Evaluating the SWW Method® Dietary Intervention for HbA1c and Weight Management as a Preventive Intervention in Women

Evaluating the SWW Method® Dietary Intervention for HbA1c and Weight Management as a Preventive Intervention in Women | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Evaluating the SWW Method® Dietary Intervention for HbA1c and Weight Management as a Preventive Intervention in Women Megan Grover, Emily Eastman, Jessica Rybka, Evan Semet, Lukasz Zdanowicz This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9003742/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 31 You are reading this latest preprint version Abstract Background Individuals with elevated blood glucose markers and an unhealthy BMI are more at risk for type II diabetes, cardiovascular disease, and metabolic syndrome. Yet, most diets addressing this are not sustainable and are typically only implemented in an unhealthy population rather than functioning as a preventative measure. There is a notable gap in dietary interventions designed for generally healthy populations seeking to optimize metabolic health before disease onset. We conducted a case-control trial to determine if the SWW Method® was an effective intervention to help lower blood glucose and weight in a healthy cohort of women aged 35–75 (baseline HbA1c: 5.31–5.4%, BMI: 23–25). The SWW Method® is a carbohydrate-insulin model dietary and lifestyle intervention. Method: We recruited 45 SWW Method® participants and 40 comparable controls. All participants completed the same questionnaire and lab testing at the start and end of the 12-week study. Results: The intervention group’s HbA1cs improved significantly (5.4% to 5.33%) compared to the control group (5.31% to 5.4%) (p < 0.05). Yet, there was no significant change in fasting blood glucose. The SWW Method® group also experienced a reduction in BMI (p < 0.0001) and weight (p < 0.0001) over the course of the 12 weeks, but not in comparison to the controls, who also lost weight. Conclusion: Integrating the SWW Method® is moderately effective in improving HbA1c and in supporting weight loss within the intervention group, indicating its potential as a preventative health intervention for optimizing metabolic health in an already healthy population. Trial Registration ClinicalTrials.gov, NCT07463417, retrospectively registered March 5, 2026. Trial registration ClinicalTrials.gov, NCT07463417, retrospectively registered March 5, 2026 carbohydrate-insulin model blood sugar balance weight loss women case-control trial Figures Figure 1 1 Introduction Obesity and elevated blood glucose markers are prevalent and predominant risk factors for chronic diseases, including type II diabetes mellitus (T2DM), cardiovascular disease, and metabolic syndrome. Fad diets are thus ever emerging. The most long-standing weight loss trend is calorie restriction, based on the Energy Balance Model (EBM) [ 1 , 2 ]. EBM is a theory based on calories in vs calories out, where intake is modulated by neural regulation. Yet, recent research has found this to be ineffective in achieving long-term weight loss [ 3 – 6 ]. The carbohydrate-insulin model (CIM) has taken the forefront in obesity and diabetes research. The CIM theorizes excess carbohydrate consumption leads to excess insulin release, which causes weight gain, due to insulin’s anabolic nature [ 4 , 7 ]. Dietary carbohydrates are the drivers of insulin secretion. Once insulin has shuttled glucose out of the bloodstream, there is less available fuel three to five hours after eating, resulting in lowered energy expenditure and a counter-regulatory response via a release of catabolic hormones [ 4 , 8 ]. The resultant drop and rise in blood sugar influences cravings, hunger, and energy intake [ 9 , 10 ]. Abiding by the EBM, individuals initially lose weight. Yet, due to deprived caloric intake, energy expenditure and metabolism decline. For continued weight loss, further caloric restriction is required [ 11 ]. Sustained weight loss requires a biological approach where the intervention influences anabolic and catabolic hormones. By limiting carbohydrate consumption, insulin release is reduced, anabolism is limited, and energy stores are accessed [ 4 , 12 ]. Modulating insulin release is essential as diseases caused by insulin resistance (IR) - T2DM, metabolic syndrome, cardiovascular disease, and obesity - are on the rise [ 6 , 8 , 13 ]. T2DM increased from 108 million in 1980 to 422 million in 2014. Worldwide incidence has risen to 8.4% of the population [ 14 ]. Increased intake of refined carbohydrates and sweetened beverages correlate with its onset [ 14 ]. Obesity currently impacts more than 93 million Americans; 39.6% of the population. Most individuals who try to lose weight gain most of it back within five years [ 15 , 16 ]. Diabetes emerges due to insulin resistance and the lack of insulin release from the pancreas [ 17 ]. As the disease progresses, glucose homeostasis cannot be maintained, and there is a 15% increased risk of all-cause mortality, with cardiovascular disease (CVD) being the greatest cause of death [ 18 ]. Blood sugar dysregulation is a precursor to CVD due to glucose’s impact on the arterial wall [ 19 ]. Excess blood glucose increases the NADH / NAD + ratio, which increases production of free radicals (and potentially oxidized low-density lipoproteins (LDL)) and reduces nitric oxide availability, precursors to endothelial dysfunction. Elevated blood glucose causes an overproduction of aldoses and an increased production of advanced glycation end products (AGEs) [ 18 ]. AGEs are created by a non-enzymatic reaction between glucose and proteins. These glycated proteins cross-link with one another and tissue proteins, leading to blood vessel and tissue damage. AGEs activate an inflammatory response, which increases expression of coagulant molecules, inhibits anticoagulant molecules, and activates thickening of the arterial wall’s basement membrane, favoring protein and lipid deposition and impaired vasodilation [ 18 – 20 ]. Hyperinsulinemia also leads to diabetic dyslipidemia. This includes: elevated triglycerides (TGs), triglyceride-rich lipoproteins, small dense LDL’s (sdLDL), and reduced high-density lipoproteins (HDL) levels, which contribute to atherosclerosis and CVD risk [ 18 , 21 ]. T2DM and IR metabolically impair the efficiency of production, metabolism, and clearance of lipoproteins. Rather, with T2DM, elevated TG’s, elevated very low-density lipoproteins (VLDL), and normal or slightly elevated TG enriched small dense LDL (sdLDL), are present [ 18 , 21 – 23 ]. sdLDL has a high propensity for atherogenesis as high levels correlate with coronary artery calcification [ 24 , 25 ]. A high-carbohydrate, high-fructose diet, as well as insulin resistance, is positively correlated with its synthesis [ 25 , 26 ]. Exercise, weight loss, carbohydrate reduction, and a balanced microbiome are effective in lowering sdLDL [ 26 ]. Finding new methods to implement sustainable weight loss and blood sugar balance is essential in disease prevention. In previous years, dietary interventions were low-calorie, high-carbohydrate diets, which initially led to weight loss, but due to a rapidly declining metabolism and blood sugar imbalances, the participants plateaued and gained all their weight back, post study participation [ 27 , 28 ]. This has been supported by recent research finding that low-carbohydrate, high-fat diets are significantly more effective in achieving weight loss and blood glucose balance than high-carbohydrate, low-fat diets [ 29 , 30 ]. Isocaloric diets based on varied macronutrient disposition have shown longer-term effectiveness at optimizing weight than calorie-restrictive diets [ 31 , 32 ]. The effectiveness is in balancing macronutrients by limiting carbohydrates and optimizing protein and fats to balance blood sugar and prevent excess insulin release, mitigating constant anabolism. This research study addresses the pandemic of obesity and T2DM by utilizing a therapeutic, preventative intervention to support and maintain weight loss and blood sugar balance: the SWW Method®, a proprietary method focused on sustainable dietary and lifestyle changes. The SWW Method® is an effective protocol as it does not abide by calorie restriction but rather implements changes to support blood sugar balance and reduce glucose’s anabolic and inflammatory impact. This isocaloric CIM approach is addressed via adequate hydration, alkalizing with a daily greens powder, intermittent fasting, conscious meal timing, prioritizing a protein-forward diet, consuming non-starchy vegetables with every meal, limiting starchy carbohydrate intake to once per day, mineralizing with magnesium, and sleep support. The benefit of this research is to provide insight for possible dietary and lifestyle modifications to address the lack of effective interventions to prevent obesity and blood sugar dysregulation. This study will primarily assess if integration of the SWW Method® results in significant weight loss and improved blood glucose markers in women 35–75 years of age. Secondly, to determine if the SWW Method® results in improved lipid markers, blood pressure, C-reactive protein, and biometrics. Lastly, to determine if it improves other lab and non-lab related metrics. 2 Materials & Methods 2.1 Study Design & Population The SWW Method® study is a non-randomized case-control trial in which participants self-selected into either the SWW Method® intervention group (cases) or a no-intervention control group through separate recruitment channels. Primary, secondary, and exploratory outcome measures were compared between the two groups. This 12-week trial was designed to evaluate the SWW Method®'s effect on blood sugar balance and weight loss in a moderately healthy population of women aged 35–75. Cases completed the six-week SWW Method® program, followed by six weeks of coach-supported accountability via the ATE™ app. Questionnaires and lab work were collected one week before and six weeks after the intervention to assess changes over the 12-week period. The second lab collection was timed to six weeks post-intervention to capture long-term outcomes, including measures such as Hemoglobin A1c (HbA1c) that require approximately three months to reflect meaningful change. Controls completed the same questionnaires and lab collections at identical timepoints for comparison. Post-study data were analyzed against baseline to evaluate the SWW Method®'s effect on all primary, secondary, and exploratory measures relative to controls. 2.1.1 Recruitment: Eligibility criteria were identical for both groups: women aged 35–75 with no history of non-skin-related cancer diagnosis or treatment, not currently taking weight loss or blood sugar medications, and with no prior enrollment in the SWW Method® or Sarah Wragge Wellness® program. Cases were recruited from women who had enrolled in the SWW Method® program. Upon completing their health history form, participants received an automated email inviting them to join the study, outlining participation requirements and compensation (six weeks of additional accountability support via the ATE™ app and fully reimbursed labs). Interested participants were directed to contact the PI for next steps. Controls were recruited from the broader community via fliers posted on social media (LinkedIn and Instagram) and distributed to SWW® employees' networks. Fliers outlined participation requirements and compensation ( $ 25 Amazon gift card and fully reimbursed labs). Interested individuals completed a screening survey, and those who qualified were contacted by the PI via email. 2.1.2 Study staff training: Study staff included the PI, consent meeting facilitator, SWW Method® Integrative Wellness Coaches, and an operations manager. Coaches had daily interaction with case participants as their program clients; the operations manager oversaw email automation and supported recruitment. All study staff completed training with the PI covering study overview, recruitment, participant compensation, coach involvement, safety protocols, and participant anonymity, as well as the Human Subjects Protection Training through Advarra. 2.2 Data Collection Questionnaires : One week before the intervention, both groups completed a baseline questionnaire covering health goals, concerns, and history; current medications, supplements, and surgeries; hereditary profile; GI health; energy; sleep; anxiety; allergies; and current diet and exercise (see Supplementary File 1). A follow-up questionnaire was completed six weeks after the intervention to assess changes over the 12-week period (see Supplementary File 2). Labs : At the same baseline timepoint, participants from both groups purchased and scheduled a Comprehensive Health Profile through Quest Diagnostics, submitting results to the PI as a PDF. Six weeks post-intervention, the PI sent instructions for the second round of labs. Upon receipt of results, participants were provided a reimbursement form to cover the cost of both lab collections. Lab results were accessible only to the PI, and were shared with participants' assigned health coaches solely at the participant's discretion. Comprehensive Health Profile : The panel included a complete blood count (CBC), comprehensive metabolic panel (CMP), lipid/cholesterol panel, urinalysis, hemoglobin A1c (HbA1c), vitamin D, and high-sensitivity C-reactive protein (hsCRP), as well as biometrics (height, weight, waist circumference, and blood pressure) and a brief online health risk assessment capturing family history and current health behaviors. Participants were advised to fast for 8–12 hours prior to collection. Quest Diagnostics collected urine and blood samples; any flagged results were followed up directly with the participant by a Quest physician. Study Administration The study was conducted remotely. Questionnaires and consent forms were completed privately online, lab work was performed at participants' nearest Quest Diagnostics location, and all coach and staff meetings were held via Zoom. Post participation : Upon study completion, participants scheduled a 15-minute meeting with the PI to address any questions, confirm reimbursement form completion, and review both sets of lab results. Lab reimbursement forms were withheld until second-round lab results were submitted to the PI, at which point participants received their compensation. 2.3 Participants Sample size determination : The power assessment for the primary end points (body mass index (BMI), HbA1cs, and fasting blood glucose) was based on data abstracted from similar case-control dietary intervention trials [ 33 , 34 ]. Assuming a 2-sided significance level of 0.05, the case-control trial needed at least 36 participants per group to provide 80% power to detect statistically significant differences in BMI, HbA1cs and fasting blood glucose of at least a standard deviation of 2.1, 1.0, and 5.4 (respectively) between the two groups. The original sample size of 102 participants (56 cases and 46 controls) allowed for a 29% dropout rate to still achieve statistical significance. In the end, 85 individuals (45 cases and 40 controls) remained in the study, indicating only a 16% dropout. This was an adequate sample size to determine if the SWW Method® has a significant impact on weight loss and blood sugar balance. Ethical approval and informed consent : All participants received an IRB-approved informed consent form outlining the study and providing sufficient information to support an informed decision about participation. Prior to signing, each participant completed a one-on-one meeting with a study staff member to review the form, address any questions, and discuss participant anonymity protections. 2.4 Dietary and Lifestyle Intervention 2.4.1 Cases: Prior to beginning the intervention, cases completed the following onboarding steps: (1) a one-on-one Zoom meeting with study staff to review study design, anonymity, and consent; (2) signing of IRB-approved consent forms via HIPAA-compliant DocuSign; (3) completion of the baseline health questionnaire; and (4) a lab appointment one week prior to the program start date. Adherence was monitored through consistent communication with the participants' assigned SWW® coach, weekly follow-up emails from the PI, daily food logs, and biomarker assessment at baseline and study completion. During the first six weeks, cases were enrolled in the SWW Method® program, which included: The SWW Method® Protocol: Participants followed a structured daily nutrition and lifestyle protocol, including: morning hydration (16–32 oz of water upon waking); alkalizing with a dehydrated greens powder, no-fruit green juice, or lemon water prior to coffee or food; 2–3L of water throughout the day; a 12–16 hour overnight fast; a feast-famine meal structure emphasizing 30 + grams of protein with adequate fat and fiber per meal, with 3–4 hours between meals and no snacking; a high-protein, low-carbohydrate mini-meal before dinner to manage evening intake; and starchy carbohydrates reserved for one meal per day, consumed last after protein and non-starchy vegetables. An evening wind-down routine by closing the kitchen two hours before bed and taking SWW Restore®. Coaching Calls: Two 45-minute one-on-one calls with an assigned integrative wellness coach via Zoom. The initial call addressed integration of the SWW Method® steps and was followed by a customized meal plan. The second call provided accountability and further individualized support. ATE™ App Access: Daily access to their coach via the ATE™ app, where participants logged food journals, water intake, and exercise. Weekly Group Coaching Calls: 90-minute live sessions led by SWW® experts covering the steps of the SWW Method®, the science of blood sugar balance, macronutrient nutrition basics, healthy exercise strategies, the impact of stress on blood sugar and hormones, and gastrointestinal health. SWW Method® Handbook: A downloadable resource outlining the protocol steps, integration guidance, recipes, shopping lists, exercise recommendations, and evening routine suggestions. Supplements: A one-month supply of SWW Alkalize® (a dehydrated green powder) and SWW Restore® (a magnesium-rich mineral supplement containing sodium and potassium bicarbonate, calcium citrate, and magnesium glycinate). Participants were advised to take Alkalize in the morning and afternoon, and Restore 30 minutes before bed. Kajabi Platform Access: Access to the SWW Method® Kajabi page, including weekly nutrition education modules, group coaching call recordings, and a community board for peer and team support. Access was maintained through the end of study participation. During the final six weeks, participants received continued daily coach access via the ATE™ app and continued access to nutrition education materials on the Kajabi platform to support ongoing adherence to the SWW Method®. Prior to study completion, the PI instructed participants to schedule their second lab appointment and complete the follow-up questionnaire. 2.4.2 Controls: Prior to enrollment, controls completed the same onboarding steps as cases: (1) a one-on-one Zoom meeting with study staff; (2) signing of IRB-approved consent forms via HIPAA-compliant DocuSign; (3) completion of the baseline questionnaire (see Supplementary File 1); and (4) a lab appointment one week prior to the study start date. Controls received no dietary or lifestyle intervention and had no interaction with SWW® integrative wellness coaches. At the end of the 12-week period, the PI notified controls to schedule their second lab appointment and complete the follow-up questionnaire (see Supplementary File 2). Upon submission of second-round lab results and completion of the reimbursement form, controls received a $ 25 Amazon gift card and full lab reimbursement. 2.5 Statistical Analysis Prior to analysis, each participant was assigned a unique study number to ensure anonymity. All statistical analyses were performed in Python (Version 3.10) [ 35 ] within a Google Collaboratory environment. Data handling was conducted with pandas [ 36 ] and numpy [ 37 ]. Between-group comparisons were performed using the Wilcoxon signed-rank test from scipy.stats [ 38 ]. Difference-in-differences regression models were conducted with statsmodels [ 39 ] and were adjusted for relevant confounders , including age and sex to account for factors that could influence both the exposure and outcome. All hypothesis tests were two-tailed , and statistical significance was set at p < 0.05. 2.6 Ethical Considerations This study was conducted in accordance with the Declaration of Helsinki, applicable U.S. federal regulations, institutional policies, and ICH Good Clinical Practice (GCP) guidelines, and was reviewed and approved by Advarra Institutional Review Board (IRB00077176; 22 Apr 2024). Participant anonymity was maintained throughout, with no personal data linked to identifying information. 3 Results 3.1 Recruitment A total of 103 individuals were officially enrolled in the study. Of the 721 individuals who responded to the control participant query, 58 did not qualify and were excluded. Of the remaining 663, 150 expressed interest and were approved to participate, of whom 46 officially enrolled and 40 completed the study. Of the 255 individuals enrolled in the SWW Method® group coaching program, 72 were interested, approved, and qualified to participate as cases, of whom 56 officially enrolled and 45 completed the study. In total, 85 participants completed the 12-week study. Please refer to Fig. 1 below (p. 8). Table 1 on page 9 displays baseline data of all primary and secondary objectives (as well as some exploratory objectives) of both the intervention group and the control group. Table 1 baseline characteristics of participants Characteristic Control group (40) Intervention group (45) Age (SD) 45.6 (9.33) 48.4 (6.98) HbA1c’s, % (SD) [95% CI] 5.31 (0.26) [5.23–5.39] 5.40 (0.23) [5.34–5.47] BMI (SD) [95% CI] 23.78 (4.19) [22.48–25.08] 24.92 (3.88) [23.78–26.05] Weight, lbs (SD) [95% CI] 149.23 (29.47) [140.09-158.36] 151.51 (23.77) [144.56-158.46] MCV, fL (SD) [95% CI] 92.41 (4.19) [91.11–93.71] 92.07 (3.81) [90.96–93.19] Cholesterol, mg/dL (SD) [95% CI] 192.8 (31.42) [183.06- 202.54] 207.9 (39.97) [196.23-219.59] Triglycerides, mg/dL (SD) [95% CI] 77.7 (50.975) [61.9–93.5] 81.2 (49.17) [66.83–95.57] LDL, mg/dL (SD) [95% CI] 103.62 (29.88) [94.36-112.88] 118.15 (37.97) [107.06-129.24] HDL, mg/dL (SD) [95% CI] 72.17 (12.42) [68.32–76.03] 72.11 (15.64) [67.54–76.68] hs-CRP, mg/L (SD) [95% CI] 1.42 (2.48) [0.65–2.19] 1.84 (1.98) [1.267–2.43] Glucose, mg/dL (SD) [95% CI] 85.35 (9.56) [82.39–88.31] 86.5 (9.14) [83.84–89.18] Waist circumference, in. (SD) [95% CI] 31.97 (4.5) [30.57–33.37] 32.27 (3.58) [31.22–33.31] Vitamin D, ng/mL (SD) [95% CI] 41.52 (14.39) [37.06–45.98] 43.91 (21.42) [37.65–50.17] WBC, thousand/uL (SD) [95% CI] 5.79 (1.7) [5.27–6.32] 5.54 (1.26) [5.17–5.92] RBC, million/uL (SD) [95% CI] 4.46 (0.39) [4.34–4.59] 4.48 (0.28) [4.4–4.56] Hgb, g/dL (SD) [95% CI] 13.52 (0.89) [13.24–13.8] 13.59 (0.97) [13.31–13.88] Hematocrit, % (SD) [95% CI] 41.16 (2.66) [40.34–41.98] 41.26 (2.83) [40.43–42.09] RDW, % (SD) [95% CI] 12.4 (0.557) [12.23–12.58] 12.44 (0.62) [12.26–12.62] Data are represented as the mean \(\pm\) SD for normally distributed continuous variables and as a 95% CI {interquartile range}. 3.2 Primary and Secondary Objectives The primary objective of this study was to determine whether integration of the SWW Method® results in significant weight loss and improved blood glucose markers in women aged 35–75. As this population was relatively healthy at baseline, the data may not accurately reflect the broader population. Mean BMI at baseline was 24.92 and 23.78 for the intervention and control groups, respectively, both within the healthy range. Mean HbA1c was 5.40% and 5.31% for the intervention and control groups, respectively, both below the 5.7% threshold for prediabetes. Mean fasting glucose was 86.5 mg/dL and 85.35 mg/dL for the intervention and control groups, respectively, both within normal range. This cohort does not represent the average population at large. For context, 11.6% of the U.S. population has been diagnosed with T2DM (HbA1c > 6.5%) and 40.3% meets criteria for obesity (BMI > 30), according to the Centers for Disease Control [ 40 , 41 ]. Despite healthy baseline measurements, statistically significant changes were observed in HbA1c, weight, BMI, and MCV over the 12-week study period, confirming achievement of the primary study objective. This is evident in Charts 1 – 4 , below. 3.2.1. HbA1c 95% confidence interval HbA1c’s : Data are presented as mean ± 95% CI. DiD: p = 0.0428; (p < 0.05); Wilcoxon: p = 0.7292. Intervention group: baseline 5.4% (SD, 0.23) [CI: 5.34–5.47]; 12 weeks 5.33% (SD, 0.22) [CI: 5.27–5.39]. Controls: baseline 5.31% (SD, 0.262) [CI: 5.23–5.39]; 12 weeks 5.39% (SD, 0.264) [CI: 5.32–5.48]. As evidenced in Chart 1 : HbA1c, above (p. 10), the intervention group's HbA1c decreased slightly over the 12-week period while controls experienced a slight increase, resulting in a statistically significant between-group difference (DiD: p = 0.0428). However, the within-group change among cases was not significant (Wilcoxon: p = 0.7292), indicating that significance was driven by divergence between groups rather than meaningful improvement within the intervention group alone. It is also notable that controls entered the study with a lower mean HbA1c (5.31%) than the intervention group (5.40%), and experienced a concurrent decrease in weight and BMI despite the rise in HbA1c. 3.2.2. BMI 95% confidence interval BMI : Data are presented as mean ± 95% CI. DiD: p = 0.5019; Wilcoxon: p < 0.0001. Intervention group: baseline 24.92 (SD, 3.881) [CI: 23.78–26.05]; 12 weeks 24.03 (SD, 3.516) [CI: 22.99–25.05]. Controls: baseline 23.78 (SD, 4.199) [CI: 22.48–25.08]; 12 weeks 23.72 (SD, 4.374) [CI: 22.36–25.08]. As evidenced in Chart 2 : BMI, above (p. 11), the intervention group experienced a statistically significant reduction in BMI over the 12-week period, declining from 24.92 to 24.03 (Wilcoxon: p < 0.0001). Controls showed a minimal reduction from 23.78 to 23.72. However, the between-group difference was not statistically significant (DiD: p = 0.5019). The high standard deviations at both timepoints indicate considerable variability within both groups. 3.2.3. Weight 95% confidence interval Weight : Data are presented as mean ± 95% CI. DiD: p = 0.5290; Wilcoxon: p < 0.0001. Intervention group: baseline 151.51lbs (SD, 23.77) [144.56-158.46]; 12 weeks 145.98lbs (SD, 21.15) [139.79-152.16]. Controls: baseline 149.23lbs (SD, 29.47) [CI: 140.09-158.36]; 12 weeks 148.82lbs (SD, 30.53) [CI: 139.36-158.28]. As evidenced in Chart 3 : Weight, above (p. 11), the intervention group experienced a statistically significant reduction in weight of 5.53 lbs over the 12-week period (Wilcoxon: p < 0.0001), while controls lost an average of 0.41 lbs. However, the between-group difference was not statistically significant (DiD: p = 0.5290). The high standard deviations at both timepoints indicate considerable variability within both groups. 3.2.4. MCV 95% confidence interval MCV : Data are presented as mean ± 95% CI. DiD: p = 0.7562; Wilcoxon: p = 0.0035 (p < 0.05). Intervention group: baseline 92.07 fL (SD, 3.81) [CI: 90.96–93.19]; 12 weeks 92.85 fL (SD, 3.868) [CI: 91.72–93.98]. Controls: baseline 92.41 fL (SD, 4.193) [CI: 91.11–93.71]; 12 weeks 92.79 fL (SD, 4.611) [CI: 91.36–94.22]. As evidenced in Chart 4 : MCV, above (p. 12), The intervention group showed a statistically significant increase in MCV over the 12-week period, from 92.07 fL to 92.85 fL (Wilcoxon: p = 0.0035). Controls also showed a slight increase, from 92.41 fL to 92.79 fL, and the between-group difference was not significant (DiD: p = 0.7562). All values remained within the normal range (80–100 fL), and the magnitude of change was not consistent with macrocytic anemia typically associated with vitamin B12 or folate deficiency. 3.2.5. Remaining Data No statistically significant differences were observed between groups for the remaining outcome measures. This is evident in Table 2 , below (p. 13). Notably, controls presented with healthier baseline values than cases across the majority of markers, including cholesterol, triglycerides, LDL, hsCRP, waist circumference, and systolic blood pressure. Table 2 Baseline to final results amongst non-significant data changes | 95% confidence intervals Biomarker Control Group Mean (SD) [95% CI] Intervention Group Mean (SD) [95% CI] Statistical result Cholesterol mg/dL Baseline : 192.8 (31.4) [183.06- 202.54] End of study : 190.46 (29.6) [181.27-199.64] Baseline : 207.9 (39.97) [196.23-219.59] End of study : 208.2 (37.66) [197.19–219.2] DiD: p = 0.8091 Wilcoxon: p = 0.525 Triglycerides* mg/dL Baseline : 77.7 (50.97) [61.9–93.5] End of study : 77.84 (49.5) [62.5-93.18] Baseline : 81.2 (49.17) [66.83–95.57] End of study : 77.24 (42.55) [64.8-89.67] DiD: p = 0.7821 Wilcoxon: p = 0.6794 LDL mg/dL Baseline : 103.62 (29.88) [94.36-112.88] End of study : 99.97 (32.08) [90.03-109.92] Baseline : 118.15 (37.97) [107.06-129.24] End of study : 118.2 (33.93) [108.28-128.11] DiD: p = 0.7221 Wilcoxon: p = 0.5962 HDL mg/dL Baseline : 72.17 (12.42) [68.32–76.03] End of study : 68.94 (14.86) [64.34–73.55] Baseline : 72.1 (15.64) [67.54–76.68] End of study : 72.46 (15.87) [67.83–77.1] DiD: p = 0.4345 Wilcoxon: p = 0.2928 hs-CRP* mg/L Baseline : 1.41 (2.48) [0.65–2.19] End of study : 1.44 (2.04) [0.81–2.07] Baseline : 1.85 (1.99) [1.267–2.43] End of study : 1.3 (1.23) [0.94–1.66] DiD: p = 0.297 Wilcoxon: p = 0.0987 Glucose mg/dL Baseline : 85.35 (9.56) [82.39–88.31] End of study : 87.7 (9.65) [84.7-90.69] Baseline : 86.5 (9.14) [83.84–89.18] End of study : 87.93 (8.17) [85.55–90.32] DiD: p = 0.74 Wilcoxon: p = 0.48 Waist Circumference* in. Baseline : 31.97 (4.5) [30.57–33.37] End of study : 31.79 (4.75) [30.32–33.27] Baseline : 32.26 (3.58) [31.22–33.31] End of study : 31.61 (3.95) [30.46–32.77] DiD: p = 0.7163 Wilcoxon: p = 0.4603 Vitamin D ng/mL Baseline : 41.52 (14.39) [37.06–45.98] End of study : 43.84 (15.3) [39.09–48.59] Baseline : 43.91 (21.42) [37.65–50.17] End of study : 45.06 (16.84) [40.15–49.99] DiD: p = 0.8278 Wilcoxon: p = 0.0825 WBC* Thousand/uL Baseline : 5.79 (1.7) [5.27–6.32] End of study : 5.93 (1.8) [5.37–6.49] Baseline : 5.54 (1.26) [5.17–5.92] End of study : 6.91 (8.96) [4.29–9.53] DiD: p = 0.4079 Wilcoxon: p = 0.2815 RBC Million/uL Baseline : 4.46 (0.39) [4.34–4.59] End of study : 4.49 (0.326) [4.39–4.59] Baseline : 4.48 (0.28) [4.4–4.56] End of study : 4.42 (0.297) [4.34–4.51] DiD: p = 0.4139 Wilcoxon: p = 0.4229 Hemoglobin g/dL Baseline : 13.52 (0.899) [13.24–13.8] End of study : 13.48 (0.86) [13.33–13.87] Baseline : 13.59 (0.98) [13.31–13.88] End of study : 13.48 (0.97) [13.2-13.77] DiD: p = 0.519 Wilcoxon: p = 0.8426 Hematocrit % Baseline : 41.16 (2.66) [40.34–41.98] End of study : 41.05 (2.45) [40.8-42.32] Baseline : 41.26 (2.83) [40.43–42.09] End of study : 41.05 (2.75) [40.25–41.86] DiD: p = 0.46 Wilcoxon: p = 0.636 RDW % Baseline : 12.4 (0.557) [12.23–12.58] End of study : 12.33 (0.65) [12.13–12.53] Baseline : 12.44 (0.62) [12.26–12.62] End of study : 12.55 (0.58) [12.38–12.72] DiD: p = 0.32 Wilcoxon: p = 0.85 Systolic** mmHg Baseline : 108.7 End of study : 103.97 Baseline : 116.33 End of study : 113.18 DiD: p = 0.788 Wilcoxon: p = 0.0735 Diastolic mmHg Baseline : 66.35 End of study : 65.9 Baseline : 72.51 End of study : 71.73 DiD: p = 0.925 Wilcoxon: p = 0.4457 Data are represented as the mean \(\pm\) SD for normally distributed continuous variables and as a 95% CI {interquartile range}. *Although not statistically significant, the intervention group demonstrated improvements in triglycerides, hs-CRP, waist circumference, and WBC count. **Systolic blood pressure: Baseline Difference Test (before treatment) | T-test Statistic: 2.7094, p-value: 0.0083 | Bias risk. Cases presented with a statistically higher systolic blood pressure at baseline (control mean: 108.7mmHg). Although not statistically significant, triglycerides improved in the intervention group from 81.2 mg/dL to 77.24 mg/dL, while remaining stable in controls (77.7 mg/dL to 77.84 mg/dL). High variability in both groups (intervention SD ± 49.17 mg/dL; control SD ± 50.97 mg/dL) likely contributed to the lack of statistical significance. hsCRP decreased slightly in the intervention group from 1.85 to 1.30 mg/L, while controls showed a marginal increase from 1.41 to 1.44 mg/L, though neither change was statistically significant. Waist circumference decreased modestly in the intervention group (mean 0.65 inches) compared to controls (mean 0.18 inches), though this difference was not statistically significant. Questionnaire data revealed notable self-reported improvements among intervention participants: 69% reported improved morning energy, 79% reported improved daytime energy, and 79% reported reduced sugar intake. 4 Discussion This study aimed to determine whether integration of the SWW Method® would result in significant weight loss and improved glycemic markers in women aged 35–75. Cases and controls were recruited to assess whether changes in blood markers and biometrics over 12 weeks were significantly greater in the intervention group compared to the control group. The SWW Method® demonstrated moderate benefit in improving prolonged blood sugar markers (HbA1c) relative to controls, while no significant changes were observed in FBG. BMI and weight improved significantly within the intervention group (p < 0.0001), but not in comparison to controls, likely due to high data variance and concurrent weight loss observed in both groups. The average baseline values for both groups were already within a healthy range, limiting the potential for significant change. Results may have differed had baseline characteristics fallen within an unhealthy range. The high degree of variability across the data further impeded the detection of statistical significance. Although the between-group change in HbA1c was statistically significant, the degree was modest, and the within-group change among cases was not significant. Baseline HbA1c values of 5.31% and 5.40% for controls and cases, respectively, were both below the prediabetes threshold of 5.7%, leaving limited room for meaningful reduction. Yet, the intervention group's HbA1c decreased from 5.40% to 5.33%, while controls increased from 5.31% to 5.39%, suggesting that the SWW Method® may have attenuated the natural progression toward worsening glycemic control. This suggests a potentially meaningful impact on HbA1c trajectories compared to a population following no dietary or lifestyle intervention. There was no significant change in FBG levels. Rather than declining they increased over the 12-week period. The intervention group’s FBG rose from 86.5mg/dL to 87.93mg/dL, while the controls’ FBG rose from 85.35mg/dL to 87.7mg/dL. High baseline variability ( \(\pm\) 9.14mg/dL (SD) and \(\pm\) 9.56mg/dL (SD) for both the cases and controls, respectively) further limited the ability to detect significance. FBG is not an accurate determinant of long-term glycemic control, as values can be transiently elevated by inadequate hydration, caffeine consumption, or stress prior to collection [ 42 – 44 ]. Although caffeine has demonstrated long-term benefits for insulin resistance, the short-term, postprandial effect is a temporary spike in blood glucose [ 45 , 46 ]. Mitigating stress is difficult, and ensuring hydration and avoiding caffeine before the lab appointment may not have been adequately conveyed by the study team. Although the intervention group achieved significant weight loss (p < 0.0001), this was not significant in comparison to the controls (p = 0.529), likely due to high data variance and weight loss observed in both groups (5.53 lbs and 0.41 lbs among cases and controls, respectively). This suggests that the weight loss may not be fully attributable to the intervention alone and could reflect general trends or external influences affecting both groups. Neither group needed significant weight loss at baseline. Additionally, receiving blood work and having biometrics tested at the start of the study may have motivated controls to make beneficial health choices over the course of 12 weeks. The intervention group experienced a significant decrease in BMI (p < 0.0001), declining from 24.92 to 24.03 (a change of 0.89), while controls showed a minimal decline from 23.78 to 23.72 (a change of 0.06). However, the between-group difference was not significant (p = 0.5019), likely due to high data variance and the fact that both cohorts had healthy baseline BMIs, leaving limited room for noticeable improvement. The control group's lower baseline BMI also suggests they were, on average, a healthier cohort at the outset. While the significant BMI reduction among cases may reflect external influences, it could also indicate the effectiveness of the SWW Method® in achieving healthy weight loss in an already healthy population. Although unrelated to the initial hypothesis, there was a significant improvement in Mean Corpuscular Volume (MCV) within the intervention group (p < 0.05). This was likely attributable in part to nightly consumption of SWW Restore®, which at the start of this study contained 500mg of methylcobalamin. Supplemental vitamin B12 may cause an early rise in MCV due to increased reticulocyte production, an effect likely to normalize with longer-term follow-up [ 47 , 48 ]. A modest improvement was also observed in controls, possibly reflecting the fact that many were followers of SWW® on social media and subscribers to their products. The transient MCV changes in both groups could reflect nutritional shifts, early hematologic recovery, or changes in alcohol intake patterns, suggesting general physiological shifts rather than a distinct intervention effect. High variability in both groups further limited the ability to extract comparable significance. Non-significant improvements were observed in the intervention group across triglycerides, high-sensitivity C-reactive protein (hsCRP), waist circumference, and white blood cell (WBC) count. Despite healthy baseline values for triglycerides (81.2 mg/dL) and hsCRP (1.85 mg/dL), both markers improved over 12 weeks (77.24 mg/dL and 1.3 mg/dL, respectively), while remaining relatively stable in controls, suggesting a potential intervention effect on blood lipids and inflammatory markers. However, the high standard deviation for triglycerides, combined with healthy baseline values in both groups, limited the ability to achieve statistical significance. The SWW Method® is an isocaloric CIM-based protocol designed to support blood sugar balance and weight loss through adequate hydration, alkalizing with a daily greens powder, intermittent fasting, conscious meal timing, a protein-forward diet, non-starchy vegetables with every meal, limiting starchy carbohydrate intake to once per day, magnesium supplementation, and optimizing circadian rhythm. This protocol is delivered through nutrition education, group coaching, and one-on-one nutrition support. Few studies have examined dietary and lifestyle interventions in tandem with educational and coaching support. However, multiple studies have identified benefits of individual components of the SWW Method® on blood glucose markers and weight loss. Consuming protein, fat, and fiber before carbohydrates can improve metabolic conditions in individuals with diabetes and obesity [ 49 ]. Eating protein and/or fat before carbohydrates promotes secretion of glucagon-like-peptide-1 (GLP-1) to improve postprandial glucose secretion and suppress appetite [ 50 ], while consuming dietary fiber before carbohydrates similarly reduces postprandial glucose secretion. Implementation of a vegetables-protein-carbohydrate meal sequence has been shown to reduce postprandial glucose levels and HbA1c in pre-diabetic and diabetic patients [ 23 , 41 – 53 ]. This is comparable with the meal sequencing recommended within the SWW Method® protocol. However, other studies have not found benefits of meal sequencing on HbA1c or FBG in diabetic patients, instead supporting a low-carbohydrate, high-protein diet as more effective for significant improvements [ 54 – 56 ]. The benefits of intermittent fasting (IF) have variable impacts. A systematic review found no benefits of IF (16–20 hour) on improving FBG levels or HbA1cs [ 57 ]. Studies that did observe glycemic improvements were conducted in individuals who were obese or diagnosed with T2DM, and IF was most effective when paired with dietary changes [ 5 , 58 – 60 ]. IF also appears more effective for weight loss than glycemic control, and primarily in obese, diabetic populations [ 61 – 63 ]. The SWW Method® protocol prioritizes protein to support glycemic markers. A 2018 randomized controlled trial found that a low-carbohydrate, high-protein diet had a positive impact on FBG in T2DM patients [ 64 ]. Multiple studies have found that when protein comprises more than 30% of total dietary intake, metabolic syndrome is better addressed compared to diets meeting the FDA-recommended protein content of 14% [ 65 – 68 ]. In a Danish case-control study of 28 diabetic participants, a carbohydrate-reduced, high-protein diet implemented over six weeks resulted in a significant reduction in HbA1c compared to a conventional diabetes diet [ 69 ]. Alternatively, to the above-mentioned studies, the SWW Method® protocol was implemented within a healthy cohort, thus the results are less drastic and more difficult to achieve significance [ 70 ]. Yet, the SWW Method® attracts a healthier population base, looking to optimize their health and prevent, rather than address, symptoms of metabolic syndrome. Hydration is a significant component of the SWW Method® protocol. Although not a primary vehicle for combating obesity, adequate hydration has been correlated with a lower BMI [ 71 ]. The SWW Method® protocol provides nutrition education and group coaching to support dietary and lifestyle changes. Other similar models have been effective in supporting weight loss goals due to the provided accountability and support, yet over a more extended period of time and in an unhealthy population [ 72 – 75 ]. The primary limitation of this study was its implementation within an already healthy cohort. As a non-randomized study in which participants self-selected into their respective groups, substantial selection bias was introduced, limiting causal inference and the ability to distinguish intervention effects from pre-existing differences between groups. At baseline, neither the cases nor controls exhibited any markers for metabolic syndrome, making significance difficult to achieve. All of the above-mentioned studies achieved the greatest results in subjects with higher baseline BMI and HbA1c. Controls presented with healthier baseline values than cases across the majority of markers, including cholesterol, triglycerides, HbA1c, FBG, LDL, weight, BMI, hsCRP, waist circumference, and systolic blood pressure, and demonstrated slight improvements in BMI, weight, cholesterol, MCV, and LDL over the study period. The Hawthorne effect may have contributed, as having blood work and biometrics tested at baseline may have motivated controls to make beneficial health choices over the 12 weeks. As an already healthy cohort, controls likely maintained their existing habits throughout, while cases enrolled specifically to improve their health. Additionally, controls' individual engagement in separate dietary or lifestyle interventions was uncontrolled and may have contributed to their observed improvements. Collectively, these factors made it difficult for cases to demonstrate significant improvement relative to controls. Regression to the mean may have contributed to the observed improvements in the intervention group. As they were enrolled specifically to improve their health markers, some baseline measurements may have represented the upper range of their natural variability and would have improved toward their typical average even without intervention. Receiving blood work may have provided further motivation. Their beneficial changes may have been independent of the intervention itself, which is reinforced by the improvement amongst the controls. The high data variance made it difficult to achieve significance, particularly in weight, BMI, triglycerides, LDL, and vitamin D levels. Recruiting a greater number of participants would have been necessary to overcome the variability observed across both groups. Without full study adherence, there is minimal to no improvement in biometrics and lab work. There was no objective monitoring of dietary intake outside of clients' self-reporting on the ATE™ app via description or photo. Without honest reporting, it was difficult for program participants to receive adequate support, reducing the likelihood of adherence to the dietary protocol. There were multiple, uncontrollable, confounding variables, which may have significantly impacted biometrics and lab results. Medication use, stress, sleep quality, concurrent illnesses, injuries, menopause, menstruation, or other interventions participants might have started during the study period could have influenced results. At Quest Diagnostics, multiple participants reported inaccurate height and waist circumference measurements, introducing inter-technician variability. Incomplete fasting prior to the lab appointment would also have impacted results [ 76 ], as would caffeine consumption or dehydration, which can affect urinalysis, CBC, and FBG levels [ 42 – 44 ]. Alcohol consumption the night prior would additionally have contributed to elevated fasting triglyceride levels [ 77 ]. Most of the recruitment was through social media, Instagram, Facebook, and LinkedIn, as well as templated emails sent from the coaches to their network. This attracted current followers of Sarah Wragge Wellness®, who are likely a healthier audience. As the program was advertised on social media and caters to a higher socioeconomic class, the resultant cohort was healthy and does not accurately represent the average population. Recruitment for controls occurred on a rolling basis, whereas the SWW Method® runs three times per year (January, April, and September), with participants recruited across multiple rounds. This temporal mismatch, combined with seasonal variations in vitamin D, physical activity, and dietary patterns, may have contributed to data variability and limited the ability to achieve statistical significance. Multiple outcome measures were assessed without correction for multiple comparisons, which increases the risk of Type I error. With approximately 20 outcomes tested at the p < 0.05 significance level, some statistically significant findings may have occurred by chance rather than reflecting true intervention effects. This study has several notable strengths. The prospective design with standardized measurements at baseline and 12 weeks allowed for accurate assessment of intervention effects while minimizing recall bias. Including a concurrent control group allowed for comparison against natural trends, strengthening causal inference. Collecting all biomarkers at Quest Diagnostics ensured standardization, reduced inter-laboratory variability, and minimized measurement error. For future evaluation, the SWW Method® protocol should be implemented in an overweight cohort exhibiting prediabetic glycemic markers. Recruiting a larger sample size would minimize excessive variability. A longer study duration would more adequately capture changes in HbA1c, FBG, and sustainable weight loss. These changes would better assess significant impact on weight and glycemic markers. Implementing the protocol amongst a diverse socioeconomic community and in similar-aged men would better address impact in the general population. 5 Conclusion This study evaluated a real-world dietary and lifestyle intervention in a generally healthy female population, demonstrating that measurable improvements in metabolic markers can occur even with baseline values within normal ranges. This was most evident in a slight but significant improvement in HbA1c relative to controls and significant improvements in weight and BMI within the intervention cohort. These findings demonstrate the potential of the SWW Method® protocol to further optimize health in an already healthy population, indicating its effectiveness as a preventative health intervention. Abbreviations The following abbreviations are used in this manuscript: T2DM Type II Diabetes Mellitus EBM Energy Balance Model CIM Carbohydrate-Insulin Model CVD Cardiovascular Disease IR Insulin Resistance LDL Low-density lipoproteins AGEs Advanced glycation end products TG Triglycerides sdLDL Small dense low-density lipoproteins HDL High density lipoproteins VLDL Very low-density lipoproteins HbA1c Hemoglobin A1c PI Principal investigator CBC Complete blood count RBC Red blood cell WBC White blood cell MCV Mean corpuscular volume MCH Mean corpuscular hemoglobin MCHC Mean corpuscular hemoglobin concentration RDW Red cell distribution width CMP Comprehensive metabolic panel EGFR Estimated glomerular filtration rate hs-CRP High sensitivity c-reactive protein MPV Mean platelet volume BMI Body mass index DiD Difference in difference SD Standard deviation FBG Fasting blood glucose GLP-1 Glucagon like peptide-1 Declarations Author Contributions: Conceptualization, M.G.; methodology, M.G., E.E.; software, E.S., L.Z.; formal analysis, M.G., E.S.; investigation, M.G., E.E.; resources, Quest Diagnostics; data curation, M.G., L.Z.; writing—original draft preparation, M.G.; writing—M.G., J.R.; visualization, M.G.; supervision, M.G.; project administration, M.G.; funding acquisition, Sarah Wragge Wellness®. All authors have read and agreed to the published version of the manuscript. Funding: This research was self-funded by Sarah Wragge Wellness®. Institutional Review Board Statement: The study was approved by the Institutional Review Board (IRB), Advarra (Pro00077176; 22 Apr 2024), and each participant signed an approved consent form. Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. Consent to Publish: Not applicable. Data Availability Statement: The data supporting the findings of this observational study are available from the corresponding author upon reasonable request. The data are not publicly available due to privacy and ethical restrictions. Trial Registration: ClinicalTrials.gov, NCT07463417, retrospectively registered March 5, 2026. Acknowledgments: The authors thank Quest Diagnostics for utilization of their labs for data collection. During the preparation of this manuscript/study, the author(s) used Python (Version 3.10) [35] within a Google Collaboratory environment for the purposes of initial code for statistical analysis. The authors also used Chat GPT 5.2 (Open AI) and Claude Sonnet 4.5 for text editing assistance. The authors have reviewed and edited the output and take full responsibility for the content of this publication. Conflicts of Interest: Megan Grover, Emily Eastman, and Jessica Rybka were employed by Sarah Wragge Wellness® (SWW®) during this study. SWW® developed, markets, and profits from the SWW Method® intervention being evaluated. This study was entirely self-funded by SWW®, which had direct financial interest in favorable outcomes. To minimize bias, statistical analysis was independently conducted by E.S., who has no financial connection to SWW®. The study design, data collection, and interpretation involved SWW® employees, representing substantial potential bias. Independent replication by researchers without commercial interests is needed to validate these findings. References Hall KD, Farooqi IS, Friedman JM, Klein S, Loos RJ, Mangelsdorf DJ, et al. The energy balance model of obesity: beyond calories in, calories out. Am J Clin Nutr. 2022;115(5):1243–54. Chen SY, Beretta M, Olzomer EM, Shah DP, Wong DYH, Alexopoulos SJ, et al. Targeting negative energy balance with calorie restriction and mitochondrial uncoupling in db/db mice. Mol Metabolism. 2023;69:101684. Flanagan SP. Carbohydrate-Insulin Model for Obesity-2021. In: Russell J, Skolnik NS, editors. Top Articles in Primary Care [Internet]. Cham: Springer International Publishing; 2023 [cited 2026 Feb 17]. pp. 197–9. Available from: https://link.springer.com/ 10.1007/978-3-031-25620-2_45 Ludwig DS, Aronne LJ, Astrup A, De Cabo R, Cantley LC, Friedman MI, et al. The carbohydrate-insulin model: a physiological perspective on the obesity pandemic. Am J Clin Nutr. 2021;114(6):1873–85. Ludwig DS, Apovian CM, Aronne LJ, Astrup A, Cantley LC, Ebbeling CB, et al. Competing paradigms of obesity pathogenesis: energy balance versus carbohydrate-insulin models. Eur J Clin Nutr. 2022;76(9):1209–21. Taubes G. The energy balance model compared with the carbohydrate-insulin model. Am J Clin Nutr. 2022;116(2):612–4. Soto-Mota A, Pereira MA, Ebbeling CB, Aronica L, Ludwig DS. Evidence for the carbohydrate–insulin model in a reanalysis of the Diet Intervention Examining The Factors Interacting with Treatment Success (DIETFITS) trial. Am J Clin Nutr. 2023;117(3):599–606. Cahill GF. Physiology of Insulin In Man: The Banting Memorial Lecture 1971. Diabetes. 1971;20(12):785–99. López-Méndez I, Maldonado-Rojas ADC, Uribe M, Juárez-Hernández E. Hunger & satiety signals: another key mechanism involved in the NAFLD pathway. Front Endocrinol. 2023;14:1213372. Petersen MC, Shulman GI. Mechanisms of Insulin Action and Insulin Resistance. Physiol Rev. 2018;98(4):2133–223. Kouda K, Nakamura H, Kohno H, Okuda T, Higashine Y, Hisamori K, et al. Metabolic response to short-term 4-day energy restriction in a controlled study. Environ Health Prev Med. 2006;11(2):89–92. Soltani S, Jayedi A, Abdollahi S, Vasmehjani AA, Meshkini F, Shab-Bidar S. Effect of carbohydrate restriction on body weight in overweight and obese adults: a systematic review and dose–response meta-analysis of 110 randomized controlled trials. Front Nutr. 2023;10:1287987. Mekala KC, Bertoni AG. Epidemiology of diabetes mellitus. In: Transplantation, Bioengineering, and Regeneration of the Endocrine Pancreas [Internet]. Elsevier; 2020 [cited 2026 Feb 17]. pp. 49–58. Available from: https://linkinghub.elsevier.com/retrieve/pii/B9780128148334000046 Alam S, Hasan MK, Neaz S, Hussain N, Hossain MF, Rahman T. Diabetes Mellitus: Insights from Epidemiology, Biochemistry, Risk Factors, Diagnosis, Complications and Comprehensive Management. Diabetology. 2021;2(2):36–50. Mann T, Tomiyama AJ, Westling E, Lew AM, Samuels B, Chatman J. Medicare’s search for effective obesity treatments: Diets are not the answer. Am Psychol. 2007;62(3):220–33. Wilding JPH, Batterham RL, Davies M, Van Gaal LF, Kandler K, Konakli K, et al. Weight regain and cardiometabolic effects after withdrawal of semaglutide: The STEP 1 trial extension. Diabetes Obes Metabolism. 2022;24(8):1553–64. Freeman AM, Pennings N. Insulin Resistance. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2018 [updated 2023]. Available from: https://www.ncbi.nlm.nih.gov/books/NBK507839/ Galicia-Garcia U, Benito-Vicente A, Jebari S, Larrea-Sebal A, Siddiqi H, Uribe KB, et al. Pathophysiology Type 2 Diabetes Mellitus IJMS. 2020;21(17):6275. Meza CA, La Favor JD, Kim DH, Hickner RC. Endothelial Dysfunction: Is There a Hyperglycemia-Induced Imbalance of NOX and NOS? IJMS. 2019;20(15):3775. Qambari H, Yu PK, Balaratnasingam C, Dickson J, Yu DY. Endothelial function can be modulated by acute hyperglycemia. Sci Rep. 2025;15(1):30559. Parhofer KG. Interaction between Glucose and Lipid Metabolism: More than Diabetic Dyslipidemia. Diabetes Metab J. 2015;39(5):353. Gao Y. Association of estimated small dense low-density lipoprotein cholesterol with diabetes mellitus: a large-scale multicenter retrospective cohort study. BMC Endocr Disord. 2025;25(1):66. He Q, Wang L, Fang Y, Liang M, Chen X, Hu R, et al. Relationship of small dense low-density lipoprotein cholesterol level with pre-diabetes and newly detected type 2 diabetes. Sci Rep. 2025;15(1):19500. Rikhi R, Schaich CL, Hafzalla GW, Patel NA, Tannenbaum JE, German CA, et al. Small dense low-density lipoprotein cholesterol and coronary artery calcification in the Multi-Ethnic Study of Atherosclerosis. Eur J Prev Cardiol. 2024;31(8):1048–54. Vekic J, Zeljkovic A, Cicero AFG, Janez A, Stoian AP, Sonmez A, et al. Atherosclerosis Development and Progression: The Role of Atherogenic Small, Dense LDL. Medicina. 2022;58(2):299. Santos HO, Earnest CP, Tinsley GM, Izidoro LFM, Macedo RCO. Small dense low-density lipoprotein-cholesterol (sdLDL-C): Analysis, effects on cardiovascular endpoints and dietary strategies. Prog Cardiovasc Dis. 2020;63(4):503–9. Benedict FG, Miles WR, Roth P, Smith HM. Human vitality and efficiency under prolonged restricted diet. Washington (DC): Carnegie Institution of Washington; 1919. Keys A, Brožek J, Henschel A, Mickelsen O, Taylor HL. The biology of human starvation. Minneapolis (MN): University of Minnesota Press; 1950. 2 vols. Antunes MM, Godoy G, De Almeida-Souza CB, Da Rocha BA, Da Silva-Santi LG, Masi LN, et al. A high-carbohydrate diet induces greater inflammation than a high-fat diet in mouse skeletal muscle. Braz J Med Biol Res. 2020;53(3):e9039. Yang Q, Lang X, Li W, Liang Y. The effects of low-fat, high-carbohydrate diets vs. low-carbohydrate, high-fat diets on weight, blood pressure, serum liquids and blood glucose: a systematic review and meta-analysis. Eur J Clin Nutr. 2022;76(1):16–27. Masood W, Annamaraju P, Suheb MZK, Uppaluri KR. Ketogenic Diet. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2023. Available from: https://www.ncbi.nlm.nih.gov/books/NBK499830/ Pescari D, Mihuta MS, Bena A, Stoian D. Quantitative analysis of the caloric restriction versus isocaloric diets models based on macronutrients composition: impacts on body weight regulation, anthropometric, and bioimpedance parameters in women with obesity. Front Nutr. 2024;11:1493954. Evangelista LS, Jose MM, Sallam H, Serag H, Golovko G, Khanipov K, et al. High-Protein vs. Standard-Protein Diets in Overweight and Obese Patients with Heart Failure and Diabetes Mellitus: Findings of the Pro-HEART Trial. ESC Heart Fail. 2021;8(2):1342–8. Perissiou M, Borkoles E, Kobayashi K, Polman R. The Effect of an 8 Week Prescribed Exercise and Low-Carbohydrate Diet on Cardiorespiratory Fitness, Body Composition and Cardiometabolic Risk Factors in Obese Individuals: A Randomised Controlled Trial. Nutrients. 2020;12(2):482. Python Software Foundation. Python. Version 3.10. Wilmington (DE): Python Software Foundation. 2023. Available from: https://www.python.org/ McKinney W. Data Structures for Statistical Computing in Python. In Austin, Texas; 2010 [cited 2026 Feb 17]. pp. 56–61. Available from: https://doi.curvenote.com/ 10.25080/Majora-92bf1922-00a Harris CR, Millman KJ, Van Der Walt SJ, Gommers R, Virtanen P, Cournapeau D, et al. Array programming with NumPy. Nature. 2020;585(7825):357–62. Virtanen P, Gommers R, Oliphant TE, Haberland M, Reddy T, Cournapeau D, et al. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat Methods. 2020;17(3):261–72. Seabold S, Perktold J, Statsmodels. Econometric and Statistical Modeling with Python. In Austin, Texas; 2010 [cited 2026 Feb 17]. pp. 92–6. Available from: https://doi.curvenote.com/ 10.25080/Majora-92bf1922-011 Centers for Disease Control and Prevention. Diabetes data and research [Internet]. Atlanta (GA): CDC; [cited 2026 Feb 17]. Available from: https://www.cdc.gov/diabetes/php/data-research/index.html Emmerich S, Fryar C, Stierman B, Ogden C. Obesity and Severe Obesity Prevalence in Adults: United States, August 2021–August 2023 [Internet]. National Center for Health Statistics (U.S.); 2024 Sep [cited 2026 Feb 17]. Available from: https://stacks.cdc.gov/view/cdc/159281 Choi MK, Ahn HS, Kim DE, Lee DS, Park CS, Kang CK. Effects of Varying Caffeine Dosages and Consumption Timings on Cerebral Vascular and Cognitive Functions: A Diagnostic Ultrasound Study. Appl Sci. 2025;15(4):1703. Khani S, Tayek JA. Cortisol increases gluconeogenesis in humans: its role in the metabolic syndrome. Clin Sci. 2001;101(6):739–47. Lane JD, Feinglos MN, Surwit RS. Caffeine increases ambulatory glucose and postprandial responses in coffee drinkers with type 2 diabetes. Diabetes Care. 2008;31(2):221–2. BaSalamah M, AlMghamsi R, AlTowairqi A, Fouda K, Mahrous A, Mujahid M, et al. The Effect of Coffee Consumption on Blood Glucose Levels. J Biochem Technol. 2022;13(2):64–9. Louie JCY, Atkinson F, Petocz P, Brand-Miller JC. Delayed effects of coffee, tea and sucrose on postprandial glycemia in lean, young, healthy adults. Asia Pac J Clin Nutr. 2008;17(4):657–62. Hall CA. Vitamin B12 Deficiency and Early Rise in Mean Corpuscular Volume. JAMA. 1981;245(11):1144. Smith TJS. Vitamin B12 Deficiency and Early Rise in Mean Corpuscular Volume. JAMA. 1982;247(8):1126. Kubota S, Liu Y, Iizuka K, Kuwata H, Seino Y, Yabe D. A Review of Recent Findings on Meal Sequence: An Attractive Dietary Approach to Prevention and Management of Type 2 Diabetes. Nutrients. 2020;12(9):2502. Gentilcore D, Chaikomin R, Jones KL, Russo A, Feinle-Bisset C, Wishart JM, et al. Effects of Fat on Gastric Emptying of and the Glycemic, Insulin, and Incretin Responses to a Carbohydrate Meal in Type 2 Diabetes. J Clin Endocrinol Metabolism. 2006;91(6):2062–7. Nitta A, Imai S, Kajiayama S, Matsuda M, Miyawaki T, Matsumoto S, et al. Impact of Dietitian-Led Nutrition Therapy of Food Order on 5-Year Glycemic Control in Outpatients with Type 2 Diabetes at Primary Care Clinic: Retrospective Cohort Study. Nutrients. 2022;14(14):2865. Serichantalergs N, Boonyavarakul A, EFFECT OF MEAL SEQUENCING, ON GLP-1 HORMONE AND POSTPRANDIAL GLUCOSE EXCURSION IN PRE-DIABETIC PATIENTS. A CROSSOVER TRIAL. J ASEAN Fed Endocr Soc. 2023;38(S3):57–8. Sun L, Goh HJ, Govindharajulu P, Leow MKS, Henry CJ. Postprandial glucose, insulin and incretin responses differ by test meal macronutrient ingestion sequence (PATTERN study). Clin Nutr. 2020;39(3):950–7. Okami Y, Tsunoda H, Watanabe J, Kataoka Y. Efficacy of a meal sequence in patients with type 2 diabetes: a systematic review and meta-analysis. BMJ Open Diab Res Care. 2022;10(1):e002534. Vlachos D, Malisova S, Lindberg FA, Karaniki G. Glycemic Index (GI) or Glycemic Load (GL) and Dietary Interventions for Optimizing Postprandial Hyperglycemia in Patients with T2 Diabetes: A Review. Nutrients. 2020;12(6):1561. Yan Y, Asemani S, Jamilian P, Yang C. The efficacy of low-carbohydrate diets on glycemic control in type 2 diabetes: a comprehensive overview of meta-analyses of controlled clinical trials. Diabetol Metab Syndr. 2025;17(1):341. Sharma SK, College of Nursing, All India Institute of Medical Sciences, Jodhpur, Rajasthan, India, Mudgal SK, College of Nursing, All India Institute of Medical Sciences, Deoghar, Jharkhand, India, Kalra S et al. Department of Endocrinology, Bharti Hospital and BRIDE, Karnal, Haryana, India,. Effect of Intermittent Fasting on Glycaemic Control in Patients With Type 2 Diabetes Mellitus: A Systematic Review and Meta-analysis of Randomized Controlled Trials. European Endocrinology. 2023;19(1):25. Anasanti MD, Hamzah N. Meta-analysis and meta-regression of intermittent fasting effects on glycaemic control in type 2 diabetes: Subgroup analyses and variability. Diabetes Metabolic Syndrome: Clin Res Reviews. 2025;19(7):103279. Duman TT, Atak Tel BM, Bilgin S, Dervisevic A, Aktas G. Evaluation of the effects of intermittent fasting on clinical and laboratory parameters in metabolic syndrome. j-ebr. 2025;2. Nowosad K, Sujka M. Effect of Various Types of Intermittent Fasting (IF) on Weight Loss and Improvement of Diabetic Parameters in Human. Curr Nutr Rep. 2021;10(2):146–54. Borgundvaag E, Mak J, Kramer CK. Metabolic Impact of Intermittent Fasting in Patients With Type 2 Diabetes Mellitus: A Systematic Review and Meta-analysis of Interventional Studies. J Clin Endocrinol Metabolism. 2021;106(3):902–11. Chair SY, Cai H, Cao X, Qin Y, Cheng HY, Ng MT. Intermittent Fasting in Weight Loss and Cardiometabolic Risk Reduction: A Randomized Controlled Trial. J Nurs Res. 2022;30(1):e185. Khalafi M, Habibi Maleki A, Symonds ME, Rosenkranz SK, Rohani H, Ehsanifar M. The effects of intermittent fasting on body composition and cardiometabolic health in adults with prediabetes or type 2 diabetes: A systematic review and meta-analysis. Diabetes Obes Metabolism. 2024;26(9):3830–41. Huhmann MB, Yamamoto S, Neutel JM, Cohen SS, Ochoa Gautier JB. Very high-protein and low-carbohydrate enteral nutrition formula and plasma glucose control in adults with type 2 diabetes mellitus: a randomized crossover trial. Nutr Diabetes. 2018;8(1):45. Azwan K, Mona R, Firdous J, David PR, Muhamad N. A whey-based, high-protein diet promotes the best body weight and blood sugar control when compared with other types of diet in male Sprague Dawley rats. In: RSU International Research Conference 2021 on Science and Technology; 2021. pp. 68–74. Flores-Hernández MN, Martínez-Coria H, López-Valdés HE, Arteaga-Silva M, Arrieta-Cruz I, Gutiérrez-Juárez R. Efficacy of a High-Protein Diet to Lower Glycemic Levels in Type 2 Diabetes Mellitus: A Systematic Review. IJMS. 2024;25(20):10959. McArthur LH, Kelly WF, Gietzen DW, Rogers QR. The Role of Palatability in the Food Intake Response of Rats Fed High-Protein Diets. Appetite. 1993;20(3):181–96. Peters JC, Harper AE. Adaptation of Rats to Diets Containing Different Levels of Protein: Effects on Food Intake, Plasma and Brain Amino Acid Concentrations and Brain Neurotransmitter Metabolism. J Nutr. 1985;115(3):382–98. Skytte MJ, Samkani A, Petersen AD, Thomsen MN, Astrup A, Chabanova E, et al. A carbohydrate-reduced high-protein diet improves HbA1c and liver fat content in weight stable participants with type 2 diabetes: a randomised controlled trial. Diabetologia. 2019;62(11):2066–78. Shea B, Bakre S, Carano K, Scharen J, Langheier J, Hu EA. Changes in Glycemic Control Among Individuals With Diabetes Who Used a Personalized Digital Nutrition Platform: Longitudinal Study. JMIR Diabetes. 2021;6(4):e32298. Chang T, Ravi N, Plegue MA, Sonneville KR, Davis MM. Inadequate Hydration, BMI, and Obesity Among US Adults: NHANES 2009–2012. Annals Family Med. 2016;14(4):320–4. Kupila SKE, Venäläinen MS, Suojanen LU, Rosengård-Bärlund M, Ahola AJ, Elo LL, et al. Weight Loss Trajectories in Healthy Weight Coaching: Cohort Study. JMIR Form Res. 2022;6(3):e26374. Fernanda Muñoz Obino K, Aguiar Pereira C, Caron Lienert R. Coaching and barriers to weight loss: an integrative review. DMSO. 2016;10:1–11. Painter SL, Ahmed R, Kushner RF, Hill JO, Lindquist R, Brunning S, et al. Expert Coaching in Weight Loss: Retrospective Analysis. J Med Internet Res. 2018;20(3):e92. Tanaka K, Sasai H, Wakaba K, Murakami S, Ueda M, Yamagata F, et al. Professional dietary coaching within a group chat using a smartphone application for weight loss: a randomized controlled trial. JMDH. 2018;11:339–47. Zaid AB, Awad SM, El-Abd MG, Saied SA, Almahdy SK, Saied AA, et al. Unraveling the controversy between fasting and nonfasting lipid testing in a normal population: a systematic review and meta-analysis of 244,665 participants. Lipids Health Dis. 2024;23(1):199. Van De Wiel A. The Effect of Alcohol on Postprandial and Fasting Triglycerides. Int J Vascular Med. 2012;2012:1. Charts Charts 1 to 4 are available in the Supplementary Files section. Additional Declarations Competing interest reported. Conflicts of Interest: Megan Grover, Emily Eastman, and Jessica Rybka were employed by Sarah Wragge Wellness® (SWW®) during this study. SWW® developed, markets, and profits from the SWW Method® intervention being evaluated. This study was entirely self-funded by SWW®, which had direct financial interest in favorable outcomes. To minimize bias, statistical analysis was independently conducted by E.S., who has no financial connection to SWW®. The study design, data collection, and interpretation involved SWW® employees, representing substantial potential bias. Independent replication by researchers without commercial interests is needed to validate these findings. Supplementary Files SupplementaryFile1.docx SupplementaryFile2.docx Charts.docx Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 29 Apr, 2026 Reviews received at journal 28 Apr, 2026 Reviews received at journal 20 Apr, 2026 Reviews received at journal 20 Apr, 2026 Reviews received at journal 20 Apr, 2026 Reviews received at journal 17 Apr, 2026 Reviews received at journal 16 Apr, 2026 Reviewers agreed at journal 15 Apr, 2026 Reviews received at journal 13 Apr, 2026 Reviews received at journal 12 Apr, 2026 Reviews received at journal 12 Apr, 2026 Reviews received at journal 12 Apr, 2026 Reviewers agreed at journal 12 Apr, 2026 Reviewers agreed at journal 11 Apr, 2026 Reviewers agreed at journal 10 Apr, 2026 Reviewers agreed at journal 10 Apr, 2026 Reviews received at journal 10 Apr, 2026 Reviewers agreed at journal 10 Apr, 2026 Reviews received at journal 10 Apr, 2026 Reviewers agreed at journal 10 Apr, 2026 Reviewers agreed at journal 10 Apr, 2026 Reviewers agreed at journal 10 Apr, 2026 Reviewers agreed at journal 10 Apr, 2026 Reviewers agreed at journal 10 Apr, 2026 Reviewers agreed at journal 08 Apr, 2026 Reviewers agreed at journal 02 Apr, 2026 Reviewers invited by journal 02 Apr, 2026 Editor assigned by journal 31 Mar, 2026 Editor invited by journal 11 Mar, 2026 Submission checks completed at journal 10 Mar, 2026 First submitted to journal 10 Mar, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9003742","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":617900559,"identity":"adcc35d0-e727-44b4-ac75-2bd6f00cc962","order_by":0,"name":"Megan Grover","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5ElEQVRIiWNgGAWjYNCCChDBQ5KWMyRrYWwjRYtue/vFz4Xz6uTN288ek66oqbXrZ2B++OgGHi1mZ84US8/cdthwzpm8NMkzx44nz2xgMzbOwaflRk6CNO+2A4wzGHLMbjawHUs2OMDDJo1Xy/03yb9559TZz+B/A9Ty71iyPUEtN9iPSfM2MCfOkADa0thWY2fAQEjLmRw2a55jh5NnSLwx/9nYdyBB4jAhvxw//vg2T02d7Qz+HGPDhm919vztzQ8f49MCjA4DZN7hxAZmvMpBgP0BMq/OnqCGUTAKRsEoGHEAAGvKTx44anPhAAAAAElFTkSuQmCC","orcid":"","institution":"Sarah Wragge Wellness","correspondingAuthor":true,"prefix":"","firstName":"Megan","middleName":"","lastName":"Grover","suffix":""},{"id":617900560,"identity":"22d00c09-71ce-44ad-aac8-3a1206b44366","order_by":1,"name":"Emily Eastman","email":"","orcid":"","institution":"Sarah Wragge Wellness","correspondingAuthor":false,"prefix":"","firstName":"Emily","middleName":"","lastName":"Eastman","suffix":""},{"id":617900562,"identity":"560b1213-bf05-48a7-bbdb-ae7e76cf41ad","order_by":2,"name":"Jessica Rybka","email":"","orcid":"","institution":"Sarah Wragge Wellness","correspondingAuthor":false,"prefix":"","firstName":"Jessica","middleName":"","lastName":"Rybka","suffix":""},{"id":617900565,"identity":"a5cda8fa-45ca-476b-96a9-71cbef215d13","order_by":3,"name":"Evan Semet","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Evan","middleName":"","lastName":"Semet","suffix":""},{"id":617900570,"identity":"e5f65f34-d134-4196-8178-93f0b2372ebd","order_by":4,"name":"Lukasz Zdanowicz","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Lukasz","middleName":"","lastName":"Zdanowicz","suffix":""}],"badges":[],"createdAt":"2026-03-01 20:24:43","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9003742/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9003742/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106534202,"identity":"d404fe98-cfe7-47d6-a9aa-590ff7527cf3","added_by":"auto","created_at":"2026-04-09 15:02:22","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":76344,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStudy Flow Diagram\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9003742/v1/cbced23b8f7362658bbbd15e.jpg"},{"id":106726591,"identity":"1aa92b8d-6941-4b19-981b-4c8761773a93","added_by":"auto","created_at":"2026-04-12 18:36:40","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1436722,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9003742/v1/1ed08a68-8342-4aee-aa97-f69291cf2d2b.pdf"},{"id":106724932,"identity":"01c2910b-caa3-4f3e-87c8-ad79cabbca65","added_by":"auto","created_at":"2026-04-12 18:30:31","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":17284,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFile1.docx","url":"https://assets-eu.researchsquare.com/files/rs-9003742/v1/fe1dc4228bd49ad72bf8adcc.docx"},{"id":106534205,"identity":"dc78cbca-9da9-4edc-b521-902200589f9b","added_by":"auto","created_at":"2026-04-09 15:02:23","extension":"docx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":16015,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryFile2.docx","url":"https://assets-eu.researchsquare.com/files/rs-9003742/v1/bff19599285cf622c6b4a534.docx"},{"id":106534204,"identity":"93df6d7f-c9ec-4036-8224-f2d45fb41db3","added_by":"auto","created_at":"2026-04-09 15:02:22","extension":"docx","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":97676,"visible":true,"origin":"","legend":"","description":"","filename":"Charts.docx","url":"https://assets-eu.researchsquare.com/files/rs-9003742/v1/7983b0f9c3292278f63b0861.docx"}],"financialInterests":"Competing interest reported. Conflicts of Interest: Megan Grover, Emily Eastman, and Jessica Rybka were employed by Sarah Wragge Wellness® (SWW®) during this study. SWW® developed, markets, and profits from the SWW Method® intervention being evaluated. This study was entirely self-funded by SWW®, which had direct financial interest in favorable outcomes. To minimize bias, statistical analysis was independently conducted by E.S., who has no financial connection to SWW®. The study design, data collection, and interpretation involved SWW® employees, representing substantial potential bias. Independent replication by researchers without commercial interests is needed to validate these findings.","formattedTitle":"Evaluating the SWW Method® Dietary Intervention for HbA1c and Weight Management as a Preventive Intervention in Women","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eObesity and elevated blood glucose markers are prevalent and predominant risk factors for chronic diseases, including type II diabetes mellitus (T2DM), cardiovascular disease, and metabolic syndrome. Fad diets are thus ever emerging. The most long-standing weight loss trend is calorie restriction, based on the Energy Balance Model (EBM) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. EBM is a theory based on calories in vs calories out, where intake is modulated by neural regulation. Yet, recent research has found this to be ineffective in achieving long-term weight loss [\u003cspan additionalcitationids=\"CR4 CR5\" citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe carbohydrate-insulin model (CIM) has taken the forefront in obesity and diabetes research. The CIM theorizes excess carbohydrate consumption leads to excess insulin release, which causes weight gain, due to insulin\u0026rsquo;s anabolic nature [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDietary carbohydrates are the drivers of insulin secretion. Once insulin has shuttled glucose out of the bloodstream, there is less available fuel three to five hours after eating, resulting in lowered energy expenditure and a counter-regulatory response via a release of catabolic hormones [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The resultant drop and rise in blood sugar influences cravings, hunger, and energy intake [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAbiding by the EBM, individuals initially lose weight. Yet, due to deprived caloric intake, energy expenditure and metabolism decline. For continued weight loss, further caloric restriction is required [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Sustained weight loss requires a biological approach where the intervention influences anabolic and catabolic hormones. By limiting carbohydrate consumption, insulin release is reduced, anabolism is limited, and energy stores are accessed [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eModulating insulin release is essential as diseases caused by insulin resistance (IR) - T2DM, metabolic syndrome, cardiovascular disease, and obesity - are on the rise [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. T2DM increased from 108\u0026nbsp;million in 1980 to 422\u0026nbsp;million in 2014. Worldwide incidence has risen to 8.4% of the population [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Increased intake of refined carbohydrates and sweetened beverages correlate with its onset [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Obesity currently impacts more than 93\u0026nbsp;million Americans; 39.6% of the population. Most individuals who try to lose weight gain most of it back within five years [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDiabetes emerges due to insulin resistance and the lack of insulin release from the pancreas [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. As the disease progresses, glucose homeostasis cannot be maintained, and there is a 15% increased risk of all-cause mortality, with cardiovascular disease (CVD) being the greatest cause of death [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBlood sugar dysregulation is a precursor to CVD due to glucose\u0026rsquo;s impact on the arterial wall [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Excess blood glucose increases the NADH / NAD\u003csup\u003e+\u003c/sup\u003e ratio, which increases production of free radicals (and potentially oxidized low-density lipoproteins (LDL)) and reduces nitric oxide availability, precursors to endothelial dysfunction. Elevated blood glucose causes an overproduction of aldoses and an increased production of advanced glycation end products (AGEs) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. AGEs are created by a non-enzymatic reaction between glucose and proteins. These glycated proteins cross-link with one another and tissue proteins, leading to blood vessel and tissue damage. AGEs activate an inflammatory response, which increases expression of coagulant molecules, inhibits anticoagulant molecules, and activates thickening of the arterial wall\u0026rsquo;s basement membrane, favoring protein and lipid deposition and impaired vasodilation [\u003cspan additionalcitationids=\"CR19\" citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHyperinsulinemia also leads to diabetic dyslipidemia. This includes: elevated triglycerides (TGs), triglyceride-rich lipoproteins, small dense LDL\u0026rsquo;s (sdLDL), and reduced high-density lipoproteins (HDL) levels, which contribute to atherosclerosis and CVD risk [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. T2DM and IR metabolically impair the efficiency of production, metabolism, and clearance of lipoproteins. Rather, with T2DM, elevated TG\u0026rsquo;s, elevated very low-density lipoproteins (VLDL), and normal or slightly elevated TG enriched small dense LDL (sdLDL), are present [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003esdLDL has a high propensity for atherogenesis as high levels correlate with coronary artery calcification [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. A high-carbohydrate, high-fructose diet, as well as insulin resistance, is positively correlated with its synthesis [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Exercise, weight loss, carbohydrate reduction, and a balanced microbiome are effective in lowering sdLDL [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFinding new methods to implement sustainable weight loss and blood sugar balance is essential in disease prevention. In previous years, dietary interventions were low-calorie, high-carbohydrate diets, which initially led to weight loss, but due to a rapidly declining metabolism and blood sugar imbalances, the participants plateaued and gained all their weight back, post study participation [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. This has been supported by recent research finding that low-carbohydrate, high-fat diets are significantly more effective in achieving weight loss and blood glucose balance than high-carbohydrate, low-fat diets [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Isocaloric diets based on varied macronutrient disposition have shown longer-term effectiveness at optimizing weight than calorie-restrictive diets [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. The effectiveness is in balancing macronutrients by limiting carbohydrates and optimizing protein and fats to balance blood sugar and prevent excess insulin release, mitigating constant anabolism.\u003c/p\u003e \u003cp\u003eThis research study addresses the pandemic of obesity and T2DM by utilizing a therapeutic, preventative intervention to support and maintain weight loss and blood sugar balance: the SWW Method\u0026reg;, a proprietary method focused on sustainable dietary and lifestyle changes.\u003c/p\u003e \u003cp\u003eThe SWW Method\u0026reg; is an effective protocol as it does not abide by calorie restriction but rather implements changes to support blood sugar balance and reduce glucose\u0026rsquo;s anabolic and inflammatory impact. This isocaloric CIM approach is addressed via adequate hydration, alkalizing with a daily greens powder, intermittent fasting, conscious meal timing, prioritizing a protein-forward diet, consuming non-starchy vegetables with every meal, limiting starchy carbohydrate intake to once per day, mineralizing with magnesium, and sleep support.\u003c/p\u003e \u003cp\u003eThe benefit of this research is to provide insight for possible dietary and lifestyle modifications to address the lack of effective interventions to prevent obesity and blood sugar dysregulation. This study will primarily assess if integration of the SWW Method\u0026reg; results in significant weight loss and improved blood glucose markers in women 35\u0026ndash;75 years of age. Secondly, to determine if the SWW Method\u0026reg; results in improved lipid markers, blood pressure, C-reactive protein, and biometrics. Lastly, to determine if it improves other lab and non-lab related metrics.\u003c/p\u003e"},{"header":"2 Materials \u0026 Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study Design \u0026amp; Population\u003c/h2\u003e \u003cp\u003eThe SWW Method\u0026reg; study is a non-randomized case-control trial in which participants self-selected into either the SWW Method\u0026reg; intervention group (cases) or a no-intervention control group through separate recruitment channels. Primary, secondary, and exploratory outcome measures were compared between the two groups.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eThis 12-week trial was designed to evaluate the SWW Method\u0026reg;'s effect on blood sugar balance and weight loss in a moderately healthy population of women aged 35\u0026ndash;75.\u003c/p\u003e\u003cp\u003eCases completed the six-week SWW Method\u0026reg; program, followed by six weeks of coach-supported accountability via the ATE\u0026trade; app. Questionnaires and lab work were collected one week before and six weeks after the intervention to assess changes over the 12-week period. The second lab collection was timed to six weeks post-intervention to capture long-term outcomes, including measures such as Hemoglobin A1c (HbA1c) that require approximately three months to reflect meaningful change. Controls completed the same questionnaires and lab collections at identical timepoints for comparison.\u003c/p\u003e\u003cp\u003ePost-study data were analyzed against baseline to evaluate the SWW Method\u0026reg;'s effect on all primary, secondary, and exploratory measures relative to controls.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003e2.1.1 Recruitment:\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eEligibility criteria were identical for both groups: women aged 35\u0026ndash;75 with no history of non-skin-related cancer diagnosis or treatment, not currently taking weight loss or blood sugar medications, and with no prior enrollment in the SWW Method\u0026reg; or Sarah Wragge Wellness\u0026reg; program.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eCases\u003c/span\u003e were recruited from women who had enrolled in the SWW Method\u0026reg; program. Upon completing their health history form, participants received an automated email inviting them to join the study, outlining participation requirements and compensation (six weeks of additional accountability support via the ATE\u0026trade; app and fully reimbursed labs). Interested participants were directed to contact the PI for next steps.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cspan type=\"BoldUnderline\" class=\"BoldUnderline\" name=\"Emphasis\"\u003eControls\u003c/span\u003e were recruited from the broader community via fliers posted on social media (LinkedIn and Instagram) and distributed to SWW\u0026reg; employees' networks. Fliers outlined participation requirements and compensation (\u003cspan\u003e$\u003c/span\u003e25 Amazon gift card and fully reimbursed labs). Interested individuals completed a screening survey, and those who qualified were contacted by the PI via email.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.1.2 Study staff training:\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eStudy staff included the PI, consent meeting facilitator, SWW Method\u0026reg; Integrative Wellness Coaches, and an operations manager. Coaches had daily interaction with case participants as their program clients; the operations manager oversaw email automation and supported recruitment.\u003c/p\u003e \u003cp\u003eAll study staff completed training with the PI covering study overview, recruitment, participant compensation, coach involvement, safety protocols, and participant anonymity, as well as the Human Subjects Protection Training through Advarra.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Data Collection\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eQuestionnaires\u003c/span\u003e: One week before the intervention, both groups completed a baseline questionnaire covering health goals, concerns, and history; current medications, supplements, and surgeries; hereditary profile; GI health; energy; sleep; anxiety; allergies; and current diet and exercise (see Supplementary File 1). A follow-up questionnaire was completed six weeks after the intervention to assess changes over the 12-week period (see Supplementary File 2).\u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eLabs\u003c/span\u003e: At the same baseline timepoint, participants from both groups purchased and scheduled a Comprehensive Health Profile through Quest Diagnostics, submitting results to the PI as a PDF. Six weeks post-intervention, the PI sent instructions for the second round of labs. Upon receipt of results, participants were provided a reimbursement form to cover the cost of both lab collections. Lab results were accessible only to the PI, and were shared with participants' assigned health coaches solely at the participant's discretion.\u003c/p\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eComprehensive Health Profile\u003c/span\u003e: The panel included a complete blood count (CBC), comprehensive metabolic panel (CMP), lipid/cholesterol panel, urinalysis, hemoglobin A1c (HbA1c), vitamin D, and high-sensitivity C-reactive protein (hsCRP), as well as biometrics (height, weight, waist circumference, and blood pressure) and a brief online health risk assessment capturing family history and current health behaviors. Participants were advised to fast for 8\u0026ndash;12 hours prior to collection. Quest Diagnostics collected urine and blood samples; any flagged results were followed up directly with the participant by a Quest physician.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eStudy Administration\u003c/strong\u003e \u003cp\u003eThe study was conducted remotely. Questionnaires and consent forms were completed privately online, lab work was performed at participants' nearest Quest Diagnostics location, and all coach and staff meetings were held via Zoom.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003ePost participation\u003c/span\u003e: Upon study completion, participants scheduled a 15-minute meeting with the PI to address any questions, confirm reimbursement form completion, and review both sets of lab results. Lab reimbursement forms were withheld until second-round lab results were submitted to the PI, at which point participants received their compensation.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Participants\u003c/h2\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eSample size determination\u003c/span\u003e:\u003c/p\u003e \u003cp\u003eThe power assessment for the primary end points (body mass index (BMI), HbA1cs, and fasting blood glucose) was based on data abstracted from similar case-control dietary intervention trials [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Assuming a 2-sided significance level of 0.05, the case-control trial needed at least 36 participants per group to provide 80% power to detect statistically significant differences in BMI, HbA1cs and fasting blood glucose of at least a standard deviation of 2.1, 1.0, and 5.4 (respectively) between the two groups. The original sample size of 102 participants (56 cases and 46 controls) allowed for a 29% dropout rate to still achieve statistical significance. In the end, 85 individuals (45 cases and 40 controls) remained in the study, indicating only a 16% dropout. This was an adequate sample size to determine if the SWW Method\u0026reg; has a significant impact on weight loss and blood sugar balance.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eEthical approval\u003c/strong\u003e \u003cp\u003e \u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eand informed consent\u003c/span\u003e:\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eAll participants received an IRB-approved informed consent form outlining the study and providing sufficient information to support an informed decision about participation. Prior to signing, each participant completed a one-on-one meeting with a study staff member to review the form, address any questions, and discuss participant anonymity protections.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Dietary and Lifestyle Intervention\u003c/h2\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.4.1 Cases:\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003ePrior to beginning the intervention, cases completed the following onboarding steps: (1) a one-on-one Zoom meeting with study staff to review study design, anonymity, and consent; (2) signing of IRB-approved consent forms via HIPAA-compliant DocuSign; (3) completion of the baseline health questionnaire; and (4) a lab appointment one week prior to the program start date.\u003c/p\u003e \u003cp\u003eAdherence was monitored through consistent communication with the participants' assigned SWW\u0026reg; coach, weekly follow-up emails from the PI, daily food logs, and biomarker assessment at baseline and study completion.\u003c/p\u003e \u003cp\u003eDuring the first six weeks, cases were enrolled in the SWW Method\u0026reg; program, which included:\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eThe SWW Method\u0026reg; Protocol: Participants followed a structured daily nutrition and lifestyle protocol, including: morning hydration (16\u0026ndash;32 oz of water upon waking); alkalizing with a dehydrated greens powder, no-fruit green juice, or lemon water prior to coffee or food; 2\u0026ndash;3L of water throughout the day; a 12\u0026ndash;16 hour overnight fast; a feast-famine meal structure emphasizing 30\u0026thinsp;+\u0026thinsp;grams of protein with adequate fat and fiber per meal, with 3\u0026ndash;4 hours between meals and no snacking; a high-protein, low-carbohydrate mini-meal before dinner to manage evening intake; and starchy carbohydrates reserved for one meal per day, consumed last after protein and non-starchy vegetables. An evening wind-down routine by closing the kitchen two hours before bed and taking SWW Restore\u0026reg;.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eCoaching Calls: Two 45-minute one-on-one calls with an assigned integrative wellness coach via Zoom. The initial call addressed integration of the SWW Method\u0026reg; steps and was followed by a customized meal plan. The second call provided accountability and further individualized support.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eATE\u0026trade; App Access: Daily access to their coach via the ATE\u0026trade; app, where participants logged food journals, water intake, and exercise.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eWeekly Group Coaching Calls: 90-minute live sessions led by SWW\u0026reg; experts covering the steps of the SWW Method\u0026reg;, the science of blood sugar balance, macronutrient nutrition basics, healthy exercise strategies, the impact of stress on blood sugar and hormones, and gastrointestinal health.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eSWW Method\u0026reg; Handbook: A downloadable resource outlining the protocol steps, integration guidance, recipes, shopping lists, exercise recommendations, and evening routine suggestions.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eSupplements: A one-month supply of SWW Alkalize\u0026reg; (a dehydrated green powder) and SWW Restore\u0026reg; (a magnesium-rich mineral supplement containing sodium and potassium bicarbonate, calcium citrate, and magnesium glycinate). Participants were advised to take Alkalize in the morning and afternoon, and Restore 30 minutes before bed.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eKajabi Platform Access: Access to the SWW Method\u0026reg; Kajabi page, including weekly nutrition education modules, group coaching call recordings, and a community board for peer and team support. Access was maintained through the end of study participation.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eDuring the final six weeks, participants received continued daily coach access via the ATE\u0026trade; app and continued access to nutrition education materials on the Kajabi platform to support ongoing adherence to the SWW Method\u0026reg;.\u003c/p\u003e \u003cp\u003ePrior to study completion, the PI instructed participants to schedule their second lab appointment and complete the follow-up questionnaire.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.4.2 Controls:\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003ePrior to enrollment, controls completed the same onboarding steps as cases: (1) a one-on-one Zoom meeting with study staff; (2) signing of IRB-approved consent forms via HIPAA-compliant DocuSign; (3) completion of the baseline questionnaire (see Supplementary File 1); and (4) a lab appointment one week prior to the study start date.\u003c/p\u003e \u003cp\u003eControls received no dietary or lifestyle intervention and had no interaction with SWW\u0026reg; integrative wellness coaches. At the end of the 12-week period, the PI notified controls to schedule their second lab appointment and complete the follow-up questionnaire (see Supplementary File 2). Upon submission of second-round lab results and completion of the reimbursement form, controls received a \u003cspan\u003e$\u003c/span\u003e25 Amazon gift card and full lab reimbursement.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Statistical Analysis\u003c/h2\u003e \u003cp\u003ePrior to analysis, each participant was assigned a unique study number to ensure anonymity. All statistical analyses were performed in Python (Version 3.10) [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] within a Google Collaboratory environment. Data handling was conducted with \u003cem\u003epandas\u003c/em\u003e [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] and \u003cem\u003enumpy\u003c/em\u003e [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Between-group comparisons were performed using the Wilcoxon signed-rank test from \u003cem\u003escipy.stats\u003c/em\u003e [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Difference-in-differences regression models were conducted with \u003cem\u003estatsmodels\u003c/em\u003e [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] and were \u003cb\u003eadjusted for relevant confounders\u003c/b\u003e, including age and sex to account for factors that could influence both the exposure and outcome. All hypothesis tests were \u003cb\u003etwo-tailed\u003c/b\u003e, and statistical significance was set at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Ethical Considerations\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThis study was conducted in accordance with the Declaration of Helsinki, applicable U.S. federal regulations, institutional policies, and ICH Good Clinical Practice (GCP) guidelines, and was reviewed and approved by Advarra Institutional Review Board (IRB00077176; 22 Apr 2024). Participant anonymity was maintained throughout, with no personal data linked to identifying information.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Recruitment\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eA total of 103 individuals were officially enrolled in the study. Of the 721 individuals who responded to the control participant query, 58 did not qualify and were excluded. Of the remaining 663, 150 expressed interest and were approved to participate, of whom 46 officially enrolled and 40 completed the study. Of the 255 individuals enrolled in the SWW Method\u0026reg; group coaching program, 72 were interested, approved, and qualified to participate as cases, of whom 56 officially enrolled and 45 completed the study. In total, 85 participants completed the 12-week study. Please refer to Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e below (p. 8).\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e on page 9 displays baseline data of all primary and secondary objectives (as well as some exploratory objectives) of both the intervention group and the control group.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ebaseline characteristics of participants\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharacteristic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl group (40)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIntervention group (45)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (SD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e45.6 (9.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e48.4 (6.98)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHbA1c\u0026rsquo;s, % (SD) [95% CI]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.31 (0.26) [5.23\u0026ndash;5.39]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.40 (0.23) [5.34\u0026ndash;5.47]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI (SD) [95% CI]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e23.78 (4.19) [22.48\u0026ndash;25.08]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24.92 (3.88) [23.78\u0026ndash;26.05]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWeight, lbs (SD) [95% CI]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e149.23 (29.47) [140.09-158.36]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e151.51 (23.77) [144.56-158.46]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMCV, fL (SD) [95% CI]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e92.41 (4.19) [91.11\u0026ndash;93.71]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e92.07 (3.81) [90.96\u0026ndash;93.19]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCholesterol, mg/dL (SD) [95% CI]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e192.8 (31.42) [183.06- 202.54]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e207.9 (39.97) [196.23-219.59]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglycerides, mg/dL (SD) [95% CI]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e77.7 (50.975) [61.9\u0026ndash;93.5]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e81.2 (49.17) [66.83\u0026ndash;95.57]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL, mg/dL (SD) [95% CI]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e103.62 (29.88) [94.36-112.88]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e118.15 (37.97) [107.06-129.24]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL, mg/dL (SD) [95% CI]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e72.17 (12.42) [68.32\u0026ndash;76.03]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e72.11 (15.64) [67.54\u0026ndash;76.68]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehs-CRP, mg/L (SD) [95% CI]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.42 (2.48) [0.65\u0026ndash;2.19]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.84 (1.98) [1.267\u0026ndash;2.43]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlucose, mg/dL (SD) [95% CI]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e85.35 (9.56) [82.39\u0026ndash;88.31]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e86.5 (9.14) [83.84\u0026ndash;89.18]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaist circumference, in. (SD) [95% CI]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31.97 (4.5) [30.57\u0026ndash;33.37]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32.27 (3.58) [31.22\u0026ndash;33.31]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVitamin D, ng/mL (SD) [95% CI]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e41.52 (14.39) [37.06\u0026ndash;45.98]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43.91 (21.42) [37.65\u0026ndash;50.17]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC, thousand/uL (SD) [95% CI]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5.79 (1.7) [5.27\u0026ndash;6.32]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.54 (1.26) [5.17\u0026ndash;5.92]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRBC, million/uL (SD) [95% CI]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.46 (0.39) [4.34\u0026ndash;4.59]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.48 (0.28) [4.4\u0026ndash;4.56]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHgb, g/dL (SD) [95% CI]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13.52 (0.89) [13.24\u0026ndash;13.8]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.59 (0.97) [13.31\u0026ndash;13.88]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHematocrit, % (SD) [95% CI]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e41.16 (2.66) [40.34\u0026ndash;41.98]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e41.26 (2.83) [40.43\u0026ndash;42.09]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRDW, % (SD) [95% CI]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12.4 (0.557) [12.23\u0026ndash;12.58]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.44 (0.62) [12.26\u0026ndash;12.62]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eData are represented as the mean \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003eSD for normally distributed continuous variables and as a 95% CI {interquartile range}.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Primary and Secondary Objectives\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThe primary objective of this study was to determine whether integration of the SWW Method\u0026reg; results in significant weight loss and improved blood glucose markers in women aged 35\u0026ndash;75. As this population was relatively healthy at baseline, the data may not accurately reflect the broader population. Mean BMI at baseline was 24.92 and 23.78 for the intervention and control groups, respectively, both within the healthy range. Mean HbA1c was 5.40% and 5.31% for the intervention and control groups, respectively, both below the 5.7% threshold for prediabetes. Mean fasting glucose was 86.5 mg/dL and 85.35 mg/dL for the intervention and control groups, respectively, both within normal range. This cohort does not represent the average population at large. For context, 11.6% of the U.S. population has been diagnosed with T2DM (HbA1c\u0026thinsp;\u0026gt;\u0026thinsp;6.5%) and 40.3% meets criteria for obesity (BMI\u0026thinsp;\u0026gt;\u0026thinsp;30), according to the Centers for Disease Control [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. Despite healthy baseline measurements, statistically significant changes were observed in HbA1c, weight, BMI, and MCV over the 12-week study period, confirming achievement of the primary study objective. This is evident in Charts \u003cspan refid=\"Str1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan refid=\"Str4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, below.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e3.2.1. HbA1c 95% confidence interval\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003e \u003cb\u003eHbA1c\u0026rsquo;s\u003c/b\u003e: Data are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;95% CI. DiD: p\u0026thinsp;=\u0026thinsp;0.0428; (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05); Wilcoxon: p\u0026thinsp;=\u0026thinsp;0.7292. Intervention group: baseline 5.4% (SD, 0.23) [CI: 5.34\u0026ndash;5.47]; 12 weeks 5.33% (SD, 0.22) [CI: 5.27\u0026ndash;5.39]. Controls: baseline 5.31% (SD, 0.262) [CI: 5.23\u0026ndash;5.39]; 12 weeks 5.39% (SD, 0.264) [CI: 5.32\u0026ndash;5.48].\u003c/p\u003e \u003cp\u003eAs evidenced in Chart \u003cspan refid=\"Str1\" class=\"InternalRef\"\u003e1\u003c/span\u003e: HbA1c, above (p. 10), the intervention group's HbA1c decreased slightly over the 12-week period while controls experienced a slight increase, resulting in a statistically significant between-group difference (DiD: p\u0026thinsp;=\u0026thinsp;0.0428). However, the within-group change among cases was not significant (Wilcoxon: p\u0026thinsp;=\u0026thinsp;0.7292), indicating that significance was driven by divergence between groups rather than meaningful improvement within the intervention group alone. It is also notable that controls entered the study with a lower mean HbA1c (5.31%) than the intervention group (5.40%), and experienced a concurrent decrease in weight and BMI despite the rise in HbA1c.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003e3.2.2. BMI 95% confidence interval\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003e \u003cb\u003eBMI\u003c/b\u003e: Data are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;95% CI. DiD: p\u0026thinsp;=\u0026thinsp;0.5019; Wilcoxon: p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001. Intervention group: baseline 24.92 (SD, 3.881) [CI: 23.78\u0026ndash;26.05]; 12 weeks 24.03 (SD, 3.516) [CI: 22.99\u0026ndash;25.05]. Controls: baseline 23.78 (SD, 4.199) [CI: 22.48\u0026ndash;25.08]; 12 weeks 23.72 (SD, 4.374) [CI: 22.36\u0026ndash;25.08].\u003c/p\u003e \u003cp\u003eAs evidenced in Chart \u003cspan refid=\"Str2\" class=\"InternalRef\"\u003e2\u003c/span\u003e: BMI, above (p. 11), the intervention group experienced a statistically significant reduction in BMI over the 12-week period, declining from 24.92 to 24.03 (Wilcoxon: p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001). Controls showed a minimal reduction from 23.78 to 23.72. However, the between-group difference was not statistically significant (DiD: p\u0026thinsp;=\u0026thinsp;0.5019). The high standard deviations at both timepoints indicate considerable variability within both groups.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003e3.2.3. Weight 95% confidence interval\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003e \u003cb\u003eWeight\u003c/b\u003e: Data are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;95% CI. DiD: p\u0026thinsp;=\u0026thinsp;0.5290; Wilcoxon: p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001. Intervention group: baseline 151.51lbs (SD, 23.77) [144.56-158.46]; 12 weeks 145.98lbs (SD, 21.15) [139.79-152.16]. Controls: baseline 149.23lbs (SD, 29.47) [CI: 140.09-158.36]; 12 weeks 148.82lbs (SD, 30.53) [CI: 139.36-158.28].\u003c/p\u003e \u003cp\u003eAs evidenced in Chart \u003cspan refid=\"Str3\" class=\"InternalRef\"\u003e3\u003c/span\u003e: Weight, above (p. 11), the intervention group experienced a statistically significant reduction in weight of 5.53 lbs over the 12-week period (Wilcoxon: p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), while controls lost an average of 0.41 lbs. However, the between-group difference was not statistically significant (DiD: p\u0026thinsp;=\u0026thinsp;0.5290). The high standard deviations at both timepoints indicate considerable variability within both groups.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003e3.2.4. MCV 95% confidence interval\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003e \u003cb\u003eMCV\u003c/b\u003e: Data are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;95% CI. DiD: p\u0026thinsp;=\u0026thinsp;0.7562; Wilcoxon: p\u0026thinsp;=\u0026thinsp;0.0035 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Intervention group: baseline 92.07 fL (SD, 3.81) [CI: 90.96\u0026ndash;93.19]; 12 weeks 92.85 fL (SD, 3.868) [CI: 91.72\u0026ndash;93.98]. Controls: baseline 92.41 fL (SD, 4.193) [CI: 91.11\u0026ndash;93.71]; 12 weeks 92.79 fL (SD, 4.611) [CI: 91.36\u0026ndash;94.22].\u003c/p\u003e \u003cp\u003eAs evidenced in Chart \u003cspan refid=\"Str4\" class=\"InternalRef\"\u003e4\u003c/span\u003e: MCV, above (p. 12), The intervention group showed a statistically significant increase in MCV over the 12-week period, from 92.07 fL to 92.85 fL (Wilcoxon: p\u0026thinsp;=\u0026thinsp;0.0035). Controls also showed a slight increase, from 92.41 fL to 92.79 fL, and the between-group difference was not significant (DiD: p\u0026thinsp;=\u0026thinsp;0.7562). All values remained within the normal range (80\u0026ndash;100 fL), and the magnitude of change was not consistent with macrocytic anemia typically associated with vitamin B12 or folate deficiency.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e \u003ch2\u003e3.2.5. Remaining Data\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eNo statistically significant differences were observed between groups for the remaining outcome measures. This is evident in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, below (p. 13). Notably, controls presented with healthier baseline values than cases across the majority of markers, including cholesterol, triglycerides, LDL, hsCRP, waist circumference, and systolic blood pressure.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cb\u003eBaseline to final results amongst non-significant data changes |\u003c/b\u003e95% confidence intervals\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBiomarker\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eControl Group Mean (SD) [95% CI]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIntervention Group Mean (SD) [95% CI]\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStatistical result\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCholesterol\u003c/p\u003e \u003cp\u003emg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eBaseline\u003c/b\u003e: 192.8 (31.4) [183.06- 202.54]\u003c/p\u003e \u003cp\u003e\u003cb\u003eEnd of study\u003c/b\u003e: 190.46 (29.6) [181.27-199.64]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eBaseline\u003c/b\u003e: 207.9 (39.97) [196.23-219.59]\u003c/p\u003e \u003cp\u003e\u003cb\u003eEnd of study\u003c/b\u003e: 208.2 (37.66) [197.19\u0026ndash;219.2]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDiD: p\u0026thinsp;=\u0026thinsp;0.8091\u003c/p\u003e \u003cp\u003eWilcoxon: p\u0026thinsp;=\u0026thinsp;0.525\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTriglycerides*\u003c/p\u003e \u003cp\u003emg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eBaseline\u003c/b\u003e: 77.7 (50.97) [61.9\u0026ndash;93.5]\u003c/p\u003e \u003cp\u003e\u003cb\u003eEnd of study\u003c/b\u003e: 77.84 (49.5) [62.5-93.18]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eBaseline\u003c/b\u003e: 81.2 (49.17) [66.83\u0026ndash;95.57]\u003c/p\u003e \u003cp\u003e\u003cb\u003eEnd of study\u003c/b\u003e: 77.24 (42.55) [64.8-89.67]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDiD: p\u0026thinsp;=\u0026thinsp;0.7821\u003c/p\u003e \u003cp\u003eWilcoxon: p\u0026thinsp;=\u0026thinsp;0.6794\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL\u003c/p\u003e \u003cp\u003emg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eBaseline\u003c/b\u003e: 103.62 (29.88) [94.36-112.88]\u003c/p\u003e \u003cp\u003e\u003cb\u003eEnd of study\u003c/b\u003e: 99.97 (32.08) [90.03-109.92]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eBaseline\u003c/b\u003e: 118.15 (37.97) [107.06-129.24]\u003c/p\u003e \u003cp\u003e\u003cb\u003eEnd of study\u003c/b\u003e: 118.2 (33.93) [108.28-128.11]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDiD: p\u0026thinsp;=\u0026thinsp;0.7221\u003c/p\u003e \u003cp\u003eWilcoxon: p\u0026thinsp;=\u0026thinsp;0.5962\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHDL\u003c/p\u003e \u003cp\u003emg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eBaseline\u003c/b\u003e: 72.17 (12.42) [68.32\u0026ndash;76.03]\u003c/p\u003e \u003cp\u003e\u003cb\u003eEnd of study\u003c/b\u003e: 68.94 (14.86) [64.34\u0026ndash;73.55]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eBaseline\u003c/b\u003e: 72.1 (15.64) [67.54\u0026ndash;76.68]\u003c/p\u003e \u003cp\u003e\u003cb\u003eEnd of study\u003c/b\u003e: 72.46 (15.87) [67.83\u0026ndash;77.1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDiD: p\u0026thinsp;=\u0026thinsp;0.4345\u003c/p\u003e \u003cp\u003eWilcoxon: p\u0026thinsp;=\u0026thinsp;0.2928\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ehs-CRP*\u003c/p\u003e \u003cp\u003emg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eBaseline\u003c/b\u003e: 1.41 (2.48) [0.65\u0026ndash;2.19]\u003c/p\u003e \u003cp\u003e\u003cb\u003eEnd of study\u003c/b\u003e: 1.44 (2.04) [0.81\u0026ndash;2.07]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eBaseline\u003c/b\u003e: 1.85 (1.99) [1.267\u0026ndash;2.43]\u003c/p\u003e \u003cp\u003e\u003cb\u003eEnd of study\u003c/b\u003e: 1.3 (1.23) [0.94\u0026ndash;1.66]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDiD: p\u0026thinsp;=\u0026thinsp;0.297\u003c/p\u003e \u003cp\u003eWilcoxon: p\u0026thinsp;=\u0026thinsp;0.0987\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlucose\u003c/p\u003e \u003cp\u003emg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eBaseline\u003c/b\u003e: 85.35 (9.56) [82.39\u0026ndash;88.31]\u003c/p\u003e \u003cp\u003e\u003cb\u003eEnd of study\u003c/b\u003e: 87.7 (9.65) [84.7-90.69]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eBaseline\u003c/b\u003e: 86.5 (9.14) [83.84\u0026ndash;89.18]\u003c/p\u003e \u003cp\u003e\u003cb\u003eEnd of study\u003c/b\u003e: 87.93 (8.17) [85.55\u0026ndash;90.32]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDiD: p\u0026thinsp;=\u0026thinsp;0.74\u003c/p\u003e \u003cp\u003eWilcoxon: p\u0026thinsp;=\u0026thinsp;0.48\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWaist Circumference*\u003c/p\u003e \u003cp\u003ein.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eBaseline\u003c/b\u003e: 31.97 (4.5) [30.57\u0026ndash;33.37]\u003c/p\u003e \u003cp\u003e\u003cb\u003eEnd of study\u003c/b\u003e: 31.79 (4.75) [30.32\u0026ndash;33.27]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eBaseline\u003c/b\u003e: 32.26 (3.58) [31.22\u0026ndash;33.31]\u003c/p\u003e \u003cp\u003e\u003cb\u003eEnd of study\u003c/b\u003e: 31.61 (3.95) [30.46\u0026ndash;32.77]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDiD: p\u0026thinsp;=\u0026thinsp;0.7163\u003c/p\u003e \u003cp\u003eWilcoxon: p\u0026thinsp;=\u0026thinsp;0.4603\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVitamin D\u003c/p\u003e \u003cp\u003eng/mL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eBaseline\u003c/b\u003e: 41.52 (14.39) [37.06\u0026ndash;45.98]\u003c/p\u003e \u003cp\u003e\u003cb\u003eEnd of study\u003c/b\u003e: 43.84 (15.3) [39.09\u0026ndash;48.59]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eBaseline\u003c/b\u003e: 43.91 (21.42) [37.65\u0026ndash;50.17]\u003c/p\u003e \u003cp\u003e\u003cb\u003eEnd of study\u003c/b\u003e: 45.06 (16.84) [40.15\u0026ndash;49.99]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDiD: p\u0026thinsp;=\u0026thinsp;0.8278\u003c/p\u003e \u003cp\u003eWilcoxon: p\u0026thinsp;=\u0026thinsp;0.0825\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC*\u003c/p\u003e \u003cp\u003eThousand/uL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eBaseline\u003c/b\u003e: 5.79 (1.7) [5.27\u0026ndash;6.32]\u003c/p\u003e \u003cp\u003e\u003cb\u003eEnd of study\u003c/b\u003e: 5.93 (1.8) [5.37\u0026ndash;6.49]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eBaseline\u003c/b\u003e: 5.54 (1.26) [5.17\u0026ndash;5.92]\u003c/p\u003e \u003cp\u003e\u003cb\u003eEnd of study\u003c/b\u003e: 6.91 (8.96) [4.29\u0026ndash;9.53]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDiD: p\u0026thinsp;=\u0026thinsp;0.4079\u003c/p\u003e \u003cp\u003eWilcoxon: p\u0026thinsp;=\u0026thinsp;0.2815\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRBC\u003c/p\u003e \u003cp\u003eMillion/uL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eBaseline\u003c/b\u003e: 4.46 (0.39) [4.34\u0026ndash;4.59]\u003c/p\u003e \u003cp\u003e\u003cb\u003eEnd of study\u003c/b\u003e: 4.49 (0.326) [4.39\u0026ndash;4.59]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eBaseline\u003c/b\u003e: 4.48 (0.28) [4.4\u0026ndash;4.56]\u003c/p\u003e \u003cp\u003e\u003cb\u003eEnd of study\u003c/b\u003e: 4.42 (0.297) [4.34\u0026ndash;4.51]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDiD: p\u0026thinsp;=\u0026thinsp;0.4139\u003c/p\u003e \u003cp\u003eWilcoxon: p\u0026thinsp;=\u0026thinsp;0.4229\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHemoglobin\u003c/p\u003e \u003cp\u003eg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eBaseline\u003c/b\u003e: 13.52 (0.899) [13.24\u0026ndash;13.8]\u003c/p\u003e \u003cp\u003e\u003cb\u003eEnd of study\u003c/b\u003e: 13.48 (0.86) [13.33\u0026ndash;13.87]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eBaseline\u003c/b\u003e: 13.59 (0.98) [13.31\u0026ndash;13.88]\u003c/p\u003e \u003cp\u003e\u003cb\u003eEnd of study\u003c/b\u003e: 13.48 (0.97) [13.2-13.77]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDiD: p\u0026thinsp;=\u0026thinsp;0.519\u003c/p\u003e \u003cp\u003eWilcoxon: p\u0026thinsp;=\u0026thinsp;0.8426\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHematocrit\u003c/p\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eBaseline\u003c/b\u003e: 41.16 (2.66) [40.34\u0026ndash;41.98]\u003c/p\u003e \u003cp\u003e\u003cb\u003eEnd of study\u003c/b\u003e: 41.05 (2.45) [40.8-42.32]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eBaseline\u003c/b\u003e: 41.26 (2.83) [40.43\u0026ndash;42.09]\u003c/p\u003e \u003cp\u003e\u003cb\u003eEnd of study\u003c/b\u003e: 41.05 (2.75) [40.25\u0026ndash;41.86]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDiD: p\u0026thinsp;=\u0026thinsp;0.46\u003c/p\u003e \u003cp\u003eWilcoxon: p\u0026thinsp;=\u0026thinsp;0.636\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRDW\u003c/p\u003e \u003cp\u003e%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eBaseline\u003c/b\u003e: 12.4 (0.557) [12.23\u0026ndash;12.58]\u003c/p\u003e \u003cp\u003e\u003cb\u003eEnd of study\u003c/b\u003e: 12.33 (0.65) [12.13\u0026ndash;12.53]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eBaseline\u003c/b\u003e: 12.44 (0.62) [12.26\u0026ndash;12.62]\u003c/p\u003e \u003cp\u003e\u003cb\u003eEnd of study\u003c/b\u003e: 12.55 (0.58) [12.38\u0026ndash;12.72]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDiD: p\u0026thinsp;=\u0026thinsp;0.32\u003c/p\u003e \u003cp\u003eWilcoxon: p\u0026thinsp;=\u0026thinsp;0.85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSystolic**\u003c/p\u003e \u003cp\u003emmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eBaseline\u003c/b\u003e: 108.7\u003c/p\u003e \u003cp\u003e\u003cb\u003eEnd of study\u003c/b\u003e: 103.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eBaseline\u003c/b\u003e: 116.33\u003c/p\u003e \u003cp\u003e\u003cb\u003eEnd of study\u003c/b\u003e: 113.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDiD: p\u0026thinsp;=\u0026thinsp;0.788\u003c/p\u003e \u003cp\u003eWilcoxon: p\u0026thinsp;=\u0026thinsp;0.0735\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiastolic\u003c/p\u003e \u003cp\u003emmHg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eBaseline\u003c/b\u003e: 66.35\u003c/p\u003e \u003cp\u003e\u003cb\u003eEnd of study\u003c/b\u003e: 65.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eBaseline\u003c/b\u003e: 72.51\u003c/p\u003e \u003cp\u003e\u003cb\u003eEnd of study\u003c/b\u003e: 71.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eDiD: p\u0026thinsp;=\u0026thinsp;0.925\u003c/p\u003e \u003cp\u003eWilcoxon: p\u0026thinsp;=\u0026thinsp;0.4457\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eData are represented as the mean \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003eSD for normally distributed continuous variables and as a 95% CI {interquartile range}. *Although not statistically significant, the intervention group demonstrated improvements in triglycerides, hs-CRP, waist circumference, and WBC count. **Systolic blood pressure: Baseline Difference Test (before treatment) | T-test Statistic: 2.7094, p-value: 0.0083 | Bias risk. Cases presented with a statistically higher systolic blood pressure at baseline (control mean: 108.7mmHg).\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eAlthough not statistically significant, triglycerides improved in the intervention group from 81.2 mg/dL to 77.24 mg/dL, while remaining stable in controls (77.7 mg/dL to 77.84 mg/dL). High variability in both groups (intervention SD\u0026thinsp;\u0026plusmn;\u0026thinsp;49.17 mg/dL; control SD\u0026thinsp;\u0026plusmn;\u0026thinsp;50.97 mg/dL) likely contributed to the lack of statistical significance.\u003c/p\u003e\u003cp\u003ehsCRP decreased slightly in the intervention group from 1.85 to 1.30 mg/L, while controls showed a marginal increase from 1.41 to 1.44 mg/L, though neither change was statistically significant.\u003c/p\u003e\u003cp\u003eWaist circumference decreased modestly in the intervention group (mean 0.65 inches) compared to controls (mean 0.18 inches), though this difference was not statistically significant.\u003c/p\u003e\u003cp\u003eQuestionnaire data revealed notable self-reported improvements among intervention participants: 69% reported improved morning energy, 79% reported improved daytime energy, and 79% reported reduced sugar intake.\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThis study aimed to determine whether integration of the SWW Method\u0026reg; would result in significant weight loss and improved glycemic markers in women aged 35\u0026ndash;75. Cases and controls were recruited to assess whether changes in blood markers and biometrics over 12 weeks were significantly greater in the intervention group compared to the control group. The SWW Method\u0026reg; demonstrated moderate benefit in improving prolonged blood sugar markers (HbA1c) relative to controls, while no significant changes were observed in FBG. BMI and weight improved significantly within the intervention group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), but not in comparison to controls, likely due to high data variance and concurrent weight loss observed in both groups.\u003c/p\u003e \u003cp\u003eThe average baseline values for both groups were already within a healthy range, limiting the potential for significant change. Results may have differed had baseline characteristics fallen within an unhealthy range. The high degree of variability across the data further impeded the detection of statistical significance.\u003c/p\u003e \u003cp\u003eAlthough the between-group change in HbA1c was statistically significant, the degree was modest, and the within-group change among cases was not significant. Baseline HbA1c values of 5.31% and 5.40% for controls and cases, respectively, were both below the prediabetes threshold of 5.7%, leaving limited room for meaningful reduction. Yet, the intervention group's HbA1c decreased from 5.40% to 5.33%, while controls increased from 5.31% to 5.39%, suggesting that the SWW Method\u0026reg; may have attenuated the natural progression toward worsening glycemic control. This suggests a potentially meaningful impact on HbA1c trajectories compared to a population following no dietary or lifestyle intervention.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003cp\u003eThere was no significant change in FBG levels. Rather than declining they increased over the 12-week period. The intervention group\u0026rsquo;s FBG rose from 86.5mg/dL to 87.93mg/dL, while the controls\u0026rsquo; FBG rose from 85.35mg/dL to 87.7mg/dL. High baseline variability (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e9.14mg/dL (SD) and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\pm\\)\u003c/span\u003e\u003c/span\u003e9.56mg/dL (SD) for both the cases and controls, respectively) further limited the ability to detect significance. FBG is not an accurate determinant of long-term glycemic control, as values can be transiently elevated by inadequate hydration, caffeine consumption, or stress prior to collection [\u003cspan additionalcitationids=\"CR43\" citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Although caffeine has demonstrated long-term benefits for insulin resistance, the short-term, postprandial effect is a temporary spike in blood glucose [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Mitigating stress is difficult, and ensuring hydration and avoiding caffeine before the lab appointment may not have been adequately conveyed by the study team.\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003eAlthough the intervention group achieved significant weight loss (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), this was not significant in comparison to the controls (p\u0026thinsp;=\u0026thinsp;0.529), likely due to high data variance and weight loss observed in both groups (5.53 lbs and 0.41 lbs among cases and controls, respectively). This suggests that the weight loss may not be fully attributable to the intervention alone and could reflect general trends or external influences affecting both groups. Neither group needed significant weight loss at baseline. Additionally, receiving blood work and having biometrics tested at the start of the study may have motivated controls to make beneficial health choices over the course of 12 weeks.\u003c/p\u003e\u003cp\u003eThe intervention group experienced a significant decrease in BMI (p\u0026thinsp;\u0026lt;\u0026thinsp;0.0001), declining from 24.92 to 24.03 (a change of 0.89), while controls showed a minimal decline from 23.78 to 23.72 (a change of 0.06). However, the between-group difference was not significant (p\u0026thinsp;=\u0026thinsp;0.5019), likely due to high data variance and the fact that both cohorts had healthy baseline BMIs, leaving limited room for noticeable improvement. The control group's lower baseline BMI also suggests they were, on average, a healthier cohort at the outset. While the significant BMI reduction among cases may reflect external influences, it could also indicate the effectiveness of the SWW Method\u0026reg; in achieving healthy weight loss in an already healthy population.\u003c/p\u003e\u003cp\u003eAlthough unrelated to the initial hypothesis, there was a significant improvement in Mean Corpuscular Volume (MCV) within the intervention group (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05). This was likely attributable in part to nightly consumption of SWW Restore\u0026reg;, which at the start of this study contained 500mg of methylcobalamin. Supplemental vitamin B12 may cause an early rise in MCV due to increased reticulocyte production, an effect likely to normalize with longer-term follow-up [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. A modest improvement was also observed in controls, possibly reflecting the fact that many were followers of SWW\u0026reg; on social media and subscribers to their products. The transient MCV changes in both groups could reflect nutritional shifts, early hematologic recovery, or changes in alcohol intake patterns, suggesting general physiological shifts rather than a distinct intervention effect. High variability in both groups further limited the ability to extract comparable significance.\u003c/p\u003e\u003cp\u003eNon-significant improvements were observed in the intervention group across triglycerides, high-sensitivity C-reactive protein (hsCRP), waist circumference, and white blood cell (WBC) count. Despite healthy baseline values for triglycerides (81.2 mg/dL) and hsCRP (1.85 mg/dL), both markers improved over 12 weeks (77.24 mg/dL and 1.3 mg/dL, respectively), while remaining relatively stable in controls, suggesting a potential intervention effect on blood lipids and inflammatory markers. However, the high standard deviation for triglycerides, combined with healthy baseline values in both groups, limited the ability to achieve statistical significance.\u003c/p\u003e\u003cp\u003eThe SWW Method\u0026reg; is an isocaloric CIM-based protocol designed to support blood sugar balance and weight loss through adequate hydration, alkalizing with a daily greens powder, intermittent fasting, conscious meal timing, a protein-forward diet, non-starchy vegetables with every meal, limiting starchy carbohydrate intake to once per day, magnesium supplementation, and optimizing circadian rhythm. This protocol is delivered through nutrition education, group coaching, and one-on-one nutrition support. Few studies have examined dietary and lifestyle interventions in tandem with educational and coaching support. However, multiple studies have identified benefits of individual components of the SWW Method\u0026reg; on blood glucose markers and weight loss.\u003c/p\u003e\u003cp\u003eConsuming protein, fat, and fiber before carbohydrates can improve metabolic conditions in individuals with diabetes and obesity [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Eating protein and/or fat before carbohydrates promotes secretion of glucagon-like-peptide-1 (GLP-1) to improve postprandial glucose secretion and suppress appetite [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e], while consuming dietary fiber before carbohydrates similarly reduces postprandial glucose secretion. Implementation of a vegetables-protein-carbohydrate meal sequence has been shown to reduce postprandial glucose levels and HbA1c in pre-diabetic and diabetic patients [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan additionalcitationids=\"CR42 CR43 CR44 CR45 CR46 CR47 CR48 CR49 CR50 CR51 CR52\" citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. This is comparable with the meal sequencing recommended within the SWW Method\u0026reg; protocol. However, other studies have not found benefits of meal sequencing on HbA1c or FBG in diabetic patients, instead supporting a low-carbohydrate, high-protein diet as more effective for significant improvements [\u003cspan additionalcitationids=\"CR55\" citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e].\u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe benefits of intermittent fasting (IF) have variable impacts. A systematic review found no benefits of IF (16\u0026ndash;20 hour) on improving FBG levels or HbA1cs [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. Studies that did observe glycemic improvements were conducted in individuals who were obese or diagnosed with T2DM, and IF was most effective when paired with dietary changes [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan additionalcitationids=\"CR59\" citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. IF also appears more effective for weight loss than glycemic control, and primarily in obese, diabetic populations [\u003cspan additionalcitationids=\"CR62\" citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe SWW Method\u0026reg; protocol prioritizes protein to support glycemic markers. A 2018 randomized controlled trial found that a low-carbohydrate, high-protein diet had a positive impact on FBG in T2DM patients [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. Multiple studies have found that when protein comprises more than 30% of total dietary intake, metabolic syndrome is better addressed compared to diets meeting the FDA-recommended protein content of 14% [\u003cspan additionalcitationids=\"CR66 CR67\" citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]. In a Danish case-control study of 28 diabetic participants, a carbohydrate-reduced, high-protein diet implemented over six weeks resulted in a significant reduction in HbA1c compared to a conventional diabetes diet [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAlternatively, to the above-mentioned studies, the SWW Method\u0026reg; protocol was implemented within a healthy cohort, thus the results are less drastic and more difficult to achieve significance [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]. Yet, the SWW Method\u0026reg; attracts a healthier population base, looking to optimize their health and prevent, rather than address, symptoms of metabolic syndrome.\u003c/p\u003e \u003cp\u003eHydration is a significant component of the SWW Method\u0026reg; protocol. Although not a primary vehicle for combating obesity, adequate hydration has been correlated with a lower BMI [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe SWW Method\u0026reg; protocol provides nutrition education and group coaching to support dietary and lifestyle changes. Other similar models have been effective in supporting weight loss goals due to the provided accountability and support, yet over a more extended period of time and in an unhealthy population [\u003cspan additionalcitationids=\"CR73 CR74\" citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe primary limitation of this study was its implementation within an already healthy cohort. As a non-randomized study in which participants self-selected into their respective groups, substantial selection bias was introduced, limiting causal inference and the ability to distinguish intervention effects from pre-existing differences between groups. At baseline, neither the cases nor controls exhibited any markers for metabolic syndrome, making significance difficult to achieve. All of the above-mentioned studies achieved the greatest results in subjects with higher baseline BMI and HbA1c.\u003c/p\u003e \u003cp\u003eControls presented with healthier baseline values than cases across the majority of markers, including cholesterol, triglycerides, HbA1c, FBG, LDL, weight, BMI, hsCRP, waist circumference, and systolic blood pressure, and demonstrated slight improvements in BMI, weight, cholesterol, MCV, and LDL over the study period. The Hawthorne effect may have contributed, as having blood work and biometrics tested at baseline may have motivated controls to make beneficial health choices over the 12 weeks. As an already healthy cohort, controls likely maintained their existing habits throughout, while cases enrolled specifically to improve their health. Additionally, controls' individual engagement in separate dietary or lifestyle interventions was uncontrolled and may have contributed to their observed improvements. Collectively, these factors made it difficult for cases to demonstrate significant improvement relative to controls.\u003c/p\u003e \u003cp\u003eRegression to the mean may have contributed to the observed improvements in the intervention group. As they were enrolled specifically to improve their health markers, some baseline measurements may have represented the upper range of their natural variability and would have improved toward their typical average even without intervention. Receiving blood work may have provided further motivation. Their beneficial changes may have been independent of the intervention itself, which is reinforced by the improvement amongst the controls.\u003c/p\u003e \u003cp\u003eThe high data variance made it difficult to achieve significance, particularly in weight, BMI, triglycerides, LDL, and vitamin D levels. Recruiting a greater number of participants would have been necessary to overcome the variability observed across both groups.\u003c/p\u003e \u003cp\u003eWithout full study adherence, there is minimal to no improvement in biometrics and lab work. There was no objective monitoring of dietary intake outside of clients' self-reporting on the ATE\u0026trade; app via description or photo. Without honest reporting, it was difficult for program participants to receive adequate support, reducing the likelihood of adherence to the dietary protocol.\u003c/p\u003e \u003cp\u003eThere were multiple, uncontrollable, confounding variables, which may have significantly impacted biometrics and lab results. Medication use, stress, sleep quality, concurrent illnesses, injuries, menopause, menstruation, or other interventions participants might have started during the study period could have influenced results.\u003c/p\u003e \u003cp\u003eAt Quest Diagnostics, multiple participants reported inaccurate height and waist circumference measurements, introducing inter-technician variability. Incomplete fasting prior to the lab appointment would also have impacted results [\u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e], as would caffeine consumption or dehydration, which can affect urinalysis, CBC, and FBG levels [\u003cspan additionalcitationids=\"CR43\" citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Alcohol consumption the night prior would additionally have contributed to elevated fasting triglyceride levels [\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMost of the recruitment was through social media, Instagram, Facebook, and LinkedIn, as well as templated emails sent from the coaches to their network. This attracted current followers of Sarah Wragge Wellness\u0026reg;, who are likely a healthier audience. As the program was advertised on social media and caters to a higher socioeconomic class, the resultant cohort was healthy and does not accurately represent the average population.\u003c/p\u003e \u003cp\u003eRecruitment for controls occurred on a rolling basis, whereas the SWW Method\u0026reg; runs three times per year (January, April, and September), with participants recruited across multiple rounds. This temporal mismatch, combined with seasonal variations in vitamin D, physical activity, and dietary patterns, may have contributed to data variability and limited the ability to achieve statistical significance.\u003c/p\u003e \u003cp\u003eMultiple outcome measures were assessed without correction for multiple comparisons, which increases the risk of Type I error. With approximately 20 outcomes tested at the p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 significance level, some statistically significant findings may have occurred by chance rather than reflecting true intervention effects.\u003c/p\u003e \u003cp\u003eThis study has several notable strengths. The prospective design with standardized measurements at baseline and 12 weeks allowed for accurate assessment of intervention effects while minimizing recall bias. Including a concurrent control group allowed for comparison against natural trends, strengthening causal inference. Collecting all biomarkers at Quest Diagnostics ensured standardization, reduced inter-laboratory variability, and minimized measurement error.\u003c/p\u003e \u003cp\u003eFor future evaluation, the SWW Method\u0026reg; protocol should be implemented in an overweight cohort exhibiting prediabetic glycemic markers. Recruiting a larger sample size would minimize excessive variability. A longer study duration would more adequately capture changes in HbA1c, FBG, and sustainable weight loss. These changes would better assess significant impact on weight and glycemic markers. Implementing the protocol amongst a diverse socioeconomic community and in similar-aged men would better address impact in the general population.\u003c/p\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThis study evaluated a real-world dietary and lifestyle intervention in a generally healthy female population, demonstrating that measurable improvements in metabolic markers can occur even with baseline values within normal ranges. This was most evident in a slight but significant improvement in HbA1c relative to controls and significant improvements in weight and BMI within the intervention cohort. These findings demonstrate the potential of the SWW Method\u0026reg; protocol to further optimize health in an already healthy population, indicating its effectiveness as a preventative health intervention.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eThe following abbreviations are used in this manuscript:\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"524\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003eT2DM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 461px;\"\u003e\n \u003cp\u003eType II Diabetes Mellitus\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003eEBM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 461px;\"\u003e\n \u003cp\u003eEnergy Balance Model\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003eCIM\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 461px;\"\u003e\n \u003cp\u003eCarbohydrate-Insulin Model\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003eCVD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 461px;\"\u003e\n \u003cp\u003eCardiovascular Disease\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003eIR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 461px;\"\u003e\n \u003cp\u003eInsulin Resistance\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003eLDL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 461px;\"\u003e\n \u003cp\u003eLow-density lipoproteins\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003eAGEs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 461px;\"\u003e\n \u003cp\u003eAdvanced glycation end products\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003eTG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 461px;\"\u003e\n \u003cp\u003eTriglycerides\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003esdLDL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 461px;\"\u003e\n \u003cp\u003eSmall dense low-density lipoproteins\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003eHDL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 461px;\"\u003e\n \u003cp\u003eHigh density lipoproteins\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003eVLDL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 461px;\"\u003e\n \u003cp\u003eVery low-density lipoproteins\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003eHbA1c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 461px;\"\u003e\n \u003cp\u003eHemoglobin A1c\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003ePI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 461px;\"\u003e\n \u003cp\u003ePrincipal investigator\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003eCBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 461px;\"\u003e\n \u003cp\u003eComplete blood count\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003eRBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 461px;\"\u003e\n \u003cp\u003eRed blood cell\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003eWBC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 461px;\"\u003e\n \u003cp\u003eWhite blood cell\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003eMCV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 461px;\"\u003e\n \u003cp\u003eMean corpuscular volume\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003eMCH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 461px;\"\u003e\n \u003cp\u003eMean corpuscular hemoglobin\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003eMCHC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 461px;\"\u003e\n \u003cp\u003eMean corpuscular hemoglobin concentration\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003eRDW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 461px;\"\u003e\n \u003cp\u003eRed cell distribution width\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003eCMP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 461px;\"\u003e\n \u003cp\u003eComprehensive metabolic panel\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003eEGFR\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 461px;\"\u003e\n \u003cp\u003eEstimated glomerular filtration rate\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003ehs-CRP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 461px;\"\u003e\n \u003cp\u003eHigh sensitivity c-reactive protein\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003eMPV\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 461px;\"\u003e\n \u003cp\u003eMean platelet volume\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003eBMI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 461px;\"\u003e\n \u003cp\u003eBody mass index\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003eDiD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 461px;\"\u003e\n \u003cp\u003eDifference in difference\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 461px;\"\u003e\n \u003cp\u003eStandard deviation\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003eFBG\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 461px;\"\u003e\n \u003cp\u003eFasting blood glucose\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 62px;\"\u003e\n \u003cp\u003eGLP-1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 461px;\"\u003e\n \u003cp\u003eGlucagon like peptide-1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor Contributions:\u003c/strong\u003e Conceptualization, M.G.; methodology, M.G., E.E.; software, E.S., L.Z.; formal analysis, M.G., E.S.; investigation, M.G., E.E.; resources, Quest Diagnostics; data curation, M.G., L.Z.; writing\u0026mdash;original draft preparation, M.G.; writing\u0026mdash;M.G., J.R.; visualization, M.G.; supervision, M.G.; project administration, M.G.; funding acquisition, Sarah Wragge Wellness\u0026reg;. All authors have read and agreed to the published version of the manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e This research was self-funded by Sarah Wragge Wellness\u0026reg;.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInstitutional Review Board Statement:\u0026nbsp;\u003c/strong\u003eThe study was approved by the Institutional Review Board (IRB), Advarra (Pro00077176; 22 Apr 2024), and each participant signed an approved consent form.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed Consent Statement:\u0026nbsp;\u003c/strong\u003eInformed consent was obtained from all subjects involved in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent to Publish:\u003c/strong\u003e Not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Availability Statement:\u003c/strong\u003e The data supporting the findings of this observational study are available from the corresponding author upon reasonable request. The data are not publicly available due to privacy and ethical restrictions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTrial Registration:\u003c/strong\u003e ClinicalTrials.gov, NCT07463417, retrospectively registered March 5, 2026.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u003c/strong\u003e The authors thank Quest Diagnostics for utilization of their labs for data collection. During the preparation of this manuscript/study, the author(s) used Python (Version 3.10) [35] within a Google Collaboratory environment for the purposes of initial code for statistical analysis. The authors also used Chat GPT 5.2 (Open AI) and Claude Sonnet 4.5 for text editing assistance. The authors have reviewed and edited the output and take full responsibility for the content of this publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest:\u003c/strong\u003e Megan Grover, Emily Eastman, and Jessica Rybka were employed by Sarah Wragge Wellness\u0026reg; (SWW\u0026reg;) during this study. SWW\u0026reg; developed, markets, and profits from the SWW Method\u0026reg; intervention being evaluated. This study was entirely self-funded by SWW\u0026reg;, which had direct financial interest in favorable outcomes. To minimize bias, statistical analysis was independently conducted by E.S., who has no financial connection to SWW\u0026reg;. The study design, data collection, and interpretation involved SWW\u0026reg; employees, representing substantial potential bias. Independent replication by researchers without commercial interests is needed to validate these findings.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eHall KD, Farooqi IS, Friedman JM, Klein S, Loos RJ, Mangelsdorf DJ, et al. The energy balance model of obesity: beyond calories in, calories out. Am J Clin Nutr. 2022;115(5):1243\u0026ndash;54.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen SY, Beretta M, Olzomer EM, Shah DP, Wong DYH, Alexopoulos SJ, et al. Targeting negative energy balance with calorie restriction and mitochondrial uncoupling in db/db mice. Mol Metabolism. 2023;69:101684.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFlanagan SP. Carbohydrate-Insulin Model for Obesity-2021. In: Russell J, Skolnik NS, editors. Top Articles in Primary Care [Internet]. Cham: Springer International Publishing; 2023 [cited 2026 Feb 17]. pp. 197\u0026ndash;9. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://link.springer.com/\u003c/span\u003e\u003cspan address=\"https://link.springer.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1007/978-3-031-25620-2_45\u003c/span\u003e\u003cspan address=\"10.1007/978-3-031-25620-2_45\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLudwig DS, Aronne LJ, Astrup A, De Cabo R, Cantley LC, Friedman MI, et al. The carbohydrate-insulin model: a physiological perspective on the obesity pandemic. Am J Clin Nutr. 2021;114(6):1873\u0026ndash;85.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLudwig DS, Apovian CM, Aronne LJ, Astrup A, Cantley LC, Ebbeling CB, et al. Competing paradigms of obesity pathogenesis: energy balance versus carbohydrate-insulin models. Eur J Clin Nutr. 2022;76(9):1209\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTaubes G. The energy balance model compared with the carbohydrate-insulin model. Am J Clin Nutr. 2022;116(2):612\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSoto-Mota A, Pereira MA, Ebbeling CB, Aronica L, Ludwig DS. Evidence for the carbohydrate\u0026ndash;insulin model in a reanalysis of the Diet Intervention Examining The Factors Interacting with Treatment Success (DIETFITS) trial. Am J Clin Nutr. 2023;117(3):599\u0026ndash;606.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCahill GF. Physiology of Insulin In Man: The Banting Memorial Lecture 1971. Diabetes. 1971;20(12):785\u0026ndash;99.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eL\u0026oacute;pez-M\u0026eacute;ndez I, Maldonado-Rojas ADC, Uribe M, Ju\u0026aacute;rez-Hern\u0026aacute;ndez E. Hunger \u0026amp; satiety signals: another key mechanism involved in the NAFLD pathway. Front Endocrinol. 2023;14:1213372.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePetersen MC, Shulman GI. Mechanisms of Insulin Action and Insulin Resistance. Physiol Rev. 2018;98(4):2133\u0026ndash;223.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKouda K, Nakamura H, Kohno H, Okuda T, Higashine Y, Hisamori K, et al. Metabolic response to short-term 4-day energy restriction in a controlled study. Environ Health Prev Med. 2006;11(2):89\u0026ndash;92.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSoltani S, Jayedi A, Abdollahi S, Vasmehjani AA, Meshkini F, Shab-Bidar S. Effect of carbohydrate restriction on body weight in overweight and obese adults: a systematic review and dose\u0026ndash;response meta-analysis of 110 randomized controlled trials. Front Nutr. 2023;10:1287987.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMekala KC, Bertoni AG. Epidemiology of diabetes mellitus. In: Transplantation, Bioengineering, and Regeneration of the Endocrine Pancreas [Internet]. Elsevier; 2020 [cited 2026 Feb 17]. pp. 49\u0026ndash;58. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://linkinghub.elsevier.com/retrieve/pii/B9780128148334000046\u003c/span\u003e\u003cspan address=\"https://linkinghub.elsevier.com/retrieve/pii/B9780128148334000046\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlam S, Hasan MK, Neaz S, Hussain N, Hossain MF, Rahman T. Diabetes Mellitus: Insights from Epidemiology, Biochemistry, Risk Factors, Diagnosis, Complications and Comprehensive Management. Diabetology. 2021;2(2):36\u0026ndash;50.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMann T, Tomiyama AJ, Westling E, Lew AM, Samuels B, Chatman J. Medicare\u0026rsquo;s search for effective obesity treatments: Diets are not the answer. Am Psychol. 2007;62(3):220\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWilding JPH, Batterham RL, Davies M, Van Gaal LF, Kandler K, Konakli K, et al. Weight regain and cardiometabolic effects after withdrawal of semaglutide: The STEP 1 trial extension. Diabetes Obes Metabolism. 2022;24(8):1553\u0026ndash;64.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFreeman AM, Pennings N. Insulin Resistance. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2018 [updated 2023]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/books/NBK507839/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/books/NBK507839/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGalicia-Garcia U, Benito-Vicente A, Jebari S, Larrea-Sebal A, Siddiqi H, Uribe KB, et al. Pathophysiology Type 2 Diabetes Mellitus IJMS. 2020;21(17):6275.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMeza CA, La Favor JD, Kim DH, Hickner RC. Endothelial Dysfunction: Is There a Hyperglycemia-Induced Imbalance of NOX and NOS? IJMS. 2019;20(15):3775.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eQambari H, Yu PK, Balaratnasingam C, Dickson J, Yu DY. Endothelial function can be modulated by acute hyperglycemia. Sci Rep. 2025;15(1):30559.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eParhofer KG. Interaction between Glucose and Lipid Metabolism: More than Diabetic Dyslipidemia. Diabetes Metab J. 2015;39(5):353.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGao Y. Association of estimated small dense low-density lipoprotein cholesterol with diabetes mellitus: a large-scale multicenter retrospective cohort study. BMC Endocr Disord. 2025;25(1):66.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHe Q, Wang L, Fang Y, Liang M, Chen X, Hu R, et al. Relationship of small dense low-density lipoprotein cholesterol level with pre-diabetes and newly detected type 2 diabetes. Sci Rep. 2025;15(1):19500.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRikhi R, Schaich CL, Hafzalla GW, Patel NA, Tannenbaum JE, German CA, et al. Small dense low-density lipoprotein cholesterol and coronary artery calcification in the Multi-Ethnic Study of Atherosclerosis. Eur J Prev Cardiol. 2024;31(8):1048\u0026ndash;54.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVekic J, Zeljkovic A, Cicero AFG, Janez A, Stoian AP, Sonmez A, et al. Atherosclerosis Development and Progression: The Role of Atherogenic Small, Dense LDL. Medicina. 2022;58(2):299.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSantos HO, Earnest CP, Tinsley GM, Izidoro LFM, Macedo RCO. Small dense low-density lipoprotein-cholesterol (sdLDL-C): Analysis, effects on cardiovascular endpoints and dietary strategies. Prog Cardiovasc Dis. 2020;63(4):503\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBenedict FG, Miles WR, Roth P, Smith HM. Human vitality and efficiency under prolonged restricted diet. Washington (DC): Carnegie Institution of Washington; 1919.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKeys A, Brožek J, Henschel A, Mickelsen O, Taylor HL. The biology of human starvation. Minneapolis (MN): University of Minnesota Press; 1950. 2 vols.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAntunes MM, Godoy G, De Almeida-Souza CB, Da Rocha BA, Da Silva-Santi LG, Masi LN, et al. A high-carbohydrate diet induces greater inflammation than a high-fat diet in mouse skeletal muscle. Braz J Med Biol Res. 2020;53(3):e9039.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYang Q, Lang X, Li W, Liang Y. The effects of low-fat, high-carbohydrate diets vs. low-carbohydrate, high-fat diets on weight, blood pressure, serum liquids and blood glucose: a systematic review and meta-analysis. Eur J Clin Nutr. 2022;76(1):16\u0026ndash;27.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMasood W, Annamaraju P, Suheb MZK, Uppaluri KR. Ketogenic Diet. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2023. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/books/NBK499830/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/books/NBK499830/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePescari D, Mihuta MS, Bena A, Stoian D. Quantitative analysis of the caloric restriction versus isocaloric diets models based on macronutrients composition: impacts on body weight regulation, anthropometric, and bioimpedance parameters in women with obesity. Front Nutr. 2024;11:1493954.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEvangelista LS, Jose MM, Sallam H, Serag H, Golovko G, Khanipov K, et al. High-Protein vs. Standard-Protein Diets in Overweight and Obese Patients with Heart Failure and Diabetes Mellitus: Findings of the Pro-HEART Trial. ESC Heart Fail. 2021;8(2):1342\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePerissiou M, Borkoles E, Kobayashi K, Polman R. The Effect of an 8 Week Prescribed Exercise and Low-Carbohydrate Diet on Cardiorespiratory Fitness, Body Composition and Cardiometabolic Risk Factors in Obese Individuals: A Randomised Controlled Trial. Nutrients. 2020;12(2):482.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePython Software Foundation. Python. Version 3.10. Wilmington (DE): Python Software Foundation. 2023. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.python.org/\u003c/span\u003e\u003cspan address=\"https://www.python.org/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcKinney W. Data Structures for Statistical Computing in Python. In Austin, Texas; 2010 [cited 2026 Feb 17]. pp. 56\u0026ndash;61. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.curvenote.com/\u003c/span\u003e\u003cspan address=\"https://doi.curvenote.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.25080/Majora-92bf1922-00a\u003c/span\u003e\u003cspan address=\"10.25080/Majora-92bf1922-00a\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHarris CR, Millman KJ, Van Der Walt SJ, Gommers R, Virtanen P, Cournapeau D, et al. Array programming with NumPy. Nature. 2020;585(7825):357\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVirtanen P, Gommers R, Oliphant TE, Haberland M, Reddy T, Cournapeau D, et al. SciPy 1.0: fundamental algorithms for scientific computing in Python. Nat Methods. 2020;17(3):261\u0026ndash;72.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSeabold S, Perktold J, Statsmodels. Econometric and Statistical Modeling with Python. In Austin, Texas; 2010 [cited 2026 Feb 17]. pp. 92\u0026ndash;6. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.curvenote.com/\u003c/span\u003e\u003cspan address=\"https://doi.curvenote.com/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.25080/Majora-92bf1922-011\u003c/span\u003e\u003cspan address=\"10.25080/Majora-92bf1922-011\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCenters for Disease Control and Prevention. Diabetes data and research [Internet]. Atlanta (GA): CDC; [cited 2026 Feb 17]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cdc.gov/diabetes/php/data-research/index.html\u003c/span\u003e\u003cspan address=\"https://www.cdc.gov/diabetes/php/data-research/index.html\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEmmerich S, Fryar C, Stierman B, Ogden C. Obesity and Severe Obesity Prevalence in Adults: United States, August 2021\u0026ndash;August 2023 [Internet]. National Center for Health Statistics (U.S.); 2024 Sep [cited 2026 Feb 17]. Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://stacks.cdc.gov/view/cdc/159281\u003c/span\u003e\u003cspan address=\"https://stacks.cdc.gov/view/cdc/159281\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChoi MK, Ahn HS, Kim DE, Lee DS, Park CS, Kang CK. Effects of Varying Caffeine Dosages and Consumption Timings on Cerebral Vascular and Cognitive Functions: A Diagnostic Ultrasound Study. Appl Sci. 2025;15(4):1703.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhani S, Tayek JA. Cortisol increases gluconeogenesis in humans: its role in the metabolic syndrome. Clin Sci. 2001;101(6):739\u0026ndash;47.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLane JD, Feinglos MN, Surwit RS. Caffeine increases ambulatory glucose and postprandial responses in coffee drinkers with type 2 diabetes. Diabetes Care. 2008;31(2):221\u0026ndash;2.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBaSalamah M, AlMghamsi R, AlTowairqi A, Fouda K, Mahrous A, Mujahid M, et al. The Effect of Coffee Consumption on Blood Glucose Levels. J Biochem Technol. 2022;13(2):64\u0026ndash;9.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLouie JCY, Atkinson F, Petocz P, Brand-Miller JC. Delayed effects of coffee, tea and sucrose on postprandial glycemia in lean, young, healthy adults. Asia Pac J Clin Nutr. 2008;17(4):657\u0026ndash;62.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHall CA. Vitamin B12 Deficiency and Early Rise in Mean Corpuscular Volume. JAMA. 1981;245(11):1144.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSmith TJS. Vitamin B12 Deficiency and Early Rise in Mean Corpuscular Volume. JAMA. 1982;247(8):1126.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKubota S, Liu Y, Iizuka K, Kuwata H, Seino Y, Yabe D. A Review of Recent Findings on Meal Sequence: An Attractive Dietary Approach to Prevention and Management of Type 2 Diabetes. Nutrients. 2020;12(9):2502.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGentilcore D, Chaikomin R, Jones KL, Russo A, Feinle-Bisset C, Wishart JM, et al. Effects of Fat on Gastric Emptying of and the Glycemic, Insulin, and Incretin Responses to a Carbohydrate Meal in Type 2 Diabetes. J Clin Endocrinol Metabolism. 2006;91(6):2062\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNitta A, Imai S, Kajiayama S, Matsuda M, Miyawaki T, Matsumoto S, et al. Impact of Dietitian-Led Nutrition Therapy of Food Order on 5-Year Glycemic Control in Outpatients with Type 2 Diabetes at Primary Care Clinic: Retrospective Cohort Study. Nutrients. 2022;14(14):2865.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSerichantalergs N, Boonyavarakul A, EFFECT OF MEAL SEQUENCING, ON GLP-1 HORMONE AND POSTPRANDIAL GLUCOSE EXCURSION IN PRE-DIABETIC PATIENTS. A CROSSOVER TRIAL. J ASEAN Fed Endocr Soc. 2023;38(S3):57\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSun L, Goh HJ, Govindharajulu P, Leow MKS, Henry CJ. Postprandial glucose, insulin and incretin responses differ by test meal macronutrient ingestion sequence (PATTERN study). Clin Nutr. 2020;39(3):950\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOkami Y, Tsunoda H, Watanabe J, Kataoka Y. Efficacy of a meal sequence in patients with type 2 diabetes: a systematic review and meta-analysis. BMJ Open Diab Res Care. 2022;10(1):e002534.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVlachos D, Malisova S, Lindberg FA, Karaniki G. Glycemic Index (GI) or Glycemic Load (GL) and Dietary Interventions for Optimizing Postprandial Hyperglycemia in Patients with T2 Diabetes: A Review. Nutrients. 2020;12(6):1561.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYan Y, Asemani S, Jamilian P, Yang C. The efficacy of low-carbohydrate diets on glycemic control in type 2 diabetes: a comprehensive overview of meta-analyses of controlled clinical trials. Diabetol Metab Syndr. 2025;17(1):341.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSharma SK, College of Nursing, All India Institute of Medical Sciences, Jodhpur, Rajasthan, India, Mudgal SK, College of Nursing, All India Institute of Medical Sciences, Deoghar, Jharkhand, India, Kalra S et al. Department of Endocrinology, Bharti Hospital and BRIDE, Karnal, Haryana, India,. Effect of Intermittent Fasting on Glycaemic Control in Patients With Type 2 Diabetes Mellitus: A Systematic Review and Meta-analysis of Randomized Controlled Trials. European Endocrinology. 2023;19(1):25.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAnasanti MD, Hamzah N. Meta-analysis and meta-regression of intermittent fasting effects on glycaemic control in type 2 diabetes: Subgroup analyses and variability. Diabetes Metabolic Syndrome: Clin Res Reviews. 2025;19(7):103279.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDuman TT, Atak Tel BM, Bilgin S, Dervisevic A, Aktas G. Evaluation of the effects of intermittent fasting on clinical and laboratory parameters in metabolic syndrome. j-ebr. 2025;2.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNowosad K, Sujka M. Effect of Various Types of Intermittent Fasting (IF) on Weight Loss and Improvement of Diabetic Parameters in Human. Curr Nutr Rep. 2021;10(2):146\u0026ndash;54.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBorgundvaag E, Mak J, Kramer CK. Metabolic Impact of Intermittent Fasting in Patients With Type 2 Diabetes Mellitus: A Systematic Review and Meta-analysis of Interventional Studies. J Clin Endocrinol Metabolism. 2021;106(3):902\u0026ndash;11.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChair SY, Cai H, Cao X, Qin Y, Cheng HY, Ng MT. Intermittent Fasting in Weight Loss and Cardiometabolic Risk Reduction: A Randomized Controlled Trial. J Nurs Res. 2022;30(1):e185.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhalafi M, Habibi Maleki A, Symonds ME, Rosenkranz SK, Rohani H, Ehsanifar M. The effects of intermittent fasting on body composition and cardiometabolic health in adults with prediabetes or type 2 diabetes: A systematic review and meta-analysis. Diabetes Obes Metabolism. 2024;26(9):3830\u0026ndash;41.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHuhmann MB, Yamamoto S, Neutel JM, Cohen SS, Ochoa Gautier JB. Very high-protein and low-carbohydrate enteral nutrition formula and plasma glucose control in adults with type 2 diabetes mellitus: a randomized crossover trial. Nutr Diabetes. 2018;8(1):45.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAzwan K, Mona R, Firdous J, David PR, Muhamad N. A whey-based, high-protein diet promotes the best body weight and blood sugar control when compared with other types of diet in male Sprague Dawley rats. In: RSU International Research Conference 2021 on Science and Technology; 2021. pp. 68\u0026ndash;74.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFlores-Hern\u0026aacute;ndez MN, Mart\u0026iacute;nez-Coria H, L\u0026oacute;pez-Vald\u0026eacute;s HE, Arteaga-Silva M, Arrieta-Cruz I, Guti\u0026eacute;rrez-Ju\u0026aacute;rez R. Efficacy of a High-Protein Diet to Lower Glycemic Levels in Type 2 Diabetes Mellitus: A Systematic Review. IJMS. 2024;25(20):10959.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMcArthur LH, Kelly WF, Gietzen DW, Rogers QR. The Role of Palatability in the Food Intake Response of Rats Fed High-Protein Diets. Appetite. 1993;20(3):181\u0026ndash;96.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePeters JC, Harper AE. Adaptation of Rats to Diets Containing Different Levels of Protein: Effects on Food Intake, Plasma and Brain Amino Acid Concentrations and Brain Neurotransmitter Metabolism. J Nutr. 1985;115(3):382\u0026ndash;98.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSkytte MJ, Samkani A, Petersen AD, Thomsen MN, Astrup A, Chabanova E, et al. A carbohydrate-reduced high-protein diet improves HbA1c and liver fat content in weight stable participants with type 2 diabetes: a randomised controlled trial. Diabetologia. 2019;62(11):2066\u0026ndash;78.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShea B, Bakre S, Carano K, Scharen J, Langheier J, Hu EA. Changes in Glycemic Control Among Individuals With Diabetes Who Used a Personalized Digital Nutrition Platform: Longitudinal Study. JMIR Diabetes. 2021;6(4):e32298.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChang T, Ravi N, Plegue MA, Sonneville KR, Davis MM. Inadequate Hydration, BMI, and Obesity Among US Adults: NHANES 2009\u0026ndash;2012. Annals Family Med. 2016;14(4):320\u0026ndash;4.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKupila SKE, Ven\u0026auml;l\u0026auml;inen MS, Suojanen LU, Roseng\u0026aring;rd-B\u0026auml;rlund M, Ahola AJ, Elo LL, et al. Weight Loss Trajectories in Healthy Weight Coaching: Cohort Study. JMIR Form Res. 2022;6(3):e26374.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eFernanda Mu\u0026ntilde;oz Obino K, Aguiar Pereira C, Caron Lienert R. Coaching and barriers to weight loss: an integrative review. DMSO. 2016;10:1\u0026ndash;11.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePainter SL, Ahmed R, Kushner RF, Hill JO, Lindquist R, Brunning S, et al. Expert Coaching in Weight Loss: Retrospective Analysis. J Med Internet Res. 2018;20(3):e92.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTanaka K, Sasai H, Wakaba K, Murakami S, Ueda M, Yamagata F, et al. Professional dietary coaching within a group chat using a smartphone application for weight loss: a randomized controlled trial. JMDH. 2018;11:339\u0026ndash;47.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZaid AB, Awad SM, El-Abd MG, Saied SA, Almahdy SK, Saied AA, et al. Unraveling the controversy between fasting and nonfasting lipid testing in a normal population: a systematic review and meta-analysis of 244,665 participants. Lipids Health Dis. 2024;23(1):199.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eVan De Wiel A. The Effect of Alcohol on Postprandial and Fasting Triglycerides. Int J Vascular Med. 2012;2012:1.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Charts","content":"\u003cp\u003eCharts 1 to 4 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-nutrition","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nutn","sideBox":"Learn more about [BMC Nutrition](http://bmcnutr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nutn/default.aspx","title":"BMC Nutrition","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"carbohydrate-insulin model, blood sugar balance, weight loss, women, case-control trial","lastPublishedDoi":"10.21203/rs.3.rs-9003742/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9003742/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIndividuals with elevated blood glucose markers and an unhealthy BMI are more at risk for type II diabetes, cardiovascular disease, and metabolic syndrome. Yet, most diets addressing this are not sustainable and are typically only implemented in an unhealthy population rather than functioning as a preventative measure. There is a notable gap in dietary interventions designed for generally healthy populations seeking to optimize metabolic health before disease onset. We conducted a case-control trial to determine if the SWW Method® was an effective intervention to help lower blood glucose and weight in a healthy cohort of women aged 35–75 (baseline HbA1c: 5.31–5.4%, BMI: 23–25). The SWW Method® is a carbohydrate-insulin model dietary and lifestyle intervention.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMethod: We recruited 45 SWW Method® participants and 40 comparable controls. All participants completed the same questionnaire and lab testing at the start and end of the 12-week study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eResults: The intervention group’s HbA1cs improved significantly (5.4% to 5.33%) compared to the control group (5.31% to 5.4%) (p \u0026lt; 0.05). Yet, there was no significant change in fasting blood glucose. The SWW Method® group also experienced a reduction in BMI (p \u0026lt; 0.0001) and weight (p \u0026lt; 0.0001) over the course of the 12 weeks, but not in comparison to the controls, who also lost weight.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eConclusion: Integrating the SWW Method® is moderately effective in improving HbA1c and in supporting weight loss within the intervention group, indicating its potential as a preventative health intervention for optimizing metabolic health in an already healthy population.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTrial Registration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eClinicalTrials.gov, NCT07463417, retrospectively registered March 5, 2026.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTrial registration\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eClinicalTrials.gov, NCT07463417, retrospectively registered March 5, 2026\u003c/p\u003e","manuscriptTitle":"Evaluating the SWW Method® Dietary Intervention for HbA1c and Weight Management as a Preventive Intervention in Women","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-09 15:01:59","doi":"10.21203/rs.3.rs-9003742/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-04-29T10:33:33+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-29T00:30:31+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-20T10:22:06+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-20T10:19:50+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-20T06:24:54+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-17T17:50:07+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-16T13:41:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"317448830306024105002403190699390718365","date":"2026-04-15T06:24:41+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-13T20:50:00+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-13T03:44:34+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-13T03:37:02+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-12T20:18:28+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"319813720340555059808264740145584457926","date":"2026-04-12T10:25:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"47677660136914002841064312008105804969","date":"2026-04-11T08:37:26+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"4779282473304374806506917462645335640","date":"2026-04-10T22:27:39+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"215454113409511993664434951948563892257","date":"2026-04-10T19:46:17+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-10T19:20:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"257528479272486000074943372124110196394","date":"2026-04-10T19:07:40+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-10T16:24:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"173480412329656144065204295549910179388","date":"2026-04-10T16:21:44+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"231446457288566840358510035844032311372","date":"2026-04-10T15:52:32+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"100107099284504621520785465159087393526","date":"2026-04-10T15:47:35+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"228724211579793904896801071724294031658","date":"2026-04-10T15:40:36+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"141047250306161982205796353173062500185","date":"2026-04-10T15:24:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"216758814946798739074494375970154991568","date":"2026-04-08T10:10:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"63147043241740029236574184535312373186","date":"2026-04-02T13:55:59+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-02T13:01:10+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-31T09:18:35+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-11T04:25:00+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-11T01:39:41+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Nutrition","date":"2026-03-10T20:01:09+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-nutrition","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"nutn","sideBox":"Learn more about [BMC Nutrition](http://bmcnutr.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/nutn/default.aspx","title":"BMC Nutrition","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"d96775a3-7089-4572-aae5-edce45b1381f","owner":[],"postedDate":"April 9th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-15T11:40:46+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-09 15:01:59","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9003742","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9003742","identity":"rs-9003742","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}