I believe that there is much skepticism to be applied in terms of weight. The low hanging fruit are the obvious woo diets, but more insidious I believe are the memes that have spread that give pseudoscientific justifications for obesity. That is the purpose of this thread. I will start a separate one on “food deserts” and public policy. This is about the mechanics.
Believing that weight is uncontrollable is, as expected, correlated with poor health habits. This fatalism encourages a self-fullfilling prophecy, and/or acts as a rationalisation.
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Believing that weight was uncontrollable was negatively related to exercise and healthful dietary practices and positively related to unhealthful eating. Lack of exercise and unhealthful eating were, in turn, associated with poor physical health. Age, but not gender, moderated the relationships between belief in weight changeability and exercise behaviors, healthful eating, and unhealthful eating. This study suggests that believing weight is unchangeable is associated with poor health behaviors and poorer physical health.
Linky.
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It is a depressing article of faith among the overweight and those who treat them that 95 percent of people who lose weight regain it -- and sometimes more -- within a few months or years.
That statistic has been quoted widely over the last four decades, in Congressional hearings, diet books, research papers and seminars. And it is the reason so many people approach dieting with a sense of hopelessness.
But in fact, obesity researchers say, no one has any idea how many people can lose weight and keep it off. Now, as researchers try to determine how many people have succeeded, they are also studying the success stories for lessons that might inspire others to try.
''That 95 percent figure has become clinical lore,'' said Dr. Thomas Wadden, a professor of psychiatry at the University of Pennsylvania. There is no basis for it, he said, ''but it's part of the mythology of obesity.''
Dr. Kelly D. Brownell, the director of the Yale Center for Eating and Weight Disorders, said the number was first suggested in a 1959 clinical study of only 100 people. The finding was repeated so often that it came to be regarded as fact, he said.
Since then, nearly all studies of weight-loss recidivism have followed patients in formal hospital or university programs, because they are the easiest to identify and keep track of. But people who turn to such programs may also be the most difficult cases, and may therefore have especially poor success rates.
To get a more accurate picture, two researchers are studying long-term dieters for a project called the National Weight Control Registry, and have found it surprisingly easy to collect success stories. About half the people who maintained a substantial weight loss for more than a year had done it on their own, they found. This suggests that many people have found ways to lose weight and keep it off, but have never been counted in formal studies.
Linky.
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J. Graham Thomas, Ph.D., is the lead author on a 10-year observational study of self-reported weight loss and behavior change in nearly 3,000 participants. The participants had lost at least 30 pounds and had kept if off for at least one year when they were enrolled in the National Weight Control Registry (NWCR).
The participants were then followed for 10 years. Thomas explains that the goal of the study was to determine how well they kept the weight off and to identify predictors of successful weight loss maintenance.
Thomas says, "On average, participants maintained the majority of their weight loss over this extended follow-up period, and better success was related to continued performance of physical activity, self-weighing, low-fat diets, and avoiding overeating."
Other findings from the study show that more than 87 percent of the participants were estimated to be still maintaining at least a 10 percent weight loss at years five and 10.
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Energy restriction produces a transient hypothyroid-hypometabolic state that normalizes on return to energy-balanced conditions. Failure to establish energy balance after weight loss gives the misleading impression that weight-reduced persons are energy conservative and predisposed to weight regain. Our findings do not provide evidence in support of adaptive metabolic changes as an explanation for the tendency of weight-reduced persons to regain weight.
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Look AHEAD's ILI produced clinically meaningful weight loss (≥5%) at year 8 in 50% of patients with type 2 diabetes and can be used to manage other obesity-related co-morbid conditions.
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Of 3,414 individuals screened, 1,280 were eligible and completed surveys. Ninety-percent were women. This descriptive analysis includes 1,110 women who lost weight through non-surgical means. Over 90 % of respondents had at least some college education. Twenty-eight percent of respondents were weight-loss maintainers. Maintainers lost an average of 24 % of their body weight and had maintained ≥ 10 % weight loss for an average of 5.1 years. Maintainers were more likely to limit their fat intake, eat breakfast most days of the week, avoid fast food restaurants, engage in moderate to high levels of physical activity, and use a scale to monitor their weight.
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These studies indicate that, in a genetically heterogeneous female population, neither the propensity to become obese nor to maintain the obese state are due to an inherent metabolic abnormality characterized by a low EE.
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The present study demonstrates, for the first time, that metabolic adaptation can occur in response to spontaneous long term weight changes, but also reveals that, on the average, these overcompensatory changes are small. We estimate that a 15-kg weight change is accompanied by a change in 24-h energy expenditure of 244 Cal/day, which is only 33 Cal/day greater than predicted from the cross-sectional relationship between 24-h energy expenditure and body weight (211 Cal/day). The change in 24-h fat oxidation after a 15-kg weight change was 53 Cal/day greater than predicted from the cross-sectional data. In practical terms, these adaptations translate into the caloric content of approximately one half of an apple, one fifth of a bagel, or one tenth of a cheeseburger (for the adaptation in 24-h energy expenditure) or the fat content of two teaspoons of peanut butter or seven potato chips (for the metabolic adaptation in 24-h fat oxidation), respectively.
These data indicate that in the long term, the defense mechanisms of the body to resist weight gain by an overcompensatory increase in energy expenditure and/or fat oxidation are relatively weak and easy to offset by small changes in food intake. The results also indicate that even a large decrease in body weight over several years is, on the average, not accompanied by a profound slowing of energy metabolism, as occasionally implied to explain the high rate of weight recidivism in the medical treatment of obesity.
Linky.
Is there such a thing as “healthy obesity”? Not really. Obviously we are dealing with population statistics and risks, but it is important to keep in mind that even those who say “I am fat and don’t have X” are ignoring that their risks have greatly increased and they are very likely to become “unhealthy obese”.
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Compared with metabolically healthy normal-weight individuals, obese persons are at increased risk for adverse long-term outcomes even in the absence of metabolic abnormalities, suggesting that there is no healthy pattern of increased weight.
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It is a tantalising hypothesis, but what does the evidence show? To find out, Australian researchers followed a group of metabolically healthy obese participants over the course of five to 10 years. Despite initially normal biomarkers, the team reported in a 2013 issue ofDiabetes Care that their subjects were more likely than non-obese control patients to develop metabolic abnormalities and diabetes. One-third of the participants who began the study as metabolically-healthy obese had become ‘unhealthy’ obese by the time the study ended. Younger people and those with low central obesity (indicated by a smaller abdominal circumference) were more likely to sustain metabolically-healthy obesity over time. But for a significant percentage of participants in the study, ‘healthy obesity’ was a transient state, a precursor to the development of medical abnormalities.
Some studies indicate that the metabolically-healthy obese have no increased risk of mortality, but these have tended to follow subjects for less than a decade. Contrast that with an article published in 2015 in the Journal of the American College of Cardiology addressing the course of metabolically-healthy obesity over two decades. Researchers found that after 20 years, roughly half of those who were initially metabolically-healthy obese adults had become unhealthy obese. The lead author, Joshua Bell, explains: ‘Even obese adults who appear to be metabolically healthy have a substantially greater risk for developing type 2 diabetes and cardiovascular disease compared with healthy, normal-weight adults. There is also a strong tendency for healthy obese adults to progress to unhealthy obesity (the highest risk group) over time. Excess fat is itself a metabolic dysfunction, with strong links to insulin resistance. Some obese adults may have a more favourable fat distribution and are considered relatively healthy, but the number of obese adults who can maintain an optimal balance of fat stores in the long-term is not high.”
Linky.
“Healthy obese” children even have altered brain structures.
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Our results indicated that obese but otherwise healthy children have different regional gray and white matter development in the brain and differences in white matter microstructures compared with healthy normal weight children.
Linky.
The low income pattern of obesity is another thing people point to to blame outside influences, but the “healthy food is more expensive” claim is very hard to demonstrate. This population is more likely to engage in nonsense attempts at weight management.
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One reason for the disparity might have to do with the tactics they used to try to shed pounds: Compared to adults making $75,000 or more, those making less than $20,000 were 50 percent less likely to exercise, 42 percent less likely to drink a lot of water, and 25 percent less likely to eat less fat and sweets. And adults making between $20,000 and $75,000 were about 50 percent more likely to use over-the-counter diet pills, which aren't proven to work.
The data for the young people were similar: The poorest among them were 33 percent less likely to exercise, but they were twice as likely to skip meals as the richest ones. Skipping meals, too, isn't a sure-fire way to slim down.
Healthy food is more expensive than junk food, and as our colleagues at Quartz reported, people on food stamps tend to purchase cheap, unhealthy products in an attempt to stretch their food budgets. But as the authors of this study point out, it's not always a financial issue. Water is (mostly) free, after all, but the low-income people drank less of it. Meanwhile, diet pills cost money.
Linky.
Does “yo yo dieting” “break” your metabolism? Nope.
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DATA SYNTHESIS:
The majority of studies do not support an adverse effect of weight cycling on metabolism. Many observational studies have shown an association between variation in body weight and increased morbidity and mortality. However, most of these studies did not examine intentional vs unintentional weight loss, nor were they designed to determine the effects of weight cycling in obese, as opposed to normal-weight, individuals.
CONCLUSIONS:
The currently available evidence is not sufficiently compelling to override the potential benefits of moderate weight loss in significantly obese patients. Therefore, obese individuals should not allow concerns about hazards of weight cycling to deter them from efforts to control their body weight. Although conclusive data regarding long-term health effects of weight cycling are lacking, nonobese individuals should attempt to maintain a stable weight. Obese individuals who undertake weight loss efforts should be ready to commit to lifelong changes in their behavioral patterns, diet, and physical activity.
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RESULTS:
At the end of treatment, patients lost 18.9 +/- 2.6 kg which was comprised of significant decreases in body fat and fat-free mass of 15.2 +/- 2.2 and 3.7 +/- 0.8 kg, respectively (both ps < .001). REE also fell during this time from 1,631 +/- 82 to 1,501 +/- 51 kcal/d (p < .03). All of these measures, however, returned to their baseline values when patients regained their lost weight. Body fat distribution was unchanged throughout the study.
DISCUSSION:
These results do not support claims that weight cycling adversely affects REE, body composition, or body fat distribution.
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Abstract
Weight cycling has been associated with an increased risk of death in some studies, but few studies differentiated weight cycling initiated by intentional weight loss from that initiated by illness. The association of weight cycling with death was examined among 55,983 men and 66,655 women in the Cancer Prevention Study II Nutrition Cohort from 1992 to 2008. A weight cycle was defined as an intentional loss of 10 or more pounds (≥4.5 kg) followed by regain of that weight, and the lifetime number of weight cycles was reported on a questionnaire administered at enrollment in 1992. A total of 15,138 men and 10,087 women died during follow-up, which ended in 2008. Hazard ratios and 95% confidence intervals were estimated using Cox proportional hazards regression models. When the models were adjusted for age only, weight cycling was positively associated with mortality (P for trend < 0.0001). However, after adjustment for body mass index and other risk factors, low numbers of weight cycles (1-4 cycles) were associated with slightly lower mortality rates (hazard ratio (HR) = 0.93, 95% confidence interval (CI): 0.89, 0.97 in men and HR = 0.93, 95% CI: 0.89, 0.98 in women), whereas high numbers of weight cycles (≥20 cycles) were not associated with mortality (HR = 1.03, 95% CI: 0.89, 1.19 in men and HR = 0.99, 95% CI: 0.88, 1.12 in women). These results do not support an increased risk of mortality associated with weight cycling.
Linky.
Now metabolism and calorie consumption. Are these increadibly exoteric and unknowable? Nope. It is just that people are very bad at self-reporting. People, by and large, have simply calculable metabolisms, and underestimate how much they consume.
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Total energy expenditure and resting metabolic rate in the subjects with diet resistance (group 1) were within 5 percent of the predicted values for body composition, and there was no significant difference between groups 1 and 2 in the thermic effects of food and exercise. Low energy expenditure was thus excluded as a mechanism of self-reported diet resistance. In contrast, the subjects in group 1 underreported their actual food intake by an average (±SD) of 47±16 percent and overreported their physical activity by 51±75 percent. Although the subjects in group 1 had no distinct psychopathologic characteristics, they perceived a genetic cause for their obesity, used thyroid medication at a high frequency, and described their eating behavior as relatively normal (all P<0.05 as compared with group 2).
CONCLUSIONS:
The failure of some obese subjects to lose weight while eating a diet they report as low in calories is due to an energy intake substantially higher than reported and an overestimation of physical activity, not to an abnormality in thermogenesis.
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CONCLUSIONS:
The FFQ produced greater under- and overestimation of energy intake. Underreporting of energy intake is a serious and prevalent error in dietary self-reports provided by Brazilian women, as has been described in studies conducted in developed countries.
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Results Factors particularly important in predicting underreporting of energy intake include factors indicating dissatisfaction with body image; for example, a 398 kcal/day underreport in subjects attempting weight loss during the past year with a nearly 500 kcal/day underreport in women. Overall, women underreported by 393 kcal/day relative to men and women evinced a social desirability bias amounting to a 26 kcal underreport for each point on the social desirability scale. Gender differences also were evident in the effect of percent body fat (with men underreporting about 16 kcal/ day/percent body fat) and in departure from self-reported ideal body weight (with women underreporting about 21 kcal/ day/kg).
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Comparing their estimated energy intake with the intake determined to maintain weight yielded mean differences of2365 and 1792 Id (565 and 428 kcal) in men and women, respectively, representing an underreporting of 18%. Twenty-two individuals (8%) overestimated and 29(11%) were accurate to within 419 Id (100 kcal) of their maintenance requirement. The remaining 2 15 individuals (8 1%) reported their habitual intake at 2930 ± 1586 kJ (700 ± 379 kcal) below that subsequently determined as their maintenance requirement. These findings suggest caution in the interpretation of food-consumption data.
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Results: Food records underreported TEEDLW by 35 ± 20%. Underreporting of energy intake was correlated with all macronutrient intake concentrations (g or kcal) (P < 0.0001). A multiple regression model showed that 86.4% of the variance in underreporting error was explained by dietary fat (g), BMI, and sex. The intrasubject CV was 3.9% for TEEDLW and 9.9% for MEI. MEI for weight stability (MEIwtstb) averaged 99 ± 11% of TEE.
Conclusions: The increased underreporting of dietary intake with increasing body weight in teens may explain in part previous reports noting that there has been an increased incidence of obesity, although energy intakes have not appeared to increase. MEIwtstb and TEEDLW gave similar estimates of energy needs. This trial was registered at clinicaltrials.gov as NCT 00592137.
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Daily metabolizable energy intake (ME) and total daily energy expenditure (TEE) were measured in 28 nonobese and 27 obese adolescents over a 2-wk period. Reported ME was significantly (p < 0.001) lower than measured TEE in both the nonobese and the obese groups (2193 ± 618 vs 2755 ± 600 kcal/d and 1 935 ± 722 vs 3390 ± 6 12 kcal/d, respectively). Reported ME as a percentage ofTEE was significantly lower in the obese than the nonobese group (58.7 ± 23.6% vs 80.6 ± 1 8.7%, respectively). When reported ME was adjusted to account for changes in body energy stores, reported ME still remained significantly lower than TEE in both groups. ME was highly reproducible over the 2-wk period. Intraclass correlation coefficients among days for subjects with complete 14-ti diaries were 0.87 and 0.89 for nonobese and obese groups, respectively. In both groups, interindividual variability in ME was significantly greater than intraindividual variability. Our data suggest that reported ME in nonobese and obese adolescents is not representative of TEE or energy requirements.
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The measurement of dietary intake by self-report has played a central role in nutritional science for decades. Despite its important role, however, little is known about the accuracy of self-reported intake. Recently, the doubly-labeled water method has been validated for the measurement of total energy expenditure in free-living subjects, and this method can serve as a reference for validating the accuracy of self-reported energy intake. Such comparisons have been made in nine recent studies, and considerable inaccuracy in self-reports of energy intake has been documented. Reported intakes tend to be lower than expenditure and thus are often underestimates of true habitual energy intake. Because the degree of underreporting increases with intake, it is speculated that individuals tend to report intakes that are closer to perceived norms than to actual intake.
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Total free living energy expenditure was compared in lean and obese women by the new doubly labelled water method and partitioned into basal metabolism and thermogenesis plus activity by whole body calorimetry. Average energy expenditure was significantly higher in the obese group (10-22 versus 7-99 MJ/day (2445 versus 1911 kcal/day); p<0-001) resulting from an increase in the energy cost of both basal metabolism and physical activity. Self recorded energy intakes were accurate in the lean subjects but underestimated expenditure by 3-5 MJ/day (837 kcal/day) in the obese group. Basal metabolic rate and energy expenditure on thermogenesis plus activity were identical in the two groups when corrected for differences in fat free mass and total body mass. In the obese women in this series there was no evidence that their obesity was caused by a metabolic or behavioural defect resulting in reduced energy expenditure.
Linky.
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Errors in dietary reporting of protein seem to occur disproportionately with respect to total energy, suggesting a differential reporting pattern of different foods. Although, on average, all subjects showed a greater underreporting of energy than of protein, this was most common in the obese subjects. Snack-type foods may be preferentially forgotten when obese people omit food items in dietary reporting. These results seem to agree with the general assumption that obese people tend to underreport fatty foods and foods rich in carbohydrates rather than underreport their total dietary intake. These results may have implications for the interpretation of studies of diet and comorbidities related to obesity.
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RESULTS:
Results indicate that subjects in the experimental group reported significantly greater intake than control subjects, when controlling for reported intake during the screening phase and weight change.
DISCUSSION:
Thus, the belief that the researcher could verify their report improved the accuracy of patients' self-report. However, all subjects continued to underreport their dietary intake. In summary, underreporting may be an intentional attempt to manage presentation to others in a society that is increasingly critical of overweight persons.
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RESULTS:
The mean EI:RMRest (s.e.m.) was 1.34 (0.02) for men, and 1.23 (0.02) for women. Overall, 21% of men and 25% of women were classified as LERs. There was a greater prevalence of LERs among people with overweight (25%), or obesity (30%) than people with normal body weight (16%, P<0.001). The oldest age group (> or =65 years) had a greater prevalence of LERs (33%) compared with all other age groups (19-24%, P<0.001). Pacific people had a greater prevalence of LERs (33%) compared with Maori (26%, P=0.007) and European (23%, P<0.001). Compared with the NNS97, a substantial increase in the prevalence of LERs was evident in most subgroups.
CONCLUSIONS:
Under-reporting of EI will continue to be a major limitation of nutrition surveys without technological innovation. Care should be taken when interpreting EI data.
Linky.
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The applicability of body composition as estimated by the bioimpedance method to predict energy expenditure (EE) was studied. Ten healthy subjects underwent measurement of body composition and 24-h energy expenditure (24-h EE) twice in a respiration chamber on a fixed program. The 24-h EE and its components, sleeping EE (SEE), basal EE (BEE), and daytime EE, for an individual were very reproducible (coefficient of variation 2.3%, 1.4%, 5.0%, & 3.1%, respectively). The variability of 24-h EE among subjects was 11.4% but only 4.1% when adjusted for differences in lean body mass (LBM). LBM was the best determinant of 24-h EE, BEE, and SEE and accounted for 91-93% of the interindividual variance of EE. The prediction equations were 24EE (kcal/d) = 390 + 33.3 LBM (r2 = 0.93, P = 0.000001), SEE (kcal/h) = 9.8 + 1.1 LBM (r2 = 0.92, P = 0.000001), and BEE (kcal/h) = -3.1 + 1.35 LBM (r2 = 0.91, P = 0.000002). In conclusion, 24EE, BEE, and SEE can be predicted with a high degree of precision from LBM as estimated by bioimpedance in normal-weight subjects.
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The Scientific Report of the 2015 Dietary Guidelines Advisory Committee was primarily informed by memory-based dietary assessment methods (M-BMs) (eg, interviews and surveys). The reliance on M-BMs to inform dietary policy continues despite decades of unequivocal evidence that M-BM data bear little relation to actual energy and nutrient consumption. Data from M-BMs are defended as valid and valuable despite no empirical support and no examination of the foundational assumptions regarding the validity of human memory and retrospective recall in dietary assessment. We assert that uncritical faith in the validity and value of M-BMs has wasted substantial resources and constitutes the greatest impediment to scientific progress in obesity and nutrition research. Herein, we present evidence that M-BMs are fundamentally and fatally flawed owing to well-established scientific facts and analytic truths. First, the assumption that human memory can provide accurate or precise reproductions of past ingestive behavior is indisputably false. Second, M-BMs require participants to submit to protocols that mimic procedures known to induce false recall. Third, the subjective (ie, not publicly accessible) mental phenomena (ie, memories) from which M-BM data are derived cannot be independently observed, quantified, or falsified; as such, these data are pseudoscientific and inadmissible in scientific research. Fourth, the failure to objectively measure physical activity in analyses renders inferences regarding diet-health relationships equivocal. Given the overwhelming evidence in support of our position, we conclude that M-BM data cannot be used to inform national dietary guidelines and that the continued funding of M-BMs constitutes an unscientific and major misuse of research resources.
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Across the 39-year history of the NHANES, EI data on the majority of respondents (67.3% of women and 58.7% of men) were not physiologically plausible. Improvements in measurement protocols after NHANES II led to small decreases in underreporting, artifactual increases in rEI, but only trivial increases in validity in subsequent surveys. The confluence of these results and other methodological limitations suggest that the ability to estimate population trends in caloric intake and generate empirically supported public policy relevant to diet-health relationships from U.S. nutritional surveillance is extremely limited.
Linky.
People also underestimate what is going on with their own bodies.
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Truesdale and Stevens found that their subjects' self-reported and actual vital statistics yielded almost identical BMI's. However, when asked to characterize their weight status—from underweight to obese—only about 70 percent of normal weight and overweight people did so accurately. When they erred, normal-weight individuals tended to think they were overweight. By contrast, overweight people who misjudged their status were most likely to round down, describing themselves as of normal weight.
The real disconnect occurred among obese volunteers. As noted earlier, 85 percent misjudged their BMIs, estimating the figures to be lower than they actually were.
To further probe perceptions on weight, Truesdale and Stevens asked each participant to estimate how many pounds he or she would have to gain or lose to fall into each respective weight category, from underweight to obese. How far off the mark an individual's estimates were tended to rise with his or her actual weight.
Consider the hypothetical 5'4" woman and 5'10" man. To be normal weight, based on federal guidelines, she should weigh between 108 and 145 pounds and he between 129 and 174 pounds. Normal-weight participants in the study came closest, on average, to estimating how many pounds they'd need to lose or gain to hit the center of their height-appropriate weight ranges. Obese individuals, however, typically underestimated how much they had to lose to get to such a point in a normal-BMI range.
Linky.
And their kids, increasingly.
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When researchers reviewed the data on how American parents perceived their kids, they were in for quite a shock. An overwhelming 94.9 percent believed the kids’ size was “just right.” As unsettling as that statistic might sound, previous studies conducted on smaller populations have yielded similar results and the researchers claim, it’s not the worst that was discovered.
When researchers compared the results with a similar survey conducted two decades earlier, they discovered the chances of a child “being appropriately perceived by the parents declined by 30%.” They even noted that African-American parents and those who were on a low-income had the most inaccurate and wildly incorrect perception about their children and their appearance, strictly from a physical health perceptive, stated Dustin T. Duncan, an assistant professor in the Department of Population Health at NYU Langone Medical Center, who led the research.
Linky.
Genetics? Thyroid? Low metabolism again? Nope.
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Genetics are one of the biggest factors examined as a cause of obesity. Some studies have found that BMI is 25–40% heritable.[18] However, genetic susceptibility often needs to be coupled with contributing environmental and behavioral factors in order to affect weight.[19] The genetic factor accounts for less than 5% of cases of childhood obesity.[18] Therefore, while genetics can play a role in the development of obesity, it is not the cause of the dramatic increase in childhood obesity.
Basal metabolic rate has also been studied as a possible cause of obesity. Basal metabolic rate, or metabolism, is the body's expenditure of energy for normal resting functions. Basal metabolic rate is accountable for 60% of total energy expenditure in sedentary adults. It has been hypothesized that obese individuals have lower basal metabolic rates. However, differences in basal metabolic rates are not likely to be responsible for the rising rates of obesity.[18]
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Combined, the 12 SNPs explained only 0.9% of variation in BMI and 0.7% of variation in waist circumference, suggesting that many more common variants with small effects, and perhaps rare variants with larger effects, remain to be identified to account for even the lower end of the reported heritability range (40–85%) (6, 21). Increasing the sample sizes of genome-wide association meta-analyses might lead to the identification of more obesity-susceptibility loci; however, these are likely to have even smaller effect sizes than those already identified.
Linky.
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WHAT IS THE RELATIONSHIP BETWEEN HYPOTHYROIDISM AND WEIGHT GAIN?
Since the BMR in the patient with hypothyroidism (see Hypothyroidism brochure) is decreased, an underactive thyroid is generally associated with some weight gain. The weight gain is often greater in those individuals with more severe hypothyroidism. However, the decrease in BMR due to hypothyroidism is usually much less dramatic than the marked increase seen in hyperthyroidism, leading to more modest alterations in weight due to the underactive thyroid. The cause of the weight gain in hypothyroid individuals is also complex, and not always related to excess fat accumulation. Most of the extra weight gained in hypothyroid individuals is due to excess accumulation of salt and water. Massive weight gain is rarely associated with hypothyroidism. In general, 5-10 pounds of body weight may be attributable to the thyroid, depending on the severity of the hypothyroidism. Finally, if weight gain is the only symptom of hypothyroidism that is present, it is less likely that the weight gain is solely due to the thyroid.
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Only 2% of the observed variability in BMR was attributable to within-subject effects, of which 0.5% was analytic error. Of the remaining variance, which reflected between-subject effects, 63% was explained by FFM, 6% by FM, and 2% by age. The effects of sex and bone mineral content were not significant (P > 0.05). Twenty-six percent of the variance remained unexplained. This variation was not associated with concentrations of circulating leptin or T3. T4 was not significant in women but explained 25% of the residual variance in men.
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Metabolic rate does vary, and technically there could be large variance. However, statistically speaking it is unlikely the variance would apply to you. The majority of the population exists in a range of 200-300kcal from each other and do not possess hugely different metabolic rates.
Linky.
Is BMI invalid? Nope. If anything, it’s largest inaccuracy is labelling obese as normal. There are more accurate, but more time-consuming and costly tests.
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Accuracy of BMI as it pertains to individuals
Youth
In a study of 1,676 young girls (aged 5-16) it was found that although ethnicity differences existed in body fat that 89.9-92.4% of girls were accurately diagnosed with BMI.[4] Results from NHANES 1999-2004 (three different NHANES surveys) found that 86.9%-89.1% of youth between the ages of 5-18 (both genders) were accurately diagnosed with BMI when compared against skin-fold calipers.[5]
Adults
A cross-sectional study of 13,601 subjects in the US[6] compared BMI against BIA (Bioelectrical Impedence Analysis). BMI defined 21% of men and 31% of women as obese, and BIA indicated 50% of men and 61% of women. Results from this study should be taken with a bit of scrutiny, as BIA is a measure of body fat with high variability based on hydration status.
A smaller scale study (1,691 persons) using DEXA scans (seen as a valid body fat measuring device) found that there was a 34.7% discrepancy between BMI and DEXA for women and 35.2% for men.[7] However, BMI appeared to misclassify women as less fat as they were by DEXA; notable misclassifications include 20.3% of women being obese via BMI while DEXA showed 37.1%, 24.8% of men being obese via BMI compared with 38.4% of men being obese via DEXA. These results have been replicated in which persons in the normal BMI range were actually obese according to body fat percentage (20% of men, 9.2% of females) and more persons in the overweight BMI range were actually obese by body fat percentage (67.2% of men, 84.2% of females).[8]
Linky.
Arguments from authority:
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What causes obesity and overweight?
The fundamental cause of obesity and overweight is an energy imbalance between calories consumed and calories expended. Globally, there has been:
an increased intake of energy-dense foods that are high in fat; and
an increase in physical inactivity due to the increasingly sedentary nature of many forms of work, changing modes of transportation, and increasing urbanization.
Changes in dietary and physical activity patterns are often the result of environmental and societal changes associated with development and lack of supportive policies in sectors such as health, agriculture, transport, urban planning, environment, food processing, distribution, marketing and education.
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Obesity is caused by a chronic energy imbalance involving both dietary intake and physical activity patterns. While these behavioral patterns and their environmental determinants are complex, important drivers of the obesity epidemic have been identified.7 Evidence indicates that increases in energy intake are driving recent obesity increases.7–12 Key drivers include changes in the global food system that moved from individual to mass preparation, “lowered the time price of food consumption,”8 produced more highly processed food (adding sugar, fats, salt and flavour enhancers), and marketed them with increasingly effective techniques. Marketing foods and beverages is especially effective among children,13,14 is associated with obesity prevalence,15 and has been a focus of policy strategies.16
Other moderators amplify or attenuate the impact of these drivers and produce observed disparities in obesity prevalence across and within populations: these include national wealth, government policy, cultural norms, the built environment,7 genetic17 and epigenetic mechanisms,18 biological bases for food preferences19 and biological mechanisms that regulate motivation for locomotion and contribute to the decline in physical activity from childhood into adulthood.20
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Question 1: Explain energy balance and imbalance in terms of a biological system in which energy intake and energy expenditure change over time in response to the environment
Human physiology complies with the first law of thermodynamics, which states that energy can be transformed from one form to another but cannot be created or destroyed. This law is usually formulated as follows: the rate of change in body ES 10 is equal to the difference between the rates of EI and EO. All of these terms are expressed as energy per unit of time.
EI primarily consists of the chemical energy from the food and fluids we consume. EO includes the radiant, conductive, and convective heat lost; any work performed; and the latent heat of evaporation. ES is the rate of change in the body's macronutrient stores. The energy balance equation (ES = EI – EO) is a statement of the principle of energy conservation.
Linky.
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What Causes Overweight and Obesity?
Lack of Energy Balance
A lack of energy balance most often causes overweight and obesity. Energy balance means that your energy IN equals your energy OUT.
Energy IN is the amount of energy or calories you get from food and drinks. Energy OUT is the amount of energy your body uses for things like breathing, digesting, and being physically active.
To maintain a healthy weight, your energy IN and OUT don't have to balance exactly every day. It's the balance over time that helps you maintain a healthy weight.
The same amount of energy IN and energy OUT over time = weight stays the same
More energy IN than energy OUT over time = weight gain
More energy OUT than energy IN over time = weight loss
Overweight and obesity happen over time when you take in more calories than you use.
Linky.
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Is it possible to be overweight because of a slow metabolism?
Answers from Donald Hensrud, M.D.
Probably not. There is such a thing as a slow metabolism. But slow metabolism is rare, and it's usually not what's behind being overweight or obese — that's ultimately a result of interactions among genetics, diet, physical activity and other factors.
Metabolism is the process by which your body converts what you eat and drink into energy. Even when you're at rest, your body needs energy for functions such as breathing, circulating blood and repairing cells. The number of calories your body uses for these basic functions is known as your basal metabolic rate.
Several factors determine your basal metabolic rate:
Your body size and composition. If you weigh more or have more muscle mass, you will burn more calories, even at rest. So people who weigh more are more likely to have a faster metabolic rate — not a slower one — because a portion of excess weight is muscle tissue.
Your sex. If you're a man, you probably have less body fat and more muscle mass than does a woman of the same age, so you burn more calories.
Your age. As you get older, your muscle mass decreases, which slows down the rate at which you burn calories.
Some more, why not?
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Conclusions: Increased energy intake appears to be more than sufficient to explain weight gain in the US population. A reversal of the increase in energy intake of ≈2000 kJ/d (500 kcal/d) for adults and of 1500 kJ/d (350 kcal/d) for children would be needed for a reversal to the mean body weights of the 1970s. Alternatively, large compensatory increases in physical activity (eg, 110–150 min of walking/d), or a combination of both, would achieve the same outcome. Population approaches to reducing obesity should emphasize a reduction in the drivers of increased energy intake.
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TEE adjusted for weight and age or PAL did not differ significantly between developing and industrialized countries, which calls into question the role of energy expenditure in the cause of obesity at the population level.
Linky.
How about that starvation mode? Not going to bother with this one. If someone wants me to.
Men vs women? You will notice many of these studies differentiate. There are differences in degree, but not kind.
Here’s a fun gif of the spread of obesity in the US.
To see these things in action, there are many (oddly usually UK) videos that have done the strict testing of people and the story is always the same. They say their metabolism is broken, they eat as much as their friends, they probably don’t eat enough to avoid “starvation mode”, etc. And in the end their metabolism is normal and they just don’t know what they are eating.
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