Evidence map›Paper›PMID 41113874›Full record

ArticleCureus2025

Comparative Analysis of Type 1 and Type 2 Diabetes Registrations and Risk Factor Correlations in the UK (2022-24).

Sayan Bandyopadhyay, Rittik Dey, Prosenjit Chakraborty, Chitrak Jharwal, Parna Chakraborty, Moitreyo Pandit

Abstract read
In one paragraph

Article in Cureus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

Who cites it

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No citing paper in PubMed yet.

4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Sayan BandyopadhyayGeneral Medicine, Mother Multispeciality Hospital, Kolkata, IND.
Rittik DeyCardiology, Glangwili General Hospital, Carmarthen, GBR.
Prosenjit ChakrabortyNeurology, Ruby General Hospital, Kolkata, IND.
Chitrak JharwalInternal Medicine, Aanch Hospital, Jaipur, IND.
Parna ChakrabortyPulmonary Medicine, Dr. D.Y. Patil Medical College, Hospital & Research Centre, Pune, IND.
Moitreyo PanditInternal Medicine, Nottingham University Hospitals NHS Trust, Nottingham, GBR.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDiabetes mellitus imposes a growing burden due to rising prevalence, complications, and costs. Type 1 Diabetes (T1D), an autoimmune condition, primarily affects younger individuals, whereas Type 2 Diabetes (T2D) is linked to lifestyle and socioeconomic factors in adults. Understanding how demographic, socioeconomic, and ethnic factors correlate with T1D and T2D registrations can guide targeted interventions.

objectivesTo compare correlations between risk factors (age, socioeconomic deprivation, ethnicity) and T1D versus T2D registrations in the UK across the 2022-23 and 2023-24 audit periods.

methodsWe conducted a retrospective, observational study using National Diabetes Audit (NDA) data from 2022-23 Q1 and 2023-24 Q1 on T1D and T2D registrations, which were cleaned and standardized. Correlation matrices and difference heatmaps were generated for numeric variables (age bands, Index of Multiple Deprivation (IMD) quintiles, ethnicity percentages, registrations) using seaborn. Trends and intergroup differences were analyzed.

resultsT1D registrations correlated strongly with the under‑40 age group (r≈0.82-0.85) and weakly with socioeconomic deprivation (IMD most deprived: r≈0.15-0.18). T2D registrations correlated most with middle‑aged and older bands (40-64: r≈0.73-0.75; 65-79: r≈0.65-0.68) and strongly with IMD most deprived (r≈0.60-0.62). Ethnicity correlations: T1D strongest in White patients (r≈0.82-0.85), while T2D showed elevated associations with Asian British (r≈0.45-0.47) and Black British (r≈0.40-0.42). Difference analyses revealed widening age‑band disparities between T1D and T2D in 2023-24.

conclusionsDistinct patterns between T1D and T2D emphasize the need for differentiated strategies: youth‑focused management for T1D and socioeconomic and culturally tailored interventions for T2D. Continued monitoring and targeted public health policies are essential to reduce disparities and improve outcomes.

Indexed as

diabetes mellitus (t2dm)diabetes type 2type 1 diabetes mellitus (t1d)type 1 diabetes (t1d)type ii diabetestypes 2 diabetes

Identifiers

PMID41113874
PMCPMC12533550

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.