diabetes and insulin signaling case study quizlet

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Diabetes and Insulin Signaling

By Kristy J. Wilson

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Cellular signaling, otherwise known as signal transduction, is the mechanism by which cellular context and environmental situation are used to regulate or adjust cellular behavior. Multicellular organisms use cellular signaling to coordinate responses to the environment, facilitate development, and maintain homeostasis. The mechanisms by which a cell receives a message and translates it into short-term or long-term effects are as varied as the responses stimulated by cellular signaling. There are, however, some generalized steps that can be applied to many different signaling situations. This case study uses insulin signaling and the pathological case of diabetes as a lens through which students will learn general signaling mechanisms like kinase cascades and second messenger pathways. The case is designed in the interrupted format and has three parts that could be used either as homework or as an in-class activity. It is written for use in a cell biology class for sophomore or junior undergraduates but could also be utilized in biochemistry, physiology, or genetics courses.

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• Divide a signaling pathway into generalized parts: signal, receptor, signaling effectors (i.e., signaling cascades or second messengers) and short-term/long-term effects.
• Explain the role of phosphorylation in protein/enzyme activity and how it contributes to signal transduction.
• Apply signal transduction mechanisms to diabetes's physiological symptoms.
• Hypothesize a mechanism to explain how signaling in diabetes might be specific for different tissue types.
• Design a line of inquiry or an experiment that could identify a cause of insulin resistance in type-2 diabetes.

Cellular signaling; signal transduction; insulin; insulin signaling; insulin resistance; diabetes; receptor; kinase; transcription factor; second messenger

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Undergraduate lower division, Undergraduate upper division, Graduate

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• Cold Spring Harb Perspect Biol
• v.6(1); 2014 Jan

Insulin Receptor Signaling in Normal and Insulin-Resistant States

Jérémie boucher.

1 Section on Integrative Physiology and Metabolism, Joslin Diabetes Center and Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, Massachusetts 02115

André Kleinridders

C. ronald kahn.

In the wake of the worldwide increase in type-2 diabetes, a major focus of research is understanding the signaling pathways impacting this disease. Insulin signaling regulates glucose, lipid, and energy homeostasis, predominantly via action on liver, skeletal muscle, and adipose tissue. Precise modulation of this pathway is vital for adaption as the individual moves from the fed to the fasted state. The positive and negative modulators acting on different steps of the signaling pathway, as well as the diversity of protein isoform interaction, ensure a proper and coordinated biological response to insulin in different tissues. Whereas genetic mutations are causes of rare and severe insulin resistance, obesity can lead to insulin resistance through a variety of mechanisms. Understanding these pathways is essential for development of new drugs to treat diabetes, metabolic syndrome, and their complications.

Insulin and IGF-1 act via tyrosine kinase receptors to produce signals that control biological processes. The signaling pathways are precisely regulated, and perturbations in them may lead to insulin resistance.

Insulin and IGF-1 control a wide variety of biological processes by acting on two closely related tyrosine kinase receptors. Receptor activation initiates a cascade of phosphorylation events that leads to the activation of enzymes that control many aspects of metabolism and growth. Insulin/IGF-1 signaling contains many different points of regulation or critical nodes, controlled both positively and negatively, to ensure proper signal duration and intensity (see the schematic in Fig. 1 ). Perturbations in these signaling pathways can lead to insulin resistance. Here we review the insulin-signaling network, its critical nodes, and how these are perturbed in insulin-resistant states.

Insulin- and IGF-1-signaling pathways. Activation of insulin and IGF-1 receptors by their ligands initiates a cascade of phosphorylation events. A conformational change and autophosphorylation of the receptors occur at the time of ligand binding, leading to the recruitment and phosphorylation of receptor substrates such as IRS and Shc proteins. Shc activates the Ras-MAPK pathway, whereas IRS proteins mostly activate the PI3K-Akt pathway by recruiting and activating PI3K, leading to the generation of second messenger PIP 3 . Membrane-bound PIP 3 recruits and activates PDK-1, which phosphorylates and activates Akt and atypical PKCs. Akt mediates most of insulin's metabolic effects, regulating glucose transport, lipid synthesis, gluconeogenesis, and glycogen synthesis. Akt also plays a role in the control of cell cycle and survival. The Shc-Grb2-Sos-Ras-Raf-MAPK pathway controls cellular proliferation and gene transcription.

INSULIN AND IGF-1 RECEPTORS

Insulin and IGF-1 mediate their biological effects via the insulin and IGF-1 receptors (IR and IGF-1R). These highly homologous tyrosine kinase receptors are members of a family that also includes the orphan insulin receptor-related receptor (IRR), which has been suggested to play a role in testis determination ( Nef et al. 2003 ) and act as an extracellular alkali sensor ( Deyev et al. 2011 ). Although insulin and IGF-1 preferentially bind to their own receptors, both ligands can also bind to the alternate receptor with reduced affinity ( Belfiore et al. 2009 ).

The IR, IGF-1R, and IRR are tetrameric proteins that consist of two extracellular α subunits and two transmembrane β subunits that are joined by disulfide bonds. Both subunits are generated from a single large precursor by proteolytic cleavage. The IR messenger RNA (mRNA) undergoes alternative splicing of exon 11 to yield two isoforms that differ by exclusion (isoform A) or inclusion (isoform B) of a 12- amino-acid sequence in the carboxy-terminal part of the α subunit ( Mosthaf et al. 1990 ). IR-A is predominantly expressed in fetal tissues and in the brain, has higher affinity for both insulin and IGF-2, has a higher rate of internalization than the type-B isoform, and tends to be up-regulated in cancer ( Frasca et al. 1999 ), whereas IR-B expression is highest in the liver. Heterotetramers composed of an α/β dimer of IR and an α/β dimer of IGF-1R can form hybrid receptor complexes that bind preferentially IGF-1 and IGF-2 over insulin ( Benyoucef et al. 2007 ). Their formation appears to occur randomly in cells expressing both receptors and depends on the relative expression level of each type of receptor ( Bailyes et al. 1997 ; Pandini et al. 1999 ). Insulin and IGF-1 differential effects in vivo reflect mostly the hormone concentration and relative expression level of receptors in different tissues rather than the capacity of IR and IGF-1R to convey different signaling ( Boucher et al. 2010 ; Siddle 2012 ).

INSULIN RECEPTOR SUBSTRATES

At the time of ligand binding to the α subunits, IR and IGF-1R undergo a conformational change-inducing activation of the kinase activity in the β subunits. This results in transphosphorylation among β subunits, further activating the kinase and allowing the recruitment of receptor substrates. The best characterized substrates are members of the insulin receptor substrate (IRS) family of proteins, simply referred to as IRS-1 through IRS-6, which act as scaffolds to organize and mediate signaling complexes ( Sun et al. 1991 , 1995 ; Lavan et al. 1997a , b ; Cai et al. 2003 ; White 2006 ; Shaw 2011 ). IRS proteins are recruited to the membrane and the activated receptors through both pleckstrin homology (PH) and phosphotyrosine binding (PTB) domains in their amino terminus ( Voliovitch et al. 1995 ). They are subsequently phosphorylated by the activated receptors on multiple tyrosine residues that form binding sites for intracellular molecules that contain Src-homology 2 (SH2) domains ( Sun et al. 1993 ).

Although these substrates have similar tyrosine phosphorylation motifs, they clearly have different functions in vivo. IRS-1 knockout (KO) mice show growth retardation and impaired insulin action, especially in muscle ( Araki et al. 1994 ), but have normal glucose tolerance. IRS-2 KO mice display growth reduction in only selective tissues, such as certain neurons and islet cells, but also have defective insulin signaling in the liver, which when combined with the loss of β cells results in the development of diabetes ( Withers et al. 1998 ). At the cellular level, IRS-1 KO preadipocytes show defects in differentiation, whereas IRS-2 KO preadipocytes differentiate normally, but have impaired insulin-stimulated glucose transport ( Miki et al. 2001 ; Tseng et al. 2005 ). In skeletal muscle cells, IRS-1, but not IRS-2, is required for myoblast differentiation and glucose metabolism, whereas IRS-2 is important for lipid metabolism and ERK activation ( Huang et al. 2005 ; Bouzakri et al. 2006 ).

IRS-3 and IRS-4 show a more restricted tissue distribution pattern. In rodents, IRS-3 is most abundant in adipocytes, liver, and lung ( Sciacchitano and Taylor 1997 ), whereas in humans, the IRS-3 gene is a pseudogene, so no protein is produced at all ( Bjornholm et al. 2002 ). In mice, disruption of the gene for IRS-3 alone does not result in abnormalities, but leads to a severe defect in adipogenesis when combined with deletion of IRS-1 ( Laustsen et al. 2002 ). IRS-4 mRNA is present in skeletal muscle, liver, heart, brain, and kidney ( Fantin et al. 1999 ), and IRS-4 KO mice show only very minimal growth retardation and glucose intolerance ( Fantin et al. 2000 ). IRS-5 (also called DOK4) and IRS-6 (DOK5) have limited tissue expression ( Cai et al. 2003 ) and are relatively poor IR substrates ( Versteyhe et al. 2010 ).

In addition to the IRS proteins, the insulin and IGF-1 receptors can phosphorylate several other substrates ( Siddle 2012 ). Shc proteins are tyrosine phosphorylated by IR and IGF-1R, and participate in the activation of the Ras/ERK pathway. Grb2-associated binder (GAB) proteins are also substrates for a variety of receptors, including IR and IGF-1R. GAB proteins resemble IRS proteins, but lack a protein tyrosine phosphatase (PTP) domain, and could play a role in insulin/IGF-1 signaling in cells expressing low IRS protein levels. APS (SHB2) and Cbl are IR/IGF-1R substrates that recruit other proteins, such as the Cbl-associated protein (CAP), to the insulin-signaling complex. The latter participates in the control of insulin-stimulated glucose uptake ( Baumann et al. 2000 ). SH2B1 directly binds to insulin receptors and IRS proteins and enhances insulin sensitivity by promoting insulin receptor catalytic activity and by inhibiting tyrosine dephosphorylation of IRS proteins.

PHOSPHATIDYLINOSITOL (3,4,5)-TRIPHOSPHATE AND PHOSPHOINOSITIDE 3-KINASE

The critical pathway linking IRS proteins to the metabolic actions of insulin is the PI3-kinase (PI3K) and Akt pathway. The class Ia PI3-kinases are heterodimers consisting of a regulatory and catalytic subunit, each of which occurs in several isoforms ( Vadas et al. 2011 ). Recruitment and activation of the PI3K depends on the binding of the two SH2 domains in the regulatory subunits to tyrosine-phosphorylated IRS proteins ( Myers et al. 1992 ; Shaw 2011 ). This results in activation of the catalytic subunit, which rapidly phosphorylates phosphatidylinositol 4,5-bisphosphate (PIP 2 ) to generate the lipid second messenger phosphatidylinositol (3,4,5)-triphosphate (PIP 3 ). The latter recruits Akt to the plasma membrane, where it is activated by phosphorylation and induces downstream signaling.

The different isoforms of the regulatory subunit of PI3K are encoded by three distinct genes. Pik3r1 encodes 65%–75% of all regulatory subunits, mostly in the form of p85α, but also the splice variants p55α and p50α. Pik3r2 encodes p85β and accounts for ∼20% of the regulatory subunits. Pik3r3 encodes p55γ, which is similar in structure to p55α, but expressed at low levels in most tissues.

The three different catalytic subunits—p110α, β, and δ—are derived from three different genes. Binding of a regulatory to a catalytic subunit increases the catalytic subunit stability and maintains it in an inhibited state. This is relieved by binding of the regulatory subunit to specific phosphotyrosine motifs in IRS proteins, resulting in its activation ( Yu et al. 1998 ; Burke et al. 2011 ; Zhang et al. 2011 ). Liver-specific ablation of p110α, and to a lesser extent p110β, in mice results in glucose intolerance and insulin resistance ( Jia et al. 2008 ; Sopasakis et al. 2010 ). Surprisingly, knockouts of the regulatory subunits of PI3K, including a heterozygous deletion of p85α, p85β KO, or p50α/p55α double KO, all display increased insulin sensitivity ( Terauchi et al. 1999 ; Ueki et al. 2002 ). Different mechanisms by which reducing concentration of regulatory subunits can increase insulin action have been identified. Regulatory subunits typically are in excess concentration to catalytic subunits and thus compete with the enzymatically competent p85/p110 heterodimer for binding to IRS proteins. The p85α monomer has also been linked to regulation of the phosphatase and tensin homolog (PTEN) ( Taniguchi et al. 2010 ). More recently, p85α has been shown to bind to the transcription factor XBP-1 and to modify the unfolded protein response, which contributes to insulin resistance ( Park et al. 2010 ; Winnay et al. 2010 ).

In addition to PI3K, IRS proteins recruit other proteins potentially contributing to insulin and IGF-1 action. Proteomics analysis of the phosphotyrosine interactome of IRS-1 and IRS-2 indicates that most interacting proteins bind to both substrates, such as adaptor proteins Grb2 or Crk, or tyrosine phosphatase SHP2. However, other interaction partners seem to bind exclusively to IRS-1 (Csk) or IRS-2 (Shc, DOCK-6, and DOCK-7) ( Hanke and Mann 2009 ).

ACTIVATION OF DOWNSTREAM KINASES

Most of the physiological effects of PI3K-generated PIP 3 are mediated by a subset of AGC protein kinase family members, which include isoforms of Akt/protein kinase B (PKB), p70 ribosomal S6 kinase (S6K), serum- and glucocorticoid-induced protein kinase (SGK), as well as several isoforms of protein kinase C (PKC), particularly the atypical PKCs. AGC kinase family members share similar structure and mechanisms of activation via phosphorylation of two serine and threonine residues ( Pearce et al. 2010 ). PDK-1 (3-phosphoinositide-dependent protein kinase 1) is the major upstream kinase responsible for the phosphorylation and activation of the AGC kinase members regulated by PI3K ( Bayascas 2010 ). PDK-1 contains a PH domain that binds to membrane-bound PIP 3 , triggering PDK-1 activation. PDK-1 phosphorylates and activates AGC protein kinases at serine/threonine residues, such as Thr-308 for Akt ( Alessi et al. 1997 ). However, Akt phosphorylation at Ser-473 is required for full activation, and this is accomplished by the mammalian target of rapamycin complex 2 (mTORC2) ( Sarbassov et al. 2005 ; Oh and Jacinto 2011 ). DNA-dependent protein kinase (DNA PK) has also been described to phosphorylate and activate Akt in response to DNA damage ( Bozulic et al. 2008 ), and is involved in insulin regulation of metabolic genes such as fatty acid synthase ( Wong et al. 2009 ).

The Akt/PKB family of proteins consists of three different isoforms of serine/threonine protein kinases encoded by different genes ( Schultze et al. 2011 ). All isoforms possess a PH domain, allowing interaction with PIP 3 and recruitment to the plasma membrane. Akt2 is most abundant in insulin-sensitive tissues and seems to play a predominant role in mediating insulin action on metabolism. Thus, Akt2 KO mice are insulin resistant and develop diabetes ( Cho et al. 2001 ), whereas Akt1 and Akt3 KO mice do not.

ACTIONS OF INSULIN DOWNSTREAM FROM AKT

Activation of Akt by PDK-1 and mTORC2 allows the phosphorylation and activation of many downstream targets. Akt phosphorylates tuberous sclerosis complex protein 2 (TSC-2), inducing the degradation of the tumor suppressor complex that consists of TSC-2 and TSC-1, which activates the mTORC1 complex. Akt-induced activation of mTORC1 can also be achieved by phosphorylation of proline-rich Akt substrate 40 KDa (PRAS40), an inhibitor of mTORC1, thereby relieving the inhibition. The mTORC1 complex then phosphorylates and inhibits 4E-binding protein 1 (4E-BP1), activates ribosomal protein S6 kinases S6K1 and S6K2 and SREBP1, and leads to the regulation of a network of genes controlling metabolism, protein synthesis, and cell growth ( Duvel et al. 2010 ).

Transcription factors of the Forkhead box O (Foxo) family control the expression of lipogenic and gluconeogenic genes. Akt phosphorylates Foxos at several sites which provides docking sites for binding proteins of the 14-3-3 family. This interaction leads to the exclusion of Foxo from the nucleus, thus blocking its transcriptional activity ( Tzivion et al. 2011 ). Interestingly, although mice lacking Akt1 and Akt2 show severe hepatic insulin resistance and high levels of hepatic glucose production, these defects are normalized when Foxo1 is concomitantly ablated in the liver. This indicates that an additional pathway exists in the control of hepatic glucose metabolism beyond the Akt/Foxo1 axis, which allows for insulin-mediated regulation of hepatic glucose production ( Lu et al. 2012 ).

There are multiple other substrates of Akt involved in insulin action. The GTPase-activating protein Akt substrate of 160 kDa (AS160), also called TBC1D4, and its homolog TBC1D1, are phosphorylated by Akt and are involved in insulin- and contraction-mediated glucose uptake ( Sano et al. 2003 ; Sakamoto and Holman 2008 ; Taylor et al. 2008 ; An et al. 2010 ). Akt also phosphorylates and inactivates glycogen synthase kinase 3, resulting in glycogen synthase activation and glycogen accumulation in liver ( Kim et al. 2004b ). Akt-dependent phosphorylation of PGC-1α impairs the ability of PGC-1α to promote gluconeogenesis and fatty acid oxidation ( Li et al. 2007 ). Phosphorylation of phosphodiesterase 3B (PDE3B) by Akt results in its activation and in a decrease in cyclic AMP levels ( Kitamura et al. 1999 ), which plays important roles in the effect of insulin to inhibit lipolysis in adipocytes and insulin secretion in β cells ( Degerman et al. 2011 ).

OTHER ACTIONS OF INSULIN DOWNSTREAM FROM PI3K

Akt plays a central role in mediating many other insulin actions by regulating the expression and activity of a wide range of proteins, including enzymes, transcription factors, cell cycle regulating proteins, or apoptosis and survival proteins ( Manning and Cantley 2007 ). Murine double minute 2 (Mdm2) is phosphorylated by Akt, which inhibits p53-mediated apoptosis and contributes to tumorigenesis ( Cheng et al. 2010 ). Akt phosphorylates cell cycle inhibitors p21Cip1/WAF1 and p27Kip1, resulting in cytoplasmic localization, cell growth, and inhibition of apoptosis ( Zhou et al. 2001 ; Motti et al. 2004 ). Akt also phosphorylates and inhibits Bax, Bad, and caspase-9, which promotes cell survival ( Datta et al. 1997 ; Cardone et al. 1998 ; Yamaguchi and Wang 2001 ; Gardai et al. 2004 ). Akt can phosphorylate and activate IkB kinase (IKK), leading to NF-κB activation ( Bai et al. 2009 ). Akt phosphorylates and activates endothelial nitric oxide synthase (eNOS), which catalyzes the production of the vasodilator and anti-inflammatory molecule nitric oxide (NO), providing a potential link between insulin resistance and cardiovascular disease ( Dimmeler et al. 1999 ; Fulton et al. 1999 ; Yu et al. 2011a ). Although less well studied in insulin action, the serum- and glucocorticoid-induced protein family of kinases (SGK) are highly homologous to Akt, are also activated by dual phosphorylation by PDK-1 and mTORC2 in a PI3K dependent manner, and have many downstream substrates in common with Akt ( Bruhn et al. 2010 ).

PROTEIN KINASES C

PKC isoforms are both mediators and modifiers of insulin's metabolic action. Of the three major classes of PKC, the atypical PKCs (aPKCs), PKC-ζ and PKC-λ/ι, are activated via phosphorylation by PDK-1. aPKCs play an important role in insulin-stimulated glucose transport and regulation of lipid synthesis, and their expression and/or activation is decreased in muscle from obese and diabetic humans ( Farese and Sajan 2010 ). Both PKC-λ and PKC-ζ have been shown to function interchangeably in mediating insulin-stimulated glucose transport ( Sajan et al. 2006 ). Muscle-specific deletion of PKC-λ in mice leads to impairment in insulin-induced glucose uptake and insulin resistance ( Farese et al. 2007 ). Mice with liver-specific deletion of PKC-λ display decreased insulin-induced expression of SREBP1c and triglyceride content in the liver, resulting in increased insulin sensitivity in these mice ( Matsumoto et al. 2003 ).

THE GRB2-SOS-RAS-MAPK PATHWAY

A second essential branch of the insulin/IGF-1-signaling pathway is the Grb2-SOS-Ras-MAPK pathway, which is activated independently of PI3K/Akt. Activated receptors and IRS proteins both possess docking sites for adaptor molecules that contain SH2 domains such as Grb2 and Shc. The carboxy-terminal SH3 domain of Grb2 binds to proteins such as Gab-1, whereas the amino-terminal SH3 domain binds to proline-rich regions of proteins such as son-of-sevenless (SOS). SOS is a guanine nucleotide exchange factor (GEF) for Ras, catalyzing the switch of membrane-bound Ras from an inactive, GDP-bound form (Ras-GDP) to an active, GTP-bound form (Ras-GTP). Ras-GTP then interacts with and stimulates downstream effectors, such as the Ser/Thr kinase Raf, which stimulates its downstream target MEK1 and 2 that phosphorylate and activate the MAP kinases ERK1 and 2. Stimulated ERK1/2 play a direct role in cell proliferation or differentiation, regulating gene expression or extra-nuclear events, such as cytoskeletal reorganization, through phosphorylation and activation of targets in the cytosol and nucleus.

NEGATIVE REGULATORS OF INSULIN SIGNALING

Insulin and IGF-1 signaling are tightly controlled because uncontrolled activity of the downstream pathways could lead to severe perturbations in metabolism and tumorigenesis. Intensity and duration of the signal play an important role in determining the specificity of the response to their pleiotropic effects. Therefore, the ability to turn off the insulin signal in a rapid manner at different levels is critical ( Fig. 2 ). On the other hand, some of these inhibitory mechanisms can be altered in pathophysiological conditions and participate in the development of insulin resistance.

Negative modulators of insulin and IGF-1 signaling. Intensity and duration of insulin and IGF-1 signaling play an important role in determining the specificity and the nature of the response to these hormones. Signaling is attenuated by action of several phosphatases, which dephosphorylate the receptors, IRS proteins, PKCs, and ERK or PIP 3 . In addition, stress kinases such as JNK, IKK, and ERK, as well as PKCs or S6K, inhibit insulin/IGF-1 signaling by inducing inhibitory serine/threonine phosphorylation of IR/IGFR and IRS proteins. Trb3 inhibits Akt, and adaptor proteins such as SOCS and Grb bind to the receptors and IRS proteins and inhibit signaling by competition.

Phosphoprotein Phosphatases as Negative Regulators of Insulin Action

Both cytoplasmic protein tyrosine phosphatases, such as PTP1B, and transmembrane phosphatases, such as LAR, have been shown to dephosphorylate the tyrosine residues on activated IR and IGF-1R, as well as IRS proteins, thereby reducing their activity ( Goldstein et al. 1998 ). Although the role of LAR in the control of insulin signaling in vivo remains controversial, PTP1B is an essential component of insulin action. PTP1B KO mice show enhanced insulin sensitivity, increased IR phosphorylation in muscle and liver, and are also resistant to high-fat-diet-induced obesity and associated insulin resistance ( Elchebly et al. 1999 ; Klaman et al. 2000 ).

The serine/threonine phosphatase protein phosphatase 1 (PP1) has been implicated in the regulation of several rate-limiting enzymes in both glucose and lipid metabolism, including glycogen synthase, hormone-sensitive lipase, or acetyl CoA carboxylase ( Brady and Saltiel 2001 ). Protein phosphatase 2A (PP2A), which accounts for ∼80% of serine/threonine phosphatase activity in cells, also regulates the activities of many protein kinases involved in insulin action, including Akt, PKC, S6K, ERK, cyclin-dependent kinases, and IKK ( Millward et al. 1999 ). Several studies indicate that PP2A is hyperactivated in diabetic states ( Kowluru and Matti 2012 ).

Other serine/threonine phosphatases have been implicated in insulin action. Protein phosphatases 2B (PP2B), also known as calcineurin, has been shown to dephosphorylate Akt ( Ni et al. 2007 ). Two novel members of the PP2C family involved in regulation of insulin action are the PH domain leucine-rich repeat protein phosphatases PHLPP-1 and -2, which dephosphorylate both Akt and PKCs ( Brognard and Newton 2008 ). Overexpression of PHLPP1 in cells impairs Akt and glycogen synthase kinase 3 activity, resulting in decreased glycogen synthesis and glucose transport ( Andreozzi et al. 2011 ). Elevated levels of PHLPP1 have been found in adipose tissue and skeletal muscle of obese and/or diabetic patients and correlate with decreased Akt2 phosphorylation ( Cozzone et al. 2008 ; Andreozzi et al. 2011 ).

Lipid Phosphatases as Negative Regulators of Insulin Action

Lipid phosphatases can regulate insulin signaling by modulating PIP 3 levels. PTEN dephosphorylates PIP 3 , thus antagonizing PI3K signaling in cells ( Cantley and Neel 1999 ; Carracedo and Pandolfi 2008 ). Muscle, adipose tissue, or liver-specific deletion of PTEN in mice increases insulin sensitivity ( Stiles et al. 2004 ; Kurlawalla-Martinez et al. 2005 ; Wijesekara et al. 2005 ), and mice with whole-body PTEN haploinsufficiency show improved glucose tolerance and increased insulin sensitivity ( Wong et al. 2007 ). Interestingly, the p85α regulatory subunit of PI3K has recently been shown to bind directly to and enhance PTEN activity, creating a unique interface between the generation and degradation of PIP 3 ( Taniguchi et al. 2006b ; Chagpar et al. 2010 ).

SH2 domain-containing inositol 5-phosphatases (SHIP) 1 and 2 also dephosphorylate PIP 3 . SHIP1 expression is restricted to hematopoietic cells, whereas SHIP2 is ubiquitously expressed and plays a role in insulin signaling ( Suwa et al. 2010 ). SHIP2 deficiency in mice results in hypoglycemia, enhanced insulin-induced Akt activation, and resistance to high-fat-diet-induced obesity, indicating that SHIP2 is a key regulator of glucose and energy homeostasis in vivo ( Clement et al. 2001 ; Sleeman et al. 2005 ). Conversely, SHIP2-overexpressing mice show reduced insulin-induced Akt activation in the liver, fat, and skeletal muscle ( Kagawa et al. 2008 ).

Other Negative Modulators (Grb, SOCS, Trb3, IP7)

Grb10 and Grb14 are cytoplasmic adaptor proteins that decrease IR and to a lesser extent IGF-1R activity, and prevent access of substrates to the activated receptors ( Holt and Siddle 2005 ). Deletion of the Grb10 gene in mice leads to increased growth, enhanced insulin signaling, and increased glucose tolerance ( Smith et al. 2007 ; Wang et al. 2007 ). Grb10 overexpression, on the other hand, results in impaired growth, glucose intolerance, and insulin resistance ( Shiura et al. 2005 ). Grb14 expression is increased in adipose tissue of insulin-resistant animal models and type-2 diabetic patients ( Cariou et al. 2004 ), and Grb14 KO mice display increased glucose tolerance and insulin sensitivity, consistent with an inhibitory role of Grb14 on insulin signaling ( Cooney et al. 2004 ). Grb10 and Grb14 share similar mechanisms as insulin signaling is not further increased in mice with deletion of both proteins ( Holt et al. 2009 ).

Proteins of the suppressor of cytokine signaling (SOCS) family are adaptor proteins that act as negative regulators of cytokine and growth factor signaling. In addition, SOCS proteins, in particular SOCS1 and SOCS3, negatively regulate insulin signaling and thus link cytokine signaling to insulin resistance. Their expression is increased in obesity, and they induce insulin resistance via either inhibition of the tyrosine kinase activity of the IR, competition for binding of the IRS proteins to the receptor, or targeting the IRS proteins to degradation ( Emanuelli et al. 2000 , 2001 ; Rui et al. 2002 ; Ueki et al. 2004a , b ; Sachithanandan et al. 2010 ).

Tribbles homolog 3 (Trb3) is a member of the family of pseudokinases that is thought to function as adaptor proteins. Trb3 expression is induced in liver in fasting and diabetes, and disrupts insulin signaling by binding to Akt and blocking its activation. Trb3 knockdown in mice improves glucose tolerance ( Du et al. 2003 ; Koo et al. 2004 ). In cultured cells, insulin-stimulated S6K activation is decreased when Trb3 is overexpressed, and increased when Trb3 levels are reduced ( Matsushima et al. 2006 ). Trb3 action in adipose tissue seems to be independent of Akt. Thus, whereas insulin promotes lipogenesis, Trb3 stimulates lipolysis by triggering the ubiquitination and degradation of acetyl-CoA carboxylase. Transgenic mice overexpressing Trb3 in adipose tissue are protected from diet-induced obesity because of enhanced fatty acid oxidation and display increased insulin sensitivity ( Qi et al. 2006 ).

A novel negative regulator of insulin signaling is the inositol phosphate IP7. It was recently shown that insulin and IGF-1 increase IP7 levels, which in turn inhibits Akt translocation to the plasma membrane and subsequent activation, creating a potential feedback mechanism that attenuates insulin signaling ( Chakraborty et al. 2010 ). Deletion of the enzyme that catalyzes IP7 formation in mice causes increased insulin responsiveness. Further studies will be needed to elucidate the contribution of this pathway in normal or pathological conditions.

Regulation by Inhibitory Serine and Threonine Phosphorylation

Tyrosine phosphorylation is essential for IR/IGF-1R and IRS activation. On the other hand, serine and threonine phosphorylation of the receptors or IRS proteins is primarily involved in turning the insulin signal down ( Fig. 3 ). Increased inhibitory Ser/Thr phosphorylation of IR and especially IRS-1 and -2 occurs in response to cytokines, fatty acids, hyperglycemia, mitochondrial dysfunction, and ER stress, and insulin itself via activation of multiple kinases, predominantly by c-Jun amino-terminal kinase (JNK), IKK, conventional and novel PKCs, but also mTORC1/S6K and MAPK ( De Fea and Roth 1997a ; Aguirre et al. 2000 ; Gao et al. 2002 ; Gual et al. 2003 ; Li et al. 2004 ; Zhang et al. 2008a ; Boura-Halfon and Zick 2009 ). Increased IR serine phosphorylation associated with decreased tyrosine kinase activity has been observed in insulin-resistant states, both in rodents and in humans ( Karasik et al. 1990 ; Dunaif et al. 1995 ; Zhou et al. 1999 ; Shao et al. 2000 ). An increase in cAMP concentration also induces inhibitory serine phosphorylation of IR in a PKA-dependent manner ( Stadtmauer and Rosen 1986 ; Roth and Beaudoin 1987 ).

Activation of Ser/Thr kinases causes inhibitory phosphorylation on insulin-signaling molecules. Lipotoxicity, inflammation, hyperglycemia, and subsequently oxidative stress, as well as mitochondrial dysfunction and ER stress, all converge on activation of Ser/Thr kinases, inducing inhibitory Ser/Thr phosphorylation of IR, IRS proteins, and Akt on multiple residues, causing insulin resistance.

Although inhibitory IRS-1 serine phosphorylation occurs at many different sites ( Boura-Halfon and Zick 2009 ), the best studied of these modifications occurs at Ser-307 ( Aguirre et al. 2002 ). IRS-1 Ser-307 phosphorylation is increased in obese and diabetic mice ( Hirosumi et al. 2002 ; Um et al. 2004 ). Although this is widely believed to contribute to insulin resistance by inhibiting insulin receptor kinase activity, recent studies have made this association less clear. Thus, insulin itself can stimulate phosphorylation of IRS-1 on Ser-307 in humans ( Yi et al. 2007 ), and mice with a knock-in of IRS-1 Ser307Ala mutant developed more severe insulin resistance than control mice when fed a high-fat diet, indicating that Ser-307 is required to maintain normal insulin signaling ( Copps et al. 2010 ). Thus, increased IRS-1 Ser-307 phosphorylation observed in insulin-resistance states may be associated with, but not cause, insulin resistance.

Lipids, through their metabolic product diacylglycerols, can activate classical (α, β, γ) and novel PKC members (δ, θ, ε) and impair insulin signaling by inducing multiple serine phosphorylation of IRS proteins and IR specifically at Thr-1336, Thr-1348, and Ser-1305/1306 ( Bollag et al. 1986 ; Karasik et al. 1990 ; Lewis et al. 1990 ; Chin et al. 1993 ; De Fea and Roth 1997b ; Turban and Hajduch 2011 ). Thus, deletion of any member of the novel PKC family prevents the development of insulin resistance in skeletal muscle and liver by decreasing IRS-1 Ser-307 phosphorylation ( Kim et al. 2004a ; Samuel et al. 2007 ; Mack et al. 2008 ; Bezy et al. 2011 ). Atypical PKC-ζ also inhibits insulin signaling by inducing serine phosphorylation of IRS-1 ( Ravichandran et al. 2001 ) and Thr-34 phosphorylation of Akt, thereby inhibiting its recruitment to the plasma membrane ( Powell et al. 2003 , 2004 ).

Another component of the negative feedback loops in insulin signaling is mTORC1. Activation of mTOR and S6K is not only downstream from insulin signaling, but also inhibits it by increasing serine phosphorylation and reducing IRS tyrosine phosphorylation. This is illustrated by the phenotype of S6K null mice, which are lean and display enhanced insulin sensitivity ( Um et al. 2004 ). In addition, IRS-1 is hyperphosphorylated and degraded in TSC-2 KO fibroblasts, which show constitutive S6K activation ( Harrington et al. 2004 ; Shah et al. 2004 ). mTORC1 also mediates phosphorylation and stabilization of Grb10, leading to feedback inhibition of insulin signaling ( Hsu et al. 2011 ; Yu et al. 2011b ).

MECHANISMS OF INSULIN RESISTANCE

A central feature of type-2 diabetes is insulin resistance, a condition in which cells cannot respond properly to insulin. This occurs primarily at the level of so-called insulin-sensitive tissues, such as liver, muscle, and fat, and can be caused by multiple mechanisms ( Fig. 3 and Table 1 ).

Molecular mechanisms of insulin resistance

Multiple molecular mechanisms of insulin resistance have been described, in addition to inhibitory Ser/Thr phosphorylation on insulin-signaling molecules ( Fig. 3 ). Genetic mutations, dephosphorylation events, posttranslational modifications, and formation of inhibitory complexes have all been shown to cause insulin resistance.

Genetic Causes of Insulin Resistance

Insulin receptor.

Mutations in the insulin receptor gene have been identified in several rare forms of severe insulin resistance, including leprechaunism, Rabson-Mendenhall syndrome, or the type-A syndrome of insulin resistance. These patients often require a hundredfold or more insulin than a typical diabetic patient ( Kahn et al. 1976 ; Cochran et al. 2005 ). Most of these patients have nonsense or missense mutations in the extracellular ligand-binding domain or intracellular tyrosine kinase domain of the receptor, which leads to severely reduced insulin binding, altered kinetics of insulin binding, or reduced tyrosine kinase activity, but some also have presumed promoter defects leading to reduced receptor mRNA expression ( Taylor et al. 1991 ; Haruta et al. 1995 ). Insulin receptor mutations have not been observed in patients with routine type-2 diabetes (T2D).

Insulin Receptor Substrate Proteins

The G972R polymorphism of IRS-1 is observed with higher frequency in patients with T2D and leads to decreased insulin signaling, mostly decreasing PI3K activity ( Almind et al. 1996 ; Hribal et al. 2008 ). Although this finding has not been confirmed in all large-scale population analyses ( Florez et al. 2004 ; van Dam et al. 2004 ), recent studies have continued to show an association between a single-nucleotide polymorphism (SNP) in IRS-1 and T2D ( Burguete-Garcia et al. 2010 ; Martinez-Gomez et al. 2011 ). A T608R missense mutation in IRS-1 resulting in decreased insulin signaling has been reported in a patient with T2D, but appears to be very rare ( Esposito et al. 2003 ). Numerous polymorphisms have been identified in the human IRS-2 gene, but a clear association between these polymorphisms and T2D has not been found ( Bernal et al. 1998 ).

Phosphoinositide 3-Kinase

An M326I polymorphism in the p85α regulatory subunit of the PI3K was identified in Pima Indian women and is associated with decreased prevalence for T2D ( Baier et al. 1998 ). However, this M326I mutation only has modest effects on insulin signaling in vitro by decreasing p85α binding to IRS-1 and increasing p85α degradation ( Almind et al. 2002 ). Another polymorphism in p85α (SNP42) is associated with fasting hyperglycemia, but its molecular mechanism so far remains elusive ( Barroso et al. 2003 ).

Phosphatase and Tensin Homolog

In diabetes, mutations of PTEN have not been reported yet. However, three Japanese type-2 diabetic subjects have been identified with polymorphisms in the PTEN gene, one of which was associated with T2D. This SNP caused a higher expression rate of PTEN and reduced insulin-induced Akt activation in cells ( Ishihara et al. 2003 ). Very recently, it has been found that individuals with PTEN haploinsufficiency are both obese and insulin sensitive, with a decreased risk of T2D but increased risk of cancer ( Pal et al. 2012 ). The impact of this in the general population is unknown.

AKT and Related Targets

A rare missense mutation (R274H) in Akt2 leading to loss of kinase activity has been identified in a patient with diabetes ( George et al. 2004 ). Two other missense mutations (R208 K and R467W) have also been identified in diabetic patients, but surprisingly, these mutant forms display unaltered insulin-stimulated kinase activities in vitro ( Tan et al. 2007 ). In type-2 diabetic patients, a gain-of-function mutation (Q84R) in Trb3 has been associated with insulin resistance and decreased insulin-stimulated Akt phosphorylation ( Prudente et al. 2005 , 2009 ). A mutation in AS160 at position 363, resulting in a premature stop codon, was identified in a patient with severe postprandial hyperinsulinemia, and acts in a dominant-negative manner to reduce glucose transport ( Dash et al. 2009 ).

Lipotoxicity

One feature of metabolic syndrome is ectopic accumulation of lipids, especially fatty acids (FA), which is believed to cause insulin resistance via multiple mechanisms. Tissue-specific increase in lipid content in nonadipose tissues provides direct evidence of lipotoxicity. Increased hydrolysis of circulating triglycerides owing to muscle-specific overexpression of lipoprotein lipase leads to skeletal muscle insulin resistance ( Ferreira et al. 2001 ), whereas increased lipid transport in heart or liver leads to lipotoxic cardiomyopathy and nonalcoholic fatty liver disease, respectively ( Chiu et al. 2005 ; Koonen et al. 2007 ). Besides the effect of increased lipid flux on insulin sensitivity, multiple lipid intermediates have been shown to promote insulin resistance.

Elevated circulating free fatty acids (FFA) are observed in obesity and induce activation of JNK, IKK, and PKC and IRS-1 Ser-307 phosphorylation ( Schenk et al. 2008 ). The fatty acid palmitate plays a particular role in promoting insulin resistance as it induces endoplasmic reticulum (ER) stress, cytokine production, and activates JNK ( Ozcan et al. 2004 ; Shi et al. 2006 ). In addition, palmitate activates NF-κB signaling while inhibition of this pathway reverses lipid-induced insulin resistance ( Kim et al. 2001a ; Sinha et al. 2004 ). Interestingly, the detrimental effect of palmitate on skeletal muscle insulin resistance can be reversed by coinfusion with oleate, thereby changing its conversion from phospholipids and diacylglycerol (DAG) to triglycerides ( Peng et al. 2011 ). This indicates that FFA induces insulin resistance through multiple mechanisms, and combinations of FA can influences insulin signaling and highlight the crucial interplay of lipids with respect to dietary interventions.

The lipid metabolite DAG has also been shown to induce insulin resistance. Increased muscle DAG (intramyocellular lipid) leads to muscle insulin resistance by activating PKC-θ and inducing IRS-1 Ser-307 phosphorylation ( Yu et al. 2002 ). Conversely, reducing DAG levels in skeletal muscle and liver protects mice against high-fat-diet-induced insulin resistance ( Liu et al. 2007 ; Ahmadian et al. 2009 ; Samuel et al. 2010 ).

Increased plasma concentration of the sphingolipid ceramide is observed in obese and diabetic patients and is associated with severe insulin resistance ( Haus et al. 2009 ). Ceramide has been shown to induce insulin resistance via PKC and JNK activation ( Westwick et al. 1995 ; Schenk et al. 2008 ) and, thus, inhibition of ceramide synthesis ameliorates insulin resistance ( Holland et al. 2007 ). Ceramides also inhibit Akt activation by increasing the interaction of PP2A with Akt, and phosphorylation of Akt at Thr-34 by PKC ζ, resulting in reduced binding of PIP 3 to Akt ( Teruel et al. 2001 ; Powell et al. 2003 ; Blouin et al. 2010 ).

In addition to effects on kinases, alteration of membrane–lipid composition affects insulin signaling. An increase in the saturated-to-unsaturated FA ratio is observed in type-2 diabetic patients and is thought to reduce membrane fluidity and insulin sensitivity ( Field et al. 1990 ; Bakan et al. 2006 ). Moreover, an increase in the phosphatidylcholine (PC) to phosphatidylethanolamine (PE) ratio in endoplasmic reticulum leads to the activation of ER stress and is associated with insulin resistance ( Fu et al. 2011 ).

Inflammation

Obesity is characterized by the development of a chronic low-grade inflammatory state, which is considered a key component in promoting obesity-associated insulin resistance ( Osborn and Olefsky 2012 ). Adipose tissue expansion occurs in response to caloric overload, and is associated with an increase in immune cell infiltration and a subsequent proinflammatory response ( Sun et al. 2011 ). Two cell types are especially important in this scenario: adipocytes and macrophages, both of them capable of secreting proinflammatory cytokines and inducing insulin resistance. Increased secretion of the chemokine MCP-1 by adipocytes drives macrophage accumulation into adipose tissues and induces insulin resistance ( Kamei et al. 2006 ). Deletion of MCP-1 or its receptor CCR2 improves insulin sensitivity and ameliorates inflammation in mice ( Kanda et al. 2006 ; Weisberg et al. 2006 ). Increased secretion of cytokines, such as TNF-α, IL1β, or IL-6, by both immune cells and adipocytes is observed with obesity and induces insulin resistance via multiple mechanisms, including activation of Ser/Thr kinases ( Ozes et al. 2001 ; Yuan et al. 2001 ; Hirosumi et al. 2002 ; Zhang et al. 2008a ; Fan et al. 2010 ), decreasing IRS-1, GLUT4, and PPARγ expression ( Rotter et al. 2003 ; Jager et al. 2007 ), or activation of SOCS3 in adipocytes ( Steppan et al. 2005 ).

Another driving factor in obesity-associated inflammation is caused by activation of Toll-like receptor (TLR), especially activation of TLR-2 and -4. TLRs belong to the innate immune system and are generally activated by pathogen-associated molecular patterns such as LPS, and induce inflammation via activation of the NF-κB pathway ( Akira and Takeda 2004 ). TLRs are ubiquitously expressed and TLR-4 is elevated in skeletal muscle ( Reyna et al. 2008 ) and adipose tissue ( Shi et al. 2006 ) with obesity. Interestingly, saturated FA can also activate this pathway ( Lee et al. 2001 ; Shi et al. 2006 ), indicating a potential role for these receptors in obesity-driven inflammation. Thus, mice with reduced TLR-2- or TLR-4-signaling proteins ( Shi et al. 2006 ; Kleinridders et al. 2009 ; Himes and Smith 2010 ) are protected from obesity and obesity-associated insulin resistance.

Negative Regulation by Hyperglycemia

Glucose itself, at supraphysiological concentrations, is able to alter insulin sensitivity in muscle and fat, as well as decrease insulin secretion from β cells ( Leahy et al. 1986 ; Hager et al. 1991 ). Hyperglycemia induced by decreased glucose transport in skeletal muscle impairs adipose and hepatic insulin action ( Zisman et al. 2000 ; Kim et al. 2001b ) and induces insulin resistance through several pathways, which are all believed to be linked to oxidative stress ( Evans et al. 2005 ). Advanced glycosylation end products (AGE) inhibit insulin signaling by increasing Ser-307 phosphorylation of IRS-1 and forming methylglyoxal-IRS-1 adducts ( Riboulet-Chavey et al. 2006 ).

Hyperglycemia increases the flux through the polyol pathway, which causes JNK activation and increases the hexosamine-biosynthetic pathway. This has been shown to promote insulin resistance in adipose tissue, skeletal muscle, liver, and pancreas in part by O -GlcNAcylation of IRS proteins ( Marshall et al. 1991 ; Patti et al. 1999 ; McClain 2002 ; McClain et al. 2002 ). Furthermore, hyperglycemia also leads to O -GlcNAcylation of IR, which impairs receptor dimerization ( Federici et al. 1999 ), and of Foxo1 leading to increased gluconeogenic gene expression ( Housley et al. 2008 ).

Hyperglycemia also activates the PKC pathway by inducing de novo synthesis of DAG ( Xia et al. 1994 ) and causes insulin resistance by forming a multimolecular complex, including receptor of AGE/IRS-1/Src, thereby activating PKC-α and increasing IRS-1 Ser-307 phosphorylation ( Miele et al. 2003 ; Cassese et al. 2008 ).

Mitochondrial Dysfunction and ROS Formation

Although low levels of reactive oxygen species (ROS) can enhance insulin action ( Krieger-Brauer et al. 1992 ; Mahadev et al. 2001 ), high concentration of ROS causes oxidative stress when unresolved. ROS formation occurs as a by-product of the electron transport chain and is a major consequence of mitochondrial dysfunction ( Chang and Chuang 2010 ). Increased ROS levels have been observed in obese and diabetic states and can be caused by an increased metabolite flux into mitochondria, alterations in mitochondrial proteins, and reduced expression of antioxidant enzymes ( West 2000 ; Rosen et al. 2001 ; Evans et al. 2005 ; Fridlyand and Philipson 2006 ). Increased oxidative stress leads to the activation of stress kinases that induce insulin resistance by serine phosphorylation of IRS proteins ( Rudich et al. 1998 ; Evans et al. 2005 ; Dokken et al. 2008 ). Besides the aspect of ROS-mediated insulin resistance, altered mitochondrial dynamics in the form of increased mitochondrial fission leads to insulin resistance and can be rescued by inhibiting fission, which decreases the activity of p38 MAP kinase and increases IRS-1 and Akt activation ( Jheng et al. 2012 ). Impairment of mitochondrial FA oxidation in liver can also lead to elevated DAG content, resulting in PKC-ε activation and decreased IRS-2 phosphorylation and PI3-kinase activity ( Koh et al. 2005 ; Zhang et al. 2007 ).

The ER stress response, also known as unfolded protein response (UPR), is an adaptive process to ensure proper protein folding, maturation, and quality control in the ER. The three crucial pathways of the UPR (PERK, IRE1α, and ATF6) are all activated with obesity and act together to reduce the burden of unfolded proteins ( Hotamisligil 2010 ). Obese mice display enhanced PERK and IRE1α activity in adipose tissue and liver, causing JNK and IKK activation and insulin resistance by phosphorylation of IRS-1 on Ser-307 ( Ozcan et al. 2004 , 2009 ; Hu et al. 2006 ; Zhang et al. 2008b ). The transcription factor XBP-1 is activated by splicing during ER stress and increases gene expression of molecular chaperones to restore ER homeostasis. Overexpression of spliced XBP-1 reduces ER stress response, decreases activation of JNK, and increases insulin signaling by decreasing IRS-1 serine phosphorylation ( Ozcan et al. 2004 ).

CONCLUDING REMARKS

Insulin and IGF-1 acting via specific tyrosine kinase receptors propagate signals via two main branches: the PI3K-PDK-1-Akt and the Grb2-SOS-Ras-MAPK pathways that control proliferation, differentiation, and survival at the cellular level, and growth and metabolism in organisms. These signaling pathways contain several points of regulation, signal divergence, and cross talk with other signaling cascades that define critical nodes ( Taniguchi et al. 2006a ). The complexity of this signaling system is essential to mediate the variety of insulin and IGF-1 biological responses. Many steps are negatively regulated by action of phosphatases or inhibitory proteins. One of the great challenges remaining is deciphering the complexity of insulin-resistance pathogenesis. Causes of insulin resistance are numerous and the mechanisms are multifactorial. In rare cases, the cause is genetic, but in most others, insulin resistance is triggered by cellular perturbations, such as lipotoxicity, inflammation, glucotoxicity, mitochondrial dysfunction, and ER stress, which lead to deregulation of genes and inhibitory protein modifications, resulting in impaired insulin and IGF-1 action. Identifying new molecules that impact insulin signaling and new levels of control, as well as better understanding the causes and mechanisms leading to insulin resistance, will be essential for a more effective treatment of type-2 diabetes and associated diseases.

Editors: Joseph Schlessinger and Mark A. Lemmon

Additional Perspectives on Signaling by Receptor Tyrosine Kinases available at www.cshperspectives.org

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Case Study 1

Case study 2, case study 3, case study 4, case study 5, case studies 6 and 7, case studies in insulin therapy: the last arrow in the treatment quiver.

The authors are from the Department of Endocrinology at the Institute of Post Graduate Medical Education & Research in Kolkata, West Bengal, India. Anubhav Thukral, MD, Chitra Selvan, MD, Partha Pratim Chakraborty, MD, Ajitesh Roy, MD, Soumik Goswami, MD, and Rana Bhattacharjee, MD, are postdoctoral trainees. Sujoy Ghosh, DM, is an assistant professor; Satinath Mukherjee, DM, is a professor; and Subhankar Chowdhury, DM, is a professor and head of the department.

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Anubhav Thukral , Chitra Selvan , Partha Pratim Chakraborty , Ajitesh Roy , Soumik Goswami , Rana Bhattacharjee , Sujoy Ghosh , Satinath Mukherjee , Subhankar Chowdhury; Case Studies in Insulin Therapy: The Last Arrow in the Treatment Quiver. Clin Diabetes 1 October 2013; 31 (4): 175–178. https://doi.org/10.2337/diaclin.31.4.175

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B ecause type 2 diabetes is a progressive disease, its natural course requires initiation of insulin in a significant proportion of patients, especially when oral agents fail to achieve glycemic targets. Most practicing physicians and endocrinologists acknowledge that neither the transition to nor the journey with insulin treatment is often as smooth as one would hope. Most have occasionally met with patients who plead for help in controlling their blood glucose levels with oral drugs alone. Often, doctors struggle to persuade such patients to start insulin.

In this article, we share some of our experiences—some common throughout the world and others unique to our country—in caring for patients who are prescribed insulin. It is important to learn from these experiences with insulin because it represents, more often than not, the last arrow in our treatment quiver.

The patient was a 45-year-old man who has had type 2 diabetes for the past 6 years and had been taking insulin for the past 2 years. His body weight was 50 kg (BMI 24 kg/m 2 ). He presented with uncontrolled and recently increased blood glucose levels and a dramatic increase in insulin dose during the past 5 months without any apparent cause. He had no history of fever, infections, or steroid use. He had had multiple hospital visits during the past few months.

Discoloration with hypertrophy on medial aspects of both thighs on the site where the patient described in Case Study 1 was injecting insulin.

The patient appeared to be well educated and concerned about this situation. To our surprise, however, we found he was injecting insulin on the medial aspect of his thighs. Examination showed lipohypertrophy with swelling in that region ( Figure 1 ). The crowded clinic with its lack of trained diabetes educators and nurses was the perfect setting for this gross oversight in checking injection sites.

The patient was a 55-year-old obese (BMI 28 kg/m 2 ) man with known type 2 diabetes for 12 years who had been taking insulin for the past 6 months. He presented with haywire blood glucose levels over several visits, with gradually increasing doses of insulin. There were no documented episodes of hypoglycemia.

The patient was injecting his own insulin, and his injection technique was confirmed to be good. The insulin injection sites were healthy. Insulin was properly stored at his home, and he was using a 40 IU syringe and a compatible vial.

Surprisingly, his A1C level was 7.2%, which was discordant with his venous blood glucose readings. He was not performing self-monitoring of blood glucose at home because of logistical issues. What was perplexing was that, although his dose of premixed insulin before breakfast had increased from 12 to 36 units in the past 6 months, his morning fasting levels were controlled with a predinner dose of 6 units, and although his post-breakfast values crept up from 200 to 340 mg/dl during the past four visits, his fasting blood glucose had decreased from 98 to 66 mg/dl in the same time period.

Through detailed questioning, we learned that dietary irregularities on the days of the test were at the heart of the matter. On a normal day, the patient injected insulin at about 7:40 a.m., ate breakfast at 8:00 a.m., and ate lunch at noon. However, on the days of the test, he had an extended fast, skipping breakfast. He then performed SMBG and took insulin at noon and, instead of a normal lunch, ended up overeating with a large meal essentially comprising both breakfast and lunch. Hence, his insulin dose was always insufficient, and his postprandial glucose values were always high. This would not have happened if the test center had been closer to his house or had opened at 8:00 a.m., which would have allowed him the flexibility to follow his normal dietary routine on test days.

The patient was asked to divide his meals, take voglibose before breakfast and lunch, and perform SMBG at the proper time. We split his doses, with 16 units of regular insulin before breakfast and 16 units before lunch and 6 units of premixed insulin before dinner. His blood glucose was then controlled.

A man with type 2 diabetes who had been taking insulin for the past 4 years with good glycemic control was started on twice-daily premixed insulin with a pen device for the past month. Although he was happy with the convenience and ease of using a pen device, his blood glucose levels became erratic. We decided to confirm his injection technique and asked him to bring his pen device to the clinic. He brought along a pen device with the cartridge in place, but the needle was missing. On questioning, we learned that the patient was injecting insulin without a needle. He thought it normal that he would not feel anything because pen devices were supposed to be painless. In actuality, he was trying to give himself insulin transdermally rather than subcutaneously. This case illustrates that a combination of less-than-adequate education and aggressive marketing by device sales companies can lead to misconceptions on the part of patients.

A woman with type 2 diabetes who had been taking insulin for the past 6 years with reasonable glycemic control presented with high glucose levels and injection site abscesses for the past month ( Figure 2 ). Through thoughtful questioning, we learned that she had been wiping the insulin injection tip with a cotton swab before and after injecting.

Insulin site abscess in patient described in Case Study 4.

A 46-year-old man with type 2 diabetes started noticing nodular swellings and subsequent discharging sinuses from these swellings at the insulin injection site for the past 6 months ( Figure 3 ). These swellings continued to appear when he took insulin from a particular vial. When that vial got finished, new lesions did not appear.

Chronic granulomatous infection at injection site in patient described in Case Study 5.

Patient was treated with multiple courses of different antibiotics with minimal response. Grams stain and multiple bacterial cultures performed on the discharge were negative for any growth. A fine needle aspiration cytology was positive for acid-fast bacilli. The patient refused a biopsy. He was started on anti-tubercular treatment with which the lesions began to regress.

The patient denied having used an insulin syringe that was used earlier for Bacillus Calmette-Guérin vaccination. However, he was sure that a particular vial of insulin was associated with these lesions because no new lesions appeared after he changed vials. Unfortunately, the vial in question could not be retrieved.

The patient was thoroughly evaluated but was not found to have any tubercular foci elsewhere in the body. Although tuberculosis is endemic in our country, tuberculosis at insulin injection sites caught us by surprise. There are no similar case reports or descriptions, although we did find case reports from Columbia of cutaneous tuberculosis after subcutaneous injections for cosmetic mesotherapy. 1  

A young woman with diabetes and uncontrolled blood glucose was found to be injecting insulin into her forearm ( Figure 4 ), and yet another man was found to be injecting insulin intradermally on his thigh with residual post-inflammatory hyperpigmentation ( Figure 5 ).

What are some of the causes of insulin therapy failure?

How can clinicians be vigilant in identifying patients with nonadherence to or problems carrying out their insulin regimen?

What are the consequences of nonadherence to insulin therapy?

How can we ensure better adherence to insulin therapy?

Forearm injection sites of the female patient described in Case Study 6.

Post-inflammatory hyperpigmentation in the thighs of the male patient described in Case Study 7.

Incomplete or incorrect initial education and a lack of continuing education are at the root of all of the cases reported above. Apart from the obvious effects they have on glycemic status, incidents such as these leave an indelible trauma in the minds of patients and act as strong deterrents to continuing insulin therapy.

Doctors and patients expend great effort to start insulin, and the manner in which we use this last arrow in our treatment quiver matters. In a busy outpatient department, the easiest thing to do for patients with uncontrolled blood glucose levels is simply to increase the dose of insulin. However, this not only destroys patients' confidence in insulin, but also leads to decreasing self-confidence and depression in patients on insulin. Hence, it would seem prudent and rewarding to devote some time to educating patients and using every visit to reinforce their knowledge and verify their insulin therapy practices. Logistical issues and test conditions need to be factored in when interpreting blood glucose data. This also highlights the importance of the role of diabetes educators and paramedical staff in care of diabetes patients. Unfortunately, these professionals and the services they provide are missing in most diabetes clinics in underdeveloped or developing countries, where the gap between the disease burden and the availability of care providers is widening rapidly.

Recent reviews on insulin adherence in Western countries show adherence rates as low as 62–64%. 2   Estimates of diabetes medication adherence in our country (India) are even worse, especially in populations in which illiteracy and poverty levels are very high. Nonadherence rates as high as 74% (95% CI 69.2–78.3) have been reported from south India. 3  

Insulin nonadherence has been shown to be a significant risk factor for increased mortality and increased costs of therapy in the diabetic population. 4 , 5   Many factors determine adherence to insulin. Misinformation and miscommunication between doctors and patients is one of the foremost causes. Illness and treatment perceptions have also been shown to be an important determinant of adherence. 6   Among various suggestions to improve treatment adherence is a participatory model in which patients are made an integral part of decision-making. This has been shown to improve adherence to insulin and antidiabetic drugs and, hence, to improve outcomes. 7  

All insulin-requiring patients must learn to position the needle perpendicular to their skin when injecting insulin, rotate injection sites, discard syringes after a single use, inject at appropriate sites, inject subcutaneously into fat, count to 10 after injections, store insulin in a cool and airy place away from direct sunlight, and discard open vials after 1 month or the prescribed shelf life indicated on the vial. 8   Apart from this basic education, they should be taught methods that may reduce the pain of injection, such as allowing the insulin to come to room temperature before injecting, making sure there are no air bubbles in the syringe, and keeping underlying muscles relaxed. This information may help to boost patients' morale. 8  

A change in temperature can change the concentration of insulin in the cartridge of a pen device. 9   Hence, it is recommended to remove the needle from the pen immediately after use so the temperature of the insulin is not affected by the leak in thermo insulation caused by the attached needle. 10   Another common mistake is using a 100 IU/ml pen cartridge but injecting insulin through a 40 IU/ml syringe. Patients also should be educated about the color coding of syringes and vials of different insulin concentrations (e.g., a red syringe is 40 IU/ml, whereas an orange syringe is 100 IU/ml).

Although the cases described here expose our shortcomings in diabetes care, highlighting such unusual clinical scenarios might alert other health care providers to recognize similar instances in their practice and enable better outcomes from insulin therapy.

CLINICAL PEARLS

Unexplained deterioration in glycemic control and discrepancies between plasma glucose, SMBG values, and A1C test results should alert physicians to the possibility of patients' nonadherence with or poor practices in carrying out their insulin regimen.

Nonadherence is associated with a significantly higher mortality, higher complication rates, low self-confidence, poor self-image, and adverse disease-related perceptions.

In addition to patient illiteracy and poverty, failure on the part of the health care system to ensure a proper, planned, and staged patient education and confidence-building program before instituting insulin therapy is a major barrier to successful insulin therapy.

A few hours spent by physicians, nurses, or other diabetes educators with patients initiating insulin may save many years of life for these patients.

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• Published: 16 April 2024

Placental IGFBP1 levels during early pregnancy and the risk of insulin resistance and gestational diabetes

• Marie-France Hivert   ORCID: orcid.org/0000-0001-7752-2585 1 , 2 , 3 ,
• Frédérique White 4 ,
• Catherine Allard   ORCID: orcid.org/0000-0002-8829-4984 3 ,
• Kaitlyn James 5 ,
• Sana Majid 1 ,
• François Aguet 6 ,
• Kristin G. Ardlie 6 ,
• Jose C. Florez   ORCID: orcid.org/0000-0002-1730-9325 2 , 6 , 7 , 8 ,
• Andrea G. Edlow   ORCID: orcid.org/0000-0003-2915-5949 5 ,
• Luigi Bouchard 3 , 9 , 10 ,
• Pierre-Étienne Jacques 3 , 4 , 11 ,
• S. Ananth Karumanchi   ORCID: orcid.org/0000-0002-2281-6831 12 &
• Camille E. Powe 2 , 5 , 6 , 8  

Nature Medicine ( 2024 ) Cite this article

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• Gene expression
• Gestational diabetes
• Predictive markers

Reduced insulin sensitivity (insulin resistance) is a hallmark of normal physiology in late pregnancy and also underlies gestational diabetes mellitus (GDM). We conducted transcriptomic profiling of 434 human placentas and identified a positive association between insulin-like growth factor binding protein 1 gene ( IGFBP1 ) expression in the placenta and insulin sensitivity at ~26 weeks gestation. Circulating IGFBP1 protein levels rose over the course of pregnancy and declined postpartum, which, together with high gene expression levels in our placenta samples, suggests a placental or decidual source. Higher circulating IGFBP1 levels were associated with greater insulin sensitivity (lesser insulin resistance) at ~26 weeks gestation in the same cohort and in two additional pregnancy cohorts. In addition, low circulating IGFBP1 levels in early pregnancy predicted subsequent GDM diagnosis in two cohorts of pregnant women. These results implicate IGFBP1 in the glycemic physiology of pregnancy and suggest a role for placental IGFBP1 deficiency in GDM pathogenesis.

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Gestational diabetes mellitus (GDM) affects one in seven pregnancies worldwide 1 and is associated with maternal and offspring adverse health outcomes during pregnancy, at delivery and over the life course 2 . Previous research has established that a defect in insulin sensitivity (that is, excess insulin resistance) contributes to GDM 3 , 4 . In addition, we and others 5 , 6 , 7 , 8 , 9 , 10 have shown previously that, among individuals with GDM, those with the lowest insulin sensitivity (insulin-resistant GDM) have the greatest risk of hyperglycemia-associated pregnancy complications, suggesting that reduced insulin sensitivity is a key contributor not only to GDM itself, but also to the negative health outcomes that accompany it.

The placenta is the main driver of marked changes in insulin physiology during pregnancy, including the drastic decline in insulin sensitivity, which occurs even in those without GDM. This has been attributed to hormonal factors released by the placenta that lead to insulin resistance 11 . The specific placental circulating factors that mediate the profound change in insulin sensitivity during pregnancy are still unknown, and the classically implicated pregnancy hormones (for example, human chorionic gonadotropin, human placental lactogen and placental growth hormone) have been found to correlate poorly with insulin sensitivity in pregnancy in human studies 12 . A better understanding of the placental factors driving the pregnancy-related decline in insulin sensitivity could lead to new therapeutic approaches to hyperglycemia, early identification of those at risk of developing GDM and recognition of those most likely to have GDM-related pregnancy complications. Despite the hallmark reduction in insulin sensitivity in all pregnancies, most pregnant individuals do not develop GDM. This phenomenon suggests that additional factors, as yet unknown, may contribute to the maintenance of euglycemia in pregnancy. Indeed, a variable improvement in insulin sensitivity has been reported in early pregnancy in several studies 13 , 14 , 15 . A systematic search for placental factors that are related to insulin sensitivity in pregnancy may also uncover those that improve it.

The overarching goal of this study was to discover new placental factors implicated in physiologic changes in insulin sensitivity during pregnancy and that contribute to GDM pathophysiology. We conducted a placental genome-wide transcriptomic study using RNA sequencing (RNA-seq) to identify genes whose expression in the placenta was associated with insulin sensitivity in pregnancy. We identified the insulin-like growth factor binding protein 1 gene ( IGFBP1 ) as the most strongly associated placental transcript. IGFBP1 is a binding protein that is produced primarily by the liver outside of pregnancy and is highly expressed by the placenta 16 . IGFBP1 has been implicated in the modulation of the biological activity of insulin-like growth factor (IGF)-1 and IGF-2, which are key regulators of growth and metabolism in postnatal and fetal life 17 . Outside of pregnancy, low IGFBP1 concentrations are correlated with insulin resistance and associated with risk of incident type 2 diabetes (T2D) 18 , 19 , 20 but this has not been shown in pregnancy. In the current study, using plasma samples collected from three different pregnancy cohorts with diverse backgrounds, we measured circulating IGFBP1 protein levels at several time points during and after gestation. Using these data, we investigated associations between circulating IGFBP1 levels and insulin sensitivity, other pregnancy-related metabolic traits, birth anthropometric measurements and risk of GDM.

Participants in the placental genome-wide RNA-seq analyses

A genome-wide RNA-seq study was conducted using placental samples collected from 434 participants in the Genetic of Glucose regulation in Gestation and Growth (Gen3G) prospective pregnancy cohort 21 (Table 1 ). At study entry (median, 9 weeks gestation), participants’ mean ± s.d. age was 28.7 ± 4.4 years, and median interquartile range (IQR) body mass index (BMI) was 23.8 (21.4–27.9) kg m −2 . Individuals with diabetes present before pregnancy were excluded. Participants underwent a fasting 75 g oral glucose tolerance (75g-OGTT) in the late second trimester (median, 26 weeks gestation), during which extra plasma samples were collected to measure glucose and insulin levels at several time points to estimate insulin sensitivity (using the Matsuda index, which has been validated previously against euglycemic clamps in pregnancy 22 ). At delivery (median (IQR) = 39.6 (38.7–40.3) weeks), trained research staff collected samples from the maternal-facing side of the placenta using standardized protocols for collection and storage for future analyses by RNA-seq ( Methods ).

Differential placental RNA expression and insulin sensitivity

After processing and quality control (QC) of the placental RNA-seq dataset, differential expression of 15,202 genes were analyzed in relation to insulin sensitivity (Matsuda index, log 2 transformed) in late second trimester. We identified 14 genes whose placental RNA expression levels were associated with insulin sensitivity ( P  < 1.0 × 10 −3 ; Extended Data Table 1 ) after accounting for technical variability (37 surrogate variables (SV)), precision variables (gestational age at delivery, fetal sex) and potential confounders (gravidity, maternal age and BMI) using multivariate linear regression models. The strongest association was found between insulin sensitivity and the IGFBP1 gene ( β  = 0.43; P  = 2.5 × 10 −5 ), where higher placental expression levels were associated with greater insulin sensitivity (Fig. 1 ). No strong associations were observed between the Matsuda index and other genes in IGF-related pathways, or genes encoding classic pregnancy-specific placental hormones (for example, human placental growth hormone ( GH2 ), human placental lactogen ( CSH1 ), prolactin ( PRL )) or genes encoding inflammatory proteins secreted by the placenta that have been found previously to be associated with insulin sensitivity in pregnancy 12 (Extended Data Table 2 ); some of the classic placental hormones ( CSH1 , GH2 ) had mappability scores <0.8, so these results should be interpreted with caution.

Linear model adjusted for maternal age, gravidity and maternal BMI at first trimester visit, sex of offspring, gestational age at delivery and 37 SVs (from SmartSVA package); gene names identified if P  < 1.0 × 10 −3 . Red dots indicate genes with association P  < 1.0 × 10 −3 (horizontal dotted line) and absolute log 2 fold changes greater than 5 s.d. from the mean (vertical dotted lines).

Circulating IGFBP1 levels during pregnancy

Given the high levels of placental expression of IGFBP1 (average transcript per million = 103.4) and its known secreted protein status, circulating levels of IGFBP1 (R&D systems enzyme-linked immunosorbent assay, catalog number DGB100) were measured in Gen3G participants ( n  = 837; Extended Data Table 3 ). Circulating levels of IGFBP1 were correlated with placental RNA expression of IGFBP1 (Pearson r  = 0.15; P  = 0.002 with IGFBP1 levels at V1; r  = 0.14; P  = 0.005 at V2; adjusted for gestational age at each visit). Furthermore, circulating levels of IGFBP1 were measured in two additional pregnancy cohorts: the Study of Pregnancy Regulation of Insulin and Glucose (SPRING) 15 and the Massachusetts General Hospital (MGH) Obstetrical Maternal Study (MOMS). Characteristics of participants included in SPRING and MOMS are presented in Extended Data Table 4 ).

In SPRING participants who remained normoglycemic throughout pregnancy ( n  = 65), the median plasma levels of IGFBP1 rose between the first trimester (66,610 pg ml −1 ) and 24 to 32 weeks gestation (79,379 pg ml −1 ), then declined dramatically postpartum (16,588 pg ml −1 ; paired t -tests P  < 0.001 for differences between plasma levels across pregnancy and postpartum; Extended Data Fig. 1 ). This pattern, combined with high placental expression levels, suggests a possible placental origin of high circulating IGFBP1 levels during pregnancy.

In a subset of Gen3G participants ( n  = 27) in whom we assayed serial IGFBP1 levels during the 75g-OGTT (Extended Data Fig. 2 ), circulating IGFPB1 levels were stable over the first hour of the OGTT (median levels, fasting = 87,008 pg ml −1 ; 1 h postload = 91,485 pg ml −1 ; paired t -test P  = 0.13), but declined 2 h postglucose-load (median = 60,920 pg ml −1 ; paired t -test P  = 0.0007 compared with fasting). The change in plasma insulin levels from baseline to 1 h (delta insulin 0–60 min) appeared to be associated inversely with the IGFBP1 levels at 1 h ( r  = −0.39; P  = 0.047) and at 2 h ( r  = −0.31; P  = 0.11) during the OGTT. This is consistent with the known negative feedback regulation of IGFBP1 expression by insulin, albeit shown previously only in hepatocytes 23 .

Circulating IGFBP1 and insulin sensitivity in pregnancy

Higher plasma IGFBP1 levels were associated with greater insulin sensitivity in all three pregnancy cohorts examined (Table 2 ). The strong positive correlations (Pearson r  = 0.5 to 0.6; P  < 0.001) between plasma IGFBP1 levels and insulin sensitivity were consistent across different periods of pregnancy, as well as in the postpartum period (SPRING). Adjusting for maternal age and gestational age at the time of blood sampling did not influence correlations. The strength of association was attenuated modestly by further adjustment for maternal BMI, but remained highly statistically significant ( r  = 0.34–0.48, P  < 0.001; Table 2 ).

In Gen3G, correlations between plasma IGFBP1 (in the first and second trimester) and various maternal metabolic traits and neonatal anthropometric measures were assessed using Pearson correlations (Extended Data Table 5 ). Higher maternal BMI was associated with lower plasma IGFBP1 in the first trimester ( r  = −0.27) and in the late second trimester ( r  = −0.54; both P  < 0.001). Plasma IGFBP1 in the late second trimester was correlated negatively with glucose ( r  = −0.28 to −0.30) and insulin levels ( r  = −0.40) during the OGTT (all P  < 0.001). Lower IGFBP1 levels at both time points were also associated with higher birthweight z -scores (standardized for gestational age and sex) at delivery ( r  = −0.15 and r  = −0.21 for IGFBP1 at the first and second trimester visits, respectively; both P  < 0.001; Extended Data Table 5 ). Adjusting for maternal BMI or for maternal glucose reduced the strength of associations, but the correlations remained statistically significant (for example, second trimester IGFBP1 partial correlations with birthweight z -score adjusted for maternal BMI r  = −0.12; P  < 0.001; or adjusted for maternal glucose (glucose area under the curve (AUC) during the OGTT) r  = −0.17; P  < 0.001). Lower IGFBP1 at the second trimester visit was associated with higher risk of large-for-gestational (LGA) birthweight (odds ratio (OR) = 0.60 (95% confidence interval (CI) = 0.46–0.78); P  = 0.0001); this association was reduced but remained statistically significant after adjustment for maternal BMI (OR = 0.73 (95% CI = 0.54–0.99); P  = 0.045).

Early pregnancy circulating IGFBP1 and GDM incidence

Prediction analyses were conducted using plasma IGFBP1 measured in early pregnancy (median 9 weeks gestation) and GDM (diagnosed with International Association of the Diabetes and Pregnancy Study Groups (IADPSG) criteria applied to a 75g-OGTT at a median of 26 weeks gestation) in Gen3G participants ( n  = 837) in addition to known clinical risk factors. Overall, 70 participants (8.4%) developed GDM (Extended Data Table 3 ). Early pregnancy IGFBP1 levels alone predicted risk of incident GDM with a modest receiver operating characteristic (ROC) AUC value of 0.64. A model including only clinical variables (maternal age, gravidity, family history of diabetes, maternal BMI, gestational week at blood sampling) without IGFBP1 levels yielded an ROC AUC of 0.66 (Fig. 2 ). A model with the same clinical variables but also incorporating early pregnancy IGFBP1 levels improved predictive ability (ROC AUC = 0.72 compared with 0.66; P  = 0.008; Fig. 2 ). Using a logistic regression model, a 1 s.d. increase in plasma IGFBP1 levels in early pregnancy was associated with a greater than 50% reduction in the risk for GDM in Gen3G (OR = 0.44; IQR = 0.30–0.64; P  < 0.001; adjusted for maternal age, gravidity, gestational age at plasma IGFBP1 measurements and maternal BMI; Table 3 ).

Red line (clinical variables only): ROC curve for GDM diagnosis based on maternal age, gravidity, family history of diabetes, gestational age at V1 and maternal BMI at V1; green line: ROC curve for GDM diagnosis based on all clinical variables plus plasma IGFBP1 levels (measured at a median of 9 weeks of gestation). GDM diagnosed by IADPSG criteria. Shaded areas: 95% CI for each curve (2,000 stratified bootstrap). Comparing AUC values with and without plasma IGFBP1 (Box–Cox transformation) using a two-sided DeLong’s test gave estimates (difference between 2 AUC) = −0.060, 95% CI = −0.104 to −0.015, z  = −2.641, P  = 0.008.

Predictive association between early pregnancy IGFBP1 levels and GDM incidence were replicated in a nested case–control study in the MOMS cohort ( n  = 55 GDM cases, diagnosed based on Carpenter–Coustan criteria at a median of 29 weeks gestation; matched 1:2 with noncases): the OR was 0.40 (95% CI, 0.24–0.67; P  < 0.001, adjusted for maternal age and BMI) per s.d. increase in plasma IGFBP1 (measured at a median of 17 weeks gestation). In the SPRING cohort, all GDM cases were combined ( n  = 44 total, diagnosed either in early pregnancy or at 24–32 weeks gestation based on IADPSG criteria) and predictive analyses showed an OR of 0.75 (95% CI, 0.46–1.25; P  = 0.28; adjusted for maternal age, BMI and gestational age at blood samples) for each SD increase in plasma IGFBP1 measured in the first trimester (median, 13 weeks gestation).

Circulating IGFBP1 in pregnancy by GDM physiologic subtype

Given the strong association between plasma IGFBP1 and insulin sensitivity in pregnancy, the longitudinal changes in plasma IGFBP1 across pregnancy in different physiologic subtypes of GDM (as defined previously 10 ) and in participants with normal glucose tolerance (NGT) were investigated in Gen3G (Fig. 3 ). All GDM subtypes had lower mean plasma IGFBP1 levels in early pregnancy compared with the NGT group. However, the insulin-resistant GDM group had a blunted increase in IGFBP1 levels between the first and second trimester; in contrast, in those with insulin-deficient GDM, IGFBP1 levels reached similar levels to those in the NGT group during the second trimester (Fig. 3 ). The group who had GDM with both insulin resistance and insulin deficiency (mixed defect GDM) showed an IGFBP1 trajectory that was intermediate between the other GDM subtypes.

Sample size in each group: NGT, n  = 767; insulin-resistant GDM, n  = 34; insulin-deficient GDM, n  = 19; and mixed defect GDM, n  = 12. Lines represent the levels of circulating IGFBP1 (not transformed) from the first trimester visit to the late second trimester visit at exact gestational age of the measure, smoothed by a generalized additive model with parameter estimation via restricted maximum likelihood using ‘stat_smooth’ function from the ggplot2 package. Shaded areas represent 95% CIs.

In Gen3G, low IGFBP1 levels in first trimester were associated with subsequent diagnosis of both insulin-resistant GDM and insulin-deficient GDM with ORs ~0.4 (in fully adjusted models, including maternal BMI) similar to prediction models where the outcome was all GDM (Model 3; Table 3 ). However, IGFBP1 levels in the second trimester were associated only with insulin-resistant GDM (OR = 0.28 (0.16–0.47) per s.d. increase in IGFBP1 levels; P  < 0.001); there was no statistically significant association between second trimester IGFBP1 plasma levels and insulin-deficient GDM (Table 3 ).

In this study, using genome-wide RNA-seq of placental tissue, we identify IGFBP1 as a key placental transcript associated with insulin sensitivity in human pregnancy. Our findings implicate IGFBP1 deficiency in GDM pathophysiology. We show that circulating IGFBP1 levels rise during pregnancy and are much higher in pregnancy than in the nonpregnant state, supporting the contribution of placental and/or decidual IGFBP1 to elevated circulating IGFBP1 in pregnancy. In three independent pregnancy cohorts, we demonstrate a strong and consistent correlation between higher circulating IGFBP1 and greater insulin sensitivity (lesser insulin resistance), uncovering a potential compensatory mechanism in euglycemic pregnancy. Moreover, we show that low plasma IGFBP1 levels in the first trimester of pregnancy predict the later diagnosis of GDM, independent of maternal clinical risk factors (including BMI). Finally, we note that the normal pregnancy rise in IGFBP1 levels is attenuated in insulin-resistant GDM, suggesting that a defect in placental IGFBP1 release may contribute specifically to this GDM physiologic subtype.

In placental tissues, IGFBP1 expression has been detected previously in decidual cells and in fetal placental macrophages or Hofbauer cells 24 ; however, there is limited knowledge of IGFBP1 regulation and actions in pregnancy. Given that our samples were collected from the maternal-facing side, it is possible that IGFBP1 expression and release from decidual cells contributed to IGFBP1 circulating levels. In vitro experiments using decidualized human endometrial stromal cells have shown IGFBP1 regulation by cAMP, progesterone and relaxin 25 , 26 —the latter two being critical hormones for the establishment and maintenance of pregnancy 27 . Outside of pregnancy, IGFBP1 is expressed almost exclusively by the liver 16 and its production is regulated by insulin, which inhibits its gene transcription in hepatocytes 28 . This is consistent with low IGFBP1 levels reflecting insulin resistance and hepatic steatosis in nonpregnant adults and children 18 , 19 , 29 . Our observations that plasma IGFBP1 levels decline after a plasma insulin rise in response to an oral glucose load introduce the possibility that insulin may downregulate the production and/or release of IGFBP1 from the placenta/decidua, similar to the downregulation observed in hepatocytes 28 . It is also possible that other insulin-sensitive endocrine factors, such as adiponectin, regulate IGFBP1 expression in placental cells 30 .

Functional studies suggest that IGFBP1 binds IGF-1 and IGF-2 with equal affinity and can either inhibit or enhance IGF actions, depending on the context 23 . In postnatal life, IGF-1 is the main active growth factor and is essential for normal growth during childhood and adolescence, whereas, during fetal development, both IGF-1 and IGF-2 are key regulators of fetal growth 23 , 31 . Outside of pregnancy, IGF-1 enhances insulin sensitivity by suppressing hepatic glucose production 32 , 33 and promoting glucose uptake in peripheral tissues 34 , 35 . IGF2 is a highly expressed placental imprinted gene that is a key regulator of fetal growth in mammals 36 . In a recent study, pregnant mice with an IGF2 deletion specific to placental endocrine cells did not develop the normal insulin resistance of pregnancy and gave birth to fetuses that were growth-restricted and hypoglycemic 37 . In general, IGFs have higher affinity for IGFBPs than for cellular IGF-receptors and, thus, IGFBPs, sometimes in combination with acid-labile subunits, often act as inhibitors of IGF biological activity 25 . PAPPA and PAPPA2 are two key proteinases released by the placenta that allow the cleavage of IGFBPs from IGFs; however, these proteinases do not cleave IGFBP1 (refs. 38 , 39 ). IGFBPs may also function as a circulating pool of IGFs by prolonging their half-lives and creating IGF reservoirs 17 , 23 . In addition, IGFBP1 has putative IGF-independent effects, and may activate PI3K/AKT signaling pathways involved in postreceptor insulin signaling directly 40 . In line with this, in vivo injection of an active IGFBP1 peptide improved insulin sensitivity in a diet-induced obesity mouse model 41 . These diverse mechanisms of action might explain some of the inconsistencies from previous animal studies attempting to establish the effects of IGFBP1 on glucose regulation 42 , 43 , 44 . Future studies of gestational glycemic pathophysiology should investigate the interrelations of IGFs with the different IGFBPs and their regulation from acid-labile subunits and PAPPAs in the context of pregnancy.

None of these previous studies provide insights into the specific role that IGFBP1 may have in pregnancy, when there are high circulating levels of IGFs, which are suspected to influence glucose metabolism 23 , 37 . We speculate that placental/decidual release of IGFBP1 may regulate insulin sensitivity in pregnancy—via direct and/or indirect effects—contributing physiologically to homeostatic mechanisms to balance maternal and fetal nutrient needs. An alternative explanation is that low levels of IGFBP1 in GDM are a consequence of hyperinsulinemia with another upstream cause, but this would not be consistent with the rise of circulating IGFBP1 throughout pregnancy (which is characterized by progressive hyperinsulinemia). In the context of GDM pathophysiology, based on our findings, in individuals with insulin-resistant GDM, we speculate that the placenta may be unable to produce increasing amounts of IGFBP1 as pregnancy progresses; this deficiency in circulating IGFBP1 could contribute to excessive insulin resistance, and thus to maternal hyperglycemia detected in the late second trimester in this GDM subtype. In individuals with insulin-deficient GDM, IGFBP1 amounts were low in the first trimester but amounts during the second semester were on par with those without GDM, suggesting that other pathophysiologic factors contribute to hyperglycemia in this GDM subtype. Given the differences in IGFBP1 in different GDM subtypes, and increasing recognition in the field that GDM is a heterogeneous condition 45 , our finding of persistently lower IGFBP1 levels in the second trimester of pregnancies affected by insulin-resistant GDM may have implications for GDM precision medicine 46 , 47 . Our findings suggest that, in cases of insulin-resistant GDM, the placenta does not increase IGFBP1 production sufficiently; if this association is demonstrated to be causal, this opens the door to a new therapeutic target for this GDM subtype. Beyond GDM, the association between lower circulating IGFBP1 levels and higher birthweight is in line with similar observations in an earlier report 48 and suggests a potential explanation for the greater risk of LGA birthweight that we observed previously in instances of insulin-resistant GDM 10 .

Accurately predicting GDM diagnosis in later pregnancy based on early pregnancy markers could allow development and implementation of interventions—such as counseling on diet and exercise—to prevent GDM and its complications. However, most predictive models that rely on established clinical risk factors perform poorly 49 , 50 and, thus, there has been a search for reliable and replicable biomarkers. We found that low levels of circulating IGFBP1 in early pregnancy predict later diagnosis of GDM in a large population-based cohort (Gen3G), with external replication and consistent effect sizes in a separate cohort (MOMS). The effect size was more modest and not statistically significant in a cohort study of participants who all had GDM risk factors (SPRING); these inclusion criteria may have diminished the predictive ability of circulating IGFBP1 in this population. Previous studies have been inconsistent with regard to circulating IGFBP1 as a predictive biomarker for GDM, with only one study reporting on IGFPB1 levels measured before 20 weeks of gestation 51 . Our ROC analyses showed that circulating IGFBP1 levels in early pregnancy have a predictive ability beyond that of established GDM risk factors (including maternal BMI and family history of diabetes); however, the moderate ROC AUC value in a model that included IGFBP1 levels along with these clinical factors suggests that additional biomarkers would be necessary for clinical utility. Future studies could also investigate whether urinary levels of IGFBP1 in pregnancy can predict GDM or characterize subtypes, which would be convenient for patients and clinicians. We do not know whether women who developed GDM in our study had low IGFBP1 levels before pregnancy, thus pre-pregnancy assessment of IGFBP1 should be considered in future studies of pre-conception interventions aiming to optimize metabolic outcomes in pregnancy.

Our investigation has several strengths. We included a large number of placental samples in our expression profiling, used transcriptome-wide RNA-seq and leveraged an agnostic approach to implicate genes and their products in insulin sensitivity during pregnancy. Furthermore, we examined not only placental expression of IGFBP1 , but also circulating IGFBP1 levels in three pregnancy cohorts. Our analyses included measurement of circulating IGFBP1 levels over a longitudinal timeframe that spanned both pregnancy and postpartum. In addition, we used an OGTT-based measure of insulin sensitivity that has been validated against euglycemic clamps in pregnancy. Our study also had some limitations. Although we had a large overall sample size, the number of GDM cases was somewhat modest, and the sample size for each GDM physiologic subtype was even more limited. We conducted RNA-seq on bulk samples, including placental and decidual cells, thus we cannot confirm the exact cellular source of IGFBP1. Although we were able to tie placental/decidual RNA expression and circulating IGFBP1 levels to detailed physiologic phenotyping, our study was observational and thus cannot establish mechanisms or causality for the associations we observed.

In conclusions, starting from agnostic and unbiased placental gene expression profiling, we implicated IGFBP1 in insulin sensitivity during pregnancy. IGFBP1 was expressed highly in our placental samples and maternal IGFBP1 levels are elevated markedly during gestation, increasing across pregnancy and dropping substantially postpartum. Both placental and circulating IGFBP1 levels are correlated strongly and consistently with maternal insulin sensitivity. A deficiency of circulating IGFBP1 in early pregnancy predicts the diagnosis of GDM in the late second trimester, independent of clinical GDM risk factors in two different pregnancy cohorts. We demonstrated distinct IGFBP1 trajectories in different physiologic subtypes of GDM, with insulin-resistant GDM lacking the expected increase in circulating IGFBP1 across gestation. Future studies should address whether IGFBP1 has direct or indirect effects on tissues that regulate maternal insulin sensitivity during pregnancy. If IGFBP1 is causally implicated in gestational glycemic regulation, new therapeutic approaches based on IGFBP1 replacement as an insulin sensitizer could be envisioned and tested for precision prevention or treatment of GDM.

All three human cohorts included in this study recruited pregnant individuals who are all female (sex as a biological determinant) given that only biological female can experience pregnancy. We acknowledge that not all pregnant individuals self-identify as women (gender). Our study aimed at understanding biology of glucose regulation in pregnancy, thus our analyses apply to female individuals. All participants provided informed consent.

Gen3G cohort

Gen3G is a prospective population-based cohort that recruited pregnant women from 1 January 2010 to 30 June 2013 at the Centre Hospitalier Universitaire de Sherbrooke (CHUS), located in the province of Quebec (Canada). Participants were demographically representative of the greater population of the region 21 . Each study participant provided informed written consent, and the study protocols were reviewed by the ethical committees from CHUS, and from the Harvard Pilgrim Health Care Institute.

We recruited 1,024 pregnant women without preexisting diabetes in the first trimester (diabetes diagnosis from self-report or biochemical screening with HbA1c ≥6.5%). Exclusion criteria for enrollment in the cohort were nonsingleton pregnancies or regular use of medications that influence glucose regulation. We collected measurements and blood samples from mothers at a first trimester visit (V1) conducted between 5 and 16 weeks of gestation (median 9 weeks), and in the late second trimester (V2) at 24 to 30 weeks of gestation (median 26 weeks; the time of universal GDM screening). We collected placental samples in addition to data on mothers and offspring at delivery.

Variables collection and measurements

At V1, we collected demographic data and previous medical and obstetric history; we performed standardized anthropometric measurements. Trained research staff measured weight with a calibrated scale and height with a standardized stadiometer. We calculated first trimester BMI as weight divided by squared height (kg m −2 ). At V1, we also collected additional blood samples that were drawn during the 50 g glucose challenge test (GCT, performed in 95% of participants). For the current study, we excluded participants who had a first trimester random glucose or 1 h-glucose post-GCT >10.3 mmol l −1 (overt hyperglycemia per national guidelines at the time) as we were interested in GDM incidence (ascertained with universal testing at 24–30 weeks).

At V2, we performed similar anthropometric measurements and questionnaires as at V1. V2 occurred at the time of the fasting 75g-OGTT, which was standard clinical practice for screening and diagnosis of GDM at CHUS. We collected additional blood samples at the fasting, 1 h and 2 h time points of the 75g-OGTT to measure insulin at each time point in addition to glucose. We measured glucose levels via the hexokinase method (Roche Diagnostics; CHUS biochemistry laboratory) as soon as samples were collected. We measured insulin levels via multiplexed particle-based flow cytometric assays (Human Milliplex MAP kits; EMD Millipore) from the previously frozen plasma samples (stored at −80°C until measurement). We estimated insulin sensitivity using the Matsuda Index 52 (using glucose and insulin values during the OGTT), as previously validated against euglycemic clamps performed in pregnancy 22 .

At delivery, we collected newborn age and sex at birth using medical records, in addition to details from the end of pregnancy and delivery complications. Trained study staff collected placentas within 30 min of delivery using a standardized protocol. In brief, a 1 cm 3 placental tissue sample was collected from the maternal-facing side, including decidual tissue (within a 5 cm radius of the corresponding location of cord insertion on the other side). Each collected sample was immediately put in RNA-Later for at least 24 h at 4 °C before storage at −80 °C until RNA extraction.

RNA extraction, sequencing and QC

We extracted total RNA (average, 19.7 ± 7.1 µg) and checked the quality of each sample using an Agilent Bioanalyzer to determine the RNA integrity number (average RNA integrity number = 6.7 ± 0.8). We shipped samples (3 µg) with an RNA integrity number value ≥5 to the Broad Institute for sequencing. In a second round of sample QC at the Broad Institute (Caliper Life Sciences LabChip GX system), the RNA quality score for each sample ranged from 3.3 to 7.8 (average RNA quality score = 5.9). We submitted all samples with an RNA quality score value of 4 or higher for RNA sequencing ( n  = 466). We completed library preparation with 250 ng of each sample, using an automated variant of the Illumina TruSeq Stranded mRNA Sample Preparation Kit (Illumina, catalog number RS-122-2103). We performed Flowcell cluster amplification and sequencing according to the manufacturer’s protocols using the Illumina HiSeq 4000, to generate 101-bp paired-end reads, average of 113 million total reads (range 33 million to 378 million) per sample.

In line with best practice and the GTEx v.8 pipeline 53 , we applied STAR v.2.5.3a 54 to align FASTQ/FASTA files to the human GRCh38 reference genome, using the parameters specified at https://github.com/broadinstitute/gtex-pipeline . Duplicate reads were marked using Picard MarkDuplicates, and expression was quantified with RNASeQC v.2.3.6 using the GENCODE v.26 annotation 55 .

Following quantification, we applied additional QC steps. Of the 466 samples sequenced, we excluded those with >1% of outlier genes (>3 times the IQR above Q3 or >3 IQR below Q1), leaving 459 samples for our final analytical dataset. Among these, we had complete data on the phenotype of interest (Matsuda index) and covariates for 434 samples. Before differential gene expression analysis, we removed genes with low abundance, keeping only those genes with at least a count of six reads and a transcript per million values >0.5 in a minimum of 20% of samples, as well as average mappability ≥0.8. After QC, 15,202 genes remained. Before differential expression analysis, we performed between-sample normalization using the R statistical software package edgeR 56 , then normalized and transformed gene counts to log 2 counts per million reads using Voom from the Limma R package 57 . The Gen3G placental RNA-seq data are available on dbGAP ( https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs003151.v1.p1 ).

Study of pregnancy regulation of insulin and glucose

SPRING is a longitudinal cohort study of pregnant participants with risk factors for diabetes that was conducted in 2015–2021. Participants were eligible if they were at <15 weeks gestation and had a history of GDM, family history of diabetes or GDM, or if they had BMI ≥ 25 kg m −2 and had one additional risk factor according to American Diabetes Association guidelines 58 . Participants gave informed consent and underwent a fasting 75g-OGTT at <15 weeks gestation, 24–28 weeks gestation and 6–12 weeks postpartum. The latter two study visit windows were widened to 24–32 weeks gestation and 6–24 weeks postpartum to promote participant retention (including during the COVID-19 pandemic). We measured glucose and insulin levels as previous described 15 . The Matsuda index was calculated from the glucose and insulin levels measured during the OGTT 15 . GDM was diagnosed according to IADPSG criteria applied to the OGTT at the pregnancy study visits. Most participants that met IADPSG criteria at the first visit were not retested at the second visit. Blood samples from each study visit were collected in EDTA plasma tubes, processed and frozen at −80 °C for future analysis. The study was approved by the Mass General Brigham Institutional Review Board.

MGH obstetrical maternal study

MOMS was conducted from 1998 to 2006 (ref. 59 ). Participants were eligible if they were receiving prenatal care at MGH. Participants provided written informed consent and were enrolled at their first prenatal visit where they donated an extra blood sample from a clinical blood draw. A subset of participants in 2001–2006 volunteered to return to donate fasting blood and urine samples at 16–20 weeks gestation. Glucose and insulin levels were measured as previously described 60 . Fasting plasma samples were frozen at −80 °C and stored for future analyses. At 24–28 weeks gestation, participants without preexisting diabetes underwent universal screening for GDM with a nonfasting 50 g GCT. If the venous blood glucose 1 h after the GCT was ≥140 mg dl −1 , patients were referred for a diagnostic 3-h 100g-OGTT. For this analysis, we included individuals whose OGTT results met Carpenter–Coustan criteria for GDM (≥2 abnormal values). Of these participants with GDM, 55 had remaining fasting samples available for analysis. We matched control participants with normal GCT results (two for each GDM case) on year of sample collection and gestational age at sample collection. We preferentially selected control samples on which fasting glucose and insulin had previously been measured on the sample collected at 16–20 weeks gestation. We calculated HOMA-2S from fasting glucose and insulin values to estimate insulin sensitivity 61 ( https://www.rdm.ox.ac.uk/about/our-clinical-facilities-and-units/DTU/software/homa ).

Institutional review board approval was obtained for participants from each of the cohorts (Gen3G, SPRING, MOMS) following the principles outlined in the Declaration of Helsinki. All enrolled participants provided written informed consent before study procedures.

Bioassays for circulating IGFBP1

We measured circulating IGFBP1 in plasma samples from all three cohorts in the same laboratory using a commercially available enzyme-linked immunosorbent assay that measures free IGFBP1 (R&D systems, catalog number DGB100). The precision for the assays were: intra-assay coefficients of variation of 5.6% and inter-assay coefficients of variation of 9.5%. We measured IGFBP1 levels in a blinded fashion, and we followed protocol for measurement per manufacturer’s instructions.

Statistical analyses

For characteristics of participants in all three cohorts, we reported normally distributed continuous variables as mean ± s.d., non-normally distributed continuous variables as median and IQR, and categorical variables as percentages. We used a log 2 transformation for Matsuda index (to approach a normal distribution) in the differential placental RNA expression analyses.

Placental differential expression analyses using RNA-seq data in Gen3G

We adjusted models for maternal age, gravidity, maternal BMI at the first trimester visit, sex of offspring and gestational age at delivery, in addition to computed SVs to account for unmeasured sources of variability, including batch effects and cell types. We used the EstDimRMT function from the R package isva 62 to estimate the number of SVs to include given the residuals from the regression of Matsuda and biological covariates from the normalized counts, which resulted in 37 SVs computed by the R package SmartSVA 63 recommended for our processed RNA-seq dataset. We used Limma 64 to identify differentially expressed genes with log 2 Matsuda as a continuous independent variable. We reported genes that had differential expression in relation to Matsuda with P  < 1.0 × 10 −3 .

Circulating IGFBP1 correlation analyses

We carefully assessed plasma IGFBP1 distribution and, after considering different potential transformations, we used a Box–Cox transformation for plasma IGFBP1 levels in Gen3G (from MASS package 65 in R) since it was the best way to approximate a normal distribution. We conducted analyses in SPRING and MOMS cohorts using plasma IGFBP1 levels without transformation, given distributions that were relatively normal. We used Pearson correlations between circulating IGFBP1 levels and Matsuda index (log transformed) in all three cohorts; we used partial correlations to assess the associations while taking into account maternal age, gestational age at blood draw and maternal BMI. In Gen3G, we also used Pearson correlations to assess associations between plasma IGFBP1 (Box–Cox transformed) and maternal metabolic markers, as well as newborn anthropometry (transformed if needed).

Circulating IGFBP1 and risk of GDM analyses

We conducted logistic regression analyses with the levels of circulating IGFBP1 as the independent variable and GDM as the dependent variable in Gen3G and SPRING; in MOMS, due to the matched case–control design of GDM cases to controls, we used conditional logistic regression. In Gen3G and SPRING, we used international criteria (IADPSG) 66 to ascertain GDM, whereas in MOMS we used the Carpenter–Coustan criteria 67 . In Gen3G, we additionally subclassified GDM by the insulin physiology defect driving hyperglycemia (insulin-resistant GDM, insulin-deficient GDM or mixed defect GDM, as previously described 10 ). We first built unadjusted logistic regression analyses (Model 1). We adjusted for maternal characteristics (maternal age, gravidity, gestational age at plasma samples) in Model 2 and additionally adjusted for maternal BMI in Model 3. We calculated profiled log-likelihood CIs along with likelihood ratio test P  values (using MASS 65 and glmglrt ( https://CRAN.R-project.org/package=glmglrt ) packages 47 in R). In SPRING and MOMS cohorts, we employed similar modeling strategies using maximum likelihood dichotomous logistic models.

We conducted GDM predictive analyses using ROC curves in Gen3G to compare the predictive ability of first trimester (V1) plasma IGFBP1 levels in addition to commonly measured GDM clinical risk factors (maternal age, gravidity, family history of diabetes, gestational age at V1 and maternal BMI at V1). We compared the ROC AUC values using all the clinical factors with and without first trimester (V1) plasma IGFBP1 levels (after Box–Cox transformation). We compared the ROC AUC values from nested models using DeLong’s test using the roc.test function from the pROC package in R 68 . We considered differences between AUC values to be statistically significant if P  < 0.05. In Gen3G, we performed analyses using R v.4.3.0 ( https://www.R-project.org ), STATA and SPSS v.28 only for partial correlations. In SPRING and MOMS, we performed analyses using Stata/IC v.16.1. Original code developed for placenta RNA-seq differential expression analysis in Gen3G available at https://github.com/labjacquespe/diff-exp .

Reporting summary

Further information on research design is available in the Nature Portfolio Reporting Summary linked to this article.

Data availability

The Gen3G placental RNA-seq data and pregnancy phenotypes are available on dbGAP ( https://www.ncbi.nlm.nih.gov/projects/gap/cgi-bin/study.cgi?study_id=phs003151.v1.p1 ).

Code availability

Original code developed for placenta RNA-seq differential expression analysis in Gen3G is available at https://github.com/labjacquespe/diff-exp .

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Acknowledgements

This work was supported by a grant from the National Institutes of Health (R01HD094150). Gen3G was initially supported by a Fonds de recherche du Québec—Santé operating grant (to M-.F.H., grant no. 20697), Canadian Institute of Health Research (CIHR) operating grants (to M.-F.H., grant no. MOP 115071, and to L.B., grant no. PJT-152989) and a Diabète Québec grant. J.C.F. is supported by National Heart, Lung, and Blood Institute grant no. K24 HL157960. L.B. and P-.E.J. are senior research scholars from the Fonds de recherche du Québec—Santé. M-.F.H. was a recipient of an American Diabetes Association Pathways To Stop Diabetes Accelerator Award (grant no. 1-15-ACE-26). The SPRING cohort was supported by the National Institute of Diabetes and Digestive and Kidney Diseases (K23DK113218), the Robert Wood Johnson Foundation’s Harold Amos Medical Faculty Development Program and the MGH Claflin Distinguished Scholar Award. SPRING data collection was also supported by grants UL1TR001102 and UL1TR000170 to the Harvard Clinical and Translational Science Center from the National Center for Advancing Translational Science. The work in the MOMS cohort was supported by the MGH Physician Scientist Development Award and the Claflin Distinguished Scholar Award.

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Department of Population Medicine, Harvard Medical School, Harvard Pilgrim Health Care Institute, Boston, MA, USA

Marie-France Hivert & Sana Majid

Diabetes Unit, Endocrine Division, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA

Marie-France Hivert, Jose C. Florez & Camille E. Powe

Centre de Recherche du Centre Hospitalier Universitaire de Sherbrooke (CRCHUS), Sherbrooke, Quebec, Canada

Marie-France Hivert, Catherine Allard, Luigi Bouchard & Pierre-Étienne Jacques

Département de Biologie, Université de Sherbrooke, Sherbrooke, Quebec, Canada

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Department of Obstetrics and Gynecology, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA

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Department of Medicine, Harvard Medical School, Boston, MA, USA

Jose C. Florez & Camille E. Powe

Department of Biochemistry and Functional Genomics, Université de Sherbrooke, Sherbrooke, Quebec, Canada

Luigi Bouchard

Department of Medical Biology, CIUSSS of Saguenay-Lac-Saint-Jean, Saguenay, Quebec, Canada

Institut de Recherche sur le Cancer de l’Université de Sherbrooke (IRCUS), Sherbrooke, Quebec, Canada

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Contributions

M-.F.H. wrote the paper, supervised analyses and obtained funding for the study. F.W., C.A., K.J. and S.M. performed analyses and prepared tables and figures. F.A. and K.G.A. supervised RNA-seq analyses, P-.E.J. supervised differential RNA expression analyses and S.A.K. supervised plasma IGFBP1 assays. C.E.P. obtained funding and directed the work in the SPRING and MOMS cohorts. F.A., J.C.F., A.G.E., L.B., P-.E.J., S.A.K. and C.E.P. provided critical input on interpretation of the findings. All authors reviewed and approved the final paper.

Corresponding author

Correspondence to Marie-France Hivert .

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Competing interests.

C.E.P. is an Associate Editor of Diabetes Care, receives payments from Wolters Kluwer for UpToDate chapters on diabetes in pregnancy and has received payments for consulting and speaking from Mediflix. M.-F.H. is co-editor of textbook ‘Essentials of Clinical Nutrition in Healthcare’ published by McGraw Hill. F.A. has been an employee of Illumina since 8 November 2021. J.C.F. has received grant funding for an investigator-initiated proposal from Novo Nordisk, a one-time consulting honorarium from AstraZeneca and speaker fees from Merck and Novo Nordisk for scientific presentations over which he had full control of content. J.C.F.’s wife has received a one-time consulting honorarium from Novartis. A.G.E. serves as a consultant for Mirvie and receives research funding from Merck Pharmaceuticals outside of this work. None of these engagements are directly relevant to this work. The other authors declare no competing interests.

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Nature Medicine thanks Amanda Sferuzzi-Perri and the other, anonymous, reviewer(s) for their contribution to the peer review of this work. Primary Handling Editor: Sonia Muliyil, in collaboration with the Nature Medicine team.

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Extended data

Extended data fig. 1 longitudinal changes in median plasma igfbp-1 levels across pregnancy and postpartum in normoglycemic spring participants (n = 65)..

IGFBP1 levels comparison using two-sided Wilcoxon signed-rank test, unadjusted for multiple comparisons. Exact P  = 5.08 ×10 -15 for differences between V3 (median 9 weeks post-partum) and V1 (median = 13 weeks gestation) and P  = 9.18 ×10 -19 for differences between V3 and V2 (median= 26 weeks gestation), denoted with *. Blue line is linking median value at each time point. Red bars represent interquartile range at each time point.

Extended Data Fig. 2 Longitudinal changes in median plasma levels of IGFBP-1, insulin, and glucose during 75g-OGTT conducted at median 26 weeks in 27 Gen3G participants.

Panel A: IGFBP1 levels comparison using two-sided Wilcoxon signed-rank test: V = 125, P  = 0.13 for differences between 60 min and fasting (0 min); V = 324, P  = 0.0007 (denoted with *) for differences between 120 min and fasting (0 min) without adjustment for multiple comparisons. Panel B: insulin levels over three time points of OGTT. Panel C: glucose levels over three time points of OGTT. Blue lines are linking median value between time points. Red bars represent interquartile range at each time point.

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Hivert, MF., White, F., Allard, C. et al. Placental IGFBP1 levels during early pregnancy and the risk of insulin resistance and gestational diabetes. Nat Med (2024). https://doi.org/10.1038/s41591-024-02936-5

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Biden sees $35 price cap for insulin as pivotal campaign issue. It’s not clear-cut.

President Joe Biden frequently cites insulin prices as he promotes a $35 price cap for Americans with diabetes who are on Medicare

WASHINGTON -- Rarely a day goes without President Joe Biden mentioning insulin prices.

He promotes a $35 price cap for the medication for Americans on Medicare — in White House speeches, campaign stops and even at non-health care events around the country. His reelection team has flooded swing-state airwaves with ads mentioning it, in English and Spanish.

All that would seemingly add up to a sweeping political and economic impact. The reality is more complicated.

As his campaign tries to emphasize what it sees as an advantage over presumptive Republican nominee Donald Trump, Biden often overstates what those people who are eligible for the price cap once paid for insulin. It’s also not clear whether the number of Americans being helped will be enough to help sway November’s election , even in the most closely contested states that could come down to a few thousand votes.

“It is about political signaling in a campaign much more than it is about demonstrating for people that they benefit from the insulin cap,” said Drew Altman, president and CEO of KFF, a nonprofit that researches health care issues. “It is a way to make concrete the fact that you are the health care candidate.”

Many who are benefiting from the price cap were already getting insulin at reduced prices, were already Biden supporters, or both. Others who need reduced-price insulin, meanwhile, cannot get it because they do not have Medicare or private health insurance .

Biden’s campaign is emphasizing the president's successful efforts to reduce insulin prices and contrasting that with Trump, who first ran for president promising to lower drug prices but took limited action in office.

“It’s a powerful and tangible contrast,” said Biden campaign spokesman Charles Lutvak. “And it’s one we are campaigning on early, aggressively, and across our coalition.”

Roughly 8.4 million people in the United States control their blood sugar levels with insulin, and more than 1 million have Type 1 diabetes and could die without regular access to it. The White House says nearly 4 million older people qualify for the new, lower price.

The price cap for Medicare recipients was part of the Inflation Reduction Act, which originally sought to cap insulin at $35 for all those with health insurance. When it passed in 2022, it was scaled back by congressional Republicans to apply only to older adults.

The Biden administration has also announced agreements with drugmakers Sanofi, Novo Nordisk and Eli Lilly, to cap insulin co-payments at $35 for those with private insurance. They account for more than 90% of the U.S. insulin market.

But Biden says constantly that many people used to pay up to $400 monthly, which is an overstatement. A Department of Health and Human Services study released in December 2022 found that people with diabetes who were enrolled in Medicare or had private insurance paid an average of $452 annually, not monthly.

The high prices the president cites mostly affected people without health insurance. But the rates of the uninsured have fallen to record lows because of the Obama administration’s signature health care law and the Biden White House’s aggressive efforts to ensure those eligible to enroll are doing so more frequently.

So, in effect, one of the administration’s policy initiatives is undermining the economic argument for another.

That effort has not reached everyone, though.

Yanet Martinez who lives in Phoenix and supports Biden. She does not work or have health insurance, but gets insulin for around $16 per month thanks to steep discounts at her local clinic.

The lower prices only apply if her husband, a landscaper, does not make enough to exceed the monthly income limit. If he does, her insulin can jump to $500-plus, she said.

“I’ve heard people talk about the price of insulin going down. I’ve not seen it,” said Martinez, 42. “It should be uniform. There are a lot of people who don’t have any way to afford it and it makes things very difficult.”

Sen. Raphael Warnock, D-Ga., is sponsoring bipartisan legislation to make the $35 insulin cap universal, even for people without health insurance. In the meantime, he said, what's been accomplished with Medicare recipients and drugmakers agreeing to reduce their prices is "literally saving lives and saving people money.”

“This is good policy because it centers the people rather than the politics," Warnock said. He said that as he travels Georgia, a pivotal swing state in November, people say “thank you for doing this for me, or for someone in my family.”

That includes people like Tommy Marshall, a 56-year-old financial services consultant in Atlanta, who has health insurance. He was diagnosed with Type 1 diabetes at age 45 and injects fast-acting insulin several times daily. He paid about $250 for four weeks to eight weeks worth of medication last November, but saw the price fall by half in February, after Novo Nordisk agreed to cut prices.

“If I was his political consultant, I’d be telling (Biden) to talk about it constantly," said Marshall, a lifelong Democrat and longtime public advocate for cutting insulin prices, including for the advocacy group Protect Our Care Georgia.

Marshall said the price caps “have meaningful emotional resonance” and could sway a close election but also conceded, “You’re talking about 18- to 65-year-olds. I can just imagine there’s probably two or three other issues that are in front of this one.”

“Maybe someone sort of on-the-fence, he added “this could maybe sway them.”

Geoff Garin, a pollster for Biden's reelection campaign, said the insulin cap is one of the president's highest performing issues. He said the data was “clear, consistent and overwhelming.”

Rich Fiesta, executive director of the Alliance for Retired Americans, which has endorsed Biden, called the insulin cap a strong issue for the president among older voters.

“For the persuadables — and there are some still out there, believe it or not — drug costs are a very important factor,” said Fiesta, whose group has 4.4-million members and advocates for health and economic security for older people.

Trump's campaign did not respond to questions. But Theo Merkel, senior fellow at the conservative Paragon Health Institute, countered that the insulin price cut an example of "policies written to fit the talking points other than the other way around.”

Merkel, who was a Trump White House adviser on health policy, said manufacturers that have long made insulin prefer caps on how much the insured pay because it gives them more leverage to secure higher prices from insurance companies.

The president's approval ratings on health care are among his highest on a range of issues, but still only 42% of U.S. adults approve of Biden’s handling of health care while 55% disapprove, according to a February poll from The Associated Press and the NORC Center for Public Affairs Research.

KFF found in its own poll in December that that 59% of U.S. adults trust the Democratic Party to do a better job addressing health care affordability issues compared to 39% for Republicans, even if only 26% of respondents in the same poll said they knew about the insulin price cap.

“In political terms, the Democrats and Biden have an advantage on health care," Altman said. “They're pressing it.”

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Biden sees a $35 price cap for insulin as a pivotal campaign issue. It's not that clear-cut

Will the number of americans being helped be enough to help sway november’s election, by will weissert | associated press • published april 21, 2024.

Rarely a day goes without President Joe Biden mentioning insulin prices.

He promotes a $35 price cap for the medication for Americans on Medicare — in White House speeches, campaign stops and even at non-health care events around the country. His reelection team has flooded swing-state airwaves with ads mentioning it, in English and Spanish.

All that would seemingly add up to a sweeping political and economic impact. The reality is more complicated.

As his campaign tries to emphasize what it sees as an advantage over presumptive Republican nominee Donald Trump , Biden often overstates what those people who are eligible for the price cap once paid for insulin. It’s also not clear whether the number of Americans being helped will be enough to help sway November’s election, even in the most closely contested states that could come down to a few thousand votes.

Get Tri-state area news and weather forecasts to your inbox. Sign up for NBC New York newsletters.

“It is about political signaling in a campaign much more than it is about demonstrating for people that they benefit from the insulin cap,” said Drew Altman, president and CEO of KFF, a nonprofit that researches health care issues. “It is a way to make concrete the fact that you are the health care candidate.”

Many who are benefiting from the price cap were already getting insulin at reduced prices, were already Biden supporters, or both. Others who need reduced-price insulin, meanwhile, cannot get it because they do not have Medicare or private health insurance.

Biden’s campaign is emphasizing the president's successful efforts to reduce insulin prices and contrasting that with Trump, who first ran for president promising to lower drug prices but took limited action in office.

“It’s a powerful and tangible contrast,” said Biden campaign spokesman Charles Lutvak. “And it’s one we are campaigning on early, aggressively, and across our coalition.”

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Roughly 8.4 million people in the United States control their blood sugar levels with insulin, and more than 1 million have Type 1 diabetes and could die without regular access to it. The White House says nearly 4 million older people qualify for the new, lower price.

The price cap for Medicare recipients was part of the Inflation Reduction Act, which originally sought to cap insulin at $35 for all those with health insurance. When it passed in 2022, it was scaled back by congressional Republicans to apply only to older adults.

The Biden administration has also announced agreements with drugmakers Sanofi, Novo Nordisk and Eli Lilly, to cap insulin co-payments at $35 for those with private insurance. They account for more than 90% of the U.S. insulin market.

But Biden says constantly that many people used to pay up to $400 monthly, which is an overstatement. A Department of Health and Human Services study released in December 2022 found that people with diabetes who were enrolled in Medicare or had private insurance paid an average of $452 annually, not monthly.

The high prices the president cites mostly affected people without health insurance. But the rates of the uninsured have fallen to record lows because of the Obama administration’s signature health care law and the Biden White House’s aggressive efforts to ensure those eligible to enroll are doing so more frequently.

So, in effect, one of the administration’s policy initiatives is undermining the economic argument for another.

That effort has not reached everyone, though.

Yanet Martinez who lives in Phoenix and supports Biden. She does not work or have health insurance, but gets insulin for around $16 per month thanks to steep discounts at her local clinic.

The lower prices only apply if her husband, a landscaper, does not make enough to exceed the monthly income limit. If he does, her insulin can jump to $500-plus, she said.

“I’ve heard people talk about the price of insulin going down. I’ve not seen it,” said Martinez, 42. “It should be uniform. There are a lot of people who don’t have any way to afford it and it makes things very difficult.”

Sen. Raphael Warnock, D-Ga., is sponsoring bipartisan legislation to make the $35 insulin cap universal, even for people without health insurance. In the meantime, he said, what's been accomplished with Medicare recipients and drugmakers agreeing to reduce their prices is "literally saving lives and saving people money.”

“This is good policy because it centers the people rather than the politics," Warnock said. He said that as he travels Georgia, a pivotal swing state in November, people say “thank you for doing this for me, or for someone in my family.”

That includes people like Tommy Marshall, a 56-year-old financial services consultant in Atlanta, who has health insurance. He was diagnosed with Type 1 diabetes at age 45 and injects fast-acting insulin several times daily. He paid about $250 for four weeks to eight weeks worth of medication last November, but saw the price fall by half in February, after Novo Nordisk agreed to cut prices.

“If I was his political consultant, I’d be telling (Biden) to talk about it constantly," said Marshall, a lifelong Democrat and longtime public advocate for cutting insulin prices, including for the advocacy group Protect Our Care Georgia.

Marshall said the price caps “have meaningful emotional resonance” and could sway a close election but also conceded, “You’re talking about 18- to 65-year-olds. I can just imagine there’s probably two or three other issues that are in front of this one.”

“Maybe someone sort of on-the-fence, he added “this could maybe sway them.”

Geoff Garin, a pollster for Biden's reelection campaign, said the insulin cap is one of the president's highest performing issues. He said the data was “clear, consistent and overwhelming.”

Rich Fiesta, executive director of the Alliance for Retired Americans, which has endorsed Biden, called the insulin cap a strong issue for the president among older voters.

“For the persuadables — and there are some still out there, believe it or not — drug costs are a very important factor,” said Fiesta, whose group has 4.4-million members and advocates for health and economic security for older people.

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Trump's campaign did not respond to questions. But Theo Merkel, senior fellow at the conservative Paragon Health Institute, countered that the insulin price cut an example of "policies written to fit the talking points other than the other way around.”

Merkel, who was a Trump White House adviser on health policy, said manufacturers that have long made insulin prefer caps on how much the insured pay because it gives them more leverage to secure higher prices from insurance companies.

The president's approval ratings on health care are among his highest on a range of issues, but still only 42% of U.S. adults approve of Biden’s handling of health care while 55% disapprove, according to a February poll from The Associated Press and the NORC Center for Public Affairs Research.

KFF found in its own poll in December that that 59% of U.S. adults trust the Democratic Party to do a better job addressing health care affordability issues compared to 39% for Republicans, even if only 26% of respondents in the same poll said they knew about the insulin price cap.

“In political terms, the Democrats and Biden have an advantage on health care," Altman said. “They're pressing it.”

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