scholarly journals Trends in prevalence of diabetes subgroups in U.S. adults: A data-driven cluster analysis spanning three decades from NHANES (1988-2018)

2020 ◽  
Author(s):  
Neftali Eduardo Antonio-Villa ◽  
Luisa Fernández-Chirino ◽  
Arsenio Vargas-Vázquez ◽  
Jessica Paola Bahena-López ◽  
Carlos A Aguilar-Salinas ◽  
...  
Keyword(s):  
Mexican Americans ◽  
Population Aging ◽  
Data Driven ◽  
Significant Heterogeneity ◽  
Obesity Prevalence ◽  
Insulin Resistant ◽  
Age Related ◽  
The Us ◽  
Mild Obesity ◽  
The U.S

AIMS: Data-driven diabetes subgroups have been proposed as an alternative to address diabetes heterogeneity; changes in trends for these subgroups have not previously been reported. Here, we analyzed trends of diabetes subgroups, stratified by sex, race, education level, and age categories in the U.S. METHODS: We used data from consecutive NHANES cycles spanning the 1988-2018 period. Diabetes subgroups (mild obesity-related [MOD], severe-insulin deficient [SIID], severe-insulin resistant [SIRD], and age-related diabetes [MARD]) were classified using self-normalizing neural networks. SAID was assessed for NHANES-III cycles. Prevalence was estimated using examination sample weights considering 2-year cycles (biannual change [B.C.]) to evaluate trends. RESULTS: Diabetes prevalence in the US increased from 7.5% (95%CI 7.1-7.9) in 1988-1989 to 13.9% (95%CI 13.4-14.4) in 2016-2018 (BC 1.09%, 95%CI 0.98-1.31, p<0.001). Non-Hispanic Blacks had the highest prevalence. Overall, MOD, MARD, and SIID had the highest increase during the studied period. Non-Hispanic Blacks had a sharp increase in MARD and SIID, Mexican Americans in SIID, and non-Hispanic Whites in MARD. Males, subjects with primary school/no-education, and adults aged 40-64 years had the highest increase in MOD prevalence. Trends in diabetes subgroups sustained after stratification by body-mass index categories. CONCLUSIONS: The prevalence of diabetes and its data-driven subgroups in the U.S. has increased from 1988-2018. These trends were different across sex, ethnicities, education, and age categories, indicating significant heterogeneity in diabetes within the U.S. Sex-specific factors, population aging, and socioeconomic aspects, together with obesity prevalence increase, could be implicated in the uprising trends of diabetes in the U.S.

scholarly journals A novel diabetes typology: towards precision diabetology from pathogenesis to treatment

Diabetologia ◽  
2022 ◽  
Author(s):  
Christian Herder ◽  
Michael Roden
Keyword(s):  
Clinical Features ◽  
Autoimmune Diabetes ◽  
Routine Care ◽  
Insulin Resistant ◽  
Open Questions ◽  
Age Related ◽  
The Stability ◽  
Mild Obesity ◽  
Insight Into

AbstractThe current classification of diabetes, based on hyperglycaemia, islet-directed antibodies and some insufficiently defined clinical features, does not reflect differences in aetiological mechanisms and in the clinical course of people with diabetes. This review discusses evidence from recent studies addressing the complexity of diabetes by proposing novel subgroups (subtypes) of diabetes. The most widely replicated and validated approach identified, in addition to severe autoimmune diabetes, four subgroups designated severe insulin-deficient diabetes, severe insulin-resistant diabetes, mild obesity-related diabetes and mild age-related diabetes subgroups. These subgroups display distinct patterns of clinical features, disease progression and onset of comorbidities and complications, with severe insulin-resistant diabetes showing the highest risk for cardiovascular, kidney and fatty liver diseases. While it has been suggested that people in these subgroups would benefit from stratified treatments, RCTs are required to assess the clinical utility of any reclassification effort. Several methodological and practical issues also need further study: the statistical approach used to define subgroups and derive recommendations for diabetes care; the stability of subgroups over time; the optimal dataset (e.g. phenotypic vs genotypic) for reclassification; the transethnic generalisability of findings; and the applicability in clinical routine care. Despite these open questions, the concept of a new classification of diabetes has already allowed researchers to gain more insight into the colourful picture of diabetes and has stimulated progress in this field so that precision diabetology may become reality in the future. Graphical abstract

scholarly journals Differences in Biomarkers of Inflammation Between Novel Subgroups of Recent-Onset Diabetes

2021 ◽  
Author(s):  
Christian Herder ◽  
Haifa Maalmi ◽  
Klaus Strassburger ◽  
Oana-Patricia Zaharia ◽  
Jacqueline M. Ratter ◽  
...  
Keyword(s):  
Autoimmune Diabetes ◽  
Serum Levels ◽  
Recent Onset ◽  
Insulin Resistant ◽  
Onset Diabetes ◽  
Age Related ◽  
Multiple Biomarkers ◽  
Adult Onset Diabetes ◽  
Clustering Approach ◽  
Mild Obesity

A novel clustering approach identified five subgroups of diabetes with distinct progression trajectories of complications. We hypothesized that these subgroups differ in multiple biomarkers of inflammation. Serum levels of 74 biomarkers of inflammation were measured in 414 individuals with recent adult-onset diabetes from the German Diabetes Study (GDS) allocated to five subgroups based on data-driven analysis. Pairwise differences between subgroups for biomarkers were assessed with generalized linear mixed models before (model 1) and after adjustment (model 2) for the clustering variables. Participants were assigned to five subgroups: severe autoimmune diabetes (SAID, 21%), severe insulin-deficient diabetes (SIDD, 3%), severe insulin-resistant diabetes (SIRD, 9%), mild obesity-related diabetes (MOD, 32%) and mild age-related diabetes (MARD, 35%). In model 1, 23 biomarkers showed ≥1 pairwise difference between subgroups (Bonferroni-corrected p<0.0007). Biomarker levels were generally highest in SIRD and lowest in SIDD. All 23 biomarkers correlated with ≥1 of the clustering variables. In model 2, three biomarkers (CASP-8, EN-RAGE, IL-6) showed at least one pairwise difference between subgroups (e.g. lower CASP8, EN-RAGE and IL-6 in SIDD vs. all other subgroups, all p<0.0007). Thus, novel diabetes subgroups show multiple differences in biomarkers of inflammation, underlining a prominent role of inflammatory pathways in particular in SIRD.

scholarly journals Differences in Biomarkers of Inflammation Between Novel Subgroups of Recent-Onset Diabetes

2021 ◽  
Author(s):  
Christian Herder ◽  
Haifa Maalmi ◽  
Klaus Strassburger ◽  
Oana-Patricia Zaharia ◽  
Jacqueline M. Ratter ◽  
...  
Keyword(s):  
Autoimmune Diabetes ◽  
Serum Levels ◽  
Recent Onset ◽  
Insulin Resistant ◽  
Onset Diabetes ◽  
Age Related ◽  
Multiple Biomarkers ◽  
Adult Onset Diabetes ◽  
Clustering Approach ◽  
Mild Obesity

A novel clustering approach identified five subgroups of diabetes with distinct progression trajectories of complications. We hypothesized that these subgroups differ in multiple biomarkers of inflammation. Serum levels of 74 biomarkers of inflammation were measured in 414 individuals with recent adult-onset diabetes from the German Diabetes Study (GDS) allocated to five subgroups based on data-driven analysis. Pairwise differences between subgroups for biomarkers were assessed with generalized linear mixed models before (model 1) and after adjustment (model 2) for the clustering variables. Participants were assigned to five subgroups: severe autoimmune diabetes (SAID, 21%), severe insulin-deficient diabetes (SIDD, 3%), severe insulin-resistant diabetes (SIRD, 9%), mild obesity-related diabetes (MOD, 32%) and mild age-related diabetes (MARD, 35%). In model 1, 23 biomarkers showed ≥1 pairwise difference between subgroups (Bonferroni-corrected p<0.0007). Biomarker levels were generally highest in SIRD and lowest in SIDD. All 23 biomarkers correlated with ≥1 of the clustering variables. In model 2, three biomarkers (CASP-8, EN-RAGE, IL-6) showed at least one pairwise difference between subgroups (e.g. lower CASP8, EN-RAGE and IL-6 in SIDD vs. all other subgroups, all p<0.0007). Thus, novel diabetes subgroups show multiple differences in biomarkers of inflammation, underlining a prominent role of inflammatory pathways in particular in SIRD.

scholarly journals The Identification of Diabetes Mellitus Subtypes Applying Cluster Analysis Techniques: A Systematic Review

2020 ◽  
Vol 17 (24) ◽  
pp. 9523
Author(s):  
Antonio Sarría-Santamera ◽  
Binur Orazumbekova ◽  
Tilektes Maulenkul ◽  
Abduzhappar Gaipov ◽  
Kuralay Atageldiyeva
Keyword(s):  
Diabetes Mellitus ◽  
Systematic Review ◽  
Cluster Analysis ◽  
Autoimmune Diabetes ◽  
Insulin Resistant ◽  
Age Related ◽  
Mild Obesity ◽  
Diabetes Patients

Diabetes Mellitus is a chronic and lifelong disease that incurs a huge burden to healthcare systems. Its prevalence is on the rise worldwide. Diabetes is more complex than the classification of Type 1 and 2 may suggest. The purpose of this systematic review was to identify the research studies that tried to find new sub-groups of diabetes patients by using unsupervised learning methods. The search was conducted on Pubmed and Medline databases by two independent researchers. All time publications on cluster analysis of diabetes patients were selected and analysed. Among fourteen studies that were included in the final review, five studies found five identical clusters: Severe Autoimmune Diabetes; Severe Insulin-Deficient Diabetes; Severe Insulin-Resistant Diabetes; Mild Obesity-Related Diabetes; and Mild Age-Related Diabetes. In addition, two studies found the same clusters, except Severe Autoimmune Diabetes cluster. Results of other studies differed from one to another and were less consistent. Cluster analysis enabled finding non-classic heterogeneity in diabetes, but there is still a necessity to explore and validate the capabilities of cluster analysis in more diverse and wider populations.

scholarly journals Trends in Diabetes Subgroups and Their Risk for All-Cause, Cardiovascular Disease and Diabetes-Specific Mortality in the US: A Data-Driven Reproducible Machine Learning Approach

2021 ◽  
Vol 5 (Supplement_1) ◽  
pp. A423-A423
Author(s):  
Neftali Eduardo Antonio-Villa ◽  
Luisa Fernández-Chirino ◽  
Arsenio Vargas-Vazquez ◽  
Jessica Paola Bahena-López ◽  
Omar Yaxmehen Bello-Chavolla
Keyword(s):  
Cardiovascular Disease ◽  
Mortality Risk ◽  
Mexican Americans ◽  
Diabetes Prevalence ◽  
Data Driven ◽  
Diabetes Diagnosis ◽  
The Us ◽  
Specific Mortality ◽  
Over Time

Abstract Background: Diabetes has been described as a heterogeneous entity which can be studied through data-driven subgroups (obesity related [MOD], severe-insulin deficient [SIID], severe-insulin resistant [SIRD] and age-related diabetes [MARD]). However, trends in prevalence and mortality risk are still unclear. Aims: To analyze diabetes subgroup trends and to evaluate mortality risk in the US. Methods: Data and follow-up causes of mortality (all-cause, cardiovascular disease, and diabetes specific) was collected from NHANES cycles 1999–2018. Subgroup diabetes classification was performed using the self-normalizing neural networks algorithm using clinical parameters (HbA1c, time since diabetes diagnosis, HOMA2-IR, HOMA2-B, and BMI) proposed by Bello-Chavolla et al (https://bit.ly/3jSm1xv). Prevalence was estimated using sample weights. 2-year cycles were used as a continuous variable to evaluate the biannual change (BC) of the overall prevalence of diabetes and subgroups. Trends were stratified according to race. Cox-proportional and Fine-Gray semiparametric hazard regression models were used to evaluate mortality risk. Results: Data from 59,204 adult subjects was extracted for trend analysis. Follow-up information was obtained for 3,980 subjects. Diabetes prevalence in the US increased from 8.2% (95%CI 7.8–8.6) in 1999–2000 to 13.9% (95% CI 13.4–14.4) in 2017–2018 (BC 1.38%, 95% CI 1.20–1.56, p&lt;0.001). Non-Hispanic Blacks had the largest increase in diabetes prevalence (BC: 1.40%, 95%CI 0.71–2.08, p=0.027), followed by Non-Hispanic Whites (BC: 1.36%, 95%CI 1.13–1.58, p&lt;0.001), and Mexican Americans (BC: 1.33%, 95%CI 1.20–1.54, p&lt;0.001). Regarding diabetes subgroups, MARD had the highest prevalence, with a moderate increase over time; however, MOD had the greatest increase over time (1.5%, [95%CI 1.2–1.8] to 4.5% [95%CI 4.0–5.0]; BC: 0.73% [95%CI 0.60–0.86], p&lt;0.01). Both SIRD and SIID had non-significant increases in prevalence during the studied period. Non-Hispanic Blacks had an increase in the prevalence in MOD and SIID, Mexican Americans in MOD and SIRD, and non-Hispanic Whites in MOD and MARD. Compared with MOD, the risk for all-cause mortality was higher for MARD (HR 2.9 95% CI: 2.1–3.9), SIRD (HR 2.0 95% CI: 1.5–2.8), and SIID (HR 1.6 95% CI: 1.1–2.3). For CVD mortality, only MARD (HR 2.8 95% CI: 1.4–5.7) and SIRD (HR 2.5 95% CI: 1.2–5.3) displayed higher risk. For diabetes-specific mortality, only MARD (HR 2.2 95% CI: 1.3–3.7) was associated. Conclusion: There is an overall increase in diabetes prevalence and its subgroups from 1999 to 2018; MORD had the highest increase. The risk for all-cause, CVD and diabetes-specific mortality was different among subgroups. Our results supports the use of diabetes subgroups for a better understanding of diabetes and its complications.

The US–Mexico War

2016 ◽  
Author(s):  
Omar Valerio-Jiménez
Keyword(s):  
United States ◽  
Mexican Americans ◽  
Mexican American ◽  
The United States ◽  
Mexican War ◽  
Mexican American War ◽  
The Us ◽  
Northern Territories ◽  
United States Mexico ◽  
The U.S

The United States–Mexico War was the first war in which the United States engaged in a conflict with a foreign nation for the purpose of conquest. It was also the first conflict in which trained soldiers (from West Point) played a large role. The war’s end transformed the United States into a continental nation as it acquired a vast portion of Mexico’s northern territories. In addition to shaping U.S.–Mexico relations into the present, the conflict also led to the forcible incorporation of Mexicans (who became Mexican Americans) as the nation’s first Latinos. Yet, the war has been identified as the nation’s “forgotten war” because few Americans know the causes and consequences of this conflict. Within fifteen years of the war’s end, the conflict faded from popular memory, but it did not disappear, due to the outbreak of the U.S. Civil War. By contrast, the U.S.–Mexico War is prominently remembered in Mexico as having caused the loss of half of the nation’s territory, and as an event that continues to shape Mexico’s relationship with the United States. Official memories (or national histories) of war affect international relations, and also shape how each nation’s population views citizens of other countries. Not surprisingly, there is a stark difference in the ways that American citizens and Mexican citizens remember and forget the war (e.g., Americans refer to the “Mexican American War” or the “U.S.–Mexican War,” for example, while Mexicans identify the conflict as the “War of North American Intervention”).

scholarly journals Clinical characterization of data-driven diabetes subgroups in Mexicans using a reproducible machine learning approach

2020 ◽  
Vol 8 (1) ◽  
pp. e001550
Author(s):  
Omar Yaxmehen Bello-Chavolla ◽  
Jessica Paola Bahena-López ◽  
Arsenio Vargas-Vázquez ◽  
Neftali Eduardo Antonio-Villa ◽  
Alejandro Márquez-Salinas ◽  
...  
Keyword(s):  
Risk Factors ◽  
Training Sample ◽  
Population Based ◽  
Data Driven ◽  
Insulin Resistant ◽  
Age Related ◽  
Adult Onset Diabetes ◽  
Design And Methods ◽  
Incident Cases

IntroductionPrevious reports in European populations demonstrated the existence of five data-driven adult-onset diabetes subgroups. Here, we use self-normalizing neural networks (SNNN) to improve reproducibility of these data-driven diabetes subgroups in Mexican cohorts to extend its application to more diverse settings.Research design and methodsWe trained SNNN and compared it with k-means clustering to classify diabetes subgroups in a multiethnic and representative population-based National Health and Nutrition Examination Survey (NHANES) datasets with all available measures (training sample: NHANES-III, n=1132; validation sample: NHANES 1999–2006, n=626). SNNN models were then applied to four Mexican cohorts (SIGMA-UIEM, n=1521; Metabolic Syndrome cohort, n=6144; ENSANUT 2016, n=614 and CAIPaDi, n=1608) to characterize diabetes subgroups in Mexicans according to treatment response, risk for chronic complications and risk factors for the incidence of each subgroup.ResultsSNNN yielded four reproducible clinical profiles (obesity related, insulin deficient, insulin resistant, age related) in NHANES and Mexican cohorts even without C-peptide measurements. We observed in a population-based survey a high prevalence of the insulin-deficient form (41.25%, 95% CI 41.02% to 41.48%), followed by obesity-related (33.60%, 95% CI 33.40% to 33.79%), age-related (14.72%, 95% CI 14.63% to 14.82%) and severe insulin-resistant groups. A significant association was found between the SLC16A11 diabetes risk variant and the obesity-related subgroup (OR 1.42, 95% CI 1.10 to 1.83, p=0.008). Among incident cases, we observed a greater incidence of mild obesity-related diabetes (n=149, 45.0%). In a diabetes outpatient clinic cohort, we observed increased 1-year risk (HR 1.59, 95% CI 1.01 to 2.51) and 2-year risk (HR 1.94, 95% CI 1.13 to 3.31) for incident retinopathy in the insulin-deficient group and decreased 2-year diabetic retinopathy risk for the obesity-related subgroup (HR 0.49, 95% CI 0.27 to 0.89).ConclusionsDiabetes subgroup phenotypes are reproducible using SNNN; our algorithm is available as web-based tool. Application of these models allowed for better characterization of diabetes subgroups and risk factors in Mexicans that could have clinical applications.

scholarly journals Type 2 Diabetes Mellitus (T2DM) may have Four Subtypes Beneficial for Adequate Treatment

2021 ◽  
Vol 4 (1) ◽  
pp. 38-41
Author(s):  
Bando H
Keyword(s):  
Type 2 Diabetes ◽  
Diabetes Type 2 ◽  
Adequate Treatment ◽  
Insulin Resistant ◽  
Age Related ◽  
Cluster A ◽  
Diabetic Treatment ◽  
Mild Obesity

Diabetes includes various heterogeneous factors. Similar to subtypes of type 1 diabetes, type 2 diabetes may show four subtype clusters. They are cluster A: severe insulin-deficient diabetes, B: severe insulin-resistant diabetes, C: mild obesity-related diabetes, and D: mild age-related diabetes. Comparing them, the prevalence of nephropathy and cardiovascular events was highest in the cluster A. Reference data are i) the ratio of cluster A-D is 18.7%, 23.7%, 21.1%, 36.4%, ii) HbA1c for A-D is 11.05%, 8.17%, 8.49%, 7.95%, iii) event ratio of MACE is 14.4%, 10.6%, 11.4%, 9.1%. Future diabetic treatment is hopefully provided suitable for each subtype.

Problems of the US Economic Policy

2013 ◽  
pp. 129-143
Author(s):  
V. Klinov
Keyword(s):  
Economic Policy ◽  
Real Estate ◽  
Full Employment ◽  
Tax Rates ◽  
Equitable Distribution ◽  
Investment Rate ◽  
The Us ◽  
Budget Systems ◽  
Enterprise Activity ◽  
The U.S

How to provide for full employment and equitable distribution of incomes and wealth are the keenest issues of the U.S. society. The Democratic and the Republican Parties have elaborated opposing views on economic policy, though both parties are certain that the problems may be resolved through the reform of the federal tax and budget systems. Globalization demands to increase incentives for labor and enterprise activity and for savings to secure proper investment rate. Tax rates for labor and enterprise incomes are to be low, but tax rates for consumption, real estate and land should be progressive.

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