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Data availability | The data that has been used is confidential. | PMC10189551 | ||
Acknowledgments | RECRUITMENT | The authors would like to thank the patients and control participants for their participation in this study. This study was supported by the Ministry of Environment, Government of Japan. The authors thank Ms. Liu X Jie, Ms. Nobuko Tabata, Mr. Ken-ichiro Miyamoto, Mr. Kouji Murao, Ms. Kiyoka Miyamoto, Ms. Akane Morimoto, Ms. Tomoko Yamashita, Ms. Mana Kamizono, Mr. Mamoru Hanada, and Ms. Miwa Matsunaga for patient recruitment, execution of the examinations, and technical assistance.Supplementary data to this article can be found online at | PMC10189551 | |
1. Introduction | obesity, decreases in fasting glucose, SSBs, cardiometabolic disease, Sugar reduction, sugar reduction | OBESITY, INFLAMMATION, INSULIN SENSITIVITY | Pediatric obesity and cardiometabolic disease disproportionately impact minority communities. Sugar reduction is a promising prevention strategy with consistent cross-sectional associations of increased sugar consumption with unfavorable biomarkers of cardiometabolic disease. Few trials have tested the efficacy of pediatric sugar reduction interventions. Therefore, in a parallel-design trial, we randomized Latino youth with obesity (BMI ≥ 95th percentile) [Pediatric obesity continues to rise, with rates disproportionately impacting Latino children [Reducing the consumption of added sugars, particularly in the form of sugar sweetened beverages (SSBs), has been identified as one modifiable dietary factor that has the potential to improve the metabolic profile in children and adolescents. Cross-sectional studies in adolescents consistently find associations between sugar consumption and markers of glucose tolerance and insulin sensitivity [While few randomized controlled trials (RCTs) have tested the efficacy of sugar reduction as a strategy to improve blood pressure, serum lipid profile, or glucose tolerance and its determinants in children and adolescents, the trials show promise. In a school-based intervention in Brazilian 9–12-year-olds, children attending schools randomized to receive education that discouraged soft drink intake had significant decreases in fasting glucose and total cholesterol compared to children at control schools [To further explore whether a dietary intervention focused on sugar reduction impacts the cardiometabolic profile, we randomized Latino adolescents with obesity to either a control group receiving standard dietary advice or an intervention group that received dietary counseling focused on sugar reduction and home-delivery of bottled water with a goal of reducing added sugar intake to 10% or less of their total calories. Outcomes of interest included measures of glucose tolerance and its determinants, the fasting serum lipid profile, and markers of inflammation. We hypothesized that participants in the intervention group would have greater improvements in cardiometabolic health. | PMC10420969 |
2. Materials and Methods | PMC10420969 | |||
2.1. Recruitment and Enrollment | RECRUITMENT | Detailed recruitment and enrollment procedures, including key inclusion and exclusion criteria, were previously described [ | PMC10420969 | |
2.2. Study Design | The study design, including randomization, study diets, and pre-specified study outcomes, were previously published [ | PMC10420969 | ||
2.3. Study Diets | Specifics of the intervention and control diets were previously presented [ | PMC10420969 | ||
2.4. Glucose Tolerance and Determinants of Glucose Tolerance | glucose tolerance | INSULIN SENSITIVITY, INSULIN RESISTANCE | At both clinic visits, participants completed a fasting blood draw and 2-h frequently samples oral glucose tolerance test (FS-OGTT) after a 10-h overnight fast. Secondary outcome measures included glucose tolerance as assessed by fasting glucose and the area under the curve (AUC) glucose; systemic insulin sensitivity as assessed by fasting insulin, the Homeostatic Model Assessment of Insulin Resistance (HOMA-IR), AUC insulin, and the Matsuda-De-Fronzo insulin sensitivity index (ISI) [ | PMC10420969 |
2.5. Fasting Serum Lipid Profile & Biomarkers | inflammation | INFLAMMATION | The fasting serum lipid profile, adipokines, and inflammatory markers were measured in fasting plasma by the DORI Metabolic Core Laboratory. The fasting serum lipid profile included triglycerides, total cholesterol, low-density lipoprotein (LDL) cholesterol, high-density lipoprotein (HDL) cholesterol, and the total cholesterol:HDL cholesterol ratio. Serum lipids were assayed using FujiFilm kits utilizing the microplate method (FUJIFILM Wako Diagnostics, Mountain View, CA, USA). The intra- and inter-assay coefficient of variation (CV) for the HDL-Cholesterol E assay is 8.23% and 3.71%, respectively; the intra- and inter-CV for the Cholesterol E assay is 8.80% and 7.75%, respectively; and the intra- and inter-assay CV for the L-Type Triglyceride M assay is 8.23% and 3.71%, respectively. Adipokines, including Monocyte Chemoattractant Protein-1 (MCP-1) and leptin, as well as markers of low-grade chronic systemic inflammation as assessed by concentrations of interleukin-6 (IL-6), and TNF-Alpha were measured in fasting plasma using the Millipore Magpix Metabolic Panel kit at the DORI Core Laboratory (intra-assay and inter-assay CV of 4.53% and 6.96%, respectively). High-sensitivity C-reactive protein (CRP) was assayed separately using the Millipore ELISA kits, with an intra- and inter-assay CV of 4.60% and 6.00%, respectively. | PMC10420969 |
2.6. Anthropometrics | Body weight and height were measured by a registered nurse or phlebotomist using standardized procedures [ | PMC10420969 | ||
2.7. Dietary Intake and Physical Activity | The methods used to assess dietary intake and physical activity were described previously [ | PMC10420969 | ||
2.8. Statistical Analysis | INFLAMMATION | We aimed to randomize 120 participants into the trial. This sample size was calculated based on the primary aim of the trial, change in liver fat [Statistical analyses were performed using the R statistical package version 4.0.3 [In exploratory analyses of the pooled data, we assessed the impact of reducing sugar intake, irrespective of randomization, on glucose tolerance and its determinants, the fasting serum lipid profile, blood pressure, and markers of inflammation. To assess the effect of reducing total sugar intake on our outcomes of interest, we split our data set into those with a reduction in total sugar over the intervention period (TS; TS | PMC10420969 | |
3. Results | PMC10420969 | |||
3.1. Description of Participants and Adverse Events | One hundred and thirteen potential participants were assessed for eligibility with 105 participants enrolled and randomized into the trial. Participants were randomly assigned to either the dietitian-led sugar reduction intervention group ( | PMC10420969 | ||
3.2. Intervention Adherence and Dietary Intakes | Intervention adherence in our study population was previously described [We previously reported dietary intake data for the 88 participants who were randomized with complete diet data [ | PMC10420969 | ||
3.3. Glucose Tolerance and Determinants of Glucose Tolerance | There were no differential effects of our intervention as compared to the control group on changes in glucose tolerance as measured by fasting glucose (adjusted | PMC10420969 | ||
3.4. Blood Pressure and Fasting Serum Lipids | We observed no differential effects of our intervention on diastolic blood pressure (adjusted | PMC10420969 | ||
3.5. Inflammatory Markers & Adipokines | Change in adipokines did not differ between the intervention and control groups including no statistically significant differential changes in MCP-1 (adjusted | PMC10420969 | ||
3.6. Changes in Cardiometabolic Health Outcomes as a Function of Change in Total Sugar Intake Regardless of Intervention Group Assignment | sugar reduction | INFLAMMATION | In exploratory analyses of the pooled data, we examined glucose tolerance and its determinants, blood pressure, fasting serum lipids, inflammation, and adipokines in all participants who successfully reduced sugar intake (There was no significant difference in the change of fasting glucose, 1-h glucose, HbA1c, AUC glucose, fasting insulin, 2-h insulin, HOMA-IR, Matsuda ISI, AUC insulin, or the Insulinogenic Index between participants who reduced total sugar intake versus those who did not (For fasting serum lipids, there was no difference in the change in total cholesterol, LDL cholesterol, or HDL cholesterol between participants with or without sugar reduction. However, there was a significant increase in total triglycerides among those without sugar reduction as compared to those with sugar reduction (There were no statistically significant differences in changes in inflammatory markers or adipokines with the exception of a greater increase in TNF- | PMC10420969 |
4. Discussion | glucose tolerance, cardiometabolic disease, sugar reduction, cardiometabolic, chronic disease | DISEASE, SECONDARY, TYPE 2 DIABETES, INSULIN SENSITIVITY, CHRONIC DISEASE | A dietitian-led intervention with the goal of reducing free sugar intake to Given that we saw sugar reductions in both our intervention and control groups, we also conducted an exploratory analysis that more specifically looked at the impact of sugar reduction on our outcomes of interest by comparing participants with and without total sugar reduction, independent of randomization. Our exploratory results indicate that reducing sugar intake as an intervention strategy may still hold promise for improving cardiometabolic health for at-risk Latino youth. Specifically, we found that participants who successfully reduced their sugar intake saw improvements in their oral-DI as compared to participants who did not reduce their sugar intake, indicating that they had improvements in their beta-cell function. Specifically, those with sugar reduction improved their oral-DI by 23% as compared to only a 9% improvement in those without sugar reduction. Oral-DI is a sensitive measure of the beta-cell function that measures beta-cell output while adjusting for insulin sensitivity. Oral-DI is a significant biomarker of disease risk and has been indicated to be more important in predicting the future development of type 2 diabetes as compared to fasting and 2-h glucose levels [In addition to our findings relevant for the risk of type 2 diabetes, our exploratory analysis revealed that participants without sugar reduction had some differences in fasting serum lipids and inflammatory markers as compared to those with sugar reduction. Specifically, we report that those without sugar reduction had a 6.5% increase in triglycerides. This outcome aligns with previous pediatric cohort studies which found that increased sugar intake is positively associated with triglycerides [There is one key factor which likely explains the difference in outcomes reported between our primary and exploratory results. As mentioned above, the difference in total sugar intake between the intervention and control groups after 12-weeks was significant but was relatively small at 3% of total calories, and we observed participants with sugar reduction in both groups. In contrast, in the exploratory analysis, the difference in total sugar intake between those who reduced their total sugar intake compared to those who did not was approximately 7% of total calories. Therefore, we conclude that the benefits of a real-world dietary intervention focused on sugar reduction, where participants met with a registered dietitian monthly and receive deliveries of bottled water, does not confer additional improvements in cardiometabolic risk factors compared to participants receiving general diet advice. However, our exploratory results confirm our hypothesis that sugar reduction in at-risk Latino adolescents results in improvements in biomarkers of cardiometabolic risk; specifically, beta-cell function, fasting serum lipids, and the inflammatory marker TNF-While we did report some findings from our exploratory analysis, many of the hypothesized changes in cardiometabolic risk factors for glucose tolerance and its determinants, serum lipids, blood pressure, and inflammatory markers were not statistically significant with non-clinically meaningful effect sizes. There are a few potential explanations for these null findings. One is that while there was a 7% difference in sugar intake as a percentage of total calories in our exploratory analysis, this difference may have still been too small to result in hypothesized shifts in these biomarkers. Additionally, while our population was overweight, they may have been too metabolically healthy at baseline to observe statistically significant improvements in some of our outcomes of interest. Further, our study was originally powered based on the study’s primary aim of liver fat, so we may have been underpowered to detect significant changes in some of our secondary and exploratory outcomes of interest. Finally, the study was not blinded, so it may be that participants in the intervention group inaccurately reported their sugar intake to be less than their actual intake, which would have biased results toward the null.Our study has some important strengths which increase our confidence in our results. First, it assessed glucose tolerance through dynamic testing. Second, we had good participant compliance, with 81.6% of intervention participants reducing their free sugar intake and 71.4% meeting the goal of a free sugar intake maximum of 10% of total calories. In addition, we controlled for changes in fat mass statistically, which provided us with greater certainty that any observed outcomes were due to dietary changes and not changes in body weight or composition. Some limitations of this study include that approximately 35% of participants included in this analysis already met the intervention free sugar intake target of ≤10% of total calories prior to study enrollment. While this decreased our ability to ascertain whether reducing sugar intake through our intervention effects our outcomes of interest, our study still provides key insight into the impact of sugar reduction on our outcomes of interest through our exploratory analysis. Also, given our 12-week study period, we are unable to capture the impact of long-term sustained sugar reduction beyond 3-months. While our study had generally good compliance, following and sustaining a low sugar diet is challenging especially in pediatric populations [In conclusion, our results help fill a critical gap in the literature by further exploring the effects of sugar reduction interventions to improve biomarkers of cardiometabolic disease risk in pediatric populations. Our outcomes indicate that dietary sugar reduction interventions have the potential to reduce the risk of chronic disease development through improvements in beta-cell function, fasting serum triglycerides, and inflammatory markers such as TNF- | PMC10420969 |
Supplementary Materials | The following supporting information can be downloaded at: Click here for additional data file. | PMC10420969 | ||
Author Contributions | Conceptualization T.L.A., H.A., K.S.N., F.R.S., R.K., W.M. and M.I.G.; Methodology P.M., E.A.H., T.L.A. and T.A.P.; Formal Analysis K.A.S., T.A.P. and W.M.; Writing—Original Draft Preparation K.A.S. and M.I.G.; Writing—Review & Editing All authors; M.I.G. has primary responsibility for final content. All authors have read and agreed to the published version of the manuscript. | PMC10420969 | ||
Institutional Review Board Statement | The study was conducted according to the guidelines of the Declaration of Helsinki, and approved the by the Institutional Review Board of the University of Southern California and Children’s Hospital Los Angeles (CHLA-21-00314). | PMC10420969 | ||
Informed Consent statement | Informed consent was obtained from all subjects involved in the study. | PMC10420969 | ||
Data Availability Statement | An anonymized dataset including all data described in the manuscript, code book, and analytic code will be made available upon request to the principal investigator (MIG). | PMC10420969 | ||
Conflicts of Interest | M.I.G. is a scientific advisor for YUMI foods and receives book royalties from Penguin Random House for Sugarproof. There are no other conflict of interests to disclose. | PMC10420969 | ||
Abbreviations | Obesity, non-alcoholic fatty liver disease, NAFLD, T2D, SSBs, FM, CVD, high-density lipoprotein, Diabetes | OBESITY, DIABETES, CARDIOVASCULAR DISEASE, CVD, NON-ALCOHOLIC FATTY LIVER DISEASE, INSULIN RESISTANCE, TYPE 2 DIABETES, INSULIN SENSITIVITY | Analysis of covariance (ANCOVA), area under the curve (AUC), body mass index (BMI), Children’s Hospital Los Angeles (CHLA), C-reactive protein (CRP), coefficient of variation (CV), cardiovascular disease (CVD), Diabetes and Obesity Research Institute (DORI), fat mass (FM), frequently sampled-oral glucose tolerance test (FS-OGTT), glycated hemoglobin (HbA1c), high-density lipoprotein (HDL), Homeostatic Model Assessment of Insulin Resistance (HOMA-IR), interleukin-6 (IL-6), intent-to-treat (ITT), Matsuda-De-Fronzo insulin sensitivity index (ISI), low-density lipoprotein (LDL), Magnetic Resonance Elastography (MRE), Magnetic Resonance Imaging (MRI), non-alcoholic fatty liver disease (NAFLD), oral disposition index (oral DI), randomized controlled trials (RCT), registered dietitian nutritionist (RDN), sugar sweetened beverages (SSBs), type 2 diabetes (T2D), University of Southern California (USC). | PMC10420969 |
References | sugar reduction | RECRUITMENT | Flow diagram of participant recruitment, enrollment, intervention allocation, follow-up, and analysis.Oral-DI increases in participants with sugar reduction. Changes (post-intervention minus the value at baseline) in the oral disposition index (Oral-DI) for participants with total sugar reduction as a percent of energy (Triglycerides and the Cholesterol to HDL ratio is lower in participants with sugar reduction. Changes (post-intervention minus the value at baseline) in Triglycerides (Baseline characteristics of study participants by study arm for individuals who were randomized and completed clinic visit 2 (Values are means ± standard deviations, or medians (25th, 75th percentiles) for non-normally distributed variables, or percentages for categorical variables. The effect of the intervention on glucose tolerance and its determinants in the primary analysis.Values are means ± standard deviations or medians (25th, 75th percentiles) for non-normally distributed variables. Raw: The effect of the intervention on serum lipids and blood pressure in the modified intent-to-treat analysis.Values are means ± standard deviations or medians (25th, 75th percentiles) for non-normally distributed variables. Raw: The effect of the intervention on adipokines and inflammatory markers in the primary analysis.Values are means ± standard deviations or medians (25th, 75th percentiles) for non-normally distributed variables. Raw: | PMC10420969 |
Rationale | impaired cognitive function | DISORDER | Chronic cannabis use is associated with impaired cognitive function. Evidence indicates cannabidiol (CBD) might be beneficial for treating cannabis use disorder. CBD may also have pro-cognitive effects; however, its effect on cognition in people with cannabis use disorder is currently unclear. | PMC9879826 |
Objectives | We aimed to assess whether a 4-week CBD treatment impacted cognitive function. We hypothesised that CBD treatment would improve cognition from baseline to week 4, compared to placebo. | PMC9879826 | ||
Methods | DSM-5 cannabis | DISORDER, SECONDARY | Cognition was assessed as a secondary outcome in a phase 2a randomised, double-blind, parallel-group and placebo-controlled clinical trial of 4-week daily 200 mg, 400 mg and 800 mg CBD for the treatment of cannabis use disorder. Participants had moderate or severe DSM-5 cannabis use disorder and intended to quit cannabis use. Our pre-registered primary cognitive outcome was delayed prose recall. Secondary cognitive outcomes were immediate prose recall, stop signal reaction time, trail-making task performance, verbal fluency and digit span. | PMC9879826 |
Results | Seventy participants were randomly assigned to placebo ( | PMC9879826 | ||
Conclusions | DISORDER | In this clinical trial for cannabis use disorder, CBD did not influence delayed verbal memory. CBD did not have broad cognitive effects but 800 mg daily treatment may improve working memory manipulation. | PMC9879826 | |
Clinical trial registration | The trial was registered with ClinicalTrials.gov (NCT02044809) and the EU Clinical Trials Register (2013–000,361-36). | PMC9879826 | ||
Supplementary Information | The online version contains supplementary material available at 10.1007/s00213-022-06303-5. | PMC9879826 | ||
Keywords | PMC9879826 | |||
Introduction | CUD, impairment in cognitive function, P., impairment or distress, verbal learning and memory | DISORDER, SECONDARY | Cannabis use disorder (CUD), a pattern of cannabis use causing significant impairment or distress, affects an estimated 22 million individuals worldwide (Degenhardt et al. Chronic cannabis use is associated with impairment in cognitive function, particularly verbal learning and memory (Broyd et al. Cannabidiol (CBD) a constituent of cannabis, shows potential as a treatment for CUD (T. P. Freeman et al. Naturalistic studies indicate that the level of CBD in the cannabis a person uses may affect verbal learning and memory performance. One study assessed prose recall performance when participants were intoxicated with their own cannabis (C. J. A. Morgan et al. Clinical trial data provide preliminary evidence that CBD may benefit cognitive performance. A randomised trial in healthy participants of single-dose vaporised CBD e-liquid (12.5 mg CBD) found better verbal episodic memory performance (but not attention or working memory) after acute CBD administration compared to placebo (Hotz et al. Here, we present data on secondary cognitive outcomes from a randomised, phase 2a, double-blind and parallel-group clinical trial of CBD for the treatment of CUD (primary outcomes on cannabis use from this trial have previously been reported (T. P. Freeman et al. | PMC9879826 |
Method | PMC9879826 | |||
Participants | CUD, DSM-5, psychotic disorder, allergies | ALLERGIES | Participants were recruited through website advertisements, forums and through flyers in the local community. They met the following inclusion criteria: aged 16–60, CUD of at least moderate severity (≥ 4 symptoms, assessed by clinical interview for DSM-5 symptoms, conducted by trained psychologists), capacity to give written informed consent, expressed a desire and intention to stop using cannabis within the upcoming month, had one or more unsuccessful prior attempts to quit their cannabis use, co-administered cannabis with tobacco, provided a positive urine sample for THC-COOH and for women, provided a negative pregnancy test within the 7 days prior to starting treatment. Women of childbearing potential and all men were required to use an effective method of contraception (oral, injected, implemented, barrier or true abstinence), from the time of consent until 6 weeks after the end of treatment. Initial criteria for participants to be aged 16–26, with vital signs within normal limits were removed early in the trial to increase the generalisability of findings. Exclusion criteria included the following: (1) current pregnancy or breastfeeding, (2) allergies to CBD, microcrystalline cellulose or gelatine, (3) prescribed psychotropic drug use, (4) use of illicit drugs (other than cannabis) 2 or more times per month at screening, (5) inaccurate self-reported drug use confirmed by a positive urine test for drugs that were not reported during screening, (6) current or previous self-reported diagnosis of a psychotic disorder, (7) physical health problem deemed clinically significant and (8) not speaking English. | PMC9879826 |
Procedures and measures | ’ | MAY | The trial was approved by the UK Health Research Authority (13/EE/0303) and the UK Medicines and Healthcare Regulatory Agency (20,363/0325/001–0001) and was prospectively registered with ClinicalTrials.gov (NCT02044809) and the EU Clinical Trials Register (2013–000,361-36). The trial was a single-centre, randomised, double-blind, placebo-controlled and parallel-group study conducted at the Clinical Psychopharmacology Unit, UCL, in central London from May 2014–June 2017. Due to a lack of funding, a subsequent phase 2b stage trial that had been planned was not initiated, and the trial ended in May 2018. The trial protocol can be found at After an initial telephone screening, participants attended an in-person screening visit to determine their eligibility prior to randomisation. The trial statistician (GB) generated the randomisation sequence using block randomisation, with a block size equivalent to the number of treatment groups in the randomisation code. The randomisation code was held by the emergency unblinding service (Sealed Envelope, London, UK) and the drug manufacturer for labelling before shipping to the trial site. Researchers and participants remained masked for the duration of the trial. Only masked investigators enrolled participants, assigned participants to interventions, did assessments and entered data. Unmasking occurred after the database had been locked by the trial statistician.Synthetic, laboratory-synthesised CBD was obtained from STI Pharmaceuticals (Brentwood, UK) and manufactured by Nova Laboratories (Leicester, UK). The first treatment stage of the trial involved twice daily at-home ingestion of two gelatine capsules containing microcrystalline cellulose filler and CBD in total doses of 200 mg, 400 mg, 800 mg or 0 mg (placebo) for 4 weeks. Capsules were identical in size and participants were instructed to take each of the two doses 12 h apart. Text reminders were sent to participants at these pre-arranged times to improve compliance. Instructions were not given for taking the doses with/without food. Participants’ adherence to the treatment schedule was monitored via the return of dosette boxes and self-report of use using diary cards.The trial was conducted to determine the most effective dose of CBD in reducing cannabis use and consisted of two stages. In the first stage, Participants attended site visits once weekly during treatment. All participants received six 30-min sessions of motivational interviewing (Miller & Rollnick | PMC9879826 |
Cognitive outcomes | PMC9879826 | |||
Prose recall | Verbal episodic memory, Wilson et al. | Verbal episodic memory performance was assessed using the prose recall task, a modified measurement from the Rivermead Behavioural Memory Test battery (Wilson et al. | PMC9879826 | |
Stop signal | Response inhibition was assessed using the stop signal task. During this computer-based task, white arrows appeared sequentially in the centre of the screen. Participants pressed a key on the keyboard based on the direction that the arrow was pointing in (left or right). In 25% of trials, the arrow turned from white to blue, following a variable delay. In these trials, participants were instructed to not press any arrow key, thereby inhibiting their initiated response. There was one block of 32 practice trials and three blocks of 64 experimental trials. Staircase tracking ensured that the delay occurred such that the participant had approximately a 50% chance of successfully inhibiting their response. The outcome (stop signal reaction time; SSRT) was generated via a computer programme ‘STOP-IT’ (Verbruggen et al. | PMC9879826 | ||
Trail-making task | Psychomotor speed, attention and task switching were assessed using the trail-making task (TMT; Reitan | PMC9879826 | ||
Digit span | Working memory was assessed using the digit span task (Wechsler | PMC9879826 | ||
Verbal fluency | Finally, verbal fluency was assessed using a letter (phonemic), category (semantic) and drug (cannabis) fluency prompts. Participants were required to generate as many words related to each prompt as they could within 1 min. The number of relevant, unique words mentioned was recorded and summed for each variation.Alternate versions of all cognitive tasks were used at each assessment, with the exception of the stop signal task which used a randomised trial design. | PMC9879826 | ||
Statistical analysis | P. | A power analysis conducted for the primary outcome of the main trial (time by group interaction on reduction in cannabis use) indicated that 12 participants per group would provide 80% power to detect an effect of CBD on cannabis use, based on a previous study of CBD on cigarette use in tobacco smokers (T. P. Freeman et al. The effect of CBD treatment compared to placebo on each cognitive outcome was analysed using linear mixed-effects models, using the “lme4” package in R. Data from all patients randomly assigned to placebo, 400 mg CBD and 800 mg CBD groups were analysed on an intention-to-treat basis. Models included data from baseline and end of treatment only (week 4) in order to focus on the effect of the treatment period, consistent with the primary endpoint analysis of the main trial (T. P. Freeman et al. | PMC9879826 | |
Results | CUD, DSM-5 | Demographic details of participants in each treatment group are provided in Table Baseline participant demographics. Data shown are frequencies and means (standard deviations) as appropriateDays of cannabis used assessed via timeline follow-back interview. CUD symptoms assessed using DSM-5 interviewCONSORT flow diagram | PMC9879826 | |
Secondary outcomes | digits recall backwards | SECONDARY | For the backwards digit span, there was a significant dose-by-time interaction at 800 mg CBD (0.76, 95%CIs: 0.01, 1.54), but not at 400 mg CBD (0.41, 95%CIs: − 0.34, 1.25). The change in performance was 0.30 (95%CIs: 0.02, 0.58) in the 800 mg group; 0.13, (95%CIs: − 0.14, 0.42) in the 400 mg group, and − 0.08 (95%CIs: − 0.35, 0.1 in the placebo group; see Fig. Group means of number of digits recall backwards, by treatment group at baseline and week 4. Error bars represent bootstrapped 95% confidence intervalsFor all other secondary outcomes (SSRT, TMT A, TMT B-A, forwards digit span, letter fluency, category fluency and drug fluency), there was no significant dose-by-time interaction at 400 mg or 800 mg CBD. There was also no main effect of dose for any secondary outcome.For immediate prose recall, TMT A and category fluency, there was a significant main effect of time. See supplementary materials for details of all analyses of secondary outcomes. | PMC9879826 |
Exploratory analyses | To determine if there was an effect of change in cannabis use on cognitive function, we added urinary THC:COOH levels (measured at both baseline and week 4) as a time-varying covariate and an interaction term of urinary THC:COOH by time as fixed effects to the models. For all models, adding these as fixed effects did not influence the main results, with no evidence for the effect of CBD compared to placebo on cognitive outcomes. The dose-by-time interaction at 800 mg for backwards digit span remained significant after adjustment (0.81, 95%CIs: 0.07–1.56). For prose recall delayed and immediate, as well as category fluency, the effect of time remained significant after adjustment for urinary THC:COOH. Time was no longer significant in the model for psychomotor speed (TMT Part A) after adjustment. | PMC9879826 | ||
Discussion | CUD, anxiety | BLIND | We used a comprehensive cognitive task battery to assess performance before and after 4-week treatment with daily oral 400 mg CBD, 800 mg CBD or placebo in a double-blind, randomised and placebo-controlled clinical trial. Contrary to our hypotheses, there was no effect of CBD on delayed prose recall compared to the placebo. There was a lack of effects of CBD on other cognitive outcomes, apart from a significant dose-by-time interaction indicating that 800 mg CBD improved performance from baseline to week 4 for backwards digit span, a measure of working memory. On the delayed prose recall task (the pre-registered primary outcome), performance increased in all groups from baseline to week 4. Taken together, these results suggest that CBD may not produce broad cognitive effects in people with CUD but could benefit working memory manipulation.Previous evidence has indicated that verbal memory is the key cognitive domain impacted by CBD treatment; however, this was not supported by the current findings. An open-label trial of 200 mg daily CBD over 10 weeks found better verbal learning and memory performance at end of treatment compared to baseline (Solowij et al. We found that 800 mg CBD improved the manipulation of information in working memory indexed via backwards digit span and did not affect maintenance indexed by forward digit span. Previous studies have indicated null effects of CBD on digit span tasks. One experimental study administering a single dose of 600 mg oral CBD did not find a significant effect on forwards or backwards digit span compared to a placebo (Bloomfield et al. Of note, performance improved across some cognitive outcomes from baseline to end of treatment, including both measurements of the prose recall task, psychomotor speed, and category fluency. There are several potential explanations for this. Firstly, cognition may have improved due to lower exposure to THC or general improvement in wellbeing caused by the reduction in CUD over the trial. However, exploratory analyses indicated that the effect of time remained significant after adjustment for urinary THC:COOH levels (except for psychomotor speed), indicating that this was not responsible for the increase in performance. Across the trial, all groups including the placebo group reduced their cannabis use considerably, which might potentially explain why the addition of a urinary marker of recent cannabis use did not alter the pattern of results. Moreover, there was no evidence for the effect of CBD on cognition being greater than placebo in the adjusted models. Secondly, the cognitive tasks used might be sensitive to practice effects, with participants scoring higher at the end of treatment as they are more familiar with the task and its instructions. It is also possible that there was an exposure effect to the testing environment over time which may reduce participants’ anxiety.This analysis benefits from robust RCT methodology including randomisation, double-blinding and placebo control. The trial used an intention-to-treat analysis, with 97% of participants providing data at baseline and end of treatment. The 4-week exposure period and two doses of CBD investigated allow for a thorough investigation of daily CBD treatment on cognition, using pre-registered hypotheses. One limitation is that the trial may have had limited power to detect potentially true effects of CBD with small effect sizes. As this was an analysis of a fixed sample from an existing dataset, an a priori power analysis could not be conducted. Given the 1.5 times larger increase for delayed prose recall and 2 times larger increase for immediate prose recall in the 800 mg group compared to placebo, there may be potential for pro-cognitive effects of daily CBD treatment on verbal memory, but larger sample sizes would be needed to detect small effect sizes. Another consideration to note is that all groups (including placebo) received motivational interviewing. This technique has demonstrated efficacy in reducing cannabis use in previous trials (Lees et al. In conclusion, this randomised, double blind and placebo-controlled trial found that 400 mg and 800 mg of CBD treatment did not significantly improve verbal learning and memory performance over 4 weeks, compared to placebo. There was evidence of a small beneficial effect of CBD on working memory as assessed by the backwards digit span.
| PMC9879826 |
Funding | The trial was supported by a UK Medical Research Council Developmental Pathway Funding Scheme award (MR/K015524/1). The funder played no role in the collection, analysis or interpretation of data, writing of the report or the decision to submit for publication. This work was supported in part by grant MR/N0137941/1 for the GW4 BIOMED MRC DTP, awarded to the Universities of Bath, Bristol, Cardiff, and Exeter from the Medical Research Council (MRC)/UKRI. | PMC9879826 | ||
Data Availability | The participants of this study did not give written consent for their data to be shared publicly, so the research supporting data is not available. | PMC9879826 | ||
Declarations | PMC9879826 | |||
Conflict of interest | CH became a full-time member of GW pharmaceuticals after the conclusion of the clinical trial. All authors declare that they have no conflict of interest. | PMC9879826 | ||
References | PMC9879826 | |||
Background | T2DM | TYPE 2 DIABETES MELLITUS | This prospective study aimed to compare telemedicine-assisted structured self-monitoring of blood glucose(SMBG) with a traditional blood glucose meter (BGM) in adults of type 2 diabetes mellitus (T2DM). | PMC10500819 |
Methods | diabetes self-management behaviors, low blood glucose, T2DM, diabetes | DIABETES | Adult participants with T2DM were assigned to an intervention group or a control group. The patients in the intervention group received a connected BGM with real-time data submission as well as individual needs-based tele-coaching to address and improve motivation and daily diabetes self-management. The patients in the control group received a traditional BGM. Changes in glycated hemoglobin(HbA1c), low blood glucose index(LBGI), and diabetes self-management behaviors were analyzed. | PMC10500819 |
Results | hypoglycemia, diabetes self-management behaviors | HYPOGLYCEMIA, DIABETES | The study demonstrated the superiority of the telemedicine-assisted structured SMBG versus the traditional BGM for improving HbA1c. Additionally, the telemedicine-assisted SMBG reduced the risk of hypoglycemia and enhanced diabetes self-management behaviors, as differences in the LBGI and the Diabetes Self-Management Questionnaire(DSMQ) results between the groups after 6 months were found to be significant. | PMC10500819 |
Conclusions | T2DM | HYPOGLYCEMIA, DISEASE PROGRESSION | Telemedicine-assisted structured SMBG helps physicians and patients to achieve a specific level of glycemic control and reduce hypoglycemia. The use of coaching applications and telemedicine-assisted SMBG indicated beneficial effects for T2DM self-management, which may help limit disease progression. | PMC10500819 |
Trial registration | Chinese Clinical Trail Registry No: ChiCTR2300072356 on 12/06/2023. Retrospectively registered. | PMC10500819 | ||
Keywords | PMC10500819 | |||
Introduction | diabetic, T1DM, T2DM, diabetes | DIABETES MELLITUS (DM), DISEASES, COMPLICATIONS, DIABETES | Diabetes mellitus (DM) is one of the world’s most serious non-infectious diseases and a major threat to human health. The World Health Organization estimates that 366 million patients will be suffering from diabetes by 2030, twice the number of patients in 2000 [DM is classified into type 1 DM (T1DM) and type 2 DM (T2DM), with T2DM accounting for nearly 95% [The incidence and severity of complications mainly depend on the course of diabetes and the control of blood glucose. Therefore, good metabolic control is very important for patients with T2DM. In China, control of blood glucose (BG) and other metabolic indicators for patients with T2DM mainly depends on doctors, which is relatively standardized. However, the management of diabetic patients outside hospitals is chaotic, lacking homogeneity, and mainly depends on the educational level and participation of patients. Therefore, the self-management of diabetic patients and appropriate management strategies are particularly important [Technology has long been used for self-management and to improve treatment compliance in people with diabetes [Telemedicine is a very promising tool for delivering personalized SMBG and healthcare at home or where it is needed, reducing the unnecessary use of healthcare resources [ | PMC10500819 |
Research design and methods | PMC10500819 | |||
Participant recruitment | T2DM, diabetes | ACUTE INFECTION, MALIGNANT TUMOR, LIVER FAILURE, DIABETES | This study was an open-label randomized (1:1) trial involving patients with T2DM with suboptimal glycemic control (7% ≤ HbA1c ≤ 11%), aged 18–75 years, with a diabetes duration of 5–10 years. The exclusion criteria were acute infection, malignant tumor, liver failure, pregnancy or lactation, long-term cortisol treatment, and inability to use smartphone applications. Participants were randomized by Qingpu Branch of Zhongshan Hospital affiliated to Fudan University, Shanghai, China from June 2021 to June 2022. All participants provided signed informed consent before enrolment in the study. And the study was registered at | PMC10500819 |
Intervention | diabetes | DIABETES | The participants were allocated randomly to an intervention group or a control group. The study was not blinded. Before the study, the patients in both groups received a self-management guide and a blood glucose meter(BGM). The name of the BGM was SINOMEDISITE as showing in Fig.
The blood glucose meter named SINOMEDISITE.Compared with the intervention group, the subjects in the control group could only examine and save their physiological parameters, which were eventually delivered to physicians for further medical advice. All the subjects had weekly phone-based coaching sessions in which they were encouraged to use the devices to actively control and manage their diabetes as part of their daily lives. The patients in both groups received routine care for medicine adjustment and condition assessment and visit hospital once a month. | PMC10500819 |
Statistical analysis | Assuming a standard deviation of HbA1c of 0.9% and considering as clinically relevant a minimum between group difference in HbA1c levels of 0.4%, the number of patients to be enrolled to ensure a power of 80% (alpha = 0.05) was 121 patients per arm. Assuming a dropout rate of 40%, 410 patients were needed. Randomization was performed through sealed envelopes. Random lists were computer generated. The baseline characteristics or data after intervention were summarized as mean and standard deviation (SD) (for continuous, normally distributed variables), median and interquartile range (for continuous, not normally distributed variables), or percentage (for categorical variables). The baseline characteristics and 6-month characteristics were compared between arms using an unpaired | PMC10500819 | ||
Results | hypoglycemic, high-density lipoprotein, SD, fasting blood glucose | As demonstrated in Fig.
Study design and flow chart
Patients’ characteristics on baselineTestingfrequency(times/month)Data are means ± SD or n (%). FBG, fasting blood glucose; BMI, body mass index; BP, blood pressure; HDL, high-density lipoprotein; LDL, low-density lipoprotein; OAD, oral hypoglycemic drugs*P<0.05 | PMC10500819 | |
Primary outcome | high-density lipoprotein, fasting blood glucose | At 6 months, the unadjusted mean HbA1c values were 7.38% for the intervention group and 7.98% for the control group(
Comparison of the differences between groupsBaseline(n = 212)3 Month(n = 136)6 Month(n = 115)Baseline(n = 206)3 Month(n = 101)6 Month(n = 65)Data is means ± SD, median and interquartile range, or n (%). FBG, fasting blood glucose; HDL, high-density lipoprotein; LDL, low-density lipoprotein; RIR, readings in-range (70 to 180 mg/dl); RIR in two weeks: RIR in the first 2 weeks of testing and in the 2 weeks prior to the 3 month and in the 2 weeks prior to the 6 month visit*P<0.05 | PMC10500819 | |
Secondary outcomes | Diabetes | DIABETES | The baseline LBGI values for the intervention group and the control group were 2.62 (SD: 1.76) and 2.71 (SD: 1.15), respectively, which decreased to 2.12 (SD: 0.96) and 2.52 (SD: 1.14) by month 6, respectively. The paired
The baseline LBGI for the intervention group and the control group were 2.62(SD 1.76) and 2.71(SD 1.15), respectively, which decreased to 2.12(SD 0.96) and 2.52(SD 1.14) by month 6. LBGI: low blood glucose indexThe baseline DSMQ results for the intervention group and the control group were 6.75 (SD: 1.80) and 6.13 (SD: 1.94), respectively, which increased to 7.79 (SD: 1.17) and 6.67 (SD: 1.22) by month 6, respectively. As shown in Table
The baseline DSMQ for the intervention group and the control group were 6.75(SD 1.80) and 6.13(SD 1.94), respectively, which decreased to 7.79(SD 1.17) and 6.67(SD 1.22) by month 6. SDMQ: Diabetes Self-Management Questionnaire | PMC10500819 |
Other outcomes | Overall, there was no difference between the intervention and the control group in terms of BMI, systolic BP, diastolic BP, TC, HDL, and LDL at 6 months. However, the independent-samples | PMC10500819 | ||
Discussion | T2DM, diabetes | DIABETES | This study examined the results of a telemedicine system (involving the use of telemedical devices and a coaching app) on the indicators of T2DM control over a 6-month period; the results demonstrated the superiority of the telemedicine system over the traditional approach for improving HbA1c. Additionally, we found a significant reduction in HbA1c levels independent of the BGM testing frequency. As shown in Table Regardless of the frequency of SMBG by patients, the positive change in HbA1c demonstrates that the telemedicine system facilitates the personalization of SMBG. A structured SMBG strategy may help patients within their daily routines to maintain as normal a BG level as possible by making appropriate food choices (with low/high carbohydrate intake) and lifestyle choices [Telemedicine-assisted structured SBMG has been shown to help patients by improving metabolic indicators, even if patients don’t monitor blood sugar frequently [
This study revealed a significant difference in TG between the intervention and control group after 6 months, which may have been due to patient lifestyle changes. Telemedicine-assisted interventions in lifestyle to change negative attitudes and promote healthy lifestyles include smoking cessation, dietary and exercise prescription, and diabetes education [
Telemedicine-assisted SBMG enhances interactions between patients with diabetes and diabetes specialists [
All the participants who were recruited in our study resided in rural areas; they had difficulty accessing hospitals and were potential users of telemedicine. Compared with urban residents, they need telemedicine systems and doctors’ guidance more. However, most previous similar studies selected urban patients [
Distribution of contracts from 0 to 6 monthsRC reference class | PMC10500819 |
Acknowledgements |
The authors thank all the physicians and technicians in the department of endocrinology and bio-chemical lab in Qingpu Branch of Zhongshan Hospital affiliated to Fudan University for their contribution. | PMC10500819 | ||
Authors’ contributions |
CH, MZ and JZ conceived the idea and conceptualised the study. JL, WX, PW, HJ and XY collected the data. CH, WX, PW, HJ, JL and XY analysed the data. CH drafted the manuscript, then MZ and JZ reviewed the manuscript. All authors read and approved the final draft. | PMC10500819 | ||
Funding |
This study was funded by Shanghai Municipal Health Commission (No. 20204Y0060), Science and Technology Development Fund Project of Qingpu District in Shanghai (No. QKY2021-03). The funding body played no role in the design of the study and collection, analysis, interpretation of data. | PMC10500819 | ||
Data Availability | The datasets used and/or analysed during the current study available from the corresponding author on reasonable request. | PMC10500819 | ||
Declarations | PMC10500819 | |||
Competing interests | The authors declare no competing interests. | PMC10500819 | ||
Ethics approval and consent to participate |
I confirm that I have read the Editorial Policy pages. This study was approved by the Medical Ethics Committee of Qingpu Branch of Zhongshan Hospital affiliated to Fudan University (Approval No: Qing Yi 2021-25). This study was conducted in accordance with the declaration of Helsinki. Written informed consent was obtained from all participants. | PMC10500819 | ||
Consent for publication | Not applicable. | PMC10500819 | ||
Abbreviations | diabetes | DIABETES | Self-monitoring of blood glucoseType 2 diabetes mellitusGlycated hemoglobinLow blood glucose indexDiabetes Self-Management QuestionnaireDiabetes mellitusType 1 diabetes mellitusApplicationsFasting blood glucoseBody mass indexBlood pressureTotal cholesterolTriglyceridesHigh density lipoproteinLow density lipoprotein2h-postprandial blood glucoseBlood glucose meterOral hypoglycemic drugsReference classReadings in-range | PMC10500819 |
References | PMC10500819 | |||
1. Introduction | Obesity, obesity, inflammation, overweight, overweight or obesity | OBESITY, OBESITY, INFLAMMATION, MALNUTRITION, PATHOGENESIS | Current address: Precision Nutrition and Cardiometabolic Health, IMDEA Food Institute, CEI UAM+CSIC, 28049 Madrid, Spain.Background and aims: Obesity is a public health problem. The usual treatment is a reduction in calorie intake and an increase in energy expenditure, but not all individuals respond equally to these treatments. Epigenetics could be a factor that contributes to this heterogeneity. The aim of this research was to determine the association between DNA methylation at baseline and the percentage of BMI loss (%BMIL) after two dietary interventions, in order to design a prediction model to evaluate %BMIL based on methylation data. Methods and Results: Spanish participants with overweight or obesity (Obesity is considered one of the main factors of morbidity due to malnutrition, and it is observed how incidences increase over the years. Obesity and overweight are defined as an excessive or abnormal accumulation of fat that can be detrimental to health. It is classified by body mass index (BMI). It is determined as overweight when presenting a BMI of 25 to 29.9 kg/mThe pathogenesis of obesity is due to a metabolic condition of disturbed adipocyte function and low-grade systemic inflammation, and this can induce epigenetic changes that perpetuate inflammation [ | PMC10746100 |
2. Materials and Methods | PMC10746100 | |||
2.1. Study Population | overweight | The population selected in this research was from the Obekit study, in which 314 Spanish individuals with overweight and obesity were initially recruited. The study lasted from October 2015 to February 2017 and was carried out in the Metabolic Unit of the Nutrition Research Center of the University of Navarra.The inclusion criteria were participants with an age range of 18–67 years old and participants with overweight (BMI: 25–29.9 kg/mAll research procedures were carried out following the ethical principles of the Declaration of Helsinki of 2013 [ | PMC10746100 | |
2.2. Study Design | HYPERTRIGLYCERIDEMIA, HYPERCHOLESTEROLEMIA, ARTERIAL HYPERTENSION, DYSLIPIDEMIA | Using the Obekit study database, the variables of interest for this research were selected from the 306 participants. General data such as sex; date of birth; and pathological history such as dyslipidemia, hypertriglyceridemia, hypercholesterolemia, and arterial hypertension were taken. The biochemical and anthropometric variables of visit 1, considered the baseline visit, and visit 3, which corresponded to the post-intervention visit, were selected. | PMC10746100 | |
2.3. Nutritional Intervention | The nutritional intervention lasted 4 months. The diets used for the study had a 30% calorie restriction. The individual energy requirements of each participant were estimated at the beginning, calculating their energy expenditure at rest and during physical activity, to prescribe the hypocaloric diets in a random manner. The macronutrient distribution for the moderately high-protein (MHP) diet was 40% carbohydrate, 30% protein, and 30% fat, and for the low-fat (LF) diet, it was 60% carbohydrate, 18% protein, and 22% fat. Both the LF and MHP diets were designed on the basis of a food exchange system. Participants received detailed information from trained dietitians on portion sizes, dietary patterns/eating schedules, and food preparation techniques. | PMC10746100 | ||
2.4. Anthropometric and Biochemical Determinations | TG | BLOOD | All participants underwent standardized procedures to measure body weight, height, waist circumference, and hip circumference, and body mass index (BMI) was calculated using the formula weight (kg) divided by height in squared meters [Blood samples were drawn after 12 h of fasting to obtain serum and plasma samples for biochemical determinations at the beginning and at the end of the intervention. Serum glucose, total cholesterol (TC), high-density lipoprotein cholesterol (HDL-C), triglycerides (TG), uric acid, and transaminases were assessed using an automated analyzer (Pentra C200, HORIBA Médica, Kyoto, Japan). Low-density lipoprotein cholesterol (LDL-C) levels were estimated using the Friedelwald formula: total cholesterol − HDL-C − (TGs/5) [ | PMC10746100 |
2.5. DNA Isolation and Bisulfite Conversion | BLOOD | Blood samples taken at the beginning of the study were centrifuged at 4 °C for 15 min to obtain plasma and isolate the buffy coat fraction. DNA extraction was performed with the “MasterPure” DNA purification kit for blood version II (Epicentre Biotechnologies, Madison, WI, USA), and it was quantified with a spectrophotometer (Nanodrop, Thermo Scientific, Wilmington, DE, USA) and stored at −80 °C. In a second step, 500 ng of DNA was treated with sodium bisulfite using the EZ-96 DNA methylation kit (Zymo Research Corporation, Irvine, CA, USA), to convert unmethylated cytosine residues to uracil, while methylated cytokines remained unchanged. | PMC10746100 | |
2.6. Array Analysis | The levels of methylated DNA were evaluated using the “Infinium MethylationEPIC BeadChip” kit (Illumina, San Diego, CA, USA), which includes 850,000 methylation sites. Samples were scanned with an “Illumina HiScanSQ” system, and image intensities were extracted with “GenomeStudio v1.9” methylation software (Illumina, CA, USA). The within-array quantile subset normalization (SWAN) method was used to improve the results obtained from the platform, reducing technical variation within and between arrays. The ComBat method was used to adjust for batch effects and remove technical variation. In addition, DNA methylation was corrected for cellular composition (granulocytes, monocytes, B cells, CD8+ cytotoxic cells, CD4+ T-helper cells, and natural killer cells) using Houseman’s algorithm [ | PMC10746100 | ||
2.8. Statistical Analysis | Variables were expressed as the mean ± SEM (standard error of the mean). To characterize the basal anthropometric and biochemical data of the general population, the | PMC10746100 | ||
2.9. Statistical Analysis for the Prediction Model | For the selection of CpG sites obtained in the methylation array, the CpG sites that presented >0.1 standard deviations were chosen. Then, a Spearman’s correlation analysis was performed with the CpG sites correlating their methylation with the percentage of BMI loss and selecting for each diet those that presented The algorithm “furnival-Wilson leaps and bounds” (vselect in Stata) [ | PMC10746100 | ||
3. Results | The results show the basal and post-intervention anthropometric and biochemical characteristics of the population with the MHP and LF diet. | PMC10746100 | ||
3.1. Anthropometric and Biochemical Data at Baseline | Statistical analysis of the baseline anthropometric and biochemical data of the 201 participants who were divided into two dietary intervention groups, 93 on the MHP diet and 108 on the LF diet ( | PMC10746100 | ||
3.2. Anthropometric and Biochemical Values after the Dietary Intervention and BMI Loss Prediction Model for the MHP and LF Diets Based on DNA Methylation Data | Of the 306 participants, 73 subjects did not complete the dietary intervention, and 32 participants had low adherence to the diets, obtaining post-intervention data from 201 participants: 93 on the MHP diet and 108 on the LF diet (The statistical analyses of the changes in the anthropometric and biochemical variables in response to dietary treatment after 4 months of intervention are shown in The analysis of the changes after the dietary intervention showed that the HDL-cholesterol in the participants with the LF diet presented a significantly greater increase than in the participants with the MHP diet (A prediction model based on basal DNA methylation data was designed to determine the percentage of BMI loss for each individual. The two types of diets and the CpG sites with methylation levels at the baseline best associated with BMI reduction were used as predictors.A selection of methylation sites was made from the 201 participants who completed the dietary intervention and had good adherence to the diets (Spearman’s correlation was performed between methylation of the selected 1233 CpG sites and the percentage of BMI loss for each of the diets after 4 months of intervention, selecting those CpG sites that presented a significant correlation with To better predict the percentage of BMI loss with each of the diets, the “furnival-Wilson leaps and bounds” algorithm (“vselect” in Stata) [With these requirements, 15 CpGs for the MHP diet and 11 CpGs for the LF diet were identified. | PMC10746100 | ||
3.3. Design of Weighted Sub-Scores That Contain the CpG Sites of Each Diet and the Calculation of the Total Methylation Score for the Prediction Model | REGRESSIONS | Weighted sub-scores were made for each diet, using the sum of the previously selected CpG sites and multiplying them by the beta coefficients obtained in each of the multiple linear regressions of the MHP diet and the LF diet (To obtain a total score for each individual that would allow to be included as a term for the interaction with the diet variable, the MHP diet sub-score was subtracted from the LF diet sub-score, as shown in A linear mixed effect model was used to predict, based on the total methylation score of each individual, which diet will be the best for the volunteers based on the greatest percentage of BMI loss. Therefore, a linear mixed effect model was designed with the percentage of BMI loss as the dependent variable and total score, diet (MHP/LF), and the interaction term between the total score and the diet as a fixed effect. Moreover, the IDs of the participants were included as a random effect. The model was adjusted for sex and age. | PMC10746100 | |
3.6. Biological Role of the Genes Related to the CpG Sites Selected for the Prediction Model | The biological role of the genes related to the CpG sites obtained in | PMC10746100 | ||
4. Discussion | In this research project, the association between DNA methylation in the blood and the reduction of the BMI percentage with an intervention with two types of diets, a moderately high-protein diet (MHP) and a low-fat diet (LF), was studied in a Spanish population. Likewise, the CpG sites which basal methylation was associated with the reduction of the percentage of BMI after 4 months of dietary intervention were identified and then used to construct a total methylation score that was included in a model together with the two types of diets, and it adequately predicted the percentage of BMI loss. | PMC10746100 | ||
4.1. Methylation Analyzed in Blood Samples Showing Association with the BMI | Thirty-four CpG sites for the MHP diet and twenty CpG sites for the LF diet were identified in blood and were associated with the percentage of BMI loss. This relationship of methylation sites with BMI has been observed in previous studies, such as in a clinical trial in a multiethnic Asian population that identified the methylation of 116 CpG sites associated with BMI and the methylation of 8 CpG sites that were associated with waist circumference [ | PMC10746100 | ||
4.2. Genes Related to CpG Sites Associated with the Percentage of BMI Loss for the MHP Diet and for the LF Diet | obesity, gastric cancer | OBESITY, GASTRIC CANCER | Another finding of this investigation is the biological role of the genes with the methylation sites in the blood associated with the percentage of BMI loss for both diets. Of these genes, those with functions in the cell cycle, the immune system, and metabolism were highlighted.In an experimental study, they analyzed the epigenetic marks related to obesity and studied the blood methylation of the Another study observed that in vitro gastric cancer cell lines infected with It is important to highlight that, in the present investigation, the study of methylation was analyzed in blood, and in some previously mentioned studies, the methylation of these same genes was studied in target tissues, which demonstrates that blood tissue can serve as a rather similar reflection of methylation status, as it has been analyzed in some studies that compared methylation in a target tissue versus identifying methylation in blood, in which methylation data were considered to be more specific if performed in the tissue of interest for the investigation. But blood is a valid option that can provide fairly accurate information on the methylation status whenever adjustments are made to the blood cell composition, since it has the advantage of being a more accessible tissue, which can be taken routinely [ | PMC10746100 |
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