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Resident memory CD8(+) T cells dominate lymphoid immune cell population in human pancreatic islets in health and type 2 diabetes.
IF 3.7 2区 医学 Q2 ENDOCRINOLOGY & METABOLISM Pub Date : 2025-03-11 DOI: 10.1136/bmjdrc-2024-004559
Miljana Radenkovic, Jeanette Arvastsson, Luis Sarmiento, Corrado M Cilio

Introduction: In type 2 diabetes (T2D), beta cell failure is often associated with islet inflammation driven by the innate immune response, with macrophages playing a significant role. However, the composition and phenotype of lymphoid immune cells in the islets of individuals with T2D have not been extensively studied. This study aims to characterize and compare the presence, phenotype, and frequency of islet-associated lymphocytes-specifically T, B, and natural killer (NK) cells-in patients with T2D and non-diabetic organ donors.

Research design and methods: Multicolor flow cytometry was employed to detect NK, B, and T cells in dissociated pancreatic islets from 13 T2D and 44 non-diabetic donors. The frequencies and phenotypes of T cell subsets were determined using markers for memory differentiation status and tissue-resident T cells. The frequencies of alpha and beta cells were assessed by flow cytometry, and the insulin secretion level was measured by ELISA.

Results: In both T2D and non-diabetic islets, CD3(+) T cells were the predominant lymphocytes, mainly central and effector memory phenotypes, with a bias toward CD8(+) T cells expressing canonical residency markers (CD69 and CD103). The frequencies of CD19(+) B cells and CD3(-) CD16(+) CD56(+) NK cells were low in both groups. The proportions of these immune and beta cells were similar between T2D and non-diabetic donors. However, T2D donors had a higher proportion of glucagon-producing alpha cells and significantly reduced glucose-stimulated insulin secretion compared with non-diabetic individuals.

Conclusions: In T2D islets, resident CD8(+) T cells with a central memory phenotype dominate the lymphoid immune cell population, similar to non-diabetic donors. These findings provide the first insights into the memory T cell composition in human pancreatic islets in T2D, suggesting that the diabetic condition does not significantly alter the lymphoid landscape of pancreatic islets.

{"title":"Resident memory CD8(+) T cells dominate lymphoid immune cell population in human pancreatic islets in health and type 2 diabetes.","authors":"Miljana Radenkovic, Jeanette Arvastsson, Luis Sarmiento, Corrado M Cilio","doi":"10.1136/bmjdrc-2024-004559","DOIUrl":"10.1136/bmjdrc-2024-004559","url":null,"abstract":"<p><strong>Introduction: </strong>In type 2 diabetes (T2D), beta cell failure is often associated with islet inflammation driven by the innate immune response, with macrophages playing a significant role. However, the composition and phenotype of lymphoid immune cells in the islets of individuals with T2D have not been extensively studied. This study aims to characterize and compare the presence, phenotype, and frequency of islet-associated lymphocytes-specifically T, B, and natural killer (NK) cells-in patients with T2D and non-diabetic organ donors.</p><p><strong>Research design and methods: </strong>Multicolor flow cytometry was employed to detect NK, B, and T cells in dissociated pancreatic islets from 13 T2D and 44 non-diabetic donors. The frequencies and phenotypes of T cell subsets were determined using markers for memory differentiation status and tissue-resident T cells. The frequencies of alpha and beta cells were assessed by flow cytometry, and the insulin secretion level was measured by ELISA.</p><p><strong>Results: </strong>In both T2D and non-diabetic islets, CD3(+) T cells were the predominant lymphocytes, mainly central and effector memory phenotypes, with a bias toward CD8(+) T cells expressing canonical residency markers (CD69 and CD103). The frequencies of CD19(+) B cells and CD3(-) CD16(+) CD56(+) NK cells were low in both groups. The proportions of these immune and beta cells were similar between T2D and non-diabetic donors. However, T2D donors had a higher proportion of glucagon-producing alpha cells and significantly reduced glucose-stimulated insulin secretion compared with non-diabetic individuals.</p><p><strong>Conclusions: </strong>In T2D islets, resident CD8(+) T cells with a central memory phenotype dominate the lymphoid immune cell population, similar to non-diabetic donors. These findings provide the first insights into the memory T cell composition in human pancreatic islets in T2D, suggesting that the diabetic condition does not significantly alter the lymphoid landscape of pancreatic islets.</p>","PeriodicalId":9151,"journal":{"name":"BMJ Open Diabetes Research & Care","volume":"13 2","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-03-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11904352/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143603921","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Medication prescribing and pregnancy-related risk factors for women with type 2 diabetes of reproductive age within primary care: a cross-sectional investigation for the PREPARED study.
IF 3.7 2区 医学 Q2 ENDOCRINOLOGY & METABOLISM Pub Date : 2025-03-05 DOI: 10.1136/bmjdrc-2024-004312
Alexandra M Famiglietti, Judith Parsons, Kia-Chong Chua, Anna Hodgkinson, Olubunmi Abiola, Anna Brackenridge, Anita Banerjee, Mark Chamley, Lily Hopkins, Katharine F Hunt, Helen Murphy, Helen Rogers, Gavin Steele, Kirsty Winkley, Angus Forbes, Rita Forde

Introduction: Women with type 2 diabetes are at risk of commencing pregnancy while using medications that are either not recommended for pregnancy or with known teratogenicity, which may contribute to adverse pregnancy outcomes. In this study, we aimed to characterize pregnancy-related risk factors and medication exposures among women with type 2 diabetes.

Research design and methods: Individual health characteristics, sociodemographic information, and prescription data were extracted from the primary care records of women aged 18-45 years with type 2 diabetes in participating general practices in the UK. Prescribed medications were categorized according to suitability for pregnancy: recommended, not recommended, or not recommended but used if clinically indicated. Logistic regression was used to estimate associations between individual characteristics and medications not recommended for pregnancy.

Results: Data on 725 women were extracted. Prescribed medications suggested the presence of numerous comorbidities, with diabetes medications (65%, n=471) and statins (20%, n=145) most frequently prescribed. 37% (n=268) of women took ≥3 medications, and a third (n=269) took medications not recommended for pregnancy. Among those not prescribed contraception (89%, n=646), no one met all clinically recommended pre-pregnancy criteria. In multivariable logistic regression analysis, polypharmacy (OR 3.49 95% CI 2.88 to 4.30) and age (OR 1.04 95% CI 1.00 to 1.09) were associated with use of medications not recommended for pregnancy.

Conclusions: Women with type 2 diabetes have suboptimal contraceptive provision despite multiple exposures to medications not recommended for pregnancy. Regular assessment of contraceptive use, reproductive intentions, and medication review is urgently needed in primary care settings to minimize pregnancy-related risks.

{"title":"Medication prescribing and pregnancy-related risk factors for women with type 2 diabetes of reproductive age within primary care: a cross-sectional investigation for the PREPARED study.","authors":"Alexandra M Famiglietti, Judith Parsons, Kia-Chong Chua, Anna Hodgkinson, Olubunmi Abiola, Anna Brackenridge, Anita Banerjee, Mark Chamley, Lily Hopkins, Katharine F Hunt, Helen Murphy, Helen Rogers, Gavin Steele, Kirsty Winkley, Angus Forbes, Rita Forde","doi":"10.1136/bmjdrc-2024-004312","DOIUrl":"10.1136/bmjdrc-2024-004312","url":null,"abstract":"<p><strong>Introduction: </strong>Women with type 2 diabetes are at risk of commencing pregnancy while using medications that are either not recommended for pregnancy or with known teratogenicity, which may contribute to adverse pregnancy outcomes. In this study, we aimed to characterize pregnancy-related risk factors and medication exposures among women with type 2 diabetes.</p><p><strong>Research design and methods: </strong>Individual health characteristics, sociodemographic information, and prescription data were extracted from the primary care records of women aged 18-45 years with type 2 diabetes in participating general practices in the UK. Prescribed medications were categorized according to suitability for pregnancy: recommended, not recommended, or not recommended but used if clinically indicated. Logistic regression was used to estimate associations between individual characteristics and medications not recommended for pregnancy.</p><p><strong>Results: </strong>Data on 725 women were extracted. Prescribed medications suggested the presence of numerous comorbidities, with diabetes medications (65%, n=471) and statins (20%, n=145) most frequently prescribed. 37% (n=268) of women took ≥3 medications, and a third (n=269) took medications not recommended for pregnancy. Among those not prescribed contraception (89%, n=646), no one met all clinically recommended pre-pregnancy criteria. In multivariable logistic regression analysis, polypharmacy (OR 3.49 95% CI 2.88 to 4.30) and age (OR 1.04 95% CI 1.00 to 1.09) were associated with use of medications not recommended for pregnancy.</p><p><strong>Conclusions: </strong>Women with type 2 diabetes have suboptimal contraceptive provision despite multiple exposures to medications not recommended for pregnancy. Regular assessment of contraceptive use, reproductive intentions, and medication review is urgently needed in primary care settings to minimize pregnancy-related risks.</p>","PeriodicalId":9151,"journal":{"name":"BMJ Open Diabetes Research & Care","volume":"13 2","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-03-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11883600/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143566139","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Quantification of the relation between continuous glucose monitoring observation period and the estimation error in assessing long-term glucose regulation.
IF 3.7 2区 医学 Q2 ENDOCRINOLOGY & METABOLISM Pub Date : 2025-02-26 DOI: 10.1136/bmjdrc-2024-004768
Stennie Zoet, Thomas Urgert, Anouk Veldhuis, Bert-Jan van Beijnum, Gozewijn D Laverman

Introduction: The integration of continuous glucose monitoring (CGM) into clinical practice has rapidly emerged in the last decade, changing the evaluation of long-term glucose regulation in patients with diabetes. When using CGM-derived metrics to evaluate long-term glucose regulation, it is essential to determine the minimal observation period necessary for a reliable estimate. The approach of this study was to calculate mean absolute errors (MAEs) for varying window lengths, with the goal of demonstrating how the CGM observation period influences the accuracy of the estimation of 90-day glycemic control.

Research design and methods: CGM data were collected from the DIABASE cohort (ZGT hospital, The Netherlands). Trailing aggregates (TAs) were calculated for four CGM-derived metrics: time in range (TIR), time below range (TBR), glucose management indicator (GMI) and glycemic variability (GV). Arbitrary MAEs for each patient were compared between the TAs of window lengths from 1 to 89 days and a reference TA of 90 days, which is assumed to reflect long-term glycemic regulation.

Results: Using 14 days of CGM data resulted in 65% of subjects having their TIR estimation being below a MAE threshold of 5%. In order to have 90% of the subjects below a TIR MAE threshold of 5%, the observation period needs to be 29 days.

Conclusions: Although there is currently no consensus on what is an acceptable MAE, this study provides insight into how MAEs of CGM-derived metrics change according to the used observation period within a population and may thus be helpful for clinical decision-making.

{"title":"Quantification of the relation between continuous glucose monitoring observation period and the estimation error in assessing long-term glucose regulation.","authors":"Stennie Zoet, Thomas Urgert, Anouk Veldhuis, Bert-Jan van Beijnum, Gozewijn D Laverman","doi":"10.1136/bmjdrc-2024-004768","DOIUrl":"10.1136/bmjdrc-2024-004768","url":null,"abstract":"<p><strong>Introduction: </strong>The integration of continuous glucose monitoring (CGM) into clinical practice has rapidly emerged in the last decade, changing the evaluation of long-term glucose regulation in patients with diabetes. When using CGM-derived metrics to evaluate long-term glucose regulation, it is essential to determine the minimal observation period necessary for a reliable estimate. The approach of this study was to calculate mean absolute errors (MAEs) for varying window lengths, with the goal of demonstrating how the CGM observation period influences the accuracy of the estimation of 90-day glycemic control.</p><p><strong>Research design and methods: </strong>CGM data were collected from the DIABASE cohort (ZGT hospital, The Netherlands). Trailing aggregates (TAs) were calculated for four CGM-derived metrics: time in range (TIR), time below range (TBR), glucose management indicator (GMI) and glycemic variability (GV). Arbitrary MAEs for each patient were compared between the TAs of window lengths from 1 to 89 days and a reference TA of 90 days, which is assumed to reflect long-term glycemic regulation.</p><p><strong>Results: </strong>Using 14 days of CGM data resulted in 65% of subjects having their TIR estimation being below a MAE threshold of 5%. In order to have 90% of the subjects below a TIR MAE threshold of 5%, the observation period needs to be 29 days.</p><p><strong>Conclusions: </strong>Although there is currently no consensus on what is an acceptable MAE, this study provides insight into how MAEs of CGM-derived metrics change according to the used observation period within a population and may thus be helpful for clinical decision-making.</p>","PeriodicalId":9151,"journal":{"name":"BMJ Open Diabetes Research & Care","volume":"13 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11865789/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143514619","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Diabetes in migrant communities: a rising healthcare priority.
IF 3.7 2区 医学 Q2 ENDOCRINOLOGY & METABOLISM Pub Date : 2025-02-26 DOI: 10.1136/bmjdrc-2024-004698
Guillermo E Umpierrez, Mohammed K Ali
{"title":"Diabetes in migrant communities: a rising healthcare priority.","authors":"Guillermo E Umpierrez, Mohammed K Ali","doi":"10.1136/bmjdrc-2024-004698","DOIUrl":"10.1136/bmjdrc-2024-004698","url":null,"abstract":"","PeriodicalId":9151,"journal":{"name":"BMJ Open Diabetes Research & Care","volume":"13 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11865790/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143514614","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Clinical phenotyping of people living with type 1 diabetes according to their levels of diabetes-related distress: results from the SFDT1 cohort.
IF 3.7 2区 医学 Q2 ENDOCRINOLOGY & METABOLISM Pub Date : 2025-02-24 DOI: 10.1136/bmjdrc-2024-004524
Dulce Canha, Gloria Aguayo, Emmanuel Cosson, Patricia Vaduva, Eric Renard, Fawaz Alzaid, Fabrice Bonnet, Samy Hadjadj, Louis Potier, Bruno Vergès, Sandrine Lablanche, Pierre Yves Benhamou, Helene Hanaire, Yves Reznik, Jean-Pierre Riveline, Guy Fagherazzi

Introduction: Type 1 diabetes is burdensome, requiring complex daily management and making people more prone to emotional distress. To better detect diabetes-related distress (DD) and identify at-risk patients, we aimed to provide an in-depth characterization of DD in people with type 1 diabetes.

Research design and methods: We included adults with type 1 diabetes from the Suivi en France des personnes avec un Diabète de Type 1 cohort who filled in the Problem Areas in Diabetes questionnaire (PAID ≥40 indicates high DD). Age and sex-adjusted multivariable logistic regression models analyzed individual characteristics, clinical indicators, diabetes-related complications and psychological factors. We further analyzed DD according to six data-driven subdimensions: emotional distress, fear of complications, social distress, eating distress, management distress, and diabetes burnout.

Results: In total, 1220 participants (50.6% female, age 42 years (SD 13.9), diabetes duration 24.7 years (13.6)) had a total mean PAID score of 39.6 (21.7) and 592 (48.5%) reported high DD. Leading subdimensions of DD included fear of complications (50.1 (24.4)) and diabetes burnout (45.9 (24.5)). Females, younger age, social vulnerability, smoking, and the presence of retinopathy were positively associated with high DD (p<0.05). We observed similar DD levels across HbA1c levels and treatment modalities, including automated insulin delivery and continuous glucose monitoring use. Several psychological factors, such as anxiety/depression, poor sleep quality, and treatment burden, were strongly associated with DD (p<0.001).

Conclusions: We provide a holistic clinical phenotyping approach that enables the identification of determinants and prevalence of DD, overall and according to key DD subdimensions, in a large and diverse population. Our results underscore the importance of developing DD-targeted prevention and intervention strategies focused specifically on high-risk groups and the most impactful distress subdimensions to reduce the impact of type 1 diabetes burden.

Trial registration number: NCT04657783.

{"title":"Clinical phenotyping of people living with type 1 diabetes according to their levels of diabetes-related distress: results from the SFDT1 cohort.","authors":"Dulce Canha, Gloria Aguayo, Emmanuel Cosson, Patricia Vaduva, Eric Renard, Fawaz Alzaid, Fabrice Bonnet, Samy Hadjadj, Louis Potier, Bruno Vergès, Sandrine Lablanche, Pierre Yves Benhamou, Helene Hanaire, Yves Reznik, Jean-Pierre Riveline, Guy Fagherazzi","doi":"10.1136/bmjdrc-2024-004524","DOIUrl":"10.1136/bmjdrc-2024-004524","url":null,"abstract":"<p><strong>Introduction: </strong>Type 1 diabetes is burdensome, requiring complex daily management and making people more prone to emotional distress. To better detect diabetes-related distress (DD) and identify at-risk patients, we aimed to provide an in-depth characterization of DD in people with type 1 diabetes.</p><p><strong>Research design and methods: </strong>We included adults with type 1 diabetes from the <i>Suivi en France des personnes avec un Diabète de Type 1</i> cohort who filled in the Problem Areas in Diabetes questionnaire (PAID ≥40 indicates high DD). Age and sex-adjusted multivariable logistic regression models analyzed individual characteristics, clinical indicators, diabetes-related complications and psychological factors. We further analyzed DD according to six data-driven subdimensions: emotional distress, fear of complications, social distress, eating distress, management distress, and diabetes burnout.</p><p><strong>Results: </strong>In total, 1220 participants (50.6% female, age 42 years (SD 13.9), diabetes duration 24.7 years (13.6)) had a total mean PAID score of 39.6 (21.7) and 592 (48.5%) reported high DD. Leading subdimensions of DD included fear of complications (50.1 (24.4)) and diabetes burnout (45.9 (24.5)). Females, younger age, social vulnerability, smoking, and the presence of retinopathy were positively associated with high DD (p<0.05). We observed similar DD levels across HbA1c levels and treatment modalities, including automated insulin delivery and continuous glucose monitoring use. Several psychological factors, such as anxiety/depression, poor sleep quality, and treatment burden, were strongly associated with DD (p<0.001).</p><p><strong>Conclusions: </strong>We provide a holistic clinical phenotyping approach that enables the identification of determinants and prevalence of DD, overall and according to key DD subdimensions, in a large and diverse population. Our results underscore the importance of developing DD-targeted prevention and intervention strategies focused specifically on high-risk groups and the most impactful distress subdimensions to reduce the impact of type 1 diabetes burden.</p><p><strong>Trial registration number: </strong>NCT04657783.</p>","PeriodicalId":9151,"journal":{"name":"BMJ Open Diabetes Research & Care","volume":"13 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-02-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11865782/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143499186","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Role of 2-hour plasma glucose in assessing pre-diabetes risk: insights from the vitamin D and type 2 diabetes (D2d) study cohort.
IF 3.7 2区 医学 Q2 ENDOCRINOLOGY & METABOLISM Pub Date : 2025-02-20 DOI: 10.1136/bmjdrc-2025-004953
Sun H Kim, Vanita R Aroda, Ranee Chatterjee, Erin S LeBlanc, Jason Nelson, Neda Rasouli, Myrlene A Staten, Ellen M Vickery, Anastassios G Pittas, Daniel S Hsia
{"title":"Role of 2-hour plasma glucose in assessing pre-diabetes risk: insights from the vitamin D and type 2 diabetes (D2d) study cohort.","authors":"Sun H Kim, Vanita R Aroda, Ranee Chatterjee, Erin S LeBlanc, Jason Nelson, Neda Rasouli, Myrlene A Staten, Ellen M Vickery, Anastassios G Pittas, Daniel S Hsia","doi":"10.1136/bmjdrc-2025-004953","DOIUrl":"https://doi.org/10.1136/bmjdrc-2025-004953","url":null,"abstract":"","PeriodicalId":9151,"journal":{"name":"BMJ Open Diabetes Research & Care","volume":"13 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143466873","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Incidence of metabolic and bariatric surgery among US adults with obesity by diabetes status: 2016-2020.
IF 3.7 2区 医学 Q2 ENDOCRINOLOGY & METABOLISM Pub Date : 2025-02-18 DOI: 10.1136/bmjdrc-2024-004614
Yiling J Cheng, Kai McKeever Bullard, Israel Hora, Brook Belay, Fang Xu, Christopher S Holliday, Roberto Simons-Linares, Stephen R Benoit

Introduction: Metabolic and bariatric surgery (MBS) is an effective intervention to manage diabetes and obesity. The population-based incidence of MBS is unknown.

Objective: To estimate the incidence of MBS among US adults with obesity by diabetes status and selected sociodemographic characteristics.

Research design and methods: This cross-sectional study used data from the 2016-2020 Nationwide Inpatient Sample and Nationwide Ambulatory Surgery Sample to capture MBS procedures. The National Health Interview Survey was used to establish the denominator for incidence calculations. Participants included US non-pregnant adults aged ≥18 years with obesity. The main outcome was incident MBS without previous MBS, defined by International Classification of Diseases, Tenth Revision Procedure Codes, Diagnosis Related Group system codes, and Current Procedural Terminology codes. Adjusted incidence and annual percentage change (2016-2019) were estimated using logistic regression.

Results: Among US adults with obesity, over 900 000 MBS procedures were performed in inpatient and hospital-owned ambulatory surgical centers in the USA during 2016-2020. The age- and sex-adjusted incidence of MBS per 1000 adults was 5.9 (95% CI 5.4 to 6.4) for adults with diabetes and 2.0 (95% CI 1.9 to 2.1) for adults without diabetes. MBS incidence was significantly higher for women and adults with class III obesity regardless of diabetes status. The highest incidence of MBS occurred in the Northeast region. Sleeve gastrectomy was the most common MBS surgical approach.

Conclusions: Incident MBS procedures were nearly threefold higher among adults with obesity and diabetes than those with obesity but without diabetes. Continued monitoring of the trends of MBS and other treatment modalities can inform our understanding of treatment accessibility to guide prevention efforts aimed at reducing obesity and diabetes.

{"title":"Incidence of metabolic and bariatric surgery among US adults with obesity by diabetes status: 2016-2020.","authors":"Yiling J Cheng, Kai McKeever Bullard, Israel Hora, Brook Belay, Fang Xu, Christopher S Holliday, Roberto Simons-Linares, Stephen R Benoit","doi":"10.1136/bmjdrc-2024-004614","DOIUrl":"10.1136/bmjdrc-2024-004614","url":null,"abstract":"<p><strong>Introduction: </strong>Metabolic and bariatric surgery (MBS) is an effective intervention to manage diabetes and obesity. The population-based incidence of MBS is unknown.</p><p><strong>Objective: </strong>To estimate the incidence of MBS among US adults with obesity by diabetes status and selected sociodemographic characteristics.</p><p><strong>Research design and methods: </strong>This cross-sectional study used data from the 2016-2020 Nationwide Inpatient Sample and Nationwide Ambulatory Surgery Sample to capture MBS procedures. The National Health Interview Survey was used to establish the denominator for incidence calculations. Participants included US non-pregnant adults aged ≥18 years with obesity. The main outcome was incident MBS without previous MBS, defined by International Classification of Diseases, Tenth Revision Procedure Codes, Diagnosis Related Group system codes, and Current Procedural Terminology codes. Adjusted incidence and annual percentage change (2016-2019) were estimated using logistic regression.</p><p><strong>Results: </strong>Among US adults with obesity, over 900 000 MBS procedures were performed in inpatient and hospital-owned ambulatory surgical centers in the USA during 2016-2020. The age- and sex-adjusted incidence of MBS per 1000 adults was 5.9 (95% CI 5.4 to 6.4) for adults with diabetes and 2.0 (95% CI 1.9 to 2.1) for adults without diabetes. MBS incidence was significantly higher for women and adults with class III obesity regardless of diabetes status. The highest incidence of MBS occurred in the Northeast region. Sleeve gastrectomy was the most common MBS surgical approach.</p><p><strong>Conclusions: </strong>Incident MBS procedures were nearly threefold higher among adults with obesity and diabetes than those with obesity but without diabetes. Continued monitoring of the trends of MBS and other treatment modalities can inform our understanding of treatment accessibility to guide prevention efforts aimed at reducing obesity and diabetes.</p>","PeriodicalId":9151,"journal":{"name":"BMJ Open Diabetes Research & Care","volume":"13 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11836783/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143448145","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Predicting diabetes self-management education engagement: machine learning algorithms and models.
IF 3.7 2区 医学 Q2 ENDOCRINOLOGY & METABOLISM Pub Date : 2025-02-18 DOI: 10.1136/bmjdrc-2024-004632
Xiangxiang Jiang, Gang Lv, Minghui Li, Jing Yuan, Z Kevin Lu

Introduction: Diabetes self-management education (DSME) is endorsed by the American Diabetes Association (ADA) as an essential component of diabetes management. However, the utilization of DSME remains limited in the USA. This study aimed to investigate current DSME participation among the older population and to identify comprehensive factors of DSME engagement through employing various machine learning (ML) models based on a US nationally representative survey linked to claims data.

Research design and methods: Data from the Medicare Current Beneficiary Survey were employed, and this study included data on US Medicare beneficiaries with diabetes from 2017 to 2019. Comprehensive variables following the National Institute on Aging Health Disparities Research Framework were employed to ensure a comprehensive evaluation of factors associated with DSME using five common ML approaches.

Results: In our study, 37.94% of participants received DSME after the application of inclusion and exclusion criteria. A total of 95 variables were used and all ML models achieved accuracy scores exceeding 70%. Random forest had better predictive performance, with an accuracy of 85%. Seventy-four of 95 variables were identified as key variables. Racial/ethnic disparities in predictors for DSME were identified in this study.

Conclusions: This study identified comprehensive and critical factors associated with DSME engagement from biological, behavioral, sociocultural, and environmental domains using different ML models, as well as related racial/ethnic disparities. Aligning these findings with the DSME National Standards from the ADA would enhance the guidelines' effectiveness, promoting tailored and equal diabetes management approaches that cater to diverse races/ethnicities.

{"title":"Predicting diabetes self-management education engagement: machine learning algorithms and models.","authors":"Xiangxiang Jiang, Gang Lv, Minghui Li, Jing Yuan, Z Kevin Lu","doi":"10.1136/bmjdrc-2024-004632","DOIUrl":"10.1136/bmjdrc-2024-004632","url":null,"abstract":"<p><strong>Introduction: </strong>Diabetes self-management education (DSME) is endorsed by the American Diabetes Association (ADA) as an essential component of diabetes management. However, the utilization of DSME remains limited in the USA. This study aimed to investigate current DSME participation among the older population and to identify comprehensive factors of DSME engagement through employing various machine learning (ML) models based on a US nationally representative survey linked to claims data.</p><p><strong>Research design and methods: </strong>Data from the Medicare Current Beneficiary Survey were employed, and this study included data on US Medicare beneficiaries with diabetes from 2017 to 2019. Comprehensive variables following the National Institute on Aging Health Disparities Research Framework were employed to ensure a comprehensive evaluation of factors associated with DSME using five common ML approaches.</p><p><strong>Results: </strong>In our study, 37.94% of participants received DSME after the application of inclusion and exclusion criteria. A total of 95 variables were used and all ML models achieved accuracy scores exceeding 70%. Random forest had better predictive performance, with an accuracy of 85%. Seventy-four of 95 variables were identified as key variables. Racial/ethnic disparities in predictors for DSME were identified in this study.</p><p><strong>Conclusions: </strong>This study identified comprehensive and critical factors associated with DSME engagement from biological, behavioral, sociocultural, and environmental domains using different ML models, as well as related racial/ethnic disparities. Aligning these findings with the DSME National Standards from the ADA would enhance the guidelines' effectiveness, promoting tailored and equal diabetes management approaches that cater to diverse races/ethnicities.</p>","PeriodicalId":9151,"journal":{"name":"BMJ Open Diabetes Research & Care","volume":"13 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11836835/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143448146","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Alterations of hepatic lipid content following COVID-19 in persons with type 2 diabetes.
IF 3.7 2区 医学 Q2 ENDOCRINOLOGY & METABOLISM Pub Date : 2025-02-18 DOI: 10.1136/bmjdrc-2024-004727
Yuliya Kupriyanova, Iryna Yurchenko, Pavel Bobrov, Frederik Bartels, Stefan Wierichs, Marc Jonuscheit, Benedict Korzekwa, Katsiaryna Prystupa, Martin Schön, Dania Mendez, Sandra Trenkamp, Volker Burkart, Robert Wagner, Vera Schrauwen-Hinderling, Michael Roden

Introduction: The study aimed to assess the effect of COVID-19 on hepatic lipid (HL) content, fibrosis risk, and adiposity in persons with type 2 diabetes.

Research design and methods: Participants with type 2 diabetes with a history of mild COVID-19 (n=15, age 58±12 years, body mass index 30.9±5.2 kg/m2) were examined before (baseline) and 1 year (12±2 months) after (follow-up) recovery from COVID-19. Investigations for changes in metabolic risk comprised clinical examination, fasting blood sampling and MR-based measurements. Potential changes were corrected with the time course of the respective parameters in a group of participants who did not contract COVID-19 over the same time course (n=14, 61±6 years, 30.0±4.6 kg/m2).

Results: COVID-19 resulted in a relative increase in HL content of 56% (95% CI 18%, 106%; p=0.04) measured as proton density fat fraction (HL-PDFF), corrected for the time course in the absence of COVID-19. While no changes in hepatic stiffness and volume, intramyocellular lipids, whole-body, subcutaneous and visceral adipose tissue volumes as well as homeostatic model assessment of insulin resistance and beta-cell function were observed.

Conclusions: History of COVID-19 in persons with type 2 diabetes is associated with higher HL-PDFF after 1 year following recovery from infection.

Trial registration number: NCT01055093.

{"title":"Alterations of hepatic lipid content following COVID-19 in persons with type 2 diabetes.","authors":"Yuliya Kupriyanova, Iryna Yurchenko, Pavel Bobrov, Frederik Bartels, Stefan Wierichs, Marc Jonuscheit, Benedict Korzekwa, Katsiaryna Prystupa, Martin Schön, Dania Mendez, Sandra Trenkamp, Volker Burkart, Robert Wagner, Vera Schrauwen-Hinderling, Michael Roden","doi":"10.1136/bmjdrc-2024-004727","DOIUrl":"10.1136/bmjdrc-2024-004727","url":null,"abstract":"<p><strong>Introduction: </strong>The study aimed to assess the effect of COVID-19 on hepatic lipid (HL) content, fibrosis risk, and adiposity in persons with type 2 diabetes.</p><p><strong>Research design and methods: </strong>Participants with type 2 diabetes with a history of mild COVID-19 (n=15, age 58±12 years, body mass index 30.9±5.2 kg/m<sup>2</sup>) were examined before (baseline) and 1 year (12±2 months) after (follow-up) recovery from COVID-19. Investigations for changes in metabolic risk comprised clinical examination, fasting blood sampling and MR-based measurements. Potential changes were corrected with the time course of the respective parameters in a group of participants who did not contract COVID-19 over the same time course (n=14, 61±6 years, 30.0±4.6 kg/m<sup>2</sup>).</p><p><strong>Results: </strong>COVID-19 resulted in a relative increase in HL content of 56% (95% CI 18%, 106%; p=0.04) measured as proton density fat fraction (HL-PDFF), corrected for the time course in the absence of COVID-19. While no changes in hepatic stiffness and volume, intramyocellular lipids, whole-body, subcutaneous and visceral adipose tissue volumes as well as homeostatic model assessment of insulin resistance and beta-cell function were observed.</p><p><strong>Conclusions: </strong>History of COVID-19 in persons with type 2 diabetes is associated with higher HL-PDFF after 1 year following recovery from infection.</p><p><strong>Trial registration number: </strong>NCT01055093.</p>","PeriodicalId":9151,"journal":{"name":"BMJ Open Diabetes Research & Care","volume":"13 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11836859/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143448144","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
Three-protein signature is associated with baseline and persistently elevated or recurrent depressive symptoms in individuals with recent-onset diabetes.
IF 3.7 2区 医学 Q2 ENDOCRINOLOGY & METABOLISM Pub Date : 2025-02-18 DOI: 10.1136/bmjdrc-2024-004396
Maria C Spagnuolo, Pascal Gottmann, Jana Sommer, Sandra Olivia Borgmann, Klaus Strassburger, Wolfgang Rathmann, Oana Patricia Zaharia, Sandra Trenkamp, Robert Wagner, Andrea Icks, Christian Herder, Michael Roden, Haifa Maalmi

Depression is associated with diabetes, but the underlying causes remain unclear. To better understand depression in diabetes, this study investigated associations between 135 inflammatory and neurological protein biomarkers and depressive symptoms in individuals with diabetes.This cross-sectional study included 430 adults with a known diabetes duration <1 year from the German Diabetes Study (GDS), in whom biomarkers were measured in serum and depressive symptoms were evaluated at baseline and annually over 5 years using the Center for Epidemiological Studies Depression Scale (CES-D). Based on the information on depressive symptoms from the baseline and follow-up visits (n=305, ≥3 time points), we subdivided the sample into individuals with persistent or recurrent and transient or never depressive symptoms. We assessed the associations of each biomarker with baseline CES-D score (continuous) and persistent/recurrent depressive symptoms using multiple linear and logistic regression models, respectively.After adjustment for covariates, we identified a three-protein signature associated with baseline CES-D score and persistent/recurrent depressive symptoms. CUB domain-containing protein 1 (CDCP1) and NAD-dependent protein deacetylase sirtuin-2 (SIRT2) were positively associated with baseline (β 1.24 (95% CI 0.19 to 2.29); β 0.89 (95% CI 0.06 to 1.72)), respectively) and persistent/recurrent depressive symptoms (OR 1.58 (95% CI 1.08 to 2.31); OR 1.32 (95% CI 1.03 to 1.71), respectively), whereas leptin receptor (LEPR) was inversely associated with baseline (β -0.99 (95% CI -1.87 to -0.11)) and persistent/recurrent depressive symptoms (OR 0.70 (95% CI 0.49 to 0.99)). However, results were not significant after adjustment for multiple testing.In conclusion, the three-protein signature identified may provide insights into mechanisms underlying depressive symptoms in diabetes and might open new therapeutic avenues.The trial registration number of the study is NCT01055093.

{"title":"Three-protein signature is associated with baseline and persistently elevated or recurrent depressive symptoms in individuals with recent-onset diabetes.","authors":"Maria C Spagnuolo, Pascal Gottmann, Jana Sommer, Sandra Olivia Borgmann, Klaus Strassburger, Wolfgang Rathmann, Oana Patricia Zaharia, Sandra Trenkamp, Robert Wagner, Andrea Icks, Christian Herder, Michael Roden, Haifa Maalmi","doi":"10.1136/bmjdrc-2024-004396","DOIUrl":"10.1136/bmjdrc-2024-004396","url":null,"abstract":"<p><p>Depression is associated with diabetes, but the underlying causes remain unclear. To better understand depression in diabetes, this study investigated associations between 135 inflammatory and neurological protein biomarkers and depressive symptoms in individuals with diabetes.This cross-sectional study included 430 adults with a known diabetes duration <1 year from the German Diabetes Study (GDS), in whom biomarkers were measured in serum and depressive symptoms were evaluated at baseline and annually over 5 years using the Center for Epidemiological Studies Depression Scale (CES-D). Based on the information on depressive symptoms from the baseline and follow-up visits (n=305, ≥3 time points), we subdivided the sample into individuals with persistent or recurrent and transient or never depressive symptoms. We assessed the associations of each biomarker with baseline CES-D score (continuous) and persistent/recurrent depressive symptoms using multiple linear and logistic regression models, respectively.After adjustment for covariates, we identified a three-protein signature associated with baseline CES-D score and persistent/recurrent depressive symptoms. CUB domain-containing protein 1 (CDCP1) and NAD-dependent protein deacetylase sirtuin-2 (SIRT2) were positively associated with baseline (β 1.24 (95% CI 0.19 to 2.29); β 0.89 (95% CI 0.06 to 1.72)), respectively) and persistent/recurrent depressive symptoms (OR 1.58 (95% CI 1.08 to 2.31); OR 1.32 (95% CI 1.03 to 1.71), respectively), whereas leptin receptor (LEPR) was inversely associated with baseline (β -0.99 (95% CI -1.87 to -0.11)) and persistent/recurrent depressive symptoms (OR 0.70 (95% CI 0.49 to 0.99)). However, results were not significant after adjustment for multiple testing.In conclusion, the three-protein signature identified may provide insights into mechanisms underlying depressive symptoms in diabetes and might open new therapeutic avenues.The trial registration number of the study is NCT01055093.</p>","PeriodicalId":9151,"journal":{"name":"BMJ Open Diabetes Research & Care","volume":"13 1","pages":""},"PeriodicalIF":3.7,"publicationDate":"2025-02-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11836832/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143448147","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
引用次数: 0
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BMJ Open Diabetes Research & Care
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