Glucose variability and mood in adults with diabetes: A systematic review

Associations between continuous glucose monitoring (CGM) metrics and psycholinguistic measures: a correlational study

AIM

Recently, the relationship between diabetes and mental health has been widely studied. With the advent of continuous glucose monitoring (CGM), some researchers have been interested in exploring the association between glucose-related metrics and psychological aspects. These studies have primarily relied on self-report questionnaires which present some limitations. Therefore, the present multicenter study aims at testing potential associations between CGM metrics and affective processes derived from narratives about using a CGM sensor.

METHODS

An exploratory correlational design was used. Fifty-eight adults with type 1 diabetes using CGM were enrolled and invited to complete an online survey, where they replied to an open-ended question regarding their personal experience with the CGM sensor. Texts derived from the answers were analyzed through Linguistic Inquiry and Word Count, a widely used text analysis tool that can automatically identify and quantify linguistic patterns related to various psychological dimensions. Psycholinguistic measures were correlated with CGM metrics.

RESULTS

Higher levels of sadness/depression correlated with lower %TIR (r = - 339; p < .01) and higher %TAR (r = .342; p < .01).

CONCLUSIONS

The study highlights the relationship between CGM metrics and psychological variables derived from patients' narratives. In particular, it is possible to hypothesize a positive role of %TIR in reducing depressive feelings in individuals with diabetes, as well as a negative role of depressive feelings in achieving desirable CGM outcomes. Additionally, there is a potential role of glycemic variability, particularly hyperglycemia, in the expression of depressive and sad feelings, which has been less studied compared to the effects of hypoglycemia.

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

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.

Type 2 diabetes mellitus is associated with manic morbidity in elderly patients with mood disorders

The association between mood disorders, especially bipolar disorder (BD), and metabolic disorders, is long known. However, to which extent metabolic disorders affect the course of mood disorders in late life is still open to inquiring. To assess the impact of type 2 diabetes mellitus (T2DM) on late-life mood disorders a retrospective chart review was performed. Elderly depressive patients (≥ 65 years) diagnosed with Major Depressive Disorder (N = 57) or BD (N = 43) and followed up for at least 18 months were included and subdivided according to the presence of T2DM comorbidity. Vascular encephalopathy (39.1% vs. 15.6%, P  = 0.021) and neurocognitive disorders (21.7% vs. 5.2%, P  = 0.028), were more frequently reported in patients with T2DM than in those without. Patients with T2DM showed a greater percentage of follow-up time in manic episodes (r = -0.23, P  = 0.020) and a higher rate of manic episode(s) during follow-up (21.7% vs. 5.2%, P  = 0.028) than those without. When restricting longitudinal analyses to patients with bipolar spectrum disorders, results were confirmed. In line with the well-known connection between BD and metabolic disorders, our data support an association between T2DM and unfavorable course of illness in the elderly with BD.

Real-time Assessment of the Bidirectional Relationship Between Affective States and Glucose: Protocol for a 14-Day Observational Study

BACKGROUND

Glucose variability increases cardiometabolic disease risk. While many factors can influence glucose levels, postprandial glucose response is the primary driver of glucose variability. Furthermore, affect may directly and indirectly impact glucose variability through its effect on eating behavior. Continuous glucose monitors (CGMs) facilitate the real-time evaluation of blood glucose, and ecological momentary assessment (EMA) can be used to assess affect in real time. Together, data collected from these sources provide the opportunity to further understand the role of affect in glucose levels.

OBJECTIVE

This paper presents the protocol for a study that aims to (1) evaluate the feasibility and acceptability of using CGMs along with EMA in nondiabetic populations and (2) examine the bidirectional relationship between affect and glucose in nondiabetic adults with overweight or obesity using a CGM and EMA.

METHODS

Eligibility criteria for the study include participants (1) aged 18 to 65 years old, (2) with a BMI of ≥25 kg/m², (3) who are able to read and write in English, and (4) who own a smartphone. Individuals will be excluded if they (1) have type 1 or 2 diabetes or have any other condition that requires glucose monitoring, (2) are pregnant, (3) use any medications that have the potential to alter blood glucose levels or interfere with the glucose sensing process, or (4) have a diagnosed gastrointestinal condition or eating disorder. In a 14-day observational study, participants will wear a FreeStyle Libre Pro CGM sensor (Abbott) and will receive mobile phone-based EMA prompts 6 times per day (randomly within six 2-hour windows between 8 AM and 8 PM) to assess positive and negative affect. Participants will also wear a Fitbit Inspire 2 (Fitbit) to continuously monitor physical activity and sleep, which will be included as covariates in the analysis. Multilevel linear regression models will be used to evaluate the acute relationship between glucose level and affect.

RESULTS

Recruitment started in October 2022 and is expected to be completed in March 2023. We will aim to recruit 100 participants. As of December 12, 2022, a total of 39 participants have been enrolled.

CONCLUSIONS

The results of this study will further elucidate the role of affect in glucose variability. By identifying affective states that may lead to glucose excursions, our findings could inform just-in-time behavioral interventions by indicating opportunities for intervention delivery.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/45104.

Ecological Momentary Assessment of Positive and Negative Affect and Associations with Blood Glucose in Teens with Type 1 Diabetes

BACKGROUND

Affect (i.e., emotions) can be associated with diabetes self-care and ambient glucose in teens with type 1 diabetes (T1D). We used momentary sampling to examine associations of daily affectwithblood glucose (BG) monitoring,BG levels,and BG variability in teens with T1D.

METHOD

Over 2 weeks, 32 teens reported positive and negative affect (Positive and Negative Affect Scale) and BG levels on handheld computers 4x/day, coordinated with planned daily BG checks. BG values were classified as: in-range (70-180 mg/dL); low (<70 mg/dL); severe low (<54 mg/dL); high (>180 mg/dL); severe high (>250 mg/dL). Daily BG variability was derived from BG coefficient of variation (BGCV). To determine associations of positive and negative affect with BG checks, BG levels, and BGCV, separate generalized estimating equations were performed, adjusting for demographic and diabetes-related variables, for the overall sample and stratified by HbA1c (≤8%, >8%).

RESULTS

Teens (44% male, ages 14-18, 63% pump-treated, HbA1c 8.8 ± 1.4%) reported 51% in-range, 6% low (2% severe low), and 44% high (19% severe high) BG. In teens with HbA1c ≤8%, positive affect was associated with in-range BG (OR = 1.08, 95% CI = 1.04-1.13, P = .0002), reduced odds of very low glucose (OR = 0.35, 95% CI = 0.16-0.74, P = .006), and less daily BGCV (β = -0.9; 95% CI = -1.6, -0.2; P = .01). In teens with HbA1c >8%, negative affect was associated with less likelihood of checking BG (OR = 0.75, 95% CI = 0.64-0.87, P = .0003).

CONCLUSIONS

Our findings shed light on individual differences in metabolic reactivity based on glycemic levels and the importance of incorporating affect into automated insulin delivery systems.

Coordination of glucose monitoring, self-care behaviour and mental health: achieving precision monitoring in diabetes

Monitoring of glucose plays an essential role in the management of diabetes. However, to fully understand and meaningfully interpret glucose levels, additional information on context is necessary. Important contextual factors include data on behaviours such as eating, exercise, medication-taking and sleep, as well as data on mental health aspects such as stress, affect, diabetes distress and depressive symptoms. This narrative review provides an overview of the current state and future directions of precision monitoring in diabetes. Precision monitoring of glucose has made great progress over the last 5 years with the emergence of continuous glucose monitoring (CGM), automated analysis of new glucose variables and visualisation of CGM data via the ambulatory glucose profile. Interestingly, there has been little progress in the identification of subgroups of people with diabetes based on their glycaemic profile. The integration of behavioural and mental health data could enrich such identification of subgroups to stimulate precision medicine. There are a handful of studies that have used innovative methodology such as ecological momentary assessment to monitor behaviour and mental health in people's everyday life. These studies indicate the importance of the interplay between behaviour, mental health and glucose. However, automated integration and intelligent interpretation of these data sources are currently not available. Automated integration of behaviour, mental health and glucose could lead to the identification of certain subgroups that, for example, show a strong association between mental health and glucose in contrast to subgroups that show independence of mental health and glucose. This could inform precision diagnostics and precision therapeutics. We identified just-in-time adaptive interventions as a potential means by which precision monitoring could lead to precision therapeutics. Just-in-time adaptive interventions consist of micro-interventions that are triggered in people's everyday lives when a certain problem is identified using monitored behaviour, mental health and glucose variables. Thus, these micro-interventions are responsive to real-life circumstances and are adaptive to the specific needs of an individual with diabetes. We conclude that, with current developments in big data analysis, there is a huge potential for precision monitoring in diabetes.

The role of mental disorders in precision medicine for diabetes: a narrative review

This narrative review aims to examine the value of addressing mental disorders as part of the care of people with type 1 and type 2 diabetes in terms of four components of precision medicine. First, we review the empirical literature on the role of common mental disorders in the development and outcomes of diabetes (precision prevention and prognostics). We then review interventions that can address mental disorders in individuals with diabetes or at risk of diabetes (precision treatment) and highlight recent studies that have used novel methods to individualise interventions, in person and through applications, based on mental disorders. Additionally, we discuss the use of detailed assessment of mental disorders using, for example, mobile health technologies (precision monitoring). Finally, we discuss future directions in research and practice and challenges to addressing mental disorders as a factor in precision medicine for diabetes. This review shows that several mental disorders are associated with a higher risk of type 2 diabetes and its complications, while there is suggestive evidence indicating that treating some mental disorders could contribute to the prevention of diabetes and improve diabetes outcomes. Using technologically enabled solutions to identify mental disorders could help individuals who stand to benefit from particular treatments. However, there are considerable gaps in knowledge and several challenges to be met before we can stratify treatment recommendations based on mental disorders. Overall, this review demonstrates that addressing mental disorders as a facet of precision medicine could have considerable value for routine diabetes care and has the potential to improve diabetes outcomes.

Relationships Among Perception of Cognitive Function, Diabetes Self-Management, and Glucose Variability in Older Adults: A Mixed Methods Study

The aim of the current study was to explore relationships among perceived cognitive function, glucose variability, and self-management in older adults with type 2 diabetes mellitus (T2DM). A mixed methods design was used with data from: (a) questionnaires on perceived cognitive function and diabetes self-management; (b) continuous glucose monitoring (CGM) reports; and (c) semi-structured interviews. Thirty adults with T2DM (47% female; mean age = 68.5 [SD = 5.2] years) participated. Those who exercised more days per week had more stable glucose. Those who reported fewer days of diet adherence had more hypoglycemia. Participants who reported higher levels of memory dissatisfaction exhibited higher levels of glucose variability. Findings point to the potential of glucose variability as a target to reduce the effect of diabetes on cognitive function. Instruction in recognition of glucose patterns found via CGM can be integrated into self-management education to improve self-management and subsequent glucose control and cognitive function. [Research in Gerontological Nursing, xx(x), xx-xx.].

Impact of anxiety, depression and disease-related distress on long-term glycaemic variability among subjects with Type 1 diabetes mellitus

BACKGROUND

Anxiety, depression, and disease-related distress are linked to worse overall glycaemic control, in terms of HbA1c. This study was aimed to evaluate whether traits of these emotional disorders are associated with long-term glycaemic variability in subjects with Type 1 diabetes.

METHODS

Longitudinal retrospective study. Six-year HbA1c data (2014-2019) from 411 subjects with Type 1 diabetes who had participated in a previous study to design a diabetes-specific quality of life questionnaire in the year 2014 were included. Scores for Spanish versions of the Hospital Anxiety and Depression Scale (HADS) and Problem Areas in Diabetes (PAID) scale were obtained at baseline, along with sociodemographic and clinical data. Long-term glycaemic variability was measured as the coefficient of variation of HbA1c (HbA1c-CV). The association between HADS and PAID scores and HbA1c-CV was analysed with Spearman correlations and multiple regression models, both linear and additive, including other covariates (age, sex, diabetes duration time, type of treatment, baseline HbA1c, use of anxiolytic or antidepressant drugs, education level and employment status).

RESULTS

Scores of depression, anxiety and distress were positively and significantly correlated to HbA1c-CV in univariate analyses. Multiple regression study demonstrated an independent association only for diabetes distress score (p < 0.001). Age, diabetes duration time, baseline HbA1c, education level and employment status were also significantly associated with HbA1c-CV. However, when subjects were analyzed separately in two age groups, distress scores were associated with HbA1c-CV only among those aged 25 years or older, while anxiety scores, but not distress, were associated with HbA1c-CV among those younger than 25 years.

CONCLUSIONS

Psychological factors, particularly disease-related distress and anxiety, are associated with long-term glycaemic variability in subjects with Type 1 diabetes.

Admission Hyperglycemia Predicts Long-Term Mortality in Critically Ill Patients With Subarachnoid Hemorrhage: A Retrospective Analysis of the MIMIC-III Database

Subarachnoid hemorrhage (SAH) is a severe subtype of stroke with high mortality. Hyperglycemia is a common phenomenon in critically ill patients and associated with poor clinical outcome. However, the predictive value of admission hyperglycemia for 30 and 90-day all-cause mortality in critically ill patients with SAH remains controversial. All SAH patients between 2001 and 2012 were included based on the MIMIC-III database and were further classified according to the tertiles of blood glucose (BG) measured on intensive care unit (ICU) admission. Clinical information including demographic data, comorbidities, and laboratory indicators were exacted and analyzed. The primary outcomes were 30- and 90-day all-cause mortality. A total of 1,298 SAH patients were included. The 30 and 90-day mortality rates were 19.80% and 22.73%, respectively. Subjects in the high glucose tertile were older, were overweight, had higher sequential organ failure assessment (SOFA) and Simplified Acute Physiology Score II (SAPS II) scores, and presented higher mortality rate. Generalized additive model revealed a U-shaped relationship between BG and 30 and 90-day all-cause mortality. Furthermore, Kaplan-Meier (K-M) survival curve also illustrated that subjects with admission hyperglycemia presented lower survival rate and shorter survival time. In Cox analysis, after adjustment for potential confounders, admission hyperglycemia was related to an increase in 30- and 90-day all-cause mortality in SAH patients. In subgroup analysis, the association between admission hyperglycemia and all-cause mortality was consistent. In conclusion, admission hyperglycemia is associated with significantly increased 30- and 90-day all-cause mortality in critically ill patients with SAH.

Function and Emotion in Everyday Life With Type 1 Diabetes (FEEL-T1D): Protocol for a Fully Remote Intensive Longitudinal Study

BACKGROUND

Although short-term blood glucose levels and variability are thought to underlie diminished function and emotional well-being in people with type 1 diabetes (T1D), these relationships are poorly understood. The Function and Emotion in Everyday Life with T1D (FEEL-T1D) study focuses on investigating these short-term dynamic relationships among blood glucose levels, functional ability, and emotional well-being in adults with T1D.

OBJECTIVE

The aim of this study is to present the FEEL-T1D study design, methods, and study progress to date, including adaptations necessitated by the COVID-19 pandemic to implement the study fully remotely.

METHODS

The FEEL-T1D study will recruit 200 adults with T1D in the age range of 18-75 years. Data collection includes a comprehensive survey battery, along with 14 days of intensive longitudinal data using blinded continuous glucose monitoring, ecological momentary assessments, ambulatory cognitive tasks, and accelerometers. All study procedures are conducted remotely by mailing the study equipment and by using videoconferencing for study visits.

RESULTS

The study received institutional review board approval in January 2019 and was funded in April 2019. Data collection began in June 2020 and is projected to end in December 2021. As of June 2021, after 12 months of recruitment, 124 participants have enrolled in the FEEL-T1D study. Approximately 87.6% (7082/8087) of ecological momentary assessment surveys have been completed with minimal missing data, and 82.0% (82/100) of the participants provided concurrent continuous glucose monitoring data, ecological momentary assessment data, and accelerometer data for at least 10 of the 14 days of data collection.

CONCLUSIONS

Thus far, our reconfiguration of the FEEL-T1D protocol to be implemented remotely during the COVID-19 pandemic has been a success. The FEEL-T1D study will elucidate the dynamic relationships among blood glucose levels, emotional well-being, cognitive function, and participation in daily activities. In doing so, it will pave the way for innovative just-in-time interventions and produce actionable insights to facilitate tailoring of diabetes treatments to optimize the function and well-being of individuals with T1D.

INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID)

DERR1-10.2196/30901.

Associations of Time in Range and Other Continuous Glucose Monitoring-Derived Metrics With Well-Being and Patient-Reported Outcomes: Overview and Trends

Time in glucose ranges is increasingly relevant for research and clinical practice. Whereas the clinical validity of these metrics has been demonstrated with regard to long-term complications, their associations with patient-reported outcomes such as well-being, diabetes distress, and fear of hypoglycemia remain an open research question. This article reviews existing evidence on links between times in glycemic ranges and patient-reported outcomes. It also describes a novel research approach of using ecological momentary assessment to analyze on a more granular level in real time possible associations of these parameters of glycemic control and patient-reported outcomes. Such an approach could further our understanding of how glucose and patient-reported outcomes may be interconnected.