GLP-1 receptor agonist for weight loss and fertility: Social media and online perception versus evidence-based medicine.

Glucagon-like peptide-1 (GLP-1) is a natural hormone that is released by the gastrointestinal system and plays an important tole in glucose homeostasis [ 1 ]. It acts on GLP-1 receptors that are abundant in the pancreas leading to increased insulin secretion and inhibition of glucagon secretion thus lowering blood glucose levels [ 1 ]. The endogenous GLP-1 also lowers gastric emptying and increases food satiety. It slows gastric emptying, thus delaying the passage of food from the stomach into the small intestine. This prolongs the feeling of fullness after meals, leading to reduced food intake. By increasing satiety and lowering the rate at which food is digested and absorbed, GLP-1 contributes to weight loss and improved glycemic control. GLP-1 receptor agonists (GLP-1 RAs) are medications that have been used in the treatment of diabetes and its comorbidities such as cardiovascular disease [ 2 ] and renal disease [ 3 ]. Additionally, GLP-1 RAs have been shown in randomized trials to cause a sustained weight loss [ 4 , 5 ] most likely by acting both centrally at the level of the brain and at the peripheral tissues [ 6 ]. Since its FDA approval for weight loss, the use of GLP-1 RAs for weight loss has rapidly gained popularity on social media, especially among obese women of reproductive-age [ 7 ]. The use of these antidiabetic medications is now easily available for purchase online and the trends on social media has caused an rapid surge in both demand and purchase of these weight loss drugs [ 8 ]. Since GLP-1 receptors exist in the reproductive organs [ 9 ], empirical results indicate that they could impact the reproductive system including the hypothalamic-pituitary-ovarian (HPO) axis. The impact of these medications on female fertility has been studied in women with polycystic ovary syndrome (PCOS) [ 10 ] where the results have shown that GLP-1 RAs could help women with PCOS both metabolically and reproductively [ 11 , 12 ]. For instance, a meta-analysis study of randomized trials compared metformin to GLP-1 RAs in women with PCOS and reported that GLP-1 RAs, in addition to lowering body weight, was significantly better than metformin in improving insulin sensitivity as well as improving menstrual cyclicity, lowering serum total testosterone, lowering total cholesterol, and lowering blood pressure [ 11 ]. As for the impact of GLP-1 RAs on fertility status in women without PCOS, at the time of writing this manuscript, a detailed literature search resulted in finding a few studies in non-PCOS female animal model but none in humans [ 9 , 13 – 15 ]. The aim of this study was to compare women’s sentiment between social media perception and online search versus evidence-based medicine toward the GLP-1 RAs and their impact on fertility.

In Group 1, on Reddit social platform, out of 46,972 total number of records analyzed including all post headers, comments, replies and excluding reposts and duplicates, there was a significantly higher Positive sentiment compared to Neutral sentiment (4,720 ± 1,669 vs. 659 ± 243; respectively, p = 0.027; Table 3 ). There was trend towards higher score for Positive sentiment compared to Negative sentiment (p = 0.068). Negative and Neutral sentiments has similar scores (p = 0.88). When looking into the data in more details, the most common topics were “IVF, Infertility, Semaglutide, and Ozempic.” Thus, we performed Communalytic on these 4 topics, with and without filter for each topic leading to 8 datasets ( Table 4 ). In the majority of scores, Positive sentiment had significantly higher score than Negative sentiment that had significantly higher score than Neutral sentiment (p < 0.0001) except in the sematuglide with filters where the Negative and Neutral sentiments were similar (p = 0.22). *p= 0.22. In Group 1, on TikTok, out of 70 posts, Positive sentiment had significantly higher score than Negative or Neutral sentiments (p = 0.006; Table 5 ) while Negative and Neutral sentiments were similar (p = 0.06). On Twitter, out of 88 posts, Positive sentiment had significantly higher score than Neutral sentiment that had significantly higher score than Negative sentiment (p = 0.001; Table 5 ). In Group 2, there was a peak spike in interest in Ozempic and fertility treatments with the top 5 states involved being Massachusetts, California, New York, Texas, and Florida. The terms “Ozempic fertility”, “Ozempic Babies” and “Ozempic getting pregnant” had significant increase in the last year reaching +600%, + 1100%, and 900% respectively. When the term “Ozempic” or “GLP-1 RA” were searched, there was a + 800% increase in the search for the term “fertility” and +650% increase in the search for the term “pregnancy” as a follow up search. Overall search showed that Positive sentiment had significantly higher score than Neutral sentiment that had significantly higher score than Negative sentiment (p < 0.0001). When the data were evaluated in more details, we found that the rank for top 5 search terms was as follows: Ozempic fertility, Ozempic pregnancy, Ozempic weight loss, Ozempic PCOS, and Ozempic babies. Table 6 shows the sentiment analysis of the most common 200 posts on Google Trends related to the two most common topics “Ozempic Fertility” and “Ozempic Pregnancy.” For “Ozempic Fertility,” the majority had a Positive sentiment which had significantly higher score than Neutral sentiment that had significantly higher score than Negative sentiment (p = 0.03; Table 6 ); however for “Ozempic Pregnancy,” the majority had a Neutral sentiment that had significantly higher score than Positive sentiment that had significantly higher score than Negative sentiment (p < 0.0001; Table 6 ). Group 1 subgroups and Group 2 has similar percentage of Positive (p = 0.9), Neutral (p = 0.8), and Negative (p = 0.7) sentiments. Among each type of sentiment, there was a strong positive correlation among all Group 1 subgroups and Group 2 (r 2  = 0.83). Positive sentiments had significantly higher score than Neutral sentiment that had significantly higher score than Negative sentiment in all ( Table 7 ). Results are shown in Table 8 . The majority of peer-reviewed manuscripts were in PCOS state both in animal and humans. In women without PCOS, there were no studies (at the time of the publication). Although majority of the posts in all three social media platforms showed positive sentiment, there are no studies in women without PCOS (n = 0) that would justify that positivity among public perception. Among the 52 original studies on PubMed in women with PCOS, they all had positive sentiments. There were no studies in humans (including case reports, case series, cohort, case-control, or trials) evaluating the impact of GLP-1 RAs on ovulation, implantation, IVF, or any fertility status in women without PCOS, but there were a few studies in animals that showed controversial results with negative impact on fertility.

Women with PCOS are a unique population that is very distinct than than those without PCOS because they could have metabolic dysfunction that understandably would benefit from the anti-diabetic actions of the GLP-1 RAs. The vulnerability of this infertility population and the wealth of online and social media misinformation (such as “#ozempicbabies” and “#ozempicpregnancy”) is causing a dramatic rise in the use of the quick fix GLP-1 RA medications with their unknown reproductive consequences [ 36 ]. There is a clear need for long-term studies in women without PCOS, to assess the impact of GLP-1 RA medications in a dose-response manner on reproduction to evaluate their safety since many women of reproductive age are resorting to these medications without knowing whether they could have any negative impact on their future fertility.

Data were collected from three main sources: from social media platfoms that included Reddit, Twitter, and TikTok (Group 1), from the large online search Google Trends (Group 2), and from Pubmed which is a free and publicly available resource provided by the US National Library of Medicine (Group 3). These three social media platforms and the Google Trends were chosen because they have a global reach, a high number of users, and significant influence on online and social media interactions and content sharing. Only publicly available data from the selected social media platforms were analysed and there was no access to any individual accounts. The same keywords pertaining to GLP-1 RAs and fertility were used for data collection for all 3 groups ( Table 1 ). Because the data was public, informed consent from participants was not needed and the study was IRB exempt. For all data collected, comments or studies that are not in English language. Addionally trivial or not relevant comments to the subject were excluded. Reddit is a web-based platform that organizes topics into subreddits (distinct forums) where each interaction/discussion is considered a thread [ 16 ]. Reddit is very popular and its users, called Redditors, discuss a variety of topics including infertility treatments and weight loss using the new injectable medications such as Ozempic and others [ 17 ]. Reddit posts, which are anonymous and voluntary, have become a common source for discussing research studies and important publications worldwide [ 16 ]. The computational social science research tool Communalytic https://edu.communalytic.org/ was used to extract data from online communities and discourse on the social platform Reddit. VADER (Valence Aware Dictionary and Sentiment Reasoner), a pre-trained sentiment analysis model that provides a sentiment score for a given text, was used to score the sentiment of the latest 200 posts as: Positive, Negative, or Neutral. Within the threads, the posts were filtered including keywords related to fertility using an advanced search query that included the keywords in Table 1 . Using Communalytic’s sorting filters, the top 200 most recent submissions were selected, including replies and comments, while excluded reposts and duplicates. Finally, we ran Communalytic on four subreddits that were analyzed with and without filter for each subreddit: r/Infertility, r/IVF, r/Ozempic, and r/Semaglutide, leading to 8 datasets. Unlike Reddit,Twitter and TikTok do not allow a third party software analysis so we manually searched the latest 200 posts on these platforms using the same keywords in Table 1 to assess whether they mention Positive, Negative, or Neutral sentiment using an objective internal scale. The internal scale included the 3 following criteria within the search process: use of evidence, language/tone, and engagement/response ( Table 2 ). Google Trends provides keyword-related data including search volume index and geographical information about search engine users. It can be used for comparative keyword research and to discover event-triggered spikes in keyword search volume. Google Trends also allows the user to compare the relative search volume of searches between two or more terms. The values in Google Trends ranges from 0 to 100, representing search interest in different regions and times. A value of 0 indicates that the search queries are not popular enough for this search term. A value of 50 indicates that the search term is half as popular. A value of 100 indicates that the search term has peak popularity. In this study, we defined the region as “United States”, and category as “Health” on the Google Trends website. A total of 126 million results using the combination of keywords in Table 1 were found. A Pubmed search was done using the keywords in Table 1 . We performed a detailed systematic review of all in vitro and in vivo studies, all prospective and retrospective studies, as well as basic science and clinical studies that are available in Pubmed in English language. The references from all relevant articles were checked and we performed a search of all abstracts of the annual meetings of the American Society for Reproductive Medicine (ASRM) and the European Society for Human Reproduction and Embryology (ESHRE). We reviewed all the titles and abstracts of all citations. The data were extracted from the text, and all the tables and graphs within the manuscripts. The reference lists of identified articles were searched for additional references. All data were abstracted and put into a table format in a systematic manner. Exclusion criteria included editorials, letters to editors, and studies pertaining to male subjects. Data are expressed as mean ± standard error of the mean. A sample size of 42 posts on TikTok or Twitter has been shown to be adequate to produce statistical difference with 80% power and two-tailed α error of 0.05. Because there were 3 groups, ANOVA with post-hoc analysis was used to determine which specific groups were statistically different. For categorial data, Chi-square test was used to compare sentiments. To assess whether there was a positive or negative association, Pearson linear regression was performed on data extracted from all three groups. R 2 was calculated to assess the strength of the correlations and p < 0.05 was considered statistically significant. Prism 10 software was used for all statistical analyses.

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