Association between Metabolic flexibility and Hepatic fat content in individuals with Non- alcoholic fatty liver disease

doi:10.21203/rs.3.rs-4336762/v1

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Association between Metabolic flexibility and Hepatic fat content in individuals with Non- alcoholic fatty liver disease

https://doi.org/10.21203/rs.3.rs-4336762/v1

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Our study investigated the relationship between metabolic flexibility (MetF) and hepatic fat content (HFC) in individuals with non-alcoholic fatty liver disease (NAFLD). Men and women who met the diagnostic criteria for NAFLD were recruited. MetF was evaluated by the change of respiratory exchange ratio (ΔRER) from resting to exercise. Body composition, hepatic fat content (HFC), and clinical blood metabolic profiles were assessed. The study included 30 subjects (16 males). Subjects were classified into HMF (higher MetF) group and LMF (lower MetF) group based on the median ΔRER of 0.12. Subjects in the LMF group demonstrated significantly higher waist circumference(p=0.048), waist to hip ratio(p=0.043), HFC (p<0.001), visceral fat(p=0.039), and android fat to gynoid fat ratio(p=0.027). The LMF group exhibited higher levels of triglycerides (p=0.040), total cholesterol(p=0.001), low-density lipoprotein cholesterol(p<0.001), and liver enzyme compared to HMF group. The AUC of glucose (p=0.030), free fat acids(p=0.024), and triglyceride (p=0.033) in LMF group were greater than those in HMF group, respectively. Metabolic flexibility reflects metabolic health in NAFLD, with lower MetF associated with higher abdominal fat and worse metabolic profiles. Enhancing fat oxidation at rest and carbohydrate oxidation during exercise may reduce HFC. High-intensity exercise is recommended to improve metabolic outcomes in NAFLD patients.

Health sciences/Endocrinology/Endocrine system and metabolic diseases/Metabolic syndrome

Health sciences/Endocrinology/Endocrine system and metabolic diseases/Obesity

Hepatic fat content

Metabolic flexibility

Exercise

Non-alcoholic fatty liver disease

Respiratory exchange ratio

Obesity is characterized by excessive fat accumulation, a key factor in triggering various metabolic diseases such as non-alcoholic fatty liver disease (NAFLD) and type 2 diabetes (T2DM). Metabolic flexibility (MetF) is defined as the ability to adaptively adjust oxidation processes based on the availability of fuel substrates[1], which is reported to be an indicator of future weight gain[2]. Under conditions of elevated free fatty acids (FFA), metabolic inflexibility(inadequate conversion to catabolism for energy production or ketogenesis) tends to promote lipid accumulation in the liver [3, 4]. The excessive accumulation of fat in the liver significantly contributor to metabolic diseases, T2DM and cardiovascular diseases (CVDs)[5]. Therefore, the evaluation of MetF may help understand the pathophysiological mechanisms underlying ectopic fat accumulation, providing valuable insights for the prevention and treatment of metabolic diseases.

Traditionally, MetF has been assessed using glucose and insulin stimulation tests alongside an indirect calorimeter[1]. Previous studies have shown a correlation between MetF, when stimulated by glucose and insulin, and adipose tissue characteristics such as volume and distribution[6]. However, no consensus exists on the relationship between MetF under exercise stimulation and adipose characteristics[6], even though exercise is an effective method to enhance MetF and reduce fat[7]. Additionally, MetF responses to exercise versus glucose stimulation demonstrate varying degrees of metabolic flexibility[8]. Thus, measuring MetF through exercise stimulation could elucidate its impact on body fat distribution. Croci, et al. found individuals with NAFLD demonstrated lower capacity of substrate oxidation during maximal fat oxidation intensity[9] However, they did not provide additional data on how MetF relates to other adipose traits. Therefore, the association between MetF (under exercise stimulation) and body fat distribution, especially hepatic fat content(HFC), need to be clarified.

The primary aim of this study is to explore the relationship between MetF(under exercise stimulation) and metabolic conditions, including body fat distribution, blood glucose levels, and lipid profiles in individuals with NAFLD. We hypothesized that MetF, assessed by the change in respiratory exchange rate (ΔRER) from rest to moderate-intensity exercise, correlates with the metabolic health status of individuals with NAFLD.

Subjects

A total of 30 adults with NAFLD were recruited at the Endocrinology Department of ChangHai Hospital in 2022. The recruitment took place in 2022. At the screening visit, individuals who met the following criteria were included, (1) Men and women aged 18–45 years with a BMI of 24 to 35, indicative of overweight or obesity, or central obesity (waist circumference ≥ 90 cm for men and ≥ 85 cm for women);(2) Individuals diagnosed with fatty liver via ultrasound who consume no more than 30 grams of alcohol per day for men and 20 grams for women [10]; (3) No cardiovascular diseases or other conditions that preclude exercise (as assessed by doctors); (4) No regular exercise (less than three times per week for at least 30 minutes per session) in the past year. (5) Drugs consumption is stable in the past 3 months (kind and dose). Individuals were excluded based on the following criteriaΛ1) Receiving insulin treatment; (2) blood pressure or glucose levels could not be controlled with medication, or who experienced rapid disease progression; (3) Unable to exercise for any reason. All participants were fully informed about the study and provided written informed consent prior to participating.

Study Design

After the screening visit, individuals who met the criteria and agreed to participate signed the informed consent form and proceeded to the subsequent assessments. HFC was assessed through magnetic resonance imaging (MRI) during a fasting state. Blood samples were collected in the fasting state and at 30, 60, and 120 minutes after consuming a high-fat drink consisting of 40% carbohydrates, 45% fats, and 15% proteins. Subsequently, the cardiorespiratory fitness assessment was conducted. MetF was assessed in the evening, subjects were given a standard meal (51% fat, 31% carbohydrates, 18% protein—accounting for 20% of total energy consumption) one hour before the test. All assessments were carried out at Shanghai University of Sport, where the experimental protocol was approved by the Ethics Committee and registered with the Chinese Clinical Trial Registry (ChiCTR2200055110, 31/12/2021), and the study adheres to the Declaration of Helsinki.

Anthropometric indicators

Height and body weight were measured using an electronic stadiometer (DC-250, TANITA, Japan), and body composition was assessed with a Dual-energy X-ray Absorptiometry (DEXA) scanner (Lunar Prodigy, GE Healthcare, US). Blood pressure was measured using an electronic blood pressure monitor following standard procedures (Omron HEM-752 Fuzzy, Omron, Dalian, China). Waist circumference(WC) was measured with a plastic tape measure at the midpoint between the iliac crest and the lower rib margin. Body Mass Index (BMI) was calculated using the formula: weight (kg) / height² (m²).

Hepatic fat content and abdominal fat

Abdominal fat and HFC were assessed using MRI with a Siemens 3T Magnetom Prisma scanner (Siemens, Germany). For the abdominal fat assessment, axial T1-weighted dual-echo (in-phase and out-of-phase) Volumetric Interpolated Breath-hold Examination (VIBE) images were obtained covering the entire abdominal area. The OsiriX software were used to segment subcutaneous and visceral abdominal fat (OsiriX Foundation, Switzerland). HFC was assessed using a VIBE Multi-echo Dixon sequence. The liver profile was automatically delineated by the scanner.

Cardiorespiratory fitness

Cardiorespiratory fitness was assessed using graded exercise testing on a cycle ergometer (Monark 839E, Sweden), and gas samples were collected and analyzed with an indirect calorimeter, the Trueone 2400 (Parvomedics, USA). Participants were instructed to pedal at 35W for women and 50W for men for 3 minutes. Subsequently, the load was increased by 15W every 60 seconds until participants reached subjective exhaustion. Peak oxygen uptake(VO_2peak) was determined by using the average of the highest value in 30 seconds.

Metabolic flexibility

MetF was assessed by the change in the respiratory exchange ratio (ΔRER). The resting respiratory exchange ratio (RER_sit) was measured starting 60 minutes after dinner over 20-minute period while participants were seated. Participants were instructed to stay relaxed and lean back in their chairs during the test. Subsequently, participants exercised on the cycle ergometer at 60% of their VO_2peak over 30 minutes to measure the exercise RER (RER_exe). Exercise intensity was monitored through oxygen uptake, and the load was adjusted to keep the oxygen levels within the target range. Data were recorded every 60 seconds, and the average value was used for analysis, excluding the first 5 minutes to ensure data stability. MetF was quantified as ΔRER (RER_exe minus RER_sit); a larger ΔRER indicates greater metabolic flexibility[11]. Substrate oxidation was calculated using the Frayn equation[12].

Blood analyzing

Whole blood samples were collected in vacutainer tubes containing sodium heparin. After thorough mixing of the blood with the contents, the samples were centrifuged in a centrifuge at 3000 rounds per minutes for 15 minutes. Total cholesterol (TC), triglycerides (TG), high-density lipoprotein cholesterol (HDL-C), low-density lipoprotein cholesterol (LDL-C), free fat acids(FFA), alanine aminotransferase (ALT), aspartate aminotransferase (AST), and serum glucose and insulin concentrations were analyzed using standard methods. All sample analyses were conducted at an independent third-party testing facility(ADICON Clinical Laboratories, China). Insulin resistance was represented by the homeostasis model assessment (HOMA)[13]. Adipose insulin sensitivity was calculated by multiplying the fasting FFA concentration (mmol/L) with the fasting insulin concentration (pmol/L)[14]. The area under the curve (AUC) for glucose, insulin, and lipids at 0, 30, 60, and 120 minutes after high-fat drink (40% carbohydrates, 45% fats, 15% proteins) was calculated using the trapezoidal rule.

Statistical analysis

The normality of continuous variables was assessed using the Shapiro-Wilk (SW) test. Variables conforming to a normal distribution were expressed as mean ± standard deviation (SD). Participants were divided into two groups based on the median value. The Student's t-test was used to analyze variables that were normally distributed, and the Wilcoxon rank-sum test was used for variables that were not normally distributed. The Chi-square test was used to compare categorical variables. Pearson's correlation was used to analyze the bivariate relationships between MetF and physical or metabolic indicators, while partial correlation analysis was used to examine the independent correlations. A multiple regression model was used to explore the determinants of MetF. The sample size in our study was powered at 95% to detect a significant association between MetF and HFC. All statistical analyses were performed using JMP Pro version 16 (SAS Institute Inc., USA). A p-value < 0.05 was considered statistically significant.

Characteristics of the subjects

A total of 30 subjects (16 males) were recruited for this study, with a mean age of 38.3 ± 4.1 years. The mean RER_sit was 0.81, the mean RER_exe was 0.94, and the median ΔRER was 0.12 (Fig. 1A). Individuals with NAFLD were categorized into a higher metabolic flexibility (HMF, ΔRER ≥ 0.12) group and a lower metabolic flexibility (LMF, ΔRER < 0.12) group based on the median ΔRER value. There was a significant difference in RER_sit between the HMF and LMF groups (0.79 ± 0.04 vs. 0.84 ± 0.04, p < 0.004), but the difference in RER_exe between the two groups was not statistically significant (0.95 ± 0.03 vs. 0.93 ± 0.03, p = 0.066) (Fig. 1B).

Figure 1. This is a figure. Schemes follow the same formatting. The characteristics of respiratory exchange ratio. The mean of 𝚫RER in two groups(A);The change characteristics of RER(B). RER: respiratory exchange ratio; LMF: Low metabolic flexibility group; HMF: High metabolic flexibility group

There was no statistically significant difference in weight and BMI between the HMF and LMF groups(Table 1). However, the WC was significantly higher in the HMF group than in the LMF group (107.5 ± 9.3 cm vs 100.3 ± 9.7 cm, p = 0.048). In addition, the HMF group demonstrated higher cardiorespiratory fitness compared to the LMF group, with VO_2peak relative to lean mass (31.5 ± 6.7 vs 37.7 ± 4.9 ml/min/kg_LBM, p = 0.008) and relative to total mass (19.4 ± 4.3 vs 23.6 ± 3.1 ml/min/kg, p = 0.004). The 60% VO_2peak load was similar between the LMF and HMF groups (77.5 ± 31.9W vs 64.1 ± 24.0W, p = 0.240).

Table 1

Descriptive statistics of anthropometric measurements between the two groups
	LMF	HMF	p
Age	38.33 ± 3.56	38.27 ± 4.77	0.966
Sex(male/female)	7/8	7/8	1
Body mass(kg)	93.10 ± 16.64	83.13 ± 10.56	0.060
BMI(kg/m²)	31.14 ± 4.15	28.36 ± 3.48	0.056
Waist circumference(cm)	107.49 ± 9.33	100.32 ± 9.68	0.048^*
Hip circumference(cm)	112.24 ± 5.97	111.11 ± 4.40	0.561
Waist to hip ratio	0.96 ± 0.06	0.90 ± 0.08	0.043^*
SBP(mmHg)	130.60 ± 14.08	124.13 ± 8.94	0.144
DBP(mmHg)	86.00 ± 7.54	81.80 ± 8.69	0.168
VO_2peak (ml/min/kg)	19.41 ± 4.25	23.64 ± 3.05	0.004^**
VO_2peak (mL/min/kg_LBM)	31.53 ± 6.66	37.66 ± 4.89	0.008^**
SBP: Systolic blood pressure; DBP: diastolic blood pressure; LBP: lean body mass. ^* means p < 0.05; ^** means p < 0.01.

Metabolic indicators

Table 2 presents the clinical metabolic indicators for the HMF and LMF groups. There were no significant differences in fasting glucose, insulin, and free fatty acids (FFA), nor in HOM-IR and adipose-IR. However, the HMF group exhibited a superior lipid profile. The triglyceride levels (2.14 ± 1.48 vs 1.62 ± 1.62 mmol/L, p = 0.040), total cholesterol (5.15 ± 0.61 vs 4.16 ± 0.82 mmol/L, p = 0.001), and LDL-C (3.28 ± 0.44 vs 2.53 ± 0.52 mmol/L, p < 0.001) were significantly lower in the HMF group than in the LMF group. The AUC for blood glucose (35.8 ± 14.4 vs 26.5 ± 9.0, p = 0.030), TG (10.3 ± 5.7 vs 7.6 ± 7.1, p = 0.033), and FFA (1453.2 ± 547.3 vs 1070.9 ± 447.3, p = 0.024) were higher in the LMF group than in the HMF group, but the AUC for insulin was comparable between the two groups (p > 0.05). ALT levels (58.9 ± 37.2 vs 31.4 ± 14.5 U/L, p = 0.020) and AST levels (41.7 ± 21.1 vs 26.1 ± 7.2 U/L, p = 0.017) were significantly higher in the LMF group than in the HMF group.

Table 2

Clinical blood indicators in participants with contrast MetF
	LMF	HMF	p
Fasting glucose ± mmol/L^†	6.08 ± 3.84	5.06 ± 0.49	0.063
Fasting insulin ± pmol/L^†	90.9 ± 86.2	88.95 ± 79.85	0.663
FFA ± Free fat acid ± umol/L^†	471.47 ± 196.68	378.54 ± 214.61	0.275
Triglyceride ± mmol/L^†	1.69 ± 1.38	1.04 ± 0.90	0.040^*
Total cholesterol ± mmol/L	5.15 ± 0.61	4.16 ± 0.82	0.001^**
HDL-C ± mmol/L	1.04 ± 0.22	0.95 ± 0.21	0.278
LDL-C ± mmol/L	3.28 ± 0.44	2.53 ± 0.52	< 0.001^**
ALT ± U/L^†	54.00 ± 54.00	31.00 ± 19.75	0.020^*
AST ± U/L^†	34.00 ± 29.00	24.00 ± 14.50	0.017^*
Glucose_AUC^†	33.15 ± 22.17	23.34 ± 4.37	0.030^*
Insulin_AUC ^†	1442.00 ± 1708.40	1026.00 ± 1430.30	0.237
Triglyceride_AUC ^†	8.94 ± 7.61	5.29 ± 4.57	0.033^*
FFA_AUC	1453.20 ± 547.29	1070.93 ± 447.32	0.024^*
HOMA-IR ^†	27.17 ± 27.83	24.92 ± 19.2	0.206
HOMA-β	50.12 ± 31.23	69.28 ± 32.81	0.106
QUICKI	0.13 ± 0.01	0.14 ± 0.01	0.206
Adipose-IR ^†	50.24 ± 52.34	27.8 ± 46.89	0.257
^† means the data does not conform to normal distribution, and presented as median ± interquartile range. ^* means p < 0.05; ^** means p < 0.01.

Hepatic fat content and Body fat distribution

The HFC of the LMF group was significantly higher than that of the HMF group (p < 0.001) (Fig. 2A), and the abdominal visceral fat was also higher in the LMF group (147.5 ± 38.2 vs 117.1 ± 47.3 cm², p = 0.039) (Fig. 2B). However, there was no significant difference in abdominal subcutaneous fat between these two groups (p > 0.05) (Fig. 2C). The whole-body composition results for the HMF and LMF groups were similar. Both groups had comparable lean body mass (58.0 ± 14.2 vs 52.2 ± 6.9 kg, p = 0.165), total fat (35.5 ± 9.5 vs 30.9 ± 8.4 kg, p = 0.173), and body fat percentage (36.84 ± 7.42 vs 36.84 ± 7.42%, p = 0.606) (Table S1). After adjusting for whole-body fat content, there was no significant difference in the android fat percentage (11.6 ± 1.9 vs 11.0 ± 1.2%, p = 0.071, Fig. 2D) between the HMF and LMF groups, but the gynoid fat percentage was higher in the HMF group (13.4 ± 2.6 vs 14.7 ± 1.8%, p = 0.053, Fig. 2E). As a result, the ratio of android to gynoid fat was significantly higher in the LMF group than in the HMF group (0.9 ± 0.3 vs 0.8 ± 0.2%, p = 0.027, Fig. 2F).

Figure 2 The difference of fat content between HMF and LMF groups(A)hepatic fat content, (B) abdominal visceral fat, (C) abdominal subutaneous fat, (D) Gynoid fat percentage, (E)Android fat percentage, (F) The ratio of Android fat(%)to Gynoid fat(%)

^† means the comparison was conducted after adjusting for the total fat.

Association between MetF and Metabolic indicators

Initially, a Pearson correlation analysis was conducted to identify metabolic indicators correlated with MetF (Table S2). Subsequently, metabolic indicators that showed statistically significant correlations with MetF were included as independent variables in a multiple regression analysis. This analysis employed a stepwise approach and adjusted for age, gender, and BMI, selecting variables based on the minimum Bayesian Information Criterion (BIC) principle. Table 3 presents the results of the multiple regression analysis. Our results indicated that HFC (β = -0.40, 95% CI: -0.7, -0.1, p = 0.01) and HOMA-β (β = 0.16, 95% CI: 0.0, 0.3, p = 0.03) were significantly associated with MetF. The results revealed that individuals with lower HFC and better pancreas function within the NAFLD population exhibit enhanced MetF.

Table 3

Multiple regression analysis of MetF and metabolic health indicator.
	β	95%CI	t	p
Age(year)	0.59	-0.31, 1.48	1.37	0.19
Gender	0.07	-0.03, 0.17	1.42	0.17
BMI(kg/m²)	-0.41	-1.28, 0.46	-0.98	0.34
Hepatic fat content	-0.40	-0.67, -0.13	-3.12	0.01^*
HOMA-β	0.16	0.02, 0.30	2.38	0.03^*
Systolic blood pressure	-0.99	-2.13, 0.17	-1.78	0.09
^* means p < 0.05; ^** means p < 0.01.

Association between substrate utilization and HFC

Figure 3 presents the volume of energy derived from fat and carbohydrates. At resting, the HMF group exhibited significantly higher energy consumption than the LMF group (37.1 ± 6.0 vs 30.6 ± 7.1 cal/kg/min, p = 0.013), derived not only from fat oxidation (FATox) (10.5 ± 2.5 vs 7.6 ± 2.4 cal/kg/min, p = 0.005) but also from carbohydrate oxidation (CHOox) (26.7 ± 3.7 vs 23.0 ± 4.8 cal/kg/min, p = 0.028). However, during exercise, the energy consumption was similar between the two groups (88.8 ± 5.2 vs 97.3 ± 5.0 cal/kg/min, p = 0.248), yet the energy derived from CHOox was higher in the HMF group compared to the LMF group (87.5 ± 4.1 vs 76.8 ± 4.2 cal/kg/min, p = 0.079). Table 4 demonstrates the association between HFC and substrate utilization both at rest and during exercise. Model 1 included CHOox and FATox at rest and during exercise as independent variables, with HFC as the dependent variable. The results indicated that FATox at rest was negatively correlated with HFC (β = -21.00, 95% CI: -38.3 to 3.7, p = 0.020), and FATox during exercise was positively correlated with HFC (β = 5.59, 95% CI: 1.8 to 9.4, p = 0.006), associated with lower HFC levels. Model 2 incorporated the 60% VO_2peak load as an adjusted factor, which reinforced the impact of substrate oxidation dynamics during exercise on HFC. The results indicated that NAFLD individuals with a higher carbohydrate oxidation ratio (β = -0.75, 95% CI: -1.5 to 0.0, p = 0.050) and a lower fat oxidation ratio (β = 5.86, 95% CI: 1.5 to 10.2, p = 0.050) during 60% VO_2peak exercise exhibited lower HFC.

Table 4

Regression of hepatic fat content and substrate oxidation at different status.
	Model 1			Model 2
Variables	β	95%CI	p	β	95%CI	p
CHOs(mg/min/kg)	2.98	-1.64, 7.59	0.195	2.23	-2.87, 7.33	0.370
CHOe(mg/min/kg)	-0.41	-1.02, 0.20	0.180	-0.75	-1.50, 0.00	0.050^*
FATs(mg/min/kg)	-20.98	-38.28, -3.67	0.020^*	-16.80	-37.99, 4.38	0.113
FATe(mg/min/kg)	5.59	1.75, 9.43	0.006^**	5.86	1.51, 10.21	0.011^**
The model 2 was adjusted for the exercise load at 60%VO_2peak in addition to model 1. ^* means p < 0.05; ^** means p < 0.01.

Figure 3 The volume of energy supply from CHO/FAT

The difference of energy supplied from CHO(*), FAT(#) and total energy consumption(†) was statistical difference between two groups during resting(p < 0.05). The total energy consumption during exercise demonstrated a trend for significant difference.

Metabolic flexibility refers to the biological capacity of an organism to adaptively regulate substrate oxidation in response to changes in substrate availability and demand, ensuring the fulfillment of metabolic requirements[15]. Different stimulation models may reflect varying levels of MetF, potentially emphasizing distinct metabolic conditions[9]. Our results s howed that changes in the RER from rest to exercise are positively associated with metabolic health in individuals with NAFLD. Additionally, a higher capacity for FATox at rest and CHOox during moderate-intensity exercise is associated with lower HFC. Therefore, strategies that increase FATox at rest and CHOox during moderate-intensity exercise, reflecting higher MetF, may help reduce hepatic fat content.

Exercise is a common stimulus for assessing substrate utilization and is also used to assess MetF[15–17]. A recent systematic review reported a strong association between adipose tissue characteristics and MetF assessed through glucose or insulin stimulation; however, no consensus currently exists on the link between adipose tissue characteristics and MetF during exercise[6]. Prior et al. reported that older individuals with impaired glucose tolerance (IGT) exhibited a lower △RER compared to their healthy counterparts[11]. Similarly, our study found that individuals with NAFLD in the LMF group had a higher glucose AUC following a high-fat diet. However, no statistically significant difference was found in visceral and subcutaneous fat between elderly individuals with or without IGT, although both types of fat were higher in those with IGT (19.5% and 24.7% higher than in elders without IGT, respectively)[11]. Júdice et al. reported that higher variability in △RER across different physical activity conditions (sitting, standing, or alternating between standing and sitting) was associated with lower body fat percentage and trunk fat[18]. Battista et al. found that obese individuals with T2DM had a lower △RER and a higher WC compared to those without T2DM [19]. However, they did not observe a significant association between WC and △RER, which may be influenced by the different exercise load methods (ergometer and treadmill). Overall, the varying assessment protocols limited the ability to establish a significant association between MetF, exercise, and adipose tissue characteristics[6]. Our results provide evidence of this association through rigorous measurement methods.

The multiple regression analysis indicated that a higher rate of FATox at rest is associated with lower HFC in individuals with NAFLD. Croci et al. reported similar findings using whole-body substrate utilization in individuals with NAFLD, they found that the RER in individuals with a NAFLD Activity Score (NAS) of ≥ 5 was significantly higher than in those with NAS ≤ 5[9]. In further analysis, CHOox during moderate-intensity exercise was found to be negatively associated with HFC after adjusting for the 60% VO_2peak load. The results suggested that individuals with higher HFC may have limited CHOox during exercise, possibly due to a reduced capacity for glucose uptake by skeletal muscles during exercise[20] and higher HFC levels [21]. Therefore, the effectiveness of exercise in reducing HFC may not rely solely on the energy expended during exercise but also on its ability to enhance MetF. Intervention strategies that aim to improve substrate oxidation could be beneficial in reducing HFC, and more vigorous exercise has been shown to be effective [22, 23].

The results concerning clinical metabolic indicators showed that individuals with NAFLD who exhibit superior MetF also demonstrate improved metabolic health. In a fasting state, the LMF group exhibits higher blood glucose levels compared to the HMF group, even with similar insulin concentrations. This may explain why individuals with T2DM show increased CHOox during fasting compared to those without the condition[24]. Gastaldelli suggested that metabolic inflexibility serves as a protective mechanism in response to excessive FFA flow, preventing NAFLD patients from increasing glucose oxidation after insulin stimulation[25]. Hence, an elevated RER_sit, indicative of increased CHOox, could potentially counterbalance the heightened glucose levels. Additionally, the AUC for plasma glucose, FFA, and triglycerides was significantly higher in the LMF group than in the HMF group, indicating reduced insulin sensitivity. Højlund et al. have reported that individuals with metabolic inflexibility exhibit reduced mitochondrial content in skeletal muscle[26]. Mitochondria play a pivotal role in determining oxidative capacity and insulin sensitivity and are also central to MetF[27]. Moreover, subjects in the low LMF group exhibit higher TC, primarily due to elevated levels of LDL-C Elevated LDL-C levels and lower cardiorespiratory fitness may jointly contribute to an increased risk of CVDs. These findings collectively suggest that improvements in MetF may be associated with a reduced risk of CVDs. Previous studies have also proposed that cardiorespiratory fitness should be a critical outcome in NAFLD treatment strategies to prevent CVDs[28]. Therefore, exercise interventions aimed at enhancing mitochondrial capacity will be beneficial for improving metabolic health in individuals with NAFLD Finally, elevated ALT and AST levels were observed in the LMF group, which may be attributed to HFC.

While the expected outcomes were achieved, it is crucial to acknowledge specific limitations inherent in this study. Notably, the assessments of MetF and the blood sample collections were not conducted simultaneously, potentially leading to the oversight of relevant information. Furthermore, the lack of isotopic labeling in this study limits the ability to conduct a more detailed investigation into the oxidation of substrates from various sources. However, despite these limitations, our study provides significant insights into MetF and metabolic health in individuals with NAFLD.

The change in the RER from rest to moderate-intensity exercise comprehensively reflects the metabolic health status of individuals with NAFLD. Among NAFLD patients, those with lower MetF tended to accumulate more abdominal fat and showed poorer indicators of glucose and lipid metabolism. Strategies that aim to increase CHOox during exercise and enhance FATox at rest may effectively mitigate hepatic fat accumulation. High-intensity exercise has emerged as a potential modality for reducing HFC.

The authors would like to thank the Shanghai Frontiers Science Research Base of Exercise, Metabolic Health and Discipline Climbing Plan of Shanghai Changhai Hospital for funding and the research start-up fund of Shanghai Customs College. The authors are also very grateful to all the doctors who contributed to the recruitment process, the administrators of the laboratory, as well as all the volunteers who participated in the project.

Funding

The work was supported by the Shanghai Frontiers Science Research Base of Exercise, Metabolic Health and 234 Discipline Climbing Plan of Shanghai Changhai Hospital(2019YXK021) and the research start-up fund of Shanghai Customs College.

Conflict of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest

Author Contributions

WH, Conceptualization, Investigating, Formal analysis, Writing- original draft; YFH, Resource, Investigating, Writing-original draft; WQR, Investigating, Validation, Writing-review & editing; HNZ, Investigating, Validation, Writing-review & editing; YYL, Investigating, Writing-review & editing; TW, Investigating, Writing-review & editing; XDD, Investigating, Writing-review & editing; CLH, Investigating, Writing-review & editing; JZ, Conceptualization, Project administration, Resource, Writing-review & editing; JL, Conceptualization, Resource, Supervision, Writing-review & editing

Data Availability Statement

The datasets used and analyzed during the current study are available from the corresponding author on reasonable request.

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Association between Metabolic flexibility and Hepatic fat content in individuals with Non- alcoholic fatty liver disease

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Abstract

Figures

Introduction

Materials and Methods

Subjects

Study Design

Anthropometric indicators

Hepatic fat content and abdominal fat

Cardiorespiratory fitness

Metabolic flexibility

Blood analyzing

Statistical analysis

Results

Characteristics of the subjects

Metabolic indicators

Hepatic fat content and Body fat distribution

Association between MetF and Metabolic indicators

Association between substrate utilization and HFC

Discussion

Conclusion

Declarations

Funding

Conflict of Interest

Author Contributions

Data Availability Statement

References

Additional Declarations

Supplementary Files

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