Can Chrono-Nutrition Help Prevent Diabetes?

This editorial aimed to put forward a question if chrono-nutrition can help prevent diabetes through optimizing circadian rhythms of glucose metabolism. With the advancing mechanization, eating behavior (timing, sequence, and frequency) has changed. People are now more willing to eat fast foods at suboptimal times of the circadian period. Growing evidence suggests that untimely eating and lack of exercise can interfere with optimal physiological rhythms of glucose and insulin metabolism that can lead to diabetes. Type 2 diabetes mellitus (T2D) is a foremost metabolic disorder worldwide occurring largely due to suboptimal eating timing and lifestyle. Consuming less sugars and carbohydrates during evening and overnight may help optimize human chrono-physiology. Chrono-nutrition via optimizing the timing of meals is a growing science that needs to be well practiced to help prevent or possibly reduce risks of T2D in today’s complicated life.

In Vitro Comparative Study of Solid Lipid and PLGA Nanoparticles Designed to Facilitate Nose-to-Brain Delivery of Insulin

The brain insulin metabolism alteration has been addressed as a pathophysiological factor underlying Alzheimer’s disease (AD). Insulin can be beneficial in AD, but its macro-polypeptide nature negatively influences the chances of reaching the brain. The intranasal (IN) administration of therapeutics in AD suggests improved brain-targeting. Solid lipid nanoparticles (SLNs) and poly(lactic-co-glycolic acid) nanoparticles (PLGA NPs) are promising carriers to deliver the IN-administered insulin to the brain due to the enhancement of the drug permeability, which can even be improved by chitosan-coating. In the present study, uncoated and chitosan-coated insulin-loaded SLNs and PLGA NPs were formulated and characterized. The obtained NPs showed desirable physicochemical properties supporting IN applicability. The in vitro investigations revealed increased mucoadhesion, nasal diffusion, and drug release rate of both insulin-loaded nanocarriers over native insulin with the superiority of chitosan-coated SLNs. Cell-line studies on human nasal epithelial and brain endothelial cells proved the safety IN applicability of nanoparticles. Insulin-loaded nanoparticles showed improved insulin permeability through the nasal mucosa, which was promoted by chitosan-coating. However, native insulin exceeded the blood-brain barrier (BBB) permeation compared with nanoparticulate formulations. Encapsulating insulin into chitosan-coated NPs can be beneficial for ensuring structural stability, enhancing nasal absorption, followed by sustained drug release.

Markers Of Glucose Metabolism And Muscle Strength Decline Among The Offspring Of Long-Lived Individuals

Diabetes has been linked to accelerated muscle strength decline with aging. However, the association between glucose metabolism and muscle strength decline among individuals without diabetes is less clear. We tested whether fasting plasma markers of glucose and insulin metabolism (glucose, insulin, hemoglobin A1c, and soluble receptor for advanced glycation end products (sRAGE)) are associated with grip strength decline among 1415 non-diabetic offspring of exceptionally long-lived individuals who have a low diabetes risk (age range 36-88; mean age ± SD = 60 ± 8 years; mean BMI ± SD = 27 ± 4.7 kg/m2; 57% women). Grip strength was assessed using a hand-held dynamometer at two clinic visits over an average of 7.9 years. Multiple linear mixed models were adjusted for age, sex, field center, lifestyle, comorbidities, body weight, height, weight change, and family relatedness. Each standard deviation higher fasting insulin (7.3 mIU/L) was related to greater grip strength decline (-0.38 ± 0.16 kg; p=0.016), while each standard deviation higher fasting sRAGE (430 pg/mL) was related to slower grip strength decline (0.36 ± 0.18 kg; p=0.04). Our findings suggest that even among non-diabetic individuals from families with a clustering of “healthier” metabolic profiles - insulin metabolism and advanced glycation end products may be important biomarkers of muscle strength decline with aging. Potential mechanisms, including genetic and metabolic mediators underlying the observed associations, warrant further investigation.

An Overview on the Therapeutic Function of Foods Enriched with Plant Sterols in Diabetes Management

Diabetes is one of the most significant health issues across the world. People identified with diabetes are more vulnerable to various infections and are at a greater risk of developing cardiovascular diseases. The plant-based food we consume often contains many sterol-based bioactive compounds. It is well documented that these compounds could effectively manage the processes of insulin metabolism and cholesterol regulation. Insulin resistance followed by hyperglycemia often results in oxidative stress level enhancement and increased reactive oxygen species production. At the molecular level, these changes induce apoptosis in pancreatic cells and hence lead to insulin insufficiency. Studies have proved that plant sterols can lower inflammatory and oxidative stress damage connected with DNA repair mechanisms. The effective forms of phyto compounds are polyphenols, terpenoids, and thiols abundant in vegetables, fruits, nuts, and seeds. The available conventional drug-based therapies for the prevention and management of diabetes are time-consuming, costly, and with life-threatening side effects. Thereby, the therapeutic management of diabetes with plant sterols available in our daily diet is highly welcome as there are no side effects. This review intends to offer an overview of the present scenario of the anti-diabetic compounds from food ingredients towards the therapeutic beneficial against diabetes.

Correlation between parity and metabolic syndrome in Chinese women aged 40 years and older: the Reaction study

Aims The purpose of the present study was to investigate the correlation between the number of live-birth pregnancies and metabolic syndrome (MetS) in Chinese women according to menstruation history. Methods Registry data for all pregnancies in a cohort of 6157 Chinese women aged 40 years or older were obtained and the number of live-birth pregnancies were enumerated. We defined MetS using five criteria: impaired insulin metabolism and glucose tolerance, obesity in the abdominal area, dyslipidemia, and hypertension. Multivariate logistic regression analysis was conducted to assess potential risk factors for MetS. Postmenopausal women with three or more of live-birth pregnancies had the highest prevalence of MetS (P < 0.05). Results Among the 6157 females aged 40 years or older in Luzhou city, 2143 (34.8%) participants had incident MetS. The number of live-birth pregnancies was significantly correlated with age and fasting blood glucose (FBG) level (P < 0.05). The prevalence of MetS increased with the number of live-birth pregnancies (P < 0.01), and the frequency in postmenopausal women was significantly higher than that in premenopausal women (P < 0.001). In the binary logistic regression model, menopausal status [OR = 0.343 (0.153–0.769), P < 0.001] were significantly associated with an increased risk of MetS. Conclusions The number of live-birth pregnancies is correlated with an increased risk of MetS in Chinese women aged 40 years and over, especially in postmenopausal women. Greater attention should be paid to postmenopausal women who have had multiple live-birth pregnancies with a view to intervening early to prevent related diseases.

Atrazine derangement of GLUT-4 and Insulin metabolism and the ameliorative effect of Virgin coconut oil in male wistar rats

We assessed the ameliorative effect of Virgin Coconut Oil following atrazine-induced metabolic derangement in rats. Adult male wistar rats weighing 180-200g were used; randomly separated into two major groups. Thirty-five rats in the test group were randomly divided into five sub-groups of 7 rats per sub-group and treated thus: Sub-group (SG) 1, 2 and 3 received 10ml/kg of distilled water, 10ml/kg VCO, 123mg/kg of Atrazine respectively, SG4 was diabetic control; SG 5 was the diabetic group treated with 10ml/kg of VCO for 2 weeks, after which the animals were sacrificed and blood collected for analysis. 35 rats for the recovery group were also divided into 5 sub-groups of 7 rats per sub-group and were treated; SG 1, 2, 3, 4 and 5 received 10ml/kg distilled water, 10ml/kg VCO, 123mg/kg of ATZ respectively. After 2 weeks, the animals were re-treated thus: SG 1,2,3,4 and 5 received 10ml/kg of distilled water, 10ml/kg of VCO, 123mg/kg of ATZ, 10ml/kg VCO and 10ml/kg distilled water respectively. After 2 weeks, the animals were also sacrificed and blood collected for analysis. ATZ reduced serum insulin and a reduced expression of GLUT4. VCO restored GLUT4 levels but did not significantly restore the insulin to the normal levels

Rate of Gestational Weight Gain and Glucose-Insulin Metabolism Among Hispanic Pregnant Women With Overweight and Obesity

Context Hispanic women are at elevated risk of gestational glucose intolerance and postpartum type 2 diabetes compared with non-Hispanic White women. Identification of potentially modifiable factors contributing to this trajectory of beta-cell dysfunction is warranted. Objective We aimed to determine the association between rate of gestational weight gain (rGWG) and glucose-insulin metabolism in Hispanic pregnant women with overweight and obesity. Methods This cross-sectional, observational study, conducted from 2018-2020 at the clinical research center at University of California, Irvine, included 33 nondiabetic Hispanic pregnant women at 28 to 30 weeks’ gestation with pre-pregnancy body mass index (BMI) 25.0 to 34.9 kg/m2. Participants consumed a standardized liquid mixed meal after an overnight fast. Serial blood samples were collected at fasting and up to 2 hours postprandial. The glucose and insulin area under the curve (AUC), insulin sensitivity index (ISI) and insulin secretion sensitivity index (ISSI)-2 were computed. Results Average rGWG (0.36 ± 0.22 kg/week) was classified as excessive in 60% of women. While rGWG was not associated with the glucose or insulin AUC or ISI, it accounted for 13.4% of the variance in ISSI-2 after controlling for covariates (maternal age, parity, and pre-pregnancy BMI); for each 1 unit increase in rGWG, ISSI-2 decreased 2.1 units (P = 0.015). Conclusion Even in the absence of gestational diabetes, rGWG was inversely associated with beta-cell function in a high-risk population of Hispanic pregnant women with overweight and obesity. Beta-cell decline is an established risk factor for transition to type 2 diabetes, and these cross-sectional findings highlight rGWG as a potentially modifiable contributor to this process.

Bayesian metamodeling of complex biological systems across varying representations

Comprehensive modeling of a whole cell requires an integration of vast amounts of information on various aspects of the cell and its parts. To divide and conquer this task, we introduce Bayesian metamodeling, a general approach to modeling complex systems by integrating a collection of heterogeneous input models. Each input model can in principle be based on any type of data and can describe a different aspect of the modeled system using any mathematical representation, scale, and level of granularity. These input models are 1) converted to a standardized statistical representation relying on probabilistic graphical models, 2) coupled by modeling their mutual relations with the physical world, and 3) finally harmonized with respect to each other. To illustrate Bayesian metamodeling, we provide a proof-of-principle metamodel of glucose-stimulated insulin secretion by human pancreatic β-cells. The input models include a coarse-grained spatiotemporal simulation of insulin vesicle trafficking, docking, and exocytosis; a molecular network model of glucose-stimulated insulin secretion signaling; a network model of insulin metabolism; a structural model of glucagon-like peptide-1 receptor activation; a linear model of a pancreatic cell population; and ordinary differential equations for systemic postprandial insulin response. Metamodeling benefits from decentralized computing, while often producing a more accurate, precise, and complete model that contextualizes input models as well as resolves conflicting information. We anticipate Bayesian metamodeling will facilitate collaborative science by providing a framework for sharing expertise, resources, data, and models, as exemplified by the Pancreatic β-Cell Consortium.

Identification of markers that distinguish adipose tissue and glucose and insulin metabolism using a multi-modal machine learning approach

The study of metabolomics has improved our knowledge of the biology behind type 2 diabetes and its related metabolic physiology. We aimed to investigate markers of adipose tissue morphology, as well as insulin and glucose metabolism in 53 non-obese male individuals. The participants underwent extensive clinical, biochemical and magnetic resonance imaging phenotyping, and we also investigated non-targeted serum metabolites. We used a multi-modal machine learning approach to evaluate which serum metabolomic compounds predicted markers of glucose and insulin metabolism, adipose tissue morphology and distribution. Fasting glucose was associated with metabolites of intracellular insulin action and beta-cell dysfunction, namely cysteine-s-sulphate and n-acetylgarginine, whereas fasting insulin was predicted by myristoleoylcarnitine, propionylcarnitine and other metabolites of beta-oxidation of fatty acids. OGTT-glucose levels at 30 min were predicted by 7-Hoca, a microbiota derived metabolite, as well as eugenol, a fatty acid. Both insulin clamp and HOMA-IR were predicted by metabolites involved in beta-oxidation of fatty acids and biodegradation of triacylglycerol, namely tartrate and 3-phosphoglycerate, as well as pyruvate, xanthine and liver fat. OGTT glucose area under curve (AUC) and OGTT insulin AUC, was associated with bile acid metabolites, subcutaneous adipocyte cell size, liver fat and fatty chain acids and derivates, such as isovalerylcarnitine. Finally, subcutaneous adipocyte size was associated with long chain fatty acids, markers of sphingolipid metabolism, increasing liver fat and dopamine-sulfate 1. Ectopic liver fat was predicted by methylmalonate, adipocyte cell size, glutathione derived metabolites and fatty chain acids. Ectopic heart fat was predicted visceral fat, gamma-glutamyl tyrosine and 2-acetamidophenol sulfate. Adipocyte cell size, age, alpha-tocopherol and blood pressure were associated with visceral fat. We identified several biomarkers associated with adipose tissue pathophysiology and insulin and glucose metabolism using a multi-modal machine learning approach. Our approach demonstrated the relative importance of serum metabolites and they outperformed traditional clinical and biochemical variables for most endpoints.