
==== Front
Ann Med
Ann Med
Annals of Medicine
0785-3890
1365-2060
Taylor & Francis

39247937
10.1080/07853890.2024.2398724
2398724
Version of Record
Research Article
Hepatology
Association of healthy lifestyles with risk of all-cause and cause-specific mortality among individuals with metabolic dysfunction-associated steatotic liver disease: results from the DFTJ cohort
Q. Deng et al.
Deng Qilin *
Zhang Yingchen *
Guan Xin
Wang Chenming
Guo Huan
Department of Occupational and Environmental Health, State Key Laboratory of Environmental Health (Incubating), School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
* Contributed equally as co-first authors.

Supplemental data for this article can be accessed online at https://doi.org/10.1080/07853890.2024.2398724.

CONTACT Huan Guo ghuan5011@hust.edu.cn Department of Occupational and Environmental Health, School of Public Health, Tongji Medical College, Huazhong University of Science and Technology, 13 Hangkong Rd, Wuhan, Hubei, China
9 9 2024
2024
9 9 2024
56 1 23987248 10 2023
18 6 2024
19 7 2024
KnowledgeWorks Global Ltd.6 9 2024
published online in a building issue6 9 2024
© 2024 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group
2024
The Author(s)
https://creativecommons.org/licenses/by-nc/4.0/ This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent.

Abstract

Aim

To examine the associations of healthy lifestyles with risk of all-cause and cause-specific mortality among adults with metabolic dysfunction-associated steatotic liver disease (MASLD), and whether the association was mediated by systemic immune-inflammatory biomarkers (SIIBs).

Methods

The study included 10,347 subjects with MASLD, who were enrolled in the Dongfeng-Tongji cohort study. The healthy lifestyles referred to non-smoking, being physically active (≥7.5 metabolic equivalents-hours/week), low-risk alcohol consumption (1–14 g/day for women and 1–28 g/day for men), and optimal sleep duration (≥6 to ≤8 h/day). Cox proportional hazard models were used to examine the relationship between each lifestyle and SIIBs with the risk of all-cause and cause-specific mortality. A mediation analysis was conducted to investigate the role of SIIBs on the association between healthy lifestyles and mortality.

Results

There were 418 MASLD subjects dead till the follow-up of 2018, including 259 deaths from cardiovascular disease (CVD). Compared to MASLD participants with 0–1 healthy lifestyle score (HLS), those with 3-4 HLS had the lowest risk of all-cause mortality [hazard ratio (HR), 0.46; 95% CI, (0.36–0.60)], and CVD mortality [HR (95%CI), 0.41 (0.29–0.58)]. Mediation analyses indicated that SIIBs mediated the association between healthy lifestyles and mortality, with proportions ranging from 2.5% to 6.1%.

Conclusions

These findings suggest that adherence to healthy lifestyles can significantly reduce mortality for MASLD patients, and the decreased SIIBs may partially explain the protection mechanism of healthy lifestyles.

Keywords

Metabolic dysfunction-associated steatotic liver disease
Healthy lifestyles
All-cause mortality
Systemic immune-inflammatory biomarkers
Cohort study
National Key Research and Development Program of China 10.13039/501100012166 2018YFC2000203 This study was supported by the funds from National Natural Science Foundation of China (grant numbers 82373667, 82021005) and National Key Research and Development Program of China (grant numbers 2018YFC2000203).
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pmc1. Introduction

Nonalcoholic fatty liver disease (NAFLD) is the major type of liver disease, affecting more than 30% of the adult population worldwide [1]. Besides, NAFLD is currently the fastest growing cause of liver-related deaths around the world, posing a serious threat to public health [2,3]. In 2023, the term ‘metabolic dysfunction-associated steatotic liver disease’ (MASLD) was proposed by three major multinational liver associations to replace NAFLD [4]. The evidence suggests that MASLD and NAFLD demonstrate good consistency in clinical characteristics and natural history [5,6]. However, compared to NAFLD, MASLD underscores the central role of metabolic dysfunction in the pathogenesis of fatty liver, encompasses a broader disease spectrum [4,7]. Consequently, it is more effective in identifying individuals who simultaneously suffer from overweight/obesity, diabetes, or multiple metabolic risk factors. This allows for earlier intervention, slows the progression of fatty liver disease, and ultimately improves the quality of life for patients [8–10].

Given the fact that MASLD is a metabolically derangement-based liver disease [10], the study shows lifestyle modification can improve the quality of life and health outcomes of MASLD patients [11]. Previous researches have highlighted that healthy lifestyle factors, including non-smoking, low-risk alcohol consumption, being physically active, and moderate sleep duration are associated with the reduced risk of overall mortality for the general population [12–15]. However, there is insufficient evidence about whether or to what extent the combined lifestyle factors affect the risk of death for patients with MASLD, let alone the effect on the risk of death from specific causes, e.g. cardiovascular disease (CVD) and cancer mortality [16,17]. Therefore, evidence from large prospective observational studies with more comprehensive lifestyle factors is still required to examine the relationship between healthy lifestyles and cause-specific mortality among MASLD.

The principal pathophysiological process of MASLD encompasses metabolic dysfunction and atherogenic lipid abnormalities [18], in which low-grade systemic inflammation plays an important role [19]. The production of inflammatory factors has been shown to contribute to MASLD progression [20]. Moreover, there have been studies conducted on the effects of healthy lifestyles such as exercise, smoking, sleep duration and alcohol consumption on inflammatory factors, which shows healthy lifestyles can reduce the inflammation levels in the body [21]. In recent years, systemic immune-inflammatory index (SII), systemic inflammatory response index (SIRI), and aggregate index of systemic inflammation (AISI) have received widespread attention as indicators of systemic inflammation, frequently employed in clinical research to assess the prognostic risks of various cardiovascular diseases. They reflect the balance between immune response and the overall inflammatory milieu [22,23]. Nevertheless, there is a lack of evidence in the current literature for the role of inflammatory factors in lifestyle and the risk of death in patients with MASLD.

Therefore, we investigated the relationships between healthy lifestyles and the risks of all-cause and cause-specific mortality in Chinese middle-aged and elderly patients with MASLD, with the stratified analysis to assess whether the relationships remain stable among subgroup populations, and further explored the possible role of systemic immune-inflammatory biomarkers (SIIBs) in the above association.

2. Methods

2.1. Study population

The Dongfeng-Tongji (DFTJ) study is an ongoing dynamic cohort that consecutively recruits retired employees of Dongfeng Motor Corporation (DMC) located in Shiyan City, Hubei, China [24]. In DFTJ cohort study, 38,295 participants were recruited from baseline assessments in 2013 and followed until December 2018. All participants provided demographic characteristics through face-to-face questionnaire interviews, as well as finishing physical examination and blood collection. We included 11,665 patients diagnosed with MASLD in 2013. MASLD was defined based on the presence of steatotic liver disease (SLD), coupled with one or more cardiometabolic risk factors, which include but are not limited to obesity, type 2 diabetes, dyslipidemia, and hypertension. Additionally, 1172 participants with cancer and 146 participants with incomplete information on overall lifestyle were also excluded. After exclusion, 10,347 participants were eligible for this analysis (Figure 1). Due to the incomplete information of SIIBs, the number of participants included in the mediation analysis differed for each marker: SII was analyzed with 8559 participants, SIRI with 7905 participants, and AISI also with 7905 participants.

Figure 1. Flowchart of study participants.

Abbreviations: SLD: steatotic liver disease; MASLD: metabolic dysfunction-associated steatotic liver disease

All participants signed the informed consent, and the study protocol was evaluated and approved by the Ethics Committee of Tongji Medical College, Huazhong University of Science and Technology (no. S335).

2.2. Assessment of healthy lifestyle factors

In the current study, we assessed four lifestyle factors: smoking status, alcohol consumption, physical activity, and sleep duration (Table S1). Non-smoking was considered as the healthy level, which was defined in the questionnaire as never smoking or having quit ≥20 years [25], while a healthy level of alcohol intake was defined as low-risk alcohol consumption: 1–14 g/day for women and 1–28 g/day for men [26,27]. Information on the frequency and average duration of physical activity, including walking, cycling, tai chi, dancing, playing ball, working out at gyms, jogging, swimming, and others, was obtained through questionnaire interviews. We utilized metabolic equivalents (METs) to classify moderate (3 to <6 METs) and vigorous (6 METs) activity, such as 3 METs for walking, 4 METs for cycling, 4.5 METs for tai chi, 5 METs for callisthenics or dancing, 6 METs for playing ball or working out at gyms, and 7.5 METs for swimming and jogging [28]. The healthy levels of physical activity were considered as moderate activity for more than 150 min per week or weekly vigorous activity for over 75 min (7.5 MET-hours per week) [28]. Since the J-shaped relationship between sleep duration and all-cause mortality has been reported previously, a healthy level of night-time sleep duration is considered to be 6-8 h/day [28,29]. Each factor was dichotomized as Table S1 illustrated, with a healthy level given a score of 1 and an unhealthy level given a score of 0. The healthy lifestyle score (HLS) was the sum of the scores of 4 lifestyle factors and ranged from 0 to 4, with higher scores indicating healthier lifestyles. Details of HLS are shown in Table S1.

2.3. Ascertainment of metabolic dysfunction-associated steatotic liver disease and mortality

According to the multisociety consensus on the nomenclature of fatty liver disease, MASLD was defined as the presence of SLD (determined by B ultrasound) combined with at least 1 of the 5 following cardiometabolic adult criteria: (1) BMI ≥23 kg/m2 or waist circumference ≥94 cm for males and ≥80 cm for females, (2) fasting glucose ≥100 mg/dl or 2-hour post-load glucose levels ≥140 mg/dl or haemoglobin A1c ≥ 5.7% or diabetes mellitus or treatment for diabetes mellitus, (3) blood pressure ≥130/85 mmHg or antihypertensive drug treatment, (4) fasting plasma triglycerides ≥150 mg/dl or lipid-lowering treatment, (5) plasma HDL-cholesterol <40 mg/dl for men and <50 mg/dl for women or lipid-lowering treatment [4]. SLD is diagnosed by ultrasound using Aplio XG (TOSHIBA, Japan) performed by an independent expert operator specializing in abdominal ultrasound [30]. Besides, the severity of SLD was further stratified through ultrasonographic assessment into three categories: mild (diffuse increase in fine hepatic echogenicity), moderate (diffuse increase in fine echogenicity with impaired visualization of intrahepatic vascular borders and the diaphragm), and severe (diffuse increase in fine echogenicity with intrahepatic vascular borders and the diaphragm being non-visible) [31]. Furthermore, due to the small proportions, moderate and severe MASLD were combined into one group for analytical purposes.

Each subject in the DFTJ cohort is allocated a unique medical insurance number in order to track vital status through DMC’s medical insurance system, which covers all retirees as of 31 December 2018. We used the International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10) to classify causes of death, including cardiovascular mortality (ICD-10 codes I00-I99), cancer mortality (ICD-10 codes C00-C97), and other causes mortality.

2.4. Ascertainment of covariates

By questionnaire visits, we also collected age, gender, education, marital status, body mass index (BMI), self-reported physician-diagnosed CVD (including coronary artery disease, myocardial infarction, and stroke), hypertension, hyperlipidaemia and diabetes, and use of hypotensive, lipid-lowering, and hypoglycaemic medications. The presence of diabetes mellitus was defined based on the use of blood glucose-lowering medications or a fasting glucose ≥7.0 mmol/l or history of diabetes. Prevalent hypertension was defined by a self-reported diagnosis, or currently taking anti-hypertensive drugs, or had a systolic/diastolic blood pressure ≥140/90 mmHg. Prevalent dyslipidemia was defined by a self-reported diagnosis, or currently using prescription drugs for lipid-modifying. Education attainment included primary school or below, middle or high school, and college or above. Marital status was classified into single (single, divorced, or widowed) or married.

In addition, the peripheral counts of blood cells were measured by certified technicians using the CELL-DYN 3700 system (Abbott Laboratories, Illinois, USA) in 2013. SIIBs levels for each participant were calculated as follows: SII (×109/L) = neutrophil count/lymphocyte count × platelet count [32,33]; SIRI (×109/L) = neutrophil counts × monocyte counts/lymphocyte counts; AISI (×109/L), neutrophil counts × platelet counts × monocyte counts/lymphocyte counts. Details were documented elsewhere [24].

2.5. Statistical analysis

Follow-up time was calculated from the date of answering the questionnaires to the date of death occurrence or the end of follow-up (31 December 2018), whichever came first. Percentages of missing values of all covariates were less than 1%, except for BMI (3.5%). We used the median to impute the missing continuous variables and the plural to replace the missing categorical variables [34].

Cox proportional hazards models were used to calculate hazard ratios (HRs) and 95% confidence intervals (CIs) for the associations of each lifestyle and the HLS (calculated as the number of low-risk factors and categorized into 0–1, 2, 3–4) with the risk of all-cause, CVD, cancer, and other causes mortality. Participants with 0–1 HLS were used as the reference group. In model 1, we adjusted for age (continuous), gender (male or female). In model 2, we further adjusted for education attainment (primary school or below, middle or high school, and college or above), and marital status (married or single, including widowed, divorced, and never married), BMI (<25.0, 25.0–29.9, or >30 kg/m2), self-reported physician-diagnosed CVD (yes or no), hypertension (yes or no), hyperlipidaemia (yes or no), and diabetes (yes or no). In model 3, we additionally adjusted for current hypotensive medication use (yes or no), current lipid-lowering medication use (yes or no) and current diabetes medication use (yes or no), alanine aminotransferase (continuous), aspartate transaminase (continuous). The selection of these covariates was based on prior literature [16,35,36]. The proportional hazard assumption was tested based on Schoenfeld residuals, and no violation was found.

To evaluate whether SIIBs could explain the beneficial effect of a healthy lifestyle on mortality risk reduction, the associations between healthy lifestyles and SIIBs were evaluated by using multiple linear regression (MLR) models. The resulting P-values were adjusted for multiple testing using Benjamini-Hochberg false discovery rate (FDR) method for multiple testing and FDR-corrected p < 0.05 was considered to be statistically significant [37]. Based on previous studies [33], we converted continuous SIIBs values to categorical variables: low SIIB: <75th percentile (SII <517, SIRI <0.97, AISI <198); and high SIIB: >75th percentile (SII ≥517, SIRI ≥0.97, AISI ≥198). Cox proportional hazards models were applied to examine the associations of SIIBs level with all-cause mortality risk. For mediation analysis, we investigated the role of SIIBs (SII, SIRI, and AISI) in the association between various lifestyle factors and mortality outcomes, including all-cause and CVD mortality. The SIIBs were dichotomized at the 75th percentile to identify their mediating effects. We employed the ‘CAUSALMED’ procedure in SAS, adhering to a counterfactual framework, which facilitated the computation of the natural indirect effects (NIE) and the proportion mediated, along with the corresponding P-values to test for significance [38,39]. Next, stratified analyses were conducted by CVD (yes or no), hypertension (yes or no), hyperlipidaemia (yes or no), diabetes (yes or no), and the severity of SLD.

Analyses were conducted using SPSS software (version 29.0, SPSS Inc., Chicago, IL), R software (version 4.3.3, R Project for Statistical Computing), and SAS program (version 9.4, SAS Institute, Carry, NC). A two-tailed P value <0.05 was considered statistically significant.

3. Results

3.1. General characteristics of study population

Table 1 presents the baseline characteristics of 10,347 participants with MASLD. The proportion of participants with 0–1, 2, 3–4 healthy lifestyle factors at MASLD diagnosis was 37.1%, 59.9%, and 70.1% in women, and 62.9%, 40.1%, and 29.9% in men, respectively. Participants with lower HLS were older, less educated, more likely to be men, higher SII, SIRI and AISI levels, had a higher prevalence of CVD, hypertension, hyperlipidaemia, and diabetes (Table 1). Compared to participants with an unhealthy lifestyle, non-smokers, low-risk drinkers, and physically active participants had lower SII, SIRI and AISI levels (Table 3).

Table 1. Baseline characteristics of the MASLD patients according to healthy lifestyle score (n = 10,347).

Characteristics	Healthy lifestyle score (HLS) a	
0–1	2	3–4	
No. of subjects	1925	4541	3881	
Age, mean (SD), years	65.2 (7.98)	64.1 (7.98)	63.5 (7.85)	
BMI, mean (SD), kg/m2	26.4 (2.76)	26.4 (2.69)	26.3 (2.66)	
Gender	 	 	 	
 Male	1210 (62.9%)	1821 (40.1%)	1159 (29.9%)	
 Female	715 (37.1%)	2720 (59.9%)	2722 (70.1%)	
Marital status	 	 	 	
 Married	1730 (90.3%)	3983 (88.1%)	3372 (87.2%)	
 Separated	180 (9.40%)	532 (11.8%)	481 (12.4%)	
 Never married	5 (0.3%)	8 (0.2%)	15 (0.4%)	
Education	 	 	 	
 Primary school or below	558 (29.1%)	1111 (24.6%)	751 (19.5%)	
 Middle or high school	734 (38.3%)	1789 (39.6%)	1505 (39.0%)	
 College or above	623 (32.5%)	1623 (35.9%)	1603 (41.5%)	
Severity of steatotic liver disease	 	 	 	
 Mild	1290 (72.7%)	3049 (73.4%)	2688 (75.1%)	
 Moderate	438 (24.7%)	987 (23.8%)	811 (22.7%)	
 Severe	46 (2.6%)	119 (2.8%)	81 (2.3%)	
Smoking status	 	 	 	
 Never	752 (39.1%)	3316 (73.0%)	3554 (91.6%)	
 Former	441 (22.9%)	540 (11.9%)	201 (5.2%)	
 Current	732 (38.0%)	685 (15.1%)	126 (3.2%)	
Alcohol consumption	 	 	 	
 Never	1180 (61.3%)	3345 (73.7%)	2757 (71.0%)	
 Former	184 (9.6%)	241 (5.3%)	99 (2.6%)	
 Current	561 (29.1%)	955 (21.0%)	1025 (26.4%)	
Physical activity	 	 	 	
 Yes	1188 (61.7%)	4086 (90.0%)	3868 (99.7%)	
 No	737 (38.3%)	455 (10.0%)	13 (0.3%)	
Sleeping, mean (SD), hours	8.91 (1.05)	8.39 (1.01)	7.63 (0.64)	
CVD	 	 	 	
 Yes	552 (28.7%)	992 (21.9%)	735 (19.0%)	
 No	1372 (71.3%)	3546 (78.1%)	3138 (81.0%)	
Hypertension	 	 	 	
 Yes	1458 (75.7%)	3329 (73.3%)	2814 (72.5%)	
 No	467 (24.3%)	1212 (26.7%)	1067 (27.5%)	
Diabetes	 	 	 	
 Yes	410 (21.3%)	987 (21.7%)	756 (19.5%)	
 No	1515 (78.7%)	3554 (78.3%)	3125 (80.5%)	
Hyperlipidemia	 	 	 	
 Yes	1123 (58.3%)	2491 (54.9%)	2091 (53.9%)	
 No	802 (41.7%)	2050 (45.1%)	1790 (46.1%)	
ALT, mean (SD), /L	23.5 (15.3)	24.2 (17.6)	24.3 (16.1)	
AST, mean (SD), /L	24.2 (19.5)	24.5 (15.9)	24.4 (11.5)	
SII, mean (SD), (×109/L)	463 (369)	425 (258)	423 (308)	
SIRI, mean (SD), (×109/L)	0.92 (0.75)	0.78 (0.55)	0.73 (0.55)	
AISI, mean (SD), (×109/L)	173 (181)	145 (124)	138 (131)	
Abbreviations: MASLD: metabolic dysfunction-associated steatotic liver disease; HLS: healthy lifestyle score; DFTJ: Dongfeng-Tongji; SD, standard deviation; ALT: alanine aminotransferase; AST: aspartate transaminase; BMI: body mass index; CVD: cardiovascular disease; MET:the metabolic equivalent; SII: systemic immune-inflammation index; SIRI: systemic inflammatory response index; AISI: aggregate index of systemic inflammation.

aHealthy lifestyle factors: non-smoking (never smoking or quitting smoking ≥20 years), low-risk alcohol consumption (1–14 g for women and 1–28 g for men), being physically active (at least 75 min of vigorous-intensity or 150 min of moderate-intensity physical activity weekly [7.5 MET-hours/week]), ideal sleep duration (6–8 h/day). Each factor was dichotomized, with a healthy level given a score of 1 and an unhealthy level given a score of 0. The healthy lifestyle score was the sum of the scores of 4 lifestyle factors and ranged from 0 to 4, with higher scores indicating healthier lifestyles.

Continuous variables were presented as mean (standard deviation), categorical variables were presented as n (%). For each subject, SII value: neutrophil count/lymphocyte count × platelet count; SIRI value: neutrophil count × monocyte count/lymphocyte count; AISI, neutrophils × platelets x monocytes/lymphocytes.

Table 2. All-cause and cause-specific mortality according to healthy lifestyle score among participants with MASLD (n = 10,347).

 	Healthy lifestyle score (HLS)	P trend a	
0–1	2	3–4	Per 1-unit increase	
Person-years	10633	25460	21856	57949	 	
No. of subjects	1925	4541	3881	10347	 	
All-cause mortality	 	 	 	 	 	
 No of Deaths	152	170	96	418	 	
 Model 1	Reference	0.54 (0.43, 0.67)	0.40 (0.31, 0.52)	0.64 (0.57, 0.71)	<0.001	
 Model 2	Reference	0.57 (0.45, 0.71)	0.45 (0.35, 0.58)	0.67 (0.60, 0.75)	<0.001	
 Model 3	Reference	0.58 (0.47, 0.73)	0.46 (0.36, 0.60)	0.68 (0.61, 0.76)	<0.001	
CVD mortality	 	 	 	 	 	
 No of Deaths	98	107	54	259	 	
 Model 1	Reference	0.53 (0.40, 0.70)	0.35 (0.25, 0.50)	0.61 (0.52, 0.70)	<0.001	
 Model 2	Reference	0.56 (0.43, 0.74)	0.40 (0.29, 0.57)	0.64 (0.55, 0.74)	<0.001	
 Model 3	Reference	0.58 (0.44, 0.76)	0.41 (0.29, 0.58)	0.65 (0.56, 0.75)	<0.001	
Cancer mortality	 	 	 	 	 	
 No of Deaths	9	11	8	28	 	
 Model 1	Reference	0.60 (0.25, 1.46)	0.56 (0.21, 1.47)	0.68 (0.44, 1.06)	0.089	
 Model 2	Reference	0.59 (0.24, 1.44)	0.51 (0.19, 1.36)	0.65 (0.42, 1.02)	0.059	
 Model 3	Reference	0.59 (0.24, 1.43)	0.52 (0.20, 1.37)	0.65 (0.42, 1.02)	0.059	
Other causes mortality	 	 	 	 	 	
 No of Deaths	45	52	34	131	 	
 Model 1	Reference	0.55 (0.37, 0.82)	0.48 (0.31, 0.75)	0.70 (0.57, 0.86)	<0.001	
 Model 2	Reference	0.57 (0.38, 0.86)	0.54 (0.34, 0.86)	0.74 (1.10, 1.16)	0.005	
 Model 3	Reference	0.59 (0.39, 0.89)	0.56 (0.36, 0.89)	0.75 (0.61, 0.93)	0.009	
Abbreviations: MASLD: metabolic dysfunction-associated steatotic liver disease; CVD: cardiovascular disease;

Model 1: adjusted age (continuous) and gender (male or female).

Model 2: further adjusted for education attainment (primary school or below, middle or high school, ≥college), marital status (single or married), body mass index (<25.0, 25.0–29.9, or >30 kg/m²), self-reported physician-diagnosed CVD (yes or no), hypertension (yes or no), hyperlipemia (yes or no) and diabetes (yes or no).

Model 3: further adjusted for current hypotensive drug use (yes or no), current hypoglycaemic drug use (yes or no), current hypolipidemic agents use, ALT (continuous), and AST (continuous).

aTest for trend based on healthy lifestyle score for each 1-point.

Table 3. The associations between lifestyle factors and SIIBs in MASLD.

Lifestyles	SII a	SIRI a	AISI a	
β (95%CI)	FDR	β (95%CI)	FDR	β (95%CI)	FDR	
Healthy lifestyle score	−0.033 (−0.055 to −0.011)	0.004	−0.055 (−0.077 to −0.032)	<0.001	−0.065 (−0.088 to −0.043)	<0.001	
 0–1	Reference	 	Reference	 	Reference	 	
 2	−0.052 (−0.078 to −0.025)	0.001	−0.058 (−0.085 to −0.030)	<0.001	−0.072 (−0.100 to −0.043)	<0.001	
 3–4	−0.051 (−0.081 to −0.020)	0.002	−0.069 (−0.100 to −0.039)	<0.001	−0.085 (−0.117 to −0.054)	<0.001	
Smoking status	 	0.002	 	<0.001	 	<0.001	
 Smokers	Reference	 	Reference	 	Reference	 	
 Non-smokers	−0.046 (−0.073 to −0.020)	 	−0.053 (−0.080 to −0.026)	 	−0.067 (−0.094 to −0.039)	 	
Alcohol consumption	 	0.597	 	0.615	 	0.063	
 high-risk alcohol consumption	Reference	 	Reference	 	Reference	 	
 low-risk alcohol consumption	−0.006 (−0.027 to 0.016)	 	−0.022 (−0.043 to 0.000)	 	−0.022 (−0.044 to 0.000)	 	
Physical activity status	 	0.002	 	<0.001	 	<0.001	
 Inactive	Reference	 	Reference	 	Reference	 	
 Active	−0.034 (−0.056 to −0.013)	 	−0.043 (−0.064 to −0.021)	 	−0.046 (−0.068 to −0.025)	 	
Sleep duration	 	0.597	 	0.625	 	0.325	
 Suboptimal	Reference	 	Reference	 	Reference	 	
 Optimal	0.007 (−0.014 to 0.028)	 	−0.005 (−0.027 to 0.016)	 	−0.011 (−0.033 to 0.011)	 	
Abbreviations: SIIBs: systemic immune-inflammatory biomarkers; SII: systemic immune-inflammation index; SIRI: systemic inflammatory response index; AISI: aggregate index of systemic inflammation; CI: confidence interval; MASLD: metabolic dysfunction-associated steatotic liver disease.

aParticipant counts for each inflammatory marker analysis were as follows: SII (n = 8559), SIRI (n = 7905), AISI (n = 7905), after excluding cases with missing information.

Multiple linear regression models were used to impute the β (95% CI), with adjusted for age (continuous), gender (male or female), education attainment (primary school or below, middle or high school, ≥college), marital status (single or married), body mass index (<25.0, 25.0–29.9, or >30 kg/m²), self-reported physician-diagnosed CVD (yes or no), hypertension (yes or no), hyperlipemia (yes or no), diabetes (yes or no), current hypotensive drug use (yes or no), current hypoglycaemic drug use (yes or no), current hypolipidemic agents use, ALT (continuous), and AST (continuous). Multiple testing corrections were performed using Benjamini-Hochberg false discovery rate (FDR) method to obtain FDR-corrected p value. Bolded values indicate statistically significant (p < 0.05).

3.2. Associations of individual and combined lifestyle factors with mortality risks

During a follow-up of 5 years, we documented 418 deaths, including 259 CVD deaths, 28 cancer deaths, and 131 other causes deaths among MASLD patients. As shown in Table S2, each individual lifestyle was significantly associated with all-cause mortality. Adoption of 4 emerging low-risk factors, compared to those with 0–1 HLS, those with 3–4 HLS had the lowest risk of all-cause mortality (HR, 0.46; 95% CI, 0.36–0.60), CVD mortality (HR, 0.41; 95% CI, 0.29–0.58) and other causes mortality (HR, 0.56; 95% CI, 0.36–0.89). However, we did not observe significant decreased risks of cancer mortality (HR, 0.52; 95% CI, 0.20–1.37) among those with 3–4 HLS (Table 2). Moreover, with the comparison of the reference group, participants with 2 HLS also had lower risks of all-cause mortality (HR, 0.58; 95% CI, 0.47–0.73), CVD mortality (HR, 0.58; 95% CI, 0.44–0.76), and other causes mortality (HR, 0.59; 95% CI, 0.39–0.89). These findings were robust in the sensitivity analysis that redefined the healthy level of alcohol consumption (other definition of low-risk alcohol consumption: 1–10 g/day for women and 1–15 g/day for men), showing no significant variation in the observed associations (Table S4). In addition, the accordant results were observed when our analyses were stratified by CVD, hypertension, hyperlipidaemia, diabetes, and SLD severity (Table S5, S6, S7, S8, S9). Subsequently, we observed significant inverse linear associations between HLS and all-cause, CVD, cancer, and other causes of mortality (Figure S1). Additionally, each 1-point increase in HLS was associated with a 32% lower risk of all-cause mortality, 35% lower risk of CVD mortality, and 25% lower risk of other causes mortality for MASLD participants (Table 2).

3.3. Associations between healthy lifestyles and SIIBs

In this study, we observed that individual healthy lifestyles and higher HLS were associated with a lower level of SIIBs. When four lifestyles were categorized into healthy and unhealthy behaviours, non-smoking and physically active subjects were separately associated with a 4.6% and 3.4% decrease in SII levels; a 5.3% and 4.3% decrease in SIRI levels; a 6.7% and 4.6% decrease in AISI levels; (Table 3). Additionally, each 1-point increase in HLS was associated with a 3.3% decrease in SII levels (95% CI: −0.055, −0.011); a 5.5% decrease in SIRI levels (95% CI: −0.077, −0.032); a 6.5% decrease in AISI levels (95% CI: −0.088, −0.043). Compared with those with an HLS of 0–1, participants adopted 2 and ≥3 healthy behaviours manifested in a 5.2% (95% CI: −0.078 to −0.025), 5.1% (95% CI: −0.081 to −0.020) decrease in SII levels; a 5.8% (95% CI: −0.085 to −0.032), 6.9% (95% CI: −0.100 to −0.039) decrease in SIRI levels and a 7.2% (95% CI: −0.100 to −0.043), 8.5% (95% CI: −0.117 to −0.054) decrease in AISI levels (Table 3).

3.4. Association of SIIBs with mortality risks

As shown in Table S3, High levels of SII (HR, 1.57; 95%CI, 1.26–1.96), SIRI (HR, 1.83; 95%CI, 1.46–2.30), and AISI (HR, 1.51; 95%CI, 1.20–1.89) demonstrated a significant association with an increased risk of all-cause mortality. Similar associations were observed for CVD mortality with high SII (HR, 1.66; 95% CI, 1.26–2.18), SIRI (HR, 1.83; 95% CI, 1.38–2.42), and AISI (HR, 1.63; 95% CI, 1.22–2.16) when compared to their respective lower levels. In terms of cancer mortality and deaths due to other causes, the associations did not reach statistical significance across the high levels of all three biomarkers.

3.5. Mediation analyses

Since healthy lifestyles causally contributed to lower SIIBs, we treated SIIBs as mediators and further carried out the mediation analyses to explore the intermediate role of SIIBs in mortality association. We found that SII, SIRI, and AISI all demonstrated mediation effects for HLS with both all-cause and CVD mortality. The proportion mediated by SIIBs for HLS ranged from 2.5% to 4.0% (p < 0.05). Moreover, all SIIBs mediated protective association between non-smoking, as well as physical activity, and all-cause mortality (p < 0.05). The SII, SIRI, and AISI mediated 5.4%, 6.1%, and 5.7% of the association between non-smoking and all-cause mortality (p < 0.05). The mediation effect was also significant for physically active, where SII, SIRI, and AISI accounted for mediation proportion of 3.6%, 4.0%, and 3.2% on all-cause mortality (p < 0.05). (Table 4).

Table 4. Mediation analysis of SIIBs for the associations between lifestyles and mortality.

Lifestyles	SII	SIRI	AISI	
NIE (95%CI)	Proportion mediated, %	p-value	NIE (95%CI)	Proportion mediated, %	p-value	NIE (95%CI)	Proportion mediated, %	p-value	
All-cause mortality	 	 	 	 	 	 	 	 	 	
 HLS	0.98 (0.97, 0.99)	3.1	0.006	0.98 (0.96, 0.99)	4.0	0.004	0.99 (0.97,1.00)	2.5	0.014	
 Non-smoking	0.96 (0.94, 0.99)	5.4	0.027	0.96 (0.94, 0.98)	6.1	0.031	0.96 (0.94, 0.99)	5.7	0.037	
 Low-risk alcohol consumption	0.98 (0.96, 1.00)	2.8	0.161	0.97 (0.95, 1.00)	3.9	0.152	0.99 (0.97, 1.01)	1.5	0.321	
 Physically active	0.97 (0.95, 0.99)	3.6	0.017	0.97 (0.95, 0.99)	4.0	0.040	0.98 (0.97, 1.00)	3.2	0.041	
 Optimal sleep	0.99 (0.98, 1.01)	1.6	0.312	0.99 (0.97, 1.00)	3.3	0.124	0.99 (0.98, 1.01)	2.5	0.133	
CVD mortality	 	 	 	 	 	 	 	 	 	
 HLS	0.98 (0.96, 0.99)	3.6	0.012	0.98 (0.96, 0.99)	4.0	0.015	0.98 (0.96, 0.99)	3.5	0.021	
 Non-smoking	0.96 (0.93, 0.98)	7.3	0.062	0.96 (0.93, 0.99)	7.9	0.095	0.95 (0.92, 0.98)	8.8	0.094	
 Low-risk alcohol consumption	0.98 (0.95, 1.00)	4.2	0.239	0.97 (0.95, 1.00)	4.8	0.237	0.99 (0.97, 1.01)	2.1	0.364	
 Physically active	0.97 (0.95, 0.99)	3.6	0.028	0.97 (0.95, 1.00)	3.3	0.059	0.97 (0.95, 0.99)	3.1	0.055	
 Optimal sleep	0.99 (0.98, 1.01)	1.6	0.322	0.99 (0.97, 1.00)	3.0	0.154	0.99 (0.97, 1.00)	2.6	0.157	
Abbreviations: SIIBs: systemic immune-inflammatory biomarkers; SII: systemic immune-inflammation index; SIRI: systemic inflammatory response index; AISI: aggregate index of systemic inflammation; HLS: healthy lifestyle score; NIE: natural indirect effect;

Note: The mediation analyses were performed using ‘CAUSALMED’ Procedure in SAS. SIIBs (SII, SIRI, and AISI) were dichotomized at the 75th percentile to determine their mediating effect on mortality.

4. Discussion

In this study, we found that adherence to overall healthy lifestyles defined as non-smoking, being physically active, low-risk alcohol consumption, and optimal sleep duration in patients with MASLD reduced the risk of all-cause mortality, and was particularly protective against the risk of CVD mortality. In addition, our study identified the mediating role of SIIBs (SII, SIRI, and AISI) in the relationship between lifestyles and mortality. These findings emphasise the importance of adopting healthy lifestyles for people with MASLD.

In previous studies, unhealthy lifestyles, such as smoking, excessive alcohol consumption, lack of exercise and poor diet, have been shown to increase the risk of all-cause mortality in the general population [40,41]. Meanwhile, prior studies have revealed that individual healthy lifestyles can reverse disease symptoms and reduce the risk of death in patients with MASLD [42–45]. Two studies from Southeast Asia and Thailand found that smoking exacerbated the risk of CVD and malignancy in patients with steatohepatitis, increasing their overall mortality approximately fivefold [42,45]. For instance, A prospective study showed that the progression of MASLD disease could be reversed by exercise and by reducing hepatic steatosis and steatohepatitis [43]. In addition, Chan-Won Kim’s study of 45,293 middle-aged workers in Korea found that short sleep duration was significantly associated with an increased risk of MASLD [15]. F. Aberg et al. observed a J-shaped association between alcohol intake and mortality in patients with fatty liver through a prospective study of 8345 participants, with moderate drinkers having the lowest risk of death [44]. However, there is still a debate about the definition of healthy drinking behaviour [46]. Yu et al. [16] defined non-drinking as healthy drinking behaviour in their study, and the 2019 Global Burden of Disease (GBD) also asserts that a safe dose of alcohol is 0 [47]. While affirming that alcohol consumption among young people poses only health risks, the 2022 GBD also suggests that small amounts of alcohol consumption may reduce the risk of ischaemic heart disease, stroke, and diabetes among people ‘over 40 with no underlying disease’ [48]. The results of the study found that a single lifestyle of non-smoking, physical activity, ideal sleep duration, and low-risk alcohol consumption reduced all-cause mortality in patients with MASLD by 32%, 40%, 22%, and 37%, respectively. Although the results support that low alcohol intake is beneficial in reducing the risk of death in patients with MASLD, more research is needed to support whether low alcohol consumption is a healthier lifestyle.

Our study referred to YB Zhang’s study and constructed HLS based on non-smoking, physically active (7.5 MET-hours per week), optimal sleep duration (6–8 h) and low-risk alcohol consumption (1–14 g/day for women and 1–28 g/day for men) [26]. Substantial evidence from cohort studies suggested that combined healthy lifestyles reduce the incidence of MASLD [49,50]. In addition, a systematic review that included 34 randomized controlled trials revealed that the healthiest lifestyle was associated with reduced risk of MASLD [51]. Nevertheless, the combined HLS approach has rarely been used to assess mortality risk in MASLD populations. Previous investigations based on the National Health Nutrition Examination Survey (NHANES) population have demonstrated a protective correlation between HLS and all-cause mortality in MASLD patients [17], which our findings affirm and extend. Besides, we further found that the 3-4 combined healthy lifestyle factor had a stronger protective effect against all-cause death in patients with MASLD (HR, 0.68; 95%CI, 0.61-0.76) compared to single lifestyle factors. Further subgroup analyses showed that the protective association between healthy lifestyle adherence and mortality rates was consistently significant across varying degrees of hepatic steatosis and concomitant comorbidities such as hypertension, diabetes, cardiovascular disease, and hyperlipidaemia.

Compelling evidence suggesting that the relationships of lifestyles with disease or mortality risks works through various metabolic and molecular alterations, such as inhibition of insulin resistance, inflammation, and oxidative stress [52–57]. However, the latent mechanisms for the associations of healthy lifestyles with reduced mortality risks remain largely unknown, which provides room for further exploration in our study. Although previous studies have demonstrated the role of a healthy lifestyle in delaying the progression of MASLD, few studies have demonstrated the existence of possible mechanisms mediating this effect. It has been shown that healthy lifestyles, such as adequate exercise, good sleep quality and non-smoking, has been found to reduce the inflammatory response [58,59]. SII, SIRI, and AISI are three novel biomarkers of systemic inflammation that assesses the inflammatory response by integrating different but complementary pathways [32,33]. These composite indices provide a comprehensive assessment of systemic inflammatory activity and offer advantages over single inflammatory markers due to their ease of detection. In recent years, many prospective studies have indicated the correlation between SIIBs and increased carcinogenesis and mortality and they are often used as novel prognostic indicators for all-cause mortality and CVD mortality [32,60–62]. In the present study, we observed significant associations between healthy lifestyles (never smoking and being physically active) and reduced SIIBs (SII, SIRI, and AISI) levels. Furthermore, we found that SIIBs (SII, SIRI, and AISI) levels were positively associated with all-cause and CVD mortality. Our observations are supported by previous studies where Kristian Karstoft et al. observed that exercise could induce strong anti-inflammatory effects directly through metabolism and indirectly by preventing visceral fat accumulation [58,59].

Moreover, numerous studies suggest that inflammation is a key factor in hepatic steatosis [63,64], and it has been suggested that the increased all-cause mortality in patients with MASLD may be mainly due to additional (systemic) inflammation in the early disease stages and liver-related complications in the late fibrosis stages [65,66]. High levels of SIIBs have been reported to be associated with elevated cytokines, such as interleukin-6 (IL-6), IL-8, and IL-10, which are associated with systemic chronic inflammatory responses [67,68]. This aligns with our findings that elevated SII, SIRI, and AISI are positively associated with increased risk of death in MASLD population.

Nevertheless, research on SIIBs in hepatic steatosis that affects chronic liver disease is scarce [64]. In our study, we found that SIIBs mediated the association between various healthy lifestyles and the risk of death among MASLD participants. This may partially explain the effect of a healthy lifestyle on the risk of death in patients with MASLD observed in the study. Our mediation analysis showed that SII, SIRI, and AISI mediated 3.1%, 4.0%, and 2.5% of the effect of HLS on the risk of all-cause mortality, respectively. Interestingly, SIIBs (SII, SIRI, and AISI) levels mediated greater proportion of the effects of the above lifestyles on CVD mortality. Besides, the effect of non-smoking on the risk of all-cause mortality was mediated by SIIBs (SII, SIRI, and AISI) from 5.4% to 6.1%. CVD is the leading cause of death in patients with MASLD. According to the current literature, tobacco components induce oxidative stress/inflammatory pathways that exacerbate MASLD in smokers due to elevated pro-inflammatory cytokines such as interleukin-1 (IL-1), IL-6 and tumour necrosis factor-α [69].

In short, our study shows that reduced SIIBs levels may play a role in the prognosis of MASLD patients, and these prognoses are improved by healthy lifestyles. Aforementioned studies may partly explain the mediation role of SIIBs played in the associations of healthy lifestyles with CVD and mortality risks. But the detailed biological mechanisms of favourable lifestyles improving health outcomes still need to be further validated in future studies.

Our study has several advantages. First, the study used a prospective cohort study design, which helped to determine causality. In addition, the sample size of our study was large, which contributed to the statistical power and reliability of the findings. Second, we advocate the adoption of a holistic healthy lifestyle rather than just one of them and call for further strengthening of lifestyle management in patients with MASLD for greater benefit. Third, to our knowledge, this is the first time to evaluate the possible mediation effect of inflammation through which lifestyle affects mortality outcomes among MASLD patients. This contributes to our insight into the mechanisms by which lifestyle factors influence mortality in patients with MASLD, and uncovering these mechanisms will help us to better understand how healthy lifestyles affect the risk of death in patients with MASLD and thus provide more targeted lifestyle advice to patients.

However, the limitations of the study require us to interpret these findings with caution. First, the present study was primarily based on the MASLD population and the conclusions drawn may not apply to the general population. Second, our participants are retired people which means average age is around 60 years, so the suitability of present findings for younger population needed further validation. Third, the healthy lifestyles considered in the current analysis may not represent all healthy behaviours. We did not include diet because the diet was assessed using a simple food frequency questionnaire and without information on portion size, so we were unable to assess total energy intake. But we included BMI as a covariate, which we believe to some extent reflects the effects of diet on the body. And the current lifestyle factors can represent the main aspects that are more accessible and modifiable in the Chinese population. In addition, we adjusted for broad covariates and the results remained concordant. Fourth, recall bias is unavoidable in self-reported assessments of lifestyle factors. Nevertheless, due to the design of prospective studies, misclassification may be undifferentiated and tend to diminish the observed association to null. Fifth, since the study design for the mediation analysis was cross-sectional, the causality of the findings is not strong and only suggests that SIIBs may be a mediator between lifestyle and mortality. Lastly, due to the observational design of the study, residual confounding effects could not be excluded entirely. Despite that, these findings provided potential new insights into the underlying mechanisms that the burden of death due to unhealthy behaviours may be partially explained by elevated inflammatory factors, and emphasise that a broad range of lifestyle strategies can be adopted to promote long-term patient survival. Further studies are needed to explore possible mechanisms.

Supplementary Material

Supplement.docx

Acknowledgments

The authors want to thank all the participants of this study and all the staff from Shiyan Dongfeng Motor Company for collecting the samples and questionnaires. we would like to appreciate all participants and staff in the cohort.

Ethical approval

This work has received approval for research ethics from the Ethics Committee of Tongji Medical College, Huazhong University of Science and Technology (no. S355).

Informed consent

All participants were provided informed consents.

Registry and the registration no. of the study/trial

N/A

Animal studies

N/A

Research involving recombinant DNA

N/A

Authors contributions

Qilin Deng: Conceptualisation, Methodology, Writing- original draft, Writing-review & editing. Yingchen Zhang: Conceptualisation, Methodology, Formal analysis, Writing-original draft. Xin Guan: Formal analysis, Writing-review & editing. Chenming Wang: Investigation, Methodology. Huan Guo: Project administration, Conceptualisation, Writing-original draft, Writing-review & editing, Funding acquisition. All authors critically reviewed the manuscript and approved the final version as submitted.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data that support the findings of this study are available from the corresponding author, Huan Guo, upon reasonable request.
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