Between 2023 and 2024, approximately ** percent of individuals registered in England with type 1 diabetes were aged between 40 and 64 years. This statistic displays the distribution of individuals registered with type 1 diabetes in England in 2023/24, by age.

This is an overview of the treatment and demographics of 227,435 adults with type 1 diabetes. From 2019 to 2022 glucose control in people with type 1 diabetes in England and Wales improved while blood pressure control deteriorated. Use of diabetes technology (wearable glucose monitoring devices in England and insulin pumps in England and Wales) was associated with lower glucose levels. Diabetes technology was used less by those in the most deprived groups and in ethnic minorities. 30% of people with type 1 diabetes did not attend specialist care in 2021-22 and were less likely to receive annual checks or achieve treatment targets as recommended by the National Institute for Health and Care Excellence (NICE). There are 3 recommendations for commissioners of care.

It was estimated that as of 2023, around **** million people in the United States had been diagnosed with diabetes. The number of people diagnosed with diabetes in the U.S. has increased in recent years and the disease is now a major health issue. Diabetes is now the seventh leading cause of death in the United States, accounting for ******percent of all deaths. What is prediabetes? A person is considered to have prediabetes if their blood sugar levels are higher than normal but not high enough to be diagnosed with type 2 diabetes. As of 2021, it was estimated that around ** million men and ** million women in the United States had prediabetes. However, according to the CDC, around ** percent of these people do not know they have this condition. Not only does prediabetes increase the risk of developing type 2 diabetes, but also increases the risk of heart disease and stroke. The states with the highest share of adults who had ever been told they have prediabetes are California, Hawaii, and New Mexico. The prevalence of diabetes in the United States As of 2023, around *** percent of adults in the United States had been diagnosed with diabetes, an increase from ****percent in the year 2000. Diabetes is much more common among older adults, with around ** percent of those aged 60 years and older diagnosed with diabetes, compared to just ****percent of those aged 20 to 39 years. The states with the highest prevalence of diabetes among adults are West Virginia, Mississippi, and Louisiana, while Utah and Colorado report the lowest rates. In West Virginia, around ** percent of adults have been diagnosed with diabetes.

Type 1 diabetes affects approximately ******* children worldwide, with ******* new cases diagnosed annually. This chronic condition, requiring lifelong insulin treatment, impacts a significant portion of the global child population of **** billion. Global diabetes trends and projections The impact of diabetes extends far beyond childhood, with the total number of diabetics worldwide expected to reach *** million by 2050. This projected increase corresponds to a rise in global diabetes prevalence from ** percent in 2024 to ** percent by 2050. The Western Pacific region currently has the highest number of diabetics, with approximately *** million people aged 20-79 affected. Africa, has the lowest number of diabetics with **** million in 2024. However, the number of diabetics in Africa is expected to increase significantly in the coming decades. Regional disparities and health concerns The global distribution of diabetes cases varies significantly, with Africa expected to see a *** percent increase in diabetes cases from 2024 to 2050, compared to a ** percent rise in North America and the Caribbean. As diabetes remains a critical health issue worldwide, it contributes to various complications and was the eighth leading cause of death globally in 2021, resulting in approximately **** million deaths that year.

The National Diabetes Audit (NDA) provides a comprehensive view of diabetes care in England and Wales. It measures the effectiveness of diabetes healthcare against NICE Clinical Guidelines and NICE Quality Standards. This is the Type 1 Diabetes report. It details the findings and recommendations relating to diabetes care process completion, treatment target achievement and structured education for people with type 1 diabetes. The 2019-20 audit covers the period 01 January 2019 to 31 March 2020. This is the first NDA report dedicated to people with type 1 diabetes. A new diagnosis validation process, which considers medication as well as recorded diagnosis, has been introduced to try to ensure that only people with true type 1 diabetes are included (see appendix). Results are to be taken in the context of low data submission from specialist services, possibly hampered due to COVID-19.

T1DiabetesGranada

A longitudinal multi-modal dataset of type 1 diabetes mellitus

Documented by:

Rodriguez-Leon, C., Aviles-Perez, M. D., Banos, O., Quesada-Charneco, M., Lopez-Ibarra, P. J., Villalonga, C., & Munoz-Torres, M. (2023). T1DiabetesGranada: a longitudinal multi-modal dataset of type 1 diabetes mellitus. Scientific Data, 10(1), 916. https://doi.org/10.1038/s41597-023-02737-4

Background

Type 1 diabetes mellitus (T1D) patients face daily difficulties in keeping their blood glucose levels within appropriate ranges. Several techniques and devices, such as flash glucose meters, have been developed to help T1D patients improve their quality of life. Most recently, the data collected via these devices is being used to train advanced artificial intelligence models to characterize the evolution of the disease and support its management. The main problem for the generation of these models is the scarcity of data, as most published works use private or artificially generated datasets. For this reason, this work presents T1DiabetesGranada, a open under specific permission longitudinal dataset that not only provides continuous glucose levels, but also patient demographic and clinical information. The dataset includes 257780 days of measurements over four years from 736 T1D patients from the province of Granada, Spain. This dataset progresses significantly beyond the state of the art as one the longest and largest open datasets of continuous glucose measurements, thus boosting the development of new artificial intelligence models for glucose level characterization and prediction.

Data Records

The data are stored in four comma-separated values (CSV) files which are available in T1DiabetesGranada.zip. These files are described in detail below.

Patient_info.csv

Patient_info.csv is the file containing information about the patients, such as demographic data, start and end dates of blood glucose level measurements and biochemical parameters, number of biochemical parameters or number of diagnostics. This file is composed of 736 records, one for each patient in the dataset, and includes the following variables:

Patient_ID – Unique identifier of the patient. Format: LIB19XXXX.

Sex – Sex of the patient. Values: F (for female), masculine (for male)

Birth_year – Year of birth of the patient. Format: YYYY.

Initial_measurement_date – Date of the first blood glucose level measurement of the patient in the Glucose_measurements.csv file. Format: YYYY-MM-DD.

Final_measurement_date – Date of the last blood glucose level measurement of the patient in the Glucose_measurements.csv file. Format: YYYY-MM-DD.

Number_of_days_with_measures – Number of days with blood glucose level measurements of the patient, extracted from the Glucose_measurements.csv file. Values: ranging from 8 to 1463.

Number_of_measurements – Number of blood glucose level measurements of the patient, extracted from the Glucose_measurements.csv file. Values: ranging from 400 to 137292.

Initial_biochemical_parameters_date – Date of the first biochemical test to measure some biochemical parameter of the patient, extracted from the Biochemical_parameters.csv file. Format: YYYY-MM-DD.

Final_biochemical_parameters_date – Date of the last biochemical test to measure some biochemical parameter of the patient, extracted from the Biochemical_parameters.csv file. Format: YYYY-MM-DD.

Number_of_biochemical_parameters – Number of biochemical parameters measured on the patient, extracted from the Biochemical_parameters.csv file. Values: ranging from 4 to 846.

Number_of_diagnostics – Number of diagnoses realized to the patient, extracted from the Diagnostics.csv file. Values: ranging from 1 to 24.

Glucose_measurements.csv

Glucose_measurements.csv is the file containing the continuous blood glucose level measurements of the patients. The file is composed of more than 22.6 million records that constitute the time series of continuous blood glucose level measurements. It includes the following variables:

Patient_ID – Unique identifier of the patient. Format: LIB19XXXX.

Measurement_date – Date of the blood glucose level measurement. Format: YYYY-MM-DD.

Measurement_time – Time of the blood glucose level measurement. Format: HH:MM:SS.

Measurement – Value of the blood glucose level measurement in mg/dL. Values: ranging from 40 to 500.

Biochemical_parameters.csv

Biochemical_parameters.csv is the file containing data of the biochemical tests performed on patients to measure their biochemical parameters. This file is composed of 87482 records and includes the following variables:

Patient_ID – Unique identifier of the patient. Format: LIB19XXXX.

Reception_date – Date of receipt in the laboratory of the sample to measure the biochemical parameter. Format: YYYY-MM-DD.

Name – Name of the measured biochemical parameter. Values: 'Potassium', 'HDL cholesterol', 'Gammaglutamyl Transferase (GGT)', 'Creatinine', 'Glucose', 'Uric acid', 'Triglycerides', 'Alanine transaminase (GPT)', 'Chlorine', 'Thyrotropin (TSH)', 'Sodium', 'Glycated hemoglobin (Ac)', 'Total cholesterol', 'Albumin (urine)', 'Creatinine (urine)', 'Insulin', 'IA ANTIBODIES'.

Value – Value of the biochemical parameter. Values: ranging from -4.0 to 6446.74.

Diagnostics.csv

Diagnostics.csv is the file containing diagnoses of diabetes mellitus complications or other diseases that patients have in addition to type 1 diabetes mellitus. This file is composed of 1757 records and includes the following variables:

Patient_ID – Unique identifier of the patient. Format: LIB19XXXX.

Code – ICD-9-CM diagnosis code. Values: subset of 594 of the ICD-9-CM codes (https://www.cms.gov/Medicare/Coding/ICD9ProviderDiagnosticCodes/codes).

Description – ICD-9-CM long description. Values: subset of 594 of the ICD-9-CM long description (https://www.cms.gov/Medicare/Coding/ICD9ProviderDiagnosticCodes/codes).

Technical Validation

Blood glucose level measurements are collected using FreeStyle Libre devices, which are widely used for healthcare in patients with T1D. Abbott Diabetes Care, Inc., Alameda, CA, USA, the manufacturer company, has conducted validation studies of these devices concluding that the measurements made by their sensors compare to YSI analyzer devices (Xylem Inc.), the gold standard, yielding results of 99.9% of the time within zones A and B of the consensus error grid. In addition, other studies external to the company concluded that the accuracy of the measurements is adequate.

Moreover, it was also checked in most cases the blood glucose level measurements per patient were continuous (i.e. a sample at least every 15 minutes) in the Glucose_measurements.csv file as they should be.

Usage Notes

For data downloading, it is necessary to be authenticated on the Zenodo platform, accept the Data Usage Agreement and send a request specifying full name, email, and the justification of the data use. This request will be processed by the Secretary of the Department of Computer Engineering, Automatics, and Robotics of the University of Granada and access to the dataset will be granted.

The files that compose the dataset are CSV type files delimited by commas and are available in T1DiabetesGranada.zip. A Jupyter Notebook (Python v. 3.8) with code that may help to a better understanding of the dataset, with graphics and statistics, is available in UsageNotes.zip.

Graphs_and_stats.ipynb

The Jupyter Notebook generates tables, graphs and statistics for a better understanding of the dataset. It has four main sections, one dedicated to each file in the dataset. In addition, it has useful functions such as calculating the patient age, deleting a patient list from a dataset file and leaving only a patient list in a dataset file.

Code Availability

The dataset was generated using some custom code located in CodeAvailability.zip. The code is provided as Jupyter Notebooks created with Python v. 3.8. The code was used to conduct tasks such as data curation and transformation, and variables extraction.

Original_patient_info_curation.ipynb

In the Jupyter Notebook is preprocessed the original file with patient data. Mainly irrelevant rows and columns are removed, and the sex variable is recoded.

Glucose_measurements_curation.ipynb

In the Jupyter Notebook is preprocessed the original file with the continuous glucose level measurements of the patients. Principally rows without information or duplicated rows are removed and the variable with the timestamp is transformed into two new variables, measurement date and measurement time.

Biochemical_parameters_curation.ipynb

In the Jupyter Notebook is preprocessed the original file with patient data of the biochemical tests performed on patients to measure their biochemical parameters. Mainly irrelevant rows and columns are removed and the variable with the name of the measured biochemical parameter is translated.

Diagnostic_curation.ipynb

In the Jupyter Notebook is preprocessed the original file with patient data of the diagnoses of diabetes mellitus complications or other diseases that patients have in addition to T1D.

Get_patient_info_variables.ipynb

In the Jupyter Notebook it is coded the feature extraction process from the files Glucose_measurements.csv, Biochemical_parameters.csv and Diagnostics.csv to complete the file Patient_info.csv. It is divided into six sections, the first three to extract the features from each of the mentioned files and the next three to add the extracted features to the resulting new file.

Data Usage Agreement

The conditions for use are as follows:

You confirm that you will not attempt to re-identify research participants for any reason, including for re-identification theory research.

You commit to keeping the T1DiabetesGranada dataset confidential and secure and will not redistribute data or Zenodo account credentials.

You will require

As of 2021, the prevalence of diabetics in the UK totaled 8.2 percent. Type 2 diabetes, highly related to unhealthy lifestyle choices, such as the overconsumption of sugar and a lack of exercise, as well as aging, affects more individuals than type 1 diabetes. Between 2023 and 2024, over 3.5 million people in England were registered with type 2 diabetes, while almost 277 thousand had type 1. Type 1 diabetes Between 2023 and 2024, most people registered with type 1 diabetes in England were aged 40 years and younger, with 44.7 percent. In 2021, the UK was the European country with the second-highest number of children and adolescents with type 1 diabetes after Germany, with over 31,600 cases. Treatment The NHS in the UK provides nine care processes annually to people with diabetes. Blood pressure checks, cholesterol monitoring, and foot surveillance are among those. Figures show that almost half of individuals in England with type 2 diabetes received all nine care processes between 2022 and 2023. This figure totaled 37 percent in the case of patients with type 1 diabetes. The same trend could be observed in Wales in 2021/22, where the share of type 2 diabetics receiving all care processes was double the type 1 patients.

Network of 43 papers and 76 citation links related to "Genetic analysis of autoimmune type 1 diabetes mellitus in mice".

The number of diabetics worldwide in 2024 was almost *** million. That number is expected to grow until at least the year 2050. The projected number of diabetics is expected to reach around *** million by that time. With an increased number of diabetics, the prevalence of diabetes is also projected to increase to around ** percent by 2050. Diabetes prevalence globally Diabetes is a chronic disease that affects the production and use of insulin in the body which affects blood glucose. Diabetes comes in two types, type 1 and type 2, which require different types of medical treatments. Globally, China, followed by India, has the largest number of diabetics as of 2024. Despite having the highest number of diabetics, China is not among the countries with the highest prevalence. Pakistan, followed by the Marshall Islands, had the highest prevalence of diabetics worldwide as of 2024. Diabetes pharmaceuticals Treatment for diabetes includes insulin, a hormone that regulates blood glucose, and pills to help regulate the effectiveness of insulin. Treatment depends on the type of diabetes. Danish drug manufacturer Novo Nordisk is one of the leading diabetes care companies in the world. In 2024, Novo Nordisk generated around *** billion kroner in revenue from its diabetes care segment.

Network of 30 papers and 46 citation links related to "The advantages of insulin pump therapy and real time glucose monitoring systems as the tools for reducing the frequency of hypoglycemic episodes in the children and adolescents with type 1 diabetes mellitus".

Network of 42 papers and 58 citation links related to "Comparison Between Continuous Subcutaneous Insulin Infusion and Multiple Insulin Injection Therapy in Type 1 Diabetes Mellitus: 18-Month Follow-Up".

United States US: Diabetes Prevalence: % of Population Aged 20-79 data was reported at 10.790 % in 2017. United States US: Diabetes Prevalence: % of Population Aged 20-79 data is updated yearly, averaging 10.790 % from Dec 2017 (Median) to 2017, with 1 observations. United States US: Diabetes Prevalence: % of Population Aged 20-79 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s USA – Table US.World Bank: Health Statistics. Diabetes prevalence refers to the percentage of people ages 20-79 who have type 1 or type 2 diabetes.; ; International Diabetes Federation, Diabetes Atlas.; Weighted average;

Type 2 diabetes is a condition that affects the amount of sugar in a person’s bloodstream and causes it to become too high. This type of diabetes can be caused by risk factors such as obesity and inactivity and, as shown in this figure, type 2 diabetes is more common among older individuals. Between 2023 and 2024, of those registered with type 2 diabetes in England, **** percent were aged between 40 and 64 years. Increasing prevalence of diabetes in England Between 2023 and 2024, there were over *** million people in England who were registered as diabetics. The number of individuals registered with diabetes in England has increased year-on-year since 2008. The vast majority of those registered with diabetes in England are diagnosed with type 2 diabetes, with over *** million, while approximately *** thousand living with type 1 diabetes. Diabetes medication By far the most used drug in England for the treatment of diabetes was metformin hydrochloride with over ** million items used in 2022 alone. Additionally, already in 2018 approximately **** percent of all prescribed drugs in primary care in England were for the treatment of diabetes. The share of prescribed diabetes drugs has steadily increased since 2005.

T1DiabetesGranada

A longitudinal multi-modal dataset of type 1 diabetes mellitus

Background

Type 1 diabetes mellitus (T1D) patients face daily difficulties in keeping their blood glucose levels within appropriate ranges. Several techniques and devices, such as flash glucose meters, have been developed to help T1D patients improve their quality of life. Most recently, the data collected via these devices is being used to train advanced artificial intelligence models to characterize the evolution of the disease and support its management. The main problem for the generation of these models is the scarcity of data, as most published works use private or artificially generated datasets. For this reason, this work presents T1DiabetesGranada, a open under specific permission longitudinal dataset that not only provides continuous glucose levels, but also patient demographic and clinical information. The dataset includes 257 780 days of measurements over four years from 736 T1D patients from the province of Granada, Spain. This dataset progresses significantly beyond the state of the art as one the longest and largest open datasets of continuous glucose measurements, thus boosting the development of new artificial intelligence models for glucose level characterization and prediction.

Data Records

The data are stored in four comma-separated values (CSV) files which are available in folder "T1DiabetesGranada". These files are described in detail below.

Patient_info.csv

Patient_info.csv is the file containing information about the patients, such as demographic data, start and end dates of blood glucose level measurements and biochemical parameters, number of biochemical parameters or number of diagnostics. This file is composed of 736 records, one for each patient in the dataset, and includes the following variables:

Patient_ID – Unique identifier of the patient. Format: LIB19XXXX.

Sex – Sex of the patient. Values: F (for female), masculine (for male)

Birth_year – Year of birth of the patient. Format: YYYY.

Initial_measurement_date – Date of the first blood glucose level measurement of the patient in the Glucose_measurements.csv file. Format: YYYY-MM-DD.

Final_measurement_date – Date of the last blood glucose level measurement of the patient in the Glucose_measurements.csv file. Format: YYYY-MM-DD.

Number_of_days_with_measures – Number of days with blood glucose level measurements of the patient, extracted from the Glucose_measurements.csv file. Values: ranging from 8 to 1463.

Number_of_measurements – Number of blood glucose level measurements of the patient, extracted from the Glucose_measurements.csv file. Values: ranging from 400 to 137292.

Initial_biochemical_parameters_date – Date of the first biochemical test to measure some biochemical parameter of the patient, extracted from the Biochemical_parameters.csv file. Format: YYYY-MM-DD.

Final_biochemical_parameters_date – Date of the last biochemical test to measure some biochemical parameter of the patient, extracted from the Biochemical_parameters.csv file. Format: YYYY-MM-DD.

Number_of_biochemical_parameters – Number of biochemical parameters measured on the patient, extracted from the Biochemical_parameters.csv file. Values: ranging from 4 to 846.

Number_of_diagnostics – Number of diagnoses realized to the patient, extracted from the Diagnostics.csv file. Values: ranging from 1 to 24.

Glucose_measurements.csv

Glucose_measurements.csv is the file containing the continuous blood glucose level measurements of the patients. The file is composed of more than 22.6 million records that constitute the time series of continuous blood glucose level measurements. It includes the following variables:

Patient_ID – Unique identifier of the patient. Format: LIB19XXXX.

Measurement_date – Date of the blood glucose level measurement. Format: YYYY-MM-DD.

Measurement_time – Time of the blood glucose level measurement. Format: HH:MM:SS.

Measurement – Value of the blood glucose level measurement in mg/dL. Values: ranging from 40 to 500.

Biochemical_parameters.csv

Biochemical_parameters.csv is the file containing data of the biochemical tests performed on patients to measure their biochemical parameters. This file is composed of 87482 records and includes the following variables:

Patient_ID – Unique identifier of the patient. Format: LIB19XXXX.

Reception_date – Date of receipt in the laboratory of the sample to measure the biochemical parameter. Format: YYYY-MM-DD.

Name – Name of the measured biochemical parameter. Values: 'Potassium', 'HDL cholesterol', 'Gammaglutamyl Transferase (GGT)', 'Creatinine', 'Glucose', 'Uric acid', 'Triglycerides', 'Alanine transaminase (GPT)', 'Chlorine', 'Thyrotropin (TSH)', 'Sodium', 'Glycated hemoglobin (Ac)', 'Total cholesterol', 'Albumin (urine)', 'Creatinine (urine)', 'Insulin', 'IA ANTIBODIES'.

Value – Value of the biochemical parameter. Values: ranging from -4.0 to 6446.74.

Diagnostics.csv

Diagnostics.csv is the file containing diagnoses of diabetes mellitus complications or other diseases that patients have in addition to type 1 diabetes mellitus. This file is composed of 1757 records and includes the following variables:

Patient_ID – Unique identifier of the patient. Format: LIB19XXXX.

Code – ICD-9-CM diagnosis code. Values: subset of 594 of the ICD-9-CM codes (https://www.cms.gov/Medicare/Coding/ICD9ProviderDiagnosticCodes/codes).

Description – ICD-9-CM long description. Values: subset of 594 of the ICD-9-CM long description (https://www.cms.gov/Medicare/Coding/ICD9ProviderDiagnosticCodes/codes).

Technical Validation

Blood glucose level measurements are collected using FreeStyle Libre devices, which are widely used for healthcare in patients with T1D. Abbott Diabetes Care, Inc., Alameda, CA, USA, the manufacturer company, has conducted validation studies of these devices concluding that the measurements made by their sensors compare to YSI analyzer devices (Xylem Inc.), the gold standard, yielding results of 99.9% of the time within zones A and B of the consensus error grid. In addition, other studies external to the company concluded that the accuracy of the measurements is adequate.

Moreover, it was also checked in most cases the blood glucose level measurements per patient were continuous (i.e. a sample at least every 15 minutes) in the Glucose_measurements.csv file as they should be.

Usage Notes

For data downloading, it is necessary to be authenticated on the Zenodo platform, accept the Data Usage Agreement and send a request specifying full name, email, and the justification of the data use. This request will be processed by the Secretary of the Department of Computer Engineering, Automatics, and Robotics of the University of Granada and access to the dataset will be granted.

The files that compose the dataset are CSV type files delimited by commas and are available in folder "T1DiabetesGranada". No request is required for data download. A Jupyter Notebook (Python v. 3.8) with code that may help to a better understanding of the dataset, with graphics and statistics, is available in folder "Usage Notes".

Graphs and Stats.ipynb

The Jupyter Notebook generates tables, graphs and statistics for a better understanding of the dataset. It has four main sections, one dedicated to each file in the dataset. In addition, it has useful functions such as calculating the patient age, deleting a patient list from a dataset file and leaving only a patient list in a dataset file.

Code availability

The dataset was generated using some custom code located in the folder "Code availability". The code is provided as Jupyter Notebooks created with Python v. 3.8. The code was used to conduct tasks such as data curation and transformation, and variables extraction.

Original_patient_info_curation.ipynb

In the Jupyter Notebook is preprocessed the original file with patient data. Mainly irrelevant rows and columns are removed, and the sex variable is recoded.

Glucose_measurements_curation.ipynb

In the Jupyter Notebook is preprocessed the original file with the continuous glucose level measurements of the patients. Principally rows without information or duplicated rows are removed and the variable with the timestamp is transformed into two new variables, measurement date and measurement time.

Biochemical_parameters_curation.ipynb

In the Jupyter Notebook is preprocessed the original file with patient data of the biochemical tests performed on patients to measure their biochemical parameters. Mainly irrelevant rows and columns are removed and the variable with the name of the

Network of 42 papers and 94 citation links related to "Genetic Dissection of Type 1 Diabetes Susceptibility Gene, Idd3, by Ancestral Haplotype Congenic Mapping".

The Moorfields DR Dataset encompasses all patients who have been referred via the NHS diabetic eye screening program (DESP) to Moorfields Eye Hospital - a leading provider of eye health services in the UK and a world-class centre of excellence for ophthalmic research and education.

The DESP invites all diabetic patients aged 12 years or over to annual primary-care-based screening. Here, two-field fundus photography (one image centred on the macula and a second image centred on the optic disc) is acquired and graded according to the English Screening Programme for Diabetic Retinopathy standards. If criteria were met (R2, R3, R3, M1, or ungradable), patients are referred to hospital eye services and suspended from screening while under secondary care. Urgently referred patients (retinopathy grade R3) are to be seen within 2 weeks, and routinely referred patients within 10 weeks.

The earliest available screening records are from 2013, however, the dataset will include any imaging or clinical metadata that is available for these patients prior to that time (for example in patients who were initially monitored for the early manifestations of the disease). Also of note, this dataset will include data from both eyes in each case. For these reasons, the dataset will include longitudinal data from a wide range of diabetic eye disease.

Clinical metadata includes information regarding: - patient demographics - visual acuities (predominantly measured with Early Treatment Diabetic Retinopathy Study (ETDRS) charts) - diabetic retinopathy grading - intravitreal therapies and ocular surgeries

Additional information is provided in the ‘technical details’ tab.

The DR dataset includes eye imaging modalities, such as: - Optical coherence tomography (CSO, Heidelberg, Optos, Topcon, Zeiss) - Colour fundus photographs (Topcon, Zeiss) - Ultra-wide field photographs (Optos, Zeiss) - Iris photographs (CSO, Zeiss) - Keratoscope topography (CSO) - Infrared photographs (Heidelberg, Topcon, Zeiss) - Fluorescein angiography (Heidelberg, Optos, Topcon, Zeiss) - Indocyanine green angiography (Heidelberg, Optos, Topcon) - Fundus autofluorescence (Heidelberg, Optos, Zeiss)

Imaging data from CSO is subject to additional approvals.

As of July 2024, the dataset consisted of 91,009 eyes with 445,792 screening readings, and over 5,537,798 ophthalmic images. This is one of the largest single centre databases from patients with DR and covers more than a decade of follow-up for these patients.

Recent clinical evidence suggests important role of lipid and amino acid metabolism in early pre-autoimmune stages of type 1 diabetes pathogenesis. We study the molecular paths associated with the incidence of insulitis and type 1 diabetes in the Non-Obese Diabetic (NOD) mouse model using available gene expression data from the pancreatic tissue from young pre-diabetic mice. We apply a graph-theoretic approach by using a modified color coding algorithm to detect optimal molecular paths associated with specific phenotypes in an integrated biological network encompassing heterogeneous interaction data types. In agreement with our recent clinical findings, we identified a path downregulated in early insulitis involving dihydroxyacetone phosphate acyltransferase (DHAPAT), a key regulator of ether phospholipid synthesis. The pathway involving serine/threonine-protein phosphatase (PP2A), an upstream regulator of lipid metabolism and insulin secretion, was found upregulated in early insulitis. Our findings provide further evidence for an important role of lipid metabolism in early stages of type 1 diabetes pathogenesis, as well as suggest that such dysregulation of lipids and related increased oxidative stress can be tracked to beta cells.

Centile charts derived from 94 healthy controls divided into 4 age groups.

In 2023, there were **** deaths from diabetes mellitus per 100,000 people in the United States. The death rate caused by this condition has fluctuated over the past decades, reaching almost ** deaths per 100,000 people in the early 2000s, and about ** deaths in 1980. Prevalence of diabetes In 2022, around *** percent of the adult population in the U.S. had diabetes. In total, around ** million adults in the United States are currently living with diabetes. Of this total, the vast majority were aged 45 years and older. The states with the highest share of adults with diabetes are West Virginia, Mississippi, and Louisiana. Cure for diabetes? Researchers are helping diabetics put their Type 2 diabetes into remission, where the blood sugar levels are kept within a healthy range. For Type 1, scientists are looking for ways to prevent the immune system’s attack on beta cells, which causes diabetes. These cells, located in the pancreas, produce the insulin people need to live. As of yet, there is no cure for diabetes mellitus; however, scientists are researching ways to make diabetes harmless one day.

ObjectiveCurrently, distinct use of clinical data, routine laboratory indicators or the detection of diabetic autoantibodies in the diagnosis and management of diabetes mellitus is limited. Hence, this study was aimed to screen the indicators, and to establish and validate a multifactorial logistic regression model nomogram for the non-invasive differential prediction of type 1 diabetes mellitus.MethodsClinical data, routine laboratory indicators, and diabetes autoantibody profiles of diabetic patients admitted between September 2018 and December 2022 were retrospectively analyzed. Logistic regression was used to select the independent influencing factors, and a prediction nomogram based on the multiple logistic regression model was constructed using these independent factors. Moreover, the predictive accuracy and clinical application value of the nomogram were evaluated using Receiver Operating Characteristic (ROC) curves, calibration curves, decision curve analysis (DCA), and clinical impact curves (CIC).ResultsA total of 522 diabetic patients were included in this study. These patients were randomized into training and validation sets in a 7:3 ratio. The predictors screened included age, prealbumin (PA), high-density lipoprotein cholesterol (HDL-C), islet cells autoantibodies (ICA), islets antigen 2 autoantibodies (IA-2A), glutamic acid decarboxylase antibody (GADA), and C-peptide levels. Based on these factors, a multivariate model nomogram was constructed, which had an Area Under Curve (AUC) of 0.966 and 0.961 for the training set and validation set, respectively. Subsequently, the calibration curves demonstrated a strong accuracy of the graph; the DCA and CIC results indicated that the graph could be used as a non-invasive valid predictive tool for the differential diagnosis of type 1 diabetes mellitus, clinically.ConclusionThe established prediction model combining patient’s age, PA, HDL-C, ICA, IA-2A, GADA, and C-peptide can assist in differential diagnosis of type 1 diabetes mellitus and type 2 diabetes mellitus and provides a basis for the clinical as well as therapeutic management of the disease.