Skip to main content
Advertisement
Browse Subject Areas
?

Click through the PLOS taxonomy to find articles in your field.

For more information about PLOS Subject Areas, click here.

  • Loading metrics

A cross-sectional questionnaire study: Impaired awareness of hypoglycaemia remains prevalent in adults with type 1 diabetes and is associated with the risk of severe hypoglycaemia

  • Faye Baxter,

    Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Writing – original draft

    Affiliation BHF Centre for Cardiovascular Science, Queen’s Medical Research Institute, University of Edinburgh, Edinburgh, United Kingdom

  • Nicola Baillie ,

    Contributed equally to this work with: Nicola Baillie, Shareen Forbes

    Roles Writing – review & editing

    Affiliation BHF Centre for Cardiovascular Science, Queen’s Medical Research Institute, University of Edinburgh, Edinburgh, United Kingdom

  • Anna Dover,

    Roles Conceptualization, Project administration

    Affiliation Department of Diabetes and Endocrinology, Outpatient Department 2, Royal Infirmary of Edinburgh, Edinburgh, United Kingdom

  • Roland H. Stimson,

    Roles Methodology

    Affiliations BHF Centre for Cardiovascular Science, Queen’s Medical Research Institute, University of Edinburgh, Edinburgh, United Kingdom, Department of Diabetes and Endocrinology, Outpatient Department 2, Royal Infirmary of Edinburgh, Edinburgh, United Kingdom

  • Fraser Gibb,

    Roles Conceptualization, Project administration, Supervision

    Affiliation Department of Diabetes and Endocrinology, Outpatient Department 2, Royal Infirmary of Edinburgh, Edinburgh, United Kingdom

  • Shareen Forbes

    Contributed equally to this work with: Nicola Baillie, Shareen Forbes

    Roles Supervision, Writing – review & editing

    Shareen.Forbes@ed.ac.uk

    Affiliations BHF Centre for Cardiovascular Science, Queen’s Medical Research Institute, University of Edinburgh, Edinburgh, United Kingdom, Department of Diabetes and Endocrinology, Outpatient Department 2, Royal Infirmary of Edinburgh, Edinburgh, United Kingdom

Abstract

Objective

Impaired awareness of hypoglycaemia (IAH) is a risk factor for severe hypoglycaemia (SH) in type 1 diabetes (T1D). Much of the IAH prevalence data comes from older studies where participants did not have the benefit of the latest insulins and technologies. This study surveyed the prevalence of IAH and SH in a tertiary adult clinic population and investigated the associated factors.

Methods

Adults (≥18 years) attending a tertiary T1D clinic completed a questionnaire, including a Gold and Clarke score. Background information was collected from health records.

Results

189 people (56.1% female) with T1D (median [IQR] disease duration 19.3 [11.5, 29.1] years and age of 41.0 [29.0, 52.0] years) participated. 17.5% had IAH and 16.0% reported ≥1 episode of SH in the previous 12 months. Those with IAH were more likely to report SH (37.5% versus 11.7%, p = 0.001) a greater number of SH episodes per person (median [IQR] 0 [0,2] versus 0 [0,0] P<0.001) and be female (72.7% versus 52.6%, p = 0.036). Socio-economic deprivation was associated with IAH (p = 0.032) and SH (p = 0.005). Use of technology was the same between IAH vs aware groups, however, participants reporting SH were more likely to use multiple daily injections (p = 0.026). Higher detectable C-peptide concentrations were associated with a reduced risk of SH (p = 0.04).

Conclusion

Insulin pump and continuous glucose monitor use was comparable in IAH versus aware groups. Despite this, IAH remains a risk factor for SH and is prevalent in females and in older people. Socioeconomic deprivation was associated with IAH and SH, making this an important population to target for interventions.

Introduction

Type 1 diabetes (T1D) affects ~35,000 people in Scotland [1]. It is characterised by autoimmune destruction of the pancreatic beta cells leading, in time, to absolute or near absolute insulin deficiency [2]. T1D is mainly managed with insulin replacement therapy which is given by multiple daily subcutaneous injections or continuous subcutaneous insulin infusion (CSII).

The landmark Diabetes Control and Complications (DCCT) trial found that the use of intensive insulin therapy in T1D reduced the risk of long-term microvascular complications, but that intensive therapy also increased the risk of hypoglycaemia [3]. A recent prospective study identified hypoglycaemia as an ongoing burden for people with T1D who experience on average 73.3 hypoglycaemic events/patient-year [4]. This effect is due in part to the inability of exogenous insulin to mimic the normal profile of endogenous insulin production, leading to relative insulin excess at inappropriate times [2], impairment of the normal compensatory hormone responses to lower blood glucose [5] and the loss of behavioural responses due to IAH [2] which affects 20–40% of people with T1D [6,7]. IAH is a risk factor for SH [8], defined as an episode of hypoglycaemia requiring external assistance for recovery. IAH increases the risk of a SH event 6-fold [6,9].

People with T1D can regain hypoglycaemia awareness through avoidance of hypoglycaemia [2,1012]. Diabetes technologies such as continuous subcutaneous insulin infusion (CSII) and continuous glucose monitoring (CGM) can reduce overall episodes of hypoglycaemia [13,14], improve glycaemic control and decrease the risk of microvascular complications [15]. Advanced diabetes technologies, such as hybrid closed-loop systems, have been shown to reduce time in hypoglycaemia both in randomised controlled trials (RCTs) [1619] and in real-world studies [20]. However, their impact on IAH is not clear due to exclusion of participants with IAH from some RCTs [17] and other trials not reporting data on IAH [18]. Additional studies investigating the effect of HCL systems on the counterregulatory response to hypoglycaemia and IAH in T1D are required [21] to further evaluate the possible benefits of these devices for those at risk of hypoglycaemia.

Technologies currently available in our clinic are: intermittently scanned CGM (isCGM), which users need to interact with in order to see their glucose data; real-time CGM (rtCGM) which transfers data in real-time to the user and CSII which can be used as part of a non-integrated system or as part of a hybrid closed-loop (HCL) system where there is automatic adjustment of insulin delivery based on readings from a rtCGM device. We surveyed an unselected population of adults with T1D to investigate associations between SH and IAH prevalence and the use of technology. Health records of respondents were then screened to identify factors associated with IAH and SH.

Methods

Participants

Between the 1st of July 2021 and the 31st of August 2022 adults (≥18 years) attending a tertiary hospital T1D clinic in person were approached to complete the study survey. People with a diagnosis of T1D documented in their health record and a length of diagnosis of ≥2 years were considered eligible. Those unable to understand or complete the survey were excluded. The study was approved by the local research and development office (2021/0092) and research ethics committee (21/WA/0149). Written informed consent was obtained from participants.

Questionnaire

The first part of the questionnaire included a Gold [9] and Clarke [22] Score. Both are validated methods for assessing hypoglycaemia awareness in people with T1D [23]. In brief, for the Gold Score the participant is asked ‘Do you know when your hypos are commencing?’. They respond using a 7-point Likert scale with 1 indicating ‘always aware’ and 7 indicating ‘never aware’. A score of ≥4 represents IAH. The Clarke score comprises 8 questions that assess exposure to moderate and severe hypoglycaemia as well as assessing the glucose level for onset of symptoms. It gives a score of 0–8 with a score of ≥4 representing IAH.

The second part of the questionnaire collected additional information on employment status, education level, time off work or education due to hypoglycaemia, history of SH in the previous 12 months, driving status and use of diabetes technology.

Additional data

Participant health records were reviewed to collect background information and demographic details including age, sex, age at diagnosis, HbA1c, insulin, date commenced CSII if applicable, date commenced intermittently scanned CGM (isCGM) or real time CGM (rtCGM) if applicable, postcode and hospital admissions in the previous 12 months related to diabetes. Socioeconomic status was assessed using the Scottish index for multiple deprivation (SIMD) quintile [24]. C-peptide data was also collected from participant’s health records. Participant’s using an isCGM had a 2-week snapshot of their data collected from Libreview consisting of: time in range (TIR) 3.9–10 mmol/L, time below range (TBR) <3.9 mmol/L, time above range (TAR) >10 mmol/L, average glucose, standard deviation (SD) of glucose and coefficient of variation (CV) of glucose. We did not collect information on the alarm functionality of the isCGM used.

C-peptide analysis

C-peptide samples obtained prior to October 2021 were analysed by Abbot Architect and after this by Roche Elecsys. Values are reported down to the limit of detection, 3pmol/L for the Abbot system and 7pmol/L for the Roche system. Results below this limit of detection are reported as 0 pmol/L in this paper. Random C-peptide levels were ascertained from medical records.

Statistical analysis

Results are reported as median (IQR) unless otherwise specified. Group differences in continuous variables were compared either using the unpaired t-test or Mann-Whitney U Test. Categorical variables were compared using the chi-square test. Logistic regression models were constructed with presence / absence of SH as the dependent variable and IAH as the independent variable. In order to explore if CSII affected the relationship between the variables, CSII was next added to the model and the relationship between the variables examined and the regression coefficients (beta co-efficients) for standardised data compared.

A p-value of <0.05 was considered significant. Statistical analysis was completed using IBM SPSS version 25. Data analysis was performed using Graph Pad Prism version 9 (Boston, USA).

Results

189 participants (56.1% female) completed the survey, IAH was defined as a Gold Score ≥4, or where this was missing (2.1%), a Clarke score ≥4. The prevalence of IAH was 17.5%. Of note the prevalence of IAH was 17.5% using either score. When analysing respondents who completed both the Gold and Clarke questionnaires (93.1%), there was a significant positive correlation between the two scores, Pearson r 0.623 (P<0.001). 15.9% of respondents reported an episode of SH in the previous 12 months with a median (IQR) 0 (0,0) (range 0–12) number of episodes per person. The Gold and Clarke Score were discordant in 16 of 189 cases and 3 of the 16 had experienced SH in the preceding 12 months, but no further statistical analyses were possible due to the small numbers. The median (IQR) HbA1c was 60.0 (51.0, 67.0) mmol/mol (7.6 [6.8, 8.3]%). 56.6% of respondents were using multiple daily injections (MDI). 70.4% of respondents were using first generation insulin analogues as their bolus insulin and, of those using a basal insulin, 62.6% were using a second-generation analogue. The most common glucose monitoring method was isCGM with 81.0% of respondents using this. Of the 11.1% who were rtCGM users, 52.4% were using an unlicenced do-it-yourself (DIY) isCGM add-on to convert the device to a rtCGM sensor. Of the respondents using CSII, 9.8% were using hybrid closed loop (HCL) systems with 25% of these being a DIY HCL system.

Of the 144 isCGM users, Libreview data was available for 95 (66%). The median (IQR) TIR was 49 (34, 63) %, TBR 2 (0, 4)% and TAR 48 (33, 64)%. TIR was significantly positively correlated with the number of scans per day, Pearson r 0.4381 (P<0.0001) and significantly negatively correlated with HbA1c (r -0.7118, P<0.0001). There was also a significant negative correlation between TBR and HbA1c, r -0.3388 (p = 0.0003). While TAR and HbA1c were positively correlated (r 0.7310, P<0.0001).

C-peptide data was available for 175 participants. The median (IQR) C-peptide was 3 (0, 16) pmol/L. C-peptide correlated significantly with the age at diagnosis, r 0.239 (p = 0.001) and the

diabetes duration, r ─0.398 (p<0.001). C-peptide did not significantly correlate with HbA1c (p = 0.895), average glucose (p = 0.254), TIR (p = 0.473), TAR (p = 0.363) and TBR (p = 0.110).

42.6% of respondents came from the three most deprived SIMD quintiles. 5.9% were from the most deprived quintile 1, 19.1% from quintile 2 and 17.6% from quintile 3. The largest proportion of respondents, 36.7%, were from the least deprived SIMD quintile (quintile 5). Technology disparities existed between SIMD quintiles. MDI use was higher in the most deprived quintiles (1–3) compared to the least deprived (4–5), 66.3% versus 49.1% (p = 0.025). Those from the three most deprived quintiles had a significantly higher median (IQR) HbA1c compared to those from the two least deprived quintiles, 64 (58, 75) mmol/mol (8 [7.4, 9) %] compared to 56 (50, 64) mmol/mol (7.3 [6.7, 8.0]%) (p<0.001).

72.7% of participants held a UK driving licence with 4.7% having previously surrendered their licence. While it did not reach statistical significance a numerically higher percentage of male respondents were drivers (77.1% versus 69.5%) and held a category of licence other than for driving a car alone (19.0% versus 12.7%).

Overall population characteristics are summarised in Table 1.

Impaired awareness of hypoglycaemia

IAH group differences are summarised in Table 2. Participants with IAH compared to aware respondents were more likely to report an episode of SH in the previous 12 months, 37.5% compared to 11.7% (odds ratio [OR] 4.5 [95% confidence interval (CI) 2.0 to 10.9]) (p = 0.001). The median (IQR) of SH episodes /person / year was higher in the IAH group, 0 (0, 2) compared to 0 (0, 0) (p<0.001) (Fig 1A and 1B).

thumbnail
Fig 1.

A. Respondents reporting severe hypoglycaemia in the previous 12 months.Percentage of participants reporting at least one episode of SH in the preceding 12 months categorised by normal vs impaired awareness of hypoglycaemia. B. Number of severe hypoglycaemia events per patient in the previous 12 months.Number of episodes of SH per participant in the preceding 12 months, categorised by normal vs impaired awareness of hypoglycaemia. (Interquartile and absolute ranges shown by violin plot).

https://doi.org/10.1371/journal.pone.0297601.g001

thumbnail
Table 2. Characteristics of subgroups of impaired awareness of hypoglycaemia (IAH).

https://doi.org/10.1371/journal.pone.0297601.t002

Participants with IAH were also more likely to be female, 72.7% compared to 52.6% (p = 0.036); older at the time of completing the survey, 44 (33, 61) years compared to 39.5 (29, 51) years (p = 0.047); unemployed, 21.2% compared to 7.7% (p = 0.005); and less likely to hold a driving licence, 56.3% compared to 76.1% (p = 0.022). They were not more likely to have surrendered their driving licence in the past.

IAH was associated with socioeconomic deprivation, 60.6% of respondents with IAH were in Scottish index of multiple deprivation (SIMD) quintile 1 to 3 compared to 39.4% of respondents with normal awareness (p = 0.032) (Fig 2). Analysing the proportion of IAH in the different SIMD categories by chi-squared analysis demonstrated a difference between the 5 socioeconomic quintiles (p = 0.01).

thumbnail
Fig 2. Impaired awareness of hypoglycaemia by SIMD quintile.

Impaired awareness by SIMD quintile. Quintile 1 is the most deprived. Chi-squared analysis showed a statistically significant difference between the proportions with IAH between quintiles, p = 0.01.

https://doi.org/10.1371/journal.pone.0297601.g002

Numerically a higher percentage of people with IAH had a diabetes-related hospital admission in the previous 12 months, 18.2% vs. 8.3%, but this did not reach statistical significance (p = 0.087). There was also a trend for people with IAH being diagnosed at an older age with a median (IQR) age at diagnosis of 20 (10.0, 37.5) years versus 16.0 (10.2, 25.0) years (p = 0.092). The proportions of people with IAH were compared between all 5 SIMD quintiles by chi-squared testing. The quintiles were amalgamated into low and high socioeconomic categories using SIMD 1–3 (low socioeconomic status) and SIMD 4–5 (high socioeconomic status).

There was no group difference between respondents with impaired and normal awareness in the use of CSII (39.4% compared to 44.2% [p = 0.701]), isCGM (75.8% compared to 82.1% [p = 0.591]) or rtCGM. (12.1% compared to 10.9% [p = 0.591]). More people with normal awareness were using a DIY isCGM add-on than in the IAH group, 64.7% compared to 0% (p = 0.035).

There was no significant group difference in C-peptide levels between those with IAH vs aware, 4.5 (0, 14.5) pmol/L compared to 3 (0, 18.5) pmol/L (p = 0.962)

Respondents reporting IAH had a higher average glucose, 11.9 (10.6, 14.7) mmol/L compared to 10 (8.8, 12.1) mmol/L (p = 0.041); a lower TIR, 35% (26.3, 46.3%) compared to 50% (36, 64.5%) (p = 0.031) and a higher TAR, 63.5% (52.5, 71%) compared to 46% (32.5, 62%) (p = 0.025).

Severe hypoglycaemia

SH group differences are summarised in Table 3. Participants reporting SH in the previous 12 months were compared to those with no history of SH and found to have: a higher median (IQR) HbA1c, 64.5 (55.7, 75.3) mmol/mol (8.1 [7.3, 9.0]%) versus 59.0 (51.0, 67.0) mmol/mol (7.5 [6.8, 8.3)%) (p = 0.024); were less likely to hold a current driving licence, 60% versus 21.2% (p<0.001); and to have previously surrendered their driving licence, 22.2% versus 2.3% (p = 0.004). They were also more likely to have had a diabetes-related hospital admission in the previous 12 months, 26.7% versus 7.1% (p = 0.004) and to have had at least one day off work/education in the previous 12 months due to hypoglycaemia, 38.1% compared to 2.2% (P<0.001). There was a trend towards those reporting a SH episode being younger at the time they were diagnosed with T1D, 11.0 (8.0, 29.0) years compared to 18 (12, 27) years (p = 0.054). In logistic regression analysis examining relationship between SH and IAH, when use of CSII was added as an independent variable to the model, there was no change in the relationship between IAH and SH and the results remained statistically significant (beta co-efficient = 0.26 in both models; p<0.001).

thumbnail
Table 3. Characteristics of subgroups of severe hypoglycaemia (SH) in the past 12 months.

https://doi.org/10.1371/journal.pone.0297601.t003

Participants reporting an episode of SH in the previous 12 months had a significantly lower C-peptide than those not reporting an episode, median (IQR) 0 (0, 6.9) pmol/L versus 3 (0, 17.8) pmol/L (p = 0.04) (Fig 3).

thumbnail
Fig 3. History of severe hypoglycaemia in the past 12 months and random C-peptide level.

Random C-peptide levels in participants with ≥1SH vs. none in the preceding 12 months. C peptide levels between the groups was statistically significant.

https://doi.org/10.1371/journal.pone.0297601.g003

SH was associated with socioeconomic deprivation. Those reporting an episode of SH were more likely to be unemployed, 26.7% compared to 6.5% (p = 0.036), and to come from the most deprived SIMD quintiles, 66.7% compared to 38.1% (p = 0.005).

People with a history of SH were more likely to be MDI users, 76.7% compared to 53.2% (p = 0.026). There was no group difference in the method for monitoring blood glucose with the majority using isCGM in both the SH group and the group with no history of SH, 73.3% and 82.2% respectively (p = 0.164).

Participants with a history of SH had a higher average glucose, 12.3 (10, 13.6) mmol/L compared to 9.9 (8.7, 11.1) mmol/L (p = 0.021). There was a non-significant lower TIR in those with a history of SH, 38 (25, 55) % compared to 50 (36, 65)% (p = 0.053) and increased TAR, 58% (39, 74%) compared to 46% (32, 63%) (p = 0.067). Active sensor time was active significantly lower in those with a history of SH, 78 (67, 87) % compared to 92 (80, 97)% (p = 0.009).

Discussion

In this cross-sectional survey study completed by 189 adults with T1D the prevalence of IAH was 17.4%. This is similar to a Norwegian study by Olsen et al in 2014 which also found a prevalence of IAH of 17% [25] and implies that the prevalence of IAH has not changed in almost 10 years despite advances in technology.

It is, however, a slightly lower rate than reported in a previous study from our centre, which surveyed 518 people with T1D and identified IAH in 19.5% of respondents [6]. However, the population characteristics between this study and ours are different. The previous study’s population was younger at the time of completing the survey, median (IQR) age 39 (31–50) years; had a shorter time since diagnosis, 16 (9–24) years and had a higher mean (SD) HbA1c, 68.3 (15.3) mmol/mol (8.4 [1.4]%). Diabetes management amongst respondents in the previous study was also different with no respondents using CSII. This may limit the comparability of these studies. Another recent cross-sectional survey study investigated SH and IAH in CGM users [26]. This survey cohort had high levels of technology use, 80% were CSII users and 61% HCL users. Despite this they report higher levels of SH and IAH in their cohort, 33% had a Gold score ≥4 and 34.6% had experienced an episode of SH. It may be that a higher proportion of people in their cohort with IAH or a history of SH were on more advanced technologies as a result of these problems. In our study, the rate of SH was greater in the IAH vs aware group despite equal access to CGM technology. However, it is possible that the IAH group, after adoption of CGM experienced an absolute decrease in the rate of SH, but our study was not set up to examine longitudinal changes within individual groups. Furthermore, it highlights the importance of these high-risk populations being included in future studies of these devices to assess the impact. Similar to our study, this survey found those with a history of SH had higher HbA1c and average glucose levels. A recent cross-sectional study among 509 individuals from the Netherlands with T1D, which used the Clarke score for assessment of IAH and SH, showed an overall prevalence of IAH of 15%, the lower prevalence of IAH compared to our study may be explained by a relatively young study population (median 32 years). It showed that participants with IAH were older, had longer diabetes duration and, interestingly, a higher age of diabetes onset and a greater proportion of those with IAH versus those that had awareness, were on glucose sensors. The study demonstrated that residual C-peptide secretion was protective, both for IAH and for SH, and that IAH was associated with a nine times higher risk of SH in the preceding year [27]. Our study showed comparable rates of IAH to this, but the C-peptide levels were similar between the IAH and hypoglycaemic aware group; We did see a reduced incidence of SH in those with higher detectable C-peptide concentrations as has been reported previously [28] demonstrating the benefit of even very low levels of C-peptide concentrations against hypoglycaemia concordant with studies in islet transplant recipients [29]. Some of the residual C-peptide levels in this reported study were much greater than in our study and overall the range of C-peptide concentrations were greater which may have accounted for the differences between the two studies. In the Netherlands individuals with T1D and IAH are eligible for both the prescription and the reimbursement of real-time glucose monitoring and use of a glucose sensor is probably a consequence of, and not a risk factor for, IAH [27].

In our study, those reporting IAH were 4 times more likely to report at least 1 episode of SH in the previous 12 months, which is concordant with other studies demonstrating an association between IAH and SH [30]. SH is linked to morbidity, mortality and reduced quality of life (QoL), making it an important target for interventions in people with T1D. While there was no difference in the type of insulin or technology used in the IAH subgroups, we did identify that those using MDI were almost 3-times more likely to have had an episode of SH in the previous 12 months, and that MDI use was most in the most deprived quintile. The data supporting a positive impact of diabetes technology on IAH is scant. A recent paper suggested CGM has reduced the prevalence of IAH, though this premise has been challenged [31]. Many studies of insulin technologies do not include people who are at risk of hypoglycaemia, that is, people with a history of SH or IAH, and so it can be difficult to comment on the impact of these devices on the risk of IAH. The lack of representation of these patient cohorts in research studies investigating these devices is a problem. One of the few studies to show improvement in IAH [32] (30) found that diabetes education was key, with no difference between technology groups. The authors acknowledge that the use of the most advanced technologies, such as HCL systems and rtCGM was low in our study, however, this is representative of our local T1D clinic population as reimbursement for isCGM is standard. We did identify a significantly higher proportion of people with normal awareness using a DIY rtCGM system than those with IAH. This is likely due to IAH being a criterion for receiving funded rtCGM in our clinic. Locally, IAH and SH are criteria for referral for more advanced diabetes technologies, however, this work highlights the need to assess patients who are not able to attend clinic using other modalities. It is this sort of data that can influence policy, improve community outreach and aid the development of strategies to help inform and improve access to technology, which may improve uptake and engagement in those difficult to reach lower socioeconomic groups. Such strategies may include reducing barriers to access technologies by offering these to all, increasing provision of information out with the hospital setting and, importantly, peer support. IAH and a history of SH was associated with the most deprived SIMD quintiles. Health disparities exist in T1D (27,30,31]) and previous studies have reported an increased risk of SH in people from more deprived socioeconomic backgrounds [31]. However, few studies have linked IAH with socioeconomic status as reported here. Only 5.7% of all respondents in this study were from the most deprived SIMD quintile (quintile 1) and 56.8% were from the least deprived quintiles (quintile 4 and 5). This means that the prevalence of IAH and SH in people from the most socioeconomically deprived areas is likely underestimated in this study. The low proportion of people from the most deprived areas completing this study may in part be due to the questionnaire being administered at a face-to-face clinic: people from the most deprived socioeconomic backgrounds often face more barriers to engaging with health appointments and so the population we have surveyed is not likely to be fully representative of this group. Previous studies, indicate that non-attenders may have poorer glycaemic control [33] and conceivably higher rates of SH and IAH. As we do not have ethical permission to collect data from non-attenders, we may have underestimated the prevalence of both SH and IAH in the general clinic population. In the IAH group there was a higher proportion of female respondents compared to males, 72.7% versus 52.2%. This may be skewed by the higher proportion of female respondents in the study, 55.7%. This is not a pattern that has been previously reported in studies investigating IAH. Of note IAH was more prevalent in older people and in females. It is recognised that SH is more prevalent in women during pregnancy. We hypothesise that in this population of women their exposure to SH may have been greater due to tight glycaemic control in pregnancy with a subsequent increase in prevalence of IAH in later years as compared to the males. However further studies are required.

We do not have information regarding renal function in patients. Renal failure is a major risk factor for SH and low eGFR is associated with IAH (27). However, intervention with diabetes technology may still positively impact this group and this could be the basis for future work.

Data from experimental studies suggest a link between neuropathy and/or cardiovascular disease and hypoglycaemic episodes in T1D [34]. The prevalence of other diabetes related complications might affect hypoglycaemia risk and outcomes, and this could also be included in future work.

As previously noted, a higher proportion of male respondents held a driving licence and a category of licence other than for driving a car alone. Some of these respondents may have had these licence categories as part of their job, which may have made them less forthcoming about their hypoglycaemia awareness status. Contrary to previous studies C-peptide levels in this patient cohort were not associated with CGM glycaemic metrics such as average glucose, TBR and TAR [35,36]. However, in these studies C-peptide levels were higher. We did not demonstrate an association between C-peptide and HbA1c in our sample. A previous study investigating the impact of random C-peptide on risk of complications and glycaemic control found a lower HbA1c in participants with a C-peptide >200pmol/L [37].

This study does have limitations: It was carried out during Covid 19 pandemic restrictions so face-to-face clinic appointment numbers were lower than usual. This study may not necessarily be representative of the wider T1D population as people attending the clinic are more likely to be compliant with treatment and prepared to embrace new technologies versus those who do not attend. Also, older people were more likely to complete our survey, which meant that there were few participants with short duration T1D. A further limitation was that the study was completed as a one-off survey and so relied on the recall of participants at a single point in time, however, recall of SH events in the previous 12 months has been shown to be robust [38]. This also means that we do not have information about the hypoglycaemia awareness status or history of SH in respondents before they started using diabetes technologies. The survey was carried out at a single site and only included participants attending a face-to-face clinic. This may have selected out more motivated and possibly a better controlled cohort than the general clinic population. It is important for future studies to reach a wider general clinic population, so that the true extent of these problems can be assessed, and management plans instigated to prevent associated morbidity and mortality.

Conclusions

IAH remains a problem for people living with T1D, with a prevalence rate of 17.4% in this study. In our cohort IAH was associated with a 4-fold increased risk of SH. Both IAH and SH were more prevalent in females and those from a more deprived socioeconomic background and respondents with these problems were more likely to be unemployed. Our study did not identify any difference in the use of diabetes technologies between groups in those who were aware vs those with IAH. However, SH was lower in those using technology.

As has been demonstrated in other studies detectable C-peptide concentrations even at very low levels are protective against SH. Similar to real-world observational studies we found that IAH and SH are associated with higher HbA1c and average glucose levels.

Randomised controlled trials are required to investigate if and how advanced diabetes technologies are beneficial for participants with IAH and, or a history of SH. Since IAH and SH were more prevalent in the most socioeconomic deprived areas, it is important that participants are actively recruited from these groups.

Acknowledgments

The authors would like to thank the staff and patients of the type 1 diabetes clinic, outpatient department 2, Royal Infirmary of Edinburgh without whom this study would not have been possible.

References

  1. 1. Scottish Diabetes Survey 2020: Scottish Diabetes Data Group; [Available from: https://www.diabetesinscotland.org.uk/wp-content/uploads/2022/01/Diabetes-Scottish-Diabetes-Survey-2020.pdf.
  2. 2. Rickels MR. Hypoglycemia-associated autonomic failure, counterregulatory responses, and therapeutic options in type 1 diabetes. Ann N Y Acad Sci. 2019;1454(1):68–79. pmid:31389033
  3. 3. Nathan DM, Genuth S, Lachin J, Cleary P, Crofford O, Davis M, et al. The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus. N Engl J Med. 1993;329(14):977–86. pmid:8366922
  4. 4. Khunti K, Alsifri S, Aronson R, Cigrovski Berković M, Enters-Weijnen C, Forsén T, et al. Rates and predictors of hypoglycaemia in 27 585 people from 24 countries with insulin-treated type 1 and type 2 diabetes: the global HAT study. Diabetes Obes Metab. 2016;18(9):907–15. pmid:27161418
  5. 5. Verhulst CEM, Fabricius TW, Teerenstra S, Kristensen PL, Tack CJ, McCrimmon RJ, et al. Glycaemic thresholds for counterregulatory hormone and symptom responses to hypoglycaemia in people with and without type 1 diabetes: a systematic review. Diabetologia. 2022;65(10):1601–12. pmid:35867127
  6. 6. Geddes J, Schopman JE, Zammitt NN, Frier BM. Prevalence of impaired awareness of hypoglycaemia in adults with Type 1 diabetes. Diabet Med. 2008;25(4):501–4. pmid:18387080
  7. 7. Pieri B, Deshmukh H, Wilmot EG, Choudhary P, Shah N, Gregory R, et al. Impaired awareness of hypoglycaemia: Prevalence and associated factors before and after FreeStyle Libre use in the Association of British Clinical Diabetologists audit. Diabetes Obes Metab. 2022.
  8. 8. Lin YK, Hung M, Sharma A, Chan O, Varner MW, Staskus G, et al. IMPAIRED AWARENESS OF HYPOGLYCEMIA CONTINUES TO BE A RISK FACTOR FOR SEVERE HYPOGLYCEMIA DESPITE THE USE OF CONTINUOUS GLUCOSE MONITORING SYSTEM IN TYPE 1 DIABETES. Endocr Pract. 2019;25(6):517–25.
  9. 9. Gold AE, MacLeod KM, Frier BM. Frequency of severe hypoglycemia in patients with type I diabetes with impaired awareness of hypoglycemia. Diabetes Care. 1994;17(7):697–703. pmid:7924780
  10. 10. Cranston I, Lomas J, Maran A, Macdonald I, Amiel SA. Restoration of hypoglycaemia awareness in patients with long-duration insulin-dependent diabetes. Lancet. 1994;344(8918):283–7. pmid:7914259
  11. 11. Fanelli CG, Epifano L, Rambotti AM, Pampanelli S, Di Vincenzo A, Modarelli F, et al. Meticulous prevention of hypoglycemia normalizes the glycemic thresholds and magnitude of most of neuroendocrine responses to, symptoms of, and cognitive function during hypoglycemia in intensively treated patients with short-term IDDM. Diabetes. 1993;42(11):1683–9. pmid:8405713
  12. 12. Yeoh E, Choudhary P, Nwokolo M, Ayis S, Amiel SA. Interventions That Restore Awareness of Hypoglycemia in Adults With Type 1 Diabetes: A Systematic Review and Meta-analysis. Diabetes Care. 2015;38(8):1592–609. pmid:26207053
  13. 13. Reddy M, Jugnee N, Anantharaja S, Oliver N. Switching from Flash Glucose Monitoring to Continuous Glucose Monitoring on Hypoglycemia in Adults with Type 1 Diabetes at High Hypoglycemia Risk: The Extension Phase of the I HART CGM Study. Diabetes Technol Ther. 2018;20(11):751–7. pmid:30265562
  14. 14. van Beers CA, DeVries JH, Kleijer SJ, Smits MM, Geelhoed-Duijvestijn PH, Kramer MH, et al. Continuous glucose monitoring for patients with type 1 diabetes and impaired awareness of hypoglycaemia (IN CONTROL): a randomised, open-label, crossover trial. Lancet Diabetes Endocrinol. 2016;4(11):893–902. pmid:27641781
  15. 15. Reid LJ, Gibb FW, Colhoun H, Wild SH, Strachan MWJ, Madill K, et al. Continuous subcutaneous insulin infusion therapy is associated with reduced retinopathy progression compared with multiple daily injections of insulin. Diabetologia. 2021;64(8):1725–36. pmid:33966091
  16. 16. Brown SA, Kovatchev BP, Raghinaru D, Lum JW, Buckingham BA, Kudva YC, et al. Six-Month Randomized, Multicenter Trial of Closed-Loop Control in Type 1 Diabetes. N Engl J Med. 2019;381(18):1707–17. pmid:31618560
  17. 17. Tauschmann M, Thabit H, Bally L, Allen JM, Hartnell S, Wilinska ME, et al. Closed-loop insulin delivery in suboptimally controlled type 1 diabetes: a multicentre, 12-week randomised trial. Lancet. 2018;392(10155):1321–9. pmid:30292578
  18. 18. Collyns OJ, Meier RA, Betts ZL, Chan DSH, Frampton C, Frewen CM, et al. Improved Glycemic Outcomes With Medtronic MiniMed Advanced Hybrid Closed-Loop Delivery: Results From a Randomized Crossover Trial Comparing Automated Insulin Delivery With Predictive Low Glucose Suspend in People With Type 1 Diabetes. Diabetes Care. 2021;44(4):969–75.
  19. 19. McAuley SA, Trawley S, Vogrin S, Ward GM, Fourlanos S, Grills CA, et al. Closed-Loop Insulin Delivery Versus Sensor-Augmented Pump Therapy in Older Adults With Type 1 Diabetes (ORACL): A Randomized, Crossover Trial. Diabetes Care. 2022;45(2):381–90. pmid:34844995
  20. 20. Noor N, Kamboj MK, Triolo T, Polsky S, McDonough RJ, Demeterco-Berggren C, et al. Hybrid Closed-Loop Systems and Glycemic Outcomes in Children and Adults With Type 1 Diabetes: Real-World Evidence From a U.S.-Based Multicenter Collaborative. Diabetes Care. 2022;45(8):e118–e9. pmid:35708494
  21. 21. Baxter F, Baillie N, Forbes S. Study protocol: a randomised controlled proof-of-concept real-world study—does maximising time in range using hybrid closed loop insulin delivery and a low carbohydrate diet restore the glucagon response to hypoglycaemia in adults with type 1 diabetes? BMJ Open. 2022;12(12):e054958. pmid:36600427
  22. 22. Clarke WL, Cox DJ, Gonder-Frederick LA, Julian D, Schlundt D, Polonsky W. Reduced awareness of hypoglycemia in adults with IDDM. A prospective study of hypoglycemic frequency and associated symptoms. Diabetes Care. 1995;18(4):517–22. pmid:7497862
  23. 23. Geddes J, Wright RJ, Zammitt NN, Deary IJ, Frier BM. An evaluation of methods of assessing impaired awareness of hypoglycemia in type 1 diabetes. Diabetes Care. 2007;30(7):1868–70. pmid:17416785
  24. 24. Government S. Scottish Index of Miltiple Deprivation 2020 2020 [Available from: https://www.gov.scot/collections/scottish-index-of-multiple-deprivation-2020/.
  25. 25. Olsen SE, Bjorgaas MR, Asvold BO, Sand T, Stjern M, Frier BM, et al. Impaired Awareness of Hypoglycemia in Adults With Type 1 Diabetes Is Not Associated With Autonomic Dysfunction or Peripheral Neuropathy. Diabetes Care. 2016;39(3):426–33. pmid:26721812
  26. 26. Lin YK, Richardson CR, Dobrin I, DeJonckheere MJ, Mizokami-Stout K, Fetters MD, et al. Beliefs Around Hypoglycemia and Their Impacts on Hypoglycemia Outcomes in Individuals with Type 1 Diabetes and High Risks for Hypoglycemia Despite Using Advanced Diabetes Technologies. Diabetes Care. 2022;45(3):520–8. pmid:35015079
  27. 27. Wellens MJ, Vollenbrock CE, Dekker P, Boesten LSM, Geelhoed-Duijvestijn PH, de Vries-Velraeds MMC, et al. Residual C-peptide secretion and hypoglycemia awareness in people with type 1 diabetes. BMJ Open Diabetes Res Care. 2021;9(1). pmid:34526306
  28. 28. Gubitosi-Klug RA, Braffett BH, Hitt S, Arends V, Uschner D, Jones K, et al. Residual β cell function in long-term type 1 diabetes associates with reduced incidence of hypoglycemia. J Clin Invest. 2021;131(3).
  29. 29. Vantyghem M-C, Raverdy V, Balavoine A-S, Defrance F, Caiazzo R, Arnalsteen L, et al. Continuous Glucose Monitoring after Islet Transplantation in Type 1 Diabetes: An Excellent Graft Function (β-Score Greater Than 7) Is Required to Abrogate Hyperglycemia, Whereas a Minimal Function Is Necessary to Suppress Severe Hypoglycemia (β-Score Greater Than 3). The Journal of Clinical Endocrinology & Metabolism. 2012;97(11):E2078–E83.
  30. 30. Choudhary P, Geddes J, Freeman JV, Emery CJ, Heller SR, Frier BM. Frequency of biochemical hypoglycaemia in adults with Type 1 diabetes with and without impaired awareness of hypoglycaemia: no identifiable differences using continuous glucose monitoring. Diabet Med. 2010;27(6):666–72. pmid:20546285
  31. 31. Ali N, El Hamdaoui S, Nefs G, Walburgh Schmidt JWJ, Tack CJ, de Galan BE. High diabetes-specific distress among adults with type 1 diabetes and impaired awareness of hypoglycaemia despite widespread use of sensor technology. Diabet Med. 2023;40(9):e15167. pmid:37347681
  32. 32. Tan HK, Little SA, Leelarathna L, Walkinshaw E, Lubina-Solomon A, Hosking J, et al. Low-Blood Glucose Avoidance Training Improves Glycemic Variability in Adults With Type 1 Diabetes Complicated by Impaired Awareness of Hypoglycemia: HypoCOMPaSS Trial. Diabetes Care. 2016;39(4):e56–8. pmid:26953169
  33. 33. Brewster S, Bartholomew J, Holt RIG, Price H. Non-attendance at diabetes outpatient appointments: a systematic review. Diabet Med. 2020;37(9):1427–42. pmid:31968127
  34. 34. Kaze AD, Yuyun MF, Ahima RS, Rickels MR, Echouffo-Tcheugui JB. Autonomic dysfunction and risk of severe hypoglycemia among individuals with type 2 diabetes. JCI Insight. 2022;7(22). pmid:36318703
  35. 35. Gibb FW, McKnight JA, Clarke C, Strachan MWJ. Preserved C-peptide secretion is associated with fewer low-glucose events and lower glucose variability on flash glucose monitoring in adults with type 1 diabetes. Diabetologia. 2020;63(5):906–14. pmid:32034440
  36. 36. Rickels MR, Evans-Molina C, Bahnson HT, Ylescupidez A, Nadeau KJ, Hao W, et al. High residual C-peptide likely contributes to glycemic control in type 1 diabetes. J Clin Invest. 2020;130(4):1850–62. pmid:31895699
  37. 37. Jeyam A, Colhoun H, McGurnaghan S, Blackbourn L, McDonald TJ, Palmer CNA, et al. Clinical Impact of Residual C-Peptide Secretion in Type 1 Diabetes on Glycemia and Microvascular Complications. Diabetes Care. 2021;44(2):390–8. pmid:33303639
  38. 38. Pedersen-Bjergaard U, Pramming S, Thorsteinsson B. Recall of severe hypoglycaemia and self-estimated state of awareness in type 1 diabetes. Diabetes Metab Res Rev. 2003;19(3):232–40. pmid:12789657