Are probiotics, prebiotics, and synbiotics beneficial in primary thyroid diseases? A systematic review with meta-analysis.

INTRODUCTION AND OBJECTIVE: A number of studies indicate the presence of a thyroid-gut axis and the important influence of the gut microbiota on thyroid function. As prebiotics, probiotics and synbiotics show therapeutic potential in the treatment of intestinal dysbiosis, the aim of this review is to evaluate the efficacy of their supplementation in primary thyroid diseases. REVIEW METHODS: Electronic databases (Ovid MEDLINE, Embase, CENTRAL), registers of clinical trials, and grey literature up to 6 October 2022 were searched for randomised controlled trials (RCTs) meeting pre-specified inclusion criteria. The protocol was registered in PROSPERO (CRD42021235054). BRIEF DESCRIPTION OF THE STATE OF KNOWLEDGE: After screening 1,721 references, two RCTs were identified, which included 136 hypothyroid participants in total. Meta-analysis of the results after eight weeks of supplementation with predominantly Lactobacillus and Bifidobacterium strains indicated a clinically and statistically nonsignificant decrease in TSH (MD -0.19 mIU/L; 95% CI -0.43 to 0.06; I2= 0%), and no effect on fT3 levels (MD 0.01 pg/mL; 95% CI-0.16 to 0.18; I2= 0%). Data from single studies indicated no significant change in the levels of fT4, thyroid auto-antibodies, BMI, levothyroxine doses, and severity of symptoms measured with validated scales. Only constipation scores showed significant improvement (MD -8.71 points in the Faecal Incontinence Questionnaire; 95% CI -15.85 to -1.57; I2= 0%). SUMMARY: Low-certainty evidence from two randomised trials, suggests that routine administration of probiotics, prebiotics or synbiotics may result in little to no benefit in patients with primary hypothyroidism.

Meeting the Unmet Needs of Individuals With Mental Disorders: Scoping Review on Peer-to-Peer Web-Based Interactions.

BACKGROUND: An increasing number of online support groups are providing advice and information on topics related to mental health. OBJECTIVE: This study aimed to investigate the needs that internet users meet through peer-to-peer interactions. METHODS: A search of 4 databases was performed until August 15, 2022. Qualitative or mixed methods (ie, qualitative and quantitative) studies investigating interactions among internet users with mental disorders were included. The φ coefficient was used and machine learning techniques were applied to investigate the associations between the type of mental disorders and web-based interactions linked to seeking help or support. RESULTS: Of the 13,098 identified records, 44 studies (analyzed in 54 study-disorder pairs) that assessed 82,091 users and 293,103 posts were included. The most frequent interactions were noted for people with eating disorders (14/54, 26%), depression (12/54, 22%), and psychoactive substance use disorders (9/54, 17%). We grouped interactions between users into 42 codes, with the empathy or compassion code being the most common (41/54, 76%). The most frequently coexisting codes were request for information and network (35 times; φ=0.5; P<.001). The algorithms that provided the best accuracy in classifying disorders by interactions were decision trees (44/54, 81%) and logistic regression (40/54, 74%). The included studies were of moderate quality. CONCLUSIONS: People with mental disorders mostly use the internet to seek support, find answers to their questions, and chat. The results of this analysis should be interpreted as a proof of concept. More data on web-based interactions among these people might help apply machine learning methods to develop a tool that might facilitate screening or even support mental health assessment.