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Darand M, Hassanizadeh S, Talebi S, Darabi Z, Bagherniya M, Yaghoubi F, et al . Comparison of the Effect of a Low-Carbohydrate Diet with a Low-Fat Diet on Anthropometric Indices and Body Fat Percentage: A Systematic Review and Meta-Analysis of Randomized Controlled Trials. JNFS 2023; 8 (3) :493-520
URL: http://jnfs.ssu.ac.ir/article-1-562-en.html
URL: http://jnfs.ssu.ac.ir/article-1-562-en.html
Mina Darand * 
, Shirin Hassanizadeh , Sepide Talebi , Zahra Darabi , Mohammad Bagherniya , Fatemeh Yaghoubi , Shahab Alizadeh , Zeinab Darand , Yasaman Azamian , Hadi Abdollahzad
, Shirin Hassanizadeh , Sepide Talebi , Zahra Darabi , Mohammad Bagherniya , Fatemeh Yaghoubi , Shahab Alizadeh , Zeinab Darand , Yasaman Azamian , Hadi Abdollahzad
Department of Clinical Nutrition, School of Nutritional Sciences and Dietetics, Isfahan University of Medical Sciences, Isfahan, Iran
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Mina Darand; PhD1,2, Shirin Hassanizadeh; MSc 3, Sepide Talebi; MSc 4,5, Zahra Darabi; MSc 6,7
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Comparison of the Effect of a Low-Carbohydrate Diet with a Low-Fat Diet on Anthropometric Indices and Body Fat Percentage: A Systematic Review and Meta-Analysis of Randomized Controlled Trials
Mina Darand; PhD1,2, Shirin Hassanizadeh; MSc 3, Sepide Talebi; MSc 4,5, Zahra Darabi; MSc 6,7
Mohammad Bagherniya; PhD 8, Fatemeh Yaghoubi; PhD 9, Shahab Alizadeh; PhD 10, Zeinab Darand; MD 11,
Yasaman Azamian; MSc 12 & Hadi Abdollahzad; PhD *13
1 Department of Clinical Nutrition, School of Nutritional Sciences and Dietetics, Isfahan University of Medical Sciences, Isfahan, Iran; 2 Nutrition and Food Security Research Center, Isfahan University of Medical Sciences, Isfahan, Iran; 3 Department of Community Nutrition, School of Nutrition and Food Science, Nutrition and Food Security Research Center, Isfahan University of Medical Science, Isfahan, Iran; 4 Students’ Scientific Research Center (SSRC), Tehran University of Medical Sciences, Tehran, Iran; 5 Department of Clinical Nutrition, School of Nutritional Sciences and Dietetics, Tehran University of Medical Sciences, Tehran, Iran; 6 Department of Nutrition, School of Public Health, Shahid Sadughi University of Medical Sciences, Yazd, Iran; 7 Nutrition and Food Security Research Center, Shahid Sadoughi University of Medical Sciences, Yazd, Iran; 8 Nutrition and Food Security Research Center and Department of Community Nutrition, School of Nutrition and Food Science, Isfahan University of Medical Sciences, Isfahan, Iran; 9 Esfarayen Faculty of Medical Sciences, Esfarayen, Iran; 10 Baharloo Hospital, Tehran University of Medical Sciences, Tehran, Iran; 11 School of Veterinary Medicine, University of Kermanshah, Kermanshah, Iran; 12 Student Research Committee, School of Nutritional Sciences and Food Technology, Kermanshah University of Medical Sciences, Kermanshah, Iran; 13 Department of Nutrition, School of Medicine, Urmia University of Medical Sciences, Urmia, Iran.
| ARTICLE INFO | ABSTRACT | |
| SYSTEMATIC REVIEW and META-ANALYSIS | Background: Although many studies have been conducted to compare the effect of adherence to a low-carbohydrate diet (LCD) with a low-fat diet (LFD) on anthropometric indices and body fat percentage, there is still no definite conclusion in this regard. Therefore, the present study aims to summarize results of studies comparing a LFD and a LCD on weight loss. Methods: A systematic search of databases including PubMed, Scopus, and Cochran Library was performed up to November 2020. All randomized controlled trials (RCTs) comparing the effect of adherence to a LCD with a LFD on anthropometric indices and body fat percentage were included. Search results were limited to English-language publications. Sixty-three RCTs, including 7660 participants, were selected for the present study. Results: Pooled analysis indicated that adherence to LCD was significantly associated with a greater reduction in BMI (SMD = - 0.07, 95% CI: -0.14,-0.001; P = 0.04), weight (kg) (SMD = - 0.22, 95% CI: - 0.31, - 0.12; P ≤ 0.001), and percentage of body fat mass (SMD = - 0.28, 95% CI: -0.48, - 0.08; P = 0.006) compared to LFD. However, no significant difference in changes of kilogram of body fat mass and waist circumference was observed between the two diet programs. Conclusion: Overall, adhering to LCD was more effective than LFD in losing weight and body fat percentage. Keywords: Low-fat; Low-carbohydrate; Weight; Obesity |
|
| Article history: Received: 17 Jan 2022 Revised: 26 Feb 2022 Accepted:19 Mar 2022 |
||
| *Corresponding author: hadi_nut@yahoo.com Department of Nutrition, School of Medicine, Urmia University of Medical Sciences, Serow Highway, Nazloo, Iran. Postal code: 5756115111 Tel: +9844 32770698 |
Introduction
obesity has become one of the most global well-known public health issue, its prevalence has nearly tripled since 1975 (Abarca-Gómez et al., 2017, Zhang et al., 2020). Although several research studies have been conducted to prevent and treat obesity, obesity prevalence is increasing worldwide and it is likely to reach maximum levels between 2026 and 2054 (Janssen et al., 2020).
Lifestyle modification is considered the first line strategy for weight loss and weight maintenance, and diet is the main component of this approach (Wadden et al., 2007, Wadden et al., 2012). Among many diet approaches for weight reduction, diets with altered macronutrient composition such as low-fat diets (LFD) and low-carbohydrate diets (LCD) have been the focus of a large body of evidence. Reduction of dietary fats (low-fat diets were defined to have ≤ 30% total fat content) (Lichtenstein and Van Horn, 1998, Nordmann et al., 2006, van Zuuren et al., 2018) are theoretically reasonable approach for weight loss for several reasons, one them is high calorie density of fats compared with carbohydrates or protein (Buchholz and Schoeller, 2004, Hite et al., 2011, Tobias et al., 2015). Furthermore, studies have examined the association between dietary fat intake and cardiovascular risk and reported that LFD was traditionally applied for weight reduction (Keys et al., 1986, Sackner-Bernstein et al., 2015). However, clinical trials results are inconsistence and evidence does not support LFD superiority to reduce weight and body composition change in comparison to other dietary interventions (Harcombe et al., 2015, Mansoor et al., 2016, Nordmann et al., 2006, Schwingshackl and Hoffmann, 2013, Tobias et al., 2015). On the other hand, LCD characterized by low amounts of carbohydrates and higher quantities of fat and protein is another popular diet for weight loss and control concentrating on macronutrient distribution of the diet (Mansoor et al., 2016). Generally, LCD contains 30-130 g of carbohydrate per day and a very low-carbohydrate ketogenic diet comprises less than 30 g of carbohydrate per day, which usually permit ketosis to occur (Hite et al., 2011). It is hypothesized that sever carbohydrate restriction results in the depletion of glycogen stores and the excretion of bound water (Astrup et al., 2004). In addition, this diet is an appetite suppressing due to its ketogenic nature, increases satiating, and reduces spontaneous food intake due to its high protein-content. Moreover, the restriction of carbohydrates limits food choices and subsequently reduces energy consumption (Astrup et al., 2004). In a recent meta-analysis, it is shown that adherence to LCD was associated with a decrease in body fat. It is hypothesized that low carbohydrate diets contain high amounts of protein, converting to amino acids in the body. Amino acids increase muscle growth, leading to an increase in muscle mass and subsequently decreasing fat mass (FM) (Hashimoto et al., 2016). On the other hand, in a previous trial, it has been shown that an energy-restricted, high-protein, LFD had no significant impact on body fat in comparison to a high-carbohydrate LFD in obese women (Noakes et al., 2005).
For a long time, debates have been existed about the effectiveness of these two kinds of very popular diets in the treatment of obesity and there is still no definite conclusion in this regard particularly in terms of body composition. Therefore, this conclusive systematic review and meta-analysis were undertaken to update previous investigations to assess the effectiveness of LFD interventions in comparison to LCD trials in terms of anthropometry parameters (body mass index (BMI), weight, waist circumference (WC)) as well as body composition (percentage and kilogram of FM).
Materials and Methods
Search strategy: The Preferred Reporting Items for Systematic Reviews and Meta-Analyses Guidelines (PRISMA) were applied for writing the current study (Moher et al., 2015). Scopus, Google Scholar, PubMed, Web of Science and Cochran Library were searched up to November 2020 without any restrictions to identify eligible trials. The effect of a low carbohydrate diet with LFD on anthropometry parameters (BMI, weight, WC) and body composition (percentage and kilogram of FM) were investigated through Medical Subject Heading (MeSH) terms and non-MeSH terms. Moreover, the references list of the included trials was checked for further possible sources as well as to ensure about the comprehensiveness of searches.
Selection criteria: The selected studies had following criteria: (1) randomized controlled trials (RCTs) design (with any duration and any sample size were included without relevance to physical activity or other factorial interventions), (2) investigating the impact of LCD with LFD on anthropometric indices and body composition (percentage and kilogram of FM) (LCD contains 30-130 g of carbohydrate per day and very low-carbohydrate ketogenic diet comprises less than 30 g of carbohydrate per day, which usually permit ketosis to occur) (Chawla et al., 2020) (these limits were chosen upon consulting literature regarding LCD (Diabetes, 2017), and LFD (Lichtenstein and Van Horn, 1998, Nordmann et al., 2006), and consensus was reached between the authors), (3) reporting macronutrients percentage in the diet, (4) encompassing participants aged ≥18 years, and (5) presenting sufficient information linking anthropometric indices and body.
Study selection: Two independent researchers (Darand M and Talebi S) carried out initial screening regarding the articles' titles and abstracts. Then, for selecting RCTs about the effect of a low carbohydrate diet with LFD on anthropometric indices and body fat percentage, full texts of all related articles were assessed by reviewers. Ultimately, any possible disagreement was negotiated and solved via consultation with other researchers (Alizadeh A and Abdollahzad H) (Figure 1).
Data extraction: Data extraction was conducted from the selected studies according to criteria including authors' family names, publication year, loss to follow-up, sample size, study duration, diet type, any other intervention, participants' gender, age, and target population, parallel or crossover study design, mean changes and standard deviation (SD) of anthropometric characteristics' levels, as well as mean and SD of anthropometric indices and body fat percentage at the beginning and the end of the trial (Table 1).
Quality assessment: The risk of bias in the included trials was evaluated based on the Cochrane criteria (Higgins et al., 2019). Items for assessing the risk of bias in each research are as follows: (1) random sequence generation; (2) allocation concealment; (3) blinding of participants and personnel; (4) blinding of outcome assessment; (5) incomplete outcome data; (6) selective outcome reporting; and (7) other potential sources of bias. According to the Cochrane Handbook recommendations, three scoring items were applied for studies including “yes”, “no” and “unclear” which respectively indicating low risk of bias, high risk of bias, and unclear or unknown risk of bias. Moreover, after determining ‘key domains’, an overall risk of bias was specified for each trial encompassing good (low risk for all items), fair (low risk for more than three items), and poor (low risk for equivalent and less than three items) (Lorzadeh et al., 2019).
Data synthesis and analysis: The Difference in mean values was defined as effect size. For computing weighted mean differences (WMDs), the mean value was divided by SD of a difference between two random values taken from each group (Higgins, 2011). If the standard error (SE) were reported in studies, SD was obtained by the following equation: SD = SE×√n (n=number of participants in each group). The WMDs with 95% confidence intervals (CIs) was calculated using a random-effects model for conducting meta-analysis (Borenstein et al., 2011). Cochran's Q test and I-squared (I2) statistic was applied to determine heterogeneity of trials which was identified by the following criteria: Q statistic P-value of <0.1; weak heterogeneity: I2 = 25-50, relatively high heterogeneity: I2 = 50-75, high heterogeneity: I2= 75-100 (Higgins and Thompson, 2002). Sub-group analysis was performed to identify possible sources of heterogeneity among the selected trials. As the findings of these trials might be affected by intervention duration, sub-group analysis was accomplished according to these variables. Publication bias was also evaluated through assessing funnel plot and asymmetry tests including Egger’s regression test and Begg’s rank correlation test at P-value of <0.05 (Duval and Tweedie, 2000). Sensitivity analysis was conducted through individual removal of each study and recalculation of pooled estimates, to determine the impact of a specific trial or a particular group of trials. Meta-regression was also carried out to assess the relationship of estimated effect size with trial duration. Stata software, version 11.2 (Stata Corp.) was used for performing statistical analyses. Statistically significant levels were specified at P-value < 0.05.
Data analysis: Sensitivity analysis revealed that the overall effects of a LCD on BMI were significantly affected by removing some studies (Brinkworth et al., 2009, de Luis et al., 2007, Frisch et al., 2009, Gardner et al., 2007, Gardner et al., 2018, Haufe et al., 2011, Jang et al., 2017, Lodi et al., 2020, Perna et al., 2019, Phillips et al., 2008, Rajaie et al., 2013, Vander Wal et al., 2007, Wycherley et al., 2010). However, sensitivity analysis did not represent any change in results for weight change, WC, change in FM percentage, and change in FM (kg).
Meta-regression analysis showed that the effect of the LCD versus the LFD on the study outcomes was not modified by the follow-up duration of studies and age of participants (BMI: slope Duration: -0.001; 95 % CI –0.004, 0.001; P = 0.21, slope Age: -0.004; 95 % CI –0.01, 0.005; P = 0.39), (weight: slope Duration: 0.001; 95 % CI –0.0004, 0.002; P = 0.14, slope Age: -0.002; 95 % CI –0.009, 0.003; P = 0.38), (Change in percentage of FM: slope Duration: 0.006; 95 % CI –0.0005, 0.01; P = 0.06, slope Age: -0.01; 95 % CI –0.03, 0.009; P = 0.28).
Publication bias: Publication bias was not observed based on funnel plots and asymmetry tests (Table 2, Figure 7)
Ethical consideration: The present study was approved by the Ethical Committee of Kermanshah University of Medical Sciences
Results
Study characteristics: The initial literature search identified a total of 2514 publications. The flow chart reporting the study selection process is presented in Figure 1. A total of 327 duplicate studies were first removed and 2077 unrelated records were excluded based on titles and abstracts. Then, 110 articles underwent full-text screening. For studies that reported results for different groups in stratified analysis, all appropriate data were extracted and compared with the control group. Accordingly, two data sets were extracted for studies conducted by Gardner et al. (Gardner et al., 2007), Rock et al. (Rock et al., 2016), Burgess et al. (Burgess et al., 2017), and Petrisko et al. (Petrisko et al., 2020). These studies were conducted on two groups of participants with different health status. Finally, 63 relevant RCT studies (67 data sets) , with a total sample size of 7660 participants (4434 for the low-carbohydrate arm and 3266 for the low-fat arm), published between 1995 and 2020, were eligible for this meta-analysis according to the inclusion criteria. The obtained data included 30 studies on BMI, 63 studies with 67 data sets on weight, 21 studies with 22 data sets on WC, 8 studies on the percentage of body FM, and 13 studies with 14 data sets on kilogram of body FM. The sample size of the analyzed studies was between 4 to 648 individuals and the duration of follow-up ranged from 2 to 144 weeks. Of the included studies, 24 data sets were on overweight/obese patients with obesity-related comorbidities and the remaining studies were on apparently healthy overweight/obese individuals. Moreover, 14 studies included only women, 1 study included only men (Sharman and Volek, 2004), and other studies included both genders. The studies quality ranged from poor to high quality. The characteristics of the analyzed studies are reported in Table 1.
Overall analysis of pooled data: The overall analysis of the included studies revealed that adherence to LCD was significantly associated with a greater reduction in BMI compared to LFD (SMD = - 0.07, 95% CI: -0.14,-0.001; P = 0.04, Figure 2), weight (SMD = - 0.22, 95% CI: - 0.31, - 0.12; P ≤ 0.001, Figure 3), and percentage of body FM (SMD = - 0.28, 95% CI: -0.48, - 0.08; P = 0.006, Figure 4). However, no significant difference in changes in kilogram of body FM (Figure 5) and WC (Figure 6) was observed between the two diet programs.
An additional analysis was performed only on parallel studies which showed similar results. (BMI: SMD = - 0.13, 95% CI: -0.21, -0.05; P = 0.002), (weight: SMD = - 0.21, 95% CI: -0.31,-0.11;
P = ≤0.001), (WC: SMD = - 0.05, 95% CI: -0.13, 0.02; P = 0.18), and (changes of kilogram of body FM: SMD = - 0.04, 95% CI: -0.22, 0.13; P = 0.61).
Evidence of heterogeneity was found among
the studies comparing the effect of a LCD with a LFD on weight and percentage of body FM. However, no significant heterogeneity was observed among the studies which evaluated BMI, WC, and kilogram of body FM (Table 2).
The results of the subgroup analysis are presented in Table 2. In subgroup analysis by the duration of follow-up, gender, age, carbohydrate percentage, and health status of participants, a significant BMI lowering LCD effect was supported by studies with ≥ 6 months follow-up (SMD = - 0.14, 95% CI: - 0.23 to - 0.04; P = 0.004), age less than 60 years (SMD = - 0.0.8, 95% CI: - 0.16 to - 0.002;
P = 0.04), and very low carbohydrate diet (SMD = - 0.19, 95% CI: - 0.31 to - 0.07; P = 0.001). However, for weight, this effect was supported by all subgroups, except for overweight or obese individuals with comorbidities, individuals over the age of 60, and diets with more than 10% carbohydrates. In all subgroups, there was no significant difference in WC, except for overweight or obese individuals without comorbidities
(SMD=-0.08, 95% CI: - 0.16 to - 0.03; P = 0.04). Moreover, for body FM percentage, the mentioned decrease was observed in subgroups of overweight/obese subjects without obesity-related comorbidities (SMD = - 0.21, 95% CI: - 0.37
to - 0.05; P = 0.009), after ≥ 6 months follow-up (SMD = - 0.29, 95%CI: - 0.52 to - 0.05; P = 0.01), in studies with less than 10% carbohydrates
(SMD = - 0.36, 95%CI: - 0.62 to - 0.10; P = 0.007), and studies on both genders (SMD = - 0.31, 95% CI: - 0.57 to - 0.05; P=0.01). In all subgroup analyses, FM changes (kg) were not significant between the two diet programs (Table 2).
Lifestyle modification is considered the first line strategy for weight loss and weight maintenance, and diet is the main component of this approach (Wadden et al., 2007, Wadden et al., 2012). Among many diet approaches for weight reduction, diets with altered macronutrient composition such as low-fat diets (LFD) and low-carbohydrate diets (LCD) have been the focus of a large body of evidence. Reduction of dietary fats (low-fat diets were defined to have ≤ 30% total fat content) (Lichtenstein and Van Horn, 1998, Nordmann et al., 2006, van Zuuren et al., 2018) are theoretically reasonable approach for weight loss for several reasons, one them is high calorie density of fats compared with carbohydrates or protein (Buchholz and Schoeller, 2004, Hite et al., 2011, Tobias et al., 2015). Furthermore, studies have examined the association between dietary fat intake and cardiovascular risk and reported that LFD was traditionally applied for weight reduction (Keys et al., 1986, Sackner-Bernstein et al., 2015). However, clinical trials results are inconsistence and evidence does not support LFD superiority to reduce weight and body composition change in comparison to other dietary interventions (Harcombe et al., 2015, Mansoor et al., 2016, Nordmann et al., 2006, Schwingshackl and Hoffmann, 2013, Tobias et al., 2015). On the other hand, LCD characterized by low amounts of carbohydrates and higher quantities of fat and protein is another popular diet for weight loss and control concentrating on macronutrient distribution of the diet (Mansoor et al., 2016). Generally, LCD contains 30-130 g of carbohydrate per day and a very low-carbohydrate ketogenic diet comprises less than 30 g of carbohydrate per day, which usually permit ketosis to occur (Hite et al., 2011). It is hypothesized that sever carbohydrate restriction results in the depletion of glycogen stores and the excretion of bound water (Astrup et al., 2004). In addition, this diet is an appetite suppressing due to its ketogenic nature, increases satiating, and reduces spontaneous food intake due to its high protein-content. Moreover, the restriction of carbohydrates limits food choices and subsequently reduces energy consumption (Astrup et al., 2004). In a recent meta-analysis, it is shown that adherence to LCD was associated with a decrease in body fat. It is hypothesized that low carbohydrate diets contain high amounts of protein, converting to amino acids in the body. Amino acids increase muscle growth, leading to an increase in muscle mass and subsequently decreasing fat mass (FM) (Hashimoto et al., 2016). On the other hand, in a previous trial, it has been shown that an energy-restricted, high-protein, LFD had no significant impact on body fat in comparison to a high-carbohydrate LFD in obese women (Noakes et al., 2005).
For a long time, debates have been existed about the effectiveness of these two kinds of very popular diets in the treatment of obesity and there is still no definite conclusion in this regard particularly in terms of body composition. Therefore, this conclusive systematic review and meta-analysis were undertaken to update previous investigations to assess the effectiveness of LFD interventions in comparison to LCD trials in terms of anthropometry parameters (body mass index (BMI), weight, waist circumference (WC)) as well as body composition (percentage and kilogram of FM).
Materials and Methods
Search strategy: The Preferred Reporting Items for Systematic Reviews and Meta-Analyses Guidelines (PRISMA) were applied for writing the current study (Moher et al., 2015). Scopus, Google Scholar, PubMed, Web of Science and Cochran Library were searched up to November 2020 without any restrictions to identify eligible trials. The effect of a low carbohydrate diet with LFD on anthropometry parameters (BMI, weight, WC) and body composition (percentage and kilogram of FM) were investigated through Medical Subject Heading (MeSH) terms and non-MeSH terms. Moreover, the references list of the included trials was checked for further possible sources as well as to ensure about the comprehensiveness of searches.
Selection criteria: The selected studies had following criteria: (1) randomized controlled trials (RCTs) design (with any duration and any sample size were included without relevance to physical activity or other factorial interventions), (2) investigating the impact of LCD with LFD on anthropometric indices and body composition (percentage and kilogram of FM) (LCD contains 30-130 g of carbohydrate per day and very low-carbohydrate ketogenic diet comprises less than 30 g of carbohydrate per day, which usually permit ketosis to occur) (Chawla et al., 2020) (these limits were chosen upon consulting literature regarding LCD (Diabetes, 2017), and LFD (Lichtenstein and Van Horn, 1998, Nordmann et al., 2006), and consensus was reached between the authors), (3) reporting macronutrients percentage in the diet, (4) encompassing participants aged ≥18 years, and (5) presenting sufficient information linking anthropometric indices and body.
Study selection: Two independent researchers (Darand M and Talebi S) carried out initial screening regarding the articles' titles and abstracts. Then, for selecting RCTs about the effect of a low carbohydrate diet with LFD on anthropometric indices and body fat percentage, full texts of all related articles were assessed by reviewers. Ultimately, any possible disagreement was negotiated and solved via consultation with other researchers (Alizadeh A and Abdollahzad H) (Figure 1).
Data extraction: Data extraction was conducted from the selected studies according to criteria including authors' family names, publication year, loss to follow-up, sample size, study duration, diet type, any other intervention, participants' gender, age, and target population, parallel or crossover study design, mean changes and standard deviation (SD) of anthropometric characteristics' levels, as well as mean and SD of anthropometric indices and body fat percentage at the beginning and the end of the trial (Table 1).
Quality assessment: The risk of bias in the included trials was evaluated based on the Cochrane criteria (Higgins et al., 2019). Items for assessing the risk of bias in each research are as follows: (1) random sequence generation; (2) allocation concealment; (3) blinding of participants and personnel; (4) blinding of outcome assessment; (5) incomplete outcome data; (6) selective outcome reporting; and (7) other potential sources of bias. According to the Cochrane Handbook recommendations, three scoring items were applied for studies including “yes”, “no” and “unclear” which respectively indicating low risk of bias, high risk of bias, and unclear or unknown risk of bias. Moreover, after determining ‘key domains’, an overall risk of bias was specified for each trial encompassing good (low risk for all items), fair (low risk for more than three items), and poor (low risk for equivalent and less than three items) (Lorzadeh et al., 2019).
Data synthesis and analysis: The Difference in mean values was defined as effect size. For computing weighted mean differences (WMDs), the mean value was divided by SD of a difference between two random values taken from each group (Higgins, 2011). If the standard error (SE) were reported in studies, SD was obtained by the following equation: SD = SE×√n (n=number of participants in each group). The WMDs with 95% confidence intervals (CIs) was calculated using a random-effects model for conducting meta-analysis (Borenstein et al., 2011). Cochran's Q test and I-squared (I2) statistic was applied to determine heterogeneity of trials which was identified by the following criteria: Q statistic P-value of <0.1; weak heterogeneity: I2 = 25-50, relatively high heterogeneity: I2 = 50-75, high heterogeneity: I2= 75-100 (Higgins and Thompson, 2002). Sub-group analysis was performed to identify possible sources of heterogeneity among the selected trials. As the findings of these trials might be affected by intervention duration, sub-group analysis was accomplished according to these variables. Publication bias was also evaluated through assessing funnel plot and asymmetry tests including Egger’s regression test and Begg’s rank correlation test at P-value of <0.05 (Duval and Tweedie, 2000). Sensitivity analysis was conducted through individual removal of each study and recalculation of pooled estimates, to determine the impact of a specific trial or a particular group of trials. Meta-regression was also carried out to assess the relationship of estimated effect size with trial duration. Stata software, version 11.2 (Stata Corp.) was used for performing statistical analyses. Statistically significant levels were specified at P-value < 0.05.
Data analysis: Sensitivity analysis revealed that the overall effects of a LCD on BMI were significantly affected by removing some studies (Brinkworth et al., 2009, de Luis et al., 2007, Frisch et al., 2009, Gardner et al., 2007, Gardner et al., 2018, Haufe et al., 2011, Jang et al., 2017, Lodi et al., 2020, Perna et al., 2019, Phillips et al., 2008, Rajaie et al., 2013, Vander Wal et al., 2007, Wycherley et al., 2010). However, sensitivity analysis did not represent any change in results for weight change, WC, change in FM percentage, and change in FM (kg).
Meta-regression analysis showed that the effect of the LCD versus the LFD on the study outcomes was not modified by the follow-up duration of studies and age of participants (BMI: slope Duration: -0.001; 95 % CI –0.004, 0.001; P = 0.21, slope Age: -0.004; 95 % CI –0.01, 0.005; P = 0.39), (weight: slope Duration: 0.001; 95 % CI –0.0004, 0.002; P = 0.14, slope Age: -0.002; 95 % CI –0.009, 0.003; P = 0.38), (Change in percentage of FM: slope Duration: 0.006; 95 % CI –0.0005, 0.01; P = 0.06, slope Age: -0.01; 95 % CI –0.03, 0.009; P = 0.28).
Publication bias: Publication bias was not observed based on funnel plots and asymmetry tests (Table 2, Figure 7)
Ethical consideration: The present study was approved by the Ethical Committee of Kermanshah University of Medical Sciences
Results
Study characteristics: The initial literature search identified a total of 2514 publications. The flow chart reporting the study selection process is presented in Figure 1. A total of 327 duplicate studies were first removed and 2077 unrelated records were excluded based on titles and abstracts. Then, 110 articles underwent full-text screening. For studies that reported results for different groups in stratified analysis, all appropriate data were extracted and compared with the control group. Accordingly, two data sets were extracted for studies conducted by Gardner et al. (Gardner et al., 2007), Rock et al. (Rock et al., 2016), Burgess et al. (Burgess et al., 2017), and Petrisko et al. (Petrisko et al., 2020). These studies were conducted on two groups of participants with different health status. Finally, 63 relevant RCT studies (67 data sets) , with a total sample size of 7660 participants (4434 for the low-carbohydrate arm and 3266 for the low-fat arm), published between 1995 and 2020, were eligible for this meta-analysis according to the inclusion criteria. The obtained data included 30 studies on BMI, 63 studies with 67 data sets on weight, 21 studies with 22 data sets on WC, 8 studies on the percentage of body FM, and 13 studies with 14 data sets on kilogram of body FM. The sample size of the analyzed studies was between 4 to 648 individuals and the duration of follow-up ranged from 2 to 144 weeks. Of the included studies, 24 data sets were on overweight/obese patients with obesity-related comorbidities and the remaining studies were on apparently healthy overweight/obese individuals. Moreover, 14 studies included only women, 1 study included only men (Sharman and Volek, 2004), and other studies included both genders. The studies quality ranged from poor to high quality. The characteristics of the analyzed studies are reported in Table 1.
Overall analysis of pooled data: The overall analysis of the included studies revealed that adherence to LCD was significantly associated with a greater reduction in BMI compared to LFD (SMD = - 0.07, 95% CI: -0.14,-0.001; P = 0.04, Figure 2), weight (SMD = - 0.22, 95% CI: - 0.31, - 0.12; P ≤ 0.001, Figure 3), and percentage of body FM (SMD = - 0.28, 95% CI: -0.48, - 0.08; P = 0.006, Figure 4). However, no significant difference in changes in kilogram of body FM (Figure 5) and WC (Figure 6) was observed between the two diet programs.
An additional analysis was performed only on parallel studies which showed similar results. (BMI: SMD = - 0.13, 95% CI: -0.21, -0.05; P = 0.002), (weight: SMD = - 0.21, 95% CI: -0.31,-0.11;
P = ≤0.001), (WC: SMD = - 0.05, 95% CI: -0.13, 0.02; P = 0.18), and (changes of kilogram of body FM: SMD = - 0.04, 95% CI: -0.22, 0.13; P = 0.61).
Evidence of heterogeneity was found among
the studies comparing the effect of a LCD with a LFD on weight and percentage of body FM. However, no significant heterogeneity was observed among the studies which evaluated BMI, WC, and kilogram of body FM (Table 2).
The results of the subgroup analysis are presented in Table 2. In subgroup analysis by the duration of follow-up, gender, age, carbohydrate percentage, and health status of participants, a significant BMI lowering LCD effect was supported by studies with ≥ 6 months follow-up (SMD = - 0.14, 95% CI: - 0.23 to - 0.04; P = 0.004), age less than 60 years (SMD = - 0.0.8, 95% CI: - 0.16 to - 0.002;
P = 0.04), and very low carbohydrate diet (SMD = - 0.19, 95% CI: - 0.31 to - 0.07; P = 0.001). However, for weight, this effect was supported by all subgroups, except for overweight or obese individuals with comorbidities, individuals over the age of 60, and diets with more than 10% carbohydrates. In all subgroups, there was no significant difference in WC, except for overweight or obese individuals without comorbidities
(SMD=-0.08, 95% CI: - 0.16 to - 0.03; P = 0.04). Moreover, for body FM percentage, the mentioned decrease was observed in subgroups of overweight/obese subjects without obesity-related comorbidities (SMD = - 0.21, 95% CI: - 0.37
to - 0.05; P = 0.009), after ≥ 6 months follow-up (SMD = - 0.29, 95%CI: - 0.52 to - 0.05; P = 0.01), in studies with less than 10% carbohydrates
(SMD = - 0.36, 95%CI: - 0.62 to - 0.10; P = 0.007), and studies on both genders (SMD = - 0.31, 95% CI: - 0.57 to - 0.05; P=0.01). In all subgroup analyses, FM changes (kg) were not significant between the two diet programs (Table 2).
CHO, carbohydrate; PRO, protein; FAT, lipid; T2DM, type 2 diabetes mellitus; NAFLD, nonalcoholic fatty liver disease; IGB, Intragastric balloon; IBW, ideal body weight; BMI, body mass index; WC, waist circumference; ND, non-defined; gr; gram, mg; milligram, d; day.
Discussion
This is a meta-analysis of RCTs investigating the effect of LCD compared to LFD on anthropometric characteristics. Treatment strategies are important in improving anthropometric indices. Abnormalities of anthropometric characteristics were strongly associated with diseases such as diabetes, hypertension, and cardiovascular disease (Furtado et al., 2018, Hadaegh et al., 2009, Khader et al., 2019). Studies have reported that different diets could affect anthropometric indices. The present meta-analysis study showed that adherence to LCD compared to LFD significantly reduced BMI in studies with ≥ 6 months follow-up, very low carbohydrate diet, and age less than 60 years. The protective effect of LCD on weight was observed except for overweight or obese individuals with comorbidities, individuals over the age of 60 and with diets more than 10% carbohydrates.
A possible reason was that older people generally had less physical activity compared to young people, so that activity-related energy expenditure was also lower. This can lead to increased BMI and weight (Elmadfa and Meyer, 2008). Also, adherence to LCD can reduce FM percentage compared to LFD, but body FM (kg) and WC did not change.
A meta-analysis was similar to the present study published in 2020, investigating the effect of LFD and LCD on weight and lipid profile. In this
study, only 38 trials were included and other anthropometric indices were not investigated (Chawla et al., 2020). Previous meta-analysis studies have reported the protective effect of LCD. Another meta-analysis study was conducted on RCTs. It was reported that LCD (50 g carbohydrate or 10% calorie from carbohydrates) can reduce body weight (BW) and FM compared to LCD (about 40% calorie from carbohydrate) (Hashimoto et al., 2016). Another meta-analysis investigated the effect of very-low-carbohydrate ketogenic diets (VLCKD) compared to LFD. Results of this study showed that VLCKD decreased BW, triglyceride and diastolic blood pressure while increased high-density lipoprotein (HDL-C) and low-density lipoproteins (LDL-C) compared to LCD (Bueno et al., 2013). Results of another meta-analysis study showed that LCD-C reduced triglycerides concentration and increased HDL cholesterol concentration (Meng et al., 2017). A meta-analysis study showed that LCD can significantly decrease BMI and serum levels of total cholesterol and LDL-C in polycystic ovary syndrome (PCOS) women (Zhang et al., 2019). Some reports have shown that LCD cannot have a protective effect (Foster et al., 2003, Yancy Jr et al., 2004). Results of a study have indicated that LCD can affect weight loss more than the conventional diet in the first six months, but the differences were not significant during a year in another study (Foster et al., 2003). It was also shown that changes in LDL-C levels did not differ statistically with the LCD and LFD (Yancy Jr et al., 2004).
The low percentage of carbohydrate intake from energy will be compensated with high fat and protein intake (Farabi and Hernandez, 2019, Frigolet et al., 2011). Protein consists of amino acids. Amino acids are necessary for preserving lean body mass and muscle growth (Coker et al., 2012, Simonson et al., 2020), also intake of protein is positively associated with lean mass (Houston et al., 2008). Ebbeling et al. reported that to decrease an individual’s BMR (basal metabolic rate) LCD is effective compared to LFD. LCD may result in the modulation of resting energy expenditure (Ebbeling et al., 2012). LCD decreases supply of glucose to the liver, muscles and brain, thereby inactivating gluconeogenesis. The main source of gluconeogenesis is glycerol (Brouns, 2018). It is caused by triglycerides breakdown (Chourpiliadis and Mohiuddin, 2020). The Decomposition of triglycerides is associated with reduced FM. The current study results showed that LCD significantly reduced body FM percentage; however, no significant difference in body FM changes was observed. In fact, LCD can change the body composition.
This study had several strengths. RCTs with less bias were included in comparison to observational studies. A large number of included studies lets us assess publication bias. This meta-analysis due to the large sample size could recognize statistically significant mean differences in outcomes. Further subgroup analyses based on the duration of follow-up, gender, and health status would provide beneficial insights into these diets.
This study had some limitations. Given that one of the inclusion criteria was English language publications, some studies might have been missed. Most studies did not prepare food for the duration of the trial, which may reduce adherence to diet protocols. Some studies did not also report any data about participant’s physical activity. Crossover trials in this study only reported overall results for low-carb and low-fat arm; so, these measures were used in the present analysis. Furthermore, diets did not account for the quality of the food consumed. The type of intake carbohydrates was not clear. Refine cereals or whole grains have different effects on the risk of disease. Other limitations of the study were heterogeneity during follow-up and the amount of carbohydrates in both the interventional and comparative diets.
Conclusion
Dietary intake is the main cause of abnormal anthropometric characteristics, and diet changes are suggested to improve this condition. Generally, LCD compared to LFD had an effect in losing BMI, weight and body fat percentage, but did not affect body FM and WC. In the future, more interventions with a specific carbohydrate dose are needed to reach a definitive conclusion.
Acknowledgment
Thanks are owed to the Research Committee of Kermanshah University of Medical Sciences.
Funding information
The present study with ID 3011110 was supported by the Nutrition Research Committee of School of Nutrition and Food Science Kermanshah University of Medical Sciences.
Conflict of interest
The authors declare that they have no conflict of interest.
Authors’ contributions
Darand M designed the research; Darand M and Alizadeh S conducted the research; Hassanizadeh S analyzed the data; Darand M, Talebi S and Darand Z extracted the data, Darabi Z, Yaghoubi F, Bagherniya M and Azamian Y wrote the paper, Abdollahzad H finalized the paper. All authors read and approved the final manuscript.
References
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Astrup A, Larsen TM & Harper A 2004. Atkins and other low-carbohydrate diets: hoax or an effective tool for weight loss? The Lancet. 364 (9437): 897-899.
Borenstein M, Hedges LV, Higgins JP & Rothstein HR 2011. Introduction to meta-analysis. John Wiley & Sons.
Brinkworth GD, Noakes M, Clifton PM & Bird AR 2009. Comparative effects of very low-carbohydrate, high-fat and high-carbohydrate, low-fat weight-loss diets on bowel habit and faecal short-chain fatty acids and bacterial populations. British journal of nutrition. 101 (10): 1493-1502.
Brouns F 2018. Overweight and diabetes prevention: is a low-carbohydrate–high-fat diet recommendable? European journal of nutrition. 57 (4): 1301-1312.
Buchholz AC & Schoeller DA 2004. Is a calorie a calorie? American journal of clinical nutrition. 79 (5): 899S-906S.
Bueno NB, de Melo ISV, de Oliveira SL & da Rocha Ataide T 2013. Very-low-carbohydrate ketogenic diet v. low-fat diet for long-term weight loss: a meta-analysis of randomised controlled trials. British journal of nutrition. 110 (7): 1178-1187.
Burgess B, Raynor HA & Tepper BJ 2017. PROP Nontaster Women Lose More Weight Following a Low-Carbohydrate Versus a Low-Fat Diet in a Randomized Controlled Trial. Obesity (Silver Spring, Md.). 25 (10): 1682-1690.
Chawla S, Tessarolo Silva F, Amaral Medeiros S, Mekary RA & Radenkovic D 2020. The Effect of Low-Fat and Low-Carbohydrate Diets on Weight Loss and Lipid Levels: A Systematic Review and Meta-Analysis. Nutrients. 12 (12): 3774.
Chourpiliadis C & Mohiuddin SS 2020. Biochemistry, gluconeogenesis. StatPearls https://www.ncbi.nlm.nih.gov/books/NBK544346/
Coker RH, Miller S, Schutzler S, Deutz N & Wolfe RR 2012. Whey protein and essential amino acids promote the reduction of adipose tissue and increased muscle protein synthesis during caloric restriction-induced weight loss in elderly, obese individuals. Nutrition journal. 11 (1): 105.
de Luis DA, et al. 2007. Effects of a low-fat versus a low-carbohydrate diet on adipocytokines in obese adults. Hormone research. 67 (6): 296-300.
Diabetes U 2017. Position statement: low-carb diets for people with diabetes. Diabetes UK: London, UK.
Duval S & Tweedie R 2000. Trim and fill: a simple funnel‐plot–based method of testing and adjusting for publication bias in meta‐analysis. Biometrics. 56 (2): 455-463.
Ebbeling CB, et al. 2012. Effects of dietary composition on energy expenditure during weight-loss maintenance. Journal of the American medical association. 307 (24): 2627-2634.
Elmadfa I & Meyer AL 2008. Body composition, changing physiological functions and nutrient requirements of the elderly. Annals of nutrition and metabolism. 52 (Suppl. 1): 2-5.
Farabi SS & Hernandez TL 2019. Low-carbohydrate diets for gestational diabetes. Nutrients. 11 (8): 1737.
Foster GD, et al. 2003. A randomized trial of a low-carbohydrate diet for obesity. New England Journal of Medicine. 348 (21): 2082-2090.
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Gardner CD, et al. 2007. Comparison of the Atkins, Zone, Ornish, and LEARN diets for change in weight and related risk factors among overweight premenopausal women: the A TO Z Weight Loss Study: a randomized trial. Journal of American medical association. 297 (9): 969-977.
Gardner CD, et al. 2018. Effect of low-fat VS low-carbohydrate diet on 12-month weight loss in overweight adults and the association with genotype pattern or insulin secretion the DIETFITS randomized clinical trial. Journal of the American medical association. 319 (7): 667-679.
Hadaegh F, Shafiee G & Azizi F 2009. Anthropometric predictors of incident type 2 diabetes mellitus in Iranian women. Annals of Saudi medicine. 29 (3): 194-200.
Harcombe Z, et al. 2015. Evidence from randomised controlled trials did not support the introduction of dietary fat guidelines in 1977 and 1983: a systematic review and meta-analysis. Open heart. 2 (1): e000196.
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This is a meta-analysis of RCTs investigating the effect of LCD compared to LFD on anthropometric characteristics. Treatment strategies are important in improving anthropometric indices. Abnormalities of anthropometric characteristics were strongly associated with diseases such as diabetes, hypertension, and cardiovascular disease (Furtado et al., 2018, Hadaegh et al., 2009, Khader et al., 2019). Studies have reported that different diets could affect anthropometric indices. The present meta-analysis study showed that adherence to LCD compared to LFD significantly reduced BMI in studies with ≥ 6 months follow-up, very low carbohydrate diet, and age less than 60 years. The protective effect of LCD on weight was observed except for overweight or obese individuals with comorbidities, individuals over the age of 60 and with diets more than 10% carbohydrates.
A possible reason was that older people generally had less physical activity compared to young people, so that activity-related energy expenditure was also lower. This can lead to increased BMI and weight (Elmadfa and Meyer, 2008). Also, adherence to LCD can reduce FM percentage compared to LFD, but body FM (kg) and WC did not change.
A meta-analysis was similar to the present study published in 2020, investigating the effect of LFD and LCD on weight and lipid profile. In this
study, only 38 trials were included and other anthropometric indices were not investigated (Chawla et al., 2020). Previous meta-analysis studies have reported the protective effect of LCD. Another meta-analysis study was conducted on RCTs. It was reported that LCD (50 g carbohydrate or 10% calorie from carbohydrates) can reduce body weight (BW) and FM compared to LCD (about 40% calorie from carbohydrate) (Hashimoto et al., 2016). Another meta-analysis investigated the effect of very-low-carbohydrate ketogenic diets (VLCKD) compared to LFD. Results of this study showed that VLCKD decreased BW, triglyceride and diastolic blood pressure while increased high-density lipoprotein (HDL-C) and low-density lipoproteins (LDL-C) compared to LCD (Bueno et al., 2013). Results of another meta-analysis study showed that LCD-C reduced triglycerides concentration and increased HDL cholesterol concentration (Meng et al., 2017). A meta-analysis study showed that LCD can significantly decrease BMI and serum levels of total cholesterol and LDL-C in polycystic ovary syndrome (PCOS) women (Zhang et al., 2019). Some reports have shown that LCD cannot have a protective effect (Foster et al., 2003, Yancy Jr et al., 2004). Results of a study have indicated that LCD can affect weight loss more than the conventional diet in the first six months, but the differences were not significant during a year in another study (Foster et al., 2003). It was also shown that changes in LDL-C levels did not differ statistically with the LCD and LFD (Yancy Jr et al., 2004).
The low percentage of carbohydrate intake from energy will be compensated with high fat and protein intake (Farabi and Hernandez, 2019, Frigolet et al., 2011). Protein consists of amino acids. Amino acids are necessary for preserving lean body mass and muscle growth (Coker et al., 2012, Simonson et al., 2020), also intake of protein is positively associated with lean mass (Houston et al., 2008). Ebbeling et al. reported that to decrease an individual’s BMR (basal metabolic rate) LCD is effective compared to LFD. LCD may result in the modulation of resting energy expenditure (Ebbeling et al., 2012). LCD decreases supply of glucose to the liver, muscles and brain, thereby inactivating gluconeogenesis. The main source of gluconeogenesis is glycerol (Brouns, 2018). It is caused by triglycerides breakdown (Chourpiliadis and Mohiuddin, 2020). The Decomposition of triglycerides is associated with reduced FM. The current study results showed that LCD significantly reduced body FM percentage; however, no significant difference in body FM changes was observed. In fact, LCD can change the body composition.
This study had several strengths. RCTs with less bias were included in comparison to observational studies. A large number of included studies lets us assess publication bias. This meta-analysis due to the large sample size could recognize statistically significant mean differences in outcomes. Further subgroup analyses based on the duration of follow-up, gender, and health status would provide beneficial insights into these diets.
This study had some limitations. Given that one of the inclusion criteria was English language publications, some studies might have been missed. Most studies did not prepare food for the duration of the trial, which may reduce adherence to diet protocols. Some studies did not also report any data about participant’s physical activity. Crossover trials in this study only reported overall results for low-carb and low-fat arm; so, these measures were used in the present analysis. Furthermore, diets did not account for the quality of the food consumed. The type of intake carbohydrates was not clear. Refine cereals or whole grains have different effects on the risk of disease. Other limitations of the study were heterogeneity during follow-up and the amount of carbohydrates in both the interventional and comparative diets.
Conclusion
Dietary intake is the main cause of abnormal anthropometric characteristics, and diet changes are suggested to improve this condition. Generally, LCD compared to LFD had an effect in losing BMI, weight and body fat percentage, but did not affect body FM and WC. In the future, more interventions with a specific carbohydrate dose are needed to reach a definitive conclusion.
Acknowledgment
Thanks are owed to the Research Committee of Kermanshah University of Medical Sciences.
Funding information
The present study with ID 3011110 was supported by the Nutrition Research Committee of School of Nutrition and Food Science Kermanshah University of Medical Sciences.
Conflict of interest
The authors declare that they have no conflict of interest.
Authors’ contributions
Darand M designed the research; Darand M and Alizadeh S conducted the research; Hassanizadeh S analyzed the data; Darand M, Talebi S and Darand Z extracted the data, Darabi Z, Yaghoubi F, Bagherniya M and Azamian Y wrote the paper, Abdollahzad H finalized the paper. All authors read and approved the final manuscript.
References
Abarca-Gómez L, et al. 2017. Worldwide trends in body-mass index, underweight, overweight, and obesity from 1975 to 2016: a pooled analysis of 2416 population-based measurement studies in 128· 9 million children, adolescents, and adults. The Lancet. 390 (10113): 2627-2642.
Astrup A, Larsen TM & Harper A 2004. Atkins and other low-carbohydrate diets: hoax or an effective tool for weight loss? The Lancet. 364 (9437): 897-899.
Borenstein M, Hedges LV, Higgins JP & Rothstein HR 2011. Introduction to meta-analysis. John Wiley & Sons.
Brinkworth GD, Noakes M, Clifton PM & Bird AR 2009. Comparative effects of very low-carbohydrate, high-fat and high-carbohydrate, low-fat weight-loss diets on bowel habit and faecal short-chain fatty acids and bacterial populations. British journal of nutrition. 101 (10): 1493-1502.
Brouns F 2018. Overweight and diabetes prevention: is a low-carbohydrate–high-fat diet recommendable? European journal of nutrition. 57 (4): 1301-1312.
Buchholz AC & Schoeller DA 2004. Is a calorie a calorie? American journal of clinical nutrition. 79 (5): 899S-906S.
Bueno NB, de Melo ISV, de Oliveira SL & da Rocha Ataide T 2013. Very-low-carbohydrate ketogenic diet v. low-fat diet for long-term weight loss: a meta-analysis of randomised controlled trials. British journal of nutrition. 110 (7): 1178-1187.
Burgess B, Raynor HA & Tepper BJ 2017. PROP Nontaster Women Lose More Weight Following a Low-Carbohydrate Versus a Low-Fat Diet in a Randomized Controlled Trial. Obesity (Silver Spring, Md.). 25 (10): 1682-1690.
Chawla S, Tessarolo Silva F, Amaral Medeiros S, Mekary RA & Radenkovic D 2020. The Effect of Low-Fat and Low-Carbohydrate Diets on Weight Loss and Lipid Levels: A Systematic Review and Meta-Analysis. Nutrients. 12 (12): 3774.
Chourpiliadis C & Mohiuddin SS 2020. Biochemistry, gluconeogenesis. StatPearls https://www.ncbi.nlm.nih.gov/books/NBK544346/
Coker RH, Miller S, Schutzler S, Deutz N & Wolfe RR 2012. Whey protein and essential amino acids promote the reduction of adipose tissue and increased muscle protein synthesis during caloric restriction-induced weight loss in elderly, obese individuals. Nutrition journal. 11 (1): 105.
de Luis DA, et al. 2007. Effects of a low-fat versus a low-carbohydrate diet on adipocytokines in obese adults. Hormone research. 67 (6): 296-300.
Diabetes U 2017. Position statement: low-carb diets for people with diabetes. Diabetes UK: London, UK.
Duval S & Tweedie R 2000. Trim and fill: a simple funnel‐plot–based method of testing and adjusting for publication bias in meta‐analysis. Biometrics. 56 (2): 455-463.
Ebbeling CB, et al. 2012. Effects of dietary composition on energy expenditure during weight-loss maintenance. Journal of the American medical association. 307 (24): 2627-2634.
Elmadfa I & Meyer AL 2008. Body composition, changing physiological functions and nutrient requirements of the elderly. Annals of nutrition and metabolism. 52 (Suppl. 1): 2-5.
Farabi SS & Hernandez TL 2019. Low-carbohydrate diets for gestational diabetes. Nutrients. 11 (8): 1737.
Foster GD, et al. 2003. A randomized trial of a low-carbohydrate diet for obesity. New England Journal of Medicine. 348 (21): 2082-2090.
Frigolet M-E, Barragán V-ER & González MT 2011. Low-carbohydrate diets: a matter of love or hate. Annals of nutrition and metabolism. 58 (4): 320-334.
Frisch S, et al. 2009. A randomized controlled trial on the efficacy of carbohydrate-reduced or fat-reduced diets in patients attending a telemedically guided weight loss program. Cardiovascular diabetology. 8: 36.
Furtado JM, et al. 2018. Anthropometric features as predictors of atherogenic dyslipidemia and cardiovascular risk in a large population of school-aged children. PloS one. 13 (6): e0197922.
Gardner CD, et al. 2007. Comparison of the Atkins, Zone, Ornish, and LEARN diets for change in weight and related risk factors among overweight premenopausal women: the A TO Z Weight Loss Study: a randomized trial. Journal of American medical association. 297 (9): 969-977.
Gardner CD, et al. 2018. Effect of low-fat VS low-carbohydrate diet on 12-month weight loss in overweight adults and the association with genotype pattern or insulin secretion the DIETFITS randomized clinical trial. Journal of the American medical association. 319 (7): 667-679.
Hadaegh F, Shafiee G & Azizi F 2009. Anthropometric predictors of incident type 2 diabetes mellitus in Iranian women. Annals of Saudi medicine. 29 (3): 194-200.
Harcombe Z, et al. 2015. Evidence from randomised controlled trials did not support the introduction of dietary fat guidelines in 1977 and 1983: a systematic review and meta-analysis. Open heart. 2 (1): e000196.
Hashimoto Y, et al. 2016. Impact of low‐carbohydrate diet on body composition: meta‐analysis of randomized controlled studies. Obesity reviews. 17 (6): 499-509.
Haufe S, et al. 2011. Randomized comparison of reduced fat and reduced carbohydrate hypocaloric diets on intrahepatic fat in overweight and obese human subjects. Hepatology (Baltimore, Md.). 53 (5): 1504-1514.
Higgins J 2011. Cochrane handbook for systematic reviews of interventions. Version 5.1. 0 [updated March 2011]. The Cochrane Collaboration.
Higgins JP, et al. 2019. Cochrane handbook for systematic reviews of interventions. John Wiley & Sons.
Higgins JP & Thompson SG 2002. Quantifying heterogeneity in a meta‐analysis. Statistics in medicine. 21 (11): 1539-1558.
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Received: 2022/01/17 | Published: 2023/08/28 | ePublished: 2023/08/28
Received: 2022/01/17 | Published: 2023/08/28 | ePublished: 2023/08/28
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