Identifying hyperinsulinaemia in the absence of impaired glucose tolerance: An examination of the Kraft database

Crofts, Catherine; Schofield, Grant; Zinn, Caryn; Wheldon, Mark C.; Kraft, Joseph

doi:10.1016/j.diabres.2016.06.007

Cited by 43 publications

(66 citation statements)

References 32 publications

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“…Although the definition of hyperinsulinaemia is commonly based on fasting insulin levels, we believed this was insufficient given a lack of consensus on fasting insulin and the evidence suggesting post-prandial insulin is crucial. 13,14 Therefore, we developed our own criteria based on examination of the graphed insulin response versus time for all 'No Diabetes' participants. This led to the division of the 'No Diabetes' group into 'Control' (n = 19) and 'Hyperinsulinaemic' (n = 3).…”

Section: Mean-variance Relationshipsmentioning

confidence: 99%

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Assessing the test–retest repeatability of insulin resistance measures: Homeostasis model assessment 2 and oral glucose insulin sensitivity

Crofts

Wheldon

Zinn

et al. 2017

JMH

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Background: Insulin resistance is commonly assessed using the homeostasis model assessment (HOMA) variants. HOMA is potentially insensitive to change because of its high coefficient of variation. The repeatability coefficient is an alternative means of assessing test repeatability. To be confident of clinical change, rather than biological variation, a subsequent test needs to differ from the former by more than the repeatability coefficient using the equation.Test 1 = Test 2 ± repeatability coefficient.The repeatability coefficients for measures of insulin resistance are unknown.Aim: To compare the repeatability coefficient of HOMA2 variants (Beta-cell function [%B], insulin sensitivity [%S], insulin resistance [IR]) to a dynamic measure of insulin resistance, and the oral glucose insulin sensitivity (OGIS) test.Setting: The raw data from a previously used data set were reanalysed.Methods: Glycaemic and insulinaemic tests were performed on 32 men and women both with (n = 10) and without type 2 diabetes (n = 22). From these data, eight fasting tests and three 50-g oral glucose tolerance tests were used to calculate HOMA2 and OGIS. The methods of Bland and Altman assessed repeatability.Results: Repeatability coefficients for all participants for the HOMA2 %B, %S and IR variants were 72.91, 189.75 and 0.9, which equates to 89%, 135% and 89% of their respective grand means. By contrast, OGIS had a repeatability coefficient of 87.13, which equates to 21% of the grand mean.Conclusion: Because of the high repeatability coefficient relative to the grand mean, use of HOMA2 measures for assessing insulin resistance in small population studies should be reconsidered.

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Section: Mean-variance Relationshipsmentioning

confidence: 99%

“…Emerging research suggests that fasting insulin levels may not predict post-prandial insulin levels, 13 and these latter levels are more important for diagnosing risk. 14 The oral glucose insulin sensitivity (OGIS) test is an alternative method for assessing insulin resistance.…”

Section: Introductionmentioning

confidence: 99%

Assessing the test–retest repeatability of insulin resistance measures: Homeostasis model assessment 2 and oral glucose insulin sensitivity

Crofts

Wheldon

Zinn

et al. 2017

JMH

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“…1,2 The aetiology of hyperinsulinemia is likely heterogeneous 2 and in the earliest stages asymptomatic. 3 Early management of hyperinsulinemia may prevent, delay, or mitigate the severity of subsequent pathologies.…”

Section: Introductionmentioning

confidence: 99%

Hyperinsulinemia: Best management practice

et al. 2016

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Chronic hyperinsulinemia associated with insulin resistance is directly and indirectly associated with many metabolic disorders that contribute to significant morbidity and mortality. Because hyperinsulinemia is not widely recognised as an independent health risk, there are few studies that assess management strategies. Medication management may not address the multiple issues associated with hyperinsulinemia. Lifestyle management includes physical activity, especially high intensity interval training, and dietary management. Reducing carbohydrate quantity and increasing nutrient density are discussed with carbohydrate-restricted and Mediterranean diets conferring additional benefits to a low-fat diet. Physical activity and dietary management provide the foundation for hyperinsulinemia management and may work synergistically. Of these principles, a combination of resistance and high intensity interval training, and carbohydrate restriction provide the two most effective frontline management strategies for managing hyperinsulinemia.

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“…2 Hyperinsulinaemia is a common aetiological factor for many metabolic diseases, especially type 2 diabetes, macro-and microvascular disease, certain cancers and dementias. Recent research suggests that hyperinsulinaemia may be present in a significant proportion of the population with both normal body mass indices and normal glucose tolerances, 6 a clinically silent harbinger of the subsequent development of type 2 diabetes over 5-10 years. 7 Research is needed to understand the factors influencing the insulin responses within the body.…”

Section: Insulin and Metabolic Diseasementioning

confidence: 99%

Sleep architecture, insulin resistance and the nasal cycle: Implications for positive airway pressure therapy

Crofts

Neill

Campbell

et al. 2018

JMH

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Aim: To explore the relationship between sleep, especially sleep architecture and the nasal cycle, on insulin secretion in obstructive sleep apnoea (OSA) with comorbid metabolic disease. This review includes a discussion of the potential role of Rest-Activity-Cycler positive airway pressure (RACer-PAP), a novel non-pharmacological OSA treatment strategy. Methods:A narrative review of all the relevant papers known to the authors was conducted. This review also included results from a polysomnographic sleep clinic pilot study (n = 3) comparing sleep efficiency of RACer-PAP to nasal continuous positive airways pressure (n-CPAP) in OSA patients.Results: Metabolic disease is strongly associated with disturbed sleep. Sleep architecture influences cerebral hormonal secretion, lateral shifts in the autonomic nervous system and nasal airflow dominance. Disturbed sleep shortens short-wave sleep periods, decreasing insulin sensitivity and glucose tolerance. Improvements to metabolic function during n-CPAP treatment are inconsistent. If RACer-PAP demonstrates superior effects on sleep architecture and autonomic function, it may offer advantages in OSA patients with comorbid metabolic disease. Conclusion:Improving sleep architecture by maintaining the nasal cycle proposes a novel non-pharmacological treatment paradigm for treating OSA with comorbid metabolic disease.Research is required to demonstrate if RACer-PAP therapy influences whole night sleep architecture, sympathovagal balance and markers of metabolic disease.

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Identifying hyperinsulinaemia in the absence of impaired glucose tolerance: An examination of the Kraft database

Cited by 43 publications

References 32 publications

Assessing the test–retest repeatability of insulin resistance measures: Homeostasis model assessment 2 and oral glucose insulin sensitivity

Assessing the test–retest repeatability of insulin resistance measures: Homeostasis model assessment 2 and oral glucose insulin sensitivity

Hyperinsulinemia: Best management practice

Sleep architecture, insulin resistance and the nasal cycle: Implications for positive airway pressure therapy

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