De Continue Suiker Analyse

G. Tabák A., Herder C., Rathmann W., et al.  (2014), Prediabetes: A high-risk state for developing diabetes. Lancet. 2012 Jun 16; 379(9833): 2279–2290. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3891203/

Abstract

Prediabetes (intermediate hyperglycaemia) is a high-risk state for diabetes that is defined by glycaemic variables that are higher than normal, but lower than diabetes thresholds. 5–10% of people per year with prediabetes will progress to diabetes, with the same proportion converting back to normoglycaemia. Prevalence of prediabetes is increasing worldwide and experts have projected that more than 470 million people will have prediabetes by 2030. Prediabetes is associated with the simultaneous presence of insulin resistance and β-cell dysfunction—abnormalities that start before glucose changes are detectable. Observational evidence shows associations between prediabetes and early forms of nephropathy, chronic kidney disease, small fibre neuropathy, diabetic retinopathy, and increased risk of macrovascular disease. Multifactorial risk scores using non-invasive measures and blood-based metabolic traits, in addition to glycaemic values, could optimise estimation of diabetes risk. For prediabetic individuals, lifestyle modification is the cornerstone of diabetes prevention, with evidence of a 40–70% relative-risk reduction. Accumulating data also show potential benefits from pharmacotherapy.

Abbott, (2016), The accuracy of the freestyle libre system. https://bit.ly/3aHXbS5

Attia P., (2021), Are continuous glucose monitors a waste of time for people without diabetes? https://peterattiamd.com/are-continuous-glucose-monitors-a-waste-of-time-for-people-without-diabetes/

Shah V. N., Dubose S. N., Li Z., et al., (2019), Continuous Glucose Monitoring Profiles in Healthy Nondiabetic Participants: A Multicenter Prospective Study. J Clin Endocrinol Metab. 2019 Oct; 104(10): 4356–4364. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7296129/

Abstract

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.

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.

Giada A., Giovanni S., Liisa H., et al., (2018),  Diabetes and Prediabetes Classification Using Glycemic Variability Indices From Continuous Glucose Monitoring Data. J Diabetes Sci Technol. 2018 Jan;12(1):105-113. doi: 10.1177/1932296817710478. Epub 2017 Jun 1. https://pubmed.ncbi.nlm.nih.gov/28569077/

Background: Tens of glycemic variability (GV) indices are available in the literature to characterize the dynamic properties of glucose concentration profiles from continuous glucose monitoring (CGM) sensors. However, how to exploit the plethora of GV indices for classifying subjects is still controversial. For instance, the basic problem of using GV indices to automatically determine if the subject is healthy rather than affected by impaired glucose tolerance (IGT) or type 2 diabetes (T2D), is still unaddressed. Here, we analyzed the feasibility of using CGM-based GV indices to distinguish healthy from IGT&T2D and IGT from T2D subjects by means of a machine-learning approach.

Methods: The data set consists of 102 subjects belonging to three different classes: 34 healthy, 39 IGT, and 29 T2D subjects. Each subject was monitored for a few days by a CGM sensor that produced a glucose profile from which we extracted 25 GV indices. We used a two-step binary logistic regression model to classify subjects. The first step distinguishes healthy subjects from IGT&T2D, the second step classifies subjects into either IGT or T2D.

Results: Healthy subjects are distinguished from subjects with diabetes (IGT&T2D) with 91.4% accuracy. Subjects are further subdivided into IGT or T2D classes with 79.5% accuracy. Globally, the classification into the three classes shows 86.6% accuracy.

Conclusions: Even with a basic classification strategy, CGM-based GV indices show good accuracy in classifying healthy and subjects with diabetes. The classification into IGT or T2D seems, not surprisingly, more critical, but results encourage further investigation of the present research.

Zheng Z., Bao S., Shiqiong H., et al., (2020), Glycemic variability: adverse clinical outcomes and how to improve it? Cardiovasc Diabetol. 2020; 19: 102. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7335439/

Glycemic variability (GV), defined as an integral component of glucose homoeostasis, is emerging as an important metric to consider when assessing glycemic control in clinical practice. Although it remains yet no consensus, accumulating evidence has suggested that GV, representing either short-term (with-day and between-day variability) or long-term GV, was associated with an increased risk of diabetic macrovascular and microvascular complications, hypoglycemia, mortality rates and other adverse clinical outcomes. In this review, we summarize the adverse clinical outcomes of GV and discuss the beneficial measures, including continuous glucose monitoring, drugs, dietary interventions and exercise training, to improve it, aiming at better addressing the challenging aspect of blood glucose management.

Gezondheid

Kolb H., Stumvoll M., Kramer W., et al. (2018), Insulin translates unfavourable lifestyle into obesity. BMC Medicine volume 16, Article number: 232 (2018).  https://bmcmedicine.biomedcentral.com/articles/10.1186/s12916-018-1225-1

Abstract

Lifestyle factors conferring increased diabetes risk are associated with elevated basal insulin levels (hyperinsulinaemia). The latter predicts later obesity in children and adolescents.

A causal role of hyperinsulinaemia for adipose tissue growth is probable because pharmacological reduction of insulin secretion lowers body weight in people who are obese. Genetic inactivation of insulin gene alleles in mice also lowers their systemic insulin levels and prevents or ameliorates high-fat diet-induced obesity. Hyperinsulinaemia causes weight gain because of a physiological property of insulin. Insulin levels that are on the high side of normal, or which are slightly elevated, are sufficient to suppress lipolysis and promote lipogenesis in adipocytes. The effect of insulin on glucose transport or hepatic glucose production requires six or two times higher hormone levels, respectively.

It seems justified to suggest a lifestyle that avoids high insulin levels in order to limit anabolic fat tissue activity.

Chan-Sik K., Sok P., Junghyun K., (2017), The role of glycation in the pathogenesis of aging and its prevention through herbal products and physical exercise. J Exerc Nutrition Biochem. 2017 Sep 30; 21(3): 55–61. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5643203/

Abstract

Purpose:Advanced glycation end products (AGEs) are non-enzymatic modifications of proteins or lipids after exposure to sugars. In this review, the glycation process and AGEs are introduced, and the harmful effects of AGEs in the aging process are discussed.

Methods: Results from human and animal studies examining the mechanisms and effects of AGEs are considered. In addition, publications addressing means to attenuate glycation stress through AGE inhibitors or physical exercise are reviewed.

Results: AGEs form in hyperglycemic conditions and/or the natural process of aging. Numerous publications have demonstrated acceleration of the aging process by AGEs. Exogenous AGEs in dietary foods also trigger organ dysfunction and tissue aging. Various herbal supplements or regular physical exercise have beneficial effects on glycemic control and oxidative stress with a consequent reduction of AGE accumulation during aging.

Conclusion: The inhibition of AGE formation and accumulation in tissues can lead to an increase in lifespan.

George S., S Sivakami., (2004), Glucose, glycation and aging. Biogerontology. 2004;5(6):365-73. doi: 10.1007/s10522-004-3189-0. https://bit.ly/3MDGgxw

Abstract

Glycation, a deleterious form of post-translational modification of macromolecules has been linked to diseases such as diabetes, cataract, Alzheimer’s, dialysis related amyloidosis (DRA), atherosclerosis and Parkinson’s as well as physiological aging. This review attempts to summarize the data on glycation in relation to its chemistry, role in macromolecular damage and disease, dietary sources and its intervention. Macromolecular damage and biochemical changes that occur in aging and age-related disorders point to the process of glycation as the common event in all of them. This is supported by the fact that several age-related diseases show symptoms manifested by hyperglycemia. Free radical mediated oxidative stress is also known to arise from hyperglycemia. There is evidence to indicate that controlling hyperglycemia by antidiabetic biguanides prolongs life span in experimental animals. Caloric restriction, which appears to prolong life span by bringing about mild hypoglycemia and increased insulin sensitivity further strengthens the idea that glucose via glycation is the primary damaging molecule.

Alejandro G., (2017),  Formation of Fructose-Mediated Advanced Glycation End Products and Their Roles in Metabolic and Inflammatory Diseases. Adv Nutr. 2017 Jan 17;8(1):54-62. doi: 10.3945/an.116.013912. Print 2017 Jan. https://pubmed.ncbi.nlm.nih.gov/28096127/

Abstract

Fructose is associated with the biochemical alterations that promote the development of metabolic syndrome (MetS), nonalcoholic fatty liver disease, and type 2 diabetes. Its consumption has increased in parallel with MetS. It is metabolized by the liver, where it stimulates de novo lipogenesis. The triglycerides synthesized lead to hepatic insulin resistance and dyslipidemia. Fructose-derived advanced glycation end products (AGEs) may be involved via the Maillard reaction. Fructose has not been a main focus of glycation research because of the difficulty in measuring its adducts, and, more importantly, because although it is 10 times more reactive than glucose, its plasma concentration is only 1% of that of glucose. In this focused review, I summarize exogenous and endogenous fructose metabolism, fructose glycation, and in vitro, animal, and human data. Fructose is elevated in several tissues of diabetic patients where the polyol pathway is active, reaching the same order of magnitude as glucose. It is plausible that the high reactivity of fructose, directly or via its metabolites, may contribute to the formation of intracellular AGEs and to vascular complications. The evidence, however, is still unconvincing. Two areas that have been overlooked so far and should be actively explored include the following: 1) enteral formation of fructose AGEs, generating an inflammatory response to the receptor for AGEs (which may explain the strong association between fructose consumption and asthma, chronic bronchitis, and arthritis); and 2) inactivation of hepatic AMP-activated protein kinase by a fructose-mediated increase in methylglyoxal flux (perpetuating lipogenesis, fatty liver, and insulin resistance). If proven correct, these mechanisms would put the fructose-mediated Maillard reaction in the limelight again as a contributing factor in chronic inflammatory diseases and MetS.

Samir S., David E. C., C Ronald K., (2016), Role of Dietary Fructose and Hepatic de novo Lipogenesis in Fatty Liver Disease. Dig Dis Sci. 2016 May;61(5):1282-93. doi: 10.1007/s10620-016-4054-0. https://pubmed.ncbi.nlm.nih.gov/26856717/

Abstract

Nonalcoholic fatty liver disease (NAFLD) is a liver manifestation of metabolic syndrome. Overconsumption of high-fat diet (HFD) and increased intake of sugar-sweetened beverages are major risk factors for development of NAFLD. Today the most commonly consumed sugar is high fructose corn syrup. Hepatic lipids may be derived from dietary intake, esterification of plasma free fatty acids (FFA) or hepatic de novo lipogenesis (DNL). A central abnormality in NAFLD is enhanced DNL. Hepatic DNL is increased in individuals with NAFLD, while the contribution of dietary fat and plasma FFA to hepatic lipids is not significantly altered. The importance of DNL in NAFLD is further established in mouse studies with knockout of genes involved in this process. Dietary fructose increases levels of enzymes involved in DNL even more strongly than HFD. Several properties of fructose metabolism make it particularly lipogenic. Fructose is absorbed via portal vein and delivered to the liver in much higher concentrations as compared to other tissues. Fructose increases protein levels of all DNL enzymes during its conversion into triglycerides. Additionally, fructose supports lipogenesis in the setting of insulin resistance as fructose does not require insulin for its metabolism, and it directly stimulates SREBP1c, a major transcriptional regulator of DNL. Fructose also leads to ATP depletion and suppression of mitochondrial fatty acid oxidation, resulting in increased production of reactive oxygen species. Furthermore, fructose promotes ER stress and uric acid formation, additional insulin independent pathways leading to DNL. In summary, fructose metabolism supports DNL more strongly than HFD and hepatic DNL is a central abnormality in NAFLD. Disrupting fructose metabolism in the liver may provide a new therapeutic option for the treatment of NAFLD.

João C. P. S., Cátia M., Fátima O. M., et al., (2019), Determining contributions of exogenous glucose and fructose to de novo fatty acid and glycerol synthesis in liver and adipose tissue. Metab Eng. 2019 Dec;56:69-76. doi: 10.1016/j.ymben.2019.08.018. Epub 2019 Aug 29. https://pubmed.ncbi.nlm.nih.gov/31473320/

Abstract

The de novo synthesis of triglyceride (TG) fatty acids (FA) and glycerol can be measured with stable isotope tracers. However, these methods typically do not inform the contribution of a given substrate to specific pathways on these synthetic processes. We integrated deuterated water (²H₂O) measurement of de novo lipogenesis (DNL) and glycerol-3-phosphate (GLY) synthesis from all substrates with a ¹³C nuclear magnetic resonance (NMR) method that quantifies TG FA and glycerol enrichment from a specific [U-¹³C]precursor. This allowed the [U-¹³C]precursor contribution to DNL and GLY to be estimated. We applied this method in mice to determine the contributions of fructose and glucose supplemented in the drinking water to DNL and GLY in liver, mesenteric adipose tissue (MAT) and subcutaneous adipose tissue (SCAT). In liver, fructose contributed significantly more to DNL of saturated fatty acids (SFA) and oleate as well as to GLY compared to glucose. Moreover, its contribution to SFA synthesis was significantly higher compared to that of oleate. MAT and SCAT had lower fractional rates of total DNL and GLY compared to liver and glucose was utilized more predominantly than fructose for TG synthesis in these tissues. This novel ²H₂O/¹³C integrated method revealed for the first time, tissue specific selection of substrates for DNL, particularly fructose in regard to glucose in liver. Also, this approach was able to resolve the distribution of specific FAs into the TG sn2 and sn1,3 sites. This stable isotope integrated approach yielded information so far uncovered by other lipidomic tools and should powerfully assist in other nutritional, pathological or environmental contexts.

Jiaoyue Z., Wen K., Pengfei X., et al., (2020), Impaired Fasting Glucose and Diabetes Are Related to Higher Risks of Complications and Mortality Among Patients With Coronavirus Disease 2019. Front Endocrinol (Lausanne). 2020; 11: 525. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7365851/

Abstract

Background: Diabetes correlates with poor prognosis in patients with COVID-19, but very few studies have evaluated whether impaired fasting glucose (IFG) is also a risk factor for the poor outcomes of patients with COVID-19. Here we aimed to examine the associations between IFG and diabetes at admission with risks of complications and mortality among patients with COVID-19.

Methods: In this multicenter retrospective cohort study, we enrolled 312 hospitalized patients with COVID-19 from 5 hospitals in Wuhan from Jan 1 to Mar 17, 2020. Clinical information, laboratory findings, complications, treatment regimens, and mortality status were collected. The associations between hyperglycemia and diabetes status at admission with primary composite end-point events (including mechanical ventilation, admission to intensive care unit, or death) were analyzed by Cox proportional hazards regression models.

Results: The median age of the patients was 57 years (interquartile range 38–66), and 172 (55%) were women. At the time of hospital admission, 84 (27%) had diabetes (and 36 were new-diagnosed), 62 (20%) had IFG, and 166 (53%) had normal fasting glucose (NFG) levels. Compared to patients with NFG, patients with IFG and diabetes developed more primary composite end-point events (9 [5%], 11 [18%], 26 [31%]), including receiving mechanical ventilation (5 [3%], 6 [10%], 21 [25%]), and death (4 [2%], 9 [15%], 20 [24%]). Multivariable Cox regression analyses showed diabetes was associated increased risks of primary composite end-point events (hazard ratio 3.53; 95% confidence interval 1.48–8.40) and mortality (6.25; 1.91–20.45), and IFG was associated with an increased risk of mortality (4.11; 1.15–14.74), after adjusting for age, sex, hospitals and comorbidities.

Conclusion: IFG and diabetes at admission were associated with higher risks of adverse outcomes among patients with COVID-19.

Emmanuelle L., Charlotte L., Cyrille F., et al., (2021), A Machine-Generated View of the Role of Blood Glucose Levels in the Severity of COVID-19. Front. Public Health, 28 July 2021 | https://doi.org/10.3389/fpubh.2021.695139 A Machine-Generated View of the R. https://www.frontiersin.org/articles/10.3389/fpubh.2021.695139/full

Abstract

SARS-CoV-2 started spreading toward the end of 2019 causing COVID-19, a disease that reached pandemic proportions among the human population within months. The reasons for the spectrum of differences in the severity of the disease across the population, and in particular why the disease affects more severely the aging population and those with specific preconditions are unclear. We developed machine learning models to mine 240,000 scientific articles openly accessible in the CORD-19 database, and constructed knowledge graphs to synthesize the extracted information and navigate the collective knowledge in an attempt to search for a potential common underlying reason for disease severity. The machine-driven framework we developed repeatedly pointed to elevated blood glucose as a key facilitator in the progression of COVID-19. Indeed, when we systematically retraced the steps of the SARS-CoV-2 infection, we found evidence linking elevated glucose to each major step of the life-cycle of the virus, progression of the disease, and presentation of symptoms. Specifically, elevations of glucose provide ideal conditions for the virus to evade and weaken the first level of the immune defense system in the lungs, gain access to deep alveolar cells, bind to the ACE2 receptor and enter the pulmonary cells, accelerate replication of the virus within cells increasing cell death and inducing an pulmonary inflammatory response, which overwhelms an already weakened innate immune system to trigger an avalanche of systemic infections, inflammation and cell damage, a cytokine storm and thrombotic events. We tested the feasibility of the hypothesis by manually reviewing the literature referenced by the machine-generated synthesis, reconstructing atomistically the virus at the surface of the pulmonary airways, and performing quantitative computational modeling of the effects of glucose levels on the infection process. We conclude that elevation in glucose levels can facilitate the progression of the disease through multiple mechanisms and can explain much of the differences in disease severity seen across the population. The study provides diagnostic considerations, new areas of research and potential treatments, and cautions on treatment strategies and critical care conditions that induce elevations in blood glucose levels.

Francisco J., M. Dolores L., b Francisco J., et al., (2020) Admission hyperglycaemia as a predictor of mortality in patients hospitalized with COVID-19 regardless of diabetes status: data from the Spanish SEMI-COVID-19 Registry. Ann Med. 2021; 53(1): 103–116. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7651248/

Abstract 

Background: Hyperglycaemia has emerged as an important risk factor for death in coronavirus disease 2019 (COVID-19). The aim of this study was to evaluate the association between blood glucose (BG) levels and in-hospital mortality in non-critically patients hospitalized with COVID-19.

Methods: This is a retrospective multi-centre study involving patients hospitalized in Spain. Patients were categorized into three groups according to admission BG levels: <140 mg/dL, 140–180 mg/dL and >180 mg/dL. The primary endpoint was all-cause in-hospital mortality.

Results: Of the 11,312 patients, only 2128 (18.9%) had diabetes and 2289 (20.4%) died during hospitalization. The in-hospital mortality rates were 15.7% (<140 mg/dL), 33.7% (140–180 mg) and 41.1% (>180 mg/dL), p<.001. The cumulative probability of mortality was significantly higher in patients with hyperglycaemia compared to patients with normoglycaemia (log rank, p<.001), independently of pre-existing diabetes. Hyperglycaemia (after adjusting for age, diabetes, hypertension and other confounding factors) was an independent risk factor of mortality (BG >180 mg/dL: HR 1.50; 95% confidence interval (CI): 1.31–1.73) (BG 140–180 mg/dL; HR 1.48; 95%CI: 1.29–1.70). Hyperglycaemia was also associated with requirement for mechanical ventilation, intensive care unit (ICU) admission and mortality.

Conclusions: Admission hyperglycaemia is a strong predictor of all-cause mortality in non-critically hospitalized COVID-19 patients regardless of prior history of diabetes.

Rachel J. P., Gerald I. S., (2020), Mechanistic Links between Obesity, Insulin, and Cancer. Trends Cancer. 2020 Feb; 6(2): 75–78. https://bit.ly/3MCwyeU

Abstract

Obesity and type 2 diabetes (T2D) increase the prevalence and worsen the prognosis of more than a dozen tumor types; however, the mechanism for this association remains hotly debated. Here we discuss a potential role for insulin as the key hormonal mediator of tumor metabolism and growth in obesity-associated insulin resistance.

Tetsuro T., Hiroshi K., Takehiro S., (2017), Association between hyperinsulinemia and increased risk of cancer death in nonobese and obese people: A population-based observational study. Int J Cancer. 2017 Jul 1;141(1):102-111. doi: 10.1002/ijc.30729. Epub 2017 Apr 22. https://pubmed.ncbi.nlm.nih.gov/28390156/

Abstract

Obesity, metabolic syndrome and type 2 diabetes are associated with cancer-related mortality. We assessed whether hyperinsulinemia is a risk factor for cancer death in nonobese people without diabetes. We conducted a prospective cohort study using data from the National Health and Nutrition Examination Survey 1999-2010 and followed up the participants until December 31, 2011. For the primary analysis of cancer mortality, we used Cox proportional hazard models to estimate hazard ratios (HRs) in the participants with hyperinsulinemia and those without. Hyperinsulinemia was defined as a fasting insulin level of ≥10 μU/mL. To identify causes of deaths, the International Classification of Diseases, Tenth Revision codes were used. This study included 9,778 participants aged 20 years or older without diabetes or a history of cancer: 6,718 nonobese participants (2,057 with hyperinsulinemia [30.6%]) and 3,060 obese participants (2,303 with hyperinsulinemia [75.3%]). A total of 99.9% completed follow-up. Among all study participants, cancer mortality was significantly higher in those with hyperinsulinemia than in those without hyperinsulinemia (adjusted HR 2.04, 95% CI 1.24-3.34, p = 0.005). Similarly, among nonobese participants, multivariable analysis showed that cancer mortality was significantly higher in those with hyperinsulinemia than in those without (adjusted HR 1.89, 95% CI 1.07-3.35, p = 0.02). Considering that nonobese people with hyperinsulinemia were at higher risk of cancer mortality than those without hyperinsulinemia, improvement of hyperinsulinemia may be an important approach for preventing cancer regardless of the presence or absence of obesity.

Biplab G., Sananda D., Tanaya D., (2018),  Chronic hyperglycemia mediated physiological alteration and metabolic distortion leads to organ dysfunction, infection, cancer progression and other pathophysiological consequences: An update on glucose toxicity. Biomed Pharmacother. 2018 Nov;107:306-328. doi: 10.1016/j.biopha.2018.07.157. Epub 2018 Aug 8. https://pubmed.ncbi.nlm.nih.gov/30098549/

Abstract

Chronic exposure of glucose rich environment creates several physiological and pathophysiological changes. There are several pathways by which hyperglycemia exacerbate its toxic effect on cells, tissues and organ systems. Hyperglycemia can induce oxidative stress, upsurge polyol pathway, activate protein kinase C (PKC), enhance hexosamine biosynthetic pathway (HBP), promote the formation of advanced glycation end-products (AGEs) and finally alters gene expressions. Prolonged hyperglycemic condition leads to severe diabetic condition by damaging the pancreatic β-cell and inducing insulin resistance. Numerous complications have been associated with diabetes, thus it has become a major health issue in the 21st century and has received serious attention. Dysregulation in the cardiovascular and reproductive systems along with nephropathy, retinopathy, neuropathy, diabetic foot ulcer may arise in the advanced stages of diabetes. High glucose level also encourages proliferation of cancer cells, development of osteoarthritis and potentiates a suitable environment for infections. This review culminates how elevated glucose level carries out its toxicity in cells, metabolic distortion along with organ dysfunction and elucidates the complications associated with chronic hyperglycemia.

Centers for Disease Control and Prevention (2021), The Surprising Truth About Prediabetes. https://www.cdc.gov/diabetes/basics/prediabetes.html

Soenens B.,(2021), Podcast – Het vette probleem van Amerika: hier komt de obesitasepidemie vandaan. https://podcast.standaard.be/episode/24027286

Burns C. M., Chen K, Kaszniak A. W., et al.,(2013), Higher serum glucose levels are associated with cerebral hypometabolism in Alzheimer regions. Neurology. 2013 Apr 23;80(17):1557-64. doi: 10.1212/WNL.0b013e31828f17de. Epub 2013 Mar 27. https://pubmed.ncbi.nlm.nih.gov/23535495/

Abstract

Objective: To investigate whether higher fasting serum glucose levels in cognitively normal, nondiabetic adults were associated with lower regional cerebral metabolic rate for glucose (rCMRgl) in brain regions preferentially affected by Alzheimer disease (AD).

Methods: This is a cross-sectional study of 124 cognitively normal persons aged 64 ± 6 years with a first-degree family history of AD, including 61 APOEε4 noncarriers and 63 carriers. An automated brain mapping algorithm characterized and compared correlations between higher fasting serum glucose levels and lower [(18)F]-fluorodeoxyglucose-PET rCMRgl measurements.

Results: As predicted, higher fasting serum glucose levels were significantly correlated with lower rCMRgl and were confined to the vicinity of brain regions preferentially affected by AD. A similar pattern of regional correlations occurred in the APOEε4 noncarriers and carriers.

Conclusions: Higher fasting serum glucose levels in cognitively normal, nondiabetic adults may be associated with AD pathophysiology. Findings suggest that the risk imparted by higher serum glucose levels may be independent of APOEε4 status. This study raises additional questions about the role of the metabolic process in the predisposition to AD and supports the possibility of targeting these processes in presymptomatic AD trials.

Willette A. A., Bendlin B. B., Starks E. J., et al., (2015), Association of Insulin Resistance With Cerebral Glucose Uptake in Late Middle-Aged Adults at Risk for Alzheimer Disease. JAMA Neurol. 2015 Sep;72(9):1013-20. doi: 10.1001/jamaneurol.2015.0613. https://pubmed.ncbi.nlm.nih.gov/26214150/

Abstract

Importance: Converging evidence suggests that Alzheimer disease (AD) involves insulin signaling impairment. Patients with AD and individuals at risk for AD show reduced glucose metabolism, as indexed by fludeoxyglucose F 18-labeled positron emission tomography (FDG-PET).

Objectives: To determine whether insulin resistance predicts AD-like global and regional glucose metabolism deficits in late middle-aged participants at risk for AD and to examine whether insulin resistance-predicted variation in regional glucose metabolism is associated with worse cognitive performance.

Design: setting, and participants: This population-based, cross-sectional study included 150 cognitively normal, late middle-aged (mean [SD] age, 60.7 [5.8] years) adults from the Wisconsin Registry for Alzheimer’s Prevention (WRAP) study, a general community sample enriched for AD parental history. Participants underwent cognitive testing, fasting blood draw, and FDG-PET at baseline. We used the homeostatic model assessment of peripheral insulin resistance (HOMA-IR). Regression analysis tested the statistical effect of HOMA-IR on global glucose metabolism. We used a voxelwise analysis to determine whether HOMA-IR predicted regional glucose metabolism. Finally, predicted variation in regional glucose metabolism was regressed against cognitive factors. Covariates included age, sex, body mass index, apolipoprotein E ε4 genotype, AD parental history status, and a reference region used to normalize regional uptake.

Main outcomes and measures: Regional glucose uptake determined using FDG-PET and neuropsychological factors.

Results: Higher HOMA-IR was associated with lower global glucose metabolism (β = -0.29; P < .01) and lower regional glucose metabolism across large portions of the frontal, lateral parietal, lateral temporal, and medial temporal lobes (P < .05, familywise error corrected). The association was especially robust in the left medial temporal lobe (R2 = 0.178). Lower glucose metabolism in the left medial temporal lobe predicted by HOMA-IR was significantly related to worse performance on the immediate memory (β = 0.317; t148 = 4.08; P < .001) and delayed memory (β = 0.305; t148 = 3.895; P < .001) factor scores.

Conclusions and relevance: Our results show that insulin resistance, a prevalent and increasingly common condition in developed countries, is associated with significantly lower regional cerebral glucose metabolism, which in turn may predict worse memory performance. Midlife may be a critical period for initiating treatments to lower peripheral insulin resistance to maintain neural metabolism and cognitive function.

Luchsinger J. A., Tang M., Shea S., (2004), Hyperinsulinemia and risk of Alzheimer disease. Neurology
. 2004 Oct 12;63(7):1187-92. doi: 10.1212/01.wnl.0000140292.04932.87. https://pubmed.ncbi.nlm.nih.gov/15477536/

Abstract

Objective: To explore the association between fasting insulin levels and dementia.

Methods: Fasting insulin levels were measured from frozen sera using solid-phase chemiluminescent enzyme immunoassay in a sample of elderly subjects chosen at random from a cohort of persons aged 65 years and older from northern Manhattan. Dementia was diagnosed using standard methods. Neuropsychiatric testing was available on all subjects at each follow-up interval.

Results: A total of 683 subjects without prevalent dementia were followed for 3,691 person-years and 149 persons developed dementia (137 Alzheimer disease [AD], 6 dementia associated with stroke, 6 other). The risk of AD doubled in the 39% of the sample with hyperinsulinemia (HR = 2.1; 95% CI: 1.5, 2.9) and was highest in people without diabetes. The HR relating presence of hyperinsulinemia or diabetes in 50% of our sample to AD was 2.2 (95% CI: 1.5, 3.1). The risk of AD attributable to the presence of hyperinsulinemia or diabetes was 39%. The HR of AD for the highest quartile of insulin compared to the lowest was 1.7 (95% CI: 1.0, 2.7; p for trend = 0.009). Hyperinsulinemia was also related to a significant decline in memory-related cognitive scores, but not to decline in other cognitive domains.

Conclusions: Hyperinsulinemia is associated with a higher risk of AD and decline in memory.

Joana A., Jianwen C., June S., (2016), Prevalence of Optimal Metabolic Health in American Adults: National Health and Nutrition Examination Survey 2009–2016. https://www.liebertpub.com/doi/10.1089/met.2018.0105

Abstract

Background: Several guidelines for cardiometabolic risk factor identification and management have been released in recent years, but there are no estimates of current prevalence of metabolic health among adults in the United States. We estimated the proportion of American adults with optimal cardiometabolic health, using different guidelines.

Methods: Data from the National Health and Nutrition Examination Survey 2009–2016 were analyzed (n = 8721). Using the most recent guidelines, metabolic health was defined as having optimal levels of waist circumference (WC <102/88 cm for men/women), glucose (fasting glucose <100 mg/dL and hemoglobin A1c <5.7%), blood pressure (systolic <120 and diastolic <80 mmHg), triglycerides (<150 mg/dL), and high-density lipoprotein cholesterol (≥40/50 mg/dL for men/women), and not taking any related medication.

Results: Changing from ATP III (Adult Treatment Panel III) guidelines to more recent cut points decreased the proportion of metabolically healthy Americans from 19.9% (95% confidence interval [CI]: 18.3–21.5) to 12.2% (95% CI: 10.9–13.6). Dropping WC from the definition increased the percentage of adults with optimal metabolic health to 17.6%. Characteristics associated with greater prevalence of metabolic health were female gender, youth, more education, never smoking, practicing vigorous physical activity, and low body mass index. Less than one-third of normal weight adults were metabolically healthy and the prevalence decreased to 8.0% and 0.5% in overweight and obese individuals, respectively.

Conclusions: Prevalence of metabolic health in American adults is alarmingly low, even in normal weight individuals. The large number of people not achieving optimal levels of risk factors, even in low-risk groups, has serious implications for public health.

Glucotypes reveal new patterns of glucose dysregulation. https://journals.plos.org/plosbiology/article?id=10.1371/journal.pbio.2005143

Abstract

Diabetes is an increasing problem worldwide; almost 30 million people, nearly 10% of the population, in the United States are diagnosed with diabetes. Another 84 million are prediabetic, and without intervention, up to 70% of these individuals may progress to type 2 diabetes. Current methods for quantifying blood glucose dysregulation in diabetes and prediabetes are limited by reliance on single-time-point measurements or on average measures of overall glycemia and neglect glucose dynamics. We have used continuous glucose monitoring (CGM) to evaluate the frequency with which individuals demonstrate elevations in postprandial glucose, the types of patterns, and how patterns vary between individuals given an identical nutrient challenge. Measurement of insulin resistance and secretion highlights the fact that the physiology underlying dysglycemia is highly variable between individuals. We developed an analytical framework that can group individuals according to specific patterns of glycemic responses called “glucotypes” that reveal heterogeneity, or subphenotypes, within traditional diagnostic categories of glucose regulation. Importantly, we found that even individuals considered normoglycemic by standard measures exhibit high glucose variability using CGM, with glucose levels reaching prediabetic and diabetic ranges 15% and 2% of the time, respectively. We thus show that glucose dysregulation, as characterized by CGM, is more prevalent and heterogeneous than previously thought and can affect individuals considered normoglycemic by standard measures, and specific patterns of glycemic responses reflect variable underlying physiology. The interindividual variability in glycemic responses to standardized meals also highlights the personal nature of glucose regulation. Through extensive phenotyping, we developed a model for identifying potential mechanisms of personal glucose dysregulation and built a webtool for visualizing a user-uploaded CGM profile and classifying individualized glucose patterns into glucotypes.

Li B., Zhang C., Zhan Y. (2018), Nonalcoholic Fatty Liver Disease Cirrhosis: A Review of Its Epidemiology, Risk Factors, Clinical Presentation, Diagnosis, Management, and Prognosis. Can J Gastroenterol Hepatol. 2018 Jul 2;2018:2784537. doi: 10.1155/2018/2784537. https://pubmed.ncbi.nlm.nih.gov/30065915/

Abstract

Cirrhosis is the common end stage of a number of chronic liver conditions and a significant cause of morbidity and mortality. With the growing epidemic of obesity and metabolic syndrome, nonalcoholic fatty liver disease (NAFLD) has become the most common cause of chronic liver disease worldwide and will become one of the leading causes of cirrhosis. Increased awareness and understanding of NAFLD cirrhosis are essential. To date, there has been no published systematic review on NAFLD cirrhosis. Thus, this article reviews recent studies on the epidemiology, risk factors, clinical presentation, diagnosis, management, and prognosis of NAFLD cirrhosis.

Younossi Z. M., Koenig A. B., Abdelatif D., et al., (2015), Global epidemiology of nonalcoholic fatty liver disease—Meta-analytic assessment of prevalence, incidence, and outcomes. Hepatology. 2016 Jul;64(1):73-84. doi: 10.1002/hep.28431. Epub 2016 Feb 22. https://pubmed.ncbi.nlm.nih.gov/26707365/

Abstract

Nonalcoholic fatty liver disease (NAFLD) is a major cause of liver disease worldwide. We estimated the global prevalence, incidence, progression, and outcomes of NAFLD and nonalcoholic steatohepatitis (NASH). PubMed/MEDLINE were searched from 1989 to 2015 for terms involving epidemiology and progression of NAFLD. Exclusions included selected groups (studies that exclusively enrolled morbidly obese or diabetics or pediatric) and no data on alcohol consumption or other liver diseases. Incidence of hepatocellular carcinoma (HCC), cirrhosis, overall mortality, and liver-related mortality were determined. NASH required histological diagnosis. All studies were reviewed by three independent investigators. Analysis was stratified by region, diagnostic technique, biopsy indication, and study population. We used random-effects models to provide point estimates (95% confidence interval [CI]) of prevalence, incidence, mortality and incidence rate ratios, and metaregression with subgroup analysis to account for heterogeneity. Of 729 studies, 86 were included with a sample size of 8,515,431 from 22 countries. Global prevalence of NAFLD is 25.24% (95% CI: 22.10-28.65) with highest prevalence in the Middle East and South America and lowest in Africa. Metabolic comorbidities associated with NAFLD included obesity (51.34%; 95% CI: 41.38-61.20), type 2 diabetes (22.51%; 95% CI: 17.92-27.89), hyperlipidemia (69.16%; 95% CI: 49.91-83.46%), hypertension (39.34%; 95% CI: 33.15-45.88), and metabolic syndrome (42.54%; 95% CI: 30.06-56.05). Fibrosis progression proportion, and mean annual rate of progression in NASH were 40.76% (95% CI: 34.69-47.13) and 0.09 (95% CI: 0.06-0.12). HCC incidence among NAFLD patients was 0.44 per 1,000 person-years (range, 0.29-0.66). Liver-specific mortality and overall mortality among NAFLD and NASH were 0.77 per 1,000 (range, 0.33-1.77) and 11.77 per 1,000 person-years (range, 7.10-19.53) and 15.44 per 1,000 (range, 11.72-20.34) and 25.56 per 1,000 person-years (range, 6.29-103.80). Incidence risk ratios for liver-specific and overall mortality for NAFLD were 1.94 (range, 1.28-2.92) and 1.05 (range, 0.70-1.56).

Conclusions: As the global epidemic of obesity fuels metabolic conditions, the clinical and economic burden of NAFLD will become enormous. (Hepatology 2016;64:73-84).

Standl E., Schnell O., Ceriello A., (2011), Postprandial hyperglycemia and glycemic variability: should we care? Diabetes Care
. 2011 May;34 Suppl 2(Suppl 2):S120-7. doi: 10.2337/dc11-s206. https://pubmed.ncbi.nlm.nih.gov/21525442/

Abstract

The aim of this article is to evaluate the pros and cons of a specific impact of postprandial hyperglycemia and glycemic variability on the–mainly cardiovascular (CV)–complications of diabetes, above and beyond the average blood glucose (BG) as measured by HbA(1c) or fasting plasma glucose (FPG). The strongest arguments in favor of this hypothesis come from impressive pathophysiological studies, also in the human situation. Measures of oxidative stress and endothelial dysfunction seem to be especially closely related to glucose peaks and even more so to fluctuating high and low glucose concentrations and can be restored to normal by preventing those glucose peaks or wide glucose excursions. The epidemiological evidence, which is more or less confined to postprandial hyperglycemia and postglucose load glycemia, is also rather compelling in favor of the hypothesis, although certainly not fully conclusive as there are also a number of conflicting results. The strongest cons are seen in the missing evidence as derived from randomized prospective intervention studies targeting postprandial hyperglycemia longer term, i.e., over several years, and seeking to reduce hard CV end points. In fact, several such intervention studies in men have recently failed to produce the intended beneficial outcome results. As this evidence by intervention is, however, key for the ultimate approval of a treatment concept in patients with diabetes, the current net balance of attained evidence is not in favor of the hypothesis here under debate, i.e., that we should care about postprandial hyperglycemia and glycemic variability. The absence of a uniformly accepted standard of how to estimate these parameters adds a further challenge to this whole debate.

Pérez-Tasigchana R. F., León-Muñoz L. M., Lopez-Garcia E., et al. (2017), Metabolic syndrome and insulin resistance are associated with frailty in older adults: a prospective cohort study. Age Ageing. 2017 Sep 1;46(5):807-812. doi: 10.1093/ageing/afx023. https://pubmed.ncbi.nlm.nih.gov/28338890/ 

Abstract

Sug S., Kim J. H., (2015), Glycemic Variability: How Do We Measure It and Why Is It Important? Diabetes Metab J. 2015 Aug;39(4):273-82. doi: 10.4093/dmj.2015.39.4.273. https://pubmed.ncbi.nlm.nih.gov/26301188/

Abstract

Chronic hyperglycemia is the primary risk factor for the development of complications in diabetes mellitus (DM); however, it is believed that frequent or large glucose fluctuations may independently contribute to diabetes-related complications. Postprandial spikes in blood glucose, as well as hypoglycemic events, are blamed for increased cardiovascular events in DM. Glycemic variability (GV) includes both of these events; hence, minimizing GV can prevent future cardiovascular events. Correcting GV emerges as a target to be pursued in clinical practice to safely reduce the mean blood glucose and to determine its direct effects on vascular complications in diabetes. Modern diabetes management modalities, including glucagon-related peptide-1-based therapy, newer insulins, modern insulin pumps and bariatric surgery, significantly reduce GV. However, defining GV remains a challenge primarily due to the difficulty of measuring it and the lack of consensus regarding the optimal approach for its management. The purpose of this manuscript was not only to review the most recent evidence on GV but also to help readers better understand the available measurement options and how the various definitions relate differently to the development of diabetic complications.

International Diabetes Federation, (2022), Diabetes around the world in 2021. https://diabetesatlas.org/

Voeding

Defries D., Taylor C.G., Appah P, et al., (2013), Consumption of buckwheat modulates the post-prandial response of selected gastrointestinal satiety hormones in individuals with type 2 diabetes mellitus. Metabolism. 2013 Jul;62(7):1021-31. doi: 10.1016/j.metabol.2013.01.021. https://pubmed.ncbi.nlm.nih.gov/23485142/

Abstract

Purpose: In healthy participants and those with diet-controlled type 2 diabetes (T2DM), to (1) compare the acute 3-hour post-prandial response of glucose, insulin and other gastrointestinal hormones known to influence food intake and glucose metabolism after consumption of a food product made from whole grain buckwheat flour versus rice flour; (2) determine the effect of daily consumption of a food product made from whole grain buckwheat flour on fasting glucose, lipids and apolipoproteins.

Methods: Healthy participants or those with T2DM consumed either buckwheat or rice crackers. Blood samples were collected at baseline and 15, 30, 45, 60, 120 and 180minutes after consumption. In a second phase of the study, participants consumed one serving of buckwheat crackers daily for 1week; fasting blood samples from day 1 and day 7 were analyzed.

Results: Post-prandial plasma glucagon-like peptide-1, glucose-dependent insulinotropic peptide and pancreatic polypeptide were altered after consuming buckwheat versus rice crackers. Interestingly, changes in these hormones did not lead to changes in post-prandial glucose, insulin or C-peptide concentrations. Significant correlations were observed between both fasting concentrations and post-prandial responses of several of the hormones examined. Interestingly, certain correlations were present only in the healthy participant group or the T2DM group. There was no effect of consuming buckwheat for one week on fasting glucose, lipids or apolipoproteins in either the healthy participants or those with T2DM.

Conclusions: Although the buckwheat cracker did not modify acute glycemia or insulinemia, it was sufficient to modulate gastrointestinal satiety hormones.

Natasha W., Feng Y., Ellen T. C., et al., (2021), Temporal Associations Among Body Mass Index, Fasting Insulin, and Systemic Inflammation. JAMA Netw Open. 2021 Mar 1;4(3):e211263. https://pubmed.ncbi.nlm.nih.gov/33710289/

Abstract

Importance: Obesity is associated with a number of noncommunicable chronic diseases and is purported to cause premature death.

Objective: To summarize evidence on the temporality of the association between higher body mass index (BMI) and 2 potential mediators: chronic inflammation and hyperinsulinemia.

Data sources: MEDLINE (1946 to August 20, 2019) and Embase (from 1974 to August 19, 2019) were searched, although only studies published in 2018 were included because of a high volume of results. The data analysis was conducted between January 2020 and October 2020.

Study selection and measures: Longitudinal studies and randomized clinical trials that measured fasting insulin level and/or an inflammation marker and BMI with at least 3 commensurate time points were selected.

Data extraction and synthesis: Slopes of these markers were calculated between time points and standardized. Standardized slopes were meta-regressed in later periods (period 2) with standardized slopes in earlier periods (period 1). Evidence-based items potentially indicating risk of bias were assessed.

Results: Of 1865 records, 60 eligible studies with 112 cohorts of 5603 participants were identified. Most standardized slopes were negative, meaning that participants in most studies experienced decreases in BMI, fasting insulin level, and C-reactive protein level. The association between period 1 fasting insulin level and period 2 BMI was positive and significant (β = 0.26; 95% CI, 0.13-0.38; I2 = 79%): for every unit of SD change in period 1 insulin level, there was an ensuing associated change in 0.26 units of SD in period 2 BMI. The association of period 1 fasting insulin level with period 2 BMI remained significant when period 1 C-reactive protein level was added to the model (β = 0.57; 95% CI, 0.27-0.86). In this bivariable model, period 1 C-reactive protein level was not significantly associated with period 2 BMI (β = -0.07; 95% CI, -0.42 to 0.29; I2 = 81%).

Conclusions and relevance: In this meta-analysis, the finding of temporal sequencing (in which changes in fasting insulin level precede changes in weight) is not consistent with the assertion that obesity causes noncommunicable chronic diseases and premature death by increasing levels of fasting insulin.

Jeff S., Erin E., Cassandra E., (2010), Low-Carbohydrate Diets Promote a More Favorable Body Composition Than Low-Fat Diets. February 2010 – Volume 32 – Issue 1 – p 42-47. https://bit.ly/39lnIEG

Abstract

A PRIMARY CONCERN WITH CONVENTIONAL WEIGHT LOSS APPROACHES IS THE LOSS OF LEAN BODY MASS THAT OCCURS WHEN FAT MASS IS DECREASED. CONSUMING MODERATE PROTEIN, WHILE RESTRICTING CARBOHYDRATE, ALLOWS FOR GREATER PRESERVATION OF LEAN BODY MASS. A LOW-CARBOHYDRATE DIET IN CONJUNCTION WITH PERIODIZED RESISTANCE TRAINING PROMOTES GREATER FAT LOSS WHILE PRESERVING LEAN BODY MASS AND PROMOTING ROBUST IMPROVEMENTS IN METABOLIC HEALTH.

#157 – AMA #22: Losing fat and gaining fat: the lessons of fat flux. https://peterattiamd.com/ama22/

Paula C., Shannon A., Laura L., (2014), Return of hunger following a relatively high carbohydrate breakfast is associated with earlier recorded glucose peak and nadir. Appetite. 2014 Sep;80:236-41. doi: 10.1016/j.appet.2014.04.031. Epub 2014 May 10. https://pubmed.ncbi.nlm.nih.gov/24819342/

Abstract

Patrick W., Sarah E B., Graham F., et al., (2021) Postprandial glycaemic dips predict appetite and energy intake in healthy individuals. Nat Metab. 2021 Apr;3(4):523-529. doi: 10.1038/s42255-021-00383-x. Epub 2021 Apr 12. https://bit.ly/39pQsMm

Abstract

Understanding how to modulate appetite in humans is key to developing successful weight loss interventions. Here, we showed that postprandial glucose dips 2-3 h after a meal are a better predictor of postprandial self-reported hunger and subsequent energy intake than peak glucose at 0-2 h and glucose incremental area under the blood glucose curve at 0-2 h. We explore the links among postprandial glucose, appetite and subsequent energy intake in 1,070 participants from a UK exploratory and US validation cohort, who consumed 8,624 standardized meals followed by 71,715 ad libitum meals, using continuous glucose monitors to record postprandial glycaemia. For participants eating each of the standardized meals, the average postprandial glucose dip at 2-3 h relative to baseline level predicted an increase in hunger at 2-3 h (r = 0.16, P < 0.001), shorter time until next meal (r = -0.14, P < 0.001), greater energy intake at 3-4 h (r = 0.19, P < 0.001) and greater energy intake at 24 h (r = 0.27, P < 0.001). Results were directionally consistent in the US validation cohort. These data provide a quantitative assessment of the relevance of postprandial glycaemia in appetite and energy intake modulation.

Kathleen A. P., Dongju S., Renata Belfort-D., et al., (2011), Circulating glucose levels modulate neural control of desire for high-calorie foods in humans. J Clin Invest. 2011 Oct;121(10):4161-9. doi: 10.1172/JCI57873. Epub 2011 Sep 19. https://pubmed.ncbi.nlm.nih.gov/21926468/

Abstract

Abstract

Postprandial hyperglycemia is an important therapeutic target for optimizing glycemic control and for mitigating the proatherogenic vascular environment characteristic of type 2 diabetes. Existing evidence indicates that the quantity and type of carbohydrate consumed influence blood glucose levels and that the total amount of carbohydrate consumed is the primary predictor of glycemic response (1). Previous studies have shown that premeal ingestion of whey protein, as well as altering the macronutrient composition of a meal, reduces postmeal glucose levels (2–4). There are limited data, however, regarding the effect of food order on postprandial glycemia in patients with type 2 diabetes (5). In this pilot study, we sought to examine the effect of food order, using a typical Western meal, incorporating vegetables, protein, and carbohydrate, on postprandial glucose and insulin excursions in overweight/obese adults with type 2 diabetes.

Kimiko N., Masaru S., Yumie T., et al., (2018),  Consuming Carbohydrates after Meat or Vegetables Lowers Postprandial Excursions of Glucose and Insulin in Nondiabetic Subjects. J Nutr Sci Vitaminol (Tokyo). 2018;64(5):316-320. doi: 10.3177/jnsv.64.316. https://pubmed.ncbi.nlm.nih.gov/30381620/

Abstract

Lesley N. L., Cynthia J. H., Sofia F. M., et al., (2018), The Effect of Added Peanut Butter on the Glycemic Response to a High-Glycemic Index Meal: A Pilot Study. J Am Coll Nutr. May-Jun 2019;38(4):351-357. doi: 10.1080/07315724.2018.1519404. Epub 2018 Nov 5. https://bit.ly/3NEq9RF

Abstract

Objective: The purpose of this pilot study was to determine whether supplementation of a high-glycemic index breakfast meal with peanut butter attenuates the glycemic response.

Methods: Sixteen healthy adults, aged 24.1 ± 3.5 years, reported in the morning to a nutrition assessment laboratory for two days of data collection, having fasted 8 to 12 hours. On day 1 (control), fasting blood glucose (BG) was measured using glucometers, then participants consumed two slices of white bread and 250 mL apple juice (60 g carbohydrate) within 15 minutes. BG was measured again at 15, 30, 60, 90, and 120 minutes after the first bite of the meal. On day 2, the protocol was repeated, except 32 g (2 tbsp) of peanut butter was added to the meal (treatment).

Results: The spike in BG was significantly lower on the treatment versus control day (35.8 ± 16.4 vs. 51.0 ± 20.8 mg/dL, respectively; p < 0.01), and BG was significantly lower on the treatment day at 15, 30, and 60 minutes post-meal consumption (p < 0.05).

Conclusions: This study indicates that supplementation with 32 g (2 tbsp) peanut butter attenuates the magnitude of BG spike and overall glycemic response to high-glycemic index meal and may be a practical, beneficial strategy to prevent undesirable elevations in BG.

David J. A. J., Cyril W. C. K., Andrea R. J., et al., (2006), Almonds Decrease Postprandial Glycemia, Insulinemia, and Oxidative Damage in Healthy Individuals. J Nutr. 2006 Dec;136(12):2987-92. doi: 10.1093/jn/136.12.2987. https://pubmed.ncbi.nlm.nih.gov/17116708/

Abstract

Strategies that decrease postprandial glucose excursions, including digestive enzyme inhibition, and low glycemic index diets result in lower diabetes incidence and coronary heart disease (CHD) risk, possibly through lower postprandial oxidative damage to lipids and proteins. We therefore assessed the effect of decreasing postprandial glucose excursions on measures of oxidative damage. Fifteen healthy subjects ate 2 bread control meals and 3 test meals: almonds and bread; parboiled rice; and instant mashed potatoes, balanced in carbohydrate, fat, and protein, using butter and cheese. We obtained blood samples at baseline and for 4 h postprandially. Glycemic indices for the rice (38 +/- 6) and almond meals (55 +/- 7) were less than for the potato meal (94 +/- 11) (P < 0.003), as were the postprandial areas under the insulin concentration time curve (P < 0.001). No postmeal treatment differences were seen in total antioxidant capacity. However, the serum protein thiol concentration increased following the almond meal (15 +/- 14 mmol/L), indicating less oxidative protein damage, and decreased after the control bread, rice, and potato meals (-10 +/- 8 mmol/L), when data from these 3 meals were pooled (P = 0.021). The change in protein thiols was also negatively related to the postprandial incremental peak glucose (r = -0.29, n = 60 observations, P = 0.026) and peak insulin responses (r = -0.26, n = 60 observations, P = 0.046). Therefore, lowering postprandial glucose excursions may decrease the risk of oxidative damage to proteins. Almonds are likely to lower this risk by decreasing the glycemic excursion and by providing antioxidants. These actions may relate to mechanisms by which nuts are associated with a decreased risk of CHD.

Lorenzo N., Alessandro M., Domenico T., (2019), Impact of Nutrient Type and Sequence on Glucose Tolerance: Physiological Insights and Therapeutic Implications. Front Endocrinol (Lausanne). 2019 Mar 8;10:144. doi: 10.3389/fendo.2019.00144. eCollection 2019. https://pubmed.ncbi.nlm.nih.gov/30906282/

Abstract

Bloedsuiker beter regelen met volgorde van eten. https://bit.ly/3H9q1qR

Quora (2019), How long does it take for an insulin spike to away after eating? https://www.quora.com/How-long-does-it-take-for-an-insulin-spike-to-away-after-eating

Vetrani C, et al., (2019), Fibre-enriched buckwheat pasta modifies blood glucose response compared to corn pasta in individuals with type 1 diabetes and celiac disease: Acute randomized controlled trial. Diabetes Res Clin Pract. 2019 Mar;149:156-162. doi: 10.1016/j.diabres.2019.02.013. Epub 2019 Feb 16. https://pubmed.ncbi.nlm.nih.gov/30779970/

Abstract 

Aim: People with type 1 diabetes and celiac disease (T1D&CD) have high blood glucose variability. Processed gluten-free foods have shown to induce a worse metabolic profile whereas naturally gluten-free foods may represent healthier options. On the other hand, dietary fibre has shown to reduce postprandial glycemic excursions in individuals with diabetes. Thus, we evaluated the acute effect of fibre-enriched buckwheat (FBP) and corn pasta (CP) on postprandial blood glucose response (PP-BGR).

Methods: Ten adult patients with T1D&CD consumed two meals with the same amount of carbohydrate while differing only for pasta type (FBP or CP) preceded by the same insulin bolus. Participants utilized continuous glucose monitoring (CGM) and data over 6 h after meal were analyzed.

Results: PP-BGR differed between the two meals, being significantly lower in the first period (0-3 h) after the CP than the FBP meal (iAUC: -38 ± 158 vs. 305 ± 209 mmol/L · 180 min, p = 0.040), whereas significantly higher in the second period (3-6 h) after the CP than the FBP meal (iAUC: 432 ± 153 vs. 308 ± 252 mmol/L · 180 min, p = 0.030). Overall, a less variable postprandial profile was observed after FBP than CP consumption.

Conclusions: In individuals with T1D&CD, the acute consumption of FBP induces significant differences in PP-BGR compared with CP that may be clinically relevant.

Glucosegoddess ,(2020), Instagram, The order you eat your food impacts glucose levels, even if it’s the exact same amount of food. https://www.instagram.com/p/CEUZGzIHCpY/?igshid=wptebfahdcbv

Tricò, D., Filice, E., Trifirò S., et al., (2016), Manipulating the sequence of food ingestion improves glycemic control in type 2 diabetic patients under free-living conditions. Nutr Diabetes. 2016 Aug; 6(8): e226. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5022147/

Abstract

Lipid and protein ingested before carbohydrate reduce postprandial hyperglycemia. We tested feasibility, safety and clinical efficacy of manipulating the sequence of nutrient ingestion in patients with type 2 diabetes (T2D). After a 4-week run-in, 17 T2D patients were randomized to either a control diet (CD) or to an experimental diet (ED) allowing the consumption of high-carbohydrate foods only after high-protein and high-fat foods at each main meal (lunch+dinner). Both diets were accurately followed and neutral on arterial blood pressure, plasma lipids and indices of hepatic and kidney function. After 8 weeks, in spite of a similar reduction of body weight (ED −1.9 95% confidence interval (−3.4/−0.4)kg, P<0.03; CD −2.0 (−3.6/−0.5)kg, P<0.02) and waist circumference (ED −2.9 (−4.3/−1.5)cm, P<0.002; CD −3.3 (−5.9/−0.7)cm, P<0.02), the ED only was associated with significant reductions of HbA1c (−0.3 (−0.50/−0.02)%, P<0.04), fasting plasma glucose (−1.0 (−1.8/−0.3)mmol l⁻¹, P<0.01), postprandial glucose excursions (lunch −1.8 (−3.2/−0.4)mmol l⁻¹, P<0.01; dinner: −1.0 (−1.9/−0.1)mmol l⁻¹, P<0.04) and other indices of glucose variability (s.d.: −0.5 (−0.7/−0.2)mmol l⁻¹, P<0.02; Coefficient of variation: −6.6 (−10.4/−2.7)%, P<0.02). When compared with the CD, the ED was associated with lower post-lunch glucose excursions (P<0.02) and lower glucose coefficients of variation (P<0.05). Manipulating the sequence of nutrient ingestion might reveal a rapid, feasible, economic and safe strategy for optimizing glucose control in T2D.

Wyatt P., Berry S. E., Finlayson G., et al., (2021), Postprandial glycaemic dips predict appetite and energy intake in healthy individuals. Nat Metab. Author manuscript; available in PMC 2021 Apr 25. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7610681/

Abstract

Understanding how to modulate appetite in humans is key to developing successful weight loss interventions. Here, we show that postprandial glucose dips 2-3h after a meal are a better predictor of postprandial self-reported hunger and subsequent energy intake than peak glucose 0-2h and glucose iAUC 0-2h. We explore the link between postprandial glucose, appetite, and subsequent energy intake in 1070 participants from a UK discovery and US validation cohort, consuming 8,624 standardised meals followed by 71,715 ad libitum meals, using continuous glucose monitors to record postprandial glycemia. For participants eating each of the standardised meals, the average postprandial glucose dip 2-3h relative to baseline level predicts an increase in hunger 2-3h (r=0.16 P=<0.001), shorter time until next meal (r=-0.14 P=<0.001), greater energy intake 3-4h (r=0.19 P=<0.001) and greater energy intake 24h (r=0.27 P<=0.001). Results aredirectionally consistent in the US validation cohort. These data provide a quantitative assessment of the relevance of postprandial glycemia in appetite and energy intake modulation.

Beweging

Biology and Medicine, (2015), Exercise Causes Muscle GLUT4 Translocation in an Insulin-Independent Manner. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.1020.9266&rep=rep1&type=pdf

Abstract

Glucose uptake in skeletal muscle is dependent on the translocation of GLUT4 glucose transporters to the
plasma membrane. The most important stimulators of glucose transport in skeletal muscle are insulin and exercise.
Glucose uptake in skeletal muscle during exercise induces acceleration of many processes compared to the resting
state. The scientific literature does not underline the role played by muscle contraction to increase glucose uptake
with insulin-independent mechanisms. Search on Pub Med (May 05, 2015) using the key words “contraction and
glucose uptake and muscle” gives 717 reports, while a search using the key words “insulin and glucose uptake and
muscle” cites 5676 publications. The present paper describes the role of exercise in the muscle glucose uptake.
Contraction of muscle induces GLUT4 translocation in the absence of insulin. There are different intracellular “pools”
of GLUT4, one stimulated by insulin and another one stimulated by exercise. The roles exerted by AMPK, AICAR,
calcium, NO, glycogen and hypoxia in the glucose uptake during exercise are emphasized. The effects of these
phenomena on human wellness are reported.

Colberg S. R., Zarrabi L., Bennington L., et al. ((2009), Postprandial Walking is Better for Lowering the Glycemic Effect of Dinner than Pre-Dinner Exercise in Type 2 Diabetic Individuals. J Am Med Dir Assoc. 2009 Jul;10(6):394-7. doi: 10.1016/j.jamda.2009.03.015. Epub 2009 May 21. https://pubmed.ncbi.nlm.nih.gov/19560716/

Abstract

Objectives: In prior studies of exercise done before or after breakfast and lunch, postprandial activity generally reduces glycemia more than pre-meal. This study sought to examine the effects of exercise before or after an evening meal.

Design: Examined the differing effects of a single bout of pre- or postprandial moderate exercise or no exercise on the glycemic response to an evening (dinner) meal in individuals with type 2 diabetes.

Setting: Community-dwelling participants tested at a research university in Virginia.

Participants: Twelve men and women subjects (mean age of 61.4+/-2.7 years) with type 2 diabetes treated with diet and/or oral medications.

Intervention: Three trials conducted on separate days consisting of a rest day when subjects consumed a standardized dinner with a moderate glycemic effect and 2 exercise days when they undertook 20 minutes of self-paced treadmill walking immediately before or 15 to 20 minutes after eating.

Measurements: Blood samples taken every 30 minutes over a 4-hour period and later assayed for plasma glucose; from these data both absolute and relative changes in glucose levels were determined, as well as the total glucose area under the curve (AUC) of the 4-hour testing period. Initial samples were additionally assayed for glycated hemoglobin and lipid levels.

Results: Twenty minutes of self-paced walking done shortly after meal consumption resulted in lower plasma glucose levels at the end of exercise compared to values at the same time point when subjects had walked pre-dinner. Total glucose AUC over 4-hours was not significantly different among trials.

Conclusion: Postprandial walking may be more effective at lowering the glycemic impact of the evening meal in individuals with type 2 diabetes compared with pre-meal or no exercise and may be an effective means to blunt postprandial glycemic excursions.

Erik A. R., Mark H., (2013), Exercise, GLUT4, and Skeletal Muscle Glucose Uptake. Physiol Rev. 2013 Jul;93(3):993-1017. doi: 10.1152/physrev.00038.2012. https://pubmed.ncbi.nlm.nih.gov/23899560/

Abstract

Andrew B., Gabriel Z., Claudio B., et al., (2018), The Effects of Postprandial Exercise on Glucose Control in Individuals with Type 2 Diabetes: A Systematic Review. Sports Med
. 2018 Jun;48(6):1479-1491. doi: 10.1007/s40279-018-0864-x. https://pubmed.ncbi.nlm.nih.gov/29396781/

Abstract

Background: Regulation of postprandial hyperglycemia is a major concern for individuals with type 2 diabetes. Exercise can reduce postprandial hyperglycemia by increasing contraction-mediated glucose uptake. However, there is no consensus with which to develop guidelines for optimal postprandial exercise timing and prescription.

Objective: The current systematic review was conducted to consolidate the literature surrounding the effects of postprandial exercise on glucose regulation in individuals with type 2 diabetes.

Methods: Electronic databases were searched on 17 February 2017. Inclusion criteria were: (1) trial was a randomized crossover trial; (2) subjects were diagnosed with type 2 diabetes; (3) a standardized meal was given; (4) exercise was initiated within three hours of the meal; (5) subjects were not treated with insulin.

Results: Twelve studies met the inclusion criteria, involving 135 participants (108 males, 20 females, seven unknown). The included studies varied greatly in their timing, duration, intensity, modality, and glucose measures. Postprandial aerobic exercise (11 studies) decreased short-term glucose area under the curve by 3.4-26.6% and 24-h prevalence of hyperglycemia by 11.9-65%. Resistance exercise (two studies) decreased the short-term glucose area under the curve by 30% and 24-h prevalence of hyperglycemia by 35%.

Conclusion: Postprandial exercise may be an effective way to improve glucose control in individuals with type 2 diabetes. The most consistent benefits were seen in long-duration (≥ 45 min), moderate-intensity aerobic exercise. Resistance training also appears to be an effective modality. We recommend that individuals with type 2 diabetes focus on increasing energy expenditure after the largest meal of the day. More research is needed in this area to confirm the results of this systematic review and to provide clinicians with specific exercise recommendations.

Lance B., (2011), Exercise and Insulin Resistance.October 2011 – Volume 33 – Issue 5 – p 44-47. https://bit.ly/3O4sxkr

Abstract

G. Perseghin., T. B. Price., K. F. Petersen., et al., (1996), Increased Glucose Transport–Phosphorylation and Muscle Glycogen Synthesis after Exercise Training in Insulin-Resistant Subjects. N Engl J Med. 1996 Oct 31;335(18):1357-62. doi: 10.1056/NEJM199610313351804. https://bit.ly/3tr8ip2

Abstract

Background: Insulin resistance in the offspring of parents with non-insulin-dependent diabetes mellitus (NIDDM) is the best predictor of development of the disease and probably plays an important part in its pathogenesis. We studied the mechanism and degree to which exercise training improves insulin sensitivity in these subjects.

Methods: Ten adult children of parents with NIDDM and eight normal subjects were studied before starting an aerobic exercise-training program, after one session of exercise, and after six weeks of exercise. Insulin sensitivity was measured by the hyperglycemic-hyperinsulinemic clamp technique combined with indirect calorimetry, and the rate of glycogen synthesis in muscle and the intramuscular glucose-6-phosphate concentration were measured by carbon-13 and phosphorus-31 nuclear magnetic resonance spectroscopy, respectively.

Results: During the base-line study, the mean (+/-SE) rate of muscle glycogen synthesis was 63 +/- 9 percent lower in the offspring of diabetic parents than in the normal subjects (P < 0.001). The mean value increased 69 +/- 10 percent (P = 0.04) and 62 +/- 11 percent (P = 0.04) after the first exercise session and 102 +/- 11 percent (P = 0.02) and 97 +/- 9 percent (P = 0.008) after six weeks of exercise training in the offspring and the normal subjects, respectively. The increment in glucose-6-phosphate during hyperglycemic-hyperinsulinemic clamping was lower in the offspring than in the normal subjects (0.039 +/- 0.013 vs. 0.089 +/- 0.009 mmol per liter, P = 0.005), reflecting reduced glucose transport-phosphorylation, but this increment was normal in the offspring after the first exercise session and after exercise training. Basal and stimulated insulin secretion was higher in the offspring than the normal subjects and was not altered by the exercise training program.

Conclusions: Exercise increases insulin sensitivity in both normal subjects and the insulin-resistant offspring of diabetic parents because of a twofold increase in insulin-stimulated glycogen synthesis in muscle, due to an increase in insulin-stimulated glucose transport-phosphorylation.

Timothy D. H., Nathan C. W., Andrea M., et al., (2015), Postdinner resistance exercise improves postprandial risk factors more effectively than predinner resistance exercise in patients with type 2 diabetes. J Appl Physiol (1985). 2015 Mar 1;118(5):624-34. doi: 10.1152/japplphysiol.00917.2014. Epub 2014 Dec 24. https://bit.ly/3myfMmq

Abstract

Abnormally elevated postprandial glucose and triacylglycerol (TAG) concentrations are risk factors for cardiovascular disease in type 2 diabetes. The most effective time to exercise to lower postprandial glucose and TAG concentrations is unknown. Thus the aim of this study was to determine what time is more effective, either pre- or postdinner resistance exercise (RE), at improving postprandial risk factors in patients with type 2 diabetes. Thirteen obese patients with type 2 diabetes completed three trials in a random order in which they consumed a dinner meal with 1) no RE (NoRE), 2) predinner RE (RE → M), and 3) postdinner RE beginning 45 min after dinner (M → RE). Clinical outcome measures included postprandial glucose and TAG concentrations. In addition, postprandial acetaminophen (gastric emptying), endocrine responses, free fatty acids, and β-cell function (mathematical modeling) were measured to determine whether these factors were related to changes in glucose and TAG. The TAG incremental area under the curve (iAUC) was ∼92% lower (P ≤ 0.02) during M → RE compared with NoRE and RE → M, an effect due in part to lower very-low-density lipoprotein-1 TAG concentrations. The glucose iAUC was reduced (P = 0.02) by ∼18 and 30% during the RE → M and M → RE trials, respectively, compared with NoRE, with no difference between RE trials. RE → M and M → RE reduced the insulin iAUC by 35 and 48%, respectively, compared with NoRE (P < 0.01). The glucagon-like peptide-1 iAUC was ∼50% lower (P ≤ 0.02) during M → RE compared with NoRE and RE → M. Given that predinner RE only improves postprandial glucose concentrations, whereas postdinner RE improves both postprandial glucose and TAG concentrations, postdinner RE may lower the risk of cardiovascular disease more effectively.

Gill J. M.R., Herd S. L., Hardmann A. E., (2010), Moderate exercise and post-prandial metabolism: issues of dose-response. J Sports Sci. 2002 Dec;20(12):961-7. doi: 10.1080/026404102321011715. https://pubmed.ncbi.nlm.nih.gov/12477005/

Abstract

In this study, we examined the effects of 1 and 2 h of brisk walking on post-prandial metabolism. Eleven pre-menopausal women participated in three oral fat tolerance tests with different pre-conditions: control (no exercise), 1 h walk (1 h of walking at 50% maximal oxygen uptake, VO2max, on the day before) and 2 h walk (2 h walking at 50% VO2max on the day before). Venous blood samples were taken in the fasted state and for 6 h after ingestion of a high-fat mixed meal. Compared with the control trial, the 1 h walk reduced post-prandial lipaemia by a mean of 9.3%, whereas the 2 h walk reduced it by 22.8% (P < 0.01 for trend). Similarly, the 2 h walk reduced the post-prandial insulin response to a greater extent than the 1 h walk (17.3 vs 7.6%; P < 0.05 for trend). The results demonstrate that the beneficial effects of exercise on post-prandial metabolism are related to the duration and, therefore, the energy expenditure of the exercise session.

Jensen J., Rustad P.I., Kolnes A.J., Lai Y. (2011), The Role of Skeletal Muscle Glycogen Breakdown for Regulation of Insulin Sensitivity by Exercise. Front Physiol. 2011; 2: 112. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3248697/

Abstract

Glycogen is the storage form of carbohydrates in mammals. In humans the majority of glycogen is stored in skeletal muscles (∼500 g) and the liver (∼100 g). Food is supplied in larger meals, but the blood glucose concentration has to be kept within narrow limits to survive and stay healthy. Therefore, the body has to cope with periods of excess carbohydrates and periods without supplementation. Healthy persons remove blood glucose rapidly when glucose is in excess, but insulin-stimulated glucose disposal is reduced in insulin resistant and type 2 diabetic subjects. During a hyperinsulinemic euglycemic clamp, 70–90% of glucose disposal will be stored as muscle glycogen in healthy subjects. The glycogen stores in skeletal muscles are limited because an efficient feedback-mediated inhibition of glycogen synthase prevents accumulation. De novo lipid synthesis can contribute to glucose disposal when glycogen stores are filled. Exercise physiologists normally consider glycogen’s main function as energy substrate. Glycogen is the main energy substrate during exercise intensity above 70% of maximal oxygen uptake (V_o2max⁡) and fatigue develops when the glycogen stores are depleted in the active muscles. After exercise, the rate of glycogen synthesis is increased to replete glycogen stores, and blood glucose is the substrate. Indeed insulin-stimulated glucose uptake and glycogen synthesis is elevated after exercise, which, from an evolutional point of view, will favor glycogen repletion and preparation for new “fight or flight” events. In the modern society, the reduced glycogen stores in skeletal muscles after exercise allows carbohydrates to be stored as muscle glycogen and prevents that glucose is channeled to de novo lipid synthesis, which over time will causes ectopic fat accumulation and insulin resistance. The reduction of skeletal muscle glycogen after exercise allows a healthy storage of carbohydrates after meals and prevents development of type 2 diabetes.

Reynolds A. N., Mann J. I., Williams S., et al. (2016), Advice to walk after meals is more effective for lowering postprandial glycaemia in type 2 diabetes mellitus than advice that does not specify timing: a randomised crossover study. Diabetologia
. 2016 Dec;59(12):2572-2578. https://pubmed.ncbi.nlm.nih.gov/27747394/

Abstract

Aims/hypothesis: Regular physical activity is a cornerstone of diabetes management. We conducted a study to evaluate whether specifying the timing of walking in relation to meals enhances the benefits conferred by current physical activity guidelines.

Methods: A total of 41 adults with type 2 diabetes mellitus (mean ± SD age 60 ± 9.9 years; mean diabetes duration 10 years) participated in this randomised, crossover study. Randomisation was by a computer-generated protocol. For periods of 2 weeks, advice to walk 30 min each day was compared with advice to walk for 10 min after each main meal. Both sets of advice met current physical activity guidelines for people with type 2 diabetes mellitus. Physical activity was measured by accelerometry over the full intervention, and glycaemia was measured using continuous glucose monitoring in 5 min intervals over 7 days. The primary outcome of postprandial glycaemia was assessed during the 3 h after a meal by the incremental area under the blood glucose curve (iAUC).

Results: The iAUC was significantly lower when participants walked after meals compared with on a single daily occasion (ratio of geometric means 0.88, 95% CI 0.78, 0.99). The improvement was particularly striking after the evening meal (0.78, 95% CI 0.67, 0.91) when the most carbohydrate was consumed and sedentary behaviours were highest. One participant died during the 30 day washout period between interventions, but participation in this study was not judged to contribute to this unfortunate event.

Conclusions/interpretation: The benefits relating to physical activity following meals suggest that current guidelines should be amended to specify post-meal activity, particularly when meals contain a substantial amount of carbohydrate.

Trial registration: ACTRN12613000832774 FUNDING: : This study was supported by grants from the University of Otago and the New Zealand Artificial Limb Service. Glycated albumin reagents were provided by Asahi Kasei.

Alcohol 

Robert H. L., (2013), Fructose: It’s “Alcohol Without the Buzz”1,2,3. Adv Nutr. 2013 Mar 1;4(2):226-35. doi: 10.3945/an.112.002998. https://pubmed.ncbi.nlm.nih.gov/23493539/

Abstract

What do the Atkins Diet and the traditional Japanese diet have in common? The Atkins Diet is low in carbohydrate and usually high in fat; the Japanese diet is high in carbohydrate and usually low in fat. Yet both work to promote weight loss. One commonality of both diets is that they both eliminate the monosaccharide fructose. Sucrose (table sugar) and its synthetic sister high fructose corn syrup consist of 2 molecules, glucose and fructose. Glucose is the molecule that when polymerized forms starch, which has a high glycemic index, generates an insulin response, and is not particularly sweet. Fructose is found in fruit, does not generate an insulin response, and is very sweet. Fructose consumption has increased worldwide, paralleling the obesity and chronic metabolic disease pandemic. Sugar (i.e., fructose-containing mixtures) has been vilified by nutritionists for ages as a source of “empty calories,” no different from any other empty calorie. However, fructose is unlike glucose. In the hypercaloric glycogen-replete state, intermediary metabolites from fructose metabolism overwhelm hepatic mitochondrial capacity, which promotes de novo lipogenesis and leads to hepatic insulin resistance, which drives chronic metabolic disease. Fructose also promotes reactive oxygen species formation, which leads to cellular dysfunction and aging, and promotes changes in the brain’s reward system, which drives excessive consumption. Thus, fructose can exert detrimental health effects beyond its calories and in ways that mimic those of ethanol, its metabolic cousin. Indeed, the only distinction is that because fructose is not metabolized in the central nervous system, it does not exert the acute neuronal depression experienced by those imbibing ethanol. These metabolic and hedonic analogies argue that fructose should be thought of as “alcohol without the buzz.”

Scientists explain how alcohol causes hypoglycemia (too low blood sugar) https://www.medicalnewstoday.com/releases/93141#1

Staff DTN, (2018), Can Alcohol Cause Low Blood Sugar. https://diabetestalk.net/blood-sugar/can-alcohol-cause-low-blood-sugar

Slaap en stress

Spiegel K., Leproult R. Van Cauter E., (1999), Impact of sleep debt on metabolic and endocrine function. Lancet. 1999 Oct 23;354(9188):1435-9. doi: 10.1016/S0140-6736(99)01376-8. https://pubmed.ncbi.nlm.nih.gov/10543671/

Abstract

Background: Chronic sleep debt is becoming increasingly common and affects millions of people in more-developed countries. Sleep debt is currently believed to have no adverse effect on health. We investigated the effect of sleep debt on metabolic and endocrine functions.

Methods: We assessed carbohydrate metabolism, thyrotropic function, activity of the hypothalamo-pituitary-adrenal axis, and sympathovagal balance in 11 young men after time in bed had been restricted to 4 h per night for 6 nights. We compared the sleep-debt condition with measurements taken at the end of a sleep-recovery period when participants were allowed 12 h in bed per night for 6 nights.

Findings: Glucose tolerance was lower in the sleep-debt condition than in the fully rested condition (p<0.02), as were thyrotropin concentrations (p<0.01). Evening cortisol concentrations were raised (p=0.0001) and activity of the sympathetic nervous system was increased in the sleep-debt condition (p<0.02).

Interpretation: Sleep debt has a harmful impact on carbohydrate metabolism and endocrine function. The effects are similar to those seen in normal ageing and, therefore, sleep debt may increase the severity of age-related chronic disorders.

Hancox R. J., Landhuis C. E., (2011), Association between sleep duration and haemoglobin A1c in young adults. J Epidemiol Community Health. 2012 Oct;66(10):957-61. doi: 10.1136/jech-2011-200217. Epub 2011 Nov 7. https://pubmed.ncbi.nlm.nih.gov/22068028/

Abstract

Background: Epidemiological and experimental evidence suggests that inadequate sleep can cause both obesity and impaired glucose tolerance. Short sleep duration in childhood appears to have a greater impact on the risk for adult obesity than adult sleep duration. The long-term effects of childhood sleep on glucose metabolism have not been investigated. The authors assessed the associations between childhood and adult sleep duration and adult glycosylated haemoglobin (HbA(1c)) levels.

Methods: An unselected cohort of 1037 individuals, born in Dunedin, New Zealand, between 1972 and 1973. Parent reports of times in bed at ages 5, 7, 9 and 11 were used to estimate childhood sleep duration. Adult sleep duration was estimated from self-reported times in bed at age 32. HbA(1c) levels were measured at age 32. Pregnant women and participants with diabetes were excluded from the analyses.

Results: Childhood sleep duration did not predict adult HbA(1c). However, less time spent in bed at age 32 was associated with higher levels of HbA(1c) (p=0.002) and an increased risk of prediabetes (p=0.015). The inverse association between adult sleep times and HbA(1c) was independent of body mass index, smoking, socioeconomic status, shift work and symptoms of obstructive sleep apnoea.

Conclusions: Short sleep duration is associated with higher levels of HbA(1c) and an increased risk of prediabetes in young adults. The findings suggest that inadequate sleep impairs glucose control in the short term and may increase the risk for long-term health problems.

Obstructive Sleep Apnea and Postprandial Glucose Differences in Type 2 Diabetes Mellitus. https://www.atsjournals.org/doi/abs/10.1164/ajrccm-conference.2020.201.1_MeetingAbstracts.A2525

Sara L. T., Tara L. Q., Jonathan B., et al., (2016), Variations in Daily Sleep Quality and Type 1 Diabetes Management in Late Adolescents. J Pediatr Psychol. 2016 Jul;41(6):661-9. doi: 10.1093/jpepsy/jsw010. Epub 2016 Mar 19. https://pubmed.ncbi.nlm.nih.gov/26994852/

Abstract

OBJECTIVE : To determine how between- and within-person variability in perceived sleep quality were associated with adolescent diabetes management.

Methods: A total of 236 older adolescents with type 1 diabetes reported daily for 2 weeks on sleep quality, self-regulatory failures, frequency of blood glucose (BG) checks, and BG values. Average, inconsistent, and daily deviations in sleep quality were examined. RESULTS : Hierarchical linear models indicated that poorer average and worse daily perceived sleep quality (compared with one’s average) was each associated with more self-regulatory failures. Sleep quality was not associated with frequency of BG checking. Poorer average sleep quality was related to greater risk of high BG. Furthermore, inconsistent and daily deviations in sleep quality interacted to predict higher BG, with more consistent sleepers benefitting more from a night of high-quality sleep. CONCLUSIONS : Good, consistent sleep quality during late adolescence may benefit diabetes management by reducing self-regulatory failures and risk of high BG.

Karine S., Kristen K., Rachel L., et al., (2005), Sleep loss: a novel risk factor for insulin resistance and Type 2 diabetes.  J Appl Physiol (1985). 2005 Nov;99(5):2008-19. doi: 10.1152/japplphysiol.00660.2005. https://pubmed.ncbi.nlm.nih.gov/16227462/

Abstract

Abstract

Objective: To examine the long-term relationship between sleep duration and type 2 diabetes or impaired glucose tolerance (IGT).

Methods: Body composition measurements and self-reported sleep duration were determined in a longitudinal sample of 276 individuals aged 21 to 64 years followed for a mean of 6 years. Risk factors of type 2 diabetes/IGT over the follow-up were determined and relative risks (RRs) calculated for the development of type 2 diabetes/IGT by sleep duration group.

Results: Independent risk factors of type 2 diabetes/IGT over the follow-up included age, obesity, sleep duration, and glucose/insulin homeostasis indicators. Using adults with 7-8h of sleep as a reference, the adjusted RR for the development of type 2 diabetes/IGT was 2.78 (1.61-4.12) for those with 6h of sleep and 2.54 (1.42-3.53) for those with 9h of sleep. These elevated RRs remained significant after adjustment for body mass index, waist circumference or percent body fat.

Conclusion: Short and long sleeping times are associated with a higher risk of developing type 2 diabetes/IGT, independent of several covariates. These results suggest that sleep duration may represent a novel risk factor for type 2 diabetes/IGT.

James E. G., Steven B. H., Bernadette B. S., et al., (2007) Duration as a Risk Factor for Diabetes Incidence in a Large US Sample. Sleep. 2007 Dec;30(12):1667-73. doi: 10.1093/sleep/30.12.1667. https://bit.ly/3HipLG3

Abstract

Study objectives: To explore the relationship between sleep duration and diabetes incidence over an 8- to 10-year follow-up period in data from the First National Health and Nutrition Examination Survey (NHANES I). We hypothesized that prolonged short sleep duration is associated with diabetes and that obesity and hypertension act as partial mediators of this relationship. The increased load on the pancreas from insulin resistance induced by chronically short sleep durations can, over time, compromise beta-cell function and lead to type 2 diabetes. No plausible mechanism has been identified by which long sleep duration could lead to diabetes.

Design: Multivariate longitudinal analyses of the NHANES I using logistic regression models.

Setting: Probability sample (n=8992) of the noninstitutionalized population of the United States between 1982 and 1992.

Participants: Subjects between the ages of 32 and 86 years.

Measurements and results: Between 1982 and 1992, 4.8% of the sample (n=430) were determined by physician diagnosis, hospital record, or cause of death to be incident cases of diabetes. Subjects with sleep durations of 5 or fewer hours (odds ratio = 1.47, 95% confidence interval 1.03-2.09) and subjects with sleep durations of 9 or more hours (odds ratio = 1.52, 95% confidence interval 1.06-2.18) were significantly more likely to have incident diabetes over the follow-up period after controlling for covariates.

Conclusions: Short sleep duration could be a significant risk factor for diabetes. The association between long sleep duration and diabetes incidence is more likely to be due to some unmeasured confounder such as poor sleep quality.

Jean-Philippe C., Jean-Pierre D., Claude B., et al., (2009), Sleep duration as a risk factor for the development of type 2 diabetes or impaired glucose tolerance: Analyses of the Quebec Family Study. Sleep Med. 2009 Sep;10(8):919-24. doi: 10.1016/j.sleep.2008.09.016. Epub 2009 Mar 29. https://pubmed.ncbi.nlm.nih.gov/19332380/

Objective: To examine the long-term relationship between sleep duration and type 2 diabetes or impaired glucose tolerance (IGT).

Methods: Body composition measurements and self-reported sleep duration were determined in a longitudinal sample of 276 individuals aged 21 to 64 years followed for a mean of 6 years. Risk factors of type 2 diabetes/IGT over the follow-up were determined and relative risks (RRs) calculated for the development of type 2 diabetes/IGT by sleep duration group.

Results: Independent risk factors of type 2 diabetes/IGT over the follow-up included age, obesity, sleep duration, and glucose/insulin homeostasis indicators. Using adults with 7-8h of sleep as a reference, the adjusted RR for the development of type 2 diabetes/IGT was 2.78 (1.61-4.12) for those with 6h of sleep and 2.54 (1.42-3.53) for those with 9h of sleep. These elevated RRs remained significant after adjustment for body mass index, waist circumference or percent body fat.

Conclusion: Short and long sleeping times are associated with a higher risk of developing type 2 diabetes/IGT, independent of several covariates. These results suggest that sleep duration may represent a novel risk factor for type 2 diabetes/IGT.

Mark T. U. B., Luiz M., (2010), Diabetes and sleep: A complex cause-and-effect relationship. Diabetes Res Clin Pract. 2011 Feb;91(2):129-37. doi: 10.1016/j.diabres.2010.07.011. https://pubmed.ncbi.nlm.nih.gov/20810183/

PubMed, (2000),, Effect of sleep deprivation on insulin sensitivity and cortisol concentration in healthy subjects. Diabetes Nutr Metab. 2000 Apr;13(2):80-3. https://bit.ly/3zwh49c

Abstract

The objective of this study was to assess insulin sensitivity and cortisol concentration in healthy subjects with 24-hr sleep deprivation. A randomised, single-blind, controlled clinical trial was performed in 28 healthy subjects. Fourteen individuals were studied before and after 24-hr sleep deprivation and 14 volunteers with normal sleep periods (NSP) as a control group. Serum creatinine, uric acid, total cholesterol, high-density lipoprotein cholesterol, and triglyceride concentrations were measured in both groups. Insulin suppression test modified with octreotide (IST) and cortisol levels were performed before and after 24-hr sleep deprivation or NSP. Clinical and metabolic characteristics of the subjects in both groups are similar. Steady-state glucose (SSG) concentration of the IST was significantly higher after 24-hr sleep deprivation (5.7+/-2.1 vs 6.7+/-2.2 mmol/l; p=0.01). SSG level was similar before and after NSP (5.0+/-2.1 vs 5.0+/-1.8 mmol/l, respectively; p=0.91). There were not significant differences in cortisol levels between initial and final tests in both groups. In conclusion, 24-hr sleep deprivation decreased the insulin sensitivity in healthy subjects without changes in cortisol levels.

Ibasaraboh D. I., Angela Y. C., Bianca M. B., et al., (2019), Associations between poor sleep and glucose intolerance in prediabetes. Psychoneuroendocrinology. 2019 Dec;110:104444. doi: 10.1016/j.psyneuen.2019.104444. Epub 2019 Sep 12. https://bit.ly/3xConLa

Abstract

Objectives: A cross-sectional study was designed to investigate the association between sleep quality and glucose metabolism among people with prediabetes, and to explore the potential pathways linking poor sleep to glucose intolerance.

Methods: One hundred fifty-five females and males, Caucasians and African Americans, aged 19-70 completed the study for data analysis. All participants were assessed for sleep quality using the Pittsburgh Sleep Quality Index (PSQI). Fasting glucose and 2-h glucose levels were collected via a 2-h oral glucose tolerance test (OGTT) and used to define prediabetes. Participants provided blood samples for measuring inflammatory markers. Associations were conducted using Pearson’s correlation with adjustments for gender, age, and body mass index (BMI). Analysis of covariance (ANCOVA) was applied to compare the two groups, prediabetes group versus the control group, after controlling for gender, age, and BMI. Regression was used to investigate predictive power of sleep subscales for inflammatory factors and glucose levels.

Results: More people with prediabetes suffered from poor sleep than in the normal glucose group (62% vs. 46%). The OGTT measures, i.e. fasting glucose and 2-h glucose levels, correlated with PSQI measures, but these associations did not maintain statistical significance after adjusting for gender, age, and BMI. The C-reactive protein (CRP) levels were greater in the prediabetes group than the normal glucose group (0.37 ± 0.07 vs. 0.18 ± 0.06 mg/L). Additionally, there was a positive correlation between sleep disturbance and CRP levels (r = 0.30, p = 0.04). Regression analysis found that sleep disturbance predicted CRP levels and significance remained after adding covariates (β = 0.20, p = 0.04). No significant difference was observed in other measured inflammatory factors, including interleukin (IL)-6, IL-8, IL-10 and tumor necrosis factor alpha (TNFα), between the two groups.

Conclusion: Prediabetes is positively associated with poor sleep. Increased CRP levels may be a potential underlying mechanism of this association between prediabetes and poor sleep which warrants further study. Our findings highlight the importance for clinicians to evaluate sleep quality as part of preventing the onset of future diabetes in this particular population.

Esther D., Marieke V. D., J. Gert V. D., et al., (2010), A single night of partial sleep deprivation induces insulin resistance in multiple metabolic pathways in healthy subjects. J Clin Endocrinol Metab
. 2010 Jun;95(6):2963-8. doi: 10.1210/jc.2009-2430. Epub 2010 Apr 6. https://pubmed.ncbi.nlm.nih.gov/20371664/

Abstract

Background: Subsequent nights with partial sleep restriction result in impaired glucose tolerance, but the effects on insulin sensitivity have not been characterized.

Objective: The aim of this study was to evaluate the effect of a single night of partial sleep restriction on parameters of insulin sensitivity.

Research design and methods: Nine healthy subjects (five men, four women) were studied once after a night of normal sleep duration (sleep allowed from 2300 to 0730 h), and once after a night of 4 h of sleep (sleep allowed from 0100 to 0500 h). Sleep characteristics were assessed by polysomnography. Insulin sensitivity was measured by hyperinsulinemic euglycemic clamp studies (from 1130 to 1430 h) with infusion of [6,6-(2)H(2)]glucose.

Results: Sleep duration was shorter in the night with sleep restriction than in the unrestricted night (226 +/- 11 vs. 454 +/- 9 min; P< 0.0001). Sleep restriction did not affect basal levels of glucose, nonesterified fatty acids, insulin, or endogenous glucose production. Sleep restriction resulted in increased endogenous glucose production during the hyperinsulinemic clamp study compared to the unrestricted night (4.4 +/- 0.3 vs. 3.6 +/- 0.2 micromol x kg lean body mass(-1) x min(-1); P = 0.017), indicating hepatic insulin resistance. In addition, sleep restriction decreased the glucose disposal rate during the clamp (32.5 +/- 3.6 vs. 40.7 +/- 5.1 micromol x kg lean body mass(-1) x min(-1); P = 0009), reflecting decreased peripheral insulin sensitivity. Accordingly, sleep restriction decreased the rate of glucose infusion by approximately 25% (P = 0.001). Sleep restriction increased plasma nonesterified fatty acid levels during the clamp study (68 +/- 5 vs. 57 +/- 4 micromol/liter; P = 0.005).

Conclusions: Partial sleep deprivation during only a single night induces insulin resistance in multiple metabolic pathways in healthy subjects. This physiological observation may be of relevance for variations in glucoregulation in patients with type 1 and type 2 diabetes.

Spiegel K., Tasali E., Leproult R., Van Cauter E. (2009), Effects of poor and short sleep on glucose metabolism and obesity risk, Juni 2009. Nat Rev Endocrinol. 2009 May; 5(5): 253–261. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4457292/

Abstract

The importance of sleep to hormones and glucose metabolism was first documented more than four decades ago. Since then, sleep curtailment has become an endemic behavior in modern society. In addition, the prevalence of sleep disorders, particularly obstructive sleep apnea (OSA), has increased. OSA is very common in endocrine and metabolic disorders, but often remains undiagnosed. This Review summarizes the laboratory and epidemiologic evidence that suggests how sleep loss, either behavioral or disease-related, and poor quality of sleep might promote the development of obesity and diabetes mellitus, and exacerbate existing endocrine conditions. Treatment of sleep disorders has the potential to improve glucose metabolism and energy balance. Screening for habitual sleep patterns and OSA might be critically important for patients with endocrine and metabolic disorders.

Van Cauter E. , Spiegel K., Tasali E., et al. (2008) Metabolic consequences of sleep and sleep loss. Sleep Med. 2008 Sep;9 Suppl 1(0 1):S23-8. doi: 10.1016/S1389-9457(08)70013-3. https://pubmed.ncbi.nlm.nih.gov/18929315/

Abstract

Reduced sleep duration and quality appear to be endemic in modern society. Curtailment of the bedtime period to minimum tolerability is thought to be efficient and harmless by many. It has been known for several decades that sleep is a major modulator of hormonal release, glucose regulation and cardiovascular function. In particular, slow wave sleep (SWS), thought to be the most restorative sleep stage, is associated with decreased heart rate, blood pressure, sympathetic nervous activity and cerebral glucose utilization, compared with wakefulness. During SWS, the anabolic growth hormone is released while the stress hormone cortisol is inhibited. In recent years, laboratory and epidemiologic evidence have converged to indicate that sleep loss may be a novel risk factor for obesity and type 2 diabetes. The increased risk of obesity is possibly linked to the effect of sleep loss on hormones that play a major role in the central control of appetite and energy expenditure, such as leptin and ghrelin. Reduced leptin and increased ghrelin levels correlate with increases in subjective hunger when individuals are sleep restricted rather than well rested. Given the evidence, sleep curtailment appears to be an important, yet modifiable, risk factor for the metabolic syndrome, diabetes and obesity. The marked decrease in average sleep duration in the last 50 years coinciding with the increased prevalence of obesity, together with the observed adverse effects of recurrent partial sleep deprivation on metabolism and hormonal processes, may have important implications for public health.

St-Onge M., Roberts A., Shechter A., et al., (2016), Fiber and Saturated Fat Are Associated with Sleep Arousals and Slow Wave Sleep. J Clin Sleep Med. 2016 Jan;12(1):19-24. doi: 10.5664/jcsm.5384. https://pubmed.ncbi.nlm.nih.gov/26156950/

Abstract

Study objectives: Sleep restriction alters food intake, but less is known about how dietary patterns affect sleep. Current goals were to determine whether: (1) sleep is different after consumption of a controlled diet vs. an ad libitum diet, and (2) dietary intake during ad libitum feeding is related to nocturnal sleep.

Methods: Twenty-six normal weight adults (30-45 y), habitually sleeping 7-9 h/night, participated in a randomized-crossover inpatient study with 2 phases of 5 nights: short (4 h in bed) or habitual (9 h in bed) sleep. Only data from the habitual sleep phase were used for the present analyses. During the first 4 days, participants consumed a controlled diet; on day 5, food intake was self-selected. Linear regression was used to determine relations between daytime food intake and nighttime sleep on day 5.

Results: Sleep duration did not differ after 3 days of controlled feeding vs. a day of ad libitum intake. However, sleep after ad libitum eating had less slow wave sleep (SWS, P = 0.0430) and longer onset latency (P = 0.0085). Greater fiber intake predicted less stage 1 (P = 0.0198) and more SWS (P = 0.0286). Percent of energy from saturated fat predicted less SWS (P = 0.0422). Higher percent of energy from sugar and other carbohydrates not considered sugar or fiber was associated with arousals (P = 0.0320 and 0.0481, respectively).

Conclusions: Low fiber and high saturated fat and sugar intake is associated with lighter, less restorative sleep with more arousals. Diet could be useful in the management of sleep disorders but this needs to be tested.

Yoda K., Inaba M. Hamamoto K., et al.,(2015), Association between poor glycemic control, impaired sleep quality, and increased arterial thickening in type 2 diabetic patients. Published: April 14, 2015
https://doi.org/10.1371/journal.pone.0122521

Abstract

Objective: Poor sleep quality is an independent predictor of cardiovascular events. However, little is known about the association between glycemic control and objective sleep architecture and its influence on arteriosclerosis in patients with type-2 diabetes mellitus (DM). The present study examined the association of objective sleep architecture with both glycemic control and arteriosclerosis in type-2 DM patients.

Methods: The subjects were 63 type-2 DM inpatients (M/F, 32/31; age, 57.5±13.1) without taking any sleeping promoting drug and chronic kidney disease. We examined objective sleep architecture by single-channel electroencephalography and arteriosclerosis by carotid-artery intima-media thickness (CA-IMT).

Results: HbA1c was associated significantly in a negative manner with REM sleep latency (interval between sleep-onset and the first REM period) (β=-0.280, p=0.033), but not with other measurements of sleep quality. REM sleep latency associated significantly in a positive manner with log delta power (the marker of deep sleep) during that period (β=0.544, p=0.001). In the model including variables univariately correlated with CA-IMT (REM sleep latency, age, DM duration, systolic blood pressure, and HbA1c) as independent variables, REM sleep latency (β=-0.232, p=0.038), but not HbA1c were significantly associated with CA-IMT. When log delta power was included in place of REM sleep latency, log delta power (β=-0.257, p=0.023) emerged as a significant factor associated with CA-IMT.

Conclusions: In type-2 DM patients, poor glycemic control was independently associated with poor quality of sleep as represented by decrease of REM sleep latency which might be responsible for increased CA-IMT, a relevant marker for arterial wall thickening.

Basse, Astrid L et al., (2018) Skeletal Muscle Insulin Sensitivity Show Circadian Rhythmicity Which Is Independent of Exercise Training Status. Front Physiol. 2018 Aug 28;9:1198. doi: 10.3389/fphys.2018.01198. eCollection 2018. https://pubmed.ncbi.nlm.nih.gov/30210362/

Abstract

Circadian rhythms can be perturbed by shift work, travel across time zones, many occupational tasks, or genetic mutations. Perturbed circadian rhythms are associated with the increasing problem of obesity, metabolic dysfunction, and insulin resistance. We hypothesized that insulin sensitivity in skeletal muscle follows a circadian pattern and that this pattern is important for overall metabolic function. This hypothesis was verified using mice as a model system. We observed circadian rhythmicity in whole body insulin tolerance, as well as in signaling pathways regulating insulin- and exercise-induced glucose uptake in skeletal muscle, including AKT, 5′-adenosine monophosphate-activated protein kinase (AMPK) and TBC1 domain family member 4 (TBC1D4) phosphorylation. Basal and insulin-stimulated glucose uptake in skeletal muscle and adipose tissues in vivo also differed between day- and nighttime. However, the rhythmicity of glucose uptake differed from the rhythm of whole-body insulin tolerance. These results indicate that neither skeletal muscle nor adipose tissue play a major role for the circadian rhythmicity in whole-body insulin tolerance. To study the circadian pattern of insulin sensitivity directly in skeletal muscle, we determined glucose uptake under basal and submaximal insulin-stimulated conditions ex vivo every sixth hour. Both insulin sensitivity and signaling of isolated skeletal muscle peaked during the dark period. We next examined the effect of exercise training on the circadian rhythmicity of insulin sensitivity. As expected, voluntary exercise training enhanced glucose uptake in skeletal muscle. Nevertheless, exercise training did not affect the circadian rhythmicity of skeletal muscle insulin sensitivity. Taken together, our results provide evidence that skeletal muscle insulin sensitivity exhibits circadian rhythmicity.

Van Cauter, E., Blackman, J. D., Roland, D., et al (1991) Modulation of glucose regulation and insulin secretion by circadian rhythmicity and sleep. J Clin Invest. 1991 Sep; 88(3): 934–942. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC295490/

Abstract

To define the roles of circadian rhythmicity (intrinsic effects of time of day independent of the sleep or wake condition) and sleep (intrinsic effects of the sleep condition, irrespective of the time of day) on the 24-h variation in glucose tolerance, eight normal men were studied during constant glucose infusion for a total of 53 h. The period of study included 8 h of nocturnal sleep, 28 h of continuous wakefulness, and 8 h of daytime sleep. Blood samples for the measurement of glucose, insulin, C-peptide, cortisol, and growth hormone were collected at 20-min intervals throughout the entire study. Insulin secretion rates were derived from C-peptide levels by deconvolution. Sleep was polygraphically monitored. During nocturnal sleep, levels of glucose and insulin secretion increased by 31 +/- 5% and 60 +/- 11%, respectively, and returned to baseline in the morning. During sleep deprivation, glucose levels and insulin secretion rose again to reach a maximum at a time corresponding to the beginning of the habitual sleep period. The magnitude of the rise above morning levels averaged 17 +/- 5% for glucose and 49 +/- 8% for calculated insulin secretion. Serum insulin levels did not parallel the circadian variation in insulin secretion, indicating the existence of an approximate 40% increase in insulin clearance during the night. Daytime sleep was associated with a 16 +/- 3% rise in glucose levels, a 55 +/- 7% rise in insulin secretion, and a 39 +/- 5% rise in serum insulin. The diurnal variation in insulin secretion was inversely related to the cortisol rhythm, with a significant correlation of the magnitudes of their morning to evening excursions. Sleep-associated rises in glucose correlated with the amount of concomitant growth hormone secreted. These studies demonstrate previously underappreciated effects of circadian rhythmicity and sleep on glucose levels, insulin secretion, and insulin clearance, and suggest that these effects could be partially mediated by cortisol and growth hormone.