Diabetic ketoacidosis at diagnosis among youth with type 1 and type 2 diabetes: Results from SEARCH (United States) and YDR (India) registries.

BACKGROUND: There is significant global variation in the prevalence of diabetic ketoacidosis (DKA) at diagnosis among youth with type 1 diabetes (T1D). However, data for youth with type 2 diabetes (T2D) are limited, even in developed countries. We compared the prevalence of DKA at diagnosis among individuals with T1D and T2D from the SEARCH for Diabetes in Youth (SEARCH) and the Registry of Youth Onset Diabetes in India (YDR) registries. METHODS: We harmonized the SEARCH and YDR registries to the structure and terminology in the Observational Medical Outcome Partnership Common Data Model. Data used were from youth with T1D and T2D diagnosed before 20 years and newly diagnosed between 2006 and 2012 in YDR and 2009 and 2012 in SEARCH. RESULTS: There were 5366 US youth (4078 with T1D, 1288 with T2D) and 2335 Indian youth (2108 with T1D, 227 with T2D). More than one third of T1D youth enrolled in SEARCH had DKA at diagnosis which was significantly higher than in YDR (35.3% vs 28.7%, P < .0001). The burden of DKA in youth with T1D was significantly higher among younger age groups; this relationship was similar across registries (P = .4). The prevalence of DKA among T2D in SEARCH and YDR were 5.5% and 6.6% respectively (P = .4). CONCLUSIONS: There is significant burden of DKA at diagnosis with T1D among youth from United States and India, especially among the younger age groups. The reasons for this high prevalence are largely unknown but are critical to developing interventions to prevent DKA at diagnosis.

Clinical profile at diagnosis with youth-onset type 1 and type 2 diabetes in two pediatric diabetes registries: SEARCH (United States) and YDR (India).

BACKGROUND: Over the last decades, diabetes in youth has increased in both India and the United States, along with the burden of long-term complications and healthcare costs. However, there are limited standardized population-based data in contemporary youth cohorts for comparison of clinical and demographic characteristics of diabetes for both type 1 (T1D) and type 2 (T2D). METHODS: In partnership, we harmonized demographic and clinical data from the SEARCH for Diabetes in Youth (SEARCH) registry in the United States and the Registry of People with Diabetes with Youth Age at Onset (YDR) in India to the structure and terminology of the Observational Medical Outcomes Partnership Common Data Model. Data were from youth with T1D and T2D, aged <20 years and newly diagnosed between 2006 and 2010. We compared key characteristics across registries using χ2 tests and t-tests. RESULTS: In total, there were 9650 youth with T1D and 2406 youth with T2D from 2006 to 2012. SEARCH youth were diagnosed at younger ages than YDR youth for T1D and T2D (10.0 vs 10.5 years, P < .001 and 14.7 vs 16.1 years, P < .001, respectively). For T2D, SEARCH had a higher proportion of females and significantly lower proportion of youth of high socioeconomic status compared to YDR. For T1D and T2D, SEARCH youth had higher BMI, lower blood pressure, and lower A1c compared to YDR youth. CONCLUSIONS: These data offer insights into the demographic and clinical characteristics of diabetes in youth across the two countries. Further research is needed to better understand why these differences exist.

Treatment regimens and glycosylated hemoglobin levels in youth with Type 1 and Type 2 diabetes: Data from SEARCH (United States) and YDR (India) registries.

OBJECTIVE: To compare treatment regimens and glycosylated hemoglobin (A1c) levels in Type 1 (T1D) and Type 2 diabetes (T2D) using diabetes registries from two countries-U.S. SEARCH for Diabetes in Youth (SEARCH) and Indian Registry of youth onset diabetes in India (YDR). METHODS: The SEARCH and YDR data were harmonized to the structure and terminology in the Observational Medical Outcomes Partnership Common Data Model. Data used were from T1D and T2D youth diagnosed <20 years between 2006-2012 for YDR, and 2006, 2008, and 2012 for SEARCH. We compared treatment regimens and A1c levels across the two registries. RESULTS: There were 4003 T1D (SEARCH = 1899; YDR = 2104) and 611 T2D (SEARCH = 384; YDR = 227) youth. The mean A1c was higher in YDR compared to SEARCH (T1D:11.0% ± 2.9% vs 7.8% ± 1.7%, P < .001; T2D:9.9% ± 2.8% vs 7.2% ± 2.1%, P < .001). Among T1D youth in SEARCH, 65.1% were on a basal/bolus regimen, whereas in YDR, 52.8% were on once/twice daily insulin regimen. Pumps were used by 16.2% of SEARCH and 1.5% of YDR youth with T1D. Among T2D youth, in SEARCH and YDR, a majority were on metformin only (43.0% vs 30.0%), followed by insulin + any oral hypoglycemic agents (26.3% vs 13.7%) and insulin only (12.8% vs 18.9%), respectively. CONCLUSION: We found significant differences between SEARCH and YDR in treatment patterns in T1D and T2D. A1c levels were higher in YDR than SEARCH youth, for both T1D and T2D, irrespective of the regimens used. Efforts to achieve better glycemic control for youth are urgently needed to reduce the risk of long-term complications.

Comparison of the incidence of diabetes in United States and Indian youth: An international harmonization of youth diabetes registries.

OBJECTIVE: Incidence of youth-onset diabetes in India has not been well described. Comparison of incidence, across diabetes registries, has the potential to inform hypotheses for risk factors. We sought to compare the incidence of diabetes in the U.S.-based registry of youth onset diabetes (SEARCH) to the Registry of Diabetes with Young Age at Onset (YDR-Chennai and New Delhi regions) in India. METHODS: We harmonized data from both SEARCH and YDR to the Observational Medical Outcomes Partnership (OMOP) Common Data Model. Data were from youth registered with incident diabetes (2006-2012). Denominators were from census and membership data. We calculated diabetes incidence by averaging the total cases across the entire follow-up period and dividing this by the estimated census population corresponding to the source population for case ascertainment. Incidence was calculated for each of the registries and compared by type and within age and sex categories using a 2-sided, skew-corrected inverted score test. RESULTS: Incidence of type 1 was higher in SEARCH (21.2 cases/100 000 [95% CI: 19.9, 22.5]) than YDR (4.9 cases/100 000 [95% CI: 4.3, 5.6]). Incidence of type 2 diabetes was also higher in SEARCH (5.9 cases/100 000 [95% CI: 5.3, 6.6] in SEARCH vs 0.5/cases/100 000 [95% CI: 0.3, 0.7] in YDR). The age distribution of incident type 1 diabetes cases was similar across registries, whereas type 2 diabetes incidence was higher at an earlier age in SEARCH. Sex differences existed in SEARCH only, with a higher rate of type 2 diabetes among females. CONCLUSION: The incidence of youth-onset type 1 and 2 diabetes was significantly different between registries. Additional data are needed to elucidate whether the differences observed represent diagnostic delay, differences in genetic susceptibility, or differences in distribution of risk factors.

Long-term Weight Loss Maintenance in the Continuation of a Randomized Diabetes Prevention Translational Study: The Healthy Living Partnerships to Prevent Diabetes (HELP PD) Continuation Trial.

OBJECTIVE: HELP PD was a clinical trial of 301 adults with prediabetes. Participants were randomized to enhanced usual care (EUC) or to a lifestyle weight loss (LWL) intervention led by community health workers that consisted of a 6-month intensive phase (phase 1) and 18 months of maintenance (phase 2). At 24 months, participants were asked to enroll in phase 3 to assess whether continued group maintenance (GM) sessions would maintain improvements realized in phases 1 and 2 compared with self-directed maintenance (SM) or EUC. RESEARCH DESIGN AND METHODS: In phase 3, LWL participants were randomly assigned to GM or SM. EUC participants remained in the EUC arm and, along with participants in SM, received monthly newsletters. All participants received semiannual dietitian sessions. Anthropometrics and biomarkers were assessed every 6 months. Mixed-effects models were used to assess changes in outcomes over time. RESULTS: Eighty-two of the 151 intervention participants (54%) agreed to participate in phase 3; 41 were randomized to GM and 41 to SM. Of the 150 EUC participants, 107 (71%) continued. Ninety percent of clinic visits were completed. Over 48 months of additional follow-up, outcomes remained relatively stable in the EUC participants; the GM group was able to maintain body weight, BMI, and waist circumference; and these measures all increased significantly (P < 0.001) in the SM group. CONCLUSIONS: Participants in the GM arm maintained weight loss achieved in phases 1 and 2, while those in the SM arm regained weight. Because group session attendance by the participants in the GM arm was low, it is unclear what intervention components led to successful weight maintenance.

The healthy living partnerships to prevent diabetes and the diabetes prevention program: a comparison of year 1 and 2 intervention results.

A number of research studies have attempted to translate the behavioral lifestyle intervention delivered in the Diabetes Prevention Program (DPP). To compare the active interventions of two trials, Diabetes Prevention Program DPP and Healthy Living Partnerships to Prevent Diabetes (HELP PD), after 1 and 2 years of intervention. DPP included 3234 adults with prediabetes randomized to intensive lifestyle intervention, metformin, troglitazone, or placebo. The lifestyle intervention, professionally delivered to individuals in a clinical setting, focused on diet and increased physical activity. HELP PD, a community-based translation of DPP, included 301 adults randomized to receive intensive lifestyle intervention or enhanced usual care. Mean weight-losses at 1 year (6.9 kg in DPP, 6.4 kg in HELP PD) and 2 years (5.5 kg in DPP, 4.4 kg in HELP PD) were similar across studies. Reductions in glucose were also similar across studies at both time points (5.2 mg/dL in DPP and 4.1 mg/dL in HELP PD at 1 year; 1.8 mg/dL and 1.6 mg/dL at 2 years). HELP PD participants achieved larger reductions in triglycerides at 1 and 2 years (38.4 mg/dL and 34.9 mg/dL, respectively) than DPP participants (24.8 mg/dL and 22.4 mg/dL). High-density lipoprotein decreased in HELP PD participants at year 1 (-0.6 mg/dL) and increased in DPP (1.2 mg/dL) but there were no significant differences in year 2. HELP PD, a community model for diabetes prevention, was similar to DPP in reducing body weight and lowering blood glucose, both important risk factors that should be controlled to reduce risk for developing type 2 diabetes.

Cost of a group translation of the Diabetes Prevention Program: Healthy Living Partnerships to Prevent Diabetes.

BACKGROUND: Although numerous studies have translated the Diabetes Prevention Program lifestyle intervention into various settings, no study to date has reported a formal cost analysis. PURPOSE: To describe costs associated with the Healthy Living Partnerships to Prevent Diabetes (HELP PD) trial. DESIGN: HELP PD was a 24-month RCT testing the impact of a lifestyle weight-loss intervention administered through a diabetes education program and delivered by community health workers (CHWs) on blood glucose and body weight among prediabetics. SETTING/PARTICIPANTS: In all, 301 participants with prediabetes were randomized in Forsyth County NC. Data reported in these analyses were collected in 2007-2011 and analyzed in 2011-2012. INTERVENTION: The lifestyle weight-loss group had a 7% weight loss goal achieved and maintained by caloric restriction and increased physical activity. The usual care group received two visits with a registered dietitian and monthly newsletters. MAIN OUTCOME MEASURES: Measures are direct medical costs, direct nonmedical costs, and indirect costs over the 2-year study period. Research costs are excluded. RESULTS: The direct medical cost (in 2010 dollars) to identify one participant was $16.85. Direct medical costs per capita for participants in the usual care group were $142 and $850 for lifestyle weight-loss participants. Per capita direct costs of care outside the study were $7454 for the usual care group and $5177 for the lifestyle weight-loss group. Per capita direct nonmedical costs were $12,881 for the usual care group and $13,836 for the lifestyle weight-loss group. The lifestyle weight-loss group in HELP PD cost $850 in direct medical costs for 2 years, compared to $2631 in direct medical costs for the first 2 years of DPP. CONCLUSIONS: A community-based translation of the DPP can be delivered effectively and with reduced costs.

The Healthy Living Partnerships to Prevent Diabetes study: 2-year outcomes of a randomized controlled trial.

BACKGROUND: Since the Diabetes Prevention Project (DPP) demonstrated that lifestyle weight-loss interventions can reduce the incidence of diabetes by 58%, several studies have translated the DPP methods to public health-friendly contexts. Although these studies have demonstrated short-term effects, no study to date has examined the impact of a translated DPP intervention on blood glucose and adiposity beyond 12 months of follow-up. PURPOSE: To examine the impact of a 24-month, community-based diabetes prevention program on fasting blood glucose, insulin, insulin resistance as well as body weight, waist circumference, and BMI in the second year of follow-up. DESIGN: An RCT comparing a 24-month lifestyle weight-loss program (LWL) to an enhanced usual care condition (UCC) in participants with prediabetes (fasting blood glucose=95-125 mg/dL). Data were collected in 2007-2011; analyses were conducted in 2011-2012. SETTING/PARTICIPANTS: 301 participants with prediabetes were randomized; 261 completed the study. The intervention was held in community-based sites. INTERVENTION: The LWL program was led by community health workers and sought to induce 7% weight loss at 6 months that would be maintained over time through decreased caloric intake and increased physical activity. The UCC received two visits with a registered dietitian and a monthly newsletter. MAIN OUTCOME MEASURES: The main measures were fasting blood glucose, insulin, insulin resistance, body weight, waist circumference, and BMI. RESULTS: Intent-to-treat analyses of between-group differences in the average of 18- and 24-month measures of outcomes (controlling for baseline values) revealed that the LWL participants experienced greater decreases in fasting glucose (-4.35 mg/dL); insulin (-3.01 µU/ml); insulin resistance (-0.97); body weight (-4.19 kg); waist circumference (-3.23 cm); and BMI (-1.40), all p-values <0.01. CONCLUSIONS: A diabetes prevention program administered through an existing community-based system and delivered by community health workers is effective at inducing significant long-term reductions in metabolic indicators and adiposity.

How good are US dermatologists at discriminating skin cancers? A number-needed-to-treat analysis.

BACKGROUND: Identification of skin cancer requires discrimination of malignant lesions from benign lesions. The number of biopsies performed to yield one cancer diagnosis can be presented as a number needed to treat (NNT), and provides an assessment of the efficiency of skin cancer detection. OBJECTIVE: To assess the clinical accuracy of US dermatologists screening for skin cancer, the NNT for both melanoma and non-melanoma skin cancer was examined. METHODS: Pathology reports from 2021 biopsies performed at the Wake Forest University Department of Dermatology were reviewed, including the physician's differential diagnosis and final pathological diagnosis. The NNT was calculated for melanoma, non-melanoma skin cancer, and all skin cancer diagnosed. RESULTS: Of 1240 biopsies suspicious for skin cancer, 559 cancers were diagnosed, yielding a NNT of 2.22 for any cancer. The NNT specifically for non-melanoma skin cancer was 1.6, while the NNT for melanoma was 15. Patient age, anatomical location, sex and physician all significantly impacted on NNT values. CONCLUSIONS: The NNT for melanoma in our study was lower compared to recently published values obtained from general practitioners in Australian skin cancer clinics (NNT of 30). Variability amongst institutions, practice settings and physicians supports the need to establish a benchmark NNT.

Cancer-prone mice expressing the Ki-rasG12C gene show increased lung carcinogenesis after CT screening exposures.

A >20-fold increase in X-ray computed tomography (CT) use during the last 30 years has caused considerable concern because of the potential carcinogenic risk from these CT exposures. Estimating the carcinogenic risk from high-energy, single high-dose exposures obtained from atomic bomb survivors and extrapolating these data to multiple low-energy, low-dose CT exposures using the Linear No-Threshold (LNT) model may not give an accurate assessment of actual cancer risk. Recently, the National Lung Cancer Screening Trial (NLST) reported that annual CT scans of current and former heavy smokers reduced lung cancer mortality by 20%, highlighting the need to better define the carcinogenic risk associated with these annual CT screening exposures. In this study, we used the bitransgenic CCSP-rtTA/Ki-ras mouse model that conditionally expresses the human mutant Ki-ras(G12C) gene in a doxycycline-inducible and lung-specific manner to measure the carcinogenic risk of exposure to multiple whole-body CT doses that approximate the annual NLST screening protocol. Irradiated mice expressing the Ki-ras(G12C) gene in their lungs had a significant (P = 0.01) 43% increase in the number of tumors/mouse (24.1 ± 1.9) compared to unirradiated mice (16.8 ± 1.3). Irradiated females had significantly (P < 0.005) more excess tumors than irradiated males. No tumor size difference or dose response was observed over the total dose range of 80-160 mGy for either sex. Irradiated bitransgenic mice that did not express the Ki-ras(G12C) gene had a low tumor incidence (≤ 0.1/mouse) that was not affected by exposure to CT radiation. These results suggest that (i) estimating the carcinogenic risk of multiple CT exposures from high-dose carcinogenesis data using the LNT model may be inappropriate for current and former smokers and (ii) any increased carcinogenic risk after exposure to fractionated low-dose CT-radiation may be restricted to only those individuals expressing cancer susceptibility genes in their tissues at the time of exposure.