Adipocyte
ISSN: 2162-3945 (Print) 2162-397X (Online) Journal homepage: https://www.tandfonline.com/loi/kadi20
Towards precision medicine: defining and
characterizing adipose tissue dysfunction to
identify early immunometabolic risk in symptomfree adults from the GEMM family study
Ernesto Rodriguez-Ayala, Esther C. Gallegos-Cabrales, Laura Gonzalez-Lopez,
Hugo A. Laviada-Molina, Rocio A. Salinas-Osornio, Edna J. Nava-Gonzalez,
Irene Leal-Berumen, Claudia Escudero-Lourdes, Fabiola Escalante-Araiza,
Fatima A. Buenfil-Rello, Vanessa-Giselle Peschard, Antonio Laviada-Nagel,
Eliud Silva, Rosa A. Veloz-Garza, Angelica Martinez-Hernandez, Francisco
M. Barajas-Olmos, Fernanda Molina-Segui, Lucia Gonzalez-Ramirez,
Rebeca Espadas-Olivera, Ricardo Lopez-Muñoz, Ruy D. Arjona-Villicaña,
Victor M. Hernandez-Escalante, Martha E. Rodriguez-Arellano, Janeth F.
Gaytan-Saucedo, Zoila Vaquera, Monica Acebo-Martinez, Judith CornejoBarrera, Huertas-Quintero Jancy Andrea, Juan Carlos Castillo-Pineda, Areli
Murillo-Ramirez, Sara P. Diaz-Tena, Benigno Figueroa-Nuñez, Melesio E.
Valencia-Rendon, Rafael Garzon-Zamora, Juan Manuel Viveros-Paredes, José
Ángeles-Chimal, Jesús Santa-Olalla Tapia, José M. Remes-Troche, Salvador
B. Valdovinos-Chavez, Eira E. Huerta-Avila, Juan Carlos Lopez-Alvarenga,
Anthony G Comuzzie, Karin Haack, Xianlin Han, Lorena Orozco, Susan
Weintraub, Jack W. Kent, Shelley A. Cole & Raul A. Bastarrachea
To cite this article: Ernesto Rodriguez-Ayala, Esther C. Gallegos-Cabrales, Laura GonzalezLopez, Hugo A. Laviada-Molina, Rocio A. Salinas-Osornio, Edna J. Nava-Gonzalez, Irene
Leal-Berumen, Claudia Escudero-Lourdes, Fabiola Escalante-Araiza, Fatima A. Buenfil-Rello,
Vanessa-Giselle Peschard, Antonio Laviada-Nagel, Eliud Silva, Rosa A. Veloz-Garza, Angelica
Martinez-Hernandez, Francisco M. Barajas-Olmos, Fernanda Molina-Segui, Lucia GonzalezRamirez, Rebeca Espadas-Olivera, Ricardo Lopez-Muñoz, Ruy D. Arjona-Villicaña, Victor M.
Hernandez-Escalante, Martha E. Rodriguez-Arellano, Janeth F. Gaytan-Saucedo, Zoila Vaquera,
Monica Acebo-Martinez, Judith Cornejo-Barrera, Huertas-Quintero Jancy Andrea, Juan Carlos
Castillo-Pineda, Areli Murillo-Ramirez, Sara P. Diaz-Tena, Benigno Figueroa-Nuñez, Melesio
E. Valencia-Rendon, Rafael Garzon-Zamora, Juan Manuel Viveros-Paredes, José ÁngelesChimal, Jesús Santa-Olalla Tapia, José M. Remes-Troche, Salvador B. Valdovinos-Chavez, Eira
E. Huerta-Avila, Juan Carlos Lopez-Alvarenga, Anthony G Comuzzie, Karin Haack, Xianlin Han,
Lorena Orozco, Susan Weintraub, Jack W. Kent, Shelley A. Cole & Raul A. Bastarrachea (2020)
Towards precision medicine: defining and characterizing adipose tissue dysfunction to identify
early immunometabolic risk in symptom-free adults from the GEMM family study, Adipocyte, 9:1,
153-169, DOI: 10.1080/21623945.2020.1743116
To link to this article: https://doi.org/10.1080/21623945.2020.1743116
© 2020 The Author(s). Published by Informa
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Published online: 09 Apr 2020.
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ADIPOCYTE
2020, VOL. 9, NO. 1, 153–169
https://doi.org/10.1080/21623945.2020.1743116
RESEARCH PAPER
Towards precision medicine: defining and characterizing adipose tissue
dysfunction to identify early immunometabolic risk in symptom-free adults from
the GEMM family study
Ernesto Rodriguez-Ayalaa*, Esther C. Gallegos-Cabralesb*, Laura Gonzalez-Lopezc, Hugo A. Laviada-Molinad,
Rocio A. Salinas-Osornioc, Edna J. Nava-Gonzaleze, Irene Leal-Berumenf, Claudia Escudero-Lourdesg,
Fabiola Escalante-Araiza a, Fatima A. Buenfil-Relloh, Vanessa-Giselle Pescharda, Antonio Laviada-Nagelh,
Eliud Silvaa, Rosa A. Veloz-Garzab, Angelica Martinez-Hernandez i, Francisco M. Barajas-Olmosi,
Fernanda Molina-Seguid, Lucia Gonzalez-Ramirezd, Rebeca Espadas-Oliverad, Ricardo Lopez-Muñozd,
Ruy D. Arjona-Villicañad, Victor M. Hernandez-Escalante h, Martha E. Rodriguez-Arellanoj, Janeth F. GaytanSaucedoh, Zoila Vaquerah, Monica Acebo-Martinezg, Judith Cornejo-Barrerak, Huertas-Quintero Jancy Andrea h,
Juan Carlos Castillo-Pinedal, Areli Murillo-Ramirezl, Sara P. Diaz-Tenal, Benigno Figueroa-Nuñezm,
Melesio E. Valencia-Rendonc, Rafael Garzon-Zamorac, Juan Manuel Viveros-Paredesc, José Ángeles-Chimal n,
Jesús Santa-Olalla Tapia n, José M. Remes-Troche o, Salvador B. Valdovinos-Chavezb, Eira E. Huerta-Avila i,
Juan Carlos Lopez-Alvarenga p, Anthony G Comuzzieq, Karin Haackh, Xianlin Hanr, Lorena Orozcoi,
Susan Weintraubs, Jack W. Kenth, Shelley A. Coleh, and Raul A. Bastarrachea h
a
Centro de Investigación en Ciencias de la Salud (CICSA), Facultad de Ciencias de la Salud, Universidad Anáhuac Norte, México City, México;
Facultad de Enfermería, Universidad Autónoma de Nuevo León (UANL), Monterrey, México; cDirección de Postgrado e Investigación,
Universidad del Valle de Atemajac (UNIVA), Zapopan, México; dEscuela de Ciencias de la Salud, Universidad Marista de Mérida, Yucatán,
Mexico; eFacultad de Salud Pública y Nutrición (Faspyn), UANL, Monterrey, México; fFacultad de Medicina y Ciencias Biomédicas, Universidad
Autónoma de Chihuahua, México; gFacultad de Ciencias Químicas, Universidad Autónoma de San Luis Potosí, México; hPopulation Health
Program, Texas Biomedical Research Institute and Southwest National Primate Research Center (SNPRC), San Antonio, TX, USA; iLaboratorio
de Inmunogenómica y Enfermedades Metabólicas, Instituto Nacional de Medicina Genómica, México City, México; jLaboratorio de Medicina
Genómica del Hospital Regional Lic, Adolfo López Mateos, ISSSTE, Mexico City, Mexico; kDepartamento de Enseñanza, Postgrado
e Investigación, Hospital Infantil de Tamaulipas, Ciudad, México; lDepartamento de Nutrición Humana, Universidad Latina de América,
Morelia, México; mClínica de Enfermedades Crónicas y Procedimientos Especiales (CECYPE), Morelia, México; nFacultad de Medicina,
Universidad Autónoma Estado de Morelos, Cuernavaca, México; oInstituto de Investigaciones Médico-Biológicas, Universidad Veracruzana,
Veracruz, México; pSchool of Medicine & South Texas Diabetes and Obesity Institute, University of Texas Rio Grande Valley, Edinburg, TX,
USA; qThe Obesity Society, Silver Spring Maryland, USA; rDepartment of Medicine, Sam and Ann Barshop Institute for Longevity and Aging
Studies, University of Texas Health San Antonio, San Antonio, TX, USA; sDepartment of Biochemistry, University of Texas Health Science
Center, San Antonio, TX, USA
b
ABSTRACT
ARTICLE HISTORY
Interactions between macrophages and adipocytes are early molecular factors influencing adipose
tissue (AT) dysfunction, resulting in high leptin, low adiponectin circulating levels and low-grade
metaflammation, leading to insulin resistance (IR) with increased cardiovascular risk. We report the
characterization of AT dysfunction through measurements of the adiponectin/leptin ratio (ALR),
the adipo-insulin resistance index (Adipo-IRi), fasting/postprandial (F/P) immunometabolic phenotyping and direct F/P differential gene expression in AT biopsies obtained from symptom-free
adults from the GEMM family study. AT dysfunction was evaluated through associations of the
ALR with F/P insulin-glucose axis, lipid-lipoprotein metabolism, and inflammatory markers.
A relevant pattern of negative associations between decreased ALR and markers of systemic lowgrade metaflammation, HOMA, and postprandial cardiovascular risk hyperinsulinemic, triglyceride
and GLP-1 curves was found. We also analysed their plasma non-coding microRNAs and shotgun
lipidomics profiles finding trends that may reflect a pattern of adipose tissue dysfunction in the
fed and fasted state. Direct gene differential expression data showed initial patterns of AT
molecular signatures of key immunometabolic genes involved in AT expansion, angiogenic
remodelling and immune cell migration. These data reinforce the central, early role of AT
dysfunction at the molecular and systemic level in the pathogenesis of IR and immunometabolic
disorders.
Received 3 December 2019
Revised 3 March 2020
Accepted 10 March 2020
KEYWORDS
Adipose tissue dysfunction;
immunometabolism;
postprandial tissue biopsies;
non-coding microRNAs;
shotgun lipidomics
CONTACT Raul A. Bastarrachea
raul@txbiomed.org
Population Health Program, Texas Biomedical Research Institute and Southwest National
Primate Research Center (SNPRC), San Antonio, TX 78227-0549, USA
*These authors contributed equally to this work.
© 2020 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted
use, distribution, and reproduction in any medium, provided the original work is properly cited.
154
E. RODRIGUEZ-AYALA ET AL.
Introduction
AT dysfunction plays a key role and is at the core of IR
development [1]. IR is also a chronic systemic inflammatory syndrome primarily triggered by macrophage
infiltration into adipose tissue [2]. Interactions between
macrophages and adipocytes [3] are the early molecular
factors influencing AT dysfunction, resulting in local
altered adipose tissue metabolism that then progresses
to altered systemic metabolism (mainly high leptin and
low adiponectin circulating levels) and chronic lowgrade inflammation, ultimately leading to IR [4]. IR,
the hallmark of immunometabolic disorders [5], contributes to glucotoxicity, lipotoxicity, metaflammation,
endothelial dysfunction, increased cardiovascular disease (CVD) risk and atherosclerosis [6].
The recently developed adiponectin/leptin ratio (ALR)
correlates with IR better than adiponectin or leptin alone.
It is strongly associated with surrogate measures of IR: the
homoeostatic model assessment (HOMA) and the hyperinsulinemic-euglycemic clamp [7]. This emerging index
decreases with increasing number of cardiometabolic risk
factors reflecting the functionality of adipose tissue and
negatively correlates with markers of low-grade chronic
subclinical inflammation. It has been proposed that an
ALR ≥ 1.0 can be considered normal, a ratio between ≥0.5
and <1.0 suggests moderate-medium increased risk, and
a low ratio <0.5 indicates a severe increase in cardiovascular and immunometabolic risk [8]. A low (L)ALR as
a marker of adipose tissue (AT) dysfunction is characterized by a lower secretion of adiponectin in relation to
leptin levels, unhealthy adipose tissue hypoxia, proinflammatory macrophage polarization, altered adipokine
profile and IR [9].
The most relevant age-related chronic immunometabolic diseases (type 2 diabetes [T2D] and CVD) associated with increased mortality are slowly progressive,
associated with traditional CVD risk factors [10] and
with a symptom-free onset [5]. These pathologies are
the leading cause of death around the world [11].
Cardiovascular risk phenotypes of immunometabolic
origin (CVRIMO) – IR, hyperinsulinemia, dysglycemia,
and dyslipidemia, elevated hsCRP and fibrinogen – are
considered major risk factors for T2D and CVD [6].
The central underlying mechanisms triggering these F/
P CVRIMO relate to localized immunometabolic processes at a cellular level during AT expansion (hypoxia,
inflammation, inappropriate extracellular matrix
(ECM) remodelling, impaired angiogenesis, fibrosis)
defined as AT dysfunction [4].
Although fasting and postprandial dysglycemia,
lipid-lipoprotein abnormalities, excess of body fat, and
elevated systolic/diastolic blood pressure [10] are wellestablished traditional cardiometabolic risk factors [12],
there is growing evidence to suggest that a metabolic
inflammatory state, termed metaflammation [13] and
defined as low-grade, chronic subclinical inflammation
[5] orchestrated by metabolic cells in response to excess
nutrients and energy [14] may be the underlying
mechanism that determines whether or not an individual would develop these CVRIMO and cardiovascular
risk abnormalities [15]. They are primarily associated
with IR and an unfavourable inflammatory state characterized by high circulating levels of high-sensitivity
C-reactive protein (hs-CRP), alpha necrosis tumour
factor (TNF-α), hyperfibrinogenemia [16] and interleukin 6 (IL-6) [17].
The GEMM family study (Genética de las
Enfermedades Metabólicas en México, Genetics of
Metabolic Diseases in Mexico) is a bi-national, multicentre collaborative study of CVRIMO related to T2D
and the risk of cardiovascular and immunometabolic
disease. GEMM’s study design characterizes detailed
dynamic and function-based metabolic and molecular
phenotypes in fasting and fed states (including the
phenome, transcriptome, proteome and metabolome)
in symptom-free volunteers. Data are acquired from the
circulation and from F/P adipose tissue and skeletal
muscle biopsies, key tissues to understand F/P metaflammation, insulin action and carbohydrate/lipid
homoeostasis. All measurements in blood are taken
over a time course of 5 h to allow fine-scale profiling
of individual postprandial responses [18].
The aim of this paper is to introduce the methodology to characterize early, key contributors triggering
the pathogenesis of AT dysfunction with data collected
from the GEMM cohort. We studied the immunometabolic characteristics in the symptom-free female volunteers and compared the relation and trends of the high
vs. low ALR with their F/P insulin-glucose axis, lipidlipoprotein metabolism, and systemic inflammatory
markers. In addition, we analysed whether their plasma
non-coding exosomal [19] microRNAs (miRNAs) [20],
advanced plasma shotgun lipidomics profiles [21], and
particularly, direct adipose tissue gene expression,
could differentially display molecular patterns reflecting
AT dysfunction in the fed and fasted state.
Materials and methods
Subjects
The GEMM study involves an ongoing recruitment from
10 University-based metabolic research units and their
ADIPOCYTE
affiliated teaching hospitals across Mexico, as described
previously [18]. The goal of our broader, current (longitudinal), ongoing recruitment is to randomly recruit 400
symptom-free adult volunteers in ~10 extended families.
Although this manuscript is only presenting data on
females, GEMM has performed the 5 h meal challenge
and biopsies of subcutaneous adipose tissue and skeletal
muscle sample collection for 125 symptom-free participants until date (80 females and 45 males) [18].
A registered nurse performed a complete family and personal medical history on each participant that included
information about allergies, illnesses, surgeries, immunizations, results of physical exams, tests, and screenings. It
accurately identified people with a higher-than-usual
chance of having common disorders, such as heart disease,
high blood pressure, stroke, certain cancers, and diabetes.
A second questionnaire was also performed to gather data
on physical activity and food intake. It is a reliable and
sensitive instrument that has been assessed and used in
Mexican population [22]. Exclusion criteria include
women that were pregnant or attempting to become pregnant, individuals with acute illness, activity-limiting unexplained illness, hypertension, dyslipidemia, prevalent
diabetes, known cardiovascular or chronic lung disease,
cancer or renal failure [23]. Individuals with signs of infection were also excluded. Participants were also ruled out for
any evidence of likely atherosclerotic disease or risk by
questioning them if they ever had coronary artery disease,
a heart attack, or congestive heart failure [23]. Local ethics
committee approval was obtained at each recruiting centre.
Subjects were given a written and an oral explanation of the
study, and all provided informed consent. Export of
GEMM samples for multiOMICS analysis to the U.S.
(Texas Biomedical Research Institute, San Antonio, TX),
has been permitted by the Mexican Federal government in
accordance with Mexican Genetic Sovereignty Law [24]
(COFEPRIS Permit No. COF187278 (DEAPE
133300CT190038/2013) issued on 19 March 2013) [25].
Study design
Limited funds were obtained from a research granting
foundation awarded to conduct a cross-sectional analysis
from 14 symptom-free female adults chosen from the
total female cohort (n = 80) to determine AT dysfunction
at a systemic and at a molecular level (Table 1). We used
the adiponectin and leptin circulating measures as phenotypes to determine the adiponectin/leptin ratio (ALR)
trait on our 14 female participants (Figure 1). It allowed
us to obtain an accurate mean to determine high (H) or
low (L) ALR ratios among our chosen females [8]. We
compared their mean (H) and (L) ALR to screen for
trends of presence or absence of cardiovascular and
immunometabolic early risk related to their AT dysfunction. Measured fasting and 2 h postprandial blood glucose, haemoglobin A1 c and insulin were used to rule out
participants with evidence of T2D, prediabetes or metabolic syndrome (triglycerides ≥ 150 mg/dL, HDL cholesterol <40 mg/dL in men and <50 mg/dL in women or
participants currently taking prescribed medicine for high
cholesterol, blood pressure ≥130/85 mmHg or any participant currently taking prescription for hypertension)
[23]. The presence of likely non-alcoholic fatty liver disease (NAFLD) or non-alcoholic steatohepatitis (NASH)
was also ruled out by finding out whether a participant
did not have alcohol dependence or laboratory data suggesting likely NASH [26].
Meal challenge, tissue biopsies and postprandial
sample collection
GEMM participants were given an innovative balanced
mixed meal challenge containing a healthy combination
of carbohydrates, fats, and protein, dosed at 30% of
each participant’s daily resting energy expenditure in
Kcal (macronutrient composition: 65% carbohydrate,
15% protein and 20% fat). They also provided blood
Table 1. Comparison of demographic and biochemical characteristics between the n = 80 vs. n = 14 GEMM
symptom-free female individuals, showing strong anthropometric and metabolic similarities and non-significant
differences.
Females
Demographic characteristics and metabolic parameters
Age
Weight (kg)
Waist Circunference (cm)
BMI (kg/m2)
% Fat Total
Systolic Pressure (mmHg)
Diastolic Pressure (mmHg)
Glucose (mg/dL)
Triglycerides (mg/dL)
Total cholesterol (md/dL)
HDL- cholesterol (md/dL)
LDL- cholesterol (md/dL)
155
n = 80
n = 14
Mean ± SD
38.8 ± 13.34
70.48 ± 15.92
88.43 ± 14.24
28.17 ± 5.84
38.58 ± 10.39
107.16 ± 10.48
68.88 ± 8.14
93.49 ± 22.92
117.73 ± 56.77
164.43 ± 43.09
47.08 ± 14.75
94.51 ± 34.22
Mean ± SD
36.39 ± 12.11
67.64 ± 13.31
87.46 ± 13.06
28.95 ± 5.78
43.9 ± 5.43
108.14 ± 10.48
69.0 ± 6.64
86.71 ± 7.18
128.78 ± 56.77
165.07 ± 39.30
43.71 ± 9.07
98.71 ± 32.08
P
0.499
0.361
0.819
0.642
0.053
0.848
0.885
0.125
0.258
0.703
0.612
0.605
156
E. RODRIGUEZ-AYALA ET AL.
Figure 1. Mean ALR in our symptom-free female participants.
samples at defined timepoints over a 5 h time course, as
well as subcutaneous adipose tissue biopsies under local
anaesthesia at fasting and 180 min postprandium to
represent fasting and peak of postprandial response.
The fasting biopsies were taken after a two-week interval rather than on the same day to avoid post-surgical
inflammatory effects on the postprandial gene and protein expression as it could happen if both biopsies are
taken the same day [18].
Postprandial biochemical phenotyping
Biochemical phenotypes were analysed on a Luminex
100IS platform (SBH Sciences, Natick, MA, USA) and
an Immulite 1000 (Diamond Diagnostics Inc.,
Holliston, MA, USA) which run enzyme-linked
immunosorbent assay (ELISA) and radioimmunoassay (RIA) analyses, consisting of an advanced optometric flux designed to analyse different cytokines
and chemokines in less than 2 h in ~25 μL of plasma.
We measured and analysed a wide range of clinical
biochemistries (including liver function enzymes),
hormones, cytokines and endophenotypes that define
the fasting and postprandial cardiovascular and
immunometabolic status of the study participants
relative to inflammation, insulin resistance, and risk
of CVD and T2DM. These analyte categories include:
Beta cell and insulin-glucose axis: F/P insulin, GLP-1
and glucose curves, HOMA-IR; Adipose tissue function: leptin, adiponectin, the adipo-insulin resistance
index (Adipo-IRi); Inflammation endophenotypes:
hsCRP, PAI-1, TNF-α, IL-6, MCP-1, IL-10; Lipidlipoprotein metabolism: non-esterified fatty acids
(NEFA), F/P triglyceride curves and total cholesterol.
Fasting and postprandial plasma shotgun
lipidomics
Lipids in biological samples were extracted with chloroform/methanol in the presence of a cocktail of internal
standards (at least one for each lipid class) using
a modification of the method of Bligh and Dyer [27],
and analysed by multi-dimensional mass spectrometrybased shotgun lipidomics (MDMS-SL), as previously
described [28]. MDMS-SL determines all of the building blocks of individual species by using the combined
analyses of multiple neutral loss scans (NLS) and/or
precursor-ion scans (PIS) and processing the NLS/PIS
data with software developed to identify the individual
species [29]. Quantification of identified lipid species
was automatically achieved through the use of this software for comparison of either survey mass spectra or
NLS/PIS spectra to the internal standard of the corresponding lipid class [29]. This strategy permitted identification and quantification of hundreds to thousands
of individual lipid species in nearly 50 lipid classes,
including the following: triglycerides, diglycerides,
monoglycerides, cholesterol and cholesteryl esters, oxysterols, acylcarnitines, acyl-CoAs, phospholipids
(including cardiolipin), lysophospholipids, eicosanoids,
4-hydroxy alkenals and retinoic acid [30].
miRNA sequencing
For miRNA Sequencing of plasma exosomes, miRNA
was isolated from 200 ul plasma using the miRNeasy
Serum/Plasma Advanced Kit (Qiagen) according to the
manufacturer protocol. Quality and quantity were
assessed by Agilent HS RNA assay and Qubit
ADIPOCYTE
microRNA assay, respectively. Conversion of miRNA
into cDNA library was performed using the NEXTflex
Illumina Small RNA-Seq Kit v3 (BiooScientific).
miRNA is heat denatured to allow ligation at the 3ʹ
then 5ʹ ends with adapters that contain 4 bp randomized end sequences, and undergo a first and second
strand cDNA synthesis resulting in dscDNA with blunt
ends. Adapter ligation products were purified and
enriched by PCR to create cDNA libraries. Gel-free
size selection was performed to remove adapter-dimer
product in the final cDNA library. Library quality and
quantity were assessed by Agilent D1000 assay and
Qubit dsDNA assay, respectively. Forty-eight sample
libraries were pooled and then quantified by qPCR
using the Kapa Library ABI Prism Quantification kit.
Library pools were shipped out for sequencing. The
sequence reads were aligned and quantified at the
Texas Biomedical Research Institute in San Antonio,
TX, [Molecular Services Core (MSC)] using an established pipeline in Partek Flow.
RNA extraction, amplification and labelling from
subcutaneous adipose tissue biopsies
Total RNA was isolated from adipose tissue using the
RiboPure kit (Applied Biosystems) after homogenizing
the RNALater-stabilized tissues in TRI Reagent. The
quantity and quality of the RNA samples were determined using a NanoDrop ND-1000 spectrophotometer.
Samples whose A260/A280 ratio deviate ±0.2 from the
accepted ratio of 2.0 were excluded. The quality/integrity of the RNA was assessed using the Agilent RNA
6000 Nano LabChip Kit and an Agilent 2100
Bioanalyzer (Agilent Technologies), ensuring that the
28 S and 18 s ribosomal RNA species are intact and that
significant degradation had not occurred. The concentration of the resulting RNA samples was determined
by using the NanaoDrop ND-1000 spectrophotometer.
For synthesis of cRNA, the Illumina TotalPrep RNA
Amplification Kit (Applied Biosystems) was used. The
quality of the cRNA was assessed using the Agilent
RNA 6000 Nano LabChip Kit and an Agilent 2100
Bioanalyzer (Agilent Technologies). cRNA quantity
was measured using a NanoDrop ND-1000
spectrophotometer.
Subcutaneous fat gene expression profiling
We used commercially available Illumina Human WG6 y3.0 Expression BeadChips for whole genome expression analysis. These BeadChips contain six arrays, each
with >48,000 probes. Each array provides genome-wide
transcriptional coverage of well-characterized genes,
157
gene candidates and splice variants. About 1.8 million
beads are available to quantify mRNA levels for each
sample providing on average a 30x redundancy and
therefore a very high precision of detection. This system uses a ‘direct hybridization’ approach, whereby
gene-specific probes attached to beads on the array
are used to capture and detect labelled cRNAs. After
hybridization, detection of cRNAs was achieved by
using an Illumina BeadArray™ reader. Gene expression
data were generated using the BeadStudio software
package. This application reports quality of performance based on built-in controls that accompany
each experiment. The resulting ~48,000 quantitative
measures were entered into a phenotypic database and
additional statistical quality control procedures were
performed to prepare these phenotypes for statistical
analysis.
Statistical analysis data
are presented as mean ± standard deviation (SD) unless
otherwise indicated. Differences between groups were
analysed by two-tailed unpaired Student’s t-tests, as
appropriate. The calculations were performed using
SPSS23 (SPSS, Chicago, IL, USA) and GraphPad
Prism 6 (GraphPad Software, Inc., La Jolla, CA, USA).
Scatter plot mathematical diagrams using Cartesian
coordinates to display two-dimensional data visualization using dots were applied to represent association
between two different variables.
Results
Table 1 shows the demographic characteristics and
metabolic parameters of the 80 female GEMM volunteers including age, weight, height, BMI, fasting glucose, total cholesterol, HDL cholesterol, triglycerides,
and blood pressure (systolic and diastolic). Deep phenotyping (HOMA-IR, postprandial metabolism curves,
F/P plasma microRNA signatures, shotgun F/P lipidomics, profiling of inflammatory markers [31] and F/P
adipose tissue transcriptomics) on 14 of our GEMM
participants is also shown in Table 1. They were chosen
for deep phenotyping because their clinical characteristics show similar metabolic patterns that accurately
represent the full database of 80 women. Figure 1 shows
a mean Adiponectin/Leptin ratio (ALR) of 1.8 ± 1.4 in
14 of our symptom-free participants. We decided to
designate the females with an ALR above and below
the mean of 1.8 as having a high (H) and a low (L)
ALR. Although this arbitrary cut-off point is above the
limits of a healthy ALR (>1.0) [8], the subgroup of
females with an ALR above and below the mean 1.8
158
E. RODRIGUEZ-AYALA ET AL.
fits our hypothesis that adipose tissue dysfunction can
be documented among symptom-free individuals.
Figure 2 and Table 2 show that our symptom-free
subjects had a mean (L) (1.0 ± 0.56) and (H)
(3.2 ± 1.3) ALR. Mean plasma concentrations of total
fasting adiponectin and leptin were 29.9 ± 21.6 ug/mL
and 9.6 ± 5.0 ng/mL in the (H)ALR group, and
15.1 ± 7.12 ug/mL and 18.4 ± 8.6 ng/mL in the (L)
ALR, respectively.
Mean values in (H) vs. (L)ALR for weight, BMI,
waist circumference and body fat percentage showed
that these anthropometric measurements were higher
in the (L)ALR group. Age showed that the (L)ALR
group was younger (Figure 2). Most mean values did
not reach statistical significance.
Proinflammatory fasting systemic biomarker levels
between the (H) and (L)ALR groups are shown in
Figure 3. Our preliminary data showed higher systemic
levels of TNF-α in the females with (L)ALR. Circulating
levels of IL-6 were elevated among the symptom-free
volunteers with (L)ALR. Our results showed a trend of
elevated systemic hs-CRP among subjects with (L)ALR.
The pattern for circulating levels of PAI-1 in the subgroup with (L)ALR showed a relevant elevation. IL-10
was decreased in the (L)ALR group. However, the
P-values were statistically non-significant.
Fasting and postprandial insulin-glucose axis phenotypes showed an increase in the (L)ALR subjects
(Figure 4) for HOMA (p = 0.01), insulin and GLP-1
curves, except for the glucose curve. The area under the
curve (AUC) for glucose in the (H) ALR group compared to the (L) ALG showed a 5% difference indicating
that they were practically similar. On the contrary, the
AUC for insulin and GLP-1 in the (H) ALR group
compared to the (L) ALG, respectively, showed an
85% and 28% differences. Systemic lipid metabolism
also showed an increase in the (L)ALR symptom-free
females for NEFA. The adipose tissue-insulin resistance
index (Adipo – IRi) (p = 0.01) was significantly higher
in individuals with a (L)ALR. The fasting liver transaminases aspartate transaminase (AST/SGOT) (p = 0.01)
and alanine transaminase (ALT/SGPT) (p = 0.01) were
Figure 2. Mean leptin, adiponectin, Adiponectin/Leptin ratio and anthropometric phenotypes of participants in the GEMM study
(ALR and Age with a P-value ≤ 0.05).
ADIPOCYTE
Table 2. High (H: >1.8) and Low (L: <1.8) Adiponectin/Leptin
ratio (ALR) according to adipokine concentrations in symptomfree female volunteers who underwent deep phenotyping.
P-values for adiponectin mean levels: 0.08; leptin mean levels:
0.08; ARL: 0.001. (A p-value ≤ 0.05 is statistically significant).
Symptom-free
female adults
(N = 14)
MTY0017
MTY0003
MTY0007
MTY0006
MTY0014
MEAN (H) ALR
MTY0013
MTY0020
MTY0018
MTY0019
MTY0021
MTY0009
MTY0015
MTY0016
MTY0010
MEAN (L) ALR
Fasting
Adiponectin
Levels (ug/ml)
66.5
18.9
10.0
27.4
26.9
29.9
13.2
24.1
23.4
17.2
21.3
7.5
15.1
10.0
3.9
15.1
ADPN/LEP
RATIO
4.89
4.36
2.54
2.24
1.95
3.2
1.71
1.48
1.48
1.40
1.05
0.90
0.60
0.36
0.12
1.0
Fasting Leptin
Levels (ng/ml)
13.6
4.3
3.9
12.2
13.8
9.6
7.7
16.3
15.8
12.2
20.3
8.4
25.0
27.5
32.4
18.4
also significantly higher in individuals with a (L)ALR
(Figure 4).
For this paper, we decided to present results from fed
and fasted samples that underwent shotgun lipidomics
only for specific classes considered proinflammatory:
Lysophos phatidylethanolamine (LPE), lysophosphatidylcholine (LPC) and Ceramide (Cer) as shown in Table 3.
The lipidomic profiling revealed distinct mean differences
in plasma lipid composition among the GEMM volunteers. We observed a discrete trend for postprandial differences in a handful of plasma bioactive lipid species at
159
180 and 300 min on proinflammatory classes of
Ceramides (CERN20:0, CERN22:0, CERN23:0), Lyso
Phosphatidylethanolamine (LPE18:1), and Lyso
Phosphatidylcholine (LPC) (LPC20:3) in the symptomfree (L)ALR compared to the (H)ALR subjects. This is
shown in cells highlighted in black from Table 3. The
AUC for CERN22:0, LPE18:1 and LPC20:3 in the (H)
ALR group compared to the (L) ALG, respectively,
showed 8%, 9% and 2.8% differences.
Comprehensive fed/fasted circulating miRNA profiling was performed in this initial sample. We detected
2974 miRNAs as described in the methods. Table 4
shows the results for the most relevant miRNAs signatures we found. They were selected from recent literature on the role of microRNAs in dysfunctional adipose
tissue [20], cardiometabolic disorders [32] and the
immune response [33]. Our mean values from subjects
with a (L)ALR for 0 and 180 min for miRNA promoting adipogenesis showed an increment for miR-27b-5p,
miR-378a-3p, miR-375 and miR-140-5p (expressed
only in postprandium). Values for miRNA promoting
anti-adipogenesis also showed increases for miR-33a5p, miR-130b-3p, miR-7-1-3p, let-7a-3p (expressed
only in postprandium).
We also quantified mRNA levels of F/P subcutaneous adipose tissue for whole genome expression analysis. We were able to directly measure and characterize
key proinflammatory (LEP, TNFaIP1, CD86, FABP4,
TGFB1) and anti-inflammatory genes (ADIPOR1,
CD163, HIF1AN, IL10, ANG) at 0 min (fasted) and
Figure 3. Immunometabolic and proinflammatory profile of participants in the GEMM study (non-significant P-values).
160
E. RODRIGUEZ-AYALA ET AL.
Figure 4. Immunometabolic, insulin resistance, liver enzyme profile, postprandial insulin-glucose axis and triglyceride curves of
female participants in the GEMM study (n = 14). Adipo-IR Index, HOMA-IR, ALT/GPT and AST/GOT with a P-value ≤ 0.05. Area under
the curve (AUC) for glucose in the (h) ALR group and (l) ALG (13,743 and 13,144, 5% difference [diff.]). AUC for insulin and GLP-1 in
the (H) ALR group (5055 and 15,143) compared to the (L) ALG (9362 and 19,327) respectively, showed an 85% and 28% diff.
180 min (fed). Values are shown in Figure 5. Most
genes in the fasted and fed state did not reach statistical
significance, except for ANG (P = 0.03).
Discussion
There is a current lack of clinically oriented indicators
to assess the complex phenomenon of AT dysfunction
for early detection of cardiovascular and immunometabolic risk before it develops into an evident systemic
(muscle and liver) IR and an overt metabolic syndrome.
It has been stated that the body mass index (BMI) [34],
waist circumference (WC) [35], MRI and CT imaging
[36] are not ideal predictors of mortality risk or cardiovascular risk factors due to inaccuracies reflecting
body fat percentage. But a deeper analysis reveals that
they all have the same common approach: they only
ascertain for the amount or excess of body fat accumulation in correlation to cardiometabolic risk without
taking into account adipose tissue appropriate
physiologic function. Moreover, they do not reflect
any metabolic or immune feature at the molecular
level regarding dysfunctional adipose tissue biology
[37]. Therefore, a useful and accurate systemic biomarker to reflect cellular AT dysfunction as an early predictor of cardiovascular and immunometabolic risk is
strongly needed.
Using deep phenotyping [38] such as the ALR, the
HOMA-IR (from measurements of fasting plasma glucose and insulin concentrations primarily reflecting
hepatic insulin resistance [39]), the Adipo-IRi and postprandial metabolism curves of the insulin-glucose axis
as anchors, we performed precision medicine screening
approaches such as F/P plasma microRNA signatures,
multi-dimensional mass spectrometry-based shotgun F/
P lipidomics, comprehensive plasma metabolic profiling of chronic low-grade subclinical inflammation markers [31] and F/P adipose tissue gene differential
expression (transcriptomics) among a selected subgroup of female adult volunteers from the GEMM
Table 3. Multi-dimensional mass spectrometry-based shotgun lipidomics. Fed and fasted shotgun lipidomics data from specific classes considered proinflammatory:
Lysophosphatidylethanolamine (LPE), Lysophosphatidylcholine (LPC) and Ceramide (Cer).
Ceramide(Cer) (nmol/ml plasma)
Table 3
TIME
0 MIN
30 MIN
180 MIN
300 MIN
TIME
0 MIN
30 MIN
180 MIN
300 MIN
TIME
0 MIN
30 MIN
180 MIN
300 MIN
A/L R
HIGH
LOW
HIGH
LOW
HIGH
LOW
HIGH
LOW
A/L R
HIGH
LOW
HIGH
LOW
HIGH
LOW
HIGH
LOW
A/L R
HIGH
LOW
HIGH
LOW
HIGH
LOW
HIGH
LOW
CERN16:0
0.34 ± 0.17
0.26 ± 0.06
0.22 ± 0.05
0.21 ± 0.05
0.19 ± 0.05
0.22 ± 0.08
0.22 ± 0.07
0.20 ± 0.06
CERN18:0
0.17 ± 0.07
0.15 ± 0.06
0.15 ± 0.06
0.13 ± 0.07
0.10 ± 0.06
0.13 ± 0.07
0.12 ± 0.04
0.12 ± 0.07
LPEP16:1
0.69 ± 0.44
0.57 ± 0.43
0.21 ± 0.12
0.28 ± 0.31
0.18 ± 0.11
0.28 ± 0.90
0.27 ± 0.10
0.22 ± 0.06
LPEP16:0
4.87 ± 2.30
3.80 ± 1.27
1.95 ± 0.12
2.04 ± 1.51
2.20 ± 0.69
2.71 ± 1.73
2.30 ± 0.37
3.86 ± 3.90
LPE16:0
9.75 ± 2.73
7.76 ± 2.31
8.85 ± 3.38
6.43 ± 1.87
8.18 ± 1.76
8.56 ± 2.80
10.00 ± 4.65
8.73 ± 6.24
LPCP16:0
37.12 ± 38.87
22.23 ± 15.58
4.23 ± 5.18
8.36 ± 21.27
1.35 ± 0.94
2.59 ± 3.34
2.49 ± 3.54
2.49 ± 3.12
LPCA16:0
3.14
2.50
2.55
2.12
2.58
2.42
2.34
2.46
±
±
±
±
±
±
±
±
0.82
0.38
0.66
0.51
0.70
0.63
0.76
1.39
LPC16:1
21.54 ± 15.27
18.24 ± 9.30
6.20 ± 3.38
8.55 ± 13.11
5.21 ± 2.16
5.53 ± 2.73
4.93 ± 2.87
5.80 ± 3.59
CERN20:0
0.16 ± 0.05
0.15 ± 0.05
0.11 ± 0.05
0.10 ± 0.06
0.06 ± 0.04
0.09 ± 0.04
0.07 ± 0.02
0.10 ± 0.07
CERN22:0
1.00 ± 0.22
1.01 ± 0.37
0.76 ± 0.31
0.73 ± 0.39
0.61 ± 0.35
0.71 ± 0.37
0.60 ± 0.14
0.70 ± 0.31
CERN23:0
0.99 ± 0.28
0.91 ± 0.30
0.83 ± 0.32
0.72 ± 0.34
0.71 ± 0.37
0.76 ± 0.33
0.63 ± 0.16
0.73 ± 0.29
CERN24:2
0.11 ± 0.02
0.11 ± 0.02
0.09 ± 0.03
0.08 ± 0.03
0.08 ± 0.05
0.09 ± 0.05
0.08 ± 0.04
0.09 ± 0.04
Lysophosphatidylethanolamine (LPE) (nmol/ml plasma)
LPEP18:1
LPEP18:0
LPE18:3
LPE18:2
2.47 ± 0.96
4.47 ± 3.01
0.30 ± 0.10
7.72 ± 2.88
2.27 ± 0.94
3.95 ± 1.33
0.36 ± 0.13
8.12 ± 1.93
0.95 ± 0.46
2.01 ± 1.01
0.30 ± 0.10
7.93 ± 2.56
1.10 ± 0.97
1.88 ± 1.40
0.30 ± 0.14
8.15 ± 2.86
1.06 ± 0.30
2.09 ± 0.41
0.34 ± 0.21
7.15 ± 1.05
1.28 ± 0.98
2.47 ± 1.45
0.43 ± 0.11
10.34 ± 2.64
1.00 ± 0.11
2.06 ± 0.25
0.63 ± 0.39
10.12 ± 4.11
1.63 ± 1.45
3.56 ± 4.03
0.47 ± 0.17
9.54 ± 2.45
Lyso Phosphatidylcholine (LPC) (nmol/ml plasma)
LPC16:0
LPC18:3
LPC18:2
LPC18:1
120.54 ± 31. 07
96.82 ± 11.93
114.05 ± 32.40
87.32 ± 12.58
117.18 ± 27.66
107.69 ± 26.04
106.19 ± 38.14
113.44 ± 71.40
0.76
0.86
0.83
0.78
0.90
0.99
0.91
0.97
±
±
±
±
±
±
±
±
0.18
0.36
0.22
0.30
0.28
0.43
0.33
0.47
42.41
39.87
45.16
39.14
48.06
53.03
52.80
52.19
±
±
±
±
±
±
±
±
12.88
10.54
15.94
10.90
13.71
16.75
22.19
16.15
24.43
20.40
23.90
18.94
22.34
22.87
23.03
23.00
±
±
±
±
±
±
±
±
5.83
5.39
6.06
5.82
5.15
8.54
8.09
9.46
CERN24:1
1.57 ± 0.21
1.32 ± 0.37
1.29 ± 0.37
1.02 ± 0.39
1.04 ± 0.44
1.06 ± 0.45
1.13 ± 0.39
1.02 ± 0.40
CERN24:0
3.30 ± 0.95
2.89 ± 0.72
2.91 ± 1.14
2.31 ± 0.95
2.32 ± 1.18
2.47 ± 0.88
2.54 ± 0.65
2.43 ± 0.83
CEROH_N24:1
0.23 ± 0.07
0.18 ± 0.05
0.18 ± 0.08
0.14 ± 0.05
0.14 ± 0.07
0.15 ± 0.04
0.14 ± 0.06
0.15 ± 0.04
CEROH_N24:0
0.09 ± 0.06
0.04 ± 0.02
0.05 ± 0.02
0.04 ± 0.02
0.03 ± 0.02
0.04 ± 0.02
0.06 ± 0.01
0.05 ± 0.04
LPE18:1
5.60 ± 1.65
5.53 ± 1.53
5.25 ± 1.72
4.89 ± 1.51
5.36 ± 0.63
7.37 ± 1.62
7.08 ± 2.44
7.47 ± 2.00
LPE18:0
11.51 ± 3.22
9.59 ± 1.88
10.44 ± 4.57
7.35 ± 1.65
9.36 ± 2.01
10.07 ± 3.23
10.46 ± 2.90
12.25 ± 10.09
LPE20:4
9.77 ± 5.47
11.52 ± 5.37
7.03 ± 1.74
8.21 ± 4.10
5.02 ± 2.88
6.32 ± 2.88
7.29 ± 4.30
6.36 ± 3.82
LPE20:3
1.00 ± 0.44
1.21 ± 0.40
0.91 ± 0.19
0.97 ± 0.51
0.63 ± 0.26
0.94 ± 0.47
0.95 ± 0.46
0.95 ± 0.55
LPC18:0
27.07 ± 7.00
21.63 ± 3.43
25.01 ± 7.90
18.70 ± 3.15
23.34 ± 4.99
22.36 ± 5.25
22.13 ± 7.34
24.93 ± 17.73
LPC20:4
9.28
7.14
9.46
6.64
9.24
7.56
9.50
8.29
±
±
±
±
±
±
±
±
2.61
1.91
3.26
1.67
3.33
1.79
3.72
2.75
LPC20:3
2.99
2.69
3.07
2.67
2.94
2.99
2.99
3.18
±
±
±
±
±
±
±
±
0.63
0.69
0.92
1.05
1.23
1.36
1.12
1.71
LPC20:2
0.93
1.08
1.32
1.29
0.98
1.23
0.95
0.93
±
±
±
±
±
±
±
±
0.39
0.40
0.33
0.61
0.78
0.57
0.35
0.60
LPE22:6
2.35 ± 1.15
2.43 ± 1.13
2.16 ± 0.86
1.97 ± 0.79
1.06 ± 0.60
1.38 ± 0.70
1.87 ± 1.36
1.45 ± 0.81
LPC20:1
0.52
0.70
0.58
0.47
0.53
0.52
0.53
0.43
±
±
±
±
±
±
±
±
0.15
0.79
0.09
0.13
0.35
0.17
0.19
0.20
ADIPOCYTE
161
162
E. RODRIGUEZ-AYALA ET AL.
Table 4. Plasma MicroRNA (miR) in symptom-free females with (H) and (L) ALR. Fed and fasted circulating miRNAs selected from
recent literature with adipogenesis-promoting or anti-adipogenic function. miR-27b-5p (fasting and postprandial), miR-375 (postprandial), miR-140-5p (postprandial), miR-130b-3p (fasting and postprandial) with a P-value ≤ 0.05.
Adiponectin/leptin ratio
Table 4
FUNCTION
ADIPOGENESIS PROMOTING miRNA
ANTI-ADIPOGENIC miRNA
TIME 0 (Fasting)
miRNA
miR-27b-5p
miR-378a-3p
miR-375
miR-140-5p
miR-33a-5p
miR-130b-3p
miR-7-1-3p
let-7a-3p
HIGH
41.2 ± 26.7
54.5 ± 39.9
18.1 ± 12.2
/
17.5 ± 8.3
24.8 ± 16.3
0.006 ± 0.01
/
LOW
65.1 ± 14.6
86.7 ± 15.5
35.0 ± 16.9
/
28.4 ± 15.0
41.2 ± 8.0
0.4 ± 0.5
/
TIME 180 (Postprandial)
P
0.02
0.11
0.08
/
0.23
0.01
0.06
/
HIGH
41.3 ± 26.6
52.7 ± 37.8
17.9 ± 11.3
11.0 ± 6.4
18.8 ± 6.6
23.6 ± 15.7
0.005 ± 0.01
20.9 ± 14.3
LOW
64.3 ± 16.0
86.1 ± 11.1
41.7 ± 21.3
16.7 ± 2.1
27.4 ± 15.0
48.1 ± 8.5
0.4 ± 0.5
35.0 ± 15.3
P
0.04
0.11
0.01
0.02
0.11
0.02
0.06
0.08
Figure 5. Direct measurements performed for fasting (0ʹ) and fed (180ʹ) subcutaneous fat transcriptomic profiling looking for
molecular trends in adipose tissue dysfunction or inflammation differential gene expression in key proinflammatory and antiinflammatory genes. The expression of the anti-inflammatory gene ANG was significantly decreased in the (l)ALR subgroup in fasting.
Family study to identify early trends of cardiovascular
and immunometabolic risks (Figures 1 and 2, and
Table 2) [40].
A pattern of increased circulating leptin concentrations along with a decreased levels of adiponectin
seems indicative of an impaired adipose tissue adipokinome, as it was found in the subgroup with a (L)
ALR (Figure 2) [41]. As stated earlier, the
adiponectin/leptin ratio (ALR) has been suggested
as a marker of adipose tissue (AT) dysfunction [42].
Data in Figure 4 show that the mean HOMA was
frankly elevated and the insulin and GLP-1 postprandial curves and their AUC were increased in the (L)
ALR group compared to the females with a (H)ALR.
Particularly, the insulin curve shows a striking elevation in the females with (L)ALR.
ADIPOCYTE
These constant elevated postprandial levels of insulin
in the symptom-free females with a (L)ALR may
explain why the postprandial glucose curve and the
AUC results in the same group were normal, corroborating that in nondiabetic individuals, the β-cells can
compensate for resistance to insulin-mediated glucose
disposal and maintain normoglycemia at the expense of
increased levels of insulin that, unfortunately, is prothrombotic [43]. These frequent postprandial daily
peaks of insulin may induce atherothrombotic mechanisms, reducing fibrinolytic balance, and impairing
endothelial function as it has been shown using the
pancreatic clamp technique in humans [44]. It should
be kept in mind that these volunteer subjects had their
postprandial metabolic response after a mixed meal
challenge with a balanced macronutrient composition
(65% carbohydrate, 15% protein and 20% fat) corresponding to 30% of their total daily energy expenditure
after a 12-hour fasting [18].
The appearance of deleterious cardiovascular and
immunometabolic risk phenotypes has been the main
concern regarding body fat accumulation [14].
However, in certain individuals, the more they accumulate an excess of body fat, the less they develop
cardiometabolic disease. They can be considered metabolically healthy despite their high degree of body fat
accumulation and their long-standing obesity. This
effect can be thought of as healthy AT expansion. On
the other hand, unhealthy AT expansion is a major
contributor to the systemic metabolic disturbances
that are characteristic of obesity and type 2 diabetes
[45]. The loss of expansion capacity can occur in
patients with normal weight, explaining the existence
of metabolically unhealthy lean subjects [46]. As
a premature event in unhealthy AT expansion, hypoxia
likely plays a fundamental role in the initiation of
inflammation, leading adipocytes to release proinflammatory factors such as TNF-α, IL-6, hsCRP, PAI-1 and
MCP-1. Ultimately, inflammation and AT dysfunction
ensues and IR develops, leading to early risk for prediabetes [47].
Metabolically driven inflammation is a hallmark of
CVD and T2D [5]. TNF-α, produced by immune cells,
was the first cytokine demonstrated to directly impede
insulin action in the adipocyte [48]. The preliminary
data showed higher systemic levels of TNF-α in the
females with (L)ALR. IL-6 also produced by inflammatory cells has been shown to inhibit insulin signalling in
the adipocyte as well [49]. Circulating levels of IL-6
were elevated among the symptom-free volunteers
with (L)ALR. This ALR has also been correlated with
markers of low-grade chronic inflammation, such as
CRP [8]. CRP has emerged as one of the best predictors
163
of vascular inflammation, metabolic syndrome and
CVD. The link between low-grade chronic subclinical
inflammation, hypoxia and adipocyte dysfunction is the
release of cytokines mainly TNF-α and IL-6 into the
circulation by adipose tissue, stimulating hepatic CRP
production [50]. Our results showed a clear trend of
elevated systemic hs-CRP among subjects with (L)ALR.
AT dysfunction is characterized by an increased secretion of plasminogen activator inhibitor (PAI)-1 contributing to impair the fibrinolytic system. It seems that
this is the link between dysfunctional AT and endothelial damage, platelet reactivity, enhanced coagulation
and impaired fibrinolysis, mechanisms currently recognized to increase arterial thrombotic risk [43]. The
pattern for circulating levels of PAI-1 in the subgroup
with (L)ALR showed a marked elevation. We also measure circulating levels of IL-10. This is a Th2-type
cytokine that inhibits the synthesis and activity of proinflammatory cytokines and counteracts Toll-like receptor-mediated inflammation. IL-10 seems to attenuate
obesity-mediated inflammation and improve insulin
sensitivity in skeletal muscle [51]. These trends of key
AT dysfunction immunometabolic phenotypes (an
increase in TNF-α, IL-6, hs-CRP, PAI-1 and
a decrease in IL-10) mirroring subclinical systemic
metaflammation, found in the symptom-free cohort
with a (L)ALR, are shown in Figure 3.
A consequence of chronic positive energy balance
leading to AT dysfunction is an ectopic deposition of
NEFA as triacylglycerols in the liver, skeletal muscle,
and pancreas promoting lipotoxicity [52]. Adipose tissue affects triglyceride metabolism by releasing free
fatty acids into the circulation, contributing to insulin
resistance and eventually leading to abnormalities in
lipid metabolism and hypertriglyceridaemia [53].
A validated adipose tissue-insulin resistance (IRi)
index (Adipo-IRi = plasma-free fatty acids (NEFA)
x fasting plasma insulin [FPI] [mmol/L/pmol/L]) is
calculated based on the linear relationship between
the rise in the FPI level and inhibition of the rate of
fasting plasma NEFA [54]. The higher the rate of fasting plasma NEFA levels, the greater the severity of
adipose tissue IR [55]. We found a triglyceride curve,
NEFA levels and an Adipo-IRi markedly elevated in the
(L)LAR subjects when compared to the (H)LAR ones
(Figure 4). Liver enzyme levels were also elevated in the
(L)ALR participants.
The lipidome is a complete set of lipid species existing
in a cell, an organ, or a biological system. Lipidomics has
become one of the most important branches of omics
[56]. The lipidomic profiling (Table 3) revealed distinct
differences in plasma lipid composition among the
GEMM volunteers. We observed a discrete trend for
164
E. RODRIGUEZ-AYALA ET AL.
a postprandial increase in some plasma bioactive lipid
species at 180 and 300 min on proinflammatory
Ceramides (CERN20:0, CERN22:0, CERN23:0), Lyso
Phosphatidylethanolamine (LPE18:1), and Lyso
Phosphatidylcholine (LPC) (LPC20:3) classes in the
symptom-free (L)ALR subjects. Of note, the multidimensional mass spectrometry-based shotgun lipidomics technique [57] has been widely used to identify
altered lipid metabolism and biomarkers under pathophysiological conditions such as prediabetics and type 2
diabetics compared to otherwise healthy subjects [58].
Here we are presenting data on the normal variation
among otherwise symptom-free individuals.
MicroRNAs (MiR) have earned great deal of attention not only for their ability to regulate adipogenesis
and adipose function, but also for their presence in
circulating blood leading to potential tools as diagnostic
biomarkers [20]. Table 4 shows the results for the most
relevant miRNAs observed, when comparing symptomfree individuals with a (H)ALP vs. a (L)ALP in this
cohort of 14 females. miR-27b-5p, miR-378a-3p and
miR-375 showed a steady increase in both fasting and
postprandial states in the (L)ALR participants. Overexpression of miR-27 results in robust and specific
inhibition of adipogenic differentiation with the blockade of PPARγ and C/EBPα expression [59]. Mir-378a3p induces adipogenesis by targeting mitogen-activated
protein kinase 1 (MAPK1) [60]. miR-375 as an important modulator of β-cell functions. miR-375 overexpression in β-cells leads to a reduction of the number
and viability of β-cells [61]. miR-140-5p was only
expressed in the fed state and showed an increase in
the (L)ALR participants. It participates in modulating
the expression of proangiogenic factors, influencing
inflammatory reactions [62]. miR-33a-5p and miR7-1-3p also showed a steady increase in both fasting
and postprandial states in the (L)ALR participants.
miR-33 is associated with adipose tissue differentiation
and development of gastrointestinal tract [63]. Lower
expression levels of miR-130 have been reported in the
abdominal subcutaneous adipose tissue and in the
plasma of obese women compared with those of lean
subjects [64]. The let-7 miRNA family plays a key role
in modulating inflammatory responses. Recent research
has documented that let-7 levels are decreased in diabetic human carotid plaques [65].
Temporal gene expression changes during the fasted
and fed state in proinflammatory LEP, TNFAIP1, CD86,
RBP4 and TGFβ and anti-inflammatory ADIPOR1,
CD163, IL10, HIF1AN and ANG activity to directly
measure the balance of the inflammatory/antiinflammatory response during the development of cellular adaptive responses in early adipose tissue
expansion and remodelling have not been fully elucidated [66]. We measured metabolic and immune molecular gene expression directly as a means to compare
differential expression features of early trends for adipose tissue dysfunction (Figure 5). We detected a pattern
of expression for proinflamatory genes. LEP and
TNFAIP1 increased in the (L)ALR subjects. In activated
macrophages, proinflammatory M1 and M2b are the
main cell types expressing and secreting TNF-α [67].
Unexpectedly, CD86 gene expression was decreased in
subjects with (L)ALR. Several studies have shown that
CD86 is expressed in and used as a marker to identify
M1 pro-inflammatory and polarized macrophages [68].
Retinol binding protein 4 (RBP4) is secreted by adipocytes, is increased in obese and insulin resistant subjects
and induces proinflammatory cytokines through the
JNK and TLR-4 pathways in macrophages [69]. RBP4
did not show any apparent change in expression in
either (H) or (L)ALR participants. TGFβ is a master
regulator and promoter of fibrosis in adipose tissue
[70]. Elevated levels of TGFβ expression were found in
fasting in (H)ALR participants, decreasing in the fed
state. TGFβ can also be anti-inflammatory and promote
M2-like macrophage activity that localize to fibrotic
areas of adipose tissue [66].
We also characterized the pattern of expression for
AT anti-inflammatory genes (Figure 5). The ADIPOR1
receptor expression showed a slight increase in the
postprandial (L)ALR group. This and several other
papers [71] have shown that circulating chronic subclinical inflammation markers are significantly associated with a systemic decrease in adiponectin
concentrations in individuals with a (L)ALR leading
to insulin resistance [8]. In addition, there is
a deleterious decline in AdipoR1/R2 mRNA expression
leading to a decrement in adiponectin binding to cell
membrane which deeply attenuates the effects of adiponectin [72]. A modest increase was also noted in
CD163 and IL10 postprandial gene expression in the
same group. Macrophages with CD163 expression are
considered anti-inflammatory M2 macrophages [73].
IL-10 is decreased in subjects with impaired glucose
tolerance and obesity [74]. This reduction plays a key
role in inflammation-mediated macrophage polarization observed in adipose tissue dysfunction [75]. ANG
is an important group of vascular remodelling angiogenic factors expressed in adipose tissue that control
vessel maturation, patterning, and stabilization [76].
Local hypoxia is a potent stimulus for new blood vessel
formation through the pro-angiogenic actions of HIF1α and HIF-2α [77]. We found a marked decrease of
ANG in the postprandial (H)ALR subjects with apparently no change in the (L)ALR group.
ADIPOCYTE
Some potential limitations of this study should be
pointed out. First, due to the small number of subjects,
these findings should be interpreted with caution and
considered as hypotheses generating. These results should
be confirmed by studies with a larger number of subjects.
Second, as we used the ALR [9] to compare the immunometabolic systemic and molecular adipose tissue phenotypes, and only total adiponectin was measured, it would be
interesting to compare the adiponectin/leptin ratio with the
measured fasting and postprandial phenotypes if highmolecular-weight adiponectin was used instead of total
adiponectin. On the other hand, leptin and adiponectin
are very stable in plasma or serum. As this study included
participants who had samples taken in the fed state, this
could be an advantage because removing the need for
fasting samples would significantly increase the efficiency
and feasibility of early immunometabolic and cardiovascular risk measurements in large population-based studies.
Third, this study was conducted with a Mexican-mestizo
population, therefore it would need to be determined
whether these findings extend to other ethnic groups.
However, Mexicans share with Mexican Americans an
elevated risk of CVD and T2D [78]. This shared, elevated
prevalence of CVRIMO suggests shared genetic factors
[79]. As the source population, Mexico reflects the allelic
diversity resulting from the conquest and subsequent confluence of European and Native American origins, and
therefore reflects the full extent of the spectrum of risk
[80]. Finally, we did not adjust for the female’s menstrual
cycle stage. Notwithstanding these limitations, this study
affirms the central role of adipose tissue dysfunction in
triggering the accumulation of predominantly proinflammatory immune cells that act as a potent stimulus
towards the immunometabolic dysfunction of this tissue
leading to adipocyte hypertrophy, fibrosis and hypoxia,
which activates macrophage infiltration that ultimately
results in insulin resistance.
We are beginning to understand the importance of the
complex interactions between inflammation, the extracellular matrix (ECM), and angiogenesis in the context of AT
dysfunction. Indeed, our study was carried out in a group
of symptom-free normoglycemic, mainly normal-weight
women with no history of age-related chronic diseases
associated with immunometabolic abnormalities.
Moreover, as IR can precede the dysglycemic states of
prediabetes and type 2 diabetes mellitus (T2DM) by
a number of years and is an early marker of risk for
immunometabolic and cardiovascular disease, our early
research findings raise several important questions: Does
the ALR predict early cardiometabolic risk? Does the elevation of the inflammatory markers (within the normal
range) in these symptom-free individuals relate to early
risk for endothelial dysfunction and cardiovascular disease?
165
Do the postprandial insulin-glucose axis abnormalities,
HOMA-IR and adipo-IRi elevations reflect early risk for
prediabetes? Could the correlations of systemic lipid species and microRNAs along with the direct molecular characterization of adipose tissue immunometabolic function
lead to early biomarkers of risk for metabolic and cardiovascular disease before the development of frank insulin
resistance? It is expected that concrete answers to this
question will pave the way for the identification of novel
biomarkers to diagnose adipose tissue dysfunction perhaps
without the need to fully account for adipose tissue accumulation in a subgroup of symptom-free individuals.
A valid take-home message relates to the 1988
American Diabetes Association Banting award lecture
[81], where the late Professor Emeritus of Medicine
Gerald ‘Jerry’ Reaven, MD, introduced the concept of
the link between IR and a constellation of lipid and
non-lipid risk factors of metabolic origin (increased
blood pressure, high blood sugar and abnormal HDL
cholesterol and triglyceride levels) [82]. This cluster
later became known as Metabolic Syndrome [83].
The main messages this outstanding scientist and educator left for the diabetes scientific community stated
that values for insulin-mediated glucose disposal vary
continuously throughout a population of apparently
healthy individuals, with at least a sixfold variation
between the most insulin sensitive and most insulin
resistant of these individuals, and that approximately
one-third of an apparently healthy population is sufficient insulin resistant to develop significant clinical
disease [84]. He brilliantly concluded that the primary
value of the concept of insulin resistance is that it
provides a conceptual framework with which to place
a substantial number of apparently unrelated biological events into a pathophysiologic construct [82]. It
must be acknowledged that his pioneering research
laid the foundations for the key role that adipose tissue
(AT) dysfunction plays in the development of IR.
Nowadays, we might be at the dawn to unravel that
in apparently symptom-free individuals we could place
a cluster of immunometabolic phenotypes related to
impaired angiogenesis and hypoxia, inflammation,
inappropriate extracellular matrix (ECM) remodelling
and macrophage polarization into a systemic and
molecular construct coined as adipose tissue dysfunction which triggers the early events leading to the
development of insulin resistance.
In conclusion, there is a major demographic and epidemiologic change taking place in the U.S. and worldwide. The preventive approach based on single diseases
towards symptom-driven medicine is becoming out-ofdate. A precision and personalized medicine linked to the
identification of early risk and prevention instead of
166
E. RODRIGUEZ-AYALA ET AL.
identification of curative pathological symptoms in
immunometabolic and cardiovascular disease is rapidly
taking place. Optimism to achieve success is in the horizon due to the overwhelming advancement of genomic
medicine, particularly integrative systems biology
through multi-OMICS technology and definitions of cardiovascular, metabolic and immune disease risk deep
phenotypes for early detection of age-related chronic diseases associated with immunometabolic pathology.
Disclosure statement
No potential conflict of interest was reported by the authors.
Funding
Research reported in this publication was supported by the
National Institute of Diabetes and Digestive and Kidney
Diseases of the National Institutes of Health under Award
Number R56DK114703-01. The content is solely the responsibility of the authors and does not necessarily represent the
official views of the National Institutes of Health.
ORCID
Fabiola Escalante-Araiza
http://orcid.org/0000-0003-05084023
http://orcid.org/0000-0001Angelica Martinez-Hernandez
9883-2988
http://orcid.org/0000Victor M. Hernandez-Escalante
0001-8574-7899
http://orcid.org/0000Huertas-Quintero Jancy Andrea
0001-6659-1980
http://orcid.org/0000-0003-0475José Ángeles-Chimal
2798
http://orcid.org/0000-0003-3436Jesús Santa-Olalla Tapia
4163
http://orcid.org/0000-0001-8478José M. Remes-Troche
9659
http://orcid.org/0000-0001-7984Eira E. Huerta-Avila
588X
http://orcid.org/0000-0002Juan Carlos Lopez-Alvarenga
0966-8766
http://orcid.org/0000-0002-4034Raul A. Bastarrachea
3062
References
[1] Goossens GH. The role of adipose tissue dysfunction in
the pathogenesis of obesity-related insulin resistance.
Physiol Behav. 2008 May 23;94(2):206–218.
[2] Gregor MF, Hotamisligil GS. Inflammatory mechanisms in obesity. Annu Rev Immunol. 2011;29:415–445.
[3] Muir LA, Kiridena S, Griffin C, et al. Frontline Science:
rapid adipose tissue expansion triggers unique proliferation and lipid accumulation profiles in adipose tissue macrophages. J Leukoc Biol. 2018 Apr;103
(4):615–628. .
[4] Crewe C, An YA, Scherer PE. The ominous triad of
adipose tissue dysfunction: inflammation, fibrosis, and
impaired angiogenesis. J Clin Invest. 2017 Jan 3;127
(1):74–82.
[5] Hotamisligil GS. Inflammation, metaflammation and
immunometabolic disorders. Nature. 2017 Feb 8;542
(7640):177–185.
[6] Ormazabal V, Nair S, Elfeky O, et al. Association
between insulin resistance and the development of
cardiovascular disease. Cardiovasc Diabetol. 2018 Aug
31;17(1):122.
[7] Bravo C, Cataldo LR, Galgani J, et al. Leptin/adiponectin ratios using either total or high-molecular-weight
adiponectin as biomarkers of systemic insulin sensitivity in normoglycemic women. J Diabetes Res.
2017;2017:9031079.
[8] Fruhbeck G, Catalan V, Rodriguez A, et al.
Adiponectin-leptin ratio is a functional biomarker of
adipose tissue inflammation. Nutrients. 2019 Feb 22;
11(2). DOI:10.3390/nu11020454.
[9] Fruhbeck G, Catalan V, Rodriguez A, et al.
Adiponectin-leptin ratio: a promising index to estimate
adipose tissue dysfunction. Relation with obesityassociated cardiometabolic risk. Adipocyte. 2018 Jan
2;7(1):57–62.
[10] Mahalle NP, Garg MK, Kulkarni MV, et al. Differences
in traditional and non-traditional risk factors with special reference to nutritional factors in patients with
coronary artery disease with or without diabetes
mellitus. Indian J Endocrinol Metab. 2013 Sep;17
(5):844–850.
[11] Bauer UE, Briss PA, Goodman RA, et al. Prevention of
chronic disease in the 21st century: elimination of the
leading preventable causes of premature death and disability in the USA. Lancet. 2014 Jul 5;384(9937):45–52.
[12] Eckel RH, Kahn R, Robertson RM, et al. Preventing
cardiovascular disease and diabetes: a call to action
from the American diabetes association and the
American heart association. Diabetes Care. 2006
Jul;29(7):1697–1699.
[13] Caputo T, Gilardi F, Desvergne B. From chronic overnutrition to metaflammation and insulin resistance:
adipose tissue and liver contributions. FEBS Lett.
2017 Oct;591(19):3061–3088.
[14] Lionetti L, Mollica MP, Lombardi A, et al. From
chronic overnutrition to insulin resistance: the role of
fat-storing capacity and inflammation. Nutr Metab
Cardiovasc Dis. 2009 Feb;19(2):146–152.
[15] Karelis AD, Rabasa-Lhoret R. Obesity: can inflammatory status define metabolic health? Nat Rev
Endocrinol. 2013 Dec;9(12):694–695.
[16] You S, Yin X, Liu H, et al. Hyperfibrinogenemia is
significantly associated with an increased risk of
in-hospital mortality in acute ischemic stroke
patients. Curr Neurovasc Res. 2017;14(3):242–249. .
[17] Tangvarasittichai S, Pongthaisong S, Tangvarasittichai O.
Tumor necrosis factor-alpha, interleukin-6, c-reactive
protein levels and insulin resistance associated with type
2 diabetes in abdominal obesity women. Indian J Clin
Biochem. 2016 Mar;31(1):68–74.
[18] Bastarrachea RA, Laviada-Molina HA, Nava-Gonzalez
EJ, et al. Deep multi-OMICs and multi-tissue
ADIPOCYTE
[19]
[20]
[21]
[22]
[23]
[24]
[25]
[26]
[27]
[28]
[29]
[30]
[31]
[32]
[33]
[34]
[35]
characterization in a pre- and postprandial state in
human volunteers: the GEMM family study research
design. Genes (Basel). 2018 Nov 2;9(11). DOI:10.3390/
genes9110532.
Hubal MJ, Nadler EP, Ferrante SC, et al. Circulating
adipocyte-derived exosomal MicroRNAs associated
with decreased insulin resistance after gastric bypass.
Obesity (Silver Spring). 2017 Jan;25(1):102–110. .
Icli B, Feinberg MW. MicroRNAs in dysfunctional
adipose tissue: cardiovascular implications. Cardiovasc
Res. 2017 Jul 1;113(9):1024–1034.
Wang M, Han X. Advanced shotgun lipidomics for
characterization of altered lipid patterns in neurodegenerative diseases and brain injury. Methods Mol
Biol. 2016;1303:405–422.
López-Alvarenga
JCC-ML,
Dávalos-Ibáñez
A,
González-Barranco J. Reproducibilidad y sensibilidad
de un cuestionario de actividad física en población
mexicana. Salud Publica de Mexico. 2001;43
(4):306–310.
Perkins BA, Caskey CT, Brar P, et al. Precision medicine
screening using whole-genome sequencing and advanced
imaging to identify disease risk in adults. Proc Natl Acad
Sci USA. 2018 Apr 3;115(14):3686–3691.
Rojas-Martinez A. Confidentiality and data sharing:
vulnerabilities of the mexican genomics sovereignty
act. J Community Genet. 2015 Jul;6(3):313–319.
Schwartz-Marin E, Mendez AA. The law of genomic
sovereignty and the protection of “Mexican genetic
patrimony”. Med Law. 2012 Jun;31(2):283–294.
Angulo P, Hui JM, Marchesini G, et al. The NAFLD
fibrosis score: a noninvasive system that identifies liver
fibrosis in patients with NAFLD. Hepatology. 2007
Apr;45(4):846–854. .
Bligh EG, Dyer WJ. A rapid method of total lipid
extraction and purification. Can J Biochem Physiol.
1959 Aug;37(8):911–917.
Wang M, Wang C, Han RH, et al. Novel advances in
shotgun lipidomics for biology and medicine. Prog
Lipid Res. 2016 Jan;61:83–108.
Yang K, Cheng H, Gross RW, et al. Automated lipid
identification and quantification by multidimensional
mass spectrometry-based shotgun lipidomics. Anal
Chem. 2009 Jun 1;81(11):4356–4368.
Han X, Yang K, Gross RW. Multi-dimensional mass
spectrometry-based shotgun lipidomics and novel strategies for lipidomic analyses. Mass Spectrom Rev. 2012
Jan–Feb;31(1):134–178.
Pietzner M, Kaul A, Henning AK, et al. Comprehensive
metabolic profiling of chronic low-grade inflammation
among generally healthy individuals. BMC Med. 2017
Nov 30;15(1):210.
Iacomino G, Siani A. Role of microRNAs in obesity
and obesity-related diseases. Genes Nutr. 2017;12:23.
Zhong H, Ma M, Liang T, et al. Role of MicroRNAs in
obesity-induced metabolic disorder and immune
response. J Immunol Res. 2018;2018:2835761.
Franzosi MG. Should we continue to use BMI as
a cardiovascular risk factor? Lancet. 2006 Aug 19;368
(9536):624–625.
Klein S, Allison DB, Heymsfield SB, et al. Waist circumference and cardiometabolic risk: a consensus
[36]
[37]
[38]
[39]
[40]
[41]
[42]
[43]
[44]
[45]
[46]
[47]
[48]
[49]
[50]
167
statement from shaping America’s health: association
for weight management and obesity prevention;
NAASO, the obesity society; the American society for
nutrition; and the American diabetes association. Am
J Clin Nutr. 2007 May;85(5):1197–1202. .
Eloi JC, Epifanio M, de Goncalves MM, et al.
Quantification of abdominal fat in obese and healthy
adolescents using 3 tesla magnetic resonance imaging
and free software for image analysis. PLoS One.
2017;12(1):e0167625. .
Jorge-Galarza E, Medina-Urrutia A, PosadasSanchez R, et al. Adipose tissue dysfunction
increases fatty liver association with pre diabetes
and newly diagnosed type 2 diabetes mellitus.
Diabetol Metab Syndr. 2016;8:73.
Robinson PN. Deep phenotyping for precision
medicine. Hum Mutat. 2012 May;33(5):777–780.
Abdul-Ghani MA, Matsuda M, Balas B, et al. Muscle
and liver insulin resistance indexes derived from the
oral glucose tolerance test. Diabetes Care. 2007 Jan;30
(1):89–94.
Mechanick JI, Garber AJ, Grunberger G, et al.
Dysglycemia-based chronic disease: an American
association of clinical endocrinologists position
2018
Nov;24
statement.
Endocr
Pract.
(11):995–1011.
Konner AC, Bruning JC. Selective insulin and leptin
resistance in metabolic disorders. Cell Metab. 2012 Aug
8;16(2):144–152.
Vega GL, Grundy SM. Metabolic risk susceptibility in
men is partially related to adiponectin/leptin ratio.
J Obes. 2013;2013:409679.
Vilahur G, Ben-Aicha S, Badimon L. New insights into
the role of adipose tissue in thrombosis. Cardiovasc
Res. 2017 Jul 1;113(9):1046–1054.
Stegenga ME, van der Crabben SN, Dessing MC, et al.
Effect of acute hyperglycaemia and/or hyperinsulinaemia on proinflammatory gene expression, cytokine
production and neutrophil function in humans.
Diabet Med. 2008 Feb;25(2):157–164. .
Stefan N, Haring HU, Hu FB, et al. Metabolically
healthy obesity: epidemiology, mechanisms, and clinical implications. Lancet Diabetes Endocrinol. 2013
Oct;1(2):152–162.
Virtue S, Vidal-Puig A. It’s not how fat you are, it’s
what you do with it that counts. PLoS Biol. 2008 Sep
23;6(9):e237.
Trayhurn P. Hypoxia and adipocyte physiology: implications for adipose tissue dysfunction in obesity. Annu
Rev Nutr. 2014;34:207–236.
Hotamisligil GS, Shargill NS, Spiegelman BM. Adipose
expression of tumor necrosis factor-alpha: direct role
in obesity-linked insulin resistance. Science. 1993 Jan
1;259(5091):87–91.
Rotter V, Nagaev I, Smith U. Interleukin-6 (IL-6) induces
insulin resistance in 3T3-L1 adipocytes and is, like IL-8
and tumor necrosis factor-alpha, overexpressed in
human fat cells from insulin-resistant subjects. J Biol
Chem. 2003 Nov 14;278(46):45777–45784.
Sutherland JP, McKinley B, Eckel RH. The metabolic
syndrome and inflammation. Metab Syndr Relat
Disord. 2004 Jun;2(2):82–104.
168
E. RODRIGUEZ-AYALA ET AL.
[51] Pestka S, Krause CD, Sarkar D, et al. Interleukin-10
and related cytokines and receptors. Annu Rev
Immunol. 2004;22:929–979.
[52] Stern JH, Rutkowski JM, Scherer PE. Adiponectin,
leptin, and fatty acids in the maintenance of metabolic
homeostasis through adipose tissue crosstalk. Cell
Metab. 2016 May 10;23(5):770–784.
[53] Aslam M, Aggarwal S, Sharma KK, et al. Postprandial
hypertriglyceridemia predicts development of insulin
resistance glucose intolerance and type 2 diabetes.
PLoS One. 2016;11(1):e0145730.
[54] Gastaldelli A, Harrison SA, Belfort-Aguilar R, et al.
Importance of changes in adipose tissue insulin
resistance to histological response during thiazolidinedione treatment of patients with nonalcoholic
steatohepatitis.
Hepatology.
2009
Oct;50(4):
1087–1093. .
[55] Lomonaco R, Ortiz-Lopez C, Orsak B, et al. Effect of
adipose tissue insulin resistance on metabolic parameters and liver histology in obese patients with nonalcoholic fatty liver disease. Hepatology. 2012 May;55
(5):1389–1397. .
[56] Han X. Lipidomics for studying metabolism. Nat Rev
Endocrinol. 2016 Nov;12(11):668–679.
[57] Wang J, Wang C, Han X. Tutorial on lipidomics. Anal
Chim Acta. 2019 Jul;11(1061):28–41.
[58] Zhong H, Fang C, Fan Y, et al. Lipidomic profiling
reveals distinct differences in plasma lipid composition
in healthy, prediabetic, and type 2 diabetic individuals.
Gigascience. 2017 Jul 1;6(7):1–12.
[59] Lin Q, Gao Z, Alarcon RM, et al. A role of miR-27 in
the regulation of adipogenesis. Febs J. 2009 Apr;276
(8):2348–2358.
[60] Huang N, Wang J, Xie W, et al. MiR-378a-3p enhances
adipogenesis by targeting mitogen-activated protein
kinase 1. Biochem Biophys Res Commun. 2015 Jan
30;457(1):37–42.
[61] Landrier JF, Derghal A, Mounien L. MicroRNAs in
obesity and related metabolic disorders. Cells. 2019
Aug 9;8(8). DOI:10.3390/cells8080859.
[62] Krist B, Florczyk U, Pietraszek-Gremplewicz K,
et al. The role of miR-378a in metabolism, angiogenesis, and muscle biology. Int J Endocrinol.
2015;2015:281756.
[63] Price NL, Fernandez-Hernando C. miRNA regulation
of white and brown adipose tissue differentiation and
function. Biochim Biophys Acta. 2016 Dec;1861(12Pt
B):2104–2110.
[64] Ortega FJ, Mercader JM, Catalan V, et al. Targeting the
circulating microRNA signature of obesity. Clin Chem.
2013 May;59(5):781–792. .
[65] Brennan E, Wang B, McClelland A, et al. Protective
effect of let-7 miRNA family in regulating inflammation in diabetes-associated atherosclerosis. Diabetes.
2017 Aug;66(8):2266–2277. .
[66] Zhang F, Wang H, Wang X, et al. TGF-beta induces
M2-like macrophage polarization via SNAIL-mediated
suppression of a pro-inflammatory phenotype.
Oncotarget. 2016 Aug 9;7(32):52294–52306.
[67] Ohlsson SM, Linge CP, Gullstrand B, et al. Serum from
patients with systemic vasculitis induces alternatively
[68]
[69]
[70]
[71]
[72]
[73]
[74]
[75]
[76]
[77]
[78]
[79]
[80]
activated macrophage M2c polarization. Clin
Immunol. 2014 May-Jun;152(1–2):10–19. .
Wang LX, Zhang SX, Wu HJ, et al. M2b macrophage
polarization and its roles in diseases. J Leukoc Biol.
2019 Aug;106(2):345–358.
Norseen J, Hosooka T, Hammarstedt A, et al. Retinolbinding protein 4 inhibits insulin signaling in adipocytes by inducing proinflammatory cytokines in
macrophages through a c-Jun N-terminal kinase- and
toll-like receptor 4-dependent and retinol-independent
2012
May;32
mechanism.
Mol
Cell
Biol.
(10):2010–2019. .
Le Dour C, Wu W, Bereziat V, et al. Extracellular
matrix remodeling and transforming growth
factor-beta signaling abnormalities induced by lamin
A/C variants that cause lipodystrophy. J Lipid Res.
2017 Jan;58(1):151–163.
Fruhbeck G, Catalan V, Rodriguez A, et al.
Involvement of the leptin-adiponectin axis in inflammation and oxidative stress in the metabolic syndrome.
Sci Rep. 2017 Jul 26;7(1):6619.
Engin A. Adiponectin-resistance in obesity. Adv Exp
Med Biol. 2017;960:415–441.
Kralova Lesna I, Kralova A, Cejkova S, et al.
Characterisation and comparison of adipose tissue
macrophages from human subcutaneous, visceral and
perivascular adipose tissue. J Transl Med. 2016 Jul
11;14(1):208.
Bluher M, Fasshauer M, Tonjes A, et al. Association of
interleukin-6, C-reactive protein, interleukin-10 and
adiponectin plasma concentrations with measures of
obesity, insulin sensitivity and glucose metabolism.
Exp Clin Endocrinol Diabetes. 2005 Oct;113
(9):534–537.
Gotoh K, Inoue M, Masaki T, et al. A novel
anti-inflammatory
role
for
spleen-derived
interleukin-10 in obesity-induced inflammation in
white adipose tissue and liver. Diabetes. 2012 Aug;61
(8):1994–2003.
Xue Y, Cao R, Nilsson D, et al. FOXC2 controls Ang-2
expression and modulates angiogenesis, vascular patterning, remodeling, and functions in adipose tissue.
Proc Natl Acad Sci U S A. 2008 Jul 22;105
(29):10167–10172.
Garcia-Martin R, Alexaki VI, Qin N, et al. Adipocytespecific hypoxia-inducible factor 2alpha deficiency
exacerbates obesity-induced brown adipose tissue dysfunction and metabolic dysregulation. Mol Cell Biol.
2016 Feb 1;36(3):376–393.
Aviles-Santa ML, Perez CM, Schneiderman N, et al.
Detecting prediabetes among hispanics/latinos from
diverse heritage groups: does the test matter?
Findings from the hispanic community health study/
study of latinos. Prev Med. 2017 Feb;95:110–118.
Morales LS, Flores YN, Leng M, et al. Risk factors for
cardiovascular disease among Mexican-American
adults in the United States and Mexico:
a comparative study. Salud Publica Mex. 2014 Apr;56
(2):197–205.
Williams AL, Jacobs SB, Moreno-Macias H, et al.
Sequence variants in SLC16A11 are a common risk
ADIPOCYTE
factor for type 2 diabetes in Mexico. Nature. 2014 Feb
6;506(7486):97–101.
[81] Reaven GM. Banting lecture 1988. Role of insulin
resistance in human disease. 1988. Nutrition. 1997
Jan;13(1):65. discussion 4, 6.
[82] Reaven GM. The metabolic syndrome: requiescat in
pace. Clin Chem. 2005 Jun;51(6):931–938.
[83] Kahn R, Buse J, Ferrannini E, et al. The metabolic
syndrome: time for a critical appraisal: joint
169
statement
from
the
American
diabetes
association and the European association for the
study of diabetes. Diabetes Care. 2005 Sep;28
(9):2289–2304.
[84] Yeni-Komshian H, Carantoni M, Abbasi F, et al.
Relationship between several surrogate estimates of insulin resistance and quantification of insulin-mediated glucose disposal in 490 healthy nondiabetic volunteers.
Diabetes Care. 2000 Feb;23(2):171–175.