Skip to main content
NCBI home page
NIHPA Author Manuscripts logoLink to NIHPA Author Manuscripts
. Author manuscript; available in PMC: 2025 Oct 1.
Published in final edited form as: Clin Neuropsychol. 2024 Feb 21;38(7):1627–1646. doi: 10.1080/13854046.2024.2315749

Nocturnal hypoglycemia is associated with next day cognitive performance in adults with type 1 diabetes: Pilot data from the GluCog Study

Miranda Zuniga-Kennedy 1, Olivia H Wang 2, Luciana M Fonseca 2,3, Michael J Cleveland 1, Jane D Bulger 4, Elizabeth Grinspoon 5, Devon Hansen 2, Zoë W Hawks 5, Laneé Jung 5, Shifali Singh 6, Martin Sliwinski 7, Alandra Verdejo 8, Kellee M Miller 9, Ruth S Weinstock 4, Laura Germine 5,*, Naomi Chaytor 2,*
PMCID: PMC11336034  NIHMSID: NIHMS1978178  PMID: 38380810

Abstract

Objective:

Individuals with type 1 diabetes (T1D) have increased risk for cognitive dysfunction and high rates of sleep disturbance. Despite associations between glycemia and cognitive performance using cross-sectional and experimental methods few studies have evaluated this relationship in a naturalistic setting, or the impact of nocturnal versus daytime hypoglycemia. Ecological Momentary Assessment (EMA) may provide insight into the dynamic associations between cognition, affective, and physiological states. The current study couples EMA data with continuous glucose monitoring (CGM) to examine the within-person impact of nocturnal glycemia on next day cognitive performance in adults with T1D. Due to high rates of sleep disturbance and emotional distress in people with T1D, the potential impacts of sleep characteristics and negative affect were also evaluated.

Methods:

This pilot study utilized EMA in 18 adults with T1D to examine the impact of glycemic excursions, measured using CGM, on cognitive performance, measured via mobile cognitive assessment using the TestMyBrain platform. Multilevel modeling was used to test the within-person effects of nocturnal hypoglycemia and hyperglycemia on next day cognition.

Results:

Results indicated that increases in nocturnal hypoglycemia were associated with slower next day processing speed. This association was not significantly attenuated by negative affect, sleepiness, or sleep quality.

Conclusions:

These results, while preliminary due to small sample size, showcase the power of intensive longitudinal designs using ambulatory cognitive assessment to uncover novel determinants of cognitive fluctuation in real world settings, an approach that may be utilized in other populations. Findings suggest reducing nocturnal hypoglycemia may improve cognition in adults with T1D.

Keywords: type 1 diabetes, ecological momentary assessment, hypoglycemia, hyperglycemia, cognition, processing speed, sleep, negative affect

Introduction

Type 1 diabetes (T1D) is a chronic medical condition caused by autoimmune destruction of insulin-producing beta cells in the pancreas, resulting in the need for exogenous insulin to treat elevated blood glucose levels (i.e., hyperglycemia). It is estimated that about 8.4 million individuals worldwide have T1D, with onset at any age (Gregory et al., 2022). Chronic hyperglycemia is associated with end-organ damage in the form of microvascular and macrovascular complications, while acute complications include diabetic ketoacidosis (Melendez et al., 2010; Klein 1995; Leske et al., 2005) and hypoglycemia. Severe hypoglycemia can lead to confusion, loss of consciousness and even death (Sayed 2022; Seaquist et al., 2013).

Cognitive impairment and decline in adults with T1D have been linked to glycemic factors (Jacobson et al., 2021; Chaytor et al., 2019; Li et al., 2017; Cox et al., 1993). In a 32-year follow-up of the Diabetes Control and Complications Trial/Epidemiology of Diabetes Interventions and Complications (DCCT/EDIC) cohort (see Nathan, 2014 for a description of this cohort), significant within-person declines in memory, as well as psychomotor and mental efficiency were observed compared to baseline cognitive assessment, even after controlling for age. Findings demonstrated that higher hemoglobin A1c (HbA1c) levels, elevated systolic blood pressure and more episodes of severe hypoglycemia were associated with greater odds of decline in psychomotor and mental efficiency (Jacobson et al., 2021). Further, cross-sectional and brain imaging studies examining cognition in adults with longstanding T1D have demonstrated diminished performance on a variety of cognitive tasks, with information processing speed and executive functioning most commonly impacted compared to baseline cognitive performance as well as compared to controls (Shalimova et al., 2019; Broadley et al., 2017; Tonoli et al., 2014; van Duinkerken et al., 2014; McCrimmon et al., 2012; Wessels et al., 2007; Brands et al., 2005).

While this research has contributed to our understanding of cognitive functioning in adults with T1D in the long-term, less is known about the acute effects of glycemic fluctuations on cognition. Research utilizing hyperinsulinemic-hypoglycemic clamp methodology (controlled insulin and glucose infusions to achieve steady state glucose levels in the hypoglycemic or hyperglycemic range) shows further evidence for acute cognitive deficits associated with hypoglycemia in several domains, including cognitive flexibility, attention, language processing, and processing speed (Allen et al., 2015; Verhulst et al., 2022; Holmes et al., 1983; Graveling et al., 2013; Ewing et al., 1998; McAulay et al., 2006). Clamp studies examining the effects of hyperglycemia on cognition have yielded mixed results, with some reporting that hyperglycemia has no immediate effect on cognitive functioning (Draelos et al., 1995) whereas others have reported that hyperglycemia is associated with slower performance across multiple tasks (Cox et al., 2005). While useful in terms of the ability to study the acute effects of glucose on cognition in a controlled manner, clamp studies lack generalizability. In real world environments, glucose levels are rarely stable for extended periods of time, as people with T1D have varying levels of physical activity, frequently monitor their blood glucose through the day, and ingest carbohydrates or administer insulin as needed to avoid sustained hypoglycemia and hyperglycemia, respectively.

Ambulatory assessment is an approach used to study intra-individual variation over time employing repeated assessments in the individual’s natural environment via electronic devices (Jungheanel & Stone., 2022; Ebner-Priemer & Trull, 2009). Ecological Momentary Assessment (EMA) is a form of ambulatory assessment that typically utilizes self-reports and, more recently, tests of cognitive performance. In the context of diabetes research, EMA has been utilized to measure affective states and self-monitoring behaviors (Warnick et al., 2020; Shapira et al., 2023). Of particular interest for diabetes researchers, EMA can be coupled with continuous glucose monitoring (CGM) technology to enable passive collection of glycemic data alongside behavioral data (including cognitive performance) in real world environments. This can be used to determine how glycemic fluctuations impact behavior acutely outside the controlled laboratory setting. Few studies using EMA coupled with CGM in adults with T1D have reported results to date, although several have published study methodologies, including Cox and colleagues (2005) as well as the FEEL-T1D study (Pyatak et al., 2021) and the Hypo-METRICS study (Søholm et al., 2022). Recently reported data from the FEEL-T1D study examining the acute effects of glycemic excursions on a wide range of aspects of daily functioning, found evidence that overnight glucose may be predictive of next day cognitive functioning (Pyatak et al., 2023). Specifically, higher glucose coefficient of variation (CV) and more time <70 mg/dL were associated with poorer sustained attention (Pyatak et al., 2023).

Individuals with T1D also have a higher prevalence of mood disturbance, including clinical diagnosis of depression and depressive symptoms compared to the general population (Anderson et al., 2001; Grigsby et al., 2002). Additionally, diabetes distress, or the emotional burden associated with managing a demanding chronic condition, is common in people with T1D (Fisher et al., 2010, Tareen & Tareen, 2017; Hagger et al., 2016; Coccaro et al., 2021; McCarthy et al., 2019; Fisher et al., 2012; Gonzalez et al; 2011; Hermanns et al., 2007). Recent data shed light on the short-term associations between glucose and negative affect in daily life (Pyatak et al., 2021; Penckofer et al., 2012; Ehrman et al., 2022; Shapira et al., 2023). EMA data have revealed negative affective states may be associated with increased variability in glucose measured via fingerstick, though the nature of the relationship between glucose and affect is likely bidirectional (Shapira et al., 2023). In a study of 36 adults with T1D, Hermanns et al. (2007) found that over the course of 48 hours, hyperglycemia measured via CGM was associated with greater negative affect. In terms of cognition, a systematic review found a significant association between the presence of depressive symptoms and poorer cognitive performance in individuals with both type 1 and type 2 diabetes that was consistent across four well-powered studies (Danna et al., 2016), though this association has been inconsistent across studies (Brands et al., 2006, Brands et al., 2005).

In addition to affective states, there is evidence of a reciprocal relationship between sleep and glycemia (Barone et al., 2014; Brandt et al., 2021, Nefs et al., 2019). Evidence suggests that individuals with T1D experience higher rates of circadian rhythm disturbances than non-diabetic controls (Perez et al., 2018; Rutters & Nefs, 2022). CGM-measured mean glucose, standard deviation in glucose, and time in range have also been found to be associated with sleep-wake variability, sleep fragmentation and daytime sleepiness in young adults (Griggs et al, 2021). Measurement of daytime sleepiness (as a proxy for sleep quality) also appears to have a significant effect on glucose levels (Søholm et al., 2022). The Hypo-METRICS study demonstrated an association between nocturnal self-reported hypoglycemia and decreased daytime mood, alertness, and sleep quality (Søholm et al., 2022). Furthermore, findings from the recently published FEEL-T1D study revealed that the relationship between glucose CV and sustained attention was partially mediated by sleep fragmentation (measured via accelerometry data), and higher overnight CV was associated with more fragmented sleep (Pyatak et al., 2023). This finding warrants further investigation into the role of sleep characteristics in nocturnal glycemic fluctuations and cognitive performance.

Additionally, results from cross-sectional studies have shown that poor sleep is associated with higher diabetes distress (Griggs et al., 2022). Increased psychological comorbidities, including diabetes distress and depressive symptoms, are associated with increased morbidity, worse glycemic control, poorer treatment outcomes and decreased quality of life (Muijis et al., 2021, Carper et al., 2013). Sleep is also known to have an impact on cognition, where poor sleep quality may be predictive of poor cognitive performance (Nebes et al., 2009; Walker et al., 2009; Lo et al., 2016). Given these findings, the ways in which emotion and sleep impact cognitive function in the context of T1D warrant further investigation.

Thus far, the short-term impacts of nocturnal glycemic excursions on daytime cognition in adults with T1D are not well characterized. EMA coupled with CGM is a powerful methodological approach that has the potential to interrogate the dynamic associations between glucose, affect, sleep and cognition in individuals with T1D. This approach has become technically feasible due to increased sophistication of mobile technology that has enabled highly precise self-administered brief cognitive assessments that can be completed in daily life, as well as the high accuracy of modern passive glucose sensor technology. Thus, the primary objective of the current study is to examine the within-person impact of nocturnal glycemia on next day cognitive performance in adults with T1D utilizing CGM and EMA data. A secondary objective was to examine the impact of sleep characteristics and negative affect states on this relationship.

Methods

Participants and Procedures

The present data were obtained from a pilot study designed to determine the optimal administration schedule of EMA tasks to yield high completion rates and detect sufficient cognitive and glycemic variability. Those data have been published previously. See Mascarenhas Fonseca et al. (2023) for greater detail regarding study methodology, additional measures utilized in the pilot study, and the results related to EMA administration frequency. Individuals aged > 18 years were recruited from the Joslin Diabetes Center at SUNY Upstate Medical University between February and May of 2020. Individuals were considered for participation if they had T1D of greater than 1 year duration, were fluent in English, and demonstrated understanding of EMA and willingness to comply with study procedures to the best of their ability. To complete the mobile assessments, participants were required to have 24-hour access to a smart phone with reliable Wi-Fi. Individuals were excluded if they were currently using real-time CGM as part of their clinical care; were unable to complete cognitive assessments due to significant visual, motor or hearing impairment; were receiving dialysis or chemotherapy; and/or had a recent myocardial infarction, inpatient psychiatric admission, organ transplant, acute neurological insult, terminal medical condition, dementia, or any medical or psychiatric condition or treatment that was judged by the principal investigators to interfere with the completion of the study. Additionally, individuals who completed less than 50% of EMA measures were excluded from analyses. The first six participants were enrolled prior to the COVID-19 pandemic and therefore had in-person enrollment visits. The protocol was then modified to be entirely remote for the remainder of data collection. A Dexcom G6 CGM with a blinded receiver was worn during everyday activities while completing 3–6 daily EMAs using personal smartphones. Participants in the pilot study were randomized to one of two groups (Group A and Group B). Participants in Group A completed 3 assessments a day for a total of 10 days for the first portion of the study, and then completed 6 assessments a day for the last 5 days of participation. The conditions in Group B were counterbalanced (6 EMAs per day for 5 days followed by 3 per day for 10 days). Each participant wore the CGM for a total of 15 days and was administered a total of 60 EMAs. EMAs were delivered between 9 am and 9 pm for 15 days. Because adherence to EMA tasks was similar for the 3 and 6 per day schedules (Mascarenhas Fonseca et al., 2023), the two counterbalanced groups were combined for the current analyses.

Materials

Baseline Questionnaires

To characterize the cohort in terms of sleep characteristics and affect, sleep quality was measured by the Pittsburgh Sleep Quality Index (PSQI), which is a self-report measure that assesses sleep duration and quality over a one-month interval (Buysse et al., 1989). The PSQI has internal homogeneity with a high reliability coefficient in both component scores (α = 0.83), and individual items (α = 0.83) as well as good test-retest reliability and has acceptable validity (Buyesse et al., 1989). Presence of depressive symptoms was assessed via the Patient Health Questionnaire (PHQ-8).

EMA

EMAs were completed by participants on a secure digital platform (TestMyBrain; TMB) via a smartphone, tablet, or computer. Measures included 1) brief cognitive tests measuring cognitive control/sustained attention, psychomotor processing speed and visual working memory developed by TMB and validated for brief mobile administration (Singh et al., 2023) and 2) questions to provide self-report data for momentary negative affect, alertness/sleepiness, and prior night sleep quality.

Cognitive EMA

The TMB Gradual Onset Continuous Performance Test (GradCPT) was used to assess sustained attention and response inhibition (Fortenbaugh, et al., 2015). Participants see a series of city or mountain scenes and are asked to tap the screen whenever they see a city scene and withhold a response whenever they see a mountain scene. The primary score of interest is sensitivity or d-prime, a measure of target discriminability not impacted by response bias, where higher scores indicate better performance as well as response accuracy. The EMA version of this task takes one minute and has been previously validated with excellent between-person reliability (Singh et al., 2023). To assess visuospatial working memory, the TMB Multiple Object Tracking (MOT) was administered (Treviño et al., 2021). In this assessment, participants track a target dot as it moves among other dots on the screen. For the current study, accuracy was the outcome measure for the MOT task. The EMA version of this task takes one minute and has been validated for EMA with excellent between-person reliability (Singh et al., 2023). Performance on this test has been shown to correlate with other tests of attention, cognitive control, and working memory (Singh et al., 2021). Lastly, the TMB Digit Symbol Matching (DSM) test was utilized to examine psychomotor processing speed (Hartshorne & Germine, 2015), and performance was measured for this assessment by participant’s median reaction time for correct responses. During this assessment, participants match a set of symbols to the numbers 1, 2, or 3, based on a key presented on screen. Despite its brief administration time (~30 seconds), this task has been validated for EMA with excellent between-person reliability (Singh et al., 2023). The TMB Digit Symbol Matching task has also been shown to correlate with other measures of cognitive processing speed (Singh et al., 2021; Singh et al., 2023). Day-level average scores were calculated (total score divided by the number of EMA administered) for each cognitive test.

Negative Affect EMA

Negative affect was assessed via seven self-report items (Sliwinski et al., 2009). Participants indicated the extent to which they felt: restless or fidgety, so sad that nothing could cheer [them] up, that everything was an effort, hopeless, irritated, worried, and depressed. Responses were based on a 5-item Likert scale from “none of the time” to “all of the time” since the last EMA. Day-level average scores were calculated for all administered affect questions.

Sleep Quality and Sleepiness EMA

The 1-item Karolinska Sleepiness Scale (Åkerstedt et al., 2014) was used to assess sleepiness at every EMA occasion, with the following rating options, ranging from extreme alertness to extreme sleepiness: 1=extremely alert, 2=very alert, 3=alert, 4=rather alert, 5=neither alert nor sleepy, 6=some signs of sleepiness, 7=sleepy, but no effort to keep awake, 8=sleepy, some effort to keep awake, and 9=very sleepy, great effort to keep awake, fighting sleep. Day-level average scores were calculated. Sleep quality was assessed via a single item administered each morning: “How well did you sleep last night?” Very well / somewhat well / somewhat poorly / very poorly (Åkerstedt et al., 2014). This measure has been shown to have acceptable reliability and construct validity (Nordin et al., 2019).

Glucose Measures

Blinded CGM

The blinded Dexcom G6 CGM (FDA-approved) consists of a sensor placed subcutaneously that is worn for a maximum of 10 days and a transmitter that transmits glucose readings to the receiver every 5 minutes. A blinded receiver was used in order to avoid knowledge of current blood glucose levels from influencing responses to the EMA tasks. Participants were free to check their glucose via finger stick as necessary to self-manage their diabetes. The CGM was inserted during the baseline visit and participants received training on how to replace the sensor at home. A second sensor was sent home with the participant so that they could replace the sensor after the first sensor session ended. Hypoglycemia and hyperglycemia were defined as the percentage of time spent with sensor glucose values <70 mg/dL or > 180 mg/dL, respectively. Daytime (8 am to 9 pm) and nocturnal (9 pm to 8 am) glycemic excursions (% hypoglycemia and hyperglycemia) were calculated separately.

Statistical Analyses

Descriptive statistics (mean, standard deviation, range) were calculated for demographic, CGM, and baseline affect and sleep variables. Multilevel models estimated person- and day-level associations between independent variables (% hypoglycemia and hyperglycemia, negative affect, sleepiness, and sleep quality) and outcomes (cognitive EMA scores) while controlling for age as a covariate. Day-level aggregate measures of each EMA measure were created by averaging across the respective items at each of the 3 or 6 repeated assessments within each day for a total of 15 day-level means for each variable. This was done for two reasons: 1) we were most interested in the impact of clinically relevant CGM metrics (i.e., % hypoglycemia, % hyperglycemia) aggregated over clinically relevant time periods (i.e., nocturnal vs daytime) on daytime cognitive performance, rather within-day glucose-cognition associations and 2) the pilot study design resulted in half the sample receiving a different number of EMAs each day.

Person-level variables were calculated as the arithmetic mean across each participant’s repeated measures. These variables were centered around the sample grand mean to provide an estimate of each participant’s usual levels relative to the overall sample. Day-level variables were calculated as deviations around each participant’s mean and, thus, represented days when a participant had higher or lower levels of the measures of interest (glucose levels, sleepiness, sleep quality, negative affect) than they did on average.

We began by estimating a series of preliminary, unconditional growth models to determine the functional form and error structure of cognitive EMA outcome scores across the fifteen days. These models assessed whether changes in EMA scores followed a quadratic or linear trend and specified a random intercept and a first-order autoregressive (AR1) error structure to account for correlated residuals across the fifteen days.

To test our hypotheses, a series of multilevel models were then estimated for each cognitive outcome separately (see Tables 2, 3 and 4), using the following model-building strategy: The first model (Model A) was designed to test the primary hypothesis and included the within-person and between-person effects of the CGM variables on each cognitive outcome. In the next step (Model B), negative affect was entered as both a within-person and between-person variable to determine if effects of CGM on cognition remained. Next, Models C and D included the sleep variables (sleep quality and sleepiness, respectively) to determine if the effects of CGM on cognition remained, after accounting for within-person and between-person effects of sleep quality (Model C) and sleepiness (Model D). Preliminary models indicated that the quadratic term for day of assessment (centered at the first day of the study) was not significant for any of the cognitive EMA outcome scores; thus, linear trends representing day of the study were included in all subsequent models. All models included the participants’ age as a between-person covariate and included a random intercept; thus, average levels of the cognitive tests were allowed to vary across the participants, but within-person effects were constrained to be fixed. The multilevel models were estimated using SAS Proc Mixed, with incomplete data treated using missing at random assumptions and restricted maximum likelihood (REML) estimation.

Table 2.

Results of Multilevel Model Examining Associations Between Continuous Glucose Monitoring and Digit Symbol Matching (n = 18)

Model A Model B Model C Model D
EST (SE) EST (SE) EST (SE) EST (SE)
Within Person Effects
Day −8.18* (1.11) −8.14* (1.10) −7.71* (1.07) −8.18* (1.11)
Day % Hypoglycemia 31.41 (73.66) 39.35 (73.73) 106.61 (80.24) 25.34 (73.92)
Day % Hyperglycemia −26.55 (20.83) −20.80 (21.21) −29.36 (22.11) −26.47 (20.84)
Night % Hypoglycemia 104.71* (41.80) 109.95* (41.90) 92.37* (43.39) 109.65* (42.08)
Night %Hyperglycemia 18.25 (13.89) 18.30 (13.86) 21.40 (14.30) 17.44 (13.90)
Negative Affect −3.03 (2.23)
Sleep Quality −14.89 (18.14)
Sleepiness 30.89 (32.78)
Between Person Effects
Age 9.18** (1.65) 9.23** (1.71) 9.23** (1.84) 7.78** (1.77)
Day % Hypoglycemia −1147.3 (661.9) −1208.6 (692.3) −932.4. (736.0) −1258.8 (623.3)
Day % Hyperglycemia −75.1 (134.0) −69.2 (138.6) −47.1 (138.1) −139.8 (131.3)
Night % Hypoglycemia 388.7 (312.8) 365.1 (326.2) 350.1 (316.7) 406.2 (293.3)
Night %Hyperglycemia −59.1 (115.8) −79.0 (123.8) −65.6 (117.2) −35.4 (109.2)
Negative Affect −0.37 (7.01)
Sleep Quality 41.70 (176.40)
Sleepiness −188.66 (116.4)
Random Components
Level 2 (Intercept) 3435* 1579 3692* 1758 3545* 1675 2948* 1449
AR1 0.22* 0.08 0.22* 0.09 0.15 0.09 0.22** 0.08
Level 1 (Residual) 3858*** 416 3841*** 416 3504*** 387 3874*** 421
Fit Indices
−2LL 2559.2 2547.9 2247.2 2535.6
AIC 2559.3 2548.1 2247.3 2535.7
BIC 2561.9 2550.6 2249.9 2538.3
Number of Observations 237 237 212 237
Open in a new tab

Note: Analyses are based on between 212 and 237 observations in a sample of 18 participants; EST = parameter estimate; SE = Standard Error; CGM = Continuous Glucose Monitoring; AR1 = 1st Order Autoregressive; −2LL = −2*loglikelihood (Deviance); AIC = Akaike information criteria; BIC = Bayesian information criteria.

*

p < 0.05

**

p < 0.01

***

p < 0.001

Table 3.

Results of Multilevel Model Examining Associations Between Continuous Glucose Monitoring and MOT (n = 18)

Model A Model B Model C Model D
EST (SE) EST (SE) EST (SE) EST (SE)
Within Person Effects
Day 0.003** (0.001) 0.00** (0.00) 0.00** (0.00) 0.00** (0.00)
Day % Hypoglycemia −0.11 (0.07) −0.10 (0.07) −0.16* (0.08) −0.10 (0.07)
Day % Hyperglycemia −0.02 (0.02) −0.02 (0.02) −0.04 (0.02) −0.02 (0.02)
Night % Hypoglycemia −0.02 (0.04) −0.02 (0.04) 0.01 (0.04) −0.02 (0.04)
Night % Hyperglycemia 0.02 (0.01) 0.01 (0.01) 0.01 (0.01) 0.02 (0.01)
Negative Affect 0.00 (0.00)
Sleep Quality 0.00 (0.02)
Sleepiness 0.00 (0.03)
Between Person Effects
Age −0.01** (0.00) −0.01** (0.00) −0.01** (0.00) −0.01** (0.00)
Day % Hypoglycemia 0.78 (0.49) 0.69 (0.49) 0.95 (0.55) 0.76 (0.51)
Day % Hyperglycemia −0.22* (0.10) −0.22* (0.10) −0.20 (0.10) −0.23* (0.11)
Night % Hypoglycemia 0.11 (0.23) 0.07 (0.23) 0.07 (0.24) 0.12 (0.24)
Night % Hyperglycemia 0.22* (0.08) 0.19* (0.09) 0.21* (0.09) 0.22* (0.09)
Negative Affect −0.01 (0.01)
Sleep Quality 0.04 (0.13)
Sleepiness −0.03 (0.09)
Random Components
Level 2 (Intercept) 0.002* 0.001 0.002* 0.001 0.002* 0.001 0.002* 0.001
AR1 0.11 0.09 0.12 0.09 0.12 0.12 0.11 0.09
Level 1 (Residual) 0.003*** 0.000 0.003*** 0.000 0.003*** 0.000 0.003*** 0.000
Fit Indices
−2LL −549.7 −532.6 −473.8 −541.8
AIC −549.6 −532.5 −473.7 −541.7
BIC −547.1 −530.0 −471.1 −539.1
Number of Observations 217 217 192 217
Open in a new tab

Note: Analyses are based on between 192 and 217 observations in a sample of 18 participants; EST = parameter estimate; SE = Standard Error; CGM = Continuous Glucose Monitoring; AR1 = 1st Order Autoregressive; −2LL = −2*log likelihood (Deviance); AIC = Akaike information criteria; BIC = Bayesian information criteria.

*

p < 0.05

**

p < 0.01

***

p < 0.001

Table 4.

Results of Multilevel Model Examining Associations Between Continuous Glucose Monitoring and GradCPT (n = 18)

Model A Model B Model C Model D
EST (SE) EST (SE) EST (SE) EST (SE)
Within Person Effects
Day 0.00 (0.01) 0.00 (0.01) 0.01 (0.01) 0.01 (0.01)
Day % Hypoglycemia −0.47 (0.58) −0.43 (0.58) 0.15 (0.65) −0.46 (0.58)
Day % Hyperglycemia −0.15 (0.16) −0.10 (0.17) −0.12 (0.18) −0.15 (0.16)
Night % Hypoglycemia −0.23 (0.33) −0.19 (0.33) −0.16 (0.34) −0.24 (0.33)
Night % Hyperglycemia 0.15 (0.11) 0.15 (0.11) 0.13 (0.11) 0.15 (0.11)
Negative Affect −0.02 (0.02)
Sleep Quality −0.16 (0.14)
Sleepiness −0.05 (0.25)
Between Person Effects
Age −0.01 (0.01) −0.01 (0.01) −0.01 (0.01) 0.01 (0.01)
Day % Hypoglycemia −0.47 (0.58) 3.80 (3.40) 2.54 (4.28) 3.53 (3.44)
Day % Hyperglycemia −0.15 (0.16) −0.96 (0.68) −0.99 (0.80) −0.75 (0.72)
Night % Hypoglycemia −0.23 (0.33) −2.09 (1.62) −2.95 (1.86) −2.37 (1.63)
Night % Hyperglycemia 0.15 (0.11) 1.27 (0.61) 0.98 (0.68) 1.04 (0.60)
Negative Affect 0.04 (0.03)
Sleep Quality −0.37 (1.03)
Sleepiness 0.54 (0.64)
Random Components
Level 2 (Intercept) 0.08* 0.04 0.08* 0.04 0.11* 0.05 0.08* 0.04
AR1 0.07 0.08 0.06 0.08 0.06 0.09 0.07 0.08
Level 1 (Residual) 0.22*** 0.02 0.22*** 0.02 0.21*** 0.02 0.22*** 0.02
Fit Indices
−2LL 353.7 362.0 305.4 353.0
AIC 353.8 362.1 305.5 353.1
BIC 356.4 364.6 308.0 355.7
Number of Observations 237 237 208 237
Open in a new tab

Note: Analyses are based on between 208 and 237 observations in a sample of 18 participants; EST = parameter estimate; SE = Standard Error; CGM = Continuous Glucose Monitoring; AR1 = 1st Order Autoregressive; −2LL = −2*loglikelihood (Deviance); AIC = Akaike information criteria; BIC = Bayesian information criteria.

*

p < 0.05

**

p < 0.01

***

p < 0.001

Associations between sleep, affect, and cognition

Additional analyses examined direct associations between the sleep quality, sleepiness, and negative affect variables and 1) cognitive outcomes and 2) CGM variables (i.e., the independent effects of negative affect, sleepiness, and sleep quality on each cognitive EMA and the independent effects of CGM variables on negative affect, sleepiness, and sleep quality).

Results

Table 1 presents the demographic, clinical and CGM characteristics of the enrolled sample of adults with T1D (n=20). Two participants were excluded in the subsequent multilevel analyses due to <50% EMA completion rate at the end of data collection. Thus, multilevel models were estimated using data from 18 participants. Sixteen of the participants had data from all 15 days; one participant had data across 14 days and one participant had data from 13 total days.

Table 1.

Participant Demographic and Clinical Characteristics (n = 20)

Variable Mean ± SD (range) or n (%)
Demographics
 Age (years) 39.8±11.0 (26–67)
 Gender, n (% female) 11 (55%)
 Race
  White or European 20 (100%)
 Ethnicity
  Hispanic/Latino 0 (0%)
 Education
  Bachelor’s degree or higher 11 (55%)
Clinical Characteristics
 HbA1c 8.3%±1.5 (5.4%-12.5%)
  HbA1c ≥ 7.0 18 (90%)
 Age of onset (years) 18.9±13.9 (4–42)
 Diabetes Duration (Years) 18±9 (4–40)
 Hours Slept 6.97±1.1 (5–9)
 Pittsburgh Sleep Quality Index (PSQI) 5.7±2.2 (2–11)
  PSQI ≥5 10 (53%)
 Karolinska Sleepiness Scale (KSS) 3.6±1.2 (1.3–5.4)
 Patient Health Questionnaire-8 (PHQ-8) 4.9±4.3 (0–15)
  PHQ-8 ≥ 10 3 (15%)
CGM Metrics
 Average glucose (mg/dL) 180.8+81.3 (39–401)
  Daytime 174.7+39.2 (102–245)
  Nocturnal 193.0+55.4 (93–305)
 Extreme Hyperglycemia (% time > 250 mg/dL) 19.0+16.8 (0–56)
  Daytime 16.3+14.8 (0–45)
  Nocturnal 24.8+23.9 (0–80)
 Hyperglycemia (% time >180 mg/dL) 44.1+21.7 (3–82)
  Daytime 40.1+21.2 (4–75)
  Nocturnal 52.3+25.7 (0–96)
 Time in Range (% time between 70–180 mg/dL) 51.7+19.0 (18–85)
  Daytime 55.9+19.0 (24–88)
  Nocturnal 42.9+21.8 (4–84)
 Hypoglycemia (% time <70 mg/dL) 4.2+4.6 (0–20.1)
  Daytime 3.9 +4.1 (0–14.5)
  Nocturnal 4.7+7.4 (0–32)
 Serious Hypoglycemia (% time <55 mg/dL) 1.2+1.9 (0–8.4)
  Daytime 1.0+1.4 (0–5.5)
Nocturnal 1.4+3.3 (0–15)
Open in a new tab

Within Person Effects

Results of multilevel models testing controlled effects of CGM on each of the cognitive test outcomes are presented in Tables 2 – 4. Across the series of models, within-person effects of day of study were significant for DSM and MOT performance, indicating that performance improved with repeated exposure to the task (i.e., there were significant practice effects across the 15 days of the study). Specific results for DSM median reaction time (Table 2) indicated within-person fluctuations in % nocturnal hypoglycemia were significantly associated with daytime processing speed. Specifically, when participants spent a higher percentage of time in hypoglycemia the night prior, relative to their average percentage the night prior, they performed slower than their average DSM performance the following day (b = 104.56, p = 0.013), while their same day percent time in hypoglycemia was not associated with within-person variation in DSM performance. This association remained significant after controlling for within-person effects of negative affect, sleep quality, and sleepiness (all ps < 0.05).

As seen in Table 3, results of the multilevel models predicting changes in daytime visual working memory (MOT) indicated that within-person fluctuations in % same day hypoglycemia were significantly associated with MOT performance. Specifically, on days when participants spent higher than their average time in hypoglycemia (during the day), they performed poorer on the MOT. This within-person effect was only significant, however, when sleep quality was included in the model (Table 3, Model C: b = −0.16, p = 0.030). No within-person effects of hypoglycemia were found for GradCPT (Table 4). Across the three cognitive tests, no significant within-person effects were found for either daytime or nocturnal hyperglycemia.

Between Person Effects

Given the small sample size, between-person effects should be interpreted with caution. Participants’ age was a significant predictor of both DSM and MOT performance, across each series of models. When accounting for age, no other significant between-person effects were found for DSM performance (Table 2). As seen in Table 3 (Model A), there was a significant negative between-person effect of daytime hyperglycemia on MOT performance (b = −0.22, p = 0.04), as well as a positive association of nocturnal hyperglycemia on MOT performance (b = 0.22, p = 0.03). Specifically, those with more daytime hyperglycemia, across the study, performed poorer on the MOT, and this effect remained significant after controlling for negative affect and sleepiness, but not when controlling for sleep quality (p = .086). However, individuals with more nocturnal hyperglycemia performed better on the MOT, and this effect remained significant even after controlling for negative affect, sleep quality, and sleepiness (Table 3, Models B-D). There were no significant between-person effects for the models’ predicting changes in GradCPT (Table 4).

Associations between sleep, affect, and cognition

The next series of multilevel models specified within-person and between-person effects of negative affect, sleep quality, and sleepiness (each entered as separate predictors) on each cognitive test outcome. Results (see Supplemental Table S1) indicated there were no significant within-person nor between-person effects of negative affect, sleep quality, or sleepiness on any of the cognitive test outcomes when controlling for age. In the final analyses, three separate multilevel models were estimated in which the CGM variables were specified as predictors of negative affect, sleep quality, and sleepiness (Supplemental Table S2). Results indicated a positive within-person effect of same-day hyperglycemia (>180 mg/dL) on negative affect (b = 1.56, p = 0.007), as well as a negative within-person effect of same-day hyperglycemia on sleep quality (b = −0.24, p = 0.005). Thus, on days when participants spent more time in hyperglycemia, relative to their average, they reported greater negative affect during that day (Model 1) and poorer sleep quality during the prior night (Model 2). There were no significant between-person effects of CGM variables on any of these outcomes, nor was day of study or age associated with daily reports of negative affect, sleep quality, or sleepiness.

Discussion

In the current real-world study using EMA and CGM to examine the impact of nocturnal glycemic excursions on cognition in 18 adults with T1D, we found that when participants had more nocturnal hypoglycemia than they typically do, their processing speed was slower the next day. The current findings of the association between nocturnal hypoglycemia and next day processing speed differs from results reported in a smaller study (n=10) involving experimental manipulation of nocturnal glucose (1 hour of hypoglycemia 41– 49 mg/dL) that found no impact on next day cognitive performance (King et al., 1998). However, recently reported results from the FEEL-T1D study using CGM and mobile cognitive assessment of sustained attention via GradCPT also found a within person association between nocturnal hypoglycemia and cognition (Pyatak et al, 2023). In terms of effect magnitude, in our sample, every 1% increase in nocturnal % time spent in hypoglycemia (<70) was associated with a 104.71 millisecond slower median reaction time the following day (e.g., If an individual went from 8% to 9% in nocturnal time spent in hypoglycemia, we would expect to see a slowing in median reaction by 104.71 milliseconds the following day). Given that one year of age was associated with 9 ms of slowing on the DSM, this degree of reaction time slowing is equivalent to 11 years of aging.

Interestingly, when participants spent more time in hypoglycemia than typical for them during the day, there was no impact on processing speed, as was expected based on clamp studies (Ewing et al., 1998; McAulay et al., 2006). It is possible that individual nocturnal hypoglycemic events were longer at night due to failure to recognize and treat hypoglycemia when asleep, however, the absolute amount of time spent in hypoglycemia during the day (30 minutes) was similar to the absolute amount of time spent in hypoglycemia at night (31 minutes). Despite prior evidence that same day hyperglycemia results in a slowing of cognitive performance in people with both type 1 and type 2 diabetes (Cox et al., 2005), there was no within-person association between percent time spent in hyperglycemia (during the day or night) on cognitive performance and no within-person impact of CGM metrics on the other cognitive EMA measures (GradCPT and MOT), in the current study.

Although our pilot study was not powered to detect reliable between-person effects of CGM metrics on cognitive performance, we found several significant associations between CGM metrics and the MOT task that require replication. Specifically, those with more daytime hyperglycemia performed worse on the MOT, as expected. However, counter to expectations, individuals with less nocturnal hyperglycemia performed worse on the MOT. Further investigation revealed a moderate negative correlation between nocturnal hyperglycemia and nocturnal hypoglycemia (r = −0.61, p = 0.01), raising the possibility that this finding is driven by the fact that those who have less nocturnal hyperglycemia tend to also have more nocturnal hypoglycemia. This finding will be explored in a larger sample.

Despite a high burden of emotional and sleep health concerns in individuals with T1D, we did not find evidence supporting a significant role of negative affect or sleep quality in the relationship between glycemic excursions and cognition in the current sample. Specifically, the significant within-person association between time spent in nocturnal hypoglycemia and processing speed the next day remained significant after controlling for age, negative affect, sleepiness, and sleep quality. In contrast, Pyatak et al. (2023) found sleep partially mediated the association between nocturnal glycemic fluctuations and sustained attention, though this relationship was observed with sleep fragmentation as a variable, which was not measured in the current study. We did find that when individuals had more hyperglycemia than their average, they also experienced more negative affect and worse sleep quality.

It is important to consider the limitations of this pilot study when interpreting our results. Failure to detect some hypothesized associations may be due to the small sample size, particularly for detecting between-person effects for which we did not have sufficient power. Therefore, the reported between-person associations are exploratory in nature and require replication. Additionally, given the sample size for the pilot study, while we controlled for age in our analyses, we were under-powered to detect meaningful age group differences in the strength of the association between nocturnal glucose and next day cognitive performance. Future research should investigate whether older adults are more vulnerable to the effects of nocturnal hypoglycemia on cognitive performance. Further, as the aim of the current pilot study was to examine the effect of clinically relevant nocturnal glucose metrics (e.g., % time <70 mg/dL) in relation to next day cognition, we aggregated data within each day of the study. This may have obscured more acute impacts of glucose, negative affect, and sleepiness on cognition during the day. Our measures of sleep quality and sleepiness were based on subjective, self-report data. Future research should consider utilizing objective measures of sleep (e.g., sleep study, actigraphy). While the use of blinded CGM makes it less likely that the current findings are attributable to expectancies based on knowledge of glucose levels, individuals with T1D often have symptoms during glucose extremes, such as blurred vision, irritability, and confusion, and finger-stick glucose testing is necessary for appropriate clinical management. Therefore, it is possible that participants were aware of glucose excursions, which may have influenced the findings presented here. Lastly, our sample was homogenous in terms of race/ethnicity- all participants were White, which limits generalizability.

Individuals with T1D are an important population in which to utilize EMA methodology coupled with passive glucose sensing given the complex behavioral and physiological processes inherent to T1D. However, this approach could be generalized to other populations characterized by dynamic physiological processes that impact cognitive and emotional health, as well as better understanding the factors that underlie normal cognitive performance fluctuations. Thus, the method described in this pilot study (passive physiological recording, mobile cognitive testing, and self-report via EMA) provides a powerful approach to studying the cognitive impacts of fluctuating physiological states and psychological symptoms in other patient populations. For example, wearable sensors that detect movement, sleep, cardiac function, respiration, or stress hormones could be substituted for glucose monitoring in future studies with participants who do not have T1D. Our approach may serve as methodological guidance for future studies that aim to examine cognitive performance via EMA. Further, assessment of cognition under naturally occurring environmental conditions is likely to provide a more ecologically valid assessment of typical cognitive performance (and how cognition is influenced by those environmental conditions) than a single clinical assessment performed under ideal conditions. Thus, our methodology has the potential to lead to more ecologically relevant findings.

The current findings provide further evidence that nocturnal hypoglycemia can affect processing speed the next day. These within-person associations have the potential to direct clinical interventions that may improve cognitive performance. Notably, commercially available automated insulin delivery via a hybrid closed-loop system has been shown to reduce the amount of time individuals spend in hypoglycemia at night by approximately 2% (Kovatchev et al., 2020), which would be equivalent to a 208 ms improvement in next day processing speed. Whether use of real-time CGM with alerts and automated insulin delivery systems result in better daytime processing speed is an important area for future study. Additionally, it is important to note that the cognitive effects that we report may differ throughout the day, perhaps due to circadian effects or other factors. Due to the aggregation of EMA data within days, these effects could not be detected in the current pilot study. Future research is needed to explore within day variation.

Supplementary Material

Supp 1

Acknowledgements

Data is from the Glycemic Variability and Fluctuations in Cognitive Status in Adults with Type 1 Diabetes (GluCog) Study. The study is funded by the National Institute of Health - National Institute of Diabetes, Digestive and Kidney Diseases, NIH- NIDDK (R01-DK121240-01). The authors would like to thank the participants who volunteered to contribute to this study. MZK, OW, and NSC wrote the manuscript and participated in the discussion. RSW, AV, KM, JB, MJS, EG contributed to the study design. MJC lead data analysis. SS, MJS, AV, LJ, ZWH, KM, DH, EG, JB, LMF and RSW edited the manuscript and participated in the discussion. LG and NSC are responsible for study design, participated in discussion and edited the manuscript. LMF is supported by an Alzheimer’s Association grant (AARFD-21-851373). The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health or the Alzheimer’s Association. All authors reviewed and approved the final manuscript for publication.

Footnotes

Conflicts of Interest

ZWH has received consulting fees from Blueprint Health, and SS has received consulting fees from Aphelion Capital. RSW has previously participated in multicenter clinical trials through SUNY Upstate Medical Center, sponsored by NIDDK, Leona M. and Harry B. Helmsley Charitable Trust, Insulet, Medtronic, Eli Lilly, Novo Nordisk, Diasome, Amgen. Tandem and DexCom provided devices for some of these studies. KMM has served as a consultant to Dexcom and has received donations of devices from Dexcom for studies funded by Helmsley Charitable Trust. LG is President of the 501c3 Many Brains Project which supports infrastructure for open-source tests used in this study. She does not receive any financial compensation for this role. NSC has received consulting fees from Adaptelligence, LLC. No conflicts of interest, financial or otherwise, are declared by the other authors.

References

  1. Akerstedt T, Anund A, Axelsson J, & Kecklund G (2014). Subjective sleepiness is a sensitive indicator of insufficient sleep and impaired waking function. Journal of Sleep Research, 23(3), 240–252. 10.1111/jsr.12158 [DOI] [PubMed] [Google Scholar]
  2. Allen KV, Pickering MJ, Zammitt NN, Hartsuiker RJ, Traxler MJ, Frier BM, & Deary IJ (2015). Effects of acute hypoglycemia on working memory and language processing in adults with and without type 1 diabetes. Diabetes Care, 38(6), 1108–1115. 10.2337/dc14-1657 [DOI] [PMC free article] [PubMed] [Google Scholar]
  3. American Diabetes Association Professional Practice Committee (2022). 2. Classification and Diagnosis of Diabetes: Standards of Medical Care in Diabetes-2022. Diabetes Care, 45(Suppl 1), S17–S38. 10.2337/dc22-S002 [DOI] [PubMed] [Google Scholar]
  4. Anderson RJ, Freedland KE, Clouse RE, & Lustman PJ (2001). The prevalence of comorbid depression in adults with diabetes: A meta-analysis. Diabetes Care, 24(6), 1069–1078. 10.2337/diacare.24.6.1069 [DOI] [PubMed] [Google Scholar]
  5. Anderson RJ, de Groot M, Grigsby AB, McGill JB, Freedland KE, Clouse RE, & Lustman PJ (2002). Anxiety and poor glycemic control: A meta-analytic review of the literature. International Journal of Psychiatry in Medicine, 32(3), 235–247. 10.2190/KLGD-4H8D-4RYL-TWQ8 [DOI] [PubMed] [Google Scholar]
  6. Barone MT, Wey D, Schorr F, Franco DR, Carra MK, Lorenzi Filho G, & Menna-Barreto L (2015). Sleep and glycemic control in type 1 diabetes. Archives of endocrinology and metabolism, 59(1), 71–78. 10.1590/2359-3997000000013 [DOI] [PubMed] [Google Scholar]
  7. Brands AM, Biessels GJ, de Haan EH, Kappelle LJ, & Kessels RP (2005). The effects of type 1 diabetes on cognitive performance: a meta-analysis. Diabetes Care, 28(3), 726–735. 10.2337/diacare.28.3.726 [DOI] [PubMed] [Google Scholar]
  8. Brands AM, Kessels RP, Hoogma RP, Henselmans JM, van der Beek Boter JW, Kappelle LJ, & Biessels GJ (2006). Cognitive performance, psychological well-being, and brain magnetic resonance imaging in older patients with type 1 diabetes. Diabetes, 55(6), 1800–1806. [DOI] [PubMed] [Google Scholar]
  9. Brandt R, Park M, Wroblewski K, Quinn L, Tasali E, & Cinar A (2021). Sleep quality and glycaemic variability in a real-life setting in adults with type 1 diabetes. Diabetologia, 64(10), 2159–2169. 10.1007/S00125-021-05500-9 [DOI] [PMC free article] [PubMed] [Google Scholar]
  10. Broadley MM, White MJ, & Andrew B (2017). A systematic review and meta-analysis of executive function performance in type 1 diabetes mellitus. Psychosomatic Medicine, 79(6), 684–696. 10.1097/PSY.0000000000000460 [DOI] [PubMed] [Google Scholar]
  11. Brosch T, Scherer KR, Grandjean D, & Sander D (2013). The impact of emotion on perception, attention, memory, and decision-making. Swiss Medical Weekly 2013 :19, 143(19). 10.4414/SMW.2013.13786 [DOI] [PubMed] [Google Scholar]
  12. Buysse DJ, Reynolds CF, Monk TH, Berman SR, Kupfer DJ. The Pittsburgh Sleep Quality Index: a new instrument for psychiatric practice and research. Psychiatry Research. May 1989;28(2):193–213. doi: 10.1016/0165-1781(89)90047-4 [DOI] [PubMed] [Google Scholar]
  13. Carpenter RW, Wycoff AM, Trull TJ. Ambulatory Assessment. Assessment. Aug 2016;23(4):414–424. doi: 10.1177/1073191116632341 [DOI] [PMC free article] [PubMed] [Google Scholar]
  14. Carper MM, Traeger L, Gonzalez JS, Wexler DJ, Psaros C, & Safren SA (2014). The differential associations of depression and diabetes distress with quality of life domains in type 2 diabetes. Journal of behavioral medicine, 37, 501–510. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Cartwright J Technology: Smartphone science. Nature. Mar 2016;531(7596):669–71. doi: 10.1038/nj7596-669a [DOI] [PubMed] [Google Scholar]
  16. Chaytor NS, Barbosa-Leiker C, Ryan CM, Germine LT, Hirsch IB, & Weinstock RS (2019). Clinically significant cognitive impairment in older adults with type 1 diabetes. Journal of Diabetes and its Complications, 33(1), 91–97. 10.1016/j.jdiacomp.2018.04.003 [DOI] [PubMed] [Google Scholar]
  17. Chaytor NS, Riddlesworth TD, Bzdick S, Odegard PS, Gray SL, Lock JP, DuBose SN, Beck RW; T1D Exchange Severe Hypoglycemia in Older Adults with Type 1 Diabetes Study Group. The relationship between neuropsychological assessment, numeracy, and functional status in older adults with type 1 diabetes (2017). Neuropsychological Rehabilitation, 27(4):507–521. doi: 10.1080/09602011.2015.1116448. Epub 2015 Nov 25. [DOI] [PubMed] [Google Scholar]
  18. Chontong S, Saetung S, & Reutrakul S (2016). Higher sleep variability is associated with poorer glycaemic control in patients with type 1 diabetes. Journal of sleep research, 25(4), 438–444. [DOI] [PubMed] [Google Scholar]
  19. Chung F, Abdullah HR, & Liao P (2016). STOP-Bang Questionnaire: A practical approach to Screen for Obstructive Sleep Apnea. Chest, 149(3), 631–638. 10.1378/chest.15-0903 [DOI] [PubMed] [Google Scholar]
  20. Chung F, Yegneswaran B, Liao P, Chung SA, Vairavanathan S, Islam S, Khajehdehi A, & Shapiro CM (2008). STOP questionnaire: a tool to screen patients for obstructive sleep apnea. Anesthesiology, 108(5), 812–821. 10.1097/ALN.0b013e31816d83e4 [DOI] [PubMed] [Google Scholar]
  21. Coccaro EF, Lazarus S, Joseph J, Wyne K, Drossos T, Phillipson L, & de Groot M (2021). Emotional Regulation and Diabetes Distress in Adults With Type 1 and Type 2 Diabetes. Diabetes Care, 44(1), 20–25. 10.2337/DC20-1059 [DOI] [PMC free article] [PubMed] [Google Scholar]
  22. Cox DJ, Kovatchev BP, Gonder-Frederick LA, Summers KH, McCall A, Grimm KJ, & Clarke WL (2005). Relationships between hyperglycemia and cognitive performance among adults with type 1 and type 2 diabetes. Diabetes Care, 28(1), 71–77 [DOI] [PubMed] [Google Scholar]
  23. Cox DJ, Gonder-Frederick LA, Schroeder DB, Cryer PE, & Clarke WL (1993). Disruptive effects of acute hypoglycemia on speed of cognitive and motor performance. Diabetes Care, 16(10), 1391–1393. 10.2337/diacare.16.10.1391 [DOI] [PubMed] [Google Scholar]
  24. Danna SM, Graham E, Burns RJ, Deschenes SS, & Schmitz N (2016). Association between depressive symptoms and cognitive function in persons with diabetes mellitus: a systematic review. PLoS One, 11(8), e0160809. [DOI] [PMC free article] [PubMed] [Google Scholar]
  25. Draelos MT, Jacobson AM, Weinger K, Widom B, Ryan CM, Finkelstein DM, & Simonson DC (1995). Cognitive function in patients with insulin-dependent diabetes mellitus during hyperglycemia and hypoglycemia. The American Journal of Medicine, 98(2), 135–144. 10.1016/S0002-9343(99)80397-0 [DOI] [PubMed] [Google Scholar]
  26. Dogra V, Mittal B, Senthil Kumaran S, Nehra A, Sagar R, Gupta A, Kalaivani M, Gupta Y, & Tandon N (2022). Evaluation of cognitive deficits in Adults with Type 1 diabetes stratified by the age of diabetes onset: A Cross-Sectional Study. Advances in Therapy, 39(4), 1711–1723. 10.1007/s12325-022-02063-y [DOI] [PubMed] [Google Scholar]
  27. Ebner-Priemer UW, Trull TJ. Ecological momentary assessment of mood disorders and mood dysregulation. Psychol Assess. 2009. Dec;21(4):463–75. doi: 10.1037/a0017075. [DOI] [PubMed] [Google Scholar]
  28. Ehrmann D, Schmitt A, Priesterroth L, Kulzer B, Haak T, Hermanns N. Time With Diabetes Distress and Glycemia-Specific Distress: New Patient-Reported Outcome Measures for the Psychosocial Burden of Diabetes Using Ecological Momentary Assessment in an Observational Study. Diabetes Care. Jul 07 2022;45(7):1522–1531. doi: 10.2337/dc21-2339 [DOI] [PubMed] [Google Scholar]
  29. Ewing FME, Deary IJ, McCrimmon RJ, Strachan MWJ, & Frier BM (1998). Effect of acute hypoglycemia on visual information processing in adults with type 1 diabetes mellitus. Physiology & Behavior, 64(5), 653–660. 10.1016/S0031-9384(98)00120-6 [DOI] [PubMed] [Google Scholar]
  30. Fisher L, Mullan JT, Arean P, Glasgow RE, Hessler D, & Masharani U (2010). Diabetes distress but not clinical depression or depressive symptoms is associated with glycemic control in both cross-sectional and longitudinal analyses. Diabetes Care, 33(1), 23–28. 10.2337/dc09-1238 [DOI] [PMC free article] [PubMed] [Google Scholar]
  31. Fisher L, Hessler DM, Polonsky WH, & Mullan J (2012). When is diabetes distress clinically meaningful? Establishing cut points for the Diabetes Distress Scale. Diabetes care, 35(2), 259–264. [DOI] [PMC free article] [PubMed] [Google Scholar]
  32. Fortenbaugh FC, DeGutis J, Germine L, Wilmer JB, Grosso M, Russo K, & Esterman M (2015). Sustained attention across the life span in a sample of 10,000: dissociating ability and strategy. Psychological Science, 26(9), 1497–1510. 10.1177/0956797615594896 [DOI] [PMC free article] [PubMed] [Google Scholar]
  33. Gabbay MAL, Rodacki M, Calliari LE et al. Time in range: a new parameter to evaluate blood glucose control in patients with diabetes. Diabetology & Metabolic Syndrome 12, 22 (2020). 10.1186/s13098-020-00529-z [DOI] [PMC free article] [PubMed] [Google Scholar]
  34. Germine L, Reinecke K, Chaytor NS. Digital neuropsychology: Challenges and opportunities at the intersection of science and software. Clinical Neuropsychology. Feb 2019;33(2):271–286. doi: 10.1080/13854046.2018.1535662 [DOI] [PubMed] [Google Scholar]
  35. Gonzalez JS, Fisher L, & Polonsky WH (2011). Depression in diabetes: have we been missing something important?. Diabetes Care, 34(1), 236–239. 10.2337/dc10-1970 [DOI] [PMC free article] [PubMed] [Google Scholar]
  36. Graveling AJ, Deary IJ, & Frier BM (2013). Acute hypoglycemia impairs executive cognitive function in adults with and without type 1 diabetes. Diabetes Care, 36(10), 3240–3246. 10.2337/dc13-0194 [DOI] [PMC free article] [PubMed] [Google Scholar]
  37. Gregory GA, Robinson TIG, Linklater SE, Wang F, Colagiuri S, de Beaufort C, Donaghue KC; International Diabetes Federation Diabetes Atlas Type 1 Diabetes in Adults Special Interest Group; Magliano DJ, Maniam J, Orchard TJ, Rai P, Ogle GD (2022). Global incidence, prevalence, and mortality of type 1 diabetes in 2021 with projection to 2040: a modelling study. Lancet Diabetes Endocrinology 10(10):741–760. doi: 10.1016/S2213-8587(22)00218-2 [DOI] [PubMed] [Google Scholar]
  38. Grigsby AB, Anderson RJ, Freedland KE, Clouse RE, & Lustman PJ (2002). Prevalence of anxiety in adults with diabetes a systematic review. In Journal of Psychosomatic Research (Vol. 53, Issue 6, pp. 1053–1060). Journal of Psychosomatic Research 10.1016/S0022-3999(02)00417-8 [DOI] [PubMed] [Google Scholar]
  39. Griggs S, Grey M, Ash GI, Li CR, Crawford SL, Hickman RL. Objective Sleep-Wake Characteristics Are Associated With Diabetes Symptoms in Young Adults With Type 1 Diabetes. The Science of Diabetes Self-Management and Care. 2022;48(3):149–156. doi: 10.1177/26350106221094521 [DOI] [PMC free article] [PubMed] [Google Scholar]
  40. Griggs S, Hickman RL, Strohl KP, Redeker NS, Crawford SL, & Grey M (2021). Sleep-wake characteristics, daytime sleepiness, and glycemia in young adults with type 1 diabetes. Journal of Clinical Sleep Medicine: JCSM : official publication of the American Academy of Sleep Medicine, 17(9), 1865–1874. 10.5664/jcsm.9402 [DOI] [PMC free article] [PubMed] [Google Scholar]
  41. Hagger V, Hendrieckx C, Sturt J, Skinner TC, & Speight J (2016). Diabetes distress among adolescents with type 1 diabetes: a Systematic Review. Current Diabetes Reports, 16(1), 1–14. 10.1007/S11892-015-0694-2 [DOI] [PubMed] [Google Scholar]
  42. Hartshorne JK, Germine LT. When does cognitive functioning peak? The asynchronous rise and fall of different cognitive abilities across the life span. Psycholgical Science. Apr 2015;26(4):433–43. doi: 10.1177/0956797614567339 [DOI] [PMC free article] [PubMed] [Google Scholar]
  43. Hermanns N, Scheff C, Kulzer B et al. Association of glucose levels and glucose variability with mood in type 1 diabetic patients. Diabetologia 50, 930–933 (2007). 10.1007/s00125-007-0643-y [DOI] [PubMed] [Google Scholar]
  44. Heller SR, & Macdonald IA (1996). The measurement of cognitive function during acute hypoglycaemia: experimental limitations and their effect on the study of hypoglycaemia unawareness. Diabetic Medicine : A Journal of the British Diabetic Association, 13(7), 607–615. 10.1002/(SICI)1096-9136(199607)13:7&lt;607::AID-DIA159&gt;3.0.CO;2-I [DOI] [PubMed] [Google Scholar]
  45. Holmes CS, Hayford JT, Gonzalez JL, & Weydert JA (1983). A survey of cognitive functioning at difference glucose levels in diabetic persons. Diabetes Care, 6(2), 180–185. 10.2337/diacare.6.2.180 [DOI] [PubMed] [Google Scholar]
  46. Jacobson AM, Ryan CM, Braffett BH, Gubitosi-Klug RA, Lorenzi GM, Luchsinger JA, Trapani VR, Bebu I, Chaytor N, Hitt SM, Farrell K, Lachin JM; DCCT/EDIC Research Group. Cognitive performance declines in older adults with type 1 diabetes: results from 32 years of follow-up in the DCCT and EDIC Study. Lancet Diabetes Endocrinology. 2021. Jul;9(7):436–445. doi: 10.1016/S2213-8587(21)00086-3. Epub 2021 May 27. [DOI] [PMC free article] [PubMed] [Google Scholar]
  47. Jauch-Chara K, Schmid SM, Hallschmid M, Born J, & Schultes B (2008). Altered neuroendocrine sleep architecture in patients with type 1 diabetes. Diabetes Care, 31(6), 1183–1188. 10.2337/dc07-1986 [DOI] [PubMed] [Google Scholar]
  48. Junghaenel DU, & Stone AA (2020). Ecological momentary assessment for the psychosocial study of health. The Wiley Encyclopedia of Health Psychology, 105–112. [Google Scholar]
  49. Kahanovitz L, Sluss PM, Russell SJ. Type 1 Diabetes - A Clinical Perspective. Point Care. 2017. Mar;16(1):37–40. doi: 10.1097/POC.0000000000000125. [DOI] [PMC free article] [PubMed] [Google Scholar]
  50. King P, Kong MF, Parkin H, Macdonald IA, & Tattersall RB (1998). Well-being, cerebral function, and physical fatigue after nocturnal hypoglycemia in IDDM. Diabetes care, 21(3), 341–345. 10.2337/diacare.21.3.341 [DOI] [PubMed] [Google Scholar]
  51. Klein R (1995). Hyperglycemia and microvascular and macrovascular disease in diabetes. Diabetes Care, 18(2), 258–268. 10.2337/diacare.18.2.258 [DOI] [PubMed] [Google Scholar]
  52. Li W, Huang E, & Gao S (2017). Type 1 diabetes mellitus and cognitive impairments: A systematic review. Journal of Alzheimer’s Disease: JAD, 57(1), 29–36. 10.3233/JAD-161250 [DOI] [PubMed] [Google Scholar]
  53. Lo JC, Ong JL, Leong RLF, Gooley JJ, & Chee MWL (2016). Cognitive Performance, Sleepiness, and Mood in Partially Sleep Deprived Adolescents: The Need for Sleep Study. Sleep, 39(3), 687–698. 10.5665/SLEEP.5552 [DOI] [PMC free article] [PubMed] [Google Scholar]
  54. Leske MC, Wu SY, Hennis A, Hyman L, Nemesure B, Yang L, Schachat AP, & Barbados Eye Study Group (2005). Hyperglycemia, blood pressure, and the 9-year incidence of diabetic retinopathy: the Barbados eye studies. Ophthalmology, 112(5), 799–805. [DOI] [PubMed] [Google Scholar]
  55. Lustman PJ, & Clouse RE (2005). Depression in diabetic patients: The relationship between mood and glycemic control. In Journal of Diabetes and its Complications (Vol. 19, Issue 2, pp. 113–122). Elsevier Inc. 10.1016/j.jdiacomp.2004.01.002 [DOI] [PubMed] [Google Scholar]
  56. Mascarenhas Fonseca L, Strong RW, Singh S, Bulger JD, Cleveland M, Grinspoon E, Janess K, Jung L, Miller K, Passell E, Ressler K, Sliwinski MJ, Verdejo A, Weinstock RS, Germine L, & Chaytor NS (2023). Glycemic Variability and Fluctuations in Cognitive Status in Adults With Type 1 Diabetes (GluCog): Observational Study Using Ecological Momentary Assessment of Cognition. JMIR Diabetes, 8, e39750. 10.2196/39750 [DOI] [PMC free article] [PubMed] [Google Scholar]
  57. McAulay V, Deary IJ, Sommerfield AJ, & Frier BM (2006). Attentional functioning is impaired during acute hypoglycaemia in people with Type 1 diabetes. Diabetic Medicine : A Journal of the British Diabetic Association, 23(1), 26–31. 10.1111/j.1464-5491.2005.01795.x [DOI] [PubMed] [Google Scholar]
  58. MacAulay GC, Galland BC, Boucher SE, Wiltshire EJ, Haszard JJ, Campbell AJ, Black SM, Smith C, Elder D, & Wheeler BJ (2020). Impact of type 1 diabetes mellitus, glucose levels, and glycemic control on sleep in children and adolescents: a case-control study. Sleep, 43(2), 1–11. 10.1093/SLEEP/ZSZ226 [DOI] [PubMed] [Google Scholar]
  59. McCrimmon RJ, Ryan CM, Frier BM. Diabetes and cognitive dysfunction. Lancet. 2012;379(9833):2291–2299 [DOI] [PubMed] [Google Scholar]
  60. Melendez-Ramirez LY, Richards RJ, & Cefalu WT (2010). Complications of type 1 diabetes. Endocrinology and Metabolism Clinics, 39(3), 625–640 [DOI] [PubMed] [Google Scholar]
  61. Muijs LT, Racca C, de Wit M, et al. Glucose variability and mood in adults with diabetes: A systematic review. Endocrinology, Diabetes and Metabolism. 2021;4(1). doi: 10.1002/edm2.152 [DOI] [PMC free article] [PubMed] [Google Scholar]
  62. Nam S, Griggs S, Ash GI, Dunton GF, Huang S, Batten J, Parekh N, & Whittemore R (2021). Ecological momentary assessment for health behaviors and contextual factors in persons with diabetes: A systematic review. Diabetes Research and Clinical Practice, 174, 108745. 10.1016/j.diabres.2021.108745 [DOI] [PubMed] [Google Scholar]
  63. Nathan DM; DCCT/EDIC Research Group. The diabetes control and complications trial/epidemiology of diabetes interventions and complications study at 30 years: overview. Diabetes Care. 2014;37(1):9–16. doi: 10.2337/dc13-2112. [DOI] [PMC free article] [PubMed] [Google Scholar]
  64. Nebes RD, Buysse DJ, Halligan EM, Houck PR, & Monk TH (2009). Self-reported sleep quality predicts poor cognitive performance in healthy older adults. Journal of Gerontology: Psychological Sciences, 64B(2), 180–187, doi: 10.1093/geronb/gbn037, Advance Access publication on February 9, 2009 [DOI] [PMC free article] [PubMed] [Google Scholar]
  65. Nefs GM, Bazelmans E, Donga E, Tack CJ, & de Galan BE (2020). Sweet dreams or bitter nightmare: a narrative review of 25 years of research on the role of sleep in diabetes and the contributions of behavioural science. Diabetic medicine: a journal of the British Diabetic Association, 37(3), 418–426. 10.1111/dme.14211 [DOI] [PubMed] [Google Scholar]
  66. Nordin M, Åkerstedt T & Nordin S Psychometric evaluation and normative data for the Karolinska Sleep Questionnaire. Sleep and Biological Rhythms 11, 216–226 (2013). 10.1111/sbr.12024 [DOI] [Google Scholar]
  67. Owens-Gary MD, Zhang X, Jawanda S, Bullard KMK, Allweiss P, & Smith BD (2019). The Importance of Addressing Depression and Diabetes Distress in Adults with Type 2 Diabetes. Journal of General Internal Medicine, 34(2), 320–324. 10.1007/S11606-018-4705-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
  68. Penckofer S, Quinn L, Byrn M, Ferrans C, Miller M, & Strange P (2012). Does glycemic variability impact mood and quality of life?. Diabetes Technology & Therapeutics, 14(4), 303–310. 10.1089/dia.2011.0191 [DOI] [PMC free article] [PubMed] [Google Scholar]
  69. Perez KM, Hamburger ER, Lyttle M, Williams R, Bergner E, Kahanda S, Cobry E, & Jaser SS (2018). Sleep in Type 1 Diabetes: Implications for Glycemic Control and Diabetes Management. Current Diabetes Reports, 18(2). 10.1007/S11892-018-0974-8 [DOI] [PMC free article] [PubMed] [Google Scholar]
  70. Phelps EA, Ling S, & Carrasco M (2006). Emotion facilitates perception and potentiates the perceptual benefits of attention. Psychological Science, 17(4), 292–299. 10.1111/J.1467-9280.2006.01701.X [DOI] [PMC free article] [PubMed] [Google Scholar]
  71. Pyatak EA, Hernandez R, Pham LT, et al. Function and Emotion in Everyday Life With Type 1 Diabetes (FEEL-T1D): Protocol for a Fully Remote Intensive Longitudinal Study. Journal of Medical Internet Research. Oct 22 2021;10(10):e30901. doi: 10.2196/30901 [DOI] [PMC free article] [PubMed] [Google Scholar]
  72. Pyatak EA, Spruijt-Metz D, Schneider S, Hernandez R, Pham LT, Hoogendoorn CJ, Peters AL, Crandall J, Jin H, Lee PJ, Gonzalez JS. Impact of overnight glucose on next-day functioning in adults with type 1 diabetes: An exploratory intensive longitudinal study. Diabetes Care. 2023. Mar 2:dc222008. doi: 10.2337/dc22-2008. Epub ahead of print. [DOI] [PMC free article] [PubMed] [Google Scholar]
  73. Rama Chandran S, Jacob P, & Choudhary P (2020). A systematic review of the effect of prior hypoglycaemia on cognitive function in type 1 diabetes. Therapeutic Advances in Endocrinology and Metabolism, 11, 2042018820906017. 10.1177/2042018820906017 [DOI] [PMC free article] [PubMed] [Google Scholar]
  74. Rutters F, & Nefs G (2022). Sleep and Circadian Rhythm Disturbances in Diabetes: A Narrative Review. Diabetes, Metabolic Syndrome and Obesity: Targets and Therapy, 3627–3637. [DOI] [PMC free article] [PubMed] [Google Scholar]
  75. Samoilova YG, Rotkank MA, Zhukova NG, Matveeva MV, Tolmachev IV, & Kudlay DA (2018). Markery kognitivnykh narusheniĭ i variabel’nost’ glikemii u patsientov s sakharnym diabetom 1-go tipa [Markers for cognitive impairments and variability of glycaemia in patients with type 1 diabetes mellitus]. Zhurnal Nevrologii i Psikhiatrii Imeni S.S. Korsakova, 118(4), 48–51. 10.17116/jnevro20181184148-51 [DOI] [PubMed] [Google Scholar]
  76. Seaquist ER, Anderson J, Childs B, Cryer P, Dagogo-Jack S, Fish L, Heller SR, Rodriguez H, Rosenzweig J, & Vigersky R (2013). Hypoglycemia and diabetes: a report of a workgroup of the American Diabetes Association and the Endocrine Society. Diabetes Care, 36(5), 1384–1395. 10.2337/dc12-2480 [DOI] [PMC free article] [PubMed] [Google Scholar]
  77. Shalimova A, Graff B, Gąsecki D, Wolf J, Sabisz A, Szurowska E, Jodzio K, & Narkiewicz K (2019). Cognitive dysfunction in type 1 diabetes mellitus. The Journal of Clinical Endocrinology and Metabolism, 104(6), 2239–2249. 10.1210/jc.2018-01315 [DOI] [PubMed] [Google Scholar]
  78. Shapira A, Volkening LK, Borus JS, & Laffel LM (2023). Ecological momentary assessment (EMA) of positive and negative affect and associations with blood glucose (BG) in teens with type 1 diabetes (T1D). Journal of Diabetes Science and Technology, 19322968211035451. Advance online publication. 10.1177/19322968211035451 [DOI] [PMC free article] [PubMed] [Google Scholar]
  79. Singh S, Strong R, Xu I, Fonseca LM, Hawks Z, Grinspoon E, Jung L, Li F, Weinstock RS, Sliwinski MJ, Chaytor NS, Germine LT (2023). Ecological momentary assessment of cognition in clinical and community samples: Reliability and validity study. Journal of Medical Internet Research Jun 2;25:e45028. doi: 10.2196/45028. [DOI] [PMC free article] [PubMed] [Google Scholar]
  80. Singh S, Strong RW, Jung L, et al. The TestMyBrain Digital Neuropsychology Toolkit: Development and Psychometric Characteristics. Journal of Clinical and Experimental Neuropsychology. 10 2021;43(8):786–795. doi: 10.1080/13803395.2021.2002269 [DOI] [PMC free article] [PubMed] [Google Scholar]
  81. Sliwinski MJ, Almeida DM, Smyth J, Stawski RS. Intraindividual change and variability in daily stress processes: findings from two measurement-burst diary studies. Psychol Aging. 2009. Dec;24(4):828–40. doi: 10.1037/a0017925. [DOI] [PMC free article] [PubMed] [Google Scholar]
  82. Søholm U, Broadley M, Zaremba N, et al. Investigating the day-to-day impact of hypoglycaemia in adults with type 1 or type 2 diabetes: design and validation protocol of the Hypo-METRICS application. BMJ Open. Feb 01 2022;12(2):e051651. doi: 10.1136/bmjopen-2021-051651 [DOI] [PMC free article] [PubMed] [Google Scholar]
  83. Sommerfield AJ, Deary IJ, & Frier BM (2004). Acute hyperglycemia alters mood state and impairs cognitive performance in people with type 2 diabetes. Diabetes Care, 27(10), 2335–2340. 10.2337/diacare.27.10.2335 [DOI] [PubMed] [Google Scholar]
  84. Sturt J, Dennick K, Due-Christensen M, & McCarthy K (2015). The detection and management of diabetes distress in people with type 1 diabetes. Current Diabetes Reports, 15(11), 1–14. [DOI] [PubMed] [Google Scholar]
  85. Syed FZ (2022). Type 1 diabetes mellitus. Annals of Internal Medicine, 175(3), ITC33–ITC48. [DOI] [PubMed] [Google Scholar]
  86. Tareen RS, & Tareen K (2017). Psychosocial aspects of diabetes management: Dilemma of diabetes distress. Translational Pediatrics, 6(4), 383. [DOI] [PMC free article] [PubMed] [Google Scholar]
  87. Tonoli C, Heyman E, Roelands B, Pattyn N, Buyse L, Piacentini MF, Berthoin S, & Meeusen R (2014). Type 1 diabetes-associated cognitive decline: a meta-analysis and update of the current literature. Journal of Diabetes, 6(6), 499–513. 10.1111/1753-0407.12193 [DOI] [PubMed] [Google Scholar]
  88. Treviño M, Zhu X, Lu YY, et al. How do we measure attention? Using factor analysis to establish construct validity of neuropsychological tests. Cognitive Research: Principles and Implications July 22 2021;6(1):51. doi: 10.1186/s41235-021-00313-1 [DOI] [PMC free article] [PubMed] [Google Scholar]
  89. van Duinkerken E, Schoonheim MM, Ijzerman RG, Klein M, Ryan CM, Moll AC, Snoek FJ, Barkhof F, Diamant M, & Pouwels PJ (2012). Diffusion tensor imaging in type 1 diabetes: decreased white matter integrity relates to cognitive functions. Diabetologia, 55(4), 1218–1220. 10.1007/s00125-012-2488-2 [DOI] [PMC free article] [PubMed] [Google Scholar]
  90. van Duinkerken E, Snoek FJ, de Wit M. The cognitive and psychological effects of living with type 1 diabetes: a narrative review. Diabetic Medicine. 2020. Apr;37(4):555–563. doi: 10.1111/dme.14216. Epub 2019 Dec 27. [DOI] [PMC free article] [PubMed] [Google Scholar]
  91. Verhulst C, Fabricius TW, Nefs G, Kessels R, Pouwer F, Teerenstra S, Tack CJ, Broadley MM, Kristensen PL, McCrimmon RJ, Heller S, Evans ML, Pedersen-Bjergaard U, & de Galan BE (2022). Consistent effects of hypoglycemia on cognitive function in people with or without diabetes. Diabetes Care, 45(9), 2103–2110. 10.2337/dc21-2502 [DOI] [PMC free article] [PubMed] [Google Scholar]
  92. Walker MP (2009). The role of sleep in cognition and emotion. Annals of the New York Academy of Sciences, 1156, 168–197. 10.1111/J.1749-6632.2009.04416.X [DOI] [PubMed] [Google Scholar]
  93. Warnick JL, Westen SC, Albanese-O’Neill A, Filipp SL, Schatz D, Haller MJ, & Janicke DM (2020). Use of ecological momentary assessment to measure self-monitoring of blood glucose adherence in youth with type 1 diabetes. Diabetes Spectrum: A Publication of the American Diabetes Association, 33(3), 280–289. 10.2337/ds19-0041 [DOI] [PMC free article] [PubMed] [Google Scholar]
  94. Wessels AM, Rombouts SARB, Remijnse PL et al. Cognitive performance in type 1 diabetes patients is associated with cerebral white matter volume. Diabetologia 50, 1763–1769 (2007). 10.1007/s00125-007-0714-0 [DOI] [PubMed] [Google Scholar]

Associated Data

This section collects any data citations, data availability statements, or supplementary materials included in this article.

Supplementary Materials

Supp 1
NIHMS1978178-supplement-Supp_1.docx (34.4KB, docx)

RESOURCES