AIMS:Cardiovascular disease is the most common complication and cause of death in people with diabetes. Hypoglycaemia is independently associated with the development of cardiovascular complications, including death. The aim of this study was to assess changes in cardiac function and workload during acute hypoglycaemia in people with and without diabetes and to explore the role of diabetes type, magnitude of the adrenaline response, and other phenotypic traits. MATERIALS AND METHOD:We enrolled people with type 1 diabetes (n = 24), people with insulin-treated type 2 diabetes (n = 15) and controls without diabetes (n = 24). All participants underwent a hyperinsulinaemic-normoglycaemic-(5.3 ± 0.3 mmol/L)-hypoglycaemic (2.8 ± 0.1 mmol/L)-glucose clamp. Cardiac function was assessed by echocardiography, with left ventricular ejection fraction (LVEF) as the primary endpoint. RESULTS:During hypoglycaemia, LVEF increased significantly in all groups compared to baseline (6.2 ± 5.2%, p < 0.05), but the increase was significantly lower in type 1 diabetes compared to controls without diabetes (5.8 ± 3.4% vs. 9.4 ± 5.0%, p = 0.03, 95% CI difference: -5.0, -0.3). In people with type 1 diabetes, ΔLVEF was inversely associated with diabetes duration (β: -0.16, 95% CI: -0.24, -0.53, p = 0.001) and recent exposure to hypoglycaemia (β: -0.30, 95% CI: -0.53, -0.07, p = 0.015). Hypoglycaemia also increased global longitudinal strain (GLS) in controls without diabetes (p < 0.05), but this did not occur in the two diabetes subgroups (p > 0.10). CONCLUSIONS:Hypoglycaemia increased LVEF in all groups, but the increase diminished with longer disease duration and prior exposure to hypoglycaemia in type 1 diabetes, suggesting adaptation to recurrent hypoglycaemia. The increment in GLS observed in controls was blunted in people with diabetes. More research is needed to determine the clinical relevance of these findings.

Postbariatric hypoglycemia (PBH) is a serious complication of Roux-en-Y gastric bypass (RYGB), characterized by severe hypoglycemia that may lead to loss of consciousness and seizures. The exact mechanism of PBH is poorly understood. One potential mechanism is β-cell expansion. To this end, we investigated β-cell mass in individuals with and without PBH after RYGB using [68Ga]Ga-NODAGA-exendin-4 positron emission tomography/computed tomography imaging (PET/CT). Individuals with PBH (n = 10) and without PBH (n = 9) after RYGB were included. PET/CT imaging was performed after infusion with 102.2 ± 6.9 MBq of the [68Ga]Ga-NODAGA-exendin-4 tracer to quantify pancreatic β-cell mass. The two groups did not differ with respect to sex, age, BMI, and total body weight loss after RYGB. Time between RYGB and inclusion was longer for individuals with PBH compared with those without. β-Cell mass did not differ between the groups. Individuals with PBH had a smaller pancreas than those without. β-Cell mass correlated neither with body weight parameters nor with metabolic parameters. Our data indicating that β-cell mass does not differ between individuals with and without PBH after RYGB argue against expansion of β-cell mass to explain PBH. ARTICLE HIGHLIGHTS:The exact mechanism of postbariatric hypoglycemia (PBH) is unclear, but β-cell mass expansion is hypothesized to play a role. We used [68Ga]Ga-NODAGA-exendin-4 positron emission tomography/computed tomography (PET/CT) to determine β-cell mass in individuals with and without PBH after Roux-en-Y gastric bypass surgery. β-Cell mass did not differ between individuals with and without PBH. Pancreas volume was lower in individuals with PBH compared with those without PBH. Our data argue against β-cell mass expansion to explain PBH after Roux-en-Y gastric bypass. Further study is required to understand PBH.

AIMS:Cognitive decline during hypoglycaemia poses a risk for severe hypoglycaemia among people with type 1 diabetes, as it may compromise the ability to self-treat and recover. Antecedent hypoglycaemia has been associated with blunted counterregulatory responses to subsequent hypoglycaemia, but whether hypoglycaemia-induced cognitive dysfunction is subject to such a process of habituation is unclear. We investigated the association between recent real-life exposure to hypoglycaemia recorded by continuous glucose monitoring (CGM) and cognitive function during a hypoglycaemic clamp. MATERIALS AND METHODS:Forty-two people with type 1 diabetes were given open intermittently scanned CGM (Freestyle Libre 1®) to record real-life hypoglycaemia for a week before participating in a hyperinsulinaemic-euglycaemic-hypoglycaemic clamp (mean ± SD) (2.8 ± 0.1 mmol/L). We assessed cognitive function at baseline and during hypoglycaemia using four validated tests: Paced Auditory Serial Addition Test (PASAT) and three subtasks of Test of Attentional Performance (TAP)-Alertness, Verbal Flexibility, and Working Memory. RESULTS:Hypoglycaemia exposure (glucose <3.9 mmol/L) in the week before the clamp averaged 5.8 (3.1-8.8) events/week. In response to hypoglycaemia during the clamp, cognitive function declined for all cognitive function tests (all p < 0.01). No associations were identified between exposure to CGM-recorded hypoglycaemia prior to the clamp and changes in cognitive function during the clamp procedure, when adjusting for sex, age, diabetes duration, HbA1c and hypoglycaemia awareness status in linear regression analyses. CONCLUSIONS:Our findings indicate that recent real-life CGM-recorded hypoglycaemia is not associated with cognitive decline during clamped hypoglycaemia in people with type 1 diabetes. This suggests that cognitive decline during hypoglycaemia is not susceptible to habituation.

Glucagon-like peptide 1 receptor (GLP-1R) agonists fail to reduce weight and improve glucose control in a sizable minority of people with type 2 diabetes. We hypothesized that stimulation of the hypothalamic-pituitary-adrenal (HPA) axis by GLP-1R agonists, thus inducing cortisol secretion, could explain this unresponsiveness to GLP-1R agonists. To assess the effects of GLP-1R agonist treatment on the HPA axis, we selected ten individuals with type 2 diabetes with (5 women/5 men) and nine without (4 women/5 men) an adequate response to GLP-1R agonists and used [68Ga]Ga-NODAGA-exendin-4 positron emission tomography (PET)/computed tomography (CT) to quantify GLP-1R expression in the pituitary. Oral glucose tolerance and 24 h urinary cortisol excretion was measured in all participants. Pituitary tracer uptake was observed in all participants with no significant difference between responders and non-responders. Pituitary tracer uptake correlated with the area under the curve for ACTH, urinary cortisol to creatinine ratio and age. Interestingly, men had higher pituitary tracer uptake than women. In conclusion, this study does not indicate a role for pituitary GLP-1R expression and HPA axis stimulation to explain the difference in treatment response to GLP-1R agonists among individuals with type 2 diabetes. The findings of substantial pituitary GLP-1R expression and the significant sex differences require further research.

BACKGROUND:The presence of low-grade inflammation has been reported in people with type 2 diabetes and related to the development of (macro)vascular complications. Whether systemic inflammation is present in type 1 diabetes and linked to long-term complications remains unknown. We used a targeted proteomics approach to compare inflammation in people with type 1 diabetes and type 2 diabetes with control subjects and linked these proteins to diabetes related characteristics and complications. METHODS:We included 233 participants with type 1 diabetes, 387 participants with type 2 diabetes and 150 healthy controls. Plasma was collected and used to determine high sensitive C-reactive proteins (hs-CRP) and an additional 92 inflammatory proteins using the Olink proteomics platform. RESULTS:Compared to healthy controls, 41 circulating inflammatory proteins were higher in type 1 diabetes (FDR < 0.05) and 64 inflammatory proteins in type 2 diabetes (FDR < 0.05) (including CXCL5, IL-15RA, MCP-4 and AXIN1 for both groups). HbA1c levels were positively associated with 21 inflammatory proteins (including CDCP1, FGF-21, HGF and IL-18R1) in type 1 diabetes (FDR < 0.05), whereas a positive association existed between body mass index (BMI) and 26 inflammatory proteins (including IL6, IL17C, FGF-23 and CSF-1) in type 2 diabetes. Inflammatory proteins associated with the presences, of complications, particularly nephropathy, were similar in both type 1 and type 2 diabetes. FlT3L and EN-RAGE were associated with the development of cardiovascular disease (CVD) in type 2 diabetes. CONCLUSIONS:Both type 1 diabetes and type 2 diabetes are associated with increased circulating inflammatory protein concentrations, but the increase is more pronounced in type 2 diabetes. These results suggest both differences in drivers of inflammation between type 1 diabetes and type 2 diabetes as well as potential similarities in pathways involved in the development of diabetes-associated complications.

Aim: Experimental hypoglycaemia blunts the counterregulatory hormone and symptom responses to a subsequent episode of hypoglycaemia. In this study, we aimed to assess the associations between antecedent exposure and continuous glucose monitoring (CGM)-recorded hypoglycaemia during a 1-week period and the counterregulatory responses to subsequent experimental hypoglycaemia in people with type 1 diabetes. Materials and Methods: Forty-two people with type 1 diabetes (20 females, mean +/- SD glycated haemoglobin 7.8% +/- 1.0%, diabetes duration median (interquartile range) 22.0 (10.5-34.9) years, 29 CGM users, and 19 with impaired awareness of hypoglycaemia) wore an open intermittently scanned CGM for 1 week to detect hypoglycaemic exposure before a standardized hyperinsulinaemic-hypoglycaemic [2.8 +/- 0.1 mmol/L (50.2 +/- 2.3 mg/dl)] glucose clamp. Symptom responses and counterregulatory hormones were measured during the clamp. The study is part of the HypoRESOLVE project. Results: CGM-recorded hypoglycaemia in the week before the clamp was negatively associated with adrenaline response [beta -0.09, 95% CI (-0.16, -0.02) nmol/L, p = .014], after adjusting for CGM use, awareness of hypoglycaemia, glycated haemoglobin and total daily insulin dose. This was driven by level 2 hypoglycaemia [<3.0 mmol/L (54 mg/dl)] [beta -0.21, 95% CI (-0.41, -0.01) nmol/L, p = .034]. CGM-recorded hypoglycaemia was negatively associated with total, autonomic, and neuroglycopenic symptom responses, but these associations were lost after adjusting for potential confounders. Conclusions: Recent exposure to CGM-detected hypoglycaemia was independently associated with an attenuated adrenaline response to experimental hypoglycaemia in people with type 1 diabetes.

Aim The sympathetic nervous and hormonal counterregulatory responses to hypoglycaemia differ between people with type 1 and type 2 diabetes and may change along the course of diabetes, but have not been directly compared. We aimed to compare counterregulatory hormone and symptom responses to hypoglycaemia between people with type 1 diabetes, insulin-treated type 2 diabetes and controls without diabetes, using a standardised hyperinsulinaemic-hypoglycaemic clamp. Materials We included 47 people with type 1 diabetes, 15 with insulin-treated type 2 diabetes, and 32 controls without diabetes. Controls were matched according to age and sex to the people with type 1 diabetes or with type 2 diabetes. All participants underwent a hyperinsulinaemic–euglycaemic-(5.2 ± 0.4 mmol/L)-hypoglycaemic-(2.8 ± 0.13 mmol/L)-clamp. Results The glucagon response was lower in people with type 1 diabetes (9.4 ± 0.8 pmol/L, 8.0 [7.0–10.0]) compared to type 2 diabetes (23.7 ± 3.7 pmol/L, 18.0 [12.0–28.0], p < 0.001) and controls (30.6 ± 4.7, 25.5 [17.8–35.8] pmol/L, p < 0.001). The adrenaline response was lower in type 1 diabetes (1.7 ± 0.2, 1.6 [1.3–5.2] nmol/L) compared to type 2 diabetes (3.4 ± 0.7, 2.6 [1.3–5.2] nmol/L, p = 0.001) and controls (2.7 ± 0.4, 2.8 [1.4–3.9] nmol/L, p = 0.012). Growth hormone was lower in people with type 2 diabetes than in type 1 diabetes, at baseline (3.4 ± 1.6 vs 7.7 ± 1.3 mU/L, p = 0.042) and during hypoglycaemia (24.7 ± 7.1 vs 62.4 ± 5.8 mU/L, p = 0.001). People with 1 diabetes had lower overall symptom responses than people with type 2 diabetes (45.3 ± 2.7 vs 58.7 ± 6.4, p = 0.018), driven by a lower neuroglycopenic score (27.4 ± 1.8 vs 36.7 ± 4.2, p = 0.012). Conclusion Acute counterregulatory hormone and symptom responses to experimental hypoglycaemia are lower in people with type 1 diabetes than in those with long-standing insulin-treated type 2 diabetes and controls.

CONTEXT:Low magnesium levels, which are common in people with type 2 diabetes, are associated with increased levels of proinflammatory molecules. It is unknown whether magnesium supplementation decreases this low-grade inflammation in people with type 2 diabetes. OBJECTIVE:We performed multidimensional immunophenotyping to better understand the effect of magnesium supplementation on the immune system of people with type 2 diabetes and low magnesium levels. METHODS:Using a randomized, double-blind, placebo-controlled, 2-period, crossover study, we compared the effect of magnesium supplementation (15 mmol/day) with placebo on the immunophenotype, including whole blood immune cell counts, T-cell and CD14+ monocyte function after ex vivo stimulation, and the circulating inflammatory proteome. RESULTS:We included 12 adults with insulin-treated type 2 diabetes (7 males, mean ± SD age 67 ± 7 years, body mass index 31 ± 5 kg/m2, HbA1c 7.5 ± 0.9%) and low magnesium levels (0.73 ± 0.05 mmol/L). Magnesium treatment significantly increased serum magnesium and urinary magnesium excretion compared with placebo. Interferon-γ production from phorbol myristate acetate/ionomycin stimulated CD8+ T-cells and T-helper 1 cells, as well as interleukin (IL) 4/IL5/IL13 production from T-helper 2 cells was lower after treatment with magnesium compared with placebo. Magnesium supplementation did not affect immune cell numbers, ex vivo monocyte function, and circulating inflammatory proteins, although we found a tendency for lower high sensitivity C-reactive protein levels after magnesium supplementation compared with placebo. CONCLUSION:In conclusion, magnesium supplementation modulates the function of CD4+ and CD8+ T-cells in people with type 2 diabetes and low serum magnesium levels.

Introduction Maturity-onset diabetes of the young (MODY) and neonatal diabetes mellitus (NDM) are the most prevalent causes of monogenic diabetes. MODY is an autosomal dominant condition with onset in childhood and young adulthood, while NDM is defined with diabetes onset within 6 months of age and can be caused by dominant, recessive, X-linked genes or by chromosomal abnormalities. Here, we describe a rare case of monogenic diabetes in a patient who is homozygous for an INS gene variant.Research design and methods The index patient, a male diagnosed with type 2 diabetes, was treated with low-dose insulin and metformin. Blood plasma was collected under fasting conditions for analysis. MODY screening was performed using a next-generation sequencing panel. In silico analysis of the insulin variant’s three-dimensional structure and its interaction with the insulin receptor was conducted. Insulin receptor affinity and downstream signaling potency were evaluated in vitro.Results Auto-immune diabetes was excluded. A homozygous missense variant of the INS gene (c.130G>A, p.Gly44Arg) was identified in the patient. The combination of three different insulin assays showed that the biosynthesis of proinsulin into insulin was intact. In silico analysis of the mutant insulin 3D structure revealed that the INS variant is likely to affect insulin receptor binding and subsequent in vitro analysis suggested reduced potency in downstream signaling.Conclusions The homozygous c.130G>A variant in the INS gene results in reduced insulin receptor binding and signaling potency. This, combined with pancreatic β-cell apoptosis or dedifferentiation supposedly, has contributed in the late-onset of monogenic diabetes in the index patient.

Aims/hypothesisThere is increasing evidence for heterogeneity in type 1 diabetes mellitus (T1D): not only the age of onset and disease progression rate differ, but also the risk of complications varies markedly. Consequently, the presence of different disease endotypes has been suggested. Impaired T and B cell responses have been established in newly diagnosed diabetes patients. We hypothesized that deciphering the immune cell profile in peripheral blood of adults with longstanding T1D may help to understand disease heterogeneity.MethodsAdult patients with longstanding T1D and healthy controls (HC) were recruited, and their blood immune cell profile was determined using multicolour flow cytometry followed by a machine-learning based elastic-net (EN) classification model. Hierarchical clustering was performed to identify patient-specific immune cell profiles. Results were compared to those obtained in matched healthy control subjects.ResultsHierarchical clustering analysis of flow cytometry data revealed three immune cell composition-based distinct subgroups of individuals: HCs, T1D-group-A and T1D-group-B. In general, T1D patients, as compared to healthy controls, showed a more active immune profile as demonstrated by a higher percentage and absolute number of neutrophils, monocytes, total B cells and activated CD4+CD25+ T cells, while the abundance of regulatory T cells (Treg) was reduced. Patients belonging to T1D-group-A, as compared to T1D-group-B, revealed a more proinflammatory phenotype characterized by a lower percentage of FOXP3+ Treg, higher proportions of CCR4 expressing CD4 and CD8 T cell subsets, monocyte subsets, a lower Treg/conventional Tcell (Tconv) ratio, an increased proinflammatory cytokine (TNFα, IFNγ) and a decreased anti-inflammatory (IL-10) producing potential. Clinically, patients in T1D-group-A had more frequent diabetes-related macrovascular complications.ConclusionsMachine-learning based classification of multiparameter flow cytometry data revealed two distinct immunological profiles in adults with longstanding type 1 diabetes; T1D-group-A and T1D-group-B. T1D-group-A is characterized by a stronger pro-inflammatory profile and is associated with a higher rate of diabetes-related (macro)vascular complications.

Background Hypoglycaemia has been shown to induce a systemic pro-inflammatory response, which may be driven, in part, by the adrenaline response. Prior exposure to hypoglycaemia attenuates counterregulatory hormone responses to subsequent hypoglycaemia, but whether this effect can be extrapolated to the pro-inflammatory response is unclear. Therefore, we investigated the effect of antecedent hypoglycaemia on inflammatory responses to subsequent hypoglycaemia in humans. Methods Healthy participants ( n = 32) were recruited and randomised to two 2-h episodes of either hypoglycaemia or normoglycaemia on day 1, followed by a hyperinsulinaemic hypoglycaemic (2.8 ± 0.1 mmol/L) glucose clamp on day 2. During normoglycaemia and hypoglycaemia, and after 24 h, 72 h and 1 week, blood was drawn to determine circulating immune cell composition, phenotype and function, and 93 circulating inflammatory proteins including hs-CRP. Results In the group undergoing antecedent hypoglycaemia, the adrenaline response to next-day hypoglycaemia was lower compared to the control group (1.45 ± 1.24 vs 2.68 ± 1.41 nmol/l). In both groups, day 2 hypoglycaemia increased absolute numbers of circulating immune cells, of which lymphocytes and monocytes remained elevated for the whole week. Also, the proportion of pro-inflammatory CD16 + -monocytes increased during hypoglycaemia. After ex vivo stimulation, monocytes released more TNF-α and IL-1β, and less IL-10 in response to hypoglycaemia, whereas levels of 19 circulating inflammatory proteins, including hs-CRP, increased for up to 1 week after the hypoglycaemic event. Most of the inflammatory responses were similar in the two groups, except the persistent pro-inflammatory protein changes were partly blunted in the group exposed to antecedent hypoglycaemia. We did not find a correlation between the adrenaline response and the inflammatory responses during hypoglycaemia. Conclusion Hypoglycaemia induces an acute and persistent pro-inflammatory response at multiple levels that occurs largely, but not completely, independent of prior exposure to hypoglycaemia. Clinical Trial information Clinicaltrials.gov no. NCT03976271 (registered 5 June 2019).

AIM:To determine whether recent repeated exposure to real-life hypoglycaemia affects the pro-inflammatory response during a hypoglycemia episode. MATERIALS AND METHODS:This was a post hoc analysis of a hyperinsulinaemic normoglycaemic-hypoglycaemic clamp study, involving 40 participants with type 1 diabetes. Glucose levels 1 week before the clamp were monitored using a Freestyle Libre 1. Blood was drawn during normoglycaemia and hypoglycaemia, and 24 hours after resolution of hypoglycaemia for measurements of inflammatory responses and counterregulatory hormone levels. We determined the relationship between the frequency and duration of spontaneous hypoglycaemia, and time below range (TBR) and the inflammatory response to experimental hypoglycaemia. RESULTS:On average, participants experienced 0.79 (0.43, 1.14) hypoglycaemia episodes per day, with a duration of 78 (47, 110) minutes and TBR of 5.5% (2.8%, 8.5%). TBR and hypoglycaemia frequency were inversely associated with the increase in circulating granulocyte and lymphocyte counts during experimental hypoglycaemia (P < .05 for all). A protein network consisting of DNER, IF-R, uPA, Flt3L, FGF-5 and TWEAK was negatively associated with hypoglycaemia frequency (P < .05), but not with the adrenaline response. Neither other counterregulatory hormones, nor hypoglycaemia awareness status, was associated with any of the inflammatory parameters markers. CONCLUSIONS:Repeated exposure to spontaneous hypoglycaemia is associated with blunted effects of subsequent experimental hypoglycaemia on circulating immune cells and the number of inflammatory proteins.

Because structured physical activities, also called sports activities, may cause substantial glycaemic disturbances in people with type 1 diabetes, specific management strategies are advised and used.1-4 Little is known about glycaemic responses to daily unstructured physical activities, such as gardening, household maintenance and snow shovelling.5 The daily amount of unstructured physical activity is highly variable within individuals and not reliably assessed by questionnaires. Moreover, most studies focus on moderate-to-vigorous-intensity physical activities (MVPA).6-8 However, most daily-life (unstructured) physical activities are light-intensity physical activities (LIPA), but few studies have evaluated LIPA in relation to glucose homeostasis. This study examined the association between daily unstructured physical activities and glucose control in adults with type 1 diabetes under free-living conditions, objectively measured by state-of-the-art continuous measuring devices. This was a post hoc analysis of the ADREM trial (clinicaltrialsregister.eu, EudraCT Number: 2019-004222-22) of insulin degludec dosing after structured exercise in 18 adults with type 1 diabetes (12 men, mean ± standard deviation [SD] age 38 ± 13 years, body mass index 25.0 ± 2.7 kg/m2, glycated haemoglobin 56 ± 8 mmol/mol [7.3 ± 0.8%], duration of diabetes 12 ± 11 years, maximum rate of oxygen consumption 40.2 ± 9.6 mL•kg−1•min−1).9 All participants wore a blinded continuous glucose monitoring device (Dexcom G6; Dexcom Inc., San Diego, California) and accelerometer (activPAL3 micro; PAL Technologies Ltd, Glasgow, UK) for 24 hours daily.10 For the current post hoc analysis, we excluded the experimental days with structured exercise, so that we included three periods of 6 days per participant. The outcome variables are defined in the Supplemental Methods 1. Participants were requested to refrain from strenuous exercise during the wearing periods and recorded sleep and wake times in a diary. We determined the association between daytime unstructured physical activity and glucose parameters during: (i) the awake period of the same day; (ii) the subsequent night (sleep period); and (iii) the next-day awake period, using mixed-regression models (Supplemental Figure S1). A linear mixed model was used for analysing continuous variables and a logistic random-effects model was used for binary outcomes. We constructed two models: Model 1, in which activity parameters were used as predictors for the glucometrics outcome (Supplemental Tables S1–S4), and Model 2, in which the associations were adjusted for demographics. We performed an additional analysis, whereby we divided total-day physical activity into early-day (ie, from wake time to 3:00 pm [awakeWT-15]) and late-day (ie, from 3:00 pm to sleep time [awake15-ST]) and we correlated these parameters with nocturnal glucose levels. Further details on the statistical analyses can be found in the Supplemental Methods 2. All data are expressed as mean ± SEM, coefficient (B) with 95% confidence interval (CI), or odds ratio [OR] and 95% CI, unless otherwise specified. Data were analysed using R version 4.1.2. P values <0.05 were taken to indicate statistical significance. All participants used multiple-dose injection as their diabetes treatment with insulin degludec as their basal insulin. Physical activity and glucose characteristics during the study period are shown in Table 1. In total, 280 measurement days were included in the analyses. More active time, higher step count, and more MVPA were associated with a lower mean glucose concentration in the same-day awake period (B −0.35 [95% CI −0.56, −0.14], p = 0.001; B −0.41 [95% CI −0.63, −0.18], p < 0.001; B −0.33 [95% CI −0.56, −0.09], p = 0.006 [Figure 1A–C, Supplemental Table S5]). More active time and more LIPA were associated with a lower mean glucose concentration during the subsequent night (B −0.45 [95% CI −0.78, −0.12], p = 0.008; B −0.49 [95% CI −0.85, −0.12], p = 0.009 [Figure 1D,E]). More early-day active time (active timeWT-15) and more LIPAWT-15 were associated with a lower mean glucose concentration during the subsequent night (B −0.60 [95% CI −0.96, −0.24], p = 0.001; B −0.81 [95% CI −1.20, −0.41], p < 0.001), whereas late-day active time (active time15-ST) and LIPA15-ST were not. No association was found between physical activity parameters and next-day mean glucose concentration. More active time and higher step count were associated with higher risks of hypoglycaemia in the same-day awake period (OR 1.56 [95% CI 1.11, 2.21], p = 0.011; OR 1.55 [95% CI 1.09, 2.21], p = 0.014 [Supplemental Figure S2A and Supplemental Table S6]). Also, more active time, higher step count, more MVPA and more LIPA were all associated with higher risks of nocturnal hypoglycaemia (OR 2.40 [95% CI 1.46, 3.96], p = 0.001; OR 2.05 [95% CI 1.30, 3.21], p = 0.002; OR 1.71 [95% CI 1.07, 2.74], p = 0.024; OR 1.88 [95% CI 1.15, 3.07], p = 0.012 [Supplemental Figure S2B]). More active timeWT-15 and active time15-ST, higher step countWT-15, more MVPAWT-15, and more LIPA15-ST were associated with higher risks of nocturnal hypoglycaemia (OR 2.08 [95% CI 1.30, 3.35], p = 0.002; OR 1.79 [95% CI 1.14, 2.83], p = 0.012; OR 1.86 [95% CI 1.22, 2.85], p = 0.004; OR 1.85 [95% CI 1.15, 2.99], p = 0.012; OR 1.80 [95% CI 1.13, 2.87], p = 0.014). Both active time and LIPA were positively associated with the occurrence of next-day hypoglycaemia (OR 1.59 [95% CI 1.12, 2.24], p = 0.009; OR 1.46 [95% CI 1.00, 2.11], p = 0.048 [Supplemental Figure S2C]). More active time was associated with a higher coefficient of variation in the same-day and next-day awake periods (B 1.14 [95% CI 0.02, 2.26], p = 0.045; B 1.22 [95% CI 0.09, 2.35], p = 0.034), but not with overnight coefficient of variation (Supplemental Table S7). No correlations were found between physical activity parameters and SDs of the same day, subsequent night or next day (Supplemental Table S8). We found that daily, unstructured, physical activity was associated with a lower mean glucose concentration during the same day and subsequent night, but also with greater glucose variability and higher risk of hypoglycaemia that persists until the next day. The association between unstructured physical activity versus mean glucose concentration and risk of hypoglycaemia during the subsequent night was largely independent of the time of the day at which physical activity was performed (early-day vs. late-day). Together, these results may indicate that, in addition to the widely known association between structured MVPA and glucose control,2, 11 unstructured physical activities, even those performed at low intensity, also affect glucose control. We found that unstructured physical activity explains up to 19% of the variation in hypoglycaemic risk, which underscores the clinical relevance of our study. Our study particularly highlights the importance of LIPA with regard to a range of glucose parameters, including (nocturnal) hypoglycaemia. Indeed, a 113-minute increase in LIPA translated into a 0.5 mmol/L drop in mean nocturnal glucose, but at the cost of an almost twofold increased risk of hypoglycaemia (Supplemental Table S9). These associations were less prominent for MVPA, which may be explained by better awareness of the association between MVPA and blood glucose concentration. Strengths of our study are the data collection under free-living conditions and the use of a continuous glucose monitor combined with a 24-hour-measuring, accurate, accelerometer specifically designed to evaluate LIPA.12 Limitations include the relatively small sample size, although this is balanced by the high number of measurement days. Also, generalization to people who are less trained or who are using automated insulin delivery systems should be performed with caution. Lastly, participation in the ADREM study9 could have made participants (more) aware of the impact of (unstructured) physical activity on glucose parameters. This may have led to underestimation of the actual association between physical activity and glucose parameters. In conclusion, unstructured physical activity, even at low intensity and performed in the morning and early afternoon, is associated with lower mean glucose concentrations during the same day and subsequent night, and with increased risks for subsequent (nocturnal) hypoglycaemia in people with type 1 diabetes. These observations suggest that daily unstructured physical activities may help to improve glucose control, provided that additional measures are considered to minimize the risk of hypoglycaemia. Linda C. A. Drenthen, Cees J. Tack, and Bastiaan E. de Galan designed the study. Linda C. A. Drenthen recruited the participants and collected the data. Esmée A. Bakker modified the activity tracker script and Linda C. A. Drenthen applied this to the patient data. Mandala Ajie analysed the data. Linda C. A. Drenthen and Mandala Ajie wrote the first version of the manuscript. All authors discussed the results and implications, commented on the manuscript at all stages and approved the final version of the manuscript. The guarantor (Bastiaan E. de Galan) accepts full responsibility for the work and conduct of the study, has access to the data, and controlled the decision to publish. The authors thank all volunteers for their participation. The authors also express their gratitude to S. Teerenstra and S. Maurits from the Department for Health Evidence, Section Biostatistics, Radboudumc, Nijmegen, the Netherlands, for their statistical advice without receiving financial support. The ADREM study (ref 9) was conducted with an unrestricted grant from Novo Nordisk, Denmark (not involved in the collection, analyses, and interpretation of data, nor in writing the report and the decision to submit for publication). They were not involved in this current post hoc analysis. Trial registry number: EudraCT number 2019-004222-22. The authors have no conflicts to disclose that are relevant to this manuscript. The peer review history for this article is available at https://www.webofscience.com/api/gateway/wos/peer-review/10.1111/dom.15277. The datasets generated during and/or analysed in the current study are available from the corresponding author upon reasonable request. Data S1: Supporting information. Please note: The publisher is not responsible for the content or functionality of any supporting information supplied by the authors. Any queries (other than missing content) should be directed to the corresponding author for the article.