Background

Type 1 diabetes mellitus (T1DM) is one of the most common pediatric chronic diseases, in which a progressive autoimmune T cell-mediated β cell destruction of pancreatic islets in genetically susceptible individuals (Wang et al. 2017), resulting in loss of insulin secretion (Larsson et al. 2016). Numerous studies have reported an increasing incidence of type 1 diabetes in children worldwide (Patterson et al. 2015; El Wakeel et al. 2017; Sabry et al. 2018). It has major health consequences for individuals and societies. Management of type I diabetes mellitus (T1DM) is lifelong and challenging, considered an overwhelming demanding disease that affects all aspects of patient’s life and thus can negatively affect the mental and physical health of the diseased children, impacting their health-related quality of life (HRQOL) (Jaser et al. 2017).

Several studies have demonstrated that diabetes has a strong negative impact on HRQOL, relative to the general population (Abuawad 2013; Jain et al. 2014; Nielsen et al. 2016), especially in the presence of complications (Hirai et al. 2013; Smith-Palmer et al. 2016). Diabetic neuropathy greatly reduces the quality of life in people with DM through increased disability and assuming more hospitalizations than other diabetic complications (Mahmood et al. 2009; Tesfaye and Selvarajah 2012; Abuawad 2013). Furthermore, greater pain levels in patients suffering distal symmetric polyneuropathy (DSPN), corresponded with higher levels of emotional distress, more sleep problems, and lower ratings of functioning and HRQOL (Vileikyte et al. 2017). The negative impact of severe hypoglycemic episodes on the QOL of young people was demonstrated in many studies, due to increased fear and anxiety, reduced productivity, and increased healthcare costs. Fear of hypoglycemia may promote compensatory behaviors in order to avoid hypoglycemia, such as decreased insulin doses, resulting in poor glycemic control, and an increased risk of serious health consequences, with further contribution to the negative impact on health and QOL (Hoey et al. 2001; Nordfeldt and Ludvigsson 2005; Fidler et al. 2011; Bahia et al. 2017).

There is a growing interest in the study of HRQOL, which has become an important end-point measure from the clinical and epidemiological point of view (Ravens-Sieberer et al. 2006; Floyd et al. 2017), commonly used as an outcome indicator because patient cooperation forms the core of health plans for incurable diseases (Lontchi-Yimagou et al. 2017).

It has been shown that poor HRQOL in patients with diabetes is associated with adverse outcomes and increased mortality (Kleefstra et al. 2008; de Souza et al. 2015). Enhancing QOL and well-being in people with diabetes is as important as metabolic control and prevention of secondary morbidity (Caferoğlu et al. 2016).

Methods

The aim of the present study was to assess health-related quality of life of a group of Egyptian children and adolescents with type 1 diabetes and to investigate the relation of microvascular complications and other sociodemographic and clinical indicators to their quality of life.

Participants

This case-control study included 60 children and adolescents with type 1 diabetes mellitus (84 type 1 diabetic patients recruited from the regular attendants of the Diabetes Clinic, Children’s Hospital, Ain Shams University and Medical Research Centre of Excellence clinics, who fulfilled the inclusion criteria and accepted to participate in the study, 24 patients dropped out through the study and 60 patients completed the study). Written informed consent was obtained from the parents after explanation of the aim of the study, its benefits and expected risks for their children if they participate in the study and informed verbal assent was taken also from all participating children after a simplified explanation of the aim and benefits of the study for them. They were 22 (36.7%) males and 38 (63.3%) females, with age range (8-18 years), with a mean of 13.03 ± 2.78 years. “The involved diabetic children fulfilled the inclusion criteria of age (8-18 years), both sexes of cases diagnosed with Type 1 diabetes mellitus who are receiving insulin therapy and on regular visits to diabetes clinic, excluding cases who are associated with another chronic disease (e.g. chronic renal failure, cardiac diseases chronic chest disease … etc.) and co-morbid psychiatric disorders (e.g. major depressive disorder, autistic disorder … etc.), that may affect quality of life”.

A control group of 60 healthy children and adolescents (recruited from the outpatient clinics and relatives of the diabetic children), with no chronic diseases or obvious medical disorders and not receiving any medications were matched for age, sex, and socioeconomic levels with the study diabetic group. They were 23 (38.3%) males and 37 (61.7%) females, with age range (Alvarado-Martel et al. 2015; American Diabetes Association 2018; Anderson et al. 2017; Arabiat and Al Jabery 2013; Bahia et al. 2017; Bai et al. 2017; Barcellos et al. 2014; Baş et al. 2011; Bisegger et al. 2005; Caferoğlu et al. 2016; Chawla 2004) years with a mean of 12.43 ± 2.66 years.

According to the presence of microvascular complications, patients were subdivided into two groups; Group I included 30 uncomplicated children and adolescents with T1DM, with age range (Alvarado-Martel et al. 2015; American Diabetes Association 2018; Anderson et al. 2017; Arabiat and Al Jabery 2013; Bahia et al. 2017; Bai et al. 2017; Barcellos et al. 2014; Baş et al. 2011) years with a mean of 11.25 ± 1.92 years. They were 12 (40.0%) males and 18 (60.0%) females. Group II included 30 children and adolescents with T1DM, and they had one or more diabetic complications, with age range (Arabiat and Al Jabery 2013; Bahia et al. 2017; Bai et al. 2017; Barcellos et al. 2014; Baş et al. 2011; Bisegger et al. 2005; Caferoğlu et al. 2016; Chawla 2004) years with a mean of 14.80 ± 2.35 years. They were 10 (33.3%) males and 20 (66.7%) females. According to age, patients and controls were subdivided into two groups (to correspond to the classification of age of PedsQL™).

Processes

All patients and controls were subjected to the following:

  1. I.

    Full history taking: laying stress upon:

    1. A.

      Sociodemographic data: (for all patients and controls) name, age, sex, and socioeconomic class.

    2. B.

      Medical history of diabetes: (for cases)

      1. 1)

        Age of onset and duration of the disease and insulin therapy.

      2. 2)

        History suggestive of acute metabolic complications (in the last year prior to the study); frequency of hypoglycemia or ketoacidosis and number of hospital admission due to severe attacks of hypoglycemia or diabetic ketoacidosis.

      3. 3)

        History suggestive of chronic diabetic complications: ocular manifestations suggestive of diabetic retinopathy (persistent blurring of vision or flashes of light), peripheral neuropathy manifestations (tingling, numbness, paresthesia, impaired or lost sensation), renal manifestations suggestive of diabetic nephropathy (polyuria, oliguria, dysuria, loin pain, or haematuria).

  2. II.

    Full clinical examination: Thorough clinical examination was done with particular emphasis on:

    1. A.

      Routine examinations for all patients and controls:

      1. 1.

        Auxological assessment; growth was assessed through auxological measurements of weight (kg) and height (cm), body mass index (BMI = weight in kg/height in m2), weight for age, height for age, and body mass index for age z scores were calculated and recorded according to World Health Organization (WHO) standards using AnthroPlus software for personal computers) (WHO 2011).

      2. 2.

        Assessment of Tanner stage using Tanner classification (Tanner 1976).

      3. 3.

        Measurement of blood pressure (mmHg) was done by a mercury sphygmomanometer.

    2. B.

      Examinations done for patients for evidence of any diabetic complications:

      1. 1.

        Complete examination including chest, heart, abdomen, and full neurological examination for evidence of any diabetic complications.

      2. 2.

        Fundus examination by direct ophthalmoscope through dilated pupils for assessment of diabetic retinopathy.

  3. III.

    Laboratory investigations: Routine investigations for all cases were done

    1. 1.

      Random blood glucose with the calculation of the mean value over the last year prior to the study.

    2. 2.

      HbA1c, the mean value over the last year was calculated.

    3. 3.

      Quantitative determination of urinary microalbumin for detection of diabetic nephropathy. Microalbuminuria was defined as the excretion rate (30–300 mg/urinary creatinine).

  4. IV.

    Psychosocial assessment:

    1. 1.

      Assessment of family socioeconomic standard by the socioeconomic level of the family scale (Egyptian version) (El-Shakhs 2013)

    2. 2.

      Assessment of health-related quality of life (HRQOL) by the Pediatric Quality of Life Inventory™ (PedsQL™), one of the international tools for assessment of HRQOL of children (Varni et al. 1999). PedsQL™ instrument provides both PedsQL™ 4.0 Generic Core Scales (for healthy and diseased children) (Varni et al. 2001) and the PedsQL™ 3.0 Diabetes Module (for diabetic children) (Varni et al. 2003), having good reliability and validity and used together.

  • PedsQL™ 4.0 Generic Core Scales (GCS) includes 23 items assessing 4 subscales (physical, emotional, social, and school functioning) with 8, 5, 5, and 5 items, respectively. Response scale with five categories, ranging from never a problem (0) to almost always a problem (Abuawad 2013). PedsQL™ 3.0 diabetes module (DM) includes 28 questions assessing five subscales (diabetes symptoms, treatments barriers, treatment adherence, worry, and communications) with 11, 4, 7, 3, and 3 items, respectively. Its response scale is similar to that of the PedsQL™ 4.0 (GCS).

  • Both child self-report and parent proxy-report of the two appropriate forms were used in the present study according to the child age (reports for children (8–12 years) and adolescents (13–18 years).

Statistical analysis

All data were coded, entered, and processed on an IBM-PC compatible computer using SPSS (version 22). The quantitative data were presented as mean, standard deviations, and ranges as their distribution found parametric, while qualitative data were presented as number and percentages. The comparison between two independent groups with quantitative data and parametric distribution was done by using independent t test, and data with non-parametric distribution was done by Mann-Whitney test. The comparison between more than two independent groups with quantitative data and parametric distribution was done by using one-way ANOVA and post hoc analyses using LSD tests. Correlation analysis: assessing the strength of association between two variables. The correlation coefficient denoted symbolically r, defined the strength and direction of the linear relationship between two variables. The confidence interval (CI) was set to 95%, and the margin of error accepted was set to 5%. So, the p value was considered significant as the following: p > 0.05 = non-significant, p < 0.05 = significant, and p < 0.01 = highly significant.

Results

The present study included 60 diabetic and 60 non-diabetic (age- and sex-matched) Egyptian children and adolescents with a mean age (13.03 ± 2.78 and 12.43 ± 2.66 years), respectively. Table 1 shows some important descriptive demographic, clinical, and laboratory data of the studied diabetic and control groups. Thirty (five children and 25 adolescents) out of 60 patients had one or more microvascular complications; the most common chronic complication encountered in the studied patients was diabetic microalbuminuria being in 22 (13 patients had nephropathy alone and nine patients had nephropathy with other complications) representing 36.7% of all diabetic patients, followed by peripheral neuropathy being in 16 patients (eight patients had neuropathy alone and eight patients had neuropathy with other complications) representing 26.7% of all diabetic patients and retinopathy being in five patients (one patient had retinopathy with nephropathy and four patients had retinopathy with nephropathy and neuropathy) representing 8.3% of all diabetic patients (Fig. 1).

Table 1 Some important descriptive demographic, clinical, and laboratory data of the studied diabetic and control groups
Full size table
Fig. 1
figure 1

Column chart showing frequency of microvascular complications in the studied diabetic group

Full size image

There was no significant difference between complicated and non-complicated patients regarding the number of DKA and hypoglycemic attacks (p > 0.05), despite of being higher in complicated patients (Table 2).

Table 2 Comparison between the two studied diabetic (non-complicated and complicated) groups in terms of their metabolic control indices
Full size table

There was significant difference (p < 0.05) between complicated and non-complicated patients regarding age of onset of disease, disease duration, mean random blood glucose, HbA1c, and mean insulin dose, being higher in complicated patients except for age of onset of disease (that means the occurrence of diabetes was at younger age in diabetic patients with microvascular complications and they had poor metabolic control indices than that of non-complicated patients).

There was no significant difference between studied diabetic and healthy children (8–12 years) concerning their PedsQL™ 4.0 Generic Core Scales (GCS) (Table 3).

Table 3 Comparison between the studied diabetic children (8–12 years) and adolescents (13–18 years) with their corresponding control groups concerning their child self-reports and parent proxy-reports of PedsQL™ 4.0 Generic Core Scales (GCS) scores
Full size table

There was a significant decrease in PedsQL™ 4.0 Generic Core Scales (GCS) for studied diabetic adolescents (13–18 years) compared to healthy adolescents.

There was no significant difference between diabetic male and female children (8–12 years) concerning their PedsQL™ 4.0 Generic Core Scales (GCS) and PedsQL™ 3.0 Diabetic Module. There was a significant decrease in emotional functioning, social functioning, worry, and communication scales for studied diabetic female adolescents (13–18 years) compared to diabetic male adolescents (Table 4).

Table 4 Differentiation between male and female diabetic children (8–12 years) and adolescents (13–18 years) concerning their PedsQL™ 4.0 Generic Core Scales (GCS) and PedsQL™ 3.0 Diabetic Module
Full size table

Table 5 and Fig. 2 indicate a significant decrease in all PedsQL™ scales of the complicated group compared to the non-complicated group except for communication and diabetes symptom scales. There was a significant difference between non-complicated and control groups regarding their PedsQL™ 4.0 GCS. Multiple regressions show that neuropathy was the most correlated complication with a total score of HRQOL (p < 0.0001).

Table 5 Comparison between complicated and non-complicated groups concerning their PedsQL™ 4.0 Generic Core Scales (GCS) and PedsQL™ 3.0 Diabetic Module Scores
Full size table
Fig. 2
figure 2

Column chart showing comparison between complicated and non-complicated groups concerning their PedsQL™ 4.0 Generic Core Scales and PedsQL™ 3.0 Diabetic Module Scores

Full size image

Table 6 shows that Pediatric Quality of Life Inventory subscales and total scales scores were found to have negative statistically significant correlations with age, disease duration, BMI, SBP, and DBP in diabetic group and positive statistically significant correlations with age of onset of diabetes (for all PedsQL™ 4.0 Generic Core Scale and only for the treatment adherence of the PedsQL™ 3.0 Diabetic Module) and socioeconomic status (the most correlated factor was maternal education as proved by multiple regression analysis, p = 0.019) (Table 6).

Table 6 Correlation between Pediatric Quality of Life Inventory scores with demographic and clinical characteristics in the studied diabetic patients
Full size table

Table 7 shows that Pediatric Quality of Life Inventory subscales and total scale scores were found to have negative statistically significant correlations with all metabolic control parameters (MRBG, HbA1c, number of DKA attacks, hypoglycemic attacks, and hospitalizations) in the studied diabetic patients, except for mean insulin dose that showed negative statistically significant correlation only with the diabetes symptom scale (Table 7).

Table 7 Correlation between Pediatric Quality of Life Inventory scores with metabolic control parameters of type 1 diabetes in the studied diabetic patients
Full size table

Discussion

The most frequent chronic complication encountered in the studied patients was diabetic microalbuminuria (nephropathy alone and with other complications) being in 22 out of 60 patients representing 36.7%, followed by peripheral neuropathy being in 16 patients representing 26.7%, then retinopathy being in five patients representing 8.3% of all diabetic patients. This came in accordance with what was reported by Gross et al. 2005, De Boer et al. 2011, and ADA 2018 that microalbuminuria was a common diagnosis in the clinical care of patients with type 1 diabetes mellitus, with a lifetime cumulative incidence of 20% to 40%.

In the present study concerning the PedsQL™ 4.0 Generic Core Scales (GCS), there was no significant difference between studied diabetic and healthy children (8–12 years) (GCS total score mean = 77.05 ± 14.58 vs. 79.32 ± 11.15, respectively). But there was a significant decrease for studied diabetic adolescents (13–18 years) compared to healthy peers (64.37 ± 14.54 vs. 74.74 ± 13.34, respectively).

The decrease in general quality of life score in the group of diabetic adolescents might be due to the high percent of microvascular complications (25 out of 36 adolescents had one or more microvascular complications, representing 69.4% of the adolescent group) among them. Furthermore, the teen challenges faced with attempts of diabetes control. There were many studies that came in accordance with the current study findings, as for children, from which the study of Nieuwesteeg et al. (2012) who reviewed a systematic literature; 17 studies were eligible for these reviews, which have compared generic quality of life of children and adolescents with T1DM with that of healthy peers and revealed that, although children and adolescents with T1DM have to live with a demanding treatment regime, overall results revealed that their generic QOL scores were not impaired compared to healthy peers. However, disease-specific QOL problems, including a negative impact of diabetes on daily functioning and diabetes-related worries, were certainly present. Recently, Murillo et al. (2017b) showed that children and adolescents with T1DM reported similar HRQOL as the general population of the same age and gender, although slightly worse physical well-being than their healthy peers. Also, the study of AlBuhairan et al. 2016 was in accordance with the present study findings, as for adolescents, they reported a mean score of (64.8) indicating poor general HRQOL after conducting a cross-sectional study of the quality of life of 315 diabetic adolescents (12–18 years) in Saudi Arabia.

In contrast, some researchers reported worse quality of life for people with diabetes compared to the general population, especially regarding physical functioning and well-being, such as the case-control study done by (Abdul-Rasoul et al. 2013) on 436 diabetic children and adolescents (2–18 years) in Kuwait, who filled the PedsQL™ 4.0 Generic Core Scales (GCS) and PedsQL 3.0 Diabetes Module. Their study revealed that children with T1DM reported lower total generic QOL (75.6 ± 8.9) than controls (83.5 ± 9.5, p = 0.001). The QOL scores were lower for physical (75.1 ± 11.6 vs. 84.2 ± 9.3, p < 0.05) and emotional (72.7 ± 10.4 vs. 84.1 ± 9.1, p < 0.001) domains. There was no difference in the school (73.5 ± 9.3 vs. 73.7 ± 8.1, p = 0.15) and social (83.2 ± 8.8 vs. 83.7 ± 8.4, p = 0.13) QOL scores between patients and controls. And de Souza et al. 2015 who conducted a retrospective, cross-sectional, and multicenter study performed by the Brazilian Type 1 Diabetes Study Group that analyzed Euro QOL scores from 3005 participants with type 1 DM in 28 public clinics in Brazil.

In the present study, there were not significant differences between diabetic male and female children (8–12 years) concerning their PedsQL™ 4.0 Generic Core Scales (GCS) and PedsQL™ 3.0 Diabetic Module Scores. There was a significant decrease in the emotional functioning, social functioning, worry, and communication scales for studied diabetic female adolescents (13–18 years) compared to diabetic male adolescents; all scores of females were lower than males.

A number of researchers have reported that quality of life is better among diabetic boys than diabetic girls, and boys are more satisfied with their diabetes treatment regimen, having lower diabetes burden (Forsander et al. 2017). The same finding reported by recent studies of Murillo et al. 2017b and Anderson et al. 2017 that enrolled lower HRQOL scores in virtually all dimensions in diabetic girls. Another study was done in Egypt by M. Hassan et al. 2017 on 150 adolescents (82 males and 68 females) (10–18 years), completing “Quality of Life for Youth” questionnaire at the Diabetes, Endocrine and Metabolism Pediatric Unit (DEMPU) clinic; males showed a significantly better mean QOL score than females (p = 0.004).

It is supposed that the decreased HRQOL in diabetic girls return to their general low perception of their QOL. Bisegger et al. 2005 reported gender differences as potential causes of greater vulnerability of teen girls for decreased HRQOL aspects than boys, from the age of 13 years on, besides being diabetic, such as the more drastic puberty for girls; the onset of menstruation, hormonal fluctuations (Toffol et al. 2014; Tanna et al. 2016), higher levels of perceived stress, and stress-related health complaints in adolescent girls than boys (Östberg et al. 2015), with handling problems and difficulties in different ways, namely girls tend to direct their coping patterns inwards, and boys outwards (Wiklund et al. 2012); and less body image satisfaction (Griffiths et al. 2017) and self-esteem (Gouveia et al. 2014), more sensitivity, empathic concern (Van der Graaff et al. 2014), and argument due to the social demands placed on girls, that are more staggering and more difficult to achieve (Barcellos et al. 2014). The difference in relationships with peers and social relations between girls and boys at the teenage (Rose and Asher 2017) is also that boys often have more privileges and are given more space than girls (Chawla 2004). Besides that, diabetic females trend to have worse metabolic control, higher frequency of microvascular complications, higher incidence of diabetic ketoacidosis (DKA), dyslipidemia, and weight problems than boys at adolescence (Forsander et al. 2017).

On the contrast, some studies reported no difference between diabetic girls and boys regarding their scores of HRQOL, as the studies done by Vanelli et al. 2003, Al-Akour et al. 2010, Baş et al. 2011, and Arabiat and Al Jabery 2013. While, the recent Egyptian study done in Assiut city by Gadallah et al. 2017 reported that gender had no significant effect on any of the QOL aspects except for the school functions where girls reported significantly higher scores, explaining these findings due to Egyptian community, especially upper Egypt that do not give enough degree of freedom for females so they spend most of their times in studying, in addition to the preoccupation of males with other social activities.

The current study declared the adverse effects of diabetic microvascular complications on the HRQOL and well-being of type 1 diabetic children and adolescents, and the the most worsen effect was associated with neuropathy (p < 0.0001). Many other studies were in accord with these findings such as the study of Abolfotouh et al. 2011, Hannula et al. 2015, and Alvarado-Martel et al. 2015 who reported lower QOL scores associated with diabetic complications. Moreover, Jacobson et al. 2013 revealed that long-term chronic diabetic complications and their associated symptoms led to decreased HRQOL, with most worsen effect associated with neuropathy (p < 0.0001). The same finding was illustrated by Bai et al. 2017 that compared to other complications, neuropathy had the greatest association with distress and depression in longstanding T1DM, that in accordance with what was previously reported by Dobrota et al. 2014 that the quality of life was expectedly lower in diabetic patients suffering painful polyneuropathy, basically caused by painful symptoms, sleeping disorders, higher intensity of depressive symptoms in these dissatisfied patients and that all these conditions require additional efforts in terms of time and energy needed for treatment and lay a more drastically burden on subject’s quality of life than the disease itself. On the other hand, Tofeeq et al. 2017 found an impact of both nephropathy and retinopathy on the physical domain of quality of life while there was no significant association between neuropathy and physical problems of children and adolescents with T1D.

In the current study, negative statistically significant correlations were found between HRQOL domains with demographic and clinical characteristics (age, disease duration, BMI, SBP, and DBP), and positive statistically significant correlations with socioeconomic status (especially maternal education, p = 0.019) and age of onset of diabetes (for all PedsQL™ 4.0 Generic Core Scale and only for the treatment adherence of the PedsQL™ 3.0 Diabetic module) were illuminated in the studied diabetic patients. As regards the effect of disease duration on HRQOL, current results were in accord with the studies of Jafari et al. 2011, Hilliard et al. 2013, Abdul-Rasoul et al. 2013, and da Costa and Vieira 2015. The negative impact of long diabetes duration on quality of life might result from significant deterioration in glycemic control, deterioration in treatment adherence over this time, and occurrence of long-term diabetes complications that reduce person’s perception of well-being and productivity. On the other hand, the studies of Stahl et al. 2012, Lawrence et al. 2012, Abd-Elall et al. 2016, and M. Hassan et al. 2017 stated that duration of diabetes had no statistically significant effect on QOL.

As regards the effect of socioeconomic status on HRQOL, current results were in accord with the study done by da Costa and Vieira 2015, reported an association between the lower education levels of parents and the deterioration of the QOL of diabetic children, as the lower education levels of parents may be associated with the insecurity of adolescents due to lack of information and the anticipation of socioeconomic difficulties in the future. Gesuita et al. 2017 found that a lower socioeconomic level was associated with poor metabolic control and QOL of young type 1 diabetic patients less than 18 years of age.

Regarding the effect of body mass index on HRQOL, current results were in agreement with the studies of Schwimmer et al. 2003 and Williams et al. 2005, but in contrast to the current findings, no significant differences in HRQOL by BMI category demonstrated by the studies of Naughton et al. 2008, Lawrence et al. 2012, and Hassan et al. 2017.

In the current study, negative statistically significant correlations were found between HRQOL domains (Pediatric Quality of Life Inventory scores) with all metabolic control parameters (MRBG, HbA1c, number of DKA, number of hypoglycemic attacks, and number of hospitalization due to acute diabetic complications); the strongest relation was between treatment adherence scale with HbA1c% (r = − 0.941, p ˂ 0.001), but no significant correlation found with mean insulin dose in the studied diabetic patients.

As regards the effect of metabolic control (indicated mainly by HbA1c%) on HRQOL, current results were in agreement with many previous studies as those done by Hassan et al. 2006, Nansel et al. 2008 (r = − 0.08), Ingerski et al. 2010 (r = − 0.28), and Lawrence et al. 2012 (r = − 0.232, p < 0.0001); all of them reported an inverse association between PedsQL-T1DM total score and HbA1c. In addition, Murillo et al. 2017a showed evidence that better HRQOL was associated with better metabolic control, and they found an association between HRQOL, HbA1c, and treatment adherence, explaining that adherence to treatment was very important to achieve a good metabolic control and was associated with better HRQOL, recommending that treatment adherence should be taken into account to improve HRQOL in diabetic patients and giving better outcome.

On the other hand, some studies did not come in context with the previous findings, such as the study conducted by Ingerski et al. 2010. On the other hand, Paula et al. 2017 found that mean glucose level (MGL), but not HbA1c or number of hypoglycemic episodes, was the glycemic control parameter that best correlated with short-term perception of HRQOL; in a sample of T1D patients with low socioeconomic status, as in this population, the perception of good daily glucose control may be more easily interpreted than biochemical parameters of glycemic control (HbA1c).

Regarding the correlation between the number of attacks of diabetic ketoacidosis and HRQOL, current results were in agreement with the previous study done by Frøisland et al. 2013 who reported that DKA significantly reduced the HRQOL of young T1DM patients. Also, Al-Hayek et al. 2014 in Saudi Arabia found that adolescents with DKA had significantly lower HRQOL than without DKA in all domains of the PedsQL 3.0 DM except for communication. Also, the regression analysis of this study showed that DKA was the independent influencing factor of the worry subscale.

Regarding the correlation between the number of severe hypoglycemic episodes and HRQOL, current results were in accord with some studies which hypothesized that the experience of suffering severe hypoglycemia could affect HRQOL for fear of their recurrence, such as the study was done by Nordfeldt and Ludvigsson 2005. Also, the study done by Abolfotouh et al. 2011 who conducted a quiz experimental study to assess the self-related quality of life and glycemic control in adolescents with type 1 diabetes in Alexandria, Egypt, they reported lower scores associated with an increasing number of hypoglycemic episodes. The previous studies came in line with the Canadian and Brazilian studies conducted by Harris et al. 2014 and Bahia et al. 2017, respectively. On the other hand, the study of Murillo et al. 2017b revealed that patients who had suffered significant hypoglycemia showed good scores in almost all dimensions of HRQOL, although these data were not valuable because of the small number (only 3 out of 136 patients) had been experiencing hypoglycemia.

Conclusion

The present study concluded that health-related quality of life (HRQOL) was similar in diabetic and non-diabetic children, while decreased in diabetic adolescents in relation to their healthy peers. It was negatively affected by the development of microvascular complications (especially diabetic neuropathy), while positively affected by achieving good metabolic control (HbA1c < 8%).