Internal disinhibition predicts weight regain following weight loss and weight loss maintenance

Objective: The disinhibition scale of the Eating Inventory predicts weight loss outcome; however, it may include multiple factors. The purpose of this study was to examine the factor structure of the disinhibition scale and determine how its factors independently relate to long‐term weight loss outcomes. Research Methods and Procedures: Exploratory factor analysis of the disinhibition scale was conducted on 286 participants in a behavioral weight loss trial (TRIM), and confirmatory factor analysis was conducted on 3345 members of the National Weight Control Registry (NWCR), a registry of successful weight loss maintainers. Multivariate regressions were used to examine the relationships between the disinhibition scale factors and weight over time in both samples. Results: Using baseline data from TRIM, two factors were extracted from the disinhibition scale: 1) an “internal” factor that described eating in response to internal cues, such as feelings and thoughts; and 2) an “external” factor that described eating in response to external cues, such as social events. This factor structure was confirmed using confirmatory factor analysis in the NWCR. In TRIM, internal disinhibition significantly predicted weight loss at 6 months (p = 0.03) and marginally significantly predicted weight loss at 18 months (p = 0.06), with higher levels of internal disinhibition at baseline predicting less weight loss; external disinhibition did not predict weight loss at any time‐point. In NWCR, internal disinhibition significantly predicted one‐year weight change (p = 0.001), while external disinhibition did not. Discussion: These results suggest that it is the disinhibition of eating in response to internal cues that is associated with poorer long‐term weight loss outcomes.     

Irregular patterns in the daily weight chart at night predict body weight regain

This study examined whether charting daily weight patterns can predict weight regain in obese patients. The subjects were 98 moderately obese Japanese women aged 23 to 66 years who were obliged to precisely record their daily weights during the initial 4-month education period, but not thereafter. The patients were followed up at 8, 12, and 16 months. Abdominal fat areas and blood samples were assessed in the outpatient clinic at 0, 4, and 16 months. The standard deviations (SDs) of the differences in body weight between "after waking up" and "after breakfast" (SDa), "after dinner" (SDb), and "before going to bed" (SDc) were calculated, which were parameters reflecting the fluctuations in the daily weight patterns during the first 4 months. SDc, but not SDa or SDb, was correlated positively with weight regain at 8, 12, and 16 months (P = 0.049, P = 0.002, and P = 0.001, respectively). There were significant differences in temporal change in body weight and abdominal visceral fat between the small SDc group (SDc 75th percentile), but not for subcutaneous abdominal fat or the serum concentrations of glucose, insulin, or lipids. The results indicate that fluctuation of body weight immediately before going to bed is useful for predicting the rebound in body weight.     

Three-year weight change in successful weight losers who lost weight on a low-carbohydrate diet

Objective: The purpose of this study was to evaluate long‐term weight loss and eating and exercise behaviors of successful weight losers who lost weight using a low‐carbohydrate diet. Research Methods and Procedures: This study examined 3‐year changes in weight, diet, and physical activity in 891 subjects (96 low‐carbohydrate dieters and 795 others) who enrolled in the National Weight Control Registry between 1998 and 2001 and reported ≥30‐lb weight loss and ≥1 year weight loss maintenance. Results: Only 10.8% of participants reported losing weight after a low‐carbohydrate diet. At entry into the study, low‐carbohydrate diet users reported consuming more kcal/d (mean ± SD, 1895 ± 452 vs. 1398 ± 574); fewer calories in weekly physical activity (1595 ± 2499 vs. 2542 ± 2301); more calories from fat (64.0 ± 7.9% vs. 30.9 ± 13.1%), saturated fat (23.8 ± 4.1 vs. 10.5 ± 5.2), monounsaturated fat (24.4 ± 3.7 vs. 11.0 ± 5.1), and polyunsaturated fat (8.6 ± 2.7 vs. 5.5 ± 2.9); and less dietary restraint (10.8 ± 2.9 vs. 14.9 ± 3.9) compared with other Registry members. These differences persisted over time. No differences in 3‐year weight regain were observed between low‐carbohydrate dieters and other Registry members in intent‐to‐treat analyses (7.0 ± 7.1 vs. 5.7 ± 8.7 kg). Discussion: It is possible to achieve and maintain long‐term weight loss using a low‐carbohydrate diet. The long‐term health effects of weight loss associated with a high‐fat diet and low activity level merits further investigation.     

Dietary adherence during weight loss predicts weight regain

This study examined the relationship between previous dietary adherence during a low-calorie diet weight loss intervention and subsequent weight change during a 2-year follow-up for weight maintenance. One hundred and sixteen healthy, recently weight reduced (lost ~12 kg, BMI 22-25 kg/m2) premenopausal women were studied. Dietary adherence was assessed by doubly labeled water (DLW) and body composition change. Comparisons were made between the upper and lower tertiles for previous dietary adherence and subsequent weight change at 1- and 2-year follow-up. Percent weight regained was significantly lower (30.9 ± 6.7% vs. 66.7 ± 9.4%; P < 0.05) in the upper compared to the lower adherence tertile for previous weight loss dietary adherence (49.9 ± 8.8% vs. 96.8 ± 12.8% P < 0.05) at 1- and 2-year follow-up, respectively. This difference was partly explained by increases in daily activity-related energy expenditure (AEE) (+95 ± 45 kcal/day vs. -44 ± 42 kcal/day, P < 0.05) and lower daily energy intake (2,066 ± 71 kcal/day vs. 2,289 ± 62 kcal/day, P < 0.05) in the higher tertile for previous dietary adherence, compared to the lower. These findings suggest that higher adherence (i.e., higher tertile) to the previous low-calorie diet predicts lower weight regain over 2-year follow-up for weight maintenance, which is explained by lower energy intake and higher physical activity. Finally, how well an individual adheres to a low-calorie diet intervention during weight loss may be a useful tool for identifying individuals who are particularly vulnerable to subsequent weight regain.     

Genetic modulation of the serotonergic pathway: influence on weight reduction and weight maintenance

The serotonergic pathway plays a major role in the development of obesity. Its activity can be modulated by the 5-HT transporter–linked polymorphic region in the SLC6A4 gene and the upstream variable number of tandem repeats polymorphism in the MAOA gene. We studied whether these genetic modulations have an influence on weight reduction and weight maintenance in a one-year weight reduction program (OPTIFAST^®52). The polymorphisms were genotyped by PCR in a sample of 135 female and 67 male subjects with severe obesity (44 ± 13 years, 122.3 ± 22.2 kg, BMI: 41.7 ± 6.7 kg/m²). The program leads to a total weight loss of 19.9 ± 9.8 kg (16.9 ± 8.3 %) in women and 27.4 ± 13.6 kg (20.4 ± 9.9 %) in men. Anthropometric measurements and blood levels were determined at the start of the program (T0), after the weight reduction phase (T1) and after the subsequent weight maintenance phase at the end of the program (T2). Each polymorphism alone did not significantly influence weight loss or weight maintenance neither in men nor in women. However, women carrying both risk genotypes (SS and 3/3) displayed a lower total weight loss during the program (p = 0.05). This effect derived mainly from difficulties in the weight maintenance phase (p = 0.11), while the weight reduction phase was not affected (p = 0.61). No influence was found in men (p = 0.93). Modulation of the serotonergic pathway by carrying both risk alleles seems to influence success of weight loss programs in women with severe obesity due to problems in stabilizing body weight after weight reduction.     

On the assessment of body weight

Body height-weight indices are useful for determining body bulk and of these Rohrer's body bulk index (weight divided by the cube of the body height) is the only one that has a theoretically acceptable base. The statistical analysis of data on 747 healthy men and women and comparison with empirical ideal weights also showed this index to be the most suitable. A nomogram for its graphical determination is therefore added.Indices are not all necessary for the determination of overweight or underweight; it is preferable to orientate the assessment on heightspecific ideal weights. Broca's rule for the determination of ideal weights is groundless and supplies unsatisfactory results. The ideal weights based on the criterion of length of life elicited by the Metropolitan Life Insurance Company are preferable. Nomograms for these weight norms are given which permit direct determination of the absolute and percentual deviation from the ideal norm.     

Seasonal relationships of ovary weight, liver weight and fat stores with body weight in the goldfish, Carassius auratus (L.)

The relationships of ovary weight, liver weight and visceral lipid stores to body weight in the goldfish were examined at different times of the year. Both arithmetic and logarithmic relationships were examined. Ovary weight significantly increased relative to increasing body weight in goldfish and the exact nature of this relationship changed from month to month. However, larger fish were found to have proportionately larger ovaries than small fish at specific times of the year. Regression lines for liver weight versus body weight were also determined. Monthly differences were found in the liver weight-body weight relationship. Visceral lipid stores expressed per gram of protein were found to be independent of body weight. Lipid stores expressed in this manner peaked in February while ovary weight peaked in April. The consequences of compensating for body size when expressing organ (liver, ovary) sizes, such as hepatosomatic index and gonosomatic index, are discussed.