When it comes to low carb diets, the quality and type of them also matter, research shows. Maria Korneeva/Getty Images
A study finds that people eating a largely plant-based, low-carbohydrate diet are more successful at avoiding weight regain.
The most successful low carb diet studied involved plant-based protein, fat, and less-refined carbohydrates.
The study demonstrates that the key is not simply the amount of carbohydrates one consumes but their type and quality as well.
Previous researchTrusted Source suggests that more than half of the weight lost in a low-carbohydrate diet returned within two years and nearly all of it by five years.
For people hoping to keep pounds at bay with a low carb sustenance diet, a new study from researchers at Harvard T.H. Chan’s School of Public Health (HSPH) in Boston, Massachusetts may help. It finds that the amount of weight one regains is influenced by the nature and quality of one’s low carb diet.
People whose low-carb diets emphasized high-quality proteins, fats, and carbohydrates from whole grains and other healthy plant-based foods were likely to regain the least amount of weight.
Conversely, people who ate more animal-sourced proteins and fats, as well as refined carbohydrates, were more likely to regain weight.
The study’s conclusions are based on data from three extensive U.S. studies, the Nurses’ Health Study (NHS) and Nurses’ Health Study II (NHSII), and the Health Professionals Follow-up Study (HPFS). The studies cumulatively extended from 1986 to 2018, with a total of 123,332 participants. Their mean age was 45, and 83% of participants were women.
Their food consumption was scored for its adherence to one of five low carb diet types:
a general, or total, low-carb diet (TLCD)
an animal-based low-carb diet (ALCD) emphasizing animal-based protein and fat
a vegetable-based low-carb diet (VLCD) emphasizing plant-sourced protein and fat
a healthy low-carb diet (HLCD) involving less-refined carbohydrates and a focus on plant protein and healthy fat
an unhealthy low-carb diet (ULCD) diet incorporating less-healthful carbohydrates along with more animal protein and unhealthy fat.
The researchers found that people who had obesity and more strictly followed a healthy low-carb diet experienced 1.63 kg less weight gain compared with people who had no change in their diet.
Dr. Qi Sun, the study’s senior author and associate professor at HSPH, said, “It’s well-appreciated that body weight maintenance after significant weight loss is challenging.”
“It’s not necessarily due to the lack of willpower. Instead, after significant weight loss, changes in multiple hormones and systems happen and together these changes lead to reduced resting metabolic rate, enhanced appetite, increased energy intake, and ultimately weight regain,” Dr. Sun said.
“For most long-term weight loss trials, it is typical that most weight loss is achieved at about six months, and after that, there is a steady weight regain.” — Dr. Qi Sun
Michelle Routhenstein, a preventive cardiology dietitian nutritionist, who was not involved in the study, mentioned another obvious reason for weight regain:
“Maintaining weight after losing it can be tough because oftentimes weight loss plans involve depriving oneself.” Doing without may be acceptable for a short duration but is difficult to sustain permanently.
As a cardiology-focused clinician, Routhenstein is also reluctant to prescribe low carb weight-loss regimens in any event.
“I do not recommend low-carbohydrate diets for weight loss because low-carb diets may pose heart related risks like increased coronary artery calcium, potential atrial fibrillation, and nutrient deficiencies.” — Michelle Routhenstein
Another reason we tend to regain weight is the “set point theory.” It suggests one’s body may have a pre-set weight to which it ultimately gravitates.
Another finding of the study is that those on an HLCD diet did even better than those on a VLCD diet. An important distinction between the two diets is that HLCD emphasizes less-refined carbohydrates.
According to Dr. Sun, “Refined carbohydrates and added sugars — especially those from sugar-sweetened beverages, are known to contribute empty calories without other nutrients — and “do not quickly lead to satiety.”
“They induce more rapid changes in postprandial glucose and insulin levels, lead to the feeling of hunger earlier than the intake of the same amount of calories in the form of whole grains,” he explained.
“I think ‘quality,” said Dr. Sun, “is the keyword in our take-home message.”
“When the low carb diets emphasize the quality of food sources of macronutrients,” he noted, “we see that such diets lead to most favorable outcomes of weight management, whereas the unhealthy version of the low carb diets show the opposite health effects.”
“As such, low-carb diets may or may not help with weight management unless they emphasize a high diet quality.” — Dr. Qi Sun
“Transitioning from weight loss to maintenance is about making lasting habits. Create an adaptable plan, stay reflective, and lean on support systems. Staying resilient and kind to yourself is also an important component for long-term success,” Routhenstein pointed out.
Dr. Sun said that low carb diets also had to think about macronutrients “increasing macronutrients to make the total energy constant.”
Plant-based vs. animal-based low carb diets
“Plant-based diets, whether very low calorie or higher-calorie, may lead to less weight gain over time compared to animal-derived low carb diets due to their high fiber content fostering satiety, lower energy density enabling larger portions with fewer calories, and lower saturated fats,” said Routhenstein.
“Additionally, their nutrient richness and potential positive impact on the gut microbiome contribute to better weight management,” she said.
Routhenstein also strongly recommends working with a qualified healthcare professional — such as a registered dietitian — to devise a food plan that meets your individual needs.
To investigate the effects of rapeseed oil on body composition, blood glucose and lipid metabolism in people with overweight and obesity compared to other cooking oils. We searched eight databases for randomized controlled studies (including randomized crossover trials). The risk of bias for the included studies was assessed using the Cochrane Risk of Bias 2.0 tool. The Grading of Recommendations Assessment Development and Evaluation (GRADE) criteria were used to evaluate the quality of the outcomes. The methodological quality of the included studies was assessed using the Physiotherapy Evidence Database (PEDro) scale. Sensitivity analysis was used to check the stability of the pooled results. Statistical analysis was carried out using Review Manager 5.3 software. As a result, fifteen randomized controlled studies (including six parallel studies and nine crossover studies) were included in this study. Compared to other edible oils, rapeseed oil significantly reduced low density lipoprotein cholesterol (LDL-C) (MD = −0.14 mmol/L, 95% CI: −0.21, −0.08, I2 = 0%, P < 0.0001), apolipoprotein B (ApoB) (MD = −0.03 g/L, 95% CI: −0.05, −0.01, I2 = 0%, P = 0.0003), ApoB/ApoA1 (MD = −0.02, 95% CI: −0.04, −0.00, I2 = 0%, P = 0.02) and insulin (MD = −12.45 pmol/L, 95% CI: −19.61, −5.29, I2 = 37%, P = 0.0007) levels, and increased fasting glucose (MD = 0.16 mmol/L, 95% CI: 0.05, 0.27, I2 = 27%, P = 0.003) levels. However, the differences in body weight and body composition between rapeseed oil and control oils were not significant. In a word, rapeseed oil is effective in reducing LDL-C, ApoB and ApoB/ApoA1 levels in people with overweight and obesity, which is helpful in preventing and reducing the risk of atherosclerosis. PROSPERO registration number: CRD42022333436.
Over the past 50 years, there has been a significant increase in obesity rates worldwide. The GBD (Global Burden of Disease) Obesity Collaborators estimated that 603.7 million adults plagued by obesity, with the obesity prevalence doubling in 73 countries between 1980 and 2015 and continuing to rise in most other countries [1]. A high-fat diet usually leads to the development of obesity [2]. With the improvement of living standards, there have been important changes in people’s eating habits, such as unhealthy diets with high sugar, oil, and fat contents, and the resulting potential for obesity problems [3,4,5]. The prevalence of obesity is increasing in younger populations, with the prevalence increasing not only in older people [6,7,8] but also in children [3, 9,10,11].
Obesity is often thought of as an excess accumulation of body fat. According to WHO standards, the diagnostic criteria for overweight and obesity in adults are as follows: overweight: 25 kg/m2≤body mass index (BMI) < 30 kg/m2 and obesity: BMI ≥ 30 kg/m2 [12]. However, these criteria do not well reflect the relationship between BMI and overweight or obesity in Asian populations (e.g., China) due to the differences in body composition of different ethnic populations. Therefore, for the Chinese population, a threshold of 24 kg/m2 for overweight and 28 kg/m2 for obesity is considered to be more appropriate [13]. Obesity can cause many negative effects on human health. There is a strong link between obesity and type 2 diabetes and cardiovascular disease [14]. In addition, obesity is considered to be an important cause of metabolic syndrome [15, 16], and can lead to hyperglycemia [17, 18], hyperlipidemia [19], and hypertension [20, 21]. Therefore, the prevention and treatment of obesity is a matter of urgency.
As mentioned earlier, unhealthy eating patterns are a potential source of obesity. Therefore, dietary interventions may be a good way to prevent and treat obesity [22, 23]. Rapeseed oil (canola oil) contains very few saturated fatty acids (SFAs) and is rich in monounsaturated fatty acids (MUFAs) and polyunsaturated fatty acids (PUFAs), including 61% oleic acid, 21% linoleic acid, and 11% alpha-linolenic acid (ALA), which are beneficial to the human body [24]. Studies have shown that, compared to SFAs, MUFAs can be involved in the regulation of cardiovascular health by regulating plasma lipids and lipoproteins, susceptibility to low-density lipoprotein (LDL) oxidation, and insulin sensitivity [25,26,27]. A MUFA-rich diet can reduce total cholesterol (TC) and LDL levels in people with moderately obese [28], as well as the body weight and fat mass [29]. The mechanism by which MUFA reduces obesity-related index may be through increased oxidation rate and energy expenditure [30]. The increase in fatty acid oxidation capacity may be due to the activation of peroxisome proliferator-activated receptor δ (PPAR-δ) by the consumption of MUFA [31, 32]. Furthermore, as a derivative of oleic acid, oleoylethanolamide can effectively activate PPAR-α and lead to lipolysis [33]. Therefore, a long-term high MUFA diet may lead to changes in body composition. Given these benefits of rapeseed oil, it is increasingly being used in the management and treatment of overweight and obesity [34, 35]. Recently, a growing number of scholars have explored the effects of rapeseed oil on body composition and oxidative metabolism in patients with obesity and overweight [30, 34, 36]. However, different studies have come to different conclusions, and some even show opposite results. A randomized double-blind crossover trial that included 44 patients with overweight and obesity showed that rapeseed oil did not have a greater beneficial effect than amaranth oil on atherosclerosis markers [37]. However, another randomized crossover trial with a larger sample size and longer intervention time showed that conventional and high oleic rapeseed oils were effective in improving lipid and lipoprotein parameters [36]. The moderate intake of rapeseed oil is an effective strategy to reduce the risk of atherosclerotic cardiovascular disease [36]. Therefore, the beneficial effects of rapeseed oil in people with overweight and obesity remain controversial. In addition, several of the trials were small sample size studies. Hence, a meta-analysis and systematic review of these studies is needed to further investigate the effects of rapeseed oil on body composition and metabolism in patients with obesity and overweight.
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Abstract
To investigate the effects of rapeseed oil on body composition, blood glucose and lipid metabolism in people with overweight and obesity compared to other cooking oils. We searched eight databases for randomized controlled studies (including randomized crossover trials). The risk of bias for the included studies was assessed using the Cochrane Risk of Bias 2.0 tool. The Grading of Recommendations Assessment Development and Evaluation (GRADE) criteria were used to evaluate the quality of the outcomes. The methodological quality of the included studies was assessed using the Physiotherapy Evidence Database (PEDro) scale. Sensitivity analysis was used to check the stability of the pooled results. Statistical analysis was carried out using Review Manager 5.3 software. As a result, fifteen randomized controlled studies (including six parallel studies and nine crossover studies) were included in this study. Compared to other edible oils, rapeseed oil significantly reduced low density lipoprotein cholesterol (LDL-C) (MD = −0.14 mmol/L, 95% CI: −0.21, −0.08, I2 = 0%, P < 0.0001), apolipoprotein B (ApoB) (MD = −0.03 g/L, 95% CI: −0.05, −0.01, I2 = 0%, P = 0.0003), ApoB/ApoA1 (MD = −0.02, 95% CI: −0.04, −0.00, I2 = 0%, P = 0.02) and insulin (MD = −12.45 pmol/L, 95% CI: −19.61, −5.29, I2 = 37%, P = 0.0007) levels, and increased fasting glucose (MD = 0.16 mmol/L, 95% CI: 0.05, 0.27, I2 = 27%, P = 0.003) levels. However, the differences in body weight and body composition between rapeseed oil and control oils were not significant. In a word, rapeseed oil is effective in reducing LDL-C, ApoB and ApoB/ApoA1 levels in people with overweight and obesity, which is helpful in preventing and reducing the risk of atherosclerosis. PROSPERO registration number: CRD42022333436.
Over the past 50 years, there has been a significant increase in obesity rates worldwide. The GBD (Global Burden of Disease) Obesity Collaborators estimated that 603.7 million adults plagued by obesity, with the obesity prevalence doubling in 73 countries between 1980 and 2015 and continuing to rise in most other countries [1]. A high-fat diet usually leads to the development of obesity [2]. With the improvement of living standards, there have been important changes in people’s eating habits, such as unhealthy diets with high sugar, oil, and fat contents, and the resulting potential for obesity problems [3,4,5]. The prevalence of obesity is increasing in younger populations, with the prevalence increasing not only in older people [6,7,8] but also in children [3, 9,10,11].
Obesity is often thought of as an excess accumulation of body fat. According to WHO standards, the diagnostic criteria for overweight and obesity in adults are as follows: overweight: 25 kg/m2≤body mass index (BMI) < 30 kg/m2 and obesity: BMI ≥ 30 kg/m2 [12]. However, these criteria do not well reflect the relationship between BMI and overweight or obesity in Asian populations (e.g., China) due to the differences in body composition of different ethnic populations. Therefore, for the Chinese population, a threshold of 24 kg/m2 for overweight and 28 kg/m2 for obesity is considered to be more appropriate [13]. Obesity can cause many negative effects on human health. There is a strong link between obesity and type 2 diabetes and cardiovascular disease [14]. In addition, obesity is considered to be an important cause of metabolic syndrome [15, 16], and can lead to hyperglycemia [17, 18], hyperlipidemia [19], and hypertension [20, 21]. Therefore, the prevention and treatment of obesity is a matter of urgency.
As mentioned earlier, unhealthy eating patterns are a potential source of obesity. Therefore, dietary interventions may be a good way to prevent and treat obesity [22, 23]. Rapeseed oil (canola oil) contains very few saturated fatty acids (SFAs) and is rich in monounsaturated fatty acids (MUFAs) and polyunsaturated fatty acids (PUFAs), including 61% oleic acid, 21% linoleic acid, and 11% alpha-linolenic acid (ALA), which are beneficial to the human body [24]. Studies have shown that, compared to SFAs, MUFAs can be involved in the regulation of cardiovascular health by regulating plasma lipids and lipoproteins, susceptibility to low-density lipoprotein (LDL) oxidation, and insulin sensitivity [25,26,27]. A MUFA-rich diet can reduce total cholesterol (TC) and LDL levels in people with moderately obese [28], as well as the body weight and fat mass [29]. The mechanism by which MUFA reduces obesity-related index may be through increased oxidation rate and energy expenditure [30]. The increase in fatty acid oxidation capacity may be due to the activation of peroxisome proliferator-activated receptor δ (PPAR-δ) by the consumption of MUFA [31, 32]. Furthermore, as a derivative of oleic acid, oleoylethanolamide can effectively activate PPAR-α and lead to lipolysis [33]. Therefore, a long-term high MUFA diet may lead to changes in body composition. Given these benefits of rapeseed oil, it is increasingly being used in the management and treatment of overweight and obesity [34, 35]. Recently, a growing number of scholars have explored the effects of rapeseed oil on body composition and oxidative metabolism in patients with obesity and overweight [30, 34, 36]. However, different studies have come to different conclusions, and some even show opposite results. A randomized double-blind crossover trial that included 44 patients with overweight and obesity showed that rapeseed oil did not have a greater beneficial effect than amaranth oil on atherosclerosis markers [37]. However, another randomized crossover trial with a larger sample size and longer intervention time showed that conventional and high oleic rapeseed oils were effective in improving lipid and lipoprotein parameters [36]. The moderate intake of rapeseed oil is an effective strategy to reduce the risk of atherosclerotic cardiovascular disease [36]. Therefore, the beneficial effects of rapeseed oil in people with overweight and obesity remain controversial. In addition, several of the trials were small sample size studies. Hence, a meta-analysis and systematic review of these studies is needed to further investigate the effects of rapeseed oil on body composition and metabolism in patients with obesity and overweight.
Methods
This study was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines [38]. This article has been registered on PROSPERO under registration number CRD42022333436.
Search strategies
We searched the published literature in PubMed, Web of Science, Embase, Cochrane Library, Scopus, SinoMed, CNKI, and Wanfang Database without imposing publication date or language restrictions. A systematic search was performed for eligible studies published through April 21, 2022. The keywords were (rapeseed oil OR oil, rapeseed OR low erucic acid rapeseed oil OR LEAR oil OR oil, LEAR OR canola oil OR oil, canola OR colza oil OR rap oil) AND (obesity OR obese OR adiposis OR overweight) AND (randomized controlled OR RCT OR controlled trial OR clinical trial). The PubMed search strategy is shown in Table S1. In addition, to prevent the omission of relevant literature, references were searched for inclusion in the study.
Study selection
Two authors (YL, HY) independently selected the literature. If any differences existed, a meeting was held to resolve them. The inclusion criteria for the studies were based on the PICOS (patients, intervention, comparison, outcomes, and study design) principle [39], as shown below:
Patients (P): People with obesity or overweight (with or without metabolic risk). There was no age limit for participants, nor was there a limit on sex or environment (such as hospitals, communities, or nursing homes).
Intervention (I): The intervention was a rapeseed oil diet.
Comparison (C): Other oils, such as amaranth oil, olive oil and so on.
Outcomes (O): Primary outcomes: body weight (BW) and body composition (e.g., waist circumference (WC), body fat % (BF%), BMI); secondary outcomes: blood glucose and lipid metabolism markers (e.g., triglyceride (TG), TC, glucose, insulin).
Study design (S): Randomized parallel controlled trials and randomized cross-controlled trials were included in this study.
Studies were excluded if they met the following criteria: (1) the participants were not patients with obesity or overweight; (2) the intervention did not include a rapeseed oil diet; (3) the comparisons were not performed according to the intervention type; (4) the outcomes did not include BW, body composition, blood glucose or lipid metabolism; and (5) the study type was not a randomized controlled trial (such as a conference paper, a protocol, a review, or a case report).
Data extraction
Two authors (YLW, QZ) independently extracted the data from the selected studies into a Microsoft Excel spreadsheet and then summarized the data in a table. Any disagreement was resolved in a consensus meeting.
The data extracted from each study included the following: (1) the first author and year of publication; (2) study design; (3) the countries of the participants; (4) the gender and age of the participants; (5) the diagnostic criteria of obesity and overweight; (6) the groups and sample size; (7) the time points of assessment; (8) the total intervention time; (9) the outcome indicators (including primary outcomes and secondary outcomes); and (10) Physiotherapy Evidence Database (PEDro) scores.
Quality assessment
Two authors (JHZ, HH) independently completed an assessment of the methodological quality of each included study using the PEDro scale. If the score was inconsistent between the two authors, a third author (YL) was consulted to determine the final score. The PEDro scale has 11 items: (1) eligibility criteria and source; (2) random allocation; (3) concealed allocation; (4) baseline comparability; (5) participant blinding; (6) therapist blinding; (7) assessor blinding; (8) adequate follow-up (>85%); (9) intention-to-treat analysis; (10) between-group statistical comparisons; and (11) point and variability measurements. A total PEDro score is achieved by adding the ratings of items (2) to (11) for a combined total score between 0 and 10. Scores of less than 4 are considered poor, scores of 4 to 5 are considered fair, scores of 6 to 8 are considered good and scores of 9 to 10 are considered excellent [40].
Furthermore, we assessed the quality of the outcomes according to the Grading of Recommendations Assessment Development and Evaluation (GRADE) criteria, which are graded as very low, low, moderate and high [41]. For randomized trials, there are five indicators to assess the quality of the outcomes: risk of bias, inconsistency, imprecision, indirectness, and publication bias.
Risk of Bias
Two authors (YL, HY) assessed the risk of bias of the included studies using the Cochrane Risk of Bias (RoB) 2.0 Tool [42]. If there was a disagreement, a third author (YLW) was involved to reach a consensus. RoB 2.0 includes six domains, which are (1) randomization process, (2) deviations from intended interventions, (3) missing outcome data, (4) measurement of the outcome, (5) selection of the reported result, and (6) overall bias. Each domain was classified as “low risk”, “some concerns”, “high risk”. It is worth noting that the overall bias is judged according to the degree of bias in the previous five domains. The overall risk of bias is judged as follows: (1) Low risk of bias: The study is judged to be at low risk of bias for all domains for this result. (2) Some concerns: The study is judged to be at some concerns in at least one domain for this result. (3) High risk of bias: The study is judged to be at high risk of bias in at least one domain for this result, or, the study is judged to have some concerns for multiple domains in a way that substantially lowers confidence in the result. In addition, if more than 8 studies were included, funnel plots were used to assess publication bias.
Data synthesis and statistical analysis
Statistical analysis was performed using Review Manager 5.3 (The Nordic Cochrane Centre, Copenhagen, Denmark). The pre-extracted mean value, standard deviation and sample size were input into the statistical software. If the standard error (SE) of the data was given in the original text, the standard deviation (SD) was calculated according to Equation ①. If a study gave the SDs of baseline, endpoint, and change for partial data, according to the 16.1.3.2 item of Review Manager 5.3 handbook, the correlation coefficient R-value of the study can be calculated according to Equation ②. Then, based on this formula, the data for the missing endpoint values in the study could be calculated. If the R-value could not be calculated based on the data in the study, R = 0.8 was estimated. If a study provided data in median (interquartile range) form, the median was treated as the mean value and the SD was calculated according to Equation ③. If a study presented results in the form of graphs (e.g., line graphs, bar graphs), the WebPlotDigitizer 4.5 software was used to extract the raw data from the graphs (https://apps.automeris.io/wpd/index.zh_CN.html). In the formulas, n represents the sample size; SD(b), SD(e), and SD(c) represent the SD of baseline, endpoint, and change, respectively; Q1 represents the lower quartile and Q3 represents the upper quartile.
In our meta-analyses, for the same continuous outcomes, if the units were the same, we used mean difference (MD) with 95% confidence intervals (95% CIs) to assess the effect size. If the units were different, they were converted to the same units. If the units could not be converted, standardized mean difference (SMD) was used to estimate the effect size. In all analyses, I2 statistics were used to analyze heterogeneity between studies. If the P value of the heterogeneity test (I2 statistic) was less than 0.05, the random effects model was used; otherwise, a fixed-effects model was used. For small sample studies and highly heterogeneous outcomes, subgroup analysis was used to look for sources of the heterogeneity, and sensitivity analysis was used to test the stability of the results.
Since this study included both parallel and crossover trials, when suitable data from crossover trials were available for meta-analysis, we used the method recommended by Elbourne et al. [43]. Where possible, we used data from paired analysis results for meta-analysis (i.e., information from participants’ own pre- and post-intervention comparisons, and appropriate SEs to estimate treatment effects). If this was not possible, we combined the data from the first stage (i.e., crossed over the previous data) as if they were derived from a parallel study design. When paired data or data from the first stage were not available, we treated the data from the crossover trials as if they were from parallel trials and then sequentially excluded the crossover trials for sensitivity analysis to assess the stability of the results.
Results
Study selection
For Chinese databases, we searched both Chinese and English literature. After a systematic search of eight databases, we identified a total of 540 articles, including 40 in PubMed, 14 in Embase, 33 in Web of Science, 75 in the Cochrane Library, 343 in Scopus, 35 in SinoMed, 0 in CNKI, and 0 in Wanfang Database. In addition, 6 papers were obtained from other sources. After deleting duplicate studies, 381 studies remained. After reading the titles and abstracts and excluding studies that did not meet the inclusion criteria, 21 papers remained. We then read the full text of these 21 papers to further eliminate studies that did not meet the requirements. As a result, 6 papers were excluded. The list of study exclusions and the reasons for exclusion are shown in Table S2. As a result, fifteen studies were included for qualitative analysis, including six randomized controlled trials [44,45,46,47,48,49] and nine randomized crossover controlled trials [30, 34, 36, 37, 50,51,52,53,54]. For the randomized crossover trials, we treated the final data from the crossover trials as coming from parallel trials because neither the paired data nor the first stage data were available. Hence, to minimize the effect of multiple phase trials on the results, we only extracted data from trials with less than or equal to 3-period crossover for the meta-analysis. Only qualitative descriptions were given for crossover studies with greater than 3-period crossover [34, 50, 51]. Therefore, we ultimately included twelve studies in the meta-analysis [30, 36, 37, 44,45,46,47,48,49, 52,53,54]. (Fig. 1)
Fig. 1
Study characteristics
Among the fifteen included studies, the subjects in three studies were from Germany [44, 46, 47], four studies were from Poland [37, 45, 48, 54], one study was from the United Kingdom [49], two studies were from Iran [30, 52], one study was from Canada [36], one study was from USA [53], and those in three studies were from Canada and USA [34, 50, 51]. Twelve studies involved both men and women [30, 34, 36, 37, 44, 45, 48,49,50,51,52, 54], and the other three studies only included men to exclude possible confounding effects of estrogen on the results [46, 47, 53]. The duration of the intervention was 3 weeks in four studies [37, 45, 48, 54], 4 weeks in four studies [34, 47, 50, 51], 6 weeks in two studies [36, 53], 8 weeks in one study [46], 9 weeks in two studies [30, 52], 12 weeks in one study [49], and 6 months in one study [44]. Three studies compared rapeseed oil with olive oil [44, 46, 47], four studies compared rapeseed oil with amaranth oil [37, 45, 48, 54], one study compared rapeseed oil with sunflower oil [49], two studies compared rapeseed oil with sesame oil [30, 52], one study compared high oleic canola oil plus medium chain triglycerides with olive oil [53], one study had two types of experimental oils: canola oil and high oleic canola oil [36], and the other three studies were 5-period randomized crossover studies involving five experimental oils [34, 50, 51]. For diagnostic criteria of overweight and obesity, three studies were based on WC [36, 44, 50], nine studies on BMI [30, 37, 45,46,47,48, 52,53,54], and three studies on WC combined with BMI [34, 49, 51]. (Table 1)Table 1 Characteristics of the included randomized controlled trials.
The funding status and conflicts of interest of the included studies are as follows. Ten studies received funding [30, 37, 44, 46, 47, 49, 50, 52,53,54], five studies had potential conflicts of interest [37, 46, 49, 50, 54], and three studies did not mention funding or conflicts of interest [34, 36, 51].
Dietary intervention protocol
One study required participants to consume 30 grams of the study oil and 20 grams of margarine per day [44]. In addition, to compensate for the lower linoleic acid content of olive oil compared to that of rapeseed oil, patients in the olive oil group consumed sunflower oil once a week [44]. One study required participants to consume 20 ml of rapeseed oil or amaranth oil per day and a diet of 1800 kcal [45]. Furthermore, these oils were given to patients at the same time of day (mid-morning) to avoid the effects of circadian rhythms [45]. Subjects in two studies had to consume 50 g of rapeseed or olive oil per day [46, 47]. Subjects in one study added 20 ml of rapeseed oil or amaranth oil to their daily diet and received aerobic exercise and physical training guided by a physiotherapist [48]. Participants in one study were asked to consume 20 ml of the experimental oil, uncooked [49]. For the randomized crossover study, one study was a three-phase crossover trial with a 6-week intervention and a 4-week washout period with an energy intake of 3,000 kcal [36]. A two-phase crossover study (two articles) for 3 weeks with 3-week washout period required participants to consume 20 ml of the experimental oil daily [37, 54]. Three studies were five-phase crossover trials containing five intervention oils, each phase of intervention for 4 weeks, with washout periods of 2 to 4 weeks [34, 50, 51]. Two articles (the same study) asked participants to intake 30 g of the experimental oil daily for 9 weeks with 4-week washout period [30, 52]. One study consisted of two separate phases, each with a 6-week intervention and a washout period of 4 to 8 weeks [53]. (Table S3) The fatty acid compositions of the treated oils of the studies by Moszak et al. 2020 [48], Kruse et al. 2020 [46] and Jones et al. 2014 [50] are shown in Table S4.
Quality of the included studies
The PEDro scores for each study are shown in Table 1, and the specific scoring details are shown in Table S5. Four studies scored 9, and the quality of the studies was considered to be excellent [30, 37, 52, 54]. Eight studies scored 6 to 8, and the quality was considered good [34, 36, 45, 47,48,49,50,51]. Three studies scored 5, and the quality was considered fair [44, 46, 53]. All fifteen studies had random allocation, baseline comparability, between-group statistical comparisons, and point and variability measurements. Eight studies used concealed allocation [30, 34, 37, 49,50,51,52, 54]. Seven studies were double-blind [34, 36, 37, 48, 50, 51, 54], two studies were triple-blind [30, 52], and two studies were single-blind [49, 53]. Ten studies had a sufficient number of follow-ups (>85%) [30, 36, 37, 44,45,46,47,48, 52, 54]. All subjects in the four studies completed the experiments according to the established study protocol [37, 45, 47, 54].
Risk of bias of the included studies
The risk of bias assessment for the fifteen studies is shown in Fig. 2 according to the Cochrane RoB 2.0 Tool. Because of the different designs of parallel and crossover trials, we used the RoB 2.0 for parallel trial and the RoB 2.0 for crossover trial to assessment risk, respectively. For the parallel trials, there was a high risk of randomization process in five studies [44,45,46,47,48], a high risk of missing data in one study [48], and some concerns about deviations from intended interventions in all six studies [44,45,46,47,48,49]. Thus, the overall bias was high risk for 5 parallel studies [44,45,46,47,48] and some concern for 1 parallel study [49]. For the crossover trials, there was a high risk for the randomization process in one study [53], some concern for the randomization process in six studies [34, 36, 37, 50, 51, 54], and some concern for missing data in four studies [34, 36, 50, 51]. Therefore, the overall bias was high risk for 1 crossover study [53], some concern for 6 crossover studies [34, 36, 37, 50, 51, 54], and low risk for 2 crossover studies [30, 52].
Fig. 2: Risk of bias graph of included studies.
Effects of rapeseed oil on primary outcomes
Compared to other cooking oils, ten studies investigated the effects of rapeseed oil on BW [30, 36, 44,45,46,47,48,49, 53, 54], eight studies investigated BMI [30, 44,45,46,47,48,49, 54], five studies assessed WC [30, 44, 48, 49, 54], five studies measured BF% [30, 44, 46,47,48], six studies investigated the waist-hip ratio (WHR) [30, 45,46,47,48, 54], and two studies measured fat free mass (FFM) [45, 48].
The forest plot results showed that rapeseed oil did not significantly improve BW, BMI, WC, the WHR or FFM in individuals with overweight or obesity compared to other cooking oils (P > 0.05). For BF%, the P value was at a critical value and had a tendency to favor the control group (MD = 0.80%, 95% CI: 0.02, 1.58, I2 = 41%, P = 0.05) (Fig. 3). Similarly, Jones et al. 2014 showed that the difference in BW between all groups after the dietary intervention was not significant [50]. However, a study by Liu et al. 2016 showed that the rapeseed oil (P = 0.007) and high oleic rapeseed oil (P = 0.02) groups significantly reduced BW compared to the Flax/Saff (flax/safflower oil) group [34].
Fig. 3
Effects of rapeseed oil on secondary outcomes
Compared to other cooking oils, nine studies investigated the effect of rapeseed oil on TC, TG, low-density lipoprotein cholesterol (LDL-C) and high-density lipoprotein cholesterol (HDL-C) levels [36, 44, 46,47,48,49, 52,53,54]. Five studies evaluated the effect of rapeseed oil on homeostatic model assessment of insulin resistance (HOMA-IR) scores [37, 46,47,48, 52], seven studies examined fasting blood glucose [37, 44, 46,47,48,49, 52], five studies measured serum insulin levels [44, 46,47,48, 52], and four studies assessed apolipoprotein A1 (ApoA1), apolipoprotein B (ApoB) levels and ApoB/ApoA1 ratio [36, 37, 44, 52].
The results of the forest plot showed that rapeseed oil significantly reduced LDL-C (MD = −0.14 mmol/L, 95% CI: −0.21, −0.08, I2 = 0%, P < 0.0001), insulin (MD = −2.45 pmol/L, 95% CI: −19.61, −5.29, I2 = 37%, P = 0.0007), ApoB (MD = −0.03 g/L, 95% CI: −0.05, −0.01, I2 = 0%, P = 0.0003) and ApoB/ApoA1 ratio (MD = −0.02, 95% CI: −0.04, −0.00, I2 = 0%, P = 0.02) compared to other oils. However, for blood glucose, the results showed in favor of the control group (MD = 0.16 mmol/L, 95% CI: 0.05, 0.27, I2 = 27%, P = 0.003). Besides these, the differences between the experimental and control groups were not significant for TC, TG, HDL-C, HOMA-IR and Apo A1 (P > 0.05) (Fig. 4). Jones et al. 2014 showed that the end of CanolaDHA (high–oleic acid canola oil with DHA) feeding resulted in higher endpoint values for TC, LDL-C, HDL-C, ApoA1, and ApoB, but resulted in lower endpoint values for TG [50]. The canola oil group was second only to the CanolaDHA group in most endpoint values, both increased and decreased. The study of Liu et al. 2018 showed that after 4 weeks, all five diets decreased TC, LDL-C, and TG concentrations (P < 0.05 for all) from baseline [51].
Fig. 4: Forest plot of the effect of rapeseed oil compared to that of the control oil on blood glucose and lipid metabolism in patients with obesity and overweight.
Quality of outcome indicators
Using the GRADE profiler 3.6.1, the level of evidence for outcome quality was categorized into four levels: very low, low, moderate and high. With the exception of ApoA1, ApoB and ApoB/ApoA1 ratio, the remaining outcomes were at serious risk of bias, as most studies were at high risk of overall bias. TC had a serious inconsistency because of the relatively high heterogeneity (I2 = 57%). WHR, FFM and HOMA-IR had serious imprecision because the sample size was less than 400 individuals. Glucose was found to have the potential for publication bias because of asymmetry on both sides of the funnel plot. BF% and insulin was found to have the potential for publication bias because one study had a small sample size and received funding, but the weight in the meta-analysis was large [47]. None of the outcomes had serious indirectness. As a consequence, for primary outcomes, the quality of evidence for BW, BMI and WC was moderate, but that for BF%, WHR and FFM was low. Regarding the secondary outcomes, the quality of evidence for ApoA1, ApoB and ApoB/ApoA1 was high, and that for LDL-C, HDL-C and TG values was moderate, but that for insulin, glucose and HOMA-IR scores was low (Table 2).Table 2 GRADE evidence profile for primary outcomes and secondary outcomes among trials included in the systematic review.
For outcomes with high heterogeneity (I2 > 50%), we performed subgroup analysis to find the source of heterogeneity and to explore the effect of different subgroups on the outcome. We noted that among all the outcomes, only TC had a high heterogeneity (I2 = 57%), and the remaining outcomes were relatively homogenous. In other words, the homogeneity between the parallel and crossover trials is better.
We established subgroups based on the different types of control oils. For CT, subgroup analysis showed that rapeseed oil had a tendency to lower TC compared to olive oil but not significantly (MD = −0.19 mmol/L, 95% CI: −0.38, 0.01, I2 = 0%, P = 0.06); compared to blended oil, rapeseed oil significantly lowered TC (MD = −0.18 mmol/L, 95% CI: −0.26, −0.10, I2 = 0%, P < 0.00001); however, compared to sunflower oil, it increased TC levels (MD = 0.60 mmol/L, 95% CI: 0.27, 0.93, I2 = 0%, P = 0.0004). Therefore, this heterogeneity mainly originated between subgroups, i.e., the types of control oils (Fig. S1).
Sensitivity analysis
To test the stability of the results of the meta-analysis, a sensitivity analysis of the outcome indicators was carried out. For BMI, when the study by Kruse et al. [47] was removed, the heterogeneity became 0%, and the difference in BMI between the experimental and control groups tended to be significant (MD = −0.70 kg/m2, 95% CI: −1.44, 0.05, I2 = 0%, P = 0.07). For WC, when the study of Baxheinrich et al. [44] was removed, rapeseed oil showed a trend of increasing WC, and P value was at critical value (MD = 2.19 cm, 95% CI: −0.01, 4.39, I2 = 0%, P = 0.05), compared to the control oil. For BF%, the result was meaningless (P > 0.05) when the study of Kruse et al. [47] was taken out. However, when any of the other studies were excluded, the results showed a BF% increase in rapeseed oil group and the results of the meta-analysis favored the control group. For TC, when the study of Nicol et al. [49] was excluded, rapeseed oil significantly reduced TC levels compared to the control oil (MD = −0.16 mmol/L, 95% CI: −0.23, −0.09, I2 = 0%, P < 0.00001), and the heterogeneity was reduced to 0%. For LDL-C, ApoB and ApoB/ApoA1, when the study of Bowen et al. 2019 [36] was removed, the difference between the experimental and control groups was not significant (P > 0.05). For glucose and insulin, when the study of Kruse et al. [47] was excluded, the difference in glucose and insulin levels between the experimental and control groups was not significant (P > 0.05) (Fig. S2).
Publication Bias
If the number of studies measuring the same outcome indicator was greater than or equal to 8, funnel plot analysis was performed to detect the presence of publication bias. The funnel plot results showed no significant publication bias for BW, BMI, TC, TG, HDL-C and LDL-C. However, for blood glucose, the funnel plot showed a significant publication bias. Besides, BF% and insulin was found to have the potential for publication bias because one study had a small sample size and received funding, but the weight in the meta-analysis was large [47] (Fig. 5).
Fig. 5: Funnel plot analysis was performed to detect publication bias of BW, BMI, TG, LDL-C, HDL-C, TC and glucose.
Discussion
This systematic review and meta-analysis of randomized controlled trials revealed that, compared to the control oils, rapeseed oil intake significantly decreased LDL-C, insulin, ApoB and ApoB/ApoA1 ratio, and increased blood glucose level in people with overweight and obesity. For body composition, TC, TG, HDL-C, ApoA1 and HOMA-IR, the differences between rapeseed oil and control oil were not significant. The results of the subgroup analysis showed that, compared to olive oil, rapeseed oil had a tendency to decrease TC level, and compared to blended oil, it significantly reduced the TC level. However, rapeseed oil increased TC level compared to sunflower oil.
Rapeseed oil is rich in PUFAs and MUFAs [24]. ALA is one of the main PUFAs of rapeseed oil, which is an essential amino acid that can be metabolized to eicosapentaenoic acid (EPA) and docosahexaenoic acid (DHA) [55]. Inflammation is the basis of many chronic diseases, including coronary heart disease, diabetes, arthritis, cancer and many others, and EPA and DHA have the most potent anti-inflammatory effects [55]. In addition, the beneficial effects of rapeseed oil may be derived from the high content of MUFAs, the high ratio of unsaturated to saturated fatty acids (15:1), and the appropriate ratio of omega-6 to omega-3 (2:1) [56]. In clinical dietary interventions, an appropriate reduction in omega-6 intake and an appropriate increase in omega-3 intake, i.e., a lower ratio of omega-6/omega-3, is thought to reduce the risk of many chronic diseases [55].
Being overweight and obese has long been a major concern. Regarding the diagnostic criteria for overweight and obesity, there are slight differences between Western countries and China, as mentioned in the Introduction section. WC measurement is considered to be a simple, inexpensive method for diagnosing abdominal obesity [57]. Similarly, there are ethnic differences in the diagnosis of abdominal obesity. For Europeans, the diagnosis of abdominal obesity is as follows: a WC ≥ 94 cm for men and ≥80 cm for women [58]. For the Chinese population, the diagnosis of abdominal obesity is as follows: WC ≥ 90 cm for men and ≥80 cm for women [58].
There are various methods that people have used to try to prevent and improve overweight and obesity, such as resistance training [59, 60], physical activity [61, 62], electrical stimulation [63], and dietary interventions [64]. As rapeseed oil has shown beneficial effects in humans, many scholars have used it for the dietary management of overweight and obesity. Our study also further summarizes the evidence on the effects of rapeseed oil on body weight, body composition and glucose lipid metabolism in people with overweight and obesity compared to other edible oils.
To our knowledge, this study is the first systematic review and meta-analysis of randomized controlled trials on the effects of rapeseed oil on body composition and glucose lipid metabolism in patients with obesity and overweight. Our results show that rapeseed oil does not produce more beneficial effects on body weight and body composition than other oils. This is similar to the findings of Raeisi-Dehkordi et al., who found that rapeseed oil had no significant effect on body composition other than reducing body weight [56]. In addition, our results showed that rapeseed oil significantly reduced LDL-C, insulin, ApoB and ApoB/ApoA1 compared to other edible oils. This has some similarities to the results of a systematic review and meta-analysis that did not restrict recruitment to populations with overweight or obesity. It found that rapeseed oil significantly reduced TC, LDL-C, ApoB and ApoB/ApoA1 compared to other oils [65]. All these results may suggest that rapeseed oil does have a lowering effect on LDL-C, ApoB and ApoB/ApoA1 levels, which has an important role in preventing and reducing the risk of developing atherosclerosis. Therefore, we recommend adding rapeseed oil to the daily diet in moderation for people with overweight or obesity to prevent and reduce the risk of developing atherosclerosis. Furthermore, our study could not see that olive oil was more effective than rapeseed oil in improving body composition and reducing cardiovascular disease, which is similar to the findings of Raeisi-Dehkordi et al. [56] and Amiri et al. [65]. For the Chinese population, the market price of olive oil is much higher than the price of rapeseed oil. Due to the advertising effect, an increasing number of Chinese people are inclined to buy olive oil because of its beneficial effects on health. However, our studies have shown that the effects of olive oil on body composition and blood glucose and lipid metabolism in people with overweight or obesity do not differ significantly from those of rapeseed oil. Therefore, people with overweight or obesity who expect to prevent and improve their cardiovascular health by consuming olive oil should be aware that olive oil does not necessarily provide more beneficial effects than rapeseed oil. Moreover, olive oil is more expensive, whereas rapeseed oil is more affordable for the general population.
Strengths and limitations
Strength: The RoB 2.0 tool was used in this study to assess the risk of bias in the included studies, and compared to RoB 1.0, it has good specificity for randomized trials with parallel, crossover, and whole-group designs, respectively.
Limitations: First, the duration of the intervention was short (3 ~ 9 weeks) in most studies, with only one study having an intervention lasting six months. Second, for parallel trials, the overall bias for most studies was high risk, because the randomization process and the deviation from intended intervention domains were high risk of bias. Third, it is important to note that the interpretation of BF%, TC, LDL-C, ApoB, ApoB/ApoA1, glucose and insulin needs some caution yet, as they were sensitive to the removal of a particular included study.
Conclusion
Compared to other edible oils, rapeseed oil significantly reduced LDL-C, ApoB, ApoB/ApoA1 and insulin levels, and increased blood glucose levels in people with overweight and obesity. However, compared to other oils, rapeseed oil does not provide additional beneficial effects on weight and body composition in individuals with overweight and obesity. In the future, randomized controlled trials with large sample sizes, long intervention durations and high quality are needed to further validate the beneficial effects of rapeseed oil on weight and body composition in patients with obesity and overweight.
The ideal body weight for age and height is a little different for everyone; it’s based on gender, body fat percentage, build, and other factors. Average American weights heights are considered obese — about 170 lbs./5’3” for women and 197 lbs./5’9” for men — so “average” doesn’t mean “healthy.”
A person’s ideal weight depends on a number of factors, such as:
Gender
Height
Frame size
Fat and muscle proportion
According to The Centers for Disease Control (CDC), the average weight, height, and waist measurement for American adults age 20 years and older is:
Though these are averages, it does not mean these weights are ideal for health. The CDC also found the average American adult’s body mass index (BMI), a calculation based on height and weight that can provide an estimate of total body fat, was 29.6 for women and 29.1 for men, both of which are considered overweight.
How Can BMI Be Used to Determine Ideal Weight?
BMI (body mass index) is a tool doctors use to determine if a person is overweight, and to what degree. BMI is often used to determine ideal weight ranges. In adults, BMI does not correlate to age.
BMI is used slightly differently in children, in that it is calculated the same way as it is for adults, but then the ranges are compared to percentiles for children of the same age and sex.
BMI is calculated from the weight and square of the height:
BMI = body weight (in kg) ÷ height (in meters) squared
Healthy vegan or low-fat omnivorous soul food diets yield no significant differences in weight loss or lipid and blood pressure levels among African American adults, according to a randomized clinical trial published in JAMA Network Open.
“Although several studies have found evidence suggesting that plant-based diets may be protective against CVD, most have been observational. The few interventions examining plant-based diets have found that they have the potential to reduce factors associated with risk of cardiovascular disease, such as trimethylamine-N-oxide, C-reactive protein and LDL cholesterol,” Gabrielle M. Turner-McGrievy, PhD, RD,professor in thedepartment of health promotion, education and behavior and the Prevention Research Center at the Arnold School of Public Health at the University of South Carolina, and colleagues wrote. “African American vegetarians and vegans have significantly lower risks of hypertension, diabetes and elevated cholesterol than African American omnivores.”
No differences in weight loss, lipids or blood pressure were observed among African American adults eating a vegan diet compared with an omnivore soul food diet. Source: Adobe Stock
The Nutritious Eating with Soul study, a randomized, 2-year clinical trial of two cohorts from 2018-2020 and 2019-2020, included 159 African American adults with overweight or obesity (mean age, 48.4 years; 79% women) from a university teaching kitchen in Columbia, South Carolina, prior to the COVID-19 pandemic and via online videoconference sessions following COVID-19. Participants received weekly nutrition classes for 6 months, biweekly classes for 6 months and monthly classes for 12 months.
All participants were randomly assigned to a dietary intervention emphasizing a vegan diet with no animal product consumption (n = 77) or a low-fat omnivorous diet (n = 82), with both diets emphasizing soul food cuisine.
The primary outcomes were change in body weight and lipid levels at 12 months.
Overall, 76% of participants had data for the primary outcome at the conclusion of the study. Researchers observed no differences between the vegan diet group and the omnivore diet group for weight (–2.39 vs. –2.03 kg), total cholesterol (–1.05 vs. 1.66 mg/dL) or LDL cholesterol (–2.56 vs. –0.79 mg/dL).
At 12 months, the first cohort — wherein weight was assessed prior to the COVID-19 pandemic — experienced greater weight loss compared with the second cohort, whose weight was assessed during the pandemic (–3.45 vs. –1.24 kg; P = .01).
“Future research should examine additional strategies to enhance adherence to [plant-based diets], such as testing the intervention in a non-university, community-based setting or providing ready-to-eat meals,” the researchers wrote.
It’s the new year, and as usual, there will be a spike in interest in dieting and weight loss. Unfortunately, these resolutions are often made in ways that definitely do not set up dieters for success. We are better at shifting habits when we can find truly meaningful motivation.
If your goals for healthy living include any aspect of managing your weight, keep the following thoughts in mind for a sustainable approach to achieving your healthiest weight yet.
Don’t Wait to Begin — Do It Now
Looking for benchmarks to start a new way of eating, quit an old habit, or begin an exercise routine is natural, but unnecessary. You can change direction at any time. If you missed the opportunity to start your new routines on January 1st, you don’t have to wait until next year or even next month to make a change. You can do it now!
If you fall off the wagon, you don’t have to wait until next week or even tomorrow to try again. Look at the next possible opportunity you have to make a healthy choice that leads you closer to your goals and make the choice, right then and there.
Make as Many Ideal Food Choices as Possible
Engin Akyurt/Pexels
It’s easy to get caught up in strict rules around eating when weight is in mind. The odds are good that if you make more ideal food choices than not, you will feel better and the weight goals will follow.
If you have the opportunity to choose your food six times throughout a day (let’s say breakfast, morning snack, lunch, afternoon snack, dinner, and after-dinner treat/snack), and you make healthy choices that meet the criteria that you have set for yourself at least three times throughout a day, you will be meeting your goal 50% of the time. If you can work up to five out of six, that’s 83%. It’s not a zero-sum game where each day becomes an automatic failure if you don’t meet 100%. Each opportunity to choose is a fresh slate and doesn’t require waiting for the next day to try again.
Choose One Change at a Time
As you set out to make a series of new choices on your journey, you will find that some choices achieve the desired results while others don’t seem to be working for you.
If you’re trying to make a million changes, think about slowing down. Give each change the attention it deserves, one at a time. You don’t want to overwhelm yourself with too many changes at once and feel discouraged if you aren’t able to keep up with a whole slew of modifications. Making more than one change at once may mean not being able to determine what results correlate with each change. Isolating the adjustments helps you to determine which steps are working, and which are not.
Put Your Carb Counting in Context
As people living with diabetes, we are always aware of our carbohydrate consumption. We count carbs, we know the difference between high-quality and low-quality carbs, and we study how different amounts and types of carb consumption affect our diabetes management and overall well-being. And it’s easy to forget that there are other pieces to the puzzle, like fat and protein.
Here are some questions to ask yourself about carb counting that may help you to dig deeper into the relationship between your diabetes management and weight loss goals:
How does my fat and protein intake affect the absorption of the carbohydrates I am eating?
What foods can I use as building blocks for my meals that will have a minimal impact on my blood glucose levels?
When does the timing of my insulin/medications and food work best for achieving my diabetes goals?
Where do I have the opportunity to make dietary changes that work for my vision of weight management, diabetes management, and quality of life?
Eat When You Are Hungry
This one seems like it could be a no-brainer, but like many of the behaviors associated with weight management, it isn’t always easy. If you are hungry, eat. Make the healthiest choice possible for your meal or snack, enjoy it slowly, and give yourself the time to sit and feel full before thinking about whether you need to go back for more. If you are not hungry, don’t eat (unless your blood sugar is low, of course!). Try to eliminate situations where you are eating for other reasons, like emotions, boredom, or habit.
While it is important to be flexible for occasions that demand it, creating a daily routine for eating is helpful for both weight and diabetes management.
Listen to Your Body
Our body gives us feedback that can guide us in healthy decision-making. For example, if your body is sluggish and reliant on caffeine to make it through the day, perhaps it is telling you it needs more “clean fuel,” like sleep and whole foods. Every individual is different, so the cues for one person may not be the same for another.
Kelvin Valerio/Pexels
Think about how sensitive you are to blood glucose fluctuations in either direction, and try to apply that kind of responsiveness to your hunger levels. If you can get more in tune with the messages your body is sending, it’ll pay off.
Mentally Prepare Yourself for Success
If you have struggled with managing your weight in the past, you might be dealing with internal messages of self-doubt. That is normal. However, embracing a positive mindset and the spirit of willing experimentation will get you far.
Look in the mirror and repeat the affirmation “I have the tools and knowledge to reach success. I am strong enough to make this journey.” You can come up with your own affirmation that speaks to you. Write it down. Say it aloud. Most importantly, believe that it is true.
Celebrate Your Journey
The road to weight loss is generally not linear. It takes a while to figure out the combination of modifications to see progress. Sometimes circumstances throw us for a loop. Results may come easily, then stagnate or reverse. In the moment, these are frustrating roadblocks. In the big picture, these are small inconveniences.
Celebrate when you make a healthy choice and follow it up with another one, make it one step closer to your goal, and see how the changes you are making are positively affecting your diabetes management. Success is not simply a destination; it can be found throughout the journey. Find joy in each of these moments as you move forward.
Monitor How Your New Habits Are Affecting Your Diabetes
As we know, eating differently or adding physical activity are just two of many factors that can affect our blood glucose levels. Lifestyle modifications may require you or your doctor to consider changes to your insulin or medication, or otherwise alter how you self-manage your diabetes. It helps to make notes of these changes so you can keep track, both for yourself and for your doctor.
Cardio exercise and lower-carbohydrate diets are two popular weightloss strategies that can quickly shift the amount of insulin or other glucose-lowering medication that you need. Be prepared to counteract hypoglycemia with fast-acting carbohydrates, like juice or glucose tabs. Losing weight increases insulin sensitivity and may affect your basal insulin or mealtime doses, making adjustments accordingly.
Remember: Your Weight Management Journey May Vary
You are unique. Your weight management journey is going to be different than that of your neighbor or the person posting about it in your diabetes Facebook group. You can try strategies that are suggested by others, but don’t be discouraged if that advice doesn’t necessarily reap rewards for you. Keep your eye on the prize (your goals!), do what is best for your body, and the rest will follow.
Randomised trial finds poor sleeping habits may contribute to regaining weight after weight loss in adults with obesity.
New research being presented at this year’s European Congress on Obesity (ECO) in Maastricht, Netherlands (4-7 May), finds that not getting enough good quality sleep undermines people’s attempts to keep weight off after dieting, and suggests that around two hours of vigorous physical activity per week can help maintain better sleep.
The study is by medical student Adrian F. Bogh, Professor Signe S. Torekov and PhD student Simon B. K. Jensen from the University of Copenhagen, Denmark and colleagues.
“It was surprising to see how losing weight in adults with obesity improved sleep duration and quality in such a short time, and how exercising while attempting to keep the weight off preserved improvements in sleep quality”, says Bogh. “Also, it was intriguing that adults who aren’t sleeping enough or getting poor quality sleep after weight loss appear less successful at maintaining weight loss than those with sufficient sleep.”
More than a third of adults in the UK [1] and the USA [2] don’t get enough sleep on a regular basis (defined as less than 6 or 7 hours per night, respectively), due to host of aspects of modern life including stress, computers, smart devices, and the blurring of work life boundaries.
Not getting enough sleep or getting poor quality sleep increases risks for high blood pressure, high cholesterol and atherosclerosis (fatty deposits building up in arteries). Not getting enough sleep is linked to obesity, diabetes and inflammation, all of which can worsen cardiovascular disease. Sleeping too much or too little also has been shown to increase the risk of stroke, heart attack and death. It has been suggested that sleep habits may be a contributing factor in weight regain after a weight loss.
For this study, researchers analysed data from the S-LiTE randomised placebo-controlled trial to investigate changes in sleep duration and quality during diet-induced weight loss. In total, 195 adults (age 18 to 65 years) with obesity (body mass index [BMI] 32 to 43 kg/m2) followed a very low-calorie diet (800 kcal/day) for eight weeks and lost an average of 12% of their body weight (figure 2 in poster).
Participants were then randomly assigned to one year of weight loss maintenance with either: daily injection of placebo (49 participants), daily 3mg injection of the weight-loss drug liraglutide (49), four exercise sessions per week (48), or a combination of both treatments (49). Everyone in the exercise groups were encouraged to participate in supervised 45-minute sessions, twice a week, doing spinning and circuit training, and two unsupervised sessions of 30 minutes.
Sleep duration was measured using data from accelerometers worn by study participants before and after the low-calorie diet and after 13, 26 and 52 weeks of weight maintenance. Sleep quality was subjectively measured with the Pittsburgh Sleep Quality Index (PSQI), a self-rated questionnaire. Lower scores on the PSQI indicate better quality sleep, ranging from 0 for the best sleep to 21 as the worst possible sleep. Scores greater than five are considered poor quality sleep.
To examine the association between sleep and weight gain, participants were grouped according to their average sleep duration (below/above 6 hrs/night) or sleep quality (below/above a PSQI score of 5) at randomisation (after low-calorie diet).
The researchers found that following the 8-week low-calorie diet, sleep quality and sleep duration improved in all participants. Notably, after one year of weight maintenance, participants in the exercise groups maintained self-reported sleep quality improvements achieved from the low-calorie diet, while non-exercise groups relapsed (average between group difference 1 PSQI score point).
Liraglutide treatment had no significant effect on any sleep quality or duration compared to placebo.
The analyses also showed that participants who slept on average less than 6 hours per night at the start of the study increased their BMI by 1.3 kg/m2 during the 1-year weight maintenance phase compared to longer sleepers (over 6 hours per night).
Similarly, poor sleepers (PSQI score 5 or higher) at the start of the study increased their BMI by 1.2 kg/m2 during the weight maintenance phase, compared to good sleepers (PSQI score less than 5).
“The fact that sleep health was so strongly related to weight loss maintenance is important since many of us don’t get the recommended amount of sleep needed for optimal health and functioning,” says Professor Torekov. “Future research examining possible ways of improving sleep in adults with obesity will be an important next step to limit weight regain. Weight loss maintained with exercise seems promising in improving sleep”
Despite the important findings, the authors note that the study is observational and cannot prove that poor sleep causes weight changes, but suggests that it is likely to contribute.
Three months of a low-calorie vegetarian and a low-calorie Mediterranean diet were equally beneficial in reducing weight and improving cardiovascular risk profiles, in the first randomized crossover trial comparing the two directly.
The vegetarian diet was more effective in reducing LDL cholesterol, whereas the Mediterranean diet led to a greater reduction in triglycerides, which was not surprising, the researchers say.
These findings from the Cardiovascular Prevention With Vegetarian Diet (CARDIVEG) by Francesco Sofi, MD, PhD (University of Florence, Italy), and colleagues were published online February 26 in Circulation.
Participants received individual, in-person counseling from nutritionists on how to adhere to each of the two evidence-based healthy eating patterns. The vegetarian diet excluded meat and fish but included dairy and eggs.
This was the first “intervention study that follows the principles of evidence-based medicine, in a general population at low risk of cardiovascular disease, that compared these two beneficial diets,” Sofi told theheart.org | Medscape Cardiology.
“We can conclude that both diets are beneficial for heart health,” she said. The result was not surprising, she said, because both diets have a strong foundation of healthy foods, such as fruits and vegetables, whole grains, and legumes.
“We [now] have data on two strategies that might be helpful in reducing cardiovascular risk and promoting cardiovascular health,” Cheryl AM Anderson, PhD, MPH, MS, University of California, San Diego, author of an accompanying editorial, told theheart.org | Medscape Cardiology.
“This is a study that provides some evidence base on which you can begin to feel reasonably reassured in counseling patients that there’s not necessarily one path to Rome, but multiple strategies can be helpful.”
“It’s easy to focus on meat vs no meat, but what’s really important is the rest of the diet,” Anderson stressed.
A healthy diet should be “nutrient dense; rich in vegetables and fruits, whole grains, legumes and nuts; low in refined grains and commercially processed foods with added sugars, saturated fats, and sodium; sustainable; culturally relevant; and enjoyable.”
There is helpful information and tools online at ChooseMyPlate.gov, she noted, which are based on the most recent US guidelines.
Two Healthy Diets
The Mediterranean diet is widely reported to be one of the healthiest eating patterns to prevent cardiovascular disease, Sofi and colleagues note. In an earlier meta-analysis, the researchers reported that a vegetarian eating pattern was associated with improvements in cardiovascular risk factors.
To compare the two diets, the CARDIVEG trial randomly assigned 118 participants (78% women) living in Florence in 2014 to 2015 who were age 18 to 75 years and overweight (BMI ≥ 25 kg/m2).
The participants also had one or more of the following risk factors for cardiovascular disease, as defined by the European Society of Cardiology (ESC) guidelines: total cholesterol greater than 190 mg/dL, LDL cholesterol greater than 115 mg/dL, triglycerides greater than 150 mg/dL, and glucose greater than 110 but less than 126 mg/dL.
On average, they were 51 years of age, weighed 84 kg, and had a BMI of 31 kg/m2. Close to half (48%) were obese (BMI > 30 kg/m2).
All participants received in-person, individual counseling as well as a detailed, 1-week menu plan for each diet and recipes for the vegetarian diet.
After a run-in period, they ate a vegetarian or Mediterranean diet for 3 months and then switched diets for another 3 months. A total of 85% of the participants (50 for each intervention) completed the study.
Both diets had similar low calories and consisted of 50% to 55% carbohydrates, 25% to 30% fat, and 15% to 20% protein.
The participants ate a similar number of servings of cereals, fruits and vegetables, potatoes, sweets, and olive oil on both diets, but on the vegetarian diet they ate more legumes, nuts, eggs, and dairy products, as well as flaxseed and avocado (to compensate for the lack of essential fatty acids from fish).
Both diets were similarly effective for weight loss. After 3 months, the participants lost a mean of 1.88 kg and 1.77 kg while on the vegetarian and Mediterranean diets, respectively.
They also lost a similar amount of fat mass (about 1.3 kg) and lowered their BMI by a similar amount (about 0.65 kg/m2).
During the vegetarian diet, 46 participants (44%) modified their cardiovascular risk category by reaching ESC-recommended target levels: Sixteen reached the target for total cholesterol, 17 for LDL cholesterol, 6 for triglycerides, and 14 for BMI.
During the Mediterranean diet, 35 participants (34%) modified their cardiovascular risk category: Seven reached the target for total cholesterol, 6 for LDL cholesterol, 8 for triglycerides, and 10 for BMI.
The vegetarian diet is low in cholesterol, total fat, and saturated fatty acid, leading to lower rates of cholesterol in the bloodstream, whereas the Mediterranean diet “can reduce triglyceride levels through its beneficial components, including olive oil, dietary fiber, and many phytonutrients,” Sofi and colleagues explain.
Vitamin B12 levels were significantly lower with the vegetarian diet.
However, there were no significant differences in levels of oxidative stress markers or inflammatory cytokines after 3 months of each diet.
The study shows that “if a person would like to follow a lacto-ovo-vegetarian diet he/she can obtain a beneficial effect as well as with the Mediterranean diet, or probably in a greater extent as for cholesterol and lipid variables,” Sofi said. “The most important thing is that he/she needs to be followed and instructed by an expert nutritionist in order to avoid possible deficiencies.”
Although these diets should be investigated in larger studies in other populations, this research suggests that “a low-calorie healthy vegetarian or Mediterranean diet pattern may offer a possible solution to the ongoing challenges to prevent and manage obesity and cardiovascular diseases,” Anderson said.
The researchers are continuing to follow the study participants, Sofi said, and “a considerable portion of them declared that they learned how to eat without meat, at least in part and for a short period. In my opinion, this is already a good result.”
Obesity is popularly linked to health issues like diabetes, cancer and cardiovascular disease, but now its effect on the brain is taking center stage.
A new study conducted by the University of Cambridge found that the composition of white matter in the brains of overweight and obese middle-age adults was similar to that of older adults. In short, the younger obese adults brains appeared far more aged.
White matter is tissue that transmits signals in the brain, allowing for communication throughout the body. Loss of white matter, however, corresponds with neurodegeneration, or the loss of neurons in the brain, which leads to the development of diseases such as Parkinson’s, Alzheimer’s and Huntington’s. And that means being overweight could literally damage your brain.
“We’re living in an aging population with increasing levels of obesity, so it’s essential that we establish how these two factors might interact, since the consequences for health are potentially serious,” Paul Fletcher, a professor in the University of Cambridge Department of Psychiatry, said in the study.
As part of the study, researchers collected data from 473 cognitively healthy subjects and split them into lean and obese groups. After gathering images of the white matter in their brains, they discovered that overweight individuals had far less white matter than their leaner counterparts. In fact, the reduced amount of white matter in their brains was in line with that of lean subjects 10 years older.
Researchers only saw the differences in white matter in middle-aged and older participants, which led to another revelation about the mind being vulnerable starting at an older age. However, the researchers stressed that obesity was not found to have an effect on intelligence or cognition, rather just the aging of the brain.
Whether these these changes in white matter could be reversible with weight loss remains in question, but the authors say it’s a starting point for a more in-depth exploration of the effects of weight.
But what we do know is that exercise has many positive impacts on the brain, including longevity.
If you are a person that is 160 cm tall and you have 65 kg – and you still think that’s the ideal weight for you, then you are deeply wrong. Even the doctors disagree with you. Well, this is not just and aesthetic problem. This is also one big health problem, for which you should be very concerned. The fat tissue can harm your health and it can cause you many health problems, especially blood problems. In this post we are going to show you the ideal weight, according to your height.
There are several ways to determine the ideal body weight and the most popular is the one called “Body mass index”. The experts warn that this method is universal so it cannot be applied to all because each person has different construction. For example there are girls who are naturally slim build or ones who exercised and have broad shoulders.
You should also have in mind that the weight never goes in the same direction as health. So, this is why you should always combine healthy diet with regular physical activities. Well, you can lose some weight by dieting, but your muscles will lose their mass. So, this is not the best or the correct way of losing weight.
However the table below will show you the number of pounds and you will see if it is time to diet again.
Overweight and underweight women with cervical cancer did not live as long as their normal-weight counterparts, according to the results of a retrospective cohort study.
The median overall survival time in overweight/obese women was 6 months shorter than in women of normal weight (22 versus 28 months). For underweight women, median overall survival time was cut in half (14 versus 28 months), reported Leslie Clark, MD, of the University of North Carolina at Chapel Hill, and colleagues.
Being overweight or underweight, as determined by body-mass index (BMI), was also associated with worse recurrence-free survival and disease-free survival, Clark and colleagues said in Gynecologic Oncology.
“In understanding the effect of BMI on cervical cancer outcomes, it is important to recognize that both extremes of weight appear to negatively impact survival. Optimizing weight in cervical cancer patients may improve outcomes in these patients.”
The study included 632 women diagnosed with cervical cancer and treated at the university from 2000 to 2013. Their BMI was calculated using height and weight measurements taken at initial presentation to the oncology clinic. Four percent of the women were underweight (n=24), 30% were normal weight (n=191), and 66% were overweight or obese (n=417).
The investigators looked for connections between BMI at time of presentation and survival, controlling for factors including age, race, smoking, cancer stage, tumor grade, and histology.
Being overweight or obese was associated with significantly reduced median overall survival time compared with normal weight (22 versus 28 months; P=0.031). For underweight women, the reduced survival time was more dramatic (14 versus 28 months;P=0.018).
Compared with for normal-weight women, median recurrence-free survival time was also significantly shorter in obese/overweight women (7.6 versus 25 months; P=0.009) and in underweight women (20 versus 25 months; P=0.026).
There was a borderline-significant trend toward worse disease-specific survival in overweight/obese women compared with those of normal weight (22 versus 28 months; P=0.089). For underweight women, the difference was significant (14 versus 28 months; P=0.042).
Potential Underlying Mechanisms
“A potential unifying hypothesis connecting both extremes of weight to poor cancer prognosis is chronic systemic inflammation,” Clark and colleagues wrote. “Both patients with cancer cachexia/sarcopenia and overweight/obese patients are in a heightened inflammatory state, which may lead to increased cell proliferation and inhibition of apoptosis.
“However, this is likely not the only mechanism of poor outcomes. Co-morbid medical conditions might account for some of the differences in survival, particularly in morbidly obese patients.”
Limitations of the study included its retrospective nature and the fact that all patients were treated at a single institution, which means the results may not be broadly generalizable, the investigators said.
“This study shows that the extremes of weight are detrimental to survival in women with cervical cancer, and further investigation regarding the cause of poor prognosis is warranted. Providers should optimize weight in underweight and overweight/obese patients to attempt to improve outcomes in these women. Interventions that target nutritional counseling and physical activity should be explored in these populations,” Clark and colleagues concluded.
That study, conducted by Aida Moeini, MD, of the University of Southern California, Los Angeles, and colleagues, examined the effect of weight change over time on disease-free survival rates in 665 women with endometrial cancer.
At 5 years, disease-free survival had fallen well below 50% for women who had either lost or gained 15% or more of their body mass. For women in the smallest weight-change category, those who had lost or gained less than 7.5% of body mass, disease-free survival was about 80%, Moeini and colleagues reported.
“Our results demonstrated that endometrial cancer patients continued to gain weight after hysterectomy, and post-treatment weight change had a bi-directional effect on survival outcome.”
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