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Bioconjug Chem
Bioconjug Chem
bc
bcches
Bioconjugate Chemistry
1043-1802
1520-4812
American Chemical Society
35833631
10.1021/acs.bioconjchem.2c00174
Article
Radiation Cleaved Drug-Conjugate Linkers Enable Local Payload Release
Quintana Jeremy M. †
https://orcid.org/0000-0002-1740-551X
Arboleda David †
Hu Huiyu †‡-
Scott Ella †
Luthria Gaurav †
Pai Sara †‡
Parangi Sareh ‡
https://orcid.org/0000-0003-0828-4143
Weissleder Ralph †§∥
https://orcid.org/0000-0001-7638-8898
Miller Miles A. *†§
† Center for Systems Biology, Massachusetts General Hospital Research Institute, Boston, Massachusetts 02114, United States
‡ Department of Surgery, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts 02114, United States
§ Department of Radiology, Massachusetts General Hospital and Harvard Medical School, Boston, Massachusetts 02114, United States
∥ Department of Systems Biology, Harvard Medical School, Boston, Massachusetts 02115, United States
* [email protected].
14 07 2022
17 08 2022
14 07 2023
33 8 14741484
© 2022 The Authors. Published by American Chemical Society
2022
The Authors
https://pubs.acs.org/page/policy/termsofuse.html Made available for a limited time for personal research and study only License.
Conjugation of therapeutic payloads to biologics including antibodies and albumin can enhance the selectively of drug delivery to solid tumors. However, achieving activity in tumors while avoiding healthy tissues remains a challenge, and payload activity in off-target tissues can cause toxicity for many such drug-conjugates. Here, we address this issue by presenting a drug–conjugate linker strategy that releases an active therapeutic payload upon exposure to ionizing radiation. Localized X-ray irradiation at clinically relevant doses (8 Gy) yields 50% drug (doxorubicin or monomethyl auristatin E, MMAE) release under hypoxic conditions that are traditionally associated with radiotherapy resistance. As proof-of-principle, we apply the approach to antibody– and albumin–drug conjugates and achieve >2000-fold enhanced MMAE cytotoxicity upon irradiation. Overall, this work establishes ionizing radiation as a strategy for spatially localized cancer drug delivery.
National Institutes of Health 10.13039/100000002 DP2CA259675 China Scholarship Council 10.13039/501100004543 201906370164 National Natural Science Foundation of China 10.13039/501100001809 81974423 National Cancer Institute 10.13039/100000054 U01CA206997 National Cancer Institute 10.13039/100000054 T32CA079443 document-id-old-9bc2c00174
document-id-new-14bc2c00174
ccc-price
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pmcIntroduction
Numerous approaches have been developed to improve the therapeutic index of potent small molecule compounds by linking them as inactive prodrugs to biologics or nanoparticles.1,2 For the treatment of metastatic cancers, cytotoxic agents have been bound to serum albumin3 and nanoparticles4 to improve systemic pharmacokinetics and, in principle, to promote tumor accumulation via molecularly targeting and/or “enhanced permeability and retention” (EPR) mechanisms of uptake. These mechanisms include oncogene-driven macropinocytosis,5,6 permeable tumor vasculature,7,8 dysfunctional tumor lymphatics, and other features that contribute to what is collectively referred to as the EPR effect.9−11 Alternatively, molecularly targeted strategies based on antibodies12,13 or functionalized nanoparticles2,14,15 have been designed to bind surface receptors selectively overexpressed by cancer cells. Multiple antibody–drug conjugates (ADCs) and therapeutic nanoparticles have received FDA-approval for clinical use. Yet despite their successes, these agents still accumulate in off-target tissues and elicit systemic toxicities.16−18 Drugs receive black box warnings when they exhibit potentially serious and deadly adverse effects, and toxicities affecting the bone marrow, liver, and other organs have led to boxed warnings on the FDA package inserts of nearly all drug-conjugates in oncology.
How can off-target payload activity be minimized for drug-conjugates? Optimization of the delivery vehicle—the biologic or nanoparticle to which the payload is conjugated—represents one set of strategies. Vehicle accumulation in clearance organs and the mononuclear phagocyte system can be minimized through PEGylation or FcRn engineering, for instance,19,20 but is nonetheless difficult to completely eliminate.21 Choosing appropriate tumor-specific molecular targets can improve selective accumulation, but one would need to first identify a tumor-specific target, and often only a subset of tumors or tumor cells may preferentially express such targets.22,23 Optimizing the chemistry by which a drug payload attaches to its delivery vehicle represents another set of strategies.24−26 Premature or off-target payload-release contributes to nonspecific and/or systemic exposure,27 while noncleavable linkers may insufficiently yield fully active payloads in tumors.28 Unfortunately, most current strategies for controlling payload release rely on pH, lysosomal degradation, protease activity, and other processes that are not reliably unique to tumor cells.29
Bio-orthogonal approaches aim to overcome limitations of biological specificity by modulating drug activity in a manner that is independent of naturally occurring chemical processes. Previously studied strategies for triggered activation of cancer prodrugs have used electrochemical,30 ultrasonic,31 optical,32,33 copper-free click chemical,34,35 and transition-metal catalytic36,37 activation schemes. It has generally been a challenge to achieve highly localized control of drug activation deep through tissue in a noninvasive manner. Methods such as heat, ultrasound, and laser irradiation may not easily penetrate deep through tissue, and bio-orthogonal chemical triggers with increased selectivity and compatibility are still in development.
In contrast to the above, ionizing radiation offers an attractive solution: radiation is routinely delivered deep through tissue via focused beams of gamma, proton, and X-ray radiation. Recent reports have described caged prodrugs that become activated with ionizing radiation, but initial studies relied on small molecule modifications with either suboptimal caging of drug activity or do not support strategies to more efficiently deliver compound to tumors, for instance, as achievable by drug conjugation to antibodies, albumin, or other delivery vehicles.38,39 To overcome these limitations, here we present a modular prodrug design containing a self-immolative linker with a cytotoxic payload, anchor for protein conjugation, and a radiation-activated trigger. A 3,5-dimethylbenzyl alcohol (DMBA) caging moiety undergoes 1–4/1–6 elimination upon radical hydroxylation that is initiated via clinically relevant doses of X-ray irradiation and, under hypoxic conditions, results in >50% localized release of the active drug and a >2000-fold increase in drug-induced cytotoxicity. The generic linker design offers flexibility in both targeting vehicles and their payload and promises to be broadly applicable to therapeutic applications that may synergize with standard-of-care radiation in the treatment of hypoxic tumors.
Results and Discussion
We designed the radiation-cleavable linker based on a tripartite structure containing three components—a drug payload, a radiation-responsive DMBA caging moiety, and a reactive anchor for conjugation to the delivery vehicle. Serum albumin (Alb) was used as an initial model vehicle, since cancer cells can accumulate high levels via oncogene-driven macropinocytosis,1,5,6 multiple albumin-based drug formulations are clinically used in the treatment of solid cancers,40,41 but off-target accumulation contributes to dose-limiting toxicity.3 A self-immolative linker (SIL) bridges the three components37 and was designed to release the drug payload upon radiation-induced reaction with DMBA, which prior work has shown to be mediated primarily by hydroxyl radical.38 We used monomethyl aurisatin E (MMAE) and doxorubicin (DOX) as model drug-conjugate payloads, since they are used in clinical-stage drug-conjugates but face known systemic toxicities. Combining these components together yielded Alb-DMBA-SIL-MMAE and Alb-DMBA-SIL-DOX as radiation-activated albumin–drug conjugates.
The overall synthesis of the Alb-DMBA-SIL-MMAE or Alb-DMBA-SIL-DOX was performed in 5 steps (Scheme 1). Self-immolative linker 3 was synthesized as previously described,42 and installation of the X-ray activated trigger was achieved by first reacting benzyl alcohol 1 with triphosgene to produce the chloroformate 2, which was then reacted directly with aniline 3 to yield DMBA-SIL 4. The benzylic alcohol on this molecule was activated by reaction with bis(4-nitrophenyl) carbonate under basic conditions with diisopropyl ethyl amine to generate carbonate 5. This carbonate could then be reacted with either MMAE or DOX to yield compounds 6 and 8, respectively.
Scheme 1 Synthesis of 3,5-Dimethyloxybenzyl Alcohol (DMBA) Prodrugs with Self-Immolative Linker (SIL) and Maleimide Anchor
Prodrugs using monomethyl auristatin E (MMAE) and doxorubicin (DOX) payloads used shared DMBA-SIL structures and precursors in the synthesis.
Maleimide was chosen as the reactive group to conjugate the drug payload to its delivery vehicle, since it is a widely used robust strategy shown to relatively selectively react with free thiol at Cysteine-34 on Alb, thereby forming a homogeneous product modified at a single site.43 It is also used for antibody conjugations, including in clinical products.44 Maleimide was installed via hydrolysis followed by amide coupling to obtain products 7 and 9. The maleimide-containing prodrugs were then conjugated to Alb by incubation at room temperature in PBS (pH 7.4, Gibco) for two hours, followed by spin filtration (30 kDa MWCO) to remove unreacted prodrug and further purification by size exclusion chromatography.
Initial experiments were performed to assess linker cleavage and payload release upon exposure to ionizing radiation. We hypothesized that the amount of dissolved oxygen in the solution would impact reaction rates, in part due to production of reactive oxygen species (ROS) other than the desired hydroxyl radicals. Although prior literature has hinted at oxygen dependent reaction,38,39 there is little data on actual effects. This is relevant for in vivo drug action, since advanced tumors are often hypoxic, and hypoxia is associated with resistance to radiation therapy in patients.45 Therefore, we tested the impact of dissolved oxygen on linker cleavage by deoxygenating solutions of DMBA-SIL-MMAE under vacuum or by bubbling the solution with an inert gas (argon) immediately prior to irradiation. Using previously reported procedures,46 we estimated that dissolved oxygen partial pressure decreased from 0.21 atm under ambient air (21% oxygen) to 0.025 atm with vacuum and to 0.005 atm with inert gas (Figure 1), as determined by the Winkler titration method. As a reference, dissolved oxygen is proportionate to its partial pressure (pO2) according to Henry’s law; tumor pO2 values can be >100-fold lower than ambient air pO2, and some tumor cells and xenografts can survive <0.1% oxygen.47 Thus, 0.005 atm oxygen partial pressure achieved by inert gas bubbling (a 40-fold decrease over ambient conditions) is likely relevant to hypoxic tumor tissue.
Figure 1 Drug release initiation via radical hydroxylation under hypoxia. (A) Radiation-induced radical hydroxylation, followed by 1,6-elimination and subsequent loss of the SIL caging group, releases the caged drug payload. (B) Measured MMAE release from DMBA-SIL-MMAE following 8 Gy irradiation with either an X-ray or gamma ray source, as a function of oxygen partial pressure. Data are means ± s.e. (n = 3). Pearson’s correlation coefficient and two-tailed t test are reported.
Results showed a correlation between oxygen levels and the efficiency of linker cleavage (R2 > 0.97, p = 0.02) at a radiation dose of 8 Gy, which is relevant to clinical radiation protocols, especially those using hypofractionated treatment schedules.48,49 No difference in cleavage efficiency was found between gamma irradiation from a sealed 137Cs source (primarily 662 keV photons at a dose rate of roughly 0.5 Gy/min) and X-ray irradiation (from a 4000 W tube generating 320 keV photons at 3 Gy/min) (Figure 1b), and little background release was observed under non-irradiated conditions (5 days, 37 °C; Figure S1). Neither MMAE nor doxorubicin themselves were substantially affected by 8 Gy irradiation, according to LC/MS chromatograph analysis (Figure S2). These data thus show that radiation can trigger drug-conjugate cleavage in a hypoxia-dependent manner without adversely degrading the drug payload.
Prior work has implicated hydroxyl radicals as the primary ROS formed from ionizing radiation that activates the DMBA trigger.38 The mechanisms for the release of doxorubicin from these prodrugs (Figure 2a) consist of a radical hydroxylation (either position 2 or 4) of the dimethoxybenzyl moiety, followed by a 1–4/1–6 elimination and loss of carbon dioxide. The self-immolative linker then releases via a further 1–6 elimination and subsequent loss of carbon dioxide to provide the free drug.37 This mechanism is supported by the observed masses of the expected intermediates in our X-ray irradiated prodrug, shortly after irradiation (Figure S3a,b). Aminophenyl fluorescein (APF) is often used as an indicator of highly reactive oxygen species including hydroxyl radicals,50 and both hydrogen peroxide and X-ray irradiation decage APF to enhance its fluorescence (Figure S4). Standard Fenton reaction conditions (100 μM H2O2 and 100 μM Fe2+) are also known to generate hydroxyl radicals and therefore can serve as a benchmark comparison to ionizing radiation effects. X-ray irradiation under the most hypoxic conditions was more efficient than Fenton conditions at cleaving the DMBA-based linker (Figure S3c), and these irradiation conditions were used in all subsequent in vitro experiments. The observed dependence on hypoxia may be considered unexpected due to the known role of oxygen in promoting ROS generation. One possible explanation may be that the absence of oxygen enhances the desired hydroxylation mechanism by reducing the quantity of alternative reactive oxygen species being formed, although further exploration of this phenomenon is needed to test this hypothesis and dissect underlying mechanisms.
Figure 2 Dose–response of payload release from drug–albumin conjugates. (A) Percentage of MMAE and DOX released from albumin-conjugates after treatment with varying doses of X-ray irradiation compared to free drug (10 μM). Data are means ± s.e. (n = 3). (B,C) Representative LC-MS chromatographs of MMAE (b; +ESI, isolated mass of 718.8 Da) and DOX (c; +ESI, isolated mass of 544.4) released from albumin.
We next examined the sensitivity of linker cleavage to varying doses of radiation. Solutions of both conjugates at a concentration of 100 μM were irradiated at an exposure of 1–16 Gy, and LC-MS analysis was performed to determine the released drug concentrations (Figure 2). Alb-DMBA-SIL-MMAE and Alb-DMBA-SIL-DOX exhibited similar dose–response behaviors, suggesting the approach generalizes to diverse drug payloads. With a standard protocol used in clinical treatments, patients typically receive ∼2 Gy radiation per day, 5 days a week, for ≥5 weeks, therefore yielding cumulative radiation doses of ≥50 Gy. Our data show that even low doses of 1–2 Gy elicit detectable drug release, and higher doses of 8–16 Gy, which are less common but still used clinically in hypofractionated schedules, release 66 ± 6% and 69 ± 8% of MMAE and DOX, respectively. These results thus show release under a range of clinically relevant doses.
We next performed experiments with cancer cells to assess the degree to which our linker strategy could shield biological activity of the drug payload under non-irradiated conditions and restore drug activity once irradiated. We focused on cancer cell lines with constitutively active oncogenic signaling in the mitogen activated protein kinase (MAPK) RAS/RAF/MEK/ERK pathway, which has been previously shown as important for oncogene-driven uptake of serum albumin;5 we furthermore focused on cancer types that are treated with cytotoxic agents and radiation therapy, including anaplastic thyroid cancer (ATC), a rare but aggressive malignancy that is associated with high degrees of resistance to traditional chemotherapy and radiation therapy. Drugs were irradiated prior to cancer cell treatment to allow assessment of drug action (caging and uncaging) independent of the biological effects of radiation to the cancer cells, which even at <10 Gy doses in cells considered radioresistant, such as human 8505c ATC cells, can substantially impact proliferation in vitro.51 Compared to the parent MMAE drug, the drug-conjugate Alb-DMBA-SIL-MMAE exhibited 5700-fold lower cytotoxicity in 8505c cells (Figure 3). Anchoring the DMBA-SIL-MMAE to its protein vehicle, Alb, enhanced the caging of drug activity: Alb-DMBA-SIL-MMAE exhibited ∼40-fold lower cytotoxicity than the unanchored compounds DMBA-SIL-MMAE and MMAE-DMBA (Figure 3). In contrast, all three prodrugs exhibited similar cytotoxicity following 8 Gy irradiation, consistent with the behavior of the parent drug and the known efficiency of drug release at this dose (52 ± 9%). Compared to prior DMBA caging approaches,38 these data show that our linker strategy exhibits a superior ability to limit drug activity under non-irradiated conditions, which is likely important for minimizing systemic toxicity. Furthermore, cytotoxicity of the parent drug is restored to a level commensurate with the fraction of released drug as measured by LC/MS, indicating that irradiation appropriately releases the payload in a fully active and intact form. Similar observations were found in cells derived from a genetically engineered mouse model of ATC (the TBP-3743 cell line) and other cancer cell lines (Figure 3c, Figure S5).
Figure 3 Caged and conjugated MMAE is selectively cytotoxic and activated by X-ray irradiation. (A) Chemical structures of Alb-DMBA-SIL-MMAE and other radiation-activated derivatives. (B) Cytotoxicity of non-irradiated and irradiated (8 Gy) MMAE and prodrug derivatives in anaplastic thyroid cancer cells (8505c), measured 72 h post-treatment by a resazurin-based assay (data are means ± s.e., n = 4). (C) Half-maximal inhibitory concentration (IC50) of non-irradiated or irradiated Alb-DMBA-SIL-MMAE in cancer cell lines of anaplastic thyroid cancer (TBP), oral squamous cell carcinoma (MOC-2), colon adenocarcinoma (MC38), and pancreatic adenocarcinoma (iKRAS). See Figure S2 for full dose responses.
Given that cytotoxicity measurements suggested parent MMAE activity was being restored following irradiation, we subsequently assessed whether the molecular effects of MMAE were similarly affected. MMAE blocks the polymerization of tubulin into microtubules, which are critical cytoskeletal components that mediate mitotic cell division and metastatic invasion of cancer cells.52,53 To directly visualize microtubule dynamics in live cancer cells, we used the HT1080 EB3-mApple cell line, which transgenically expresses the fluorescent protein mApple fused to the protein EB3 (microtubule-associated protein RP/EB family member 3, MAPRE3). EB3 binds plus-end tips of growing microtubules, and time-lapse microscopy allows growing microtubules to be quantified for their abundance, growth velocities, and other features.37 Using this approach, non-irradiated Alb-DMBA-SIL-MMAE elicited no significant impacts on cancer cell microtubule dynamics; in contrast, dynamics were totally eliminated with irradiated drug (Figure 4). These results confirm that the linker strategy efficiently cages drug activity and show that the activity of MMAE to disrupt microtubule dynamics is restored upon radiation-mediated drug release.
Figure 4 Radiation restores the microtubule-disrupting activity of caged MMAE. (A) Reporter cell line for tracking +TIP (microtubule plus-end tracking protein) was imaged over time via confocal microscopy to visualize microtubule dynamics. After drug treatment, microtubule “comets” were automatically detected, computationally tracked, and visualized with pseudocoloring according to comet speed. (B) Corresponding to representative data in part A, features of microtubule dynamics were averaged across individual cells (n > 10 per condition). Prodrug = Alb-DMBA-SIL-MMAE, n.d. = none (no comets) detected.
Similar experiments were performed to test the caging and activation efficiencies of Alb-DMBA-SIL-DOX, compared to the parent compound doxorubicin (Figure 5). The concentration at which drug inhibited 50% of ATC cell growth was >10 μM for non-irradiated Alb-DMBA-SIL-DOX and DMBA-SIL-DOX. This was roughly 100-fold higher than that observed for the parent drug (IC50 = 94 nM). Irradiation restored prodrug cytotoxicity to a level expected based on the fraction of drug released (0.5 ± 0.1). Similar results were observed in other cancer cell lines (Figure 5c, Figure S6). Compared to MMAE, the DMBA-SIL-DOX did not benefit further from conjugation to serum albumin, in terms of limiting the activity of non-irradiated compound. This is potentially due to the distinct DOX mechanism of action compared to MMAE (Figure 5b).
Figure 5 Caged and conjugated DOX is selectively cytotoxic and activated by X-ray irradiation. (A) Structures of DOX prodrug derivatives. (B) Cytotoxicity of each prodrug/conjugate ±8 Gy X-ray irradiation in comparison to free DOX in anaplastic thyroid cancer cells (8505c). Data are means ± s.e., n = 4. (C) Half-maximal inhibitory concentration (IC50) of non-irradiated or irradiated Alb-DMBA-SIL-DOX in cancer cell lines of anaplastic thyroid cancer (TBP), oral squamous cell carcinoma (MOC-2), and colon adenocarcinoma (MC38). See Figure S3 for full dose responses.
Doxorubicin and prodrug derivatives exhibit intrinsic fluorescence that is visible by confocal microscopy, and imaging can therefore be used to assess drug accumulation and colocalization with its target in the nuclei of live cancer cells.54 Doxorubicin is an anthracycline that intercalates DNA, inhibits topoisomerase II, and therefore generates DNA damage leading to cell death. The 3′-amino of the daunosamine moiety on doxorubicin forms a covalent bond with the exocyclic amino of guanine, and this site is frequently modified to cage drug activity,37 as done here as well. ATC cells show nuclear accumulation of doxorubicin and irradiated Alb-DMBA-SIL-DOX (Figure 6, Figure S7). In contrast, non-irradiated prodrug is confined to the cytoplasm, therefore suggesting the non-irradiated drug-conjugate remains intact, such that doxorubicin is unable to freely enter the nucleus and interact with DNA. Overall, these results indicate that the radiation-cleavable linker blocks the ability of doxorubicin to intercalate DNA and elicit cytotoxic effects, and that drug irradiation can appropriately release it to facilitate nuclear localization and cytotoxicity.
Figure 6 Intrinsic doxorubicin fluorescence quantifies nuclear uptake following X-ray mediated payload release. (A) Representative images of intrinsic DOX fluorescence in 8505c cells treated with DOX, non-irradiated Alb-DMBA-SIL-DOX, or X-ray-irradiated (8 Gy) conjugate for 24 h; Hoechst 33342 counterstains cell nuclei. (B) Ratio of nuclear to cytoplasmic DOX fluorescence was quantified to evaluate the subcellular drug accumulation. Data are means ± s.e., n > 15 single cells per condition (one-way ANOVA with Dunnett’s T3 multiple comparisons test).
To examine the generalizability of the linker strategy beyond albumin-conjugates, we next tested whether the approach could also yield an antibody–drug conjugate (ADC) with a radiation-cleavable cytotoxic payload. We prepared conjugates of the DMBA-SIL-DOX/MMAE prodrugs to a model tumor-targeted monoclonal antibody that binds epidermal growth factor receptor (αEGFR mAb), since multiple αEGFR-mAb are used clinically including cetuximab and panitumumab for a variety of solid tumors, and αEGFR-ADC are under development. We first reduced antibody disulfiide bonds with tris(2-carboxyethyl)phosphine hydrogen chloride (TCEP-HCl), followed by conjugation with the respective prodrugs via thiol–maleimide Michael addition. The resulting mAb-DMBA-SIL-DOX and mAb-DMBA-SIL-MMAE were then subjected to the same in vitro analyses as the albumin conjugates to demonstrate X-ray activation and determine drug release efficiencies. These experiments showed that antibody-conjugates performed similarly to the previous albumin-conjugates, releasing with radiation 64 ± 7% and 56 ± 4% of the estimated MMAE and DOX, respectively (Figure 7a). In a cytotoxicity assay using the 8505c ATC cell line, the mAb-DMBA-SIL-MMAE conjugate demonstrated a 70-fold increase in cytotoxicity after X-ray irradiation (8 Gy) compared to the nonirradiated conjugate (Figure 7b).
Figure 7 Radiation-mediated release of antibody–drug conjugate (ADC) payloads. (A) Chemical structures of ADCs, here using anti-EGFR mAb as a model tumor-targeted antibody (shown as purple). (B) MMAE and DOX payload release from their respective antibody conjugates after X-ray irradiation (8 Gy), measured by LC-MS. Data are means ± s.e., n = 3. (C) Cytotoxicity of non-irradiated or X-ray-irradiated (8 Gy) mAb-DMBA-SIL-MMAE conjugate in comparison to free MMAE in anaplastic thyroid cancer (8505c cell line). (D) Ratio of nuclear to cytoplasmic DOX fluorescence was quantified as in Figure 6 in anaplastic thyroid cancer cells (8505c) treated for 24 h with DOX, non-irradiated conjugate, or irradiated (8 Gy) conjugate. Prodrug = mAb-DMBA-SIL-DOX. Data are means ± s.e., n > 16 single-cells per condition (one-way ANOVA with Dunnett’s T3 multiple comparisons test).
Similarly, irradiated mAb-DMBA-SIL-Dox demonstrated an increased nuclear-to-cytoplasm ratio in the subcellular distribution of the doxorubicin payload relative to its non-irradiated counterpart (Figure 7c). Taken together, these data show that mAb conjugation is effective in blocking payload activity and that radiation exposure releases payload from the mAb-conjugate and unleashes payload activity in the cancer cells.
Conclusions
This work extends radiation-activated chemistry to support triggered release of biologic drug-conjugates. As with traditional ADC linkers, the approach can be applied to diverse delivery vehicles such as serum albumin or antibodies as we demonstrate here; it furthermore can be applied to diverse drug payloads, including the widely used cytotoxic chemotherapies doxorubicin and MMAE. We show that the linker cleavage can be triggered by multiple forms of ionizing radiation and is accelerated under hypoxic conditions thought to stimulate macropinocytosis of albumin-bound agents.55 Radiation itself has been shown to improve drug penetration into tumors8,37,56 and therefore offers the possibility of self-amplifying the local payload activity in this case. This work sets the stage for future work to investigate mechanisms of in vivo drug activation and potential off-target payload release, which will require (i) careful analysis of chemical and biological drug behaviors in tumors and off-target tissue, with and without radiation; (ii) comparison with free (nonconjugated) drugs and prodrugs, as well as noncleavable drug-conjugates; and (iii) assessment of drug behavior in orthotopic, patient-derived, and/or autochthonous tumor models that recapitulate the hypoxic tumor microenvironments often found in patients.57 This work also establishes a foundation from which to assess drug-conjugate payloads, beyond MMAE and doxorubicin, that may synergistically combine with the biological effects of radiation. Although the therapeutic radiation doses used here are known to elicit strong impacts on cancer cell behavior in vitro—for instance, by reducing proliferation of the cancer cells used in this work (8505c, MC38, HT1080) by >95% in clonogenic assays—such doses nonetheless fail to eradicate tumors and block disease progression.8,51,58 To this end, the modular design strategy lends itself to further optimization of the X-ray activated trigger, the conjugation anchor, and the drug payload.
Our presented method of prodrug engineering may be applied to a variety of therapeutics including immunomodulatory agents and targeted inhibitors that may be chosen to synergistically combine with radiation administered as part of the standard of care. Extensive research has already gone into understanding how DNA- and microtubule-targeted drugs may best combine with radiation therapy, with special focus on optimizing cancer cells to be in the radiosensitive G2/M phase of their cell cycle and on tumor microenvironment effects that maximize oxygenation. In contrast, our linker approach offers a distinct way of considering synergistic effects under conditions of hypoxia, and local drug delivery may allow higher local drug concentrations or more potent drug payloads to be considered.
Experimental Procedures
Synthetic Procedures
Detailed synthetic protocols and characterization data can be found in the Supporting Information.
Conjugate Preparation
To a solution of bovine serum albumin (BSA, 47.1 mg in 1 mL PBS) was added 100 μL of a 10 mg/mL solution of Mal-containing prodrug (9 or 11) in DMF. These mixtures were then incubated at room temperature with gentle mixing for 3 h, followed by removal of the unreacted prodrugs by spin filtration (20,000g for 5 min, × 3) using 30,000 kDa MWCO spin filters (Amicon, 0.5 mL). The conjugates were further purified by size-exclusion chromatography in PBS using a SEC column on an Agilent 1260 HPLC. Collected fractions eluting between 8.5 and 10.5 min were concentrated by further spin filtration to provide a final conjugate concentration of 10 mg/mL in PBS. Alb is generally well tolerated in patients, given at high doses, but can undergo accelerated clearance if extensively modified; therefore, we performed conjugation with a low degree of labeling, achieving on average 0.12 drug molecules per Alb molecule (Table S1).
Conjugates of the DMBA-SIL-DOX/MMAE prodrugs to an anti-epidermal growth factor receptor (EGFR) antibody (BioXcell, mab225) were prepared by first reducing the disulfiide bonds in the antibody by incubating in 100 μM tris(2-carboxyethyl)phosphine hydrogen chloride (TCEP-HCl) for one hour at room temperature, followed by removal of the reducing agent by spin filtration (10,000g, 50,000 kDa MWCO, Amicon/Sigma). While leaving a small fraction of the TCEP-HCl (∼50 μL) to maintain the reduced state of the antibody, the prodrugs were added to separate aliquots and incubated at room temperature for 2 h, after which unreacted prodrug was removed by further spin filtration (10,000g × 3, 50,000 kDa MWCO). The degree of labeling was estimated to be on average 5.6 drug molecules per antibody calculated following manufacturer guidelines and using a NanoDrop spectrophotometer (Table S1).
Drug Release
Determination of drug release efficiency was performed by preparing solutions of each prodrug in deionized water (0.1% DMF) to a final concentration of 10 μM. Vacuum degassing was performed by gently stirring the samples at ∼200 mbar for 30 min at room temperature, while degassing by argon was performed by bubbling ultrahigh-purity-grade gas (Airgas, Lynn, MA, 01902, USA) into the solution at a rate of approximately 5 mL s–1. After these preparations, X-ray irradiation was performed using a Precision (Madison, CT, USA) X-Rad320 at a rate of 385 ± 10 cGy/min until the desired dosage was achieved. Gamma irradiation was similarly performed on a dual source 137Cs Gammacell 40 Exactor (Best Theratronics) with a dose rate of roughly 50 cGy/min. After irradiation or other treatment, the concentrations of the released drug and the intact prodrug were determined by LC-MS on a Waters instrument equipped with a Waters 2424 ELS Detector, Waters 2998 UV–vis Diode Array Detector, and a Waters 3100 Mass Detector. Drug concentrations were determined by comparing the AUC from the ELSD or isolated mass chromatographs (+ESI, 718.8 Da for MMAE, 544.4 Da for DOX) of the analyzed sample to a standard calibration curve.
For the measurement of released products relative to the X-ray dosage, DMBA-SIL-MMAE protein conjugate was prepared to a concentration of 50 μM in PBS (0.1% DMF, 5 mL), then purged with ultrapure-grade argon (5 mL s–1) for 15 min. The solution was then X-ray irradiated using the X-Rad320 to deliver 8 Gy irradiation and analyzed by LC-MS as described above.
Determination of the oxygen concentrations for each degassing condition was achieved through the use of the Winkler titration method46 and is reported as parts per million (ppm, mg O2 per kg dI H2O). Briefly, water samples were degassed by either vacuum or argon purging as described above, with untreated deionized water used as a control. To the samples were then added solutions of manganese sulfate monohydrate (100 μL, 2 mM) and alkaline potassium iodide (100 μL, 12.5 M NaOH, 0.8 M KI, 0.15 M NaN3). The solutions were mixed and allowed to stand for 15 min before concentrated sulfuric acid (100 μL) was added and again mixed. Addition of 100 μL of a starch solution (50 mg mL–1) turned the solution blue, and the mixture was then titrated with a solution of sodium thiosulfate (2.5 mM) until it reached a colorless end point. The volume of the thiosulfate solution added (in mL) was equivalent to the initial concentration of dissolved oxygen in ppm.
Cytotoxicity Assays
TBP-3743 (derived from a genetically engineered mouse model of anaplastic thyroid cancer) and 8505c (human anaplastic thyroid cancer) are described previously,59 as are HT1080-EB3-mApple cells.52 Raw264.7 (mouse macrophage model, Raw-MΦ) were from ATCC. MC38 cells provided by M. Smyth (Peter MacCallum Cancer Centre, Victoria, Australia). iKras cells were derived from a genetically engineered mouse model of KrasG12D pancreatic adenocarcinoma and were routinely cultured in Dulbecco’s Modified Eagle Medium/Nutrient Mixture F-12 media (DMEM/F12, Invitrogen) supplemented with 2 μg mL–1 doxycycline (Sigma) to maintain mutant Kras expression (provided as a gift from H. Ying, MD Anderson Cancer Center, by way of N. Bardeesy, Massachusetts General Hospital (MGH)).60 MOC2 (mouse oral cavity squamous cell cancer – aggressive growth phenotype) (RRID:CVCL_ZD33) cell lines from Kerafast and were cultured according to provider guidelines using IMDM/F12, supplemented with 5 ng/mL EGF (EMD Millipore), 400 ng/mL hydrocortisone (Sigma-Aldrich), and 5 mg/mL insulin (Sigma-Aldrich). All cells were routinely evaluated for mycoplasma contamination and cultured following provider guidelines using 10% FBS (Bio-Techne Sales), 100 IU mL–1 penicillin, 100 μg mL–1 streptomycin (Invitrogen), with incubation at 37 °C and 5% CO2. Cytotoxicity experiments were performed by seeding 5000 cells per well overnight in a 96-well plate (Corning) before the addition of each drug/conjugate. Empty wells with only media or vehicle-treated cells were used as controls. After addition of the corresponding drugs and a 72 h incubation, the number of live cells was determined by PrestoBlue (ThermoFisher, USA) staining according to the provider’s protocols.
Cell Imaging
Subcellular localization of DOX in the 8505c cell line was measured using a modified BX63 (Olympus) inverted microscopy system equipped with an environmental chamber and robotic stage. DOX images were collected using a 40× air objective (PLAPO 40×/0.95 numerical aperture), with excitation and emission wavelengths of 489 and 508 nm, respectively. All images were processed using cellSens Dimension 3.1.1 (Olympus, USA) and ImageJ 1.53k (NIH, USA) software. Nuclear to cytoplasm fluorescence ratios were calculated and plotted using Excel (Microsoft) and Prism (GraphPad). 20 μM concentration of free (or released) DOX was used, and non-irradiated prodrug used the same total (caged and uncaged) DOX concentration as in the irradiated sample. Following treatment for 24 h, live cells were immediately imaged.
EB3 imaging was performed on an FV1000 confocal laser scanning microscope equipped with a 37 °C heated stage, XLUMPLFLN 20× (NA 1.0) water-immersion objective, 559 nm diode laser, and BA575-620 emission filter (all Olympus America). Cells were treated with 1 μM MMAE, Alb-DMBA-SIL-MMAE, or Alb-DMBA-SIL-MMAE after argon purging and X-ray irradiation (8 Gy) 1 h prior to imaging. MT tracks were obtained by detecting and linking EB3 comets using the U-track software.61 Cell masks were constructed using ImageJ. For MT tracks to be included in the downstream analyses, they must pass a strict set of filters: (1) located within a cell boundary, (2) be present in a minimum of 3 consecutive frames, (3) have a path length less than 10 μm, (4) track persistence greater than 0.5 (measured on a scale of 0 to 1, with 1 indicating a line). Additionally, tracks with outlier speeds (greater than or less than 1.5 *IQR) within their respective cells were not included in the analysis.
Supporting Information Available
The Supporting Information is available free of charge at https://pubs.acs.org/doi/10.1021/acs.bioconjchem.2c00174.Figure S1, MMAE release from DMBA-SIL-Mal after degassing and irradiation; Figure S2, Drug stability after irradiation; Figure S3, Release intermediates after irradiation; Figure S4, Release via reactive oxygen species; Figure S5, MMAE conjugate cytotoxicity; Figure S6, DOX conjugate cytotoxicity; Figure S7, Subcellular DOX conjugate distribution; Figure S8, LC chromatographs of DMBA prodrugs; Table S1, Conjugate degree of labeling; Organic Synthesis and Characterization (PDF)
Supplementary Material
bc2c00174_si_001.pdf
The authors declare the following competing financial interest(s): RW is a consultant to ModeRNA, Tarveda Pharmaceuticals, Lumicell, Seer, Earli, Aikili Biosystems and Accure Health. MAM is a scientific advisor for January Therapeutics and has received past support from Ionis Pharmaceuticals, Pfizer, and via material transfer agreement from Genentech/Roche. None of these activities are related to the manuscript. The other authors declare that they have no competing interests.
Acknowledgments
This work was supported in part by NIH grants T32CA079443 (J.Q.), DP2CA259675 (M.A.M.), and U01CA206997 (R.W.). H.H. was supported by the China Scholarship Council 201906370164, and the National Natural Science Foundation of China 81974423 and is affiliated with the Department of General Surgery, Xiangya Hospital, Central South University, China. We acknowledge Rainer H. Kohler, Ph.D. (Massachusetts General Hospital) for expertise and assistance in microscopy experiments. We also acknowledge the Harvard Center for Mass Spectrometry (Harvard University) for conducting high-resolution mass spectrometry measurements of selected prodrugs.
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==== Front
Clin Diabetes
Clin Diabetes
clinical diabetes
Clinical Diabetes : A Publication of the American Diabetes Association
0891-8929
1945-4953
American Diabetes Association
CD220090
10.2337/cd22-0090
Feature Articles
Effectiveness of RADAR: An Innovative Model to Organize Diabetes Care in First Nations Communities
https://orcid.org/0000-0003-2197-0463
Eurich Dean T. 1 2
Wozniak Lisa A. 1 2
Soprovich Allison 1 2
Minhas-Sandhu Jasjeet K. 1 2
Crowshoe Lynden 3
Johnson Jeffrey A. 1 2
Samanani Salim 4
1 School of Public Health, University of Alberta, Edmonton, Alberta, Canada
2 Alliance for Canadian Health Outcomes Research in Diabetes, University of Alberta, Edmonton, Alberta, Canada
3 Cumming School of Medicine and Indigenous, Local, and Global Health Office, University of Calgary, Calgary, Alberta, Canada
4 OKAKI Health Intelligence, Inc., Calgary, Alberta, Canada
Corresponding author: Dean T. Eurich, [email protected]
Summer 2023
27 1 2023
27 1 2023
41 3 351358
© 2023 by the American Diabetes Association
2023
https://www.diabetesjournals.org/journals/pages/license Readers may use this article as long as the work is properly cited, the use is educational and not for profit, and the work is not altered. More information is available at https://www.diabetesjournals.org/journals/pages/license.
Challenges exist for the management of diabetes care in First Nations populations. RADAR (Reorganizing the Approach to Diabetes through the Application of Registries) is a culturally appropriate, innovative care model that incorporates a disease registry and electronic health record for local care provision with remote coordination, tailored for First Nations people. This study assessed the effectiveness of RADAR on patient outcomes and diabetes care organization in participating communities in Alberta, Canada. It revealed significant improvements in outcomes after 2 years, with 91% of patients achieving a primary combined end point of a 10% improvement in or persistence at target for A1C, systolic blood pressure, and/or LDL cholesterol. Qualitative assessment showed that diabetes care organization also improved. These multimethod findings support tailored diabetes care practices in First Nations populations.
The Lawson Foundation Canadian Institutes of Health Research 10.13039/501100000024 143562 Alberta Innovates - Health Solutions 10.13039/501100000145
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pmcIn Canada, chronic diseases have reached critical imbalances for First Nations (FN) people (1). Diabetes is three to five times more common in FN than in non-FN populations (2,3). Higher rates of diabetes-related complications are observed in FN populations (4,5). The high burden of disease, complications, and comorbidity that FN people with diabetes experience is compounded by the significant and historical social, physical, and economic challenges they face (5,6).
Primary care for people with diabetes is typically delivered using a chronic care model (7), although its implementation within FN communities is challenging. FN communities are often remote, with limited access to comprehensive diabetes care. Current federal and provincial approaches to managing diabetes care among FN people has led to suboptimal and fragmented care for people living both on and off reserves. Indeed, we previously found that such diabetes care was reactive, relying on FN people to navigate a complicated health care system (8).
Within a chronic care model, one framework for organizing and delivering optimal diabetes care is called the 5Rs, which stands for Recognize (performing screening/risk factor assessment), Register (systematically tracking patients), Relay (facilitating information-sharing), Recall (providing timely review and reassessment), and Resource (supporting self-management) (7). Furthermore, FN-specific clinical practice guidelines (CPGs) recommend that diabetes prevention, care, and education be grounded in communities’ social, cultural, and health service contexts (6). The organization of diabetes care (7) and the principles of self-determination and governance of FN communities (9) guided the development of RADAR (Reorganizing the Approach to Diabetes through the Application of Registries) (10), in partnership with FN communities and OKAKI Health Intelligence, Inc., a private, social enterprise in Alberta, Canada, with >12 years working with FN communities.
The RADAR model (10) uses remote care coordinators (CCs), who are registered nurses or dietitians, to support local health care providers (HCPs) (e.g., registered nurses, licensed practical nurses, and dietitians) in FN communities through telehealth to 1) populate an electronic medical record (EMR) diabetes registry called the CARE platform, which was developed specifically for FN communities; 2) coordinate population-level care to identify priorities and gaps in care; and 3) apply current CPGs (11) through regular client review and case conferencing to consider therapeutic changes and coordinate referrals (Figure 1).
FIGURE 1 RADAR infographic.
RADAR was implemented collaboratively with FN communities from Treaty 6, 7, and 8 territories in Alberta, Canada, where local HCPs found the model appropriate, acceptable, and valuable for both HCPs and patients (12). Here, we describe the effectiveness of RADAR with regard to patient outcomes and the organization of diabetes care in these communities.
Design and Methods
Design
We used a modified stepped-wedge design supplemented with a descriptive qualitative assessment to evaluate RADAR’s effectiveness (10). Eligible patients were ≥18 years of age, diagnosed with type 2 diabetes, and actively engaged in and received care from the FN health facility on a reserve and had provided verbal consent for HCPs to manage their diabetes (10). For the qualitative assessment, we purposefully sampled remote care coordinators and local HCPs who were implementing RADAR.
The decision to participate in RADAR was made by FN leaders (e.g., chiefs and council, chief executive officers, and/or community health directors) in each participating community, who reviewed and endorsed the project. OCAP (Ownership, Control, Access, and Possession) principles were followed (13,14). Findings were shared with FN health leadership to incorporate their feedback. RADAR was approved by the University of Alberta’s Health Research Ethics Board (Study ID Pro00048714).
Data Collection and Analysis
The primary outcome was a 10% improvement in A1C, systolic blood pressure (SBP), or LDL cholesterol during the 2-year follow-up relative to baseline control periods, representing a clinically important difference (15). For patients who were already at target at baseline, we considered persistence at target (i.e., maintaining values within 10% of baseline) as achieving the primary outcome (10). Outcomes were measured continuously throughout the follow-up period and at the time of each community transition (i.e., step) as per traditional stepped-wedge designs.
The primary outcome was first analyzed using an intention-to-treat (ITT) framework, which included all patients identified by the communities in their baseline assessments, irrespective of the follow-up care they received. Additionally, we used a per-protocol (PP) framework, removing patients who died, moved, or subsequently refused care at FN community health centers. We fit a generalized linear mixed model with random effect for cluster (i.e., community) and fixed effect for each step (i.e., transitions from baseline to follow-up intervention) (16). We also included calendar year in our models as a fixed effect to control for any temporal confounding effects, although calendar year was not significant in the models (P >0.10). Patients with insufficient primary outcome data were assumed to be nonresponders (i.e., individuals who had failed to achieve the primary end point). In addition, the mean change in A1C, SBP, and LDL was assessed for patients with A1C ≥7.5% (58 mmol/mol), SBP ≥140 mmHg, and LDL cholesterol ≥2.5 mmol/L (i.e., at-risk patients), after accounting for the baseline value, year, and community clustering in alignment with Diabetes Canada CPGs (10,11). Outcome data are not reported by community to maintain confidentiality as per our data-sharing agreements; however, the consistency of effects across the communities was assessed and is expressed as ranges. Quantitative analyses were performed using Stata, v. 14.2, statistical software (StataCorp, College Station, TX).
We used a descriptive qualitative approach (17) to assess the effectiveness of RADAR on the organization of diabetes care within participating communities, which were diverse by treaty, geography, population, and proximity to urban centers. Data sources included individual interviews with remote CCs and local HCPs. Qualitative data were managed using ATLAS.ti, v. 8, software (Scientific Software Development, Berlin, Germany) and analyzed using summative content analysis within the 5Rs framework, including negative case analysis for meaning saturation to understand the diversity of experiences (18,19). We followed the Consolidated Criteria for Reporting Qualitative Research framework (Supplementary Table S1) (20).
Results
Primary Outcomes
The quantitative assessment included seven communities at the end of 2 years. At baseline, 516 patients with type 2 diabetes were registered in RADAR, ranging from 28 to 129 per community. The mean age was 60 years (SD 13.6 years, mean community range [MCR] 55–65 years), 57% were female (MCR 50–66%), the mean A1C was 8.3% (67 mmol/mol) (SD 2.0%, MCR 8.0–8.7%), the mean SBP was 131 mmHg (SD 19.1 mmHg, MCR 122–137 mmHg), and the mean LDL cholesterol was 2.0 mmol/L (36 mg/dL) (SD 0.9, MCR 1.7–2.3 mmol/L [30.6–41.4 mg/dL]) (Table 1).
TABLE 1 Clustered Baseline Demographics of RADAR Participants (n = 516)
Characteristic Value
Age, years 60 ± 13.6
Female sex 292 (57)
Smoker 119 (23.1)
A1C, % 8.3 ± 2.0
Cholesterol, mmol/L
LDL cholesterol
HDL cholesterol
Triglycerides
Total cholesterol–to–HDL cholesterol ratio
2.0 ± 0.9
1.1 ± 0.4
2.5 ± 2.7
3.9 ± 1.8
Estimated glomerular filtration rate, mL/min/1.73 m2*
15–30
30–45
45–60
60–90
>90
15 ± 3.1
31 ± 6.5
44 ± 9.2
143 ± 29.9
246 ± 51.4
Data are mean ± SD or n (%).
* n = 479.
Overall, after 2 years of RADAR across all communities, we found significant improvements for the primary combined end point and in all three outcomes individually (Figure 2). Achievement of the combined end point was 91% (95% CI 89–94%) in the ITT population (n = 516) and 93% (95% CI 91–95%) in the PP population (n = 419) (intracluster correlation ρ = 0.008). Within the combined primary end point (ITT population), the majority (377 of 465, or 80%) had a 10% improvement in any one parameter (A1C, SBP, or LDL cholesterol) over baseline, while 78% of the remaining patients (40 of 51) were persistently at target (Supplementary Figure S1). When examining the primary combined outcome by community, achievement ranged from 87 to 100% (Supplementary Figure S2).
FIGURE 2 Primary combined end point of achieving a 10% reduction or maintaining values within 10% of target for A1C, SBP, or LDL cholesterol during 2 years of follow-up.
When examining the individual components of the primary end point, the biggest driver of the improvement was A1C. Two-thirds of patients achieved a 10% reduction in or persistence at target for A1C (ITT 66% [95% CI 55–77%] and PP 65% [95% CI 54–76]). The proportion of patients achieving a 10% reduction (221 of 336, or 64%) was similar to the number of patients with persistence at target (130 of 180, or 71%) in the ITT population. The mean change in A1C during follow-up was −0.93% (95% CI −0.59 to −1.28%, P = 0.001; MCR −1.26 to −0.16%). Among those with an A1C ≥7.5% (58 mmol/mol) at baseline (mean 9.70%, SD 1.66%; n = 303), mean change in A1C during follow-up was −1.62% (95% CI −2.18 to −1.06%, P <0.001; MCR −2.43 to −0.54%).
With regard to the other individual outcomes, SBP improved for 54% (95% CI 39–68%) of the ITT population and 58% (95% CI 46–69%) of the PP population, and LDL cholesterol improved for 40% (95% CI 32–48%) of the ITT population and 42% (95% CI 34–50%) of the PP population. Unlike A1C, achieving the SBP or LDL cholesterol target was driven by achieving a 10% reduction: 134 of 187 (70%) for SBP and 159 of 236 (66%) for LDL cholesterol in ITT populations, whereas a smaller proportion persisted at target for SBP (150 of 329, or 45%) or LDL cholesterol (55 of 280, or 18%). The mean change in SBP during follow-up was −6.03 mmHg (95% CI −2.68 to −9.38 mmHg, P = 0.005; MCR −19.64 to −0.82 mmHg). Among those with an SBP ≥140 mmHg at baseline (mean 156 mmHg, SD 14.9 mmHg; n = 193), mean change in SBP during follow-up was −14.19 mmHg (95% CI −22.55 to −5.82, P = 0.006; MCR −36.17 to −3.45 mmHg). The mean change in LDL cholesterol during follow-up was −0.17 mmol/L (95% CI −0.10 to −0.24 mmol/L, P = 0.001; MCR −0.38 to −0.02 mmol/L). Among those with LDL cholesterol ≥2.5 mmol/L at baseline (mean 3.17 mmol/L, SD 0.57 mmol/L; n = 215), mean change in LDL during follow-up was −0.33 mmol/L (95% CI −0.46 to −0.21 mmol/L, P = 0.01; MCR −0.71 to −0.03 mmol/L).
Organization of Diabetes Care
We conducted 21 semistructured interviews with 11 individual participants: three remote CCs and eight local HCPs (e.g., registered nurses, licensed practical nurses, and dietitians) from May 2015 to February 2019 (Table 2). Overall, we found improvements in the organization of diabetes care by the 5Rs, as presented below, with illustrative quotes including position (i.e., CC or HCP) and anonymous study code number for each participant.
TABLE 2 Interview Timing and Participant Roles
Participant Role Interviewed at 6 Months, n Interviewed at 24 Months, n Total, n (%)*
Remote CC 2 3 3 (27)†
Local dietitian — 3 3 (27)
Local licensed practical nurse — 2 2 (18)
Local registered nurse — 3 3 (27)
* Percentages do not sum to 100 due to rounding.
† Two CCs were interviewed at both 6 and 24 months.
Recognize and Register
Overall, RADAR facilitated the recognition and registration of patients with diabetes. RADAR “identified our [diabetes] population” (HCP 5), including “younger people that we didn’t know had diabetes” (HCP 4), thus capturing “a lot more of the population” (HCP 7). Nevertheless, RADAR was limited in identifying new patients because only physicians can diagnose. “I don’t know if RADAR had any impact on recognizing clients . . . [because] we never can diagnose” (HCP 6). As such, “the registry was only those [patients] already diagnosed with type 2 diabetes” (HCP 3).
Regardless, RADAR resulted in a “client registry that has been created” (HCP 1). The act of centrally registering patients in CARE with CC support confirmed the type of diabetes for some patients because “many patients had been indicated as ‘other’ diabetes because the HCPs weren’t sure [of the diagnosis]” (CC 2). CCs helped identify type of diabetes by “looking on [the provincial EMR] through discharge summaries to find data to support whether [patients had] type 2 diabetes” (CC 2) or asking “the nurse to confirm with [the patient’s] physician” (CC 1). However, centrally registering patients also resulted in “increasing our overall client load” (HCP 1) or feeling overwhelmed by “seeing my whole population and saying, ‘I should be doing all these things for everybody’” (HCP 6) in light of limited resources.
Relay
Overall, RADAR improved HCP access to and sharing of information. Within health centers, local HCPs described efficient and comprehensive access to information through CARE versus relying on patients’ recollections or paper charts. “You don’t have to go into the [paper] chart every time. Even when you’re unfamiliar with a client, all that information is [in CARE] . . . . Because, sometimes, your clients are not great historians” (HCP 7). CCs also enabled access of local HCPs to clinical information contained in other EMRs. “It can take a lot of time to get access to [the provincial EMR], if we are given it at all. With RADAR, [the CCs] populated CARE with medical information that is important to the level of care we can provide” (HCP 1). Moreover, CARE resulted in “improved communication amongst the team” (HCP 7), allowing local HCPs “to communicate everything you’ve done with a client” (HCP 5). This feature was especially important given staffing challenges. “We really have a good flow [of information] regardless of the staff turnover” (CC 1).
Finally, RADAR improved communication with providers outside the health centers. “Prior to RADAR, there was no communication between on- and off-reserve medical services,” but the CCs helped “connect us to the off-reserve services, such as the physicians . . . [and] the communication has become more open” (HCP 1). Furthermore, CARE facilitated communication and coordinated diabetes specialty care such as ophthalmology (HCP 7) and endocrinology (HCP 6) through report and letter templates. “I use the [patient] report summary, and I can easily fax that to the [physician] clinic” (HCP 2). However, improved relay of information was not reported in all communities, indicating that more work is needed.
Recall
RADAR improved timely review and reassessment of patients with diabetes by local HCPs through CARE’s features (e.g., tasks, reminders, and patient summaries) and CC support. “You have very specific tasks that you need to accomplish to improve people’s health, and they’re right in the forefront [in CARE]” (HCP 7). CCs facilitated recall by “looking at the total population, including the ones not accessing care” (CC 1) and identifying for HCPs which patients “should be targeted at this time” (HCP 4). As a result, “we have been able to reach out and connect with a lot more community members who may not have been utilizing our services for diabetes care” (HCP 1). In addition, CCs reviewed “the diabetes guidelines and asked us to follow up on as much as we can with that client” (HCP 6). As a result, processes of care were completed. “Blood pressures were taken; foot exams and lab work were done” (CC 1). Recall support was crucial in the context of busy clinics. “It gets busy, and you get sidetracked, so it helps to have that person saying, ‘This is who you’ve got to follow up with next; this person needs [this]’” (HCP 4).
However, not all HCPs believed CARE improved recall beyond what existed “because we already set up regular reminders, like [for A1Cs], foot exams, eye exams . . . into the [physician] EMR” (HCP 3). Furthermore, in some communities, “we only case-reviewed half the patients” (CC 1), thus limiting recall. Some HCPs were uncomfortable recalling patients, saying, “That’s not our job’” (CC 1). HCPs resisted recalling patients they believed did not want their care. “Some of these clients that [the CCs] asked me to review were ones that don’t access care here regularly. We know their [glucose] is high [and] that they have other things going on in their life, so they don’t come in for appointments . . . . Chasing them down, [is] not the best use of my time” (HCP 3). Another HCP explained, “We tell [the CC] this person is not engaged . . . and [to] just leave them alone” (HCP 6).
Resource
RADAR helped improve local HCPs’ ability to support patients’ diabetes self-management by informing patients of health center services and supports, developing care plans, and increasing local HCPs’ diabetes knowledge. For example, one respondent said, “Based on the registry, we would call clients, introduce ourselves, [and] introduce the program” (HCP 1) or inform patients that “we have a diabetes management support system . . . [and ask] ‘Would you like some help?’” (CC 2). This resulted in patients accessing services. “There’s probably 50% more people coming for their monthly foot care” (HCP 5).
Furthermore, CCs helped local HCPs develop care plans informed by CPGs. “[The CC] reviewed the guidelines with us and helped us incorporate them into our day-to-day care with clients” (HCP 1). This, in part, helped to increase local HCPs’ diabetes knowledge and “build local capacity” (CC 1). For example, one respondent said, “[The CC showed us] the gold standards for diabetes treatment . . . . I am not an expert on diabetes. I have learned so much” (HCP 7). Diabetes education was especially valuable to HCPs who did not have time or resources to stay current in their knowledge. “Even though we’re close to the nearest town and physicians, you’re still isolated from information . . . . There may be new information, and [the CC is] helpful that way, with new stuff or if we have a question” (HCP 4). As a result, local HCPs reported increased confidence in their diabetes knowledge. “[The CC] has made a huge difference in my confidence with diabetes knowledge” (HCP 1). However, not all HCPs believed RADAR improved their diabetes knowledge. “I have a lot of knowledge in diabetes, so it is not anything new” (HCP 2). One respondent noted that the community already had a certified diabetes care and education specialist (HCP 3). Finally, some HCPs requested further education, such as “10-minute education sessions on new [CPGs]” (HCP6), indicating an ongoing need for up-to-date diabetes knowledge.
Discussion
We found that RADAR improved patient-level outcomes through improved organization of diabetes care in FN communities. In addition, we found significant achievement of our primary outcomes for RADAR participants over 2 years, with the largest effect observed for change in A1C. This finding is similar to an intervention study of rural (non-FN) patients with type 2 diabetes, in which improvements in SBP and/or LDL cholesterol were smaller than improvements in A1C (21). Although glycemic control is essential, further improvements in SBP and/or LDL cholesterol would provide substantial macro- and microvascular benefits (22).
Our results also demonstrated improved diabetes care organization (8), which is foundational to improving patient-level outcomes (7). With regard to the 5Rs, RADAR resulted in central registries, helping communities verify, visualize, and recall their diabetes population. The use of patient registries for diabetes management is associated with better processes and outcomes of care (23,24). Furthermore, FN health directors can use their own registry data as a resource to inform decision-making for local program planning, meet federal reporting requirements, and support funding requests based on the needs of and trends in the local patient population. RADAR allows for a community-led response based in data sovereignty (25) and aligned with the principles of self-governance and self-determination (9).
Second, RADAR facilitated relay of clinical information essential for continuity and coordination of team-based diabetes care (11). This effort included overcoming the restricted access of local HCPs to patients’ provincial EMR data in the current fragmented system, with multiple isolated technologies hindering clinical information-sharing necessary for diabetes care (26,27). Within health centers, centralized compilation of patients’ clinical information and care plans in CARE was crucial in the context of the high staff turnover that FN communities face (27). In addition, team-based care was enhanced through relationship-building among HCPs through CCs’ professional networks, including diabetes specialists. This was especially important for remote communities with irregular access to services necessary for comprehensive diabetes care (11,27).
Finally, RADAR increased local HCP capacity (i.e., knowledge and confidence) to support diabetes self-management (resource) through education (e.g., of current CPGs) and peer support, promoting the long-term sustainability of primary outcomes. This is important because lack of adequate diabetes knowledge among local HCPs is a barrier to diabetes care and is largely affected by time/competing priorities, staff turnover, and changing guidelines (4,28). Furthermore, local HCPs, who are the experts in their communities, can apply RADAR, including enhanced knowledge of diabetes care, with local patients in appropriate ways that recognize contextual and cultural factors (12).
Although RADAR improved diabetes care organization overall, challenges remain. Centrally registering patients does not necessarily recognize new patients through screening, thereby limiting the population health impact. Future RADAR activities may include recommended earlier and more frequent screening of type 2 diabetes for FN people (6). Furthermore, centrally registering patients with recall functionality increased local HCPs’ workload, contributing to feelings of stress. In addition, although RADAR encouraged local HCPs to actively engage or re-engage patients, some HCPs resisted recalling patients, thus limiting the proactive care essential to improving patient outcomes (11). This challenge is not unique to FN communities, as HCPs must balance proactive care with patients’ right to decline care. However, it can become a greater challenge in FN communities, as patients may have competing priorities that take precedence over diabetes self-management (resource), such as housing or food insecurity related to historic and social injustices (5,9). Although local HCPs are experts in their communities, including knowing which patients want to engage or re-engage with diabetes care, some HCPs may believe it is patients’ responsibility to seek care and outside of their role as care providers to facilitate patient engagement proactively. This opinion may be related to local HCPs’ feelings of stress, which is understandable in the context of their limited resources. Nonetheless, this belief may limit community-level comprehensive diabetes care and requires further exploration. Indeed, we previously found that the 5Rs of diabetes care organization are interrelated and influenced by financial and human resources (the sixth R) (8). Regardless, RADAR created the conditions for local HCPs and patients to collaborate in diabetes self-management in this resource-challenged context.
Strengths and Limitations
Our incorporation of quantitative and qualitative components to comprehensively measure effectiveness for both clinical outcomes and diabetes care organization is a strength. However, this study is not without limitations. First, given our follow-up of 2 years, we used intermediate outcomes as opposed to hard clinical end points (e.g., occurrence of heart attack or stroke). Nevertheless, A1C, SBP, and LDL cholesterol are well established diabetes care targets within guidelines (11) and have been shown in many large-scale randomized controlled trials (RCTs) to confer substantial benefits with regard to macro- or microvascular outcomes (11,21). Second, we designed a pragmatic, controlled, community-based intervention aimed at local HCPs as opposed to patients per se. Individual patients or local HCPs within the community could not be randomized to care with and without RADAR because of the threat of “contamination” via exposure to some aspects of the intervention within the same community team or health facility. Moreover, our community partners were not interested in a traditional RCT; a stepped-wedge design was used, as few communities would accept being the control community for an extended duration. Although the participating FN communities were diverse, they may not be representative of all FN communities. Additionally, the participating local HCPs who were interviewed for the qualitative component of the study are a sample of the health care workers involved in the implementation of RADAR. Although almost all FN communities are dealing with similar issues in providing diabetes care, it is possible that some of our findings may not be applicable to all communities, depending on local resources.
Conclusion
RADAR is an effective, tailored approach to support the organization of and capacity building in diabetes care for FN communities. From our findings, it is reasonable to assume that this intervention could be applied to other indigenous populations in jurisdictions around the globe.
Article Information
Acknowledgments
The authors thank their FN partners for their ongoing and generous support and recognize that this work was implemented in the traditional territories of their Treaty 6, 7, and 8 FN partners. The authors thank OKAKI for their support and participation in the evaluation of RADAR. They also acknowledge the significant contribution of Dr. Sumit (Me2) R. Majumdar, who died in 2018, to the study design.
Funding
This work was funded by the Canadian Institutes of Health Research (MOP #143562), Alberta Innovates Health Solutions, and the Lawson Foundation.
Duality of Interest
No potential conflicts of interest relevant to this article were reported.
Author Contributions
D.T.E. and S.S. conceived the study, designed its protocols, and received ethics approval and funding. D.T.E. led the quantitative data collection and analysis, with support from J.K.M.-S. L.A.W. led the evaluation design and conducted qualitative data collection and analysis. D.T.E., L.A.W., and A.S. drafted the manuscript. L.C. and J.A.J. actively contributed to the study design. All authors read and approved the final manuscript. D.T.E. is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.
This article contains supplementary material online at https://doi.org/10.2337/figshare.21872040.
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Clin Diabetes
Clin Diabetes
clinical diabetes
Clinical Diabetes : A Publication of the American Diabetes Association
0891-8929
1945-4953
American Diabetes Association
CD220085
10.2337/cd22-0085
Case Study
Weight Loss With Rising Blood Glucose: Challenges in Distinguishing Conventional Type 2 Diabetes From Pancreatic Cancer–Associated Hyperglycemia
Johannes Laura 1
https://orcid.org/0000-0002-4321-3992
Westcott Gregory P. 2
1 Independent medical journalist, Boston, MA
2 Division of Endocrinology, Diabetes & Metabolism, Beth Israel Deaconess Medical Center, Joslin Diabetes Center, and Harvard Medical School, Boston, MA
Corresponding author: Gregory Westcott, [email protected]
Summer 2023
20 1 2023
20 1 2023
41 3 477480
© 2023 by the American Diabetes Association
2023
https://www.diabetesjournals.org/journals/pages/license Readers may use this article as long as the work is properly cited, the use is educational and not for profit, and the work is not altered. More information is available at https://www.diabetesjournals.org/journals/pages/license.
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pmcDiabetes is both a risk factor for pancreatic cancer and a potential early warning sign of a growing tumor. Approximately 1% of people with diabetes who are ≥50 years of age will be diagnosed with pancreatic cancer within 3 years of first meeting the criteria for diabetes, a rate that is eight times higher than would have been expected for people of similar age and sex in the general population (1). Distinguishing typical diabetes from hyperglycemia associated with pancreatic cancer can be difficult. There is no validated, widely available, noninvasive test for pancreatic cancer, and, unlike colon and breast cancer, there is no standard screening process. Additionally, given its low prevalence compared with type 2 diabetes, pancreatic cancer is often low on the list of differential diagnoses when patients present with hyperglycemia. While the lifetime risk of diabetes in U.S. adults is estimated at ∼40% (2), the lifetime risk of pancreatic cancer is 1.7% (3). However, the prevalence of hyperglycemia in patients with pancreatic adenocarcinoma may be as high as 75% (4), indicating a relationship between pancreatic cancer and diabetes.
In many cases, hyperglycemia may precede detection of a tumor and be the first clinical indication of malignancy, so hyperglycemia can provide a critical clue to diagnosis if it prompts early evaluation. Although surgical resection of pancreatic cancer offers the best chance at a cure, some 80% of pancreatic tumors are not detected until they are unresectable. The transition of a growing pancreatic tumor from resectable to unresectable occurs over a period of ∼6 months before diagnosis, suggesting that detection even as little as 6 months earlier would lead to an increase in the resectability rate (5).
Case Presentation
A 56-year-old female recreational cyclist presented to her primary care provider (PCP) with postprandial hyperglycemia, new-onset upper abdominal pain worse with fatty meals, and weight loss.
She had a history of type 2 diabetes, with an A1C of 7.4% 3 years before presentation. At that time, she had a BMI of 36.5 kg/m2 and subsequently lost 51 lb via lifestyle interventions, with a corresponding reduction in A1C to 5.0% and durable remission of diabetes.
At presentation, her A1C had risen to 6.1%, with associated postprandial glucose excursions into the 200-mg/dL range. Her fasting C-peptide concentration was 0.84 ng/mL and tests for pancreatic autoantibodies (GAD65, IA-2, and anti-insulin) were negative. Her PCP was initially concerned about gastritis and prescribed omeprazole. The patient had lost 23 lb in 11 weeks since the onset of abdominal pain, although this was thought to be explained by dietary modifications to address her gastritis. A DEXA body composition scan demonstrated that her weight loss had been entirely accounted for by loss of lean mass when compared with prior studies.
Four months after presentation, the patient reported that, despite a weight loss of an additional 5 lb, or a total of 28 lb since the abdominal pain began, a home A1C measurement was 6.4%, and she was recording fingerstick blood glucose values as high as 321 mg/dL. She was prescribed metformin.
Because of severe hyperglycemia from glycemic excursions, worsening abdominal pain, and pruritus, the patient presented to a local emergency department, where urinalysis revealed moderate bilirubin. In follow-up with the PCP, her A1C had risen to 7%, her serum C-peptide concentration was 1.39 ng/mL, and her blood glucose was 285 mg/dL. Insulin glargine 20 units daily was initiated. A Helicobacter pylori urea breath test was negative, and upper endoscopy was recommended. Given her high postprandial blood glucose levels, insulin aspart with meals was initiated, and metformin was discontinued because of abdominal pain and loose stools. Her fasting glucose levels and weight trends over time are shown in Figure 1.
FIGURE 1 Weight and fasting blood glucose trends from day of initial presentation (day 0). Blood glucose trendline represents 7-day running average.
The patient was subsequently noted by friends to appear jaundiced, and she presented to the emergency department, where an abdominal CT scan revealed a hypodense mass in the pancreatic head, highly concerning for malignancy, as well as several ill-defined lesions in the liver, enlarged lymph nodes, and small nodules in the lungs. She was admitted and received a biliary stent. The tumor was biopsied and found to be adenocarcinoma, which was deemed nonresectable, and she sought specialist care for her malignancy and diabetes.
Questions
When should pancreatic cancer be suspected in a patient with hyperglycemia?
How should hyperglycemia be evaluated and managed in patients with suspected pancreatic cancer?
Commentary
Distinguishing typical type 2 diabetes from tumor- related hyperglycemia can be challenging in clinical practice. This case highlights how difficult it is to make the connection between new-onset hyperglycemia and pancreatic cancer, as classic symptoms of cancer such as abdominal pain and weight loss can easily be attributed to other causes.
In certain clinical circumstances, new-onset diabetes or sudden and unexplained worsening of glucose control may be a red flag for possible underlying malignancy. As previously noted, ∼75% of pancreatic cancer patients present with either diabetes or impaired glucose tolerance. In this case, significant postprandial glucose excursions appear not to have been reflected by the A1C, which remained mostly in the prediabetes range despite intermittent blood glucose readings >300 mg/dL. The mechanisms by which pancreatic cancer contribute to hyperglycemia are not fully understood but appear to include both an increase in insulin resistance and an impaired β-cell response (6).
Type 2 diabetes is a gradually progressive disease and does not typically advance as rapidly or dramatically as seen in this case, and the worsening in blood glucose despite significant weight loss and a healthy low-glycemic diet should suggest a possible secondary cause. Given that patients are often instructed to lose weight, this sign can be overlooked by both patients and clinicians, but successful weight loss—16% of body weight in this case—should have resulted in a significant improvement in glycemia. The onset of abdominal pain, although nonspecific, is also not typical of type 2 diabetes and, in the setting of unexplained worsening glycemia, should be investigated more aggressively.
Despite the established link between pancreatic cancer and new-onset diabetes, the American Diabetes Association recommends against routine evaluation for pancreatic cancer in patients without clinical signs or symptoms such as weight loss or abdominal pain (7), and the low prevalence of the disease paired with the current lack of a low-cost screening modality limits the practicality of widespread screening. Clinical tools such as the Enriching New-Onset Diabetes for Pancreatic Cancer (END-PAC) score have been developed to aid clinicians in identifying patients who may warrant further workup (8), but this score is not in widespread clinical use and requires knowledge of blood glucose values before the onset of diabetes, which may limit its practicality. In this case, the patient had a complete remission of diabetes for 3 years before her diagnosis of cancer, and, at the time of diabetes relapse, an END-PAC score would have been 9, suggesting a high risk of malignancy (although this score has not been validated in the setting of diabetes relapse). The glucagon-to-insulin ratio has been proposed as a method for discriminating pancreatic cancer–associated hyperglycemia from type 2 diabetes (9), but the specificity of this approach is not adequate to be used in clinical practice, and this ratio was not determined in this patient. There is interest in developing better biomarkers to identify patients with new-onset hyperglycemia who are at high risk of pancreatic adenocarcinoma; a large clinical study is currently enrolling patients to determine the role of measuring carbohydrate antigen 19–9 and potentially other markers in this population (10), and Bluestar Genomics is developing a blood test to screen for pancreatic cancer based on DNA hydroxy-methylation signatures (11). For now, however, it is up to health care providers to clinically assess patients with a new diagnosis of diabetes or suddenly worsening diabetes for signs or symptoms of pancreatic cancer that warrant further evaluation.
In patients with suspected or confirmed pancreatic adenocarcinoma, it is important to recognize that the secretion of multiple islet hormones may be affected, with therapeutic implications. Extensive pancreatic destruction results in pancreatogenic, or type 3c, diabetes, which is characterized not only by decreased insulin production, but also by lower glucagon and pancreatic polypeptide levels (12). In terms of therapy, studies of particular antihyperglycemic agents, including incretin-based therapy (i.e., dipeptidyl peptidase 4 inhibitors and glucagon-like peptide 1 receptor agonists), generally have been unable to establish a strong causal relationship between any agent and pancreatic cancer (13). It may be helpful to estimate insulin secretory function at the time of cancer diagnosis by measuring fasting or stimulated C-peptide level to assess the need for insulin therapy, although this can also be determined based on a patient’s response to noninsulin therapy and the clinical scenario, as patients who are perioperative or have impaired renal or hepatic function may have limited therapeutic options. Note that patients with substantial metastatic liver involvement may have diminished gluconeogenic capacity and therefore may be prone to fasting hypoglycemia, and, rarely, paraneoplastic IGF-II production can result in noninsulin-mediated hypoglycemia (14), presenting management challenges.
In conclusion, risks of pancreatic cancer and diabetes are bidirectional; pancreatic cancer is a risk factor for new-onset diabetes, and longstanding type 2 diabetes is a risk factor for pancreatic cancer. Careful clinical assessment is critical at the time of diabetes diagnosis or a change in diabetes status that is characterized by atypical features such as unintentional weight loss, abdominal pain, or dramatic postprandial glucose excursions out of proportion to overall glycemic control, since early detection of pancreatic adenocarcinoma is likely to be lifesaving.
Clinical Pearls
At the time of diabetes diagnosis, worsening blood glucose despite significant weight loss with negative pancreatic autoantibodies, particularly in patients >65 years of age, should prompt assessment for secondary causes, including pancreatic cancer.
Abdominal pain in the context of new-onset or worsening diabetes should trigger expedient assessment.
Patients with pancreatic cancer and hyperglycemia can be treated with standard diabetes therapy, although there may be high risk for hypoglycemia, particularly in patients with substantial pancreatic destruction or resection.
Article Information
Duality of Interest
No potential conflicts of interest relevant to this article were reported.
Author Contributions
L.J. drafted the initial manuscript. G.P.W. critically reviewed and revised the manuscript. G.P.W. is the guarantor of the work and, as such, had full access to all data in the case study and takes responsibility for the contents of this article.
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PMC010xxxxxx/PMC10338274.txt
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==== Front
Clin Diabetes
Clin Diabetes
clinical diabetes
Clinical Diabetes : A Publication of the American Diabetes Association
0891-8929
1945-4953
American Diabetes Association
36920750
CD220128
10.2337/cd22-0128
Practical Pointers
Personalized Virtual Care Using Continuous Glucose Monitoring in Adults With Type 2 Diabetes Treated With Less Intensive Therapies
Reddy Sushma
Wu Calvin C.
José Aimée
Hsieh Jennifer L.
Rautela Shetal Desai
Carbon Health Virtual Diabetes Care, Oakland CA
Corresponding author: Sushma Reddy, [email protected]
Summer 2023
15 3 2023
15 3 2023
41 3 452457
© 2023 by the American Diabetes Association
2023
https://www.diabetesjournals.org/journals/pages/license Readers may use this article as long as the work is properly cited, the use is educational and not for profit, and the work is not altered. More information is available at https://www.diabetesjournals.org/journals/pages/license.
Abbott Diabetes Care provided funding for the development of this manuscript
==== Body
pmcDuring the past 5 years, an increasing number of individuals with diabetes have transitioned from traditional blood glucose monitoring (BGM) to continuous glucose monitoring (CGM). Numerous studies have demonstrated the safety and efficacy of CGM use in improving overall glycemic control in type 1 diabetes and intensively treated type 2 diabetes (1–12).
When used in conjunction with virtual telehealth visits and telemonitoring interventions, CGM not only improves glycemic management (13–18), but has also been shown to reduce diabetes-related distress (19) and increase medication adherence (20). Importantly, use of CGM in conjunction with telehealth visits and remote monitoring can serve to connect patients remotely with their health care providers and facilitate timely clinical care (21–23).
Despite innovations in glucose monitoring and other diabetes technologies, suboptimal glycemic control persists (24–26) and continues to worsen (27) among a substantial percentage of individuals with type 2 diabetes, ∼95% of whom are treated with basal insulin only, noninsulin medications, and/or lifestyle interventions (28–30). As reported in numerous studies, therapeutic inertia is a significant driver of poor diabetes management (31–36). Several factors contribute to therapeutic inertia, including lack of sufficient data to make clinical decisions, restrictions on clinicians’ time and resources, lack of training/education, and patient-level obstacles to treatment adherence (37).
Carbon Health (San Francisco, CA) recently launched a virtual, endocrinologist-led diabetes program that addresses these factors. In this article, we discuss how this approach was used to personalize therapy and improve outcomes in two patients with newly diagnosed type 2 diabetes treated with nonintensive therapies.
Evidence for CGM Use in Nonintensively Treated Type 2 Diabetes
Large database analyses have demonstrated strong associations between CGM use and significant reductions in A1C, acute diabetes events, and diabetes- related hospitalizations in people with diabetes treated with basal insulin only or noninsulin medications (38,39). Although these types of studies have limitations, their findings are supported by one randomized controlled trial (40) and smaller observational cohort studies (18,19,23,41).
In a large retrospective database study of 1,034 adults with nonintensively treated type 2 diabetes, Wright et al. (38) reported a significant A1C reduction (from 10.1% to 8.6%, P <0.001) with the use of CGM. Importantly, individuals treated with noninsulin medications showed a greater A1C reduction compared with those treated with basal insulin only (−1.6 vs. −1.1%, respectively, P <0.001).
A similar database analysis by Miller et al. (39) reported significant reductions in acute diabetes events (ADEs) and all-cause hospitalizations (ACHs) among 10,282 adults with nonintensively treated type 2 diabetes using CGM. As reported, the ADE rate decreased from 0.076 to 0.052 events per patient-year (P <0.001). The rate of ACHs decreased from 0.177 to 0.151 events per patient-year (P = 0.002) during the 6 months after acquisition of a CGM system.
In the randomized, controlled MOBILE study, Martens et al. (40) followed 175 adults with type 2 diabetes treated with basal insulin with or without noninsulin medications who were randomized to CGM or BGM. Mean baseline A1C was 9.1%. At 8 months, A1C decreased 1.1% in the CGM group compared with a 0.5% reduction in the BGM group (P = 0.02). The mean percentage of time in the established target glucose range (time in range [TIR]) of 70–180 mg/dL (42) was 59% with CGM versus 43% with BGM. Although neither group achieved the recommended goal of >70% TIR, the difference was significant (P <0.001).
More recently, Grace and Salyer (41) reported findings from a 6-month, prospective, interventional, single-arm study of 38 adults with type 2 diabetes and a mean baseline A1C of 10.1%. Twenty-two (58%) were treated with noninsulin medications, and 16 were treated with basal insulin with or without noninsulin medications. At 6 months, mean A1C decreased to 7.3% (P <0.001), with significant reductions in body weight (−3.1 kg, P = 0.002) and significant increases in TIR, from 57.0% at baseline to 72.2% at study end.
Persistent CGM use (>6 days/week) is generally recommended for individuals treated with intensive insulin regimens (42). However, this level of CGM use may not be necessary for those treated with less intensive regimens.
A recent observational study of 594 adults with type 2 diabetes who were enrolled in the Onduo Virtual Diabetes Care (VDC) clinic found that this cohort achieved significant A1C reductions (mean 0.6%, P <0.001) with intermittent use of CGM supported by coaching and clinical advice delivery via telehealth visits (23). Survey results revealed that 94.7% of respondents agreed or strongly agreed that they were comfortable inserting the sensor via remote training, and the majority agreed or strongly agreed that intermittent use of CGM improved their understanding of the impact of eating (97.0%), increased their diabetes knowledge (95.7%), and helped them improve their diabetes control when not wearing the CGM sensor (79.4%). Additional surveys of the Onduo cohort have found similar glycemic improvements (18,43), and one study by Polonsky et al. (19) showed associations between participation in the VDC and reductions in diabetes-related distress.
Clinical Setting
Carbon Health designed its endocrinologist-led program to integrate with primary care physicians (PCPs) throughout its primary care and urgent care (UC) network. The program uses virtual telehealth visits, telemonitoring of CGM data, nutrition and physical activity counseling, and support in making other lifestyle changes through a completely virtual platform. A specialized care team of endocrinologists and diabetes care and education specialists (DCESs) interprets the data and creates individualized treatment plans optimized for each patient’s health status, lifestyle, and preferences. The digital delivery of this care provides patients with greater accessibility, with no waiting or travel time involved. The following case examples demonstrate how we used this approach to address the needs of two patients with newly diagnosed type 2 diabetes.
Case Example 1
At presentation, patient 1 was a 56-year-old man with a BMI of 31.2 kg/m2, weight of 97.8 kg, fasting glucose of 431 mg/dL, and blood pressure of 136/78 mmHg. On 1 November 2021, the patient presented to his PCP with fatigue and polyuria. Laboratory results showed that his A1C was 12.9%. The PCP provided basic education on diabetes management, started the patient on metformin 1,000 mg once daily, and prescribed CGM, which was initiated the next day with an intermittently scanned CGM system.
On 16 November, he returned to his PCP for follow-up. His blood pressure was significantly lower (110/66 mmHg) and his albumin-to-creatinine ratio was normal. The patient explained that he was no longer consuming sugar, had switched to a low-carbohydrate diet (≤35 g at each meal), and was exercising almost daily. He indicated that he did not want to do fingerstick BGM testing and liked being able to see his glucose levels at any time with CGM. His PCP increased his metformin dose to 1,000 mg twice daily; however, the patient declined treatment with an angiotensin receptor blocker for high blood pressure or a statin for elevated lipids.
His CGM glucose profiles revealed notable improvements in glycemic control during the first 4 days of sensor wear (Figure 1A), which corresponded with daily progressive reductions in average glucose of 275, 227, 180, and 167 mg/dL on 2–5 November, respectively. These improvements continued throughout the next week (Figure 1B). During the same period, his TIR increased from 63.2 to 99.4%.
FIGURE 1 Glucose profiles during patient 1’s first 4 days of CGM use (A) and during the subsequent week (B).
Additional history was obtained when the patient met with the endocrinologist on 30 November 2021 in a telehealth visit. The endocrinologist learned that the patient had been “addicted to soda pop” (drinking six bottles per day) and had his glucose checked annually because, he said, he “knew he would get diabetes some day.” The patient explained that he had given away all of his candy and kept only one can of sugar-sweetened soft drink for possible episodes of low glucose. Additionally, the patient said he had stayed at a health and wellness retreat and met with a nutritionist and physician to learn how to eat more healthfully. The patient also expressed interest in using health-related technologies and wears a sleep and fitness tracking ring, which provides personalized health metrics. Laboratory results obtained in February 2022 revealed that the patient’s A1C had decreased from 12.9% to 6.7%.
Case Example 2
At presentation, patient 2 was a 54-year-old man with a BMI of 24.2 kg/m2 and weight of 63.5 kg. On 6 April 2021, he presented at a Carbon Health UC clinic complaining of pain in his left hand with cramping and numbness, which had started 3 weeks before the visit. The patient also described symptoms of polyuria and polydipsia. Random glucose was 467 mg/dL. The UC provider scheduled a virtual telehealth visit with the endocrinologist for the next day.
On 7 April, the endocrinologist provided education on the pathophysiology of diabetes and appropriate lifestyle changes and prescribed the patient CGM. Laboratory results showed that the patient’s A1C was 14.5%. The patient was started on metformin 750 mg extended release (ER) once daily and insulin glargine 15 units (0.24 units/kg) once daily to treat the glucotoxicity. A telehealth visit with the DCES was scheduled for the next day.
At that visit, the DCES reviewed proper insulin injection technique, instructed the patient on CGM use and sensor placement, and provided additional nutrition guidance. The patient started an intermittently scanned CGM system on 11 April.
At the follow-up visit 4 days later, the patient reported that he had discontinued the insulin on 11 April. As shown in Figure 2, the patient’s glucose profile showed significant improvement, with 99% of CGM values within the target range.
FIGURE 2 Patient 2’s glucose profile during the first 2 weeks of CGM use.
Throughout April, the DCES kept in touch with the patient through frequent messaging and provided patient-appropriate articles about various aspects of diabetes self-management. At a follow-up visit with the endocrinologist on 1 August, the patient’s A1C was 5.8%, a decrease of 8.7% from his initial visit.
Discussion
Through use of telehealth visits in conjunction with CGM data, our diabetes program has been effective in improving glycemic outcomes in our patients with type 2 diabetes. Our patients with a baseline A1C >9.0% have achieved an average 4.3% reduction through participation in our program. Our approach allows us to provide comprehensive, personalized diabetes care that is timely and efficient for both patients and our health care team. Whereas the average wait time for an initial nonurgent endocrinologist consultation is 37 days (44), patient 2 was able to meet virtually with an endocrinologist 1 day after being seen by the UC provider. Importantly, our approach facilitates personalized care in a way that allows patients to engage with their self-management.
With patient 1, engagement was immediate. Although initiating metformin effectively lowered his overall glucose levels throughout the day, the notable reductions in glycemic spikes and rapid improvement in TIR suggests that he quickly recognized the causes and effects of his eating pattern and physical activity level and responded appropriately with significant lifestyle changes. Moreover, given his significant improvement in overall diabetes management and his affinity for using health technologies, it is likely that he will persist in using CGM.
With patient 2, we recognized his need for ongoing follow-up, education, and reassurance. We were able to provide this with two telehealth visits with the DCES and frequent messaging during his first month of diabetes care, which reinforced his successes and kept him engaged with his diabetes self-management.
Use of CGM data is an essential component of our approach to care. As we have seen with our patients, CGM facilitates their understanding of diabetes and promotes needed changes in their lifestyle behaviors in addition to providing our team with meaningful information to guide therapy decisions. The patients described here traditionally would have required multiple medications and/or insulin, but with the use of this care model, they achieved optimal glycemic control within 2 weeks with metformin alone by incorporating CGM into their regimens.
Based on our experiences, we believe that CGM should be an integral part of the treatment algorithm for individuals with type 2 diabetes regardless of their treatment regimen. Unfortunately, many individuals with diabetes who are treated with nonintensive therapies are denied access to CGM because of overly restrictive insurance coverage policies. Given the significant and increasing worldwide prevalence of type 2 diabetes, it only makes sense that current and future diabetes technologies be made available to all people with diabetes regardless of type and therapy.
Article Information
Acknowledgments
The authors thank Christopher G. Parkin, MS, of CGParkin Communications, Inc., for his assistance in developing this article.
Funding
Abbott Diabetes Care provided funding for the development of this article.
Duality of Interest
S.R. receives speaker fees from Dexcom. J.L.H. receives speaker fees from Dexcom and Eli Lilly. No other potential conflicts of interest relevant to this article were reported.
Author Contributions
S.R. and C.C.W. conceptualized the article and contributed to writing, reviewing, and editing the manuscript. All authors curated the data. S.R. is the guarantor of this work and, as such, takes responsibility for the integrity of the data and the accuracy of the information included in the article.
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PMC010xxxxxx/PMC10338275.txt
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==== Front
Clin Diabetes
Clin Diabetes
clinical diabetes
Clinical Diabetes : A Publication of the American Diabetes Association
0891-8929
1945-4953
American Diabetes Association
CD220099
10.2337/cd22-0099
Feature Articles
Severe Hypoglycemia and the Use of Glucagon Rescue Agents: An Observational Survey in Adults With Type 1 Diabetes
https://orcid.org/0000-0003-1054-7096
Hughes Allyson S. 1
Chapman Katherine S. 2
Nguyen Huyen 2
Liu Jingwen 2
Bispham Jeoffrey 2
Winget Melissa 3
Weinzimer Stuart A. 4
Wolf Wendy A. 2
1 Department of Primary Care, Ohio University Heritage College of Osteopathic Medicine, Athens, OH
2 T1D Exchange, Boston, MA
3 Zealand Pharma A/S, Boston, MA
4 Department of Pediatrics, Yale School of Medicine, New Haven, CT
Corresponding author: Allyson S. Hughes, [email protected]
Summer 2023
06 3 2023
06 3 2023
41 3 399410
© 2023 by the American Diabetes Association
2023
https://www.diabetesjournals.org/journals/pages/license Readers may use this article as long as the work is properly cited, the use is educational and not for profit, and the work is not altered. More information is available at https://www.diabetesjournals.org/journals/pages/license.
Severe hypoglycemia (SH) is the most frequent and potentially serious complication affecting individuals with type 1 diabetes and can have major clinical and psychosocial consequences. Glucagon is the only approved treatment for SH that can be administered by non–health care professionals (HCPs); however, reports on the experiences and emotions of people with type 1 diabetes associated with SH and glucagon rescue use are limited. This survey study demonstrated that an increasing number of individuals with type 1 diabetes have current and filled prescriptions for glucagon and have been educated about glucagon rescue use by an HCP. Despite this positive trend, challenges with SH remain, including a high level of health care resource utilization, considerable out-of-pocket expenses for glucagon kits, a high prevalence of hypoglycemia unawareness, and a negative emotional impact on individuals with diabetes. Nocturnal and exercise-related hypoglycemia were concerns for most survey participants.
Zealand Pharma A/S
==== Body
pmcInsulin therapy carries an increased risk of hypoglycemia, a significant and potentially fatal complication of diabetes management (1–4). The American Diabetes Association (ADA) defines level 2 hypoglycemia as a blood glucose level <54 mg/dL (3.0 mmol/L). It is associated with impaired counterregulatory mechanisms that may lead to reduced awareness of hypoglycemic events, potentially leading to a cycle of recurrent hypoglycemia (3,4). Level 3 hypoglycemia (also called severe hypoglycemia [SH]) is defined by an altered mental and/or physical status that requires assistance from another person for recovery (3). In the United States, SH results in almost 300,000 emergency department visits per year (5). Untreated SH may lead to adverse clinical outcomes that can include cognitive dysfunction, loss of consciousness, seizures, coma, and death (2,3,6).
Long-term clinical studies have shown that SH remains an ongoing challenge for people with type 1 diabetes, occurring at an average rate of 0.36–0.41 episodes per person per year (7). A history of SH is a strong indicator for the risk of subsequent SH events (7), and people with type 1 diabetes are likely to experience several SH events in their lifetime. A clinical history of SH is associated with an increased risk of all-cause mortality and cardiovascular disease (CVD) in people with type 1 diabetes (8,9), which may lead to substantial direct and indirect costs (10–12) and constitutes a significant part of the economic burden associated with diabetes (13–16). Hypoglycemia can also significantly compromise a person’s emotional state and quality of life (2,3).
The potential negative impacts of SH are significant sources of distress for many individuals with type 1 diabetes and may create a strong aversion to situations in which SH might occur. This fear of hypoglycemia is prevalent among individuals with type 1 diabetes and may have substantial implications for diabetes management and subsequent health outcomes (3,17). Many people with diabetes who are worried about hypoglycemia maintain their blood glucose at higher than recommended levels to avoid the adverse effects of hypoglycemic episodes (18), thus contributing to the metabolic complications of hyperglycemia.
Glucagon has been the first-line emergency treatment for SH in insulin-treated people with diabetes since the 1960s (19). The ADA recommends prescribing glucagon for all individuals at an increased risk of level 2 and level 3 hypoglycemia so that it is available when needed (3). The ability to administer glucagon for the treatment of SH is not limited to health care professionals (HCPs); it can be administered by family members, caregivers, or bystanders (3). The significant burden of SH could be partially alleviated with the use of glucagon as recommended by the ADA (3). However, despite its favorable safety and efficacy profile, glucagon is underutilized as a rescue treatment for SH owing to a lack of routine prescribing of glucagon among HCPs, people with diabetes not filling their glucagon prescriptions, and caregivers not administering or not correctly administering glucagon in the event of SH (5,20–23). First-generation glucagon kits (24,25) require reconstitution of lyophilized native human glucagon before use. The complexity of this multistep process is a known barrier to the rapid and efficient administration of glucagon (21,26,27). Other barriers include lack of appropriate education and training (21,22,26,27) and reluctance to administer rescue glucagon in an emergency for fear of harming the individual with SH (21,22,27).
Over many decades, there has been an unmet need for easy-to-use glucagon products to treat SH to aid in alleviating fear related to hypoglycemia, reduce training needs, and increase overall utilization of glucagon (27). Second-generation glucagon products do not require reconstitution and have been available on the U.S. market since 2019. These products include Baqsimi (Eli Lilly and Company) (28), a native human glucagon powder for nasal administration; Gvoke (Xeris Pharmaceuticals) (29), a ready-to-use premixed solution of native human glucagon for subcutaneous injection; and Zegalogue (Zealand Pharma) (30), a ready-to-use pre-mixed solution of the glucagon analog dasiglucagon for subcutaneous injection.
To date, there has been limited research on the experiences and emotions of individuals with diabetes regarding hypoglycemia and glucagon rescue use. This dearth of research is concerning considering the potentially severe consequences that a lack of glucagon use can have for SH recovery (19). To address these questions, we surveyed adults with type 1 diabetes to gain a real-world understanding of their experiences with hypoglycemia and the use of glucagon rescue products, identify barriers to glucagon use, and determine their preferences for glucagon rescue product characteristics. The objective of this study was to provide an updated real-world perspective on how the landscape of emergency glucagon use has changed since the introduction of second-generation, ready-to-use glucagon products in 2019.
Research Design and Methods
Focus Groups Study to Inform Survey Development
The survey questions for this research were informed by focus groups conducted virtually from October to December 2020. In total, 38 adults with type 1 diabetes consented and participated. Seven focus groups with five to seven participants each were asked about their experiences with hypoglycemia and glucagon use. Participants were encouraged to interact with each other to gain robust insights. Each 90-minute session was recorded and transcribed for analysis. Each focus group participant was remunerated with a $100 Amazon gift card. The questions included in the survey for this study were developed from the key concepts that emerged during these focus groups.
Recruitment
Participants for the survey were recruited through the T1D Exchange registry (31–33), a longitudinal study collecting information on type 1 diabetes management and outcomes. To be enrolled in the registry, individuals with type 1 diabetes must either receive insulin or have had a pancreas or islet cell transplant. In May 2021, recruitment emails were sent to the adult members of the T1D Exchange registry community, and posts were set up on social media (Facebook and Twitter) containing a link directing potential participants to a brief description of the research study.
Informed Consent
An institutional review board (IRB) exemption as well as a partial waiver of the Health Insurance Portability and Accountability Act authorization signature requirement for use and disclosure of protected health information were obtained from the WCG IRB on 22 April 2021. Potential study participants from the T1D Exchange registry community were provided with a link to an electronic consent form for their review and electronic signature. Participants who provided consent were then emailed a copy of their informed consent form for their records.
Inclusion Criteria
Participants enrolled in the study were included in the data analysis if they satisfied the following criteria: ≥18 years of age, diagnosis of type 1 diabetes for at least 1 year, at least one episode of SH defined as a low blood glucose event during which assistance was required, familiarity with current glucagon options on the market, currently residing in the United States, fluent in written and spoken English, and agreed to provide digital informed consent. Participants were excluded from the analysis if they were currently pregnant or refused to declare whether they were pregnant.
Survey
Consenting participants were directed to the online survey conducted via Alchemer (34). After completing the survey, each participant was remunerated with a $25 Amazon gift card. For the purposes of the survey, participants’ experiences with hypoglycemia were classified as “mild” (blood glucose <70 mg/dL [3.9 mmol/L] to ≥54 mg/dL [3.0 mmol/L]), “moderate” (blood glucose <54 mg/dL [3.0 mmol/L]), and “severe” (an altered mental and/or physical status requiring assistance for treatment of hypoglycemia). Definitions of these classifications were provided to participants as they completed the survey. Some survey questions allowed for multiple answers to be given, resulting in an overall percentage in excess of 100%. These questions are noted in the results tables.
Statistical Analysis
All statistical analyses were performed using R software, v. 4.0.5 or later (R Core Team, Vienna, Austria). Summary statistics, including mean, SD, frequency, and percentage, were calculated for general demographic and diabetes-related health information. Bivariate statistical analyses were performed using Welch two-sample t tests, Pearson χ2 tests, and Fisher exact tests to assess differences in demographic and clinical characteristics between participants who, at the time of the survey, had a current and filled glucagon prescription and those who did not and between participants who had impaired awareness of hypoglycemia and those who did not. A P value ≤0.05 was considered statistically significant.
Results
Participants’ Demographics and Clinical Characteristics
In total, 428 individuals consented and were enrolled in the study. Of these, 316 individuals met the inclusion criteria and completed the survey. The baseline participant characteristics are shown in Table 1.
TABLE 1 Participants’ Demographics (N = 316)
Characteristic Value
Age, years 35.6 ± 8.9
Duration of type 1 diabetes, years 17.2 ± 12.7
Gender
Female
Male
Other
187 (59.2)
128 (40.5)
1 (0.3)
Race (multiple answers possible)
White
Black or African American
Asian
American Indian or Alaskan Native
Native Hawaiian or other Pacific Islander
Other
290 (91.8)
12 (3.8)
9 (2.8)
8 (2.5)
1 (0.3)
4 (1.3)
Ethnicity
Not Hispanic or Latino
Hispanic or Latino
302 (95.6)
14 (4.4)
Employment status (multiple answers possible)
Working full time
Working part time
Student
Temporarily unemployed or on leave from work
Unemployed, looking for work
Volunteer
Disabled
Unemployed, not looking for work
Retired
195 (61.7)
65 (20.6)
36 (11.4)
13 (4.1)
12 (3.8)
9 (2.8)
8 (2.5)
6 (1.9)
2 (0.6)
Health insurance (multiple answers possible)
Private health insurance (e.g., commercial, fee-for-service, HMO, PPO, or POS)
Medicare
Medicaid
Other government-sponsored health coverage plan
Affordable Care Act plan
Medigap
Single service plan (e.g., dental, vision, or prescriptions)
Military health care (e.g., TRICARE, CHAMPUS, CHAMPVA, or VA)
Other state-sponsored health coverage plan
No health insurance or health care coverage of any type
Not known
187 (59.2)
71 (22.5)
34 (10.8)
12 (3.8)
8 (2.5)
7 (2.2)
6 (1.9)
6 (1.9)
5 (1.6)
4 (1.3)
1 (0.3)
Education
Bachelor’s degree
Some college
Master’s degree
Associate’s degree
Doctoral degree
High school graduate/GED
Some high school
111 (35.1)
77 (24.4)
57 (18.0)
33 (10.4)
14 (4.4)
12 (3.8)
12 (3.8)
Annual household income
<$30,000
$30,000 to <$50,000
$50,000 to <$75,000
$75,000 to <$100,000
$100,000 to <$200,000
≥$200,000
Do not wish to provide
Not known
44 (13.9)
41 (13.0)
85 (26.9)
53 (16.8)
54 (17.1)
18 (5.7)
12 (3.8)
9 (2.8)
Data are mean ± SD or n (%). CHAMPUS, Civilian Health and Medical Program of the Uniformed Services; CHAMPVA, Civilian Health and Medical Program of the Department of Veterans Affairs; GED, general education diploma; HMO, health maintenance organization; POS, point of service; PPO, preferred provider organization; VA, Veterans Affairs.
Participants had a mean ± SD age of 35.6 ± 8.9 years and a mean duration of type 1 diabetes of 17.2 ± 12.7 years since their diagnosis. Of the participants, 59.2% were female, most were White and identified as not Hispanic or Latino, and the majority were in full-time or part-time employment. Overall, 59.2% had private health insurance; 22.5% and 10.8% were enrolled in Medicare or Medicaid, respectively; 1.3% did not have health insurance of any type; and the remainder received health insurance from other sources. In terms of education, 57.5% reported having a bachelor’s degree or higher, and 66.5% had an annual household income ≥$50,000.
The clinical characteristics of participants are summarized in Table 2. The most frequently used method of glucose monitoring was real-time continuous glucose monitoring (CGM; 80.4%), followed by traditional fingerstick blood glucose monitoring (BGM; 47.2%) and intermittently scanning CGM, formerly called flash CGM (FGM; 0.9%). Most participants (76.6%) reported using an insulin pump, 30.4% used injectable insulin, and 4.1% used inhalable insulin. Participants reported comorbid conditions, the most frequent of which were joint issues (27.5%), hypothyroidism (25.3%), CVD (19.3%), and retinopathy (19.3%). Overall, 69.3% were seen by an adult or pediatric endocrinologist, and 22.8% were seen by other specialized diabetes HCPs, with the remainder seen by primary care HCPs.
TABLE 2 Participants’ Clinical Characteristics (N = 316)
Characteristic Value
Glucose monitoring method (multiple answers possible)
CGM
BGM
FGM
254 (80.4)
149 (47.2)
3 (0.9)
Duration of CGM use (n = 254)
<6 months
6 months to <1 year
1 year to <3 years
3 years to <5 years
≥5 years
10 (3.9)
20 (7.9)
105 (41.3)
49 (19.3)
70 (27.6)
Blood glucose tests
A1C, %
Number of daily fingerstick blood glucose checks
Number of daily glucose checks by CGM/FGM
7.4 ± 2.5
2.4 ± 2.6
20.7 ± 25
Insulin delivery method (multiple answers possible)
Insulin pump
Multiple daily injections using an insulin pen
Multiple daily injections using vial/syringe
Inhalable insulin
Other
242 (76.6)
59 (18.7)
37 (11.7)
13 (4.1)
1 (0.3)
Reported diabetes-related comorbidities (multiple answers possible)
Joint issues
Hypothyroidism
Retinopathy
CVD
Gastroparesis
Neuropathy
Sexual dysfunction
Nephropathy
Hyperthyroidism
87 (27.5)
80 (25.3)
61 (19.3)
61 (19.3)
51 (16.1)
46 (14.6)
44 (13.9)
22 (7.0)
18 (5.7)
Type of diabetes HCP
Adult endocrinologist
Diabetes nurse practitioner
Diabetes physician assistant
Pediatric endocrinologist
Primary care physician
Primary care, nurse practitioner
Primary care, physician assistant
203 (64.2)
48 (15.2)
24 (7.6)
16 (5.1)
14 (4.4)
7 (2.2)
4 (1.3)
Frequency of visits with diabetes HCP
Every month
Every 2–3 months
Every 6 months
Once per year
Once every 1–2 years
Other
9 (2.8)
201 (63.6)
92 (29.1)
6 (1.9)
2 (0.6)
6 (1.9)
Data are n (%) or mean ± SD.
Participants’ Experiences With Hypoglycemia
Participants’ reported experiences with hypoglycemia are presented in Table 3. Participants disclosed experiencing a mean of 3.6 ± 6.0 SH events in the preceding 12 months and a mean of 15.3 ± 60.4 (median 5, range 1–1,000) SH events in total in their lifetime. Participants noticed hypoglycemia in a variety of ways, most commonly by experiencing symptoms (80.7%), but also by using the functions of their CGM system (alert [63.3%], glucose reading [49.7%], or predictive low glucose warning [48.1%]). About one-fourth of the participants also made use of fingerstick BGM.
TABLE 3 Participants’ Experiences With Hypoglycemia (N = 316)
Hypoglycemia-Related Survey Data Value
Hypoglycemia data
Blood glucose level at which hypoglycemia is being treated, mg/dL
Number of mild hypoglycemic events per week
Number of moderate hypoglycemic events per week
Number of SH events in the previous 12 months
Number of SH events in the lifetime
70.2 ± 21.8
7.4 ± 13.2
5.0 ± 13.3
3.6 ± 6.0
15.3 ± 60.4
How hypoglycemia is usually noticed (multiple answers possible)
Feeling symptoms
CGM low alert
Looking at the CGM reading
CGM predictive low glucose warning
Fingerstick BGM
Other
255 (80.7)
200 (63.3)
157 (49.7)
152 (48.1)
82 (25.9)
5 (1.6)
Number of SH events
In the previous 12 months
CGM users (n = 254)
Non-CGM users (n = 62)
In the lifetime
CGM users (n = 254)
Non-CGM users (n = 62)
In the lifetime, excluding those with >100 lifetime events (n = 3)
CGM users (n = 251)
Non-CGM users (n = 62)
3.1 ± 4.2
5.7 ± 10.6
15.9 ± 66.5
13.2 ± 21.7
10.4 ± 16.7
13.2 ± 21.7
Awareness of hypoglycemia
1 (always aware)
2
3
4
5
6
7 (never aware)
30 (9.5)
77 (24.4)
68 (21.5)
85 (26.9)
43 (13.6)
7 (2.2)
6 (1.9)
Times that hypoglycemia is of most concern (multiple answers possible)
Overnight
During exercise
After exercise
During the day
Other
238 (75.3)
139 (44.0)
105 (33.2)
87 (27.5)
17 (5.4)
Occurrence of the most recent SH event
<1 month ago
1–3 months ago
3 months to <6 months ago
6 months to 1 year ago
1–2 years ago
2–5 years ago
>5 years ago
44 (13.9)
76 (24.1)
51 (16.1)
48 (15.2)
30 (9.5)
22 (7.0)
45 (14.2)
Type of emergency resulting from SH (multiple answers possible)
Change in mental state requiring assistance
Passing out/loss of consciousness or seizure
Calling a paramedic
Emergency department visit(s)
Requiring glucagon
Hospitalizations with at least 1 night spent in the hospital
258 (81.6)
196 (62.0)
195 (61.7)
185 (58.5)
181 (57.3)
115 (36.4)
Emotions experienced in connection with SH (multiple answers possible)
Worry/concern
Anxiety
Stress
Fear
Panic
Weakness
Embarrassment
Anger
Guilt
Sadness
Shame
214 (67.7)
203 (64.2)
202 (63.9)
172 (54.4)
166 (52.5)
159 (50.3)
160 (50.6)
112 (35.4)
106 (33.5)
89 (28.2)
67 (21.2)
Data are mean ± SD or n (%).
When comparing SH events in the previous 12 months for participants who used or did not use CGM, CGM users experienced a mean of 3.1 ± 4.2 (median 1.0, range 0–30) SH events, and non-CGM users experienced a mean of 5.7 ± 10.6 (median 2.5, range 0–58) SH events. Excluding participants who reported having had >100 lifetime SH events (n = 3), CGM users reported a mean of 10.4 ± 17.7 (median 5.0, range 1–100) SH events in their lifetime, and non-CGM users reported a mean of 13.2 ± 21.7 (median 4.5, range 0–100) SH events in their lifetime.
Using the criteria employed by Gold et al. (35), participants’ awareness of hypoglycemia was classified as “normal” (scores of 1–2) or “impaired” (scores of 4–7) on a visual analog scale. “Indeterminant” awareness (score of 3) was not included in either of these categories. Overall, 33.9% of the participants reported having normal awareness of hypoglycemia, and 44.6% reported having impaired awareness of hypoglycemia. Most participants (75.3%) considered the night or the time during or after exercise (77.2%) as the time of most concern about hypoglycemia.
SH events were associated with several emergency situations, including calling an ambulance (61.7%), visiting an emergency department (58.5%), and staying for at least 1 night at the hospital (36.4%). More than half of the participants stated that they had felt either worry, anxiety, stress, fear, panic, weakness, or embarrassment during an SH episode.
Participants’ Experiences With Glucagon Rescue Treatments
Participants’ experiences with glucagon rescue treatments are reported in Table 4. Almost all participants (97.2%) had been prescribed glucagon either at the time of completing the survey or in the past, with 80.4% reporting a current glucagon prescription. Overall, 74.7% of participants had a current and filled glucagon prescription, and 25.3% did not. Participants reported a mean of 1.7 ± 5.3 uses of glucagon to treat SH in the previous 12 months.
TABLE 4 Participants’ Experiences With Glucagon Rescue Treatments (N = 316)
Glucagon-Related Survey Data Value
Current prescription for glucagon
Yes, currently have a glucagon prescription and have filled it
Yes, have been prescribed glucagon in the past but not recently
Yes, currently have a glucagon prescription but have not filled it
No, have never had a glucagon prescription
236 (74.7)
53 (16.8)
18 (5.7)
9 (2.8)
Number of times glucagon was used to treat SH in the previous 12 months 1.7 ± 5.3
Likelihood of having a current and filled glucagon prescription
Impaired awareness of hypoglycemia (scores 4–7; n = 141)
Having a current and filled glucagon prescription
Not having a current and filled glucagon prescription
Not having impaired awareness of hypoglycemia (scores 1–3; n = 175)
Having a current and filled glucagon prescription
Not having a current and filled glucagon prescription
113 (80.1)
28 (19.9)
123 (70.3)
52 (29.7)
Current ownership of a glucagon kit
Yes
No
Unsure
251 (79.4)
62 (19.6)
3 (0.9)
Age of glucagon kit if currently owning one (n = 251)
<1 year old
1–2 years old
>2 years old
114 (45.4)
117 (46.6)
20 (8.0)
Feelings associated with having a glucagon kit at home (n = 316; multiple answers possible)
Safe
Prepared for hypoglycemia
Confident about hypoglycemia
The same as not having it in my home
151 (47.8)
127 (40.2)
90 (28.5)
87 (27.5)
Educated by an HCP about glucagon use
Yes
No
Unsure
263 (83.2)
47 (14.9)
6 (1.9)
Data are n (%) or mean ± SD.
Participants who had impaired awareness of hypoglycemia (scores 4–7, n = 141) were significantly more likely to have a current and filled glucagon prescription than those who did not have impaired awareness of hypoglycemia (scores 1–3, n = 175): 80.1% (113 of 141) versus 70.3% (123 of 175) (Pearson χ2 test P = 0.05). There were no significant differences between those with or without a current and filled glucagon prescription in A1C (P = 0.31) or age (P = 0.19). In addition, having a current and filled glucagon prescription was not associated with a greater likelihood of receiving care from an endocrinologist (P = 0.70).
Overall, 79.4% of the participants (n = 251) reported that they currently owned a glucagon kit, with 92.0% (n = 231) owning a kit <2 years old and 8.0% (n = 20) owning a kit >2 years old. Approximately half of the participants stated that having a glucagon kit in their home made them feel safe, 40.2% felt that they were prepared for hypoglycemia, and 28.5% felt confident about hypoglycemia; however, 27.5% reported that having a glucagon kit in their home made no difference in how they felt about hypoglycemia. HCPs had provided education about glucagon use to 83.2% of the participants, but 16.8% of participants had not received education by an HCP or were unsure about whether they had received such education. Compared with other HCPs, care from an endocrinologist was not associated with better education about glucagon use (P = 0.60).
Barriers to Glucagon Use as a Rescue Treatment
Barriers to glucagon rescue use reported by the participants are included in Table 5. Importantly, the 251 participants who currently owned a glucagon kit had a mean out-of-pocket cost of $73.40 ± $85.80 (median $30, range $0–400) for their kit. When queried, 85.1% of the 316 participants reported that rescue glucagon was administered when needed. In the remaining 14.9% (n = 47) for whom glucagon was not administered when needed, the reasons were diverse, including problems with the reconstitution and administration process (n = 33), inability to locate the kit (n = 23), lack of training (n = 15), an expired kit (n = 14), the rescuing individual being unaware of the existence of the kit (n = 13), and lack of confidence of the rescuing individual (n = 7).
TABLE 5 Barriers to Using Glucagon Rescue Treatment
Barrier-Related Survey Data Value
Out-of-pocket cost for a glucagon kit, $ (n = 251) 73.40 (85.80)
Ability to have available glucagon administered (n = 316)
Able
Unable
269 (85.1)
47 (14.9)
Reasons for inability to have available glucagon administered (n = 47; multiple answers possible)
The rescuing individual was not able to locate the glucagon kit.
The rescuing individual was not trained to use the glucagon kit.
The glucagon was expired.
The rescuing individual was not aware of the glucagon kit.
There was a problem with mixing.
The rescuing individual was not able to use the glucagon kit correctly.
The rescuing individual was not comfortable administering the glucagon.
The rescuing individual who delivered glucagon broke the needle.
The process was too complex.
Other
23 (48.9)
15 (31.9)
14 (29.8)
13 (27.7)
11 (23.4)
10 (21.3)
7 (14.9)
7 (14.9)
5 (10.6)
3 (6.4)
Reasons for not currently having a glucagon prescription (n = 9; multiple answers possible)
My doctor has never discussed glucagon with me.
I carry other supplies instead (glucose tablets, juice, etc.).
My CGM device gives me alerts before my blood glucose gets too low.
I don’t need it.
I am worried about temperature changes affecting the kit.
It costs too much to fill the prescription.
The kit has too many steps to be useful.
There is nobody to administer the glucagon (e.g., living alone).
Those around me would call 911 if I became severely hypoglycemic.
Other
6 (66.7)
6 (66.7)
5 (55.6)
1 (11.1)
1 (11.1)
1 (11.1)
1 (11.1)
1 (11.1)
1 (11.1)
1 (11.1)
Data are n (%).
The most important reasons for not having a glucagon prescription stated by participants who have never been prescribed glucagon (n = 9) were that their doctor had never discussed it with them (n = 6), they usually carried oral carbohydrates (n = 6), and their CGM device alerted them to low blood glucose levels (n = 5).
Participants’ Preferences for Characteristics of Glucagon Rescue Products
Most participants preferred ready-to-use glucagon rescue products, with 47.8% favoring an intranasal spray and 38.0% a premixed autoinjector (Table 6). The remaining 14.2% of the participants preferred injectable glucagon that requires reconstitution before use. Valued characteristics of glucagon rescue treatments included the ease of use of ready-to-use products (76.3%), intuitive modes of administration such as a nasal spray or ready-to-use injection (55.7%), a fast onset of action (53.5%), and storage at room temperature (42.1%).
TABLE 6 Participants’ Preferences for Characteristics of Glucagon Products (N = 316)
Preference-Related Survey Data Value
Preferred method of glucagon administration
Intranasal—spray in the nose
Premixed autoinjector—already reconstituted
Injection—needs to be reconstituted
151 (47.8)
120 (38.0)
45 (14.2)
Preferred characteristics of glucagon rescue treatments (multiple answers possible)
Ease of use (ready-to-use treatment)
The way it is administered (nasal spray, ready-to-use injection)
How fast the glucagon works compared with others on the market
Treatment that can be carried at room temperature (vs. stored in a refrigerator)
Having multiple kits available
241 (76.3)
176 (55.7)
169 (53.5)
133 (42.1)
102 (32.3)
Main improvements to be made in glucagon rescue treatments (multiple answers possible)
Ease of use (premixed dosing)
Cost
Administration type (nasal, syringe)
Storage options
Time to physical recovery
Fewer steps in the instructions
Size of kit
All of the above
Other
198 (62.7)
169 (53.5)
159 (50.3)
122 (38.6)
126 (39.9)
96 (30.4)
99 (31.3)
39 (12.3)
3 (0.9)
Importance of time to plasma glucose recovery in glucagon rescue treatments
Very important
Fairly important
Important
Slightly important
Not at all important
148 (46.8)
84 (26.6)
57 (18.0)
26 (8.2)
1 (0.3)
Data are n (%).
The participants also discussed various ways to improve glucagon rescue products, including greater ease of use (62.7%), reduced cost (53.5%), and more intuitive modes of administration (50.3%). The time to plasma glucose recovery was rated as a “very important” or “fairly important” characteristic of glucagon rescue products by 73.4% of the participants (Table 6).
Discussion
The burden of SH remains a challenge throughout the lives of people with type 1 diabetes and has a substantial impact on their emotional well-being, diabetes management, and health outcomes (17,27). Although previous studies have examined the emotional impact of SH and glucagon rescue treatment on caregivers of individuals with type 1 diabetes (36–38), literature is limited about the views and emotions connected with hypoglycemia and glucagon rescue use of people with type 1 diabetes themselves (19). For this reason, the objective of this research study was to provide an updated real-world perspective on emergency glucagon use of individuals with type 1 diabetes, as available glucagon products have become more diversified with the introduction of second-generation, ready-to-use rescue products.
The surveyed sample of individuals with type 1 diabetes had a mean age of 35.6 ± 8.9 years and a mean diabetes duration of 17.2 ± 12.7 years since diagnosis. Participants experienced a mean of 7.4 mild and 5.0 moderate hypoglycemic events per week and a mean of 3.6 SH events in the past year and 15.3 SH events (median 5 SH events) in their lifetime. Approximately half of the most recent SH events occurred in the 6 months immediately before the survey, emphasizing the relative frequency with which SH episodes occurred in the surveyed sample. The data on SH events in the 12 months before survey completion align with recent surveys of individuals with type 1 diabetes in other countries that reported means of 1.5–4.2 SH events in the previous 12 months (39,40). Importantly, CGM users among the surveyed sample continued to experience SH events, albeit fewer than non-CGM users. The reasons for this observation may be manifold, including lack of functional data because of system warm-up time, early termination, transmitter or receiver failure, or, more commonly, insufficient supply as a result of insurance problems.
Of note, the majority of participants in this study had received care from a paramedic (61.7%) or had an emergency department visit (58.5%), and more than one-third of participants (36.4%) had experienced a hospital stay of at least 1 night as a result of an SH event. These data illustrate the high level of health resource utilization as a direct consequence of SH, which is associated with significant costs to the U.S. health care system.
Importantly, the survey highlighted the emotional burden of SH on those affected, showing a considerable proportion of participants expressing worry or concern (67.7%), fear (54.4%), panic (52.5%), embarrassment (50.6%), and shame (21.2%) in connection with SH events. These findings are reflective of the findings of other recent survey studies on the negative emotional impact of SH on individuals with type 1 diabetes (39–43).
Participants reported being aware of their hypoglycemia episodes primarily by experiencing symptoms and through the use of CGM; however, a substantial proportion (44.6%) reported impaired awareness of hypoglycemia. A high prevalence of hypoglycemia unawareness, even in those using CGM, has previously been reported; yet, the result from this survey was slightly higher than the rates noted in people with type 1 diabetes in other studies (39,40,42,44,45).
Nocturnal hypoglycemia and hypoglycemia during or after exercise were concerns for approximately three-fourths of the participants. Although we did not measure how often participants experienced nocturnal or exercise-related hypoglycemia, current research supports our participants’ concerns, indicating that 28.7–51.4% of participants’ most recent SH events occurred during the night (39,40,42). Both nocturnal hypoglycemia and exercise-related hypoglycemia are associated with immediate clinical consequences (46,47). Moreover, nocturnal hypoglycemia has a long-term impact on glucose counterregulatory mechanisms that may lead to cognitive impairment, reduced hypoglycemia awareness, and autonomic failure (47).
Importantly, this research study showed encouraging results, with 83.2% of the participants receiving education from their HCP pertaining to glucagon rescue use, although receiving care from an endocrinologist compared with other HCPs was not associated with perceived better education about glucagon use. However, this is an increase from the 71.0% of adults with type 1 diabetes who reported being educated by an HCP on this topic in a 2019 U.S. survey (21). Furthermore, almost all of the participants in the current study (97.2%) had been prescribed a glucagon rescue product at the time of the survey or in the past, 80.4% had a current prescription for glucagon, and 74.7% had a current prescription and had filled it. Participants with hypoglycemia unawareness were significantly more likely to have a current and filled glucagon prescription than those who had normal awareness of hypoglycemia. A possible explanation is that those with hypoglycemia unawareness may be more vigilant about having glucagon at their disposal because they are conscious of their own difficulty in recognizing symptoms of an oncoming SH episode. Those with a normal awareness of SH may be reassured that they can recognize symptoms of an oncoming SH episode and take evasive action (e.g., consuming oral carbohydrates) and thus may feel less reliant on glucagon.
The U.S. survey by Haymond et al. (21) noted that 85.2% (n = 225 of 264) of participating adults with type 1 diabetes had been prescribed glucagon and that 58.3% (n = 154 of 264) had a current glucagon prescription (21); 51% of those participants who had experienced an SH event in the past had not been able to have glucagon administered to them when needed, even if the kit was close by. In contrast, 14.9% of the participants in the present survey were unable to have glucagon administered to them when required. Thus, in addition to an increased number of people with type 1 diabetes being prescribed glucagon rescue therapy since 2019, the proportion of people who have been administered rescue glucagon when required has improved substantially. Nonetheless, this survey showed that known obstacles to the appropriate use of rescue glucagon (21,22,27) continue to pose challenges, including problems with the reconstitution and administration process, unawareness of the existence of the kit or inability to locate it, lack of training, kit expiration, and lack of confidence of the rescuing individual.
Participants who had never received a glucagon prescription (n = 9) stated that the main reasons for this were that their doctor had never discussed it with them, they carried oral carbohydrates with them, or they relied on CGM. A global survey report suggests differences among countries regarding the proportion of individuals with type 1 diabetes who were unable to have glucagon administered during an SH event because they did not have a glucagon prescription or it was not filled (39,40,42), ranging from 25.0% in a French cohort (42) to 68.8% in a Japanese cohort (39).
One of the most critical barriers to the use of glucagon rescue products identified in this study was a mean out-of-pocket cost of $73.40 ± $85.80 for glucagon kits. Cost was identified by 53.5% of participants as one of the improvements they would like to see in glucagon rescue products. Financial barriers may therefore contribute to people with type 1 diabetes not filling their glucagon prescriptions or relying on other means of achieving normoglycemia.
Our survey data further demonstrated that most participants preferred ready-to-use glucagon products over first-generation glucagon products that require reconstitution because of the former’s ease of use, ready-to-use administration format, fast onset of action, and storage at room temperature. Participants favored a nasal mode of administration (47.8%) and a premixed autoinjector (38.0%) over reconstituted first-generation glucagon (14.2%). When queried, at least half of the participants indicated that they would also like to see improvements in ease of use (62.7%) and more intuitive modes of administration (50.3%). This finding suggests that a large proportion of the participants may still use first-generation glucagon products that require complex reconstitution and administration steps.
Advice for Primary Care Providers
Despite the increasing use of CGM, SH and fear of SH continue to pose challenges for individuals with type 1 diabetes. Although the typical automated insulin delivery system responds by adjusting the insulin dose when an SH event occurs, this process does not obviate the immediate need for administration of counterregulatory glucagon. Thus, glucagon continues to be a necessary and potentially life-saving component of therapy for type 1 diabetes.
To provide the best possible care for individuals with type 1 diabetes, primary care providers are advised to follow the ADA’s guidelines for prescribing glucagon (3) and to address the following topics with their patients. Ask whether the individual has a supply of glucagon at home. If so, ascertain that the glucagon is not out of date.
If not, determine the barriers to accessing glucagon that the individual may face.
Ask about who in the individual’s support network is trained in glucagon use. Ascertain whether these trusted individuals know where the glucagon is stored and are confident about administering it.
Given that ready-to-use glucagon products are now available, determine whether these individuals need to be retrained or whether additional people can be added to the individual’s circle of trusted individuals to optimize safety.
Limitations
A limitation of this study is that the surveyed sample is not representative of the overall population of people with type 1 diabetes in the United States. The survey sample was predominantly White and female and relatively young, affluent, and well-educated. The study design made use of an online survey format, and recruitment was conducted via digital media, which may have excluded people with limited computer skills or restricted computer or Internet access. The study included only adults with type 1 diabetes who had experienced at least one SH event in their lifetime and who were aware of the glucagon products on the market; therefore, the survey sample may be skewed toward people who were more aware of the available glucagon products than those who had never experienced an SH episode. In addition, the survey was conducted retrospectively, and data were self-reported, which carries the potential for misclassification of hypoglycemic events as well as recall bias, especially for the number of SH events in the participants’ lifetime. It was noted that a small number of participants reported a very high number of SH events in their lifetime (up to 1,000 events), which may have skewed the mean; the median may be more representative of the overall survey sample.
Conclusion
This study highlighted several important findings about the experiences of individuals with type 1 diabetes in the United States with SH and glucagon rescue use. The survey results suggest that the majority of individuals with type 1 diabetes have a current and filled prescription for rescue glucagon and have received education about glucagon rescue use from their HCP. The proportion of individuals with type 1 diabetes who were unable to have glucagon administered to them when required has decreased substantially compared with previous surveys. These data show a promising trend toward increased utilization of rescue glucagon and improved training in its use by HCPs in the past few years.
Despite these positive findings, there are continued challenges, including a high level of costly health care resource utilization directly resulting from SH events, substantial out-of-pocket expenses for glucagon kits, and a substantial prevalence of hypoglycemia unawareness. This study also highlighted the emotional burden of SH for individuals with type 1 diabetes.
Ready-to-use glucagon products do not yet seem to be fully adopted by HCPs and the diabetes community. Greater dissemination of these products among individuals with type 1 diabetes could improve the use of glucagon for SH rescue even more in the future. Further research is needed to understand experiences with and emotional effects of hypoglycemia and glucagon rescue use in both individuals with type 1 diabetes and their caregivers to improve the health and quality of life of people with type 1 diabetes.
Article Information
Acknowledgments
The authors thank the people with type 1 diabetes who contributed to the focus groups and survey data. Medical writing and editorial assistance for this manuscript were provided by Claudia Brockmeyer, DPhil, Katharine Timberlake, DPhil, and Marlena Radomska, BSc, of Oxford PharmaGenesis, Oxford, U.K.
Funding
This study and the preparation of this article were funded by Zealand Pharma.
Duality of Interest
S.A.W. has received consulting fees from Zealand Pharma independent of the submitted work. M.W. is a former employee of Zealand Pharma. No other potential conflicts of interest relevant to this article were reported.
Author Contributions
A.S.H., K.S.C., J.L., J.B., and W.A.W. participated in the conceptualization and design of the study, enrolled the survey participants, and conducted the survey. A.S.H., K.S.C., and H.N. analyzed the survey data. H.N. conducted the correlation analysis. M.W. obtained funding for medical writing and editorial assistance. All of the authors interpreted the data, reviewed the manuscript critically for intellectual content, provided feedback for incorporation, and approved the final version for publication. A.S.H. is the guarantor of this work and, as such, had full access to all the data in the study and takes responsibility for the integrity of the data and the accuracy of the data analysis.
J.L. is currently affiliated with Ironwood Pharmaceuticals, Boston, MA.
J.B. is currently affiliated with PatientsLikeMe, Boston, MA.
M.W. is currently affiliated with Market Access and RWE Consulting and Contract Services, Winget LLC, Boston, MA.
==== Refs
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