Targeting microbiota in dietary obesity management: a systematic review on randomized control trials in adults

Figures & data

Table 1. Characteristics of included studies.

Author, journal, year	Study/Country	Study length	Study subjects	Diet before intervention	Intervention	Adherence to diet evaluation
Beaumont et al. 2017	ClinicalTrial.gov number NCT02351297 France	2 weeks run-in diet + 3 weeks intervention period	N = 42 overweight subjects 38 subjects completed the study MD group (aged 31 ± 2 years, BMI 27.10 ± 0.41 kg/m^2, 9 F, 4 M) CAS group (aged 28 ± 1 years, BMI 28.27 ± 0.41 kg/m^2, 9 F, 3 M) SOY group (aged 31 ± 2 years, BMI 27.32 ± 0.42 kg/m^2, 7 F, 6 M)	Not specified	Run-in diet (normal protein diet) Intervention: SOY group CAS group MD group	3d food diary after the run -in and the intervention period
Bendtsen et al., 2018	ClinicalTrial.gov number NCT01199835 MEPEB study Denmark	24 weeks	N = 80 (11 M/69F) 40 HD (aged 42 ± 1 years, BMI 31.5 ± 0.4 kg/m^2, F 35,88%) 40 LD (aged 45 ± 2 years, BMI 30.8 ± 0.4 kg/m^2, F 34,85 %) 52 subjects completed all the 24 weeks of dietary intervention period (HD: M/F, 2/20; LD: M/F, 5/25)		Hypocaloric high dairy diet (HD) Hypocaloric low dairy diet (LD) 7 individual dietary counseling visit and 1 group session scheduled at week 0, 2, 4, 8, 12, 16, 20, and 24	7-day dietary records at 12 and 24 weeks
Christensen et al. 2013	ClinicalTrial.gov number NCT00869531 Denmark	2 weeks run in + 12 weeks intervention	N = 79 postmenopausal women (aged 45-70 years, BMI 27–37 kg/m^2) 72 subjects completed the study	Not specified	whole-grain wheat (WW) refined wheat (RF)
Fava et al. 2013	ISRCTN registry ISRCTN29111298 RISCK trial United Kingdom	4-weeks run-in reference diet + 24 weeks of treatment	N = 130 subjects with a minimum of two features of MetS 88 subjects completed the study (45 F, aged 59.1 ± 9.0 years, BMI 29.4 ± 5.5 kg/m^2; 43 M, aged 52.7 ± 9.8 years, BMI 28.2 ± 4.0 kg/m^2)	Not specified	Control diet: high saturated fat diet- high glycemic index (GI) diet (HS/HGI) high monounsaturated fat (MUFA)/high GI (HM/HGI) high MUFA/low GI (HM/LGI) high carbohydrate (CHO)/high GI (HC/HGI) high CHO/ low GI (HC/LGI) products specifically chosen or formulated to provide the required fat/carbohydrate intake and composition in each of the intervention diets.
Fragiadakis et al. 2020	ClinicalTrial.gov number NCT01826591 DIETFITS study USA	52 weeks of treatment	N = 609 generally healthy, nondiabetic participants [BMI (kg/m^2): 28–40] 49 subjects included in the study on microbiota Low carb (aged 42.6 ± 5.8 years, BMI 32.8 ± 3.9 kg/m^2, 20 F, 5 M) Low fat (aged 39.2 ± 5.5 years, BMI 33.7 ± 3.5 kg/m^2, 19 F, 5 M)	Not specified	1 year healthy low-carb vs healthy low-fat diet: “limbo” phase: 20 g/d carb or fat (8w) “titrate” phase: carb or fat increase (5 to 15 g/d) to reach the amount for a lifelong eating pattern 22 evening session with health educators Maximization of vegetable and whole-food intake and minimization or elimination of added sugars and refined grains Diet-tracking tools Support group created by the participants to share successful strategies
Grembi et al. 2020	ClinicaTrial.gov number NCT01826591 DIETFITS study USA	12 months 52 weeks	Discovery cohort N = 66 (39 F, 27 M) Low carb (N = 32, aged 43.1 ± 6 years, BMI 33.4 ± 3.7 kg/m^2, 22 F, 10 M) Low fat (N = 34, aged 40.5 ± 6.9 years, BMI 33.2 ± 3.3 kg/m^2, 17 F, 17 M) Validation cohort N = 56 (44 F, 12 M) Low carb (N = 31, aged 42.5 ± 6 years, BMI 32.8 ± 3.5 kg/m^2, 25 F, 6 M) Low fat (N = 25, aged 39.6 ± 6 years, BMI 33.2 ± 3.5 kg/m^2,19F, 6 M)	Not specified (US population: western)	Low carb diet low fat diet	3 unannounced 24-hour dietary recalls at T0, 3, 6, 12 months of dietary intervention Dietary adherence: % kcal consumed from non-restricted food dietary change: the difference in percentage of total daily kcals consumed from restricted foods between baseline and during the dietary intervention (average 12 months) In-person instructional sessions related to nutrition, behavior, emotions, and physical activity
Haro et al. 2016a	ClinicalTrial.gov number NCT00924937 CORDIOPREV study Spain	1 year 52 weeks	N = 20 obese men with coronary heart disease (M, aged f 63.3 ± 2.0 years, BMI 32.2 ± 0.5 kg/m^2)	Not specified	Mediterranean diet low-fat high-complex carbohydrate diet (LFHCC) Olive oil and food pack provided by researchers	Mediterranean diet: 14-item questionnaire Low-fat diet: 9-point score
Haro et al. 2016b	ClinicalTrial.gov number NCT00924937 CORDIOPREV study Spain	2 years 104 weeks	N = 239 subjects with coronary heart disease: 138 MetS patients (aged 60.16 ± 0.74 years, 111 M, 27 F) 101 subjects without MetS (aged 61.41 ± 0.94 years, 87 M, 14 F)	Not specified	Mediterranean diet low-fat high-complex carbohydrate diet Olive oil and food pack provided by researchers	Mediterranean diet: 14-item questionnaire Low-fat diet: 9-point score
Haro et al. 2017	ClinicalTrial.gov number NCT00924937 CORDIOPREV study Spain	2 years 104 weeks	N = 106 men with coronary heart disease: 33 obese patients with MetS (5 criteria for metabolic syndrome) (aged 59.03 ± 1.83 years, BMI 32.42 ± 0.76 kg/m^2) (MetS–OB) 32 obese patients without Mets (2 or less criteria for metabolic syndrome) (aged 63.72 ± 1.70 years, BMI 32.88 ± 0.56 kg/m^2) (non-MetS–OB) 41 non-obese subjects (aged 61.73 ± 1.39 years, BMI 27.05 ± 0.27 kg/m^2) (non-MetS–non-OB).	Not specified	Mediterranean diet low-fat high-complex carbohydrate diet (LF) Dietary counseling	Mediterranean diet: 14-item questionnaire Low-fat diet: 9-point score
Hjorth et al. 2018	ClinicalTrial.gov number NCT01195610 Denmark	26 weeks treatment + 12 months follow up (52 weeks) with NND	N = 181 Fecal analysis of 62 subjects: High P/B group (N = 28, aged 41.9 (30.4; 56.7) years, BMI 31.0 ± 4.7 2.8 kg/m^2, %F/M 64.3/35.7) Low P/B group (N = 34, aged 47.5 (33; 55.6) years, BMI 29.0 ± 4.4 kg/m^2, %F/M 69.2/30.8)	Average Danish Diet (ADD)	New Nordic Diet (NND) Average Danish Diet (ADD) Follow up (1 year) with NND without food provision Food and beverage provided from a study shop
Johnstone et al. 2020	ClinicalTrial.gov number NCT01724411 within-subject crossover design United Kingdom	49 days	N = 24 19 subjects completed the study (BMI 32.8 ± 4.07 kg/m^2, 8 F, 11 M)	UK diet	days 1–4, habitual diet consumed ad libitum (4 d) days 5–7, maintenance diet (M; 3 d) days 8–28, high-protein WL diet (WL; 21 d); days 29–38 and 39–48, randomly assigned WM diets (10 d), RS (RSWM) or control (C-WM).	breakfast test meal on 4 occasions, at the end of each dietary phase, corresponding to the morning of day 8, 29, 39, and 49.
Kopf et al. 2018	ClinicalTrial.gov number NCT02602496 USA	6 weeks	N = 52 49 subjects completed the study: 14 controls (aged 27.6 ± 5.9 years, BMI 30.1 ± 5.2 kg/m^2, 7 F, 7 M) 17 WG (aged 39.2 ± 13.5 years, BMI 33.7 ± 6.3 kg/m^2, 11 F, 6 M) 18 FV (aged 29.4 ± 12.8 years, BMI 30.3 ± 6.0 kg/m^2, 12 F, 6 M)	Western diet	Whole grains intervention (WG) vs fruits and vegetables intervention (FV) vs control diet	Weekly visits: food diary and gastrointestinal symptom questionnaire
Lappi et al. 2013	ClinicalTrial.gov number NCT00573781 Sysdimet study Finland	12 weeks of treatment	N = 106 subjects with MetS 51 studied for effects of diet on the microbiota composition and clinical variables (aged 60 ± 6 years, BMI 31 ± 4 kg/m^2, 26 F, 25 M)	Finnic diet (rich in WG)	rye bread (RB) diet refined white wheat bread (WWB) diet	Daily questionnaires 4-d food records at baseline and after 11 weeks
Mayengbam et al. 2019	ClinicalTrial.gov number NCT01719900 Canada	12 weeks	N = 53 (aged 18-70 years, BMI 25–38 kg/m^2, 43 F, 10 M)	Not specified	24 Control group (CO) 29 Pea fiber group (PF)
Meslier et al. 2020	ClinicalTrial.gov number NCT03071718 Italy	8 weeks	N = 82 (aged 43 ± 12 years, BMI 31.1 ± 4.5 kg/m^2, 43 F, 39 M)	Western	Isocaloric Mediterranean diet (MedD) Control diet (CoD: habitual diet, western)	11-unit dietary score MD index Self-recorded 7-day food diaries and physical activity questionnaires every 2 weeks
Roager et al. 2019	ClinicalTrials.gov, NCT01731366 Denmark	8 weeks	N = 60 (BMI of 25–35 kg/m2 and/or increased WC (M ≥ 94 cm and F ≥ 80 cm)) with at least one of the following criteria: -non-diabetic dysglycaemia (fasting plasma glucose 6.1–6.9 mmol/L) -dyslipidaemia (HDL ≤1.03 mmol/L for M and ≤1.29 mmol/L for F) -hypertension (systolic BP >130mmHg or medical treatment of hypertension). 50 subjects completed the two dietary intervention period (aged 48.6 ± 11.1 years, BMI 28.9 ± 3.6 kg/m^2, 32 F, 18 M)	Not specified	Two 8-weeks dietary intervention, separated by a washout period of at least 6 weeks, comprising: whole grain diet refined grain diet	study diary fasting concentrations of plasma alkylresorcinols 4-day precoded dietary registration (National Food Institute at the Technical University of Denmark)
Russell et al. 2011	Not registered United Kingdom	2-5 days run-in + 9 weeks intervention (28d HPLC + 28d HPMC)	N = 17 obese males (aged 56.38 ± 12.1 years, BMI 36.15 ± 5.68 kg/m^2)	Not specified	Weight maintenance diet (M diet) high-protein and low-carbohydrate diet (HPLC) high-protein and moderate-carbohydrate diet (HPMC)	daily intakes were recorded by weighing the food components provided and that remained after each meal
Salonen et al. 2014	Not registered United Kingdom	1-week run-in diet + 9 weeks of intervention	N = 14 males with MetS (aged 54 ± 4 years, BMI 39.37 ± 1.49 kg/m^2) (SEM)	Not specified	1-week M diet + 3 weeks RS or NPSs diet + 3 weeks NPSs or RS diet + 3 weeks weight loss diet
Schutte et al. 2018	ClinicalTrial.gov number NCT02385149 Netherlands	4-weeks run-in + 12 weeks intervention	N = 50: 25 RW (aged 61 [51 − 70] years, BMI 27.6 ± 2.6 kg/m^2, 9F, 16M) 25 WGW (aged 61 [47 − 69] years, BMI 28.0 ± 2.1 kg/m^2, 10F, 15M)	Netherland diet (quite high in WGW products)	Whole-grain wheat (WGW) diet refined wheat (RW) diet	Diary to report all deviations from the protocol, a weekly recall of empty product packages as well as by measuring total plasma alkylresorcinols
Vitaglione et al. 2015	ClinicalTrial.gov number NCT01293175 Italy	8 weeks	N = 80 68 subjects completed the study 32 controls (aged 37 ± 2 years, BMI 29.5 ± 0.4 kg/m^2, 20 F, 12 M) 36 WG (aged 40 ± 2 years, BMI 30.0 ± 0.5 kg/m^2, 25 F, 11 M)	limited fruit and vegetable intake	Whole grains intervention (WG) vs control diet	self-recorded 4-d (3 working days and 1 weekend day) food diaries every 2 weeks weighing the uneaten foods every 4 weeks phone call interviews by expert dietitian International Physical Activity Questionnaire
Vuholm et al. 2017	ClinicalTrial.gov number NCT02358122 Denmark	6 weeks	N = 75 70 subjects completed the study 24 WGR (aged 53.0 ± 8.9 years, BMI 28.0 ± 1.9 kg/m^2, 13 F, 11 M) 24 WGW (aged 48.2 ± 9.9 years, BMI 27.7 ± 1.9 kg/m^2, 14 F, 10 M) 22 RW (aged 51.8 ± 9.0 years, BMI 27.8 ± 2.0 kg/m^2, 11 F, 11 M)	Danish diet	whole-grain rye (WGR) whole-grain wheat (WGW) refined wheat (RW)	study diary with the amount and type of study products eaten daily concentration of plasma alkylresorcinols
Walker et al. 2011	Not registered United Kingdom	1-week run-in diet + 9 weeks of intervention	N = 14 males with MetS (aged 54 ± 4 years, BMI 39.37 ± 1.49 kg/m^2) (SEM)	Not specified	1-week M diet + 3 weeks RS or NPSs diet + 3 weeks NPSs or RS diet + 3 weeks weight loss diet

Figure 1. Flow diagram for study retrieval and selection.

Figure 2. Traffic light plot presenting the risk of bias within the RCTs included in the systematic review.

Figure 3. Summary plot presenting the risk of bias within the RCTs included in the systematic review.

Table 2. Diets compositions of the included studies.

Study	Study diets	Diet composition 1	Diet composition 2	Diet composition 3	Diet composition 4	Diet composition 5
Beaumont et al. 2017	run-in diet SOY group CAS group MD group	Run-in diet: individualized normal-protein diet with 15%, 50%, and 35% of energy provided by proteins, carbohydrates, and lipids, respectively.	SOY group: substitution of 15% of energy intake from starchy food with isolated soy protein	CAS group: substitution of 15% of energy intake from starchy food with isolated milk protein enriched in micellar casein	MD group: substitution of 15% of energy intake from starchy food with maltodextrin
Bendtsen et al. 2018	Low dairy product diet (LD) High dairy product diet (HD)	LD diet: daily 500-kcal (2100 kJ) energy deficit vs EER: 30 energy percentage (E%) fat, 52 E% carbohydrate and 18 E% protein. ≤ 600 mg calcium/day	HD diet: daily 500-kcal (2100 kJ) energy deficit vs EER: 30 energy percentage (E%) fat, 52 E% carbohydrate and 18 E% protein. ≈1500 mg calcium/day (1200 mg from dairy products)
Christensen et al. 2013	run-in diet whole-grain wheat (WW) refined wheat (RF)	Run-in diet: consumption of RW-containing food	WW: deficit of ∼1250 kJ/d. Bread, pasta and biscuits providing 105 g of whole grain daily (2 MJ/day)	RW: deficit of ∼1250 kJ/d. Bread, pasta and biscuits providing 105 g of refined wheat daily (2 MJ/day)
Fava et al. 2013	Control diet: high saturated fat diet- high glycemic index (GI) diet (HS/HGI) high monounsaturated fat (MUFA)/high GI (HM/HGI) high MUFA/low GI (HM/LGI) high carbohydrate (CHO)/high GI (HC/HGI) high CHO/ low GI (HC/LGI)	HS/HGI: total fat 38%E, high saturated fat (SFA) 18%E, high monounsaturated fat (MUFA) 12%E, polyunsaturated fatty acid (PUFA) 6%E, CHO 45%E, GI 64%	HM/HGI: total fat 38%E, SFA 10%E, MUFA 20%E, PUFA 6%E, carbohydrate (CHO) 45%E, GI 64%	HM/LGI: total fat 38%E, SFA 10%E, MUFA 20%E, PUFA 6%E, CHO 45%E, GI 53%	HC/HGI: total fat 28%E, SFA 10%E, MUFA 11%E, PUFA 6%E, CHO 55%E, GI 64%	HC/LGI: total fat 28%E, SFA 10%E, MUFA 11%E, PUFA 6%E, CHO 55%E, GI 51%
Fragiadakis et al. 2020	healthy low carb diet healthy low fat diet	HLC diet: ↓cereals, grains, rice, starchy vegetables, and legumes	HLF diet: ↓edible oils, fatty meats, whole-fat dairy, and nuts
Grembi et al. 2020	Low carb or low fat diet with no specific caloric, fat or carbohydrates restriction. Phase I (8 weeks): Limbo (go as low as you can go): Reduction fat or carbohydrate (20 g/day); participants were instructed to progressively cut back on fat or carbohydrate intake until they had achieved a daily intake of no more than 20 grams of fat or carbohydrate per day, depending on their group assignment. Phase II (Titration): increase fat or carbohydrate consumption by increments of 5-15 g/daily with no set endpoint goal for a specific level of fat or carbohydrate (increase level of fat or carbohydrate affected both their satisfaction with their daily intake and their weight loss progress). If satisfaction and weight loss progress were acceptable, they had the option of maintaining that level of fat or carb intake for another week or adding an incremental 5-15 grams/day. Both diet groups were instructed to (1) maximize vegetable intake; (2) minimize intake of added sugars, refined flours, and trans fats; and (3) focus on whole foods that were minimally processed, nutrient dense, and prepared at home whenever possible.	Low-carb with no specific caloric restriction but promoting high dietary quality (i.e. high-quality oils and fats, avocados, hard cheeses, nut butters, and nuts & seeds).	Low-fat with no specific caloric restriction but promoting high dietary quality (whole-grain foods, i.e. rather than whole wheat flour products, steel cut oats, farro, barley, quinoa, brown rice, and wild rice, wide range of legumes and beans, fresh fruit, low-fat dairy products, and lean meats).
Haro et al. 2016a	Mediterranean diet Low-fat high-carbohydrate diet	Mediterranean diet: 35% fat (22% monounsaturated; 6% polyunsaturated and 7% saturated)	Low-fat high-carbohydrate diet 28% fat (12% monounsaturated; 8% polyunsaturated and 8% saturated)
Haro et al. 2016b	Mediterranean diet Low-fat high-carbohydrate diet	Mediterranean diet: 35% fat (22% monounsaturated, 6% polyunsaturated, and 7% saturated)	Low-fat high-carbohydrate diet: 28% fat (12% monounsaturated, 8% polyunsaturated, and 8% saturated)
Haro et al. 2017	Mediterranean diet Low-fat high-carbohydrate diet	Mediterranean diet minimum 35% of calories as fat (22% MUFA fat, 6% PUFA fat and <10% saturated fat), 15% proteins and a maximum of 50% carbohydrates. Cholesterol content adjusted to < 300 mg/day	Low-fat diet: <30% total fat (<10% saturated fat, 12–14% MUFA fat and 6–8% PUFA fat), 15% protein and a minimum of 55% carbohydrates. Cholesterol content adjusted to < 300 mg/day
Hjorth et al. 2018	Intervention: New Nordic Diet (NDD) Control: Average Danish Diet (ADD)	NDD: very high in dietary fiber, whole grain, fruit and vegetables; dietary fiber 43.3 g/MJ, protein 18.1%, fat 30.4%, carbohydrates 46.6%	ADD: dietary fiber 28.6 g/MJ, protein 16.4%, fat 33.8%, carbohydrates 45.3%
Johnstone et al. 2020	Maintenance diet (M diet) high-protein weight loss diet (WL diet) control weight maintenance diet (C-WM diet) resistant-starch weight maintenance diet (RS-WM)	M diet: 15% protein, 30% fat, and 55% carbohydrate fed to 1.5 × measured rest metabolic rate (RMR)	WL diet: fed as 100% RMR as a 7-d rotation menu, as 3 meals/d, each 30% protein, 30% fat, and 40% carbohydrate	C-WM diet: 1.2 × RMR with 20% protein, 30% fat, and 50% carbohydrate. Digestible starch was incorporated as control	RS-WM diet: 1.2 × RMR with 20% protein, 30% fat, and 50% carbohydrate. daily amount of 22 g for females and 26 g for males of resistant starch type 3 was provided
Kopf et al. 2018	Control diet Fruits and vegetables intervention Whole grains intervention The serving sizes supplied to each group were set according to the Nutrition Labeling and Education Act (NLEA) standards	Control diet: 3 servings/day of refined grain	Fruits and vegetables intervention: 3 servings/day of fruits and vegetable	Whole grain intervention: 3 servings/day of whole grains
Lappi et al. 2013	rye bread (RB) diet refined white wheat bread (WWB) diet	RB diet: rye breads with a high-fiber content (7–15%). The breads are a selection of commercial WG rye breads (50% share of all the breads), endosperm rye bread (40% share), and a whole-meal wheat bread (10% share). They consumed whole-meal pasta [3.5 dL/wk (measured as uncooked)] and were given high-fiber oat biscuits for voluntary intake	WWB diet: refined wheat breads with a low fiber content (4%). The test breads were a selection of commercial refined wheat breads and the intake of rye products was restricted to 1–2 portions/d.
Mayengbam et al. 2019	control (CO) pea fiber (PF)	CO group: isocaloric amount of control wafer with no fiber. The wafers provided the participants with 204 kcal/d derived from 55.2% carbohydrate, 5.2% protein and 39.6% fat in the control wafers and 53.1% carbohydrate, 5.2% protein and 41.7% fat in the fiber wafers (expressed as a percent of total energy content). Wafers consumed 30 min before 3 main meals	PF group: wafers containing 5 g per serving of yellow pea fiber thrice per day. The wafers provided the participants with 204 kcal/d derived from 55.2% carbohydrate, 5.2% protein and 39.6% fat in the control wafers and 53.1% carbohydrate, 5.2% protein and 41.7% fat in the fiber wafers (expressed as a percent of total energy content). Wafers consumed 30 min before 3 main meals
Meslier et al. 2020	Intervention: Isocaloric Mediterranean diet (MedD) Control diet (CoD): habitual western diet	Isocaloric Mediterranean diet (MedD): inclusion of fruit and vegetables and nuts (at least 5 portions, ∼500 g/day) and nuts (30 g/day) as well as calorie-adjusted replacement of refined cereal products with wholegrain products (at least 2 portions, ∼200 g/day between wholegrain pasta, bread and breakfast cereal); replacement of meat, eggs and dairy products with fish and legumes (at least 2 portions, ∼300 g/week of fish and 3 portions, ∼300 g/week of legumes); replacement of butter/margarine with extra-virgin olive oil.	Control diet (CoD): habitual western diet
Roager et al. 2019	whole grain diet refined grain diet	Whole grain diet: intact, ground, cracked or flaked caryopses, where the starchy endosperm, germ and bran are present in the same relative proportions as in the intact caryopsis according to the definition proposed by the HEALTHGRAIN consortium in 2013
Russell et al. 2011	weight maintenance diet high-protein and low-carbohydrate diet (HPLC) high-protein and moderate-carbohydrate diet (HPMC)	weight maintenance diet: 13% protein, 50% carbohydrates, and 37% fat as calories. 2 fixed meal	HPLC: 29% protein, 5% carbohydrate, and 66% fat as calories	HPMC: 28% protein, 35% carbohydrate, and 37% fat
Salonen et al. 2014	Run-in maintenance diet Resistant starch diet (RS) non-starch polysaccharides diet (NSPs) Weight loss diet	Run-in maintenance diet: protein/carbohydrate/fat % as 13:52:35 of metabolizable energy and 27.7 g per day NSP	Resistant starch diet (RS): high in type 3 resistant starch	non-starch polysaccharides diet (NSPs): high in non-starch polysaccharides	Weight loss diet: high protein and medium carbohydrate levels
Schutte et al. 2018	Run-in diet Whole-grain wheat (WGW) diet refined wheat (RW) diet	Run-in diet: RW products. Absence of any whole-grain food products including products from other grain sources.	WGW diet: 98 g/d WGW products. 4 slices of bread (in total 100 g) and 1 serving of ready-to-eat-cereals of 33.4 g in total, adding up to a total of 98 g of WGW flour per day. Small buns, currant buns, and currant bread were provided on the weekends instead of the slices of bread	RW diet: 98 g/d RW products. 4 slices of bread (in total 100 g) and 1 serving of ready-to-eat-cereals of 33.4 g in total, adding up to a total of 98 g of RW flour per day. Small buns, currant buns, and currant bread were provided on the weekends instead of the slices of bread
Vitaglione et al. 2015	Control diet Whole grains intervention	Control diet: 60 g refined wheat products/day	Whole grain intervention: 70 g whole grains wheat/day
Vuholm et al. 2017	whole-grain rye (WGR) whole-grain wheat (WGW) refined wheat (RW)	WGR: one choice of breakfast cereal, pasta, kernels, crisp bread and two choices of bread. Bread produced by university of Copenhagen while other study products are commercially available. Products consumed ad libitum	WGW: one choice of breakfast cereal, pasta, kernels, crisp bread and two choices of bread. Bread produced by university of Copenhagen while other study products are commercially available. Products consumed ad libitum	RW: one choice of breakfast cereal, pasta, kernels, crisp bread and two choices of bread. Study products are commercially available and consumed ad libitum
Walker et al. 2011	Run-in maintenance diet Resistant starch diet (RS) non-starch polysaccharides diet (NSPs) Weight loss diet	Run-in maintenance diet: protein/carbohydrate/fat % as 13:52:35 of metabolizable energy and 27.7 g per day NSP	Resistant starch diet (RS): high in type 3 resistant starch	non-starch polysaccharides diet (NSPs): high in non-starch polysaccharides	Weight loss diet: high protein and medium carbohydrate levels

Figure 4. Microbiota main changes in individuals with obesity following classical or healthy dietary regimens in comparison to several control diets. MD: Mediterranean Diet, compared to low-fat high carbohydrate diet in ¹Haro et al. (2016a, 2016b, 2017) and to a habitual western diet in ²Meslier et al. (2020). LFHC: Low-fat high-carbohydrate diet shows changes in comparison to MD (¹Haro et al. 2016a, 2016b, 2017; P. diastonis only in the non-MetS group; Streptococcus and Clostridium in MetS-OB) and in microbiome plasticity (³Fragiadakis et al. 2020; ⁴Grembi et al. 2020). HFD: High fat diet; F. prausnitzii increased in high saturated fat diet with a high glycemic index as opposed to baseline (⁵Fava et al. 2013). HPD: high protein diet presents variations in Roseburia/Eubacterium rectale as proportion of total bacteria and decreased of Bacteroidetes versus MD (⁶Russell et al. 2011; ⁷Beaumont et al. 2017). *Others: dietary interventions with resistant-starch weight maintenance diet (RS-WM) versus control weight maintenance diet (C-WM diet) (⁸Johnstone et al. 2020); whole grain (WG) and fruits and vegetables (FV) interventions versus baseline (⁹Kopf et al. 2018). WG diet was also studied against refined grain (RG) consumption (¹⁰Roager et al. 2019; ¹²Vitaglione et al. 2015), with correlation in women (¹³Vuholm et al. 2017); resistant starch (RS) diet, non-starch polysaccharides (NSPs) diet and weight loss (WL) diet versus Run-in maintenance diet (protein/carbohydrate/fat % as 13:52:35 of metabolizable energy) (¹¹Salonen et al. 2014, ¹⁴ Walker et al. 2011). Created with BioRender.com.

Table 3. Results of the included studies.

Author, journal, year	Outcomes	Method	Results on microbiota	Microbiota-mediated metabolic changes	Clinical results
Beaumont et al. 2017	effects of the quantity and source of dietary proteins on the gut microbiota composition and metabolic activity and the consequences on the large intestinal mucosa	V3 - V4 region of 16S rRNA gene sequencing analysis Mucosal transcriptome GAS chromatography and HPLC analysis	↔ α and β diversity between dietary treatments	Fecal metabolome: ↑fecal concentrations branched-chain AAs in CAS group vs SOY and MD groups ↓ relative concentration of butyrate in CAS and SOY groups vs MD group ↑ fecal pH in CAS group ↑isoleucine-derived bacterial metabolite 2-methylbutyrate in CAS and SOY groups vs MD group ↑relative concentration of several AA-derived bacterial metabolites (valerate, phenylacetate, and tyramine) in SOY group ↑acetoin in SOY group Urinary metabolome: ↑ relative concentrations of AA-derived bacterial metabolites isobutyrate, microbiota-host cometabolites indoxyl sulfate and phenylacetylglutamine, and phenylacetylglutamine in SOY and CAS groups vs MD group ↑ 3-hydroxyisovalerate, 3-hydroxybutyrate, and the cometabolite p-cresyl sulfate in CAS group ↑acetoin and isoflavone in SOY group	↑Blood and urinary urea in SOY and CAS groups ↓systolic blood pressure in SOY group
Bendtsen et al., 2018	increase BW loss, FM loss, and/or fecal fat excretion over 24 weeks of HD or LD. effects of dairy intake on fecal calcium and energy excretion, resting energy expenditure (REE), blood pressure, and lipid metabolism HD and LD effects on gut microbiota composition	V3 + V4 region of 16S rRNA gene sequencing analysis	↔ α diversity ↓Veillonella genus in LD POS correlation between relative abundance of Papillibacter (family Ruminococcaceae) and the total FM loss, independent of diet group	↔ fecal fat excretion in LD vs HD diet ↔ fecal energy excretion in LD vs HD diet	24 weeks ↓ body weight, ↓fat mass, ↓hip and waist circumference in LD and HD diet. ↑ hip circumference loss in LD vs HD diet ↔ SBP, DBP, total-, LDL-, and HDL-cholesterol, TAG, glucose, insulin, NEFA, and hsCRP The between-group change in RQ differed over 24 weeks HD diet: ↑Fecal and total calcium excretion ↓ respiratory quotient (RQ) LD diet: ↓ Fecal and total calcium excretion ↓ resting energy expenditure
Christensen et al. 2013	changes in gut bacterial composition caused by the energy-restricted diet enriched with WW or RW	quantitative real time PCR targeting regions within the 16 S rRNA genes	↔ microbiota composition ↑Bifidobacterium in WW ↓Bacteroides in RW ↓ Firmicutes from baseline in all subjects		↔ effects of fecal water on trans-epithelial resistance POS correlation in run-in samples (RW diet): Fecal pH of the original sample and Bacteroidetes relative abundance of Bifidobacterium with fat mass percentage and trunk fat percentage NEG correlation in run-in samples (RW diet): relative abundance of the phylum Bacteroidetes and fat mass percentage and trunk fat percentage systolic and diastolic blood pressure with the relative abundance of Firmicutes Firmicutes with IL-6
Fava et al. 2013	effects of the type and quantity of dietary fat and carbohydrate on gut microbiota and colonic fermentation in human subjects ‘at risk’ or suffering from the MetS. Impact of any gut microbial changes on metabolic and cardiovascular disease biomarkers	FISH	↓ total bacteria in HM/HGI and HM/LGI vs HC/HGI and baseline ↔ total bacteria in HC/HGI and HC/LGI ↑Bifidobacterium spp. in HC/HGI and HC/LGI vs baseline or control HS (only for HC/HGI) ↑ Bacteroides spp. in HC/HGI vs baseline, associated with ↓ in body weight, BMI, waist circumference ↑Faecalibacterium prasnutzii in control HS vs baseline	↔ SCFA comparing 5 diets ↑ fecal acetate, propionate, and n-butyrate in HS vs baseline	↔ anthropometric, blood pressure, biochemical parameters (TG, TC, LDL, HDL, glucose, insulin, leptin, PAI-1, ICAM-1, CRP), and insulin sensitivity from IVGTT between the 5 diets ↓ % body fat in HC/HGI vs baseline ↑waist circumference in HM/LGI vs baseline ↓NEFA in HC/LGI vs control HS and HC/HGI ↓Plasma total and LDL cholesterol in all diets vs baseline ↓HDL cholesterol in HC/HGI vs baseline ↓ Fasting plasma glucose with both HC diets vs baseline ↓ plasma insulin concentration with HC/HGI ↑ Soluble ICAM-1 with HM/HGI vs baseline
Fragiadakis et al. 2020	effect of a healthy low-fat (HLF) diet vs a healthy low-carbohydrate (HLC) diet on weight change at 12 months	V4 region of 16S rRNA gene sequencing analysis	After 3 m: HLC diet: ↑Proteobacteria, Bacteroidetes, and Firmicutes phyla (Bilophila and Desulfovibrio genera - Desulfovibrionaceae family) HLF diet: ↓ Actinobacteria and the Firmicutes phyla (Dorea and Blautia genera - Lachnospiraceae family) Bacteroidetes ↑ both in low carb and low fat diet Oscillospira and Lachnospira were NEG associated with weight Firmicutes were POS associated with weight ↔ α diversity		↓ BMI, % body fat, waist circumference, blood pressure, insulin, and glucose in both diets ↑ LDL in HLC ↑ HDL and ↓ TG > in HLC vs HLF diet ↓Respiratory exchange ratio in HLC vs HLF diet ↓ Resting energy expenditure in both diets
Grembi et al. 2020	Explore whether attributes of gut microbiota predispose individuals to successful 12-month weight loss. Identifying pre-diet features of the gut microbiota that can predict adherence and/or success on a specific diet for personalization of dietary intervention strategies to maximize weight loss	V4 region of 16S rRNA gene sequencing analysis	Pre-diet microbiota composition did not cluster by 12-month weight loss success category, age, gender, pre-diet weight, body mass index or dietary adherence ↑pre-diet microbiota plasticity in very successful (VS) vs unsuccessful (US) subjects on low-fat diet ↑microbiota plasticity between T0 and 10 weeks in VS in both diets Pre-diet plasticity ↔ independently from baseline fiber, fat, protein or carbohydrate consumption. Daily pre-diet plasticity was POS correlated with plasticity over 10 weeks for subjects on the low-fat diet		↑ 12-month weight loss for high P/B vs low P/B subjects on low-carb diet in discovery but not validation cohort
Haro et al. 2016a	changes in microbiota of obese people with CHD after long term consumption of healthy diets relationship between specific bacteria and the metabolomics profile found in feces and plasma	16S rRNA gene sequencing analysis	LF diet: ↑ Prevotella bacterial genus ↓Roseburia, Faecalibacterium prausnitzii ↔ Oscillospira bacterial genera Med diet: ↓ Prevotella bacterial genus ↑Roseburia and Oscillospira bacterial genera, Parabacteroides distasonis	Changes in: Roseburia: POS correlation with changes in N-Acetylaspartate in feces and XHWESASXXR in plasma NEG correlation with changes in Arginylproline in feces Prevotella: POS correlation with changes in Glutamate, Arginylproline, and 3,7-Dimethylurate in feces Oscillospira: NEG correlation with changes in Glutamate and Pantothenate in feces POS correlation with changes in cis-Vaccenate (18:1n7) in feces and Valylglutamine in plasma Faecalibacterium prausnitzii: POS correlation with changes in Leucylvaline in feces Bacteroides uniformis: POS correlation with changes in Glycocholenate sulfate in plasma ↔ SCFA and secondary bile acids	1 year MED or LF diet: ↑insulin sensitivity index (OGTT) ↔ Glucose, HbA1c, TG, TC HDL, LDL
Haro et al. 2016b	differences in the bacterial community structure in MetS and nonMets patients effect of the long-term consumption of two healthy diets in restoring the gut microbiota composition.	16S rRNA gene sequencing analysis	T0 ↑Bacteroides, Eubacterium, and Lactobacillus genera in MetS vs non-MetS ↓Bacteroides fragilis group, Parabacteroides distasonis, Bacteroides thetaiotaomicron, Faecalibacterium prausnitzii, Fusobacterium nucleatum, Bifidobacterium longum, Bifidobacterium adolescentis, Ruminococcus flavefaciens subgroup, and Eubacterium rectal MetS vs non-MetS Met diet: ↑ P. distasonis, B. thetaiotaomicron, F. prausnitzii, B. adolescentis, and B. longum in the MetS ↑ E. rectale in the non-MetS Low fat diet: ↓ P. distasonis in the non-MetS group ↔ P. distasonis in MetS patient group
Haro et al. 2017	restoration of gut microbiome dysbiosis in obese patients depending on the degree of metabolic dysfunction	16S rRNA gene sequencing analysis	T0: ↔ bacterial diversity or microbiota composition MetS-B vs NonMetS-OB vs NonMetS-NonOB ↓Actinobacteria and Bacteroidetes phyla in MetS-OB vs NonMetS-NonOB ↑Firmicutes/Bacteroidetes ratio MetS-OB vs NonMetS-NonOB ↓ Bacteroides, Prevotella, Roseburia, Faecalibacterium, and Ruminococcus MetS-OB vs NonMetS-NonOB ↑ Streptococcus and Clostridium genera MetS-OB vs NonMetS-NonOB ↓ P.distasonis and F.prausnitzii MetS-OB vs NonMetS-NonOB 2 years: ↔ bacterial diversity or microbiota composition MetS-B vs NonMetS-OB vs NonMetS-NonOB ↑ Bacteroides, Prevotella and Faecalibacterium genera in MetS-OB with MED or LF vs T0 ↓ Firmicutes/ Bacteroidetes ratio, Streptococcus and Clostridium genera after LF in MetS-OB ↑ Roseburia and Ruminococcus genera and P. distasonis and F. prausnitzii bacterial species after MED diet in MetS-OB ↔ microbiota in NonMetS-NonOB and NonMetS-OB vs T0		↓ TG levels in MetS-OB after MED or LF diet ↔ TG levels NonMetS-Ob and NonMetS-NonOB groups
Hjorth et al. 2018	pre-treatment Prevotella-to-Bacteroides (P/B) ratio as a prognostic marker for successful body fat loss on two diets differing greatly in dietary fiber and whole-grain content.	16S rRNA gene sequencing analysis			26 weeks follow-up: High P/B ratio: ↑3.15 kg body fat loss, ↑3.49 kg BW loss, and ↓4.75 cm WC in NND vs ADD Low P/B ratio: ↔ body fat, BW, and WC between NND and ADD Difference in responsiveness to diets between the P/B groups high-low): 2.27 kg body fat 3.95 cm waist circumference 1 year follow up: 3.99 kg difference in responsiveness to NDD between P/B groups (↓1.23 Kg in high P/B; ↑ 2.76 Kg in low P/B) ↔ weight in high P/B ratio subjects ↑2.76 kg in low P/B subjects
Johnstone et al. 2020	investigate the effect of nondigestible fibers on weight maintenance after weight loss. Assess the impact of the fibers on potential mechanisms via the gut microbiota and plasma gut hormone profiles in healthy but obese and overweight volunteers in free-living conditions.	16S rRNA gene sequencing analysis SCFA content of fecal samples was determined by capillary GC following conversion to t-butyldimethylsilyl derivatives	Microbial composition (alla data): Firmicutes (72.5% ± 10.7%), Bacteroidetes (17% ± 9.1%), and Actinobacteria (6.7% ± 8.1%) Principal bacterial genera (all data): Faecalibacterium (12.3% ± 6%), Ruminococcus (8.9% ± 6.3%), Bacteroides (8.4% ± 5.6%), Blautia (7.9% ± 4.4%), Bifidobacterium (6.7% ± 8.2%), and Roseburia (5.5 ± 4.3%) Addition of RS: modification of Roseburia, Ruminococcus, and Faecalibacterium. ↑ Ruminococcus and Roseburia after RS-WM vs C-WM diet. ↑ Ruminococcus (especially Ruminococcus bromii) after RS-WM diet vs WL diet ↑ Eubacterium rectale Roseburia faecis after RS-WM diet vs C-WM diet ↓ Blautia in RS-WM vs WL diet ↑Faecalibacterium prausnitzii in RS-WM POS association between the genus Anerostipes and blood glucose	↔ acetate, propionate, or butyrate in fecal samples ↑ iso-valerate and iso-butyrate after WL or C-WM vs M or RS-WM ↔ N-nitroso compounds in fecal water	Mean weight loss: 3.64 kg ± 1.93 ↔ loss of lean and fat mass ↓ systolic (6%) and diastolic (5%) blood pressure after WL ↓ 2.57 cm in waist circumference and 2.35 cm in hip circumference in WL ↓ −3.57 cm during C-WM and RS-WM ↔ motivation to eat ↓ 3.8% fasting glucose after WL ↓ by 0.5% after RS-WM vs after C-WM ↓ total cholesterol, LDL, and fasting triglycerides after WL ↓ Triglycerides after C-WM and RS-WM vs M diet. ↓Nonesterified fatty acids after C-WM and RS-WM vs WL diet period ↔ 180-min plasma glucose profile after test meal ↓ insulin after WL diet vs M diet period. This effect was maintenance after RS-WM while ↑ in C-WM ↔ inflammatory biomarkers
Kopf et al. 2018	impact of increasing intake of WG or FV against the background of a typical Western diet on inflammatory makers and gut microbiota composition in individuals affected by overweight or obesity	V4 + V5 region of 16S rRNA gene sequencing analysis	↑ α-diversity in FV ↔ β-Diversity Change in dominant bacteria from baseline to the end of the study (mean): Bacteroides: ↓ in C and FV; ↑ in WG Ruminococcus (Ruminococcaceae): ↑ in C; ↓ in FV­­ and WG Ruminococcus (Lachnospiraceae): ↑ in C, FV and WG Bifidobacterium: ↓ in C and WG, and FV Fecalibacterium: ↓ in C, FV and WG	↔ fecal SCFAs	↔ BMI, hs-CRP ↓LPB in WG and FV (individuals with higher Firmicutes and lower Bacteroidetes at baseline showed a greater decrease in LBP during the study) ↓ TNFα in WG ↓IL6 in FV
Lappi et al. 2013	effects of refined low-fiber wheat bread and WG and high-fiber rye bread intake on the intestinal microbiota composition subjects with metabolic syndrome	HITChip on V1 and V6 regions of 16S rRNA	↔ microbiota composition between RB and WWB except for ↑Bryantella formatexigens in WWB WWB vs baseline: ↑ Clostridium cluster IV (Clostridium leptum et rel., Clostridium cellulosi et rel., and Anaerotruncus colihominis et rel.), and XI (Anaerovoranx odorimutans et rel.) and Actinobacteria (Collinsella and Atopobium spp.) ↓ Bacteroidetes phylum (Bacteroides plebeius et rel, B. vulgatus et rel. and Prevotella tannerae et rel.) Largest mean ↓ in B. vulgatus et rel. (19%) which also ↓ in ∼50% RB participants Dietary variables associated with Bacteroides cluster variation (>10%): % energy intake from PUFA and 18:2n6 (∼30% variation) % energy from 18:3n3 WG and refined breads Dietary variables associated with butyrate producers variation (40-50%): WG and refined white breads total fiber and grain fiber Correlations between individual bacterial group and single nutrient/food intakes (all data): B. vulgatus et rel., B. ovatus et rel., P. tannerae et rel., and P. oralis et rel. POS correlated with the intake of 18:2n6, other fat-derived compounds, and margarine F. prausnitzii et rel. NEG correlated with the intake of refined white breads R. intestinalis et rel. POS correlated with the intake of total fiber, grain fiber, and WG breads, while NEG correlated with that of refined white breads E. ventriosum POS correlated with energy intake from alcohol		↔ body weight, markers of glucose metabolism and inflammation ↓ in the high sensitivity C-reactive protein concentration within the RB group
Mayengbam et al. 2019	investigate the potential gut related mechanisms by which pea-derived mixed fiber improves body weight, fat mass, glucose tolerance and appetite regulation	16S rRNA gene sequencing analysis	PF group: ↓ Actinomycetaceae ↑ Barnesiellaceae ↓ Actinomyces, Holdermania and Oscillospira ↑ Lachnospira CO group: ↑Prevotellaceae, Porphyromonadaceae, Rikenellaceae and S24-7 ↑Anaerostipes, Collinsella, Paludibacter and Prevotella, ↓ Lachnospira and Paraprevotella ↓α diversity in CO e PF group (Chao1 metric) NEG correlation (all data): body weight and Lachnospira abundance Anaerostipes and 2-hydroxybutyrate, 3-hydroxybutyrate and proline levels Actinomyces and Collinsella with propionate and butyrate Oscillospira and BAs such as DCA and isoLCA. POS correlation (all data): Lachnospira abundance with acetate level. Prevotella with branched SCFAs (isovalerate and isobutyrate) Paraprevotella abundance and Propionate, total SCFA, acetoacetate concentrations Holdermania and glucose-AUC	PF group after 12w: ↑acetate ↓isovalerate ↓fecal primary bile acid (BAs) cholic acid and chenodeoxycholic acid, and secondary BAs deoxycholic acid and total Bas CO group after 12w: ↔ BAs
Meslier et al. 2020	Primary outcomes Plasma lipids fecal levels SCFAs Secondary outcomes: changes in gut microbiota changes in markers of metabolic disease (blood pressure, fasting blood glucose, hs-CRP, urinary and plasma TMAO, plasma gastrointestinal peptides and urinary polyphenols)	16S rRNA gene sequencing analysis shotgun metagenomics Metagenomic species pangenome (MSP)	↑ differentially abundant Metagenomic species pangenome (MSP) between MedD and ConD: ↓Ruthenibacterium lactatiformans, Flavonifractor plautii, Parabacteroides merdae, Ruminococcus torques and Ruminococcus gnavus, Streptococcus thermophilus in MedD vs ConD ↑ Faecalibacterium prausnitzii, Roseburia and Lachnospiraceae taxa in MedD vs ConD ↑ baseline levels of several Bacteroides species and ↓ Prevotella sp. and P. copri levels in subjects showing HOMA reduction at 4 weeks on MedD	18% variation in the metabolic potential captured by gut metabolic modules after 4 weeks of treatment. Enrichment in amino acid, carbohydrate, triglyceride and glycoprotein degradation, and conversion of acetyl-CoA and glutamate degradation to crotonyl-CoA in MedD group. 6% variation after 8 weeks. Enrichment in glutamate degradation to crotonyl-CoA maintained in the MedD group. Metabolites changes: ↑ urinary urolithin glucuronides (↑Eggerthellaceae family) in MedD vs ConD ↓ fecal BAs in MedD during intervention vs baseline ↓ fecal deoxycholic and lithocholic acid after 4 and 8 weeks in MedD ↔ SCFAs acetate, butyrate, propionate ↓ BCFAs valerate, isovalerate, isobutyrate and 2- methylbutyrate in MedD over intervention	↓total plasma cholesterol and (HDL)-cholesterol after 4 weeks in MedD vs ConD (proportional to MD adherence rate) ↔ blood glucose, HOMA, serum hs- CRP, plasma insulin, TMAO, glucagon, ghrelin, GIP, GLP-1, leptin, C- peptide, resistin, visfatin and PAI-1
Roager et al. 2019	Primary outcomes: effects of replacing refined grains with whole grains in improving insulin sensitivity and alterations on gut microbiome Secondary outcomes: biomarkers of metabolic health and gut functionality	16S rRNA gene sequencing analysis Shotgun metagenomic sequencing	↔ Microbial gene diversity and richness ↔ abundance of individual bacterial species four distinct strains of Faecalibacterium prausnitzii and one Prevotella copri ↑ after whole grain and ↓ after refined grain consumption Bacteroides thetaiotaomicron ↓ after whole grain and ↑ after refined grain consumption several strains of Clostridiales and F. prausnitzii were POS associated with whole grain and fiber intake, B. thetaiotaomicron was NEG associated with whole grain and fiber intake Erysipelatoclostridium ramosum was POS associated with IL-6 ↔ fasting nor postprandial breath hydrogen excretion between the intervention periods	↔ fasting plasma SCFA concentrations ↑ plasma concentrations of butyrate after the whole grain intervention ↑ five urinary metabolites (DHPPA-glucuronide, 2-aminophenol-sulfate, pyrocatechol-glucuronide, pyrocatechol-sulfate, 3-methyladipic acid) during whole grain period. NEG association between: urinary concentrations of DHPPA-glucuronide and serum concentrations of both IL-6 and CRP urinary concentrations of pyrocatechol-glucuronide and 2-aminophenol-sulfate with CRP urinary concentrations of 3-methyladipic acid, and body weight ↑lactose/L-arabinose transport system substrate-binding protein (KO K10188) and a glycosyl-1-phosphate transferase (KO K15915) with whole grain diet ↔ carbohydrate active enzymes ↔ acetate CoA-transferase genes	↔ HOMA-IR, HbA1c, fasting serum C-peptide, fasting glucose and insulin, lipid metabolism (fasting serum concentrations of TG, HDL, LDL and total cholesterol and FFA), AST, ALT, fasting plasma concentrations of leptin, GLP-1 and GLP-2 and systolic/diastolic BP ↓body weight, fat-free mass and tendency for ↓in fat mass (p = 0.057) in whole grain ↓sagittal abdomen diameter and a similar tendency was observed for WC in whole grain ↓body weight and energy intake both significantly associated with ↓plasma leptin in whole grain ↓CRP, IL-6, IL1 in whole grain ↔ TNF alpha
Russell et al. 2011	monitor the effect of a high-protein and reduced-carbohydrate weight-loss diets on a wide range of fecal metabolites	FISH analysis	↑ total number of bacteria in M diet vs HPLC and HPMC ↓Roseburia/Eubacterium rectale group as proportion of total bacteria in HPLC ↔ Lachnospiraceae and Faecalibacterium prausnitzii. ↓Bacteroides spp in HPLC vs M diet	↓ total SCFA in HPLC ↓butyrate in HPLC vs M diet ↑isovalerate and isobutyrate in HPLC and HPMC vs M diet ↑pH of fecal water in HPLC vs HPMC and M diet ↑N-nitroso compounds in HPLC and HPMC vs M diet ↓ ferulic acid and its metabolites in HPLC vs M diet ↓ gentisic acid (2,5-dihydroxybenzoic acid), 3,5-dihydroxybenzoic acid, and a 4-hydroxy3-methoxy-, 3,4-dihydroxy-substituted phenylacetic acid in HPLC diet ↓ phenylpropionic acid concentrations in HPLC diet ↑ PAA concentrations in HPLC and HPMC vs M diet ↓Indole and ↑ indole-3-pyruvic acid in HPMC diet POS association: fecal NOCs, BCFAs, valerate, lactate, and phenyl acetic acid concentration with high protein diets phenolic metabolites, including FA derivatives, as well as various bacterial groups plus acetate, butyrate, and propionate with M diet
Salonen et al. 2014	Effects of the changes in the major type of non-digestible carbohydrates in the diets on dominant bacterial phylotypes.	HITChip microarray on V1 and V6 hypervariable regions of the 16S rRNA	↓ β diversity in M and RS diet ↓ α diversity in RS diet RS diet: ↑ Oscillospira guillermondii, R. bromii. Sporobacter termitis, Clostridium leptum, C. cellulosi (Clostridium cluster IV). ↓Alistipes spp. (Bacteroidetes), Lachnospiraceae (Clostridium cluster XIVa: in particular, ↑E. rectale and the uncultured bacterium D522 and ↓ related uncultured phylotypes and R. inulinivorans L1-83) ↑Lachnospiraceae in NSP diet vs M and SP diet NSP diet: ↑Eggerthella, Collinsella and Corynebacterium (Actinobacteria), ↑bacteria related to Bacteroides vulgatus and Prevotella oralis. ↓ C. leptum, C. cellulosi, Oscillospira spp. and Sprorobacter spp. (Ruminococcaceae) ↓Bifidobacteria, Bacilli and Proteobacteria; ↑ Lactococci, Sutterella wadsworthia in WL diet	↓ SCFA acetate, propionate, butyrate, and succinate and ↑minor fermentation acids in RS and WL vs NSP and M diet POS correlation between 16 s rRNA signal and concentrations of both total and major SCFA in the three maintenance diets (RS, NSP, M) NEG correlation between SCFA and cluster IV Ruminococcus group SCFA in the three maintenance diets (RS, NSP, M) ↑branched chain SCFA in RS and WL; NEG correlated with 16 s rRNA signal in the three maintenance diets (RS, NSP, M)	WL diet ↑ SCFA and insulin sensitivity POS correlation between fecal Bifidobacteria and insulin
Schutte et al. 2018	investigate the potential benefits of WGW consumption compared with RW consumption on liver health and associated parameters.	V4 region of 16S rRNA gene sequencing analysis	↓α diversity in RW ↔ α diversity in WGW ↔ Lachnospiraceae and Ruminococcaceae between RW and WGW		↔ total cholesterol, HDL cholesterol, TGs, NEFAs, glucose, insulin, and HOMA-IR ↔ abdominal subcutaneous adipose tissue (SAT) and visceral adipose tissue (VAT) mass ↑49% in intrahepatic triglyceride in RW ↑ %subjects with steatosis in RW ↓%subjects with steatosis in WGW ↔ fasting plasma ALT, AST, GGT, and postprandial NEFA ↓serum amyloid A and CRP in WGW ↑TG levels 4 h postprandially in WGW after intervention vs baseline ↔ adipocyte size, expression of the genes LPL, ANGPTL4, CD36, CD68, TGFB1, MMP9, and LOX
Vitaglione et al. 2015	assess circulating concentration, excretion, and the physiologic role of WG wheat polyphenols in subjects with suboptimal dietary and lifestyle behaviors	V4 region of 16S rRNA gene sequencing analysis	↑Prevotella (1.8% to 3.5%) in WG ↓ Dialister (from 2.5% to 0.6%), Bifidobacterium (from 6.6% to 5.3%), Blautia (from 9.7% to 6.7%), and Collinsella (from 1.8% to 0.9%) in WG ↑ Bacteroides from 9.9% to 14.7% and Lactobacillus from 0.03% to 0.12% correlate with ↓ TNFα in WG after 8 weeks	↔ single and total phenolic acids in biological samples from WG and control subjects at T0 WG intervention: 4.2- and 5-fold increase in serum DHFA after 4 and 8 weeks 1.3- and 0.8-fold increase in fecal FA after 4 and 8 weeks 0.8 fold increase in FA urinary excretion after 8 weeks FA serum concentrations at 4 and 8 week POS correlated with serum DHFA Urinary FA POS correlated with fecal and urinary DHFA in those ↑ urinary FA, a ↑ fecal FA after 8-wk urinary FA and DHFA NEG correlated with PAI-1 ↓ relative abundance at T0 of Bifidobacteriales (Actinobacteria) of 5.0% and Bacteroidetes of 9.6% associated with ↑ FA in the gut and urinary excretion, respectively after treatment, fecal FA POS associated with Bacteroidetes (↑ from 9.6% to 14.5%) and Firmicutes (↑from 75.3% to 79.7%), whereas NEG associated with Clostridium (↓from 3.1% to 1.6%)	↔ anthropometric data, body composition, plasma lipids, and glycemia ↓ TNFα in WG after 8 weeks ↑ IL10 in WG after 4 weeks
Vuholm et al. 2017	evaluate whether consumption of whole grains compared to refined grains for 6 wk improved gut health	V3-V4 region of 16S rRNA gene sequencing analysis	↔ UniFrac distance matrices ↔ bacterial diversity ↔ genera relative abundance ↑ unassigned genus of Ruminococcaceae in WGR vs RW in women	↔ fecal pH, total SCFAs, propionate among diets RW group: ↓ fecal concentration of butyrate, total SCFA, and propionate	GI symptoms: ↑ flatulence in WGW and WGR vs RW ↓bloating in WGW and WGR vs RW ↓fatigue in WGW and WGR vs RW ↑stool frequency, ↓ stool consistency in WGR group at weeks 2 and 4 vs RW ↔ intestinal permeability ↔ breath hydrogen
Walker et al. 2011	Effects of the changes in the major type of non-digestible carbohydrates in the diets on dominant bacterial phylotypes.	V2 – V5 region of 16S rRNA gene sequencing analysis Quantitative real time PCR denaturing gradient gel electrophoresis of V3 region of 16S	↔ proportion of Bacteroidetes, Firmicutes, Actinobacteria or Proteobacteria ↑ E. rectale and Ruminococcus bromii in RS diet ↓ Collinsella aerofaciens in WL diet ↑ Ruminococcaceae (R. bromii, R. flavefaciens and R. albus) in RS diet vs NPSs diet ↑Oscillibacter valericigenes in RS and WL vs M and NPS diets ↔ F. prausnitzii Roseburia and E. rectale ↑ RS diet and ↓WL diet ↔ Methanogenic archaea

HbA_1c: glycated hemoglobin A_1c, TG: triglycerides, TC: total cholesterol, HDL: High density lipoprotein, LDL: low density lipoprotein, LD: Low dairy product diet, HD: High dairy product diet, SBP: systolic blood pressure, DBP: diastolic blood pressure, hsCRP: high sensitivity C-reactive peptide, NEFA: plasma nonesterified fatty acids TMAO: trimethylamine N- oxide, LBP: lipopolysaccharide binding protein, SCFAs: short chain fatty acids, DHFA: dihydroferulic acid, FA: ferulic acid, GGT: γ-glutamyl transferase, DCA: deoxycholic acid, LCA: lithocholic acid, NOCs: N-nitroso compounds, BCFAs: branched-chain fatty acids.

Beaumont, M., K. J. Portune, N. Steuer, A. Lan, V. Cerrudo, M. Audebert, F. Dumont, G. Mancano, N. Khodorova, M. Andriamihaja, et al. 2017. Quantity and source of dietary protein influence metabolite production by gut microbiota and rectal mucosa gene expression: A randomized, parallel, double-blind trial in overweight humans. The American Journal of Clinical Nutrition 106 (4):1005–19. doi: 10.3945/ajcn.117.158816.

 PubMed Web of Science ®Google Scholar

Bendtsen, L. Q., T. Blaedel, J. B. Holm, J. K. Lorenzen, A. B. Mark, P. Kiilerich, K. Kristiansen, A. Astrup, and L. H. Larsen. 2018. High intake of dairy during energy restriction does not affect energy balance or the intestinal microflora compared with low dairy intake in overweight individuals in a randomized controlled trial. Applied Physiology, Nutrition, and Metabolism = Physiologie Appliquee, Nutrition et Metabolisme 43 (1):1–10. doi: 10.1139/apnm-2017-0234.

 PubMed Web of Science ®Google Scholar

Christensen, E. G., T. R. Licht, M. Kristensen, and M. I. Bahl. 2013. Bifidogenic effect of whole-grain wheat during a 12-week energy-restricted dietary intervention in postmenopausal women. European Journal of Clinical Nutrition 67 (12):1316–21. doi: 10.1038/ejcn.2013.207.

 PubMed Web of Science ®Google Scholar

Fava, F., R. Gitau, B. A. Griffin, G. R. Gibson, K. M. Tuohy, and J. A. Lovegrove. 2013. The type and quantity of dietary fat and carbohydrate alter faecal microbiome and short-chain fatty acid excretion in a metabolic syndrome ‘at-risk’ population. International Journal of Obesity 37 (2):216–23. doi: 10.1038/ijo.2012.33.

 PubMed Web of Science ®Google Scholar

Fragiadakis, G. K., H. C. Wastyk, J. L. Robinson, E. D. Sonnenburg, J. L. Sonnenburg, and C. D. Gardner. 2020. Long-term dietary intervention reveals resilience of the gut microbiota despite changes in diet and weight. The American Journal of Clinical Nutrition 111 (6):1127–36. doi: 10.1093/ajcn/nqaa046.

 PubMed Web of Science ®Google Scholar

Grembi, J. A., L. H. Nguyen, T. D. Haggerty, C. D. Gardner, S. P. Holmes, and J. Parsonnet. 2020. Gut microbiota plasticity is correlated with sustained weight loss on a low-carb or low-fat dietary intervention. Scientific Reports 10 (1):1405. doi: 10.1038/s41598-020-58000-y.

 PubMed Web of Science ®Google Scholar

Haro, C., M. Montes-Borrego, O. A. Rangel-Zúñiga, J. F. Alcalá-Díaz, F. Gómez-Delgado, P. Pérez-Martínez, J. Delgado-Lista, G. M. Quintana-Navarro, F. J. Tinahones, B. B. Landa, et al. 2016a. Two healthy diets modulate gut microbial community improving insulin sensitivity in a human obese population. The Journal of Clinical Endocrinology and Metabolism 101 (1):233–42. doi: 10.1210/jc.2015-3351.

 PubMed Web of Science ®Google Scholar

Haro, C., S. Garcia-Carpintero, J. F. Alcala-Diaz, F. Gomez-Delgado, J. Delgado-Lista, P. Perez-Martinez, O. A. Rangel Zuñiga, G. M. Quintana-Navarro, B. B. Landa, J. C. Clemente, et al. 2016b. The gut microbial community in metabolic syndrome patients is modified by diet. Journal of Nutritional Biochemistry. 27:27–31. doi: 10.1016/j.jnutbio.2015.08.011.

 PubMed Web of Science ®Google Scholar

Haro, C., S. García-Carpintero, O. A. Rangel-Zúñiga, J. F. Alcalá-Díaz, B. B. Landa, J. C. Clemente, P. Pérez-Martínez, J. López-Miranda, F. Pérez-Jiménez, and A. Camargo. 2017. Consumption of two healthy dietary patterns restored microbiota dysbiosis in obese patients with metabolic dysfunction. Molecular Nutrition & Food Research. (12):61. doi: 10.1002/mnfr.201700300.

 Web of Science ®Google Scholar

Hjorth, M. F., H. M. Roager, T. M. Larsen, S. K. Poulsen, T. R. Licht, M. I. Bahl, Y. Zohar, and A. Astrup. 2018. Pre-treatment microbial Prevotella-to-Bacteroides ratio, determines body fat loss success during a 6-month randomized controlled diet intervention. International Journal of Obesity ). 42 (3):580–3. doi: 10.1038/ijo.2017.220.

 Web of Science ®Google Scholar

Johnstone, A. M., J. Kelly, S. Ryan, R. Romero-Gonzalez, H. McKinnon, C. Fyfe, E. Naslund, R. Lopez-Nicolas, D. Bosscher, A. Bonnema, et al. 2020. Nondigestible carbohydrates affect metabolic health and gut microbiota in overweight adults after weight loss. The Journal of Nutrition 150 (7):1859–70. PMID: 32510158. doi: 10.1093/jn/nxaa124.

 PubMed Web of Science ®Google Scholar

Kopf, J. C., M. J. Suhr, J. Clarke, S. I. Eyun, J. M. Riethoven, A. E. Ramer-Tait, and D. J. Rose. 2018. Role of whole grains versus fruits and vegetables in reducing subclinical inflammation and promoting gastrointestinal health in individuals affected by overweight and obesity: A randomized controlled trial. Nutrition Journal 17 (1):72. doi: 10.1186/s12937-018-0381-7.

 PubMedGoogle Scholar

Lappi, J., M. Kolehmainen, H. Mykkänen, and K. Poutanen. 2013. Do large intestinal events explain the protective effects of whole grain foods against type 2 diabetes? Critical Reviews in Food Science and Nutrition 53 (6):631–40. doi: 10.1080/10408398.2010.550388.

 PubMed Web of Science ®Google Scholar

Mayengbam, S., J. E. Lambert, J. A. Parnell, J. M. Tunnicliffe, A. C. Nicolucci, J. Han, T. Sturzenegger, J. Shearer, B. Mickiewicz, H. J. Vogel, et al. 2019. Impact of dietary fiber supplementation on modulating microbiota-host-metabolic axes in obesity. The Journal of Nutritional Biochemistry 64:228–36. doi: 10.1016/j.jnutbio.2018.11.003.

 PubMed Web of Science ®Google Scholar

Meslier, V., M. Laiola, H. M. Roager, F. De Filippis, H. Roume, B. Quinquis, R. Giacco, I. Mennella, R. Ferracane, N. Pons, et al. 2020. Mediterranean diet intervention in overweight and obese subjects lowers plasma cholesterol and causes changes in the gut microbiome and metabolome independently of energy intake. Gut 69 (7):1258–68. doi: 10.1136/gutjnl-2019-320438.

 PubMed Web of Science ®Google Scholar

Roager, H. M., J. K. Vogt, M. Kristensen, L. B. S. Hansen, S. Ibrügger, R. B. Maerkedahl, M. I. Bahl, M. V. Lind, R. L. Nielsen, H. Frøkiaer, et al. 2019. Whole grain-rich diet reduces body weight and systemic low-grade inflammation without inducing major changes of the gut microbiome: A randomised cross-over trial. Gut 68 (1):83–93. doi: 10.1136/gutjnl-2017-314786.

 PubMed Web of Science ®Google Scholar

Russell, W. R., S. W. Gratz, S. H. Duncan, G. Holtrop, J. Ince, L. Scobbie, G. Duncan, A. M. Johnstone, G. E. Lobley, R. J. Wallace, et al. 2011. High-protein, reduced-carbohydrate weight-loss diets promote metabolite profiles likely to be detrimental to colonic health. The American Journal of Clinical Nutrition 93 (5):1062–72. doi: 10.3945/ajcn.110.002188.

 PubMed Web of Science ®Google Scholar

Salonen, A., L. Lahti, J. Salojärvi, G. Holtrop, K. Korpela, S. H. Duncan, P. Date, F. Farquharson, A. M. Johnstone, G. E. Lobley, et al. 2014. Impact of diet and individual variation on intestinal microbiota composition and fermentation products in obese men. The ISME Journal 8 (11):2218–30. doi: 10.1038/ismej.2014.63.

 PubMed Web of Science ®Google Scholar

Schutte, S., D. Esser, F. P. M. Hoevenaars, G. Hooiveld, M. G. Priebe, R. J. Vonk, S. Wopereis, and L. A. Afman. 2018. A 12-wk whole-grain wheat intervention protects against hepatic fat: The Graandioos study, a randomized trial in overweight subjects. The American Journal of Clinical Nutrition 108 (6):1264–74. doi: 10.1093/ajcn/nqy204.

 PubMed Web of Science ®Google Scholar

Vuholm, S., D. S. Nielsen, K. N. Iversen, J. Suhr, P. Westermann, L. Krych, J. R. Andersen, and M. Kristensen. 2017. Whole-grain rye and wheat affect some markers of gut health without altering the fecal microbiota in healthy overweight adults: A 6-week randomized trial. The Journal of Nutrition 147 (11):2067–75. doi: 10.3945/jn.117.250647.

 PubMed Web of Science ®Google Scholar

Walker, A. W., J. Ince, S. H. Duncan, L. M. Webster, G. Holtrop, X. Ze, D. Brown, M. D. Stares, P. Scott, A. Bergerat, et al. 2011. Dominant and diet-responsive groups of bacteria within the human colonic microbiota. The ISME Journal 5 (2):220–30. doi: 10.1038/ismej.2010.118.

 PubMed Web of Science ®Google Scholar

Vitaglione, P., I. Mennella, R. Ferracane, A. A. Rivellese, R. Giacco, D. Ercolini, S. M. Gibbons, A. La Storia, J. A. Gilbert, S. Jonnalagadda, et al. 2015. Whole-grain wheat consumption reduces inflammation in a randomized controlled trial on overweight and obese subjects with unhealthy dietary and lifestyle behaviors: Role of polyphenols bound to cereal dietary fiber. The American Journal of Clinical Nutrition 101 (2):251–61. doi: 10.3945/ajcn.114.088120.

 PubMed Web of Science ®Google Scholar