Obesity in Type 1 Diabetes: Pathophysiology, Clinical Impact, and Mechanisms

Key Enteroendocrine Hormones That Regulate Food Intake Through Central Nervous System Interactions

Enteroendocrine Hormone	Tissue of Origin	Impact on Appetite
Ghrelin	Gastrointestinal tract	↑
Amylin	Pancreas	↓
Cholecystokinin	Gastrointestinal tract	↓
GIP	Gastrointestinal tract	↓
GLP-1	Gastrointestinal tract	↓
Insulin	Pancreas	↓
Leptin	Adipose	↓
Oxyntomodulin	Gastrointestinal tract	↓
Pancreatic Peptide	Pancreas	↓
PYY	Gastrointestinal tract	↓

Enteroendocrine Hormone	Tissue of Origin	Impact on Appetite
Ghrelin	Gastrointestinal tract	↑
Amylin	Pancreas	↓
Cholecystokinin	Gastrointestinal tract	↓
GIP	Gastrointestinal tract	↓
GLP-1	Gastrointestinal tract	↓
Insulin	Pancreas	↓
Leptin	Adipose	↓
Oxyntomodulin	Gastrointestinal tract	↓
Pancreatic Peptide	Pancreas	↓
PYY	Gastrointestinal tract	↓

Adapted from Begg DP, Woods SC. The endocrinology of food intake. Nat Rev Endocrinol. 2013; 9:584; and Cummings DE, Overduin J. Gastrointestinal regulation of food intake. J Clin Invest. 2007; 117:13–23.

Table 1.

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Key Enteroendocrine Hormones That Regulate Food Intake Through Central Nervous System Interactions

Enteroendocrine Hormone	Tissue of Origin	Impact on Appetite
Ghrelin	Gastrointestinal tract	↑
Amylin	Pancreas	↓
Cholecystokinin	Gastrointestinal tract	↓
GIP	Gastrointestinal tract	↓
GLP-1	Gastrointestinal tract	↓
Insulin	Pancreas	↓
Leptin	Adipose	↓
Oxyntomodulin	Gastrointestinal tract	↓
Pancreatic Peptide	Pancreas	↓
PYY	Gastrointestinal tract	↓

Enteroendocrine Hormone	Tissue of Origin	Impact on Appetite
Ghrelin	Gastrointestinal tract	↑
Amylin	Pancreas	↓
Cholecystokinin	Gastrointestinal tract	↓
GIP	Gastrointestinal tract	↓
GLP-1	Gastrointestinal tract	↓
Insulin	Pancreas	↓
Leptin	Adipose	↓
Oxyntomodulin	Gastrointestinal tract	↓
Pancreatic Peptide	Pancreas	↓
PYY	Gastrointestinal tract	↓

Adapted from Begg DP, Woods SC. The endocrinology of food intake. Nat Rev Endocrinol. 2013; 9:584; and Cummings DE, Overduin J. Gastrointestinal regulation of food intake. J Clin Invest. 2007; 117:13–23.

The mechanisms controlling satiety and appetite in diabetes and obesity are dysfunctional and involved in the pathobiology of disease. Defective communication circuits between the gut and brain have been implicated in the pathogenesis of obesity and T2D (180). Although the data are limited in T1D, a comprehensive assessment of gut peptides in children with T1D (age at diagnosis 6.87 ± 2.47 years; duration of diagnosis 6.5 ± 2.22. years), levels of ghrelin and amylin were reduced, GIP was upregulated, and GLP-1 and PYY were unchanged as compared with healthy controls (181), indicating that there is dysregulation of enteroendocrine signaling in T1D. The sections below highlight the canonical functions of a subset of satiety hormones with an emphasis on how they are regulated in T1D.

Ghrelin and its isoforms acyl-ghrelin, des-acylghrelin, and obestatin are enteroendocrine hormones that also modulate food intake and insulin responsiveness. The ultimate actions of these hormones are to stimulate food intake (182). Plasma ghrelin concentrations are reduced in obesity (183) and insulin resistance (184, 185). Ghrelin levels are reduced in adults (186) and children (187) with T1D. Ghrelin levels have been shown to decrease (188) or have no response (187) to insulin treatment. There is an inverse relationship between ghrelin and BMI in T1D (187, 188), which is consistent with the reduced ghrelin seen in obese populations (183, 186).

Upon central nervous system activation in response to a meal stimulus, the incretin hormone GLP-1 regulates body weight by diminishing appetite and delaying gastric emptying (174) in concert with the actions of PYY and CCK on gastric emptying and satiety (162, 189, 190). In the proinflammatory, insulin-resistant state that is common in obesity and T2D, the incretin effect is decreased as a result of increased degradation of GLP-1 by the enzyme DPP-4 (191). GLP-1 is also a key mediator for the regulation of glucose homeostasis (192). This is exemplified in bariatric surgery patients. The weight loss and T2D resolution that can occur after bariatric surgery are accompanied by restoration of the function of several enteroendocrine molecules, including GLP-1, ghrelin, and bile acids (193, 194). The functional profile of GLP-1 in T1D is not well characterized, and levels of GLP-1 are similar to healthy controls (181). There is no difference between healthy controls and T1D cases with respect to the postmeal rise in GLP-1 (195).

The most severe defects in food intake regulation in T1D are due to impairments in release of the pancreatic hormones insulin, glucagon, and amylin (55, 196). Insulin regulates both appetite and body weight via central nervous system interactions in response to circulating nutrients and in proportion to level of adiposity (197, 198). Insulin, in conjunction with leptin, regulates energy balance by minimizing the impact of acute changes in the flux of energy. This occurs via a balancing of anabolic and catabolic hypothalamic controls [reviewed in Refs. (151) and (165)]. A small amount of circulating insulin reaches the brain. Because insulin is secreted in proportion to adiposity, its role in the brain is to activate a negative feedback loop to adjust food intake in the direction that will promote body weight homeostasis. Importantly, similar populations of neurons in the hypothalamus control both food intake and the impact of pancreatic hormones on glucose homeostasis (198). Exogenous insulin is given in response to anticipated food intake and the expected amount needed to normalize glucose, which bypasses the endogenous control of insulin release in response to adiposity and the signals generated upon a meal stimulus. Additionally, when exogenous insulin leads to hypoglycemia, there is an increased tendency to eat, which negates the expected reduction on food intake from a stronger insulin signal in the brain (198).

Under physiological conditions amylin is cosecreted with insulin from pancreatic β-cells in response to a meal stimulus and functions to decrease food intake, suppress glucagon secretion, regulate body weight, and increase energy expenditure (199). In obesity, insulin resistance, and T2D, there is a dampened postmeal release of amylin. In T1D, there is a dual defect: levels are markedly lower than in the general population and do not respond to a meal stimulus. Because of this, the homeostatic controls on food intake are dramatically impaired (200).

During fasting conditions, glucagon is released from α-cells to promote satiety. This satiety response appears to be preserved in T1D but not in obesity (186). Intramuscular glucagon administration decreases hunger in lean individuals and those with T1D, but not in individuals with obesity. This experimental hyperglucagonemia leads to a decrease in hunger, coincident with a reduction in ghrelin levels, in T1D and healthy controls only (186). Given this discordance between the effects of glucagon on hunger in T1D/lean individuals vs those with obesity, it is unclear how dysregulation of ghrelin secretion will impact hunger when obesity and T1D are concurrent phenotypes.

In addition to the more canonical pathways of food intake regulation, bile acids are well recognized for their important endocrine functions. They correlate positively with BMI, and evidence is emerging on their role in food intake via effects on eating behaviors (201) and interactions with gut hormones such ghrelin and GLP-1 (202, 203). The bile acid pathway is under active investigation as a mediator of the metabolic aberrations in obesity and T2D (194, 204, 205). The role of bile acids on obesity in T1D has not been investigated, particularly with respect to a dual role in glycemic control and energy homeostasis.

Beyond the role of the enteroendocrine system in controlling energy intake, fundamental defects in gut function have been linked to T1D. For example, PYY, which is released from the gut in response to food intake to suppress appetite, is also important for islet cell development and in regeneration of pancreatic cells (206). Suppression of the bactericidal function of intestinal Paneth cells was identified in the streptozotocin (STZ) mouse model of T1D, which could have deleterious effect on innate immunity (207). This dysfunction was reversed upon insulin treatment, suggesting a dual role for insulin in glycemic control in gut health. The importance of the gut and its released peptides as therapeutic modalities was elegantly demonstrated in both cell-based and mouse experiments where ablation of forkhead box O (Foxo)1 gave rise to insulin-producing cells in the gut that displayed some of the characteristics of β-cells (208). This concept of reprogramming the gut to restore insulin production has been demonstrated in several other studies (209–211), highlighting the unique role of a healthy gut for T1D therapeutics.

Given the aberrant functionality the gastrointestinal hormones in obesity, T1D and T2D could impact appetitive behavior, energy intake, and body weight. It is not known how the effects of obesity on the homeostatic controls that regulate food intake and satiety impact health and metabolism in individuals with T1D. Given the negative impact of obesity on gut health, preventing and treating obesity aggressively in people with T1D, or in those at risk for developing it, could be a critical determinant of long-term health outcomes.

Energy expenditure

The main component of energy expenditure is resting energy expenditure, which accounts for ~55% to 70% of total daily energy expenditure. Energy expended by daily activities (~30%) and the thermic effect of food (~10%) are the major remaining components of energy expenditure (212). Energy expenditure is influenced by a number of biological factors, including age, daily activity, and body composition, particularly fat-free mass (153). There is interindividual variability in energy expenditure, and the specific mechanisms contributing to this variability are the subject of intense study (152, 153). T1D is a catabolic state, particularly in insulin deficiency (213). Standard equations for estimating energy expenditure and dietary intake requirements in healthy individuals are not adequate for those with T1D. First, in patients with uncontrolled T1D, glycosuria can account for 300 to 400 kcal/d in obligate energy losses (158). Second, most studies demonstrate a modest increase in resting energy expenditure (range, 100 to 300 kcal/d) in people with T1D relative to healthy controls or predicted values (158, 214–217). A study that manipulated glucagon levels via insulin deprivation/replacement experiments revealed that the resting energy expenditure increase in people with T1D appears to be proportionate to the degree of hyperglycemia and attributed to increased hepatic gluconeogenesis driven by hyperglucagonemia (218). One possible explanation for this is that in the absence of insulin, excess glucagon leads to gluconeogenesis and increased protein turnover, resulting in a net negative energy balance (218–220). There are limited data available on the impact of systemic vs portal insulin delivery on energy expenditure. One study in a canine model of T1D demonstrated that 24-hour energy expenditure (assessed by ventilated hood) is higher with portal vs systemic insulin administration (221). Other factors, including increased sympathetic nervous system activity (222), increased protein turnover and substrate transport across cellular membranes (218), alterations in the gut microbiome (223), and increases in size of metabolically active organs (224), may contribute to alterations in energy expenditure in T1D.

Very few studies have been conducted to fully understand the energy balance equation in T1D. Most of the available literature dates back several decades. Initial studies showed conflicting evidence regarding energy balance status in T1D. In a controlled metabolic ward study, basal energy expenditure was higher than predicted in the absence of insulin and was restored to predicted values with insulin replenishment in poorly controlled individuals with T1D (n = 10) (217). In individuals with poorly controlled T1D, resting metabolic rate (assessed via ventilated hood) decreased, the thermic response to high-fat overfeeding and norepinephrine infusion was blunted, and weight increased by an average of 3.5 kg after changing from conventional therapy to intensive insulin therapy (225). In one of the few studies conducted in individuals with T1D using whole-room calorimetry, lean individuals with well-controlled T1D had significantly lower diet-induced thermogenesis compared with well-matched healthy controls. This dampened thermic effect of food was inversely correlated with 24-hour glycemia, suggesting that that the energy cost of thermogenesis may be reduced, at least in part, by hyperglycemia. There were no significant differences on other energy and substrate oxidation profiles including 24-hour energy expenditure and respiratory quotient (a measure of whole-body substrate oxidation) between groups (216). The energy expenditure finding corroborates what was seen in a previous study where the increased energy expenditure in poorly controlled T1D normalized after initiation of insulin therapy (217). The energy expenditure during exercise was also lower in the T1D group, but the net work efficiency was not, meaning that the response to exercise was similar (216). A recent crossover design study comparing the effects of insulin glargine with peglispro, a novel hepatoselective insulin, in 15 patients with T1D demonstrated a small (80 kcal/24 h) increment in 24-hour energy expenditure with peglispro, accompanied by increases in nocturnal lipid oxidation rates (226). Additional studies are needed to understand the energy expenditure profile in T1D across the spectrum of body weight and glycemic control to inform calorie prescriptions to achieve weight goals. A theoretical model of the impacts of glycemic control on the components of energy expenditure is shown in Figure 2.

Figure 2.

Theoretical model of the currently known impacts of glycemic control on energy balance. The largest component of the energy balance equation is resting energy expenditure (REE), of which the largest constituent is sleep energy expenditure (SEE). It accounts for 50% to 70% of total daily energy expenditure. The remaining major components of energy expenditure are thermic effect of food (TEF) and activity energy expenditure (AEE). AEE includes exercise, activities of daily living (ADL), work, and fidgeting. There are multiple other components that can influence energy expenditure differentially, including the gut microbiome, energy losses in urine, stool, or volatile organic compounds, thermogenesis, changes in energy expenditure from metabolically active organs (brain, kidney, liver, muscle, heart), and sympathetic nervous system activity. Achievement of well-controlled T1D due to insulin intensification reduces urinary glucose losses, REE, and TEF but does not affect food intake, resulting in a net positive energy balance and weight gain. Uncontrolled T1D is associated with an increase in resting energy expenditure due to increases in metabolically active organ weights and substrate cycling, as well as glucosuria resulting in a net negative energy balance (due to an overall increase in 24-hour energy expenditure) and weight loss.

Metabolic flexibility

Metabolic flexibility is defined as the ability of an organism to rapidly shift substrate oxidation rates to accommodate changes in substrate availability due to dietary inputs, energy demands, environmental changes, and biological signals (227). Notably, an inability to switch fuel oxidation in response to dietary macronutrients and metabolic demands (such as fasting) is a central component of both optimal glycemic control and weight regulation (228, 229). Metabolic inflexibility has been demonstrated in individuals who are obese, with and without T2D (166, 230). In newly diagnosed individuals with well-controlled T2D, metabolic flexibility is one key determinant of insulin sensitivity (231). Although most research in diabetes has focused on the central role of dysregulated carbohydrate metabolism, lack of homeostatic control of lipid synthesis and oxidation are also critically important (232). The mechanisms that control metabolic flexibility in healthy and metabolically compromised individuals are the subject of intense study.

T1D is characterized by marked differences in substrate oxidation rates with an increase in lipid oxidation rates during basal conditions, a blunted ability to shift to carbohydrate oxidation during meals, and a decrease in the thermic effect of food compared with controls without T1D (158, 214–218). In the insulin-deficient STZ mouse model of T1D, treatment with bezafibrate, a peroxisome proliferator-activated receptor pan-agonist, results in an improvement in insulin sensitivity, metabolic flexibility, and liver physiology (233). A mouse model of maturity-onset diabetes of the young generated by ablation of the three Foxo genes displays metabolic inflexibility characterized by preferential oxidation of lipids. This inflexibility leads to impaired insulin secretion (234). Recently, the concept of β-cell dedifferentiation has revealed that there are windows of opportunity for preserving β-cell function. It is thought that an early event in the decline of β-cell action is metabolic inflexibility (235). It is not known whether whole-body metabolic flexibility interacts with β-cell metabolic flexibility. Importantly, the health implications of obesity plus T1D in this paradigm, including the potential connection between obesity, insulin resistance, and progression/age of onset of T1D, have not been studied.

In sharp contrast to the wealth of evidence about energy metabolism in obesity and T2D, there have been virtually no published randomized clinical trials to systematically, comprehensively, and precisely study energy balance and metabolic flexibility as insulin therapy is modified to achieve glycemic control. In one of the studies reviewed above, an increase in metabolic flexibility assessed in response to a high carbohydrate breakfast meal was noted in patients with T1D during treatment with peglispro compared with glargine (226). Collectively, this literature suggests that studies are needed to target various aspects of the energy balance equation and metabolic inflexibility in T1D through lifestyle or medical interventions to understand the impact on glycemic control, weight regulation, and overall health.

Potential Mechanisms Modulating the Obese Phenotype in T1D

Obesity is a complex phenotype that involves many pathophysiological pathways. The causes of obesity are both biological and environmental in nature (236). With the possible exception of genetics (237, 238), the mechanisms thought to be causal for obesity can also be modulated by the obese phenotype. The sections below provide a framework for how many of the key mechanisms thought to be important in obesity causality and pathophysiology are involved in non-T1D obesity, describe the literature related to these mechanisms in T1D (if any), and use the currently available knowledge to highlight the gaps that need to be addressed to prevent and treat obesity in people with T1D.

Genetics

The genetic underpinnings of obesity and T2D and how these two metabolic diseases could have shared genetic control points have been a subject of intense study (239–243). The combined effect of genetic variants associated with both diseases on pathogenesis is poorly understood and points toward few shared genetic features but many shared mechanistic pathways (244). Indeed, even the well-studied genetic variants in fat mass and obesity-associated protein (FTO) may not be the causal variants associated with obesity and T2D, but perhaps instead (or additionally) there are roles for neighboring genes (245). This suggests there is still much work that needs to be done to harness the power of genetics for predicting disease risk and informing on therapeutic options.

The genetics of T1D have also been intensively studied, particularly with respect to alleles that increase susceptibility (246). The data with respect the genetics of obesity and T1D are emerging. In the DCCT, it was demonstrated that individuals with T1D on intensive insulin therapy with a family history of T2D gained more weight than did those without a family history. Weight gain was similar on conventional therapy, regardless of family history (100). This implies that a genetic predisposition to T2D may interact with the environmental input of glycemic control to influence the phenotypic outcome of obesity in T1D.

Some of the genes that are relevant for obesity and T2D are connected to T1D. As discussed earlier, one key pathway in the regulation of energy balance is the central melanocortin system where a tightly orchestrated set of signals converges on the arcuate nucleus to regulate food intake and satiety [reviewed in Ref. (247)]. Three well-studied examples of genes within this pathway that are associated with obesity are FTO (248, 249), melanocortin 4 receptor (MC4R) (250, 251), and proopiomelanocortin (252). These genes are tied to neuronal controls of appetite and eating behaviors, which points to a biologically plausible connection to obesity (247). In a cohort of 1119 children with T1D, the association between BMI and known obesity susceptibility genes was studied. Polymorphisms in multiple genes were tested, including FTO and MC4R. Only the A allele of rs9939609 in the FTO gene was associated with higher BMI in T1D (253). The TCF7L2 gene has been associated with both obesity and T2D, but one study found no association with T1D incidence (254). However, in a cohort of 810 individuals with T1D, carriers of the T allele at the rs4506565 locus of TCF7L2 were more likely to have only a single autoantibody present, higher C-peptide AUC, and lower glucose AUC during an oral glucose tolerance test after adjustment of several parameters, including BMI z score, suggesting that this gene contributes to a distinct T1D phenotype with milder immune and metabolic characteristics (255). These studies suggest that genetic screening for obesity in T1D may allow for stratification of the individuals at the highest risk for developing obesity (FTO) or more advanced disease (TCF7L2).

In addition to the overlap between genetics of T1D, T2D, and obesity, unique relationships in T1D have been identified. Given the challenges in establishing the causality of genetic variants on disease phenotypes due to the effects of confounding factors or reverse causality, Mendelian randomization approaches have emerged to exploit the random distribution of alleles at meiosis to essentially implement randomization based on normal distributions of alleles in populations. The alleles serve as instrumental variables, and the success of these methods depends on the strength of the association between the genetic variants and the phenotype of interest (256, 257). This method was used to test the role of childhood adiposity on T1D etiology using 23 single nucleotide polymorphisms (SNPs) as instrumental variables. The SNPs were initially selected from a previous genome-wide association study on SNPs associated with childhood adiposity. A set of additional criteria and validation steps was implemented to select the final set of 23 SNPs. They found that genetic risk for higher childhood BMI increased the risk of T1D by 32% (OR 1.32; 95% CI, 1.06 to 1.64) per SD score of BMI (256). Additionally, Mendelian randomization analysis with a genetic risk score comprised of 30 validated BMI loci showed a U-shaped relationship between BMI over lifespan and diabetic kidney disease, microalbuminuria, and end-stage renal disease in a cohort of 6049 individuals with T1D (257). This suggests that being underweight or overweight impacts risk of complications.

Inflammation is a phenotype that is prominent in both obesity and T1D (258). Although the direct connection between these three phenotypes has not been studied, there are several lines of evidence that connect risk alleles for T1D and inflammation. The most fundamental genetic connection between T1D and inflammation is the variation in human leukocyte antigen (HLA) genes, which are part of the major histocompatibility complex and key regulators adaptive immunity. These alleles account for about half of the genetic risk in T1D (259). TNF-induced protein 3 (TNFAIP3) is a susceptibility locus for T1D, and variation in this gene (at the rs2327832 locus) is associated with lower stimulated C-peptide and higher HbA1c 12 months after diagnosis (260). Loss-of-function polymorphisms in tyrosine kinase 2 areassociated with an increased risk of T1D. Several cell-based experiments to dampen tyrosinekinase 2 function demonstrate antiapoptotic and anti-inflammatory effects in β-cells (261). Based on these data, it is possible that the proinflammatory phenotypes of obesity and T1D could synergize to negatively impact outcomes. The combined effects of susceptibility variants for obesity and T1D, within the context of inflammation, could provide valuable insight into disease risk and severity.

The genetic underpinnings of T1D risk are, with a few notable exceptions, distinct from T2D and obesity. Although it is reasonable to postulate that the genetic defects that promote obesity susceptibility might coexist with genetic variants known to be risk factors for T1D, how these concurrent genetic aberrations will impact the health of individuals with T1D is unclear. Studies are needed to compare the genetic risk factors for T1D in lean individuals vs those who are obese. Whether genetic susceptibility to obesity or T2D interacts with the genes known to be causal for T1D has not been reported in the literature. Therefore, there are many gaps in knowledge that prevent the application of genetics for disease prediction and therapeutics, including limited data in multiethnic groups. These foundational hurdles need to be overcome before the connection between genetics, obesity, and T1D can be elucidated.

Epigenetics

Genetics is an essentially static biological fingerprint that exerts substantial control over metabolic disease susceptibility and pathophysiology. Epigenetics, in contrast, is a dynamic set of modifications to DNA and histones that can have profound effects on gene expression. Epigenetic modifications integrate environmental cues throughout the lifespan and modify biological pathways in response to the actual or expected environment. These modifications impact obesity, T2D (262), and T1D (263) susceptibility and pathophysiology. A critical window of time where these modifications are established is in utero through the early postnatal period.

In utero exposures to obesity and diabetes have been shown to affect fetal development and risk for future metabolic disease, including obesity, metabolic syndrome, and T2D (264–266). For example, maternal nutritional status, both overnutrition and undernutrition, and maternal weight have been associated with increased risk for obesity in offspring (265, 267–269). Specific nutritional components can also influence offspring health. The agouti viable yellow mouse model displays a phenotype of increasing weight and yellowing coat color as the agouti viable yellow locus becomes increasingly demethylated. Hypermethylation represses the normal agouti signaling functions, which normally promotes a yellow color and suppresses satiety signaling via MC4R. When females with obesity are weaned onto a hypermethylating methyl-supplemented diet, the transgenerational amplification of the obese phenotype to subsequent generations of offspring does not occur as it does in females weaned to a control diet (270). Similar to T2D and obesity, there is a relationship between in utero exposure to maternal T1D and future health of the offspring, including increased risk of metabolic disease (271), increased adiposity and adipocyte dysfunction (272, 273), altered concentrations of metabolic hormones such as leptin (271, 272) and ghrelin (274), and future metabolic disease, including prediabetes and T2D (266, 275). The risk of T1D is higher in children born to fathers vs mothers with T1D due to differential inheritance of risk alleles, maternal vs paternal imprinting (an epigenetic phenomenon), and reduced risk of islet autoimmunity in the first year of life when the mother has T1D (276). The increased risk of T1D in children of mothers who are overweight or obese is confined to mothers that are nondiabetic (277, 278).

The early postnatal period is also a critical window of exposure that impacts metabolic disease. In rats, an overfeeding paradigm during the early postnatal window created by dividing offspring into small litters (overfed), normal litters (normal feeding), and large litters (underfeeding) led to obesity, hypertension, and hyperinsulinemia in adulthood. Furthermore, these overfed adult rats developed a T1D-like phenotype, characterized by hyperglycemia, glucosuria, and ketonuria on a low dose of STZ (279). Another study, also in rats, showed that postnatal overfeeding led to reduced glucose-stimulated insulin secretion (GSIS) at weaning that persisted into adulthood. This was recapitulated in isolated islets. Although glucose tolerance was normal, the work shows the importance of postnatal nutrition on metabolic responses that have relevance to T1D (280). In female mice, early postnatal overnutrition reduced energy expenditure, in part due to reduced physical activity. This was associated with higher body weight and fat mass that persisted into adulthood (281). Given the close connection between the brain and the gut in modulating eating behaviors and energy balance, these mechanisms could prove to be relevant for obesity in both T1D and T2D. Therefore, the window between in utero development and the early postnatal period is a critical time frame that connects nutritional and environmental factors with future health outcomes of offspring.

The mechanisms driving the early programming of metabolic disease are incompletely understood, but there is abundant evidence pointing toward epigenetics [reviewed in Ref. (282)]. The changes in GSIS due to postnatal overfeeding in rats described above were accompanied by persistent changes in gene expression that were likely due to epigenetic mechanisms (280). There are multiple types of epigenetic modifications, each with unique mechanistic consequences. Perhaps the most common type of epigenetic modification is DNA methylation, which is most often associated with gene silencing. The changes in energy expenditure, physical activity, body weight, and fat mass due to postnatal overnutrition in rats described in the previous paragraph were accompanied by modest alterations in hypothalamic methylation at genes that are relevant for neural development or function. These changes in methylation persisted into adulthood (281). It is plausible that these epigenetic marks were responsible for the subsequent metabolic phenotype given the neurobiological regulation of physical activity (283). Leptin (284) and adiponectin (285), adipokines that mediate both insulin action and adiposity, have been shown to be differentially methylated based on glycemic status of the mother. Leptin gene methylation was assessed in placental tissues in normal vs impaired glucose-tolerant females. In the impaired glucose-tolerant females, reduced leptin methylation on the maternal-facing side of the placenta was negatively correlated with maternal 2-hour glycemia during a glucose tolerance test (284). Lower methylation of the adiponectin gene promoter in both the fetal- and maternal-facing placental tissue was associated with compromised glycemic control in the mother (285). It is conceivable that aberrant methylation of leptin and adiponectin could predispose offspring to metabolic disease and that this will hold true in individuals with obesity and T1D.

“Metabolic flexibility is defined as the ability of an organism to rapidly shift substrate oxidation rates.”

As described earlier, genetic variation in the FTO locus is associated with obesity, T1D, and T2D. Another regulatory mechanism that modulates the impact of FTO on metabolic disease is epigenetics. In adults with T2D, methylation of the FTO obesity susceptibility haplotype (tagged by SNP rs8050136) was increased (286). FTO expression was not analyzed because there has not been a correlation between FTO SNPs and expression levels (287), making causality difficult to ascertain. FTO methylation changes in lean individuals vs those with obesity and T1D have not been reported. Despite the lack of data on the role of methylation on the expression of metabolic regulators in T1D, there have been unique methylation mechanisms uncovered in T1D. For instance, in nonobese diabetic (NOD) mice, progression of T1D correlated with changes in methylation of insulin DNA in β-cells that was inversely associated with gene transcription. This methylation was induced by proinflammatory cytokines (288).

Acetylation is another important epigenetic mechanism that impacts gene expression and influences health. The effects of acetylation and deacetylation on transcriptional activity depend on the site of the epigenetic modification. Sirtuins are energy sensors that are activated in conditions of energy restriction. They control gene expression through histone deacetylation of target genes with profound effects on metabolism (289). One mechanistic explanation for the beneficial effects of sirtuins was demonstrated in a sirtuin 1 (SirT1) gain-of-function transgenic mouse model. As compared with control mice, gain of function of SirT1 leads to decreased energy requirements (mirroring a calorie-restricted state), increased adiponectin, and improved insulin sensitivity in response to a high-fat diet despite equivalent weight gain. Part of the mechanism of action involved activation of Foxo1 via deacetylation (290). Pharmacological activation of SirT1 in mice leads to a similar plethora of metabolic benefits (291). These benefits of SitT1 on insulin sensitivity and obesity have made it an attractive target of investigation as a pharmacological option for T2D (292). The knowledge in T1D is limited, but suggests that this pathway could also be a target for therapeutics in T1D. In NOD mice, SirT1 activation via subcutaneous delivery of resveratrol led to an 80% reduction in the development of T1D. This was accompanied by reduced insulitis, higher number of islets, and alterations in immune mediators (e.g., reduction in interferon γ) (293). Similarly, in NOD mice, administration of lysine deacetylase inhibitors, which have shown promise in immunomodulation, at doses that are tolerated clinically led to reduced T1D cumulative incidence, delayed T1D onset, diminished insulitis, and normalized immune function. Lysine deacetylase inhibition also diminished human islet cell apoptosis by reducing inflammatory mediators (294). This points to a unique set of mechanisms by which acetylation could impact immune system function that could be potentially beneficial when obesity and T1D coexist.

Another epigenetic mechanism with a prominent effect on metabolism involves noncoding RNAs, such as microRNA, that regulate the expression of metabolic gene networks. In a study of 2317 individuals from the Framingham Heart Study Offspring Cohort, 16 microRNAs were found to be associated with insulin levels, and two additional microRNAs were also associated with homeostatic model assessment of insulin resistance after adjusting for age, sex, and BMI. Of those 18 microRNAs, miR-122 was also associated with greater BMI, waist circumference, and visceral fat (295). Multiple studies [reviewed in Ref. (296)] have identified microRNAs that are dysregulated in adipose tissue of individuals with obesity. MicroRNAs are also important in regulating physiological processes that are central to the pathogenesis of T1D. A systematic review identified 11 microRNAs that were consistently different in T1D vs healthy controls (297). A review of microRNAs associated with either T1D or T2D reveals a unique fingerprint of microRNAs in T1D (298). Additional studies have demonstrated the impact of microRNA on insulin secretion (299–302), insulin synthesis (303, 304), and pancreatic physiology (305–309) in T1D. Investigations on the differences in miRNA profiles across the spectrum of BMI and body composition could reveal important biomarkers to drive prevention and treatment strategies (310).

The translational relevance of epigenetic control points in T1D has been demonstrated in several studies. In cohorts from DCCT/EDIC with and without diabetes complications by study year 10, profiling of peripheral blood monocytes showed differential epigenetic marks in people on conventional therapy as compared with intensive therapy, and these patterns were associated with higher HbA1c and epigenetic alterations in an inflammatory pathway known to be associated with complications (nuclear factor κ-light-chain-enhancer of activated B cells) (311). In a follow-up study, it was uncovered that these biologically relevant epigenetic marks persisted at study years 16 to 17 in individuals with complications (312). The relationship between epigenetics and T1D complications is supported by another study where genome-wide changes in methylation were identified in individuals with retinopathy as compared with controls (313). Other studies support the role of differential epigenetic marks in individuals with T1D with and without complications (314, 315). These studies are intriguing, as they suggest a role for epigenetics in metabolic memory, which aside from its importance in glycemic control and complications, could modulate the physiological relationships between T1D and obesity (316–318).

Based on the current knowledge of the role of epigenetic mechanisms on energy balance and metabolism, it is likely that a unique set of epigenetic marks will be relevant in scenarios where T1D and obesity coexist. Importantly, these epigenetic fingerprints could serve as biomarkers for disease risk and future complications, making them viable therapeutic targets. Studies to specifically address the gaps in our understanding of the epigenetics of obesity in the context of T1D are essential for maximizing the modifiable influence of epigenetics on physiology.

Microbiome

The human gut microbiome has emerged as a modulator of health outcomes of the host, particularly metabolic diseases with shared pathogenic mechanisms such as obesity (319), NAFLD (320), and diabetes (321). One of the landmark studies that demonstrated the importance of the gut microbiome on metabolic disease showed that fat pads and adipocytes were smaller in germ-free mice vs conventionally raised mice. When gut microbes from conventionally raised mice were transferred to germ-free mice, the obese phenotype was recapitulated (322). Shortly after that discovery, the findings in mice were translated to humans, where it was demonstrated that there are shifts in the proportions of the bacterial phyla Firmicutes and Bacteroidetes in individuals with obesity. (323, 324). The plethora of evidence connecting the gut microbiome to obesity led to many studies showing associations with other metabolic diseases. One prominent example is the connection of the microbiome with NAFLD in both mice (325) and humans (326).

Based on these findings, it is not surprising that the alterations in gut microbiota composition and function are associated with diabetes. Individuals with T2D display moderate dysbiosis, reduced abundance of butyrate-producing bacteria, an increase in opportunistic pathogens, and increased sulfate reduction and drug resistance functions (327). A metagenome analysis in individuals with T2D, as compared with controls, confirmed dysbiotic conditions (decreased Firmicutes, Clostridia, and butyrate-producing bacteria) (328). An important connection between obesity, T2D, and the microbiome is illustrated in bariatric surgery patients. The improvements in T2D that are associated with bariatric surgery are attributable, at least in part, to weight-independent changes in the gut microbiome, incretins, and bile acid signaling (329). As obesity in T1D increases, the likelihood of implementation of bariatric surgery as a therapeutic modality may increase. Studies are needed to understand the unique metabolic outcomes and microbiome phenotypes of bariatric surgery in T1D.

Considerable advances have been made to our understanding of the microbiome in individuals with T1D. Although the microbiome will likely be a major mechanistic connector between obesity and T1D, a full review of the literature is outside the scope of the present review [see Refs. (330) and (331) for reviews]. A subset of key literature is discussed below, and a summary of the progression of the knowledge base in T1D with additional references is provided in Table 2 (332–347). Early rodent studies demonstrated the interaction between innate immunity and the microbiome in the development of T1D. A knockout mouse model of the Toll-like receptor adaptor protein myeloid differentiation primary response 88 on a NOD background was protected from T1D, but when the mice were raised in germ-free conditions, T1D ensued. When germ-free mice were colonized with a complement of microbes that mirror what is found in the human gut, there was a reduction in the development of T1D (332).

Table 2.

Summary of Major Developments in Microbiome Research in T1D

Rodent Model	Human Model	Key Findings	References
	X	Correlation between dysbiosis and glycemic control	(445, 446)
X		Microbiome imparts protection from T1D	(332, 447, 448)
X	X	Microbiome differences based on T1D presence or susceptibility	(333–336, 449–452)
X	X	Dysbiosis prior to T1D onset	(337–341, 453)
X	X	Dysbiosis at or after T1D onset	(333, 342, 452, 454)
X	X	Interactions between the microbiome and immunity in T1D	(207, 343, 344, 447, 455–458)
X		Antibiotics impact microbiome and T1D onset	(459–461)
X		Microbial metabolites and peptides impact T1D pathophysiology	(344–347)
X	X	Lifestyle or environmental exposures and microbiome composition in T1D	(446, 455, 462–464)
X	X	Interaction between genetics, microbiome, and T1D	(465–467)

Rodent Model	Human Model	Key Findings	References
	X	Correlation between dysbiosis and glycemic control	(445, 446)
X		Microbiome imparts protection from T1D	(332, 447, 448)
X	X	Microbiome differences based on T1D presence or susceptibility	(333–336, 449–452)
X	X	Dysbiosis prior to T1D onset	(337–341, 453)
X	X	Dysbiosis at or after T1D onset	(333, 342, 452, 454)
X	X	Interactions between the microbiome and immunity in T1D	(207, 343, 344, 447, 455–458)
X		Antibiotics impact microbiome and T1D onset	(459–461)
X		Microbial metabolites and peptides impact T1D pathophysiology	(344–347)
X	X	Lifestyle or environmental exposures and microbiome composition in T1D	(446, 455, 462–464)
X	X	Interaction between genetics, microbiome, and T1D	(465–467)

Table 2.

Open in new tab Download slide

Summary of Major Developments in Microbiome Research in T1D

Rodent Model	Human Model	Key Findings	References
	X	Correlation between dysbiosis and glycemic control	(445, 446)
X		Microbiome imparts protection from T1D	(332, 447, 448)
X	X	Microbiome differences based on T1D presence or susceptibility	(333–336, 449–452)
X	X	Dysbiosis prior to T1D onset	(337–341, 453)
X	X	Dysbiosis at or after T1D onset	(333, 342, 452, 454)
X	X	Interactions between the microbiome and immunity in T1D	(207, 343, 344, 447, 455–458)
X		Antibiotics impact microbiome and T1D onset	(459–461)
X		Microbial metabolites and peptides impact T1D pathophysiology	(344–347)
X	X	Lifestyle or environmental exposures and microbiome composition in T1D	(446, 455, 462–464)
X	X	Interaction between genetics, microbiome, and T1D	(465–467)

Rodent Model	Human Model	Key Findings	References
	X	Correlation between dysbiosis and glycemic control	(445, 446)
X		Microbiome imparts protection from T1D	(332, 447, 448)
X	X	Microbiome differences based on T1D presence or susceptibility	(333–336, 449–452)
X	X	Dysbiosis prior to T1D onset	(337–341, 453)
X	X	Dysbiosis at or after T1D onset	(333, 342, 452, 454)
X	X	Interactions between the microbiome and immunity in T1D	(207, 343, 344, 447, 455–458)
X		Antibiotics impact microbiome and T1D onset	(459–461)
X		Microbial metabolites and peptides impact T1D pathophysiology	(344–347)
X	X	Lifestyle or environmental exposures and microbiome composition in T1D	(446, 455, 462–464)
X	X	Interaction between genetics, microbiome, and T1D	(465–467)

The potential mechanistic driver connecting immunity and the microbiome was recently uncovered, also in NOD mice, with associated translation to clinical populations. Mucosal-associated invariant T (MAIT) cells are a specialized type of innate-like T-cells that recognize microbially derived riboflavin derivatives and promote inflammation and cell death. MAIT cells were highly suppressed in germ-free NOD mice and were found to have protective effects in the development of T1D. Importantly, MAIT cells were highly suppressed in newly diagnosed children with T1D as compared with healthy controls. Modeling with MAIT cell parameters was sufficient to distinguish healthy controls, early onset T1D, and established T1D, suggesting that MAIT cells could be an early biomarker of T1D (343). The work in MAIT cells shows a strong connection between the microbiome and autoimmunity. It is not known whether obesity, in which MAIT cells are also reduced (348, 349), will further deteriorate MAIT cell function in T1D.

In humans, when comparing T1D cases to healthy controls, there is lower functional diversity, as well as a dampening of butyrate production capacity, both of which may contribute to gut permeability and autoimmunity (333). Children with β-cell autoimmunity have a distinct microbiome composition when compared with children matched for age, sex, HLA-DQB1 genotype, and breastfeeding history (337). Several other studies have confirmed the difference in gut microbes in healthy vs T1D cohorts (334, 335, 338, 342). In a study of 33 HLA-matched Estonian infants followed longitudinally from birth to age 3 years, the diversity of the gut microbiota flattens just prior to seroconversion, whereas it increases in nonconverters. The converter microbial profile is characterized by a spike in inflammatory pathways, despite consistent metabolic pathway activity (339). The gut mucosa of individuals with long-standing T1D has proinflammatory phenotype and microbiome composition that are distinct from healthy controls or those with celiac disease. Although causality could not be established, the findings highlight the duodenum as a therapeutic target for the inflammatory processes that accompany autoimmunity (336). In support of this notion, increased intestinal permeability is thought to be an important mediator of T1D onset (340, 350), and this permeability could be remedied by restoring a healthy gut flora (341, 351).

There are several mechanisms whereby the microbiome modulates human physiology. One such method is via the production of metabolites from diet, drugs, or bacterial metabolic activities. The most prominent and well-studied microbial metabolites are short-chain fatty acids (SCFAs) such as acetate, propionate, and butyrate, which are produced from the fermentation of indigestible carbohydrates. SCFAs reduce energy harvest, reduce adiposity, and improve insulin sensitivity, which makes them attractive therapeutic targets for obesity and T2D (352). The metabolic phenotypes thought to be attributable to the fermentation of SCFAs could prove beneficial in individuals with obesity and T1D, but there are no published reports on this. However, a metabolic connection in T1D is plausible based on the role of SCFAs on GSIS and immunity. One study found that acetate turnover and plasma/fecal concentrations are markedly increased in high-fat fed, obese, insulin-resistant rats. Additionally, there was a causal relationship between acetate turnover and stimulation of GSIS, which resulted in increased ghrelin secretion, hyperphagia, ectopic deposition, and insulin resistance (345). It is reasonable to hypothesize that individuals with obesity and T1D would also have increased acetate turnover and associated metabolic derangements. Furthermore, in NOD mice, dietary interventions that promote SCFA production protected against the development of T1D via reductions in autoimmunity and protection of gut integrity (346). In Kilham rat virus–induced T1D, provision of SFCAs in drinking water to mothers prior to pregnancy reduced the incidence of virus-induced T1D and insulitis. This was due, at least in part, to SCFA-mediated alterations in gut microbial taxa, restoration of microbial diversity, and a reduction in inflammation. These benefits of SCFAs were not seen when provided after weaning, suggesting the potential for an epigenetic mechanism (347).

Nutrients are life-long modulators of the gut microbiome, and it is not surprising that there is a constantly growing body of literature in this area, including the impact of sugars, resistant starch, fiber, fats, proteins, and micronutrients (e.g., antioxidants, choline, carnitine, betaine) on microbial diversity and metabolism. Recent and comprehensive reviews on this topic are available elsewhere (353, 354), and only a few examples are discussed in this review. Glycemic responses to foods with equivalent glycemic load are highly individualized, and one of the modulators of this individuality is the gut microbiome (355). Artificial sweeteners, which are commonly consumed by individuals with diabetes, alter the microbiome and negatively impact glycemia (356). Dietary and endogenous branched-chain amino acids (BCAAs) are associated with increased risk of developing T2D (357). The plasma metabolome of individuals with insulin resistance is characterized by an increase in BCAAs and a concomitant enrichment of gut microbes that synthesize BCAAs (358). This could be relevant in T1D given the significant positive association between dietary BCAAs and fasting C-peptide in individuals with preserved β-cell function at baseline (fasting C-peptide ≥0.23ng/mL) that was identified in a prospective analysis of the SEARCH for Diabetes in Youth Study (359). The efficacy of customized nutritional interventions in T1D for amelioration of hyperglycemia, promoting insulin sensitivity, modulating energy balance, and preventing autoimmunity via interactions with the microbiome is an area that requires intense research attention.

Much like nutrients that reach the gut microbiota are subject to metabolism, drugs can be subject to alterations in efficacy and bioavailability due to microbial interactions. Drugs used to treat T2D have unique microbial interactions (360). For example, metformin, a commonly used medication for T2D and the most intensively studied adjunctive therapy in T1D, impacts gut microbiome composition (77, 361). It is thought that at least part of the therapeutic efficacy of metformin is due to its effect on the microbiome, particularly on the species Akkermansia muciniphila (362, 363), which improves insulin sensitivity, reduces adipocyte size, and increases fecal energy losses (364). The gut microbes of individuals with T2D differ based on metformin treatment status (328). Emerging evidence is delineating a central role of the microbiome in drug metabolism (365), and this will impact our current understanding of the variability in response to diabetes therapeutics. Studies are needed to uncover the potential intersections between the microbiome, obesity, and T1D therapeutics.

Despite the many advances in the field of the gut microbiome in T1D, many questions remain unanswered, including ascertainment of causality, the functional connection between the autoimmune process and the gut microbiome, and the therapeutic efficacy of microbiome modulators (366). Importantly, there are no published studies characterizing the microbiome in lean individuals vs those who are obese and have T1D or deciphering how differences in the microbiome could affect T1D and obesity-related complications.

Adipose tissue dysfunction

In contrast to the historical characterization of adipose tissue as an inactive organ with a primary role of storing excess energy, it is now undeniable that it is an endocrine organ with far-reaching effects on physiology. The beneficial actions of adipose tissue become dysregulated in obesity [reviewed in Refs. (367) and (368)]. Total adiposity only reveals a fraction of the physiological impact of obesity (369). Adipose distribution, particularly higher central localization in the abdominal region and accumulation in muscle, is associated with insulin resistance, T2D (370–372), and myocardial infarction (373). Skeletal muscle infiltration of adipose tissue (intramuscular lipid content) correlates with insulin resistance in obesity and T2D (374). In lean individuals (BMI 25.1 ± 2.5 kg/m²) with well-controlled T1D compared with people with T2D and obesity, deep thigh fat was similar and the ratio of mid-thigh deep fat/total fat was higher. This phenotype might be associated with insulin resistance (372). In support of this notion, in lean individuals (BMI 22.5 ± 0.7 kg/m²) with poorly controlled T1D, higher intramuscular lipid content was a marker of insulin resistance (375). Adipose tissue that surrounds the coronary arteries, that is, pericardial adipose tissue, is associated with heart disease and T2D (376, 377). In people with and without T1D, the ratio of pericardial adipose tissue to subcutaneous adipose tissue is associated with insulin resistance irrespective of total adipose volume (378). These studies highlight the importance of adipose tissue distribution in the metabolic phenotype of obesity, T1D, and T2D. The mechanisms mediating the role of specific adipose depots on health are active areas of investigation, and epigenetic control points seem to explain some of the functional differences (379, 380). Additional research is needed to define the relationships between adipose distribution and in-depth metabolic phenotypes in lean individuals vs those with obesity and T1D compared with matched healthy controls.

Adipose tissue is modulated during obesity progression and its dysfunction, which involves inflammation, fibrosis, disruptions in angiogenesis, and alterations in release of adipokines, influences insulin sensitivity, energy balance, and other key metabolic control points, creating a perpetuating cycle of disease advancement (368). The functional importance of adipose tissue for obesity, T1D, and T2D, described elsewhere in this review, includes its roles in metabolic flexibility, β-cell physiology, and mitochondrial function. A few additional examples of the metabolic consequences of unhealthy adipose tissue are discussed below.

Release of adequate amounts of functional adipokines, such as leptin and adiponectin, is necessary for glucose metabolism (381, 382), and one of the target tissues of insulin-sensitizing drugs is adipose tissue (383). Adiponectin has a unique regulatory paradigm in T1D. Adiponectin correlates with insulin sensitivity in T1D similarly to controls but at slightly higher adiponectin concentrations for a similar degree of insulin resistance (384). Leptin function is also altered in T1D; leptin levels are lower at onset of T1D as compared with healthy controls, but are rapidly restored with insulin treatment (385). Additionally, leptin levels are higher in individuals treated with intensive insulin therapy vs conventional therapy (386). The mechanistic impact of leptin in T1D was demonstrated in the Akita mouse, a nonobese model of T1D that displays β-cell failure, hyperglycemia, and a shortened lifespan (387). In these mice, expression of leptin in the hypothalamus restored euglycemia (388). Transgenic Akita mice with hyperleptinemia are normoglycemic, have improved renal function, and have a longer lifespan (389). Several other studies have highlighted the role of restoring adipokine function as a means to treat T1D in rodent models (390–394). The potential benefits of restoring the hormonal output of adipose tissue in T1D was demonstrated via the use of recombinant methionyl human leptin (metreleptin) in individuals with T1D. After 20 weeks of therapy, there were modest reductions in weight (6.6%), adipose (8%), and insulin dose (15%), without an improvement in glycemic control (395). Despite the lack of efficacy with respect to glycemic control in this pilot study, the benefits on weight and reduction in insulin dose support the idea that healthy adipose function is important for T1D.

“Studies are needed to compare the genetic risk factors for T1D in lean individuals vs those who are obese.”

Adipose tissue fibrosis is also a hallmark of obesity and T2D. It is characterized by excessive extracellular matrix deposition and is a pathological consequence of long-term adipose dysfunction (368). In obesity, adipose tissue fibrosis is associated with accumulation of the adipokine endotrophin, inflammation, and insulin resistance (396). In db/db mice, there is an upregulation of most adipose tissue collagens, and this is abrogated in wild-type mice treated with peroxisome proliferator-activated receptor γ agonists or in mice overexpressing adiponectin. One of the most enriched collagens in adipose tissue is collagen VI, and it is overexpressed in both ob/ob and db/db mice (397). Given the relevance of adipose tissue fibrosis on obesity, insulin resistance, and T2D, the impact of T1D on the metabolic aberrations that accompany adipose tissue fibrosis is an area that could reveal novel mechanistic pathways and therapeutic options.

The role of adipose tissue function, and not merely changes in adipose tissue mass or distribution, was demonstrated in a study where transplantation of healthy adipose into C57BL6/J mice led to an improvement in glucose tolerance and insulin sensitivity (398). Metabolic benefits of adipose tissue transplantation have also been demonstrated in T1D models. The BioBreeding/Ottawa Karlsburg rat develops T1D spontaneously. One research group found that transplantation of visceral white adipose tissue led to a reduction in the development of T1D in females more than males (399). This dichotomy parallels the findings related to greater metabolic flexibility in females, which is due, at least in part, to adipose tissue function (400). These results are supported by a study showing that transplantation of embryonic brown adipose tissue (BAT) leads to weight gain and euglycemia in STZ-treated mice. This euglycemia persists for >6 months. Interestingly, this improvement in glucose metabolism is not dependent on insulin. Restoration of healthy adipose tissue seemed to be the main driver of these benefits as evidenced by reduced adipose tissue inflammation, increased leptin, adiponectin, and IGF-1, glucagon suppression, and insulin receptor activation (401). The importance of BAT for T1D is supported by the finding that STZ mice have both BAT dysfunction and fibroblast growth factor 21 (FGF21) deficiency. Treatment with an FGF21 analog (LY2405319) reduced blood glucose levels and improved BAT function (402). These findings are particularly intriguing given that these are the first preclinical data in T1D of an FGF21 analog that is under active investigation in clinical trials for its glycemic and other metabolic benefits in T2D and obesity (403). Once sufficient preclinical data are available, clinical trials will be needed to understand whether LY2405319 will be beneficial in people with T1D, particularly in the context of obesity. Although the studies presented related to restoration of adipose tissue function are restricted to preclinical models, they highlight a potential therapeutic avenue for treatment of human disease that could be beneficial in individuals with T1D. There are ongoing clinical trials of transplantation of adipose-derived stem cells for the treatment of various diseases. Therefore, the potential benefits of restoration of adipose tissue function on metabolic disease merit additional investigation into the long-term safety and efficacy of transplantation of adipose tissue and/or cell derivatives in humans (404).

Overall, the studies reviewed above highlight the potential importance of adipose tissue burden, distribution, and function for glycemic control in T1D and suggests that adipose-targeted therapeutics that are independent of insulin could be highly beneficial in T1D. This makes the adipose dysfunction seen in obesity a major concern for people with T1D that warrants targeted investigation.

β-Cell dysfunction

T1D is characterized by a total or near lack of insulin secretion due to the destruction of β-cells by autoimmune or (rarely) other processes. Although T1D often presents as an acute illness due to marked hyperglycemia and metabolic decompensation, studies have demonstrated that the β-cell phenotype develops gradually over time, although with a more rapid course than in T2D (405). Before overt β-cell failure occurs, there is evidence of β-cell dysfunction and insulin resistance, and this sometimes correlates with the presence of excess adiposity (406).

Considerable evidence links obesity-mediated pathways to β-cell dysfunction in T2D, including a chronic state of elevated free fatty acids, which leads to lipotoxicity in β-cells (407) along with CD95 receptor–mediated inflammation and apoptosis in human islets (408). Additionally, weight loss improves β-cell function (409), and multiple studies on populations around the globe have shown the benefits of weight loss for long-term diabetes prevention (410–412). Therefore, achieving or maintaining a healthy weight is an important therapeutic mechanism to prevent or ameliorate T2D via preservation of β-cell function.

There are few studies addressing the β-cell phenotype in individuals with obesity and T1D. One study in an ethnically diverse cohort of children with newly diagnosed T1D found that children with obesity had higher C-peptide levels than did overweight and lean counterparts (149), and this relationship was confirmed by other studies (143, 413). This may reflect obesity-related insulin resistance and resulting β-cell compensation preceding the onset of the autoimmune process in T1D. This apparent paradox may be due to the fact that healthy adipose tissue is essential for β-cell function [discussed above and reviewed in Ref. (414)]. Additional studies are needed to understand this association between BMI, C-peptide, and β-cell decline over time.

As discussed previously, the microbiome is relevant to both T1D and obesity. Recent evidence suggests that the microbiome impacts β-cell function. Cathelicidin antimicrobial peptide (CAMP) is a secreted gut peptide that modulates immune function and interacts with the microbiome. CAMP is expressed in β-cells and δ-cells, but not α-cells, of rats, mice, and humans. In a rat model of T1D, the BioBreeding diabetes-prone rat, the secretion of this peptide was diminished prior to the onset of T1D as compared with the BioBreeding control rat. Treatment with CAMP restored islet function as evidenced by insulin and glucagon secretion. It also induced a potentially beneficial gut microbiome profile (344).

Evidence exists that in T1D, a large proportion of individuals display residual β-cell function (415, 416) and, importantly, that insulin secretion can be partially restored (417, 418). Preserving or restoring this function is a central focus of novel T1D therapeutic efforts (419). This is of key importance when considering the potential impact of obesity on the long-term outcomes of people with T1D, as the β-cell hostile environment that accompanies the obese phenotype may exacerbate the loss of this residual functionality. The effects that obesity plus T1D have on β-cell longevity are not well understood, and maximizing the efficacy of novel therapeutics requires this knowledge.

Mitochondrial dysfunction

GSIS from β-cells depends on mitochondrial generation of ATP. Because mitochondrial function is essential for maintaining glucose homeostasis, it is not surprising that mitochondrial dysfunction is a common underlying mechanism in metabolic diseases (420, 421). A major component of one’s ability to metabolically adapt to fuel availability and demand is thought to be driven primarily by mitochondria in skeletal muscle and other tissues (227).

In twins discordant for obesity, the obese phenotype is characterized by downregulation of mitochondrial oxidative phosphorylation and reduced mitochondrial DNA. These defects are present before the onset of metabolic disease, suggesting a role in pathogenesis (422). In T2D and insulin-resistant states, there is a reduction in the functionality of the mitochondrial electron transport chain (423, 424) via multiple mechanisms, including changes in gene expression (425, 426), reduced enzyme activity (427), and shifts in lipid metabolism in skeletal muscle (428, 429), liver, and adipose tissue (430). It is likely that mitochondrial dysfunction precedes the onset of T2D because lean, insulin-resistant offspring of individuals with T2D demonstrate reduced mitochondrial ATP production (431). More support for this early causative role of mitochondrial dysfunction on metabolic disease comes from studies that demonstrated that elevated free fatty acids after a lipid infusion are associated with diminished skeletal muscle mitochondrial membrane potential, insulin resistance (432), and altered nuclear-encoded mitochondrial genes (433).

In agreement with what is known in T2D and obesity, there have been documented mitochondrial deficiencies associated with T1D. In the Akita^+/Ins2 model of monogenic T1D, mitochondrial dysfunction was evidenced by defects in mitochondrial bioenergetics (reduced ATP respiration, maximal respiration, and reserve capacity), dysregulation of mitochondrial respiratory chain proteins, dysmorphology, increased oxidative stress (427), and remodeling of the mitochondrial proteome (434). There is a relationship between PDX1, a gene that is essential for islet development, β-cell maintenance, GSIS (435), and mitochondrial function (436). This gene has been implicated in both T1D and T2D pathogenesis and may shed light onto mitochondrial therapeutics to preserve or reactivate β-cells. Insulin itself has profound effects on mitochondrial function in multiple tissues by enhancing production of ATP in skeletal muscle mitochondria, increasing activity of skeletal muscle mitochondrial oxidative enzymes (437), signaling through Foxo1 in liver (438), and reducing mitochondrial uncoupling in BAT (439). The ability to restore insulin modulation of mitochondrial function fully with exogenous insulin is likely to be suboptimal.

In clinical studies of T1D, the impact of mitochondrial function on diabetes complications has been revealed. Mitochondrial DNA haplogroups are patterns of point mutations in mitochondrial DNA that are thought to impact mitochondrial function. In comparison with people with T1D free from retinopathy, mitochondrial DNA haplogroups have been associated with higher severity of diabetic retinopathy (440). Diabetic kidney disease is associated with genetic risk factors for mitochondrial function (441), and this mirrors findings in mice where glomerular endothelial mitochondrial function is a central control point for kidney disease progression (442). The epigenetic patterns of mitochondrial function genes in circulating DNA differ in individuals with T1D with and without renal disease (443), suggesting that mitochondrial dysfunction is indeed an early event that impacts kidney health.

“It is likely that a unique set of epigenetic marks will be relevant in scenarios where T1D and obesity coexist.”

There is robust evidence that unhealthy mitochondria are a hallmark of obesity, T1D, and T2D. It is unclear how the potential additive effects of obesity and T1D will impact mitochondrial dysfunction and T1D progression. It is also plausible that T1D-mediated mitochondrial dysfunction could promote obesity and insulin resistance. Understanding both paradigms is necessary to effectively ameliorate or prevent obesity in T1D and to develop therapies that address the unique mitochondrial characteristics when obesity is concurrent with T1D.

Future Directions and Perspectives

The collective knowledge presented in this review strongly suggests that there is a pressing need to generate the scientific evidence base to develop weight management guidelines that are specific to T1D. Several major gaps in our understanding need to be addressed so that weight management strategies specific for people with T1D can be developed, including:

What are the unique behavioral, psychological, and social characteristics that underlie obesity in T1D?
What are the biological and behavioral mechanisms that contribute to the obese phenotype in T1D?
Does obesity impact the natural history of T1D, from seroconversion through the appearance of complications?
- When obesity occurs before or after T1D diagnosis, how is the health of a person with T1D modified?
Could obesity-related pathways serve as therapeutic or prevention targets that are insulin-independent or act synergistically with intensive insulin therapy?
How do energy and macronutrient balance shift in response to advanced medical therapies (pharmacologic and technologic), dietary challenges, exercise, and glycemic control in the context of obesity?
- How do these shifts influence therapeutic requirements and the subsequent response to those therapies?
What lifestyle approaches will be most efficacious for preventing and addressing obesity in people with T1D while concurrently achieving glycemic control?
What is the role for medications that promote weight loss?
- Can these be used as adjunctive therapy in patients with T1D to counterbalance the effects of intensive insulin therapy to improve glycemic control?

A proposed framework to traverse the continuum from the lack of scientific evidence to the implementation of efficacious health care paradigms that address the maintenance or achievement of a healthy weight while optimizing glycemic control is presented in Figure 3. A key first step for building this framework is the collaboration between synergistic research experts with a comprehensive understanding of existing knowledge to design and execute research approaches that will define individual metabolic, biological, and behavioral characteristics of obesity in T1D. Identification of subgroups of individuals with T1D who respond differentially to interventions will allow for the design and testing of innovative individualized approaches. Accomplishing this will require the use of novel trial designs that are more efficient, especially relative to subgroups who may respond differently to various approaches. These designs need to consider, at minimum, sex, glycemic control, and therapeutic regimen (including use of insulin pump therapy, multiple daily injections, and automated insulin delivery systems). This can be accomplished through adaptive interventions and sequential multiple assignment randomized trial designs (444). Studies are needed to uncover medical approaches to promote glycemic control while maintaining energy balance, barriers and facilitators of behavior change, unique aspects of ingestive behavior, and the phenotype of energy and macronutrient balance. Finally, efforts will be needed to ensure effective implementation and dissemination of proven interventions. In concert, such efforts will advance capacity to improve outcomes for individuals living with T1D and obesity.

Figure 3.

A framework for defining transdisciplinary research paradigms aimed at the development of T1D-specific weight management recommendations. This framework requires the merger of complementary expertise and existing knowledge to design and implement transdisciplinary research approaches that will generate the data needed to develop T1D-specific weight management recommendations.

Abbreviations

ADA
American Diabetes Association

AUC
area under the curve

BAT
brown adipose tissue

BCAA
branched-chain amino acid

BMI
body mass index

CAMP
cathelicidin antimicrobial peptide

CCK
cholecystokinin

DCCT
Diabetes Control and Complications Trial

DPP-4
dipeptidyl peptidase-4

EDIC
Epidemiology of Diabetes Interventions and Complications

FGF21
fibroblast growth factor 21

Foxo
forkhead box O

FTO
fat mass and obesity-associated protein

GIP
glucose-dependent insulinotropic polypeptide

GLP-1
glucagon-like peptide-1

GSIS
glucose-stimulated insulin secretion

HLA
human leukocyte antigen

MAIT
mucosal-associated invariant T

MC4R
melanocortin 4 receptor

NAFLD
nonalcoholic fatty liver disease

NHANES
National Health and Nutrition Examination Survey

NOD
nonobese diabetic

Pittsburgh EDC
Pittsburgh Epidemiology of Diabetes Complications

PYY
peptide tyrosine-tyrosine

SCFA
short-chain fatty acid

SGLT
sodium-glucose cotransporter

SirT1
sirtuin 1

SNP
single nucleotide polymorphism

STZ
streptozotocin

T1D
type 1 diabetes

T2D
type 2 diabetes

TIDX
T1D Exchange Registry

Acknowledgments

We thank Anna Kahkoska for assistance with creating the graphics for Figure 3 that summarize the key message in this review.

Advancing Care for Type 1 Diabetes and Obesity Network (ACT1ON) is a consortium of clinical, behavioral, and basic scientists comprising expertise in endocrinology, epidemiology, nutrition, exercise physiology, clinical psychology, and biostatistics from multiple institutions led by the University of North Carolina, Stanford University, the Florida Hospital Translational Medicine Research Institute for Metabolism and Diabetes, and the Barbara Davis Center. ACT1ON aims to elucidate drivers of overweight and obesity in youth and adults with T1D. The overarching goal of ACT1ON is to develop evidence-based guidelines for individuals with T1D and their diabetes care team regarding weight management and weight loss. Members of the consortium for ACT1ON include the following: University of Colorado Denver Barbara Davis Center: Franziska K. Bishop, MS, CPHS CDE, and Kimberly A. Driscoll, PhD; Stanford University Division of Endocrinology: David M. Maahs, MD, PhD; Ananta Addala, DO, MPH; Laya Ekhlaspour, MD; Korey K. Hood, PhD; and Marissa Town, RN, BSN, CDE; Florida Hospital Translational Research Institute for Metabolism and Diabetes: Richard Pratley, MD; Karen D. Corbin, PhD, RD, Steven R. Smith, MD; and Keri Whitaker, RN, CCRP; North Carolina State University: Eric Laber, PhD; University of North Carolina at Chapel Hill, Glillings School of Global Public Health, Departments of Nutrition and Medicine: Elizabeth Mayer-Davis, PhD; Kyle Burger, PhD; Ian Carroll, PhD; Daria Igudesman; Anna Kahkoska; Michael R. Kosorok, PhD; Grace Shearrer, PhD; and Joan Thomas, MS, RD; Boston Children’s Hospital: Garry Steil, PhD; York University School of Kinesiology and Health Science: Michael Riddell, PhD.

Disclosure Summary: D.M.M. has received institutional research funding from Dexcom, Medtronic, Bigfoot Biomedical, Insulet, and Roche; has served on the advisory board for Insulet; and has acted as a consultant for Abbott Diabetes Care, Sanofi, and Eli Lilly. R.E.P. received research grants, honoraria, speaker’s bureau fees, and consulting fees paid directly to Florida Hospital, a nonprofit organization, from AstraZeneca, Boehringer Ingelheim, Gilead Sciences, GlaxoSmithKline, Hanmi Pharmaceutical, Eli Lilly and Company, ICON Clinical Research, Janssen Pharmaceuticals, Lexicon Pharmaceuticals, Ligand Pharmaceuticals, Merck & Company, Novo Nordisk, Pfizer, Sanofi-Aventis, Takeda, and Eisai. S.R.S. serves on scientific advisory boards or consults for Takeda, Arena Pharmaceuticals, AstraZeneca, Boehringer Ingelheim Pharma, Bristol-Myers Squibb, Eisai, Elcelyx, Eli Lilly and Company, Five Prime Therapeutics, GlaxoSmithKline US Processing/Genpact AP, NGM Biopharmaceuticals, Novo Nordisk, Orexigen Therapeutics, Piramal Life Sciences, Takeda Global Research and Development, and Zafgen; receives research support from Amylin Pharmaceuticals, BirdRock Bio, Eli Lilly and Company, and Takeda; and has equity in Jenrin Discovery and Zafgen. The remaining authors have nothing to disclose.

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