The DOI matches its Crossref record; 58 of the 58 references it cites were found in a registry or an index; 3 of the 74 sources its claims cite were checked for support.
Glycemic variability and cardiovascular disease in patients with type 2 diabetes
Open accessAbstract
Glycated hemoglobin is currently the gold standard for assessment of long-term glycemic control and response to medical treatment in patients with diabetes. Glycated hemoglobin, however, does not address fluctuations in blood glucose. Glycemic variability (GV) refers to fluctuations in blood glucose levels. Recent clinical data indicate that GV is associated with increased risk of hypoglycemia, microvascular and macrovascular complications, and mortality in patients with diabetes, independently of glycated hemoglobin level. The use of continuous glucose monitoring devices has markedly improved the assessment of GV in clinical practice and facilitated the assessment of GV as well as hypoglycemia and hyperglycemia events in patients with diabetes. We review current concepts on the definition and assessment of GV and its association with cardiovascular complications in patients with type 2 diabetes.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
This review summarizes definitions and assessment of glycemic variability (GV) and evidence linking GV to cardiovascular complications in type 2 diabetes, noting associations independent of HbA1c in many studies but not all. It concludes that clear data confirming GV as an independent risk factor for diabetic complications are still lacking and calls for prospective randomized CGM trials.
Key findings
- GV refers to blood glucose swings, including hypoglycemia and postprandial hyperglycemia; CGM metrics include time in range 70–180 mg/dL, time above/below range, and GV expressed as coefficient of variation (CV), with a recommended CV target ≤36%. The International Consensus on Use of Continuous Glucose Monitoring recommended CV as the preferred measure and SD as secondary.
- A meta-analysis by Gorst et al. found that in patients with T2D, higher HbA1c variability was associated with renal disease (risk ratio 1.34, 95% CI 1.15 to 1.57), macrovascular events (1.21, 1.06 to 1.38), cardiovascular disease (1.27, 1.15 to 1.40), and mortality (1.34, 1.18 to 1.53).
- In ADVANCE, HbA1c visit-to-visit variation was associated with increased risk of vascular events (p=0.01, HR 1.64, 1.05–2.55), macrovascular events (p=0.02), and mortality (p<0.001, HR 3.31, 1.57–6.98); fasting glucose visit-to-visit variation was associated with cardiovascular events (p<0.001, HR 2.70, 1.65–4.42) and macrovascular and microvascular events.
- In DEVOTE 2, high day-to-day fasting GV was associated with severe hypoglycemia (HR 4.11, 3.15–5.35), MACE (HR 1.36, 1.12–1.65), and all-cause mortality (HR 1.58, 1.23–2.03). However, HEART2D, DIGAMI 2, and FLAT-SUGAR reported no association or no significant difference in cardiovascular outcomes despite GV changes.
Methods
- Narrative review of current concepts on the definition and assessment of GV and its association with cardiovascular complications in patients with type 2 diabetes.
- Synthesizes experimental, clinical, observational, meta-analytic, and post-hoc trial evidence, including a table of studies assessing GV and cardiovascular outcomes in T2D, acute coronary syndrome, and acute myocardial infarction populations.
- No systematic search strategy, primary data collection, or formal risk-of-bias assessment is described.
Limitations
- The paper states there is currently no consensus on metrics to assess GV; heterogeneity between studies, including different GV metrics, limits understanding of relationships between GV and diabetes complications.
- The paper states that clear data confirming GV as an independent risk factor of diabetic complications are still lacking, and future prospective randomized clinical trials using CGM devices are needed.
- Evident from the text: this is a narrative review, and the cited studies vary in populations, GV measures (SMBG, CGM, HbA1c, fasting glucose), follow-up duration, and outcomes, with some reporting null associations.
Model-written summary. Check it against the paper before relying on it.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
64 Not checked yet · 7 No basis given · 5 Own interpretation · 1 Supported so far · 1 Partly supported · accuracy not yet measured
The sources with a verdict support this claim; the rest weren’t checked yet, have no full text we could read, or their verdicts aren’t rated yet.
In the paper
“Recent clinical data indicate that GV is associated with increased risk of hypoglycemia, microvascular and macrovascular complications, and mortality in patients with diabetes, independently of glycated hemoglobin level.”
- Also stated in: § Introduction · plain
“increased risk of cardiovascular disease18–20 and mortality in patients with T2D.21–23”
§ Glycemic variability and cardiovascular disease in patients with type 2 diabetes > Abstract
What the sources say
- Not checked yetcites [21] source
- Supportedcites [22] source
The Discussion reports that greater long-term glycemic variability was associated with higher mortality in adults with type 2 diabetes, above and beyond the degree of glycemic control. It specifically reports an association for HbA1C variability after accounting for average HbA1C over follow-up. No page number is provided in our copy. (LLM, conf=0.94)
- Not checked yetcites [23] Scott, 2020, Long-term glycemic variability and vascular complications in type 2 diabetes: post hoc analysis of the field study, J Clin Endocrinol Metab source
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Also stated in: § Introduction · plain
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“increased risk of cardiovascular disease18–20”
§ Glycemic variability and cardiovascular disease in patients with type 2 diabetes > Introduction
What the sources say
- Partly supportedcites [18] sourceExperimental — accuracy not yet measured
The Conclusions (page unavailable) associate glycemic variability with endothelial dysfunction and increased cardiac mass in patients with short-term, well-controlled type 2 diabetes. These are surrogate measures of cardiovascular damage; the study does not establish an increased risk of cardiovascular disease events in type 2 diabetes patients generally. (LLM, conf=0.86)
- Not checked yetcites [19] Ceriello, 2019, Glycaemic variability in diabetes: clinical and therapeutic implications, Lancet Diabetes Endocrinol source
- Supportedcites [20] source
The Discussion reports a positive relationship between glycaemic variability and cardiovascular disease in type 2 diabetes. The paper describes an association, not a causal effect, consistent with the claim. (LLM, conf=0.94)
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Partly supported
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
“The combination of reduced NO availability and increased vasoconstrictor production has been shown to play a role in the development of vascular atherosclerotic changes.”
§ Glycemic variability and cardiovascular disease in patients with type 2 diabetes > Definitions and methods of assessment > GV and tissue damage
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“GV has emerged as an important tool in the evaluation of glycemic control, which may be a better predictor of complications than average glucose by SMBG and HbA1c values.20 25 31”
§ Glycemic variability and cardiovascular disease in patients with type 2 diabetes > Definitions and methods of assessment > Methods of GV assessment
What the sources say
- Not checked yetcites 20 25 31
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“Recent clinical data indicate that GV is associated with increased risk of hypoglycemia, microvascular and macrovascular complications, and mortality in patients with diabetes, independently of glycated hemoglobin level.”
- Also stated in: § Methods of GV assessment · plain
“GV metrics have been associated with microvascular complications15 32 33 as well as increased risk of adverse cardiovascular outcomes in patients with diabetes.19 21 23 25 34”
§ Glycemic variability and cardiovascular disease in patients with type 2 diabetes > Abstract
What the sources say
- Not checked yetcites 19 21 23 25 34
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Also stated in: § Methods of GV assessment · plain
None of the sources cited for this claim have been checked yet.
In the paper
“In these studies, long-term GV has shown to be a better predictor of adverse outcomes than the average HbA1c.”
§ Glycemic variability and cardiovascular disease in patients with type 2 diabetes > Definitions and methods of assessment > Methods of GV assessment
boostedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“Long-term GV has been reported in observational studies and meta-analyses to predict diabetic complications in both T1D and T2D.15 19 21 31”
§ Glycemic variability and cardiovascular disease in patients with type 2 diabetes > Definitions and methods of assessment > Methods of GV assessment
What the sources say
- Not checked yetcites 15 19 21 31
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“In vitro studies in human umbilical vein endothelial cells reported that increases in nitrotyrosine and 8-hydroxydeoxyguanosine, markers of oxidative stress, were more pronounced when they were exposed to intermittent high glucose than in constant high glucose.24”
§ Glycemic variability and cardiovascular disease in patients with type 2 diabetes > Definitions and methods of assessment > GV and tissue damage
What the sources say
- Not checked yetcites [24] source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
In the paper
“The association between HbA1c variability by different metrics as SD and CV has been strongly associated with microvascular complications and increased cardiovascular disease, as well as increased all-cause mortality, in patients with T2D.15 19–21 31”
§ Glycemic variability and cardiovascular disease in patients with type 2 diabetes > Definitions and methods of assessment > Methods of GV assessment
What the sources say
- Not checked yetcites 15 19–21 31
boostedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
None of the sources cited for this claim have been checked yet.
In the paper
“Recent clinical data indicate that GV is associated with increased risk of hypoglycemia, microvascular and macrovascular complications, and mortality in patients with diabetes, independently of glycated hemoglobin level.”
- Also stated in: § Introduction · plain
“High GV has been associated with increased risk of hypoglycemia,13–16”
§ Glycemic variability and cardiovascular disease in patients with type 2 diabetes > Abstract
What the sources say
- Not checked yetcites [13] Umpierrez, 2018, Glycemic variability: how to measure and its clinical implication for type 2 diabetes, Am J Med Sci source
- Not checked yetcites [14] source
- Not checked yetcites [15] source
- Not checked yetcites [16] Jin, 2014, Clinical factors associated with absolute and relative measures of glycemic variability determined by continuous glucose monitoring: an analysis of 480 subjects, Diabetes Res Clin Pract source
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Also stated in: § Introduction · plain
- Probably not a substantial claim. 5
- It may state two separate claims as one. 24
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
“The use of continuous glucose monitoring devices has markedly improved the assessment of GV in clinical practice and facilitated the assessment of GV as well as hypoglycemia and hyperglycemia events in patients with diabetes.”
§ Glycemic variability and cardiovascular disease in patients with type 2 diabetes > Abstract
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
In the paper
“Hyperglycemia and PKC activation-induced ROS production result in inflammatory changes in the vascular endothelium including monocytes adhesion, formation of foam cells, and activation of major biochemical paths including polyol pathway flux and increased formation of advanced glycation end products, further contributing to the development of atherosclerosis.40”
§ Glycemic variability and cardiovascular disease in patients with type 2 diabetes > Definitions and methods of assessment > GV and tissue damage
What the sources say
- Not checked yetcites [40] source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“increasing evidence indicates that daily fluctuations in blood glucose are associated with endothelial dysfunction,23–25 inflammation and oxidative stress,22 24”
§ Glycemic variability and cardiovascular disease in patients with type 2 diabetes > Introduction
What the sources say
hedgedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“In addition, hyperglycemia triggers the activation of protein kinase C (PKC), which has an effect on vascular cell growth and apoptosis, extracellular matrix synthesis, and altered vascular homeostasis.24 38”
§ Glycemic variability and cardiovascular disease in patients with type 2 diabetes > Definitions and methods of assessment > GV and tissue damage
What the sources say
- Not checked yetcites 24 38
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“PKC also increases production of ROS, which decreases NO availability and overproduction of vasoconstrictors such as endothelin-1 and cyclo-oxygenase-2 expression, molecules involved in platelet aggregation and vasoconstriction.39”
§ Glycemic variability and cardiovascular disease in patients with type 2 diabetes > Definitions and methods of assessment > GV and tissue damage
What the sources say
- Not checked yetcites [39] source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a 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
“factors associated with the pathogenesis of vascular damage and atherosclerosis.”
§ Glycemic variability and cardiovascular disease in patients with type 2 diabetes > Introduction
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
In the paper
“Hyperglycemia increases the production of reactive oxygen species (ROS), which inactivates nitric oxide (NO),19 38 leading to endothelial dysfunction and vascular complications.”
§ Glycemic variability and cardiovascular disease in patients with type 2 diabetes > Definitions and methods of assessment > GV and tissue damage
What the sources say
- Not checked yetcites 19 38
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“MAGE was associated with increased MACCE (23.5% vs 11.6%, p=0.002) and was an independent predictor of poor prognosis (p= 0.045, HR 1.84, 1.01–3.36).”
§ Glycemic variability and cardiovascular disease in patients with type 2 diabetes > Definitions and methods of assessment > GV and cardiovascular disease
What the sources say
- Not checked yetcites [50] Takahashi, 2018, Glycemic variability determined with a continuous glucose monitoring system can predict prognosis after acute coronary syndrome, Cardiovasc Diabetol source
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Increased HbA1c visit-to-visit variation was associated with increased risk of vascular events (p=0.01, HR 1.64, 1.05–2.55), macrovascular events (p=0.02), and mortality (p<0.001, HR 3.31, 1.57–6.98).”
§ Glycemic variability and cardiovascular disease in patients with type 2 diabetes > Definitions and methods of assessment > GV and cardiovascular disease
What the sources say
- Not checked yetcites [21] source
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“Sustained hyperglycemia measured by high HbA1c or by CGM and increased GV are important factors associated with cardiovascular outcomes in patients with T2D.21 34 48”
§ Glycemic variability and cardiovascular disease in patients with type 2 diabetes > Definitions and methods of assessment > GV and cardiovascular disease
What the sources say
- Not checked yetcites 21 34 48
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“FG visit-to-visit variation was associated with increased risk of CV events (p<0.001, HR 2.70, 1.65–4.42) and macrovascular (p=0.005) and microvascular (p<0.001) events.”
§ Glycemic variability and cardiovascular disease in patients with type 2 diabetes > Definitions and methods of assessment > GV and cardiovascular disease
What the sources say
- Not checked yetcites [21] source
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“High day-to-day fasting GV was significantly associated with severe hypoglycemia, MACE and all-cause mortality; risks raised 2.7-fold, 1.2-fold and 1.4-fold, respectively, when fasting GV was duplicated.49”
§ Glycemic variability and cardiovascular disease in patients with type 2 diabetes > Definitions and methods of assessment > GV and cardiovascular disease
What the sources say
- Not checked yetcites [49] Zinman, 2018, Day-to-day fasting glycaemic variability in DEVOTE: associations with severe hypoglycaemia and cardiovascular outcomes (DEVOTE 2), Diabetologia source
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“Increasing evidence indicates that high glucose fluctuations also increase oxidative stress in heart tissue and can induce cardiomyocyte apoptosis.”
§ Glycemic variability and cardiovascular disease in patients with type 2 diabetes > Definitions and methods of assessment > GV and tissue damage
hedgeddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“High day-to-day fasting GV was significantly associated with severe hypoglycemia, MACE and all-cause mortality; risks raised 2.7-fold, 1.2-fold and 1.4-fold, respectively, when fasting GV was duplicated.49”
§ Glycemic variability and cardiovascular disease in patients with type 2 diabetes > Definitions and methods of assessment > GV and cardiovascular disease
What the sources say
- Not checked yetcites [49] Zinman, 2018, Day-to-day fasting glycaemic variability in DEVOTE: associations with severe hypoglycaemia and cardiovascular outcomes (DEVOTE 2), Diabetologia source
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“In the same meta-analysis were included 13 studies of patients with T2D, where higher HbA1c variability was associated with higher risk of renal disease (1.34, 1.15 to 1.57), macrovascular events (1.21, 1.06 to 1.38), ulceration/gangrene (1.50, 1.06 to 2.12), cardiovascular disease (1.27, 1.15 to 1.40), and mortality (1.34, 1.18 to 1.53).20”
§ Glycemic variability and cardiovascular disease in patients with type 2 diabetes > Definitions and methods of assessment > GV and cardiovascular disease
What the sources say
- Not checked yetcites [20] source
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“High HbA1c variability among patients with T1D was associated with increased risk of renal disease (risk ratio 1.56, 95% CI 1.08 to 2.25), cardiovascular events (1.98, 1.39 to 2.82), and retinopathy (2.11, 1.54 to 2.89).”
§ Glycemic variability and cardiovascular disease in patients with type 2 diabetes > Definitions and methods of assessment > GV and cardiovascular disease
What the sources say
- Not checked yetcites [20] source
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
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
“Clinical studies and meta-analyses have shown that GV is positively associated with macrovascular complications and mortality (table 1), independently of the HbA1c level.”
§ Glycemic variability and cardiovascular disease in patients with type 2 diabetes > Definitions and methods of assessment > GV and cardiovascular disease
boostedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“In addition, chronic hyperglycemia and exposure of endothelial cells to intermittent high glucose produce an overproduction of several adhesion molecules such as intracellular adhesion molecule-1, vascular cell adhesion molecule-1, and E-selectin, which regulate the adhesion of leukocytes to the endothelium, leading to atheroma pathogenesis.24 38”
§ Glycemic variability and cardiovascular disease in patients with type 2 diabetes > Definitions and methods of assessment > GV and tissue damage
What the sources say
- Not checked yetcites 24 38
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“High day-to-day fasting GV was significantly associated with severe hypoglycemia, MACE and all-cause mortality; risks raised 2.7-fold, 1.2-fold and 1.4-fold, respectively, when fasting GV was duplicated.49”
§ Glycemic variability and cardiovascular disease in patients with type 2 diabetes > Definitions and methods of assessment > GV and cardiovascular disease
What the sources say
- Not checked yetcites [49] Zinman, 2018, Day-to-day fasting glycaemic variability in DEVOTE: associations with severe hypoglycaemia and cardiovascular outcomes (DEVOTE 2), Diabetologia source
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
None of the sources cited for this claim have been checked yet.
In the paper
“It is also associated with an accelerating atherosclerosis and cardiovascular disease,21 23 34 42 which accounts for 52% of deaths in patients with T2D and 44% in patients T1D.3–5”
§ Glycemic variability and cardiovascular disease in patients with type 2 diabetes > Definitions and methods of assessment > GV and clinical implications
What the sources say
- Not checked yetcites 21 23 34 42
- Not checked yetcites [3] Chan, 2021, The Lancet Commission on diabetes: using data to transform diabetes care and patient lives, Lancet source
- Not checked yetcites [4] Huang, 2017, Macrovascular complications in patients with diabetes and prediabetes, Biomed Res Int
- Not checked yetcites [5] Morrish, 2001, Mortality and causes of death in the who multinational study of vascular disease in diabetes, Diabetologia source
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
In the paper
“This risk is increased when the mean blood glucose concentration is low or if deviations around the mean glucose concentrations are large, suggesting the need to reduce short-term GV.46”
§ Glycemic variability and cardiovascular disease in patients with type 2 diabetes > Definitions and methods of assessment > GV and clinical implications
What the sources say
- Not checked yetcites [46] source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“GV was associated with severe hypoglycemia: HR 4.11 (3.15–5.35); MACE: HR 1.36 (1.12–1.65); and all cause mortality: HR 1.58 (1.23–2.03).”
§ Glycemic variability and cardiovascular disease in patients with type 2 diabetes > Definitions and methods of assessment > GV and cardiovascular disease
What the sources say
- Not checked yetcites [49] Zinman, 2018, Day-to-day fasting glycaemic variability in DEVOTE: associations with severe hypoglycaemia and cardiovascular outcomes (DEVOTE 2), Diabetologia source
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
In the paper
“An analysis to assess the effect of intraday GV from 7-point SMBG reported no difference on cardiovascular outcomes despite reduced intraday GV and postprandial hyperglycemia.52”
§ Glycemic variability and cardiovascular disease in patients with type 2 diabetes > Definitions and methods of assessment > GV and cardiovascular disease
What the sources say
- Not checked yetcites [52] source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a 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
“During the past three decades, HbA1c, a measure of sustained hyperglycemia, has been the main target to assess response to diabetes treatment”
§ Glycemic variability and cardiovascular disease in patients with type 2 diabetes > Conclusions
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
- Rests on an assumption: Poor glycemic control is positively associated with acute and long-term complications of diabetes.
- Rests on an assumption: Reduction in HbA1c leads to lower rates of diabetic complications.
In the paper
“Despite the improvement in GV, there was no statistically significant difference in cardiac arrhythmias”
§ Glycemic variability and cardiovascular disease in patients with type 2 diabetes > Definitions and methods of assessment > GV and cardiovascular disease
What the sources say
- Not checked yetcites [54] source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a 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
“The results of experimental and clinical studies demonstrate that glucose fluctuations are associated with oxidative stress, endothelial dysfunction, and inflammation”
§ Glycemic variability and cardiovascular disease in patients with type 2 diabetes > Conclusions
boostedcorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: unclear — It is unclear why the paper says it holds.
It may state two separate claims as one.
In the paper
“minimally invasive CGM sensors are becoming an increasingly adopted technology for the management of patients with T1D and T2D.55”
§ Glycemic variability and cardiovascular disease in patients with type 2 diabetes > Conclusions
What the sources say
- Not checked yetcites [55] Rodbard, 2017, Continuous glucose monitoring: a review of recent studies demonstrating improved glycemic outcomes, Diabetes Technol Ther source
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper relies on this without support.
HbA1c is the gold standard for assessment of long-term glycemic control and response to medical treatment in patients with diabetes.
“Glycated hemoglobin is currently the gold standard for assessment of long-term glycemic control and response to medical treatment in patients with diabetes.”
The paper relies on this without support.
HbA1c does not address fluctuations in blood glucose.
“Glycated hemoglobin, however, does not address fluctuations in blood glucose.”
The paper relies on this without support.
There is currently no consensus on the use of metrics to assess glycemic variability.
“There is currently no consensus on the use of metrics to assess GV.”
The paper relies on this without support.
Mechanistic findings from in vitro and animal models of glucose fluctuation apply to the pathogenesis of vascular damage in human type 2 diabetes.
“In vitro studies in human umbilical vein endothelial cells reported that increases in nitrotyrosine and 8-hydroxydeoxyguanosine, markers of oxidative stress, were more pronounced when they were exposed to intermittent high glucose than in constant high glucose.24”
“In an animal model, Ying and colleagues41 evaluated markers of tissue oxidative stress in heart ventricles and found that blood glucose fluctuation accelerated cardiac injury via intracellular signal transduction pathways including protein kinase B (AKT), key enzyme in cell death.”
The paper relies on this without support.
Poor glycemic control is positively associated with acute and long-term complications of diabetes.
“based on the positive association between poor glycemic control and acute and long-term complications of diabetes”
The paper relies on this without support.
Reduction in HbA1c leads to lower rates of diabetic complications.
“that reduction in HbA1c leads to lower rates in diabetic complications.”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The cited source says what the claim attributes to it.
In the citing paper
“new literature evidence links glycemic variability to oxidative stress, endothelial dysfunction, and cardiovascular outcomes independent of mean glucose levels [[51], [52], [53], [54]].”
From Effectiveness of continuous glucose monitoring systems on glycemic control in adults with type 1 diabetes: A systematic review and meta-analysis (2025) · cites this paper as [51]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Glycemic variability and cardiovascular disease in patients with type 2 diabetes > Definitions and methods of assessment > GV and cardiovascular disease
“OR 2.21”
§ Glycemic variability and cardiovascular disease in patients with type 2 diabetes > Definitions and methods of assessment > GV and cardiovascular disease
“(1.64–0.98), p<0.001.”
Citations
Cites
International Diabetes Federation 2017
Carracher, A. M., Marathe, P. H., & Close, K. L. (2018) · Journal of Diabetes · doi:10.1111/1753-0407.12644 · Cited by 13,379
Intensive blood glucose control and vascular outcomes in patients with type 2 diabetes
(2008) · New England Journal of Medicine · doi:10.1056/nejmoa0802987 · Cited by 7,397
Oxidative stress and diabetic complications
Giacco, F., & Brownlee, M. (2010) · Circulation Research · doi:10.1161/circresaha.110.223545 · Cited by 5,461
Intensive diabetes treatment and cardiovascular disease in patients with type 1 diabetes
(2005) · New England Journal of Medicine · doi:10.1056/nejmoa052187 · Cited by 5,138
Glucose control and vascular complications in veterans with type 2 diabetes
Duckworth, W., Abraira, C., Moritz, T., Reda, D., Emanuele, N., Reaven, P. D., Zieve, F. J., Marks, J., Davis, S. N., Hayward, R., et al. (2009) · New England Journal of Medicine · doi:10.1056/nejmoa0808431 · Cited by 4,762
Battelino, T., Danne, T., Bergenstal, R. M., Amiel, S. A., Beck, R., Biester, T., Bosi, E., Buckingham, B. A., Cefalu, W. T., Close, K. L., et al. (2019) · Diabetes Care · doi:10.2337/dci19-0028 · Cited by 4,237
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
Diabetes and vascular disease: pathophysiology, clinical consequences, and medical therapy: part I
Paneni, F., Beckman, J. A., Creager, M. A., & Cosentino, F. (2013) · European Heart Journal · doi:10.1093/eurheartj/eht149 · Cited by 1,455
Mortality and causes of death in the WHO Multinational Study of Vascular Disease in Diabetes
and the WHO Multinational Study Group, Morrish, N. J., Wang, S.-L., Stevens, L. K., Fuller, J. H., & Keen, H. (2001) · Diabetologia · doi:10.1007/pl00002934 · Cited by 1,288
Skjerdal, T., Gefferth, A., Spajic, M., Gaston, E., Cesare, A. D., Vitali, S. F., Pasquali, F., Bovo, F., Manfreda, G., Mancusi, R., et al. (2017) · BioMed Research International · doi:10.1155/2017/6353510 · Cited by 1,178
Cited by
Sorted by citations
Optimized glycemic control of type 2 diabetes with reinforcement learning: a proof-of-concept trial
Wang, G., Liu, X., Ying, Z., Yang, G., Chen, Z., Liu, Z., Zhang, M., Yan, H., Lu, Y., Gao, Y., et al. (2023) · Nature Medicine · doi:10.1038/s41591-023-02552-9 · Cited by 156
Glycaemic management in diabetes: old and new approaches
Ceriello, A., Prattichizzo, F., Phillip, M., Hirsch, I. B., Mathieu, C., & Battelino, T. (2021) · The Lancet Diabetes & Endocrinology · doi:10.1016/s2213-8587(21)00245-x · Cited by 149
Diabetes mellitus and perioperative outcomes: a scoping review of the literature
Drayton, D. J., Birch, R. J., D'souza-Ferrer, C., Ayres, M., Howell, S. J., & Ajjan, R. (2022) · British Journal of Anaesthesia · doi:10.1016/j.bja.2022.02.013 · Cited by 74
Glucose variability: a new risk factor for cardiovascular disease
Belli, M., Bellia, A., Sergi, D., Barone, L., Lauro, D., & Barillà, F. (2023) · Acta Diabetologica · doi:10.1007/s00592-023-02097-w · Cited by 68
Vitamin C supplementation for diabetes management: A comprehensive narrative review
Mason, S. A., Parker, L., van der Pligt, P., & Wadley, G. D. (2023) · Free Radical Biology and Medicine · doi:10.1016/j.freeradbiomed.2022.12.003 · Cited by 53
Morales, A. M., Yamamoto, M., Inoue, M., Vu, T., & Araki, M. (2023) · Nutrients · doi:10.3390/nu15183937 · Cited by 49
The double burden: type 1 diabetes and heart failure—a comprehensive review
Julián, M. T., de, A. P.-M., Julve, J., & Alonso, N. (2024) · Cardiovascular Diabetology · doi:10.1186/s12933-024-02136-y · Cited by 53
Djeujo, F. M., Ragazzi, E., Urettini, M., Sauro, B., Cichero, E., Tonelli, M., & Froldi, G. (2022) · Pharmaceuticals · doi:10.3390/ph15020205 · Cited by 46
Mo, Y., Wang, C., Lu, J., Shen, Y., Chen, L., Zhang, L., Lu, W., Zhu, W., Xia, T., & Zhou, J. (2022) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2022.109940 · Cited by 45
Significance of Glycemic Variability in Diabetes Mellitus
Kusunoki, Y., Konishi, K., Tsunoda, T., & Koyama, H. (2022) · Internal Medicine · doi:10.2169/internalmedicine.8424-21 · Cited by 40
Aroda, V. R., & Eckel, R. H. (2022) · Diabetes Obesity and Metabolism · doi:10.1111/dom.14830 · Cited by 40
Sheng, L., Yang, G., Chai, X., Zhou, Y., Sun, X., & Xing, Z. (2024) · Frontiers in Endocrinology · doi:10.3389/fendo.2024.1323571 · Cited by 35
Turton, J. L., Brinkworth, G. D., Parker, H. M., Lim, D. M., Lee, K., Rush, A., Johnson, R., & Rooney, K. B. (2023) · PLoS ONE · doi:10.1371/journal.pone.0288440 · Cited by 36
Psoma, O., Makris, M., Tselepis, A. D., & Tsimihodimos, V. (2022) · Journal of Diabetes Science and Technology · doi:10.1177/19322968221146808 · Cited by 32
Personalized diabetes monitoring platform leveraging IoMT and AI for non-invasive estimation
Padmavilochanan, D., Pathinarupothi, R. K., Menon, K. A. U., Kumar, H., Guntha, R., Ramesh, M. V., & Rangan, P. V. (2023) · Smart Health · doi:10.1016/j.smhl.2023.100428 · Cited by 29
Yang, X., Su, G., Zhang, T., Yang, H., Tao, H., Du, X., & Dong, J. (2024) · Journal of Translational Internal Medicine · doi:10.2478/jtim-2024-0006 · Cited by 25
Gerbaud, É., de la Poterie, A. B., Baudinet, T., Montaudon, M., Beauvieux, M.-C., Lemaître, A.-I., Cetran, L., Séguy, B., Picard, F., Vélayoudom, F.-L., et al. (2022) · Journal of Clinical Medicine · doi:10.3390/jcm11061549 · Cited by 24
Mario, C. D., Genovese, S., Lanza, G. A., Mannucci, E., Marenzi, G., Sciatti, E., Pitocco, D., the Expert Panel Group, Avogaro, A., Bertuzzi, F., et al. (2022) · Cardiovascular Diabetology · doi:10.1186/s12933-022-01598-2 · Cited by 24
How to Measure Glycemic Variability? A Literature Review
Lazăr, S., Ioniță, I., Reurean‐Pintilei, D., & Timar, B. (2023) · Medicina · doi:10.3390/medicina60010061 · Cited by 22
Perioperative Glycemic Management in Cardiac Surgery: A Narrative Review
Thongsuk, Y., & Hwang, N. C. (2023) · Journal of Cardiothoracic and Vascular Anesthesia · doi:10.1053/j.jvca.2023.08.149 · Cited by 20
Dawson, M. A., Cheung, S. N., Frano, M. R. L., Nagpal, R., & Berryman, C. E. (2024) · Cell Reports Medicine · doi:10.1016/j.xcrm.2023.101363 · Cited by 21
A distributional reinforcement learning model for optimal glucose control after cardiac surgery
Desman, J. M., Hong, Z.-W., Sabounchi, M., Sawant, A. S., Gill, J., Costa, A. C., Kumar, G., Sharma, R., Gupta, A., McCarthy, P. J., et al. (2025) · npj Digital Medicine · doi:10.1038/s41746-025-01709-9 · Cited by 20
Almagthali, A., Alsohimi, S., Alkhalaf, A., Sulaiman, K. A. A., & Aljuhani, O. A. (2024) · Scientific Reports · doi:10.1038/s41598-024-57403-5 · Cited by 17
Huang, Q., Zou, X., Lian, Z., Zhou, X., Han, X., Luo, Y., Chen, S., Wang, Y., Wu, S., & Ji, L. (2025) · Cardiovascular Diabetology · doi:10.1186/s12933-025-02611-0 · Cited by 15
Bruno, J., Walker, J. M., Nasserifar, S., Upadhyay, D., Ronning, A., Vanegas, S. M., Popp, C. J., Barua, S., & Alemán, J. O. (2024) · iScience · doi:10.1016/j.isci.2024.111501 · Cited by 16
Koh, S. M., Chung, S. H., Yum, Y., Park, S.-J., Joo, H. J., Kim, Y., & Kim, E. J. (2022) · Cardiovascular Diabetology · doi:10.1186/s12933-022-01681-8 · Cited by 15
Chen, H., Lai, Y., & Jiang, Y. (2025) · Journal of Diabetes Investigation · doi:10.1111/jdi.70019 · Cited by 13
Hayek, A. A., Klonoff, D. C., Zahrani, W. M. A., Ibrahim, S. E., & Dawish, M. A. A. (2025) · Journal of Diabetes and its Complications · doi:10.1016/j.jdiacomp.2025.109064 · Cited by 13
Wang, S., Gu, L., Zhu, J. W., Shan, T., Sun, J., Jiang, Q., Wang, H., Zhao, D., Wang, Q., & Wang, L. (2022) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2022.110116 · Cited by 13
Senior, P. A. (2021) · Atherosclerosis · doi:10.1016/j.atherosclerosis.2021.09.001 · Cited by 12
Alfadli, S. F., Alotaibi, Y. S., Aqdi, M. J., Almozan, L. A., Alzubaidi, Z. B., Altemani, H. A., Almutairi, S. D., Alabdullah, H. A., Almehmadi, A. A., Alanzi, A. L., et al. (2025) · Metabolism Open · doi:10.1016/j.metop.2025.100382 · Cited by 16
Ma, H., Qian, X., Song, X., Jiang, R., Li, J., Fang, X., Dou, R., Guan, X., Lui, K. Y., Li, S., et al. (2025) · Frontiers in Immunology · doi:10.3389/fimmu.2025.1610519 · Cited by 10
Glycaemic fluctuations across the menstrual cycle: possible effect of the brain
Hummel, J., Kullmann, S., Wagner, R. J., & Heni, M. (2023) · The Lancet Diabetes & Endocrinology · doi:10.1016/s2213-8587(23)00286-3 · Cited by 10
Scheen, A. J. (2023) · Diabetes & Metabolism · doi:10.1016/j.diabet.2023.101508 · Cited by 10
Kaddech, N., Guelmami, N., Bonsaksen, T., Doggui, R., Beji, C., & Ati, J. E. (2022) · Healthcare · doi:10.3390/healthcare10050951 · Cited by 10
Chen, J., Huang, W., & Liang, N. (2024) · PLoS ONE · doi:10.1371/journal.pone.0300323 · Cited by 9
Lind, N., Christensen, M. B., & Nørgaard, K. (2024) · PEC Innovation · doi:10.1016/j.pecinn.2024.100324 · Cited by 10
Hayek, A. A., & Dawish, M. A. A. (2024) · Journal of Diabetes Science and Technology · doi:10.1177/19322968241301750 · Cited by 9
Abdel‐Rahman, N., Calderon‐Margalit, R., Cohen, A. D., Elran, E., Golan‐Cohen, A., Krieger, M., Paltiel, O., Valinsky, L. J., Ben‐Yehuda, A., & Manor, O. (2022) · Journal of the American Heart Association · doi:10.1161/jaha.122.025603 · Cited by 9
Sun, R., Duan, Y., Zhang, Y., Feng, L., Ding, B., Yan, R., Ma, J., & Su, X. (2023) · Diabetes Therapy · doi:10.1007/s13300-023-01432-2 · Cited by 9
Zhu, Q., Zong, Q., Guo, S., Ye, H., Ma, Z., Zhang, R., Zou, H., & Ba, Y. (2025) · Diabetes Obesity and Metabolism · doi:10.1111/dom.16410 · Cited by 8
Hayek, A. A., Klonoff, D. C., Zahrani, W. M. A., Saeed, A. H. A., & Dawish, M. A. A. (2025) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2025.112458 · Cited by 8
Sun, B., Gao, Y., He, F., Liu, Z., Zhou, J., Wang, X., & Zhang, W. (2022) · Frontiers in Public Health · doi:10.3389/fpubh.2022.1052485 · Cited by 8
Mhaimeed, O., Pillai, K. V., Dargham, S. R., Suwaidi, J. M. S. A., Jneid, H. M., & Khalil, C. A. (2023) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2023.1175731 · Cited by 8
Mahin, D., Lavasani, S. M., Cristobal, L., Patel, N. T., Sedrak, M. S., Stewart, D. B., Waisman, J. R., Yuan, Y., Yu, W., Samoa, R. A., et al. (2023) · Journal of Clinical Medicine · doi:10.3390/jcm12051906 · Cited by 7
Pham, C. T., Ali, A. S., Churilov, L. P., Baqar, S., Hendrieckx, C., O'Neal, D. N., Howard, M. E., & Ekinci, E. I. (2024) · Diabetic Medicine · doi:10.1111/dme.15485 · Cited by 7
Lu, J., Ou, H., Wu, C., Yang, C.-Y., Jiang, J., Lu, C., Jiang, Y., Chang, T., Chang, Y., Hsieh, M.-L., et al. (2023) · Scientific Reports · doi:10.1038/s41598-023-29791-7 · Cited by 7
Kim, S., Hwang, J., Yon, D. K., Sang, H., Woo, S., Woo, H. G., Lim, H. J., Park, J., Hsiao, F., Chen, L., et al. (2025) · Journal of Cachexia Sarcopenia and Muscle · doi:10.1002/jcsm.70028 · Cited by 8
Patients’ Perspective on Barriers to Utilization of a Diabetic Retinopathy Screening Service
Owusu-Afriyie, B., Gende, T., Tapilas, M., Zimbare, N., & Kewande, J. (2023) · Diabetology · doi:10.3390/diabetology4030033 · Cited by 8
Hayek, A. A., Mashali, M. A., & Dawish, M. A. A. (2025) · Journal of Diabetes & Metabolic Disorders · doi:10.1007/s40200-025-01569-w · Cited by 6
Guo, X., Guo, S., Zhang, H., & Li, Z. (2022) · Journal of Clinical Medicine · doi:10.3390/jcm11154257 · Cited by 6
Lahouel, A. (2024) · Metabolic Brain Disease · doi:10.1007/s11011-024-01352-5 · Cited by 6
Schwartz, S. S., & Herman, M. E. (2024) · Frontiers in Endocrinology · doi:10.3389/fendo.2024.1394805 · Cited by 6
Bariatric surgery normalizes diabetes risk index by one month post-operation
Sinatra, V. J., Lin, B., Parikh, M., Berger, J. S., Fisher, E. A. A., & Heffron, S. P. (2022) · Acta Diabetologica · doi:10.1007/s00592-022-02002-x · Cited by 6
Low Blood Glucose Index Associated with Cardiovascular Events in Diabetic Hemodialysis Patients
He, R., Xuan, Y., Zhu, L., Pang, S., Qin, L., Tian, J., & Yuan, J. (2023) · Blood Purification · doi:10.1159/000531964 · Cited by 5
Huang, Q., Cui, M., Yin, Y., Hui, C., Cheng, Y., Wang, S., Hu, X., Yin, X., Zhang, X., Sun, X., et al. (2024) · BMC Geriatrics · doi:10.1186/s12877-024-05616-8 · Cited by 5
Renal Foot in Diabetes: Implications for Wound Healing and Solutions
Rastogi, A., Gupta, R. K., Ghosh, J., & Jude, E. B. (2025) · Advances in Wound Care · doi:10.1089/wound.2025.0074 · Cited by 5
Jadav, R. K., Yee, K. C., Turner, M. R., & Mortazavi, R. (2024) · Healthcare · doi:10.3390/healthcare12151542 · Cited by 5
Pantanetti, P., Cangelosi, G., Sguanci, M. E., Palomares, S. M., Nguyen, C. T. T., Morresi, G., Mancin, S., & Petrelli, F. (2024) · Nutrients · doi:10.3390/nu16162602 · Cited by 5
Alkhuzam, K. A., Li, P., Abuloha, S., Xue, Q., Shi, L., Fonseca, V. A., Zhang, Y., & Shao, H. (2025) · Diabetes Obesity and Metabolism · doi:10.1111/dom.16168 · Cited by 5
Ebrahimi, M., Thompson, P. M., Kafashan, Z., Ceriello, A., Kolko, M., & Grauslund, J. (2025) · Journal of Endocrinological Investigation · doi:10.1007/s40618-025-02600-w · Cited by 5
CHANDRAN, S. R., Tan, H. C., Chen, Q., Lee, P. C., Gardner, D. S.-L., Chin, Y. A., Lam, A. Y. R., Teh, M. M., & Bee, Y. M. (2023) · Journal of Diabetes Science and Technology · doi:10.1177/19322968231157387 · Cited by 5
Bender, B. F., & Berry, J. A. (2023) · Advances in Geriatric Medicine and Research · doi:10.20900/agmr20230002 · Cited by 5
Vijayakumar, V., Mavathur, R. N., Kannan, S., Sharma, M. N. K., Raguram, N., & Kuppusamy, M. (2023) · Diabetes & Metabolism · doi:10.1016/j.diabet.2023.101457 · Cited by 5
Jiang, J., Zhang, X., Zheng, D., Li, Y., Li, F. R., Wang, W., Ding, S., Zhang, J., Su, X., Zhai, Q., et al. (2023) · Journal of Endocrinological Investigation · doi:10.1007/s40618-023-02142-z · Cited by 5
Sreenivasan, C., Parikh, A., Francis, A. J., Kanthajan, T., Pandey, M., AlQassab, O., & Nath, T. S. (2024) · Cureus · doi:10.7759/cureus.66697 · Cited by 5
Diabetes distress, the mediator of the poor glycemic control and depression: A meta-analysis
Mirghani, H. O. (2024) · World Journal of Meta-Analysis · doi:10.13105/wjma.v12.i4.97779 · Cited by 4
Hayek, A. A., Zahrani, W. M. A., & Dawish, M. A. A. (2025) · Journal of Diabetes Science and Technology · doi:10.1177/19322968251321860 · Cited by 4
Kleckner, A. S., Clingan, C. L., Youngblood, S. M., Kleckner, I. R., Quick, L., Elrod, R. D., Zhu, S., Manoogian, E. N. C., Panda, S., Badros, A. Z., et al. (2025) · Supportive Care in Cancer · doi:10.1007/s00520-025-09394-w · Cited by 4
Xu, M.-R., Li, M., Zhang, Y.-W., Lu, J., Li, L., & Zhang, Y.-R. (2025) · Diabetes Metabolic Syndrome and Obesity · doi:10.2147/dmso.s548509 · Cited by 4
Screening for Impaired Glucose Homeostasis: A Novel Metric of Glycemic Control
Kaufman, J. M., VEEN, L. V., & Fossat, Y. (2023) · Mayo Clinic Proceedings Digital Health · doi:10.1016/j.mcpdig.2023.02.008 · Cited by 4
Steck, M., Wells, D. A., Stoffel, J. M., Hudson, J. Q., Saeed, O., Elangovan, C., Krishnaiah, B., & Shah, S. P. (2023) · The Neurohospitalist · doi:10.1177/19418744231200048 · Cited by 4
Wang, Y., Zhou, Y., Hu, H., Liu, C., Wang, P., Zhang, L., Chu, J., Lu, Z., Guo, Z., Jing, W., et al. (2024) · Journal of Clinical Neuroscience · doi:10.1016/j.jocn.2024.03.028 · Cited by 4
Solanki, J. D., Hirani, C. N., Vohra, A. S., Panjwani, S. J., Senta, V. M., & Rudani, D. K. (2024) · PubMed Central · doi:10.60787/nmj-64-3-204 · Cited by 4
Deng, Z., Wang, S., Lu, J., Zhang, R., Zhang, L. J., Lu, W., Zhu, W., Bao, Y., Zhou, J., & Hu, C. (2023) · Endocrine · doi:10.1007/s12020-023-03484-7 · Cited by 4
Delevatti, R. S., Leonel, L. D. S., & Wolin, I. A. V. (2024) · Medical Hypotheses · doi:10.1016/j.mehy.2024.111379 · Cited by 4
Martínez-Díaz, F. M., Jiménez-Cuevas, E. A., Ramírez-Mejía, M. M., Gómez-Camacho, S., & Méndez‐Sánchez, N. (2026) · Exploration of Digestive Diseases · doi:10.37349/edd.2026.1005115 · Cited by 3
Tee, C., Xu, H., Fu, X., Cui, D., Jafar, T. H., & Bee, Y. M. (2023) · PLoS ONE · doi:10.1371/journal.pone.0275610 · Cited by 3
Gan, Y., Chen, M., Kong, L., Wu, J., Pu, Y. D., Wang, X., Zhou, J., Fan, X., Xiong, Z., & Qi, H. (2023) · Frontiers in Endocrinology · doi:10.3389/fendo.2023.1216897 · Cited by 4
Htun, H. L., Lian, W., Phua, H. P., Lim, M. Y., Chew, D. E. K., Quek, T. P. L., & Lim, W. (2024) · Diabetes Obesity and Metabolism · doi:10.1111/dom.16026 · Cited by 3
Continuous glucose monitoring metrics in pregnancy with type 1 diabetes mellitus
Jeeyavudeen, M. S., Crosby, M., & Pappachan, J. M. (2024) · World Journal of Methodology · doi:10.5662/wjm.v14.i1.90316 · Cited by 3
Liang, X., Yin, K., Fu, Y., Xu, G., Feng, X., & Lv, P. (2025) · Frontiers in Neurology · doi:10.3389/fneur.2025.1516274 · Cited by 2
He, H., Ying, Z., Li, B., Fan, Y., Wang, P., Lu, J., Wu, L., Zhao, H., Guo, Y., Wang, G., et al. (2026) · Communications Medicine · doi:10.1038/s43856-026-01449-1 · Cited by 2
Lazić, A., & Trajković, N. (2026) · Reviews in Endocrine and Metabolic Disorders · doi:10.1007/s11154-026-10073-2 · Cited by 2
Dwipajati, D., & Kaswari, S. R. T. (2024) · Amerta Nutrition · doi:10.20473/amnt.v8i1.2024.58-66 · Cited by 3
Initiating insulin therapy in adults with type 2 diabetes: a practical guide
Stewart, M. (2024) · Primary Health Care · doi:10.7748/phc.2024.e1829 · Cited by 2
Hyperglycemic Patterns in Acute Stroke Patients
Fountouki, A., Tegos, T. J., Ztriva, E., Kaiafa, G. D., Didangelos, T., Theofanidis, D., & Savopoulos, C. G. (2024) · Cureus · doi:10.7759/cureus.62039 · Cited by 2
Tecce, N., Menafra, D., Proganò, M., Tecce, M. F., Pivonello, R., & Colao, A. (2024) · Healthcare · doi:10.3390/healthcare12181823 · Cited by 2
Kim, H. J., Kim, K. W., Jung, C. W., Lee, J. Y., Choi, J. Y., Kim, B. S., Kim, M. S., & Yang, J. (2024) · Scientific Reports · doi:10.1038/s41598-024-78079-x · Cited by 2
Kah, T. A. (2026) · Advancements in health research. · doi:10.4081/ahr.2026.131 · Cited by 2
Rooney, M. R., Wang, D., McEvoy, J. W., Juraschek, S. P., Chalmers, J. P., Woodward, M., & Selvin, E. (2021) · Diabetes Research and Clinical Practice · doi:10.1016/j.diabres.2021.109148 · Cited by 2
Glycemic Variability and the Risk of Diabetic Peripheral Neuropathy: A Meta-Analysis
Song, Y., Zhang, H., Sun, J., Long, Y., Zhang, K., Yin, Q., & Duan, X. (2023) · Hormone and Metabolic Research · doi:10.1055/a-2165-3579 · Cited by 2
Enhancement of Ambulatory Glucose Profile for Decision Assistance and Treatment Adjustments
Satuluri, V. K. R. R., & Ponnusamy, V. (2024) · Diagnostics · doi:10.3390/diagnostics14040436 · Cited by 2
Beattie, S. (2023) · The Nurse Practitioner · doi:10.1097/01.npr.0000000000000100 · Cited by 1
Ismail, A. E., & Howell, S. J. (2025) · British Journal of Anaesthesia · doi:10.1016/j.bja.2025.04.036 · Cited by 1
Yuan, J., Wang, J., Wang, Y., Chen, P., Wu, S., Shen, Y., Zhao, C., Reutrakul, S., Park, C. G., Chen, X., et al. (2025) · Sleep Medicine · doi:10.1016/j.sleep.2025.106754 · Cited by 2
Abdelmaksoud, A. A., Hayek, A. A., Matter, R. M., Salah, N. Y., Shahin, H. H. A. E., Reyad, A. R., & Tantawy, N. M. (2025) · Cureus · doi:10.7759/cureus.91864 · Cited by 1
Andishmand, A., Montazeri, M., & Bahrami, H. (2025) · Journal of Research in Medical Sciences · doi:10.4103/jrms.jrms_305_24 · Cited by 1
Chisbert, M., Castell, A., Berghe, L. V. D., Breyton, A.-E., Feugier, N., Cuerq, C., Chikh, K., Brack, O., Laville, M., Goux, A., et al. (2025) · American Journal of Clinical Nutrition · doi:10.1016/j.ajcnut.2025.10.007 · Cited by 8
Lee, K.-S., Cho, H., Moon, Y. R., Kim, J., Yang, S., Park, S., Hwang, D., Kang, J., Han, J., Yang, H. M., et al. (2026) · Korean Circulation Journal · doi:10.4070/kcj.2025.0288 · Cited by 1
Metadata
- Authors
- Martinez, M., Santamarina, J., Pavesi, A., Musso, C., & Umpierrez, G. E.
- Affiliations
- Fundacion Favaloro Hospital Universitario (AR)
- Diabetes and Endocrinology, Favaloro Foundation University Hospital, Buenos Aires, Federal District, Argentina
- Emory University (US)
- Corresponding author
- Umpierrez, Guillermo E
- Venue
- BMJ Open Diabetes Research & Care
- Publisher
- BMJ
- Published
- 2021-03
- Volume
- 9
- Issue
- 1
- Pages
- e002032
- ISSN
- 2052-4897 (ISSN-L)
- PMC
- PMC7993171
- OpenAlex
- W3138553431
- Semantic Scholar
- 64c8482c3eb134fca5ead0912e1852c256d5243b
- Cited by
- 155
- References
- 58
- Citations by year
- 2026: 23 · 2025: 39 · 2024: 37 · 2023: 32 · 2022: 19 · 2021: 5
- FWCI
- 3.56
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- BMJ Open Diabetes Research & Care (publishedVersion, cc-by-nc)
- PubMed Central (publishedVersion, cc-by-nc)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- National Institutes of Health (UL1TR002378)
- National Center for Advancing Translational Sciences
- Topics
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Management and Research
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes Treatment and Management
- Health Sciences › Medicine › Endocrinology, Diabetes and Metabolism › Diabetes, Cardiovascular Risks, and Lipoproteins
- Keywords
- glycemic variability, type 2 diabetes, cardiovascular disease, HbA1c, continuous glucose monitoring, hypoglycemia, hyperglycemia, microvascular complications, macrovascular complications, mortality
- MeSH terms
- Blood Glucose, Cardiovascular Diseases, Diabetes Mellitus, Type 2, Glycated Hemoglobin, Humans, Blood Glucose Self-Monitoring
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 2026-10-09, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-09 (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 ›
Are you an author, or do you see a problem with this page? Request a review