Global obesity rates have dramatically increased, with significant health implications. Weight loss through calorie restriction is a common intervention, but it's often hampered by reductions in total daily energy expenditure (TDEE), primarily due to decreases in resting metabolic rate (RMR). RMR constitutes a substantial portion (60-70%) of TDEE. Therefore, preserving RMR during weight loss is crucial for preventing weight regain. Traditionally, maintaining fat-free mass (FFM) was considered key to RMR preservation, as FFM is a primary determinant of RMR. However, FFM is heterogeneous, and the impact of FFM loss on RMR depends on the specific tissues lost. While vital organs maintain metabolic activity, losses in skeletal muscle are common during weight loss. Adipose tissue, though less metabolically active, is typically lost in larger quantities. Even accounting for tissue loss, RMR declines beyond what's expected, indicating metabolic adaptations in the remaining tissues. These adaptations are linked to changes in hormones like leptin and thyroid hormones. This study aimed to quantify the individual contributions of tissue loss and metabolic adaptations to RMR reduction during prolonged weight loss and their interrelationship.

Existing literature indicates that reductions in RMR following weight loss are a consistent observation across various studies. While some research emphasizes the role of fat-free mass (FFM) loss in explaining RMR reduction, others highlight the importance of metabolic adaptations independent of tissue changes. Studies examining the contribution of specific tissues within FFM to RMR reductions are limited. There's a lack of clarity on the interrelationship between tissue-specific losses and the magnitude of metabolic adaptations. Previous research has shown a relationship between adaptive reductions in RMR and changes in key metabolic hormones, such as leptin and thyroid hormones. However, a comprehensive investigation into the relative contributions of tissue loss and metabolic adaptations, along with their interdependencies, is needed to inform effective weight management strategies.

This study conducted a secondary analysis of data from the Comprehensive Assessment of Long-term Effects of Reducing Intake of Energy (CALERIE) study. Data from 109 participants (77 women, 32 men) with complete baseline and 12-month data were included. Participants underwent a 25% calorie restriction for 12 months. Body weight, body composition (via dual-energy X-ray absorptiometry or DXA), RMR (indirect calorimetry), and metabolic hormone concentrations (leptin, triiodothyronine (T3), insulin, and insulin-like growth factor 1 (IGF-1)) were assessed at baseline and 12 months. The contribution of tissue losses to RMR reduction was calculated by weighing changes in the size of energy-expending tissues (skeletal muscle, adipose tissue, bone, brain, inner organs, residual mass) with their tissue-specific metabolic rates. Metabolic adaptations were quantified as the remaining RMR reduction not explained by tissue loss. Statistical analyses included paired t-tests, linear regression, Pearson's correlation, and generalized linear model analyses.

Over 12 months, participants lost 7.3 ± 0.2 kg (p < 0.001) and experienced a 101 ± 12 kcal/d RMR reduction (-7.6%, p < 0.001). On average, 60% of the total RMR reduction was explained by energy-expending tissue losses, while 40% was attributed to metabolic adaptations. Adipose tissue loss (7.2 ± 3.0 kg) showed a positive association with both RMR reduction (r = 0.42, p < 0.001) and metabolic adaptations (r = 0.31, p < 0.001). In contrast, skeletal muscle mass loss (1.0 ± 0.7 kg) was not significantly related to RMR changes (r = 0.14, p = 0.16). Metabolic adaptations were correlated with declines in leptin (r = 0.27, p < 0.01), T3 (r = 0.19, p < 0.05), and insulin (r = 0.25, p < 0.05). Analysis stratified by quartiles of skeletal muscle and adipose tissue loss further reinforced the stronger association between adipose tissue loss and RMR reduction and metabolic adaptations. In individuals with the greatest adipose tissue loss, adipose tissue loss accounted for 31.3% of RMR reduction, while other tissue losses accounted for another 18.3%, totaling 49.6%.

This study confirms that RMR reduction following weight loss is a complex process involving both tissue loss and metabolic adaptations. The finding that adipose tissue loss, rather than skeletal muscle loss, is more strongly associated with RMR reduction challenges conventional wisdom. The significant contribution of metabolic adaptations, coupled with their correlation with key metabolic hormones, highlights the regulatory mechanisms involved in energy expenditure reduction. The substantial interindividual variability observed in the contributions of tissue loss and metabolic adaptations emphasizes the need for personalized interventions. The study’s results align with some previous research but contrast with others that emphasize the predominant role of FFM loss in RMR reduction. This discrepancy might stem from the heterogeneous nature of FFM and the different methodologies used in previous studies.

This study demonstrates that RMR reduction after weight loss occurs through a combination of tissue loss (primarily adipose tissue) and metabolic adaptations. The variability in the contribution of these factors underscores the need for personalized strategies to address RMR reduction for effective weight management. Future research should explore the impact of preserving specific tissues or manipulating metabolic hormones on RMR and weight maintenance. Investigating more targeted interventions based on individual profiles of tissue loss and metabolic adaptations could lead to more effective weight-loss and weight-maintenance strategies.

This study utilized data from a non-obese population, limiting the generalizability of the findings to individuals with obesity. The study also did not tightly control the methods of caloric restriction employed by participants. The reliance on indirect calorimetry and DXA for measuring RMR and body composition, respectively, introduces potential limitations associated with these methodologies. Further, the study’s reliance on correlation does not establish direct causality between the identified factors.