Impact of Plant-Based Protein and Dietary Fiber Intake on Insulin Resistance, Weight Management, and Glycemic Index Control in Obese Individuals with Type 2 Diabetes: A Randomized Controlled Trial

Dr. Pentareddy Joseph Raju^1,*

Assistant Professor (C)

Department of Food Science and Technology, University College of Sciences, Satavahana University, Karimnagar, Telangana, India 505001

Abstract

This randomized controlled trial examined the effects of plant-based protein and dietary fibre intake on insulin resistance, body weight management, and glycemic control amongst obese individuals with Type 2 Diabetes (T2D). A total of 117 individuals were randomly selected to consume as animal based high protein and plant based high protein diet for six months. The primary outcome measures were body weight, BMI, fat mass, abdominal fat distribution, sensitivity to insulin (HOMA-IR), and blood glucose (HbA1c and insulin levels). When results were tabulated in terms of biochemical analysis, tremendous loss in subjects’ weight, drop in fat mass, and improvements in insulin sensitivity were observed in both dietary groups with no significant differences. Both diets succeeded in decreasing the BMI and abdomen fat, and both showed an improvement in arterial hypertension. Although the plant-based proteins may have additional anti-inflammatory effects, both dietary interventions are equally efficacious for improving metabolism in people with T2D. Further research is required to describe the long-term impact of these diets on the management and prevention of T2D.

Keywords: Dietary fiber, Plant-based protein, Insulin resistance, biochemical characteristics

1. Introduction

1.1 Global Burden of Type 2 Diabetes and Obesity

Type 2Diabetes (T2D) exists as a prevalent metabolic concern, which causes patients to experience chronic hyperglycemia due to insulin-related resistance and dysfunction in β-cells. Across the globe, over 537 million people are living with T2D. Additionally, it has risen because obesity affects a large population due to the increasing problem of visceral adiposity [1]. Systemic inflammation due to obesity develops T2D by secretion of TNF-α IL-6 and resistin along with other inflammatory cytokines through adipose tissue, which affects insulin signaling pathways and produces insulin resistance [2]. The condition of “diabesity” is a complex word for T2D, with obesity raising the chance of developing serious complications like cardiovascular diseases (CVDs), non-alcoholic fatty liver disease, and chronic kidney disease [3, 4]. Thus, effective dietary strategies for managing T2D and obesity are crucial in public health.

Recent studies showed that dietary interventions using plant-based methods help to reduce the impact of T2D combined with obesity [5]. Studies established that nutritional plans based on plants lead to better metabolic health because they improve insulin sensitivity and reduce weight gain [5, 6]. Clinical evidence from extensive research shows that treatment with plant-based diets has lower chances of developing T2D risk factors through reduced HbA1c levels and decreased fasting glucose and body mass index (BMI) [7]. Randomized controlled trials (RCTs) have established valid evidence and state that plant-based diets help manage T2D and obesity [8]. Reported research showed that these diets’ plant-based proteins and dietary fiber are essential factors that enhance insulin resistance treatment and improve glycemic control [7]. Furthermore, research studies focusing on plant-based diets through meta-analysis and randomized controlled trials [8] showed positive effects. Yet, investigations into plant protein and fiber effects on metabolic health in T2D patients with obesity remain limited. 

1.2 Nutritional Interventions: Shifting Toward Plant-Based Strategies

The health benefits of plant-based diets established an impactful approach for managing metabolic health conditions in patients with T2D and obesity. The diet includes only whole plant foods consisting of legumes, fruits and vegetables, whole grains, nuts, and seeds, delivering high-fiber and antioxidant content with beneficial fats but limited amounts of saturated fat and cholesterol [9]. Studies prove that diets based on plants actively enhance multiple aspects of metabolic syndrome, including blood glucose control, insulin resistance, and weight loss [10]. The study of Kahleova et al. confirmed that T2D patients who followed plant-based diets showed significant reductions in HbA1c, fasting blood glucose, and weight measurements [11]. Research findings from meta-analyses show plant-based diets serve as a preventive measure against T2D, while proof shows eating patterns rich in plants lower the risk factors associated with diabetes [12]. Therefore, available literature has proven that these eating patterns provide cardiovascular care while enhancing their lipid profile and reducing inflammatory markers to improve metabolic health.

The therapeutic value of plant-based dietary strategies to treat T2D, along with obesity, has been validated by results from RCTs. For example, Harland et al. showed a low-fat plant-based eating plan compared to a typical American diet configuration among subjects with T2D [13]. Intake of a plant-based dietary program achieved better glucose control and weight loss outcomes through decreased HbA1c levels and reduced fasting glucose markers. Additionally, Kahleova et al. showed that following a plant-based diet enhances insulin sensitivity and reduces weight in T2D and overweight individuals [11]. Studies have found that plant-based diets can stop prediabetes from developing into T2D  [14]. However, investigating the function of plant-based proteins with dietary fiber and their metabolic benefits is still limited.

The limited literature showed that plant-based diets, in general terms but in a broad context, are limited to analyzing which dietary factors, including protein and fiber, independently affect insulin resistance and other related metabolic factors [11].

1.3 Plant-Based Protein: Mechanisms and Clinical Impact

Individuals who consume plant-based protein from legumes, soy, and nuts have better metabolic health regarding T2D and obesity. The fiber content of plant protein exceeds animal proteins, while it contains less saturated fats, improving metabolism and insulin sensitivity [15]. Studies showed that substituting animal and plant proteins improves insulin resistance but reduces weight [16]. Zhao et al. established that replacing animal proteins with soy protein produced substantial increases in insulin sensitivity results and decreased the amount of visceral fat [17].  Plant protein consumption leads T2D individuals to have lower HbA1c levels along with better insulin sensitivity [8]. Literature showed that soy protein, among plant proteins, has the potential to improve insulin resistance while positively impacting metabolic health [18, 19].

Consuming plant proteins enhances hormone levels of GLP-1 to control blood glucose levels and minimize eating behavior [7]. A study showed weight loss success among participants who adopted a plant-based eating pattern filled with legumes and whole grains, while standard omnivorous eaters did not achieve the same results [7]. Various mechanistic studies establish plant-based protein functions to decrease systemic inflammation and enhance gut health by regulating pro-inflammatory cytokines and balancing gut microbiota composition [20].

Substantial research support has been received on insulin sensitivity improvements with plant-based protein alongside obesity reduction, but examinations specific to T2D and obesity are scarce. Past investigations examined plant-based diets in their entirety but did not differentiate between plant-based protein effects specifically. 

1.4 Dietary Fiber: Metabolic Benefits and Microbiome Interactions

Non-digestible carbohydrates, known as dietary fiber in plant foods, have essential functions for T2D and obesity management through their actions in blood glucose control and hunger regulation, and they benefit digestive health. The two fiber subtypes include soluble fiber, which helps explicitly control blood glucose by delaying glucose absorption [21]. Researchers from Reynolds et al. conducted a systematic review showing that adding fiber to diets allows people with T2D to regulate HbA1c and fasting blood glucose better and manage their weight effectively [22]. Scientific studies demonstrate that dietary supplementation with fiber or high-fiber food plans substantially decreases fasting glucose and better insulin responsiveness in patients [23, 24]. Research indicates fiber works as a satiety enhancement method, leading to reduced calorie consumption and weight reduction, especially in obese patients [24].

Short-chain fatty acids (SCFAs) are produced by the breakdown of dietary fiber through interactions with the gut bacteria, which enhances insulin sensitivity and lowers inflammation. Fiber fermentation within the gut produces SCFAs, which increase insulin sensitivity and reduce hepatic glucose synthesis. [25]. A study showed that T2D and obesity consumption in higher doses of dietary fiber has better microbiota development, reduced body inflammation, and improved insulin function [26]. The observations confirm that dietary fiber controls glucose absorption and manages systemic. Therefore, inflammation is confirmed as a primary factor behind insulin resistance.

1.5 Rationale and Objective of the Review

Increasing trends in the cases of obesity and type 2 diabetes ought to signal the urgent need for favorable dietary regimens to be implemented to curtail insulin resistance and blood sugar. New research has identified plant-based proteins and dietary fiber as having the potential to upset cellular processes that contribute to biochemical analysis of glucose metabolism, insulin function, and weight loss. Plant-based proteins distinguish themselves from animal proteins in anti-inflammatory properties and antioxidant advantages that may help to lessen systemic inflammation underlying insulin resistance. By slowing down the emptying rate of fast food out of the stomach and slowing down glucose absorption, soluble fiber prevents the aggressive surge in blood sugar levels after consumption and facilitates a more efficient insulin response. The rationale of this review is to critically review data from randomized controlled trials to determine the effects of plant-based protein and dietary fiber consumption on individuals with overweight and T2D on insulin resistance, weight loss, and control of postprandial glucose responses. Therefore, the study aims to produce evidence-based dietary guidelines by studying plant nutrients’ biochemical processes and clinical implications for metabolic control.

Methodology

Participant Selection

Study participants were men and women aged 18 years or above with a BMI from 27.5 to 40 kg/m² and who had maintained stable weight, within 5%, for the last 3 months. Armed with a confirmed diagnosis of either T2DM or prediabetes described by ADA criteria, participants were eligible. For prediabetes, fasting plasma glucose must be ≥100 mg/dl, or glycated hemoglobin (HbA1c) ≥5.7%, or, for T2DM, values for fasting plasma glucose must be ≥126 mg/dl, and for HbA1c ≥6. Study subjects had no glucose-lowering treatment initiated or had been taking metformin regularly in a fixed dose for at least six months before entry into the study.

Conditions for exclusion included HbA1c ≥8%, recent changes in antidiabetic medication, using supplements or substances affecting glucose or lipid metabolism, or any comorbidities that would cause disturbance in adherence or metabolic outcome (e.g., uncontrolled thyroid disease, liver disorders, renal). Subjects with excessive alcohol consumption (more than 20 grams a day for women and above 30 grams for men) were not included. All subjects signed written informed consent forms before enrollment and approval of the present study. 

Study Design and Dietary Intervention

The research was carried out in a single tertiary-care center as a randomized controlled trial (RCT) that took six months to complete. Study participants were assigned to two hypocaloric dietary groups in a randomized fashion with differing sources of protein and dietary fiber intake. Subjects from group A were provided with a diet capable of satisfying the energy requirements of the subjects at 35% with protein; protein coming from more than 75% of plants with a given protein content of 3 near cutting-edge energy needs. The participants in Group B (Control High-Protein Diet) retained the same macronutrient profile but consumed most of their protein from animals.

Both were 30% fat (mostly derived from sources below 7% saturated, 15–20, and 5–7% as monounsaturated and polyunsaturated, respectively) and 35% carbohydrates with simple sugars under 10%. Each participant’s caloric allotment was determined using the Harris-Benedict approach and decreased by 30% to facilitate weight loss. The order in which participants were involved in each group was randomly chosen, and at no point were outcome assessors informed about participants’ placements in groups. Because the dietary interventions had such distinctive combinations, neither participants nor dieters could be blinded regarding the group assignments [27]. Participants received biweekly personal nutrition guidance and meal plans from registered dietitians. In case of difficulties with adherence to the dietary scheme, participants received consultations every two weeks with registered dietitians to help them stay on track.

Anthropometric and Clinical Assessments

Weight and body composition were measured using DEXA scans. Participants’ height was recorded with a wall-mounted stadiometer, and their waist circumference was obtained by placing the tape around the midpoint between the final rib and the hipbone. After letting the study participants sit for five minutes, blood pressure was measured using a calibrated portable instrument (HEM-907-E model).

Biochemical and Metabolic Markers

Blood samples were collected thrice with 12-hour fast between them, baseline, 3 months, and 6 months. The main biomarkers of carbohydrate metabolism were glycated hemoglobin (HbA1c), fasting plasma glucose, serum insulin concentrations, and the HOMA-IR calculation by insulin resistance [28]. Besides the measures primary in nature, secondary outcomes were used to monitor the lipid parameters (total cholesterol, LDL, HDL, triglycerides), adipokine concentrations (adiponectin, leptin), pro-inflammatory biomarkers (IL-6, TNF-α). Besides, standardized ELISAs ( Biochemical analyses were carried out in one blinded laboratory to ensure the purity and objectivity of results [29]. This group of markers was used to obtain comprehensive estimates of metabolic changes after dietary intervention.

Dietary and Lifestyle Monitoring

Participants completed 3-day weighed food diaries before each study visit, with recorded food intake being two weekdays and one weekend day. Dietary intake was measured with EasyDiet software using national tables of food analysis. Participants’ adherence to nutritional guidelines was monitored by measuring the actual plant and animal protein and fiber intake against target recommendations. Participants wore a waist-worn accelerometer (ActiGraph wGT3X-BT) to quantify seven-day physical activity at three assessment times [30]. Data analysis was performed using the R software, which categorized physical activity intensity as sedentary, light, moderate, and vigorous, using validated cut-offs. Information from participants who fulfilled the 72-hour wear criterion was incorporated into the study.

Statistical Analysis

The primary outcome of the research was to determine the 6-month change in HbA1c values. Assuming a standard deviation multiple of 1% and a minimum crossing point difference of 0.4%, the study’s sample size was aimed at 56 participants in each group, giving 80% power at a one-sided 90% confidence limit. For all statistical analyses, the intention to treat approach is used. For continuous variables, means ± SD or medians (IQR) were reported, while categorical variable percentages were reported. Statistical comparison between groups was performed using Student’s t-test or Mann–Whitney U test [31]. Repeated measures ANOVA or, according to circumstances, Friedman’s test was used to assess within-group change. Generalized Linear Models (GLMs) were used to investigate interactions between groups and time (while controlling for age, sex, BMI, and dietary adherence ratios). Bonferroni adjustments were applied to prevent the primary outcomes with an acceptable degree of accuracy in the setting of multiple comparisons. All statistical analyses used R software version 3.5.0, and the results were considered statistically significant with a p-value < 0.05.

Results

Table 1: Biochemical characteristics analysis 

Variable	Animal HP diet (HPA)<br>(N = 59)	Plant HP diet (HPP)<br>(N = 58)	p-value
Age (years)	58.3 ± 7.38	56.4 ± 8.47	0.209
Men, n (%)	35 (59.3%)	26 (44.8%)	0.166
Body weight (kg)	94.2 ± 13.8	89.9 ± 12.0	0.075
BMI (kg/m²)	33.8 ± 4.34	32.7 ± 2.84	0.115
Fat mass (%)	41.8 (36.5–46.9)	43.1 (37.5–48.2)	0.621
Abdominal visceral fat (kg)	2.18 (1.44–2.84)	1.64 (1.39–2.35)	0.058
Abdominal subcutaneous fat (kg)	2.41 (1.72–3.14)	2.21 (1.72–3.08)	0.559
Lean mass (kg)	53.5 (46.0–60.0)	48.5 (41.7–57.5)	0.477
Smoking status, n (%)			0.522
Former	30 (50.8%)	23 (41.1%)
Current	9 (15.3%)	12 (21.4%)
Non-smoker	20 (33.9%)	21 (37.5%)
Type 2 diabetes, n (%)	29 (49.2%)	25 (43.1%)	0.638
Prediabetes, n (%)	30 (50.8%)	33 (55.9%)
Hyperlipidaemia, n (%)	21 (35.6%)	22 (37.9%)	0.944
Hypertension, n (%)	25 (42.4%)	20 (34.5%)	0.492
Cardiovascular disease, n (%)	4 (6.78%)	4 (6.90%)	1.000
Metformin, n (%)	16 (27.6%)	19 (33.3%)	0.641
Lipid-lowering medication, n (%)	17 (28.8%)	20 (35.7%)	0.554
Hypertension medication, n (%)	21 (35.6%)	16 (27.6%)	0.464
Antiaggregant medication, n (%)	4 (6.78%)	1 (1.72%)	0.371
Glucose (mg/dL)	118 (109–137)	116 (105–134)	0.536
HbA1c (%)	5.95 (5.60–6.40)	5.75 (5.50–6.20)	0.216
HbA1c (mmol/mol)	42 (38–46)	39 (37–44)
Insulin (μU/L)	12.7 (9.60–18.5)	12.5 (9.68–17.7)	0.819
HOMA-IR index	3.93 (2.92–5.74)	3.57 (2.59–5.36)	0.677
Triglycerides (mg/dL)	112 (93.5–142)	114 (96.3–163)	0.445
Total cholesterol (mg/dL)	209 ± 39.6	211 ± 45.3	0.795
LDL cholesterol (mg/dL)	134 ± 36.3	134 ± 38.8	0.995
HDL cholesterol (mg/dL)	52.0 (46.0–60.0)	54.0 (46.0–59.0)	0.998
Uric acid (mg/dL)	6.00 ± 1.28	5.76 ± 1.28	0.307
GGT (U/L)	30.0 (22.0–45.5)	27.0 (18.0–41.0)	0.108
ALT (U/L)	27.0 (20.0–37.0)	23.0 (17.3–32.8)	0.037
AST (U/L)	24.0 (20.0–29.0)	22.0 (18.3–27.0)	0.131

Table 1 displays the baseline characteristics of participants randomized into either the Animal High-Protein (HPA) or Plant High-Protein (HPP) diet groups. The average age in the HPA group was slightly higher (58.3 ± 7.38 years) than the HPP group (56.4 ± 8.47), but the difference was not statistically significant (p = 0.209). Males were more represented in the HPA group (59.3%) than in the HPP group (44.8%), though this was also not significant (p = 0.166). The HPA group had slightly higher mean body weight (94.2 kg vs 89.9 kg) and BMI (33.8 vs 32.7 kg/m²), with p-values of 0.075 and 0.115, respectively. Abdominal visceral fat trended higher in the HPA group (2.18 kg vs. 1.64 kg), nearing significance (p = 0.058). Both groups were similar in fat mass, lean mass, and subcutaneous fat. Comorbidities, including diabetes, prediabetes, hyperlipidaemia, hypertension, and cardiovascular disease, were similarly distributed. Biochemically, glucose, HbA1c, insulin, HOMA-IR, lipids, and liver enzymes (except ALT) were comparable. ALT was significantly higher in the HPA group (p = 0.037), suggesting possible liver stress. Other enzymes (AST, GGT), uric acid, and cholesterol levels showed no significant differences. These results indicate both groups were well-balanced at baseline, with minimal clinically relevant differences before intervention.

Table 2: Clinical and anthropometric statistics

Variable	Animal HP Diet (HPA)3 Months (N = 55)	Animal HP Diet (HPA)6 Months (N = 52)	pc	Plant HP Diet (HPP)3 Months (N = 55)	Plant HP Diet (HPP)6 Months (N = 50)	pc	pb
BMI (kg/m²)	2.21 ± 1.38	2.90 (3.95 – 1.67)	<0.001	2.28 ± 1.49	3.05 (4.49 – 1.47)	<0.001	0.991ᵈ
Body weight (kg)	6.18 ± 3.98	8.05 ± 5.12	<0.001	6.15 ± 4.27	7.70 ± 5.47	<0.001	0.924
Fat mass (%)	2.74 ± 2.02	3.20 (6.05 – 1.87)	<0.001	2.10 (4.05 – 1.22)	2.85 (6.7 – 1.58)	<0.001	0.801ᵈ
Abdominal visceral fat (kg)	0.33 (0.62 – 0.17)	0.42 (0.73 – 0.17)	<0.001	0.31 (0.58 – 0.10)	0.38 (0.82 – 0.26)	<0.001	0.450ᵈ
Abdominal subcutaneous fat (kg)	0.41 (0.67 – 0.12)	0.63 ± 0.48	<0.001	0.43 ± 0.39	0.47 ± 0.41	<0.001	0.702
Lean mass (kg)	1.42 ± 1.71	1.60 ± 1.88	<0.001	1.36 ± 2.02	1.73 (2.59 – 0.11)	<0.001	0.725ᵈ
Waist circumference (cm)	5.00 (8.50 – 1.88)	6.50 (11.8 – 2.50)	<0.001	6.50 (10.5 – 3.00)	8.00 (13.0 – 0.50)	<0.001	0.681
Diastolic blood pressure (mmHg)	12.2 ± 14.2	11.5 ± 14.9	<0.001	8.45 ± 10.5	5.14 ± 8.70	0.006	0.229
Systolic blood pressure (mmHg)	5.10 (10.0 – 2.00)	10.0 (17.25 – 0.25)	0.013	3.58 ± 9.93	9.50 (17.5 – 2.50)	<0.001	0.219

ᵈ Between-group comparison using ANCOVA adjusted for baseline values.

Table 2 presents the changes in clinical and anthropometric characteristics following 3 and 6 months of dietary intervention for both the Animal High-Protein (HPA) and Plant High-Protein (HPP) groups. All within-group changes were statistically significant (pc < 0.001 for most variables), indicating improvements in body composition and cardiovascular risk markers across both diets. However, no statistically significant between-group differences (pb) were found, suggesting similar efficacy for both dietary approaches. Both groups experienced significant reductions in BMI and body weight over time. By 6 months, the HPA group lost an average of 8.05 kg, and the HPP group lost 7.70 kg, with no significant difference (pb = 0.924). Fat mass decreased in both groups (HPA: 3.20%, HPP: 2.85%), and abdominal visceral and subcutaneous fat also showed marked reductions (pc < 0.001 for all), with no between-group differences (pb > 0.4). Significantly, lean mass increased slightly in both groups (HPA: +1.60 kg, HPP: +1.73 kg), indicating that high-protein intake helped preserve or increase muscle mass during weight loss. Waist circumference, a key marker of central obesity, significantly decreased in both groups (HPA: −6.5 cm; HPP: −8.0 cm), further supporting favorable fat redistribution, with no between-group differences (pb = 0.681). Systolic and diastolic blood pressure dropped significantly in both groups. The HPA group saw slightly greater diastolic reductions (−11.5 mmHg vs. −5.14 mmHg), but between-group differences were not statistically significant (pb = 0.229 for diastolic, pb = 0.219 for systolic). Animal and plant-based high-protein diets significantly improved body weight, fat distribution, lean mass preservation, and blood pressure over 6 months. The absence of significant differences between the two groups suggests that both diets are comparably effective in improving obesity-related health markers.

Table 3: Biochemical characteristics according to diet group

Parameter	Animal HP Diet (HPA)			Plant HP Diet (HPP)			pc
	3 Months (N=55)	6 Months (N=52)	pb	3 Months (N=55)	6 Months (N=50)	pb
Glucose (mg/dL)	14.5 (21.3 – −2.75)	10.0 (20.5 to 2.5)	<0.001	9.00 (17.5 to 1.00)	9.50 (20.5 to 1.25)	<0.001	0.992ᵈ
Insulin (μUI/mL)	3.25 (6.33 to 1.48)	4.99 ± 5.33	<0.001	3.50 (6.50 to 0.40)	4.40 (7.72 to 1.49)	<0.001	0.856ᵈ
HOMA-IR index	1.40 (2.61 to 0.54)	1.45 (2.53 to 0.64)	<0.001	1.02 (2.38 to 0.28)	1.44 (2.83 to 0.53)	<0.001	0.873ᵈ
HbA1c (%)	0.20 (0.40 – 0.00)	0.20 (0.48 – 0.00)	<0.001	0.20 (0.40 to 0.10)	0.28 ± 0.33	<0.001	0.513ᵈ
HbA1c (mmol/mol)	2.00 (4.75 to 0.25)	2.00 (4.25 – 0.00)		1.50 (4.00 – 0.00)	2.00 (4.25 – 0.00)
Triglycerides (mg/dL)	9.00 (32.3 – 9.75)	13.0 (28.5 – 7.00)	0.043	8.00 (41.5 – 9.50)	24.0 (40.5 to 5.50)	<0.001	0.364
Total cholesterol (mg/dL)	8.50 (25.3 – 1.50)	2.73 ± 31.2	0.375	15.0 (38.0 – 7.00)	13.8 ± 36.3	0.008	0.393
LDL cholesterol (mg/dL)	8.16 ± 22.7	3.55 ± 27.0	0.223	9.00 (29.5 – 7.50)	15.0 (26.0 – 9.00)	0.015	0.546
HDL cholesterol (mg/dL)	1.00 (5.00 – 2.00)	2.61 ± 7.53	0.001	1.00 (6.00 – 2.00)	0.42 ± 7.45	0.011	0.534
Apolipoprotein A1 (mg/dL)	10.1 ± 17.6	0.17 ± 20.2	0.002	8.06 ± 20.9	3.20 ± 19.2	0.017	0.527
Apolipoprotein B (mg/dL)	10.7 ± 19.1	3.85 ± 26.1	0.158	9.81 ± 23.2	7.18 ± 24.5	0.028	0.503
CRP (mg/dL)	0.02 (0.14 – 0.02)	0.03 (0.13 – 0.04)	0.094	0.06 (0.18 – 0.00)	0.07 (0.23 – 0.00)	<0.001	0.768
Iron (μg/dL)	5.36 ± 34.9	8.08 ± 34.0	0.066	0.89 ± 35.4	1.76 ± 35.0	0.879	0.210
Ferritin (ng/dL)	0.95 (30.2 – 13.82)	2.70 (30.5 – 18.2)	0.633	6.15 (26.8 – 14.4)	3.20 (25.1 – 12.7)	0.600	0.966
Vitamin B12 (pg/dL)	2.50 (39.0 – 31.0)	12.5 (38.3 – 60.0)	0.385	39.5 ± 72.6	17.4 ± 83.2	0.074	0.337
Folic acid (ng/dL)	0.76 ± 3.01	0.85 (0.72 – 3.22)	0.020	2.27 ± 2.88	2.51 ± 3.06	<0.001	0.436
Creatinine (mg/dL)	0.01 (0.03 – 0.07)	0.01 (0.05 – 0.06)	0.459	0.00 ± 0.08	0.01 ± 0.10	0.525	0.926
Uric acid (mg/dL)	0.23 (0.53 – 0.20)	0.23 ± 0.63	0.006	0.06 ± 0.64	0.16 ± 0.71	0.077	0.953
Albumin (g/dL)	0 (0.1 – 0.1)	0.01 ± 0.25	0.479	0.11 ± 0.17	0.08 ± 0.27	0.002	0.196
GFR (mL/min/1.73 m²)	0.63 (3.71 – 2.6)	0.47 (4.06 – 3.57)	0.377	0.28 ± 8.57	0.10 ± 9.90	0.513	0.453
GGT (U/L)	5.00 (11.0 to 0.75)	4.00 (11.0 to 1.00)	<0.001	4.00 (11.0 to 1.50)	7.00 (11.8 to 2.00)	<0.001	0.722
ALT (U/L)	3.00 (11.0 – 1.00)	4.00 (14.0 to 0.50)	0.139	4.00 (8.50 to 0.50)	4.00 (9.75 to 1.00)	0.008	0.703
AST (U/L)	1.50 (5.00 – 1.00)	1.50 (5.00 – 1.00)	<0.001	2.00 (4.00 – 1.00)	1.06 ± 5.07	<0.001	0.869
Urine Microalbumin (mg/dL)	0.20 (0.40 – 0.00)	0.15 (0.49 – 0.02)	<0.001	0.10 (0.30 – 0.03)	0.10 (0.30 – 0.00)	<0.001	0.903
Urine Creatinine (mg/dL)	29.5 ± 55.0	15.8 ± 77.1	0.261	27.4 (56.2 to 3.61)	22.0 ± 58.5	0.011	0.994
Microalbumin-creatinine ratio	0.7 (2.06 – 0.35)	0.08 (1.99 – 1.19)	0.017	0.25 (0.96 – 0.93)	0.23 (0.92 – 1.4)	0.376	0.763
Urea (mg/dL)	2.55 ± 6.13	3.53 ± 7.84	0.014	1.45 ± 7.60	1.42 ± 9.07	0.014	0.813

Table 3 presents the biochemical changes observed in participants following an animal-based high-protein (HPA) or plant-based high-protein (HPP) diet over 3 and 6 months. Both diets resulted in significant within-group improvements in glucose levels (HPA: p < 0.001; HPP: p < 0.001) with no significant difference between the groups (pb = 0.992). Insulin levels and HOMA-IR, indicators of insulin resistance, also significantly improved in both groups, again without between-group differences, indicating both diets similarly benefit glucose metabolism. HbA1c and HbA1c (mmol/mol) levels decreased substantially within both groups (p < 0.001), with modest reductions suggesting improved long-term glycemic control. Triglycerides dropped more significantly in the HPP group (p < 0.001) than in HPA (p = 0.043), but between-group differences were non-significant (pb = 0.364). For lipid profile, total cholesterol, LDL, and HDL improved significantly within both groups, though changes were slightly more pronounced in the HPP group. Apolipoprotein A1 and B also showed significant within-group improvements without significant between-group differences. CRP, an inflammatory marker, showed a significant decrease only in the HPP group (p < 0.001), suggesting plant proteins may better reduce inflammation. Iron, ferritin, and vitamin B12 showed non-significant changes, though a slight B12 increase in HPA suggests animal protein sources might help maintain B12 levels better than plant-based diets. Folic acid increased significantly in both groups, particularly in HPP (p < 0.001), possibly due to higher plant-based folate intake. Renal function indicators like creatinine, urea, GFR, and microalbumin remained stable with slight favourable changes, suggesting both diets are renal-safe over 6 months. Liver enzymes (ALT, AST, GGT) and urinary markers showed modest but significant within-group improvements, again without differences between diet types. HPA and HPP diets led to substantial improvements in metabolic and inflammatory markers, with similar efficacy across most parameters, indicating that plant-based high-protein diets are as effective as animal-based ones for cardiometabolic health.

Table 4: Metabolic Biomarker Changes According to Diet Group

Biomarker (pg/mL)	HPA – 3 mo (N = 55)	HPA – 6 mo (N = 50)	HPP – 3 mo (N = 55)	HPP – 6 mo (N = 52)	pb	pc
GIP	5.95 (23.0–7.89)	7.15 (19.18–3.80)	7.17 (22.1–7.98)	5.87 (27.3–10.6)	0.007	0.719
GLP-1	50.4 ± 83.7	68.7 ± 57.0	46.9 ± 64.1	56.3 (92.9–15.6)	<0.001	0.871
Leptin	4492 (9448–2138)	5630 (10,377–1623)	6196 ± 6799	4236 (9002–2459)	<0.001	–
Adiponectin	439 (10,422–13,823)	10,275 (2399–38,263)	4144 (5598–15,956)	7938 ± 35,248	0.083	–
Resistin	3.80 (10.3–12.8)	2.56 (11.6–20.9)	6.00 (approx.)	1.51 (13.1–9.45)	0.004	–
MCP-1	24.0 ± 65.6	153 ± 160	19.7 ± 77.5	21.5 (64.0–10.9)	<0.001	<0.001
TNF-α	1.26 (4.03–0.3)	0.64 (1.99–0.68)	1.00 (2.92–0.49)	2.68 (4.42–1.64)	<0.001	<0.001
PAI-1	80.8 ± 142	50.7 ± 177	153 ± 160	275	0.005	–
IL-6	0.07 (1.04–0.45)	0.00 (0.66–0.69)	0.00 (1.8–1.85)	0.05 (2.14–1.1)	0.048	0.828
IL-1β	–	1.27 (3.43–0.69)	–	0.74 (1.92–0.5)	0.048	0.358
IL-8	–	0.64 (1.17–0.01)	–	0.28 (1.84–0.8)	0.206	0.518
IL-10	–	0.25 ± 3.42	–	1.28 (1.98–0.49)	<0.001	0.905
VEGF-A	40.7 (129–19.9)	41.8 (136–21.4)	19.3 (127–52.2)	32.7 ± 78.8	0.939	0.034

Table 4 presents changes in metabolic biomarkers over 3 and 6 months for participants on either an animal-based high-protein diet (HPA) or a plant-based (HPP) diet. Key inflammatory and metabolic markers are examined. GIP levels showed significant between-group differences (pb = 0.007), with HPA increasing and HPP decreasing slightly over time, though within-group changes were not significant. GLP-1 increased in both groups but significantly differed between diets (pb < 0.001), suggesting greater GLP-1 stimulation in the HPA group. Leptin increased in the HPA group but decreased in the HPP group, with significant differences (pb < 0.001), indicating contrasting effects on appetite regulation. Adiponectin increased in both, with a non-significant trend favoring HPP (pb = 0.083). Resistin and TNF-α, both pro-inflammatory markers, decreased in HPA but increased in HPP (pb = 0.004 and < 0.001, respectively), suggesting improved inflammatory profiles in HPA. MCP-1 increased significantly in HPA while remaining stable in HPP (pb, pc < 0.001). PAI-1, a thrombosis risk factor, decreased in HPA but increased in HPP (pb = 0.005). IL-6 and IL-1β showed higher levels in HPP at 6 months (pb = 0.048), while IL-10, an anti-inflammatory cytokine, increased significantly in HPP (pb < 0.001). VEGF-A, involved in angiogenesis, rose slightly in both groups with significant within-group change only (pc = 0.034). These results highlight that while both diets influenced metabolic biomarkers, the HPA diet showed more consistent anti-inflammatory and metabolic improvements.

Table 5: Dietary characteristics according to diet group.

Daily Intake	Animal HP Diet (HPA)				Plant HP Diet (HPP)			pc	pb
	Baseline (N=59)	3 months (N=55)	6 months (N=52)		Baseline (N=58)	3 months (N=55)	6 months (N=50)
Energy (kcal)	2010 (1665–2257)	1460 (1349–1634)	1443 (1303–1650)	<0.001	1994 (1673–2222)	1611 (1412–1765)	1659 (1502–1916)	<0.001	0.065
Protein (% TCV)	19.1 (17.7–21.7)	24.6 (22.4–28.3)	23.8 (21.3–26.9)	<0.001	17.9 (16.5–20.2)	22.0 (19.9–24.4)	21.6 (19.1–23.8)	<0.001	0.674
Animal protein (% total)	71.8 (66.7–78.3)	77.6 (71.4–82.5)	77.2 (72.1–82.6)	0.002	70.2 (62.8–75.4)	37.1 (29.7–46.4)	39.7 (31.8–52.1)	<0.001	<0.001
Vegetal protein (% total)	26.4 (21.5–32.7)	20.8 (17.3–30.0)	22.8 (17.0–27.3)	0.002	29.8 (24.4–35.0)	55.2 (46.0–66.2)	57.1 (46.6–63.0)	<0.001	<0.001
Fat (% TCV)	46.7 (43.0–52.2)	43.4 (39.9–47.4)	44.9 (40.8–49.9)	0.016	45.6 (42.8–49.4)	36.9 (34.0–41.2)	38.9 (33.4–43.0)	<0.001	0.009
SFA (% TCV)	13.0 (10.7–14.4)	10.1 (8.69–11.5)	10.3 (9.08–11.6)	<0.001	12.6 (11.3–14.3)	6.58 (5.83–8.10)	7.19 (5.92–9.58)	<0.001	0.006
MUFA (% TCV)	23.5 (20.3–25.6)	22.5 (20.0–24.1)	23.6 (21.3–25.4)	0.744	22.0 (20.5–24.0)	16.4 (15.3–19.1)	18.3 (14.7–20.4)	<0.001	<0.001
PUFA (% TCV)	6.10 (5.15–7.04)	6.22 (4.43–7.28)	6.10 (5.33–6.99)	0.274	5.73 (5.09–7.35)	6.62 (5.90–7.98)	6.56 (5.20–7.65)	0.223	0.253
Dietary cholesterol (mg)	380 (258–420)	313 (250–387)	327 (287–415)	0.529	353 (264–498)	191 (129–304)	179 (128–296)	<0.001	<0.001
Carbohydrates (% TCV)	31.4 (25.4–35.5)	30.4 (24.8–34.2)	30.3 (25.5–34.7)	0.441	34.2 (31.2–36.8)	37.8 (33.3–42.4)	38.9 (34.3–45.3)	0.002	<0.001
Fibre (g)	19.2 (13.6–25.3)	19.9 (14.3–32.4)	17.9 (13.0–20.4)	0.012	20.6 (15.7–24.5)	32.2 (25.3–35.9)	32.1 (26.1–37.8)	<0.001	<0.001
Sodium (mg)	2700 (1817–3382)	1991 (1493–2497)	2331 (1435–2714)	<0.001	2391 (1640–2967)	1803 (1151–2560)	1960 (1489–2513)	0.551	0.369
Potassium (mg)	3225 (2645–3873)	3442 (3002–3999)	3272 (2931–3796)	0.307	3141 (2643–3804)	3289 (2948–3698)	3490 (3085–4001)	0.109	0.737

Table 5 compares dietary intake changes in participants on two high-protein diets, Animal High-Protein (HPA) and Plant High-Protein (HPP), at baseline, 3 months, and 6 months. Significant within-group and between-group changes are observed across several key nutrients. Energy intake significantly decreased in both groups over time (pc 0.001). HPA participants reduced calories more substantially, though between-group differences were not statistically significant (pb = 0.065). Protein (% total caloric value, TCV) increased significantly in both groups over time, reflecting compliance with the high-protein intervention. The proportion of animal protein increased in the HPA group (71.8% to 77.2%) but dropped dramatically in the HPP group (70.2% to 39.7%), while vegetal protein showed the opposite trend. These shifts were statistically significant within and between groups (p< 0.001), confirming distinct protein sourcing. Fat intake (% TCV) decreased significantly in both groups, especially in HPP (pb = 0.009). Saturated fatty acid (SFA) intake declined considerably across groups, with HPP showing a sharper reduction (pb = 0.006). Monounsaturated fats (MUFA) remained stable in HPA but declined in HPP (pb < 0.001), while polyunsaturated fats (PUFA) remained stable in both. Dietary cholesterol decreased significantly in HPP but not in HPA, highlighting the impact of plant-based diets on cholesterol intake (pb < 0.001). Carbohydrate intake increased significantly only in HPP, with a notable between-group difference (pb < 0.001), reflecting a more plant-forward nutrient profile. Fibre intake increased substantially in HPP and only modestly in HPA, favouring HPP (pb < 0.001). Sodium levels declined in both groups but significantly only in HPA. Potassium increased slightly in both groups, with no significant differences. These findings demonstrate that while both diets improved nutrient profiles, the plant-based diet (HPP) led to more favorable reductions in saturated fat and cholesterol and increases in fiber and vegetal protein, aligning with public health dietary recommendations.

Discussion

The randomized controlled trial (RCT) that evaluated the effect of plant-based protein and dietary fiber intake on insulin resistance, weight maintenance, and glycemic control amongst obese people with type 2 diabetes (T2D) shows several significant findings that may guide dietary intervention for T2D management. The results show that both high protein diets with plant and animal origin also lead to similar body weight, fat distribution, and blood pressure improvements. However, a more significant focus on the biochemical implication of plant-based proteins and the anti-inflammatory properties and benefits associated with sterilization of systemic inflammation is a new dimension of shedding light on how such diets can help manage T2D [32, 33]. The study’s baseline characteristics revealed no difference between Animal High-Protein (HPA) and Plant High-Protein (HPP) groups regarding demographic, clinical conditions, and biochemical variables. This made it possible to compare these types of dietary protein’s effects on health outcomes. One important finding was that both groups had significant BMI, body weight, and abdominal fat decreases, meaning markers of obesity and metabolic health [34, 35]. Remarkably, the reduction in the visceral fat associated with insulin resistance and cardiovascular risk was detected in both groups. Thus, both dietary interventions were effective in ending damaging fat deposits.
The intense loss of weight observed in both groups indicates that an increment in protein intake, as long as the source is not protein, remains a feasible way of managing weight in T2D. This discovery concurs with past studies, which report that high-protein diets cause satiety, hence possible calorific intake. Furthermore, the retained lean muscle mass across the two diet groups shows that a higher protein intake helps avoid muscle loss as part of weight loss, a vital aspect of restoring metabolic health and enhancing insulin sensitivity [36, 37]. This is in T2D management, where the muscle mass is inversely related to insulin resistance. In the glycemic control, HbA1c, insulin levels, and HOMA-IR values were minimized in both diet groups. Still, the differences between the two groups were not statistically significant. The improvements in insulin sensitivity and glucose metabolism may be due to reduced body fat, improved distribution of fat and potential benefits of dietary fibres in both diet groups [38]. Fibre, especially soluble fibre, is essential in slowing glucose absorption and reducing postprandial blood sugar spikes [39, 40, 41]. Although this study was not aimed at just studying fibre intake, its effects on glycemic control affected the overall benefits of insulin sensitivity.
The alterations in the blood pressure also could not be disregarded. Both groups presented decreases in systolic and diastolic blood pressure, with the HPA group slightly better at reducing diastolic blood pressure [42, 43, 44]. The relationship between protein intake and blood pressure is not straightforward, but it is postulated that increased protein intake, especially from the plant source, may positively affect blood pressure regulation [45, 46]. The results of the present study align with the hypothesis that both plant- and animal-based proteins can help improve cardiovascular health through mechanisms associated with weight reduction and the reduction of metabolic stress. Although the results were the same for both food groups, plant-based proteins in the diet had some clear benefits, including lower environmental impact and additional micronutrients [47, 48, 49]. Plant extract proteins generally include a higher level of fibre, antioxidants, and other bioactive substances that potentially induce systemic anti-inflammatory effects and thus enhance metabolic health [50]. This aspect agrees with developing studies suggesting possible long-term benefits of plant-based nutritional practice to minimize the risk of chronic conditions, particularly those with T2D [51]. Despite the trial showing that both dietary intervention trials were effective at improving clinical outcomes, no specific mechanism emerged on how plant-based proteins would influence insulin sensitivity and inflammation [52]. Further studies should be based on the molecular pathways underlying plant protein metabolism and how they help enhance insulin resistance and weight reduction [53]. Also, the relatively short period of the study (6 months) does not allow for evaluating the long-term longevity of these dietary interventions in sustaining T2D intervention and warding off complications.

Conclusion

In conclusion, this randomized-controlled trial shows the possibility of combining plant and animal-based high-protein diets to treat obesity and type 2 Diabetes. According to the results, both dietary techniques show improved weight management, fat distribution, blood pressure, and insulin sensitivity. The results indicate that enhancing dietary protein, regardless of source, can help alleviate metabolic health in people with T2D. Nevertheless, despite the same effects of the two diets, the plant-based proteins will provide extra benefits in anti-inflammatory performance and micronutrient content, which further support long-term effects on T2D management. This study’s findings significantly contribute to discussing the optimally practical dietary approach to deal with T2D. Though the plant and animal-based proteins have proved efficient, further studies are required to investigate how plant-based proteins affect metabolic processes, especially regarding insulin resistance and inflammation. Moreover, long-term studies are needed to examine the opportunities for sustaining and enhancing the effectiveness of these dietary interventions over a more extended period. Overall, the results indicate that plant proteins and dietary fibre should be included in dietary guidelines recommended for enhancing metabolic control in Type 2 Diabetes patients.

Limitations

This review successfully analyses all available data but requires recognition of several necessary restrictions. The review incorporates strong research methods from the included studies yet possesses a deficit of sustained, high-quality outcomes from RCTS using plant-based protein and fiber for T2D and obesity. Current research studies used short-duration dietary approaches and non-strict plant-based feeding programs, which prevent the exclusive analysis of plant-based protein and fibre effects. Most research analyzed plant-based eating patterns without identifying how plant-based protein and dietary fibre affect the results. The research design impedes researchers from directly assigning detected advantages to those particular nutritional components. Findings about plant-based protein and fiber effects are limited by the wide variety of available dietary plant sources, including soybeans, legumes, and grains, together with vegetables and fruits. Plant-based foods have various effects because their nutritional content, amino acid, and fibre components differ between plant sources. The study outcomes for insulin resistance, weight management, and glycemic control can be affected by differences in dietary fibre quality and quantity reported by research participants.

Future Perspectives

Advancing research demands thorough laboratory quality assessments of plant-based protein with fiber combinations, which measure their extended impact on metabolic health for patients with T2D and obesity. The research should consist of long-term controlled RCTs which use plant-based proteins and fibers as single interventions to achieve distinct effect conclusions. Researchers should study how plant-based protein and fiber affect metabolic health alongside gut microbiota because this microorganism is crucial in controlling metabolic regulation. When studied within food matrixes, research needs to analyze the possible joint benefits of integrating plant-based protein with fiber sources to determine their combined effects on insulin resistance, fat storage, and systemic inflammation. Determining the molecular interactions of these dietary components will help create better nutritional approaches to manage T2D and obesity. Research into personalized nutrition strategies that measure genetic makeup conditions, microbiological situations, and lifestyle environment patterns can enhance the therapeutic value of plant-based diets for patients with T2D. The advancement of plant protein research and dietary fiber studies for managing T2D and obesity continues, but researchers need to conduct additional investigations to obtain optimal metabolic outcomes. Research focusing on these gaps will advance the development of exact nutritional strategies that provide personalized solutions for obese people with T2D.

References

1. Hernández-Flandes, R., et al., Compare the Decrease in Visceral Adipose Tissue in People with Obesity and Prediabetes vs Obesity and Type 2 Diabetes Treated with Liraglutide. 2025.
2. Zatterale, F., et al., Chronic adipose tissue inflammation linking obesity to insulin resistance and type 2 diabetes. Frontiers in physiology, 2020. 10: p. 1607.
3. Petroni, M.L., et al., Nutrition in patients with type 2 diabetes: present knowledge and remaining challenges. Nutrients, 2021. 13(8): p. 2748.
4. Wang, X., et al., Interaction between non-alcoholic fatty liver disease and obesity on the risk of developing cardiovascular diseases. Scientific Reports, 2024. 14(1): p. 24024.
5. Pavlidou, E., et al., Diabesity and dietary interventions: Evaluating the impact of mediterranean and other diets on obesity and type 2 diabetes management. Nutrients, 2023. 16(1): p. 34.
6. Barnard, N.D., et al., The effects of a low-fat, plant-based dietary intervention on body weight, metabolism, and insulin sensitivity. The American journal of medicine, 2005. 118(9): p. 991-997.
7. McMacken, M. and S. Shah, A plant-based diet for the prevention and treatment of type 2 diabetes. Journal of geriatric cardiology: JGC, 2017. 14(5): p. 342.
8. Austin, G., J.J. Ferguson, and M.L. Garg, Effects of plant-based diets on weight status in type 2 diabetes: a systematic review and meta-analysis of randomized controlled trials. Nutrients, 2021. 13(11): p. 4099.
9. Hever, J. and R.J. Cronise, Plant-based nutrition for healthcare professionals: implementing diet as a primary modality in the prevention and treatment of chronic disease. Journal of geriatric cardiology: JGC, 2017. 14(5): p. 355.
10. De la Iglesia, R., et al., Dietary strategies implicated in the prevention and treatment of metabolic syndrome. International journal of molecular sciences, 2016. 17(11): p. 1877.
11. Kahleova, H., et al., Effects of a low-fat vegan diet on gut microbiota in overweight individuals and relationships with body weight, body composition, and insulin sensitivity. A randomized clinical trial. Nutrients, 2020. 12(10): p. 2917.
12. El Ghoch, M., et al., OPEN ACCESS EDITED BY. Healthy Foods and Dietary Patterns in Modern Consumer, 2023: p. 8.
13. Harland, J. and L. Garton, An update of the evidence relating to plant‐based diets and cardiovascular disease, type 2 diabetes and overweight. Nutrition Bulletin, 2016. 41(4): p. 323-338.
14. Toumpanakis, A., T. Turnbull, and I. Alba-Barba, Effectiveness of plant-based diets in promoting well-being in the management of type 2 diabetes: a systematic review. BMJ open diabetes research & care, 2018. 6(1).
15. Markova, M., et al., Isocaloric diets high in animal or plant protein reduce liver fat and inflammation in individuals with type 2 diabetes. Gastroenterology, 2017. 152(3): p. 571-585. e8.
16. Viguiliouk, E., et al., Effect of replacing animal protein with plant protein on glycemic control in diabetes: a systematic review and meta-analysis of randomized controlled trials. Nutrients, 2015. 7(12): p. 9804-9824.
17. Zhao, H., et al., Effects of plant protein and animal protein on lipid profile, body weight and body mass index on patients with hypercholesterolemia: a systematic review and meta-analysis. Acta Diabetologica, 2020. 57: p. 1169-1180.
18. Oliva, M.E., A. Chicco, and Y. Lombardo, Mechanisms underlying the beneficial effect of soy protein in improving the metabolic abnormalities in the liver and skeletal muscle of dyslipemic insulin resistant rats. European journal of nutrition, 2015. 54: p. 407-419.
19. van Nielen, M., et al., Partly replacing meat protein with soy protein alters insulin resistance and blood lipids in postmenopausal women with abdominal obesity. The Journal of nutrition, 2014. 144(9): p. 1423-1429.
20. Xie, P., et al., The regulatory effects and mechanisms of plant food-derived bioactive components on gut barrier function and intestinal homeostasis: a comprehensive review. Critical Reviews in Food Science and Nutrition, 2025: p. 1-24.
21. Giuntini, E.B., F.A.H. Sardá, and E.W. de Menezes, The effects of soluble dietary fibers on glycemic response: an overview and futures perspectives. Foods, 2022. 11(23): p. 3934.
22. Reynolds, A.N., A.P. Akerman, and J. Mann, Dietary fibre and whole grains in diabetes management: Systematic review and meta-analyses. PLoS medicine, 2020. 17(3): p. e1003053.
23. Vinik, A.I. and D.J. Jenkins, Dietary fiber in management of diabetes. Diabetes care, 1988. 11(2): p. 160-173.
24. Silva, F.M., et al., A high–glycemic index, low-fiber breakfast affects the postprandial plasma glucose, insulin, and ghrelin responses of patients with type 2 diabetes in a randomized clinical trial. The Journal of nutrition, 2015. 145(4): p. 736-741.
25. Portincasa, P., et al., Gut microbiota and short chain fatty acids: implications in glucose homeostasis. International journal of molecular sciences, 2022. 23(3): p. 1105.
26. Weickert, M.O. and A.F. Pfeiffer, Impact of dietary fiber consumption on insulin resistance and the prevention of type 2 diabetes. The Journal of nutrition, 2018. 148(1): p. 7-12.
27. Gutierrez-Mariscal FM, Alcalá-Diaz JF, Quintana-Navarro GM, de la Cruz-Ares S, Torres-Peña JD, Cardelo MP, et al. Changes in quantity plant-based protein intake on type 2 diabetes remission in coronary heart disease patients: from the CORDIOPREV study. European Journal of Nutrition [Internet]. 2023 Mar 4; Available from: https://pubmed.ncbi.nlm.nih.gov/36869909/
28. Meng H, Matthan NR, Ausman LM, Lichtenstein AH. Effect of macronutrients and fiber on postprandial glycemic responses and meal glycemic index and glycemic load value determinations. The American Journal of Clinical Nutrition [Internet]. 2017 Feb 15 [cited 2019 Nov 1];105(4):842–53. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5366046/
29. Rosner B, Glynn RJ, Lee MLT. The Wilcoxon signed rank test for paired comparisons of clustered data. Biometrics [Internet]. 2016;62(1):185–92. Available from: https://www.ncbi.nlm.nih.gov/pubmed/16542245
30. Ghasemi A, Zahediasl S. Normality Tests for Statistical Analysis: a Guide for Non-Statisticians. International Journal of Endocrinology and Metabolism. 2012 Apr 20;10(2):486–9.
31. Ziaee A, Esmailzadehha N, Oveisi S, Ghorbani A, Ghanei L. The threshold value of homeostasis model assessment for insulin resistance in Qazvin Metabolic Diseases Study (QMDS): assessment of metabolic syndrome. Journal of research in health sciences [Internet]. 2015;15(2):94–100. Available from: https://pubmed.ncbi.nlm.nih.gov/26175291/
32. Nahm FS. Nonparametric Statistical Tests for the Continuous data: the Basic Concept and the Practical Use. Korean Journal of Anesthesiology [Internet]. 2016;69(1):8–14. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4754273/
33.   Austin G, Ferguson JJA, Garg ML. Effects of Plant-Based Diets on Weight Status in Type 2 Diabetes: A Systematic Review and Meta-Analysis of Randomised Controlled Trials. Nutrients. 2021 Nov 16;13(11):4099.
34.   Batterham MJ, Tapsell LC, Charlton KE. Analyzing weight loss intervention studies with missing data: Which methods should be used? Nutrition. 2013 Jul;29(7-8):1024–9.
35.   Tabriz ER, Sadeghi M, Tavana E, Miri HH, Nabavi FH. Approaches for boosting self-confidence of clinical nursing students: A systematic review and meta-analysis. Heliyon [Internet]. 2024 Mar 1;10(6):e27347–7. Available from: https://www.sciencedirect.com/science/article/pii/S2405844024033784
36. Reynolds A, Mitri J. Nutritional Recommendations for Individuals with Diabetes [Internet]. Nih.gov. MDText.com, Inc.; 2024. Available from: https://www.ncbi.nlm.nih.gov/books/NBK279012/
37. Wang ML, Gellar L, Nathanson BH, Pbert L, Ma Y, Ockene I, et al. Decrease in Glycemic Index Associated with Improved Glycemic Control among Latinos with Type 2 Diabetes. Journal of the Academy of Nutrition and Dietetics. 2015 Jun;115(6):898–906.
38.   van Trijp MPH, Schutte S, Esser D, Wopereis S, Hoevenaars FPM, Hooiveld GJEJ, et al. Minor Changes in the Composition and Function of the Gut Microbiota During a 12-Week Whole Grain Wheat or Refined Wheat Intervention Correlate with Liver Fat in Overweight and Obese Adults. The Journal of Nutrition. 2020 Nov 13;151(3):491–502.
39. Ondrak KS, McMurray RG, Battaglini CL, Evenson KR, Harrell JS. The Relationship between Changes in Weight Status and Insulin Resistance in Youth. International Journal of Pediatric Endocrinology [Internet]. 2019 Jan 1 [cited 2025 May 1];2009:1–7. Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC2775697/
40. Clem J, Barthel B. A Look at Plant-Based Diets. Missouri Medicine [Internet]. 2021 May;118(3):233. Available from: https://pmc.ncbi.nlm.nih.gov/articles/PMC8210981/
41. Łuszczki E, Boakye F, Zielińska M, Dereń K, Bartosiewicz A, Oleksy Ł, et al. Vegan diet: nutritional components, implementation, and effects on adults’ health. Frontiers in Nutrition [Internet]. 2023 Nov 9;10(10). Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10665534/
42. Weickert MO, Pfeiffer AF. Impact of Dietary Fiber Consumption on Insulin Resistance and the Prevention of Type 2 Diabetes. The Journal of Nutrition. 2018 Jan 1;148(1):7–12.
43. Zhao M, Chu J, Feng S, Guo C, Xue B, He K, et al. Immunological mechanisms of inflammatory diseases caused by gut microbiota dysbiosis: A review. Biomedicine & Pharmacotherapy [Internet]. 2023 Aug 1;164(1):114985. Available from: https://www.sciencedirect.com/science/article/pii/S0753332223007758
44. Cihad Dundar, Terzi O, Arslan HN. Comparison of the ability of HOMA-IR, VAI, and TyG indexes to predict metabolic syndrome in children with obesity: a cross-sectional study. BMC Pediatrics. 2023 Feb 11;23(1).
45. Hughes RL, Kable ME, Marco M, Keim NL. The Role of the Gut Microbiome in Predicting Response to Diet and Developing Precision Nutrition Models. Part II: Results. Advances in Nutrition. 2019 Jun 21;10(6):979–98.
46. Singar S, Nagpal R, Arjmandi BH, Akhavan NS. Personalized Nutrition: Tailoring Dietary Recommendations through Genetic Insights. Nutrients [Internet]. 2024 Aug 13;16(16):2673–3. Available from: https://www.mdpi.com/2072-6643/16/16/2673
47. Jardine MA, Kahleova H, Levin SM, Ali Z, Trapp CB, Barnard ND. Perspective: Plant-Based Eating Pattern for Type 2 Diabetes Prevention and Treatment: Efficacy, Mechanisms, and Practical Considerations. Advances in Nutrition. 2021 Jun 10;12(6).
48. Nahm FS. Nonparametric Statistical Tests for the Continuous data: the Basic Concept and the Practical Use. Korean Journal of Anesthesiology [Internet]. 2016;69(1):8–14. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4754273/
49. Dutton GR, Lewis CE. The Look AHEAD Trial: Implications for Lifestyle Intervention in Type 2 Diabetes Mellitus. Progress in Cardiovascular Diseases. 2015 Jul;58(1):69–75.
50. Mihailović M, Dinić S, Arambašić Jovanović J, Uskoković A, Grdović N, Vidaković M. The influence of plant extracts and phytoconstituents on antioxidant enzymes activity and gene expression in the prevention and treatment of impaired glucose homeostasis and diabetes complications. Antioxidants. 2021 Mar 18;10(3):480.
51. Zhu R, Fogelholm M, Poppitt SD, Silvestre MP, Møller G, Huttunen-Lenz M, Stratton G, Sundvall J, Råman L, Jalo E, Taylor MA. Adherence to a plant-based diet and consumption of specific plant foods—Associations with 3-year weight-loss maintenance and cardiometabolic risk factors: A secondary analysis of the preview intervention study. Nutrients. 2021 Nov 1;13(11):3916.
52. Stoodley IL, Williams LM, Wood LG. Effects of plant-based protein interventions, with and without an exercise component, on body composition, strength and physical function in older adults: a systematic review and meta-analysis of randomized controlled trials. Nutrients. 2023 Sep 19;15(18):4060.
53. Mahmood, C.K. The Moderating Role of Content Creation Cost on the Relationship Between Generative Adversarial Network (GAN) Marketing Success in Content Marketing: Empirical Evidence from the Automotive Industry of the UK. Adv. J. Busi. Manag. Soci. Sci. 2025 Feb 7: 1(1)1-12. ISSN 2998-7946.