Diabetes

Posted on Diabetes

Researchers suggest stress explains how obesity causes diabetes

The findings highlight the importance of analyzing the impact of life stressors on those with MS

A study from Rutgers Health and other institutions indicates that stress hormones – not impaired cellular insulin signalling – may be the primary driver of obesity-related diabetes.

“We have been interested in the basic mechanisms of how obesity induces diabetes. Given that the cost of the diabetes epidemic in the U.S. alone exceeds $300 billion per year, this is a critically important question,” said Christoph Buettner, chief of endocrinology, metabolism and nutrition at Rutgers Robert Wood Johnson Medical School and the study’s senior author.

Scientists have long thought obesity causes diabetes by impairing insulin signalling within the liver and fat cells. However, new research shows that overeating and obesity increase the body’s sympathetic nervous system—the “fight or flight” response—and that the increased levels of the stress hormones norepinephrine and epinephrine counteract insulin’s effects even though cellular insulin signalling still works.

The authors observed that overeating in normal mice increases the stress hormone norepinephrine within days, indicating how quickly surplus food stimulates the sympathetic nervous system.

To see what effect this excess hormone production has in spurring disease development, the authors then deployed a new type of genetically engineered mice that are normal in every way but one: They cannot produce stress hormones catecholamines outside of their brains and central nervous systems.

The researchers fed these mice the obesity-inducing high-fat and high-sugar diet, but although they ate as many calories and got just as obese as normal mice, they did not develop metabolic disease.

“We were delighted to see that our mice ate as much because it indicates that the differences in insulin sensitivity and their lack of metabolic disease are not due to reduced food intake or reduced obesity but the greatly reduced stress hormones. These mice cannot increase stress hormones that counteract insulin; hence, insulin resistance does not develop during obesity development.”

The new findings may help explain why some obese individuals develop diabetes while others don’t and why stress can worsen diabetes even with little weight gain.

“Many types of stress – financial stress, marital stress, the stress associated with living in dangerous areas or suffering discrimination or even the physical stress that comes from excessive alcohol consumption — all increase diabetes and synergize with the metabolic stress of obesity,” Buettner said.

“Our finding that even obesity principally induces metabolic disease via increased stress hormones provides new insight into the common basis for all these factors that increase the risk of diabetes. Stress and obesity, in essence, work through the same basic mechanism in causing diabetes, through the actions of stress hormones.”

While it is well known that catecholamines can impair insulin action, the new study suggests that this may be the fundamental mechanism underlying insulin resistance in obesity. The dynamic interplay between stress hormones, which work in opposition to insulin, has long been known. Stress hormones increase glucose and lipids in the bloodstream, while insulin lowers these. However, an unexpected finding of the new study is that insulin signaling can remain intact even in insulin-resistant states like obesity. It’s just that the heightened activity of stress hormones effectively “push the gas pedal harder,” resulting in increased blood sugar and fat levels. Even though the level of insulin’s “braking” effect remains the same, the accelerated gas pedal effect of catecholamines overwhelms the brake effect of insulin and results in relatively diminished insulin action.

“Some colleagues are at first surprised that insulin resistance can exist even though cellular insulin signaling is intact. But let’s not forget that the gas pedal effects of stress hormones are exerted through very different signaling pathways than insulin signaling. That explains why the ability of insulin to ‘brake’ and reduce the release of sugar and fat into the bloodstream is impaired even though insulin signaling is intact because stress signaling is predominant.”

The findings suggest that medications that reduce catecholamines, a term for all the stress-related hormones and neurotransmitters produced by the SNS and the adrenal gland, might help prevent or treat diabetes. However, medicines that block catecholamines, as they are currently used to treat high blood pressure, haven’t shown major benefits for diabetes. This may be because current drugs don’t block the relevant receptors or because they affect the brain and body in complex ways, Buettner said.

Buettner and the study’s first author, Kenichi Sakamoto, an assistant professor of endocrinology at Robert Wood Johnson Medical School, are planning human studies to confirm their findings. They’re also examining the role of the sympathetic nervous system and other forms of diabetes, including Type 1 diabetes.

“We would like to study if short-term overfeeding, as some of us experience during the holidays by gaining five to 10 pounds, increases insulin resistance with heightened sympathetic nervous system activation,” Buettner said.

The findings may ultimately lead to new therapeutic approaches to tackle insulin resistance, diabetes and metabolic disease, focused on reducing stress hormones rather than targeting insulin signaling.

“We hope this paper provides a different take on insulin resistance,” Buettner said. “It may also explain why none of the drugs currently used to treat insulin resistance, except insulin itself, directly increases cellular insulin signaling.”

Posted on Diabetes

Could a genetic flaw be the key to stopping people craving sugary treats?

Diabetes is an increasingly pervasive disease, currently affecting over 500 million adults worldwide. Since there is as yet no cure for type 1 or type 2 diabetes, patients must regularly monitor their BGLs to keep them in check. Though BGL-measuring devices relying on painful finger pricks have been the gold standard for decades, modern technology is slowly opening doors to better alternatives. Many researchers have proposed noninvasive methods to monitor BGLs using widely available wearable devices, such as smartwatches. For example, by placing the LEDs and photodetectors present in certain smartwatches against the skin, oxyhemoglobin and hemoglobin pulse signals can be measured to calculate a metabolic index, from which BGLs can be estimated. However, given the small size and limited power of smartwatches and similar wearables, the data quality of the measured signals tends to be quite low. Moreover, as these devices are worn on extremities, daily movements introduce measurement errors. These problems limit the accuracy and clinical applicability of such wearables for diabetes management. A team from Hamamatsu Photonics K.K., Japan, has been actively researching this issue, looking for effective solutions. In a recent study led by Research and Development Engineer Tomoya Nakazawa, published in the Journal of Biomedical Optics (JBO), they conducted an in-depth theoretical analysis of the sources of errors in the metabolic-index-based method. Based on this analysis, they implemented a novel signal quality index to filter out low-quality data as a preprocessing step and thereby enhance the accuracy of estimated BGLs. “As smartwatches are widely adopted across different regions and age groups, and with the global rise in diabetes cases, a signal quality enhancing method that is easy to implement and apply regardless of personal and individual differences is absolutely essential for meeting the increasing worldwide demand for noninvasive glucose monitoring devices,” remarks Nakazawa, explaining the motivation behind the study. First, the researchers mathematically showed that discrepancy between the two types of phase delays in the oxyhemoglobin and hemoglobin pulse signal calculated by different methods provides a good measure of the influence of noise. They then considered two main sources of phase error, namely, a background noise level and the estimation errors introduced via sampling at discrete intervals. After formalizing these sources of errors, they calculated the effect on the estimated metabolic index. The proposed screening approach involves implementing thresholds for the phase estimation and metabolic index errors. Data chunks that exceed the set thresholds are discarded, and the missing values are approximated using other means based on the rest of the data. To test this strategy, the researchers conducted a long-term experiment in which the sensors in a commercial smartwatch were used to monitor the BGLs of a healthy individual during “oral challenges.” In each of the 30 tests conducted over four months, the subject would fast for two hours before consuming high-glucose foods. Their BGLs were measured using the smartwatch and a commercial continuous glucose monitoring sensor, the latter of which was used to capture the reference values. Notably, preprocessing the data with the proposed screening method led to a notable increase in accuracy. Using the Parkes error grid technique to categorize measurement errors, a substantially higher percentage of data points ended up in Zone A when screening was applied. This refers to clinically accurate values that would lead to correct treatment decisions. “Adopting the screening process improved BGL estimation accuracy in our smartwatch-based prototype,” remarks Nakazawa, “Our technique could facilitate the integration of wearable and continuous BGL monitoring into devices such as smartwatches and smart rings, which are typically constrained in terms of size and signal quality,” he adds, highlighting the impact of the research work. The research team also noted some of the current limitations of smartwatches that lead to inferior performance compared to smartphone camera-based techniques. Though the proposed method could certainly help enhance the performance of the former, hardware improvements in the photodetector and amplifier circuits could go a long way to make wearable electronics a more attractive and clinically acceptable option to monitor BGLs.

The work provides novel genetic insights into dietary preferences and opens the possibility of selectively targeting SI to reduce sucrose intake at the population level.

The study was led by Dr. Peter Aldiss, “Excess calories from sugar are an established contributor to obesity and type 2 diabetes. In the UK, we consume 9-12% of our dietary intake from free sugars, such as sucrose, with 79% consuming up to three sugary snacks daily. At the same time, genetic defects in sucrose digestion have been associated with irritable bowel syndrome, a common functional disorder affecting up to 10% of the population.

“Our study suggests that genetic variation in our ability to digest dietary sucrose may impact not only how much sucrose we eat, but how much we like sugary foods.”

The experts began by investigating the dietary behaviours in mice lacking the SI gene. Here, mice developed a rapid reduction in sucrose intake and preference. This was confirmed in two large population-based cohorts involving 6,000 individuals in Greenland and 134,766 in the UK BioBank.

The team took a nutrigenetics approach to understanding how genetic variation in the SI gene impacts sucrose intake and preference in humans. Strikingly, individuals with a complete inability to digest dietary sucrose in Greenland consumed significantly less sucrose-rich foods, while individuals with a defective, partially functional SI gene in the UK liked sucrose-rich foods less.

“These findings suggest that genetic variation in our ability to digest dietary sucrose can influence our intake, and preference, for sucrose-rich foods whilst opening up the possibility of targeting SI to reduce sucrose intake at the population level selectively,” says Dr Aldiss.

“In the future, understanding how defects in the SI gene act to reduce the intake, and preference, of dietary sucrose will facilitate the development of novel therapeutics to help curb population-wide sucrose intake to improve digestive and metabolic health.”

Posted on Diabetes

The role of digital technology in diabetes prevention and management

the transformative role digital health technologies play in diabetes management and prevention
The transformative role digital health technologies play in diabetes management and prevention.

The editorial, written by Dr. Gang Hu and Dr. Yun Shen from Pennington Biomedical, along with Dr. Xiantong Zou from Peking University, emphasizes studies that demonstrate how innovations in digital technology enhance self-management, enable personalized treatments, and facilitate seamless communication between patients and healthcare providers.

“Digital tools provide unique opportunities to enhance patient outcomes through improved monitoring, personalized care, and more effective communication between patients and healthcare providers,” the authors stated.

Digital health tools have the potential to enhance diabetes care by making it more accessible, effective, and tailored to patients’ individual needs. Advances in wearable devices, mobile applications, and telemedicine can empower patients to manage their own health, personalize their treatment, and ultimately improve health outcomes. This editorial highlights key challenges associated with these technologies, such as data privacy and accessibility, and emphasizes the importance of ongoing research and development in this promising field.

“As the field evolves, digital health innovations are set to play an increasingly vital role in preventing and managing diabetes, leading to more efficient and equitable healthcare delivery,” the authors concluded.

Posted on Diabetes

New AI-ready dataset released in type 2 diabetes study

Early results suggest broader participant diversity and novel measures will enable new, artificial intelligence-driven insights
Early results suggest broader participant diversity and novel measures will enable new, artificial intelligence-driven insights.

Researchers are releasing the main dataset from an ambitious study exploring the biomarkers and environmental factors that may influence the development of type 2 diabetes. This study involves participants with no diabetes and those at various stages of the condition. The initial findings suggest a rich and unique set of information that differs from previous research.

Data from customized environmental sensors installed in participants’ homes reveal a clear link between disease states and exposure to fine particulate pollution. The collected information also includes survey responses, depression scale scores, eye imaging scans, traditional glucose measurements, and various other biological variables.

These data are intended to be mined by artificial intelligence for novel insights about risks, preventive measures, and pathways between disease and health.

“We observe evidence of diversity among patients with type 2 diabetes, indicating that their experiences and challenges are not uniform. With access to increasingly large and detailed datasets, researchers will have the opportunity to explore these differences in depth,” stated Dr. Cecilia Lee, a professor of ophthalmology at the University of Washington School of Medicine.

She expressed excitement at the quality of the collected data, representing 1,067 people, just 25% of the study’s total expected enrollees.

Lee is the program director of AI-READI (Artificial Intelligence Ready and Equitable Atlas for Diabetes Insights), a National Institutes of Health-supported initiative that aims to collect and share AI-ready data for global scientists to analyze for new clues about health and disease.

The authors restated their aim to gather health information from a more racially and ethnically diverse population than previously measured, and to make the resulting data ready, technically and ethically, for AI mining.

“This discovery process has been invigorating,” said Dr. Aaron Lee, a UW Medicine professor of ophthalmology and the project’s principal investigator. “We’re a consortium of seven institutions and multidisciplinary teams that had never worked together. But we have shared goals of drawing on unbiased data and protecting the security of that data as we make it accessible to colleagues everywhere.”

At study sites in Seattle, San Diego, and Birmingham, Alabama, recruiters are collectively enrolling 4,000 participants, with inclusion criteria promoting balance:

  • race/ethnicity (1,000 each – white, Black, Hispanic and Asian)
  • disease severity (1,000 each – no diabetes, prediabetes, medication/non-insulin-controlled and insulin-controlled type 2 diabetes)
  • sex (equal male/female split)

“Conventionally, scientists are examining pathogenesis — how people become diseased — and risk factors,” Aaron Lee said. “We want our datasets also to be studied for salutogenesis, or factors that contribute to health. So if your diabetes improves, what factors might contribute to that? We expect that the flagship dataset will lead to novel discoveries about type 2 diabetes in both of these ways.”

He added that by collecting more deeply characterizing data from many people, the researchers hope to create pseudo health histories of how a person might progress from disease to full health and from full health to disease. 

The data are hosted on a custom online platform and produced in two sets: a controlled-access set requiring a usage agreement and a registered, publicly available version stripped of HIPAA-protected information.

Posted on Cardiovascular disease, Diabetes

Study shows how high blood sugar increases risk of thrombosis

Discoveries by Brazilian researchers belonging to a FAPESP-supported research center could lead to strategies to prevent cardiovascular disease associated with diabetes
Discoveries could lead to strategies to prevent cardiovascular disease associated with diabetes.

A study conducted at the Center for Research on Redox Processes in Biomedicine (Redoxoma) has enhanced our understanding of how high blood sugar levels (hyperglycemia), a common symptom of diabetes, can lead to thrombosis. The findings, published in the Journal of Thrombosis and Haemostasis, could inform the development of strategies to prevent cardiovascular issues in individuals with diabetes.

“The primary causes of death in Brazil and many other Latin American countries are ischemic events, including heart attacks and strokes, where arterial thrombosis plays a significant role. These cardiovascular disorders can result from various risk factors such as high blood sugar (hyperglycemia), abnormal lipid levels (dyslipidemia), and high blood pressure (hypertension). Among these factors, hyperglycemia is notably associated with an increased risk of cardiovascular disease,” stated Renato Simões Gaspar, the article’s lead author.

The investigation was conducted with support from FAPESP during Gaspar’s postdoctoral research and led by Francisco Laurindo, the last author of the article. Laurindo is a professor at the University of São Paulo’s Medical School (FM-USP) in Brazil and is also a member of Redoxoma, a Research, Innovation, and Dissemination Center (RIDC) established by FAPESP at the Institute of Chemistry (IQ-USP). Gaspar currently teaches at the State University of Campinas (UNICAMP).

The authors state that prolonged hyperglycemia and diabetic ketoacidosis increase the risk of thrombosis. This is due to their effects on endothelial dysfunction, which refers to changes in the inner lining of blood vessels. These changes can lead to the binding of platelets to the endothelial cells, triggering the formation of blood clots.

The study showed that peri/epicellular protein disulfide isomerase A1 (pecPDI) regulates platelet-endothelium interaction in hyperglycemia through adhesion-related proteins and alterations in endothelial membrane biophysics.

“We found that a pathway for this PDI in endothelial cells mediates thrombosis in diabetes when hyperglycemia is present, involving a specific molecular mechanism, which we identified,” Laurindo said.

PDI is an enzyme that resides in the endoplasmic reticulum and has the classic function of catalyzing the insertion of disulfide bridges into nascent proteins so that they merge in the correct shape, i.e. so that the amino acid chain folds to form the three-dimensional structure that makes the molecule functional. It is also found in the extracellular space as pecPDI, a pool secreted or bound to the cell surface, in various cell types including platelets and endothelial cells. Studies have shown that pecPDI regulates thrombosis in several models. 

Biochemical and biophysical modifications

To investigate platelet-endothelium interaction in hyperglycemia, the researchers created a model with human umbilical vein endothelial cells cultured in different glucose concentrations to produce normoglycemic and hyperglycemic cells. They assessed PDI’s contribution using whole-cell PDI or pecPDI inhibitors.

The cells were incubated with platelets derived from healthy donors. The platelets adhered almost three times more in hyperglycemic than normoglycemic cells. PDI inhibition reversed this effect, and the researchers concluded that the process is regulated by endothelial pecPDI.

To better understand the result, they investigated biophysical processes such as endothelial cell cytoskeleton remodelling and found that hyperglycemic cells had more well-structured actin filament fibres than normoglycemic cells. They also measured the production of hydrogen peroxide, an oxidizing compound, because reactive oxygen species are mediators of cytoskeleton reorganization and cell adhesion—hyperglycemic cells produced twice as much hydrogen peroxide as normoglycemic cells.

The researchers then investigated whether cytoskeleton reorganization affected cell membrane stiffness since substrate stiffness increases platelet adhesion. Using atomic force microscopy, they demonstrated that hyperglycemic cells were stiffer than normoglycemic cells.

The microscope images also showed the formation of cell elongations with extracellular vesicles that appeared to separate from the elongations. This observation led the researchers to investigate the secretome – the set of proteins secreted by an organism into the extracellular space – to find out whether it included proteins that enhanced platelet adhesion. “The purpose of this experiment was to detect proteins exclusively expressed by or present in hyperglycemic cells and not in controls or cells treated with PDI inhibitors,” Gaspar explained.

They found 947 proteins in the secretome, from which they selected eight with a role in cellular adhesion. They then silenced gene expression for three of these proteins using RNA interference and arrived at two proteins, SLC3A2 and LAMC1, as modulators of platelet adhesion. SLC3A2 is a membrane protein, and LAMC1 is the gamma subunit of laminin 1, a key extracellular matrix component.