In 2024, Pakistan had the highest prevalence of diabetes worldwide, with around ** percent of the population suffering from the disease. Diabetes mellitus, or simply, diabetes, refers to a group of metabolic disorders that cause high blood sugar levels. Diabetes can be prevented and treated though exercise, maintaining normal body weight, and healthy eating, but is usually managed with insulin injections. Costs As of 2024, there were almost *** million people worldwide who had diabetes. With such a huge number of people suffering from this disease, it is no surprise that spending on diabetes can be very high. It is estimated that the United States alone spent around ***** billion U.S. dollars on diabetes health expenditure in 2024. The countries with the highest spending per patient with diabetes include Switzerland, the United States, and Norway. Death Diabetes is among the leading ten causes of death worldwide, accounting for around **** million deaths in 2021. Complications resulting from diabetes include chronic kidney disease, stroke, and cardiovascular disease. The risk of early death is at least doubled among those with diabetes. The Western Pacific reports the highest number of deaths from diabetes, followed by North America and the Caribbean.

China is the country with the highest number of diabetics worldwide, with around *** million people suffering from the disease. By the year 2050, it is predicted that China will have around *** million people with diabetes. Death from diabetes Diabetes is one of the leading causes of death worldwide, accounting for **** million deaths in 2021. Diabetes at least doubles one’s chance of dying prematurely, and many places in the world lack appropriate treatment options. The highest number of deaths from diabetes comes from the Western Pacific, where around *** million people died from the disease in 2024. Obesity One of the biggest risk factors for developing diabetes is being overweight or obese. Rates of obesity have increased in recent years in many countries around the world. In the United States, for example, it is estimated that around ** percent of the adult population was obese in 2023, compared to ** percent of the population in 2011.

Detailed dataset comprising health and demographic data of 100,000 individuals, aimed at facilitating diabetes-related research and predictive modeling. This dataset includes information on gender, age, location, race, hypertension, heart disease, smoking history, BMI, HbA1c level, blood glucose level, and diabetes status.

Dataset Use Cases

This dataset can be used for various analytical and machine learning purposes, such as:

1. Predictive Modeling: Build models to predict the likelihood of diabetes based on demographic and health-related features.
2. Health Analytics: Analyze the correlation between different health metrics (e.g., BMI, HbA1c level) and diabetes.
3. Demographic Studies: Examine the distribution of diabetes across different demographic groups and locations.
4. Public Health Research: Identify risk factors for diabetes and target interventions to high-risk groups.
5. Clinical Research: Study the relationship between comorbid conditions like hypertension and heart disease with diabetes.

Potential Analyses

• Descriptive Statistics: Summarize the dataset to understand the central tendencies and dispersion of features.
• Correlation Analysis: Identify the relationships between features.
• Classification Models: Use machine learning algorithms to classify individuals as diabetic or non-diabetic.
• Trend Analysis: Analyze trends over the years to see how diabetes prevalence has changed.

In 2024, around 16 percent of adults between the ages of 20 and 79 had diabetes in Turkey. Other selected countries with a high prevalence of diabetes that year included Mexico, the United States, and Portugal. Diabetes is a metabolic disease that causes high blood sugar levels. Diabetes worldwide In 2024, an estimated 11 percent of the global adult population had diabetes. In concrete numbers, there were about 589 million diabetic adults (20-79 years) worldwide in 2024, and this total is predicted to grow to approximately 852.5 million by the year 2050. Spending per patient The country that spent the most on patients with diabetes in 2024 was Switzerland. At that time, providing for a diabetic patient in Switzerland cost an average of over 12 thousand U.S. dollars. The United States stood in second place, spending about 10,500 U.S. dollars per patient. In the same year, the ten countries by lowest average spending per person with diabetes were all African and Asian countries. Bangladesh had the lowest annual diabetes-related health expenditures per person, with just 74 U.S. dollars.

South Africa is experiencing a rapidly growing diabetes epidemic that threatens its healthcare system. Research on the determinants of diabetes in South Africa receives considerable attention due to the lifestyle changes accompanying South Africa’s rapid urbanization since the fall of Apartheid. However, few studies have investigated how segments of the Black South African population, who continue to endure Apartheid’s institutional discriminatory legacy, experience this transition. This paper explores the association between individual and area-level socioeconomic status and diabetes prevalence, awareness, treatment, and control within a sample of Black South Africans aged 45 years or older in three municipalities in KwaZulu-Natal. Cross-sectional data were collected on 3,685 participants from February 2017 to February 2018. Individual-level socioeconomic status was assessed with employment status and educational attainment. Area-level deprivation was measured using the most recent South African Multidimensional Poverty Index scores. Covariates included age, sex, BMI, and hypertension diagnosis. The prevalence of diabetes was 23% (n = 830). Of those, 769 were aware of their diagnosis, 629 were receiving treatment, and 404 had their diabetes controlled. Compared to those with no formal education, Black South Africans with some high school education had increased diabetes prevalence, and those who had completed high school had lower prevalence of treatment receipt. Employment status was negatively associated with diabetes prevalence. Black South Africans living in more deprived wards had lower diabetes prevalence, and those residing in wards that became more deprived from 2001 to 2011 had a higher prevalence diabetes, as well as diabetic control. Results from this study can assist policymakers and practitioners in identifying modifiable risk factors for diabetes among Black South Africans to intervene on. Potential community-based interventions include those focused on patient empowerment and linkages to care. Such interventions should act in concert with policy changes, such as expanding the existing sugar-sweetened beverage tax.

Population-based county-level estimates for diagnosed (DDP), undiagnosed (UDP), and total diabetes prevalence (TDP) were acquired from the Institute for Health Metrics and Evaluation (IHME) for the years 2004-2012 (Evaluation 2017). Prevalence estimates were calculated using a two-stage approach. The first stage used National Health and Nutrition Examination Survey (NHANES) data to predict high fasting plasma glucose (FPG) levels (≥126 mg/dL) and/or hemoglobin A1C (HbA1C) levels (≥6.5% [48 mmol/mol]) based on self-reported demographic and behavioral characteristics (Dwyer-Lindgren, Mackenbach et al. 2016). This model was then applied to Behavioral Risk Factor Surveillance System (BRFSS) data to impute high FPG and/or A1C status for each BRFSS respondent (Dwyer-Lindgren, Mackenbach et al. 2016). The second stage used the imputed BRFSS data to fit a series of small area models, which were used to predict the county-level prevalence of each of the diabetes-related outcomes (Dwyer-Lindgren, Mackenbach et al. 2016). Diagnosed diabetes was defined as the proportion of adults (age 20+ years) who reported a previous diabetes diagnosis, represented as an age-standardized prevalence percentage. Undiagnosed diabetes was defined as proportion of adults (age 20+ years) who have a high FPG or HbA1C but did not report a previous diagnosis of diabetes. Total diabetes was defined as the proportion of adults (age 20+ years) who reported a previous diabetes diagnosis and/or had a high FPG/HbA1C. The age-standardized diabetes prevalence (%) was used as the outcome. The EQI was constructed for 2000-2005 for all US counties and is composed of five domains (air, water, built, land, and sociodemographic), each composed of variables to represent the environmental quality of that domain. Domain-specific EQIs were developed using principal components analysis (PCA) to reduce these variables within each domain while the overall EQI was constructed from a second PCA from these individual domains (L. C. Messer et al., 2014). To account for differences in environment across rural and urban counties, the overall and domain-specific EQIs were stratified by rural urban continuum codes (RUCCs) (U.S. Department of Agriculture, 2015). This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: Human health data are not available publicly. EQI data are available at: https://edg.epa.gov/data/Public/ORD/NHEERL/EQI. Format: Data are stored as csv files. This dataset is associated with the following publication: Jagai, J., A. Krajewski, S. Shaikh, D. Lobdell, and R. Sargis. Association between environmental quality and diabetes in the U.S.A.. Journal of Diabetes Investigation. John Wiley & Sons, Inc., Hoboken, NJ, USA, 11(2): 315-324, (2020).

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.

Context Diabetes is one of the most prevalent chronic diseases in the United States, affecting millions of Americans each year and placing a substantial financial burden on the economy. It is a serious chronic condition in which the body loses the ability to effectively regulate blood glucose levels, leading to a reduced quality of life and decreased life expectancy. During digestion, food is broken down into sugars, which enter the bloodstream. This triggers the pancreas to release insulin, a hormone that helps cells in the body use these sugars for energy. Diabetes is typically characterized by either insufficient insulin production or the body's inability to use insulin effectively.

Chronic high blood sugar levels in individuals with diabetes can lead to severe complications, including heart disease, vision loss, kidney disease, and lower-limb amputation. Although there is no cure for diabetes, strategies such as maintaining a healthy weight, eating a balanced diet, staying physically active, and receiving medical treatments can help mitigate its effects. Early diagnosis is crucial, as it allows for lifestyle modifications and more effective treatment, making predictive models for assessing diabetes risk valuable tools for public health officials.

The scale of the diabetes epidemic is significant. According to the Centers for Disease Control and Prevention (CDC), as of 2018, approximately 34.2 million Americans have diabetes, while 88 million have prediabetes. Alarmingly, the CDC estimates that 1 in 5 individuals with diabetes and about 8 in 10 individuals with prediabetes are unaware of their condition. Type II diabetes is the most common form, and its prevalence varies based on factors such as age, education, income, geographic location, race, and other social determinants of health. The burden of diabetes disproportionately affects those with lower socioeconomic status. The economic impact is also substantial, with the cost of diagnosed diabetes reaching approximately $327 billion annually, and total costs, including undiagnosed diabetes and prediabetes, nearing $400 billion each year.

Content The Behavioral Risk Factor Surveillance System (BRFSS) is a health-related telephone survey that is collected annually by the CDC. Each year, the survey collects responses from over 400,000 Americans on health-related risk behaviors, chronic health conditions, and the use of preventative services. It has been conducted every year since 1984. For this project, a XPT of the dataset available on CDC website for the year 2023 was used. This original dataset contains responses from 433,323 individuals and has 345 features. These features are either questions directly asked of participants, or calculated variables based on individual participant responses.

I have selected 20 features from this dataset that are suitable for working on the topic of diabetes, and I have saved them in a CSV file without making any changes to the data. The goal of this is to make it easier to work with the data. For more information or to access updated data, you can refer to the CDC website. I initially examined the original dataset from the CDC and found no duplicate entries. That dataset contains 330 columns and features. Therefore, the duplicate cases in this dataset are not due to errors but rather represent individuals with similar conditions. In my opinion, removing these entries would both introduce errors and reduce accuracy.

Explore some of the following research questions: - Can survey questions from the BRFSS provide accurate predictions of whether an individual has diabetes? - What risk factors are most predictive of diabetes risk? - Can we use a subset of the risk factors to accurately predict whether an individual has diabetes? - Can we create a short form of questions from the BRFSS using feature selection to accurately predict if someone might have diabetes or is at high risk of diabetes?

Acknowledgements It is important to reiterate that I did not create this dataset, it is simply a summarized and reformatted dataset derived from the BRFSS 2023 dataset available on the CDC website. It is also worth noting that none of the data in this dataset discloses individuals' identities.

Inspiration Zidian Xie et al for Building Risk Prediction Models for Type 2 Diabetes Using Machine Learning Techniques using the 2014 BRFSS, and Alex Teboul for building Diabetes Health Indicators dataset based on BRFSS 2015 were the inspiration for creating this dataset and exploring the BRFSS in general.

Population-based county-level estimates for prevalence of DC were obtained from the Institute for Health Metrics and Evaluation (IHME) for the years 2004-2012 (16). DC prevalence rate was defined as the propor-tion of people within a county who had previously been diagnosed with diabetes (high fasting plasma glu-cose 126 mg/dL, hemoglobin A1c (HbA1c) of 6.5%, or diabetes diagnosis) but do not currently have high fasting plasma glucose or HbA1c for the period 2004-2012. DC prevalence estimates were calculated using a two-stage approach. The first stage used National Health and Nutrition Examination Survey (NHANES) data to predict high fasting plasma glucose (FPG) levels (≥126 mg/dL) and/or HbA1C levels (≥6.5% [48 mmol/mol]) based on self-reported demographic and behavioral characteristics (16). This model was then applied to Behavioral Risk Factor Surveillance System (BRFSS) data to impute high FPG and/or HbA1C status for each BRFSS respondent (16). The second stage used the imputed BRFSS data to fit a series of small area models, which were used to predict county-level prevalence of diabetes-related outcomes, including DC (16). The EQI was constructed for 2006-2010 for all US counties and is composed of five domains (air, water, built, land, and sociodemographic), each composed of variables to represent the environmental quality of that domain. Domain-specific EQIs were developed using principal components analysis (PCA) to reduce these variables within each domain while the overall EQI was constructed from a second PCA from these individual domains (L. C. Messer et al., 2014). To account for differences in environment across rural and urban counties, the overall and domain-specific EQIs were stratified by rural urban continuum codes (RUCCs) (U.S. Department of Agriculture, 2015). Results are reported as prevalence rate differences (PRD) with 95% confidence intervals (CIs) comparing the highest quintile/worst environmental quality to the lowest quintile/best environmental quality expo-sure metrics. PRDs are representative of the entire period of interest, 2004-2012. Due to availability of DC data and covariate data, not all counties were captured, however, the majority, 3134 of 3142 were utilized in the analysis. This dataset is not publicly accessible because: EPA cannot release personally identifiable information regarding living individuals, according to the Privacy Act and the Freedom of Information Act (FOIA). This dataset contains information about human research subjects. Because there is potential to identify individual participants and disclose personal information, either alone or in combination with other datasets, individual level data are not appropriate to post for public access. Restricted access may be granted to authorized persons by contacting the party listed. It can be accessed through the following means: Human health data are not available publicly. EQI data are available at: https://edg.epa.gov/data/Public/ORD/NHEERL/EQI. Format: Data are stored as csv files. This dataset is associated with the following publication: Jagai, J., A. Krajewski, K. Price, D. Lobdell, and R. Sargis. Diabetes control is associated with environmental quality in the USA. Endocrine Connections. BioScientifica Ltd., Bristol, UK, 10(9): 1018-1026, (2021).

The Diabetes Dataset is commonly used for predictive modeling and medical research, aiming to analyze factors contributing to diabetes and predict its occurrence based on various health metrics. - Pregnancies – More pregnancies may increase the risk of gestational diabetes, which can lead to Type 2 diabetes.
- Glucose – High blood sugar levels indicate poor insulin regulation, a key factor in diabetes.
- Skin Thickness – Measures subcutaneous fat; higher values may indicate insulin resistance.
- Insulin – Low or high insulin levels can signal improper glucose metabolism, leading to diabetes.
- Blood Pressure (BP) – Hypertension is often linked with insulin resistance and diabetes complications.
- Diabetes Pedigree Function – Estimates genetic predisposition to diabetes based on family history.
- Age – Older individuals have a higher risk of developing Type 2 diabetes.
- BMI (Body Mass Index) – High BMI is associated with obesity, a major risk factor for diabetes.

Would you like further insights on how these factors interact in diabetes prediction? 😊

1. Source of information: National Health Administration national nutrition and health survey.2. Definition of hyperglycemia/diabetes: Fasting blood sugar level 126mg/dL for 8 hours or more, or taking hypoglycemic medications in the past month.

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

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Diabetes Management Devices Market Size 2024-2028

The diabetes management devices market size is forecast to increase by USD 13.98 bn at a CAGR of 7.68% between 2023 and 2028.

The market is witnessing significant growth due to the rising global burden of diabetes and the increasing focus on advanced technologies such as artificial pancreas systems. These systems utilize insulin pumps and continuous glucose monitoring sensors to automatically adjust insulin delivery based on real-time glucose levels. Additionally, the integration of artificial intelligence and data analytics in diabetes management devices is revolutionizing the industry, enabling remote patient monitoring and personalized treatment plans. Other trends include the development of insulin pens with advanced features and the adoption of spectroscopy technology for non-invasive blood glucose monitoring. However, the prohibitive cost of diabetes care devices remains a major challenge for market growth.Overall, the market is expected to experience robust growth In the coming years, driven by technological advancements and the increasing prevalence of diabetes.

What will be the Size of the Diabetes Management Devices Market during the Forecast Period?

Request Free SampleThe market encompasses a range of technologies designed to assist individuals in managing their diabetes, including insulin delivery devices, mobile health applications, and disease management tools. With the global diabetic population projected to reach over 592 million by 2035, driven by factors such as obesity rates, smoking, and high cholesterol levels, the market for diabetes care devices is experiencing significant growth. Hospitals and specialty clinics are increasingly adopting minimally invasive devices, such as diabetes lancet devices and diabetes tracker devices, to improve patient care and outcomes. Type 1 diabetes, an autoimmune disease characterized by insulin deficiency, and Type 2 diabetes, often associated with insulin resistance, both require ongoing management to prevent complications, including kidney failure, gangrene, and lower limb amputation.Insulin delivery devices, including insulin pens and blood glucose level monitoring systems, are essential tools in managing both types of diabetes. The market for diabetes management devices is expected to continue expanding as technology advances and the global population ages.

How is this Diabetes Management Devices Industry segmented and which is the largest segment?

The diabetes management devices industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD billion' for the period 2024-2028, as well as historical data from 2018-2022 for the following segments. ProductBlood glucose monitoring devicesInsulin delivery systemsDistribution ChannelOfflineOnlineGeographyNorth AmericaUSEuropeGermanyUKFranceAsiaChinaRest of World (ROW)

By Product Insights

The blood glucose monitoring devices segment is estimated to witness significant growth during the forecast period. Diabetes management devices play a crucial role in monitoring and managing blood glucose levels for individuals with diabetes. These devices include a range of products such as insulin delivery systems, continuous monitoring systems, and glucose monitoring devices. Insulin delivery devices, including insulin pumps and pens, facilitate precise insulin administration. Continuous monitoring systems, such as continuous glucose monitoring systems (CGMs) and Mobi insulin pumps, provide real-time glucose level data through wireless transmission and dedicated apps. Glucowear, a continuous glucose monitoring system, offers non-invasive transdermal sensors and spectroscopy technology. Hospital pharmacies, retail pharmacies, and online pharmacies stock these diabetes care devices. The increasing prevalence of diabetes among the obese, elderly population, and those with conditions like high cholesterol levels, smoking, and inactive lifestyles necessitates effective diabetes management.Disease management is essential to prevent complications like kidney failure, gangrene, lower limb amputation, heart attack, blindness, and stroke. Diabetes tracker devices, such as diabetes monitoring software and artificial pancreas systems, help healthcare providers analyze glucose patterns and create treatment plans. User-friendly interfaces, visual representations, and medication adherence features enhance the efficacy of treatment. In summary, diabetes management devices, including insulin delivery devices, continuous monitoring systems, and glucose monitoring devices, are essential tools for managing diabetes and preventing complications. These devices offer precision, flexibility, and smart features, making diabetes care more accessible and convenient for individuals with diabetes. Hospital segment, diagnostic centers, diabetes clinics, and healthcare professionals uti

Diabetic Meal Replacement Shakes Market Outlook

According to our latest research, the global diabetic meal replacement shakes market size in 2024 reached USD 2.38 billion, reflecting a robust demand from both developed and emerging economies. The market is advancing at a CAGR of 6.7% from 2025 to 2033, driven by the rising prevalence of diabetes, increased health awareness, and innovation in nutritional food products. By 2033, the market is expected to attain a value of USD 4.33 billion, as per our comprehensive analysis. This growth trajectory is underpinned by the increasing adoption of convenient dietary solutions among diabetic populations and the ongoing expansion of retail and online distribution channels.

A significant growth factor for the diabetic meal replacement shakes market is the escalating global prevalence of diabetes, which currently affects over 537 million adults worldwide, according to the International Diabetes Federation. This growing diabetic population is fueling demand for specialized nutritional products that can help manage blood sugar levels and support weight management. Consumers are increasingly seeking meal replacement options that are not only low in sugar but also fortified with essential nutrients, catering to the dietary restrictions and nutritional needs of diabetics. Furthermore, the rising incidence of obesity and sedentary lifestyles is contributing to the onset of diabetes, thereby amplifying the need for convenient, healthy meal alternatives like diabetic meal replacement shakes.

Another key driver is the surge in health consciousness among consumers, particularly in urban areas. The modern consumer is more informed about the importance of nutrition and proactive health management, leading to a greater demand for functional foods and beverages. Diabetic meal replacement shakes, with their balanced macronutrient profiles and added vitamins and minerals, are increasingly perceived as an effective solution for managing calorie intake and blood glucose levels. The inclusion of high-quality proteins, dietary fibers, and low glycemic index ingredients further enhances their appeal. This trend is particularly pronounced among working professionals and elderly populations, who prioritize convenience without compromising on health benefits.

Product innovation and technological advancements are also propelling market growth. Manufacturers are investing in research and development to formulate shakes that cater to diverse taste preferences, dietary needs, and cultural inclinations. The introduction of plant-based and allergen-free variants, along with the use of natural sweeteners and clean label ingredients, is expanding the consumer base. Additionally, advancements in packaging technology have improved shelf life and portability, making diabetic meal replacement shakes more accessible than ever before. The expansion of e-commerce platforms and digital health solutions has also played a pivotal role in enhancing product visibility and consumer reach, further stimulating market growth.

From a regional perspective, North America continues to dominate the diabetic meal replacement shakes market, accounting for the largest share in 2024, followed closely by Europe and Asia Pacific. The well-established healthcare infrastructure, high diabetes prevalence, and strong presence of leading market players in North America are key contributors to its dominance. Meanwhile, Asia Pacific is emerging as a high-growth region due to increasing diabetes incidence, rising disposable incomes, and rapid urbanization. Latin America and the Middle East & Africa are also witnessing steady growth, driven by improving healthcare awareness and expanding retail networks. The global market landscape is thus characterized by both established demand in mature markets and burgeoning opportunities in emerging economies.

Product Type Analysis

The diabetic meal replacement shakes market is segmente

Diabetes prevalence in Massachusetts has been steadily increasing.

With diabetes, your body either doesn't make enough insulin or can't use it as well as it should. Diabetes is a chronic (long-lasting) health condition that affects how your body turns food into energy. Most of the food you eat is broken down into sugar (also called glucose) and released into your bloodstream.

Women age 15 years and above with high (141-160 mg/dl) Blood sugar level23 (%)

Women age 15 years and above wih very high (>160 mg/dl) Blood sugar level23 (%)

Women age 15 years and above wih high or very high (>140 mg/dl) Blood sugar level or taking

medicine to control blood sugar level23 (%)

Men age 15 years and above wih high (141-160 mg/dl) Blood sugar level23 (%)

Men (age 15 years and above wih very high (>160 mg/dl) Blood sugar level23 (%)

Men age 15 years and above wih high or very high (>140 mg/dl) Blood sugar level or taking medicine to control

blood sugar level23 (%)

Diabetes Management Software Market Outlook

The global diabetes management software market size was valued at approximately USD 5.4 billion in 2023, and it is anticipated to grow at a compound annual growth rate (CAGR) of 12.6% from 2024 to 2032, reaching an estimated USD 13.9 billion by 2032. The growth of this market is predominantly driven by the increasing prevalence of diabetes worldwide, technological advancements in diabetes management solutions, and the growing demand for efficient and user-friendly software applications.

The increase in the prevalence of diabetes is a major driving factor for the diabetes management software market. With diabetes affecting more than 422 million people globally, there is a growing need for better management solutions. The high incidence of diabetes-related complications such as cardiovascular diseases, neuropathy, and nephropathy further necessitates the adoption of effective management tools. Diabetes management software offers diabetic patients and healthcare providers accurate and efficient ways to monitor and manage blood glucose levels, thereby reducing the risk of complications.

Technological advancements in the healthcare industry have significantly contributed to the growth of the diabetes management software market. Innovations such as continuous glucose monitoring (CGM) systems, artificial intelligence, and machine learning algorithms have enhanced the capability of diabetes management solutions. These advanced technologies enable real-time data collection, analysis, and personalized treatment plans, which are critical for effective diabetes management. The integration of these technologies with diabetes management software has transformed the way diabetes is monitored and managed.

The rising demand for user-friendly and efficient diabetes management solutions is another key growth factor. As patients become more health-conscious and proactive in managing their conditions, there is an increasing preference for software that is easy to use and provides comprehensive data analytics. Mobile-based diabetes management applications, in particular, have gained popularity due to their convenience and accessibility. These applications allow patients to track their blood glucose levels, diet, exercise, and medication schedules seamlessly, thus improving adherence to treatment plans and overall health outcomes.

Regionally, the diabetes management software market is witnessing substantial growth across various geographies. North America holds a significant share of the market, driven by the high prevalence of diabetes, well-established healthcare infrastructure, and the presence of key market players. Meanwhile, the Asia Pacific region is experiencing rapid market growth, attributed to the rising diabetic population, increasing healthcare expenditure, and growing awareness about diabetes management. Europe and Latin America are also notable markets, with continuous advancements in healthcare technologies and increasing adoption of digital health solutions.

Type Analysis

The diabetes management software market can be categorized into web-based, mobile-based, and cloud-based solutions. Each type has its unique features and functionalities catering to the diverse needs of users. Web-based diabetes management software is widely adopted due to its accessibility and ease of use. Users can access their data from any device with an internet connection, making it convenient for both patients and healthcare providers. The web-based platforms often come with robust data analytics and reporting tools, which are essential for effective diabetes management.

Mobile-based diabetes management software has gained significant traction in recent years, thanks to the proliferation of smartphones and mobile health applications. These mobile apps offer a high level of convenience, allowing users to monitor their diabetes on-the-go. They usually come with features such as blood glucose tracking, medication reminders, and dietary recommendations. The popularity of mobile-based applications is particularly high among younger, tech-savvy populations who prefer managing their health through digital means.

Cloud-based diabetes management software is another rapidly growing segment in the market. Cloud-based solutions offer the advantage of seamless data storage and sharing across multiple devices. This is particularly beneficial for healthcare providers as they can access patient data in real-time, regardless of location, facilitating better coordination of care. Additionally, cloud-based platforms often include

Significant variants associated with high fasting blood glucose (FBG level ≥100 mg/ dL or currently on diabetes medication).

Around ** percent of the global adult population suffered from diabetes in 2024 - by the year 2050 this number is expected to rise to ** percent. Diabetes, or diabetes mellitus, refers to a group of metabolic disorders that result in chronic high blood sugar levels. Diabetes can lead to serious health complications, such as cardiovascular disease, chronic kidney disease, and stroke, and is now among the top ten leading causes of death worldwide. Prevalence Diabetes is a global problem affecting many countries. China currently has the largest number of diabetics worldwide, with some *** million people suffering from the disease. However, the highest prevalence of diabetes is found in Pakistan, followed by the Marshall Islands and Kuwait. Rates of diabetes have increased in many countries in recent years, as have rates of obesity, one of the leading risk factors for the disease. Outlook It is predicted that diabetes will continue to be a problem in the future. Africa is expected to see a *** percent increase in the number of diabetics in the region from 2024 to 2050, while North America and the Caribbean are expected to see an increase of ** percent. In 2050, China is predicted to be the country with the highest number of diabetics worldwide, with the United States accounting for the fourth-highest number.