UNASSIGNED: This manuscript performed a meta-analysis to compare the effects of a low-fat diet (LFD) and a low-carbohydrate diet (LCD) on body weight and lipid levels in adolescents with overweight and obesity.
UNASSIGNED: PubMed and other databases were searched for the full-text literature comparing LFD and LCD up to November 2023 using a subject plus free word strategy, with search terms such as \"low-fat diet\", \"low-carbohydrate diet\", \"obesity\", \"weight\", \"adolescents\", \"RCT\", and so on. Two independent reviewers selected promising candidate trials, collected the data, and assessed the quality of the trials. RevMan 5.3 software was utilized to conduct a meta-analysis of the randomized controlled trials (RCTs) that were included.
UNASSIGNED: 5 RCTs with 192 participants were included in this meta-analysis. Weight (mean difference -2.81; 95% CI -5.38 to -0.25), Body Mass Index (BMI) (-1.13; 95% CI -2.14 to -0.11) and Triglyceride (TG) (-0.36; 95% CI -0.46 to -0.27) of the LCD were significantly lower than that of the LFD. At the same time, the high-density lipoprotein cholesterol (HDL) levels of the LCD were significantly higher than those of the LFD (0.08; 95% CI 0.04 to 0.12) (P < 0.05). However, there was no significant difference in the Homeostasis Model Assessment of Insulin Resistance (HOMA-IR), percent body fat, total cholesterol (TC), and low-density lipoprotein cholesterol (LDL) between the two groups (P>0.05).
UNASSIGNED: According to this study, LCD is more helpful in improving weight loss, HDL and TG. Thus, LCD may serve as an effective intervention for weight management in adolescents with overweight and obesity, although further research is needed to determine its long-term effects.

OBJECTIVE: There is limited research on the proportion of individuals with epilepsy who maintain response to ketogenic diet therapy (KDT) after discontinuing treatment. We aimed to determine the proportion of individuals who did / did not maintain response post KDT and explore factors that may influence the likelihood of maintaining response.
METHODS: Retrospective data were collected from 97 individuals from 9 KDT centres. Individuals had achieved ≥50 % seizure reduction on KDT for at least 12 months, with seizure frequency data available at 3 months+ post diet. Outcome 1 was: recurrence of seizures or increase in seizure frequency post diet; outcome 2: recurrence of seizures, increase in seizure frequency or an additional anti-seizure treatment started post diet.
RESULTS: 61/97 (62.9 %) individuals maintained response at latest follow-up (mean 2.5[2.0] years since stopping KDT). Approximately one third maintained response without further anti-seizure treatments. One quarter of individuals had an increase in frequency or recurrence of seizures within 6 months (95 %CI 4, 12) for outcome 1 and within 3 months (3, 6) for outcome 2. Individuals who did not achieve seizure freedom on diet were significantly more likely to have an increase in seizures or to require additional anti-seizure treatments post diet compared to those who were seizure-free on diet (hazard ratio 4.02, 95 %CI (1.46, 11.16) p < 0.01).
CONCLUSIONS: Our findings should help guide clinical teams with the information they provide patients and their families regarding likelihood of long-term seizure response to KDT. Realistic costings for KDT services may need to be considered.

BACKGROUND: Mischaracterization of dietary intake by patients and study participants is a common problem that presents challenges to clinical and public health approaches to improve diet quality, identify healthy eating patterns, and reduce the risk of chronic disease.
OBJECTIVE: This study examined participants\' self-reported adherence to low-carbohydrate and low-fat diets compared with their estimated adherence using up to 2 24-hour recalls.
METHODS: This cross-sectional study acquired data on dietary intake from respondents in the National Health and Nutrition Examination Survey, 2007-2018.
METHODS: This study included 30 219 respondents aged 20 years and older who had complete and reliable dietary data and were not pregnant or breastfeeding.
METHODS: The main outcome was prevalence of self-reported and estimated adherence to low-carbohydrate or low-fat diet patterns.
METHODS: Self-reported adherence to low-carbohydrate or low-fat diets was evaluated using responses to questionnaires. Estimated adherence to these diets was assessed using data from up to 2 24-hour recalls and usual intake methodology developed by the National Cancer Institute.
RESULTS: Of the 1.4% of participants who reported following a low-carbohydrate diet, estimated adherence (<26% energy from carbohydrates) using 24-hour recalls was 4.1%, whereas estimated adherence among those that did not report following a low-carbohydrate diet was <1% (P value for difference = .014). Of the 2.0% of participants who reported following a low-fat diet, estimated adherence (<30% energy from fat) was 23.0%, whereas estimated adherence among those who did not report following a low-fat diet was 17.8% (P value for difference = .048).
CONCLUSIONS: This research demonstrates that most individuals mischaracterized their diet pattern when compared with up to 2 24-hour recalls. These findings emphasize the need for clinicians and public health professionals to be cautious when interpreting individuals\' self-reported diet patterns, and should aim to collect more detailed dietary data when possible.

OBJECTIVE: Considering the high prevalence of obesity and related metabolic impairments in the population, the unique role nutrition has in weight loss, reversing metabolic disorders, and maintaining health cannot be overstated. Normal weight and well-being are compatible with varying dietary patterns, but for the last half century there has been a strong emphasis on low-fat, low-saturated fat, high-carbohydrate based approaches. Whereas low-fat dietary patterns can be effective for a subset of individuals, we now have a population where the vast majority of adults have excess adiposity and some degree of metabolic impairment. We are also entering a new era with greater access to bariatric surgery and approval of anti-obesity medications (glucagon-like peptide-1 analogues) that produce substantial weight loss for many people, but there are concerns about disproportionate loss of lean mass and nutritional deficiencies.
RESULTS: No matter the approach used to achieve major weight loss, careful attention to nutritional considerations is necessary. Here, we examine the recent findings regarding the importance of adequate protein to maintain lean mass, the rationale and evidence supporting low-carbohydrate and ketogenic dietary patterns, and the potential benefits of including exercise training in the context of major weight loss. While losing and sustaining weight loss has proven challenging, we are optimistic that application of emerging nutrition science, particularly personalized well-formulated low-carbohydrate dietary patterns that contain adequate protein (1.2 to 2.0 g per kilogram reference weight) and achieve the beneficial metabolic state of euketonemia (circulating ketones 0.5 to 5 mM), is a promising path for many individuals with excess adiposity.

The ketogenic diet (KD) is a distinctive dietary regimen known for its low-carbohydrate and high-fat composition. Recently, it has garnered considerable interest from the scientific community and the general population because of its claimed efficacy in facilitating weight reduction, improving the management of glucose levels, and raising overall energy levels. The core principle of the KD is the substantial decrease in carbohydrate consumption, which is subsequently substituted by ingesting nourishing fats. While the KD has promising advantages and is gaining popularity, it must be acknowledged that this dietary method may not be appropriate for all individuals. The dietary regimen may give rise to adverse effects, including constipation, halitosis, and imbalances in electrolyte levels, which may pose a potential risk if not adequately supervised. Hence, thorough and meticulous inquiry is needed to better comprehend the possible hazards and advantages linked to the KD over prolonged durations. By obtaining a more comprehensive perspective, we can enhance our ability to make well-informed judgments and suggestions as to implementation of this specific dietary regimen.

There is a substantial body of clinical evidence supporting the beneficial effects of lower-carbohydrate dietary patterns on multiple established risk factors associated with insulin resistance and cardiovascular diseases in adult populations. Nutrition and health researchers, clinical practitioners, and stakeholders gathered for, \"The Scientific Forum on Nutrition, Wellness, and Lower-Carbohydrate Diets: An Evidence- and Equity-Based Approach to Dietary Guidance\" to discuss the evidence base around lower-carbohydrate diets, health outcomes, and dietary guidance. Consensus statements were agreed upon to identify current areas of scientific agreement and spotlight gaps in research, education, and practice to help define and prioritize future pathways. Given the evidence base and considering that most American adults are living with at least one nutrition-related chronic disease, there was consensus that including a lower-carbohydrate dietary pattern as one part of the Dietary Guidelines for Americans could help promote health equity among the general population.

BACKGROUND: The Healthy Eating Index 2010 (HEI-2010) and Alternative Healthy Eating Index 2010 (AHEI-2010) are commonly used to measure dietary quality in research settings. Neither index is designed specifically to compare diet quality between low-carbohydrate (LC) and low-fat (LF) diets. It is unknown whether biases exist in making these comparisons.
OBJECTIVE: The aim was to determine whether HEI-2010 and AHEI-2010 contain biases when scoring LC and LF diets.
METHODS: Secondary analyses of the Diet Intervention Examining the Factors Interacting With Treatment Success (DIETFITS) weight loss trial were conducted. The trial was conducted in the San Francisco Bay Area of California between January 2013 and May 2016. Three approaches were used to investigate whether biases existed for HEI-2010 and AHEI-2010 when scoring LC and LF diets.
METHODS: DIETFITS participants were assigned to follow healthy LC or healthy LF diets for 12 months (n = 609).
METHODS: Mean diet quality index scores for each diet were measured.
METHODS: Approach 1 examined both diet quality indices\' scoring criteria. Approach 2 compared scores garnered by exemplary quality LC and LF menus created by registered dietitian nutritionists. Approach 3 used 2-sided t tests to compare the HEI-2010 and AHEI-2010 scores calculated from 24-hour dietary recalls of DIETFITS trial participants (n = 608).
RESULTS: Scoring criteria for both HEI-2010 (100 possible points) and AHEI-2010 (110 possible points) were estimated to favor an LF diet by 10 points. Mean scores for exemplary quality LF menus were higher than for LC menus using both HEI-2010 (91.8 vs 76.8) and AHEI-2010 (71.7 vs 64.4, adjusted to 100 possible points). DIETFITS participants assigned to a healthy LF diet scored significantly higher on HEI and AHEI than those assigned to a healthy LC diet at 3, 6, and 12 months (all, P < .001). Mean baseline scores were lower than mean scores at all follow-up time points regardless of diet assignment or diet quality index used.
CONCLUSIONS: Commonly used diet quality indices, HEI-2010 and AHEI-2010, showed biases toward LF vs LC diets. However, both indices detected expected changes in diet quality within each diet, with HEI-2010 yielding greater variation in scores. Findings support the use of these indices in measuring diet quality differences within, but not between, LC and LF diets.

Eating a high-quality diet or adhering to a given dietary strategy may influence weight loss. However, these 2 factors have not been examined concurrently for those following macronutrient-limiting diets.
To determine whether improvement in dietary quality, change in dietary macronutrient composition, or the combination of these factors is associated with differential weight loss when following a healthy low-carbohydrate (HLC) or healthy low-fat (HLF) diet.
Generally healthy adults were randomly assigned to HLC or HLF diets for 12 mo (n = 609) as part of a randomized controlled weight loss study. Participants with complete 24-h dietary recall data at baseline and 12-mo were included in this secondary analysis (total N = 448; N = 224 HLC, N = 224 HLF). Participants were divided into 4 subgroups according to 12-mo change in HEI-2010 score [above median = high quality (HQ) and below median = low quality (LQ)] and 12-mo change in macronutrient intake [below median = high adherence (HA) and above median = low adherence (LA) for net carbohydrate (g) or fat (g) for HLC and HLF, respectively]. Baseline to 12-mo changes in mean BMI were compared for those in HQ/HA, HQ/LA, LQ/HA subgroups with the LQ/LA subgroup within HLC and HLF.
For HLC, changes (95 % confidence level [CI]) in mean BMI were -1.15 kg/m2 (-2.04, -0.26) for HQ/HA, -0.30 (-1.22, 0.61) for HQ/LA, and -0.80 (-1.74, 0.14) for LQ/HA compared with the LQ/LA subgroup. For HLF, changes (95% CI) in mean BMI were -1.11kg/m2 (-2.10, -0.11) for HQ/HA, -0.26 (-1.26, 0.75) for HQ/LA, and -0.66 (-1.74, 0.41) for LQ/HA compared with the LQ/LA subgroup.
Within both HLC and HLF diet arms, 12-mo decrease in BMI was significantly greater in HQ/HA subgroups relative to LQ/LA subgroups. Neither HQ nor HA alone were significantly different than LQ/LA subgroups. Results of this analysis support the combination of dietary adherence and high-quality diets for weight loss.
clinicaltrials.gov (Identifier: NCT01826591).

Despite the known effects of diet on gut microbiota composition, not many studies have evaluated the relationship between distinct dietary patterns and gut microbiota. The aim of our study was to determine whether gut microbiota composition could be a useful indicator of a long-term dietary pattern. We collected data from 89 subjects adhering to omnivorous, vegetarian, vegan, and low-carbohydrate, high-fat diet that were equally distributed between groups and homogenous by age, gender, and BMI. Gut microbiota composition was analyzed with a metabarcoding approach using V4 hypervariable region of the 16S rRNA gene. K-means clustering of gut microbiota at the genus level was performed and the nearest neighbor classifier was applied to predict microbiota clustering classes. Our results suggest that gut microbiota composition at the genus level is not a useful indicator of a subject\'s dietary pattern, with the exception of a vegan diet that is represented by a high abundance of Prevotella 9. Based on our model, a combination of 26 variables (anthropometric measurements, serum biomarkers, lifestyle factors, gastrointestinal symptoms, psychological factors, specific nutrients intake) is more important to predict an individual\'s microbiota composition cluster, with 91% accuracy, than the dietary intake alone. Our findings could serve to develop strategies to educate individuals about changes of some modifiable lifestyle factors, aiming to classify them into clusters with favorable health markers, independent of their dietary pattern.

UNASSIGNED: All Veterans Affairs (VA) Medical Centers offer the MOVE! Weight Management Program to help patients achieve and maintain a healthy weight through a calorie-restricted, low-fat diet and increased physical activity. Yet, most MOVE! participants do not achieve clinically significant weight loss of ≥5%. A carbohydrate-restricted diet may help more Veterans to achieve ≥5% weight loss.
UNASSIGNED: This was a single-arm explanatory sequential mixed methods pilot study conducted in one VA health care system. Veterans with prediabetes and body mass index ≥25 kg/m2 were invited to participate in a group-based, virtual, very low-carbohydrate Diabetes Prevention Program (VLC-DPP) consisting of 23 sessions over 12 months. Participants were taught to follow a very low-carbohydrate eating pattern, defined as 20-35 grams of net carbohydrates per day. The primary outcomes were measures of feasibility and acceptability, including program uptake and session attendance. Secondary outcomes included change in weight, hemoglobin A1c, lipids, and patient-reported measures of food cravings, stress eating, perceived health status, and motivation. Interviews were conducted at 6 months to identify factors that facilitated or hindered participants\' achievement of ≥5% weight loss.
UNASSIGNED: Among 108 screened Veterans, 21 enrolled in the study (19%), and 18 were included in the analytic cohort. On average, participants attended 12.4/16 weekly sessions and 3.6/8 bimonthly or monthly sessions. At 12 months, mean percent weight loss was 9.4% (SD = 10.7) with 9 participants (50%) achieving ≥5% weight loss. Three factors facilitated achievement of ≥5% weight loss among 10/16 interviewees: (1) enjoyment of low-carbohydrate foods; (2) careful monitoring of carbohydrate intake; and (3) reduced hunger and food cravings. Three factors hindered achievement of ≥5% weight loss among 6/16 interviewees: (1) food cravings, particularly for sweets; (2) challenges with maintaining a food log; and (3) difficulty with meal planning.
UNASSIGNED: A VLC-DPP is feasible and acceptable and shows preliminary efficacy among Veterans with prediabetes. The program\'s weight loss effectiveness compared to standard MOVE! should be evaluated in a larger-scale trial. Such a program may be offered in addition to the standard MOVE! program to expand the menu of evidence-based lifestyle counseling options for Veterans.
UNASSIGNED: https://clinicaltrials.gov/ct2/show/NCT04881890, identifier NCT04881890.