
==== Front
Diabetol Metab Syndr
Diabetol Metab Syndr
Diabetology & Metabolic Syndrome
1758-5996
BioMed Central London

1457
10.1186/s13098-024-01457-y
Research
High-density lipoprotein cholesterol trajectory and new-onset metabolic dysfunction-associated fatty liver disease incidence: a longitudinal study
Zhang Mengting 12
Chang Dongchun 2
Guan Qing 2
Dong Rui 2
Zhang Ru 3
Zhang Wei 4
Wang Hongliang 5
Wang Jie wangjienjmu@126.com

2
1 https://ror.org/051jg5p78 grid.429222.d 0000 0004 1798 0228 Department of Gastroenterology, the Third Affiliated Hospital of Soochow University, Changzhou, 213003 Jiangsu China
2 https://ror.org/059gcgy73 grid.89957.3a 0000 0000 9255 8984 Department of Fundamental and Community Nursing, School of Nursing, Nanjing Medical University, Nanjing, 211166 Jiangsu China
3 School of Nursing and Midwifery, Jiangsu College of Nursing, Huai’an, 223003 Jiangsu China
4 https://ror.org/01ty4bg86 grid.419087.3 0000 0004 1789 563X Department of Epidemiology, Shanghai Cancer Institute, Shanghai, 200032 China
5 Department of General Practice, Ninghai Road Community Health Service Center, Gulou District, Nanjing, 210024 Jiangsu China
11 9 2024
11 9 2024
2024
16 22320 1 2024
25 8 2024
© The Author(s) 2024
2024
https://creativecommons.org/licenses/by-nc-nd/4.0/ Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Background

Although high-density lipoprotein cholesterol (HDL-C) exerts a significant influence on the development of metabolic dysfunction-associated fatty liver disease (MAFLD), the association of dynamic changes in HDL-C levels with the risk of MAFLD remains unclear. Thus, the aim of the current study was to explore the association between the changing trajectories of HDL-C and new-onset MAFLD. The findings of this study may provide a theoretical basis for future personalized intervention and prevention targeting MAFLD.

Methods

A total of 1507 participants who met the inclusion criteria were recruited from a community-based physical examination population in Nanjing, China from 2017 to 2021. Group-based trajectory models were constructed to determine the heterogeneous HDL-C trajectories. The incidence of MAFLD in each group in 2022 was followed up, and the Cox proportional hazards regression model was applied to investigate the associations between different HDL-C trajectories and the risk of new-onset MAFLD.

Results

The incidences of MAFLD in the low-stable, moderate-stable, moderate-high-stable, and high-stable groups of HDL-C trajectory were 26.5%, 13.8%, 7.2% and 2.6%, respectively. The incidence rate of MAFLD in the order of the above trajectory groups exhibited a decreasing trend (χ2 = 72.55, Ptrend<0.001). After adjusting for confounders, the risk of MAFLD onset in HDL-C low-stable group was still 5.421 times (95%CI: 1.303–22.554, P = 0.020) higher than that in the high-stable group. Subgroup analyses of the combined (moderate high-stable and high-stable groups combined), moderate-stable and low-stable groups showed that sex, age, and overweight/obesity did not affect the association between HDL-C trajectory and MAFLD risk.

Conclusions

Persistently low HDL-C level is a risk factor for the onset of MAFLD. Long-term monitoring of HDL-C levels and timely intervention for those experiencing persistent declines are crucial for early prevention of MAFLD.

Keywords

Metabolic dysfunction-associated fatty liver disease (MAFLD)
High-density lipoprotein cholesterol (HDL-C)
Trajectory
Longitudinal study
the Six Talent Peaks Project in Jiangsu Province, China2019, WSN-049 2019, WSN-049 2019, WSN-049 2019, WSN-049 2019, WSN-049 “333 Project” of Jiangsu Provincehttp://dx.doi.org/10.13039/501100012246 Priority Academic Program Development of Jiangsu Higher Education Institutions Nursing Science, 2018, No.87 Nursing Science, 2018, No.87 Nursing Science, 2018, No.87 Nursing Science, 2018, No.87 Nursing Science, 2018, No.87 Research and Innovation Team Project of School of Nursing, Nanjing Medical UniversityWisdom Kangyang Industry Institute Key Research Project, Nanjing Medical Universityissue-copyright-statement© BioMed Central Ltd., part of Springer Nature 2024
==== Body
pmcBackground

Metabolic dysfunction-associated fatty liver disease (MAFLD), formerly termed non-alcoholic fatty liver disease (NAFLD) [1], has a wide disease spectrum including simple fatty liver, steatohepatitis, liver fibrosis, cirrhosis, and hepatocellular carcinoma. According to the Global Burden of Disease dataset, MAFLD is the most rapidly increasing cause of cirrhosis, liver failure and liver cancer [2]. It is not merely a liver disease but rather one component of a multi-faceted, multi-organ collection of diseases resulting in a dysfunctional metabolic milieu with diverse effects [3], such as a significantly increased risk of metabolic syndrome, type 2 diabetes, atherosclerosis cardio-renal and peripheral vascular disease [4–6]. In recent years, the prevalence of MAFLD has been escalating annually, currently affecting approximately one-third of the global population [7]. In China, the reported prevalence of MAFLD ranges from 21.0–46.7% [8–12]. It is anticipated that by 2030, the total number of MAFLD patients in China will rise to 314.58 million, becoming the country with the largest increase in prevalence of this disease globally [13]. MAFLD deteriorates the quality of life of patients, imposes a heavy economic burden on society, and has become a serious public health issue. Nevertheless, the clinical development of MAFLD is insidious and frequently marked by an asymptomatic onset. Additionally, there is a scarcity of accurate and reliable non-invasive diagnostic tests, as well as tailored treatments specifically designed and approved for use in patients with MAFLD [14]. Thus, further research on predictors of MAFLD and identification of the high-risk population are essential steps for strengthening the prevention, control, and standardized management of this disease.

There are numerous factors that give rise to an increased risk of having MAFLD, including abnormal levels of blood lipids [15, 16]. Various types of abnormal blood lipid profiles exist, with abnormal cholesterol levels being one of them. Dysregulated cholesterol metabolism is not only a significant contributor to the pathogenesis of MAFLD [17], but also plays an important role in the transition from nonalcoholic steatohepatitis to hepatocellular carcinoma [18]. Cholesterol is transported through lipoproteins in the bloodstream, and high-density lipoprotein (HDL) is one of the most prominent lipoproteins. High-density lipoprotein cholesterol (HDL-C) serves as an indicator for gauging the concentration of cholesterol within HDL. HDL-C possesses the capability to facilitate reverse cholesterol transport (RCT) [19], as HDL absorbs free cholesterol from the cell surface and transports it back to the liver for processing and elimination [16, 20, 21]. Therefore, HDL-C, often regarded as the good cholesterol and known as the “blood vessel scavenger”, lower concentrations of it can be detrimental. The less cholesterol HDL absorbs, the lower the concentration of HDL-C will be, and the more free cholesterol there is, making it more prone to accumulate in the liver, leading to fatty liver [22]. Previous cross-sectional studies have indicated an inverse association between HDL-C and the prevalence of MAFLD [23, 24]. Patients with MAFLD have lower HDL-C levels, especially those with severe MAFLD [25, 26]. In prospective studies, lower baseline HDL-C levels predict the risk of MAFLD during follow-up [27, 28] and are associated with an increased risk of developing hepatocellular carcinoma in patients with liver fibrosis [29]. Based on the evidence above, it can be observed that lower levels of HDL-C are associated with an elevated risk of MAFLD occurrence and disease progression [28, 30–32]. However, these studies [23–32] regarding the relationship between the risk of MAFLD and HDL-C employed only a single measurement of HDL-C levels and did not explore the longitudinal association between changes in HDL-C over time and new-onset of MAFLD. It is widely known that HDL-C levels undergo continuous changes, and long-term monitoring of HDL-C levels is of considerable significance.

Therefore, based on an existing MAFLD population cohort and method of group-based trajectory model (GBTM), the current longitudinal study aimed to investigate the dynamic alternations of HDL-C concentrations and their associations with the risk of MAFLD onset. The findings of the current study may further enhance the understanding of the impact of HDL-C changes on the risk of MAFLD onset and provide more evidence for the identification of appropriate targets for early intervention.

Materials and methods

Study population

A retrospective file review of all individuals aged ≥ 18 years who underwent health examinations in a community in Nanjing, China between 2017 and 2021 was undertaken. The participants were considered as the baseline population when they underwent their initial health examination between 2017 and 2019. A total of 4,283 participants were recorded as having had at least three health examinations over a five-year period. In 2022, they were followed up for one year, and 2,921 participants ultimately attended the health examinations. The exclusion criteria were as follows: (1) participants diagnosed with MAFLD from 2017 to 2021; (2) individuals lacking complete HDL-C data; (3) individuals with autoimmune liver disease, viral hepatitis, cirrhosis, primary liver cancer, etc. As shown in Fig. 1, a total of 1,507 participants were incorporated for analysis. The current study was conducted in accordance with the Declaration of Helsinki, and the study protocol was approved by the Institutional Ethics Review Board of Nanjing Medical University (Nanjing, China). Written informed consent was obtained from all participants.

Fig. 1 Flowchart of the participant inclusion process. Abbreviations: HDL-C, high-density lipoprotein cholesterol; MAFLD, metabolic dysfunction-associated fatty liver disease

Data collection

Demographic information (e.g., age, sex) and anthropometric data (e.g., height, weight, and blood pressure) were collected using a self-designed questionnaire and an electronic medical record system. The surveys were performed by trained investigators. Height, weight, and blood pressure were measured by professional medical staff at the medical examination center using standardized procedures. Body mass index (BMI) was calculated as weight (kg)/height (m)2. Blood pressure was measured in the seated position using a mercury sphygmomanometer at 5-minute intervals, and the average of the two readings was recorded as systolic and diastolic blood pressure.

Blood samples were obtained from the median forearm vein after an 8-hour fasting period. The collected blood samples were promptly analyzed to determine the required biochemical parameters. The main measures included high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), triglyceride (TG), total cholesterol (TC), γ-glutamyl transpeptidase (GGT), alanine aminotransferase (ALT), aspartate transaminase (AST), fasting plasma glucose (FPG), serum uric acid (SUA), total bilirubin (TBil), direct bilirubin (DBil), serum creatinine (Scr), blood urea nitrogen (BUN), and alkaline phosphatase (ALP). All biochemical assays were measured in the same laboratory using an automated biochemical analyzer (Mindray BC-860; Mindray, Shenzhen, China) and standardized methods.

Hepatic steatosis screenings were conducted on the participants using a standardized abdominal ultrasound apparatus (Logiq E9 ultrasound system, General Electric Healthcare, Milwaukee, WI, USA). Examinations were performed by trained sonographers. Two experienced sonographers jointly examined and confirmed the results, observing the near and far-field echogenic changes in the liver and the intrahepatic ductal structures to determine the presence of hepatocellular steatosis.

Definition

According to the diagnostic criteria of MAFLD proposed by the international expert group, MAFLD was defined by the presence of hepatic steatosis (HS) on ultrasound and meeting at least one of three conditions: overweight/obesity (defined as BMI ≥ 23 kg/m2 in Asians), presence of type 2 diabetes mellitus (T2DM), or presence of metabolic disorder [1]. Lean/normal-weight individuals with HS but no T2DM were considered to have a metabolic disorder if two or more of the following metabolic risk abnormalities were present:1) waist circumference ≥ 90/80 cm in Asian males and females; 2) blood pressure ≥ 130/85 mmHg or on antihypertensive therapy; 3) plasma triglycerides ≥ 150 mg/dl (≥ 1.70 mmol/L) or on specific drug therapy; 4) plasma HDL-C < 40 mg/dl (< 1.0 mmol/L) for males and < 50 mg/dl (< 1.3 mmol/L) for females or specific drug therapy; 5) prediabetes (i.e., fasting glucose levels 5.6–6.9 mmol/L, or 2-hour post-load glucose levels 7.8–11.0 mmol/L or HbA1c 5.7-6.4%); 6) homeostasis model assessment of insulin resistance score ≥ 2.5; 7) plasma high-sensitivity C-reactive protein level > 2 mg/L.

Trajectory modelling

The trajectory modelling process was completed in STATA software (Version 16.0, STATA Corporation Houston, TX, USA). Group-Based Trajectory Model (GBTM) was constructed using the traj plugin (https://www.andrew.cmu.edu/user/bjones/traj) in STATA to identify the latent trajectory groups of HDL-C among the included participants [33, 34]. This method identified cluster subgroups of events or outcomes that had similar courses over time or age [34, 35]. In our study, time was equal to years from baseline to the last follow-up visit. The nature of the dependent variable HDL-C (normal distribution) prompted us to employ a censored normal model (CNORM) [34]. The probability of group membership was estimated for each individual, and they were assigned to the group with the highest probability. Model fitting was iteratively performed by comparing models with different numbers of subgroup (1 to 6 groups) and different trajectory shapes (linear, quadratic and cubic). Among the statistically significant models, the model with the smallest absolute value of Bayesian Information Criterion (BIC), the average posterior probability (AvePP) of each trajectory group was not less than 0.7 and the membership of each group was not less than 5% was selected as the best-fit model.

Statistical analysis

All statistical analyses were processed using SPSS (Version 21.0, SPSS Inc., Chicago, IL, USA) and R software (Version 4.3.1). A two-tailed P < 0.05 was regarded as statistically significant. Continuous variables with normal and skewed distributions were described by mean ± standard deviation (SD) and median and interquartile range (IQR), respectively. Categorical variables were described by frequencies and constituent ratios. According to distribution, the Kruskal-Wallis test or one-way analysis of variance was used for comparisons of continuous variables and the chi-square test was conducted to analyze categorical variables. The trend between multiple groups was analyzed by the χ2 test for linear trend. Bonferroni correction was used to control the family-wise type I error rate in all multiple comparisons, and the P-value threshold for significance after correction was 0.05/6 = 0.008. Cox proportional hazard regression was used to estimate the hazard ratio (HR) and 95% confidence interval (95% CI) for quantifying the associations of different HDL-C trajectory groups with the risk of new-onset MAFLD. Kaplan-Meier analysis was used for a graphical presentation of the time to the development of MAFLD, and the Log rank test was used to assess differences among groups. Finally, combined groups showed no statistically significant difference in prevalence. Subgroups and interaction analyses were conducted in accordance with sex, age and BMI because those grouping variables have been reported to be associated with MAFLD risk [36, 37]. Age was classified as < 38 and ≥ 38 years which were determined based on the mean values. The BMI groups were defined in line with the definition of overweight/obesity in the MAFLD diagnostic criteria.

Result

The trajectory of HDL-C

In this study, GBTM identified four distinct trajectory patterns based on the HDL-C change from 2017 to 2021, as illustrated in Fig. 2. The best-fit patterns exhibited a BIC of 817.53 and AvePP of 88.13%, 83.80%, 90.38%, and 92.82% (AvePP > 70.0%), respectively. From low to high, they were named low-stable group (n = 480, 31.85%), moderate-stable group (n = 545, 36.16%), moderate high-stable group (n = 405, 26.88%) and high-stable group (n = 77, 5.11%). The HDL-C moderate-stable group demonstrated a gradual and steady increase. The trends of the low-stable group, moderate high-stable group, and high-stable group were basically the same, with an increasing trend followed by a decreasing trend. Figure 3 showed that the mean HDL-C ranges were 1.06–1.30,1.36–1.66, 1.60–2.10, and 2.04–2.70 in the low-stability, moderate-stable, moderate high-stable and high-stable groups, respectively.

Fig. 2 HDL-C trajectory during 2017–2021. Abbreviations: HDL-C, high-density lipoprotein cholesterol

Fig. 3 HDL-C mean during 2017–2021. Abbreviations: HDL-C, high-density lipoprotein cholesterol

Baseline characteristics

The demographic and clinical baseline characteristics of the participants, stratified by HDL-C trajectories, were presented in Table 1. A total of 1507 participants (956 males and 551 females) with an average age of 42.44 ± 8.51 years were enrolled in this study. There were 399 (83.1%) males and 81 (16.9%) females in low-stable group; 363 (66.6%) males and 182 (33.4%) females in moderate-stable group; 178 (44.0%) males and 227 (56.0%) females in moderate high-stable group; 16 (25.7%) males and 61 (79.2%) females in high-stable group. Significant statistical differences were observed in age, sex, BMI, SBP, DBP, ALT, AST, Scr, BUN, SUA, ALP, FPG, GGT, LDL-C, HDL-C, TG, and TC among different HDL-C trajectories (all P values < 0.05). However, no significant differences were detected between the groups in DBIL and TBIL (all P values > 0.05).

Table 1 Baseline characteristics in different HDL-C trajectory groups

Variables	Total	Low-stable group	Moderate-stable group	Moderate high-stable group	High-stable group	F/χ2/Z	P	
(n=1507)	(n=480)	(n=545)	(n=405)	(n=77)	
Sex						209.288	<0.001 a	
 Male	956(63.4)	399(83.1)	363(66.6)	178(44.0)	16(25.7)			
 Female	551(36.6)	81(16.9)	182(33.4)	227(56.0)	61(79.2)			
Age (years)	37.93±8.37	37.84±8.47	37.71±8.35	37.77±8.23	41.00±8.17	3.666	0.012 b	
BMI (kg/m2)	21.79±2.25	22.64±2.10	21.70±2.13	21.21±2.23	20.32±2.25	42.638	<0.001 b	
SBP (mmHg)	122.79±14.19	124.89±13.75	122.75±14.00	121.70±14.01	121.10±17.28	20.467	<0.001 c	
DBP (mmHg)	74.31±9.66	75.30±9.68	74.37±9.53	73.15±9.51	73.86±10.70	3.402	0.017 b	
ALT (U/L)	15.00(12.00, 21.00)	18.00(13.00, 25.75)	15.00(12.00, 20.00)	14.00(11.00, 18.00)	13.00(11.00, 18.00)	76.734	<0.001 d	
AST (U/L)	18.34±5.97	18.84±6.51	18.37±5.78	17.68±5.54	18.48±5.74	2.795	0.039 b	
DBIL (µmol/L)	4.42±1.65	4.30±1.54	4.54±1.71	4.40±1.66	4.40±1.83	1.815	0.143b	
TBIL (µmol/L)	14.79±6.25	14.80±5.91	15.26±6.57	14.31±6.12	13.94±6.61	2.308	0.075b	
Scr (µmol/L)	65.75±13.33	69.43±12.16	65.93±12.66	62.45±14.02	58.98±14.42	91.526	<0.001 c	
BUN (mmol/L)	5.20±1.27	5.28±1.33	5.26±1.24	5.10±1.19	4.88±1.38	3.528	0.014 b	
SUA (µmol/L)	310.02±75.50	336.76±71.97	310.62±71.59	285.59±75.36	267.66±62.60	46.097	<0.001 b	
ALP (IU/L)	65.41±16.85	67.44±15.89	66.30±16.97	62.84±16.55	59.91±20.45	8.813	<0.001 b	
FPG (mmol/L)	4.34±1.17	4.81±1.19	4.26±1.06	4.00±1.14	3.80±0.90	145.866	<0.001 c	
GGT (mmol/L)	15.00(11.00, 21.00)	17.00(13.00, 23.00)	14.00(11.00, 21.00)	13.00(10.00, 19.00)	11.00(10.00, 18.00)	52.91	<0.001 d	
LDL-C (mmol/L)	2.45±0.68	2.50±0.69	2.52±0.69	2.34±0.66	2.27±0.58	8.376	<0.001 b	
HDL-C (mmol/L)	1.41±0.34	1.09±0.16	1.39±0.18	1.66±0.22	2.12±0.32	1036.604	<0.001 c	
TG (mmol/L)	0.93(0.71, 1.21)	1.17(1.90, 1.48)	0.90(0.72, 1.16)	0.79(0.61, 1.00)	0.70(0.54, 0.88)	253.348	<0.001 d	
TC (mmol/L)	4.31±0.77	4.17±0.76	4.34±0.77	4.37±0.76	4.70±0.68	13.309	<0.001 b	
Notes: Data were present as mean±SD, median (interquartile range) or n (%). aThe variables were tested by χ2 test; bMean±SD, and the variables met the assumption of homogeneity of variance were tested by one-way analysis of variance; cMean±SD, and the variables did not meet the assumption of homogeneity of variance were tested by Kruskal-Wallis test; dMedian (interquartile range), and the variables were tested by Kruskal-Wallis test

Abbreviations: BMI: body mass index; SBP: systolic blood pressure; DBP: diastolic blood pressure; ALT: alanine aminotransferase; AST: aspartate transaminase; DBil: direct bilirubin; TBil: total bilirubin; Scr: serum creatinine; BUN: blood urea nitrogen; SUA: serum uric acid; ALP: alkaline phosphatase; FPG: fasting plasma glucose; GGT: γ-glutamyl transpeptidase; HDL-C: high-density lipoprotein cholesterol; LDL-C: low-density lipoprotein cholesterol; TG: triglyceride; TC: total cholesterol

Incidence of MAFLD in different trajectory groups

The findings of the study in Fig. 4 demonstrated that during an average follow-up period of 4.49 ± 0.62 years, a total of 233 (15.5%, 233/1507) incident cases of MAFLD were detected. The incidence of MAFLD by the end of the final follow-up was 26.5%, 13.8%, 7.2%, and 2.6% in the low-stable, moderate-stable, moderate high-stable, and high-stable groups, respectively. In the order of the above groups, the incidence rate of MAFLD showed a decreasing trend (χ2 = 72.55, Ptrend<0.001). The results from multiple comparisons revealed that the incidence of MAFLD in the low-stable group was significantly higher than that in the other three groups (all P for Bonferroni correction < 0.008), and the moderate-stable group exhibited a higher incidence than both the moderate high-stable and high-stable groups (all P values for Bonferroni correction < 0.008). However, no significant difference was observed in the incidence of MAFLD between the high-stable and moderate high-stable groups (P for Bonferroni correction = 0.135).

Fig. 4 Incidence of MAFLD among different HDL-C trajectory groups. Abbreviations: MAFLD, metabolic dysfunction-associated fatty liver disease

Univariate analysis of predictive factors for MAFLD

The presence of MAFLD at the follow-up health examination (no = 0, yes = 1) was employed as the dependent variable, while independent variables such as sex, age, and BMI were considered predictive factors. A univariate analysis of potential predictive factors for MAFLD was conducted using the Cox hazard regression method, with the results presented in Table 2. The findings indicated that sex, BMI, SBP, DBP, ALT, ASL, DBIL, TBIL, Scr, SUA, ALP, GGT, LDL-C, and TG were influencing factors for the incidence of MAFLD (all P values < 0.05).

Table 2 Univariate Cox proportional hazard associations of MAFLD risk in the study population

Variables	β	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:{S}_{\bar{\chi}}$$\end{document}	Waldχ2	HR (95%CI)	P	
Sex (male vs. female)	1.397	0.184	57.637	4.042(2.818, 5.797)	< 0.001	
Age (years)	-0.004	0.008	0.287	0.996(0.980 1.012)	0.592	
BMI (kg/m2)	0.267	0.031	72.330	1.306(1.228, 1.389)	< 0.001	
SBP (mmHg)	0.014	0.005	8.336	1.014(1.004, 1.023)	0.004	
DBP (mmHg)	0.020	0.007	8.575	1.020(1.007, 1.034)	0.003	
ALT (U/L)	0.020	0.004	27.572	1.020(1.013, 1.028)	< 0.001	
AST (U/L)	0.028	0.009	9.963	1.028(1.010, 1.046)	0.002	
DBIL (µmol/L)	0.086	0.041	4.535	1.090(1.007, 1.180)	0.033	
TBIL (µmol/L)	0.028	0.010	7.674	1.028(1.008, 1.049)	0.006	
Scr (µmol/L)	0.030	0.005	38.035	1.030(1.020, 1.040)	< 0.001	
BUN (mmol/L)	0.093	0.050	3.413	1.097(0.994, 1.211)	0.065	
SUA (µmol/L)	0.007	0.001	63.539	1.007(1.005, 1.008)	< 0.001	
ALP (IU/L)	0.018	0.004	26.166	1.018(1.011, 1.025)	< 0.001	
FPG (mmol/L)	0.019	0.076	0.060	1.019(0.878, 1.182)	0.806	
GGT (mmol/L)	0.007	0.002	12.620	1.007(1.003 1.011)	0.002	
LDL-C (mmol/L)	0.245	0.093	6.946	1.277(1.065, 1.532)	0.008	
TG (mmol/L)	0.541	0.075	51.342	1.717(1.481, 1.991)	< 0.001	
TC (mmol/L)	0.123	0.083	2.190	1.130(0.961, 1.329)	0.139	
Abbreviations: BMI: body mass index; SBP: systolic blood pressure; DBP: diastolic blood pressure; ALT: alanine aminotransferase; AST: aspartate transaminase; DBil: direct bilirubin; TBil: total bilirubin; Scr: serum creatinine; BUN: blood urea nitrogen; SUA: serum uric acid; ALP: alkaline phosphatase; FPG: fasting plasma glucose; GGT: γ-glutamyl transpeptidase; LDL-C: low-density lipoprotein cholesterol; TG: triglyceride; TC: total cholesterol

Relationship between HDL-C trajectory and MAFLD risk

The results of the Kaplan-Meier analysis across the 4 trajectory groups of HDL-C level are depicted in Fig. 5. The MAFLD risk was significantly different between the four trajectory groups (log rank test χ2 = 84.22, P < 0.001). Further multiple comparisons showed that the MAFLD risk was lower in the moderate-stable, moderate high-stable, and high-stable groups than in the low-stable group, and lower in both the moderate high-stable and high-stable groups than in the moderate high-stable group.

Fig. 5 Kaplan-Meier analysis of MAFLD risk according to HDL-C trajectories

A Cox proportional risk regression model was conducted to assess the association between HDL-C trajectories and MAFLD risk. The variables that exhibited a significant association with the new-onset of MAFLD through univariate Cox proportional hazards regression analysis were included as confounding factors and adjusted for in this analysis. The results are shown in Table 3. In the unadjusted model (Model 1), the risk of MAFLD in the low-stable and moderate-stable groups were 13.459 (95% CI: 3.328–54.438) and 6.677 (95% CI: 1.639–27.204) times higher than that in the high-stable group, respectively. After adjusting for sex, BMI, SBP, and DBP (Model 2), the risk of MAFLD in the low-stable and moderate-stable groups were 7.835 (95% CI: 1.924–31.907) and 4.838 (95% CI: 1.183–19.780) times higher than that in the high-stable group. In the fully adjusted model (Model 3), the risk of MAFLD in the low-stable group was 5.421 (95% CI: 1.303–22.554) times higher than that in the high-stable group.

Table 3 Relationship between HDL-C trajectory and MAFLD risk

Variables	β	\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\:{S}_{\bar{\chi}}$$\end{document}	Waldχ2	HR (95%CI)	P	
Model 1	
 high-stable group				1.000		
 moderate high-stable group	1.131	0.731	2.393	3.009(0.739, 12.989)	0.122	
 moderate-stable group	1.899	0.717	7.018	6.677(1.639, 27.204)	0.008	
 low-stable group	2.600	0.713	13.296	13.459(3.328, 54.438)	< 0.001	
Model 2	
 high-stable group				1.000		
 moderate high-stable group	0.806	0.735	1.203	2.238(0.530, 9.445)	0.273	
 moderate-stable group	1.576	0.718	4.814	4.838(1.183, 19.780)	0.028	
 low-stable group	2.059	0.716	8.257	7.835(1.924, 31.907)	0.004	
Model 3	
 high-stable group				1.000		
 moderate high-stable group	0.524	0.743	0.497	1.688(0.394, 7.239)	0.481	
 moderate-stable group	1.200	0.725	2.738	3.320(0.801, 13.754)	0.098	
 low-stable group	1.690	0.727	5.401	5.421(1.303, 22.554)	0.020	
Notes: Model 1 was unadjusted for baseline variables; Model 2 was adjusted for sex, BMI, SBP, and DBP; Model 3 was adjusted for ALT, AST, DBIL, TBIL, Scr, SUA, ALP, GGT, LDL-C and TG on the basis of Model 2

Abbreviations: BMI: body mass index; SBP: systolic blood pressure; DBP: diastolic blood pressure; ALT: alanine aminotransferase; AST: aspartate transaminase; DBil: direct bilirubin; TBil: total bilirubin; Scr: serum creatinine; SUA: serum uric acid; ALP: alkaline phosphatase; GGT: γ-glutamyl transpeptidase; LDL-C: low-density lipoprotein cholesterol; TG: triglyceride

Subgroup and interaction analyses

In light of the absence of statistically significant disparities in incidence between the moderate high-stable and high-stable groups, these two groups were consolidated and designated as the combined group. Subgroup analyses were performed according to sex, age, and BMI. As shown in Table 4, the association between HDL-C trajectories and MAFLD risk was evident in all subgroups (all Ptrend<0.05). Interaction analyses revealed no significant differences in the MAFLD risk among trajectory groups across sex, age, and BMI subgroups (all Pinteraction>0.05).

Table 4 Subgroup analyses for the association between HDL-C trajectories and MAFLD risk

Variables	Combined group	Moderate-stable group	Low-stable group	P trend	P interaction	
Sex	0.217	
 Male	1.000	1.469(0.844, 2.555)	2.436(1.429, 4.153) **	< 0.001		
 Female	1.000	5.077(1.820, 14.166) **	6.145(1.914, 19.724) **	0.001		
Age (years)	0.276	
 < 38	1.000	1.309(0.619, 2.768)	2.805(1.383, 5.688) **	0.001		
 ≥ 38	1.000	2.462(1.290, 4.699) **	2.948(1.518, 5.727) **	0.002		
BMI (kg/m2)	0.665	
 < 23	1.000	2.661(1.366, 5.184) **	4.066(2.030, 8.142) **	< 0.001		
 ≥ 23	1.000	1.184(0.564, 2.485)	2.480(1.237, 4.972) *	0.003		
Notes: The age groups were determined based on the mean values. The BMI groups were defined according to the definition of overweight/obesity in the MAFLD diagnostic criteria. All analyses were adjusted for sex, BMI, SBP, DBP, ALT, AST, DBIL, TBIL, Scr, SUA, ALP, GGT, LDL-C and TG, except for subgroup variables. Date was shown as HR with 95%CI

Abbreviations: BMI: body mass index; SBP: systolic blood pressure; DBP: diastolic blood pressure; ALT: alanine aminotransferase; AST: aspartate transaminase; DBil: direct bilirubin; TBil: total bilirubin; Scr: serum creatinine; SUA: serum uric acid; ALP: alkaline phosphatase; GGT: γ-glutamyl transpeptidase; LDL-C: low-density lipoprotein cholesterol; TG: triglyceride

*P < 0.05, **P < 0.01

Discussion

In this longitudinal study examining participants without MAFLD at baseline, 15.5% developed MAFLD over an average follow-up of 4.51 ± 0.61 years. We conducted a longitudinal analysis of HDL-C changing trajectories from 2017 to 2021 in a community cohort, employing GBTM analysis. Four distinct HDL-C trajectory patterns for 5 years were identified. Compared with those in the moderate-stable group, the moderate high-stable group, and the high-stable group, participants with a persistently low HDL-C level demonstrated a significantly increased risk of MAFLD during the follow-up period.

The incidence of MAFLD has been widely investigated. A global meta-analysis, encompassing 17 countries, revealed an overall estimated incidence of 46.9 cases per 1000 person-years for MAFLD [38]. A meta-analysis focused on MAFLD in Asia reported an incidence of 50.9 per 1,000 person-years, with the highest incidence of 63 per 1,000 person-years in mainland China and the lowest incidence of 29 per 1,000 person-years in Japan [39]. During a Korean cohort study, with a follow-up duration of 348,193.5 person-years, 10,340 participants developed MAFLD at an incidence rate of 29.7 per 1,000 person-years [40]. In addition, a study of 565 participants from Hong Kong with a 3–5 year follow-up estimated that MAFLD incidence was 13.5% [41]. The incidence rate of MAFLD in this study was slightly higher than that in the Hong Kong area, confirming the higher incidence of MAFLD in mainland China.

In order to identify 5-year trajectory groupings and trends in HDL-C, this study employed GBTM for trajectory construction and finalized four different trajectory patterns. In a study involving 330 participants and exploring the effect of lifestyle and biochemical measurements on HDL-C trajectories, the trajectory of HDL-C change was categorized into 3 trajectories [42]. The trajectory trends of Group 1 and Group 2 exhibited a pattern similar to those of the low-stable group, moderate high-stable group and high-stable group in this study, which was characterized by an initial increase and then a subsequent decrease. The trajectory of Group 3 displayed a decreasing trend followed by an increasing trend, and no trajectories similar to it were found in this study. The different number of trajectory groups might be attributed to the different number of study participants.

The above study [42] also reported significant differences in sex, BMI, SBP, DBP, FPG, HDL-C, TG, and TC among the three groups at the baseline, which was consistent with the results in our study. However, in this report, no significant difference in age and LDL-C was observed among different groups, which was inconsistent with our study. Additionally, in our study, it was found that ALT, AST, Scr, BUN, SUA, ALP, and GGT were significantly different at baseline. Previous prospective studies have demonstrated that factors such as sex, BMI, SBP, DBP, ALT, Scr, SUA, GGT, DBIL, TG, and LDL-C contribute to the risk of MAFLD [43, 44]. These observations were consistent with this study. Furthermore, our study also discovered that AST, ALP, and TBIL were also risk factors for the risk of MAFLD. The elevation of AST indicated liver damage, and related studies showed that in instances where FibroScan was unavailable, the AST to platelet ratio index (APRI) was one of the best indicators for evaluating liver fibrosis in MAFLD patients [45]. ALP and TBIL were also crucial indicators reflecting liver function. However, the specific mechanisms through which these parameters contributed to the development of MAFLD remain unclear and necessitate further exploration.

In further analysis, after adjusting for sex, BMI, SBP, and DBP, the risk of MAFLD in the low-stable group of HDL-C trajectory was 7.835 times higher than that in the high-stable group. On this basis, after adjusting for ALT, AST, Scr, SUA, ALP, GGT, LDL-C and TG, the risk of MAFLD in the low-stable group of HDL-C trajectory was 5.421 times higher than that in the high-stable group. Therefore, a persistent reduction in HDL-C level could be an important clue to the development of MAFLD, and the risk of MAFLD was the highest when HDL-C was 1.06–1.30 mmol/L in the low-stable group. In a cohort study with a 6-year follow-up, baseline HDL-C remained significantly and independently associated with MAFLD incidence [27]. In another health screening cohort study, it was shown that the incidence of MAFLD decreased as HDL-C quartiles rose [28]. This finding was consistent with the results of our study. MAFLD was regarded as a manifestation of the metabolic syndrome in the liver [44], and a decreased HDL level was itself a feature of the metabolic syndrome [46]. Many studies showed that the imbalance of cholesterol efflux activity in HDL-C function might be the main cause of metabolic abnormality [26, 47, 48], which might support the results of this study to a certain extent.

The findings from the subgroup analysis demonstrated that the association between HDL-C trajectory and the risk of MAFLD was unaffected by sex, age, and overweight/obesity. In fact, the impact of sex on MAFLD remained inconclusive, and the mechanisms underlying sex differences were still unclear [49]. Previous research reports had indicated that overweight/obesity was a significant risk factor for MAFLD [50]. However, a meta-analysis indicated that MAFLD might also occur in non-obese populations [51]. Our study, for the first time, found a correlation between HDL-C changing trajectory and the incidence of MAFLD, independent of sex and overweight/obesity. These results and the involved mechanisms warrant further investigation.

Predicting the progression of MAFLD using biochemical indicators has been a prominent research topic in recent years. Owing to the insufficiency of a single indicator test, liver function indices such as ALT, AST, and GGT are subject to individual reactivity, resulting in limited sensitivity and specificity. Furthermore, serum lipid indices TC and TG are susceptible to interferon levels within the patient’s body, leading to fluctuations in serum concentrations. Recent studies have instead concentrated on the combination of HDL-C with other biochemical indices. A cross-sectional study indicated that GGT/HDL-C ratio could serve as a predictive factor for the prevalence of MAFLD [26]. A secondary prospective cohort study conducted among non-obese Chinese adults revealed a positive and non-linear relationship between the GGT/HDL-C ratio and the risk of MAFLD [52]. TG/HDL-C was a potential risk factor for MAFLD and could predict the incident fatty liver [53–55]. SUA/HDL-C was inexpensive and easy-to-assess, thereby being regarded as a useful tool in diagnosing hepatic steatosis [56]. In addition, research has indicated that small dense low-density lipoprotein cholesterol (sdLDL-C) levels were specifically elevated in patients with diabetes and MAFLD [57]. The elevated ratio of sdLDL-C to HDL‐C (SHR) is independently linked to an increased risk of MAFLD in patients with T2DM, and may serve as practical indicators for assessing the risk of MAFLD in T2DM patients [58]. However, most studies only considered HDL-C levels at baseline or at a point in time, ignoring the continuity of HDL-C. In China, researchers explored the relationship between HDL-C trajectory and the new-onset of diabetes mellitus (DM) and clarified the role of HDL-C changes in the process of DM [59]. This showed that HDL-C indicators might be closely associated with metabolic-related diseases. Therefore, in this study, GBTM was employed to develop a dynamic HDL-C trajectory for non-MAFLD patients. To the best of our knowledge, this is the first exploration of the interrelation between diverse HDL-C alteration trajectories and the progression of MAFLD.

The main strengths of the study lied in the longitudinal study design and the application of GBTM trajectory analysis, which provided robust epidemiological evidence for the dynamic change of HDL-C level and its relationship with the incidence of MAFLD in Chinese population. The high-risk population of MAFLD can be screened early by monitoring the change of HDL-C. However, this study had some limitations. Firstly, we were incapable of employing liver tissue biopsy as a diagnostic criterion for hepatic steatosis. Given the difficulties associated with liver biopsy in general population screening, abdominal ultrasound was utilized to identify steatosis. Secondly, our study was deficient in relevant information regarding lifestyle factors, such as exercise and diet, that may affect HDL-C levels. Thirdly, our sample was confined to Chinese adults in a community, and it remains ambiguous whether the study findings can be extrapolated to other populations. Consequently, further research is required to adjust for possible confounding factors and corroborate these results.

In summary, our findings revealed a significant correlation between HDL-C changing trajectory and the risk of developing MAFLD. The incidence of MAFLD in the low-stable HDL-C dynamic level was significantly higher than that in the other 3 trajectory groups. This suggested that monitoring longitudinal patterns for HDL-C change and lipid management may provide an inspiration for targeted intervention and prevention of MAFLD.

Acknowledgements

We thank all doctors and nurses who dedicated to the data collection and kind suggestions.

Author contributions

Mengting Zhang: Formal analysis; Visualization; Writing—original draft. Dongchun Chang: Formal analysis; Visualization; Writing—review and editing. Qing Guan: Data curation. Rui Dong: Data curation. Ru Zhang: Methodology; Writing—review and editing. Wei Zhang: Writing—review and editing. Hongliang: Wang: Investigation. Jie Wang: Supervision; Writing - review & editing; Funding acquisition. All authors reviewed and approved the final version of the submitted manuscript.

Funding

This work was supported by the Six Talent Peaks Project in Jiangsu Province, China (2019, WSN-049), “333 Project” of Jiangsu Province, Priority Academic Program Development of Jiangsu Higher Education Institutions (Nursing Science, 2018, No.87), Research and Innovation Team Project of School of Nursing, Nanjing Medical University, and Wisdom Kangyang Industry Institute Key Research Project, Nanjing Medical University.

Data availability

For reasons of personal privacy, the data supporting the results of this study cannot be made public. The data are available from the authors.

Declarations

Ethics approval

The study protocol was approved by the Institutional Ethics Review Board of Nanjing Medical University (Nanjing, China).

Consent to participate

Informed written consents were obtained from all participants.

Consent for publication

Not applicable.

Competing interests

The authors declare no competing interests.

Abbreviations

ALP alkaline phosphatase

ALT alanine aminotransferase

AST aspartate transaminase

AvePP average posterior probability

BIC bayesian information criterion

BMI body mass index

BUN blood urea nitrogen

CNORM censored normal model

DBil direct bilirubin

DBP diastolic blood pressure

FPG fasting plasma glucose

GGT γ-glutamyl transpeptidase

GBTM group-based trajectory model

HDL-C high-density lipoprotein cholesterol

LDL-C low-density lipoprotein cholesterol

MAFLD metabolic dysfunction-associated fatty liver disease

NAFLD non-alcoholic fatty liver disease

SBP systolic blood pressure

Scr serum creatinine

SUA serum uric acid

T2DM diabetes mellitus

TBil total bilirubin

TC total cholesterol

TG triglyceride

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Mengting Zhang and Dongchun Chang contributed equally to this work.
==== Refs
References

1. Eslam M Newsome PN Sarin SK Anstee QM Targher G Romero-Gomez M Zelber-Sagi S Wai-Sun Wong V Dufour JF Schattenberg JM A new definition for metabolic dysfunction-associated fatty liver disease: an international expert consensus statement J Hepatol 2020 73 202 9 10.1016/j.jhep.2020.03.039 32278004
Eslam M, Newsome PN, Sarin SK, Anstee QM, Targher G, Romero-Gomez M, Zelber-Sagi S, Wai-Sun Wong V, Dufour JF, Schattenberg JM, et al. A new definition for metabolic dysfunction-associated fatty liver disease: an international expert consensus statement. J Hepatol. 2020;73:202–9. https://www.ncbi.nlm.nih.gov/pubmed/32278004.32278004 10.1016/j.jhep.2020.03.039
2. Paik JM, Henry L, De Avila L, Younossi E, Racila A, Younossi ZM. Mortality related to nonalcoholic fatty liver disease is increasing in the United States. Hepatol Commun 2019, 3:1459–71https://www.ncbi.nlm.nih.gov/pubmed/31701070
3. Eslam M George J Genetic contributions to NAFLD: leveraging shared genetics to uncover systems biology Nat Rev Gastroenterol Hepatol 2020 17 40 52 10.1038/s41575-019-0212-0 31641249
Eslam M, George J. Genetic contributions to NAFLD: leveraging shared genetics to uncover systems biology. Nat Rev Gastroenterol Hepatol. 2020;17:40–52. https://www.ncbi.nlm.nih.gov/pubmed/31641249.31641249 10.1038/s41575-019-0212-0
4. Byrne CD Targher G NAFLD: a multisystem disease J Hepatol 2015 62 S47 64 10.1016/j.jhep.2014.12.012 25920090
Byrne CD, Targher G. NAFLD: a multisystem disease. J Hepatol. 2015;62:S47–64. https://www.ncbi.nlm.nih.gov/pubmed/25920090.25920090 10.1016/j.jhep.2014.12.012
5. Targher G, Byrne CD, Lonardo A, Zoppini G, Barbui C. Non-alcoholic fatty liver disease and risk of incident cardiovascular disease: a meta-analysis. J Hepatol 2016, 65:589–600https://www.ncbi.nlm.nih.gov/pubmed/27212244
6. Younossi ZM Non-alcoholic fatty liver disease - A global public health perspective J Hepatol 2019 70 531 44 10.1016/j.jhep.2018.10.033 30414863
Younossi ZM. Non-alcoholic fatty liver disease - A global public health perspective. J Hepatol. 2019;70:531–44. https://www.ncbi.nlm.nih.gov/pubmed/30414863.30414863 10.1016/j.jhep.2018.10.033
7. Eslam M El-Serag HB Francque S Sarin SK Wei L Bugianesi E George J Metabolic (dysfunction)-associated fatty liver disease in individuals of normal weight Nat Rev Gastroenterol Hepatol 2022 19 638 51 10.1038/s41575-022-00635-5 35710982
Eslam M, El-Serag HB, Francque S, Sarin SK, Wei L, Bugianesi E, George J. Metabolic (dysfunction)-associated fatty liver disease in individuals of normal weight. Nat Rev Gastroenterol Hepatol. 2022;19:638–51. https://www.ncbi.nlm.nih.gov/pubmed/35710982.35710982 10.1038/s41575-022-00635-5
8. Zeng J Qin L Jin Q Yang RX Ning G Su Q Yang Z Fan JG Prevalence and characteristics of MAFLD in Chinese adults aged 40 years or older: a community-based study Hepatobiliary Pancreat Dis Int 2022 21 154 61 10.1016/j.hbpd.2022.01.006 35153138
Zeng J, Qin L, Jin Q, Yang RX, Ning G, Su Q, Yang Z, Fan JG. Prevalence and characteristics of MAFLD in Chinese adults aged 40 years or older: a community-based study. Hepatobiliary Pancreat Dis Int. 2022;21:154–61. https://www.ncbi.nlm.nih.gov/pubmed/35153138.35153138 10.1016/j.hbpd.2022.01.006
9. Wang X Wu S Yuan X Chen S Fu Q Sun Y Lan Y Hu S Wang Y Lu Y Metabolic dysfunction-associated fatty liver disease and mortality among Chinese adults: a prospective cohort study J Clin Endocrinol Metab 2022 107 e745 55 10.1210/clinem/dgab644 34467980
Wang X, Wu S, Yuan X, Chen S, Fu Q, Sun Y, Lan Y, Hu S, Wang Y, Lu Y, et al. Metabolic dysfunction-associated fatty liver disease and mortality among Chinese adults: a prospective cohort study. J Clin Endocrinol Metab. 2022;107:e745–55. https://www.ncbi.nlm.nih.gov/pubmed/34467980.34467980 10.1210/clinem/dgab644
10. Liang Y Chen H Liu Y Hou X Wei L Bao Y Yang C Zong G Wu J Jia W Association of MAFLD with Diabetes, chronic kidney Disease, and Cardiovascular Disease: A 4.6-Year Cohort Study in China J Clin Endocrinol Metab 2022 107 88 97 10.1210/clinem/dgab641 34508601
Liang Y, Chen H, Liu Y, Hou X, Wei L, Bao Y, Yang C, Zong G, Wu J, Jia W. Association of MAFLD with Diabetes, chronic kidney Disease, and Cardiovascular Disease: A 4.6-Year Cohort Study in China. J Clin Endocrinol Metab. 2022;107:88–97. https://www.ncbi.nlm.nih.gov/pubmed/34508601.34508601 10.1210/clinem/dgab641
11. Fan J Luo S Ye Y Ju J Zhang Z Liu L Yang J Xia M Prevalence and risk factors of metabolic associated fatty liver disease in the contemporary South China population Nutr Metab (Lond) 2021 18 82 10.1186/s12986-021-00611-x 34496912
Fan J, Luo S, Ye Y, Ju J, Zhang Z, Liu L, Yang J, Xia M. Prevalence and risk factors of metabolic associated fatty liver disease in the contemporary South China population. Nutr Metab (Lond). 2021;18:82. https://www.ncbi.nlm.nih.gov/pubmed/34496912.34496912 10.1186/s12986-021-00611-x
12. Li H, Guo M, An Z, Meng J, Jiang J, Song J, Wu W. Prevalence and risk factors of metabolic Associated fatty liver disease in Xinxiang, China. Int J Environ Res Public Health. 2020;17. https://www.ncbi.nlm.nih.gov/pubmed/32168920
13. Zhou J Zhou F Wang W Zhang XJ Ji YX Zhang P She ZG Zhu L Cai J Li H Epidemiological features of NAFLD from 1999 to 2018 in China Hepatology 2020 71 1851 64 10.1002/hep.31150 32012320
Zhou J, Zhou F, Wang W, Zhang XJ, Ji YX, Zhang P, She ZG, Zhu L, Cai J, Li H. Epidemiological features of NAFLD from 1999 to 2018 in China. Hepatology. 2020;71:1851–64.32012320 10.1002/hep.31150
14. Barber TM, Kabisch S, Pfeiffer AFH, Weickert MO. Metabolic-Associated fatty liver disease and insulin resistance: a review of Complex interlinks. Metabolites. 2023;13. https://www.ncbi.nlm.nih.gov/pubmed/37367914
15. Malhotra P Gill RK Saksena S Alrefai WA Disturbances in Cholesterol Homeostasis and non-alcoholic fatty liver diseases Front Med (Lausanne) 2020 7 467 10.3389/fmed.2020.00467 32984364
Malhotra P, Gill RK, Saksena S, Alrefai WA. Disturbances in Cholesterol Homeostasis and non-alcoholic fatty liver diseases. Front Med (Lausanne). 2020;7:467. https://www.ncbi.nlm.nih.gov/pubmed/32984364.32984364 10.3389/fmed.2020.00467
16. Francque SM Marchesini G Kautz A Walmsley M Dorner R Lazarus JV Zelber-Sagi S Hallsworth K Busetto L Fruhbeck G Non-alcoholic fatty liver disease: a patient guideline JHEP Rep 2021 3 100322 10.1016/j.jhepr.2021.100322 34693236
Francque SM, Marchesini G, Kautz A, Walmsley M, Dorner R, Lazarus JV, Zelber-Sagi S, Hallsworth K, Busetto L, Fruhbeck G, et al. Non-alcoholic fatty liver disease: a patient guideline. JHEP Rep. 2021;3:100322. https://www.ncbi.nlm.nih.gov/pubmed/34693236.34693236 10.1016/j.jhepr.2021.100322
17. Vos DY van de Sluis B Function of the endolysosomal network in cholesterol homeostasis and metabolic-associated fatty liver disease (MAFLD) Mol Metab 2021 50 101146 10.1016/j.molmet.2020.101146 33348067
Vos DY, van de Sluis B. Function of the endolysosomal network in cholesterol homeostasis and metabolic-associated fatty liver disease (MAFLD). Mol Metab. 2021;50:101146.33348067 10.1016/j.molmet.2020.101146
18. Gutiérrez-Cuevas J, Lucano-Landeros S, López-Cifuentes D, Santos A, Armendariz-Borunda J. Epidemiologic, Genetic, Pathogenic, Metabolic, Epigenetic Aspects Involved in NASH-HCC: Current Therapeutic Strategies. Cancers (Basel). 2022;15.
19. Gutiérrez-Cuevas J, Santos A, Armendariz-Borunda J. Pathophysiological molecular mechanisms of obesity: a link between MAFLD and NASH with Cardiovascular diseases. Int J Mol Sci. 2021;22.
20. Mocciaro G, Allison M, Jenkins B, Azzu V, Huang-Doran I, Herrera-Marcos LV, Hall Z, Murgia A, Susan D, Frontini M et al. Non-alcoholic fatty liver disease is characterised by a reduced polyunsaturated fatty acid transport via free fatty acids and high-density lipoproteins (HDL). Mol Metab. 2023;73:101728https://www.ncbi.nlm.nih.gov/pubmed/37084865
21. Heeren J Scheja L Metabolic-associated fatty liver disease and lipoprotein metabolism Mol Metab 2021 50 101238 10.1016/j.molmet.2021.101238 33892169
Heeren J, Scheja L. Metabolic-associated fatty liver disease and lipoprotein metabolism. Mol Metab. 2021;50:101238. https://www.ncbi.nlm.nih.gov/pubmed/33892169.33892169 10.1016/j.molmet.2021.101238
22. Konings M, Baumgartner S, Mensink RP, Plat J. Investigating microRNAs to explain the link between Cholesterol Metabolism and NAFLD in humans: a systematic review. Nutrients; 2022. p. 14.
23. Speliotes EK, Massaro JM, Hoffmann U, Vasan RS, Meigs JB, Sahani DV, Hirschhorn JN, O’Donnell CJ, Fox CS. Fatty liver is associated with dyslipidemia and dysglycemia independent of visceral fat: the Framingham Heart Study. Hepatology. 2010;51:1979–87. https://www.ncbi.nlm.nih.gov/pubmed/20336705.
24. DeFilippis AP Blaha MJ Martin SS Reed RM Jones SR Nasir K Blumenthal RS Budoff MJ Nonalcoholic fatty liver disease and serum lipoproteins: the multi-ethnic study of atherosclerosis Atherosclerosis 2013 227 429 36 10.1016/j.atherosclerosis.2013.01.022 23419204
DeFilippis AP, Blaha MJ, Martin SS, Reed RM, Jones SR, Nasir K, Blumenthal RS, Budoff MJ. Nonalcoholic fatty liver disease and serum lipoproteins: the multi-ethnic study of atherosclerosis. Atherosclerosis. 2013;227:429–36. https://www.ncbi.nlm.nih.gov/pubmed/23419204.23419204 10.1016/j.atherosclerosis.2013.01.022
25. Wu KT Kuo PL Su SB Chen YY Yeh ML Huang CI Yang JF Lin CI Hsieh MH Hsieh MY Nonalcoholic fatty liver disease severity is associated with the ratios of total cholesterol and triglycerides to high-density lipoprotein cholesterol J Clin Lipidol 2016 10 420 e425421 10.1016/j.jacl.2015.12.026 27055973
Wu KT, Kuo PL, Su SB, Chen YY, Yeh ML, Huang CI, Yang JF, Lin CI, Hsieh MH, Hsieh MY, et al. Nonalcoholic fatty liver disease severity is associated with the ratios of total cholesterol and triglycerides to high-density lipoprotein cholesterol. J Clin Lipidol. 2016;10:420–e425421. https://www.ncbi.nlm.nih.gov/pubmed/27055973.27055973 10.1016/j.jacl.2015.12.026
26. Feng G, Feng L, Zhao Y. Association between ratio of gamma-glutamyl transpeptidase to high-density lipoprotein cholesterol and prevalence of nonalcoholic fatty liver disease and metabolic syndrome: a cross-sectional study. Ann Transl Med. 2020;8:634https://www.ncbi.nlm.nih.gov/pubmed/32566571
27. Wu J He S Xu H Chi X Sun J Wang X Gao X Wu R Shao M Zhao H Non-alcoholic fatty liver disease incidence, remission and risk factors among a general Chinese population with a 6-year follow-up Sci Rep 2018 8 7557 10.1038/s41598-018-25641-z 29765064
Wu J, He S, Xu H, Chi X, Sun J, Wang X, Gao X, Wu R, Shao M, Zhao H, et al. Non-alcoholic fatty liver disease incidence, remission and risk factors among a general Chinese population with a 6-year follow-up. Sci Rep. 2018;8:7557. https://www.ncbi.nlm.nih.gov/pubmed/29765064.29765064 10.1038/s41598-018-25641-z
28. Ren XY, Shi D, Ding J, Cheng ZY, Li HY, Li JS, Pu HQ, Yang AM, He CL, Zhang JP et al. Total cholesterol to high-density lipoprotein cholesterol ratio is a significant predictor of nonalcoholic fatty liver: Jinchang cohort study. Lipids Health Dis, 2019;18:47https://www.ncbi.nlm.nih.gov/pubmed/30744645
29. Crudele L De Matteis C Piccinin E Gadaleta RM Cariello M Di Buduo E Piazzolla G Suppressa P Berardi E Sabba C Moschetta A Low HDL-cholesterol levels predict hepatocellular carcinoma development in individuals with liver fibrosis JHEP Rep 2023 5 100627 10.1016/j.jhepr.2022.100627 36561127
Crudele L, De Matteis C, Piccinin E, Gadaleta RM, Cariello M, Di Buduo E, Piazzolla G, Suppressa P, Berardi E, Sabba C, Moschetta A. Low HDL-cholesterol levels predict hepatocellular carcinoma development in individuals with liver fibrosis. JHEP Rep. 2023;5:100627. https://www.ncbi.nlm.nih.gov/pubmed/36561127.36561127 10.1016/j.jhepr.2022.100627
30. Katsiki N Mikhailidis DP Mantzoros CS Non-alcoholic fatty liver disease and dyslipidemia: an update Metabolism 2016 65 1109 23 10.1016/j.metabol.2016.05.003 27237577
Katsiki N, Mikhailidis DP, Mantzoros CS. Non-alcoholic fatty liver disease and dyslipidemia: an update. Metabolism. 2016;65:1109–23. https://www.ncbi.nlm.nih.gov/pubmed/27237577.27237577 10.1016/j.metabol.2016.05.003
31. Xie J Huang H Liu Z Li Y Yu C Xu L Xu C The associations between modifiable risk factors and nonalcoholic fatty liver disease: a comprehensive mendelian randomization study Hepatology 2023 77 949 64 35971878
Xie J, Huang H, Liu Z, Li Y, Yu C, Xu L, Xu C. The associations between modifiable risk factors and nonalcoholic fatty liver disease: a comprehensive mendelian randomization study. Hepatology. 2023;77:949–64. https://www.ncbi.nlm.nih.gov/pubmed/35971878.35971878
32. Mato JM Alonso C Noureddin M Lu SC Biomarkers and subtypes of deranged lipid metabolism in non-alcoholic fatty liver disease World J Gastroenterol 2019 25 3009 20 10.3748/wjg.v25.i24.3009 31293337
Mato JM, Alonso C, Noureddin M, Lu SC. Biomarkers and subtypes of deranged lipid metabolism in non-alcoholic fatty liver disease. World J Gastroenterol. 2019;25:3009–20. https://www.ncbi.nlm.nih.gov/pubmed/31293337.31293337 10.3748/wjg.v25.i24.3009
33. Jones BL Nagin DS A note on a Stata Plugin for Estimating Group-based Trajectory models Sociol Methods Res 2013 42 608 13 10.1177/0049124113503141
Jones BL, Nagin DS. A note on a Stata Plugin for Estimating Group-based Trajectory models. Sociol Methods Res. 2013;42:608–13. ://WOS:000330315100007.10.1177/0049124113503141
34. Nagin DS Odgers CL Group-based trajectory modeling in clinical research Annu Rev Clin Psychol 2010 6 109 38 10.1146/annurev.clinpsy.121208.131413 20192788
Nagin DS, Odgers CL. Group-based trajectory modeling in clinical research. Annu Rev Clin Psychol. 2010;6:109–38. https://www.ncbi.nlm.nih.gov/pubmed/20192788.20192788 10.1146/annurev.clinpsy.121208.131413
35. Nagin DS Jones BL Passos VL Tremblay RE Group-based multi-trajectory modeling Stat Methods Med Res 2018 27 2015 23 10.1177/0962280216673085 29846144
Nagin DS, Jones BL, Passos VL, Tremblay RE. Group-based multi-trajectory modeling. Stat Methods Med Res. 2018;27:2015–23. https://www.ncbi.nlm.nih.gov/pubmed/29846144.29846144 10.1177/0962280216673085
36. Tian T, Zhang J, Xie W, Ni Y, Fang X, Liu M, Peng X, Wang J, Dai Y, Zhou Y. Dietary Quality and relationships with Metabolic Dysfunction-Associated fatty liver Disease (MAFLD) among United States adults, results from NHANES 2017–2018. Nutrients; 2022. p. 14.
37. Lim GEH Tang A Ng CH Chin YH Lim WH Tan DJH Yong JN Xiao J Lee CW Chan M An Observational Data Meta-Analysis on the differences in prevalence and risk factors between MAFLD vs NAFLD Clin Gastroenterol Hepatol 2023 21 619 e629617 10.1016/j.cgh.2021.11.038 34871813
Lim GEH, Tang A, Ng CH, Chin YH, Lim WH, Tan DJH, Yong JN, Xiao J, Lee CW, Chan M, et al. An Observational Data Meta-Analysis on the differences in prevalence and risk factors between MAFLD vs NAFLD. Clin Gastroenterol Hepatol. 2023;21:619–e629617.34871813 10.1016/j.cgh.2021.11.038
38. Riazi K Azhari H Charette JH Underwood FE King JA Afshar EE Swain MG Congly SE Kaplan GG Shaheen AA The prevalence and incidence of NAFLD worldwide: a systematic review and meta-analysis Lancet Gastroenterol Hepatol 2022 7 851 61 10.1016/S2468-1253(22)00165-0 35798021
Riazi K, Azhari H, Charette JH, Underwood FE, King JA, Afshar EE, Swain MG, Congly SE, Kaplan GG, Shaheen AA. The prevalence and incidence of NAFLD worldwide: a systematic review and meta-analysis. Lancet Gastroenterol Hepatol. 2022;7:851–61. https://www.ncbi.nlm.nih.gov/pubmed/35798021.35798021 10.1016/S2468-1253(22)00165-0
39. Li J Zou B Yeo YH Feng Y Xie X Lee DH Fujii H Wu Y Kam LY Ji F Prevalence, incidence, and outcome of non-alcoholic fatty liver disease in Asia, 1999–2019: a systematic review and meta-analysis Lancet Gastroenterol Hepatol 2019 4 389 98 10.1016/S2468-1253(19)30039-1 30902670
Li J, Zou B, Yeo YH, Feng Y, Xie X, Lee DH, Fujii H, Wu Y, Kam LY, Ji F, et al. Prevalence, incidence, and outcome of non-alcoholic fatty liver disease in Asia, 1999–2019: a systematic review and meta-analysis. Lancet Gastroenterol Hepatol. 2019;4:389–98. https://www.ncbi.nlm.nih.gov/pubmed/30902670.30902670 10.1016/S2468-1253(19)30039-1
40. Chang Y Jung HS Cho J Zhang Y Yun KE Lazo M Pastor-Barriuso R Ahn J Kim CW Rampal S Metabolically healthy obesity and the development of nonalcoholic fatty liver disease Am J Gastroenterol 2016 111 1133 40 10.1038/ajg.2016.178 27185080
Chang Y, Jung HS, Cho J, Zhang Y, Yun KE, Lazo M, Pastor-Barriuso R, Ahn J, Kim CW, Rampal S, et al. Metabolically healthy obesity and the development of nonalcoholic fatty liver disease. Am J Gastroenterol. 2016;111:1133–40. https://www.ncbi.nlm.nih.gov/pubmed/27185080.27185080 10.1038/ajg.2016.178
41. Wong VW Wong GL Yeung DK Lau TK Chan CK Chim AM Abrigo JM Chan RS Woo J Tse YK Incidence of non-alcoholic fatty liver disease in Hong Kong: a population study with paired proton-magnetic resonance spectroscopy J Hepatol 2015 62 182 9 10.1016/j.jhep.2014.08.041 25195550
Wong VW, Wong GL, Yeung DK, Lau TK, Chan CK, Chim AM, Abrigo JM, Chan RS, Woo J, Tse YK, et al. Incidence of non-alcoholic fatty liver disease in Hong Kong: a population study with paired proton-magnetic resonance spectroscopy. J Hepatol. 2015;62:182–9. https://www.ncbi.nlm.nih.gov/pubmed/25195550.25195550 10.1016/j.jhep.2014.08.041
42. Lee YC, Chien KL, Lee BC, Lin HJ, Hsu HC, Chen MF. High-density lipoprotein-cholesterol trajectory pattern, associated lifestyle and biochemical factors among Taiwanese. Circ J. 2009;73:1887–92https://www.ncbi.nlm.nih.gov/pubmed/19661721
43. Ji L Cai X Bai Y Li T Application of a Novel Prediction Model for Predicting 2-Year risk of non-alcoholic fatty liver disease in the non-obese Population with normal blood lipid levels: a large prospective cohort study from China Int J Gen Med 2021 14 2909 22 10.2147/IJGM.S319759 34234521
Ji L, Cai X, Bai Y, Li T. Application of a Novel Prediction Model for Predicting 2-Year risk of non-alcoholic fatty liver disease in the non-obese Population with normal blood lipid levels: a large prospective cohort study from China. Int J Gen Med. 2021;14:2909–22. https://www.ncbi.nlm.nih.gov/pubmed/34234521.34234521 10.2147/IJGM.S319759
44. Xing Y Chen J Liu J Ma H Associations between GGT/HDL and MAFLD: a cross-sectional study Diabetes Metab Syndr Obes 2022 15 383 94 10.2147/DMSO.S342505 35177915
Xing Y, Chen J, Liu J, Ma H. Associations between GGT/HDL and MAFLD: a cross-sectional study. Diabetes Metab Syndr Obes. 2022;15:383–94. https://www.ncbi.nlm.nih.gov/pubmed/35177915.35177915 10.2147/DMSO.S342505
45. Amernia B Moosavy SH Banookh F Zoghi G FIB-4, APRI, and AST/ALT ratio compared to FibroScan for the assessment of hepatic fibrosis in patients with non-alcoholic fatty liver disease in Bandar Abbas, Iran BMC Gastroenterol 2021 21 453 10.1186/s12876-021-02038-3 34861841
Amernia B, Moosavy SH, Banookh F, Zoghi G. FIB-4, APRI, and AST/ALT ratio compared to FibroScan for the assessment of hepatic fibrosis in patients with non-alcoholic fatty liver disease in Bandar Abbas, Iran. BMC Gastroenterol. 2021;21:453. https://www.ncbi.nlm.nih.gov/pubmed/34861841.34861841 10.1186/s12876-021-02038-3
46. Cho J, Hong H, Park S, Kim S, Kang H. Insulin Resistance and its Association with metabolic syndrome in Korean Children. Biomed Res Int. 2017;2017:8728017https://www.ncbi.nlm.nih.gov/pubmed/29457038
47. Di Costanzo A, Ronca A, D’Erasmo L, Manfredini M, Baratta F, Pastori D, Di Martino M, Ceci F, Angelico F, Del Ben M et al. HDL-Mediated Cholesterol Efflux and Plasma Loading Capacities Are Altered in Subjects with Metabolically- but Not Genetically Driven Non-Alcoholic Fatty Liver Disease (NAFLD). Biomedicines, 2020;8.https://www.ncbi.nlm.nih.gov/pubmed/33352841
48. Verwer BJ Scheffer PG Vermue RP Pouwels PJ Diamant M Tushuizen ME NAFLD is related to post-prandial triglyceride-enrichment of HDL Particles in Association with endothelial and HDL dysfunction Liver Int 2020 40 2439 44 10.1111/liv.14597 32652824
Verwer BJ, Scheffer PG, Vermue RP, Pouwels PJ, Diamant M, Tushuizen ME. NAFLD is related to post-prandial triglyceride-enrichment of HDL Particles in Association with endothelial and HDL dysfunction. Liver Int. 2020;40:2439–44. https://www.ncbi.nlm.nih.gov/pubmed/32652824.32652824 10.1111/liv.14597
49. Pan JJ Fallon MB Gender and racial differences in nonalcoholic fatty liver disease World J Hepatol 2014 6 274 83 10.4254/wjh.v6.i5.274 24868321
Pan JJ, Fallon MB. Gender and racial differences in nonalcoholic fatty liver disease. World J Hepatol. 2014;6:274–83. https://www.ncbi.nlm.nih.gov/pubmed/24868321.24868321 10.4254/wjh.v6.i5.274
50. Younossi Z Anstee QM Marietti M Hardy T Henry L Eslam M George J Bugianesi E Global burden of NAFLD and NASH: trends, predictions, risk factors and prevention Nat Rev Gastroenterol Hepatol 2018 15 11 20 10.1038/nrgastro.2017.109 28930295
Younossi Z, Anstee QM, Marietti M, Hardy T, Henry L, Eslam M, George J, Bugianesi E. Global burden of NAFLD and NASH: trends, predictions, risk factors and prevention. Nat Rev Gastroenterol Hepatol. 2018;15:11–20. https://www.ncbi.nlm.nih.gov/pubmed/28930295.28930295 10.1038/nrgastro.2017.109
51. Ye Q Zou B Yeo YH Li J Huang DQ Wu Y Yang H Liu C Kam LY Tan XXE 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 5 739 52 10.1016/S2468-1253(20)30077-7 32413340
Ye Q, Zou B, Yeo YH, Li J, Huang DQ, Wu Y, Yang H, Liu C, Kam LY, Tan XXE, 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;5:739–52. https://www.ncbi.nlm.nih.gov/pubmed/32413340.32413340 10.1016/S2468-1253(20)30077-7
52. Li Q, Han Y, Hu H, Zhuge Y. Gamma-Glutamyl transferase to high-density lipoprotein cholesterol ratio has a non-linear association with non-alcoholic fatty liver disease: a secondary prospective cohort study in non-obese Chinese adults. Front Med (Lausanne). 2022;9:995749https://www.ncbi.nlm.nih.gov/pubmed/36465946
53. Liu Z He H Dai Y Yang L Liao S An Z Li S Comparison of the diagnostic value between triglyceride-glucose index and triglyceride to high-density lipoprotein cholesterol ratio in metabolic-associated fatty liver disease patients: a retrospective cross-sectional study Lipids Health Dis 2022 21 55 10.1186/s12944-022-01661-7 35752830
Liu Z, He H, Dai Y, Yang L, Liao S, An Z, Li S. Comparison of the diagnostic value between triglyceride-glucose index and triglyceride to high-density lipoprotein cholesterol ratio in metabolic-associated fatty liver disease patients: a retrospective cross-sectional study. Lipids Health Dis. 2022;21:55. https://www.ncbi.nlm.nih.gov/pubmed/35752830.35752830 10.1186/s12944-022-01661-7
54. Fan N Peng L Xia Z Zhang L Song Z Wang Y Peng Y Triglycerides to high-density lipoprotein cholesterol ratio as a surrogate for nonalcoholic fatty liver disease: a cross-sectional study Lipids Health Dis 2019 18 39 10.1186/s12944-019-0986-7 30711017
Fan N, Peng L, Xia Z, Zhang L, Song Z, Wang Y, Peng Y. Triglycerides to high-density lipoprotein cholesterol ratio as a surrogate for nonalcoholic fatty liver disease: a cross-sectional study. Lipids Health Dis. 2019;18:39. https://www.ncbi.nlm.nih.gov/pubmed/30711017.30711017 10.1186/s12944-019-0986-7
55. Fukuda Y Hashimoto Y Hamaguchi M Fukuda T Nakamura N Ohbora A Kato T Kojima T Fukui M Triglycerides to high-density lipoprotein cholesterol ratio is an independent predictor of incident fatty liver; a population-based cohort study Liver Int 2016 36 713 20 10.1111/liv.12977 26444696
Fukuda Y, Hashimoto Y, Hamaguchi M, Fukuda T, Nakamura N, Ohbora A, Kato T, Kojima T, Fukui M. Triglycerides to high-density lipoprotein cholesterol ratio is an independent predictor of incident fatty liver; a population-based cohort study. Liver Int. 2016;36:713–20. https://www.ncbi.nlm.nih.gov/pubmed/26444696.26444696 10.1111/liv.12977
56. Kosekli MA, Kurtkulagii O, Kahveci G, Duman TT, Tel BMA, Bilgin S, Demirkol ME, Aktas G. The association between serum uric acid to high density lipoprotein-cholesterol ratio and non-alcoholic fatty liver disease: the abund study. Rev Assoc Med Bras (1992). 2021;67:549–554.https://www.ncbi.nlm.nih.gov/pubmed/34495059
57. Hirano T Satoh N Ito Y Specific increase in small dense low-density lipoprotein-cholesterol levels beyond triglycerides in patients with diabetes: implications for Cardiovascular Risk of MAFLD J Atheroscler Thromb 2024 31 36 47 10.5551/jat.64271 37438123
Hirano T, Satoh N, Ito Y. Specific increase in small dense low-density lipoprotein-cholesterol levels beyond triglycerides in patients with diabetes: implications for Cardiovascular Risk of MAFLD. J Atheroscler Thromb. 2024;31:36–47.37438123 10.5551/jat.64271
58. Yang S Xu J Elevated small dense low-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio is associated with an increased risk of metabolic dysfunction associated fatty liver disease in Chinese patients with type 2 diabetes mellitus J Diabetes Investig 2024 15 634 42 10.1111/jdi.14148 38251808
Yang S, Xu J. Elevated small dense low-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio is associated with an increased risk of metabolic dysfunction associated fatty liver disease in Chinese patients with type 2 diabetes mellitus. J Diabetes Investig. 2024;15:634–42.38251808 10.1111/jdi.14148
59. Pan H Liu J Liu Y Li H Tao L Ping Z Xi G Correlation between trajectories of high density lipoprotein cholesterol and the risk of diabetes in middle-aged and elderly population in Beijing Chin J Cardiovasc Med 2021 26 483 7
Pan H, Liu J, Liu Y, Li H, Tao L, Ping Z, Xi G. Correlation between trajectories of high density lipoprotein cholesterol and the risk of diabetes in middle-aged and elderly population in Beijing. Chin J Cardiovasc Med. 2021;26:483–7.
