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      Heterogeneity in elevated glucose and A1C as predictors of the prediabetes to diabetes transition: Framingham Heart Study, Multi-Ethnic Study on Atherosclerosis, Jackson Heart Study, and Atherosclerosis Risk In Communities

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      , PhD 1 , , MD, DSc 2 , , PhD 3 , , MD 4 , , PhD 1
      medRxiv
      Cold Spring Harbor Laboratory

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          Abstract

          Aims/hypothesis:

          There are a number of glycemic definitions for prediabetes; however, the heterogeneity in diabetes transition rates from prediabetes across different glycemic definitions in major US cohorts has been unexplored. We hypothesize that a significant source of variation in the transition rate are cohorts themselves. We estimate the variability in risk and relative risk of diabetes based on diagnostic criteria like fasting glucose and hemoglobin A1C% (HbA1c%).

          Methods:

          We estimated transition rate from prediabetes, as defined by fasting glucose between 100–125 and/or 110–125 mg/dL, and HbA1c% between 5.7–6.5% in participant data from the Framingham Heart Study (FHS) Generation 2, FHS Generation 3, Multi-Ethnic Study on Atherosclerosis, Atherosclerosis Risk in Communities, and the Jackson Heart Study. We estimated the heterogeneity and prediction interval across cohorts, stratifying by age, sex, and body mass index. Among individuals with prediabetes, we estimated the relative risk for obesity, blood pressure, education, age, and sex for diabetes.

          Results:

          There is substantial heterogeneity in diabetes transition rates across cohorts and prediabetes definitions with large prediction intervals. We observed the individuals with fasting glucose of 100–125 range from 4–14% per 100 person years and 110–125 mg/dL ranging from 2–18 per 100 person-years. For HbA1C between 5.7–6.5%, the transition rate ranged from 2.5–11 per 100 person years (I 2 for heterogeneity was greater than 93% for all definitions). Obesity and hypertension did not explain the differences in risk.

          Conclusion:

          The absolute transition rate from prediabetes to diabetes significantly depends on both cohort and prediabetes definitions.

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          Most cited references22

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          Reduction in the incidence of type 2 diabetes with lifestyle intervention or metformin.

          Type 2 diabetes affects approximately 8 percent of adults in the United States. Some risk factors--elevated plasma glucose concentrations in the fasting state and after an oral glucose load, overweight, and a sedentary lifestyle--are potentially reversible. We hypothesized that modifying these factors with a lifestyle-intervention program or the administration of metformin would prevent or delay the development of diabetes. We randomly assigned 3234 nondiabetic persons with elevated fasting and post-load plasma glucose concentrations to placebo, metformin (850 mg twice daily), or a lifestyle-modification program with the goals of at least a 7 percent weight loss and at least 150 minutes of physical activity per week. The mean age of the participants was 51 years, and the mean body-mass index (the weight in kilograms divided by the square of the height in meters) was 34.0; 68 percent were women, and 45 percent were members of minority groups. The average follow-up was 2.8 years. The incidence of diabetes was 11.0, 7.8, and 4.8 cases per 100 person-years in the placebo, metformin, and lifestyle groups, respectively. The lifestyle intervention reduced the incidence by 58 percent (95 percent confidence interval, 48 to 66 percent) and metformin by 31 percent (95 percent confidence interval, 17 to 43 percent), as compared with placebo; the lifestyle intervention was significantly more effective than metformin. To prevent one case of diabetes during a period of three years, 6.9 persons would have to participate in the lifestyle-intervention program, and 13.9 would have to receive metformin. Lifestyle changes and treatment with metformin both reduced the incidence of diabetes in persons at high risk. The lifestyle intervention was more effective than metformin.
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            Novel subgroups of adult-onset diabetes and their association with outcomes: a data-driven cluster analysis of six variables

            Diabetes is presently classified into two main forms, type 1 and type 2 diabetes, but type 2 diabetes in particular is highly heterogeneous. A refined classification could provide a powerful tool to individualise treatment regimens and identify individuals with increased risk of complications at diagnosis.
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              Plea for routinely presenting prediction intervals in meta-analysis

              Objectives Evaluating the variation in the strength of the effect across studies is a key feature of meta-analyses. This variability is reflected by measures like τ2 or I2, but their clinical interpretation is not straightforward. A prediction interval is less complicated: it presents the expected range of true effects in similar studies. We aimed to show the advantages of having the prediction interval routinely reported in meta-analyses. Design We show how the prediction interval can help understand the uncertainty about whether an intervention works or not. To evaluate the implications of using this interval to interpret the results, we selected the first meta-analysis per intervention review of the Cochrane Database of Systematic Reviews Issues 2009–2013 with a dichotomous (n=2009) or continuous (n=1254) outcome, and generated 95% prediction intervals for them. Results In 72.4% of 479 statistically significant (random-effects p 0), the 95% prediction interval suggested that the intervention effect could be null or even be in the opposite direction. In 20.3% of those 479 meta-analyses, the prediction interval showed that the effect could be completely opposite to the point estimate of the meta-analysis. We demonstrate also how the prediction interval can be used to calculate the probability that a new trial will show a negative effect and to improve the calculations of the power of a new trial. Conclusions The prediction interval reflects the variation in treatment effects over different settings, including what effect is to be expected in future patients, such as the patients that a clinician is interested to treat. Prediction intervals should be routinely reported to allow more informative inferences in meta-analyses.
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                Author and article information

                Journal
                medRxiv
                MEDRXIV
                medRxiv
                Cold Spring Harbor Laboratory
                18 March 2024
                : 2024.03.16.24304398
                Affiliations
                [1 ]Department of Biomedical Informatics, Harvard Medical School, Boston, MA. 02215
                [2 ]Department of Prevention Research, Stanford University School of Medicine, Stanford, CA. 94305
                [3 ]School of Population Health, Royal College of Surgeons in Ireland, Dublin, Ireland
                [4 ]University of Texas School of Public Health, San Antonio TX. 78249
                Author notes

                Contribution Statement

                CJP, WEG, AKM, RV, JPAI designed the study and contributed to drafting of the manuscript.. CJP attained the data and conducted the analysis. CJP takes full responsibility of the work and is the guarantor.

                Correspondence to: Chirag J Patel, chirag_patel@ 123456hms.harvard.edu
                Author information
                http://orcid.org/0000-0002-8756-8525
                Article
                10.1101/2024.03.16.24304398
                10984063
                38562763
                be70bba1-cde2-466a-849d-0b510c4717cf

                This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which allows reusers to copy and distribute the material in any medium or format in unadapted form only, for noncommercial purposes only, and only so long as attribution is given to the creator.

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