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.

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).

Based on data from January 2017 to March 2020, it was estimated that around 41 percent of men and 32 percent of women in the United States had prediabetes. Those with prediabetes have blood sugar levels higher than normal, but not high enough to yet be diagnosed with diabetes. This statistic shows the percentage of adults in the United States with prediabetes, by gender.

This is a source dataset for a Let's Get Healthy California indicator at "https://letsgethealthy.ca.gov/. This table displays the prevalence of diabetes in California. It contains data for California only. The data are from the California Behavioral Risk Factor Surveillance Survey (BRFSS). The California BRFSS is an annual cross-sectional health-related telephone survey that collects data about California residents regarding their health-related risk behaviors, chronic health conditions, and use of preventive services. The BRFSS is conducted by Public Health Survey Research Program of California State University, Sacramento under contract from CDPH. This prevalence rate does not include pre-diabetes, or gestational diabetes. This is based on the question: "Has a doctor, or nurse or other health professional ever told you that you have diabetes?" The sample size for 2014 was 8,832. NOTE: Denominator data and weighting was taken from the California Department of Finance, not U.S. Census. Values may therefore differ from what has been published in the national BRFSS data tables by the Centers for Disease Control and Prevention (CDC) or other federal agencies.

Based on data from January 2017 to March 2020, it was estimated that around 36 percent of non-Hispanic white adults in the United States had prediabetes. Those with prediabetes have blood sugar levels higher than normal, but not high enough to yet be diagnosed with diabetes. This statistic shows the percentage of adults in the United States with prediabetes from 2017 to 2020, by race/ethnicity.

In 2021, it was estimated that a total of 97.6 million adults in the United States had prediabetes, with men accounting for around 53.2 million of these cases. Those with prediabetes have blood sugar levels higher than normal, but not high enough to yet be diagnosed with diabetes. This statistic shows the number of adults in the United States with prediabetes in 2021, by gender.

Rationale:

The accrual of data from the laboratory and from epidemiologic and prevention trials has improved the understanding of the etiology and pathogenesis of type 1 diabetes mellitus (T1DM). Genetic and immunologic factors play a key role in the development of T1DM, and characterization of the early metabolic abnormalities in T1DM is steadily increasing. However, information regarding the natural history of T1DM remains incomplete. The TrialNet Natural History Study of the Development of T1DM (Pathway to Prevention Study) has been designed to clarify this picture, and in so doing, will contribute to the development and implementation of studies aimed at prevention of and early treatment in T1DM.

Purpose:

TrialNet is an international network dedicated to the study, prevention, and early treatment of type 1 diabetes. TrialNet sites are located throughout the United States, Canada, Finland, United Kingdom, Italy, Germany, Sweden, Australia, and New Zealand. TrialNet is dedicated to testing new approaches to the prevention of and early intervention for type 1 diabetes.

The goal of the TrialNet Natural History Study of the Development of Type 1 Diabetes is to enhance our understanding of the demographic, immunologic, and metabolic characteristics of individuals at risk for developing type 1 diabetes.

The Natural History Study will screen relatives of people with type 1 diabetes to identify those at risk for developing the disease. Relatives of people with type 1 diabetes have about a 5% percent chance of being positive for the antibodies associated with diabetes. TrialNet will identify adults and children at risk for developing diabetes by testing for the presence of these antibodies in the blood. A positive antibody test is an early indication that damage to insulin-secreting cells may have begun. If this test is positive, additional testing will be offered to determine the likelihood that a person may develop diabetes. Individuals with antibodies will be offered the opportunity for further testing to determine their risk of developing diabetes over the next 5 years and to receive close monitoring for the development of diabetes.

Abstract (en): Nearly 9 million Americans live in extreme-poverty neighborhoods, places that also tend to be racially segregated and dangerous. Yet, the effects on the well-being of residents of moving out of such communities into less distressed areas remain uncertain. Moving to Opportunity (MTO) is a randomized housing experiment administered by the United States Department of Housing and Urban Development that gave low-income families living in high-poverty areas in five cities the chance to move to lower-poverty areas. Families were randomly assigned to one of three groups: (1) the low-poverty voucher (LPV) group (also called the experimental group) received Section 8 rental assistance certificates or vouchers that they could use only in census tracts with 1990 poverty rates below 10 percent. The families received mobility counseling and help in leasing a new unit. One year after relocating, families could use their voucher to move again if they wished, without any special constraints on location; (2) the traditional voucher (TRV) group (also called the Section 8 group) received regular Section 8 certificates or vouchers that they could use anywhere; these families received no special mobility counseling; (3) the control group received no certificates or vouchers through MTO, but continued to be eligible for project-based housing assistance and whatever other social programs and services to which they would otherwise be entitled. Families were tracked from baseline (1994-1998) through the long-term evaluation survey fielding period (2008-2010) with the purpose of determining the effects of "neighborhood" on participating families. This data collection includes data from the 3,273 adult interviews completed as part of the MTO long-term evaluation. Using data from the long-term evaluation, the associated article reports that moving from a high-poverty to lower-poverty neighborhood was associated in the long-term (10 to 15 years) with modest, but potentially important, reductions in the prevalence of extreme obesity and diabetes. The data contain all outcomes and mediators analyzed for the associated article (with the exception of a few mediator variables from the interim MTO evaluation) as well as a variety of demographic and other baseline measures that were controlled for in the analysis. All analysis of the data should be weighted using the total survey weight. The cell-level file includes a separate weight for each outcome and mediator measure that is the sum of weights for all observations in the cell with valid data for the measure (for example, wt_f_db_hba1c_diab_final is the weight for the glycated hemoglobin measure, mn_f_db_hba1c_diab_final). In the pseudo-individual file, mn_f_wt_totsvy is the average of the total survey weight variable for all observations in the cell. In the original individual-level file, the total survey weight (f_wt_totsvy) is calculated as the product of three component weights: (1) Randomization ratio weight -- At the start of the MTO program, random assignment (RA) ratios were set to produce equal numbers of leased-up families in the low-poverty and traditional voucher groups based on expected leased-up rates. The initial ratios were "8 to 3 to 5": eight low-poverty voucher group families to three traditional voucher families to five control families. During the demonstration program, these RA ratios were adjusted to accommodate higher than anticipated leased-up rates among low-poverty voucher group families. This weight ensures that the proportion of families in a given site is the same across all three treatment groups. This component weight value ranges from 0.59 to 2.09. (2) Survey sample selection weight -- For budgetary reasons, adults from only a random two-thirds of traditional voucher group households were selected for the long-term survey interview sample (while adults from all low-poverty voucher and control group families were selected), so this component weights up the selected traditional voucher group adults so that they are representative of all traditional voucher group adults. This weight component is equal to the inverse probability of selection into the subsample (~1.52). (3) Phase 2 subsample weight -- The long-term survey data collection was completed as a two-phase process. In the first phase, we sought to interview all selected respondents. Phase 2 of fielding was triggered when the response rate reached approximately 74 percent. In the second phase, we su...

IntroductionNo study has comprehensively quantified the individual and collective contributions of various risk factors to the growing burden of diabetes in the United States.MethodsThis study aimed to determine the extent to which an increase in the prevalence of diabetes was related to concurrent changes in the distribution of diabetes-related risk factors among US adults (aged 20 years or above and not pregnant). Seven cycles of series of cross-sectional National Health and Nutrition Examination Survey data between 2005–2006 and 2017–2018 were included. The exposures were survey cycles and seven domains of risk factors, including genetic, demographic, social determinants of health, lifestyle, obesity, biological, and psychosocial domains. Using Poisson regressions, percent reduction in the β coefficient (the logarithm used to calculate the prevalence ratio for prevalence of diabetes in 2017–2018 vs. 2005–2006) was computed to assess the individual and collective contribution of the 31 prespecified risk factors and seven domains to the growing burden of diabetes.ResultsOf the 16,091 participants included, the unadjusted prevalence of diabetes increased from 12.2% in 2005–2006 to 17.1% in 2017–2018 [prevalence ratio: 1.40 (95% CI, 1.14–1.72)]. Individually, genetic domain [17.3% (95% CI, 5.4%−40.8%)], demographic domain [41.5% (95% CI, 24.4%−76.8%)], obesity domain [35.3% (95% CI, 15.8%−70.2%)], biological domain [46.2% (95% CI, 21.6%−79.1%)], and psychosocial domain [21.3% (95% CI, 9.5%−40.1%)] were significantly associated with a different percent reduction in β. After adjusting for all seven domains, the percent reduction in β was 97.3% (95% CI, 62.7%−164.8%).ConclusionThe concurrently changing risk factors accounted for the increasing diabetes prevalence. However, the contribution of each risk factor domain varied. Findings may inform planning cost-effective and targeted public health programs for diabetes prevention.

IntroductionRacial and ethnic minority groups and individuals with limited educational attainment experience a disproportionate burden of diabetes. Prediabetes represents a high-risk state for developing type 2 diabetes, but most adults with prediabetes are unaware of having the condition. Uncovering whether racial, ethnic, or educational disparities also occur in the prediabetes stage could help inform strategies to support health equity in preventing type 2 diabetes and its complications. We examined the prevalence of prediabetes and prediabetes awareness, with corresponding prevalence ratios according to race, ethnicity, and educational attainment.MethodsThis study was a pooled cross-sectional analysis of the National Health and Nutrition Examination Survey data from 2011 to March 2020. The final sample comprised 10,262 U.S. adults who self-reported being Asian, Black, Hispanic, or White. Prediabetes was defined using hemoglobin A1c and fasting plasma glucose values. Those with prediabetes were classified as “aware” or “unaware” based on survey responses. We calculated prevalence ratios (PR) to assess the relationship between race, ethnicity, and educational attainment with prediabetes and prediabetes awareness, controlling for sociodemographic, health and healthcare-related, and clinical characteristics.ResultsIn fully adjusted logistic regression models, Asian, Black, and Hispanic adults had a statistically significant higher risk of prediabetes than White adults (PR:1.26 [1.18,1.35], PR:1.17 [1.08,1.25], and PR:1.10 [1.02,1.19], respectively). Adults completing less than high school and high school had a significantly higher risk of prediabetes compared to those with a college degree (PR:1.14 [1.02,1.26] and PR:1.12 [1.01,1.23], respectively). We also found that Black and Hispanic adults had higher rates of prediabetes awareness in the fully adjusted model than White adults (PR:1.27 [1.07,1.50] and PR:1.33 [1.02,1.72], respectively). The rates of prediabetes awareness were consistently lower among those with less than a high school education relative to individuals who completed college (fully-adjusted model PR:0.66 [0.47,0.92]).DiscussionDisparities in prediabetes among racial and ethnic minority groups and adults with low educational attainment suggest challenges and opportunities for promoting health equity in high-risk groups and expanding awareness of prediabetes in the United States.

Introduction

Global Diabetes Drugs Market size is expected to be worth around USD 153.4 Billion by 2033, from USD 69.1 Billion in 2023, growing at a CAGR of 8.6% during the forecast period from 2024 to 2033. In 2023, North America led the market, achieving over 51.3% share with a revenue of US$ 35.4 Million.

Several factors are driving the growth of the diabetes drug market. A key factor is the rising prevalence of diabetes globally, especially type 2 diabetes. Data from the International Diabetes Federation indicates that the number of adults with diabetes is expected to increase from 537 million in 2021 to 783 million by 2045. Furthermore, advancements in diabetes medications, such as the introduction of GLP-1 receptor agonists and SGLT-2 inhibitors, offer effective alternatives to traditional therapies, enhancing market expansion.

Recent developments in the sector include the introduction of new drugs and formulations. For example, Glenmark Pharmaceuticals launched Lirafit, a biosimilar of the popular anti-diabetic drug Liraglutide, in India in January 2024. Additionally, companies like Akums Drugs and Pharmaceuticals have been developing novel combination therapies aimed at improving treatment effectiveness and adherence among the elderly with type 2 diabetes.

Despite these advancements, the market faces challenges such as the high costs associated with diabetes drug therapies, which pose significant barriers, particularly in less developed regions. Nonetheless, the ongoing focus on diabetes care, coupled with increasing awareness and substantial research and development investments by leading companies, are anticipated to maintain the market's growth trajectory. North America leads this market, propelled by high obesity rates and sedentary lifestyles, with the United States holding the largest share due to its significant demand for insulin and other diabetes medications.

Based on data from January 2017 to March 2020, it was estimated that around 38 percent of adults in the United States had prediabetes. Among those aged 65 years and older almost 50 percent were estimated to have prediabetes. Those with prediabetes have blood sugar levels higher than normal, but not high enough to yet be diagnosed with diabetes. This statistic shows the percentage of adults in the United States with prediabetes, by age.

Introduction

Intensive care has played a pivotal role during the COVID-19 pandemic as many patients developed severe pulmonary complications. The availability of information in pediatric intensive care (PICUs) remains limited. The purpose of this study is to characterize COVID-19 positive admissions (CPAs) in the United States and to determine factors that may impact those admissions.

Materials and Methods

This is a retrospective cohort study using data from the COVID-19 dashboard virtual pediatric system) containing information regarding respiratory support and comorbidities for all CPAs between March and April 2020. The state level data contained 13 different factors from population density, comorbid conditions and social distancing score. The absolute CPAs count was converted to frequency using the state’s population. Univariate and multivariate regression analyses were performed to assess the association between CPAs frequency and endpoints.

Results

A total of 205 CPAs were reported by 167 PICUs across 48 states. The estimated CPAs frequency was 2.8 per million children. A total of 3,235 tests were conducted with 6.3% positive tests. Children above 11 years of age comprised 69.7% of the total cohort and 35.1% had moderated or severe comorbidities. The median duration of a CPA was 4.9 days [1.25-12.00 days]. Out of the 1,132 total CPA days, 592 [52.2%] were for mechanical ventilation. The inpatient mortalities were 3 [1.4%]. Multivariate analyses demonstrated an association between CPAs with greater population density [beta-coefficient 0.01, p<0.01] and increased percent of children receiving the influenza vaccination [beta-coefficient 0.17, p=0.01].

Conclusions

Inpatient mortality during PICU CPAs is relatively low at 1.4%. CPA frequency seems to be impacted by population density while characteristics of illness severity appear to be associated with ultraviolet index, temperature, and comorbidities such as Type 1 diabetes. These factors should be included in future studies using patient-level data.

Methods This study utilized only publicly available, deidentified, state-level data. As such, no institutional review board review or approval was sought.

Endpoint identification and data collection

The following data was identified for collection regarding the CPAs themselves: number, duration, need for various ventilatory support measures, severity of comorbidities, and the total number of COVID-19 tests conducted. The following data was collected regarding US states: pediatric population, state population (pediatric and adult) density, air and drinking water quality, average temperature, average ultraviolet index, prevalence of pediatric obesity, type 1 diabetes mellitus (DM) and asthma, the proportion of children who smoke cigarettes, received the influenza vaccine, had health insurance, and received home health care, race, percent of households with children below the poverty line, highest education level of adults in homes with children, and the social distancing score by global positional satellite data (Supplementary Table 1).

The data regarding the CPAs themselves was collected from the publicly available COVID-19 dashboard provided by the Virtual Pediatric System (VPS), which collects data from several PICUs in the US. COVID-19 data was collected from March 14th through April 14th, 2020, in order to represent one full month of data. Data regarding number of centers, number of tests, and number of CPAs was captured in absolute counts. Data regarding CPAs duration was collected in days. The respiratory support modalities for which data was available were room air (RA), nasal cannula (NC) and for the advanced respiratory support modalities (i.e. other than RA and NC) there was available data for high flow nasal cannula (HFNC), non-invasive positive pressure ventilation (NIPPV), conventional mechanical ventilation (MCV), high frequency oscillatory ventilation (HFOV), and extracorporeal membrane oxygenation (ECMO), and was captured in duration (days) of their use. Data regarding severity of comorbidities is reported in the VPS dashboard and the percentage of CPAs with moderate or severe degree of comorbidities was collected.

State-wide data for the analyses were collected from a variety of sources with the complete list of sources provided as Supplementary Material 1. Children’s population data and pediatric comorbidity data was obtained from 2018, as these were the most recent and comprehensive data available. The sources for these other data points were generally US government-based efforts to capture state-level data on various medical issues, however, not all states reported data for all the endpoints (Supplementary Table 2).

Endpoints were assigned to the authors for collection. One author was responsible for collecting data for each state for the variables assigned. Once these data were collected a different author, who did not primarily collect data for that specific endpoint, verified the numbers for accuracy. Finally, values in the top and bottom 10th percentile were identified and verified by a third author.

Statistical analyses

As the data was collected for each state and intended for state-level analyses, and each state has a different pediatric population, the absolute numbers of CPAs for each state were not directly comparable. Thus, the absolute CPAs count for each state was first converted to a frequency of CPAs per 1,000,000 children using the specific state’s population. This CPAs frequency was then used as the dependent variable in a series of single-independent variable linear regressions to determine the univariate association between CPAs frequency and the other endpoints. Multivariate regression was conducted with CPAs frequency as the dependent variable and with other variables entered as independent variables. Forward stepwise regression was utilized with the model with greatest R-squared value being used for the analyses.

Next, a composite endpoint called “percent of PICUs days requiring advanced respiratory support” was created. This consisted of the total duration of HFNC, NIPPV, MCV, HFOV, and ECMO divided by the total PICUs admission duration. This was then modeled similarly to CPAs frequency. Next, a composite outcome called “percent of PICU days requiring intubation” was created. This consisted of the total duration of MCV and HFOV divided by the total PICU admission duration. This, too, was then modeled similarly as CPA frequency. Lastly, an endpoint called “PICUs duration per admission” was created for each state and consisted of the total CPAs PICUs duration for that specific state divided by the number of CPAs reported by that state. This was also then modeled similarly to CPA frequency.

All statistical analyses were done using the user-coded, syntax-based interface of SPSS Version 23.0. A p-value of 0.05 was considered statistically significant. All statistical analyses were done at the state-level with state-level data. Analyses were not conducted at a patient-level with patient-level data. Any use of the word significant here-on in the manuscript refers to “statistically significant” unless explicitly specified otherwise.

Originally, the dataset come from the CDC and is a major part of the Behavioral Risk Factor Surveillance System (BRFSS), which conducts annual telephone surveys to gather data on the health status of U.S. residents. As the CDC describes: "Established in 1984 with 15 states, BRFSS now collects data in all 50 states as well as the District of Columbia and three U.S. territories. BRFSS completes more than 400,000 adult interviews each year, making it the largest continuously conducted health survey system in the world.". The most recent dataset (as of February 15, 2022) includes data from 2020. It consists of 401,958 rows and 279 columns. The vast majority of columns are questions asked to respondents about their health status, such as "Do you have serious difficulty walking or climbing stairs?" or "Have you smoked at least 100 cigarettes in your entire life? [Note: 5 packs = 100 cigarettes]".

To improve the efficiency and relevance of our analysis, we removed certain attributes from the original BRFSS dataset. Many of the 279 original attributes included administrative codes, metadata, or survey-specific variables that do not contribute meaningfully to heart disease prediction—such as respondent IDs, timestamps, state-level identifiers, and detailed lifestyle questions unrelated to cardiovascular health. By focusing on a carefully selected subset of 18 attributes directly linked to medical, behavioral, and demographic factors known to influence heart health, we streamlined the dataset. This not only reduced computational complexity but also improved model interpretability and performance by eliminating noise and irrelevant information. All predicting variables could be divided into 4 broad categories:

1. Demographic factors: sex, age category (14 levels), race, BMI (Body Mass Index)

2. Diseases: weather respondent ever had such diseases as asthma, skin cancer, diabetes, stroke or kidney disease (not including kidney stones, bladder infection or incontinence)

3. Unhealthy habits:

   • Smoking - respondents that smoked at least 100 cigarettes in their entire life (5 packs = 100 cigarettes)
   • Alcohol Drinking - heavy drinkers (adult men having more than 14 drinks per week and adult women having more than 7 drinks per week

4. General Health:

   • Difficulty Walking - weather respondent have serious difficulty walking or climbing stairs
   • Physical Activity - adults who reported doing physical activity or exercise during the past 30 days other than their regular job
   • Sleep Time - respondent’s reported average hours of sleep in a 24-hour period
   • Physical Health - number of days being physically ill or injured (0-30 days)
   • Mental Health - number of days having bad mental health (0-30 days)
   • General Health - respondents declared their health as ’Excellent’, ’Very good’, ’Good’ ,’Fair’ or ’Poor’

Below is a description of the features collected for each patient:

According to the Juvenile Diabetes Research Foundation (JDRF), almost 1.25 million people in the United States (US) have type 1 diabetes, which makes them dependent on insulin injections. Nationwide, type 2 diabetes rates have nearly doubled in the past 20 years resulting in more than 29 million American adults with diabetes and another 86 million in a pre-diabetic state. The International Diabetes Federation (IDF)has estimated that there will be almost 650 million adult diabetic patients worldwide at the end of the next 20 years (excluding patients over the age of 80). At this time, pancreas transplantation is the only available cure for selected patients, but it is offered only to a small percentage of them due to organ shortage and the risks linked to immunosuppressive regimes. Currently, exogenous insulin therapy is still considered to be the gold standard when managing diabetes, though stem cell biology is recognized as one of the most promising strategies for restoring endocrine pancreatic function. However, many issues remain to be solved, and there are currently no recognized treatments for diabetes based on stem cells. In addition to stem cell research, severalβ-cell substitutive therapies have been explored in the recent era, including the use of acellular extracellular matrix scaffolding as a template for cellular seeding, thus providing an empty template to be repopulated with β-cells. Although this bioengineering approach still has to overcome important hurdles in regard to clinical application (including the origin of insulin producing cells as well as immune-related limitations), it could theoretically provide an inexhaustible source of bio-engineered pancreases Date Submitted: 2021-01-13

The North America Oral Anti-Diabetic Drug Market report segments the industry into By Drug Types (Biguanides, Alpha-Glucosidase Inhibitors, Dopamine D2 Receptor Agonist, SGLT-2 Inhibitors, DPP-4 Inhibitors, Sulfonylureas, Meglitinides), By Patient Type (Adults, Geriatric, Pediatric), and Geography (United States, Canada, Mexico). Get five years of historical data alongside five-year market forecasts.

The United States self-monitoring blood glucose (SMBG) market, valued at $7.62 billion in 2025, is projected to experience robust growth, driven by the increasing prevalence of diabetes and the rising adoption of advanced SMBG devices. The market's Compound Annual Growth Rate (CAGR) of 6.40% from 2019 to 2024 indicates a steady expansion, which is expected to continue through 2033. Key growth drivers include the increasing diabetic population, particularly among older adults, the rising awareness about diabetes management, and technological advancements leading to more accurate, user-friendly, and convenient glucose monitoring devices. The market is segmented into glucometer devices, test strips, and lancets, with glucometer devices likely representing the largest segment due to the technological innovations leading to smaller, more sophisticated devices and integration with mobile apps for data tracking and management. Furthermore, the growing demand for continuous glucose monitoring (CGM) systems, though not explicitly stated in the provided data, is a significant emerging trend that is likely contributing to market expansion. While challenges exist, such as the high cost of treatment and the potential for inaccuracies with some devices, the market's overall trajectory remains positive due to the sustained need for effective diabetes management. The competitive landscape is characterized by established players like Abbott Diabetes Care, Roche Holding AG, and LifeScan, who hold significant market share. These companies are engaged in continuous innovation to maintain their market dominance by developing technologically advanced devices and expanding their global reach. Smaller companies contribute significantly to innovation and competition, particularly in the development of less expensive and more accessible devices. However, the market's success is closely tied to the broader healthcare landscape, including government regulations, insurance coverage policies, and public health initiatives aimed at diabetes prevention and management. Further research is required to fully quantify the impact of these factors on specific market segments and individual companies within the US SMBG market. Recent developments include: January, 2023: LifeScan announced that the peer-reviewed Journal of Diabetes Science and Technology published Improved Glycemic Control Using a Bluetooth Connected Blood Glucose Meter and a Mobile Diabetes App: Real-World Evidence From Over 144,000 People With Diabetes, detailing results from a retrospective analysis of real-world data from over 144,000 people with diabetes-one of the largest combined blood glucose meter and mobile diabetes app datasets ever published., January 20, 2022: Roche announced the launch of the COBAS pulse system in selected countries accepting the CE mark. The COBAS pulse system marks Roche Diagnostics' newest generation of connected point-of-care solutions for professional blood glucose management. The COBAS pulse system combines the form factor of a high-performance blood glucose meter with simple usability and expanded digital capabilities like those of a smartphone. Following first commercial availability under the CE mark in select markets, Roche plans to seek CE IVDR and FDA clearance for the Cobas Pulse System in other global markets.. Notable trends are: Rising Diabetes Prevalence in the United States.

The country projected to have the highest number of diabetics in 2050 is China, with some *** million people between the ages of 20 and 79 expected to be suffering from diabetes at that time. It is forecast that the number of adults with the condition in Pakistan will exceed that of the United States by 2050. Number of diabetics worldwide Diabetes is a condition that causes the pancreas to stop (or severely reduce) the production of insulin, a hormone needed to regulate blood sugar levels. Many people worldwide live with diabetes: in 2024, a total of almost *** million people had diabetes. This number is projected to reach an estimated ***** million by the year 2050. Global diabetes costs In 2024, just over************* U.S. dollars were spent on diabetes-related healthcare treatment worldwide. Of 2024’s global total, around ***** billion U.S. dollars were spent in the United States alone. Global healthcare expenditures for diabetes are expected to increase by ** billion U.S. dollars by 2050.

The dataset comes from Kaggle and contains information on food choices, preferences, and consumption patterns of college students in the United States. The data can help identify dietary patterns and provide insights into factors that influence food choices, such as health concerns, convenience, taste, and cultural factors. This dataset can be used by nutritionists, policymakers, and health educators to develop effective interventions and programs to promote healthy eating habits among college students. The data can also be used by food manufacturers and marketers to understand the factors that influence food choices among young adults and develop products that align with their preferences. Researchers can use this dataset to investigate the relationship between dietary patterns and various health outcomes, such as obesity, diabetes, and cardiovascular diseases. Additionally, this dataset can be used to compare food consumption patterns and preferences across different demographic groups, such as age, gender, and ethnicity.

Diabetes Care Market Outlook

According to our latest research, the global diabetes care market size reached USD 78.6 billion in 2024, reflecting robust growth driven by the rising global prevalence of diabetes and technological advancements in care solutions. The market is expected to expand at a CAGR of 7.2% from 2025 to 2033, projecting a value of USD 146.7 billion by 2033. This significant growth is primarily fueled by increasing awareness about diabetes management, expanding access to advanced medical devices, and a surge in the adoption of digital health solutions worldwide.

A key growth factor for the diabetes care market is the escalating incidence of diabetes globally, particularly type 2 diabetes, which is closely linked to sedentary lifestyles, unhealthy dietary habits, and rising obesity rates. According to the International Diabetes Federation, more than 537 million adults were living with diabetes in 2024, a number expected to rise steadily over the next decade. This surge in patient population is creating a substantial demand for diabetes care products, including blood glucose monitoring devices, insulin delivery systems, and diabetes management software. The growing emphasis on early diagnosis and continuous monitoring is also prompting healthcare providers and patients alike to invest in advanced and user-friendly diabetes care solutions.

Technological advancements are another critical driver of the diabetes care market. Innovations such as continuous glucose monitoring (CGM) systems, smart insulin pens, and integrated diabetes management platforms are transforming the way patients manage their condition. These technologies offer real-time data, improved accuracy, and enhanced patient convenience, leading to better glycemic control and overall health outcomes. The integration of artificial intelligence and data analytics in diabetes management software further enables personalized treatment plans and remote patient monitoring, contributing to improved patient adherence and reduced complications.

In addition, the increasing penetration of digital health and telemedicine is reshaping the diabetes care landscape. The COVID-19 pandemic accelerated the adoption of remote healthcare solutions, making it easier for patients to access diabetes care services from the comfort of their homes. This trend is particularly beneficial for patients in remote or underserved areas, where access to specialized diabetes care is limited. The proliferation of online pharmacies and direct-to-consumer sales channels is also facilitating the availability of diabetes care products, thereby boosting market growth. Furthermore, supportive government initiatives, favorable reimbursement policies, and growing investments in diabetes research and development are creating a conducive environment for market expansion.

Regionally, North America continues to dominate the diabetes care market, owing to its large diabetic population, well-established healthcare infrastructure, and high adoption of advanced medical technologies. However, the Asia Pacific region is emerging as a lucrative market, driven by rapid urbanization, increasing healthcare expenditure, and rising awareness about diabetes management. Europe also holds a significant market share, supported by robust government initiatives and a high prevalence of diabetes. Meanwhile, Latin America and the Middle East & Africa are witnessing steady growth, fueled by improving healthcare access and rising investments in healthcare infrastructure. Overall, the global diabetes care market is poised for sustained growth, supported by demographic trends, technological innovation, and evolving patient needs.

Product Type Analysis

The diabetes care market is segmented by product type into blood glucose monitoring devices, insulin delivery devices, diabetes management software, and others. Blood glucose monitoring devices constitute the largest segment, driven by the necessity for regular glucose level checks among diabetic patients. These devices have evolved f