BackgroundSeveral prospective studies have been conducted to examine the relationship between fruit juice intake and risk of incident type 2 diabetes, but results have been mixed. In the present study, we aimed to estimate the association between fruit juice intake and risk of type 2 diabetes.MethodsPubMed and Embase databases were searched up to December 2013. All prospective cohort studies of fruit juice intake with risk of type 2 diabetes were included. The pooled relative risks (RRs) with 95% confidence intervals (CIs) for highest vs. lowest category of fruit juice intake were estimated using a random-effects model.ResultsA total of four studies (191,686 participants, including 12,375 with type 2 diabetes) investigated the association between sugar-sweetened fruit juice and risk of incident type 2 diabetes, and four studies (137,663 participants and 4,906 cases) investigated the association between 100% fruit juice and risk of incident type 2 diabetes. A higher intake of sugar-sweetened fruit juice was significantly associated with risk of type 2 diabetes (RR = 1.28, 95%CI = 1.04–1.59, p = 0.02), while intake of 100% fruit juice was not associated with risk of developing type 2 diabetes (RR = 1.03, 95% CI = 0.91–1.18, p = 0.62).ConclusionsOur findings support dietary recommendations to limit sugar-sweetened beverages, such as fruit juice with added sugar, to prevent the development of type 2 diabetes.

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Association between fruit intake and risk of type 2 diabetes is inconsistent. In this study, we performed a meta-analysis of all prospective cohort studies to clarify the association between fruit intake and risk of type 2 diabetes. Relevant studies were identified by searches of the PubMed and Embase databases up to November 2013. The summary relative risks of association were obtained using a fixed- or random-effects model. A total of nine prospective studies (403,259 participants, including 27,940 with incident type 2 diabetes) from seven publications were included in the meta-analysis of fruit intake and risk of type 2 diabetes. We found that individuals in the highest category of fruit intake had a reduced risk of type 2 diabetes (relative risk 0.92, 95 % confidence interval 0.86-0.97, p = 0.003) compared to those in the lowest category, with moderate evidence of between-study heterogeneity (I (2) = 37.6 %, p = 0.12). There was an evident non-linear association of fruit intake with type 2 diabetes (P for nonlinearity <0.001). A non-linear threshold of 200 g/day of fruit intake was identified and the risk of type 2 diabetes reduced by about 13 % at this cut-off. Our findings are consistent with diet recommendations to consume about 200 g/day of fruits to prevent type 2 diabetes.

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The current mental health crisis is a growing public health issue requiring a large-scale response that cannot be met with traditional services alone. Digital support tools are proliferating, yet most are not systematically evaluated, and we know little about their users and their needs. Shout is a free mental health text messaging service run by the charity Mental Health Innovations, which provides support for individuals in the UK experiencing mental or emotional distress and seeking help. Here we study a large data set of anonymised text message conversations and post-conversation surveys compiled through Shout. This data provides an opportunity to hear at scale from those experiencing distress; to better understand mental health needs for people not using traditional mental health services; and to evaluate the impact of a novel form of crisis support. We use natural language processing (NLP) to assess the adherence of volunteers to conversation techniques and formats, and to gain insight into demographic user groups and their behavioural expressions of distress. Our textual analyses achieve accurate classification of conversation stages (weighted accuracy = 88%), behaviours (1-hamming loss = 95%) and texter demographics (weighted accuracy = 96%), exemplifying how the application of NLP to frontline mental health data sets can aid with post-hoc analysis and evaluation of quality of service provision in digital mental health services.