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Aug 10

Does Kratom Cause Weight Loss?

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Weight loss is not the main reason that most people consume this very popular herbal medicinal. The modern benefits of the plant have been widely documented for decades, including incredible pain relief, energy regulation, easing of symptoms of depression and anxiety. You almost never hear people talking about kratom for weight loss in the top five benefits, but this is probably more a function of just how many benefits there are.

Many users have reported that kratoms effects include the suppression of appetite. This, coupled with the natural, all-day energy that the leaves promote, can lead to the type of healthy, active lifestyle that goes hand in hand with weight loss. Over time, kratom users may find that they are doing less sedentary activities, and less snacking out of boredom.

More importantly, kratom use relaxes other deeper factors which may contribute to weight gain. These other factors leading to unhealthy diet, lifestyle, and weight problems include stress, depression, hopelessness, and lack of energy. Kratom famously goes to work right away inspiring feelings of euphoria and optimism. The bottom line is, happier people are healthier people. Energetic people are more active. In this way, regular use of kratom caps has an incredible therapeutic value, softening all of the ways in which we hold ourselves down. So, we can add healthy weight to the list of kratom benefits.

Read User Reviews on the Best Kratom Strains.

Of course, there are some detractors who consider kratoms long term effects of weight loss to be an unwanted side effect. This brings up an important question is it safe to use kratom with weight loss in mind? The answer is yes, but this should never be the sole intention for an herbal medicinal that has so many far-reaching benefits. Focusing too squarely on using kratom for weight loss could lead to an unhealthy pattern or perhaps too much kratom use for you to enjoy. Its best to think of the comprehensive benefits that kratom brings, with weight loss being just one happy symptom of widespread internal happiness and relaxation. Doesnt that sound like a low-stress weight loss strategy?

Furthermore, one is more likely to experience some side effects from kratom at doses which are too high for your body to happily handle. Side effects include stomach upset and brain fog. These are not serious, but are just uncomfortable enough that most people will stop taking kratom voluntarily in reaction to them, effectively preventing anyone from getting to a larger dose.

Weight loss is neither a major symptom nor goal for most kratom users. With responsible use, you may find your weight coming down slightly over a long period of time. If you find that you have lost your appetite completely in conjunction with regular kratom use, this is a good sign from your body to make an adjustment in your dosage or frequency of use. The idea is to use kratom to feel better, healthier, stronger, happier, and calmer. If you keep these intentions in mind and adjust your use accordingly, you cant lose.

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Does Kratom Cause Weight Loss?


Jun 21

Monitoring and Feedback for Long-term Weight Loss – JAMA

Importance Effective long-term treatments are needed to address the obesity epidemic. Numerous wearable technologies specific to physical activity and diet are available, but it is unclear if these are effective at improving weight loss.

Objective To test the hypothesis that, compared with a standard behavioral weight loss intervention (standard intervention), a technology-enhanced weight loss intervention (enhanced intervention) would result in greater weight loss.

Design, Setting, Participants Randomized clinical trial conducted at the University of Pittsburgh and enrolling 471 adult participants between October 2010 and October 2012, with data collection completed by December 2014.

Interventions Participants were placed on a low-calorie diet, prescribed increases in physical activity, and had group counseling sessions. At 6 months, the interventions added telephone counseling sessions, text message prompts, and access to study materials on a website. At 6 months, participants randomized to the standard intervention group initiated self-monitoring of diet and physical activity using a website, and those randomized to the enhanced intervention group were provided with a wearable device and accompanying web interface to monitor diet and physical activity.

Main Outcomes and Measures The primary outcome of weight was measured over 24 months at 6-month intervals, and the primary hypothesis tested the change in weight between 2 groups at 24 months. Secondary outcomes included body composition, fitness, physical activity, and dietary intake.

Results Among the 471 participants randomized (body mass index [BMI], 25 to

Conclusions and Relevance Among young adults with a BMI between 25 and less than 40, the addition of a wearable technology device to a standard behavioral intervention resulted in less weight loss over 24 months. Devices that monitor and provide feedback on physical activity may not offer an advantage over standard behavioral weight loss approaches.

Trial Registration clinicaltrials.gov Identifier: NCT01131871

Overweight and obesity have high prevalence1 and are associated with numerous health conditions.2 Interventions emphasizing both diet and physical activity are effective for weight loss, resulting in 6-month weight loss of 8% to 10% of initial weight.3 However, challenges remain to sustaining weight loss long-term.3

There is wide availability of commercial technologies for physical activity and diet.4 These technologies include wearable devices to monitor physical activity, with many also including an interface to monitor diet. Short-term studies have shown these technologies to result in modest improvements in weight loss when added to a behavioral intervention.5,6 These technologies may provide a method to improve longer-term weight loss; however, there are limited data on the effectiveness of such technologies for modifying health behaviors long term.4

This randomized trial examined whether adding wearable technology to a behavioral intervention would improve weight loss across 24 months among young adults aged 18 to 35 years. Additional outcomes included body composition, fitness, physical activity, and dietary intake.

Question Is the addition of a wearable device to monitor and provide feedback on physical activity effective for improving weight loss within the context of a behavioral weight loss intervention?

Findings In this randomized trial that included 470 young adults, weight loss was significantly less (by 2.4 kg) in response to a behavioral intervention when a wearable device that monitored and provided feedback on physical activity was included within the intervention.

Meaning Devices that monitor and provide feedback on physical activity may not offer an advantage over standard behavioral weight loss approaches.

IDEA (Innovative Approaches to Diet, Exercise and Activity) was a randomized clinical trial conducted at the University of Pittsburgh and was one of the studies within the EARLY (Early Adult Reduction of Weight Through Lifestyle Intervention)Trials consortium, with each study implementing a unique intervention in young adults.7 The IDEA study protocol is available in Supplement 1. Participants were randomized to 1 of 2 groups. Both groups received a behavioral weight loss intervention for 6 months; at 6 months, both interventions added telephone counseling sessions, text message prompts, and access to study materials on a website. However, after the initial 6 months, participants randomized to the standard behavioral weight loss intervention (standard intervention) group initiated self-monitoring of diet and physical activity behaviors, and those in the technology-enhanced weight loss intervention (enhanced intervention) group used the study website to access education materials only, and wearable technology was provided along with a web-based interface to monitor physical activity and diet. Randomization was stratified by sex and race (white or nonwhite) using a computer program that applied randomly selected block sizes of 2 and 4 with the sequence of randomization kept confidential to the other investigators. The primary outcome was weight change at 24 months.

Recruitment occurred across 10 recruitment periods that took place between October 2010 and October 2012 at the University of Pittsburgh using direct mail, mass media advertisements, or referral from clinical research registries. Eligibility was assessed based on self-reported medical history, and clearance from the participants physician was also obtained. Procedures were approved by the University of Pittsburgh institutional review board, and all participants provided informed consent.

Eligibility criteria included age between 18 to 35 years, body mass index (BMI) of 25.0 to less than 40.0 (calculated as weight in kilograms divided by height in meters squared), access to a cellular telephone that could receive text messages, and a computer with internet access. Exclusion criteria have been published.8

Both the standard intervention group and the enhanced intervention group received regular intervention contact. Group-based sessions were scheduled weekly for the initial 6 months and monthly between months 7 to 24. If a participant was unable to attend a scheduled group session, attempts were made to engage the participant in a makeup session. Theory-based strategies were used to promote adherence to weight loss behaviors.9-13 At each session, participants were given feedback on weight change and were provided materials to complement the topic of the session. Beginning with month 7, these materials were posted on the study website, along with a weekly behavioral tip.

During months 7 to 24, participants were also scheduled to receive a brief (10 minutes) individual telephone contact once per month and weekly text messages. The telephone contacts were conducted by intervention staff and followed a standard script. Text messages were provided once or twice per week and were used to prompt engagement in weight loss behaviors or to remind participants of upcoming intervention sessions. Participants were compensated $5 per month to offset the cost of receiving text messages.

Calorie intake in both intervention groups was prescribed based on baseline weight at 1200 kcal/d for individuals who weighed less than 90.7 kg, 1500 kcal/d for those who weighed 90.7 to less than 113.4 kg, and 1800 kcal/d for those who weighed 113.4 kg or more. If weight loss exceeded 6% during each 4-week period or if BMI was 22 or less, prescribed individual calorie intake was increased. Dietary fat was prescribed at 20% to 30% of total calorie intake, and sample meal plans were provided to facilitate adoption of the prescribed dietary recommendations. During months 1 to 6, participants were instructed to self-monitor dietary intake in a diary that was returned to the interventionists at the conclusion of each week, and the intervention staff provided feedback prior to returning diaries to the participants. During months 7 to 24, participants in the standard intervention group self-reported their daily intake using a website designed for this study, and this information was available to the staff during the intervention telephone contacts. Participants in the enhanced intervention group self-monitored their dietary patterns using the technology described below.

Nonsupervised moderate-to-vigorous physical activity (MVPA) in both intervention groups was initially prescribed at 100 minutes per week and increased at 4-week intervals until a prescription of 300 minutes per week was achieved. Participants were instructed to engage in structured forms of MVPA that were 10 minutes or longer in duration. During months 1 to 6, participants were instructed to self-monitor their MVPA in a diary returned to the interventionists at the conclusion of each week. The intervention staff provided feedback on these diaries. During months 7 to 24, participants in the standard intervention group self-reported their daily MVPA using a website designed for this study, and this information was available to the staff during the intervention telephone contacts. Participants in the enhanced intervention group self-monitored their MVPA using the technology described below.

Technology Used by the Enhanced Intervention Group

The enhanced intervention group was provided and encouraged to use a commercially available wearable technology that included a web-based interface (FIT Core; BodyMedia). This system included a multisensor device worn on the upper arm that provided feedback to the participant on energy expenditure and physical activity through a small display or through web-based software developed by the manufacturer. While the display provided information about total MVPA, the web-based software also provided feedback on MVPA performed in durations of 10 minutes or longer. The web-based software also allowed for self-monitoring of dietary intake. Intervention staff had access to this information during the scheduled telephone contacts.

Measures occurred at 0, 6, 12, 18, and 24 months. Participants received $100 for completing each of the 4 postbaseline assessments. Assessment staff were masked to prior data at each assessment to minimize potential bias.

Weight was assessed to the nearest 0.1 kg with the participant clothed in a hospital gown or lightweight clothing. Height was measured only at baseline to the nearest 0.1 cm with shoes removed.

Body composition was assessed using dual-energy x-ray absorptiometry from a total body scan. Prior to this scan, women had a urine pregnancy test; a positive result excluded the participant from further study participation.

Cardiorespiratory fitness was assessed with a submaximal graded exercise test performed on a motorized treadmill.8 Oxygen consumption was assessed using a metabolic cart.

Physical activity was assessed using a portable device worn for 1 week.14,15 Data were considered valid if the participant wore the device for 10 or more hours per day for 4 or more days during the observation period.16,17 Minute-by-minute data were used to identify minutes and metabolic equivalent (MET)minutes per week of sedentary behavior (awake time,

Diet over the past month was assessed using the web-based version of the Diet History Questionnaire18,19 and DietCalc software (version 1.5.0).

Percent weight loss was included as a post hoc outcome.

For safety, depressive symptoms were assessed using the 10-item Center for Epidemiology Studies questionnaire.20 Participants with a score of 13 or greater were referred to their primary care physician and provided a list of community resources to assist in obtaining treatment. Resting blood pressure was assessed following a 5-minute seated resting period using an automated system; participants with systolic blood pressure of 140 mm Hg or greater or diastolic blood pressure of 90 mm Hg or greater were referred to their primary care physician. Participants were queried regarding the occurrence of overnight hospitalizations and conditions to assess for adverse and serious adverse events.

Sex, education, income, employment status, smoking status, alcohol consumption, and depressive symptoms20 were assessed by self-report using questionnaires. Race and ethnicity, measures included in the early trials consortium, were assessed by self-report using questionnaires with fixed categories.

The mean weight loss from baseline to month 24 in the standard intervention group was projected to be approximately 3.4 kg at 24 months, with these estimates based on data from prior weight loss studies that included young adults.21-23 We specified 2.3-kg or more mean weight loss for the enhanced intervention compared with the standard intervention, so that the mean weight loss in the enhanced intervention group was expected to be 5.7 kg at the end of month 24. This would allow participants in the enhanced intervention group to maintain a clinically meaningful weight loss of at least 5%.3 Using a standard deviation of 6.8 kg for both groups, a 2-sided t test at 5% level of significance had 90% power to detect a mean difference of 2.3 kg (effect size, 0.33) between the enhanced intervention and standard intervention groups if 24-month data were available for at least 191 patients in each group. Based on an expected attrition rate of 20%, the recruitment goal was 238 participants per group.

Descriptive statistics were used to describe the participants in the 2 groups. Statistical significance of group differences in distributions was tested using Wilcoxon test for continuous variables and Pearson 2 test or exact tests for categorical variables, as appropriate.

It was expected that the likelihood of missingness could be predicted by the observed data, so missing data were assumed to be at random and a likelihood-based analysis was used. Thus, the primary hypothesis of participants in the enhanced intervention group achieving weight loss different from those in the standard intervention group was tested by fitting a linear mixed-effects model via maximum likelihood with weight over time as the outcome, including race, sex, time (assessment, treated as discrete, at baseline and at 6, 12, 18, and 24 months), intervention (enhanced intervention vs standard intervention), and interventiontime interaction as fixed effects and participants and recruitment periods as random effects. Weights measured during or after pregnancy were excluded from the analyses. Significance of the difference in distributions of weight was tested with a likelihood ratio test of the null hypothesis H0: =0, with as the coefficient of the intervention by 24-month visit interaction in the linear mixed-effects model.

For all of the models, if the interventiontime interaction was statistically significant (P<.05 the equality of mean changes in intervention groups at each intermediate time point was tested. change estimated using least-square means are presented by along with corresponding confidence intervals. p values were adjusted holm method for multiplicity when differences tested multiple points.24 no adjustments comparisons made primary outcome. all other secondary outcome analyses method.>

Multiple imputation was used for sensitivity analysis. Specifically, 10 Monte Carlo Markov Chain imputations based on the observed variables (intervention group, sex, race, ethnicity, education, income, employment status, waist circumference, smoking status, alcohol consumption, depression, and weight) at previous assessments were used to impute the missing weights for the sensitivity analysis. The estimates from the imputed data sets were averaged to see if they were similar to the likelihood-based estimates from the primary analysis. A similar approach was used for the secondary outcomes.

Fisher exact test conducted separately for each time interval was used for comparing adverse events and other alerts. All tests were 2-sided, and P<.05 was used as the cutoff for statistical significance. all analyses were conducted using sas version institute inc>

This study randomized 471 participants (BMI, 25 to

There was significant change in weight over time (P<.001 for time and the change differed significantly between enhanced intervention standard groups grouptime interaction with less weight loss in group estimated mean weights were kg ci to at baseline months resulting a of corresponding values was lower compared results from sensitivity analysis using multiple imputation similar group.>

In post hoc analysis, percent weight loss differed significantly between the standard intervention and enhanced intervention groups (P<.001 although there was no significant difference between groups at months means for standard intervention vs enhanced p=".15)," percent weight loss significantly greater in the group compared with and>

Participants in the standard intervention and enhanced intervention groups did not differ significantly for fat mass, lean mass, percent body fat, bone mineral content, bone mineral density, or cardiorespiratory fitness (P.05 for all), although there were significant changes across time among all participants (P<.01 for all time>

Differences between intervention groups for physical activity and dietary intake were not significant (Table 3). Regardless of the intervention conditions, there was a significant change in percent sedentary time, sedentary time, and light-intensity physical activity across time (P<.001 for all although total mvpa per week or met-minutes did not change significantly over time performed in bouts of minutes longer changed across the intervention and approximately participants providing weight data also provided valid physical activity assessment periods supplement>

Total calorie intake and the percent of energy intake consumed as dietary fat, carbohydrates, and protein changed significantly over time (P<.001 for all>

Of the 237 participants randomized to enhanced intervention, 191 participants received the wearable device that was a component of the intervention starting after month 6 and wore the device for 1 day or longer (median days worn, 170.0 [25th-75th percentile: 68.0-347]). On days that the device was worn, the median wear time was 241.1 min/d (25th-75th percentile: 99.3-579.1). User experience with this technology is reported in eTable 2 in Supplement 2.

There were no significant differences between groups in the number of safety alerts, nonserious adverse events, and serious adverse events (Table 4).

In this study, the addition of wearable technology to a behavioral intervention was less effective for 24-month weight loss. This may be a result of the technology not being as effective for changing diet or physical activity behaviors compared with what was achieved with the standard intervention; however, the study found no significant difference in these measures between the standard intervention and enhanced intervention groups. Thus, the reason for this difference in weight loss between the standard intervention and enhanced intervention groups warrants further investigation.

The few studies that have shown promise for adding wearable technology at the onset of a weight loss intervention have been short in duration and have included relatively small samples of participants.5,6 However, in one 9-month intervention, combining a group-based weight loss intervention with wearable technology improved weight loss compared with the group-based treatment alone.25 Furthermore, the group-based treatment resulted in a mean weight loss of approximately 2 kg, whereas our standard intervention resulted in mean weight loss of approximately 8 kg at both 6 and 12 months. Thus, questions remain regarding the effectiveness of wearable technologies over and above a standard intervention and how to best use them to modify physical activity and diet behaviors in adults seeking weight loss.

Although this study showed weight loss across the 24-month intervention in young adults, similar to trials of middle-aged and older-aged adults,22,23,26,27 the benefits achieved at 6 months were not fully sustained long term. Thus, regardless of age, challenges remain to preventing or minimizing weight regain following initial weight loss in adults. These findings are important because of the lack of data to support the effectiveness of approaches for weight loss in young adults, who have a high prevalence of overweight and obesity.1 The interventions used in this study resulted in substantially greater weight loss than what was recently reported for young adults in response to a 24-month low-intensity, technology-based intervention.28 Given that there was not a no-treatment control condition in this study, the degree to which the observed change in weight is a direct result of the intervention vs other factors cannot be determined. However, the importance of examining effective weight loss strategies for young adults is supported by a recent report showing that this age demographic has a prevalence of obesity (32.3%) higher than the prevalence in youth 12 to 19 years of age (20.5%) but lower than that found in middle-aged adults (40.2%).29 This may suggest that young adulthood is an important transition period for weight gain and the development of obesity.29

There were limitations to this study. The study sample was restricted to young adults, so results cannot be generalized to other ages. The multisensor wearable device was worn on the upper arm, which may not reflect the effectiveness of more contemporary devices worn on the wrist. However, the accuracy of wrist-worn devices to monitor physical activity and energy expenditure compared with the arm-worn device has been questioned,30 which may also limit their effectiveness, and this may not be consequential. Moreover, the use of wearable technology was not initiated at the onset of the intervention, which may have influenced how the participants adopted and used the technology during their weight loss efforts. The device used was also commercially available, and therefore the investigators did not have control over any additional information that may have been provided through the website available for use with this device. Dietary intake was assessed using self-report, which may have affected the accuracy of this measure and therefore influenced the understanding of how the intervention influenced this aspect of energy balance. Additional investigation is also needed to examine for whom wearable devices and other technologies may be effective within the context of weight loss efforts and how these technologies influence other components of weight loss, namely, eating behavior and dietary intake.

Approximately 75% of the participants provided outcome data at the 24-month assessment. Of the 120 participants missing 24-month weight, approximately one-third (n=38) had missing weight due to either being excluded for pregnancy (n=29) or moving out of the area (n=9), which are unlikely to bias the results. Linear mixed models used all available data from participants with missing data (ie, from earlier time points) to gain efficiency. Although multiple imputation was used to account for missing data in a sensitivity analysis, the loss of outcome data most likely resulted in reduced precision for the parameter estimates. Moreover, it is possible that the results could be biased in the event that the data lost to follow-up were not missing at random. Assessment staff were also aware that individuals were engaged in a weight loss trial, which may have introduced additional bias.

Among young adults with a BMI between 25 and less than 40, the addition of a wearable technology device to a standard behavioral intervention resulted in less weight loss over 24 months. Devices that monitor and provide feedback on physical activity may not offer an advantage over standard behavioral weight loss approaches.

Corresponding Author: John M. Jakicic, PhD, University of Pittsburgh, Department of Health and Physical Activity, Physical Activity and Weight Management Research Center, 32 Oak Hill Ct, Pittsburgh, PA 15261 (jjakicic@pitt.edu).

Correction: This article was corrected online on September 22, 2016, to correct transposed data in the abstract.

Author Contributions: Dr Jakicic had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.

Study concept and design: Jakicic, Davis, Marcus, Rickman, Wahed, Belle.

Acquisition, analysis, or interpretation of data: Jakicic, Davis, Rogers, King, Helsel, Rickman, Wahed, Belle.

Drafting of the manuscript: Jakicic, Marcus, Wahed.

Critical revision of the manuscript for important intellectual content: Davis, Rogers, King, Helsel, Rickman, Wahed, Belle.

Statistical analysis: King, Wahed, Belle.

Obtained funding: Jakicic, Wahed, Belle.

Administrative, technical, or material support: Jakicic, Davis, Marcus, Helsel, Rickman.

Study supervision: Jakicic, Davis, Rogers, Rickman, Belle.

Conflict of Interest Disclosures: Dr Jakicic reported receiving an honorarium for serving on the Scientific Advisory Board for Weight Watchers International; serving as principal investigator on a grant to examine the validity of activity monitors awarded to the University of Pittsburgh by Jawbone Inc; and serving as a co-investigator on grants awarded to the University of Pittsburgh by HumanScale, Weight Watchers International, and Ethicon/Covidien. Dr Rogers reported serving as principal investigator on a grant awarded to the University of Pittsburgh by Weight Watchers International. Dr Marcus reported receiving an honorarium for serving on the Scientific Advisory Board for Weight Watchers International. No other disclosures were reported.

Funding Support: This study was supported by grant U01 HL096770 from the National Institutes of Health and the National Heart, Lung, and Blood Institute (NHLBI).

Role of the Funders/Sponsors: The funders/sponsors had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; or decision to submit the manuscript for publication. However, because this grant was funded as a cooperative agreement (U-award), the sponsor provided input on outcome measurements prior to implementation, and the program officers of the sponsor (NHLBI) were invited to participate in meetings of the data and safety monitoring board.

Additional Contributions: We recognize the contribution of the staff and graduate students at the Physical Activity and Weight Management Research Center and the Epidemiology Data Center at the University of Pittsburgh, who received salary support for their effort on this project.

Bandura A. Social Foundations of Thought and Action: A Social Cognitive Theory. Englewood Cliffs, NJ: Prentice Hall; 1986.

Janz KF. Use of heart rate monitors to assess physical activity. In: Welk GJ, ed. Physical Activity Assessment for Health-Related Research. Champaign, IL: Human Kinetics; 2002.

Marlatt GA, Gordon JR. Relapse Prevention. New York, NY: Guilford Press; 1985.

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Monitoring and Feedback for Long-term Weight Loss – JAMA


Jun 20

Best Weight Loss Plan for Long Term Results – bistroMD

If you don’t want to waste time searching for the best weight loss plan, and especially if you are looking for long term results, look no further. Here are few things that will make deciding on the best plan for weight loss quick and easy. You’ll want to make sure you investigate a few key things that the best weight loss plans in the country all seem to have in common.

We’ve come up with a set of essential rules for the best ways to lose weight when following a plan for weight loss. You will absolutely want to follow these if you want to lose weight and keep it off.

Guidelines to follow for the best weight loss plan:

1. Don’t cut out a whole food group.

It is vitally important choose a balance of foods to make sure you don’t miss out on any key essential nutrients. When you completely cut out a whole food group for example, grains, breads, potatoes, cereals, pastayou might miss out on the fortified vitamins and minerals that are found in these foods. It’s better to reduce your portion size of these foods, rather than to completely cut them out for months on end to lose weight. Also, it’s nearly impossible to cut out an entire food group for your whole lifetime, and this could set you up for future failure when it comes time to begin maintaining your weight.

2. No fewer than 1000 calories, per day, unless recommended and supervised by your doctor.

While keeping your diet at 900 calories per day may seem like the best way to lose weight in the moment, it’s actually the worst. Why? Because you will without a doubt begin to lose lean muscle tissue. This is the last thing any of us want when we are attempting to achieve a healthy weight. Muscle protein is the largest contributor to metabolic rate, meaning it burns the most calories, even at rest. When you lose muscle, you’ll see the scale drop, but this is not from losing fat! The fat tissue is still there, so it is extremely misleading when you inspect the scale. So steer clear of plans that decrease daily calories to under 1,000 kcals per day.

3. Focus on habits and lifestyle change.

The best weight loss plans all have one thing in common. They help you switch your entire lifestyle, not just your diet. Normally they will include lots of education, portion control, and will list ways to help you develop skills in food preparation. They should absolutely provide tips on how to order at restaurants, and what a healthy, balanced meal looks like. The best weight loss plans almost always help you with grocery shopping lists, or planning menus for a family. Most importantly, these plans will focus on changing how you live your life around food not just making temporary changes. If a program claims they have a fast way to lose weight, then you might want to think twice, because you may not develop the skills you need to keep the weight off once it’s gone. And no one wants to have to lose weightagain.

4. Skip diets that skip protein, or recommend severely limiting protein intake.

If a diet is dropping your lean protein intake drastically, such as a vegan or vegetarian diet, you might want to skip it. You can easily add in all the healthy foods that a vegan diet containsand achieve all the same health benefitswithout dodging lean proteins. Contrary to popular belief, beans are NOT a complete source of protein. We’re not sure where this rumor began circulating, but it’s absolutely false and here’s why: Beans contain a very, very small amount of lysine. Too small to count toward your daily amino acid needs. And so you MUST combine them with another food source, such as rice or bread, in order to get the complete amino acid profile your body requires. But the real problem is this: any of the amino acids found in plant foods will never be absorbed as well as amino acids from lean animal proteins. Plants contain fibers and indigestible complexes that bind amino acids and minerals, making them much more difficult to absorb. Lean proteins are easily broken down into separate amino acids, and are readily absorbed along with minerals like iron and zinc. So if a plan encourages a total vegan diet, you might want to reconsider the protein part, and embrace all of the healthy foods present in a vegan diet.

5. No fat and ultra-low carb diets are a no-go.

Most of us have moved on from the 80’s and 90’s no-fat dieting craze. However, there are still diets that recommend removing all the fat you can from your diet, which is truly crazy. You absolutely need certain essential fats. Without them, you might develop dry skin, texture changes in your hair, and deficiencies in certain vitamins, such as Vitamin A, D, E, and K. Additionally, your brain is composed mainly of fat, and healthy fat intake is crucial to maintaining healthy brain function. The essential fats you need are found in a variety of foods, such as flax seed, chia seeds, soybeans, pumpkin seeds, walnuts, salmon, and avocados. Any diet that does not include foods that contain essential fats is one you will not want to explore.

In summary, the best way to lose weight and keep it off is to follow a weight loss plan, such as bistroMD, that focuses on lifestyle changes, and helps you learn the skills you need to achieve a normal weight. Explore our menu and get started today!

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Best Weight Loss Plan for Long Term Results – bistroMD


Jun 20

2 Science-Backed Strategies to Avoid Long-Term Weight Gain


Jun 5

Which diet is best for long-term weight loss? – Harvard …

Much has been made of the recently published results of the DIETFITS (Diet Intervention Examining the Factors Interacting with Treatment Success) study. Most of the headlines emphasized the fact that the two diets involved low-fat and low-carb ended up having the same results across almost all end points studied, from weight loss to lowering blood sugar and cholesterol.

Whats most interesting, however, is how these two diets are similar.

The authors wanted to compare low-fat vs. low-carb diets, but they also wanted to study genetic and physical makeups that purportedly (their word) could influence how effective each type of diet will be for people. Previous studies had suggested that a difference in a particular genetic sequence could mean that certain people will do better with a low-fat diet. Other studies had suggested that insulin sensitivity may mean that certain people will do better with a low-carb diet.

The study began with 609 relatively healthy overweight and obese people, and 481 completed the whole year. For the first month, everyone did what they usually did. Then, for the next eight weeks, the low-fat group reduced their total fat intake to 20 grams per day, and the low-carb group reduced their total carbohydrate intake to 20 grams per day. These are incredibly restricted amounts, considering that there are 26 grams of carbs in the yogurt drink Im enjoying as I write this, and 21 grams of fat in my half of the dark chocolate bar my husband and I split for dessert last night.

That kind of dietary restriction is impossible to maintain over the long term and, as this study showed, unnecessary. Participants were instructed to slowly add back fats or carbs until they reached a level they felt could be maintained for life. In addition, both groups were instructed to

People were not asked to count calories at all. Over the course of a year, both groups attended 22 classes reinforcing these very sound principles and all participants had access to health educators who guided them in behavioral modification strategies, such as emotional awareness, setting goals, developing self-efficacy (also known as willpower), and utilizing social support networks, all to avoid falling back into unhealthy eating patterns.

Participants in both groups also were encouraged to maintain current US government physical activity recommendations, which are 150 minutes of moderate intensity aerobic physical activity (2 hours and 30 minutes) each week.

Get all that? Basically, the differences between groups were minimal. Yes, the low-fat group dropped their daily fat intake and the low-carb group dropped their daily carb intake. But both groups ended up taking in 500 to 600 calories less per day than they had before, and both lost the same average amount of weight (12 pounds) over the course of a year. Those genetic and physical makeups didnt result in any differences either. The only measure that was different was that the LDL (low density lipoprotein) was significantly lower in the low-fat group, and the HDL (high density lipoprotein) was significantly higher in the low-carb group.

I love this study because it examined a realistic lifestyle change rather than just a fad diet. Both groups, after all, were labeled as healthy diets, and they were, because study investigators encouraged eating high-quality, nutritious whole foods, unlimited vegetables, and avoiding flours, sugars, bad fats, and processed foods. Everyone was encouraged to be physically active at a level most Americans are not. And this is a big one everyone had access to basic behavioral counseling aimed at reducing emotional eating.

This whole study could just as well be called a study of sustainable healthy lifestyle change. The results jibe very much with prior research about healthy lifestyle. The end message is the same one that we usually end with:

The best diet is the one we can maintain for life and is only one piece of a healthy lifestyle. People should aim to eat high-quality, nutritious whole foods, mostly plants (fruits and veggies), and avoid flours, sugars, trans fats, and processed foods (anything in a box). Everyone should try to be physically active, aiming for about two and a half hours of vigorous activity per week. For many people, a healthy lifestyle also means better stress management, and perhaps even therapy to address emotional issues that can lead to unhealthy eating patterns.

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Which diet is best for long-term weight loss? – Harvard …


May 28

4 Tips to Find Long-Term Weight-Loss Success | MyFitnessPal

While the oft-touted advice to get to our goal weight is simply to cut back on calories, achieving long-term weight-loss goals is actually much more complex. What we dont realize is our goal weight can actually be hard to achieve and maintain, especially without an eye toward a lifestyle shift.

Research has proven time and again that restricting and dieting often dont work. In fact, traditional dieting methods often lead to long-term weight gain, rather than weight loss because it doesnt teach the necessary behaviors for developing sustainable healthy eating habits. Research also shows that restricting foods and calories alone often leads to increased cravings for them, resulting in potential bingeing, a cycle known as the restrict-binge cycle.

Eating mindfully and intuitively, on the other hand, focus on tuning into your body and trusting it to know what it needs. While intuitive eating doesnt focus on calories or weight, research shows it may stabilize and reduce weight. A 2015 study in the American Journal of Health Promotion found women who reported being intuitive eaters had significantly lower BMI scores than non-intuitive eaters.

Rather than focusing exclusively on counting calories or cutting out entire food groups, try these mindful ways to tune in to your body and hunger:

Ironically, it can be the rigidity established by cutting calories that causes us to crave certain foods even more, potentially making us crave foods we dont normally think about. The only way to learn how foods make you feel is not to feel restricted by them.

You may find that when you remove chocolate from the pedestal and give yourself permission to enjoy a piece of chocolate each afternoon, you may stop craving it altogether. You may determine it doesnt make you feel energized the rest of the afternoon or that allowing one piece removes the taboo and you can stop there. By paying attention, you may discover how foods affect you in different ways.

Picking an arbitrary number of calories to eat each day wont necessarily account for your bodys needs but listening to your hunger and fullness cues will. Because our bodies are fluid and adaptable, our calorie needs are constantly changing.

Recovering from an illness or injury, being under a significant amount of stress or recovering from an intense workout are just some examples of when your body may need more calories and specific macronutrients. Being able to trust your hunger cues, rather than ignoring or suppressing them is one way to become more trusting and intuitive.

READ MORE > ESSENTIAL GUIDE TO LOSING WEIGHT

A low-fat granola bar may taste better in the moment and have fewer calories than, say, an apple and an ounce of nuts. However, because of its higher sugar content, you may find yourself hungrier only to snack more afterward and ultimately consume more calories than if you ate the apple and nuts. This is your bodys way of seeking satisfaction. While our bodies dont monitor calories, they know what foods are satisfying and filling.

Theres a plethora of research indicating that there are different ranges of optimal Body Mass Index (BMI), including that we can be healthy outside of the normal BMI range. In addition, the correlation of mortality rates with BMI often fail to take into consideration such critical factors as family history, mental disorders and our social environment.

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4 Tips to Find Long-Term Weight-Loss Success | MyFitnessPal


Apr 3

Improving adherence to healthy dietary patterns, genetic …

Abstract

Objective To investigate whether improving adherence to healthy dietary patterns interacts with the genetic predisposition to obesity in relation to long term changes in body mass index and body weight.

Design Prospective cohort study.

Setting Health professionals in the United States.

Participants 8828 women from the Nurses Health Study and 5218 men from the Health Professionals Follow-up Study.

Exposure Genetic predisposition score was calculated on the basis of 77 variants associated with body mass index. Dietary patterns were assessed by the Alternate Healthy Eating Index 2010 (AHEI-2010), Dietary Approach to Stop Hypertension (DASH), and Alternate Mediterranean Diet (AMED).

Main outcome measures Five repeated measurements of four year changes in body mass index and body weight over follow-up (1986 to 2006).

Results During a 20 year follow-up, genetic association with change in body mass index was significantly attenuated with increasing adherence to the AHEI-2010 in the Nurses Health Study (P=0.001 for interaction) and Health Professionals Follow-up Study (P=0.005 for interaction). In the combined cohorts, four year changes in body mass index per 10 risk allele increment were 0.07 (SE 0.02) among participants with decreased AHEI-2010 score and 0.01 (0.02) among those with increased AHEI-2010 score, corresponding to 0.16 (0.05) kg versus 0.02 (0.05) kg weight change every four years (P

Conclusions These data indicate that improving adherence to healthy dietary patterns could attenuate the genetic association with weight gain. Moreover, the beneficial effect of improved diet quality on weight management was particularly pronounced in people at high genetic risk for obesity.

Obesity is a multifactorial disorder that has a genetic predisposition but requires environmental influences for it to manifest.12 In the US, the past decades witnessed considerable transition of habitual dietary habits from a traditional pattern high in complex carbohydrates and fiber toward one high in sugar, fat, and animal products, which has played a key role in triggering the surge of obesity.34 Compelling evidence has shown that certain dietary factors such as sugar sweetened drinks, fried foods, and coffee might modify the genetic susceptibility to elevated body mass index, supporting potential interactions between genetic predisposition and overall dietary patterns on the risk of obesity.567

On the basis of scientific evidence and dietary recommendations, several diet quality scores have been developed to evaluate the healthfulness of dietary patterns.8910 One such score is the Alternate Healthy Eating Index 2010 (AHEI-2010), which has been consistently associated with lower risk of chronic disease in clinical and epidemiological investigations.8 The other two commonly studied scores are the Dietary Approach to Stop Hypertension (DASH), which represents the DASH-style diet aimed at reducing blood pressure,9 and the Alternate Mediterranean Diet (AMED), which focuses on a Mediterranean dietary pattern.10 Improving adherence to healthy dietary patterns, as assessed by these three diet quality scores, has been associated with less weight gain in previous studies.111213 However, no study has assessed the interactions between changes in adherence to healthy dietary patterns over time and genetic susceptibility to obesity on long term weight gain.

In this study, we prospectively examined the interactions of changes in the AHEI-2010, DASH, and AMED over up to 20 years with genetic predisposition to obesity, as evaluated by a genetic risk score based on 77 genetic variants associated with body mass index, on long term changes in body mass index and body weight in US men and women from two independent, prospective cohorts: the Nurses Health Study and the Health Professionals Follow-up Study.

The Nurses Health Study is a cohort of 121701 female registered nurses aged 30-55 years at enrollment in 1976.14 The Health Professionals Follow-up Study is a cohort of 51529 male health professionals aged 40-75 years at enrollment in 1986.15 Participants were followed with application of biennial validated questionnaires about medical history and lifestyle. For this study, the baseline year in both studies was 1986, when detailed information of diet and lifestyle was available. Between 1989 and 1990, 32826 women in the Nurses Health Study provided blood samples; likewise, between 1993 and 1995, a blood sample was obtained from 18225 men in the Health Professionals Follow-up Study. This analysis included 8828 women and 5218 men of European ancestry who had complete baseline information and available genotype data based on genome-wide association studies1617181920 and were free from diabetes, cancer, or cardiovascular at baseline.

Height was assessed by questionnaires administered at enrollment, and body weight was requested by questionnaires administered at enrollment and at each follow-up. Weights reported in questionnaires and measured by technicians were highly correlated (r=0.97 in both studies) in a validation subsample.21 Body mass index was calculated as weight in kilograms divided by the square of height in meters. Changes in body mass index and weight were evaluated every four years as the differences in body mass index and weight between the beginning and the end of each four year interval, with positive differences representing weight gain and negative differences weight loss.

Dietary intake information was collected by a validated 131 item semiquantitative food frequency questionnaire, administered in 1986 and every four years thereafter.22 Participants were asked how often on average they had consumed each food of a standard portion size over the previous 12 months. The responses had nine frequency categories ranging from never or less than once per month to six or more times per day. The reproducibility and validity of the food frequency questionnaire showed good correlation of food intake with that measured by multiple diet records.2324 Diet quality scores were calculated from the food frequency questionnaires every four years. Criteria for computation of each diet quality score are given in supplementary table A.

The AHEI-2010 score was based on 11 foods and nutrients predictive of chronic disease risk,8 emphasizing higher intake of vegetables (excluding potatoes), fruits, whole grains, nuts and legumes, long chain (n-3) fats, and polyunsaturated fatty acids; moderate intake of alcohol; and lower intake of sugar sweetened drinks and fruit juice, red and processed meats, trans fat, and sodium. Each component was scored from 0 (unhealthiest) to 10 (healthiest) points, with intermediate values scored proportionally. All component scores were summed to obtain a total score ranging from 0 (non-adherence) to 110 (best adherence) points.

The DASH score was based on eight foods and nutrients that were either emphasized or de-emphasized in the DASH-style diet.9 Each component was scored from 1 to 5 points according to fifths of intake, with 5 being the best score for higher intake of vegetables, fruits, nuts and legumes, whole grains, and low fat dairy products and for lower intake of sugar sweetened drinks, red and processed meats, and sodium. The total score ranged from 8 to 40 points.

The AMED score was modified and adapted to a Mediterranean diet in a Greek population.25 This score included nine components and awarded 1 point for an intake equal to or above the cohort specific median for vegetables, fruits, whole grains, nuts, legumes, fish, and ratio of monounsaturated to saturated fat and 1 point for an intake below the cohort specific median for red and processed meat and for alcohol intake 5-15 g/d for women and 10-25 g/d for men.10 The total score ranged from 0 to 9 points, with a higher score representing higher resemblance to the Mediterranean diet.

Changes in the diet quality scores were calculated as their differences between the beginning and the end of each four year interval. Therefore, positive differences represented increased adherence to a high quality diet and negative differences decreased adherence to a high quality diet.

We selected 77 single nucleotide polymorphisms (SNPs) that represent all 77 loci associated with body mass index identified in people of European descent (supplementary table B).26 The detailed information on SNP genotyping and imputation have been described previously.1617181920 Most of the SNPs were genotyped or had a high imputation quality score (r20.8), as assessed with the use of MACH software, version 1.0.16. No proxy SNPs were used.

Consistent with our previous study,27 we used a weighted method to calculate the genetic risk score on the basis of the 77 SNPs. Each SNP was recoded as 0, 1, or 2 according to the number of risk alleles (body mass index increasing alleles), and each SNP was weighted by its relative effect size ( coefficient) on body mass index obtained from the previous genome-wide association study.26 We calculated the genetic risk score by using the equation: GRS=(1SNP1+2SNP2++77SNP77) (77/sum of the coefficients), where SNPi is the risk allele number of each SNP. The genetic risk score ranges from 0 to 154, with each unit corresponding to one risk allele and higher scores indicating a higher genetic predisposition to obesity.

Information on demographics, lifestyle, and medical history came from the biennial questionnaires. We converted leisure time physical activity to metabolic equivalent hours (METs) per week.28 The reproducibility and validity of physical activity have been described previously.29 Alcohol intake was updated on the food frequency questionnaires every four years, and total energy intake was derived from these questionnaires.

In the Nurses Health Study and Health Professionals Follow-up Study, data were analyzed within five intervals of four years during a follow-up of 20 years from 1986 to 2006.27 We used multivariable generalized linear models with repeated measures analyses to assess the main associations of the genetic risk score and changes in the AHEI-2010, DASH, and AMED scores with change in body mass index within each four year interval, the associations between each additional 10 risk allele and change in body mass index according to thirds of changes in the three diet quality scores, and the associations between each 1 SD increase in diet scores and change in body mass index according to genetic risk subgroups. We classified genetic risk as low risk, intermediate risk, and high risk on the basis of thirds of the genetic risk score. We tested interactions of the genetic risk score with changes in the three diet quality scores and each dietary components on change in body mass index by including the respective interaction terms in the models (for example, change in the AHEI-2010genetic risk score), with the main effects included in the models as well. We also examined the genetic associations and interactions on weight change. We used multivariable models to adjust for age, genotyping source, baseline levels of body mass index, respective diet quality scores, physical activity, and other dietary and lifestyle factors at the beginning of each four year interval, as well as concurrent changes in these dietary and lifestyle factors within each four year interval. Missing values for diet, body mass index, and body weight were carried forward only once, and after that the follow-up was censored; for other variables, we coded missing data during any follow-up period as a missing indicator category for categorical variables (for example, smoking status) or used carried forward values for continuous variables.

In sensitivity analyses, considering potential confounding caused by age related or smoking related weight change, we assessed the genetic associations and interactions in participants younger than 65 years by censoring participants who were aged 65 years and in participants who had never smoked throughout the follow-up period. Moreover, we repeated the analyses of genetic association and interactions by using an extensive genetic risk score based on 97 SNPs comprising the 77 SNPs identified in people of European descent and 20 more SNPs identified in a combination of people of European and non-European descent (supplementary table B).26 We pooled the findings across the two cohorts by means of inverse variance weighted fixed effects meta-analysis. All reported P values are nominal and two sided. We used SAS software, version 9.4, for statistical analyses.

No patients were involved in setting the research question or the outcome measures, nor were they involved in recruitment or the design and implementation of the study. No patients were asked to advice on interpretation or writing up of results. There are no plans to disseminate the results of the research to study participants or the relevant patient community.

Table 1 shows characteristics at baseline and the first four year changes in characteristics of women in the Nurses Health Study and men in the Health Professionals Follow-up Study. Compared with participants with relatively stable adherence to diet quality scores, participants with the greatest increases in diet quality scores seemed to have lower diet quality scores at baseline and increased physical activity and less weight gain during the first four year period. The mean genetic risk score was 69.5 (SD 5.5) in the Nurses Health Study and 69.3 (SD 5.6) in the Health Professionals Follow-up Study; the genetic risk score was significantly correlated with body mass index and showed normal distributions across the two cohorts (supplementary figure A).

Characteristics according to first four year changes in three diet quality scores in thirds among 14046 US men and women in Nurses Health Study and Health Professional Follow-up Study

In general, the genetic risk score was associated with increases in body mass index and body weight every four years: in the two cohorts combined, each additional 10 risk allele was associated with 0.02 (SE 0.01) increase in body mass index and 0.05 (SE 0.03) kg increase in body weight (supplementary tables C and D). The difference in body mass index change between people at high genetic risk and those at low genetic risk was more prominent among participants with decreased adherence to the AHEI-2010 (0.12) than those with increased adherence to the AHEI-2010 (0.03); a similar pattern was observed for DASH but not for AMED (fig 1). When viewed jointly, the genetic associations with change in body mass index attenuated in participants who increased adherence to the AHEI-2010 and DASH; from another perspective, the inverse associations of increased adherence to the AHEI-2010 and DASH with change in body mass index were more prominent in participants at high genetic risk. Similar results were observed for weight change (supplementary figure B).

Pooled, multivariable adjusted means of change in body mass index (BMI) every four years, according to categories of genetic risk and changes in diet quality scores in thirds. AHEI-2010=Alternate Healthy Eating Index 2010; AMED=Alternate Mediterranean Diet; DASH=Dietary Approach to Stop Hypertension. Histograms and bars are means and SEs. Decreased, stable, and increased adherence to each diet quality score refers to third 1, 2, and 3 of each score, respectively. Data were derived from repeated measurements analyses for women in Nurses Health Study (five intervals of four years from 1986 to 2006) and men in Health Professionals Follow-up Study (five intervals of four years from 1986 to 2006). Results were adjusted for same set of variables as in table 2. Results for two cohorts were pooled by means of inverse variance weighted fixed effects meta-analysis

The genetic associations with change in body mass index were significantly attenuated with increased AHEI-2010 score in the Nurses Health Study (P=0.001 for interaction) and Health Professionals Follow-up Study (P=0.005 for interaction) (table 2). In the combined cohorts, changes in body mass index per 10 risk allele increment were 0.07 (SE 0.02) among participants in the lowest third with decreased AHEI-2010 score and 0.01 (0.02) among those in the highest third with increased AHEI-2010 score (P

Body mass index change every four years per 10 risk allele increment, according changes in diet quality scores in thirds*

Increase in each diet quality score was associated with decreases in body mass index and body weight every four years in total participants (supplementary tables C and D), and such association seemed to be more prominent in participants at high genetic risk (fig 2). Changes in body mass index per 1 SD increase in AHEI-2010 score were 0.12 (SE 0.01), 0.14 (0.01), and 0.18 (0.01) among participants at low, intermediate, and high genetic risk, respectively, corresponding to weight changes of 0.35 (0.03), 0.36 (0.04), and 0.50 (0.04) kg, respectively (supplementary figure C). Similarly, changes in body mass index per 1 SD increase in DASH score were 0.14 (0.01), 0.16 (0.01), and 0.19 (0.02) across these genetic risk subgroups. Differences in body mass index changes associated with change in the AMED across these subgroups were not evident. Similar results were observed for weight changes (supplementary figure C).

Pooled, multivariable adjusted body mass index (BMI) change every four years per 1 SD increment of each diet quality score, according to genetic risk. AHEI-2010=Alternate Healthy Eating Index 2010; AMED=Alternate Mediterranean Diet; DASH=Dietary Approach to Stop Hypertension. Histograms and bars are coefficients and SEs. Value of 1 SD: AHEI-2010: 8.38; DASH: 3.71; AMED: 1.72. Data were derived from repeated measurements analyses for women in Nurses Health Study (five intervals of four years from 1986 to 2006) and men in Health Professionals Follow-up Study (five intervals of four years from 1986 to 2006). Results were adjusted for same set of variables as in table 2. Results for two cohorts were pooled by means of inverse variance weighted fixed effects meta-analysis

In the combined cohorts, increases in AHEI-2010 and DASH scores significantly attenuated the genetic association with change in body mass index: each 1 SD increase in the AHEI-2010 and DASH score was associated with 0.05 (95% confidence interval 0.08 to 0.03; P

Interaction of genetic risk score with changes in diet quality scores and dietary components on change in body mass index (BMI) every four years. AHEI-2010=Alternate Healthy Eating Index 2010; AMED=Alternate Mediterranean Diet; DASH=Dietary Approach to Stop Hypertension; NHS=Nurses Health Study; HPFS=Health Professionals Follow-up Study. Histograms and bars are coefficients and 95% CIs for interactions between genetic risk score (per 10 risk allele) and changes in diet quality scores and dietary components (per 1 SD increment) on BMI change. Value of 1 SD: AHEI-2010: 8.38; DASH: 3.71; AMED: 1.72; fruits (servings/d): 1.12; vegetables (servings/d): 2.06; long chain (n-3) fats (mg/d): 300.7; whole grains (g/d): 17.34; low fat dairy (servings/d): 0.88; legumes (servings/d): 0.27; fish (servings/d): 0.38; alcohol (drinks/d): 0.70; sodium (mg/d): 3.10; red and processed meats (servings/d): 0.26; nuts (servings/d): 0.52; ratio of monounsaturated to saturated fat: 0.21; polyunsaturated fatty acids (% of energy): 1.68; sugar sweetened drinks and fruit juice (servings/d): 0.92; trans fat (% of energy): 0.01. Data were derived from repeated measurements analyses for women in Nurses Health Study (five intervals of four years from 1986 to 2006) and men in Health Professionals Follow-up Study (five intervals of four years from 1986 to 2006). Results were adjusted for same set of variables as in table 2. Results for two cohorts were pooled by means of inverse variance weighted fixed effects meta-analysis

In participants younger than 65 years and in those who had never smoked throughout the follow-up period, we observed similar but weaker results for genetic associations and interactions between the genetic risk score and changes in diet quality scores on change in body mass index (supplementary tables F and G). Moreover, analyses using the genetic risk score comprising 97 SNPs yielded consistent results (supplementary table H).

In this study, we found consistent interactions between changes in diet quality scores and genetic predisposition related to long term changes in body mass index and body weight in two independent prospective cohorts of US women and men. Our findings show that improving adherence to healthy dietary patterns assessed according to the AHEI-2010 and DASH could significantly attenuate the genetic association with increases in body mass index and body weight. Viewed differently, improving diet quality over time was associated with decreases in body mass index and body weight, and such favorable effect was more prominent in people at high genetic risk for obesity than in those with low genetic risk.

The dramatic alternations in dietary patterns over the past decades have paralleled the rapid rise in the prevalence of obesity in the US.34 Emerging evidence supports a protective effect of improved adherence to healthy dietary patterns on weight gain and other health outcomes such as cardiovascular disease and total and cardiovascular disease mortality.1112133031 In previous studies, we have shown that dietary factors such as sugar sweetened drinks and fried foods could amplify the genetic associations with elevated body mass index.56 Similar interactions have also been reported by another group.32 Our findings in this study are consistent with these previous reports and for the first time indicate that improving adherence to healthy dietary patterns might diminish the genetic association with weight gain. Here, we evaluated healthy dietary patterns by diet quality scores. Instead of considering individual diets in isolation, diet quality scores provide comprehensive measures of diets incorporating nutrients and foods and therefore represent a broader picture of dietary intake.3334 In this study, the AHEI-2010 showed the most significant interaction with genetic predisposition to obesity on changes in body mass index and body weight, and we also found a similar interaction pattern for DASH but not for AMED. When evaluating changes over time, the continuous scale and wider range of the AHEI-2010 may allow for greater sensitivity to differentiate dietary changes; in contrast, the wider scale and narrower range of AMED may limit its ability to detect the differences in dietary changes. Additionally, the AHEI-2010 captured all four dietary components (fruits, vegetables, long chain (n-3) fats, and trans fat) that contributed to significant interactions with the genetic risk score at a nominal significance threshold, whereas DASH and AMED each captured two, which might also account for the observed differences between the three diet quality scores.

From another point of view, our findings indicate that people with a greater genetic predisposition seem to be more susceptible to the favorable effect of improving diet quality on weight management. Our results are in line with the findings of a meta-analysis (including 6951 participants from 10 studies) showing that people carrying the homozygous FTO allele predisposing to obesity may lose more weight than non-carriers through diet and lifestyle interventions.35 In a more recent meta-analysis of 9563 participants from eight randomized controlled trials, each copy of the FTO obesity predisposing allele was associated with non-significant reductions in body mass index (0.02, 95% confidence interval 0.13 to 0.09) and body weight (0.04, 0.34 to 0.26, kg) (indicative of gene by treatment interactions) after weight loss intervention in the treatment versus control arm.36 Of note, the effect sizes of gene by treatment (dietary, physical activity, or drug based intervention) interaction in this meta-analysis are in similar ranges to the effect sizes of gene by dietary patterns interaction shown in our study, supporting the generalization of the effect sizes yielded by our study.

The precise mechanisms underlying the observed interactions remain unclear. The beneficial bioactivities of healthy dietary patterns, such as balancing energy intake, regulating metabolism, and reducing cardiometabolic risk,3738 may partly explain their modifying effect on genetic predisposition to weight gain. In addition, several genes associated with body mass index have been shown to be involved in central appetite regulation and energy homeostasis,26 which may also be responsible for the observed interactions. However, we cannot exclude the involvement of other biological pathways, and future functional studies are needed to provide biological insights into the gene by diet interactions on weight change.

The strengths of our study include the cross validation from two independent prospective cohorts of men and women, the well validated measures of dietary factors and body weight within five repeated four year periods of a 20 year follow-up, and the reliable findings improved by several sensitivity analyses. Notably, we evaluated changes in diet quality scores and changes in body mass index and body weight during the same four year intervals in discrete periods, because this change-on-change analytic approach has been shown to generate more robust, consistent, and biologically plausible relations between diet and long term weight change than the approaches of prevalent diet with weight change (prevalent analysis) or change in diet with weight change in the subsequent four years (lagged changes analysis).39

Our study also has several potential limitations. Firstly, although we have carefully controlled for baseline and concurrent changes of lifestyle and dietary factors in the analyses, unmeasured or unknown confounders may also exist. Secondly, because adherence to healthy dietary patterns was not randomized, the association between dietary factors and weight change may not imply a causal relation. Thirdly, the results could be underestimated by potential reverse causality; for example, people who gained weight might tend to adopt healthier eating patterns to lose weight. Fourthly, our study was restricted to health professionals of European descent in the US, and the generalizability of our findings should be tested in other demographic and racial/ethnic populations.

Our results suggest that weight gain associated with genetic predisposition can be at least partly counteracted by improving adherence to healthy dietary patterns. Importantly, for people who are genetically predisposed to obesity, improving adherence to a healthy diet is more likely to lead to greater weight loss. Our findings support recommendation of adherence to healthy dietary patterns,37 particularly for people at high genetic risk of obesity. The observed genetic effects were modest in magnitude, compared with lifestyle risk factors. Of note, the changes in body mass index and body weight reported in our study were changes per four years. Because changes in body mass index and body weight are essentially cumulative during the life course, the long term effect size would be substantial. Furthermore, long term, dramatic weight loss is difficult to achieve, even in the context of weight loss interventions. Therefore, even modest weight loss or simply maintaining weight from adulthood onward, compared with gaining weight, may have a substantial effect on population health.

Our study provides reproducible evidence from two prospective cohorts of US men and women that improving adherence to healthy dietary patterns could attenuate the genetic association with body mass index increment and weight gain, and the beneficial effect of improving diet quality on weight management was more prominent in people at high genetic risk. Our findings highlight the importance of improving adherence to a healthy diet in the prevention of weight gain, particularly in people genetically predisposed to obesity.

Improving adherence to healthy dietary patterns, as assessed by various diet scores, has been associated with weight loss in several studies

No study has assessed the interactions between changes in these diet quality scores and genetic predisposition to obesity in relation to long term changes in body mass index and body weight

Improving adherence to healthy dietary patterns as assessed by the Alternate Healthy Eating Index 2010 and Dietary Approach to Stop Hypertension can counteract part of gene related, long term weight gain

People at high genetic risk for obesity are more susceptible to the beneficial effect of improving diet quality on weight loss

This underlines the importance of improving adherence to healthy dietary patterns in the prevention of weight gain, especially in people with greater genetic predisposition to obesity

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Improving adherence to healthy dietary patterns, genetic …


Mar 15

Long Term Weight Loss Tips – Shape Magazine

Discover how to modify your balanced healthy diet for long term effective weight loss.

No matter where it comes from, a calorie is a calorie, and it takes 3,500 calories to gain or lose a pound. Want to shed a perfectly respectable 1 pound per week? Carve 500 calories off your day by thinking through your healthy food choices and upping your exercise.

But make sure you’re not cutting too many calories. Drastically reducing food intake could slow your metabolism and sabotage your efforts to build muscle, which is needed to burn maximum calories and be toned. To make calories count, choose nutrient-rich healthy foods with a mix of protein, carbs and fats at every meal for an overall balanced healthy diet.

Eat plenty of healthy foods full of the six nutrients most often lacking in women’s diets. The quality and length of our lives depend on our health; our bodies thrive only when nourished with optimal amounts of the more than 40 nutrients and 12,000 phytochemicals found in minimally processed foods.

And, these also aid in weight loss. If you focus more on your health and less on your waistline, you will automatically eat more low-calorie, nutrient-packed healthy foods, like vegetables, fruits, whole grains, nonfat dairy products and legumes, which help you maintain your weight.

Cut out the specific items in whatever categorycarbs, sweets, meatgives you difficulty without cutting out the nutrition. Give pretzels the heave-ho but don’t dismiss whole-grain breads. Has ice cream become a problem? Instead, snack on single-serving portions of yogurt.

The best way to achieve that same clarity when you’re trying to lose weight is to set some rules. Find yourself snacking on cereal at night? Make an only-for-breakfast rule. If you slip, no cereal in your house for a month! Tend to dive into the breadbasket as soon as the waiter brings it around? Set a one-starchy-carb-per-restaurant-meal rule. If you want the bread, tell yourself before you head out that you’ll skip the potato or pasta that comes with your meal. To make the rules official, write them down.

Discover three more tips from Shape about a balanced healthy diet strategy that will help lead to effective long term weight loss.

[header = Find more long term weight loss tips for your balanced healthy diet at Shape.]

Fill up on wholesome, fiber-rich, water-filled healthy foods like vegetables, fruits, whole grains and beans, which fill you up faster for longer on fewer calories. And how you eat is as important as what you eat. A growing body of research shows that the best way to keep your metabolism revved and body-fat levels low is to feed yourself in small amounts. Eat airline-size (rather than restaurant-size) portions of healthy foods like meat, fish, pasta, grains and desserts. And eat every three to four hours, for a total of five mini-meals per day.

You’re much more likely to stick with a diet if your food looks, tastes and smells delicious. Feeling deprived will only backfire. Make a plan you can live with by livening up healthy foods with herbs and spices like basil, cilantro, curry and ginger; aromatic veggies like garlic and onions; and condiments like mustard, hot pepper sauce or salsa. Experiment with new nutritious foods: Tantalize your taste buds with two new fruits or vegetables at each meal. Try different cold/hot cereals and breads. Don’t declare high-fat favorites “off limits”; savor them in small amounts to maintain a balanced healthy diet.

For energy, satisfaction, staying power and good health, aim to eat a healthy balance of protein (15-20 percent of your total daily calories), fat (less than 30 percent of your total daily calories) and carbohydrate (50-55 percent of your total daily calories) each day. Rule of thumb: Fill three-quarters of your plate with plant foods, leaving the rest for small amounts of fish, nonfat milk products and nuts or seeds.

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Long Term Weight Loss Tips – Shape Magazine


Dec 17

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Nov 9

The China Study: Revised and Expanded Edition: The Most …

The revised and expanded edition of the bestseller that changed millions of lives

The science is clear. The results are unmistakable.

You can dramatically reduce your risk of cancer, heart disease, and diabetes just by changing your diet.

More than 30 years ago, nutrition researcher T. Colin Campbell and his team at Cornell, in partnership with teams in China and England, embarked upon the China Study, the most comprehensive study ever undertaken of the relationship between diet and the risk of developing disease. What they found when combined with findings in Colins laboratory, opened their eyes to the dangers of a diet high in animal protein and the unparalleled health benefits of a whole foods, plant-based diet.

In 2005, Colin and his son Tom, now a physician, shared those findings with the world in The China Study, hailed as one of the most important books about diet and health ever written.

Featuring brand new content, this heavily expanded edition of Colin and Toms groundbreaking book includes the latest undeniable evidence of the power of a plant-based diet, plus updated information about the changing medical system and how patients stand to benefit from a surging interest in plant-based nutrition.

The China StudyRevised and Expanded Edition presents a clear and concise message of hope as it dispels a multitude of health myths and misinformation. The basic message is clear. The key to a long, healthy life lies in three things: breakfast, lunch, and dinner.

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The China Study: Revised and Expanded Edition: The Most …



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