Abstract
Objective
Linear growth is impaired in children with type 1 diabetes (T1D) and poor metabolic control. A good metabolic control is a key therapeutic goal to prevent vascular complications and also to ensure appropriate anthropometric development during childhood. In this study, we aimed to identify and characterize the effects of glycemic variability on linear growth in children with T1D.
Methods
Data from 144 prepubertal children with T1D were evaluated. Anthropometric measurements (weight, weight-SDS, height, height-SDS, BMI, BMI-SDS) were collected and glycosylated hemoglobin (HbA1c) was measured at admission and every 4 months over a 2-year period. Glycemic variability indexes (glycemic coefficient of variation (CV), glycemic CV percentage (CV%), and the product between HbA1c-mean and HbA1c-SDS/100 (M*SDS-HbA1c/100)) were calculated. According to height-SDS changes after 2 years of follow-up, the study population was divided into three tertile groups and differences across groups were investigated for variables of interest.
Results
The three groups were similar in terms of age, gender, and follow-up period. After 2 years, all prepubertal children showed a significant positive trend of anthropometric data. Across the three tertile groups, HbA1c-SDS, CV, CV%, and M*SDS-HbA1c significantly decreased from the first to the third tertile of height-SDS. During follow-up, children with lower Δheight-SDS values reported higher values of HbA1c-SDS, CV, CV%, and M*SDS-HbA1c than subjects with higher linear growth.
Conclusions
Glycemic variability correlates with linear growth in children with T1D. Low glycemic variability indexes were reported in higher height-SDS tertiles. Δheight-SDS is inversely correlated with glycemic CV, CV%, and M*SDS-HbA1c.
Introduction
Type 1 diabetes (T1D) and other chronic diseases are well recognized to adversely affect linear growth in childhood. Indeed, early studies have shown that suboptimal glycemic control and longer disease duration can impair anthropometric development in children with T1D (1, 2). The Mauriac syndrome, characterized by growth failure and hepatomegaly, constitutes the most critical growth-related condition characterized by long-term and poorly controlled T1D in relation to chronic under-insulinization (3). These effects are mainly related to the role of insulin in the regulation of the growth hormone (GH)/insulin-like growth factor-1 (IGF-1) axis. In fact, normal insulin secretion and adequate portal insulin levels are necessary to obtain normal serum concentrations of IGF-1 and IGF-binding proteins (IGFBPs), thus indirectly ensuring physiological growth in children. In particular, many studies have demonstrated that insulin could influence GH receptor (GHR) expression in the liver and interact with GH post-receptor signaling, thereby regulating the hepatic synthesis of IGF-1 and IGFBPs (4, 5). Thus, in children with T1D, portal insulin deficiency frequently occurs and determines GH hypersecretion, low circulating concentrations of IGF-1 and IGFBP-3, with high serum levels of IGFBP-1 consequently. Indeed, exogenous s.c. insulin therapy seems not able to replace pancreatic insulin secretion in portal circulation (6). These impaired hormonal patterns have been largely described not only in an actually rare condition such as Mauriac syndrome but also in many children and adolescents with poorly controlled T1D (7, 8, 9). In particular, many authors have detected an association between the lowest IGF-1 levels in those children with the highest glycated hemoglobin (HbA1c) levels (9, 10). Other studies have also documented an inversely strict correlation between height velocity and HbA1c value in prepubertal children with T1D (11). However, modern diabetes care, particularly intensified insulin regimens and novel technologies, can improve metabolic control in patients with T1D, therefore preventing abnormalities of the GH–IGF-I axis, thus potentially leading to normal growth and final height similar to unaffected peers (12). Bizzarri and colleagues described that HbA1c levels modulated the growth pattern in diabetic children and demonstrated that height velocity after T1D diagnosis was directly associated with pancreatic beta cell residual activity, evaluated as C-peptide levels (8). However, although a large number of data have clearly shown that glucose variability strongly affects the risk of diabetes-related microvascular and macrovascular complications (13, 14, 15), no data characterizing its effect on growth pattern are available. Therefore, the aim of the study was to identify and characterize the effects of glycemic variability on linear growth in a well-selected group of prepubertal children with T1D, by using accurate glycemic variability indexes during a 2-year follow-up period.
Materials and methods
Study population
We performed a retrospective study evaluating charts of children with T1D at the Department of Pediatrics, University of Chieti, Chieti, Italy. In the outpatient clinic, subjects with T1D are followed every 4 months, and during each visit, data regarding weight, height, BMI, and pubertal development are routinely evaluated and HbA1c values are measured. In particular, we included data of 144 prepubertal Caucasian children (76 male/68 female) with T1D, older than 5 years at onset. In addition, only children younger than 9 years and 10 years for female and male, respectively, thus without signs of pubertal development according to the Tanner stage at baseline and during follow-up, were included in order to avoid confounding effects of puberty on growth pattern. Only data after 6 months from the diagnosis of T1D (T0) were included. In addition, only children with complete data during a 2-year (T24) follow-up were included. Furthermore, in order to exclude the potential effects on growth, children with other autoimmune complications or genetic disorders, patients with chronic diseases (i.e. celiac disease, thyroid abnormalities), or using corticosteroid therapy or with genetic syndromes (i.e. Prader-Willi syndrome, Down syndrome) were excluded.
All children have had a diagnosis of T1D according to American Diabetes Association (ADA) criteria (16). All selected patients were treated with intensive insulin therapy consisting of injections of rapid insulin (lispro or aspart) plus long-acting insulin (degludec) once a day. Periodical adjustments were made every 3 or 6 months. Children using continuous s.c. insulin infusion were not included.
Ethics approval for this study was not required, since (i) it was a retrospective study confined to anonymized and unidentifiable data that are routinely collected at the outpatient clinic for T1D at the University Hospital in Chieti; and (ii) the study findings would not affect patient care retrospectively.
Physical characteristics
Anthropometric measurements (height, height-SDS, weight, weight-SDS, BMI, BMI-SDS) were determined in all children at admission (T0) and every 4 months during the 2-year follow-up (T24). Body weight was taken with a digital scale to the nearest ±0.1 kg. Height was evaluated in triplicate with a wall-mounted Harpenden stadiometer to the nearest ±0.1 cm. BMI was used as the fatness index and calculated as the ratio of weight (kg)/height2 (m). Weight-SDS, height-SDS, BMI-SDS for age and sex were also obtained using the reference data for Italian population (17). Pubertal stage was defined according to Tanner’s stage for both sexes (18).
Laboratory procedures and glycemic variability indexes
In all subjects, HbA1c values were obtained at admission (T0) and every 4 months during the 2 years (T24) by using the high-performance liquid chromatography (HPLC) technique. Moreover, specific indexes to characterize the glycemic variations of T1D children were also calculated. In particular, according to HbA1c values during the follow-up, we calculated the glycemic mean evaluated as mean-HbA1c (M-HbA1c), the standard deviation score of HbA1c (HbA1c-SDS), the coefficient of variation (CV) of HbA1c (CV = HbA1c-SDS/meanHbA1c), the percentage of CV (CV%), the product between HbA1c-mean and HbA1c-SDS divided by 100 (M*SDS-HbA1c).
Statistical analysis
Statistical analysis was performed using SPSS version 23.0 software for Windows (IBM). Values were expressed as mean ± s.d. A P-value < 0.05 was considered statistically significant. The variations of the main anthropometric and laboratory parameters (weight, weigh-SDS, height, height-SDS, BMI, BMI-SDS, HbA1c) in the entire population between admission (T0) and 2-year period (T24) were calculated by using the Wilcoxon test. In addition, in order to explore differences in terms of glycemic variability, according to different growth patterns, the height-SDS changes after 2 years of follow-up (height-SDS after 2 years − height-SDS at baseline) were calculated and the study population was then divided into three tertile groups (first tertile: height-SDS ≤ −0.29; second tertile: −0.29 < height-SDS < 0.20; third tertile: height-SDS ≥ 0.20). Thus, differences across the three groups were investigated by Kruskal–Wallis test, while the Mann–Whitney U test was used for post hoc analysis. In order to further characterize the variations of anthropometric SDS values across the three tertile groups, the delta changes for age, weight-SDS, height-SDS, and BMI-SDS were calculated as variables at follow (T24) − variable at baseline (T0) (Δ(T24 − T0)) and differences across the three groups were investigated by Kruskal–Wallis test while the Mann–Whitney U test was used for post hoc analysis. The Spearman test was also performed to evaluate the correlation between height-SDS and the glycemic variability indexes.
Results
Physical and biochemical characteristics at admission and during follow-up
The main anthropometric and biochemical characteristics of the study population including 144 prepubertal children with T1D, recruited and followed up for 2 years, at 4-month intervals are summarized in Table 1.
Clinical and biochemical characteristics of all children with T1D at admission (T0) and after 2years of follow-up (T24).
| Variables | Admission (T0) | Follow-up (T24) | P-value |
|---|---|---|---|
| Sample size | 144 | 144 | |
| Gender (male/female) | 76(53%)/68(47%) | 76(53%)/68(47%) | |
| Age (years) | 7.7 ± 2.0 | 9.8 ± 2.0 | |
| Weight (kg) | 30.8 ± 8.7 | 40.5 ± 11.6 | <0.01 |
| Weight-SDS | 0.44 ± 0.99 | 0.55 ± 1.0 | 0.02 |
| Height (cm) | 127.6 ± 12.5 | 139.6 ± 12.8 | <0.01 |
| Height-SDS | 0.25 ± 1.0 | 0.26 ± 1.1 | 0.82 |
| BMI (kg/m2) | 18.5 ± 2.6 | 20.4 ± 3.4 | <0.01 |
| BMI-SDS | 0.50 ± 0.92 | 0.61 ± 0.95 | 0.07 |
| HbA1c (%) | 7.7 ± 1.7 | 7.3 ± 1.2 | 0.07 |
Bold indicates statistical significance. HbA1c, glycosylated hemoglobin.
In particular, data from 76 boys and 68 girls were evaluated with a mean age at admission of 7.7 ± 2.0 years. At admission, the mean value of HbA1c was 7.7 ± 1.7%. In the entire population, as expected after 2 years of follow-up (T24), mean weight (P < 0.01) and weight-SDS (P = 0.02), the mean height (P < 0.01), and the mean BMI (P < 0.01) significantly improved, while the BMI-SDS and height-SDS increased and the mean HbA1c decreased although they did not reach a significant value.
Main anthropometric and laboratory data of the study population divided according to changes in height-SDS at follow-up (height-SDS tertiles)
The main data of the three groups were divided into tertiles according to the changes of height-SDS at follow-up (height-SDS at T24−height-SDS at T0) are reported in Table 2. In particular, at admission (T0), the three groups were similar in terms of the main anthropometric measurements (weight, weight-SDS, height, height-SDS, BMI, and BMI-SDS) and in terms of metabolic control as defined by HbA1c values (all P > 0.05). Therefore, in order to characterize the variations of anthropometric SDS values across the three tertile groups, the delta changes were calculated and reported in Table 3. As expected, Δ-height-SDS results were statistically different across the three groups, thus increasing from the first to the third tertile of height-SDS (P < 0.001) during follow-up. In addition, no difference was reported for Δ-age and Δ-BMI-SDS across the three tertile groups. In contrast, Δ-weight-SDS values were significantly different across the three groups (P < 0.001).
Main clinical and biochemical characteristics at admission (T0) and after 2 years of follow-up (T24) of children with T1D divided according to height-SDS tertile.
| Height-SDS 1° tertile height-SDS ≤ −0.29 | Height-SDS 2° tertile −0.29 < Height-SDS < 0.20 | Height-SDS 3° tertile height-SDS ≥ 0.20 | P-value(at T0) | ||||
|---|---|---|---|---|---|---|---|
| T0 | T24 | T0 | T24 | T0 | T24 | ||
| Sample size | 48 | 48 | 48 | ||||
| Gender (male/female) | 27/21 | 27/21 | 27/21 | 0.69 | |||
| Age (years) | 8.2 ± 2.0 | 10.2 ± 2.0 | 7.6 ± 2.0 | 9.6 ± 2.0 | 7.4 ± 1.9 | 9.5 ± 1.9 | 0.14 |
| Weight (kg) | 32.4 ± 8.6 | 40.8 ± 11.4 | 30.5 ± 10.1 | 39.7 ± 12.9 | 29.7 ± 7.2 | 41.0 ± 10.5 | 0.24 |
| Weight-SDS | 0.43 ± 0.96 | 0.30 ± 1.0 | 0.39 ± 1.0 | 0.52 ± 0.97 | 0.49 ± 0.93 | 0.83 ± 0.92 | 0.83 |
| Height (cm) | 130 ± 12 | 139 ± 12 | 126 ± 12 | 138 ± 12 | 125 ± 12 | 141 ± 14 | 0.18 |
| Height-SDS | 0.29 ± 0.95 | −0.18 ± 0.98 | 0.28 ± 0.95 | 0.21 ± 0.92 | 0.18 ± 1.1 | 0.76 ± 1.2 | 0.87 |
| BMI (kg/m2) | 18.7 ± 2.5 | 20.5 ± 3.5 | 18.4 ± 3.2 | 20.3 ± 3.8 | 18.6 ± 2.1 | 20.3 ± 2.8 | 0.66 |
| BMI-SDS | 0.48 ± 0.87 | 0.52 ± 0.99 | 0.39 ± 1.0 | 0.59 ± 0.97 | 0.63 ± 0.81 | 0.71 ± 0.89 | 0.51 |
| Mean HbA1c (%) | 7.3 ± 1.4 | 7.2 ± 0.9 | 7.6 ± 1.5 | 7.3 ± 1.6 | 7.6 ± 1.4 | 7.4 ± 0.9 | 0.19 |
Data are expressed as mean ± s.d. P significant <0.05.
HbA1c, glycosylated hemoglobin.
Delta values (T24−T0) of the main anthropometric variables in the three groups of height-SDS tertiles.
| Height-SDS 1° tertile height-SDS ≤ −0.29 | Height-SDS 2° tertile −0.29< height-SDS < 0.20 | Height-SDS 3° tertile height-SDS ≥ 0.20 | P-value | |
|---|---|---|---|---|
| Δ(T24−T0)-age (years) | 2.0 ± 0.14 | 1.9 ± 0.10 | 2.0 ± 0.16 | 0.10 |
| Δ(T24−T0)-weight-SDS | −0.13 ± 0.36 | 0.13 ± 0.55 | 0.33 ± 0.43 | <0.001 |
| Δ(T24−T0)-BMI-SDS | 0.04 ± 0.45 | 0.19 ± 0.71 | 0.07 ± 0.51 | 0.60 |
| Δ(T24−T0)-height-SDS | −0.47 ± 0.17 | −0.04 ± 0.14 | 0.57 ± 0.29 | <0.001 |
Bold indicates statistical significance.
Main glucose variability indexes in the study population divided according to changes in height-SDS at follow-up (height-SDS tertiles)
Figure 1 shows data regarding the main glucose variability indexes across height-SDS tertiles (Fig. 1). In detail, glucose variability indexes significantly improved with the increase of height-SDS tertiles. In fact, HbA1c-SDS significantly decreased from the first to the third tertile group (P for trend< 0.001); in addition, HbA1c-SDS was also higher in the second group than the third tertile (P = 0.01) (Fig. 1A). In addition, CV (P for trend< 0.001: first tertile, 0.12, second tertile 0.11, and third tertile 0.09, P0) and the CV% significantly decreased from the first to the third group (P for trend < 0.001), showing higher values in the second tertile compared to the third (P = 0.01) and as well as in the second compared to the first tertile (P = 0.16) (Fig. 1B). Similarly, the M*SDS-HbA1c progressively and significantly decreased across the three tertiles (P for trend= 0.001), showing lower values in the third tertile compared to the second (P = 0.02) and first (P < 0.001) as well as in the second compared to the first tertile (P = 0.19) (Fig. 1C). Of note, no statistically significant difference in terms of mean HbA1c values across the three tertiles (P for trend =0.99) was documented.
Main glucose variability indexes across height-SDS tertiles. HbA1c-SDS (A), CV percentage (CV%) (B), and M*SDS-HbA1c (C) across the three tertiles of height-SDS.
Citation: Endocrine Connections 12, 4; 10.1530/EC-22-0370
Correlation analyses
An indirect and statistically significant correlation was documented between HbA1c-SDS and Δ(T24−T0)-height-SDS values (P = 0.006, β = −0.22). In particular, a higher HbA1c-SDS value was observed in those children who had a lower Δ(T24−0)-height-SDS (Fig. 2A). In addition, Δ(T24−T0)-height-SDS correlated significantly and indirectly with CV (P = 0.003, β = −0.24) and CV% (P = 0.003, β = −0.24). Indeed, children with lower Δ(T24−T0)-height-SDS showed higher CV and CV% values (Fig. 2B), thus the higher was the glucose variability the lower was the increase of height-SDS at follow-up. Moreover, an indirect and statistically significant association between M*SDS-HbA1c and Δ(T24−T0)-height-SDS values (P = 0.01, β = −0.22) was found. In particular, children with lower values of Δ(T24−T0)-height-SDS reported higher values of M*SDS-HbA1c (Fig. 2C). Of note, in contrast, no significant correlation was documented between mean-HbA1c and Δ(T24−T0)-height-SDS (P = 0.74).
Association between Δheight-SDS and main glucose variability indexes. (A) Association between Δheight-SDS and HbA1c-SDS (P = 0.006). (B) Association between Δheight-SDS and CV% (P = 0.003). (C) Association between Δheight-SDS and M*SDS-HbA1c (P = 0.01).
Citation: Endocrine Connections 12, 4; 10.1530/EC-22-0370
Discussion
In this study, by evaluating a highly selected population of only pre-pubertal children with T1D, we showed for the first time a direct association between glycemic variability and linear growth. In fact, the worse was the increase of height-SDS during a 2-year follow-up the higher was the glucose variability during follow-up, thus documenting a positive effect of low glucose variability in the linear group.
Linear growth in children is a complex multifactorial physiological process influenced by nutritional, endocrinological, and psychological elements. Many studies reported that T1D duration and its metabolic control could modulate children’s linear growth pattern and final adult height (19). In particular, the GH-IGF-1 axis has been described to be impaired in these patients. These effects are directly related to low IGF-1 serum concentrations caused by low intraportal insulin concentration (4). Therefore, in poorly controlled T1D, exogenous s.c. insulin therapy could be unable to replace pancreatic insulin secretion in portal circulation, thus impairing linear growth in children (20). Previously, Van Sickle and colleagues confirmed that adolescents with T1D who reported higher HbA1c values presented higher levels of interleukin-8 (IL-8) and lower IGF-1 serum concentrations than diabetic peers with better metabolic control (21). Bonfig and colleagues evaluating anthropometric parameters and glycemic control in children with T1D demonstrated that children with HbA1c < 7.0% had a better final adult height-SDS, while the groups with HbA1c 7.0–8.0% and suboptimal metabolic profile (HbA1c > 8.0%) reported a worst final adult height-SDS (22). In addition to these previous studies, we were able to confirm that not only metabolic control is defined by HbA1c values but also glucose variability is related to growth pattern. In fact, in our study, we have confirmed an indirect and statistically significant association between indexes of glycemic variability and height-SDS values in prepubertal children with T1D during the prepubertal period. In particular, children with lower height-SDS values at follow-up reported higher glycemic variability indexes, namely, HbA1c-SDS, CV, CV%, and M*SDS-HbA1c. Interestingly, correlations were also found between glycemic index (HbA1c-SDS, CV, CV%, and M*SDS-HbA1c) and Δheight-SDS, confirming the possible link between glucometabolic profile and linear growth in the pediatric population with T1D during pre-puberty. Therefore, these data suggest a potential link between glycemic control and growth parameters. To date, several studies have shown the direct correlation of these indexes with the risk of microvascular and macrovascular complications (23, 24). Of note, these indexes, independently of the overall HbA1c values, better predict the risk of complications. In fact, it is largely known that children with similar mean glucose or HbA1c levels reported differences in terms of both the number and degree of glucose excursions that have been largely associated with diabetic complications (25). It is important to highlight that in our study, delta in weight-SDS and height-SDS overtime significantly increased across the three tertile groups, although no differences in terms of overall adiposity were documented as expressed by no statistically significant differences in terms of BMI-SDS. Thus, since weight might also drive height, in order to properly characterize whether the differences could be due to higher weight and not necessarily the glycemic control itself, or the two effects are independent, it is necessary to confirm our results in further studies including a larger population, thus allowing to categorize the tertile groups according to the degree of weight gain.
We are aware of some limitations of the present study related to the retrospective nature of the study, which strongly affect the availability of data regarding food diary or physical activity. In addition, a relevant limitation is the absence of IGF-1, IGFBP3, and ALS measurements able to show a direct effect of glucose variability on hormonal patterns. In addition, a longer follow-up evaluating data up to the final height is needed in order to confirm our results. As well, evaluating a highly selected population, it is not possible to extend these data to other ethnic groups; therefore, further studies in this sense will be necessary to confirm such results according to this relevant factor. Finally, the lack of data regarding parental height and especially bone age might not offer a complete view regarding its relevant effects on growth and particularly do not allow to differentiate fast growers (bone age>calendar age) from late bloomers (bone age<calendar age). A strength of our study is the highly selected population of prepubertal children with a close age range evaluated. In fact, by evaluating the data of subject older than 5 years and with the absence of signs of pubertal development during the entire follow-up, we were able to minimize several hormonal effects on growth related to puberty. In addition, by excluding subjects with other diseases able to affect growth such as celiac disease or thyroid dysfunction, we were able to strongly minimize additional confounding factors on growth.
Conclusions
In conclusion, not only metabolic control but also glucose variability is able to affect the growth pattern in prepubertal children with T1D. Therefore, new insulin regimes and new technologies able to minimize glucose variability are needed in children with T1D in order to improve linear growth in prepubertal children with T1D.
Declaration of interest
The authors declare that the research was performed in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Funding
This work did not receive any specific grant from any funding agency in the public, commercial or not-for-profit sector.
Author contribution statement
All authors contributed to the research of the articles and to the writing of the manuscript.
References
- 1↑
Penfold J, Chase HP, Marshall G, Walravens CF, Walravens PA, & Garg SK. Final adult height and its relationship to blood glucose control and microvascular complications in IDDM. Diabetic Medicine 1995 12 129–133. (https://doi.org/10.1111/j.1464-5491.1995.tb00443.x)
- 2↑
Mauras N, Merimee T, & Rogol AD. Function of the growth hormone-insulin-like growth factor i axis in the profoundly growth-retarded diabetic child: evidence for defective target organ responsiveness in the Mauriac syndrome. Metabolism 1991 40 1106–1111. (https://doi.org/10.1016/0026-0495(9190138-M)
- 3↑
Giordano S, Martocchia A, Toussan L, Stefanelli M, Pastore F, Devito A, Risicato MG, Ruco L, & Falaschi P. Diagnosis of hepatic glycogenosis in poorly controlled type 1 diabetes mellitus. World Journal of Diabetes 2014 5 882–888. (https://doi.org/10.4239/wjd.v5.i6.882)
- 4↑
Dunger DB, & Cheetham TD. Growth hormone insulin-like growth factor I Axis in insulin-Dependent diabetes mellitus. Hormone Research 1996 46 2–6. (https://doi.org/10.1159/000184969)
- 5↑
Santi E, Tascini G, Toni G, Berioli MG, & Esposito S. Linear growth in children and adolescents with type 1 diabetes mellitus. International Journal of Environmental Research and Public Health 2019 16 3677. (https://doi.org/10.3390/ijerph16193677)
- 6↑
Shishko PI, Dreval AV, Abugova IA, Zajarny IU, & Goncharov VC. Insulin-like growth factors and binding proteins in patients with recent-onset type 1 (insulin-dependent) diabetes mellitus: influence of diabetes control and intraportal insulin infusion. Diabetes Research and Clinical Practice 1994 25 1–12. (https://doi.org/10.1016/0168-8227(9490155-4)
- 7↑
Ljungkrantz M, Ludvigsson J, & Samuelsson U. Type 1 diabetes: increased height and weight gains in early childhood. Pediatric Diabetes 2008 9 50–56. (https://doi.org/10.1111/j.1399-5448.2007.00360.x)
- 8↑
Bizzarri C, Benevento D, Giannone G, Bongiovanni M, Anziano M, Patera IP, Cappa M, & Cianfarani S. Sexual dimorphism in growth and insulin-like growth factor-I in children with type 1 diabetes mellitus. Growth Hormone and IGF Research 2014 24 256–259. (https://doi.org/10.1016/j.ghir.2014.08.004)
- 9↑
Dills DG, Allen C, Palta M, Zaccaro DJ, Klein R, & D'Alessio D. Insulin-like growth factor-I is related to glycemic control in children and adolescents with newly diagnosed insulin-dependent diabetes. Journal of Clinical Endocrinology and Metabolism 1995 80 2139–2143. (https://doi.org/10.1210/jcem.80.7.7608267)
- 10↑
Raisingani M, Preneet B, Kohn B, & Yakar S. Skeletal growth and bone mineral acquisition in type 1 diabetic children; abnormalities of the GH/IGF-1 axis. Growth Hormone and IGF Research 2017 34 13–21. (https://doi.org/10.1016/j.ghir.2017.04.003)
- 11↑
Bizzarri C, Benevento D, Patera IP, Bongiovanni M, Boiani A, Fusco C, Cianfarani S, & Cappa M. Residual β-cell mass influences growth of prepubertal children with Type 1 diabetes. Hormone Research in Paediatrics 2013 80 287–292. (https://doi.org/10.1159/000355116)
- 12↑
Chiarelli F, Giannini C, & Mohn A. Growth, growth factors and diabetes. European Journal of Endocrinology 2004 151(Supplement 3) U109–U117. (https://doi.org/10.1530/eje.0.151u109)
- 13↑
Zhou Z, Sun B, Huang S, Zhu C, & Bian M. Glycemic variability: adverse clinical outcomes and how to improve it? Cardiovascular Diabetology 2020 19 102. (https://doi.org/10.1186/s12933-020-01085-6)
- 14↑
Hirsch IB. Glycemic variability and diabetes complications: does it matter? Of course it does! Diabetes Care 2015 38 1610–1614. (https://doi.org/10.2337/dc14-2898)
- 15↑
Zhang X, Yang X, Sun B, & Zhu C. Perspectives of glycemic variability in diabetic neuropathy: a comprehensive review. Communications Biology 2021 4 1366. (https://doi.org/10.1038/s42003-021-02896-3)
- 16↑
American Diabetes Association (ADA). Classification and diagnosis of diabetes: standards of medical care in diabetes. Diabetes Care 2021 44 S15–S33. (https://doi.org/10.2337/dc21-S002)
- 17↑
Cacciari E, Milani S, Balsamo A, Spada E, Bona G, Cavallo L, Cerutti F, Gargantini L, Greggio N, Tonini G, et al.Italian cross-sectional growth charts for height, weight and BMI (2 to 20 yr). Journal of Endocrinological Investigation 2006 29 581–593. (https://doi.org/10.1007/BF03344156)
- 19↑
Mitchell DM. Growth in patients with type 1 diabetes. Current Opinion in Endocrinology, Diabetes, and Obesity 2017 24 67–72. (https://doi.org/10.1097/MED.0000000000000310)
- 20↑
Daughaday WH, Phillips LS, & Muller MC. The effects of insulin and growth hormone on the release of somatomedin by the isolated rat liver. Endocrinology 1976 98 1214–1219. (https://doi.org/10.1210/endo-98-5-1214)
- 21↑
Van Sickle BJ, Simmons J, Hall R, Raines M, Ness K, & Spagnoli A. Increased circulating IL-8 is associated with reduced IGF-1 and related to poor metabolic control in adolescents with type 1 diabetes mellitus. Cytokine 2009 48 290–294. (https://doi.org/10.1016/j.cyto.2009.08.011)
- 22↑
Bonfig W, Kapellen T, Dost A, Fritsch M, Rohrer T, Wolf J, & Holl RW. Diabetes Patienten. Growth in children and adolescents with Type 1 diabetes. Jurnalul Pediatrului 2012 160 900-3.e2.
- 23↑
Nusca A, Tuccinardi D, Albano M, Cavallaro C, Ricottini E, Manfrini S, Pozzilli P, & Di Sciascio G. Glycemic variability in the development of cardiovascular complications in diabetes. Diabetes/Metabolism Research and Reviews 2018 34 e3047. (https://doi.org/10.1002/dmrr.3047)
- 24↑
Ceriello A, Monnier L, & Owens D. Glycaemic variability in diabetes: clinical and therapeutic implications. Lancet. Diabetes and Endocrinology 2019 7 221–230. (https://doi.org/10.1016/S2213-8587(1830136-0)
- 25↑
Saisho Y. Glycemic variability and oxidative stress: a link between diabetes and cardiovascular disease? International Journal of Molecular Sciences, 2014 15 18381–18406. (https://doi.org/10.3390/ijms151018381)
