Association between multifactorial control and excess risk of liver diseases in type 2 diabetes: a prospective cohort study

Rui Chen; Ying Zhou; Minzhi Xu; Yanhong Gong; Wenfei Xia; Xiaoxv Yin

doi:10.1136/bmjdrc-2025-005336

. 2025 Sep 8;13(5):e005336. doi: 10.1136/bmjdrc-2025-005336

Association between multifactorial control and excess risk of liver diseases in type 2 diabetes: a prospective cohort study

Rui Chen ^1,⁰, Ying Zhou ^1,⁰, Minzhi Xu ¹, Yanhong Gong ¹, Wenfei Xia ^2,^*, Xiaoxv Yin ^1,^✉

PMCID: PMC12421187 PMID: 40921491

Abstract

Introduction

To examine the association of the number of controlled risk factors with the excess risk of severe metabolic dysfunction-associated steatotic liver disease (MASLD) and major adverse liver outcomes (MALO) among patients with type 2 diabetes.

Research design and methods

In this cohort study, a total of 307,688 participants from the UK Biobank were included. Participants with baseline type 2 diabetes were categorized according to the number of risk factors within the guideline-recommended ranges (diet, smoking, drinking, exercise, sedentary behavior, body mass index, glycated hemoglobin, blood pressure, and low-density lipoprotein cholesterol).

Results

During a median (IQR) of 12.5 (11.8–13.2) years of follow-up, 519 (3.9%) participants with type 2 diabetes and 2718 (0.9%) participants without diabetes developed severe MASLD. Patients with type 2 diabetes had an increased risk of severe MASLD compared with participants without diabetes (HR 3.93, 95% CI 3.56 to 4.33), but the excess risk decreased stepwise with an increasing number of risk factors on target (HR (95% CI) for zero to two controlled risk factors: 5.44 (4.09 to 7.25); three controlled risk factors: 4.47 (3.59 to 5.57); four controlled risk factors: 4.16 (3.49 to 4.96); five controlled risk factors: 3.91 (3.28 to 4.66); six controlled risk factors: 3.50 (2.80 to 4.38); seven to nine controlled risk factors: 2.61 (1.92 to 3.56)). Similar patterns were observed in the analysis of MALO.

Conclusions

Patients with type 2 diabetes who had more controlled risk factors showed progressively lower excess risk of severe MASLD and MALO. Comprehensive interventions targeting multiple risk factors may be associated with reduced liver lesions in patients with type 2 diabetes.

Keywords: Cohort Studies; Gastroenterology; Life Style; Diabetes Mellitus, Type 2

WHAT IS ALREADY KNOWN ON THIS TOPIC

The control of multiple risk factors has been shown to be associated with lower excess risk of several diabetes-associated diseases, but whether and to which extent multifactorial modification can mitigate the risk of severe metabolic dysfunction-associated steatotic liver disease (MASLD) and major adverse liver outcomes (MALO) in patients with type 2 diabetes remains unclear.

WHAT THIS STUDY ADDS

Patients with type 2 diabetes who had more controlled risk factors showed progressively lower excess risk of severe MASLD and MALO.
Multifactorial control may be effective to reduce excess risk of diabetes-associated liver diseases.

HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY

Comprehensive interventions based on the control of multiple risk factors may reduce liver lesions in patients with type 2 diabetes.

Introduction

Diabetes is a global public health threat, affecting 589 million adults and causing more than 3.4 million deaths worldwide in 2024.¹ By 2030, the global economic burden of diabetes (mainly type 2 diabetes) and its complications will increase to a massive $2.5 trillion.² About two-thirds of patients with type 2 diabetes worldwide also suffer from non-alcoholic fatty liver disease (NAFLD),³ defined as the presence of steatosis in more than 5% of hepatocytes and in the absence of excessive alcohol consumption or other chronic liver diseases.⁴ In 2020, metabolic dysfunction-associated fatty liver disease was introduced to replace NAFLD, emphasizing its metabolic basis; this terminology was subsequently updated to metabolic dysfunction-associated steatotic liver disease (MASLD) in the 2023 expert consensus to better define the disease.⁵ Without effective intervention, MASLD may progress to liver fibrosis, cirrhosis, and hepatocellular carcinoma stepwise.⁶ Considering that there are no well-established and generalizable therapies for hepatic steatosis and fibrosis,^{7 8} it is crucial to identify effective measures for the prevention of diabetes-associated liver diseases.

The latest guidelines for diabetes care mentioned several priorities (such as diet, smoking, drinking, exercise, sedentary behavior, body mass index (BMI), glycated hemoglobin (HbA_1c), blood pressure (BP), and low-density lipoprotein cholesterol (LDL-C)) for attention and suggested keeping the risk factors within the recommended ranges to reduce the incidence of diabetes-associated diseases,^{9 10} which have been shown to be potentially beneficial for some complications.11,15 However, there are three gaps in the current studies. First, most existing studies on multifactorial control in type 2 diabetes have focused primarily on cardiovascular outcomes,11,13 with little attention given to hepatic outcomes. Second, while some research has examined the association between individual risk factors and liver disease risk in patients with diabetes,^{11 16 17} few have considered the effect of multiple modifiable risk factors. Third, to the best of our knowledge, no previous study has specifically quantified the extent to which achieving multiple guideline-recommended targets can mitigate the excess risk of liver diseases among individuals with type 2 diabetes.

To fill these knowledge gaps, we aimed to examine the associations between the number of risk factors within the guideline-recommended target ranges and the risk of liver diseases among patients with type 2 diabetes, compared with participants without diabetes from the UK Biobank.

Materials and methods

Study population

The UK Biobank is a population-based prospective cohort study, consisting of more than 500,000 adults from England, Scotland, and Wales. Participants completed touchscreen questionnaires, verbal interviews, and physical measurements, as well as provided biological samples. The study design and other detailed information on the UK Biobank have been fully illustrated in previous studies.¹⁸

Participants with baseline diabetes (type 2 diabetes, type 1 diabetes, and gestational diabetes) were identified according to the algorithm developed by the UK Biobank, which incorporates multiple aspects of information such as self-reported medical history and medication.¹⁹ In this study, we excluded participants with baseline diagnoses of type 1 diabetes, gestational diabetes, liver diseases, or alcohol/drug use disorders, and those with missing values of exposure variables. The liver diseases and alcohol/drug use disorders were defined according to the International Classification of Diseases (ICD) codes provided by the Expert Panel Consensus statement²⁰ and relative reference (online supplemental table S1).⁷ Finally, 307,688 participants (13,380 with type 2 diabetes and 294,308 without diabetes) were included in this study (online supplemental figure S1).

Before enrollment, all participants gave consent in writing form.

Assessment of risk factors

Information on risk factors was collected at baseline through self-report, physical measures, or biochemistry assays. Risk factors were selected based on clinical guidelines and management recommendations for diabetes care and defined based on guideline-recommended ranges^{9 10} and previous studies21,24: diet (meeting at least 5 of 10 intake requirements for fruit, vegetable, fish, processed meats, unprocessed meats, whole grains, refined grains, vegetable oils, dairy, and sugar-sweetened beverages), smoking status (non-current smokers), alcohol consumption (no more than 14 g/day for women and 28 g/day for men), exercise (at least 150 min/week of moderate activity or 75 min/week of vigorous activity), sedentary behavior (less than 4 hours/day of television watching), BMI (20–25 kg/m²), HbA_1c (less than 53 mmol/mol), BP (less than 140/90 mmHg), and LDL-C (less than 2.6 mmol/L). Details on the assessment of these risk factors are shown in online supplemental tables S2 and S3.

Ascertainment of outcomes

The outcomes of this study were severe MASLD (hospitalization or death due to MASLD or metabolic dysfunction-associated steatohepatitis)²⁴ and major adverse liver outcomes (MALO, a composite of compensated or decompensated liver cirrhosis, liver failure, hepatocellular carcinoma, and liver transplantation).⁷ Specifically, the outcomes were identified on the basis of hospital inpatient records through ICD-10 and ICD-9 codes. The specific ICD codes for severe MASLD and MALO refer to the Expert Panel Consensus statement²⁰ and relative reference (online supplemental table S1).⁷ The follow-up time for each participant was calculated from the baseline to the date of severe MASLD or MALO diagnosis, date of death, date of loss to follow-up, or the end of follow-up (20210930), whichever came first.

Assessment of covariates

Sociodemographic characteristics were included as potential confounders in this study: age (years), sex (male or female), ethnicity (white British or others), Townsend Deprivation Index (continuous variable; a lower score is indicative of higher socioeconomic status), and educational attainment (college/university degree or other qualification).

Statistical analysis

To ensure balanced group sizes and statistical robustness, patients with type 2 diabetes were categorized into six groups according to the number of risk factors within the recommended ranges (0–2, 3, 4, 5, 6, 7–9).^{14 25} Participants without diabetes were seen as the reference group. Baseline characteristics of these seven study groups were shown as n (%) for categorical variables, mean (SD) for normally distributed continuous variables, and median (IQR) for non-normally distributed continuous variables. Differences between groups were tested by the χ² test, analysis of variance, and the Kruskal-Wallis test, respectively.

Cox proportional hazards models were applied to estimate the associations (expressed as HRs) between the number of risk factors on target and severe MASLD and MALO among patients with type 2 diabetes, compared with participants without diabetes. The Schoenfeld residual method was used to test the proportional hazards assumption, and no violation was detected. All analyses were adjusted for baseline age, sex, ethnicity, Townsend Deprivation Index, and educational attainment. The percentage of missing values for each covariate was less than 1% and multiple imputation was employed to fill these missing values. To quantify observed disease frequencies, we calculated the incidence rates and absolute rate differences per 1000 person-years of severe MASLD and MALO across study groups, and estimated their 95% CIs using Poisson regression.^{14 26 27} Furthermore, to explore the proportion of patients with severe MASLD and MALO out of the participants with type 2 diabetes who could theoretically be avoided if all participants with type 2 diabetes maintained seven to nine risk factors on target at baseline, we calculated the population attributable fraction (PAF) under the assumption of a causal relationship.^{25 28} The PAF values were estimated using the well-validated AFcoxph function from the AF package, which has been widely adopted in similar studies.^{25 29}

We also examined the association of each risk factor with the occurrence of severe MASLD and MALO among patients with type 2 diabetes. The Cox models were adjusted for age, sex, ethnicity, Townsend Deprivation Index, educational attainment, diabetes duration, and diabetes medication use. Additionally, the relative importance of these risk factors was measured by the R² values of the Cox regression models, using coxphERR function of R software.^{30 31} The R² essentially quantifies the proportion of independent contribution of a risk factor to the overall explanatory power of the model, reflecting the relative importance of that variable in explaining survival risk variation.

Analyses among patients with type 2 diabetes were performed with stratification by age (≤60 or >60 years), sex (female or male), educational attainment (college/university degree or other), diabetes duration (<5 or ≥5 years), and diabetes medication use (only oral medication pills, insulin or others, or neither). Interactions between the score of risk factors on target and stratified factors on the risk of severe MASLD and MALO were examined by the likelihood ratio test.

Several sensitivity analyses were performed to test the robustness of our results. First, participants who were diagnosed with severe MASLD or MALO within the first 2 years after recruitment were excluded to reduce potential reverse causation. Second, competing risk models were employed with death regarded as a competitive event. Third, considering the varying degrees of association of different risk factors with severe MASLD and MALO, we constructed a weighted score of risk factors and examined its association with disease outcomes.²⁵ Fourth, analyses were repeated by sequentially excluding each of the nine risk factors. Fifth, different cut-off values for BMI (20–30 kg/m²), HbA_1c (less than 48 mmol/mol), BP (less than 130/80 mm Hg), and LDL-C (less than 1.8 mmol/L) were used. Sixth, we additionally included sleep duration (7–8 hours/day was recommended)¹⁵ and albuminuria (absence of albuminuria was recommended)¹⁴ as additional risk factors, which may be related to diabetes care and liver diseases.^{9 16 32 33} Seventh, multiple imputations with chained equations were applied to assign missing values of exposure to test the influence of missing data. Finally, the reference group was defined as participants without diabetes who had up to three risk factors on target, four or five risk factors on target, and six to nine risk factors on target, respectively.

All statistical analyses were performed using R V.4.3.0. A two-sided p<0.05 was considered statistically significant.

Results

Baseline characteristics

In this study, 13,380 participants with type 2 diabetes and 294,308 participants without diabetes were included (median (IQR) baseline age: 57.0 (50.0–63.0) years; 52.4% females). Patients with type 2 diabetes with a lower number of risk factors on target were more socioeconomically deprived and less educated (table 1). The baseline characteristics of the included participants and excluded ones were similar (online supplemental table S4).

Table 1. Baseline characteristics of participants without diabetes and participants with type 2 diabetes.

	Participants without diabetes	Participants with type 2 diabetes
		Overall	Number of risk factors within the recommended ranges^*
		Overall	0–2	3	4	5	6	7–9
Participants	294,308 (100.0%)	13,380 (100.0%)	882 (6.6%)	1866 (13.9%)	3231 (24.1%)	3428 (25.6%)	2336 (17.5%)	1637 (12.2%)
Age, years	57.0 (49.0, 63.0)	62.0 (56.0, 65.0)	61.0 (55.0, 65.0)	61.0 (56.0, 65.0)	62.0 (56.0, 65.0)	62.0 (56.0, 66.0)	62.0 (56.0, 66.0)	62.0 (57.0, 66.0)
Female	156,588 (53.2%)	4497 (33.6%)	257 (29.1%)	587 (31.5%)	1061 (32.8%)	1215 (35.4%)	792 (33.9%)	585 (35.7%)
White British	263,447 (89.5%)	11,146 (83.3%)	769 (87.2%)	1603 (85.9%)	2730 (84.5%)	2863 (83.5%)	1923 (82.3%)	1258 (76.8%)
Townsend Deprivation Index	−2.3 (−3.7, 0.2)	−1.5 (−3.3, 1.7)	−0.6 (−3.0, 2.7)	−1.0 (−3.1, 2.2)	−1.5 (−3.3, 1.6)	−1.4 (−3.2, 1.7)	−1.9 (−3.5, 1.0)	−1.8 (−3.4, 1.2)
College/university degree	104,868 (35.6%)	3409 (25.5%)	157 (17.8%)	389 (20.8%)	788 (24.4%)	874 (25.5%)	641 (27.4%)	560 (34.2%)
Healthy diet^†	53,888 (18.3%)	3759 (28.1%)	22 (2.5%)	154 (8.3%)	479 (14.8%)	960 (28.0%)	1045 (44.7%)	1099 (67.1%)
No current smoking	265,335 (90.2%)	11,997 (89.7%)	531 (60.2%)	1526 (81.8%)	2889 (89.4%)	3193 (93.1%)	2247 (96.2%)	1611 (98.4%)
Non-excessive drinking^‡	215,295 (73.2%)	10,722 (80.1%)	372 (42.2%)	1218 (65.3%)	2462 (76.2%)	2975(86.8%)	2108 (90.2%)	1587 (96.9%)
Sufficient physical activity^§	161,442 (54.9%)	6099 (45.6%)	65 (7.4%)	396 (21.2%)	1147 (35.5%)	1666 (48.6%)	1547 (66.2%)	1278 (78.1%)
Less sedentary behavior^¶	217,568 (73.9%)	7559 (56.5%)	120 (13.6%)	607 (32.5%)	1486 (46.0%)	2138 (62.4%)	1734 (74.2%)	1474 (90.0%)
BMI, kg/m²	26.4 (23.9, 29.4)	30.5 (27.4, 34.3)	32.4 (29.3, 35.8)	32 (28.9, 35.7)	31.2 (28.1, 34.8)	30.5 (27.6, 34.5)	29.5 (26.4, 33.2)	26.9 (24.0, 31.1)
HbA_1c, mmol/mol	34.9 (32.5, 37.3)	49.7 (43.2, 58.2)	59 (53.7, 68.0)	55.8 (47.5, 64.6)	51.8 (44.3, 60.7)	48.5 (42.7, 56.2)	46.9 (41.6, 52.1)	45.3 (40.6, 49.7)
Systolic blood pressure, mm Hg	135.5 (123.5, 148.5)	140.0 (129.0, 151.5)	149.3 (142.0, 158.1)	147.0 (138.0, 158.0)	143.0 (132.0, 153.5)	139.0 (128.5, 150.5)	134.5 (126.0, 144.5)	130.5 (122.0, 137.5)
Diastolic blood pressure, mm Hg	81.5 (75.0, 88.5)	81.0 (75.0, 87.5)	86.5 (80.0, 92.0)	84.5 (78.5, 91.4)	82.5 (76.5, 89.0)	81.0 (75.0, 87.0)	79.0 (73.5, 85.0)	76.0 (71.0, 82.0)
LDL-C, mmol/L	3.5 (3.0, 4.1)	2.6 (2.2, 3.1)	3.0 (2.7, 3.5)	2.9 (2.5, 3.3)	2.7 (2.2, 3.2)	2.5 (2.1, 3.0)	2.4 (2.0, 2.7)	2.2 (1.9, 2.5)

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Data are n (%) for categorical variables, mean (SD) for normally distributed continuous variables, and median (IQR) for non-normally distributed continuous variables, with differences between groups tested by the χ2 test, analysis of variance, and the Kruskal-Wallis test, respectively. p<0.001 for all comparisons across groups.

Nine risk factors within the recommended ranges include healthy diet, no current smoking, non-excessive drinking, sufficient physical activity, less sedentary behavior, BMI 20–25 kg/m2, HbA_1c less than 53 mmol/mol (less than 7%), blood pressure less than 140/90 mm Hg, LDL-C less than 2.6 mmol/L.

^†

Satisfying at least five of ten elements regarding fruit, vegetable, fish, processed meats, unprocessed meats, whole grains, refined grains, vegetable oils, dairy, and sugar-sweetened beverages.

^‡

No more than 14 g/day for women and 28 g/day for men.

^§

At least 150 min/week of moderate activity or 75 minutes/week of vigorous activity.

^¶

Less than 4 hours/day of television watching.

BMI, body mass index; HbA_1c, glycated hemoglobin; LDL-C, low-density lipoprotein cholesterol.

Association of type 2 diabetes according to the number of risk factors within the recommended ranges with severe MASLD

During a median (IQR) follow-up of 12.5 (11.8–13.2) years, 519 (3.9%) participants with type 2 diabetes and 2718 (0.9%) participants without diabetes developed severe MASLD. The risk of severe MASLD in patients with type 2 diabetes was about four times higher than in participants without diabetes (HR 3.93; 95% CI 3.56 to 4.33). Figure 1 shows the incidence rates and HRs (95% CIs) for severe MASLD among seven groups. With an increasing number of risk factors within the recommended ranges, the excess risk of severe MASLD in patients with type 2 diabetes decreased stepwise. After adjusting for covariates, HRs (95% CIs) of severe MASLD for patients with type 2 diabetes with zero to two risk factors on target, three risk factors on target, four risk factors on target, five risk factors on target, six risk factors on target, and seven to nine risk factors on target, as compared with participants without diabetes, were 5.44 (4.09 to 7.25), 4.47 (3.59 to 5.57), 4.16 (3.49 to 4.96), 3.91 (3.28 to 4.66), 3.50 (2.80 to 4.38), and 2.61 (1.92 to 3.56), respectively (P for trend<0.001). Each 1-point increase in the number of risk factors on target was linked to an HR of 0.76 (95% CI 0.74 to 0.78) (online supplemental table S5). The associations between each risk factor and severe MASLD among participants with type 2 diabetes are shown in online supplemental table S6. The relative importance of the risk factors in predicting the risk of developing severe MASLD in patients with type 2 diabetes is shown in online supplemental figure S2A. The estimate of the PAF of severe MASLD was 33.3% (95% CI 11.9% to 54.6%) for patients with type 2 diabetes with seven to nine risk factors on target.

Association of type 2 diabetes according to the number of risk factors within the recommended ranges with MALO

During a median (IQR) of 12.5 (11.8–13.2) years, 423 (3.2%) participants with type 2 diabetes and 2758 (0.9%) participants without diabetes developed MALO. Patients with type 2 diabetes had 2.71 times the risk of developing MALO than participants without diabetes (HR 2.71; 95% CI 2.44 to 3.01). Likewise, the excess risk of MALO in participants with type 2 diabetes decreased stepwise as the number of risk factors on target increased (figure 2). Compared with participants without diabetes, the corresponding adjusted HRs for patients with type 2 diabetes with zero to two risk factors on target, three risk factors on target, four risk factors on target, five risk factors on target, six risk factors on target, and seven to nine risk factors on target were 4.56 (3.39 to 6.12), 3.35 (2.65 to 4.23), 2.73 (2.24 to 3.33), 2.65 (2.18 to 3.22), 2.16 (1.67 to 2.80), and 1.92 (1.39 to 2.66), respectively (P for trend<0.001). The HR (95% CI) was 0.85 (0.82 to 0.89) for each 1-point increment in the number of risk factors on target (online supplemental table S5). The associations between each risk factor and MALO among patients with type 2 diabetes are presented in online supplemental table S6. Online supplemental figure S2B illustrates the relative contribution of each risk factor in predicting severe MALO risk among patients with type 2 diabetes. Estimate of the PAF of MALO was 27.6% (95% CI 3.2% to 52.1%) for patients with type 2 diabetes with seven to nine risk factors on target.

Interaction analyses and sensitivity analyses

Among participants with type 2 diabetes, consistent results were observed in analyses with stratification by age, sex, educational attainment, diabetes duration, and diabetes medication use on severe MASLD and MALO (figure 3).

Our results were robust in a series of sensitivity analyses excluding participants developing severe MASLD or MALO during the first 2 years of follow-up, considering the competing risk of death, applying the weighted score of risk factors, excluding each of the nine risk factors sequentially, using different cut-off values for BMI, HbA_1c, BP, or LDL-C, including sleep duration or albuminuria as potential exposure, assigning missing values of exposure, and defining the reference group as general population with different number of risk factors on target (online supplemental table S7).

Discussion

In this large cohort study of 307,688 participants from the UK Biobank, we found that the excess risk of severe MASLD and MALO in patients with type 2 diabetes decreased gradually with an increasing number of risk factors within the guideline-recommended ranges. Patients with type 2 diabetes with zero to two risk factors on target had 5.4-fold and 4.6-fold higher risks of severe MASLD and MALO, respectively, compared with participants without diabetes, while those achieving seven to nine risk factors on target showed substantially lower risks (2.6-fold for severe MASLD and 1.9-fold for MALO).

Prior evidence on risk factors and liver diseases in type 2 diabetes

Previous studies have reported that some of the risk factors mentioned in the guidelines for diabetes care may contribute to an increased risk of MASLD and MALO in patients with type 2 diabetes. The Look AHEAD (Action for Health in Diabetes) trial in 5145 patients with type 2 diabetes demonstrated that the 1-year intensive intervention based on diet and exercise was associated with reductions in steatosis and MASLD.³⁴ In a cohort study conducted in China, random plasma glucose of individuals with type 2 diabetes compared with those without diabetes was positively associated with the incidence of MASLD, cirrhosis, and liver cancer.³⁵ Besides, in a case-control study, patients with type 2 diabetes and comorbid MASLD were found to have significantly higher BMI, BP, and LDL-C compared with patients with type 2 diabetes but without MASLD.³³ Based on the above studies, we conducted this research with a more comprehensive inclusion of risk factors to investigate the combined effect of guideline-recommended risk factors targets achieved on the excess risk of MASLD and MALO in patients with type 2 diabetes.

Biological pathways in diabetes-related liver diseases

The underlying mechanism of diabetes-related MASLD and MALO is complex. Several hypotheses have been proposed, including insulin resistance, oxidative stress, and chronic inflammation.³⁶ Risk factors included in this study, such as unhealthy diet, smoking, excessive drinking, inadequate exercise, sedentary behavior, and high BP, have been proven to be associated with insulin resistance,^{37 38} resulting in an increased influx of free fatty acids into the liver.³⁹ Diet, smoking, alcohol consumption, and exercise have been found to affect oxidative stress,⁴⁰ thereby impairing mitochondria,⁴¹ which plays an essential role in fatty acid oxidation, lipogenesis, and gluconeogenesis.⁴² In addition, unhealthy diet, smoking, heavy drinking, insufficient exercise, sedentary behavior, obesity, hypertension, and disordered lipid metabolism are all linked to low-grade systemic inflammation,43,46 which characterizes MASLD and may promote tumorigenesis.³⁹ Thus, controlling these risk factors may reduce the accumulation of fat in the liver, promote liver repair and regeneration, and decrease liver inflammation, which in turn reduces the occurrence of diabetes-related MASLD and MALO. If causal, our study would emphasize the significance of comprehensive interventions to prevent MASLD and MALO in patients with type 2 diabetes, enrich the great value of multifactorial management of the guideline-recommended risk factors in diabetes care, and increase reliable evidence on integrated management of diabetes.

Potential multidimensional intervention strategies for liver diseases in type 2 diabetes

Current interventions for liver disease among patients with type 2 diabetes mainly focus on single-dimensional approaches (such as lifestyle modifications and medical management).^{47 48} Based on our findings, a multidimensional intervention strategy is proposed to address this issue. A mobile health application (App) tailored for individuals with type 2 diabetes could be developed to support the monthly self-monitoring of key health indicators, including diet, smoking status, drinking status, physical activity, sedentary behavior, BMI, HbA_1c, BP, and LDL-C. When any of these risk factors deviate from guideline-recommended ranges, the App would provide immediate, personalized recommendations to guide users in making appropriate behavioral or lifestyle adjustments. If multiple risk factors are found to be outside the recommended ranges simultaneously, or if a single factor significantly exceeds the normal threshold, the system would automatically prompt the user and assist in scheduling an appointment with a primary care physician for further evaluation and preventive action. However, this proposal is based solely on findings from our observational study, and its feasibility and effectiveness require further validation through future interventional research.

Strengths and limitations

To the best of our knowledge, this study may be the first to examine the association between multifactorial control and excess risk of severe MASLD and MALO among patients with type 2 diabetes. We underscore the feasibility of multifactorial modification in diabetes care to prevent liver diseases through a long follow-up study in a large sample of participants. Our study has several limitations. First, participants in the UK biobank were primarily Caucasians, which limits the generalizability of our findings to other ethnic groups. Second, lifestyle factors were self-reported, which may inevitably induce misclassification and measurement errors. Of note, the lack of accelerometer-derived physical activity measurements in the UK Biobank dataset during our study period limits the accuracy of exercise-related assessments. Third, all the risk factors were measured at recruitment, and changes during follow-up could not be observed. Fourth, assigning the same weight to each risk factor may ignore the varying degrees of association between each factor and the occurrence of severe MASLD and MALO, yet the application of weighted scores in sensitivity analysis yielded similar results. Fifth, we identified the outcomes from hospital inpatient records, which may have been limited to cases of more advanced or severe disease, with some relatively mild MASLD being missed. However, given that MASLD severity is positively associated with the risk of subsequent adverse outcomes, severe MASLD may be more clinically important. Finally, due to the nature of observational studies, residual confounding is inevitable and causal associations cannot be ascertained.

Conclusions

In conclusion, we found that higher numbers of controlled risk factors were associated with lower excess risk of severe MASLD and MALO in patients with type 2 diabetes. These findings suggest the potential benefit of multifactorial interventions for liver health in patients with type 2 diabetes and support the incorporation of multiple controlled risk factors into guidelines for diabetes care to reduce the burden of diabetes-associated diseases.

Supplementary material

online supplemental file 1

bmjdrc-13-5-s001.docx^{(160.1KB, docx)}

DOI: 10.1136/bmjdrc-2025-005336

Acknowledgements

We especially thank all the participants and staff of the UK Biobank for their dedication and contribution to the research.

Footnotes

Funding: The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

Provenance and peer review: Not commissioned; externally peer reviewed.

Patient consent for publication: Not applicable.

Ethics approval: The UK Biobank was approved by the North West Multi-Centre Research Ethical Committee (11/NW/0382). Participants gave informed consent to participate in the study before taking part.

Data availability free text: The data that support the findings of this study are available from the UK Biobank but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are, however, available from the authors on reasonable request and with permission of the UK Biobank. Our analyses were conducted under the UKB application no. 88159.

Data availability statement

Data are available upon reasonable request.

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8.Brennan PN, Elsharkawy AM, Kendall TJ, et al. Antifibrotic therapy in nonalcoholic steatohepatitis: time for a human-centric approach. Nat Rev Gastroenterol Hepatol. 2023;20:679–88. doi: 10.1038/s41575-023-00796-x. [DOI] [PMC free article] [PubMed] [Google Scholar]
9.Marx N, Federici M, Schütt K, et al. 2023 ESC Guidelines for the management of cardiovascular disease in patients with diabetes. Eur Heart J. 2023;44:4043–140. doi: 10.1093/eurheartj/ehad192. [DOI] [PubMed] [Google Scholar]
10.American Diabetes Association Lifestyle management: standards of medical care in diabetes-2019. Diabetes Care. 2019;42:S46–60. doi: 10.2337/dc19-S005. [DOI] [PubMed] [Google Scholar]
11.Rawshani A, Rawshani A, Franzén S, et al. Risk Factors, Mortality, and Cardiovascular Outcomes in Patients with Type 2 Diabetes. N Engl J Med. 2018;379:633–44. doi: 10.1056/NEJMoa1800256. [DOI] [PubMed] [Google Scholar]
12.Wang T, Lu J, Su Q, et al. Ideal Cardiovascular Health Metrics and Major Cardiovascular Events in Patients With Prediabetes and Diabetes. JAMA Cardiol. 2019;4:874–83. doi: 10.1001/jamacardio.2019.2499. [DOI] [PMC free article] [PubMed] [Google Scholar]
13.Wright AK, Suarez-Ortegon MF, Read SH, et al. Risk Factor Control and Cardiovascular Event Risk in People With Type 2 Diabetes in Primary and Secondary Prevention Settings. Circulation. 2020;142:1925–36. doi: 10.1161/CIRCULATIONAHA.120.046783. [DOI] [PMC free article] [PubMed] [Google Scholar]
14.van Gennip ACE, Schram MT, Köhler S, et al. Association of type 2 diabetes according to the number of risk factors within the recommended range with incidence of major depression and clinically relevant depressive symptoms: a prospective analysis. Lancet Healthy Longev. 2023;4:e63–71. doi: 10.1016/S2666-7568(22)00291-4. [DOI] [PubMed] [Google Scholar]
15.Wang B, Fu Y, Tan X, et al. Assessing the impact of type 2 diabetes on mortality and life expectancy according to the number of risk factor targets achieved: an observational study. BMC Med. 2024;22:114. doi: 10.1186/s12916-024-03343-w. [DOI] [PMC free article] [PubMed] [Google Scholar]
16.Mallet V, Parlati L, Martinino A. Burden of liver disease progression in hospitalized patients with type 2 diabetes mellitus. J Hepatol. 2022;76:265–74. doi: 10.1016/j.jhep.2021.09.030. [DOI] [PubMed] [Google Scholar]
17.Mao X, Cheung K-S, Tan J-T, et al. Glycaemic control is a modifiable risk factor for hepatocellular carcinoma and liver-related mortality in patients with diabetes. Aliment Pharmacol Ther. 2024;60:1398–408. doi: 10.1111/apt.18254. [DOI] [PubMed] [Google Scholar]
18.Sudlow C, Gallacher J, Allen N, et al. UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 2015;12:e1001779. doi: 10.1371/journal.pmed.1001779. [DOI] [PMC free article] [PubMed] [Google Scholar]
19.Eastwood SV, Mathur R, Atkinson M, et al. Algorithms for the Capture and Adjudication of Prevalent and Incident Diabetes in UK Biobank. PLoS ONE. 2016;11:e0162388. doi: 10.1371/journal.pone.0162388. [DOI] [PMC free article] [PubMed] [Google Scholar]
20.Hagström H, Adams LA, Allen AM, et al. Administrative Coding in Electronic Health Care Record-Based Research of NAFLD: An Expert Panel Consensus Statement. Hepatology. 2021;74:474–82. doi: 10.1002/hep.31726. [DOI] [PMC free article] [PubMed] [Google Scholar]
21.Wang B, Wang N, Sun Y, et al. Association of Combined Healthy Lifestyle Factors With Incident Dementia in Patients With Type 2 Diabetes. Neurology (ECronicon) 2022;99:e2336–45. doi: 10.1212/WNL.0000000000201231. [DOI] [PubMed] [Google Scholar]
22.Foster HME, Celis-Morales CA, Nicholl BI, et al. The effect of socioeconomic deprivation on the association between an extended measurement of unhealthy lifestyle factors and health outcomes: a prospective analysis of the UK Biobank cohort. Lancet Public Health. 2018;3:e576–85. doi: 10.1016/S2468-2667(18)30200-7. [DOI] [PubMed] [Google Scholar]
23.Wong I. Study of type 2 diabetes management among patients in a Macau primary care setting. Fam Med Community Health. 2019;7:e000031. doi: 10.1136/fmch-2018-000031. [DOI] [PMC free article] [PubMed] [Google Scholar]
24.Petermann-Rocha F, Gray SR, Forrest E, et al. Associations of muscle mass and grip strength with severe NAFLD: A prospective study of 333,295 UK Biobank participants. J Hepatol. 2022;76:1021–9. doi: 10.1016/j.jhep.2022.01.010. [DOI] [PubMed] [Google Scholar]
25.Han H, Cao Y, Feng C, et al. Association of a Healthy Lifestyle With All-Cause and Cause-Specific Mortality Among Individuals With Type 2 Diabetes: A Prospective Study in UK Biobank. Diabetes Care. 2022;45:319–29. doi: 10.2337/dc21-1512. [DOI] [PubMed] [Google Scholar]
26.Frome EL, Checkoway H. Epidemiologic programs for computers and calculators. Use of Poisson regression models in estimating incidence rates and ratios. Am J Epidemiol. 1985;121:309–23. doi: 10.1093/oxfordjournals.aje.a114001. [DOI] [PubMed] [Google Scholar]
27.Cabral NL, Freire AT, Conforto AB, et al. Increase of Stroke Incidence in Young Adults in a Middle-Income Country: A 10-Year Population-Based Study. Stroke. 2017;48:2925–30. doi: 10.1161/STROKEAHA.117.018531. [DOI] [PubMed] [Google Scholar]
28.Rockhill B, Newman B, Weinberg C. Use and misuse of population attributable fractions. Am J Public Health. 1998;88:15–9. doi: 10.2105/ajph.88.1.15. [DOI] [PMC free article] [PubMed] [Google Scholar]
29.Lv J, Yu C, Guo Y, et al. Adherence to a healthy lifestyle and the risk of type 2 diabetes in Chinese adults. Int J Epidemiol. 2017;46:1410–20. doi: 10.1093/ije/dyx074. [DOI] [PMC free article] [PubMed] [Google Scholar]
30.Heller G. A measure of explained risk in the proportional hazards model. Biostatistics. 2012;13:315–25. doi: 10.1093/biostatistics/kxr047. [DOI] [PMC free article] [PubMed] [Google Scholar]
31.Zhou J, Tang R, Wang X, et al. Improvement of Social Isolation and Loneliness and Excess Mortality Risk in People With Obesity. JAMA Netw Open. 2024;7:e2352824. doi: 10.1001/jamanetworkopen.2023.52824. [DOI] [PMC free article] [PubMed] [Google Scholar]
32.Liu Z, Huang H, Xie J, et al. Modifiable lifestyle factors, genetic and acquired risk, and the risk of severe liver disease in the UK Biobank cohort: (Lifestyle factors and SLD) Dig Liver Dis. 2024;56:130–6. doi: 10.1016/j.dld.2023.06.025. [DOI] [PubMed] [Google Scholar]
33.Amin RF, El Bendary AS, Ezzat SE, et al. Serum Ferritin level, microalbuminuria and non-alcoholic fatty liver disease in type 2 diabetic patients. Diabetes Metab Syndr. 2019;13:2226–9. doi: 10.1016/j.dsx.2019.05.030. [DOI] [PubMed] [Google Scholar]
34.Lazo M, Solga SF, Horska A, et al. Effect of a 12-month intensive lifestyle intervention on hepatic steatosis in adults with type 2 diabetes. Diabetes Care. 2010;33:2156–63. doi: 10.2337/dc10-0856. [DOI] [PMC free article] [PubMed] [Google Scholar]
35.Pang Y, Kartsonaki C, Turnbull I, et al. Diabetes, Plasma Glucose, and Incidence of Fatty Liver, Cirrhosis, and Liver Cancer: A Prospective Study of 0.5 Million People. Hepatology. 2018;68:1308–18. doi: 10.1002/hep.30083. [DOI] [PMC free article] [PubMed] [Google Scholar]
36.Bellini MI, Urciuoli I, Del Gaudio G, et al. Nonalcoholic fatty liver disease and diabetes. World J Diabetes. 2022;13:668–82. doi: 10.4239/wjd.v13.i9.668. [DOI] [PMC free article] [PubMed] [Google Scholar]
37.Li M, Chi X, Wang Y, et al. Trends in insulin resistance: insights into mechanisms and therapeutic strategy. Signal Transduct Target Ther. 2022;7:216. doi: 10.1038/s41392-022-01073-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
38.Petrie JR, Guzik TJ, Touyz RM. Diabetes, Hypertension, and Cardiovascular Disease: Clinical Insights and Vascular Mechanisms. Can J Cardiol. 2018;34:575–84. doi: 10.1016/j.cjca.2017.12.005. [DOI] [PMC free article] [PubMed] [Google Scholar]
39.Targher G, Byrne CD, Tilg H. MASLD: a systemic metabolic disorder with cardiovascular and malignant complications. Gut. 2024;73:691–702. doi: 10.1136/gutjnl-2023-330595. [DOI] [PubMed] [Google Scholar]
40.Aseervatham GSB, Sivasudha T, Jeyadevi R, et al. Environmental factors and unhealthy lifestyle influence oxidative stress in humans--an overview. Environ Sci Pollut Res Int. 2013;20:4356–69. doi: 10.1007/s11356-013-1748-0. [DOI] [PubMed] [Google Scholar]
41.Kumar V, Xin X, Ma J, et al. Therapeutic targets, novel drugs, and delivery systems for diabetes associated NAFLD and liver fibrosis. Adv Drug Deliv Rev. 2021;176:113888. doi: 10.1016/j.addr.2021.113888. [DOI] [PMC free article] [PubMed] [Google Scholar]
42.Fromenty B, Roden M. Mitochondrial alterations in fatty liver diseases. J Hepatol. 2023;78:415–29. doi: 10.1016/j.jhep.2022.09.020. [DOI] [PubMed] [Google Scholar]
43.Furman D, Campisi J, Verdin E, et al. Chronic inflammation in the etiology of disease across the life span. Nat Med. 2019;25:1822–32. doi: 10.1038/s41591-019-0675-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
44.Bapat SP, Whitty C, Mowery CT, et al. Obesity alters pathology and treatment response in inflammatory disease. Nature New Biol. 2022;604:337–42. doi: 10.1038/s41586-022-04536-0. [DOI] [PMC free article] [PubMed] [Google Scholar]
45.Zhao TV, Li Y, Liu X, et al. ATP release drives heightened immune responses associated with hypertension. Sci Immunol. 2019;4:eaau6426. doi: 10.1126/sciimmunol.aau6426. [DOI] [PMC free article] [PubMed] [Google Scholar]
46.Hornburg D, Wu S, Moqri M, et al. Dynamic lipidome alterations associated with human health, disease and ageing. Nat Metab. 2023;5:1578–94. doi: 10.1038/s42255-023-00880-1. [DOI] [PMC free article] [PubMed] [Google Scholar]
47.Jin L-Y, Wang K, Xu B-T. High metabolic dysfunction-associated steatotic liver disease prevalence in type 2 diabetes: Urgent need for integrated screening and lifestyle intervention. World J Hepatol. 2025;17:103409. doi: 10.4254/wjh.v17.i1.103409. [DOI] [PMC free article] [PubMed] [Google Scholar]
48.Oser TK, Varney C, McCall AL, et al. Current and new treatment options for adults recently diagnosed with type 2 diabetes. Fam Med Community Health. 2025;13:e003154. doi: 10.1136/fmch-2024-003154. [DOI] [PMC free article] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

online supplemental file 1

bmjdrc-13-5-s001.docx^{(160.1KB, docx)}

DOI: 10.1136/bmjdrc-2025-005336

Data Availability Statement

Data are available upon reasonable request.

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[R9] 9.Marx N, Federici M, Schütt K, et al. 2023 ESC Guidelines for the management of cardiovascular disease in patients with diabetes. Eur Heart J. 2023;44:4043–140. doi: 10.1093/eurheartj/ehad192. [DOI] [PubMed] [Google Scholar]

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[R12] 12.Wang T, Lu J, Su Q, et al. Ideal Cardiovascular Health Metrics and Major Cardiovascular Events in Patients With Prediabetes and Diabetes. JAMA Cardiol. 2019;4:874–83. doi: 10.1001/jamacardio.2019.2499. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R13] 13.Wright AK, Suarez-Ortegon MF, Read SH, et al. Risk Factor Control and Cardiovascular Event Risk in People With Type 2 Diabetes in Primary and Secondary Prevention Settings. Circulation. 2020;142:1925–36. doi: 10.1161/CIRCULATIONAHA.120.046783. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R14] 14.van Gennip ACE, Schram MT, Köhler S, et al. Association of type 2 diabetes according to the number of risk factors within the recommended range with incidence of major depression and clinically relevant depressive symptoms: a prospective analysis. Lancet Healthy Longev. 2023;4:e63–71. doi: 10.1016/S2666-7568(22)00291-4. [DOI] [PubMed] [Google Scholar]

[R15] 15.Wang B, Fu Y, Tan X, et al. Assessing the impact of type 2 diabetes on mortality and life expectancy according to the number of risk factor targets achieved: an observational study. BMC Med. 2024;22:114. doi: 10.1186/s12916-024-03343-w. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R16] 16.Mallet V, Parlati L, Martinino A. Burden of liver disease progression in hospitalized patients with type 2 diabetes mellitus. J Hepatol. 2022;76:265–74. doi: 10.1016/j.jhep.2021.09.030. [DOI] [PubMed] [Google Scholar]

[R17] 17.Mao X, Cheung K-S, Tan J-T, et al. Glycaemic control is a modifiable risk factor for hepatocellular carcinoma and liver-related mortality in patients with diabetes. Aliment Pharmacol Ther. 2024;60:1398–408. doi: 10.1111/apt.18254. [DOI] [PubMed] [Google Scholar]

[R18] 18.Sudlow C, Gallacher J, Allen N, et al. UK biobank: an open access resource for identifying the causes of a wide range of complex diseases of middle and old age. PLoS Med. 2015;12:e1001779. doi: 10.1371/journal.pmed.1001779. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R19] 19.Eastwood SV, Mathur R, Atkinson M, et al. Algorithms for the Capture and Adjudication of Prevalent and Incident Diabetes in UK Biobank. PLoS ONE. 2016;11:e0162388. doi: 10.1371/journal.pone.0162388. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R20] 20.Hagström H, Adams LA, Allen AM, et al. Administrative Coding in Electronic Health Care Record-Based Research of NAFLD: An Expert Panel Consensus Statement. Hepatology. 2021;74:474–82. doi: 10.1002/hep.31726. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R21] 21.Wang B, Wang N, Sun Y, et al. Association of Combined Healthy Lifestyle Factors With Incident Dementia in Patients With Type 2 Diabetes. Neurology (ECronicon) 2022;99:e2336–45. doi: 10.1212/WNL.0000000000201231. [DOI] [PubMed] [Google Scholar]

[R22] 22.Foster HME, Celis-Morales CA, Nicholl BI, et al. The effect of socioeconomic deprivation on the association between an extended measurement of unhealthy lifestyle factors and health outcomes: a prospective analysis of the UK Biobank cohort. Lancet Public Health. 2018;3:e576–85. doi: 10.1016/S2468-2667(18)30200-7. [DOI] [PubMed] [Google Scholar]

[R23] 23.Wong I. Study of type 2 diabetes management among patients in a Macau primary care setting. Fam Med Community Health. 2019;7:e000031. doi: 10.1136/fmch-2018-000031. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R24] 24.Petermann-Rocha F, Gray SR, Forrest E, et al. Associations of muscle mass and grip strength with severe NAFLD: A prospective study of 333,295 UK Biobank participants. J Hepatol. 2022;76:1021–9. doi: 10.1016/j.jhep.2022.01.010. [DOI] [PubMed] [Google Scholar]

[R25] 25.Han H, Cao Y, Feng C, et al. Association of a Healthy Lifestyle With All-Cause and Cause-Specific Mortality Among Individuals With Type 2 Diabetes: A Prospective Study in UK Biobank. Diabetes Care. 2022;45:319–29. doi: 10.2337/dc21-1512. [DOI] [PubMed] [Google Scholar]

[R26] 26.Frome EL, Checkoway H. Epidemiologic programs for computers and calculators. Use of Poisson regression models in estimating incidence rates and ratios. Am J Epidemiol. 1985;121:309–23. doi: 10.1093/oxfordjournals.aje.a114001. [DOI] [PubMed] [Google Scholar]

[R27] 27.Cabral NL, Freire AT, Conforto AB, et al. Increase of Stroke Incidence in Young Adults in a Middle-Income Country: A 10-Year Population-Based Study. Stroke. 2017;48:2925–30. doi: 10.1161/STROKEAHA.117.018531. [DOI] [PubMed] [Google Scholar]

[R28] 28.Rockhill B, Newman B, Weinberg C. Use and misuse of population attributable fractions. Am J Public Health. 1998;88:15–9. doi: 10.2105/ajph.88.1.15. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R29] 29.Lv J, Yu C, Guo Y, et al. Adherence to a healthy lifestyle and the risk of type 2 diabetes in Chinese adults. Int J Epidemiol. 2017;46:1410–20. doi: 10.1093/ije/dyx074. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R30] 30.Heller G. A measure of explained risk in the proportional hazards model. Biostatistics. 2012;13:315–25. doi: 10.1093/biostatistics/kxr047. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R31] 31.Zhou J, Tang R, Wang X, et al. Improvement of Social Isolation and Loneliness and Excess Mortality Risk in People With Obesity. JAMA Netw Open. 2024;7:e2352824. doi: 10.1001/jamanetworkopen.2023.52824. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R32] 32.Liu Z, Huang H, Xie J, et al. Modifiable lifestyle factors, genetic and acquired risk, and the risk of severe liver disease in the UK Biobank cohort: (Lifestyle factors and SLD) Dig Liver Dis. 2024;56:130–6. doi: 10.1016/j.dld.2023.06.025. [DOI] [PubMed] [Google Scholar]

[R33] 33.Amin RF, El Bendary AS, Ezzat SE, et al. Serum Ferritin level, microalbuminuria and non-alcoholic fatty liver disease in type 2 diabetic patients. Diabetes Metab Syndr. 2019;13:2226–9. doi: 10.1016/j.dsx.2019.05.030. [DOI] [PubMed] [Google Scholar]

[R34] 34.Lazo M, Solga SF, Horska A, et al. Effect of a 12-month intensive lifestyle intervention on hepatic steatosis in adults with type 2 diabetes. Diabetes Care. 2010;33:2156–63. doi: 10.2337/dc10-0856. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R35] 35.Pang Y, Kartsonaki C, Turnbull I, et al. Diabetes, Plasma Glucose, and Incidence of Fatty Liver, Cirrhosis, and Liver Cancer: A Prospective Study of 0.5 Million People. Hepatology. 2018;68:1308–18. doi: 10.1002/hep.30083. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R36] 36.Bellini MI, Urciuoli I, Del Gaudio G, et al. Nonalcoholic fatty liver disease and diabetes. World J Diabetes. 2022;13:668–82. doi: 10.4239/wjd.v13.i9.668. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R37] 37.Li M, Chi X, Wang Y, et al. Trends in insulin resistance: insights into mechanisms and therapeutic strategy. Signal Transduct Target Ther. 2022;7:216. doi: 10.1038/s41392-022-01073-0. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R38] 38.Petrie JR, Guzik TJ, Touyz RM. Diabetes, Hypertension, and Cardiovascular Disease: Clinical Insights and Vascular Mechanisms. Can J Cardiol. 2018;34:575–84. doi: 10.1016/j.cjca.2017.12.005. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R39] 39.Targher G, Byrne CD, Tilg H. MASLD: a systemic metabolic disorder with cardiovascular and malignant complications. Gut. 2024;73:691–702. doi: 10.1136/gutjnl-2023-330595. [DOI] [PubMed] [Google Scholar]

[R40] 40.Aseervatham GSB, Sivasudha T, Jeyadevi R, et al. Environmental factors and unhealthy lifestyle influence oxidative stress in humans--an overview. Environ Sci Pollut Res Int. 2013;20:4356–69. doi: 10.1007/s11356-013-1748-0. [DOI] [PubMed] [Google Scholar]

[R41] 41.Kumar V, Xin X, Ma J, et al. Therapeutic targets, novel drugs, and delivery systems for diabetes associated NAFLD and liver fibrosis. Adv Drug Deliv Rev. 2021;176:113888. doi: 10.1016/j.addr.2021.113888. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R42] 42.Fromenty B, Roden M. Mitochondrial alterations in fatty liver diseases. J Hepatol. 2023;78:415–29. doi: 10.1016/j.jhep.2022.09.020. [DOI] [PubMed] [Google Scholar]

[R43] 43.Furman D, Campisi J, Verdin E, et al. Chronic inflammation in the etiology of disease across the life span. Nat Med. 2019;25:1822–32. doi: 10.1038/s41591-019-0675-0. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R44] 44.Bapat SP, Whitty C, Mowery CT, et al. Obesity alters pathology and treatment response in inflammatory disease. Nature New Biol. 2022;604:337–42. doi: 10.1038/s41586-022-04536-0. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R45] 45.Zhao TV, Li Y, Liu X, et al. ATP release drives heightened immune responses associated with hypertension. Sci Immunol. 2019;4:eaau6426. doi: 10.1126/sciimmunol.aau6426. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R46] 46.Hornburg D, Wu S, Moqri M, et al. Dynamic lipidome alterations associated with human health, disease and ageing. Nat Metab. 2023;5:1578–94. doi: 10.1038/s42255-023-00880-1. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R47] 47.Jin L-Y, Wang K, Xu B-T. High metabolic dysfunction-associated steatotic liver disease prevalence in type 2 diabetes: Urgent need for integrated screening and lifestyle intervention. World J Hepatol. 2025;17:103409. doi: 10.4254/wjh.v17.i1.103409. [DOI] [PMC free article] [PubMed] [Google Scholar]

[R48] 48.Oser TK, Varney C, McCall AL, et al. Current and new treatment options for adults recently diagnosed with type 2 diabetes. Fam Med Community Health. 2025;13:e003154. doi: 10.1136/fmch-2024-003154. [DOI] [PMC free article] [PubMed] [Google Scholar]

PERMALINK

Association between multifactorial control and excess risk of liver diseases in type 2 diabetes: a prospective cohort study

Rui Chen

Ying Zhou

Minzhi Xu

Yanhong Gong

Wenfei Xia

Xiaoxv Yin

Abstract

Introduction

Research design and methods

Results

Conclusions

WHAT IS ALREADY KNOWN ON THIS TOPIC

WHAT THIS STUDY ADDS

HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY

Introduction

Materials and methods

Study population

Assessment of risk factors

Ascertainment of outcomes

Assessment of covariates

Statistical analysis

Results

Baseline characteristics

Table 1. Baseline characteristics of participants without diabetes and participants with type 2 diabetes.

Association of type 2 diabetes according to the number of risk factors within the recommended ranges with severe MASLD

Association of type 2 diabetes according to the number of risk factors within the recommended ranges with MALO

Interaction analyses and sensitivity analyses

Discussion

Prior evidence on risk factors and liver diseases in type 2 diabetes

Biological pathways in diabetes-related liver diseases

Potential multidimensional intervention strategies for liver diseases in type 2 diabetes

Strengths and limitations

Conclusions

Supplementary material

Acknowledgements

Footnotes

Data availability statement

References

Associated Data

Supplementary Materials

Data Availability Statement

ACTIONS

PERMALINK

RESOURCES

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