Next Article in Journal
Polyphenols as a Diet Therapy Concept for Endometriosis—Current Opinion and Future Perspectives
Next Article in Special Issue
Role of High Energy Breakfast “Big Breakfast Diet” in Clock Gene Regulation of Postprandial Hyperglycemia and Weight Loss in Type 2 Diabetes
Previous Article in Journal
Palmitoylethanolamide and Its Biobehavioral Correlates in Autism Spectrum Disorder: A Systematic Review of Human and Animal Evidence
 
 
Comment published on 21 October 2021, see Nutrients 2021, 13(11), 3694.
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Effects of Low-Carbohydrate versus Mediterranean Diets on Weight Loss, Glucose Metabolism, Insulin Kinetics and β-Cell Function in Morbidly Obese Individuals

1
Department of Surgical, Medical and Molecular Pathology and Critical Care Medicine, University of Pisa, 56126 Pisa, Italy
2
Bariatric Surgery Division, University Hospital of Pisa, 56124 Pisa, Italy
3
Department of Clinical and Experimental Medicine, University of Pisa, 56126 Pisa, Italy
4
Fondazione Toscana Gabriele Monasterio, 56126 Pisa, Italy
5
Institute of Clinical Physiology, National Research Council, 56124 Pisa, Italy
6
Institute of Neuroscience, National Research Council, 35127 Padua, Italy
*
Author to whom correspondence should be addressed.
These authors contributed equally to the study.
Nutrients 2021, 13(4), 1345; https://doi.org/10.3390/nu13041345
Received: 30 March 2021 / Revised: 6 April 2021 / Accepted: 16 April 2021 / Published: 18 April 2021
(This article belongs to the Special Issue Nutrients and Cardiometabolic Health in Type 2 Diabetes)

Abstract

:
Low-calorie Mediterranean-style or low-carbohydrate dietary regimens are widely used nutritional strategies against obesity and associated metabolic diseases, including type 2 diabetes. The aim of this study was to compare the effectiveness of a balanced Mediterranean diet with a low-carbohydrate diet on weight loss and glucose homeostasis in morbidly obese individuals at high risk to develop diabetes. Insulin secretion, insulin clearance, and different β-cell function components were estimated by modeling plasma glucose, insulin and C-peptide profiles during 75-g oral glucose tolerance tests (OGTTs) performed at baseline and after 4 weeks of each dietary intervention. The average weight loss was 5%, being 58% greater in the low-carbohydrate-group than Mediterranean-group. Fasting plasma glucose and glucose tolerance were not affected by the diets. The two dietary regimens proved similarly effective in improving insulin resistance and fasting hyperinsulinemia, while enhancing endogenous insulin clearance and β-cell glucose sensitivity. In summary, we demonstrated that a low-carbohydrate diet is a successful short-term approach for weight loss in morbidly obese patients and a feasible alternative to the Mediterranean diet for its glucometabolic benefits, including improvements in insulin resistance, insulin clearance and β-cell function. Further studies are needed to compare the long-term efficacy and safety of the two diets.

1. Introduction

The obesity pandemic and the dramatic increase in obesity-related metabolic diseases, including type 2 diabetes mellitus [1], underscore the urgent need to compare the effectiveness of widely used nutritional strategies on weight loss and glucose metabolism.
Low-calorie Mediterranean-style (Med) diets with balanced macronutrient composition are recommended by current guidelines for weight loss [2,3] and for their additional cardiometabolic benefits [4,5,6,7,8,9,10,11]. The traditional Med diet is characterized by high proportion of vegetables, legumes, fruits, grains, nuts, and olive oil, moderate consumption of fish and red wine, and reduced intake of whole-fat dairy products, red meat, and processed foods [12,13]. Prospective studies have shown a reduced risk of developing diabetes [10,14] and a lower need for glucose-lowering medications for diabetes management [11] in people following a Med diet. In the Dietary Intervention Randomized Controlled Trial (DIRECT) [6], a Med-style diet induced a more sustained weight loss compared with a low-fat diet with similar calorie restriction. Furthermore, in a subgroup of participants with type 2 diabetes, the Med diet reduced fasting plasma glucose, insulin and markers of insulin resistance at 24 months to a greater extent compared with the low-fat diet.
A low-carbohydrate (LC), high-protein diet has been proposed as a feasible alternative to Med diets, particularly in the short term, for its greater ability to reduce body weight [6,15,16,17,18] and inhibit weight regain [19], to maintain lean body mass [20,21], to induce satiety and suppress hunger [22], to reduce liver fat content [23], and to increase thermogenesis [20,24,25]. However, studies comparing the efficacy of LC diets with standard eating patterns on weight loss and glucose metabolism yielded conflicting results [18,21,25]. During the first 6 months of the DIRECT study [6], subjects on a LC diet without calorie restriction achieved a 40–50% greater weight loss compared with the calorie-restricted Med and low-fat diets. However, they tend to regain weight at 12 months during the weight maintenance phase, reaching a plateau towards the mean weight change observed with the Med diet. Moreover, 24-month changes in plasma insulin and insulin resistance were numerically greater in non-diabetic subjects following the LC diet, but smaller in patients with type 2 diabetes, compared with the Med diet.
Along with the improvement in insulin resistance associated with weight-loss, an amelioration of β-cell function may contribute to the glucometabolic benefits of the diet. In fact, both the Med diet [26] and LC diet [25] produced a greater increase in surrogate markers of β-cell function, compared with the respective control diets, in insulin-resistant subjects without diabetes. Previous studies on β-cell function however are limited by the poor characterization of insulin kinetics, which requires the implementation of C-peptide deconvolution methods to estimate insulin secretion and to dissect the separate contribution of insulin clearance on the resulting plasma insulin levels.
The aim of this study was to compare the effectiveness of a calorie-restricted LC diet with a Med diet on weight loss and on the main determinant of glucose homeostasis in morbidly obese, insulin-resistant individuals at high risk to develop diabetes. To overcome the limitation of previous studies, insulin secretion, insulin clearance, and the different components of β-cell function were estimated by modeling plasma glucose, insulin and C-peptide profiles during 75-g oral glucose tolerance tests (OGTT) performed at the beginning and at the end of each dietary intervention.

2. Materials and Methods

2.1. Study Participants

Thirty-six morbidly obese patients on the waiting list for bariatric surgery were recruited from the outpatients’ clinic for metabolic diseases and bariatric surgery of the University Hospital of Pisa in 2018–2019. Main inclusion criteria were a BMI equal or higher than 35 kg/m2 (obesity grade II–III), weight stable (±1%) for at least 6 months prior to the study, age between 25 and 60 years, both women and men. We excluded patients with known diabetes, chronic kidney disease, heart failure, liver failure or NASH, chronic diarrhea (including inflammatory bowel disease), endocrine diseases including secondary causes of obesity, or taking medications known to affect glucose metabolism or body weight. All participants had a detailed medical history and a complete physical examination. The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Institutional Ethics Committee. Informed written consent was obtained from all subjects before enrollment.

2.2. Study Protocol

In this parallel-arm, open, randomized clinical trial, patients were randomly assigned to two types of low-calorie diets designed by a qualified dietician for 4 weeks: a LC diet and a Med diet (Figure 1). All participants underwent two sessions of behavioral dietary counseling. At randomization, the session focused on nutrition education and dietary errors to avoid, with specific recommendations for each type of diet. The second visit was performed after 2 weeks to verify dietary adherence and reinforce nutritional recommendations, reducing the prescribed daily calorie intake in case of inadequate weight loss. At the beginning and at the end of each dietary intervention, subjects were admitted to our Clinical Research Unit for metabolic assessments.

2.3. Dietary Interventions

The LC diet was primarily made of beef, veal, cold cuts (carpaccio, cured ham), eggs and seasoned cheese (e.g., parmesan), vegetables and fruits. The energy distribution of macronutrients was 30% carbohydrate, 30% protein and 40% fat. The Med diet was rich in whole grains (pasta, bread, whole wheat), eggs, poultry, fish, vegetables, legumes, fruits and olive oil as the main condiment, and low in red and processed meat, according to the Mediterranean-style diet pyramid [12]. The energy distribution of macronutrients was 55% carbohydrate, 15% protein and 30% lipids. A detailed dietary plan containing food to be favored or avoided was provided to each participant, including a table of possible substitutions with variable equicaloric amounts of foods. The proposed food substitutions were designed to keep the daily macronutrient intake within the desired macronutrient ratio. The daily calorie intake was tailored on each patient by calculating a 50% energy deficit from the Resting Energy Expenditure (REE) derived from the indirect calorimetry.

2.4. OGTT

At baseline and after 4 weeks of diet, participants were admitted to our Clinical Research Unit at 08:00 a.m. after an overnight fast (10–12 h) to undergo a 75 g OGTT. A 20-gauge polyethylene cannula was inserted into a wrist vein for blood sampling, and the forearm was kept wrapped into a heated blanket to achieve arterialization of venous blood. After two baseline blood samples were drawn, participants consumed an oral glucose drink consisting of 150 mL of 50% dextrose solution (w/v) within 5 min. Timed arterialized blood samples were collected at times −15, 0, 15, 30, 45, 60, 90, 120, 150 and 180 min during the test to measure plasma glucose, insulin, C-peptide, GLP-1, and GIP. Plasma glucose was measured immediately by the glucose-oxidase technique (Beckman Glucose Analyzer II, Fullerton, CA, USA). Blood samples were centrifuged for 15 min (3000× g at 4 °C) and frozen at −20 °C before analysis. Insulin and C-peptide measurements were performed by electrochemiluminescence on a COBAS e411 instrument (Roche, Indianapolis, IN, USA). Plasma GLP-1 and GIP were assessed by multiplex immunoassays (Milliplex® Map kit, Merck KGaA, Darmstadt, Germany).

2.5. Body Composition

Body composition was determined by conventional bioimpedance analysis (BIA) with a single frequency (0.4 mA, 50 KHz) electrical impedance plethysmograph (EFG-Akern, Firenze, Italy) according to the standard tetrapolar technique, in patients lying in supine position. Two electrodes were placed on the dorsal surface of the right wrist and hand, and two on the dorsal surface of the right foot. Patients were evaluated after an overnight fasting, after emptying the bladder. Body composition was calculated from the values of resistance and reactance combined with anthropometric data by applying the software provided by the manufacturer, which incorporated validated predictive equations for total body water, fat mass and fat-free mass. Waist circumference was measured at the narrowest circumference between the lower rib margin and anterior superior iliac crest. Hip circumference was measured around the widest portion of the buttocks, and the waist/hip ratio was calculated.

2.6. Indirect Calorimetry

Resting Energy Expenditure (REE) was measured using indirect calorimetry (Vmax Series 29, SensorMedics, Yorba Linda, CA, USA). The parameters used for its calculation were the oxygen consumed and the carbon dioxide produced, according with the modified Weir formula [27]. Before measurement, the equipment was calibrated, controlling the environmental temperature (22–23 °C), to allow thermal homeostasis. The test was performed between 8:00 and 9:00 a.m. after an overnight fast. During calorimetry, patients were awake and in the supine position. The calorimetry lasted for at least 20 min after achieving steady state.

2.7. Mathematical Modelling and Calculations

Insulin sensitivity was estimated by the homeostasis model assessment (HOMA) index [28] and the oral glucose insulin sensitivity (OGIS) index [29].
Insulin secretion rate (ISR) was estimated via C-peptide deconvolution using the Van Cauter’s model of C-peptide kinetics [30].
Endogenous insulin clearance, which largely reflects hepatic insulin clearance, was calculated as the ratio between fasting ISR and plasma insulin levels (ISRfast/Ifast) and as the ratio of their areas under the curve (AUC) calculated by the trapezoidal rule over the duration of the OGTT (ISRAUC/IAUC) [31,32].
The different parameters of β-cell function were calculated by modelling insulin secretion and glucose concentration, as previously reported in detail [33,34]. Briefly, this model describes insulin secretion as the sum of two components. The first represents the dependence of insulin secretion on absolute glucose concentration. The quasi-linear dose–response function relating the two variables is described by a slope and an intercept, named β-cell glucose sensitivity and ISR at 5 mmol/L glucose ([email protected]), respectively, and is modulated by a time-dependent factor termed potentiation. The second component represents the dependence of ISR on the rate of change of glucose concentration and is named β-cell rate sensitivity.

2.8. Statistical Analysis

Continuous variables are presented as means ± standard deviations (SD) and nominal variables are reported as counts and/or percentages. Variables with a skewed distribution are presented as median [interquartile range]. Baseline differences between groups were tested by Mann Whitney test or Fisher’s exact test, as appropriate. Repeated measures were analyzed by multivariate analysis of variance (MANOVA). The p-values for differences between groups (diet), between time-points (time), and of the effect of the interaction between diet and time are reported. Statistical tests were performed using JMP Pro 14.3.0 (SAS Institute Inc., Cary, NC, USA) using a two-sided α level of 0.05.

3. Results

3.1. Study Participants

Thirty-six morbidly obese patients attended for a screening visit, all of whom were randomized to the LC diet or Med diet (Figure 1). One participant withdrew before beginning the intervention and 3 were excluded for the presence of unknown diabetes at the baseline OGTT, diagnosed according to the current guidelines [35]. Thirty-two (88.9%) participants completed the study and were included in the analysis, of whom 17 (53.1%) in the LC-group and 15 (46.9%) in the Med-group. Baseline clinical and metabolic characteristics were similar between the two groups (Table 1).

3.2. Body Weight and Composition

Overall, a 4.8% weight loss was achieved (range 2.0% to 7.7%), with a BMI reduction of 2.36 kg/m2 (range 0.87 kg/m2 to 4.01 kg/m2). The average weight loss was 58% greater in the LC-group compared with the Med-group (5.7 ± 1.8% and 3.6 ± 1.6%, respectively; p = 0.001), with similar changes in waist circumference, fat mass, and basal metabolic rate (Table 2).

3.3. Glucose Tolerance and Insulin Sensitivity

Fasting plasma glucose and glucose tolerance were not affected by the diet (Table 3). Fasting plasma insulin was similarly reduced by the two diets while mean insulin levels during the OGTT were unchanged. Consistently, there was a significant improvement in insulin resistance under fasting conditions, as measured by the HOMA-IR, without significant improvements during the OGTT, as measured by the OGIS index.

3.4. B-Cell Function and Insulin Clearance

Baseline and glucose-stimulated insulin secretion estimated by C-peptide deconvolution were similarly increased by the dietary interventions (Figure 2 and Table 3). Among model-derived parameters of β-cell function, the β-cell glucose sensitivity showed a similar improvement after the two diets, whereas incretin hormones were reduced. Similar or even lower peripheral insulin levels despite enhanced insulin secretion were explained by a greater fasting and total insulin clearance, whose increase tended to be greater after the LC diet compared with the Med diet (fasting: +16.1 [137.6] % and +8.5 [37.7] %, respectively, p = 0.06; total: +21.4 [81] % and −3.0 [33.1] %, respectively, p = 0.06).

4. Discussion

In this study involving morbidly obese, insulin-resistant individuals at high risk to develop type 2 diabetes, we demonstrated that a LC/high-protein diet is a feasible alternative to a Med diet with balanced macronutrient composition for weight loss and glucose management. The subjects on LC diet exhibited greater absolute and percentage body weight reduction than the Med diet, despite same daily calorie restriction. The two dietary regimens proved similarly effective in improving insulin resistance and fasting hyperinsulinemia, which are key pathogenetic mechanisms of diabetes progression [36,37,38], while enhancing β-cell function and endogenous insulin clearance.
The subjects on LC diet achieved a ~60% greater weight loss than the Med diet, to an extent (>5%) that has proven effective in decreasing diabetes incidence and improving cardiovascular risk factors in obese patients [2]. Compared with standard dietary regimens, a LC/high-protein diet has generally demonstrated greater effectiveness in reducing body weight in the short term [6,15,16,17,18], which was attributed to the greater ability of protein to induce satiety and suppress hunger [22] and to increase thermogenesis [20,24,25] than carbohydrate and fat. In fact, in morbidly obese patients eligible to bariatric surgery, a LC diet is recommended as a feasible approach to obtain a 5–10% weight loss in the immediate preoperative period, which facilitates surgery and reduces the risk of complications [39]. The higher efficacy of LC diets on weight loss and maintenance was not consistently reported in longer-term studies conducted over a 6- to 24-month period [18,21,25]. Therefore, combined nutritional approaches have been proposed implementing LC diets to achieve a rapid weight loss followed by Mediterranean-style dietary regimens for long-term weight maintenance [40].
Plasma glucose levels at fasting and in response to the oral glucose ingestion were not substantially affected by the two diets. There was however a significant improvement in insulin sensitivity in fasting conditions, likely related to the weight loss, which translates into the lower need for insulin to obtain the same glucose levels after the dietary intervention. These findings are mostly in agreement with previous studies testing similar dietary approaches. In the DIRECT study [6], a calorie-restricted Mediterranean-style diet reduced plasma insulin and markers of insulin resistance at 24 months, without improvements in fasting glucose and glycated hemoglobin except in participants with type 2 diabetes. In insulin resistant women, a 6-month LC/ high-protein diet without calorie restriction yielded a greater reduction in body weight compared with the standard dietary prescription, which again occurred without significant improvements in glucose levels [18]. Furthermore, a randomized clinical trial in overweight and obese individuals comparing four low-calorie diets with different macronutrient intake observed a larger decrease in fasting insulin levels with high-protein diets (protein 25%) than average-protein diets (protein 15%), despite similar weight reduction and no changes in fasting glucose at 24 months [41]. Other studies support the glucose-lowering effects of both diets. In the Prevención con Dieta Mediterránea (PREDIMED) study, a Mediterranean diet enriched with olive oil or nuts was associated with a 52% reduction in the incidence of diabetes over a 4-year follow-up period in participants without diabetes at baseline [10], and with lower need for glucose-lowering medications in those with diabetes [11]. A 6-month LC/high-protein diet produced a greater increase in glucose tolerance and insulin sensitivity compared with a standard diet in obese people with normal [25] and impaired [21] glucose tolerance, even despite similar weight loss. These studies are hardly comparable due to different patients’ characteristics, dietary prescriptions, study duration, and weight loss achieved. However, the improvement in fasting and postprandial hyperglycemia appears proportional to baseline plasma glucose levels, being negligible in patients with only minor glucose alterations, while the improvement in insulin sensitivity is consistent across studies and seems similar between the two diets, as also supported by our data.
Using an accurate C-peptide-based estimation of insulin secretion and clearance, we could characterize and compare for the first time the effects of each diet on insulin kinetics. Unexpectedly, given the lower peripheral insulin levels, we observed an enhancement of fasting and glucose-stimulated insulin secretion after the diet, whose extent was numerically greater for the LC diet than Med diet. The enhanced insulin secretion however did not result in higher plasma insulin levels as it was associated with a more than compensatory increase in insulin clearance at fasting and after the glucose load, which again tended to be greater with the LC diet. Endogenous insulin clearance mostly occurs in the liver, which accounts for the majority (up to 80%) of secreted insulin removal during its first-pass transit into the portal vein [42]. A reduced insulin clearance has been recently identified as a major pathogenetic mechanism of diabetes progression [43,44,45,46] and is typically associated with insulin-resistance [45,46,47], visceral obesity [47,48,49,50,51], and non-alcoholic fatty liver disease [46,47,51,52,53]. Therefore, the increase in insulin clearance observed in our study may be interpreted as a positive effect of the diet associated with the reduced body weight and whole-body/hepatic insulin resistance.
We hypothesized that an improvement in β-cell function would occur after the two diets. Indeed, dietary intake of monounsaturated fatty acids (MUFA) and n-3 polyunsaturated fatty acids (n-3 PUFA), which is especially high in the Mediterranean area, was associated with positive 7-year longitudinal changes in model-derived markers of β-cell function (i.e., β-cell glucose sensitivity and rate sensitivity) in a population-based study [54]. Furthermore, protein and amino acid consumption is known to enhance insulin secretion in both healthy [55,56,57,58] and diabetic individuals [59,60,61,62,63]. In line with this indirect evidence, previous randomized clinical trials in insulin-resistant subjects demonstrated an increase in surrogate markers of β-cell function after a Med diet [26] or LC diet [25]. Definite conclusions however could not be drawn from these studies, lacking an adequate characterization of β-cell function independent of insulin sensitivity and glucose levels. By using a mathematical model of β-cell function, here we demonstrated a significant and equal improvement in β-cell glucose sensitivity after the two dietary interventions. An increase in β-cell glucose sensitivity is graphically represented by a steeper dose-response function of the insulin secretion rates plotted against plasma glucose levels (Figure 2), meaning that the β-cell secretes more insulin secretion in response to the same glucose levels.
We acknowledge that this study has some limitations. We relied on self-reported dietary compliance, but we estimated that the adherence to the diet was adequate given that the actual weight loss was close to the predicted and that participants returned after 2 weeks for reinforcement of dietary prescriptions. While subjects on the LC/ high-protein diet lost more weight than those on the Med diet, we could not clearly identify group differences in glucometabolic parameters, possibly due to the small sample size. In fact, numerical differences emerged suggesting greater benefits of the LC diet on insulin secretion and clearance, which however did not reach statistical significance. Finally, our findings should not be generalized to subjects with overweight or low-grade obesity and in those with type 2 diabetes.

5. Conclusions

This study demonstrates that a LC/ high-protein diet is a successful short-term approach for weight loss in morbidly obese patients and a feasible alternative to the Med diet for its glucometabolic benefits, including rapid improvements in insulin resistance, insulin clearance and β-cell function. Further studies are needed to compare efficacy and safety of LC and Med diets in the long term.

Author Contributions

Conceptualization, D.T., D.M., R.B., A.Q.-G., M.N.; methodology, D.T., D.M., S.B., A.Q.-G., L.G., M.N.; formal analysis, D.T.; mathematical modeling, D.T., A.M.; resources, R.B., S.B., M.N.; data curation, D.M., S.B., A.Q.-G., L.G., M.N.; writing—original draft preparation, D.T., D.M.; review and editing, R.B., S.B., A.Q.-G., L.G., S.T., A.M., M.N.; supervision, S.T., M.N.; funding acquisition, M.N. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Institutional Ethics Committee.

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

The data presented in this study are available on request from the corresponding author.

Acknowledgments

The authors thank all the volunteers who participated in the study.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Saeedi, P.; Petersohn, I.; Salpea, P.; Malanda, B.; Karuranga, S.; Unwin, N.; Colagiuri, S.; Guariguata, L.; Motala, A.A.; Ogurtsova, K.; et al. Global and regional diabetes prevalence estimates for 2019 and projections for 2030 and 2045: Results from the International Diabetes Federation Diabetes Atlas, 9th edition. Diabetes Res. Clin. Pract. 2019, 157. [Google Scholar] [CrossRef] [PubMed][Green Version]
  2. Evert, A.B.; Dennison, M.; Gardner, C.D.; Garvey, W.T.; Lau, K.H.K.; MacLeod, J.; Mitri, J.; Pereira, R.F.; Rawlings, K.; Robinson, S.; et al. Nutrition therapy for adults with diabetes or prediabetes: A consensus report. Diabetes Care 2019, 42, 731–754. [Google Scholar] [CrossRef] [PubMed][Green Version]
  3. Fernandez, M.L.; Raheem, D.; Ramos, F.; Carrascosa, C.; Saraiva, A.; Raposo, A. highlights of current dietary guidelines in five continents. Int. J. Environ. Res. Public Health 2021, 18, 2814. [Google Scholar] [CrossRef] [PubMed]
  4. Esposito, K.; Marfella, R.; Ciotola, M.; Di Palo, C.; Giugliano, F.; Giugliano, G.; D’Armiento, M.; D’Andrea, F.; Giugliano, D. Effect of a Mediterranean-style diet on endothelial dysfunction and markers of vascular inflammation in the metabolic syndrome. JAMA 2004, 292. [Google Scholar] [CrossRef] [PubMed][Green Version]
  5. Covas, M.-I.; Nyyssönen, K.; Poulsen, H.E.; Kaikkonen, J.; Zunft, H.-J.F.; Kiesewetter, H.; Gaddi, A.; de la Torre, R.; Mursu, J.; Bäumler, H.; et al. The effect of polyphenols in olive oil on heart disease risk factors. Ann. Intern. Med. 2006, 145. [Google Scholar] [CrossRef]
  6. Shai, I.; Schwarzfuchs, D.; Henkin, Y.; Shahar, D.R.; Witkow, S.; Greenberg, I.; Golan, R.; Fraser, D.; Bolotin, A.; Vardi, H.; et al. Weight loss with a low-carbohydrate, mediterranean, or low-Fat diet. N. Engl. J. Med. 2008, 359, 229–241. [Google Scholar] [CrossRef][Green Version]
  7. Estruch, R.; Ros, E.; Salas-Salvadó, J.; Covas, M.-I.; Corella, D.; Arós, F.; Gómez-Gracia, E.; Ruiz-Gutiérrez, V.; Fiol, M.; Lapetra, J.; et al. Primary prevention of cardiovascular disease with a Mediterranean diet supplemented with extra-virgin olive oil or nuts. N. Engl. J. Med. 2018, 378. [Google Scholar] [CrossRef]
  8. Salas-Salvadó, J.; Fernández-Ballart, J.; Ros, E.; Martínez-González, M.-A.; Fitó, M.; Estruch, R.; Corella, D.; Fiol, M.; Gómez-Gracia, E.; Arós, F.; et al. Effect of a Mediterranean diet supplemented with nuts on metabolic syndrome status. Arch. Intern. Med. 2008, 168. [Google Scholar] [CrossRef][Green Version]
  9. Elhayany, A.; Lustman, A.; Abel, R.; Attal-Singer, J.; Vinker, S. A low carbohydrate Mediterranean diet improves cardiovascular risk factors and diabetes control among overweight patients with type 2 diabetes mellitus: A 1-year prospective randomized intervention study. Diabetes Obes. Metab. 2010, 12, 204–209. [Google Scholar] [CrossRef]
  10. Salas-Salvado, J.; Bullo, M.; Babio, N.; Martinez-Gonzalez, M.A.; Ibarrola-Jurado, N.; Basora, J.; Estruch, R.; Covas, M.I.; Corella, D.; Aros, F.; et al. Reduction in the incidence of type 2 diabetes with the Mediterranean diet: Results of the PREDIMED-Reus nutrition intervention randomized trial. Diabetes Care 2011, 34, 14–19. [Google Scholar] [CrossRef][Green Version]
  11. Basterra-Gortari, F.J.; Ruiz-Canela, M.; Martinez-Gonzalez, M.A.; Babio, N.; Sorli, J.V.; Fito, M.; Ros, E.; Gomez-Gracia, E.; Fiol, M.; Lapetra, J.; et al. Effects of a Mediterranean eating plan on the need for glucose-lowering medications in participants with type 2 diabetes: A subgroup analysis of the PREDIMED trial. Diabetes Care 2019, 42, 1390–1397. [Google Scholar] [CrossRef][Green Version]
  12. Willett, W.C.; Sacks, F.; Trichopoulou, A.; Drescher, G.; Ferro-Luzzi, A.; Helsing, E.; Trichopoulos, D. Mediterranean diet pyramid: A cultural model for healthy eating. Am. J. Clin. Nutr. 1995, 61, 1402S–1406S. [Google Scholar] [CrossRef] [PubMed]
  13. Serra-Majem, L.; Tomaino, L.; Dernini, S.; Berry, E.M.; Lairon, D.; Ngo de la Cruz, J.; Bach-Faig, A.; Donini, L.M.; Medina, F.-X.; Belahsen, R.; et al. Updating the Mediterranean diet pyramid towards sustainability: Focus on environmental concerns. Int. J. Environ. Res. Public Health 2020, 17, 8758. [Google Scholar] [CrossRef] [PubMed]
  14. Martinez-Gonzalez, M.A.; de la Fuente-Arrillaga, C.; Nunez-Cordoba, J.M.; Basterra-Gortari, F.J.; Beunza, J.J.; Vazquez, Z.; Benito, S.; Tortosa, A.; Bes-Rastrollo, M. Adherence to Mediterranean diet and risk of developing diabetes: Prospective cohort study. BMJ 2008, 336, 1348–1351. [Google Scholar] [CrossRef][Green Version]
  15. Yancy, W.S., Jr.; Olsen, M.K.; Guyton, J.R.; Bakst, R.P.; Westman, E.C. A low-carbohydrate, ketogenic diet versus a low-fat diet to treat obesity and hyperlipidemia: A randomized, controlled trial. Ann. Intern. Med. 2004, 140, 769–777. [Google Scholar] [CrossRef] [PubMed]
  16. Foster, G.D.; Wyatt, H.R.; Hill, J.O.; McGuckin, B.G.; Brill, C.; Mohammed, B.S.; Szapary, P.O.; Rader, D.J.; Edman, J.S.; Klein, S. A randomized trial of a low-carbohydrate diet for obesity. N. Engl. J. Med. 2003, 348, 2082–2090. [Google Scholar] [CrossRef][Green Version]
  17. Skov, A.R.; Toubro, S.; Rønn, B.; Holm, L.; Astrup, A. randomized trial on protein vs carbohydrate in ad libitum fat reduced diet for the treatment of obesity. Int. J. Obes. 1999, 23, 528–536. [Google Scholar] [CrossRef][Green Version]
  18. McAuley, K.A.; Hopkins, C.M.; Smith, K.J.; McLay, R.T.; Williams, S.M.; Taylor, R.W.; Mann, J.I. Comparison of high-fat and high-protein diets with a high-carbohydrate diet in insulin-resistant obese women. Diabetologia 2004, 48, 8–16. [Google Scholar] [CrossRef] [PubMed]
  19. Westerterp-Plantenga, M.S.; Nieuwenhuizen, A.; Tomé, D.; Soenen, S.; Westerterp, K.R. Dietary protein, weight loss, and weight maintenance. Annu. Rev. Nutr. 2009, 29, 21–41. [Google Scholar] [CrossRef][Green Version]
  20. Bray, G.A.; Smith, S.R.; de Jonge, L.; Xie, H.; Rood, J.; Martin, C.K.; Most, M.; Brock, C.; Mancuso, S.; Redman, L.M. Effect of dietary protein content on weight gain, energy expenditure, and body composition during overeating: A randomized controlled trial. JAMA 2012, 307, 47–55. [Google Scholar] [CrossRef][Green Version]
  21. Stentz, F.B.; Brewer, A.; Wan, J.; Garber, C.; Daniels, B.; Sands, C.; Kitabchi, A.E. Remission of pre-diabetes to normal glucose tolerance in obese adults with high protein versus high carbohydrate diet: Randomized control trial. BMJ Open Diabetes Res. Care 2016, 4, e000258. [Google Scholar] [CrossRef][Green Version]
  22. Stentz, F.B.; Mikhael, A.; Kineish, O.; Christman, J.; Sands, C. High protein diet leads to prediabetes remission and positive changes in incretins and cardiovascular risk factors. Nutr. Metab. Cardiovasc Dis. 2020. [Google Scholar] [CrossRef]
  23. Trico, D.; Biancalana, E.; Solini, A. Protein and amino acids in nonalcoholic fatty liver disease. Curr. Opin. Clin. Nutr. Metab. Care 2021, 24, 96–101. [Google Scholar] [CrossRef] [PubMed]
  24. Eisenstein, J.; Roberts, S.B.; Dallal, G.; Saltzman, E. High-protein weight-loss diets: Are they safe and do they work? A review of the experimental and epidemiologic data. Nutr. Rev. 2002, 60, 189–200. [Google Scholar] [CrossRef]
  25. Kitabchi, A.E.; McDaniel, K.A.; Wan, J.Y.; Tylavsky, F.A.; Jacovino, C.A.; Sands, C.W.; Nyenwe, E.A.; Stentz, F.B. Effects of high-protein versus high-carbohydrate diets on markers of beta-cell function, oxidative stress, lipid peroxidation, proinflammatory cytokines, and adipokines in obese, premenopausal women without diabetes: A randomized controlled trial. Diabetes Care 2013, 36, 1919–1925. [Google Scholar] [CrossRef] [PubMed][Green Version]
  26. Blanco-Rojo, R.; Alcala-Diaz, J.F.; Wopereis, S.; Perez-Martinez, P.; Quintana-Navarro, G.M.; Marin, C.; Ordovas, J.M.; van Ommen, B.; Perez-Jimenez, F.; Delgado-Lista, J.; et al. The insulin resistance phenotype (muscle or liver) interacts with the type of diet to determine changes in disposition index after 2 years of intervention: The CORDIOPREV-DIAB randomised clinical trial. Diabetologia 2016, 59, 67–76. [Google Scholar] [CrossRef][Green Version]
  27. Weir, J.B. New methods for calculating metabolic rate with special reference to protein metabolism. J. Physiol. 1949, 109, 1–9. [Google Scholar] [CrossRef]
  28. Matthews, D.R.; Hosker, J.P.; Rudenski, A.S.; Naylor, B.A.; Treacher, D.F.; Turner, R.C. Homeostasis model assessment: Insulin resistance and beta-cell function from fasting plasma glucose and insulin concentrations in man. Diabetologia 1985, 28, 412–419. [Google Scholar] [CrossRef][Green Version]
  29. Mari, A.; Pacini, G.; Murphy, E.; Ludvik, B.; Nolan, J.J. A model-based method for assessing insulin sensitivity from the oral glucose tolerance test. Diabetes Care 2001, 24, 539–548. [Google Scholar] [CrossRef]
  30. Van Cauter, E.; Mestrez, F.; Sturis, J.; Polonsky, K.S. Estimation of insulin secretion rates from C-peptide levels. Comparison of individual and standard kinetic parameters for C-peptide clearance. Diabetes 1992, 41, 368–377. [Google Scholar] [CrossRef]
  31. Trico, D.; Galderisi, A.; Mari, A.; Santoro, N.; Caprio, S. One-hour post-load plasma glucose predicts progression to prediabetes in a multi-ethnic cohort of obese youths. Diabetes Obes. Metab. 2019, 21, 1191–1198. [Google Scholar] [CrossRef] [PubMed]
  32. Trico, D.; Natali, A.; Mari, A.; Ferrannini, E.; Santoro, N.; Caprio, S. Triglyceride-rich very low-density lipoproteins (VLDL) are independently associated with insulin secretion in a multiethnic cohort of adolescents. Diabetes Obes. Metab. 2018, 20, 2905–2910. [Google Scholar] [CrossRef] [PubMed]
  33. Mari, A.; Ferrannini, E. Beta-cell function assessment from modelling of oral tests: An effective approach. Diabetes Obes. Metab. 2008, 10 (Suppl. 4), 77–87. [Google Scholar] [CrossRef]
  34. Trico, D.; Mengozzi, A.; Nesti, L.; Hatunic, M.; Gabriel Sanchez, R.; Konrad, T.; Lalic, K.; Lalic, N.M.; Mari, A.; Natali, A.; et al. Circulating palmitoleic acid is an independent determinant of insulin sensitivity, beta cell function and glucose tolerance in non-diabetic individuals: A longitudinal analysis. Diabetologia 2020, 63, 206–218. [Google Scholar] [CrossRef][Green Version]
  35. American Diabetes Association. 2. Classification and Diagnosis of Diabetes: Standards of Medical Care in Diabetes—2021. Diabetes Care 2020, 44, S15–S33. [Google Scholar] [CrossRef]
  36. Trico, D.; Natali, A.; Arslanian, S.; Mari, A.; Ferrannini, E. Identification, pathophysiology, and clinical implications of primary insulin hypersecretion in nondiabetic adults and adolescents. JCI Insight 2018, 3. [Google Scholar] [CrossRef] [PubMed][Green Version]
  37. Corkey, B.E. Banting lecture 2011: Hyperinsulinemia: Cause or consequence? Diabetes 2012, 61, 4–13. [Google Scholar] [CrossRef] [PubMed][Green Version]
  38. Ferrannini, E. A journey in diabetes: From clinical physiology to novel therapeutics: The 2020 Banting Medal for Scientific Achievement Lecture. Diabetes 2021, 70, 338–346. [Google Scholar] [CrossRef]
  39. Bettini, S.; Belligoli, A.; Fabris, R.; Busetto, L. Diet approach before and after bariatric surgery. Rev. Endocr. Metab. Disord. 2020, 21, 297–306. [Google Scholar] [CrossRef]
  40. Paoli, A.; Bianco, A.; Grimaldi, K.A.; Lodi, A.; Bosco, G. Long term successful weight loss with a combination biphasic ketogenic Mediterranean diet and Mediterranean diet maintenance protocol. Nutrients 2013, 5, 5205–5217. [Google Scholar] [CrossRef]
  41. Sacks, F.M.; Bray, G.A.; Carey, V.J.; Smith, S.R.; Ryan, D.H.; Anton, S.D.; McManus, K.; Champagne, C.M.; Bishop, L.M.; Laranjo, N.; et al. Comparison of weight-loss diets with different compositions of fat, protein, and carbohydrates. N. Engl. J. Med. 2009, 360, 859–873. [Google Scholar] [CrossRef][Green Version]
  42. Ferrannini, E.; Wahren, J.; Faber, O.K.; Felig, P.; Binder, C.; DeFronzo, R.A. Splanchnic and renal metabolism of insulin in human subjects: A dose-response study. Am. J. Physiol. 1983, 244, E517–E527. [Google Scholar] [CrossRef]
  43. Bergman, R.N.; Piccinini, F.; Kabir, M.; Kolka, C.M.; Ader, M. Hypothesis: Role of reduced hepatic insulin clearance in the pathogenesis of type 2 diabetes. Diabetes 2019, 68, 1709–1716. [Google Scholar] [CrossRef]
  44. Galderisi, A.; Polidori, D.; Weiss, R.; Giannini, C.; Pierpont, B.; Trico, D.; Caprio, S. Lower insulin clearance parallels a reduced insulin sensitivity in obese youths and is associated with a decline in beta-cell function over time. Diabetes 2019. [Google Scholar] [CrossRef] [PubMed]
  45. Gastaldelli, A.; Abdul Ghani, M.; DeFronzo, R.A. Adaptation of insulin clearance to metabolic demand is a key determinant of glucose tolerance. Diabetes 2021, 70, 377–385. [Google Scholar] [CrossRef] [PubMed]
  46. Trico, D.; Galderisi, A.; Mari, A.; Polidori, D.; Galuppo, B.; Pierpont, B.; Samuels, S.; Santoro, N.; Caprio, S. Intrahepatic fat, irrespective of ethnicity, is associated with reduced endogenous insulin clearance and hepatic insulin resistance in obese youths: A cross-sectional and longitudinal study from the Yale Pediatric NAFLD cohort. Diabetes Obes. Metab. 2020, 22, 1628–1638. [Google Scholar] [CrossRef] [PubMed]
  47. Gastaldelli, A.; Cusi, K.; Pettiti, M.; Hardies, J.; Miyazaki, Y.; Berria, R.; Buzzigoli, E.; Sironi, A.M.; Cersosimo, E.; Ferrannini, E.; et al. Relationship between hepatic/visceral fat and hepatic insulin resistance in nondiabetic and type 2 diabetic subjects. Gastroenterology 2007, 133, 496–506. [Google Scholar] [CrossRef] [PubMed]
  48. Kim, M.K.; Reaven, G.M.; Kim, S.H. Dissecting the relationship between obesity and hyperinsulinemia: Role of insulin secretion and insulin clearance. Obesity (Silver Spring) 2017, 25, 378–383. [Google Scholar] [CrossRef][Green Version]
  49. Valera Mora, M.E.; Scarfone, A.; Calvani, M.; Greco, A.V.; Mingrone, G. Insulin clearance in obesity. J. Am. Coll. Nutr. 2003, 22, 487–493. [Google Scholar] [CrossRef]
  50. Umano, G.R.; Shabanova, V.; Pierpont, B.; Mata, M.; Nouws, J.; Trico, D.; Galderisi, A.; Santoro, N.; Caprio, S. A low visceral fat proportion, independent of total body fat mass, protects obese adolescent girls against fatty liver and glucose dysregulation: A longitudinal study. Int. J. Obes. 2019, 43, 673–682. [Google Scholar] [CrossRef]
  51. Matsubayashi, Y.; Yoshida, A.; Suganami, H.; Ishiguro, H.; Yamamoto, M.; Fujihara, K.; Kodama, S.; Tanaka, S.; Kaku, K.; Sone, H. Role of fatty liver in the association between obesity and reduced hepatic insulin clearance. Diabetes Metab. 2018, 44, 135–142. [Google Scholar] [CrossRef]
  52. Trico, D.; Caprio, S.; Rosaria Umano, G.; Pierpont, B.; Nouws, J.; Galderisi, A.; Kim, G.; Mata, M.M.; Santoro, N. Metabolic features of nonalcoholic fatty liver (NAFL) in Obese Adolescents: Findings from a multiethnic cohort. Hepatology 2018, 68, 1376–1390. [Google Scholar] [CrossRef] [PubMed][Green Version]
  53. Kotronen, A.; Juurinen, L.; Tiikkainen, M.; Vehkavaara, S.; Yki-Jarvinen, H. Increased liver fat, impaired insulin clearance, and hepatic and adipose tissue insulin resistance in type 2 diabetes. Gastroenterology 2008, 135, 122–130. [Google Scholar] [CrossRef]
  54. den Biggelaar, L.; Eussen, S.; Sep, S.J.S.; Mari, A.; Ferrannini, E.; van Greevenbroek, M.M.; van der Kallen, C.J.; Schalkwijk, C.G.; Arts, I.C.W.; Stehouwer, C.D.A.; et al. Prospective associations of dietary carbohydrate, fat, and protein intake with beta-cell function in the CODAM study. Eur. J. Nutr. 2019, 58, 597–608. [Google Scholar] [CrossRef]
  55. Trico, D.; Baldi, S.; Tulipani, A.; Frascerra, S.; Macedo, M.P.; Mari, A.; Ferrannini, E.; Natali, A. Mechanisms through which a small protein and lipid preload improves glucose tolerance. Diabetologia 2015, 58, 2503–2512. [Google Scholar] [CrossRef]
  56. Nesti, L.; Mengozzi, A.; Trico, D. Impact of nutrient type and sequence on glucose tolerance: Physiological insights and therapeutic implications. Front. Endocrinol. (Lausanne) 2019, 10, 144. [Google Scholar] [CrossRef] [PubMed]
  57. Carr, R.D.; Larsen, M.O.; Winzell, M.S.; Jelic, K.; Lindgren, O.; Deacon, C.F.; Ahren, B. Incretin and islet hormonal responses to fat and protein ingestion in healthy men. Am. J. Physiol. Endocrinol. Metab. 2008, 295, E779–E784. [Google Scholar] [CrossRef] [PubMed]
  58. van Loon, L.J.; Saris, W.H.; Verhagen, H.; Wagenmakers, A.J. Plasma insulin responses after ingestion of different amino acid or protein mixtures with carbohydrate. Am. J. Clin. Nutr. 2000, 72, 96–105. [Google Scholar] [CrossRef] [PubMed][Green Version]
  59. Trico, D.; Filice, E.; Baldi, S.; Frascerra, S.; Mari, A.; Natali, A. Sustained effects of a protein and lipid preload on glucose tolerance in type 2 diabetes patients. Diabetes Metab. 2016, 42, 242–248. [Google Scholar] [CrossRef]
  60. Nuttall, F.Q.; Gannon, M.C. Plasma glucose and insulin response to macronutrients in nondiabetic and NIDDM subjects. Diabetes Care 1991, 14, 824–838. [Google Scholar] [CrossRef]
  61. Trico, D.; Frascerra, S.; Baldi, S.; Mengozzi, A.; Nesti, L.; Mari, A.; Natali, A. The insulinotropic effect of a high-protein nutrient preload is mediated by the increase of plasma amino acids in type 2 diabetes. Eur. J. Nutr. 2019, 58, 2253–2261. [Google Scholar] [CrossRef] [PubMed]
  62. Ma, J.; Stevens, J.E.; Cukier, K.; Maddox, A.F.; Wishart, J.M.; Jones, K.L.; Clifton, P.M.; Horowitz, M.; Rayner, C.K. Effects of a protein preload on gastric emptying, glycemia, and gut hormones after a carbohydrate meal in diet-controlled type 2 diabetes. Diabetes Care 2009, 32, 1600–1602. [Google Scholar] [CrossRef] [PubMed][Green Version]
  63. Trico, D.; Filice, E.; Trifiro, S.; Natali, A. Manipulating the sequence of food ingestion improves glycemic control in type 2 diabetic patients under free-living conditions. Nutr. Diabetes 2016, 6, e226. [Google Scholar] [CrossRef] [PubMed][Green Version]
Figure 1. Study design. BIA, bioimpedance analysis; OGTT, oral glucose tolerance test.
Figure 1. Study design. BIA, bioimpedance analysis; OGTT, oral glucose tolerance test.
Nutrients 13 01345 g001
Figure 2. Insulin secretion rate during a 75-g OGTT (left panel) and insulin secretion rate against plasma glucose levels (right panel) in morbidly obese individuals before (dotted lines) and after (continuous lines) a 4-week low-carbohydrate diet (red lines) or Mediterranean diet (blue lines). Thick lines indicate mean and shaded areas indicate SEM.
Figure 2. Insulin secretion rate during a 75-g OGTT (left panel) and insulin secretion rate against plasma glucose levels (right panel) in morbidly obese individuals before (dotted lines) and after (continuous lines) a 4-week low-carbohydrate diet (red lines) or Mediterranean diet (blue lines). Thick lines indicate mean and shaded areas indicate SEM.
Nutrients 13 01345 g002
Table 1. Baseline clinical and metabolic characteristics of morbidly obese individuals randomized to a low-carbohydrate diet (LC) or Mediterranean (Med) diet.
Table 1. Baseline clinical and metabolic characteristics of morbidly obese individuals randomized to a low-carbohydrate diet (LC) or Mediterranean (Med) diet.
LC Diet
(n = 17)
Med Diet
(n = 15)
p
Age (years)41.4 ± 10.546.9 ± 10.5ns
Women (n; %)12; 70.610; 66.7ns
Systolic Blood Pressure (mmHg)131 ± 9143 ± 160.07
Diastolic Blood Pressure (mmHg)79 ± 683 ± 7ns
Body Mass Index (kg/m2)48.9 ± 5.449.9 ± 8.8ns
Body Weight (kg)137.0 ± 19.3136.0 ± 34.6 ns
Waist Circumference (cm)131 ± 13132 ± 15ns
Waist-to-hip Ratio (ratio)1.07 ± 0.141.07 ± 0.12ns
Fat mass (%)45.6 ± 3.244.1 ± 5.3ns
BMR (calories/day)2350 ± 4902490 ± 655ns
HbA1c (mmol/mol)38 ± 538 ± 4ns
Fasting glucose (mmol/L)5.5 ± 0.55.7 ± 0.7ns
Total cholesterol (mg/dL)193 ± 41178 ± 31ns
HDL cholesterol (mg/dL)51 ± 957 ± 15ns
LDL cholesterol (mg/dL)124 ± 33 93 ± 340.08
Triglycerides (mg/dL)114 ± 31125 ± 63ns
Alanine Aminotransferase (U/L)28 [16]24 [31]ns
Aspartate Aminotransferase (U/L)19 [14]19 [11]ns
γ-Glutamyl Transferase (U/L)22 [20]22 [15]ns
Creatinine (mg/dL)0.76 ± 0.190.76 ± 0.20ns
eGFR (ml min−1 1.73 m2)108 ± 21102 ± 17ns
Urea (mg/dL)28.4 ± 5.930.7 ± 11.5ns
Data are mean ± SD or median [interquartile range]. p values below 0.10 are shown.
Table 2. Changes in body weight, adiposity and basal metabolic rate (BMR) induced by a low-carbohydrate diet (LC) or Mediterranean (Med) diet in morbidly obese individuals.
Table 2. Changes in body weight, adiposity and basal metabolic rate (BMR) induced by a low-carbohydrate diet (LC) or Mediterranean (Med) diet in morbidly obese individuals.
LC Diet
(n = 17)
Med Diet
(n = 15)
p
Day 0Day 28Day 0Day 28DietTimeInteraction
Body Mass Index (kg/m2)48.9 ± 5.446.1 ± 5.149.9 ± 8.848.0 ± 8.4ns<0.0010.005
Body Weight (kg)137.0 ± 19.3129.0 ± 18.0136.0 ± 34.6 131.0 ± 32.4ns<0.0010.01
Waist Circumference (cm)131 ± 13124 ± 11132 ± 15127 ± 14ns<0.001ns
Waist-to-hip Ratio (ratio)1.07 ± 0.140.92 ± 0.111.07 ± 0.120.93 ± 0.10ns0.003ns
Fat mass (%)45.6 ± 3.243.3 ± 2.944.1 ± 5.341.0 ± 5.7ns<0.001ns
BMR (calories/day)2350 ± 4902169 ± 3172490 ± 6552234 ± 671ns0.007ns
Data are mean ± SD. p values below 0.10 are shown.
Table 3. Changes in glucose and insulin metabolism induced by a low-carbohydrate diet (LC) or Mediterranean (Med) diet in morbidly obese individuals.
Table 3. Changes in glucose and insulin metabolism induced by a low-carbohydrate diet (LC) or Mediterranean (Med) diet in morbidly obese individuals.
LC Diet
(n = 17)
Med Diet
(n = 15)
p
Day 0Day 28Day 0Day 28DietTimeInteraction
Glucose Control
Fasting Glucose (mmol/L)5.5 ± 0.55.6 ± 0.55.7 ± 0.75.5 ± 0.7nsnsns
2-h Glucose (mmol/L)7.3 ± 1.67.4 ± 1.87.0 ± 1.57.4 ± 2.1nsnsns
Mean Glucose (mmol/L)7.3 ± 1.17.6 ± 1.27.3 ± 1.17.6 ± 1.7nsnsns
Fasting Insulin (pmol/L)139 [105]110 [75]137 [99]109 [67]ns0.02ns
Mean Insulin (pmol/L)473 [399]411 [183]447 [271]508 [512]nsnsns
Insulin Sensitivity
HOMA-IR (unit)5.42 [5.22]4.05 [2.94]5.79 [5.54]4.90 [4.64]ns0.04ns
OGIS (ml min−1 m−2)331 [90]320 [97]342 [93]306 [141]nsnsns
Beta-cell Function
Fasting ISR (pmol m−2 min−1)119 [58]135 [61]123 [147]130 [72]ns0.0003ns
Total ISR (nmol/m2)52 [25]60 [23]65 [48]66 [33]ns0.05ns
[email protected] (pmol min−1 m−2)105 [59]106 [48]106 [90]119 [97]nsnsns
β-GS (pmol min−1 m−2 mM−1)57 [80]82 [38]64 [53]86 [65]ns0.03ns
β-RS (nmol m−2 mM−1)1000 [898]898 [1067]1187 [1216]1019 [1055]nsnsns
Potentiation Factor (ratio)1.15 [0.24]1.15 [0.48]1.42 [0.71]1.41 [0.76]nsnsns
Incretin Hormones
GLP-1 AUC (nmol/L × min)9.5 [4.8]7.3 [5.2]7.6 [4.6]6.3 [3.5]ns0.002ns
GIP AUC (nmol/L × min)36.6 [28.9]35.8 [25.8]43.2 [31.6]38.7 [42.2]nsnsns
Insulin Clearance
Fasting Clearance (l min−1 m−2)0.92 [0.50]1.26 [0.56]1.02 [0.66]1.15 [0.46]ns0.0050.06
OGTT Clearance (l min−1 m−2)0.62 [0.29]0.81 [0.30]0.74 [0.33]0.71 [0.29]ns0.050.06
Data are mean ± SD or median [interquartile range]. p values below 0.10 are shown.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Tricò, D.; Moriconi, D.; Berta, R.; Baldi, S.; Quinones-Galvan, A.; Guiducci, L.; Taddei, S.; Mari, A.; Nannipieri, M. Effects of Low-Carbohydrate versus Mediterranean Diets on Weight Loss, Glucose Metabolism, Insulin Kinetics and β-Cell Function in Morbidly Obese Individuals. Nutrients 2021, 13, 1345. https://doi.org/10.3390/nu13041345

AMA Style

Tricò D, Moriconi D, Berta R, Baldi S, Quinones-Galvan A, Guiducci L, Taddei S, Mari A, Nannipieri M. Effects of Low-Carbohydrate versus Mediterranean Diets on Weight Loss, Glucose Metabolism, Insulin Kinetics and β-Cell Function in Morbidly Obese Individuals. Nutrients. 2021; 13(4):1345. https://doi.org/10.3390/nu13041345

Chicago/Turabian Style

Tricò, Domenico, Diego Moriconi, Rossana Berta, Simona Baldi, Alfredo Quinones-Galvan, Letizia Guiducci, Stefano Taddei, Andrea Mari, and Monica Nannipieri. 2021. "Effects of Low-Carbohydrate versus Mediterranean Diets on Weight Loss, Glucose Metabolism, Insulin Kinetics and β-Cell Function in Morbidly Obese Individuals" Nutrients 13, no. 4: 1345. https://doi.org/10.3390/nu13041345

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop