The Clinical and Public Health Challenges of Diabetes Prevention: A Search for Sustainable Solutions.

In an Editorial accompanying PLOS Medicine's Special Issue on Diabetes Prevention, Guest Editors Nicholas Wareham and William Herman discuss some of the challenges for researchers and policy makers in developing effective and equitable solutions to the worldwide problem of type 2 diabetes

Genetic Inactivation of ANGPTL4 Improves Glucose Homeostasis and Reduces Risk of Type 2 Diabetes

Abstract Angiopoietin-like 4 (ANGPTL4) is an endogenous inhibitor of lipoprotein lipase that modulates lipid levels, coronary atherosclerosis risk, and nutrient partitioning. We hypothesize that loss of ANGPTL4 function might improve glucose homeostasis and decrease risk of type 2 diabetes (T2D). We investigate protein-altering variants in ANGPTL4 among 58,124 participants in the DiscovEHR human genetics study, with follow-up studies in 82,766 T2D cases and 498,761 controls. Carriers of p.E40K, a variant that abolishes ANGPTL4’s ability to inhibit lipoprotein lipase, have lower odds of T2D (odds ratio 0.89, 95% confidence interval 0.85-0.92, p=6.3x10-10), lower fasting glucose, and greater insulin sensitivity. Predicted loss-of-function variants are associated with lower odds of T2D among 32,015 cases and 84,006 controls (odds ratio 0.71, 95% confidence interval 0.49-0.99, p=0.041). Functional studies in Angptl4-deficient mice confirm improved insulin sensitivity and glucose homeostasis. In conclusion, genetic inactivation of ANGPTL4 is associated with improved glucose homeostasis and reduced risk of T2D

A combination of metabolites predicts adherence to the Mediterranean diet pattern and its associations with insulin sensitivity and lipid homeostasis in the general population: The Fenland Study, United Kingdom

BACKGROUND: Cardiometabolic benefits of the Mediterranean diet have been recognized, but underlying mechanisms are not fully understood. OBJECTIVES: We aimed to investigate how the Mediterranean diet could influence circulating metabolites and how the metabolites could mediate the associations of the diet with cardiometabolic risk factors. METHODS: Among 10,806 participants (58.9% women, mean age = 48.4 y) in the Fenland Study (2004-2015) in the United Kingdom, we assessed dietary consumption with FFQs and conducted a targeted metabolomics assay for 175 plasma metabolites (acylcarnitines, amines, sphingolipids, and phospholipids). We examined cross-sectional associations of the Mediterranean diet score (MDS) and its major components with each metabolite, modeling multivariable-adjusted linear regression. We used the regression estimates to summarize metabolites associated with the MDS into a metabolite score as a marker of the diet. Subsequently, we assessed how much metabolite subclasses and the metabolite score would mediate the associations of the MDS with circulating lipids, homeostasis model assessment of insulin resistance (HOMA-IR), and other metabolic factors by comparing regression estimates upon adjustment for the metabolites. RESULTS: Sixty-six metabolites were significantly associated with the MDS (P ≤ 0.003, corrected for false discovery rate) (Spearman correlations, r: -0.28 to +0.28). The metabolite score was moderately correlated with the MDS (r = 0.43). Of MDS components, consumption of nuts, cereals, and meats contributed to variations in acylcarnitines; fruits, to amino acids and amines; and fish, to phospholipids. The metabolite score was estimated to explain 37.2% of the inverse association of the MDS with HOMA-IR (P for mediation < 0.05). The associations of the MDS with cardiometabolic factors were estimated to be mediated by acylcarnitines, sphingolipids, and phospholipids. CONCLUSIONS: Multiple metabolites relate to the Mediterranean diet in a healthy general British population and highlight the potential to identify a set of biomarkers for an overall diet. The associations may involve pathways of phospholipid metabolism, carnitine metabolism, and development of insulin resistance and dyslipidemia

The association between a biomarker score for fruit and vegetable intake and incident type 2 diabetes: the EPIC-Norfolk study.

BACKGROUND/OBJECTIVES: Biomarkers for a mixed fruit and vegetable (FV) diet are needed to provide a better understanding of the association between FV intake and type 2 diabetes. We aimed to examine the prospective association between a composite score comprised of three biomarkers of FV intake in free-living populations and incident diabetes. SUBJECTS/METHODS: A total of 318 incident diabetes cases and 926 controls from the EPIC (European Prospective Investigation of Cancer)-Norfolk study aged 40-79 years at baseline (1993-1997), completed 7-day prospective food diary and had plasma vitamin C and carotenoid measures. A composite biomarker score (CB-score) comprising the sum of plasma vitamin C, beta-carotene and lutein was derived. Odds ratios (ORs) and 95% confidence intervals (CIs) for incident diabetes were estimated using multivariable logistic regression. RESULTS: A strong inverse association was found between the CB-score and incident diabetes. The ORs (95% CI) of diabetes comparing quartiles Q2, Q3 and Q4 of the CB-score with Q1 (reference category) were 0.70 (0.49, 1.00), 0.34 (0.23, 0.52) and 0.19 (0.12, 0.32), respectively, and 0.49 (0.40, 0.58) per s.d. change in CB-score in a model adjusted for demographic and lifestyle factors. The association was marginally attenuated after additionally adjusting for body mass index and waist circumference (0.60 (0.49 and 0.74) per s.d. change in CB-score). CONCLUSIONS: A combination of biomarkers representing the intake of a mixed FV diet was strongly inversely associated with incident diabetes. These findings provide further support for measuring dietary biomarkers in studies of diet-disease associations and highlight the importance of consuming FV for the prevention of diabetes.The EPIC Norfolk study is supported by programme grants from the Medical Research Council UK and Cancer Research UK. The sponsors did not participate in the design or conduct of this study; in the collection, management, analysis, or interpretation of data; in the writing of the manuscript; or in the preparation, review, approval, or decision to submit this manuscript for publication. We acknowledge support from the MRC Epidemiology Unit (MC_UU_12015/5).This is the author accepted manuscript. The final version is available from NPG via http://dx.doi.org/10.1038/ejcn.2014.24

Increasing overall physical activity and aerobic fitness is associated with improvements in metabolic risk: cohort analysis of the ProActive trial.

AIMS/HYPOTHESIS: Our aim was to examine the association between change in physical activity energy expenditure (PAEE), total body movement (counts per day) and aerobic fitness (maximum oxygen consumption [VO2max] over 1 year and metabolic risk among individuals with a family history of diabetes. METHODS: Three hundred and sixty-five offspring of people with type 2 diabetes underwent measurement of energy expenditure (PAEE measured using the flex heart rate method), total body movement (daily activity counts from accelerometry data), [VO2max] predicted from a submaximal graded treadmill exercise test and anthropometric and metabolic status at baseline and 1 year (n = 321) in the ProActive trial. Clustered metabolic risk was calculated by summing standardised values for waist circumference, fasting triacylglycerol, insulin and glucose, blood pressure and the inverse of HDL-cholesterol. Linear regression was used to quantify the association between changes in PAEE, total body movement and fitness and clustered metabolic risk at follow-up. RESULTS: Participants increased their activity by 0.01 units PAEE kJ kg(-1) day(-1) over 1 year. Total body movement increased by an average of 9,848 counts per day. Change in total body movement (beta = -0.066, p = 0.004) and fitness (beta = -0.056, p = 0.003) was associated with clustered metabolic risk at follow-up, independently of age, sex, smoking status, socioeconomic status and baseline metabolic score. CONCLUSIONS/INTERPRETATION: Small increases in activity and fitness were associated with a reduction in clustered metabolic risk in this cohort of carefully characterised at-risk individuals. Further research to quantify the reduction in risk of type 2 diabetes associated with feasible changes in these variables should inform preventive interventions

Diurnal Profiles of Physical Activity and Postures Derived From Wrist-Worn Accelerometry in UK Adults

Background: Wrist-worn accelerometry is the commonest objective method for measuring physical activity in large-scale epidemiological studies. Research-grade devices capture raw triaxial acceleration which, in addition to quantifying movement, facilitates assessment of orientation relative to gravity. No population-based study has yet described the interrelationship and variation of these features by time and personal characteristics. Methods: 2,043 United Kingdom adults (35–65 years) wore an accelerometer on the non-dominant wrist and a chest-mounted combined heart-rate-and-movement sensor for 7 days free-living. From raw (60 Hz) wrist acceleration, we derived movement (non-gravity acceleration) and pitch and roll (forearm) angles relative to gravity. We inferred physical activity energy expenditure (PAEE) from combined sensing and sedentary time from approximate horizontal arm angle coupled with low movement. Results: Movement differences by time-of-day and day-of-week were associated with forearm angles; more movement in downward forearm positions. Mean (SD) movement was similar between sexes ∼31 (42) mg, despite higher PAEE in men. Women spent longer with the forearm pitched &gt;0°, above horizontal (53% vs 36%), and less time at &lt;0° (37% vs 53%). Diurnal pitch was 2.5–5° above and 0–7.5°below horizontal during night and daytime, respectively; corresponding roll angles were ∼0° (hand flat) and ∼20° (thumb-up). Differences were more pronounced in younger participants. All diurnal profiles indicated later wake-times on weekends. Daytime pitch was closer to horizontal on weekdays; roll was similar. Sedentary time was higher (17 vs 15 hours/day) in obese vs normal-weight individuals. Conclusions: More movement occurred in forearm positions below horizontal, commensurate with activities including walking. Findings suggest time-specific population differences in behaviors by age, sex, and BMI.The authors were supported by the UK Medical Research Council (MC_UU_12015/3) and the NIHR Biomedical Research Centre in Cambridge (IS-BRC-1215-20014). We would also like to thank EPSRC and GlaxoSmithKline for their support through graduate fellowships (iCase 17100053)

SelfHAR: Improving Human Activity Recognition through Self-training with Unlabeled Data

Machine learning and deep learning have shown great promise in mobile sensing applications, including Human Activity Recognition. However, the performance of such models in real-world settings largely depends on the availability of large datasets that captures diverse behaviors. Recently, studies in computer vision and natural language processing have shown that leveraging massive amounts of unlabeled data enables performance on par with state-of-the-art supervised models. In this work, we present SelfHAR, a semi-supervised model that effectively learns to leverage unlabeled mobile sensing datasets to complement small labeled datasets. Our approach combines teacher-student self-training, which distills the knowledge of unlabeled and labeled datasets while allowing for data augmentation, and multi-task self-supervision, which learns robust signal-level representations by predicting distorted versions of the input. We evaluated SelfHAR on various HAR datasets and showed state-of-the-art performance over supervised and previous semi-supervised approaches, with up to 12% increase in F1 score using the same number of model parameters at inference. Furthermore, SelfHAR is data-efficient, reaching similar performance using up to 10 times less labeled data compared to supervised approaches. Our work not only achieves state-of-the-art performance in a diverse set of HAR datasets, but also sheds light on how pre-training tasks may affect downstream performance

Multi-omic prediction of incident type 2 diabetes.

AIMS/HYPOTHESIS: The identification of people who are at high risk of developing type 2 diabetes is a key part of population-level prevention strategies. Previous studies have evaluated the predictive utility of omics measurements, such as metabolites, proteins or polygenic scores, but have considered these separately. The improvement that combined omics biomarkers can provide over and above current clinical standard models is unclear. The aim of this study was to test the predictive performance of genome, proteome, metabolome and clinical biomarkers when added to established clinical prediction models for type 2 diabetes. METHODS: We developed sparse interpretable prediction models in a prospective, nested type 2 diabetes case-cohort study (N=1105, incident type 2 diabetes cases=375) with 10,792 person-years of follow-up, selecting from 5759 features across the genome, proteome, metabolome and clinical biomarkers using least absolute shrinkage and selection operator (LASSO) regression. We compared the predictive performance of omics-derived predictors with a clinical model including the variables from the Cambridge Diabetes Risk Score and HbA1c. RESULTS: Among single omics prediction models that did not include clinical risk factors, the top ten proteins alone achieved the highest performance (concordance index [C index]=0.82 [95% CI 0.75, 0.88]), suggesting the proteome as the most informative single omic layer in the absence of clinical information. However, the largest improvement in prediction of type 2 diabetes incidence over and above the clinical model was achieved by the top ten features across several omic layers (C index=0.87 [95% CI 0.82, 0.92], Δ C index=0.05, p=0.045). This improvement by the top ten omic features was also evident in individuals with HbA1c <42 mmol/mol (6.0%), the threshold for prediabetes (C index=0.84 [95% CI 0.77, 0.90], Δ C index=0.07, p=0.03), the group in whom prediction would be most useful since they are not targeted for preventative interventions by current clinical guidelines. In this subgroup, the type 2 diabetes polygenic risk score was the major contributor to the improvement in prediction, and achieved a comparable improvement in performance when added onto the clinical model alone (C index=0.83 [95% CI 0.75, 0.90], Δ C index=0.06, p=0.002). However, compared with those with prediabetes, individuals at high polygenic risk in this group had only around half the absolute risk for type 2 diabetes over a 20 year period. CONCLUSIONS/INTERPRETATION: Omic approaches provided marginal improvements in prediction of incident type 2 diabetes. However, while a polygenic risk score does improve prediction in people with an HbA1c in the normoglycaemic range, the group in whom prediction would be most useful, even individuals with a high polygenic burden in that subgroup had a low absolute type 2 diabetes risk. This suggests a limited feasibility of implementing targeted population-based genetic screening for preventative interventions

Associations of serum folate and holotranscobalamin with cardiometabolic risk factors in rural and urban cameroon

A low intake of fruit and vegetables and a high intake of meat are associated with higher cardiometabolic disease risk; however much prior research has relied on subjective methods for dietary assessment and focused on Western populations. We aimed to investigate the association of blood folate as an objective marker of fruit and vegetable intake and holotranscobalamin (holoTC) as a marker of animal-sourced food intake with cardiometabolic risk factors. We con-ducted a population-based cross-sectional study on 578 adults (mean ± SD age = 38.2 ± 8.6 years; 64% women). The primary outcome was a continuous metabolic syndrome score. The median serum folate was 12.9 (IQR: 8.6–20.5) nmol/L and the mean holoTC was 75 (SD: 34.3) pmol/L. Rural residents demonstrated higher serum folate concentrations (15.9 (9.8–25.9) nmol/L) than urban residents (11.3 (7.9–15.8) nmol/L), but lower holoTC concentrations (rural: 69.8 (32.9) pmol/L; urban: 79.8 (34.9)) pmol/L, p < 0.001 for both comparisons. There was an inverse association between serum folate and metabolic syndrome score by −0.20 in the z-score (95% CI, −0.38 to −0.02) per 10.8 (1 SD) of folate) in a model adjusted for socio-demographic factors, smoking status, alcohol intake, BMI, and physical activity. HoloTC was positively associated with the metabolic syndrome score in unadjusted analysis (0.33 (95% CI, 0.10 to 0.56)) but became non-significant (0.17 (−0.05 to 0.39)) after adjusting for socio-demographic and behavioural characteristics. In conclusion, serum folate and holoTC were associated with the metabolic syndrome score in opposite directions. The positive association between serum holoTC and the metabolic syndrome score was partly dependent on sociodemographic characteristics. These findings suggest that, based on these biomarkers reflecting dietary intakes, public health approaches promoting a higher intake of fruit and vegetables may lower cardiometabolic risk factors in this population

Comparison of the EPIC Physical Activity Questionnaire with combined heart rate and movement sensing in a nationally representative sample of older British adults

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