Next Article in Journal
Avoiding High Pressure Abdominal Closure of Congenital Abdominal Wall Defects—One Step Further to Improve Outcomes
Previous Article in Journal
The Difference in the Creativity of People Who Are Deaf or Hard of Hearing and Those with Typical Hearing: A Scoping Review
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Children
Volume 10
Issue 8
10.3390/children10081382
children-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Mediation Analysis of Waist Circumference in the Association of Gut Microbiota with Insulin Resistance in Children

by
Juan Carlos Ayala-García
1,†,
Cinthya Estefhany Díaz-Benítez
1,†,
Alfredo Lagunas-Martínez
1,
Yaneth Citlalli Orbe-Orihuela
1,
Ana Cristina Castañeda-Márquez
2,
Eduardo Ortiz-Panozo
3,
Víctor Hugo Bermúdez-Morales
1,
Miguel Cruz
4 and
Ana Isabel Burguete-García
1,*
1
Centro de Investigación Sobre Enfermedades Infecciosas, Instituto Nacional de Salud Pública, Cuernavaca 62100, Mexico
2
Instituto de Investigación Científica, Universidad Juárez del Estado de Durango, Durango 34000, Mexico
3
Centro de Investigación en Salud Poblacional, Instituto Nacional de Salud Pública, Cuernavaca 62100, Mexico
4
Unidad de Investigación Médica en Bioquímica, Centro Médico Nacional Siglo XXI, Ciudad de México 06720, Mexico
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this manuscript.
Children 2023, 10(8), 1382; https://doi.org/10.3390/children10081382
Submission received: 11 July 2023 / Revised: 4 August 2023 / Accepted: 10 August 2023 / Published: 14 August 2023
(This article belongs to the Section Pediatric Endocrinology & Diabetes)
Browse Figure
Review Reports Versions Notes

Abstract

:
Background: Persistent gut microbiota (GM) imbalance has been associated with metabolic disease development. This study evaluated the mediating role of waist circumference in the association between GM and insulin resistance (IR) in children. Methods: This cross-sectional study included 533 children aged between 6 and 12. The anthropometry, metabolic markers, and relative abundance (RA) of five intestinal bacterial species were measured. Path coefficients were estimated using path analysis to assess direct, indirect (mediated by waist circumference), and total effects on the association between GM and IR. Results: The results indicated a positive association mediated by waist circumference between the medium and high RA of S. aureus with homeostatic model assessments for insulin resistance (HOMA-IR) and for insulin resistance adiponectin-corrected (HOMA-AD). We found a negative association mediated by waist circumference between the low and medium RA of A. muciniphila and HOMA-IR and HOMA-AD. Finally, when we evaluated the joint effect of S. aureus, L. casei, and A. muciniphila, we found a waist circumference-mediated negative association with HOMA-IR and HOMA-AD. Conclusions: Waist circumference is a crucial mediator in the association between S. aureus and A. muciniphila RA and changes in HOMA-IR and HOMA-AD scores in children.

1. Introduction

Insulin resistance (IR) is defined as an increase in the production and secretion of insulin in the beta cells of the pancreas as a compensatory process for the alteration in the elimination of glucose in the target tissues—mainly the liver, muscles, and adipose tissue [1]. The childhood obesity epidemic worldwide has directly impacted the increase in IR in this population. The factors associated with the development of IR in children include sex, ethnic origin, chronic low-grade inflammation, cellular dysfunction, and mainly excess visceral adipose tissue [2,3].
During the last decade, the role of gut microbiota (GM) in developing metabolic diseases has been studied. GM has evolved with the human being, generating a symbiotic relationship that allows the host to perform essential physiological functions such as protection against pathogens, development of the immune response, participation in metabolism, digestion, and neuronal development [4,5].
GM is transferred from the mother to the fetus during the birthing process, depending on the route of birth, and colonization continues during the first few years of life. The composition of GM is largely dependent on dietary and environmental factors. However, antibiotic use, stress, and breastfeeding can disturb the composition of the microbiota, altering the physiological functions of the host [6,7]. This alteration in the composition of GM, known as gut dysbiosis, produces different activations of signaling pathways and phenotypic changes in humans, triggering chronic inflammatory processes and cardiometabolic diseases such as obesity, type 2 diabetes (T2D), and cardiovascular diseases [8]. We aimed to evaluate the direct, indirect (mediated by waist circumference), and total effects on the association between GM and IR in Mexican children.
In the international literature, some species such as Akkermansia muciniphila (A. muciniphila), Lactobacillus casei (L. casei), Lactobacillus paracasei (L. paracasei), Lactobacillus reuteri (L. reuteri), and Staphylococcus aureus (S. aureus) have attracted considerable attention due to their association with the development of metabolic diseases. A. muciniphila has been proposed as a potential probiotic in animal and human models; daily supplementation decreases total body weight, fat mass, waist circumference, and metabolic endotoxemia; it inhibits proinflammatory pathways and improves glucose tolerance [9,10].
Some Lactobacillus species are associated with protection, while others increase the risk of developing IR; this is due to differences in carbohydrate metabolism. While some species, such as L. casei and L. paracasei, can store carbohydrates as glycogen because they encode genes for glucose permease, L. reuteri lacks these enzymes involved in fructose catabolism [11,12]. Supplementation with L. casei and L. paracasei affects sirtuin 1 (SIRT 1) and fetuin-A levels, decreases blood glucose and insulin levels, and reduces inflammatory status in subjects with T2D [13,14].
S. aureus belongs to the phylum Firmicutes; these bacteria produce enzymes involved in the energetic extraction of food and its deposit in fat reserves. Overweight children present a higher abundance of S. aureus than normal-weight children, and S. aureus produces superantigens (Sags) that induce the production of proinflammatory cytokines in adipocytes, contributing to the development of chronic low-grade inflammation, which plays an essential role in the development of peripheral IR [15,16,17].
Adipose tissue plays an essential role in the effect of gut bacteria on insulin action, secreting several adipokines that participate in metabolic regulation, inflammation, immune function, and processes related to cardiometabolic diseases [18]. Compared to the main adipokines, adiponectin has an inverse relationship with obesity; it has been described that the reduction of body fat is associated with an increase in circulating levels of adiponectin [19]. It is also an insulin sensitizer, promoting insulin action by improving insulin resistance, and it increases the secretion of anti-inflammatory cytokines [20]. Because of its potential benefits, evaluating its role in the development of IR is relevant.
GM participates in the health–disease process of its host, so the present work aimed to evaluate the mediating role of waist circumference in the association between A. muciniphila, L. casei, L. paracasei, L.reuteri, and S. aureus with homeostatic model assessment for insulin resistance (HOMA-IR) and homeostatic model assessment for insulin resistance adiponectin-corrected (HOMA-AD) in Mexican children.

2. Materials and Methods

2.1. Design and Study Population

This cross-sectional study was conducted between 2012 and 2014 and approved by the Ethics, Research, and Biosafety Commissions of the Instituto Nacional de Salud Pública (INSP) with the approval number CI:1129, No. 1294. The analysis included the information of 533 unrelated children from 6 to 12 years old, living in four geographic zones of Mexico City (north, south, east, and west). Children diagnosed with infectious diseases or gastrointestinal disorders and those undergoing antibiotic treatment two months before the study were excluded. The data were obtained from previous work [21].
All children and parents signed their assent and informed consent, respectively. We performed the power calculation to detect indirect effects using the Monte Carlo method with an application in the R statistical language. We used the methodology described by Alexander M. Schoemann; using our sample size of n = 533 with 5000 simulations, 20,000 repetitions, and a confidence level of 95%, we obtained a statistical power of 1 [22].

2.2. Gut Microbiota

DNA was extracted from a 200 mg stool sample using the commercial kit QIAamp® DNA stool (Qiagen, Hilden, Germany). The DNA concentration and purity was determined using a spectrophotometer NanoDropTM Lite Thermo ScientificTM (Madison, Wisconsin, U.S.A.) with an absorbance of 260 nm and 260/280 nm. The relative abundance (RA) of A. muciniphila, L. casei, L. paracasei, L. reuteri, and S. aureus, was determined via quantitative polymerase chain reaction (qPCR) using specific universal primers (Table S1).
Each qPCR reaction was performed in duplicate using 5 μL of 2× Maxima SYBR Green/ROX qPCR Master Mix Thermo ScientificTM (Carlsbad, California, U.S.A.), 1 μL (five pmol) from each primer, 5 ng (S. aureus) and 10 ng (rest of species) of the DNA template, and 2 μL of nucleic acid-free water (Fermentas®, Carlsbad, California, U.S.A.), resulting in a final volume of 10 μL. The amplifications were performed with the StepOnePlusTM Real-Time PCR System (Applied Biosystems, Woodlands, Singapore, Singapore) under the following conditions: initial thermal cycling of ten minutes at 95 °C, 40 cycles with a denaturation phase at 95 °C for 15 s, an alignment phase at 56–60 °C for 20 s, and an elongation phase at 72 °C for 20 s.
The RA obtained with the following formula was calculated: UAR = 2−ΔCt, where UAR = Units of Relative Abundance and ΔCt = Ct specific primer-Ct universal primer [23].

2.3. Waist Circumference and Body Mass Index

The personnel who performed the measurement were trained and standardized in the correct measurement of children with calibrated instruments. Waist circumference (cm) was measured using the lower edge of the last palpable rib and the upper edge of the iliac crest around the waist as equidistant reference points. The weight (kg), height (cm), and body mass index (BMI, kg/m2) were measured. The children were tested without shoes on and with as little clothing as possible.

2.4. Biochemical Determination

Blood samples were collected via antecubital venipuncture under overnight fasting conditions for 12 h. The samples were centrifuged to separate the serum and stored at −80 °C until use.
Serum levels of glucose (mg/dL), insulin (mU/L), total cholesterol (mg/dL), high-density lipoproteins (HDL, mg/dL), low-density lipoproteins (LDL, mg/dL), and triglycerides (mg/dL) were measured via chemiluminescence to evaluate the metabolic status.
Serum adiponectin (µg/mL) concentrations were measured using commercial ELISA kits according to the manufacturer (PeproTech, Rocky Hill, New Jersey, U.S.A.), and absorbance was determined using a Labsystems Multiskan MS®, Vantaa, Finland.

2.5. Insulin Resistance

We converted glucose units from mg/dL to mmol/L to calculate the HOMA-IR and HOMA-AD, using the methodology of Sullara Vilela, as follows:
HOMA-IR = [glucose (mmol/L) × insulin (mU/L)]/22.5 and HOMA-AD = [glucose (mmol/L) × insulin (mU/L)]/[22.5 × adiponectin (µg/mL)] [24].

2.6. Hereditary Family History and Sociodemographic Data

Trained personnel used a questionnaire to collect information from the children and their parents or guardians regarding family history of T2D, high blood pressure, and overweight/obesity, as well as sociodemographic, socioeconomic, and pathological data.

2.7. Physical Activity

To collect information on physical activity (METs/hour/week) and inactivity, we used a validated questionnaire for Mexican students (CAINM) consisting of 40 questions [25].

2.8. Diet

Dietary intake was obtained through a validated semi-quantitative questionnaire focused on food consumption frequency. For more precise measurements, the questionnaire was administered to the children in the presence of their parents or guardians. The questionnaire included questions about the frequency and portions of 111 foods divided into sections. According to the reported frequency, grams of lipids, proteins, and carbohydrates were calculated as the average daily consumption.

2.9. Statistical Analysis

To describe the relevant variables in the population, we stratified children into two groups: normal weight and overweight/obese (OW/OB), according to BMI Z-scores for age [26]. We performed the Shapiro–Wilk test to evaluate normality. As the distribution of the variables was non-normal, between-group comparison was performed using the Mann–Whitney U test for continuous variables and chi-square (X2) for categorical variables. For the inferential analysis, we obtained the tertiles of RA for each bacterial species and dichotomized the waist circumference variable above and below the median of our sample (63.6 cm).
We used a logistic regression model to evaluate the association between GM and waist circumference and evaluated the association between GM and waist circumference with IR using linear regression. To consider mediation, we obtained the direct, indirect, and total effects via path analysis, taking the RA tertiles of each bacterial species as the independent variable, waist circumference as the mediator, and the HOMA-IR and HOMA-AD indexes as the dependent variables.
Next, we built a latent variable with the tertiles of the RA of the bacterial species in which we found an association with HOMA-IR and HOMA-AD; we used this latent variable as the independent variable, waist circumference as a mediator, and IR indexes as dependent variables. Figure 1 shows the Directed Acyclic Diagram (DAG), in which we can observe the causal relationship between GM and IR mediated by waist circumference. The confounding adjustment set included a family history of T2D, a family history of overweight/obesity (OW/OB), age, physical activity, sex, diet, and U (unmeasured) genes. However, the history of T2D and physical activity did not add variability to our estimator, so we decided not to include them as adjustment variables in order to ensure we had the most parsimonious models possible. We established statistical significance with a value of p < 0.05. All analyses were performed using the Stata® software version 17.

3. Results

We evaluated the general characteristics of 533 children according to the BMI Z-scores. Table 1 shows the median, 25th percentile, and 75th percentile for continuous variables and proportions for categorical variables.
The combined prevalence of (OW/OB) was 49%. Children with OW/OB had a higher median age, a higher prevalence of a history of OW/OB, a higher prevalence of a history of T2D, a greater waist circumference, and a greater median in the score of the HOMA-IR and HOMA-AD indexes compared to normal-weight children. We found no difference between the two groups in the distribution by physical activity, sex, macronutrient consumption, or RA of bacterial species.
The prevalence of children with HOMA-IR > 2 was 7% (n = 38), and the prevalence of children with HOMA-IR < 2 was 93% (n = 495).
When evaluating the metabolic status of both groups, we found that children with OW/OB had higher serum levels of cholesterol, triglycerides, LDL, and insulin and lower serum levels of HDL than normal-weight children (Table S2).
We analyzed the association between the RA of bacterial species and waist circumference, and as shown in Table 2, children with medium and high RA of S. aureus had higher odds of having a higher waist circumference than children with low RA of S. aureus [OR 2.30 (95% CI: 1.44, 3.65) and OR 1.72 (95% CI: 1.08, 2.73)]. Children with medium RA of L. casei had lower odds of having a higher waist circumference than children with high RA of L. casei [OR 0.63 (95% CI: 0.40, 1.00)]. Finally, children with low and medium RA of A. muciniphila had lower odds of having a higher waist circumference than children with high RA of A. muciniphila [OR 0.62 (95% CI: 0.39, 0.99) and OR 0.50 (95% CI: 0.32, 0.80)].
Through linear regression, we evaluated the association between the tertiles of RA of each bacterial species and waist circumference with IR indexes. After adjusting for confounders, we determined that children with higher waist circumferences showed a 62% increase in their HOMA-IR score [β = 0.62 (95% CI: 0.49, 0.75)] and a 16% increase in their HOMA-AD score [β = 0.16 (95% CI: 0.13, 0.20)] than children with lower waist circumferences (Table 3).
Table 4 shows the direct, indirect (mediated by waist circumference), and total effects obtained via path analysis (Figure S1). Children with medium RA of L. paracasei showed a 15% increase in their HOMA-IR score compared to children with high RA of L. paracasei [PC = 0.15 (95% CI: 0.002, 0.30)].
Regarding indirect effects, the waist circumference measurements show that children with medium RA of S. aureus had an 11% increase in HOMA-IR score [PC = 0.11 (95% CI: 0.04, 0.17)] and a 3% increase in HOMA-AD score [PC = 0.03 (95% CI: 0.01, 0.05)] compared to children with low RA of S. aureus. The results also showed that children with high RA of S. aureus exhibited a 7% increase in HOMA-IR score [PC = 0.07 (95% CI: 0.01, 0.13)] and a 2% increase in HOMA-AD score [PC = 0.02 (95% CI: 0.002, 0.3)] compared to children with low RA of S. aureus.
We found that children with low RA of A. muciniphila had a 6% decrease in HOMA-IR score [PC = −0.06 (95% CI: −0.13, −0.0013)] and a 2% decrease in HOMA-AD score [PC= -0.02 (95% CI: −0.03, −0.0004)] compared to children with high RA of A. muciniphila. Finally, children with medium RA of A. muciniphila had a 9% decrease in HOMA-IR score [PC= −0.09 (95% CI: −0.16, −0.03)] and a 2% decrease in HOMA-AD score [PC= −0.02 (95% CI: −0.04, −0.007)] compared to children with high RA of A. muciniphila.
To evaluate the joint effect of the bacterial species under study, we constructed a latent variable (microbiota) with the observed RA of S. aureus, A. muciniphila, and L. casei.
We performed structural equation modeling to evaluate the direct, indirect (mediated by waist circumference), and total effects of the latent variable microbiota with HOMA-IR and HOMA-AD (Figure S2). We found that waist circumference is a mediator in children with a profile characterized by high RA of S. aureus, medium RA of L. casei, and low RA of A. muciniphila; there was a 63% increase in HOMA-IR score [PC = 0.63 (95% CI: 0.50, 0.78)] and a 17% increase in HOMA-AD score [PC = 0.17 (95% CI: 0.13, 0.20)].
Finally, based on waist circumference measurements in children with a profile characterized by high RA of S. aureus, medium RA of L. casei, and medium RA of A. muciniphila, we identified a 66% increase in HOMA-IR score [PC = 0.66 (95% CI: 0.51, 0.81)] and a 17% increase in HOMA-AD score [PC = 0.17 (95% CI: 0.13, 0.20)] (Table 5).

4. Discussion

In our study, S. aureus was indirectly associated with increased scores for the HOMA-IR and HOMA-AD indexes. A study conducted in mice showed that S. aureus impairs glucose tolerance through the secretion of an extracellular domain of insulin-binding proteins that block insulin-mediated glucose uptake [27]. Our research revealed that S. aureus is associated with increased waist circumference, triglyceride and LDL levels, and lower HDL levels [28].
In prospective studies, the results of the characterization of the intestinal microbiota in women according to their body mass index (BMI) before pregnancy showed that pregnant women who were overweight had a higher RA of S. aureus than normal-weight women. After delivery, the composition of the fecal microbiota in the infants was analyzed. The children of women who were overweight before pregnancy had a higher RA of S. aureus than the children of normal-weight women, which implies a direct transmission of the maternal microbiota to the child depending on the degree of adiposity [29,30]. Our results were concordant since we did not find a direct association between S. aureus, HOMA-IR, and HOMA-AD. The association was observed only when we included waist circumference as a mediator.
We also found that A. muciniphila was indirectly associated with decreased HOMA-IR and HOMA-AD scores. A study carried out in mice revealed that 5 weeks of supplementation with A. muciniphila reduced visceral fat, which is closely related to the pathogenesis of IR [31]. In a cohort study in the Chinese population, it was reported that a decrease in the abundance of A. muciniphila was associated with impaired IR in subjects with T2D because A. muciniphila modulates insulin secretion through the levels of 3 β—serum chenodeoxycholic acid, inducing glycogen synthesis and suppressing gluconeogenesis, thereby improving glucose tolerance [32].
Our findings are similar to those of other authors; in overweight and obese children, a decrease in the abundance of A. muciniphila was found compared to that of normal-weight children [33]. A. muciniphila can restore intestinal barrier function and proper expression of tight junctions, causing thickening of the intestinal mucosal layer, decreasing chronic low-grade inflammation, and improving the metabolic health of the host [34].
We identified an association between GM and IR mediated by waist circumference, as GM is an ecosystem in equilibrium that can be altered to modify the relative abundances of bacteria compared to their normal abundance; this persistent imbalance of the microbiota is known as dysbiosis [35].
Intestinal dysbiosis facilitates the disorganization of the tight junction proteins of the colonic epithelial cells ZO-1 and occludins and a reduction in the intestinal mucus layer, favoring intestinal permeability and a more significant endotoxin load. Under these conditions, lipopolysaccharide (LPS) from the outer membrane of Gram-negative bacteria can translocate through the epithelial layers into the bloodstream, activating toll-like receptor (TLR) 4 and thus inducing the production of proinflammatory cytokines and interferons [36,37,38,39].
In addition to the inflammation caused by LPS, obese individuals have hypertrophic adipocytes and a more significant number of macrophages; because of adipocyte hypertrophy, the blood supply is compromised, causing oxygen deficiency and macrophage infiltration towards adipose tissue, inducing the overexpression of proinflammatory cytokines, and reducing the expression of adiponectin [40,41]. Therefore, excess abdominal adiposity would potentiate the effect of LPS-induced endotoxemia and the subsequent metabolic effects of low-grade inflammation, such as IR and dyslipidemia [42].
When evaluating the joint effect of bacterial profiles, we observed a dominant effect of S. aureus over L. casei and A. muciniphila; even the high relative abundance of the latter does not offset the increase in HOMA-IR and HOMA-AD scores. Therefore, studies must evaluate how S. aureus inhibits the protective effect of L. casei and A. muciniphila on RI.
Regarding HOMA-IR and HOMA-AD, we noted no difference between using one index or the other; some studies have reported similar results in which children with metabolic syndrome have higher scores in both indexes than healthy children. Additionally, they have achieved efficient screening for metabolic risk using both indexes without observing significant differences [43,44]. When adjusting the HOMA index for adiponectin levels, slightly more accurate estimates are observed than those obtained using HOMA-IR, but there is no difference in the direction or magnitude of the association.
Within the limitations of our study, we evaluated RI using the HOMA index, with the awareness that the hyperinsulinemic–euglycemic clamp (HEC) is the gold standard for assessing insulin sensitivity. The HOMA index imputes the dynamic function of β-cells, which prevents direct measurement of these cells’ proper insulin secretion function. Nevertheless, the HOMA index is an inexpensive quantitative tool that works adequately in population studies. The validity between the HOMA index and the HEC in the pediatric population has also been reported, allowing us to use this index as a valuable tool for classification in epidemiological studies [45,46,47].
Among anthropometric markers of central adiposity, waist circumference is the best predictor of insulin resistance in children. Although dual-energy X-ray absorptiometry (DXA) is the gold standard for measuring body composition, waist circumference has been chosen for its simple, fast, and cheap measurement in population-based studies. Several studies have reported a high correlation between waist circumference and metabolic diseases. During the last few years, waist circumference has been encouraged in clinical practice to detect subjects at increased risk of metabolic diseases [48]. If there were a measurement error among the anthropometrists, it would be non-differential.
Finally, due to the nature of our design, we consider the probability of reverse causality; however, our analysis strategy (path analysis and structural equation modeling) allows us to evaluate the fit of theoretical models in which a set of dependency relationships between variables is proposed. These methods do not prove causality but allow selection or inference between causal hypotheses [49]. In addition, in prospective studies, it has been determined that intestinal dysbiosis leads to an increased risk of developing IR [50].
The results allow us to consider GM as a potentially useful biomarker for identifying children at risk of developing IR and, later, T2D, considering the effect of waist circumference. Measuring the RA of intestinal bacteria will help to carry out interventions for at-risk children in a timely and effective manner to reduce the prevalence of IR in the child population, directly impacting their quality of life.

5. Conclusions

Waist circumference mediates the association between the RA of S. aureus and A. muciniphila and insulin resistance. When evaluating microbiota profiles’ joint effect, we observed that S. aureus predominates over L. paracasei and A. muciniphila, increasing HOMA-IR and HOMA-AD scores. However, more studies are needed to elucidate the mechanism by which the gut microbiota is associated with insulin resistance in children, allowing potential areas of opportunity in the treatment and approach to the care of children with abdominal obesity.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/children10081382/s1, Table S1: Specific universal primers [51,52,53]; Table S2: Metabolic status of participants according to BMI Z-scores; Figure S1: Path analysis; Figure S2: Structural equation modeling.

Author Contributions

Conceptualization, J.C.A.-G., C.E.D.-B., A.L.-M. and A.I.B.-G.; data curation, J.C.A.-G., Y.C.O.-O. and A.C.C.-M.; formal analysis, J.C.A.-G., C.E.D.-B., A.L.-M., E.O.-P. and A.I.B.-G.; funding acquisition, M.C. and A.I.B.-G.; investigation, C.E.D.-B., A.L.-M., V.H.B.-M. and A.I.B.-G.; methodology, J.C.A.-G., C.E.D.-B., A.L.-M. and A.I.B.-G.; project administration, A.I.B.-G.; resources, M.C. and A.I.B.-G.; supervision, A.L.-M. and A.I.B.-G.; visualization, J.C.A.-G. and C.E.D.-B.; writing—original draft, J.C.A.-G., C.E.D.-B., A.L.-M. and A.I.B.-G.; writing—review and editing, Y.C.O.-O., A.C.C.-M., E.O.-P. and V.H.B.-M. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Consejo Nacional de Ciencia y Tecnología (CONACYT), grant numbers SSA/IMSS/ISSSTE-CONACYT 2015-262133 and FSSEP02-CB-2018, solicitud A1-S-33221.

Institutional Review Board Statement

This study was approved by the Ethics, Research, and Biosafety Commissions of the Instituto Nacional de Salud Pública (INSP) with the approval number CI:1129, No. 1294. Approval date: 27 August 2012. Annual approval date: 4 October 2013. This study was conducted in accordance with the Declaration of Helsinki.

Informed Consent Statement

All children and parents signed assent and informed consent, respectively.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Brunetti, A.; Chiefari, E. Recent advances in the molecular genetics of type 2 diabetes mellitus. World J. Diabetes 2014, 5, 128–140. [Google Scholar] [CrossRef] [PubMed]
  2. Maffeis, C.; Morandi, A. Body composition and insulin resistance in children. Eur. J. Clin. Nutr. 2018, 72, 1239–1245. [Google Scholar] [CrossRef] [PubMed]
  3. DeBoer, M.D. Assessing and Managing the Metabolic Syndrome in Children and Adolescents. Nutrients 2019, 11, 1788. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  4. Jandhyala, S.M.; Talukdar, R. Role of the normal gut microbiota. World J. Gastroenterol. 2015, 21, 8787–8803. [Google Scholar] [CrossRef]
  5. Buccigrossi, V.; Nicastro, E. Functions of intestinal microflora in children. Curr. Opin. Gastroenterol. 2013, 29, 31–38. [Google Scholar] [CrossRef]
  6. Rodríguez, J.M.; Murphy, K. The composition of the gut microbiota throughout life, with an emphasis on early life. Microb. Ecol. Health Dis. 2015, 26, 26050. [Google Scholar] [CrossRef]
  7. Hawrelak, J.A.; Myers, S.P. The causes of intestinal dysbiosis: A review. Altern. Med. Rev. 2004, 9, 180–197. [Google Scholar]
  8. Pascale, A.; Marchesi, N. Microbiota and metabolic diseases. Endocrine 2018, 61, 357–371. [Google Scholar] [CrossRef]
  9. Zhao, S.; Liu, W. Akkermansia muciniphila improves metabolic profiles by reducing inflammation in chow diet-fed mice. J. Mol. Endocrinol. 2017, 58, 1–14. [Google Scholar] [CrossRef]
  10. Depommier, C.; Everard, A. Supplementation with Akkermansia muciniphila in overweight and obese human volunteers: A proof-of-concept exploratory study. Nat. Med. 2019, 25, 1096–1103. [Google Scholar] [CrossRef]
  11. Drissi, F.; Merhej, V. Comparative genomics analysis of Lactobacillus species associated with weight gain or weight protection. Nutr. Diabetes 2014, 24, e109. [Google Scholar] [CrossRef] [Green Version]
  12. Castañeda-Márquez, A.C.; Díaz-Benítez, C.E.; Bahena-Roman, M.; Campuzano-Benítez, G.E.; Galván-Portillo, M.; Campuzano-Rincón, J.C.; Lagunas-Martínez, A.; Bermudez-Morales, V.H.; Orbe-Orihuela, Y.C.; Peralta-Romero, J.; et al. Lactobacillus paracasei as a protective factor of obesity induced by an unhealthy diet in children. Obes. Res. Clin. Pract. 2020, 14, 271–278. [Google Scholar] [CrossRef] [PubMed]
  13. Khalili, L.; Alipour, B. The Effects of Lactobacillus casei on Glycemic Response, Serum Sirtuin1 and Fetuin-A Levels in Patients with Type 2 Diabetes Mellitus: A Randomized Controlled Trial. Iran Biomed. J. 2019, 23, 68–77. [Google Scholar]
  14. Toejing, P.; Khampithum, N. Influence of Lactobacillus paracasei HII01 Supplementation on Glycemia and Inflammatory Biomarkers in Type 2 Diabetes: A Randomized Clinical Trial. Foods 2021, 10, 1455. [Google Scholar]
  15. Venegas, D.P.; De La Fuente, M.K. Short chain fatty acids (SCFAs)mediated gut epithelial and immune regulation and its relevance for inflammatory bowel diseases. Front. Immunol. 2019, 10, 277. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  16. Kalliomäki, M.; Collado, M.C. Early differences in fecal microbiota composition in children may predict overweight. Am. J. Clin. Nutr. 2008, 87, 534–538. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  17. Vu, B.G.; Stach, C.S. Chronic superantigen exposure induces systemic inflammation, elevated bloodstream endotoxin, and abnormal glucose tolerance in rabbits: Possible role in diabetes. mBio 2015, 24, e02554. [Google Scholar]
  18. Clemente-Suárez, V.J.; Redondo-Flórez, L.; Beltrán-Velasco, A.I.; Martín-Rodríguez, A.; Martínez-Guardado, I.; Navarro-Jiménez, E.; Laborde-Cárdenas, C.C.; Tornero-Aguilera, J.F. The Role of Adipokines in Health and Disease. Biomedicines 2023, 11, 1290. [Google Scholar] [CrossRef]
  19. Nguyen, T.M.D. Adiponectin: Role in Physiology and Pathophysiology. Int. J. Prev. Med. 2020, 11, 136. [Google Scholar] [CrossRef]
  20. Ruan, H.; Dong, L.Q. Adiponectin signaling and function in insulin target tissues. J. Mol. Cell Biol. 2016, 8, 101–109. [Google Scholar] [CrossRef] [Green Version]
  21. Estrada-Velasco, B.I.; Cruz, M. La obesidad infantil como consecuencia de la interacción entre firmicutes y el consumo de alimentos con alto contenido energético [Childhood obesity is associated to the interaction between firmicutes and high energy food consumption]. Nutr. Hosp. 2014, 31, 1074–1081. [Google Scholar] [PubMed]
  22. Schoemann, A.M.; Boulton, A.J. Determining Power and Sample Size for Simple and Complex Mediation Models. Soc. Psychol. Personal. Sci. 2017, 8, 379–386. [Google Scholar] [CrossRef]
  23. Schmittgen, T.D.; Livak, K.J. Analyzing real-time PCR data by the comparative C(T) method. Nat. Protoc. 2008, 3, 1101–1108. [Google Scholar] [CrossRef] [PubMed]
  24. Vilela, B.S.; Vasques, A.C. The HOMA-Adiponectin (HOMA-AD) Closely Mirrors the HOMA-IR Index in the Screening of Insulin Resistance in the Brazilian Metabolic Syndrome Study (BRAMS). PLoS ONE 2016, 11, e0158751. [Google Scholar] [CrossRef] [Green Version]
  25. Hernández, B.; Gortmaker, S.L. Validez y reproducibilidad de un cuestionario de actividad e inactividad física para escolares de la ciudad de México [Validity and reproducibility of a questionnaire on physical activity and non-activity for school children in Mexico City]. Salud Publica Mex. 2000, 42, 315–323. [Google Scholar] [CrossRef] [Green Version]
  26. de Onis, M.; Onyango, A.W. Development of a WHO growth reference for school-aged children and adolescents. Bull. World Health Organ 2007, 85, 660–667. [Google Scholar] [CrossRef]
  27. Liu, Y.; Liu, F.J. The extracellular domain of Staphylococcus aureus LtaS binds insulin and induces insulin resistance during infection. Nat. Microbiol. 2018, 3, 622–631. [Google Scholar] [CrossRef]
  28. Ayala-García, J.C.; Lagunas-Martínez, A. High Relative Abundance of Staphylococcus aureus and Serum Cytokines Are Associated with Cardiometabolic Abnormalities in Children. Metab. Syndr. Relat. Disord. 2022, 20, 303–311. [Google Scholar]
  29. Collado, M.C.; Isolauri, E. Distinct composition of gut microbiota during pregnancy in overweight and normal-weight women. Am. J. Clin. Nutr. 2008, 88, 894–899. [Google Scholar] [CrossRef] [Green Version]
  30. Collado, M.C.; Isolauri, E. Effect of mother’s weight on infant’s microbiota acquisition, composition, and activity during early infancy: A prospective follow-up study initiated in early pregnancy. Am. J. Clin. Nutr. 2010, 92, 1023–1030. [Google Scholar] [CrossRef] [Green Version]
  31. Depommier, C.; Van Hul, M. Pasteurized Akkermansia muciniphila increases whole-body energy expenditure and fecal energy excretion in diet-induced obese mice. Gut Microbes 2020, 11, 1231–1245. [Google Scholar] [CrossRef] [Green Version]
  32. Zhang, J.; Ni, Y. Decreased Abundance of Akkermansia muciniphila Leads to the Impairment of Insulin Secretion and Glucose Homeostasis in Lean Type 2 Diabetes. Adv. Sci. 2021, 8, e2100536. [Google Scholar] [CrossRef] [PubMed]
  33. Karlsson, C.L.; Onnerfält, J. The microbiota of the gut in preschool children with normal and excessive body weight. Obesity 2012, 20, 2257–2261. [Google Scholar] [PubMed]
  34. Cani, P.D.; Vos, W.M. Next-Generation Beneficial Microbes: The Case of Akkermansia muciniphila. Front. Microbiol. 2017, 8, 1765. [Google Scholar] [PubMed] [Green Version]
  35. Roberfroid, M.; Gibson, G.R. Prebiotic effects: Metabolic and health benefits. Br. J. Nutr. 2010, 104 (Suppl. S2), S1–S63. [Google Scholar] [CrossRef] [Green Version]
  36. Martínez-Oca, P.; Robles-Vera, I. Gut DYSBIOSIS and altered barrier function precedes the appearance of metabolic syndrome in a rat model of nutrient-induced catch-up growth. J. Nutr. Biochem. 2020, 81, 108383. [Google Scholar]
  37. Stadlbauer, V.; Engertsberger, L. Dysbiosis, gut barrier dysfunction and inflammation in dementia: A pilot study. BMC Geriatr. 2020, 20, 248. [Google Scholar] [CrossRef]
  38. Schumann, R.R. Function of lipopolysaccharide (LPS)-binding protein (LBP) and CD14, the receptor for LPS/LBP complexes: A short review. Res. Immunol. 1992, 143, 11–15. [Google Scholar]
  39. Park, B.S.; Lee, J.O. Recognition of lipopolysaccharide pattern by TLR4 complexes. Exp. Mol. Med. 2013, 45, e66. [Google Scholar] [CrossRef] [Green Version]
  40. De Loera-Rodriguez, C.O.; Delgado-Rizo, V. Over-expression of TLR4-CD14, pro-inflammatory cytokines, metabolic markers and NEFAs in obese non-diabetic Mexicans. J. Inflamm. 2014, 11, 39. [Google Scholar] [CrossRef] [Green Version]
  41. Ellulu, M.S.; Patimah, I. Obesity and inflammation: The linking mechanism and the complications. Arch. Med. Sci. 2017, 13, 851–863. [Google Scholar] [CrossRef] [PubMed]
  42. Clemente-Postigo, M.; Oliva-Olivera, W. Metabolic endotoxemia promotes adipose dysfunction and inflammation in human obesity. Am. J. Physiol. Endocrinol. Metab. 2019, 316, E319–E332. [Google Scholar] [CrossRef]
  43. Cândido, A.P.C.; Geloneze, B. Adiponectin, HOMA-Adiponectin, HOMA-IR in Children and Adolescents: Ouro Preto Study. Indian J. Pediatr. 2021, 88, 336–344. [Google Scholar] [PubMed]
  44. Makni, E.; Moalla, W. The Homeostasis Model Assessment-adiponectin (HOMA-AD) is the most sensitive predictor of insulin resistance in obese children. Ann. D’endocrinol. 2012, 73, 26–33. [Google Scholar] [CrossRef] [PubMed]
  45. Yeckel, C.W.; Ram, W. Validation of insulin sensitivity indices from oral glucose tolerance test parameters in obese children and adolescents. J. Clin. Endocrinol. Metab. 2004, 89, 1096–1101. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  46. Conwell, L.S.; Trost, S.G. Indexes of insulin resistance and secretion in obese children and adolescents: A validation study. Diabetes Care 2004, 27, 314–319. [Google Scholar] [CrossRef] [Green Version]
  47. Keskin, M.; Kurtoglu, S.; Kendirci, M.; Atabek, M.E.; Yazici, C. Homeostasis model assessment is more reliable than the fasting glucose/insulin ratio and quantitative insulin sensitivity check index for assessing insulin resistance among obese children and adolescents. Pediatrics 2005, 115, e500–e503. [Google Scholar] [CrossRef] [Green Version]
  48. Ross, R.; Neeland, I.J. Waist circumference as a vital sign in clinical practice: A Consensus Statement from the IAS and ICCR Working Group on Visceral Obesity. Nat. Rev. Endocrinol. 2020, 16, 177–189. [Google Scholar]
  49. VanderWeele, T.J. Invited commentary: Structural equation models and epidemiologic analysis. Am. J. Epidemiol. 2012, 176, 608–612. [Google Scholar]
  50. Li, W.Z.; Stirling, K. Gut microbiota and diabetes: From correlation to causality and mechanism. World J. Diabetes 2020, 11, 293–308. [Google Scholar]
  51. Collado, M.C.; Derrien, M. Intestinal integrity and Akkermansia muciniphila, a mucin-degrading member of the intestinal microbiota present in infants, adults, and the elderly. Appl. Environ. Microbiol. 2007, 73, 7767–7770. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  52. Haarman, M.; Knol, J. Quantitative real-time PCR analysis of fecal Lactobacillus species in infants receiving a prebiotic infant formula. Appl. Environ. Microbiol. 2006, 72, 2359–2365. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  53. Bacchetti De Gregoris, T.; Aldred, N. Improvement of phylum- and class-specific primers for real-time PCR quantification of bacterial taxa. J. Microbiol. Methods 2011, 86, 351–356. [Google Scholar] [CrossRef] [PubMed]
Children 10 01382 g001
Figure 1. Directed acyclic graph (DAG) representing the hypothetical causal structure.
Figure 1. Directed acyclic graph (DAG) representing the hypothetical causal structure.
Children 10 01382 g001
Table 1. General characteristics of participants according to BMI Z-scores.
Table 1. General characteristics of participants according to BMI Z-scores.
Characteristics a
n = 533		Normal WeightOverweight/Obesityp-Value
n = 265 (51%)n = 268 (49%)
Age (years)9 (7–10)9 (8–10)0.013
Physical activity (METs/hour/week)301 (150–577)337 (162–625)0.330
Sex
   Boy (%)52480.368
   Girl (%)4852
Family History of OW/OB
      Yes (%)43570.001
      No (%)5842
Family History of T2D
      Yes (%)37630.020
      No (%)5248
Macronutrient Consumption
      Carbohydrates (g/day)272.01 (210.27–339.53)272.64 (211.07–357.14)0.571
      Lipids (g/day)74.84 (59.28–93.39)74.81 (58.18–93.66)0.989
      Proteins (g/day)69.66 (55.15–83.43)68.13 (53.48–86.88)0.963
Exposure Variables
S. aureus (RA)0.0000368 (7.54 × 10−6–0.0002222)0.0000558 (9.83 × 10−6–0.000277)0.140
L. paracasei (RA)0.0002953 (0.0000239–0.0025059)0.0003333 (0.0000212–0.0044035)0.622
L. casei (RA)0.0004417 (0.0000628–0.0064059)0.0004487 (0.0000457–0.0075155)0.858
L. reuteri (RA)0.0001371 (0.0000198–0.0009797)0.0001141 (0.0000202–0.0007388)0.521
A. muciniphila (RA)0.0033518 (0.0000323–0.0585953)0.0034792 (0.0000413–0.0742626)0.539
Mediator Variable
      Waist circumference (cm)56.7 (53.5–61.2)74.4 (67.7–79.9)<0.001
Outcome Variables
   HOMA-IR0.36 (0.28–0.53)0.81 (0.41–1.55)<0.001
   HOMA-AD0.06 (0.05–0.10)0.17 (0.06–0.21)<0.001
a Values represented the median (p25 and p75) or percentages. The Mann–Whitney U test is used for continuous variables and the chi-square for categorical variables. OW/OB: overweight/obesity; T2D: type 2 diabetes; RA: relative abundance; HOMA-IR: homeostatic model assessment for insulin resistance; HOMA-AD: homeostatic model assessment for insulin resistance adiponectin-corrected. Statistically significant differences are marked in bold: p < 0.05.
Table 2. Association between gut microbiota and waist circumference.
Table 2. Association between gut microbiota and waist circumference.
Waist Circumference ≥ 63.6 cm
OR95% CIp-Value
RA of Staphylococcus aureus *
      Medium tertile2.301.44, 3.65<0.001
      High tertile1.721.08, 2.730.022
RA of Lactobacillus reuteri *
      Medium tertile1.190.76, 1.890.447
      High tertile1.210.77, 1.900.417
RA of Lactobacillus paracasei **
      Low tertile0.820.52, 1.290.393
      Medium tertile0.890.57, 1.410.639
RA of Lactobacillus casei **
      Low tertile0.780.49, 1.230.283
      Medium tertile0.630.40, 1.000.050
RA of Akkermansia muciniphila **
      Low tertile0.620.39, 0.990.046
      Medium tertile0.500.32, 0.800.004
RA: relative abundance. Reference group: * Low tertile, ** High tertile. Logistic regression adjusted by age, sex, family history of OW/OB, and daily consumption of carbohydrates, lipids, and proteins. Statistically significant differences are marked in bold: p < 0.05.
Table 3. Association between waist circumference and gut microbiota with HOMA-IR and HOMA-AD.
Table 3. Association between waist circumference and gut microbiota with HOMA-IR and HOMA-AD.
HOMA-IRHOMA-AD
β95% CIp-Valueβ95% CIp-Value
Waist circumference ≥ 63.6 cm *0.620.49, 0.75<0.0010.160.13, 0.20<0.001
RA of Staphylococcus aureus **
      Medium tertile0.15−0.01, 0.310.0660.03−0.01, 0.070.111
      High tertile0.04−0.12, 0.200.6160.01−0.03, 0.060.459
RA of Lactobacillus reuteri **
      Medium tertile0.05−0.11, 0.210.5440.006−0.03, 0.050.766
      High tertile−0.08−0.24, 0.080.345−0.005−0.05, 0.040.801
RA of Lactobacillus paracasei ***
      Low tertile−0.10−0.27, 0.060.210−0.02−0.06, 0.020.407
      Medium tertile0.13−0.03, 0.290.1050.03−0.01, 0.070.141
RA of Lactobacillus casei ***
      Low tertile−0.06−0.22, 0.110.492−0.01−0.05, 0.030.585
      Medium tertile−0.07−0.23, 0.100.445−0.02−0.06, 0.020.289
RA of Akkermansia muciniphila ***
      Low tertile0.04−0.12, 0.210.6110.02−0.02, 0.060.410
      Medium tertile0.05−0.11, 0.210.5270.002−0.04, 0.040.907
RA: relative abundance. Reference group: * Waist circumference <63.6 cm; ** Low tertile; *** High tertile. Linear regression adjusted by age, sex, family history of OW/OB, and daily consumption of carbohydrates, lipids, and proteins. Statistically significant differences are marked in bold: p < 0.05.
Table 4. Direct, indirect, and total effects of gut microbiota on HOMA-IR and HOMA-AD.
Table 4. Direct, indirect, and total effects of gut microbiota on HOMA-IR and HOMA-AD.
HOMA-IRHOMA-AD
Path Coefficient
(PC)		95% CIp-ValuePath Coefficient
(PC)		95% CIp-Value
Direct effect
RA of Staphylococcus aureus *
      Medium tertile0.043−0.11, 0.190.5740.0048−0.03, 0.040.807
      High tertile−0.03−0.18, 0.120.696−0.0033−0.04, 0.030.868
RA of Lactobacillus reuteri *
      Medium tertile0.025−0.12, 0.170.744−0.00042−0.04, 0.040.983
      High tertile−0.11−0.25, 0.040.165−0.013−0.05, 0.020.518
RA of Lactobacillus paracasei **
      Low tertile−0.076−0.22, 0.070.317−0.01−0.04, 0.030.598
      Medium tertile0.150.002, 0.300.0470.036−0.002, 0.070.066
RA of Lactobacillus casei **
      Low tertile−0.036−0.19, 0.110.639−0.0023−0.04, 0.040.906
      Medium tertile−0.0016−0.15, 0.150.983−0.0063−0.04, 0.030.748
RA of Akkermansia muciniphila **
      Low tertile0.11−0.04, 0.260.1580.035−0.003, 0.070.074
      Medium tertile0.15−0.003, 0.300.0560.027−0.01, 0.060.164
Indirect effect
RA of Staphylococcus aureus *
      Medium tertile0.110.04, 0.170.0010.030.01, 0.050.001
      High tertile0.070.01, 0.130.0240.020.002, 0.030.024
RA of Lactobacillus reuteri *
      Medium tertile0.02−0.04, 0.090.4170.007−0.009, 0.020.417
      High tertile0.03−0.03, 0.090.3870.007−0.01, 0.020.386
RA of Lactobacillus paracasei **
      Low tertile−0.03−0.09, 0.030.373−0.007−0.02, 0.0090.373
      Medium tertile−0.02−0.08, 0.040.610−0.004−0.02, 0.010.610
RA of Lactobacillus casei **
      Low tertile−0.03−0.10, 0.030.260−0.009−0.03, 0.0070.260
      Medium tertile−0.06−0.01, 0.00060.052−0.02−0.03, 0.00010.052
RA of Akkermansia muciniphila **
      Low tertile−0.06−0.13, −0.00130.045−0.02−0.03, −0.00040.045
      Medium tertile−0.09−0.16, −0.030.005−0.02−0.04, −0.0070.005
Total effect
RA of Staphylococcus aureus *
      Medium tertile0.15−0.01, 0.310.0630.035−0.007, 0.070.108
      High tertile0.04−0.12, 0.200.6120.017−0.02, 0.060.455
RA of Lactobacillus reuteri *
      Medium tertile0.05−0.11, 0.210.5140.006−0.03, 0.050.764
      High tertile−0.08−0.24, 0.080.385−0.005−0.05, 0.040.799
RA of Lactobacillus paracasei **
      Low tertile−0.10−0.26, 0.050.205−0.02−0.06, 0.020.402
      Medium tertile0.13−0.03, 0.290.1010.03−0.01, 0.070.136
RA of Lactobacillus casei **
      Low tertile−0.07−0.24, 0.090.387−0.01−0.05, 0.030.582
      Medium tertile−0.06−0.22, 0.100.441−0.02−0.06, 0.020.284
RA of Akkermansia muciniphila **
      Low tertile0.04−0.12, 0.200.6070.02−0.02, 0.060.406
      Medium tertile0.05−0.11, 0.210.5230.002−0.04, 0.040.906
RA: relative abundance. Reference group: * Low tertile, ** High tertile. Path analysis was adjusted by age, sex, family history of OW/OB, and daily consumption of carbohydrates, lipids, and proteins. Statistically significant differences are marked in bold: p < 0.05.
Table 5. Direct, indirect, and total effects of gut microbiota profiles on HOMA-IR and HOMA-AD.
Table 5. Direct, indirect, and total effects of gut microbiota profiles on HOMA-IR and HOMA-AD.
HOMA-IRHOMA-AD
Path Coefficient
(PC)		95% CIp-ValuePath Coefficient
(PC)		95% CIp-Value
Direct effect
Gut microbiota
Profile 1−1.2−4.6, 2.10.479−0.4−1.5, 0.760.500
Profile 2−0.98−2.45, 0.490.190−0.092−0.42, 0.240.588
Indirect effect
Gut microbiota
Profile 10.630.50, 0.78<0.0010.170.13, 0.20<0.001
Profile 20.660.51, 0.81<0.0010.170.13, 0.20<0.001
Total effect
Gut microbiota
Profile 1−0.57−3.9, 2.80.736−0.23−1.38, 0.930.699
Profile 2−0.32−1.76, 1.120.6640.07−0.24, 0.400.642
Profile 1: high RA of S. aureus, medium RA of L. casei, and low RA of A. muciniphila. Profile 2: high RA of S. aureus, medium RA of L. casei, and medium RA of A. muciniphila. Structural equation modeling is adjusted by age, sex, family history of OW/OB, and daily consumption of carbohydrates, lipids, and proteins. Statistically significant differences are marked in bold: p < 0.05.
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

Share and Cite

MDPI and ACS Style

Ayala-García, J.C.; Díaz-Benítez, C.E.; Lagunas-Martínez, A.; Orbe-Orihuela, Y.C.; Castañeda-Márquez, A.C.; Ortiz-Panozo, E.; Bermúdez-Morales, V.H.; Cruz, M.; Burguete-García, A.I. Mediation Analysis of Waist Circumference in the Association of Gut Microbiota with Insulin Resistance in Children. Children 2023, 10, 1382. https://doi.org/10.3390/children10081382

AMA Style

Ayala-García JC, Díaz-Benítez CE, Lagunas-Martínez A, Orbe-Orihuela YC, Castañeda-Márquez AC, Ortiz-Panozo E, Bermúdez-Morales VH, Cruz M, Burguete-García AI. Mediation Analysis of Waist Circumference in the Association of Gut Microbiota with Insulin Resistance in Children. Children. 2023; 10(8):1382. https://doi.org/10.3390/children10081382

Chicago/Turabian Style

Ayala-García, Juan Carlos, Cinthya Estefhany Díaz-Benítez, Alfredo Lagunas-Martínez, Yaneth Citlalli Orbe-Orihuela, Ana Cristina Castañeda-Márquez, Eduardo Ortiz-Panozo, Víctor Hugo Bermúdez-Morales, Miguel Cruz, and Ana Isabel Burguete-García. 2023. "Mediation Analysis of Waist Circumference in the Association of Gut Microbiota with Insulin Resistance in Children" Children 10, no. 8: 1382. https://doi.org/10.3390/children10081382

APA Style

Ayala-García, J. C., Díaz-Benítez, C. E., Lagunas-Martínez, A., Orbe-Orihuela, Y. C., Castañeda-Márquez, A. C., Ortiz-Panozo, E., Bermúdez-Morales, V. H., Cruz, M., & Burguete-García, A. I. (2023). Mediation Analysis of Waist Circumference in the Association of Gut Microbiota with Insulin Resistance in Children. Children, 10(8), 1382. https://doi.org/10.3390/children10081382

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