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Nutrition, Metabolism, and Obesity: Novel Strategies and Molecular Mechanisms—Future Perspectives

A special issue of Biomolecules (ISSN 2218-273X). This special issue belongs to the section "Molecular Medicine".

Deadline for manuscript submissions: 30 April 2025 | Viewed by 16192

Special Issue Editor


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Guest Editor
Science for Life Laboratory, Department of Gene Technology, School of Engineering Sciences in Chemistry, Biotechnology and Health (CBH), KTH Royal Institute of Technology, Karolinska Institute Science Park, SE-171 21 Solna, Sweden
Interests: lipid metabolism; lipidomic; sex differences; sex hormones; nutrition; metabolic syndrome
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Special Issue Information

Dear Colleagues,

Obesity is a significant public health concern due to its close link with metabolic comorbidities, such as type 2 diabetes, dyslipidemia, liver diseases, and cardiovascular diseases. Evidence suggests that excessive or inappropriate nutrient intake triggers chronic inflammatory responses in tissues that regulate metabolic homeostasis, leading to altered metabolic pathways, insulin resistance, and systemic metabolic dysfunction. Nutritional interventions have emerged as effective strategies for managing obesity and associated metabolic disorders.

In this Special Issue, we aim to cover the molecular significance of both macro- and micro-nutrients, as well as the current dietary recommendations. We will explore the primary metabolic pathways and molecular mechanisms in which each nutrient plays a role, and their contribution to the development of obesity and/or metabolic diseases. Importantly, gut microbiota are crucial in regulating the metabolic response to nutrient intake. As such, we aim to gain a mechanistic understanding of how nutrients and the intestinal microbiota may contribute to the metabolic health of the human host. By doing so, we hope to shed light on the pathogenesis of various common metabolic disorders including, but not limited to, obesity, type 2 diabetes, non-alcoholic liver disease, cardiometabolic diseases and malnutrition.

This Special Issue will focus on novel strategies and molecular mechanisms, as well as nutritional approaches to tackle obesity and its associated metabolic disturbances.

This Special Issue of the Biomolecules Journal, entitled “Nutrition, Metabolism, and Obesity: Novel Strategies and Molecular Mechanisms—Future Perspectives”, aims to solicit original research papers or review articles on the current state of research in this field, including both discovery and preclinical studies.

We look forward to your contributions to this research field.

Dr. Marion Korach-André
Guest Editor

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Keywords

  • diet
  • nutrients
  • obesity
  • inflammation
  • fat
  • glucose intolerance
  • insulin resistance
  • fatty liver disease
  • metabolic syndrome

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Published Papers (8 papers)

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11 pages, 1495 KiB  
Article
Effect of Acute Nutritional Ketosis on Circulating Levels of Growth Differentiation Factor 15: Findings from a Cross-Over Randomised Controlled Trial
by Sanjali Charles, Yutong Liu, Sakina H. Bharmal, Wandia Kimita and Maxim S. Petrov
Biomolecules 2024, 14(6), 665; https://doi.org/10.3390/biom14060665 - 6 Jun 2024
Viewed by 886
Abstract
Exogenous supplementation with ketone beverages has been shown to reduce plasma glucose levels during acute nutritional ketosis. It remains to be investigated whether growth differentiation factor 15 (GDF-15)—an anorexigenic hormone—is involved in this process. The aim was to investigate the effect of a [...] Read more.
Exogenous supplementation with ketone beverages has been shown to reduce plasma glucose levels during acute nutritional ketosis. It remains to be investigated whether growth differentiation factor 15 (GDF-15)—an anorexigenic hormone—is involved in this process. The aim was to investigate the effect of a ketone ester beverage delivering β-hydroxybutyrate (KEβHB) on plasma levels of GDF-15, as well as assess the influence of eating behaviour on it. The study was a randomised controlled trial (registered at clinicaltrials.gov as NCT03889210). Individuals were given a KEβHB beverage or placebo in a cross-over fashion. Blood samples were collected at baseline, 30, 60, 90, 120, and 150 min after ingestion. Eating behaviour was assessed using the three-factor eating questionnaire. GDF-15 levels were not significantly different (p = 0.503) after the KEβHB beverage compared with the placebo. This finding remained consistent across the cognitive restraint, emotional eating, and uncontrolled eating domains. Changes in the anorexigenic hormone GDF-15, irrespective of eating behaviour, do not appear to play a major role in the glucose-lowering effect of exogenous ketones. Full article
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Figure 1

Figure 1
<p>Associations between GDF-15 and changes in glucose and triglycerides. Footnotes: * indicates statistical significance (<span class="html-italic">p</span> &lt; 0.05). Spearman’s correlation analysis was conducted between two variables: 1. GDF-15 levels at 30, 60, 90, 120, and 150 min; 2. change in glucose (<b>A</b>) and triglycerides (<b>B</b>) from baseline to 30, 60, 90, 120, and 150 min. Analysis was conducted on data from the KEβHB group only. Abbreviations: GDF-15 = growth differentiation factor 15; KeβHB = ketone monoester β-hydroxybutyrate.</p>
Full article ">Figure 2
<p>Change in the levels of GDF-15 after ingestion of the KEβHB and placebo beverages. Footnotes: mean absolute concentrations of GDF-15 from 0 to 150 min are presented in panel (<b>i</b>). Log-transformed AUCs of GDF-15 are presented in panel (<b>ii</b>). In both panels, error bars represent standard deviation. Abbreviations: AUC = area under the curve; GDF-15 = growth differentiation factor 15; KEβHB = ketone monoester β-hydroxybutyrate.</p>
Full article ">Figure 3
<p>Change in the levels of GDF-15 after ingestion of KEβHB and placebo beverages according to the eating behaviour domains. Footnotes: mean absolute concentrations of GDF-15 from 0 to 150 min and log-transformed AUCs of GDF-15 grouped by high and low uncontrolled eating categories are presented in panels (<b>A</b>(<b>i</b>,<b>ii</b>)), respectively. Mean absolute concentrations of GDF-15 from 0 to 150 min and log-transformed AUCs of GDF-15 grouped by high and low cognitive restraint categories are presented in panels (<b>B</b>(<b>i</b>,<b>ii</b>)), respectively. Mean absolute concentrations of GDF-15 from 0 to 150 min and log-transformed AUCs of GDF-15 grouped by high and low EE categories are presented in panels (<b>C</b>(<b>i</b>,<b>ii</b>)), respectively. In all panels, error bars represent standard deviation. Abbreviations: AUC = area under the curve; GDF-15 = growth differentiation factor 15; KEβHB = ketone monoester β-hydroxybutyrate.</p>
Full article ">
30 pages, 9872 KiB  
Article
Bariatric Surgery Induces Alterations in the Immune Profile of Peripheral Blood T Cells
by Pedro Barbosa, Aryane Pinho, André Lázaro, Diogo Paula, José G. Tralhão, Artur Paiva, Maria J. Pereira, Eugenia Carvalho and Paula Laranjeira
Biomolecules 2024, 14(2), 219; https://doi.org/10.3390/biom14020219 - 12 Feb 2024
Cited by 1 | Viewed by 2366
Abstract
Low-grade inflammation is closely linked to obesity and obesity-related comorbidities; therefore, immune cells have become an important topic in obesity research. Here, we performed a deep phenotypic characterization of circulating T cells in people with obesity, using flow cytometry. Forty-one individuals with obesity [...] Read more.
Low-grade inflammation is closely linked to obesity and obesity-related comorbidities; therefore, immune cells have become an important topic in obesity research. Here, we performed a deep phenotypic characterization of circulating T cells in people with obesity, using flow cytometry. Forty-one individuals with obesity (OB) and clinical criteria for bariatric surgery were enrolled in this study. We identified and quantified 44 different circulating T cell subsets and assessed their activation status and the expression of immune-checkpoint molecules, immediately before (T1) and 7–18 months after (T2) the bariatric surgery. Twelve age- and sex-matched healthy individuals (nOB) were also recruited. The OB participants showed higher leukocyte counts and a higher percentage of neutrophils. The percentage of circulating Th1 cells were negatively correlated to HbA1c and insulin levels. OB Th1 cells displayed a higher activation status and lower PD-1 expression. The percentage of Th17 and Th1/17 cells were increased in OB, whereas the CD4+ Tregs’ percentage was decreased. Interestingly, a higher proportion of OB CD4+ Tregs were polarized toward Th1- and Th1/17-like cells and expressed higher levels of CCR5. Bariatric surgery induced the recovery of CD4+ Treg cell levels and the expansion and activation of Tfh and B cells. Our results show alterations in the distribution and phenotype of circulating T cells from OB people, including activation markers and immune-checkpoint proteins, demonstrating that different metabolic profiles are associated to distinct immune profiles, and both are modulated by bariatric surgery. Full article
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Figure 1

Figure 1
<p>Distribution of the participants according to the time-point of sample collection. Thirty-five individuals were studied before bariatric surgery (T1). Eight of them, with BMI ≥50 kg/m<sup>2</sup> at T1, were also studied 9 to 18 months after the surgery (T2). Six individuals were only studied at T2. N/A: not analyzed.</p>
Full article ">Figure 2
<p>White blood cells count (WBC) in peripheral blood (<b>A</b>) and percentage of neutrophils (<b>B</b>) and monocytes within peripheral blood (PB) leukocytes (<b>C</b>) in nOB and OB, as well as among OB participants stratified by obesity class and metabolic profile. Percentage of monocytes expressing CCR5<sup>+</sup> within all the studied groups (<b>D</b>). nOB: healthy participants (without obesity); OB: participants with obesity; IS: insulin sensitive; IRn: insulin resistant and normoglycemic; Pre-T2D: pre-diabetes; T2D: type 2 diabetes. Statistical differences were considered when <span class="html-italic">p</span> &lt; 0.05. <sup>A</sup> <span class="html-italic">p</span> &lt; 0.05 vs. nOB; <sup>B</sup> <span class="html-italic">p</span> &lt; 0.05 vs. Class II; <sup>C</sup> <span class="html-italic">p</span> &lt; 0.05 vs. Class III; <sup>D</sup> <span class="html-italic">p</span> &lt; 0.05 vs. IS; <sup>E</sup> <span class="html-italic">p</span> &lt; 0.05 vs. IRn; <sup>F</sup> <span class="html-italic">p</span> &lt; 0.05 vs. Pre-T2D.</p>
Full article ">Figure 3
<p>Spearman’s correlation analysis between immune and anthropometric or metabolic parameters. BMI: body mass index; WBC: white blood cells. Statistical differences were considered when <span class="html-italic">p</span> &lt; 0.05.</p>
Full article ">Figure 4
<p>Percentage of lymphocytes (<b>A</b>), T lymphocytes (<b>B</b>), and B lymphocytes (<b>C</b>) within peripheral blood (PB) leukocytes and distribution of T lymphocytes into CD4<sup>+</sup> (<b>D</b>), CD8<sup>+</sup> (<b>E</b>), CD4<sup>+</sup>CD8<sup>+</sup> (<b>F</b>), and CD4<sup>−</sup>CD8<sup>−</sup> (<b>G</b>) compartments among all the studied groups. nOB: healthy participants (without obesity); OB: participants with obesity; IS: insulin sensitive; IRn: insulin resistant and normoglycemic; Pre-T2D: pre-diabetes; T2D: type 2 diabetes. Statistical differences were considered when <span class="html-italic">p</span> &lt; 0.05. <sup>A</sup> <span class="html-italic">p</span> &lt; 0.05 vs. nOB; <sup>B</sup> <span class="html-italic">p</span> &lt; 0.05 vs. Class II; <sup>C</sup> <span class="html-italic">p</span> &lt; 0.05 vs. Class III; <sup>D</sup> <span class="html-italic">p</span> &lt; 0.05 vs. IS; <sup>E</sup> <span class="html-italic">p</span> &lt; 0.05 vs. IRn; <sup>F</sup> <span class="html-italic">p</span> &lt; 0.05 vs. Pre-T2D.</p>
Full article ">Figure 5
<p>Spearman’s correlation analysis between immune and anthropometric or metabolic parameters. BMI: body mass index; PB: peripheral blood. Statistical differences were considered when <span class="html-italic">p</span> &lt; 0.05.</p>
Full article ">Figure 6
<p>Percentage of Th1 (<b>A</b>), Th17 (<b>B</b>), Th1/17 (<b>C</b>), and CCR5<sup>−</sup>CCR6<sup>−</sup> (<b>D</b>) T cells within CD4<sup>+</sup> T cells. Percentage of activated (CD25<sup>+</sup>) Th1 (<b>E</b>) and CCR5<sup>+</sup>CCR6<sup>+</sup> Th cells (<b>F</b>). Percentage of PD-1<sup>+</sup> Th1 (<b>G</b>), TIM-3<sup>+</sup> Th1 (<b>H</b>), and TIM-3<sup>+</sup> Th1/17 cells (<b>I</b>) in nOB and OB, grouped according to obesity class and metabolic profile. nOB: healthy participants (without obesity); OB: participants with obesity; IS: insulin sensitive; IRn: insulin resistant and normoglycemic; Pre-T2D: pre-diabetes; T2D: type 2 diabetes; Th1—T helper 1 (CCR5<sup>+</sup>CCR6<sup>−</sup>); Th17—T helper 17 (CCR5<sup>−</sup>CCR6<sup>+</sup>); Th1/17—T helper 1/17 (CCR5<sup>+</sup>CCR6<sup>+</sup>). Statistical differences were considered when <span class="html-italic">p</span> &lt; 0.05. <sup>A</sup> <span class="html-italic">p</span> &lt; 0.05 vs. nOB; <sup>B</sup> <span class="html-italic">p</span> &lt; 0.05 vs. Class II; <sup>C</sup> <span class="html-italic">p</span> &lt; 0.05 vs. Class III; <sup>D</sup> <span class="html-italic">p</span> &lt; 0.05 vs. IS; <sup>E</sup> <span class="html-italic">p</span> &lt; 0.05 vs. IRn; <sup>F</sup> <span class="html-italic">p</span> &lt; 0.05 vs. Pre-T2D.</p>
Full article ">Figure 7
<p>Spearman’s correlation analysis between immune and anthropometric or metabolic parameters. BMI: body mass index; MFI: mean fluorescence intensity. Statistical differences were considered when <span class="html-italic">p</span> &lt; 0.05.</p>
Full article ">Figure 8
<p>Percentage of CD4<sup>+</sup> Tregs within CD4<sup>+</sup> T cells (<b>A</b>) and within peripheral blood (PB) leukocytes (<b>B</b>). Polarization of CD4<sup>+</sup> Tregs into Th1-like (<b>C</b>), Th17-like (<b>D</b>), Th1/17-like (<b>E</b>), and CCR5<sup>−</sup>CCR6<sup>−</sup> (<b>F</b>) CD4<sup>+</sup> Tregs. Percentage of PD-1<sup>+</sup> and TIM-3<sup>+</sup> expressed by CD4<sup>+</sup> Tregs (<b>G</b>) and by Th1-like (<b>H</b>,<b>I</b>), Th17-like (<b>J</b>,<b>L</b>), and Th1/17-like (<b>M</b>,<b>N</b>) CD4<sup>+</sup> Tregs among the studied groups. nOB: healthy participants (without obesity); OB: participants with obesity; IS: insulin sensitive; IRn: insulin resistant and normoglycemic; Pre-T2D: pre-diabetes; T2D: type 2 diabetes. Statistical differences were considered when <span class="html-italic">p</span> &lt; 0.05. <sup>A</sup> <span class="html-italic">p</span> &lt; 0.05 vs. nOB; <sup>B</sup> <span class="html-italic">p</span> &lt; 0.05 vs. Class II; <sup>C</sup> <span class="html-italic">p</span> &lt; 0.05 vs. Class III; <sup>D</sup> <span class="html-italic">p</span> &lt; 0.05 vs. IS; <sup>E</sup> <span class="html-italic">p</span> &lt; 0.05 vs. IRn; <sup>F</sup> <span class="html-italic">p</span> &lt; 0.05 vs. Pre-T2D.</p>
Full article ">Figure 9
<p>Treg cells with follicular phenotype and their polarization into Th1-like Tfr cells. Percentage of follicular CD4<sup>+</sup> Treg cells (Tfr) within CD4<sup>+</sup> Treg cells (<b>A</b>). Percentage of CD4<sup>+</sup> Tfr cells with Th1-like phenotype (<b>B</b>). Percentage of PD-1<sup>+</sup> (<b>C</b>) and TIM-3<sup>+</sup> (<b>D</b>) cells within Th1-like CD4<sup>+</sup> Tfr cells. nOB: healthy participants (without obesity); OB: participants with obesity; IS: insulin sensitive; IRn: insulin resistant and normoglycemic; Pre-T2D: pre-diabetes; T2D: type 2 diabetes. Statistical differences were considered when <span class="html-italic">p</span> &lt; 0.05. <sup>A</sup> <span class="html-italic">p</span> &lt; 0.05 vs. nOB; <sup>B</sup> <span class="html-italic">p</span> &lt; 0.05 vs. Class II; <sup>C</sup> <span class="html-italic">p</span> &lt; 0.05 vs. Class III; <sup>D</sup> <span class="html-italic">p</span> &lt; 0.05 vs. IS; <sup>E</sup> <span class="html-italic">p</span> &lt; 0.05 vs. IRn; <sup>F</sup> <span class="html-italic">p</span> &lt; 0.05 vs. Pre-T2D.</p>
Full article ">Figure 10
<p>Frequency and expression of immune regulatory molecules by follicular CD4<sup>+</sup> T cells (Tfh). Percentage of follicular CD4<sup>+</sup> T cells (Tfh) within CD4<sup>+</sup> T cells (<b>A</b>). Percentage of PD-1<sup>+</sup> (<b>B</b>) and TIM-3<sup>+</sup> (<b>C</b>) cells within Tfh cells, in the different studied groups. nOB: healthy participants (without obesity); OB: participants with obesity; IS: insulin sensitive; IRn: insulin resistant and normoglycemic; Pre-T2D: pre-diabetes; T2D: type 2 diabetes. Statistical differences were considered when <span class="html-italic">p</span> &lt; 0.05. <sup>A</sup> <span class="html-italic">p</span> &lt; 0.05 vs. nOB; <sup>B</sup> <span class="html-italic">p</span> &lt; 0.05 vs. Class II; <sup>C</sup> <span class="html-italic">p</span> &lt; 0.05 vs. Class III; <sup>D</sup> <span class="html-italic">p</span> &lt; 0.05 vs. IS; <sup>E</sup> <span class="html-italic">p</span> &lt; 0.05 vs. IRn; <sup>F</sup> <span class="html-italic">p</span> &lt; 0.05 vs. Pre-T2D.</p>
Full article ">Figure 11
<p>The Tc1 (<b>A</b>), Tc17 (<b>B</b>), Tc1/17 (<b>C</b>), and CCR5<sup>−</sup>CCR6<sup>−</sup> (<b>D</b>) cell percentages within CD8<sup>+</sup> T cells, as well as the percentages of PD-1<sup>+</sup> Tc1 (<b>E</b>) and Tc1/17 (<b>F</b>) cells and of the activated CCR5<sup>−</sup>CCR6<sup>−</sup> Tc cells (<b>G</b>) in nOB and OB, grouped according to obesity class and metabolic profile. nOB: healthy participants (without obesity); OB: participants with obesity; IS: insulin sensitive; IRn: insulin resistant and normoglycemic; Pre-T2D: pre-diabetes; T2D: type 2 diabetes; Tc1—T cytotoxic 1 (CCR5<sup>+</sup>CCR6<sup>−</sup>); Tc17—T cytotoxic 17 (CCR5<sup>−</sup>CCR6<sup>+</sup>); Tc1/17—T cytotoxic 1/17 (CCR5<sup>+</sup>CCR6<sup>+</sup>). Statistical differences were considered when <span class="html-italic">p</span> &lt; 0.05. <sup>A</sup> <span class="html-italic">p</span> &lt; 0.05 vs. nOB; <sup>B</sup> <span class="html-italic">p</span> &lt; 0.05 vs. Class II; <sup>C</sup> <span class="html-italic">p</span> &lt; 0.05 vs. Class III; <sup>D</sup> <span class="html-italic">p</span> &lt; 0.05 vs. IS; <sup>E</sup> <span class="html-italic">p</span> &lt; 0.05 vs. IRn; <sup>F</sup> <span class="html-italic">p</span> &lt; 0.05 vs. Pre-T2D.</p>
Full article ">Figure 12
<p>Spearman’s correlation analysis between immune and anthropometric or metabolic parameters. BMI: body mass index; MFI: mean fluorescence intensity. Statistical differences were considered when <span class="html-italic">p</span> &lt; 0.05.</p>
Full article ">Figure 13
<p>Frequency, activation profile, and immune regulatory markers expression by follicular CD8<sup>+</sup> T cells (Tfc). Percentage of follicular CD8<sup>+</sup> T cells (Tfc) within CD8<sup>+</sup> T cells (<b>A</b>). Percentage of activated Tfc (<b>B</b>) and PD-1<sup>+</sup> Tfc (<b>C</b>) cells in the different groups under study. nOB: healthy participants (without obesity); OB: participants with obesity; IS: insulin sensitive; Irn: insulin resistant and normoglycemic; Pre-T2D: pre-diabetes; T2D: type 2 diabetes. Statistical differences were considered when <span class="html-italic">p</span> &lt; 0.05. <sup>A</sup> <span class="html-italic">p</span> &lt; 0.05 vs. nOB; <sup>B</sup> <span class="html-italic">p</span> &lt; 0.05 vs. Class II; <sup>C</sup> <span class="html-italic">p</span> &lt; 0.05 vs. Class III; <sup>D</sup> <span class="html-italic">p</span> &lt; 0.05 vs. IS; <sup>E</sup> <span class="html-italic">p</span> &lt; 0.05 vs. IRn; <sup>F</sup> <span class="html-italic">p</span> &lt; 0.05 vs. Pre-T2D.</p>
Full article ">Figure 14
<p>Percentage of CD4<sup>+</sup>CD8<sup>+</sup> T cells presenting a T1-like (<b>A</b>), T17-like (<b>B</b>), T1/17-like (<b>C</b>), and CCR5<sup>−</sup>CCR6<sup>−</sup> (<b>D</b>), Treg (<b>E</b>), and Tf (<b>F</b>) phenotype, within CD4<sup>+</sup>CD8<sup>+</sup> T cells, in nOB and OB, grouped according to obesity class and metabolic profile. nOB: healthy participants (without obesity); OB: participants with obesity; IS: insulin sensitive; IRn: insulin resistant and normoglycemic; Pre-T2D: pre-diabetes; T2D: type 2 diabetes; T1-like—CCR5<sup>+</sup>CCR6<sup>−</sup>; T17-like—CCR5<sup>−</sup>CCR6<sup>+</sup>; T1/17-like—CCR5<sup>+</sup>CCR6<sup>+</sup>. Statistical differences were considered when <span class="html-italic">p</span> &lt; 0.05. <sup>A</sup> <span class="html-italic">p</span> &lt; 0.05 vs. nOB; <sup>B</sup> <span class="html-italic">p</span> &lt; 0.05 vs. Class II; <sup>C</sup> <span class="html-italic">p</span> &lt; 0.05 vs. Class III; <sup>D</sup> <span class="html-italic">p</span> &lt; 0.05 vs. IS; <sup>E</sup> <span class="html-italic">p</span> &lt; 0.05 vs. IRn; <sup>F</sup> <span class="html-italic">p</span> &lt; 0.05 vs. Pre-T2D.</p>
Full article ">Figure 15
<p>Spearman’s correlation analysis between immune and anthropometric or metabolic parameters. BMI: body mass index. Statistical differences were considered when <span class="html-italic">p</span> &lt; 0.05.</p>
Full article ">Figure 16
<p>Phenotypical changes in T cells from individuals with obesity, before (T1) and after (T2) bariatric surgery. The analysis of unpaired samples comprised all individuals with Class IV obesity in T1 (<span class="html-italic">n</span> = 15) and 14 individuals after bariatric surgery (T2). The analysis of paired samples consisted in a follow-up of 8 individuals before (T1) and after (T2) bariatric surgery. The nOB group was used as reference. Distribution of white blood cells count (WBC) (cells/µL) before (T1) and after (T2) surgery (<b>A</b>). Percentage of lymphocytes (<b>B</b>), T lymphocytes (<b>C</b>), CD4<sup>+</sup> Treg cells (<b>D</b>), and B lymphocytes (<b>E</b>), within peripheral blood leukocytes (PB), before (T1) and after (T2) surgery. Percentage of activated B cells (measured within B cells) (<b>F</b>) and Tfh cells (within CD4<sup>+</sup> T cells) (<b>G</b>), activated Th1 cells (within Th1 cells) (<b>H</b>), and CD4<sup>+</sup> Tfr cells (within CD4<sup>+</sup> Treg cells) (<b>I</b>) before (T1) and after (T2) surgery. Percentages of PD-1<sup>+</sup> CD4<sup>+</sup> Tregs (<b>J</b>) and Th1-like Tregs (<b>K</b>) and percentage of TIM-3<sup>+</sup>CD4<sup>+</sup> T cells (<b>L</b>) before (T1) and after (T2) bariatric surgery. Percentage of Tc1/17, within CD8<sup>+</sup> T cells (<b>M</b>) and Tc1/17-like Tfc cells, within Tfc (<b>N</b>), at T1 and T2. Percentages of PD-1<sup>+</sup> CD8<sup>+</sup> T cells (<b>O</b>) and Tfc cells (<b>P</b>) and percentage of TIM-3<sup>+</sup>CD8<sup>+</sup> T cells (<b>Q</b>) before (T1) and after (T2) surgery. nOB: healthy participants (without obesity). Statistical differences were considered when <span class="html-italic">p</span> &lt; 0.05. <sup>A</sup> <span class="html-italic">p</span> &lt; 0.05 vs. nOB; <sup>B</sup> <span class="html-italic">p</span> &lt; 0.05 vs. T1 unpaired; <sup>C</sup> <span class="html-italic">p</span> &lt; 0.05 vs. T1 paired.</p>
Full article ">
17 pages, 4482 KiB  
Article
Integrated Analysis of Gut Microbiome and Adipose Transcriptome Reveals Beneficial Effects of Resistant Dextrin from Wheat Starch on Insulin Resistance in Kunming Mice
by Xinyang Chen, Yinchen Hou, Aimei Liao, Long Pan, Shengru Yang, Yingying Liu, Jingjing Wang, Yingchun Xue, Mingyi Zhang, Zhitong Zhu and Jihong Huang
Biomolecules 2024, 14(2), 186; https://doi.org/10.3390/biom14020186 - 4 Feb 2024
Cited by 1 | Viewed by 2116
Abstract
Systemic chronic inflammation is recognized as a significant contributor to the development of obesity-related insulin resistance. Previous studies have revealed the physiological benefits of resistant dextrin (RD), including obesity reduction, lower fasting glucose levels, and anti-inflammation. The present study investigated the effects of [...] Read more.
Systemic chronic inflammation is recognized as a significant contributor to the development of obesity-related insulin resistance. Previous studies have revealed the physiological benefits of resistant dextrin (RD), including obesity reduction, lower fasting glucose levels, and anti-inflammation. The present study investigated the effects of RD intervention on insulin resistance (IR) in Kunming mice, expounding the mechanisms through the gut microbiome and transcriptome of white adipose. In this eight-week study, we investigated changes in tissue weight, glucose–lipid metabolism levels, serum inflammation levels, and lesions of epididymal white adipose tissue (eWAT) evaluated via Hematoxylin and Eosin (H&E) staining. Moreover, we analyzed the gut microbiota composition and transcriptome of eWAT to assess the potential protective effects of RD intervention. Compared with a high-fat, high-sugar diet (HFHSD) group, the RD intervention significantly enhanced glucose homeostasis (e.g., AUC-OGTT, HOMA-IR, p < 0.001), and reduced lipid metabolism (e.g., TG, LDL-C, p < 0.001) and serum inflammation levels (e.g., IL-1β, IL-6, p < 0.001). The RD intervention also led to changes in the gut microbiota composition, with an increase in the abundance of probiotics (e.g., Parabacteroides, Faecalibaculum, and Muribaculum, p < 0.05) and a decrease in harmful bacteria (Colidextribacter, p < 0.05). Moreover, the RD intervention had a noticeable effect on the gene transcription profile of eWAT, and KEGG enrichment analysis revealed that differential genes were enriched in PI3K/AKT, AMPK, in glucose-lipid metabolism, and in the regulation of lipolysis in adipocytes signaling pathways. The findings demonstrated that RD not only ameliorated IR, but also remodeled the gut microbiota and modified the transcriptome profile of eWAT. Full article
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Graphical abstract

Graphical abstract
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<p>The animal experimental design.</p>
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<p>RD ameliorates glucose–lipid metabolism disorders in HFHSD Kunming mice (<span class="html-italic">n</span> = 10). (<b>A</b>) FBG every two weeks. (<b>B</b>) OGTT at the end of intervention. (<b>C</b>) HOMA-IR at the end of intervention. (<b>D</b>) Area under the curve (AUC) of OGTT. (<b>E</b>–<b>H</b>) Serum lipid profiles. ** <span class="html-italic">p</span> &lt; 0.01, and *** <span class="html-italic">p</span> &lt; 0.001 vs. HFHSD group.</p>
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<p>RD ameliorates eWAT weight, cellular hypertrophy and serum inflammation levels in HFHSD Kunming mice (<span class="html-italic">n</span> = 10). (<b>A</b>) The eWAT weight. (<b>B</b>) H&amp;E staining. (<b>C</b>,<b>D</b>) Serum IL-1β and IL-6 levels. * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01 and *** <span class="html-italic">p</span> &lt; 0.001 vs. HFHSD group.</p>
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<p>RD intervention modifies the eWAT transcriptome profiles in HFHSD Kunming mice (<span class="html-italic">n</span> = 2). (<b>A</b>) Principal component analysis, ND group: A1 and A9; HFHSD group: B1 and B3; HFHSD+RD group: D2 and D10. (<b>B</b>) Venn diagram showing the total number of DEGs in the HFHSD vs. ND and HFHSD+RD vs. HFHSD comparisons. (<b>C</b>) Volcano plot showing the significant DEGs for the HFHSD and ND groups. (<b>D</b>) Volcano plot showing the significant DEGs for HFHSD+RD and HFHSD groups. (<b>E</b>) KEGG enrichment analysis showing the top 20 signaling pathways affected by DEGs between HFHSD and ND groups (FDR &lt; 0.01 |log<sub>2</sub>FC| ≥ 2). (<b>F</b>) KEGG enrichment analysis showing the top 20 signaling pathways affected by DEGs between HFHSD+RD and HFHSD groups (FDR &lt; 0.01 |log<sub>2</sub>FC| ≥ 2).</p>
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<p>RD alters gut microbiota composition in HFHSD Kunming mice (<span class="html-italic">n</span> = 6). (<b>A</b>) Venn diagram depicting the number of bacteria at the OTU level between ND, HFHSD, HFHSD+RD, and HFHSD+MC groups. (<b>B</b>) Principal coordinate analyses based on OTUs of the microbial community in the four groups. (<b>C</b>) Gut microbiota composition at the genus level between the four groups of mice (abundance &gt; 20). (<b>D</b>) Variances in the genus-level flora between HFHSD and HFHSD+RD mice employing the Wilcoxon rank-sum test. (<b>E</b>) LDA scores of differentially abundant taxa among HFHSD, HFHSD+RD, and HFHSD+MC mice, using the LEfSE method (LDA score &gt; 3.5).</p>
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<p>Correlation analysis of gut microbiota and glucose–lipid metabolism factors. (<span class="html-italic">n</span> = 6, * <span class="html-italic">p</span> &lt; 0.05, ** <span class="html-italic">p</span> &lt; 0.01, *** <span class="html-italic">p</span> &lt; 0.001 indicates a significant difference).</p>
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12 pages, 3180 KiB  
Article
Effect of Metformin Treatment on Serum Metabolic Profile Changes in Lean and Obese Zucker Rat Model for Fatty Liver Disease
by Stepan Melnyk and Reza Hakkak
Biomolecules 2023, 13(8), 1234; https://doi.org/10.3390/biom13081234 - 10 Aug 2023
Viewed by 1304
Abstract
Excessive weight and obesity are the leading risk factors for the development of chronic diseases, including diabetes. Metformin is capable of significantly improving coexisting complications of diabetes. We used a metabolomics approach to examine the effects of metformin administration on lean and obese [...] Read more.
Excessive weight and obesity are the leading risk factors for the development of chronic diseases, including diabetes. Metformin is capable of significantly improving coexisting complications of diabetes. We used a metabolomics approach to examine the effects of metformin administration on lean and obese (fa/fa) Zucker rats. After 1 week of acclimation, twenty-eight 5-week-old female lean and obese rats were randomly assigned to and maintained in the following four groups (seven rats/group) for 10 weeks: (1) lean control (LC); (2) obese control (OC); (3) lean metformin (LM); and (4) obese metformin (OM). At the end of 10 weeks, serum was collected and analyzed using HPLC with electrochemical detection, HPLC with UV detection, and liquid chromatography mass spectrometry. We selected 50 metabolites’ peaks that were shared by all four groups of rats. Peak heights, as a defining factor, generally decreased in metformin-treated lean rats vs. untreated lean controls (3 LM:16 LC). Peak heights generally increased in metformin-treated obese rats vs. untreated obese controls (14 OM:5 OC). Overall, individual peaks were distributed as 11 that represented only lean rats, 11 that represented only obese rats, and 8 that were common among both lean and obese rats. In future studies, we will use a targeted metabolomics approach to identify those metabolites, map them to biochemical pathways and create a list of biomarkers. In summary, the current study contributed to a better understanding of the basic metabolic changes of lean and obese rats and demonstrated that both obesity and metformin make a significant impact on the metabolome of Zucker rats. Full article
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<p>Metformin mechanism of action. (green color means decreasing risk and red is increasing risk).</p>
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<p>Heat map of metabolic differences in serum between lean and obese, control and metformin-treated Zucker rats. <span class="html-fig-inline" id="biomolecules-13-01234-i001"><img alt="Biomolecules 13 01234 i001" src="/biomolecules/biomolecules-13-01234/article_deploy/html/images/biomolecules-13-01234-i001.png"/></span> Statistically higher (<span class="html-italic">p</span> &lt; 0.05); <span class="html-fig-inline" id="biomolecules-13-01234-i002"><img alt="Biomolecules 13 01234 i002" src="/biomolecules/biomolecules-13-01234/article_deploy/html/images/biomolecules-13-01234-i002.png"/></span> Statistically lower (<span class="html-italic">p</span> &lt; 0.05); <span class="html-fig-inline" id="biomolecules-13-01234-i003"><img alt="Biomolecules 13 01234 i003" src="/biomolecules/biomolecules-13-01234/article_deploy/html/images/biomolecules-13-01234-i003.png"/></span> Marginal (0.1 &gt; <span class="html-italic">p</span> &gt; 0.05); <span class="html-fig-inline" id="biomolecules-13-01234-i004"><img alt="Biomolecules 13 01234 i004" src="/biomolecules/biomolecules-13-01234/article_deploy/html/images/biomolecules-13-01234-i004.png"/></span> No difference (<span class="html-italic">p</span> &gt; 0.05).</p>
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<p>Peaks distribution in lean control (LC), lean metformin (LM), obese control (OC), and obese metformin (OM) rats. <span class="html-fig-inline" id="biomolecules-13-01234-i005"><img alt="Biomolecules 13 01234 i005" src="/biomolecules/biomolecules-13-01234/article_deploy/html/images/biomolecules-13-01234-i005.png"/></span> <span class="html-italic">p</span> &lt; 0.05, <span class="html-fig-inline" id="biomolecules-13-01234-i006"><img alt="Biomolecules 13 01234 i006" src="/biomolecules/biomolecules-13-01234/article_deploy/html/images/biomolecules-13-01234-i006.png"/></span> 0.1 &gt; <span class="html-italic">p</span> &gt; 0.05, <span class="html-fig-inline" id="biomolecules-13-01234-i007"><img alt="Biomolecules 13 01234 i007" src="/biomolecules/biomolecules-13-01234/article_deploy/html/images/biomolecules-13-01234-i007.png"/></span> <span class="html-italic">p</span> &gt; 0.05.</p>
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<p>Metabolic differences in individual peak heights between lean and obese Zucker rats. <span class="html-fig-inline" id="biomolecules-13-01234-i008"><img alt="Biomolecules 13 01234 i008" src="/biomolecules/biomolecules-13-01234/article_deploy/html/images/biomolecules-13-01234-i008.png"/></span> Peaks more pronounced in obese animals. <span class="html-fig-inline" id="biomolecules-13-01234-i009"><img alt="Biomolecules 13 01234 i009" src="/biomolecules/biomolecules-13-01234/article_deploy/html/images/biomolecules-13-01234-i009.png"/></span> Peaks more pronounced in lean animals. <span class="html-fig-inline" id="biomolecules-13-01234-i010"><img alt="Biomolecules 13 01234 i010" src="/biomolecules/biomolecules-13-01234/article_deploy/html/images/biomolecules-13-01234-i010.png"/></span> Peak that are similar among lean and obese animals.</p>
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<p>Metabolic differences in individual peak heights between lean and obese Zucker rats. <span class="html-fig-inline" id="biomolecules-13-01234-i008"><img alt="Biomolecules 13 01234 i008" src="/biomolecules/biomolecules-13-01234/article_deploy/html/images/biomolecules-13-01234-i008.png"/></span> Peaks more pronounced in obese animals. <span class="html-fig-inline" id="biomolecules-13-01234-i009"><img alt="Biomolecules 13 01234 i009" src="/biomolecules/biomolecules-13-01234/article_deploy/html/images/biomolecules-13-01234-i009.png"/></span> Peaks more pronounced in lean animals. <span class="html-fig-inline" id="biomolecules-13-01234-i010"><img alt="Biomolecules 13 01234 i010" src="/biomolecules/biomolecules-13-01234/article_deploy/html/images/biomolecules-13-01234-i010.png"/></span> Peak that are similar among lean and obese animals.</p>
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<p>Changes of individual peak heights of metformin-treated lean and obese Zucker rats. <span class="html-fig-inline" id="biomolecules-13-01234-i011"><img alt="Biomolecules 13 01234 i011" src="/biomolecules/biomolecules-13-01234/article_deploy/html/images/biomolecules-13-01234-i011.png"/></span> Peaks higher in the lean group. <span class="html-fig-inline" id="biomolecules-13-01234-i012"><img alt="Biomolecules 13 01234 i012" src="/biomolecules/biomolecules-13-01234/article_deploy/html/images/biomolecules-13-01234-i012.png"/></span> Peaks higher in the obese group.</p>
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Review

Jump to: Research

50 pages, 3269 KiB  
Review
Anti-Diabetic Therapies and Cancer: From Bench to Bedside
by Dimitris Kounatidis, Natalia G. Vallianou, Irene Karampela, Eleni Rebelos, Marina Kouveletsou, Vasileios Dalopoulos, Petros Koufopoulos, Evanthia Diakoumopoulou, Nikolaos Tentolouris and Maria Dalamaga
Biomolecules 2024, 14(11), 1479; https://doi.org/10.3390/biom14111479 - 20 Nov 2024
Viewed by 113
Abstract
Diabetes mellitus (DM) is a significant risk factor for various cancers, with the impact of anti-diabetic therapies on cancer progression differing across malignancies. Among these therapies, metformin has gained attention for its potential anti-cancer effects, primarily through modulation of the AMP-activated protein kinase/mammalian [...] Read more.
Diabetes mellitus (DM) is a significant risk factor for various cancers, with the impact of anti-diabetic therapies on cancer progression differing across malignancies. Among these therapies, metformin has gained attention for its potential anti-cancer effects, primarily through modulation of the AMP-activated protein kinase/mammalian target of rapamycin (AMPK/mTOR) pathway and the induction of autophagy. Beyond metformin, other conventional anti-diabetic treatments, such as insulin, sulfonylureas (SUs), pioglitazone, and dipeptidyl peptidase-4 (DPP-4) inhibitors, have also been examined for their roles in cancer biology, though findings are often inconclusive. More recently, novel medications, like glucagon-like peptide-1 (GLP-1) receptor agonists, dual GLP-1/glucose-dependent insulinotropic polypeptide (GIP) agonists, and sodium-glucose co-transporter-2 (SGLT-2) inhibitors, have revolutionized DM management by not only improving glycemic control but also delivering substantial cardiovascular and renal benefits. Given their diverse metabolic effects, including anti-obesogenic properties, these novel agents are now under meticulous investigation for their potential influence on tumorigenesis and cancer advancement. This review aims to offer a comprehensive exploration of the evolving landscape of glucose-lowering treatments and their implications in cancer biology. It critically evaluates experimental evidence surrounding the molecular mechanisms by which these medications may modulate oncogenic signaling pathways and reshape the tumor microenvironment (TME). Furthermore, it assesses translational research and clinical trials to gauge the practical relevance of these findings in real-world settings. Finally, it explores the potential of anti-diabetic medications as adjuncts in cancer treatment, particularly in enhancing the efficacy of chemotherapy, minimizing toxicity, and addressing resistance within the framework of immunotherapy. Full article
56 pages, 3906 KiB  
Review
Metabolic Syndrome and Biotherapeutic Activity of Dairy (Cow and Buffalo) Milk Proteins and Peptides: Fast Food-Induced Obesity Perspective—A Narrative Review
by Kenbon Beyene Abdisa, Emőke Szerdahelyi, Máté András Molnár, László Friedrich, Zoltán Lakner, András Koris, Attila Toth and Arijit Nath
Biomolecules 2024, 14(4), 478; https://doi.org/10.3390/biom14040478 - 14 Apr 2024
Viewed by 2268
Abstract
Metabolic syndrome (MS) is defined by the outcome of interconnected metabolic factors that directly increase the prevalence of obesity and other metabolic diseases. Currently, obesity is considered one of the most relevant topics of discussion because an epidemic heave of the incidence of [...] Read more.
Metabolic syndrome (MS) is defined by the outcome of interconnected metabolic factors that directly increase the prevalence of obesity and other metabolic diseases. Currently, obesity is considered one of the most relevant topics of discussion because an epidemic heave of the incidence of obesity in both developing and underdeveloped countries has been reached. According to the World Obesity Atlas 2023 report, 38% of the world population are presently either obese or overweight. One of the causes of obesity is an imbalance of energy intake and energy expenditure, where nutritional imbalance due to consumption of high-calorie fast foods play a pivotal role. The dynamic interactions among different risk factors of obesity are highly complex; however, the underpinnings of hyperglycemia and dyslipidemia for obesity incidence are recognized. Fast foods, primarily composed of soluble carbohydrates, non-nutritive artificial sweeteners, saturated fats, and complexes of macronutrients (protein-carbohydrate, starch-lipid, starch-lipid-protein) provide high metabolic calories. Several experimental studies have pointed out that dairy proteins and peptides may modulate the activities of risk factors of obesity. To justify the results precisely, peptides from dairy milk proteins were synthesized under in vitro conditions and their contributions to biomarkers of obesity were assessed. Comprehensive information about the impact of proteins and peptides from dairy milks on fast food-induced obesity is presented in this narrative review article. Full article
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<p>Risk factors of MS and associated metabolic diseases (self-developed, concept was adopted from Mendrick et al., 2018 [<a href="#B7-biomolecules-14-00478" class="html-bibr">7</a>]).</p>
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<p>Interconnection between genetic factors, environmental factors, dietary pattern and gut microbiota associated with the incidence of obesity (self-developed, concept was adopted from Ussar et al., 2016 [<a href="#B36-biomolecules-14-00478" class="html-bibr">36</a>]).</p>
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<p>Metabolic pathways for diabetes and associated complications, induced by the consumption of high-calorie fast foods (self-developed, concept was adopted from Kang and Yang, 2020 [<a href="#B108-biomolecules-14-00478" class="html-bibr">108</a>], and Naveen and Baskaran, 2018 [<a href="#B109-biomolecules-14-00478" class="html-bibr">109</a>]).</p>
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<p>Biochemical pathways of (<b>A</b>) formation of exogenous and endogenous AGEs (self-developed, concepts were adopted from Inan-Eroglu et al., 2020 [<a href="#B124-biomolecules-14-00478" class="html-bibr">124</a>] and Takeuchi et al., 2020 [<a href="#B125-biomolecules-14-00478" class="html-bibr">125</a>]), and (<b>B</b>) AGE/RAGE-mediated signaling and metabolic pathways for DM (self-developed, concept was adopted from Salazar et al., 2021 [<a href="#B126-biomolecules-14-00478" class="html-bibr">126</a>] and Dong et al., 2023 [<a href="#B127-biomolecules-14-00478" class="html-bibr">127</a>]). Abbreviations: Fru-AGEs: Fructose-derived glycation end products, Glycer-AGEs: Glyceraldehyde-derived glycation end products, MGO-AGEs: Methylglyoxal-derived glycation end products, 3-DG-AGEs: 3-Deoxyglucosone-derived glycation end products, Glu-AGEs: Glucose-derived glycation end products, Glycol-AGEs: Glycol-derived glycation end products, GO-AGEs: Glyoxal-derived glycation end products.</p>
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<p>Metabolic pathways for dyslipidemia caused by soluble carbohydrates (glucose and fructose) and dietary fats in high-calorie fast foods (self-developed, concept was adopted from Fukushima et al., 2015 [<a href="#B154-biomolecules-14-00478" class="html-bibr">154</a>], and Mato et al., 2019 [<a href="#B155-biomolecules-14-00478" class="html-bibr">155</a>]).</p>
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<p>Physiological functions of milk protein-derived peptides (self-developed, concept was adopted from Park and Nam, 2015 [<a href="#B202-biomolecules-14-00478" class="html-bibr">202</a>]).</p>
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<p>Interconnections between inflammation, oxidative stress, and dysregulation of glucose and lipid metabolism (self-developed; concept was adopted from Putnam et al., 2012 [<a href="#B293-biomolecules-14-00478" class="html-bibr">293</a>]).</p>
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19 pages, 2445 KiB  
Review
Polyphenols: Role in Modulating Immune Function and Obesity
by Md Abdullah Al Mamun, Ahmed Rakib, Mousumi Mandal, Santosh Kumar, Bhupesh Singla and Udai P. Singh
Biomolecules 2024, 14(2), 221; https://doi.org/10.3390/biom14020221 - 14 Feb 2024
Cited by 6 | Viewed by 3530
Abstract
Polyphenols, long-used components of medicinal plants, have drawn great interest in recent years as potential therapeutic agents because of their safety, efficacy, and wide range of biological effects. Approximately 75% of the world’s population still use plant-based medicinal compounds, indicating the ongoing significance [...] Read more.
Polyphenols, long-used components of medicinal plants, have drawn great interest in recent years as potential therapeutic agents because of their safety, efficacy, and wide range of biological effects. Approximately 75% of the world’s population still use plant-based medicinal compounds, indicating the ongoing significance of phytochemicals for human health. This study emphasizes the growing body of research investigating the anti-adipogenic and anti-obesity functions of polyphenols. The functions of polyphenols, including phenylpropanoids, flavonoids, terpenoids, alkaloids, glycosides, and phenolic acids, are distinct due to changes in chemical diversity and structural characteristics. This review methodically investigates the mechanisms by which naturally occurring polyphenols mediate obesity and metabolic function in immunomodulation. To this end, hormonal control of hunger has the potential to inhibit pro-obesity enzymes such as pancreatic lipase, the promotion of energy expenditure, and the modulation of adipocytokine production. Specifically, polyphenols affect insulin, a hormone that is essential for regulating blood sugar, and they also play a role, in part, in a complex web of factors that affect the progression of obesity. This review also explores the immunomodulatory properties of polyphenols, providing insight into their ability to improve immune function and the effects of polyphenols on gut health, improving the number of commensal bacteria, cytokine production suppression, and immune cell mediation, including natural killer cells and macrophages. Taken together, continuous studies are required to understand the prudent and precise mechanisms underlying polyphenols’ therapeutic potential in obesity and immunomodulation. In the interim, this review emphasizes a holistic approach to health and promotes the consumption of a wide range of foods and drinks high in polyphenols. This review lays the groundwork for future developments, indicating that the components of polyphenols and their derivatives may provide the answer to urgent worldwide health issues. This compilation of the body of knowledge paves the way for future discoveries in the global treatment of pressing health concerns in obesity and metabolic diseases. Full article
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<p>Polyphenols play a role in modifying obesity. They hinder the production of free fatty acids and mitigate the hypersecretion of insulin associated with obesity. Natural polyphenols facilitate BAT thermogenesis and lipolysis, aiding in the regulation of adiposity by enhancing the expression of UCP1 and SIRT1. Furthermore, polyphenols downregulate gene transcription of FAS and PPARγ, thereby inhibiting lipogenesis and adipocyte differentiation. Additionally, they prevent TNF-α and IL-6-mediated NF- κB expression, subsequently averting inflammation.</p>
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<p>Polyphenols enhance the well-being of the digestive system by promoting a healthy community of beneficial bacteria and preserving an equilibrium between Th17 and Treg. Harmful microorganisms can harm the mucosal lining of the gut, allowing the invasion of antigens and triggering the activation of antigen-presenting cells, such as dendritic cells. Polyphenols intervene in this process, obstructing the pathway and averting subsequent inflammation driven by proinflammatory cytokines such as IL-10, IL-6, and IL-17.</p>
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<p>(<b>A</b>) The initiation of M1 differentiation is prompted by the activation of toll-like receptors (TLRs) and the stimulation of IFN through bacterial LPS. Conversely, M2 polarization is initiated by IL-4 and IL-13. Polyphenols promote the conversion of macrophages to anti-inflammatory M2 phenotype. (<b>B</b>) The generation of ROS is facilitated by the NADPH oxidase family. Polyphenols, particularly flavonols, can reduce NADPH activity, leading to a subsequent reduction in ROS production and inflammation.</p>
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27 pages, 2508 KiB  
Review
Trace Amine-Associated Receptors and Monoamine-Mediated Regulation of Insulin Secretion in Pancreatic Islets
by Anastasia N. Vaganova, Taisiia S. Shemyakova, Karina V. Lenskaia, Roman N. Rodionov, Charlotte Steenblock and Raul R. Gainetdinov
Biomolecules 2023, 13(11), 1618; https://doi.org/10.3390/biom13111618 - 5 Nov 2023
Cited by 1 | Viewed by 2563
Abstract
Currently, metabolic syndrome treatment includes predominantly pharmacological symptom relief and complex lifestyle changes. Trace amines and their receptor systems modulate signaling pathways of dopamine, norepinephrine, and serotonin, which are involved in the pathogenesis of this disorder. Trace amine-associated receptor 1 (TAAR1) is expressed [...] Read more.
Currently, metabolic syndrome treatment includes predominantly pharmacological symptom relief and complex lifestyle changes. Trace amines and their receptor systems modulate signaling pathways of dopamine, norepinephrine, and serotonin, which are involved in the pathogenesis of this disorder. Trace amine-associated receptor 1 (TAAR1) is expressed in endocrine organs, and it was revealed that TAAR1 may regulate insulin secretion in pancreatic islet β-cells. For instance, accumulating data demonstrate the positive effect of TAAR1 agonists on the dynamics of metabolic syndrome progression and MetS-associated disease development. The role of other TAARs (TAAR2, TAAR5, TAAR6, TAAR8, and TAAR9) in the islet’s function is much less studied. In this review, we summarize the evidence of TAARs’ contribution to the metabolic syndrome pathogenesis and regulation of insulin secretion in pancreatic islets. Additionally, by the analysis of public transcriptomic data, we demonstrate that TAAR1 and other TAAR receptors are expressed in the pancreatic islets. We also explore associations between the expression of TAARs mRNA and other genes in studied samples and demonstrate the deregulation of TAARs’ functional associations in patients with metabolic diseases compared to healthy donors. Full article
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<p>The role of monoamine neurotransmitters in the insulin secretion of β-cells. (<b>a</b>) The main sources of monoamine neurotransmitters in pancreatic islets; all monoamine neurotransmitters may be synthesized in islets (1), the dopamine synthesis in islets needs L-DOPA which is realized from acini (2) or other sources, dopamine or norepinephrine are realized from the nerve endings (3), or acquired from the circulation (4). (<b>b</b>) Monoamines and their receptors are involved in the pancreatic islet hormone production. The activation effect is marked by red arrows, inhibition is marked by blue blunt arrows, and the dotted line indicates bioactive molecules’ release. Parts of the figure were drawn by using pictures from Servier Medical Art. Servier Medical Art by Servier is licensed under a Creative Commons Attribution 3.0 Unported License.</p>
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<p>The interactions between dopamine and insulin signaling. (<b>a</b>) The crosstalk between insulin signaling and dopamine signaling in the brain where the D1R and D2R receptors in the nucleus accumbens and VTA and dopamine receptors D1R in striatonigral pathway neurons regulate the reward system, which contributes to the normalization of eating behavior (1, 2, 3). The dopamine interaction with D2R receptor in the hypothalamus prevents prolactin production and consequent hyperinsulinemia (4). At the same time, insulin dualistically affects dopamine transmission by the activation of dopamine reuptake by DAT and prevention of dopamine degradation by MAO enzymes. (<b>b</b>) Intracellular interaction of insulin signaling and dopamine signaling resulting from common downstream targets, D2R inhibits Akt signaling by the β-arrestin-mediated signaling cascade (1) and activates MEK signaling (2) while D1R stimulation activates MEK via PKA-DARPP32 pathway (3) instead. Parts of the figure were drawn by using pictures from Servier Medical Art. Servier Medical Art by Servier is licensed under a Creative Commons Attribution 3.0 Unported License. DR1—dopamine receptor 1, DR2—dopamine receptor 2, IR—insulin receptor, IRS—insulin receptor substrate, SHC—<span class="html-italic">Src</span> homology 2 domain containing, PP2A—protein phosphatase 2A, PI3K—phosphatidylinositol 3-kinase, PDK1—pyruvate dehydrogenase kinase 1, GSK3—glycogen synthase kinase 3, MEK—mitogen-activated protein kinase kinase, ERK 1/2—extracellular signal-regulated kinase 1/2, PKA—protein kinase A, DARPP32—dopamine- and cAMP-regulated phosphoprotein with an apparent Mr of 32,000, PP1—protein phosphatase 1, STEP—striatal-enriched protein tyrosine phosphatase.</p>
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<p>The associations between TAAR-mediated signaling and mechanisms involved in metabolic syndrome development. (1) TAAR1 expression was identified in brain structure involved in the reward system regulation which is involved in food consumption regulation; (2) TAAR1 was identified in the stomach epithelium, its activation on the D-cells leads to the somatostatin realizing, downregulation of ghrelin production, and reduces the feeling of hunger; (3) in the duodenum, TAAR1, TAAR2, and TAAR9 were identified; it is suggested that these receptors are involved in the regulation of neuroendocrine secretory cells that control appetite and glucose metabolism; (4) TAAR1 is expressed in β-cells and regulates insulin secretion, TAAR1 agonists could stimulate insulin secretion, but if TAAR1 dimerizes with ADRA2A, the effect of its activation on insulin secretion becomes inhibitory; (5) TAAR9 seems to be involved in the cholesterol metabolism regulation, but this regulatory mechanism is not clearly understood yet. Parts of the figure were drawn using pictures from Servier Medical Art. Servier Medical Art by Servier is licensed under a Creative Commons Attribution 3.0 Unported License.</p>
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<p><span class="html-italic">TAARs</span> and <span class="html-italic">TAAR</span> co-expressed gene clusters in pancreatic islets. (<b>a</b>) Expression levels of <span class="html-italic">TAAR1</span>, <span class="html-italic">TAAR5</span>, <span class="html-italic">TAAR6</span>, <span class="html-italic">TAAR8</span>, and <span class="html-italic">TAAR9</span> in pancreatic islets isolated from healthy donors; (<b>b</b>) Venn diagram representing overlaps between <span class="html-italic">TAAR1</span>, <span class="html-italic">TAAR5</span>, <span class="html-italic">TAAR6</span>, <span class="html-italic">TAAR8</span>, and <span class="html-italic">TAAR9</span> co-expressed gene clusters; (<b>c</b>) analysis of Gene Ontology (GO) enrichment of <span class="html-italic">TAAR</span> co-expressed gene clusters; (<b>d</b>) analysis of KEGG pathway enrichment of <span class="html-italic">TAAR</span> co-expressed gene clusters.</p>
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<p><span class="html-italic">TAAR1</span> and <span class="html-italic">TAAR1</span> co-expressed gene clusters in pancreatic islets isolated from healthy donors and patients with metabolic diseases. (<b>a</b>) Expression levels of <span class="html-italic">TAAR1</span> healthy donors and patients with metabolic diseases; (<b>b</b>) functional similarity of <span class="html-italic">TRAA1</span> co-expressed genes in islets isolated from healthy donors and patients with metabolic diseases. ND—non-diabetic (healthy donors), PreD—pre-diabetic, T2D—type 2 diabetes, T3cD—type 3c patients. *—<span class="html-italic">p</span> &lt; 0.05, **—<span class="html-italic">p</span> &lt; 0.01, ****—<span class="html-italic">p</span> &lt; 0.001</p>
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