Abstract
Objective:
To investigate if baseline (pre-weight loss) metabolic variables can predict weight regain.
Methods:
117 women with overweight completed a weight loss program to achieve a BMI <25 kg/m2, and were followed for 2 years. Resting metabolic rate (RMR), respiratory quotient (RQ), insulin sensitivity and serum leptin concentration were measured pre-weight loss, while on energy balance, and examined as predictors of weight regain at 1 and 2 years. We also examined rate and amount of weight loss as predictors, as these outcomes may reflect metabolic phenotype.
Results:
Average weight loss was 12 ±2.5 kg, and regain was 48±35% and 80±52% at 1 and 2 years, respectively. In regression modeling, metabolic variables (both pre-weight loss and changes with weight loss) did not predict weight regain. However, initial weight loss and time to achieve a BMI<25 were significant predictors of weight regain at 1 and 2 years, even after adjusting for confounders.
Conclusion:
Baseline (pre-weight loss) RMR, RQ, insulin sensitivity and leptin did not predict weight regain. However, a larger and faster weight loss was associated with a lower weight regain. Understanding the mechanisms behind inter-individual variation in magnitude and rate of weight loss is needed to ensure better weight loss maintenance.
Keywords: relapse, weight regain, RMR, leptin, insulin
Introduction
Obesity is a chronic, progressive and relapsing condition (1) affecting approximately 13% of the world’s population (2). Lifestyle interventions can lead to clinically relevant weight loss (WL) in the short-term, but long-term results are disappointing (3). According to data from the National Health and Nutrition Examination Survey (NHANES, 1999–2006), only 1 in 6 individuals with overweight or obesity report ever having maintained a WL of at least 10% for 1-year, after a lifestyle intervention (4). Therefore, relapse represents the biggest challenge in obesity management.
Identification of baseline (prior to weight loss) predictors of weight regain would allow for the early identification of individuals at risk, with the possibility of channeling more resources to this group of individuals and/or individualized treatment, aiming at a better use of resources and better long-term outcomes. However, up to date very few baseline variables have been found to correlate with long-term weight regain after WL induced by lifestyle interventions (5–7). Several metabolic predictors of weight gain have been proposed, namely: 1) a low resting metabolic rate (RMR), 2) low fat oxidation (indicated by a high respiratory quotient (RQ)); 3) high insulin sensitivity and 4) low plasma leptin concentrations (8). However, the majority of the studies have looked at weight gain over time (5, 9–20), not weight regain after an initial weight loss intervention, and results are controversial. The only study looking into weight regain was the one from Vogels et al who found that a low baseline RMR was predictive of weight regain at 2 years follow up (5). Additionally, most of the above mentioned studies, including the Vogels study, are limited by the fact that no effort was made for measurements to be performed under energy balance.
Therefore the main aim of this study was to determine if baseline (pre-WL) metabolic variables, namely RMR, RQ, insulin sensitivity and serum leptin concentration, measured under energy balance conditions, predict weight regain over a 2 year period following a tightly controlled weight loss intervention. A secondary aim was to assess if WL and WL rate were predictors of weight regain. The main hypothesis was that individuals with the lowest RMR, fat oxidation (higher RQ), and leptin concentration, and the highest insulin sensitivity at baseline (pre-WL), would gain more weight during the follow up period. Additionally, it was hypothesized that a larger and faster WL would be associated with a better WL maintenance.
Methods
Participants
Participants in this study were premenopausal women with overweight. Participants were 20–41 years of age, sedentary (no more than one time per week regular exercise), had normal glucose tolerance (2–h glucose ≤140 mg/dL following 75g oral dose), family history of overweight/obesity in at least one first-degree relative, and no use of medications that affect body composition or metabolism. All women were nonsmokers and reported a regular menstrual cycle. Those eligible for inclusion in this analysis also had to have completed at least a one-year follow-up weight maintenance assessment. The study was approved by the Institutional Review Board for Human Use at the University of Alabama at Birmingham (UAB). All women provided informed consent before participating in the study.
Study design
All participants were tested at baseline after a 4-week weight stabilization period during which the participants were weighed 3 times/week with food provided during the last 2 weeks. After evaluation they were randomly assigned to one of three groups: 1) Weight loss with aerobic exercise training 3 times/week; 2) Weight loss with resistance exercise training 3 times/week; and 3) Weight loss with diet alone. During weight loss, all participants were provided an 800 kcal diet until reaching a BMI <25 kg/m2. Food was provided (20–22% fat, 20–22% protein, and 56–58% carbohydrate) by the General Clinical Research Center (GCRC) Kitchen. All testing was conducted in the follicular phase of the menstrual cycle during a 4-day GCRC in-patient stay (to ensure that physical activity and diet was standardized). Testing was done in a fasted state in the morning after spending the night in the GCRC.
During the 1-year follow-up period, participants were encouraged, but not mandated, to attend regular support group meetings (bimonthly dietary education classes aimed at weight maintenance for the first 6 months, followed by monthly meetings for months 6 to 12) and to continue with their exercise program, if applicable. For detailed information about the intervention see Hunter et al, 2008 (21).
Measurements
Body composition (% FM) was assessed with (dual-energy X-ray absorptiometry; Lunar Prodigy, GE-Lunar, Madison, WI). RMR and RQ were measured as previously described (21). Briefly, RMR was measured after an overnight stay in the GCRC and 12-h fast, immediately after awakening between 6 and 7 AM. The RMR was measured for 30 min with a computerized, open-circuit, indirect calorimetry system with a ventilated canopy (Delta Trac II; Sensor Medics, Yorba Linda, CA).
Insulin sensitivity was assessed by a frequently sampled intravenous glucose tolerance test (IVGTT) with minimal modeling. Details of this test have been previously described (22). Glucose was measured in the serum using an Ektachem DT II System (Johnson and Johnson Clinical Diagnostics). In our laboratory, this analysis has a mean intra-assay coefficient of variation (c.v.) of 0.61%, and a mean inter-assay c.v. of 1.45%.
Insulin and leptin in the serum were measured in duplicate using double antibody radio-immunoassays (RIA) from Linco/Millipore (Billerica, MA). The intra- and interassay coefficient of variation (CV) for insulin and leptin were 6.26% and 3.49% and 5.0% and 5.6%, respectively.
Statistics
Statistical analysis was performed with SPSS version 22 (SPSS Inc., Chicago, IL), and data presented as means and standard deviations. Weight loss was defined as weight loss (in kg) from baseline to the end of the intervention (i.e. when BMI<25 kg/m2 was achieved). Rate of weight loss was defined as weight loss (kg) divided by the duration of weight loss (time to reach a BMI <25 kg/m2). There were three outliers for duration of weight loss (with 77, 58 and 49 weeks). Their exclusion did not change the outcomes of the analysis. Weight regain was expressed as a % of the initial weight loss. Simple Pearson and Spearmen correlations were used to identify potential predictors of weight regain. Potential predictors of weight regain were then analyzed using multiple linear regression, after adjusting for age, race, group (diet only, diet +aerobic exercise and diet + resistance exercise) and baseline body weight.
Results
117 participants were included in the present analysis (53 European-American and 64 African-American), aged 35.0 ± 6.5 years. Summary characteristics are reported in Table 1. Average weight loss was 12 ±2.5 kg and weight regain at 1 and 2 years follow-up 48±35 and 80±52%, respectively.
Table 1.
Descriptive statistics (n=117)
| Characteristics | Mean ± SD |
|---|---|
| Age (years) | 35.0 ± 6.4 |
| Body weight (Kg) | 77.1 ± 6.9 |
| Fat mass (%) | 45.0 ±3.7 |
| BMI (kg/m2) | 28.3 ± 1.3 |
| RMR (kcal/day) | 1344.8 ± 126.5 |
| RQ | 0.86 ± 0.06 |
| Insulin sensitivity | 2.94 ± 1.69 |
| Leptin plasma concentration | 23.0 ± 8.6 |
| Initial weight loss (kg) | −12.1 ± 2.5 kg |
| Time to reach weight loss goal (weeks) | 21.2±7.8 |
| Weight loss rate (kg/week) | 0.64±0.30 |
| Weight regain at 1 year (%) (n=117) | 47.7 ±34.6 |
| Weight regain at 2 years (%) (n=63) | 80.4 ± 52.0 |
No significant correlation was found between any of the metabolic variables of interest and weight regain at any time point (1 or 2 years) (Table 2). The same happened when RMR was expressed /kg body weight or kg FFM (Table 2). When these variables were included in a model to predict weight regain at 1or 2 years, none of the metabolic variables were significant predictors of weight regain and the overall model was not significant. Changes in RMR, RQ, insulin sensitivity and serum leptin concentration with weight loss were also not correlated with weight regain at 1 or 2 years follow up (Table 3).
Table 2.
Correlation analysis between metabolic variables at baseline (pre-weight loss) and weight regain at 1 and 2 years follow up
| Weight regain at 1 year (n=117) | Weight regain at 2 years (n=63) | |
|---|---|---|
| RMR (kcal/day) | r=−0.005, p=0.960 | r=0.106, p=0.414 |
| RMR (kcal/kg/day) | r=−0.086, p=0.368 | r=−0.010, p=0.938 |
| RMR (kcal/kg FFM/day) | r=−0.021, p=0.114 | r=0.026, p=0.842 |
| RQ | r=−0.010, p=0.917 | r=−0.050, p=0.699 |
| Insulin sensitivity | r=−0.103, p=0.295 | r=0.036, p=0.799 |
| Serum leptin concentration | r=−0.111, p=0.4 | r=−0.109, p=0.566 |
Table 3.
Correlation analysis between changes in metabolic variables with weight loss and weight regain at 1 and 2 years follow up
| Weight regain at 1 year (n=109) | Weight regain at 2 years (n=62) | |
|---|---|---|
| Δ RMR (kcal/day) | r=0.094, p=0.331 | r=0.090, p=0.492 |
| Δ RMR (kcal/kg/day) | r=−0.095, p=0.325 | r=0.004, p=0.976 |
| Δ RMR (kcal/kg FFM/day) | r=0.134, p=0.166 | r=0.026, p=0.842 |
| Δ RQ | r=−0.010, p=0.917 | r=−0.050, p=0.699 |
| Δ Insulin sensitivity | r=0.150, p=0.150 | r=0.197, p=0.171 |
| Δ Serum leptin concentration | r=0.173, p=0.634 | r=0.058, p=0.913 |
The only variables found to correlate with weight regain at 1 and 2 years were initial WL, WL rate, and time to reach WL goal, such that women who lost more weight, lost it faster, and reached WL goals earlier, re-gained less weight over the next 2 years (Table 4). Initial WL was moderately correlated with WL rate (r=0.365, p<0.001), but not correlated with time for WL goal (r=−0.033, p=0.698). The regression model with the largest R2 was obtained when WL and time to reach WL were included, with the model explaining 32% (R2 =0.321, p<0.001) of the variability in weight regain at 1 year and 25% (R2 =0.251, p=0.009) of the variability in weight regain at 2 years (Table 5).
Table 4.
Correlation analysis between magnitude and rate of weight loss and time to reach weight loss goal and weight regain at 1 and 2 years follow up.
| Weight regain at 1 year | Weight regain at 2 years | |
|---|---|---|
| Weight loss (kg) | r=−0.316, p=0.001 | r=−0.340, p=0.006 |
| Weight loss rate (kg/week) | r=−0.433, p<0.001 | r=−0.359, p=0.004 |
| Time to achieve weight loss goal (weeks) | r=0.339, p<0.001 | r=0.240, p=0.058 |
Table 5.
Regression models for predicting weight regain at 1 and 2 years in previously women with overweight
| Model | B | R2 | β | P |
|---|---|---|---|---|
| Model 1. Weight regain at 1 year (n=116) | R2=0.321 | <0.001 | ||
| 31.389 | R2 adjusted=0.288 | |||
| Intercept | 0.100 | 0.019 | 0.814 | |
| Age | 0.778 | 0.011 | 0.889 | |
| Race | 6.376 | 0.141 | 0.081 | |
| Group | 0.370 | 0.074 | 0.416 | |
| Baseline body weight | 5.133 | 0.371 | <0.001 | |
| Initial weight loss | 1.511 | 0446 | <0.001 | |
| Time to achieve a BMI<25 kg/m2 | ||||
| Model 2. Weight regain at 2 years (n=63) | R2=0.251 | 0.009 | ||
| R2 adjusted=0.176 | ||||
| Intercept | 87.633 | |||
| Age | −1.144 | −0.130 | 0.297 | |
| Race | 13.601 | 0.130 | 0.287 | |
| Group | 12.300 | 0.192 | 0.136 | |
| Baseline body weight | 0.655 | 0.078 | 0.561 | |
| Initial weight loss | 7.109 | 0.361 | 0.010 | |
| Time to achieve a BMI<25 kg/m2 | 1.715 | 0.289 | 0.022 | |
Discussion
The results of this study do not support the hypothesis that low RMR, fat oxidation, and leptin concentration, or a high insulin sensitivity measured at baseline (pre-WL), predict weight regain over a 2 year period. Even when all variables were included in a regression model, the model was not significant at predicting weight regain. This was the case even though the weight regain at 2 years was 80% of the initial WL and inter-individual variation was large (SD =52%). However, losing more weight, losing it faster and reaching WL goals earlier were all associated with less weight regain over the next 2 years. This implies that there might be other metabolic variables, not looked at in the present analysis, which make some individuals more resistant to WL and therefore more susceptible to relapse.
Baseline metabolic variables have been suggested to modulate long-term weight gain (8), however, the evidence is inconclusive and cannot be generalized to weight regain after WL. Even though positive energy balance is ultimately responsible for both weight gain in the general population and weight regain after WL, it remains to be determined whether the mechanisms driving these phenomena are the same. Baseline (pre-WL) RMR has been found to be a predictor of weight gain over time in some (9, 10), but not all studies (11–14). Similarly, some have found that a high RQ (low fat oxidation) was a risk factor for weight gain (11, 12, 14, 15), while others found no association (9, 13). A high baseline insulin sensitivity has also been reported as a risk factor for weight gain in populations such as the Pima Indians (16), Mexican Americans and non-Hispanic whites (17) and Caucasians (19). Low plasma concentrations of leptin have also been found to predict weight gain, even after adjusting for fat mass or BMI (18, 20).
In a recent analysis by Sumithran and colleagues (2018) (23), male sex, higher % weight loss required to achieve “dream” weight, lower depression scores, lower age and lower perceived stress (pre-weight loss) were consistently associated with less weight regain at 1 year follow-up. However, the prediction model did not have enough accuracy for it to be of clinical relevance and combining biological and psychological data did not improve the prediction model (to explain weight regain) compared with when simple anthropometric variables were used. Very few studies have, from our knowledge, reported metabolic variables measured at baseline (pre-WL) to be predictors of weight regain. Kong and colleagues (2013) showed that weight regain could be predicted by a combination of high insulin resistance and inflammatory markers at baseline (pre-weight loss) (6). Additionally, some studies have suggested that diet x phenotype interactions might affect weight loss maintenance (7). Moreover, Vogels and colleagues, reported weight regain at 2 years follow-up to correlate negatively with baseline (pre-WL) RMR in 91 individuals with overweight and obesity who had initially lost an average of 8 kg through a very-low energy diet (VLED) (5). However, in a recently published study from our group, in a mixed sample of men and women, who were not examined in energy balance, baseline (pre-WL) RMR or RQ were not correlated with weight regain at 1 year follow-up, after an initial 17% WL induced by a VLED (24). It is interesting to see that these findings were reproduced in the present analysis in a very different study population of pre-menopausal women with family history of overweight/obesity who were assessed under energy balance conditions.
Moreover, changes in metabolic variables with WL were also not correlated with weight regain at either 1 or 2 years follow-up. The evidence linking a reduction in RMR, seen with WL, with subsequent weight regain is scarce and conflicting. Wang et al reported that the reduction in RMR that accompanies WL was not predictive of weight regain at 12-months follow-up in women who underwent an initial 20-week hypocaloric diet with or without exercise (25). Pasman et al., on the other hand, reported that the amount of weight regained at 14-month follow-up in premenopausal women with obesity was larger in those who experienced the greatest decrease in RMR and physical activity EE (measured with an activity monitor) in response to a 2-month low-energy diet (26). We have recently shown that the reduction in RMR observed with a 17% WL (followed by 4 weeks of weight stabilization), was not predictive of weight regain at 1-year follow-up (27). The search for accurate and clinical relevant baseline predictors of weight regain remains, therefore, a pressing area of research.
In the present study, we found that women who lost more weight, lost it faster and reached WL goals earlier re-gained less weight over the next 2 years, with initial WL and time to reach WL explaining approximately 32 and 25% of the variability in weight regain at 1 and 2 years follow-up, respectively. Our findings are in line with a meta-analysis on WL maintenance from 2001 showing that those who lost >20 kg during WL intervention were better at maintaining WL at 5-year follow up, compared with those who lost <10 kg (28). Moreover, losing weight fast has also been shown to be associated with a better WL maintenance (29–32), even though the evidence remains controversial (32, 33). The findings of the present analysis are even more striking if we have into account that the dietary intervention was controlled (everyone was offered a 800 kcal-diet with all food provided), that exercise was supervised (on those randomized to aerobic and resistance exercise, in addition to diet) and that baseline body weight was a covariate in the model (as heavier people should be expected to lose more weight and reach WL targets earlier in response to a fixed energy deficit). Weight loss and rate of weight loss are likely to reflect some aspect of metabolism, not captured by the present analysis, by which some individuals would be more sensitive and others more resistance to WL given the same intervention. Variation in WL has been observed even under tightly controlled conditions, and is likely to reflect genetic factors (34). Even though behavioral aspects cannot be completely ruled out, as this was an outpatient intervention, physiological variables are likely to play a larger role in explaining inter-individual variation in WL in the present study.
This study presents with several strengths. First, baseline measurements of RMR, RQ, insulin sensitivity and leptin were done after a 4-week weight stabilization period, with all food provided to the participants. Second, we have adjusted for important confounders in our regression model (age, race, baseline body weight, and group (diet only, diet +aerobic exercise and diet + resistance exercise). The study also has several limitations. The participants in this study were all pre-menopausal women with overweight, which prevents the generalization of our results to populations outside this narrow window. Moreover, the women included in this study were relatively homogeneous for the amount of overweight, i.e. they all had a BMI of between 27 and 30 kg/m2 prior to WL. This may have limited our ability to observe differences at baseline that predicted weight regain. In other words, they may have been metabolically too similar prior to WL for differences to be detectable. Also, we examined only baseline variables. It is possible that post-WL measures have an independent predictive effect on weight regain, as we (22, 35) and others have previously observed (36, 37).
The findings of the present analysis have important practical implications, as they suggest that everybody has a chance to succeed in long-term WL maintenance regardless of their baseline (pre-WL) metabolic make-up. Moreover, losing more weight and faster, even after adjusting for baseline body weight and regardless of the method used to induce WL (diet alone or diet in combination with resistance or aerobic exercise), seems to be protective in terms of long-term weight regain. It remains to be determined if the reasons why some individuals are able to lose more weight and lose it faster are physiological or behavioral. Regardless of the mechanisms involved, patients who struggle to lose weight (both in terms of magnitude and speed) should be offered additional support or alternative intervention approaches, as the present analysis shows that these individuals are at increased risk of weight regain in the long-term.
In conclusion, findings from the present analysis show that RMR, RQ, insulin sensitivity and serum leptin plasma concentration at baseline (pre-weight loss) do not predict weight regain over a 2-year follow-up period. However a larger and faster WL was associated with a lower weight regain at both 1 and 2 years follow up. Elucidating the underlying mechanisms responsible for the inter-individual variation in magnitude and rate of initial WL is needed to develop measures that can help weight-reduced individuals to maintain WL in the long-term.
What is already known about the subject?
Relapse (weight regain) remains the biggest challenge in obesity management.
The reasons for relapse in obesity management remain largely unknown.
Although metabolic factors such as resting metabolic rate (RMR), respiratory quotient (RQ), insulin sensitivity and circulating leptin may affect success with weight loss maintenance, the literature is discrepant regarding their role in predicting weight regain.
What are the new findings in this manuscript?
RMR, RQ, insulin sensitivity and circulating serum leptin measured at baseline (pre-weight loss), under carefully controlled energy balance conditions, do not predict weight regain at 1 or 2 years follow-up.
A larger and faster weight loss was associated with a lower weight regain at both 1 and 2 years follow up.
Understanding the mechanisms behind inter-individual variation in magnitude and rate of initial weight loss may help to ensure better weight loss maintenance in the long-term.
How might your results change the direction of research or the focus of clinical practice?
More focus needs to be put in understanding the reasons behind inter-individual variation in magnitude and rate of weight loss, so that relapse can be minimized.
Acknowledgments
Funding: R01 DK049779, P30 DK56336, P60 DK079626, UL 1RR025777. Catia Martins was supported by a Sabbatical grant by the Liaison Committee for education, research and innovation in Central Norway and the Norwegian University of Science and Technology (NTNU).
Footnotes
Trial registration name: Clinicaltrials.gov
Study registration ID: NCT00067873,
Data sharing: Data described in the manuscript will not be made available because that would violate IRB and HIPAA rules.
Competing interests: The authors declare no competing financial interests.
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