TYPE Original Research PUBLISHED 02 December 2022 DOI 10.3389/fendo.2022.1015614 Obesity and related OPEN ACCESS comorbidities in a large EDITED BY Guilherme Zweig Rocha, State University of Campinas, Brazil population-based cohort REVIEWED BY Roberto Codella, of subjects with type 1 University of Milan, Italy Bart Van Der Schueren, KU Leuven, Belgium diabetes in Catalonia Conxa Castell, Public Health Agency of Catalonia, Spain Idoia Genua 1,2,3, Josep Franch-Nadal 4,5,6, Elena Navas 7, *CORRESPONDENCE Manel Mata-Cases 4,6,8, Gabriel Giménez-Pérez 9,10, Didac Mauricio Bogdan Vlacho 3,4,6, Didac Mauricio 1,4,6,11* didacmauricio@gmail.com and Albert Goday 2,12,13 SPECIALTY SECTION This article was submitted to 1 Department of Endocrinology and Nutrition, Hospital de la Santa Creu i Sant Pau, Obesity, Barcelona, Spain, 2 Department of Medicine, Autonomous University of Barcelona, Barcelona, Spain, a section of the journal 3 Institut d’Investigació Biomèdica Sant Pau (IIB SANT PAU), Barcelona, Spain, 4 Diabetis en Atenció Frontiers in Endocrinology Primaria-Catalunya (DAP-Cat) group, Unitat de Suport a la Recerca Barcelona, Fundació Institut RECEIVED 09 August 2022 Universitari per a la recerca a l’Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), Barcelona, ACCEPTED 16 November 2022 Spain, 5 Primary Health Care Center Raval Sud, Gerència d’Atenció Primaria, Institut Català de la PUBLISHED 02 December 2022 Salut, Barcelona, Spain, 6 Centro de Investigación Biomédica en Red (CIBER) of Diabetes and Associated Metabolic Diseases (CIBERDEM), Instituto de Salud Carlos III (ISCIII), Barcelona, Spain, CITATION 7 Unitat de Suport a la Recerca|, Barcelona, Spain, 8 Primary Health Care Center La Mina, Gerència Genua I, Franch-Nadal J, Navas E, d’Atenció Primària Barcelona Ciutat, Institut Català de la Salut, Sant Adrià de Besòs, Spain, Mata-Cases M, Giménez-Pérez G, 9 Endocrinology Section, Department of Medicine, Hospital General de Granollers, Granollers, Spain, Vlacho B, Mauricio D and Goday A 10 School of Medicine and Health Sciences, Universitat Internacional de Catalunya, Sant Cugat del (2022) Obesity and related Vallès, Spain, 11 Department of Medicine, Universitat de Vic - Universitat Central de Catalunya, comorbidities in a large population- Barcelona, Spain, 12 Deparment of Endocrinology & Nutrition, Hospital del Mar, IMIM Institut Mar based cohort of subjects with type 1 d’Investigacions Mediques. Parc de Salut Mar, Barcelona, Spain, 13 CIBERobn, Instituto de Salud diabetes in Catalonia. Carlos III (ISCIII), Madrid, Spain Front. Endocrinol. 13:1015614. doi: 10.3389/fendo.2022.1015614 COPYRIGHT © 2022 Genua, Franch-Nadal, Navas, Mata-Cases, Giménez-Pérez, Vlacho, Introduction: Obesity, an increasing global health problem, can affect people Mauricio and Goday. This is an open- with other disease conditions. The prevalence of obesity in people with type 1 access article distributed under the terms of the Creative Commons diabetes (T1D) is not well known. The aim of this study was to describe Attribution License (CC BY). The use, extensively the characteristics and prevalence of different classes of obesity distribution or reproduction in other according to BMI (body mass index) categories in a large cohort of patients forums is permitted, provided the original author(s) and the copyright with T1D. owner(s) are credited and that the original publication in this journal is Material and methods: This was a retrospective, cross-sectional study in cited, in accordance with accepted academic practice. No use, Catalonia. We reviewed all patients with T1D diagnosis, ≥ 18 years old and distribution or reproduction is with BMI data from the SIDIAP database. Sociodemographic and clinical data, permitted which does not comply with these terms. cardiovascular risk factors, laboratory parameters and concomitant medications were collected. Results: A total of 6,068 patients with T1D were analyzed. The prevalence of obesity in the total sample was 18% (13.8% with class 1 obesity [BMI 30-34.9 kg/ m2]). Patients with obesity had a higher prevalence of other cardiovascular risk factors (i.e. hypertension was 61.4% vs. 37.5%; dyslipidemia 63.6% vs 44%, and chronic kidney disease 38.4% vs. 24.4%; p<0.001 in all cases) and poorer Frontiers in Endocrinology 01 frontiersin.org Genua et al. 10.3389/fendo.2022.1015614 control of them. The higher prevalence was regardless of sex, age and duration of diabetes. The increase in these comorbidities was noticeable from a BMI > 25 kg/m2. Patients with obesity did not have poorer glycemic control. Conclusion: The presence of obesity in people with T1D is frequent and cardiovascular risk factors are more common and more poorly controlled in T1D patients with obesity. KEYWORDS Obesity, type 1 diabetes, cardiovascular risk factors, metabolic syndrome, comorbidities Introduction The aim of this study was to describe extensively the characteristics and prevalence of obesity (including according While it is well-known that obesity is commonly linked to to different degrees of obesity) in a large population-based type 2 diabetes (T2D), obesity may also be associated with type 1 cohort of patients with T1D living in a Mediterranean area, i.e. diabetes (T1D) in a bidirectional way (1). With the ongoing Catalonia, with a high prevalence of obesity in the general obesity epidemic and the improvement in T1D treatments, the population, and to analyze the impact of the obesity on concern about possible consequences of this relationship has metabolic control and other CV risk factors. increased in the recent years. The prevalence of obesity in patients with T1D is not well known. Furthermore, it differs widely among the available Material and methods studies, varying from 8.9% to 28.4% across large cohort studies made in the USA and west and northern European countries (2– We performed a cross-sectional analysis of retrospective 7), and reported to be only 2% in a smaller cohort in an Asian routinely collected pseudo-anonymized health data from population (8). These variations can probably be explained people who attended primary healthcare centers from the because the incidence and prevalence of both conditions in the leading public healthcare provider (Institut Català de la Salut, general population vary by country and by regions and, also, the ICS) in Catalonia (Spain). This pseudo-anonymized health data fact that the prevalence of both T1D and obesity has increased in was obtained from the SIDIAP database (Sistema d’Informació the last decades in the general population (9–11). It is therefore per al desenvolupament de la Investigació en Atenció Primària, important to have current, region-specific data. Overall, the https://www.sidiap.org/), which is a well validated primary association between obesity and T1D appears to be frequent healthcare database used in diabetes epidemiological research and the current prevalence of different classes of obesity in T1D (14). The SIDIAP database collects healthcare data from more and its characteristics need to be further studied. than 74% (5,564,292 persons) of the Catalonian population. The The deleterious effect of obesity in the general population is cross-sectional analysis was performed on the cut-off date well described (12). Nevertheless, data about the characteristics (December 31st, 2016) for 12 months (from January 1st, 2016). and consequences in patients with T1D are scarce. The association of obesity in people with T1D may entail additional problems such as poor metabolic control, increased Study participants cardiovascular (CV) risk factors and a consequent increase in related complications (13). For our analysis, we included all of the subjects in the In a previous study (14), we described the clinical database with a diagnosis of T1D, defined as the presence of characteristics of a large cohort of patients with T1D in the diagnostic ICD-10 (International Classification of Diseases Catalonia, finding a high prevalence of obesity (16.5%). 10) code E10 and sub-codes, as described in the previous study However, the different degrees of obesity, its characteristics (14). Only patients ≥18 years old and with body mass index and its implications were not addressed in that initial study. (BMI) data (directly measured and recorded by a healthcare Considering its prevalence and its possible implications, we professional) were considered for the analysis. Those with an strongly felt that a more comprehensive analysis was needed; E10 diagnosis treated with any glucose-lowering agents other this led us to plan the current sub-study. than insulin and those who had not been treated with short- Frontiers in Endocrinology 02 frontiersin.org Genua et al. 10.3389/fendo.2022.1015614 acting insulins for more than two years after the recorded date of was used. Statistical analysis was performed using R software for diagnosis were excluded, as described previously (14). Windows version 3.6.1, Vienna, Austria. No formal sample size calculation was done, as the current study is a sub-analysis of the initial study on the characteristics of type 1 diabetes in Catalonia (14). Results A description of the whole cohort of patients with T1D in Study variables Catalonia has been published previously by this group elsewhere (14). From the overall number of subjects with T1D (15,008), we On the cut-off date, we collected different sociodemographic included those older than 18 years old (13,547) who had BMI data (age, sex), clinical data (BMI, blood pressure, age at T1D data available (6,610). A total of 6,068 patients with T1D were diagnosis, duration of T1D), CV risk factors (hypertension, analyzed in the current study. The sociodemographic and dyslipidemia, smoking, and obesity), laboratory parameters clinical characteristics of the studied population according to (HbA1c, blood lipids, estimated glomerular filtration rate sex are shown in Table 1. The mean age of the entire cohort was (GFR; using the CKD-EPI formula) and insulin therapy and 48.7 ± 15.9 years old with mean glycosylated hemoglobin other concomitant medications related to CV risk factors. (HbA1c) value of 8.0 ± 1.4%. The mean age at T1D diagnosis Dyslipidemia and hypertension were defined by the ICD-10 was 35.0 years, and 54.6% were men. Only 8.3% of the patients diagnostic code and/or a record of lipid-lowering or achieved all 3 risk factor target level for primary prevention for antihypertensive drugs, respectively. Chronic kidney disease cardiovascular diseases (HbA1c <7%, BP<140/90 and (CKD) was defined by the ICD-10 diagnostic code and/or LDLc <100 mg/dl). GFR <60 or urine albumin to creatinine ratio (ACR) >30 mg/ The mean BMI was 26.2 ± 4.5 kg/m2, with a prevalence of g. Diagnostic codes and proxies used to define the study variables obesity (BMI ≥30 kg/m2) of 18%. There were no statistically are presented in Supplementary Appendix 1 (code list). significant differences between sexes (20.1% in women and Objectives for CV prevention were established according to 16.2% in men). Regarding different degrees of obesity, 13.7% the European Society of Cardiology guidelines (15) of patients with T1D had class 1 obesity (BMI between 30-34.9 (HbA1c <7% and blood pressure <140/90 mmHg and low- kg/m2), 3.3% had class 2 obesity (BMI between 35-39.9 kg/m2) density lipoprotein cholesterol (LDLc) <100 mg/dl for primary and only 1% had class 3 obesity (BMI ≥40 kg/m2) (Table 2). prevention and HbA1c <7% and blood pressure <140/90mmHg Compared to patients with T1D without obesity, those with and LDLc <70mg/dl for secondary prevention). obesity were older at the time of the study (53.6 ± 15.8 vs 47.7 ± A complete-case analysis was performed. 15.7 years old; p<0.001), and also at the time of T1D diagnosis (39.4 ± 18.7 vs 34.1 ± 17.2 years old; p<0.001), but there was no difference in the duration of T1D from diagnosis (14.8 vs 14.2 Statistical analysis years). The prevalence of obesity gradually increased with age (6.2% in ≤20 years old, 9.7% in 21-30 years old, 14.6% in 31-40 Descriptive statistics were calculated to summarize the years old, 16.1% in 41-50 years old, 20.9% in 51-60 years old, sociodemographic characteristics, as well as the study variables 21.8% in 61-70 years old and 29.6% in >70 years old; p<0.001). (clinics and treatments). Continuous variables were expressed as Patients with obesity were more likely to be non-smokers. means and standard deviations or medians and interquartile Concerning glycemic control, patients with obesity did not range, and categorical variables were expressed as frequencies have a poorer control compared to those without obesity (mean and percentages. Descriptive analyses were conducted for the HbA1c 8.0 ± 1.3 vs 8.0 ± 1.5, respectively; p=0.233). Moreover, overall population, and stratified by sex and BMI groups (<30, 30 having obesity did not make it less likely to have glycemic – 34.9, 35 – 39.9 and ≥40 kg/m2) for all the study variables. In control within the optimal range (defined as HbA1c <7%), addition, for the clinical variables, analyses were stratified by sex, with 21.9% of patients with obesity and 21.5% without obesity age groups (<40 and ≥40 years) and diabetes duration (<10, 10– having HbA1c < 7%. 19 and >19 years). To evaluate the differences between the Table 2 shows the clinical characteristics of patients with characteristics (variables) according to the generated groups, T1D according to different degrees of obesity. Compared to non- the Chi-square test and the F-Fisher test were used when obese patients, a higher proportion of those with obesity had CV appropriate. To analyze continuous variables, the tests used risk factors (hypertension was 61.4% vs. 37.5%; dyslipidemia were the T-Student test or analysis of variance (Anova). 63.6% vs 44%, and chronic kidney disease 38.4% vs. 24.4%; Additionally, the Bonferroni test was performed for multiple p<0.001 in all cases). Furthermore, obesity was associated with comparisons. To obtain the optimal cut-off points of the BMI in poorer control of some CV risk factors; fewer patients with the ROC analysis, the “Concordance Probability Method (CZ)” obesity than those without had fair blood pressure control Frontiers in Endocrinology 03 frontiersin.org Genua et al. 10.3389/fendo.2022.1015614 TABLE 1 Characteristics of the whole study population and also according to sex. Total population Women Men p-value N 6068 (100.0%) 2756 (45.4%) 3312 (54.6%) Current age (years) 48.7 (15.9) 49.3 (16.8) 48.3 (15.1) 0.272 Age at diagnosis (years) 35.0 (17.6) 35.3 (18.9) 34.8 (16.4) >0.999 Diabetes duration (years) >0.999 <10 years 2084 (34.3%) 925 (33.6%) 1159 (35.0%) 10 – 19 years 2703 (44.5%) 1227 (44.5%) 1476 (44.6%) >19 years 1281 (21.1%) 604 (21.9%) 677 (20.4%) HbA1c % (n = 4249) 8.0 (1.4) 8.0 (1.4) 8.0 (1.4) >0.999 BMI (kg/m2) (n= 6068) 26.2 (4.5) 26.1 (5.0) 26.2 (4.1) 0.003 Non obesity (BMI < 30 kg/m2) 4976 (82.0%) 2202 (79.9%) 2774 (83.8%) Obesity (BMI ≥ 30 kg/m2) 1092 (18.0%) 554 (20.1%) 538 (16.2%) Smoking status: <0.001 Non-smoker 2885 (47.5%) 1598 (58.0%) 1287 (38.9%) Ex-smoker 1638 (27.0%) 625 (22.7%) 1013 (30.6%) Smoker 1545 (25.5%) 533 (19.3%) 1012 (30.6%) BP Systolic BP (mmHg) (n= 5711) 127 (14.0) 125 (14.7) 129 (13.0) <0.001 Diastolic BP (mmHg) (n= 5711) 73.6 (9.1) 72.4 (8.8) 74.6 (9.2) <0.001 Total cholesterol (mg/dl) (n=4331) 182 (37.9) 187 (36.4) 178 (38.7) <0.001 HDLc (mg/dl) (n= 4145) 59.1 (17.3) 65.4 (17.2) 53.7 (15.5) <0.001 LDLc (mg/dl) (n= 3933) 103 (30.2) 103 (29.0) 102 (31.2) >0.999 Triglycerides (mg/dl) (n= 4005) 111 (91.3) 100 (64.3) 120 (108.0) <0.001 Microalbuminuria (n= 3252) 68.2 (275) 61.5 (278) 74.0 (272) >0.999 Estimated GFR (CKDEPI) (n= 3632) 79.2 (22.4) 78.4 (22.3) 79.9 (22.4) 0.929 Other variables Chronic kidney disease (n= 2962) 805 (27.2%) 366 (26.9%) 439 (27.4%) >0.999 Dyslipidemia (%), (n= 6068) 2881 (47.5%) 1225 (44.4%) 1656 (50.0%) <0.001 Hypertension (%), (n= 6068) 2534 (41.8%) 1082 (39.3%) 1452 (43.8%) 0.009 Cardiovascular prevention Primary (%), (n = 4434) 368 (8.3%) 170 (8.5%) 198 (8.2%) >0.999 Secondary (%), (n = 4434) 114 (2.5%) 38 (1.8%) 76 (3.1%) 0.256 More treatments BP lowering (%) 2366 (39.0%) 1005 (36.5%) 1361 (41.1%) 0.006 Antithrombotic (%) 1675 (27.6%) 673 (24.4%) 1002 (30.3%) <0.001 Lipid-lowering (%) 2500 (41.2%) 1055 (38.3%) 1445 (43.6%) <0.001 Insulin therapy Short-acting insulin (%) 5603 (92.3%) 2576 (93.5%) 3027 (91.4%) 0.068 Long-acting insulin (%) 5093 (83.9%) 2275 (82.5%) 2818 (85.1%) 0.198 Mixed insulin (%) 873 (14.4%) 378 (13.7%) 495 (14.9%) >0.999 Intermediate-acting insulin (%) 316 (5.2%) 135 (4.9%) 181 (5.4%) >0.999 P-value<0.05 is considered to be statistically significant. Data are mean (SD) or n(%). Where indicated, the n value denotes the number of study patients with available data during the study period. BMI, body mass index; BP, blood pressure; HDLc, HDL-cholesterol; LDLc, LDL-cholesterol; GFR, glomerular filtration rate. Primary prevention includes the composite target achievement of HbA1c<7%, blood pressure <140/90 and LDL-C<100. Secondary prevention includes the composite target achievement of HbA1c<7%, blood pressure <140/90 and LDL-C<70. Bold values correspond to statistically significant p-values. (defined as <140/90 mmHg) (75% vs. 84% (p <0.001), factors of patients with T1D according to the different obesity triglyceride levels were higher in the obese group (134 ± 113 categories, and the same differences were observed, as shown vs 106 ± 84.3 mg/dl; p<0.001), and high density lipoprotein in Table 2. cholesterol (HDLc) levels were lower in the obese group (53.1 ± No differences were found in the percentage of patients that 15.2 vs 60.5 ± 17.5 mg/dl; p<0.001). No differences were found in met the established targets for primary (HbA1c <7% and blood total cholesterol or LDLc levels. We have also compared CV risk pressure <140/90 and LDLc <100 mg/dl) and secondary Frontiers in Endocrinology 04 frontiersin.org Genua et al. 10.3389/fendo.2022.1015614 TABLE 2 Characteristics of T1D according to the different categories of body mass index. No Obesity Obesity Class 1 Obesity Class 2 Obesity Class 3 p-value < 30kg/m2 30-34.9 kg/m2 35-39.9 kg/m2 ≥ 40 kg/m2 N (%) 4976 (82.0%) 830 (13.8%) 199 (3.2%) 63 (1.0%) Current age (years) 47.7 (15.7) 53.0 (15.7) 55.9 (15.9) 53.7 (16.6) <0.001 Age at diagnosis (years) 34.1 (17.2) 38.4 (18.4) 42.5 (19.6) 41.9 (19.1) <0.001 Diabetes duration (years) >0.999 <10 years 1722 (34.6%) 258 (31.1%) 80 (40.2%) 24 (38.1%) 10 – 19 years 2221 (44.6%) 380 (45.8%) 73 (36.7%) 29 (46.0%) >19 years 1033 (20.8%) 192 (23.1%) 46 (23.1%) 10 (15.9%) HbA1c % (n = 4249) 8.0 (1.5) 7.9 (1.3) 8.1 (1.3) 8.1 (1.6) >0.999 BMI (kg/m2) (n= 6068) 24.6 (2.9) 31.9 (1.3) 36.9 (1.4) 43.3 (3.2) <0.001 Smoking status <0.001 Non smoker 2321 (46.6%) 422 (50.8%) 105 (52.8%) 37 (58.7%) Former Smoker 1209 (24.3%) 252 (30.4%) 68 (34.2%) 16 (25.4%) Smoker 1446 (29.1%) 156 (18.8%) 26 (13.1%) 10 (15.9%) BP Systolic BP (mmHg) (n= 5711) 126 (13.9) 131 (13.3) 131 (12.4) 132 (16.6) <0.001 Diastolic BP (mmHg) (n= 5711) 73.2 (9.0) 75.3 (9.2) 75.5 (9.2) 74.2 (12.0) <0.001 Total cholesterol (mg/dl) (n=4331) 183 (37.6) 180 (38.0) 179 (41.0) 194 (48.0) 0.709 HDLc (mg/dl) (n= 4145) 60.5 (17.5) 53.6 (15.3) 51.2 (15.1) 51.8 (13.5) <0.001 LDLc (mg/dl) (n= 3933) 103 (30.1) 102 (31.4) 98.8 (30.6) 103 (25.2) >0.999 Triglycerides (mg/dl) (n= 4005) 106 (84.3) 129 (119) 141 (82.9) 164 (122) <0.001 Microalbuminuria (n= 3252) 64.7 (273) 64.5 (251) 108 (301) 224 (452) 0.024 Estimated GFR (CKDEPI) (n= 3632) 80.4 (21.7) 76.4 (23.3) 69.1 (24.3) 62.9 (28.8) <0.001 Other variables Chronic kidney disease (n= 2962) 577 (24.4%) 155 (34.9%) 51 (46.8%) 22 (53.7%) <0.001 Dyslipidemia (%), (n= 6068) 2187 (44.0%) 519 (62.5%) 135 (67.8%) 40 (63.5%) <0.001 Hypertension (%), (n= 6068) 1864 (37.5%) 485 (58.4%) 139 (69.8%) 46 (73.0%) <0.001 Cardiovascular Prevention Primary (%), (n = 4434) 303 (8.5%) 54 (8.3%) 9 (5.9%) 2 (3.9%) >0.999 Secondary (%), (n = 4434) 86 (2.4%) 24 (3.7%) 4 (2.6%) 0 (0.0%) >0.999 More treatments BP lowering (%) 1735 (34.9%) 455 (54.8%) 132 (66.3%) 44 (69.8%) <0.001 Antithrombotic (%) 1259 (25.3%) 307 (37.0%) 80 (40.2%) 29 (46.0%) <0.001 Lipid-lowering (%) 1868 (37.5%) 472 (56.9%) 124 (62.3%) 36 (57.1%) <0.001 Insulin therapy Short-acting insulin (%) 4626 (93.0%) 747 (90.0%) 176 (88.4%) 54 (85.7%) 0.016 Long-acting insulin (%) 4220 (84.8%) 672 (81.0%) 155 (77.9%) 46 (73.0%) 0.008 Mixed insulin (%) 673 (13.5%) 144 (17.3%) 37 (18.6%) 19 (30.2%) 0.001 Intermediate-acting insulin (%) 258 (5.1%) 42 (5.0%) 12 (6.0%) 4 (6.3%) >0.999 P-value<0.05 is considered to be statistically significant. Data are mean (SD) or n(%). Where indicated, the n value denotes the number of study patients with available data during the study period. BMI, body mass index; BP, blood pressure; GFR, glomerular filtration rate; HDLc, HDL-cholesterol; LDLc, LDL-cholesterol. Primary prevention includes the composite target achievement of HbA1c<7%, blood pressure <140/90 and LDL-C<100. Secondary prevention includes the composite target achievement of HbA1c<7%, blood pressure <140/90 and LDL-C<70. Bold values correspond to statistically significant p-values. Frontiers in Endocrinology 05 frontiersin.org Genua et al. 10.3389/fendo.2022.1015614 (HbA1c <7% and blood pressure <140/90 and LDLc <70 mg/dl) few studies have evaluated the association of obesity with other CV prevention when comparing distinct BMI categories. CV risk factors in people with T1D. A study that assessed this in The prevalence of obesity and clinical characteristics 11,348 children in the USA, with a 14% prevalence of obesity, according to gender, current age and diabetes duration are also found a higher prevalence of hypertension and dyslipidemia presented in Tables 3A–C. The higher prevalence of in the obese group, with no differences in HbA1c values (20). In hypertension and dyslipidemia in the obesity group was contrast, a Swedish study in 26,125 people with T1D, found maintained irrespective of sex, age and duration of diabetes. higher HbA1c levels in patients with obesity at baseline, as well Table 4 represents the relation between BMI, as a continuous as higher mean systolic blood pressure (21). Considering that the variable, and the degree of control of CV risk factors showing a main cause of mortality in T1D is CV disease, having an increase progressive worsening in their control from a BMI of >25 kg/m2. and poorer control of CV risk factors in those with obesity is a The BMI cut-off values at which the progressive worsening relevant finding. Further, a study found higher major occurred were 25.6, 25.6, 25.4, 26.6, 25.2 and 26.5 kg/m2 for complication outcomes (an aggregate of mortality, coronary systolic and diastolic blood pressure, total cholesterol, HDLc, artery disease, and renal failure) in T1D patients with LDLc and triglycerides, respectively. metabolic syndrome (22). Interestingly, patients with obesity were more frequently non-smokers, a fact that is favorable and does not further increase the adverse CV risk profile of Discussion these subjects. Whether people with T1D and obesity have poorer glycemic The present study confirms a high prevalence of obesity in control remains unclear. We did not find differences in chronic people with T1D, with a concomitant higher prevalence and glycemic control (HbA1c). The T1D exchange clinic registry poorer control of other CV risk factors. The prevalence of T1D (23) found that adolescents who had better metabolic control in Spain is medium-high, being around 15 cases per 1,000 in were at higher risk of obesity. However, a study of the SWEET people under 19 years old (16) and 0.3% in the working registry in children and adolescents found that underweight and population (17). The estimated prevalence of obesity in Spain obese children had significantly higher HbA1c (24). Although is also medium-high at around 23% (18). With the prevalence of these studies were conducted in pediatric populations that might both conditions being medium-high in our area, it is relevant to not be comparable to adults, results from the few studies in adult explore the association between both diseases. To our populations are also inconsistent (21, 25). The fact that a more knowledge, this is the largest cohort that describes extensively intensive insulin therapy is related to weight gain could go some the prevalence and clinical characteristics of obesity, including way to explaining why obesity does not clearly worsen metabolic different degrees of obesity, in adults with T1D. control (26). The prevalence of obesity observed in patients with T1D The established cut-off points by WHO for overweight and (18%, mainly class 1 obesity) is similar or a bit lower than the obesity in the general population are based on the relationship prevalence of obesity in the general population in Spain (23%) between BMI and morbidity and mortality. However, it is well (18). Both epidemiological data are based on BMI according to known that these cut-offs are inaccurate for some groups (27). the direct measurement of weight and height, and therefore are Nevertheless, we found a good correlation between the classic methodologically homogeneous. These data are more accurate BMI cut-off points and the risk of comorbidities in this specific than data on self-declaration of weight and height, as obtained in subgroup of patients. We acknowledge that obtained AUC, national health surveys. The prevalence found in our cohort is in sensitivity and specificity are not very discriminant. However, line with another recently published European study in Belgium we did not aim at building a screening tool, but at exploring the in a cohort of 32,809 patients (between 2017 and 2018) with T1D threshold upon which the overweight-related risk factor profile that reported a prevalence of obesity of around 17%, similar to was unfavorable. The major strengths of the study are the that in the Belgium general population (4). Another recent large extensive description of patients and the large cohort used for European cohort study found a prevalence of obesity of 15.3% in the analysis. Data are representative and robust, encompassing a T1D (3). As mentioned in a recent review (1), the prevalence large proportion of the Catalonian population, with the SIDIAP found in the current study is close to the prevalence of obesity database having been used in several other diabetes studies (14, seen in the general population. On the other hand, other recent 28, 29). The restrictive criteria used to define the diagnosis of studies in large cohorts from non-European countries with a T1D ensured the correct diagnosis of T1D. higher prevalence of obesity in the general population, observed The study has some limitations. First, due to the cross- a markedly lower prevalence of obesity in T1D (2, 19). Despite sectional retrospective nature of the study, inherent limitations these differences, it is definitely clear that obesity in people with of this type of study are presented. We cannot establish T1D is a prevalent comorbidity that we cannot disregard. tendencies or causality. In addition, obesity was frequently not As expected, considering the general population, patients well documented compared to other diseases, and BMI was with obesity were older (18). Unlike in the general population, probably more often recorded in more complicated and older Frontiers in Endocrinology 06 frontiersin.org Genua et al. 10.3389/fendo.2022.1015614 TABLE 3A Prevalence of obesity and clinical characteristics according to sex. Sex Women (n = 2756) Men (n = 3312) <30kg/m 2 ≥30kg/m 2 p-value <30kg/m 2 ≥30kg/m2 p-value n (%) 2,202 (79.8%) 554 (20.2%) 2774 (83.7%) 538 (16.2%) Systolic BP, mmHg 123 (14.5) 130 (14.1) <0.001 129 (13.0) 132 (12.4) <0.001 Diastolic BP, mmHg 72.0 (8.7) 73.7 (9.4) <0.001 74.1 (9.2) 76.8 (9.1) <0.001 Total cholesterol (mg/dl) 188 (36.2) 185 (37.1) >0.999 178 (38.2) 176 (41.2) >0.999 HDLc (mg/dl) 67.4 (17.2) 58.0 (15.0) <0.001 54.9 (15.6) 47.8 (13.5) <0.001 LDLc (mg/dl) 103 (28.8) 103 (29.8) >0.999 103 (31.1) 99.9 (32.0) >0.999 Triglycerides (mg/dl) 94.7 (60.5) 120 (72.9) <0.001 114 (98.2) 148 (143) <0.001 HbA1c % 8.0 (1.5) 8.0 (1.3) >0.999 8.0 (1.5) 7.9 (1.3) >0.999 Dyslipidemia 903 (41.0%) 322 (58.1%) <0.001 1284 (46.3%) 372 (69.1%) <0.001 Hypertension 756 (34.3%) 326 (58.8%) <0.001 1108 (39.9%) 344 (63.9%) <0.001 Chronic kidney disease 239 (22.7%) 127 (41.5%) <0.001 338 (25.7%) 101 (35.1%) 0.018 BP, blood pressure, HbA1c, glycosylate haemoglobin; HDLc, HDL-cholesterol; LDLc, LDL-cholesterol. P-value<0.05 is considered to be statistically significant. Bold values correspond to statistically significant p-values. TABLE 3B Prevalence of obesity and clinical characteristics according to current age. Current age (years) <40 years (n = 1843) ≥40 years (n = 4225) <30kg/m2 ≥30kg/m2 p-value <30kg/m2 ≥30kg/m2 p-value N (%) 1618 (87.8%) 225 (12.2%) 3358 (79.4%) 867 (20.6%) Systolic BP, mmHg 121 (12.6) 125 (12.0) <0.001 129 (13.8) 133 (13.2) <0.001 Diastolic BP, mmHg 72.9 (8.6) 77.9 (7.3) <0.001 73.4 (9.2) 74.6 (9.7) 0.007 Total cholesterol (mg/dl) 184 (39.1) 183 (36.8) >0.999 182 (36.9) 180 (40.0) >0.999 HDLc (mg/dl) 59.4 (15.3) 51.3 (13.6) <0.001 61.0 (18.3) 53.5 (15.5) <0.001 LDLc (mg/dl) 105 (30.5) 108 (30.1) >0.999 102 (29.9) 99.9 (30.9) >0.999 Triglycerides (mg/dl) 104 (88.2) 133 (187) 0.618 106 (82.5) 132 (86.6) <0.001 HbA1c % 8.2 (1.7) 7.9 (1.3) 0.028 8.0 (1.3) 8.0 (1.3) >0.999 Dyslipidemia 200 (12.4%) 53 (23.6%) <0.001 1987 (59.2%) 641 (73.9%) <0.001 Hypertension 154 (9.5%) 45 (20.0%) <0.001 1710 (50.9%) 625 (72.1%) <0.001 Chronic kidney disease 87 (12.7%) 18 (17.1%) >0.999 490 (29.1%) 210 (42.9%) <0.001 BP, blood pressure; HbA1c, glycosylate haemoglobin; HDLc, HDL-cholesterol; LDLc, LDL-cholesterol. P-value<0.05 is considered to be statistically significant. Bold values correspond to statistically significant p-values. TABLE 3C Prevalence of obesity and clinical characteristics according to diabetes duration. Diabetes duration (years) <10 years (n = 2084) 10 – 19 years (n = 2703) >19 years (n = 1281) <30kg/m2 ≥30kg/m2 p-value <30kg/m2 ≥30kg/m2 p-value <30kg/m2 ≥30kg/m2 p-value N(%) 1722 (82.7%) 362 (17.3%) 2221 (82.1%) 482 (17.9%) 1033 (80.6%) 248 (19.4%) Systolic BP, mmHg 125 (14.6) 131 (14.4) <0.001 127 (13.7) 132 (12.6) <0.001 128 (13.1) 131 (13.2) 0.036 Diastolic BP, mmHg 72.8 (9.0) 75.7 (9.3) <0.001 73.6 (8.9) 75.5 (9.5) <0.001 73.0 (9.3) 74.2 (9.2) >0.999 Total cholesterol (mg/dl) 183 (39.2) 181 (41.0) >0.999 183 (36.6) 182 (38.1) >0.999 181 (36.9) 179 (39.4) >0.999 HDLc (mg/dl) 58.7 (17.6) 50.2 (14.3) <0.001 61.3 (17.2) 53.7 (15.1) <0.001 62.0 (17.7) 56.3 (15.9) <0.001 LDLc (mg/dl) 103 (31.4) 103 (30.7) >0.999 103 (29.7) 103 (31.3) >0.999 100 (28.4) 95.3 (29.8) 0.725 Triglycerides (mg/dl) 114 (108) 154 (157) <0.001 102 (69.7) 123 (76.1) <0.001 99.0 (60.5) 123 (85.3) 0.008 HbA1c % 8.0 (1.7) 7.8 (1.5) 0.405 8.1 (1.3) 8.1 (1.3) >0.999 8.0 (1.3) 7.9 (1.2) >0.999 (Continued) Frontiers in Endocrinology 07 frontiersin.org Genua et al. 10.3389/fendo.2022.1015614 TABLE 3C Continued Diabetes duration (years) <10 years (n = 2084) 10 – 19 years (n = 2703) >19 years (n = 1281) <30kg/m2 ≥30kg/m2 p-value <30kg/m2 ≥30kg/m2 p-value <30kg/m2 ≥30kg/m2 p-value Dyslipidemia 640 (37.2%) 211 (58.3%) <0.001 980 (44.1%) 301 (62.4%) <0.001 567 (54.9%) 182 (73.4%) <0.001 Hypertension 528 (30.7%) 216 (59.7%) <0.001 808 (36.4%) 277 (57.5%) <0.001 528 (51.1%) 177 (71.4%) <0.001 Chronic kidney disease 201 (23.9%) 102 (48.1%) <0.001 234 (23.6%) 75 (29.4%) 0.675 142 (26.5%) 51 (40.2%) 0.036 P-value<0.05 is considered to be statistically significant. BP, blood pressure; HDLc, HDL-cholesterol; LDLc, LDL-cholesterol. Bold values correspond to statistically significant p-values. TABLE 4 Cut-off points of the body mass index that show optimal or suboptimal control according to clinical variables. Variables Suboptimal Patients with Optimal Patients with BMI AUC Sensitivity or Specificity or control suboptimal control optimal control cut-off (95% True Positive True Negative control N (%) N (%) points CI) Rate Rate Systolic BP, mmHg ≥130 mmHg 2709 (47.4%) <130 3002 (52.5%) 25.6 kg/m2 0.62 60.1% 57.4% mmHg (0.60; 0.63) Diastolic BP, ≥80 mmHg 1570 (27.4%) <80 mmHg 4141 (72.5%) 25.6 kg/m2 0.58 60.3% 52.6% mmHg (0.56; 0.59) Total cholesterol ≥240 mg/dl 307 (7.1%) <240 mg/dl 4024 (92.9%) 25.4 kg/m2 0.50 56.4% 46.3% (mg/dl) (0.46; 0.53) HDLc (mg/dl) Men <40 mg/dl 351 (15.6%) ≥40 mg/dl 1891 (84.3%) 26.6 kg/m2 0.61 59.8% 57.9% (0.57; 0.64) Women <50 mg/dl 329 (17.2%) ≥50 mg/dl 1574 (82.7%) 26.1 kg/m2 0.67 69.9% 59.4% (0.63; 0.70) LDLc (mg/dl) ≥100 mg/dl 1987 (50.5%) <100 mg/dl 1946 (49.4%) 25.2 kg/m2 0.51 57.9% 46.8% (0.49; 0.53) Triglycerides ≥150 mg/dl 662 (16.5%) <150 mg/dl 3343 (84.4%) 26.5 kg/m2 0.60 59.8% 57.7% (mg/dl) (0.58; 0.62) Glomerular <60 mg/dL 471 (12.9%) ≥60 mg/dL 3161 (87.0%) 27.6 kg/m2 0.60 51.6% 66.4% Filtration (mg/dL) (0.57; 0.63) Microalbuminuria >30 mg 542 (16.6%) ≤30 mg 2710 (83.3%) 27.2 kg/m2 0.53 45.8% 62.1% (0.51; 0.56) AUC, area under the curve; BP, blood pressure; CI, confidence interval; HDLc, high-density lipoprotein cholesterol; LDLc, low-density lipoprotein cholesterol; BMI, body mass index. patients. The study relied on the recorded codes without external In conclusion, the prevalence of obesity in T1D is relevant validation and because of the restrictive definition criteria we and is associated with a higher risk of having other CV risk may have excluded a few subjects with T1D who were also factors with a suboptimal control of them. It seems that glycemic treated with glucose-lowering agents other than insulin. control is not related to the degree of obesity. Frontiers in Endocrinology 08 frontiersin.org Genua et al. 10.3389/fendo.2022.1015614 Data availability statement Conflict of interest Restrictions apply to the availability of some or all data IG received support for attending meetings form generated or analyzed during this study because they were used Boehringer Ingelheim, Lilly, MSD, Novo-Nordisk and under license. Requests to access these datasets should be Sanofi. JF-N received advisory and/or speaking fees from directed to DM, didacmauricio@gmail.com. Astra-Zeneca, Ascensia, Boehringer Ingelheim, GSK, Lilly, MSD, Novartis, Novo Nordisk and Sanofi. Received research grants to the institution from Astra-Zeneca. GSK, Lilly, MSD, Ethics statement Novartis, Novo Nordisk, Sanofi and Boehringer. DM has received advisory and/or speaking fees from Almirall, The studies involving human participants were reviewed and Esteve, Ferrer, Janssen, Lilly, Menarini, MSD, NovoNordisk approved by Primary Health Care University Research Institute and Sanofi. GG-P has received speaking fees from Lilly and Jordi Gol (number P17/115). Written informed consent for Astra-Zeneca. MM-C has received advisory honorarium from participation was not required for this study in accordance for Astra-Zeneca, Bayer, Boehringer Ingelheim, GSK, Lilly, with the national legislation and the institutional requirements. MSD, NOVARTIS, NovoNordisk, Sanofi; speaker honorarium from Astra- Zeneca, Bayer, Boehringer Ingelheim, GSK, Lilly, Menarini, MSD, Novartis, Author contributions NovoNordisk, and Sanofi; and research grants to institution from Astra-Zeneca, GSK, Lilly, MSD, Novartis, NovoNordisk, Conceptualization: AG, JF-N and DM; formal analysis: EN; and Sanofi. resources and data curation, EN; writing—original draft The remaining authors declare that the research was preparation IG; writing—review and editing: BV, IG, AG, EN, conducted in the absence of any commercial or financial JF-N, GG-P, MM-C and DM; supervision: DM, AG, and JF-N; relationships that could be construed as a potential conflict project administration: JF-N. All authors contributed to the of interest. article and approved the submitted version. Funding Publisher’s note All claims expressed in this article are solely those of the This research was partially funded by an unrestricted grant authors and do not necessarily represent those of their by Lilly S.A. The funder was not involved in the study design, affiliated organizations, or those of the publisher, the collection, analysis, interpretation of data, the writing of this editors and the reviewers. Any product that may be article or the decision to submit it for publication. This evaluated in this article, or claim that may be made by research was supported by CIBER-Consorcio Centro its manufacturer, is not guaranteed or endorsed by de Investigació n Biomé dica en Red (leading group: the publisher. CIBERDEM - CB15/00071), Instituto de Salud Carlos III, Ministerio de Ciencia e Innovació n and Unió n Europea – European Regional Development Fund. Supplementary material Acknowledgments The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/ We thank Amanda Prowse for her support in linguistics. fendo.2022.1015614/full#supplementary-material References 1. van der Schueren B, Ellis D, Faradji RN, Al-Ozairi E, Rosen J, Mathieu C. 3. van Mark G, Lanzinger S, Barion R, Degenhardt M, Badis S, Noll H, et al. Obesity in people living with type 1 diabetes. Lancet Diabetes Endocrinol (2021) Patient and disease characteristics of adult patients with type 1 diabetes in 9:776–85. doi: 10.1016/S2213-8587(21)00246-1 Germany: An analysis of the DPV and DIVE databases. Ther Adv Endocrinol 2. Foster NC, Beck RW, Miller KM, Clements MA, Rickels MR, Dimeglio LA, Metab (2019) 10:1–12. doi: 10.1177/2042018819830867 et al. State of type 1 diabetes management and outcomes from the T1D exchange in 4. Lavens A, Nobels F, de Block C, Oriot P, Verhaegen A, Chao S, et al. Effect of 2016-2018. Diabetes Technol Ther (2019) 21:66–72. doi: 10.1089/dia.2018.0384 an integrated, multidisciplinary nationwide approach to type 1 diabetes care on Frontiers in Endocrinology 09 frontiersin.org Genua et al. 10.3389/fendo.2022.1015614 metabolic outcomes: An observational real-world study. Diabetes Technol Ther 18. Gutié rrez-Fisac JL, Guallar-Castilló n P, Leó n-Muñoz LM, Graciani A, (2021) 23(8):565–76. doi: 10.1089/dia.2021.0003 Banegas JR, Rodrı́guez-Artalejo F. Prevalence of general and abdominal obesity 5. Vestberg D, Rosengren A, Olsson M, Gudbjörnsdottir S, Svensson AM, Lind in the adult population of Spain, 2008-2010: The ENRICA study. Obes Rev (2012) M. Relationship between overweight and obesity with hospitalization for heart 13(4):388–92. doi: 10.1111/j.1467-789X.2011.00964.x failure in 20,985 patients with type 1 diabetes: A population-based study from the 19. Faradji-Hazá n RN, Valenzuela-Lara M, Dı́az-Barriga Menchaca AP, Swedish national diabetes registry. Diabetes Care (2013) 36(9):2857–61. doi: Almeda-Valdes P, Antonio-Villa NE, Vidrio-Velá zquez M, et al. Type 1 diabetes 10.2337/dc12-2007 care in Mexico: An analysis of the RENACED-DT1 national registry. Rev 6. Hill CJ, Cardwell CR, Maxwell AP, Young RJ, Matthews B, O’Donoghue DJ, investigacion clinica organo del Hosp Enfermedades la Nutricion (2021) 73 et al. Obesity and kidney disease in type 1 and 2 diabetes: An analysis of the (4):222–30. doi: 10.24875/RIC.20000498 national diabetes audit. QJM (2013) 106(10):933–42. doi: 10.1093/qjmed/hct123 20. Redondo MJ, Foster NC, Libman IM, Mehta SN, Hathway JM, Bethin KE, 7. Dahlström EH, Sandholm N, Forsblom CM, Thorn LM, Jansson FJ, et al. Prevalence of cardiovascular risk factors in youth with type 1 diabetes and Harjutsalo V, et al. Body mass index and mortality in individuals with type 1 elevated body mass index. Acta Diabetol (2016) 53(2):271–7. doi: 10.1007/s00592- diabetes. J Clin Endocrinol Metab (2019) 104(11):5195–204. doi: 10.1210/jc.2019- 015-0785-1 00042 21. Edqvist J, Rawshani A, Adiels M, Björck L, Lind M, Svensson AM, et al. 8. Arai K, Yokoyama H, Okuguchi F, Yamazaki K, Takagi H, Hirao K, et al. BMI, mortality, and cardiovascular outcomes in type 1 diabetes: Findings against Association between body mass index and core components of metabolic an obesity paradox. Diabetes Care (2019) 42(7):1297–304. doi: 10.2337/dc18-1446 syndrome in 1486 patients with type 1 diabetes mellitus in Japan (JDDM 13). 22. Pambianco G, Costacou T, Orchard TJ. The prediction of major outcomes of Endoc J (2008) 55(6):1025-1032. doi: 10.1507/endocrj.K08E-167 type 1 diabetes: a 12-year prospective evaluation of three separate definitions of the 9. DiMeglio LA, Evans-Molina C, Oram RA. Type 1 diabetes. Lancet (2018) metabolic syndrome and their components and estimated glucose disposal rate the 391:2449–62. doi: 10.1016/S0140-6736(18)31320-5 Pittsburgh epidemiology of diabetes complications study experience (2007). Available at: 10. Chooi YC, Ding C, Magkos F. The epidemiology of obesity. Metabol: Clin http://diabetesjournals.org/care/article-pdf/30/5/1248/596117/zdc00507001248.pdf. Exp (2019) 92:6–10. doi: 10.1016/j.metabol.2018.09.005 23. Minges KE, Whittemore R, Weinzimer SA, Irwin ML, Redeker NS, Grey M. 11. Herná ez Á , Zomeño MD, Dé gano IR, Pé rez-Ferná ndez S, Goday A, Vila J, Correlates of overweight and obesity in 5529 adolescents with type 1 diabetes: The et al. Excess weight in Spain: Current situation, projections for 2030, and estimated T1D exchange clinic registry. Diabetes Res Clin Pract (2017) 126:68–78. doi: direct extra cost for the Spanish health system. Rev Española Cardiol (English 10.1016/j.diabres.2017.01.012 Edition) (2019) 72(11):916–24. doi: 10.1016/j.rec.2018.10.010 24. Maffeis C, Birkebaek NH, Konstantinova M, Schwandt A, Vazeou A, 12. Haslam DW, James PT. (2005). Watton Place Clinic: National Obesity Casteels K, et al. Prevalence of underweight, overweight, and obesity in children Forum. Available at: www.thelancet.com. and adolescents with type 1 diabetes: Data from the international SWEET registry. Pediatr Diabetes (2018) 19(7):1211–20. doi: 10.1111/pedi.12730 13. Corbin KD, Driscoll KA, Pratley RE, Smith SR, Maahs DM, Mayer-Davis EJ. Obesity in type 1 diabetes: Pathophysiology, clinical impact, and mechanisms. 25. Price SA, Gorelik A, Fourlanos S, Colman PG, Wentworth JM. Obesity is Endoc Rev (2018) 39:629–63. doi: 10.1210/er.2017-00191 associated with retinopathy and macrovascular disease in type 1 diabetes. Obes Res Clin Pract (2014) 8(2):e178–82. doi: 10.1016/j.orcp.2013.03.007 14. Gimenez-Perez G, Franch-Nadal J, Ortega E, Mata-Cases M, Goday A, Real J, et al. Clinical characteristics and degree of glycemic and cardiovascular risk factor 26. Weight gain associated with intensive therapy in the diabetes contraband control in patients with type 1 diabetes in catalonia (Spain). J Clin Med (2021) 10 complications trial . Available at: http://diabetesjournals.org/care/article-pdf/11/7/ (7):1536. doi: 10.3390/jcm10071536 567/438558/11-7-567.pdf. 15. Catapano AL, Graham I, de Backer G, Wiklund O, Chapman MJ, Drexel H, 27. Caleyachetty R, Barber TM, Mohammed NI, Cappuccio FP, Hardy R, et al. ESC/EAS guidelines for the management of dyslipidaemias. Eur Heart J Mathur R, et al. Ethnicity-specific BMI cutoffs for obesity based on type 2 (2016) 37(39):2999–3058. doi: 10.1093/eurheartj/ehw272 diabetes risk in England: A population-based cohort study. Lancet Diabetes Endocrinol (2021) 9(7):419–26. doi: 10.1016/S2213-8587(21)00088-7 16. Patterson CC, Karuranga S, Salpea P, Saeedi P, Dahlquist G, Soltesz G, et al. Worldwide estimates of incidence, prevalence and mortality of type 1 diabetes in 28. Mata-Cases M, Franch-Nadal J, Real J, Mauricio D. Glycaemic control and children and adolescents: Results from the international diabetes federation antidiabetic treatment trends in primary care centres in patients with type 2 diabetes atlas, 9th edition. Diabetes Res Clin Pract (2019) 157:107842. doi: diabetes mellitus during 2007–2013 in Catalonia: a population-based study. BMJ 10.1016/j.diabres.2019.107842 Open (2016) 6(10):e012463. doi: 10.1136/bmjopen-2016-012463 17. Reviriego J, Vá zquez LA, Goday A, Cabrera M, Garcı́a-Margallo MT, Calvo 29. Vinagre I, Mata-Cases M, Hermosilla E, Morros R, Fina F, Rosell M, et al. E. Prevalence of impaired fasting glucose and type 1 and 2 diabetes mellitus in a Control of glycemia and cardiovascular risk factors in patients with type 2 diabetes large nationwide working population in Spain. Endocrinol y Nutricion (2016) 63 in primary care in Catalonia (Spain). Diabetes Care (2012) 35(4):774–9. doi: (4):157–63. doi: 10.1016/j.endonu.2015.12.006 10.2337/dc11-1679 Frontiers in Endocrinology 10 frontiersin.org