Optimal Metabolic Health Through Continuous Glucose Monitoring

Overview

The primary focus of this study is to evaluate the role of Continuous Glucose Monitoring (CGM) with Levels Health software as a tool to provide feedback and accountability necessary to create sustainable behavioral changes in nutrition associated with improved metabolic health and resilience against chronic and infectious diseases.

Full Title of Study: “Improving Cognitive-Behavioral and Cardio-Metabolic Health Through Continuous Glucose Monitoring (CGM)”

Study Type

  • Study Type: Interventional
  • Study Design
    • Allocation: Randomized
    • Intervention Model: Parallel Assignment
    • Primary Purpose: Treatment
    • Masking: Single (Investigator)
  • Study Primary Completion Date: March 16, 2022

Detailed Description

Achieving optimal metabolic health and glycemic control is a common goal among not only diabetics, but also for healthy individuals, athletes, elite military operators and for infectious disease prevention and resilience. No isolated biomarker is currently ubiquitously accepted as a marker of overall metabolic health and most rely on isolated snapshot (single time point) analyses and not a continuous closed-loop biomarker data assessment. Glycosylated hemoglobin (A1c) provides limited characterization of glycemic variability, which contributes to the progression of glycemic dysregulation. For example, emerging evidence links the amplitude and duration of glycemic variability as an independent risk factor linked to cardiovascular disease (CVD) (Di Flaviani 2011, Monnier 2006). Hyperglycemia-induced endothelial dysfunction and oxidative stress are greater with larger glycemic variability (Monnier 2006, Buscemi 2010). Glycemic variability is more deleterious for the cardiovascular system than sustained hyperglycemia (Nalysnyk 2010). Few technologies allow for continuous biomarker monitoring over time, and under a range of conditions like daily activities, swimming, exercise, sleep, etc. Multiple lines of evidence strongly suggest the predictive impact and value of monitoring glycemic variability on acute and chronic health of diabetes populations and non-diabetes populations (Rodriguez-Segade 2018, Zeevi 2015). Thus, there has been emerging interest in therapeutic approaches that seek to reduce glycemic variability. This potential for early detection of glycemic dysregulation is likely to be the single most beneficial effect of using CGM as an informational device, especially in the context of other biomarkers measures periodically. It is likely that people will make lifestyle modifications if they are aware of an impending health problem, detected through real time GCM-tracked glycemic variability. Lifestyle modifications are proven to be the most effective intervention for restoring normal fasting glucose levels and preventing diabetes among dysglycemic subjects, reducing the conversion to diabetes by 58% over placebo, and by 39% over metformin in one large US study (Diabetes Prevention Program Research Group 2002). Long terms follow-ups on other international studies have shown equally significant results at 4 years (Tuomilehto 2001) and 14 years (Li 2008) after the controlled lifestyle interventions ended, including reductions in diabetes incidence of 58%, and 43% respectively. It is known that metabolic health is on a spectrum and long-term studies in diabetic populations have demonstrated that reducing glycemic variability is more important than lowering baseline hyperglycemia in terms of reducing cardiovascular complications (Hall 2018). Therefore, there exists a scientific rationale to study interventions that can optimize metabolic health in non-diabetics since the potential benefits of metabolic awareness extend beyond the diabetic population. Emerging technology that can provide tight feedback on lifestyle effects could be a valuable mechanism for non-diabetics seeking to improve education and reduce their lifetime risk of disease. Though such outcomes have not yet been demonstrated in long term studies, the existing research reveals promising results, including improved screening for metabolic risk (Rodriguez-Segade 2018), clear observability of effects of lifestyle intervention (Hall 2018, Brynes 2005, Freckmann 2007), and acceptance of a minimal-risk strategy for use as a preventative tool in a non-diabetic population (Liao 2018). The Diabetes Prevention Program Research Group called for a shift in response in order to reverse these trends, stating that: "methods of treating diabetes remain inadequate and that prevention is preferable (Diabetes Prevention Program Research Group 2002)." Though unproven as a preventative measure, monitoring of glycemic variability is – at worst - unlikely to exacerbate the problem. At best, however, if it becomes a widespread lifestyle tool, the benefits of improved individual metabolic awareness and educated action could have compounding effects at a larger societal scale. Therefore, there exists a scientific rationale to study interventions that can optimize metabolic health with improved glycemic monitoring technologies (Danne 2017). It is becoming clear, that in addition to diabetic populations, normal, healthy populations can benefit from stable, controlled blood sugar levels, and that feedback mechanisms, including wearable technologies, can be employed. Thus, CGM could be a promising method of improving biomarkers of metabolic health for virtually anyone. In addition, optimal metabolic health is typically associated with improved behavioral health and cognitive resilience and decision making (Hadj-Abo 2020). Thus, optimizing and monitoring glycemic control may be useful for mental health and may be a valuable tool for military personnel and first responders under metabolic stress. Advances in software and hardware technologies have been developed to measure, analyze and predict glycemic variability and provides insight on how this dynamic biomarker correlates to metabolic fitness. Specifically, new advances in CGM technologies offer the potential to monitor, predict and change behavior through a closed-loop feedback system. By comparing CGM data with blood markers of metabolic health (eg.HbA1c , Insulin, etc.), and inflammation (e.g. hsCRP, cytokines) and along with assessments of emotion, cognition and behavior, a more robust interpretation and deconvolution of CGM data with experimental interventions may be possible.

Interventions

  • Device: Continuous Glucose Monitor
    • Continuous glucose monitor – a device that monitors blood glucose levels in a continuous closed-loop manner. This can also refer to the process of continuous glucose monitoring
  • Other: <Active Comparator?>
    • <describe, Comprehensive Wellness Program incorporating a low carbohydrate diet (<50g/day) and associated education >

Arms, Groups and Cohorts

  • Experimental: Wellness Program combined with Continuous Glucose Monitoring (CGM)
    • Continuous Glucose Monitoring (CGM) sensor combined with Levels CGM software that provides real-time visualization, analysis and feedback will be added to a Wellness Program incorporating a low carbohydrate diet (<50 g carbohydrate). Subjects in the group will be manually randomized and listed in a sealed envelope by someone who is not part of the study team
  • Active Comparator: Wellness Program
    • Wellness Program incorporating a low carbohydrate diet (<50 g carbohydrate). Subjects in the group will be manually randomized and listed in a sealed envelope by someone who is not part of the study team

Clinical Trial Outcome Measures

Primary Measures

  • Glucose stability from baseline to 12 weeks as measured by Continuous Glucose Monitoring (CGM)
    • Time Frame: 12 weeks
    • The intervention arm will have Continuous Glucose Monitoring (CGM) data collected over 12 weeks per protocol design. Subjects will be considered stable with no more than a 10% increase in average CGM from baseline. This outcome with be presented as mean glucose and Hba1c concentration as well as the number of subjects that improved average CGM from baseline.
  • Glucose stability from baseline to 12 weeks as measured by hemoglobin A1c (HbA1c)
    • Time Frame: 12 weeks
    • Both arms will have HbA1c collected over 12 weeks per protocol design. HbA1c is considered pre-diabetes when between 5.7-6.4% and abnormally high when above 6.4%. Subjects will be considered stable with no more than a 10% increase in HbA1c from baseline. This outcome with be presented as mean Hba1c concentration as well as the number of subjects that improved average HbA1c from baseline.

Secondary Measures

  • Changes in depression severity from baseline to 12 weeks as measured by Patient Health Questionnaire-9 (PHQ-9) assessment
    • Time Frame: 12 weeks
    • Both arms will complete the PHQ-9 assessment at baseline and at the end of the 12 week study per protocol design. PHQ-9 score of depression severity ranges from 0-27 as follows: 0-4 none, 5-9 mild, 10-14 moderate, 15-19 moderately severe, 20-27 severe. Subjects will be considered stable if they remain within 2 points of their baseline range. This outcome will be presented as the mean PHQ-9 assessment score as well as the number of subjects that remained stable, increased, or decreased on the scale.
  • Changes in anxiety from baseline to 12 weeks as measured by GAD-7 assessment
    • Time Frame: 12 weeks
    • Both arms will complete the Generalised Anxiety Disorder Assessment (GAD-7) over the 12 week study per protocol design. GAD-7 total score ranges from 0 to 21. 0-4: minimal anxiety. 5-9: mild anxiety. 10-14: moderate anxiety. 15-21: severe anxiety. Subjects will be considered stable if they remain within 2 points of their baseline range. This outcome with be presented as the mean GAD-7 assessment score as well as the number of subjects that remained stable, increased, or decreased on the scale.
  • Changes in daily stress from baseline to 12 weeks as measured by Short Stress State Questionnaire (SSSQ) assessment
    • Time Frame: 12 weeks
    • Daily stress will be assessed by the SSSQ. It is a 1min questionnaire consisting of 24 simple questions regarding their stress level perception. It can be performed on an iPad. Conscious appraisals of stress, or stress states, are an important aspect of human performance. Therefore, we will use a short multidimensional self-report measure of stress state, the SSSQ (Helton, 2004) to evaluate the changes in stress level during the mission. The SSSQ measures task engagement, distress, and worry.
  • Changes in circulating ghrelin from baseline to 12 weeks
    • Time Frame: 12 weeks
    • Both arms will have blood drawn for analysis of circulating ghrelin over the 12 week study per protocol design. This outcome will be presented as the mean concentration of ghrelin (pg/mL) as well as the number of patients who remained stable, increased, or decreased from baseline over time.
  • Changes in circulating glucagon from baseline to 12 weeks
    • Time Frame: 12 weeks
    • Both arms will have blood drawn for analysis of circulating glucagon over the 12 week study per protocol design. This outcome will be presented as the mean concentration of glucagon (pg/mL) as well as the number of patients who remained stable, increased, or decreased from baseline over time.
  • Changes in circulating leptin from baseline to 12 weeks
    • Time Frame: 12 weeks
    • Both arms will have blood drawn for analysis of circulating leptin over the 12 week study per protocol design. This outcome will be presented as the mean concentration of leptin (pg/mL) as well as the number of patients who remained stable, increased, or decreased from baseline over time.
  • Changes in circulating insulin from baseline to 12 weeks
    • Time Frame: 12 weeks
    • Both arms will have blood drawn for analysis of circulating insulin over the 12 week study per protocol design. This outcome will be presented as the mean concentration of insulin (pg/mL) as well as the number of patients who remained stable, increased, or decreased from baseline over time.
  • Changes in circulating GLP-1 from baseline to 12 weeks
    • Time Frame: 12 weeks
    • Both arms will have blood drawn for analysis of circulating GLP-1 over the 12 week study per protocol design. This outcome will be presented as the mean concentration of GLP-1 (pg/mL) as well as the number of patients who remained stable, increased, or decreased from baseline over time.
  • Changes in circulating hsCRP from baseline to 12 weeks
    • Time Frame: 12 weeks
    • Both arms will have blood drawn for analysis of circulating hsCRP over the 12 week study per protocol design. This outcome will be presented as the mean concentration of hsCRP (mg/L) as well as the number of patients who remained stable, increased, or decreased from baseline over time.
  • Changes in circulating total cholesterol from baseline to 12 weeks
    • Time Frame: 12 weeks
    • Both arms will have blood drawn for analysis of circulating total cholesterol over the 12 week study per protocol design. This outcome will be presented as the mean concentration of total cholesterol (mg/dL) as well as the number of patients who remained stable, increased, or decreased from baseline over time.
  • Changes in circulating HDL from baseline to 12 weeks
    • Time Frame: 12 weeks
    • Both arms will have blood drawn for analysis of circulating HDL over the 12 week study per protocol design. This outcome will be presented as the mean concentration of HDL (mg/dL) as well as the number of patients who remained stable, increased, or decreased from baseline over time.
  • Changes in circulating LDL and ApoB from baseline to 12 weeks
    • Time Frame: 12 weeks
    • Both arms will have blood drawn for analysis of circulating LDL and ApoB over the 12 week study per protocol design. This outcome will be presented as the mean concentration of LDL (mg/dL) as well as the number of patients who remained stable, increased, or decreased from baseline over time.
  • Changes in circulating triglycerides from baseline to 12 weeks
    • Time Frame: 12 weeks
    • Both arms will have blood drawn for analysis of circulating triglycerides over the 12 week study per protocol design. This outcome will be presented as the mean concentration of triglycerides (mg/dL) as well as the number of patients who remained stable, increased, or decreased from baseline over time.
  • Changes in blood glucose from baseline to 12 weeks using POC finger stick glucometer.
    • Time Frame: 12 weeks
    • Subjects in the treatment arm will use a point of care (POC) finger stick glucometer to test their blood glucose levels over the 12 week study per protocol design. Glucose in the range of 70-120mg/dL will be considered normal. This outcome will be presented as the mean glucose concentration, the percent of subjects that remained in the normal range, and the number of patients who remained stable, increased, or decreased from baseline over time.
  • Changes in blood ketones (beta hydroxybutyrate) from baseline to 12 weeks using POC finger stick ketone meter.
    • Time Frame: 12 weeks
    • Subjects in the treatment arm will use a POC finger stick ketone meter to test their blood ketone levels over the 12 week study per protocol design. Beta-hydroxybutyrate in the range of 0-5mM will be considered normal. This outcome will be presented as the mean beta-hydroxybutyrate concentration, the percent of subjects that remained in the normal range, and the number of patients who remained stable, increased, or decreased from baseline over time.
  • Changes in hepatic steatosis from baseline to 12 weeks as measured by abdominal ultrasound (US).
    • Time Frame: 12 weeks
    • Both arms will undergo an abdominal US pre- and post- the 12 week study for assessment of hepatic steatosis as a marker of fatty liver disease. Hepatic fat content will be estimated by assessment of radiographic findings and measurement of liver echogenicity scored by a qualified ultrasound technologist.. This outcome with be presented as none, mild, moderate, or severe for individual subjects as well as the number of subjects that remained stable, increased, or decreased in severity from pre- to post- study.

Participating in This Clinical Trial

Inclusion Criteria

  • Ages 18-69 years of age – Desire to improve metabolic health through nutritional, fitness, cognitive, and behavioral therapies. – Voluntarily participate in either a live or virtual 12-week, multidisciplinary wellness program created and led by Allison Hull, DO. – Body Mass Index (BMI) > 20 kg/m2 – Fasting Blood Glucose (FBG) of 85-125 mg/dl – HbA1c of 5.0-6.4 % Exclusion Criteria:

  • Type 1 or 2 Diabetes. – Chronic Kidney Disease – End Stage Liver Disease – Use of any weight loss medications currently or in the past 3 months. – Disordered Eating – anorexia or bulimia nervosa. – Pregnant or Breastfeeding females.

Gender Eligibility: All

Minimum Age: 18 Years

Maximum Age: 69 Years

Are Healthy Volunteers Accepted: Accepts Healthy Volunteers

Investigator Details

  • Lead Sponsor
    • University of South Florida
  • Provider of Information About this Clinical Study
    • Sponsor
  • Overall Official(s)
    • Dominic D’Agostino, PhD, Principal Investigator, University of South Florida
  • Overall Contact(s)
    • Allison Hull, DO, 813-528-4898, ahull@floridamedicalclinic.com

References

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Monnier L, Mas E, Ginet C, Michel F, Villon L, Cristol JP, Colette C. Activation of oxidative stress by acute glucose fluctuations compared with sustained chronic hyperglycemia in patients with type 2 diabetes. JAMA. 2006 Apr 12;295(14):1681-7.

Buscemi S, Re A, Batsis JA, Arnone M, Mattina A, Cerasola G, Verga S. Glycaemic variability using continuous glucose monitoring and endothelial function in the metabolic syndrome and in Type 2 diabetes. Diabet Med. 2010 Aug;27(8):872-8. doi: 10.1111/j.1464-5491.2010.03059.x.

Nalysnyk L, Hernandez-Medina M, Krishnarajah G. Glycaemic variability and complications in patients with diabetes mellitus: evidence from a systematic review of the literature. Diabetes Obes Metab. 2010 Apr;12(4):288-98. doi: 10.1111/j.1463-1326.2009.01160.x. Review.

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Li G, Zhang P, Wang J, Gregg EW, Yang W, Gong Q, Li H, Li H, Jiang Y, An Y, Shuai Y, Zhang B, Zhang J, Thompson TJ, Gerzoff RB, Roglic G, Hu Y, Bennett PH. The long-term effect of lifestyle interventions to prevent diabetes in the China Da Qing Diabetes Prevention Study: a 20-year follow-up study. Lancet. 2008 May 24;371(9626):1783-9. doi: 10.1016/S0140-6736(08)60766-7.

Hall H, Perelman D, Breschi A, Limcaoco P, Kellogg R, McLaughlin T, Snyder M. Glucotypes reveal new patterns of glucose dysregulation. PLoS Biol. 2018 Jul 24;16(7):e2005143. doi: 10.1371/journal.pbio.2005143. eCollection 2018 Jul.

Brynes AE, Adamson J, Dornhorst A, Frost GS. The beneficial effect of a diet with low glycaemic index on 24 h glucose profiles in healthy young people as assessed by continuous glucose monitoring. Br J Nutr. 2005 Feb;93(2):179-82.

Freckmann G, Hagenlocher S, Baumstark A, Jendrike N, Gillen RC, Rössner K, Haug C. Continuous glucose profiles in healthy subjects under everyday life conditions and after different meals. J Diabetes Sci Technol. 2007 Sep;1(5):695-703.

Liao Y, Schembre S. Acceptability of Continuous Glucose Monitoring in Free-Living Healthy Individuals: Implications for the Use of Wearable Biosensors in Diet and Physical Activity Research. JMIR Mhealth Uhealth. 2018 Oct 24;6(10):e11181. doi: 10.2196/11181.

Danne T, Nimri R, Battelino T, Bergenstal RM, Close KL, DeVries JH, Garg S, Heinemann L, Hirsch I, Amiel SA, Beck R, Bosi E, Buckingham B, Cobelli C, Dassau E, Doyle FJ 3rd, Heller S, Hovorka R, Jia W, Jones T, Kordonouri O, Kovatchev B, Kowalski A, Laffel L, Maahs D, Murphy HR, Nørgaard K, Parkin CG, Renard E, Saboo B, Scharf M, Tamborlane WV, Weinzimer SA, Phillip M. International Consensus on Use of Continuous Glucose Monitoring. Diabetes Care. 2017 Dec;40(12):1631-1640. doi: 10.2337/dc17-1600. Review.

Hadj-Abo A, Enge S, Rose J, Kunte H, Fleischhauer M. Individual differences in impulsivity and need for cognition as potential risk or resilience factors of diabetes self-management and glycemic control. PLoS One. 2020 Jan 29;15(1):e0227995. doi: 10.1371/journal.pone.0227995. eCollection 2020.

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