Successful implementation of a stakeholder engagement program for pharmacoepidemiologic research.

PURPOSE: Rigorously conducted pharmacoepidemiologic research requires methodologically complex study designs and analysis yet evaluates problems of high importance to patients and clinicians. Despite this, participation in and mechanisms for stakeholder engagement in pharmacoepidemiologic research are not well-described. Here, we describe our approach and lessons learned from engaging stakeholders, of varying familiarity with research methods, in a rigorous multi-year pharmacoepidemiologic research program evaluating the comparative effectiveness of diabetes medications. METHODS: We recruited 5 patient and 4 clinician stakeholders; each was compensated for their time. Stakeholders received initial formal training in observational research and pharmacoepidemiologic methods sufficient to enable contribution to the research project. After onboarding, stakeholder engagement meetings were held virtually, in the evening, 2-3 times annually. Each was approximately 90 min and focused on 1-2 specific questions about the project, with preparatory materials sent in advance. RESULTS: Stakeholder meeting attendance was high (89%-100%), and all stakeholders engaged with the research project, both during and between meetings. Stakeholders reported positive experiences with meetings, satisfaction, and interest in the research project and its findings, and dedication to the success of the project's goals. They affirmed the value of receiving materials to review in advance and the effectiveness of a virtual platform. Their contributions included prioritizing and suggesting research questions, optimizing written evidence briefs for a lay audience, and guidance on broader topics such as research audience and methods of dissemination. CONCLUSIONS: Stakeholder engagement in pharmacoepidemiologic research using complex study designs and analysis is feasible, acceptable, and positively impacts the research project.

Racial and Ethnic Disparities in Metabolic Bone Disease.

Racial and ethnic disparities exist in the prevalence and management of osteoporosis, metastatic cancer, and sickle cell disease. Despite being the most common metabolic bone disease, osteoporosis remains underscreened and undertreated among Black women. Skeletal-related events in metastatic cancer include bone pain, pathologic fractures, and spinal cord compression. Disparities in screening for and treating skeletal-related events disproportionately affect Black patients. Metabolic bone disease contributes significantly to morbidity in sickle cell disease; however, clinical guidelines for screening and treatment do not currently exist. Clinical care recommendations are provided to raise awareness, close health care gaps, and guide future research efforts.

Precision subclassification of type 2 diabetes: a systematic review.

BACKGROUND: Heterogeneity in type 2 diabetes presentation and progression suggests that precision medicine interventions could improve clinical outcomes. We undertook a systematic review to determine whether strategies to subclassify type 2 diabetes were associated with high quality evidence, reproducible results and improved outcomes for patients. METHODS: We searched PubMed and Embase for publications that used 'simple subclassification' approaches using simple categorisation of clinical characteristics, or 'complex subclassification' approaches which used machine learning or 'omics approaches in people with established type 2 diabetes. We excluded other diabetes subtypes and those predicting incident type 2 diabetes. We assessed quality, reproducibility and clinical relevance of extracted full-text articles and qualitatively synthesised a summary of subclassification approaches. RESULTS: Here we show data from 51 studies that demonstrate many simple stratification approaches, but none have been replicated and many are not associated with meaningful clinical outcomes. Complex stratification was reviewed in 62 studies and produced reproducible subtypes of type 2 diabetes that are associated with outcomes. Both approaches require a higher grade of evidence but support the premise that type 2 diabetes can be subclassified into clinically meaningful subtypes. CONCLUSION: Critical next steps toward clinical implementation are to test whether subtypes exist in more diverse ancestries and whether tailoring interventions to subtypes will improve outcomes.

In people with type 2 diabetes there may be differences in the way people present, including for example, their symptoms, body weight or how much insulin they make. We looked at recent publications describing research in this area to see whether it is possible to separate people with type 2 diabetes into different subgroups and, if so, whether these groupings were useful for patients. We found that it is possible to group people with type 2 diabetes into different subgroups and being in one subgroup can be more strongly linked to the likelihood of developing complications over others. This might mean that in the future we can treat people in different subgroups differently in ways that improves their treatment and their health but it requires further study.

Second international consensus report on gaps and opportunities for the clinical translation of precision diabetes medicine.

Precision medicine is part of the logical evolution of contemporary evidence-based medicine that seeks to reduce errors and optimize outcomes when making medical decisions and health recommendations. Diabetes affects hundreds of millions of people worldwide, many of whom will develop life-threatening complications and die prematurely. Precision medicine can potentially address this enormous problem by accounting for heterogeneity in the etiology, clinical presentation and pathogenesis of common forms of diabetes and risks of complications. This second international consensus report on precision diabetes medicine summarizes the findings from a systematic evidence review across the key pillars of precision medicine (prevention, diagnosis, treatment, prognosis) in four recognized forms of diabetes (monogenic, gestational, type 1, type 2). These reviews address key questions about the translation of precision medicine research into practice. Although not complete, owing to the vast literature on this topic, they revealed opportunities for the immediate or near-term clinical implementation of precision diabetes medicine; furthermore, we expose important gaps in knowledge, focusing on the need to obtain new clinically relevant evidence. Gaps include the need for common standards for clinical readiness, including consideration of cost-effectiveness, health equity, predictive accuracy, liability and accessibility. Key milestones are outlined for the broad clinical implementation of precision diabetes medicine.

Racial/ethnic and socioeconomic disparities in achievement of treatment goals within a clinical trial: a secondary analysis of the ACCORD trial.

AIMS/HYPOTHESIS: Clinical trial participation should theoretically reduce barriers to care by ensuring medication and healthcare access. We aimed to evaluate disparities in achieving diabetes treatment targets by race/ethnicity and educational attainment within the Action to Control Cardiovascular Risk in Diabetes (ACCORD) trial (ClinicalTrials.gov NCT00000620). METHODS: The ACCORD trial included three interventions of varying participant burden: glycaemic (high burden), blood pressure (medium burden) and triglyceride-lowering (low burden). We examined adjusted odds ratios (aORs) for achievement of glycaemic targets, blood pressure targets and a ≥25% reduction in triglyceride levels (a proxy for adherence to fenofibrate therapy) in the first year, and for hypoglycaemia requiring medical assistance at any time, by treatment arm, race/ethnicity and educational attainment using multivariable models adjusted for demographics and clinical characteristics. We explored whether disparities in glycaemic goal achievement were mediated by hypoglycaemia, medication use, change in BMI or number of study visits attended. RESULTS: Compared with White participants, participants who identified as Black, Hispanic and Other race/ethnicity were less likely to achieve glycaemic targets (aOR [95% CI]) 0.63 [0.55,0.71], 0.73 [0.61, 0.88], 0.82 [0.71, 0.96], respectively); Black participants but not Hispanic and Other race/ethnicity participants were less likely to achieve blood pressure targets (aOR [95% CI] 0.77 [0.65, 0.90], 1.01 [0.78, 1.32], 1.01 [0.81, 1.26], respectively); and Black, Hispanic and Other race/ethnicity participants were equally or more likely to achieve triglyceride reduction (aOR [95% CI] 1.77 [1.38, 2.28], 1.34 [0.98, 1.84], 1.43 [1.10, 1.85], respectively). Differences in goal achievement by educational attainment were generally not significant after adjusting for baseline characteristics. Rates of hypoglycaemia requiring medical assistance were highest among Black individuals and those with lower educational attainment. Associations between race/ethnicity and glycaemic control were partially mediated by differences in insulin dosing and oral medication use. CONCLUSIONS/INTERPRETATION: Racially/ethnically minoritised participants in the ACCORD trial were less likely to achieve high-burden (glycaemic) treatment goals but were generally similarly likely to achieve goals of less intensive interventions. Differences in glycaemic treatment goal achievement were partially mediated by differences in medication use but not mediated by hypoglycaemia, change in BMI or study visit attendance.

Systematic review of precision subclassification of type 2 diabetes.

Heterogeneity in type 2 diabetes presentation, progression and treatment has the potential for precision medicine interventions that can enhance care and outcomes for affected individuals. We undertook a systematic review to ascertain whether strategies to subclassify type 2 diabetes are associated with improved clinical outcomes, show reproducibility and have high quality evidence. We reviewed publications that deployed 'simple subclassification' using clinical features, biomarkers, imaging or other routinely available parameters or 'complex subclassification' approaches that used machine learning and/or genomic data. We found that simple stratification approaches, for example, stratification based on age, body mass index or lipid profiles, had been widely used, but no strategy had been replicated and many lacked association with meaningful outcomes. Complex stratification using clustering of simple clinical data with and without genetic data did show reproducible subtypes of diabetes that had been associated with outcomes such as cardiovascular disease and/or mortality. Both approaches require a higher grade of evidence but support the premise that type 2 diabetes can be subclassified into meaningful groups. More studies are needed to test these subclassifications in more diverse ancestries and prove that they are amenable to interventions.

Association and Interaction of Genetics and Area-Level Socioeconomic Factors on the Prevalence of Type 2 Diabetes and Obesity.

OBJECTIVE: Quantify the impact of genetic and socioeconomic factors on risk of type 2 diabetes (T2D) and obesity. RESEARCH DESIGN AND METHODS: Among participants in the Mass General Brigham Biobank (MGBB) and UK Biobank (UKB), we used logistic regression models to calculate cross-sectional odds of T2D and obesity using 1) polygenic risk scores for T2D and BMI and 2) area-level socioeconomic risk (educational attainment) measures. The primary analysis included 26,737 participants of European genetic ancestry in MGBB with replication in UKB (N = 223,843), as well as in participants of non-European ancestry (MGBB N = 3,468; UKB N = 7,459). RESULTS: The area-level socioeconomic measure most strongly associated with both T2D and obesity was percent without a college degree, and associations with disease prevalence were independent of genetic risk (P < 0.001 for each). Moving from lowest to highest quintiles of combined genetic and socioeconomic burden more than tripled T2D (3.1% to 22.2%) and obesity (20.9% to 69.0%) prevalence. Favorable socioeconomic risk was associated with lower disease prevalence, even in those with highest genetic risk (T2D 13.0% vs. 22.2%, obesity 53.6% vs. 69.0% in lowest vs. highest socioeconomic risk quintiles). Additive effects of genetic and socioeconomic factors accounted for 13.2% and 16.7% of T2D and obesity prevalence, respectively, explained by these models. Findings were replicated in independent European and non-European ancestral populations. CONCLUSIONS: Genetic and socioeconomic factors significantly interact to increase risk of T2D and obesity. Favorable area-level socioeconomic status was associated with an almost 50% lower T2D prevalence in those with high genetic risk.

Enrollment of underrepresented racial and ethnic groups in the Rare and Atypical Diabetes Network (RADIANT).

Introduction: Diabetes mellitus in underrepresented racial and ethnic groups (URG) is rapidly increasing in incidence and has worse outcomes than diabetes in non-Hispanic White individuals. Rare and Atypical Diabetes Network (RADIANT) established recruitment targets based on the racial and ethnic distribution of the USA to enroll a diverse study population. We examined participation of URG across RADIANT study stages and described strategies to enhance recruitment and retention of URG. Materials and Methods: RADIANT is a multicenter NIH-funded study of people with uncharacterized forms of atypical diabetes. RADIANT participants consent online and progress through three sequential study stages, as eligible. Results: We enrolled 601 participants with mean age 44 ± 16.8 years, 64.4% female. At Stage 1, 80.6% were White, 7.2% African American (AA), 12.2% other/more than one race, and 8.4% Hispanic. Enrollment of URG was significantly below preset targets across most stages. Referral sources differed by race (p < 0.001) but not ethnicity (p = 0.15). Most AA participants were referred by RADIANT investigators (58.5% vs. 24.5% in Whites), whereas flyers, news, social media, and family or friends were more frequent referral sources for White individuals (26.4% vs. 12.2% in AA). Ongoing initiatives to increase enrollment of URG in RADIANT include engaging with clinics/hospitals serving URG, screening electronic medical records, and providing culturally competent study coordination and targeted advertisement. Conclusions: There is low participation of URG in RADIANT, potentially limiting the generalizability of its discoveries. Investigations into barriers and facilitators for recruitment and retention of URG in RADIANT, with implications for other studies, are ongoing.

Deficits and Disparities in Early Uptake of Glucagon-Like Peptide 1 Receptor Agonists and SGLT2i Among Medicare-Insured Adults Following a New Diagnosis of Cardiovascular Disease or Heart Failure.

OBJECTIVE: To examine the association of race/ethnicity and socioeconomic deprivation with initiation of guideline-recommended diabetes medications with cardiovascular benefit (glucagon-like peptide 1 receptor agonists [GLP1-RA] and sodium-glucose cotransporter 2 inhibitors [SGLT2i]) among older adults with type 2 diabetes (T2D) and either incident atherosclerotic cardiovascular disease (ASCVD) or congestive heart failure (CHF). RESEARCH DESIGN AND METHODS: Using Medicare data (2016-2019), we identified 4,057,725 individuals age >65 years with T2D and either incident ASCVD or CHF. We estimated incidence rates and hazard ratios (HR) of GLP1-RA or SGLT2i initiation within 180 days by race/ethnicity and zip code-level Social Deprivation Index (SDI) using adjusted Cox proportional hazards models. RESULTS: Incidence rates of GLP1-RA or SGLT2i initiation increased over time but remained low (<0.6 initiations per 100 person-months) in all years studied. Medication initiation was less common among those of Black or other race/ethnicity (HR 0.81 [95% CI 0.79-0.84] and HR 0.84 [95% CI 0.75-0.95], respectively) and decreased with increasing SDI (HR 0.96 [95% CI 0.96-0.97]). Initiation was higher in ASCVD than CHF (0.35 vs. 0.135 initiations per 100 person-months). Moderate (e.g., nephropathy, nonalcoholic fatty liver disease) but not severe (e.g., advanced chronic kidney disease, cirrhosis) comorbidities were associated with higher probability of medication initiation. CONCLUSIONS: Among older adults with T2D and either ASCVD or CHF, initiation of GLP1-RA or SGLT2i was low, suggesting a substantial deficit in delivery of guideline-recommended care or treatment barriers. Individuals of Black and other race/ethnicity and those with higher area-level socioeconomic deprivation were less likely to initiate these medications.

Algorithmic identification of atypical diabetes in electronic health record (EHR) systems.

AIMS: Understanding atypical forms of diabetes (AD) may advance precision medicine, but methods to identify such patients are needed. We propose an electronic health record (EHR)-based algorithmic approach to identify patients who may have AD, specifically those with insulin-sufficient, non-metabolic diabetes, in order to improve feasibility of identifying these patients through detailed chart review. METHODS: Patients with likely T2D were selected using a validated machine-learning (ML) algorithm applied to EHR data. "Typical" T2D cases were removed by excluding individuals with obesity, evidence of dyslipidemia, antibody-positive diabetes, or cystic fibrosis. To filter out likely type 1 diabetes (T1D) cases, we applied six additional "branch algorithms," relying on various clinical characteristics, which resulted in six overlapping cohorts. Diabetes type was classified by manual chart review as atypical, not atypical, or indeterminate due to missing information. RESULTS: Of 114,975 biobank participants, the algorithms collectively identified 119 (0.1%) potential AD cases, of which 16 (0.014%) were confirmed after expert review. The branch algorithm that excluded T1D based on outpatient insulin use had the highest percentage yield of AD (13 of 27; 48.2% yield). Together, the 16 AD cases had significantly lower BMI and higher HDL than either unselected T1D or T2D cases identified by ML algorithms (P<0.05). Compared to the ML T1D group, the AD group had a significantly higher T2D polygenic score (P<0.01) and lower hemoglobin A1c (P<0.01). CONCLUSION: Our EHR-based algorithms followed by manual chart review identified collectively 16 individuals with AD, representing 0.22% of biobank enrollees with T2D. With a maximum yield of 48% cases after manual chart review, our algorithms have the potential to drastically improve efficiency of AD identification. Recognizing patients with AD may inform on the heterogeneity of T2D and facilitate enrollment in studies like the Rare and Atypical Diabetes Network (RADIANT).

Racial/ethnic and socioeconomic disparities in weight outcomes, cardiovascular events, and mortality in the look AHEAD trial.

BACKGROUND: Intensive lifestyle interventions (ILI) for type 2 diabetes (T2D) improve health outcomes, but participants from different races/ethnicities or socioeconomic status may not benefit equally. METHODS: Within the Look AHEAD trial, we examined achievement of the 7% weight loss goal, as well as secondary weight, cardiovascular, and mortality outcomes, by race/ethnicity and educational attainment (EA). RESULTS: Among 4,640 participants (31 % Black or Hispanic, 13 % with less than a high school degree), Black and Hispanic participants were less likely than White participants to achieve 7 % weight loss in both the ILI (45.8 % v. 60.7 %, p < 0.001 and 53.0 % v. 60.7 %, p = 0.01, respectively) and diabetes support and education (DSE) arms. Contrastingly, participants with less than a high school degree were more likely in the ILI but less likely in the DSE arm to achieve this goal, with a significant arm by EA interaction. Hispanic participants and those with lowest EA also experienced decreased mortality in the ILI versus the DSE arm. CONCLUSIONS: All Look AHEAD participant subgroups achieved greater weight loss from ILI; however, Black and Hispanic participants lost less weight than White participants in both arms, while those with lowest EA benefited disproportionately from the ILI compared to participants with higher EA.

Proteomic analysis of cardiometabolic biomarkers and predictive modeling of severe outcomes in patients hospitalized with COVID-19.

BACKGROUND: The high heterogeneity in the symptoms and severity of COVID-19 makes it challenging to identify high-risk patients early in the disease. Cardiometabolic comorbidities have shown strong associations with COVID-19 severity in epidemiologic studies. Cardiometabolic protein biomarkers, therefore, may provide predictive insight regarding which patients are most susceptible to severe illness from COVID-19. METHODS: In plasma samples collected from 343 patients hospitalized with COVID-19 during the first wave of the pandemic, we measured 92 circulating protein biomarkers previously implicated in cardiometabolic disease. We performed proteomic analysis and developed predictive models for severe outcomes. We then used these models to predict the outcomes of out-of-sample patients hospitalized with COVID-19 later in the surge (N = 194). RESULTS: We identified a set of seven protein biomarkers predictive of admission to the intensive care unit and/or death (ICU/death) within 28 days of presentation to care. Two of the biomarkers, ADAMTS13 and VEGFD, were associated with a lower risk of ICU/death. The remaining biomarkers, ACE2, IL-1RA, IL6, KIM1, and CTSL1, were associated with higher risk. When used to predict the outcomes of the future, out-of-sample patients, the predictive models built with these protein biomarkers outperformed all models built from standard clinical data, including known COVID-19 risk factors. CONCLUSIONS: These findings suggest that proteomic profiling can inform the early clinical impression of a patient's likelihood of developing severe COVID-19 outcomes and, ultimately, accelerate the recognition and treatment of high-risk patients.

Newly diagnosed diabetes vs. pre-existing diabetes upon admission for COVID-19: Associated factors, short-term outcomes, and long-term glycemic phenotypes.

AIMS: High rates of newly diagnosed diabetes mellitus (NDDM) have been reported in association with coronavirus disease-2019 (COVID-19). Factors associated with NDDM and long-term glycemic outcomes are not known. METHODS: Retrospective review of individuals admitted with COVID-19 and diabetes mellitus (DM; based on labs, diagnoses, outpatient insulin use, or severe inpatient hyperglycemia) between March and September 2020, with follow-up through July 2021. RESULTS: Of 1902 individuals admitted with COVID-19, 594 (31.2%) had DM; 77 (13.0%) of these had NDDM. Compared to pre-existing DM, NDDM was more common in younger patients and less common in those of non-Hispanic White race/ethnicity. Glycemic parameters were lower and inflammatory markers higher in patients with NDDM. In adjusted models, NDDM was associated with lower insulin requirements, longer length of stay, and intensive care unit admission but not death. Of 64 survivors with NDDM, 36 (56.3%) continued to have DM, 26 (40.6%) regressed to normoglycemia or pre-diabetes, and 2 were unable to be classified at a median follow-up of 323 days. CONCLUSIONS: Diabetes diagnosed at COVID-19 presentation is associated with lower glucose but higher inflammatory markers and ICU admission, suggesting stress hyperglycemia as a major physiologic mechanism. Approximately half of such individuals experience regression of DM.

Challenges and Opportunities for Osteoporosis Care During the COVID-19 Pandemic.

PURPOSE: The coronavirus disease 2019 (COVID-19) has both directly and indirectly affected osteoporosis diagnosis and treatment throughout the world. METHODS: This mini-review summarizes the available evidence regarding the effects of COVID-19, its treatment, and the consequences of the pandemic itself on bone health. Additionally, we review evidence and expert recommendations regarding putative effects of osteoporosis medications on COVID-19 outcomes and vaccine efficacy and summarize recommendations for continuation of osteoporosis treatment during the pandemic. RESULTS: The use of standard screening procedures to assess for osteoporosis and fracture risk declined dramatically early in the pandemic, while rates of fragility fractures were largely unchanged. COVID-19, its treatments, and public health measures to prevent viral spread are each likely to negatively affect bone health. Osteoporosis treatments are not known to increase risk of adverse events from COVID-19, and preclinical data suggest possible beneficial effects of some therapies. Vitamin D deficiency is clearly associated with adverse outcomes from COVID-19, but it remains unclear whether vitamin D supplementation may improve outcomes. Osteoporosis treatment should be continued whenever possible, and recommendations for substituting therapies, if required, are available. CONCLUSION: The COVID-19 pandemic has decreased screening and disrupted treatment for osteoporosis. Osteoporosis medications are safe and effective during the pandemic and should be continued whenever possible. Further studies are needed to fully understand the impact of the COVID-19 pandemic on long-term bone health.

Correlates of analog vs human basal insulin use among individuals with type 2 diabetes: A cross-sectional study.

BACKGROUND: Given the shift from use of less expensive human to costlier analog insulins for treatment of type 2 diabetes (T2D), we examine characteristics and glycemic control associated with type of basal insulin use. METHODS: We analyzed respondents with T2D in six consecutive National Health and Nutrition Examination Survey (NHANES) cycles (2005-2016). Logistic regression models examined associations between demographics, socioeconomic factors, and NHANES cycle with (1) type of basal insulin use and (2) hemoglobin A1c <8.0% and <7.0% according to basal insulin type. FINDINGS: Basal insulin use increased from 9.6% to 17.2% of respondents with T2D between 2005 and 2016. Among 723 respondents meeting inclusion criteria, the proportion using analog basal insulin rose from 58% to 88%. African American (aOR 0.42, 95% CI 0.24-0.74) and Hispanic (aOR 0.54, 95% CI 0.30-0.96) respondents had lower odds of analog basal insulin use than non-Hispanic White respondents in adjusted and unadjusted models. Older age and having health insurance, but not type of basal insulin use, associated with meeting HbA1c targets. INTERPRETATION: Non-White NHANES respondents were less likely to use analog basal insulin than White respondents. Increased analog basal insulin use between 2005 and 2016 was not associated with improved glycemic control.

Geospatial Analysis of Individual and Community-Level Socioeconomic Factors Impacting SARS-CoV-2 Prevalence and Outcomes.

Background: The SARS-CoV-2 pandemic has disproportionately affected racial and ethnic minority communities across the United States. We sought to disentangle individual and census tract-level sociodemographic and economic factors associated with these disparities. Methods and Findings: All adults tested for SARS-CoV-2 between February 1 and June 21, 2020 were geocoded to a census tract based on their address; hospital employees and individuals with invalid addresses were excluded. Individual (age, sex, race/ethnicity, preferred language, insurance) and census tract-level (demographics, insurance, income, education, employment, occupation, household crowding and occupancy, built home environment, and transportation) variables were analyzed using linear mixed models predicting infection, hospitalization, and death from SARS-CoV-2.Among 57,865 individuals, per capita testing rates, individual (older age, male sex, non-White race, non-English preferred language, and non-private insurance), and census tract-level (increased population density, higher household occupancy, and lower education) measures were associated with likelihood of infection. Among those infected, individual age, sex, race, language, and insurance, and census tract-level measures of lower education, more multi-family homes, and extreme household crowding were associated with increased likelihood of hospitalization, while higher per capita testing rates were associated with decreased likelihood. Only individual-level variables (older age, male sex, Medicare insurance) were associated with increased mortality among those hospitalized. Conclusions: This study of the first wave of the SARS-CoV-2 pandemic in a major U.S. city presents the cascade of outcomes following SARS-CoV-2 infection within a large, multi-ethnic cohort. SARS-CoV-2 infection and hospitalization rates, but not death rates among those hospitalized, are related to census tract-level socioeconomic characteristics including lower educational attainment and higher household crowding and occupancy, but not neighborhood measures of race, independent of individual factors.

Does the effect of lifestyle intervention for individuals with diabetes vary by food insecurity status? A preplanned subgroup analysis of the REAL HEALTH randomized clinical trial.

INTRODUCTION: We aimed to test the effectiveness of a lifestyle intervention (LI) for individuals with food insecurity and type 2 diabetes. RESEARCH DESIGN AND METHODS: Adults with type 2 diabetes, body mass index ≥25 kg/m2 (or ≥23 kg/m2 if Asian), hemoglobin A1c of 6.5%-11.5% (48-97 mmol/mol) and who were willing to lose 5%-7% bodyweight were enrolled in REAL HEALTH-Diabetes. This practice-based randomized clinical trial compared LI (delivered inperson or by telephone) with medical nutrition therapy (MNT) on weight loss at 6 and 12 months. Two or more affirmative responses on the six-item US Department of Agriculture Food Security Survey Module indicated food insecurity. In this prespecified subgroup analysis, we tested using linear mixed effects models whether the intervention effect varied by food security status. RESULTS: Of 208 participants, 13% were food insecure. Those with food insecurity were more likely to be racial/ethnic minorities (p<0.001) and have lower education (p<0.001). LI, versus MNT, led to greater weight loss at 6 months (5.1% lost vs 1.1% lost; p<0.0001) and 12 months (4.7% lost vs 2.0% lost; p=0.0005). The intervention effect was similar regardless of food security status (5.1% bodyweight lost vs 1.1% in food secure participants and 5.1% bodyweight lost vs 1.3% in food insecure participants at 6 months; 4.7% bodyweight lost vs 2.1% in food secure participants and 4.5% bodyweight lost vs 0.9% in food insecure participants at 12 months; p for interaction=0.99). CONCLUSIONS: The REAL HEALTH-Diabetes lifestyle intervention led to meaningful weight loss for individuals with food insecurity and type 2 diabetes. TRIAL REGISTRATION NUMBER: NCT02320253.