Obesity-related comorbidities are a significant public health concern. Lifestyle interventions, particularly those focusing on weight loss through reduction of excess adipose tissue (AT) and ectopic fat, are crucial preventative strategies. Different fat depots possess distinct metabolic profiles, influencing the effectiveness of weight loss interventions. Short-term weight loss success significantly impacts long-term weight maintenance. This success varies substantially between individuals, due to factors including intervention adherence, insulin resistance, genetics, gut microbiota, sleep habits, and basal metabolic rate. AT distribution also plays a role; abdominal obesity (more visceral adipose tissue, VAT) is often associated with greater weight loss benefits than gluteal-femoral obesity (more subcutaneous adipose tissue, SAT). Anthropometric measurements are commonly used to assess AT characteristics, but imaging techniques like MRI offer more precise and detailed assessments of AT volume and distribution. While some studies have linked baseline AT characteristics to weight loss, many are limited by small sample sizes, investigation of single depots, or use of single-slice measurements. This study utilized MRI to assess AT volume changes following an 8-week LCD and identify baseline predictors of short-term AT loss in adults with obesity.

The literature highlights the importance of weight loss interventions in preventing obesity-related comorbidities. Studies emphasize the diverse metabolic profiles of different adipose tissue depots and the impact of short-term weight loss on long-term success. Numerous factors influence weight loss outcomes, including adherence, insulin resistance, genetics, gut microbiota, sleep, and basal metabolic rate. While some research links abdominal obesity and high VAT to better weight loss responses, inconsistencies exist regarding the role of age and sex. Imaging techniques, particularly MRI, offer advantages over anthropometric measurements in assessing AT characteristics but are often limited by sample size, focus on single depots, or utilization of single-slice measurements rather than volumetric assessment. The existing research lacks comprehensive analyses of the association between baseline AT characteristics and the outcomes of weight loss interventions, particularly using advanced MRI techniques.

This prospective study enrolled 81 adults with obesity (mean BMI 34.08 ± 2.75 kg/m², mean age 46.3 ± 10.97 years, 49 females). Participants underwent baseline MRI (liver dome to femoral head) and anthropometric measurements (BMI, waist-to-hip ratio, body fat). An 8-week formula-based low-calorie diet (LCD) of 800 kcal (with optional additional 200 g non-starchy vegetables) was implemented, followed by a post-LCD MRI examination. MRI data included visceral and subcutaneous AT (VAT and SAT) volumes and AT fat fraction. Apparent lipid volumes were calculated. SAT and VAT volumes were divided into equidistant thirds along the craniocaudal axis and normalized by length. T-tests compared baseline and follow-up measurements and sex differences. Effect sizes on subdivided AT volumes were compared using standardized mean differences (SMD). Spearman Rank correlation and multiple regression analysis explored associations between baseline parameters and AT loss, using age and sex as covariates. The statistical software MedCalc was used for data analysis.

Following the LCD, participants showed significant weight loss (-11.61 ± 3.07 kg, *p* < 0.01) and reductions in all MRI-based AT parameters (*p* < 0.01). Absolute SAT loss (-3.24 ± 1.07 L) was significantly greater than VAT loss (-1.24 ± 0.66 L, *p* < 0.01). Relative apparent lipid loss was higher in VAT (*p* < 0.01). The lower abdominopelvic third exhibited the most significant SAT and VAT reduction. The normalized baseline SAT volume in the lower abdominopelvic third was the strongest predictor of AT and apparent lipid loss (*p* < 0.01 for SAT and VAT loss and SAT apparent lipid volume loss). Smaller baseline volumes in this region were associated with greater AT loss. Correlation analyses revealed strong associations between relative AT and apparent lipid volume losses and various baseline parameters, including BMI, body fat percentage, and SAT volume (particularly in the lower third). Multiple regression models confirmed that the normalized SAT volume in the lower third and baseline body fat percentage were significant predictors of SAT and VAT loss. A smaller baseline SAT volume in the lower third was associated with greater SAT and VAT loss.

The study's findings demonstrate the effectiveness of the 8-week LCD in reducing AT in individuals with obesity, confirming the expected outcome given the intervention. The greater absolute SAT loss compared to VAT loss aligns with previous research. The higher relative apparent lipid loss in VAT may be explained by the greater responsiveness of VAT to fasting-induced changes in lipid metabolism, as suggested by rodent model studies. The greater reduction in AT volume in the lower abdominopelvic third highlights the importance of regional AT distribution. The identification of lower abdominopelvic SAT volume as a significant predictor of AT loss offers valuable insights for personalized weight management strategies. This predictor's association with both SAT and VAT loss suggests a potential interdependency between these depots in response to the LCD. Differences in sex-specific responses to the LCD and AT distribution warrant further investigation.

This study demonstrates significant AT loss following an 8-week LCD, with the greatest reduction observed in the lower abdominopelvic SAT. Baseline SAT volume in this region emerged as a strong predictor of both SAT and VAT loss. Measuring this parameter may enhance prediction of short-term AT loss success following an LCD. Further research should explore the long-term effects and assess the implications of these findings for personalized weight loss interventions.

Limitations include potential partial volume effects in PDFF measurements, the use of 'apparent' lipid volume calculations, and a time gap between anthropometric and MRI measurements. The segmentation extended only to the middle of the femoral heads, potentially omitting lower body fat. The lack of a control group and various normalization approaches considered (BMI, height, or body surface area) represent further limitations.