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Sebastian Kinnberg Nielsen,1 Nina Nouhravesh,1 Mads Hashiba Jensen,1 Rawia Farah Gedde Jensen,1 Mads Falk Klein,2 Zaigham Saghir,3,4 Dorte Nielsen,5 Morten Schou,1 Morten Lamberts1 1Department of Cardiology, Herlev-Gentofte University Hospital, Copenhagen, Denmark; 2Department of Surgery, Herlev-Gentofte University Hospital, Copenhagen, Denmark; 3Department of Respiratory Medicine, Herlev-Gentofte University Hospital, Copenhagen, Denmark; 4Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark; 5Department of Oncology, Herlev-Gentofte University Hospital, Copenhagen, DenmarkCorrespondence: Sebastian Kinnberg Nielsen, Department of Cardiology, Herlev-Gentofte University Hospital, Gentofte Hospitalsvej 1, Opgang 6, 3. sal 2900, Hellerup, Copenhagen, Denmark, Tel +45 40479556, Email Sebastian.Kinnberg.Nielsen03@regionh.dkBackground/Aim: The Danish National Patient Registry (DNPR) provides unique epidemiological insight, but often lacks granular data. We propose a procedure-based definition of cancer status in patients with breast-, lung- and colorectal cancer, which can be applied to administrative health databases. New definitions of cancer status are needed as mortality and morbidity are closely linked to cancer status, yet most studies only use duration since cancer diagnosis as a severity marker. The aim of the study was to validate a new pragmatic definition.Methods: Medical journals of 600 patients, with breast-, lung- and colorectal cancer from the Department of Oncology at Herlev-Gentofte Hospital were retrospectively reviewed. We defined active cancer as a cancer diagnosis, not followed by a potentially curative procedure within 6 months of the diagnosis. The remaining patients were characterized as having non-active cancer. This dichotomization was then compared to a cancer status assessment based on treatment received and paraclinical test such as their first post-procedural control scan. Based on this comparison, we calculated the positive predictive value (PPV) of our definitions of active and non-active cancer.Results: The calculated PPVs for active breast-, lung- and colorectal cancer were 87% (CI 95%: 0.74– 0.99), 91% (CI 95%: 0.87– 0.96) and 82% (CI 95%: 0.73– 0.91). The PPVs for non-active breast-, lung- and colorectal cancer were 95% (CI 95%: 0.92– 0.99), 91% (CI 95%: 0.82– 0.99) and 73% (CI 95%: 0.66– 0.81), respectively.Conclusion: We found an overall high PPV for both active and non-active cancer across all three types of cancer.Keywords: active cancer, non-active cancer, DNPR, validation, epidemiology

The tumor suppressor p53 responds to cellular stress and activates transcription programs critical for regulating cell fate. DNA damage triggers oscillations in p53 levels with a robust period. Guided by the theory of synchronization and entrainment, we developed a mathematical model and experimental system to test the ability of the p53 oscillator to entrain to external drug pulses of various periods and strengths. We found that the p53 oscillator can be locked and entrained to a wide range of entrainment modes. External periods far from p53's natural oscillations increased the heterogeneity between individual cells whereas stronger inputs reduced it. Single-cell measurements allowed deriving the phase response curves (PRCs) and multiple Arnold tongues of p53. In addition, multi-stability and non-linear behaviors were mathematically predicted and experimentally detected, including mode hopping, period doubling, and chaos. Our work revealed critical dynamical properties of the p53 oscillator and provided insights into understanding and controlling it. A record of this paper's transparent peer review process is included in the supplemental information., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2024 Elsevier Inc. All rights reserved.)

Background and Objective: The aim of this study was to develop and validate explainable prediction models based on continuous glucose monitoring (CGM) and baseline data to identify a week-to-week risk of CGM key metrics (hyperglycemia, hypoglycemia, glycemic variability). By having a weekly prediction of CGM key metrics, it is possible for the patient or health care personnel to take immediate preemptive action., Methods: We analyzed, trained, and internally tested three prediction models (Logistic regression, XGBoost, and TabNet) using CGM data from 187 type 1 diabetes patients with long-term CGM monitoring. A binary classification approach combined with feature engineering deployed on the CGM signals was used to predict hyperglycemia, hypoglycemia, and glycemic variability based on consensus targets (time above range ≥5%, time below range ≥4%, coefficient of variation ≥36%). The models were validated in two independent cohorts with a total of 223 additional patients of varying ages., Results: A total of 46 593 weeks of CGM data were included in the analysis. For the best model (XGBoost), the area under the receiver operating characteristic curve (ROC-AUC) was 0.9 [95% confidence interval (CI) = 0.89-0.91], 0.89 [95% CI = 0.88-0.9], and 0.8 [95% CI = 0.79-0.81] for predicting hyperglycemia, hypoglycemia, and glycemic variability in the interval validation, respectively. The validation test showed good generalizability of the models with ROC-AUC of 0.88 to 0.95, 0.84 to 0.89, and 0.80 to 0.82 for predicting the glycemic outcomes., Conclusion: Prediction models based on real-world CGM data can be used to predict the risk of unstable glycemic control in the forthcoming week. The models showed good performance in both internal and external validation cohorts., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: The research was funded by i-SENS, Inc (Seoul, South Korea) and SLC’s involvement with the company did not influence the design, implementation, or interpretation of the study. SLC have received research funding from i-SENS, Inc (Seoul, South Korea), which manufactures some of the product types discussed in this paper. However, the study was conducted independently, and the authors declare that their involvement with i-SENS, Inc (Seoul, South Korea) did not influence the findings or conclusions of the study.

Objectives: Obesity represents a global health crisis with significant patient burdens and healthcare costs. Despite the advances with glucagon-like peptide-1 (GLP-1) receptor agonists in treating obesity, unmet needs remain. This study characterizes a novel glucose-dependent insulinotropic polypeptide receptor (GIPR) peptide antagonist, AT-7687, evaluating its potential to enhance obesity treatment., Methods: We assessed the in vitro potency and pharmacokinetics of AT-7687, alongside its therapeutic effects when administered subcutaneously (SC) alone and in combination with liraglutide to high-fat-diet-fed obese non-human primates (NHP). The study spanned a 42-day treatment period and a 15-day washout period., Results: AT-7687 demonstrated a subnanomolar cAMP antagonistic potency (pKB of 9.5) in HEK-293 cells and a 27.4 h half-life in NHPs. It effectively maintained weight stability in obese monkeys, whereas placebo recipients had an 8.6% weight increase by day 42 (P = 0.01). Monotherapy with liraglutide resulted in a 12.4% weight reduction compared to placebo (P = 0.03) and combining AT-7687 with liraglutide led to a 16.3% weight reduction (P = 0.0002). The combination therapy significantly improved metabolic markers, reducing insulin levels by 52% (P = 0.008), glucose by 30% (P = 0.02), triglycerides by 39% (P = 0.05), total cholesterol by 29% (P = 0.03), and LDL cholesterol by 48% (P = 0.003) compared to placebo. AT-7687 treatment was well tolerated and not associated with any side effects., Conclusions: This study underscores the potential of AT-7687 as a promising addition to current obesity treatments., Competing Interests: Declaration of competing interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests:MHJ, SJS, DR, and AHS-U are or were previously employees of Antag Therapeutics Aps. MHJ, SJS, DR, JJH, MMR and AHS-U hold warrants in Antag Therapeutics Aps. JJH, MMR, and AHS-U are founders of Antag Therapeutics Aps. JJH, MMR, and AHS-U hold the following patent: WO 2018/220123. DR, MMR and AHS-U hold the following patents: WO 2020/115048 and WO 2020/115049. SJS, DR, MMR, and AHS-U hold the following patent: WO 2021/110845. JJH is a board member of Antag Therapeutics Aps and has served as a consultant or advisor to Novo Nordisk, Roche, Novartis Pharmaceuticals, Merck Sharp & Dohme and received fees for lectures from Merck Sharp & Dohme, and Novo Nordisk. MMR is a consultant for Antag Therapeutics Aps., (Copyright © 2024 The Authors. Published by Elsevier GmbH.. All rights reserved.)

Background and Aims: Glycemic control is crucial for people with type 2 diabetes. However, only about half achieve the advocated HbA1c target of ≤7%. Identifying those who will probably struggle to reach this target may be valuable as they require additional support. Thus, the aim of this study was to develop a model to predict people with type 2 diabetes not achieving HbA1c target after initiating fast-acting insulin., Methods: Data from a randomized controlled trial (NCT01819129) of participants with type 2 diabetes initiating fast-acting insulin were used. Data included demographics, clinical laboratory values, self-monitored blood glucose (SMBG), health-related quality of life (SF-36), and body measurements. A logistic regression was developed to predict HbA1c target nonachievers. A potential of 196 features was input for a forward feature selection. To assess the performance, a 20-repeated stratified 5-fold cross-validation with area under the receiver operating characteristics curve (AUROC) was used., Results: Out of the 467 included participants, 98 (21%) did not achieve HbA1c target of ≤7%. The forward selection identified 7 features: baseline HbA1c (%), mean postprandial SMBG at all meals 3 consecutive days before baseline (mmol/L), sex, no ketones in urine, baseline albumin (g/dL), baseline low-density lipoprotein cholesterol (mmol/L), and traces of protein in urine. The model had an AUROC of 0.745 [95% CI = 0.734, 0.756]., Conclusions: The model was able to predict those who did not achieve HbA1c target with promising performance, potentially enabling early identification of people with type 2 diabetes who require additional support to reach glycemic control., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Type 2 diabetes (T2D) is associated with a systemic increase in the pro-inflammatory cytokine IL-1β. While transient exposure to low IL-1β concentrations improves insulin secretion and β-cell proliferation in pancreatic islets, prolonged exposure leads to impaired insulin secretion and collective β-cell death. IL-1 is secreted locally by islet-resident macrophages and β-cells; however, it is unknown if and how the two opposing modes may emerge at single islet level. We investigated the duality of IL-1β with a quantitative in silico model of the IL-1 regulatory network in pancreatic islets. We find that the network can produce either transient or persistent IL-1 responses when induced by pro-inflammatory and metabolic cues. This suggests that the duality of IL-1 may be regulated at the single islet level. We use two core feedbacks in the IL-1 regulation to explain both modes: First, a fast positive feedback in which IL-1 induces its own production through the IL-1R/IKK/NF-κB pathway. Second, a slow negative feedback where NF-κB upregulates inhibitors acting at different levels along the IL-1R/IKK/NF-κB pathway-IL-1 receptor antagonist and A20, among others. A transient response ensues when the two feedbacks are balanced. When the positive feedback dominates over the negative, islets transit into the persistent inflammation mode. Consistent with several observations, where the size of islets was implicated in its inflammatory state, we find that large islets and islets with high density of IL-1β amplifying cells are more prone to transit into persistent IL-1β mode. Our results are likely not limited to IL-1β but are general for the combined effect of multiple pro-inflammatory cytokines and chemokines. Generalizing complex regulations in terms of two feedback mechanisms of opposing nature and acting on different time scales provides a number of testable predictions. Taking islet architecture and cellular heterogeneity into consideration, further dynamic monitoring and experimental validation in actual islet samples will be crucial to verify the model predictions and enhance its utility in clinical applications., (© 2024. The Author(s).)

Background: Real-world studies of people with type 2 diabetes (T2D) have shown insufficient dose adjustment during basal insulin titration in clinical practice leading to suboptimal treatment. Thus, 60% of people with T2D treated with insulin do not reach glycemic targets. This emphasizes a need for methods supporting efficient and individualized basal insulin titration of people with T2D. However, no systematic review of basal insulin dose guidance for people with T2D has been found., Objective: To provide an overview of basal insulin dose guidance methods that support titration of people with T2D and categorize these methods by characteristics, effect, and user experience., Methods: The review was conducted according to the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines. Studies about basal insulin dose guidance, including adults with T2D on basal insulin analogs published before September 7, 2022, were included. Joanna Briggs Institute critical appraisal checklists were applied to assess risk of bias., Results: In total, 35 studies were included, and three categories of dose guidance were identified: paper-based titration algorithms, telehealth solutions, and mathematical models. Heterogeneous reporting of glycemic outcomes challenged comparison of effect between the three categories. Few studies assessed user experience., Conclusions: Studies mainly used titration algorithms to titrate basal insulin as telehealth or in paper format, except for studies using mathematical models. A numerically larger proportion of participants seemed to reach target using telehealth solutions compared to paper-based titration algorithms. Exploring capabilities of machine learning may provide insights that could pioneer future research while focusing on holistic development., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: Author P.V. is head of research at the Steno Diabetes Center North Denmark, funded by the Novo Nordisk Foundation. Author M.H.J is a former Novo Nordisk employee and holds Novo Nordisk shares.

Objective: This study aims to investigate the continuum of glucose control from normoglycemia to dysglycemia (HbA1c ≥ 5.7%/39 mmol/mol) using metrics derived from continuous glucose monitoring (CGM). In addition, we aim to develop a machine learning-based classification model to classify dysglycemia based on observed patterns. Methods: Data from five distinct studies, each featuring at least two days of CGM, were pooled. Participants included individuals classified as healthy, with prediabetes, or with type 2 diabetes mellitus (T2DM). Various CGM indices were extracted and compared across groups. The data set was split 70/30 for training and testing two classification models (XGBoost/Logistic Regression) to differentiate between prediabetes or dysglycemia and the healthy group. Results: The analysis included 836 participants (healthy: n = 282; prediabetes: n = 133; T2DM: n = 432). Across all CGM indices, a progressive shift was observed from the healthy group to those with diabetes ( P < 0.001). Statistically significant differences ( P < 0.01) were noted in mean glucose, time below range, time above 140 mg/dl, mobility, multiscale complexity index, and glycemic risk index when transitioning from health to prediabetes. The XGBoost models achieved the highest receiver operating characteristic area under the curve values on the test data set ranging from 0.91 [confidence interval (CI): 0.87-0.95] (prediabetes identification) to 0.97 [CI: 0.95-0.98] (dysglycemia identification). Conclusion: Our findings demonstrate a gradual deterioration of glucose homeostasis and increased glycemic variability across the spectrum from normo- to dysglycemia, as evidenced by CGM metrics. The performance of CGM-based indices in classifying healthy individuals and those with prediabetes and diabetes is promising.

This study developed and validated a machine learning model for predicting glycemic control in children with type 1 diabetes at the time of diagnosis, revealing age at diagnosis as the most informative predictor.

Context: About 30% of patients with active acromegaly experience paradoxically increased growth hormone (GH) secretion during the diagnostic oral glucose tolerance test (OGTT). Endogenous glucose-dependent insulinotropic polypeptide (GIP) is implicated in this paradoxical secretion., Objective: We used the GIP receptor (GIPR) antagonist GIP(3-30)NH2 to test the hypothesis that GIP mediates this paradoxical response when GIPR is abundantly expressed in somatotropinomas., Design, Patients, Setting, Interventions: 25 treatment-naïve patients with acromegaly were enrolled. Each patient underwent one OGTT during simultaneous placebo infusion and one OGTT during a GIP(3-30)NH2 infusion. Blood samples were drawn at baseline and regularly after infusions to measure GH. We assessed pituitary adenoma size by magnetic resonance imaging and GIPR expression by immunohistochemistry on resected somatotropinomas. For mechanistic confirmation, we applied in vitro and ex vivo approaches., Main Outcome Measure: The effect of GIP(3-30)NH2 on paradoxical GH secretion during OGTT as a measure of GIP involvement., Results: In four of seven patients with paradoxical GH secretion, GIP(3-30)NH2 infusion completely abolished the paradoxical response (P = 0.0003). Somatotrophs were available from three of four of these patients, all showing abundant GIPR expression. Adenoma size did not differ between patients with and without paradoxical GH secretion., Conclusions: Of 25 patients with acromegaly, seven had paradoxical GH secretion during OGTT, and pharmaceutical GIPR blockade abolished this secretion in four. Corresponding somatotroph adenomas abundantly expressed GIPR, suggesting a therapeutic target in this subpopulation of patients. In vitro and ex vivo analyses confirmed the role of GIP and the effects of the antagonist., (© The Author(s) 2024. Published by Oxford University Press on behalf of the Endocrine Society.)

The increased utilization of continuous glucose monitors (CGM) and smart insulin pens (SIP) among people with type 2 diabetes generates significant health data. This study explored possible patterns in long term CGM and SIP data.

Protocol for a three-arm randomized controlled trial investigating telemonitoring with decision support in insulin-treated people with type 2 diabetes.

The study aims to develop a system for insulin titration in type 2 diabetes to assist primary healthcare providers. Usability testing with end-users is ongoing.

Healthy lifestyle behaviors are essential in the treatment of type 2 diabetes, and meal registration is therefore important. Manual meal registration is cumbersome and could be automated using continuous glucose monitoring (CGM). If such an algorithm is based on patient-reported meals, potential errors might be induced. Thus, the aim was to investigate potential errors in patient-reported mealtimes and the effect on automatic meal detection. Two healthcare professionals (HCPs) reported the mealtimes of the 18 included patients based on the patients' CGM data to assess the agreement between HCP- and patient-reported mealtimes. A developed meal detection algorithm based on detecting the post-prandial glucose response using cross-correlation was used to assess the impact of errors in patient-reported meals. The results showed poor disagreement between HCP- and patient-reported meals and that the meal detection algorithm had a moderately better performance on the HCP-reported meals. Therefore, the possibility of errors in patient-reported mealtimes should be considered in the development of meal detection algorithms. However, more research is needed to confirm the results of this study.

Background: Comorbidities such as cardiovascular disease (CVD) and diabetic kidney disease (DKD) are major burdens of type 1 diabetes (T1D). Predicting people at high risk of developing comorbidities would enable early intervention. This study aimed to develop models incorporating socioeconomic status (SES) to predict CVD, DKD, and mortality in adults with T1D to improve early identification of comorbidities., Methods: Nationwide Danish registry data were used. Logistic regression models were developed to predict the development of CVD, DKD, and mortality within five years of T1D diagnosis. Features included age, sex, personal income, and education. Performance was evaluated by five-fold cross-validation with area under the receiver operating characteristic curve (AUROC) and the precision-recall area under the curve (PR-AUC). The importance of SES was assessed from feature importance plots., Results: Of the 6572 included adults (≥21 years) with T1D, 379 (6%) developed CVD, 668 (10%) developed DKD, and 921 (14%) died within the five-year follow-up. The AUROC (±SD) was 0.79 (±0.03) for CVD, 0.61 (±0.03) for DKD, and 0.87 (±0.01) for mortality. The PR-AUC was 0.18 (±0.01), 0.15 (±0.03), and 0.49 (±0.02), respectively. Based on feature importance plots, SES was the most important feature in the DKD model but had minimal impact on models for CVD and mortality., Conclusions: The developed models showed good performance for predicting CVD and mortality, suggesting they could help in the early identification of these outcomes in individuals with T1D. The importance of SES in individual prediction within diabetes remains uncertain., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Background: Limited knowledge of antihypertensive treatment of the elderly potentially impedes effective strategies for hypertension management in this growing patient group. We aimed to investigate temporal trends for first-line drug choice for antihypertensive treatment and treatment continuity among patients ≥75 years from 2000 to 2021., Methods: Using nationwide Danish registers, patients ≥75 years initiated for the first time on antihypertensive drugs: Angiotensin converting enzyme inhibitors (ACEi), angiotensin II receptor blockers (ARB), beta blockers (BB), calcium channel blockers (CCB), thiazides, or combinations, were identified. Patients with other indications than hypertension were excluded. Treatment continuity was described using claimed prescriptions the first 180 days following study entry., Results: From 2000 to 2021, 170,769 patients (median age 80 years [interquartile range:77-84], 60.3% female) were included. From 2000 to 2003 to 2015-2021 the proportion of first-line drug choice increased for ACEi (8.7% to 14.9%), ARB (4.1% to 23.9%), and CCB (10.7% to 27.6%), decreased for thiazides (60.6% to 15.9%) and remained stable for BB (12.9% to 14.1%) and combinations (2.9% to 3.6%). For 157,457 patients alive after 180 days, discontinuation was highest among patients initiated on thiazides (28.3%) whereas most patients continued the same single drug regimen if they started on ACEi (55.2%), ARB (65.0%), BB (57.2%) or CCB (59.3%)., Conclusions: From 2000 to 2021 thiazides have been replaced by ACEi, ARB and CCB. Thiazides had the lowest treatment continuity while ARB appeared preferred slightly over ACEi. Differences in adherence in relation to first-line drug choice may warrant scrutiny regarding recommendations for the elderly., Competing Interests: Declaration of competing interest ML reports research grants from Karen Elise Jensen Fonden, Danish Heart Foundation, Bayer, and BMS; speaker fees from BMS/Pfizer, Bayer, Astra Zeneca, MSD, and Merck; and advisory board positions with BMS/Pfizer, Bayer, and Mundipharma. NN reports speaker fees from Astra Zeneca and Bayer. SKN, AH, JES, MHJ, GG, and PM report no conflict of interest., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Accumulating data suggest a role for the lysosomal protease cathepsin S (CTSS) in type 1 diabetes. Circulating CTSS is increased in type 1 diabetes; however, whether CTSS has protective or deleterious effects is unclear. The study's objectives were to examine the biomarker potential of CTSS in new-onset type 1 diabetes, and to investigate the expression and secretion of CTSS in human islets and β-cells. The CTSS level was analyzed in serum from children with new-onset type 1 diabetes and autoantibody-positive and -negative siblings by ELISA. The expression and secretion of CTSS were evaluated in isolated human islets and EndoC-βH5 cells by real-time qPCR, immunoblotting, and ELISA. The CTSS serum level was elevated in children with new-onset type 1 diabetes and positively associated with autoantibody status in healthy siblings. Human islets and EndoC-βH5 cells demonstrated induction and secretion of CTSS after exposure to proinflammatory cytokines, a model system of islet inflammation. Analysis of publicly available single-cell RNA sequencing data on human islets showed that elevated CTSS expression was exclusive for the β-cells in donors with type 1 diabetes as compared with nondiabetic donors. These findings suggest a potential of CTSS as a diagnostic biomarker in type 1 diabetes., (© 2024 by the American Diabetes Association.)

The gut microbiome affects brain and neuronal development and may contribute to the pathophysiology of neurodevelopmental disorders. However, it is unclear how risk genes associated with such disorders affect gut physiology in a manner that could impact microbial colonization and how the mechanical properties of the gut tissue might play a role in gut-brain bidirectional communication. To address this, we used Drosophila melanogaster with a null mutation in the gene kismet, an ortholog of chromodomain helicase DNA-binding protein (CHD) family members CHD7 and CHD8. In humans, these are risk genes for neurodevelopmental disorders with co-occurring gastrointestinal symptoms. We found that kismet mutant flies have a significant increase in gastrointestinal transit time, indicating the functional homology of kismet with CHD7/CHD8 in vertebrates. Rheological characterization of dissected gut tissue revealed significant changes in the mechanics of kismet mutant gut elasticity, strain stiffening behavior, and tensile strength. Using 16S rRNA metagenomic sequencing, we also found that kismet mutants have reduced diversity and abundance of gut microbiota at every taxonomic level. To investigate the connection between the gut microbiome and behavior, we depleted gut microbiota in kismet mutant and control flies and quantified the flies' courtship behavior. Depletion of gut microbiota rescued courtship defects of kismet mutant flies, indicating a connection between gut microbiota and behavior. In striking contrast, depletion of the gut microbiome in the control strain reduced courtship activity, demonstrating that antibiotic treatment can have differential impacts on behavior and may depend on the status of microbial dysbiosis in the gut prior to depletion. We propose that Kismet influences multiple gastrointestinal phenotypes that contribute to the gut-microbiome-brain axis to influence behavior. We also suggest that gut tissue mechanics should be considered as an element in the gut-brain communication loop, both influenced by and potentially influencing the gut microbiome and neurodevelopment., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2024 Biophysical Society. Published by Elsevier Inc. All rights reserved.)

Background and Aims: Hypoglycemia may lead to anxiety, poor adherence, and hypoglycemia unawareness and is especially a threat during the night in patients with insulin-treated type 2 diabetes (T2D). It would therefore be beneficial to warn patients at risk of hypoglycemia at bedtime so they can react accordingly and avoid the episode. Hence, the aim of the present study was to develop a model for predicting nocturnal hypoglycemia., Methods: Continuous glucose monitoring (CGM), mealtime, and insulin data were collected from 67 insulin-treated patients with T2D (NCT01819129). Data were structured into 24-hour periods and labeled as nocturnal hypoglycemia or not depending on whether 15 consecutive minutes were spent below 3.0 mmol/L (54 mg/dL) during the following night. Each period was divided into "last night," "morning," "day," and "evening" for feature extraction purposes, and 72 potential features were extracted for every period. A five-fold cross-validation was used to select features by forward selection and for training and validating a model based on logistic regression., Results: The prediction model was based on 30 patients with 60/496 periods resulting in nocturnal hypoglycemia. Forward selection revealed that the best features were based on CGM and involved the last value and mean value during the evening, as well as the relative difference in maximum value during the day between the present period and previous periods. The model obtained a mean area under the receiver operating characteristics curve (AUC) of 0.82 with an accuracy of 0.79., Conclusions: The model was able to predict nocturnal hypoglycemia with an acceptable accuracy and could therefore prevent such cases., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

The adherence to oral antidiabetic drugs (OADs) among people with type 2 diabetes (T2D) is suboptimal. However, new OADs have been marketed within the last 10 years. As these new drugs differ in mechanism of action, treatment complexity, and side effects, they may influence adherence. Thus, the aim of this study was to assess the adherence to newer second-line OADs, defined as drugs marketed in 2012-2022, among people with T2D. A systematic review was performed in CINAHL, Cochrane Trials, Embase, PubMed, PsycINFO, and Scopus. Articles were included if they were original research of adherence to newer second-line OADs and reported objective adherence quantification. The quality of the articles was assessed using JBI's critical appraisal tools. The overall findings were reported according to the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines and summarized in a narrative synthesis. All seven included articles were European retrospective cohort studies investigating alogliptin, canagliflozin, dapagliflozin, empagliflozin, and unspecified types of SGLT2i. Treatment discontinuation and medication possession ratio (MPR) were the most frequently reported adherence quantification measures. Within the first 12 months of treatment, 29%-44% of subjects on SGLT2i discontinued the treatment. In terms of MPR, 61.7%-94.9% of subjects on either alogliptin, canagliflozin, dapagliflozin, empagliflozin or an unspecified SGLT2i were adherent. The two investigated adherence quantification measures, treatment discontinuation and MPR, suggest that adherence to the newer second-line OADs may be better than that of older OADs. However, a study directly comparing older and newer OADs should be done to verify this., (© 2024 The Authors. Pharmacology Research & Perspectives published by British Pharmacological Society and American Society for Pharmacology and Experimental Therapeutics and John Wiley & Sons Ltd.)

Baleen whales (mysticetes) use vocalizations to mediate their complex social and reproductive behaviours in vast, opaque marine environments 1 . Adapting to an obligate aquatic lifestyle demanded fundamental physiological changes to efficiently produce sound, including laryngeal specializations 2-4 . Whereas toothed whales (odontocetes) evolved a nasal vocal organ 5 , mysticetes have been thought to use the larynx for sound production 1,6-8 . However, there has been no direct demonstration that the mysticete larynx can phonate, or if it does, how it produces the great diversity of mysticete sounds 9 . Here we combine experiments on the excised larynx of three mysticete species with detailed anatomy and computational models to show that mysticetes evolved unique laryngeal structures for sound production. These structures allow some of the largest animals that ever lived to efficiently produce frequency-modulated, low-frequency calls. Furthermore, we show that this phonation mechanism is likely to be ancestral to all mysticetes and shares its fundamental physical basis with most terrestrial mammals, including humans 10 , birds 11 , and their closest relatives, odontocetes 5 . However, these laryngeal structures set insurmountable physiological limits to the frequency range and depth of their vocalizations, preventing them from escaping anthropogenic vessel noise 12,13 and communicating at great depths 14 , thereby greatly reducing their active communication range., (© 2024. The Author(s), under exclusive licence to Springer Nature Limited.)

Background: There has been an increasing interest in the use of digital health lifestyle interventions for people with prediabetes, as these interventions may offer a scalable approach to preventing type 2 diabetes. Previous systematic reviews on digital health lifestyle interventions for people with prediabetes had limitations, such as a narrow focus on certain types of interventions, a lack of statistical pooling, and no broader subgroup analysis of intervention characteristics. The identified limitations observed in previous systematic reviews substantiate the necessity of conducting a comprehensive review to address these gaps within the field. This will enable a comprehensive understanding of the effectiveness of digital health lifestyle interventions for people with prediabetes., Objective: The objective of this systematic review, meta-analysis, and meta-regression is to systematically investigate the effectiveness of digital health lifestyle interventions on prediabetes-related outcomes in comparison with any comparator without a digital component among adults with prediabetes., Methods: This systematic review will include randomized controlled trials that investigate the effectiveness of digital health lifestyle interventions on adults (aged 18 years or older) with prediabetes and compare the digital interventions with nondigital interventions. The primary outcome will be change in body weight (kg). Secondary outcomes include, among others, change in glycemic status, markers of cardiometabolic health, feasibility outcomes, and incidence of type 2 diabetes. Embase, PubMed, CINAHL, and CENTRAL (Cochrane Central Register of Controlled Trials) will be systematically searched. The data items to be extracted include study characteristics, participant characteristics, intervention characteristics, and relevant outcomes. To estimate the overall effect size, a meta-analysis will be conducted using the mean difference. Additionally, if feasible, meta-regression on study, intervention, and participant characteristics will be performed. The Cochrane risk of bias tool will be applied to assess study quality, and the GRADE (Grading of Recommendations Assessment, Development and Evaluation) approach will be used to assess the certainty of evidence., Results: The results are projected to yield an overall estimate of the effectiveness of digital health lifestyle interventions on adults with prediabetes and elucidate the characteristics that contribute to their effectiveness., Conclusions: The insights gained from this study may help clarify the potential of digital health lifestyle interventions for people with prediabetes and guide the decision-making regarding future intervention components., Trial Registration: PROSPERO CRD42023426919; http://tinyurl.com/d3enrw9j., International Registered Report Identifier (irrid): PRR1-10.2196/50340., (©Tanja Fredensborg Holm, Flemming Witt Udsen, Kristine Færch, Morten Hasselstrøm Jensen, Bernt Johan von Scholten, Ole Kristian Hejlesen, Stine Hangaard. Originally published in JMIR Research Protocols (https://www.researchprotocols.org), 09.02.2024.)

Objective: To develop a machine-learning model that can predict the risk of pancreatic ductal adenocarcinoma (PDAC) in people with new-onset diabetes (NOD)., Methods: From a population-based sample of individuals with NOD aged >50 years, patients with pancreatic cancer-related diabetes (PCRD), defined as NOD followed by a PDAC diagnosis within 3 years, were included (n = 716). These PCRD patients were randomly matched in a 1:1 ratio with individuals having NOD. Data from Danish national health registries were used to develop a random forest model to distinguish PCRD from Type 2 diabetes. The model was based on age, gender, and parameters derived from feature engineering on trajectories of routine biochemical variables. Model performance was evaluated using receiver operating characteristic curves (ROC) and relative risk scores., Results: The most discriminative model included 20 features and achieved a ROC-AUC of 0.78 (CI:0.75-0.83). Compared to the general NOD population, the relative risk for PCRD was 20-fold increase for the 1 % of patients predicted by the model to have the highest cancer risk (3-year cancer risk of 12 % and sensitivity of 20 %). Age was the most discriminative single feature, followed by the rate of change in haemoglobin A1c and the latest plasma triglyceride level. When the prediction model was restricted to patients with PDAC diagnosed six months after diabetes diagnosis, the ROC-AUC was 0.74 (CI:0.69-0.79)., Conclusion: In a population-based setting, a machine-learning model utilising information on age, sex and trajectories of routine biochemical variables demonstrated good discriminative ability between PCRD and Type 2 diabetes., Competing Interests: Declaration of Competing Interest No potential conflicts of interest relevant to this article were reported, (Copyright © 2023 The Author(s). Published by Elsevier B.V. All rights reserved.)

Background and Objectives: Predicting glucose levels in individuals with diabetes offers potential improvements in glucose control. However, not all patients exhibit predictable glucose dynamics, which may lead to ineffective treatment strategies. We sought to investigate the efficacy of a 7-day blinded screening test in identifying diabetes patients suitable for glucose forecasting., Methods: Participants with type 1 diabetes (T1D) were stratified into high and low initial error groups based on screening results (eligible and non-eligible). Long-term glucose predictions (30/60 min lead time) were evaluated among 334 individuals who underwent continuous glucose monitoring (CGM) over a total of 64,460,560 min., Results: A strong correlation was observed between screening accuracy and long-term mean absolute relative difference (MARD) (0.661-0.736; p < 0.001), suggesting significant predictability between screening and long-term errors. Group analysis revealed a notable reduction in predictions falling within zone D of the Clark Error Grid by a factor of three and in zone C by a factor of two., Conclusions: The identification of eligible patients for glucose prediction through screening represents a practical and effective strategy. Implementation of this approach could lead to a decrease in adverse glucose predictions., Competing Interests: Declaration of competing interest None to report., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Background and Objectives: In most neurosurgical centers, irrigation is an essential part of the surgical procedure for chronic subdural hematoma (CSDH). However, it is unknown whether the volume of irrigation fluid affects the risk of CSDH recurrence. This study aimed to investigate a potential association between the volume of irrigation fluid used during burr hole evacuation of CSDH and the risk of CSDH recurrence., Methods: This study is a subanalysis of 2 randomized trials (Drain Time & Drain Time 2) designed to investigate the effect of drainage duration on the recurrence of CSDH. Intraoperative irrigation volume was measured, and patients were followed for 90 days for recurrent CSDH., Results: A total of 525 patients with CSDH were included. There was no significant difference in the volume of irrigation fluid used between patients with recurrence (mean = 938 mL, SD = ±552) and without recurrence (mean = 852 mL, SD = ±454) ( P -value = .15). Patients with recurrent CSDH had larger primary CSDH volumes (mean = 134 cm 3 , SD = ±69) than patients without recurrence (mean = 119 cm 3 , SD = ±58) ( P = .04). Multiple logistic regression analysis revealed no association between irrigation volume and recurrence, also when stratified for hematoma size., Conclusion: There was no significant association between irrigation volume and recurrent CSDH within 90 days in patients undergoing burr hole surgery for CSDH., (Copyright © Congress of Neurological Surgeons 2023. All rights reserved.)

Objective: Post-pancreatitis diabetes mellitus (PPDM) is a frequent complication of pancreatitis and is associated with an increased risk of adverse outcomes. Metformin is recommended for the treatment of PPDM, but evidence of its risk-benefit profile is limited. In a pharmaco-epidemiologic study, we investigated the association between metformin treatment and adverse outcomes in patients with PPDM., Design and Methods: In a Danish nationwide population-based cohort study, we included adults (≥18 years) with incident PPDM or type 2 diabetes between 2009 and 2018. Post-pancreatitis diabetes mellitus was categorised into acute and chronic subtypes (PPDM-A and PPDM-C). Associations between metformin treatment and severe hypoglycaemia, major adverse cardiovascular events (MACE), and all-cause mortality were examined across the diabetes subgroups using Cox regression analysis. Treatments with metformin, insulin, and other glucose-lowering therapies were handled as time-varying exposures., Results: We included 222 337 individuals with new-onset type 2 diabetes and 3781 with PPDM, of whom 2305 (61%) were classified as PPDM-A and 1476 (39%) as PPDM-C. Treatment with metformin was associated with a lower risk of severe hypoglycaemia (adjusted hazard ratio [HR] 0.41, 95% CI 0.27-0.62, P < .0001), MACE (HR 0.74, 95% CI 0.60-0.92, P = .0071), and all-cause mortality (HR 0.56, 95% CI 0.49-0.64, P < .0001) in patients with PPDM. In sensitivity analyses and among individuals with type 2 diabetes, metformin treatment exhibited comparable trends of risk reduction., Conclusions: Metformin is associated with a lower risk of adverse outcomes, including all-cause mortality in patients with PPDM, supporting the use of metformin as a glucose-lowering therapy for these patients., Competing Interests: Conflict of interest: M.H.J. is an employee at Novo Nordisk and holds shares in Novo Nordisk. Co-author F.K.K. is on the editorial board of EJE. He was not involved in the review or editorial process for this paper, on which he is listed as author. No other potential conflicts of interest relevant to this article were reported., (© The Author(s) 2023. Published by Oxford University Press on behalf of European Society of Endocrinology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.)

Background: While health care providers (HCPs) are generally aware of the challenges concerning insulin adherence in adults with insulin-treated type 2 diabetes (T2D), data guiding identification of insulin nonadherence and understanding of injection patterns have been limited. Hence, the aim of this study was to examine detailed injection data and provide methods for assessing different aspects of basal insulin adherence., Method: Basal insulin data recorded by a connected insulin pen and prescribed doses were collected from 103 insulin-treated patients (aged ≥18 years) with T2D from an ongoing clinical trial (NCT04981808). We categorized the data and analyzed distributions of correct doses, increased doses, reduced doses, and missed doses to quantify adherence. We developed a three-step model evaluating three aspects of adherence (overall adherence, adherence distribution, and dose deviation) offering HCPs a comprehensive assessment approach., Results: We used data from a connected insulin pen to exemplify the use of the three-step model to evaluate overall, adherence, adherence distribution, and dose deviation using patient cases., Conclusion: The methodology provides HCPs with detailed access to previously limited clinical data on insulin administration, making it possible to identify specific nonadherence behavior which will guide patient-HCP discussions and potentially provide valuable insights for tailoring the most appropriate forms of support., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: MHJ is an employee of, and holds stock in, Novo Nordisk A/S and PV is head of research at Steno Diabetes Center North Denmark funded by an unrestricted grant from the Novo Nordisk Foundation. Apart from that, we declare that no conflicts of interest are associated with this publication and that Novo Nordisk A/S did not influence the research or its presentation.

Aims: This systematic review aims to identify current methods used for the assessment of insulin adherence in adults with insulin-treated type 2 diabetes. The primary goal is to offer recommendations for clinical practice to improve quantification of adherence., Methods: The review was conducted in accordance with PRISMA 2020 and registered at PROSPERO (CRD42022334134). PubMed, Embase, CINAHL, and PsycINFO were searched on 15 November 2022 and included three blocks: Type 2 diabetes, insulin, and adherence. We considered primary full-text studies describing an assessment method and a threshold for assessment of insulin adherence in adults with insulin-treated type 2 diabetes., Results: A final sample of 50 studies were included. Identified methods fell into four categories: self-report, pharmacy claims, inulin count, and data from an insulin pen device. Commonly reported methods included: The Morisky Medication Adherence Scale, the (adjusted) Medication Possession Ratio, and the Proportions of Days Covered. A threshold of <80% was used to define non-adherence in nearly half of the studies. Yet, several thresholds were reported., Conclusions: Most available methods for assessing insulin adherence in adults with insulin-treated type 2 diabetes are severely limited in providing in-depth insights into timing, dosing size, injection patterns, and adherence behavior. However, recognizing diverse types of non-adherence is crucial, as they denote unique behavioral entities requiring targeted intervention. Employing insulin injection data (e.g., from a smart insulin pen cap) to underlie an assessment method is a potential new approach to objectively assess insulin timing and dosing adherence in adults with insulin-treated type 2 diabetes., Competing Interests: Declaration of competing interest M.H.J. is an employee of, and holds stock in, Novo Nordisk A/S. Apart from that, we declare that no conflicts of interest are associated with this publication and that Novo Nordisk A/S did not influence the research or its presentation., (Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Background: Machine-learning models may improve prediction of length of stay (LOS) and morbidity after surgery. However, few studies include fast-track programs, and most rely on administrative coding with limited follow-up and information on perioperative care. This study investigates potential benefits of a machine-learning model for prediction of postoperative morbidity in fast-track total hip (THA) and knee arthroplasty (TKA)., Methods: Cohort study in consecutive unselected primary THA/TKA between 2014-2017 from seven Danish centers with established fast-track protocols. Preoperative comorbidity and prescribed medication were recorded prospectively and information on length of stay and readmissions was obtained through the Danish National Patient Registry and medical records. We used a machine-learning model (Boosted Decision Trees) based on boosted decision trees with 33 preoperative variables for predicting "medical" morbidity leading to LOS > 4 days or 90-days readmissions and compared to a logistical regression model based on the same variables. We also evaluated two parsimonious models, using the ten most important variables in the full machine-learning and logistic regression models. Data collected between 2014-2016 (n:18,013) was used for model training and data from 2017 (n:3913) was used for testing. Model performances were analyzed using precision, area under receiver operating (AUROC) and precision recall curves (AUPRC), as well as the Mathews Correlation Coefficient. Variable importance was analyzed using Shapley Additive Explanations values., Results: Using a threshold of 20% "risk-patients" (n:782), precision, AUROC and AUPRC were 13.6%, 76.3% and 15.5% vs. 12.4%, 74.7% and 15.6% for the machine-learning and logistic regression model, respectively. The parsimonious machine-learning model performed better than the full logistic regression model. Of the top ten variables, eight were shared between the machine-learning and logistic regression models, but with a considerable age-related variation in importance of specific types of medication., Conclusion: A machine-learning model using preoperative characteristics and prescriptions slightly improved identification of patients in high-risk of "medical" complications after fast-track THA and TKA compared to a logistic regression model. Such algorithms could help find a manageable population of patients who may benefit most from intensified perioperative care., (© 2023. The Author(s).)

Background: Hypoglycemia is common in insulin-treated type 2 diabetes (T2D) patients, which can lead to decreased quality of life or premature death. Deep learning models offer promise of accurate predictions, but data scarcity poses a challenge. This study aims to develop a deep learning model utilizing transfer learning to predict hypoglycemia., Methods: Continuous glucose monitoring (CGM) data from 226 patients with type 1 diabetes (T1D) and 180 patients with T2D were utilized. Data were structured into one-hour samples and labeled as hypoglycemia or not depending on whether three consecutive CGM values were below 3.9 [mmol/L] (70 mg/dL) one hour after the sample. A convolutional neural network (CNN) was pre-trained with the T1D data set and subsequently fitted using a T2D data set, all while being optimized toward maximizing the area under the receiver operating characteristics curve (AUC) value, and it was externally validated on a separate T2D data set., Results: The developed model was externally validated with 334 711 one-hour CGM samples, of which 15 695 (4.69%) were labeled as hypoglycemic. The model achieved an AUC of 0.941 and a positive predictive value of 40.49% at a specificity of 95% and a sensitivity of 69.16%., Conclusions: The transfer learned CNN model showed promising performance in predicting hypoglycemic episodes and with slightly better results than a non-transfer learned CNN model., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: M.H.J. is employed at Novo Nordisk A/S

Aims: For people with type 2 diabetes treated with basal insulin, suboptimal glycemic control due to clinical inertia is a common issue. Determining the optimal basal insulin dose can be difficult, as it varies between individuals. Thus, insulin titration can be slow and cautious which may lead to treatment fatigue and non-adherence. A model that predicts changes in fasting blood glucose (FBG) after adjusting basal insulin dose may lead to more optimal titration, reducing some of these challenges., Objective: To predict the change in FBG following adjustment of basal insulin in people with type 2 diabetes using a machine learning framework., Methods: A multiple linear regression model was developed based on 786 adults with type 2 diabetes. Data were divided into training (80%) and testing (20%) sets using a ranking approach. Forward feature selection and fivefold cross-validation were used to select features., Results: Participants had a mean age of approximately 59 years, a mean duration of diabetes of 12 years, and a mean HbA 1c at screening of 65 mmol/mol (8.1%). Chosen features were FBG at week 2, basal insulin dose adjustment from week 2 to 7, trial site, hemoglobin level, and alkaline phosphatase level. The model achieved a relative absolute error of 0.67, a Pearson correlation coefficient of 0.74, and a coefficient of determination of 0.55., Conclusions: A model using FBG, insulin doses, and blood samples can predict a five-week change in FBG after adjusting the basal insulin dose in people with type 2 diabetes. Implementation of such a model can potentially help optimize titration and improve glycemic control., Competing Interests: Declaration of Conflicting InterestsThe author(s) declared the following potential conflicts of interest with respect to the research, authorship, and/or publication of this article: Author P.V. is the head of research at the Steno Diabetes Center North Denmark, funded by the Novo Nordisk Foundation. Author M.H.J holds Novo Nordisk shares.

Aims: To investigate if HLA risk haplotypes and HbA1c levels are associated with the expression levels of innate anti-viral immune pathway genes in type 1 diabetes., Materials and Methods: We investigated RNA expression levels of innate anti-viral immune pathway genes in laser-dissected islets from two to five tissue sections per donor from the Diabetes Virus Detection study and the network of Pancreatic Organ Donors in relation to HLA risk haplotypes (non-predisposed and predisposed) and HbA1c levels (normal, elevated, and high)., Results: The expression of innate anti-viral immune genes (TLR7, OAS1, OAS3 etc.) was significantly increased in individuals with predisposing vs non-predisposing HLA haplotypes. Also, the expression of several of the innate anti-viral immune genes from the HLA risk haplotype analysis was significantly increased in the group with high vs normal HbA1c. Furthermore, the gene expression of OAS2 was significantly increased in the group with high HbA1c vs elevated HbA1c., Conclusions: Expression of innate anti-viral immune pathway genes was increased in individuals with predisposing HLA risk haplotypes and those with high HbA1c. This indicates that type 1 diabetes might well begin with alterations in innate anti-viral immunity, and already at this stage be associated with HLA risk haplotypes., (© 2023 The Authors. Diabetes/Metabolism Research and Reviews published by John Wiley & Sons Ltd.)

Background: New onset diabetes (NOD) in people 50 years or older may indicate underlying pancreatic ductal adenocarcinoma (PDAC). The cumulative incidence of PDAC among people with NOD remains uncertain on a population-based level., Methods: This was a nationwide population-based retrospective cohort study based on the Danish national health registries. We investigated the 3-year cumulative incidence of PDAC in people 50 years or older with NOD. We further characterised people with pancreatic cancer-related diabetes (PCRD) in relation to demographic and clinical characteristics, including trajectories of routine biochemical parameters, using people with type 2 diabetes (T2D) as a comparator group., Results: During a 21-year observation period, we identified 353,970 people with NOD. Among them, 2105 people were subsequently diagnosed with pancreatic cancer within 3 years (0.59%, 95% CI [0.57-0.62%]). People with PCRD were older than people with T2D at diabetes diagnosis (median age 70.9 vs. 66.0 years (P < 0.001) and had a higher burden of comorbidities (P = 0.007) and more prescriptions of medications used to treat cardiovascular diseases (all P < 0.001). Distinct trajectories of HbA1c and plasma triglycerides were observed in PCRD vs. T2D, with group differences observed for up to three years prior to NOD diagnosis for HbA1c and up to two years for plasma triglyceride levels., Conclusions: The 3-year cumulative incidence of PDAC is approximately 0.6% among people 50 years or older with NOD in a nationwide population-based setting. Compared to T2D, people with PCRD are characterised by distinct demographic and clinical profiles, including distinctive trajectories of plasma HbA1c and triglyceride levels., Competing Interests: Declaration of competing interest No potential conflicts of interest relevant to this article were reported., (Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved.)

Introduction/aim: People with type 1 diabetes (T1D) and type 2 diabetes (T2D) have an increased risk of fractures due to skeletal fragility. We aimed to compare areal bone mineral density (aBMD), volumetric BMD (vBMD), cortical and trabecular measures, and bone strength parameters in participants with diabetes vs. controls., Methods: In a cross-sectional study, we included adult participants with T1D (n = 111, MA = 52.9 years), T2D (n = 106, MA = 62.1 years) and controls (n = 328, MA = 57.7 years). The study comprised of DXA scans and HR-pQCT scans, biochemistry, handgrip strength (HGS), Timed Up and GO (TUG), vibration perception threshold (VPT), questionnaires, medical histories, alcohol use, and previous fractures. Group comparisons were performed after adjustment for sex, age, BMI, diabetes duration, HbA1c, alcohol, smoking, previous fractures, postmenopausal, HGS, TUG, and VPT., Results: We found decreased aBMD in participants with T1D at the femoral neck (p = 0.028), whereas T2D had significantly higher aBMD at peripheral sites (legs, arms, p < 0.01) vs. controls. In T1D we found higher vBMD (p < 0.001), cortical vBMD (p < 0.001), cortical area (p = 0.002) and thickness (p < 0.001), lower cortical porosity(p = 0.008), higher stiffness (p = 0.002) and failure load (p = 0.003) at radius and higher vBMD (p = 0.003), cortical vBMD(p < 0.001), bone stiffness (p = 0.023) and failure load(p = 0.044) at the tibia than controls. In T2D we found higher vBMD (p < 0.001), cortical vBMD (p < 0.001), trabecular vBMD (p < 0.001), cortical area (p < 0.001) and thickness (p < 0.001), trabecular number (p = 0.024), lower separation (p = 0.010), higher stiffness (p < 0.001) and failure load (p < 0.001) at the radius and higher total vBMD (p < 0.001), cortical vBMD (p < 0.011), trabecular vBMD (p = 0.001), cortical area (p = 0.002) and thickness (p = 0.021), lower trabecular separation (p = 0.039), higher stiffness (p < 0.001) and failure load (p = 0.034) at tibia compared with controls., Conclusion: aBMD measures were as expected lower in T1D and higher in T2D than controls. Favorable bone microarchitecture and strength parameters were seen at the tibia and radius for T1D and T2D., Competing Interests: Declaration of competing interest Peter Vestergaard is head of research in the Steno Diabetes Center North Denmark, sponsored by the Novo Nordisk Foundation. Joop van den Bergh is involved in research that is sponsored by Amgen, Eli Lilly, and UCB. Morten Hasselstrøm Jensen is a former employee of Novo Nordisk and holds shares in Novo Nordisk. Nicklas H. Rasmussen holds shares in Novo Nordisk and lecture fees from Boehringer. Jakob Dal: unrestricted research grants and lecture fee from Pfizer and IPSEN. Annika Vestergaard Kvist declares that she has no conflict of interest., (Copyright © 2023 Elsevier Inc. All rights reserved.)