European Journal of Clinical Nutritionhttps://doi.org/10.1038/s41430-020-0607-6

REVIEW ARTICLE

Profile of the gut microbiota of adults with obesity: a systematic review

Louise Crovesy1 ● Daniele Masterson2● Eliane Lopes Rosado1

Received: 27 May 2019 / Revised: 4 March 2020 / Accepted: 6 March 2020© The Author(s), under exclusive licence to Springer Nature Limited 2020

AbstractRecently, relationship between gut microbiota composition and development of obesity has been pointed. However, thegut microbiota composition of individual with obesity is not known yet. Therefore, this systematic review aimed toevaluate differences in profile of gut microbiota between individuals with obesity and individuals with normal weight. Asearch performed on August 2019 in the databases Pubmed, Scopus, Web of Science, Cochrane library, Lilacs and grayliterature using the terms: “microbiota”, “microbiome”, “obesity”, “obesity morbid”, and “humans”. Studies assessingthe gut microbiota composition in adults with obesity and lean were included. Quality assessment was performed byNewcastle–Ottawa Quality Assessment Scale. Of the 12,496 studies, 32 were eligible and included in this review.Individuals with obesity have a greater Firmicutes/Bacteroidetes ratio, Firmicutes, Fusobacteria, Proteobacteria,Mollicutes, Lactobacillus (reuteri), and less Verrucomicrobia (Akkermansia muciniphila), Faecalibacterium(prausnitzii), Bacteroidetes, Methanobrevibacter smithii, Lactobacillus plantarum and paracasei. In addition, somebacteria had positive correlation and others negative correlation with obesity. Individuals with obesity showed profile ofgut microbiota different than individual lean. These results may help in advances of the diagnosis and treatment ofobesity.

Introduction

Obesity is a growing public health problem throughout theworld, affecting 650 million adults worldwide. Obesity isdifficult to control due to multiple etiological factors [1]and amongst the causes the gut microbiota has beenhighlighted [2].

The gut microbiota is composed mainly of bacteria fol-lowed by smaller proportions of fungi, virus and Archea [3].The main phyla of bacteria in the gut microbiota are Fir-micutes, Bacteroidetes, Proteobacteria, Actinobacteria, andVerrucomicrobia. Firmicutes and Bacteroidetes are the

most frequent in the microbiota, corresponding to 90% ofthe gut bacteria [4, 5].

On 2004, Bäckhed et al. [6] published the first articleabout the role of gut microbiota in the host metabolism,including body weight control. Animal model and humans’studies have been carried out to discover the composition ofthe gut microbiota associated with obesity [2, 7–10].

Experimental studies with animals have shown successin determining the association between the Firmicutes/Bacteroidetes ratio and obesity [2, 7, 8]. However, inhumans this affirmation has not been confirmed [9–12]. Inaddition, other phyla and bacteria genera have been asso-ciated with obesity [11–14]. Therefore, we carried out asystematic review aimed to evaluate the differences betweenthe profile of the gut microbiota of individuals with obesityand individuals with lean.

Methods

Search strategy and registration

A systematic review was carried out to answer the question:“Is the gut microbiota profile of individuals with obesitydifferent of lean individuals?”.

* Louise Crovesylouisecrovesy@yahoo.com.br

1 Instituto de Nutrição Josué de Castro, Universidade Federal do Riode Janeiro, Rio de Janeiro, Rio de Janeiro, Brazil

2 Library of Health Sciences Center, Universidade Federal do Rio deJaneiro, Rio de Janeiro, Brazil

Supplementary information The online version of this article (https://doi.org/10.1038/s41430-020-0607-6) contains supplementarymaterial, which is available to authorized users.

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The eligibility criteria were based on the PICOS strat-egy: Population (P), Intervention (I), Comparison (C),Outcome (O), Study design (S). The literature search wascarried out on August 2019 using five scientific databases:Medline via Pubmed, Scopus, Web of Science, Food Sci-ence and Technology Abstracts (FSTA), Cochrane library,Lilacs, and gray literature (Tripdatabase, Open Gray,Google Scholar, Digital Library of Theses and Disserta-tions and the Universidade de São Paulo). We followed thePRISMA statement to carry out the present systematicreview [15].

The search strategy combined MESH (Medline), DeCs(VHL) terms, and free terms using the boolean operators“AND” and “OR”. “Microbiota”, “microbiome”, “obesity”,and “morbid obesity” were the terms used in the search. Thesearch protocols for each scientific database can be found inthe supplementary material. A complementary search wascarried out in the references of studies included.

The studies identified were imported to the EndNoteWeb Software (Thomson Reuters, New York, USA), andduplicate articles were identified and excluded.

The present systematic review was registered in theInternational Prospective Register of Systematic Reviews(PROSPERO) under the number: CRD42018107414(https://www.crd.york.ac.uk/PROSPERO/).

Eligibility criteria

Observational human studies and clinical trials that eval-uated the gut microbiota composition in adults with obe-sity and in those of normal weight by any validatedtechnique, written in English, Spanish or Portuguese,were included.

Studies with animal models, children, teenagers andpregnant; clinical trials with no gut microbiota compositionbaseline data; evaluation of oral, stomach, skin or oro-pharyngeal microbiota; studies not compared with leanindividuals; and studies with a high risk of bias (poorquality), were excluded.

Quality assessment and risk of bias

The studies included were submitted to a quality assess-ment by the Newcastle–Ottawa Quality Assessment Scale[16]. This instrument includes three domains: selection,comparability, and outcomes. The selection domain iscomposed of by four items, comparability of one item,and outcomes of three items. The article could receiveone star in each item, receiving a maximum four starsin selection, one or two in comparability, and three inoutcomes [16]. A high risk of bias occurred when somedomain did not receive star, and in this case, the articlewas excluded.

Data extraction

The data extracted from the studies included in this sys-tematic review was summarized in Table 1, giving thefollowing information: author and year of publication,country and period of study/seasons (when informed),sample size and characterization of the study population,method used to evaluate the gut microbiota and bacteriaanalyzed (if applicable), and outcomes.

Results

Literature search

Of the 12,496 studies found in the search (scientific literature(n= 9881—Pubmed (n= 2800), Scopus (n= 3393), Web ofscience (n= 2426), FSTA (n= 887), Cochrane library (n=300), Lilacs (n= 75)), and gray literature (n= 2615)), the fulltexts of only 53 articles were read, after the removal ofduplicates and screening of the title and abstract (Fig. 1). Onethesis was not available for full-text reading. Thirty-twoarticles are included and are described in Table 1.

Quality and risk of bias

The quality scores of the studies can be found in Table 1.Three articles [17–19] received the maximum score andseven [17, 20, 21] showed good quality with eight points.Two articles did not explain the recruitment process[22, 23]. So they were excluded from the systematic review,due to a high risk of bias.

Studies characteristics

Of the 32 studies included, three found no differencebetween the gut microbiota of individuals with obesity andnormal weight [17–19]. Differences found between the gutmicrobiota compositions are shown in the next sections.

Diversity, richness, and total number of bacteria

Verdam et al. [24], Dorminianni et al. [20], and De laCuesta-Zuluaga et al. [25] identified less diversity in indi-viduals with obesity, while Kocelak et al. [12] and Kasaiet al. [26] found greater diversity and a larger total numberof bacteria. The microbiota was richer in individuals withobesity [26, 27].

Firmicutes/bacteroidetes ratio

One study showed a lower Firmicutes/Bacteroidetes ratio inindividuals with obesity [11], but other studies found the

L. Crovesy et al.

Table 1 Characteristics, summary, and quality score of studies included in systematic review.

Author CountryPeriodof study

Characteristics of population n(n men) BMI (kg/m²) age (years)

Method of gut microbiota evaluation(bacteria analyzed)

Outcomes NOS score

Schwiertzet al. [11]

Germany 98 individuals (♂ n= 34)Age: 47 ± 13Lean (n= 30 BMI 18.5–24.9)Overweight (n= 35, BMI 25–30)Obese (n= 33, BMI > 30)

qPCR (Clostridium leptum, Clostridiumcoccoides, E. cylindroide, Lactobacilli/Enterococci, Ruminococcus flavefaciensspp., Veilonella, Bacteroides,Prevotella, Bifidobacterium,Methanobrevibacter)

Ruminococcus flavefaciens spp,Firmicutes, and Firmicutes/Bacteroidetes ratio were lower andBacteroidetes higher in individuals withoverweight and obesity, andBifidobacterium, Methanobrevibacterand Clostridium leptum were lower inindividuals with obesity, comparedwith lean.Positive correlation between BMI andBifidobacterium, Methanobrevibacter.

8

Zuo et al.[33]

China 104 individualsLean (n= 52, ♂ n= 26, BMI20.26 ± 1.50, age 33.02 ± 10.37)Obese (n= 52, ♂ n= 34, BMI30.79 ± 2.80, age 34.65 ± 11.91)

Quantification of bacteria isolated fromfeces and incubated (Escherichia coli,Enterococci, Lactobacilli,Bifidobacteria, Clostridium perfringens,Bacteroides)

Bacteroides and Clostridiumperfringens were lower in individualswith obesity. Enterococci tended to behigher in individuals affected by obesity(no significance).

8

Million et al.[41]

France 115 individualsLean (n= 47, ♂ n= 24, BMI 22.1 ±1.8, age 42.6 ± 17.5)Obese (n= 68, ♂ n= 31, BMI43.6 ± 7.8, age 50.5 ± 14.4)

qPCR (Methanobrevibacter smithii,Bacteroidetes, Firmicutes,Lactobacillus, Lactococcus latis, andBifidobacterium)

Individuals with obesity showed lowerMethanobrevibacter smithii,Lactobacillus paracasei, Lactobacillusplantarum and Bifidoacterium animalis,and higher Lactobacillus andLactobacillus reuteri, comparedwith lean.Lactobacillus reuteri increased,Bifidobacterium animalis andMethanobrevibacter smithii decreasedwere associated with obesity.

7

Munukkaet al. [40]

Finland 85 womenLean (n= 11, BMI 21.6 ± 2.0, age31 ± 14)Obese without metabolic disorder(n= 47, BMI 29.1 ± 2.9, age 39 ± 9)Obese with metabolic disorder(n= 27, BMI 30.9 ± 2.9, age 42 ± 8)

Flow cytometry, 16S rRNAhybridization, and DNA staining(Atopobium cluster, Bacteroides group,Bifidobacterium spp., enteric bacteria,Eubacterium rectale- Clostridiumcoccoides group, and Faecalibacteriumprausnitzii)

Women with obesity and metabolicdisorder showed higher Eubacteriumrectale-Clostridium coccoides,Firmicutes/Bacteroidetes ratio andEubacterium rectale/Bacteroidetescompared with other groups and highergram-negative bacteria compared withlean only. Eubacterium rectale/Bacteroidetes ratio was higher inwomen affected by obesity withoutmetabolic disorder compared with lean.Positive correlation between BMI andEubacterium rectale-Clostridiumcoccoides and Eubacterium rectale/Bacteroidetes ratio, and negativecorrelation between BMI andBacteroides.

7

Patil et al.[35]

India 20 individualsUnderweight (n= 5, BMI 16.46 ±1.34, median age 23)Lean (n= 5, BMI 23.56 ± 0.82,median age 44)Obese (n= 5, BMI 44.62 ± 7.49,median age 45)Obese post bariatric surgery (n= 5,BMI 31.63 ± 3.68, median age 50)

Sequencing and qPCR (Archaea andBacteroides)

Archaea and Bacteroides were higher inindividuals with obesity, decreasingafter bariatric surgery and becoming likethe lean individual.

5

Bezerra et al.[18]

Brazil Octoberto April

♀ n= 32Lean (n= 13, BMI 21.9 ± 2.1, age26 (24–27))Obese (n= 19, BMI 45.8 ± 4.7, age34 (31–47))

Quantification of bacteria isolated offeces and incubated (Bifidobacteriumspp. and Lactobacillus spp.)

No difference in gut microbiotacomposition between differentnutritional status.

6

Kocelaket al. [12]

Poland 80 individualsLean (n= 30, BMI 23.2 (22.5–23.9),age 42.6 (38.1–47.1))obese (n= 50, BMI 35.7(34.3–37.1), age 51.9 (48.1–55.7))

Quantification of bacteria isolated offeces and incubated (Bacteroidesovatus, Clostridium septicum,Clostridium perfringens, staphylococcusaureus, echerichia coli)

Individuals with obesity showed higherbacteria total. Gram-positive and gram-variables cocci only cultured on fecalsample of obese.

5

Million et al.[14]

France 263 individualsAnorexia (n= 15, ♂ n= 1, BMI13.5 (11.7–14.6), age 27.3 ± 10.8)Lean (n= 76, ♂ n= 40, BMI 22.4(20.7–23.7), age 49.5 ± 18.6)Overweight (n= 38, ♂ n= 32, BMI27.1 (25.9–28.6), age 54.1 ± 17.8)Obese (n= 134), ♂ n= 65, BMI40.0 (36.4–46.8), age 51.8 ± 14.7

qPCR (Firmicutes, Bacteroidetes,Escherichia coli, Metanobevibactersmithii, Latobacillus reuteri,Lactobacillus plantarum, Lactobacillushamnosus, Lactobacillus fermentum andLactobacillus acidophilus,Bifidobacterium animalis)

Lactobacillus reuteri was higher inindividuals with overweight and obesityand Lactobacillus reuteri was associatedwith overweight and obesity.Bacteroidetes and Escherichia coli werelower in individual with obesitycompared with overweight and lean, andMetanobrevibacter smithii andBifidobacterium animalis were lower

5

Profile of the gut microbiota of adults with obesity: a systematic review

Table 1 (continued)

Author CountryPeriodof study

Characteristics of population n(n men) BMI (kg/m²) age (years)

Method of gut microbiota evaluation(bacteria analyzed)

Outcomes NOS score

compared with lean.Negative correlation between BMI andBifidobacterium animalis, Escherichiacoli.

Simões et al.[17]

Finland Years:1975–1979

20 twin pairs (♂ pairs n= 9)Lean (BMI 22.9 ± 2.2, age 26 ± 3)Overweight (BMI 26.5 ± 1.2, age29 ± 3)Obese (BMI 32.74 ± 2.1, age 28 ± 4)

qPCR (Bacteroides spp. (Bacteoroidesthetataiotaomicron), E. rectale(Roseburia intestinalis), C. leptum(Anaerotruncus colibominis),Atopobium (A. parulim),Bifidobacterium, (B. longum)Lactobacillus (Lactobacillus casei))

No difference in gut microbiotacomposition between differentnutritional status.

9

Teixeiraet al. [39]

Brazil ♀ n= 32Lean (n= 17, BMI 21.2 (20.6–21.9),age 28.05 ± 6.9)Obese (n= 15, BMI 34,5(32.8–36.7), age 30.7 ± 5.7)

qPCR (Bifidobacterium longum,Bifidobacterium catenulatum,Bifidobacterium bifidum,Bifidobacterium adolescentis,Bifidobacterium breve, Akkermansiamuciniphila, Clostridium coccoides,Clostridium leptum, Lactobacillusacidophilus, Lactobacillus paracase,Lactobacillus plantarum, Lactobacillusrhamnosus, Lactobacillus casei)

Women with obesity showed lowerBifidobacterium, Bifidobacteriumlongum, Clostridium coccoides andClostridium leptum. Lactobacillusplantarum and Akkermansia (p= 0.006)tended to be lower.Negative correlation between BMI andAkkermansia muciphinila,Bifidobacterium, Bifidobacteriumlongum, Clostridium coccoides,Clostridium leptum, Lactobacillusplantarum.

6

Verdam et a.[24]

NetherlandMay toSeptember

28 individualsLean (n= 9, ♂ n= 3, BMI 22.2 ±0.7, age 23.3 ± 3.3)Obese (n= 19, ♂ n= 5, BMI 40.4 ±2.5, age 36.2 ± 2.4)

Sequencing Individuals with obesity showed lowerdiversity and Bacteroidetes/Firmicutesratio, and higher amount of Clostridiumcluster IV and XIVa.Negative correlation between BMI andBacteroidetes/Firmicutes ratio,Allistipes, and positive correlationbetween BMI and Roseburiaintestinalis, Enterobacter aerogenes,Klebsiella pneumniea, Vibrio,Yersina spp.

7

Zak-Golabet al. [19]

Poland 80 individualsLean (n= 30, ♂ n= 6, BMI 23.7(21.8–24.8), age 42.5 (32.0–52.0))Obese (n= 50, ♂ n= 11, BMI 35.4(30.6–38.7), age 53.5 (42.0–63.0))

Quantification of bacteria isolated offeces and incubated (Bacteroides ovatusATCC BAA-1296, Clostridiumsepticum ATCC 12464, Clostridiumperfringens ATCC 13124,Staphylococcus aureus ATCC 25923,and Escherichia coli ATCC 25922)

No difference in gut microbiotacomposition between differentnutritional status.

5

Escobar et al.[34]

– 126 individualsColombian (n= 30, ♂ n= 16 - Lean(BMI 22.6 ± 1.7, age 33 ± 11),Overweight (BMI 27.1 ± 1.3, age39 ± 9), Obese (BMI 32.6 ± 2.3, age43 ± 12))European (n= 13, ♂ n= 10 - Lean(BMI 22.5 ± 1.2, age 56 ± 9),Overweight (BMI 28.4 ± 0.8, age56 ± 9), Obese (BMI 32.8 ± 1.7, age59 ± 6))Japanese (n= 11, ♂ n= 5 - Lean(n= 10, BMI 20.1 ± 0.8, age 21 ± 1),Overweight (n= 1, BMI 28, age 33))South Korea (n= 18, ♂ n= 12 -Lean (n= 14, BMI 22.5 ± 1.2, age43 ± 16), Overweight (n= 4, BMI28.5 ± 0.6, age 58 ± 13))USA (♀ n= 54 twin - Lean (BMI21.3 ± 1.0, age 26 ± 2), Overweight(BMI 28.3 ± 0.6, age 26 ± 3), Obese(BMI 41.7 ± 7.8, age 26 ± 3))

Sequencing Americans with obesity showed lowerFirmicutes, Bacteroides, Coprococcus,Oscillospira, Parabacteroides,Clostridiales, Rikenellaceae,Ruminococcaceae, and higherCatenibacterium, while Colombiansaffected with obesity showed lowerRuminococcaceae, Clostridiales,Dialister, Oscillospira, Akkermansia.Europeans with obesity showed lowerBacteroides and higher Veillonellaceae.

7

Fernandeset al. [42]

Canada 94 individualsLean (n= 52, ♂ n= 22, BMI 21.8 ±0.3, age 32.0 ± 1.8)Overweight and obese (n= 42, ♂n= 21, BMI 30.3 ± 0.7, ager 37.9 ±2.0)

qPCR (Bacteroidetes/Prevotella(Bacteroidetes), Clostridium coccoides,Clotridium leptum, Bifidobacteria,Escherichia coli, Archea)

Individuals with overweight and obesityshowed lower Escherichia coli.Negative correlation between BMI andBacteroidetes.

7

Rahat-rozenblooet al. [28]

Canada 22 individualsLean (n= 11, ♂ n= 6, BMI 22.6 ±0.6, age 35.8 ± 4.2)

Sequencing Individuals with obesity showed higherFirmicutes and Firmicutes/Bacteroidetes ratio.

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L. Crovesy et al.

Table 1 (continued)

Author CountryPeriodof study

Characteristics of population n(n men) BMI (kg/m²) age (years)

Method of gut microbiota evaluation(bacteria analyzed)

Outcomes NOS score

Obese (n= 11, ♂ n= 9, BMI 30.1 ±0.8, age 42.5 ± 3.9)

Dorminianniet al. [20]

USA April1985 toJune 1987

82 individuals (♂ n= 51)♀ (n= 31, BMI 23.8 ± 4.69, age59.2 ± 12.67)♂ (n= 51, BMI 25.0 ± 4.06, age58.4 ± 13.18)

Sequencing Women with overweight had lowerdiversity.

9

Kasai et al.[26]

Japan2012–2013

56 individualsLean (n= 23, ♂ n= 11, BMI 18.6 ±1.2, age 45.6 ± 9.6)obese (n= 33, ♂ n= 20, BMI27.8 ± 2.5, age 54.4 ± 8.2)

Terminal restriction fragment lengthpolymorphism

Individuals with obesity showed higherdiversity, richness, Firmicutes/Bacteroidetes ratio, and lower amountsof Bacteroidetes.Blautia hydrogenotorophica,Coprococcus catus, Eubacteriumventriosum, Ruminococcus bromiii andRuminococcus obeum were associatedwith obesity.While Bacteroidetes faecichinchilar,Bacteroides thetaiotaomicron, Blautiawexlerae, Clostridium bolteae,Flavonifractor plautii. Bacteroidetesfaecichinchil and Bacteroidesthetaiotaomicron were associatedwith lean.

7

Santos et al.[29]

Brazil Octoberto December

♀ n= 40Lean (n= 20, BMI 22.41 ± 2.04, age44 ± 9.3)Obese (n= 20, BMI 36.45 ± 4.52,age 33 ± 12.04)

qPCR (Mollicutes classes, Firmicutesand Bacteroidetes)

Women with obesity showed higheramount of Mollicutes and Firmicutes/Bacteroidetes ratio.Positive correlation between BMI andMollicutes.

8

Yasir et al.[13]

Saudi Arabiaand France

46 individualsSaudi (♂ n= 18 men – Lean (n= 9,BMI 24.5 ± 3.2, age 28 ± 4), Obese(n= 9, BMI 46.0 ± 5.9, age 26 ± 3))French (n= 28 – Lean (n= 16, ♂n= 7, BMI no data, age 34 ± 5),Obese (n= 12, ♂ n= 7, BMI 38.3 ±7.9, age 39 ± 13))

Sequencing Lean French showed higher bacterialspecies than other groups.French with obesity showed higherProteobacteria, Bacteroidetes,Lactobacillus, Escherichia-Shiguela,Bacteroides, Bacteroides fragilis,Blautia wexlerae, Echerichia coli andlower Clostridium, Faecalibacterium,Bifidobacterium adolescentis, breve,and Ruminococcus lactaris comparedwith lean French, and lowerRuminococcus, Verrucomicrobia,Faecalibacterium, Bifidobacteriumadolescentis, and higher Bacteroidesfragilis, and Bifidobacterium bifidumcompared with lean Saudi.Saudi with obesity showed higherFirmicutes and Dorea compared withlean Saudi, and lower Clostridium,Bifidobacterium brevee and higherDorea, Blautiawex lerae and Rothiamucilaginosa compared with leanFrench.Gemmatimonadetes, Lactobacillusgasseri and reuteri were detected onlyFrench with obesity, and Spirochaetaeand Elusimicrobia detected only leanFrench.French with obesity had higherVerrucobacteria, Rothia mucilaginosaand Ruminococcus bromii, and lowerFaecalibacterium, Blautia andBifidobacterium compared with Saudiwith obesity.

6

Andoh et al.[32]

Japan 20 individualsLean (n= 10, ♂ n= 5, BMI 16.6 ±1.0, age 45 (31–58))Obese (n= 10, ♂ n= 5, BMI 38.1 ±3.5, age 41 (35–55))

Sequencing Individuals with obesity showed higherFirmicutes, Fusobacteria, Alistipes,Anaerococcus, Coprococcus,Fusobacterium, Parvimonas,Acidaminococcus intestine, Actinomycesmeyeri, Atopobium parvulum,Bacteroides vulgates, Eubacteriumhadrum, Klebsiella pneumonia,Roseburia faecis and lower Bacteroides,Desulfovibrio, Faecalibacterium,Finegoldia, Lachnoanaero baculum,

6

Profile of the gut microbiota of adults with obesity: a systematic review

Table 1 (continued)

Author CountryPeriodof study

Characteristics of population n(n men) BMI (kg/m²) age (years)

Method of gut microbiota evaluation(bacteria analyzed)

Outcomes NOS score

Olsenella, Clostridium ramosum,Clostridium citroniae, Faecalibacteriumprausnitzii, Eubacterium desmolans,Eubacterium fissicatena, andHaldemania filiformis.

Hippe et al.[43]

– 68 individualsLean (n= 18, ♂ n= 3, BMI 21.2 ±1.9, age 26 ± 3)Obese with T2D (n= 24, ♂ n= 14,BMI 38.2 ± 5.1, age 58 ± 9)Obese (n= 26, ♂ n= 4, BMI 43.9 ±12.1, age 42 ± 15)

qPCR (Faecalibacterium prausnitzii) Individuals with obesity with or withoutT2D showed lower Faecalibacteriumprausnitzii compared with lean.

5

Selma et al.[30]

SpainClinical trial

69 individualsLean (n= 20, ♂ n= 11, BMI 22.5 ±2.1)Overweight-obesity (n= 49, ♂ n=32, BMI 30.3 ± 3.4)

qPCR (Echerichia coli, Bifidobacterium(B. longum), Lactobacillus/Leuconostoc/Pedicoccus (Lactobacillusplantarum), Firmicutes, Clostridiumleptum, Blautiacoccoides/Eubacteriumrectale (Blautia coccoides),Bacteroides (Bacteroides ovatus),Prevotella (Prevotella bucalis), andGordonibacter (Gordonibacterpamelaeae))

Individuals with overweight-obesityshowed higher Firmicutes, Clostridiumleptum, Lactobacillus/Leuconostoc/Predicoccus, Bifidobacterium andFirmicutes/Bacteroidetes ratio, andlower Prevotella.Positive correlation between BMI andFirmicutes, Clostridium leptum,Lactobacillus/Leuconostoc/Predicoccus,and negative correlation between BMIand Prevotella.

5

Blasco et al.[37]

Spain Januaryto September

35 individualsLean (n= 18, ♂ n= 10, BMI 23.3(21.6–25.82), age 50 (39–56.25))Obese (n= 17, ♂ n= 11, BMI 45.1(39.1–47.45), age 53 (48–58))

Sequencing Individuals with obesity showed lowerIgnavibacteriae.

6

Davis et al.[45]

USA May toDecember

81individuals, ♂ n= 36BMI 28.3 ± 7.0Lean (27)Overweight (27)Obese (27)Age 33 ± 13.3

Sequencing Firmicutes (Dialister sp., Ruminococcusgnavus, Blautiaobeum,Megasphaerasp., Oscillospira sp.) andVerrucobacteria (Akkermansiamuciniphila) had association withobesity.

8

Fernandéz-Navarro et al.[21]

Spain 2009to 2015

68 individualsLean (n= 20, ♂ n= 4, BMI 23.0 ±1.5, age 56.4 ± 10.1)Overweight (n= 35, ♂ n= 17, BMI27.5 ± 1.4, age 51.7 ± 11.7)Obese (n= 13, ♂ n= 6, BMI 34.1 ±2.7, age 47.8 ± 10.2)

qPCR (Akkermansia (Akkermansiamuciniphila CIP 107961), Bacteroidetesgroup and Bacteroidetes-Prevotella-Porphiromonas (Bacteroidesthetaiotaomicron DSMZ 2079),Bifidobacterium (Bifidobacteriumlongum NCIMB 8809),Faecalibacterium (Faecalibacteriumprausnitzii DSMZ 17977), ClostridiaXIVa and Blautia coccoides-Eubacterium rectale (Blautia coccoidesDSMZ 935), Lactobacillus group(Lactobacillus gasseriIPLA IR7/5))

Lactobacillus was higher in individualswith obesity.Positive correlation between BMI andLactobacillus.

9

Kolida et al.[10]

UkraineMarch to May

61 individualsUnderweight (n= 7, ♂ n= 2,BMI < 18.5)Lean (n= 27, ♂ n= 7, BMI18.5–24.9)Overweight (n= 16, ♂ n= 2, BMI25–29.9)Obese (n= 11, ♂ n= 4, BMI ≥ 30)Age 44.2

qPCR (Bacteroidetes, Firmicutes,Actinobacteria)

Individuals with obesity showed lowerBacteroidetes and higher Firmicutes andFirmicutes/Bacteroidetes ratio.Positive correlation between BMI andFirmicutes and Firmicutes/Bacteroidetes ratio, and negativecorrelation between BMI andBacteroidetes.

6

Nistal et al.[27]

Spain 73 individualsLean (n= 20)Obese (n= 53)No data about BMIAge (20–60)

Sequencing Individuals with obesity showed higherbacterial richness and amount ofProteobacteria, Prevotella,Megasphaera, Lactobacillus,Meganomas and Acidaminococcus, andlower amount of Blautia, Osillospira,Flavobacterium, Alkaliphillys andEubacterium, compared with lean.

7

De la Cuesta-Zuluaga et al.[25]

Colombia Julyto November

441 individualsHealthy lean (n= 91, ♂ n= 41, BMI22.5 ± 1.6, age 36.8 ± 10.8)Non-healthy lean (n= 47, ♂ n= 22,BMI 23.0 ± 1.6, age 43.3 ± 11.8)Healthy overweight (n= 60, ♂ n=26, BMI 27.2 ± 1.3, age 38.4 ± 10.8)

Sequencing Diversity was lower in individuals withobesity and individuals withcardiovascular disease.Pathobiont-CAG and Lachnospiraceae-CAG were associated withcardiovascular disease and obesity,

6

L. Crovesy et al.

inverse result, with higher Firmicutes/Bacteroidetes ratio inindividual with obesity [10, 24, 26, 28–30].

Microbiota profile and obesity

Individuals with obesity showed higher Firmicutes counts[10, 13, 28, 30–32] and lower Bacteroidetes counts[10, 14, 26, 32–34]. However, some studies found lowerFirmicutes [11, 34] and higher Bacteroidetes[11, 13, 31, 35, 36] in individual with obesity. Fusobacteria[32, 36] and Proteobacteria [13, 27, 36] phyla increased,while the Ignavibacteriae [37] and Verrucobacteria(Akkermansia municiphila) [13] decreased. In addition, onestudy showed higher counts for the Archaea kingdom [32],

and another study found higher Mollicutes class [29], whilea third showed lower Methanobacteria [31] in individualswith obesity.

Rikenellaceae, Ruminococcaceae, and Veillonellaceaefamilies were less present in individuals with obesity [34]. Atthe genus level, individuals with obesity had higher count ofAlistipes, Anaerococcus, Coprococcus, Parvimonas [32],Fusobacterium [32, 38], Enterococcus [38], Prevotella,Megasphaera, Meganomas, Acidaminococcus [27], Bifido-bacterium [30, 39], Lactobacillus [13, 14, 21, 27], Cateni-bacterium [34], Dorea, Bacteroides, Escherichia-Shiguela[13], Lactobacillus/Leuconostoc/Predicoccus [30], and theEubacteriumrectale/Bacteroidetes ratio [40], and lowercounts of Desulfovibrio, Finegoldia, Lachnoanaerobaculum,

Table 1 (continued)

Author CountryPeriodof study

Characteristics of population n(n men) BMI (kg/m²) age (years)

Method of gut microbiota evaluation(bacteria analyzed)

Outcomes NOS score

Non-healthy overweight (n= 111, ♂n= 63, BMI 27.6 ± 1.4, age 41.5 ±10.9)Healthy obese (n= 21, ♂ n= 6,BMI 33.5 ± 2.6, age 43.1 ± 8.8)Non-healthy obese (n= 111, ♂ n=54, BMI 34.1 ± 3.6, age 42.7 ± 11.1)

while Ruminococcaceae andAkkermansia were associated with lean.

Gao et al.[36]

China 551 individualsUnderweight (n= 62 ♀ n= 49,♂ n= 13, BMI ♀ 17.5 ± 1.0, ♂16.7 ± 1.1, age ♀ 38.0 ± 25.6, ♂21.5 ± 5.5)Lean (n= 261 ♀ n= 168, ♂ n= 93,BMI ♀ 20.7 ± 1.3, ♂ 21.3 ± 1.3, age♀ 35.6 ± 14.3, ♂ 37.8 ± 17.3)Overweight (n= 170 ♀ n= 55,♂ n= 115, BMI ♀ 24.7 ± 1.3, ♂25.1 ± 1.2, age ♀ 38.1 ± 12.6, ♂41.7 ± 15.9)Obese (n= 58 ♀ n= 20, ♂ n= 38,BMI ♀ 31.7 ± 4.3, ♂ 31.2 ± 3.2, age♀ 35.5 ± 4.3, ♂ 34.7 ± 3.2)

Sequencing Underweight showed higher alphadiversity compared with the othergroups. Bacteroidetes, Fusobacteria andProteobacteria were higher inindividuals with obesity, compared withunderweight.

7

Jinathamet al. [31]

Thailand 42 individualsLean (n= 21, BMI 20.7 ± 0.4)Overweight (n= 10, BMI 27.4 ±0.5)Obese (n= 11, BMI 33.6 ± 1.0)Age 27.6 ± 1.3

qPCR (Bacteroidetes, Bacteroides,Prevotella, Firmicutes, Roseburia eEubacterium rectale, Ruminococcus,Lactobacillus, Enterococcus,Staphylococcus, Oscillospira,Faecalibacterium prausnitzii,Chistensenella minuta, Actinobacteria,γ-Proteobacteria, Fusobacterium,Akkermansia muciniphila,Methanobacteria)

Bacteroidetes, Firmicutes,Staphylococcus, Akkermansiamuciniphila, Methanobacteria werelower in individuals with obesity,compared with lean. Ruminoccocus,Christensenella minuta, γ-Proteobacteria, Akkermansiamuniciphila was lower in individualsaffected by obesity, compared withoverweight.Negative correlation between BMI,waist circumference and Firmicutes,Staphylococcus, Akkermansiamuniciphila, Methanobacteria.

8

Ottossonet al. [44]

Sweden 674 individualsLean (BMI 22.3 ± 1.8)Overweight (BMI 27.1 ± 1.4)Obese (BMI 33.6 ± 3.3) Age 39.4 ±13.5

Sequencing Positive correlation between BMI andLachnospiraceae (Blautia, Dorea, andRuminococcus), and negativecorrelation between BMI and SHA-98.

7

Sarmientoet al. [38]

Brazil 72 individualsLean (n= 24, BMI 22.2 ± 1.8)Overweight (n= 24, BMI 27.1 ±1.3)Obese (n= 24, BMI 36.9 ± 6.0)Age 39.6 ± 13.5

FISH (Bacteroidetes fragilis,Escherichia cpli, Enterococcus,Fusobacterium, Prevotella intermedia,prevotella nigrescens, Pseudomonas,Staphylococcus, Streptococcus,Acinetobacter)

Individuals with obesity showed higherbacterial density followed by individualswith overweigh and lean.Fusobacterium, Enterococcus,Echerichia coli were higher inindividuals with obesity, compared withlean individuals.

8

♀ women, ♂ men, BMI body mass index, DNA desoxyribonucleic acid, FISH fluorescence in situ hybridization, NOS Newcastle–Ottawa Scale,rRNA ribosomal ribonucleic acid, qPCR quantitative polymerase chain reaction quantitative, T2D type 2 diabetes, USA United State of America.

Profile of the gut microbiota of adults with obesity: a systematic review

Olsenella [32], Faecalibacterium [13, 32], Bifidobacterium,Methanobrevibacter [11], Prevotella [30], Blautia, Flavo-bacterium, Alkaliphillys, Eubacterium [27], Staphylococcus[31], Osillospira [27, 34], Dialister, Akkermansia, Copro-coccus, Parabacteroides, Clostridiales [34], Clostridium, andRuminococcus [13].

The species more present in individuals with obesitywere Lactobacillus reuteri [14, 41], Clostridium cluster IVand XIVa [24], Clostridium leptum [30], Acidaminococcusintestine, Actinomyces meyeri, Atopobium parvulum, Bac-teroides vulgates, Eubacterium hadrum, Klebsiella pneu-monia, Roseburia faecis [32], Bacteroides fragilis, Blautiawexlerae, Escherichia coli, and Bifidobacterium bifidum[13]. On the other hand, Akkermansia muciniphila [13, 31],Clostridium leptum, Ruminococcus flavefaciens spp. [11],Clostridium perfringens [33], Lactobacillus paracasei,Lactobacillus plantarum [41], Methanobrevibacter smithii,Bifidoacterium animalis [14, 41], Escherichia coli [14, 42],Ruminococcus lactaris, Rothia mucilaginosa, Bifido-bacterium adolescentis Bifidobacterium breve [13], Bifido-bacterium longum, Clostridium coccoides, Clostridiumleptum [37], Clostridium ramosum, Clostridium citroniae,Faecalibacterium prausnitzii [32, 43], Eubacterium des-molans, Eubacterium fissicatena, and Haldemania filiformis[32] were less present in individuals with obesity.

Correlation between gut microbiota and obesity

Some studies carried out correlations between the gutmicrobiota composition and the BMI (n= 15). Positive

correlations were found between the BMI and Bifido-bacterium, Methanobrevibacter [11], Eubacterium rectale-Clostridium coccuides, Eubacterium rectale/Bacteroidetesratio [40], Roseburia intestinalis, Enterobacter aerogenes,Klebsiella pneumniea, Vibrio, Yersina spp. [24], Mollicutes[29], Firmicutes, Clostridium leptum, Lactobacillus/Leu-conostoc/Predicoccus [30], Lactobacillus [21], Proteo-bacteria, Actinobacteria, Spitochaetes, Blautia, Dorea,Ruminococcus [44], and Firmicutes/Bacteroidetes ratio[26].

Negative correlations were found between the BMI andBacteroides [40, 42], Clostridium coccoides, Clostridiumleptum, Lactobacillus plantarum, Bifidobacterium, Bifido-bacterium longum [39], Akkermansia muciphinila [31, 39],Firmicutes, Staphylococcus, Methanobacteria [31], Bifido-bacterium animalis, Escherichia coli [14], Prevotella [30],SHA-98 [44], Allistipes, and Bacteroidetes/Firmicutes ratio[24].

Bacteroidetes faecichinchilar, Bacteroides thetaiotao-micron, Blautia wexlerae, Clostridium bolteae, Flavoni-fractor plautii [26], Ruminococcaceae, and Akkermansia[25] were associated with normal weight. On the otherhand, increases in Blautia hydrogenotorophica, Copro-coccus catus, Eubacterium ventriosum, Ruminococcusbromii, Ruminococcus obeum [26], Firmicutes, Verruco-bacteria [45] and Lactobacillus reuteri [14, 41], anddecreases in Bifidobacterium animalis and Methano-brevibacter smithii were associated with obesity [41], andPathobiont-CAG and Lachnospiraceae-CAG were asso-ciated with cardiovascular disease and obesity [41].

Discussion

Over the years the role that the gut microbiota compositionexerts on human health influencing body weight control,has been shown [46]. This review observed controversy inthe diversity of the gut microbiota associated with obesitywith respect to higher or lower counts in individual withobesity, compared with lean. Some bacteria of the gutmicrobiota were highlighted as higher in individual withobesity, such as Firmicutes, Proteobacteria, Fusobacteria,Firmicutes/Bacteroidetes ratio, and Lactobacillus, and oth-ers as lower, such as Bacteroidetes, Faecalibacteriumprausnitzii, Akkermansia muciniphila, Methanobrevibactersmithii, and Bifidobacterium animalis.

The relationship between the gut microbiota and bodyweight control was discovered in the last decade [6]. Later astudy showed weight gains in germ-free mice receivingtransplants of gut microbiota from individuals with obesity[47]. The literature indicates an important influence of themicrobiota on body weight control. This changes in the gutmicrobiota composition causes a greater extraction and

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Records identified through database searching

(n=9881)

2615 records identified through grey literature

Records after duplicates removed (n=8336)

Full text assessed for eligibility (n=53)

Records excluded after reading title and abstract (n=8283)

Full-text articles excluded (n=21): −Short communication (n=2) −Thesis was not available (n=1) −Did not compared lean and obese (n=4) −Included children and teenagers (n=2) − Did not evaluated gut microbiota (n=2) −Intervention study (n=2) −Obesity group had overweight BMI (n= 5) −Protocol (n=1) −Poor quality (n=2)

Records duplicates excluded (n=4160)

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Fig. 1 Flow chart of selection process.

L. Crovesy et al.

absorption of calories, a reduction in the secretion of theanorexigenic hormones (GLP-1, PYY, and leptin) andsatiety, increases fat storage in the adipose tissue, anddamage to the gut barrier contributing with translocation oflipopolysaccharide and inflammation. These changes con-tribute to the development of obesity [46, 48]. Although themechanism causing an imbalance in the gut microbiota isknown, the bacteria involved in this process have not beendetermined yet [48].

The relationship between Firmicutes/Bacteroidetes ratioand obesity appears to have also been confirmed in humans.Only one study showed lower Firmicutes/Bacteroidetesratio in individuals with obesity; however, these authorsconsidered Bacteroides and Prevotella as Bacteroidetesphylum [11], which may have resulted in this phylum beingoverestimated. Firmicutes and Bacteroidetes are dominantin the gut microbiota, corresponding to 90% of bacteria [4].The Bacteroidetes have been associated with adequate bodyweight but the Firmicutes with obesity. The Bacteroideteshave a positive correlation with a reduction of body fat [2],whereas the relationship between Firmicutes and obesitycan be associated with a greater energy harvest. The Fir-micutes have more carbohydrate metabolism enzymes,which contribute to the metabolization of this macronutrientallowed for a greater energy absortion [49].

Lactobacillus genus belongs to Firmicutes phylum, anincrease in this genus has been associated with obesity [49].Amongst the bacteria of this genus, Lactobacillus reuteri[14, 40] has been correlated with a higher BMI. However,despite the association between Lactobacillus and obesity, itappears that some of the bacteria (Lactobacillus paracaseiand Lactobacillus plantarum) of this phylum have a pro-tective effect against weight gain. Lactobacillus paracaseiand Lactobacillus plantarum produce bacteriocins withantibacterial action, preventing the growth of bacterialpathogens that cause dysbiosis [50].

The Proteobacteria is associated with dysbiosis, leadingto metabolic diseases such as obesity. When the Proteo-bacteria increases there is a reduction in mucus productioncausing damage to the gut barrier and a low-grade inflam-mation [51]. Fusobacteria and Fusobacterium are oppor-tunist pathogenic bacteria, increasing in individuals withobesity. This result was also found by Goa et al. [23].

Faecalibacterium prausnitzii is a butyrate producingbacterium with anti-inflammatory and protective effectsagainst obesity [52]. Akkermansia muciniphila (Verruco-microbia) is involved in the metabolism of mucin,degrading and stimulating the mucin in host gut. Besidesthe control of mucus, this bacterium also interacts with thehost metabolism, maintaining the integrity of the intestinalbarrier, and modulating other bacteria of the gut microbiota,favoring eubiosis [53].

The presence of divergent results between different stu-dies can be caused by many factors, such as the techniquesused to analyze the gut microbiota, different primer designsand different DNA extraction techniques [11, 54], thepopulation studied, diet, gender, latitude of the study site,and the season. The gut microbiota is easily affected, and itis difficult to control all the elements that affect the gutmicrobiota composition [55]. Most studies did not evaluatethe components that influence the gut microbiota, andtherefore it is difficult to understand the possible differencesfound between the results of the different studies.

Amongst these factors, the diet appears to have a greaterinfluence on the gut microbiota composition. Thus, thefocus of the present article was a comparison between thegut microbiota of individuals with obesity and normalweight individuals, who have different dietary patterns. Inaddition, the studies were carried out in different countrieswith distinct food habits that affect the gut microbiotacomposition [55]. Most of the studies were carried out withsingle population except those of Yasir et al. [13] andEscobar et al. [34]. From the information reported in thesearticles, it was possible to show different dietary patternsexerted different influences on the gut microbiota compo-sition, not only on body weight, making the study of themicrobiota with respect to obesity more complex.

Other important point to discuss is the metabolites pro-duces by the bacteria of the microbiota. Of the metaboliteshighlighted, the short-chain fatty acids can exert differenteffects on the host and can help or hinder the host meta-bolism. Short-chain fatty acids influence the metabolism ofenergy, lipids, glucose, and cholesterol, in addition con-tribute to fat storage in the adipose tissue and the immunesystem function [56]. Patil et al. [35] observed that the gutmicrobiota of individuals with obesity produced twice asmuch short-chain fatty acids as compared with that of leanindividuals, propionate being present in the greatest pro-portion [11]. Few studies have explored the role of meta-bolites in the host metabolism.

Furthermore, the impact of the gut microbiota composi-tion on body weight must be considered. Although somestudies have associated alterations in the gut microbiotawith metabolic changes, leading to weight gain and con-sequently to the development of obesity [2, 8, 9], there arestill doubts as to whether obesity leads to changes in the gutmicrobiota composition or whether changes in the gutmicrobiota lead to obesity [57]. Studies evaluating theimpact of weight loss in individual with obesity have shownchanges in the gut microbiota [58, 59]. Pajecki et al. [59]showed alteration in the gut microbiota composition inindividual with severe obesity after bariatric surgery, andthe bacteria showed different contributions to the weightloss process. In addition, the diet appears to have greater

Profile of the gut microbiota of adults with obesity: a systematic review

impact on changes in the gut microbiota than surgery alone[60].

From the gut microbiota composition can predict theresults of the intervention on weight loss. In addition,knowledge of the microbiota composition of individual withobesity can maximize the weight loss produced by a per-sonalized diet [58]. It is undeniable that the gut microbiotais related to obesity, and that weight loss interventionsreflect on changes in the microbiota [58]. It is important tounderstand the role of the bacteria involved in the obesityprocess and how can change this scenario to generateweight loss in individuals with overweight.

Conclusion

Individual with obesity showed different gut microbiotaprofiles the those of individuals with normal weight. Indi-viduals affected by obesity had higher counts of Firmicutes,Fusobacteria, Proteobacteria, and Lactobacillus, and lowercounts of Bacteroidetes, Akkermansia muciniphila, Faeca-libacterium prausnitzii, Lactobacillus plantarum, and Lac-tobacillus paracasei. In addition, an increase in theFirmicutes/Bacteroidetes ratio appeared to be related toobesity, as in animal studies.

The results found in this systematic review indicated thepossible involvement of bacteria in the development ofobesity, guiding the choice of strain probiotics for gutmicrobiota modulation in the treatment of obesity. How-ever, the study of the microbiota is complex, and it is dif-ficult to define a standard for the gut microbiotacomposition in populations or in specific groups. Manyfactors influence the microbiota composition, such as sea-son, diet, exercise, drugs, country, and gender. Some ofthese factors cannot be controlled, but with advances in thescience of the microbiota and analytical techniques, it willbe possible to discover the role that each bacterium plays inthe human metabolism and in the disease-health process.

Funding This research was financed in part by the Coordenação deAperfeiçoamento de Pessoal de Nível Superior—Brasil (CAPES)—Finance code 001, and Fundação de Amparo à Pesquisa do Estado doRio de Janeiro (Faperj). The funders were not involved in the designuntil publication of study.

Author contributions LC and ELR: conceived and design of the study;LC and DM: protocol of search and acquisition of data; LC: draftingthe article; All authors: revised and approval of the version to besubmitted.

Compliance with ethical standards

Conflict of interest The authors declare that they have no conflict ofinterest.

Publisher’s note Springer Nature remains neutral with regard tojurisdictional claims in published maps and institutional affiliations.

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