Future Type 2 Diabetes Risk Research Articles

Testosterone in prevention and treatment of type 2 diabetes in men: Focus on recent randomized controlled trials.

In epidemiological studies, lowered serum testosterone concentrations are common in men with obesity, prediabetes, and established type 2 diabetes (T2D). In men with prediabetes, lowered serum testosterone also predicts a future risk of T2D in men. Administration of testosterone consistently reduces fat mass and increases skeletal muscle mass-body compositional changes expected to be metabolically favorable. In men with established T2D, the effects of testosterone treatment on glycemic measures are inconsistent. Irrespective of baseline serum testosterone concentration in men with prediabetes or newly diagnosed early-onset T2D, testosterone treatment prescribed in conjunction with a lifestyle program has been reported to reduce the risk of T2D by 40% after 2 years, suggesting that either a lifestyle program is required to facilitate the glycemic benefit of testosterone treatment and/or that testosterone treatment has more favorable effects on glycemia in men early in the evolution or onset of the disease. The durability of the benefit and longer-term safety of testosterone treatment have not been established. Therefore, more studies are required before testosterone treatment can be recommended for the prevention and/or treatment of men with or at elevated risk of T2D who do not have hypogonadism due to an established disease of the hypothalamic-pituitary-testicular axis.

Tailoring of Health-Promotion Video Messaging for Reproductive-Aged Women at Risk for Developing Cardiometabolic Disease: Qualitative Focus-Groups Study.

Targeting reproductive-aged women at high risk for type 2 diabetes (T2D) provides an opportunity for prevention earlier in the life course. A woman's experiences during her reproductive years may have a large impact on her future risk of T2D. Her risk is 7 to 10 times higher if she has had gestational diabetes (GDM). Despite these risks, T2D is preventable. Evidence-based programs, such as the National Diabetes Prevention Program (DPP), can reduce the risk of developing T2D by nearly 60%. However, only 0.4% of adults with prediabetes have participated in the DPP to date and reproductive-aged women are 50% less likely to participate than older women. In prior work, our team developed a mobile 360° video to address diabetes risk awareness and promote DPP enrollment among at-risk adults; this video was not designed, however, for reproductive-aged women. This study aims to obtain feedback from reproductive-aged women with cardiometabolic disease risk about a 360° video designed to promote enrollment in the DPP, and to gather suggestions about tailoring video messages to reproductive-aged women. Focus groups and a qualitative descriptive approach were used. Women with at least 1 previous pregnancy, aged 18 to 40 years, participated in one of three focus groups stratified by the following health risks: (1) a history of GDM or a hypertensive disorder of pregnancy, (2) a diagnosis of prediabetes, or (3) a BMI classified as obese. Focus-group questions addressed several topics; this report shared findings regarding video feedback. The 3 focus-group discussions were conducted via Zoom and were recorded and transcribed for analysis. Deductive codes were used to identify concepts related to the research question and inductive codes were created for novel insights shared by participants. The codes were then organized into categories and themes. The main themes identified were positive feedback, negative feedback, centering motherhood, and the importance of storytelling. While some participants said the video produced a sense of urgency for health-behavior change, all participants agreed that design changes could improve the video's motivating effect on health-behavior change in reproductive-aged women. Participants felt a tailored video should recognize the complexities of being a mother and how these dynamics contribute to women's difficulty engaging in healthy behaviors without stirring feelings of guilt. Women desired a video with a positive, problem-solving perspective, and recommended live links as clickable resources for practical solutions promoting health behavior change. Women suggested using storytelling, both to describe how complications experienced during pregnancy impact long-term health and to motivate health behavior change. Reproductive-aged women require tailored lifestyle-change messaging that addresses barriers commonly encountered by this population (eg, parenting or work responsibilities). Moreover, messaging should prioritize a positive tone that harnesses storytelling and human connection while offering realistic solutions.

Future diabetes risk can be predicted by the number of abnormal oral glucose tolerance test values during pregnancy.

To quantify the future risk of type 2 diabetes (T2D) in women with gestational diabetes (GD) based on baseline metabolic characteristics and the number of abnormal values during a 3-hour 100-g oral glucose tolerance test (OGTT). We conducted a population-based retrospective cohort study of 10 023 pregnant women who underwent testing for GD in a large health maintenance organization in Israel using a 100-g OGTT. Glucose values were obtained at four time points, 0, 60, 120 and 180 minutes. We identified 9939 women who met the study criteria. Median follow-up was 3.25 (interquartile range 1.5-5.1; maximum 10.1) years. Using women without GD as reference, women with GD were at an increased risk of future T2D (hazard ratio [HR] 5.33 [95% confidence interval {CI} 3.86-7.34]). This risk increased with a greater number of abnormal OGTT values, with the highest risk seen in women with four abnormal values (HR 16.67 [95% CI 7.94-35.01]). In a multivariate model, a higher number of abnormal values, Arab ethnicity, higher body mass index, triglycerides and prepregnancy glucose were significantly associated with increased risk. Future T2D risk was also affected by the type of OGTT abnormality; an abnormal fasting value had the greatest risk, whereas an abnormal 3-hour value had the lowest risk (HR 3.61 [95% CI 2.42-5.38] vs. 1.50 [95% CI 0.93-2.43], respectively). GD isa heterogenous disease, with varying degrees of glucose intolerance and subsequent T2D risk. Targeting interventions to womenat the highest risk may help to improve postpartum adherence and effective long-term follow-up strategies.

Abstract 13308: Complementary Investigations of Plasma Proteomics Identify Novel Protein Associations With Incident Diabetes and Glucose Metabolism

Introduction: Type 2 diabetes (T2D) is a cardiovascular disease risk equivalent and likely results from broad metabolic changes, which high throughput proteomics have helped to unravel. Prior studies are limited by proteomic coverage, cross sectional design, and lack of physiologic phenotyping. Hypothesis: Complementary proteomic studies of incident T2D and physiologic responses to an intravenous glucose tolerance test (IVGTT) will identify novel proteins with roles in glucose homeostasis and future risk of T2D. Methods: Cardiovascular Health Study (CHS) and HERITAGE study participants without diabetes underwent SomaScan ® profiling of 4,776 plasma proteins. HERITAGE participants underwent IVGTT, from which insulin sensitivity index (S I ), acute insulin response to glucose (AIR G ), and glucose effectiveness (S G ) were derived. We used Cox regression to test protein associations with 18-year incident T2D in CHS, and multivariable linear regression to test protein associations with IVGTT measures in HERITAGE. Results: In CHS (N = 2631, 74 ± 5 years, 62% female, 14% Black), 57 proteins were significantly associated with incident T2D after comprehensive covariate and multiple testing adjustment. Of these, 44, 9, and 8 were associated with S I , AIR G , and S G respectively in HERITAGE (N = 752, 35 ± 14 years, 55% female, 38% Black) (Figure). Notable findings include beta-glucuronidase, which associated with increased T2D risk (HR 1.46 per SD increase in log 2 protein level) and lower S G , suggesting a role in insulin-independent glucose disposal, and thrombospondin-2, which associated with increased T2D risk (HR 1.26 per SD), lower AIR G , and not with S I , indicating that it may be a marker of pancreatic dysfunction. Conclusions: By integrating proteomics from two complementary prospective cohorts using different but related outcomes, we identified 34 novel protein-T2D associations, and characterized their relationship with physiologic axes of glucose metabolism.

Risk prediction models for incident type 2 diabetes in Chinese people with intermediate hyperglycemia: a systematic literature review and external validation study

BackgroundPeople with intermediate hyperglycemia (IH), including impaired fasting glucose and/or impaired glucose tolerance, are at higher risk of developing type 2 diabetes (T2D) than those with normoglycemia. We aimed to evaluate the performance of published T2D risk prediction models in Chinese people with IH to inform them about the choice of primary diabetes prevention measures.MethodsA systematic literature search was conducted to identify Asian-derived T2D risk prediction models, which were eligible if they were built on a prospective cohort of Asian adults without diabetes at baseline and utilized routinely-available variables to predict future risk of T2D. These Asian-derived and five prespecified non-Asian derived T2D risk prediction models were divided into BASIC (clinical variables only) and EXTENDED (plus laboratory variables) versions, with validation performed on them in three prospective Chinese IH cohorts: ACE (n = 3241), Luzhou (n = 1333), and TCLSIH (n = 1702). Model performance was assessed in terms of discrimination (C-statistic) and calibration (Hosmer–Lemeshow test).ResultsForty-four Asian and five non-Asian studies comprising 21 BASIC and 46 EXTENDED T2D risk prediction models for validation were identified. The majority were at high (n = 43, 87.8%) or unclear (n = 3, 6.1%) risk of bias, while only three studies (6.1%) were scored at low risk of bias. BASIC models showed poor-to-moderate discrimination with C-statistics 0.52–0.60, 0.50–0.59, and 0.50–0.64 in the ACE, Luzhou, and TCLSIH cohorts respectively. EXTENDED models showed poor-to-acceptable discrimination with C-statistics 0.54–0.73, 0.52–0.67, and 0.59–0.78 respectively. Fifteen BASIC and 40 EXTENDED models showed poor calibration (P < 0.05), overpredicting or underestimating the observed diabetes risk. Most recalibrated models showed improved calibration but modestly-to-severely overestimated diabetes risk in the three cohorts. The NAVIGATOR model showed the best discrimination in the three cohorts but had poor calibration (P < 0.05).ConclusionsIn Chinese people with IH, previously published BASIC models to predict T2D did not exhibit good discrimination or calibration. Several EXTENDED models performed better, but a robust Chinese T2D risk prediction tool in people with IH remains a major unmet need.

The role of NMR-based circulating metabolic biomarkers in development and risk prediction of new onset type 2 diabetes

Associations of circulating metabolic biomarkers with type 2 diabetes (T2D) and their added value for risk prediction are uncertain among Chinese adults. A case-cohort study included 882 T2D cases diagnosed during 8-years’ follow-up and a subcohort of 789 participants. NMR-metabolomic profiling quantified 225 plasma biomarkers in stored samples taken at recruitment into the study. Cox regression yielded adjusted hazard ratios (HRs) for T2D associated with individual biomarkers, with a set of biomarkers incorporated into an established T2D risk prediction model to assess improvement in discriminatory ability. Mean baseline BMI (SD) was higher in T2D cases than in the subcohort (25.7 [3.6] vs. 23.9 [3.6] kg/m2). Overall, 163 biomarkers were significantly and independently associated with T2D at false discovery rate (FDR) controlled p < 0.05, and 138 at FDR-controlled p < 0.01. Branched chain amino acids (BCAA), apolipoprotein B/apolipoprotein A1, triglycerides in VLDL and medium and small HDL particles, and VLDL particle size were strongly positively associated with T2D (HRs 1.74–2.36 per 1 SD, p < 0.001). HDL particle size, cholesterol concentration in larger HDL particles and docosahexaenoic acid levels were strongly inversely associated with T2D (HRs 0.43–0.48, p < 0.001). With additional adjustment for plasma glucose, most associations (n = 147 and n = 129 at p < 0.05 and p < 0.01, respectively) remained significant. HRs appeared more extreme among more centrally adipose participants for apolipoprotein B/apolipoprotein A1, BCAA, HDL particle size and docosahexaenoic acid (p for heterogeneity ≤ 0.05). Addition of 31 selected biomarkers to an established T2D risk prediction model modestly, but significantly, improved risk discrimination (c-statistic 0.86 to 0.91, p < 0.001). In relatively lean Chinese adults, diverse metabolic biomarkers are associated with future risk of T2D and can help improve established risk prediction models.

Metabolic and Genetic Markers Improve Prediction of Incident Type 2 Diabetes: A Nested Case-Control Study in Chinese.

It is essential to improve the current predictive ability for type 2 diabetes (T2D) risk. We aimed to identify novel metabolic markers for future T2D in Chinese individuals of Han ethnicity and to determine whether the combined effect of metabolic and genetic markers improves the accuracy of prediction models containing clinical factors. A nested case-control study containing 220 incident T2D patients and 220 age- and sex- matched controls from normoglycemic Chinese individuals of Han ethnicity was conducted within the Wuxi Non-Communicable Disease cohort with a 12-year follow-up. Metabolic profiling detection was performed by high-performance liquid chromatography‒mass spectrometry (HPLC-MS) by an untargeted strategy and 20 single nucleotide polymorphisms (SNPs) associated with T2D were genotyped using the Iplex Sequenom MassARRAY platform. Machine learning methods were used to identify metabolites associated with future T2D risk. We found that abnormal levels of 5 metabolites were associated with increased risk of future T2D: riboflavin, cnidioside A, 2-methoxy-5-(1H-1, 2, 4-triazol-5-yl)- 4-(trifluoromethyl) pyridine, 7-methylxanthine, and mestranol. The genetic risk score (GRS) based on 20 SNPs was significantly associated with T2D risk (OR = 1.35; 95% CI, 1.08-1.70 per SD). The area under the receiver operating characteristic curve (AUC) was greater for the model containing metabolites, GRS, and clinical traits than for the model containing clinical traits only (0.960 vs 0.798, P = 7.91 × 10-16). In individuals with normal fasting glucose levels, abnormal levels of 5 metabolites were associated with future T2D. The combination of newly discovered metabolic markers and genetic markers could improve the prediction of incident T2D.

Machine Learning-Derived Prenatal Predictive Risk Model to Guide Intervention and Prevent the Progression of Gestational Diabetes Mellitus to Type 2 Diabetes: Prediction Model Development Study.

BackgroundThe increasing prevalence of gestational diabetes mellitus (GDM) is concerning as women with GDM are at high risk of type 2 diabetes (T2D) later in life. The magnitude of this risk highlights the importance of early intervention to prevent the progression of GDM to T2D. Rates of postpartum screening are suboptimal, often as low as 13% in Asian countries. The lack of preventive care through structured postpartum screening in several health care systems and low public awareness are key barriers to postpartum diabetes screening.ObjectiveIn this study, we developed a machine learning model for early prediction of postpartum T2D following routine antenatal GDM screening. The early prediction of postpartum T2D during prenatal care would enable the implementation of effective strategies for diabetes prevention interventions. To our best knowledge, this is the first study that uses machine learning for postpartum T2D risk assessment in antenatal populations of Asian origin.MethodsProspective multiethnic data (Chinese, Malay, and Indian ethnicities) from 561 pregnancies in Singapore’s most deeply phenotyped mother-offspring cohort study—Growing Up in Singapore Towards healthy Outcomes—were used for predictive modeling. The feature variables included were demographics, medical or obstetric history, physical measures, lifestyle information, and GDM diagnosis. Shapley values were combined with CatBoost tree ensembles to perform feature selection. Our game theoretical approach for predictive analytics enables population subtyping and pattern discovery for data-driven precision care. The predictive models were trained using 4 machine learning algorithms: logistic regression, support vector machine, CatBoost gradient boosting, and artificial neural network. We used 5-fold stratified cross-validation to preserve the same proportion of T2D cases in each fold. Grid search pipelines were built to evaluate the best performing hyperparameters.ResultsA high performance prediction model for postpartum T2D comprising of 2 midgestation features—midpregnancy BMI after gestational weight gain and diagnosis of GDM—was developed (BMI_GDM CatBoost model: AUC=0.86, 95% CI 0.72-0.99). Prepregnancy BMI alone was inadequate in predicting postpartum T2D risk (ppBMI CatBoost model: AUC=0.62, 95% CI 0.39-0.86). A 2-hour postprandial glucose test (BMI_2hour CatBoost model: AUC=0.86, 95% CI 0.76-0.96) showed a stronger postpartum T2D risk prediction effect compared to fasting glucose test (BMI_Fasting CatBoost model: AUC=0.76, 95% CI 0.61-0.91). The BMI_GDM model was also robust when using a modified 2-point International Association of the Diabetes and Pregnancy Study Groups (IADPSG) 2018 criteria for GDM diagnosis (BMI_GDM2 CatBoost model: AUC=0.84, 95% CI 0.72-0.97). Total gestational weight gain was inversely associated with postpartum T2D outcome, independent of prepregnancy BMI and diagnosis of GDM (P=.02; OR 0.88, 95% CI 0.79-0.98).ConclusionsMidgestation weight gain effects, combined with the metabolic derangements underlying GDM during pregnancy, signal future T2D risk in Singaporean women. Further studies will be required to examine the influence of metabolic adaptations in pregnancy on postpartum maternal metabolic health outcomes. The state-of-the-art machine learning model can be leveraged as a rapid risk stratification tool during prenatal care.Trial RegistrationClinicalTrials.gov NCT01174875; https://clinicaltrials.gov/ct2/show/NCT01174875

Prolactin and Maternal Metabolism in Women With a Recent GDM Pregnancy and Links to Future T2D: The SWIFT Study.

Prolactin is a multifaceted hormone known to regulate lactation. In women with gestational diabetes mellitus (GDM) history, intensive lactation has been associated with lower relative risk of future type 2 diabetes (T2D). However, the role of prolactin in T2D development and maternal metabolism in women with a recent GDM pregnancy has not been ascertained. We examined the relationships among prolactin, future T2D risk, and key clinical and metabolic parameters. We utilized a prospective GDM research cohort (the SWIFT study) and followed T2D onset by performing 2-hour 75-g research oral glucose tolerance test (OGTT) at study baseline (6-9 weeks postpartum) and again annually for 2 years, and also by retrieving clinical diagnoses of T2D from 2 years through 10 years of follow up from electronic medical records. Targeted metabolomics and lipidomics were applied on fasting plasma samples collected at study baseline from 2-hour 75-g research OGTTs in a nested case-control study (100 future incident T2D cases vs 100 no T2D controls). Decreasing prolactin quartiles were associated with increased future T2D risk (adjusted odds ratio 2.48; 95% CI, 0.81-7.58; P = 0.05). In women who maintained normoglycemia during the 10-year follow-up period, higher prolactin at baseline was associated with higher insulin sensitivity (P = 0.038) and HDL-cholesterol (P = 0.01), but lower BMI (P = 0.001) and leptin (P = 0.002). Remarkably, among women who developed future T2D, prolactin was not correlated with a favorable metabolic status (all P > 0.05). Metabolomics and lipidomics showed that lower circulating prolactin strongly correlated with a T2D-high risk lipid profile, with elevated circulating neutral lipids and lower concentrations of specific phospholipids/sphingolipids. In women with recent GDM pregnancy, low circulating prolactin is associated with specific clinical and metabolic parameters and lipid metabolites linked to a high risk of developing T2D.

Prognostic Value of Triglyceride and Glucose Index for Incident Type 2 Diabetes beyond Metabolic Health and Obesity.

BackgroundMetabolically healthy obese (MHO) phenotype is metabolically heterogeneous in terms of type 2 diabetes (T2D). Previously, the triglyceride and glucose (TyG) index has been considered for identifying metabolic health and future risk of T2D. This study aimed to evaluate the risk of incident T2D according to obesity status and metabolic health, categorized by four different criteria and the TyG index.MethodsThe study included 39,418 Koreans without T2D at baseline. The risk of T2D was evaluated based on four different definitions of metabolic health and obesity status and according to the baseline TyG index within each metabolic health and obesity group.ResultsDuring the median follow-up at 38.1 months, 726 individuals developed T2D. Compared with the metabolically healthy non-obese (MHNO) group with low TyG index, the MHO group with high TyG index showed increased risk of T2D in all four definitions of metabolic health with multivariate-adjusted hazard ratios of 2.57 (95% confidence interval [CI], 1.76 to 3.75), 3.72 (95% CI, 2.15 to 6.43), 4.13 (95% CI, 2.67 to 6.38), and 3.05 (95% CI, 2.24 to 4.15), when defined by Adult Treatment Panel III, Wildman, Karelis, and homeostasis model assessment (HOMA) criteria, respectively.ConclusionMHO subjects with high TyG index were at an increased risk of developing T2D compared with MHNO subjects, regardless of the definition of metabolic health. TyG index may serve as an additional factor for predicting the individual risk of incident T2D in MHO subjects.

Intensive lactation among women with recent gestational diabetes significantly alters the early postpartum circulating lipid profile: the SWIFT study

BackgroundWomen with a history of gestational diabetes mellitus (GDM) have a 7-fold higher risk of developing type 2 diabetes (T2D). It is estimated that 20-50% of women with GDM history will progress to T2D within 10 years after delivery. Intensive lactation could be negatively associated with this risk, but the mechanisms behind a protective effect remain unknown.MethodsIn this study, we utilized a prospective GDM cohort of 1010 women without T2D at 6-9 weeks postpartum (study baseline) and tested for T2D onset up to 8 years post-baseline (n=980). Targeted metabolic profiling was performed on fasting plasma samples collected at both baseline and follow-up (1-2 years post-baseline) during research exams in a subset of 350 women (216 intensive breastfeeding, IBF vs. 134 intensive formula feeding or mixed feeding, IFF/Mixed). The relationship between lactation intensity and circulating metabolites at both baseline and follow-up were evaluated to discover underlying metabolic responses of lactation and to explore the link between these metabolites and T2D risk.ResultsWe observed that lactation intensity was strongly associated with decreased glycerolipids (TAGs/DAGs) and increased phospholipids/sphingolipids at baseline. This lipid profile suggested decreased lipogenesis caused by a shift away from the glycerolipid metabolism pathway towards the phospholipid/sphingolipid metabolism pathway as a component of the mechanism underlying the benefits of lactation. Longitudinal analysis demonstrated that this favorable lipid profile was transient and diminished at 1-2 years postpartum, coinciding with the cessation of lactation. Importantly, when stratifying these 350 women by future T2D status during the follow-up (171 future T2D vs. 179 no T2D), we discovered that lactation induced robust lipid changes only in women who did not develop incident T2D. Subsequently, we identified a cluster of metabolites that strongly associated with future T2D risk from which we developed a predictive metabolic signature with a discriminating power (AUC) of 0.78, superior to common clinical variables (i.e., fasting glucose, AUC 0.56 or 2-h glucose, AUC 0.62).ConclusionsIn this study, we show that intensive lactation significantly alters the circulating lipid profile at early postpartum and that women who do not respond metabolically to lactation are more likely to develop T2D. We also discovered a 10-analyte metabolic signature capable of predicting future onset of T2D in IBF women. Our findings provide novel insight into how lactation affects maternal metabolism and its link to future diabetes onset.Trial registrationClinicalTrials.gov NCT01967030.

Identification of Inflammatory and Disease-Associated Plasma Proteins that Associate with Intake of Added Sugar and Sugar-Sweetened Beverages and Their Role in Type 2 Diabetes Risk.

It has been suggested that high intake of added sugar and sugar-sweetened beverages (SSBs) increase the level of circulating inflammatory proteins and that chronic inflammation plays a role in type 2 diabetes (T2D) development. We aim to examine how added sugar and SSB intake associate with 136 measured plasma proteins and C-reactive protein (CRP) in the Malmö Diet and Cancer–Cardiovascular Cohort (n = 4382), and examine if the identified added sugar- and SSB-associated proteins associate with T2D incidence. A two-step iterative resampling approach was used to internally replicate proteins that associated with added sugar and SSB intake. Nine proteins were identified to associate with added sugar intake, of which only two associated with T2D incidence (p < 0.00045). Seven proteins were identified to associate with SSB intake, of which six associated strongly with T2D incidence (p < 6.9 × 10−8). No significant associations were observed between added sugar and SSB intake and CRP concentrations. In summary, our elucidation of the relationship between plasma proteome and added sugar and SSB intake, in relation to future T2D risk, demonstrated that SSB intake, rather than the total intake of added sugar, was related to a T2D-pathological proteomic signature. However, external replication is needed to verify the findings.

Development and validation of a model for predicting incident type 2 diabetes using quantitative clinical data and a Bayesian logistic model: A nationwide cohort and modeling study.

Obesity is closely related to the development of insulin resistance and type 2 diabetes (T2D). The prevention of T2D has become imperative to stem the rising rates of this disease. Weight loss is highly effective in preventing T2D; however, the at-risk pool is large, and a clinically meaningful metric for risk stratification to guide interventions remains a challenge. The objective of this study is to predict T2D risk using full-information continuous analysis of nationally sampled data from white and black American adults age ≥45 years. A sample of 12,043 black (33%) and white individuals from a population-based cohort, REasons for Geographic And Racial Differences in Stroke (REGARDS) (enrolled 2003-2007), was observed through 2013-2016. The mean participant age was 63.12 ± 8.62 years, and 43.7% were male. Mean BMI was 28.55 ± 5.61 kg/m2. Risk factors for T2D regularly recorded in the primary care setting were used to evaluate future T2D risk using Bayesian logistic regression. External validation was performed using 9,710 participants (19% black) from Atherosclerotic Risk in Communities (ARIC) (enrolled 1987-1989), observed through 1996-1998. The mean participant age in this cohort was 53.86 ± 5.65 years, and 44.6% were male. Mean BMI was 27.15 ± 4.92 kg/m2. Predictive performance was assessed using the receiver operating characteristic (ROC) curves and area under the curve (AUC) statistics. The primary outcome was incident T2D. By 2016 in REGARDS, there were 1,602 incident cases of T2D. Risk factors used to predict T2D progression included age, sex, race, BMI, triglycerides, high-density lipoprotein, blood pressure, and blood glucose. The Bayesian logistic model (AUC = 0.79) outperformed the Framingham risk score (AUC = 0.76), the American Diabetes Association risk score (AUC = 0.64), and a cardiometabolic disease system (using Adult Treatment Panel III criteria) (AUC = 0.75). Validation in ARIC was robust (AUC = 0.85). Main limitations include the limited generalizability of the REGARDS sample to black and white, older Americans, and no time to diagnosis for T2D. Our results show that a Bayesian logistic model using full-information continuous predictors has high predictive discrimination, and can be used to quantify race- and sex-specific T2D risk, providing a new, powerful predictive tool. This tool can be used for T2D prevention efforts including weight loss therapy by allowing clinicians to target high-risk individuals in a manner that could be used to optimize outcomes.

Handgrip strength-A risk indicator for type 2 diabetes: Systematic review and meta-analysis of observational cohort studies.

Evolving debate suggests that handgrip strength (HGS), a measure of muscular strength, might be associated with the risk of type 2 diabetes (T2D); however, the evidence is conflicting. Using a systematic review and meta-analysis of published observational cohort studies in general populations, we aimed to assess the association of HGS with the future risk of T2D. Relevant studies were sought from inception until April 2020 in MEDLINE, Embase, Web of Science, and manual search of relevant articles. Transformed or extracted relative risks (RRs) with 95% confidence intervals (CIs) comparing the top vs bottom thirds of HGS levels were pooled using random effects meta-analysis. A total of 10 unique observational cohort studies comprising of 177 826 participants and >5167 T2D cases were eligible. The pooled multivariable RR (95% CI) for T2D comparing the top vs bottom thirds of HGS levels was 0.73 (0.63-0.84). This association was consistent across several relevant subgroups except for evidence of effect modification by sample size (P value for meta-regression <.001): evidence of an association in smaller studies (<250 events) 0.50 (0.40-0.63), with no significant association in bigger studies (≥250 events) 0.87 (0.73-1.05). There was no evidence of small study effects using formal tests such as funnel plots and Egger's regression symmetry test. Pooled analysis of observational cohort studies suggests that HGS may be a risk indicator for T2D in the general population. The role of utilizing HGS measurements in T2D prevention strategies warrants further investigation.

Identifying hotspots of type 2 diabetes risk using general practice data and geospatial analysis: an approach to inform policy and practice.

The prevalence of type 2 diabetes (T2D) is increasing worldwide and there is a need to identify communities with a high-risk profile and to develop appropriate primary care interventions. This study aimed to predict future T2D risk and identify community-level geographic variations using general practices data. The Australian T2D risk assessment (AUSDRISK) tool was used to calculate the individual T2D risk scores using 55693 clinical records from 16 general practices in west Adelaide, South Australia, Australia. Spatial clusters and potential 'hotspots' of T2D risk were examined using Local Moran's I and the Getis-Ord Gi* techniques. Further, the correlation between T2D risk and the socioeconomic status of communities were mapped. Individual risk scores were categorised into three groups: low risk (34.0% of participants), moderate risk (35.2% of participants) and high risk (30.8% of participants). Spatial analysis showed heterogeneity in T2D risk across communities, with significant clusters in the central part of the study area. These study results suggest that routinely collected data from general practices offer a rich source of data that may be a useful and efficient approach for identifying T2D hotspots across communities. Mapping aggregated T2D risk offers a novel approach to identifying areas of unmet need.

Lysine pathway metabolites and the risk of type 2 diabetes and cardiovascular disease in the PREDIMED study: results from two case-cohort studies

BackgroundThe pandemic of cardiovascular disease (CVD) and type 2 diabetes (T2D) requires the identification of new predictor biomarkers. Biomarkers potentially modifiable with lifestyle changes deserve a special interest. Our aims were to analyze: (a) The associations of lysine, 2-aminoadipic acid (2-AAA) or pipecolic acid with the risk of T2D or CVD in the PREDIMED trial; (b) the effect of the dietary intervention on 1-year changes in these metabolites, and (c) whether the Mediterranean diet (MedDiet) interventions can modify the effects of these metabolites on CVD or T2D risk.MethodsTwo unstratified case-cohort studies nested within the PREDIMED trial were used. For CVD analyses, we selected 696 non-cases and 221 incident CVD cases; for T2D, we included 610 non-cases and 243 type 2 diabetes incident cases. Metabolites were quantified using liquid chromatography–tandem mass spectrometry, at baseline and after 1-year of intervention.ResultsIn weighted Cox regression models, we found that baseline lysine (HR+1 SD increase = 1.26; 95% CI 1.06–1.51) and 2-AAA (HR+1 SD increase = 1.28; 95% CI 1.05–1.55) were both associated with a higher risk of T2D, but not with CVD. A significant interaction (p = 0.032) between baseline lysine and T2D on the risk of CVD was observed: subjects with prevalent T2D and high levels of lysine exhibited the highest risk of CVD. The intervention with MedDiet did not have a significant effect on 1-year changes of the metabolites.ConclusionsOur results provide an independent prospective replication of the association of 2-AAA with future risk of T2D. We show an association of lysine with subsequent CVD risk, which is apparently diabetes-dependent. No evidence of effects of MedDiet intervention on lysine, 2-AAA or pipecolic acid changes was found.Trial registration ISRCTN35739639; registration date: 05/10/2005; recruitment start date 01/10/2003

The Impact of Dairy Products in the Development of Type 2 Diabetes: Where Does the Evidence Stand in 2019?

ABSTRACTThe prevalence of type 2 diabetes (T2D) has increased rapidly. Adopting a heathy diet is suggested as one of the effective behaviors to prevent or delay onset of T2D. Dairy consumption has been recommended as part of a healthy diet, but there remains uncertainty in both the scientific community and the public about the effect of different dairy products on T2D risk. In a recent workshop, the evidence on dairy products and T2D risk was presented and discussed by a group of experts. The main conclusions from the workshop are presented in this position paper and are as follows. 1) Available evidence from large prospective cohort studies and limited randomized controlled trials (RCTs) suggests that total dairy consumption has a neutral or moderately beneficial effect on T2D risk. 2) Increasing evidence from prospective cohort studies indicates that yogurt is most strongly associated with a lower T2D risk, but evidence from RCTs is scarce. 3) Fatty acids from dairy (medium-chain, odd, and very long-chain SFAs as well as trans-palmitoleic acid) are associated with lower T2D risk and improved metabolic health, but more research is needed on studies that explore cause and effect relations to exclude the possibility that the dairy fatty acids simply serve as markers of overall dairy consumption. 4) The food matrix can be a stronger determinant of health effects than SFA content. This review further identifies research gaps in the existing knowledge and highlights key research questions that need to be addressed to better understand the impact of dairy consumption on future T2D risk.

OR31-5 Characterization of the One-Hour Plasma Glucose and Plasma Insulin during an Oral Glucose Tolerance Test (OGTT) in Obese Hispanic Children and Adolescents Diagnosed with Prediabetes (Hba1c ≥ 5.7% &amp; ≤ 6.4%)

Background: OGTT is still considered the gold standard test to detect prediabetes and /or Type 2 diabetes (T2D). American Diabetes Association criteria for prediabetes: a) Hba1c 5.7- 6.4% b) fasting plasma glucose (FPG) ≥ 100 mg/dl and/or post-prandial PG ≥ 140, Impaired glucose tolerance (IGT) during an OGTT. In adults the 1-hour(1hr) glucose level (≥ 155 mg/dl) is a strong predictor of future T2D risk than the two-hour level and is associated with diabetes complication. The ideal cut-off for pediatrics is not established though studies have found 1hPG ≥ 132.5 mg/dl, as predictive for risk of T2D (1) Objective: a. To study the dynamic profile of Plasma Glucose (PG) and plasma Insulin (PI) values at time 0,1 and 2 hour during an OGTT b. to assess whether degree of insulin resistance in Hispanic children and adolescents can be predict an abnormal I hr glucose level. Methods: We reviewed OGTT results of 43 obese Hispanic youths; 7-18 years of age with prediabetes. PG and PI values were obtained at 0, 1 and 2hr during OGTT (glucola: dose of 1.75 g/kg body weight up to a maximum of 75 g). The Homeostasis Model Assessment of fasting insulin resistance (HOMA-IR), an index of insulin resistance was calculated using (fasting glucose (mg/dl) X fasting insulin(µIU/ml)/405) and values ≥3.4 were taken as insulin resistance(2). Independent T test was used to study differences between means for group1: HOMA-IR< 3.4 and group 2: HOMA-IR≥ 3.4. Results: Forty-three obese Hispanic youths; 7-18 years of age; mean age of 11.5 ± 0.7; BMI=42.2 ± 4.9; 47% male and 53% female. a) OGTT results: Only one subject had abnormal fasting plasma glucose, 3 subjects had IGT (7%), and 11(26%) had an abnormal 1 h PG during the OGTT b) 1hr glucose and 1hr insulin was different between HOMA-IR groups (p=0.001) even though 2-hr was not. PG: grp#1: 1hr=108 ± 27, 2hr=107 ± 33 mg/dl; grp #2: 1hr=136 ± 33, 2hr=120 ± 32. PI: grp#1: 1hr= 80 ± 61, 2hr= 78 ± 70; grp#2: 1hr= 213 ±171, 2hr= 99 ± 375. Conclusion: In our Hispanic children and adolescents, 1& 2-hr plasma insulin and plasma glucose were clearly abnormal, more so, in the HOMA- IR group ≥ 3.4. This demonstrate that at least in the IR children and adolescents, 1-hour values add value to the interpretation of the OGTT. 26 % of Hispanic children and adolescents had an abnormal 1-hr glucose which is a novel finding of our study. These results validate the recent reports of the plasma 1-hr glucose threshold ≥132.5 mg/dl in Pediatrics and prospective follow up of these Hispanic patients will be able to demonstrate the predictive value of 1 hr values of insulin and glucose to development of T2D.Reference: ADDIN EN.REFLIST 1. Manco M, Miraglia Del Giudice E, et al. 1-Hour plasma glucose in obese youth. Acta diabetologica. 2012;49(6):435-43. 2. Brar PC, Screening obese children and adolescents for prediabetes and/or type 2 diabetes in pediatric practices: a validation study. Clinical pediatrics. 2014;53(8):771-6.

Using MetS Severity to Track Risk for T2D during Intervention with Lifestyle and Metformin

While a metabolic syndrome severity Z-score (MetS-Z) predicts incident type 2 diabetes (T2D) in observational cohorts, it is unclear whether MetS-Z can track response to medical intervention and whether changes in MetS-Z during treatment reflect altered risk for T2D. We evaluated data from the Diabetes Prevention Program, which randomized individuals with glucose intolerance (age 51±11 years) to receive: placebo (n=882), metformin 850 mg bid (n=867), or an intensive lifestyle modification program (n=827). We assessed levels of MetS-Z and the individual MetS components during 3 years of intervention (Figure), revealing 1-year decreases in MetS-Z among those treated with lifestyle (effect size -0.6), metformin (-0.3) and placebo (-0.1), driven by improvements in the individual MetS components. We assessed how change in MetS-Z affected T2D risk, finding that each 1-point increase in a participant’s effect size of MetS Z between 0 and 1 year (difference per participant/baseline SD) increased odds of incident T2D by year 3 by 70% (adjusting for intervention group; Figure). This positive association was found in individual models and in a joint model that included changes in MetS-Z and in each MetS component—suggesting independent associations of changes in MetS-Z with future T2D risk. These data support MetS-Z as an important outcome to follow for response to medical treatment in research studies and clinical care. Disclosure M.D. DeBoer: None. S.L. Filipp: None. M.J. Gurka: None.

One-Hour Post-OGTT Glucose Improves the Early Prediction of Type 2 Diabetes beyond Metabolic Markers

Background: We previously evaluated metabolic markers (alpha-hydroxybutyrate, bradykinin hydroxyproline, mannose, alpha-tocopherol, 10:1 carnitine, and X-12063) for the early prediction of type 2 diabetes (T2D). The present study compared glucose measurements at different time points during an oral glucose tolerance test (OGTT) with the metabolites for predicting future T2D. Methods: Fasting (FPG), 30, 60, and 120 minute plasma glucose (PG) levels were measured in 543 individuals from the Botnia Prospective Study, 146 of whom progressed to T2D within a 10 year period. Machine learning based predictive models for the future risk of T2D were built based on (1) OGTT-derived glucose measurements, (2) clinical risk factors (age, sex, body mass index, and family history of T2D, (3) metabolites, (4) combinations of PG levels and metabolites, (5) combinations of PG levels and clinical risk factors, and (6) combinations of PG levels, clinical risk factors, and metabolites. The metabolites were also correlated with the PG levels. Results: The 1-h PG predictive performance (area under the receiver operating characteristic curve, AUC = 0.76, sensitivity = 0.70, specificity = 0.69) was superior to FPG, 2-h PG, or individual metabolites, and was as good as the multivariate model with all six metabolites (AUC = 0.78, DeLong’s p = 0.24). The combined predictive model that included the 1-h PG, clinical risk factors, and six metabolites (AUC = 0.81, sensitivity = 0.74, specificity = 0.75) significantly improved the predictive performance beyond the 1-h PG alone (p = 0.02) and the combined model with clinical risk factors and six metabolites (p = 0.02). Conclusion: The 1-h PG predicts risk for T2D better than FPG or the 2-h PG. Although several multivariate models containing the metabolites improved the prediction performance beyond the 1-h PG, the 1-h PG alone was on par, and is a more practical biomarker. Shortening the OGTT to 1 hour provides a better and simpler assessment of T2D risk than the current OGTT. Disclosure G. Peddinti: None. T. Tuomi: None. M. Bergman: None. L. Groop: None.