INSULIN RESISTANCE IN CHILDREN AND

ADOLESCENTS;

MECHANISMS AND CLINICAL

EFFECTS. 

THESIS FOR DOCTORAL DEGREE (Ph.D.)

For the degree of Ph.D. at Karolinska Institutet.

This thesis was defended in Karolinska University Hospital, Huddinge Friday May 7th, 2021, 10:00 

By Anna E Ek

 

Läs HELA avhandlingen i sin helhet inkl alla figurer och tabeller

om pdf med full layout utan lösenord

https://openarchive.ki.se/xmlui/bitstream/handle/10616/47573/Thesis_Anna_Ek.pdf?sequence=3&isAllowed=y

 

Nyhetsinfo

www red DiabetologNytt

______________________

Förkortat utdrag ur avhandlingen

The emergence of obesity among children and adolescents during the last 30 years has brought new diseases, earlier only present in adults, into the field of pediatrics. There is overwhelming evidence of the risk of obesity, and this thesis follows a trail from the early risk factors associated with obesity in children to type 2 diabetes in youth.

Diabetes is a chronic metabolic disease characterized by elevated levels of glucose and is commonly divided into two categories. Type 1 diabetes is the dominant type of diabetes in children, with an immunological destruction of the insulin-producing b-cells leading to insulin deficiency. Type 2 diabetes is by far the most prevalent diabetes type in adults. It is associated with obesity and insulin resistance and has lately also appeared in children. The treatment and management of these two disorders differs considerably and poses new challenges to pediatric diabetes specialist teams, who are currently more familiar with the management of type 1 diabetes. There is an increased risk of micro- and macrovascular complications in the heart, kidney, eyes, and nervous system in both diabetes types. However, in recent years there have been increasing reports that the metabolic consequences associated with early-onset type 2 diabetes seem to be more severe than those associated with type 1 diabetes. There is also emerging evidence that early-onset type 2 diabetes is associated with comorbidities and complications at the time of diagnosis and seems to be more aggressive disease than later-onset type 2 diabetes.

The theme in this thesis circles around insulin resistance: an insensitivity toward the effects of insulin which is believed to be one of the key players in the development of obesity complications such as type 2 diabetes. Many obese children will be obese as adults and remain under the burden of obesity for a long time unless lifestyle changes are made. Hence, they have an increased risk of complications associated with insulin resistance, both as adolescents and young adults. Besides being a pathologic reaction to obesity, insulin resistance increases during puberty in both healthy and obese children. It is a dynamic interplay between insulin secretion and insulin resistance, depending on the demand in different tissues and conditions, making insulin resistance a volatile phenomenon. Extensive research into insulin resistance and insulin secretion in both adults and children has been carried out in the recent decades, which has led to an enormous increase in the knowledge in of type 2 diabetes development. Not all adult studies are directly applicable to the growing child, as the risk of being obese as a child does not equal being obese as an adult. Not all obese children have the same risk of developing complications, and the cause of this is unclear.

This thesis provides some insight into the factors causing prediabetes in severe obesity in childhood, the occurrence of early-onset type 2 diabetes in Sweden, and the prevalence of diabetes related complications in both type 1 and 2 diabetes in youth.

2 BACKGROUND

2.1 OBESITY IN CHILDREN AND ADULTS

2.1.1 Definition of obesity

Definitions of overweight and obesity are different for children and adults, depending on growth patterns in the child. The standard classification of overweight and obesity in adults is based on body mass index (BMI), and is defined as a person’s weight in kilograms divided by their height in meters squared (kg/m2). Overweight is defined by as BMI Ñ25 and obesity as BMI Ñ30, according to the World Health Organization (WHO) [1]. BMI classification does not distinguish between fat mass and fat free mass, varies with height at certain ages, and can also vary in different populations, which can lead to misclassification. However, it is a simple and widely used measure of adiposity.

As BMI in children varies considerably with age, height, and to a certain degree gender, specific BMI cut-offs for obesity are used in children. Different definitions and national

references in regard to childhood weight patterns have been used in previous studies, making assessments of prevalence of childhood obesity difficult. In 2002 the International Obesity Task Force (IOTF) implemented the most commonly used international classification ofchildhood obesity, based on Cole’s age- and gender-specific cut-off points corresponding to the adult criteria of a BMI of 25 for overweight (ISO-BMI 25) and 30 for obesity (ISO-BMI 30) [2]. An extended international reference was created in 2012 in an effort to make comparisons between different populations easier and facilitate division into SD scores and centiles [3].

The Body Mass Index Standard Deviation Score (BMI SDS) is often used as a measure of relative weight in children, aged between 2 and 18 years of age. It is calculated based on the weight, height, age, and sex, using a reference population. In this thesis we use a Swedish and an international reference population [3, 4]. In many other countries, for example the USA, growth and weight are commonly described in percentiles instead of BMI SDS, with overweight and obesity in childhood being defined as a BMI above the 85th and 95th percentile for children of the same age and sex, using national reference growth charts [5].

2.1.2 Prevalence of obesity

2.1.2.1 Children

The rates of overweight and obesity continue to grow among children worldwide. The WHO

reports that, since 1975, the prevalence of overweight or obese children between 5 and 19

years of age increased from 4 % to 18 % globally, and the rise is evident in both developed

and non-developed countries [6]. The prevalence of obesity is difficult to assess, since not all

children, adolescents, or adults with obesity seek health care. In Sweden children are

routinely measured in primary health care and at school at certain ages but reports on weight

in older children are often self-assessments, which makes assessment of the prevalence more

complicated.

In Sweden we can see the same pattern of increasing weight among children: the prevalence

of overweight and obesity in children and adolescents has doubled over the past 30 years, and

the risk of overweight and obesity increases with age. A national health report on selfreported

childhood weight patterns in Sweden revealed an increase from 7% to 15%

overweight among 11–15-year-old school children between 1989 and 2018, with a

corresponding increase in obesity from 0.8% to 4% [7, 8]. In a recent report dated 2019,

based on data from the WHO Childhood Obesity Surveillance Initiative, the overall

prevalence of overweight among Swedish primary school children was 17%, of whom 6%

were obese and 1% had severe obesity [7, 9].

In 2018 a survey of Swedish national data was conducted to estimate the prevalence of

overweight and obesity in four-year-old children found that 9.4% were overweight and 2.3 %

were obese [10]. In the Stockholm region in 2020 the prevalence of overweight and obesity

were 8.4% and 1.8 % respectively among four-year-old children [11]. The overweight and

obesity rates differ substantially in different regions in Sweden and also in different

municipalities in Stockholm, in the case of overweight ranging between 4.6% and 18.2 % in

certain areas [11].

The rates of obesity are higher in southern Europe than in Sweden, with a very high

prevalence (40–47%) of overweight and obesity among children in countries such as Greece,

Spain, and Italy and a high rate of severe obesity, as seen in Figure 1 [9]. In the USA the

overall prevalence of obesity, although high among the adolescents (20.5%), has reached a

plateau, however, the prevalence of severe obesity has continued to increase [12].

Figure 1. Prevalence by country of overweight, obesity, and severe obesity among in children

aged 6-9 based on WHO definitions in 21 European countries. Spinelli et al. 2019 Obesity

Facts.

2.1.2.2 Adults

According to the WHO’s report in 2016, more than 1.9 billion adults worldwide were

overweight or obese, with prevalence rates of obesity varying across countries and regions

[13]. In a large national health survey in 2020 carried out in Sweden, H.lsa p. lika Villkor,

the self-reported prevalence of overweight or obesity was 52%, with a higher prevalence

among men (57%) than in women (46%) [7]. As in children, BMI increases with age, and

there are large variations in the prevalence of overweight and obesity in adults according to

region and evident sociodemographic differences.

2.1.2.3 Causes of obesity

The underlying cause of obesity is, in a simplified version, an imbalance between calorie

intake and the calories used. There is an evident genetic susceptibility, but also psychosocial,

endocrine, environmental, and nutritional factors are also among the components that

contributes to the development of the overweight/obese condition. Society, the availability of

healthcare, and life conditions are also important factors in the development of childhood

obesity.

2.1.3 Some consequences of obesity

Obesity affects both children and adults and has several social and health-related

consequences. Obesity-related disease can be detected at an early age, and obesity in all age

groups accounts for over 70 % of premature deaths worldwide according to the WHO, as

being one of the most important risk factors for cardiovascular diseases, cancer and diabetes

mellitus [13, 14]. The risk of obesity has also become even more apparent during the Covid-

19 pandemic, with an increased risk of severe disease and increased mortality [15, 16].

Obesity in childhood often continues into adulthood [17], so children with obesity are at risk

for a long time. As obesity is becoming more prevalent in children, there is significant risk of

both present comorbidity and the development of future disease in children with obesity.

Children with obesity are more likely to have many psychosocial consequences related to

obesity such as a lower self-esteem, lower quality of life, depression, and a high prevalence of

comorbidity with neuropsychiatric disorders [18-20]. It is also more common for a child with

obesity to have asthma [21], obstructive sleep apnea, and musculoskeletal disorders [22-24].

Even before comorbidity is present, signs of endothelial dysfunction and cardiovascular risk

factors can be detected among children with obesity [25-27]. Like adults with obesity,

children with obesity can have several comorbidities, such as hypertension, prediabetes,

dyslipidemia, elevated liver enzymes and non-alcoholic fatty liver disease [26, 28-31].

Obesity is the most important risk factor for type 2 diabetes in adults [32, 33], and during

recent years evidence has emerged that a greater risk is associated with early-onset type 2

diabetes compared with a later-onset of type 2 diabetes [34]. The earlier the onset of type 2

diabetes, the greater loss of life years, and there is a higher morbidity among those who are

younger at diagnosis [34]. Both obesity and type 2 diabetes are associated with insulin

resistance, which is one of the main abnormalities in disturbed glucose regulation.

2.2 GLUCOSE REGULATION

2.2.1 Normal glucose homeostasis

Normal glucose homeostasis in the feeding state is regulated by insulin, an endocrine

hormone with the ability to “open the door” to glucose to act as energy, primarily in skeletal

muscle, adipose tissue, and the liver. Blood glucose levels are regulated by an intricate

communication between endocrine and neurological factors in a feed-back loop system both

in the fed state and in periods of fasting. Fasting glucose is normally maintained at between

3.9 and 5.6 mmol/L [35], and the increase after a meal rarely exceeds 3 mmol/L in young and

healthy individuals [36].

Low blood glucose is especially dangerous for the brain cells, and glucose transport to brain

cells is therefore not dependent on insulin, instead glucose is transported by facilitated

diffusion through the cell membrane. To secure sufficient glucose for brain cells, there are

several other mechanisms to elevate blood glucose. In the fasting state the insulin counterpart

hormones; glucagon, cortisol, growth hormone (GH), and the catecholamines adrenaline and

noradrenaline are involved in glucose homeostasis. All these hormones raise blood sugar

levels through different effects.

2.2.2 Insulin and insulin signaling

Insulin is an anabolic and regulatory hormone, discovered by Frederick Banting and Charles

Best in 1921, for which they received the Nobel Prize in 1923. Insulin is synthesized in the bcells

in the islets of Langerhans of the pancreas and has a regulatory effect on blood glucose

levels, mainly by promoting the entry of glucose into cells, especially skeletal muscle. It is

the only hormone with a glucose lowering capacity [37, 38]. The pharmacological half-life is

short, approximately 5-8 minutes [39].

Since the discovery of insulin, extensive research has been conducted to understand the

intricate insulin signaling pathways, providing increasing knowledge about the different

complex regulations of carbohydrate, protein, and lipid metabolism. The process of

understanding these biologic patterns is ongoing and offers possibilities of understanding the

pathogenesis of type 2 diabetes and finding new potential treatment strategies.

Elevated levels of blood glucose trigger pancreatic insulin release; upon binding to the insulin

receptor (INSR) insulin promotes glucose uptake and glycogen storage. Virtually all cells in

the body express INSRs, but the effect on glucose homeostasis is exerted mainly in skeletal

muscle, white adipocytes, and hepatocytes. When insulin binds to receptors in these tissues, a

signaling phosphorylation cascade is activated, promoting glucose uptake and a variety of

metabolic actions. Insulin binds preferentially to INSR , but insulin-like growth factor-1 and

2 (IGF-1, IGF-2) can also bind to INSR with a reduced affinity [40]. Insulin action is mainly

on glucose uptake but also affects lipid and protein metabolism, while IGF-1 and IGF-2

primarily promote cell differentiation and growth [41].

The INSR is composed of two extracellular a-subunits, which binds insulin, and two

transmembrane b-subunits which contains tyrosine kinase [42]. Two isoforms of the INSR, A

and B; where binding to the B isoform is more specific to insulin binding and is highly

expressed in liver, muscle, and adipose tissue [40]. The A isoform of INSR is predominantly

expressed in fetal tissues and in the brain and has comparable affinity for both insulin and

IGF-2 [43].

The activation of INSR leads to a cascade of cellular phosphorylation by activating tyrosine

kinase in the b-subunits [25]. This initiates metabolic signaling by recruiting INSR substrates

(IRS 1-6), which leads to further downstream activation [44-47]. Among the most important

steps in this cascade of events are the subsequent activation of phosphoinositide 3-kinase

(PI3K), leading to the activation of phosphatidylinositol dependent kinase (PDK-1),

mammalian target of rapamycin (mTOR) complex, and subsequently the serine/threonine

kinase Akt pathway (also known as protein kinase B, PKB) [48-50].

These are necessary steps in the insulin signaling cascade to promote the translocation of

glucose transporters (GLUT) to the cell membrane [47, 51, 52], and a critical pathway to link

the IRS proteins to the metabolic actions of insulin. Transport of glucose over the cell

membrane is facilitated by the GLUT family members, and several different GLUT

transporters have been identified. The most important in glucose homeostasis are GLUT 1-4

[53].

The Akt/PKB family of proteins consists of three different isoforms of serine/threonine

protein kinases. The Akt 2 isoform is the most abundant form in insulin-sensitive tissues and

appear to play a role in mediating insulin action on metabolism [54]. Activation of Akt

complex allows the activation of many downstream targets besides glucose uptake: glycogen

synthesis by glycogen synthase kinase 3 (GSK3), and protein and fat synthesis through the

mTOR complex, which regulates a network of genes controlling metabolism, protein

synthesis, and cell growth [55].

Additionally, it seems that Akt is involved in the activation of the transcription factor family

Forkhead box O transcription factor (FOXO), which controls lipogenic and gluconeogenic

genes [56, 57] and also affects other transcription factors and survival proteins [55, 58, 59].

Insulin also has a role in cell differentiation by activating non-INSR substrates, independently

of the Akt pathway, for example heterotrimeric G protein, SOS, RAS, MAPK factors [48].

An overview of the myriad of activities in the INSR region, with cascade phosphorylationÅLs

and allosteric reactions, is shown in Figure 2.

Figure 2. Insulin and IGF-1-signaling pathways. J Boucher et al. 2014. Cold Spring

Harbour Perspectives in Biology

2.2.3 Major insulin actions in different tissues

2.2.3.1 Insulin action in skeletal muscle

Skeletal muscle is an energy-consuming tissue. It stores glucose as glycogen when glucose is

abundant, for later use. Most of the postprandial glucose uptake in humans (approximately

60-80%) is made in skeletal muscle, and proper insulin action is important to maintain

glucose homeostasis [60-62]. The effect of insulin in skeletal muscle myocytes is mainly the

promotion of glucose uptake, through the translocation of GLUT 4 to the cell membrane.

Physical activity also increases the translocation of GLUT 4, hence also glucose uptake,

independently of insulin [63]. GLUT 1 transporters are responsible for basal glucose uptake

and are not expressed to a great extent in adults but probably more in infants and young

children.

The primary INSR substrate in skeletal muscle appears to be IRS-1 and the most important

part seems to be Akt 2, since studies in mice have observed that mice lacking Akt 2 develop

insulin resistance [64, 65]. Akt phosphorylates several proteins involved in glucose uptake in

the myocytes. Among the best characterized are TBC1D1/TBC1D4, which seem to have an

effect of the translocation of GLUT 4 to the cell membrane [66].

2.2.3.2 Insulin action in adipocytes

The white adipocytes are cells responsible for the storage of lipids and mobilization of energy

by releasing fatty acids. In adipose tissue insulin enhances glucose uptake through increased

GLUT 4 translocation to the cell surface by stimulation of the INSR complex and increased

lipogenesis; thereby, insulin regulates the secretion of free fatty acids (FFA) in the blood

stream. White adipose tissue (WAT) accounts for only <5 % of glucose uptake after a meal

[67].

2.2.3.3 Insulin action in hepatocytes

Insulin is released from the pancreas into the portal vein, so the liver is exposed to higher

insulin levels than the general circulation [68]. Glucose uptake in the liver is not directly

dependent on insulin; rather, it is mainly dependent on the glucose gradient and facilitated by

GLUT 2 [69, 70]. The main effect of insulin on the hepatocytes is the activation of glycogen

synthesis, lipogenesis and protein synthesis. Both the direct and indirect action of insulin

leads to suppressed gluconeogenesis as an effect of postprandial insulin [71-73], however, the

mechanisms are not clear.

The major isoforms of IRS in hepatocytes are IRS-1 and 2 [74]. Although glucose transport

in the hepatocytes is mainly independent of insulin, insulin stimulates glycogen synthesis

through the cascade of phosphorylation and dephosphorylation in a similar way as the

signaling cascade in skeletal muscle [75, 76]. Insulin also facilitates glucokinase

translocation, which is necessary for hepatic glucose regulation [77].

The pathway of hepatic insulin signaling appears to diversify distal to Akt activation,

involving substrates regulating glycogen synthesis and mTOR activity, leading to lipogenesis

and protein synthesis [57].

2.3 INSULIN RESISTANCE

2.3.1 Pathophysiology of insulin resistance

In 1939 the first notion of insulin resistance came from Himsworth who pointed out that

diabetes should be subdivided into two categories “according to which of these disorders

predominates into insulin-sensitive and insulin-insensitive types”. Reaven and colleagues

further explored this proposition and suggested in the Banting Lecture in 1988 that insulin

resistance was a link between obesity, hypertension, dyslipidemia and type 2 diabetes in a

description of the metabolic syndrome [78].

Insulin resistance is defined as a failure in target tissues to respond to insulin, and the

mechanisms underlying is both on the level of the whole body and on cellular level in

different tissues [33, 79]. Obesity contributes to the development of insulin resistance in both

adults and children and is thought to be one of the major factors in obesity leading to type 2

diabetes. In children adiposity is one of the most important determinants of insulin resistance

irrespective of age, ethnicity, and gender [80, 81].

Although extremely rare, a few conditions with mutations in the insulin receptor gene exist

and are associated with insulin resistance in childhood. These conditions have varying

degrees of severity, ranging from mild to severe phenotypes such as Insulin Resistance

syndrome type A, Rabson-Mendenhall syndrome, and Leprechaunism. Severe insulin

resistance, in for example Leprechaunism, results in a variety of characteristics such as

growth deficiency, hyper-glycemia and other endocrine abnormalities [82, 83]. A milder type

of insulin resistance occurs at different stages of life, with variations in insulin resistance

during puberty, pregnancy, and ageing [84-86].

Normal glucose regulation is maintained by a feedback loop involving insulin secretion from

the islet b-cells and insulin-sensitive tissues. When insulin sensitivity declines, the insulin

secretion increases to keep glucose levels in the normal range. When the b-cells cannot

secrete enough insulin to compensate for the insulin resistance, the glucose homeostasis is

disturbed and progresses to prediabetes and type 2 diabetes [87, 88].

Sophisticated communication is taking place between different tissues, such as skeletal

muscle, liver and adipose tissue, and the severity of insulin resistance may vary between

organs. It is proposed that impaired insulin secretion from the b-cells in the pancreas affects

the brain, liver, skeletal muscle and adipocytes in the form of increasing weight, increased

insulin resistance and higher levels of non-esterified fatty acids (NEFA) [33]. The resulting

increases of blood glucose and NEFA levels further stress the b-cells, causing a phenomenon

referred to as glucolipotoxicity [89, 90], visually presented in Figure 3.

Figure

The causes of insulin resistance are not fully known, but the effects are enormous and manyof the alterations work in concert with each other. Insulin and IGF-1 signaling are constrictedby several mechanisms; uncontrolled signaling would lead to severe alternations inmetabolism and also have negative effects on cell differentiation and cell growth. On theother hand, too much inhibition of these regulatory functions leads to a cascade of adverseevents and can play a role in the development of insulin resistance.

A diverse group of bioactive factors and molecules are proposed as potential mediators inimpairing insulin sensitivity such as hyperinsulinemia per se, low grade inflammation, defectsin insulin signaling pathways, lipid metabolites, endoplasmic reticulum stress, oxidativestress, and mitochondrial dysfunction

.2.3.2 Molecular mechanisms behind insulin resistance

The molecular mechanisms in the development of insulin resistance are not easilyinvestigated nor fully understood, possibly due to the intricate nature of the pathways and thefact that many co-exist and interact with each other. A description of all the pathways is notfeasible in this thesis. However, some of the cellular and molecular mechanisms underlyinginsulin resistance in specific tissues are described below.

2.3.2.1 Skeletal muscle insulin resistance

Physiological and molecular studies have observed that the translocation of GLUT 4 isdefective in insulin resistance. It seems the most proximal levels of insulin signaling areaffected, namely INSR, the IRS-family, PI3K and Akt [91, 92].

Defects in phosphorylation ofthe IRS substrates are associated with reduced insulin signaling activity, for examplephosphorylation of IRS-1 at serine 307, and by some kinases such as IkB kinase (IKK),nuclear factor kappa B (NFkB), and c-Jun N-terminal kinase (JNK) [93]. Some evidence11exists that low-grade inflammation is specifically related to muscle insulin resistance and nothepatic insulin resistance [94], but this association is not fully understood.

The inflammatorypathways seem to affect insulin sensitivity in particular by the NF-kB cascade [95, 96], butmany other different pathways have also been investigated. The alterations withphosphorylation of the AKT complex, are associated with insulin resistance, and there is alsoevidence of effects on endothelial dysfunction through endothelial-derived nitric oxidesynthetase (eNOS) [97-99].Impaired insulin signaling in skeletal muscle is also believed to be affected by increased fattyacid intermediates, via impaired IRS-1 phosphorylation and subsequent lower GLUT 4translocation to the myocyte membrane surface [100-102].

The accumulation ofintramyocellular lipid content (IMCL) in skeletal muscle and its metabolites diacylglycerol(DAG) is proposed to be the potential mediator in the development of insulin resistance.There is some evidence that DAG affects PKC, one of the mediators in the INSR complex,and the activation thereby results in an cascade of events leading to a blockage of the Aktpathway, causing impaired insulin signaling [103] [60].

Physical activity mainly targetsmuscle insulin sensitivity, by promoting GLUT 4 translocation to the cell membrane, thusfacilitating glucose uptake [104].

2.3.2.2 Adipose tissue insulin resistance

Visceral adipose tissue and adipose tissue dysfunction, rather than the overweight or obesecondition per se, are proposed to be associated with insulin resistance in both adults andchildren [105, 106].

Adipose tissue dysfunction is characterized by adipocyte hypertrophyrather than increased adipocyte number and is associated with systemic inflammation withaltered levels of adipokines, chemokines and macrophage infiltration [107]. The activation ofmacrophages and subsequent released of chemokines such as TNF-a and interleukin-6 (IL-6)are associated with obesity and correlated with the risk of type 2 diabetes [108-111].

TNF-aaffects the insulin signaling pathways through phosphorylation of IRS, which leads to areduced glucose uptake, and this process is associated with an increased transcription ofinflammatory genes via NFkB and JNK [112, 113].

Adipocyte dysfunction is proposed to be an essential event in the development of insulinresistance, increasing the inflammatory responses and FFA delivery to skeletal muscle andliver, promoting development of insulin resistance in those tissues [114, 115]. The specificmolecular defects in adipocyte insulin resistance in humans are not well known; most studieshave focused on the endocrine and autocrine functions in white adipocytes.

2.3.2.3 Hepatic insulin resistance

Insulin directly and indirectly promotes hepatic glucose uptake, in combination withhyperglycemia. This also involves suppression of gluconeogenesis and glycogenolysis andthe activation of glycogen synthesis. In hepatic insulin resistance the effect of insulin’s abilityto suppress gluconeogenesis is diminished, causing fasting hyperglycemia and also increasedlipid synthesis [116-118].

Studies in knockout mouse models have described that changes in insulin signaling in the Aktcomplex leads to insulin resistance and impaired glucose tolerance (IGT), in liver as well asin skeletal muscle and adipose tissue [119, 120]. Besides exerting effects on glucosemetabolism in the liver, insulin also regulates lipid metabolism by promoting lipogenesis.Individuals with insulin resistance should according to this assumption have a decreased lipid synthesis in the liver, which is seen in genetic mice models with a knockout of the INSRwhere a decreased lipid synthesis is evident [121-123]. However, in the insulin-resistant statethere is often an association with increased lipogenesis and hepatic steatosis suggesting thathepatic insulin resistance is not selective but maybe more a part of an integrated biologicalsystem.

Overnutrition and the subsequent insulin resistance and increasing insulin levels has effectson skeletal muscle, adipose tissue, hepatocytes, b-cells in pancreas, and the brain assummarized in Figure 4.Figure 4. The integrative biology of type 2 diabetes. Roden et al. Nature Dec 2019.2.3.3 The gut

2.3.3.1 Incretin effect

Oral glucose induces insulin secretion, in spite of equally elevated blood glucose levels,more than intravenous glucose [124]. This phenomenon is called the incretin effect and ismediated by the gut-derived hormones glucose-dependent insulinotropic polypeptide (GIP)and glucagon-like polypeptide (GLP-1)[125].

GIP is released from enteroendocrine K cells,which resides mainly in the duodenum and upper jejunum and mediates most of the incretineffect in healthy individuals [126, 127]. GLP-1 is produced by enteroendocrine L cells,residing mostly in the mucosa in the ileum and colon [128]. In both adults and adolescentswith prediabetes and type 2 diabetes, a reduced incretin effect can be seen [129-134].

Patientswith type 2 diabetes have a reduced b-cell mass [135], and it is hypothesized that thisreduction in b-cell mass is a contributor to the diminished incretin response. This hypothesisis supported by findings that such patients have lost their GIP-mediated insulin response13[136]. The transcription factor 7 like 2 (TCF7L2) gene variant is associated with anincreased risk of prediabetes and type 2 diabetes, possibly through impaired hepatic insulinsensitivity and b-cell function [137, 138].

There are indications that a reduced incretin effectis associated with the TCF7L2 gene variant, in both healthy and obese adults as well as inadolescents [139, 140], not because of reduced secretion of GLP-1 and GIP, but rather dueto the effect of TCF7L2 on the sensitivity of the b-cell to incretins. Incretin-based drugs arewidely used in adults with type 2 diabetes, with positive effects on glycemic control and BMIand are also of beneficial effect in patients with kidney and heart failure [125].

2.3.3.2 Gut microbiotaIn

1908, the Nobel Prize for Medicine was awarded to Elia Metchnikoff and Paul Erlich whowere the first to pay attention to human gut flora. Having observed that mountain farmerswho consumed fermented milk lived longer, the recipients formulated the oralbacteriotherapy theory [141]. The intestinal microbiota plays a part in the metabolism, partlyby having an effect on the immune system but also through neuroendocrine signaling [142,143]. An altered gut microbiota has been proposed as a driver of the inflammation associatedwith insulin resistance and the development of type 2 diabetes [144, 145], but themechanisms are largely unknown.

2.3.4 Genetic background

The relationship between obesity and insulin resistance exists regardless of ethnicity [146].However, the relation between adiposity and insulin resistance varies across ethnic groups[147, 148]. Ethnic differences appear to exist in the relationship between insulin sensitivityand insulin response in cohorts with normal glucose tolerance as presented in Figure 5,making some ethnic populations more susceptible to diabetes [149].

A longitudinal study oninsulin resistance and insulin secretion in normal-weight children and adolescents, revealedthat both insulin sensitivity and insulin secretion diminish transiently during puberty;however, only the African Americans in this cohort of children had a low disposition index(DI), which might reflect a higher risk of developing type 2 diabetes [150]

Figure 5. Ethnic differences in the relationship between insulin sensitivity and insulinresponse. Kodama et al. Diabetes Care June 2013.Genetic background is of importance in the development of both type 1 and 2 diabetes,which has led to an extensive search for the gene variants associated with diabetes duringthe past decade.

In type 1 diabetes genes in the human leukocyte antigen (HLA) regionconfer 50 % of the genetic risk of T1D [151, 152], and more than 60 non-HLA risk variantshave been identified [153-155].

Finding genetic markers for the early detection of prediabetes or diabetes has beenproposed as a tool to identify individuals at high risk, but only 10 % of the type 2heritability is explained by gene variants [156, 157]. While obesity is the strongest predictorof type 2 diabetes, heritability is also a predictor, varying from 26% to 69 % depending onage at onset [158-160].

The first discovered type 2 risk alleles were three genetic variants inKNJ11, PPARy, and TCF7L2 [161]. Over recent decades, over 400 risk gene variantsassociated with type 2 diabetes risk have been discovered through extensive research bylarge-scale genetic studies [162].2.3.5 PubertyIn healthy children the insulin sensitivity decreases with the onset of puberty, and recoversby the end of puberty, and insulin secretion increases to compensate for the higher insulinresistance [84, 163-165]. IGF-1 levels follow the rise and fall in insulin resistance duringpuberty, suggesting an effect of the GH/IGF-1 axis on pubertal insulin resistance [166]

2.3.6 Physical activity

A sedentary lifestyle is clearly associated with increased insulin resistance and obesity as wellas an increased risk of death due to any cause [167]. The level of physical activity decreasesduring the transition from childhood to adolescence [168], and there is evidence thatphysically active youth remain active in adulthood [169].

A recent randomized study provedthe positive effects of increased physical activity in different modalities in increasing insulinsensitivity and reducing ectopic fat in obese adolescents [170].2.3.7 DietMany trials on different diets have been conducted during the last decades, with differentcompositions, with conflicting results on how to achieve the most successful weightmanagement.

The diversity of results is due to a great number of causes, such as variations inthe duration of trial, type of diet, difficulties in adherence to dietary advice, and also theheterogeneity of trial settings. It is evident that changes in dietary patterns are extremelydifficult to achieve and maintain: a recent metanalysis of 121 randomized trials in obeseadults enrolled on different diets showed that all diets (low carbohydrate, moderatemacronutrients and low-fat diet) had similar effects on weight loss and improvement incardiovascular risk factors after six months and that all these effects had disappeared aftertwelve months [171].

Studies in lifestyle alterations and diet in obese children have shown that a very-lowcarbohydratediet, a reduced intake of carbohydrates limited to 20-50 g/day or 5-15 % of totalcalories, have proven more positive results on short-term weight loss and improved insulinlevels and insulin resistance than the traditional low-fat diet [172-174].

Studies of whichdietary pattern is beneficial for children with obesity are limited, but it appears that a15reduction in carbohydrate intake, may be beneficial in reducing risk factors for type 2diabetes in youth with obesity [174, 175]. However, another study presented positive effectson weight independent of type of diet, which was interpreted as showing that any structureddiet program can have positive effects [176].

2.4 MEASUREMENTS OF INSULIN RESISTANCE AND INSULIN SECRETION

Many methods are available for the estimation of insulin resistance, from simple fastingblood measures to elaborate protocols involving intravenous infusion and repeated testing.All methods are approximations of insulin resistance in a multicompartment system, thehuman body, and several different methodologies and models are used to create indexes ofinsulin resistance.

A wide range of cut-off values to define insulin resistance exists, whichmakes comparison between studies and populations complicated. A few studies have usedarbitrary cut-off levels from population estimates, but there is no uniform categoricaldefinition of insulin resistance.

2.4.1 Direct measures of insulin resistance

2.4.1.1 Hyperinsulinemic-Euglycemic Clamp

The gold standard to determine insulin resistance in vivo was developed by DeFronzo et al[177]. This technique assesses whole-body and tissue insulin sensitivity and estimates wholebodyglucose disposal under steady-state conditions. In the fasting state the glucoseproduction, mainly from the liver, is equal to glucose uptake in peripheral tissues. Insulinsuppresses hepatic glucose production and stimulates glucose uptake.

The clamp techniqueuses this by creating a steady-state level of exogenous insulin infusion suppressingendogenous hepatic glucose production, while the plasma glucose concentration is heldconstant at normal glucose levels in a one-compartment model. The rate of variable glucoseinfusion necessary to maintain normal glucose levels, “clamp”, provides a measure of theeffect of insulin on glucose production and utilization. Subjects who require a higher amountof glucose infusion to remain euglycemic are more insulin-sensitive, giving a measure ofinsulin-stimulated glucose disposal (M) and insulin sensitivity (M/I), where I is the steadystateinsulin concentration [177, 178].

This technique requires infusion of both insulin andglucose via two intravenous lines, and frequent blood sampling to control the hyperinsulinemicand euglycemic state.2.4.2 Indirect measures of insulin resistance

2.4.2.1 Fs-IVGTT minimal model

The Frequently Sampled Intravenous Glucose Tolerance Test (fs-IVGTT) was developed byBergman et al in 1979 [179]. It is a validated method to assess insulin sensitivity in bothadults and in children [180, 181]. Fs-IVGTT data are most commonly analyzed in a minimalmodel assessment [182], in which the glucose and insulin data are entered in the MINMODcomputer program generating an insulin sensitivity index (Si), acute insulin response (AIR)and glucose effectiveness (Sg).The fs-IVGTT is performed after an overnight fast, and the subjects receive an intravenouscatheter in each arm.

Baseline blood samples of insulin and glucose are drawn, and at time 0minutes an intravenous bolus of glucose is given. Frequent sampling of glucose and insulin16concentrations is performed every minute during the first 10 minutes to measure the acute(first-phase) insulin response and used to derive the late (second-) phase response, andmeasurements of insulin and glucose continued throughout the test. Insulin is administered at20 minutes, a given amount of insulin per kg body weight as an intravenous bolus, andmeasurements of glucose and insulin are taken over a period of 180 minutes.

An overview ofthe test is displayed in Figure 6.Figure 6. Schematic display of the fs-IVGTT test. Initially, a repeated baseline sampling ofglucose and insulin is performed; thereafter an intravenous glucose bolus is given. A periodof frequent sampling period of glucose and insulin levels follows, and after 20 minutes anintravenous bolus of insulin is given. Sampling is performed frequently during the initialphase, and thereafter more spaced out over the 180-minute period of the test.The minimal model is a mathematical model investigating glucose and insulin kinetics bytwo differential equations assuming two compartments: dynamics of glucose uptake after anexternal stimulus and assuming that insulin is the driving function; and in a separate, remotecompartment in which the dynamics of insulin release in response to glucose is assumed,where glucose drives the function.

Si is calculated from two of these model parameters and isdefined as the fractional glucose disappearance per insulin concentration unit [179, 182].???????????? ??????? 1 =??(?)??= −?(?) × (?? + ?(?)) + ?? × ??(0) = ?0???????????? ???????? 2 =??(?)??= −?2 × ?(?) + ?3 × ?(?)?(0) = 0?(?) = 0 ?? ?(?) ≤ ???????(?) − ?Explanation of the equations: G(t) is plasma glucose at time t. Sg is glucose effectiveness,X(t) is insulin action at time t. Gb is the basal glucose concentration. I(t) is the plasma insulinconcentration at time t. Ib is the basal insulin concentration. F(t) is a function that representsthe elevation of plasma insulin above basal insulin. P2 describes the removal rate of insulinfrom the interstitial space. P3 describes the movement of insulin to the interstitial space.17An advantage of the fs-IVGTT is that it is easier to perform than the clamp-method since itdoes not rely on steady-state conditions, so constant adjustments of infusions are not needed.

The index of insulin sensitivity, Si, is comparable to clamp-derived insulin sensitivitymeasures in healthy subjects but displays slightly weaker associations in insulin-resistantpopulations [180, 183-185]. As insulin resistance varies, the endogenous hepatic glucoseproduction may contribute to the variation in Si [186]. Another disadvantage is that when bcelldysfunction is severely affected, the first-phase insulin response can be very low orunmeasurable [187, 188], although the insulin-modified protocol was intended to overcomethis [182].

Figure 7.

Results of an fs-IVTT investigation with minimal modeling in MINMODMillennium. Green dots represent insulin levels, and the peak after 20 min is displays the AIRto the glucose bolus. Blue dots represent glucose levels.

2.4.2.2 Oral Glucose Tolerance Test

The Oral Glucose Tolerance Test (OGTT) is most often used as a test to determine glucosetolerance but can also be used to assess b-cell function and insulin resistance. The insulin,glucose and C-peptide levels can be measured after glucose ingestion, to calculate the earlyinsulin and insulinogenic response [189, 190].

The surrogate markers of insulin secretion andinsulin resistance derived from these modified OGTTs are, for example, SI index-Matsuda,Insulin Sensitivity Index [191, 192]. Extended OGTT models can also be used, with thesimultaneous use of surrogate measures such as HOMA%S as well as minimal modelingtechniques [193, 194].182.4.3 Simple measures of insulin action and sensitivityHomeostasis model assessment (HOMA) was developed in 1985 by Matthews et al. and isused to quantify insulin resistance and b-cell function from basal fasting glucose and insulinconcentrations [195].

The HOMA model is robust in clinical and epidemiological studieswhen only fasting levels of glucose and insulin are available, and correlates well with insulinsensitivity determined by the euglycemic clamp [196-198]. Several other methods are used,but HOMA-IR is the most widely used surrogate measure and seems to be a reliable measurein children as well [198, 199].

A brief summary of methods defining insulin resistance is displayed in Table 1.Table 1. Measures of insulin resistance and insulin secretionMethod MeasureDirect HyperinsulinemicEuglycemic ClampInsulin sensitivity (M/I) Steady stateGold standardIndirect Fs-IVGTT withminimal modelingOral glucose tolerancetest (OGTT)Insulin sensitivity (Si)Acute insulin response(AIR)Glucose effectiveness (Sg)Disposition indexDetermining glucosetoleranceDynamic dataTwocompartmentassumptionPhysiologicconditionsSimplesurrogatemeasuresHOMA-IRHOMA%BHOMA%SQUICKIHOMA-IR=(fasting insulin(μU/mL) x fasting glucose(mmol/l)/22.5QUICKI= 1/log (fastinginsulin μU/mL)+fastingglucose (mg/dL)Based onfasting levels ofinsulin, cpeptideandglucoseSurrogatesderived fromdynamic testsOGTTMixed MealTolerance-Matsuda index-Stumvoll index-Gutt indexInsulin sensitivity(ISIMatsuda), insulinsecretion, DI(ISIStumvoll, ISI)Dynamic dataCorrelates wellwith clamp19

Figure 8. To assess insulin resistance, different mathematical models of glucose and insulinfluxes have been created, all of which are assumptions of reality. This is a scheme of the mainmechanisms in glucose homeostasis. The colored dashed arrows represent control signalsthat regulate glucose fluxes of insulin and glucagon secretion. Mathematical modeling ofGlucose homeostasis. Mari et al. Frontiers in Physiology Nov 2020.

2.5 PREDIABETES

Impaired fasting glucose (IFG) and IGT are intermediate stages in the development fromnormal glucose tolerance to diabetes. There is some evidence that IFG and IGT representdifferent and distinct phenotypes [200]. Studies of tissue-specific insulin-resistant phenotypesare rare, possibly since measures are not easily taken when multiple simultaneous biologicalprocesses are at play at the same time. IFG is characterized by a more pronounced hepaticinsulin resistance and a measure of glucose disturbance in the basal state [201].

IGT ischaracterized by a peripheral or skeletal muscle insulin resistance [201, 202]. In individualswith more pronounced muscle insulin resistance, an increased inflammatory gene expressionhas been noted, and plasma markers of low-grade inflammation are increased [94].2.5.1 Diagnosis of prediabetesThe cut-off level for diagnosing IFG has been widely debated.

The American DiabetesAssociation (ADA) guidelines were changed in 2003, when the cut-off level was loweredfrom plasma glucose 6.1 mmol/L to 5.6 mmol/L (referred to as IFGADA in this thesis) toincrease the sensitivity of testing to identify individuals at risk of type 2 diabetes. WHOguidelines categorize IFG as a fasting blood glucose level of 6.1-6.9 mmol/L (referred to asIFGWHO in this thesis). IGT is diagnosed after a two-hour OGTT glucose load if glucose is7.8-<11.1 mmol/L.202.5.2 Epidemiology of prediabetesDepending on the cut-off level of glucose used and the population studied, the prevalence ofprediabetes varies widely. However, 7.3% of the global population were estimated to haveprediabetes in 2017 [203]. In a recent population-based study in healthy adolescents aged 12–18 years in the USA, 18% had prediabetes, among them IFGADA was the most prevalent typeof glucose dysregulation, and the prevalence was higher among males and associated withobesity [204]. In a comparison between Swedish and German obese children, the prevalenceof IFGADA was much higher among the Swedish children than the German cohort (17.1% vs.5.7%) [205].

2.6 DIABETES2.6.1 Diagnosis of diabetes

Diabetes is a disease characterized by elevated blood glucose, and the diagnostic criteria arebased on plasma blood glucose and dependent on the presence or absence of symptoms.Diabetes is diagnosed in both children and adults by a combination of classic symptoms and arandom plasma glucose ³11.1 mmol/L, fasting plasma glucose ³ 7.0 mmol/L, or a plasmaglucose ³ 11.1 mmol/L two hours after an OGTT.

The International Society for Pediatric andAdolescent Diabetes (ISPAD) 2018 guidelines also suggest the use of HbA1c as an aid indiagnosing diabetes, and a HbA1c level >48 mmol/mol indicates diabetes [206].Diabetes in young people usually present with symptoms such as polyuria, polydipsia andweight loss.

The initial presentation can vary among patients: some present with symptomsand are clinically stable and others present with severe symptoms and ketoacidosis. Assessingdiabetes type can sometimes depend on the circumstances present at the time of diagnosis,since not all patients present with symptoms and the clinical picture can be very diverse.

Some individuals with newly diagnosed diabetes cannot be easily categorized since theetiology of diabetes is heterogenous, although the majority of all diabetes cases can beclassified into two categories of diabetes, as further discussed below.

2.6.2 Diabetes classification2.6.2.1 Type 1 diabetes

Type 1 diabetes is the most common type of diabetes among children and adolescents, and itis considered to be an autoimmune disease. It is characterized by insulin deficiency, causedby an immunologic destruction of the insulin-producing b-cells in the pancreas. In 85-90 %of these individuals autoantibodies to GAD, as well as the tyrosine phosphatases IAA, IA2 orZnt8 autoantibodies, are present at diagnosis [207]. Type 1 diabetes has a strong associationwith the HLA system, with a linkage to DQA and DQB genes [208].

These patients are alsopredisposed for other autoimmune mediated diseases such as for example coeliac disease andHashimoto´s disease.2.6.2.2 Type 2 diabetesType 2 diabetes is caused by a combination of insulin resistance and the failure of the b-cellsto produce sufficient insulin to maintain normal glucose levels and is associated withoverweight and obesity.21

2.6.2.3 MODY

There are several forms of inherited diabetes, they are referred to as maturity-onset of thediabetes in the young (MODY). They are inherited in an autosomal dominant pattern and areusually detected at an early age and accounts for 1-4 % of pediatric diabetes patients [209].Several variants are known, the most common form is caused by a mutation in HNF-1 alfa(MODY 3) in chromosome 12, which is responsive to sulfonylurea treatment [210, 211].

Amutation in the glucokinase gene in chromosome 7 (MODY 2) leads to elevated glucoselevels, where the glucose sensor is set to high and leads to elevated “normal” levels ofglucose. It is often randomly discovered and does not need any treatment [212, 213].2.6.2.4 LADALatent autoimmune diabetes (LADA) in adults, represents <10 % of all diabetes in adults.LADA initially presents as type 2 diabetes and becomes increasingly more similar to type 1diabetes. It is associated with the presence of glutamic acid decarboxylase autoantibodies(GAD), and in time insulin treatment is needed [214].2.6.2.5

Other types of diabetesAlthough other diabetes types are rare in children, they can be a part of other diseases such ascystic fibrosis, metabolic or mitochondrial diseases or drug-induced diabetes, for exampleduring cancer treatment. A few other endocrine disorders are associated with diabetes, forexample polycystic ovary syndrome, Cushing´s syndrome, GH-producing tumors, andautoimmune polyendocrine syndromes. Genetic syndromes can also be associated withdiabetes, for example Downs, Laurence-Moon-Biedl, and Prader-Willi syndromes

.2.6.3 Epidemiology of diabetes2.6.3.1 Prevalence of type 1 diabetes

The incidence of type 1 diabetes is increasing. There is evidence suggesting that the trend inincidence varies in across countries, and it seems the incidence has shifted to a peak at ayounger age [215, 216]. The highest incidence is in Finland, with an incidence of 60 cases per100,000 children per year [217]. In Sweden the incidence is 43 per 100, 000 children per yearand the peak incidence is 10–14 years [218, 219].2.6.3.2 Prevalence of type 2 diabetesThe prevalence of type 2 diabetes among children and adolescents is increasing in severalparts of the world.

Large variations in the prevalence and incidence are apparent, with higherrates among certain ethnicities and populations. In the USA the overall incidence of type 2diabetes in youth has increased from 9 to 12.5 cases per 100,000 youth per year from 2002-2003 to 2011-2012, and among Pima Indians 330 per 100,000 person-years [215, 220, 221].

The European countries have the lowest incidence rates and prevalence of early-onset type 2diabetes; Germany, for example, has a low prevalence rate of 2.42 per 100,000 youth under20 years of age [222, 223]

REFERENCES

1. WHO. Preventing and Managing the Global Epidemic of Obesity. . 1997.
2. Cole, T.J., et al., Establishing a standard definition for child overweight and obesity

worldwide: international survey. BMJ, 2000. 320(7244): p. 1240-3.

3. Cole, T.J. and T. Lobstein, Extended international (IOTF) body mass index cut-offs

for thinness, overweight and obesity. Pediatr Obes, 2012. 7(4): p. 284-94.

4. Karlberg, J., C.W. Kwan, and K. Albertsson-Wikland, Reference values for change in

body mass index from birth to 18 years of age. Acta Paediatr, 2003. 92(6): p. 648-52.

5. Kuczmarski, R.J., et al., 2000 CDC Growth Charts for the United States: methods

and development. Vital Health Stat 11, 2002. 11(246): p. 1-190.

6. Ng, M., et al., Global, regional, and national prevalence of overweight and obesity in

children and adults during 1980-2013: a systematic analysis for the Global Burden of

Disease Study 2013. Lancet, 2014. 384(9945): p. 766-81.

7. Folkh.lsomyndigheten, Årsrapport 2020. 2020.
8. WHO, Spotlight on adolescent health and well-being. Findings from the the

2017/2018 Health Behaviour in School-aged Children (HBSC) survey in Europe and

Canada. International Report. . 2019.

9. Spinelli, A., et al., Prevalence of Severe Obesity among Primary School Children in

21 European Countries. Obes Facts, 2019. 12(2): p. 244-258.

10. Nylander C, M.J., Övervikt och fetma bland 4-åringar i Sverige.

Barnh.lsov.rdsrapporten. Region S.rmland. , 2018.

11. Landsting, S.L., Barnhälsovårdsrapport 2019. . 2019.
12. Skinner, A.C. and J.A. Skelton, Prevalence and trends in obesity and severe obesity

among children in the United States, 1999-2012. JAMA Pediatr, 2014. 168(6): p.

561-6.

13. WHO, World Health Report 2016. www.who.int, 2016.
14. Lozano, R., et al., Global and regional mortality from 235 causes of death for 20 age

groups in 1990 and 2010: a systematic analysis for the Global Burden of Disease

Study 2010. Lancet, 2012. 380(9859): p. 2095-128.

15. Svensson, P., et al., Association between cardiometabolic disease and severe COVID-

19: a nationwide case-control study of patients requiring invasive mechanical

ventilation. BMJ Open, 2021. 11(2): p. e044486.

16. Petrilli, C.M., et al., Factors associated with hospital admission and critical illness

among 5279 people with coronavirus disease 2019 in New York City: prospective

cohort study. BMJ, 2020. 369: p. m1966.

17. Simmonds, M., et al., Predicting adult obesity from childhood obesity: a systematic

review and meta-analysis. Obes Rev, 2016. 17(2): p. 95-107.

18. Griffiths, L.J., T.J. Parsons, and A.J. Hill, Self-esteem and quality of life in obese

children and adolescents: a systematic review. Int J Pediatr Obes, 2010. 5(4): p. 282-

304.

19. Lindberg, L., et al., Anxiety and depression in children and adolescents with obesity:

a nationwide study in Sweden. BMC Med, 2020. 18(1): p. 30.

20. Cortese, S., et al., Association Between ADHD and Obesity: A Systematic Review and

Meta-Analysis. Am J Psychiatry, 2016. 173(1): p. 34-43.

21. Ford, E.S., The epidemiology of obesity and asthma. J Allergy Clin Immunol, 2005.

115(5): p. 897-909; quiz 910.

22. Kumar, S. and A.S. Kelly, Review of Childhood Obesity: From Epidemiology,

Etiology, and Comorbidities to Clinical Assessment and Treatment. Mayo Clin Proc,

2017. 92(2): p. 251-265.

66

23. Pomerantz, W.J., N.L. Timm, and M.A. Gittelman, Injury patterns in obese versus

nonobese children presenting to a pediatric emergency department. Pediatrics, 2010.

125(4): p. 681-5.

24. Sharma, V., et al., A systematic review and meta-analysis estimating the population

prevalence of comorbidities in children and adolescents aged 5 to 18 years. Obes

Rev, 2019. 20(10): p. 1341-1349.

25. Baker, J.L., L.W. Olsen, and T.I. Sorensen, Childhood body-mass index and the risk

of coronary heart disease in adulthood. N Engl J Med, 2007. 357(23): p. 2329-37.

26. Berenson, G.S., Childhood risk factors predict adult risk associated with subclinical

cardiovascular disease. The Bogalusa Heart Study. Am J Cardiol, 2002. 90(10C): p.

3L-7L.

27. Hedvall Kallerman, P., et al., Obese children without comorbidities have impaired

microvascular endothelial function. Acta Paediatr, 2014. 103(4): p. 411-7.

28. Weiss, R., et al., Obesity and the metabolic syndrome in children and adolescents. N

Engl J Med, 2004. 350(23): p. 2362-74.

29. Arslanian, S.A., Type 2 diabetes mellitus in children: pathophysiology and risk

factors. J Pediatr Endocrinol Metab, 2000. 13 Suppl 6: p. 1385-94.

30. Cruz, M.L. and M.I. Goran, The metabolic syndrome in children and adolescents.

Curr Diab Rep, 2004. 4(1): p. 53-62.

31. Sinha, R., et al., Prevalence of impaired glucose tolerance among children and

adolescents with marked obesity. N Engl J Med, 2002. 346(11): p. 802-10.

32. Van Gaal, L.F., I.L. Mertens, and C.E. De Block, Mechanisms linking obesity with

cardiovascular disease. Nature, 2006. 444(7121): p. 875-80.

33. Kahn, S.E., R.L. Hull, and K.M. Utzschneider, Mechanisms linking obesity to insulin

resistance and type 2 diabetes. Nature, 2006. 444(7121): p. 840-6.

34. Sattar, N., et al., Age at Diagnosis of Type 2 Diabetes Mellitus and Associations With

Cardiovascular and Mortality Risks. Circulation, 2019. 139(19): p. 2228-2237.

35. Abdul-Ghani, M.A., et al., Risk of progression to type 2 diabetes based on

relationship between postload plasma glucose and fasting plasma glucose. Diabetes

Care, 2006. 29(7): p. 1613-8.

36. Ferrannini, E., A. Gastaldelli, and P. Iozzo, Pathophysiology of prediabetes. Med Clin

North Am, 2011. 95(2): p. 327-39, vii-viii.

37. Topp, B.G., L.L. Atkinson, and D.T. Finegood, Dynamics of insulin sensitivity, -cell

function, and -cell mass during the development of diabetes in fa/fa rats. Am J

Physiol Endocrinol Metab, 2007. 293(6): p. E1730-5.

38. Weiss, M., D.F. Steiner, and L.H. Philipson, Insulin Biosynthesis, Secretion,

Structure, and Structure-Activity Relationships, in Endotext, K.R. Feingold, et al.,

Editors. 2000: South Dartmouth (MA).

39. Matthews, D.R., et al., The half-life of endogenous insulin and C-peptide in man

assessed by somatostatin suppression. Clin Endocrinol (Oxf), 1985. 23(1): p. 71-9.

40. Belfiore, A., et al., Insulin Receptor Isoforms in Physiology and Disease: An Updated

View. Endocr Rev, 2017. 38(5): p. 379-431.

41. Dupont, J., et al., Insulin and IGF-1 induce different patterns of gene expression in

mouse fibroblast NIH-3T3 cells: identification by cDNA microarray analysis.

Endocrinology, 2001. 142(11): p. 4969-75.

42. Hubbard, S.R., The insulin receptor: both a prototypical and atypical receptor

tyrosine kinase. Cold Spring Harb Perspect Biol, 2013. 5(3): p. a008946.

43. Frasca, F., et al., Insulin receptor isoform A, a newly recognized, high-affinity insulinlike

growth factor II receptor in fetal and cancer cells. Mol Cell Biol, 1999. 19(5): p.

3278-88.

44. Sun, X.J., et al., Structure of the insulin receptor substrate IRS-1 defines a unique

signal transduction protein. Nature, 1991. 352(6330): p. 73-7.

67

45. Cai, D., et al., Two new substrates in insulin signaling, IRS5/DOK4 and IRS6/DOK5.

J Biol Chem, 2003. 278(28): p. 25323-30.

46. White, M.F., Regulating insulin signaling and beta-cell function through IRS

proteins. Can J Physiol Pharmacol, 2006. 84(7): p. 725-37.

47. Shaw, L.M., The insulin receptor substrate (IRS) proteins: at the intersection of

metabolism and cancer. Cell Cycle, 2011. 10(11): p. 1750-6.

48. Taniguchi, C.M., B. Emanuelli, and C.R. Kahn, Critical nodes in signalling

pathways: insights into insulin action. Nat Rev Mol Cell Biol, 2006. 7(2): p. 85-96.

49. Stephens, L., et al., Protein kinase B kinases that mediate phosphatidylinositol 3,4,5-

trisphosphate-dependent activation of protein kinase B. Science, 1998. 279(5351): p.

710-4.

50. Latva-Rasku, A., et al., A Partial Loss-of-Function Variant in AKT2 Is Associated

With Reduced Insulin-Mediated Glucose Uptake in Multiple Insulin-Sensitive Tissues:

A Genotype-Based Callback Positron Emission Tomography Study. Diabetes, 2018.

67(2): p. 334-342.

51. Vadas, O., et al., Structural basis for activation and inhibition of class I

phosphoinositide 3-kinases. Sci Signal, 2011. 4(195): p. re2.

52. Myers, M.G., Jr., et al., IRS-1 activates phosphatidylinositol 3′-kinase by associating

with src homology 2 domains of p85. Proc Natl Acad Sci U S A, 1992. 89(21): p.

10350-4.

53. Huang, S., et al., Insulin stimulates membrane fusion and GLUT4 accumulation in

clathrin coats on adipocyte plasma membranes. Mol Cell Biol, 2007. 27(9): p. 3456-

69.

54. Schultze, S.M., et al., Promiscuous affairs of PKB/AKT isoforms in metabolism. Arch

Physiol Biochem, 2011. 117(2): p. 70-7.

55. Duvel, K., et al., Activation of a metabolic gene regulatory network downstream of

mTOR complex 1. Mol Cell, 2010. 39(2): p. 171-83.

56. Tzivion, G., M. Dobson, and G. Ramakrishnan, FoxO transcription factors;

Regulation by AKT and 14-3-3 proteins. Biochim Biophys Acta, 2011. 1813(11): p.

1938-45.

57. Lu, M., et al., Insulin regulates liver metabolism in vivo in the absence of hepatic Akt

and Foxo1. Nat Med, 2012. 18(3): p. 388-95.

58. Degerman, E., et al., From PDE3B to the regulation of energy homeostasis. Curr

Opin Pharmacol, 2011. 11(6): p. 676-82.

59. Takata, M., et al., Requirement for Akt (protein kinase B) in insulin-induced

activation of glycogen synthase and phosphorylation of 4E-BP1 (PHAS-1). J Biol

Chem, 1999. 274(29): p. 20611-8.

60. DeFronzo, R.A. and D. Tripathy, Skeletal muscle insulin resistance is the primary

defect in type 2 diabetes. Diabetes Care, 2009. 32 Suppl 2: p. S157-63.

61. DeFronzo, R.A., et al., Effects of insulin on peripheral and splanchnic glucose

metabolism in noninsulin-dependent (type II) diabetes mellitus. J Clin Invest, 1985.

76(1): p. 149-55.

62. Ferrannini, E., et al., Effect of insulin on the distribution and disposition of glucose in

man. J Clin Invest, 1985. 76(1): p. 357-64.

63. Ojuka, E.O. and V. Goyaram, Mechanisms in exercise-induced increase in glucose

disposal in skeletal muscle. Med Sport Sci, 2014. 60: p. 71-81.

64. Bouzakri, K., et al., siRNA-based gene silencing reveals specialized roles of IRS-

1/Akt2 and IRS-2/Akt1 in glucose and lipid metabolism in human skeletal muscle. Cell

Metab, 2006. 4(1): p. 89-96.

65. Cho, H., et al., Insulin resistance and a diabetes mellitus-like syndrome in mice

lacking the protein kinase Akt2 (PKB beta). Science, 2001. 292(5522): p. 1728-31.

68

66. Klip, A., et al., Signal transduction meets vesicle traffic: the software and hardware

of GLUT4 translocation. Am J Physiol Cell Physiol, 2014. 306(10): p. C879-86.

67. Jackson, R.A., et al., The influence of graded hyperglycemia with and without

physiological hyperinsulinemia on forearm glucose uptake and other metabolic

responses in man. J Clin Endocrinol Metab, 1986. 63(3): p. 594-604.

68. Sindelar, D.K., et al., Basal hepatic glucose production is regulated by the portal vein

insulin concentration. Diabetes, 1998. 47(4): p. 523-9.

69. Radziuk, J. and S. Pye, Hepatic glucose uptake, gluconeogenesis and the regulation

of glycogen synthesis. Diabetes Metab Res Rev, 2001. 17(4): p. 250-72.

70. Kruszynska, Y.T., P.D. Home, and K.G. Alberti, In vivo regulation of liver and

skeletal muscle glycogen synthase activity by glucose and insulin. Diabetes, 1986.

35(6): p. 662-7.

71. Baba, H., X.J. Zhang, and R.R. Wolfe, Glycerol gluconeogenesis in fasting humans.

Nutrition, 1995. 11(2): p. 149-53.

72. Cherrington, A.D., D. Edgerton, and D.K. Sindelar, The direct and indirect effects of

insulin on hepatic glucose production in vivo. Diabetologia, 1998. 41(9): p. 987-96.

73. Edgerton, D.S., et al., Insulin’s direct hepatic effect explains the inhibition of glucose

production caused by insulin secretion. JCI Insight, 2017. 2(6): p. e91863.

74. Dong, X., et al., Irs1 and Irs2 signaling is essential for hepatic glucose homeostasis

and systemic growth. J Clin Invest, 2006. 116(1): p. 101-14.

75. Newgard, C.B., P.K. Hwang, and R.J. Fletterick, The family of glycogen

phosphorylases: structure and function. Crit Rev Biochem Mol Biol, 1989. 24(1): p.

69-99.

76. Ros, S., et al., Control of liver glycogen synthase activity and intracellular

distribution by phosphorylation. J Biol Chem, 2009. 284(10): p. 6370-8.

77. Iynedjian, P.B., Molecular physiology of mammalian glucokinase. Cell Mol Life Sci,
78. 66(1): p. 27-42.
79. Reaven, G.M., Banting lecture 1988. Role of insulin resistance in human disease.

Diabetes, 1988. 37(12): p. 1595-607.

79. Czech, M.P., Insulin action and resistance in obesity and type 2 diabetes. Nat Med,
80. 23(7): p. 804-814.
81. Pankow, J.S., et al., Insulin resistance and cardiovascular disease risk factors in

children of parents with the insulin resistance (metabolic) syndrome. Diabetes Care,

2004. 27(3): p. 775-80.
2005. Lee, J.M., et al., Prevalence and determinants of insulin resistance among U.S.

adolescents: a population-based study. Diabetes Care, 2006. 29(11): p. 2427-32.

82. Kosztolanyi, G., Leprechaunism/Donohue syndrome/insulin receptor gene mutations:

a syndrome delineation story from clinicopathological description to molecular

understanding. Eur J Pediatr, 1997. 156(4): p. 253-5.

83. Musso, C., et al., Clinical course of genetic diseases of the insulin receptor (type A

and Rabson-Mendenhall syndromes): a 30-year prospective. Medicine (Baltimore),

2004. 83(4): p. 209-222.
2005. Moran, A., et al., Insulin resistance during puberty: results from clamp studies in 357

children. Diabetes, 1999. 48(10): p. 2039-44.

85. Buchanan, T.A., et al., Insulin sensitivity and B-cell responsiveness to glucose during

late pregnancy in lean and moderately obese women with normal glucose tolerance

or mild gestational diabetes. Am J Obstet Gynecol, 1990. 162(4): p. 1008-14.

86. Defronzo, R.A., Glucose intolerance and aging: evidence for tissue insensitivity to

insulin. Diabetes, 1979. 28(12): p. 1095-101.

87. Gungor, N. and S. Arslanian, Progressive beta cell failure in type 2 diabetes mellitus

of youth. J Pediatr, 2004. 144(5): p. 656-9.

69

88. Bacha, F., et al., In vivo insulin sensitivity and secretion in obese youth: what are the

differences between normal glucose tolerance, impaired glucose tolerance, and type 2

diabetes? Diabetes Care, 2009. 32(1): p. 100-5.

89. Prentki, M., et al., Malonyl-CoA signaling, lipid partitioning, and glucolipotoxicity:

role in beta-cell adaptation and failure in the etiology of diabetes. Diabetes, 2002. 51

Suppl 3: p. S405-13.

90. Leung, N., et al., Prolonged increase of plasma non-esterified fatty acids fully

abolishes the stimulatory effect of 24 hours of moderate hyperglycaemia on insulin

sensitivity and pancreatic beta-cell function in obese men. Diabetologia, 2004. 47(2):

13. 204-13.
14. Cusi, K., et al., Insulin resistance differentially affects the PI 3-kinase- and MAP

kinase-mediated signaling in human muscle. J Clin Invest, 2000. 105(3): p. 311-20.

92. Griffin, M.E., et al., Free fatty acid-induced insulin resistance is associated with

activation of protein kinase C theta and alterations in the insulin signaling cascade.

Diabetes, 1999. 48(6): p. 1270-4.

93. Aguirre, V., et al., Phosphorylation of Ser307 in insulin receptor substrate-1 blocks

interactions with the insulin receptor and inhibits insulin action. J Biol Chem, 2002.

277(2): p. 1531-7.

94. van der Kolk, B.W., et al., Subcutaneous Adipose Tissue and Systemic Inflammation

Are Associated With Peripheral but Not Hepatic Insulin Resistance in Humans.

Diabetes, 2019. 68(12): p. 2247-2258.

95. Yuan, M., et al., Reversal of obesity- and diet-induced insulin resistance with

salicylates or targeted disruption of Ikkbeta. Science, 2001. 293(5535): p. 1673-7.

96. Gao, Z., et al., Serine phosphorylation of insulin receptor substrate 1 by inhibitor

kappa B kinase complex. J Biol Chem, 2002. 277(50): p. 48115-21.

97. Manning, B.D. and L.C. Cantley, AKT/PKB signaling: navigating downstream. Cell,
98. 129(7): p. 1261-74.
99. Dimmeler, S., et al., Activation of nitric oxide synthase in endothelial cells by Aktdependent

phosphorylation. Nature, 1999. 399(6736): p. 601-5.

99. Fulton, D., et al., Regulation of endothelium-derived nitric oxide production by the

protein kinase Akt. Nature, 1999. 399(6736): p. 597-601.

100. Chavez, J.A., et al., A role for ceramide, but not diacylglycerol, in the antagonism of

insulin signal transduction by saturated fatty acids. J Biol Chem, 2003. 278(12): p.

10297-303.

101. Itani, S.I., et al., Lipid-induced insulin resistance in human muscle is associated with

changes in diacylglycerol, protein kinase C, and IkappaB-alpha. Diabetes, 2002.

51(7): p. 2005-11.

102. Montell, E., et al., DAG accumulation from saturated fatty acids desensitizes insulin

stimulation of glucose uptake in muscle cells. Am J Physiol Endocrinol Metab, 2001.

280(2): p. E229-37.

103. Samuel, V.T. and G.I. Shulman, The pathogenesis of insulin resistance: integrating

signaling pathways and substrate flux. J Clin Invest, 2016. 126(1): p. 12-22.

104. Bird, S.R. and J.A. Hawley, Update on the effects of physical activity on insulin

sensitivity in humans. BMJ Open Sport Exerc Med, 2016. 2(1): p. e000143.

105. Maffeis, C., et al., Fat cell size, insulin sensitivity, and inflammation in obese

children. J Pediatr, 2007. 151(6): p. 647-52.

106. Hanley, A.J., et al., Insulin resistance, beta cell dysfunction and visceral adiposity as

predictors of incident diabetes: the Insulin Resistance Atherosclerosis Study (IRAS)

Family study. Diabetologia, 2009. 52(10): p. 2079-86.

107. Caprio, S., R. Perry, and R. Kursawe, Adolescent Obesity and Insulin Resistance:

Roles of Ectopic Fat Accumulation and Adipose Inflammation. Gastroenterology,

2017. 152(7): p. 1638-1646.

70

108. Vozarova, B., et al., High white blood cell count is associated with a worsening of

insulin sensitivity and predicts the development of type 2 diabetes. Diabetes, 2002.

51(2): p. 455-61.

109. Kwon, H. and J.E. Pessin, Adipokines mediate inflammation and insulin resistance.

Front Endocrinol (Lausanne), 2013. 4: p. 71.

110. Emanuelli, B., et al., SOCS-3 inhibits insulin signaling and is up-regulated in

response to tumor necrosis factor-alpha in the adipose tissue of obese mice. J Biol

Chem, 2001. 276(51): p. 47944-9.

111. Plomgaard, P., et al., Associations between insulin resistance and TNF-alpha in

plasma, skeletal muscle and adipose tissue in humans with and without type 2

diabetes. Diabetologia, 2007. 50(12): p. 2562-71.

112. Lee, Y.H., et al., c-Jun N-terminal kinase (JNK) mediates feedback inhibition of the

insulin signaling cascade. J Biol Chem, 2003. 278(5): p. 2896-902.

113. Hirosumi, J., et al., A central role for JNK in obesity and insulin resistance. Nature,
114. 420(6913): p. 333-6.
115. Chen, Y.D., et al., Resistance to insulin suppression of plasma free fatty acid

concentrations and insulin stimulation of glucose uptake in noninsulin-dependent

diabetes mellitus. J Clin Endocrinol Metab, 1987. 64(1): p. 17-21.

115. Roden, M. and G.I. Shulman, The integrative biology of type 2 diabetes. Nature,
116. 576(7785): p. 51-60.
117. Gastaldelli, A., et al., Influence of obesity and type 2 diabetes on gluconeogenesis and

glucose output in humans: a quantitative study. Diabetes, 2000. 49(8): p. 1367-73.

117. Hundal, R.S., et al., Mechanism by which metformin reduces glucose production in

type 2 diabetes. Diabetes, 2000. 49(12): p. 2063-9.

118. Magnusson, I., et al., Increased rate of gluconeogenesis in type II diabetes mellitus. A

13C nuclear magnetic resonance study. J Clin Invest, 1992. 90(4): p. 1323-7.

119. Leavens, K.F., et al., Akt2 is required for hepatic lipid accumulation in models of

insulin resistance. Cell Metab, 2009. 10(5): p. 405-18.

120. Jaiswal, N., et al., The role of skeletal muscle Akt in the regulation of muscle mass

and glucose homeostasis. Mol Metab, 2019. 28: p. 1-13.

121. Biddinger, S.B., et al., Hepatic insulin resistance is sufficient to produce dyslipidemia

and susceptibility to atherosclerosis. Cell Metab, 2008. 7(2): p. 125-34.

122. Vatner, D.F., et al., Insulin-independent regulation of hepatic triglyceride synthesis by

fatty acids. Proc Natl Acad Sci U S A, 2015. 112(4): p. 1143-8.

123. Brown, J.M., et al., CGI-58 knockdown in mice causes hepatic steatosis but prevents

diet-induced obesity and glucose intolerance. J Lipid Res, 2010. 51(11): p. 3306-15.

124. Elrick, H., et al., Plasma Insulin Response to Oral and Intravenous Glucose

Administration. J Clin Endocrinol Metab, 1964. 24: p. 1076-82.

125. Drucker, D.J. and M.A. Nauck, The incretin system: glucagon-like peptide-1 receptor

agonists and dipeptidyl peptidase-4 inhibitors in type 2 diabetes. Lancet, 2006.

368(9548): p. 1696-705.

126. Buchan, A.M., et al., Electronimmunocytochemical evidence for the K cell

localization of gastric inhibitory polypeptide (GIP) in man. Histochemistry, 1978.

56(1): p. 37-44.

127. Meier, J.J. and M.A. Nauck, Is the diminished incretin effect in type 2 diabetes just an

epi-phenomenon of impaired beta-cell function? Diabetes, 2010. 59(5): p. 1117-25.

128. Eissele, R., et al., Glucagon-like peptide-1 cells in the gastrointestinal tract and

pancreas of rat, pig and man. Eur J Clin Invest, 1992. 22(4): p. 283-91.

129. Knop, F.K., et al., Reduced incretin effect in type 2 diabetes: cause or consequence of

the diabetic state? Diabetes, 2007. 56(8): p. 1951-9.

71

130. Knop, F.K., et al., Impaired incretin effect and fasting hyperglucagonaemia

characterizing type 2 diabetic subjects are early signs of dysmetabolism in obesity.

Diabetes Obes Metab, 2012. 14(6): p. 500-10.

131. Hansen, K.B., et al., Impaired incretin-induced amplification of insulin secretion after

glucose homeostatic dysregulation in healthy subjects. J Clin Endocrinol Metab,

2012. 97(4): p. 1363-70.
2013. Nauck, M.A. and J.J. Meier, The incretin effect in healthy individuals and those with

type 2 diabetes: physiology, pathophysiology, and response to therapeutic

interventions. Lancet Diabetes Endocrinol, 2016. 4(6): p. 525-36.

133. Michaliszyn, S.F., et al., beta-cell function, incretin effect, and incretin hormones in

obese youth along the span of glucose tolerance from normal to prediabetes to type 2

diabetes. Diabetes, 2014. 63(11): p. 3846-55.

134. Aulinger, B.A., et al., The incretin effect in obese adolescents with and without type 2

diabetes: impaired or intact? Am J Physiol Endocrinol Metab, 2016. 310(9): p. E774-

81.

135. Butler, A.E., et al., Beta-cell deficit and increased beta-cell apoptosis in humans with

type 2 diabetes. Diabetes, 2003. 52(1): p. 102-10.

136. Vilsboll, T., et al., Defective amplification of the late phase insulin response to

glucose by GIP in obese Type II diabetic patients. Diabetologia, 2002. 45(8): p. 1111-

9.

137. Cropano, C., et al., The rs7903146 Variant in the TCF7L2 Gene Increases the Risk of

Prediabetes/Type 2 Diabetes in Obese Adolescents by Impairing beta-Cell Function

and Hepatic Insulin Sensitivity. Diabetes Care, 2017. 40(8): p. 1082-1089.

138. Florez, J.C., et al., TCF7L2 polymorphisms and progression to diabetes in the

Diabetes Prevention Program. N Engl J Med, 2006. 355(3): p. 241-50.

139. Galderisi, A., et al., A Reduced Incretin Effect Mediated by the rs7903146 Variant in

the TCF7L2 Gene Is an Early Marker of beta-Cell Dysfunction in Obese Youth.

Diabetes Care, 2020. 43(10): p. 2553-2563.

140. Villareal, D.T., et al., TCF7L2 variant rs7903146 affects the risk of type 2 diabetes by

modulating incretin action. Diabetes, 2010. 59(2): p. 479-85.

141. Metchnikoff, E., The Prolongation of life. An optimistic view. 1907.
142. Sanna, S., et al., Causal relationships among the gut microbiome, short-chain fatty

acids and metabolic diseases. Nat Genet, 2019. 51(4): p. 600-605.

143. Petersen, C., et al., T cell-mediated regulation of the microbiota protects against

obesity. Science, 2019. 365(6451).

144. Qin, J., et al., A metagenome-wide association study of gut microbiota in type 2

diabetes. Nature, 2012. 490(7418): p. 55-60.

145. Larsen, N., et al., Gut microbiota in human adults with type 2 diabetes differs from

non-diabetic adults. PLoS One, 2010. 5(2): p. e9085.

146. Gower, B.A., T.R. Nagy, and M.I. Goran, Visceral fat, insulin sensitivity, and lipids in

prepubertal children. Diabetes, 1999. 48(8): p. 1515-21.

147. Bacha, F., et al., Obesity, regional fat distribution, and syndrome X in obese black

versus white adolescents: race differential in diabetogenic and atherogenic risk

factors. J Clin Endocrinol Metab, 2003. 88(6): p. 2534-40.

148. Nightingale, C.M., et al., Influence of adiposity on insulin resistance and glycemia

markers among U.K. Children of South Asian, black African-Caribbean, and white

European origin: child heart and health study in England. Diabetes Care, 2013.

36(6): p. 1712-9.

149. Kodama, K., et al., Ethnic differences in the relationship between insulin sensitivity

and insulin response: a systematic review and meta-analysis. Diabetes Care, 2013.

36(6): p. 1789-96.

72

150. Ball, G.D., et al., Longitudinal changes in insulin sensitivity, insulin secretion, and

beta-cell function during puberty. J Pediatr, 2006. 148(1): p. 16-22.

151. Redondo, M.J., et al., Concordance for islet autoimmunity among monozygotic twins.

N Engl J Med, 2008. 359(26): p. 2849-50.

152. Kuo, C.F., et al., Familial aggregation and heritability of type 1 diabetes mellitus and

coaggregation of chronic diseases in affected families. Clin Epidemiol, 2018. 10: p.

1447-1455.

153. Pociot, F., Type 1 diabetes genome-wide association studies: not to be lost in

translation. Clin Transl Immunology, 2017. 6(12): p. e162.

154. Barrett, J.C., et al., Genome-wide association study and meta-analysis find that over

40 loci affect risk of type 1 diabetes. Nat Genet, 2009. 41(6): p. 703-7.

155. Onengut-Gumuscu, S., et al., Fine mapping of type 1 diabetes susceptibility loci and

evidence for colocalization of causal variants with lymphoid gene enhancers. Nat

Genet, 2015. 47(4): p. 381-6.

156. Yang, J., et al., Common SNPs explain a large proportion of the heritability for

human height. Nat Genet, 2010. 42(7): p. 565-9.

157. Manolio, T.A., et al., Finding the missing heritability of complex diseases. Nature,
158. 461(7265): p. 747-53.
159. Poulsen, P., et al., Heritability of type II (non-insulin-dependent) diabetes mellitus

and abnormal glucose tolerance–a population-based twin study. Diabetologia, 1999.

42(2): p. 139-45.

159. Tuomilehto, J., et al., Prevention of type 2 diabetes mellitus by changes in lifestyle

among subjects with impaired glucose tolerance. N Engl J Med, 2001. 344(18): p.

1343-50.

160. Almgren, P., et al., Heritability and familiality of type 2 diabetes and related

quantitative traits in the Botnia Study. Diabetologia, 2011. 54(11): p. 2811-9.

161. Lyssenko, V., et al., Clinical risk factors, DNA variants, and the development of type

2 diabetes. N Engl J Med, 2008. 359(21): p. 2220-32.

162. Mahajan, A., et al., Fine-mapping type 2 diabetes loci to single-variant resolution

using high-density imputation and islet-specific epigenome maps. Nat Genet, 2018.

50(11): p. 1505-1513.

163. Caprio, S., et al., Increased insulin secretion in puberty: a compensatory response to

reductions in insulin sensitivity. J Pediatr, 1989. 114(6): p. 963-7.

164. Amiel, S.A., et al., Impaired insulin action in puberty. A contributing factor to poor

glycemic control in adolescents with diabetes. N Engl J Med, 1986. 315(4): p. 215-9.

165. Goran, M.I. and B.A. Gower, Longitudinal study on pubertal insulin resistance.

Diabetes, 2001. 50(11): p. 2444-50.

166. Moran, A., et al., Association between the insulin resistance of puberty and the

insulin-like growth factor-I/growth hormone axis. J Clin Endocrinol Metab, 2002.

87(10): p. 4817-20.

167. Ekelund, U., et al., Does physical activity attenuate, or even eliminate, the detrimental

association of sitting time with mortality? A harmonised meta-analysis of data from

more than 1 million men and women. Lancet, 2016. 388(10051): p. 1302-10.

168. Troiano, R.P., et al., Physical activity in the United States measured by

accelerometer. Med Sci Sports Exerc, 2008. 40(1): p. 181-8.

169. Telama, R., et al., Tracking of physical activity from early childhood through youth

into adulthood. Med Sci Sports Exerc, 2014. 46(5): p. 955-62.

170. Lee, S., et al., Effects of Exercise Modality on Insulin Resistance and Ectopic Fat in

Adolescents with Overweight and Obesity: A Randomized Clinical Trial. J Pediatr,

2019. 206: p. 91-98 e1.
2020. Ge, L., et al., Comparison of dietary macronutrient patterns of 14 popular named

dietary programmes for weight and cardiovascular risk factor reduction in adults:

73

systematic review and network meta-analysis of randomised trials. BMJ, 2020. 369:

696. m696.
697. Krebs, N.F., et al., Efficacy and safety of a high protein, low carbohydrate diet for

weight loss in severely obese adolescents. J Pediatr, 2010. 157(2): p. 252-8.

173. Kirk, S., et al., Role of carbohydrate modification in weight management among

obese children: a randomized clinical trial. J Pediatr, 2012. 161(2): p. 320-7 e1.

174. Gow, M.L., et al., The Effectiveness of Different Diet Strategies to Reduce Type 2

Diabetes Risk in Youth. Nutrients, 2016. 8(8).

175. Garnett, S.P., et al., Improved insulin sensitivity and body composition, irrespective of

macronutrient intake, after a 12 month intervention in adolescents with pre-diabetes;

RESIST a randomised control trial. BMC Pediatr, 2014. 14: p. 289.

176. Truby, H., et al., A Randomized Controlled Trial of Two Different Macronutrient

Profiles on Weight, Body Composition and Metabolic Parameters in Obese

Adolescents Seeking Weight Loss. PLoS One, 2016. 11(3): p. e0151787.

177. DeFronzo, R.A., J.D. Tobin, and R. Andres, Glucose clamp technique: a method for

quantifying insulin secretion and resistance. Am J Physiol, 1979. 237(3): p. E214-23.

178. Elahi, D., In praise of the hyperglycemic clamp. A method for assessment of beta-cell

sensitivity and insulin resistance. Diabetes Care, 1996. 19(3): p. 278-86.

179. Bergman, R.N., Lilly lecture 1989. Toward physiological understanding of glucose

tolerance. Minimal-model approach. Diabetes, 1989. 38(12): p. 1512-27.

180. Bergman, R.N., et al., Equivalence of the insulin sensitivity index in man derived by

the minimal model method and the euglycemic glucose clamp. J Clin Invest, 1987.

79(3): p. 790-800.

181. Cutfield, W.S., et al., The modified minimal model: application to measurement of

insulin sensitivity in children. J Clin Endocrinol Metab, 1990. 70(6): p. 1644-50.

182. Bergman, R.N., Minimal model: perspective from 2005. Horm Res, 2005. 64 Suppl

3: p. 8-15.

183. Beard, J.C., et al., The insulin sensitivity index in nondiabetic man. Correlation

between clamp-derived and IVGTT-derived values. Diabetes, 1986. 35(3): p. 362-9.

184. Steil, G.M., et al., Repeatability of insulin sensitivity and glucose effectiveness from

the minimal model. Implications for study design. Diabetes, 1994. 43(11): p. 1365-71.

185. Saad, M.F., et al., A comparison between the minimal model and the glucose clamp in

the assessment of insulin sensitivity across the spectrum of glucose tolerance. Insulin

Resistance Atherosclerosis Study. Diabetes, 1994. 43(9): p. 1114-21.

186. Muniyappa, R., et al., Current approaches for assessing insulin sensitivity and

resistance in vivo: advantages, limitations, and appropriate usage. Am J Physiol

Endocrinol Metab, 2008. 294(1): p. E15-26.

187. Hannon, T.S., et al., Profound defects in beta-cell function in screen-detected type 2

diabetes are not improved with glucose-lowering treatment in the Early Diabetes

Intervention Program (EDIP). Diabetes Metab Res Rev, 2014. 30(8): p. 767-76.

188. Brunzell, J.D., et al., Relationships between fasting plasma glucose levels and insulin

secretion during intravenous glucose tolerance tests. J Clin Endocrinol Metab, 1976.

42(2): p. 222-9.

189. Burns, S.F., et al., Declining beta-cell function relative to insulin sensitivity with

escalating OGTT 2-h glucose concentrations in the nondiabetic through the diabetic

range in overweight youth. Diabetes Care, 2011. 34(9): p. 2033-40.

190. Utzschneider, K.M., et al., Oral disposition index predicts the development of future

diabetes above and beyond fasting and 2-h glucose levels. Diabetes Care, 2009.

32(2): p. 335-41.

191. Matsuda, M. and R.A. DeFronzo, Insulin sensitivity indices obtained from oral

glucose tolerance testing: comparison with the euglycemic insulin clamp. Diabetes

Care, 1999. 22(9): p. 1462-70.

74

192. Gutt, M., et al., Validation of the insulin sensitivity index (ISI(0,120)): comparison

with other measures. Diabetes Res Clin Pract, 2000. 47(3): p. 177-84.

193. Cobelli, C., et al., The oral minimal model method. Diabetes, 2014. 63(4): p. 1203-13.
194. Breda, E., et al., Oral glucose tolerance test minimal model indexes of beta-cell

function and insulin sensitivity. Diabetes, 2001. 50(1): p. 150-8.

195. Matthews, D.R., et al., Homeostasis model assessment: insulin resistance and betacell

function from fasting plasma glucose and insulin concentrations in man.

Diabetologia, 1985. 28(7): p. 412-9.

196. Gungor, N., et al., Validation of surrogate estimates of insulin sensitivity and insulin

secretion in children and adolescents. J Pediatr, 2004. 144(1): p. 47-55.

197. Bonora, E., et al., Homeostasis model assessment closely mirrors the glucose clamp

technique in the assessment of insulin sensitivity: studies in subjects with various

degrees of glucose tolerance and insulin sensitivity. Diabetes Care, 2000. 23(1): p.

57-63.

198. Uwaifo, G.I., et al., Indices of insulin action, disposal, and secretion derived from

fasting samples and clamps in normal glucose-tolerant black and white children.

Diabetes Care, 2002. 25(11): p. 2081-7.

199. Keskin, M., et al., Homeostasis model assessment is more reliable than the fasting

glucose/insulin ratio and quantitative insulin sensitivity check index for assessing

insulin resistance among obese children and adolescents. Pediatrics, 2005. 115(4): p.

e500-3.

200. Stefan, N., et al., Phenotypes of prediabetes and stratification of cardiometabolic risk.

Lancet Diabetes Endocrinol, 2016. 4(9): p. 789-798.

201. Blaak, E.E., Characterisation of fatty acid metabolism in different insulin-resistant

phenotypes by means of stable isotopes. Proc Nutr Soc, 2017: p. 1-7.

202. Goossens, G.H., et al., Altered Skeletal Muscle Fatty Acid Handling in Subjects with

Impaired Glucose Tolerance as Compared to Impaired Fasting Glucose. Nutrients,

2016. 8(3): p. 164.
2017. International Diabetes Federation, B., Belgium, IDF Atlas 8 edition. 2017.
2018. Andes, L.J., et al., Prevalence of Prediabetes Among Adolescents and Young Adults in

the United States, 2005-2016. JAMA Pediatr, 2020. 174(2): p. e194498.

205. Hagman, E., et al., Blood sugar levels are higher in obese young children in Sweden

than in Poland. Acta Paediatr, 2014. 103(11): p. 1174-8.

206. Mayer-Davis, E.J., et al., ISPAD Clinical Practice Consensus Guidelines 2018:

Definition, epidemiology, and classification of diabetes in children and adolescents.

Pediatr Diabetes, 2018. 19 Suppl 27: p. 7-19.

207. Pihoker, C., et al., Autoantibodies in diabetes. Diabetes, 2005. 54 Suppl 2: p. S52-61.
208. Howson, J.M., et al., Evidence that HLA class I and II associations with type 1

diabetes, autoantibodies to GAD and autoantibodies to IA-2, are distinct. Diabetes,

2011. 60(10): p. 2635-44.
2012. Carlsson, A., et al., Absence of Islet Autoantibodies and Modestly Raised Glucose

Values at Diabetes Diagnosis Should Lead to Testing for MODY: Lessons From a 5-

Year Pediatric Swedish National Cohort Study. Diabetes Care, 2020. 43(1): p. 82-89.

210. Locke, J.M., et al., The Common HNF1A Variant I27L Is a Modifier of Age at

Diabetes Diagnosis in Individuals With HNF1A-MODY. Diabetes, 2018. 67(9): p.

1903-1907.

211. Rubio-Cabezas, O., et al., ISPAD Clinical Practice Consensus Guidelines 2014. The

diagnosis and management of monogenic diabetes in children and adolescents.

Pediatr Diabetes, 2014. 15 Suppl 20: p. 47-64.

212. Steele, A.M., et al., Prevalence of vascular complications among patients with

glucokinase mutations and prolonged, mild hyperglycemia. JAMA, 2014. 311(3): p.

279-86.

75

213. Froguel, P., et al., Familial hyperglycemia due to mutations in glucokinase. Definition

of a subtype of diabetes mellitus. N Engl J Med, 1993. 328(10): p. 697-702.

214. Tuomi, T., et al., Antibodies to glutamic acid decarboxylase reveal latent autoimmune

diabetes mellitus in adults with a non-insulin-dependent onset of disease. Diabetes,

1993. 42(2): p. 359-62.
1994. Mayer-Davis, E.J., et al., Incidence Trends of Type 1 and Type 2 Diabetes among

Youths, 2002-2012. N Engl J Med, 2017. 376(15): p. 1419-1429.

216. Patterson, C.C., et al., Trends and cyclical variation in the incidence of childhood type

1 diabetes in 26 European centres in the 25 year period 1989-2013: a multicentre

prospective registration study. Diabetologia, 2019. 62(3): p. 408-417.

217. Harjutsalo, V., L. Sjoberg, and J. Tuomilehto, Time trends in the incidence of type 1

diabetes in Finnish children: a cohort study. Lancet, 2008. 371(9626): p. 1777-82.

218. Berhan, Y., et al., Thirty years of prospective nationwide incidence of childhood type

1 diabetes: the accelerating increase by time tends to level off in Sweden. Diabetes,

2011. 60(2): p. 577-81.
2012. Rawshani, A., et al., The incidence of diabetes among 0-34 year olds in Sweden: new

data and better methods. Diabetologia, 2014. 57(7): p. 1375-81.

220. Dabelea, D., et al., Prevalence of type 1 and type 2 diabetes among children and

adolescents from 2001 to 2009. JAMA, 2014. 311(17): p. 1778-86.

221. Pavkov, M.E., et al., Changing patterns of type 2 diabetes incidence among Pima

Indians. Diabetes Care, 2007. 30(7): p. 1758-63.

222. Neu, A., et al., Type 2 diabetes mellitus in children and adolescents is still a rare

disease in Germany: a population-based assessment of the prevalence of type 2

diabetes and MODY in patients aged 0-20 years. Pediatr Diabetes, 2009. 10(7): p.

468-73.

223. Neu, A., et al., No change in type 2 diabetes prevalence in children and adolescents

over 10 years: Update of a population-based survey in South Germany. Pediatr

Diabetes, 2018. 19(4): p. 637-639.

 

Läs HELA avhandlingen i sin helhet inkl alla figurer och tabeller

https://openarchive.ki.se/xmlui/bitstream/handle/10616/47573/Thesis_Anna_Ek.pdf?sequence=3&isAllowed=y