Effects of Popular Diets on Anthropometric and Cardiometabolic Parameters: An Umbrella Review of Meta-Analyses of Randomized Controlled Trials

Monica Dinu; Giuditta Pagliai; Donato Angelino; Alice Rosi; Margherita Dall'Asta; Letizia Bresciani; Cinzia Ferraris; Monica Guglielmetti; Justyna Godos; Cristian Del Bo’; Daniele Nucci; Erika Meroni; Linda Landini; Daniela Martini; Francesco Sofi

doi:10.1093/advances/nmaa006

. 2020 Feb 14;11(4):815–833. doi: 10.1093/advances/nmaa006

Effects of Popular Diets on Anthropometric and Cardiometabolic Parameters: An Umbrella Review of Meta-Analyses of Randomized Controlled Trials

Monica Dinu ¹, Giuditta Pagliai ², Donato Angelino ³, Alice Rosi ⁴, Margherita Dall'Asta ⁵, Letizia Bresciani ⁶, Cinzia Ferraris ⁷, Monica Guglielmetti ⁸, Justyna Godos ⁹, Cristian Del Bo’ ¹⁰, Daniele Nucci ¹¹, Erika Meroni ¹², Linda Landini ¹³, Daniela Martini ^14,^✉, Francesco Sofi ^15,^16,¹⁷, The Working Group “Young Members” of the Italian Society of Human Nutrition (SINU)

PMCID: PMC7360456 PMID: 32059053

ABSTRACT

The prevalence of overweight, obesity, and their related complications is increasing worldwide. The purpose of this umbrella review was to summarize and critically evaluate the effects of different diets on anthropometric parameters and cardiometabolic risk factors. Medline, Embase, Scopus, Cochrane Database of Systematic Reviews, and Web of Science, from inception to April 2019, were used as data sources to select meta-analyses of randomized controlled trials that examined the effects of different diets on anthropometric parameters and cardiometabolic risk factors. Strength and validity of the evidence were assessed through a set of predefined criteria. Eighty articles reporting 495 unique meta-analyses were examined, covering a wide range of popular diets: low-carbohydrate (n = 21 articles), high-protein (n = 8), low-fat (n = 9), paleolithic (n = 2), low-glycemic-index/load (n = 12), intermittent energy restriction (n = 6), Mediterranean (n = 11), Nordic (n = 2), vegetarian (n = 9), Dietary Approaches to Stop Hypertension (DASH) (n = 6), and portfolio dietary pattern (n = 1). Great variability in terms of definition of the intervention and control diets was observed. The methodological quality of most articles (n = 65; 81%), evaluated using the “A MeaSurement Tool to Assess systematic Reviews-2” questionnaire, was low or critically low. The strength of evidence was generally weak. The most consistent evidence was reported for the Mediterranean diet, with suggestive evidence of an improvement in weight, BMI, total cholesterol, glucose, and blood pressure. Suggestive evidence of an improvement in weight and blood pressure was also reported for the DASH diet. Low-carbohydrate, high-protein, low-fat, and low-glycemic-index/load diets showed suggestive and/or weak evidence of a reduction in weight and BMI, but contrasting evidence for lipid, glycemic, and blood pressure parameters, suggesting potential risks of unfavorable effects. Evidence for paleolithic, intermittent energy restriction, Nordic, vegetarian, and portfolio dietary patterns was graded as weak. Among all the diets evaluated, the Mediterranean diet had the strongest and most consistent evidence of a beneficial effect on both anthropometric parameters and cardiometabolic risk factors. This review protocol was registered at www.crd.york.ac.uk/PROSPERO/ as CRD42019126103.

Keywords: diet, review, meta-analysis, weight, risk factors

Introduction

With the increasing numbers of overweight and obese people worldwide (1), there is a growing public health concern on body size and dietary habits. Current data show that ∼42% of adults worldwide have tried to lose weight at some point in life (2). In response to the ubiquity of weight-loss efforts, diets that promise rapid and easy weight loss by limiting certain foods or macronutrients are constantly emerging, attracting public attention, and generating considerable debate. The effectiveness of a diet, however, is measured not only by its ability to induce weight loss in a short time. Several other factors such as their overall nutritional quality and the long-term effects on cardiometabolic risk factors should be carefully considered (3). As reported by both observational and intervention studies, there is supporting evidence for potential causal relations between dietary patterns, health status, and occurrence of chronic degenerative diseases (4, 5).

Numerous epidemiological studies and clinical trials have evaluated the impact of dietary interventions on weight and biomarkers related to metabolic disorders so far (6), and many meta-analyses have been published (5, 7–9). Meta-analyses are powerful tools that can overcome difficulties in performing large-scale randomized controlled trials (RCTs), but are subject to the possibility of bias related to variation in quality and empirical validation. It has been reported that over half of the meta-analyses published are flawed and unnecessary (10), and that the production of poor-quality and redundant meta-analyses can contribute to the spread of misleading dietary concepts (11).

The assessment of the quality and credibility of existing evidence may have implications for both clinical practice and public health. Umbrella reviews are overviews of systematic reviews and meta-analyses that provide a comprehensive and systematic evaluation of the scientific literature available for a specific research topic and offer the possibility to understand the strength of evidence and extent of potential biases (12). To the best of our knowledge, no previous umbrella reviews have assessed the strength and validity of the evidence available on dietary approaches to the treatment of obesity and overweight. Our aim, therefore, was to describe and critically evaluate the impact of different diets and/or dietary patterns on human health, by considering their effects on anthropometric parameters and cardiometabolic risk factors.

Methods

An umbrella review of meta-analyses of RCTs (CRD42019126103) was conducted according to the Joanna Briggs Institute Umbrella Review Methodology (13).

Search strategy

The systematic literature search was independently conducted by 2 authors (DM and DA). Any discrepancy was resolved through consultation with a third independent reviewer (LL). The systematic computerized literature search was performed in the Medline, Embase, Scopus, Cochrane Database of Systematic Reviews, and Web of Science databases, from inception to April 2019. Additional studies were searched by checking references of the identified articles and by consulting experts in the field. The following search terms were used in combination as Medical Subject Headings (MeSH) terms and text words: “diet*” and its variants, with the words “weight,” “body mass index,” “BMI,” “plasma lipids,” “cholesterol,” “LDL-cholesterol,” “HDL-cholesterol,” “triglycerides,” “glycated hemoglobin,” “insulin,” “blood pressure,” and their variants, and the words “meta-analysis,” “systematic reviews,” and their variants. A more exhaustive search strategy list, for each database, is provided in Supplemental Table 1. The most updated or complete publication was used when >1 article was present for a meta-analysis. If an article presented meta-analyses for >1 health outcome, each of these was included separately. Missing data or additional information were requested from the corresponding authors of the articles.

Data selection

Supplemental Table 2 summarizes the eligibility criteria, following the PICOS (Population, Intervention, Comparison, Outcome, Study design) format. Inclusion criteria were the following: 1) Population: adults (aged ≥18 y); 2) Intervention: all diets or dietary patterns; 3) Comparison: any other dietary intervention; 4) Outcome: weight, BMI, total cholesterol, LDL cholesterol, HDL cholesterol, triglycerides, glucose, insulin, glycated hemoglobin (HbA1c), systolic blood pressure, or diastolic blood pressure; 5) Study design: meta-analyses of RCTs.

Exclusion criteria were the following: 1) Population: nonadults (aged < 18 y), pregnancy, or postpartum; 2) Intervention: not a specific diet or dietary pattern; 3) Outcome: any other outcome outside of the inclusion criteria; 4) Study design: systematic reviews of RCTs without quantitative analysis, meta-analyses not reporting comprehensive data (e.g., effect sizes and 95% CIs), or meta-analyses of observational studies. The decision to include studies was based on the title, abstract, and full-text screening.

Data extraction and quality assessment

Three independent researchers (AR, MDA, and LB) achieved consensus on which data to extract from each eligible meta-analysis, using a standard form. The following data were extracted: first author and year of publication, number of included studies, intervention diet, control diet, number of subjects assigned to the intervention group, number of subjects assigned to the control group, duration of the intervention, study population, outcomes of interest, effect size measurements, and quality of the studies included in each meta-analysis. Data were grouped according to the type of dietary intervention. Within each diet, outcomes were categorized as follows: body weight (kg), BMI (kg/m²), total cholesterol (mmol/L), LDL cholesterol (mmol/L), HDL cholesterol (mmol/L), triglycerides (mmol/L), glucose (mmol/L), insulin (μU/mL), HbA1c (%), systolic blood pressure (mm Hg), and diastolic blood pressure (mm Hg). When data were provided in milligrams per deciliter or picomoles per liter, they were transformed into millimoles per liter or micro-International Units per milliliter for consistency of results.

Three authors (CDB, DN, and EM) independently evaluated the methodological quality of the included meta-analyses. Disagreements were resolved by discussion with a fourth investigator (MD). The “A MeaSurement Tool to Assess systematic Reviews 2” (AMSTAR-2) questionnaire was used to identify the high-quality meta-analyses (14). This instrument has 16 items in total, with an overall rating based on weaknesses in critical domains. Critical domains were as follows: adequacy of the literature search, risk of bias from individual studies included in the review, appropriateness of meta-analytical methods, consideration of risk of bias when interpreting the results of the review, and assessment of presence of publication bias.

Data analysis

For each unique meta-analysis, we estimated the summary effect and 95% CIs using both fixed-effect and random-effect models (DerSimonian and Laird method). Heterogeneity among studies was evaluated using the I² statistic (15). Where I² exceeded 50% or 75%, the heterogeneity was considered substantial or considerable, respectively. The 95% prediction interval (PI) was calculated to predict the range of effect sizes that would be expected in a new original study, after accounting for both the uncertainty of the summary effect estimated in the random-effect model and the heterogeneity among individual studies (16). The possible presence of small-study effects was estimated by using Egger's regression asymmetry test (17). We investigated if small studies tended to give larger estimates of effect size than large studies by calculating the SE of the effect size (under the random-effect model) for the largest study of each meta-analysis. The largest study was defined on the basis of the smallest SE. If the P value for Egger's test was <0.10 and the largest study had a smaller effect size than the summary effect size, both criteria for the existence of small-study effects were fulfilled (18). All statistical analyses were conducted using Review Manager (RevMan, version 5.3 for Macintosh; The Cochrane Collaboration) and the statistical package PASW 20.0 for Macintosh (SPSS Inc.).

As previously proposed (19, 20), observed associations were categorized as convincing or not by using the following criteria: significance at P ≤ 0.05 and P ≤ 0.001; inclusion of ≥2500 or ≥5000 total participants; absence of considerable heterogeneity (I² < 50%); 95% PI excluding the null value; and absence of small-study effects. Convincing evidence was assigned to associations with a significance of P ≤ 0.001 for both random- and fixed-effect models, ≥5000 total participants, not large heterogeneity between studies (I² < 50%), 95% PI excluding the null value, and no evidence of small-study effects (if it could be tested). Highly suggestive evidence was assigned to associations with a significance of P ≤ 0.001 for both random- and fixed-effect models, ≥5000 total participants, and not considerable heterogeneity between studies (I² = 50–75%). Suggestive evidence was assigned to associations with a significance of P ≤ 0.001 for the random-effect model and 2500–5000 total participants. Weak evidence was assigned to associations with a significance of P ≤ 0.05 for the random-effect model. No-evidence was assigned to associations where the significance threshold was not reached (P > 0.05).

Results

Search results

The selection process is shown in Figure 1, in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Initial database and other searches yielded 27,627 articles. After eliminating duplicates, 12,469 articles were excluded on the basis of their title and abstract, and 105 on the basis of full-text assessment. A total of 80 articles (7–9, 21–97) met the inclusion criteria and were included in the analysis, covering a wide range of diets: low-carbohydrate (n = 21 articles), high-protein (n = 8), low-fat (n = 9), paleolithic (n = 2), low-glycemic-index/load (n = 12), intermittent energy restriction (n = 6), Mediterranean (n = 11), Nordic (n = 2), vegetarian (n = 9), Dietary Approaches to Stop Hypertension (DASH) (n = 6), and portfolio dietary pattern (n = 1).

Flow diagram of the study selection process. RCT, randomized controlled trial.

Study characteristics and quality

Table 1 reports the characteristics and methodological quality of the meta-analyses included. There was great variability in terms of definition of the intervention diets: as regards low-carbohydrate diets, for example, some studies defined as “low-carbohydrate” diets containing ≤45% of total energy from carbohydrates (23, 28, 34, 36, 38), others diets that included carbohydrates totalling ≤26% (33) or even less (≤10%) (26) of the total energy, whereas others did not define the amount of carbohydrates included (22, 24, 25, 27, 31, 32, 35, 40). Similarly, for high-protein diets, in some meta-analyses the high-protein content was defined as >20% of total energy (42), in others >25% (43) or between 25% and 35% (41, 45), and in others it was not defined at all (8, 25, 44, 46). High variability was also observed among vegetarian diets, where some meta-analyses included lacto-ovo-vegetarian and vegan diets altogether (85, 86, 89–91), whereas others considered lacto-ovo-vegetarian (84, 87, 88) or vegan (87, 88, 92) diets specifically. A consistent heterogeneity was also present for control diets. In fact, most meta-analyses had as “control” any other dietary intervention, without specific indication. The study population was mainly composed of subjects with overweight/obesity or type 2 diabetes. Overweight was defined as a BMI between 25 and 29.9 and obesity as a BMI ≥ 30. A greater number of RCTs and a bigger sample size (≥2500 subjects) were observed in meta-analyses on Mediterranean (74–76, 79, 80, 82) and low-carbohydrate (23, 24, 28, 38, 39) diets. Conversely, the number of RCTs and the study population were small (≤500 subjects) in meta-analyses on paleolithic (55, 56), intermittent energy restriction (68–73), Nordic (81, 83), and portfolio dietary patterns (97). The methodological quality of the included meta-analyses, determined by the AMSTAR-2 questionnaire, was moderate-to-high only in 6 meta-analyses on low-carbohydrate diets (7, 26, 27, 36, 37, 39), in 2 meta-analyses on low-glycemic-index/load (58, 64) and vegetarian diets (91, 92), and in 1 meta-analysis on each of low-fat diet (52), intermittent energy restriction (71), Mediterranean diet (9), Nordic diet (83), and portfolio dietary pattern (97). There were no meta-analyses with moderate or high methodological quality for high-protein, paleolithic, and DASH diets. Although most meta-analyses (n = 73; 91%) performed a quality/risk of bias assessment using validated tools or criteria set by the authors, only 27 (34%) accounted for risk of bias in individual studies when interpreting/discussing the results of the meta-analysis.

TABLE 1.

Characteristics of meta-analyses of RCTs included in the umbrella review according to dietary interventions¹

Meta-analyses	Intervention diet	Control diet	n Intervention	n Control	Study population (age ≥ 18 y)	Duration	Quality/risk of bias assessment	Outcomes	Quality of meta-analyses (AMSTAR-2)
LCs
Nordmann et al. (21)	LC (≤60 g CHO)	LF (≤30% of TE)	222	225	OW/OB	>6 mo, >12 mo	Criteria set by authors	Weight, TC, LDL-C, HDL-C, TG, SBP, DBP	Critically low
Hession et al. (22)	LC/HP²	HC/LF³	375	367	OW/OB	>6 mo, >12 mo	Criteria set by authors	Weight, TC, LDL-C, HDL-C, TG, glucose, SBP, DBP	Critically low
Hu et al. (23)	LC (≤45% of TE)	LF (≤30% of TE)	1396	1392	OW/OB	6–24 mo	No	Weight, TC, LDL-C, HDL-C, TG, glucose, insulin, SBP, DBP	Critically low
Santos et al. (24)	LC⁴	Other	2394	4346	OB	3–24 mo	Criteria set by authors	Weight, BMI, LDL-C, HDL-C, TG, glucose, insulin, HbA1c, SBP, DBP	Critically low
Ajala et al. (25)	LC⁴	Other	NA	NA	T2DM	6–12 mo	Cochrane RoB	HbA1c	Critically low
Bueno et al. (26)	VLCKD (≤50 g CHO or ≤10% of TE)	LF (≤30% of TE)	712	703	OW/OB	12–24 mo	Cochrane RoB	Weight, LDL-C, HDL-C, TG, SBP, DBP	High
Naude et al. (27)	LC⁴	Balanced energy-restricted diets	837	872	OW/OB, T2DM	3–6 mo, 12–24 mo	Cochrane RoB	Weight	Moderate
Alexandraki et al. (28)	LC (≤45% of TE)	LF (≤30% of TE)	1548	1543	OW/OB	6 mo, 12 mo	Cochrane RoB	Weight	Critically low
Sackner-Bernstein et al. (29)	LC (≤120 g CHO)	LF (≤30% of TE)	895	902	OW/OB	2–24 mo	No	Weight	Critically low
Fan et al. (30)	LC (≤130 g CHO)	Other	567	569	T2DM	3–48 mo	Jadad scale	Weight, HbA1c	Critically low
Hashimoto et al. (31)	LC⁴	Other	697	719	OW/OB	2–24 mo	AMSTAR	Weight	Critically low
Mansoor et al. (7)	LC (≤20% of TE)	LF (≤30% of TE)	688	681	OW/OB	6–24 mo	Cochrane RoB	Weight, TC, LDL-C, HDL-C, TG, glucose, insulin, SBP, DBP	High
Steckhan et al. (32)	LC⁴	Other	96	90	MetS	1–24 mo	Cochrane RoB	Weight, insulin	Low
Meng et al. (33)	LC (≤26% of TE) or ≤130 g CHO/d	HC (45–60% of TE)	366	368	T2DM	3–24 mo	Jadad scale	Weight, TC, LDL-C, HDL-C, TG, glucose, HbA1c	Critically low
Snorgaard et al. (34)	LC (≤45% of TE)	HC (45–60% of TE)	414	425	T2DM	<12 mo, ≥12 mo	Cochrane RoB	Weight, BMI, LDL-C, HbA1c	Critically low
Huntriss et al. (35)	LC⁴	Other	330	315	T2DM	12 mo	Cochrane RoB	Weight, TC, LDL-C, HDL-C, TG, HbA1c, SBP, DBP	Critically low
Sainsbury et al. (36)	LC (≤45% of TE)	HC (>45% of TE)	NA	NA	T2DM	6 mo, 12 mo	Cochrane RoB	Weight, HbA1c	Moderate
van Zuuren et al. (37)	LC (≤40% of TE)	LF (≤30% of TE)	269	270	T2DM	<2 mo, 2–4 mo, 4–6 mo, >6 mo, 24 mo	Cochrane RoB/ROBINS-I tool	Weight, BMI, LDL-C, HDL-C, TG, glucose, HbA1c, SBP, DBP	Moderate
Gjuladin-Hellon et al. (38)	LC (≤45% of TE)	LF (≤35% of TE)	1680	1678	OW/OB	6–24 mo	Cochrane RoB	TC, LDL-C, HDL-C, TG	Critically low
Korsmo-Haugen et al. (39)	LC (≤40% of TE)	HC (>40% of TE)	1587⁵	NA	T2DM	3–24 mo	Cochrane RoB	Weight, TC, LDL-C, HDL-C, TG, HbA1c, SBP, DBP	High
McArdle et al. (40)	LC⁴	Other	1006	1126	T2DM	3–52 mo	Cochrane RoB	Weight, HbA1c	Critically low
HPs
Santesso et al. (8)	HP⁴	LP	1158	1160	Different health status	>1 mo	No	Weight, BMI, TC, LDL-C, HDL-C, TG, glucose, insulin, HbA1c, SBP, DBP	Critically low
Wycherley et al. (41)	HP (25–35% of TE)	LP (12–18% of TE)	494	516	Different health status	1–13 mo	Cochrane RoB	Weight, TC, LDL-C, HDL-C, TG, glucose, insulin, SBP, DBP	Critically low
Ajala et al. (25)	HP4⁴	Other	72	65	T2DM	6–12 mo	Cochrane RoB	HbA1c	Critically low
Dong et al. (42)	HP (>20% of TE)	LP (15–20% of TE)	NA	NA	T2DM	1–6 mo	Criteria set by authors	Weight, TC, LDL-C, HDL-C, TG, glucose, HbA1c, SBP, DBP	Critically low
Schwingshackl and Hoffmann (43)	HP (≥25% of TE)	LP (≤20% of TE)	533	599	Different health status	12–24 mo	Cochrane RoB/Jadad scale	Weight, TC, LDL-C, HDL-C, TG, glucose, insulin, HbA1c, SBP, DBP	Critically low
Clifton et al. (44)	HP⁴	LCD	1681	1811	Different health status	13–52 mo	Cochrane RoB	Weight, TC, LDL-C, HDL-C, TG, glucose, insulin, HbA1c, SBP, DBP	Critically low
Johansson et al. (45)	HP (25–30% of TE)	Other	451	414	Different health status	0.8–2 mo	Criteria set by authors	Weight	Critically low
Zhao et al. (46)	HP⁴	LP	520	539	T2DM	1–24 mo	Cochrane RoB	Weight, BMI, TC, LDL-C, HDL-C, TG, glucose, insulin, HbA1c, SBP, DBP	Critically low
LFs
Astrup et al. (47)	Reduced fat⁴	Other	1101	869	Nondiabetic	2–12 mo	No	Weight	Critically low
Avenell et al. (48)	LF⁴	Other	665	688	OW/OB	12 mo	Criteria set by authors	Weight	Critically low
Schwingshackl and Hoffmann (49)	LF (≤30% of TE)	Other, HF (>30% of TE), LC (<50 g CHO), LGI/LGL, MUFA	3793	4249	OW/OB	>3 mo	Cochrane RoB/Jadad scale	TC, LDL-C, HDL-C, TG	Critically low
Wu et al. (50)	LF (≤30% of TE)	Usual diet	900	636	Women	1–12 mo	Jadad scale	TC, LDL-C, HDL-C, TG	Critically low
Boaz et al. (51)	LF (≤30% of TE)	LC (≤45% of TE)	569	592	OW/OB	1–8.7 y	No	Weight	Critically low
Hooper et al. (52)	LF (≤30% of TE)	HF (>30% of TE)	22,316	31,331	Different health status	0.5–8 y	Cochrane RoB	Weight, BMI, TC, LDL-C, HDL-C, TG, SBP, DBP	High
Tobias et al. (53)	LF⁴	Other, HF, LC, usual diet	NA	NA	Different health status	1–10 y	Cochrane RoB	Weight	Low
Steckhan et al. (32)	LF⁴	Other	116	111	MetS	1–24 mo	Cochrane RoB	Weight	Low
Lu et al. (54)	LF (≤30% of TE)	HF (>30% of TE)	NA	NA	OW/OB	2–24 mo	Cochrane RoB/Jadad scale	TC, LDL-C, HDL-C, TG, SBP, DBP	Low
Paleolithic diet
Manheimer et al. (55)	Paleolithic	Other	73	64	MetS	0.5–6 mo	Cochrane RoB	HDL-C, TG, glucose, SBP, DBP	Low
Ghaedi et al. (56)	Paleolithic	Other	115	98	Different health status	0.5–24 mo	Cochrane RoB	Weight, BMI, TC, LDL-C, HDL-C, TG, SBP, DBP	Low
LGI/LGLs
Opperman et al. (57)	LGI⁴	HGI	206	200	Different health status, T2DM	<6 mo	Criteria adapted from the Cochrane EPOC Group	TC, LDL-C, HDL-C, TG, HbA1c	Critically low
Thomas et al. (58)	LGI/LGL⁴	HGI/HGL	82	81	OW/OB	1.3–6 mo	Criteria set by authors	Weight, BMI, TC, HDL-C, TG, glucose, insulin	High
Thomas and Elliott (59)	LGI⁴	HGI	238	219	T2DM	1–6 mo	Criteria set by authors	HbA1c	Critically low
Ajala et al. (25)	LGI/LGL⁴	Other	181	172	T2DM	6–12 mo	Cochrane RoB	HbA1c	Critically low
Fleming and Godwin (60)	LGI⁴	HGI	107	105	OW/OB	<3 mo	US Preventive Services Task Force Quality Rating Criteria	TC, LDL-C, HDL-C, TG	Critically low
Goff et al. (61)	LGI⁴	HGI	733	679	Different health status, T2DM	>1 mo	Jadad scale	TC, LDL-C, HDL-C, TG	Low
Schwingshackl and Hoffmann (62)	LGI/LGL⁴	HGI/HGL	913	857	Different health status	6–17 mo	Cochrane RoB	Weight, TC, LDL-C, HDL-C, TG, glucose, insulin, HbA1c, SBP, DBP	Critically low
Wang et al. (63)	LGI⁴	HGI	421	409	T2DM	<36 mo	Jadad scale	HbA1c	Critically low
Clar et al. (64)	LGI⁴	HGI	NA	NA	CVD	>3 mo	Cochrane RoB	Weight, BMI, TC, LDL-C, HDL-C, TG, SBP, DBP	High
Evans et al. (65)	LGI/LGL⁴	HGI/HGL	NA	NA	Healthy adults	<18 mo	Cochrane RoB	SBP, DBP	Low
Ojo et al. (66)	LGI⁴	HGI	291	283	T2DM	<22 mo	Cochrane RoB/CASP RCT Checklist	Glucose, HbA1c	Critically low
Zafar et al. (67)	LGI⁴	Other	3333	3241	OW/OB	<26 mo	Cochrane RoB	Weight, BMI, TC, LDL-C, HDL-C, TG, glucose	Low
IER
Alhamdan et al. (68)	ADF	VLCD	91⁵	NA	OW/OB	2–3 mo	Downs and Black checklist	Weight	Critically low
Headland et al. (69)	IER	CER	230	216	Different health status	>12 mo	Cochrane RoB	Weight	Critically low
Cioffi et al. (70)	IER⁶	CER	343	222	Different health status	2–6 mo	Cochrane RoB	Weight, TC, LDL-C, HDL-C, TG, glucose, insulin, HbA1c, SBP, DBP	Critically low
Harris et al. (71)	IER⁷	Ad libitum/CER⁸	180	137	OW/OB	3–12 mo	Cochrane RoB	Weight	Moderate
Harris et al. (72)	IER⁷	Ad libitum/CER⁸	161	126	OW/OB	3 mo	JBI SUMARI	Weight, TC, LDL-C, HDL-C, TG, glucose, insulin	Critically low
Roman et al. (73)	IER	CER	329	337	OW/OB	3–13 mo	Cochrane RoB	Weight	Critically low
MDs
Esposito et al. (74)	MD	Other	1937	1588	Different health status	1–60 mo	Jadad scale	Weight, BMI	Critically low
Kastorini et al. (75)	MD	Other	2202	1903	OW/OB	1–48 mo	Criteria set by authors	HDL-C, TG, glucose, SBP, DBP	Critically low
Nordmann et al. (76)	MD	LF (≤30% of TE)	1641	1009	OW/OB	24 mo	Criteria set by authors	Weight, BMI, TC, LDL-C, HDL-C, glucose, insulin, SBP, DBP	Critically low
Ajala et al. (25)	MD	Other	308	280	T2DM	6–12 mo	Cochrane RoB	HbA1c	Critically low
Huo et al. (77)	MD	Other	568	521	T2DM	1–48 mo	Cochrane RoB	Weight, BMI, TC, LDL-C, HDL-C, TG, glucose, insulin, HbA1c, SBP, DBP	Critically low
Esposito et al. (78)	MD	Other	395	278	Different health status	1–60 mo	Cochrane RoB	HbA1c	Low
Garcia et al. (79)	MD	Other	3262⁵	NA	Different health status	1–52 mo	Cochrane RoB	HDL-C, TG, glucose, SBP, DBP	Low
Gay et al. (80)	MD	Other	5148	5013	Different health status	6–48 mo	Cochrane RoB	SBP, DBP	Critically low
Ndanuko et al. (81)	MD	Other	310	225	Different health status	2–24 mo	Cochrane RoB	SBP, DBP	Critically low
Nissensohn et al. (82)	MD	Other	5226	5111	OW/OB	24 mo	Cochrane RoB	SBP, DBP	Critically low
Rees et al. (9)	MD	Other	692	662	Primary/secondary prevention	≥3 mo	Cochrane RoB	TC, LDL-C, HDL-C, TG, SBP, DBP	High
Nordic diet
Ndanuko et al. (81)	Nordic	Other	306	NA	Different health status	2–24 mo	Cochrane RoB	SBP, DBP	Critically low
Ramezani-Jolfaie et al. (83)	Nordic	Typical/Danish diets	513⁵	NA	Different health status	0.5–6 mo	Cochrane RoB	TC, LDL-C, HDL-C, TG, SBP, DBP	High
VGTs
Yokoyama et al. (84)	VGT, LOV	Non-VGT	210	195	Different health status	1.5–13 mo	No	SBP, DBP	Critically low
Yokoyama et al. (85)	VGT	Non-VGT	120	174	T2DM	1–18.5 mo	Cochrane RoB	Glucose, HbA1c	Critically low
Barnard et al. (86)	VGT	Non-VGT	689⁵	NA	Different health status	3–26 mo	Cochrane RoB	Weight	Low
Huang et al. (87)	VGT, LOV, VGN	Non-VGT	1151⁵	NA	Different health status	2.3–24 mo	Jadad scale	Weight	Critically low
Wang et al. (88)	VGT, LOV, VGN	Non-VGT	785⁵	NA	Different health status	2.3–24 mo	Cochrane RoB	Weight, TC, LDL-C, HDL-C, TG	Critically low
Yokoyama et al. (89)	VGT	Non-VGT	1329⁵	NA	Different health status	>1.5 mo	Jadad scale	TC, LDL-C, HDL-C, TG	Critically low
Picasso et al. (90)	VGT	Non-VGT	350	339	Different health status	1.5–18.5 mo	Cochrane RoB	HDL-C, TG, glucose, SBP, DBP	Low
Viguiliouk et al. (91)	VGT	Non-VGT	329	337	T2DM	1–18.5 mo	Cochrane RoB	Weight, BMI, LDL-C, HDL-C, TG, glucose, HbA1c, SBP, DBP	Moderate
Lopez et al. (92)	VGN	Non-VGN	1078⁵	NA	Different health status	0.8–18.5 mo	Cochrane RoB	SBP, DBP	High
DASH diet
Shirani et al. (93)	DASH	Other	815	813	Different health status	0.8–6 mo	No	Glucose, insulin	Critically low
Saneei et al. (94)	DASH	Other	1281	1280	Different health status	0.5–6.5 mo	Criteria set by authors	SBP, DBP	Critically low
Siervo et al. (95)	DASH	Other	964	964	Different health status	0.5–6 mo	Jadad scale	TC, LDL-C, HDL-C, TG, glucose, SBP, DBP	Critically low
Gay et al. (80)	DASH	Other	NA	NA	Different health status	6–48 mo	Cochrane RoB	SBP, DBP	Critically low
Ndanuko et al. (81)	DASH	Other	1399	1399	Different health status	2–24 mo	Cochrane RoB	SBP, DBP	Critically low
Soltani et al. (96)	DASH	LCD	1291	1291	Different health status	2–13 mo	Cochrane RoB	Weight, BMI	Low
Portfolio dietary pattern⁹
Chiavaroli et al. (97)	Portfolio	Energy-matched diets	439⁵	NA	Dyslipidemia	1–6 mo	Cochrane RoB	Weight, TC, LDL-C, HDL-C, TG, SBP, DBP	High

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ADF, alternate day fasting; AMSTAR, A MeaSurement Tool to Assess systematic Reviews; CASP, Critical Appraisal Skills Programme; CER, continuous energy restriction; CHO, carbohydrates; Cochrane RoB, the Cochrane risk-of-bias tool for randomized trials; CVD, cardiovascular disease; DASH, Dietary Approaches to Stop Hypertension; DBP, diastolic blood pressure; EPOC, Effective Practice and Organisation of Care; HbA1c, glycated hemoglobin; HC, high-carbohydrate diet; HDL-C, HDL cholesterol; HF, high-fat diet; HGI, high-glycemic-index; HGL, high-glycemic-load; HP, high-protein diet; IER, intermittent energy restriction; JBI SUMARI, Joanna Briggs Institute's System for the Unified Management, Assessment, and Review of Information critical appraisal tool; LC, low-carbohydrate diet; LCD, low-calorie diet; LDL-C, LDL cholesterol; LF, low-fat diet; LGI/LGL, low-glycemic-index/low-glycemic-load diet (total fat >30% of daily energy consumption, CHO <50% of daily energy consumption, and low-glycemic-index foods); LOV, lacto-ovo-vegetarian diet; LP, low-protein diet; MD, Mediterranean diet; MetS, metabolic syndrome; MUFA, high-MUFA diet (total fat >30% of daily energy consumption and MUFA >12% of daily energy consumption); NA, not available; OB, obese; OW, overweight; RCT, randomized controlled trial; ROBINS-I, Risk of Bias In Non-randomized Studies of Interventions; SBP, systolic blood pressure; TC, total cholesterol; TE, total energy; TG, triglyceride; T2DM, type 2 diabetes mellitus; VGN, vegan diet; VGT, vegetarian diet; VLCD, very-low-calorie dieting (<800 kcal/d); VLCKD, very-low-carbohydrate ketogenic diets (≤50 g/d of CHO or ≤10% of daily energy from CHO).

Low-carbohydrate (≤60 g/d of CHO)/ketogenic diets (<40 g/d of CHO).

Low-fat (≤30% of daily energy from fat)/high-carbohydrate conventional diets, energy restricted.

⁴

As defined by the investigators of each trial.

⁵

Number of total participants.

⁶

IER defined as 75% energy restriction on “fast” days, with a maximum cutoff of 500 and 660 kcal/d for females and males, respectively.

⁷

IER defined as consumption of ≤800 kcal on ≥1 d, but no more than 6 d in 1 wk.

⁸

Control defined as “ad libitum” diet (no intervention) or advice to continuously follow a reduced-calorie diet of ∼25% of estimated daily energy requirements.

⁹

Portfolio dietary pattern was defined as including the following components: 1–3 g plant sterols/d, 15–25 g viscous fibers/d (from oats, barley, psyllium, legumes, eggplants, and okra), 35–50 g plant protein/d, and 25–50 g nuts/d.

Supplemental Table 3 reports the effects of all the diets studied on body weight and cardiometabolic risk factors. By applying our evidence classification criteria, based on the evaluation of the level of significance for both random- and fixed-effect calculations, the sample size, the heterogeneity, the 95% PI, and the presence of small study effects, only a limited number of meta-analyses provided suggestive evidence and no meta-analyses provided highly suggestive or convincing evidence.

Anthropometric parameters

Figure 2 summarizes the characteristics and the strength of evidence of the meta-analyses of RCTs that evaluated the effects of diets on anthropometric parameters. With regard to body weight, suggestive evidence for a decrease in body weight was observed for low-carbohydrate, low-fat, Mediterranean, and DASH diets. The mean difference between intervention and control diets in meta-analyses reporting suggestive evidence ranged from −0.98 to −7.05 kg for low-carbohydrate diets (23, 24, 28), from −1.75 to −2.24 kg for the Mediterranean diet (74, 76), was −1.54 kg (95% CI: −1.97 to −1.12 kg) for low-fat diets (52), and −1.42 kg (95% CI: −2.03 to −0.82 kg) for the DASH diet (96). Weak or no evidence was reported for high-protein, paleolithic, low-glycemic-index/load, and vegetarian diets, as well as for intermittent energy restriction and portfolio dietary pattern. When the outcome BMI was analyzed, suggestive evidence was observed only in 1 meta-analysis (52) on low-fat diets (mean difference: −0.50; 95% CI: −0.74 to −0.26) and in 2 meta-analyses on the Mediterranean diet (74, 76) (mean difference: −0.57; 95% CI: −0.93 to −0.21 and mean difference: −0.56; 95% CI: −1.01 to −0.11, respectively).

Lipid profile

Figure 3 summarizes the characteristics and the strength of evidence of the meta-analyses of RCTs that evaluated the effects of diets on lipid profile. With regard to total cholesterol, suggestive evidence for a difference between intervention and control diets was reported for low-fat (mean difference: −0.20 mmol/L; 95% CI: −0.29 to −0.11 mmol/L) (52), low-glycemic-index/load (mean difference: −0.14 mmol/L; 95% CI: −0.22 to −0.09 mmol/L) (67), and Mediterranean (mean difference: −0.19 mmol/L; 95% CI: −0.27 to −0.11 mmol/L) (76) diets. Meta-analyses evaluating LDL cholesterol reported suggestive evidence for low-fat (mean difference: −0.08 mmol/L; 95% CI: −0.12 to −0.04 mmol/L) ( 49) and low-glycemic-index/load (mean difference: −0.14 mmol/L; 95% CI: −0.22 to −0.07 mmol/L) (67) diets. Meta-analyses evaluating HDL cholesterol reported suggestive evidence for low-carbohydrate (mean difference: 0.02–0.08 mmol/L) (23, 24, 38), low-fat (mean difference: −0.06 mmol/L; 95% CI: −0.09 to −0.03 mmol/L) (49), and Mediterranean (mean difference: 0.03 mmol/L; 95% CI: 0.01–0.05 mmol/L) (75) diets. Finally, suggestive evidence for triglycerides was reported in meta-analyses comparing low-carbohydrate with other dietary interventions (mean difference: −0.34 mmol/L; 95% CI: −0.36 to −0.31 mmol/L) (24) or low-fat diets (mean difference: −0.14 mmol/L; 95% CI: −0.18 to −0.11 mmol/L) (23, 38), in 1 meta-analysis (44) comparing high-protein with low-calorie diets (mean difference: −0.18 mmol/L; 95% CI: −0.30 to −0.07 mmol/L), and in 1 meta-analysis (49) comparing low-fat with other dietary interventions (mean difference: 0.09 mmol/L; 95% CI: 0.04–0.15 mmol/L).

Glycemic profile

Figure 4 summarizes the characteristics and the strength of evidence of the meta-analyses of RCTs that evaluated the effects of diets on glycemic profile. With regard to glucose, suggestive evidence for a difference between intervention and control diets was reported only for Mediterranean diet (mean difference: −0.37 mmol/L; 95% CI: −0.41 to −0.33 mmol/L) (79). On the other hand, 1 meta-analysis (24) comparing low-carbohydrate diets (as defined by the investigators of each trial) with other dietary interventions reported suggestive evidence for insulin (mean difference: −2.24 μU/mL; 95% CI: −2.66 to −1.82 μU/mL). Weak or no evidence was reported by all the meta-analyses evaluating HbA1c.

Blood pressure

Figure 5 summarizes the characteristics and the strength of evidence of the meta-analyses of RCTs that evaluated the effects of diets on systolic and diastolic blood pressure. Suggestive evidence for a difference between intervention and control diets was reported for low-carbohydrate, Mediterranean, and DASH diets. In particular, evidence from 1 meta-analysis (24) comparing low-carbohydrate diets (as defined by the investigators of each trial) with other dietary interventions (mean difference: −4.81 mm Hg; 95% CI: −5.33 to −4.29 mm Hg), 4 meta-analyses (75, 76, 79, 80) on the Mediterranean diet (ranging from −0.37 to −2.35 mm Hg), and 2 meta-analyses (81, 94) on the DASH diet (ranging from −2.63 to −6.74 mm Hg) were graded as suggestive.

Evaluation of methodological quality, bias, heterogeneity, and strength of evidence

Detailed information on the evaluation of the methodological quality of included meta-analyses and the assessment of the quality and/or risk of bias of original studies as reported by the authors of the meta-analyses is summarized in Supplemental Tables 4 and 5. Detailed information on the assessment of the strength of evidence is reported in Supplemental Tables 6 and 7.

Figure 6 depicts a summary of the results reported in the meta-analyses of RCTs included. Among all the diets evaluated, only the Mediterranean diet showed significant beneficial effects (i.e., reduction for all the outcomes except for HDL cholesterol, for which an increase is considered as beneficial) for all the parameters analyzed, without evidence of detrimental effects (i.e., increase for any of the outcomes except for HDL cholesterol). Figure 7 reports a forest plot with the summary effect for each outcome evaluated. When for an outcome overlapping meta-analyses existed, we retained the meta-analysis with the highest methodological quality as determined by the AMSTAR-2 questionnaire. When the overlapping meta-analyses had the same methodological quality, we reported the meta-analysis with the largest number of studies.

Summary of the results reported in meta-analyses of randomized controlled trials in adults according to dietary interventions. Green = evidence of a beneficial effect (i.e., lowering for all outcomes except HDL-C); grey = evidence of no effect; red = evidence of a detrimental effect (i.e., increasing for all outcomes except HDL-C). The size of the circles reflects the number of unique meta-analyses available. BP, blood pressure; DASH, Dietary Approaches to Stop Hypertension; ER, energy restriction; GI, glycemic index; GL, glycemic load; HbA1c, glycated hemoglobin; HDL-C, HDL cholesterol; LDL-C, LDL cholesterol; TC, total cholesterol; TG, triglyceride.

Forest plot of all nonoverlapping meta-analyses of randomized controlled trials in adults according to dietary interventions. *Number of total participants. BP, blood pressure; CER, continuous energy restriction; Crit., critically; DASH, Dietary Approaches to Stop Hypertension; HC, high-carbohydrate; HDL-C, HDL cholesterol; HF, high-fat; HGI, high-glycemic-index; HGL, high-glycemic-load; HP, high-protein; IER, intermittent energy restriction; LC, low-carbohydrate; LDL-C, LDL cholesterol; LF, low-fat; LGI, low-glycemic-index; LGL, low-glycemic-load; LP, low-protein; MD, mean difference; Med., Mediterranean; NA, not available; TC, total cholesterol; TG, triglyceride; Veg., vegetarian.

Discussion

The present is the first umbrella review providing a comprehensive overview and a critical evaluation of the effects of different popular diets on body weight and cardiometabolic risk factors. The overall analysis comprised 80 different meta-analyses of RCTs that evaluated low-carbohydrate, high-protein, low-fat, paleolithic, low-glycemic-index/load, intermittent energy restriction, Mediterranean, Nordic, vegetarian, DASH, and portfolio dietary patterns. Over 80% of the meta-analyses included showed low methodological quality and the strength of evidence, assessed using evidence classification criteria, was generally weak. Notably, the Mediterranean diet was the only diet that demonstrated significant and beneficial effects for all the parameters analyzed, without evidence of potential adverse effects.

Over the past few decades, a wide range of dietary strategies have been promoted to reduce body weight. Some of these diets have been characterized by the modulation of macronutrients (e.g., low-carbohydrate, high-protein, and low-fat diets), whereas others focused on dietary patterns as a whole (e.g., Mediterranean, Nordic, vegetarian, DASH, and portfolio dietary models). To date, several meta-analyses including dietary intervention trials have been published, but to the best of our knowledge no umbrella reviews evaluating the strength of evidence for such meta-analyses have been performed.

In the present umbrella review, the largest number of meta-analyses was found for low-carbohydrate diets. Their definition varied greatly, and cutoffs ranged from 50 to 130 g/d, or 26–45% energy from carbohydrates. Four meta-analyses (33, 34, 36, 39), conducted on participants with type 2 diabetes, compared low-carbohydrate with high-carbohydrate diets, reporting no significant effects on weight. The other meta-analyses compared low-carbohydrate with low-fat diets (7, 21–23, 28, 29, 37) or other dietary interventions (24, 27, 30–32, 35, 40), reporting contrasting results. Evidence of a significant reduction in body weight was observed, especially in the short term (6 mo) and in studies with more extreme carbohydrate restriction. When the follow-up period or the amount of carbohydrates increased, the effect was attenuated. As to the other parameters, we observed weak or suggestive evidence of an improvement in glycemic profile and blood pressure, and conflicting results for lipid profile, with an increase in total and LDL cholesterol reported in 12 meta-analyses. The detrimental effects of low-carbohydrate diets on lipid parameters may be related to the fact that people on low-carbohydrate diets tend to eat less vegetables and fruits rich in micronutrients and fiber, and more animal-derived foods (98).

As for high-protein diets, they are one of the most popular weight-loss strategies. Several mechanisms have been proposed to explain their supposed superiority over conventional weight-loss diets, including higher satiety and an increase in energy expenditure (99). Our analysis showed that the quality of published meta-analyses on high-protein diets is critically low and the number of participants is relatively small. Weak or no evidence of a reduction in anthropometric parameters and blood pressure was reported, whereas data on lipid and glycemic profiles were discordant. Increased saturated fat and lower fiber intake can potentially contribute to the observed increase in LDL cholesterol, glucose, and HbA1c, questioning the safety of high-protein diets in the long term.

With regard to low-fat diets, the proportion of fat in the present umbrella review was ≤30% of energy intake, according to the dietary recommendations from the WHO Healthy Diet Fact Sheet. Suggestive evidence of weight and BMI reduction was reported in the meta-analysis by Hooper et al. (52), which included the Women's Health Initiative Dietary Modification Trial and compared low-fat with high-fat diets. The other meta-analyses comparing low-fat with high-fat (53), low-carbohydrate (51, 53), and other dietary interventions (32, 47, 48, 53) reported weak or no evidence. As to the lipid profile, low-fat diets resulted in a greater reduction in total and LDL cholesterol than high-fat diets or other dietary interventions, but also in a significant worsening of HDL cholesterol and triglycerides. This negative effect is probably determined by the type of fat and the quality of carbohydrates consumed (100).

The most consistent findings were observed in studies that included dietary patterns such as the Mediterranean and DASH diets. Both dietary patterns are high in fruits, vegetables, fish, and nuts, and indexes measuring adherence to these diets have been associated with lower risk of cardiovascular events, diabetes, and cancer in epidemiological studies (5, 20). In the present analysis, the Mediterranean diet showed suggestive evidence of a reduction in weight, BMI, total cholesterol, glucose, and blood pressure, and weak evidence of an improvement in LDL and HDL cholesterol, triglycerides, insulin, and HbA1c. No meta-analyses reported detrimental effects. The DASH diet, on the other hand, reported suggestive evidence of a beneficial effect on weight and blood pressure, and weak evidence for BMI and total cholesterol. With regard to the other dietary patterns, the evidence was less consistent, because most studies had a limited sample size and many meta-analyses were of low methodological quality. We found weak evidence of an improvement in total, LDL cholesterol, and blood pressure with the Nordic diet; weak evidence of an improvement in anthropometric parameters, total and LDL cholesterol, glucose, HbA1c, and blood pressure with vegetarian diets; and weak evidence of an improvement in total and LDL cholesterol, triglycerides, and blood pressure with the portfolio dietary pattern. Altogether, these results corroborate observational findings indicating that dietary patterns that emphasize vegetables, fruits, whole grains, and plant-based protein, and limit sugar, sodium, and red and processed meat, are consistently associated with decreased risk of cardiovascular and metabolic diseases (20, 101).

As to the other popular diets studied, the present umbrella review showed many criticisms. For the paleolithic diet, a weight-loss plan based upon the premise of consuming only foods available during the Stone Age (102), the number of participants was very small and the follow-up was short. In addition, extensive publication bias, selective outcome reporting, and potential conflict of interests were detected. With regard to intermittent energy restriction, a dietary approach that has gained greater popularity as a way for losing weight alternative to conventional weight-loss diets, our systematic literature search led to the identification of 6 meta-analyses of RCTs published in the last 3 y. Intermittent energy restriction includes diverse interventions such as alternate day fasting, the 5:2 diet, and longer cyclic periods of restricting energy intake or fasting, interchanged by periods of ad libitum energy intake. The number of clinical trials and participants, however, was very small, most studies were performed by the same authors, and the follow-up was generally short. With the exception of a meta-analysis that reported weak evidence of a greater reduction in insulin (70), all the other meta-analyses evaluating weight, lipid profile, glucose metabolism, and blood pressure reported no evidence of a superiority of intermittent energy restriction over continuous energy restriction.

The present umbrella review has several limitations. First of all, the included meta-analyses showed relevant differences in terms of populations, methods, duration of interventions, study quality, and definition of intervention and control diets. Most meta-analyses included studies conducted on participants with overweight/obesity or other diseases, and this should be considered before extending these results to the general population. In any case, the choice of the diet should be made via a critical approach, by considering the effects of the diet on all the factors that may have a role in the development of the disease. Second, despite the relatively high number of meta-analyses published, a limited number of clinical trials were available for many diets evaluated. Third, when multiple meta-analyses of RCTs existed for an outcome, often the results were not concordant in terms of direction of effect and/or statistical significance. Such a difference in the final results could be explained mainly by the framing of the question and differences in the inclusion criteria, comparisons, populations, and statistical methods used. Lastly, as with any other systematic review, an umbrella review is dependent on the reporting of the included meta-analyses and does not account for potential omissions or overlapping of original studies.

Because meta-analyses have become an indispensable tool in clinical application for evidence-based decision making, it is extremely important to define and carefully standardize the criteria and the strategies to adopt. Although the number of meta-analyses included in the present umbrella review is high, their methodological quality appears to be mainly low or critically low. More efforts are needed to improve the quality of published articles and further research on the effects of popular diets on anthropometric and cardiometabolic parameters is needed before firm conclusions can be drawn. This will facilitate the understanding, meaning, and applicability of findings in clinical practice.

In conclusion, through a systematic and comprehensive search we were able to include a vast number of meta-analyses that assessed the effects of different popular diets on weight and cardiometabolic risk factors. Among all the diets and dietary patterns evaluated, the Mediterranean diet had the strongest and most consistent evidence, with no meta-analyses reporting detrimental effects. Suggestive evidence of an improvement in body weight and blood pressure was also reported for the DASH diet. Low-carbohydrate, high-protein, low-fat and low-glycemic-index/load diets, on the other hand, showed beneficial effects on weight loss, but also potential risks of unfavorable lipid, glycemic, or blood pressure parameters. The strength of evidence for the other diets evaluated was weak or not statistically significant. Overall, these findings highlight the strengths and limitations of most popular diets, confirming that the best results, in terms of weight and cardiometabolic risk amelioration, are obtained with balanced dietary patterns such as the Mediterranean diet.

Supplementary Material

nmaa006_Supplemental_File

Click here for additional data file.^{(448.1KB, docx)}

ACKNOWLEDGEMENTS

The authors’ responsibilities were as follows—MD and FS: designed the study protocol; DM, DA, and LL: conducted the systematic literature search; CDB, DN, and EM: performed the quality assessment; AR, MDA, and LB: performed the data extraction; MD and GP: performed the statistical analysis; MD, GP, CF, MG, and JG: wrote the first draft of the manuscript; FS: critically reviewed the manuscript and contributed important intellectual content; MD: is the guarantor of the paper; and all authors: contributed to writing and reviewing the manuscript and read and approved the final manuscript.

Notes

The authors reported no funding received for this study.

Author disclosures: LL is employed by Janssen. All other authors report no conflicts of interest.

Janssen did not have any role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Supplemental Tables 1–7 are available from the “Supplementary data” link in the online posting of the article and from the same link in the online table of contents at https://academic.oup.com/advances/.

Abbreviations used: AMSTAR-2, A MeaSurement Tool to Assess systematic Reviews 2; DASH, Dietary Approaches to Stop Hypertension; HbA1c, glycated hemoglobin; MeSH, Medical Subject Headings; PI, prediction interval; RCT, randomized controlled trial.

Contributor Information

Monica Dinu, Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy.

Giuditta Pagliai, Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy.

Donato Angelino, Faculty of Bioscience and Technology for Food, Agriculture, and Environment, University of Teramo, Teramo, Italy.

Alice Rosi, Human Nutrition Unit, Department of Food and Drug, University of Parma, Parma, Italy.

Margherita Dall'Asta, Department of Animal Science, Food, and Nutrition, Università Cattolica del Sacro Cuore, Piacenza, Italy.

Letizia Bresciani, Human Nutrition Unit, Department of Veterinary Science, University of Parma, Parma, Italy.

Cinzia Ferraris, Human Nutrition and Eating Disorder Research Center, Department of Public Health, Experimental and Forensic Medicine, University of Pavia, Pavia, Italy.

Monica Guglielmetti, Human Nutrition and Eating Disorder Research Center, Department of Public Health, Experimental and Forensic Medicine, University of Pavia, Pavia, Italy.

Justyna Godos, Oasi Research Institute, Troina, Italy.

Cristian Del Bo’, Department of Food, Environmental, and Nutritional Sciences, Università degli Studi di Milano, Milan, Italy.

Daniele Nucci, Digestive Endoscopy Unit, Veneto Institute of Oncology, Padua, Italy.

Erika Meroni, Department of Food, Environmental, and Nutritional Sciences, Università degli Studi di Milano, Milan, Italy.

Linda Landini, Medical Affairs Janssen, Cologno-Monzese, Milan, Italy.

Daniela Martini, Department of Food, Environmental, and Nutritional Sciences, Università degli Studi di Milano, Milan, Italy.

Francesco Sofi, Department of Experimental and Clinical Medicine, University of Florence, Florence, Italy; Unit of Clinical Nutrition, University Hospital of Careggi, Florence, Italy; Don Carlo Gnocchi Foundation Italy, Onlus, Florence, Italy.

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PERMALINK

Effects of Popular Diets on Anthropometric and Cardiometabolic Parameters: An Umbrella Review of Meta-Analyses of Randomized Controlled Trials

Monica Dinu

Giuditta Pagliai

Donato Angelino

Alice Rosi

Margherita Dall'Asta

Letizia Bresciani

Cinzia Ferraris

Monica Guglielmetti

Justyna Godos

Cristian Del Bo’

Daniele Nucci

Erika Meroni

Linda Landini

Daniela Martini

Francesco Sofi

ABSTRACT

Introduction

Methods

Search strategy

Data selection

Data extraction and quality assessment

Data analysis

Results

Search results

Figure 1.

Study characteristics and quality

TABLE 1.

Anthropometric parameters

FIGURE 2.

Lipid profile

FIGURE 3.

Glycemic profile

FIGURE 4.

Blood pressure

FIGURE 5.

Evaluation of methodological quality, bias, heterogeneity, and strength of evidence

FIGURE 6.

FIGURE 7.

Discussion

Supplementary Material

ACKNOWLEDGEMENTS

Notes

Contributor Information

References

Associated Data

Supplementary Materials

ACTIONS

PERMALINK

RESOURCES

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