Pancreatic α-cell dysfunction in horses with insulin dysregulation

Pancreatic α-cell dysfunction in horses with insulin dysregulation | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 9 February 2026 V1 Latest version Share on Pancreatic α-cell dysfunction in horses with insulin dysregulation Authors : Sanna Lindåse 0000-0001-6124-4015 [email protected] , Emma Strage , Josefin Söder , and Johan Bröjer Authors Info & Affiliations https://doi.org/10.22541/au.177064400.03583820/v1 122 views 81 downloads Contents Abstract 1. Introduction Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract Background: Insulin dysregulation (ID) in horses is characterized by insulin resistance and exaggerated insulin responses, yet the underlying endocrine mechanisms remain incompletely understood. In humans, α-cell dysfunction, characterized by exaggerated glucagon secretion, contributes to metabolic disease; however, the role of pancreatic α-cell function in equine ID has not previously been investigated. Objectives: To determine whether horses with moderate to severe ID exhibit α-cell dysfunction, assessed by fasting and postprandial glucagon concentrations and postprandial suppression of glucagon secretion, compared with horses with normal insulin regulation (NIR). Study Design: Cross-sectional experimental study. Methods: Insulin dysregulated horses (n = 19) and NIR horses (n = 19) underwent a 180-minute seven-sampled oral sugar test. Plasma glucose, insulin, and glucagon concentrations were measured at baseline and every 30-minute for 180 minutes postprandially. Fasting glucagon concentrations (Fasting GLUCAGON ), total area under the curve for glucagon (AUC GLUCAGON) and postprandial suppression of glucagon secretion were used as primary outcomes. Results: Horses with ID exhibited higher geometric mean (95% confidence interval) values compared to NIR horses for the α-cell dysfunction indices Fasting GLUCAGON (10.3 (8.0 – 13.4) vs 4.1 (3.5 – 4.8) pmol/L, P < 0.0001) and AUC GLUCAGON (1351.5 (1016.1 – 1797.7) vs 624.7 (529.8 – 736.6) pmol/L x min, P < 0.0001). Geometric least squares mean (95% confidence interval) glucagon concentrations were higher (P = 0.01) at 180 minutes after oral sugar administration in ID horses (6.3 (5.0 – 8.0) pmol/L) compared with baseline concentrations in NIR horses (4.1 (3.3 – 5.2) pmol/L), indicating impaired postprandial glucagon suppression in the ID group. Main Limitations: Cross-sectional design and unmatched groups limit causal inference. Conclusions: Horses with moderate to severe ID exhibit marked α-cell dysfunction characterized by fasting and postprandial hyperglucagonemia and impaired postprandial suppression of glucagon secretion. These findings identify α-cell dysregulation as a novel component of equine ID. Pancreatic α-cell dysfunction in horses with insulin dysregulation Summary Background: Insulin dysregulation (ID) in horses is characterized by insulin resistance and exaggerated insulin responses, yet the underlying endocrine mechanisms remain incompletely understood. In humans, α-cell dysfunction, characterized by exaggerated glucagon secretion, contributes to metabolic disease; however, the role of pancreatic α-cell function in equine ID has not previously been investigated. Objectives: To determine whether horses with moderate to severe ID exhibit α-cell dysfunction, assessed by fasting and postprandial glucagon concentrations and postprandial suppression of glucagon secretion, compared with horses with normal insulin regulation (NIR). Study Design: Cross-sectional experimental study. Methods: Insulin dysregulated horses (n = 19) and NIR horses (n = 19) underwent a 180-minute seven-sampled oral sugar test. Plasma glucose, insulin, and glucagon concentrations were measured at baseline and every 30-minute for 180 minutes postprandially. Fasting glucagon concentrations (Fasting GLUCAGON ), total area under the curve for glucagon (AUC GLUCAGON) and postprandial suppression of glucagon secretion were used as primary outcomes. Results: Horses with ID exhibited higher geometric mean (95% confidence interval) values compared to NIR horses for the α-cell dysfunction indices Fasting GLUCAGON (10.3 (8.0 – 13.4) vs 4.1 (3.5 – 4.8) pmol/L, P < 0.0001) and AUC GLUCAGON (1351.5 (1016.1 – 1797.7) vs 624.7 (529.8 – 736.6) pmol/L \(x\) min, P < 0.0001). Geometric least squares mean (95% confidence interval) glucagon concentrations were higher (P = 0.01) at 180 minutes after oral sugar administration in ID horses (6.3 (5.0 – 8.0) pmol/L) compared with baseline concentrations in NIR horses (4.1 (3.3 – 5.2) pmol/L), indicating impaired postprandial glucagon suppression in the ID group. Main Limitations: Cross-sectional design and unmatched groups limit causal inference. Conclusions: Horses with moderate to severe ID exhibit marked α-cell dysfunction characterized by fasting and postprandial hyperglucagonemia and impaired postprandial suppression of glucagon secretion. These findings identify α-cell dysregulation as a novel component of equine ID. 1. Introduction Insulin dysregulation (ID) is a nonspecific term describing horses with abnormalities in insulin metabolism, encompassing fasting and postprandial hyperinsulinemia as well as insulin resistance (IR). 1,2 Despite being intensively studied over the past decade, the pathophysiology of ID in horses remains incompletely understood. Glucagon is a peptide hormone secreted by pancreatic α-cells that acts counter-regulatory to insulin by stimulating hepatic glucose production, playing a central role in maintaining glucose homeostasis. 3 Glucagon secretion is primarily stimulated by hypoglycemia but is also modulated by paracrine and autocrine hormonal signaling, autonomic neural inputs, and incretin hormones released from the gastrointestinal tract. 3,4 In humans, abnormal glucagon secretion was identified over 40 years ago as a factor contributing to the development of pre-diabetes and type 2 diabetes mellitus (T2DM). 5,6 Although species-specific differences exist, parallels between ID in horses and pre-diabetes in humans are evident, particularly with respect to IR and hyperinsulinemia. 7 In humans, α-cell dysfunction manifests as fasting hyperglucagonemia, excessive postprandial glucagon secretion, and inadequate suppression of glucagon during the postprandial phase. 5,8-10 This dysregulation contributes to elevated plasma glucose concentrations through increased hepatic glycogenolysis and gluconeogenesis. 11-13 These hyperglycemic excursions may further exacerbate β-cell hypersecretion of insulin, creating a feedback loop that worsens glucose intolerance. 12 Given these parallels, it is plausible that horses with ID exhibit α-cell dysfunction alongside an upregulated β-cell response, with resulting hyperglucagonemia potentially contributing to the hyperinsulinemia observed in affected horses. The main objectives of this study were to investigate whether horses with moderate to severe ID display elevated fasting and postprandial hyperglucagonemia, as well as impaired postprandial glucagon suppression, compared with a cohort of horses with normal insulin regulation (NIR). Additional objectives included evaluation of the relationships between indices for insulin sensitivity (IS), β-cell response and α-cell function. Elucidating the role of α-cell function in equine ID may provide novel insights into disease pathophysiology and highlight potential therapeutic targets for managing hyperinsulinemia and its associated complications. 2. Materials and Methods 2.1 Enrollment of study subjects Nineteen horses and ponies with ID and 19 horses and ponies with NIR were included in the study. Horses and ponies (hereafter collectively termed horses) were enrolled to the study based on results from a single-sample oral sugar test (OST) 14 performed in the field, using insulin concentrations > 45 µIU/mL at blood sampling 60 to 90 min after oral sugar administration as diagnostic cut-off for ID. Horses with insulin concentrations 100 µIU/mL were included in the ID group. Horses were excluded if they were < 4 years of age, had ongoing acute episode of laminitis based on clinical examination or had clinical evidence of PPID including assessment of EDTA plasma ACTH concentrations adjusted for season. Additional exclusion criteria included systemic disease other than ID and treatment with drugs and/or access to grass pasture for at least one month before enrolment. The included horses were recruited from the research herd owned by the Swedish University of Agricultural Sciences or were client-owned. Written informed client consent were provided for all client-owned horses. All horses in the study were fed a hay or haylage diet supplemented with minerals. During the study horses were housed in individual box stalls and allowed daily turnout in a dirt or sand paddock. 2.2 Experimental design In this single-center, cross-sectional experimental study horses were examined with a 180-minute seven-sampled OST. 14 The majority of client-owned horses (19 out of 26) were examined at the (masked for review). The horses owned by the (masked for review) (n = 12) and seven of the client-owned horses were examined in their home environment. All horses were acclimatized for at least 48 hours to the environment where sampling was to take place. The day before the OST an area over one of the jugular veins was clipped, desensitized with local anesthetic gel (EMLA, (masked for review)) and a catheter (Intranule, 2.0 x 105 mm. Vygon, Ecouen, France) was aseptically inserted. The OST commenced after 8 hours of feed withdrawal overnight, but with free access to water. 2.3 Oral Sugar Test The OST was conducted as described previously. 14 Briefly, (masked for review) was syringed orally at a dosage of 0.2 ml/kg body weight. Blood samples were collected from the jugular catheter into evacuated tubes (Vacuette 9 ml, Greiner Bio-One GmbH, Kremsmünster, Austria) containing lithium heparin and EDTA, immediately before (fasting blood sampling), and every 30-minute for 180 minutes after oral syrup administration. Tubes were placed on ice immediately after sampling and centrifuged within 5 min for 10 min at 2700 × g. Plasma was subsequently harvested, frozen within 5 minutes and stored at -80 °C until later analysis of plasma glucose, plasma insulin and plasma glucagon concentrations. 2.4 Blood samples analysis Plasma glucose concentrations were measured enzymatically with an automated clinical chemistry analyser 15 (YSI 2300 Stat Plus Analyzer, YSI Incorporated, Yellow Spring, Ohio). Plasma insulin concentrations were determined using a commercial equine-optimized ELISA 15 ( Mercodia equine insulin ELISA, Mercodia AB, Uppsala, Sweden) evaluated for use in horses 16 ( Mercodia AB, Uppsala, Sweden). Plasma glucagon concentrations were analysed using a commercially available ELISA (Mercodia Glucagon ELISA – 10 µL, 10-1281-01, Mercodia AB, Uppsala, Sweden). The intra-, inter-, and total assay coefficient of variation (CV) for glucagon were calculated according to Clinical and Laboratory Standards Institute (CSLI) EP15-A3 guidelines. 17 For a low concentration sample (mean 2.1 pmol/L), the intra-, inter-, and total assay CVs were 9.1%, 7.9%, and 12.0%, respectively. For a high concentration sample (mean 12.4 pmol/L), the corresponding CVs were 8.9%, 3.7%, and 9.7%. Linearity upon dilution, expressed as the observed-to-expected (O/E) ratio, ranged from 87% to 108%. 2.5 Calculations and endpoints Total area under the curve (AUC) for OST response data for glucose ( AUC GLU ), insulin (AUC INS ), glucagon (AUC GLUCAGON ) were calculated by use of the trapezoid method (GraphPad Prism, version 9.2.0 for windows; GraphPad Software Inc, San Diego, CA). Fasting and OST derived glucose and insulin concentrations were used to calculate indices for IS and β-cell response as previously described. 14,18 Insulin sensitivity was estimated using the fasting-index RISQI and the OST-derived index ISI COMP whereas the β-cell response was estimated using fasting insulin concentration (Fasting INS ), the fasting index HOMA-β and the OST derived AUC INS/GLU . The pancreatic α-cell function was estimated using Fasting glucagon concentrations (Fasting GLUCAGON ) and the OST derived AUC GLUCAGON . Primary endpoints included group comparison of pancreatic α-cell indices (Fasting GLUCAGON and AUC GLUCAGON ) and evaluation of the postprandial suppression of glucagon secretion. Secondary endpoints included group comparison of physical characteristics (gender, age, BCS and CNS), indices for IS ( RISQI and ISI COMP ), indices for β-cell response ( Fasting INS , HOMA-β and AUC INS/GLU ) , Fasting GLU , AUC GLU , AUC INS , OST glucose, insulin and glucagon concentrations over time as well as correlations between indices for IS, β-cell response and α-cell function . 2.6 Statistical analysis A priori sample size calculation was performed using the commercially available software G*Power 3.1.9.7 (Heinrich-Heine-Universität Düsseldorf, Germany). Based on the assumption of large effect size, 80% power (1-β error probability) and 5% α-error probability a total of 36 (18 per group) horses were considered sufficient to detect differences (independent 2-tailed t-test) between groups for the primary outcome AUC GLUCAGON . Differences in physical characteristics (gender, age, BCS, CNS) were compared between groups (NIR vs ID) by use of an independent 2-tailed t-test or a nonparametric test (2-tailed fisher´s exact test or Wilcoxon rank-sum test) as appropriate. Fasting concentrations ( Fasting GLU , Fasting INS and Fasting GLUCAGON ), AUCs (AUC GLU , AUC INS , AUC GLUCAGON and AUC INS/GLU ), RISQI, ISI COMP and HOMA- β were compared between groups (NIR vs ID) by use of an independent 2-tailed t-test. Data were analysed for normality and homoscedasticity by visual assessment of residual vs predicted plots and Q–Q plots. Normally distributed data are reported as mean ± SD. Non-normally distributed data were log-transformed prior to analysis and presented as geometric means and 95% confidence interval after back-transformation to the original scale. Categorical data are presented as median and interquartile range. The mixed model procedure was used for group comparison (NIR vs ID) for the OST glucose, insulin and glucagon concentrations over time. The full model included group (NIR and ID), time and the interaction term (group\(x\) time) as fixed effects and the individual horses were used as a random effect. Autoregressive model of order 1 (AR1) was selected as covariance structure based on the lowest BIC for all individual models that were run. Tukey-Kramer post hoc test was used to identify simple effect differences. Variables were tested for homogeneity of variances by use of the Levene’s-test prior to analysis. Residual vs predicted plots and Q-Q plots were assessed for normality and homoscedasticity. In case of non-normally distributed residuals, data were log transformed prior to analysis. Data with normally distributed residuals are reported as least squares (LS) means and 95% confidence interval whereas data log-transformed prior to analysis are presented as geometric LS means and 95% confidence interval after back-transformation to the original scale. A rectangular hyperbolic relationship exist between insulin secretion and IS in horses, based on the equation y = constant • 1/x, where y and x represent β-cell response and IS respectively. 15,19 The hyperbolic equation can be re-expressed to a linear model through logarithmic transformation: Ln (y) = constant + β • Ln (x), where β is the regression coefficient. If β is close to -1 (if the 95 % CI for β included − 1 but excluded 0) the rectangular hyperbolic relationship is considered to be fulfilled. 15,20 This linear function was used to determine the β, and further to describe the relationship between indices for β-cell response (dependent variable) and indices for IS (independent variable) and between indices for α-cell function (dependent variable) and indices for IS (independent variable). The linear regressions were run using the fit orthogonal command in the bivariate platform. The non-parametric Spearman´s rank correlation coefficient (Spearman´s p) was used to evaluate the correlation between indices for α- cell function measures and indices for β-cell response. All data were analysed using a commercially available statistical software ( JMP® Pro 17.2.0, SAS Institute Inc., Cary, North Carolina, USA). Values of P < 0.05 were considered as statistically different for all analyses. 3. Results 3.1 Study subjects All horses fulfilled the inclusion criteria for the study. Physical characteristics for horses enrolled are reported in Table 1. Horses in the ID group had higher BCS (P = 0.0002) and CNS (P < 0.0001) than horses in the NIR group. None of the horses included in the NIR group but 84% (16 out of 19) of the horses in the ID group had at least one previous episode of laminitis. Icelandic horses (n = 13) and (masked for review) native pony breeds (n = 6) were represented in the NIR group. The ID group included Icelandic horses (n = 10), (masked for review) (n = 1), (masked for review) (n = 2) and (masked for review) native (n = 4) pony breeds and crossbreed horses (n = 1) and ponies (n = 1). 3.2 Group comparison of insulin sensitivity, β-cell response and α-cell function indices Fasting concentrations and AUCs for insulin, glucose and glucagon derived from the OST were all higher in the ID vs NIR group ( P ≤ 0.003; Table 2 ). Compared to the NIR group, the ID group had on average 3.1 and 10.9 times lower geometric means for the IS indices RISQI and ISI COMP respectively and on average 9.6, 7.0 and 8.3 times higher geometric means for the β-cell indices Fasting INS, HOMA-β and AUC INS/GLU respectively. Geometric means for the α-cell function indices Fasting GLUCAGON and AUC GLUCAGON were on average 2.5 and 2.2 times higher in the ID vs NIR group respectively. 3.3 Group comparison of oral sugar test response data The ID group had higher geometric LS mean insulin and glucagon concentrations at all single time points during the OST compared to the NIR group ( P ≤ 0.006; Figure 1 ). There was a postprandial suppression of glucagon secretion in both the NIR and ID groups, but the magnitude of suppression was lower in the NIR compared to the ID group (Figure 1). At 180 minutes after oral glucose administration, the geometric LS mean glucagon concentration was 74% (95% CI: 57 – 97%; P = 0.01) and 61% (95% CI: 47 – 80; P < 0.0001) of the geometric LS mean fasting glucagon concentration in the NIR and ID groups respectively. That corresponds to a relative postprandial glucagon suppression of 26% and 39% in the NIR and ID groups respectively at the end of the OST. Despite a larger relative postprandial suppression in the ID group compared to the NIR group, the suppression was insufficient in the ID horses as geometric LS mean (95% CI) glucagon concentrations were higher in the ID group (6.3 (5.0 – 8.0) pmol/L) at 180 minutes after oral sugar administration compared with baseline concentrations in NIR horses (4.1 (3.3 – 5.2) pmol/L, P = 0.04). 3.4 Relationships between insulin sensitivity, β-cell response and α-cell function indices A non-linear inverse relationship was identified between indices for β-cell response (Fasting INS , HOMA-β and AUC INS/GLU ) and indices for IS (RISQI and ISI COMP ) (P ≤ 0.05). A decrease in IS was mirrored by a reciprocal increase in β-cell response for all three comparisons (Figure 2). The criteria for a rectangular hyperbolic relationship was fulfilled for one of the three comparisons. The indices for β-cell response were highly correlated with the indices for IS for all three comparisons (Table 3). A non-linear inverse relationship was identified between indices for IS (RISQI and ISI COMP ) and indices for α-cell function (Fasting GLUCAGON and AUC GLUCAGON ; P ≤ 0.05). Increased glucagon secretion was mirrored by a reciprocal decrease in IS for all four comparisons (Figure 3). The criteria for a rectangular hyperbolic relationship was fulfilled for two of four comparisons. The indices for IS were highly correlated with Fasting GLUCAGON and moderately correlated with AUC GLUCAGON (Table 3). Scatter plots showing the correlation between indices for β-cell response (Fasting INS , HOMA-β and AUC INS/GLU ) and indices for α-cell function (Fasting GLUCAGON and AUC GLUCAGON ) show heteroscedasticity (Figure 4). The indices for β-cell response were moderately to highly correlated (Spearman’s p 0.61 – 0.81; P < 0.0001) to indices for α-cell function. 4. Discussion To the best of our knowledge, this study is the first to investigate the role of pancreatic α-cell dysfunction in the pathophysiology of ID in horses. Our findings demonstrate that horses with moderate to severe ID exhibit pronounced α-cell dysfunction, characterized by fasting and postprandial hyperglucagonemia, as well as insufficient suppression of glucagon secretion during the postprandial phase. These findings provide novel insights into the pathophysiology of ID in horses, suggesting that α-cell dysfunction may be a key player in the disease process. 4.1 Pancreatic α-cell function and dysfunction In healthy humans, glucagon secretion is inhibited during the postprandial phase, with multiple mechanisms thought to contribute including hyperglycemia and elevated plasma concentrations of insulin, somatostatin, and the incretin hormone GLP-1. 3-5,9 In patients with impaired glucose tolerance and TDM2, this regulation is impaired, resulting in insufficient postprandial suppression of glucagon secretion. 5,8-10 In this study, glucagon secretion was suppressed during the postprandial phase in both groups of horses, but the suppression was insufficient in the ID horses. The glucagon concentrations remained significantly elevated in the ID horses compared to the NIR horses throughout the entire postprandial phase. Notably, this insufficient glucagon suppression occurred despite markedly elevated postprandial insulin concentrations, which under normal circumstances exert negative feedback on glucagon production from the α-cells. 21 In humans, mechanisms proposed to explain α-cell dysfunction and the resulting relative hyperglucagonemia include reduced paracrine and autocrine inhibition of glucagon secretion due to impaired β-cell insulin release, α-cell IR, or a failure of α-cells to respond to hyperglycemia. 5,8,21,22 In the equine context, characterized by severe hyperinsulinemia without significant hyperglycemia, it is plausible that α-cell IR is the main factor driving the relative hyperglucagonemia observed in ID horses. Interestingly, a delayed glucagon suppression was observed in the healthy group of horses, becoming evident at 120 minutes, whereas in healthy humans it typically occurs within 30 – 60 minutes after meal ingestion. 23,24 Horses are adapted to grazing for extended periods throughout the day, leading to mild and prolonged postprandial increases in plasma glucose and insulin concentrations. 25,26 Consequently, horses may have evolutionarily adapted their metabolism to rely less on rapid and pronounced fluctuations in glucagon secretion for maintaining glucose homeostasis, in contrast to humans and carnivores, whose feed intake is typically confined to discrete meals. 4.2 Role of α-cell dysfunction in the pathophysiology of insulin dysregulation Alpha-cell dysfunction and hyperglucagonemia contribute to the development and exacerbation of hyperglycemia in humans with T2DM 8 mainly by promoting increased hepatic gluconeogenesis. 11-13,27 In humans with T2DM, elevated glucagon concentrations and impaired suppression of glucagon following a meal are estimated to account for approximately 50% of the pathological postprandial hyperglycemic response. 9,13,28 In contrast to humans, horses with ID rarely progress to T2DM. Instead, they typically exhibit IR accompanied by a sustained compensatory upregulated β-cell activity, resulting in excessive postprandial hyperinsulinemia and preserved glucose tolerance. 1,2,15,29 A key distinction between horses with ID and humans with overt T2DM is thus the preservation of insulin production and the absence of sustained hyperglycemia in horses with ID. Although overt hyperglycemia is uncommon in horses with ID, the individuals in this study with moderate to severe disease exhibited higher fasting and postprandial glucose concentrations compared to the healthy controls, which is consistent with previous research evaluating horses with moderate to severe ID. 14,30 This observation indicate a progression of α-cell dysfunction associated with advanced ID, leading to increased glucose production during both fasting and postprandial states. This, in turn, may contribute to hypersecretion of insulin by the β-cells. Supporting this hypothesis, the present study identified moderate to strong positive correlations between indices of α-cell function (fasting and postprandial glucagon concentrations) and indices of β-cell response in horses with ID. While the temporal relationship between these processes remains uncertain, our findings suggest that α -cell dysfunction may play a causative role in the development of equine hyperinsulinemia, rather than being a consequence of it. Human in vitro studies have demonstrated that glucagon can stimulate insulin secretion in depolarized β-cells, acting as an incretin hormone during the postprandial phase when blood glucose levels are elevated. 21 If a similar mechanism exists in horses with ID, this could provide an additional explanation for the excessive postprandial hyperinsulinemia observed in horses with pronounced ID. 4.3 Relationships between insulin sensitivity, β-cell response and α-cell function Previous studies have demonstrated that β-cell responsiveness and IS are inversely related in both horses and humans. 15,20 Consequently, substantial changes in IS among horses with normal IS result in only minor alterations in insulin secretion, whereas even small reductions in IS in severely IR individuals (i.e., those with low IS) lead to pronounced changes in β-cell responsiveness. Our findings support this reciprocal relationship between IS and β-cell responsiveness in the present cohort of horses. While views differ on the causal relationship, the prevailing theory holds that altered β-cell activity and compensatory insulin hypersecretion arise as an adaptive response to peripheral IR. 15,31,32 In this study, we observed a non-linear inverse relationship between indices of α-cell function and IS, with two of four comparisons fulfilling the criteria for a hyperbolic relationship. This suggests that basal α-cell function relates to the prevailing degree of IS and that worsening α-cell dysfunction is associated with reduced IS. In humans, a reciprocal relationship between α-cell hypersecretion and decreased IS has been established, indicating that α-cell dysfunction occurs in parallel with increased β-cell hypersecretion in response to declining tissue IS. 8,11 However, the causal direction of this interplay remains unclear, even in humans, and it is uncertain whether impaired α-cell function arises as a consequence of reduced IS or contributes to it. As previously discussed, IR at the level of α-cells has been proposed as a mechanism underlying α-cell dysfunction in humans, 5,22 and may represent a similar contributory mechanism in horses. If so, IR contributes to both α- and β-cell dysfunction, with subsequent α-cell dysregulation further promoting the marked hyperinsulinemia observed in ID horses through stimulation of gluconeogenesis. Further studies are required to clarify this interplay and to advance our understanding of the pathophysiology of ID. 4.4 Evaluation of glucagon assay and study limitations To accurately assess α-cell function and glucagon dynamics in horses, reliable measurement of plasma glucagon is essential. However, no equine-specific assays are currently available. In this study, plasma glucagon was analysed using a high-sensitivity, high-specificity human glucagon ELISA. The in-house assay evaluation yielded acceptable results, aligning with physiologically expected values based on other species. 23,33,34 Additionally, the 29 long amino acid sequence of equine glucagon is identical to that of both human and feline glucagon, 35 and the Mercodia glucagon assay has been validated for use in cats. 33 Therefore, despite the absence of a gold standard assay or equine control material for direct comparison, the results were considered reliable. According to the manufacturer, the assay does not cross-react with glicentin, oxyntomodulin, proglucagon, GLP-1, or other glucagon-related peptides, making it highly specific for the measurement of glucagon concentrations. In contrast, glucagon measurements obtained using conventional radioimmunoassays (RIA) typically yield substantially higher apparent glucagon concentrations due to cross-reactivity with glucagon-related peptides and degradation fragments. 36,37 Some limitations of the present study should be acknowledged. Insulin sensitivity was estimated using surrogate indices rather than quantified using a euglycemic–hyperinsulinemic clamp or a frequently sampled intravenous glucose tolerance test. However, the indices used in the current study have been shown to correlate well with IS measured by the euglycemic–hyperinsulinemic clamp. 18 The study groups were not matched for sex or age, as this was not feasible within the current study design. In addition, the cross-sectional design precludes conclusions regarding causality between α-cell dysfunction and IR, underscoring the need for longitudinal studies to confirm these relationships. 5. Conclusion Our results demonstrate that horses with ID exhibit α-cell dysfunction, characterized by fasting and postprandial hyperglucagonemia and impaired postprandial suppression of glucagon secretion. A non-linear inverse relationship between indices of α-cell function and IS was identified, linking α-cell dysfunction to IR, consistent with previously established relationships for β-cell function. The data suggest that α-cell dysfunction may act as a key mediator, and potentially a primary driver, of increased β-cell activity, ultimately contributing to the hyperinsulinemia that predisposes moderately to severely ID horses to laminitis. These findings represent a significant advance in understanding the pathophysiology of ID in horses and highlight the potential for new therapeutic strategies targeting glucagon regulation, providing avenues to improve the management of ID and its associated complications. Tables Table 1. Physical characteristics for study subjects in the NIR and ID groups. Gender (g / m) 15 / 4 † 5 / 14 § Age (year) 7.6 ± 2.7 † 13.6 ± 4.6 § BCS (1 – 9) 5.5 (5 – 6) † 7 (6 – 8) § CNS (0 – 5) 2.5 (2 – 2.5) † 4 (3.5 – 4) § Data are reported as mean ± SD (age) or as median (interquartile range; BCS, CNS). †,§ Different superscript symbols indicate statistical differences within row (P ≤ 0.003). NIR, normal insulin regulation; ID, insulin dysregulation; g, gelding; m, mare; BCS, body condition score; CNS, cresty neck score. Table 2. Indices for insulin sensitivity, β-cell response and α-cell function for the NIR and ID groups. Insulin sensitivity RISQI 0.53 (0.47 – 0.59) † 0.17 (0.15 – 0.20) § ISI COMP 27.5 (21.7 – 34.7) † 2.5 (1.9 – 3.4) § β-cell response AUC INS/GLU (mIU/mmol x min) 19.4 (16.2 – 23.2) † 160.5 (121.6 – 212.0) § Fasting INS (µIU/mL) 3.5 (2.9 – 4.5) † 34.2 (25.7 – 45.5) § HOMA-β 46.8 (36.7 – 59.6) † 325.9 (250.1 – 424.7) § α-cell function Fasting GLUCAGON (pmol/L) 4.1 (3.5 – 4.8) † 10.3 (8.0 – 13.4) § AUC GLUCAGON (pmol/L \(x\) min) 624.7 (529.8 – 736.6) † 1351.5 (1016.1 – 1797.7) § Data are presented as geometric means (95% confidence interval) . †,§ Different superscript symbols indicate statistical differences within row (P < 0.0001). NIR, normal insulin regulation; ID, insulin dysregulation; RISQI, reciprocal of the square root of insulin; ISI COMP , composite whole-body insulin sensitivity index; AUC INS/GLU , area under the curve for insulin/glucose; Fasting INS , fasting insulin concentration; HOMA-β, homeostatic model assessment of β-cell function; Fasting GLUCAGON , fasting glucagon concentration; AUC GLUCAGON , area under the curve for glucagon. Table 3. Correlation coefficient (r), regression coefficient (β) and 95% confidence interval for combinations of fasting- and oral sugar test derived indices of insulin sensitivity (x variable), β-cell response (y variable) and α-cell function (y variable) used in the regression model: Ln (y) = constant + β • Ln (x). RISQI AUC INS/GLU -0.95 -1.92 (-1.73 – -2.14) ISI COMP Fasting INS -0.99 -0.95 * (-0.90 – -1.00) ISI COMP HOMA-β -0.93 -0.83 (-0.73 – -0.94) RISQI Fasting GLUCAGON -0.79 -1.02 * (-0.78 – -1.34) RISQI AUC GLUCAGON -0.67 -0.97 * (-0.65 – -1.43) ISI COMP Fasting GLUCAGON -0.77 -0.40 (-0.29 – -0.52) ISI COMP AUC GLUCAGON -0.65 -0.34 (-0.21 – -0.48) * Indicate rectangular hyperbolic relationship within row ( P < 0.05). RISQI, reciprocal of the square root of insulin; ISI COMP , composite whole-body insulin sensitivity index; AUC INS/GLU , area under the curve for insulin/glucose; Fasting INS , fasting insulin concentration; HOMA-β, homeostatic model assessment of β-cell function; Fasting GLUCAGON , fasting glucagon concentration; AUC GLUCAGON , area under the curve for glucagon. Figure legends Figure 1. Plasma glucose (A), plasma insulin (C) and plasma glucagon (E) responses; AUC GLU (B), AUC INS (D) and AUC GLUCAGON (F) derived from an oral sugar test performed in the NIR (n = 19, orange circles ) and the ID (n = 19, blue squares ) groups . Data are presented as least squares means with 95% confidence interval for plasma glucose responses and as geometric least squares means; 95% confidence interval for plasma insulin, plasma glugacon and plasma insulin/glucagon responses during the OST. Data are presented as geometric means with 95% confidence interval for AUC GLU , AUC INS and AUC GLUCAGON . * Statistical difference between groups within time point at P ≤ 0.043. Range of postprandial time points within group (NIR, orange solid arrow; ID, blue solid arrow) that differ (P ≤ 0.027 ) from time 0 (fasting glucagon). Abbreviations: AUC GLU , area under the curve for glucose; AUC INS , area under the curve for insulin; AUC GLUCAGON , area under the curve for glucagon; OST, oral sugar test; NIR, normal insulin regulation; ID, insulin dysregulation. Figure 2. Scatterplots of individual horse data in the NIR (n = 19, orange circles) and ID (n = 19, blue squares) groups showing the relationship between indices for β-cell response: Fasting INS (A), HOMA-β (B) and AUC INS/GLU (C) and indices for IS: ISI COMP and RISQI. The line represents the regression line for logarithmically transformed data (Ln (y) = constant + β • Ln (x)). Data presented in figure A fulfil the statistical criteria of a hyperbolic relationship. Abbreviations: Fasting INS , fasting insulin concentration; HOMA-β, homeostatic model assessment of β-cell function; AUC INS/GLU , area under the curve for insulin/glucose; ISI COMP , composite whole-body insulin sensitivity index; RISQI, reciprocal of the square root of insulin; NIR, normal insulin regulation; ID, insulin dysregulation. Figure 3. Scatterplots of individual horse data in the NIR (n = 19, orange circles) and ID (n = 19, blue squares) groups showing the relationship between indices for IS: RISQI, (A and C) and ISI COMP (B and D) and indices for α-cell function: Fasting GLUCAGON and AUC GLUCAGON . The line represents the regression line for logarithmically transformed data (Ln (y) = constant + β • Ln (x)). Data presented in figure A and C fulfil the statistical criteria of a hyperbolic relationship. Abbreviations: RISQI, reciprocal of the square root of insulin; ISI COMP , composite whole-body insulin sensitivity index; Fasting GLUCAGON , fasting glucagon concentration; AUC GLUCAGON , area under the curve for glucagon, NIR, normal insulin regulation; ID, insulin dysregulation. Figure 4. Scatterplots of individual horse data in the NIR (n = 19, orange circles) and ID (n = 19, blue squares) groups showing the relationship between indices for β-cell response: Fasting INS , (A and D), HOMA-β (B and E) and AUC INS/GLU (C and F) and indices for α-cell function: Fasting GLUCAGON and AUC GLUCAGON . Abbreviations: Fasting INS , fasting insulin concentration; HOMA-β, homeostatic model assessment of β-cell function; AUC INS/GLU , area under the curve for insulin/glucose; Fasting GLUCAGON , fasting glucagon concentration; AUC GLUCAGON , area under the curve for glucagon; Spearman´s p, Spearman´s rank correlation coefficient, NIR, normal insulin regulation; ID, insulin dysregulation. Referenses 1. Frank N, Tadros E. Insulin dysregulation. Equine Vet J. 2014;46:103-112. 2. Durham AE, Frank N, McGowan CM, Menzies‐Gow NJ, Roelfsema E, Vervuert I, et al. ECEIM consensus statement on equine metabolic syndrome. J Vet Intern Med. 2019;33:335-349. 3. Ahren B. Glucagon–Early breakthroughs and recent discoveries. Peptides. 2015;67:74-81. 4. Burcelin R, Knauf C, Cani PD. Pancreatic α-cell dysfunction in diabetes. Diabetes Metab. 2008;34:S49-S55. 5. Martínez MS, Manzano A, Olivar LC, Nava M, Salazar J, D’marco L, et al. The Role of the α Cell in the Pathogenesis of Diabetes: A World beyond the Mirror. Int J Mol Sci. 2021;22:9504. 6. Unger R, Orci L. The essential role of glucagon in the pathogenesis of diabetes mellitus. Lancet. 1975;305:14-16. 7. Armato J, DeFronzo RA, Abdul-Ghani M, Ruby R. Pre-Prediabetes: Insulin Resistance Is Associated With Cardiometabolic Risk in Nonobese Patients (STOP DIABETES). J Clin Endocrinol Metab. 2025;110:e1481-e1487. 8. Mohan S, Christie HE, Laurenti MC, Egan AM, Bailey KR, Cobelli C, et al. Abnormal Glucagon Secretion Contributes to a Longitudinal Decline in Glucose Tolerance. J Clin Endocrinol Metab. 2025;110:e2956-e2965. 9. Moon JS, Won KC. Pancreatic α-cell dysfunction in type 2 diabetes: old kids on the block. Diabetes Metab. 2015;39:1-9. 10. Yoo S, Kim D, Koh G. The Change in Glucagon Following Meal Ingestion Is Associated with Glycemic Control, but Not with Incretin, in People with Diabetes. J Clin Med. 2021; 10(11):2487. 11. Færch K, Vistisen D, Pacini G, Torekov SS, Johansen NB, Witte DR, et al. Insulin resistance is accompanied by increased fasting glucagon and delayed glucagon suppression in individuals with normal and impaired glucose regulation. Diabetes. 2016;65:3473-3481. 12. Girard J. Glucagon, a key factor in the pathophysiology of type 2 diabetes. Biochimie. 2017;143:33-36. 13. Shah P, Vella A, Basu A, Basu R, Schwenk WF, Rizza RA. Lack of suppression of glucagon contributes to postprandial hyperglycemia in subjects with type 2 diabetes mellitus. J Clin Endocrinol Metab. 2000;85:4053-4059. 14. Lindåse S, Nostell K, Bröjer J. A modified oral sugar test for evaluation of insulin and glucose dynamics in horses. Acta Vet Scand. 2016;58(suppl 1):64. 15. Lindåse S, Nostell K, Söder J, Bröjer J. Relationship Between β-cell Response and Insulin Sensitivity in Horses based on the Oral Sugar Test and the Euglycemic Hyperinsulinemic Clamp. J Vet Intern Med. 2017;31:1541-1550. 16. Öberg J, Bröjer J, Wattle O, Lilliehöök I. Evaluation of an equine-optimized enzyme-linked immunosorbent assay for serum insulin measurement and stability study of equine serum insulin. Comp Clin Path. 2012;21:1291-1300. 17. Clinical and Laboratory Standards Institute. User verification of precision and estimation of bias; approved guideline. 3rd ed. CLSI document EP15-A3. Wayne, PA, USA. 2014. 18. Lindåse S, Nostell K, Bergsten P, Forslund A, Bröjer J. Evaluation of fasting plasma insulin and proxy measurements to assess insulin sensitivity in horses. BMC Vet Res. 2021;17:1-10. 19. Bamford NJ, Potter SJ, Harris PA, Bailey SR. Breed differences in insulin sensitivity and insulinemic responses to oral glucose in horses and ponies of moderate body condition score. Dom Anim Endocrinol. 2014;47:101-107. 20. Kahn SE, Prigeon RL, McCulloch DK, Boyko EJ, Bergman RN, Schwartz MW, et al. Quantification of the relationship between insulin sensitivity and β-cell function in human subjects: evidence for a hyperbolic function. Diabetes. 1993;42:1663-1672. 21. Caruso I, Marrano N, Biondi G, Genchi VA, D’Oria R, Sorice GP, et al. Glucagon in type 2 diabetes: Friend or foe? Diabetes Metab Res Rev. 2023;39:e3609. 22. Ferrannini E, Muscelli E, Natali A, Gabriel R, Mitrakou A, Flyvbjerg A, et al. Association of fasting glucagon and proinsulin concentrations with insulin resistance. Diabetologia. 2007;50:2342-2347. 23. Manell H, Staaf J, Manukyan L, Kristinsson H, Cen J, Stenlid R, et al. Altered plasma levels of glucagon, GLP-1 and glicentin during OGTT in adolescents with obesity and type 2 diabetes. J Clin Endocrinol Metab. 2016;101:1181-1189. 24. Ahren B, Larsson H. Impaired glucose tolerance (IGT) is associated with reduced insulin-induced suppression of glucagon concentrations. Diabetologia. 2001;44:1998-2003. 25. Fitzgerald DM, Walsh DM, Sillence MN, Pollitt CC, de Laat MA. Insulin and incretin responses to grazing in insulin-dysregulated and healthy ponies. J Vet Intern Med. 2019;33:225-232. 26. Williams CA, Kenny LB, Burk AO. Effects of grazing system, season, and forage carbohydrates on glucose and insulin dynamics of the grazing horse. J Anim Sci. 2019;97:2541-2554. 27. Shah P, Vella A, Basu A, Basu R, Schwenk WF, Rizza RA. Lack of suppression of glucagon contributes to postprandial hyperglycemia in subjects with type 2 diabetes mellitus. J Clin Endocrinol Metab. 2000;85:4053-4059. 28. Reed J, Bain S, Kanamarlapudi V. A review of current trends with type 2 diabetes epidemiology, aetiology, pathogenesis, treatments and future perspectives. Diabetes Metab Syndr Obes. 2021:3567-3602. 29. Morgan R, Keen J, McGowan C. Equine metabolic syndrome. Vet Rec. 2015;177:173-179. 30. Schuver A, Frank N, Chameroy KA, Elliott SB. Assessment of Insulin and Glucose Dynamics by Using an Oral Sugar Test in Horses. J Equine Vet Sci. 2014;34:465-470. 31. Hoffman R, Boston R, Stefanovski D, Kronfeld D, Harris P. Obesity and diet affect glucose dynamics and insulin sensitivity in Thoroughbred geldings. J Anim Sci. 2003;81:2333-2342. 32. Toth F, Frank N, Martin-Jimenez T, Elliott SB, Geor RJ, Boston RC. Measurement of C-peptide concentrations and responses to somatostatin, glucose infusion, and insulin resistance in horses. Equine Vet J. 2010;42:149-155. 33. Rudinsky A, Adin C, Borin-Crivellenti S, Rajala-Schultz P, Hall M, Gilor C. Pharmacology of the glucagon-like peptide-1 analog exenatide extended-release in healthy cats. Dom Anim Endocrinol. 2015;51:78-85. 34. Adams JD, Dalla Man C, Laurenti MC, Andrade MDH, Cobelli C, Rizza RA, et al. Fasting glucagon concentrations are associated with longitudinal decline of β-cell function in non-diabetic humans. Metabolism. 2020;105:154175. 35. UniProt Consortium [accessed 12 December 2025]. UniProt: a worldwide hub of protein knowledge, 2002–2026. Available at: https://www.uniprot.org. 36. Brunner M, Moser O, Raml R, Haberlander M, Boulgaropoulos B, Obermayer-Pietsch B, et al. Assessment of two different glucagon assays in healthy individuals and type 1 and type 2 diabetes patients. Biomolecules. 2022;12:466. 37. Holst JJ, Wewer Albrechtsen NJ. Methods and guidelines for measurement of glucagon in plasma. Int J Mol Sci. 2019;20:5416. Information & Authors Information Version history V1 Version 1 09 February 2026 Copyright This work is licensed under a Non Exclusive No Reuse License. Authors Affiliations Sanna Lindåse 0000-0001-6124-4015 [email protected] Sveriges lantbruksuniversitet View all articles by this author Emma Strage Sveriges lantbruksuniversitet View all articles by this author Josefin Söder Sveriges lantbruksuniversitet View all articles by this author Johan Bröjer Sveriges lantbruksuniversitet View all articles by this author Metrics & Citations Metrics Article Usage 122 views 81 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Sanna Lindåse, Emma Strage, Josefin Söder, et al. Pancreatic α-cell dysfunction in horses with insulin dysregulation. Authorea . 09 February 2026. DOI: https://doi.org/10.22541/au.177064400.03583820/v1 If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download. For more information or tips please see 'Downloading to a citation manager' in the Help menu . Format Please select one from the list RIS (ProCite, Reference Manager) EndNote BibTex Medlars RefWorks Direct import Tips for downloading citations document.getElementById('citMgrHelpLink').addEventListener('click', function() { popupHelp(this.href); return false; }); $(".js__slcInclude").on("change", function(e){ if ($(this).val() == 'refworks') $('#direct').prop("checked", false); $('#direct').prop("disabled", ($(this).val() == 'refworks')); }); View Options View options PDF View PDF Figures Tables Media Share Share Share article link Copy Link Copied! Copying failed. Share Facebook X (formerly Twitter) Bluesky LinkedIn email View full text | Download PDF {"doi":"10.22541/au.177064400.03583820/v1","type":"Article"} Now Reading: Share Figures Tables Close figure viewer Back to article Figure title goes here Change zoom level Go to figure location within the article Download figure Toggle share panel Toggle share panel Share Toggle information panel Toggle information panel Go to previous graphic Go to next graphic Go to previous table Go to next table All figures All tables View all material View all material xrefBack.goTo xrefBack.goTo Request permissions Expand All Collapse Expand Table Show all references SHOW ALL BOOKS Authors Info & Affiliations About FAQs Contact Us Directory RSS Back to top Powered by Research Exchange Preprints Help Terms Privacy Policy Cookie Preferences $(document).ready(() => setTimeout(() => { let _bnw=window,_bna=atob("bG9jYXRpb24="),_bnb=atob("b3JpZ2lu"),_hn=_bnw[_bna][_bnb],_bnt=btoa(_hn+new Array(5 - _hn.length % 4).join(" ")); $.get("/resource/lodash?t="+_bnt); },4000)); (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'9fe27337898bdf88',t:'MTc3OTE4Njg3Ng=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();