Effect of way of life intervention on the mobilization of fats depots and

Introduction

Obesity has turn into a worldwide epidemic, affecting over one billion people worldwide.¹ Obesity, significantly central weight problems, has been firmly established as a serious threat issue for metabolic problems comparable to sort 2 diabetes mellitus (T2DM).² A key pathological characteristic of central weight problems is physique fats redistribution and ectopic fats deposition.³ This ectopic fats deposition in non-adipose tissues, significantly the liver and pancreas, differs essentially from that in visceral fats (eg, mesenteric and perirenal fats) and subcutaneous fats (outlined as fats saved beneath the pores and skin).² Fat accumulation in each the liver and pancreas is related to impaired glucose metabolism.⁴ Emerging proof means that along with fats accumulation, iron overload contributes to metabolic problems.^5,6 Elevated serum ferritin predicts diabetes threat,^5,6 however its specificity is proscribed as an acute-phase reactant influenced by irritation, liver illness, and insulin resistance.^7–9 The function of organ-specific iron deposition (eg, hepatic and pancreatic iron overload) in metabolic dysfunction continues to be unclear.

Traditional anthropometrics comparable to physique mass index (BMI), waist circumference, or bioelectrical impedance evaluation have important limitations in assessing ectopic fats deposition. Magnetic resonance imaging (MRI), significantly the mDIXON approach, gives a noninvasive gold normal for quantifying each fats (through the proton density fats fraction, PDFF) and iron deposition (through R2*).¹⁰ Importantly, the MRI-PDFF evaluation is extremely concordant with liver biopsy evaluation relating to adjustments in liver histology.¹¹ Existing research have examined fats deposition inside particular person organs individually,^12,13 failing to deal with the mixed metabolic results of multisite fats distribution and organ-specific iron deposition. This research makes use of MRI-based multiorgan (liver, pancreas, visceral and subcutaneous adipose tissue) and multiparametric (fat-iron) quantitative assessments to comprehensively consider the relationships amongst region-specific ectopic fats deposition, organ-specific iron accumulation, and glucose metabolism in people with weight problems. Previous research have demonstrated {that a} 6-month way of life intervention interval can induce measurable adjustments in visceral and ectopic fats depots, in addition to enhancements in glucose metabolism.^14–17 Therefore, in our research, all overweight sufferers underwent a 6-month way of life intervention (together with a caloric-restricted balanced food plan and train intervention). We carried out MRI-based quantification of the adjustments in fats and iron contents throughout key organs (liver, pancreas, visceral and subcutaneous adipose tissue) after intervention and analyzed their relationships with enhancements in glucose metabolism.

Materials and Methods

Study Design and Participants

Study Design

This potential research enrolled 128 people with weight problems (68 males, 60 females; median age 40.8 years [interquartile range (IQR) 29.5–43.9]) from the Third Xiangya Hospital of Central South University, China between April 2024 and April 2025. The contributors obtained way of life interventions solely, with no use of hypoglycemic drugs all through the research. Baseline and 6-month post-intervention assessments—together with magnetic resonance imaging (MRI) and oral glucose tolerance assessments (OGTTs)—had been carried out for all contributors. The research was accepted by the Institutional Review Board of the Third Xiangya Hospital (Approval No. Quick I 22218) and registered at ClinicalTrials.gov (NCT06441409, https://register.clinicaltrials.gov/prs/beta/records) in April 2024. All contributors offered written knowledgeable consent. This research adheres to the Consolidated Standards of Reporting Trials (CONSORT).¹⁸

Inclusion and Exclusion Criteria

The contributors had been required to satisfy all the next inclusion standards: (1) aged 18–70 years; (2) BMI ≥28 kg/m²; (3) capability to finish the protocol necessities; and (4) dedication to a 6-month way of life intervention. Key exclusions included the next: preexisting diabetes with glucose-lowering remedy; diabetes problems or main systemic ailments; energetic malignancy; acute/power infections; hemochromatosis; hepatitis (drug-induced/autoimmune/viral); unexplained transaminase elevation (>2×ULN); pancreatic problems; iron deficiency anemia requiring supplementation; latest (6-month) blood donation/transfusion or cardiovascular occasions; extreme alcohol consumption (>140 g/week [men], >70 g/week [women]); latest (3-month) weight-affecting drugs or >5% weight fluctuation; and being pregnant/lactation.

Grouping of Participants

The 128 contributors had been stratified by an oral glucose tolerance check (OGTT) into regular glucose regulation (NGR, n=54), prediabetes (IFG and IGT, n=30), and sort 2 diabetes (T2DM, n=44) teams. The standards defining NGR had been glycated hemoglobin (HbA1c) <5.7%, fasting plasma glucose (FPG) <110 mg/dl (6.1 mmol/L) and 2-h plasma glucose (PG) throughout the 75-g OGTT <140 mg/dl (7.8 mmol/L). The standards defining prediabetes had been an HbA1c stage of 5.7–6.4%, an FPG stage of 110 mg/dl (6.1 mmol/L) to 125 mg/dl (6.9 mmol/L) (IFG) or a 2-h PG throughout the 75-g OGTT 140 mg/dl (7.8 mmol/L) to 200 mg/dl (11.0 mmol/L) (IGT). The standards for the prognosis of diabetes had been HbA1c ≥6.5%, FPG ≥126 mg/dl (≥7.0 mmol/L), 2-h PG ≥200 mg/dl (≥11.1 mmol/L) or a random PG ≥200 mg/dl (≥11.1 mmol/L) in a person with traditional signs of hyperglycemia or hyperglycemic disaster. The criterion for T2DM remission after a 6-month way of life intervention was outlined as an HbA1c stage <6.5%. For prediabetes remission to NGR, the factors require each OGTT outcomes and HbA1c to completely meet the aforementioned NGR requirements.

Research Methods

Lifestyle Intervention

Dietary Assessment

Dietary consumption was evaluated in all contributors with weight problems through three consecutive 24-hour dietary recollects (together with two weekdays and one weekend day) administered by licensed dietitians based on the Chinese Health Industry Standard (WS/T 426.1–2013) at baseline and on the follow-up visits. Energy and nutrient intakes had been calculated through the China Food Composition Table (Standard Edition, sixth model), with evaluation specializing in macronutrient (carbohydrates, proteins, fat), dietary fiber, and iron consumption.

Caloric-Restricted Balanced Diet

All contributors obtained a 6-month individualized calorie-restricted balanced food plan (CRD) designed by licensed dietitians, with the resting vitality expenditure measured through oblique calorimetry (COSMED C00600-01-11, USA) and every day vitality consumption set at 500 kcal under the estimated whole vitality expenditure (calculated through bodily exercise ranges). The CRD offered 45–55% carbohydrates from low-glycemic index sources (entire grains/legumes), 15–20% protein from high-quality sources (lean meat/fish/eggs/soy/dairy), and 20–30% fats predominantly from unsaturated fat with restricted trans/saturated fat.

Exercise Intervention

All enrolled people with weight problems underwent a 6-month structured train program consisting of (1) ≥150 minutes/week of moderate-intensity cardio train (eg, brisk strolling, biking, swimming) and (2) ≥2 classes/week of whole-body resistance coaching concentrating on main muscle teams (eg, squats, bench press, rowing), with 3 units of 8‒6 repetitions per session.

Implementation Protocol

The contributors joined WeDiscussion groups (≤30 members/group) supervised by 1–2 dietitians for every day meal photograph monitoring, with month-to-month follow-ups, together with anthropometric measurements (weight and waist‒hip circumference), physique composition evaluation through bioelectrical impedance evaluation (BIA), and individualized food plan plan changes on the premise of progress monitoring. Patients lacking scheduled month-to-month visits will obtain cellphone or WeChat reminders. Those failing to attend the 6-month closing go to regardless of reminders and nonetheless absent inside 1 week can be excluded from closing evaluation.

Biochemical Indicators

Demographic and anthropometric knowledge (age, intercourse, peak, weight, physique mass index [BMI], waist‒hip circumference, waist‒hip ratio [WHR]), train habits, and present drugs had been extracted from digital medical information, with 6-month adjustments denoted as Δweight, ΔBMI, and ΔWHR. Laboratory analyses included (1) liver/kidney operate, triglyceride (TG), whole ldl cholesterol (TC), and ferritin ranges measured by an automatic biochemistry analyzer (Hitachi 7600, Japan); (2) HbA1c and OGTT (0,1,2-h glucose) through the enzymatic electrode technique (Arkray GA-1172); and (3) insulin ranges measured through electrochemiluminescence (Roche 601).

Calculation Formulas

Homeostasis mannequin evaluation of insulin resistance (HOMA-IR)¹⁹ = (fasting plasma glucose [FPG] × fasting insulin)/22.5; HOMA of β-cell operate (HOMA-β)¹⁹ = 20 × fasting insulin/(FPG – 3.5); insulin sensitivity index (ISI)¹⁹ = 10,000/(FPG × fasting insulin × 2-h glucose × 2-h insulin)⁰·⁵; triglyceride‒glucose index (TyG)¹⁹ = Ln[fasting TG (mg/dL) × FPG (mg/dL)/2] (FPG/insulin models: mmol/L/μU/mL). Six-month adjustments had been recorded as ΔTG, ΔTC, ΔHOMA-IR, ΔHOMA-β, and ΔISI.

Magnetic Resonance Imaging (MRI)

All contributors underwent fasting MRI scans at baseline and after the 6-month intervention through a 3.0T system (Philips INGENIA ELITION X, Netherlands) with multiecho Dixon sequences by knowledgeable radiologist with 9 years of expertise. The hepatic fats content material [proton density fat fraction (PDFF), %] and iron deposition (iron/s) had been derived from eight 100-mm² areas of curiosity (ROIs) in the appropriate lobe (excluding main vessels/bile ducts). Hepatic steatosis was outlined as a PDFF ≥5.0%.²⁰ The pancreatic fats content material (PDFF,%) and iron deposition (iron,/s) had been averaged from three 100 mm² ROIs (head/physique/tail). The belly subcutaneous fats space (SFA) and visceral fats space (VFA, cm²) on the L3 vertebra stage had been analyzed through Slice O matic software program on T2-weighted photographs. Changes (Δ) and proportion adjustments (Δ%) had been calculated as follows: Δ = baseline – follow-up; Δ% = (Δ/baseline)×100.

Outcome Measurements

The main consequence of the research was the adjustments of site-specific fats and organ-specific iron deposition in people with weight problems outlined by MRI, calculated as (preliminary measurement − follow-up measurement) / (preliminary measurement) × 100 and predefined to be assessed at 6-month follow-up. The secondary consequence of the research was to analyze whether or not these adjustments can function indicators of glycemic remission in overweight sufferers with dysglycemia.

Sample Size

The pattern measurement was calculated based mostly on the next assumptions: (1) a two‐sided significance stage of 0.05, (2) a statistical energy of a minimum of 90%, and (3) a priori speculation that way of life intervention would preferentially mobilize hepatic fats, adopted by pancreatic fats, VAT, and SAT. Based on these assumptions, a minimal of 89 contributors was required for the ultimate evaluation. Sample measurement estimation was carried out utilizing PASS software program, model 22 (NCSS, LLC). To account for an anticipated dropout charge of 30%, the preliminary variety of enrolled contributors was set at 128 to make sure that the ultimate achieved pattern measurement would meet the research necessities.

Statistical Analysis

Data had been analyzed through R 3.5.3 and SPSS 27.0. The categorical knowledge are offered as n(%). Normality was assessed through the Shapiro‒Wilk normality check, and homogeneity of variance was assessed through Levene’s check. Normally distributed steady variables are offered because the means ± SDs. Nonnormally distributed knowledge are offered as medians (Q1‒Q3). Chi-square assessments (χ²-tests) had been used to check categorical knowledge. Independent t assessments or Mann‒Whitney U-tests had been used to check steady variables between contributors who accomplished follow-up and people who had been misplaced to follow-up. A paired t check or Wilcoxon signed-rank check was used to check steady variables between baseline and the 6-month follow-up. Analysis of variance (ANOVA) or the Kruskal‒Wallis check was used to check steady variables among the many NGR, prediabetes, and T2DM teams. Pairwise comparisons among the many three teams had been carried out with put up hoc Bonferroni correction. A Sankey diagram was used to visualise adjustments in glucose metabolism varieties from baseline to the 6-month follow-up. Pearson correlation evaluation or Spearman’s rank correlation was used to evaluate the relationships between adjustments in hepatic and pancreatic fats or iron content material, physique composition and anthropometric measures, and insulin sensitivity or resistance indices.

Multinomial logistic regression analyses with multivariable adjustment had been performed to evaluate the unbiased associations of potential indicators with prediabetes and T2DM. Additionally, binary multivariable logistic regression was employed to evaluate the unbiased associations of potential indicators with two distinct binary outcomes: diabetes remission and the reversal of prediabetes to NGR. For steady variables with potential prognostic worth, optimum cutoffs had been decided through receiver working attribute (ROC) curve evaluation, and these steady variables had been dichotomized on the premise of their respective optimum cutoffs. ROC curves had been plotted to judge the power of adjustments in these parameters (Δhepatic fats%, Δhepatic iron%, Δpancreatic fats%, and Δpancreatic iron%, ΔVFA%, ΔSFA%, ΔBMI%) to replicate T2DM remission and reversal of prediabetes to NGR in people with weight problems.

Results

Study Flowchart

Among the 128 people with weight problems at baseline, 24 people had been misplaced to follow-up on the 1-, 2-, 3-, 4-, 5-, or 6-month visits, and a complete of 104 people accomplished each the 6-month MRI and OGTT assessments (stream diagram in Figure 1). There was no important distinction in baseline traits between people who had been misplaced to follow-up and people who accomplished follow-up (Supplementary Table 1). After 6-month way of life intervention, among the many 104 contributors, 41 people with NGR at baseline maintained NGR standing. Among the 23 contributors with prediabetes at baseline, 21 achieved NGR, whereas 2 remained prediabetic. Among the 40 contributors with T2DM at baseline, 16 achieved NGR, 2 regressed to prediabetes, and 22 endured with T2DM (sankey diagram in Figure 2).

Figure 1 Flowchart of participant enrollment.

Figure 2 Transitions in glucose metabolism types from baseline to the 6-month follow-up.

Associations Between Regional Fat Content, Iron Deposition, and Glucose Metabolism at Baseline

At baseline, the 104 participants were stratified into NGR (n=41, 39.4%), prediabetes (n=23, 22.1%) and T2DM (n=40, 38.5%) groups. No significant differences in age, BMI, or WHR were observed among the groups (all P > 0.05). Compared with the NGR or prediabetes group, the T2DM group presented significantly greater ferritin, TyG index and HOMA-IR values but lower HOMA-β values (all P < 0.05). Additionally, SFA, VFA, hepatic and pancreatic fat, and pancreatic iron deposition were also elevated in the T2DM group compared with the NGR or prediabetes group (all P < 0.05) (Table 1 and Supplementary Table 2).

Table 1 Clinical Characteristics of Obese Patients with Different Glycemic States

In individuals with obesity, hepatic fat content was positively correlated with HOMA-IR (r = 0.49, P < 0.001) and hepatic iron deposition (r = 0.67, P < 0.001) but negatively correlated with the ISI (r = −0.54, P < 0.001); pancreatic fat content was negatively correlated with HOMA-β (r = −0.27, P < 0.001) and positively correlated with pancreatic iron deposition (r = 0.77, P < 0.001). Hepatic iron deposition was positively associated with HOMA-IR (r = 0.24, P = 0.002) and negatively associated with the ISI (r = −0.29, P < 0.001), whereas pancreatic iron deposition was negatively correlated with HOMA-β (r = −0.26, P = 0.001).

Initially, we employed ordinal multinomial logistic regression to analyze independent risk factors for prediabetes and T2DM at baseline. However, as the parallel regression assumption was not satisfied, we ultimately adopted unordered multinomial logistic regression for the final analysis. The dependent variable comprised three glycemic status categories: normal glucose regulation (reference group), prediabetes, and T2DM. Independent variables included sex (categorical variable with males as the reference group), age, BMI, ferritin, hepatic fat content (categorical variable with ≥ 5% as the hepatic steatosis, <5% as the reference group), hepatic iron deposition, pancreatic fat content, pancreatic iron content, visceral fat area, and subcutaneous fat area. All independent variables except sex and hepatic fat content were continuous variables. We found that only hepatic fat content was identified as an independent risk factor for T2DM in individuals with obesity [OR = 1.11, 95% confidence interval (CI): 1.02–1.21] in the multivariable-adjusted model (Supplementary Table 3).

Changes in Regional Fat Mobilization and Organ-Specific Iron Deposition After Intervention

After a 6-month lifestyle intervention, significant reductions in weight, BMI, WHR, TG, TC, ferritin, and the TyG index were detected in obese individuals (all P<0.05). Glycemic status improved markedly: the number of T2DM patients decreased to 22, the IGR decreased to 4, and the NGR increased to 78. Concurrently, HOMA-IR decreased, whereas HOMA-β and ISI increased significantly (all P<0.05; Table 2).

Table 2 Changes in the Clinical Characteristics of Individuals with Obesity After Intervention

After a 6-month lifestyle intervention, MRI revealed significant reductions in the SFA, VFA, hepatic fat, pancreatic fat, and hepatic and pancreatic iron contents in individuals with obesity (all P < 0.05, Supplementary Table 3). The hepatic fat content decreased the most (median reduction: 42.9% [IQR 32.4–52.4%]), followed by the pancreatic fat content (34.1% [20.0–43.1%]), VFA (19.5% [10.2–28.9%]), and SFA (12.7% [5.4–16.1%]) (P < 0.001). Moreover, the reduction in hepatic iron deposition was greater than that in pancreatic iron deposition in obese individuals (P < 0.05; Supplementary Figure 1A). Representative MR images are shown in Supplementary Figures 1B–F.

Pearson correlation analyses were conducted between site-specific reductions in adipose tissue (ΔVFA%, ΔSFA%, Δhepatic fat%, and Δpancreatic fat%), improvements in organ iron deposition (Δhepatic iron% and Δpancreatic iron%) and changes in glycemic markers (ΔHOMA-IR%, ΔISI%, and ΔHOMA-β%). The results revealed that the changes in hepatic fat content (Δhepatic fat%) and hepatic iron deposition (Δhepatic iron%) were positively correlated with ΔHOMA-IR% (insulin resistance) but negatively correlated with ΔISI% (insulin sensitivity) (all P < 0.05) in obese individuals. Conversely, the changes in pancreatic fat content (Δpancreatic fat%) and pancreatic iron deposition (Δpancreatic iron%) were negatively correlated with ΔHOMA-β% (β-cell function) in individuals with obesity (P < 0.05, Supplementary Table 4).

Markers of Remission in Diabetes and Prediabetes Among Individuals with Obesity

Binary multivariable logistic regression was employed to assess the independent associations of potential indicators with two distinct binary outcomes: diabetes remission and the reversal of prediabetes to normal glucose regulation. For continuous variables with potential prognostic value, optimal cutoffs were determined via ROC curve analysis, and these continuous variables were dichotomized on the basis of their respective optimal cutoffs. The optimal cutoffs for reflecting T2DM remission were as follows: Δweight% ≥7.0%, ΔBMI% ≥6.5%, ΔWHR% ≥5.5%, ΔVFA% ≥20.3%, Δhepatic fat% ≥38.8%, Δhepatic iron% ≥17.4%, Δpancreatic fat% ≥28.2%, Δpancreatic iron% ≥15.0%, and ΔSFA% ≥9.2%. For reflecting the reversal of prediabetes to NGR, the optimal cutoffs were Δweight% ≥5.5%, ΔBMI% ≥5.0%, ΔWHR% ≥5.0%, ΔVFA% ≥15.5%, Δhepatic fat% ≥30.0%, Δhepatic iron% ≥15.0%, Δpancreatic fat% ≥25.5%, Δpancreatic iron% ≥10.2%, and ΔSFA% ≥7.4%.

The results indicated that in the multivariable-adjusted model, none of the variables were confirmed as independent risk factors for prediabetes remission, whereas only a Δhepatic fat% ≥38.8% was identified as an independent indicator for T2DM remission (OR = 2.50, 95% CI: 1.59–5.60) (Table 3).

Table 3 Binary Logistic Regression with Multivariable Adjustment Was Used to Assess Indicators for Diabetes Remission and the Reversal of Prediabetes

We then constructed ROC curves for reflecting the remission of T2DM in individuals with obesity on the basis of BMI reduction and fat mobilization in different body regions. The areas under the ROC curve (AUCs) and 95% CIs for the percentage changes in liver fat, visceral fat, pancreatic fat, liver iron, pancreatic iron, BMI, and subcutaneous fat were 0.819 (95% CI: 0.681–0.957), 0.724 (95% CI: 0.564–0.884), 0.647 (95% CI: 0.492–0.803), 0.628 (95% CI: 0.472–0.785), 0.588 (95% CI: 0.421–0.756), 0.552 (95% CI: 0.380–0.724), and 0.547 (95% CI: 0.372–0.721), respectively (Figure 3). These outcomes additional show that the proportional discount in liver fats is a powerful indicator of T2DM remission.

Figure 3 ROC curves reflecting the remission of type 2 diabetes in obese individuals on the basis of BMI reduction and fat mobilization in different body regions.

Discussion

Lifestyle intervention is the first-line treatment for managing obesity. Previous studies have confirmed the effects of lifestyle intervention on the mobilization of fat from different body regions in obese individuals, as well as its role in remission of diabetes or prediabetes.^21–24 This research innovatively discovered that, along with mobilizing fats from numerous physique areas, way of life intervention additionally led to a sure diploma of enchancment in hepatic and pancreatic iron load. While earlier research have demonstrated an affiliation between hepatic iron deposition and irregular glucose metabolism,^25,26 our longitudinal cohort research gives the primary proof that the discount in hepatic and pancreatic iron load performs a sure function in enhancing insulin resistance and insulin sensitivity in people with weight problems after way of life intervention. Furthermore, by incorporating hepatic fats, pancreatic fats, visceral fats, and subcutaneous fats into the identical analysis mannequin, we discovered that hepatic fats deposition has a higher influence on diabetes in overweight people (OR=1.11, 95% CI: 1.02–1.21) than pancreatic fats (OR=0.99, 95% CI: 0.88–1.12), and much more so than visceral fats (OR=1.00, 95% CI: 0.99–1.01). Compared to fats mobilization from different areas, we discovered that the diploma of hepatic fats mobilization was most strongly related to the remission of T2DM in people with weight problems (OR=2.50, 95% CI: 1.59–5.60) after way of life intervention. Our research additionally validated earlier findings: when each hepatic fats and visceral fats had been included concurrently, liver fats was extra strongly related to metabolic illness development (OR=2.7, 95% CI: 2.3–4.9) than was visceral fats (OR=2.0, 95% CI: 1.2–2.8),²⁷ whereas the decision of fatty liver lowered the T2DM incidence.^28–30 The potential mechanism could also be associated to visceral fats driving systemic irritation, whereas liver fats instantly regulates glucose metabolism. Hepatic fats accumulation promotes hepatic insulin resistance and will increase hepatic glucose output.^31–33 Therefore, for overweight sufferers, prioritizing interventions concentrating on liver fats throughout weight reduction could also be simpler for enhancing diabetes. The findings of the current research point out that way of life interventions (a caloric–restricted balanced food plan mixed with train) are efficient approaches for preferentially mobilizing liver fats [intrahepatic (‒42.9%), pancreatic fat (‒34.1%), VAT (‒19.5%), and SAT (‒12.7%)]. Although each the CENTRAL³⁴ and DiRECT³⁵ trials focused a number of fats depots, hepatic fats exhibited a extra marked decline than did VAT (−29% vs −22%) within the former, and hepatic fats decreased extra considerably than did pancreatic fats (13% ± 1% vs 0.9% ± 0.2%) within the latter. These findings present a rationale for future pharmacological methods that selectively cut back liver fats to forestall or deal with diabetes.

The findings of the current research revealed the differential roles of hepatic fats throughout completely different phases of glucose metabolism in people with weight problems. In the diabetic stage, hepatic fats serves as each an unbiased threat issue and an indicator of remission, which corroborates the next mechanistic pathway:³⁶ hepatic fats accumulation → hepatic insulin resistance → elevated hepatic glucose output → compensatory hyperfunction of pancreatic β-cells → improvement of diabetes. Conversely, a discount in hepatic fats can enhance hepatic insulin sensitivity, thereby facilitating diabetes reversal. Our research additionally revealed that amongst people with prediabetes, 91.3% returned to regular glucose metabolism after a 6-month way of life intervention. However, within the prediabetic stage, hepatic fats didn’t emerge as an unbiased threat issue or an indicator of remission. This could replicate a buffering impact throughout the compensatory part: in people with prediabetes, pancreatic islet operate stays comparatively preserved, and compensatory hyperinsulinemia could briefly counteract the metabolic hurt attributable to hepatic fats. Long-term follow-up could also be wanted to uncover the delayed protecting impact of hepatic fats discount in people who transition from prediabetes to normoglycemia. Similarly, the Tübingen Lifestyle Intervention Program (TULIP)²¹ reported that the reversal of prediabetes to NGR was unrelated to adjusted physique fats loss however was related to a high-risk phenotype at baseline, outlined as low insulin sensitivity (under the cohort median) mixed with elevated liver fats (1H-MRS with a strict threshold of >5.56% liver fats). Following this classification, we additionally stratified prediabetic people into high-risk (these with low insulin sensitivity: ISI < median + liver fats >5.56%) and low-risk phenotypes at baseline. However, after 6 months of follow-up, we didn’t observe important variations within the charge of prediabetes reversal to NGR between the 2 teams. This discrepancy could also be attributed to interethnic variability. Future research ought to make use of extra refined phenotypic stratification and establish core regulatory targets to elucidate the heterogeneity in prediabetes intervention outcomes and optimize threat prediction fashions.

Hepatic lipid overflow to pancreatic tissue below power saturated fatty acid publicity impairs β-cell operate.^37–39 While our research confirmed that larger pancreatic fats content material was correlated with worse β-cell operate and elevated glucose ranges, it was not related to HOMA-IR or ISI. After 6 months of way of life intervention, pancreatic fats mobilization (Δpancreatic fats%) correlated solely with β-cell enchancment (ΔHOMA-β%), unbiased of insulin resistance adjustments, which aligns with prior proof that pancreatic fats disrupts glucose metabolism primarily through β-cell dysfunction relatively than insulin resistance.³⁸ Notably, though univariate evaluation linked pancreatic fats discount to glycemia remission, this affiliation turned nonsignificant after multivariable adjustment. Similarly, a 706-subject cohort revealed no unbiased metabolic profit from pancreatic fats discount,¹³ suggesting that its results could also be secondary to hepatic fats mobilization. These findings additional underscore the important significance of concentrating on hepatic fats for glycemic enchancment.

The liver, as a significant organ for iron metabolism within the physique, can endure injury on account of iron overload via the induction of oxidative stress and promotion of intracellular lipid peroxidation, resulting in hepatic fibrosis and cirrhosis.⁴⁰ Our current research revealed that iron deposition in each the liver and pancreas was intently related to fats deposition. Additionally, hepatic iron deposition was considerably correlated with glucose metabolism indicators, exhibiting a constructive correlation with the insulin resistance index (HOMA-IR) and a unfavourable correlation with the insulin sensitivity index. However, the connection between hepatic iron deposition and these glucose metabolism indicators disappeared within the multivariate-adjusted logistic mannequin, presumably on account of collinearity between hepatic fats deposition and hepatic iron deposition. The TREND research,⁴¹ which included 1746 German adults, revealed that hepatic iron overload was considerably positively correlated with 2-hour OGTT glucose ranges. After adjusting for confounding elements, people with remoted hepatic iron overload confirmed no important variations in fasting glucose, 2-hour glucose, or insulin ranges or HOMA-IR scores, whereas these with each hepatic steatosis and iron overload offered considerably higher values of those indicators. However, as a cross-sectional research, TREND38 couldn’t set up the causal influence of dynamic adjustments in hepatic iron or fats deposition on glucose metabolism. Our current research revealed that hepatic and pancreatic iron deposition considerably decreased after a 6-month way of life intervention, with the discount in hepatic iron deposition exhibiting a major constructive correlation with the lower in HOMA-IR and a major unfavourable correlation with the lower within the insulin sensitivity index. Therefore, in sufferers with weight problems, hepatic steatosis and iron overload collectively exacerbate metabolic threat (eg, via fat-induced irritation and iron toxicity).^25,26 For people with mixed hepatic steatosis and iron deposition, way of life interventions (food plan + train) could also be simpler than iron chelation remedy. Obese people with iron overload needs to be monitored for dynamic adjustments in OGTTs to mitigate the danger of diabetes development. This research has some limitations, comparable to a small pattern measurement and the absence of a management group of way of life intervention. Future analysis incorporating randomized managed designs would assist validate these findings.

Our findings underscore the scientific significance of liver fats evaluation in weight problems administration, whereas concurrently highlighting sensible challenges in implementing MRI-based quantification in routine follow. Although MRI-PDFF stays the gold normal for hepatic fats quantification, its widespread scientific adoption faces important limitations together with time-intensive guide evaluation, the necessity for specialised radiological experience, and restricted availability of superior 3T MRI techniques with acceptable sequences in lots of scientific settings. In mild of those technical limitations, a number of sensible approaches have emerged for real-world monitoring: risk-stratified MRI utilization prioritizing high-risk sufferers with mixed steatosis and iron overload, surrogate biomarkers incorporating FibroScan measurements and ALT traits for resource-limited settings. Particularly promising are latest advances in AI-assisted quantification which have achieved 0.99 correlation with guide segmentation whereas decreasing evaluation time to below 2 minutes per case.⁴² For scientific implementation, we suggest a tiered monitoring framework starting with complete baseline MRI evaluation, adopted by quarterly biomarker surveillance (ALT, ferritin) and annual FibroScan evaluations for secure sufferers, reserving repeat MRI for circumstances exhibiting important metabolic adjustments. This method strategically balances precision with practicality, addressing the important translational problem of implementing hepatic fats monitoring throughout numerous healthcare settings. While these technological developments in automated evaluation are enhancing MRI’s scalability, additional cost-effectiveness analyses stay essential to information widespread adoption in routine weight problems administration protocols.

In conclusion, dynamic adjustments in hepatic fats deposition (accumulation and mobilization) signify key pathophysiological mechanisms underlying each the event and remission of dysglycemia in people with weight problems. Our findings show that focused interventions addressing hepatic steatosis can successfully ameliorate obesity-related metabolic dysfunction. Specifically, hepatic fats quantification needs to be included into routine metabolic threat stratification for overweight people, significantly these with T2DM. Therapeutic methods ought to prioritize liver-specific fats discount via structured way of life interventions combining reasonable caloric restriction (−500 kcal/day) and supervised train (≥150 min/week). Future multi-center analysis ought to validate these thresholds throughout numerous populations whereas creating cost-effective monitoring protocols, comparable to speedy MRI strategies, for widespread scientific implementation.

Data Sharing Statement

The knowledge supporting the findings of this research can be found inside the article and its supplementary supplies.

Ethics Approval and Informed Consent

This research complies with the Declaration of Helsinki. All research topics signed an knowledgeable consent type. Ethical approval was granted by the Ethics Committee of The Third Xiangya Hospital of Central South University (approval quantity I 22218) and registered at ClinicalTrials.gov (No. NCT06441409).

Consent for Publication

We affirm that the specifics of any photographs, movies, recordings, and many others, are licensed for publication, and the people granting consent have been knowledgeable in regards to the content material of the article that can be printed.

Acknowledgments

We want to thank all of the contributors on this research for his or her cooperation. We additionally acknowledge the efforts of our nutritionists Ms. Min Liu and Ms. Yufang Luo for following up with sufferers.

Author Contributions

Hong Liu: Data curation (equal); formal evaluation (equal); investigation (equal); venture administration (equal); writing – unique draft (lead). Junhong Duan: Data curation (equal); formal evaluation (equal); venture administration (equal). Gaopeng Guan: Data curation (equal); venture administration (equal). Pengfei Rong: Data curation (equal); formal evaluation (equal). Ping Jin: Data curation (equal); formal evaluation (equal); investigation (equal); venture administration (equal); writing – assessment and enhancing (equal). All authors took half in drafting, revising or critically reviewing the article; gave closing approval of the model to be printed; have agreed on the journal to which the article has been submitted; and comply with be accountable for all facets of the work.

Funding

This research was supported by the Natural Science Foundation of Hunan Province, China (2024JJ9052, 2022JJ40749).

Disclosure

The writer(s) report no conflicts of curiosity on this work.

References

1. NCD Risk Factor Collaboration (NCD-RisC). Worldwide traits in underweight and weight problems from 1990 to 2022: a pooled evaluation of 3663 population-representative research with 222 million kids, adolescents, and adults. Lancet. 2024;403(10431):1027–1050. doi:10.1016/S0140-6736(23)02750-2

2. Jamjl J. The causal function of ectopic fats deposition within the pathogenesis of metabolic syndrome. Int J Mol Sci. 2024;25(24):13238. doi:10.3390/ijms252413238

3. Cao MJ, Wu WJ, Chen JW, et al. Quantification of ectopic fats storage within the liver and pancreas utilizing six-point Dixon MRI and its affiliation with insulin sensitivity and β-cell operate in sufferers with central weight problems. Eur Radiol. 2023;33(12):9213–9222. doi:10.1007/s00330-023-09856-x

4. Mk S, Saucedo A, Darwin CH, et al. Noninvasive evaluation of belly adipose tissues and quantification of hepatic and pancreatic fats fractions in sort 2 diabetes mellitus. Magn Reson Imaging. 2020;72:95–102. doi:10.1016/j.mri.2020.07.001

5. Gao H, Yang J, Pan W, et al. Iron overload and the danger of diabetes within the common inhabitants: outcomes of the Chinese Health and Nutrition Survey Cohort Study. Diabetes Metab J. 2022;46(2):307–318. doi:10.4093/dmj.2020.0287

6. Liu J, Li Q, Yang Y, et al. Iron metabolism and sort 2 diabetes mellitus: a meta-analysis and systematic assessment. J Diabetes Investig. 2020;11(4):946–955. doi:10.1111/jdi.13216

7. Naim M, Amal A, Zeynep Okay, et al. Ferritin – from iron, via irritation and autoimmunity, to COVID-19. J Autoimmun. 2022;126:102778. doi:10.1016/j.jaut.2021.102778

8. Moreira AC, Mesquita G, Gomes MS. Ferritin: an Inflammatory participant preserving iron on the core of pathogen interactions. Microorganisms. 2020;8(4):589. doi:10.3390/microorganisms8040589

9. Brzezinski RY, Wasserman A, Sasson N, et al. An exploratory evaluation of routine ferritin measurement upon admission and the prognostic implications of low-grade ferritinemia throughout irritation. Am J Med. 2024;137(9):865–871.e1. doi:10.1016/j.amjmed.2024.04.033

10. Kim JW, Lee CH, Yang Z, et al. The spectrum of magnetic resonance imaging proton density fats fraction (MRI-PDFF), magnetic resonance spectroscopy (MRS), and two completely different histopathologic strategies (synthetic intelligence vs. pathologist) in quantifying hepatic steatosis. Quant Imaging Med Surg. 2022;12(11):5251–5262. doi:10.21037/qims-22-393

11. Karolina G, Michał G, Olgierd R. Usefulness of various imaging modalities in analysis of sufferers with non-alcoholic fatty liver illness. Biomedicines. 2020;8(9):298. doi:10.3390/biomedicines8090298

12. London A, Lundsgaard A, Kiens B, et al. The function of hepatic fats accumulation in glucose and insulin homeostasis-dysregulation by the liver. J Clin Med. 2021;10(3):390. doi:10.3390/jcm10030390

13. Deng M, Li Z, Chen SY, et al. Exploring the heterogeneity of hepatic and pancreatic fats deposition in weight problems: implications for metabolic well being. Front Endocrinol. 2024;8:1447750. doi:10.3389/fendo.2024.1447750

14. Eckard C, Cole R, Lockwood J, et al. Prospective histopathologic analysis of way of life modification in nonalcoholic fatty liver illness: a randomized trial. Therap Adv Gastroenterol. 2013;6(4):249–259. doi:10.1177/1756283X13484078

15. Mascaró CM, Bouzas C, Montemayor S, et al. Effect of a six-month way of life intervention on the bodily exercise and health standing of adults with NAFLD and metabolic syndrome. Nutrients. 2022;14(9):1813. doi:10.3390/nu14091813

16. Walden P, Jiang Q, Jackson EA, et al. Assessing the incremental good thing about an prolonged period way of life intervention for the parts of the metabolic syndrome. Diabetes Metab Syndr Obes. 2016;9:177–184. doi:10.2147/DMSO.S94772

17. Saha S, Leijon M, Gerdtham U, et al. A culturally tailored way of life intervention addressing a Middle Eastern immigrant inhabitants susceptible to diabetes, the MEDIM (influence of Migration and Ethnicity on Diabetes In Malmö): research protocol for a randomized managed trial. Trials. 2013;14:279. doi:10.1186/1745-6215-14-279

18. Hopewell S, Chan AW, Collins GS, et al. CONSORT 2025 assertion: up to date guideline for reporting randomised trials.BMJ. 2025; 389: e081123.

19. Fpb Okay, Dh C, Callaghan BC, et al. The prevalence of polyneuropathy in sort 2 diabetes subgroups based mostly on HOMA2 indices of β-cell operate and insulin sensitivity. Diabetes Care. 2023;46(8):1546–1555. doi:10.2337/dc23-0079

20. Brunt EM, Janney CG, AMDi B, et al. Nonalcoholic steatohepatitis: a proposal for grading and staging the histological lesions. Am J Gastroenterol. 1999;94(9):2467–2474. doi:10.1111/j.1572-0241.1999.01377.x

21. Stefan N, Staiger H, Wagner R, et al. A high-risk phenotype associates with lowered enchancment in glycemia throughout a way of life intervention in prediabetes. Diabetologia. 2015;58(12):2877–2884. doi:10.1007/s00125-015-3760-z

22. Kabisch S, Meyer NMT, Honsek C, et al. Predicting elements for metabolic non-response to a posh way of life intervention-A replication evaluation to a randomized-controlled trial. Nutrients. 2022;14(22):4721. doi:10.3390/nu14224721

23. Grune E, Nattenmüller J, Kiefer LS, et al. Subphenotypes of physique composition and their affiliation with cardiometabolic threat – magnetic resonance imaging in a population-based pattern. Metabolism. 2025;164:156130. doi:10.1016/j.metabol.2024.156130

24. Linge J, Borga M, West J, et al. Body composition profiling within the UK Biobank imaging research. Obesity. 2018;26(11):1785–1795. doi:10.1002/oby.22210

25. Pitchika A, Kühn JP, Schipf S, et al. Hepatic steatosis and hepatic iron overload modify the affiliation of iron markers with glucose metabolism problems and metabolic syndrome. Liver Int. 2021;41(8):1841–1852. doi:10.1111/liv.14868

26. Niedermayer F, Su YQ, Krüchten RV, et al. Trajectories of glycemic traits exhibit sex-specific associations with hepatic iron and fats content material: outcomes from the KORA-MRI research. Liver Int. 2023;43(10):2153–2166. doi:10.1111/liv.15635

27. Basheer M, Saad E, Jeries H, et al. Liver fats storage is a greater predictor of coronary artery illness than visceral fats. Metabolites. 2023;13(8):896. doi:10.3390/metabo13080896

28. Yang M, Chen J, Yue J, et al. Liver fats is superior to visceral and pancreatic fats as a threat biomarker of impaired glucose regulation in obese/overweight topics. Diabetes Obes Metab. 2023;25(3):716–725. doi:10.1111/dom.14918

29. Fukuda T, Hamaguchi M, Kojima T, et al. Transient remission of nonalcoholic fatty liver illness decreases the danger of incident sort 2 diabetes mellitus in Japanese males. Eur J Gastroenterol Hepatol. 2016;28(12):1443–1449. doi:10.1097/MEG.0000000000000736

30. Chen CL, Zhang YC, Fan YJ, et al. The change of nonalcoholic fatty liver illness is related to threat of incident diabetes. Front Endocrinol. 2023;14:1108442. doi:10.3389/fendo.2023.1108442

31. Fan ZX, Yang CJ, Zhao XJ, et al. Association of cardiometabolic markers with hepatic steatosis and liver fibrosis in inhabitants with out weight problems and diabetes. Sci Rep. 2025;15(1):15695. doi:10.1038/s41598-025-01003-4

32. Gangireddy VGR, Pilkerton C, Xiang J, et al. Hepatic fibrosis and steatosis in metabolic syndrome. J Obes Metab Syndr. 2022;31(1):61–69. doi:10.7570/jomes21062

33. Liang ZT, Huang RH, Zhang LY. Correlation between hepatic steatosis severity identified by ultrasound and metabolic indices in aged sufferers with MAFLD. Front Med Lausanne. 2025;11:1467773. doi:10.3389/fmed.2024.1467773

34. Gepner Y, Shelef I, Schwarzfuchs D, et al. Effect of distinct way of life interventions on mobilization of fats storage swimming pools: CENTRAL magnetic resonance imaging randomized managed trial. Circulation. 2018;137(11):1143–1157. doi:10.1161/CIRCULATIONAHA.117.030501

35. Taylor R, Leslie WS, Barnes AC, et al. Clinical and metabolic options of the randomized managed diabetes remission scientific trial (DiRECT) cohort. Diabetologia. 2018;61(3):589–598. doi:10.1007/s00125-017-4503-0

36. Taylor R, Al-Mrabeh A, Sattar N. Understanding the mechanisms of reversal of sort 2 diabetes. Lancet Diabetes Endocrinol. 2019;7(9):726–736. doi:10.1016/S2213-8587(19)30076-2

37. Al-Mrabeh A, Zhyzhneuskaya SV, Peters C, et al. Hepatic lipoprotein export and remission of human sort 2 diabetes after weight reduction. Cell Metab. 2020;31(2):233–249.e4. doi:10.1016/j.cmet.2019.11.018

38. Wu WJ. Diabetes remission and nonalcoholic fatty pancreas illness. World J Diabetes. 2024;15(7):1390–1393. doi:10.4239/wjd.v15.i7.1390

39. Wagner R, Eckstein SS, Yamazaki H, et al. Metabolic implications of pancreatic fats accumulation. Nat Rev Endocrinol. 2022;18(1):43–54. doi:10.1038/s41574-021-00573-3

40. Anderson ER, Shah YM. Iron homeostasis within the liver. Compr Physiol. 2013;3(1):315–330. doi:10.1002/j.2040-4603.2013.tb00494.x

41. Naeem M, Schipf S, Bülow R, et al. Association between hepatic iron overload assessed by magnetic resonance imaging and glucose intolerance states within the common inhabitants. Nutr Metab Cardiovasc Dis. 2022;32(6):1470–1476. doi:10.1016/j.numecd.2022.02.013

42. Wei JY, Chen HL, Yao LJR, et al. BioCompNet: a deep studying workflow enabling automated physique composition evaluation towards precision administration of cardiometabolic problems. Cyborg Bionic Syst. 2025;20:381. doi:10.34133/cbsystems.0381