Experimental studies suggested potential adverse effects of preservative food additives, but epidemiological data are lacking. We aim to investigate associations between exposure to these compounds and type 2 diabetes incidence in the NutriNet-Santé prospective cohort (n = 108,723; 79.2

BACKGROUND:Despite recent progress in insulin delivery, carbohydrate counting is essential for determining prandial insulin doses and is, therefore, a key component of education for patients living with type 1 diabetes (PwT1D). This study aimed to evaluate the relationship between carbohydrate knowledge and glycemic control. METHODS:Carbohydrate knowledge was assessed using GluciQuizz, a validated, self-administered questionnaire totaling 36 points submitted to participants in the SFDT1 cohort. Glycemic control was determined using a 14-day time in range (TIR, 70-180 mg/dl) RESULTS: The median age of the 635 participants was 43 [interquartile range 32-54] years, T1D duration of 25 [14-36] years. The median overall GluciQuizz score was 25 [23-28]: 24 [21-27] among the 158 participants treated with multiple daily injections (MDI), 25 [22-27] among the 255 users of continuous subcutaneous insulin infusion (CSII), and 26 [24-29] among the 222 users of automated insulin delivery (AID) systems. Median TIR (%) was: overall 68 [55-77]; MDI 60 [50-72]; CSII 62 [49-73]; AID 75 [69-82]. The total GluciQuizz score was positively associated with TIR in the overall population (β ± SE = 0.04 ± 0.01; P < 0.001). This association remained significant in CSII users (β ± SE = 0.06 ± 0.02; P < 0.001) but not in those treated with MDI (P = 0.19) or AID (P = 0.55). CONCLUSIONS:Better carbohydrate knowledge is independently associated with improved glycemic control in PwT1D using CSII but not among those using MDI or AID systems. These findings highlight the continued relevance of structured nutritional education, particularly for individuals using CSII.

Background: Effective glycemic control in diabetes management relies heavily on dietary carbohydrate knowledge. This study aimed to assess carbohydrate knowledge in individuals with type 1 diabetes (T1D) and insulin-treated type 2 diabetes (itT2D) using the GluciQuizz tool. Methods: A total of 465 persons (96 with T1D, 153 with itT2D; 89 and 127 matched controls without diabetes, respectively) from the French NutriNet-Santé prospective cohort were included. Participants completed the GluciQuizz questionnaire, which evaluates carbohydrate knowledge across five domains: carbohydrate food recognition; carbohydrate food content; nutrition label reading; glycemic targets and hypoglycemia prevention and treatment; and carbohydrate content of meals. Results: The mean age ± standard deviation of participants with diabetes was 65.8 ± 11.2 years, 44.2% male, with a diabetes duration of 23.3 ± 12.9 years. T1D participants scored significantly higher on the GluciQuizz compared to those with itT2D (23.9 ± 5.0 vs. 17.5 ± 5.6, p < 0.001). In secondary analysis, T1D participants showed superior knowledge to their matched controls without diabetes, whereas itT2D participants showed similar knowledge to their matched controls without diabetes. Conclusions: T1D participants demonstrated the best carbohydrate knowledge compared to those with itT2D. Targeted educational interventions in itT2D populations may improve dietary management and clinical outcomes.

Objective To address whether eating disorders (ED) or insulin omission (IOM) in adult persons living with type 1 diabetes (pwT1D) are associated with impaired glycaemic control.Design Cross-sectional analysis.Settings The French-Speaking Diabetes Society—Type 1 Diabetes Cohort (SFDT1) is an ongoing epidemiological cohort study that includes pwT1D in France who attend hospitals or private ambulatory diabetes centres.Participants Adult participants from the SFDT1 study, with data on ED and IOM. The current analysis was performed on data collected during the baseline visit in participants enrolled between December 2020 and March 2024.Main outcome measures Using the SCOFF, a self-reported questionnaire to screen for ED, and a single question on IOM to screen for IOM, we described four categories of pwT1D: no ED & no IOM, ED & no IOM, no ED & IOM and ED & IOM. We performed unadjusted and adjusted (for age, sex, diabetes duration, social vulnerability, smoking, alcohol status and insulin treatment) multinomial logistic regression models with the four categories as the outcome and glycaemic variables as explanatory variables, including continuous glucose monitoring (CGM) variables and HbA1c. No ED & no IOM was the reference outcome for all comparisons. We stratified each model by sex and fear of hypoglycaemia.Results We included 1113 participants, 51% males, median (IQR) age 38 (29–50) years, diabetes duration 21 (12–32) years. Prevalences were as follows: no ED & no IOM: 68% (n=758), ED & no IOM: 11% (n=124), no ED & IOM: 16% (n=177) and ED & IOM: 5% (n=54). With the fully adjusted model, and compared with the group no ED & no IOM, time in range (OR (95% CI) 0.5 (0.4 to 0.7)) and time below range (0.5 (0.3 to 0.8)) were inversely associated with ED & IOM. Moreover, time in range (0.4 (0.4 to 0.5)) was associated with IOM & no ED. Time above range (2.2 (1.6 to 2.9)), Glycaemic Risk Index (1.8 (1.3 to 2.5)), glucose monitoring indicator (2.2 (1.7 to 2.9)) and HbA1c (2.0 (1.5 to 2.5)) were directly associated with ED & IOM. We did not observe associations between CGM variables and ED & no IOM. Most associations were valid in both men and women. The associations were stronger in participants with a fear of hypoglycaemia. However, the associations remained even in people with a fear of hypoglycaemia.Conclusions Both ED and IOM are frequent in pwT1D, and IOM seems to be associated with impaired glycaemic control. As our analysis was cross-sectional, we cannot infer causality and cannot know whether IOM was a result of glycaemic control or the inverse (reverse causality). Our results suggest that IOM should be systematically screened in clinical practice. Further research is needed to better identify and care for EDs, with or without IOM, in T1D.Trial registration number NCT04657783.

Objective To investigate potential association between exposure to food colouring additives and type 2 diabetes incidence. Research Design and Methods 108,723 participants (79.2% female, mean age= 42.5 y, SD= 14.6) from the French NutriNet-Santé cohort were followed (2009-2023). Dietary data were assessed using repeated 24h-dietary records, including industrial food brands. Cumulative time-dependent exposure to food additives was evaluated through multiple composition databases and ad-hoc laboratory assays in food matrices. Associations between exposures to food colouring additives (sex-specific tertiles if proportion of exposed participants >2/3, or non-exposed/lower/higher exposed based on sex-specific median otherwise) and type 2 diabetes incidence were assessed using multivariable Cox proportional hazards models. Results 1,131 incident type 2 diabetes cases were diagnosed (median follow-up=8.05 y). After False Discovery Rate correction, intakes of following colours were associated with higher type 2 diabetes incidence: total food colouring additives (hazard ratio [HR]higher versus non/lower consumers (95% CI)= 1.38 [1.17-1.63], p=0.0002), total caramel (1.43 [1.21-1.67], p=0.0002), plain caramel (1.46 [1.26-1.70], p=0.0002), sulphite ammonia caramel (1.30 [1.07-1.59], p= 0.007), total carotene (1.27 [1.08-1.48], p=0.007), carotenoids (1.39 [1.19-1.62], p=0.0002), beta-carotene (1.44 [1.23-1.68], p=0.0002), paprika, capsanthin, and capsorubin (1.26 [1.08-1.46], p=0.004), lutein (1.20 [1.02-1.40], p=0.0002), curcumin (1.49 [1.29-1.73], p=0.0002), cochineal, carminic acid, and carmines (1.27 [1.10-1.48], p=0.003), and anthocyanins (1.40 [1.17-1.68], p=0.0002). Conclusion Several positive associations were observed between exposure to natural synthetic food colouring additives and type 2 diabetes incidence. Further studies are needed to gain insights into underlying mechanisms, and if confirmed, call for re-evaluation of food colouring additives to protect consumer health.

Epicardial adipose tissue (EAT), a visceral adipose tissue located between the pericardium and the myocardium, has been linked to cardiac issues. The present study aimed to evaluate the association between EAT volume and the presence of nephropathy and its components, i.e. albuminuria/creatininuria ratio (ACR) and estimated glomerular filtration rate (eGFR) classes, in patients living with type 2 diabetes (PwT2D). The present study is a cross-sectional analysis of a retrospective cohort of 700 PwT2D who had a computed tomography scan to measure both their coronary artery calcium score and EAT volume (proprietary prototype, GE HealthCare), and available data for both eGFR and ACR. A total of 700 PwT2D (332 women) were included, of whom 295 patients had nephropathy (42.1

Epicardial adipose tissue (EAT) and arterial stiffness are determinants of excess risk of cardiovascular disease in persons with diabetes. This study aimed to evaluate the relationship between both of these conditions in a cohort of patients with diabetes. A part retrospective, part prospective non-interventional cohort study of people living with diabetes who had (i) a computed tomography scan to measure both their coronary artery calcium score and EAT volume (proprietary prototype, GE HealthCare), and (ii) a finger-to-toe pulse wave velocity (PWV) measurement to assess arterial stiffness. The study’s ClinicalTrials.gov identifier is NCT05681533. A total of 345 participants (198 men, mean age (± standard deviation (SD)) 55.6 ± 12.6 years) were included; 73.6

As in the general population, people living with type 1 diabetes (PWT1D) are faced with overweight and obesity, which contribute to cardiovascular (CV) risk. However, the role of visceral adiposity, due to its adverse metabolic profile, should also be addressed in PWT1D. We aimed to assess the 10-year CV risk of PWT1D according to body mass index (BMI) and waist-to-height ratio (WHtR), a parameter for estimating visceral adiposity. In this cross-sectional study, PWT1D in primary CV prevention from the SFDT1 cohort were categorized by BMI status, either normal (18.5–24.9 kg/m2) or overweight/obesity (≥ 25 kg/m2), and by WHtR according to the validated threshold of 0.5. The 10-year CV risk was estimated using the Steno Type 1 Risk Engine and classified into three categories: low (< 10

Introduction:Smoking and hyperglycemia first diagnosed during pregnancy (H1inP) have opposing effects on fetal growth. The aim of this study was to explore adverse pregnancy outcomes, particularly fetal growth, according to the smoking and H1inP status. Methods:We included 13,958 women from a large French dataset (2012-2018). Using multivariable regression analyses, we retrospectively evaluated the risk of large-for-gestational-age (LGA) babies and other adverse outcomes according to the H1inP and smoking status in four groups: no H1inP/non-smoker (group A: n = 10,454, 88.2%), no H1inP/smoker (group B: n = 819, 5.9%), H1inP/non-smoker (group C: n = 2,570, 18.4%), and H1inP/smoker (group D: n = 115, 0.8%). Results:The rates of LGA were 8.9%, 4.0%, 14.6%, and 8.7% in groups A, B, C, and D, respectively (global ANOVA p < 0.0001, factor H1inP p = 0.0003, factor smoking p = 0.0002, and interaction p = 0.48). After adjustment for potential confounders including age, body mass index, employment, ethnicity, parity, hypertension before pregnancy, gestational weight gain, and alcohol and drug consumption, H1inP was associated with a higher risk [odds ratio (OR) = 1.50, 95% confidence interval (95%CI) = 1.30-1.74] and smoking with a lower risk (OR = 0.35, 95%CI = 0.25-0.50) of LGA. In addition, H1inP was associated with a lower total gestational weight gain and a lower rate of small-for-gestational-age (SGA) babies, but higher rates of hypertensive disorders and more frequent caesarean sections and admissions in the neonatal intensive care unit. Smoking was associated with higher rates of SGA, including severe SGA (<3rd centile), and this despite a higher total gestational weight gain. Smoking increased the risk of hypertensive disorders only in women with H1inP. Discussion:Smoking among women with H1inP could mask the risk of maternal hyperglycemia for LGA babies. This could provide a false sense of security for women with H1inP who smoke, particularly when assessing for LGA alone, but these women still face other risks to their health, such as hypertensive disorders and the health of the fetus.

BACKGROUND:Mixtures of food additives are daily consumed worldwide by billions of people. So far, safety assessments have been performed substance by substance due to lack of data on the effect of multiexposure to combinations of additives. Our objective was to identify most common food additive mixtures, and investigate their associations with type 2 diabetes incidence in a large prospective cohort. METHODS AND FINDINGS:Participants (n = 108,643, mean follow-up =  7.7 years (standard deviation (SD) =  4.6), age =  42.5 years (SD =  14.6), 79.2% women) were adults from the French NutriNet-Santé cohort (2009-2023). Dietary intakes were assessed using repeated 24h-dietary records, including industrial food brands. Exposure to food additives was evaluated through multiple food composition databases and laboratory assays. Mixtures were identified through nonnegative matrix factorization (NMF), and associations with type 2 diabetes incidence were assessed using Cox models adjusted for potential socio-demographic, anthropometric, lifestyle and dietary confounders. A total of 1,131 participants were diagnosed with type 2 diabetes. Two out of the five identified food additive mixtures were associated with higher type 2 diabetes incidence: the first mixture included modified starches, pectin, guar gum, carrageenan, polyphosphates, potassium sorbates, curcumin, and xanthan gum (hazard ratio (HR)per an increment of 1SD of the NMF mixture score = 1.08 [1.02, 1.15], p = 0.006), and the other mixture included citric acid, sodium citrates, phosphoric acid, sulphite ammonia caramel, acesulfame-K, aspartame, sucralose, arabic gum, malic acid, carnauba wax, paprika extract, anthocyanins, guar gum, and pectin (HR = 1.13 [1.08,1.18], p < 0.001). No association was detected for the three remaining mixtures: HR =  0.98 [0.91, 1.06], p = 0.67; HR =  1.02 [0.94, 1.10], p = 0.68; and HR =  0.99 [0.92, 1.07], p = 0.78. Several synergistic and antagonist interactions between food additives were detected in exploratory analyses. Residual confounding as well as exposure or outcome misclassifications cannot be entirely ruled out and causality cannot be established based on this single observational study. CONCLUSIONS:This study revealed positive associations between exposure to two widely consumed food additive mixtures and higher type 2 diabetes incidence. Further experimental research is needed to depict underlying mechanisms, including potential synergistic/antagonist effects. These findings suggest that a combination of food additives may be of interest to consider in safety assessments, and they support public health recommendations to limit nonessential additives. TRIAL REGISTRATION:The NutriNet-Santé cohort is registered at clinicaltrials.gov (NCT03335644). https://clinicaltrials.gov/study/NCT03335644.

BACKGROUND:Most studies on bariatric patients to date have only examined mortality and morbidities in terms of surgery or no surgery. Few have investigated loss to follow-up in post-surgery patients. PURPOSE:This study aimed to describe the dynamics behind non-adherence to follow-up in bariatric patients postsurgery. DESIGN:Using semi-structured interviews, we performed a qualitative study. Using a thematic analysis, we described themes involved in patient adherence to postsurgery follow-up. SETTING:Participants were recruited from a university hospital near Paris and via social networks. PARTICIPANTS:17 patients who had undergone surgery, some of whom were lost to follow-up, 15 women and 2 men, were interviewed, during a mean time of 90 min. 10 were adherent, and 7 were lost to follow-up. RESULTS:Follow-up was seen as a support in which the care provider-patient relationship can act on the four following themes: (1) regaining control, (2) knowledge acquisition, (3) management of fears and (4) overall restructuring of one's life postsurgery. CONCLUSIONS:Patients' experiences and representations of postsurgery follow-up should be documented in detail in order to define the specific roles of the various care providers offering support to this population, and to strengthen the coordination of care pathways between these actors. In addition, improving the quality of communication could improve adherence to follow-up after bariatric surgery.

Prior studies have shown that plant-based diets are associated with lower cardiovascular risk. However, these diets encompass a large diversity of foods with contrasted nutritional quality that may differentially impact health. We aimed to investigate the pooled cross-sectional association between metabolic syndrome (MetS), its components and healthy and unhealthy plant-based diet indices (hPDI and uPDI), using data from two French cohorts and one representative study from the French population. This study included 16 358 participants from the NutriNet-Santé study, 1769 participants from the Esteban study and 1565 participants from the STANISLAS study who underwent a clinical visit. The MetS was defined according to the International Diabetes Federation definition. The associations between these plant-based diet indices and MetS were estimated by multivariable Poisson and logistic regression models, stratified by gender. Meta-analysis enabled the computation of a pooled prevalence ratio. A higher contribution of healthy plant foods (higher hPDI) was associated with a lower probability of having MetS (PRmen: 0·85; 95 % CI: 0·75, 0·94, PRwomen: 0·72; 95 % CI: 0·67, 0·77), elevated waist circumferences and elevated blood pressure. In women, a higher hPDI was associated with a lower probability of having elevated triacylglyceride (TAG), low HDL-cholesterolaemia and hyperglycaemia; and a higher contribution of unhealthy plant foods was associated with a higher prevalence of MetS (PRwomen: 1·13; 95 % CI: 1·01, 1·26) and elevated TAG. A greater contribution of healthy plant floods was associated with protective effects on metabolic syndrome, especially in women. Gender differences should be further investigated in relation to the current sustainable nutrition transition.

In France, 0.2% of women who gave birth in 2021 had type 1 diabetes, and 0.3% had type 2 diabetes. Regarding preconception care, it is recommended that women with any type of diabetes achieve an HbA1c level of less than 6.5%. For women using continuous glucose monitoring (CGM), the recommended target range is 0.70-1.80g/L (3.9-10mmol/L), and it is recommended to achieve this range at least 70% of the time. The preconception assessment includes: 1) an HbA1c measurement, 2) an assessment of microangiopathic impact, 3) an assessment of macroangiopathic complications, 4) screening for associated cardiovascular risk factors, and 5) a TSH measurement in women with type 1 diabetes (T1D), as well as screening for obstructive sleep apnea syndrome during questioning in cases of type 2 diabetes (T2D) and obesity in women with T1D. To improve preconception glycemic control, implementation of a CGM system is recommended for all women with T1D. Implementation of automated insulin delivery (AID) in anticipation of pregnancy should also be discussed as part of a shared medical decision. For type 2 diabetes, treatment with metformin and/or insulin therapy is recommended if necessary. Other antidiabetic treatments should be discontinued before conception. The following is recommended: 1) Discontinuing statin and potentially teratogenic antihypertensive treatments, replacing them with treatments compatible with pregnancy; 2) systematically providing smoking cessation advice to women who smoke, offering support from a healthcare professional trained in tobacco addiction; and 3) starting folic acid supplementation at 0.4mg per day before conception. Finally, women of childbearing age should be regularly advised of the importance of planning their pregnancies during follow-up visits. They should also be provided with dietary care to improve glycemic control, and, in some cases, encouraged to lose weight prior to pregnancy. Women should be encouraged to engage in physical activity to improve glycemic control. Regarding care during pregnancy, the following metabolic targets are recommended: Fasting blood glucose should be less than 0.95g/dL (less than 5.3mmol/L), and postprandial blood glucose should be less than 1.20g/dL (less than 6.7mmol/L) two hours after eating. Time spent in the target range (0.63-1.40g/dL [3.5-7.8mmol/L]) should be greater than 70% for type 1 diabetes (T1D) and greater than 90% for type 2 diabetes (T2D). The HbA1c level should be less than 6% during pregnancy, and hypoglycemia should be limited as much as possible. An CGM is recommended for T1D during pregnancy. For women with T2D, an CGM is recommended or they should maintain multiple daily capillary self-monitoring of blood glucose as part of individualized management. For women with type 1 diabetes, treatment with an insulin pump infusion device (IUD) is recommended during pregnancy. For type 2 diabetes, insulin therapy is recommended. The addition or continuation of metformin should be discussed on a case-by-case basis, depending on the diabetes phenotype and glycemic control. Regular monitoring by a diabetes specialist and monthly monitoring by an obstetrician-gynecologist, in collaboration with a maternity ward, are recommended from the first trimester. Depending on the patient's history, treatment, pregnancy progress, and glycemic control, monitoring may be intensified in the third trimester. Regarding ultrasound monitoring, an ultrasound should be performed between 36 and 37 weeks of gestation to assess fetal growth, guide the mode of delivery, and determine gestational age at birth. Regarding fetal heart rate monitoring, there is insufficient data to recommend its use in predicting fetal death. Similarly, there is insufficient data to recommend routine aspirin prescriptions during pregnancy to prevent maternal or perinatal morbidity. Prenatal treatment with corticosteroids is recommended according to the same indications as for non-diabetic women. This treatment involves close monitoring of maternal blood glucose control during hospitalization and an increase in the usual dose of insulin during the days following corticosteroid administration. Regarding acute diabetes complications, women who do not perceive their hypoglycemia should be identified to adapt monitoring and alert women with type 1 diabetes mellitus (T1DM) to the increased risk of hypoglycemia during the first trimester of pregnancy. Regarding diabetic ketoacidosis, capillary ketonemia should be measured when clinical signs of ketoacidosis are present (e.g., nausea, vomiting, and abdominal pain) and systematically when blood glucose levels are greater than or equal to 2g/dL (11mmol/L). Women should be screened for diabetic retinopathy (DR) through quarterly ophthalmological monitoring during pregnancy, which may increase to monthly monitoring if risk factors are present. An initial assessment of kidney function is recommended for screening for diabetic nephropathy before pregnancy or during the first trimester. If diabetic nephropathy is diagnosed, then monthly monitoring is recommended. In cases of high blood pressure, the target blood pressure should be below 140/90mmHg. In the context of pre-existing diabetes, a cesarean section is recommended for delivery if fetal weight is suspected to be greater than 4,500g to reduce the risk of brachial plexus palsy and other associated neonatal injuries. Due to the risk of fetal mortality, delivery should be considered between 37 and 38+6 weeks of gestation. The gestational age at birth depends on the presence of comorbidities, blood glucose levels, and estimated fetal weight (macrosomia or intrauterine growth restriction). The obstetrician-gynecologist, anesthesiologist, and pediatrician should be present in the maternity ward during delivery. Recommended blood glucose targets during labor and delivery are 0.8g/L to 1.4g/L (4.4mmol/L to 7.8mmol/L). Monitoring can be performed using capillary blood glucose measurements or continuous glucose monitoring (CGM). Rapid-acting insulin is the preferred treatment for managing labor. According to an advance protocol agreed upon with the diabetes specialist, insulin can be administered via an insulin pump, IUD, continuous intravenous infusion, or multiple injections. In the event of glycemic imbalance, continuous intravenous insulin therapy should be used as rescue therapy. For postpartum management of T1D, a reduction in insulin doses is recommended in the immediate postpartum period. For women with T2D, oral or injectable antidiabetic drugs should be reintroduced. If the woman is breastfeeding, metformin is the only acceptable oral antidiabetic drug. Women should be informed during pregnancy about the benefits of breastfeeding, and breastfeeding should be actively supported if desired. Breastfeeding should be encouraged in the delivery room. If the woman wishes to use contraception in the immediate postpartum period, it is recommended that she be prescribed either long-acting reversible contraception (such as a copper or hormonal intrauterine device or a subcutaneous implant) or a microgestin-only pill. An initial consultation with a diabetes specialist should take place within six months after birth. Regarding neonatal care, active measures should be taken to prevent hypoglycemia, including: 1) thermoregulation (e.g., early skin-to-skin contact and rapid drying of the newborn after birth), 2) feeding within one hour of birth, and 3) encouraging breastfeeding if desired. Blood glucose monitoring should begin before the infant's second feeding and no later than 4hours after birth, or earlier if the infant exhibits symptoms such as tremors, hypothermia, or irritability. Monitoring should continue before each feeding every three hours for at least 24hours. Each team caring for these newborns should have a protocol for preventing, monitoring, and treating hypoglycemia. The child's medical record should indicate that the pregnancy occurred in the context of preexisting diabetes and specify the type of diabetes.

Given the increasing prevalence of type 2 diabetes (T2D) worldwide, it is important to better understand its risk and protective factors in order to guide future prevention and disease management policies. Observational studies on a large scale provide valuable information in this regard. The NutriNet-Sante study is a French prospective online cohort, launched in 2009. Since then, 12 original articles focused on T2D have been published. Of those studies, 11 used a prospective design (mean follow-up: 4 to 9 years), analyzing risk or protective factors for T2D related to dietary intake, with samples ranging from 33,256 to 107,377 participants. Consumption of ultra-processed foods, exposure to certain pesticide mixtures or the late timing of the first food intake were associated with a higher risk of T2D. On the other hand, greater adherence to French dietary guidelines and higher consumption of organic food were associated with a lower risk of T2D. Other studies in relation to T2D are currently underway in the NutriNet-Sante cohort, focusing on adherence to sustainable diets, exposure to certain additives and contaminants linked to food processing, and mental health disorders. (c) 2024 The Author(s). Published by Elsevier Masson SAS on behalf of Societe franc,aise de nutrition. This is an open access article under the CC BY license (http://creativecommons. org/licenses/by/4.0/).

INTRODUCTION:We explored the association between epicardial adipose tissue (EAT) volume and diabetic retinopathy. METHODS:We used clinical data from a monocentric mixed retrospective and prospective observational study of 1093 individuals living with diabetes who had a computed tomography (CT) scan in order to calculate their coronary artery calcium (CAC) score. This scan was also used to compute EAT volume. For the present study, only persons whose diabetic retinopathy status was known (i.e., yes/no) were included. RESULTS:We included 1037 individuals living with diabetes (type 2 79.1 %, type 1 14.8 %, other types 6.2 %) for 14.6 ± 9.9 years. Mean body mass index was 29.4 ± 5.9 kg/m², HbA1c was 8.7 ± 2.2 %, 38.2 % had diabetic retinopathy, and EAT volume was 93 ± 40 cm3. Diabetic retinopathy was positively associated with North African ethnicity, type 1 diabetes, longer diabetes duration, higher HbA1c levels, and more hypertension and diabetes-related complications (nephropathy, neuropathy, macroangiopathy and a high CAC score). EAT volume was lower in patients with diabetic retinopathy than in those without (87 ± 37 vs 97 ± 42 cm3, P < 0.0001), independently of confounders (per 10cm3 increase: odds ratio 0.89 [95 % confidence interval 0.84;0.93], P < 0.0001). CONCLUSION:We found an unexpected negative association between the volume of EAT-a proinflammatory tissue-and diabetic retinopathy prevalence. This finding warrants further mechanistic investigation.

Chez les femmes atteintes de diabète de type 1 ayant des cycles ovulatoires réguliers, la sensibilité à l’insuline et les besoins en insuline varient selon un rythme prévisible, dicté par les hormones. Les jours de phase folliculaire précoce, lorsque les niveaux d’estradiol et de progestérone sont tous deux bas, sont les plus « favorables » au contrôle glycémique : la glycémie moyenne mesurée par capteur est la plus basse, le temps dans la cible est maximal, et la dose quotidienne totale d’insuline est généralement inférieure de 10 à 20 % par rapport aux phases ultérieures du cycle. Après l’ovulation, la montée de la progestérone perturbe la signalisation insulinique, si bien que les études de clamp en phases lutéales moyenne et tardive montrent une baisse de 15 à 25 % de la sensibilité à l’insuline. Les données de surveillance continue du glucose indiquent une élévation de 6 à 12mg/dL de la glycémie moyenne et une réduction de 4 à 8 points de pourcentage du temps dans la cible. La plupart des femmes augmentent alors leur insuline basale d’environ 0,02U/kg/j–1. Les pompes en boucle fermée amortissent partiellement cette détérioration, mais nécessitent le plus souvent un ajustement manuel du débit basal ou du facteur de correction. Cet effet s’inverse rapidement avec les menstruations, lorsque le taux de progestérone chute brutalement, exposant parfois les utilisatrices sensibles à un risque d’hypoglycémie nocturne en phase folliculaire précoce. Environ un quart des femmes présentent peu de variations cycliques, tandis qu’un autre quart subit des fluctuations marquées, pouvant aller jusqu’à de rares épisodes d’acidocétose diabétique cataméniale, soulignant ainsi l’importance d’ajustements d’insuline personnalisés et sensibles au cycle, en attendant l’adoption généralisée d’algorithmes de prochaine génération intégrant les données hormonales.