the United States population ","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eHyperlipidemia was a condition that is characterized by abnormally elevated blood lipids, which could be attributed to a variety of genetic predispositions or acquired health conditions. In adults, hyperlipidemia was recognized to contribute to the development of cardiovascular disease (CVD) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. The available evidence suggested an association between hyperlipidemia which was characterized by the increase of triglycerides (TG) and low-density lipoprotein cholesterol (LDL-C) in plasma, and the prevalence of coronary artery disease [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Not only did it promote atherosclerosis of blood vessels, hyperlipidemia could also act directly on the heart leading to ischemia-reperfusion injury [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Moreover, the CVD accounted for the highest percentage of all causes of death among adults in the United States, and the risk of developing CVD in people with hyperlipidemia was about twice than those without dyslipidemia [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe link between atherosclerosis and hyperlipidemia, as well as the role of persistent low-grade inflammation and lipid abnormalities, has prompted research into the potential association between elevated lipid levels and inflammatory states [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. The cholesterol has been proved to directly induce inflammation by the activation of the NLRP3 inflammasomes, possibly contributing to the initiation and exacerbation of local and systemic immune inflammatory responses [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe pan-immune-inflammation value (PIV) represented a novel biomarker for predicting inflammatory status, including four cell types in peripheral blood [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. The definition of it relied on the counts of neutrophils, monocytes, lymphocytes, and platelets [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In 2020, it was initially used in metastatic colorectal cancer patients, showing a good ability to assess the prognosis of them and it even exceeded that of previously established markers related to inflammation [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. PIV was a potent and independent predictor of coronary slow flow, superior to other inflammatory markers [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. In a cross-sectional study, elevated PIV was associated with an increased risk of all-cause mortality (1.37 (1.20\u0026ndash;1.55); \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and cardiovascular mortality (1.62 (1.22\u0026ndash;2.15); \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) in hypertensive patients [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. The systemic immune-inflammation index (SII) was applied to the assessment of the risk of hyperlipidemia previously, but the correlation between them was relatively small (Model 2: OR (95%CI): 1.03 (1.01\u0026ndash;1.05); Model 3: OR (95%CI): 1.02 (1.00-1.04)) [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe association between novel composite biomarker PIV and hyperlipidemia has not been reported. Therefore, the study is aimed to explore the association between PIV and hyperlipidemia and answer whether PIV can be used to assess the risk of hyperlipidemia more effectively.\u003c/p\u003e"},{"header":"2 Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Data source and participants\u003c/h2\u003e \u003cp\u003eThe NHANES, administered by the National Center for Health Statistics (NCHS), represents a biennial nationwide survey dedicated to examining the health and nutritional status of citizens in the United States. The purpose of this program is to understand as fully as possible the contemporary disease patterns and better help the development of public health services and the optimization of public health policies. The NHANES data is open to the public and able to be freely downloaded via the official website: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cdc.gov/nchs/nhanes/index.htm\u003c/span\u003e\u003cspan address=\"https://www.cdc.gov/nchs/nhanes/index.htm\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe authors initially included statistics of 59,842 participants of the NHANES (2007\u0026ndash;2018) in this study. Data screening for inclusion and exclusion followed the prescribed procedures: (1) participants without PIV data (N\u0026thinsp;=\u0026thinsp;10,705); (2) participants without lipid data (N\u0026thinsp;=\u0026thinsp;30,664); (3) participants who had extreme data and missing covariate data (N\u0026thinsp;=\u0026thinsp;10,758). A total of 7,715 participants were ultimately selected for subsequent analyses after manual data filtration. The complete flow chart of participant being included and excluded was presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Assessment of Hyperlipidemia\u003c/h2\u003e \u003cp\u003e Hyperlipidemia was defined in accordance with the guidelines developed by the National Cholesterol Education Program Adult Treatment Panel III (NCEP-ATP3). Specifically, hyperlipidemia was defined as a total cholesterol (TC) level of 200 mg/dL or higher, TG of 150 mg/dL or higher, high-density lipoprotein cholesterol (HDL-C) less than 40 mg/dL for males and 50 mg/dL for females, or LDL-C of 130 mg/dL or higher. Furthermore, subjects who reported taking lipid-lowering drugs were also defined as having hyperlipidemia [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Assessment of PIV\u003c/h2\u003e \u003cp\u003eThe formula for calculating PIV was as follows: neutrophil count \u0026times; platelet count \u0026times; monocyte count / lymphocyte count. The numeric expression of cell counts was indicated as \u0026times;1000 cells/\u0026micro;L [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Because it was a study based on data of multiple periods, the measurements of target cells in the blood were operated with different instruments in the NHANES mobile examination centers.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Covariates\u003c/h2\u003e \u003cp\u003eIn order to reduce the influences of confounders on the final results, covariates with known or potential associations with hyperlipidemia were selected in the analysis. Demographic variables consisted of gender, race, age, education, body mass index (BMI), poverty-to-income ratio (PIR), smoking, and alcohol use. Hypertension and diabetes were included as medical conditions. Age was stratified into three groups as follows: 20\u0026ndash;39, 40\u0026ndash;59, and \u0026gt;\u0026thinsp;59 years. Five types of races were divided: Mexican American, non-Hispanic black, non-Hispanic white, other Hispanic and other races. Education levels were classified into three types: above high school, high school or GED, and less than high school. The economic situation was based on the PIR, which was categorized into three levels: \u0026lt;1.3, 1.3\u0026ndash;3.5, and \u0026gt;\u0026thinsp;3.5. The \u0026lt;\u0026thinsp;25, 25\u0026ndash;30, and \u0026gt;\u0026thinsp;30 kg/㎡BMI degrees were also established. The definition of smoking was having smoked 100 or more cigarettes over the lifetime. The definition of alcohol use was the consumption of at least 12 cups of alcoholic beverages in the last 12 months or had a drink of any kind of alcohol over the lifetime. Hypertension was diagnosed as an average SBP\u0026thinsp;\u0026ge;\u0026thinsp;140 mmHg, or DBP\u0026thinsp;\u0026ge;\u0026thinsp;90 mmHg based on at least three standard consecutive seated measurements. The definition for diabetes consisted of fasting serum glucose\u0026thinsp;\u0026ge;\u0026thinsp;126 mg/dL, self-reported diabetes or any insulin use, and glycated hemoglobin\u0026thinsp;\u0026ge;\u0026thinsp;6.5%.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Statistical analyses\u003c/h2\u003e \u003cp\u003eAll statistical analyses took into account the complex, multistage probabilistic sampling design of NHANES through the utilization of appropriate sampling weights. The R software version 4.3.3 (R foundation) was operated for statistical analyses of this study. In the baseline, weighted means and standard deviations (SD) or interquartile ranges (IQR) were presented for continuous variables, while categorical variables were presented as weighted proportions. The comparative analyses of weighted t-tests and Rao-Scott chi-square tests were carried out to assess the baseline clinical characteristics of each group of patients. The PIV performed a log10 transformation before regression analysis due to its right-skewed nature of the distribution. Weighted logistic regressions were carried out to explore the association between PIV and hyperlipidemia. The odd ratio (OR) and 95% confidence interval (CI) were calculated for each one-unit increase in PIV as well as for each PIV quartile. The study constructed three regression models: Model 1, adjusted for non-covariates; Model 2, adjusted for age, gender, race; and Model 3, adjusted for all covariates (added BMI, PIR, education, smoking, alcohol use, medical conditions to Model 2). Additionally, the restricted cubic splines (RCS) were performed to assess the nonlinear relationships between PIV and hyperlipidemia. Stratified analyses for age, gender, BMI, education level, PIR, hypertension, diabetes, smoking, and alcohol use, were conducted to explore the potential heterogeneity in different subgroups. The receiver operating characteristic (ROC) curve analysis was applied to compare the predictive value of PIV and SII for hyperlipidemia. Only bilateral \u003cem\u003eP\u003c/em\u003e-value of \u0026lt;\u0026thinsp;0.05 for all statistical tests were considered significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Statistics of baseline\u003c/h2\u003e \u003cp\u003eThere were all baseline characteristics presented in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e of 7,715 individuals categorized by whether they had hyperlipidemia or not, 48.25% of whom were male. And there were 70.2% of participants having hyperlipidemia. In all variables, age, race, education level, BMI, smoking, hypertension, diabetes, and PIV had significant associations with hyperlipidemia (with or without) (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). The characteristics of people who were susceptible to hyperlipidemia were as follows: non-Hispanic white, middle-aged (40\u0026ndash;59 years), above high school, 25\u0026thinsp;\u0026le;\u0026thinsp;BMI\u0026thinsp;\u0026le;\u0026thinsp;30 kg/㎡, non-diabetic, non-hypertensive, nonsmokers, as well as having higher PIV. However, for the three covariates of gender, PIR, and alcohol use, their associations with lipid status were not statistically significant (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eClinical characteristics of participants by groups whether had hyperlipidemia or not.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e Characteristics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eHyperlipidemia\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLevel\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOverall\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e129345805.2 (100.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e38594494.9 (29.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e90751310.3 (70.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.676\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e66336938.6 (51.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19983471.8 (51.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e46353466.8 (51.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e63008866.6 (48.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e18611023.0 (48.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e44397843.5 (48.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRace (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMexican American\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10801263.9 (8.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3218968.4 (8.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7582295.5 (8.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther Hispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7197721.0 (5.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2328087.0 (6.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4869634.0 (5.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-Hispanic White\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e89706135.6 (69.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e25542297.8 (66.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e64163837.8 (70.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNon-Hispanic Black\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12610752.1 (9.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4776419.6 (12.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7834332.5 (8.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOther Races\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9029932.6 (7.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2728722.0 (7.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6301210.6 (6.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation level (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLess than high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e19866317.4 (15.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4873646.3 (12.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e14992671.1 (16.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHigh school or GED\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27684145.5 (21.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7710050.4 (20.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e19974095.1 (22.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAbove high school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e81795342.3 (63.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e26010798.2 (67.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e55784544.1 (61.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u0026ndash;39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e49827024.5 (38.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e22632924.2 (58.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e27194100.3 (30.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e40\u0026ndash;59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e50414266.0 (39.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11055196.8 (28.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e39359069.2 (43.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29104514.7 (22.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4906373.9 (12.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e24198140.8 (26.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePIR (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;1.3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27159891.8 (21.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8933721.4 (23.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e18226170.4 (20.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.3\u0026ndash;3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e45208780.9 (35.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13209528.8 (34.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e31999252.1 (35.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;3.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e56977132.5 (44.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16451244.6 (42.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e40525887.8 (44.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (kg/m2, %)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e39555612.0 (30.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17657635.0 (45.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e21897977.0 (24.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25\u0026ndash;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47655054.0 (36.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e12746203.9 (33.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e34908850.2 (38.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e42135139.1 (32.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8190656.0 (21.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e33944483.2 (37.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14769743.2 (11.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1887554.6 (4.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12882188.6 (14.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e114576062.0 (88.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e36706940.2 (95.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e77869121.8 (85.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e15411816.3 (11.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2440017.7 (6.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12971798.6 (14.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e113933988.9 (88.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e36154477.2 (93.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e77779511.7 (85.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSBP (mmHg, mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e120.15 (15.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e116.14 (14.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e121.85 (15.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBP (mmHg, mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e70.09 (10.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e68.03 (10.12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e70.97 (10.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e58236737.2 (45.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15165170.0 (39.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e43071567.2 (47.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e71109068.0 (55.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23429324.8 (60.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e47679743.1 (52.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol use (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.175\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e103219245.2 (79.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e31214542.0 (80.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e72004703.3 (79.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e26126560.0 (20.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7379952.9 (19.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e18746607.1 (20.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePIV (median [IQR])\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e221.54 [148.40, 328.22]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e202.71 [135.85, 301.92]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e230.00 [155.07, 342.83]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLog10 (PIV) (mean (SD))\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.35 (0.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.31 (0.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.36 (0.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eBaseline data shown above were weighted by Fasting Subsample 2 Year MEC Weight/6.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003ePIR, poverty-to-income ratio; BMI, body mass index; SBP, systolic blood pressure; DBP, diastolic blood pressure; PIV, pan-immune-inflammation value.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Association between PIV and Hyperlipidemia\u003c/h2\u003e \u003cp\u003eAll results of the multivariable logistic regression analyses between PIV and hyperlipidemia were presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. The associations in three models were all statistically significant: model 1 (2.28 (1.75\u0026ndash;2.98)), model 2 (2.00 (1.51\u0026ndash;2.66)) and model 3 (1.55 (1.17\u0026ndash;2.06)). In addition, when the independent variable-PIV was divided into four quartiles, Q1 (\u0026lt;\u0026thinsp;2.16), Q2 (2.16\u0026ndash;2.33), Q3 (2.33\u0026ndash;2.51) and Q4 (\u0026ge;\u0026thinsp;2.51), the multivariable logistic regression analysis adjusted for all covariates revealed that the ORs for the risk of hyperlipidemia compared to Q1 were 1.28, 1.38, and 1.47 respectively. Meanwhile, the RCS curve (included all variables adjusted in model 3) did not display a nonlinear relationship of PIV with hyperlipidemia (\u003cem\u003eP\u003c/em\u003e-nonlinear\u0026thinsp;=\u0026thinsp;0.0633, \u003cem\u003eP\u003c/em\u003e-overall\u0026thinsp;\u0026lt;\u0026thinsp;0.001) in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation between PIV and hyperlipidemia.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePIV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c5\" namest=\"c3\"\u003e \u003cp\u003eHyperlipidemia OR (95%CI), \u003cem\u003eP\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLog10 (PIV)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7751\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.28 (1.75, 2.98) \u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.00 (1.51, 2.66) \u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.55 (1.17, 2.06) 0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLog10 (PIV) Tertile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ1 (\u0026lt;\u0026thinsp;2.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1929\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eReference\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ2 (2.16\u0026ndash;2.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1928\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.38 (1.16, 1.65) \u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.33 (1.10, 1.61) \u0026lt;0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.28 (1.05, 1.57) \u0026lt;0.050\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ3 (2.33\u0026ndash;2.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1929\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.49 (1.27, 1.75) \u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.49 (1.26, 1.75) \u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.38 (1.18, 1.62) \u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQ4 (\u0026ge;\u0026thinsp;2.51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1929\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.88 (1.57, 2.24) \u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.71 (1.42, 2.07) \u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.47 (1.21, 1.79) \u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e for trend\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eData are presented as OR (95%CI). OR, odds ratio; CI, confidence interval; PIV, pan-immune-inflammation value; Q1, 1st quartile; Q2, 2nd quartile; Q3, 3rd quartile; Q4, 4th quartile.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eModel 1: adjusted for non-covariates.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eModel 2: adjusted for adjusted for age, gender, race.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eModel 3: further adjusted for BMI, PIR, education, smoking, alcohol use, medical conditions (diabetes and hypertension).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Stratified analysis\u003c/h2\u003e \u003cp\u003eIn order to investigate whether the positive relationship between PIV and hyperlipidemia was heterogeneous among all subgroups, stratified analysis were conducted within fully adjusted models. All results were shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The findings revealed that most of the covariates-gender, education level, PIR, smoking, alcohol use, and hypertension did not affect the stable association between PIV and hyperlipidemia (\u003cem\u003eP\u003c/em\u003e for interaction for each group\u0026thinsp;\u0026gt;\u0026thinsp;0.05), except for age, BMI and diabetes. For these three confounders, patients with hyperlipidemia were more likely to be overweight (BMI 25\u0026ndash;30; OR (95%CI): 2.13 (1.40\u0026ndash;3.26)), young (aged 20\u0026ndash;39 years; OR (95%CI): 2.27 (1.59\u0026ndash;3.23)), and non-diabetic (OR (95%CI): 1.66 (1.23\u0026ndash;2.24)).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.4 ROC analysis\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e showed the comparison of predictive value of PIV and SII for hyperlipidemia. The area under curve (AUC) of PIV (0.547 (0.532\u0026ndash;0.561)) and SII (0.542 (0.528\u0026ndash;0.556)) were almost equal. There was no significance in predictive value between the two indexes (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.267). Compared with SII, PIV had higher sensitivity (0.769 vs 0.631) but lower specificity (0.302 vs 0.441).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eComparison of the predictive value of PIV and SII for hyperlipidemia based on the ROC curve.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAUC (95%CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSensitivity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSpecificity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value (PIV - SII)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePIV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.547 (0.532, 0.561)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.769\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.302\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.267\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.542 (0.528, 0.556)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.631\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.441\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003ePIV, pan-immune-inflammation value; SII, systemic immune-inflammation index; ROC, receiver operating characteristic; AUC, area under curve.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eIn the present study, the authors selected 7,715 adults in United States from the NHANES datasets. The important discovers of this cross-sectional study were as follows: (1) PIV index had an significantly positive association with the risk of hyperlipidemia. And this effect maintained stable in both continuous and quartile independent variables even after adjustment for all confounders. The prevalence of hyperlipidemia was relatively significant in female, 20\u0026ndash;39 years old, 25-30kg/㎡, PIR\u0026thinsp;\u0026gt;\u0026thinsp;3.5, above high school, non-diabetic, non-hypertensive, non-smoking, and non-drinking individuals. (2) In the stratified analysis, except for BMI, age, and diabetes condition, the rest were all not statistically significant. Patients with hyperlipidemia were concentrated in young (aged 20\u0026ndash;39 years) and overweight (BMI 25\u0026ndash;30). Therefore, it might be necessary to call on young overweight individuals to reduce their weight reasonably to avoid the risk of hyperlipidemia in the future. Meanwhile, this new index might be a reliable predictor of dyslipidemia indirectly.\u003c/p\u003e \u003cp\u003eTo our knowledge, the association between PIV and hyperlipidemia was first reported in this study based on a national population of the United States. The PIV was derived from the four important immune cells in plama, neutrophils, monocytes, lymphocytes, and platelets [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAcute inflammation was initially considered a compensatory mechanism for injury repair, but when it progressed to a chronic state, the direction of lipid synthesis altered, manifesting in a reduction of HDL and an elevation in very low-density lipoprotein (VLDL) levels [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. The long-term existence of infection and inflammation could cause abnormal lipid metabolism [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. In individuals with primary Sjogren\u0026rsquo;s syndrome, interleukin-2 and LDL-C were positively correlated (r\u0026thinsp;=\u0026thinsp;0.7, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.02) [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. In addition, interleukin-6 (r\u0026thinsp;=\u0026thinsp;0.39, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01) and tumor necrosis factor-alpha (TNF-α) levels had significant associations with TG (r\u0026thinsp;=\u0026thinsp;0.4, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007) and HDL-C (r = -0.4, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Among individuals with heterozygous familial hypercholesterolemia, nuclear factor-kappa B (NF-kB) activity of mononuclear cells in blood was independently associated with apolipoprotein B (r\u0026thinsp;=\u0026thinsp;0.287, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.03) and oxidized LDL (r\u0026thinsp;=\u0026thinsp;0.300, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.02) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Meanwhile, modified LDLs had the ability to activate the toll-like receptors, thereby priming the Nod-like receptor protein 3 inflammasomes and ultimately lead to the activation of interleukin-1β and secondary inflammatory responses [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. In newly diagnosed patients with metabolic syndrome, there were associations between TNF-α and fasting blood glucose (r\u0026thinsp;=\u0026thinsp;0.179, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.021), LDL-C (r\u0026thinsp;=\u0026thinsp;0.199, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.01), atherogenic index (r\u0026thinsp;=\u0026thinsp;0.219, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.004), TG (r\u0026thinsp;=\u0026thinsp;0.351, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and HDL-C (r = -0.244, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001) [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Among individuals without severe cardiovascular risks, there was a positive association between serum TG and high-sensitivity C-reactive protein (CRP) (r\u0026thinsp;=\u0026thinsp;0.298, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. In a Korean cross-sectional study, elevated CRP levels were positively associated with hypertriglyceridaemia (OR (95%CI): 1.157 (1.040\u0026ndash;1.287); \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.007) [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. In Inner Mongolia of China, individuals with the highest quartile of inflammatory biomarkers were more likely to have dyslipidemia (High-sensetivity CRP: OR (95%CI): 3.215 (2.551\u0026ndash;4.116)) [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The administration of pro-atherogenic cytokines, such as TNF-α, interleukin-1β, and interleukin-6 in rats could result in an elevation of plasma VLDL-TG levels [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Excessive migration of LDL to the artery wall triggered an inflammatory cascade, which then accelerated the accumulation of cholesterol, further exacerbating the inflammatory response. This vicious cycle ultimately accelerated the formation of atherosclerotic plaque [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. It was also reported that the lipoprotein-mediated enhancement of inflammation was mainly mediated by TG-rich lipoproteins, not LDL [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Hypertriglyceridemia enriched with apolipoproteins C-III, could activate NF-kB inflammatory signaling pathways, leading to development of atherosclerosis [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. In mastitis mice, elevated LDL-C, TG, and TC in plasma were observed possibly due to decreased expression of lipoprotein lipase and increased expression of ANGPTL which was a liver-specific secretory protein with homology to angiopoietin [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. In addition, the TG and cholesterol levels in plasma were elevated in mice with double-knockout genes (Tribbles homolog 1 and LDL receptor), leading to systemic inflammation and progression of atherosclerosis [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. And the level of secretory phospholipase A2 increased during inflammation, potentially leading to an acceleration in HDL catabolism [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. The acute-phase protein serum amyloid A1 in inflammation could remarkably alter the composition of HDL. Additionally, HDL at this stage had a decreased ability in mediating cholesterol transport and protecting LDL from oxidative stress [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe co-existence of inflammation and hyperlipidemia has been identified as important factors in the progression of atherosclerosis. Anti-inflammatory therapy and lipid-lowering interventions were not mutually exclusive, but had a synergistic effect [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Colchicine, a well-known anti-inflammatory drug, could reduce lipid levels and inflammatory markers in rats [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Compared with monotherapy, colchicine combined with atorvastatin further reduced inflammatory markers and lipoprotein associated phospholipase A2 in rats [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. In individuals treated with statins, high-sensitivity CRP had a potent predictive ability of cardiovascular event (1.31 (1.20\u0026ndash;1.43); \u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.0001) than LDL-C [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. In addition, women were more susceptible to hyperlipidemia in this study (1.71 (1.15\u0026ndash;2.55) vs 1.53 (1.01\u0026ndash;2.34); \u003cem\u003eP\u003c/em\u003e for interaction\u0026thinsp;=\u0026thinsp;0.4). Among patients aged 20 to 39 years with moderate to severe hypercholesterolemia in the United States, lipid control was worse in women than in men. It was possibly because reproductive-aged women were concerned about teratogenicity of drugs (such as statins) and underestimated the long-term cardiovascular risks [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Moreover, the overweight patients were more likely to have hyperlipidemia, which was similar to a cross-sectional study from the United States and Spain (The relationship between BMI and LDL had an inverted U shape) [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. The imbalance between body weight and dyslipidemia might be related to the functional failure of adipose tissues in a state of obesity [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. The majority of people in this study were non-diabetic, so this might explain why the proportion of non-diabetics was higher in patients with hyperlipidemia.\u003c/p\u003e"},{"header":"5 Strengths and limitations","content":"\u003cp\u003eThere were several strengths and limitations in the study. The authors performed statistical analyses using a large, nationally representative sample with adjustment for demographic, examination, questionnaire, and laboratory covariates to ensure that the findings are generalizable to practice. In addition, the study used a novel inflammatory biomarker PIV, which might be more reliable and effective in the assessment of inflammation. Lastly, the definitions of independent and dependent variables in the study were based on standardized laboratory tests, which largely ensured the objectivity and accuracy of the data and avoided recall bias. Some limitations were also considered. First, it was a cross-sectional study which had statistical significance between both variables, but could not verify their causal connection. Second, the study only considered the population in United States, and it was unclear whether the findings could be generalized to other races in the world. Third, although this study adjusted the relevant confounders, there might be other unknown confounders. Fourth, there was not a stratified analysis by race due to the lack of some data of racial sampling survey, the study could not explore the effect of PIV on different races. However, the overall interaction for all races was not significant (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.7).\u003c/p\u003e"},{"header":"6 Conclusion","content":"\u003cp\u003eThe study discovered a significantly positive association between PIV and hyperlipidemia in United States adults, particularly in young overweight population. Our findings might provide more solid evidence for the synergistic treatment of lipid-lowering and anti-inflammatory drugs in the future.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eBMI body\u0026nbsp;mass\u0026nbsp;index\u003c/p\u003e\n\u003cp\u003eCRP\u0026nbsp;C-reactive protein\u003c/p\u003e\n\u003cp\u003eCVD\u0026nbsp;cardiovascular disease\u003c/p\u003e\n\u003cp\u003eHDL-C\u0026nbsp;high-density lipoprotein\u0026nbsp;cholesterol\u003c/p\u003e\n\u003cp\u003eLDL-C\u0026nbsp;low-density lipoprotein\u0026nbsp;cholesterol\u003c/p\u003e\n\u003cp\u003eNF-kB\u0026nbsp;nuclear factor-kappa B\u003c/p\u003e\n\u003cp\u003eNHANES National Health and Nutrition Examination Surveys\u003c/p\u003e\n\u003cp\u003ePIR\u0026nbsp;poverty-to-income ratio\u003c/p\u003e\n\u003cp\u003ePIV\u0026nbsp;pan-immune-inflammation value\u003c/p\u003e\n\u003cp\u003eSII\u0026nbsp;systemic immune-inflammation index\u003c/p\u003e\n\u003cp\u003eTC\u0026nbsp;total cholesterol\u003c/p\u003e\n\u003cp\u003eTG\u0026nbsp;triglyceride\u003c/p\u003e\n\u003cp\u003eTNF-α\u0026nbsp;tumor\u0026nbsp;necrosis\u0026nbsp;factor-alpha\u003c/p\u003e\n\u003cp\u003eVLDL\u0026nbsp;very low-density lipoprotein\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003eData availability statement\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe current study involved the analysis of publicly available datasets. These datasets are accessible at the following URL: https://www.cdc.gov/nchs/nhanes/index.htm.\u003c/p\u003e\n\u003cp\u003eEthics statement\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe studies involving human subjects\u0026nbsp;were\u0026nbsp;approved by the Ethics Review Board of the National Center for Health Statistics. These studies complied\u0026nbsp;with both local legislation and institutional regulations. Prior to participating in NHANES, all participants provided\u0026nbsp;written informed consent. The data\u0026nbsp;was\u0026nbsp;anonymised and all participants\u0026apos; information\u0026nbsp;was\u0026nbsp;drawn from the publicly accessible NHANES datasets.\u0026nbsp;Therefore, the\u0026nbsp;study did not\u0026nbsp;require\u0026nbsp;further approval and followed ethical guidelines.\u003c/p\u003e\n\u003cp\u003eAuthor contributions\u003c/p\u003e\n\u003cp\u003eYu\u0026nbsp;Yan: Conceptualization, Data curation,\u0026nbsp;Software, Methodology, Investigation, Writing\u0026nbsp;-\u0026nbsp;original draft, Writing\u0026nbsp;-\u0026nbsp;review\u0026nbsp;\u0026amp;\u0026nbsp;editing. Shanshan\u0026nbsp;Jia: Conceptualization,\u0026nbsp;Methodology,\u0026nbsp;Investigation,\u0026nbsp;Writing\u0026nbsp;-\u0026nbsp;review\u0026nbsp;\u0026amp;\u0026nbsp;editing. Xingwei\u0026nbsp;Huo: Investigation,\u0026nbsp;Methodology,\u0026nbsp;Writing\u0026nbsp;-\u0026nbsp;review\u0026nbsp;\u0026amp;\u0026nbsp;editing. Lu\u0026nbsp;Liu: Investigation, Writing\u0026nbsp;-\u0026nbsp;review\u0026nbsp;\u0026amp;\u0026nbsp;editing. Lirong\u0026nbsp;Sun: Investigation, Writing\u0026nbsp;-\u0026nbsp;review\u0026nbsp;\u0026amp;\u0026nbsp;editing. Shuangliang\u0026nbsp;Ma: Investigation, Writing\u0026nbsp;-\u0026nbsp;review\u0026nbsp;\u0026amp;\u0026nbsp;editing. Xiaoping\u0026nbsp;Chen: Investigation, Funding acquisition, Supervision, Writing\u0026nbsp;-\u0026nbsp;review\u0026nbsp;\u0026amp;\u0026nbsp;editing.\u003c/p\u003e\n\u003cp\u003eAcknowledgments\u003c/p\u003e\n\u003cp\u003eThe study\u0026nbsp;thanks\u0026nbsp;all NHANES staff and CDC for data collection\u0026nbsp;and processing.\u003c/p\u003e\n\u003cp\u003eFunding\u003c/p\u003e\n\u003cp\u003eThe study was funded by\u0026nbsp;the National Natural Science Foundation of China (Grant No. 81970355).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConflict of interest\u003c/p\u003e\n\u003cp\u003eEach author claimed to have no conflict of interest in the study.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eStewart, J., et al., \u003cem\u003eHyperlipidemia.\u003c/em\u003e Pediatr Rev, 2020. \u003cstrong\u003e41\u003c/strong\u003e(8): p. 393-402.\u003c/li\u003e\n \u003cli\u003eSu, X., et al., \u003cem\u003eMicroRNA in cardio-metabolic disorders.\u003c/em\u003e Clin Chim Acta, 2021. \u003cstrong\u003e518\u003c/strong\u003e: p. 134-141.\u003c/li\u003e\n \u003cli\u003eYao, Y.S., T.D. 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Fern\u0026aacute;ndez-Real, \u003cem\u003eDyslipidemia and inflammation: an evolutionary conserved mechanism.\u003c/em\u003e Clin Nutr, 2005. \u003cstrong\u003e24\u003c/strong\u003e(1): p. 16-31.\u003c/li\u003e\n \u003cli\u003eKhovidhunkit, W., et al., \u003cem\u003eEffects of infection and inflammation on lipid and lipoprotein metabolism: mechanisms and consequences to the host.\u003c/em\u003e J Lipid Res, 2004. \u003cstrong\u003e45\u003c/strong\u003e(7): p. 1169-96.\u003c/li\u003e\n \u003cli\u003eYang, L., et al., \u003cem\u003eDysregulated serum lipid profile is associated with inflammation and disease activity in primary Sj\u0026ouml;gren\u0026apos;s syndrome: a retrospective study in China.\u003c/em\u003e Immunol Lett, 2024. \u003cstrong\u003e267\u003c/strong\u003e: p. 106865.\u003c/li\u003e\n \u003cli\u003eReal, J.T., et al., \u003cem\u003eCirculating mononuclear cells nuclear factor-kappa B activity, plasma xanthine oxidase, and low grade inflammatory markers in adult patients with familial hypercholesterolaemia.\u003c/em\u003e Eur J Clin Invest, 2010. \u003cstrong\u003e40\u003c/strong\u003e(2): p. 89-94.\u003c/li\u003e\n \u003cli\u003eTu\u0026ntilde;\u0026oacute;n, J., et al., \u003cem\u003eInterplay between hypercholesterolaemia and inflammation in atherosclerosis: Translating experimental targets into clinical practice.\u003c/em\u003e Eur J Prev Cardiol, 2018. \u003cstrong\u003e25\u003c/strong\u003e(9): p. 948-955.\u003c/li\u003e\n \u003cli\u003eMahdavi-Roshan, M., et al., \u003cem\u003eInflammatory Markers and Atherogenic Coefficient: Early Markers of Metabolic Syndrome.\u003c/em\u003e Int J Endocrinol Metab, 2022. \u003cstrong\u003e20\u003c/strong\u003e(4): p. e127445.\u003c/li\u003e\n \u003cli\u003eRaposeiras-Roubin, S., et al., \u003cem\u003eTriglycerides and Residual Atherosclerotic Risk.\u003c/em\u003e J Am Coll Cardiol, 2021. \u003cstrong\u003e77\u003c/strong\u003e(24): p. 3031-3041.\u003c/li\u003e\n \u003cli\u003eJeong, H., et al., \u003cem\u003eC reactive protein level as a marker for dyslipidaemia, diabetes and metabolic syndrome: results from the Korea National Health and Nutrition Examination Survey.\u003c/em\u003e BMJ Open, 2019. \u003cstrong\u003e9\u003c/strong\u003e(8): p. e029861.\u003c/li\u003e\n \u003cli\u003eTang, L., et al., \u003cem\u003eAssociation of biomarkers of inflammation with dyslipidemia and its components among Mongolians in China.\u003c/em\u003e PLoS One, 2014. \u003cstrong\u003e9\u003c/strong\u003e(2): p. e89023.\u003c/li\u003e\n \u003cli\u003evan Diepen, J.A., et al., \u003cem\u003eInteractions between inflammation and lipid metabolism: relevance for efficacy of anti-inflammatory drugs in the treatment of atherosclerosis.\u003c/em\u003e Atherosclerosis, 2013. \u003cstrong\u003e228\u003c/strong\u003e(2): p. 306-15.\u003c/li\u003e\n \u003cli\u003eManduteanu, I. and M. Simionescu, \u003cem\u003eInflammation in atherosclerosis: a cause or a result of vascular disorders?\u003c/em\u003e J Cell Mol Med, 2012. \u003cstrong\u003e16\u003c/strong\u003e(9): p. 1978-90.\u003c/li\u003e\n \u003cli\u003eWadstr\u0026ouml;m, B.N., et al., \u003cem\u003eInflammation compared to low-density lipoprotein cholesterol: two different causes of atherosclerotic cardiovascular disease.\u003c/em\u003e Curr Opin Lipidol, 2023. \u003cstrong\u003e34\u003c/strong\u003e(3): p. 96-104.\u003c/li\u003e\n \u003cli\u003eWelty, F.K., \u003cem\u003eHow do elevated triglycerides and low HDL-cholesterol affect inflammation and atherothrombosis?\u003c/em\u003e Curr Cardiol Rep, 2013. \u003cstrong\u003e15\u003c/strong\u003e(9): p. 400.\u003c/li\u003e\n \u003cli\u003eXiao, H.B., J.Y. Wang, and Z.L. Sun, \u003cem\u003eANGPTL3 is part of the machinery causing dyslipidemia majorily via LPL inhibition in mastitis mice.\u003c/em\u003e Exp Mol Pathol, 2017. \u003cstrong\u003e103\u003c/strong\u003e(3): p. 242-248.\u003c/li\u003e\n \u003cli\u003eArndt, L., et al., \u003cem\u003eTrib1 Deficiency Promotes Hyperlipidemia, Inflammation, and Atherosclerosis in LDL Receptor Knockout Mice.\u003c/em\u003e Arterioscler Thromb Vasc Biol, 2023. \u003cstrong\u003e43\u003c/strong\u003e(6): p. 979-994.\u003c/li\u003e\n \u003cli\u003ede Beer, F.C., et al., \u003cem\u003eSecretory non-pancreatic phospholipase A2: influence on lipoprotein metabolism.\u003c/em\u003e J Lipid Res, 1997. \u003cstrong\u003e38\u003c/strong\u003e(11): p. 2232-9.\u003c/li\u003e\n \u003cli\u003eMarsche, G., et al., \u003cem\u003eInflammation alters HDL composition and function: implications for HDL-raising therapies.\u003c/em\u003e Pharmacol Ther, 2013. \u003cstrong\u003e137\u003c/strong\u003e(3): p. 341-51.\u003c/li\u003e\n \u003cli\u003eRidker, P.M., \u003cem\u003eTargeting residual inflammatory risk: The next frontier for atherosclerosis treatment and prevention.\u003c/em\u003e Vascul Pharmacol, 2023. \u003cstrong\u003e153\u003c/strong\u003e: p. 107238.\u003c/li\u003e\n \u003cli\u003eZălar, D.M., et al., \u003cem\u003eEffects of Colchicine in a Rat Model of Diet-Induced Hyperlipidemia.\u003c/em\u003e Antioxidants (Basel), 2022. \u003cstrong\u003e11\u003c/strong\u003e(2).\u003c/li\u003e\n \u003cli\u003eHuang, C., et al., \u003cem\u003eSynergistic effects of colchicine combined with atorvastatin in rats with hyperlipidemia.\u003c/em\u003e Lipids Health Dis, 2014. \u003cstrong\u003e13\u003c/strong\u003e: p. 67.\u003c/li\u003e\n \u003cli\u003eRidker, P.M., et al., \u003cem\u003eInflammation and cholesterol as predictors of cardiovascular events among patients receiving statin therapy: a collaborative analysis of three randomised trials.\u003c/em\u003e Lancet, 2023. \u003cstrong\u003e401\u003c/strong\u003e(10384): p. 1293-1301.\u003c/li\u003e\n \u003cli\u003eNewton, S.L., et al., \u003cem\u003eManagement of Severe and Moderate Hypercholesterolemia in Young Women and Men.\u003c/em\u003e JAMA Cardiol, 2022. \u003cstrong\u003e7\u003c/strong\u003e(2): p. 227-230.\u003c/li\u003e\n \u003cli\u003eLaclaustra, M., et al., \u003cem\u003eLDL Cholesterol Rises With BMI Only in Lean Individuals: Cross-sectional U.S. and Spanish Representative Data.\u003c/em\u003e Diabetes Care, 2018. \u003cstrong\u003e41\u003c/strong\u003e(10): p. 2195-2201.\u003c/li\u003e\n \u003cli\u003eLaclaustra, M., D. Corella, and J.M. Ordovas, \u003cem\u003eMetabolic syndrome pathophysiology: the role of adipose tissue.\u003c/em\u003e Nutr Metab Cardiovasc Dis, 2007. \u003cstrong\u003e17\u003c/strong\u003e(2): p. 125-39.\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":"pan-immune-inflammation value, hyperlipidemia, NHANES, inflammation, cross-sectional study","lastPublishedDoi":"10.21203/rs.3.rs-4662107/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4662107/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eObject: \u003c/strong\u003eTo investigate the possible association between pan-immune-inflammation value (PIV) and hyperlipidemia.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e The authors selected the relevant data from National Health and Nutrition Examination Survey (NHANES) for a detailed cross-sectional study. The independent variable used the logarithmic form of PIV-log10 (PIV). The definition of dependent variable-hyperlipidemiawas based on the National Cholesterol Education Program standards. Both variables were calculated from measured laboratory data. Weighted multivariate logistic regression analyses and restricted cubic splines (RCS) were conducted to analyze the association between PIV and hyperlipidemia. Stratified analyses were used to identify potential associations between PIV and hyperlipidemia with other covariates. The study also constructed the receiver operating characteristic (ROC) curve to assess the predictive value for hyperlipidemia of PIV compared to systemic immune-inflammation index (SII).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003eIn the study, 7,715 participants from NHANES were included. After adjusting for all confounders, PIV and hyperglycemia had an significantly positive association (OR (95%CI): 1.55 (1.17-2.06); \u003cem\u003eP =\u003c/em\u003e 0.002). Compared to participants with lowest quartile (Q1) of PIV, participants with the highest quartile (Q4) had a significantly higher risk of hyperlipidemia (OR (95%CI): 1.47 (1.21-1.79); \u003cem\u003eP \u003c/em\u003e\u0026lt; 0.001). The RCS curve showed a linear relationship between PIV and hyperlipidemia (\u003cem\u003eP\u003c/em\u003e-nonlinear = 0.0633, \u003cem\u003eP\u003c/em\u003e-overall \u0026lt; 0.001). The ROC curve found that compared with SII, PIV had a slightly higher predictive value (0.547 vs 0.542, \u003cem\u003eP\u003c/em\u003e = 0.267).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion: \u003c/strong\u003eThis national cross-sectional study discovered that PIV had a significantly positive relationship with hyperlipidemia, particularly in young overweight individuals. More prospective studies are needed to verify whether the PIV is a more reliable and effective index for assessing the risk of hyperlipidemia.\u003c/p\u003e","manuscriptTitle":"Association between pan-immune-inflammation value and hyperlipidemia in the United States population","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-23 16:57:33","doi":"10.21203/rs.3.rs-4662107/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":"764eaf55-77ed-4b29-8cd4-9d0ff5b64c4c","owner":[],"postedDate":"July 23rd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-08-13T02:41:34+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-23 16:57:33","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4662107","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4662107","identity":"rs-4662107","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}