Instituto Tecnológico y de Estudios Superiores de Monterrey
Campus Monterrey
School of Medicine and Health Sciences
Identification and Association of Fatty Acid Profile and Inflammation in Pediatric Type 2 Diabetes Mellitus and Metabolic Syndrome
Dissertation presented by
Mariana Navarro Guerra
submitted to theSchool of Medicine and Health Sciences as a partial requirement to obtain the degree of
Master of Science in
Biomedical Sciences
Monterrey, Nuevo León, June 11th 2020
5 Dedication
To my parents, thank you for your unconditional support, patience, confidence and encouragement.
6 Acknowledgements
To my advisors, Dr. Leticia Elizondo and Dr. Carmen Hernández for offering me their guidance and tutorship through these years and for the chance of being part of this project.
To MsC. Martín Marín and PhD Dariana Rodríguez for their assistance, guidance and tutorship in experimental methodology.
To Dr. Romeo Villarreal for his assistance, guidance and tutorship in statistical analysis, and especially for his unconditional support.
To my fellow students Francisco Velázquez, Marcelo González, Itzel Treviño, Bianca Nieblas and Nikita Campa for their support in different parts of this process.
During the period of the two years of the study and work for this thesis, Tecnológico de Monterrey provided tuition support and CONACYT provided funding for maintenance.
7
Identification and Association of Fatty Acid Profile and Inflammation in Pediatric
Type 2 Diabetes Mellitus and Metabolic Syndrome
byMariana Navarro Guerra Abstract
Plasma fatty acid composition reflects dietary intake, as well as endogenous metabolism of fatty acids, which may be impaired in metabolic diseases. In adults, analysis of plasma fatty acids and their metabolism have been used to characterize their role in inflammation and obesity-related diseases such as Type 2 Diabetes Mellitus (T2DM), however evidence in the pediatric population is scarce. To the best of knowledge, there are no studies in T2DM pediatric patients focused on the quantification of fatty acid profiles and their potential relationship with inflammation markers.
The objective of this study was to determine the association between the plasma fatty acid composition and inflammatory markers in pediatric Mexican patients with T2DM, Metabolic syndrome (MetS) and healthy controls (HC). Anthropometric and biochemical parameters were determined. Plasma fatty acid profiles were quantified by gas chromatography and plasma cytokines by flow-cytometry. Univariate and multivariate statistical analyses were conducted to establish differences and relationships between response variables investigated in the clinical groups. Patients with T2DM and MetS had distinct fatty acid profiles despite similarities between anthropometric and metabolic parameters. Higher proportions of C8:0 and n-6 polyunsaturated fatty acids (PUFA), lower n-3 PUFA and estimated delta-5-desaturase (D5D) activity could place MetS patients at higher risk of developing T2DM and cardiovascular disease. Despite having higher proportions of anti-inflammatory n-3 PUFAs, patients with T2DM had a pro-inflammatory profile characterized by higher proportions of C16:0 and elevated chemokines MCP-1, IL-8 and IL-18 concentrations. Higher percent contributions of odd chain saturated fatty acid (OCSFA) C17:0 were observed in plasma of patients with MetS and T2DM; its metabolic significance requires further research but a possible protective role in the reduction of inflammation seems to be supported by prior literature. Plasma monounsaturated fatty acids (MUFA) 18:1 n-9 and 16:1 n-7 appear to have a dual role in inflammation depending on the obese state. Characterization of the fatty acid profiles of a pediatric population with MetS and T2DM generated new knowledge of specific compounds such as C8:0 and C17:0, which may play a role in progression of obesity induced IR and inflammation to T2DM.
8 Table of Contents
Abstract 7
Chapter 1. Problem Statement 10
1.1 Epidemiology of Obesity, Metabolic Syndrome and Type 2 Diabetes Mellitus 10
1.2 The Role of Fatty Acids in Disease 11
1.3 Hypothesis 16
1.4 General Objective 16
1.4.1 Specific Objectives 16
1.5 Rationale 17
1.6 Scope of the study 18
Chapter 2. Theoretical Framework 19
2.1 Obesity and Inflammation 19
2.2 Metabolic Syndrome and Inflammation 20
2.3 Type 2 Diabetes Mellitus and Inflammation 22
2.4 Fatty Acids: Properties and Metabolism 24
2.5 Fatty Acid Profile in the Pediatric Population with Obesity, Metabolic Syndrome and Type 2 Diabetes
Mellitus 29
2.6 Fatty Acid Profile and Inflammation 32
Chapter 3. Methodology 34
3.1 Study Population 34
3.2 Anthropometric Measurements 35
3.3 Biological Samples 35
3.4 Quantification of Plasma Fatty Acid Profiles 36
3.5 Plasma Cytokine Analysis 38
3.6 Statistical Analysis 38
Chapter 4. Results 40
4.1 Cohort Description 40
4.2 Anthropometric, Metabolic, Clinical and Biochemical Parameters of the Population 41
4.3 Fatty Acid Profile 43
9 4.4 Fatty Acids Profile for the Type 2 Diabetes Mellitus, Metabolic Syndrome, and Healthy Control
Groups. 48
4.5 Inflammatory Profile for the Type 2 Diabetes Mellitus, Metabolic Syndrome, and Healthy Control
Groups. 57
4.6 Fatty Acid Profile and Inflammation 58
Chapter 5. Discussion 63
Chapter 6. Conclusion 82
References 84
Currículum Vitae Único (CVU) 103
10 Chapter 1. Problem Statement
1.1 Epidemiology of Obesity, Metabolic Syndrome and Type 2 Diabetes Mellitus
Worldwide, the prevalence of obesity in children, adolescents, and adults has triplicated between 1975 and 2016. Specifically, the prevalence of 15 to 19 years old adolescents with overweight or obesity increased from 4% in 1975 to 18% in 2016, impacting 340 million children and adolescents (WHO, 2020). According to the National Health and Nutrition Survey (ENSANUT) of 2016, in Mexico 1 in 3 children aged 5-11 years has a combined prevalence of overweight and obesity. Furthermore, the percentage of adolescents with overweight and obesity (aged 12-19 years) increased from 34.9% in 2012 to 38.4% in 2018 (23.8% overweight and 14.6%
obese) (ENSANUT et al., 2018). Diseases such as type 2 diabetes mellitus (T2DM) and Metabolic Syndrome (MetS) were found, by prior reports, to be highly correlated with obesity (Lakshman et al., 2012).
A literature review by Pierlot et al. (2017) assessed the prevalence of MetS in children and adolescents in the American continent, in their report, Mexico had a prevalence of 6%. However, a significant limitation for the comparison of studies has been the differences in criteria for MetS diagnosis. For instance, an analysis conducted by the National Health and Nutrition Examination Survey (NHANES) 1999-2002 showed that depending on the definition used, the prevalence of MetS among 12-19-year-olds in the US varied from 2 to 9.4%. However, using Cook et al. (2003) MetS definition criteria, the prevalence was 7.8% in adolescents with overweight and 44% in adolescents with obesity (Messiah et al., 2019).
11 According to the American Diabetes Association (ADA, 2000), in the pediatric population, overweight and obesity are risk factors for T2DM. Their data showed that 85% of pediatric patients with new-onset T2DM had overweight or obesity at diagnosis. Even though for decades, T2DM has been viewed as a disease of adults, during the last decade, its prevalence in children and adolescents has been rising. In the United States, this population alone represents 20-50% of newly diagnosed patients (Evert et al., 2004), with an increase in the prevalence of up to 30.5%, in 10- 19-year-olds from 2001 to 2009 (Dabelea et al., 2014). Mayer-Davis et al. (2017) observed a 7.1%
increase in the incidence of T2DM in children and adolescents 10-19 years of age between the years 2002-2012. Furthermore, the annual rate of T2DM diagnosis was more prominent in Hispanic-American population compared to non-Hispanic White Americans (4.2% vs 1.2%, respectively). Although formal data on the epidemiology of T2DM disease in the Mexican pediatric population is not available, recent publications suggest that it is a growing public health issue and that it is also affecting younger children. T2DM has been estimated to represent an
$11,516 million USD burden on society, and to cause 85,000 deaths per year for the adult population in Mexico (IDF, 2019).
1.2 The Role of Fatty Acids in Disease
Fatty Acids and Inflammation
As a result of diet or obesity, the increase of different lipid species may contribute to adipose tissue inflammation. In cell culture models, SFA such as lauric acid (C12:0) and C16:0 have been shown to induce activation and translocation of nuclear factor kappa beta (NF-κβ) to the nucleus by binding to toll-like receptors (TLR4 and TLR2) (Calder, 2015b). Activated NF-κβ induces synthesis and secretion of chemokines (i.e. MCP1) by adipocytes, resulting in infiltration
12 of proinflammatory monocytes. TLR4 and TLR2 are both associated with obesity-associated inflammatory signaling, and their receptor have been observed to increase in adipose tissue of patients with obesity (Reilly & Saltiel, 2017).
IR, present in most patients with MetS, as well as in almost all patients with T2DM, is a metabolic dysfunction often mediated by increased inflammation, which may be in part induced via the role of several fatty acids. Generally, n-6 AA and C16:0 are viewed as proinflammatory molecules, while n-3 fatty acids (mainly EPA and DHA) are viewed as anti-inflammatory molecules (Sears & Perry, 2015). An in vitro study in adipocytes, n-6 AA combined with LPS, exerted a strong synergistic effect on pro-inflammation monocyte chemoattractant protein-1 (MCP-1) and interleukin-6 (IL-6) protein secretion. In this study, n-3 PUFA EPA and DHA were capable to reduce MCP-1 and IL-1β in an LPS-induced inflammatory state, through GPR120 signaling, resulting in a reduction of NF-κβ activity (Cranmer-Byng et al., 2015). This study suggests that marine-derived longer chain n-3 PUFA (DHA and EPA) may compete with an inflammatory stimulus via potent anti-inflammatory effects, while n-6 AA further promotes inflammation. Additionally, intervention studies in healthy patients, described marine n-3 supplementation decreased proinflammatory cytokines TNF-α, IL-1β and IL-6 in monocytes and mononuclear cells (Calder, 2015a). Therefore, opposite effects on inflammation are observed between n-3 and n-6 PUFA; while n-3 EPA and DHA exert anti-inflammatory effects via reduction of NF-κβ activity followed by reduced synthesis of pro-inflammatory cytokines, n-6 AA upregulates inflammation.
13 Moreover, intake of SFAs has also been related to activation of pro-inflammatory pathways including gene up-regulation for CD16A, MCP-1, matrix metalloproteinase-9 (MMP-9), IL-1β, IL-6, TNF-α and nuclear p65 protein (p65), in peripheral blood mononuclear cells (PBMCs) and subcutaneous adipose tissue of adults with obesity. While intake of MUFA or PUFA (irrespective of the type) showed anti-inflammatory profiles, or at least a less pronounced pro-inflammatory response than SFA consumption (Rocha et al., 2017). Finucane et al. (2015) demonstrated that high-fat diet enriched with MUFA (45% kcal oleic acid) may reduce adipose tissue secretion of IL-1β, stimulate hyperplasia and reduce IR, compared with a high fat SFA enriched diet (45% kcal palmitic acid) in mice. Subjects with T2DM with high habitual SFA intake had reduced insulin sensitivity compared with high-MUFA consumers (Finucane et al., 2015). Also, an intervention study that involved a regimen supplemented with extra-virgin olive oil (which main component is 18:1n-9) was conducted in subjects that presented high cardiovascular risk factors (T2DM or at least 3 major risk factors. They found a reduced incidence of major cardiovascular events in the treatment subjects when compared to those assigned to a reduced-fat diet, as a control treatment (Estruch et al., 2018). Therefore, MUFA (mainly 18:1 n-9 oleic acid) may limit SFA-induced inflammation and associated adverse metabolic effects observed in comorbidities associated with obesity.
Fatty Acids, Insulin resistance and T2DM
One of the mechanisms influencing insulin resistance (IR), one of the main characteristics of MetS and T2DM, appears to be directly or indirectly associated with inflammation induced through lipotoxicity caused by chronic exposure to free fatty acids (FFA) (Acosta-Montaño &
García-González, 2018). Under normal physiological circumstances, fatty acids are stored in
14 adipose tissue as triglycerides (TG), which following hydrolysis, produces a FFA efflux for their utilization as an energy source in different tissues (Mika & Sledzinski, 2017). In an obese state, a large increase in plasma FFA, as a result of adipose tissue lipolysis, favors their deposition in non- adipose tissues such as liver, muscle, heart and pancreas (Aganović & Dušek, 2007) (Ferreri et al., 2016). This ectopic deposition of fatty acids results in cellular metabolic dysfunction, it has been described that FFA are capable of activating c-Jun-N-terminal kinases (JNK) via TLR2 and TLR4, which cause a cascade obstruction in the insulin signaling pathway, and induce IR in skeletal muscle, adipose tissue and liver (Gonzalez et al., 2018).
Among FFAs capable of inhibiting insulin signaling the SFA C16:0 has been described to attenuate insulin signaling pathway through several mechanisms that ultimately lead to IR in vivo.
Under circumstances of excess FFA, and their ectopic delivery to non-adipose tissues such as obesity, MetS and T2DM, the elevated C16:0 concentrations are known to exceed their mitochondrial β-oxidation levels, resulting in its conversion to complex deleterious lipids such as diacylglycerol (DAG) and ceramides. Such complex lipids have been described to activate protein kinase C (PKC), phosphorylation of serine residue in insulin receptor substrate 1 (IRS-1), inhibition of nuclear factor NF-κβ (IKKB) kinase, which further attenuate insulin action and can increase levels of pro-inflammatory cytokines. In addition, C16:0 itself may activate pro- inflammatory pathways through TLR4 or fetuin A, an adaptor protein that mediates interactions between SFA and TLR4. On the other hand, oleic acid (C18:1 n-9) has been shown to prevent the deleterious effects of C16:0 by reducing the synthesis of complex lipids, and inhibiting phosphorylation of IRS-1 (Palomer et al., 2018). Hence, in the context of obesity-mediated inflammation observed in patients with MetS and T2DM, SFA (C16:0) and MUFA (C18:1 n-9)
15 may exert opposite effects on inflammation and IR, where C18:1 n-9 may confer protection over C16:0-induced adverse metabolic actions.
FFAs may also play a role in pancreatic β-cells, although the mechanism through which they have a beneficial or deleterious effect is not clear.(Acosta-Montaño & García-González, 2018). However, β-cell response to nutrient overload is known to involve membrane phospholipid remodeling and lipid peroxidation. Cohen et al. (2015) observed that increased glucose levels altered the quantity of SFA, MUFA and PUFA in membrane phospholipids of β-cells. C16:0 and palmitoleic acid (C16:1n-7) incorporation into phospholipids was positively correlated to ambient glucose, while the abundance of stearic acid (C18:0) and C18:1n-9 was significantly reduced.
Furthermore, C16:0 further intensified the depletion of AA (C20:4 n-6) and n-6 LA at all glucose concentrations, conditions which resulted in peroxidation of these PUFA to produce 4-HNE, amplifying insulin secretion in a PPARδ-dependent manner (Cohen et al., 2015). Thus, increased glucose concentrations observed in patients with MetS and T2DM may alter membrane phospholipids of β-cells making them prone to lipid peroxidation of PUFA leading to cell membrane damage.
Plasma fatty acid composition reflects dietary intake, as well as endogenous metabolism of fatty acids, which may be impaired in metabolic diseases. Plasma lipids circulate mainly in complex lipids including phospholipids (PL), cholesteryl esters (CE) and triglycerides (TG). Each complex lipid in plasma differs in their fatty acid profile, displaying unique metabolic processes that cause the incorporation of specific fatty acids in favor of others (Furtado et al., 2019). Plasma PL and CE reflect dietary intake of fatty acids during previous weeks, but also endogenous metabolism, synthesis by lipogenesis de novo, desaturation, elongation, retro-conversion, and oxidation of fatty acids
16 (Poreba et al., 2018). Prior works suggest plasma CE fraction is to some extent more stable than TG or PL and therefore a better marker for dietary assessment (Yu et al., 2014). Fatty acids may have a role on inflammation and IR depending on their characteristics such as chain length, number of unsaturations and their position on the carbon chain. Therefore, it is relevant to characterize fatty acid profiles and their association with obesity induced-low-grade chronic inflammation in diseases such as MetS and T2DM in the pediatric population.
1.3 Hypothesis
Pediatric patients with Type 2 Diabetes Mellitus will have a worse fatty acid profile associated with inflammation compared with patients with Metabolic Syndrome and Healthy Controls.
1.4 General Objective
To determine the association between the plasma fatty acid composition and inflammatory markers in pediatric Mexican patients with Type 2 Diabetes Mellitus, Metabolic Syndrome patients and Healthy Controls.
1.4.1 Specific Objectives
1. To characterize and quantify the fatty acid profile in two plasma lipid sub-fractions (CE and PL) in children and adolescents with normal-weight, MetS and T2DM.
2. To determine the systemic concentrations of the cytokine profile in children and adolescents with normal-weight, MetS and T2DM.
17 3. To determine the association between the plasma fatty acid composition and inflammation
in children and adolescents with normal-weight, MetS and T2DM.
1.5 Rationale
Obesity is a disease characterized by low-grade chronic inflammation and associated with the development of IR and metabolic diseases (Reilly & Saltiel, 2017). The onset of disease in the young population is possibly a multifactor and complex phenomenon, that can in part be aggravated because during puberty a marked physiological IR occurs; therefore, youth with obesity are particularly at risk of increased cardiometabolic risk and related comorbidities such as T2DM (Kelsey & Zeitler, 2016). Different fatty acids may exert either proinflammatory or anti- inflammatory effects, depending on their characteristics such as chain length, number of unsaturations and their position on the carbon chain. SFA such as C16:0 and C12:0 have been described as proinflammatory, as it may activate NF-κβ and increase the synthesis of pro- inflammatory cytokines and attenuate insulin signaling pathway. Furthermore, generally, n-6 AA is described as a proinflammatory molecule, mediated pro-inflammatory eicosanoid production.
MUFA (mainly 18:1 n-9) seem to counteract the SFA induced effect on inflammation and IR.
Also, n-3 PUFA (DHA and EPA) have been observed to be potent anti-inflammatory effectors.
Fatty acid profiles of children and adolescents with obesity are characterized by elevated SFA (generally describing C16:0) and low anti-inflammatory MUFA, while PUFA content differ between studies. Moreover, the few studies assesing the fatty acid profile of children with MetS agree on a profile high in levels of DGLA and 16:1 n-7 and lower n-6 LA, while the rest of individual fatty acid associated with disease vary between studies and therefore remain controversial. Additionally, only a few studies have researched the correlation between fatty acid
18 composition and markers of inflammation in the pediatric population with obesity and observed inflammatory plasma cytokine concentrations are positively associated with obesity, and at the same time with a profile high in SFA and low in total MUFA and PUFA. Nonetheless, these profiles generally group families instead of individual fatty acids ….. The association of fatty acid profiles and plasma concentrations of cytokines in children and adolescents with T2DM have not been described in the literature. Because of the growing trend in obesity and diagnosis of T2DM in children and adolescents, the characterization of fatty acid profiles to improve the understanding of their impact on health is considered a relevant area of research.
1.6 Scope of the study
The results of this study allowed us to identify the plasma fatty acid profile in two lipid sub-fractions (CE and PL) and their association with inflammatory markers of healthy controls and patients with MetS and T2DM in the pediatric population. Additionally, prior works suggest plasma CE fraction is to some extent more stable than TG or PL and therefore a better marker for dietary assessment (Yu et al., 2014). Nonetheless, parallel evaluation of two lipid sub-fractions in the same subject improves the diagnostic power of the targeted fatty acid analysis, mainly when interfering factors such as diet and lifestyle are difficult to control in patients and control groups (Ferreri et al., 2016). To our knowledge, this was the first time the plasma fatty acid profile of pediatric patients with T2DM and its association with inflammatory markers was assessed.
19 Chapter 2. Theoretical Framework
2.1 Obesity and Inflammation
Overweight classification for children and adolescents (≥2 years of age) is commonly determined, by clinicians, when the body mass index (BMI) is ≥85th percentile but <95th percentile, whereas obesity classification is assigned when the BMI is ≥95th percentile for age and gender (CDC, 2018). Experts agree on the fact that overweight and obesity, in pediatric populations, are associated with significant comorbidities that include T2DM, dyslipidemia, hypertension, early subclinical atherosclerosis and non-alcoholic fatty liver disease (NAFLD) Other comorbidities include sleep apnea, proteinuria, focal segmental glomerulosclerosis, hyperandrogenemia/polycystic ovary syndrome (PCOS), slipped capital femoral epiphysis and pseudotumor cerebri, increased cardiovascular disease (CVD), and premature mortality in adulthood. Moreover, the greater the obesity, the higher the risks of cardiometabolic disease factors, particularly among boys (Styne et al., 2017).
Evidence suggests that comorbidities of obesity, such as T2DM, NAFLD, and CVD, are also correlated with low-grade chronic inflammation. Cytokines are low molecular weight proteins with autocrine and paracrine functions; they are products and effectors of the immune and inflammatory systems (Coppack, 2001). The degree of inflammation also correlates closely with the severity of IR in T2DM. A relationship that is generally associated with macrophage increases, as a result of M1-polarized macrophage recruitment, thus adopting a pro-inflammatory phenotype and secreting the pro-inflammatory cytokines TNF-a and IL-1β (Reilly & Saltiel, 2017). Quantity of macrophages in human adipose tissue (subcutaneous and visceral) depends on the degree of adiposity, and increased adiposity also leads to increased macrophage infiltration (Bourlier et al.,
20 2008). An increased ratio of M1/M2 macrophages is characteristic of adipose tissue inflammation in obesity and is also associated with the development of IR and metabolic diseases (Reilly &
Saltiel, 2017).
An analysis performed in human adipose tissue reported that macrophage classes switched from M2 to M1 in an obese state, resulting in secretion of pro-inflammatory cytokines TNF-ɑ, IL- 6, IL1-β, IL-12, IL-23 (Bourlier et al., 2008; Chehimi et al., 2017). M1 macrophage primary markers of inflammation included TNF-α and IL-6, while M2 macrophages mainly expressed anti- inflammatory IL-10 and transforming growth factor-β (TGF-β) (Bourlier et al., 2008). Adipose tissue of patients with obesity has higher concentrations of TNF-α (Gonzalez et al., 2018), this pro-inflammatory cytokine is known to be secreted from adipose tissue or immune cells as a central regulator and adaptor to the metabolic demands associated with overnutrition (Reilly & Saltiel, 2017). It may also indirectly promote naive T cell differentiation into Th1 and Th17 cells by inducing differentiation of monocytes to mature dendritic cells (Chehimi et al., 2017). Moreover, a review of the literature by Gonzalez et al. (2018) indicated that in humans, adipose tissue contributes to around 10-35% of circulating IL-6, and its concentrations positively correlate with adipocytes hypertrophy.
2.2 Metabolic Syndrome and Inflammation
Based on adult criteria for MetS by NCEP’s ATP III, Cook et al. (2003) adapted the criteria for the development of a definition of MetS in adolescents. The author’s definition for adolescents included waist circumference (WC) ≥ 90th percentile (for age and sex), fasting glucose level ≥ 100mg/dL, blood pressure (systolic or diastolic) ≥ 90th percentile (for age and height), fasting
21 triglyceride (TG) ≥110mg/dL and HDL cholesterol (HDL-c) < 40mg/dL. Abdominal obesity has been pointed out as a hallmark characteristic of MetS, where excess visceral adipose tissue, followed by low-grade chronic inflammation and IR, are crucial in the pathogenesis of MetS (Wittcopp & Conroy, 2016). In this context, excess FFA secreted from adipose tissue lipolysis leads to the development of IR in tissues such as liver, skeletal muscle, and pancreas. In the liver, FFA increases the synthesis of glucose, triglycerides (TG), and secretion of very low-density lipoproteins (VLDL), leading to a decreased conversion of glucose to glycogen and increased TG accumulation (Aganović & Dušek, 2007). In skeletal muscle, FFA reduces glucose uptake by inhibiting protein kinase activation, and in the pancreas, FFA lipotoxic effect decreases insulin secretion (Rochlani et al., 2017). This ectopic deposition of lipids together with its lipotoxic and glucotoxic effects may either induce an inflammatory response or be exacerbated with inflammation (Donath & Shoelson, 2011). In the liver, NAFLD often observed with abdominal obesity is associated with increased expression and synthesis of proinflammatory cytokines TNF- ɑ, IL-6 and IL-1β. Additionally, in mice, M2 macrophage activation in hepatic Kupffer cells results in decreased IR which further slows progression of NAFLD to nonalcoholic steatohepatitis (NASH) (Esser et al., 2014). Therefore, inflammation appears to play an important role in disease progression, which is mediated by lipotoxic and glucotoxic effects of excess FFA in non-adipose tissues. Certainly, other researchers have observed that progression from obesity-induced IR to T2DM involves incapacity of β-pancreatic cells to compensate IR, ultimately resulting in sustained chronic hyperglycemia. Indeed, approximately 1 out of 3 patients with obesity develop chronic hyperglycemia and T2DM (Donath & Shoelson, 2011). Other authors have concluded, from their works, that inflammation also plays a role in disease progression; their observations indicated that pro-inflammatory cytokine IL-1β regulated β-cells by increasing expression of other pro-
22 inflammatory cytokines and chemokines which further promoted immune cell recruitment. IL-1β also increased in patients with T2DM, and inflammation progressively reduced insulin secretion, followed by a decrease in β-cell mass via apoptosis (Esser et al., 2014).
Cardoso-Saldaña et al. (2007) assessed the relationship between systemic inflammation and MetS in 325 mexican adolescents aged 12-16 years. They observed hs-CRP values were highest among patients with MetS. As expected, hs-CRP concentrations correlated positively with parameters of the MetS including WC, TG and fasting insulin, while it correlated negatively with HDL-c. Furthermore, they performed a multiple regression analysis in which BMI and HOMA-IR accounted for 10.4% and 12.7% (respectively) of hs-CRP concentration. A limitation of this study was the use of NCEP-ATP III adult definition of MetS, instead of the adapted Cook et al. (2003) definition for adolescents.
2.3 Type 2 Diabetes Mellitus and Inflammation
Type 2 Diabetes Mellitus (T2DM) is a metabolic disorder characterized by chronic hyperglycemia and alterations in carbohydrate, lipid, and protein metabolism, progressing to defects in insulin secretion and/or its action (WHO, 1999).
Diagnosis of T2DM at a young age has been associated, in prior studies, with a rapid and aggressive progression of beta-pancreatic cell dysfunction (Lakshman et al., 2012). Several studies have observed that at the time of diagnosis, microvascular complications and risk markers for macrovascular complications such as high blood pressure, microalbuminuria, low HDL cholesterol, and elevated triglycerides were present (Copeland et al., 2011). Moreover, renal and
23 neurological complications begin to appear five years post-diagnosis, while significant complications such as dialysis, blindness, and limb amputations arise ten years post-diagnosis in this population (Nadeau et al., 2016).
During puberty, adolescents develop a lower physiologic insulin sensitivity, where adolescents with obesity tend to be more insulin resistant compared with lean ones. This decline in insulin sensitivity, together with compensation in insulin secretion, recovers after puberty in healthy youth. In contrast, in youth with obesity, IR does not resolve and may lead to increased cardiometabolic risk and obesity-related comorbidities such as T2DM (Kelsey & Zeitler, 2016).
Therefore, special attention must be given to this vulnerable population and the mechanisms and implications of the disease's development.
An increase of inflammatory biomarkers has been observed in adolescents with obesity and T2DM, including CRP, TNF-a, and IL-1β, suggesting a causal relationship between low-grade chronic inflammation, obesity, and T2DM (Reinehr, 2013). According to prior authors, T2DM causes inflammation and cellular dysfunction in adipose tissue and β-pancreatic cells by enhancing the production of proinflammatory cytokines and recruiting macrophages through secretion of chemokines. Inflammatory mediators are thought to be not only abnormal metabolic biomarkers in T2DM; but also contribute to apoptosis, decreased function, and mass of β-pancreatic cells (Gonzalez et al., 2018). Pro-inflammatory cytokine IL-1β, which is expressed by macrophages in adipose tissue (Gonzalez et al., 2018) is involved in the pathogenesis of glucotoxicity in T2DM patients (Maedler et al., 2017). Described mechanisms observed that IL-1β promotes the synthesis of pro-inflammatory cytokines (through receptor IL-1), which included TNF-α, IL-6, and more IL-
24 1β by activating NF-κβ pathway, and created a positive inflammatory loop (Gonzalez et al., 2018).
Indeed, prior studies have observed that T2DM patients contained elevated blood levels of pro- inflammatory molecules, including IL-1β dependent, and the inhibition of IL-1β activity reduced their concentrations (Donath & Shoelson, 2011). Besides, secreted TNF-α in human adipocytes has also been clinically associated, by authors, with impairment of insulin signaling and reduced insulin sensitivity. Moreover, the deletion of TNF-α expression or its receptor improved insulin sensitivity in mice. Concentrations of chemokines such as IL-8 and MCP-1, have also been correlated with T2DM by prior studies (Gonzalez et al., 2018). In a study by Zaharieva et al. (2018) in subjects with T2DM, a significant correlation between concentrations of IL-18 and postprandial glucose and inflammation markers including IL-6 and hs-CRP and a negative correlation with HDL-c was observed. Thus, β-pancreatic cell apoptosis, decreased function, and mass observed in patients with T2DM appears to be mediated by the action of several inflammatory markers, including chemokines MCP-1 and IL-8 and pro-inflammatory cytokines IL-1β, TNF-α, IL-18.
2.4 Fatty Acids: Properties and Metabolism
Fatty acids are fundamental components of lipids; they constitute structural elements of phospholipids and glycolipids, which are essential components of cellular membranes and triglycerides (Mika & Sledzinski, 2017). Fatty acids are classified, by analysts, in three major groups according to their double bonds which include, saturated fatty acids (SFA) that don't have double bonds or unsaturations, monounsaturated fatty acids (MUFA) with one double bond, and polyunsaturated fatty acids (PUFA), which are composed of two or more double bonds. The first double bond position, starting from the methyl-end, indicates another standard classification of
25 PUFA, which is commonly referred to as omega notations (n-3 or n-6). Free fatty acids (FFAs) can bind to either the cell membrane or its receptor, and depending on their level of saturation, exert different metabolic responses (Papackova & Cahova, 2015).
SFA and MUFA may be endogenously formed as a result of excess dietary carbohydrate intake, which may be converted to fat via gluconeogenesis in liver and adipose tissue to produce primarily 16:0-CoA, which then translocates into the endoplasmic reticulum (ER). Once in that location, a combination of desaturase enzymes including stearoyl CoA desaturase Δ9-desaturase (SCD), fatty acid desaturase 2 (FADS2), and the carboxylase enzymatic system for fatty acid chain elongation take action. These catalyze the conversion of palmitic acid (C16:0) into stearic acid (C18:0) prior to the action of SCD via conversion of C18:0 to C18:1n-9 (Park et al., 2016) (Figure 1).
26
Figure 1. Fatty acid biosynthesis. A) Long-chain saturated and unsaturated fatty acids of the n-7 and n-9 families can be synthesized endogenously from palmitic acid (C16:0). B) Long-chain fatty acids of the n-6 and n-3 families can be synthesized only from their respective precursors obtained from diet. D6D=Δ6-desaturase. D5D=Δ5-desaturase.
SCD= stearoyl CoA desaturase Δ9-desaturase. (X. Han, 2016)
While SFAs and MUFAs can be endogenously formed through de novo lipogenesis, PUFAs are essential fatty acids and that humans need to acquire through dietary intake. The precursor for other n-3 PUFAs is alpha-linolenic acid (n-3 ALA; 18: 3n-3), found primarily in green leafy vegetables, nuts, soybeans, chia and vegetable oils. On the other hand, linoleic acid (n- 6 LA; 18: 2n-6) is the precursor for other n-6 fatty acids and is found in vegetable oils, seeds and nuts, as well as in fats from animal origin. Through several steps of elongation and desaturation, n-3 ALA is metabolized into eicosapentaenoic acid (EPA; 20:5n-3) and docosahexaenoic acid (DHA; 22:6n-3), mainly known for their anti-inflammatory actions; whereas n-6 LA is
27 metabolized into arachidonic acid (AA; 20:4n-6), mainly known for being a precursor of pro- inflammatory eicosanoids.
Fatty acids, n-3 ALA and n-6 LA directly compete, in their metabolic pathways, for the same elongation and desaturation enzymes (Elovl5 and desaturase D6D, respectively), a competition that is influenced by their concentrations (Acosta-Montaño & García-González, 2018) (Figure 1). The main PUFA in most Western diets is n-6 LA which is typically consumed in 7 to 20-fold greater amounts than n-3 ALA. Although the preferred substrate for delta (Δ)-6 desaturase (D6D) is n-3 ALA, n-6 LA is much more prevalent in most human diets; therefore, the n-6 fatty acid metabolic pathway is favored. As a result, conversion of n-3 ALA to EPA is poor in humans and conversion to DHA is also extremely limited (Calder, 2015a). Δ-5 desaturase (D5D) and D6D catalyze the synthesis of long chain n-6 and n-3 PUFA, therefore their activity is commonly estimated using PUFA product-to-precursor ratios (S. Imamura et al., 2014) (Figure 1). The direct measurement of desaturase activity has been reported to be difficult and expensive; since accurate in vivo and non-invasive desaturase measurements are challenging because of the competing reactions for precursors and products. On the other hand, measurement of major fatty acid profiles and concentrations can provide a rather simple assessment, thus ratios of precursor-product concentrations are widely utilized (Park et al., 2016).
Fatty acids are essential in biophysical, biochemical and signaling processes and are known to act as sensing mechanisms and stimuli transduction, functions that have been linked to relevant roles in epigenetic control pathways (Ferreri et al., 2016). As has been mentioned, different fatty acids may exert either pro-inflammatory or anti-inflammatory effects, depending on their
28 characteristics such as chain length, the number of unsaturations and their position on the carbon chain. Therefore, it is relevant to characterize fatty acid profiles and their association with obesity induced-low-grade chronic inflammation and its associated diseases including MetS and T2DM in the pediatric population.
Adipose tissue gives a stable and accurate estimate of the measurement of fatty acid profile status because of its nature as a long-term storage pool, however obtainment of an adipose aspirate is invasive and not suitable for most epidemiological clinical studies. Therefore, measurement of fatty acids in blood serves as a less invasive assessment technique of fatty acid profiling, despite the fact that as a pool of fatty acids may be more influenced by recent dietary exposures when compared to adipose tissue (Furtado et al., 2019). For instance, plasma fatty acid composition in phospholipids (PL) and cholesteryl esters (CE) has been reported to reflect dietary intake of fatty acids during previous weeks, but also endogenous metabolism, synthesis by lipogenesis de novo, desaturation, elongation, retro-conversion, and oxidation of fatty acids (Poreba et al., 2018).
Plasma fatty acids are primarily derived from FFAs and lipoproteins circulating in the blood, the latter in the form of more complex lipids such as: TG, PL and CE. Each complex lipid in plasma differs in their fatty acid profile, displaying unique metabolic processes that cause the incorporation of specific fatty acids in favor of others. Differences in the types of fatty acids is thought to be explained, at least partially, by their synthesis. PL are synthesized in the cytosolic side of the ER membrane, and usually have either a SFA or MUFA in the sn-1 position and a PUFA in the sn-2 position. In contrast, CE are produced in the body by the transfer of fatty acids from PLs to cholesterol. This is achieved either by the enzyme lecithin:cholesterol acyl transferase (LCAT), which transfers a fatty acid from the sn-2 position (frequently a PUFA) of the PL lecithin
29 (phosphatidylcholine) or by acyl-CoA:cholesterol acyltransferase (ACAT), resulting in lipid species that are particularly rich in PUFAs (Furtado et al., 2019). For instance, Risé et al. (2007) observed that human plasma PL fraction had higher concentrations of SFA (including C16:0 and C18:0) and n-6 PUFA (including AA and LA). Whereas, the CE fractions had higher concentrations of PUFA (mainly n-3 18:2) and lower concentrations of SFA. In their study, concentrations of overall fatty acids in TGs fractions were lower. Another study described that in human serum, n-6 PUFAs (omega-6) represented 22% of the fatty acids in TAGs, 38% in PLs and 60% in CEs. On the other hand, n-3 (omega-3) PUFAs represented only 18 % of fatty acids in TAGs, 35 % in PLs and 17 % in CE (Sergeant et al., 2016). Hence, parallel evaluation of two lipid species in the same individual can represent a significant improvement of the targeted fatty acid analysis diagnostic power, mainly when interfering factors such as diet and lifestyle are difficult to control in patients and control groups (Ferreri et al., 2016).
2.5 Fatty Acid Profile in the Pediatric Population with Obesity, Metabolic Syndrome and Type 2 Diabetes Mellitus
Obesity
Studies describing a relationship between fatty acids in the pediatric population with obesity mostly define a profile high in SFA and low in MUFA (especially 18:1 n-9), while PUFA levels either n-3 or n-6 findings have been more inconsistent. A case-control study in 100 patients with obesity and 100 normal-weight children aged between 6–12 years observed that when compared to normal-weight children, children with obesity had significantly higher proportions of total SFA, especially 18:0 and increased proportion of 16:1n-7, 20:3 n-6 (DGLA) and total n-3;
30 followed by lower proportions of total MUFA, especially 18:1n-9, and n-6 fatty acids (LA-18:2n- 6 and 22:5n-6). Children with obesity also had significantly higher ratios of 20:3n-6/18:2n-6, higher values of LA-18:3n-6 + DGLA/LA (20:3n-6/18:2n-6), and lower ratios of AA/DGLA (20:4n-6/20:3n-6) and n-6/n-3 (Elizondo-Montemayor et al., 2010). Similarly, Aguilar Cordero et al. (2012) observed that patients with obesity had significantly lower concentrations of total MUFA and PUFA and n-6 PUFA. Also, they had significantly higher SFA/MUFA, SFA/PUFA and C16:0/C18:2 ratios. Children with obesity have an inflammatory fatty acid profile, characterized by elevated SFA (mainly C16:0) and low anti-inflammatory MUFA, while PUFA content differ between studies. Additionally, weight loss may contribute to a healthier fatty acid profile. In a weight loss intervention study following 127 adolescents with overweight and/or obesity aged 12–17 years old, by 6 months, plasma SFA and n-6 PUFA levels decreased significantly, while MUFA (mainly 18:1 n9) and n-3 PUFA increased significantly (Guerendiain et al., 2018).
Metabolic Syndrome
Few studies have described the fatty acid profile of MetS pediatric patients. In a cross- sectional study Bermúdez-Cardona & Velásquez-Rodríguez (2016) assessed the fatty acid profile in 96 adolescent patients with MetS, patients with obesity without MetS and normal-weight controls. Compared with normal-weight controls, patients with obesity with MetS and without MetS had significantly higher concentrations of C16:0 and its product 16:1 n-7. Also, patients with MetS had higher DGLA (C20:3n-6) and lower n-6 LA (18:2 n-6) concentrations compared with normal-weight patients. Moreover, a study of 264 adolescents (mean age 15±1.2) observed DGLA was correlated with MetS metabolic parameters, including a positive correlation with WC, insulin
31 and TG concentrations and a negative correlation with HDL-c and similarly to the latter study, n- 6 LA was inversely correlated with WC and TG levels (Steffen et al., 2008). Results from another clinical study that assessed the fatty acid profile of 120 normal-weight and overweight adolescents indicated that of the 60 overweight patients assessed, 15 had MetS (25%). In their study, presence of MetS was associated with an increase of 16:1 n-7 (in both PL and CE fractions) and reduced n- 6 LA concentrations in PL fraction. Nonetheless, fatty acid profile was not found to be directly associated with IR by measurement of HOMA-IR (Klein-Platat et al., 2005). Therefore, generally, the fatty acid profile of children and adolescents with MetS consisted of higher DGLA and 16:1 n-7 and lower n-6 LA concentrations.
Type 2 Diabetes mellitus
In adults, literature on the role of fatty acid composition on physiopathology of T2DM is limited and inconsistent (Poreba et al., 2018). A case-control study including 396 adult patients with T2DM and a healthy control group with 122 subjects assessed n-3 and n-6 PUFA concentrations in plasma. Compared to healthy controls, subjects with T2DM had higher n-6 PUFA given by AA and its precursor DGLA, and n-3 DHA; and lower EPA/AA, DHA/AA, and (EPA + DHA)/AA ratios (S. Imamura et al., 2014). The combination of fatty acid profile as a potential risk factor for incidence of T2DM in adults was evaluated in a large European cohort study. Composition of plasma 27 plasma fatty acids was evaluated in a 12 year period. A lower incidence of T2DM was associated with a fatty acid profile composed of high concentrations of n- 6 LA, stearic acid (C18:0) and very long chain saturated fatty acids (VLCSFA) and lower concentrations of SFA including C14:0 and C16:0, total MUFA and ɣ-linoleic acid (C18:3 n-6) (F. Imamura et al., 2017). Another large case-cohort study including around 28,000 adults,
32 observed even chain saturated fatty acids (ECSFA) C14:0, C16:0 and C18:0 and DGLA positively associated with incident T2DM (Forouhi et al., 2016). The association of fatty acid composition in plasma and T2DM has been fairly described in the adult population. However, to our knowledge, the fatty acid profile in children and adolescents with T2DM has not been described.
2.6 Fatty Acid Profile and Inflammation
A study assessing the relationship between fatty acids and low-grade inflammation in 958 children (mean age 10.2 years and mean BMI% of 16.6) observed that fatty acid concentrations associated with significantly higher low-grade inflammation (measured by hs-CRP or IL-6 levels) included: C16:0 (IL-6: p-value<0.001), AA (20:2 n-6) (hs-CRP: p-value= 0.002), ratio AA/LA (hs-CRP: p-value<0.001) and total SFA (IL-6: p-value<0.001). Moreover, fatty acids associated with reduced levels of inflammatory markers included n-6 LA (hs-CRP: p-value= 0.001; IL-6: p- value<0.001) and total PUFA (IL-6: p-value<0.001) (C. Harris et al., 2017). According to Imamura et al. (2014) differential inflammatory effects of n-6 AA vs n-3 EPA appears to be mediated in part by eicosanoid production. Eicosanoids derived from n-6 AA are reported to be pro- inflammatory, pro-aggregative, and vasoconstrictive, while eicosanoids derived from EPA have opposing cardioprotective effects. Associated with these biological characteristics, a higher EPA/AA ratio in circulation has been found to be predictive against major cardiac events in the general population (S. Imamura et al., 2014). Another clinical study assessed fatty acid profile and inflammation of 120 normal-weight and overweight adolescents and observed that normal-weight adolescents had significantly lower total SFAs in both PL and CE fractions, and significantly higher PL-DHA. Regarding fatty acid ratios, PUFA/SFA in both fractions was inversely associated with IL-6. While other fatty acid such as EPA (PL; p-value<0.06 and CE p-value<0.05) and CE-
33 LA n-6 were negatively associated with CRP (p-value<0.05) (Klein-Platat et al., 2005). Therefore, lower concentrations of n-3 PUFA in plasma may favor development of cardiometabolic complications associated with low-grade inflammation in the pediatric population with obesity.
Similarly, a case-control study evaluated the association between markers of inflammation in individuals with obesity with fatty acid composition of erythrocyte membranes. The population studied included 26 adolescents between 12-16 years of age, 13 normal weight and 13 with obesity according to age and sex. Results indicated that in comparison to the control group, adolescents with obesity had higher levels of inflammation markers including, C-reactive protein (CRP), IL-6 and rheumatoid factor. Furthermore, adolescents with obesity had higher concentrations of SFA and lower concentrations MUFA, total PUFA and n-6 PUFA, as well as higher ratios of SFA/MUFA, SFA/PUFA and C16:0/C18:2 (Aguilar Cordero, 2012). Hence, inflammatory plasma cytokine concentrations are positively associated with obesity, and at the same time with higher concentrations of SFA and lower concentrations of total MUFA and PUFA.
34 Chapter 3. Methodology
3.1 Study Population
Patients were recruited by Dr. Leticia Elizondo-Montemayor, Dr. Nora Rodríguez and Dr.
Oscar Tamez. The study population consisted of 60 pediatric Mexican patients aged 7-17 years of age, 21 healthy normal weight controls (HC), 19 patients with Metabolic Syndrome (MetS) and 20 patients with Type 2 Diabetes Mellitus (T2DM). T2DM was diagnosed according to the following American Diabetes Association (2019) criteria: fasting plasma glucose (FPG) level of 126 mg/dL (7.0 mmol/L) or higher, or a 2-hour plasma glucose level of 200 mg/dL (11.1 mmol/L) or a higher during a 75-g oral glucose tolerance test (OGTT), or a random plasma glucose of 200 mg/dL (11.1 mmol/L) or a higher in a patient with classic symptoms of hyperglycemia or hyperglycemic crisis, or a hemoglobin A1c (HbA1c) level of ≥ 6.5% (48 mmol/mol). Cook et al.
(2003) criteria was used for patients with pediatric MetS, which include ≥3 of the following criteria: abdominal obesity (waist circumference ≥ 90th percentile for age and sex), glucose intolerance (FPG ≥100 mg/dL, elevated blood pressure (≥90th percentile for age and sex) and dyslipidemia (elevated triglycerides ≥110 mg/dL and/or low HDL cholesterol ≤40 mg/dL). For healthy control (HC) patients, a normal weight given by a BMI between 15 and 85th percentile according to CDC growth charts for age and sex (CDC, 2013) was considered. Exclusion criteria for HC group included: previous diagnosis of cardiac or metabolic diseases, metabolic abnormalities found on blood sample, and use of medications (i.e. antihypertensives, hypolipidemic or hypoglycemic drugs). Before inclusion to the study, parents and/or legal guardians gave written informed consent. This study was approved by the Ethics and Research Committee of the School of Medicine at Tecnológico de Monterrey. Dr. Leticia Elizondo-
35 Montemayor and Dr. Oscar Tamez-Rivera wrote the manuscript and submissions forms and submitted them to the Ethics and Research Committees.
3.2 Anthropometric Measurements
Anthropometric variables and blood pressure were taken by a medical professional. Blood pressure was obtained by triplicate with a mercury sphygmomanometer for children. Utilizing standardized protocols (NHANES, 2007), anthropometric parameters were obtained. Weight (kg) was measured using (TANITA ® BF-689; TANITA Corporation of America Inc, IL, USA) and height (cm), was measured using a stadiometer (SECA® 217, SECA Mexico, CDMX, Mexico);
waist circumference (WC) and hip circumference (cm) were measured with a standard fiber optic measuring tape. BMI was calculated as kg/m2.
3.3 Biological Samples
Blood samples were collected by peripheral venipuncture after an overnight 12-hour fast.
Plasma and serum were collected by centrifugation of blood samples and then stored at −80°C for further processing. Processing of the samples was done by Dr. Leticia Elizodo-Montemayor and other collaborators. Samples were analyzed at an external laboratory at Hospital San Jose with a budget granted by Dr. Leticia Elizondo-Montemayor, from the Center for Research in Obesity and Clinical Nutrition at School of Medicine and Health Sciences of Tecnológico de Monterrey.
Fasting serum glucose levels were measured by the Hexokinase (HK)/Glucose-6-Phosphate dehydrogenase (G-6-PDH) method, with Glucose 3L82 reagent kit on the Architects cSystems™
(304772/R02; DENKA SEIKEN CO., LTD. TYO, Japan). Concentrations of serum insulin were
36 measured with chemiluminescent microparticle immunoassay (ARCHITECT Insulin Reagent kit 8K41-27 (G6-2892/R03) from Abbot Laboratories Diagnostic Division (IL USA). Overall, lipid profiles were characterized using various diagnostic kits from Abbot Laboratories Diagnostic Division (IL USA) and quantified on their Architect cSystems™. Total cholesterol (TC) levels were determined with the Cholesterol Reagent kit 7D62 (304796/R02), high-density lipoprotein cholesterol (HDL-c) levels were determined using the Ultra HDL 3K33-21 assay (306571/R03), and plasma triglyceride (TG) levels were determined through the glycerol-phosphate-oxidase reaction, using the Triglyceride 7D74-20 (30-3140/R3).
3.4 Quantification of Plasma Fatty Acid Profiles
Plasma samples (200 µL) were extracted by the Folch method (Folch et al., 1957). Each sample was further separated into five lipid fractions utilizing a solid phase extraction aminopropyl column (500 mg, 3mL, Bond Elut NH2, Agilent Technologies Inc., CA, USA). Using the procedure described by Agren et al. (1992), five lipid fractions obtained: cholesteryl esters (CE), triglycerides (TG), mono and diglycerides (MG+DG), free fatty acids (FFA) and phospholipid esters (PL). Cholesteryl nonadecanoate (400 ppm) from Nu Chek Prep Inc. (MA, USA), and Diheptadecanoyl phosphorylcholine (400 ppm) from Abcam® (Cambridge, UK), were added as an extraction internal standards for CE and PL, respectively. Eluted fractions were stored at -80°C for further analyses. CE and PL fractions were evaporated to dryness in a vacuum concentrator (Centrivap, Labconco, MO, USA) and resuspended in a 0.6 mL toluene-hexane 1:1 mixture.
Fractions were trans-methylated using 1 mL of methanol-sulfuric acid (MeOH:H2SO4; 93:7) in tightly capped vials and incubated in a water-bath for 1 hr at 80°C. Afterward, 3 mL of hexane were added to the tubes and shaken for 1 min.
37 Fatty acid methyl esters (FAMEs) of CE and PL fractions were concentrated to 1mL and transferred to vials for gas chromatography analysis using an Agilent 6850A gas chromatograph (Agilent Technologies Inc., CA, USA) equipped with a 100 m x 0.25 mm i.d. (film thickness 0.2 m) SP-2380 fused silica capillary column (Supelco, PA, USA). The temperature of the oven was set up from 140 °C to 240°C at 4°C/min and held for 45 min until stabilization; injector and flame ionization detector temperatures were 260°C and 300°C, respectively. The carrier gas used was helium (4 mL/min) at a split ratio of 5:1. For internal quantification, cholesteryl undecanoate (600 ppm), from Nu Chek Prep Inc. (Elysian, MA, USA), was used. Response factors for individual fatty acids were determined according to AOAC method 996.06 (AOAC, 1998), with the use of a standard mix of 40 fatty acids (GLC 566, Nu Chek Prep Inc., MA, USA). Individual chromatographic peaks were quantified and their concentrations were standardized according to each patient as a percentage of the total amount of fatty acids.
Desaturase activities in plasma CE and PL were estimated using product to precursor relation (Elizondo-Montemayor et al., 2010). In CE fraction, ratios were determined as: stearoyl- CoA-desaturase (SCD)=(16:1n-7/16:0), Δ6-desaturase (D6D)=(18:3n-6/18:2n-6), Δ5-desaturase (D5D)=(20:4n-6/20:3n-6). In PL they were calculated similarly apart from D6D=(20:3n-6/18:2n- 6) (Warensjö et al., 2008). Quantification and analysis of plasma fatty acids was performed by me, with technical support from MsC. Martín Marín and Dr. Dariana Rodríguez, and was supervised by Dr. Carmen Hernández-Brenes. Funding and analytical infrastructure was provided by Dr. Carmen Hernández-Brenes from Centro de Biotecnología-FEMSA and Traslational Omics (GIEE) Research Chair. We are also grateful to MsC. Martín Marín and Dr. Dariana Rodríguez for their technical support in plasma fatty acid analyses.
38 3.5 Plasma Cytokine Analysis
Plasma cytokine concentrations were obtained by Ana Sofía Huerta and Daniel Roffe under supervision of Dr. Elena González, by the method of flow cytometry with a bead-based multiplex assay (LEGENDplex Human Inflammatory Panel (13-plex) (BioLegend, CA, USA). The assay used beads that were specific for each analyte, depending on their size and APC signal. Plasma samples were incubated with beads and biotinylated specific antibodies in FACS tubes for 2 hours at room temperature and protected from light, followed by the addition of Streptavidin-PE fluorophore and incubation for 30 minutes at the same conditions. Tubes were then washed with a wash buffer, provided in the kit, and quantified in the flow cytometer BD FACSCanto II (Becton Dickinson, USA). Data was analyzed using LEGENDplex software version 8.0. Funding for the cytokines kits was provided with a budget granted by Dr. Leticia Elizondo-Montemayor, from the Center for Research in Obesity and Clinical Nutrition located at School of Medicine and Health Sciences of Tecnológico de Monterrey.
3.6 Statistical Analysis
The statistical analysis was done by me, with support from Dr. Romeo Villarreal-Calderon and Dr. Carmen Hernandez-Brenes. Univariate and Multivariate analyses were conducted with JMP® software version 14.0 from SAS Institute (Cary, NC, USA). Distributions of all response variables were studied for normality with Shapiro-Wilk test and declared non-normal if a p ≥0.05 was obtained. Differences between groups were tested by One-way ANOVA and mean separations were conducted with Tukey post-hoc test and Kruskal-Wallis with Nemenyi post-hoc test for parametric and non-parametric variables, respectively.
39 Hierarchical clustering with the method euclidean distance was used to visualize fatty acid profiles. Fatty acids were log transformed and then scaled to a mean of 0 and a sd of 1. Only those fatty acids with a p-value ≤ 0.05 in one-way ANOVA or Kruskal-Wallis (for parametric and non- parametric variables, respectively) were used for hierarchical clustering. This analysis was done with R software version 3.6.1 (R Core Team, 2019).
Discriminant multivariate analyses models were also constructed for the classification of clinical groups (T2DM, MetS and HC) by their differences in fatty acids and cytokine concentration. For each model, the number of misclassified subjects was assessed as well as the R-square entropy of the model. Quadratic discriminant analysis was fitted, and the stepwise procedure was used for the selection of the variables that were included as predictors of membership to each experimental group, for inclusion of variables in the models p-values ≤ 0.05 were required. Furthermore, a classification and regression tree (CART) analysis was also used to predict clinical groups using fatty acid proportions and, for visualization purposes, a scatter plot of the best predictors was constructed using log-transformed data. Lastly, bivariate correlations were calculated with Spearman Rho, and variables were grouped with hierarchical clustering with the method euclidean distance. CART analysis and bivariate correlation analysis were done with R software version 3.6.1 (R Core Team, 2019).
40 Chapter 4. Results
4.1 Cohort Description
The population studied consisted of 60 subjects, 20 with T2DM, 19 with MetS and 21 healthy normal-weight controls (HC). As shown in Table 1, we found no statistical difference with respect to gender among groups; in the HC group 38% were boys and 62% girls; 53% boys and 47% girls for the MetS group; and equal parts of boys and girls for T2DM group. Regarding overweight and obesity, 90% of T2DM had either overweight (20%) or obesity (70%); similarly 89% of MetS group had overweight (63%) or obesity (26%), while none of the patients in HC group had overweight or obesity. The mean age also was not significantly different among the three groups. Acanthosis was observed in the majority of both MetS and T2DM subjects (89% and 95%, respectively) and none of the HC subjects. All T2DM patients presented Tanner developmental stages between 3-5; MetS had 68% of patients in stages 3-5, and the rest (32%) in stages 1-2, while in HC group, 38% were tanner stages 1-2 and 62% were stages 3-5. The demographic and clinical characteristics of the population are summarized in Table 1.
41
Table 1. Demographic and clinical characteristics of the population
Parameter HC (n=21) MetS (n=19) T2DM (n=20) p-value
Male 8 (38) 10 (53) 10 (50)
0.851
Female 13 (62) 9 (47) 10 (50)
Age (years) 12.48 (±3.16) 12.42 (±2.19) 13.9 (±1.52) 0.097
Obesity 0 (0) AB 12 (63) A 14 (70) B
<0.001
Overweight 0 (0) AB 5 (26) A 4 (20) B
Normal weight 21(100) AB 2 (11) A 2 (10) B
Acanthosis nigricans 0 (0) AB 17 (89) A 19 (95) B 0.002
Tanner 1-2 8 (38) B 6 (32) C 0 (0) B C
<0.001 Tanner 3-5 13 (62) B 13 (68) C 20 (100) B C
Data is presented as absolute number and percentage (%) unless specified otherwise. A represents statistical difference between HC and MetS; B HC vs T2DM; C MetS vs T2DM.
HC= Healthy controls, MetS= Metabolic syndrome and T2DM= Type 2 diabetes mellitus.
Obesity was defined as BMI ≥ 95th percentile according to the CDC criteria. Overweight was defined as BMI ≥ 85th percentile and < 95th percentile according to CDC criteria. Significance was established as a p-value < 0.05
4.2 Anthropometric, Metabolic, Clinical and Biochemical Parameters of the Population
Anthropometric, clinical, and biochemical parameters of the population are summarized in Table 2. Parameters such as weight, BMI (kg/m²), BMI (%), WC, hip circumference, and waist/height ratio (W/ht) were statistically higher in T2DM and MetS when compared with HC (p- value <0.001). As for height, T2DM patients had statistically higher values compared with controls (p-value= 0.015). Regarding lipid profile, T2DM and MetS patients had statistically higher concentrations of triglycerides (TG) and lower total HDL cholesterol (HDL-c) when compared with control subjects (p-value <0.001). T2DM patients had lower LDL cholesterol (LDL-c) than HC (p-value= 0.019). For IR related parameters, both T2DM and MetS patients had statistically
42 higher concentrations of glucose (Glc), serum insulin and higher Homeostatic model assessment of insulin resistance (HOMA-IR) values when compared with HC group (p-value < 0.001).
Table 2. Anthropometric, clinical and biochemical parameters for Healthy Controls, Metabolic Syndrome and Diabetes Mellitus Type 2
Parameter HC (n=21) MetS (n=19) T2DM (n=20) p-value Weight, kg 43.9 ( 30.1 - 54 ) A B 66.8 ( 52.75 - 80.2 ) A 68.4 ( 62 - 88.55 ) B < 0.001
Height, m 149.33 ± 15.28 B 155.82 ± 12.41 161.15 ± 9.41 B 0.015
BMI, kg/m² 18 ( 17.2 - 21.6 ) A B 26 ( 24 - 31.55 ) A 27.8 ( 25.48 - 32.23 ) B < 0.001
BMI% 63 ( 42 - 70 ) A B 98 ( 92.5 - 99 ) A 96.5 ( 92 - 99 ) B < 0.001
WC % 25 ( 25 - 25 ) A B 85 ( 75 - 90 ) A 85 ( 75 - 95 ) B < 0.001
Hip circumference, cm 70 ( 62.5 - 78.3 ) A B 94.6 ( 89.5 - 104.5 ) A 99 ( 90.75 - 112.5 ) B < 0.001 WHI 0.96 ( 0.95 - 0.97 ) 0.92 ( 0.84 - 0.96 ) 0.96 ( 0.9 - 0.97 ) 0.353 W/ht 0.45 ( 0.44 - 0.46 ) A B 0.57 ( 0.54 - 0.62 ) A 0.59 ( 0.55 - 0.63 ) B < 0.001
SBP% 40 ( 32.5 - 45 ) 71.5 ( 33 - 86 ) 58 ( 34.75 - 88.75 ) 0.067
DBP% 61.55 ± 11.94 58.11 ± 25.91 63.65 ± 25.35 0.737
TC, mg/dL 152.29 ± 21.18 166.11 ± 39.22 149.5 ± 29.24 0.2
TG, mg/dL 87 ( 55 - 96 ) A B 143 ( 118 - 208.5 ) A 157 ( 114.25 - 189.75 ) B < 0.001
HDL-c, mg/dL 53.43 ± 12.01 A B 39.58 ± 8.21 A 37.8 ± 7.66 B < 0.001
LDL-c, mg/dL 97.38 ± 22.08 B 94.42 ± 35.51 74.9 ± 20.63 B 0.019
Glc, mg/dL 87 ( 78 - 89 ) A B 94 ( 88.5 - 99.5 ) A 114.5 ( 92 - 173.75 ) B < 0.001 Serum insulin, mlU/L 7.64 ( 5.78 - 8.82 ) A B 19.2 ( 14.65 - 25.75 ) A 23.3 ( 11.48 - 30.18 ) B < 0.001 HOMA-IR 1.5 ( 1.06 - 1.89 ) A B 4.76 ( 3.46 - 5.9 ) A 8.37 ( 3.94 - 12.53 ) B < 0.001 Data is presented as median and interquartile range for nonparametric data and as mean ± SD for parametric data. Arepresents statistical difference between HC and MetS; BHC vs T2DM. BMI = Body mass index. T2DM= Type 2 diabetes mellitus. DBP = Diastolic blood pressure. HC = Healthy Controls. HDL-c = High density lipoprotein cholesterol. Glc = Fasting glucose. LDL = Low density lipoprotein cholesterol. HOMA-IR= Homeostatic model assessment of insulin resistance. MetS= Metabolic syndrome. SBP = Systolic blood pressure. TC = Total cholesterol. TG = Triglycerides. WC = Waist circumference. WHI = Waist-hip index. W/ht = Waist-to-height ratio. % = Percentile for age and gender. Significance was established as a p-value <
0.05
43 4.3 Fatty Acid Profile
Phospholipid (PL) fraction in the pediatric population with T2DM, MetS and HC
Proportion of octanoic acid (C8:0) bound to PL was statistically higher in HC and MetS patients than T2DM patients (p-value<0.001, Table 3). HC and T2DM patients shared statistically higher proportions of SFA lauric acid (C12:0), stearic acid (C18:0), and tricosanoic acid (C23:0) than MetS patients. While, HC group had higher ECSFA myristic acid (C14:0) and OCSFA pentadecanoic acid (C15:0) vs MetS patients (p-value <0.001 and 0.018, respectively); and T2DM patients had significantly higher palmitic acid (C16:0) (p-value= 0.013) than MetS patients.
Furthermore, both MetS and T2DM patients had statistically higher proportions of OCSFA heptadecanoic acid (C17:0) (p-value<0.001). Higher proportions of total MUFAs were observed in the HC when compared with MetS patients (p-value= 0.014), mainly driven by the higher 18:1 (n-9) and 16:1 (n-7) proportions in the HC group (p-value= 0.019 and <0.001, respectively).
Moreover, T2DM patients also had statistically higher proportions of 16:1 n-7 when compared with MetS. PUFA profiles bound to PL were also different among groups, MetS patients had statistically higher total n-6 PUFA compared with T2DM (p-value= 0.019), mainly given by the n-6 DGLA and docosatetraenoic acid (22:4 n-6) (p-value<0.001 and p-value=0.05, respectively).
Furthermore, y-linoleic acid (18:3 n-6), the precursor of DGLA, was significantly lower in MetS patients when compared with HC and T2DM patients (p-value<0.001). No significant differences were observed between groups for n-6 AA, a product of DGLA desaturation. Eicosenoic acid (20:1 n-9) was statistically higher among HC and T2DM patients compared with those with MetS, and docosapentaenoic (22:5 n-6) was statistically lower among HC compared with T2DM and MetS patients. In addition, the estimated enzymatic activity of SDS and D5D was lower in MetS patients
