Journal of Diabetes Science and Technology最新文献

{"title":"Marked Increases in Continuous Glucose Monitor-Detected Hypoglycemia During a Seven-Day Water-Only Fast in Healthy Men and Women.","authors":"Kristoffer J Kolnes, Lauren V Turner, Steffen Brufladt, Emelie T F Nilsen, Anders J Kolnes, Stephen O'Rahilly, Jørgen Jensen, Michael C Riddell","doi":"10.1177/19322968261421956","DOIUrl":"10.1177/19322968261421956","url":null,"abstract":"<p><strong>Background: </strong>Multiday fasting is practiced globally for various health or religious reasons which can cause marked declines in circulating glucose levels. Yet, the extent of hypoglycemia exposure (ie, blood glucose <70 mg/dL), as measured by continuous glucose monitoring (CGM) during a prolonged fast is unclear. We aimed to determine the distribution of interstitial glucose data as measured by CGM, during a seven-day water-only fast in healthy men and women.</p><p><strong>Methods: </strong>This study used interstitial glucose levels from CGM (Dexcom G4 Platinum) to profile hypoglycemic exposure during a seven-day water-only fast in 12 healthy adults (seven men; age 29.7 ± 6.1 years; body mass index [BMI] 25.0 ± 3.3 kg/m²) that also included physical performance tests (day 6) and an oral glucose tolerance test (day 7).</p><p><strong>Results: </strong>Time <70 mg/dL increased from 3.0% ± 7.1% at baseline to 66.0% ± 25.7% by day 5 (<i>P</i> < .001). Minimum daily glucose levels also declined from 76 ± 14 mg/dL at baseline to 50 ± 7 mg/dL by day 5 (<i>P</i> < .001). The performance tests and the oral glucose tolerance test markedly increased glycemia. No symptoms of hypoglycemia were reported.</p><p><strong>Conclusions: </strong>This research demonstrated considerable hypoglycemia exposure occurs without symptoms in heathy men and women who undertake multiday fasting.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"659-663"},"PeriodicalIF":3.7,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12929083/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147271259","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting the Risk of Adverse Neonatal Outcomes in Women With Insulin-Treated Diabetes: Is It Time for a Pregnancy-Specific Glycemic Risk Index?","authors":"Fabrizia Citro, Cristina Bianchi, Tommaso Belcari, Federico Galleano, Caterina Venturi, Lorella Battini, Piero Marchetti, Alessandra Bertolotto, Michele Aragona","doi":"10.1177/19322968241289957","DOIUrl":"10.1177/19322968241289957","url":null,"abstract":"<p><strong>Background: </strong>The Glycemia Risk Index (GRI) describes the quality of glycemic control, emphasizing extreme hypoglycemia and hyperglycemia more than less extreme values. However, a pregnancy-specific GRI (pGRI), tailored to the tighter target glucose range required during pregnancy, has not been established.</p><p><strong>Methods: </strong>We retrospectively evaluated clinical, metabolic, and Continuous Glucose Monitoring (CGM) data across pregnancy in women with insulin-treated diabetes, managed between September 2021 and March 2024 at the University Hospital of Pisa. First and second levels of hyperglycemia (TAR1: 140-180 mg/dL, TAR2: >180 mg/dL) and hypoglycemia (TBR1: 63-54 mg/dL, TBR2: <54 mg/dL) were used to calculate the pGRI at each trimester. Logistic regression analysis investigated the association between pGRI and risk of at least one adverse neonatal outcome (among preterm delivery, macrosomia, large for gestational age, small for gestational age, neonatal hypoglycemia, neonatal jaundice, and neonatal intensive care unit admission).</p><p><strong>Results: </strong>Of 45 pregnant women, 25 (56%) experienced at least one adverse neonatal outcome. In the third trimester, women with adverse outcomes had significantly higher total TAR (26 [12-32]% vs 10 [4-23]%, <i>P</i> = .018) and lower TIR (71 [64-83]% vs 88 [75-92]%, <i>P</i> = .007). Specifically, the difference was notable in TAR2 (6 [2-15]% vs 1 [0-4]%, <i>P</i> = .004), whereas TAR1 was comparable between the 2 groups. Accordingly, third trimester pGRI was higher in women with adverse neonatal outcomes (38 [18-49]% vs 18 [10-31]%, <i>P</i> = .013) and, at logistic regression, slightly but significantly increased the risk of adverse neonatal outcomes (1.044 [1.004-1.086], <i>P</i> = .024).</p><p><strong>Conclusions: </strong>Pregnant women with insulin-treated diabetes reporting adverse neonatal outcomes spent more time in hyperglycemia, particularly in extreme hyperglycemia. Therefore, the level of hyperglycemia should always be assessed during pregnancy. The pGRI, emphasizing extreme hyperglycemia, may be a novel comprehensive tool for assessing the risk of adverse neonatal outcomes.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"736-742"},"PeriodicalIF":3.7,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11571631/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142466696","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Do Metrics of Temporal Glycemic Variability Reveal Abnormal Glucose Rates of Change in Type 1 Diabetes?","authors":"Robert Richardson","doi":"10.1177/19322968241298248","DOIUrl":"10.1177/19322968241298248","url":null,"abstract":"<p><strong>Background: </strong>We aimed to identify the normal range of glucose rates of change (RoC) observed in health and assess whether existing metrics of temporal glycemic variability (GV-timing), such as mean absolute glucose change (MAG) and continuous overlapping net glycemic action (CONGA), are predictive of abnormally rapid RoC in type 1 diabetes (T1D).</p><p><strong>Methods: </strong>We identified the normal range of RoC over one-hour intervals from continuous glucose monitoring (CGM) data of healthy individuals. Rapidly rising glucose was defined as RoC values above percentiles 99% (level 1, L1) or 99.9% (level 2, L2), and rapidly falling glucose as below 1% (L1) or 0.1% (L2). The percentage of time these thresholds are exceeded in a given individual is referred to as time in fluctuation (TIF). In a separate CGM dataset of 736 T1D individuals, we calculated TIF-L1 and TIF-L2, and compared them against corresponding values of MAG and CONGA.</p><p><strong>Results: </strong>The extremum percentiles of RoC observed in health are 0.1%: -80 mg/dL/h, 1%: -50 mg/dL, 99%: +56 mg/dL/h, and 99.9%: +89 mg/dL/h. The T1D individuals spend significantly more TIF at rates exceeding these thresholds (TIF-L1: median, 16.7% [interquartile range, 12.7-21.5], TIF-L2: 5.0% [3.1-7.8]) than healthy individuals (TIF-L1: 1.4% [0.6-2.8], TIF-L2: 0.0% [0.0-0.2]). Both MAG and CONGA are highly correlated with TIF-L1 and TIF-L2 (<i>r</i> > .95 in each pairwise comparison).</p><p><strong>Conclusions: </strong>Individuals with T1D spend significant time with glucose RoC exceeding those observed in health. Existing GV-timing metrics are strongly correlated with time with abnormal RoC. Incorporation of a GV-timing metric in clinical practice is recommended.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"794-802"},"PeriodicalIF":3.7,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11571577/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142621214","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improvement in Newly Defined Continuous Glucose Monitor Metrics, Extended Hypoglycemia, and Extended Hyperglycemia With Automated Insulin Delivery Initiation in Adults With Type 1 Diabetes.","authors":"Kagan E Karakus, Janet Snell-Bergeon, Emma Mason, Halis K Akturk","doi":"10.1177/19322968241301429","DOIUrl":"10.1177/19322968241301429","url":null,"abstract":"<p><strong>Objective: </strong>Extended hypoglycemia (Ehypo) and extended hyperglycemia (Ehyper) are recently defined continuous glucose monitoring (CGM) metrics by the International Consensus for clinical trials as secondary endpoints for continuous outcomes. This study aims to evaluate the changes in Ehypo and Ehyper before and after automated insulin delivery (AID) initiation in adults with type 1 diabetes (T1D).</p><p><strong>Research methods: </strong>This is a retrospective single-center study that evaluated Ehypo and Ehyper in addition to other CGM metrics in 154 adults that initiated an AID system. Metrics were compared before and after AID initiation by Wilcoxon signed-rank test.</p><p><strong>Results: </strong>Median (interquartile range) Ehypo (<70 mg/dL) events/week decreased from 0.1 (0-0.4) to 0 (0-0.1) and Ehyper (>250 mg/dL) events/week decreased from 2.2 (0.9-4.5) to 0.8 (0.3-1.7) (both <i>P</i> < .001) after AID initiation compared with before AID initiation. All other CGM metrics improved after AID initiation. There was a strong positive correlation between Ehyper (>250 mg/dL) and mean glucose (before AID: <i>r</i> = 0.947, after AID: <i>r</i> = 0.894), glucose management indicator (before AID: <i>r</i> = 0.947, after AID: <i>r</i> = 0.887), and time above range (TAR; >180 mg/dL) (before AID: <i>r</i> = 0.957, after AID: <i>r</i> = 0.917) and a strong positive correlation between Ehypo (<70 mg/dL) and time below range (TBR; <70 mg/dL) (before AID: <i>r</i> = 0.823, after AID: <i>r</i> = 0.608) before and after AID initiation, respectively.</p><p><strong>Conclusion: </strong>Automated insulin delivery initiation significantly improved Ehypo and Ehyper metrics. Ehypo and Ehyper had a strong positive correlation with TBR and TAR, respectively. Ehypo and Ehyper events can be used in addition to TBR and TAR metrics in clinical studies as secondary outcomes.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"721-726"},"PeriodicalIF":3.7,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11618842/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142780241","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Effectiveness of Mobile Health Applications for Cardiometabolic Risk Reduction in Urban and Rural India: A Pilot, Randomized Controlled Study.","authors":"Harish Ranjani, Parizad Avari, Sharma Nitika, Narayanaswamy Jagannathan, Nick Oliver, Jonathan Valabhji, Viswanathan Mohan, John Campbell Chambers, Ranjit Mohan Anjana","doi":"10.1177/19322968241310861","DOIUrl":"10.1177/19322968241310861","url":null,"abstract":"<p><strong>Introduction: </strong>mHealth technology has the potential to deliver personalized health care; however, data on cardiometabolic risk factors are limited. This study aims to assess the effectiveness of mobile health applications (apps) on cardiometabolic risk factor reduction in adults aged 25 to 60 years in urban and rural India.</p><p><strong>Methods: </strong>The study design was a pilot randomized controlled trial conducted in Tamil Nadu, India. Smartphone users (25-60 years) with basic literacy and at high risk of developing diabetes (Indian Diabetes Risk Score ≥30 and/or fasting blood sugar [FBS] 100-125 mg/dL) were recruited. Four mobile apps (two commercially available, two novel) for cardiometabolic risk reduction were evaluated. Primary outcome (weight loss) was analyzed using intention-to-treat analysis with post hoc analysis and logistic regression models adjusted for confounders.</p><p><strong>Results: </strong>A total of 5264 participants were screened, and 610 were recruited into the study. Participants (7%) dropped out largely due to the COVID-19 pandemic. Data from 567 participants were used for the final analysis. In the intention-to-treat analysis, a significant reduction in body weight was observed in the intervention group as compared with control, more so in the urban (-2.40 kg, 95% confidence interval [CI] = [-3.10, -1.69], <i>P</i> < .001) compared with rural population (-1.19 kg, 95% CI = [-1.55, -0.82], <i>P</i> < .001). Intervention group participants showed significant reductions in body mass index, waist circumference, blood pressure, FBS, total serum cholesterol, and a positive effect on dietary and physical activity behaviors compared with controls.</p><p><strong>Conclusions: </strong>mHealth interventions can reduce diabetes risk, improve cardiometabolic health, and improve lifestyle behaviors in South Asian populations.</p><p><strong>Trial registration: </strong>The trial is registered with the Central Trials Registry, India (CTRI/2020/03/024327).</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"962-976"},"PeriodicalIF":3.7,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11733870/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142983733","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cost-Effective Quality of Life Improvement While Reducing Health Care Professional Burnout With an AI-Driven Intervention for Personalized Medicine.","authors":"Ryan Charles Kelly, Richard I G Holt, Hermione Price, Peter Phiri, Michael Cummings, Amar Ali, Mayank Patel, Ethan Barnard, Sharon Allard, Victoria Hunter, Jana Rojkova, Clare Bolger, Daniela Georgieva, Maren Schinz, Martina Rothenbühler, Aritz Lizoain, Katharine Barnard-Kelly","doi":"10.1177/19322968241310879","DOIUrl":"10.1177/19322968241310879","url":null,"abstract":"<p><strong>Background and aims: </strong>Burnout affects >50% of physicians and nurses. Spotlight-AQ is a personalized digital health platform designed to improve routine diabetes visits. We assessed cost-effectiveness, visit length, and association with health care professional (HCP) burnout.</p><p><strong>Materials and methods: </strong>Complete case within-trial cost-effectiveness analysis embedded within a multicenter, parallel-group randomized controlled trial. Adults with diabetes were recruited from primary and secondary care. Intervention group participants completed the Spotlight-AQ pre-clinic assessment before each routine visit. Health status was assessed with EQ-5D-5L to calculate quality-adjusted life years (QALYs). Client Service Receipt Inventory measured downstream resource use. Total costs and QALYs were calculated using baseline-controlled seemingly unrelated regression with bootstrapping. Haemoglobin (HbA_1c) data were collected. Health care professionals completed the Maslach Burnout Inventory at baseline and study end.</p><p><strong>Results: </strong>A total of 98 adults (49 intervention) and 18 HCPs participated. Total costs: £243 (US$310) intervention arm versus £230 (US$293) control arm; incremental cost: £13 (US$16). Total QALYs: 0.362 intervention arm and 0.358 control arm, with an incremental QALY: 0.004. Spotlight-AQ intervention dominated usual care with a 68% probability of cost-effectiveness at a threshold of £30 000 (US$38 294) per QALY gained. Health care professionals reported reduced burnout, emotional exhaustion, depersonalization, and a greater sense of personal achievement. Doctors are more so than nurses.</p><p><strong>Conclusion: </strong>Spotlight-AQ has demonstrated cost-effective while delivering improved care and reduced HCP burnout.</p><p><strong>Trial registration: </strong>ISRCTN15511689, registration date: November 1, 2021.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"848-854"},"PeriodicalIF":3.7,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11783410/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143066194","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantification of Different Types of Waste and Batteries Associated With the Widespread Usage of Continuous Glucose Monitoring Systems.","authors":"Sebastian F Petry, Marie Bienhaus, Friedrich W Petry, Johannes K Petry, Lutz Heinemann, Stefan Gäth","doi":"10.1177/19322968241305161","DOIUrl":"10.1177/19322968241305161","url":null,"abstract":"<p><strong>Background: </strong>People with diabetes benefit from continuous glucose monitoring (CGM) systems. A downside of these valuable aids for diabetes management is the generation of a tremendous amount of waste. This study aimed to quantify this CGM-related waste.</p><p><strong>Method: </strong>Twenty-four used CGM sensors from two different manufacturers (8× FreeStyle Libre 2, 11× FreeStyle Libre 3, and 5× Dexcom G7) were dismantled manually and separated in case, circuit board, and battery. Each component as well as included packaging, applicator, and leaflet were weighed separately.</p><p><strong>Results: </strong>Packaging, applicators, and leaflets accounted for most of the waste (FL2: 93.4 g; FL3: 58 g; G7: 108.1 g). The plastic case contributed mainly to the total sensor weight (FL2: 1.9 g/63% of 3.3 g; FL3: 0.5 g/49% of 1.1 g; G7: 1.9 g/59% of 3.2 g), whereas the weight of the electronic circuit board and battery varied (FL2: 0.8 g/25%, 0.4 g/12%; FL3: 0.2 g/17%, 0.4 g/34%; G7: 0.7 g/22%, 0.6 g/19%). Extrapolating these data based on annual worldwide usage of around 230 million glucose sensors, approximately 20,000 tons of packaging, applicators, and leaflets and 580 tons of glucose sensors are disposed of, including about 340 tons of casings, 130 tons of circuit boards, and 110 tons of batteries.</p><p><strong>Conclusions: </strong>Our data highlight the potential for optimized resource utilization by reduction of packaging, sensor size, longer application periods, implementation of multiuse applicators, and the need for recycling options.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"787-793"},"PeriodicalIF":3.7,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11662339/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142872256","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Addressing Disparities Using Continuous Glucose Monitors and Remote Patient Monitoring for Youth With Type 1 Diabetes.","authors":"Ming Yeh Lee, Victor Ritter, Blake Shaw, Johannes O Ferstad, Ramesh Johari, David Scheinker, Franziska Bishop, Manisha Desai, David M Maahs, Priya Prahalad","doi":"10.1177/19322968241305612","DOIUrl":"10.1177/19322968241305612","url":null,"abstract":"<p><strong>Background: </strong>Youth with type 1 diabetes (T1D) and public insurance have lower diabetes technology use. This pilot study assessed the feasibility of a program to support continuous glucose monitor (CGM) use with remote patient monitoring (RPM) to improve glycemia for youth with established T1D and public insurance.</p><p><strong>Methods: </strong>From August 2020 to June 2023, we provided CGM with RPM support via patient portal messaging for youth with established T1D on public insurance with challenges obtaining consistent CGM supplies. We prospectively collected hemoglobin A_1c (HbA_1c), standard CGM metrics, and diabetes technology use over 12 months.</p><p><strong>Results: </strong>The cohort included 91 youths with median age at enrollment 14.7 years, duration of diabetes 4.4 years, 33% non-English speakers, and 44% Hispanic. Continuous glucose monitor data were consistently available (≥70%) in 23% of the participants. For the 64% of participants with paired HbA_1c values at enrollment and study end, the median HbA_1c decreased from 9.8% to 9.0% (<i>P</i> < .001). Insulin pump users increased from 31 to 48 and automated insulin delivery users increased from 11 to 38.</p><p><strong>Conclusions: </strong>We established a program to support CGM use in youth with T1D and barriers to consistent CGM supplies, offering lessons for other clinics to address disparities with team-based, algorithm-enabled, remote T1D care. This real-world pilot and feasibility study noted challenges with low levels of protocol adherence and obtaining complete data in this cohort. Future iterations of the program should explore RPM communication methods that better align with this population's preferences to increase participant engagement.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"985-993"},"PeriodicalIF":3.7,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11664559/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142877339","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modifiable Factors Affecting the Postprandial Glycemic Response.","authors":"Yue Wu, Tracey McLaughlin, Sayra Gorgani, Agatha F Scheideman, Mandy M Shao, Brady David Hislop, Khoa Hoang, Dalia Perelman, Curtis McGinity, Majid Rodgar, Heyjun Park, Tao Wang, Caleb Mayer, Ashley DuNova, Alessandra Ayers, Cindy Ho, Helge Ræder, David C Klonoff, Michael P Snyder","doi":"10.1177/19322968261418614","DOIUrl":"10.1177/19322968261418614","url":null,"abstract":"<p><p>The postprandial glycemic response (PPGR) is associated with diabetes and cardiovascular disease and is highly individualized. The PPGR is affected by both physiological and behavioral factors. Attention to the PPGR has dramatically increased recently with the widespread use of continuous glucose monitors. It is expected that individualized control of PPGRs will be important in the prevention of diabetes and its associated complications. In this article, we discuss six modifiable factors associated with the PPGRs, including (1) the glucoregulatory hormones, (2) gastric emptying, (3) salivary or pancreatic amylase, (4) diet, (5) physical exercise, and (6) sleep and circadian rhythm. Modifying these factors may allow for personalized intervention strategies to control the PPGR-to reduce the risk for cardiovascular disease in individuals with varying degrees of glycemia.</p>","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"1041-1047"},"PeriodicalIF":3.7,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12885966/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146142532","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Glucodensity-Based Models Outperform Time in Range and Glycemia Risk Index in Prediction Models.","authors":"Mandy M Shao, Agatha F Scheideman, David C Klonoff, Francisco Gude, Marcos Matabuena","doi":"10.1177/19322968261421954","DOIUrl":"10.1177/19322968261421954","url":null,"abstract":"","PeriodicalId":15475,"journal":{"name":"Journal of Diabetes Science and Technology","volume":" ","pages":"1054-1055"},"PeriodicalIF":3.7,"publicationDate":"2026-05-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12932125/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147275473","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}