Atherogenic index of plasma is a novel and strong predictor of non-alcoholic fatty liver disease in non-obese participants: a retrospective cohort research of China

Atherogenic index of plasma is a novel and strong predictor of non-alcoholic fatty liver disease in non-obese participants: a retrospective cohort research of China | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Atherogenic index of plasma is a novel and strong predictor of non-alcoholic fatty liver disease in non-obese participants: a retrospective cohort research of China Jingnan Li, Kemin Li This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-1485769/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Non-alcoholic fatty liver disease (NAFLD) in the non-obese population accounts for a large proportion of NAFLD. The association between the atherogenic index of plasma (AIP) with the risk of NAFLD was limited, especially among non-obese participants. Methods: We performed a post-hoc analysis of data obtained from the Dryad data repository and explored the predictive value of AIP on the risk of NAFLD among non-obese participants. Results: 16173 participants with AIP were included in this study, and 2322(14.4%) non-obese participants developed into individuals with NAFLD with the 5-year follow-up examination. The difference between AIP quartiles in the cumulative estimation of new-onset NAFLD was significant. And with increased AIP, the cumulative primary outcomes gradually increased. Participants in higher AIP quartiles had a significantly increased risk of NAFLD. In the fully adjusted Model 3, HRs of the primary outcome for subjects in Q2, Q3, and Q4 of AIP were 1.56 (1.29, 1.89), 2.12 (1.78, 2.53), and 2.78 (2.35, 3.92) respectively. Meanwhile, the trend test for the association between AIP quartiles and the primary outcome presented that AIP quartile was positively and strongly associated with the primary outcome (adjusted HR (95%CI) in Model 3: 1.37 (1.31, 1.44), P<0.001). In the subgroup analysis, we revealed a significant interaction between systolic blood pressure categories (=120mmHg; P for interaction =0.011) and NAFLD. Conclusion : This study found AIP was a strong independent risk factor for new-onset NAFLD among non-obese individuals and screening for AIP in this population can be used to prevent future NAFLD. atherogenic index of plasma Non-alcoholic fatty liver disease non-obese participants Figures Figure 1 Background Non-alcoholic fatty liver disease (NAFLD) is a chronic liver disease where hepatic fat accumulation in the absence of quantities of alcohol and any other secondary cause, diagnosed by pathology or imaging( 1 ). Nowadays, NAFLD is gradually becoming the leading cause of chronic liver disease, the prevalence is estimated to be 25% in the worldwide( 2 ), and ranges from 15–40% in Asia( 3 ). The pathogenesis of NAFLD was not clearly, however, steatosis was a key factor in its development( 4 ). The atherogenic index of plasma (AIP), calculated by the logarithm of the ratio between the level of triglyceride and high-density lipoprotein cholesterol (TG/HDL-C), was an indicator reflecting the characteristics and the degree of abnormal lipid metabolism( 5 ). Previous researches suggested that AIP was strongly associated with NAFLD in obese and Chinese Han populations, but it was not studied in non-obese populations. It is wide known that obesity was strongly associated with NAFLD, but the non-obese population was still a very large population suffering from NAFLD. It was reported that the rates of NAFLD in non-obese population average 10%-30% in Western and Eastern countries( 6 ), and as high as 17.5% in China( 7 ). Moreover, non-obese individuals with NAFLD had a significantly higher cardiovascular disease (CVD) risk compared with obese subjects with NAFLD( 8 ). Hence, it was crucial to concentrate on NAFLD in the non-obese population. Therefore, in this study, we aimed to investigate the predictive value of AIP on the risk of non-obese patients with NAFLD. Methods Data we used in this study was derived from the Dryad data repository at http://datadryad.org/ with the doi:10.5061/dryad.1n6c4. Study population and design The participants in the longitudinal studies were individuals who received a health examination in Wenzhou Medical Center of Wenzhou People's Hospital from January 2010 to December 2014. The protocol and main outcomes were previously published(8).The major outcomes were that increased normal low-density lipoprotein cholesterol (LDL-C) levels were related to an elevated risk of NAFLD in non-obese populations independently. And this study was approved by the ethics committee of Wenzhou People's Hospital. Our aim of this analysis was to assess the association between baseline AIP (calculated by TG and HDL-C) and the NAFLD outcomes in non-obese individuals. And 16371 participants were included in the final analysis. Evaluation of atherogenic index of plasma and Study outcomes The AIP was calculated by the logarithm of TG/HDL-C mole ratio base 10. In total, 16173participants were grouped according to AIP quartiles: Q1 (n=4042), Q2 (n=4044), Q3 (n=4043), Q4 (n=4044) and the Q1 group was used as the reference. NAFLD was the primary outcome of this analysis. The definition of the outcomes was published in the Sun et al. study protocol(9). Statistical analysis Included participants were grouped by the AIP quartiles. Continuous variables were expressed as mean (standard deviation) or median (Q1-Q3) based on the distribution of data. The difference between the quartiles were tested using ANOVA and Kruskal-Wallis H test for normal distribution data and skew distribution data respectively. Chi-square test or Fisher test were applied to compare the categorical variables. All categorical variables were expressed as frequency (percentile). The Kaplan-Meier analysis with log-rank test was used to estimate cumulative incidence of new-onset NAFLD in non-obese populations and to compare in the AIP quartiles. The Cox model was applied to evaluate the association between AIP quartiles and the occurrence of the NAFLD in three models. Model 1 was adjusted for none. Model 2 was adjusted for age, sex and body mass index (BMI). Model 3 was adjusted for age, sex, BMI, systolic blood pressure (SBP), diastolic blood pressure (DBP), alkaline phosphatase (ALP), Gamma glutamyl transferase (GGT), alanine aminotransferase (ALT), aspartate aminotransferase (AST), serum creatinine (CR), and uric acid (UA).We performed Schoenfeld residuals test to test the proportional hazard assumption in the Cox model. The relationship between AIP and NAFLD according to various subgroups were assessed with stratified analysis and interaction test by using Model 3. All analyses were performed using the statistical software packages R (The R Foundation; http://www.R-project.org). Statistical significance was set at P <0.05. Results Baseline characteristics of the participants In total,16173 participants with AIP were included in this analysis, and 2322(14.4%) non-obese participants developed into individuals with NAFLD with the 5-year follow-up examination. The baseline characteristics of the included participants according to the AIP quartiles were shown in Table 1. Participants in higher quartiles of AIP tended to be male, elder and were more likely to have a higher BMI, SBP, DBP liver enzymes (including ALP, ALT, AST), CR, UA, blood lipids (including TG, HDL-C, LDL-C), fasting triglycerides, glucose, and NAFLD risk than in lower quartiles. Kaplan-Meier curves analysis Figure 1 showed the Kaplan-Meier curves of the cumulative incidence of new-onset NAFLD stratified by AIP quartiles. NAFLD incident risk was significantly different between each level of AIP(P<0.001). With increased AIP, the cumulative primary outcomes gradually increased. And the top quartile group performed the maximum risk of NAFLD with no doubt. Relationship between AIP and primary outcome Table 2 exhibited the HRs (95%CI) of primary outcome among the included subjects grouped by quartiles of AIP. Participants in higher AIP quartiles all had a significantly increased risk of NAFLD compared with the lowest group with the HRs of 1.92, 3.06 and 5.0, respectively ( P < 0.001). After adjustment for potential confounding factors, including age, sex, BMI, SBP, DBP, ALP, GGT, ALT, AST, CR, UA, the associations remained significant ( P < 0.001). Especially in the fully adjusted Model 3, the adjusted HRs of the primary outcome for subjects in Q2, Q3, and Q4 of AIP were 1.56 (1.29, 1.89), 2.12 (1.78, 2.53), and 2.78 (2.35, 3.92) respectively. For the purpose of sensitivity analysis, we also handled AIP as categorical variable (Quartile), the top quartile had 78% increment of primary outcome risk when compared with the bottom quartile in the fully adjusted Model 3. Meanwhile, the trend test for the association between AIP quartiles and the primary outcome presented that AIP quartile was positively and strongly associated with the primary outcome (adjusted HR (95%CI) in Model 3: 1.37 (1.31, 1.44), P<0.001). Subgroup analysis for the risk of primary outcome by baseline AIP quartiles We further explored other risks in the associations between AIP (per SD) and NAFLD by performing a subgroup analysis to estimate the factors that might influence the results. As shown in Table 3，we used sex (male vs. female), age (=40 years), BMI (=21.5 years), ALT（=16 U/L) and SBP (=120mmHg). There was a significant interaction between SBP (=120mmHg; P for interaction =0.011) and NAFLD. The effect of AIP on the risk of NAFLD was smaller in >=120mmHg group [HR =1.03, 95%CI (1.01, 1.05), P = 0.004] than in <120mmHg group [HR =1.06, 95%CI (1.04, 1.08), P 0.05. (Table 3) Discussion In this study, we pointed out that AIP was an independent risk factor for NAFLD in non-obese population. Moreover, AIP was positively associated with the occurrence of NAFLD in non-obese individuals after adjusting for other covariates. In the non-obese population, more attention should be paid to AIP to prevent NAFLD. The mechanisms of NAFLD pathogenesis were extremely complicated and there were no specific drugs available to treat it. The imbalance between the production of triglycerides(TG) and its uptake and clearance in the liver caused steatosis, which was the key to the occurrence and development of NAFLD( 4 ). It was widely accepted that NAFLD patients were at elevated risk of developing CVD, in turn, CVD was the leading cause of death in NAFLD patients (12–30%)( 10 , 11 ). Notably, atherosclerosis caused by dyslipidemia in NAFLD was the basis for the incidence and development of CVD, and the dyslipidemia associated with NAFLD was mainly manifested as increased plasma concentrations of TG and decreased high density lipoprotein cholesterol (HDL-C)( 12 ). In addition, with the ongoing awareness of NAFLD disease, non-obese NAFLD was not rare and its prevalence was reported to be up to 12.6% in Korea adults( 13 ). Interestingly, approximately 40% of the global NAFLD population was classified as non-obese NAFLD( 14 ). But since most of the NAFLD with non-obese patients were asymptomatic and prone to the possibility of underdiagnosis, their prevalence may be higher than actual( 15 ). In addition, compared to the obese population with NAFLD, the non-obese population with NAFLD had a higher risk of prostate hyperplasia( 16 ), diabetes mellitus( 17 ), and also had a similar risk of CVD and malignancy( 18 ). However, for the non-obese population, the incidence and severity of dyslipidemia were lower than those of the obese population. Therefore, it was essential to be aware of the non-obese populations with NAFLD and to find appropriate predictors. AIP, as the logarithm of the TG/HDL-C ratio, combines lipid abnormalities into a lipid complex that can provide a stronger reflection of dyslipidemia and atherosclerosis( 5 ). Currently, the relationship between AIP and NAFLD was not adequately well-studied and limited. To our knowledge, there were only three cross sectional researches on the relationship between AIP and NAFLD, one study concluded that the AIP sensitivity and specificity in predicting NAFLD in Chinese population values 80.8% and 65.4%( 19 ) and may have higher diagnostic capability for women, but still weaker than BMI, which was similar to the results of a study in Chinese Han population( 20 ). Whereas, in another study of NAFLD of obese population, AIP was found to have stronger predictive abilities compared to other indicators, and better in men( 21 ).While in our study, we did not identify the strengths and weaknesses between male and female. The type of study and the size of the sample may contribute to the difference. Nevertheless, in the consideration of the advantages of long-term follow-up retrospective cohort and the large number participants in our study, the credibility of our conclusion was stronger. Furthermore, in the subgroup analysis, we revealed a strengthened interaction between AIP and primary outcome in the population with SBP = 120mmHg. This may be explained by the fact that when individuals with SBP > = 120mmHg may have abnormal in other metabolic indicators, which in turn led to a diminished role of AIP. This study not merely had a large sample size, but also had the advantages of long-term follow-up, retrospective design, and population specificity. Moreover, we used rigorous statistical adjustment to reduce confounding factors to draw more stable conclusions. However, the study still had some limitations. Firstly, our population was only the Chinese non-obese NAFLD population, and there were no data on other ethnic and regional non-obese NAFLD. Secondly, information on smoking, alcohol consumption, waist circumference, hip circumference, etc. was missing from the data, and these may affect the study conclusion. Therefore, a larger population and more comprehensive information are needed to further validate the relationship between AIP and NAFLD in non-obese populations. Conclusion In conclusion, this study found elevated AIP is independently and positively associated with the risk of NAFLD among non-obese patients. Screening for AIP could be considered in non-obese people to prevent the new-onset NAFLD in the future, and more studies were needed to further confirm the association between AIP and NAFLD in non-obese populations. Declarations Ethics approval and consent to participate Not applicable. Consent for publication Not applicable. Availability of data and materials Data are available from the Dryad data repository at http://datadryad.org/ Competing interests The authors declare that they have no competing interests. Funding This work was funded by the Natural Science Foundation of China (Grant No. 81370500 and 81170364 to JNL) and CAMS Initiative for Innovative Medicine (CAMS-2020-I2M-2-013 to Jingnan Li). Author’s contributions KL and JL completed the writing of the paper. KL applied for the database and made statistical analysis. JL was responsible for the revision of the paper. All authors confirmed the final version of the paper. Acknowledgements Not applicable. References Neuschwander-Tetri BA. Non-alcoholic fatty liver disease. BMC Med. 2017;15(1):45. Powell EE, Wong VW, Rinella M. Non-alcoholic fatty liver disease. Lancet. 2021;397(10290):2212-24. Younossi Z, Tacke F, Arrese M, Chander Sharma B, Mostafa I, Bugianesi E, et al. Global Perspectives on Nonalcoholic Fatty Liver Disease and Nonalcoholic Steatohepatitis. Hepatology. 2019;69(6):2672-82. Esler WP, Bence KK. Metabolic Targets in Nonalcoholic Fatty Liver Disease. Cell Mol Gastroenterol Hepatol. 2019;8(2):247-67. Dobiásová M, Frohlich J. The plasma parameter log (TG/HDL-C) as an atherogenic index: correlation with lipoprotein particle size and esterification rate in apoB-lipoprotein-depleted plasma (FER(HDL)). Clin Biochem. 2001;34(7):583-8. Kim D, Kim WR. Nonobese Fatty Liver Disease. Clin Gastroenterol Hepatol. 2017;15(4):474-85. Zeng J, Yang RX, Sun C, Pan Q, Zhang RN, Chen GY, et al. Prevalence, clinical characteristics, risk factors, and indicators for lean Chinese adults with nonalcoholic fatty liver disease. World J Gastroenterol. 2020;26(15):1792-804. Yoshitaka H, Hamaguchi M, Kojima T, Fukuda T, Ohbora A, Fukui M. Nonoverweight nonalcoholic fatty liver disease and incident cardiovascular disease: A post hoc analysis of a cohort study. Medicine (Baltimore). 2017;96(18):e6712. Sun DQ, Wu SJ, Liu WY, Wang LR, Chen YR, Zhang DC, et al. Association of low-density lipoprotein cholesterol within the normal range and NAFLD in the non-obese Chinese population: a cross-sectional and longitudinal study. BMJ Open. 2016;6(12):e013781. Chatrath H, Vuppalanchi R, Chalasani N. Dyslipidemia in patients with nonalcoholic fatty liver disease. Semin Liver Dis. 2012;32(1):22-9. Adams LA, Lymp JF, St Sauver J, Sanderson SO, Lindor KD, Feldstein A, et al. The natural history of nonalcoholic fatty liver disease: a population-based cohort study. Gastroenterology. 2005;129(1):113-21. Speliotes EK, Massaro JM, Hoffmann U, Vasan RS, Meigs JB, Sahani DV, et al. Fatty liver is associated with dyslipidemia and dysglycemia independent of visceral fat: the Framingham Heart Study. Hepatology. 2010;51(6):1979-87. Kwon YM, Oh SW, Hwang SS, Lee C, Kwon H, Chung GE. Association of nonalcoholic fatty liver disease with components of metabolic syndrome according to body mass index in Korean adults. Am J Gastroenterol. 2012;107(12):1852-8. Ye Q, Zou B, Yeo YH, Li J, Huang DQ, Wu Y, et al. Global prevalence, incidence, and outcomes of non-obese or lean non-alcoholic fatty liver disease: a systematic review and meta-analysis. Lancet Gastroenterol Hepatol. 2020;5(8):739-52. Li Y, Chen Y, Tian X, Zhang S, Jiao J. Comparison of Clinical Characteristics Between Obese and Non-Obese Patients with Nonalcoholic Fatty Liver Disease (NAFLD). Diabetes Metab Syndr Obes. 2021;14:2029-39. Shao C, Ye J, Li F, Lin Y, Wu T, Wang W, et al. Early Predictors of Cardiovascular Disease Risk in Nonalcoholic Fatty Liver Disease: Non-obese Versus Obese Patients. Dig Dis Sci. 2020;65(6):1850-60. Kim SS, Cho HJ, Kim HJ, Kang DR, Berry JR, Kim JH, et al. Nonalcoholic fatty liver disease as a sentinel marker for the development of diabetes mellitus in non-obese subjects. Dig Liver Dis. 2018;50(4):370-7. Ahmed OT, Gidener T, Mara KC, Larson JJ, Therneau TM, Allen AM. Natural History of Nonalcoholic Fatty Liver Disease With Normal Body Mass Index: A Population-Based Study. Clin Gastroenterol Hepatol. 2021. Xie F, Pei Y, Zhou Q, Cao D, Wang Y. Comparison of obesity-related indices for identifying nonalcoholic fatty liver disease: a population-based cross-sectional study in China. Lipids Health Dis. 2021;20(1):132. Xie F, Zhou H, Wang Y. Atherogenic index of plasma is a novel and strong predictor associated with fatty liver: a cross-sectional study in the Chinese Han population. Lipids Health Dis. 2019;18(1):170. Wang Q, Zheng D, Liu J, Fang L, Li Q. Atherogenic index of plasma is a novel predictor of non-alcoholic fatty liver disease in obese participants: a cross-sectional study. Lipids Health Dis. 2018;17(1):284. Tables Table 1 Baseline characteristic of the participants included in the analysis according to AIP quartiles. Variable AIP P value Q1 Q2 Q3 Q4 N 4042 4044 4043 4044 AIP, mean ± SD 0.37 ± 0.05 0.47 ± 0.02 0.54 ± 0.02 0.65 ±0.06 <0.001 Female, n (%) 2039(50.45%) 1917(47.40%) 1920(47.49%) 1814(44.86%) <0.001 BMI(Kg/m 2 ), mean ± SD 20.55 ± 2.01 21.14±2.01 21.62±1.95 22.21±1.84 Age, mean ± SD 42.60±14.86 42.60±14.75 43.34 ±14.91 44.36±15.25 <0.001 SBP (mm Hg), mean ± SD 116.80 ±16.22 119.50±16.68 121.59±16.48 125.02±16.39 <0.001 DBP (mm Hg), mean ± SD 70.43 ± 9.83 72.14 ± 10.23 73.23 ± 10.17 75.43 ± 10.53 <0.001 ALT(U/L), median (Q1-Q3) 14.00 (11.00-20.00) 15.00 (12.00-21.00) 17.00 (13.00-23.00) 20.00 (14.00-27.00) <0.001 AST(U/L), median (Q1-Q3) 20.00 (18.00-24.00) 21.00 (18.00-24.00) 21.00 (18.00-25.00) 23.00 (19.00-26.00) <0.001 ALP(U/L), mean ± SD 67.34 ± 21.83 70.71 ± 23.29 73.97 ± 25.03 77.01 ± 21.43 <0.001 GGT(U/L), median (Q1-Q3) 18.00 (14.00-25.00) 20.00 (16.00-27.00) 22.00 (17.00-32.00) 27.00 (20.00-41.00) <0.001 CR (umol/L), median (Q1-Q3) 71.00 (62.00-83.00) 75.00 (65.00-89.00) 78.00 (65.00-92.00) 80.00 (65.00-93.00) <0.001 UA (umol/L),median (Q1-Q3) 233.00 (191.00-292.00) 257.00 (209.00-317.00) 280.00 (227.00-344.00) 311.00 (253.00-369.00) <0.001 TC(mmol/L), mean ± SD 4.28 ± 0.72 4.59 ± 0.72 4.68 ± 0.68 4.95 ± 0.69 <0.001 TG(mmol/L),median (Q1-Q3) 0.79 (0.65-1.00) 0.99 (0.78-1.25) 1.20 (0.93-1.54) 1.67(1.21-2.35) <0.001 HDL-C (mmol/L), mean ± SD 1.83 ± 0.32 1.57 ± 0.26 1.35 ± 0.21 1.10 ± 0.18 <0.001 LDL-C( mmol/L), mean ± SD 1.88 ± 0.41 2.25 ± 0.40 2.40 ± 0.40 2.52 ± 0.38 <0.001 Fasting glucose (mmol/L), mean ± SD 5.02 ± 0.70 5.11 ± 0.74 5.17 ± 0.73 5.27 ± 0.92 <0.001 NAFLD 194 (4.80%) 370(9.15%) 632 (15.63%) 1126(27.84%) <0.001 AIP: atherogenic index of plasma, BMI: body mass index (BMI), SBP: systolic blood pressure, DBP: diastolic blood pressure, ALP: alkaline phosphatase, GGT: gamma glutamyl transferase, ALT: alanine aminotransferase, AST: aspartate aminotransferase, CR: serum creatinine, UA: uric acid, TC: total cholesterol, TG: total triglycerides, HDL-C: high-density lipoprotein cholesterol, LDL-C: low-density lipoprotein cholesterol. Table 2 Association between AIP and primary outcome in different models. AIP quartiles Model 1 Model 2 Model 3 HR (95%CI) P value 1 Ref. Ref. Ref. 2 1.92 (1.62, 2.29) P<0.001 1.55 (1.30, 1.84) P<0.001 1.56 (1.29, 1.89) P<0.001 3 3.06 (2.60, 3.59) P<0.001 2.14 (1.82, 2.51) P<0.001 2.12 (1.78, 2.53) P<0.001 4 5.00 (4.29, 5.83) P<0.001 2.80 (2.40, 3.27) P<0.001 2.78 (2.35, 3.29) P<0.001 P for trend (1 Q increment) 1.67 (1.60, 1.74) P<0.001 1.38 (1.33, 1.44) P<0.001 1.37 (1.31, 1.44) P<0.001 Model were adjusted for none. Model 2 were adjusted for age, sex and BMI. Model 3 were adjusted for age, sex , BMI, SBP, DBP, ALP, GGT, ALT, AST, CR, UA. Table 3 Subgroup analysis for the risk of primary outcome by baseline AIP quartiles (per SD). AIP Quartiles (per SD) Subgroup HR, 95%CI P value P for interaction Sex 0.580 Male 1.31 (1.23, 1.40) <0.001 Female 1.34 (1.27, 1.41) <0.001 Age group 0.617 <40 1.34 (1.27, 1.41) =40 1.31 (1.24, 1.39) <0.001 BMI 0.126 <21.5 1.49 (1.32, 1.68) =21.5 1.34 (1.29, 1.40) <0.001 ALT 0.495 <16 1.35 (1.22, 1.48) =16 1.30 (1.24, 1.36) <0.001 SBP 0.011 <120 1.45 (1.34, 1.56) =120 1.29 (1.23, 1.35) <0.001 Model were adjusted for all covariates in Model 3 except stratifications itself. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-1485769","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":93484203,"identity":"9a7f96ab-9d40-4686-8733-cd6e0ae7da9a","order_by":0,"name":"Jingnan Li","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAArElEQVRIiWNgGAWjYLACCQYLOTb25gMkaZEw5uM5lkCaPYnzJHIUiFNrLpGd+MFyh0R6G0MOA8OPim2EtVjOyN0sIXlGIreN4ewBxp4ztwlrMbiRu41Bsg2ohbEvgZmxjQQt6WzMPAakaUlgYyNWi2XPW6Bf2iQM23jYEg4S5Rdz9tyNnyXbbOTl5z8++OBHBTEOA2JmCSjnAGH1UC2MH4hSOgpGwSgYBSMWAAA0ITXIeiM/BQAAAABJRU5ErkJggg==","orcid":"","institution":"Peking Union Medical College Hospital, Chinese Academy of Medical Sciences","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Jingnan","middleName":"","lastName":"Li","suffix":""},{"id":93484198,"identity":"9eae070c-11e9-410b-812b-6571730fbac2","order_by":1,"name":"Kemin Li","email":"","orcid":"","institution":"Peking Union Medical College Hospital, Chinese Academy of Medical Sciences","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Kemin","middleName":"","lastName":"Li","suffix":""}],"badges":[],"createdAt":"2022-03-24 13:59:19","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-1485769/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-1485769/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":19684969,"identity":"1c8083ef-402c-4e18-84ae-2a20b1f51441","added_by":"auto","created_at":"2022-03-28 13:39:36","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":11906,"visible":true,"origin":"","legend":"\u003cp\u003eKaplan–Meier estimation of new-onset NAFLD by AIP quartiles.\u003c/p\u003e\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-1485769/v1/68913e05d5a7c17c3e06ae62.png"},{"id":19685732,"identity":"b1685ac2-03e6-4675-b4d1-c4b8dce81890","added_by":"auto","created_at":"2022-03-28 13:44:39","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":319705,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-1485769/v1/f053fcce-6794-4cc1-af19-9bd73cb8842d.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Atherogenic index of plasma is a novel and strong predictor of non-alcoholic fatty liver disease in non-obese participants: a retrospective cohort research of China","fulltext":[{"header":"Background","content":"\u003cp\u003eNon-alcoholic fatty liver disease (NAFLD) is a chronic liver disease where hepatic fat accumulation in the absence of quantities of alcohol and any other secondary cause, diagnosed by pathology or imaging(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Nowadays, NAFLD is gradually becoming the leading cause of chronic liver disease, the prevalence is estimated to be 25% in the worldwide(\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e), and ranges from 15\u0026ndash;40% in Asia(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe pathogenesis of NAFLD was not clearly, however, steatosis was a key factor in its development(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). The atherogenic index of plasma (AIP), calculated by the logarithm of the ratio between the level of triglyceride and high-density lipoprotein cholesterol (TG/HDL-C), was an indicator reflecting the characteristics and the degree of abnormal lipid metabolism(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Previous researches suggested that AIP was strongly associated with NAFLD in obese and Chinese Han populations, but it was not studied in non-obese populations.\u003c/p\u003e \u003cp\u003eIt is wide known that obesity was strongly associated with NAFLD, but the non-obese population was still a very large population suffering from NAFLD. It was reported that the rates of NAFLD in non-obese population average 10%-30% in Western and Eastern countries(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e), and as high as 17.5% in China(\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). Moreover, non-obese individuals with NAFLD had a significantly higher cardiovascular disease (CVD) risk compared with obese subjects with NAFLD(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e). Hence, it was crucial to concentrate on NAFLD in the non-obese population. Therefore, in this study, we aimed to investigate the predictive value of AIP on the risk of non-obese patients with NAFLD.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eData we used in this study was derived from the Dryad data repository at http://datadryad.org/ with the doi:10.5061/dryad.1n6c4.\u003c/p\u003e\n\u003ch2\u003eStudy population and design\u003c/h2\u003e\n\u003cp\u003eThe participants in the longitudinal studies were individuals who received a health examination in Wenzhou Medical Center of Wenzhou People\u0026apos;s Hospital from January 2010 to December 2014. The protocol and main outcomes were previously published(8).The major outcomes were that increased normal\u0026nbsp;low-density lipoprotein cholesterol (LDL-C) levels were related to an elevated risk of NAFLD in non-obese populations independently. And this study was approved by the ethics committee of Wenzhou People\u0026apos;s Hospital.\u003c/p\u003e\n\u003cp\u003eOur aim of this analysis was to assess the association between baseline AIP (calculated by TG and HDL-C) and the NAFLD outcomes in non-obese individuals. And 16371 participants were included in the final analysis.\u003c/p\u003e\n\u003ch2\u003eEvaluation of atherogenic index of plasma and Study outcomes\u003c/h2\u003e\n\u003cp\u003eThe AIP was calculated by the logarithm of TG/HDL-C mole ratio base 10. In total, 16173participants were grouped according to AIP quartiles: Q1 (n=4042), Q2 (n=4044), Q3 (n=4043), Q4 (n=4044) and the Q1 group was used as the reference. NAFLD was the primary outcome of this analysis. The definition of the outcomes was published in the Sun et al. study protocol(9).\u003c/p\u003e\n\u003ch2\u003eStatistical analysis\u003c/h2\u003e\n\u003cp\u003eIncluded participants were grouped by the AIP quartiles. Continuous variables were expressed as mean (standard deviation) or median (Q1-Q3) based on the distribution of data. The difference between the quartiles were tested using ANOVA and Kruskal-Wallis H test for normal distribution data and skew distribution data respectively. Chi-square test or Fisher test were applied to compare the categorical\u0026nbsp;variables. All\u0026nbsp;categorical variables were expressed as frequency (percentile).\u003c/p\u003e\n\u003cp\u003eThe Kaplan-Meier analysis with log-rank test was used to estimate cumulative incidence of new-onset NAFLD in non-obese populations and to compare in the AIP quartiles.\u003c/p\u003e\n\u003cp\u003eThe Cox model was applied to evaluate the association between AIP quartiles and the occurrence of the\u0026nbsp;NAFLD\u0026nbsp;in three models. Model 1 was adjusted for none. Model 2 was adjusted for age, sex and body mass index (BMI). Model 3 was adjusted for age,\u0026nbsp;sex,\u0026nbsp;BMI, systolic blood pressure (SBP), diastolic blood pressure (DBP),\u0026nbsp;alkaline phosphatase\u0026nbsp;(ALP), Gamma glutamyl transferase (GGT), alanine aminotransferase (ALT), aspartate aminotransferase (AST), serum creatinine (CR), and uric acid (UA).We performed Schoenfeld residuals test to test the proportional hazard assumption in the Cox model.\u0026nbsp;The relationship between AIP and\u0026nbsp;NAFLD according to various subgroups were assessed with stratified analysis and interaction test\u0026nbsp;by using Model 3.\u003c/p\u003e\n\u003cp\u003eAll analyses were performed using the statistical software packages R (The R Foundation; http://www.R-project.org). Statistical significance was set at \u003cem\u003eP\u003c/em\u003e\u0026lt;0.05.\u003c/p\u003e"},{"header":"Results","content":"\u003ch2\u003eBaseline characteristics of the participants\u003c/h2\u003e\n\u003cp\u003eIn total,16173 participants with AIP were included in this analysis, and 2322(14.4%) non-obese participants developed into individuals with NAFLD with the 5-year follow-up examination. The baseline characteristics of the included participants according to the AIP quartiles were shown in Table 1. Participants in higher quartiles of AIP tended to be male, elder and were more likely to have a higher BMI, SBP, DBP liver enzymes (including ALP, ALT, AST), CR, UA, blood lipids (including TG, HDL-C, LDL-C), fasting triglycerides, glucose, and\u0026nbsp;NAFLD\u0026nbsp;risk than in lower quartiles.\u003c/p\u003e\n\u003ch2\u003eKaplan-Meier curves analysis\u003c/h2\u003e\n\u003cp\u003eFigure 1 showed the Kaplan-Meier curves of the cumulative incidence of new-onset NAFLD stratified by AIP quartiles. NAFLD incident risk was significantly different between each level of AIP(P\u0026lt;0.001). With increased AIP, the cumulative primary outcomes gradually increased. And the top quartile group performed the maximum risk of NAFLD with no doubt.\u003c/p\u003e\n\u003ch2\u003eRelationship between AIP and primary outcome\u003c/h2\u003e\n\u003cp\u003eTable 2 exhibited the HRs (95%CI) of primary outcome among the included subjects grouped by quartiles of AIP. Participants in higher AIP quartiles all had a significantly increased risk of\u0026nbsp;NAFLD\u0026nbsp;compared with the lowest group with the HRs of 1.92, 3.06 and 5.0, respectively (\u003cem\u003eP\u003c/em\u003e \u0026lt;\u0026thinsp;0.001). After adjustment for potential confounding factors, including age, sex, BMI, SBP, DBP, ALP, GGT, ALT, AST, CR, UA, the associations remained significant (\u003cem\u003eP\u003c/em\u003e\u0026lt;\u0026thinsp;0.001). Especially in the fully adjusted Model 3, the adjusted HRs of the primary outcome for subjects in Q2, Q3, and Q4 of AIP were 1.56 (1.29, 1.89), 2.12 (1.78, 2.53), and 2.78 (2.35, 3.92) respectively. For the purpose of sensitivity analysis, we also handled AIP as categorical variable (Quartile), the top quartile had 78% increment of primary outcome risk when compared with the bottom quartile in the fully adjusted Model 3. Meanwhile, the trend test for the association between AIP quartiles and the primary outcome presented that AIP quartile was positively and strongly associated with the primary outcome (adjusted HR (95%CI) in Model 3: 1.37 (1.31, 1.44), P\u0026lt;0.001).\u003c/p\u003e\n\u003ch2\u003eSubgroup analysis for the risk of primary outcome by baseline AIP quartiles\u003c/h2\u003e\n\u003cp\u003eWe further explored other risks in the associations between AIP (per SD) and NAFLD by performing a subgroup analysis to estimate the factors that might influence the results. As shown in Table 3，we used sex (male vs. female), age (\u0026lt;40 years vs. \u0026gt;=40 years), BMI (\u0026lt;21.5 vs. \u0026gt;=21.5 years), ALT（\u0026lt;16U/L vs. \u0026gt;=16 U/L) and SBP (\u0026lt;120mmHg vs. \u0026gt;=120mmHg). There was a significant interaction between SBP (\u0026lt;120mmHg vs. \u0026gt;=120mmHg; P for interaction =0.011) and NAFLD. The effect of AIP on the risk of NAFLD was smaller in \u0026gt;=120mmHg group [HR =1.03, 95%CI (1.01, 1.05), P = 0.004] than in \u0026lt;120mmHg group [HR =1.06, 95%CI (1.04, 1.08), P \u0026lt; 0.001]. The interaction of other subgroups and AIP had no significant effect on the risk of NAFLD, and the P values of interaction were all \u0026gt;0.05. (Table 3)\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we pointed out that AIP was an independent risk factor for NAFLD in non-obese population. Moreover, AIP was positively associated with the occurrence of NAFLD in non-obese individuals after adjusting for other covariates. In the non-obese population, more attention should be paid to AIP to prevent NAFLD.\u003c/p\u003e \u003cp\u003eThe mechanisms of NAFLD pathogenesis were extremely complicated and there were no specific drugs available to treat it. The imbalance between the production of triglycerides(TG) and its uptake and clearance in the liver caused steatosis, which was the key to the occurrence and development of NAFLD(\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). It was widely accepted that NAFLD patients were at elevated risk of developing CVD, in turn, CVD was the leading cause of death in NAFLD patients (12\u0026ndash;30%)(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). Notably, atherosclerosis caused by dyslipidemia in NAFLD was the basis for the incidence and development of CVD, and the dyslipidemia associated with NAFLD was mainly manifested as increased plasma concentrations of TG and decreased high density lipoprotein cholesterol (HDL-C)(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). In addition, with the ongoing awareness of NAFLD disease, non-obese NAFLD was not rare and its prevalence was reported to be up to 12.6% in Korea adults(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Interestingly, approximately 40% of the global NAFLD population was classified as non-obese NAFLD(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). But since most of the NAFLD with non-obese patients were asymptomatic and prone to the possibility of underdiagnosis, their prevalence may be higher than actual(\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). In addition, compared to the obese population with NAFLD, the non-obese population with NAFLD had a higher risk of prostate hyperplasia(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e), diabetes mellitus(\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e), and also had a similar risk of CVD and malignancy(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e). However, for the non-obese population, the incidence and severity of dyslipidemia were lower than those of the obese population. Therefore, it was essential to be aware of the non-obese populations with NAFLD and to find appropriate predictors.\u003c/p\u003e \u003cp\u003eAIP, as the logarithm of the TG/HDL-C ratio, combines lipid abnormalities into a lipid complex that can provide a stronger reflection of dyslipidemia and atherosclerosis(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Currently, the relationship between AIP and NAFLD was not adequately well-studied and limited. To our knowledge, there were only three cross sectional researches on the relationship between AIP and NAFLD, one study concluded that the AIP sensitivity and specificity in predicting NAFLD in Chinese population values 80.8% and 65.4%(\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e) and may have higher diagnostic capability for women, but still weaker than BMI, which was similar to the results of a study in Chinese Han population(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Whereas, in another study of NAFLD of obese population, AIP was found to have stronger predictive abilities compared to other indicators, and better in men(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e).While in our study, we did not identify the strengths and weaknesses between male and female. The type of study and the size of the sample may contribute to the difference. Nevertheless, in the consideration of the advantages of long-term follow-up retrospective cohort and the large number participants in our study, the credibility of our conclusion was stronger. Furthermore, in the subgroup analysis, we revealed a strengthened interaction between AIP and primary outcome in the population with SBP\u0026thinsp;\u0026lt;\u0026thinsp;120mmHg, which in turn was weaker in the population with SBP\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;120mmHg. This may be explained by the fact that when individuals with SBP\u0026thinsp;\u0026gt;\u0026thinsp;=\u0026thinsp;120mmHg may have abnormal in other metabolic indicators, which in turn led to a diminished role of AIP.\u003c/p\u003e \u003cp\u003eThis study not merely had a large sample size, but also had the advantages of long-term follow-up, retrospective design, and population specificity. Moreover, we used rigorous statistical adjustment to reduce confounding factors to draw more stable conclusions. However, the study still had some limitations. Firstly, our population was only the Chinese non-obese NAFLD population, and there were no data on other ethnic and regional non-obese NAFLD. Secondly, information on smoking, alcohol consumption, waist circumference, hip circumference, etc. was missing from the data, and these may affect the study conclusion. Therefore, a larger population and more comprehensive information are needed to further validate the relationship between AIP and NAFLD in non-obese populations.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eIn conclusion, this study found elevated AIP is independently and positively associated with the risk of NAFLD among non-obese patients. Screening for AIP could be considered in non-obese people to prevent the new-onset NAFLD in the future, and more studies were needed to further confirm the association between AIP and NAFLD in non-obese populations.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData\u0026nbsp;are available from the\u0026nbsp;Dryad data repository at http://datadryad.org/\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was funded by the Natural Science Foundation of China (Grant No. 81370500 and 81170364 to JNL) and CAMS Initiative for Innovative Medicine (CAMS-2020-I2M-2-013 to Jingnan Li).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor\u0026rsquo;s contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKL and JL completed the writing of the paper. KL applied for the database and made statistical analysis. JL was responsible for the revision of the paper. All authors confirmed the final version of the paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003e Neuschwander-Tetri BA. Non-alcoholic fatty liver disease. BMC Med. 2017;15(1):45.\u003c/li\u003e\n \u003cli\u003e Powell EE, Wong VW, Rinella M. Non-alcoholic fatty liver disease. Lancet. 2021;397(10290):2212-24.\u003c/li\u003e\n \u003cli\u003e Younossi Z, Tacke F, Arrese M, Chander Sharma B, Mostafa I, Bugianesi E, et al. Global Perspectives on Nonalcoholic Fatty Liver Disease and Nonalcoholic Steatohepatitis. Hepatology. 2019;69(6):2672-82.\u003c/li\u003e\n \u003cli\u003e Esler WP, Bence KK. Metabolic Targets in Nonalcoholic Fatty Liver Disease. Cell Mol Gastroenterol Hepatol. 2019;8(2):247-67.\u003c/li\u003e\n \u003cli\u003e Dobi\u0026aacute;sov\u0026aacute; M, Frohlich J. The plasma parameter log (TG/HDL-C) as an atherogenic index: correlation with lipoprotein particle size and esterification rate in apoB-lipoprotein-depleted plasma (FER(HDL)). Clin Biochem. 2001;34(7):583-8.\u003c/li\u003e\n \u003cli\u003e Kim D, Kim WR. Nonobese Fatty Liver Disease. Clin Gastroenterol Hepatol. 2017;15(4):474-85.\u003c/li\u003e\n \u003cli\u003e Zeng J, Yang RX, Sun C, Pan Q, Zhang RN, Chen GY, et al. Prevalence, clinical characteristics, risk factors, and indicators for lean Chinese adults with nonalcoholic fatty liver disease. World J Gastroenterol. 2020;26(15):1792-804.\u003c/li\u003e\n \u003cli\u003e Yoshitaka H, Hamaguchi M, Kojima T, Fukuda T, Ohbora A, Fukui M. Nonoverweight nonalcoholic fatty liver disease and incident cardiovascular disease: A post hoc analysis of a cohort study. Medicine (Baltimore). 2017;96(18):e6712.\u003c/li\u003e\n \u003cli\u003e Sun DQ, Wu SJ, Liu WY, Wang LR, Chen YR, Zhang DC, et al. Association of low-density lipoprotein cholesterol within the normal range and NAFLD in the non-obese Chinese population: a cross-sectional and longitudinal study. BMJ Open. 2016;6(12):e013781.\u003c/li\u003e\n \u003cli\u003e Chatrath H, Vuppalanchi R, Chalasani N. Dyslipidemia in patients with nonalcoholic fatty liver disease. Semin Liver Dis. 2012;32(1):22-9.\u003c/li\u003e\n \u003cli\u003e Adams LA, Lymp JF, St Sauver J, Sanderson SO, Lindor KD, Feldstein A, et al. The natural history of nonalcoholic fatty liver disease: a population-based cohort study. Gastroenterology. 2005;129(1):113-21.\u003c/li\u003e\n \u003cli\u003e Speliotes EK, Massaro JM, Hoffmann U, Vasan RS, Meigs JB, Sahani DV, et al. Fatty liver is associated with dyslipidemia and dysglycemia independent of visceral fat: the Framingham Heart Study. Hepatology. 2010;51(6):1979-87.\u003c/li\u003e\n \u003cli\u003e Kwon YM, Oh SW, Hwang SS, Lee C, Kwon H, Chung GE. Association of nonalcoholic fatty liver disease with components of metabolic syndrome according to body mass index in Korean adults. Am J Gastroenterol. 2012;107(12):1852-8.\u003c/li\u003e\n \u003cli\u003e Ye Q, Zou B, Yeo YH, Li J, Huang DQ, Wu Y, et al. Global prevalence, incidence, and outcomes of non-obese or lean non-alcoholic fatty liver disease: a systematic review and meta-analysis. Lancet Gastroenterol Hepatol. 2020;5(8):739-52.\u003c/li\u003e\n \u003cli\u003e Li Y, Chen Y, Tian X, Zhang S, Jiao J. Comparison of Clinical Characteristics Between Obese and Non-Obese Patients with Nonalcoholic Fatty Liver Disease (NAFLD). Diabetes Metab Syndr Obes. 2021;14:2029-39.\u003c/li\u003e\n \u003cli\u003e Shao C, Ye J, Li F, Lin Y, Wu T, Wang W, et al. Early Predictors of Cardiovascular Disease Risk in Nonalcoholic Fatty Liver Disease: Non-obese Versus Obese Patients. Dig Dis Sci. 2020;65(6):1850-60.\u003c/li\u003e\n \u003cli\u003e Kim SS, Cho HJ, Kim HJ, Kang DR, Berry JR, Kim JH, et al. Nonalcoholic fatty liver disease as a sentinel marker for the development of diabetes mellitus in non-obese subjects. Dig Liver Dis. 2018;50(4):370-7.\u003c/li\u003e\n \u003cli\u003e Ahmed OT, Gidener T, Mara KC, Larson JJ, Therneau TM, Allen AM. Natural History of Nonalcoholic Fatty Liver Disease With Normal Body Mass Index: A Population-Based Study. Clin Gastroenterol Hepatol. 2021.\u003c/li\u003e\n \u003cli\u003e Xie F, Pei Y, Zhou Q, Cao D, Wang Y. Comparison of obesity-related indices for identifying nonalcoholic fatty liver disease: a population-based cross-sectional study in China. Lipids Health Dis. 2021;20(1):132.\u003c/li\u003e\n \u003cli\u003e Xie F, Zhou H, Wang Y. Atherogenic index of plasma is a novel and strong predictor associated with fatty liver: a cross-sectional study in the Chinese Han population. Lipids Health Dis. 2019;18(1):170.\u003c/li\u003e\n \u003cli\u003e Wang Q, Zheng D, Liu J, Fang L, Li Q. Atherogenic index of plasma is a novel predictor of non-alcoholic fatty liver disease in obese participants: a cross-sectional study. Lipids Health Dis. 2018;17(1):284.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp style=\"text-align: center;\"\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e\u003c/p\u003e\n\u003cp style=\"text-align: center;\"\u003e Baseline characteristic of the participants included in the analysis according to AIP quartiles.\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" width=\"22.43975903614458%\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"4\" width=\"68.37349397590361%\"\u003e\n \u003cp\u003eAIP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" width=\"9.186746987951807%\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"24.889867841409693%\"\u003e\n \u003cp\u003eQ1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.110132158590307%\"\u003e\n \u003cp\u003eQ2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"24.889867841409693%\"\u003e\n \u003cp\u003eQ3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.110132158590307%\"\u003e\n \u003cp\u003eQ4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.43975903614458%\"\u003e\n \u003cp\u003eN\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.018072289156628%\"\u003e\n \u003cp\u003e4042\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.16867469879518%\"\u003e\n \u003cp\u003e4044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.018072289156628%\"\u003e\n \u003cp\u003e4043\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.16867469879518%\"\u003e\n \u003cp\u003e4044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.186746987951807%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.43975903614458%\"\u003e\n \u003cp\u003eAIP, mean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.018072289156628%\"\u003e\n \u003cp\u003e0.37 \u0026plusmn; 0.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.16867469879518%\"\u003e\n \u003cp\u003e0.47 \u0026plusmn; 0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.018072289156628%\"\u003e\n \u003cp\u003e0.54 \u0026plusmn; 0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.16867469879518%\"\u003e\n \u003cp\u003e0.65 \u0026plusmn;0.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.186746987951807%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.43975903614458%\"\u003e\n \u003cp\u003eFemale, n (%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.018072289156628%\"\u003e\n \u003cp\u003e2039(50.45%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.16867469879518%\"\u003e\n \u003cp\u003e1917(47.40%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.018072289156628%\"\u003e\n \u003cp\u003e1920(47.49%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.16867469879518%\"\u003e\n \u003cp\u003e1814(44.86%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.186746987951807%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.43975903614458%\"\u003e\n \u003cp\u003eBMI(Kg/m\u003csup\u003e2\u003c/sup\u003e), mean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.018072289156628%\"\u003e\n \u003cp\u003e20.55 \u0026plusmn; 2.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.16867469879518%\"\u003e\n \u003cp\u003e21.14\u0026plusmn;2.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.018072289156628%\"\u003e\n \u003cp\u003e21.62\u0026plusmn;1.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.16867469879518%\"\u003e\n \u003cp\u003e22.21\u0026plusmn;1.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.186746987951807%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.43975903614458%\"\u003e\n \u003cp\u003eAge, mean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.018072289156628%\"\u003e\n \u003cp\u003e42.60\u0026plusmn;14.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.16867469879518%\"\u003e\n \u003cp\u003e42.60\u0026plusmn;14.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.018072289156628%\"\u003e\n \u003cp\u003e43.34 \u0026plusmn;14.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.16867469879518%\"\u003e\n \u003cp\u003e44.36\u0026plusmn;15.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.186746987951807%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.43975903614458%\"\u003e\n \u003cp\u003eSBP\u0026nbsp;(mm Hg), mean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.018072289156628%\"\u003e\n \u003cp\u003e116.80 \u0026plusmn;16.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.16867469879518%\"\u003e\n \u003cp\u003e119.50\u0026plusmn;16.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.018072289156628%\"\u003e\n \u003cp\u003e121.59\u0026plusmn;16.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.16867469879518%\"\u003e\n \u003cp\u003e125.02\u0026plusmn;16.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.186746987951807%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.43975903614458%\"\u003e\n \u003cp\u003eDBP (mm Hg), mean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.018072289156628%\"\u003e\n \u003cp\u003e70.43 \u0026plusmn; 9.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.16867469879518%\"\u003e\n \u003cp\u003e72.14 \u0026plusmn; 10.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.018072289156628%\"\u003e\n \u003cp\u003e73.23 \u0026plusmn; 10.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.16867469879518%\"\u003e\n \u003cp\u003e75.43 \u0026plusmn; 10.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.186746987951807%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.43975903614458%\"\u003e\n \u003cp\u003eALT(U/L), median (Q1-Q3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.018072289156628%\"\u003e\n \u003cp\u003e14.00\u003c/p\u003e\n \u003cp\u003e(11.00-20.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.16867469879518%\"\u003e\n \u003cp\u003e15.00 (12.00-21.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.018072289156628%\"\u003e\n \u003cp\u003e17.00 (13.00-23.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.16867469879518%\"\u003e\n \u003cp\u003e20.00 (14.00-27.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.186746987951807%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.43975903614458%\"\u003e\n \u003cp\u003eAST(U/L), median (Q1-Q3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.018072289156628%\"\u003e\n \u003cp\u003e20.00\u003c/p\u003e\n \u003cp\u003e(18.00-24.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.16867469879518%\"\u003e\n \u003cp\u003e21.00 (18.00-24.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.018072289156628%\"\u003e\n \u003cp\u003e21.00 (18.00-25.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.16867469879518%\"\u003e\n \u003cp\u003e23.00 (19.00-26.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.186746987951807%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.43975903614458%\"\u003e\n \u003cp\u003eALP(U/L), mean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.018072289156628%\"\u003e\n \u003cp\u003e67.34 \u0026plusmn; 21.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.16867469879518%\"\u003e\n \u003cp\u003e70.71 \u0026plusmn; 23.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.018072289156628%\"\u003e\n \u003cp\u003e73.97 \u0026plusmn; 25.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.16867469879518%\"\u003e\n \u003cp\u003e77.01 \u0026plusmn; 21.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.186746987951807%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.43975903614458%\"\u003e\n \u003cp\u003eGGT(U/L), median (Q1-Q3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.018072289156628%\"\u003e\n \u003cp\u003e18.00 (14.00-25.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.16867469879518%\"\u003e\n \u003cp\u003e20.00 (16.00-27.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.018072289156628%\"\u003e\n \u003cp\u003e22.00 (17.00-32.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.16867469879518%\"\u003e\n \u003cp\u003e27.00 (20.00-41.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.186746987951807%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.43975903614458%\"\u003e\n \u003cp\u003eCR (umol/L), median (Q1-Q3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.018072289156628%\"\u003e\n \u003cp\u003e71.00 (62.00-83.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.16867469879518%\"\u003e\n \u003cp\u003e75.00 (65.00-89.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.018072289156628%\"\u003e\n \u003cp\u003e78.00 (65.00-92.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.16867469879518%\"\u003e\n \u003cp\u003e80.00 (65.00-93.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.186746987951807%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.43975903614458%\"\u003e\n \u003cp\u003eUA (umol/L),median (Q1-Q3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.018072289156628%\"\u003e\n \u003cp\u003e233.00\u003c/p\u003e\n \u003cp\u003e(191.00-292.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.16867469879518%\"\u003e\n \u003cp\u003e257.00\u003c/p\u003e\n \u003cp\u003e(209.00-317.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.018072289156628%\"\u003e\n \u003cp\u003e280.00\u003c/p\u003e\n \u003cp\u003e(227.00-344.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.16867469879518%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e311.00\u003c/p\u003e\n \u003cp\u003e(253.00-369.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.186746987951807%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.43975903614458%\"\u003e\n \u003cp\u003eTC(mmol/L), mean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.018072289156628%\"\u003e\n \u003cp\u003e4.28 \u0026plusmn; 0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.16867469879518%\"\u003e\n \u003cp\u003e4.59 \u0026plusmn; 0.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.018072289156628%\"\u003e\n \u003cp\u003e4.68 \u0026plusmn; 0.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.16867469879518%\"\u003e\n \u003cp\u003e4.95 \u0026plusmn; 0.69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.186746987951807%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.43975903614458%\"\u003e\n \u003cp\u003eTG(mmol/L),median (Q1-Q3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.018072289156628%\"\u003e\n \u003cp\u003e0.79 (0.65-1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.16867469879518%\"\u003e\n \u003cp\u003e0.99 (0.78-1.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.018072289156628%\"\u003e\n \u003cp\u003e1.20 (0.93-1.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.16867469879518%\"\u003e\n \u003cp\u003e1.67(1.21-2.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.186746987951807%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.43975903614458%\"\u003e\n \u003cp\u003eHDL-C (mmol/L), mean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.018072289156628%\"\u003e\n \u003cp\u003e1.83 \u0026plusmn; 0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.16867469879518%\"\u003e\n \u003cp\u003e1.57 \u0026plusmn; 0.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.018072289156628%\"\u003e\n \u003cp\u003e1.35 \u0026plusmn; 0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.16867469879518%\"\u003e\n \u003cp\u003e1.10 \u0026plusmn; 0.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.186746987951807%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.43975903614458%\"\u003e\n \u003cp\u003eLDL-C( mmol/L), mean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.018072289156628%\"\u003e\n \u003cp\u003e1.88 \u0026plusmn; 0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.16867469879518%\"\u003e\n \u003cp\u003e2.25 \u0026plusmn; 0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.018072289156628%\"\u003e\n \u003cp\u003e2.40 \u0026plusmn; 0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.16867469879518%\"\u003e\n \u003cp\u003e2.52 \u0026plusmn; 0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.186746987951807%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.43975903614458%\"\u003e\n \u003cp\u003eFasting glucose (mmol/L), mean \u0026plusmn; SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.018072289156628%\"\u003e\n \u003cp\u003e5.02 \u0026plusmn; 0.70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.16867469879518%\"\u003e\n \u003cp\u003e5.11 \u0026plusmn; 0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.018072289156628%\"\u003e\n \u003cp\u003e5.17 \u0026plusmn; 0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.16867469879518%\"\u003e\n \u003cp\u003e5.27 \u0026plusmn; 0.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.186746987951807%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"22.43975903614458%\"\u003e\n \u003cp\u003eNAFLD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.018072289156628%\"\u003e\n \u003cp\u003e194 (4.80%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.16867469879518%\"\u003e\n \u003cp\u003e370(9.15%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.018072289156628%\"\u003e\n \u003cp\u003e632 (15.63%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"17.16867469879518%\"\u003e\n \u003cp\u003e1126(27.84%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.186746987951807%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eAIP: atherogenic index of plasma, BMI:\u0026nbsp;body mass index (BMI), SBP: systolic blood pressure, DBP: diastolic blood pressure, ALP:\u0026nbsp;alkaline phosphatase, GGT: gamma glutamyl transferase, ALT: alanine aminotransferase, AST: aspartate aminotransferase, CR: serum creatinine, UA: uric acid, TC: total cholesterol, TG: total triglycerides, HDL-C:\u0026nbsp;high-density lipoprotein cholesterol, LDL-C: low-density lipoprotein cholesterol.\u003c/p\u003e\n\u003cp style=\"text-align: center;\"\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e\u003c/p\u003e\n\u003cp style=\"text-align: center;\"\u003e Association between AIP and primary outcome in different models.\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.280423280423282%\"\u003e\n \u003cp\u003eAIP quartiles\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.573192239858905%\"\u003e\n \u003cp\u003eModel 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"25.573192239858905%\"\u003e\n \u003cp\u003eModel 2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.573192239858905%\"\u003e\n \u003cp\u003eModel 3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" width=\"49.91181657848325%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"50.08818342151675%\"\u003e\n \u003cp\u003eHR (95%CI) P value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.280423280423282%\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.573192239858905%\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"25.573192239858905%\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.573192239858905%\"\u003e\n \u003cp\u003eRef.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.280423280423282%\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.573192239858905%\"\u003e\n \u003cp\u003e1.92 (1.62, 2.29) P\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"25.573192239858905%\"\u003e\n \u003cp\u003e1.55 (1.30, 1.84) P\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.573192239858905%\"\u003e\n \u003cp\u003e1.56 (1.29, 1.89) P\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.280423280423282%\"\u003e\n \u003cp\u003e3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.573192239858905%\"\u003e\n \u003cp\u003e3.06 (2.60, 3.59) P\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"25.573192239858905%\"\u003e\n \u003cp\u003e2.14 (1.82, 2.51) P\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.573192239858905%\"\u003e\n \u003cp\u003e2.12 (1.78, 2.53) P\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.280423280423282%\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.573192239858905%\"\u003e\n \u003cp\u003e5.00 (4.29, 5.83) P\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"25.573192239858905%\"\u003e\n \u003cp\u003e2.80 (2.40, 3.27) P\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.573192239858905%\"\u003e\n \u003cp\u003e2.78 (2.35, 3.29) P\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.280423280423282%\"\u003e\n \u003cp\u003eP for trend (1 Q increment)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.573192239858905%\"\u003e\n \u003cp\u003e1.67 (1.60, 1.74) P\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" width=\"25.573192239858905%\"\u003e\n \u003cp\u003e1.38 (1.33, 1.44) P\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"25.573192239858905%\"\u003e\n \u003cp\u003e1.37 (1.31, 1.44) P\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.280423280423282%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"25.573192239858905%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"1.0582010582010581%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"24.514991181657848%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd width=\"25.573192239858905%\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003eModel were adjusted for none. Model 2 were adjusted for age, sex and BMI. Model 3 were adjusted for age, sex , BMI, SBP, DBP, ALP, GGT, ALT, AST, CR, UA.\u003c/p\u003e\n\u003cp style=\"text-align: center;\"\u003eTable 3\u003c/p\u003e\n\u003cp style=\"text-align: center;\"\u003eSubgroup analysis for the risk of primary outcome by baseline AIP quartiles (per SD).\u003c/p\u003e\n\u003cdiv align=\"center\"\u003e\n \u003ctable border=\"0\" cellpadding=\"0\" cellspacing=\"0\" width=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.693121693121693%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"3\" width=\"78.3068783068783%\"\u003e\n \u003cp\u003eAIP Quartiles (per SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.731448763250885%\"\u003e\n \u003cp\u003eSubgroup\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.57243816254417%\"\u003e\n \u003cp\u003eHR, 95%CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.081272084805654%\"\u003e\n \u003cp\u003eP value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.614840989399294%\"\u003e\n \u003cp\u003eP for interaction\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.731448763250885%\"\u003e\n \u003cp\u003eSex\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.57243816254417%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.081272084805654%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.614840989399294%\"\u003e\n \u003cp\u003e0.580\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.731448763250885%\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.57243816254417%\"\u003e\n \u003cp\u003e1.31 (1.23, 1.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.081272084805654%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.614840989399294%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.731448763250885%\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.57243816254417%\"\u003e\n \u003cp\u003e1.34 (1.27, 1.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.081272084805654%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.614840989399294%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.731448763250885%\"\u003e\n \u003cp\u003eAge group\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.57243816254417%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.081272084805654%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.614840989399294%\"\u003e\n \u003cp\u003e0.617\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.731448763250885%\"\u003e\n \u003cp\u003e\u0026lt;40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.57243816254417%\"\u003e\n \u003cp\u003e1.34 (1.27, 1.41)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.081272084805654%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.614840989399294%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.731448763250885%\"\u003e\n \u003cp\u003e\u0026gt;=40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.57243816254417%\"\u003e\n \u003cp\u003e1.31 (1.24, 1.39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.081272084805654%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.614840989399294%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.731448763250885%\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.57243816254417%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.081272084805654%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.614840989399294%\"\u003e\n \u003cp\u003e0.126\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.731448763250885%\"\u003e\n \u003cp\u003e\u0026lt;21.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.57243816254417%\"\u003e\n \u003cp\u003e1.49 (1.32, 1.68)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.081272084805654%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.614840989399294%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.731448763250885%\"\u003e\n \u003cp\u003e\u0026gt;=21.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.57243816254417%\"\u003e\n \u003cp\u003e1.34 (1.29, 1.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.081272084805654%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.614840989399294%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.731448763250885%\"\u003e\n \u003cp\u003eALT\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.57243816254417%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.081272084805654%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.614840989399294%\"\u003e\n \u003cp\u003e0.495\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.731448763250885%\"\u003e\n \u003cp\u003e\u0026lt;16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.57243816254417%\"\u003e\n \u003cp\u003e1.35 (1.22, 1.48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.081272084805654%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.614840989399294%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.731448763250885%\"\u003e\n \u003cp\u003e\u0026gt;=16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.57243816254417%\"\u003e\n \u003cp\u003e1.30 (1.24, 1.36)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.081272084805654%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.614840989399294%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.731448763250885%\"\u003e\n \u003cp\u003eSBP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.57243816254417%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.081272084805654%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.614840989399294%\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.731448763250885%\"\u003e\n \u003cp\u003e\u0026lt;120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.57243816254417%\"\u003e\n \u003cp\u003e1.45 (1.34, 1.56)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.081272084805654%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.614840989399294%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"21.731448763250885%\"\u003e\n \u003cp\u003e\u0026gt;=120\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"36.57243816254417%\"\u003e\n \u003cp\u003e1.29 (1.23, 1.35)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"19.081272084805654%\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.614840989399294%\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp style=\"text-align: center;\"\u003eModel were adjusted for all covariates in Model 3 except stratifications itself.\u003c/p\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":"atherogenic index of plasma, Non-alcoholic fatty liver disease, non-obese participants","lastPublishedDoi":"10.21203/rs.3.rs-1485769/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-1485769/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Non-alcoholic fatty liver disease (NAFLD) in the non-obese population accounts for a large proportion of NAFLD. The association between the atherogenic index of plasma (AIP) with the risk of NAFLD was limited, especially among non-obese participants.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e We performed a post-hoc analysis of data obtained from the Dryad data repository and explored the predictive value of AIP on the risk of NAFLD among non-obese participants. \u003c/p\u003e\u003cp\u003e\u003cstrong\u003eResults: \u003c/strong\u003e16173 participants with AIP were included in this study, and 2322(14.4%) non-obese participants developed into individuals with NAFLD with the 5-year follow-up examination. The difference between AIP quartiles in the cumulative estimation of new-onset NAFLD was significant. And with increased AIP, the cumulative primary outcomes gradually increased. Participants in higher AIP quartiles had a significantly increased risk of NAFLD. In the fully adjusted Model 3, HRs of the primary outcome for subjects in Q2, Q3, and Q4 of AIP were 1.56 (1.29, 1.89), 2.12 (1.78, 2.53), and 2.78 (2.35, 3.92) respectively. Meanwhile, the trend test for the association between AIP quartiles and the primary outcome presented that AIP quartile was positively and strongly associated with the primary outcome (adjusted HR (95%CI) in Model 3: 1.37 (1.31, 1.44), P\u0026lt;0.001). In the subgroup analysis, we revealed a significant interaction between systolic blood pressure categories (\u0026lt;120mmHg vs. \u0026gt;=120mmHg; P for interaction =0.011) and NAFLD.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e: This study found AIP was a strong independent risk factor for new-onset NAFLD among non-obese individuals and screening for AIP in this population can be used to prevent future NAFLD.\u0026nbsp;\u003c/p\u003e","manuscriptTitle":"Atherogenic index of plasma is a novel and strong predictor of non-alcoholic fatty liver disease in non-obese participants: a retrospective cohort research of China","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2022-03-28 13:39:34","doi":"10.21203/rs.3.rs-1485769/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":"3f16e53c-d350-4121-a555-60f0e1714012","owner":[],"postedDate":"March 28th, 2022","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2022-03-28T13:44:35+00:00","versionOfRecord":[],"versionCreatedAt":"2022-03-28 13:39:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-1485769","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-1485769","identity":"rs-1485769","version":["v1"]},"buildId":"WrCJVZZCHTDjtuVLN7oU0","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}