DOI: https://doi.org/10.21203/rs.3.rs-1485769/v1
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 vs. >=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.
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.
Data we used in this study was derived from the Dryad data repository at http://datadryad.org/ with the doi:10.5061/dryad.1n6c4.
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.
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).
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.
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.
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.
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).
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 vs. >=40 years), BMI (<21.5 vs. >=21.5 years), ALT（<16U/L vs. >=16 U/L) and SBP (<120mmHg vs. >=120mmHg). There was a significant interaction between SBP (<120mmHg vs. >=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 < 0.001]. The interaction of other subgroups and AIP had no significant effect on the risk of NAFLD, and the P values of interaction were all >0.05. (Table 3)
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, which in turn was weaker 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.
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.
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.
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/m2), 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) |
<0.001 |
|
|
>=40 |
1.31 (1.24, 1.39) |
<0.001 |
|
|
BMI |
|
|
0.126 |
|
<21.5 |
1.49 (1.32, 1.68) |
<0.001 |
|
|
>=21.5 |
1.34 (1.29, 1.40) |
<0.001 |
|
|
ALT |
|
|
0.495 |
|
<16 |
1.35 (1.22, 1.48) |
<0.001 |
|
|
>=16 |
1.30 (1.24, 1.36) |
<0.001 |
|
|
SBP |
|
|
0.011 |
|
<120 |
1.45 (1.34, 1.56) |
<0.001 |
|
|
>=120 |
1.29 (1.23, 1.35) |
<0.001 |
|
Model were adjusted for all covariates in Model 3 except stratifications itself.