The DOI matches its Crossref record; 20 of the 20 references it cites were found in a registry or an index; 13 of the 17 sources its claims cite were checked for support.
Continuous Glucose Monitoring Profiles in Healthy Nondiabetic Participants: A Multicenter Prospective Study
Open accessAbstract
Context: Use of continuous glucose monitoring (CGM) is increasing for insulin-requiring patients with diabetes. Although data on glycemic profiles of healthy, nondiabetic individuals exist for older sensors, assessment of glycemic metrics with new-generation CGM devices is lacking. Objective: To establish reference sensor glucose ranges in healthy, nondiabetic individuals across different age groups using a current generation CGM sensor. Design: Multicenter, prospective study. Setting: Twelve centers within the T1D Exchange Clinic Network. Patients or Participants: Nonpregnant, healthy, nondiabetic children and adults (age ≥6 years) with nonobese body mass index. Intervention: Each participant wore a blinded Dexcom G6 CGM, with once-daily calibration, for up to 10 days. Main Outcome Measures: CGM metrics of mean glucose, hyperglycemia, hypoglycemia, and glycemic variability. Results: A total of 153 participants (age 7 to 80 years) were included in the analyses. Mean average glucose was 98 to 99 mg/dL (5.4 to 5.5 mmol/L) for all age groups except those over 60 years, in whom mean average glucose was 104 mg/dL (5.8 mmol/L). The median time between 70 to 140 mg/dL (3.9 to 7.8 mmol/L) was 96% (interquartile range, 93 to 98). Mean within-individual coefficient of variation was 17 ± 3%. Median time spent with glucose levels >140 mg/dL was 2.1% (30 min/d), and median time spent with glucose levels <70 mg/dL (3.9 mmol/L) was 1.1% (15 min/d). Conclusion: By assessing across age groups in a healthy, nondiabetic population, normative sensor glucose data have been derived and will be useful as a benchmark for future research studies.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
In 153 healthy nondiabetic children and adults aged 7 to 80 years, blinded Dexcom G6 CGM showed a mean 24-hour glucose of 99 ± 7 mg/dL and median 96% time in 70 to 140 mg/dL. Adults ≥60 years had higher mean glucose and less time in range than younger participants.
Key findings
- Overall mean 24-hour CGM glucose was 99 ± 7 mg/dL, mean within-individual coefficient of variation was 17 ± 3%, and median time between 70 and 140 mg/dL was 96% (IQR 93–98).
- Mean average glucose was 98–99 mg/dL for all age groups except those ≥60 years, in whom it was 104 mg/dL (P < 0.001); median time in 70–140 mg/dL was lowest at 93% in adults ≥60 years (P < 0.001).
- Median time >140 mg/dL was 2.1% (30 min/d) and <70 mg/dL was 1.1% (15 min/d); values >180 mg/dL and <54 mg/dL were uncommon, but 28% of participants had at least one hypoglycemic event overall and 14% had an overnight event.
- The correlation between mean glucose and HbA1c was weak (r = 0.27; 95% CI, 0.12–0.41).
Methods
- Multicenter, prospective study at 12 T1D Exchange Clinic Network centers; participants were healthy, nondiabetic, nonpregnant children and adults age ≥6 years with nonobese BMI, HbA1c <5.7%, and no islet autoantibodies. Of 201 screened, 153 were analyzed (age 7–80 years; mean 31 years).
- Participants wore a blinded Dexcom G6 CGM for up to 10 days with once-daily calibration; ≥72 total hours of CGM data with ≥24 overnight hours were required. Mean CGM use was 192 hours (8 days), and MARD versus blood glucose meter was 6.0% (n = 1555 paired measurements).
- Glucose outcomes were summarized overall and by age group and by daytime/nighttime; analyses included t tests for ≥60 vs <60 years, Pearson correlation for mean glucose and HbA1c, and sensitivity analyses removing implausible values.
Limitations
- The paper states that 93% of participants were white, so it could not evaluate whether results were consistent across races; possible sensor pressure during sleep may have caused inaccurate CGM readings, and the study could not determine how many nocturnal hypoglycemic events were due to pressure on the sensor.
- The paper states that after removing implausible data, mean glucose, time in 70–140 mg/dL, and CV were unchanged, but the percentage of participants with a hypoglycemic event was much lower; results may not be generalizable to capillary or laboratory-measured glucose profiles.
- The paper notes the weak HbA1c–glucose correlation may relate to HbA1c being obtained before CGM data collection and to having at most 10 days of CGM data. Evident from the text: participants were primarily recruited from family, friends, and neighbors of patients seen in diabetes clinics, which may limit generalizability to the broader nondiabetic population.
Model-written summary. Check it against the paper before relying on it.
Integrity signals
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Verification
Claims the paper makes
13 of 17 cited sources checked · 7 supported · 0 flagged · accuracy not yet measured
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“The overall results of our study are in agreement with previous studies (3)(4)(5)(6)(7)17) despite the different generations of CGM systems used in most of the studies.”
§ Continuous Glucose Monitoring Profiles in Healthy Nondiabetic Participants: A Multicenter Prospective Study > Discussion
What the sources say
- Partly supportedcites [3] Borg, 2010, Real-life glycaemic profiles in non-diabetic individuals with low fasting glucose and normal HbA1c: the A1C-Derived Average Glucose (ADAG) study, Diabetologia sourceExperimental — accuracy not yet measured
The Discussion says previous smaller CGM studies found similar glucose patterns, supporting the claim of overall agreement. The cited paper does not compare generations of CGM systems, so it does not support that qualification. (LLM, conf=0.83)
- Supportedcites [4] Freckmann, 2007, Continuous glucose profiles in healthy subjects under everyday life conditions and after different meals, J Diabetes Sci Technol source
The Discussion (page unavailable) reports glucose profiles in healthy subjects and says earlier studies found similar fasting and postprandial glucose levels. It also compares its continuous-monitoring results with results obtained using other sensor types. The agreement with the citing authors’ own study is their conclusion, not a finding attributed to Freckmann et al. (LLM, conf=0.86)
- Supportedcites [5] Fox, 2010, Variation of interstitial glucose measurements assessed by continuous glucose monitors in healthy, nondiabetic individuals, Diabetes Care source
The Methods identify this as a study of CGM measurements in healthy, nondiabetic people, so it is a previous study of the kind cited in the sentence. Its Conclusions also report that its mean sensor data were similar to those of an earlier CGM study. Whether the citing authors’ own results agree across device generations is their comparison, not a finding attributed to this source. (LLM, conf=0.86)
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- Partly supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“However, the correlation between HbA1c and glucose is lower than what others have found (15).”
§ Continuous Glucose Monitoring Profiles in Healthy Nondiabetic Participants: A Multicenter Prospective Study > Discussion
What the sources say
- Supportedcites [15] Nathan, 2008, Translating the A1C assay into estimated average glucose values, Diabetes Care source
The citation supplies the comparison study, not the citing authors’ own correlation. The source’s Results report a strong relationship between A1C and average glucose (R²=0.84), and its Conclusions describe that relationship as strong. Those passages support its use as a comparator for the citing authors’ lower correlation. (LLM, conf=0.87)
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Supported
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Overall, median time spent with glucose levels .140mg/dL was 2.1% (30 min/d)”
§ Continuous Glucose Monitoring Profiles in Healthy Nondiabetic Participants: A Multicenter Prospective Study > Results
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“and ,70 mg/dL was 1.1% (15 min/d).”
§ Continuous Glucose Monitoring Profiles in Healthy Nondiabetic Participants: A Multicenter Prospective Study > Results
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“and 99% (IQR, 95 to 100), respectively”
§ Continuous Glucose Monitoring Profiles in Healthy Nondiabetic Participants: A Multicenter Prospective Study > Results
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“the mean of the within-individual coefficient of variation (CV), a metric of glucose variability, was 17 6 3% (Table 2).”
§ Continuous Glucose Monitoring Profiles in Healthy Nondiabetic Participants: A Multicenter Prospective Study > Results
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“but only 1% of participants spent $2% of time ,54 mg/dL.”
§ Continuous Glucose Monitoring Profiles in Healthy Nondiabetic Participants: A Multicenter Prospective Study > Results
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Yet, 14% of participants had a hypoglycemic event overnight (Table 3).”
§ Continuous Glucose Monitoring Profiles in Healthy Nondiabetic Participants: A Multicenter Prospective Study > Results
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The percentage of time between 70 to 120 mg/dL was quite different for daytime vs nighttime in the 601 age group (78% vs 92%, respectively).”
§ Continuous Glucose Monitoring Profiles in Healthy Nondiabetic Participants: A Multicenter Prospective Study > Discussion
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Overall, the correlation was weak (r 5 0.27; 95% confidence interval, 0.12 to 0.41) between mean glucose concentration and HbA1c levels”
§ Continuous Glucose Monitoring Profiles in Healthy Nondiabetic Participants: A Multicenter Prospective Study > Results
Tests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes
- Probably not a substantial claim. 21
- It may state two separate claims as one. 16
- The quote only partly states this claim. 3
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Our findings support the recent consensus conference recommendation (12, 13) that sensor glucose values ,54 mg/dL represent a more meaningful cut-off than 70 mg/dL to define clinically important hypoglycemia because CGM glucose levels below 54 mg/dL were uncommon, whereas most participants had some glucose levels of 55 to 69 mg/dL.”
§ Continuous Glucose Monitoring Profiles in Healthy Nondiabetic Participants: A Multicenter Prospective Study > Discussion
What the sources say
- Supportedcites [12] Danne, 2017, International consensus on use of continuous glucose monitoring, Diabetes Care source
The hypoglycemia recommendations classify sensor glucose below 54 mg/dL as clinically significant and 54–70 mg/dL as an alert range. They also recommend giving values below 54 mg/dL more weight in CGM outcome reports. The citing paper’s observation about how often participants entered each range is its own finding, not part of the recommendation attributed to this source. (LLM, conf=0.94)
- Partly supportedcites [13] Maahs, 2016, Outcome measures for artificial pancreas clinical trials: a consensus report, Diabetes Care sourceExperimental — accuracy not yet measured
In Additional Recommendations and Limitations (page unavailable), the consensus report suggests reporting CGM excursions below either 70 or 55 mg/dL as possible measures of hypoglycemia. It does not recommend a cutoff below 54 mg/dL or say that cutoff is more clinically meaningful than 70 mg/dL. (LLM, conf=0.85)
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Most studies of CGM profiles in healthy, nondiabetic individuals have had small sample sizes or used early-generation CGM systems, which were less accurate than current devices (3)(4)(5)(6)(7).”
§ Continuous Glucose Monitoring Profiles in Healthy Nondiabetic Participants: A Multicenter Prospective Study > Abstract
What the sources say
- Partly supportedcites [3] Borg, 2010, Real-life glycaemic profiles in non-diabetic individuals with low fasting glucose and normal HbA1c: the A1C-Derived Average Glucose (ADAG) study, Diabetologia sourceExperimental — accuracy not yet measured
The Discussion describes earlier CGM studies of 32 and 15 people, supporting the small-sample part of the claim; Methods reports that this study used CGM in 80 nondiabetic participants. Neither section says the systems were early-generation or compares their accuracy with current devices. (LLM, conf=0.84)
- Partly supportedcites [4] Freckmann, 2007, Continuous glucose profiles in healthy subjects under everyday life conditions and after different meals, J Diabetes Sci Technol sourceExperimental — accuracy not yet measured
The Methods report CGM profiles from 24 healthy volunteers using SCGM1 devices, supporting this paper as an example of a small study using an older system. The paper reports the SCGM1’s measurement error but does not compare its accuracy with current devices or establish that most such studies share these limitations. (LLM, conf=0.84)
- Partly supportedcites [5] Fox, 2010, Variation of interstitial glucose measurements assessed by continuous glucose monitors in healthy, nondiabetic individuals, Diabetes Care sourceExperimental — accuracy not yet measured
The Abstract’s Research Design and Methods describes a CGM study of 74 healthy participants. Research Design and Methods identifies the devices used and reports missing or implausible readings from one sensor, noting that a commercially available version had newer software. The paper does not establish that most such studies had small samples or that earlier devices were less accurate than current devices. (LLM, conf=0.83)
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Partly supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“As the assessment of outcomes of treatment of T1D and type 2 diabetes in clinical practice and research trials moves beyond the narrow focus of HbA1c levels to include the results of sensor glucose profiles (14), there is an increased need for a repository of”
§ Continuous Glucose Monitoring Profiles in Healthy Nondiabetic Participants: A Multicenter Prospective Study > Discussion
What the sources say
- Supportedcites [14] Beck, 2017, The fallacy of average: how using HbA1c alone to assess glycemic control can be misleading, Diabetes Care source
The citation is attached to the clause about assessing diabetes outcomes beyond HbA1c using sensor glucose profiles, not to the subsequent need for a normative-data repository. The Introduction says CGM profiles provide information missed by HbA1c, and the Conclusions say a patient’s CGM glucose profile has considerable value for diabetes management. (LLM, conf=0.88)
Tests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may be too vague to check against evidence.
- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“This is similar to what has been observed in the T1D population and highlights the limitation of HbA1c to reflect true glycemic status for some individuals (14).”
§ Continuous Glucose Monitoring Profiles in Healthy Nondiabetic Participants: A Multicenter Prospective Study > Discussion
What the sources say
- Supportedcites [14] Beck, 2017, The fallacy of average: how using HbA1c alone to assess glycemic control can be misleading, Diabetes Care source
The Introduction states that a wide range of mean glucose concentrations can accompany a given HbA1c and that this has been demonstrated in people with type 1 diabetes. It also explains that HbA1c alone may therefore misrepresent an individual’s glycemic control. Page numbers were not supplied for these passages. (LLM, conf=0.91)
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“The current consensus conferences have somewhat arbitrarily set time in ranges for CGM sensor glucose levels for patients with T1D, with time in target identified as being between 70 to 180 mg/dL (12,13).”
§ Continuous Glucose Monitoring Profiles in Healthy Nondiabetic Participants: A Multicenter Prospective Study > Discussion
What the sources say
- Supportedcites [12] Danne, 2017, International consensus on use of continuous glucose monitoring, Diabetes Care source
The ATTD consensus table (p. 1636) lists 70–180 mg/dL as the default CGM time-in-target range, supporting the attributed range for patients with type 1 diabetes. The paper does not describe that cutoff as “somewhat arbitrary”; that appears to be the citing authors’ characterization. (LLM, conf=0.90)
- Partly supportedcites [13] Maahs, 2016, Outcome measures for artificial pancreas clinical trials: a consensus report, Diabetes Care sourceExperimental — accuracy not yet measured
The Objective and Rationale section advocates standardized CGM glucose metrics for artificial-pancreas studies and says they are broadly applicable to type 1 diabetes studies. Additional Recommendations and Limitations mentions arbitrary glycemic ranges, but the retrieved passages do not identify 70–180 mg/dL as the target range or say that consensus conferences set that range somewhat arbitrarily. Page numbers were not available in our copy. (LLM, conf=0.80)
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may state two separate claims as one.
- Supported
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“A total of 153 participants (age 7 to 80 years) were included in the analyses.”
- Also stated in: § Results · plain
“Therefore, 153 participants were included in the analyses, ranging from 7 to 80 years (mean, 31 years).”
§ Continuous Glucose Monitoring Profiles in Healthy Nondiabetic Participants: A Multicenter Prospective Study > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
- Also stated in: § Results · plain
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Median sensor glucose between 70 and 140 mg/dL during the daytime and nighttime was 96% (IQR, 92 to 97%)”
§ Continuous Glucose Monitoring Profiles in Healthy Nondiabetic Participants: A Multicenter Prospective Study > Results
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesThe quote only partly states this claim.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Sensor glucose values .180mg/dL (10.0 mmol/L) and ,54 mg/dL (3.0 mmol/L) were uncommon.”
§ Continuous Glucose Monitoring Profiles in Healthy Nondiabetic Participants: A Multicenter Prospective Study > Results
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In a sensitivity analysis after removing participants with first-degree family members with T1D (n 5 101), mean CGM glucose, time spent in the 70 to 140 mg/dL range, and glycemic variability as measured by CV were not different from the main analysis (data not shown).”
§ Continuous Glucose Monitoring Profiles in Healthy Nondiabetic Participants: A Multicenter Prospective Study > Results
Strength:plain· not yet calibratedn = 101Tests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Mean average glucose was 98 to 99 mg/dL (5.4 to 5.5 mmol/L) for all age groups except those over 60 years, in whom mean average glucose was 104 mg/dL (5.8 mmol/L).”
- Also stated in: § Results · plain
“The mean average glucose level was 98 to 99 mg/dL (5.4 to 5.5 mmol/L) for all age groups except those $60 years old in whom mean average glucose a The underweight, normal weight, and overweight BMI categories for participants aged $18 years are ,18.5, 18.5 to ,24.9, and $24.9, respectively.”
§ Continuous Glucose Monitoring Profiles in Healthy Nondiabetic Participants: A Multicenter Prospective Study > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one. The quote only partly states this claim.
- Also stated in: § Results · plain
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Similarly, in a sensitivity analysis after removing implausible data, mean glucose, time spent in the 70 to 140 mg/dL range, and CV were not different from the main analysis”
§ Continuous Glucose Monitoring Profiles in Healthy Nondiabetic Participants: A Multicenter Prospective Study > Results
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“mean glycemic variability measured by CV during the daytime and nighttime was 17% 6 3%”
§ Continuous Glucose Monitoring Profiles in Healthy Nondiabetic Participants: A Multicenter Prospective Study > Results
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesThe quote only partly states this claim.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Mean CGM use by the participants was 192 hours (8 days)”
§ Continuous Glucose Monitoring Profiles in Healthy Nondiabetic Participants: A Multicenter Prospective Study > Results
Strength:plain· not yet calibratedn = 153Tests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“with participants 6 to 11 years old having a median of 90% of their readings in this range, as compared with only 81% in the cohort over age 60 years.”
§ Continuous Glucose Monitoring Profiles in Healthy Nondiabetic Participants: A Multicenter Prospective Study > Discussion
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
In the paper
“There are two possible factors that may have contributed to this weaker-than-expected correlation: (1) HbA1c was obtained prior to collection of CGM glucose data and ( 2) there are at most 10 days of CGM data (16).”
§ Continuous Glucose Monitoring Profiles in Healthy Nondiabetic Participants: A Multicenter Prospective Study > Discussion
What the sources say
- Not checked — no full text of the sourcecites [16] Riddlesworth, 2018, Optimal sampling duration for continuous glucose monitoring to determine long-term glycemic control, Diabetes Technol Ther source
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Understanding CGM metrics in healthy participants without diabetes is needed to serve as a benchmark for research studies to define impaired glycemic status and for future technologies aiming to help individuals achieve near-normal glycemic profiles.”
§ Continuous Glucose Monitoring Profiles in Healthy Nondiabetic Participants: A Multicenter Prospective Study > Abstract
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may be too vague to check against evidence.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Strengths of our study include the large sample size crossing the lifespan, multiple centers conducting the study, use of the latest generation technology, and up to 10 days of CGM wear time.”
§ Continuous Glucose Monitoring Profiles in Healthy Nondiabetic Participants: A Multicenter Prospective Study > Discussion
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Although our study fills the current gap in the data”
§ Continuous Glucose Monitoring Profiles in Healthy Nondiabetic Participants: A Multicenter Prospective Study > Discussion
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may be too vague to check against evidence.
In the paper
“The reported mean absolute relative difference (MARD) for the Dexcom G6 in accuracy studies comparing sensor glucose values with reference blood glucose values is 9.0% (9).”
§ Continuous Glucose Monitoring Profiles in Healthy Nondiabetic Participants: A Multicenter Prospective Study > Participants
What the sources say
- Not checked — no full text of the sourcecites [9] Shah, 2018, Performance of a factory-calibrated real-time continuous glucose monitoring system utilizing an automated sensor applicator, Diabetes Technol Ther source
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Although pressure on the sensor during sleep may have resulted in inaccuracy of some CGM readings”
§ Continuous Glucose Monitoring Profiles in Healthy Nondiabetic Participants: A Multicenter Prospective Study > Discussion
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“CGM profiles also provide the best window available to date to evaluate the frequency of biochemical hypoglycemia in clinical trials of new drugs and devices for the treatment of diabetes.”
§ Continuous Glucose Monitoring Profiles in Healthy Nondiabetic Participants: A Multicenter Prospective Study > Discussion
Strength:plain· not yet calibratedTests✓ Asserted: passes✗ Checkable: fails✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“However, HbA1c does not capture clinically relevant hyperglycemic and hypoglycemic patterns, which limits its utility in personalizing insulin-dosing decisions.”
§ Continuous Glucose Monitoring Profiles in Healthy Nondiabetic Participants: A Multicenter Prospective Study > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Although data on glycemic profiles of healthy, nondiabetic individuals exist for older sensors, assessment of glycemic metrics with new-generation CGM devices is lacking.”
§ Continuous Glucose Monitoring Profiles in Healthy Nondiabetic Participants: A Multicenter Prospective Study > Abstract
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may state two separate claims as one.
In the paper
“This is due to the fact that HbA1c is a reflection of overall glycemic control from the prior 60-90 days, as well as clear evidence from the DCCT establishing it as a surrogate marker for diabetes microvascular complications risk (1).”
§ Continuous Glucose Monitoring Profiles in Healthy Nondiabetic Participants: A Multicenter Prospective Study > Abstract
What the sources say
- Not checked — no full text of the sourcecites [1] Nathan, 1993, The effect of intensive treatment of diabetes on the development and progression of long-term complications in insulin-dependent diabetes mellitus, N Engl J Med source
Strength:boosted· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Use of continuous glucose monitoring (CGM) is increasing for insulin-requiring patients with diabetes.”
§ Continuous Glucose Monitoring Profiles in Healthy Nondiabetic Participants: A Multicenter Prospective Study > Abstract
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Participants were healthy, nondiabetic children and adults, primarily recruited from family, friends, and neighbors of patients seen in the diabetes clinics.”
§ Continuous Glucose Monitoring Profiles in Healthy Nondiabetic Participants: A Multicenter Prospective Study > Participants
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Moreover, CGM metrics are being used to examine and compare the relative effectiveness and safety of new therapeutic agents and modern technologies in clinical diabetes research.”
§ Continuous Glucose Monitoring Profiles in Healthy Nondiabetic Participants: A Multicenter Prospective Study > Abstract
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“Continuous glucose monitoring (CGM) provides information on trends for hypoglycemia, hyperglycemia, and glycemic variability that helps patients and providers optimize glycemic control without increasing the risk for hypoglycemia.”
§ Continuous Glucose Monitoring Profiles in Healthy Nondiabetic Participants: A Multicenter Prospective Study > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Study participants provided written informed consent prior to study participation.”
§ Continuous Glucose Monitoring Profiles in Healthy Nondiabetic Participants: A Multicenter Prospective Study > Participants
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Only participants with $72 total hours of CGM data (with at least 24 hours overnight) were included in the analysis.”
§ Continuous Glucose Monitoring Profiles in Healthy Nondiabetic Participants: A Multicenter Prospective Study > Participants
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Only participants with no islet autoantibodies and a central laboratory HbA1c level ,5.7% were included in the analysis.”
§ Continuous Glucose Monitoring Profiles in Healthy Nondiabetic Participants: A Multicenter Prospective Study > Participants
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may state two separate claims as one.
In the paper
“The study was conducted at 12 diabetes centers within the T1D Exchange Clinic Network after approval by institutional review boards (8).”
§ Continuous Glucose Monitoring Profiles in Healthy Nondiabetic Participants: A Multicenter Prospective Study > Participants
What the sources say
- Not checked — no full text of the sourcecites [8] Beck, 2012, The T1D Exchange clinic registry, J Clin Endocrinol Metab source
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
The paper relies on this without support.
The 153 participants are representative of healthy nondiabetic individuals across age groups.
“Participants were healthy, nondiabetic children and adults, primarily recruited from family, friends, and neighbors of patients seen in the diabetes clinics.”
“Because the majority of our participants were white (93%), we were unable to evaluate whether results were consistent across races.”
The paper relies on this without support.
CGM profiles over up to 10 days reflect usual glucose profiles in healthy nondiabetic individuals.
“An early-generation Dexcom G6 CGM (Dexcom, Inc., San Diego, CA) in blinded mode (i.e., the participant was unable to see the glucose values) was worn for up to 10 days with one daily calibration using a study-provided blood glucose meter (Contour Next EZ; Ascensia Diabetes Care, Parsippany, NJ).”
“there are at most 10 days of CGM data (16).”
The authors state this as an assumption.
Low CGM readings <50 mg/dL during sleep are likely due to the participant lying on the CGM sensor.
“any low CGM readings ,50 mg/dL (2.8 mmol/L) during sleep time as defined by logs (in which case the participant was likely to be lying on the CGM sensor)”
The paper relies on this without support.
Participants' daily logs accurately recorded sleep and wake times.
“Participants completed a daily log indicating times and intensity of any exercise, start times of meals and snacks, and sleep and wake times.”
Citations
Cites
(1993) · New England Journal of Medicine · doi:10.1056/nejm199309303291401 · Cited by 24,667
International consensus on use of continuous glucose monitoring
Danne, T., Nimri, R., Battelino, T., Bergenstal, R. M., Close, K. L., DeVries, J. H., Garg, S., Heinemann, L., Hirsch, I., Amiel, S. A., et al. (2017) · Diabetes Care · doi:10.2337/dc17-1600 · Cited by 2,069
Self-citationTranslating the A1C assay into estimated average glucose values
Nathan, D. M., Kuenen, J., Borg, R., Zheng, H., Schoenfeld, D., Heine, R. J., & for the A1c-Derived Average Glucose (ADAG) Study Group (2008) · Diabetes Care · doi:10.2337/dc08-0545 · Cited by 1,812
The fallacy of average: how using HbA1c alone to assess glycemic control can be misleading
Beck, R. W., Connor, C. G., Mullen, D. M., Wesley, D. M., & Bergenstal, R. M. (2017) · Diabetes Care · doi:10.2337/dc17-0636 · Cited by 477
Self-citationThe T1D exchange clinic registry
Beck, R. W., Tamborlane, W. V., Bergenstal, R. M., Miller, K. M., DuBose, S. N., Hall, C. A., & for the T1D Exchange Clinic Network (2012) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/jc.2012-1561 · Cited by 456
Self-citationBergenstal, R. M., Ahmann, A. J., Bailey, T., Beck, R. W., Bissen, J., Buckingham, B., Deeb, L., Dolin, R. H., Garg, S. K., Goland, R., et al. (2013) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2013.0051 · Cited by 307
Optimal Sampling Duration for Continuous Glucose Monitoring to Determine Long-Term Glycemic Control
Riddlesworth, T. D., Beck, R. W., Gal, R. L., Connor, C. G., Bergenstal, R. M., Lee, S., & Willi, S. M. (2018) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2017.0455 · Cited by 266
Self-citationDeSalvo, D. J., Miller, K. M., Hermann, J. M., Maahs, D. M., Hofer, S. E., Clements, M. A., Lilienthal, E., Sherr, J. L., Tauschmann, M., Holl, R. W., et al. (2018) · Pediatric Diabetes · doi:10.1111/pedi.12711 · Cited by 233
Self-citationOutcome measures for artificial pancreas clinical trials: a consensus report
Maahs, D. M., Buckingham, B. A., Castle, J. R., Cinar, A., Damiano, E. R., Dassau, E., DeVries, J. H., Doyle, F. J., Griffen, S. C., Haidar, A., et al. (2016) · Diabetes Care · doi:10.2337/dc15-2716 · Cited by 211
Shah, V. N., Laffel, L. M., Wadwa, R. P., & Garg, S. K. (2018) · Diabetes Technology & Therapeutics · doi:10.1089/dia.2018.0143 · Cited by 197
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Avner, S., & Robbins, T. (2025) · Clinical Medicine Insights Endocrinology and Diabetes · doi:10.1177/11795514251381409 · Cited by 3
“23 This helps to understand that despite inevitable post-prandial glucose spikes, people studied were still within the determined healthy range for glucose levels, therefore – this poses the question of do glucose spikes affect overall health in people without diabetes?”
Kinny, F., Läer, S., & Obarčanin, E. (2025) · PLOS Digital Health · doi:10.1371/journal.pdig.0001087 · Cited by 2
Richardson, R. R. (2025) · Diabetes Technology & Therapeutics · doi:10.1177/15209156251390822 · Cited by 2
Defining non-diabetic hypoglycemia: is there an appropriate plasma glucose threshold?
González‐Vidal, T., Delgado, E., & Torre, E. L. M. (2025) · Clinical Chemistry and Laboratory Medicine (CCLM) · doi:10.1515/cclm-2025-1496 · Cited by 2
Piona, C. A., Aiello, E. M., Mancioppi, V., Caiazza, E., Olivieri, F., Passanisi, S., Lombardo, F., Mastropasqua, C., Giannini, C., Riccardi, G., et al. (2025) · Diabetes Technology & Therapeutics · doi:10.1177/15209156251407959 · Cited by 2
Passanisi, S., Bombaci, B., Longo, A., Alibrandi, A., Salzano, G., & Lombardo, F. (2026) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2026.113160 · Cited by 3
Skroče, K., Turner, L. V., Zignoli, A., Lipman, D. J., Zisser, H., & Riddell, M. C. (2026) · Sensors · doi:10.3390/s26051624 · Cited by 2
“A multicenter prospective study involving 153 healthy participants aged 7 to 80 years reported a mean 24 h glucose concentration of 99 ± 7 mg/dL (5.5 ± 0.4 mmol/L), with the median percentage of time spent within the TITR was 96% [9].”
InfluentialMahapatra, A., Agarwal, M., Yadav, V., et al. (2026) · Pediatric Obesity · doi:10.1111/ijpo.70110 · Cited by 2
Acute Effects of Cannabis Inhalation on Cardiac Ectopy, Physical Activity, Sleep, and Glucose
Elias, A., Montenegro, G. C., Oo, H. H., Peña, I., Lowe, D. A., Lee, C., Tang, J., Lynch, K. L., Lim, L., Maamou, M., et al. (2026) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2026.07.014 · Cited by 2
Rakotoarisoa, L., Weiss, L. M., Lefebvre, F., et al. (2022) · Hormone and Metabolic Research · doi:10.1055/a-1794-5496 · Cited by 2
Zignoli, A., Martinez‐Gonzalez, B., Skroče, K., Lipman, D. J., Zisser, H., & Giorgi, A. (2024) · International Journal of Sport Nutrition and Exercise Metabolism · doi:10.1123/ijsnem.2024-0119 · Cited by 2
Jones, L., Park, J., Dliso, S., Hawcutt, D. B., Shantsila, A., Lip, G. Y. H., & Blair, J. (2025) · American Journal of Medical Genetics Part C Seminars in Medical Genetics · doi:10.1002/ajmg.c.32130 · Cited by 2
“In this small cohort of participants, mean interstitial glucose concentration was 10% higher than in age- and- sex-matched control subjects (Shah et al. 2019).”
Nomura, N., Iizuka, K., Goshima, E., et al. (2021) · Endocrine Journal · doi:10.1507/endocrj.ej21-0526 · Cited by 2
Bally, L., Khoo, B. C. E., Knop, F. K., Madsbad, S., Patti, M., & Tan, T. (2023) · The Lancet Diabetes & Endocrinology · doi:10.1016/s2213-8587(23)00228-0 · Cited by 2
Galderisi, A., Kariyawasam, D., Stoupa, A., Quoc, A. N., Pinto, G., Viaud, M., Brabant, S., Beltrand, J., Polak, M., & Samara‐Boustani, D. (2023) · European Journal of Endocrinology · doi:10.1093/ejendo/lvad147 · Cited by 3
Glucose variability in 6–12-month-old healthy infants
Hauschild, M., Monnard, C. R., Eldridge, A. L., et al. (2023) · Frontiers in Nutrition · doi:10.3389/fnut.2023.1128389 · Cited by 1
Park, S. (2025) · 대한지역사회영양학회지 · doi:10.5720/kjcn.2025.00073 · Cited by 1
“While CGM has demonstrated efficacy in managing glycemic control and promoting dietary behavior change in individuals with and without diabetes [10-14], the application of CGM outside diabetic populations remains debated [15], with emerging evidence suggesting heightened dietary awareness and…”
Using off-the-shelf biosensors to implement gateways for alarm-system nanonetworks
Lau, F.-L. A., Prange, L. J., Wendt, R., Scheer, S., Hyttrek, C., Pal, S., Gómez, J. T., Dressler, F., & Fischer, S. (2025) · Nano Communication Networks · doi:10.1016/j.nancom.2025.100584 · Cited by 1
Cossu, L., Facchinetti, A., & Bally, L. (2025) · Diabetes Obesity and Metabolism · doi:10.1111/dom.70059 · Cited by 1
Monnier, L., Colette, C., Benhamou, P., & Owens, D. R. (2025) · Diabetes Obesity and Metabolism · doi:10.1111/dom.70179 · Cited by 1
“10 Therefore, this value of 27% can be considered the upper limit of normal.”
Harnessing Personalized Nutrition and Clinical Chemistry: Opportunities to Improve Public Health
Mathers, J. C. (2025) · Clinical Chemistry · doi:10.1093/clinchem/hvaf114 · Cited by 1
García, I. A., Pérez, P. V., Plaza, B. L., et al. (2025) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2025.112952 · Cited by 1
Post-bariatric Hypoglycemia Management: A Gulf Cooperation Council Consensus Statement
Abdelgadir, E., Rashid, F., Awadi, F. A. A., Busaidi, N. A., Alfaris, N., Hadad, M. A., Alkaabi, J., Kharusi, A. A., Al-Ozairi, E., Alromaihi, D., et al. (2025) · Journal of the Endocrine Society · doi:10.1210/jendso/bvaf225 · Cited by 3
Evaluating the Accuracy of Declared Eating Schedules by Continuous Glucose Monitoring
González-Romero, P., Madrid, J. A., Pagán, P., & Rol, M. A. (2026) · Nutrients · doi:10.3390/nu18050772 · Cited by 1
Skroče, K., Zignoli, A., Turner, L. V., Lipman, D. J., Riddell, M. C., & Zisser, H. (2026) · Journal of Diabetes Science and Technology · doi:10.1177/19322968261426376 · Cited by 1
“14 Similarly, Jarvis et al 15 highlighted heterogeneous postprandial glycemic responses in individuals without diabetes.”
CGM-Led Multimodal Tracking with Chatbot Support: An Autoethnography in Sub-Health
Pan, D. P., Luo, L., Wang, Y., & Hui, P. (2025) · International Conference on Human-Engaged Computing (ICHEC) · doi:10.1145/3786995.3787067 · Cited by 1
“Beyond diagnosed populations, biomedical studies show that non-diabetic individuals exhibit substantial glycemic variability shaped by sleep, stress, and meal timing [23, 48].”
Afeef, S., Salvaggio, R., Barrett, L. A., Zakrzewski-Fruer, J. K., Smith, L. R., Newell, M. L., Thackray, A. E., Kingsnorth, A. P., & Tolfrey, K. (2026) · Pediatric Exercise Science · doi:10.1123/pes.2024-0173 · Cited by 1
Reutrakul, S., Simon, S. L., Wang, Q., Manoogian, E. N. C., Panda, S., Ryu, S., Gao, Z., Griffiths, C., Helgeson, E. S., Mashek, D. G., et al. (2026) · Nutrients · doi:10.3390/nu18111824 · Cited by 1
“Although prolonged AM fasting was associated with a slight increase in time 70 mg/dL, this remained 1% in participants without diabetes, consistent with healthy ranges [24].”
Haschka, S. J., Gar, C., Potzel, A. L., Sacco, V., Kern-Matschilles, S., Benz, I., Then, C., Seißler, J., & Lechner, A. (2022) · Cureus · doi:10.7759/cureus.23916 · Cited by 1
“Additionally, a normal range for real-life glucose profiles has now been established through the study of healthy populations [11].”
BackgroundNontraditional Uses of Continuous Glucose Monitoring
Kapsar, P., Chao, C., & Walker, T. C. (2022) · The Journal for Nurse Practitioners · doi:10.1016/j.nurpra.2022.06.013 · Cited by 1
Defining Outcomes for β-Cell Replacement Therapy
Rickels, M. R. (2023) · Springer eBooks · doi:10.1007/978-3-031-20999-4_65 · Cited by 1
CGM auch bei Hochaltrigen ohne Diabetes sinnvoll?
Müssig, K. (2025) · Info Diabetologie · doi:10.1007/s15034-025-5103-6 · Cited by 1
La variabilité glycémique : un facteur de risque singulier à conjuguer au pluriel
Monnier, L., Colette, C., Bonnet, F., & Owens, D. R. (2021) · Médecine des Maladies Métaboliques · doi:10.1016/j.mmm.2021.11.003 · Cited by 3
Hyperglycaemia converting to persistent hypoglycaemia
Chin, Y. A., & Gardner, D. S.-L. (2021) · Diabetic Medicine · doi:10.1111/dme.14519
Glucose control, cardiorespiratory fitness, and dietary composition in healthy men.
Moller, J. C. (2020) · SUNScholar (Stellenbosch University)
Afeef, S., Barrett, L. A., Zakrzewski-Fruer, J. K., & Tolfrey, K. (2022) · Recent Progress in Nutrition · doi:10.21926/rpn.2203020
Efficience et précision du contrôle glycémique dans le diabète : symphonie inachevée ?
Monnier, L., Halimi, S., & Colette, C. (2022) · Médecine des Maladies Métaboliques · doi:10.1016/j.mmm.2022.11.005
Linton, K., Seckl, J. R., & Gibb, F. W. (2023) · Endocrine · doi:10.1007/s12020-023-03314-w
“9 mM (70 mg/dl) are present in 35% of non-diabetic individuals when using continuous glucose monitoring (CGM) [3].”
BackgroundInfluentialRöhl, K. N., & Wirsam, J. (2023) · Springer eBooks · doi:10.1007/978-3-658-41618-8_16
MIZOGUCHI, M., & TAKENAKA, M. (2023) · Journal of Nutritional Science and Vitaminology · doi:10.3177/jnsv.69.485
Перспективи використання безперервного моніторингу рівня глюкози у хворих з інсуліномою
Kondratyshyn, A. R., Fik, M. S., Коваль, А.-М., Naumova, L. V., & Khomitskiy, S. B. (2023) · Endokrynologia · doi:10.31793/1680-1466.2023.28-1.90
Tecnología aplicada a la diabetes
Cristoforetti, M. F. (2024) · doi:10.47196/0577
Continuous glucose monitoring in children and adolescents with congenital adrenal hyperplasia
Dubinski, I., Pozza, S. B., Debor, B., et al. (2024) · European Journal of Endocrinology · doi:10.1093/ejendo/lvae036
Mesure continue du glucose : quels sont les nouveaux indicateurs de suivi ?
Joubert, M. (2024) · Médecine des Maladies Métaboliques · doi:10.1016/j.mmm.2024.05.001
Grotta, R. L. (2024) · The Journal of AMD · doi:10.36171/jamd24.27.1.3
Babayeva, A., Coşkun, M., Yalçın, M. M., Aktürk, M., Törüner, F. B., Karakoç, M. A., & Altınova, A. E. (2024) · International Journal of Diabetes in Developing Countries · doi:10.1007/s13410-024-01367-9
Patient Selection, Clinical Considerations, and Intensive Diabetes Management Goals
(2021) · American Diabetes Association eBooks · doi:10.2337/9781580407694.ch05
Wang, W., Shang, J., Yuan, Z., Zhang, Z., Zhou, Q., & Zou, Y. (2024) · doi:10.2196/preprints.67012
Identifying Type 1 Diabetes from Daily Continuous Glucose Monitoring profiles Using Machine Learning
Belkacem, I. (2024) · International Conference on Electrical, Computer and Energy Technologies (ICECET) · doi:10.1109/icecet61485.2024.10698385
“The second is the result of studies of glucose sensor profiles in healthy non-diabetic subjects [8].”
BackgroundDing, J. J., Milley, L., & Son, M. (2024) · American Journal of Perinatology · doi:10.1055/a-2419-8476
Tarocchi, M., Pellegrino, A., Skroče, K., Zignoli, A., Cavadini, L. C., Bodini, C., Pagliai, G., Toncelli, L., Stefani, L., Vanni, S., et al. (2024) · Nutrients · doi:10.3390/nu16223824 · Cited by 2
“The sample size was calculated to assess differences in the 24 h glucose level standard deviation (SD), a metric derived by the CGM, based on data obtained from a healthy population [24].”
Kut, R. E., Vuraloğlu, E., Kut, A., & İyidir, Ö. T. (2024) · Research Square · doi:10.21203/rs.3.rs-5318071/v1
Pei, L., & Patti, M. (2024) · Springer eBooks · doi:10.1007/978-3-031-72309-4_34
Monnier, L., Colette, C., Renard, É., & Benhamou, P. (2025) · Médecine des Maladies Métaboliques · doi:10.1016/j.mmm.2025.03.010
Zeit im Zielbereich: Welche Werte erreichen Stoffwechselgesunde?
Schmidt, L., & Adamek, H. E. (2025) · Info Diabetologie · doi:10.1007/s15034-025-5124-1
“Bisherige Studien zeigen, dass die Glukosekonzentrationen bei Gesunden nahe-zu vollständig in einem Bereich von 70 bis 140 mg/dl liegen [4, 5].”
Proimou, A., Sotiriou, G., Panagopoulou, P., Tsimtsiou, Z., & Christoforidis, A. (2025) · Journal of Diabetes Science and Technology · doi:10.1177/19322968251352554
“Our findings during regular working days align with the multicenter CGM study by Shah et al, 5 who reported a 96% time in normoglycemia in healthy adults.”
Herranz-Antolín, S., Ramos-Garrido, S., Esteban-Monge, V., Cotón-Batres, C., López-Virgos, M. C., Lallena-Pérez, S., & Torralba, M. (2025) · Acta Diabetologica · doi:10.1007/s00592-025-02563-7
Göksoy, E., & Kumru, B. (2025) · Frontiers in Endocrinology · doi:10.3389/fendo.2025.1664863
Hypophosphatasia and Type 1 Diabetes: A Pilot Study and Review of Literature
Tariq, Z., Tinsley, J. P., Carpenter, R. D., Snell‐Bergeon, J. K., & Shah, V. N. (2025) · Current Osteoporosis Reports · doi:10.1007/s11914-025-00938-x
Self-citationGlucose data interpretation in pediatric diabetes using an artificial intelligence approach
Paragliola, G., Campanella, S., Cherubini, V., Tiberi, V., Pierleoni, P., Belli, A., Iannilli, A., & Palma, L. (2025) · Smart Health · doi:10.1016/j.smhl.2025.100616 · Cited by 1
Anaglycemia and Cataglycemia: Proposed Terminology for Glucose Dynamics
Richardson, R. (2025) · Journal of Diabetes Science and Technology · doi:10.1177/19322968251383919
Jiang, H., Henley, D., & Jiang, F. (2025) · World Journal of Transplantation · doi:10.5500/wjt.v15.i4.101926
Li, Q.-H., Qin, J., Lamberg, S., Brakenridge, C. J., Pesola, A. J., & Gao, Y. (2025) · International Journal of Behavioral Medicine · doi:10.1007/s12529-025-10414-6 · Cited by 1
“Glycemic outcomes derived from continuous glucose monitoring (CGM), such as glycemic variability [1] and timein target glucoserange (TIR) [2], capture short-term, dynamic glucose regulation more sensitively than conventional markers like fasting glucose and glycated hemoglobin (HbA1c).”
Arce, N. N., Vu, M. H., Barnett, B., & Black, D. S. (2025) · Journal of Diabetes Science and Technology · doi:10.1177/19322968251398876 · Cited by 2
“5,7 Furthermore, existing studies often pool across wide age ranges, obscuring developmental differences, and sex-specific benchmarks are rarely reported.”
InfluentialFløde, M., Hermann, M., Haugstvedt, A., Søfteland, E., Igland, J., Åsberg, A., Jenssen, T. G., & Graue, M. (2023) · Figshare · doi:10.6084/m9.figshare.24286550
Critically Appraising the Wellness Movement: Cost Without Value, A Narrative Review
Ho, M., Topol, E. J., & Rajpurkar, P. (2026) · Journal of General Internal Medicine · doi:10.1007/s11606-025-10099-3
Continuous Blood Glucose Monitoring: Potential Applications from Diabetes Management
Manaf, F. A., Amin, M. Q. A. M., Ahmad, A., Farizan, N. H., Sukri, N. M., & Maliki, A. B. H. M. (2025) · Malaysian Journal of Medicine and Health Sciences · doi:10.47836/mjmhs.21.4.41
Harawa, P. P., Bourdon, C., Khoshnevisan, F., et al. (2026) · medRxiv · doi:10.64898/2026.01.30.26344670
Prediabetes is a window of opportunity for preventive interventions. Part 2
Adasheva, T. V., Gubernatorova, E. E., Fomina, E. I., et al. (2026) · Meditsinskiy sovet = Medical Council · doi:10.21518/ms2025-559
Torimoto, K., Okada, Y., Tomoya, M. I. T. A., Tanaka, K., Sato, F., Katakami, N., Yoshii, H., Nishida, K., Nakayamada, S., Ishii, R., et al. (2026) · Diabetes Technology & Therapeutics · doi:10.1177/15209156251403563
Cooper, H. C., & Chan, C. L. (2026) · Journal of Diabetes Science and Technology · doi:10.1177/19322968261422204
Schoonhoven, A. D., Bakker, J. J., Mauro, A. D., van der Vaart, A., Been, R. A., van Beek, A. P., Choudhary, P., Bouma, H. R., & van Dijk, P. R. (2026) · PLoS ONE · doi:10.1371/journal.pone.0343703
Sotiriou, G., Proimou, A., Panagopoulou, P., Tsimtsiou, Z., & Christoforidis, A. (2026) · Journal of Diabetes & Metabolic Disorders · doi:10.1007/s40200-026-01871-1
“The comparison our results with data from Shah’s et al., multicenter study on CGM profiles of nondiabetic participants is interesting [ 8].”
InfluentialFlint, J., & Dittmar, M. (2026) · Chronobiology International · doi:10.1080/07420528.2026.2638528
“The separate analysis of mean glucose by day and night blocks showed lower glucose concentrations at night than during daytime for each chronotype, consistent with results in healthy adults (Shah et al. 2019).”
Arethiya, N., Krammer, L., David, J. A., Bakshi, V., Basuchoudhary, A., Bhuiyan, U., Sen, S., Mazumder, R., & McNeely, P. M. (2026) · medRxiv · doi:10.64898/2026.03.04.26347604
Time to redefine success in continuous glucose monitoring
Albert, L., Capel, I., & Rigla, M. (2026) · Trends in Endocrinology and Metabolism · doi:10.1016/j.tem.2026.02.007
González-Maroto, I., Moreno-Fernández, J., Beato-Víbora, P. I., & Quirós, C. (2026) · Diabetes & Metabolism · doi:10.1016/j.diabet.2026.101757
Continuous Glucose Monitoring-Derived Glycemic Profiles in Adolescents With Obesity Without Diabetes
Vidmar, A. P., Vu, M. H., Bakhsh, J. A., & Black, D. S. (2026) · Journal of Diabetes Science and Technology · doi:10.1177/19322968261427488
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“Nondiabetic participants of Western descent have an average time-in-tight-range of 96% ‐ 97% [24,25].”
InfluentialSummarizing data from continuous glucose monitors using the cgmstats command
Malek, N. D., Wang, D., Zhang, S., Fang, M., Wallace, A., Zeger, S. L., & Selvin, E. (2026) · The Stata Journal Promoting communications on statistics and Stata · doi:10.1177/1536867x261450271 · Cited by 1
Cichosz, S. L., Færch, K., & Jensen, M. H. (2026) · Diabetes Obesity and Metabolism · doi:10.1111/dom.71008
Continuous Glucose Monitoring Profiles in Children Receiving High-Dose Glucocorticoid Treatment
Lukka, M., Tillmann, V., & Peet, A. (2026) · Hormone Research in Paediatrics · doi:10.1159/000552876
Bauddh, N. K., Biswas, K., Jindal, R., & Agrawal, P. (2026) · Indian Journal of Endocrinology and Metabolism · doi:10.4103/ijem.ijem_41_26
“[12] (US) reported an MBG of 99 ± 7 mg/dl in healthy individuals.”
InfluentialSchwensfeier, L., & Brinkmann, C. K. (2026) · Sports · doi:10.3390/sports14070274
“Interest in CGM has also increased among non-diabetic populations for a range of possible lifestyle applications, including improving dietary habits, increasing physical activity and optimizing athletic performance [2,8–10].”
Mohsenipour, R., Abbasi, F., Ghaderian, M. S., Khalili, A., Abdolahpour, S., & Akbari, K. (2026) · Endocrinology Diabetes & Metabolism · doi:10.1002/edm2.70274
Firmansyah, B. S., Sinha, R., Sabharwal, A., Stults, C. D., Wilcox, J., Gillespie, K., Viswanathan, R., Chapman, R., MCCLENDON, S., & Kerr, D. E. (2026) · Journal of Diabetes Science and Technology · doi:10.1177/19322968261463562
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INSIGHT: Ingest and Standardize Glucose Harmonization Tool
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Sleep Quality and Glycemic Variability Among Nondiabetic African American Pregnant Women
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Interpretation of CGM and Insulin Pump Downloads
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Post-exercise hypoglycaemia in adults with cystic fibrosis compared to healthy control adults
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“…free-living conditions in children and young adults with CF and healthy controls (2.5 – 4.0% and 1.6 – 1.8% respectively) [34,38], the largest study of healthy controls aged 7-80 yr at 1.1% [39], and adolescents [36] and adults [37] with T1DM (1.1 – 1.9% and 3.1% on exercise days respectively).”
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Symptomatic reactive hypoglycemia is highly prevalent post-esophagectomy
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Metadata
- Authors
- Shah, V. N., DuBose, S. N., Li, Z., Beck, R. W., Peters, A. L., Weinstock, R. S., Kruger, D., Tansey, M., Sparling, D., Woerner, S., Vendrame, F., Bergenstal, R., Tamborlane, W. V., Watson, S. E., & Sherr, J.
- Affiliations
- Barbara Davis Center for Diabetes, Aurora, Colorado
- Jaeb Center for Health Research (US)
- University of Southern California (US)
- SUNY Upstate Medical University (US)
- Henry Ford Health (US)
- University of Iowa (US)
- University of Oklahoma Health Sciences Center (US)
- Indiana University School of Medicine
- University of Miami (US)
- Park Nicollet Clinic (US)
- Yale University (US)
- University of Louisville (US)
- Corresponding author
- DuBose, Stephanie N
- ORCID
- Venue
- The Journal of Clinical Endocrinology & Metabolism
- Publisher
- The Endocrine Society
- Published
- 2019-04-25
- Volume
- 104
- Issue
- 10
- Pages
- 4356-4364
- ISSN
- 0021-972X (ISSN-L)
- 1945-7197
- PMC
- PMC7296129
- OpenAlex
- W2941246007
- Semantic Scholar
- 22170f815dcf3fd4385ff1485a7902d3413b5ae7
- Cited by
- 315
- References
- 17
- Citations by year
- 2026: 57 · 2025: 71 · 2024: 63 · 2023: 41 · 2022: 38 · 2021: 26 · 2020: 15 · 2019: 4
- FWCI
- 14.29
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- The Journal of Clinical Endocrinology & Metabolism (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (submittedVersion)
- Funders
- Leona M. and Harry B. Helmsley Charitable Trust
- National Institutes of Health
- Topics
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
- Health Sciences › Health Professions › Medical Laboratory Technology › Clinical Laboratory Practices and Quality Control
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes, Cardiovascular Risks, and Lipoproteins
- Keywords
- continuous glucose monitoring, glycemic variability, hypoglycemia, hyperglycemia, Dexcom G6, reference values
- MeSH terms
- Adolescent, Adult, Age Factors, Aged, Blood Glucose, Child, Circadian Rhythm, Female, Humans, Hyperglycemia, Hypoglycemia, Male, Middle Aged, Monitoring, Physiologic, Prospective Studies, Reference Values, Sex Factors, Blood Glucose Self-Monitoring, Benchmarking, Young Adult, Healthy Volunteers
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-10-14, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-14 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Cited sources are checked with a language model’s help (gpt-6-sol).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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