Sustained visceral fat loss is associated with attenuated brain atrophy and improved cognitive function in late midlife

Domain: biology
Published: 2026-03-26
Editorial outcome: minor_revision

Abstract
We examined whether long-term exposure to visceral-adipose-tissue (VAT) influences brain atrophy and cognitive performance years after lifestyle intervention. In the Follow-Interventions-Trial (FIT) project, 533 adults (age=61.4y, 86% men) from four prior 18-24-month lifestyle randomized-clinical-trials underwent abdominal/brain magnetic-resonance-imaging (MRI)s and Montreal-Cognitive-Assessment (MoCA) testing 5-16y after interventions. Lower VAT exposure, calculated by area-under-the-curve, from baseline, post-intervention, and follow-up, independently resulted in higher MoCA scores. VAT loss during intervention predicted higher brain volumes at follow-up, independent of weight loss. Among participants with three brain and VAT MRI scans, lower long-term VAT was associated with a slower rate of brain atrophy. These patterns were not observed for deep/superficial subcutaneous-adipose-tissues. Improved glycemic control parameters, rather than lipid or inflammatory markers, were mostly related to the favorable longitudinal brain outcomes. This long-term, large-scale intervention and follow-up MRI study suggests that sustained visceral fat loss, rather than weight loss, is linked to better cognition and attenuation of brain atrophy years later, mainly via improved glycemic control.

Introduction
Visceral adipose tissue (VAT) has emerged as a critical factor in brain aging. Large studies and systematic reviews consistently find strong associations between VAT and adverse brain outcomes.1-7 Specifically, higher VAT levels predict greater brain atrophy, including lower hippocampal,2,8 gray matter,2,3 and white matter volumes,2 larger ventricular volume,8 and cognitive decline.1,4,6-8 Despite robust observational evidence, there is a limited understanding of whether long-term exposure to lower visceral adiposity, independent of weight loss, can attenuate brain atrophy and protect cognitive function over time.

The Magnetic Resonance Imaging (MRI) Follow-up Intervention Trials (FIT) project addresses this gap by leveraging multiple abdominal adipose depot measurements obtained via MRI in two completed randomized controlled trials (RCTs): CENTRAL9 and DIRECT-PLUS.10-12 In addition, we conducted cross-sectional analyses of VAT, brain atrophy, and cognitive function using follow-up data from all four RCTs (DIRECT,13 CASCADE,14,15 CENTRAL,9 and DIRECT-PLUS10-12). By combining repeated VAT measurements, brain MRI, and cognitive testing, we investigated how visceral adiposity influences brain atrophy and cognitive performance, and whether reductions in VAT preserved brain structure and function years after intervention.

Results
Participant Retention and Follow-Up
Of the 881 eligible RCT participants from the DIRECT, CASCADE, CENTRAL, and DIRECT-PLUS trials, 647 participants (73.4%) were successfully identified for the FIT project (The study timeline and data acquisition are illustrated in Figure 1. Figure 1A created with BioRender.com. The study flow diagram is provided in Figure 1B). Of these, 599 (92.6% of 647) completed assessments, and 48 were deceased cases. Among the 599 participants with 5-16 years of follow-up data, brain MRI structural analyses were performed for 533 (89% of 599) participants (Table 1).

Participant Baseline Characteristics at the Start of the Interventions
At RCT baseline, the cohort was 86% male, with a mean age of 52.0 years, a body mass index (BMI) of 30.4, and a waist circumference (WC) of 106.4 cm. Among participants from CENTRAL and DIRECT-PLUS who had baseline adipose depot measurements (n=344), the mean VAT area was 139.3 (VAT proportion = 28.7%). Participants from DIRECT-PLUS also had baseline brain MRI measures (n = 211): Total Brain (TB) = 74.2% of Intracranial Volume (ICV), Gray Matter (GM) = 30.6% of ICV, White Matter (WM) = 30.8% of ICV, Total Ventricles (TV) = 1.7% of ICV, and Hippocampal Occupancy Score (HOC) = 0.9. Among DIRECT-PLUS participants with available genetic data, 17% were apolipoprotein E ε4 allele (APOE ε4) carriers.

5-16-year Follow-Up Characteristics
At follow-up (Table 1), the mean participant age was 61.4 years, with an average BMI of 30.1 kg/m², and WC of 102.5 cm. VAT area averaged 131 cm² (VAT proportion = 29.9%). Brain structure measurements included TB = 72.2% of ICV, GM = 29.6% of ICV, WM = 30.0% of ICV, TV = 2.5% of ICV, and HOC = 0.8. The mean Montreal Cognitive Assessment (MoCA) score was 23.6. Dietary assessment at follow-up showed a mean Mediterranean Diet (MED) diet adherence score of 7.0.

Cross-sectional Analysis of the Interaction Between Visceral Adipose Tissue (VAT) and Brain Volumes in Association with Cognitive Function (MoCA) at Follow-Up
We examined the associations between VAT and cognitive performance and assessed whether brain structural volumes modified these associations (n = 527, Figure 2A). Linear regression models revealed significant interactions between VAT and several brain volume parameters in relation to MoCA scores. Significant interaction terms were observed between VAT and TB (VAT × TB; β = -0.30, p = 0.032), HOC (VAT × HOC; β = -0.29, p = 0.048), and WM (VAT × WM; β = -0.3, p = 0.028). In the model including TB, VAT showed a negative main effect (β = -0.52, p = 0.003), whereas TB showed a positive one (β = 0.39, p = 0.068). In the model including WM, VAT again demonstrated a negative effect (β = -0.5, p = 0.004), while WM was positively associated (β = 0.42, p = 0.012). Similarly, in the model including HOC, VAT showed a negative association (β = -0.5, p = 0.004), while HOC showed a positive association (β = 0.39, p = 0.068). Conversely, interactions involving deep subcutaneous adipose tissue (DSAT) and superficial subcutaneous adipose tissue (SSAT) levels were not significantly associated with MoCA scores. As illustrated in Figure 2A, participants with mid or high brain volumes exhibited a more pronounced inverse association between VAT and MoCA scores. In contrast, among individuals with low brain volumes, MoCA scores remained relatively stable across the range of VAT values. All statistical analyses tested interactions between continuous standardized measures of VAT and brain volumes in linear regression models, with MoCA score as the outcome. These models were adjusted for age, sex, BMI, and MED diet adherence score (full model results are provided in Supplementary Data 1). In an additional categorical analysis, adjusted for age, sex, BMI, and MED score, participants with 18-25 MoCA scores had significantly higher VAT compared with those with a normal cognition score (β = 0.21, p < 0.01). In contrast, differences in SSAT and DSAT across MoCA categories were not statistically significant (18-25 vs. >=26 MoCA score: β = 0.07 for SSAT and β = 0.11 for DSAT). Likewise, none of the adipose tissue depots showed significant differences between individuals with dementia-range MoCA scores (<18) and those with normal cognition.

In parallel models in which BMI, rather than VAT, was specified as the main adiposity exposure, BMI did not show significant interactions with brain volumes in relation to cognitive performance, either in models without VAT or in models additionally adjusted for VAT (model results shown in Supplementary Data 2). The BMI-brain interaction terms were consistently non-significant, in contrast to the significant VAT-brain interactions observed in our primary analyses. In sensitivity analyses adjusted for incretin-based anti-obesity medication use, no significant associations were observed, and the inclusion of this covariate did not change the MoCA-related findings (Supplementary Data 3).

Longitudinal Analysis of Cumulative Abdominal Adipose Tissue Exposure and Cognitive Function at Follow-Up
Among participants with complete longitudinal adiposity measurements across three time points (baseline, 18 months, and follow-up) from the DIRECT-PLUS and CENTRAL trials (n = 295), we examined the associations between cumulative abdominal adipose tissue exposure (area under the curve (AUC)) and cognitive outcomes at follow-up (Figure 2B). A significant inverse association was found between cumulative VAT exposure and cognitive performance, as reflected by both MoCA (β = -0.49, p = 0.032) and MoCA-Memory Index Score (MIS) (β = -0.75, p = 0.028), using a fixed-effect meta-analysis for the two trials. However, using the same fixed-effect model, no significant associations were found for either deep or superficial SAT levels. These associations remained significant after adjustment for age, sex, BMI, diet adherence score, and brain anatomy structure (HOC). Full model results are provided in Supplementary Data 4.

Similarly, in longitudinal models using BMI area under the curve (BMI AUC) as the adiposity exposure, no significant associations were observed between BMI AUC and cognitive scores (Supplementary Data 5).

Longitudinal Analysis of Visceral Adipose Tissue (VAT) Change During the Intervention and Brain Structural Outcomes at Follow-Up
Among participants with available baseline and end-of-intervention VAT measurements from the CENTRAL and DIRECT-PLUS trials (n = 297), the mean VAT change during the 18-month intervention was -24% ± 23.4%. We examined the associations between changes in abdominal adipose depots during the intervention period and brain structure and cognitive function at 5 and 10 years post-intervention using a fixed-effect meta-analysis (Figure 3). A greater reduction in VAT area during the intervention was associated with better preservation of brain structure at follow-up, as reflected by higher TB (β = -0.13, p = 0.05) and higher GM (β = -0.19, p = 0.01). Additionally, a reduction in VAT proportion was significantly associated with a higher HOC score (β = -0.10, p = 0.028) at follow-up. These associations remained robust after adjustment for multiple potential confounders, including age, baseline fat depot levels, BMI changes during intervention, and follow-up, MED diet adherence score at follow-up, dual trial participation, sex, diet group, and their interaction. In contrast, reductions in deep and superficial SAT during the intervention were not associated with brain structure outcomes at follow-up (model details in Supplementary Data 6). However, in additional analyses, relative change in BMI during the intervention was not significantly associated with brain structural measures at follow-up (model details in Supplementary Data 7). In contrast, this BMI change was negatively associated with MoCA score at follow-up (β = -0.153, p = 0.014). Sex-by-intervention interaction did not reach statistical significance in any of the models (Supplementary Data 8).

Interaction Between Longitudinal Visceral Adipose Tissue (VAT) Exposure and Brain Atrophy Rate
Among participants from the DIRECT-PLUS trial with repeated brain MRI measurements over three time points: baseline, 18 months, and 5 years (n = 188), we evaluated the association between cumulative VAT exposure and the rate of brain structural change over time (Figure 4). Interaction analyses using linear mixed-effects models (LMM) revealed that the association between VAT exposure and brain volume significantly differed according to the rate of brain change. In these longitudinal models, a significant main effect of time was observed across all brain volume structures: TB (β = -0.02, p < 0.001), GM (β = -0.01, p < 0.001), WM (β = -0.005, p < 0.001), and HOC (β = -0.003, p < 0.001) and TV (β = 0.003, p < 0.001).

VAT AUC did not exhibit a significant main effect in any of the models.

For HOC, significant interaction was observed between time and VAT AUC (Time × VAT AUC; β = -0.0001, p < 0.001) and for GM (Time × VAT AUC; β = -0.001, p =0.042). Similar interactions were observed for TB (β =- 0.002, p = 0.06) and WM (β = -0.001, p = 0.062). For TV, the interaction was also significant, but in the opposite (positive) direction (β = 0.0007, p < 0.001), consistent with ventricular expansion in the context of brain atrophy. All models were adjusted for age, sex, BMI, and MED diet adherence score (full model details in Supplementary Data 9). As shown in the density plots (Figure 4), distributions of VAT AUC differed across brain change rate groups (slow, moderate, fast), with higher mean VAT values consistently observed in participants exhibiting faster atrophy trajectories. When the linear mixed-effects models included BMI AUC as the main exposure together with time to predict trajectories of brain structure, the interaction between BMI AUC and time was not significant (Supplementary Data 10). Also, APOE ε4 carrier status showed no significant main effect and no significant interactions with time, VAT AUC, or their combination in models across all brain outcomes.

In addition, we conducted sensitivity analyses in which baseline glycemic, lipid, and inflammatory biomarkers were individually entered as interaction terms with time in these linear mixed-effects models. All models were adjusted for visceral adipose tissue accumulation, age, sex, and MED score, and included a random intercept for participant. After false discovery rate (FDR) correction, only fasting glucose and HbA1c remained significantly associated with longitudinal brain changes through their interaction with time (time × fasting glucose: β = -8.7×10⁻⁵, p = 0.001; time × HbA1c: β = -9.2×10⁻⁵, p < 0.001). All other baseline glycemic, lipid, and inflammatory biomarkers showed no significant interactions (Supplementary Data 11).

Methods
Study Population
The DIRECT study was conducted from 2005 to 2007, enrolling 322 moderately obese adults (BMI ≥27 kg/m²), or participants with type 2 diabetes (T2D) aged 40 to 65 years, or those with coronary heart disease. The 24-month retention rate was 84.6%.13 The CASCADE trial was conducted from 2010 to 2012, enrolling 224 adults aged 40 to 75 years with T2D, and achieved a 24-month retention rate of 87%.14,15 The CENTRAL trial, conducted from 2012 to 2014, enrolled 278 participants with abdominal obesity (WC>102 cm in men, >88 cm in women) and/or dyslipidemia (triglycerides>150 mg/dL, HDL-C<40 mg/dL in men, <50 mg/dL in women), with an 18-month retention rate of 86.3%.9 The DIRECT-PLUS trial was conducted from 2017 to 2018, enrolling 294 participants with abdominal obesity over the age of 30, and achieved an 18-month retention rate of 89.8%.10-12 Detailed exclusion criteria for each trial and sample size calculation are provided in Supplementary Methods 1. Lifestyle intervention descriptions are provided in Supplementary Methods 2. All trials were approved by the Medical Ethics Board and Helsinki Committee of Soroka University Medical Center, and all participants provided written informed consent. No financial compensation or gifts were provided. All 4 randomized controlled trials (DIRECT, CASCADE, CENTRAL, and DIRECT-PLUS) included in this work have been completed, and their primary outcomes have been published previously. Pooled data from the four RCTs comprised 1,118 observations. However, 237 participants had participated in more than one trial, yielding a final unique sample of 881 eligible participants. For the FIT follow-up study, we successfully reached 647 participants (73.4%) for follow-up assessments, including 48 deceased cases. Brain MRI structural analyses were completed for 533 participants at follow-up.

Data Collection
Abdominal adipose depots were quantified using 3-Tesla MRI scanners in CENTRAL and DIRECT-PLUS RCTs and in the FIT follow-up study, using validated protocols described in the original publications.9,10 VAT, deep SAT, and superficial SAT areas were measured at standardized anatomical levels (L4-L5, L5-S1) and quantified using semi-automated MATLAB-based software. Manual tracing of the fascia superficialis distinguished superficial from deep SATS. Observers were blinded to intervention assignment and time point. Reproducibility of VAT quantification was high (inter- and intra-rater reliability for n=30: r>0.96; p<0.001).9,10 Further details on MRI acquisition parameters are provided in Supplementary Methods 2.

Brain structure volumes were assessed at three time points: baseline and 18 months during the DIRECT-PLUS trial, and at the FIT follow-up. Imaging was performed using a 3D T1-weighted turbo field echo (T1-TFE) sequence with 1.0 mm isotropic voxel resolution. Brain volumes were quantified using NeuroQuant® Software Version 4.1.2 (https://www.cortechs.ai/), an FDA-approved, fully automated software for volumetric brain analysis. Key metrics included brain structural metrics modeled as normalized region-specific volumes of the intracranial volume (%ICV), including TB volume, cortical GM, WM, TV volume, and HOC. Further details on MRI acquisition parameters and the NeuroQuant analysis pipeline are provided in Supplementary Methods 3.

Cognitive performance was assessed using MoCA with a score range: 0-30, and the MoCA-MIS with a score range: 0-15. Anthropometric indices, demographic data (including sex, as determined based on medical records), fasting blood biomarkers (further detailed in Supplementary Methods 4), and structured lifestyle questionnaires regarding diet, smoking habits, clinical data, and physical activity (PA) levels (quantified as metabolic equivalent of task (MET)-hours per week)43 were collected at baseline, end of intervention, and follow-up (5-16 years post-RCT), with dietary patterns additionally evaluated using the validated 14-item MED Diet Adherence Screener at follow-up.44 Missing MED score data (n=10, 1.9%) were imputed using k-nearest neighbours (KNN) imputation.45

Statistical Analyses
Outcomes and Data Presentation
The primary outcome variables were the MoCA cognitive test score and brain structural measures, including TB, GM, WM, TV, and HOC. Abdominal adipose tissue depots, including VAT, deep SAT, and superficial SAT, were also considered as primary outcomes as well as exposures. Secondary outcome variables included demographic parameters, anthropometric, and blood biomarker measurements. Continuous variables are presented as means with standard deviations (SD), and categorical variables as frequencies and percentages. The normality of continuous variables was assessed using histograms and the Shapiro-Wilk test. Non-normally distributed variables were log-transformed prior to modeling. For analyses involving multiple comparisons, FDR correction was applied using the Benjamini-Hochberg procedure. Statistical significance was set a priori at p<0.05.

Cross-sectional Analyses
Using data from FIT follow-up study, linear regression models were used cross-sectionally to examine the associations between abdominal adipose depots and MoCA score as the outcome variable. Each adipose depot served as the primary exposure, and interaction terms between adipose depot and brain volume measures were included to assess whether associations with MoCA were modified by brain volumes. Models were adjusted for age, sex, BMI, and MED diet adherence score. We additionally performed a sensitivity analysis in which BMI replaced VAT as the primary exposure while VAT was included as an adjustment covariate. All continuous predictors were standardized (z-scores) prior to analysis. While adipose depots were modeled as continuous variables in statistical analyses, tertile stratification was applied in figures to aid visual interpretation of trends.

Longitudinal Visceral Adipose Tissue (VAT) Exposure and AUC Calculation
For the CENTRAL and DIRECT-PLUS trials, cumulative adiposity exposure was calculated as AUC using the trapezoidal method,46 based on participants with complete data from all three time points (baseline, 18 months, and 5 or 10 years of follow-up, in the DIRECT-PLUS and CENTRAL trials, respectively). AUC values were normalized by follow-up duration (years) to account for differences in time intervals. Changes in anthropometric and abdominal adipose depots were calculated as relative percent changes from baseline to 18 months, and from 18 months to FIT follow-up, using the formula: ((Time_later - Time_earlier) / Time_earlier) × 100.

Longitudinal Pooled Data Analysis
In the CENTRAL and DIRECT-PLUS subset analyses, fixed-effect meta-analysis based on linear regression models assessed associations between adiposity AUCs and cognitive outcomes (MoCA total and MoCA-MIS scores assessed at the FIT follow-up, 5 and 10 years post-intervention). For participants who joined both trials, only data from their initial intervention were included in these analyses. Models included age, sex, BMI, MED score, and brain volume at follow-up as covariates. We additionally performed a sensitivity analysis in which BMI AUC replaced adiposity AUCs as the primary exposure while VAT was included as an adjustment covariate. All continuous predictors were standardized (z-scores) prior to analysis, and beta estimates with 95% confidence intervals (CI) were reported.

Additional fixed-effect meta-analysis models were used to examine whether reductions in each adipose depot predicted brain structure and cognitive performance outcomes at 5 and 10 year follow-up. All brain outcomes and adipose depots predictors were standardized (Z-scores) prior to modeling. Models were adjusted for baseline fat depot, BMI changes during intervention, and follow-up, MED score assessed at follow-up, dual participation in both trials, sex, diet group, and their interactions.

Longitudinal Analysis of Brain Atrophy and Adiposity Exposure (DIRECT-PLUS Subset)
For the DIRECT-PLUS subset analysis, longitudinal changes in brain volumes were modeled using LMMs,47 which included random intercepts for participants. Brain volume measures at baseline, end of intervention (18 months), and 5-year follow-up were modeled as the dependent variable, with time between brain assessments (in months), adipose depot AUC, and their interaction (Time × adipose depot AUC) as predictors. All continuous predictors were standardized (z-scores) prior to analysis. Covariates included age, sex, BMI, and MED score assessed at follow-up time. APOE ε4 carrier status was additionally included as a covariate, and interactions with time and VAT AUC were tested.

Changes in brain volumes were analyzed as continuous variables; however, for clarity of presentation, stratifications were applied in the figures to illustrate three categories of brain volume decline: fast, moderate, and slow. For that end, individual slopes of brain volume change were computed using subject-level linear models, with time as the independent variable. These slopes were annualized by standardizing time intervals and multiplying by 12. Participants were then stratified into three categories based on their TB slope distribution: Fast Decline (lowest quartile), Moderate Decline (middle 50%), and Slow Decline (highest quartile). Thresholds for stratification were based on the observed distribution of annualized brain volume slopes, with the lower quartile (Fast Decline- reflecting approximately-0.3 per year) and the upper quartile (Slow Decline -reflecting approximately-0.12% per year). The intermediate 50% (Moderate Decline) fell between these two values, aligning well with previously reported ranges of age-related brain atrophy in the literature (see Supplementary Table 1, and Supplementary Table 2 for the calculation of the biological rationale underlying this classification). To ensure consistency in visualization, the same quartile-based classification was applied across all brain regions. For brain regions expected to increase in volume with atrophy (TV), the categories were reversed: "Fast Increase", "Moderate Increase", and "Slow Increase". p-values presented in figures reflect the interaction term from the LMM (Time × VAT AUC). We additionally performed a sensitivity analysis in which BMI AUC replaced VAT AUC as the primary exposure while VAT was included as an adjustment covariate.

All statistical analyses were performed using R version 4.4.1. Key packages included metafor,48 ggplot2,49 dplyr,50 tidyr,51 lme4,52 MESS,53 ggridge,54 DiagrammerR,55 and gtsummary.56

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "Visceral fat has been increasingly recognized as an independent factor affecting brain health and cognition. In the Follow-Interventions-Trial (FIT) project, investigators evaluated long-term outcomes from four lifestyle randomized clinical trials to determine whether persistent reductions in visceral adipose tissue (VAT) influence brain structure and cognitive performance years after intervention. Among participants followed for 5–16 years, lower cumulative VAT exposure was associated with greater total brain, gray matter, and hippocampal volumes, higher cognitive scores, and slower rates of brain atrophy. Importantly, reductions in VAT during intervention predicted better brain outcomes independent of overall weight loss, suggesting that specifically targeting visceral fat may be a key strategy for promoting healthy brain aging. This important study demonstrates that sustained reductions in VAT resulting from lifestyle interventions are associated with slower brain atrophy over time, which in turn supports better cognitive outcomes, in this study with a maximal follow-up of 10 years. Several methodological limitations hamper enthusiasm.\n\nThe MoCA is a limited screening tool with relatively low sensitivity for detecting cognitive impairment, particularly in younger individuals such as those in this cohort (average age 61 years at follow up). In this context, the reported mean MoCA score of 23.6 is unexpectedly low, suggesting cognitive performance within the MCI to early dementia range for a relatively young sample. Please verify that this value is correct or clarify any methodological explanations for this finding. If accurate, consider analyzing the data using established MoCA cutoffs (26–30 for normal cognition, 18–25 for MCI, and below 18 for dementia) to evaluate whether visceral fat (and its changes) predicts incidence of MCI and dementia.\n\nThe interactions of brain volume with VAT on cognition suggest that once brain atrophy is substantial, additional visceral fat may no longer meaningfully affect cognition. The focus of the authors in the discussion is on the group with greater brain volumes. However, an alternative explanation is that cognitive decline may have already progressed beyond the range where VAT contributes detectably. That would once again suggest particularly low cognitive functioning in a rather young sample.\n\nWere other regions assessed for fat quantification in the MRI analysis? Evidence suggests that fatty liver and pancreas are associated with reduced brain volumes and poorer cognitive performance. Exploring whether lifestyle-induced changes in fat levels within these regions affect cognition would be of great interest and would help clarify if the effects found are specific to VAT.\n\nThe authors appropriately adjusted for BMI in all analyses, ensuring that the assessment of VAT reflected effects independent of overall adiposity. However, the manuscript would benefit from parallel analyses using BMI to determine whether the observed relationships are truly specific to VAT (although admittedly, the lack of associations with SAT support a specific effect of VAT). The evidence linking BMI in midlife with cognitive decline and incident dementia is robust, and BMI measurement is considerably easier and more cost-effective than VAT assessment.\n\nAdjustment for multiple comparisons was not applied in a study with a very large number of analyses.\n\nThe FIT cohort is predominantly male. It is important to address whether there is a selection bias. Also, despite the male/female disproportionate ratio, exploring interactions between the effects of the lifestyle intervention and sex on each outcome is essential, given prior evidence suggesting stronger effects in men and the importance of tailoring personalized interventions in the future.", "reviewer_2": "The resuls of your manuscript are undeniably compelling and warrant publication in this journal. A couple of key points:\n1. You don't really know in your specific cohort what is driving the increased atrophy rate as you don't have AD biomarker data (PET, blood biomarkers). This is a limitation you should note in your discussion.\n2. You need to clarify if any of your cohort were on anti-obesity medications, specifically the incretin analogs (semgalutide, tirzepatide, etc.) at the time of their evaluations. I doubt it given when these medications came out but you need to state this clearly in the manuscript.\n3. Your literature citations double count this citation (#2, #45):\nRaji CA, Meysami S, Hashemi S, et al. Visceral and subcutaneous abdominal 785 fat predict brain volume loss at midlife in 10,001 individuals. Aging and Disease. 786 2024;15(4):1831.\nI'd keep that referennce for #2 and for #45 you need to modify that sentence to note that i) VAT has been linked to amyloid deposition as noted in this paper from the same group:\nDolatshahi M, Commean PK, Rahmani F, Liu J, Lloyd L, Nguyen C, Hantler N, Ly M, Yu G, Ippolito JE, Sirlin C, Morris JC, Benzinger TLS, Raji CA. Alzheimer Disease Pathology and Neurodegeneration in Midlife Obesity: A Pilot Study. Aging Dis. 2024 Aug 1;15(4):1843-1854. doi: 10.14336/AD.2023.0707. PMID: 37548931; PMCID: PMC11272197.\nYou also need to note foundational literature linking BMI to brain atropy to being with while also acknowledging that this original work lacked the benefit for the more detailed evaluations you have now:\nRaji CA, Ho AJ, Parikshak NN, Becker JT, Lopez OL, Kuller LH, Hua X, Leow AD, Toga AW, Thompson PM. Brain structure and obesity. Hum Brain Mapp. 2010 Mar;31(3):353-64. doi: 10.1002/hbm.20870. PMID: 19662657; PMCID: PMC2826530.\n4. You should also cite the Lancet Commission, noting obesity as a dementia risk factor while also taking care to note that it does not separately characterize VISCERAL obesity as the main component of obesity driving the risk:\nLivingston G, Huntley J, Liu KY, Costafreda SG, Selbæk G, Alladi S, Ames D, Banerjee S, Burns A, Brayne C, Fox NC, Ferri CP, Gitlin LN, Howard R, Kales HC, Kivimäki M, Larson EB, Nakasujja N, Rockwood K, Samus Q, Shirai K, Singh-Manoux A, Schneider LS, Walsh S, Yao Y, Sommerlad A, Mukadam N. Dementia prevention, intervention, and care: 2024 report of the Lancet standing Commission. Lancet. 2024 Aug 10;404(10452):572-628. doi: 10.1016/S0140-6736(24)01296-0. Epub 2024 Jul 31. PMID: 39096926.\nThis can be separately aligned with newer Lancet guidelines noting that obesity has to be identified by more than BMI and other measures such as waist circumference, DEXA and even MRI may be considered in refining our obesity defintions:\nRubino F, Cummings DE, Eckel RH, Cohen RV, Wilding JPH, Brown WA, Stanford FC, Batterham RL, Farooqi IS, Farpour-Lambert NJ, le Roux CW, Sattar N, Baur LA, Morrison KM, Misra A, Kadowaki T, Tham KW, Sumithran P, Garvey WT, Kirwan JP, Fernández-Real JM, Corkey BE, Toplak H, Kokkinos A, Kushner RF, Branca F, Valabhji J, Blüher M, Bornstein SR, Grill HJ, Ravussin E, Gregg E, Al Busaidi NB, Alfaris NF, Al Ozairi E, Carlsson LMS, Clément K, Després JP, Dixon JB, Galea G, Kaplan LM, Laferrère B, Laville M, Lim S, Luna Fuentes JR, Mooney VM, Nadglowski J Jr, Urudinachi A, Olszanecka-Glinianowicz M, Pan A, Pattou F, Schauer PR, Tschöp MH, van der Merwe MT, Vettor R, Mingrone G. Definition and diagnostic criteria of clinical obesity. Lancet Diabetes Endocrinol. 2025 Mar;13(3):221-262. doi: 10.1016/S2213-8587(24)00316-4. Epub 2025 Jan 14. Erratum in: Lancet Diabetes Endocrinol. 2025 Mar;13(3):e6. doi: 10.1016/S2213-8587(25)00006-3. PMID: 39824205; PMCID: PMC11870235."}, "author_response": "We greatly appreciate the recognition that our work supports targeting visceral fat as a potential strategy to promote healthy brain aging, and we hope that the revised manuscript satisfactorily addresses the remaining methodological concerns of the reviewer.\n\n1. The MoCA is a limited screening tool with relatively low sensitivity for detecting cognitive impairment, particularly in younger individuals such as those in this cohort (average age 61 years at follow up). In this context, the reported mean MoCA score of 23.6 is unexpectedly low, suggesting cognitive performance within the MCI to early dementia range for a relatively young sample. Please verify that this value is correct or clarify any methodological explanations for this finding. If accurate, consider analyzing the data using established MoCA cutoffs (26–30 for normal cognition, 18–25 for MCI, and below 18 for dementia) to evaluate whether visceral fat (and its changes) predicts incidence of MCI and dementia.\nResponse: We thank the reviewer for this important and insightful comment. We have carefully re-checked our data and confirm that the mean MoCA score of 23.6 at follow-up is correct.\nRegarding the relatively low mean MoCA score observed in our FIT population, we agree that this value may appear below the normative range typically described in general population samples. According to the original MoCA validation study, a score of ≥26 is commonly used as the cutoff for normal cognition (Nasreddine et al., 2005, https://doi.org/10.1111/j.1532-5415.2005.53221.x). More recent work, however, has shown that a cutoff score of 23/30, rather than the initially recommended 26/30, reduces the false-positive rate and provides better overall diagnostic accuracy (Carson et al., 2017, https://doi.org/10.1002/gps.4756).\nIn relation to the important point raised by the reviewer regarding the relatively low mean MoCA score, the FIT cohort is not a general population sample. Participants were selected on the basis of abdominal obesity/dyslipidemia, or type 2 diabetes (CASCADE trial). Specifically, the follow-up analyses in the CENTRAL and DIRECT-PLUS intervention trials included individuals who were enrolled because of abdominal obesity and/or dyslipidemia.\nRecent studies have reported that visceral adiposity is associated with lower cognitive scores, such as in a cross-sectional analysis of adults around 60 years of age (Anand et al., 2022. doi:10.1001/jamanetworkopen.2021.46324). Thus, although this may appear to be a relatively “healthy” community-dwelling population, prior abdominal obesity or diabetes is, according to the literature, consistently linked to lower cognitive performance.\nIn our study, we analyzed MoCA as a continuous variable to preserve statistical power and to capture the full range of cognitive performance, rather than relying solely on categorical groupings. However, as suggested by the reviewer, we conducted a complementary analysis in which MoCA scores were categorized into three clinically relevant groups (26–30, 18–25, and <18). Because MoCA data were available only at the follow-up visit, this analysis reflects cognitive status at the follow-up time point rather than the incidence of new events. In this analysis, only higher visceral adipose tissue (VAT) was significantly associated with being in the 18-25 range (β = 0.21, p < 0.01) compared with the normal cognition group after adjustment for age, sex, BMI, and MED score. In a manner familiar from our main analyses in which MoCA was modeled as a continuous variable, among participants with already low MoCA score (<18), further differences in VAT were no longer meaningfully associated with cognitive status. No significant associations were observed for SSAT or DSAT. Attached below the results based on these MoCA score groups:\nFatType 18–25 <18\nVAT 0.21** 0.11\nSSAT 0.07 0.15\nDSAT 0.11 0.02\nThese results have been added as a complementary analysis in the Results section (Results lines 322-329):\"In an additional categorical analysis, adjusted for age, sex, BMI, and MED score, participants with 18-25 MoCA scores had significantly higher VAT compared with those with a normal cognition score (β = 0.21, p < 0.01). In contrast, differences in SSAT and DSAT across MoCA categories were not statistically significant (18-25 vs. >=26 MoCA score: β = 0.07 for SSAT and β = 0.11 for DSAT). Likewise, none of the adipose tissue depots showed significant differences between individuals with dementia-range MoCA scores (<18) and those with normal cognition.”\nIn light of the reviewer’s comment, we have added a dedicated paragraph in the Discussion that directly addresses the relatively low MoCA distribution (Discussion lines 688-692):\n\"The mean MoCA score in our cohort was relatively low, according to the original MoCA validation study. Although this distribution may appear unexpected for a relatively young late–midlife sample, it is consistent with the metabolic profile of the FIT population, which was enriched with individuals with long-standing abdominal obesity, dyslipidemia, and type 2 diabetes.”\nTaken together, the clinical characteristics of our participants ,who were recruited on the basis of abdominal obesity and dyslipidemia, and the evidence linking obesity with lower cognitive performance support the plausibility of the observed MoCA mean score in our trial. These complementary categorical findings are fully consistent with our primary continuous-scale analyses and further reinforce the visceral adipose–cognitive associations reported in the manuscript.\nWe hope that this clarification and the additional analyses address the reviewer’s concerns and meet the reviewer’s expectations.\n\n2. The interactions of brain volume with VAT on cognition suggest that once brain atrophy is substantial, additional visceral fat may no longer meaningfully affect cognition. The focus of the authors in the discussion is on the group with greater brain volumes. However, an alternative explanation is that cognitive decline may have already progressed beyond the range where VAT contributes detectably. That would once again suggest particularly low cognitive functioning in a rather young sample.\nResponse: We agree with the reviewer thoughtful comment and for highlighting the implications of the brain volume × VAT interaction for interpreting cognitive performance in these relatively young participants.\nWe have also incorporated the reviewer’s alternative interpretation of the interaction between brain volume, VAT, and cognition. Specifically, in addition to our original view that VAT appears most salient as a cognitive correlate in individuals with relatively preserved brain volume, we now note that, among participants with more pronounced brain atrophy, cognitive decline may already have progressed to a level at which additional VAT no longer makes a detectable incremental contribution to performance.\nDiscussion (lines 679-688): “Another interpretation is that the interaction between brain volume and visceral adiposity suggests that VAT is most strongly related to cognition among participants with relatively preserved brain structure, whereas in those with more pronounced atrophy, structural, vascular, and metabolic factors may already be the main determinants of cognitive performance. Thus, the VAT × brain volume interaction may reflect both the particular vulnerability of individuals with less advanced structural brain changes to metabolic insults and the damage among those with lower brain volumes, in whom cognitive impairment may already be largely determined by these factors, such that further effects of VAT are no longer detectable.”\n\n3. Were other regions assessed for fat quantification in the MRI analysis? Evidence suggests that fatty liver and pancreas are associated with reduced brain volumes and poorer cognitive performance. Exploring whether lifestyle-induced changes in fat levels within these regions affect cognition would be of great interest and would help clarify if the effects found are specific to VAT.\nResponse: We appreciate the reviewer’s insightful comment and agree that ectopic fat in organs such as the liver and pancreas is relevant to brain health and cognition. Unfortunately, in the present study, quantitative MRI measures of hepatic and pancreatic fat were not available. However, within the adipose tissue measures available in our trial, a clear pattern emerged: visceral adipose tissue (VAT) showed the strongest and most consistent associations with brain volumes and cognition, whereas subcutaneous depots (superficial and deep SAT) did not. We clarified this point in the Discussion and explicitly acknowledge the lack of liver and pancreatic fat quantification as a limitation and a key direction for future research.\nDiscussion (lines 633-641): “A further limitation is the absence of quantitative MRI measures of ectopic fat in organs such as the liver and pancreas, which are recognized as relevant to cardiometabolic and brain health. Future studies including hepatic and pancreatic fat will be needed to determine whether our findings are specific to VAT or reflect a broader ectopic fat burden. Nevertheless, within the abdominal adipose depots assessed here, VAT consistently showed stronger associations with brain atrophy and cognitive performance than superficial or deep subcutaneous fat, supporting a distinct role for visceral adiposity in the adipose-brain axis.”\n\n4. The authors appropriately adjusted for BMI in all analyses, ensuring that the assessment of VAT reflected effects independent of overall adiposity. However, the manuscript would benefit from parallel analyses using BMI to determine whether the observed relationships are truly specific to VAT (although admittedly, the lack of associations with SAT supports a specific effect of VAT). The evidence linking BMI in midlife with cognitive decline and incident dementia is robust, and BMI measurement is considerably easier and more cost-effective than VAT assessment.\nResponse: The reviewer raises an important point, and we fully agree that BMI is an accessible and clinically relevant measure. In our primary analyses, we adjusted all VAT models for BMI to ensure that the reported associations reflect effects of visceral adiposity that are independent of overall adiposity. In response to the reviewer’s suggestion, we conducted additional parallel analyses in which BMI was entered as the primary adiposity exposure instead of VAT, both (i) without adjustment for VAT and (ii) in models that additionally included VAT.\nAcross these complementary analyses, BMI showed no significant interactions with brain structure in relation to cognitive outcomes, in contrast to the VAT–brain interactions observed in our cross-sectional analyses. In the longitudinal models, the interaction between BMI exposure and time of brain scan was not significant, with only a trend-level association for total ventricular volume (p < 0.10). These findings may further support the specific relevance of abdominal adiposity beyond overall BMI.\nApproximately 43% of the FIT cohort were aged 65 years or older, an age range commonly classified as older adults by the National Institute on Aging (https://www.nih.gov/nih-style-guide/age, last reviewed on June 12, 2025). In this population, prior literature has described a BMI “paradox,” whereby both low and very high BMI can be associated with increased risk, while central adiposity remains detrimental even among older adults with relatively low BMI. The absence of significant BMI-brain interactions, alongside the observed VAT-brain associations, is therefore consistent with the notion that visceral fat more directly captures the adiposity-related risk for brain health. Finally, BMI change during the 18-month intervention was significantly associated only with MoCA score at follow-up, and not with brain structural measures at FIT follow-up. This pattern may suggest that changes in BMI relate to cognitive performance through additional pathways that are not fully mediated by brain atrophy, such as vascular, metabolic, or functional mechanisms.\nThese new analyses have been added to the Statistics and Results section, and the extended models are summarised in the Supplementary Material.\nCross-sectional Analysis, Fig 2A, Results, lines 330–335:\n“In parallel models in which BMI, rather than VAT, was specified as the main adiposity exposure, BMI did not show significant interactions with brain volumes in relation to cognitive performance, either in models without VAT or in models additionally adjusted for VAT (model results shown in Supplementary Figure 2A, Table 1.2). The BMI-brain interaction terms were consistently non-significant, in contrast to the significant VAT-brain interactions observed in our primary analyses.”\nStatistics, lines 832-834: “We additionally performed a sensitivity analysis in which BMI replaced VAT as the primary exposure while VAT was included as an adjustment covariate. “\nLongitudinal BMI AUC Analysis, Fig 2B, Results, lines 404–406:\n“Similarly, in longitudinal models using BMI area under the curve (BMI AUC) as the adiposity exposure, no significant associations were observed between BMI AUC and cognitive scores (Supplementary Figure 2B, Supplementary Table 2.2).\nStatistics, lines 853-855: “We additionally performed a sensitivity analysis in which BMI AUC replaced adiposity AUCs as the primary exposure while VAT was included as an adjustment covariate. “\nLongitudinal change in BMI during the intervention and cognition Fig 3 (Results, lines 424–428):\n“However, in additional analyses, relative change in BMI during the intervention was not significantly associated with brain structural measures at follow-up (model details in Supplementary Figure 3, Table 3.2). In contrast, this BMI change was negatively associated with MoCA score at follow-up (β = -0.153, p = 0.014). “\nLongitudinal LMM with BMI AUC and brain structure, Fig 4 (Results, 545-548):\n“When the linear mixed-effects models included BMI AUC as the main exposure together with time to predict trajectories of brain structure, the interaction between BMI AUC and time was not significant (Supplementary Figure 4, Table 4.2).“\nStatistics, lines (894-896): ”We additionally performed a sensitivity analysis in which BMI AUC replaced VAT AUC as the primary exposure while VAT was included as an adjustment covariate.“\nNew Discussion Paragraph Derived From the Additional BMI Analyses (Discussion, lines 652–660) :\n“In parallel analyses that substituted BMI for VAT as the primary adiposity measure, BMI showed no significant associations with brain structures, which may help explain why VAT, rather than BMI, emerges as a more specific adiposity-related risk factor in our cohort despite the expected correlation between these measures. However, changes in BMI during the dietary intervention were associated with subsequent MoCA performance at long-term follow-up, although these BMI changes were not related to brain structural outcomes. This may indicate that BMI relates to cognitive performance through additional mechanisms, such as metabolic or vascular improvements, that are not fully reflected in brain imaging measures.“\n\n5. Adjustment for multiple comparisons was not applied in a study with a very large number of analyses.\nResponse: We thank the reviewer for this important point regarding correction for multiple comparisons. Studies have demonstrated associations between visceral fat and both brain structure and cognitive function, providing a biological and empirical rationale for focusing on VAT as the main exposure of interest.\nOur hypothesis was that long-term exposure to visceral adiposity may influence the rate of brain atrophy and cognitive decline over time. We performed false-discovery rate (FDR) in analyses involving multiple comparisons, which is also added to the statistical section (lines 823-825: ”For analyses involving multiple comparisons, FDR correction was applied using the Benjamini-Hochberg procedure” (. However, we were concerned that applying (FDR) correction across a very small set of five brain regions would substantially increase the probability of false-negative findings. Analyses of the SAT and DSAT measures did not yield significant associations with brain structure, reinforcing the specificity of our VAT-based hypothesis.\nNevertheless, in response to the reviewer’s request, we applied FDR correction across the five brain outcomes at FIT follow-up. After correction, only gray matter volume, white matter volume, and hippocampal occupancy score remained significant at FDR < 0.10 in relation to VAT, whereas all associations with SAT and DSAT remained clearly non-significant, as shown in Supplementary Figure 2A. This additional step is fully detailed in the extended models presented in the Supplementary Material. For example, in the longitudinal models, both hippocampal occupancy score and lateral ventricle volume remained significant after FDR correction (FDR < 0.001; Supplementary Figure 4, Supplementary Table 4.1).\n\n6. The FIT cohort is predominantly male. It is important to address whether there is a selection bias. Also, despite the male/female disproportionate ratio, exploring interactions between the effects of the lifestyle intervention and sex on each outcome is essential, given prior evidence suggesting stronger effects in men and the importance of tailoring personalized interventions in the future.\nResponse: This comment highlights the importance of considering sex-specific responses when aiming to develop personalized lifestyle interventions.\nIt is true that the FIT cohort includes a higher proportion of men, a limitation shared by all of our intervention studies conducted in this unique workplace setting. This reflects the actual demographic profile of the research site rather than a recruitment bias. At the same time, the closed and highly controlled workplace environment offers important methodological advantages, including the ability to directly monitor and standardize the provided lunch and to deliver intensive, multidisciplinary on-site dietary guidance throughout the intervention.\nIn response to the reviewer’s request, we conducted sex-by-intervention interaction analyses in both DIRECT-PLUS and CENTRAL. These analyses did not identify significant sex-intervention interactions for any of the outcomes (Supplementary Figure 2 and Supplementary Table 3.3). We observed significant main effects of sex on Total brain volumes (women showed lower total brain volume at follow-up, b=-0.617, p=0.004), consistent with well-established sex differences in neuroanatomy reported in the literature. However, the dietary intervention effects themselves, as well as sex-by-intervention interactions, were not significant.\nWe have added the results of the sex-by-intervention interaction analyses to the Results section (Results, lines 428–429):”Sex-by-intervention interaction did not reach statistical significance in any of the models (Supplementary Figure 3, Table 3.3)”\n\nReviewer #2:\nThe results of your manuscript are undeniably compelling and warrant publication in this journal. A couple of key points:\nWe thank the reviewer for the positive feedback on the importance and strength of our results, and we appreciate the thoughtful comments.\n\n1. You don't really know in your specific cohort what is driving the increased atrophy rate as you don't have AD biomarker data (PET, blood biomarkers). This is a limitation you should note in your discussion.\nResponse: We agree that the absence of AD biomarkers is a limitation, and in accordance with your suggestion, we have added a statement addressing this limitation in the Discussion section, lines 641-644): “Additional limitation of the study is the lack of Alzheimer’s disease biomarker data (e.g., PET or blood-based markers), which prevents us from determining whether the accelerated atrophy rate observed in our cohort reflects underlying AD-related neurodegeneration biomarkers.”\n\n2. You need to clarify if any of your cohort were on anti-obesity medications, specifically the incretin analogs (semgalutide, tirzepatide, etc.) at the time of their evaluations. I doubt it given when these medications came out but you need to state this clearly in the manuscript.\nResponse: We appreciate the reviewer for this highly relevant and timely comment. In our RCT trials, data on incretin-based anti-obesity medications were not collected, as these agents were not in common clinical use during the study period. In our current follow-up dataset, however, self-reported information regarding the use of these medications is available. To address this, we conducted additional sensitivity analyses adjusting for incretin medication use. As shown in the extended models in the Supplementary Material, medication use was not significantly associated with MoCA scores and did not alter the results. A clarifying sentence has been added to the Results section (Results, lines 335–338). “In sensitivity analyses adjusted for incretin-based anti-obesity medication use, no significant associations were observed, and the inclusion of this covariate did not change the MoCA-related findings (Supplementary Figure 2A Table 1.3).”\n\n3. Your literature citations double count this citation (#2, #45):\nRaji CA, Meysami S, Hashemi S, et al. Visceral and subcutaneous abdominal 785 fat predict brain volume loss at midlife in 10,001 individuals. Aging and Disease. 786 2024;15(4):1831.\nI'd keep that referennce for #2 and for #45 you need to modify that sentence to note that i) VAT has been linked to amyloid deposition as noted in this paper from the same group:\nDolatshahi M, Commean PK, Rahmani F, Liu J, Lloyd L, Nguyen C, Hantler N, Ly M, Yu G, Ippolito JE, Sirlin C, Morris JC, Benzinger TLS, Raji CA. Alzheimer Disease Pathology and Neurodegeneration in Midlife Obesity: A Pilot Study. Aging Dis. 2024 Aug 1;15(4):1843-1854. doi: 10.14336/AD.2023.0707. PMID: 37548931; PMCID: PMC11272197.\nResponse: We thank the reviewer for bringing our attention to the duplicated citation. We have corrected this error by keepingthe original reference as citation #2 and revising citation #45 accordingly. In line with the reviewer’s suggestion, we have now added the Dolatshahi et al. (2024) study, which links visceral adiposity to amyloid pathology and neurodegeneration in midlife obesity.\n(Discussion lines 739-741): \"Recent data in cognitively normal midlife adults indicate that visceral obesity is associated with greater amyloid burden and cortical thinning in Alzheimer’s disease-related regions.\"\nYou also need to note foundational literature linking BMI to brain atropy to being with while also acknowledging that this original work lacked the benefit for the more detailed evaluations you have now:\nRaji CA, Ho AJ, Parikshak NN, Becker JT, Lopez OL, Kuller LH, Hua X, Leow AD, Toga AW, Thompson PM. Brain structure and obesity. Hum Brain Mapp. 2010 Mar;31(3):353-64. doi: 10.1002/hbm.20870. PMID: 19662657; PMCID: PMC2826530.\n\n4. You should also cite the Lancet Commission, noting obesity as a dementia risk factor while also taking care to note that it does not separately characterize VISCERAL obesity as the main component of obesity driving the risk:\nLivingston G, Huntley J, Liu KY, Costafreda SG, Selbæk G, Alladi S, Ames D, Banerjee S, Burns A, Brayne C, Fox NC, Ferri CP, Gitlin LN, Howard R, Kales HC, Kivimäki M, Larson EB, Nakasujja N, Rockwood K, Samus Q, Shirai K, Singh-Manoux A, Schneider LS, Walsh S, Yao Y, Sommerlad A, Mukadam N. Dementia prevention, intervention, and care: 2024 report of the Lancet standing Commission. Lancet. 2024 Aug 10;404(10452):572-628. doi: 10.1016/S0140-6736(24)01296-0. Epub 2024 Jul 31. PMID: 39096926.\nThis can be separately aligned with newer Lancet guidelines noting that obesity has to be identified by more than BMI and other measures such as waist circumference, DEXA and even MRI may be considered in refining our obesity defintions:\nRubino F, Cummings DE, Eckel RH, Cohen RV, Wilding JPH, Brown WA, Stanford FC, Batterham RL, Farooqi IS, Farpour-Lambert NJ, le Roux CW, Sattar N, Baur LA, Morrison KM, Misra A, Kadowaki T, Tham KW, Sumithran P, Garvey WT, Kirwan JP, Fernández-Real JM, Corkey BE, Toplak H, Kokkinos A, Kushner RF, Branca F, Valabhji J, Blüher M, Bornstein SR, Grill HJ, Ravussin E, Gregg E, Al Busaidi NB, Alfaris NF, Al Ozairi E, Carlsson LMS, Clément K, Després JP, Dixon JB, Galea G, Kaplan LM, Laferrère B, Laville M, Lim S, Luna Fuentes JR, Mooney VM, Nadglowski J Jr, Urudinachi A, Olszanecka-Glinianowicz M, Pan A, Pattou F, Schauer PR, Tschöp MH, van der Merwe MT, Vettor R, Mingrone G. Definition and diagnostic criteria of clinical obesity. Lancet Diabetes Endocrinol. 2025 Mar;13(3):221-262. doi: 10.1016/S2213-8587(24)00316-4. Epub 2025 Jan 14. Erratum in: Lancet Diabetes Endocrinol. 2025 Mar;13(3):e6. doi: 10.1016/S2213-8587(25)00006-3. PMID: 39824205; PMCID: PMC11870235.\nResponse: We thank the reviewer for highlighting these important references, which enrich the conceptual of the manuscript. In response, we have incorporated the suggested references and revised the discussion accordingly. Below we provide the updated paragraph (Discussion lines 660-667):\n“Our MRI-based VAT measure extends prior BMI-based literature by isolating the metabolically active component of obesity most relevant to neurodegeneration, aligning with updated obesity definitions that call for adiposity measures beyond BMI. While the 2024 Lancet Commission on dementia prevention identifies obesity as a modifiable midlife risk factor for dementia and does not separately characterize visceral adiposity, our findings suggest that visceral fat represents a particularly harmful obesity phenotype that may disproportionately drive neurodegenerative risk.“"}, {"round": 2, "reviewer_comments": {"reviewer_1": "This reviewer appreciates the authors’ efforts to respond fully to the concerns raised.", "reviewer_2": "I feel the authors did an excellent job at addressing all of my comments."}, "author_response": ""}]

Decision letter