| Research Topics: |
| INDEX |
| AI-chat 2 |
| PRIME 15 |
| aging 2 |
| brain 3 |
| brainwave 1 |
| cancer 1 |
| cognition 2 |
| culture 3 |
| diabetes 4 |
| economy 1 |
| exercise 1 |
| genetics 4 |
| heart 6 |
| immune 4 |
| metabolism 2 |
| microbiome 2 |
| misc |
| nutrition 2 |
| obesity 1 |
| politics 1 |
| pollution 3 |
| privacy 2 |
| psychology 3 |
| reproduction 3 |
| rights 2 |
| sleep 1 |
| vaccine 1 |
| virus 2 |
| war-confl |
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Scanned: 1695 Evaluated: 92 Selected: 36 |
| PRIME DIABETES METABOLISM MICROBIOME NUTRITION | ||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
Aaa08-003 | How Different Types of Fiber Improve Blood Sugar Control and Metabolic Health Download PDF Copy | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Quote: | Dietary fiber intake is essential for maintaining optimal metabolic health by influencing viscosity, microbial fermentation, and short-chain fatty acid SCFA production. In fact, higher fiber intake is frequently associated with improved glucose regulation, favorable lipid profiles, reduced obesity risk, and lower incidence of cardiometabolic diseases.2 However, the magnitude and durability of these benefits vary by fiber type, dose, and individual metabolic status. Large prospective analyses link higher habitual fiber intake with reduced risk of type 2 diabetes, cardiovascular disease, metabolic syndrome, and all-cause mortality. | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Article: | https://www.news-medical.net/health/How-Different-Types-of-Fiber-Improve-Blood-Sugar-Control-and-Metabolic-Health.aspx | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| Search Similar: | Brave Mojeek Startpage Dogpile Bing Googl Lycos Utub Yaho | |||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||||
| See Also: |
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PRIME
AGING
DIABETES
| Mdb08-002Accounting for age-related increases in HbA1c more accurately quantifies risk of Type 1 Diabetes progression in islet autoantibody-positive adultsQuote: | Conclusions Age-related variation in HbA1c influences dysglycemia classification in adults at risk for type 1 diabetes. Age-adjusted HbA1c or a higher HbA1c threshold 6.0 42 mmol/mol in adults 30 years identifies individuals with progression risk comparable to children and may improve age-specific risk stratification in prevention seungs.
| Abstract:
| https://www.medrxiv.org/content/10.64898/2026.02.19.26346463v1 | Full Text:
| https://www.medrxiv.org/content/10.64898/2026.02.19.26346463v1.full-text | Search Similar: |
Brave Mojeek Startpage Dogpile Bing Googl Lycos Utub Yaho | Search Authors: |
Erin L. Templeman Nick Thomas Susan Martin Diane K. Wherett Maria J. Redondo Jennifer Sherr Alessandra Petrelli Laura M. Jacobsen Falastin Salami Jacqueline Lonier Carmella Evans Molina Jay Sosenko Ins Barroso Richard A. Oram Emily K. Sims Lauric A. Ferrat,
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PRIME
DIABETES
| Mep08-018Large-Scale Multi-Omics Enhance Risk Prediction for Type 2 DiabetesQuote: | Conclusion Integrating proteomics, metabolomics, and a diabetes-PRS into a clinical model substantially improves type 2 diabetes risk prediction beyond single-omics extensions. However, the C-index difference between the proteomics extended and full multi-omics extended models is small, and the clinical models extended with proteomics data would be easier to translate into routine care because it needs only the measurement of 15 proteins.
| Abstract:
| https://www.medrxiv.org/content/10.64898/2026.02.19.26346636v1 | Full Text:
| https://www.medrxiv.org/content/10.64898/2026.02.19.26346636v1.full-text | Search Similar: |
Brave Mojeek Startpage Dogpile Bing Googl Lycos Utub Yaho | Search Authors: |
Ruijie Xie Christian Herder Ben Schttker,
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DIABETES
| Mxa08-016Divergent uric acid responses to traditional Japanese diet and the DPP-4 inhibitor alogliptin in drug-naive subjects with type 2 diabetesQuote: | Uric acid UA is traditionally regarded as a metabolic risk marker; however, its dynamic behavior during glucose-lowering therapy remains incompletely understood. ... Patients exhibiting the greatest UA increases demonstrated the most marked improvements in beta-cell function and, with MJDD, the greatest weight loss. These findings indicate that MJDD and alogliptin exert distinct metabolic effects in early T2DM, yet both link rising UA to enhanced beta-cell function, suggesting that UA may serve as a dynamic pharmacometabolic biomarker reflecting therapy-specific metabolic adaptation rather than metabolic deterioration.
| Abstract:
| https://www.medrxiv.org/content/10.64898/2026.02.21.26346799v1 | Full Text:
| https://www.medrxiv.org/content/10.64898/2026.02.21.26346799v1.full-text | Search Similar: |
Brave Mojeek Startpage Dogpile Bing Googl Lycos Utub Yaho | Search Authors: |
Eiji Kuto Alexandra N Kuto Nozomi Urushibara Rumiko Okada Sachiko Ito,
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02/25/2026-08:05:42 PM
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